{"text": "# ------------------------------------------------------------------------------------------\n# # Multiple dispatch\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# In this notebook we'll explore **multiple dispatch**, which is a key feature of Julia.\n#\n# Multiple dispatch makes software *generic* and *fast*!\n#\n# #### Starting with the familiar\n#\n# To understand multiple dispatch in Julia, let's start with what we've already seen.\n#\n# We can declare functions in Julia without giving Julia any information about the types of\n# the input arguments that function will receive:\n# ------------------------------------------------------------------------------------------\n\nf(x) = x^2\n\n# ------------------------------------------------------------------------------------------\n# and then Julia will determine on its own which input argument types make sense and which\n# do not:\n# ------------------------------------------------------------------------------------------\n\nf(10)\n\nf([1, 2, 3])\n\n# ------------------------------------------------------------------------------------------\n# #### Specifying the types of our input arguments\n#\n# However, we also have the *option* to tell Julia explicitly what types our input arguments\n# are allowed to have.\n#\n# For example, let's write a function `foo` that only takes strings as inputs.\n# ------------------------------------------------------------------------------------------\n\nfoo(x::String, y::String) = println(\"My inputs x and y are both strings!\")\n\n# ------------------------------------------------------------------------------------------\n# We see here that in order to restrict the type of `x` and `y` to `String`s, we just follow\n# the input argument name by a double colon and the keyword `String`.\n#\n# Now we'll see that `foo` works on `String`s and doesn't work on other input argument\n# types.\n# ------------------------------------------------------------------------------------------\n\nfoo(\"hello\", \"hi!\")\n\nfoo(3, 4)\n\n# ------------------------------------------------------------------------------------------\n# To get `foo` to work on integer (`Int`) inputs, let's tack `::Int` onto our input\n# arguments when we declare `foo`.\n# ------------------------------------------------------------------------------------------\n\nfoo(x::Int, y::Int) = println(\"My inputs x and y are both integers!\")\n\nfoo(3, 4)\n\n# ------------------------------------------------------------------------------------------\n# Now `foo` works on integers! But look, `foo` also still works when `x` and `y` are\n# strings!\n# ------------------------------------------------------------------------------------------\n\nfoo(\"hello\", \"hi!\")\n\n# ------------------------------------------------------------------------------------------\n# This is starting to get to the heart of multiple dispatch. When we declared\n#\n# ```julia\n# foo(x::Int, y::Int) = println(\"My inputs x and y are both integers!\")\n# ```\n# we didn't overwrite or replace\n# ```julia\n# foo(y::String, y::String)```\n#\n# Instead, we just added an additional ***method*** to the ***generic function*** called\n# `foo`.\n#\n# A ***generic function*** is the abstract concept associated with a particular operation.\n#\n# For example, the generic function `+` represents the concept of addition.\n#\n# A ***method*** is a specific implementation of a generic function for *particular argument\n# types*.\n#\n# For example, `+` has methods that accept floating point numbers, integers, matrices, etc.\n#\n# We can use the `methods` to see how many methods there are for `foo`.\n# ------------------------------------------------------------------------------------------\n\nmethods(foo)\n\nmethods(+)\n\n# ------------------------------------------------------------------------------------------\n# So, we now can call `foo` on integers or strings. When you call `foo` on a particular set\n# of arguments, Julia will infer the types of the inputs and dispatch the appropriate\n# method. *This* is multiple dispatch.\n#\n# Multiple dispatch makes our code generic and fast. Our code can be generic and flexible\n# because we can write code in terms of abstract operations such as addition and\n# multiplication, rather than in terms of specific implementations. At the same time, our\n# code runs quickly because Julia is able to call efficient methods for the relevant types.\n#\n# To see which method is being dispatched when we call a generic function, we can use the\n# @which macro:\n# ------------------------------------------------------------------------------------------\n\n@which foo(3, 4)\n\n@which 3.0 + 3.0\n\n# ------------------------------------------------------------------------------------------\n# Given that a method written specifically for floating point numbers is dispatched on `3.0\n# + 3.0`, the LLVM code generated is extremely terse:\n# ------------------------------------------------------------------------------------------\n\n@code_llvm 3.0 + 3.0\n\n# ------------------------------------------------------------------------------------------\n# Note that Julia is fast even when we write generic function definitions because, at the\n# end of the day, specific/tailored methods are called under the hood.\n#\n# For example, note that we can declare the adding function `myadd` without providing any\n# type annotations -\n# ------------------------------------------------------------------------------------------\n\nmyadd(x, y) = x + y\n\n# ------------------------------------------------------------------------------------------\n# and though we haven't constrained the types of `x` and `y`, we'll see that the LLVM code\n# generated for `myadd(3.0, 3.0)` looks like that of `3.0 + 3.0`\n# ------------------------------------------------------------------------------------------\n\n@code_llvm myadd(3.0, 3.0)\n", "meta": {"hexsha": "bbd3b8e278c9a68a5fe32c4cb9090b049bf6aa32", "size": 5929, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/short-version/06.Multiple_dispatch.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/short-version/06.Multiple_dispatch.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/short-version/06.Multiple_dispatch.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 42.9637681159, "max_line_length": 92, "alphanum_fraction": 0.4456063417, "num_tokens": 1033, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.21469142916152645, "lm_q1q2_score": 0.09981038277759216}}
{"text": "# # Instructions for Assignment02\n#\n# ## Introduction\n# \n# In this assignment,\n# you will start to put the pieces together.\n# Note: this file is runnable in its current state,\n# but is incomplete.\n# You can run the file from the command line,\n# or use the VS Code julia extension to run individual lines.\n\n# The following code is used for set up;\n# there is no need to change it.\n# If you _do_ change it to figure out what it's doing,\n# make sure you change it back or your assignment may not work correctly\n\n## To view additional instructions for this assignment, #jl\n## see https://wellesley-bisc195.github.io/BISC195.jl/stable/Assignments/assignment02.html #jl\n\n## To view a rendered version of this document, #jl\n## see https://wellesley-bisc195.github.io/BISC195.jl/stable/Assignments/assignment02_code.html #jl\n\nusing Random\nRandom.seed!(42)\n\nfunction generate_sequence(len)\n    seq = join(rand(['A','C','G','T'], len))\n    println(\"Your sequence is: \", seq)\n    return seq\nend\n\nmy_seq = generate_sequence(20)\n\n# ## Question 1\n# \n# The code above generates a random 20nt DNA sequence,\n# and assigns it to the variable `my_seq`.\n# What kind of data type is `my_seq` (eg Float64, Int64, String, or something else)?\n# Assign the variable `question1` to the correct type.\n#\n# Hint1: rather than guess, you can just use the `typeof()` function.\n\nquestion1 = \"\"\n\n# Hint2: If you're having trouble, \n# note that your answer should not contain quotes.\n# Eg. if you thing that `seq` has the type `MyType`,\n# your code should read `question1 = MyType` and not `question1 = \"MyType\"`\n\n# ## Question 2\n# \n# The function bellow has a \"doc string\",\n# which explains what the function should do.\n# Many julia functions have doc strings, \n# which makes it easy to get help right from the REPL\n# \n# Open a julia REPL and type `?`.\n# You will see the `julia>` prompt change to `help?>`.\n# Now type `println` and hit enter.\n# The docstring of `println` is printed\n# and you're returned to a julia prompt\n#\n# Evaluate the following function, including the docstring, in the REPL\n# (that is, copy from the tripple quotes down to the \"end\"\n# and paste it into the REPL, then hit enter)\n\n\"\"\"\n    question2(sequence)\n\nChecks if `sequence` is a String.\n\nExample\n\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\n\n    julia> question2(\"hello\")\n    true\n\n    julia> question2(1001)\n    false\n\"\"\"\nfunction question2()\n    ## put your code here\nend\n\n# At the moment, this function doesn't do what it says it should.\n# Actually, it doesn't do anything. \n# Fix the function so that it takes one argument\n# and returns `true` if the argument is a String,\n# and returns `false` otherwise.\n# You should be able to run the examples in the docstring\n# and get the correct answer,\n# and to run this function using `my_seq` as the argument\n# (it should return `true`).\n#\n# Hint: You haven't explicitly encountered a way to check if a type is another type.\n# Learning to search for answers is a key programming skill!\n# Try searching \"check if type is string julia\".\n# Typically, when I see search results, I prioritize in this order:\n# 1. stackoverflow.com\n# 2. docs.julialang.org\n# 3. discourse.julialang.org\n# 4. github.com\n\n# ## Question 3\n# \n# Let's try something a bit more complicated.\n# The `question3()` function has a doc string,\n# but is also incomplete. \n# I've written some code to get you started,\n# complete it so that the examples work as expected in the docstring.\n# If you use this function on `my_seq`,\n# you should get the answer `0.3`\n\n\"\"\"\n    question3(sequence)\n\nCalculates the GC ratio of a DNA sequence.\nThe GC ratio is the total number of G and C bases divided by the total length of the sequence.\nFor more info about GC content, see here:\n\nExample\n\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\n\n    julia> question3(\"AATG\")\n    0.25\n\n    julia> question3(\"CCCGG\")\n    1.0\n\n    julia> question3(\"ATTA\")\n    0.0\n\"\"\"\nfunction question3(sequence)\n    ## throw an error if the string contains anything other than ACGT\n    if any(c-> !in(c, ['A','C','G','T']), sequence)\n        throw(ArgumentError(\"Sequence must only contain ACGT\"))\n    end\n\n    ## change line to assign `seqlength` to the length of `sequence` instead of `1`\n    ## If you're stuck, search for \"length of string julia\"\n    seqlength = 1\n\n    ## count the number of G's\n    gs = count(==('G'), sequence)\n    ## count the number of C's\n    cs = count(==('C'), sequence)\n\n    return gs + cs / seqlength # something is wrong with this line...\nend\n\n# ## Question 4\n# \n# This process should be familiar by now.\n#\n# Hint: you do not need to re-write code if you already have it.\n# That is, don't copy any code from `question3`,\n# just call it!\n\n\"\"\"\n    question4(sequence)\n\nCalculates the GC content of a DNA sequence\nand prints it to the screen.\n\nExample\n\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\n\n    julia> question4(\"AATC\")\n    Sequence: \n    AATC\n    GC Content:\n    0.25\n\n    julia> question4(\"CCCGG\")\n    Sequence:\n    CCCGG\n    GC Content:\n    1.0\n\"\"\"\nfunction question4(sequence)\n    ## Your code here\nend\n\n", "meta": {"hexsha": "bbdbb7a07e5dfd252fd3ea3b72c808959787f896", "size": 4960, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/literate/assignment_scripts/assignment02_code.jl", "max_stars_repo_name": "vanjakle/BISC195.jl", "max_stars_repo_head_hexsha": "348ebd63742b948dc3bafc86f92dd09ea8169d44", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/literate/assignment_scripts/assignment02_code.jl", "max_issues_repo_name": "vanjakle/BISC195.jl", "max_issues_repo_head_hexsha": "348ebd63742b948dc3bafc86f92dd09ea8169d44", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "docs/literate/assignment_scripts/assignment02_code.jl", "max_forks_repo_name": "vanjakle/BISC195.jl", "max_forks_repo_head_hexsha": "348ebd63742b948dc3bafc86f92dd09ea8169d44", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.9565217391, "max_line_length": 99, "alphanum_fraction": 0.69375, "num_tokens": 1385, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.26588047309981694, "lm_q2_score": 0.3738758297482025, "lm_q1q2_score": 0.09940628249403868}}
{"text": "################################################################################\n###  Introduction to Scientific Programming and Machine Learning with Julia  ###\n###                                                                          ###\n### Run each script on a new clean Julia session                             ###\n### GitHub: https://github.com/sylvaticus/IntroSPMLJuliaCourse               ###\n### Licence (apply to all material of the course: scripts, videos, quizes,..)###\n### Creative Commons By Attribution (CC BY 4.0), Antonello Lobianco          ###\n################################################################################\n\n# # 0106 Further Topics\n\n\n# ## Some stuff to set-up the environment..\n\ncd(@__DIR__)         \nusing Pkg             \nPkg.activate(\".\")     \n## If using a Julia version different than 1.7 please uncomment and run the following line (reproductibility guarantee will however be lost)\n## Pkg.resolve()   \n## Pkg.instantiate() # run this if you didn't in Segment 01.01\nusing Random\nRandom.seed!(123)\nusing InteractiveUtils # loaded automatically when working... interactively\n\n# ## Metaprogramming and macros\n\n# \"running\" some code include the following passages (roughly):\n# - parsing of the text defining the code and its translation in hierarchical expressions to the Abstract syntax Tree (AST) (syntax errors are caugth at this time)\n# - on the first instance required (\"just in time\") compilation of the AST expressions into object code (using the LLVM compiler)\n# - execution of the compiled object code\n\n# \"Macros\" in many other language (e.g. C or C++) refer to the possibility to \"pre-process\" the textual representation of the code statements before it is parsed. In julia instead it refers to the possibility to alter the expression once has already being parsed in the AST, allowing a greater expressivity as we are no longer limited by the parsing syntax\n\n# The AST is organised in a hierarchical tree of _expressions_ where each element (including the operators) is a _symbol_\n# For variables, you can use symbols to refer to the actual identifiers instad to the variable's value\n\n# Expressions themselves are objects representing unevaluated computer expressions\n\n# ### Expressions and symbols\n\nexpr1 = Meta.parse(\"a = b + 2\") # What the parser do when reading the source code. b doesn't need to actually been defined, it's just a namebinding without the reference to any object, not even `nothing`\ntypeof(expr1) # expressions are first class objects\nexpr2 = :(a = b + 1)\nexpr3 = quote a = b + 1 end\nexpr3\ndump(expr1)   # The AST ! Note this is already a nested statement, an assignment of the result of an expression (the sum call between the symbol `:b` and 1) to the symbol `a`  \nexpr4 = Expr(:(=),:a,Expr(:call,:+,:b,1)) # The AST using the \"Expr\" constructor\n\nsymbol1   = :(a)               # as for expressions\nsymbol2   = Meta.parse(\"a\")    # as for expressions\nsymbol3   = Symbol(\"a\")        # specific for symbols only\nothsymbol = Symbol(\"aaa\",10,\"bbb\")\ntypeof(symbol1)\n\n# I can access any parts of my expression before evaluating it (indeed, that's what macro will do...)\nmyASymbol = expr1.args[2].args[1]\nexpr1.args[2].args[1] = :(*)\nb = 2\n## a # error, a not defined\neval(expr1)\na # here now is defined and it has an object associated... 4!\n\n# !!! danger\n#     The capability to evaluate expressions is very powerfull but due to obvious secutiry implications never evaluate expressions you aren't sure of their provenience. For example if you develop a Julia web app (e.g. using [Genie.jl](https://github.com/GenieFramework/Genie.jl)) never evaluate user provided expressions.\n\n# Note that evaluation of expressions happens always at global scope, even if it done inside a function:\nfunction foo()\n    locVar = 1\n    expr = :(locVar + 1)\n    return eval(expr)\nend\n## a = foo() # error locVar not defined \n\n# To refer to the _value_ of a variable rather than the identifier itself within an expression, interpolate the variable using the dollar sign:\n\nexpr = :($a + b) # here the identifier 'a' has been replaced with its numerical value, `4`\ndump(expr)\neval(expr)\na = 10\neval(expr) # no changes\nb = 100\neval(expr) # here it change, as it is at eval time that the identifier `b` is \"replaced\" with its value\n\n# ### Macros\n\n# One of the best usage of macros is they allow package developers to provide a very flexible API to their package, suited for the specific needs of the package, making life easier for the users. Compare for example [the API of JuMP](https://jump.dev/JuMP.jl/stable/manual/constraints/) with [those of Pyomo](https://pyomo.readthedocs.io/en/stable/pyomo_modeling_components/Constraints.html) to define model constraints !\n\n# Some examples of macros:\n# [MultiDimEquations.jl](https://github.com/sylvaticus/MultiDimEquations.jl)\n# - from: `@meq par1[d1 in DIM1, d2 in DIM2, dfix3] =  par2[d1,d2]+par3[d1,d2]`\n# - to:   `[par1[d1,d2,dfix3] =  par2[d1,d2]+par3[d1,d2] for d1 in DIM1, d2 in DIM2]`\n# [Pipe.jl:](https://github.com/oxinabox/Pipe.jl)\n# - from: `@pipe  10 |> foo(_,a) |> foo2(b,_,c) |> foo3(_)`\n# - to:   `foo3(foo2(b,foo(10,a),c))`\n# Brodcasting (Base):\n# - from: `@. a + b * D^2`\n# - to:   `a .+ b .* D.^2`\n\n# Defining a macro...\n\n# Like functions, but both the arguments and the returned output are expressions\nmacro customLoop(controlExpr,workExpr) \n    return quote\n      for i in $controlExpr\n        $workExpr\n      end\n    end\nend\n\n# Invoking a macro....\na = 5\n@customLoop 1:4 println(i) #note that \"i\" is in the macro\n@customLoop 1:a println(i)\n@customLoop 1:a if i > 3 println(i) end\n@customLoop [\"apple\", \"orange\", \"banana\"]  println(i)\n@customLoop [\"apple\", \"orange\", \"banana\"]  begin print(\"i: \"); println(i)  end\n@macroexpand @customLoop 1:4 println(i) # print what the macro does with the specific expressions provided\n\n\n# String macros (aka \"non-standard string literals\")\n# Invoked with the syntax `xxx\" ...text...\"` or `xxx\"\"\" ...multi-line text...\"\"\"` where `xxx` is the name of the macro and the macro must be defined as `macro  xxx_str`.\n# Used to perform textual modification o nthe given text, for example this print the given text on a 8 characters  \n\nmacro print8_str(mystr)                                 # input here is a string, not an expression\n    limits = collect(1:8:length(mystr))\n    for (i,j) in enumerate(limits)\n      st = j\n      en = i==length(limits) ? length(mystr) : j+7\n      println(mystr[st:en])\n    end\nend\n\nprint8\"123456789012345678\"\nprint8\"\"\"This is a text that once printed in 8 columns with terminal will be several lines. Ok, no rammar rules relating to carriage returns are emploied here...\"\"\"\n\n# While normally used to modify text, string macros are \"true\" macros:\n\nmacro customLoop_str(str) \n    exprs = Meta.parse(str)\n    controlExpr,workExpr = exprs.args[1],exprs.args[2]\n    return quote\n      for i in $controlExpr\n        $workExpr\n      end\n    end\nend\n\ncustomLoop\"\"\"1:4; println(i)\"\"\"\n\n# ## Interfacing with other languages\n\n# There are 3 ways to interface Julia with programs or libraries wrote in other languages.\n# At the lowest level, Julia allows to directly interface with C or Fortran libraries, and this means, aside using directly libraries written in C, to be able to interface with any programming language that offer also a C interface (R, Python...)\n# Using this low level C Interface, users have created specific packages to interface many languages using a simple, Julian-way syntax. We will see these interfaces for R and Python.\n# Finally, at the highest level, many common packages of other languages have been already \"interfaced\", so that the user can use the Julia Package without even knowing that this is an interface for an other package, for example `SymPy.jl` is a large interface to the Python package `SymPy`.\n\n# ### Using C libraries\n\n# Let's start by seing how to use a C library. For this example to work you will need to have the GCC compiler installed on your machine\n# First let's write the header and source C files and write them to the disk:\n\ncheader = \"\"\"\nextern int get5();\nextern double mySum(float x, float y);\n\"\"\"\ncsource = \"\"\"\nint get5(){\n    return 5;\n}\n\ndouble mySum(float x, float y){\n    return x+y;\n}\n\"\"\"\n\nopen(f->write(f,cheader),\"myclib.h\",\"w\")  # We open a stream to file with the \"w\" parameter as for \"writing\", and we pass the stream to the anonymous function to actually write to the stream. If this funcitons is many lines of code, consider rewriting the `open` statement using a `do` block\nopen(f->write(f,csource),\"myclib.c\",\"w\") \n\n# Now let's run the command to compile the C code we saved as shared library using gcc, a C compiler.\n# The following example assume that GCC is installed in the machine where this example is run and available as `gcc`.\n\ncompilationCommand1 = `gcc -o myclib.o -c myclib.c` # the actual compilation, note the backticks used to define a command\ncompilationCommand2 = `gcc -shared -o libmyclib.so myclib.o -lm -fPIC` # the linking into a shared library\nrun(compilationCommand1)\nrun(compilationCommand2)\n\n# This should have created the C library `libmyclib.so` on disk. Let's gonna use it:\nconst myclib = joinpath(@__DIR__, \"libmyclib.so\")  # we need the full path\n# ccall arguments:\n# 1. A tuple with the funcion name to call and the library path. For both, if embedded in a variable, the variable must be set constant.  \n# 2. The Julia type that map to the C type returned by the function.\n#    - `int` \u2192 `Int32` or `Int64` (or the easy-to remmeber `Cint` alias)\n#    - `float` \u2192 `Float32` (or the `Cfloat` alias)\n#    - `double` \u2192 `Float64` (or the `Cdouble` alias)\n# 3. A tuple with the Julia types of the parameters passed to the C function\n# 4. Any other argument are the values of the parameter passed\na = ccall((:get5,myclib), Int32, ())       \nb = ccall((:mySum,myclib), Float64, (Float32,Float32), 2.5, 1.5)\n\n# More details on calling C or Fortran code can be obtained [in the official Julia documentation](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/).  \n\n# ### Using Python in Julia \n\n# The \"default\" way to use Python code in Julia is trough the [PyCall.jl](https://github.com/JuliaPy/PyCall.jl) package. It automatically take care of convert between Python types (including numpy arrays) and Julia types (types that can not be converted automatically are converted to the generic `PyObject` type).\nENV[\"PYTHON\"] = \"\" # will force PyCall to download and use a \"private to Julia\" (conda based) version of Python. use \"/path/to/python\" if you want to reuse a version already installed on your system\n## using Pkg\n## Pkg.add(\"PyCall\")\n## Pkg.build(\"PyCall\")\nusing PyCall\n\n# #### Embed short python snippets in Julia\npy\"\"\"\ndef sumMyArgs (i, j):\n  return i+j\ndef getNthElement (vec,n):\n  return vec[n]\n\"\"\"\na = py\"sumMyArgs\"(3,4)             # 7 - here we call the Python object (a function) with Julia parameters\nb = py\"getNthElement\"([1,2,3],1)   # 2 - attention to the diffferent convention for starting arrays!. Note the Julia Array ahas been converted automatically to a Python list\nd = py\"getNthElement([1,$a,3],1)\"  # 7 - here we interpolate the Python call\n\n# Alternativly, use `@pyinclude(\"pythonScript.py\")`\npythonCode = \"\"\"\ndef sumMyArgs (i, j, z):\n  return i+j+z\n\"\"\"\nopen(f->write(f,pythonCode),\"pythonScript.py\",\"w\")\n@pyinclude(\"pythonScript.py\")\na = py\"sumMyArgs\"(3,4,5)\n\n# !!! tip\n#     Note thaat the 3 arguments definition of `sumMyArgs` has _replaced_ the 3-arguments one. This would now error `py\"sumMyArgs\"(3,4)` \n\n\n# #### Use Python libraries\n\n# Add a package to the local Python installation using Conda:\npyimport_conda(\"ezodf\", \"ezodf\", \"conda-forge\") # pyimport_conda(module, package, channel)\n\nconst ez = pyimport(\"ezodf\")  # Equiv. of Python `import ezodf as ez`\ndestDoc  = ez.newdoc(doctype=\"ods\", filename=\"anOdsSheet.ods\")\n# Both `ez` and `destDoc` are `PyObjects` for which we can access attributes and call the methods using the usual `obj.method()` syntax as we would do in Python\nsheet    = ez.Sheet(\"Sheet1\", size=(10, 10))\ndestDoc.sheets.append(sheet)\n## dcell1 = sheet[(2,3)] # This would error because the index is a tuple. Let's use directly the `get(obj,key)` function instead:\ndcell1   = get(sheet,(2,3)) # Equiv. of Python `dcell1 = sheet[(2,3)]`. Attention again to Python indexing from zero: this is cell \"D3\", not \"B3\" !\ndcell1.set_value(\"Hello\")\nget(sheet,\"A9\").set_value(10.5) # Equiv. of Python `sheet['A9'].set_value(10.5)`\ndestDoc.backup = false\ndestDoc.save()\n\n# ### Using Julia in Python\n\n# #### Installation of the Python package `PyJulia`\n\n# [PyJulia](https://github.com/JuliaPy/pyjulia) can be installed using `pip`, taking note that its name using `pip` is `julia` not `PyJulia`:\n# ```$ python3 -m pip install --user julia```\n \n# We can now open a Python terminal and initialise PyJulia to work with our Julia version:\n# ```python\n# >>> import julia\n# >>> julia.install() # Only once to set-up in julia the julia packages required by PyJulia\n# ```\n \n# If we have multiple Julia versions, we can specify the one to use in Python passing julia=\"/path/to/julia/binary/executable\" (e.g. julia = \"/home/myUser/lib/julia-1.1.0/bin/julia\") to the install() function.\n\n# #### Running Julia libraries and code in Python\n\n# On each Python session we need to run the following code: \n# ```python\n# from julia import Julia\n# Julia(compiled_modules=False)\n# ```\n\n# This is a workaround to the common situation when the Python interpreter is statically linked to libpython, but it will slow down the interactive experience, as it will disable Julia packages pre-compilation, and every time we will use a module for the first time, this will need to be compiled first. Other, more efficient but also more complicate, workarounds are given in the package documentation, under the [Troubleshooting section](https://pyjulia.readthedocs.io/en/stable/troubleshooting.html).\n\n# We can now direcltly load a Julia module, including `Main`, the global namespace of Julia\u2019s interpreter, with `from julia import ModuleToLoad` and access the module objects directly or using the `Module.evel()` interface.\n\n# ##### Add a Julia package...\n# ```python\n# >>> from julia import Pkg\n# >>> Pkg.add(\"BetaML\")\n# ```\n# Of course we can add a package alternatively from within Julia\n\n# ##### \"Direct\" calling of Julia functions...\n# ```python\n# >>> from julia import BetaML\n# >>> import numpy as np\n# >>> model = BetaML.buildForest([[1,10],[2,12],[12,1]],[\"a\",\"a\",\"b\"])\n# >>> predictions = BetaML.predict(model,np.array([[2,9],[13,0]]))\n# >>> predictions\n# [{'b': 0.36666666666666664, 'a': 0.6333333333333333}, {'b': 0.7333333333333333, 'a': 0.26666666666666666}]\n# ```\n\n# ##### Access using the `eval()` interface...\n# If we are using the jl.eval() interface, the objects we use must be already known to julia. To pass objects from Python to Julia, we can import the julia Main module (the root module in julia) and assign the needed variables, e.g.\n\n# ```python\n# >>> X_python = [1,2,3,2,4]\n# >>> from julia import Main\n# >>> Main.X_julia = X_python\n# >>> Main.eval('BetaML.gini(X_julia)')\n# 0.7199999999999999\n# >>> Main.eval(\"\"\"\n# ...   function makeProd(x,y)\n# ...       return x*y\n# ...   end\n# ...   \"\"\"\n# ... )\n# >>> Main.eval(\"makeProd(2,3)\") # or Main.makeProd(2,3)\n# ```\n# For large scripts instead of using `eval()` we can equivalently use `Main.include(\"aJuliaScript.jl\")`\n\n# ### Using R in Julia \n\n# To use R from within Julia we use the [RCall](https://github.com/JuliaInterop/RCall.jl) package. \nENV[\"R_HOME\"] = \"*\" #  # will force RCall to download and use a \"private to Julia\" (conda based) version of R. use \"/path/to/R/directory\" (e.g. `/usr/lib/R`) if you want to reuse a version already installed on your system\n## using Pkg\n## Pkg.add(\"RCall\")\n## Pkg.build(\"RCall\")\nusing RCall\n\n\nR\"\"\"\nsumMyArgs <- function(i,j) i+j\ngetNthElement <- function(vec,n) {\n  return(vec[n])\n}\n\"\"\"\na = rcopy(R\"sumMyArgs\"(3,4))             # 7 - here we call the R object (a function) with Julia parameters\nb = rcopy(R\"getNthElement\"([1,2,3],1))   # 1 - no differences in array indexing here\nd = rcopy(R\"as.integer(getNthElement(c(1,$a,3),2))\")  # 7 - here we interpolate the R call\nd = convert(Int64,R\"getNthElement(c(1,$a,3),2)\")  \n\n# While we don't have here the problem of different array indexing convention (both Julia and R start indexing arrays at 1), we have the \"problem\" that the output returned by using `R\"...\"` is not yet an exploitable Julia object but it remains as an `RObject` that we can convert with `rcopy()` or explicitly with `convert(T,obj)`. Also, R elements are all floats by default, so if we need an integer in Julia we need to explicitly convert it, either in R or in Julia.\n\n# If the R code is on a script, we don't have here a sort of @Rinclude macro, so let's implement it ourselves by loading the file content as a file and evaluating it using the function `reval` provided by `RCall`:\n\nmacro Rinclude(fname)\n    quote\n        rCodeString = read($fname,String)\n        reval(rCodeString)\n        nothing\n    end\nend\n\nRCode = \"\"\"\nsumMyArgs <- function(i, j, z) i+j+z\n\"\"\"\nopen(f->write(f,RCode),\"RScript.R\",\"w\")\n@Rinclude(\"RScript.R\")\na = rcopy(R\"sumMyArgs\"(3,4,5))  # 12\n## a = rcopy(R\"sumMyArgs\"(3,4)) # error !  The 3-arguments version of `sumMyArgs` has _replaced_ the 2-arguments one\n\n# ### Using Julia in R\n\n# #### Installation of the R package `JuliaCall`\n\n# [JuliaCall](https://github.com/Non-Contradiction/JuliaCall) can be installed from CRAN:\n\n# ```{r}\n# > install.packages(\"JuliaCall\")\n# > library(JuliaCall)\n# install_julia()\n# ```\n\n# `install_julia()` will force the download of R and install a private copy of julia. If you prefer to use instead an existing version of julia and having R default to download a private version only if it can't find a version already installed, use `julia_setup(installJulia = TRUE)` instead of `install_julia()`, eventually passing the `JULIA_HOME = \"/path/to/julia/binary/executable/directory\"` (e.g. `JULIA_HOME = \"/home/myUser/lib/julia-1.7.0/bin\"`) parameter to the `julia_setup` call.\n\n# `JuliaCall` depends for some things (like object conversion between Julia and R) from the Julia `RCall` package. If we don't already have it installed in Julia, it will try to install it automatically.\n\n# #### Running Julia libraries and code in R\n\n# On each R session we need to run the `julia_setup` function:\n# ```R\n# library(JuliaCall)\n# julia_setup() # If we have already downloaded a private version of Julia for R it will be retrieved automatically\n# ```\n\n# We can now load a Julia module and access the module objects directly or using the `Module.evel()` interface.\n\n# ##### Add a Julia package...\n\n# ```{r}\n# > julia_eval('using Pkg; Pkg.add(\"BetaML\")')\n# ```\n# Of course we can add a package alternatively from within Julia\n\n\n# Let's load some data from R and do some work with this data in Julia:\n\n# ```{r}\n# > library(datasets)\n# > X <- as.matrix(sapply(iris[,1:4], as.numeric))\n# > y <- sapply(iris[,5], as.integer)\n# ```\n\n# ##### Calling of Julia functions with `julia_call`...\n\n# With `JuliaCall`, differently than `PyJulia`, we can't call direclty the julia functions but we need to employ the R function `julia_call(\"juliaFunction\",args)`:\n\n# ```{r}\n# > julia_eval(\"using BetaML\")\n# > yencoded <- julia_call(\"integerEncoder\",y)\n# > ids      <- julia_call(\"shuffle\",1:length(y))\n# > Xs       <- X[ids,]\n# > ys       <- yencoded[ids]\n# > cOut     <- julia_call(\"kmeans\",Xs,3L)    # kmeans expects K to be an integer\n# > y_hat    <- sapply(cOut[1],as.integer)[,] # We need a vector, not a matrix\n# > acc      <- julia_call(\"accuracy\",y_hat,ys)\n# > acc\n# [1] 0.8933333\n# ```\n# ##### Access using the `eval()` interface...\n\n# As alternative, we can embed Julia code directly in R using the `julia_eval()` function:\n\n# ```{r}\n# > kMeansR  <- julia_eval('\n# +      function accFromKmeans(x,k,y_true)\n# +        cOut = kmeans(x,Int(k))\n# +        acc = accuracy(cOut[1],y_true)\n# +        return acc\n# +      end\n# + ')\n# ```\n\n# We can then call the above function in R in one of the following three ways:\n# 1. `kMeansR(Xs,3,ys)`\n# 2. `julia_assign(\"Xs_julia\", Xs); julia_assign(\"ys_julia\", ys); julia_eval(\"accFromKmeans(Xs_julia,3,ys_julia)\")`\n# 3. `julia_call(\"accFromKmeans\",Xs,3,ys)`.\n\n# While other \"convenience\" functions are provided by the package, using  `julia_call` or `julia_assign` followed by `julia_eval` should suffix to accomplish most of the task we may need in Julia.\n\n\n# ## Some performance tips\n\n# ### Type stability\n\n# \"Type stable\" functions guarantee to the compiler that given a certain method (i.e. with the arguments being of a given type) the object returned by the function is also of a certain fixed type. Type stability is fundamental to allow type inference continue across the function call stack.\n\nfunction f1(x)    # Type unstable\n    outVector = [1,2.0,\"2\"]\n    if x < 0\n        return outVector[1]\n    elseif x == 0\n        return outVector[2]\n    else\n        return outVector[3]\n    end\nend\n\nfunction f2(x)   # Type stable\n    outVector = [1,convert(Int64,2.0),parse(Int64,\"2\")]\n    if x < 0\n        return outVector[1]\n    elseif x == 0\n        return outVector[2]\n    else\n        return outVector[3]\n    end\nend\n\na = f1(0)\nb = f1(1)\ntypeof(a)\ntypeof(b)\n\nc = f2(0)\nd = f2(1)\ntypeof(c)\ntypeof(d)\n\nusing BenchmarkTools\n@btime f1(0) # 661 ns 6 allocations\n@btime f2(0) #  55 ns 1 allocations\n\n@code_warntype f1(0) # Body::Any \n@code_warntype f2(0) # Body::Int64 \n\n# While in general is NOT important to annotate function parameters for performance, it is important to annotate struct fields with concrete types \nabstract type Goo end\nstruct Foo <: Goo\n    x::Number\nend\nstruct Boo <: Goo\n    x::Int64\nend\n\nfunction f1(o::Goo)\n    return o.x +2\nend\n\nfobj = Foo(1)\nbobj = Boo(1)\n@btime f1($fobj) # 17.1 ns 0 allocations\n@btime f1($bobj) #  2.8 ns 0 allocations\n\n# Here the same function under some argument types is type stable, under other argument types is not\n@code_warntype f1(fobj)\n@code_warntype f1(bobj) \n\n# #### Avoid (non-constant) global variables\n\ng        = 2\nconst cg = 1   # we can't change the _type_ of the object binded to a constant variable \ncg       = 2   # we can rebind to an other object of the same type, but we get a warning\n## cg    = 2.5 # this would error !\nf1(x,y) = x+y\nf2(x)   = x + g\nf3(x)   = x + cg\n\n@btime f1(3,2)  \n@btime f2(3)    # 22 times slower !!!\n@btime f3(3)    # as f1\n\n# #### Loop arrays with the inner loop by rows\n\n# Julia is column mayor (differently than Python) so arrays of bits types are contiguous in memory across the different rows of the same column\n\na = rand(1000,1000);\nfunction f1(x)\n    (R,C) = size(x)\n    cum = 0.0\n    for r in 1:R\n        for c in 1:C\n            cum += x[r,c]\n        end\n    end\n    return cum\nend\nfunction f2(x)\n    (R,C) = size(x)\n    cum = 0.0\n    for c in 1:C\n        for r in 1:R\n            cum += x[r,c]\n        end\n    end\n    return cum\nend\n@btime f1($a) # 2.3 ms 0 allocations\n@btime f2($a) # 1.3 ms 0 allocations\n\n# #### Use low-level optimisation when possible\n\nfunction f1(x)\n    s = 0.0\n    for i in 1:length(x)\n        s += i * x[i] \n    end\n    return s\nend\n\nfunction f2(x)\n    s = 0.0\n    for i in 1:length(x)\n        @inbounds s += i * x[i] # remove bound checks\n    end\n    return s\nend\n\nfunction f3(x)\n    s = 0.0\n    @simd for i in 1:length(x) # tell compiler it is allowed to run the loop in whatever order, allowing in-thread paralllelism of modern CPUs\n        s += i * x[i] \n    end\n    return s\nend\n\nx = rand(10000);\n@btime f1($x)\n@btime f2($x)\n@btime f3($x)\n\nX = rand(100,20);\nfunction f1(x)\n    s = 0.0\n    for i in 1:size(x,1)\n        s += sum(x[i,:])\n    end\n    return s\nend\nfunction f2(x)\n    s = 0.0\n    @views for i in 1:size(x,1)\n        s += sum(x[i,:])   # the slice operator copy the data.. the views macro force to have instead to have a view (reference) \n    end\n    return s\nend\n\n@btime f1($X)\n@btime f2($X)\n\n# !!! warning\n#     Attention that while the `@views` macro \"save time\" by not copying the data, the resulting array has a pretty messy layout. If you need to use it for many subsequent operations it may be more efficient to \"pay\" the copy cost once and then have an array with a nicelly continuous block of memory..\n\n\nfunction f1(x,y)\n    if x+y > 100\n        return x + y + 2\n    else\n        return x + 1\n    end\nend\n\n@inline function f2(x,y)   # the function is \"inlined\", its whole definition copied at each calling place rather than being called\n    if x+y > 100\n        return x + y + 2\n    else\n        return x + 1\n    end\nend\n\nfunction f3(y)\n    s = 0.0\n    for i in 2:y\n       s += f1(i,i-1)\n       s += f1(i,i)\n    end\n    return s\n end\n \nfunction f4(y)\n    s = 0.0\n    for i in 2:y\n       s += f2(i,i-1)\n       s += f2(i,i)\n    end\n    return s\nend\n \nx = 1000\n@btime f3($x)\n@btime f4($x)\n\n# But attention! Not always a good idea:\nfunction f3(y)\n    s = 0.0\n    for i in 2:y\n       s += sum(f1(a,a-1) for a in 2:i)\n    end\n    return s\n end\n \nfunction f4(y)\n    s = 0.0\n    for i in 2:y\n       s += sum(f2(a,a-1) for a in 2:i)\n    end\n    return s\nend\n\nx = 1000\n@btime f3($x)\n@btime f4($x)\n\n# Note that the Julia compiles already inline small functions automatically when it thinks it will improve performances\n\n# ## Profiling the code to discover bootlenecks\n\n# We already see `@btime` and `@benchmark` from the package [BenchmarkTools.jl](https://github.com/JuliaCI/BenchmarkTools.jl)\n# Remember to quote the global variables used as parameter of your function with the dollar sign to have accurate benchmarking of the function execution.\n# Julia provide the macro `@time` but we should run on a second call to a given function (with a certain parameter types) or it will include compilation time in its output:\n\nfunction fb(x)\n    out = Union{Int64,Float64}[1,2.0,3]\n    push!(out,4)\n    if x > 10\n        if ( x > 100)\n            return [out[1],out[2]] |>  sum\n        else\n            return [out[2],out[3]] |>  sum\n        end\n    else\n        return [out[1],out[3]] |>  sum\n    end\nend\n\n\n@time fb(3)\n@time fb(3)\n\n# We can use `@profile function(x,y)` to use a sample-based profiling\n\nusing Profile # in the stdlib\nfunction foo(n)\n    a = rand(n,n)\n    b = a + a\n    c = b * b\n    return c\nend\n@profile (for i = 1:100; foo(1000); end) # too fast otherwise\nProfile.print() # on my pc: 243 rand, 174 the sum, 439 the matrix product\nProfile.clear()\n\n# ## Introspection and debugging\n\n# To discover problems on the code more in general we can use several introspection functions that Julia provide us (some of which we have already saw):\n\n## @less rand(3)  # Show the source code of the specific method invoked - use `q` to quit\n## @edit rand(3)  # Like @loss but it opens the source code in an editor\nmethods(foo)\n@which foo(2)          # which method am I using when I call foo with an integer?\ntypeof(a)\neltype(a)\nfieldnames(Foo)\ndump(fobj)\nnames(Main, all=false) # available (e.g. exported) identifiers of a given module\nsizeof(2)              # bytes\ntypemin(Int64)\ntypemax(Int64)\nbitstring(2)\n# Various low-level interpretation of an expression\n@code_native foo(3)\n@code_llvm foo(3)\n@code_typed foo(3)\n@code_lowered foo(3)\n\n# We can use a debugger, like e.g. the one integrated in Juno or VSCode.\n# Graphical debuggers allow to put a _breakpoint_ on some specific line of code, run the code in debug mode (yes, it will be slower), let the program arrive to the breakpoint and inspect the state of the system at that point of the code, including local variables. In Julia we can also _change_ the program interactively !\n# Other typycal functions are running a single line, running inside a function, running until the current function return, ecc..\n\n# ## Runtime exceptions\n\n# As many (all?) languages, Julia when \"finds\" an error issues an exception, that if it is not caugth at higher level in the call stack (i.e. recognised and handled) lead to an error and return to the prompt or termination of the script (and rarely with the Julia process crashing altogether).\n\n# The idea is that we _try_ some potentially dangerous code and if some error is raised in this code we _catch_ it and handle it.\n\nfunction customIndex(vect,idx;toReturn=0)\n    try\n        vect[idx]\n    catch e  \n        if isa(e,BoundsError)\n            return toReturn\n        end\n        rethrow(e)\n    end\nend\n\na = [1,2,3]\n## a[4] # Error (\"BoundsError\" to be precise)\ncustomIndex(a,4)\n\n# Note that handling exceptions is computationally expensive, so do not use exceptions in place of conditional statements\n\n# ## [Parallel computation](@id parallel_computation)\n\n# Finally one note on parallel computation. We see only some basic usage of multithreading and multiprocesses in this course, but with Julia it is relativelly easy to parallelise the code either using multiple threads or multiple processes. What's the difference ?\n# - **multithread**\n#   - advantages: computationally \"cheap\" to create (the memory is shared)\n#   - disadvantages: limited to the number of cores within a CPU, require attention in not overwriting the same memory or doing it at the intended order (\"data race\"), we can't add threads dynamically (within a script) \n# - **multiprocesses**\n#   - advantages: unlimited number, can be run in different CPUs of the same machine or differnet nodes of a cluster, even using SSH on different networks, we can add processes from within our code with `addprocs(nToAdd)`\n#   - disadvantages: the memory being copied (each process will have its own memory) are computationally expensive (you need to have a gain higher than the cost on setting a new process) and require attention to select which memory a given process will need to \"bring with it\" for its functionality\n\n# Note that if you are reading this document on the github pages, this script is compiled using GitHub actions where a single thread and process are available, so you will not see performance gains.\n\n# ### Multithreading\n\n# !!! warning\n#     It is not possible to add threads dinamically, either we have to start Julia with the parameter `-t` (e.g. `-t 8` or `-t auto`) in the command line or use the VSCode Julia externsion setting `Julia: Num Threads`\n\nfunction inner(x)\n    s = 0.0\n    for i in 1:x\n        for j in 1:i\n            if j%2 == 0\n                s += j\n            else\n                s -= j\n            end\n        end\n    end\n    return s\nend\n\nfunction parentSingleThread(x,y)\n    toTest = x .+ (1:y)\n    out = zeros(length(toTest))\n    for i in 1:length(toTest)\n        out[i] = inner(toTest[i])\n    end\n    return out\nend\nfunction parentThreaded(x,y)\n    toTest = x .+ (1:y)\n    out = zeros(length(toTest))\n    Threads.@threads for i in 1:length(toTest)\n        out[i] = inner(toTest[i])\n    end\n    return out\nend\n\n\nx = 100\ny = 20\n\nstr = parentSingleThread(x,y)\nmtr = parentThreaded(x,y)   \n\nstr == mtr # true\nThreads.nthreads() # 4 in my case \nThreads.threadid()\n@btime parentSingleThread(100,20) # 140 \u03bcs on my machine\n@btime parentThreaded(100,20)     #  47 \u03bcs \n\n# ### Multiprocessing\n\n# _**NOTE**_\n# _The code on multiprocessing is commented out (not executed, so you don't see the output) as GitHub actions (that are used to run this code and render the web pages you are reading) have problems running multi-process functions:_\n\n# ```julia\n# using Distributed     # from the Standard Library\n# addprocs(3)           # 2,3,4\n# ```\n# The first process is considered a sort of \"master\" process, the other one are the \"workers\"\n# We can add processes on other machines by providing the SSH connection details directly in the `addprocs()` call (Julia must be installed on that machines as well)\n# We can alternativly start Julia directly with _n_ worker processes using the armument `-p n` in the command line.\n\n# ```julia\n# println(\"Worker pids: \")\n# for pid in workers()  # return a vector to the pids\n#     println(pid)      # 2,3,4\n# end\n# rmprocs(workers()[1])    #  remove process pid 2\n# println(\"Worker pids: \")\n# for pid in workers() \n#     println(pid) # 3,4 are left\n# end\n# @everywhere begin using Distributed end # this is needed only in GitHub action\n# @everywhere println(myid()) # 4,3\n# ```\n\n\n# #### Run heavy tasks in parallel\n\n# ```julia\n# using Distributed, BenchmarkTools\n# a = rand(1:35,100)\n# @everywhere function fib(n)\n#     if n == 0 return 0 end\n#     if n == 1 return 1 end\n#     return fib(n-1) + fib(n-2)\n# end\n# ```\n\n# The macro `@everywhere` make available the given function (or functions with `@everywhere begin [shared function definitions] end` or `@everywhere include(\"sharedCode.jl\")`) to all the current workers.\n\n## result  = map(fib,a)\n\n# The pmap function (\"parallel\" map) automatically pick up the free processes, assign them the job prom the \"input\" array and merge the results in the returned array. Note that the order is preserved:\n\n# ```julia\n# result2 = pmap(fib,a)\n# result == result2\n# @btime map(fib,$a)  # serialised:   median time: 514 ms    1 allocations\n# @btime pmap(fib,$a) # parallelised: median time: 265 ms 4220 allocations # the memory of `a` need to be copied to all processes\n# ```\n\n# #### Divide and Conquer\n\n# Rather than having a \"heavy operation\" and being interested in the individual results, here we have a \"light\" operation and we want to aggregate the results of the various computations using some aggreagation function.\n# We can then use `@distributed (aggregationfunction) for [forConditions]` macro:\n\n# ```julia\n# using Distributed, BenchmarkTools\n# function f(n)   # our single-process benchmark\n#   s = 0.0\n#   for i = 1:n\n#     s += i/2\n#   end\n#     return s\n# end\n# function pf(n)\n#   s = @distributed (+) for i = 1:n # aggregate using sum on variable s\n#         i/2                        # the last element of the for cycle is used by the aggregator\n#   end\n#   return s\n# end\n# @btime  f(10000000) # median time: 11.1 ms   0 allocations\n# @btime pf(10000000) # median time:  5.7 ms 145 allocations\n# ```\n\n# Note that also in this case the improvement is less than proportional with the number of processes we add\n\n# Details on parallel comutation can be found [on the official documentation](https://docs.julialang.org/en/v1/manual/parallel-computing/), including information to run nativelly Julia on GPUs or TPUs.", "meta": {"hexsha": "8ffa40b780091af75569ff7b81cd49244ffd9d7e", "size": 34255, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0106_-_Further_topics.jl", "max_stars_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_stars_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2022-02-26T15:44:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T13:05:52.000Z", "max_issues_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0106_-_Further_topics.jl", "max_issues_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_issues_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0106_-_Further_topics.jl", "max_forks_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_forks_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-26T08:06:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T08:06:40.000Z", "avg_line_length": 38.618940248, "max_line_length": 503, "alphanum_fraction": 0.6832579186, "num_tokens": 9596, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "### A Pluto.jl notebook ###\n# v0.17.4\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 bbe90636-9d1f-4ee0-8e35-c285f7b922d0\nbegin\n\timport CSV, DataFrames\n\tusing EvoTrees\n  \tusing MLJ  \n \tusing MLJLinearModels\n\tusing MLJBase\n \tusing MLJMultivariateStatsInterface\n  \tusing UrlDownload\n\tusing PlutoUI\nend\n\n\n# \u2554\u2550\u2561 83a7b4d7-6442-44b2-9388-8330b19cd537\nhtml\"\"\"\n<div style=\"\nposition: absolute;\nwidth: calc(100% - 30px);\nborder: 50vw solid SteelBlue;\nborder-top: 500px solid SteelBlue;\nborder-bottom: none;\nbox-sizing: content-box;\nleft: calc(-50vw + 15px);\ntop: -500px;\nheight: 300px;\npointer-events: none;\n\"></div>\n\n<div style=\"\nheight: 300px;\nwidth: 100%;\nbackground: SteelBlue;\ncolor: #88BBD6;\npadding-top: 68px;\npadding-left: 5px;\n\">\n\n<span style=\"\nfont-family: Vollkorn, serif;\nfont-weight: 700;\nfont-feature-settings: 'lnum', 'pnum';\n\"> \n\n<p style=\"\nfont-family: Alegreya sans;\nfont-size: 1.4rem;\nfont-weight: 300;\nopacity: 1.0;\ncolor: #CDCDCD;\n\">Tutorial - Part 3</p>\n<p style=\"text-align: left; font-size: 2.5rem;\">\nMachine Learning in Julia\n</p>\n\"\"\"\n\n# \u2554\u2550\u2561 24abafc3-0e26-4cd2-9bca-7c69b794f8ce\nPlutoUI.TableOfContents(title = \"MLJ Tutorial - Part 3\")\n\n# \u2554\u2550\u2561 b67c62c2-15ab-4328-b795-033f6f2a0674\nmd\"\"\"\nAn introduction to the\n[MLJ](https://alan-turing-institute.github.io/MLJ.jl/stable/)\ntoolbox.\n\"\"\"\n\n# \u2554\u2550\u2561 bc689638-fd19-4c9f-935f-ddf6a6bfbbdd\nmd\"## Set-up\"\n\n# \u2554\u2550\u2561 197fd00e-9068-46ea-af2a-25235e544a31\nmd\"Inspect Julia version:\"\n\n# \u2554\u2550\u2561 f6d4f8c4-e441-45c4-8af5-148d95ea2900\nVERSION\n\n# \u2554\u2550\u2561 45740c4d-b789-45dc-a6bf-47194d7e8e12\nmd\"The following instantiates a package environment.\"\n\n# \u2554\u2550\u2561 42b0f1e1-16c9-4238-828a-4cc485149963\nmd\"\"\"\nThe package environment has been created using **Julia 1.6** and may not\ninstantiate properly for other Julia versions.\n\"\"\"\n\n# \u2554\u2550\u2561 499cbc31-83ba-4583-ba1f-6363f43ec697\nmd\"## General resources\"\n\n# \u2554\u2550\u2561 db5821e8-956a-4a46-95ea-2955abd45275\nmd\"\"\"\n- [MLJ Cheatsheet](https://alan-turing-institute.github.io/MLJ.jl/dev/mlj_cheatsheet/)\n- [Common MLJ Workflows](https://alan-turing-institute.github.io/MLJ.jl/dev/common_mlj_workflows/)\n- [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/)\n- [Data Science Tutorials in Julia](https://juliaai.github.io/DataScienceTutorials.jl/)\n\"\"\"\n\n# \u2554\u2550\u2561 1e248249-5629-412f-b1b3-dd47e367ba54\nmd\"# Part 3 - Transformers and Pipelines\"\n\n# \u2554\u2550\u2561 d055d24f-1b2d-48a6-8d7e-8e449afd1c48\nmd\"## Transformers\"\n\n# \u2554\u2550\u2561 0dc40881-6163-4948-a949-7f3bd292fe31\nmd\"\"\"\nUnsupervised models, which receive no target `y` during training,\nalways have a `transform` operation. They sometimes also support an\n`inverse_transform` operation, with obvious meaning, and sometimes\nsupport a `predict` operation (see the clustering example discussed\n[here](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Transformers-that-also-predict-1)).\nOtherwise, they are handled much like supervised models.\n\"\"\"\n\n# \u2554\u2550\u2561 0d066909-c9bf-4ae3-8514-99938a2932db\nmd\"Here's a simple standardization example:\"\n\n# \u2554\u2550\u2561 29ea6531-1337-4527-92bd-6ebccf75659a\n x = rand(100)\n\n# \u2554\u2550\u2561 ad63ad4b-04c0-491b-a0de-1721c1bc2df2\n(mean(x), std(x))\n\n# \u2554\u2550\u2561 f86b62eb-6853-482a-bca8-7eec7950fd82\nbegin\n  model = Standardizer() # a built-in model\n  mach1 = machine(model, x)\n  fit!(mach1)\n  xhat = MLJ.transform(mach1, x);\n  (mean(xhat), std(xhat))\nend\n\n# \u2554\u2550\u2561 58686fe6-088a-4751-9873-1b1430e635cc\nmd\"This particular model has an `inverse_transform`:\"\n\n# \u2554\u2550\u2561 8b90b209-cb98-4116-b43e-d0ebe87da176\ninverse_transform(mach1, xhat) \u2248 x\n\n# \u2554\u2550\u2561 f1b47e75-0ce3-4e90-9009-4cfb2012998f\nmd\"## Re-encoding the King County House data as continuous\"\n\n# \u2554\u2550\u2561 e904fcb3-7603-40d2-abd3-cb77d7a79683\nmd\"\"\"\nFor further illustrations of transformers, let's re-encode *all* of the\nKing County House input features (see [Ex\n3](#exercise-3-fixing-scitypes-in-a-table)) into a set of `Continuous`\nfeatures. We do this with the `ContinuousEncoder` model, which, by\ndefault, will:\n\"\"\"\n\n# \u2554\u2550\u2561 e93966db-0dab-42ca-879e-dc128f3db7b3\nmd\"\"\"\n- one-hot encode all `Multiclass` features\n- coerce all `OrderedFactor` features to `Continuous` ones\n- coerce all `Count` features to `Continuous` ones (there aren't any)\n- drop any remaining non-Continuous features (none of these either)\n\"\"\"\n\n# \u2554\u2550\u2561 2564f31e-4704-4820-a367-9aa3c6cf34d0\nmd\"First, we reload the data and fix the scitypes (Exercise 3):\"\n\n# \u2554\u2550\u2561 7e23f8f6-1634-45c8-bf32-94657e65284e\nbegin\n \n  house_csv = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                          \"MachineLearningInJulia2020/for-MLJ-version-0.16/\"*\n                          \"data/house.csv\");\n  house = DataFrames.DataFrame(house_csv)\n  coerce!(house, autotype(house_csv));\n  coerce!(house, Count => Continuous, :zipcode => Multiclass);\n  schema(house)\nend\n\n# \u2554\u2550\u2561 348ff9a2-89ab-434a-9afd-65a5b5facac9\nyHouse, XHouse = unpack(house, ==(:price), rng=123);\n\n# \u2554\u2550\u2561 c7e83ce7-f067-4049-b6c8-54f5ed90a964\nmd\"Instantiate the unsupervised model (transformer):\"\n\n# \u2554\u2550\u2561 a6106065-1baf-4720-9292-fe822525fc77\nencoder = ContinuousEncoder() # a built-in model; no need to @load it\n\n# \u2554\u2550\u2561 703d4ac3-116e-4e05-ae5e-4b83dcbc9675\nmd\"Bind the model to the data and fit!\"\n\n# \u2554\u2550\u2561 5d976f7e-45ee-4cbb-8a29-32f11452654a\nmach2 = machine(encoder, XHouse) |> fit!\n\n# \u2554\u2550\u2561 aad55579-14f3-439a-a5f1-151fcbe229a2\nmd\"Transform and inspect the result:\"\n\n# \u2554\u2550\u2561 a6c1d3ea-6f43-42c7-8950-2f4327f23c1f\nXHouseCont = MLJ.transform(mach2, XHouse)\n\n# \u2554\u2550\u2561 9f2f583a-e90a-43f6-81bc-2d5603785a09\nschema(XHouseCont)\n\n# \u2554\u2550\u2561 828460a7-7d4e-444e-9d87-1d1fbb0e3997\nmd\"## More transformers\"\n\n# \u2554\u2550\u2561 12631f23-8262-475d-b950-fddef2a1fb10\nmd\"Here's how to list all of MLJ's unsupervised models:\"\n\n# \u2554\u2550\u2561 d9069be8-d8c5-43ab-951b-64cb2a36c8e3\nmodels(m->!m.is_supervised)\n\n# \u2554\u2550\u2561 4dee7ef6-b472-4b9c-b0e6-441461cc8770\nmd\"Some commonly used ones are built-in (do not require `@load`ing):\"\n\n# \u2554\u2550\u2561 7a6df9e6-89f1-4117-8cb0-de601961bc02\nmd\"\"\"\nmodel type                  | does what?\n:----------------------------|:----------------------------------------------\nContinuousEncoder | transform input table to a table of `Continuous` features (see above)\nFeatureSelector | retain or dump selected features\nFillImputer | impute missing values\nOneHotEncoder | one-hot encoder `Multiclass` (and optionally `OrderedFactor`) features\nStandardizer | standardize (whiten) a vector or all `Continuous` features of a table\nUnivariateBoxCoxTransformer | apply a learned Box-Cox transformation to a vector\nUnivariateDiscretizer | discretize a `Continuous` vector, and hence render its elscitypw `OrderedFactor`\n\"\"\"\n\n# \u2554\u2550\u2561 8ff17b93-2830-41e2-a87d-950c50fb2955\nmd\"\"\"\nIn addition to \"dynamic\" transformers (ones that learn something\nfrom the data and must be `fit!`) users can wrap ordinary functions\nas transformers, and such *static* transformers can depend on\nparameters, like the dynamic ones. See\n[here](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Static-transformers-1)\nfor how to define your own static transformers.\n\"\"\"\n\n# \u2554\u2550\u2561 d45a81ec-f978-4483-8448-4bcb089096cd\nmd\"## Pipelines\"\n\n# \u2554\u2550\u2561 b454845b-3b3b-4c46-a012-a84240254fb6\nlength(schema(XHouseCont).names)\n\n# \u2554\u2550\u2561 28cf44d4-3f4b-485f-bbdd-9799f7bb2e60\nmd\"\"\"\nLet's suppose that additionally we'd like to reduce the dimension of\nour data.  A model that will do this is `PCA` from\n`MultivariateStats.jl`:\n\"\"\"\n\n# \u2554\u2550\u2561 1e780939-abf0-4203-97a7-88e3af4f10ee\nPCA = @load PCA\n\n# \u2554\u2550\u2561 09ece795-72e1-4240-a833-1b098ab27d3f\nreducer = PCA()\n\n# \u2554\u2550\u2561 8e76b5a5-7c80-4684-b372-8c9866e51852\nmd\"\"\"\nNow, rather simply repeating the work-flow above, applying the new\ntransformation to `XHouseCont`, we can combine both the encoding and the\ndimension-reducing models into a single model, known as a\n*pipeline*. While MLJ offers a powerful interface for composing\nmodels in a variety of ways, we'll stick to these simplest class of\ncomposite models for now. The simplest way to construct a pipeline is using the Julia's `|>` syntax:\n\"\"\"\n\n# \u2554\u2550\u2561 5ed77309-afa6-4c94-88c0-761fe6b5a5d4\npipe1 = encoder |> reducer\n\n# \u2554\u2550\u2561 d45ff6ec-2dd6-4a9c-a97e-79337b0be5c8\nmd\"\"\"\nNotice that the model `pipe` has other models as hyperparameters (with names automatically generated based on the mode type name). The hyperparameters of the component models are are now nested, but we can still access them:\n\"\"\"\n\n# \u2554\u2550\u2561 4a0b7419-d9ae-4570-b9b6-029e155045c0\npipe1.pca.pratio\n\n# \u2554\u2550\u2561 3958b3df-51cd-4920-8f28-382c4a088a8f\npipe1.pca.pratio = 0.85\n\n# \u2554\u2550\u2561 e68044d8-42b4-49cd-86cf-35420d9e6995\nmd\"The pipeline model behaves like any other transformer:\"\n\n# \u2554\u2550\u2561 26d649a8-3d96-4479-a29a-8d0445351914\nbegin\n  mach3 = machine(pipe1, XHouse)\n  fit!(mach3)\n  XHouseSmall = transform(mach3, XHouse)\n  schema(XHouseSmall)\nend\n\n# \u2554\u2550\u2561 7f60c74e-b708-46f4-be65-28d0fcca50a8\nmd\"Want to combine this pre-processing with ridge regression?\"\n\n# \u2554\u2550\u2561 c8c960f0-2a46-42c2-8754-8bd531c8db8e\nRidgeRegressor = @load RidgeRegressor pkg=MLJLinearModels\n\n# \u2554\u2550\u2561 4e327bb5-8abc-4e68-9832-d4c16415df30\nrgs = RidgeRegressor()\n\n# \u2554\u2550\u2561 f96a84ec-1cc0-400c-9493-9c2a2e3c5b51\npipe2 = pipe1 |> rgs\n\n# \u2554\u2550\u2561 fbb24be1-fe01-4489-b5fa-cb79ebf595ef\nmd\"\"\"\nNow our pipeline is a supervised model, instead of a transformer,\nwhose performance we can evaluate:\n\"\"\"\n\n# \u2554\u2550\u2561 958f9a9a-23d5-446b-aa54-f7a086cb0264\nmach4 = machine(pipe2, XHouse, yHouse)\n\n# \u2554\u2550\u2561 ea64dbb5-963a-4da4-91c5-524da38aa391\nevaluate!(mach4, measure=mae, resampling=Holdout()) # CV(nfolds=6) is default\n\n# \u2554\u2550\u2561 1ce36a90-ea25-48d6-ad90-b4405b216771\nmd\"## Training of composite models is \\\"smart\\\"\"\n\n# \u2554\u2550\u2561 4270976f-1207-4086-895b-997a92b731e0\nmd\"\"\"\nNow notice what happens if we train on all the data, then change a\nregressor hyper-parameter and retrain:\n\"\"\"\n\n# \u2554\u2550\u2561 1d0bce6b-ffff-4f30-a525-e9b04a4bd246\nfit!(mach4)\n\n# \u2554\u2550\u2561 64a648e0-d5c9-4c3d-80f0-ba7781e173cf\nwith_terminal() do\n  pipe2.ridge_regressor.lambda = 0.1\n  fit!(mach4)\nend\n\n# \u2554\u2550\u2561 5dcd0d55-bb5d-459a-9cbc-9a2c39793333\nmd\"Second time only the ridge regressor is retrained!\"\n\n# \u2554\u2550\u2561 234c586e-862f-4216-b887-2b16710e5502\nmd\"\"\"\nMutate a hyper-parameter of the `PCA` model and every model except\nthe `ContinuousEncoder` (which comes before it will be retrained):\n\"\"\"\n\n# \u2554\u2550\u2561 b8e84a2c-2c7f-41f5-9452-5298a8a4a401\nwith_terminal() do\n  pipe2.pca.pratio = 0.9999\n  fit!(mach4)\nend\n\n# \u2554\u2550\u2561 95b75a71-47b5-49b1-b017-96f1602f2cad\nmd\"## Inspecting composite models\"\n\n# \u2554\u2550\u2561 5de9f9a0-ad8f-47de-8be2-2a4017c45345\nmd\"\"\"\nThe dot syntax used above to change the values of *nested*\nhyper-parameters is also useful when inspecting the learned\nparameters and report generated when training a composite model:\n\"\"\"\n\n# \u2554\u2550\u2561 aa7bdff7-7d1b-43b1-a7ad-80d84f5b0070\nfitted_params(mach4).ridge_regressor\n\n# \u2554\u2550\u2561 92c5bc62-b430-41b8-8378-5aa686f0b407\nreport(mach4).pca\n\n# \u2554\u2550\u2561 adcb0506-e89d-43b0-9f43-49763e8691a1\nmd\"## Incorporating target transformations\"\n\n# \u2554\u2550\u2561 667917e3-25d2-435c-bb0e-3a45761c733a\nmd\"\"\"\nNext, suppose that instead of using the raw `:price` as the\ntraining target, we want to use the log-price (a common practice in\ndealing with house price data). However, suppose that we still want\nto report final *predictions* on the original linear scale (and use\nthese for evaluation purposes). Then we wrap our unsupervised model using `TransformedTargetModel`, which has\nthe key-word arguments `target` and `inverse`.\n\"\"\"\n\n# \u2554\u2550\u2561 f71e4957-b6aa-480f-8acf-0c1d237fe9f9\nmd\"\"\"\nFirst we'll overload `log` and `exp` for broadcasting:\n\"\"\"\n\n# \u2554\u2550\u2561 dc119abd-72a1-4781-811d-5998beb56406\nbegin\n\tBase.log(v::AbstractArray) = log.(v)\n\tBase.exp(v::AbstractArray) = exp.(v)\nend\n\n# \u2554\u2550\u2561 0398adfe-721a-4b97-910b-f8a9a217eff2\nmd\"Now for the new pipeline:\"\n\n# \u2554\u2550\u2561 85f71232-0ec4-40a8-81b7-e11a56b42313\nrgs_log = TransformedTargetModel(rgs, target = log, inverse = exp)\n\n# \u2554\u2550\u2561 d1af0582-9074-4355-9d03-7ceec3db5d7f\npipe3 = pipe1 |> rgs_log\n\n# \u2554\u2550\u2561 06fac83d-ec17-4503-b3b1-670e16d4251a\nmach5 = machine(pipe3, XHouse, yHouse)\n\n# \u2554\u2550\u2561 9335bbb6-7169-47a2-8ccb-85c839a68433\nevaluate!(mach5, measure = mae)\n\n# \u2554\u2550\u2561 fbe0163a-ed06-49ea-8faa-017fa7aeca69\nmd\"\"\"\nMLJ will also allow you to insert *learned* target\ntransformations. For example, we might want to apply\n`Standardizer()` to the target, to standardize it, or\n`UnivariateBoxCoxTransformer()` to make it look Gaussian. Then\ninstead of specifying a *function* for `target`, we specify a\nunsupervised *model* (or model type). One does not specify `inverse`\nbecause only models implementing `inverse_transform` are\nallowed.\n\"\"\"\n\n# \u2554\u2550\u2561 4fd0807d-61e5-4b4b-a1cb-54e34396a855\nmd\"Let's see which of these two options results in a better outcome:\"\n\n# \u2554\u2550\u2561 1957f387-aee4-4c08-9838-327b915082f8\nbox = UnivariateBoxCoxTransformer(n=20)\n\n# \u2554\u2550\u2561 7b59b396-daac-4dea-bc51-54fafe346cd8\nstand = Standardizer()\n\n# \u2554\u2550\u2561 2efdfa9c-8e70-4c09-b9f9-85c873edc16a\nrgs_box = TransformedTargetModel(rgs, target=box)\n\n# \u2554\u2550\u2561 dcf51860-a300-453e-87fc-345e741f94d0\npipe4 = pipe1 |> rgs_box\n\n# \u2554\u2550\u2561 c54f41ee-75e6-4181-8de7-d194004c9700\nmach6 = machine(pipe4, XHouse, yHouse)\n\n# \u2554\u2550\u2561 cb026565-6c5c-4f7a-91b6-417cfea74d41\nevaluate!(mach6, measure = mae)\n\n# \u2554\u2550\u2561 e073f936-f2a0-4609-83fe-bd8afb87cfc1\npipe4.transformed_target_model_deterministic.target = stand\n\n# \u2554\u2550\u2561 91cca9cb-db5f-4d9f-983b-3f2b20444662\nevaluate!(mach6, measure = mae)\n\n# \u2554\u2550\u2561 a0488295-9642-4156-b7cc-46f4ef988c68\nmd\"# Resources for Part 3\"\n\n# \u2554\u2550\u2561 d59d9e91-b26d-4342-90fb-7f2721f6fcc7\nmd\"\"\"\n- From the MLJ manual:\n    - [Transformers and other unsupervised models](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/)\n    - [Linear pipelines](https://alan-turing-institute.github.io/MLJ.jl/dev/linear_pipelines/#Linear-Pipelines)\n- From Data Science Tutorials:\n    - [Composing models](https://juliaai.github.io/DataScienceTutorials.jl/getting-started/composing-models/)\n\"\"\"\n\n# \u2554\u2550\u2561 c4f30527-c9b4-44e9-af65-ccd25ecb9818\nmd\"# Exercises for Part 3\"\n\n# \u2554\u2550\u2561 25a6fd3c-60d1-44dd-8890-979691212d9b\nmd\"#### Exercise 7\"\n\n# \u2554\u2550\u2561 aed2b274-cc0c-42f2-a6fa-eaf14df5d44b\nmd\"\"\"\nConsider again the Horse Colic classification problem considered in\nExercise 6, but with all features, `Finite` and `Infinite`:\n\"\"\"\n\n# \u2554\u2550\u2561 0df6be04-24fe-4417-8025-5084804a9f6c\nbegin\n  csv_file = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                     \"MachineLearningInJulia2020/\"*\n                     \"for-MLJ-version-0.16/data/horse.csv\");\n  horse = DataFrames.DataFrame(csv_file); # convert to data frame\n  coerce!(horse, autotype(horse));\n  coerce!(horse, Count => Continuous);\n  coerce!(horse,\n          :surgery               => Multiclass,\n          :age                   => Multiclass,\n          :mucous_membranes      => Multiclass,\n          :capillary_refill_time => Multiclass,\n          :outcome               => Multiclass,\n          :cp_data               => Multiclass);\n  \n  yHorse, XHorse = unpack(horse, ==(:outcome));\n  schema(XHorse)\nend\n\n# \u2554\u2550\u2561 9abf25a8-7231-43f5-9e90-09023d201062\nmd\"\"\"\n(a) Define a pipeline that:\n- uses `Standardizer` to ensure that features that are already\n  continuous are centered at zero and have unit variance\n- re-encodes the full set of features as `Continuous`, using\n  `ContinuousEncoder`\n- uses the `KMeans` clustering model from `Clustering.jl`\n  to reduce the dimension of the feature space to `k=10`.\n- trains a `EvoTreeClassifier` (a gradient tree boosting\n  algorithm in `EvoTrees.jl`) on the reduced data, using\n  `nrounds=50` and default values for the other\n   hyper-parameters\n\n(b) Evaluate the pipeline on all data, using 6-fold cross-validation\nand `cross_entropy` loss.\n\n $\\star$ (c) Plot a learning curve which examines the effect on this loss\nas the tree booster parameter `max_depth` varies from 2 to 10.\n\"\"\"\n\n# \u2554\u2550\u2561 6cce6530-0d12-4862-af4f-11c7de9e21dd\nhtml\"<a id='part-4-tuning-hyper-parameters'></a>\"\n\n# \u2554\u2550\u2561 135dac9b-0bd9-4e1d-8dba-241d9b744683\nmd\"\"\"\n---\n\n*This notebook was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nEvoTrees = \"f6006082-12f8-11e9-0c9c-0d5d367ab1e5\"\nMLJ = \"add582a8-e3ab-11e8-2d5e-e98b27df1bc7\"\nMLJBase = \"a7f614a8-145f-11e9-1d2a-a57a1082229d\"\nMLJLinearModels = \"6ee0df7b-362f-4a72-a706-9e79364fb692\"\nMLJMultivariateStatsInterface = \"1b6a4a23-ba22-4f51-9698-8599985d3728\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nUrlDownload = \"856ac37a-3032-4c1c-9122-f86d88358c8b\"\n\n[compat]\nCSV = \"~0.9.11\"\nDataFrames = \"~1.3.1\"\nEvoTrees = \"~0.9.1\"\nMLJ = \"~0.17.0\"\nMLJBase = \"~0.19.2\"\nMLJLinearModels = \"~0.5.7\"\nMLJMultivariateStatsInterface = \"~0.2.2\"\nPlutoUI = \"~0.7.27\"\nUrlDownload = \"~1.0.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ARFFFiles]]\ndeps = [\"CategoricalArrays\", \"Dates\", \"Parsers\", \"Tables\"]\ngit-tree-sha1 = \"e8c8e0a2be6eb4f56b1672e46004463033daa409\"\nuuid = \"da404889-ca92-49ff-9e8b-0aa6b4d38dc8\"\nversion = \"1.4.1\"\n\n[[AbstractFFTs]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"485ee0867925449198280d4af84bdb46a2a404d0\"\nuuid = \"621f4979-c628-5d54-868e-fcf4e3e8185c\"\nversion = \"1.0.1\"\n\n[[AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[Adapt]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"af92965fb30777147966f58acb05da51c5616b5f\"\nuuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\nversion = \"3.3.3\"\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Arpack]]\ndeps = [\"Arpack_jll\", \"Libdl\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"2ff92b71ba1747c5fdd541f8fc87736d82f40ec9\"\nuuid = \"7d9fca2a-8960-54d3-9f78-7d1dccf2cb97\"\nversion = \"0.4.0\"\n\n[[Arpack_jll]]\ndeps = [\"Libdl\", \"OpenBLAS_jll\", \"Pkg\"]\ngit-tree-sha1 = \"e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7\"\nuuid = \"68821587-b530-5797-8361-c406ea357684\"\nversion = \"3.5.0+3\"\n\n[[ArrayInterface]]\ndeps = [\"Compat\", \"IfElse\", \"LinearAlgebra\", \"Requires\", \"SparseArrays\", \"Static\"]\ngit-tree-sha1 = \"1ee88c4c76caa995a885dc2f22a5d548dfbbc0ba\"\nuuid = \"4fba245c-0d91-5ea0-9b3e-6abc04ee57a9\"\nversion = \"3.2.2\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[BFloat16s]]\ndeps = [\"LinearAlgebra\", \"Printf\", \"Random\", \"Test\"]\ngit-tree-sha1 = \"a598ecb0d717092b5539dbbe890c98bac842b072\"\nuuid = \"ab4f0b2a-ad5b-11e8-123f-65d77653426b\"\nversion = \"0.2.0\"\n\n[[BSON]]\ngit-tree-sha1 = \"ebcd6e22d69f21249b7b8668351ebf42d6dc87a1\"\nuuid = \"fbb218c0-5317-5bc6-957e-2ee96dd4b1f0\"\nversion = \"0.3.4\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[CEnum]]\ngit-tree-sha1 = \"215a9aa4a1f23fbd05b92769fdd62559488d70e9\"\nuuid = \"fa961155-64e5-5f13-b03f-caf6b980ea82\"\nversion = \"0.4.1\"\n\n[[CSV]]\ndeps = [\"CodecZlib\", \"Dates\", \"FilePathsBase\", \"InlineStrings\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"49f14b6c56a2da47608fe30aed711b5882264d7a\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.9.11\"\n\n[[CUDA]]\ndeps = [\"AbstractFFTs\", \"Adapt\", \"BFloat16s\", \"CEnum\", \"CompilerSupportLibraries_jll\", \"ExprTools\", \"GPUArrays\", \"GPUCompiler\", \"LLVM\", \"LazyArtifacts\", \"Libdl\", \"LinearAlgebra\", \"Logging\", \"Printf\", \"Random\", \"Random123\", \"RandomNumbers\", \"Reexport\", \"Requires\", \"SparseArrays\", \"SpecialFunctions\", \"TimerOutputs\"]\ngit-tree-sha1 = \"429a1a05348ce948a96adbdd873fbe6d9e5e052f\"\nuuid = \"052768ef-5323-5732-b1bb-66c8b64840ba\"\nversion = \"3.6.2\"\n\n[[CategoricalArrays]]\ndeps = [\"DataAPI\", \"Future\", \"Missings\", \"Printf\", \"Requires\", \"Statistics\", \"Unicode\"]\ngit-tree-sha1 = \"c308f209870fdbd84cb20332b6dfaf14bf3387f8\"\nuuid = \"324d7699-5711-5eae-9e2f-1d82baa6b597\"\nversion = \"0.10.2\"\n\n[[CategoricalDistributions]]\ndeps = [\"CategoricalArrays\", \"Distributions\", \"Missings\", \"OrderedCollections\", \"Random\", \"ScientificTypesBase\", \"UnicodePlots\"]\ngit-tree-sha1 = \"a5734a58e5dc8c749b5507d03ba5e457d077181b\"\nuuid = \"af321ab8-2d2e-40a6-b165-3d674595d28e\"\nversion = \"0.1.4\"\n\n[[ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"926870acb6cbcf029396f2f2de030282b6bc1941\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.11.4\"\n\n[[ChangesOfVariables]]\ndeps = [\"ChainRulesCore\", \"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"bf98fa45a0a4cee295de98d4c1462be26345b9a1\"\nuuid = \"9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0\"\nversion = \"0.1.2\"\n\n[[CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[CommonSubexpressions]]\ndeps = [\"MacroTools\", \"Test\"]\ngit-tree-sha1 = \"7b8a93dba8af7e3b42fecabf646260105ac373f7\"\nuuid = \"bbf7d656-a473-5ed7-a52c-81e309532950\"\nversion = \"0.3.0\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", 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\"f28f55f0-a522-5efc-85c2-fe41dfb9b2d9\"\nversion = \"1.3.0\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"bf210ce90b6c9eed32d25dbcae1ebc565df2687f\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.2\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[MultivariateStats]]\ndeps = [\"Arpack\", \"LinearAlgebra\", \"SparseArrays\", \"Statistics\", \"StatsBase\"]\ngit-tree-sha1 = \"8d958ff1854b166003238fe191ec34b9d592860a\"\nuuid = \"6f286f6a-111f-5878-ab1e-185364afe411\"\nversion = \"0.8.0\"\n\n[[NLSolversBase]]\ndeps = [\"DiffResults\", \"Distributed\", \"FiniteDiff\", \"ForwardDiff\"]\ngit-tree-sha1 = \"50310f934e55e5ca3912fb941dec199b49ca9b68\"\nuuid = \"d41bc354-129a-5804-8e4c-c37616107c6c\"\nversion = \"7.8.2\"\n\n[[NaNMath]]\ngit-tree-sha1 = \"f755f36b19a5116bb580de457cda0c140153f283\"\nuuid = \"77ba4419-2d1f-58cd-9bb1-8ffee604a2e3\"\nversion = \"0.3.6\"\n\n[[NetworkLayout]]\ndeps = [\"GeometryBasics\", \"LinearAlgebra\", \"Random\", \"Requires\", \"SparseArrays\"]\ngit-tree-sha1 = \"cac8fc7ba64b699c678094fa630f49b80618f625\"\nuuid = \"46757867-2c16-5918-afeb-47bfcb05e46a\"\nversion = \"0.4.4\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[OpenLibm_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"05823500-19ac-5b8b-9628-191a04bc5112\"\n\n[[OpenML]]\ndeps = [\"ARFFFiles\", \"HTTP\", \"JSON\", \"Markdown\", \"Pkg\"]\ngit-tree-sha1 = \"06080992e86a93957bfe2e12d3181443cedf2400\"\nuuid = \"8b6db2d4-7670-4922-a472-f9537c81ab66\"\nversion = \"0.2.0\"\n\n[[OpenSpecFun_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"13652491f6856acfd2db29360e1bbcd4565d04f1\"\nuuid = \"efe28fd5-8261-553b-a9e1-b2916fc3738e\"\nversion = \"0.5.5+0\"\n\n[[Optim]]\ndeps = [\"Compat\", \"FillArrays\", \"ForwardDiff\", \"LineSearches\", \"LinearAlgebra\", \"NLSolversBase\", \"NaNMath\", \"Parameters\", \"PositiveFactorizations\", \"Printf\", \"SparseArrays\", \"StatsBase\"]\ngit-tree-sha1 = \"916077e0f0f8966eb0dc98a5c39921fdb8f49eb4\"\nuuid = \"429524aa-4258-5aef-a3af-852621145aeb\"\nversion = \"1.6.0\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[PDMats]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\", \"SuiteSparse\"]\ngit-tree-sha1 = \"ee26b350276c51697c9c2d88a072b339f9f03d73\"\nuuid = \"90014a1f-27ba-587c-ab20-58faa44d9150\"\nversion = \"0.11.5\"\n\n[[Parameters]]\ndeps = [\"OrderedCollections\", \"UnPack\"]\ngit-tree-sha1 = \"34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe\"\nuuid = \"d96e819e-fc66-5662-9728-84c9c7592b0a\"\nversion = \"0.12.3\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"d7fa6237da8004be601e19bd6666083056649918\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.1.3\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"fed057115644d04fba7f4d768faeeeff6ad11a60\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.27\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"db3a23166af8aebf4db5ef87ac5b00d36eb771e2\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.4.0\"\n\n[[PositiveFactorizations]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"17275485f373e6673f7e7f97051f703ed5b15b20\"\nuuid = \"85a6dd25-e78a-55b7-8502-1745935b8125\"\nversion = \"0.2.4\"\n\n[[Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"2cf929d64681236a2e074ffafb8d568733d2e6af\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.3\"\n\n[[PrettyPrinting]]\ngit-tree-sha1 = \"a5db8a42938bc65c2679406c51a8f5fe9597c6e7\"\nuuid = \"54e16d92-306c-5ea0-a30b-337be88ac337\"\nversion = \"0.3.2\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"dfb54c4e414caa595a1f2ed759b160f5a3ddcba5\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.3.1\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[ProgressMeter]]\ndeps = [\"Distributed\", \"Printf\"]\ngit-tree-sha1 = \"afadeba63d90ff223a6a48d2009434ecee2ec9e8\"\nuuid = \"92933f4c-e287-5a05-a399-4b506db050ca\"\nversion = \"1.7.1\"\n\n[[QuadGK]]\ndeps = [\"DataStructures\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"78aadffb3efd2155af139781b8a8df1ef279ea39\"\nuuid = \"1fd47b50-473d-5c70-9696-f719f8f3bcdc\"\nversion = \"2.4.2\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Random123]]\ndeps = [\"Libdl\", \"Random\", \"RandomNumbers\"]\ngit-tree-sha1 = \"0e8b146557ad1c6deb1367655e052276690e71a3\"\nuuid = \"74087812-796a-5b5d-8853-05524746bad3\"\nversion = \"1.4.2\"\n\n[[RandomNumbers]]\ndeps = [\"Random\", \"Requires\"]\ngit-tree-sha1 = \"043da614cc7e95c703498a491e2c21f58a2b8111\"\nuuid = \"e6cf234a-135c-5ec9-84dd-332b85af5143\"\nversion = \"1.5.3\"\n\n[[RecipesBase]]\ngit-tree-sha1 = \"6bf3f380ff52ce0832ddd3a2a7b9538ed1bcca7d\"\nuuid = \"3cdcf5f2-1ef4-517c-9805-6587b60abb01\"\nversion = \"1.2.1\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"8f82019e525f4d5c669692772a6f4b0a58b06a6a\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.2.0\"\n\n[[Rmath]]\ndeps = [\"Random\", \"Rmath_jll\"]\ngit-tree-sha1 = \"bf3188feca147ce108c76ad82c2792c57abe7b1f\"\nuuid = \"79098fc4-a85e-5d69-aa6a-4863f24498fa\"\nversion = \"0.7.0\"\n\n[[Rmath_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"68db32dff12bb6127bac73c209881191bf0efbb7\"\nuuid = \"f50d1b31-88e8-58de-be2c-1cc44531875f\"\nversion = \"0.3.0+0\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[ScientificTypes]]\ndeps = [\"CategoricalArrays\", \"ColorTypes\", \"Dates\", \"Distributions\", \"PrettyTables\", \"Reexport\", \"ScientificTypesBase\", \"StatisticalTraits\", \"Tables\"]\ngit-tree-sha1 = \"ba70c9a6e4c81cc3634e3e80bb8163ab5ef57eb8\"\nuuid = \"321657f4-b219-11e9-178b-2701a2544e81\"\nversion = \"3.0.0\"\n\n[[ScientificTypesBase]]\ngit-tree-sha1 = \"a8e18eb383b5ecf1b5e6fc237eb39255044fd92b\"\nuuid = \"30f210dd-8aff-4c5f-94ba-8e64358c1161\"\nversion = \"3.0.0\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"244586bc07462d22aed0113af9c731f2a518c93e\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.10\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[SpecialFunctions]]\ndeps = [\"ChainRulesCore\", \"IrrationalConstants\", \"LogExpFunctions\", \"OpenLibm_jll\", \"OpenSpecFun_jll\"]\ngit-tree-sha1 = \"e08890d19787ec25029113e88c34ec20cac1c91e\"\nuuid = \"276daf66-3868-5448-9aa4-cd146d93841b\"\nversion = \"2.0.0\"\n\n[[StableRNGs]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"3be7d49667040add7ee151fefaf1f8c04c8c8276\"\nuuid = \"860ef19b-820b-49d6-a774-d7a799459cd3\"\nversion = \"1.0.0\"\n\n[[Static]]\ndeps = [\"IfElse\"]\ngit-tree-sha1 = \"7f5a513baec6f122401abfc8e9c074fdac54f6c1\"\nuuid = \"aedffcd0-7271-4cad-89d0-dc628f76c6d3\"\nversion = \"0.4.1\"\n\n[[StaticArrays]]\ndeps = [\"LinearAlgebra\", \"Random\", \"Statistics\"]\ngit-tree-sha1 = \"de9e88179b584ba9cf3cc5edbb7a41f26ce42cda\"\nuuid = \"90137ffa-7385-5640-81b9-e52037218182\"\nversion = \"1.3.0\"\n\n[[StatisticalTraits]]\ndeps = [\"ScientificTypesBase\"]\ngit-tree-sha1 = \"271a7fea12d319f23d55b785c51f6876aadb9ac0\"\nuuid = \"64bff920-2084-43da-a3e6-9bb72801c0c9\"\nversion = \"3.0.0\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[StatsAPI]]\ngit-tree-sha1 = \"d88665adc9bcf45903013af0982e2fd05ae3d0a6\"\nuuid = \"82ae8749-77ed-4fe6-ae5f-f523153014b0\"\nversion = \"1.2.0\"\n\n[[StatsBase]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"LogExpFunctions\", \"Missings\", \"Printf\", \"Random\", \"SortingAlgorithms\", \"SparseArrays\", \"Statistics\", \"StatsAPI\"]\ngit-tree-sha1 = \"51383f2d367eb3b444c961d485c565e4c0cf4ba0\"\nuuid = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nversion = \"0.33.14\"\n\n[[StatsFuns]]\ndeps = [\"ChainRulesCore\", \"InverseFunctions\", \"IrrationalConstants\", \"LogExpFunctions\", \"Reexport\", \"Rmath\", \"SpecialFunctions\"]\ngit-tree-sha1 = \"bedb3e17cc1d94ce0e6e66d3afa47157978ba404\"\nuuid = \"4c63d2b9-4356-54db-8cca-17b64c39e42c\"\nversion = \"0.9.14\"\n\n[[StructArrays]]\ndeps = [\"Adapt\", \"DataAPI\", \"StaticArrays\", \"Tables\"]\ngit-tree-sha1 = \"2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3\"\nuuid = \"09ab397b-f2b6-538f-b94a-2f83cf4a842a\"\nversion = \"0.6.3\"\n\n[[SuiteSparse]]\ndeps = [\"Libdl\", \"LinearAlgebra\", \"Serialization\", \"SparseArrays\"]\nuuid = \"4607b0f0-06f3-5cda-b6b1-a6196a1729e9\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"bb1064c9a84c52e277f1096cf41434b675cd368b\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.1\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TimerOutputs]]\ndeps = [\"ExprTools\", \"Printf\"]\ngit-tree-sha1 = \"7cb456f358e8f9d102a8b25e8dfedf58fa5689bc\"\nuuid = \"a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f\"\nversion = \"0.5.13\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[URIs]]\ngit-tree-sha1 = \"97bbe755a53fe859669cd907f2d96aee8d2c1355\"\nuuid = \"5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4\"\nversion = \"1.3.0\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[UnPack]]\ngit-tree-sha1 = \"387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b\"\nuuid = \"3a884ed6-31ef-47d7-9d2a-63182c4928ed\"\nversion = \"1.0.2\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[UnicodePlots]]\ndeps = [\"Crayons\", \"Dates\", \"SparseArrays\", \"StatsBase\"]\ngit-tree-sha1 = \"3cb994143aba28cfe66615702505b2d294cebd3e\"\nuuid = \"b8865327-cd53-5732-bb35-84acbb429228\"\nversion = \"2.5.1\"\n\n[[UrlDownload]]\ndeps = [\"HTTP\", \"ProgressMeter\"]\ngit-tree-sha1 = \"05f86730c7a53c9da603bd506a4fc9ad0851171c\"\nuuid = \"856ac37a-3032-4c1c-9122-f86d88358c8b\"\nversion = \"1.0.0\"\n\n[[WeakRefStrings]]\ndeps = [\"DataAPI\", \"InlineStrings\", \"Parsers\"]\ngit-tree-sha1 = \"c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa\"\nuuid = \"ea10d353-3f73-51f8-a26c-33c1cb351aa5\"\nversion = \"1.4.1\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250083a7b4d7-6442-44b2-9388-8330b19cd537\n# \u255f\u250024abafc3-0e26-4cd2-9bca-7c69b794f8ce\n# \u255f\u2500b67c62c2-15ab-4328-b795-033f6f2a0674\n# \u255f\u2500bc689638-fd19-4c9f-935f-ddf6a6bfbbdd\n# \u255f\u2500197fd00e-9068-46ea-af2a-25235e544a31\n# \u2560\u2550f6d4f8c4-e441-45c4-8af5-148d95ea2900\n# \u255f\u250045740c4d-b789-45dc-a6bf-47194d7e8e12\n# \u255f\u250042b0f1e1-16c9-4238-828a-4cc485149963\n# \u2560\u2550bbe90636-9d1f-4ee0-8e35-c285f7b922d0\n# \u255f\u2500499cbc31-83ba-4583-ba1f-6363f43ec697\n# \u255f\u2500db5821e8-956a-4a46-95ea-2955abd45275\n# \u255f\u25001e248249-5629-412f-b1b3-dd47e367ba54\n# \u255f\u2500d055d24f-1b2d-48a6-8d7e-8e449afd1c48\n# \u255f\u25000dc40881-6163-4948-a949-7f3bd292fe31\n# \u255f\u25000d066909-c9bf-4ae3-8514-99938a2932db\n# \u2560\u255029ea6531-1337-4527-92bd-6ebccf75659a\n# \u2560\u2550ad63ad4b-04c0-491b-a0de-1721c1bc2df2\n# \u2560\u2550f86b62eb-6853-482a-bca8-7eec7950fd82\n# \u255f\u250058686fe6-088a-4751-9873-1b1430e635cc\n# \u2560\u25508b90b209-cb98-4116-b43e-d0ebe87da176\n# \u255f\u2500f1b47e75-0ce3-4e90-9009-4cfb2012998f\n# \u255f\u2500e904fcb3-7603-40d2-abd3-cb77d7a79683\n# \u255f\u2500e93966db-0dab-42ca-879e-dc128f3db7b3\n# \u255f\u25002564f31e-4704-4820-a367-9aa3c6cf34d0\n# \u2560\u25507e23f8f6-1634-45c8-bf32-94657e65284e\n# \u2560\u2550348ff9a2-89ab-434a-9afd-65a5b5facac9\n# \u255f\u2500c7e83ce7-f067-4049-b6c8-54f5ed90a964\n# \u2560\u2550a6106065-1baf-4720-9292-fe822525fc77\n# \u255f\u2500703d4ac3-116e-4e05-ae5e-4b83dcbc9675\n# \u2560\u25505d976f7e-45ee-4cbb-8a29-32f11452654a\n# \u255f\u2500aad55579-14f3-439a-a5f1-151fcbe229a2\n# \u2560\u2550a6c1d3ea-6f43-42c7-8950-2f4327f23c1f\n# \u2560\u25509f2f583a-e90a-43f6-81bc-2d5603785a09\n# \u255f\u2500828460a7-7d4e-444e-9d87-1d1fbb0e3997\n# \u255f\u250012631f23-8262-475d-b950-fddef2a1fb10\n# \u2560\u2550d9069be8-d8c5-43ab-951b-64cb2a36c8e3\n# \u255f\u25004dee7ef6-b472-4b9c-b0e6-441461cc8770\n# \u255f\u25007a6df9e6-89f1-4117-8cb0-de601961bc02\n# \u255f\u25008ff17b93-2830-41e2-a87d-950c50fb2955\n# \u255f\u2500d45a81ec-f978-4483-8448-4bcb089096cd\n# \u2560\u2550b454845b-3b3b-4c46-a012-a84240254fb6\n# \u255f\u250028cf44d4-3f4b-485f-bbdd-9799f7bb2e60\n# \u2560\u25501e780939-abf0-4203-97a7-88e3af4f10ee\n# \u2560\u255009ece795-72e1-4240-a833-1b098ab27d3f\n# \u255f\u25008e76b5a5-7c80-4684-b372-8c9866e51852\n# \u2560\u25505ed77309-afa6-4c94-88c0-761fe6b5a5d4\n# \u255f\u2500d45ff6ec-2dd6-4a9c-a97e-79337b0be5c8\n# \u2560\u25504a0b7419-d9ae-4570-b9b6-029e155045c0\n# \u2560\u25503958b3df-51cd-4920-8f28-382c4a088a8f\n# \u255f\u2500e68044d8-42b4-49cd-86cf-35420d9e6995\n# \u2560\u255026d649a8-3d96-4479-a29a-8d0445351914\n# \u255f\u25007f60c74e-b708-46f4-be65-28d0fcca50a8\n# \u2560\u2550c8c960f0-2a46-42c2-8754-8bd531c8db8e\n# \u2560\u25504e327bb5-8abc-4e68-9832-d4c16415df30\n# \u2560\u2550f96a84ec-1cc0-400c-9493-9c2a2e3c5b51\n# \u255f\u2500fbb24be1-fe01-4489-b5fa-cb79ebf595ef\n# \u2560\u2550958f9a9a-23d5-446b-aa54-f7a086cb0264\n# \u2560\u2550ea64dbb5-963a-4da4-91c5-524da38aa391\n# \u255f\u25001ce36a90-ea25-48d6-ad90-b4405b216771\n# \u255f\u25004270976f-1207-4086-895b-997a92b731e0\n# \u2560\u25501d0bce6b-ffff-4f30-a525-e9b04a4bd246\n# \u2560\u255064a648e0-d5c9-4c3d-80f0-ba7781e173cf\n# \u255f\u25005dcd0d55-bb5d-459a-9cbc-9a2c39793333\n# \u255f\u2500234c586e-862f-4216-b887-2b16710e5502\n# \u2560\u2550b8e84a2c-2c7f-41f5-9452-5298a8a4a401\n# \u255f\u250095b75a71-47b5-49b1-b017-96f1602f2cad\n# \u255f\u25005de9f9a0-ad8f-47de-8be2-2a4017c45345\n# \u2560\u2550aa7bdff7-7d1b-43b1-a7ad-80d84f5b0070\n# \u2560\u255092c5bc62-b430-41b8-8378-5aa686f0b407\n# \u255f\u2500adcb0506-e89d-43b0-9f43-49763e8691a1\n# \u255f\u2500667917e3-25d2-435c-bb0e-3a45761c733a\n# \u255f\u2500f71e4957-b6aa-480f-8acf-0c1d237fe9f9\n# \u2560\u2550dc119abd-72a1-4781-811d-5998beb56406\n# \u255f\u25000398adfe-721a-4b97-910b-f8a9a217eff2\n# \u2560\u255085f71232-0ec4-40a8-81b7-e11a56b42313\n# \u2560\u2550d1af0582-9074-4355-9d03-7ceec3db5d7f\n# \u2560\u255006fac83d-ec17-4503-b3b1-670e16d4251a\n# \u2560\u25509335bbb6-7169-47a2-8ccb-85c839a68433\n# \u255f\u2500fbe0163a-ed06-49ea-8faa-017fa7aeca69\n# \u255f\u25004fd0807d-61e5-4b4b-a1cb-54e34396a855\n# \u2560\u25501957f387-aee4-4c08-9838-327b915082f8\n# \u2560\u25507b59b396-daac-4dea-bc51-54fafe346cd8\n# \u2560\u25502efdfa9c-8e70-4c09-b9f9-85c873edc16a\n# \u2560\u2550dcf51860-a300-453e-87fc-345e741f94d0\n# \u2560\u2550c54f41ee-75e6-4181-8de7-d194004c9700\n# \u2560\u2550cb026565-6c5c-4f7a-91b6-417cfea74d41\n# \u2560\u2550e073f936-f2a0-4609-83fe-bd8afb87cfc1\n# \u2560\u255091cca9cb-db5f-4d9f-983b-3f2b20444662\n# \u255f\u2500a0488295-9642-4156-b7cc-46f4ef988c68\n# \u255f\u2500d59d9e91-b26d-4342-90fb-7f2721f6fcc7\n# \u255f\u2500c4f30527-c9b4-44e9-af65-ccd25ecb9818\n# \u255f\u250025a6fd3c-60d1-44dd-8890-979691212d9b\n# \u255f\u2500aed2b274-cc0c-42f2-a6fa-eaf14df5d44b\n# \u2560\u25500df6be04-24fe-4417-8025-5084804a9f6c\n# \u255f\u25009abf25a8-7231-43f5-9e90-09023d201062\n# \u255f\u25006cce6530-0d12-4862-af4f-11c7de9e21dd\n# \u255f\u2500135dac9b-0bd9-4e1d-8dba-241d9b744683\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "d59159be7a9a67ac535d279bbba945fe1b8d33ed", "size": 51801, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/03_pipelines/notebook.pluto.jl", "max_stars_repo_name": "roland-KA/MLJTutorial.jl", "max_stars_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2021-11-02T10:46:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:36:21.000Z", "max_issues_repo_path": "notebooks/03_pipelines/notebook.pluto.jl", "max_issues_repo_name": "roland-KA/MLJTutorial.jl", "max_issues_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, 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{"text": "### A Pluto.jl notebook ###\n# v0.18.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 58c3936e-c10a-40bd-b8f9-a15804c27e59\nusing DataFrames\n\n# \u2554\u2550\u2561 636f1f75-2055-4cd3-95bc-3e4f1885f79c\nhtml\"\"\"\n<style>\n\tmain {\n\t\tmargin: 0 auto;\n\t\tmax-width: 2000px;\n    \tpadding-left: max(160px, 10%);\n    \tpadding-right: max(160px, 10%);\n\t}\n</style>\n\"\"\"\n\n\n# \u2554\u2550\u2561 2a70a1c7-054f-43d1-84ac-0c1b737233cb\ndf = DataFrame(a=1:3, b=4:6)\n\n# \u2554\u2550\u2561 8993cf19-ae9b-4add-95bc-78207dd6ec9c\nmd\" #### `getindex` (used in df on the right hand side of =).\"\n\n# \u2554\u2550\u2561 d2959067-58d9-487f-af6f-2ce2980d63ae\nlet\n\tcopyOfa = df[:, :a]\n\tcopyOfa .= 0\n\tcopyOfa\nend\n\n# \u2554\u2550\u2561 2434c37c-a7d4-4e1a-985d-7824885fbab4\ndf\n\n# \u2554\u2550\u2561 f4ef304f-2ca5-4e95-86b0-d83865d74aeb\nmd\" ##### Note that column `:a` has not changed.\"\n\n# \u2554\u2550\u2561 fb181af4-b916-4aac-b997-1b89dbb99fb5\nmd\" #### `setindex` (used in df on left hand side of =).\"\n\n# \u2554\u2550\u2561 2b49dcd4-4054-4300-b75a-c109b40fc815\nbegin\n\tdf[:, :a] .= 0\n\tdf\nend\n\n# \u2554\u2550\u2561 957207cf-47ce-4f2d-ba3b-53b609401c8d\nmd\" ##### Now df has changed!\"\n\n# \u2554\u2550\u2561 a373977d-c27b-4478-8beb-58b15b7995a9\nbegin\n\tdf .= 0\n\tdf\nend\n\n# \u2554\u2550\u2561 6111833d-6c40-4de1-9692-93a276a5730a\nmd\" ##### In `getindex` context a user can ask for the following behaviors:\n\n1. get a vector \u201cas is\u201d, without copying; this is achieved with df[!, :a] and df.a;\n2. get a vector with copying; that is achieved with df[:, :a];\n3. get a view of the vector; this is achieved with view(df, :, :a) or view(df, !, :a)\n\"\n\n# \u2554\u2550\u2561 331bde26-9892-4045-8355-85f666514487\nmd\"\"\" ##### In `setindex!` + broadcasting assignment context the user might ask for the following:\n\n1. set an existing vector in-place, this is done by: df[:, :a] = vec and df[:, :a] .= something\n\n2. replace an existing vector without copying: df[!, :a] = vec or df.a = vec\n\n3. replace an existing vector with copying: df[!, :a] .= vec and df[!, :a] .= something\n\n4. add a new vector without copying: df[!, :new] = vec or df.new = vec\n\n5. add a new vector with copying: df[:, :new] = vec or df[:, :new] .= something\n\nor \n\n6. df[!, :new] .= something\n\n(this last rule breaks things a bit as ! and : behave here the in same way, but it was added for user convenience)\n\"\"\"\n\n# \u2554\u2550\u2561 9e510dbd-2e9c-40b3-a5ca-4ab4aa02d019\nbegin\n\ta = [7; 8; 9]\n\tdf[:, :a] = a\n\tdf\nend\n\n# \u2554\u2550\u2561 2d15edc9-1f27-4d89-8824-7f6f27385b67\nlet\n\tb = [-1; -2; -3]\n\tdf[!, :a] = b\n\tdf\nend\n\n# \u2554\u2550\u2561 5e631910-7a4f-4651-b605-c12966468757\nbegin\n\tb = 10:12\n\tdf\nend\n\n# \u2554\u2550\u2561 2b33122b-4e96-42b4-a2f3-83b6f231fd1c\ndf\n\n# \u2554\u2550\u2561 9d8a3a01-7c1c-4505-b7d1-42849d44ce3d\n\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\n\n[compat]\nDataFrames = \"~1.3.2\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.8.0-beta1\"\nmanifest_format = \"2.0\"\nproject_hash = \"c5a2b0816e3f46eedf015440dc2fa38edb4deba5\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\nversion = \"1.1.1\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"96b0bc6c52df76506efc8a441c6cf1adcb1babc4\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.42.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\nversion = \"0.5.0+0\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"ae02104e835f219b8930c7664b8012c93475c340\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.3.2\"\n\n[[deps.DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"3daef5523dd2e769dad2365274f760ff5f282c7d\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.11\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[deps.Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"FileWatching\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\nversion = \"1.6.0\"\n\n[[deps.FileWatching]]\nuuid = \"7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee\"\n\n[[deps.Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[deps.Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[deps.IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\nversion = \"0.6.3\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", 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\"1.0.1\"\n\n[[deps.Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"OrderedCollections\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"5ce79ce186cc678bbb5c5681ca3379d1ddae11a1\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.7.0\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\nversion = \"1.10.0\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\nversion = \"1.2.12+1\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\nversion = \"5.0.1+0\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\nversion = \"1.41.0+1\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\nversion = \"16.2.1+1\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550636f1f75-2055-4cd3-95bc-3e4f1885f79c\n# \u2560\u255058c3936e-c10a-40bd-b8f9-a15804c27e59\n# \u2560\u25502a70a1c7-054f-43d1-84ac-0c1b737233cb\n# \u255f\u25008993cf19-ae9b-4add-95bc-78207dd6ec9c\n# \u2560\u2550d2959067-58d9-487f-af6f-2ce2980d63ae\n# \u2560\u25502434c37c-a7d4-4e1a-985d-7824885fbab4\n# \u255f\u2500f4ef304f-2ca5-4e95-86b0-d83865d74aeb\n# \u255f\u2500fb181af4-b916-4aac-b997-1b89dbb99fb5\n# \u2560\u25502b49dcd4-4054-4300-b75a-c109b40fc815\n# \u255f\u2500957207cf-47ce-4f2d-ba3b-53b609401c8d\n# \u2560\u2550a373977d-c27b-4478-8beb-58b15b7995a9\n# \u255f\u25006111833d-6c40-4de1-9692-93a276a5730a\n# \u255f\u2500331bde26-9892-4045-8355-85f666514487\n# \u2560\u25509e510dbd-2e9c-40b3-a5ca-4ab4aa02d019\n# \u2560\u25502d15edc9-1f27-4d89-8824-7f6f27385b67\n# \u2560\u25505e631910-7a4f-4651-b605-c12966468757\n# \u2560\u25502b33122b-4e96-42b4-a2f3-83b6f231fd1c\n# \u2560\u25509d8a3a01-7c1c-4505-b7d1-42849d44ce3d\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "e990a91c196f9bff10ec4167a88bfef0474632b0", "size": 11660, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/DataFramesPlayground.jl", "max_stars_repo_name": "StatisticalRethinkingJulia/SR2TuringPluto.jl", "max_stars_repo_head_hexsha": "bac316572c5a1a59f7b444ab5cf5601c71437fce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2021-12-27T21:58:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T11:10:10.000Z", "max_issues_repo_path": "notebooks/DataFramesPlayground.jl", "max_issues_repo_name": 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{"text": "# This file is a part of Julia. License is MIT: https://julialang.org/license\n\nusing Test, LinearAlgebra, SparseArrays\nlet ambig = detect_ambiguities(SparseArrays; recursive=true)\n    @test_broken isempty(ambig)\n    ambig = Set{Any}(((m1.sig, m2.sig) for (m1, m2) in ambig))\n    expect = []\n\n    push!(expect, (Tuple{typeof(dot),AbstractVector{Tx},Union{SparseVector{Ty,Ti}, SubArray{Ty,1,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},Int},false} where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{Ty,Ti}, SubArray{Ty,1,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}}},false} where var\"#s843\"<:AbstractSparseVector{Ty,Ti}} where Ti} where Ty<:Number where Tx<:Number, Tuple{typeof(dot),Union{SparseVector{Tx,Ti}, SubArray{Tx,1,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},Int},false} where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{Tx,Ti}, SubArray{Tx,1,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}}},false} where var\"#s843\"<:AbstractSparseVector{Tx,Ti}} where Ti,AbstractVector{Ty}} where Ty<:Number where Tx<:Number))\n    push!(expect, (Tuple{typeof(hcat),Vararg{AbstractSparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(hcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Hermitian{T,A} where A<:(Matrix{T} where T) where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\"},N} where N}))\n    push!(expect, (Tuple{typeof(hcat),Vararg{AbstractSparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(hcat),Vararg{SparseArrays.AbstractSparseMatrixCSC,N} where N}, Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(hcat),Vararg{SparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(hcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Hermitian{T,A} where A<:(Matrix{T} where T) where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\"},N} where N}))\n    push!(expect, (Tuple{typeof(hcat),Vararg{SparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(hcat),Vararg{Union{Vector{T} where T, AbstractSparseVector{Tv,Ti} where Ti where Tv},N} where N}, Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(hcat),Vararg{BitVector,N} where N}))\n    push!(expect, (Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(hcat),Vararg{Number,N} where N}))\n    push!(expect, (Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(hcat),Vararg{Union{BitMatrix, BitVector},N} where N}))\n    push!(expect, (Tuple{typeof(hcat),Vararg{Vector{T} where T,N} where N}, Tuple{typeof(hcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(hvcat),Tuple{Vararg{Int,N} where N},Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(hvcat),Tuple{Vararg{Int,N} where N},Vararg{Number,N} where N}))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s806\",var\"#s805\"} where var\"#s805\"<:(LowerTriangular{T,var\"#s804\"} where var\"#s804\"<:(StridedMatrix{T} where T)) where var\"#s806\",StridedVecOrMat{T}} where T<:Union{ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s806\",var\"#s805\"} where var\"#s805\"<:(UnitLowerTriangular{T,var\"#s804\"} where var\"#s804\"<:(StridedMatrix{T} where T)) where var\"#s806\",StridedVecOrMat{T}} where T<:Union{ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s806\",var\"#s805\"} where var\"#s805\"<:(UnitUpperTriangular{T,var\"#s804\"} where var\"#s804\"<:(StridedMatrix{T} where T)) where var\"#s806\",StridedVecOrMat{T}} where T<:Union{ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s806\",var\"#s805\"} where var\"#s805\"<:(UpperTriangular{T,var\"#s804\"} where var\"#s804\"<:(StridedMatrix{T} where T)) where var\"#s806\",StridedVecOrMat{T}} where T<:Union{ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Diagonal{T,V} where V<:AbstractVector{T}) where var\"#s843\",AbstractVecOrMat{T}} where T, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(LU{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(StridedMatrix{T} where T) where var\"#s841\") where var\"#s843\",StridedVecOrMat{T} where T}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:Hessenberg where var\"#s843\",AbstractVecOrMat{T} where T}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s812\",var\"#s811\"} where var\"#s811\"<:(LowerTriangular{T,var\"#s810\"} where var\"#s810\"<:(StridedMatrix{T} where T)) where var\"#s812\",StridedVecOrMat{T}} where T<:Union{Float32, Float64, ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s812\",var\"#s811\"} where var\"#s811\"<:(UnitLowerTriangular{T,var\"#s810\"} where var\"#s810\"<:(StridedMatrix{T} where T)) where var\"#s812\",StridedVecOrMat{T}} where T<:Union{Float32, Float64, ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s812\",var\"#s811\"} where var\"#s811\"<:(UnitUpperTriangular{T,var\"#s810\"} where var\"#s810\"<:(StridedMatrix{T} where T)) where var\"#s812\",StridedVecOrMat{T}} where T<:Union{Float32, Float64, ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s812\",var\"#s811\"} where var\"#s811\"<:(UpperTriangular{T,var\"#s810\"} where var\"#s810\"<:(StridedMatrix{T} where T)) where var\"#s812\",StridedVecOrMat{T}} where T<:Union{Float32, Float64, ComplexF32, ComplexF64}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Diagonal{T,V} where V<:AbstractVector{T}) where var\"#s843\",AbstractVecOrMat{T}} where T, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(LU{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(StridedMatrix{T} where T) where var\"#s841\") where var\"#s843\",StridedVecOrMat{T} where T}, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(ldiv!),Transpose{var\"#s843\",var\"#s842\"} where var\"#s842\"<:LU{T,Tridiagonal{T,V}} where var\"#s843\",AbstractVecOrMat{T} where T} where V where T, Tuple{typeof(ldiv!),Union{Adjoint{T,var\"#s843\"} where var\"#s843\"<:(UpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitUpperTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s843\"} where var\"#s843\"<:(LowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Adjoint{T,var\"#s841\"} where var\"#s841\"<:(UnitLowerTriangular{T,var\"#s840\"} where var\"#s840\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), LowerTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), Transpose{T,var\"#s839\"} where var\"#s839\"<:(UpperTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitUpperTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s839\"} where var\"#s839\"<:(LowerTriangular{T,var\"#s838\"} where var\"#s838\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), Transpose{T,var\"#s837\"} where var\"#s837\"<:(UnitLowerTriangular{T,var\"#s836\"} where var\"#s836\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)), UnitLowerTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UnitUpperTriangular{T,var\"#s842\"} where var\"#s842\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti), UpperTriangular{T,var\"#s843\"} where var\"#s843\"<:(Union{SparseArrays.AbstractSparseMatrixCSC{T,Ti}, SubArray{T,2,var\"#s843\",Tuple{Base.Slice{Base.OneTo{Int}},I},L} where L where var\"#s843\"<:SparseArrays.AbstractSparseMatrixCSC{T,Ti} where I<:AbstractUnitRange} where Ti)},StridedVecOrMat{T}} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{AbstractSparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Hermitian{T,A} where A<:(Matrix{T} where T) where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\"},N} where N}))\n    push!(expect, (Tuple{typeof(vcat),Vararg{AbstractSparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{SparseArrays.AbstractSparseMatrixCSC,N} where N}, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{SparseVector,N} where N}, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{SparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Hermitian{T,A} where A<:(Matrix{T} where T) where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\"},N} where N}))\n    push!(expect, (Tuple{typeof(vcat),Vararg{SparseVector{Tv,Ti},N} where N} where Ti<:Integer where Tv, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, AbstractSparseVector{Tv,Ti} where Ti where Tv},N} where N}, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Hermitian{T,A} where A<:(Matrix{T} where T) where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\"},N} where N}, Tuple{typeof(vcat),Vararg{AbstractRange{T},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, Matrix{T} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:Union{Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:SparseVector where var\"#s843\", Bidiagonal, Diagonal, SymTridiagonal, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:SparseVector where var\"#s841\", Tridiagonal, SparseArrays.AbstractSparseMatrixCSC, SparseVector} where T, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T) where T, Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:(Vector{T} where T) where var\"#s843\", Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:SparseVector where var\"#s843\", Bidiagonal, Diagonal, Hermitian{T,A} where A<:Union{Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:SparseVector where var\"#s843\", Bidiagonal, Diagonal, SymTridiagonal, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:SparseVector where var\"#s841\", Tridiagonal, SparseArrays.AbstractSparseMatrixCSC, SparseVector} where T, Hermitian{T,A} where A<:(Matrix{T} where T) where T, SymTridiagonal, Symmetric{T,A} where A<:Union{Adjoint{var\"#s843\",var\"#s842\"} where var\"#s842\"<:SparseVector where var\"#s843\", Bidiagonal, Diagonal, SymTridiagonal, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:SparseVector where var\"#s841\", Tridiagonal, SparseArrays.AbstractSparseMatrixCSC, SparseVector} where T, Symmetric{T,A} where A<:(Matrix{T} where T) where T, Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:(Vector{T} where T) where var\"#s841\", Transpose{var\"#s841\",var\"#s840\"} where var\"#s840\"<:SparseVector where var\"#s841\", Tridiagonal, SparseArrays.AbstractSparseMatrixCSC, SparseVector},N} where N}, Tuple{typeof(vcat),Vararg{AbstractRange{T},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T} where T, SparseVector},N} where N}, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(vcat),Vararg{AbstractRange{T},N} where N} where T))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(vcat),Vararg{BitMatrix,N} where N}))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(vcat),Vararg{BitVector,N} where N}))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T, Tuple{typeof(vcat),Vararg{Number,N} where N}))\n    push!(expect, (Tuple{typeof(vcat),Vararg{Vector{T} where T,N} where N}, Tuple{typeof(vcat),Vararg{Union{Vector{T}, Matrix{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, LinearAlgebra.AbstractTriangular{T,A} where A<:(Matrix{T} where T), Hermitian{T,A} where A<:(Matrix{T} where T), Symmetric{T,A} where A<:(Matrix{T} where T)},N} where N} where T))\n\n    good = true\n    while !isempty(ambig)\n        sigs = pop!(ambig)\n        i = findfirst(==(sigs), expect)\n        if i === nothing\n            println(stderr, \"push!(expect, (\", sigs[1], \", \", sigs[2], \"))\")\n            good = false\n            continue\n        end\n        deleteat!(expect, i)\n    end\n    @test isempty(expect)\n    @test good\nend\n", "meta": {"hexsha": "f4b7e5d07d578ea35017d126945cf845fbdabed6", "size": 67892, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "stdlib/SparseArrays/test/ambiguous_exec.jl", "max_stars_repo_name": "ExpandingMan/julia", "max_stars_repo_head_hexsha": "0bff5bf0c5afc6ccc05d00572e3ebca4c698db5d", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "stdlib/SparseArrays/test/ambiguous_exec.jl", "max_issues_repo_name": "ExpandingMan/julia", "max_issues_repo_head_hexsha": "0bff5bf0c5afc6ccc05d00572e3ebca4c698db5d", "max_issues_repo_licenses": ["Zlib"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stdlib/SparseArrays/test/ambiguous_exec.jl", "max_forks_repo_name": "ExpandingMan/julia", "max_forks_repo_head_hexsha": "0bff5bf0c5afc6ccc05d00572e3ebca4c698db5d", "max_forks_repo_licenses": ["Zlib"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 1044.4923076923, "max_line_length": 3941, "alphanum_fraction": 0.7480557356, "num_tokens": 21657, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.1871326866957783, "lm_q1q2_score": 0.09794905616769607}}
{"text": "module MathJaxRenderer\n\nusing Artifacts, Librsvg_jll, NodeJS, JSON3\n\nexport Math\n\n\"\"\"\n    Math(src::AbstractString)\n\nWrap a string `src` containing LaTeX math syntax for conversion to one of the\nfollowing formats: `svg`, `png`, `pdf`, or `eps`. The MathJax NodeJS library\nis used to typeset `src`.\n\nWhen writing to either a file (via `write(filename, math)`) or an `IO` object\n(via `show(io, mime, math)`) the following keyword options are available for\neither `show` or `write`:\n\n  - The following apply to the conversion from `tex` to `svg`:\n\n      - `inline = false`, whether to typeset as inline or display math.\n      - `em = 16`, a number giving the number of pixels in an `em` for the surrounding font.\n      - `ex = 8`, a number giving the number of pixels in an `ex` for the surrounding font.\n      - `width = 80em`, a number giving the width of the container, in pixels.\n\n  - and the following apply to conversion from `tex` to `svg`, `png`, `pdf`, and `eps`:\n\n      - `dpi_x = 90`, pixels per inch (horizontal).\n      - `dpi_y = 90`, pixels per inch (vertical).\n      - `x_zoom = 1.0`, zoom factor (horizontal).\n      - `y_zoom = 1.0`, zoom factor (vertical).\n      - `zoom = 1.0`, zoom factor (both horizontal and vertical).\n      - `keep_aspect_ratio = false`, whether to preserve the aspect ratio.\n      - `background_color = :white`, set the background color, CSS color names and syntax.\n      - `unlimited = false`, allow for huge SVG files.\n\nWhen writing to file with `write` the follow extensions are supported:\n\n```julia-repl\njulia> m = Math(\"\\\\sqrt{x^2 + y^2}\");\n\njulia> write(\"math.svg\", m); # MIME(\"image/svg+xml\")\n\njulia> write(\"math.png\", m); # MIME(\"image/png\")\n\njulia> write(\"math.pdf\", m); # MIME(\"application/pdf\")\n\njulia> write(\"math.eps\", m); # MIME(\"application/postscript\")\n\n```\n\nand when using `show` use the noted `MIME` types for each extension.\n\"\"\"\nstruct Math\n    src::String\nend\n\nBase.read(fn::String, ::Type{Math}) = Math(read(fn, String))\nBase.write(fn::String, d::Math; kws...) = open(io -> show(io, mimetype(fn)(), d; kws...), fn, \"w\")\n\nBase.show(io::IO, d::Math) = print(io, \"$Math(...)\")\n\n### Keep next section in sync.\n\nconst SVG = MIME\"image/svg+xml\"\nconst PNG = MIME\"image/png\"\nconst PDF = MIME\"application/pdf\"\nconst EPS = MIME\"application/postscript\"\n\nconst SUPPORTED_MIMES = Union{SVG,PNG,PDF,EPS}\n\nextension(::SVG) = \"svg\"\nextension(::PNG) = \"png\"\nextension(::PDF) = \"pdf\"\nextension(::EPS) = \"eps\"\nextension(other) = error(\"unknown extension '$other'.\")\n\nfunction mimetype(filename::AbstractString)\n    _, ext = splitext(filename)\n    return mimetype(Val{Symbol(ext[2:end])}())\nend\nmimetype(::Val{:svg}) = SVG\nmimetype(::Val{:png}) = PNG\nmimetype(::Val{:pdf}) = PDF\nmimetype(::Val{:eps}) = EPS\nmimetype(::Val{s}) where s = error(\"unknown format '$s'.\")\n\n###\n\nBase.show(io::IO, mime::SUPPORTED_MIMES, d::Math; kws...) = write(io, converter(d, mime; kws...))\n\nmathjax_bin() = joinpath(artifact\"mathjax\", \"index.js\")\nmathjax() = `$(nodejs_cmd()) $(mathjax_bin())`\n\nfunction exec(cmd::Cmd, input::IOBuffer)\n    output, errors = IOBuffer(), IOBuffer()\n    yes = success(pipeline(cmd; stdout = output, stdin = input, stderr = errors))\n    return (yes = yes, io = yes ? output : errors)\nend\nexec(cmd::Cmd, input=\"\") = exec(cmd, IOBuffer(input))\n\nfunction extract_svg(s::String)\n    fn(str, a::UnitRange, b::UnitRange) = String(str[a[begin]:b[end]])\n    fn(str, ::Any, ::Any) = str\n    return fn(s, findfirst(\"<svg\", s), findlast(\"</svg>\", s))\nend\nextract_svg(vec::Vector{UInt8}) = extract_svg(String(vec))\n\nfunction svg_converter(\n    f::Math;\n    inline = false,\n    em = 16,\n    ex = 8,\n    width = 80em,\n    kws...,\n)\n    conf = (\n        src = f.src,\n        config = (\n            display = !inline,\n            em = em,\n            ex = ex,\n            containerWidth = width,\n        ),\n    )\n    str = take!(check(exec(mathjax(), JSON3.write(conf))).io)\n    return IOBuffer(extract_svg(str))\nend\n\nfunction converter(\n    f::Math,\n    m::SUPPORTED_MIMES;\n    dpi_x = 90,\n    dpi_y = 90,\n    x_zoom = 1.0,\n    y_zoom = 1.0,\n    zoom = 1.0,\n    keep_aspect_ratio = false,\n    background_color = :white,\n    unlimited = false,\n    kws...,\n)\n    Librsvg_jll.rsvg_convert() do bin\n        ext = extension(m)\n        cmd = [\n            bin,\n            \"--format=$(extension(m))\",\n            \"--dpi-x=$dpi_x\",\n            \"--dpi-y=$dpi_y\",\n            \"--x-zoom=$x_zoom\",\n            \"--y-zoom=$y_zoom\",\n            \"--zoom=$zoom\",\n            \"--background-color=$background_color\",\n        ]\n        keep_aspect_ratio && push!(cmd, \"--keep-aspect-ratio\")\n        unlimited && push!(cmd, \"--unlimited\")\n        take!(check(exec(Cmd(cmd), svg_converter(f; kws...))).io)\n    end\nend\ncheck(result) = result.yes ? result : error(String(take!(result.io)))\n\nend # module\n", "meta": {"hexsha": "945bc09d0d5d1a3c8b11a3d00c7ba68e91db0094", "size": 4813, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MathJaxRenderer.jl", "max_stars_repo_name": "originalsouth/MathJaxRenderer.jl", "max_stars_repo_head_hexsha": "a993b0bf7ba28e52deeb7b00be275f57cc32d246", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/MathJaxRenderer.jl", "max_issues_repo_name": "originalsouth/MathJaxRenderer.jl", "max_issues_repo_head_hexsha": "a993b0bf7ba28e52deeb7b00be275f57cc32d246", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/MathJaxRenderer.jl", "max_forks_repo_name": "originalsouth/MathJaxRenderer.jl", "max_forks_repo_head_hexsha": "a993b0bf7ba28e52deeb7b00be275f57cc32d246", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7098765432, "max_line_length": 98, "alphanum_fraction": 0.6147932682, "num_tokens": 1356, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.22000709463169618, "lm_q1q2_score": 0.09717119475352295}}
{"text": "for i in 1:10\n    println(\"i is $i\")\nend\n\nfor name in [\"Richard\", \"Sam\", \"Dawn\"]\n    println(name)\nend\n\n# nested loops\nfor i in 1:5, j in 1:3\n    println(i, \" / \", j)\nend\n\n# enumerating over a list\nnames = (\"Richard\", \"Dawn\", \"Sam\", \"Eleanor\", \"Lizzy\")\nfor (idx, val) in enumerate(names)\n    println(idx, \" / \", val)\nend \n\n# break or continue\nfor a in 1:10\n    if isodd(a)\n        continue\n    elseif a > 8\n        break\n    else\n        println(\"a is $a\")\n    end\nend\n\n# global values\nb = 1\nwhile b < 10\n    print(b)\n    global b += 1\nend", "meta": {"hexsha": "47abe2049e5190a072140a0b5872327dce413a72", "size": 539, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "loops.jl", "max_stars_repo_name": "rpillar/JuliaExamples", "max_stars_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "loops.jl", "max_issues_repo_name": "rpillar/JuliaExamples", "max_issues_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "loops.jl", "max_forks_repo_name": "rpillar/JuliaExamples", "max_forks_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.9722222222, "max_line_length": 54, "alphanum_fraction": 0.5584415584, "num_tokens": 190, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.373875808818685, "lm_q2_score": 0.2598256436924554, "lm_q1q2_score": 0.09714252268735223}}
{"text": "\n    # Functions and methods for cascade computation\n\n\n    \"\"\"\n    `Cascade.computeSteps(scheme::Cascade.PhotonIonizationScheme, comp::Cascade.Computation, stepList::Array{Cascade.Step,1})` \n        ... computes in turn all the requested transition amplitudes as well as PhotoIonization.Line's, etc. for all pre-specified \n            (photo-) ionizing steps of the cascade. When compared with standard computations of these atomic processes, however, \n            the amount of output is largely reduced and often just printed into the summary file. A set of  \n            data::Cascade.PhotoIonData  is returned.\n    \"\"\"\n    function computeSteps(scheme::Cascade.PhotonIonizationScheme, comp::Cascade.Computation, stepList::Array{Cascade.Step,1})\n        data = Cascade.PhotoIonData[]\n        printSummary, iostream = Defaults.getDefaults(\"summary flag/stream\")\n        \n        # Perform computations in turn for all photon energies\n        for  photonEnergy in  scheme.photonEnergies\n            nt = 0;   st = 0;   linesP = PhotoIonization.Line[]  \n            settings = PhotoIonization.Settings(PhotoIonization.Settings(), photonEnergies=[Defaults.convertUnits(\"energy: from atomic\", photonEnergy)])\n            println(\"\\n\\n* Perform ionizing computations for the photon energy $photonEnergy  [a.u.]\")\n            if  printSummary   println(iostream, \"\\n\\n* Perform ionizing computations for the photon energy $photonEnergy  [a.u.]\")   end \n            #\n            for  step  in  stepList\n                st = st + 1\n                nc = length(step.initialMultiplet.levels) * length(step.finalMultiplet.levels)\n                println(\"\\n  $st) Perform $(string(step.process)) amplitude computations for up to $nc ionizing lines (without selection rules): \")\n                if  printSummary   println(iostream, \"\\n* $st) Perform $(string(step.process)) amplitude computations for \" *\n                                                     \"up to $nc ionizing lines (without selection rules): \")   end \n          \n                if      step.process == Basics.Photo()\n                    newLines = PhotoIonization.computeLinesCascade(step.finalMultiplet, step.initialMultiplet, comp.nuclearModel, comp.grid, \n                                                                   settings, output=true, printout=true) \n                    append!(linesP, newLines);    nt = length(linesP)\n                else   error(\"Unsupported atomic process for cascade computations.\")\n                end\n                println(\"     Step $st:: A total of $(length(newLines)) $(string(step.process)) lines are calculated, giving now rise \" *\n                        \"to a total of $nt $(string(step.process)) ionizing lines.\" )\n                if  printSummary   println(iostream, \"\\n*    Step $st:: A total of $(length(newLines)) $(string(step.process)) lines are calculated, \" *\n                                                     \"giving now rise to a total of $nt $(string(step.process)) ionizing lines.\" )   end      \n            end\n            #\n            push!(data, Cascade.PhotoIonData(photonEnergy, linesP))\n        end\n        \n        return( data )\n    end\n\n\n    \"\"\"\n    `Cascade.determineSteps(scheme::Cascade.PhotonIonizationScheme, comp::Cascade.Computation, \n                            initialList::Array{Cascade.Block,1}, ionizedList::Array{Cascade.Block,1})`  \n        ... determines all steps::Cascade.Step's that need to be computed for this ionizing cascade. It cycles through all processes \n            of the given ionizing scheme and selects all pairs of initial and ionized blocks due to the selected photon energies and \n            cascade approaches.             It also checks that the (averaged) energies of the configuration support a photoionization \n            energetically.             A stepList::Array{Cascade.Step,1} is returned\n    \"\"\"\n    function determineSteps(scheme::Cascade.PhotonIonizationScheme, comp::Cascade.Computation, \n                            initialList::Array{Cascade.Block,1}, ionizedList::Array{Cascade.Block,1})\n        stepList = Cascade.Step[]\n        if  comp.approach  in  [Cascade.AverageSCA(), Cascade.SCA()]\n            for  initialBlock in initialList\n                for  ionizedBlock in ionizedList\n                    for  process  in  comp.scheme.processes\n                        if      process == Basics.Photo()  \n                            if  initialBlock.NoElectrons == ionizedBlock.NoElectrons + 1\n                                settings = PhotoIonization.Settings()   ##x settings, photonEnergies=[1.0], printBefore=false)\n                                push!( stepList, Cascade.Step(process, settings, initialBlock.confs, ionizedBlock.confs, \n                                                                                 initialBlock.multiplet, ionizedBlock.multiplet) )\n                            end\n                        else    error(\"stop a\")\n                        end\n                    end\n                end\n            end\n            #\n        else  error(\"Unsupported cascade approach.\")\n        end\n        return( stepList )\n    end\n\n\n    \"\"\"\n    `Cascade.generateConfigurationsForPhotoionization(multiplets::Array{Multiplet,1},  maxPhotoElectrons::Int64, \n                                                      maxPhotonEnergy::Float64, nm::Nuclear.Model)`  \n        ... generates all possible (photoionized) configurations with upto maxPhotoElectrons less electrons and whose averaged energy\n            is NOT higher than maxPhotoEnergy above of the given configuration. In the present version, no shake contributions are taken\n            into account. A Tuple(initialConfList::Array{Configuration,1}, confList::Array{Configuration,1}) is returned.\n    \"\"\"\n    function generateConfigurationsForPhotoionization(multiplets::Array{Multiplet,1}, maxPhotoElectrons::Int64, maxPhotonEnergy::Float64, \n                                                      nm::Nuclear.Model)\n        # Determine all (different) configurations from multiplets\n        newconfList = Configuration[]\n        for mp  in  multiplets   \n            confList = Basics.extractNonrelativisticConfigurations(mp.levels[1].basis)\n            for  conf in confList   if  conf in newconfList   nothing   else   push!(newconfList, conf)      end      end\n        end\n        en     = Float64[];   for conf in confList    push!(en, -Semiempirical.estimate(\"binding energy\", round(Int64, nm.Z), conf))   end\n        maxen  = maximum(en)\n        shList = Basics.generate(\"shells: ordered list for NR configurations\", newconfList)\n        \n        # Collect all configuration with up to maxPhotoElectrons less electrons\n        initialConfList = deepcopy(newconfList);      \n        allnewconfList  = Configuration[];    confList = deepcopy(newconfList);    newconfList = Configuration[]\n        for ne = 1:maxPhotoElectrons\n            for  sh in shList\n                for conf in confList\n                    shells = deepcopy(conf.shells);     \n                    if shells[sh] > 0   shells[sh] = shells[sh] - 1;     push!(newconfList, Configuration(shells, conf.NoElectrons - 1))  end\n                end\n            end\n            append!(allnewconfList, newconfList)\n            confList = deepcopy(newconfList);    newconfList = Configuration[]\n        end\n        \n        # Exclude double configurations as well as those with too high average energy\n        ##x confList = Basics.excludeDoubles(allnewconfList)\n        confList = unique(allnewconfList)\n        newconfList = Configuration[]\n        for  conf in confList  \n            enconf = -Semiempirical.estimate(\"binding energy\", round(Int64, nm.Z), conf)\n            if enconf - maxen < maxPhotonEnergy   push!(newconfList, conf)    end\n        end\n\n        return( (initialConfList, newconfList)  )\n    end\n\n\n    \"\"\"\n    `Cascade.perform(scheme::PhotonIonizationScheme, comp::Cascade.Computation)`  \n        ... to set-up and perform a cascade computation that starts from a given set of initial configurations xor initial multiplets\n            and proceeds via photoionizing processes to configurations with a given No of photoelectrons. The results of all individual \n            photoionization steps are comprised into (output) data::PhotoIonData, although all data are only printed and nothing \n            is returned.\n\n    `Cascade.perform(scheme::PhotonIonizationScheme, comp::Cascade.Computation; output=true, outputToFile::Bool=true)`   \n        ... to perform the same but to return the complete output in a dictionary that is written to disk and can be used in subsequent\n            cascade simulation. The particular output also depends on the specifications of the cascade.\n    \"\"\"\n    function perform(scheme::PhotonIonizationScheme, comp::Cascade.Computation; output::Bool=false, outputToFile::Bool=true)\n        if  output    results = Dict{String, Any}()    else    results = nothing    end\n        printSummary, iostream = Defaults.getDefaults(\"summary flag/stream\")\n        #\n        # Perform the SCF and CI computation for the intial-state multiplets if initial configurations are given\n        if  comp.initialConfigs != Configuration[]\n            basis      = Basics.performSCF(comp.initialConfigs, comp.nuclearModel, comp.grid, comp.asfSettings; printout=false)\n            multiplet  = Basics.performCI(basis, comp.nuclearModel, comp.grid, comp.asfSettings; printout=false)\n            multiplets = [Multiplet(\"initial states\", multiplet.levels)]\n        else\n            multiplets = comp.initialMultiplets\n        end\n        # Print out initial configurations and levels \n        Cascade.displayLevels(stdout, multiplets, sa=\"initial \")\n        if  printSummary   Cascade.displayLevels(iostream, multiplets, sa=\"initial \")                            end\n        #\n        # Generate subsequent cascade configurations as well as display and group them together\n        maxen = maximum(comp.scheme.photonEnergies) + 20\n        wa  = Cascade.generateConfigurationsForPhotoionization(multiplets, comp.scheme.maxPhotoElectrons, maxen, comp.nuclearModel)\n        wb1 = Cascade.groupDisplayConfigurationList(comp.nuclearModel.Z, wa[1], sa=\"(initial part of the) ionizing \")\n        wb2 = Cascade.groupDisplayConfigurationList(comp.nuclearModel.Z, wa[2], sa=\"(generated part of the) ionizing \")\n        #\n        # Determine first all configuration 'blocks' and from them the individual steps of the cascade\n        wc1 = Cascade.generateBlocks(comp, wb1, basis.orbitals, sa=\"for the ionizing cascade \")\n        wc2 = Cascade.generateBlocks(comp, wb2, basis.orbitals, sa=\"for the ionizing cascade \", printout=false)\n        Cascade.displayBlocks(stdout, wc1, sa=\"for the (initial part of the) ionizing cascade \");      \n        Cascade.displayBlocks(stdout, wc2, sa=\"for the (generated part of the) ionizing cascade \")\n        if  printSummary   Cascade.displayBlocks(iostream, wc1, sa=\"for the (initial part of the) ionizing cascade \")\n                           Cascade.displayBlocks(iostream, wc2, sa=\"for the (generated part of the) ionizing cascade \")    end      \n        # Determine, modify and compute the transition data for all steps, ie. the PhotoIonization.Line's, etc.\n        gMultiplets = Multiplet[];     for block in wc2  push!(gMultiplets, block.multiplet)    end\n        we = Cascade.determineSteps(scheme, comp, wc1, wc2)\n        Cascade.displaySteps(stdout, we, sa=\"ionizing \")\n        if  printSummary   Cascade.displaySteps(iostream, we, sa=\"ionizing \")    end      \n        wf   = Cascade.modifySteps(we)\n        data = Cascade.computeSteps(scheme, comp, wf)\n        if output    \n            results = Base.merge( results, Dict(\"name\"                       => comp.name) ) \n            results = Base.merge( results, Dict(\"cascade scheme\"             => comp.scheme) ) \n            results = Base.merge( results, Dict(\"initial multiplets:\"        => multiplets) )    \n            results = Base.merge( results, Dict(\"generated multiplets:\"      => gMultiplets) )    \n            results = Base.merge( results, Dict(\"photo-ionizing line data:\"  => data) )\n            #\n            #  Write out the result to file to later continue with simulations on the cascade data\n            filename = \"zzz-cascade-ionizing-computations-\" * string(Dates.now())[1:13] * \".jld\"\n            println(\"\\n* Write all results to disk; use:\\n   JLD.save(''$filename'', results) \\n   using JLD \" *\n                    \"\\n   results = JLD.load(''$filename'')    ... to load the results back from file.\")\n            if  printSummary   println(iostream, \"\\n* Write all results to disk; use:\\n   JLD.save(''$filename'', results) \\n   using JLD \" *\n                                                 \"\\n   results = JLD.load(''$filename'')    ... to load the results back from file.\" )      end      \n            JLD2.@save filename results\n        end\n        ## return( results )\n        return( results )\n    end\n", "meta": {"hexsha": "ea8e49837c5b7ca0d98763770f52939492428a19", "size": 12982, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/module-Cascade-inc-photoionization.jl", "max_stars_repo_name": "mfherbst/JAC.jl", "max_stars_repo_head_hexsha": "43563c049a995817ada86ce82908714d4e1a034b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 115, "max_stars_repo_stars_event_min_datetime": "2019-03-11T11:24:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T22:33:28.000Z", "max_issues_repo_path": "src/module-Cascade-inc-photoionization.jl", "max_issues_repo_name": "mfherbst/JAC.jl", "max_issues_repo_head_hexsha": "43563c049a995817ada86ce82908714d4e1a034b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2019-04-02T22:04:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-06T06:21:57.000Z", "max_forks_repo_path": "src/module-Cascade-inc-photoionization.jl", "max_forks_repo_name": "mfherbst/JAC.jl", "max_forks_repo_head_hexsha": "43563c049a995817ada86ce82908714d4e1a034b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2019-03-20T10:28:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-04T12:51:19.000Z", "avg_line_length": 67.2642487047, "max_line_length": 152, "alphanum_fraction": 0.6188568788, "num_tokens": 2805, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.17781086512112404, "lm_q1q2_score": 0.09583707275624684}}
{"text": "__precompile__()\nmodule TinySegmenter\n\nexport tokenize\n\n# make a tuple of Char from a string\nmacro c_str(s)\n  tuple(s...)\nend\n\n# make a tuple of UInt8s from an ASCII string\nmacro i_str(s)\n  tuple(UInt8[UInt8(c) for c in s]...)\nend\n\n# make a Dict{UInt8,Int} from Char=>Int pairs\ndict_c2i(p::Pair{Char,Int}...) = Dict{UInt8,Int}(map(p -> Pair(UInt8(p[1]),p[2]), p))\n\n# Use out of range of Unicode code point. See also: https://en.wikipedia.org/wiki/Code_point\nconst B1 = Char(0x110001)\nconst B2 = Char(0x110002)\nconst B3 = Char(0x110003)\nconst E1 = Char(0x110004)\nconst E2 = Char(0x110005)\n\nconst BIAS = -332\n\nconst BC1 = Dict{Tuple{UInt8,UInt8},Int}(i\"HH\" => 6, i\"II\" => 2461, i\"KH\" => 406, i\"OH\" => -1378)\nconst BC2 = Dict{Tuple{UInt8,UInt8},Int}(i\"AA\" => -3267, i\"AI\" => 2744, i\"AN\" => -878, i\"HH\" => -4070, i\"HM\" => -1711, i\"HN\" => 4012, i\"HO\" => 3761, i\"IA\" => 1327, i\"IH\" => -1184, i\"II\" => -1332, i\"IK\" => 1721, i\"IO\" => 5492, i\"KI\" => 3831, i\"KK\" => -8741, i\"MH\" => -3132, i\"MK\" => 3334, i\"OO\" => -2920)\nconst BC3 = Dict{Tuple{UInt8,UInt8},Int}(i\"HH\" => 996, i\"HI\" => 626, i\"HK\" => -721, i\"HN\" => -1307, i\"HO\" => -836, i\"IH\" => -301, i\"KK\" => 2762, i\"MK\" => 1079, i\"MM\" => 4034, i\"OA\" => -1652, i\"OH\" => 266)\nconst BP1 = Dict{Tuple{UInt8,UInt8},Int}(i\"BB\" => 295, i\"OB\" => 304, i\"OO\" => -125, i\"UB\" => 352)\nconst BP2 = Dict{Tuple{UInt8,UInt8},Int}(i\"BO\" => 60, i\"OO\" => -1762)\nconst BQ1 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"BHH\" => 1150, i\"BHM\" => 1521, i\"BII\" => -1158, i\"BIM\" => 886, i\"BMH\" => 1208, i\"BNH\" => 449, i\"BOH\" => -91, i\"BOO\" => -2597, i\"OHI\" => 451, i\"OIH\" => -296, i\"OKA\" => 1851, i\"OKH\" => -1020, i\"OKK\" => 904, i\"OOO\" => 2965)\nconst BQ2 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"BHH\" => 118, i\"BHI\" => -1159, i\"BHM\" => 466, i\"BIH\" => -919, i\"BKK\" => -1720, i\"BKO\" => 864, i\"OHH\" => -1139, i\"OHM\" => -181, i\"OIH\" => 153, i\"UHI\" => -1146)\nconst BQ3 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"BHH\" => -792, i\"BHI\" => 2664, i\"BII\" => -299, i\"BKI\" => 419, i\"BMH\" => 937, i\"BMM\" => 8335, i\"BNN\" => 998, i\"BOH\" => 775, i\"OHH\" => 2174, i\"OHM\" => 439, i\"OII\" => 280, i\"OKH\" => 1798, i\"OKI\" => -793, i\"OKO\" => -2242, i\"OMH\" => -2402, i\"OOO\" => 11699)\nconst BQ4 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"BHH\" => -3895, i\"BIH\" => 3761, i\"BII\" => -4654, i\"BIK\" => 1348, i\"BKK\" => -1806, i\"BMI\" => -3385, i\"BOO\" => -12396, i\"OAH\" => 926, i\"OHH\" => 266, i\"OHK\" => -2036, i\"ONN\" => -973)\nconst BW1 = Dict{Tuple{Char,Char},Int}(c\",\u3068\" => 660, c\",\u540c\" => 727, (B1,'\u3042') => 1404, (B1,'\u540c') => 542, c\"\u3001\u3068\" => 660, c\"\u3001\u540c\" => 727, c\"\u300d\u3068\" => 1682, c\"\u3042\u3063\" => 1505, c\"\u3044\u3046\" => 1743, c\"\u3044\u3063\" => -2055, c\"\u3044\u308b\" => 672, c\"\u3046\u3057\" => -4817, c\"\u3046\u3093\" => 665, c\"\u304b\u3089\" => 3472, c\"\u304c\u3089\" => 600, c\"\u3053\u3046\" => -790, c\"\u3053\u3068\" => 2083, c\"\u3053\u3093\" => -1262, c\"\u3055\u3089\" => -4143, c\"\u3055\u3093\" => 4573, c\"\u3057\u305f\" => 2641, c\"\u3057\u3066\" => 1104, c\"\u3059\u3067\" => -3399, c\"\u305d\u3053\" => 1977, c\"\u305d\u308c\" => -871, c\"\u305f\u3061\" => 1122, c\"\u305f\u3081\" => 601, c\"\u3063\u305f\" => 3463, c\"\u3064\u3044\" => -802, c\"\u3066\u3044\" => 805, c\"\u3066\u304d\" => 1249, c\"\u3067\u304d\" => 1127, c\"\u3067\u3059\" => 3445, c\"\u3067\u306f\" => 844, c\"\u3068\u3044\" => -4915, c\"\u3068\u307f\" => 1922, c\"\u3069\u3053\" => 3887, c\"\u306a\u3044\" => 5713, c\"\u306a\u3063\" => 3015, c\"\u306a\u3069\" => 7379, c\"\u306a\u3093\" => -1113, c\"\u306b\u3057\" => 2468, c\"\u306b\u306f\" => 1498, c\"\u306b\u3082\" => 1671, c\"\u306b\u5bfe\" => -912, c\"\u306e\u4e00\" => -501, c\"\u306e\u4e2d\" => 741, c\"\u307e\u305b\" => 2448, c\"\u307e\u3067\" => 1711, c\"\u307e\u307e\" => 2600, c\"\u307e\u308b\" => -2155, c\"\u3084\u3080\" => -1947, c\"\u3088\u3063\" => -2565, c\"\u308c\u305f\" => 2369, c\"\u308c\u3067\" => -913, c\"\u3092\u3057\" => 1860, c\"\u3092\u898b\" => 731, c\"\u4ea1\u304f\" => -1886, c\"\u4eac\u90fd\" => 2558, c\"\u53d6\u308a\" => -2784, c\"\u5927\u304d\" => -2604, c\"\u5927\u962a\" => 1497, c\"\u5e73\u65b9\" => -2314, c\"\u5f15\u304d\" => -1336, c\"\u65e5\u672c\" => -195, c\"\u672c\u5f53\" => -2423, c\"\u6bce\u65e5\" => -2113, c\"\u76ee\u6307\" => -724, (B1,'\u3042') => 1404, (B1,'\u540c') => 542, c\"\uff63\u3068\" => 1682)\nconst BW2 = Dict{Tuple{Char,Char},Int}(c\"..\" => -11822, c\"11\" => -669, c\"\u2015\u2015\" => -5730, c\"\u2212\u2212\" => -13175, c\"\u3044\u3046\" => -1609, c\"\u3046\u304b\" => 2490, c\"\u304b\u3057\" => -1350, c\"\u304b\u3082\" => -602, c\"\u304b\u3089\" => -7194, c\"\u304b\u308c\" => 4612, c\"\u304c\u3044\" => 853, c\"\u304c\u3089\" => -3198, c\"\u304d\u305f\" => 1941, c\"\u304f\u306a\" => -1597, c\"\u3053\u3068\" => -8392, c\"\u3053\u306e\" => -4193, c\"\u3055\u305b\" => 4533, c\"\u3055\u308c\" => 13168, c\"\u3055\u3093\" => -3977, c\"\u3057\u3044\" => -1819, c\"\u3057\u304b\" => -545, c\"\u3057\u305f\" => 5078, c\"\u3057\u3066\" => 972, c\"\u3057\u306a\" => 939, c\"\u305d\u306e\" => -3744, c\"\u305f\u3044\" => -1253, c\"\u305f\u305f\" => -662, c\"\u305f\u3060\" => -3857, c\"\u305f\u3061\" => -786, c\"\u305f\u3068\" => 1224, c\"\u305f\u306f\" => -939, c\"\u3063\u305f\" => 4589, c\"\u3063\u3066\" => 1647, c\"\u3063\u3068\" => -2094, c\"\u3066\u3044\" => 6144, c\"\u3066\u304d\" => 3640, c\"\u3066\u304f\" => 2551, c\"\u3066\u306f\" => -3110, c\"\u3066\u3082\" => -3065, c\"\u3067\u3044\" => 2666, c\"\u3067\u304d\" => -1528, c\"\u3067\u3057\" => -3828, c\"\u3067\u3059\" => -4761, c\"\u3067\u3082\" => -4203, c\"\u3068\u3044\" => 1890, c\"\u3068\u3053\" => -1746, c\"\u3068\u3068\" => -2279, c\"\u3068\u306e\" => 720, c\"\u3068\u307f\" => 5168, c\"\u3068\u3082\" => -3941, c\"\u306a\u3044\" => -2488, c\"\u306a\u304c\" => -1313, c\"\u306a\u3069\" => -6509, c\"\u306a\u306e\" => 2614, c\"\u306a\u3093\" => 3099, c\"\u306b\u304a\" => -1615, c\"\u306b\u3057\" => 2748, c\"\u306b\u306a\" => 2454, c\"\u306b\u3088\" => -7236, c\"\u306b\u5bfe\" => -14943, c\"\u306b\u5f93\" => -4688, c\"\u306b\u95a2\" => -11388, c\"\u306e\u304b\" => 2093, c\"\u306e\u3067\" => -7059, c\"\u306e\u306b\" => -6041, c\"\u306e\u306e\" => -6125, c\"\u306f\u3044\" => 1073, c\"\u306f\u304c\" => -1033, c\"\u306f\u305a\" => -2532, c\"\u3070\u308c\" => 1813, c\"\u307e\u3057\" => -1316, c\"\u307e\u3067\" => -6621, c\"\u307e\u308c\" => 5409, c\"\u3081\u3066\" => -3153, c\"\u3082\u3044\" => 2230, c\"\u3082\u306e\" => -10713, c\"\u3089\u304b\" => -944, c\"\u3089\u3057\" => -1611, c\"\u3089\u306b\" => -1897, c\"\u308a\u3057\" => 651, c\"\u308a\u307e\" => 1620, c\"\u308c\u305f\" => 4270, c\"\u308c\u3066\" => 849, c\"\u308c\u3070\" => 4114, c\"\u308d\u3046\" => 6067, c\"\u308f\u308c\" => 7901, c\"\u3092\u901a\" => -11877, c\"\u3093\u3060\" => 728, c\"\u3093\u306a\" => -4115, c\"\u4e00\u4eba\" => 602, c\"\u4e00\u65b9\" => -1375, c\"\u4e00\u65e5\" => 970, c\"\u4e00\u90e8\" => -1051, c\"\u4e0a\u304c\" => -4479, c\"\u4f1a\u793e\" => -1116, c\"\u51fa\u3066\" => 2163, c\"\u5206\u306e\" => -7758, c\"\u540c\u515a\" => 970, c\"\u540c\u65e5\" => -913, c\"\u5927\u962a\" => -2471, c\"\u59d4\u54e1\" => -1250, c\"\u5c11\u306a\" => -1050, c\"\u5e74\u5ea6\" => -8669, c\"\u5e74\u9593\" => -1626, c\"\u5e9c\u770c\" => -2363, c\"\u624b\u6a29\" => -1982, c\"\u65b0\u805e\" => -4066, c\"\u65e5\u65b0\" => -722, c\"\u65e5\u672c\" => -7068, c\"\u65e5\u7c73\" => 3372, c\"\u66dc\u65e5\" => -601, c\"\u671d\u9bae\" => -2355, c\"\u672c\u4eba\" => -2697, c\"\u6771\u4eac\" => -1543, c\"\u7136\u3068\" => -1384, c\"\u793e\u4f1a\" => -1276, c\"\u7acb\u3066\" => -990, c\"\u7b2c\u306b\" => -1612, c\"\u7c73\u56fd\" => -4268, c\"\uff11\uff11\" => -669, c\"\uff78\uff9e\" => 1319)\nconst BW3 = Dict{Tuple{Char,Char},Int}(c\"\u3042\u305f\" => -2194, c\"\u3042\u308a\" => 719, c\"\u3042\u308b\" => 3846, c\"\u3044.\" => -1185, c\"\u3044\u3002\" => -1185, c\"\u3044\u3044\" => 5308, c\"\u3044\u3048\" => 2079, c\"\u3044\u304f\" => 3029, c\"\u3044\u305f\" => 2056, c\"\u3044\u3063\" => 1883, c\"\u3044\u308b\" => 5600, c\"\u3044\u308f\" => 1527, c\"\u3046\u3061\" => 1117, c\"\u3046\u3068\" => 4798, c\"\u3048\u3068\" => 1454, c\"\u304b.\" => 2857, c\"\u304b\u3002\" => 2857, c\"\u304b\u3051\" => -743, c\"\u304b\u3063\" => -4098, c\"\u304b\u306b\" => -669, c\"\u304b\u3089\" => 6520, c\"\u304b\u308a\" => -2670, c\"\u304c,\" => 1816, c\"\u304c\u3001\" => 1816, c\"\u304c\u304d\" => -4855, c\"\u304c\u3051\" => -1127, c\"\u304c\u3063\" => -913, c\"\u304c\u3089\" => -4977, c\"\u304c\u308a\" => -2064, c\"\u304d\u305f\" => 1645, c\"\u3051\u3069\" => 1374, c\"\u3053\u3068\" => 7397, c\"\u3053\u306e\" => 1542, c\"\u3053\u308d\" => -2757, c\"\u3055\u3044\" => -714, c\"\u3055\u3092\" => 976, c\"\u3057,\" => 1557, c\"\u3057\u3001\" => 1557, c\"\u3057\u3044\" => -3714, c\"\u3057\u305f\" => 3562, c\"\u3057\u3066\" => 1449, c\"\u3057\u306a\" => 2608, c\"\u3057\u307e\" => 1200, c\"\u3059.\" => -1310, c\"\u3059\u3002\" => -1310, c\"\u3059\u308b\" => 6521, c\"\u305a,\" => 3426, c\"\u305a\u3001\" => 3426, c\"\u305a\u306b\" => 841, c\"\u305d\u3046\" => 428, c\"\u305f.\" => 8875, c\"\u305f\u3002\" => 8875, c\"\u305f\u3044\" => -594, c\"\u305f\u306e\" => 812, c\"\u305f\u308a\" => -1183, c\"\u305f\u308b\" => -853, c\"\u3060.\" => 4098, c\"\u3060\u3002\" => 4098, c\"\u3060\u3063\" => 1004, c\"\u3063\u305f\" => -4748, c\"\u3063\u3066\" => 300, c\"\u3066\u3044\" => 6240, c\"\u3066\u304a\" => 855, c\"\u3066\u3082\" => 302, c\"\u3067\u3059\" => 1437, c\"\u3067\u306b\" => -1482, c\"\u3067\u306f\" => 2295, c\"\u3068\u3046\" => -1387, c\"\u3068\u3057\" => 2266, c\"\u3068\u306e\" => 541, c\"\u3068\u3082\" => -3543, c\"\u3069\u3046\" => 4664, c\"\u306a\u3044\" => 1796, c\"\u306a\u304f\" => -903, c\"\u306a\u3069\" => 2135, c\"\u306b,\" => -1021, c\"\u306b\u3001\" => -1021, c\"\u306b\u3057\" => 1771, c\"\u306b\u306a\" => 1906, c\"\u306b\u306f\" => 2644, c\"\u306e,\" => -724, c\"\u306e\u3001\" => -724, c\"\u306e\u5b50\" => -1000, c\"\u306f,\" => 1337, c\"\u306f\u3001\" => 1337, c\"\u3079\u304d\" => 2181, c\"\u307e\u3057\" => 1113, c\"\u307e\u3059\" => 6943, c\"\u307e\u3063\" => -1549, c\"\u307e\u3067\" => 6154, c\"\u307e\u308c\" => -793, c\"\u3089\u3057\" => 1479, c\"\u3089\u308c\" => 6820, c\"\u308b\u308b\" => 3818, c\"\u308c,\" => 854, c\"\u308c\u3001\" => 854, c\"\u308c\u305f\" => 1850, c\"\u308c\u3066\" => 1375, c\"\u308c\u3070\" => -3246, c\"\u308c\u308b\" => 1091, c\"\u308f\u308c\" => -605, c\"\u3093\u3060\" => 606, c\"\u3093\u3067\" => 798, c\"\u30ab\u6708\" => 990, c\"\u4f1a\u8b70\" => 860, c\"\u5165\u308a\" => 1232, c\"\u5927\u4f1a\" => 2217, c\"\u59cb\u3081\" => 1681, c\"\u5e02 \" => 965, c\"\u65b0\u805e\" => -5055, c\"\u65e5,\" => 974, c\"\u65e5\u3001\" => 974, c\"\u793e\u4f1a\" => 2024, c\"\uff76\u6708\" => 990)\nconst TC1 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"AAA\" => 1093, i\"HHH\" => 1029, i\"HHM\" => 580, i\"HII\" => 998, i\"HOH\" => -390, i\"HOM\" => -331, i\"IHI\" => 1169, i\"IOH\" => -142, i\"IOI\" => -1015, i\"IOM\" => 467, i\"MMH\" => 187, i\"OOI\" => -1832)\nconst TC2 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"HHO\" => 2088, i\"HII\" => -1023, i\"HMM\" => -1154, i\"IHI\" => -1965, i\"KKH\" => 703, i\"OII\" => -2649)\nconst TC3 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"AAA\" => -294, i\"HHH\" => 346, i\"HHI\" => -341, i\"HII\" => -1088, i\"HIK\" => 731, i\"HOH\" => -1486, i\"IHH\" => 128, i\"IHI\" => -3041, i\"IHO\" => -1935, i\"IIH\" => -825, i\"IIM\" => -1035, i\"IOI\" => -542, i\"KHH\" => -1216, i\"KKA\" => 491, i\"KKH\" => -1217, i\"KOK\" => -1009, i\"MHH\" => -2694, i\"MHM\" => -457, i\"MHO\" => 123, i\"MMH\" => -471, i\"NNH\" => -1689, i\"NNO\" => 662, i\"OHO\" => -3393)\nconst TC4 = Dict{Tuple{UInt8,UInt8,UInt8},Int}(i\"HHH\" => -203, i\"HHI\" => 1344, i\"HHK\" => 365, i\"HHM\" => -122, i\"HHN\" => 182, i\"HHO\" => 669, i\"HIH\" => 804, i\"HII\" => 679, i\"HOH\" => 446, i\"IHH\" => 695, i\"IHO\" => -2324, i\"IIH\" => 321, i\"III\" => 1497, i\"IIO\" => 656, i\"IOO\" => 54, i\"KAK\" => 4845, i\"KKA\" => 3386, i\"KKK\" => 3065, i\"MHH\" => -405, i\"MHI\" => 201, i\"MMH\" => -241, i\"MMM\" => 661, i\"MOM\" => 841)\nconst TQ1 = Dict{Tuple{UInt8,UInt8,UInt8,UInt8},Int}(i\"BHHH\" => -227, i\"BHHI\" => 316, i\"BHIH\" => -132, i\"BIHH\" => 60, i\"BIII\" => 1595, i\"BNHH\" => -744, i\"BOHH\" => 225, i\"BOOO\" => -908, i\"OAKK\" => 482, i\"OHHH\" => 281, i\"OHIH\" => 249, i\"OIHI\" => 200, i\"OIIH\" => -68)\nconst TQ2 = Dict{Tuple{UInt8,UInt8,UInt8,UInt8},Int}(i\"BIHH\" => -1401, i\"BIII\" => -1033, i\"BKAK\" => -543, i\"BOOO\" => -5591)\nconst TQ3 = Dict{Tuple{UInt8,UInt8,UInt8,UInt8},Int}(i\"BHHH\" => 478, i\"BHHM\" => -1073, i\"BHIH\" => 222, i\"BHII\" => -504, i\"BIIH\" => -116, i\"BIII\" => -105, i\"BMHI\" => -863, i\"BMHM\" => -464, i\"BOMH\" => 620, i\"OHHH\" => 346, i\"OHHI\" => 1729, i\"OHII\" => 997, i\"OHMH\" => 481, i\"OIHH\" => 623, i\"OIIH\" => 1344, i\"OKAK\" => 2792, i\"OKHH\" => 587, i\"OKKA\" => 679, i\"OOHH\" => 110, i\"OOII\" => -685)\nconst TQ4 = Dict{Tuple{UInt8,UInt8,UInt8,UInt8},Int}(i\"BHHH\" => -721, i\"BHHM\" => -3604, i\"BHII\" => -966, i\"BIIH\" => -607, i\"BIII\" => -2181, i\"OAAA\" => -2763, i\"OAKK\" => 180, i\"OHHH\" => -294, i\"OHHI\" => 2446, i\"OHHO\" => 480, i\"OHIH\" => -1573, i\"OIHH\" => 1935, i\"OIHI\" => -493, i\"OIIH\" => 626, i\"OIII\" => -4007, i\"OKAK\" => -8156)\nconst TW1 = Dict{Tuple{Char,Char,Char},Int}(c\"\u306b\u3064\u3044\" => -4681, c\"\u6771\u4eac\u90fd\" => 2026)\nconst TW2 = Dict{Tuple{Char,Char,Char},Int}(c\"\u3042\u308b\u7a0b\" => -2049, c\"\u3044\u3063\u305f\" => -1256, c\"\u3053\u308d\u304c\" => -2434, c\"\u3057\u3087\u3046\" => 3873, c\"\u305d\u306e\u5f8c\" => -4430, c\"\u3060\u3063\u3066\" => -1049, c\"\u3066\u3044\u305f\" => 1833, c\"\u3068\u3057\u3066\" => -4657, c\"\u3068\u3082\u306b\" => -4517, c\"\u3082\u306e\u3067\" => 1882, c\"\u4e00\u6c17\u306b\" => -792, c\"\u521d\u3081\u3066\" => -1512, c\"\u540c\u6642\u306b\" => -8097, c\"\u5927\u304d\u306a\" => -1255, c\"\u5bfe\u3057\u3066\" => -2721, c\"\u793e\u4f1a\u515a\" => -3216)\nconst TW3 = Dict{Tuple{Char,Char,Char},Int}(c\"\u3044\u305f\u3060\" => -1734, c\"\u3057\u3066\u3044\" => 1314, c\"\u3068\u3057\u3066\" => -4314, c\"\u306b\u3064\u3044\" => -5483, c\"\u306b\u3068\u3063\" => -5989, c\"\u306b\u5f53\u305f\" => -6247, c\"\u306e\u3067,\" => -727, c\"\u306e\u3067\u3001\" => -727, c\"\u306e\u3082\u306e\" => -600, c\"\u308c\u304b\u3089\" => -3752, c\"\u5341\u4e8c\u6708\" => -2287)\nconst TW4 = Dict{Tuple{Char,Char,Char},Int}(c\"\u3044\u3046.\" => 8576, c\"\u3044\u3046\u3002\" => 8576, c\"\u304b\u3089\u306a\" => -2348, c\"\u3057\u3066\u3044\" => 2958, c\"\u305f\u304c,\" => 1516, c\"\u305f\u304c\u3001\" => 1516, c\"\u3066\u3044\u308b\" => 1538, c\"\u3068\u3044\u3046\" => 1349, c\"\u307e\u3057\u305f\" => 5543, c\"\u307e\u305b\u3093\" => 1097, c\"\u3088\u3046\u3068\" => -4258, c\"\u3088\u308b\u3068\" => 5865)\nconst UC1 = dict_c2i('A' => 484, 'K' => 93, 'M' => 645, 'O' => -505)\nconst UC2 = dict_c2i('A' => 819, 'H' => 1059, 'I' => 409, 'M' => 3987, 'N' => 5775, 'O' => 646)\nconst UC3 = dict_c2i('A' => -1370, 'I' => 2311)\nconst UC4 = dict_c2i('A' => -2643, 'H' => 1809, 'I' => -1032, 'K' => -3450, 'M' => 3565, 'N' => 3876, 'O' => 6646)\nconst UC5 = dict_c2i('H' => 313, 'I' => -1238, 'K' => -799, 'M' => 539, 'O' => -831)\nconst UC6 = dict_c2i('H' => -506, 'I' => -253, 'K' => 87, 'M' => 247, 'O' => -387)\nconst UP1 = dict_c2i('O' => -214)\nconst UP2 = dict_c2i('B' => 69, 'O' => 935)\nconst UP3 = dict_c2i('B' => 189)\nconst UQ1 = Dict{Tuple{UInt8,UInt8},Int}(i\"BH\" => 21, i\"BI\" => -12, i\"BK\" => -99, i\"BN\" => 142, i\"BO\" => -56, i\"OH\" => -95, i\"OI\" => 477, i\"OK\" => 410, i\"OO\" => -2422)\nconst UQ2 = Dict{Tuple{UInt8,UInt8},Int}(i\"BH\" => 216, i\"BI\" => 113, i\"OK\" => 1759)\nconst UQ3 = Dict{Tuple{UInt8,UInt8},Int}(i\"BA\" => -479, i\"BH\" => 42, i\"BI\" => 1913, i\"BK\" => -7198, i\"BM\" => 3160, i\"BN\" => 6427, i\"BO\" => 14761, i\"OI\" => -827, i\"ON\" => -3212)\nconst UW1 = Dict{Char,Int}(',' => 156, '\u3001' => 156, '\u300c' => -463, '\u3042' => -941, '\u3046' => -127, '\u304c' => -553, '\u304d' => 121, '\u3053' => 505, '\u3067' => -201, '\u3068' => -547, '\u3069' => -123, '\u306b' => -789, '\u306e' => -185, '\u306f' => -847, '\u3082' => -466, '\u3084' => -470, '\u3088' => 182, '\u3089' => -292, '\u308a' => 208, '\u308c' => 169, '\u3092' => -446, '\u3093' => -137, '\u30fb' => -135, '\u4e3b' => -402, '\u4eac' => -268, '\u533a' => -912, '\u5348' => 871, '\u56fd' => -460, '\u5927' => 561, '\u59d4' => 729, '\u5e02' => -411, '\u65e5' => -141, '\u7406' => 361, '\u751f' => -408, '\u770c' => -386, '\u90fd' => -718, '\uff62' => -463, '\uff65' => -135)\nconst UW2 = Dict{Char,Int}(',' => -829, '\u3001' => -829, '\u3007' => 892, '\u300c' => -645, '\u300d' => 3145, '\u3042' => -538, '\u3044' => 505, '\u3046' => 134, '\u304a' => -502, '\u304b' => 1454, '\u304c' => -856, '\u304f' => -412, '\u3053' => 1141, '\u3055' => 878, '\u3056' => 540, '\u3057' => 1529, '\u3059' => -675, '\u305b' => 300, '\u305d' => -1011, '\u305f' => 188, '\u3060' => 1837, '\u3064' => -949, '\u3066' => -291, '\u3067' => -268, '\u3068' => -981, '\u3069' => 1273, '\u306a' => 1063, '\u306b' => -1764, '\u306e' => 130, '\u306f' => -409, '\u3072' => -1273, '\u3079' => 1261, '\u307e' => 600, '\u3082' => -1263, '\u3084' => -402, '\u3088' => 1639, '\u308a' => -579, '\u308b' => -694, '\u308c' => 571, '\u3092' => -2516, '\u3093' => 2095, '\u30a2' => -587, '\u30ab' => 306, '\u30ad' => 568, '\u30c3' => 831, '\u4e09' => -758, '\u4e0d' => -2150, '\u4e16' => -302, '\u4e2d' => -968, '\u4e3b' => -861, '\u4e8b' => 492, '\u4eba' => -123, '\u4f1a' => 978, '\u4fdd' => 362, '\u5165' => 548, '\u521d' => -3025, '\u526f' => -1566, '\u5317' => -3414, '\u533a' => -422, '\u5927' => -1769, '\u5929' => -865, '\u592a' => -483, '\u5b50' => -1519, '\u5b66' => 760, '\u5b9f' => 1023, '\u5c0f' => -2009, '\u5e02' => -813, '\u5e74' => -1060, '\u5f37' => 1067, '\u624b' => -1519, '\u63fa' => -1033, '\u653f' => 1522, '\u6587' => -1355, '\u65b0' => -1682, '\u65e5' => -1815, '\u660e' => -1462, '\u6700' => -630, '\u671d' => -1843, '\u672c' => -1650, '\u6771' => -931, '\u679c' => -665, '\u6b21' => -2378, '\u6c11' => -180, '\u6c17' => -1740, '\u7406' => 752, '\u767a' => 529, '\u76ee' => -1584, '\u76f8' => -242, '\u770c' => -1165, '\u7acb' => -763, '\u7b2c' => 810, '\u7c73' => 509, '\u81ea' => -1353, '\u884c' => 838, '\u897f' => -744, '\u898b' => -3874, '\u8abf' => 1010, '\u8b70' => 1198, '\u8fbc' => 3041, '\u958b' => 1758, '\u9593' => -1257, '\uff62' => -645, '\uff63' => 3145, '\uff6f' => 831, '\uff71' => -587, '\uff76' => 306, '\uff77' => 568)\nconst UW3 = Dict{Char,Int}(',' => 4889, '1' => -800, '\u2212' => -1723, '\u3001' => 4889, '\u3005' => -2311, '\u3007' => 5827, '\u300d' => 2670, '\u3013' => -3573, '\u3042' => -2696, '\u3044' => 1006, '\u3046' => 2342, '\u3048' => 1983, '\u304a' => -4864, '\u304b' => -1163, '\u304c' => 3271, '\u304f' => 1004, '\u3051' => 388, '\u3052' => 401, '\u3053' => -3552, '\u3054' => -3116, '\u3055' => -1058, '\u3057' => -395, '\u3059' => 584, '\u305b' => 3685, '\u305d' => -5228, '\u305f' => 842, '\u3061' => -521, '\u3063' => -1444, '\u3064' => -1081, '\u3066' => 6167, '\u3067' => 2318, '\u3068' => 1691, '\u3069' => -899, '\u306a' => -2788, '\u306b' => 2745, '\u306e' => 4056, '\u306f' => 4555, '\u3072' => -2171, '\u3075' => -1798, '\u3078' => 1199, '\u307b' => -5516, '\u307e' => -4384, '\u307f' => -120, '\u3081' => 1205, '\u3082' => 2323, '\u3084' => -788, '\u3088' => -202, '\u3089' => 727, '\u308a' => 649, '\u308b' => 5905, '\u308c' => 2773, '\u308f' => -1207, '\u3092' => 6620, '\u3093' => -518, '\u30a2' => 551, '\u30b0' => 1319, '\u30b9' => 874, '\u30c3' => -1350, '\u30c8' => 521, '\u30e0' => 1109, '\u30eb' => 1591, '\u30ed' => 2201, '\u30f3' => 278, '\u30fb' => -3794, '\u4e00' => -1619, '\u4e0b' => -1759, '\u4e16' => -2087, '\u4e21' => 3815, '\u4e2d' => 653, '\u4e3b' => -758, '\u4e88' => -1193, '\u4e8c' => 974, '\u4eba' => 2742, '\u4eca' => 792, '\u4ed6' => 1889, '\u4ee5' => -1368, '\u4f4e' => 811, '\u4f55' => 4265, '\u4f5c' => -361, '\u4fdd' => -2439, '\u5143' => 4858, '\u515a' => 3593, '\u5168' => 1574, '\u516c' => -3030, '\u516d' => 755, '\u5171' => -1880, '\u5186' => 5807, '\u518d' => 3095, '\u5206' => 457, '\u521d' => 2475, '\u5225' => 1129, '\u524d' => 2286, '\u526f' => 4437, '\u529b' => 365, '\u52d5' => -949, '\u52d9' => -1872, '\u5316' => 1327, '\u5317' => -1038, '\u533a' => 4646, '\u5343' => -2309, '\u5348' => -783, '\u5354' => -1006, '\u53e3' => 483, '\u53f3' => 1233, '\u5404' => 3588, '\u5408' => -241, '\u540c' => 3906, '\u548c' => -837, '\u54e1' => 4513, '\u56fd' => 642, '\u578b' => 1389, '\u5834' => 1219, '\u5916' => -241, '\u59bb' => 2016, '\u5b66' => -1356, '\u5b89' => -423, '\u5b9f' => -1008, '\u5bb6' => 1078, '\u5c0f' => -513, '\u5c11' => -3102, '\u5dde' => 1155, '\u5e02' => 3197, '\u5e73' => -1804, '\u5e74' => 2416, '\u5e83' => -1030, '\u5e9c' => 1605, '\u5ea6' => 1452, '\u5efa' => -2352, '\u5f53' => -3885, '\u5f97' => 1905, '\u601d' => -1291, '\u6027' => 1822, '\u6238' => -488, '\u6307' => -3973, '\u653f' => -2013, '\u6559' => -1479, '\u6570' => 3222, '\u6587' => -1489, '\u65b0' => 1764, '\u65e5' => 2099, '\u65e7' => 5792, '\u6628' => -661, '\u6642' => -1248, '\u66dc' => -951, '\u6700' => -937, '\u6708' => 4125, '\u671f' => 360, '\u674e' => 3094, '\u6751' => 364, '\u6771' => -805, '\u6838' => 5156, '\u68ee' => 2438, '\u696d' => 484, '\u6c0f' => 2613, '\u6c11' => -1694, '\u6c7a' => -1073, '\u6cd5' => 1868, '\u6d77' => -495, '\u7121' => 979, '\u7269' => 461, '\u7279' => -3850, '\u751f' => -273, '\u7528' => 914, '\u753a' => 1215, '\u7684' => 7313, '\u76f4' => -1835, '\u7701' => 792, '\u770c' => 6293, '\u77e5' => -1528, '\u79c1' => 4231, '\u7a0e' => 401, '\u7acb' => -960, '\u7b2c' => 1201, '\u7c73' => 7767, '\u7cfb' => 3066, '\u7d04' => 3663, '\u7d1a' => 1384, '\u7d71' => -4229, '\u7dcf' => 1163, '\u7dda' => 1255, '\u8005' => 6457, '\u80fd' => 725, '\u81ea' => -2869, '\u82f1' => 785, '\u898b' => 1044, '\u8abf' => -562, '\u8ca1' => -733, '\u8cbb' => 1777, '\u8eca' => 1835, '\u8ecd' => 1375, '\u8fbc' => -1504, '\u901a' => -1136, '\u9078' => -681, '\u90ce' => 1026, '\u90e1' => 4404, '\u90e8' => 1200, '\u91d1' => 2163, '\u9577' => 421, '\u958b' => -1432, '\u9593' => 1302, '\u95a2' => -1282, '\u96e8' => 2009, '\u96fb' => -1045, '\u975e' => 2066, '\u99c5' => 1620, '\uff11' => -800, '\uff63' => 2670, '\uff65' => -3794, '\uff6f' => -1350, '\uff71' => 551, '\uff7d' => 874, '\uff84' => 521, '\uff91' => 1109, '\uff99' => 1591, '\uff9b' => 2201, '\uff9d' => 278)\nconst UW4 = Dict{Char,Int}(',' => 3930, '.' => 3508, '\u2015' => -4841, '\u3001' => 3930, '\u3002' => 3508, '\u3007' => 4999, '\u300c' => 1895, '\u300d' => 3798, '\u3013' => -5156, '\u3042' => 4752, '\u3044' => -3435, '\u3046' => -640, '\u3048' => -2514, '\u304a' => 2405, '\u304b' => 530, '\u304c' => 6006, '\u304d' => -4482, '\u304e' => -3821, '\u304f' => -3788, '\u3051' => -4376, '\u3052' => -4734, '\u3053' => 2255, '\u3054' => 1979, '\u3055' => 2864, '\u3057' => -843, '\u3058' => -2506, '\u3059' => -731, '\u305a' => 1251, '\u305b' => 181, '\u305d' => 4091, '\u305f' => 5034, '\u3060' => 5408, '\u3061' => -3654, '\u3063' => -5882, '\u3064' => -1659, '\u3066' => 3994, '\u3067' => 7410, '\u3068' => 4547, '\u306a' => 5433, '\u306b' => 6499, '\u306c' => 1853, '\u306d' => 1413, '\u306e' => 7396, '\u306f' => 8578, '\u3070' => 1940, '\u3072' => 4249, '\u3073' => -4134, '\u3075' => 1345, '\u3078' => 6665, '\u3079' => -744, '\u307b' => 1464, '\u307e' => 1051, '\u307f' => -2082, '\u3080' => -882, '\u3081' => -5046, '\u3082' => 4169, '\u3083' => -2666, '\u3084' => 2795, '\u3087' => -1544, '\u3088' => 3351, '\u3089' => -2922, '\u308a' => -9726, '\u308b' => -14896, '\u308c' => -2613, '\u308d' => -4570, '\u308f' => -1783, '\u3092' => 13150, '\u3093' => -2352, '\u30ab' => 2145, '\u30b3' => 1789, '\u30bb' => 1287, '\u30c3' => -724, '\u30c8' => -403, '\u30e1' => -1635, '\u30e9' => -881, '\u30ea' => -541, '\u30eb' => -856, '\u30f3' => -3637, '\u30fb' => -4371, '\u30fc' => -11870, '\u4e00' => -2069, '\u4e2d' => 2210, '\u4e88' => 782, '\u4e8b' => -190, '\u4e95' => -1768, '\u4eba' => 1036, '\u4ee5' => 544, '\u4f1a' => 950, '\u4f53' => -1286, '\u4f5c' => 530, '\u5074' => 4292, '\u5148' => 601, '\u515a' => -2006, '\u5171' => -1212, '\u5185' => 584, '\u5186' => 788, '\u521d' => 1347, '\u524d' => 1623, '\u526f' => 3879, '\u529b' => -302, '\u52d5' => -740, '\u52d9' => -2715, '\u5316' => 776, '\u533a' => 4517, '\u5354' => 1013, '\u53c2' => 1555, '\u5408' => -1834, '\u548c' => -681, '\u54e1' => -910, '\u5668' => -851, '\u56de' => 1500, '\u56fd' => -619, '\u5712' => -1200, '\u5730' => 866, '\u5834' => -1410, '\u5841' => -2094, '\u58eb' => -1413, '\u591a' => 1067, '\u5927' => 571, '\u5b50' => -4802, '\u5b66' => -1397, '\u5b9a' => -1057, '\u5bfa' => -809, '\u5c0f' => 1910, '\u5c4b' => -1328, '\u5c71' => -1500, '\u5cf6' => -2056, '\u5ddd' => -2667, '\u5e02' => 2771, '\u5e74' => 374, '\u5e81' => -4556, '\u5f8c' => 456, '\u6027' => 553, '\u611f' => 916, '\u6240' => -1566, '\u652f' => 856, '\u6539' => 787, '\u653f' => 2182, '\u6559' => 704, '\u6587' => 522, '\u65b9' => -856, '\u65e5' => 1798, '\u6642' => 1829, '\u6700' => 845, '\u6708' => -9066, '\u6728' => -485, '\u6765' => -442, '\u6821' => -360, '\u696d' => -1043, '\u6c0f' => 5388, '\u6c11' => -2716, '\u6c17' => -910, '\u6ca2' => -939, '\u6e08' => -543, '\u7269' => -735, '\u7387' => 672, '\u7403' => -1267, '\u751f' => -1286, '\u7523' => -1101, '\u7530' => -2900, '\u753a' => 1826, '\u7684' => 2586, '\u76ee' => 922, '\u7701' => -3485, '\u770c' => 2997, '\u7a7a' => -867, '\u7acb' => -2112, '\u7b2c' => 788, '\u7c73' => 2937, '\u7cfb' => 786, '\u7d04' => 2171, '\u7d4c' => 1146, '\u7d71' => -1169, '\u7dcf' => 940, '\u7dda' => -994, '\u7f72' => 749, '\u8005' => 2145, '\u80fd' => -730, '\u822c' => -852, '\u884c' => -792, '\u898f' => 792, '\u8b66' => -1184, '\u8b70' => -244, '\u8c37' => -1000, '\u8cde' => 730, '\u8eca' => -1481, '\u8ecd' => 1158, '\u8f2a' => -1433, '\u8fbc' => -3370, '\u8fd1' => 929, '\u9053' => -1291, '\u9078' => 2596, '\u90ce' => -4866, '\u90fd' => 1192, '\u91ce' => -1100, '\u9280' => -2213, '\u9577' => 357, '\u9593' => -2344, '\u9662' => -2297, '\u969b' => -2604, '\u96fb' => -878, '\u9818' => -1659, '\u984c' => -792, '\u9928' => -1984, '\u9996' => 1749, '\u9ad8' => 2120, '\uff62' => 1895, '\uff63' => 3798, '\uff65' => -4371, '\uff6f' => -724, '\uff70' => -11870, '\uff76' => 2145, '\uff7a' => 1789, '\uff7e' => 1287, '\uff84' => -403, '\uff92' => -1635, '\uff97' => -881, '\uff98' => -541, '\uff99' => -856, '\uff9d' => -3637)\nconst UW5 = Dict{Char,Int}(',' => 465, '.' => -299, '1' => -514, E2 => -32768, ']' => -2762, '\u3001' => 465, '\u3002' => -299, '\u300c' => 363, '\u3042' => 1655, '\u3044' => 331, '\u3046' => -503, '\u3048' => 1199, '\u304a' => 527, '\u304b' => 647, '\u304c' => -421, '\u304d' => 1624, '\u304e' => 1971, '\u304f' => 312, '\u3052' => -983, '\u3055' => -1537, '\u3057' => -1371, '\u3059' => -852, '\u3060' => -1186, '\u3061' => 1093, '\u3063' => 52, '\u3064' => 921, '\u3066' => -18, '\u3067' => -850, '\u3068' => -127, '\u3069' => 1682, '\u306a' => -787, '\u306b' => -1224, '\u306e' => -635, '\u306f' => -578, '\u3079' => 1001, '\u307f' => 502, '\u3081' => 865, '\u3083' => 3350, '\u3087' => 854, '\u308a' => -208, '\u308b' => 429, '\u308c' => 504, '\u308f' => 419, '\u3092' => -1264, '\u3093' => 327, '\u30a4' => 241, '\u30eb' => 451, '\u30f3' => -343, '\u4e2d' => -871, '\u4eac' => 722, '\u4f1a' => -1153, '\u515a' => -654, '\u52d9' => 3519, '\u533a' => -901, '\u544a' => 848, '\u54e1' => 2104, '\u5927' => -1296, '\u5b66' => -548, '\u5b9a' => 1785, '\u5d50' => -1304, '\u5e02' => -2991, '\u5e2d' => 921, '\u5e74' => 1763, '\u601d' => 872, '\u6240' => -814, '\u6319' => 1618, '\u65b0' => -1682, '\u65e5' => 218, '\u6708' => -4353, '\u67fb' => 932, '\u683c' => 1356, '\u6a5f' => -1508, '\u6c0f' => -1347, '\u7530' => 240, '\u753a' => -3912, '\u7684' => -3149, '\u76f8' => 1319, '\u7701' => -1052, '\u770c' => -4003, '\u7814' => -997, '\u793e' => -278, '\u7a7a' => -813, '\u7d71' => 1955, '\u8005' => -2233, '\u8868' => 663, '\u8a9e' => -1073, '\u8b70' => 1219, '\u9078' => -1018, '\u90ce' => -368, '\u9577' => 786, '\u9593' => 1191, '\u984c' => 2368, '\u9928' => -689, '\uff11' => -514, E2 => -32768, '\uff62' => 363, '\uff72' => 241, '\uff99' => 451, '\uff9d' => -343)\nconst UW6 = Dict{Char,Int}(',' => 227, '.' => 808, '1' => -270, E1 => 306, '\u3001' => 227, '\u3002' => 808, '\u3042' => -307, '\u3046' => 189, '\u304b' => 241, '\u304c' => -73, '\u304f' => -121, '\u3053' => -200, '\u3058' => 1782, '\u3059' => 383, '\u305f' => -428, '\u3063' => 573, '\u3066' => -1014, '\u3067' => 101, '\u3068' => -105, '\u306a' => -253, '\u306b' => -149, '\u306e' => -417, '\u306f' => -236, '\u3082' => -206, '\u308a' => 187, '\u308b' => -135, '\u3092' => 195, '\u30eb' => -673, '\u30f3' => -496, '\u4e00' => -277, '\u4e2d' => 201, '\u4ef6' => -800, '\u4f1a' => 624, '\u524d' => 302, '\u533a' => 1792, '\u54e1' => -1212, '\u59d4' => 798, '\u5b66' => -960, '\u5e02' => 887, '\u5e83' => -695, '\u5f8c' => 535, '\u696d' => -697, '\u76f8' => 753, '\u793e' => -507, '\u798f' => 974, '\u7a7a' => -822, '\u8005' => 1811, '\u9023' => 463, '\u90ce' => 1082, '\uff11' => -270, E1 => 306, '\uff99' => -673, '\uff9d' => -496)\n\nconst CHARDICT = Dict{Char, UInt8}()\nfor (chars,cat) in (\n    (\"\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d\u5341\u767e\u5343\u4e07\u5104\u5146\",'M'),\n    (\"\u3005\u3006\u30f5\u30f6\", 'H'),\n    ('\u3041':'\u3093','I'),\n    ('\u30a1':'\u30f4','K'),\n    (\"\u30fc\uff70\\uff9e\",'K'),\n    ('\uff71':'\uff9d','K'),\n    (['a':'z';'A':'Z';'\uff41':'\uff5a';'\uff21':'\uff3a'],'A'),\n    (['0':'9';'\uff10':'\uff19'],'N')\n  )\n  for c in chars\n      CHARDICT[c] = cat\n  end\nend\nconst Achar = UInt8('A')\nconst Ichar = UInt8('I')\nconst Hchar = UInt8('H')\nconst Ochar = UInt8('O')\nconst Uchar = UInt8('U')\nconst Bchar = UInt8('B')\nfunction ctype(c::Char)\n  return get(CHARDICT, c, '\u4e00' <= c <= '\u9fa0' ? Hchar : Ochar)\nend\n\n\"\"\"\n    tokenize(text::AbstractString)\n\nGiven a `text` string, `tokenize` attempts to segment it into a list\nof words, and in particular tries to segment Japanese text\ninto words (\"tokens\" or \"segments\"), using the TinySegmenter algorithm.\nIt returns an array of substrings consisting of the guessed tokens in\n`text`, in the order that they appear.\n\"\"\"\nfunction tokenize{T<:AbstractString}(text::T)\n  result = SubString{T}[]\n  isempty(text) && return result\n\n  wordstart = start(text)\n  pos = wordstart\n\n  p1 = Uchar\n  p2 = Uchar\n  p3 = Uchar\n  w1, w2, w3 = B3, B2, B1\n  c1, c2, c3 = Ochar, Ochar, Ochar\n  w4 = text[pos]\n  c4 = ctype(w4)\n\n  pos1 = nextind(text, pos) # pos + 1 character\n  pos2 = nextind(text, pos1) # pos + 2 characters\n  if pos == endof(text)\n    w5, w6 = E1, E2\n    c5, c6 = Ochar, Ochar\n  else\n    w5 = text[pos1]\n    c5 = ctype(w5)\n    if pos1 == endof(text)\n      w6 = E1\n      c6 = Ochar\n    else\n      w6 = text[pos2]\n      c6 = ctype(w6)\n    end\n  end\n\n  while pos < endof(text)\n    score = BIAS\n    w1,w2,w3,w4,w5 = w2,w3,w4,w5,w6\n    c1,c2,c3,c4,c5 = c2,c3,c4,c5,c6\n    pos3 = nextind(text, pos2) # pos + 3\n    if pos3 <= endof(text)\n      w6 = text[pos3]\n      c6 = ctype(w6)\n    elseif pos2 == endof(text)\n      w6 = E1\n      c6 = Ochar\n    else # pos1 == endof(text)\n      w6 = E2\n      c6 = Ochar\n    end\n\n    if p1 == Ochar; score += -214; end # score += get(UP1, p1, 0)\n    if p2 == Bchar; score += 69; elseif p2 == Ochar; score += 935; end # score += get(UP2, p2, 0)\n    if p3 == Bchar; score += 189; end # score += get(UP3, p3, 0)\n    score += get(BP1, (p1, p2), 0)\n    score += get(BP2, (p2, p3), 0)\n    score += get(UW1, w1, 0)\n    score += get(UW2, w2, 0)\n    score += get(UW3, w3, 0)\n    score += get(UW4, w4, 0)\n    score += get(UW5, w5, 0)\n    score += get(UW6, w6, 0)\n    score += get(BW1, (w2, w3), 0)\n    score += get(BW2, (w3, w4), 0)\n    score += get(BW3, (w4, w5), 0)\n    score += get(TW1, (w1, w2, w3), 0)\n    score += get(TW2, (w2, w3, w4), 0)\n    score += get(TW3, (w3, w4, w5), 0)\n    score += get(TW4, (w4, w5, w6), 0)\n    score += get(UC1, c1, 0)\n    score += get(UC2, c2, 0)\n    if c3 == Achar; score += -1370; elseif c3 == Ichar; score += 2311; end # score += get(UC3, c3, 0)\n    score += get(UC4, c4, 0)\n    score += get(UC5, c5, 0)\n    score += get(UC6, c6, 0)\n    score += get(BC1, (c2, c3), 0)\n    score += get(BC2, (c3, c4), 0)\n    score += get(BC3, (c4, c5), 0)\n    score += get(TC1, (c1, c2, c3), 0)\n    score += get(TC2, (c2, c3, c4), 0)\n    score += get(TC3, (c3, c4, c5), 0)\n    score += get(TC4, (c4, c5, c6), 0)\n    score += get(UQ1, (p1, c1), 0)\n    score += get(UQ2, (p2, c2), 0)\n    score += get(UQ3, (p3, c3), 0)\n    score += get(BQ1, (p2, c2, c3), 0)\n    score += get(BQ2, (p2, c3, c4), 0)\n    score += get(BQ3, (p3, c2, c3), 0)\n    score += get(BQ4, (p3, c3, c4), 0)\n    score += get(TQ1, (p2, c1, c2, c3), 0)\n    score += get(TQ2, (p2, c2, c3, c4), 0)\n    score += get(TQ3, (p3, c1, c2, c3), 0)\n    score += get(TQ4, (p3, c2, c3, c4), 0)\n\n    p = Ochar\n    if score > 0\n      push!(result, SubString(text, wordstart, pos))\n      wordstart = pos1\n      p = Bchar\n    end\n\n    p1 = p2\n    p2 = p3\n    p3 = p\n    pos = pos1\n    pos1 = pos2\n    pos2 = pos3\n  end\n\n  push!(result, SubString(text, wordstart, pos))\n  return result\nend\n\nend # module\n", "meta": {"hexsha": "04f1f1b098801737810464c8d76f37309f4b0281", "size": 24968, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TinySegmenter.jl", "max_stars_repo_name": "JuliaPackageMirrors/TinySegmenter.jl", "max_stars_repo_head_hexsha": "5f48dcf2076c1d8043bd0b8fcd4442a63a12231b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/TinySegmenter.jl", "max_issues_repo_name": "JuliaPackageMirrors/TinySegmenter.jl", "max_issues_repo_head_hexsha": "5f48dcf2076c1d8043bd0b8fcd4442a63a12231b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/TinySegmenter.jl", "max_forks_repo_name": "JuliaPackageMirrors/TinySegmenter.jl", "max_forks_repo_head_hexsha": "5f48dcf2076c1d8043bd0b8fcd4442a63a12231b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 115.5925925926, "max_line_length": 2936, "alphanum_fraction": 0.4440483819, "num_tokens": 13932, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.18010667288817792, "lm_q1q2_score": 0.09567435284536109}}
{"text": "import Flatten: flattenable\n@metadata initial_value nothing\nimport FieldMetadata: @units, units\nimport FieldMetadata: @limits, limits\nimport FieldMetadata: @prior, prior\nimport FieldMetadata: @description, description\n@metadata bounds nothing\nimport FieldMetadata: @logscaled, logscaled\n@metadata reference nothing\n\n\n\"\"\"\n    AbstractParameters{T} <: AbstractVector{T}\n\nAn abstract type for AIBECS model parameters.\n\nParameters in AIBECS use the following convenience packages:\n\n- Parameters\n- FieldMetadata\n- FieldDefaults\n- Flatten\n- Unitful\n- DataFrames\n- Distributions\n\nThese aim to allow for some nice features, which include\n\n- nice syntax for unpacking parameters in functions via the `@unpack` macro (fron UnPack.jl)\n- additional metadata on parameters\n- easy conversion to and from vectors\n- use of units and automatic conversions if necessary\n- pretty table-format displays\n- loading and saving to and from CSV files\n- prior estimates for bayesian inference and optimization\n\nSee the list of examples to get an idea of how to generate parameters for your model.\n\n# Examples\n\nGenerate a simple parameter type via\n\n```jldoctest\njulia> struct SimpleParams{T} <: AbstractParameters{T}\n           \u03b1::T\n           \u03b2::T\n           \u03b3::T\n       end\nSimpleParams\n```\n\nTo create an instance of the `SimpleParams(Float64)` type, you can do\n\n```jldoctest\njulia> p = SimpleParams(1.0, 2.0, 3.0)\nSimpleParams{Float64}\n\u2502 Row \u2502 Symbol \u2502 Value   \u2502\n\u2502     \u2502 Symbol \u2502 Float64 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 1   \u2502 \u03b1      \u2502 1.0     \u2502\n\u2502 2   \u2502 \u03b2      \u2502 2.0     \u2502\n\u2502 3   \u2502 \u03b3      \u2502 3.0     \u2502\n```\n\nOne of the core features from Parameters is unpacking in functions, e.g.,\n\n```jldoctest\njulia> function simplef(p)\n           @unpack \u03b1, \u03b3 = p\n           return \u03b1 + \u03b3\n       end\nsimplef (generic function with 1 method)\n\njulia> simplef(p) # 1.0 + 3.0\n4.0\n```\n\nMore complex examples are permitted by adding metadata (thanks to FieldMetadata.jl).\nYou can add units\n\n```jldoctest\njulia> @units struct UnitParams{T} <: AbstractParameters{T}\n           \u03b1::T | u\"km\"\n           \u03b2::T | u\"hr\"\n           \u03b3::T | u\"m/s\"\n       end ;\n\njulia> p = UnitParams(1.0, 2.0, 3.0)\nUnitParams{Float64}\n\u2502 Row \u2502 Symbol \u2502 Value   \u2502 Unit     \u2502\n\u2502     \u2502 Symbol \u2502 Float64 \u2502 Unitful\u2026 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 1   \u2502 \u03b1      \u2502 1.0     \u2502 km       \u2502\n\u2502 2   \u2502 \u03b2      \u2502 2.0     \u2502 hr       \u2502\n\u2502 3   \u2502 \u03b3      \u2502 3.0     \u2502 m s^-1   \u2502\n```\n\nNote that when adding units to your parameters, they will be converted to SI\nwhen unpacked, as in, e.g.,\n\n```jldoctest\njulia> function speed(p)\n           @unpack \u03b1, \u03b2, \u03b3 = p\n           return \u03b1 / \u03b2 + \u03b3\n       end\nspeed (generic function with 1 method)\n\njulia> speed(p) # (1.0 km / 2.0 hr + 3 m/s) in m/s\n3.138888888888889\n```\n\nAnother example for optimizable/flattenable parameters\n\n```jldoctest\njulia> @initial_value @units @flattenable struct OptParams{T} <: AbstractParameters{T}\n           \u03b1::T | 3.6 | u\"km\"  | true\n           \u03b2::T | 1.0 | u\"hr\"  | false\n           \u03b3::T | 1.0 | u\"m/s\" | true\n       end ;\n\njulia> p = OptParams(initial_value(OptParams)...)\nOptParams{Float64}\n\u2502 Row \u2502 Symbol \u2502 Value   \u2502 Initial value \u2502 Unit     \u2502 Optimizable \u2502\n\u2502     \u2502 Symbol \u2502 Float64 \u2502 Float64       \u2502 Unitful\u2026 \u2502 Bool        \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 1   \u2502 \u03b1      \u2502 3.6     \u2502 3.6           \u2502 km       \u2502 1           \u2502\n\u2502 2   \u2502 \u03b2      \u2502 1.0     \u2502 1.0           \u2502 hr       \u2502 0           \u2502\n\u2502 3   \u2502 \u03b3      \u2502 1.0     \u2502 1.0           \u2502 m s^-1   \u2502 1           \u2502\n```\n\nThanks to the FieldMetaData interface, you can chain the following preloaded metadata:\n\n- initial_value\n- units (from Unitful.jl)\n- prior (from Distributions.jl)\n- description (`String`)\n- bounds (2-element `Tuple`)\n- logscaled (`Bool`)\n- flattenable (to convert to vectors of optimizable parameters only)\n- reference (`String`)\n\nHere is an example of parameter with all the possible metadata available in AIBECS:\n\n```jldoctest\njulia> @initial_value @units @prior @description @bounds @logscaled @flattenable @reference struct FullParams{T} <: AbstractParameters{T}\n           \u03b1::T | 1.0 | u\"km\"  | Normal(0,1)    | \"The distance\"   | (-Inf, Inf) | false | false | \"Jean et al., 2042\"\n           \u03b2::T | 2.0 | u\"hr\"  | LogNormal(0,1) | \"The time\"       | (   0, Inf) | true  | true  | \"Claude et al. 1983\"\n           \u03b3::T | 3.0 | u\"mol\" | Normal(1,2)    | \"The # of moles\" | (  -1,   1) | false | true  | \"Dusse et al. 2000\"\n       end ;\n\njulia> FullParams(4.0, 5.0, 6.0)\nFullParams{Float64}\n\u2502 Row \u2502 Symbol \u2502 Value   \u2502 Initial value \u2502 Unit     \u2502 Prior                            \u2502 Description    \u2502 Bounds      \u2502 Logscaled \u2502 Optimizable \u2502 Reference          \u2502\n\u2502     \u2502 Symbol \u2502 Float64 \u2502 Float64       \u2502 Unitful\u2026 \u2502 Distribu\u2026                        \u2502 String         \u2502 Tuple\u2026      \u2502 Bool      \u2502 Bool        \u2502 String             \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 1   \u2502 \u03b1      \u2502 4.0     \u2502 1.0           \u2502 km       \u2502 Normal{Float64}(\u03bc=0.0, \u03c3=1.0)    \u2502 The distance   \u2502 (-Inf, Inf) \u2502 0         \u2502 0           \u2502 Jean et al., 2042  \u2502\n\u2502 2   \u2502 \u03b2      \u2502 5.0     \u2502 2.0           \u2502 hr       \u2502 LogNormal{Float64}(\u03bc=0.0, \u03c3=1.0) \u2502 The time       \u2502 (0, Inf)    \u2502 1         \u2502 1           \u2502 Claude et al. 1983 \u2502\n\u2502 3   \u2502 \u03b3      \u2502 6.0     \u2502 3.0           \u2502 mol      \u2502 Normal{Float64}(\u03bc=1.0, \u03c3=2.0)    \u2502 The # of moles \u2502 (-1, 1)     \u2502 0         \u2502 1           \u2502 Dusse et al. 2000  \u2502\n```\n\nNote that there is no check that the metadata you give is consistent.\nThese metadata will hopefully be useful for advanced usage of AIBECS, e.g., using prior information and/or bounds for optimization.\n\"\"\"\nabstract type AbstractParameters{T} <: AbstractVector{T} end\n\n\"\"\"\n    symbols(p)\n\nReturns the symbols in `p`.\n\nCan also be used directly on the type of `p`\n(because the symbols of `p::T` are contained in the type `T`).\n\"\"\"\nsymbols(::Type{T}) where {T <: AbstractParameters} = fieldnames(T)\nsymbols(::T) where {T <: AbstractParameters} = symbols(T)\n\n\"\"\"\n    flattenable_symbols(p)\n\nReturns the flattenable symbols in `p`.\n\nThe flattenable symbols are those symbols that are kepth when using `p` as a vector,\ne.g., when doing `vec(p)`.\n(Useful when passing parameters to an optimization routine expeting a vector of optimizable parameters.)\n\nCan also be used directly on the type of `p`\n(because the flattenable symbols of `p::T` are contained in the type `T`).\n\"\"\"\nflattenable_symbols(::T) where {T <: AbstractParameters} = flattenable_symbols(T)\nflattenable_symbols(::Type{T}) where {T <: AbstractParameters} = fieldnames(T)[collect(flattenable(T))]\n\n\"\"\"\n    values(p::T) where {T <: AbstractParameters}\n\nReturns a vector of **all** the values of `p`.\n\nNote that `values(p)` is different from `vec(p)`.\n\"\"\"\nBase.values(p::T) where {T <: AbstractParameters} = [getfield(p, s) for s in symbols(T)]\n\n\"\"\"\n    flattenable_values(p::T) where {T <: AbstractParameters}\n\nReturns a vector of the **flattenable** values of `p`.\n\nNote that `vec(p)` is different from `values(p)`.\n\"\"\"\nflattenable_values(p::T) where {T <: AbstractParameters} = [getfield(p, s) for s in flattenable_symbols(T)]\n\n\"\"\"\n    vec(p::T) where {T <: AbstractParameters}\n\nReturns a **SI-unit-converted** vector of flattenable values of `p`.\n\nNote that `vec(p) \u2260 flattenable_values(p)` if `p` has units.\n\"\"\"\nBase.vec(p::T) where {T <: AbstractParameters} = [UnPack.unpack(p, Val(s)) for s in flattenable_symbols(T)]\n\n\n\"\"\"\n    length(p::AbstractParameter)\n\nReturns the length of the **flattened/optimzable** vector of `p`.\n\nMay be different from the number of parameters.\nCan also be used directly on the type of `p`.\n\"\"\"\nBase.length(::T) where {T <: AbstractParameters} = sum(flattenable(T))\nBase.length(::Type{T}) where {T <: AbstractParameters} = sum(flattenable(T))\n\n\"\"\"\n    size(p::AbstractParameter)\n\nReturns the size of the **flattened/optimzable** vector of `p`.\n\nMay be different from the number of parameters.\nCan also be used directly on the type of `p`.\n\"\"\"\nBase.size(::T) where {T <: AbstractParameters} = (length(T),)\nBase.size(::Type{T}) where {T <: AbstractParameters} = (length(T),)\n\nfunction Base.show(io::IO, p::T) where T <: AbstractParameters\n    print(T)\n    t = table(p)\n    show(io, t; summary=false)\nend\nfunction Base.show(io::IO, m::MIME\"text/plain\", p::T) where T <: AbstractParameters\n    print(T)\n    t = table(p)\n    show(io, m, t; summary=false)\nend\n\nfunction table(p::T, nf::NamedTuple) where {T <: AbstractParameters}\n    t = DataFrame(Symbol = collect(symbols(p)), Value = values(p))\n    for (n,f) in zip(keys(nf), nf)\n        setproperty!(t, n, collect(f(p)))\n    end\n    return t\nend\n\n\"\"\"\n    table(p)\n\nReturns a `DataFrame` (a table) of `p`.\nUseful for printing and saving into an actual text/latex table.\n\"\"\"\nfunction table(p::AbstractParameters)\n    ks, vs = Symbol[], Function[]\n    all(isnothing, initial_value(p)) || (push!(ks, Symbol(\"Initial value\")); push!(vs, initial_value))\n    all(isequal(1), units(p)) || (push!(ks, :Unit); push!(vs, units))\n    all(isnothing, prior(p)) || (push!(ks, Symbol(\"Prior\")); push!(vs, prior))\n    all(isempty, description(p)) || (push!(ks, Symbol(\"Description\")); push!(vs, description))\n    all(isnothing, bounds(p)) || (push!(ks, Symbol(\"Bounds\")); push!(vs, bounds))\n    any(logscaled(p)) && (push!(ks, Symbol(\"Logscaled\")); push!(vs, logscaled))\n    all(flattenable(p)) || (push!(ks, Symbol(\"Optimizable\")); push!(vs, flattenable))\n    all(isnothing, reference(p)) || (push!(ks, Symbol(\"Reference\")); push!(vs, reference))\n    nf = (; zip(ks, vs)...)\n    t = table(p, nf)\nend\n\n\n\"\"\"\n    latex(p)\n\nReturns a LaTeX-formatted table of the parameters.\n\"\"\"\nlatex(p::AbstractParameters) = show(stdout, MIME(\"text/latex\"), table(p))\n\n\"\"\"\n    unpack(p <: AbstractParameters, s)\n\nUnpacks the parameter `s` from `p`.\n\nNote this is specialized and will convert the parameter value to SI units.\n\"\"\"\n@inline UnPack.unpack(p::T, ::Val{f}) where {T<:AbstractParameters,f} = ustrip(upreferred(getproperty(p, f) * units(T, f)))\n\n\"\"\"\n    +(p::T, v::Vector) where {T <: AbstractParameters}\n\nAdds the flattened vector `v` to `p`.\n\n**Warning:** This method for `+` is implemented only for differentiation\nusing dual and hyperdual numbers.\nIf you want to change the values of `p`, you should do so explicitly\nrather than use this `+` method.\n\"\"\"\nBase.:+(p::T, v::Vector) where {T <: AbstractParameters} = reconstruct(T, vec(p) + v)\n\n\"\"\"\n    reconstruct(T, v)\n\nReconstructs the parameter of type `T` from flattenable vector `v`.\n\"\"\"\nfunction reconstruct(::Type{T}, v::Tv) where {T <: AbstractParameters, Tv <: Vector}\n    all(isnothing, initial_value(T)) && error(\"Can't reconstruct without initial values\")\n    vunits = units(T)[[flattenable(T)...]]\n    v = @. v * upreferred(vunits) |> vunits |> ustrip\n    reconstructed_v = convert(Tv, collect(initial_value(T)))\n    reconstructed_v[collect(flattenable(T))] .= v\n    return T.name.wrapper(reconstructed_v...)\nend\n\n\"\"\"\n    getindex(p::T, i) where {T <: AbstractParameters}\n\nReturns the i-th element of vec(p).\n\nThis is not efficient and only used for testing the derivatives with ForwardDiff.\n\"\"\"\nBase.getindex(p::T, i) where {T <: AbstractParameters} = getindex(vec(p), i)\n\n\nfunction (::Type{T})(args::Quantity...) where {T <: AbstractParameters}\n    all(isequal(1), units(T)) && error(\"$T needs `units` for this construction to work\")\n    return T([ustrip(x |> units(T, f)) for (x,f) in zip(args, fieldnames(T))]...)\nend\n\nfunction (::Type{T})(;kwargs...) where {T <: AbstractParameters}\n    all(isnothing, initial_value(T)) && length(kwargs) \u2260 length(symbols(T)) && error(\"$T needs `initial_value` if one of the parameters is not supplied\")\n    value(f::Symbol, v::Quantity) = ustrip(v |> units(T, f))\n    value(f::Symbol, v) = v\n    return T([f \u2208 keys(kwargs) ? value(f, kwargs[f]) : initial_value(T, f) for f in fieldnames(T)]...)\nend\n\n#===============================\nWriting to savable formats\n===============================#\n\nfunction Base.Dict(p::T, s=symbols(p)) where {T<:AbstractParameters}\n    v = [getfield(p,s) for s in s]\n    u = [units(p,s) for s in s]\n    return Dict([(s,v*u) for (s,v,u) in zip(s,v,u)])\nend\nfunction Base.NamedTuple(p::T, s=symbols(p)) where {T<:AbstractParameters}\n    v = [getfield(p,s) for s in s]\n    u = [units(p,s) for s in s]\n    return (; zip(s, v .* u)...)\nend\n\n#=====================\nmismatch of parameters\n=====================#\n\"\"\"\n    mismatch(p::AbstractParameters)\n\nReturns the sum of the negative log-likelihood of each flattenable parameter.\n\"\"\"\nfunction mismatch(p::AbstractParameters)\n    return sum(-logpdf(prior(p,k), UnPack.unpack(p,Val(k))) for k in flattenable_symbols(p))\nend\nfunction \u2207mismatch(p::AbstractParameters)\n    return transpose([-gradlogpdf(prior(p,k), UnPack.unpack(p,Val(k))) for k in flattenable_symbols(p)])\nend\n\n# The functions below is just for ForwardDiff to work with vectors instead of p\n# which requires `mismatch` to know about the priors, which are containted in `T`\nfunction generate_objective(\u03c9s, \u03bcx, \u03c3\u00b2x, v, \u03c9p, ::Type{T}) where {T<:AbstractParameters}\n    nt, nb = length(\u03c9s), length(v)\n    tracers(x) = state_to_tracers(x, nb, nt)\n    f(x, p) = \u03c9p * mismatch(T, p) +\n        sum([\u03c9\u2c7c * mismatch(x\u2c7c, \u03bc\u2c7c, \u03c3\u2c7c\u00b2, v) for (\u03c9\u2c7c, x\u2c7c, \u03bc\u2c7c, \u03c3\u2c7c\u00b2) in zip(\u03c9s, tracers(x), \u03bcx, \u03c3\u00b2x)])\n    return f\nend\nfunction mismatch(::Type{T}, v) where {T<:AbstractParameters}\n    return sum(-logpdf(prior(T,k), v[i]) for (i,k) in enumerate(flattenable_symbols(T)))\nend\n\n#==================\nChange of variables\n==================#\n\"\"\"\n    subfun\n\nReturns the substitution function for the change of variables of parameters.\n\nIf the prior of parameter `p\u1d62` is `LogNormal`, then the substitution function is `exp`.\nIf the prior is `Uniform`, then the change of variables is the logit function.\nOtherwise, it's `identity`.\n\"\"\"\nfunction subfun(::Type{T}) where {T<:AbstractParameters}\n    return \u03bb -> reconstruct(T, [subfun(T, s)(\u03bb\u1d62) for (\u03bb\u1d62,s) in zip(\u03bb, flattenable_symbols(T))])\nend\nfunction \u2207subfun(::Type{T}) where {T<:AbstractParameters}\n    return \u03bb -> reconstruct(T, [\u2207subfun(T, s)(\u03bb\u1d62) for (\u03bb\u1d62,s) in zip(\u03bb, flattenable_symbols(T))])\nend\nfunction \u2207\u00b2subfun(::Type{T}) where {T<:AbstractParameters}\n    return \u03bb -> reconstruct(T, [\u2207\u00b2subfun(T, s)(\u03bb\u1d62) for (\u03bb\u1d62,s) in zip(\u03bb, flattenable_symbols(T))])\nend\nfunction invsubfun(::Type{T}) where {T<:AbstractParameters}\n    return p -> [invsubfun(T, s)(p\u1d62) for (p\u1d62,s) in zip(vec(p), flattenable_symbols(T))]\nend\n# substitution function (change of variables) is determined from prior distribution\nsubfun(::Type{T}, s::Symbol) where {T<:AbstractParameters} = subfun(prior(T,s))\n\u2207subfun(::Type{T}, s::Symbol) where {T<:AbstractParameters} = \u2207subfun(prior(T,s))\n\u2207\u00b2subfun(::Type{T}, s::Symbol) where {T<:AbstractParameters} = \u2207\u00b2subfun(prior(T,s))\ninvsubfun(::Type{T}, s::Symbol) where {T<:AbstractParameters} = invsubfun(prior(T,s))\n# Fallback rule for change of variables is identity\nsubfun(::Distribution) = identity\n\u2207subfun(::Distribution) = x -> one(x)\n\u2207\u00b2subfun(::Distribution) = x -> zero(x)\ninvsubfun(::Distribution) = identity\n# p = exp(\u03bb) for LogNormal\nsubfun(::LogNormal) = exp\n\u2207subfun(::LogNormal) = exp\n\u2207\u00b2subfun(::LogNormal) = exp\ninvsubfun(::LogNormal) = log\n# p = logistic(\u03bb) for Uniform\nsubfun(d::Uniform) = \u03bb -> d.a + (d.b - d.a) / (exp(-\u03bb) + 1)\n\u2207subfun(d::Uniform) = \u03bb -> (d.b - d.a) * exp(-\u03bb) / (exp(-\u03bb) + 1)^2\n\u2207\u00b2subfun(d::Uniform) = \u03bb -> (d.a - d.b) * exp(-\u03bb) / (exp(-\u03bb) + 1)^2 + 2(d.b - d.a) * exp(-2\u03bb) / (exp(-\u03bb) + 1)^3\ninvsubfun(d::Uniform) = p -> -log((d.b - d.a) / (p - d.a) - 1)\n\nexport subfun, \u2207subfun, \u2207\u00b2subfun, invsubfun\n\n\nexport AbstractParameters, latex\n\n", "meta": {"hexsha": "481fac812d70b9460d04e9336910f15128cc0216", "size": 15637, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Parameters.jl", "max_stars_repo_name": "seamanticscience/AIBECS.jl", "max_stars_repo_head_hexsha": "ff64544aa637a7324f513be697897384efbec4e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Parameters.jl", "max_issues_repo_name": "seamanticscience/AIBECS.jl", "max_issues_repo_head_hexsha": "ff64544aa637a7324f513be697897384efbec4e6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Parameters.jl", "max_forks_repo_name": "seamanticscience/AIBECS.jl", "max_forks_repo_head_hexsha": "ff64544aa637a7324f513be697897384efbec4e6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6195899772, "max_line_length": 166, "alphanum_fraction": 0.6213468057, "num_tokens": 4680, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.18713268669577832, "lm_q1q2_score": 0.09356634334788916}}
{"text": "module TOPAS\nusing ..AGFFileReader: Glass, GlassID, AGF\nexport _5013X16, _6015S_04, _5013LS_01\n\nconst _5013X16 = Glass(GlassID(AGF, 1771), 1, 1.7607057, 0.300332361, 0.396393537, -0.127731385, 0.0205062, -0.001262104, 0.0, 0.0, 0.0, 0.0, 0.4, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 25.0, 0.0, -1.0, -1.0, 60.0, -1.0, 0, -1.0, [], 1.533102, -1.0, -1.0, 0, 56.14146, 0, 1.02, 0)\nconst _6015S_04 = Glass(GlassID(AGF, 1772), 1, 0.918248132, 0.79083987, 0.948682804, -0.301710833, 0.046922024, -0.00281335, 0.0, 0.0, 0.0, 0.0, 0.4, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 25.0, 0.0, -1.0, -1.0, 60.0, -1.0, 0, -1.0, [], 1.533002, -1.0, -1.0, 0, 54.975411, 0, 1.02, 0)\nconst _5013LS_01 = Glass(GlassID(AGF, 1773), 1, 2.12785831, 0.094547938, 0.14488717, -0.044888007, 0.007401338, -0.000464875, 0.0, 0.0, 0.0, 0.0, 0.4, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 25.0, 0.0, -1.0, -1.0, 60.0, -1.0, 0, -1.0, [], 1.533202, -1.0, -1.0, 0, 56.150461, 0, 1.02, 0)\nend # module\n", "meta": {"hexsha": "528bf1f7bfb58e8769a7ce129c704f6daa7b5119", "size": 949, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data/jl/TOPAS.jl", "max_stars_repo_name": "rambunctiousapple/GlassCat", "max_stars_repo_head_hexsha": "5e10affbed57311088f78f461e8f83f6f5874c87", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/data/jl/TOPAS.jl", "max_issues_repo_name": "rambunctiousapple/GlassCat", "max_issues_repo_head_hexsha": "5e10affbed57311088f78f461e8f83f6f5874c87", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-12-12T17:20:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-12T17:20:35.000Z", "max_forks_repo_path": "src/data/jl/TOPAS.jl", "max_forks_repo_name": "rambunctiousapple/GlassCat", "max_forks_repo_head_hexsha": "5e10affbed57311088f78f461e8f83f6f5874c87", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-29T23:16:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T23:16:46.000Z", "avg_line_length": 105.4444444444, "max_line_length": 281, "alphanum_fraction": 0.5869336143, "num_tokens": 643, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.16885695841556153, "lm_q1q2_score": 0.0923205413938669}}
{"text": "### A Pluto.jl notebook ###\n# v0.15.1\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 7a8e5f68-beb1-41d4-af1f-4b89e5bbf388\nbegin\n\tusing PlutoUI\n\tmd\"\ud83e\udd86 : $(@bind ducksize Slider(20:500, default=200))\"\nend\n\n# \u2554\u2550\u2561 e703ea17-554a-4932-a20c-5e5975f4372a\nbegin\nusing HypertextLiteral\nmd\"\"\"Select your **settings** below:\\\n*Background color*: $(@bind bg_color html\"<input type=color value='#00eeff'>\")\\\n*Font*: $(@bind font_type Select([\"cursive\",\"serif\",\"sans-serif\",\"monospace\",\"fantasy\"]))\\\n*Font size*: $(@bind font_size NumberField(10:40, default=20))\\\n\"\"\"\nend\n\n# \u2554\u2550\u2561 b5094370-ee11-11eb-3e48-b3a77979f99a\nmd\"\"\"# An Introduction To Pluto\nWelcome, dear reader, to this short introduction to Pluto. Knowing how to use Pluto is your first step towards working in an interactive environment with the `Julia` language!\n\nIn the rest of this notebook you'll find some handy **tips** and **tricks** to get you up and running in no time. \n\nGood luck!\n\"\"\"\n\n# \u2554\u2550\u2561 2dd30617-8deb-4762-9cc3-55f6ef341505\nmd\"\"\"## The Very Beginning\nIf you're here that means that you have already done this part!\nBut just for completion let us repeat what you need to do to start a `Pluto` notebook.\n1. Open your `Julia` PERL (the application you downloaded from the Julia site)\n2. Type `using Pluto` : This will load the Pluto module for you.\n3. Type `Pluto.run()` : This starts your Pluto environment in your standard browser.\n4. Select the notebook you want to open by typing the **path** to it or by inserting the **url** to the notebook. \n\"\"\"\n\n# \u2554\u2550\u2561 bc216d83-20ba-4efb-b201-bff73a83f5af\nmd\"\"\"## Interacting With Pluto \nIn this section we will quickly explain to you how to run notebooks and interact with them.\n\nTo begin with when you open a Pluto notebook, every cell is loaded and executed. So you may want to wait a couple of seconds before typing/working in a notebook.\n\"\"\"\n\n# \u2554\u2550\u2561 8d9a48e4-382f-49a1-9f51-ab549a5b7039\nmd\"\"\"If you want to load a cell yourself you have two options. The first one is to just click the grey \u25b6 button in the bottom right corner. The second way is by pressing `Ctrl + Shift`. This will cause your current cell to be executed but it will also cause all the *related* cells to update. This way Pluto always keep your whole notebook up to date. Try to run the cell below. See how the other cell changes its output as well?\"\"\"\n\n# \u2554\u2550\u2561 288f2701-aa8e-4a81-b334-b58bcdc74ae7\nduckcount = 2\n\n# \u2554\u2550\u2561 5bb6c26d-4d0f-47b4-8a47-e7d436be4892\nrepeat(\"\ud83e\udd86\",duckcount)\n\n# \u2554\u2550\u2561 75f401fc-03eb-465a-a218-68b6ccc544d1\nmd\"\"\"Want to see the code behind a certain cell? Easy, just click on the \ud83d\udc41 button to the left of this cell. Go on! Try it with this cell.\"\"\"\n\n# Congrats! See how easy this is?\n\n# \u2554\u2550\u2561 22a6d095-e48f-4a56-9587-bca63beedf99\nmd\"\"\"If you're feeling ready to start typing yourself then you can create a new cell by clicking on the `+` icon on the upper or lower left corner of this cell.\n\nLastly we want to mention that if you want to remove a cell or just disable it temporary, you can so so by clicking on the `...` circle on the left side of your cell. Not seeing this button? That's normal! You'll only see this symbol when you are inside a code cell. (remember you can do this with the \ud83d\udc41 button).\n\"\"\"\n\n# \u2554\u2550\u2561 1bffea5a-dd8d-4156-88d4-b6b614436881\nmd\"\"\"## Markdown and Pluto \nYou may have noticed that there is a bit of markup inside this notebook. The nice thing in Pluto is that it's really easy to do this. You can just use the `Markdown` language inside these notebooks. To start a `Markdown` part, just type `md\"your text here\"`. Want to type a big chunck of markdown text? Use `md\\\"\"\"Your big chunk of text\\\"\"\"` this way you can also use backspaces inside your markdown. \n\nNot really familiar with the `Markdown` language? Don't worry it is really easy to learn, all the info you'll ever need can be found [here](https://www.markdownguide.org/cheat-sheet/).\n\nAnother nice thing to know is that you can use `Latex` and `unicode` symbols in Pluto. `Latex` gives you easy access to symbols like `\u03b1` (just type `\\alpha<TAB>`). `Unicode` makes it so you can use all the smileys you love in your text and even as variable names! (big \ud83e\udde0 time). \nAgain if you don't know any Latex or Unicode symbols you can just look them up on there respective pages. Just click [here](https://oeis.org/wiki/List_of_LaTeX_mathematical_symbols) for **Latex** and [here](https://unicode.org/emoji/charts/full-emoji-list.html) for **Unicode**. \n\"\"\"\n\n# \u2554\u2550\u2561 4b0bca9a-426f-4157-935d-cf67cd03f201\nmd\"\"\"## Export Modes\nIf you want to export your current notebook there are a couple of ways and formats to do this with.\n\n### Formats\nThere are 3 different formats as which you can export a notebook:\n1. A notebook\n2. A static HTML file (if you want to make static sites in Pluto)\n3. A PDF file\n\n### How to do it?\nThere are (at the moment) two ways to do this:\n1. The most easy way to do this is by just scrolling to the top of your notebook, clicking the export button (looks like a triangle on top of a circle).\n\n2. To automatically export your notebook to a pdf you first need to install the PlutoPDF package. This can be done with the following code `import Pkg; Pkg.add(url=\"https://github.com/JuliaPluto/PlutoPDF.jl\")`. Next you just need to run the following code in Julia: `import PlutoPDF` followed by `PlutoPDF.pluto_to_pdf(\"notebook.jl\")`. More info about this package can be found [here](https://github.com/JuliaPluto/PlutoPDF.jl).\n\n\"\"\"\n\n# \u2554\u2550\u2561 c6363449-3b51-4f25-ab41-c0cde7f0565c\nmd\"\"\"## Making It Interactive\nTo end the introduction, you will learn how to give your notebook that extra portion of interactivity.\n\nPluto allows you to use `Javascript` inside your notebook. Most people don't know `Javascript` so that's where `PlutoUI` comes to the rescue! Activate this by typing `using PlutoUI` at the top of your new notebook. By simply typing `@bind varname Slider(5:10)` you can make a nice slider. The value of the variable `varname` will now change depending on where you put the slider. Inside the `Slider` function you put `(minimal value : maximal value)`. Sliders are of course not the only thing you can use, there is a whole range interaction tools. You can learn about all of them inside the sample notebook *Sample Interactivity*. These sample notebooks can be found by clicking on the **Pluto.jl** logo at the top of your notebook and then selecting the *sample notebooks* option at the start menu.\n\nTo give you a little example, just play with the slider below and see what happens.\n\"\"\"\n\n# \u2554\u2550\u2561 34971682-5962-4e58-aa18-bf155256c49f\nResource(\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/24/Male_mallard_duck_2.jpg/1920px-Male_mallard_duck_2.jpg\", :width => ducksize)\n\n# \u2554\u2550\u2561 b3b85049-733d-4d05-b734-051949a61468\nmd\"\"\"# The Advanced Stuff\nIf you just want to use Pluto as you Julia environment then you know everything you need to know by now \ud83e\udd73. However if you would like to know how to change the layout of your Pluto notebooks, add extra functionality, make a presentation in Pluto and how to publish it only as an interactive notebook. Then the next parts are for you!\n\"\"\"\n\n# \u2554\u2550\u2561 f5630f3c-b0eb-4b60-a428-da21b8a68416\nmd\"\"\"## Presentation Mode\nYes you are reading this correctly! Next to being very interactive and easy to learn you can also make presentations in Pluto.\nThis is not yet implemented in a 'nice' way but we can access it through a backdoor.\nBy typing the following code in an empty cell you'll create a button that allows you to enter presentation mode. `html\"<button onclick='present()'>present</button>\"`\n\nWhen in presentation mode, all second order headings or higher will start a new slide. So to start a new slide just type `md\"## cool title\"` in an empty cell and you are good to go!\n\nTo exit presentation mode, just click again on the same button.\n\"\"\"\n\n# \u2554\u2550\u2561 ec19c690-5784-4cc4-a63b-5d2485eeafa9\nhtml\"<button onclick='present()'>present</button>\"\n\n# \u2554\u2550\u2561 2a83d1e3-79c2-4a36-93b2-1bf2f89b27f3\nmd\"## This starts a new slide\"\n\n# \u2554\u2550\u2561 5a1f85e6-f9c6-44b7-a2d4-6b70d65aeaef\nmd\"### This doesn't\"\n\n# \u2554\u2550\u2561 6b20de32-f606-45d1-a800-b4cdadc33017\nmd\"\"\"## Javascript, HTML and CSS \nAs the title already tells you, it is indeed possible to tailor Pluto notebooks to your own preferences. Because you can actually use `Javascript, HTML and CSS` in Pluto notebooks. To do this you just install the module called `HypertextLiteral` by typing `using HypertextLiteral` at the beginning of your notebook. This allows you to use the `@htl()` macro in which you can start typing your Hypertext code encapsulated by \\\"\"\".  If you would like some more info about this or a sneakpeak of its applications, then take a look at the sample notebook called *sample JavaScript* that you can find at the sample notebook section. \n\nBelow you will find an example of what you can do by combining these languages in Pluto.\n\"\"\"\n\n# \u2554\u2550\u2561 d1739e3b-6363-4a77-8d7c-61b6ba8978bd\n@htl(\"\"\"\n\n<article class=\"learning\">\n\t<p>\n\t\tThis is a little example of how you can use the automatic update function of Pluto to your advantage. By binding the right variables you can completely let the user choose how their notebook looks! Isn't that great?\n\t</p>\n</article>\n\n\n<style>\n\n\tarticle.learning {\n\t\tbackground: $(bg_color);\n\t\tpadding: 1em;\n\t\tborder-radius: 5px;\n\t\tfont-size: $(font_size)px;\n\t\tfont-family: $(font_type);\n\t}\n</style>\n\"\"\")\n\n# \u2554\u2550\u2561 3acc7d2e-7c54-423c-aac8-1f8f61963d77\nmd\"\"\"And this is how the above cell looks in `HTML` and `CSS` code:\n```htmlmixed\n<article class=\"learning\">\n\t<p>\n\t\tThis is a little example of how you can use the automatic update function of Pluto to your advantage. By binding the right variables you can completely let the user choose how their notebook looks! Isn't that great?\n\t</p>\n</article>\n\n\n<style>\n\n\tarticle.learning {\n\t\tbackground: $(bg_color);\n\t\tpadding: 1em;\n\t\tborder-radius: 5px;\n\t\tfont-size: $(font_size)px;\n\t\tfont-family: $(font_type);\n\t}\n</style>\n```\n\n\"\"\"\n\n# \u2554\u2550\u2561 4c1ea155-df57-4b1b-94f7-c638e7f747c2\nmd\"\"\"## Publishing Your Notebook Online \nLastly we will take a small look at how you could post your interactive notebook online so that other people can also access it without using Julia.\n\nAt the moment this still isn't implemented in Pluto. It is however as we mentioned earlier possible to make a static version of your notebook.\nBut if you really want to have an up and running version of your notebook that everyone can access, then there are two main ways at the moment.\n\n1. The first and easiest option is to use [Binder](https://github.com/fonsp/pluto-on-binder). This gives an easy and free way of running your notebook online. However it can take a long time to load.\n\n\n2. The second way is a bit more complicated. In short you will run your notebook through `GitHub` and use a (payed) server to take care of the interactive part. If you don't have a server laying around this may not be for you. If you want to find out more about this you can read all about it [here](https://cotangent.dev/how-to-publish-pluto-jl-notebooks-online-interactively/)\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nHypertextLiteral = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\n\n[compat]\nHypertextLiteral = \"~0.9.0\"\nPlutoUI = \"~0.7.9\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[HypertextLiteral]]\ngit-tree-sha1 = \"72053798e1be56026b81d4e2682dbe58922e5ec9\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.0\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"81690084b6198a2e1da36fcfda16eeca9f9f24e4\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.1\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"c8abc88faa3f7a3950832ac5d6e690881590d6dc\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"1.1.0\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"Suppressor\"]\ngit-tree-sha1 = \"44e225d5837e2a2345e69a1d1e01ac2443ff9fcb\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.9\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"5f6c21241f0f655da3952fd60aa18477cf96c220\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.1.0\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[Suppressor]]\ngit-tree-sha1 = \"a819d77f31f83e5792a76081eee1ea6342ab8787\"\nuuid = \"fd094767-a336-5f1f-9728-57cf17d0bbfb\"\nversion = \"0.2.0\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500b5094370-ee11-11eb-3e48-b3a77979f99a\n# \u255f\u25002dd30617-8deb-4762-9cc3-55f6ef341505\n# \u255f\u2500bc216d83-20ba-4efb-b201-bff73a83f5af\n# \u255f\u25008d9a48e4-382f-49a1-9f51-ab549a5b7039\n# \u2560\u2550288f2701-aa8e-4a81-b334-b58bcdc74ae7\n# \u255f\u25005bb6c26d-4d0f-47b4-8a47-e7d436be4892\n# \u255f\u250075f401fc-03eb-465a-a218-68b6ccc544d1\n# \u255f\u250022a6d095-e48f-4a56-9587-bca63beedf99\n# \u255f\u25001bffea5a-dd8d-4156-88d4-b6b614436881\n# \u255f\u25004b0bca9a-426f-4157-935d-cf67cd03f201\n# \u255f\u2500c6363449-3b51-4f25-ab41-c0cde7f0565c\n# \u255f\u25007a8e5f68-beb1-41d4-af1f-4b89e5bbf388\n# \u255f\u250034971682-5962-4e58-aa18-bf155256c49f\n# \u255f\u2500b3b85049-733d-4d05-b734-051949a61468\n# \u255f\u2500f5630f3c-b0eb-4b60-a428-da21b8a68416\n# \u255f\u2500ec19c690-5784-4cc4-a63b-5d2485eeafa9\n# \u255f\u25002a83d1e3-79c2-4a36-93b2-1bf2f89b27f3\n# \u255f\u25005a1f85e6-f9c6-44b7-a2d4-6b70d65aeaef\n# \u255f\u25006b20de32-f606-45d1-a800-b4cdadc33017\n# \u255f\u2500e703ea17-554a-4932-a20c-5e5975f4372a\n# \u255f\u2500d1739e3b-6363-4a77-8d7c-61b6ba8978bd\n# \u255f\u25003acc7d2e-7c54-423c-aac8-1f8f61963d77\n# \u255f\u25004c1ea155-df57-4b1b-94f7-c638e7f747c2\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "643910ba986b4c66bfd173501b33783a2cece3f0", 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{"text": "################################################################################\n###  Introduction to Scientific Programming and Machine Learning with Julia  ###\n###                                                                          ###\n### Run each script on a new clean Julia session                             ###\n### GitHub: https://github.com/sylvaticus/IntroSPMLJuliaCourse               ###\n### Licence (apply to all material of the course: scripts, videos, quizes,..)###\n### Creative Commons By Attribution (CC BY 4.0), Antonello Lobianco          ###\n################################################################################\n\n# # 0105 Custom Types\n\n\n# ## Some stuff to set-up the environment..\n\ncd(@__DIR__)         \nusing Pkg             \nPkg.activate(\".\")     \n## If using a Julia version different than 1.7 please uncomment and run the following line (reproductibility guarantee will however be lost)\n## Pkg.resolve()   \n## Pkg.instantiate() # run this if you didn't in Segment 01.01\nusing Random\nRandom.seed!(123)\nusing InteractiveUtils # loaded automatically when working... interactively\n\n# ## \"Type\" of types\n\n# In Julia primitive, composite and abstract types can all be defined by the user\n\nprimitive type APrimitiveType 819200 end # name and size in bit - multiple of 8 and below 8388608 (1MB)\nprimitive type APrimitiveType2 819200 end\n819200/(8*1024)\nstruct ACompositeType end                # fields, constructors.. we'll see this later in details\nabstract type AnAbstractType end         # no objects, no instantialisation of objects\n\n\n# ## Composite types\nmutable struct Foo # starting with a capital letter\n    field1\n    field2::String\n    field3::ACompositeType\nend\n\n# mutable struct Foo # error! can't change a struct after I defined it\n# end\n\nfieldnames(Foo)\n\no = Foo(123,\"aaa\",ACompositeType()) # call the default constructor (available automatically) - order matters!\ntypeof(o)\n\n\n# Outer constructor\nfunction Foo(f2,f3=ACompositeType()) # \"normal\" functions just happens it has the name of the object to create\n    if startswith(f2,\"Booo\")         # put whatever logic you wish\n        return nothing\n    end\n    return Foo(123,f2,f3)            # call the default constructor\nend\n\no = Foo(123,\"aaa\", ACompositeType()) # call the default constructor\no = Foo(\"blaaaaa\")                   # call the outer constructor we defined\n\no.field1       # access fields\no.field1 = 321 # modify field (because type defined as \"mutable\" !!!)\no\n\n\nfunction Base.show(io::IO, x::Foo)             # function(o) rather than o.function() \n    println(io,\"My custom representation for Foo objects\")\n    println(io,\"Field1: $(o.field1)\")\n    println(io,\"Field2: $(o.field2)\")\nend\no\n\n# Inner constructor\nmutable struct Foo2\n    field1::Int64\n    field2::String\n    function Foo2(f1,f2,f3)\n        ## ... logic\n        return new(f1+f2,f3)\n    end\nend\nFoo2(1,2,\"aaa\")\n\n# !!! tip\n#     If any inner constructor method is defined, no default constructor method is provided.\n\n## Foo2(1,\"aaa\") # Error, no default constructor !\n\n# ## Parametric types\n\nstruct Point{T<:Number} # T must be a child of type \"Number\"\n   x::T\n   y::T\nend\n\no = Point(1,2)\nPoint(1.0,2.)\n## Point(1,2.0) # error !\n\nfunction Point(x::T, y::T=zero(T)) where {T}\n    return Point(x,y)\nend\nPoint(2)\nPoint(1.5)\n\nabstract type Figure{T<:Number} end\n\na = Array{Int64,2}(undef,2,2) # Array is nothing else than a parametric type with 2 parameters\ntypeof(a)\neltype(a)\n\n# As we see for arrays, parameters doesn't need to be _types_, but can be any value of a bits type (in practice an integer value) :\n\nstruct MyType{T,N}\n  data::Array{T,N}\nend\n\nintMatrixInside = MyType([1 2 3; 4 5 6])\nfloatVectorInside = MyType([1 2 3])\n\nfunction getPlane(o::MyType{T,N},dim,pos) where {T,N}\nsizes = size(o.data)\nif length(sizes) > N\n  error(\"Dim over the dimensions of the data\")\nelseif sizes[dim] < pos\n  error(\"Non enought elements in dimension $dim to cut at $pos\")\nend\nreturn selectdim(o.data,dim,pos)\nend\n\ngetPlane(intMatrixInside,1,2)\n\n# A package where non-type parameters are emploied to boost speed is [StaticArray.jl](https://github.com/JuliaArrays/StaticArrays.jl) where one parameter is the _size_ of the array that hence become known at compile time\n\n\n# ## Inheritance\n\nabstract type MyOwnGenericAbstractType end                       # the highest-level\nabstract type MyOwnAbstractType1 <: MyOwnGenericAbstractType end # child of MyOwnGenericAbstractType \nabstract type MyOwnAbstractType2 <: MyOwnGenericAbstractType end # also child of MyOwnGenericAbstractType\nmutable struct AConcreteTypeA <: MyOwnAbstractType1\n  f1::Int64\n  f2::Int64\nend\nmutable struct AConcreteTypeB <: MyOwnAbstractType1\n  f1::Float64\nend\nmutable struct AConcreteTypeZ <: MyOwnAbstractType2\n  f1::String\nend\noA = AConcreteTypeA(2,10)\noB = AConcreteTypeB(1.5)\noZ = AConcreteTypeZ(\"aa\")\n\nsupertype(AConcreteTypeA)\nsubtypes(MyOwnAbstractType1)\n\n\n# !!! tip\n#     When multiple methods are available for an object, function calls are dispatched to the most stricter method, i.e. the one defined over the exact parameter's type or their immediate parents\n\nfunction foo(a :: MyOwnGenericAbstractType)                      # good for everyone \n  println(\"Default implementation: $(a.f1)\")\nend\nfoo(oA) # Default implementation: 2\nfoo(oB) # Default implementation: 1.5\nfoo(oZ) # Default implementation: aa\nfunction foo(a :: MyOwnAbstractType1)                            # specialisation for MyOwnAbstractType1\n  println(\"A more specialised implementation: $(a.f1*4)\")\nend\nfoo(oA) # A more specialised implementation: 8\nfoo(oB) # A more specialised implementation: 6.0\nfoo(oZ) # Default implementation: aa                             # doesn't match the specialisation, default to foo(a :: MyOwnGenericAbstractType)\nfunction foo(a :: AConcreteTypeA)\n     println(\"A even more specialised implementation: $(a.f1 + a.f2)\")\nend\nfoo(oA) # A even more specialised implementation: 12\nfoo(oB) # A more specialised implementation: 6.0\nfoo(oZ) # Default implementation: aa\n\n# !!! warning \n#     Attention to the inheritance for parametric types. If it is true that `Vector{Int64} <: AbstractVector{Int64}` and `Int64 <: Number`, it is FALSE that `AbstractVector{Int64} <: AbstractVector{Number}`. If you want to allow a function parameter to be a vector of numbers, use instead templates explicitly, e.g. `foo(x::AbstractVector{T}) where {T<:Number} = return sum(x)`\n\nVector{Int64} <: AbstractVector{Int64}\nInt64 <: Number\nVector{Int64} <: Vector{Number}\nAbstractVector{Int64} <: AbstractVector{Number}\n\n\n# ## Object-oriented model\n\n# OO model based on _composition_ \n\nstruct Shoes\n   shoesType::String\n   colour::String\nend\nstruct Person\n  myname::String\n  age::Int64\nend\nstruct Student\n   p::Person        # by referencing a `Person`` object, we do not need to repeat its fields\n   school::String\n   shoes::Shoes     # same for `shoes`\nend\nstruct Employee\n   p::Person\n   monthlyIncomes::Float64\n   company::String\n   shoes::Shoes\nend\ngymShoes = Shoes(\"gym\",\"white\")\nproShoes = Shoes(\"classical\",\"brown\")\nMarc     = Student(Person(\"Marc\",15),\"Divine School\",gymShoes)\nMrBrown  = Employee(Person(\"Brown\",45),3200.0,\"ABC Corporation Inc.\", proShoes)\n\nfunction printMyActivity(self::Student)\n   println(\"Hi! I am $(self.p.myname), I study at $(self.school) school, and I wear $(self.shoes.colour) shoes\") # I can use the dot operator chained...\nend\nfunction printMyActivity(self::Employee)\n  println(\"Good day. My name is $(self.p.myname), I work at $(self.company) company and I wear $(self.shoes.colour) shoes\")\nend\n\nprintMyActivity(Marc)     # Hi! I am Marc, ...\nprintMyActivity(MrBrown)  # Good day. My name is MrBrown, ...\n\n# OO models based on Specialisation (Person \u2192 Student) or Weack Relation (Person \u2192 Shoes) instead of Composition (Person \u2192 Arm) can be implemented using third party packages, like e.g. [SimpleTraits.jl](https://github.com/mauro3/SimpleTraits.jl) or [OOPMacro.jl](https://github.com/ipod825/OOPMacro.jl)\n", "meta": {"hexsha": "747b1b34f5c65b272b430a69702a7e19bf8bc3ab", "size": 7968, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0105_-_Custom_types.jl", "max_stars_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_stars_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2022-02-26T15:44:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T13:05:52.000Z", "max_issues_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0105_-_Custom_types.jl", "max_issues_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_issues_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0105_-_Custom_types.jl", "max_forks_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_forks_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-26T08:06:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T08:06:40.000Z", "avg_line_length": 34.4935064935, "max_line_length": 377, "alphanum_fraction": 0.6848644578, "num_tokens": 2066, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.33807711081162, "lm_q2_score": 0.2720245510940225, "lm_q1q2_score": 0.09196527430369504}}
{"text": "## Exercise 6-6\n## A palindrome is a word that is spelled the same backward and forward, like \u201cnoon\u201d and \u201credivider\u201d. Recursively, a word is a palindrome if the first and last letters are the same and the middle is a palindrome.\n\n## The following are functions that take a string argument and return the first, last, and middle letters:\n\nfunction first(word)\n    first = firstindex(word)\n    word[first]\nend\n\nfunction last(word)\n    last = lastindex(word)\n    word[last]\nend\n\nfunction middle(word)\n    first = firstindex(word)\n    last = lastindex(word)\n    word[nextind(word, first) : prevind(word, last)]\nend\n\n## We\u2019ll see how they work in Strings\n\n## 1. Test these functions out. What happens if you call middle with a string with two letters? One letter? What about the empty string, which is written \"\" and contains no letters?\nprintln(\"Ans 1: \")\n\nprintln(first(\"first\")) # returns 'f'\nprintln(last(\"last\")) # returns 't'\nprintln(middle(\"middle\")) # returns \"iddl\"\n\nprintln(middle(\"as\")) # returns ''\nprintln(middle(\"A\")) # returns ''\n# println(middle(\"\")) # error, empty string\n\n## 2. Write a function called ispalindrome that takes a string argument and returns true if it is a palindrome and false otherwise. Remember that you can use the built-in function length to check the length of a string.\nprintln(\"Ans 2: \")\n\nfunction ispalindrome(word)\n    if length(word) == 0\n        return true\n    end\n\n    if first(word) == last(word)\n        return ispalindrome(middle(word))\n    else\n        return false\n    end\nend\n\nprintln(ispalindrome(\"noon\"))\nprintln(ispalindrome(\"redivider\"))\nprintln(ispalindrome(\"name\"))\n\nprintln(\"End.\")\n", "meta": {"hexsha": "b72d4e22099b0f281db1187a6c0a552962e5daa5", "size": 1637, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter6/ex6.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter6/ex6.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter6/ex6.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7636363636, "max_line_length": 219, "alphanum_fraction": 0.7067806964, "num_tokens": 394, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.19930800503802076, "lm_q1q2_score": 0.09188433457379117}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.4\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 d7967150-5a59-4199-83ad-4e79476c0d44\nusing PlutoUI: with_terminal\n\n# \u2554\u2550\u2561 1a45c71a-4204-461b-8f85-61d9a79f8cf8\nusing BenchmarkTools\n\n# \u2554\u2550\u2561 bdacad08-a9b1-11ec-1013-45393b4e19c9\n# hideall\ntitle = \"Benchmarking\";\n\n# \u2554\u2550\u2561 031e949e-e784-4d9d-b767-0a3e2d29d3a2\n\"\"\"\n+++\ntitle = \"$title\"\n+++\n\"\"\" |> Base.Text\n\n# \u2554\u2550\u2561 8a859941-0f2d-45fb-b56b-a3f58f97a36a\nmd\"\"\"\n# $title\n\nThis notebook shows some `@benchmark` and `@code_warntype` output.\nBoth outputs are shown via the `with_terminal` from [PlutoUI.jl](https://github.com/JuliaPluto/PlutoUI.jl), see below.\n\nWe define some function `double` and a dictionary of numbers in order to show type inference problems via `@code_warntype`:\n\"\"\"\n\n# \u2554\u2550\u2561 90cb7685-09fc-4f2a-88c9-64fada9e50d0\nnumbers = Dict(:one => 1f0, :two => 2.0)\n\n# \u2554\u2550\u2561 3f0e2049-8597-4dac-b499-4d7a8a35978e\nfunction double(mapping, key::Symbol)\n    return 2 * mapping[key]\nend;\n\n# \u2554\u2550\u2561 faef1ae5-eeac-4e23-ba75-23ae2f1ca629\nmd\"\"\"\nNow, the code works.\n\"\"\"\n\n# \u2554\u2550\u2561 a3c923e5-2873-48a2-ad0f-0087585136a7\ndouble(numbers, :one)\n\n# \u2554\u2550\u2561 a16c4a67-8666-4dc5-b100-4c2c7c51f981\ndouble(numbers, :two)\n\n# \u2554\u2550\u2561 187e21bc-db38-48c9-97cd-7894685e1b43\nmd\"\"\"\nBut the `@code_warntype` shows some big red warnings:\n\"\"\"\n\n# \u2554\u2550\u2561 a3b4bcc6-4aa1-48b2-b598-0613794beab2\nwith_terminal() do\n    @code_warntype double(numbers, :one)\nend\n\n# \u2554\u2550\u2561 8c147812-f0e7-4b14-9082-3ac4d5f07000\nmd\"\"\"\nWe can fix this by forcing all elements in the dictionary to have the same type.\nSpecifically, to we force all elements to be of type `Float64`:\n\"\"\"\n\n# \u2554\u2550\u2561 0f78717a-ccbd-4af1-a150-adcce25cddb4\ntypednumbers = Dict{Symbol, Float64}(:one => 1f0, :two => 2.0)\n\n# \u2554\u2550\u2561 48d6f2fa-04e8-475c-92b6-43418f6a2c6c\nmd\"\"\"\nThis gets rid of all the type warnings:\n\"\"\"\n\n# \u2554\u2550\u2561 c678d759-9a68-439e-9cb3-3cfe94088794\nwith_terminal() do\n    @code_warntype double(typednumbers, :one)\nend\n\n# \u2554\u2550\u2561 3cf79c82-9d43-41d3-b968-e2d283bc32ea\nmd\"\"\"\nAnd makes the method more quick:\n\"\"\"\n\n# \u2554\u2550\u2561 b52f7449-aed4-4eba-905f-3c078fba4f3f\nmd\"\"\"\n## Appendix\n\"\"\"\n\n# \u2554\u2550\u2561 064395ff-2cc6-4be1-a6f0-0c3ccefdf8ce\nfunction with_benchmark_terminal(f)\n    out = sprint(show, \"text/plain\", f())\n    with_terminal() do\n        print(out)\n    end\nend;\n\n# \u2554\u2550\u2561 bc815bd5-1f5e-4641-81f4-80e0306398bf\nwith_benchmark_terminal() do\n    @benchmark double(numbers, :one)\nend\n\n# \u2554\u2550\u2561 b8f4d737-4647-47f1-8c45-c3ed30994e1f\nwith_benchmark_terminal() do\n    @benchmark double(typednumbers, :one)\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nBenchmarkTools = \"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\n\n[compat]\nBenchmarkTools = \"~1.3.1\"\nPlutoUI = \"~0.7.37\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.2\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.BenchmarkTools]]\ndeps = [\"JSON\", \"Logging\", \"Printf\", \"Profile\", \"Statistics\", \"UUIDs\"]\ngit-tree-sha1 = \"4c10eee4af024676200bc7752e536f858c6b8f93\"\nuuid = \"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf\"\nversion = \"1.3.1\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[deps.HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[deps.IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"3c837543ddb02250ef42f4738347454f95079d4e\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.3\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"85b5da0fa43588c75bb1ff986493443f821c70b7\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.3\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"bf0a1121af131d9974241ba53f601211e9303a9e\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.37\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.Profile]]\ndeps = [\"Printf\"]\nuuid = \"9abbd945-dff8-562f-b5e8-e1ebf5ef1b79\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[deps.Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550bdacad08-a9b1-11ec-1013-45393b4e19c9\n# \u2560\u2550031e949e-e784-4d9d-b767-0a3e2d29d3a2\n# \u2560\u25508a859941-0f2d-45fb-b56b-a3f58f97a36a\n# \u2560\u255090cb7685-09fc-4f2a-88c9-64fada9e50d0\n# \u2560\u25503f0e2049-8597-4dac-b499-4d7a8a35978e\n# \u2560\u2550faef1ae5-eeac-4e23-ba75-23ae2f1ca629\n# \u2560\u2550a3c923e5-2873-48a2-ad0f-0087585136a7\n# \u2560\u2550a16c4a67-8666-4dc5-b100-4c2c7c51f981\n# \u2560\u2550187e21bc-db38-48c9-97cd-7894685e1b43\n# \u2560\u2550d7967150-5a59-4199-83ad-4e79476c0d44\n# \u2560\u2550a3b4bcc6-4aa1-48b2-b598-0613794beab2\n# \u2560\u25508c147812-f0e7-4b14-9082-3ac4d5f07000\n# \u2560\u25500f78717a-ccbd-4af1-a150-adcce25cddb4\n# \u2560\u255048d6f2fa-04e8-475c-92b6-43418f6a2c6c\n# \u2560\u2550c678d759-9a68-439e-9cb3-3cfe94088794\n# \u2560\u25503cf79c82-9d43-41d3-b968-e2d283bc32ea\n# \u2560\u25501a45c71a-4204-461b-8f85-61d9a79f8cf8\n# \u2560\u2550bc815bd5-1f5e-4641-81f4-80e0306398bf\n# \u2560\u2550b8f4d737-4647-47f1-8c45-c3ed30994e1f\n# \u2560\u2550b52f7449-aed4-4eba-905f-3c078fba4f3f\n# \u2560\u2550064395ff-2cc6-4be1-a6f0-0c3ccefdf8ce\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "a4a8f68150736615165df68f208cb1a468f833c0", "size": 9640, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/benchmarking.jl", "max_stars_repo_name": "rikhuijzer/JuliaTutorialsTemplate", "max_stars_repo_head_hexsha": "2c2bcc92aee2c9cd092ffa32e9b68ba0abf0c7e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2022-01-24T09:17:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-27T08:44:53.000Z", "max_issues_repo_path": "tutorials/benchmarking.jl", "max_issues_repo_name": "rikhuijzer/JuliaTutorialsTemplate", "max_issues_repo_head_hexsha": "2c2bcc92aee2c9cd092ffa32e9b68ba0abf0c7e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 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{"text": "### A Pluto.jl notebook ###\n# v0.18.4\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 49a44e94-29c0-4004-8d0b-e37008eafc1c\nusing BenchmarkTools, PlutoUI, Wordlegames\n\n# \u2554\u2550\u2561 f9816eb0-078e-41f2-bf7c-29ed5c663fd0\n# hideall\ntitle = \"Scoring guesses in Wordle\";\n\n# \u2554\u2550\u2561 a9678ae8-48c0-4bb9-920d-7de176105bd0\n\"\"\"\n+++\ntitle = \"$title\"\n+++\n\"\"\" |> Base.Text\n\n# \u2554\u2550\u2561 4eabf46e-f75c-4b15-8583-6abe17c0fd85\nmd\"\"\"\n# $title\n\n[Wordle](https://en.wikipedia.org/wiki/Wordle) is a recently developed, extremely popular word game that has already spawned many imitators such as [Primel](https://converged.yt/primel/).\n\nThese tutorials illustrate some [Julia](https://julialang.org) programming concepts using functions in the [Wordlegames](https://github.com/dmbates/Wordlegames.jl) package for illustration.\nPart of the purpose is to illustrate the unique nature of Julia as a dynamically-typed language with a just-in-time (JIT) compiler.\nIt allows you to write \"generic\", both in the common meaning of \"general purpose\" and in the technical meaning of generic functions, and performative code.\n\nThis posting originated from a conversation on the Julia [discourse channel](https://discourse.julialang.org/t/rust-julia-comparison-post/75403) referring to a case where Julia code to perform a certain Wordle-related task - determine the \"best\" initial guess in a Wordle game - was horribly slow.\nJulia code described in a [Hacker News](https://news.ycombinator.com/) posting took several hours to do this.\n\nIn situations like this the Julia community inevitably responds with suggested modifications to make the code run faster.\nSomeone joked that we wouldn't be satisfied until we could do that task in less than 1 second, and we did.\n\nThe code in these postings can be used to solve a Wordle game very rapidly, as well as related games like Primel.\n\nBefore beginning we attach some packages that will be used in this notebook.\n\"\"\"\n\n# \u2554\u2550\u2561 03b41118-5756-447b-9900-15af37667529\nmd\"\"\"\n## Target pools\n\nIf you are not familiar with the rules of Wordle, please check the [Wikipedia page](https://en.wikipedia.org/wiki/Wordle).\nIt is a word game with the objective of guessing a 5-letter English word, which we will call the \"target\".\nThe target word is changed every day but it is always chosen from a set of 2315 words, which we will call the \"target pool\".\n\nThe original target pool is available with the `Wordlegames` package.  (Apparently the New York Times removed a few of these words after they purchased the rights to Wordle.)\n\"\"\"\n\n# \u2554\u2550\u2561 73e3e38b-0935-4ef9-97b1-8a8cdfa2feb7\ndatadir = joinpath(pkgdir(Wordlegames), \"data\");\n\n# \u2554\u2550\u2561 4c5e781c-fd10-425f-95d2-1fec5bf1015f\nwordlestrings = collect(readlines(joinpath(datadir, \"Wordletargets.txt\")))\n\n# \u2554\u2550\u2561 2046a4ca-c4d3-4804-9373-930d9cbb58fd\nmd\"We call this pool `wordlestrings` because it is stored as a vector of `String`s\"\n\n# \u2554\u2550\u2561 560ad4d4-945c-4d21-8947-cac68c1880a1\ntypeof(wordlestrings)\n\n# \u2554\u2550\u2561 ddd7f90c-7cad-4279-a275-b1800b4ae83b\nmd\"\"\"\nThe `Wordlegames` package defines a `GamePool` struct for playing Wordle or related games.\nIn that `struct` the `String`s are converted to a more efficient storage mode as a vector of `NTuple{5,Char}`, which takes advantage of the fact that each string is exactly 5 characters long.\n\nSpeaking of which, it would be a good idea to check that this collection has the properties we were told it had.\nIt should be a vector of 2315 strings, each of which is 5 characters.\n\"\"\"\n\n# \u2554\u2550\u2561 49500976-7ab8-43cc-9902-b25ad73ff42e\nlength(wordlestrings)\n\n# \u2554\u2550\u2561 f6681018-f729-48de-9342-a5bcb151aca2\nall(w -> length(w) == 5, wordlestrings)\n\n# \u2554\u2550\u2561 22f7e1ad-3a2b-4a88-8f81-91feada88621\nmd\"\"\"\nThat last expression may look, well, \"interesting\".\nIt is a way of checking that a function, in this case an anonymous function expressed using the [stabby lambda](https://dev.to/keithrbennett/why-i-prefer-stabby-lambda-notation-5gcj) notation, returns `true` for each element of an iterator, in this case the vector `wordlestrings`.\nYou can read the whole expression as \"is `length(w)` equal to `5` for each word `w` in `wordlestrings`\".\n\nThese words are supposed to be exactly 5 letters long but it never hurts to check.\nI've been a data scientist for several decades and one of the first lessons in the field is to [trust, but verify](https://en.wikipedia.org/wiki/Trust%2C_but_verify) any claims about the data you are provided.\n\nIt turns out this check is redundant because the property is checked when creating a `GamePool`.\n\"\"\"\n\n# \u2554\u2550\u2561 5787eef2-7a2c-46ad-8fa0-7313c07be84b\nwordle = GamePool(wordlestrings);\n\n# \u2554\u2550\u2561 8df0e0c0-cdae-450c-abc1-5cea1d520ec9\npropertynames(wordle)\n\n# \u2554\u2550\u2561 8580bdcf-24ce-4eb6-ac5a-4c77b729b38b\ntypeof(wordle.guesspool)\n\n# \u2554\u2550\u2561 9ed9e8ab-3ebc-4c6e-b2c3-46ad1987561e\nfirst(wordle.guesspool, 3)\n\n# \u2554\u2550\u2561 fdd6631e-8b3a-4b4f-b8a7-11135f13cc23\nmd\"\"\"\n## Game play\n\nA Wordle game is a dialog between the player and an \"oracle\", which, for the official game, is the web site.\nThe player submits a question to the oracle and the oracle responds, using information to which the player does not have access.\nIn this case the information is the target word.\nThe question is the player's guess - a 5-letter word - and the response is a score for that word.\nThe score indicates, for each character, whether it matches the character in the same position in the target or it is in the target in another position or it is not in the target at all.\n\nUsing the sample game for Wordle #196 from the Wikipedia page for illustration\n\"\"\"\n\n# \u2554\u2550\u2561 9b911b3c-17d5-42d5-b7e5-a4187ce5787c\nPlutoUI.Resource(\n    \"https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Wordle_196_example.svg/440px-Wordle_196_example.svg.png\",\n)\n\n# \u2554\u2550\u2561 4d5681f9-56a5-4f83-9b31-810bdef26d98\nmd\"\"\"\nThe target is \"rebus\".\n\nThe player's first guess is \"arise\" and the response, or score, from the oracle is coded as \ud83d\udfeb\ud83d\udfe8\ud83d\udfeb\ud83d\udfe8\ud83d\udfe8 where \ud83d\udfeb indicates that the letter is not in the target (neither `a` nor `i` occur in \"rebus\") and \ud83d\udfe8 indicates that the letter is in the target but not at that position.\n(I'm using \ud83d\udfeb instead of a gray square because I can't find a gray square Unicode character.)\n\nThe second guess is \"route\" for which the response is \ud83d\udfe9\ud83d\udfeb\ud83d\udfe8\ud83d\udfeb\ud83d\udfe8 indicating that the first letter in the guess occurs as the first letter in the target.\nNotice that this guess does not include an \"s\", which is known from the score of the first guess to be one of the characters in the target.\nThis guess would not be allowed if playing the game under the \"Hard Mode\" setting.\n\nOf course, the colors are just one way of summarizing the response to a guess.\nWithin a computer program it is easier to use an integer to represent each of the 243 = 3\u2075 possible scores.\nAn obvious way of mapping the result to an integer in the (decimal) range 0:242 is by mapping the response for each character to 2 (in target at that position), 1 (in target not at that position), or 0 (not in target) and regarding the pattern as a base-3 number.\n\nIn this coding the response for the first guess, \"arise\", is 01011 in base-3 or 31 in decimal.\nThe response for the second guess, \"route\", is 20101 in base-3 or 172 in decimal.\n\nA function to evaluate this score can be written as\n\"\"\"\n\n# \u2554\u2550\u2561 db787d98-d2a4-4d4a-8e13-8b61e80e48dd\nfunction score(guess, target)\n    s = 0\n    for (g, t) in zip(guess, target)\n        s *= 3\n        s += (g == t) ? 2 : Int(g \u2208 target)\n    end\n    return s\nend\n\n# \u2554\u2550\u2561 e9e23c37-e4d8-4b77-ab6f-264bcba4ebec\nmd\"\"\"\nThese numeric scores are not on a scale where \"smaller is better\" or \"larger is better\".\n(It happens that the best score is 242, corresponding to a perfect match, or five green tiles, but that's incidental.)\n\nThe score is just a way of representing each of the 243 patterns that can be produced.\n\nWe can convert back to colored tiles if desired using the `tiles` function from the `Wordlegames` package, defined as \n```julia\nfunction tiles(sc, ntiles)\n    result = Char[]       # initialize to an empty array of Char\n    for _ in 1:ntiles     # _ indicates the value of the iterator is not used\n        sc, r = divrem(sc, 3)\n        push!(result, iszero(r) ? '\ud83d\udfeb' : (isone(r) ? '\ud83d\udfe8' : '\ud83d\udfe9'))\n    end\n    return String(reverse(result))\nend\n```\n\"\"\"\n\n# \u2554\u2550\u2561 b0970feb-103d-4560-b577-f611171e8da6\ntiles(31, 5)\n\n# \u2554\u2550\u2561 39ae1224-9cdf-4d4b-b543-985a5667a601\nmd\"\"\"\n## Examining the score function\n\nIn the Sherlock Holmes story [The Adventure of Silver Blaze](thttps://en.wikipedia.org/wiki/The_Adventure_of_Silver_Blaze) there is a famous exchange where Holmes remarks on \"the curious incident of the dog in the night-time\" (see the link).\nThe critical clue in the case is not what happened but what didn't happen - the dog didn't bark.\n\nJust as Holmes found it interesting that the dog didn't bark, we should find some of the functions in this notebook interesting for what they don't include.\nIn many of the functions shown here the arguments aren't given explicit types.\n\nKnowing the concrete types of arguments is very important when compiling functions, as is done in Julia, but these functions are written without explicit types.\n\nConsider the `score` function which we reproduce here\n\n```julia\nfunction score(guess, target)\n    s = 0\n    for (g, t) in zip(guess, target)\n        s *= 3\n        s += (g == t) ? 2 : Int(g \u2208 target)\n    end\n    return s\nend\n```\n\nThe arguments to `score` can be any type.\nIn fact, formally they are of an abstract type called `Any`.\n\nSo how do we make sure that the actual arguments make sense for this function?\nWell, the first thing that is done with the arguments is to pass them to `zip(guess, target)` to produce pairs of values, `g` and `t`, that can be compared for equality, `g == t`.\nIn a sense `score` delegates the task of checking that the arguments are sensible to the `zip` function.\n\nFor those unfamiliar with zipping two or more iterators, we can check what the result is.\n\"\"\"\n\n# \u2554\u2550\u2561 8a1cc31b-f747-49b4-9860-a2c381d98c44\ncollect(zip(\"arise\", \"rebus\"))\n\n# \u2554\u2550\u2561 6830dbd6-edc9-4193-8d73-becf3be3855a\nmd\"\"\"\nOne of the great advantages of dynamically-typed languages with a REPL (read-eval-print-loop) like Julia is that we can easily check what `zip` produces in a couple of examples (or even read the documentation returned by `?zip`, if we are desperate).\n\nThe rest of the function is a common pattern - initialize `s`, which will be the result, modify `s` in a loop, and return it.\nThe Julia expression\n```julia\ns *= 3\n```\nindicates, as in several other languages, that `s` is to be multiplied by 3 in-place.\n\nAn expression like\n```julia\n(g == t) ? 2 : Int(g  \u2208 target)\n```\nis a *ternary operator* expression (the name comes from the operator taking three operands).\nIt evaluates the condition, `g == t`, and returns `2` if the condition is `true`.\nIf `g == t` is `false` the operator returns the value of the Boolean expression `g  \u2208 target`, converted to an `Int`.\n(The expression could also be written `g in target`.\nIn the Julia REPL the `\u2208` character is created by typing `\\in<tab>`.)\nThe Boolean expression will return `false` or `true`, which is promoted to `0` or `1` for the `+=` operation.\n\nThe operation of multiplying by 3 and adding 2 or 1 or 0 is an implementation of [Horner's method](https://en.wikipedia.org/wiki/Horner%27s_method) for evaluating a polynomial.\n\nThe function is remarkable because it is both general and compact.\nEven more remarkable is that it will be very, very fast after its first usage triggers compilation.\nThat's important because this function will be in a \"hot loop\".\nIt will be called many, many times when evaluating the next guess.\n\n(Unfortunately, this version doesn't properly account for cases where a character is repeated in the guess - an example of [Kernighan and Plauger's](https://en.wikipedia.org/wiki/The_Elements_of_Programming_Style) aphorism, \"Efficiency often means getting the wrong answer quickly.\"\nWe will return to this issue later.)\n\nWe won't go into detail about the Julia compiler except to note that compilation is performed for specific *method signatures* (or \"method instances\") not for general method definitions.\n\nThere are several functions and macros in Julia that allow for inspection at different stages of compilation.\nOne of the most useful is the macro `@code_warntype` which is used to check for situations where type inference has not been successful.\nApplying it as\n```julia\njulia> @code_warntype score(\"arise\", \"rebus\")\nMethodInstance for score(::String, ::String)\n  from score(guess, target) in Main at REPL[1]:1\nArguments\n  #self#::Core.Const(score)\n  guess::String\n  target::String\nLocals\n  @_4::Union{Nothing, Tuple{Tuple{Char, Char}, Tuple{Int64, Int64}}}\n  s::Int64\n  @_6::Int64\n  t::Char\n  g::Char\n  @_9::Int64\nBody::Int64\n...\n```\nshows that type inference is based on concrete types (`String`) for the arguments.\n\nSome argument types are handled more efficiently than others.\nWithout going in to details we note that we can take advantage of the fact that we have exactly 5 characters and convert the elements of `words` from `String` to `NTuple{5,Char}`, which is an ordered, fixed-length homogeneous collection.\n\nUsing the `@benchmark` macro from the `BenchmarkTools` package gives run times of a few tens of nanoseconds for these arguments, and shows that the function applied to the fixed-length collections is faster.\n\"\"\"\n\n# \u2554\u2550\u2561 e4410d39-6680-4eac-9f70-a425df352998\nmd\"\"\"\nNew methods can be defined for a generic function like `score`.\nA reason for this could be, for example, that the method can more effectively use information from the type.\n\nFor example, the `NTuple{N,Char}` type has exactly `N` characters - information that can be used in a loop where we can turn off bounds checking.\n\"\"\"\n\n# \u2554\u2550\u2561 a031c3db-6630-4deb-a90d-d2b13202dec6\nfunction score(guess::NTuple{N,Char}, target::NTuple{N,Char}) where {N}\n    s = 0\n    @inbounds for i = 1:N\n        s *= 3\n        gi = guess[i]\n        s += (gi == target[i]) ? 2 : Int(gi \u2208 target)\n    end\n    return s\nend\n\n# \u2554\u2550\u2561 9e1d6017-f79e-49b5-916e-8e3a9e49ab1d\nscore(\"arise\", \"rebus\")\n\n# \u2554\u2550\u2561 a69f62bd-ed6d-42e7-8405-9321d13a52d1\n@benchmark score(guess, target) setup = (guess = \"arise\"; target = \"rebus\")\n\n# \u2554\u2550\u2561 1b9261b3-c90c-4db1-9e70-51bf364072b8\nmd\"\"\"\nThis method returns the same result as the other method, only faster.\n\"\"\"\n\n# \u2554\u2550\u2561 1887c091-46a0-4e8c-9a66-2ab17968729c\nscore(('a', 'r', 'i', 's', 'e'), ('r', 'e', 'b', 'u', 's'))\n\n# \u2554\u2550\u2561 39202f1a-e7d6-4961-9776-ec67c3fd9743\n@benchmark score(guess1, target1) setup =\n    (guess1 = ('a', 'r', 'i', 's', 'e'); target1 = ('r', 'e', 'b', 'u', 's'))\n\n# \u2554\u2550\u2561 2b73c7f4-0203-4e2b-b634-eb310152834b\nmd\"\"\"\n## Repeated characters in the guess\n\nThe simple `score` methods shown above don't give the correct score (meaning the score that would be returned on the web site) when there are repeated characters in the guess.\nFor example, a guess of `\"sheer\"` for the target `\"super\"` is scored as\n\"\"\"\n\n# \u2554\u2550\u2561 79f0ceab-0c9f-42f7-b3ec-a58b5ac1c083\ntiles(score(\"sheer\", \"super\"), 5)\n\n# \u2554\u2550\u2561 b92abc9c-2a86-47ab-a6e8-127c4d7de52e\nmd\"\"\"\nbut the score should be `\"\ud83d\udfe9\ud83d\udfeb\ud83d\udfeb\ud83d\udfe9\ud83d\udfe9\"` because there is only one `e` in the target `\"super\"`.\nIn a case like this where a character occurs multiple times in a guess but only once in the target the rules about which position in the guess is marked are that \"correct position\" takes precedence over \"in the word\" and, if none of the guess positions are correct, then leftmost takes precedence.\n\nThis makes for a considerably more complex score evaluation.\nEssentially there have to be two passes over the score and target - the first to check for correct position and the second to check for \"in the target, not in the correct position\".\n\nHowever, the simple algorithm in the current `score` methods works if there are no duplicate characters in the guess.\nThus it is probably worthwhile checking for duplicates, using the simple function\n\n```julia\nfunction hasdups(guess::NTuple{N,Char}) where {N}\n    @inbounds for i in 1:(N - 1)\n        gi = guess[i]\n        for j in (i + 1):N\n            gi == guess[j] && return true\n        end\n    end\n    return false\nend\n```\n\nand choose the simple scoring algorithm when there are no duplicates.\n\nIn the [Wordlegames](https://github.com/dmbates/Wordlegames.jl) package these operations are combined in a `scorecolumn!` function that updates a vector of scores on a single guess against a vector of targets.\n\n```julia\nfunction scorecolumn!(\n    col::AbstractVector{<:Integer},\n    guess::NTuple{N,Char},\n    targets::AbstractVector{NTuple{N,Char}},\n) where {N}\n    if axes(col) \u2260 axes(targets)\n        throw(\n            DimensionMismatch(\n                \"axes(col) = $(axes(col)) \u2260 $(axes(targets)) = axes(targets)\",\n            )\n        )\n    end\n    if hasdups(guess)\n        onetoN = (1:N...,)           # 1:N as a Tuple\n        svec = zeros(Int, N)         # scores for characters in guess\n        unused = trues(N)            # unused positions in targets[i]\n        @inbounds for i in axes(targets, 1)\n            targeti = targets[i]\n            fill!(unused, true)      # reset to all unused\n            fill!(svec, 0)           # reset to all guess characters not in target\n            for j = 1:N              # first pass for target in same position\n                if guess[j] == targeti[j]\n                    unused[j] = false\n                    svec[j] = 2\n                end\n            end\n            for j = 1:N              # second pass for match in unused position\n                if iszero(svec[j])\n                    for k in onetoN[unused]\n                        if guess[j] == targeti[k]\n                            svec[j] = 1\n                            unused[k] = false\n                            break\n                        end\n                    end\n                end\n            end\n            sc = 0                   # Horner's method to evaluate the score\n            for s in svec\n                sc *= 3\n                sc += s\n            end\n            col[i] = sc\n        end\n    else                             # simplified alg. for guess w/o duplicates\n        @inbounds for i in axes(targets, 1)\n            sc = 0\n            targeti = targets[i]\n            for j = 1:N\n                sc *= 3\n                gj = guess[j]\n                sc += (gj == targeti[j]) ? 2 : Int(gj \u2208 targeti)\n            end\n            col[i] = sc\n        end\n    end\n    return col\nend\n```\n\nThis is \"production code\" which has gone through several refinement steps so it may seem a bit daunting at first.\nHowever, we can break it down.\n\nFirst, does it give the desired result?\n\"\"\"\n\n# \u2554\u2550\u2561 2f32604f-b64c-47f5-bf14-a3cbde36cc13\nscores1 = zeros(Int, 1)  # initialize a vector of 1 integer to zero \n\n# \u2554\u2550\u2561 f899d69c-ee68-47e3-963f-90bc401558b2\nscorecolumn!(scores1, ('s', 'h', 'e', 'e', 'r'), [('s', 'u', 'p', 'e', 'r')])\n\n# \u2554\u2550\u2561 643bb3f5-c8e7-446c-b7d4-ea40952c4f59\ntiles(first(scores1), 5)\n\n# \u2554\u2550\u2561 4742dec1-1a46-4095-bc49-467182beb163\nmd\"\"\"\nWe see that the call to `scorecolumn!` overwrites the contents of the `scores1` vector with the score for the guess on the first (and only) target.\n\nThus `scorecolumn!` is a \"mutating function\", meaning that it changes the contents of one or more of its arguments.\nBy convention we give such functions names ending in `\"!\"`, as a warning to the user that the function may mutate its arguments.\n(This is merely a convention; the `\"!\"` has no syntactic significance.)\nFurthermore, the convention is to list any arguments that may be modified first.\n\nThe reason this function is called `scorecolumn!` is because the scores for all possible guesses on all possible targets are evaluated and cached as a matrix in a `GamePool` object.\nThis may seem extravagant but most methods for determining an initial guess algorithmically will end up evaluating all these scores so it makes sense to save them in an array.\nIn this case the rows correspond to targets and the columns to guesses and evaluating the scores for a single guess against all possible targets updates a column of this matrix.\n\nA section from the upper left corner of this matrix\n\"\"\"\n\n# \u2554\u2550\u2561 225c813a-e63e-44fc-bc08-caf4cf36e21b\nview(wordle.allscores, 1:7, 1:10)\n\n# \u2554\u2550\u2561 875f7d47-b71f-4f67-90ae-755b00e2b464\nmd\"\"\"\nshows that the scores, which are in the range `0:242`, are stored as unsigned, 8-bit integers to conserve storage.\nEven so, the storage required is (2315)\u00b2 bytes, or over 5 megabytes.\n\"\"\"\n\n# \u2554\u2550\u2561 7c6e0762-de57-4041-a683-7ed6f0715057\nBase.summarysize(wordle.allscores)\n\n# \u2554\u2550\u2561 178cfcd4-e198-4e44-b5bd-7da6c8b21456\nmd\"\"\"\nFive megabytes is not a large amount of memory by today's standards, but for games with larger pools of guesses or targets the storage may start to mount up.\nIn those cases there is provision for [memory-mapping](https://en.wikipedia.org/wiki/Memory-mapped_file) the array.\nThe evaluation of the array is multi-threaded when Julia is running with multiple threads.\n\nThe scores in the first column,\n\"\"\"\n\n# \u2554\u2550\u2561 90f09143-4046-4e2f-967e-1df115411b32\ntiles.(view(wordle.allscores, 1:7, 1), 5)\n\n# \u2554\u2550\u2561 628fb954-e44f-4c6d-af03-c41e9e2894d5\nmd\"\"\"\nare for the first guess, \"aback\", against the first 7 targets\n\"\"\"\n\n# \u2554\u2550\u2561 c976042e-2e91-401b-acfa-88199c22326c\n[String(collect(t)) for t in view(wordle.targetpool, 1:7)]\n\n# \u2554\u2550\u2561 197e3fbe-6172-47b8-99c5-c8293223747c\nmd\"\"\"\nThe `scorecolumn!` function itself uses the `axes` function in several places.\nBy default Julia uses 1-based indexing but other forms of indexing are allowed.\n(I am obligated at this point to mention [StarWarsArrays](https://github.com/giordano/StarWarsArrays.jl) which begins indexing at 4, 5, 6 then 1, 2, 3 then 7, 8, and 9.)\n\nThe call to `axes(targets, 1)` returns the indices in the first (and only) axis of the `target` vector.\nThe `col` and `targets` arguments are typed as `AbstractVector`, not `Vector`, because `Vector` is a concrete, specific type and we wish to allow for \"vector-like\" objects such as a one-dimensional view in a multi-dimensional array.\n\nThe call to `scorecolumn!` in the constructor for a `GamePool` is in the code segment\n\n```julia\n    S = scoretype(N)\n    vtargs = view(guesspool, validtargets)\n    allscores = Array{S}(undef, length(vtargs), length(guesspool))\n    Threads.@threads for j in axes(allscores, 2)\n        scorecolumn!(view(allscores, :, j), guesspool[j], vtargs)\n    end\n```\n\nThere are two arrays, `svec` and `unused` allocated within the `scorecolumn!` function when guesses have repeated characters.\nThese are very small arrays but nonetheless we would want to minimize the number of allocations if feasible.\nThis is why the check for duplicate characters is carried out and the allocation of these arrays is done only once per column of `allscores`.\nThe allocation is done within the function so that it can be called from multiple threads simultaneously without the threads interfering with each other.\n\nThe branch for a guess without duplicates is still much faster than for a guess with duplicate characters but neither case is horribly slow.\n\"\"\"\n\n# \u2554\u2550\u2561 de2cac98-2ca9-4d27-8b02-ff152a73df7e\n@benchmark scorecolumn!(col, ('a', 'r', 'i', 's', 'e'), $(wordle.guesspool)) setup =\n    (col = zeros(UInt8, length(wordle.guesspool)))\n\n# \u2554\u2550\u2561 04cbc640-70be-402b-8a7e-3e9fc8e2cb00\nmd\"\"\"\nNotice that there are no allocations of memory when there are no duplicated characters in the guess.\nThere are allocations, and consequently some garbage collection (GC), when the guess has duplicated characters.\n\"\"\"\n\n# \u2554\u2550\u2561 01d24948-480d-4eb5-9cc4-7c1e806f22fd\n@benchmark scorecolumn!(col1, ('a', 'b', 'a', 'c', 'k'), $(wordle.guesspool)) setup =\n    (col1 = zeros(UInt8, length(wordle.guesspool)))\n\n# \u2554\u2550\u2561 2c6d5aa5-7a23-4c67-8ff0-27b95ef58375\nmd\"\"\"\n## Conclusion\n\nThese few examples have introduced, at least in passing, several advanced programming concepts - multi-threading, memory-mapping, control of storage allocation and garbage collection - that one typically would not associate with a dynamically-typed, REPL-based language like Julia.\n\nOf course, all of these facilities are available in compiled languages like C/C++ or Rust but usually without the \"rapid development and testing\" capability of a language like Julia.\n\nJulia provides a wide range of tools so that a programmer can start at a very simple level, like the original `score` method and refine as needed to reach speeds previously only achievable with compiled, statically-typed languages.\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nBenchmarkTools = \"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nWordlegames = \"1cb69566-e1cf-455f-a587-fd79a2e00f5a\"\n\n[compat]\nBenchmarkTools = \"~1.3.1\"\nPlutoUI = \"~0.7.38\"\nWordlegames = \"~0.3.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.8.0-beta3\"\nmanifest_format = \"2.0\"\nproject_hash = \"045a6e2c86e27f8d763b85c40450eb9e85eed07e\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.AbstractTrees]]\ngit-tree-sha1 = \"03e0550477d86222521d254b741d470ba17ea0b5\"\nuuid = \"1520ce14-60c1-5f80-bbc7-55ef81b5835c\"\nversion = \"0.3.4\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\nversion = \"1.1.1\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.BenchmarkTools]]\ndeps = [\"JSON\", \"Logging\", \"Printf\", \"Profile\", \"Statistics\", \"UUIDs\"]\ngit-tree-sha1 = \"4c10eee4af024676200bc7752e536f858c6b8f93\"\nuuid = \"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf\"\nversion = \"1.3.1\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"96b0bc6c52df76506efc8a441c6cf1adcb1babc4\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.42.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\nversion = \"0.5.2+0\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"ae02104e835f219b8930c7664b8012c93475c340\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.3.2\"\n\n[[deps.DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"3daef5523dd2e769dad2365274f760ff5f282c7d\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.11\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[deps.Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"FileWatching\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\nversion = \"1.6.0\"\n\n[[deps.FileWatching]]\nuuid = \"7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[deps.Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[deps.Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[deps.HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[deps.IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[deps.IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"3c837543ddb02250ef42f4738347454f95079d4e\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.3\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\nversion = \"0.6.3\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\nversion = \"7.81.0+0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\nversion = \"1.10.2+0\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\nversion = \"2.28.0+0\"\n\n[[deps.Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"bf210ce90b6c9eed32d25dbcae1ebc565df2687f\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.2\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\nversion = \"2022.2.1\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\nversion = \"1.2.0\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\nversion = \"0.3.20+0\"\n\n[[deps.OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"621f4f3b4977325b9128d5fae7a8b4829a0c2222\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.4\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\nversion = \"1.8.0\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"670e559e5c8e191ded66fa9ea89c97f10376bb4c\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.38\"\n\n[[deps.PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"28ef6c7ce353f0b35d0df0d5930e0d072c1f5b9b\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.4.1\"\n\n[[deps.PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"dfb54c4e414caa595a1f2ed759b160f5a3ddcba5\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.3.1\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.Profile]]\ndeps = [\"Printf\"]\nuuid = \"9abbd945-dff8-562f-b5e8-e1ebf5ef1b79\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[deps.Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\nversion = \"0.7.0\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\nversion = \"1.0.0\"\n\n[[deps.TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[deps.Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"OrderedCollections\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"5ce79ce186cc678bbb5c5681ca3379d1ddae11a1\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.7.0\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\nversion = \"1.10.0\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.Wordlegames]]\ndeps = [\"AbstractTrees\", \"DataFrames\", \"Random\", \"Tables\"]\ngit-tree-sha1 = \"4c463de78d2f3f9447b695e241eba43cc945f866\"\nuuid = \"1cb69566-e1cf-455f-a587-fd79a2e00f5a\"\nversion = \"0.3.0\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\nversion = \"1.2.12+1\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\nversion = \"5.1.0+0\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\nversion = \"1.41.0+1\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\nversion = \"16.2.1+1\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500f9816eb0-078e-41f2-bf7c-29ed5c663fd0\n# \u255f\u2500a9678ae8-48c0-4bb9-920d-7de176105bd0\n# \u255f\u25004eabf46e-f75c-4b15-8583-6abe17c0fd85\n# \u2560\u255049a44e94-29c0-4004-8d0b-e37008eafc1c\n# \u255f\u250003b41118-5756-447b-9900-15af37667529\n# \u2560\u255073e3e38b-0935-4ef9-97b1-8a8cdfa2feb7\n# \u2560\u25504c5e781c-fd10-425f-95d2-1fec5bf1015f\n# \u255f\u25002046a4ca-c4d3-4804-9373-930d9cbb58fd\n# \u2560\u2550560ad4d4-945c-4d21-8947-cac68c1880a1\n# \u255f\u2500ddd7f90c-7cad-4279-a275-b1800b4ae83b\n# \u2560\u255049500976-7ab8-43cc-9902-b25ad73ff42e\n# \u2560\u2550f6681018-f729-48de-9342-a5bcb151aca2\n# \u255f\u250022f7e1ad-3a2b-4a88-8f81-91feada88621\n# \u2560\u25505787eef2-7a2c-46ad-8fa0-7313c07be84b\n# \u2560\u25508df0e0c0-cdae-450c-abc1-5cea1d520ec9\n# \u2560\u25508580bdcf-24ce-4eb6-ac5a-4c77b729b38b\n# \u2560\u25509ed9e8ab-3ebc-4c6e-b2c3-46ad1987561e\n# \u255f\u2500fdd6631e-8b3a-4b4f-b8a7-11135f13cc23\n# \u255f\u25009b911b3c-17d5-42d5-b7e5-a4187ce5787c\n# \u255f\u25004d5681f9-56a5-4f83-9b31-810bdef26d98\n# \u2560\u2550db787d98-d2a4-4d4a-8e13-8b61e80e48dd\n# \u2560\u25509e1d6017-f79e-49b5-916e-8e3a9e49ab1d\n# \u255f\u2500e9e23c37-e4d8-4b77-ab6f-264bcba4ebec\n# \u2560\u2550b0970feb-103d-4560-b577-f611171e8da6\n# \u255f\u250039ae1224-9cdf-4d4b-b543-985a5667a601\n# \u2560\u25508a1cc31b-f747-49b4-9860-a2c381d98c44\n# \u255f\u25006830dbd6-edc9-4193-8d73-becf3be3855a\n# \u2560\u2550a69f62bd-ed6d-42e7-8405-9321d13a52d1\n# \u255f\u2500e4410d39-6680-4eac-9f70-a425df352998\n# \u2560\u2550a031c3db-6630-4deb-a90d-d2b13202dec6\n# \u255f\u25001b9261b3-c90c-4db1-9e70-51bf364072b8\n# \u2560\u25501887c091-46a0-4e8c-9a66-2ab17968729c\n# \u2560\u255039202f1a-e7d6-4961-9776-ec67c3fd9743\n# \u255f\u25002b73c7f4-0203-4e2b-b634-eb310152834b\n# \u2560\u255079f0ceab-0c9f-42f7-b3ec-a58b5ac1c083\n# \u255f\u2500b92abc9c-2a86-47ab-a6e8-127c4d7de52e\n# \u2560\u25502f32604f-b64c-47f5-bf14-a3cbde36cc13\n# \u2560\u2550f899d69c-ee68-47e3-963f-90bc401558b2\n# \u2560\u2550643bb3f5-c8e7-446c-b7d4-ea40952c4f59\n# \u255f\u25004742dec1-1a46-4095-bc49-467182beb163\n# \u2560\u2550225c813a-e63e-44fc-bc08-caf4cf36e21b\n# \u255f\u2500875f7d47-b71f-4f67-90ae-755b00e2b464\n# \u2560\u25507c6e0762-de57-4041-a683-7ed6f0715057\n# \u255f\u2500178cfcd4-e198-4e44-b5bd-7da6c8b21456\n# \u2560\u255090f09143-4046-4e2f-967e-1df115411b32\n# \u255f\u2500628fb954-e44f-4c6d-af03-c41e9e2894d5\n# \u2560\u2550c976042e-2e91-401b-acfa-88199c22326c\n# \u255f\u2500197e3fbe-6172-47b8-99c5-c8293223747c\n# \u2560\u2550de2cac98-2ca9-4d27-8b02-ff152a73df7e\n# \u255f\u250004cbc640-70be-402b-8a7e-3e9fc8e2cb00\n# \u2560\u255001d24948-480d-4eb5-9cc4-7c1e806f22fd\n# \u255f\u25002c6d5aa5-7a23-4c67-8ff0-27b95ef58375\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "37828a9c0b97ca60997bc67ef45b332b36d31cd0", "size": 37383, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/scoring.jl", "max_stars_repo_name": "dmbates/WordleTutorials", "max_stars_repo_head_hexsha": "db855a5d18b33ef65d27533ea8dafbf83c89e395", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2022-03-05T15:19:39.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T22:03:06.000Z", "max_issues_repo_path": "tutorials/scoring.jl", "max_issues_repo_name": "dmbates/WordleTutorials", "max_issues_repo_head_hexsha": "db855a5d18b33ef65d27533ea8dafbf83c89e395", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tutorials/scoring.jl", "max_forks_repo_name": "dmbates/WordleTutorials", "max_forks_repo_head_hexsha": "db855a5d18b33ef65d27533ea8dafbf83c89e395", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-07T18:54:15.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T18:54:15.000Z", "avg_line_length": 39.2678571429, "max_line_length": 297, "alphanum_fraction": 0.7228954338, "num_tokens": 13314, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4416729909662417, "lm_q2_score": 0.2068940390354276, "lm_q1q2_score": 0.09137950903386367}}
{"text": "# You might not want to run this file completely, as the Pkg-commands can take a\n# long time to complete.\n\n# list all available packages:\nPkg.available()\n\n# install one package (e.g. [Calculus](https://github.com/johnmyleswhite/Calculus.jl)) and all its dependencies:\nPkg.add(\"Calculus\")\n\n# to list all installed packages\nPkg.installed()\n\n# to update all packages to their newest version\nPkg.update()\n\n# to use a package:\nusing Calculus\n# will import all functions of that package into the current namespace, so that\n# it is possible to call\nderivative(x -> sin(x), 1.0)\n# without specifing the package it is included in.\n\nimport Calculus\n# will enable you to specify which package the function is called from\nCalculus.derivative(x -> cos(x), 1.0)\n\n# Using `import` is especially useful if there are conflicts in function/type-names\n# between packages.\n# Example:\n# Winston as well as Gadfly provide a plot() function (see below).\nPkg.add(\"Winston\")\nPkg.add(\"Gadfly\")\n\n# If you were to \"import\" both of the packages with `using`, there would be a conflict.\n# That can be prevented by using `import`, as follows:\nimport Winston\nimport Gadfly\n\nWinston.plot(rand(4))\nGadfly.plot(x=[1:10], y=rand(10))\n", "meta": {"hexsha": "43046cd5c92a9a680aa790190579be6217399e40", "size": 1198, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "common_usage/using_packages.jl", "max_stars_repo_name": "floswald/JuliaByExample", "max_stars_repo_head_hexsha": "62429621187e190724f7c4dac2b490dcd64edcd3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-05-15T23:51:17.000Z", "max_stars_repo_stars_event_max_datetime": "2019-02-15T18:50:46.000Z", "max_issues_repo_path": "common_usage/using_packages.jl", "max_issues_repo_name": "floswald/JuliaByExample", "max_issues_repo_head_hexsha": "62429621187e190724f7c4dac2b490dcd64edcd3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "common_usage/using_packages.jl", "max_forks_repo_name": "floswald/JuliaByExample", "max_forks_repo_head_hexsha": "62429621187e190724f7c4dac2b490dcd64edcd3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-05-15T23:51:19.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-06T13:09:53.000Z", "avg_line_length": 29.2195121951, "max_line_length": 112, "alphanum_fraction": 0.7445742905, "num_tokens": 310, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.18952108904081677, "lm_q1q2_score": 0.09106084232453132}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.0\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 15b8afb9-ace9-4b5b-894e-dce9dda5bf5f\nbegin\n\tusing Pkg\n\t\n# \ttry\n# \t\tusing UnicodePlots\n\t\t\n# \tcatch ArgumentError\n# \t\tPkg.add(\"UnicodePlots\")\n# \t\tusing UnicodePlots\n# \tend\n\t\n\tusing Test\n\tusing PlutoUI\nend\n\n# \u2554\u2550\u2561 45d72214-919c-11eb-2e69-23c0786baacb\nmd\"\"\"\n### Scope of variables\n\nSrc: [Julia 1.0 Cookbook -  Bogumi\u0142 Kami\u0144ski , Przemys\u0142aw Szufel - 2018](https://www.packtpub.com/product/julia-1-0-programming-cookbook/9781788998369)\n\"\"\"\n\n# \u2554\u2550\u2561 1cab379d-d483-43fc-84f7-3bad9e583e77\nmd\"\"\"\nA variable can be defined in one of two scopes \u2014 either *local* or *global*.\n\"\"\"\n\n# \u2554\u2550\u2561 bbc9bdb0-3b28-4f89-a980-d597b98e7880\na, b = 1, 2;\n\n# \u2554\u2550\u2561 c2f716f9-98be-4bdf-be71-d915b02fc8a6\nwith_terminal() do\n\n\tlet a=30, b=40\n\t\tlet b=500\n\t\t\tprintln(\"inner scope $a $b\")\n\t\tend\n\t\tprintln(\"outer scope $a $b\")\n\tend\nend\n\n# \u2554\u2550\u2561 ee5589bd-3ee5-4a19-9870-b9ef03122639\nwith_terminal() do\n\tprintln(\"global scope $a $b\")\nend\n\n# \u2554\u2550\u2561 e091acba-3fb1-4d96-a976-58cb051656a6\nx = 5;\n\n# \u2554\u2550\u2561 f73be67c-5989-4be2-8102-0c4023815756\nwith_terminal() do\n\tlet\n\t\tprintln(x+1)   ## Expect 6\n\tend\nend\n\n# \u2554\u2550\u2561 a6d11cc6-0628-4a2b-9e46-89823839786f\nmd\"\"\"\nHowever:\n\"\"\"\n\n# \u2554\u2550\u2561 05276c9e-b4f7-400e-bb17-2ef4211c19cd\nlet\n\tx = x + 1   ## No Error, with Julia 1.6 / PLuto \nend\n\n# \u2554\u2550\u2561 2687c6b0-727d-4e0d-aed3-377a57867279\nwith_terminal() do\n\tlet\n\t\tprintln(x)   ## Expect 5\n\tend\nend\n\n# \u2554\u2550\u2561 82bd8ebd-63af-4de2-a673-d2b147c82bca\n## Will fail\nfunction twogram(s::AbstractString) \n\ttwograms = String[] \n\tfor (i, c) in enumerate(s)\n\t\tprev = if i == 1\n\t\t\tc\n\t\telse\n\t\t\tpush!(twograms, string(prev, c))\n\t\t\tc\n\t\tend\n\tend\n\ttwograms\nend\n\n# \u2554\u2550\u2561 82c70730-535f-45ac-ac8a-f2d9e0ab5454\ntwogram(\"ABCD\")   ## Error expected\n\n# \u2554\u2550\u2561 1ae57919-8890-4bac-b80e-fb1981ce2552\nmd\"\"\"\nThe `prev` variable has been declared in a loop scope, and hence its value is lost in each pass of the loop. \n\nThis can be corrected (we have added `local prev` before the loop in order to declare prev in an *outer local scope*):\n\"\"\"\n\n# \u2554\u2550\u2561 d3a817d8-3b7b-4a12-b305-cb756d839524\nfunction twogram2(s::AbstractString)\n\ttwograms = String[]\n\tlocal prev\n\n\tfor (i, c) \u2208 enumerate(s)\n\t\tprev = if i==1\n\t\t\tc\n\t\telse\n\t\t\tpush!(twograms, string(prev, c))\n\t\t\tc\n\t\tend\n\tend\n\ttwograms\nend\n\n# \u2554\u2550\u2561 5c69604a-63a3-4288-bb7a-bf47f614d917\ntwogram2(\"ABCD\")\n\n# \u2554\u2550\u2561 2e481224-fed9-417e-9d4e-89c190fab2d2\nmd\"\"\"\nNote however that the code of `twograms2` will not run as expected, if copied directly to the Julia command line.\n\nWe need to define a local scope to make it work using a `let` construct or prepending `global` in front of `prev` inside the function.\n\"\"\"\n\n# \u2554\u2550\u2561 bbce4433-d567-43f4-8b04-ba4aa242d5c5\nmd\"\"\"\nNote that each module has its own global scope. The `Julia` command line works in the global scope of the `Main` module. If you define some code within a module, it will be global within this module. All the presented rules are valid. For example, consider the following module:\n\"\"\"\n\n# \u2554\u2550\u2561 7895d933-105b-4618-a9dd-368c17cef35c\nmodule B\n\tx=1\n\n\tfunction getxplusone()\n\t\tx + 1\n\tend\n\n\tfunction increasex()\n\t\tx += 1\n\tend\n\n\tfunction increasexglob()\n\t\tglobal x += 1\n\tend\nend\n\n# \u2554\u2550\u2561 1d409947-442f-42e8-9524-e99a245c00d8\nB.getxplusone()\n\n# \u2554\u2550\u2561 27714280-7dff-40ee-8442-023c5182e1b5\nB.increasex()   ## Error expected\n\n# \u2554\u2550\u2561 61b1330e-7fcd-483d-bbee-000ef0b479a1\nB.increasexglob()\n\n# \u2554\u2550\u2561 b522199d-ef3b-4be6-8aee-d828037d202b\nmd\"\"\"\nAny function creates a new local scope. \nScoping within a function (*local scope*) and within the `Julia` command line (*global scope*) exposes completely different behavior. \n\nConsider the following code:\n\"\"\"\n\n# \u2554\u2550\u2561 b1be6114-5cd1-47bf-b400-e996ee0a9181\nfunction f()\n\tx = 1\n\tfor a \u2208 1:10\n\t\tx += 1\n\tend\n\tx\nend\n\n# \u2554\u2550\u2561 9bb77ec4-3857-4a27-a74b-37b8bdbf368f\nf()\n\n# \u2554\u2550\u2561 cb51498d-baf4-42f8-975d-ef08ee7271d1\nz = 1\n\n# \u2554\u2550\u2561 c3529a34-c2e4-45d9-8aef-d91786f342e4\nfor _ \u2208 1:10\n\tz += 1       ## No problem anynore with `Julia \u2265 1.6`\nend\n\n# \u2554\u2550\u2561 48d99279-a9a0-448d-b23d-2511d1c2fd52\nu = 5; [(x = u + i; x) for i \u2208 1:2]\n\n# \u2554\u2550\u2561 bb20dc58-b9c8-447b-a7a2-1fb553d6888b\nu\n\n# \u2554\u2550\u2561 dce5f294-48db-4ea6-a405-4f74b1aa3f97\nv = 5; [(v = v + i; v) for i in 1:2]  # No problem anynore\n\n# \u2554\u2550\u2561 03ee4abd-268a-4a1c-9be0-5dbfdb50b41a\nv\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250045d72214-919c-11eb-2e69-23c0786baacb\n# \u2560\u255015b8afb9-ace9-4b5b-894e-dce9dda5bf5f\n# \u255f\u25001cab379d-d483-43fc-84f7-3bad9e583e77\n# \u2560\u2550bbc9bdb0-3b28-4f89-a980-d597b98e7880\n# \u2560\u2550c2f716f9-98be-4bdf-be71-d915b02fc8a6\n# \u2560\u2550ee5589bd-3ee5-4a19-9870-b9ef03122639\n# \u2560\u2550e091acba-3fb1-4d96-a976-58cb051656a6\n# \u2560\u2550f73be67c-5989-4be2-8102-0c4023815756\n# \u255f\u2500a6d11cc6-0628-4a2b-9e46-89823839786f\n# \u2560\u255005276c9e-b4f7-400e-bb17-2ef4211c19cd\n# \u2560\u25502687c6b0-727d-4e0d-aed3-377a57867279\n# \u2560\u255082bd8ebd-63af-4de2-a673-d2b147c82bca\n# \u2560\u255082c70730-535f-45ac-ac8a-f2d9e0ab5454\n# \u255f\u25001ae57919-8890-4bac-b80e-fb1981ce2552\n# \u2560\u2550d3a817d8-3b7b-4a12-b305-cb756d839524\n# \u2560\u25505c69604a-63a3-4288-bb7a-bf47f614d917\n# \u255f\u25002e481224-fed9-417e-9d4e-89c190fab2d2\n# \u255f\u2500bbce4433-d567-43f4-8b04-ba4aa242d5c5\n# \u2560\u25507895d933-105b-4618-a9dd-368c17cef35c\n# \u2560\u25501d409947-442f-42e8-9524-e99a245c00d8\n# \u2560\u255027714280-7dff-40ee-8442-023c5182e1b5\n# \u2560\u255061b1330e-7fcd-483d-bbee-000ef0b479a1\n# \u255f\u2500b522199d-ef3b-4be6-8aee-d828037d202b\n# \u2560\u2550b1be6114-5cd1-47bf-b400-e996ee0a9181\n# \u2560\u25509bb77ec4-3857-4a27-a74b-37b8bdbf368f\n# \u2560\u2550cb51498d-baf4-42f8-975d-ef08ee7271d1\n# \u2560\u2550c3529a34-c2e4-45d9-8aef-d91786f342e4\n# \u2560\u255048d99279-a9a0-448d-b23d-2511d1c2fd52\n# \u2560\u2550bb20dc58-b9c8-447b-a7a2-1fb553d6888b\n# \u2560\u2550dce5f294-48db-4ea6-a405-4f74b1aa3f97\n# \u2560\u255003ee4abd-268a-4a1c-9be0-5dbfdb50b41a\n", "meta": {"hexsha": "23cd279ee0ff2c88bae51be3c02d66e656d46a9c", "size": 5462, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia_Investigation/cb07-Var_Scope.jl", "max_stars_repo_name": "pascal-p/julia-notebooks", "max_stars_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-01T20:34:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-01T20:34:56.000Z", "max_issues_repo_path": "Julia_Investigation/cb07-Var_Scope.jl", "max_issues_repo_name": "pascal-p/julia-notebooks", "max_issues_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Julia_Investigation/cb07-Var_Scope.jl", "max_forks_repo_name": "pascal-p/julia-notebooks", "max_forks_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-10T09:03:18.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-10T09:03:18.000Z", "avg_line_length": 23.2425531915, "max_line_length": 278, "alphanum_fraction": 0.7112779202, "num_tokens": 2671, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.38491213037224875, "lm_q2_score": 0.23651623106411435, "lm_q1q2_score": 0.0910379663665033}}
{"text": "# ---\n# title: 94. Binary Tree Inorder Traversal\n# id: problem94\n# author: zhwang\n# date: 2022-02-19\n# difficulty: Medium\n# categories: Hash Table, Stack, Tree\n# link: <https://leetcode.com/problems/binary-tree-inorder-traversal/description/>\n# hidden: true\n# ---\n# \n# Given the `root` of a binary tree, return _the inorder traversal of its nodes\n# ' values_.\n# \n# \n# \n# **Example 1:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_1.jpg)\n# \n#     \n#     \n#     Input: root = [1,null,2,3]\n#     Output: [1,3,2]\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: root = []\n#     Output: []\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: root = [1]\n#     Output: [1]\n#     \n# \n# **Example 4:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_5.jpg)\n# \n#     \n#     \n#     Input: root = [1,2]\n#     Output: [2,1]\n#     \n# \n# **Example 5:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_4.jpg)\n# \n#     \n#     \n#     Input: root = [1,null,2]\n#     Output: [1,2]\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * The number of nodes in the tree is in the range `[0, 100]`.\n#   * `-100 <= Node.val <= 100`\n# \n# \n# \n# **Follow up:**\n# \n# Recursive solution is trivial, could you do it iteratively?\n# \n# \n# \n# \n## @lc code=start\nusing LeetCode\n\ninorder_traversal(::Nothing) = Int[]\nfunction inorder_traversal(root::TreeNode)::Vector{Int}\n    res, stack = Int[], []\n    while !isempty(stack) || !isnothing(root)\n        while !isnothing(root)\n            push!(stack, root)\n            root = root.left\n        end\n        root = pop!(stack) \n        push!(res, root.val) ## inorder search\n        root = root.right\n    end\n    res\nend\n\n##### code template for inorder traversal and preorder traversal #####\n## traversal(::Nothing) = Int[]\n## function traversal(root::TreeNode)::Vector{Int}\n##     res, stack = Int[], []\n##     while !isempty(stack) || !isnothing(root)\n##         while !isnothing(root)\n##             push!(stack, root)\n##             ## preorder traversal: put codes here\n##             root = root.left\n##         end\n##         root = pop!(stack) \n##         ## inorder traversal: put codes here\n##         root = root.right\n##     end\n##     res\n## end\n\n## @lc code=end\n## @lc test=start\n@testset \"94.binary-tree-inorder-traversal.jl\" begin\n    @test inorder_traversal(TreeNode{Int}([1])) == [1]\n    @test inorder_traversal(nothing) == Int[]\n    @test inorder_traversal(TreeNode{Int}([1,nothing,2,3])) == [1,3,2]\n    @test inorder_traversal(TreeNode{Int}([1, 2, 3, 4, 5, 6])) == [4,2,5,1,6,3]\n    @test inorder_traversal(TreeNode{Int}([3,9,4,nothing, nothing, 5,7])) == [9,3,5,4,7]\nend\n## @lc test=end\n\n\n## @lc info=start\n# link: [solution to 94](https://leetcode-cn.com/problems/binary-tree-preorder-traversal/solution/144-er-cha-shu-de-qian-xu-bian-li-by-rex-frkw/)\n## @lc info=end\n", "meta": {"hexsha": "ed89ca0534823e9c8b77b718ec03171160f8c9fe", "size": 2859, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-03/94.binary-tree-inorder-traversal.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/submited/date-03/94.binary-tree-inorder-traversal.jl", "max_issues_repo_name": "RexWzh/leetcode_note.jl", "max_issues_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/submited/date-03/94.binary-tree-inorder-traversal.jl", "max_forks_repo_name": "RexWzh/leetcode_note.jl", "max_forks_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.1627906977, "max_line_length": 145, "alphanum_fraction": 0.5627841903, "num_tokens": 910, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.18713268216242657, "lm_q1q2_score": 0.09064334435681431}}
{"text": "# Start version of HelloWorld for Learning Julia course\n\n# Define a function that prints out a message\n# This creates a function with a single \"method\"\nfunction helloWorld()\n    println(\"Hello world!\")\nend\n\n# By defining another version of the function with different\n# arguments, we create another \"method\" - Julia will choose\n# the right one based on the arguments. This is called \n# \"multiple dispatch\"\nfunction helloWorld(count::Int)\n    for i in 1:count\n        println(\"Hello world \", i, \"!\")\n    end\nend\n\n# Call the version with no arguments\nhelloWorld()\n\n# Call the version that takes a number\nhelloWorld(3)", "meta": {"hexsha": "4f1694779779206f11e2db93f798a630dcd3694d", "size": 615, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "1_overview/HelloWorld.jl", "max_stars_repo_name": "scaotravis/Julia-Course", "max_stars_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "1_overview/HelloWorld.jl", "max_issues_repo_name": "scaotravis/Julia-Course", "max_issues_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1_overview/HelloWorld.jl", "max_forks_repo_name": "scaotravis/Julia-Course", "max_forks_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7391304348, "max_line_length": 60, "alphanum_fraction": 0.7349593496, "num_tokens": 142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.31405054499180746, "lm_q2_score": 0.28776780965284365, "lm_q1q2_score": 0.09037363745257426}}
{"text": "#nb # %% slideshow [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n#md # # [Lecture 5 - Container types and indexing](@id lecture5)\n#!md # # Lecture 5 - Container types and indexing\n#\n# **Essential Skills For Computational Biology**  \n#\n# *2020-06-16*  \n#\n# Kevin Bonham, PhD\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## Scalars and Containers\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# - A \"scalar\" is a singular value\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# - Most of the types you've seen so far are scalars\n#   - Eg. `Float64`, `Int64`, `Bool`, `Char`\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# - There are also many container types, which store multiple values\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# - Containers can usually be accessed by an \"index\"\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## A Tuple is a simple, ordered container\n\nmy_tuple = (1, 1.2, \"a string\")\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# Each position has an integer index, \n# 1 for the first position, 2 for the second, and so on\nmy_tuple[1]\n#-\nmy_tuple[3]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Attempting to access an index that doesn't exist is an error\nlength(my_tuple)\n#-\nmy_tuple[4]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## A Vector is a mutable, ordered container\n# \n# Vectors can by easily created with `[` and `]`\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nmy_vector = [1,3,5,7]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# Each position has an integer index, \n# 1 for the first position, 2 for the second, and so on\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nmy_vector[1]\n#-\nmy_vector[2]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Add to the end of a `Vector` with `push!()`\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\npush!(my_vector, 50);\n#-\nmy_vector\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"subslide\"}}\nother_vector = [] ## an empty vector\nfor i in 1:2:10\n    push!(other_vector, i)\nend\n#-\nother_vector\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Change the value of a vector by assignment (`=`)\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nmy_vector[1] = 42;\n#-\nmy_vector\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Index a \"slice\" with a range\nmy_vector\n#-\nmy_vector[2:4]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Attempting to access an index that doesn't exist is an error\nlength(my_vector)\n#-\nmy_vector[6]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# # Dictionaries are containers with \"key\" => \"value\" pairs\n# \n# - Keys can be any scalar type\n# - Keys are used as indexes\n# - Values can be scalars or other containers\n# - Keys are unordered\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Create a Dictionary with `Dict()`\nmy_dict = Dict(\"key1\"=> 42, \"key2\"=> 1.8, 5=> \"a value\")\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## The keys are used to index\nmy_dict[\"key1\"]\n#-\nmy_dict[5]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Values can be reassigned\nmy_dict[\"key1\"] = 3.14\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Attempting to access with a key that doesn't exist is an error\nmy_dict[\"I don't exist\"]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# But you can assign a new key-value pair\nmy_dict[\"I don't exist... yet\"] = \"Now I do!\"\n#-\nmy_dict[\"I don't exist... yet\"]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# You can use `haskey()` (a Boolean function) to see if a dictionary already has a key\nhaskey(my_dict, \"key1\")\n#-\nhaskey(my_dict, \"key1000\")\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# # Questions?\n", "meta": {"hexsha": "9cc37823906bd39fafd31a95327914870f7e7c10", "size": 4185, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/literate/lectures/lecture5.jl", "max_stars_repo_name": "wellesley-bisc195/BISC195.jl", "max_stars_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-12-16T22:45:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T23:23:35.000Z", "max_issues_repo_path": "docs/literate/lectures/lecture5.jl", "max_issues_repo_name": "wellesley-bisc195/BISC195.jl", "max_issues_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-06-05T15:30:36.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-13T18:25:22.000Z", "max_forks_repo_path": "docs/literate/lectures/lecture5.jl", "max_forks_repo_name": "wellesley-bisc195/BISC195.jl", "max_forks_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-06-05T14:59:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-10T00:45:02.000Z", "avg_line_length": 30.3260869565, "max_line_length": 86, "alphanum_fraction": 0.6430107527, "num_tokens": 1259, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3276683008207139, "lm_q2_score": 0.275129717879598, "lm_q1q2_score": 0.09015128716289027}}
{"text": "using InstantiateFromURL\nactivate_github(\"QuantEcon/QuantEconLecturePackages\", tag = \"v0.9.8\");\n", "meta": {"hexsha": "4cc2adc1a661e111f3da77961750f9ef225f41b0", "size": 96, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_static/includes/deps_no_using.jl", "max_stars_repo_name": "sbacelar/quantecon-notebooks-jl", "max_stars_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "_static/includes/deps_no_using.jl", "max_issues_repo_name": "sbacelar/quantecon-notebooks-jl", "max_issues_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "_static/includes/deps_no_using.jl", "max_forks_repo_name": "sbacelar/quantecon-notebooks-jl", "max_forks_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0, "max_line_length": 70, "alphanum_fraction": 0.8125, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.18713267989575072, "lm_q1q2_score": 0.08991326265587245}}
{"text": "#nb # %% slideshow [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n#md # # [Lecture 4 - Scope and Fruitful Functions](@id lecture4)\n#!md # # Lecture 4 - Scope and Fruitful Functions\n#\n# **Essential Skills For Computational Biology**  \n#\n# *2020-06-16*  \n#\n# Kevin Bonham, PhD\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## \"Scope\" is where references live\n# \n# - A reference is a human-readable name for data or action\n#   - eg. a variable, argument, or function name\n# - All references have scope\n#   - sometimes the scope is \"global\" (available everywhere)\n#   - sometimes the scope is narrow (eg. inside a loop)\n# - In different scopes, the same name may reference different things\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"subslide\"}}\ni = 3\n\nfor i in 1:5\n    print(i, \" \")\nend\n\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\ni\n\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"subslide\"}}\nmyvar = \"Green eggs\"\nfunction ham(myvar)\n    myvar = \"ham\"\n    return myvar\nend\n\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nmyvar\n\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"subslide\"}}\nfunction decrement4()\n    j = 4\n    while j > 0\n        print(j, \" \")\n        j = j - 1\n    end\n    return j\nend\ndecrement4()\n\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n\n## copy to REPL\nj = 4\n\nwhile j > 0\n    print(j, \" \")\n    j = j - 1\nend\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## Fruitful functions and the perils of printing\n#\n# - returned values are printed in the REPL\n# - printed values are not (necessarily) returned\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n\nfunction verbose(thing)\n    println(thing)\n    return thing\nend\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nverbose(true)\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nverbose(1)\nverbose(2)\nverbose(3)\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nverbose(\"woah\");\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## A reminder: I make mistakes\n# \n# - Several people have encountered tests that failed even\n#   though they had correct answers\n# - Don't bang your head against the wall (for too long)\n# - We will still make you work for it\n# - Not all difficulty is desirable\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## One other reminder: Helping eachother is ok\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# ### (Just don't copy-paste code from one another)\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Final project ideas\n#\n# - COVID data analysis\n# - SARS-CoV2 evolution / strain analysis\n# - Police violence data analysis\n# - Your idea here\n", "meta": {"hexsha": "9243836cc088c1ff895b940f38d3cfa6a4e426cb", "size": 2847, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/literate/lectures/lecture4.jl", "max_stars_repo_name": "wellesley-bisc195/BISC195.jl", "max_stars_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-12-16T22:45:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T23:23:35.000Z", "max_issues_repo_path": "docs/literate/lectures/lecture4.jl", "max_issues_repo_name": "wellesley-bisc195/BISC195.jl", "max_issues_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-06-05T15:30:36.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-13T18:25:22.000Z", "max_forks_repo_path": "docs/literate/lectures/lecture4.jl", "max_forks_repo_name": "wellesley-bisc195/BISC195.jl", "max_forks_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-06-05T14:59:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-10T00:45:02.000Z", "avg_line_length": 25.1946902655, "max_line_length": 69, "alphanum_fraction": 0.6385669125, "num_tokens": 833, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.19193279338050545, "lm_q1q2_score": 0.08773952701749418}}
{"text": "using InstantiateFromURL\nactivate_github(\"QuantEcon/QuantEconLectureAllPackages\", tag = \"v0.9.8\");\n", "meta": {"hexsha": "eed1e4e8e8d871620dfe6db047c36c95ff1efd9c", "size": 99, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_static/includes/alldeps_no_using.jl", "max_stars_repo_name": "sbacelar/quantecon-notebooks-jl", "max_stars_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "_static/includes/alldeps_no_using.jl", "max_issues_repo_name": "sbacelar/quantecon-notebooks-jl", "max_issues_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "_static/includes/alldeps_no_using.jl", "max_forks_repo_name": "sbacelar/quantecon-notebooks-jl", "max_forks_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0, "max_line_length": 73, "alphanum_fraction": 0.8181818182, "num_tokens": 31, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.1919327887583263, "lm_q1q2_score": 0.0877395249045264}}
{"text": "using Helium, DelimitedFiles, Test\n\n\n# Create testing matrix\ntestmat = [\n        1.5   8  12  24\n          7  22  24  75\n]\n\n# File name in .he format and CSV\nfileName = string(@__DIR__,\"/data/testFile.he\")\ncsvFileName = string(fileName[1:end-2], \"csv\")\n\n##########################\n# TEST 1 Writing/Reading #\n##########################\n\nHelium.writehe(testmat, abspath(fileName))\nrsltMat = Helium.readhe(fileName)\nprintln(\"IO test 1: \", @test rsltMat == testmat)\n\n#############################################################################\n# TEST 2 Converting CSV to He: no row names, no column names, no supplement #\n#############################################################################\n\nopen(csvFileName, \"w\") do io\n    writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64, hasColNames = false)\nrsltMat = Helium.readhe(fileName)\nprintln(\"CSV to He test 2: \", @test rsltMat == testmat)\n\n##########################################################################\n# TEST 3 Converting CSV to He: row names, no column names, no supplement #\n##########################################################################\ntestmat = [\n        \"ID1\"  1.5   8  12  24\n        \"ID2\"    7  22  24  75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\n\nHelium.csv2he(csvFileName, fileName, Float64, hasColNames = false,\n                hasRowNames = true)\nrsltMat = Helium.readhe(fileName)\nprintln(\"CSV to He with row names test 3: \",\n        @test rsltMat == testmat[:, 2:end])\n\n\n##########################################################################\n# TEST 4 Converting CSV to He: no row names, column names, no supplement #\n##########################################################################\ntestmat = [\n        \"col1\"  \"col2\"  \"col3\"  \"col4\"\n           1.5       8      12      24\n             7      22      24      75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64)\nrsltMat = Helium.readhe(fileName)\nprintln(\"CSV to He with column names test 4: \",\n        @test rsltMat == testmat[2:end, :])\n\n#######################################################################\n# TEST 5 Converting CSV to He: row names, column names, no supplement #\n#######################################################################\ntestmat = [\n    \"ID\"  \"col1\"  \"col2\"  \"col3\"  \"col4\"\n   \"ID1\"     1.5       8      12      24\n   \"ID2\"       7      22      24      75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64, hasRowNames = true)\nrsltMat = Helium.readhe(fileName)\nprintln(\"CSV to He with column and row names test 5: \",\n        @test rsltMat == testmat[2:end, 2:end])\n\n#####################################################\n# TEST 6 Converting CSV to He: getting column names #\n#####################################################\ncolnames = Helium.getcolnames(fileName);\nprintln(\"Get column names test 6: \",\n        @test colnames == string.(testmat[1,2:end]))\n\n###################################################\n# TEST 7 Converting CSV to He: getting  row names #\n###################################################\nrownames = Helium.getrownames(fileName);\n println(\"Get row names test 7: \",\n         @test rownames == string.(testmat[2:end, 1]))\n\n#####################################################################\n# TEST 8 Converting CSV to He: missing option during CSV conversion #\n#####################################################################\ntestmat = [\n      \"X\"   1.5    8   12   24\n     \"NA\"     7   22   24   75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64, hasColNames = false, strMiss = \"x\")\nrsltMat = Helium.readhe(fileName)\nprintln(\"Missing customization test 8: \",\n        @test isnan(rsltMat[1,1]) && isnan(rsltMat[2,1]))\n\n#######################################################################\n# TEST 9 Converting CSV to He: separator option during CSV conversion #\n#######################################################################\ntestmat = [\n        \"col1\"  \"col2\"  \"col3\"  \"col4\"\n           1.5       8      12      24\n             7      22      24      75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ' ')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64, sep = \" \")\nrsltMat = Helium.readhe(fileName)\nprintln(\"CSV to He with customized delimeter test 9: \",\n        @test rsltMat == testmat[2:end, :])\n\n#######################################################################\n# TEST 10 Converting CSV to He: skipping option during CSV conversion #\n#######################################################################\ntestmat = [\n     \"ID1\"    \"Xtra1\"    \"X\"    1.5      8      12     24\n     \"ID2\"    \"Xtra2\"    \"NA\"   7.0      22     24     75\n]\n\nopen(csvFileName, \"w\") do io\n          writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName, Float64, hasColNames = false,\n        hasRowNames = true, strMiss = \"x\", skipCol = 1)\nrsltMat = Helium.readhe(fileName)\nprintln(\"Skipping columns test 10: \",\n         @test rsltMat[:,2:end] == convert(Array{Float64,2}, testmat[:, 4:end]))\n\n###########################################################\n# TEST 11 Converting CSV to He: getting supplemental data #\n###########################################################\ntestmat = [\n      \"ID\"     \"var1\" \"var2\"  \"var3\"  \"var4\"  \"var5\"  \"var6\"\n     \"ID1\"    \"Xtra1\"      3     1.5     \"X\"      12      24\n     \"ID2\"    \"Xtra2\"     10     7.0      22      24      75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName,  Float64, hasRowNames = true,\n               strMiss = \"x\", skipCol = 2)\nrsltMat = Helium.getsupp(fileName)\nprintln(\"Getting supplement test 11: \",\n         @test rsltMat == convert(Array{String,2}, string.(testmat[:, 2:3])))\n\n#############################################################################\n# TEST 12 Converting CSV to He: getting supplemental data without row names #\n#############################################################################\ntestmat = [\n         \"var1\" \"var2\"  \"var3\"  \"var4\"  \"var5\"  \"var6\"\n        \"Xtra1\"    3.0     1.5     \"X\"      12      24\n        \"Xtra2\"   10.0     7.0    22.0      24      75\n]\n\nopen(csvFileName, \"w\") do io\n        writedlm(io, testmat, ',')\nend;\n\nHelium.csv2he(csvFileName, fileName,  Float64, strMiss = \"x\", skipCol = 2)\nrsltMat = Helium.getsupp(fileName)\nprintln(\"Getting supplement without row names test 12: \",\n        @test rsltMat == convert(Array{String,2}, string.(testmat[:, 1:2])))\n\n###########################################################\n# TEST 13 Converting He to CSV: getting supplemental data #\n###########################################################\nHelium.he2csv( fileName, csvFileName, strMiss = \"X\")\nrsltMat = readdlm(csvFileName, ',')\nprintln(\"Converting He to CSV test 13: \",\n         @test rsltMat == testmat)\n\n ###########################################################\n # TEST 14 Converting He to CSV: getting supplemental data #\n ###########################################################\n Helium.he2csv( fileName, csvFileName, strMiss = \"X\", sep = \" \")\n rsltMat = readdlm(csvFileName, ' ')\n println(\"Converting He to CSV with customized delimiter test 14: \",\n          @test rsltMat == testmat)\n", "meta": {"hexsha": "539f9bf723ae67df089fb73d072ccf8b2f576802", "size": 7393, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hetests.jl", "max_stars_repo_name": "GregFa/Helium.jl", "max_stars_repo_head_hexsha": "2a1cc4241dd42da442abb0c4c5634eb9d9240cdc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-28T12:28:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-28T12:28:10.000Z", "max_issues_repo_path": "test/hetests.jl", "max_issues_repo_name": "GregFa/Helium.jl", "max_issues_repo_head_hexsha": "2a1cc4241dd42da442abb0c4c5634eb9d9240cdc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-02-27T23:39:14.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-13T03:44:02.000Z", "max_forks_repo_path": "test/hetests.jl", "max_forks_repo_name": "GregFa/Helium.jl", "max_forks_repo_head_hexsha": "2a1cc4241dd42da442abb0c4c5634eb9d9240cdc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-02-23T21:07:18.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-15T19:42:09.000Z", "avg_line_length": 35.3732057416, "max_line_length": 81, "alphanum_fraction": 0.4612471257, "num_tokens": 1885, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4493926344647596, "lm_q2_score": 0.19436780401867254, "lm_q1q2_score": 0.08734745950308134}}
{"text": "# Bogumi\u0142 Kami\u0144ski, 2022\n\n# Codes for chapter 6\n\n# Code for listing 6.1\n\nimport Downloads\nDownloads.download(\"https://raw.githubusercontent.com/\" *\n                   \"sidooms/MovieTweetings/\" *\n                   \"44c525d0c766944910686c60697203cda39305d6/\" *\n                   \"snapshots/10K/movies.dat\",\n                   \"movies.dat\")\n\n# Code for string interpolation examples\n\nx = 10\n\"I have $x apples\"\n\n\"I have $(2 * x) apples\"\n\n\"I have \\$100.\"\n\"I have $100.\"\n\n# Code for multiline strings\n\nDownloads.download(\"https://raw.githubusercontent.com/\\\n                    sidooms/MovieTweetings/\\\n                    44c525d0c766944910686c60697203cda39305d6/\\\n                    snapshots/10K/movies.dat\",\n                   \"movies.dat\")\n\n\"a\\\n b\\\n c\"\n\n# Code for raw strings\n\n\"C:\\my_folder\\my_file.txt\"\n\nraw\"C:\\my_folder\\my_file.txt\"\n\n# Code for listing 6.2\n\nmovies = readlines(\"movies.dat\")\n\n# Code for section 6.2\n\nmovie1 = first(movies)\n\nmovie1_parts = split(movie1, \"::\")\n\nsupertype(String)\nsupertype(SubString{String})\n\n# Code for section 6.3\n\nmovie1_parts[2]\n\nrx = r\"(.*) \\((\\d{4})\\)$\"\n\nm = match(rx, movie1_parts[2])\n\nm[1]\nm[2]\n\nparse(Int, m[2])\n\n# Code for listing 6.3\n\nfunction parseline(line::AbstractString)\n    parts = split(line, \"::\")\n    m = match(r\"(.*) \\((\\d{4})\\)\", parts[2])\n    return (id=parts[1],\n            name=m[1],\n            year=parse(Int, m[2]),\n            genres=split(parts[3], \"|\"))\nend\n\n# Code for parsing one line of movies data\n\nrecord1 = parseline(movie1)\n\n# Code for listing 6.4\n\ncodeunits(\"a\")\ncodeunits(\"\u03b5\")\ncodeunits(\"\u2200\")\n\n# Codes for different patterns of string subsetting\n\nword = first(record1.name, 8)\n\nrecord1.name[1:8]\n\nfor i in eachindex(word)\n    println(i, \": \", word[i])\nend\n\ncodeunits(\"\u00f4\")\n\ncodeunits(\"Fant\u00f4mas\")\n\nisascii(\"Hello world!\")\nisascii(\"\u2200 x: x\u22650\")\n\nword[1]\nword[5]\n\n# Code for section 6.5\n\nrecords = parseline.(movies)\n\ngenres = String[]\nfor record in records\n    append!(genres, record.genres)\nend\ngenres\n\nusing FreqTables\ntable = freqtable(genres)\nsort!(table)\n\nyears = [record.year for record in records]\nhas_drama = [\"Drama\" in record.genres for record in records]\ndrama_prop = proptable(years, has_drama; margins=1)\n\n# Code for listing 6.5\n\nusing Plots\nplot(names(drama_prop, 1), drama_prop[:, 2]; legend=false,\n     xlabel=\"year\", ylabel=\"Drama probability\")\n\n# Code for section 6.6.1\n\ns1 = Symbol(\"x\")\ns2 = Symbol(\"hello world!\")\ns3 = Symbol(\"x\", 1)\n\ntypeof(s1)\ntypeof(s2)\ntypeof(s3)\n\nSymbol(\"1\")\n\n:x\n:x1\n\n:hello world\n:1\n\n# Code for section 6.6.2\n\nsupertype(Symbol)\n\n:x == :x\n:x == :y\n\n# Code for listing 6.6\n\nusing BenchmarkTools\nstr = string.(\"x\", 1:10^6)\nsymb = Symbol.(str)\n@btime \"x\" in $str;\n@btime :x in $symb;\n\n# Code for section 6.7\n\nusing InlineStrings\ns1 = InlineString(\"x\")\ntypeof(s1)\ns2 = InlineString(\"\u2200\")\ntypeof(s2)\nsv = inlinestrings([\"The\", \"quick\", \"brown\", \"fox\", \"jumps\",\n                    \"over\", \"the\", \"lazy\", \"dog\"])\n\n# Code for listing 6.7\n\nusing Random\nusing BenchmarkTools\nRandom.seed!(1234);\ns1 = [randstring(3) for i in 1:10^6]\ns2 = inlinestrings(s1)\n\n# Code for analyzing properties of InlineStrings.jl\n\nBase.summarysize(s1)\nBase.summarysize(s2)\n\n@btime sort($s1);\n@btime sort($s2);\n\n# Code for listing 6.8\n\nopen(\"iris.txt\", \"w\") do io\n    for i in 1:10^6\n        println(io, \"Iris setosa\")\n        println(io, \"Iris virginica\")\n        println(io, \"Iris versicolor\")\n    end\nend\n\n# Code for section 6.8.2\n\nuncompressed = readlines(\"iris.txt\")\n\nusing PooledArrays\ncompressed = PooledArray(uncompressed)\n\nBase.summarysize(uncompressed)\nBase.summarysize(compressed)\n\n# Code for section 6.8.3\n\ncompressed.invpool\ncompressed.pool\n\ncompressed[10]\ncompressed.pool[compressed.refs[10]]\n\nBase.summarysize.(compressed.pool)\n\nv1 = string.(\"x\", 1:10^6)\nv2 = PooledArray(v1)\nBase.summarysize(v1)\nBase.summarysize(v2)\n", "meta": {"hexsha": "5d0b2e85551fe20a458943e5c7df4a92ab32f512", "size": 3813, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ch06.jl", "max_stars_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_stars_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ch06.jl", "max_issues_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_issues_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ch06.jl", "max_forks_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_forks_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.8716814159, "max_line_length": 64, "alphanum_fraction": 0.6475216365, "num_tokens": 1176, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42632159254749036, "lm_q2_score": 0.20434190478229486, "lm_q1q2_score": 0.08711536627097557}}
{"text": "# Copyright (c) 2017: Miles Lubin and contributors\n# Copyright (c) 2017: Google Inc.\n#\n# Use of this source code is governed by an MIT-style license that can be found\n# in the LICENSE.md file or at https://opensource.org/licenses/MIT.\n\nmodule Test\n\nusing MathOptInterface\nconst MOI = MathOptInterface\nconst MOIU = MOI.Utilities\n\nusing Test\n\n# Be wary of adding new fields to this Config struct. Always think: can it be\n# achieved a different way?\nmutable struct Config{T<:Real}\n    atol::T\n    rtol::T\n    optimal_status::MOI.TerminationStatusCode\n    infeasible_status::MOI.TerminationStatusCode\n    exclude::Vector{Any}\nend\n\n\"\"\"\n    Config(\n        ::Type{T} = Float64;\n        atol::Real = Base.rtoldefault(T),\n        rtol::Real = Base.rtoldefault(T),\n        optimal_status::MOI.TerminationStatusCode = MOI.OPTIMAL,\n        infeasible_status::MOI.TerminationStatusCode = MOI.INFEASIBLE,\n        exclude::Vector{Any} = Any[],\n    ) where {T}\n\nReturn an object that is used to configure various tests.\n\n## Configuration arguments\n\n  * `atol::Real = Base.rtoldefault(T)`: Control the absolute tolerance used\n    when comparing solutions.\n  * `rtol::Real = Base.rtoldefault(T)`: Control the relative tolerance used\n    when comparing solutions.\n  * `optimal_status = MOI.OPTIMAL`: Set to `MOI.LOCALLY_SOLVED` if the solver\n    cannot prove global optimality.\n  * `infeasible_status = MOI.INFEASIBLE`: Set to `MOI.LOCALLY_INFEASIBLE` if the\n    solver cannot prove global infeasibility.\n  * `exclude = Vector{Any}`: Pass attributes or functions to `exclude` to skip\n    parts of tests that require certain functionality. Common arguments include:\n     - `MOI.delete` to skip deletion-related tests\n     - `MOI.optimize!` to skip optimize-related tests\n     - `MOI.ConstraintDual` to skip dual-related tests\n     - `MOI.VariableName` to skip setting variable names\n     - `MOI.ConstraintName` to skip setting constraint names\n\n## Examples\n\nFor a nonlinear solver that finds local optima and does not support finding\ndual variables or constraint names:\n```julia\nConfig(\n    Float64;\n    optimal_status = MOI.LOCALLY_SOLVED,\n    exclude = Any[\n        MOI.ConstraintDual,\n        MOI.VariableName,\n        MOI.ConstraintName,\n        MOI.delete,\n    ],\n)\n```\n\"\"\"\nfunction Config(\n    ::Type{T} = Float64;\n    atol::Real = Base.rtoldefault(T),\n    rtol::Real = Base.rtoldefault(T),\n    optimal_status::MOI.TerminationStatusCode = MOI.OPTIMAL,\n    infeasible_status::MOI.TerminationStatusCode = MOI.INFEASIBLE,\n    exclude::Vector{Any} = Any[],\n) where {T<:Real}\n    return Config{T}(atol, rtol, optimal_status, infeasible_status, exclude)\nend\n\nfunction Base.copy(config::Config{T}) where {T}\n    return Config{T}(\n        config.atol,\n        config.rtol,\n        config.optimal_status,\n        config.infeasible_status,\n        copy(config.exclude),\n    )\nend\n\n\"\"\"\n    setup_test(::typeof(f), model::MOI.ModelLike, config::Config)\n\nOverload this method to modify `model` before running the test function `f` on\n`model` with `config`. You can also modify the fields in `config` (e.g., to\nloosen the default tolerances).\n\nThis function should either return `nothing`, or return a function which, when\ncalled with zero arguments, undoes the setup to return the model to its\nprevious state. You do not need to undo any modifications to `config`.\n\nThis function is most useful when writing new tests of the tests for MOI, but it\ncan also be used to set test-specific tolerances, etc.\n\nSee also: [`runtests`](@ref)\n\n## Example\n\n```julia\nfunction MOI.Test.setup_test(\n    ::typeof(MOI.Test.test_linear_VariablePrimalStart_partial),\n    mock::MOIU.MockOptimizer,\n    ::MOI.Test.Config,\n)\n    MOIU.set_mock_optimize!(\n        mock,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(mock, [1.0, 0.0]),\n    )\n    mock.eval_variable_constraint_dual = false\n\n    function reset_function()\n        mock.eval_variable_constraint_dual = true\n        return\n    end\n    return reset_function\nend\n```\n\"\"\"\nsetup_test(::Any, ::MOI.ModelLike, ::Config{T}) where {T} = nothing\n\n\"\"\"\n    version_added(::typeof(function_name))\n\nReturns the version of MOI in which the test `function_name` was added.\n\nThis method should be implemented for all new tests.\n\nSee the `exclude_tests_after` keyword of [`runtests`](@ref) for more details.\n\"\"\"\nversion_added(::F) where {F} = v\"0.10.5\"  # The default for any unlabeled tests.\n\n\"\"\"\n    runtests(\n        model::MOI.ModelLike,\n        config::Config;\n        include::Vector{String} = String[],\n        exclude::Vector{String} = String[],\n        warn_unsupported::Bool = false,\n        exclude_tests_after::VersionNumber = v\"999.0.0\",\n    )\n\nRun all tests in `MathOptInterface.Test` on `model`.\n\n## Configuration arguments\n\n * `config` is a [`Test.Config`](@ref) object that can be used to modify the\n   behavior of tests.\n * If `include` is not empty, only run tests that contain an element from\n   `include` in their name.\n * If `exclude` is not empty, skip tests that contain an element from `exclude`\n   in their name.\n * `exclude` takes priority over `include`.\n * If `warn_unsupported` is `false`, `runtests` will silently skip tests that\n   fail with `UnsupportedConstraint` or `UnsupportedAttribute`. When\n   `warn_unsupported` is `true`, a warning will be printed. For most cases the\n   default behavior (`false`) is what you want, since these tests likely test\n   functionality that is not supported by `model`. However, it can be useful to\n   run  `warn_unsupported = true` to check you are not skipping tests due to a\n   missing `supports_constraint` method or equivalent.\n * `exclude_tests_after` is a version number that excludes any tests to MOI\n   added after that version number. This is useful for solvers who can declare a\n   fixed set of tests, and not cause their tests to break if a new patch of MOI\n   is released with a new test.\n\nSee also: [`setup_test`](@ref).\n\n## Example\n\n```julia\nconfig = MathOptInterface.Test.Config()\nMathOptInterface.Test.runtests(\n    model,\n    config;\n    include = [\"test_linear_\"],\n    exclude = [\"VariablePrimalStart\"],\n    warn_unsupported = true,\n    exclude_tests_after = v\"0.10.5\",\n)\n```\n\"\"\"\nfunction runtests(\n    model::MOI.ModelLike,\n    config::Config;\n    include::Vector{String} = String[],\n    exclude::Vector{String} = String[],\n    warn_unsupported::Bool = false,\n    exclude_tests_after::VersionNumber = v\"999.0.0\",\n)\n    for name_sym in names(@__MODULE__; all = true)\n        name = string(name_sym)\n        if !startswith(name, \"test_\")\n            continue  # All test functions start with test_\n        elseif !isempty(include) && !any(s -> occursin(s, name), include)\n            continue\n        elseif !isempty(exclude) && any(s -> occursin(s, name), exclude)\n            continue\n        end\n        test_function = getfield(@__MODULE__, name_sym)\n        if version_added(test_function) > exclude_tests_after\n            continue\n        end\n        @testset \"$(name)\" begin\n            c = copy(config)\n            tear_down = setup_test(test_function, model, c)\n            # Make sure to empty the model before every test!\n            MOI.empty!(model)\n            try\n                test_function(model, c)\n            catch err\n                _error_handler(err, name, warn_unsupported)\n            end\n            if tear_down !== nothing\n                tear_down()\n            end\n        end\n    end\n    return\nend\n\n\"\"\"\n    RequirementUnmet(msg::String) <: Exception\n\nAn error for throwing in tests to indicate that the model does not support some\nrequirement expected by the test function.\n\"\"\"\nstruct RequirementUnmet <: Exception\n    msg::String\nend\n\nfunction Base.show(io::IO, err::RequirementUnmet)\n    print(io, \"RequirementUnmet: $(err.msg)\")\n    return\nend\n\n\"\"\"\n    @requires(x)\n\nCheck that the condition `x` is `true`. Otherwise, throw an [`RequirementUnmet`](@ref)\nerror to indicate that the model does not support something required by the test\nfunction.\n\n## Examples\n\n```julia\n@requires MOI.supports(model, MOI.Silent())\n@test MOI.get(model, MOI.Silent())\n```\n\"\"\"\nmacro requires(x)\n    msg = string(x)\n    return quote\n        if !$(esc(x))\n            throw(RequirementUnmet($msg))\n        end\n    end\nend\n\nfunction _error_handler(\n    err::Union{MOI.NotAllowedError,MOI.UnsupportedError,RequirementUnmet},\n    name::String,\n    warn_unsupported::Bool,\n)\n    if warn_unsupported\n        @warn(\"Skipping $(name): $(err)\")\n    end\n    return\nend\n\n_error_handler(err, ::String, ::Bool) = rethrow(err)\n\n###\n### The following are helpful utilities for writing tests in MOI.Test.\n###\n\n\"\"\"\n    Base.isapprox(x, y, config::Config)\n\nA three argument version of `isapprox` for use in MOI.Test.\n\"\"\"\nfunction Base.isapprox(x, y, config::Config{T}) where {T}\n    return Base.isapprox(x, y; atol = config.atol, rtol = config.rtol)\nend\n\n\"\"\"\n    _supports(config::Config, attribute::MOI.AnyAttribute)\n\nReturn `true` if the `attribute` is supported by the `config`.\n\nThis is helpful when writing tests.\n\n## Example\n\n```julia\nif MOI.Test._supports(config, MOI.Silent)\n    @test MOI.get(model, MOI.Silent()) == true\nend\n```\n\"\"\"\n_supports(config::Config, T::Any)::Bool = !(T in config.exclude)\n\n\"\"\"\n    _test_model_solution(\n        model::MOI.ModelLike,\n        config::Config;\n        objective_value = nothing,\n        variable_primal = nothing,\n        constraint_primal = nothing,\n        constraint_dual = nothing,\n    )\n\nSolve, and then test, various aspects of a model.\n\nFirst, check that `TerminationStatus == config.optimal_status`.\n\nIf `objective_value` is not nothing, check that the attribute `ObjectiveValue()`\nis approximately `objective_value`.\n\nIf `variable_primal` is not nothing, check that the attribute  `PrimalStatus` is\n`MOI.FEASIBLE_POINT`. Then for each `(index, value)` in `variable_primal`, check\nthat the primal value of the variable `index` is approximately `value`.\n\nIf `constraint_primal` is not nothing, check that the attribute  `PrimalStatus`\nis `MOI.FEASIBLE_POINT`. Then for each `(index, value)` in `constraint_primal`,\ncheck that the primal value of the constraint `index` is approximately `value`.\n\nFinally, if `config.duals = true`, and if `constraint_dual` is not nothing,\ncheck that the attribute  `DualStatus` is `MOI.FEASIBLE_POINT`. Then for each\n`(index, value)` in `constraint_dual`, check that the dual of the constraint\n`index` is approximately `value`.\n\n### Example\n\n```julia\nMOIU.loadfromstring!(model, \\\"\\\"\\\"\n    variables: x\n    minobjective: 2.0x + 1.0\n    c: x >= 1.0\n\\\"\\\"\\\")\nx = MOI.get(model, MOI.VariableIndex, \"x\")\nc = MOI.get(\n    model,\n    MOI.ConstraintIndex{MOI.VariableIndex,MOI.GreaterThan{Float64}},\n    \"c\",\n)\n_test_model_solution(\n    model,\n    config;\n    objective_value = 3.0,\n    variable_primal = [(x, 1.0)],\n    constraint_primal = [(c, 1.0)],\n    constraint_dual = [(c, 2.0)],\n)\n```\n\"\"\"\nfunction _test_model_solution(\n    model::MOI.ModelLike,\n    config::Config{T};\n    objective_value = nothing,\n    variable_primal = nothing,\n    constraint_primal = nothing,\n    constraint_dual = nothing,\n) where {T}\n    if !_supports(config, MOI.optimize!)\n        return\n    end\n    MOI.optimize!(model)\n    # No need to check supports. Everyone _must_ implement ObjectiveValue.\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    if objective_value !== nothing && _supports(config, MOI.ObjectiveValue())\n        @test isapprox(\n            MOI.get(model, MOI.ObjectiveValue()),\n            objective_value,\n            config,\n        )\n    end\n    # No need to check supports. Everyone _must_ implement VariablePrimal.\n    if variable_primal !== nothing\n        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n        for (index, solution_value) in variable_primal\n            @test isapprox(\n                MOI.get(model, MOI.VariablePrimal(), index),\n                solution_value,\n                config,\n            )\n        end\n    end\n    if constraint_primal !== nothing && _supports(config, MOI.ConstraintPrimal)\n        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n        for (index, solution_value) in constraint_primal\n            @test isapprox(\n                MOI.get(model, MOI.ConstraintPrimal(), index),\n                solution_value,\n                config,\n            )\n        end\n    end\n    if constraint_dual !== nothing && _supports(config, MOI.ConstraintDual)\n        @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT\n        for (index, solution_value) in constraint_dual\n            @test isapprox(\n                MOI.get(model, MOI.ConstraintDual(), index),\n                solution_value,\n                config,\n            )\n        end\n    end\n    return\nend\n\n# TODO(odow): The following are helper functions for testing the value types of\n#     different attributes. The following attributes are not tested:\n# BarrierIterations()\n# CallbackNodeStatus()\n# ConflictStatus()\n# ConstraintBridgingCost()\n# ConstraintConflictStatus()\n# LazyConstraintCallback()\n# NodeCount\n# RelativeGap\n# SimplexIterations\n# VariableBridgingCost()\n\nfunction _test_attribute_value_type(\n    model::MOI.ModelLike,\n    attribute::Union{MOI.AbstractModelAttribute,MOI.AbstractOptimizerAttribute},\n)\n    T = MOI.attribute_value_type(attribute)\n    @test MOI.get(model, attribute) isa T\n    return\nend\n\nfunction _test_attribute_value_type(\n    model::MOI.ModelLike,\n    attribute::MOI.AbstractConstraintAttribute,\n    ci::MOI.ConstraintIndex,\n)\n    T = MOI.attribute_value_type(attribute)\n    @test @inferred(T, MOI.get(model, attribute, ci)) isa T\n    return\nend\n\nfunction _test_attribute_value_type(\n    model::MOI.ModelLike,\n    attribute::MOI.AbstractVariableAttribute,\n    x::MOI.VariableIndex,\n)\n    T = MOI.attribute_value_type(attribute)\n    @test @inferred(T, MOI.get(model, attribute, x)) isa T\n    return\nend\n\nfunction _test_variablenames_equal(model, variable_names)\n    seen_name = Dict(name => false for name in variable_names)\n    for index in MOI.get(model, MOI.ListOfVariableIndices())\n        name = MOI.get(model, MOI.VariableName(), index)\n        @test haskey(seen_name, name)\n        @test seen_name[name] == false\n        seen_name[name] = true\n    end\n    @test all(values(seen_name))\n    return\nend\n\nfunction _test_constraintnames_equal(model, constraint_names)\n    seen_name = Dict(name => false for name in constraint_names)\n    for (F, S) in MOI.get(model, MOI.ListOfConstraintTypesPresent())\n        if F == MOI.VariableIndex\n            continue\n        end\n        for index in MOI.get(model, MOI.ListOfConstraintIndices{F,S}())\n            name = MOI.get(model, MOI.ConstraintName(), index)\n            @test haskey(seen_name, name)\n            @test seen_name[name] == false\n            seen_name[name] = true\n        end\n    end\n    @test all(values(seen_name))\n    return\nend\n\n\"\"\"\n    util_test_models_equal(\n        model1::ModelLike,\n        model2::ModelLike,\n        variable_names::Vector{String},\n        constraint_names::Vector{String},\n        single_variable_constraints::Vector{Tuple{String,<:MOI.AbstractScalarSet}}\n    )\n\nTest that `model1` and `model2` are identical using `variable_names` as keys for\nthe variable names and `constraint_names` as keys for the constraint names.\n\nIn addition, it checks that there is a VariableIndex-in-Set constraint for each\n`(name, set)` tuple in `single_variable_constraints`, where `name` is the name\nof the corresponding variable.\n\n!!! warning\n    This is not a generic function that works in all cases. It is mainly\n    intended for writing tests in which all variables and constraints have\n    unique names.\n\"\"\"\nfunction util_test_models_equal(\n    model1::MOI.ModelLike,\n    model2::MOI.ModelLike,\n    variable_names::Vector{String},\n    constraint_names::Vector{String},\n    single_variable_constraints::Vector{<:Tuple} = Tuple[],\n)\n    for (name, set) in single_variable_constraints\n        x1 = MOI.get(model1, MOI.VariableIndex, name)\n        ci1 = MOI.ConstraintIndex{MOI.VariableIndex,typeof(set)}(x1.value)\n        @test MOI.is_valid(model1, ci1)\n        @test MOI.get(model1, MOI.ConstraintSet(), ci1) == set\n        x2 = MOI.get(model2, MOI.VariableIndex, name)\n        ci2 = MOI.ConstraintIndex{MOI.VariableIndex,typeof(set)}(x2.value)\n        @test MOI.is_valid(model2, ci2)\n        @test MOI.get(model2, MOI.ConstraintSet(), ci2) == set\n    end\n    _test_variablenames_equal(model1, variable_names)\n    _test_variablenames_equal(model2, variable_names)\n    _test_constraintnames_equal(model1, constraint_names)\n    _test_constraintnames_equal(model2, constraint_names)\n    map_2to1 = Dict{MOI.VariableIndex,MOI.VariableIndex}()\n    for name in variable_names\n        index2 = MOI.get(model2, MOI.VariableIndex, name)\n        map_2to1[index2] = MOI.get(model1, MOI.VariableIndex, name)\n    end\n    for name in constraint_names\n        c1 = MOI.get(model1, MOI.ConstraintIndex, name)\n        c2 = MOI.get(model2, MOI.ConstraintIndex, name)\n        f1 = MOI.get(model1, MOI.ConstraintFunction(), c1)\n        f2 = MOI.get(model2, MOI.ConstraintFunction(), c2)\n        @test isapprox(f1, MOI.Utilities.map_indices(map_2to1, f2))\n        @test MOI.get(model1, MOI.ConstraintSet(), c1) ==\n              MOI.get(model2, MOI.ConstraintSet(), c2)\n    end\n    attrs1 = MOI.get(model1, MOI.ListOfModelAttributesSet())\n    attrs2 = MOI.get(model2, MOI.ListOfModelAttributesSet())\n    for attr in union(attrs1, attrs2)\n        value1 = MOI.get(model1, attr)\n        value2 = MOI.get(model2, attr)\n        if value1 isa MOI.AbstractFunction\n            @test value2 isa MOI.AbstractFunction\n            @test isapprox(value1, MOI.Utilities.map_indices(map_2to1, value2))\n        else\n            @test !(value2 isa MOI.AbstractFunction)\n            @test value1 == value2\n        end\n    end\n    return\nend\n\n###\n### Include all the test files!\n###\n\nfor file in readdir(@__DIR__)\n    if startswith(file, \"test_\") && endswith(file, \".jl\")\n        include(file)\n    end\nend\n\nend # module\n", "meta": {"hexsha": "a632366b4241b0cb31d986f687d05155f41b14c4", "size": 18027, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Test/Test.jl", "max_stars_repo_name": "JuliaOpt/MathOptInterface", "max_stars_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 123, "max_stars_repo_stars_event_min_datetime": "2017-06-30T01:25:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-26T12:21:22.000Z", "max_issues_repo_path": "src/Test/Test.jl", "max_issues_repo_name": "JuliaOpt/MathOptInterface", "max_issues_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 918, "max_issues_repo_issues_event_min_datetime": "2017-06-30T05:09:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-03T15:13:02.000Z", "max_forks_repo_path": "src/Test/Test.jl", "max_forks_repo_name": "JuliaOpt/MathOptInterface", "max_forks_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 76, "max_forks_repo_forks_event_min_datetime": "2017-06-30T03:18:15.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-26T12:22:28.000Z", "avg_line_length": 31.296875, "max_line_length": 86, "alphanum_fraction": 0.6757086592, "num_tokens": 4564, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.414898860266261, "lm_q2_score": 0.20946968133032526, "lm_q1q2_score": 0.08690873204428884}}
{"text": "# Some GUI is sending a command from the user clicking on a menu\n# e.g. \"open\", \"close\", \"exit\", \"help\"\n\n# Depending on the command, the program does different things.\nfunction process_command(command::String, args...)\n    if command == \"open\"\n        # open a file\n    elseif command == \"close\"\n        # close current file\n    elseif command == \"exit\"\n        # exit program\n    elseif command == \"help\"\n        # pops up a help dialog\n    else\n        error(\"bug - this should have never happened.\")\n    end\nend\n\n# problem - this function can become very long\n# problem - this function must be changed every time a new command is added\n\n\nstruct OpenCommand end\n\noc1 = OpenCommand()\noc2 = OpenCommand()\noc1 === oc2\n\n# what is Val?\nVal(1)\nVal(1) |> typeof\n\n#=\njulia> Val(1)\nVal{1}()\n\njulia> Val(1) |> typeof\nVal{1}\n=#\n\n#= as defined in Julia, it's nothing but:\nstruct Val{x}\nend\n=#\n\n# is it a singleton?\nVal(1) === Val(1)\nVal(:foo) === Val(:foo)\n\n#=\njulia> Val(1) === Val(1)\ntrue\n\njulia> Val(:foo) === Val(:foo)\ntrue\n=#\n\n# only bits-type is allowed, otherwise...\n#=\njulia> Val(\"julia\")\nERROR: TypeError: in Type, in parameter, expected Type, got String\n=#\n\nVal(:open)\n#=\njulia> Val(:open)\nVal{:open}()\n=#\n\nfunction process_command(::Val{:open}, filename) \n    println(\"opening file $filename\")\nend\n\nfunction process_command(::Val{:close}, filename) \n    println(\"closing file $filename\")\nend\n\nprocess_command(Val(:open), \"julia.pdf\")\n#=\njulia> process_command(Val(:open), \"julia.pdf\")\nopening file julia.pdf\n=#\n\n# look ma, there's no if-then-else statements!\nfunction process_command(command::String, args...)\n    process_command(Val(Symbol(command)), args...)\nend\n\nprocess_command(\"open\", \"julia.pdf\")\nprocess_command(\"close\", \"julia.pdf\")\n#=\njulia> process_command(\"open\", \"julia.pdf\")\nopening file julia.pdf\n\njulia> process_command(\"close\", \"julia.pdf\")\nclosing file julia.pdf\n=#\n\n# Let's build your own parametric type \n# A parametric type that represents a specific command\nstruct Command{T} end\n\n# Constructor function to create a new Command instance from a string\nCommand(s::AbstractString) = Command{Symbol(s)}()\n\n# Dispatcher function\nfunction process_command(command::String, args...)\n    process_command(Command(command), args...)\nend\n\n# Actions\nfunction process_command(::Command{:open}, filename) \n    println(\"opening file $filename\")\nend\n\nfunction process_command(::Command{:close}, filename) \n    println(\"closing file $filename\")\nend\n\n# Test\nprocess_command(\"open\", \"julia.pdf\")\nprocess_command(\"close\", \"julia.pdf\")\n\n\n# -----------------------------------------------\n\nntuple(i -> 2i, 10)\n\nusing BenchmarkTools\n@btime ntuple(i->2i, 10);\n@btime ntuple(i->2i, 11);\n#=\njulia> ntuple(i -> 2i, 10)\n(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)\n\njulia> @btime ntuple(i->2i, 10);\n  0.032 ns (0 allocations: 0 bytes)\n\njulia> @btime ntuple(i->2i, 11);\n  812.209 ns (4 allocations: 336 bytes)\n=#\n\n@btime ntuple(i->2i, Val(10));\n@btime ntuple(i->2i, Val(11));\n@btime ntuple(i->2i, Val(100));\n#= singleton type\njulia> @btime ntuple(i->2i, Val(10));\n  0.032 ns (0 allocations: 0 bytes)\n\njulia> @btime ntuple(i->2i, Val(11));\n  0.031 ns (0 allocations: 0 bytes)\n\njulia> @btime ntuple(i->2i, Val(100));\n  17.270 ns (0 allocations: 0 bytes)\n=#\n\n# An extreme example from Julia Base\n# This is a huge if/then/else block of code for performance reason\nfunction ntuple(f::F, n::Integer) where F\n    t = n == 0  ? () :\n        n == 1  ? (f(1),) :\n        n == 2  ? (f(1), f(2)) :\n        n == 3  ? (f(1), f(2), f(3)) :\n        n == 4  ? (f(1), f(2), f(3), f(4)) :\n        n == 5  ? (f(1), f(2), f(3), f(4), f(5)) :\n        n == 6  ? (f(1), f(2), f(3), f(4), f(5), f(6)) :\n        n == 7  ? (f(1), f(2), f(3), f(4), f(5), f(6), f(7)) :\n        n == 8  ? (f(1), f(2), f(3), f(4), f(5), f(6), f(7), f(8)) :\n        n == 9  ? (f(1), f(2), f(3), f(4), f(5), f(6), f(7), f(8), f(9)) :\n        n == 10 ? (f(1), f(2), f(3), f(4), f(5), f(6), f(7), f(8), f(9), f(10)) :\n        _ntuple(f, n)\n    return t\nend\n\nfunction _ntuple(f, n)\n    @_noinline_meta\n    (n >= 0) || throw(ArgumentError(string(\"tuple length should be \u2265 0, got \", n)))\n    ([f(i) for i = 1:n]...,)\nend\n\n# Using singleton type dynamic dispatch\n# inferrable ntuple (enough for bootstrapping)\nntuple(f, ::Val{0}) = ()\nntuple(f, ::Val{1}) = (@_inline_meta; (f(1),))\nntuple(f, ::Val{2}) = (@_inline_meta; (f(1), f(2)))\nntuple(f, ::Val{3}) = (@_inline_meta; (f(1), f(2), f(3)))\n\n@inline function ntuple(f::F, ::Val{N}) where {F,N}\n    N::Int\n    (N >= 0) || throw(ArgumentError(string(\"tuple length should be \u2265 0, got \", N)))\n    if @generated\n        quote\n            @nexprs $N i -> t_i = f(i)\n            @ncall $N tuple t\n        end\n    else\n        Tuple(f(i) for i = 1:N)\n    end\nend\n\n# Optional: let's understand what's going on here\n\nusing Base.Cartesian\n@nexprs 3 i -> t_i = i\n\n# It's the same thing as:\n# t_1 = 1\n# t_2 = 2\n# t_3 = 3\n\n#=\njulia> @macroexpand(@nexprs 3 i -> t_i = i)\nquote\n    t_1 = 1\n    t_2 = 2\n    t_3 = 3\nend\n=#\n\n#=\njulia> @macroexpand(@ncall 3 tuple t)\n:(tuple(t_1, t_2, t_3))\n=#\n\n# of course, we cannot hard code the number 3.\n# but the macro can only work wth an integer not a variable...\n# that's why the implementation wraps it in a @generated block\n# and interpolate the value of N into the @ncall and @nexprs calls.\n\n#=\njulia> @macroexpand(@ncall N tuple t)\nERROR: LoadError: MethodError: no method matching @ncall(::LineNumberNode, ::Module, ::Symbol, ::Symbol, ::Symbol)\nClosest candidates are:\n  @ncall(::LineNumberNode, ::Module, ::Int64, ::Any, ::Any...) at cartesian.jl:105\n=#\n\n", "meta": {"hexsha": "223a9d855dbd072d170462b9c7785ee7a32c3760", "size": 5564, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter09/SingletonTypes/val.jl", "max_stars_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_stars_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 167, "max_stars_repo_stars_event_min_datetime": "2020-02-07T14:38:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T09:33:00.000Z", "max_issues_repo_path": "Chapter09/SingletonTypes/val.jl", "max_issues_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_issues_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter09/SingletonTypes/val.jl", "max_forks_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_forks_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 33, "max_forks_repo_forks_event_min_datetime": "2020-02-14T05:17:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-24T07:38:59.000Z", "avg_line_length": 23.4767932489, "max_line_length": 114, "alphanum_fraction": 0.6053199137, "num_tokens": 1890, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4687906266262437, "lm_q2_score": 0.184767510648, "lm_q1q2_score": 0.08661727709684708}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.21\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 62f36be0-64cf-11eb-0b86-9d4b55165cbf\nbegin\n\tusing PlutoUI\n\tusing NativeSVG\nend\n\n# \u2554\u2550\u2561 46a00ea0-64cd-11eb-3e3f-b7cfa00d69ce\nmd\"\"\"# Variables, Expressions, and Statements\n\nOne of the most powerful features of a programming language is the ability to manipulate *variables*. A variable is a name that refers to a value.\"\"\"\n\n# \u2554\u2550\u2561 63956320-64cd-11eb-30d5-b1f23c40108f\nmd\"\"\"## Assignment Statements\n\nAn assignment statement creates a new variable and gives it a value:\n\n```julia\njulia> message = \"And now for something completely different\"\n\"And now for something completely different\"\njulia> n = 17\n17\njulia> \u03c0_val = 3.141592653589793\n3.141592653589793\n```\n\nThis example makes three assignments. The first assigns a string to a new variable named `message`, the second assigns the integer `17` to `n`, and the third assigns the\n(approximate) value of \u03c0 to `\u03c0_val` (**`\\pi TAB`**).\n\nA common way to represent variables on paper is to write the name of each with an arrow pointing to its value. This kind of figure is called a *state diagram* because it shows what state each of the variables is in (think of it as the variable\u2019s state of mind). Figure 2-1 shows the result of the previous example.\n\"\"\"\n\n# \u2554\u2550\u2561 58ba9f20-64d1-11eb-30e7-71ec2290f593\nDrawing(width=720, height=110) do\n    rect(x=110, y=10, width=500, height=90, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=180, y=30, font_family=\"JuliaMono, monospace\", text_anchor=\"end\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"message\") \n\tend\n\ttext(x=190, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"->\") \n\tend\n\ttext(x=220, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"\\\"And now for something completely different\\\"\") \n\tend\n\ttext(x=180, y=60, font_family=\"JuliaMono, monospace\", text_anchor=\"end\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\ttext(x=190, y=60, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"->\") \n\tend\n\ttext(x=220, y=60, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"17\") \n\tend\n\ttext(x=180, y=90, font_family=\"JuliaMono, monospace\", text_anchor=\"end\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"\u03c0_val\") \n\tend\n\ttext(x=190, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"->\") \n\tend\n\ttext(x=220, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"3.141592653589793\") \n\tend\nend\n\n# \u2554\u2550\u2561 7f908a10-64d6-11eb-1629-9179dc49bad7\nmd\"\"\"\n*Figure 2-1. State diagram.*\n\"\"\"\n\n# \u2554\u2550\u2561 5cb86662-64e1-11eb-0c04-41643ac1fc94\nmd\"\"\"\n!!! languages\n    Both the C programming language and Python use also the symbol `=` as assignment operator.\n\n    In the C Programming Language, you have to specify the type as a *declaration* before the name of the variable:\n    ```c\n    char[] message = \"And now for something completely different\";\n    int n = 17;\n    double pi = 3.141592653589793;\n    ```\n    or you can separate declaration from assignment:\n    ```c\n    int n;\n    n = 17;\n    ```\n\n    Python:\n    ```python\n    >>> message = 'And now for something completely different'\n    >>> n = 17\n    >>> pi = 3.141592653589793\n    ```\n\"\"\"\n\n# \u2554\u2550\u2561 60d71100-64ce-11eb-2a71-dd40eded5474\nmd\"\"\"## Variable Names\n\nProgrammers generally choose names for their variables that are meaningful\u2014they document what the variable is used for.\n\nVariable names can be as long as you like. They can contain almost all Unicode characters (see [\u201cCharacters\u201d]()), but they can\u2019t begin with a number. It is legal to use uppercase letters, but it is conventional to use only lowercase for variable names.\n\nUnicode characters can be entered via tab completion of LaTeX-like abbreviations in\nthe Julia REPL.\n\nThe underscore character, `_`, can appear in a name. It is often used in names with multiple words, such as `your_name` or `airspeed_of_unladen_swallow`.\n\nIf you give a variable an illegal name, you get a syntax error:\n\n```julia\njulia> 76trombones = \"big parade\"\nERROR: syntax: \"76\" is not a valid function argument name\njulia> more@ = 1000000\nERROR: syntax: extra token \"@\" after end of expression\njulia> struct = \"Advanced Theoretical Zymurgy\"\nERROR: syntax: unexpected \"=\"\n```\n\n`76trombones` is illegal because it begins with a number. `more@` is illegal because it contains an illegal character, `@`. But what\u2019s wrong with `struct`?\n\nIt turns out that `struct` is one of Julia\u2019s keywords. The REPL uses *keywords* to recognize the structure of the program, and they cannot be used as variable names.\n\nJulia has these keywords:\n\n```\nabstract  type      baremodule  begin       break      catch\nconst     continue  do          else        elseif     end \nexport    finally   for         false       function   global \nif        import    in          let         local      macro \nmodule    mutable   primitive   type        quote      return\ntrue      try       using       struct      where      while\n```\n\nYou don\u2019t have to memorize this list. In most development environments, keywords are displayed in a different color; if you try to use one as a variable name, you\u2019ll know.\"\"\"\n\n# \u2554\u2550\u2561 14697060-64d8-11eb-105d-d39759df9d42\nmd\"\"\"## Expressions and Statements\nAn *expression* is a combination of values, variables, and operators. A value all by itself is considered an expression, and so is a variable, so the following are all legal expressions:\n\n```julia\njulia> 42\n42\njulia> n\n17\njulia> n + 25\n42\n```\n\nWhen you type an expression at the prompt, the REPL *evaluates* it, which means that it finds the value of the expression. In this example, `n` has the value `17` and `n + 25` has the value `42`.\n\nA *statement* is a unit of code that has an effect, like creating a variable or displaying a value:\n\n```julia\njulia> n = 17\n17\njulia> println(n)\n17\n```\n\nThe first line here is an assignment statement that gives a value to `n`. The second line is a print statement that displays the value of `n`.\n\nWhen you type a statement, the REPL *executes* it, which means that it does whatever\nthe statement says.\n\"\"\"\n\n# \u2554\u2550\u2561 567fc950-64dc-11eb-22e2-df958738bfff\nmd\"\"\"## Script Mode\n\nSo far we have run Julia in *interactive mode*, which means that you interact directly with the REPL. Interactive mode is a good way to get started, but if you are working with more than a few lines of code, it can be clumsy.\n\nThe alternative is to save code in a file called a *script* and then run Julia in *script mode* to execute the script. By convention, Julia scripts have names that end with *.jl*.\n\nIf you know how to create and run a script on your computer, you are ready to go. Otherwise I recommend installing Visual Studio Code. Open a text file, write the script, and save the file with a *.jl* extension. The script can be executed in a terminal with the command **`julia name_of_the_script.jl`**.\n\nBecause Julia provides both modes, you can test bits of code in interactive mode before you put them in a script. But there are differences between interactive mode and script mode that can be confusing.\n\nFor example, if you are using Julia as a calculator, you might type:\n\n```julia\njulia> miles = 26.2\n26.2\njulia> miles * 1.61\n42.182\n```\n\nThe first line assigns a value to `miles` and displays the value. The second line is an expression, so the REPL evaluates it and displays the result. It turns out that a mara thon is about 42 kilometers.\n\nBut if you type the same code into a script and run it, you get no output at all. In script mode an expression, all by itself, has no visible effect. Julia actually evaluates the expression, but it doesn\u2019t display the value unless you tell it to:\n\n```julia\nmiles = 26.2\nprintln(miles * 1.61)\n```\n\nThis behavior can be confusing at first.\n\nA script usually contains a sequence of statements. If there is more than one statement, the results appear one at a time as the statements execute.\n\nFor example, the script:\n\n```julia\nprintln(1)\nx = 2\nprintln(x)\n```\n\nproduces the output:\n\n```\n1\n2\n```\n\nThe assignment statement produces no output.\n\"\"\"\n\n# \u2554\u2550\u2561 ff42a18e-64e4-11eb-0d79-b139fff2bcfd\nmd\"\"\"\n!!! languages\n    The C programming language has no script mode. All statements have to be in a file:\n    ```c\n    #include <stdio.h>\n\n    int main(void)\n    {\n        printf(\"%i\\n\", 1);\n        int x = 2;\n        printf(\"%i\\n\", x);\n    }\n    ```\n\"\"\"\n\n# \u2554\u2550\u2561 9f56ada0-64dd-11eb-2689-89413775adb1\nmd\"\"\"#### Exercise 2-1\n\nTo check your understanding, type the following statements in the Julia REPL and see\nwhat they do:\n\n```julia\n5\nx = 5\nx + 1\n```\n\nNow put the same statements in a script and run it. What is the output? Modify the script by transforming each expression into a print statement and then run it again.\n\"\"\"\n\n# \u2554\u2550\u2561 c95baab0-64dd-11eb-00c7-af9957ec1614\nmd\"\"\"## Operator Precedence\n\nWhen an expression contains more than one operator, the order of evaluation depends on the *operator precedence*. For mathematical operators, Julia follows mathematical convention. The acronym *PEMDAS* is a useful way to remember the rules:\n\n* *P*arentheses have the highest precedence and can be used to force an expression\nto evaluate in the order you want. Since expressions in parentheses are evaluated\nfirst, `2*(3-1)` is `4`, and `(1+1)^(5-2)` is `8`. You can also use parentheses to make an expression easier to read, as in `(minute * 100) / 60`, even if it doesn\u2019t change the result.\n* *E*xponentiation has the next highest precedence, so `1+2^3` is `9`, not `27`, and `2*3^2` is `18`, not `36`.\n* *M*ultiplication and *D*ivision have higher precedence than *A*ddition and *S*ubtraction. So, `2*3-1` is `5`, not `4`, and `6+4/2` is `8`, not `5`.\n* Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression `degrees / 2 * \u03c0`, the division happens first and the result is multiplied by \u03c0. To divide by 2\u03c0, you can use parentheses, or write `degrees / 2 / \u03c0` or `degrees / 2\u03c0`.\n\n!!! tip\n    I don\u2019t work very hard to remember the precedence of operators. If I can\u2019t tell by looking at the expression, I use parentheses to make it obvious.\n\"\"\"\n\n# \u2554\u2550\u2561 d619bb60-64de-11eb-3d7b-572676f08d00\nmd\"\"\"## String Operations\n\nIn general, you can\u2019t perform mathematical operations on strings, even if the strings look like numbers, so the following are illegal:\n\n```julia\n\"2\" - \"1\" \n\"eggs\" / \"easy\" \n\"third\" + \"a charm\"\n```\n\nBut there are two exceptions, `*` and `^`.\n\nThe `*` operator performs *string concatenation*, which means it joins the strings by linking them end-to-end. For example:\n\n```julia\njulia> first_str = \"throat\"\n\"throat\"\njulia> second_str = \"warbler\"\n\"warbler\"\njulia> first_str * second_str\n\"throatwarbler\"\n```\n\nThe `^` operator also works on strings; it performs repetition. For example, `\"Spam\"^3` is `\"SpamSpamSpam\"`. If one of the values is a string, the other has to be an integer.\n\nThis use of `*` and `^` makes sense by analogy with multiplication and exponentiation.\nJust as ``4^3`` is equivalent to ``4\\cdot4\\cdot4``, we expect `\"Spam\"^3` to be the same as `\"Spam\"*\"Spam\"*\"Spam\"`, and it is.\n\"\"\"\n\n# \u2554\u2550\u2561 0579eaa0-64e0-11eb-3fba-211e3857675f\nmd\"\"\"\n!!! languages\n    Python uses the `+` and the `*` operator to perform string concatenation and repetition:\n\n    ```python\n    >>> first_str = 'throat'\n    >>> second_str = 'warbler'\n    >>> first_str + second_str\n    'throatwarbler'\n    >>> 'spam'*3\n    'spamspamspam'\n    ```\n\"\"\"\n\n# \u2554\u2550\u2561 68695430-64df-11eb-2c75-bd24a518cceb\nmd\"\"\"## Comments\n\nAs programs get bigger and more complicated, they get more difficult to read. Formal languages are dense, and it is often difficult to look at a piece of code and figure out what it is doing, or why.\n\nFor this reason, it is a good idea to add notes to your programs to explain in natural language what the program is doing. These notes are called *comments*, and they start with the `#` symbol:\n\n```julia\n# compute the percentage of the hour that has elapsed\npercentage = (minute * 100) / 60\n```\n\nIn this case, the comment appears on a line by itself. You can also put comments at the end of a line:\n\n```julia\npercentage = (minute * 100) / 60 # percentage of an hour\n```\n\nEverything from the `#` to the end of the line is ignored\u2014it has no effect on the execution of the program.\n\nComments are most useful when they document nonobvious features of the code. It is reasonable to assume that the reader can figure out what the code does; it is more useful to explain why.\n\nThis comment is redundant with the code and useless:\n\n```julia\nv = 5 # assign 5 to v\n```\n\nThis comment contains useful information that is not in the code:\n\n```julia\nv = 5 # velocity in meters/second\n```\n\n!!! warning\n    Good variable names can reduce the need for comments, but long names can make complex expressions hard to read, so there is a trade-off.\n\"\"\"\n\n# \u2554\u2550\u2561 e11055b0-64e3-11eb-2bdd-6702d0a3fe84\nmd\"\"\"\n!!! languages\n    Comments in the C programming language start with `//`:\n    ```c\n    // compute the percentage of the hour that has elapsed\n    double percentage = (minute * 100) / 60;\n    percentage = (minute * 100) / 60; // percentage of an hour\n    ```\n\n    Python uses also the `#` symbol to start a comment:\n    ```python\n    # compute the percentage of the hour that has elapsed\n    percentage = (minute * 100) / 60 # percentage of an hour\n    ```\n\"\"\"\n\n# \u2554\u2550\u2561 d42b0690-64e5-11eb-16c8-e96f57c5dab8\nmd\"\"\"## Debugging\n\nThree kinds of errors can occur in a program: syntax errors, runtime errors, and semantic errors. It is useful to distinguish between them in order to track them down more quickly:\n\n* *Syntax error*:\n  \u201cSyntax\u201d refers to the structure of a program and the rules about that structure. For example, parentheses have to come in matching pairs, so `(1 + 2)` is legal, but `8)` is a syntax error.\n\n  If there is a syntax error anywhere in your program, Julia displays an error message and quits, and you will not be able to run the program. During the first few weeks of your programming career, you might spend a lot of time tracking down syntax errors. As you gain experience, you will make fewer errors and find them faster.\n\n* *Runtime error*:\n  The second type of error is a runtime error, so called because the error does not appear until after the program has started running. These errors are also called exceptions because they usually indicate that something exceptional (and bad) has happened.\n\n  Runtime errors are rare in the simple programs you will see in the first few chapters, so it might be a while before you encounter one.\n\n* *Semantic error*:\n  The third type of error is \u201csemantic,\u201d which means related to meaning. If there is a semantic error in your program, it will run without generating error messages, but it will not do the right thing. It will do something else. Specifically, it will do what you told it to do.\n\n  Identifying semantic errors can be tricky because it requires you to work backward by looking at the output of the program and trying to figure out what it is doing.\n\"\"\"\n\n# \u2554\u2550\u2561 c40b1b50-64e6-11eb-24ce-33b1cc5c6d0d\nmd\"\"\"## Glossary\n\n*variable*:\nA name that refers to a value.\n\n*assignment*:\nA statement that assigns a value to a variable.\n\n*state diagram*:\nA graphical representation of a set of variables and the values they refer to.\n\n*keyword*:\nA reserved word that is used to parse a program; you cannot use keywords like\n`if`, `function`, and `while` as variable names.\n\n*expression*:\nA combination of variables, operators, and values that represents a single result.\n\n*evaluate*:\nTo simplify an expression by performing the operations in order to yield a single value.\n\n*statement*:\nA section of code that represents a command or action. So far, the statements we have seen are assignments and print statements.\n\n*execute*:\nTo run a statement and do what it says.\n\n*interactive mode*:\nA way of using the Julia REPL by typing code at the prompt.\n\n*script mode*:\nA way of using Julia to read code from a script and run it.\n\n*script*:\nA program stored in a file.\n\n*operator precedence*:\nRules governing the order in which expressions involving multiple mathematical operators and operands are evaluated.\n\n*concatenate*:\nTo join two strings end-to-end.\n\n*comment*:\nInformation in a program that is meant for other programmers (or anyone reading the source code) and has no effect on the execution of the program.\n\n*syntax error*:\nAn error in a program that makes it impossible to parse (and therefore impossible to interpret).\n\n*runtime error* or *exception*:\nAn error that is detected while the program is running.\n\n*semantics*:\nThe meaning of a program.\n\n*semantic error*:\nAn error in a program that makes it do something other than what the programmer intended.\n\"\"\"\n\n# \u2554\u2550\u2561 763c5320-64e7-11eb-16e0-1bdc148f2e24\nmd\"\"\"## Exercises\n\n#### Exercise 2-2\n\nRepeating my advice from the previous chapter, whenever you learn a new feature, you should try it out in interactive mode and make errors on purpose to see what goes wrong.\n\n1. We\u2019ve seen that `n = 42` is legal. What about `42 = n`?\n2. How about `x = y = 1`?\n3. In some languages every statement ends with a semicolon, `;`. What happens if you put a semicolon at the end of a Julia statement?\n4. What if you put a period at the end of a statement?\n5. In math notation you can multiply ``x`` and ``y`` like this: ``xy``. What happens if you try that in Julia? What about `5x`?\n\"\"\"\n\n# \u2554\u2550\u2561 c3de97a2-64e7-11eb-077e-a1692cd76929\nmd\"\"\"#### Exercise 2-3\n\nPractice using the Julia REPL as a calculator:\n\n1. The volume of a sphere with radius ``r`` is ``\\frac{4}{3}\\pi r^3``. What is the volume of a sphere with radius 5?\n2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?\n3. If I leave my house at 6:52 a.m. and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250062f36be0-64cf-11eb-0b86-9d4b55165cbf\n# \u255f\u250046a00ea0-64cd-11eb-3e3f-b7cfa00d69ce\n# \u255f\u250063956320-64cd-11eb-30d5-b1f23c40108f\n# \u2560\u255058ba9f20-64d1-11eb-30e7-71ec2290f593\n# \u255f\u25007f908a10-64d6-11eb-1629-9179dc49bad7\n# \u255f\u25005cb86662-64e1-11eb-0c04-41643ac1fc94\n# \u255f\u250060d71100-64ce-11eb-2a71-dd40eded5474\n# \u255f\u250014697060-64d8-11eb-105d-d39759df9d42\n# \u255f\u2500567fc950-64dc-11eb-22e2-df958738bfff\n# \u255f\u2500ff42a18e-64e4-11eb-0d79-b139fff2bcfd\n# \u255f\u25009f56ada0-64dd-11eb-2689-89413775adb1\n# \u255f\u2500c95baab0-64dd-11eb-00c7-af9957ec1614\n# \u255f\u2500d619bb60-64de-11eb-3d7b-572676f08d00\n# \u255f\u25000579eaa0-64e0-11eb-3fba-211e3857675f\n# \u255f\u250068695430-64df-11eb-2c75-bd24a518cceb\n# \u255f\u2500e11055b0-64e3-11eb-2bdd-6702d0a3fe84\n# \u255f\u2500d42b0690-64e5-11eb-16c8-e96f57c5dab8\n# \u255f\u2500c40b1b50-64e6-11eb-24ce-33b1cc5c6d0d\n# \u255f\u2500763c5320-64e7-11eb-16e0-1bdc148f2e24\n# \u255f\u2500c3de97a2-64e7-11eb-077e-a1692cd76929\n", "meta": {"hexsha": "4bd3028c085c11f5c0a82d53949381844971718b", "size": 18928, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lectures/Lecture02.jl", "max_stars_repo_name": "BenLauwens/ES123", "max_stars_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2018-03-07T18:04:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-10T15:07:23.000Z", "max_issues_repo_path": "Lectures/Lecture02.jl", "max_issues_repo_name": "BenLauwens/ES123", "max_issues_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lectures/Lecture02.jl", "max_forks_repo_name": "BenLauwens/ES123", "max_forks_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-08-12T05:29:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-02T21:38:27.000Z", "avg_line_length": 38.3935091278, "max_line_length": 329, "alphanum_fraction": 0.7237426036, "num_tokens": 5536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.37022539259558657, "lm_q2_score": 0.23370635691404026, "lm_q1q2_score": 0.08652402774058483}}
{"text": "\"\"\"\n    Shuffle\n\nSupport for a number of deterministic and random shuffling algorithms. Provides functions\n[`shuffle`](@ref), [`shuffle!`](@ref), [`nshuffle`](@ref) and [`nshuffle!`](@ref)\nas well as the following shuffling algorithms:\n- [faro (or weave) shuffle](https://luapulu.github.io/Shuffle.jl/stable/reference/#Shuffle.Faro),\n- a [cut](https://luapulu.github.io/Shuffle.jl/stable/reference/#Shuffle.Cut),\n- [random shuffle](https://luapulu.github.io/Shuffle.jl/stable/reference/#Shuffle.RandomShuffle)\n    (uses [`Random.shuffle`](https://docs.julialang.org/en/v1/stdlib/Random/#Random.shuffle)) and\n- [Gilbert-Shannon-Reeds model](https://luapulu.github.io/Shuffle.jl/stable/reference/#Shuffle.GilbertShannonReeds).\n\"\"\"\nmodule Shuffle\n\nimport Base: show\nimport Random # used so Random.shuffle! can be seperated from shuffle!\n\nusing Random: AbstractRNG, default_rng\nusing Base: copymutable\n\nexport AbstractShuffle, AbstractDeterministicShuffle, AbstractRandomShuffle\nexport Faro, Weave, Cut, RandomShuffle, GilbertShannonReeds\nexport infaro, outfaro, inweave, outweave, randshuffle, gsrshuffle\nexport shuffle, shuffle!, nshuffle, nshuffle!\n\n## Abstract Types ##\n\n\"\"\"\n    AbstractShuffle\n\nSupertype for [`AbstractDeterministicShuffle`](@ref) and [`AbstractRandomShuffle`](@ref).\n\n# Implementation\n\nNew shuffling algorithm types should be sub types of either\n[`AbstractDeterministicShuffle`](@ref) or [`AbstractRandomShuffle`](@ref). If an algorithm\ncan be defined in-place, only [`shuffle!`](@ref) needs to be extended. [`shuffle`](@ref),\n[`nshuffle`](@ref) and [`nshuffle!`](@ref) will make a copy / repeatedly call\n[`shuffle!`](@ref) as needed. If the algorithm can not be defined in-place, define\n[`shuffle`](@ref) and [`nshuffle!`](@ref). [`nshuffle`](@ref) will make a copy and call\n[`nshuffle!`](@ref).\n\"\"\"\nabstract type AbstractShuffle end\n\n\"\"\"\n    AbstractDeterministicShuffle <: AbstractShuffle\n\nSupertype for fully deterministic shuffle algorithms such as [`Faro`](@ref) and\n[`Cut`](@ref).\n\n# Implementation\n\nNew deterministic shuffling algorithms should implement either [`shuffle`](@ref) or\n[`shuffle!`](@ref), as appropriate. The new shuffle method should take a collection to be\nshuffled as the first argument and an instance of the new shuffling algorithm type as the\nsecond argument.\n\nFor example:\n```julia\nstruct MyShuffle <: AbstractDeterministicShuffle\n    parameter\nend\n\nfunction shuffle(c::AbstractArray, s::MyShuffle, prealloc_out=similar(c))\n    # Do the shuffling\n\n    return prealloc_out\nend\n```\n\nPre-allocating the output makes it easy to also write an efficient [`nshuffle!`](@ref)\nfunction for algorithms that cannot be written in-place.\n\nSee also: [`AbstractShuffle`](@ref)\n\"\"\"\nabstract type AbstractDeterministicShuffle <: AbstractShuffle end\n\n\"\"\"\n    AbstractRandomShuffle <: AbstractShuffle\n\nSupertype for partly or fully random shuffle algorithms such as [`RandomShuffle`](@ref) and\n[`GilbertShannonReeds`](@ref).\n\n# Implementation\n\nNew random shuffling algorithms should implement either [`shuffle`](@ref) or\n[`shuffle!`](@ref), as appropriate. The new shuffle method should take a\n[`Random.AbstractRNG`](https://docs.julialang.org/en/v1/stdlib/Random/#Random.AbstractRNG)\nas the first argument, a collection to be shuffled as the second argument and an instance of\nthe new shuffling algorithm type as the third argument.\n\nFor example:\n```julia\nstruct MyShuffle <: AbstractRandomShuffle end\n\nfunction shuffle!(r::AbstractRNG, c, s::MyShuffle)\n    # Do the shuffling, passing r to any random number generating functions\n\n    return c\nend\n```\n\nSee also: [`AbstractShuffle`](@ref)\n\"\"\"\nabstract type AbstractRandomShuffle <: AbstractShuffle end\n\n## Include files ##\n\ninclude(\"DeterministicShuffles.jl\")\ninclude(\"RandomShuffles.jl\")\n\n## Defaults ##\n\nmutable struct Defaults\n    shuffle::AbstractShuffle\nend\n\n\"\"\"\n    DEFAULTS\n\nbinding to a mutable struct containing the default shuffling algorithm.\n[`RandomShuffle`](@ref) is set initially.\n\n# Examples\n```jldoctest\njulia> Shuffle.DEFAULTS.shuffle\nRandomShuffle()\n\njulia> mt1 = MersenneTwister(1234); mt2 = MersenneTwister(1234);\n\njulia> shuffle(mt1, collect(1:100)) == shuffle(mt2, collect(1:100), RandomShuffle())\ntrue\n\njulia> Shuffle.DEFAULTS.shuffle = infaro; Shuffle.DEFAULTS.shuffle\nFaro{:in}()\n\njulia> shuffle([1, 2, 3, 4, 5, 6, 7, 8])\n8-element Vector{Int64}:\n 5\n 1\n 6\n 2\n 7\n 3\n 8\n 4\n\njulia> Shuffle.DEFAULTS.shuffle = RandomShuffle();\n```\n\"\"\"\nconst DEFAULTS = Defaults(RandomShuffle())\n\n## Shuffle! and shuffle ##\n\n\"\"\"\n    shuffle!([rng=GLOBAL_RNG,], c, s::AbstractShuffle=RandomShuffle())\n\nshuffle collection `c` in-place using shuffling algorithm `s`. A random-number generator `rng`\nmay be supplied for random shuffling algorithms.\n\n# Examples\n```jldoctest\njulia> mt = MersenneTwister(1234);\n\njulia> a = collect(1:6);\n\njulia> shuffle!(mt, a); a\n6-element Vector{Int64}:\n 2\n 1\n 3\n 6\n 4\n 5\n\njulia> shuffle!(a, Faro{:out}()); a\n6-element Vector{Int64}:\n 2\n 6\n 1\n 4\n 3\n 5\n```\n\"\"\"\nshuffle!(c) = shuffle!(c, DEFAULTS.shuffle)\nshuffle!(r::AbstractRNG, c) = shuffle!(r, c, DEFAULTS.shuffle)\nshuffle!(c, s::AbstractRandomShuffle) = shuffle!(default_rng(), c, s)\n\n\"\"\"\n    shuffle([rng=GLOBAL_RNG,], c, s::AbstractShuffle=RandomShuffle())\n\nshuffle collection `c` using shuffling algorithm `s`. To shuffle `c` in-place,\nsee [`shuffle!`](@ref). A random-number generator `rng` may be supplied for random shuffling\nalgorithms.\n\n# Examples\n```jldoctest\njulia> mt = MersenneTwister(1234);\n\njulia> shuffle(mt, [1, 2, 3, 4, 5, 6, 7, 8], GilbertShannonReeds())\n8-element Vector{Int64}:\n 6\n 7\n 1\n 2\n 8\n 3\n 4\n 5\n\njulia> shuffle([6, 5, 4, 3, 2, 1], Faro{:in}())\n6-element Vector{Int64}:\n 3\n 6\n 2\n 5\n 1\n 4\n```\n\"\"\"\nshuffle(c, s::AbstractShuffle=DEFAULTS.shuffle) = shuffle!(copymutable(c), s)\nshuffle(r::AbstractRNG, c, s::AbstractRandomShuffle=DEFAULTS.shuffle) = shuffle!(r, copymutable(c), s)\nshuffle(c, s::AbstractRandomShuffle) = shuffle(default_rng(), c, s)\n\n## nshuffle! and nshuffle ##\n\n\"\"\"\n    nshuffle!([rng=GLOBAL_RNG,], c, n::Integer, s::AbstractShuffle=RandomShuffle())\n\nshuffle collection `c` in-place `n` times using shuffling algorithm `s`. A random-number\ngenerator `rng` may be supplied for random shuffling algorithms.\n\n# Examples\n```jldoctest\njulia> mt = MersenneTwister(1234);\n\njulia> a = collect(1:7);\n\njulia> nshuffle!(mt, a, 3, GilbertShannonReeds()); a\n7-element Vector{Int64}:\n 5\n 6\n 1\n 7\n 2\n 3\n 4\n```\n\"\"\"\nnshuffle!(c, n::Integer) = nshuffle!(c, n, DEFAULTS.shuffle)\n\nfunction nshuffle!(r::AbstractRNG, c, n::Integer, s::AbstractRandomShuffle=DEFAULTS.shuffle)\n    for i in 1:n\n        shuffle!(r, c, s)\n    end\n    return c\nend\n\nnshuffle!(c, n::Integer, s::AbstractRandomShuffle) = nshuffle!(default_rng(), c, n, s)\n\nfunction nshuffle!(c, n::Integer, s::AbstractDeterministicShuffle)\n    for i in 1:n\n        shuffle!(c, s)\n    end\n    return c\nend\n\n\"\"\"\n    nshuffle([rng=GLOBAL_RNG,], c, n::Integer, s::AbstractShuffle=RandomShuffle())\n\nshuffle collection `c` `n` times using shuffling algorithm `s`. A random-number generator\n`rng` may be supplied for random shuffling algorithms.\nTo shuffle `c` in-place see [`nshuffle!`](@ref)\n\n# Examples\n```jldoctest\njulia> nshuffle(collect(1:8), 3, Faro{:in}())\n8-element Vector{Int64}:\n 8\n 7\n 6\n 5\n 4\n 3\n 2\n 1\n```\n\"\"\"\nnshuffle(c, n::Integer, s::AbstractShuffle=DEFAULTS.shuffle) = nshuffle!(copymutable(c), n, s)\nnshuffle(c, n::Integer, s::AbstractRandomShuffle) = nshuffle(default_rng(), c, n, s)\nnshuffle(r::AbstractRNG, c, n::Integer, s::AbstractRandomShuffle=DEFAULTS.shuffle) =\n    nshuffle!(r, copymutable(c), n, s)\n\nend # module\n", "meta": {"hexsha": "4ab98bad2756e71f4a761ae8869fa3692d588a93", "size": 7577, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Shuffle.jl", "max_stars_repo_name": "Luapulu/Shuffle.jl", "max_stars_repo_head_hexsha": "2cb70e084813fc55fe3ed24e3482e27909183a8a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-10-13T04:43:13.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-11T18:43:29.000Z", "max_issues_repo_path": "src/Shuffle.jl", "max_issues_repo_name": "Luapulu/Shuffle.jl", "max_issues_repo_head_hexsha": "2cb70e084813fc55fe3ed24e3482e27909183a8a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2020-10-13T03:02:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-25T08:29:51.000Z", "max_forks_repo_path": "src/Shuffle.jl", "max_forks_repo_name": "Luapulu/Shuffle.jl", "max_forks_repo_head_hexsha": "2cb70e084813fc55fe3ed24e3482e27909183a8a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.597972973, "max_line_length": 116, "alphanum_fraction": 0.7203378646, "num_tokens": 2252, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4610167645017354, "lm_q2_score": 0.1871326889624542, "lm_q1q2_score": 0.08627130679798024}}
{"text": "## No * `QUIZ_BOWL`: \n\n\n\"\"\"\n    question(io::IO, question_type::Symbol, q, answers...)\n\nWrite a question as a line in a tab separated file specified to `io`\n\n* `io`: IO object specifying the file\n* `question_type`: a symbol to dispatch on. \n* qanswers...`: Each question type has a different structure for answers\n\n\nCurrently these question types are supported \n\n* `MC`: multiple choice: Answer is tuple of pairs, e.g.: `(\"ans1\" => true, \"ans2\" => false, ...)` -- exactly one is `true`\n* `MA`  multiple answer: Answer is tuple of pairs, e.g.: `(\"ans1\" => true, \"ans2\" => false, \"ans3\" => true, ...)` -- one or more is `true`\n* `TF`  true false: answer is either `true` or `false`\n* `ESS` Essay: no answer given\n* `ORD` Ordered list.  Answer  is a tuple of options, e.g.: `(\"option 1\", \"option 2\", ...)`\n* `MAT` Matching. Answer is a tuple of answer-match pairs `(\"answer1\"=>\"match1\", \"answer2\"=>\"match2\")`\n* `FIB` fill in blank: question uses `____` for blank;  answer is a tuple of possible answers, e.g. `(\"ans1\", \"ans2\", ...)`.\n* `FIB_PLUS`  fill in blank plus. Question has variables marked as [var1], .... Answer is collection of pairs of the form `\"var1\" => (\"possible\", \"exact\", \"matches\")`\n* `FIL`  File upload: no answer\n* `NUM`: numeric answer. Answer specified as `value, [tolerance]`\n* `SR` short reponse \"answer\" is a prompt\n* `OP` Likert scale. No answer\n* `JUMBLED_SENTENCE`: Question marks blanks with [var1], [var2] (variable names enclosed in []); answer is a collection of pairs of the form `\"var\"=>\"choice\"` with a special pair possible of the type `nothing=>(\"distractor 1\", \"distractor 2\", ...)` for 1 or more distractors\n\n\"\"\"\nfunction question(io::IO, qt::Symbol, q, answers...)\n\n    qq = create_html(q)\n    print(io, string(qt))\n    print(io, \"\\t\")\n    print(io, qq)\n    _write_answer(io, Val(qt), answers...)\n    println(io)\n    \nend\n\n## helpers\n## XXX Deprecate these\n@deprecate lquestion(io::IO, qt::Symbol, q, answers...)  question(io, qt, LaTeX(q), answers...)\n@deprecate mdquestion(io::IO, qt::Symbol, q, answers...) question(io, qt, LaTeX\u2032(q), answers...)\n\n\n\n\n## https://help.blackboard.com/Learn/Instructor/Tests_Pools_Surveys/Reuse_Questions/Upload_Questions\n## upload question types\n## https://help.blackboard.com/Learn/Instructor/Tests_Pools_Surveys/Question_Types\n\nconst MC = :MC   # multiple choice: (\"ans1\" => true, \"ans2\" => false, ...)\nconst MA = :MA   # multiple answer: (\"ans1\" => true, \"ans2\" => false, \"ans3\" => true, ...)\nconst TF = :TF   # true false: answer::Bool\nconst ESS = :ESS # Essay: no answer given\nconst ORD = :ORD # answer = answers, in order, in container, `(\"one\", \"two\", \"three\")`\nconst MAT = :MAT # answer-match pairs, e.g. `(\"answer\"=>\"match\", \"answer\"=>\"match\"...)`\nconst FIB = :FIB # fill in blank: user answer \u2208 (\"ans1\", \"ans2\", ...)\nconst FIB_PLUS = :FIB_PLUS # variable list answers: variable, answer as tuples\nconst FIL = :FIL # File upload: no answer\nconst SR = :SR   # short reponse: answer = short_response\nconst OP = :OP   # opinion\nconst NUM = :NUM # numeric: answer, [tolerance]\nconst JUMBLED_SENTENCE = :JUMBLED_SENTENCE # choices, variables as tuples\nconst QUIZ_BOWL = :QUIZ_BOWL # question_words, phrases as tuple\n\n\n# answers = (\"ans1\"=>true, \"ans2\"=>false, ...)\nfunction _write_answer(io::IO, ::Val{:MC}, answers)\n    for (k,v) in answers\n        print(io, \"\\t\")\n        print(io, create_html(k, strip=true))\n        print(io, \"\\t\")\n        print(io, v ? \"correct\" : \"incorrect\")\n    end\nend\n\n# answers = (\"ans1\"=>true, \"ans2\"=>false, ...)\nfunction _write_answer(io::IO, ::Val{:MA}, answers)\n    for (k,v) in answers\n        print(io, \"\\t\")\n        print(io, create_html(k, strip=true))\n        print(io, \"\\t\")\n        print(io, v ? \"correct\" : \"incorrect\")\n    end\nend\n\n# answer [true | false]\nfunction _write_answer(io::IO, ::Val{:TF}, answer::Bool)\n    print(io, \"\\t\")        \n    print(io, string(answer))\nend\n\n# no answer\nfunction _write_answer(io::IO, ::Val{:ESS}, answers...)\n    # nothing\nend\n\n# answers = (ans1, ..., ansn)\nfunction _write_answer(io::IO, ::Val{:ORD}, answers)\n    print(io, \"\\t\")        \n    print(io, join(answers, \"\\t\"))\nend\n\n## Matching answer is answer-match pairs\nfunction _write_answer(io::IO, ::Val{:MAT}, answers)\n    for (a,m) in answers\n        print(io, \"\\t\")        \n        print(io, create_html(a, strip=false))\n        print(io, \"\\t\")\n        print(io, create_html(m,strip=false))\n    end\nend\n\n\n# question has ____ marking blank\n# answers = (ans1, ..., ansn)\nfunction _write_answer(io::IO, ::Val{:FIB}, answers)\n    print(io, \"\\t\")            \n    print(io, join(answers, \"\\t\"))\nend\n\n# quesstin has [var1], [var2], ...\n# answers = (\"var1\" => (\"possible\", \"exact\", matches\"), \"var2\"=> ...)\nfunction _write_answer(io::IO, ::Val{:FIB_PLUS}, answers)\n    n = length(answers)\n    for i in eachindex(answers)\n        (a, choices) = answers[i]\n        print(io, \"\\t\")        \n        print(io, a)\n        for choice in choices\n            print(io,\"\\t\")\n            print(io, choice)\n        end\n        i < n && print(io, \"\\t\")\n    end\n    out = String(take!(io))\n    out\nend\n\n\nfunction _write_answer(io::IO, ::Val{:FIL}, answers...)\n    # nothing\nend\n\n\nfunction _write_answer(io::IO, ::Val{:SR}, answer = \"A short response...\")\n    print(io, \"\\t\")        \n    print(io, create_html(answer, strip=true))\nend\n\nfunction _write_answer(io::IO, ::Val{:OP}, answers...)\n    # nothing\nend\n\n\n# answer, [tolerance]\nfunction _write_answer(io::IO, ::Val{:NUM},  answer, tolerance=nothing)\n    print(io, \"\\t\")        \n    print(io, answer)\n    if !isnothing(tolerance)\n        print(io, \"\\t\")\n        print(io, tolerance)\n    end\nend\n\n\n\n\n# (\"var\"=>\"choice\", \"var1\"=>\"choice\", nothing=>(distractors,))\n# no markup for \"choice\"\nfunction _write_answer(io::IO, ::Val{:JUMBLED_SENTENCE},  answers)\n    do_last = ()\n    for (var, choice) in answers\n        if var == nothing\n            do_last = choice\n            continue\n        end\n        print(io, \"\\t\")\n        print(io, choice)\n        print(io, \"\\t\")\n        print(io, var)\n        print(io, \"\\t\")\n    end\n    for choice in do_last\n        print(io, \"\\t\")\n        print(io, choice)\n        print(io, \"\\t\")\n    end\nend\n\n\n## Student gets answers, they provide question\n# function _write_answer(io::IO, ::Val{:QUIZ_BOWL}, answers)\n#     for (q,ph) in answers\n#         print(io, \"\\t\")\n        \n#         print(io, q)\n#     end\n#     print(io, \"\\t\") #??? \n#     for (q,ph) in answers\n#         print(io, \"\\t\")\n#         print(io, ph)\n#     end\n#     out = String(take!(io))\n#     out\n# end\n", "meta": {"hexsha": "3dd526747c87d016a23f5f2cb0c33c31d999c1e6", "size": 6539, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/questions.jl", "max_stars_repo_name": "mth229/JuliaBlackBoard.jl", "max_stars_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-10T04:33:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-10T04:33:36.000Z", "max_issues_repo_path": "src/questions.jl", "max_issues_repo_name": "mth229/JuliaBlackBoard.jl", "max_issues_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/questions.jl", "max_forks_repo_name": "mth229/JuliaBlackBoard.jl", "max_forks_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-10T04:30:39.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-10T04:30:39.000Z", "avg_line_length": 31.1380952381, "max_line_length": 274, "alphanum_fraction": 0.6022327573, "num_tokens": 1910, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3208213138121609, "lm_q2_score": 0.26588048473393133, "lm_q1q2_score": 0.08530012642935404}}
{"text": "## Exercise 4-2\n## Write a function called square that takes a parameter named t, which is a turtle. It should use the turtle to draw a square.\nusing ThinkJulia\n\nprintln(\"Ans: \")\n\nfunction square(turtle::Turtle, distance::Int = 100)\n    @svg begin\n        for i in 1:3\n            forward(turtle, distance)\n            turn(turtle, -90)\n        end\n        \n        forward(turtle, distance)\n    end\nend\n\nturtle = Turtle()\nsquare(turtle)\n\nprintln(\"End.\")\n", "meta": {"hexsha": "0f171eccd4cd88cb7df34d9d5b5c2f4d937f0607", "size": 455, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter4/ex2.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter4/ex2.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter4/ex2.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6818181818, "max_line_length": 127, "alphanum_fraction": 0.6285714286, "num_tokens": 116, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.18952109132967757, "lm_q1q2_score": 0.08443724370030364}}
{"text": "using  Literate\nusing  Plots\n\nfiles =  filter( f -> startswith(f, \"0\"), readdir(\"src\")) |> collect\n\nfor file in files\n    Literate.notebook(\"src/$file\", \"notebooks\",  execute=false)\nend\n", "meta": {"hexsha": "5b1755e1d91adaaf14f77182cac9bc826782cea8", "size": 186, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "generate_nb.jl", "max_stars_repo_name": "pnavaro/JuliaSMAI2021", "max_stars_repo_head_hexsha": "1ba72998ada9b8fee19f0547def441df0c51a119", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate_nb.jl", "max_issues_repo_name": "pnavaro/JuliaSMAI2021", "max_issues_repo_head_hexsha": "1ba72998ada9b8fee19f0547def441df0c51a119", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate_nb.jl", "max_forks_repo_name": "pnavaro/JuliaSMAI2021", "max_forks_repo_head_hexsha": "1ba72998ada9b8fee19f0547def441df0c51a119", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6666666667, "max_line_length": 68, "alphanum_fraction": 0.6720430108, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.171061193791558, "lm_q1q2_score": 0.08419429006644066}}
{"text": "module TestMockOptimizer\n\nusing Test\nimport MathOptInterface\nconst MOI = MathOptInterface\nconst MOIU = MOI.Utilities\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$(name)\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\n    return\nend\n\n# function test_optimizer_solve_no_result()\n#     optimizer = MOIU.MockOptimizer(MOIU.Model{Float64}())\n\n#     v1 = MOI.add_variable(optimizer)\n\n#     # Load fake solution\n#     MOI.set(optimizer, MOI.TerminationStatus(), MOI.INFEASIBLE)\n#     MOI.optimize!(optimizer)\n#     @test MOI.get(optimizer, MOI.TerminationStatus()) == MOI.INFEASIBLE\n\n#     MOI.set(optimizer, MOI.ConflictStatus(), MOI.CONFLICT_FOUND)\n#     MOI.compute_conflict!(optimizer)\n#     @test MOI.get(optimizer, MOI.ConflictStatus()) == MOI.CONFLICT_FOUND\n# end\n\nfunction test_optimizer_solve_with_result()\n    optimizer = MOIU.MockOptimizer(\n        MOIU.Model{Float64}(),\n        eval_objective_value = false,\n        eval_variable_constraint_dual = false,\n    )\n\n    v = MOI.add_variables(optimizer, 2)\n    c1 = MOI.add_constraint(optimizer, v[1], MOI.GreaterThan(1.0))\n    soc = MOI.add_constraint(\n        optimizer,\n        MOI.VectorOfVariables(v),\n        MOI.SecondOrderCone(2),\n    )\n    MOI.set(optimizer, MOI.ObjectiveFunction{MOI.VariableIndex}(), v[1])\n    MOI.set(optimizer, MOI.ResultCount(), 1)\n    @test_throws(\n        ErrorException(\"No mock primal is set for variable `$(v[1])`.\"),\n        MOI.get(optimizer, MOI.VariablePrimal(), v[1])\n    )\n    @test_throws(\n        MOI.InvalidIndex(MOI.VariableIndex(-1)),\n        MOI.get(optimizer, MOI.VariablePrimal(), MOI.VariableIndex(-1))\n    )\n    MOI.set(optimizer, MOI.ResultCount(), 0)\n    err = MOI.ResultIndexBoundsError(MOI.VariablePrimal(1), 0)\n    @test_throws err MOI.get(optimizer, MOI.VariablePrimal(), v[1])\n    err = MOI.ResultIndexBoundsError(MOI.ConstraintDual(1), 0)\n    @test_throws err MOI.get(optimizer, MOI.ConstraintDual(), c1)\n\n    # Load fake solution\n    # TODO: Provide a more compact API for this.\n    MOI.set(optimizer, MOI.TerminationStatus(), MOI.OPTIMAL)\n    MOI.set(optimizer, MOI.ObjectiveValue(), 1.0)\n    MOI.set(optimizer, MOI.DualObjectiveValue(2), 2.0)\n    MOI.set(optimizer, MOI.ResultCount(), 2)\n    MOI.set(optimizer, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)\n    MOI.set(optimizer, MOI.DualStatus(2), MOI.FEASIBLE_POINT)\n    MOI.set(optimizer, MOI.VariablePrimal(), v, [1.0, 2.0])\n    MOI.set(optimizer, MOI.VariablePrimal(), v[1], 3.0)\n    MOI.set(optimizer, MOI.ConstraintDual(2), c1, 5.9)\n    MOI.set(optimizer, MOI.ConstraintDual(2), soc, [1.0, 2.0])\n\n    MOI.optimize!(optimizer)\n    @test MOI.get(optimizer, MOI.TerminationStatus()) == MOI.OPTIMAL\n    @test MOI.get(optimizer, MOI.ResultCount()) == 2\n    @test MOI.get(optimizer, MOI.ObjectiveValue()) == 1.0\n    @test isnan(MOI.get(optimizer, MOI.ObjectiveValue(2)))\n    optimizer.eval_objective_value = true\n    @test MOI.get(optimizer, MOI.ObjectiveValue()) == 3.0\n    @test_throws(\n        ErrorException(\n            \"No mock primal is set for variable `$(v[1])` at result index `2`.\",\n        ),\n        MOI.get(optimizer, MOI.ObjectiveValue(2))\n    )\n    @test_throws(\n        ErrorException(\n            \"No mock dual is set for constraint `$c1` at result index `1`.\",\n        ),\n        MOI.get(optimizer, MOI.DualObjectiveValue())\n    )\n    @test MOI.get(optimizer, MOI.DualObjectiveValue(2)) == 5.9\n    @test MOI.get(optimizer, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    @test MOI.get(optimizer, MOI.DualStatus(2)) == MOI.FEASIBLE_POINT\n    @test MOI.get(optimizer, MOI.VariablePrimal(), v) == [3.0, 2.0]\n    @test MOI.get(optimizer, MOI.VariablePrimal(), v[1]) == 3.0\n    @test_throws(\n        ErrorException(\n            \"No mock primal is set for variable `$(v[1])` at result index `2`.\",\n        ),\n        MOI.get(optimizer, MOI.VariablePrimal(2), v[1])\n    )\n    @test MOI.get(optimizer, MOI.ConstraintPrimal(), c1) == 3.0\n    @test MOI.get(optimizer, MOI.ConstraintPrimal(), soc) == [3.0, 2.0]\n    @test_throws(\n        ErrorException(\n            \"No mock primal is set for variable `$(v[1])` at result index `2`.\",\n        ),\n        @show MOI.get(optimizer, MOI.ConstraintPrimal(2), c1)\n    )\n    @test_throws(\n        ErrorException(\n            \"No mock primal is set for variable `$(v[1])` at result index `2`.\",\n        ),\n        MOI.get(optimizer, MOI.ConstraintPrimal(2), soc)\n    )\n    @test MOI.get(optimizer, MOI.ConstraintDual(2), c1) == 5.9\n    @test MOI.get(optimizer, MOI.ConstraintDual(2), soc) == [1.0, 2.0]\n    @test_throws(\n        ErrorException(\n            \"No mock dual is set for constraint `$c1` at result index `1`.\",\n        ),\n        MOI.get(optimizer, MOI.ConstraintDual(1), c1)\n    )\nend\n\nfunction test_CanonicalConstraintFunction()\n    mock = MOIU.MockOptimizer(MOIU.Model{Int}())\n    fx, fy = MOI.add_variables(mock, 2)\n    cx = MOI.add_constraint(mock, fx, MOI.LessThan(0))\n    c = MOI.add_constraint(mock, 1fx + fy, MOI.LessThan(1))\n    @test MOIU.is_canonical(MOI.get(mock, MOI.ConstraintFunction(), cx))\n    @test MOIU.is_canonical(\n        MOI.get(mock, MOI.CanonicalConstraintFunction(), cx),\n    )\n    @test !MOIU.is_canonical(MOI.get(mock, MOI.ConstraintFunction(), c))\n    func = MOI.get(mock, MOI.CanonicalConstraintFunction(), c)\n    @test MOIU.is_canonical(func)\n    # Check that indices have been xored\n    for term in func.terms\n        @test MOI.is_valid(mock, term.variable)\n    end\nend\n\nfunction test_conflict_access()\n    mock = MOIU.MockOptimizer(MOIU.Model{Int}())\n    fx, fy = MOI.add_variables(mock, 2)\n    cx = MOI.add_constraint(mock, fx, MOI.LessThan(0))\n    c = MOI.add_constraint(mock, 1fx + fy, MOI.LessThan(1))\n    MOI.set(mock, MOI.ConstraintConflictStatus(), cx, MOI.NOT_IN_CONFLICT)\n    MOI.set(mock, MOI.ConstraintConflictStatus(), c, MOI.IN_CONFLICT)\n    MOI.compute_conflict!(mock)\n\n    @test MOI.get(mock, MOI.ConstraintConflictStatus(), cx) ==\n          MOI.NOT_IN_CONFLICT\n    @test MOI.get(mock, MOI.ConstraintConflictStatus(), c) == MOI.IN_CONFLICT\nend\n\nfunction test_MockVariableAttribute()\n    mock = MOIU.MockOptimizer(MOIU.Model{Int}())\n    x = MOI.add_variable(mock)\n    MOI.set(mock, MOI.Utilities.MockVariableAttribute(), x, 1)\n    @test MOI.get(mock, MOI.Utilities.MockVariableAttribute(), x) == 1\n    return\nend\n\nfunction test_MockConstraintAttribute()\n    mock = MOIU.MockOptimizer(MOIU.Model{Int}())\n    x = MOI.add_variable(mock)\n    c = MOI.add_constraint(mock, x, MOI.LessThan(0))\n    MOI.set(mock, MOI.Utilities.MockConstraintAttribute(), c, 1)\n    @test MOI.get(mock, MOI.Utilities.MockConstraintAttribute(), c) == 1\n    return\nend\n\nfunction test_DualObjectiveValue()\n    mock =\n        MOIU.MockOptimizer(MOIU.Model{Int}(); eval_dual_objective_value = false)\n    @test isnan(MOI.get(mock, MOI.DualObjectiveValue()))\n    return\nend\n\nend  # module\n\nTestMockOptimizer.runtests()\n", "meta": {"hexsha": "961af840f6e70ef1f77fd2230e9c66319090a2d4", "size": 6980, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Utilities/mockoptimizer.jl", "max_stars_repo_name": "goulart-paul/MathOptInterface.jl", "max_stars_repo_head_hexsha": "f96f7cb7947af4546d0f1aa1d18c482024aaf9fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-29T16:27:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-29T16:27:46.000Z", "max_issues_repo_path": "test/Utilities/mockoptimizer.jl", "max_issues_repo_name": "goulart-paul/MathOptInterface.jl", "max_issues_repo_head_hexsha": "f96f7cb7947af4546d0f1aa1d18c482024aaf9fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-02-24T21:11:26.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-24T21:11:26.000Z", "max_forks_repo_path": "test/Utilities/mockoptimizer.jl", "max_forks_repo_name": "guilhermebodin/MathOptInterface.jl", "max_forks_repo_head_hexsha": "6b50fa0f9e31ef97a06facaad60af7d0b6ae3d2a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7368421053, "max_line_length": 80, "alphanum_fraction": 0.6585959885, "num_tokens": 2053, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.17106119167858436, "lm_q1q2_score": 0.0841942890264601}}
{"text": "import Pkg\n\nPkg.add(Pkg.PackageSpec(url=\"https://github.com/JuliaComputing/JuliaAcademyData.jl\"))\nPkg.add(\"Weave\")\nPkg.add(\"IJulia\")\nPkg.add(\"Images\")\nPkg.add(\"ImageMagick\")\nPkg.add(\"ImageIO\")\nPkg.add(\"Interact\")\nPkg.add(\"Statistics\")\nPkg.add(\"Plots\")\nPkg.add(\"GR\")\nPkg.add(\"Plotly\")\nPkg.add(\"Glob\")\nPkg.add(\"WebIO\")\nPkg.add(\"Conda\")\nPkg.add(\"PyCall\")\nPkg.add(\"PyPlot\")\nPkg.add(\"Revise\")\n\n# Force PyCall to use Conda.jl\n#ENV[\"PYTHON\"] = \"\"\n#Pkg.build(\"PyCall\")\n\n# Interact\n#import WebIO\n#WebIO.install_jupyter_nbextension()\n\n", "meta": {"hexsha": "bd85ba076f590b5ca1d448c364f3fb85bb3cb7c6", "size": 525, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JuliaAcademy/FundMachLearn/package.jl", "max_stars_repo_name": "ykyang/org.allnix.julia", "max_stars_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "JuliaAcademy/FundMachLearn/package.jl", "max_issues_repo_name": "ykyang/org.allnix.julia", "max_issues_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "JuliaAcademy/FundMachLearn/package.jl", "max_forks_repo_name": "ykyang/org.allnix.julia", "max_forks_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.1034482759, "max_line_length": 85, "alphanum_fraction": 0.7028571429, "num_tokens": 186, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.32766830082071396, "lm_q2_score": 0.256831980010821, "lm_q1q2_score": 0.0841556984865653}}
{"text": "\nusing HDF5, JLD, Test, ProgressMeter, Pkg\n\ninclude(\"pkgversions.jl\");\n\npkgsVersion()\n\narr1 = rand(1000,1000)\narr2 = rand(10,25)\n\nh5write(\"testfile.h5\", \"arr1\", arr1) \n# @write \"testfile.h5\" arr1 \n# h5write(\"testfile.h5\", \"arr1\", arr1, \"arr2\", arr2) for several datasets at the same time \n# the first option is more specific (you can tell the name of the dataset to be saved), so I liked more. \n\narr_load = h5read(\"testfile.h5\", \"arr1\")\n\nfarr = h5open(\"testfile.h5\", \"r\")\n\ndump(farr)\n\nnames(farr)\n\nreadmmap(farr[\"arr1\"])\n\nclose(farr)\n\n@time arr_load = h5read(\"testfile.h5\", \"arr1\");\n\n@time data = h5read(\"testfile.h5\", \"arr1\", (2:15, 2:15))\n\nh5write(\"testfile.h5\", \"arr2\", arr2) \n\nfarr2 = h5open(\"testfile.h5\", \"r\")\n\nnames(farr2)\n\nread(farr2[\"arr2\"])\n\nclose(farr2)\n\nfunction szprint(szfile)\n    if szfile <= 10^6\n        println(\"File size = $(szfile/10^3) KB\")\n    elseif 10^6 < szfile <= 10^9\n        println(\"File size = $(szfile/10^6) MB\")\n    elseif 10^9 < szfile <= 10^12\n        println(\"File size = $(szfile/10^9) GB\")\n    elseif 10^12 < szfile <= 10^15\n        println(\"File size = $(szfile/10^12) TB\")\n    else\n        println(\"File size is really big. Petabytes or more!\")\n    end\nend\n\nszprint(700001000)\n\nf = h5open(\"incrementalFilev1.h5\", \"w\")\ndset_A = d_create(f,\"arr1\",datatype(Float64), dataspace(10^6,1))\nfor i in 1:10^4:10^6\n    dset_A[i:i + 10^4 - 1,1] = rand(10^4)\n    szfile = filesize(\"incrementalFilev1.h5\")\n    szprint(szfile)\n    sleep(.1)\n    IJulia.clear_output(true)\nend\nflush(f)\nclose(f)\n\nfunction szprintp(szfile)\n    if szfile <= 10^6\n        return (szfile/10^3, \"KB\") \n    elseif 10^6 < szfile <= 10^9\n        return (szfile/10^6, \"MB\")\n    elseif 10^9 < szfile <= 10^12\n        return (szfile/10^9, \"GB\")\n    elseif 10^12 < szfile <= 10^15\n        return(szfile/10^12, \"TB\")\n    else\n        return (\"=> Petabytes\")\n    end\nend\n\nszprintp(1000)\n\nh5open(\"incrementalFilev2.h5\", \"w\") do file\n    dset = d_create(file, \"arr1k\", datatype(Float64), dataspace(10^6,1))\n    p = Progress(100, dt=0.1, desc=\"Saving...\", color=:blue)\n    for iter in 1:10^4:10^6\n        dset[iter:iter + 10^4 - 1,1] = rand(10^4)\n        szfile = filesize(\"incrementalFilev2.h5\")        \n        ProgressMeter.next!(p; showvalues = [(:Filesize, szprintp(szfile))])\n        sleep(0.1)\n        IJulia.clear_output(true)\n    end\n    end;\n\nf1 = h5open(\"incrementalFilev1.h5\",\"r\")\n\nnames(f1)\n\nA_mmaped = readmmap(f1[\"arr1\"])\n\n#rand(100,100)\n\nfm = h5open(\"MatrixFile.h5\", \"w\")\ndset_fm = d_create(fm,\"arraym\",datatype(Float64), dataspace(100,100,100)) #, \"chunk\", (100,100,1))\np = Progress(100, dt=0.1, desc=\"Saving...\", color=:blue)\nfor i in 1:100\n    dset_fm[:, :, i] = rand(100,100)\n    szfilem = filesize(\"MatrixFile.h5\")\n    ProgressMeter.next!(p; showvalues = [(:File_Size, szprintp(szfilem))])\n    sleep(0.1)\n    IJulia.clear_output(true)\nend\nflush(fm)\nclose(fm)\n\nfmatrix = h5open(\"MatrixFile.h5\", \"r\")\n\nnames(fmatrix)\n\nclose(fmatrix)\n\nh5read(\"MatrixFile.h5\", \"arraym\", (:, :, 1))\n\nsave(\"testfile.jld\", \"arr1\", arr1)\n#@save \"testfile.jld\" arr1\n# again, I prefer the first option, just for control. \n\nopenfile = jldopen(\"testfile.jld\",\"r\", mmaparrays=true)\n#load(\"testfile.jld\", \"arr1\")\n#@load \"testfile.jld\" arr1\n# eval(:arr1)\n# these last options are also valid, but for big files the first option is better. \n\n@time read(openfile[\"arr1\"])[1:15, 2:5]\n\n@load \"testfile.jld\"\n\nnames(openfile)\n\nclose(openfile)\n", "meta": {"hexsha": "92e879e308fa683859bf1d36ea8c106294cbfdf2", "size": 3413, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "HDF5savingFiles.jl", "max_stars_repo_name": "lazarusA/helpfulJulia", "max_stars_repo_head_hexsha": "7f0165db0de6515e0a07549e418620b7deafcb51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-20T02:16:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-20T02:16:53.000Z", "max_issues_repo_path": "HDF5savingFiles.jl", "max_issues_repo_name": "lazarusA/helpfulJulia", "max_issues_repo_head_hexsha": "7f0165db0de6515e0a07549e418620b7deafcb51", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "HDF5savingFiles.jl", "max_forks_repo_name": "lazarusA/helpfulJulia", "max_forks_repo_head_hexsha": "7f0165db0de6515e0a07549e418620b7deafcb51", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.7013888889, "max_line_length": 105, "alphanum_fraction": 0.6325813068, "num_tokens": 1230, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.16885695841556156, "lm_q1q2_score": 0.08376889513318773}}
{"text": "# Copyright (c) 2019 Arpit Bhatia and contributors                               #src\n#                                                                                #src\n# Permission is hereby granted, free of charge, to any person obtaining a copy   #src\n# of this software and associated documentation files (the \"Software\"), to deal  #src\n# in the Software without restriction, including without limitation the rights   #src\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #src\n# copies of the Software, and to permit persons to whom the Software is          #src\n# furnished to do so, subject to the following conditions:                       #src\n#                                                                                #src\n# The above copyright notice and this permission notice shall be included in all #src\n# copies or substantial portions of the Software.                                #src\n#                                                                                #src\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #src\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #src\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #src\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #src\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #src\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #src\n# SOFTWARE.                                                                      #src\n\n# # Getting started with JuMP\n\n# This tutorial is aimed at providing a quick introduction to writing JuMP code.\n\n# ## What is JuMP?\n\n# JuMP (\"Julia for Mathematical Programming\") is an open-source modeling\n# language that is embedded in Julia. It allows users to formulate various\n# classes of optimization problems (linear, mixed-integer, quadratic, conic\n# quadratic, semidefinite, and nonlinear) with easy-to-read code. These problems\n# can then be solved using state-of-the-art open-source and commercial solvers.\n\n# JuMP also makes advanced optimization techniques easily accessible from a\n# high-level language.\n\n# ## Installation\n\n# JuMP is a package for Julia. From Julia, JuMP is installed by using the\n# built-in package manager.\n\n# ```julia\n# import Pkg\n# Pkg.add(\"JuMP\")\n# ```\n\n# You also need to include a Julia package which provides an appropriate solver.\n# One such solver is `GLPK.Optimizer`, which is provided by the\n# [GLPK.jl package](https://github.com/jump-dev/GLPK.jl).\n# ```julia\n# import Pkg\n# Pkg.add(\"GLPK\")\n# ```\n# See [Installation Guide](@ref) for a list of other solvers you can use.\n\n# ## An example\n\n# Let's try to solve the following linear programming problem by using JuMP and\n# GLPK. We will first look at the complete code to solve the problem and then go\n# through it step by step.\n\n# ```math\n# \\begin{aligned}\n# & \\min & 12x + 20y \\\\\n# & \\;\\;\\text{s.t.} & 6x + 8y \\geq 100 \\\\\n# & & 7x + 12y \\geq 120 \\\\\n# & & x \\geq 0 \\\\\n# & & y \\in [0, 3] \\\\\n# \\end{aligned}\n# ```\n\nusing JuMP\nusing GLPK\nmodel = Model(GLPK.Optimizer)\n@variable(model, x >= 0)\n@variable(model, 0 <= y <= 3)\n@objective(model, Min, 12x + 20y)\n@constraint(model, c1, 6x + 8y >= 100)\n@constraint(model, c2, 7x + 12y >= 120)\nprint(model)\noptimize!(model)\n@show termination_status(model)\n@show primal_status(model)\n@show dual_status(model)\n@show objective_value(model)\n@show value(x)\n@show value(y)\n@show shadow_price(c1)\n@show shadow_price(c2)\n\n# ## Step-by-step\n\n# Once JuMP is installed, to use JuMP in your programs, we just need to write:\n\nusing JuMP\n\n# We also need to include a Julia package which provides an appropriate solver.\n# We want to use `GLPK.Optimizer` here which is provided by the `GLPK.jl`\n# package.\n\nusing GLPK\n\n# A model object is a container for variables, constraints, solver options, etc.\n# Models are created with the [`Model`](@ref) function. The model can be created\n# with an optimizer attached with default arguments by calling the constructor\n# with the optimizer type, as follows:\n\nmodel = Model(GLPK.Optimizer)\n\n# Variables are modeled using [`@variable`](@ref):\n\n@variable(model, x >= 0)\n\n# They can have lower and upper bounds.\n\n@variable(model, 0 <= y <= 30)\n\n# The objective is set using [`@objective`](@ref):\n\n@objective(model, Min, 12x + 20y)\n\n# Constraints are modeled using [`@constraint`](@ref). Here `c1` and `c2` are\n# the names of our constraint.\n\n@constraint(model, c1, 6x + 8y >= 100)\n\n#-\n\n@constraint(model, c2, 7x + 12y >= 120)\n\n#- Call `print` to display the model:\n\nprint(model)\n\n# To solve the optimization problem, call the `optimize!` function.\n\noptimize!(model)\n\n# !!! info\n#     The `!` after optimize is just part of the name. It's nothing special.\n#     Julia has a convention that functions which mutate their arguments should\n#     end in `!`. A common example is `push!`.\n\n# Now let's see what information we can query about the solution.\n\n# [`termination_status`](@ref) tells us why the solver stopped:\n\ntermination_status(model)\n\n# In this case, the solver found an optimal solution. We should also check\n# [`primal_status`](@ref) to see if the solver found a primal feasible point:\n\nprimal_status(model)\n\n# and [`dual_status`](@ref) to see if the solver found a dual feasible point:\n\ndual_status(model)\n\n# Now we know that our solver found an optimal solution, and has a primal and a\n# dual solution to query.\n\n# Query the objective value using [`objective_value`](@ref):\n\nobjective_value(model)\n\n# The primal solution using [`value`](@ref):\n\nvalue(x)\n\n#-\n\nvalue(y)\n\n# and the dual solution using [`shadow_price`](@ref):\n\nshadow_price(c1)\n\n#-\n\nshadow_price(c2)\n\n# ## Variable basics\n\nmodel = Model()\n\n\n# ### Variable bounds\n\n# All of the variables we have created till now have had a bound. We can also\n# create a free variable.\n\n@variable(model, free_x)\n\n# While creating a variable, instead of using the <= and >= syntax, we can also\n# use the `lower_bound` and `upper_bound` keyword arguments.\n\n@variable(model, keyword_x, lower_bound = 1, upper_bound = 2)\n\n# We can query whether a variable has a bound using the `has_lower_bound` and\n# `has_upper_bound` functions. The values of the bound can be obtained using the\n# `lower_bound` and `upper_bound` functions.\n\nhas_upper_bound(keyword_x)\n\n#-\n\nupper_bound(keyword_x)\n\n# Note querying the value of a bound that does not exist will result in an error.\n\nlower_bound(free_x)\n\n# JuMP also allows us to change the bounds on variable. We will learn this in\n# the problem modification tutorial.\n\n# ### [Containers](@id tutorial_variable_container)\n\n# We have already seen how to add a single variable to a model using the\n# [`@variable`](@ref) macro. Let's now look at more ways to add variables to a\n# JuMP model.\n\n# JuMP provides data structures for adding collections of variables to a model.\n# These data structures are referred to as Containers and are of three types:\n# `Arrays`, `DenseAxisArrays`, and `SparseAxisArrays`.\n\n# #### Arrays\n\n# JuMP arrays are created in a similar syntax to Julia arrays with the addition\n# of specifying that the indices start with 1. If we do not tell JuMP that the\n# indices start at 1, it will create a `DenseAxisArray` instead.\n\n@variable(model, a[1:2, 1:2])\n\n# An n-dimensional variable $x \\in {R}^n$ having a bound $l \\preceq x \\preceq u$\n# ($l, u \\in {R}^n$) is added in the following manner.\n\nn = 10\nl = [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]\nu = [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]\n\n@variable(model, l[i] <= x[i = 1:n] <= u[i])\n\n# Note that while working with Containers, we can also create variable bounds\n# depending upon the indices:\n\n@variable(model, y[i = 1:2, j = 1:2] >= 2i + j)\n\n# #### DenseAxisArrays\n\n# `DenseAxisArrays` are used when the required indices are not one-based integer\n# ranges. The syntax is similar except with an arbitrary vector as an index as\n# opposed to a one-based range.\n\n# An example where the indices are integers but do not start with one.\n\n@variable(model, z[i = 2:3, j = 1:2:3] >= 0)\n\n# Another example where the indices are an arbitrary vector.\n\n@variable(model, w[1:5, [\"red\", \"blue\"]] <= 1)\n\n# #### SparseAxisArrays\n\n# `SparseAxisArrays` are created when the indices do not form a rectangular set.\n# For example, this applies when indices have a dependence upon previous indices\n# (called triangular indexing).\n\n@variable(model, u[i = 1:3, j = i:5])\n\n# We can also conditionally create variables by adding a comparison check that\n# depends upon the named indices and is separated from the indices by a\n# semi-colon (;).\n\n@variable(model, v[i = 1:9; mod(i, 3) == 0])\n\n# ### Variable types\n\n# The last argument to the `@variable` macro is usually the variable type. Here\n# we'll look at how to specify the variable type.\n\n# #### Integer variables\n\n# Integer optimization variables are constrained to the set $x \\in {Z}$\n\n@variable(model, integer_x, Int)\n\n# or\n\n@variable(model, integer_z, integer = true)\n\n# #### Binary variables\n\n# Binary optimization variables are constrained to the set $x \\in \\{0, 1\\}$.\n\n@variable(model, binary_x, Bin)\n\n# or\n\n@variable(model, binary_z, binary = true)\n\n# ## Constraint basics\n\nmodel = Model()\n@variable(model, x)\n@variable(model, y)\n@variable(model, z[1:10]);\n\n# ### Constraint references\n\n# While calling the `@constraint` macro, we can also set up a constraint\n# reference. Such a reference is useful for obtaining additional information\n# about the constraint, such as its dual solution.\n\n@constraint(model, con, x <= 4)\n\n# ### [Containers](@id tutorial_constraint_container)\n\n# Just as we had containers for variables, JuMP also provides `Arrays`,\n# `DenseAxisArrays`, and `SparseAxisArrays` for storing collections of\n# constraints. Examples for each container type are given below.\n\n# #### Arrays\n\n@constraint(model, [i = 1:3], i * x <= i + 1)\n\n# #### DenseAxisArrays\n\n@constraint(model, [i = 1:2, j = 2:3], i * x <= j + 1)\n\n# #### SparseAxisArrays\n\n@constraint(model, [i = 1:2, j = 1:2; i != j], i * x <= j + 1)\n\n# ### Constraints in a loop\n\n# We can add constraints using regular Julia loops\n\nfor i in 1:3\n    @constraint(model, 6x + 4y >= 5i)\nend\n\n# or use for each loops inside the `@constraint` macro.\n\n@constraint(model, [i in 1:3], 6x + 4y >= 5i)\n\n# We can also create constraints such as $\\sum _{i = 1}^{10} z_i \\leq 1$\n\n@constraint(model, sum(z[i] for i in 1:10) <= 1)\n\n# ## Objective functions\n\n# While the recommended way to set the objective is with the [`@objective`](@ref)\n# macro, the functions [`set_objective_sense`](@ref) and [`set_objective_function`](@ref)\n# provide an equivalent lower-level interface.\n\nusing GLPK\n\nmodel = Model(GLPK.Optimizer)\n@variable(model, x >= 0)\n@variable(model, y >= 0)\nset_objective_sense(model, MOI.MIN_SENSE)\nset_objective_function(model, x + y)\n\noptimize!(model)\n\n#-\n\nobjective_value(model)\n\n# To query the objective function from a model, we use the [`objective_sense`](@ref),\n# [`objective_function`](@ref), and [`objective_function_type`](@ref) functions.\n\nobjective_sense(model)\n\n#-\n\nobjective_function(model)\n\n#-\n\nobjective_function_type(model)\n\n# ## Vectorized syntax\n\n# We can also add constraints and an objective to JuMP using vectorized linear\n# algebra. We'll illustrate this by solving an LP in standard form i.e.\n\n# ```math\n# \\begin{aligned}\n# & \\min & c^T x \\\\\n# & \\;\\;\\text{s.t.} & A x = b \\\\\n# & & x \\succeq 0 \\\\\n# & & x \\in \\mathbb{R}^n\n# \\end{aligned}\n# ```\n\nvector_model = Model(GLPK.Optimizer)\n\nA = [\n    1 1 9 5\n    3 5 0 8\n    2 0 6 13\n]\n\nb = [7; 3; 5]\n\nc = [1; 3; 5; 2]\n\n@variable(vector_model, x[1:4] >= 0)\n@constraint(vector_model, A * x .== b)\n@objective(vector_model, Min, c' * x)\n\noptimize!(vector_model)\n\n#-\n\nobjective_value(vector_model)\n\n", "meta": {"hexsha": "64ed4a2fb44726d901bf724b73a3b2de9ce88440", "size": 11793, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/getting_started_with_JuMP.jl", "max_stars_repo_name": "odow/jump-training-materials", "max_stars_repo_head_hexsha": "0ed2164c7625ce66b59881f1ea6c5b491daae013", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2021-08-31T05:32:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-30T08:33:50.000Z", "max_issues_repo_path": "src/getting_started_with_JuMP.jl", "max_issues_repo_name": "odow/jump-training-materials", "max_issues_repo_head_hexsha": "0ed2164c7625ce66b59881f1ea6c5b491daae013", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/getting_started_with_JuMP.jl", "max_forks_repo_name": "odow/jump-training-materials", "max_forks_repo_head_hexsha": "0ed2164c7625ce66b59881f1ea6c5b491daae013", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-08T15:45:59.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T05:37:06.000Z", "avg_line_length": 28.2129186603, "max_line_length": 89, "alphanum_fraction": 0.6809972017, "num_tokens": 3145, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.18010666848732088, "lm_q1q2_score": 0.08373187319458142}}
{"text": "using Pkg\r\n\r\nPkg.add(\"NPZ\")\r\nPkg.add(\"Profile\")\r\nPkg.add(\"BenchmarkTools\")\r\nPkg.add(\"Interpolations\")\r\nPkg.add(\"LinearAlgebra\")\r\nPkg.add(\"Distributed\")\r\nPkg.add(\"Statistics\")\r\nPkg.add(\"DifferentialEquations\")\r\nPkg.add(\"OrdinaryDiffEq\")\r\nPkg.add(\"Plots\")\r\n", "meta": {"hexsha": "f6e7c962f100ffd77a6eef51092a769e7648610b", "size": 255, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "quick_install.jl", "max_stars_repo_name": "dtoconnor/Annealing-Samplers", "max_stars_repo_head_hexsha": "65f85cc911b4cdfa0ae7b00924143a5e017f00dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "quick_install.jl", "max_issues_repo_name": "dtoconnor/Annealing-Samplers", "max_issues_repo_head_hexsha": "65f85cc911b4cdfa0ae7b00924143a5e017f00dc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "quick_install.jl", "max_forks_repo_name": "dtoconnor/Annealing-Samplers", "max_forks_repo_head_hexsha": "65f85cc911b4cdfa0ae7b00924143a5e017f00dc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6153846154, "max_line_length": 33, "alphanum_fraction": 0.7058823529, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4532618480153862, "lm_q2_score": 0.18242551936144571, "lm_q1q2_score": 0.0826865280309355}}
{"text": "#######################\n###### Variables ######\n#######################\n\n# Variable names must begin with a letter (A-Z or a-z), \n# underscore, or a subset of Unicode code points greater than 00A0\n\n# Assign the value 10 to the variable x\nx = 10\n# println(x)\n\n# Doing math with x's value\nx = x + 1\n# println(x)\n\n# Reassign x's value\nx = 1 + 1\n# println(x)\n\n# You can assign values of other types, like strings of text\nx = \"Hello World!\"\n# println(x)\n\n# Variable names are case-sensitive, and have no semantic meaning\n\u03b4 = 0.00001\n# println(\u03b4)\n\n\uc548\ub155\ud558\uc138\uc694 = \"Hello\"\n# println(\uc548\ub155\ud558\uc138\uc694)\n\n# You can type many Unicode math symbols by typing the backslashed LaTeX symbol name followed by tab.\n# For example, the variable name \u03b4 can be entered by typing \\delta-tab, \n# or even \u03b1\u0302\u207d\u00b2\u207e by \\alpha-tab-\\hat- tab-\\^(2)-tab.\n\n###################################\n###### Stylistic Conventions ######\n###################################\n\n# 1) Names of variables are in lower case.\n# 2) Word separation can be indicated by underscores ('_'), \n#    but use of underscores is discouraged unless the name would be hard to read otherwise.\n# 3) Names of Types and Modules begin with a capital letter and word separation is shown \n#    with upper camel case instead of underscores.\n# 4) Names of functions and macros are in lower case, without underscores.\n# 5) Functions that write to their arguments have names that end in !. \n#    These are sometimes called \"mutating\" or \"in-place\" functions because they are intended \n#    to produce changes in their arguments after the function is called, not just return a value.\n\n", "meta": {"hexsha": "6e6b64cc24606de42e1f114f9b996b296315f532", "size": 1589, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "basics/variables.jl", "max_stars_repo_name": "mauricioriva/learn-julia", "max_stars_repo_head_hexsha": "f4ff81ff412ba4f1243a8cee1123ebc8b3335f01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "basics/variables.jl", "max_issues_repo_name": "mauricioriva/learn-julia", "max_issues_repo_head_hexsha": "f4ff81ff412ba4f1243a8cee1123ebc8b3335f01", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "basics/variables.jl", "max_forks_repo_name": "mauricioriva/learn-julia", "max_forks_repo_head_hexsha": "f4ff81ff412ba4f1243a8cee1123ebc8b3335f01", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4285714286, "max_line_length": 101, "alphanum_fraction": 0.6601636249, "num_tokens": 395, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.1732882037945951, "lm_q1q2_score": 0.08258563169787932}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Introduction to DataFrames\n# **[Bogumi\u0142 Kami\u0144ski](http://bogumilkaminski.pl/about/), May 13, 2018**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames\n\n# ------------------------------------------------------------------------------------------\n# ## Extras - selected functionalities of selected packages\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### FreqTables: creating cross tabulations\n# ------------------------------------------------------------------------------------------\n\nusing FreqTables\ndf = DataFrame(a=rand('a':'d', 1000), b=rand([\"x\", \"y\", \"z\"], 1000))\nft = freqtable(df, :a, :b) # observe that dimensions are sorted if possible\n\nft[1,1], ft['b', \"z\"] # you can index the result using numbers or names\n\nprop(ft, 1) # getting proportions - 1 means we want to calculate them in rows (first dimension)\n\nprop(ft, 2) # and columns are normalized to 1.0 now\n\nx = categorical(rand(1:3, 10))\nlevels!(x, [3, 1, 2, 4]) # reordering levels and adding an extra level\nfreqtable(x) # order is preserved and not-used level is shown\n\nfreqtable([1,1,2,3,missing]) # by default missings are listed\n\nfreqtable([1,1,2,3,missing], skipmissing=true) # but we can skip them\n\n# ------------------------------------------------------------------------------------------\n# ### DataFramesMeta - working on `DataFrame`\n# ------------------------------------------------------------------------------------------\n\nusing DataFramesMeta\ndf = DataFrame(x=1:8, y='a':'h', z=repeat([true,false], outer=4))\n\n@with(df, :x+:z) # expressions with columns of DataFrame\n\n@with df begin # you can define code blocks\n    a = :x[:z]\n    b = :x[.!:z]\n    :y + [a; b]\nend\n\na # @with creates hard scope so variables do not leak out\n\ndf2 = DataFrame(a = [:a, :b, :c])\n@with(df2, :a .== ^(:a)) # sometimes we want to work on raw Symbol, ^() escapes it\n\ndf2 = DataFrame(x=1:3, y=4:6, z=7:9)\n@with(df2, _I_(2:3)) # _I_(expression) is translated to df2[expression]\n\n@where(df, :x .< 4, :z .== true) # very useful macro for filtering\n\n@select(df, :x, y = 2*:x, z=:y) # create a new DataFrame based on the old one\n\n@transform(df, a=1, x = 2*:x, y=:x) # create a new DataFrame adding columns based on the old one\n\n@transform(df, a=1, b=:a) # old DataFrame is used and :a is not present there\n\n@orderby(df, :z, -:x) # sorting into a new data frame, less powerful than sort, but lightweight\n\n@linq df |> # chaining of operations on DataFrame\n    where(:x .< 5) |>\n    orderby(:z) |>\n    transform(x\u00b2=:x.^2) |>\n    select(:z, :x, :x\u00b2)\n\nf(df, col) = df[col] # you can define your own functions and put them in the chain\n@linq df |> where(:x .<= 4) |> f(:x)\n\n# ------------------------------------------------------------------------------------------\n# ### DataFramesMeta - working on grouped `DataFrame`\n# ------------------------------------------------------------------------------------------\n\ndf = DataFrame(a = 1:12, b = repeat('a':'d', outer=3))\ng = groupby(df, :b)\n\n@by(df, :b, first=first(:a), last=last(:a), mean=mean(:a)) # more convinient than by from DataFrames\n\n@based_on(g, first=first(:a), last=last(:a), mean=mean(:a)) # the same as by but on grouped DataFrame\n\n@where(g, mean(:a) > 6.5) # filter gropus on aggregate conditions\n\n@orderby(g, -sum(:a)) # order groups on aggregate conditions\n\n@transform(g, center = mean(:a), centered = :a - mean(:a)) # perform operations within a group and return ungroped DataFrame\n\nDataFrame(g) # a nice convinience function not defined in DataFrames\n\n@transform(g) # actually this is the same\n\n@linq df |> groupby(:b) |> where(mean(:a) > 6.5) |> DataFrame # you can do chaining on grouped DataFrames as well\n\n# ------------------------------------------------------------------------------------------\n# ### DataFramesMeta - rowwise operations on `DataFrame`\n# ------------------------------------------------------------------------------------------\n\ndf = DataFrame(a = 1:12, b = repeat(1:4, outer=3))\n\n# such conditions are often needed but are complex to write\n@transform(df, x = ifelse.((:a .> 6) .& (:b .== 4), \"yes\", \"no\"))\n\n# one option is to use a function that works on a single observation and broadcast it\nmyfun(a, b) = a > 6 && b == 4 ? \"yes\" : \"no\"\n@transform(df, x = myfun.(:a, :b))\n\n# or you can use @byrow! macro that allows you to process DataFrame rowwise\n@byrow! df begin\n    @newcol x::Vector{String}\n    :x = :a > 6 && :b == 4 ? \"yes\" : \"no\"\nend\n\n# ------------------------------------------------------------------------------------------\n# ### Visualizing data with StatPlots\n# ------------------------------------------------------------------------------------------\n\nusing StatPlots # you might need to setup Plots package and some plotting backend first\n\n# we present only a minimal functionality of the package\n\nsrand(1)\ndf = DataFrame(x = sort(randn(1000)), y=randn(1000), z = [fill(\"b\", 500); fill(\"a\", 500)])\n\n@df df plot(:x, :y, legend=:topleft, label=\"y(x)\") # a most basic plot\n\n@df df density(:x, label=\"\") # density plot\n\n@df df histogram(:y, label=\"y\") # and a histogram\n\n@df df boxplot(:z, :x, label=\"x\")\n\n@df df violin(:z, :y, label=\"y\")\n", "meta": {"hexsha": "fc54b39dc44f6232473abbf9b56dbd431b32d8b2", "size": 5395, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/13_extras.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/13_extras.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/13_extras.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 39.0942028986, "max_line_length": 124, "alphanum_fraction": 0.4986098239, "num_tokens": 1363, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.16885696050685436, "lm_q1q2_score": 0.08245004999562151}}
{"text": "# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,jl:hydrogen\n#     text_representation:\n#       extension: .jl\n#       format_name: hydrogen\n#       format_version: '1.3'\n#       jupytext_version: 1.11.2\n#   kernelspec:\n#     display_name: Julia 1.8.0-DEV\n#     language: julia\n#     name: julia-1.8\n# ---\n\n# %% [markdown]\n# Since there is no minimal working example (MWE), I could not figure out what you really wanted to do.\n# (Please read https://discourse.julialang.org/t/please-read-make-it-easier-to-help-you/14757)\n#\n# However, I thought that possibly you would like to do something like this. Please take a look at the MWE below.\n\n# %%\nmodule SCI\n\nexport Problem, Algorithm_A, Algorithm_B, solve\n\nabstract type AbstractProblem end\nstruct Problem{T} <: AbstractProblem x::T end\n\nabstract type AbstractAlgorithm end\nBase.@kwdef struct Algorithm_A{T} <: AbstractAlgorithm a::T = 2.0 end\nBase.@kwdef struct Algorithm_B{T} <: AbstractAlgorithm a::T = 3.0 end\ndefault_algorithm(prob::Problem) = Algorithm_A()\n\nstruct Solution{R, P<:AbstractProblem, A<:AbstractAlgorithm} result::R; prob::P; alg::A end\nsolve(prob::AbstractProblem) = solve(prob, default_algorithm(prob))\nsolve(prob::AbstractProblem, alg::AbstractAlgorithm) = Solution(alg.a * prob.x, prob, alg)\n\n\"\"\"\nHere, `Base` module plays the role of `COM` package.\nSCI module can define the method of \n`Base.show` = `COM.show` function for `SCI.Solution` type.\n\"\"\"\nfunction Base.show(io::IO, sol::Solution)\n    result = \"\"\"\n    Problem:   $(sol.prob)\n    Algorithm: $(sol.alg)\n    Result:    $(sol.result)\n    \"\"\"\n    print(io, result)\nend\n\nend\n\nusing .SCI\n@show prob = Problem(1.5)\nprintln()\nprintln(solve(prob))\nprintln(solve(prob, Algorithm_B()))\n\n# %%\nmodule SCI2 # extension of SCI module\n\nexport Problem2, Algorithm_C\n\nusing ..SCI: SCI, AbstractProblem, AbstractAlgorithm, Solution\n\nstruct Problem2{T} <: AbstractProblem x::T; y::T end\nBase.@kwdef struct Algorithm_C{T} <: AbstractAlgorithm a::T = 2.0; b = 3.0 end\nSCI.default_algorithm(prob::Problem2) = Algorithm_C()\nSCI.solve(prob::Problem2, alg::Algorithm_C) = Solution(alg.a * prob.x + alg.b * prob.y, prob, alg)\n\nend\n\nusing .SCI2\n@show prob2 = Problem2(10.0, 1.0)\nprintln()\nprintln(solve(prob2))\n\n# %% [markdown]\n# In the above example I don't define `AbstractSolution` type, nor did I make `Solution` a subtype of it. But you can define it, and then define another Solution type as a subtype of it in `SCI2` module, and also define how to display objects of another Solution type.   Many other extensions are also possible.\n#\n# See also https://discourse.julialang.org/t/function-depending-on-the-global-variable-inside-module/64322/10\n\n# %%\n", "meta": {"hexsha": "8db9d22125bf917099d084db86ea604dced65648", "size": 2664, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "0011/Handling of package inter-dependency.jl", "max_stars_repo_name": "genkuroki/public", "max_stars_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2021-06-06T00:33:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T06:56:08.000Z", "max_issues_repo_path": "0011/Handling of package inter-dependency.jl", "max_issues_repo_name": "genkuroki/public", "max_issues_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "0011/Handling of package inter-dependency.jl", "max_forks_repo_name": "genkuroki/public", "max_forks_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-08-02T11:58:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-11T11:46:05.000Z", "avg_line_length": 30.976744186, "max_line_length": 311, "alphanum_fraction": 0.7090840841, "num_tokens": 737, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.16885694586780495, "lm_q1q2_score": 0.08245004284761677}}
{"text": "activate_github(\"QuantEcon/QuantEconLectureAllPackages\", tag = \"v0.9.8\", add_default_environment = true)\n", "meta": {"hexsha": "171e8cdb05864da4286cedffdd156523388e0200", "size": 105, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_static/includes/add_default_env.jl", "max_stars_repo_name": "sbacelar/quantecon-notebooks-jl", "max_stars_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "_static/includes/add_default_env.jl", "max_issues_repo_name": "sbacelar/quantecon-notebooks-jl", "max_issues_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "_static/includes/add_default_env.jl", "max_forks_repo_name": "sbacelar/quantecon-notebooks-jl", "max_forks_repo_head_hexsha": "8a6361f07e0893019cff477c24e46641f2fdb736", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.5, "max_line_length": 104, "alphanum_fraction": 0.8095238095, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.17781086512112398, "lm_q1q2_score": 0.08197379236487719}}
{"text": "module DIPTutorials\n\nexport DIP3e\n\ninclude(\"DIP3e.jl\")\nusing .DIP3e\n\n\"\"\"\nA helper package for DIP tutorials: https://johnnychen94.github.io/Image-Processing-in-Julia/dev/\n\n# Examples\n\n```julia\nusing DIPTutorials, FileIO\nimgfile = DIP3e.get_filepath(\"0101\") # figure 1.1\nimg = load(imgfile)\n```\n\"\"\"\nDIPTutorials\n\nend\n", "meta": {"hexsha": "5d5e6910f5fc4d511622ff3354a1c483178be0e8", "size": 316, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DIPTutorials.jl", "max_stars_repo_name": "johnnychen94/DIP3E_Gonzalez_Julia", "max_stars_repo_head_hexsha": "4f2707a86a63b317d1efab5de4ae9730f214b866", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 33, "max_stars_repo_stars_event_min_datetime": "2018-11-07T08:16:08.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-20T14:24:47.000Z", "max_issues_repo_path": "src/DIPTutorials.jl", "max_issues_repo_name": "johnnychen94/Digital-Image-Processing-Gonzalez-Julia", "max_issues_repo_head_hexsha": "4f2707a86a63b317d1efab5de4ae9730f214b866", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2018-10-18T13:48:03.000Z", "max_issues_repo_issues_event_max_datetime": "2019-03-20T01:56:47.000Z", "max_forks_repo_path": "src/DIPTutorials.jl", "max_forks_repo_name": "johnnychen94/Digital-Image-Processing-Gonzalez-Julia", "max_forks_repo_head_hexsha": "4f2707a86a63b317d1efab5de4ae9730f214b866", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2018-09-19T11:43:30.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-11T04:07:16.000Z", "avg_line_length": 14.3636363636, "max_line_length": 97, "alphanum_fraction": 0.7373417722, "num_tokens": 102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47268347662043275, "lm_q2_score": 0.17328820806405806, "lm_q1q2_score": 0.08191047264504388}}
{"text": "# using <package name>\nusing Random\n\n# Print without new line\nprint(\"Hello World\")\n\n# Print with new line\nprintln(\"Hello World\")\n\nfor i in range(1, 10, step = 1)\n    println(i)\nend\n\nfunction square(a::Int)\n    return a*a\nend\n\n# Call the function\nprint(square(6))\n\n# Vectorization \nvec_sq = square.([1, 2, 3, 4])\n\n\n\n# Mapping\nmapped_vec = map(x->square(x)+4, [1,2,3,4])", "meta": {"hexsha": "8f612686de02957873eeec04cb13d31e502732fb", "size": 368, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Study_Session_1/Introduction.jl", "max_stars_repo_name": "khanfarhan10/Study-Group", "max_stars_repo_head_hexsha": "5e5e9c74890a2f31ad9cb67b199db9eac6b6444a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2021-01-15T11:06:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-31T20:22:13.000Z", "max_issues_repo_path": "Study_Session_1/Introduction.jl", "max_issues_repo_name": "chaitak-gorai/Study-Group", "max_issues_repo_head_hexsha": "add27a3c6b4d890816ea210b7377f1c5db3c1853", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-01-30T18:32:28.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-25T18:15:41.000Z", "max_forks_repo_path": "Study_Session_1/Introduction.jl", "max_forks_repo_name": "chaitak-gorai/Study-Group", "max_forks_repo_head_hexsha": "add27a3c6b4d890816ea210b7377f1c5db3c1853", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-01-31T14:11:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-23T11:12:53.000Z", "avg_line_length": 13.6296296296, "max_line_length": 43, "alphanum_fraction": 0.6576086957, "num_tokens": 120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.38491214448393346, "lm_q2_score": 0.21206881431678098, "lm_q1q2_score": 0.08162786209683726}}
{"text": "### A Pluto.jl notebook ###\n# v0.19.8\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 1f0cb625-d1ec-4775-bc75-59d4675d181e\n# \u2560\u2550\u2561 show_logs = false\nbegin\n\tusing Logging\n\tglobal_logger(NullLogger())\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 25448e4a-345b-4bd6-9cdb-a6a280cfd22c\n# \u2560\u2550\u2561 show_logs = false\n#Set-up packages\nbegin\n\t\n\tusing PlutoUI, DataFrames, HTTP, CSV, Dates, Printf, LaTeXStrings, HypertextLiteral, XLSX\n\t\n\t# gr();\n\t# Plots.GRBackend()\n\n\n\t#Define html elements\n\tnbsp = html\"&nbsp\" #non-breaking space\n\tvspace = html\"\"\"<div style=\"margin-bottom:0.05cm;\"></div>\"\"\"\n\tbr = html\"<br>\"\n\n\t#Sets the width of cells, caps the cell width by 90% of screen width\n\t#(setting overwritten by cell below)\n\t# @bind screenWidth @htl(\"\"\"\n\t# \t<div>\n\t# \t<script>\n\t# \t\tvar div = currentScript.parentElement\n\t# \t\tdiv.value = screen.width\n\t# \t</script>\n\t# \t</div>\n\t# \"\"\")\n\n\t\n\t# cellWidth= min(1000, screenWidth*0.9)\n\t# @htl(\"\"\"\n\t# \t<style>\n\t# \t\tpluto-notebook {\n\t# \t\t\tmargin: auto;\n\t# \t\t\twidth: $(cellWidth)px;\n\t# \t\t}\n\t# \t</style>\n\t# \"\"\")\n\t\n\n\t#Sets the width of the cells\n\t#begin\n\t#\thtml\"\"\"<style>\n\t#\tmain {\n\t#\t\tmax-width: 900px;\n\t#\t}\n\t#\t\"\"\"\n\t#end\n\n\n\t#Sets the height of displayed tables\n\thtml\"\"\"<style>\n\t\tpluto-output.scroll_y {\n\t\t\tmax-height: 550px; /* changed this from 400 to 550 */\n\t\t}\n\t\t\"\"\"\n\t\n\n\t#Two-column cell\n\tstruct TwoColumn{A, B}\n\t\tleft::A\n\t\tright::B\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", tc::TwoColumn)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t<div style=\"display: flex;\">\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.left)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.right)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t</div>\n\t\t\"\"\")\n\tend\n\n\t#Creates a foldable cell\n\tstruct Foldable{C}\n\t\ttitle::String\n\t\tcontent::C\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", fld::Foldable)\n\t\twrite(io,\"<details><summary>$(fld.title)</summary><p>\")\n\t\tshow(io, mime, fld.content)\n\t\twrite(io,\"</p></details>\")\n\tend\n\t\n\t\n\t#helper functions\n\t#round to digits, e.g. 6 digits then prec=1e-6\n\troundmult(val, prec) = (inv_prec = 1 / prec; round(val * inv_prec) / inv_prec); \n\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 a2f22b9d-13f8-4e39-b7dd-14b905d987ab\n#add button to trigger presentation mode\nhtml\"<button onclick='present()'>present</button>\"\n\n# \u2554\u2550\u2561 731c88b4-7daf-480d-b163-7003a5fbd41f\nbegin \n\thtml\"\"\"\n\t<p align=left style=\"font-size:25px; font-family:family:Georgia\"> <b> UD/ISCTE-IUL Trading and Bloomberg Program</b> <p>\n\t\"\"\"\nend\n\n\n# \u2554\u2550\u2561 a5de5746-3df0-45b4-a62c-3daf36f015a5\nbegin \n\thtml\"\"\"\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:25px; font-family:family:Georgia\">Fixed Income Securities </div>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> <b> U.S. Treasury Securities Market</b> <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> Summer 2022 <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> Prof. Matt Fleckenstein </div>\n\t<p style=\"padding-bottom:0.0cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> University of Delaware, \n\tLerner College of Business and Economics </div>\n\t<p style=\"padding-bottom:0.5cm\"> </p>\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 69dca09e-b572-436f-90c6-187af17cf027\nTableOfContents(aside=true, depth=1)\n\n# \u2554\u2550\u2561 6498b10d-bece-42bf-a32b-631224857753\nmd\"\"\"\n# Overview\n\"\"\"\n\n# \u2554\u2550\u2561 95db374b-b10d-4877-a38d-1d0ac45877c4\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Our goals for today</legend>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\">Introduction to the U.S. Treasury Securities \t\tMarket<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\">Treasury Bills<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\">Treasury Notes and Bonds<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\">Treasury STRIPS<br>           \n\t\t<br>\n\t\t<input type=\"checkbox\" value=\"\">Treasury TIPS<br> \n        <br>\n\t    <input type=\"checkbox\" value=\"\">Inflation Swaps<br>      \n\t\t<br>\n\t  \t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 61346ddf-bea9-442d-9086-040b78073884\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 10a73b76-b6ad-4081-b8b1-21363608618e\nmd\"\"\"\n# The U.S. Treasury Market\n- The Department of the Treasury is the largest single issuer of debt in the world.\n- The large volume of total debt and the large size of any single issue have contributed to making the Treasury market the most active and hence the most liquid market in the world\n\n\n[SIFMA Fixed Income Statistics](https://www.sifma.org/resources/research/fixed-income-chart)\n\"\"\"\n\n# \u2554\u2550\u2561 1bf762d9-5652-4a2b-9eda-ad1231c1c83b\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 b33e1473-e374-441b-a6a4-6c2a7b079c98\nmd\"\"\"\n## U.S. Treasury Debt Amount Outstanding\n\"\"\"\n\n# \u2554\u2550\u2561 f89f6fc4-cb6f-4d7b-84c9-f8a18e764427\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 313959e0-eea6-449a-a6b8-29c9394e50b6\nLocalResource(\"./Assets/TreasuryOutstandingSIFMA.svg\",:width => 900)\n\n# \u2554\u2550\u2561 e20b95fa-e038-4d3b-82ca-8a38262c64c4\nmd\"\"\"\n[Link to SIFMA](https://www.sifma.org/resources/research/fixed-income-chart/)\n\"\"\"\n\n# \u2554\u2550\u2561 95318eb1-357f-4880-a83d-732d8dc5f7de\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 ae67f236-60ec-4c2e-abd3-9ea537200ca3\nmd\"\"\"\n# Types of Treasury Securities\n\"\"\"\n\n# \u2554\u2550\u2561 fa1dcebd-4a54-433b-897c-de2929fb8233\nmd\"\"\"\n>- Where to get information about U.S. Treasury securities?\n>- Go to webpage of the [U.S. Treasury](https://www.treasurydirect.gov/).\n>- In the middle panel, click on `Treasury securities Overview`.\n\"\"\"\n\n# \u2554\u2550\u2561 2452e56b-2a61-4608-9ca0-55c8cdca89c6\nLocalResource(\"./Assets/TreasuryDirect_01.png\",:width => 900)\n\n# \u2554\u2550\u2561 f688e93c-e8b1-4718-b56e-d76bf70eef57\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 4914c857-5dc8-43b8-8e70-377debbc3d0c\nLocalResource(\"./Assets/TreasuryDirect_02.png\",:width => 900)\n\n# \u2554\u2550\u2561 49aeda6d-b718-46d0-92ca-79f59a81ae14\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 d25744ad-fbe3-4f08-b17a-d00d51bcb2b8\nmd\"\"\"\n# Treasury Bill (T-bill)\n- Short-term securities with maturities of 4, 13, 26, and 52 weeks.\n- Treasury bills do not pay interest before maturity.\n  - This is often referred to as a _discount security_ or _zero-coupon security_.\n- Instead, Treasury bills are issued at a price less than their par value and at maturity, Treasury bills pay back their par value.\n  - Intuitively, the \u201cinterest\u201d to the investor is the difference between par value and the purchase price.\n  - Example: a 52-week T-bill with par value of \\$100 has a price of \\$98.\n\"\"\"\n\n# \u2554\u2550\u2561 382a9c58-4baa-4ad2-9f32-7189d4881c50\nmd\"\"\"\n## Example of 52-week Treasury Bill\n\"\"\"\n\n# \u2554\u2550\u2561 deb54591-8769-480d-98ad-32157abac2c0\nLocalResource(\"./Assets/BloombergTBill_01.png\", :width=>900)\n\n# \u2554\u2550\u2561 3578793a-e0b6-42de-941b-2db09ef99e49\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - Open a terminal and on the keyboard type `T Bill`.\n>  - In the popup window,  select `B Govt - United States Treasury Bill (Multiple Matches)`.\n>  - Next, click on one of the different Treasury bills in the list.\n>  - Then, click on `DES` on the top-right, or type `DES` on the keyboard and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 82db2847-a1a1-45a3-8d29-c3ac4213591c\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 d296b99d-588d-48ac-8e0c-a0bcbde72c56\nmd\"\"\"\n# Treasury Note (T-Note)\n- Medium-term securities with maturities of 2, 3, 5, 7, and 10 years.\n- Treasury notes pay interest every six months up to and including the maturity date.\n  - Example: A 2-year T-note has its last interest payment in two years, and it pays interest after 6 months, 12 months, and 18 months.\n- At maturity, Treasury notes pay back their par value.\n\"\"\"\n\n# \u2554\u2550\u2561 1c4db2fa-7187-4966-aa3f-9a7f56036497\nmd\"\"\"\n## Example of 10-year Treasury Note\n\"\"\"\n\n# \u2554\u2550\u2561 3a3b7641-ed63-4cce-a98c-a4cf1b6e603d\nLocalResource(\"./Assets/BloombergTNote_01.png\")\n\n# \u2554\u2550\u2561 2384b88b-e8dc-4e9c-9488-27828da0289d\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - Open a terminal and on the keyboard type `T Note`.\n>  - In the popup window,  select `T Govt - United States Treasury Note/Bond (Multiple Matches)`.\n>  - Next, click on one of the different Treasury notes in the list.\n>  - Then, click on `DES` on the top-right, or type `DES` on the keyboard and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 0918383a-9a0b-4f8c-ba63-aaad1cf6d72c\nmd\"\"\"\n- Let's look at price quotes for this Treasury note.\n- If you were to purchase this Treasury note, would you pay \\$95.02?\n- The answer is _no_. Prices for Treasury notes are quoted in a specific way, which we will discuss in the next lecture.\n\"\"\"\n\n# \u2554\u2550\u2561 c9b876e2-cb29-4a70-9c1f-abb2dedf3fe2\nLocalResource(\"./Assets/BloombergTNote_02.png\")\n\n# \u2554\u2550\u2561 d912c3cb-c1a6-46fe-af9d-25a75074b31d\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - From the `Description` page where you are currently at, type `GP` and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 40df1177-48cf-4cb2-9860-11d2d9f0a450\nmd\"\"\"\n# Treasury Bond (T-Bond)\n- Long-term securities with maturities of 20 and 30 years.\n  - Currently, the Treasury does not issue 15-year Treasury bonds.\n- Treasury bonds notes pay interest every six months up to and including the maturity date. At maturity, Treasury notes pay back their par value.\n  - Similar to Treasury notes.\n\n[Treasury Marketable Securities](https://www.treasurydirect.gov/instit/marketables/marketables.htm)\n\"\"\"\n\n# \u2554\u2550\u2561 4567e3a2-24fb-494f-8005-801b03ffeabf\n#https://www.treasurydirect.gov/indiv/research/history/histtime/histtime_bonds.htm\n\n# \u2554\u2550\u2561 76774fcb-f806-4db2-b653-fe69720f43a7\nmd\"\"\"\n## Coupon bonds\n- Treasury notes and bonds are referred to as **coupon** securities.\n- Why?\n\"\"\"\n\n# \u2554\u2550\u2561 a440a28f-04af-4248-8f2b-c8e6a726c07a\nLocalResource(\"./Assets/TreasuryCouponPicture.svg\",:width => 900)\n\n# \u2554\u2550\u2561 344b41a7-7257-4dc0-babd-456ca3cdc4c6\nmd\"\"\"\n# Example of Treasury Note Cash Flows\n\"\"\"\n\n# \u2554\u2550\u2561 fee10643-3176-45b3-b424-3f479874561e\nLocalResource(\"./Assets/TreasuryNoteCashflowExample.png\",:width => 900)\n\n# \u2554\u2550\u2561 c32458ce-5884-4385-8b65-dc322445aaf6\n# md\"\"\"\n# - Par Value $F$ = \\$100.\n# - Coupon Rate $c$ [% p.a.]:  $(@bind CTnote Slider(0:0.1:10.0, default=10, show_value=true))\n# - Time to maturity $T$ [years]:  $(@bind TTnote Slider(1.0:1:10.0, default=5.0,show_value=true))\n# \"\"\"\n\n# \u2554\u2550\u2561 47558761-7341-40e0-bdda-7d30f5527f4b\n# let\n# \tCF = 0.5*CTnote.*ones(convert(Int64,TTnote*2))\n# \tCF[end] += 100\n# \tdt = collect(0.5:0.5:TTnote)\n# \tbar(dt,CF,label=\"\", ylim=(0,120), xlim=(0,TTnote+1), xticks=collect(0.0:0.5:TTnote), xlabel=\"Years\", ylabel=\"Coupon Cash Flow\")\n# end\n\n# \u2554\u2550\u2561 fef5577e-025f-4afc-9fd9-aa025146858e\nmd\"\"\"\n# Treasury Floating Rate Note (FRN)\n- First issued in 2014 by the U.S. Treasury.\n- Maturity of 2 years.\n- Pay interest every three months up to and including the maturity date.\n  - At maturity, FRNs pay back their par value.\n- The interest on an FRN varies with interest rate on 13-week Treasury bills.\n\"\"\"\n\n# \u2554\u2550\u2561 992074c6-d6d5-4417-9edc-0229a0333070\nmd\"\"\"\n# Treasury STRIPS\n\n- The Treasury does not issue zero-coupon notes or bonds.\n- However, because of the demand for zero-coupon instruments with no credit risk, the private sector has created such securities.\n- The process of separating the interest on a bond from the underlying principal is called coupon stripping\n- Zero-coupon Treasury securities were first created in August 1982 by large Wall-Street firms.\n  - The problem with these securities was that they were identified with particular dealers and therefore reduced liquidity.\n  - Moreover, the process involved legal and insurance costs. \n  - Today, all Treasury notes and bonds (fixed-principal and inflation-indexed) are eligible for stripping. \n- The zero-coupon Treasury securities created under the STRIPS program are direct obligations of the U.S. government\n\"\"\"\n\n# \u2554\u2550\u2561 cc282e0d-9faa-4808-b506-335e9097f2fe\nmd\"\"\"\n## Treasury STRIPS in Bloomberg\n\"\"\"\n\n# \u2554\u2550\u2561 d44298e3-5763-4249-8140-5eefb80adb49\nLocalResource(\"./Assets/TreasurySTRIPSBloomberg.png\",:width => 900)\n\n# \u2554\u2550\u2561 57759a33-f026-4c15-91ba-f6af45627f1e\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - Open a terminal and on the keyboard type `Treasury STRIP`.\n>  - In the popup window,  select `S Govt - United States Treasury Strip Coupon (Multiple Matches)`.\n>  - Next, click on one of the different Treasury bills in the list.\n>  - Then, click on `DES` on the top-right, or type `DES` on the keyboard and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 9c596473-76f6-4e9a-b3dd-32add39eaf92\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 9db71771-d778-4d43-af7a-80d8cfff97ff\nLocalResource(\"./Assets/BloombergSTRIPS_01.png\")\n\n# \u2554\u2550\u2561 9aa2c9f6-5a3e-4138-8352-e8d293f340a8\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 cf1fa1d8-da24-4f48-8762-affa1f58279a\nmd\"\"\"\n# Treasury Inflation Protected Securities (TIPS)\n- First issued in 1997 by the U.S. Treasury.\n- Maturities of 5, 10, and 30 years.\n- TIPS pay interest every six months up to and including the maturity date. At maturity, Treasury notes pay back their par value.\n  - Similar to Treasury notes and bonds.\n- Key difference is that both par value and interest go up with the rate of inflation.\n\"\"\"\n\n# \u2554\u2550\u2561 3000e0f6-381f-4a9c-85c1-c517af617f14\nmd\"\"\"\n- Why inflation matters ...\n  - [WSJ, June 10, 2022: U.S. Inflation Hit 8.6% in May. Energy, groceries, shelter costs drive fastest rise in consumer-price index since December 1981](https://www.wsj.com/articles/us-inflation-consumer-price-index-may-2022-11654810079)\n  - [WSJ, January 12, 2022: U.S. Inflation Hit 7% in December, Fastest Pace Since 1982](https://www.wsj.com/articles/us-inflation-consumer-price-index-december-2021-11641940760?mod=article_inline)\n  - [WSJ, February 10, 2022: U.S. Inflation Rate Accelerates to a 40-Year High of 7.5%](https://www.wsj.com/articles/us-inflation-consumer-price-index-january-2022-11644452274?mod=hp_lead_pos1)\n  - [WSJ, February 6, 2022: What Investors Should Know About TIPS](https://www.wsj.com/articles/tips-what-investors-should-know-treasury-inflation-protected-securities-11643849892?mod=hp_lista_pos1)\"\"\"\n\n# \u2554\u2550\u2561 80a54df2-24e1-4c9e-a292-8b3c57e797dd\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 889c4a84-b6c3-4495-8ef9-cd2d862259a6\nLocalResource(\"./Assets/TreasuryAmountOutstanding.png\")\n\n# \u2554\u2550\u2561 8ebcfea4-53a2-47dc-b4fa-868bb2a570b4\n# #US Treasury Market\n# begin\n# \tTreasMkt = DataFrame()\n# \tplot_Treas = plot()\n# \tlet\n# \t\tdata_url = \"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/US-Treasury-Securities-Statistics-SIFMA_Outstanding.csv\"\n# \t\tmy_file = CSV.File(HTTP.get(data_url).body; missingstring=\"-999\")\n# \t\t# my_file = CSV.File(\"./Assets/US-Treasury-Securities-Statistics-SIFMA_Outstanding.csv\")\n#  \t\tTreasMkt = DataFrame(my_file)\n\t\t\n# \t\ttransform!(TreasMkt, [:Notes,:Bonds] => (+) => :NotesAndBonds)\n# \t\ttransform!(TreasMkt, \n# \t\t  [:TIPS, :Total] => ByRow( (x,y)->any(ismissing.([x,y])) ? \n# \t\t \t\tmissing :  (x/y*100)) => :TIPSPctTreas)\n\t\t\n# \t\tminX = 1997\n# \t\tmaxX = 2020\n# \t\tminY = 0.0\n# \t\tmaxY = 2000.0\n# \t\tplot!(plot_Treas, TreasMkt.Year,TreasMkt.TIPS, \n# \t\t  \txlim=(minX,maxX), ylim=(minY, maxY), \n# \t\t  \tylabel=\"Billions of Dollars\",label=\"TIPS\",\n# \t\t  \tlegend = :topleft, title=\"Amount Outstanding\",right_margin = 15Plots.mm)\t\n# \t\tsubplot=twinx()\n# \t\tplot!(subplot, TreasMkt.Year,TreasMkt.TIPSPctTreas, color=:red,\n# \t\t\txlim=(minX,maxX), ylim=(0,20), ylabel = \"Percent\", \n# \t\t\tyticks=(0:5:20),label = \"TIPS to Treasury Debt\")\t\n# \t\tplot(plot_Treas)\n# \tend\n# end\n\n# \u2554\u2550\u2561 5c8f4910-7b54-414a-8eb9-52104c0ff36e\nmd\"\"\"\n## Example of a Treasury TIPS\n\"\"\"\n\n# \u2554\u2550\u2561 972e44da-4614-4349-bcb7-edb0518fe24d\nLocalResource(\"./Assets/BloombergTIPS_01.png\")\n\n# \u2554\u2550\u2561 a1c74635-2fb9-492c-8243-4232b771718b\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - Open a terminal and on the keyboard type `Treasury TIPS`.\n>  - In the popup window,  select `TII Govt - United States Treasury Inflation Indexed Bonds (Multiple Matches)`.\n>  - Next, click on one of the different Treasury bills in the list.\n>  - Then, click on `DES` on the top-right, or type `DES` on the keyboard and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 45ffcfef-16f3-464a-b868-0d3f64cb6019\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 874afd1d-eba3-4295-9f96-7502c187b171\nmd\"\"\"\n- Let's look at price quotes for this Treasury TIPS.\n- If you were to purchase this Treasury TIPS, would you pay \\$105.28?\n- The answer is _no_. Prices for Treasury TIPS are quoted in a specific way, which we will discuss in the next lecture.\n\"\"\"\n\n# \u2554\u2550\u2561 95110a89-1d2b-4c83-9b1c-d7dc95ab89c8\nLocalResource(\"./Assets/BloombergTIPS_02.png\")\n\n# \u2554\u2550\u2561 aa70d2f6-c726-45cc-89cb-bb6afb85f065\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - From the `Description` page where you are currently at, type `GP` and press enter.\n\"\"\"\n\n# \u2554\u2550\u2561 7930b421-9b9d-4029-aad1-affd0b36b432\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 d69cce2c-dce4-4369-b3db-968c0b4d3dc6\nmd\"\"\"\n## Inflation-Linked notes date back centuries\n\"\"\"\n\n# \u2554\u2550\u2561 ccba5159-b6bb-4c98-8f04-8dd91c032146\nmd\"\"\"\n> Both Principal and Interest to be paid in the then current Money of said State, in a greater or less Sum, according as Five Bushels of Corn, Sixty-eight Pounds and four-seventh Parts of a Pound of Beef, Ten Pounds of Sheeps Wool, and Sixteen Pounds of Sole Leather shall then cost more or less than One Hundred and Thirty Pounds current Money, at the then current Prices of said Articles.\n_Source: \u201cInflation-indexed Securities: Bonds, Swaps and Other Derivatives\u201d, 2nd Edition, M. Deacon, A. Derry, D. Mirfendereski, Wiley._\n\n\n\"\"\"\n\n# \u2554\u2550\u2561 f50d144b-6d62-4b3f-b85a-d76861ea2ae0\nLocalResource(\"./Assets/SoldiersDepreciationNote.svg\",:width => 900)\n\n# \u2554\u2550\u2561 8dff2987-d375-441c-b6fe-5fceb32f9117\nmd\"\"\"\n# Indexing Bonds to Inflation\n\"\"\"\n\n# \u2554\u2550\u2561 ce096e20-bed9-4de1-8af5-b9eefe898ebd\nmd\"\"\"\n- Treasury Inflation-Protected Securities (TIPS) are _index-linked_ bonds.\n  - An index-linked bond is one whose _cash flows_ are linked to movements in a specific price index.\n- The _principal amount_ of a TIPS is indexed to the price level. \n  - Since a fixed coupon rate is applied to the principal that varies with the price level, the actual coupon cash flows vary in response to the realized rate of inflation.\n- Index-linked bonds are usually indexed to a broad measure of prices, typically a domestic _Consumer Price Index (CPI)_.\n\"\"\"\n\n# \u2554\u2550\u2561 89b1d772-517c-48b5-8928-db6e053576f1\nmd\"\"\"\n# U.S. Consumer Price Index\n- In the U.S. this price index is the Consumer Price Index for All Urban Consumers (_CPI-U_).\n- The CPI-U measures the level of prices paid by consumers for goods and services. \n- This index is published by the Bureau of Labor Statistics (BLS) every month.\n- [Bureau of Labor Statistics](https://www.bls.gov/schedule/news_release/cpi.htm); [Bureau of Labor Statistics Release](https://www.bls.gov/news.release/pdf/cpi.pdf)\n\"\"\"\n\n# \u2554\u2550\u2561 813ffddc-3273-4fd0-9469-e78b9175335b\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/USCPIYoY.svg\",:width => 900)\n\n\n# \u2554\u2550\u2561 5f87817d-a6d0-4506-b8a2-5327ef0734bd\nmd\"\"\"\nSource: [BLS.gov](https://www.bls.gov/charts/consumer-price-index/consumer-price-index-by-category-line-chart.htm)\n\"\"\"\n\n# \u2554\u2550\u2561 b51b9209-76f7-43b9-b539-a7ff17f9148d\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 c4aa9ab9-5502-4676-8b6a-5b55cc645e03\nmd\"\"\"\n## TIPS Inflation-Adjustment\n\"\"\"\n\n# \u2554\u2550\u2561 f481451a-6949-4604-811b-6b4c530fd644\nLocalResource(\"./Assets/TIPSInflationAdjustment.svg\",:width => 900)\n\n# \u2554\u2550\u2561 36678746-8926-451d-b692-b57c620170c5\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 4e0d6bde-f652-4260-a2bf-03da8c2012c3\nmd\"\"\"\n# TIPS Inflation Adjustment Details\n\n- The principal amount of a TIPS (assume $100 at issuance) is adjusted daily based on the CPI-U. \n- The inflation adjustment $I_t$ is computed as the ratio of the **Reference CPI** at the time $t$ divided by the reference CPI when the TIPS was first issued ($t=0$). \n$$I_t = \\frac{\\textrm{Reference CPI at time } t}{\\textrm{Reference CPI at TIPS issue date}}$$\n\n\"\"\"\n\n# \u2554\u2550\u2561 95162814-820b-402e-b8e2-c2a7e511ef8b\nmd\"\"\"\n- The **Reference CPI** for a particular date $t$ during a month is linearly interpolated from the **Reference CPI** for the beginning of that month and the **Reference CPI** for the beginning of the subsequent month.\n  - The **Reference CPI** for the first day of _any_ calendar month is the CPI-U index for the third preceding calendar month. \n\"\"\"\n\n# \u2554\u2550\u2561 86ed68e9-e097-4385-abdf-172763ee6281\nmd\"\"\"\n- _Example 1_: the **Reference CPI** for _April 1_ is the CPI-U index for the month of _January_ (which is reported by the BLS during February).\n- _Example 2_: the **Reference CPI** for _April 15_ is roughly the average of the CPI-U index for the month of _January_ and the CPI-U index for the month of February.\n\"\"\"\n\n# \u2554\u2550\u2561 358b0a71-e722-4622-90e5-a875acaf5e21\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/ReferenceCPI_Des.png\",:width => 900)\n\n\n# \u2554\u2550\u2561 def2b50e-a660-4d98-9efe-67413ef76d37\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 dd30bcd0-939d-4907-b4e9-6a559a71426d\nmd\"\"\"\n# Deflation Protection\n- TIPS have an embedded option that protects against deflation.\n- The Treasury guarantees that the _final redemption value is no less than \\$100 per \\$100 nominal_ amount, irrespective of the movements in the CPI over the life of the bond.\n- Let $F$ be the TIPS principal amount and $T$ the time to maturity of the TIPS.\n- The principal cash flow at maturity $T$ is\n$$F \\times \\max\\left[\\, I_T, 1 \\,\\right]$$\n- This deflation protection does not apply to coupon cash flows.\n\"\"\"\n\n# \u2554\u2550\u2561 82ba10db-c126-4355-9311-26c1d0ad707c\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 22c5f11f-d1b0-4780-ae3d-eb0a5c7d5f14\nmd\"\"\"\n# Inflation-Adjusted Coupon Interest\n\"\"\"\n\n# \u2554\u2550\u2561 51ea0a30-8558-455b-81e3-79fbc8fe59e9\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/of698cpi_crop.svg\",:width => 500)\n\n\n# \u2554\u2550\u2561 8921aae4-2d39-4ddd-bf19-6b395253e488\nmd\"\"\"\n- The Reference CPI is then turned into a ratio to caclulate the inflation adjustment by taking the Reference CPI on the date and dividing by the Reference CPI at issue. \n  - For example, the Reference CPI for January 15, the official issue date of the inaugural TIPS bond is 158.43548. Assume \\$100 par value at issuance.\n  - Suppose we are on July 15 and the first coupon cash flow is about to be paid. Suppose that the reference CPI on July 15 turns out to be 168.53226.\n  - Then, the inflation adjustment factor is 168.53226/158.43458 = 1.063734\n  - This means coupon rate is paid on par value of \\$100 $\\times$ 1.063734 = \\$106.3734.\n\"\"\"\n\n# \u2554\u2550\u2561 4ce2544f-c66d-4d56-a39d-fc7d5ee6d633\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 f69d0a46-263e-4526-a8b2-ef4f4ee5b762\nmd\"\"\"\n# Inflation-Adjusted Coupon Interest Example\n\"\"\"\n\n# \u2554\u2550\u2561 68942dbc-0cdc-4201-a10e-bfbb2b56c73f\nmd\"\"\"\n- Let $F$ be the TIPS principal value.\n- Let $c$ denote the (fixed) _real_ coupon rate on the TIPS.\n- Let $T$ denote the time to maturity of the TIPS (in years).\n\n\"\"\"\n\n# \u2554\u2550\u2561 8ffac44e-56cd-435e-be5c-d72e8be35941\nmd\"\"\"\n- Principal $F$ [$]: $(@bind F Slider(100:100:1000, default=100, show_value=true))\n- Real Coupon Rate c [%]: $(@bind c Slider(0:0.25:10, default=3, show_value=true))\n- Time to Maturity $T$ [years]: $(@bind T Slider(1:1:30, default=5, show_value=true))\n- Reference CPI at issue date: $(@bind I\u2080 Slider(100:1:300, default=100, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 d6e02f5e-cb9e-440c-a335-a487db0d0930\nMarkdown.parse(\"\n- Suppose, \\$c= $c \\\\%\\$ and \\$F=$F\\$.\n- In real terms, the coupon cash flows at each coupon date are \n\t\t\n\\$\\\\frac{c}{2} \\\\, F = \\\\frac{$(c/100)}{2} \\\\, $F = $(c/200*F)\\$\n\t\n- Suppose there is inflation (or deflation).\n- The actual cash flows (in nominal terms) of the TIPS are:\n\")\n\n# \u2554\u2550\u2561 5b9336cd-e88d-478f-8711-bde0da5724be\nbegin\n\tcfDates = collect(0.5:0.5:T)\n\trandNormal = randn(length(cfDates))\n\trefCPI = zeros(length(cfDates))\n\trefCPI[1] = I\u2080 + (1+randNormal[1])\n\tfor ii=2:length(refCPI)\n    \trefCPI[ii] = refCPI[ii-1] + (0+randNormal[ii]);\n\tend\n\tInfl = (refCPI./I\u2080 .-1)*100\n\tII\u209c = refCPI./I\u2080\n\tadjPrin = F.*II\u209c\n\tadjPrin[end] = maximum([100,F.*II\u209c[end]])\n\tcfAmounts = (c/200)*F.*II\u209c.*vec(ones(length(cfDates),1))\n\tcfAmounts[end] = cfAmounts[end]+100*maximum([II\u209c[end],1])\n\tdf = DataFrame(Time=cfDates, Reference_CPI=refCPI, I\u209c=II\u209c, Adjusted_Principal=adjPrin, Cashflows=cfAmounts,)\nend\n\n# \u2554\u2550\u2561 b401991e-ef32-4cee-8630-d9f54635aec3\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 0dee4432-9118-428a-9b49-ca2648e14fcd\nmd\"\"\"\n# Inflation Derivatives\n\n- In addition to the cash inflation market, there is an active derivatives market that consists mainly of inflation swap contracts and inflation options. \n\"\"\"\n\n# \u2554\u2550\u2561 6e529940-7f36-4bdd-8adb-1f34b2825d32\nmd\"\"\"\n# Inflation Swap Basics\n\"\"\"\n\n# \u2554\u2550\u2561 be39cbe4-9b57-4827-b649-45655cb49270\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/InflSwap_Chart.svg\",:width => 400)\n\n\n# \u2554\u2550\u2561 27a48326-0265-4334-b7cd-0c2fe3595999\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/InflSwap1Yr_Des.png\",:width => 900)\n\n\n# \u2554\u2550\u2561 9ffda585-493f-4ffe-b360-dcf1420f107c\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/InflSwap1Yr_PxQuotes.png\",:width => 900)\n\n\n# \u2554\u2550\u2561 2533a3d3-d368-4036-bea4-79645fe9adf2\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/InflSwap1Yr_Graph.png\",:width => 900)\n\n\n# \u2554\u2550\u2561 19eceb43-c982-4e99-a9d4-de229ac948e7\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 636cdd0c-88c9-4c5a-833a-db2a49014c75\nmd\"\"\"\n# Inflation Swap Cash Flows\n\"\"\"\n\n# \u2554\u2550\u2561 29718094-db53-434b-ae91-5d93d06d857b\nmd\"\"\"\n- The swap is executed between two counterparties at time $t=0$ and has only one cash flow that occurs at maturity in $T$ years.\n\"\"\"\n\n# \u2554\u2550\u2561 e4458558-d2be-4d88-82cc-db1335c49aa8\nmd\"\"\"\n- For example, imagine that at time $t=0$, the five-year zero-coupon inflation swap rate is 200 basis points and that the inflation swap has a notional of \\$1. \n- There are no cash flows at time $t=0$ when the swap is executed. \n- At the maturity of the swap in $T=5$ years, suppose that realized inflation is $I_T$, then the counterparties to the inflation swap exchange a cash flow of \n\n$$\\left[ (1 + 0.0200)^5 -1 \\right] \u2212 \\left[I_T -1 \\right],$$\n\"\"\"\n\n# \u2554\u2550\u2561 89646676-3a58-437b-ba49-df1732b43ba7\nmd\"\"\"\n- Thus, if the realized inflation rate was 1.50% per year over the five-year horizon of the swap, \n\n$$I_T = 1.015^5 = 1.077284$$ \n\n- In this case, the net cash flow per \\$1 notional of the swap from the swap would be \n\n\n$$\\left[ (1 + 0.0200)^5 -1\\right] \u2212 \\left[1.077284 -1 \\right]= 0.026797$$ \n\"\"\"\n\n# \u2554\u2550\u2561 19706e83-7306-4383-ac7f-55346d977d6e\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 688eae45-6d4f-467a-a00b-911355528f66\nmd\"\"\"\n# Inflation Swap Example\n\"\"\"\n\n# \u2554\u2550\u2561 38b29bc7-bd8c-4fe8-bc1a-17e3dc6c79f5\nmd\"\"\"\n- Notional $N$ [$]: $(@bind Nswap Slider(100:100:1000, default=100, show_value=true))\n- Inflation Swap Rate $f$ [%]: $(@bind fswap Slider(0:0.25:10, default=3, show_value=true))\n- Time to Maturity $T$ [years]: $(@bind Tswap Slider(1:1:30, default=5, show_value=true))\n- Annual Inflation Rate $I$ [%]: $(@bind Iswap Slider(0:0.25:10, default=2, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 d320b509-675c-45a8-8b75-bdc449bbc6fe\nMarkdown.parse(\"\n- Cash flow on the fixed leg of the inflation swap:\n\n\\$N \\\\times \\\\left[ (1+f)^T - 1 \\\\right] = $(Nswap) \\\\times \\\\left[ (1+ $(fswap/100))^{$(Tswap)} -1 \\\\right]= $(roundmult(Nswap*( (1+fswap/100)^Tswap -1),1e-4))\\$\n\t\n- Cash flow on the floating leg of the swap:\n\t\n\\$N \\\\times \\\\left[ (1+I)^T - 1 \\\\right]]= $(Nswap) \\\\times \\\\left[ (1+$(Iswap/100))^{$(Tswap)} - 1 \\\\right]=$(roundmult(Nswap*((1+Iswap/100)^Tswap-1),1e-4))\\$\n\t\n- Net cash flow of inflation buyer: $(roundmult(Nswap*(1+Iswap/100)^Tswap - Nswap*(1+fswap/100)^Tswap,1e-4))\n\")\n\n# \u2554\u2550\u2561 71606cb8-7ed2-4a70-b229-017cc0d21714\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 53c77ef1-899d-47c8-8a30-ea38380d1614\nmd\"\"\"\n## Wrap-Up\n\"\"\"\n\n# \u2554\u2550\u2561 670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Our goals for today</legend>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\" checked>Introduction to the U.S. Treasury Securities \t\tMarket<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\" checked>Treasury Bills<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\" checked>Treasury Notes and Bonds<br>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\" checked>Treasury STRIPS<br>           \n\t\t<br>\n\t\t<input type=\"checkbox\" value=\"\" checked>Treasury TIPS<br> \n        <br>\n\t    <input type=\"checkbox\" value=\"\" checked>Inflation Swaps<br>      \n\t\t<br>\n\t  \t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 2ee2c328-5ebe-488e-94a9-2fce2200484c\nmd\"\"\"\n# Reading \nFabozzi, Fabozzi, 2021, Bond Markets, Analysis, and Strategies, 10th Edition\\\nChapter 7\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nDates = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\nHTTP = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\nHypertextLiteral = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nLaTeXStrings = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\nLogging = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPrintf = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\nXLSX = \"fdbf4ff8-1666-58a4-91e7-1b58723a45e0\"\n\n[compat]\nCSV = \"~0.9.11\"\nDataFrames = \"~1.3.1\"\nHTTP = \"~0.9.17\"\nHypertextLiteral = \"~0.9.3\"\nLaTeXStrings = \"~1.3.0\"\nPlutoUI = \"~0.7.39\"\nXLSX = \"~0.7.8\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.3\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.CSV]]\ndeps = [\"CodecZlib\", \"Dates\", \"FilePathsBase\", \"InlineStrings\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"49f14b6c56a2da47608fe30aed711b5882264d7a\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.9.11\"\n\n[[deps.CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"44c37b4636bc54afac5c574d2d02b625349d6582\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.41.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"ae02104e835f219b8930c7664b8012c93475c340\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.3.2\"\n\n[[deps.DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"3daef5523dd2e769dad2365274f760ff5f282c7d\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.11\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[deps.Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"FileWatching\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.EzXML]]\ndeps = [\"Printf\", \"XML2_jll\"]\ngit-tree-sha1 = \"0fa3b52a04a4e210aeb1626def9c90df3ae65268\"\nuuid = \"8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615\"\nversion = \"1.1.0\"\n\n[[deps.FilePathsBase]]\ndeps = [\"Compat\", \"Dates\", \"Mmap\", \"Printf\", \"Test\", \"UUIDs\"]\ngit-tree-sha1 = \"04d13bfa8ef11720c24e4d840c0033d145537df7\"\nuuid = \"48062228-2e41-5def-b9a4-89aafe57970f\"\nversion = \"0.9.17\"\n\n[[deps.FileWatching]]\nuuid = \"7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[deps.Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[deps.HTTP]]\ndeps = [\"Base64\", \"Dates\", \"IniFile\", \"Logging\", \"MbedTLS\", \"NetworkOptions\", \"Sockets\", \"URIs\"]\ngit-tree-sha1 = \"0fa77022fe4b511826b39c894c90daf5fce3334a\"\nuuid = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\nversion = \"0.9.17\"\n\n[[deps.Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[deps.HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[deps.IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[deps.IniFile]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"098e4d2c533924c921f9f9847274f2ad89e018b8\"\nuuid = \"83e8ac13-25f8-5344-8a64-a9f2b223428f\"\nversion = \"0.5.0\"\n\n[[deps.InlineStrings]]\ndeps = [\"Parsers\"]\ngit-tree-sha1 = \"61feba885fac3a407465726d0c330b3055df897f\"\nuuid = \"842dd82b-1e85-43dc-bf29-5d0ee9dffc48\"\nversion = \"1.1.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[deps.IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[deps.JLLWrappers]]\ndeps = [\"Preferences\"]\ngit-tree-sha1 = \"abc9885a7ca2052a736a600f7fa66209f96506e1\"\nuuid = \"692b3bcd-3c85-4b1f-b108-f13ce0eb3210\"\nversion = \"1.4.1\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[deps.LaTeXStrings]]\ngit-tree-sha1 = \"f2355693d6778a178ade15952b7ac47a4ff97996\"\nuuid = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\nversion = \"1.3.0\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.Libiconv_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"42b62845d70a619f063a7da093d995ec8e15e778\"\nuuid = \"94ce4f54-9a6c-5748-9c1c-f9c7231a4531\"\nversion = \"1.16.1+1\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MbedTLS]]\ndeps = [\"Dates\", \"MbedTLS_jll\", \"Random\", \"Sockets\"]\ngit-tree-sha1 = \"1c38e51c3d08ef2278062ebceade0e46cefc96fe\"\nuuid = \"739be429-bea8-5141-9913-cc70e7f3736d\"\nversion = \"1.0.3\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[deps.Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"bf210ce90b6c9eed32d25dbcae1ebc565df2687f\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.2\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[deps.OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"0b5cfbb704034b5b4c1869e36634438a047df065\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.1\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"8d1f54886b9037091edf146b517989fc4a09efec\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.39\"\n\n[[deps.PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"db3a23166af8aebf4db5ef87ac5b00d36eb771e2\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.4.0\"\n\n[[deps.Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"2cf929d64681236a2e074ffafb8d568733d2e6af\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.3\"\n\n[[deps.PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"dfb54c4e414caa595a1f2ed759b160f5a3ddcba5\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.3.1\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = 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\u255f\u2500def2b50e-a660-4d98-9efe-67413ef76d37\n# \u255f\u2500dd30bcd0-939d-4907-b4e9-6a559a71426d\n# \u255f\u250082ba10db-c126-4355-9311-26c1d0ad707c\n# \u255f\u250022c5f11f-d1b0-4780-ae3d-eb0a5c7d5f14\n# \u255f\u250051ea0a30-8558-455b-81e3-79fbc8fe59e9\n# \u255f\u25008921aae4-2d39-4ddd-bf19-6b395253e488\n# \u255f\u25004ce2544f-c66d-4d56-a39d-fc7d5ee6d633\n# \u255f\u2500f69d0a46-263e-4526-a8b2-ef4f4ee5b762\n# \u255f\u250068942dbc-0cdc-4201-a10e-bfbb2b56c73f\n# \u255f\u25008ffac44e-56cd-435e-be5c-d72e8be35941\n# \u255f\u2500d6e02f5e-cb9e-440c-a335-a487db0d0930\n# \u255f\u25005b9336cd-e88d-478f-8711-bde0da5724be\n# \u255f\u2500b401991e-ef32-4cee-8630-d9f54635aec3\n# \u255f\u25000dee4432-9118-428a-9b49-ca2648e14fcd\n# \u255f\u25006e529940-7f36-4bdd-8adb-1f34b2825d32\n# \u255f\u2500be39cbe4-9b57-4827-b649-45655cb49270\n# \u255f\u250027a48326-0265-4334-b7cd-0c2fe3595999\n# \u255f\u25009ffda585-493f-4ffe-b360-dcf1420f107c\n# \u255f\u25002533a3d3-d368-4036-bea4-79645fe9adf2\n# \u255f\u250019eceb43-c982-4e99-a9d4-de229ac948e7\n# \u255f\u2500636cdd0c-88c9-4c5a-833a-db2a49014c75\n# \u255f\u250029718094-db53-434b-ae91-5d93d06d857b\n# \u255f\u2500e4458558-d2be-4d88-82cc-db1335c49aa8\n# \u255f\u250089646676-3a58-437b-ba49-df1732b43ba7\n# \u255f\u250019706e83-7306-4383-ac7f-55346d977d6e\n# \u255f\u2500688eae45-6d4f-467a-a00b-911355528f66\n# \u255f\u250038b29bc7-bd8c-4fe8-bc1a-17e3dc6c79f5\n# \u255f\u2500d320b509-675c-45a8-8b75-bdc449bbc6fe\n# \u255f\u250071606cb8-7ed2-4a70-b229-017cc0d21714\n# \u255f\u250053c77ef1-899d-47c8-8a30-ea38380d1614\n# \u255f\u2500670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\n# \u255f\u25002ee2c328-5ebe-488e-94a9-2fce2200484c\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "e040dc327ce6c8db498c5707914d0ef991b78ba7", "size": 47039, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/Lecture_02_TreasuryMarket.jl", "max_stars_repo_name": 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NO\n2. NO", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.175538067153742, "lm_q1q2_score": 0.08160792325562205}}
{"text": "\"\"\"\nPackage: Forecast\n\n    co2(full = false)\n\nReturn dataset with atmospheric Carbon Dioxide Dry Air Mole Fractions from quasi-continuous measurements at Mauna Loa, Hawaii.\n\nK.W. Thoning, A.M. Crotwell, and J.W. Mund (2020), Atmospheric Carbon Dioxide Dry Air Mole Fractions from continuous measurements at Mauna Loa, Hawaii, Barrow, Alaska, American Samoa and South Pole. 1973-2019, Version 2020-08 National Oceanic and Atmospheric Administration (NOAA), Global Monitoring Laboratory (GML), Boulder, Colorado, USA https://doi.org/10.15138/yaf1-bk21 FTP path: ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/co2/in-situ/surface/\n\n# Arguments\n- `full`: if `true` Returns the full original dataset from 1973 to 2020 in a DataFrame, otherwise returns the subset used in \"STL: A Seasonal-Trend Decomposition Procedure Based on Loess\" from Cleveland et. al. Its default value is `false`.\n\n# Returns\nDataframe containing the descrived dataset.\n\n# Examples\n```julia-repl\njulia> co2()\n[ Info: Dataset used in Cleveland et al. paper\n4612\u00d72 DataFrame\n  Row \u2502 date        co2        \n      \u2502 Date        Float64?   \n\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    1 \u2502 1974-05-17      333.38\n    2 \u2502 1974-05-18      333.11\n    3 \u2502 1974-05-19      333.46\n   [...]\n```\n\"\"\"\nfunction co2(full::Bool = false)\n\n    data = \"data/co2.csv.gz\"\n    path = joinpath(splitdir(@__DIR__)[1], data)\n    \n    co2_df = GZip.open(path, \"r\") do io\n        CSV.read(io,DataFrame)\n    end\n\n    if full\n        @info \"Full dataset from 1973 to 2020\"\n        return co2_df\n    else\n        @info \"Dataset used in Cleveland et al. paper\"\n    end\n\n    # recode missing values\n    co2_df.value = replace(co2_df.value, -999.99 => missing)\n\n    dates_co2 = Dates.Date(1973,1,1):Dates.Day(1):Dates.Date(2019,12,31)\n    co2_sdf = DataFrame([dates_co2, co2_df.value],[:date,:co2])\n\n    # paper dates\n    # using 17 may instead 17 april (as the paper claims)\n    # since there seems to be no data for april in 1974\n    dates_co2_stl = Dates.Date(1974,5,17):Dates.Day(1):Dates.Date(1986,12,31)\n\n    # revome leap year day\n    dates_co2_stl = filter(dates_co2_stl) do x\n        (Dates.isleapyear(x) & (Dates.month(x) == 2)) ? Dates.day(x) != 29 : true\n    end\n\n    @where(co2_sdf, in.(:date,  Ref(dates_co2_stl)))\n    \nend\n\n\n\"\"\"\nPackage: Forecast\n\n    seaborne(full = false)\n\nReturn estimates of world seaborne trade from AIS data collected by Marine Traffic.\n\nBy default a DataFrame containing deadweight imports for France, Germany and the United Kingdom from 2015-04-01 to 2021-05-02 is returned, otherwise a DataFrame is returned for the same countries with import and exports for the below fields:\n\nnum_pc:     number of port calls\nmtc:        metric tons of cargo\ndwt:        deadweight tonnage\nsuffix_ma:  30-day moving averages\n\nData available at UN COMTRADE Monitor.Cerdeiro, Komaromi, Liu and Saeed (2020). \n\n# Returns\n Dataframe containing the seaborne dataset.\n\n# Examples\n```julia-repl\njulia> seaborne()\n[ Info: Seaborne deadweight trade imports from AIS\n2199\u00d74 DataFrame\n  Row \u2502 Date        France   Germany  UK     \n      \u2502 Date        Int64    Int64    Int64  \n\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    1 \u2502 2015-04-01   507946   878377  599573\n    2 \u2502 2015-04-02   332043  1501614  772714\n    3 \u2502 2015-04-03   810077   941663  262994\n   [...]\n```\n\"\"\"\nfunction seaborne(full::Bool = false)\n\n    data = \"data/seaborne.csv.gz\"\n    path = joinpath(splitdir(@__DIR__)[1], data)\n    \n    sb_df = GZip.open(path, \"r\") do io\n        CSV.read(io, DataFrame, dateformat = \"mm/dd/yyyy HH:MM:SS pp\", types = Dict(:date => Date))\n    end\n\n    if full\n        @info \"Full dataset with estimates of world seaborne trade from AIS\"\n        return sb_df\n    else\n        @info \"Seaborne deadweight trade imports from AIS\"\n    end\n\n    # Group by Country and Flow to select Imports\n    gbt = groupby(sb_df, [:country_name, :flow], sort=true)\n    DataFrame(Dict(:Date => gbt[1].date ,\n                   :UK => gbt[1].dwt, :Germany => gbt[3].dwt, :France => gbt[5].dwt))\nend\n\n\n\"\"\"\nPackage: Forecast\n\n    quakes()\n\nReturn the number of earthquakes per year on earth with a magnitude higher or equal to six from 1950 to 2020. The data has been collected from https://earthquake.usgs.gov/ and aggregated.\n\n# Examples\n```julia-repl\njulia> quakes()\n71\u00d72 DataFrame\n Row \u2502 year        quakes \n     \u2502 Date        Int64  \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 1950-01-01     138\n   2 \u2502 1951-01-01     151\n   3 \u2502 1952-01-01     181\n   [...]\n```\n\"\"\"\nfunction quakes()\n\n    data = \"data/quakes.csv.gz\"\n    path = joinpath(splitdir(@__DIR__)[1], data)\n    \n    qk_df = GZip.open(path, \"r\") do io\n        CSV.read(io,DataFrame, dateformat = \"yyyy\", types = Dict(:year => Date))\n    end\n\n    qk_df\n    \nend\n\n\"\"\"\nPackage: Forecast\n\n    air()\n\nReturn the classic Box & Jenkins airline data. Monthly totals of international airline passengers from 1949 to 1960.\n\nBox, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) _Time Series Analysis, Forecasting and Control._ Third Edition. Holden-Day. Series G.\n\n# Returns\nDataframe containing the descrived dataset.\n\n# Examples\n```julia-repl\njulia> air()\n71\u00d72 DataFrame\n Row \u2502 year        quakes \n     \u2502 Date        Int64  \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 1950-01-01     138\n   2 \u2502 1951-01-01     151\n   3 \u2502 1952-01-01     181\n   [...]\n```\n\"\"\"\nfunction air()\n\n    data = \"data/air.csv.gz\"\n    path = joinpath(splitdir(@__DIR__)[1], data)\n    \n    df = GZip.open(path, \"r\") do io\n        CSV.read(io,DataFrame)\n    end\n\n    t =  DataFrame([Date.(df.year,df.month)],[:Date])\n    x = DataFrame([df.passengers],[:Passengers])\n\n    [t x]\nend\n\n\n\"\"\"\nPackage: Forecast\n\n    london()\n\nReturn ten years of monthly data about weather and crime in Greater London from 2008 to 2018.\n\nData has been collected and joined from london.gov.uk and metoffice.gov.uk (Heathrow Station).\n\n# Weather Variables\n\n- `MaxTemp`:  Mean daily maximum temperature in C\u00b0\n- `MinTemp`:  Mean daily minimum temperature in C\u00b0\n- `AirFrost`: Days of air frost\n- `Rain`:     Total rainfall in mm\n- `Sun`:      Total sunshine durationin hours\n\n# Crime Variables and its aggregated categories\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502                       Crime \u2502                               Category \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502                    Burglary \u2502            Burglary in Other Buildings \u2502\n\u2502                             \u2502                 Burglary in a Dwelling \u2502\n\u2502                      Damage \u2502            Criminal Damage To Dwelling \u2502\n\u2502                             \u2502       Criminal Damage To Motor Vehicle \u2502\n\u2502                             \u2502      Criminal Damage To Other Building \u2502\n\u2502                             \u2502                  Other Criminal Damage \u2502\n\u2502                       Drugs \u2502                       Drug Trafficking \u2502\n\u2502                             \u2502                            Other Drugs \u2502\n\u2502                             \u2502                    Possession Of Drugs \u2502\n\u2502                       Fraud \u2502                     Counted per Victim \u2502\n\u2502                             \u2502                  Other Fraud & Forgery \u2502\n\u2502                       Other \u2502                         Going Equipped \u2502\n\u2502                             \u2502                       Other Notifiable \u2502\n\u2502                     Robbery \u2502                      Business Property \u2502\n\u2502                             \u2502                      Personal Property \u2502\n\u2502                      Sexual \u2502                           Other Sexual \u2502\n\u2502                             \u2502                                   Rape \u2502\n\u2502                       Theft \u2502                  Handling Stolen Goods \u2502\n\u2502                             \u2502 Motor Vehicle Interference & Tampering \u2502\n\u2502                             \u2502                            Other Theft \u2502\n\u2502                             \u2502                     Other Theft Person \u2502\n\u2502                             \u2502               Theft From Motor Vehicle \u2502\n\u2502                             \u2502                       Theft From Shops \u2502\n\u2502                             \u2502          Theft/Taking Of Motor Vehicle \u2502\n\u2502                             \u2502            Theft/Taking of Pedal Cycle \u2502\n\u2502                    Violence \u2502                    Assault with Injury \u2502\n\u2502                             \u2502                         Common Assault \u2502\n\u2502                             \u2502                   Grievous Bodily Harm \u2502\n\u2502                             \u2502                             Harassment \u2502\n\u2502                             \u2502                                 Murder \u2502\n\u2502                             \u2502                       Offensive Weapon \u2502\n\u2502                             \u2502                         Other violence \u2502\n\u2502                             \u2502                           Wounding/GBH \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n# Returns\nDataframe containing the descrived dataset.\n\n# Examples\n```julia-repl\njulia> london()\n132\u00d715 DataFrame\n Row \u2502 Date        MaxTemp  MinTemp  AirFrost  \n     \u2502 Date        Float64  Float64  Int64     \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500[...]\n   1 \u2502 2008-01-01     10.4      4.7         0  \n   2 \u2502 2008-02-01     11.0      2.0         7  \n   3 \u2502 2008-03-01     10.6      3.7         2\n   [...]\n```\n\"\"\"\nfunction london()\n\n    data = \"data/london.csv.gz\"\n    path = joinpath(splitdir(@__DIR__)[1], data)\n    \n    df = GZip.open(path, \"r\") do io\n        CSV.read(io,DataFrame)\n    end\n\n    df = GZip.open(path, \"r\") do io\n        CSV.read(io, DataFrame, dateformat = \"yyyy-mm-dd\", types = Dict(:Date => Date))\n    end\n\n    df\n    \nend\n", "meta": {"hexsha": "47a4c5071a29696d5a6426d4f88f2df603d19fa7", "size": 9581, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets.jl", "max_stars_repo_name": "pitmonticone/Forecast.jl", "max_stars_repo_head_hexsha": "6827f904b9d197f5dcc27e8507931d586a37b968", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-01T00:13:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-01T00:13:29.000Z", "max_issues_repo_path": "src/datasets.jl", "max_issues_repo_name": "pitmonticone/Forecast.jl", "max_issues_repo_head_hexsha": "6827f904b9d197f5dcc27e8507931d586a37b968", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datasets.jl", "max_forks_repo_name": "pitmonticone/Forecast.jl", "max_forks_repo_head_hexsha": "6827f904b9d197f5dcc27e8507931d586a37b968", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3832752613, "max_line_length": 453, "alphanum_fraction": 0.5161256654, "num_tokens": 2474, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.16451645880021026, "lm_q1q2_score": 0.08097304915226256}}
{"text": "function prefix_bounds(a, b)\nfor i = 2:size(a, 1)\na[i] = b[i-1] + b[i]\nend\nend\nfunction prefix_inbounds(a, b)\n@inbounds for i = 2:size(a, 1)\na[i] = b[i-1] + b[i]\nend\nend", "meta": {"hexsha": "89f181d0b35e4caecffd05962c68537809c6974a", "size": 169, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Module 2/Chapter06/bound_check.jl", "max_stars_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_stars_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_stars_repo_stars_event_min_datetime": "2017-02-12T15:36:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T03:30:39.000Z", "max_issues_repo_path": "Module 2/Chapter06/bound_check.jl", "max_issues_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_issues_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Module 2/Chapter06/bound_check.jl", "max_forks_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_forks_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2017-02-10T16:19:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T11:46:44.000Z", "avg_line_length": 16.9, "max_line_length": 30, "alphanum_fraction": 0.6153846154, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.16885694795909767, "lm_q1q2_score": 0.08047378533802405}}
{"text": "# cache such that creating svgs from LaTeX don't need to be created every time\n# this is also used for test cases such that `tex2svg` doesn't need to be installed on Github Objects\ninclude(\"latexsvgfile.jl\")\nlatex(text::LaTeXString) = latex(text, O)\nlatex(text::LaTeXString, pos::Point, valign::Symbol, halign::Symbol) =\n    latex(text, pos, :stroke, valign = valign, halign = halign)\nlatex(text::LaTeXString, x, y) = latex(text, Point(x, y), :stroke)\nlatex(text::LaTeXString, x, y, valign::Symbol, halign::Symbol) =\n    latex(text, Point(x, y), :stroke, valign = valign, halign = halign)\nlatex(text::LaTeXString, pos::Point; valign = :top, halign = :left) =\n    latex(text, pos, :stroke, valign = valign, halign = halign)\n\n\"\"\"\n    latex(text::LaTeXString, pos::Point, object::Symbol; valign = :top, halign = :left)\n\nAdd the latex string `text` to the top left corner of the LaTeX path.\nCan be added to `Luxor.jl` graphics via [`Video`](@ref).\n\n**NOTES:**\n- **This only works if `tex2svg` is installed.**\n    It can be installed using the following command (you may have to prefix this command with `sudo` depending on your installation):\n\n        npm install -g mathjax-node-cli\n\n- **The `latex` method must be called from within an [`Object`](@ref).**\n\n# Arguments\n- `text::LaTeXString`: a LaTeX string to render.\n- `pos::Point`: position of the upper left corner of the latex text. Default: `O`\n    - can be written as `x, y` instead of `Point(x, y)`\n- `object::Symbol`: graphics objects defined by `Luxor.jl`. Default `:stroke`.\nAvailable objects:\n  - `:stroke` - Draws the latex string on the canvas. For more info check `Luxor.strokepath`\n  - `:path` - Creates the path of the latex string but does not render it to the canvas.\n\n# Keywords:\n  - `valign::Symbol=:top`: vertical alignment with respect to the specified `pos` parameter.\n      - Options available are `:top`, `:middle`, `:bottom`\n  - `halign::Symbol=:left`: horizontal alignment with respect to the specified `pos` parameter.\n      - Options available are `:left`, `:center/:centre`, `:right`\n\n# Throws\n- `IOError`: mathjax-node-cli is not installed\n\n# Warning\nShows a warning if either of the alignment options are unrecognised.\n\n# Example\n\n```julia\nusing Javis\nusing LaTeXStrings\n\nfunction ground(args...)\n    background(\"white\")\n    sethue(\"black\")\nend\n\nfunction draw_latex(video, object, frame)\n    fontsize(50)\n    x = 100\n    y = 120\n    latex(L\"\\\\sqrt{5}\", x, y)\nend\n\ndemo = Video(500, 500)\nBackground(1:2, ground)\nObject(draw_latex)\nrender(demo; pathname = \"latex.gif\")\n```\n\n\"\"\"\nfunction latex(\n    text::LaTeXString,\n    pos::Point,\n    draw_object::Symbol;\n    valign = :top,\n    halign = :left,\n)\n    object = CURRENT_OBJECT[1]\n    opts = object.opts\n    t = get(opts, :draw_text_t, 1.0)\n    return animate_latex(text, pos, t, valign, halign, draw_object)\nend\n\nfunction animate_latex(text, pos::Point, t, valign::Symbol, halign::Symbol, object)\n    svg = get_latex_svg(text)\n    object == :stroke && (object = :fill)\n\n    w, h = svgwh(svg)\n    halignment = findfirst(isequal(halign), (:left, :center, :right, :centre))\n\n    if halignment === nothing\n        @warn \"Unknown horizontal alignment option: $(halign). Defaulting to left alignment\"\n        halignment = 1\n    elseif halignment == 4\n        halignment = 2\n    end\n\n    textpointx = pos.x - (0, w / 2, w)[halignment]\n    valignment = findfirst(isequal(valign), (:top, :middle, :bottom))\n\n    if valignment === nothing\n        @warn \"Unknown vertical alignment option: $(valign). Defaulting to top alignment\"\n        valignment = 1\n    end\n\n    textpointy = pos.y - (0, h / 2, h)[valignment]\n    upperleft = Point(textpointx, textpointy)\n\n    if t >= 1\n        translate(upperleft)\n        pathsvg(svg)\n        do_action(object)\n        translate(-upperleft)\n        return\n    end\n\n    translate(upperleft)\n    pathsvg(svg)\n    do_action(:clip)\n    r = t * sqrt(w^2 + h^2)\n    circle(O, r, :fill)\n    translate(-upperleft)\nend\n\"\"\"\n    strip_eq(text::LaTeXString)\n\nStrips `\\$\\$` from `text.s` if present and returns the resulting string. \n\n# Arguments\n- `text::LaTeXString`: a LaTeX string\n\"\"\"\nfunction strip_eq(text::LaTeXString)\n    ts = text.s\n    if ts[1] == '$'\n        ts = ts[2:(end - 1)]\n    end\n    ts\nend\n\n# \\todo update LaTeXSVG cache to use output of strip_eq as the key. See https://github.com/JuliaAnimators/Javis.jl/pull/307#issuecomment-749616375\nfunction get_latex_svg(text::LaTeXString)\n    # check if it's cached\n    if haskey(LaTeXSVG, text)\n        svg = LaTeXSVG[text]\n    else\n        ts = replace(strip_eq(text), \"\\n\" => \" \")\n        command = if Sys.iswindows()\n            `cmd /C tex2svg $ts`\n        else\n            `tex2svg $ts`\n        end\n        try\n            svg = read(command, String)\n        catch e\n            @warn \"Using LaTeX needs the program `tex2svg` which might not be installed\"\n            @info \"It can be installed using `npm install -g mathjax-node-cli`\"\n            throw(e)\n        end\n        LaTeXSVG[text] = svg\n    end\n    return svg\nend\n", "meta": {"hexsha": "1295d842146248905c0a995286835d025253a9fe", "size": 5035, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/latex.jl", "max_stars_repo_name": "ArbitRandomUser/Javis.jl", "max_stars_repo_head_hexsha": "e4e108770ca4093e891df9c5b8209bd5c8ffb802", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 97, "max_stars_repo_stars_event_min_datetime": "2021-11-01T22:54:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T02:08:55.000Z", "max_issues_repo_path": "src/latex.jl", "max_issues_repo_name": "ArbitRandomUser/Javis.jl", "max_issues_repo_head_hexsha": "e4e108770ca4093e891df9c5b8209bd5c8ffb802", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41, "max_issues_repo_issues_event_min_datetime": "2021-11-04T22:18:09.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-17T21:45:20.000Z", "max_forks_repo_path": "src/latex.jl", "max_forks_repo_name": "ArbitRandomUser/Javis.jl", "max_forks_repo_head_hexsha": "e4e108770ca4093e891df9c5b8209bd5c8ffb802", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2021-11-15T13:23:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-28T20:53:50.000Z", "avg_line_length": 30.5151515152, "max_line_length": 146, "alphanum_fraction": 0.6490566038, "num_tokens": 1414, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.1645164608483867, "lm_q1q2_score": 0.08033065608510452}}
{"text": "## top-level submission file\n\n\n\"\"\"\n    f(a, b) = c\nA function that adds the inputs `a` and `b`\n\"\"\"\nfunction f(a, b)\n    # write your code here. Remember to return the sum of a and b!\nend\n", "meta": {"hexsha": "b4a8b4b9debba41a97db6921ee1d8c68c311b28b", "size": 187, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "AA222_spring_2021/AA222Project0/project0_jl/project0.jl", "max_stars_repo_name": "wo315/AA222_Collections", "max_stars_repo_head_hexsha": "c7845f05f09df052ad81b1a1350a2294ed7e8c16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2021-11-14T05:57:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T08:10:03.000Z", "max_issues_repo_path": "AA222_spring_2021/AA222Project0/project0_jl/project0.jl", "max_issues_repo_name": "xieguangci/AA222_Collections", "max_issues_repo_head_hexsha": "2b4a8f6e96966ad7ed50b7071c8eded6f642eb9c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AA222_spring_2021/AA222Project0/project0_jl/project0.jl", "max_forks_repo_name": "xieguangci/AA222_Collections", "max_forks_repo_head_hexsha": "2b4a8f6e96966ad7ed50b7071c8eded6f642eb9c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2021-11-14T06:44:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T04:09:39.000Z", "avg_line_length": 17.0, "max_line_length": 66, "alphanum_fraction": 0.6256684492, "num_tokens": 55, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.39233683016710835, "lm_q2_score": 0.2043418950913969, "lm_q1q2_score": 0.08017085139049847}}
{"text": "# Author: Ashwani Rathee\n# Program to swap two numbers\n\nprintln(\"Enter 1st number: \")\nfirst=readline()\nfirst=parse(Int64,first)\nprintln(\"Enter 2nd number: \")\nsecond=readline()\nsecond=parse(Int64,second)\ntemporary=first\nfirst=second\nsecond=temporary\nprintln(\"First number: \",first,\" Second Number: \",second)\n\n", "meta": {"hexsha": "8b7e9b03f74bc26b51c2410e8969de647ab666a5", "size": 308, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Examples/5-SwapNumbers.jl", "max_stars_repo_name": "ashwani-rathee/Learning--Julia", "max_stars_repo_head_hexsha": "db4b4fd10f90540760098c5609901952922f62ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Examples/5-SwapNumbers.jl", "max_issues_repo_name": "ashwani-rathee/Learning--Julia", "max_issues_repo_head_hexsha": "db4b4fd10f90540760098c5609901952922f62ae", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Examples/5-SwapNumbers.jl", "max_forks_repo_name": "ashwani-rathee/Learning--Julia", "max_forks_repo_head_hexsha": "db4b4fd10f90540760098c5609901952922f62ae", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-12T02:24:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-12T02:24:38.000Z", "avg_line_length": 20.5333333333, "max_line_length": 57, "alphanum_fraction": 0.7597402597, "num_tokens": 84, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.1688569500503904, "lm_q1q2_score": 0.0798158902013413}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.21\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 bcaefa4e-85c6-11eb-3316-0db4881ebd30\nbegin\n\tusing PlutoUI\n\tusing NativeSVG\nend\n\n# \u2554\u2550\u2561 13d62b06-85c7-11eb-00b9-0399467b1ab5\nmd\"\"\"# Dictionaries\n\nThis chapter presents another built-in type called a dictionary.\"\"\"\n\n# \u2554\u2550\u2561 1f1c885e-85c7-11eb-1452-13a8943ebc90\nmd\"\"\"## A Dictionary Is a Mapping\n\nA *dictionary* is like an array, but more general. In an array, the indices have to be integers; in a dictionary they can be (almost) any type.\n\nA dictionary contains a collection of indices, which are called *keys*, and a collection of *values*. Each key is associated with a single value. The association of a key and a value is called a *key-value* pair, or sometimes an *item*.\n\nIn mathematical language, a dictionary represents a *mapping* from keys to values, so you can also say that each key \u201cmaps to\u201d a value. As an example, we\u2019ll build a dictionary that maps from English to Spanish words, so the keys and the values are all strings.\n\nThe function `Dict` creates a new dictionary with no items (because Dict is the name of a built-in function, you should avoid using it as a variable name):\n\n```julia\njulia> eng2sp = Dict() \nDict{Any,Any} with 0 entries\n```\n\nThe types of the keys and values in the dictionary are specified in curly braces: here, both are of type `Any`.\n\n\nThe dictionary is empty. To add items to the dictionary, you can use square brackets:\n\n```julia\njulia> eng2sp[\"one\"] = \"uno\";\n```\n\nThis line creates an item that maps from the key `\"one\"` to the value `\"uno\"`. If we print the dictionary again, we see a key-value pair with an arrow `=>` between the key and value:\n\n```julia\njulia> eng2sp \nDict{Any,Any} with 1 entry:\n  \"one\" => \"uno\"\n```\n\nThis output format is also an input format. For example, you can create a new dictionary with three items as follows:\n\n```julia\njulia> eng2sp = Dict(\"one\" => \"uno\", \"two\" => \"dos\", \"three\" => \"tres\") Dict{String,String} with 3 entries:\n  \"two\"   => \"dos\"\n  \"one\"   => \"uno\"\n  \"three\" => \"tres\"\n```\n\nHere all the initial keys and values are strings, so a `Dict{String,String}` is created. \n\nThe order of the items in a dictionary is unpredictable. If you type the same example\non your computer, you might get a different result.\n\nBut that\u2019s not a problem because the elements of a dictionary are never indexed with integer indices. Instead, you use the keys to look up the corresponding values:\n\n```julia\njulia> eng2sp[\"two\"] \n\"dos\"\n```\n\nThe key `\"two\"` always maps to the value `\"dos\"`, so the order of the items doesn\u2019t matter.\n\nIf the key isn\u2019t in the dictionary, you get an exception:\n\n```julia\njulia> eng2sp[\"four\"]\nERROR: KeyError: key \"four\" not found\n```\n\nThe `length` function works on dictionaries; it returns the number of key-value pairs: \n\n```julia\njulia> length(eng2sp)\n3\n```\n\nThe function keys returns a collection with the keys of the dictionary: \n\n```julia\njulia> ks = keys(eng2sp);\n\njulia> print(ks) \n[\"two\", \"one\", \"three\"]\n```\n\nNow you can use the `\u2208` operator to see whether something appears as a key in the dictionary:\n\n```julia\njulia> \"one\" \u2208 ks \ntrue\njulia> \"uno\" \u2208 ks \nfalse\n```\n\nTo see whether something appears as a value in a dictionary, you can use the function `values`, which returns a collection of values, and then use the `\u2208` operator:\n\n```julia\njulia> vs = values(eng2sp); \n\njulia> \"uno\" \u2208 vs\ntrue\n```\n\nThe `\u2208` operator uses different algorithms for arrays and dictionaries. For arrays, it searches the elements of the array in order, as described in \u201cSearching\u201d. As the array gets longer, the search time gets longer in direct proportion.\n\nFor dictionaries, Julia uses an algorithm called a *hash table* that has a remarkable property: the `\u2208` operator takes about the same amount of time no matter how many items are in the dictionary.\n\"\"\"\n\n# \u2554\u2550\u2561 27e4cf0c-85c8-11eb-0d95-ab9ddc845387\nmd\"\"\"## Dictionaries as Collections of Counters\n\nSuppose you are given a string and you want to count how many times each letter appears. There are several ways you could do it:\n\n* You could create 26 variables, one for each letter of the alphabet. Then you could traverse the string and, for each character, increment the corresponding counter, probably using a chained conditional.\n* You could create an array with 26 elements. Then you could convert each character to a number (using the built-in function Int), use the number as an index into the array, and increment the appropriate counter.\n* You could create a dictionary with characters as keys and counters as the corresponding values. The first time you see a character, you would add an item to the dictionary. After that you would increment the value of an existing item.\n\nEach of these options performs the same computation, but each of them implements that computation in a different way.\n\nAn *implementation* is a way of performing a computation. Some implementations are better than others. For example, an advantage of the dictionary implementation is that we don\u2019t have to know ahead of time which letters appear in the string and we only have to make room for the letters that do appear.\n\nHere is what the code might look like:\n\n```julia\nfunction histogram(s) \n\td = Dict()\n\tfor c in s\n\t\tif c \u2209 keys(d)\n\t\t\td[c] = 1 \n\t\telse\n\t\t\td[c] += 1 \n\t\tend\n\tend\n\td\nend\n```\n\nThe name of the function is `histogram`, which is a statistical term for a collection of counters (or frequencies).\n\n\nThe first line of the function creates an empty dictionary. The `for` loop traverses the string. Each time through the loop, if the character `c` is not in the dictionary, we create a new item with key `c` and the initial value `1` (since we have seen this letter once). If `c` is already in the dictionary we increment `d[c]`.\n\nHere\u2019s how it works:\n\n```julia\njulia> h = histogram(\"brontosaurus\") Dict{Any,Any} with 8 entries:\n  'n' => 1\n  's' => 2\n  'a' => 1\n  'r' => 2\n  't' => 1\n  'o' => 2\n  'u' => 2\n  'b' => 1\n```\n\nThe histogram indicates that the letters *a* and *b* appear once, *o* appears twice, and so on.\n\nDictionaries have a function called `get` that takes a key and a default value. If the key appears in the dictionary, `get` returns the corresponding value; otherwise, it returns the default value. For example:\n\n```julia\njulia> h = histogram(\"a\") \nDict{Any,Any} with 1 entry:\n  'a' => 1\njulia> get(h, 'a', 0) \n1\njulia> get(h, 'b', 0) \n0\n```\n\"\"\"\n\n# \u2554\u2550\u2561 c8dff15e-85c8-11eb-1230-bd39702cf460\nmd\"\"\"#### Exercise 11-1\n\nUse `get` to write `histogram` more concisely. You should be able to eliminate the `if` statement. What does the function `get!` do? Can you use it to simplify `histogram`?\n\"\"\"\n\n# \u2554\u2550\u2561 0b2e93b0-85c9-11eb-0dc3-61b1cbfb022b\nmd\"\"\"## Looping and Dictionaries\n\nYou can traverse the keys of a dictionary in a for statement. For example, `printhist` prints each key and the corresponding value:\n\n```julia\nfunction printhist(h) \n\tfor c in keys(h)\n\t\tprintln(c, \" \", h[c]) \n\tend\nend\n```\n\nHere\u2019s what the output looks like:\n\n```julia\njulia> h = histogram(\"parrot\");\n\njulia> printhist(h) \na 1\nr 2\np 1\no 1 \nt 1\n```\n\nAgain, the keys are in no particular order. To traverse the keys in sorted order, you can combine `sort` and `collect`:\n\n```julia\njulia> for c in sort(collect(keys(h))) \n\t       println(c, \" \", h[c])\n       end\na 1 \no 1 \np 1 \nr 2 \nt 1\n```\n\"\"\"\n\n# \u2554\u2550\u2561 82a2f936-85c9-11eb-1d33-0d9dc98fdac0\nmd\"\"\"## Reverse Lookup\n\nGiven a dictionary `d` and a key `k`, it is easy to find the corresponding value `v = d[k]`. This operation is called a *lookup*.\n\nBut what if you have `v` and you want to find `k`? You have two problems. First, there might be more than one key that maps to the value `v`. Depending on the application, you might be able to pick one, or you might have to make an array that contains all of them. Second, there is no simple syntax to do a *reverse lookup*; you have to search.\n\n!!! danger\n    A reverse lookup is much slower than a forward lookup; if you have to do it often, or if the dictionary gets big, the performance of your program will suffer.\n\nHere is a function that takes a value and returns the first key that maps to that value:\n\n```julia\nfunction reverselookup(d, v) \n\tfor k in keys(d)\n\t\tif d[k]==v \n\t\t\treturn k\n\t\tend \n\tend\n\terror(\"LookupError\") \nend\n```\n\nThis function is yet another example of the search pattern, but it uses a function we haven\u2019t seen before: `error`. The `error` function is used to produce an `ErrorException` that interrupts the normal flow of control. In this case it has the message `\"LookupError\"`, indicating that a key does not exist.\n\n\nIf we get to the end of the loop, that means `v` doesn\u2019t appear in the dictionary as a value, so we throw an exception.\n\nHere is an example of a successful reverse lookup:\n\n```julia\njulia> h = histogram(\"parrot\"); \n\njulia> key = reverselookup(h, 2)\n'r': ASCII/Unicode U+0072 (category Ll: Letter, lowercase)\n```\n\nAnd an unsuccessful one:\n\n```julia\njulia> key = reverselookup(h, 3) \nERROR: LookupError\n```\n\nThe effect when you generate an exception is the same as when Julia throws one: it prints a stacktrace and an error message.\n\n!!! tip\n    Julia provides an optimized way to do a reverse lookup: findall(isequal(3), h).\n\"\"\"\n\n# \u2554\u2550\u2561 6d87ad02-85ca-11eb-2a3d-999d28a7c4b7\nmd\"\"\"## Dictionaries and Arrays\n\nArrays can appear as values in a dictionary. For example, if you are given a dictionary that maps from letters to frequencies, you might want to invert it\u2014that is, create a dictionary that maps from frequencies to letters. Since there might be several letters with the same frequency, each value in the inverted dictionary should be an array of letters.\n\nHere is a function that inverts a dictionary:\n\n```julia\nfunction invertdict(d) \n\tinverse = Dict()\n\tfor key in keys(d) \n\t\tval = d[key]\n\t\tif val \u2209 keys(inverse) \n\t\t\tinverse[val] = [key]\n\t\telse\n\t\t\tpush!(inverse[val], key) \n\t\tend\n\tend\n\tinverse\nend\n```\n\nEach time through the loop, `key` gets a key from `d` and `val` gets the corresponding value. If `val` is not in `inverse`, that means we haven\u2019t seen it before, so we create a new item and initialize it with a *singleton* (an array that contains a single element). Otherwise, we have seen this value before, so we append the corresponding key to the array.\n\nHere is an example:\n\n```julia\njulia> hist = histogram(\"parrot\");\n\njulia> inverse = invertdict(hist) Dict{Any,Any} with 2 entries:\n  2 => ['r']\n  1 => ['a', 'p', 'o', 't']\n```\n\nFigure 11-1 is a state diagram showing `hist` and `inverse`. A dictionary is represented as a box with the key-value pairs inside. If the values are integers, floats, or strings, I draw them inside the box, but I usually draw arrays outside the box, just to keep the diagram simple.\n\"\"\"\n\n# \u2554\u2550\u2561 4fb90040-85cb-11eb-23dd-ad9ea62b94c3\nDrawing(width=720, height=150) do\n\tdefs() do\n        marker(id=\"arrow\", markerWidth=\"10\", markerHeight=\"10\", refX=\"0\", refY=\"3\", orient=\"auto\", markerUnits=\"strokeWidth\") do\n      \t\tpath(d=\"M0,0 L0,6 L9,3 z\", fill=\"black\")\n\t\tend\n\tend\n\ttext(x=105, y=80, font_family=\"JuliaMono, monospace\", text_anchor=\"end\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"hist ->\") \n\tend\n\trect(x=120, y=20, width=150, height=110, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=140, y=40, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'o'\") \n\tend\n\tline(x1=170, y1=35, x2=220, y2=35, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=240, y=40, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\ttext(x=140, y=60, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'a'\") \n\tend\n\tline(x1=170, y1=55, x2=220, y2=55, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=240, y=60, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\ttext(x=140, y=80, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'p'\") \n\tend\n\ttext(x=140, y=100, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'t'\") \n\tend\n\ttext(x=140, y=120, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'r'\") \n\tend\n\tline(x1=170, y1=115, x2=220, y2=115, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=240, y=120, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=170, y1=95, x2=220, y2=95, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=240, y=100, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=170, y1=75, x2=220, y2=75, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=240, y=80, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\ttext(x=410, y=80, font_family=\"JuliaMono, monospace\", text_anchor=\"end\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"inverse ->\") \n\tend\n    rect(x=520, y=10, width=150, height=90, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=540, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=560, y1=25, x2=610, y2=25, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=630, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'o'\") \n\tend\n\ttext(x=540, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=560, y1=45, x2=610, y2=45, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=630, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'a'\") \n\tend\n\ttext(x=540, y=70, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"3\") \n\tend\n\tline(x1=560, y1=65, x2=610, y2=65, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=630, y=70, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'p'\") \n\tend\n\ttext(x=540, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"4\") \n\tend\n\tline(x1=560, y1=85, x2=610, y2=85, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=630, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'t'\") \n\tend\n\trect(x=520, y=110, width=150, height=30, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=540, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=560, y1=125, x2=610, y2=125, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=630, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"'r'\") \n\tend\n\trect(x=420, y=10, width=50, height=130, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=440, y=60, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=460, y1=55, x2=510, y2=55, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=440, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=460, y1=125, x2=510, y2=125, stroke=\"black\", marker_end=\"url(#arrow)\")\nend\n\n# \u2554\u2550\u2561 26c0d704-85cf-11eb-2a13-293b42210e70\nmd\"*Figure 11-1. State diagram.*\"\n\n# \u2554\u2550\u2561 35e8ee92-85cf-11eb-215c-21912c5fc92b\nmd\"\"\"!!! note\n    I mentioned earlier that a dictionary is implemented using a hash table. That means that the keys have to be *hashable*.\n    \n    A *hash* is a function that takes a value (of any kind) and returns an integer. Dictionaries use these integers, called hash values, to store and look up key-value pairs.\n\"\"\"\n\n# \u2554\u2550\u2561 8316e7a0-85cf-11eb-2ecb-df63279cadc6\nmd\"\"\"## Memos\n\nIf you played with the `fibonacci` function from \u201cOne More Example\u201d, you might have noticed that the bigger the argument you provide, the longer the function takes to run. Furthermore, the runtime increases quickly.\n\nTo understand why, consider Figure 11-2, which shows the *call graph* for `fibonacci` with `n = 4`.\n\"\"\"\n\n# \u2554\u2550\u2561 c1e519e8-85cf-11eb-2ce1-4bcd389f56bf\nDrawing(width=720, height=310) do\n\tdefs() do\n        marker(id=\"arrow\", markerWidth=\"10\", markerHeight=\"10\", refX=\"0\", refY=\"3\", orient=\"auto\", markerUnits=\"strokeWidth\") do\n      \t\tpath(d=\"M0,0 L0,6 L9,3 z\", fill=\"black\")\n\t\tend\n\tend\n\trect(x=310, y=10, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=325, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=325, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=340, y1=45, x2=375, y2=45, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=390, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"4\") \n\tend\n\tline(x1=310, y1=60, x2=260, y2=85, stroke=\"black\", marker_end=\"url(#arrow)\")\n\tline(x1=410, y1=60, x2=460, y2=85, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=150, y=90, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=165, y=110, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=165, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=180, y1=125, x2=215, y2=125, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=230, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"3\") \n\tend\n\tline(x1=150, y1=140, x2=130, y2=162, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=75, y=170, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=90, y=190, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=90, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=105, y1=205, x2=140, y2=205, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=155, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=250, y1=140, x2=270, y2=162, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=225, y=170, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=240, y=190, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=240, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=255, y1=205, x2=290, y2=205, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=305, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=75, y1=220, x2=53, y2=242, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=0, y=250, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=15, y=270, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=15, y=290, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=30, y1=285, x2=65, y2=285, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=80, y=290, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=175, y1=220, x2=197, y2=242, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=150, y=250, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=150+15, y=270, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=150+15, y=290, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=150+30, y1=285, x2=150+65, y2=285, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=150+80, y=290, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"0\") \n\tend\n\trect(x=470, y=90, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=485, y=110, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=485, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=500, y1=125, x2=535, y2=125, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=550, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=570, y1=140, x2=590, y2=162, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=720-175, y=170, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=720-175+15, y=190, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=720-175+15, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=720-175+30, y1=205, x2=720-175+65, y2=205, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=720-175+80, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"0\") \n\tend\n\tline(x1=470, y1=140, x2=450, y2=162, stroke=\"black\", marker_end=\"url(#arrow)\")\n\trect(x=720-325, y=170, width=100, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=720-325+15, y=190, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"fibonacci\") \n\tend\n\ttext(x=720-325+15, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"n\") \n\tend\n\tline(x1=720-325+30, y1=205, x2=720-325+65, y2=205, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=720-325+80, y=210, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\nend\n\n# \u2554\u2550\u2561 2a7646fe-85d9-11eb-3bbe-95f975f3901f\nmd\"*Figure 11-2. Call graph.*\"\n\n# \u2554\u2550\u2561 388a8d90-85d9-11eb-08f1-6b622cfb2c11\nmd\"\"\"A call graph shows a set of function frames, with lines connecting each frame to the frames of the functions it calls. At the top of the graph, `fibonacci` with `n = 4` calls `fibonacci` with `n = 3` and `n = 2`. In turn, `fibonacci` with `n = 3` calls `fibonacci` with `n = 2`and `n = 1`, and so on.\n\nCount how many times `fibonacci(0)` and `fibonacci(1)` are called. This is an inefficient solution to the problem, and it gets worse as the argument gets bigger.\n\nOne solution is to keep track of values that have already been computed by storing them in a dictionary. A previously computed value that is stored for later use is called a *memo*. Here is a \u201cmemoized\u201d version of `fibonacci`:\n\n```julia\nconst known = Dict(0=>0, 1=>1)\n\nfunction fibonacci(n) \n\tif n \u2208 keys(known)\n\t\treturn known[n] \n\tend\n    res = fibonacci(n-1) + fibonacci(n-2)\n\tknown[n] = res\n\tres\nend\n```\n\n`known` is a constant dictionary that keeps track of the Fibonacci numbers we already know. It starts with two items: `0` maps to `0` and `1` maps to `1`.\n\nWhenever `fibonacci` is called, it checks `known`. If the result is already there, it can return immediately. Otherwise, it has to compute the new value, add it to the dictionary, and return it.\n\nIf you run this version of `fibonacci` and compare it with the original, you will find that it is much faster.\n\"\"\"\n\n# \u2554\u2550\u2561 170c40ea-85da-11eb-2079-a912e41da021\nmd\"\"\"## Global Variables\n\nIn the previous example, `known` is created outside the function, so it belongs to the special frame called `Main`. Variables in `Main` are sometimes called *global* because they can be accessed from any function. Unlike local variables, which disappear when their function ends, global variables persist from one function call to the next.\n\nFor performance reasons, you should declare a global variable *constant* with the keyword `const`. You can no longer reassign the variable, but if it refers to a mutable value, you can modify the value.\n\n!!! danger\n    Global variables can be useful, but if you have a lot of them, and you modify them frequently, they can make programs hard to debug and perform badly.\n\"\"\"\n\n# \u2554\u2550\u2561 698eb48a-85da-11eb-272f-157bb3fec731\nmd\"\"\"## Debugging\n\nAs you work with bigger datasets, it can become unwieldy to debug by printing and checking the output by hand. Here are some suggestions for debugging large datasets:\n* Scale down the input.\n  \n  If possible, reduce the size of the dataset. For example, if the program reads a text file, start with just the first 10 lines, or with the smallest example you can find that errors. You should not edit the files themselves, but rather modify the pro\u2010 gram so it reads only the first n lines.\n    \n  If there is an error, you can reduce n to the smallest value that manifests the error, and then increase it gradually as you find and correct errors.\n\n* Check summaries and types.\n  \n  Instead of printing and checking the entire dataset, consider printing summaries of the data: for example, the number of items in a dictionary or the total of an array of numbers.\n  \n  A common cause of runtime errors is a value that is not the right type. For debugging this kind of error, it is often enough to print the type of a value.\n  \n* Write self-checks.\n  \n  Sometimes you can write code to check for errors automatically. For example, if you are computing the average of an array of numbers, you could check that the result is not greater than the largest element in the array or less than the smallest. This is called a \u201csanity check.\u201d\n  \n  Another kind of check compares the results of two different computations to see if they are consistent. This is called a \u201cconsistency check.\u201d\n  \n* Format the output.\n\n  Formatting debugging output can make it easier to spot an error. We saw an example in \u201cDebugging\u201d.\n  \n  Again, time you spend building scaffolding can reduce the time you spend debugging.\n\"\"\"\n\n# \u2554\u2550\u2561 057b7444-85db-11eb-1dc3-179e19732c1c\nmd\"\"\"## Glossary\n\n*dictionary*:\nA mapping from keys to their corresponding values.\n\n*key*:\nAn object that appears in a dictionary as the first part of a key-value pair.\n\n*value*:\nAn object that appears in a dictionary as the second part of a key-value pair. This is more specific than our previous use of the word \u201cvalue.\u201d\n\n*key-value pair*:\nThe representation of the mapping from a key to a value.\n\n*item*:\nIn a dictionary, another name for a key-value pair.\n\n*mapping*:\nA relationship in which each element of one set corresponds to an element of another set.\n\n*hash table*:\nThe algorithm used to implement Julia dictionaries.\n\n*implementation*:\nA way of performing a computation.\n\n*lookup*:\nA dictionary operation that takes a key and finds the corresponding value.\n\n*reverse lookup*:\nA dictionary operation that takes a value and finds one or more keys that map to it.\n\n*singleton*:\nAn array (or other sequence) with a single element.\n\n*hashable*:\nA type that has a hash function.\n\n*hash function*:\nA function used by a hash table to compute the location for a key.\n\n*call graph*:\nA diagram that shows every frame created during the execution of a program, with an arrow from each caller to each callee.\n\n*memo*:\nA computed value stored to avoid unnecessary future computation.\n\n*global variable*:\nA variable defined outside a function. Global variables can be accessed from any function.\n\n*constant global variable*\nA global variable that cannot be reassigned.\n\"\"\"\n\n# \u2554\u2550\u2561 64397616-85db-11eb-0150-fd07faf0ee9b\nmd\"\"\"## Exercises \n\n#### Exercise 11-2\n\nWrite a function that reads the words in words.txt and stores them as keys in a dictionary. It doesn\u2019t matter what the values are. Then you can use the `\u2208` operator as a fast way to check whether a string is in the dictionar.\n\nIf you did \u201cExercise 10-10\u201d, you can compare the speed of this implementation with the array `\u2208` operator and the bisection search.\n\"\"\"\n\n# \u2554\u2550\u2561 9513aed2-85db-11eb-19ee-dd547267503b\nmd\"\"\"#### Exercise 11-3\n\nRead the documentation of the dictionary function `get!` and use it to write a more concise version of `invertdict`.\n\"\"\"\n\n# \u2554\u2550\u2561 a5cd8176-85db-11eb-193c-75864c42871e\nmd\"\"\"#### Exercise 11-4\n\nMemoize the Ackermann function from \u201cExercise 6-5\u201d and see if memoization makes it possible to evaluate the function with bigger arguments.\n\"\"\"\n\n# \u2554\u2550\u2561 b8989c78-85db-11eb-3a4a-c3bb2dc3a988\nmd\"\"\"#### Exercise 11-5\n\nIf you did \u201cExercise 10-7\u201d, you already have a function named `hasduplicates` that takes an array as a parameter and returns true if there is any object that appears more than once in the array.\n\n\nUse a dictionary to write a faster, simpler version of `hasduplicates`.\n\"\"\"\n\n# \u2554\u2550\u2561 d4c39484-85db-11eb-0ed1-a3268690454e\nmd\"\"\"#### Exercise 11-6\n\nTwo words are \u201crotate pairs\u201d if you can rotate one of them and get the other (see rotateword in \u201cExercise 8-11\u201d).\n\nWrite a program that reads a word array and finds all the rotate pairs.\n\"\"\"\n\n# \u2554\u2550\u2561 e388e7f8-85db-11eb-0451-df6af9de2adc\nmd\"\"\"#### Exercise 11-7\n\nHere\u2019s another Puzzler from Car Talk:\n\n> [A contributor] came upon a common one-syllable, five-letter word recently that has the following unique property. When you remove the first letter, the remaining letters form a homophone of the original word, that is a word that sounds exactly the same. Replace the first letter, that is, put it back and remove the second letter and the result is yet another homophone of the original word. And the question is, what\u2019s the word?\n> \n> Now I\u2019m going to give you an example that doesn\u2019t work. Let\u2019s look at the five-letter word, \u2018wrack.\u2019 W-R-A-C-K, you know like to \u2018wrack with pain.\u2019 If I remove the first letter, I am left with a four-letter word, \u2018R-A-C-K.\u2019 As in, \u2018Holy cow, did you see the rack on that buck! It must have been a nine-pointer!\u2019 It\u2019s a perfect homophone. If you put the \u2018w\u2019 back, and remove the \u2018r,\u2019 instead, you\u2019re left with the word, \u2018wack,\u2019 which is a real word, it\u2019s just not a homophone of the other two words.\n> \n> But there is, however, at least one word that [I] know of, which will yield two homophones if you remove either of the first two letters to make two, new four-letter words. The question is, what\u2019s the word?\n\nYou can use the dictionary from \u201cExercise 11-2\u201d to check whether a string is in the word array.\n!!! tip\n    To check whether two words are homophones, you can use the Carnegie Mellon University Pronouncing Dictionary.\n\nWrite a program that lists all the words that solve the Puzzler.\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500bcaefa4e-85c6-11eb-3316-0db4881ebd30\n# \u255f\u250013d62b06-85c7-11eb-00b9-0399467b1ab5\n# \u255f\u25001f1c885e-85c7-11eb-1452-13a8943ebc90\n# \u255f\u250027e4cf0c-85c8-11eb-0d95-ab9ddc845387\n# \u255f\u2500c8dff15e-85c8-11eb-1230-bd39702cf460\n# \u255f\u25000b2e93b0-85c9-11eb-0dc3-61b1cbfb022b\n# \u255f\u250082a2f936-85c9-11eb-1d33-0d9dc98fdac0\n# \u255f\u25006d87ad02-85ca-11eb-2a3d-999d28a7c4b7\n# \u255f\u25004fb90040-85cb-11eb-23dd-ad9ea62b94c3\n# \u255f\u250026c0d704-85cf-11eb-2a13-293b42210e70\n# \u255f\u250035e8ee92-85cf-11eb-215c-21912c5fc92b\n# \u255f\u25008316e7a0-85cf-11eb-2ecb-df63279cadc6\n# \u255f\u2500c1e519e8-85cf-11eb-2ce1-4bcd389f56bf\n# \u255f\u25002a7646fe-85d9-11eb-3bbe-95f975f3901f\n# \u255f\u2500388a8d90-85d9-11eb-08f1-6b622cfb2c11\n# \u255f\u2500170c40ea-85da-11eb-2079-a912e41da021\n# \u255f\u2500698eb48a-85da-11eb-272f-157bb3fec731\n# \u255f\u2500057b7444-85db-11eb-1dc3-179e19732c1c\n# \u255f\u250064397616-85db-11eb-0150-fd07faf0ee9b\n# \u255f\u25009513aed2-85db-11eb-19ee-dd547267503b\n# \u255f\u2500a5cd8176-85db-11eb-193c-75864c42871e\n# \u255f\u2500b8989c78-85db-11eb-3a4a-c3bb2dc3a988\n# \u255f\u2500d4c39484-85db-11eb-0ed1-a3268690454e\n# \u255f\u2500e388e7f8-85db-11eb-0451-df6af9de2adc\n", "meta": {"hexsha": "331020eb76b3a3e8193d7543cccf495d74fcbe6c", "size": 31050, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lectures/Lecture11.jl", "max_stars_repo_name": "BenLauwens/ES123", "max_stars_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2018-03-07T18:04:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-10T15:07:23.000Z", "max_issues_repo_path": "Lectures/Lecture11.jl", "max_issues_repo_name": "BenLauwens/ES123", "max_issues_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lectures/Lecture11.jl", "max_forks_repo_name": "BenLauwens/ES123", "max_forks_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-08-12T05:29:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-02T21:38:27.000Z", "avg_line_length": 41.1803713528, "max_line_length": 500, "alphanum_fraction": 0.7150402576, "num_tokens": 9936, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3849121303722487, "lm_q2_score": 0.20689405370611846, "lm_q1q2_score": 0.07963603097337249}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.0\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 451ae3a0-8068-4747-95a7-d31955808f29\nbegin\n\n\t# external packages -\n\tusing PlutoUI\n\tusing Plots\n\tusing PrettyTables\nend\n\n# \u2554\u2550\u2561 8ba6a00a-7a53-4dcc-af4d-cb85aa9b4d68\nmd\"\"\"\n### Simple and Complex Models of Enzyme Kinetics\n\nThe flux bounds are essential constraints in flux balance analysis calculations and the convex decomposition of the stoichiometric array. Beyond their role in the flux estimation problem, the flux bounds are _integrative_, i.e., these constraints integrate many types of genetic and biochemical information into the flux estimation problem. \n\nFlux bounds constrain the values that each reaction in a metabolic network can take. A general model for these bounds is given by:\n\n$$-\\delta_{j}\\left[{V_{max,j}^{\\circ}}\\left(\\frac{e}{e^{\\circ}}\\right)\\theta_{j}\\left(\\dots\\right){f_{j}\\left(\\dots\\right)}\\right]\\leq{v_{j}}\\leq{V_{max,j}^{\\circ}}\\left(\\frac{e}{e^{\\circ}}\\right)\\theta_{j}\\left(\\dots\\right){f_{j}\\left(\\dots\\right)}$$\n\nwhere $V_{max,j}^{\\circ}$ denotes the maximum reaction velocity (units: flux) computed at some characteristic enzyme abundance (units: concentration), the ratio $e/e^{\\circ}$ is a correction for enzyme abundance (units: dimensioness), $\\theta_{j}\\left(\\dots\\right)\\in\\left[0,1\\right]$ is the fraction of maximial enzyme activity (a function or measurement producing units: dimensionless), and $f_{j}\\left(\\dots\\right)$ is a function describing the substrate dependence of the reaction rate $j$ (units: dimensionless). Both $\\theta_{j}\\left(\\dots\\right)$ and $f_{j}\\left(\\dots\\right)$ could have associated parameters, e.g., saturation or binding constants, etc.  Finally, the quanity $\\delta_{j}\\in\\left\\{0,1\\right\\}$ is a _binary_ variable: \n* If reaction $j$ is __reversible__ $\\delta_{j}=1$ or,\n* If reaction $j$ is __irreversible__ $\\delta_{j}=0$\n\nToday, let's focus on approaches for computing the value of these bounds, i.e., the form and value of the terms in the brackets. In this lecture, we will:\n\n1. Develop simple models of enzyme kinetics using the Michaelis\u2013Menten approach\n1. Introduce the fundamental concepts underlying more complex models of allosteric regulation (the fast control mechanisms that we mentioned previously)\n1. Introduce effective discrete regulation models to capture allosteric regulation\n\"\"\"\n\n# \u2554\u2550\u2561 8869f117-1fc6-4cc5-9803-b8ec72a109a6\nmd\"\"\"\n### What is allosteric regulation?\nAllosteric regulation modulates enzyme activity by binding effector molecules to sites other than the enzyme's active site. These events can activate (enhances rate) and inhibit (decreases rate). Allosteric regulation operates on a fast time scale compared to gene expression (synthesis of the enzyme). \n\nAllosteric mechanisms in Central Carbon Metabolism:\n\n* [Reznik E, Christodoulou D, Goldford JE, Briars E, Sauer U, Segr\u00e8 D, Noor E. Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Rep. 2017 Sep 12;20(11):2666-2677. doi: 10.1016/j.celrep.2017.08.066. PMID: 28903046; PMCID: PMC5600504.](https://pubmed.ncbi.nlm.nih.gov/28903046/)\n\n\"\"\"\n\n# \u2554\u2550\u2561 17724757-604e-4c77-a42b-238ba121e88f\nmd\"\"\"\n### Simple: Michaelis\u2013Menten kinetics\nLet's assume we have a well-mixed test tube containing an enzyme $E$ (a protein that catalyzes chemical reactions), which converts substrate $S$ (the starting compound) \ninto product $P$ according to three elementary reactions:\n\n$$\\begin{eqnarray}\n\tE+S&\\rightleftharpoons&{E:S}\\\\\n\t{E:S}&\\longrightarrow&E+P\n\\end{eqnarray}$$\n\nThe kinetics of each elementary step can be written using mass-action kinetics, i.e.,\n\n$$\\begin{eqnarray}\n\tr_{1} & = & k_{1}\\left[E\\right]\\left[S\\right]\\\\\n\tr_{2} & = & k_{2}\\left[E:S\\right]\\\\\n\tr_{3} & = & k_{3}\\left[E:S\\right]\n\\end{eqnarray}$$\n\nwhere $\\left[\\cdot\\right]$ denotes a species concentration, and $k_{j}$ denotes the rate constant governing the $jth$ elementary reaction:\n\n* The rate $r_{1}$ describes the _association_ rate between the enzyme and substrate,\n* The rate $r_{2}$ represents the rate of _dissociation_ of the enzyme-substrate complex, and\n* The $r_{3}$ denotes the rate of _chemical conversion_ of the bound substrate into the product (we assume the dissociation of the product from the enzyme is fast).\n\nThe enzyme must obey the relationship:\n\n$$\\left[E_{T}\\right] = \\left[E\\right] + \\left[E:S\\right]$$\n\nwhere $\\left[E_{T}\\right]$ denotes the total enzyme concentration in the tube, \n$\\left[E\\right]$ denotes the free enzyme concentration (not bound to substrate) while\n$\\left[E:S\\right]$ denotes the enzyme-substrate complex. \n\nTo estimate the _overall_ rate $v$, we stipulate a single rate-limiting step out of the set of elementary reactions. Let's assume that the rate of chemical conversion ($r_{3}$) is the slowest step, i.e., the substrate bounces on/off the enzyme quickly with only a tiny fraction of these binding events resulting in a successful chemical transformation. Thus, the overall rate is then given by:\n\n$$v = k_{3}\\left[E:S\\right]$$\n\nLet's also assume that we already know (or can estimate) the rate constants $k_{1},k_{2}$ and $k_{3}$. When this is true, the only unknown is $\\left[E:S\\right]$.\nHowever, we can relate $\\left[E:S\\right]$ to variables we know ($E_{T}$ and at least initially $S$) through the enzyme balance, and the _pseudo-steady-state assumption_ for the reaction intermediate \n$\\left[E:S\\right]$:\n\n$$\\frac{d\\left[E:S\\right]}{dt} = k_{1}\\left[E\\right]\\left[S\\right] - k_{2}\\left[E:S\\right] - k_{3}\\left[E:S\\right]\\simeq{0}$$\n\nRearranging and solving for $\\left[E:S\\right]$ gives the relationship:\n\n$$\\left[E:S\\right]\\simeq\\frac{k_{1}}{k_{2}+k_{3}}\\left[E\\right]\\left[S\\right]$$\n\nwhere the ratio of constants is defined as the Michaelis-Menten saturation coefficient or $K_{M}$:\n\n$$\\frac{1}{K_{M}}\\equiv\\frac{k_{1}}{k_{2}+k_{3}}$$\n\nSubstituting the definition of $K_{M}$ into the overall rate yields:\n\n$$v = k_{3}\\frac{\\left[E\\right]\\left[S\\right]}{K_{M}}$$\n\nHowever, we do not know the free enzyme concentration of $\\left[E\\right]$; to find $\\left[E\\right]$\nwe subsitute $\\left[E:S\\right]$ into the enzyme balance and solving for $\\left[E\\right]$:\n\n$$\\left[E\\right] = \\frac{\\left[E_T\\right]K_{M}}{K_{M}+\\left[S\\right]}$$\n\nLastly, we substitute $\\left[E\\right]$ into the overall rate to arrive at the final expression for $v$:\n\n$$v = V_{max}\\frac{\\left[S\\right]}{K_{M}+\\left[S\\right]}$$\n\nwhere $V_{max}\\equiv{k_{3}}\\left[E_{T}\\right]$. \n\n__Limiting cases:__\n* when $S\\gg{K}_{M}$, the rate becomes close to $V_{max}$.\n* when $S\\ll{K}_{M}$ the rate appears to be linear with respect to substrate concentration.\n* when $K_{M}\\simeq S$ the reaction rate equals $v\\simeq 1/2V_{max}$. \n\"\"\"\n\n# \u2554\u2550\u2561 431ccdf0-93a9-4b3c-9576-8854ba2f1fad\n@bind MM_parameters PlutoUI.combine() do Child\n\tmd\"\"\"\n\t##### Michaelis\u2013Menten Parameters\n\t\n\t\\[E\\] $(\n\t\tChild(Slider(1:5))\n\t) (\u03bcM) and K\u2098 $(\n\t\tChild(Slider(5:100))\n\t) (mM)\n\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 7efaf27c-8e0a-40f9-ac28-af90357450a3\nbegin\n\n\t# get MM parameter values -\n\tE = MM_parameters[1]\n\tK\u2098 = MM_parameters[2]\n\tkcat = 13.7 # units: s^-1\n\tnumber_of_steps = 1000\n\n\t# substrate range -\n\tS_array = range(0.0,stop=100.0,length=number_of_steps) |> collect;\n\n\t# initialize space -\n\tv_array = Array{Float64,1}(undef,number_of_steps)\n\n\t# compute the rate -\n\tfor (i,S) \u2208 enumerate(S_array)\n\n\t\t# compute the rate -\n\t\tv_array[i] = (kcat*E)*(S/(S+K\u2098))\n\tend\n\n\t# plot -\n\tplot(S_array,v_array,xlims=(0.0,100.0),ylims=(0.0,14), label=\"v: E = $(E) \u03bcM and K\u2098 = $(K\u2098) (mM)\")\n\txlabel!(\"Substrate S (mM)\",fontsize=18)\n\tylabel!(\"Rate v (\u03bcM/s)\",fontsize=18)\nend\n\n# \u2554\u2550\u2561 55ea8324-f36d-40bd-99e3-92e7fcbafca9\nmd\"\"\"\n### Complex: MWC and Sequential kinetic models\nThe Monod-Wyman-Changeux model (MWC model, also known as the symmetry model) describes allosteric transitions of proteins made up of identical subunits. Effector binding modulates the state of the entire protein.\n\n* [MONOD J, WYMAN J, CHANGEUX JP. ON THE NATURE OF ALLOSTERIC TRANSITIONS: A PLAUSIBLE MODEL. J Mol Biol. 1965 May;12:88-118. doi: 10.1016/s0022-2836(65)80285-6. PMID: 14343300.](https://pubmed.ncbi.nlm.nih.gov/14343300/)\n\nThe sequential model (KNF model) of allosteric regulation posits that enzyme subunits are independent.\nThus, binding substrate (or effector) to a subunit results in only slight conformational changes to adjacent subunits. KNF model can do describe both positive and negative cooperativity: \n\n* [Koshland D Jr, N\u00e9methy G, Filmer D. Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochemistry. 1966 Jan;5(1):365-85. doi: 10.1021/bi00865a047. PMID: 5938952.](https://pubmed.ncbi.nlm.nih.gov/5938952/)\n\n  \n\"\"\"\n\n# \u2554\u2550\u2561 3502da52-7d2b-4c79-82ce-7424d756cd9b\nmd\"\"\"\n### Effective discrete state kinetic models\n\nMichaelis\u2013Menten kinetics are easy to understand, but they neglect many factors, e.g., the influence of allosteric regulators. On the other hand, MWC/sequential models are detailed but case-specific (and too complex). It would be great if we could correct Michaelis\u2013Menten kinetics to capture the influence of allosteric factors. \n\nSuppose we model the rate $v_{j}$ as the product of a kinetic limit (a simple model of the rate) and a correction term that accounts for the missing regulation:\n\n$$v_{j} = r_{j}\\theta\\left(...\\right)_{j}$$\n\nwhere $v_{j}$ denotes the overall rate (units: $\\mu$M/time), $r_{j}$ denotes the kinetic limit i.e., the maximum rate of conversion (units: $\\mu$M/time) and \n$0\\leq \\theta\\left(...\\right)_{j}\\leq 1$ (units: dimensionless) is a control function that describes the influence of effector molecules. \n\"\"\"\n\n# \u2554\u2550\u2561 c392c8a9-361d-47e4-933e-2e51b793e069\nmd\"\"\"\n##### Discrete state control function model\nThere is a wide variety of different possible ways we can build the $\\theta\\left(...\\right)_{j}$ control functions. Ultimately, it doesn't matter what we choose, as long as it gives a good performance. However, let's introduce an idea that we will revisit later, namely a discrete probabilistic approach.\n\n###### Theory\nSuppose an enzyme $E$ can exits in one of $s=1,2\\dots,\\mathcal{S}$ possible microstates, where each microstate $s$ has some pseudo energy $\\epsilon_{s}$. Some microstates will lead to activity (the ability to carry out the chemical reactions, while others will not). For each microstate $s$, let's assign a pseudo energy $\\epsilon_{s}$, where by definition $\\epsilon_{1}=0$; we assume the base state has the lowest energy. Next, suppose the probability that enzyme $E$ is in microstate $s$ follows a [Boltzmann distribution](https://en.wikipedia.org/wiki/Boltzmann_distribution) which says:\n\n$$p_{i} = \\frac{1}{Z} \\times f_{i}\\exp\\left(-\\beta\\epsilon_{i}\\right)\\qquad{i=1,2,\\dots,\\mathcal{S}}$$\n\nwhere $p_{i}$ denotes the probability that enzyme $E$ is in microstate $i=1,2,\\dots,\\mathcal{S}$, $f_{i}$ denotes a state-specific factor $f_{i}\\in\\left[0,1\\right]$, $\\beta$ denotes the [thermodynamic beta](https://en.wikipedia.org/wiki/Thermodynamic_beta) and $Z$ denotes a normalization factor (called the [Partiton function](https://en.wikipedia.org/wiki/Partition_function_(statistical_mechanics)) in the statistical physics community). We can find $Z$ using the summation law of discrete probolity e.g.,  $\\sum_{s}p_{s} = 1$ which gives:\n\n$$Z = \\sum_{s=1}^{\\mathcal{S}}f_{i}\\exp\\left(-\\beta\\epsilon_{i}\\right)$$\n\nwhich gives:\n\n$$p_{i} = \\frac{f_{i}\\exp\\left(-\\beta\\epsilon_{i}\\right)}{\\displaystyle \\sum_{s=1}^{\\mathcal{S}}f_{i}\\exp\\left(-\\beta\\epsilon_{i}\\right)}\\qquad{i=1,2,\\dots,\\mathcal{S}}$$.\n\nFinally, we relate the probability that enzyme $E$ is in microstate $s$ back to the $\\theta$ control function by computing the overall probability that the desired event happens, e.g., enzyme $E$ catalyzes the reaction of interests. We know if $\\Omega = \\left\\{1,2,\\dots,\\mathcal{S}\\right\\}$, then we can define the subset $\\mathcal{A}\\subseteq\\Omega$ in which the desired event happens. Given $\\mathcal{A}$, the $\\theta$ function becomes:\n\n$$\\theta=\\sum_{s\\in{\\mathcal{A}}}p_{s}$$\n\n###### Conceptual exampleTo illustrate this idea, consider an enzyme inhibited by a downstream product (this is a common allosteric motif known as [feedback inhibition](Pedre\u00f1o S, Pisco JP, Larrouy-Maumus G, Kelly G, de Carvalho LP. Mechanism of feedback allosteric inhibition of ATP phosphoribosyltransferase. Biochemistry. 2012;51(40):8027-8038. doi:10.1021/bi300808b)). In this case, suppose enzyme $E$, which is inhibited by compound $I$, can exist in one of three possible microstates:\n\n* __State s = 1__: No substrate $S$ is bound, $E$ is floating around in solution minding its own business (base state, no reaction)\n* __State s = 2__: Substrate $S$ is bound to enzyme $E$, but inhibitor $I$ is not bound (reaction possible)\n* __State s = 3__: Both the substrate $S$ and inhibitor $I$ are bound to enzyme $E$ (no reaction possible)\n\n\nGiven these microstates (and their functional assignment) we know that enzyme $E$ can only catalyze its reaction in microstate $s=2$, thus:\n\n$$\\theta = \\frac{f_{2}\\exp\\left(-\\beta\\epsilon_{i}\\right)}{\\displaystyle \\sum_{s=1}^{\\mathcal{3}}f_{s}\\exp\\left(-\\beta\\epsilon_{s}\\right)}$$\n\n###### What are the state-specific factors?\nThe state-specific factors $f_{i}\\in\\left[0,1\\right]$ control the reachability of a microstate:\n\n* if $f_{\\star} = 0$, then microstate $\\star$ can __never__ be obtained\n* if $f_{\\star} = 1$, then microstate $\\star$ can __always__ be obtained\n\nAs a modeler, you can choose the form (or value) that $f_{\\star}$ takes. These factors can\nbe set to a specific value by definition (depending upon the state) or can be used to describe events associated with state $i$, e.g., such as binding events. For example, suppose state $i$ involved binding an effector molecule $x$ to the enzyme $E$. In this case, we could model the state-specific factor $f_{i}$ as:\n\n$$f_{i} = (x/K_{i})^{n_{i}}/(1+(x/K_{i})^{n_{i}})$$\n\nwhere $x\\geq{0}$ denotes effector abundance, $K_{i}\\geq{0}$ denotes a binding constant and $n_{i}\\geq{0}$ denotes a binding order parameter. \n\"\"\"\n\n# \u2554\u2550\u2561 91345d04-f502-4e90-935e-a4dc250244db\n@bind DSM_parameters PlutoUI.combine() do Child\n\t\n\tmd\"\"\"\n\t\\[I\\] $(\n\t\tChild(Slider(0:100))\n\t) (\u03bcM)  K $(\n\t\tChild(Slider(1:1:100))\n\t) (mM)  \u03f5\u2082 $(\n\t\tChild(Slider(0.001:0.1:100))\n\t) (J/mol) \u03f5\u2083 $(\n\t\tChild(Slider(0.001:0.1:100))\n\t) (J/mol) \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 719784fe-3ef6-4e53-ad52-82140e3d0b5e\nbegin\n\n\t# get I -\n\tI\u2092 = DSM_parameters[1]\n\tKd = DSM_parameters[2]\n\t\u03f5\u2082 = (DSM_parameters[3])/100\n\t\u03f5\u2083 = (DSM_parameters[4])/100\n\t\n\t# setup system -\n\tR = 8.314 \t\t\t# units: J/mol-K\n\tT = 273.15 + 25.0 \t# units: K\n\t\u03b2 = 1/R*T\n\n\t# setup binding parameters for state 3 -\n\tn = 2.0\n\t\n\t# setup energy array -\n\t\u03f5_array = [\n\t\t0.0 \t; # state 1 (just E)\n\t\t-\u03f5\u2082 \t; # state 2 (E bound to S, but no I)\n\t\t-\u03f5\u2083 \t; # state 3 (E bound to I)\n\t];\n\n\t# compute W -\n\tW_array = exp.(-\u03b2*\u03f5_array)\n\n\t# let's compute the state-specific factor array -\n\tf_array = [\n\t\t1.0 ; # state 1 \n\t\t1.0 ; # state 2\n\t\t((I\u2092/Kd)^(n))/(1+(I\u2092/Kd)^(n))\n\t];\n\n\t# compute the \u03b8 variable -\n\tmicrostate_array = f_array.*W_array;\n\tZ = sum(microstate_array)\n\tp_array = (1/Z)*microstate_array\n\t\u03b8 = p_array[2]\n\n\t# show -\n\twith_terminal() do\n\t\tprintln(\"\u03b8 = $(\u03b8)\")\n\tend\nend\n\n# \u2554\u2550\u2561 1d4d6059-5b79-43cd-ac17-ef2aa057ecad\nwith_terminal() do\n\n\t# initialize -\n\t\ud835\udcae = 3\n\tstate_table = Array{Any,2}(undef,\ud835\udcae,4)\n\t\n\t# populate the state array -\n\tfor s \u2208 1:\ud835\udcae\n\n\t\tstate_table[s,1] = s\n\t\tstate_table[s,2] = f_array[s]\n\t\tstate_table[s,3] = W_array[s]\n\t\tstate_table[s,4] = p_array[s]\n\tend\n\n\t# header -\n\theader_row = ([\"s\",\"f\u209b\",\"W\u209b\",\"p\u209b\"])\n\tpretty_table(state_table;header=header_row)\n\t\nend\n\n# \u2554\u2550\u2561 d46ced14-a5d3-45a9-9d43-38dc7879b0c7\nlet\n\n\t# get MM parameter values -\n\tE = 1.0 # units: \u03bcM\n\tK\u2098 = 5 \t# units: mM\n\tkcat = 13.7 # units: s^-1\n\tnumber_of_steps = 1000\n\n\t# substrate range -\n\tS_array = range(0.0,stop=100.0,length=number_of_steps) |> collect;\n\n\t# initialize space -\n\tv_array = Array{Float64,1}(undef,number_of_steps)\n\n\t# compute the rate -\n\tfor (i,S) \u2208 enumerate(S_array)\n\n\t\t# compute the rate -\n\t\tv_array[i] = (kcat*E)*(S/(S+K\u2098))*\u03b8\n\tend\n\n\t# plot -\n\tplot(S_array,v_array,xlims=(0.0,100.0),ylims=(0.0,14), label=\"v: I = $(I\u2092) mM and Kd = $(Kd) (mM)\")\n\txlabel!(\"Substrate S (mM)\",fontsize=18)\n\tylabel!(\"Rate v (\u03bcM/s)\",fontsize=18)\n\t\nend\n\n# \u2554\u2550\u2561 d3568c5d-16bf-4698-9891-0be65d62b36c\nmd\"\"\"\n### Summary and Conclusions\n\nIn this lecture we:\n\n1. Developed simple models of enzyme kinetics using the Michaelis\u2013Menten approach\n1. Introduced the fundamental concepts underlying more complex models of allosteric regulation (the fast control mechanisms that we mentioned previously)\n1. Introduced effective discrete regulation models to simulate allosteric regulation\n\n\"\"\"\n\n# \u2554\u2550\u2561 55d4ab48-9589-4fd9-ac06-3338fdb418c4\nmd\"\"\"\n### Next Time\n\n* What about multiple substrates?\n* Does the discrete state model work?\n* How do we implement these bounds models in a flux balance analysis calculation?\n\n\"\"\"\n\n# \u2554\u2550\u2561 54370424-3add-4f93-91b0-e136166842ae\nTableOfContents(title=\"\ud83d\udcda Lecture Outline\", indent=true, depth=5, aside=true)\n\n# \u2554\u2550\u2561 06a90381-b4cb-4d32-af0d-2f084364129c\nhtml\"\"\"\n<script>\n\t// initialize -\n\tvar section = 0;\n\tvar subsection = 0;\n\tvar subsubsection = 0;\n\tvar headers = document.querySelectorAll('h3, h5, h6');\n\t\n\t// main loop -\n\tfor (var i=0; i < headers.length; i++) {\n\t    \n\t\tvar header = headers[i];\n\t    var text = header.innerText;\n\t    var original = header.getAttribute(\"text-original\");\n\t    if (original === null) {\n\t        \n\t\t\t// Save original header text\n\t        header.setAttribute(\"text-original\", text);\n\t    } else {\n\t        \n\t\t\t// Replace with original text before adding section number\n\t        text = header.getAttribute(\"text-original\");\n\t    }\n\t\n\t    var numbering = \"\";\n\t    switch (header.tagName) {\n\t        case 'H3':\n\t            section += 1;\n\t            numbering = section + \".\";\n\t            subsection = 0;\n\t            break;\n\t        case 'H5':\n\t            subsection += 1;\n\t            numbering = section + \".\" + subsection;\n\t            break;\n\t\t\tcase 'H6':\n\t            subsubsection += 1;\n\t            numbering = section + \".\" + subsection + \".\" + subsubsection;\n\t            break;\n\t    }\n\t\t// update the header text \n\t\theader.innerText = numbering + \" \" + text;\n\t};\n</script>\"\"\"\n\n# \u2554\u2550\u2561 b6890de0-8923-11ec-3552-3113bdd53f86\nhtml\"\"\"\n<style>\nmain {\n    max-width: 860px;\n    width: 70%;\n    margin: auto;\n    font-family: \"Roboto, monospace\";\n}\n\na {\n    color: blue;\n    text-decoration: none;\n}\n</style>\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nPlots = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPrettyTables = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\n\n[compat]\nPlots = \"~1.25.8\"\nPlutoUI = \"~0.7.34\"\nPrettyTables = \"~1.3.1\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.2\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.Adapt]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"af92965fb30777147966f58acb05da51c5616b5f\"\nuuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\nversion = \"3.3.3\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.Bzip2_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"19a35467a82e236ff51bc17a3a44b69ef35185a2\"\nuuid = \"6e34b625-4abd-537c-b88f-471c36dfa7a0\"\nversion = \"1.0.8+0\"\n\n[[deps.Cairo]]\ndeps = [\"Cairo_jll\", \"Colors\", \"Glib_jll\", \"Graphics\", \"Libdl\", \"Pango_jll\"]\ngit-tree-sha1 = \"d0b3f8b4ad16cb0a2988c6788646a5e6a17b6b1b\"\nuuid = \"159f3aea-2a34-519c-b102-8c37f9878175\"\nversion = \"1.0.5\"\n\n[[deps.Cairo_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"Fontconfig_jll\", \"FreeType2_jll\", \"Glib_jll\", \"JLLWrappers\", \"LZO_jll\", \"Libdl\", \"Pixman_jll\", \"Pkg\", \"Xorg_libXext_jll\", \"Xorg_libXrender_jll\", \"Zlib_jll\", \"libpng_jll\"]\ngit-tree-sha1 = \"4b859a208b2397a7a623a03449e4636bdb17bcf2\"\nuuid = \"83423d85-b0ee-5818-9007-b63ccbeb887a\"\nversion = \"1.16.1+1\"\n\n[[deps.ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"f9982ef575e19b0e5c7a98c6e75ee496c0f73a93\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.12.0\"\n\n[[deps.ChangesOfVariables]]\ndeps = [\"ChainRulesCore\", \"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"bf98fa45a0a4cee295de98d4c1462be26345b9a1\"\nuuid = \"9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0\"\nversion = \"0.1.2\"\n\n[[deps.ColorSchemes]]\ndeps = [\"ColorTypes\", \"Colors\", \"FixedPointNumbers\", \"Luxor\", \"Random\"]\ngit-tree-sha1 = \"5b7d2a8b53c68dfdbce545e957a3b5cc4da80b01\"\nuuid = \"35d6a980-a343-548e-a6ea-1d62b119f2f4\"\nversion = \"3.17.0\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.Colors]]\ndeps = [\"ColorTypes\", \"FixedPointNumbers\", \"Reexport\"]\ngit-tree-sha1 = \"417b0ed7b8b838aa6ca0a87aadf1bb9eb111ce40\"\nuuid = \"5ae59095-9a9b-59fe-a467-6f913c188581\"\nversion = \"0.12.8\"\n\n[[deps.Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"44c37b4636bc54afac5c574d2d02b625349d6582\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.41.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.Contour]]\ndeps = [\"StaticArrays\"]\ngit-tree-sha1 = \"9f02045d934dc030edad45944ea80dbd1f0ebea7\"\nuuid = \"d38c429a-6771-53c6-b99e-75d170b6e991\"\nversion = \"0.5.7\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"3daef5523dd2e769dad2365274f760ff5f282c7d\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.11\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[deps.Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[deps.DocStringExtensions]]\ndeps = [\"LibGit2\"]\ngit-tree-sha1 = \"b19534d1895d702889b219c382a6e18010797f0b\"\nuuid = \"ffbed154-4ef7-542d-bbb7-c09d3a79fcae\"\nversion = \"0.8.6\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.EarCut_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"3f3a2501fa7236e9b911e0f7a588c657e822bb6d\"\nuuid = \"5ae413db-bbd1-5e63-b57d-d24a61df00f5\"\nversion = \"2.2.3+0\"\n\n[[deps.Expat_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"ae13fcbc7ab8f16b0856729b050ef0c446aa3492\"\nuuid = \"2e619515-83b5-522b-bb60-26c02a35a201\"\nversion = \"2.4.4+0\"\n\n[[deps.FFMPEG]]\ndeps = [\"FFMPEG_jll\"]\ngit-tree-sha1 = \"b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8\"\nuuid = \"c87230d0-a227-11e9-1b43-d7ebe4e7570a\"\nversion = \"0.4.1\"\n\n[[deps.FFMPEG_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"FreeType2_jll\", \"FriBidi_jll\", \"JLLWrappers\", \"LAME_jll\", \"Libdl\", \"Ogg_jll\", \"OpenSSL_jll\", \"Opus_jll\", \"Pkg\", \"Zlib_jll\", \"libass_jll\", \"libfdk_aac_jll\", \"libvorbis_jll\", \"x264_jll\", \"x265_jll\"]\ngit-tree-sha1 = \"d8a578692e3077ac998b50c0217dfd67f21d1e5f\"\nuuid = \"b22a6f82-2f65-5046-a5b2-351ab43fb4e5\"\nversion = \"4.4.0+0\"\n\n[[deps.FileIO]]\ndeps = [\"Pkg\", \"Requires\", \"UUIDs\"]\ngit-tree-sha1 = \"80ced645013a5dbdc52cf70329399c35ce007fae\"\nuuid = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nversion = \"1.13.0\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Fontconfig_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"Expat_jll\", \"FreeType2_jll\", \"JLLWrappers\", \"Libdl\", \"Libuuid_jll\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = 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[\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Ogg_jll\", \"Pkg\"]\ngit-tree-sha1 = \"b910cb81ef3fe6e78bf6acee440bda86fd6ae00c\"\nuuid = \"f27f6e37-5d2b-51aa-960f-b287f2bc3b7a\"\nversion = \"1.3.7+1\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\n[[deps.x264_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4fea590b89e6ec504593146bf8b988b2c00922b2\"\nuuid = \"1270edf5-f2f9-52d2-97e9-ab00b5d0237a\"\nversion = \"2021.5.5+0\"\n\n[[deps.x265_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"ee567a171cce03570d77ad3a43e90218e38937a9\"\nuuid = \"dfaa095f-4041-5dcd-9319-2fabd8486b76\"\nversion = \"3.5.0+0\"\n\n[[deps.xkbcommon_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"ece2350174195bb31de1a63bea3a41ae1aa593b6\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"0.9.1+5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25008ba6a00a-7a53-4dcc-af4d-cb85aa9b4d68\n# \u255f\u25008869f117-1fc6-4cc5-9803-b8ec72a109a6\n# \u255f\u250017724757-604e-4c77-a42b-238ba121e88f\n# \u255f\u2500431ccdf0-93a9-4b3c-9576-8854ba2f1fad\n# \u255f\u25007efaf27c-8e0a-40f9-ac28-af90357450a3\n# \u255f\u250055ea8324-f36d-40bd-99e3-92e7fcbafca9\n# \u255f\u25003502da52-7d2b-4c79-82ce-7424d756cd9b\n# \u255f\u2500c392c8a9-361d-47e4-933e-2e51b793e069\n# \u255f\u250091345d04-f502-4e90-935e-a4dc250244db\n# \u255f\u2500719784fe-3ef6-4e53-ad52-82140e3d0b5e\n# \u255f\u25001d4d6059-5b79-43cd-ac17-ef2aa057ecad\n# \u255f\u2500d46ced14-a5d3-45a9-9d43-38dc7879b0c7\n# \u255f\u2500d3568c5d-16bf-4698-9891-0be65d62b36c\n# \u255f\u250055d4ab48-9589-4fd9-ac06-3338fdb418c4\n# \u255f\u250054370424-3add-4f93-91b0-e136166842ae\n# \u2560\u2550451ae3a0-8068-4747-95a7-d31955808f29\n# \u2560\u255006a90381-b4cb-4d32-af0d-2f084364129c\n# \u2560\u2550b6890de0-8923-11ec-3552-3113bdd53f86\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "7319b22a8e4288dfb1d8848c9238eb6392cff3a3", "size": 53910, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lectures/Lecture-6-5440-7770-S2022.jl", "max_stars_repo_name": "mavisbrown/CHEME-5440-7770-Cornell-Spring-2022", "max_stars_repo_head_hexsha": "6bbfa8f58f75df4bc8d2146ae768a816a1273a46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-02-07T23:40:03.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-14T19:06:26.000Z", "max_issues_repo_path": "lectures/Lecture-6-5440-7770-S2022.jl", "max_issues_repo_name": "mavisbrown/CHEME-5440-7770-Cornell-Spring-2022", "max_issues_repo_head_hexsha": "6bbfa8f58f75df4bc8d2146ae768a816a1273a46", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lectures/Lecture-6-5440-7770-S2022.jl", "max_forks_repo_name": "mavisbrown/CHEME-5440-7770-Cornell-Spring-2022", "max_forks_repo_head_hexsha": "6bbfa8f58f75df4bc8d2146ae768a816a1273a46", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2022-02-03T15:15:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T22:46:36.000Z", "avg_line_length": 37.0006863418, "max_line_length": 742, "alphanum_fraction": 0.7253941755, "num_tokens": 22209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.17553807793655438, "lm_q1q2_score": 0.07956471360259151}}
{"text": "using Test \nusing clima_demo \n\n@testset \"My test\" begin \n\t@test 1 + 1 == 2\nend\n", "meta": {"hexsha": "02a7dfc9b7cd27b64fb622d9b476d51ebab07f1e", "size": 79, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "costachris/clima_demo", "max_stars_repo_head_hexsha": "0b132414dd6effe035bcdcdf6a045af42e51436b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "costachris/clima_demo", "max_issues_repo_head_hexsha": "0b132414dd6effe035bcdcdf6a045af42e51436b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "costachris/clima_demo", "max_forks_repo_head_hexsha": "0b132414dd6effe035bcdcdf6a045af42e51436b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.2857142857, "max_line_length": 25, "alphanum_fraction": 0.6582278481, "num_tokens": 29, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.1602660403494035, "lm_q1q2_score": 0.07950699369107649}}
{"text": "# NOTE: All tests of this file can be run with any number of processes.\r\n# Nearly all of the functionality can however be verified with one single process\r\n# (thanks to the usage of periodic boundaries in most of the full halo update tests).\r\n\r\npush!(LOAD_PATH, \"../src\")\r\nusing Test\r\nusing ImplicitGlobalGrid; GG = ImplicitGlobalGrid\r\nimport MPI\r\nusing CUDA\r\nimport ImplicitGlobalGrid: @require, longnameof\r\n\r\ntest_gpu = CUDA.functional()\r\n\r\nif test_gpu\r\n\tglobal cuzeros = CUDA.zeros\r\n\tglobal allocators = [zeros, cuzeros]\r\n\tglobal ArrayConstructors = [Array, CuArray]\r\nelse\r\n\tglobal cuzeros = nothing  # To enable statements like: if zeros==cuzeros\r\n\tglobal allocators = [zeros]\r\n\tglobal ArrayConstructors = [Array]\r\nend\r\n\r\n\r\n## Test setup\r\nMPI.Init();\r\nnprocs = MPI.Comm_size(MPI.COMM_WORLD); # NOTE: these tests can run with any number of processes.\r\nndims_mpi = GG.NDIMS_MPI;\r\nnneighbors_per_dim = GG.NNEIGHBORS_PER_DIM; # Should be 2 (one left and one right neighbor).\r\nnx = 7;\r\nny = 5;\r\nnz = 6;\r\ndx = 1.0\r\ndy = 1.0\r\ndz = 1.0\r\n\r\n@testset \"$(basename(@__FILE__)) (processes: $nprocs)\" begin\r\n\t@testset \"1. argument check (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\tinit_global_grid(nx, ny, nz, quiet=true, init_MPI=false);\r\n\t\tP   = zeros(nx,  ny,  nz  );\r\n\t\tSxz = zeros(nx-2,ny-1,nz-2);\r\n\t\tA   = zeros(nx-1,ny+2,nz+1);\r\n\t    A2  = A;\n\t\tZ   = zeros(ComplexF64, nx-1,ny+2,nz+1);\n\t\tZ2  = Z;\r\n\t    @test_throws ErrorException update_halo!(P, Sxz, A)       # Error: Sxz has no halo.\r\n\t    @test_throws ErrorException update_halo!(P, Sxz, A, Sxz)  # Error: Sxz and Sxz have no halo.\r\n\t    @test_throws ErrorException update_halo!(P, A, A)         # Error: A is given twice.\r\n\t    @test_throws ErrorException update_halo!(P, A, A2)        # Error: A2 is duplicate of A (an alias; it points to the same memory).\r\n\t    @test_throws ErrorException update_halo!(P, A, A, A2)     # Error: the second A and A2 are duplicates of the first A.\n\t\t@test_throws ErrorException update_halo!(Z, Z2)           # Error: Z2 is duplicate of Z (an alias; it points to the same memory).\n\t\t@test_throws ErrorException update_halo!(Z, P)            # Error: P is of different type than Z.\n\t\t@test_throws ErrorException update_halo!(Z, P, A)         # Error: P and A are of different type than Z.\r\n\t\tfinalize_global_grid(finalize_MPI=false);\r\n\tend;\r\n\r\n\t@testset \"2. buffer allocation (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\tP = zeros(nx,  ny,  nz  );\r\n\t\tA = zeros(nx-1,ny+2,nz+1);\r\n\t\tB = zeros(Float32,    nx+1, ny+2, nz+3);\r\n\t\tC = zeros(Float32,    nx+1, ny+1, nz+1);\n\t\tZ = zeros(ComplexF16, nx,   ny,   nz  );\n\t\tY = zeros(ComplexF16, nx-1, ny+2, nz+1);\r\n\t    @testset \"free buffers\" begin\r\n\t        @require GG.get_sendbufs_raw() === nothing\r\n\t        @require GG.get_recvbufs_raw() === nothing\r\n\t        GG.allocate_bufs(P);\r\n\t        @require GG.get_sendbufs_raw() !== nothing\r\n\t        @require GG.get_recvbufs_raw() !== nothing\r\n\t        GG.free_update_halo_buffers();\r\n\t        @test GG.get_sendbufs_raw() === nothing\r\n\t        @test GG.get_recvbufs_raw() === nothing\r\n\t    end;\r\n\t    @testset \"allocate single\" begin\r\n\t        GG.free_update_halo_buffers();\r\n\t        GG.allocate_bufs(P);\r\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\r\n\t            @test length(bufs_raw)       == 1                                    # 1 array\r\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\r\n\t            for n = 1:nneighbors_per_dim\r\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(P)...])[2:end])  # required length: max halo elements in any of the dimensions\r\n\t            end\r\n\t        end\r\n\t    end;\n\t\t@testset \"allocate single (Complex)\" begin\n\t        GG.free_update_halo_buffers();\n\t        GG.allocate_bufs(Z);\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\n\t            @test length(bufs_raw)       == 1                                    # 1 array\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\n\t            for n = 1:nneighbors_per_dim\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(Z)...])[2:end])  # required length: max halo elements in any of the dimensions\n\t            end\n\t        end\n\t    end;\r\n\t    @testset \"keep 1st, allocate 2nd\" begin\r\n\t        GG.free_update_halo_buffers();\r\n\t        GG.allocate_bufs(P);\r\n\t        GG.allocate_bufs(A, P);\r\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\r\n\t            @test length(bufs_raw)       == 2                                    # 2 arrays\r\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\r\n\t            @test length(bufs_raw[2])    == nneighbors_per_dim                   # 2 neighbors per dimension\r\n\t            for n = 1:nneighbors_per_dim\r\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(A)...])[2:end])  # required length: max halo elements in any of the dimensions\r\n\t                @test length(bufs_raw[2][n]) >= prod(sort([size(P)...])[2:end])  # ...\r\n\t            end\r\n\t        end\r\n\t    end;\n\t\t@testset \"keep 1st, allocate 2nd (Complex)\" begin\n\t        GG.free_update_halo_buffers();\n\t        GG.allocate_bufs(Z);\n\t        GG.allocate_bufs(Y, Z);\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\n\t            @test length(bufs_raw)       == 2                                    # 2 arrays\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\n\t            @test length(bufs_raw[2])    == nneighbors_per_dim                   # 2 neighbors per dimension\n\t            for n = 1:nneighbors_per_dim\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(Y)...])[2:end])  # required length: max halo elements in any of the dimensions\n\t                @test length(bufs_raw[2][n]) >= prod(sort([size(Z)...])[2:end])  # ...\n\t            end\n\t        end\n\t    end;\r\n\t    @testset \"reinterpret (no allocation)\" begin\r\n\t        GG.free_update_halo_buffers();\r\n\t        GG.allocate_bufs(A, P);\r\n\t        GG.allocate_bufs(B, C);                                                  # The new arrays contain Float32 (A, and P were Float64); B and C have a halo with more elements than A and P had, but they require less space in memory\r\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\r\n\t            @test length(bufs_raw)       == 2                                    # Still 2 arrays: B, C (even though they are different then before: was A and P)\r\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\r\n\t            @test length(bufs_raw[2])    == nneighbors_per_dim                   # 2 neighbors per dimension\r\n\t            for n = 1:nneighbors_per_dim\r\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(B)...])[2:end])  # required length: max halo elements in any of the dimensions\r\n\t                @test length(bufs_raw[2][n]) >= prod(sort([size(C)...])[2:end])  # ...\r\n\t            end\r\n\t            @test all([eltype(bufs_raw[i][n]) == Float32 for i=1:length(bufs_raw), n=1:nneighbors_per_dim])\r\n\t        end\r\n\t    end;\n\t\t@testset \"reinterpret (no allocation) (Complex)\" begin\n\t        GG.free_update_halo_buffers();\n\t        GG.allocate_bufs(A, P);\n\t        GG.allocate_bufs(Y, Z);                                                  # The new arrays contain Float32 (A, and P were Float64); B and C have a halo with more elements than A and P had, but they require less space in memory\n\t        for bufs_raw in [GG.get_sendbufs_raw(), GG.get_recvbufs_raw()]\n\t            @test length(bufs_raw)       == 2                                    # Still 2 arrays: B, C (even though they are different then before: was A and P)\n\t            @test length(bufs_raw[1])    == nneighbors_per_dim                   # 2 neighbors per dimension\n\t            @test length(bufs_raw[2])    == nneighbors_per_dim                   # 2 neighbors per dimension\n\t            for n = 1:nneighbors_per_dim\n\t                @test length(bufs_raw[1][n]) >= prod(sort([size(Y)...])[2:end])  # required length: max halo elements in any of the dimensions\n\t                @test length(bufs_raw[2][n]) >= prod(sort([size(Z)...])[2:end])  # ...\n\t            end\n\t            @test all([eltype(bufs_raw[i][n]) == ComplexF16 for i=1:length(bufs_raw), n=1:nneighbors_per_dim])\n\t        end\n\t    end;\r\n\t    @testset \"(cu)sendbuf / (cu)recvbuf\" begin\r\n\t\t\tsendbuf, recvbuf = (GG.sendbuf, GG.recvbuf);\r\n\t\t\t@static if (test_gpu && zeros == cuzeros) sendbuf, recvbuf = (GG.cusendbuf, GG.curecvbuf); end\r\n\t        GG.free_update_halo_buffers();\r\n\t        GG.allocate_bufs(A, P);\r\n\t        for dim = 1:ndims(A), n = 1:nneighbors_per_dim\r\n\t            @test all(size(sendbuf(n,dim,1,A)) .== size(A)[1:ndims(A).!=dim])\r\n\t            @test all(size(recvbuf(n,dim,1,A)) .== size(A)[1:ndims(A).!=dim])\r\n\t        end\r\n\t        for dim = 1:ndims(P), n = 1:nneighbors_per_dim\r\n\t            @test all(size(sendbuf(n,dim,2,P)) .== size(P)[1:ndims(P).!=dim])\r\n\t            @test all(size(recvbuf(n,dim,2,P)) .== size(P)[1:ndims(P).!=dim])\r\n\t        end\r\n\t    end;\n\t\t@testset \"(cu)sendbuf / (cu)recvbuf (Complex)\" begin\n\t\t\tsendbuf, recvbuf = (GG.sendbuf, GG.recvbuf);\n\t\t\t@static if (test_gpu && zeros == cuzeros) sendbuf, recvbuf = (GG.cusendbuf, GG.curecvbuf); end\n\t        GG.free_update_halo_buffers();\n\t        GG.allocate_bufs(Y, Z);\n\t        for dim = 1:ndims(Y), n = 1:nneighbors_per_dim\n\t            @test all(size(sendbuf(n,dim,1,Y)) .== size(Y)[1:ndims(Y).!=dim])\n\t            @test all(size(recvbuf(n,dim,1,Y)) .== size(Y)[1:ndims(Y).!=dim])\n\t        end\n\t        for dim = 1:ndims(Z), n = 1:nneighbors_per_dim\n\t            @test all(size(sendbuf(n,dim,2,Z)) .== size(Z)[1:ndims(Z).!=dim])\n\t            @test all(size(recvbuf(n,dim,2,Z)) .== size(Z)[1:ndims(Z).!=dim])\n\t        end\n\t    end;\r\n\t\tfinalize_global_grid(finalize_MPI=false);\r\n\tend;\r\n\r\n\t@testset \"3. data transfer components\" begin\r\n\t\t@testset \"iwrite_sendbufs! / iread_recvbufs!\" begin\r\n\t\t\t@testset \"sendranges / recvranges (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\tP   = zeros(nx,  ny,  nz  );\r\n\t\t\t\tA   = zeros(nx-1,ny+2,nz+1);\r\n\t\t\t\t@test GG.sendranges(1, 1, P) == [                    2:2,             1:size(P,2),             1:size(P,3)]\r\n\t\t\t\t@test GG.sendranges(2, 1, P) == [size(P,1)-1:size(P,1)-1,             1:size(P,2),             1:size(P,3)]\r\n\t\t\t\t@test GG.sendranges(1, 2, P) == [            1:size(P,1),                     2:2,             1:size(P,3)]\r\n\t\t\t\t@test GG.sendranges(2, 2, P) == [            1:size(P,1), size(P,2)-1:size(P,2)-1,             1:size(P,3)]\r\n\t\t\t\t@test GG.sendranges(1, 3, P) == [            1:size(P,1),             1:size(P,2),                     3:3]\r\n\t\t\t\t@test GG.sendranges(2, 3, P) == [            1:size(P,1),             1:size(P,2), size(P,3)-2:size(P,3)-2]\r\n\t\t\t\t@test GG.recvranges(1, 1, P) == [                    1:1,             1:size(P,2),             1:size(P,3)]\r\n\t\t\t\t@test GG.recvranges(2, 1, P) == [    size(P,1):size(P,1),             1:size(P,2),             1:size(P,3)]\r\n\t\t\t\t@test GG.recvranges(1, 2, P) == [            1:size(P,1),                     1:1,             1:size(P,3)]\r\n\t\t\t\t@test GG.recvranges(2, 2, P) == [            1:size(P,1),     size(P,2):size(P,2),             1:size(P,3)]\r\n\t\t\t\t@test GG.recvranges(1, 3, P) == [            1:size(P,1),             1:size(P,2),                     1:1]\r\n\t\t\t\t@test GG.recvranges(2, 3, P) == [            1:size(P,1),             1:size(P,2),     size(P,3):size(P,3)]\r\n\t\t\t\t@test_throws ErrorException  GG.sendranges(1, 1, A)\r\n\t\t\t\t@test_throws ErrorException  GG.sendranges(2, 1, A)\r\n\t\t\t\t@test GG.sendranges(1, 2, A) == [            1:size(A,1),                     4:4,             1:size(A,3)]\r\n\t\t\t\t@test GG.sendranges(2, 2, A) == [            1:size(A,1), size(A,2)-3:size(A,2)-3,             1:size(A,3)]\r\n\t\t\t\t@test GG.sendranges(1, 3, A) == [            1:size(A,1),             1:size(A,2),                     4:4]\r\n\t\t\t\t@test GG.sendranges(2, 3, A) == [            1:size(A,1),             1:size(A,2), size(A,3)-3:size(A,3)-3]\r\n\t\t\t\t@test_throws ErrorException  GG.recvranges(1, 1, A)\r\n\t\t\t\t@test_throws ErrorException  GG.recvranges(2, 1, A)\r\n\t\t\t\t@test GG.recvranges(1, 2, A) == [            1:size(A,1),                     1:1,             1:size(A,3)]\r\n\t\t\t\t@test GG.recvranges(2, 2, A) == [            1:size(A,1),     size(A,2):size(A,2),             1:size(A,3)]\r\n\t\t\t\t@test GG.recvranges(1, 3, A) == [            1:size(A,1),             1:size(A,2),                     1:1]\r\n\t\t\t\t@test GG.recvranges(2, 3, A) == [            1:size(A,1),             1:size(A,2),     size(A,3):size(A,3)]\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"write_h2h! / read_h2h!\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, quiet=true, init_MPI=false);\r\n\t\t\t\tP  = zeros(nx,  ny,  nz  );\r\n\t\t\t\tP .= [iz*1e2 + iy*1e1 + ix for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\tP2 = zeros(size(P));\r\n\t\t\t\t# (dim=1)\r\n\t\t\t\tbuf = zeros(size(P,2), size(P,3));\r\n\t\t\t\tranges = [2:2, 1:size(P,2), 1:size(P,3)];\r\n\t\t\t\tGG.write_h2h!(buf, P, ranges, 1);\r\n\t\t\t\t@test all(buf[:] .== P[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\tGG.read_h2h!(buf, P2, ranges, 1);\r\n\t\t\t\t@test all(buf[:] .== P2[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\t# (dim=2)\r\n\t\t\t\tbuf = zeros(size(P,1), size(P,3));\r\n\t\t\t\tranges = [1:size(P,1), 3:3, 1:size(P,3)];\r\n\t\t\t\tGG.write_h2h!(buf, P, ranges, 2);\r\n\t\t\t\t@test all(buf[:] .== P[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\tGG.read_h2h!(buf, P2, ranges, 2);\r\n\t\t\t\t@test all(buf[:] .== P2[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\t# (dim=3)\r\n\t\t\t\tbuf = zeros(size(P,1), size(P,2));\r\n\t\t\t\tranges = [1:size(P,1), 1:size(P,2), 4:4];\r\n\t\t\t\tGG.write_h2h!(buf, P, ranges, 3);\r\n\t\t\t\t@test all(buf[:] .== P[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\tGG.read_h2h!(buf, P2, ranges, 3);\r\n\t\t\t\t@test all(buf[:] .== P2[ranges[1],ranges[2],ranges[3]][:])\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@static if test_gpu\r\n\t\t\t\t@testset \"write_d2x! / write_d2h_async! / read_x2d! / read_h2d_async!\" begin\r\n\t\t\t\t\tinit_global_grid(nx, ny, nz, quiet=true, init_MPI=false);\r\n\t\t\t\t\tP  = zeros(nx,  ny,  nz  );\r\n\t\t\t\t\tP .= [iz*1e2 + iy*1e1 + ix for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\t\tP  = CuArray(P);\r\n\t\t\t\t\t# (dim=1)\r\n\t\t\t\t\tdim = 1;\r\n\t\t\t\t\tP2  = cuzeros(eltype(P),size(P));\r\n\t\t\t\t\tbuf = zeros(size(P,2), size(P,3));\r\n\t\t\t\t\tbuf_d, buf_h = GG.register(buf);\r\n\t\t\t\t\tranges = [2:2, 1:size(P,2), 1:size(P,3)];\r\n\t\t\t\t\tnthreads = (1, 1, 1);\r\n\t                halosize = [r[end] - r[1] + 1 for r in ranges];\r\n\t\t\t\t\tnblocks  = Tuple(ceil.(Int, halosize./nthreads));\r\n\t                @cuda blocks=nblocks threads=nthreads GG.write_d2x!(buf_d, P, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\t@cuda blocks=nblocks threads=nthreads GG.read_x2d!(buf_d, P2, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tbuf .= 0.0;\r\n\t\t\t\t\tP2  .= 0.0;\r\n\t\t\t\t\tcustream = stream();\r\n\t\t\t\t\tGG.write_d2h_async!(buf, P, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tGG.read_h2d_async!(buf, P2, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tCUDA.Mem.unregister(buf_h);\r\n\t\t\t\t\t# (dim=2)\r\n\t\t\t\t\tdim = 2;\r\n\t\t\t\t\tP2  = cuzeros(eltype(P),size(P));\r\n\t\t\t\t\tbuf = zeros(size(P,1), size(P,3));\r\n\t\t\t\t\tbuf_d, buf_h = GG.register(buf);\r\n\t\t\t\t\tranges = [1:size(P,1), 3:3, 1:size(P,3)];\r\n\t\t\t\t\tnthreads = (1, 1, 1);\r\n\t\t\t\t\thalosize = [r[end] - r[1] + 1 for r in ranges];\r\n\t\t\t\t\tnblocks  = Tuple(ceil.(Int, halosize./nthreads));\r\n\t\t\t\t\t@cuda blocks=nblocks threads=nthreads GG.write_d2x!(buf_d, P, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\t@cuda blocks=nblocks threads=nthreads GG.read_x2d!(buf_d, P2, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tbuf .= 0.0;\r\n\t\t\t\t\tP2  .= 0.0;\r\n\t\t\t\t\tcustream = stream();\r\n\t\t\t\t\tGG.write_d2h_async!(buf, P, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tGG.read_h2d_async!(buf, P2, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tCUDA.Mem.unregister(buf_h);\r\n\t\t\t\t\t# (dim=3)\r\n\t\t\t\t\tdim = 3\r\n\t\t\t\t\tP2  = cuzeros(eltype(P),size(P));\r\n\t\t\t\t\tbuf = zeros(size(P,1), size(P,2));\r\n\t\t\t\t\tbuf_d, buf_h = GG.register(buf);\r\n\t\t\t\t\tranges = [1:size(P,1), 1:size(P,2), 4:4];\r\n\t\t\t\t\tnthreads = (1, 1, 1);\r\n\t\t\t\t\thalosize = [r[end] - r[1] + 1 for r in ranges];\r\n\t\t\t\t\tnblocks  = Tuple(ceil.(Int, halosize./nthreads));\r\n\t\t\t\t\t@cuda blocks=nblocks threads=nthreads GG.write_d2x!(buf_d, P, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\t@cuda blocks=nblocks threads=nthreads GG.read_x2d!(buf_d, P2, ranges[1], ranges[2], ranges[3], dim); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tbuf .= 0.0;\r\n\t\t\t\t\tP2  .= 0.0;\r\n\t\t\t\t\tcustream = stream();\r\n\t\t\t\t\tGG.write_d2h_async!(buf, P, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tGG.read_h2d_async!(buf, P2, ranges, dim, custream); CUDA.synchronize();\r\n\t\t\t\t\t@test all(buf[:] .== Array(P2[ranges[1],ranges[2],ranges[3]][:]))\r\n\t\t\t\t\tCUDA.Mem.unregister(buf_h);\r\n\t\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\t\tend;\r\n\t\t\tend\r\n\t\t\t@testset \"iwrite_sendbufs! (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\tP = zeros(nx,  ny,  nz  );\r\n\t\t\t\tA = zeros(nx-1,ny+2,nz+1);\r\n\t\t\t\tif zeros == cuzeros\r\n\t\t\t\t\tP = CuArray([iz*1e2 + iy*1e1 + ix for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)]);\r\n\t\t\t\t\tA = CuArray([iz*1e2 + iy*1e1 + ix for ix=1:size(A,1), iy=1:size(A,2), iz=1:size(A,3)]);\r\n\t\t\t\telse\r\n\t\t\t\t\tP .= [iz*1e2 + iy*1e1 + ix for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\t\tA .= [iz*1e2 + iy*1e1 + ix for ix=1:size(A,1), iy=1:size(A,2), iz=1:size(A,3)];\r\n\t\t\t\tend\r\n\t\t\t\tGG.allocate_bufs(P, A);\r\n\t\t\t\tif (zeros == cuzeros)\r\n\t\t\t\t\tGG.allocate_custreams(P, A);\r\n\t\t\t\telse\r\n\t\t\t\t\tGG.allocate_tasks(P, A);\r\n\t\t\t\tend\r\n\t\t\t\tdim = 1\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[2,:,:][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== 0.0)\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[2,:,:][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== 0.0)\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[end-1,:,:][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== 0.0)\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[end-1,:,:][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== 0.0)\r\n\t\t\t\tend\r\n\t\t\t\tdim = 2\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[:,2,:][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== CuArray(A[:,4,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[:,2,:][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== Array(A[:,4,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[:,end-1,:][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== CuArray(A[:,end-3,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[:,end-1,:][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== Array(A[:,end-3,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tdim = 3\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[:,:,3][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== CuArray(A[:,:,4][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[:,:,3][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== Array(A[:,:,4][:]))\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iwrite_sendbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iwrite(n, P, 1);\r\n\t\t\t\tGG.wait_iwrite(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,1,P) .== CuArray(P[:,:,end-2][:]))\r\n\t\t\t\t\t@test all(GG.cusendbuf_flat(n,dim,2,A) .== CuArray(A[:,:,end-3][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,1,P) .== Array(P[:,:,end-2][:]))\r\n\t\t\t\t\t@test all(GG.sendbuf_flat(n,dim,2,A) .== Array(A[:,:,end-3][:]))\r\n\t\t\t\tend\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"iread_recvbufs! (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\tP = zeros(nx,  ny,  nz  );\r\n\t\t\t\tA = zeros(nx-1,ny+2,nz+1);\r\n\t\t\t\tGG.allocate_bufs(P, A);\r\n\t\t\t\tif (zeros == cuzeros)\r\n\t\t\t\t\tGG.allocate_custreams(P, A);\r\n\t\t\t\telse\r\n\t\t\t\t\tGG.allocate_tasks(P, A);\r\n\t\t\t\tend\r\n\t\t\t\tdim = 1\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\tend\r\n\t\t\t\tend\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[1,:,:][:]))\r\n\t\t\t\t\t@test all(                         0.0 .== CuArray(A[1,:,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[1,:,:][:]))\r\n\t\t\t\t\t@test all(                       0.0 .== Array(A[1,:,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[end,:,:][:]))\r\n\t\t\t\t\t@test all(                         0.0 .== CuArray(A[end,:,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[end,:,:][:]))\r\n\t\t\t\t\t@test all(                       0.0 .== Array(A[end,:,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tdim = 2\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\tend\r\n\t\t\t\tend\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[:,1,:][:]))\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,2,A) .== CuArray(A[:,1,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[:,1,:][:]))\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,2,A) .== Array(A[:,1,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[:,end,:][:]))\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,2,A) .== CuArray(A[:,end,:][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[:,end,:][:]))\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,2,A) .== Array(A[:,end,:][:]))\r\n\t\t\t\tend\r\n\t\t\t\tdim = 3\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.curecvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\tGG.recvbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\tend\r\n\t\t\t\tend\r\n\t\t\t\tn = 1\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[:,:,1][:]))\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,2,A) .== CuArray(A[:,:,1][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[:,:,1][:]))\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,2,A) .== Array(A[:,:,1][:]))\r\n\t\t\t\tend\r\n\t\t\t\tn = 2\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, P, 1);\r\n\t\t\t\tGG.iread_recvbufs!(n, dim, A, 2);\r\n\t\t\t\tGG.wait_iread(n, P, 1);\r\n\t\t\t\tGG.wait_iread(n, A, 2);\r\n\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,1,P) .== CuArray(P[:,:,end][:]))\r\n\t\t\t\t\t@test all(GG.curecvbuf_flat(n,dim,2,A) .== CuArray(A[:,:,end][:]))\r\n\t\t\t\telse\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,1,P) .== Array(P[:,:,end][:]))\r\n\t\t\t\t\t@test all(GG.recvbuf_flat(n,dim,2,A) .== Array(A[:,:,end][:]))\r\n\t\t\t\tend\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\tif (nprocs==1)\r\n\t\t\t\t@testset \"sendrecv_halo_local (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\t\tP = zeros(nx,  ny,  nz  );\r\n\t\t\t\t\tA = zeros(nx-1,ny+2,nz+1);\r\n\t\t\t\t\tGG.allocate_bufs(P, A);\r\n\t\t\t\t\tdim = 1\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\telse\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\tend\r\n\t\t\t\t\tend\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, P, 1);\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, A, 2);\r\n\t\t\t\t\tend\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,1,P) .== GG.cusendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,2,A) .== 0.0);  # There is no halo (ol(dim,A) < 2).\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,1,P) .== GG.cusendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,2,A) .== 0.0);  # There is no halo (ol(dim,A) < 2).\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,1,P) .== GG.sendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,2,A) .== 0.0);  # There is no halo (ol(dim,A) < 2).\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,1,P) .== GG.sendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,2,A) .== 0.0);  # There is no halo (ol(dim,A) < 2).\r\n\t\t\t\t\tend\r\n\t\t\t\t\tdim = 2\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\telse\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\tend\r\n\t\t\t\t\tend\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, P, 1);\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, A, 2);\r\n\t\t\t\t\tend\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,1,P) .== GG.cusendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,2,A) .== GG.cusendbuf_flat(2,dim,2,A));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,1,P) .== GG.cusendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,2,A) .== GG.cusendbuf_flat(1,dim,2,A));\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,1,P) .== GG.sendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,2,A) .== GG.sendbuf_flat(2,dim,2,A));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,1,P) .== GG.sendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,2,A) .== GG.sendbuf_flat(1,dim,2,A));\r\n\t\t\t\t\tend\r\n\t\t\t\t\tdim = 3\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.cusendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\telse\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,1,P) .= dim*1e2 + n*1e1 + 1;\r\n\t\t\t\t\t\t\tGG.sendbuf_flat(n,dim,2,A) .= dim*1e2 + n*1e1 + 2;\r\n\t\t\t\t\t\tend\r\n\t\t\t\t\tend\r\n\t\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, P, 1);\r\n\t\t\t\t\t\tGG.sendrecv_halo_local(n, dim, A, 2);\r\n\t\t\t\t\tend\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,1,P) .== GG.cusendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(1,dim,2,A) .== GG.cusendbuf_flat(2,dim,2,A));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,1,P) .== GG.cusendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.curecvbuf_flat(2,dim,2,A) .== GG.cusendbuf_flat(1,dim,2,A));\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,1,P) .== GG.sendbuf_flat(2,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(1,dim,2,A) .== GG.sendbuf_flat(2,dim,2,A));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,1,P) .== GG.sendbuf_flat(1,dim,1,P));\r\n\t\t\t\t\t\t@test all(GG.recvbuf_flat(2,dim,2,A) .== GG.sendbuf_flat(1,dim,2,A));\r\n\t\t\t\t\tend\r\n\t\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\t\tend\r\n\t\t\tend\r\n\t\tend;\r\n\t\tif (nprocs>1)\r\n\t\t\t@testset \"irecv_halo! / isend_halo (allocator: $(longnameof(zeros)))\" for zeros in allocators\r\n\t\t\t\tme, dims, nprocs, coords, comm = init_global_grid(nx, ny, nz, dimy=1, dimz=1, periodx=1, quiet=true, init_MPI=false);\r\n\t\t\t\tP   = zeros(nx,ny,nz);\r\n\t\t\t\tA   = zeros(nx,ny,nz);\r\n\t\t\t\tdim = 1;\r\n\t\t\t\tGG.allocate_bufs(P, A);\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\tGG.cusendbuf(n,dim,1,P) .= 9.0;\r\n\t\t\t\t\t\tGG.curecvbuf(n,dim,1,P) .= 0;\r\n\t\t\t\t\t\tGG.cusendbuf(n,dim,2,A) .= 9.0;\r\n\t\t\t\t\t\tGG.curecvbuf(n,dim,2,A) .= 0;\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\tGG.sendbuf(n,dim,1,P) .= 9.0;\r\n\t\t\t\t\t\tGG.recvbuf(n,dim,1,P) .= 0;\r\n\t\t\t\t\t\tGG.sendbuf(n,dim,2,A) .= 9.0;\r\n\t\t\t\t\t\tGG.recvbuf(n,dim,2,A) .= 0;\r\n\t\t\t\t\tend\r\n\t\t\t\tend\r\n\t\t\t\treqs  = fill(MPI.REQUEST_NULL, 2, nneighbors_per_dim, 2);\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t                reqs[1,n,1] = GG.irecv_halo!(n, dim, P, 1);\r\n\t\t\t\t\treqs[2,n,1] = GG.irecv_halo!(n, dim, A, 2);\r\n\t                reqs[1,n,2] = GG.isend_halo(n, dim, P, 1);\r\n\t\t\t\t\treqs[2,n,2] = GG.isend_halo(n, dim, A, 2);\r\n\t            end\r\n\t\t\t\t@test all(reqs .!= [MPI.REQUEST_NULL])\r\n\t            MPI.Waitall!(reqs[:]);\r\n\t\t\t\tfor n = 1:nneighbors_per_dim\r\n\t\t\t\t\tif zeros == cuzeros && GG.cudaaware_MPI(dim)\r\n\t\t\t\t\t\t@test all(GG.curecvbuf(n,dim,1,P) .== 9.0)\r\n\t\t\t\t\t\t@test all(GG.curecvbuf(n,dim,2,A) .== 9.0)\r\n\t\t\t\t\telse\r\n\t\t\t\t\t\t@test all(GG.recvbuf(n,dim,1,P) .== 9.0)\r\n\t\t\t\t\t\t@test all(GG.recvbuf(n,dim,2,A) .== 9.0)\r\n\t\t\t\t\tend\r\n\t\t\t\tend\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\tend\r\n\tend;\r\n\r\n\t# (Backup field filled with encoded coordinates and set boundary to zeros; then update halo and compare with backuped field; it should be the same again, except for the boundaries that are not halos)\r\n\t@testset \"4. halo update (allocator: $(longnameof(Array)))\" for Array in ArrayConstructors\r\n\t\t@testset \"basic grid (default: periodic)\" begin\r\n\t\t\t@testset \"1D\" begin\r\n\t\t     \tinit_global_grid(nx, 1, 1, periodx=1, quiet=true, init_MPI=false);\r\n\t\t\t\tP     = zeros(nx);\r\n\t\t\t\tP    .= [x_g(ix,dx,P) for ix=1:size(P,1)];\r\n\t\t\t\tP_ref = copy(P);\r\n\t\t\t\tP[[1, end]] .= 0.0;\r\n\t\t\t\tP     = Array(P);\r\n\t\t\t\tP_ref = Array(P_ref);\r\n\t\t\t\t@require !all(P .== P_ref)\r\n\t\t\t\tupdate_halo!(P);\r\n\t\t\t\t@test all(P .== P_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"2D\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, 1, periodx=1, periody=1, quiet=true, init_MPI=false);\r\n\t\t\t\tP     = zeros(nx, ny);\r\n\t\t\t\tP    .= [y_g(iy,dy,P)*1e1 + x_g(ix,dx,P) for ix=1:size(P,1), iy=1:size(P,2)];\r\n\t\t\t\tP_ref = copy(P);\r\n\t\t\t\tP[[1, end],       :] .= 0.0;\r\n\t\t\t\tP[       :,[1, end]] .= 0.0;\r\n\t\t\t\tP     = Array(P);\r\n\t\t\t\tP_ref = Array(P_ref);\r\n\t\t\t\t@require !all(P .== P_ref)\r\n\t\t\t\tupdate_halo!(P);\r\n\t\t\t\t@test all(P .== P_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\t\t\tP     = zeros(nx, ny, nz);\r\n\t\t\t\tP    .= [z_g(iz,dz,P)*1e2 + y_g(iy,dy,P)*1e1 + x_g(ix,dx,P) for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\tP_ref = copy(P);\r\n\t\t\t\tP[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tP[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tP[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tP     = Array(P);\r\n\t\t\t\tP_ref = Array(P_ref);\r\n\t\t\t\t@require !all(P .== P_ref)\r\n\t\t\t\tupdate_halo!(P);\r\n\t\t\t\t@test all(P .== P_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D (non-default overlap)\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapx=4, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\tP     = zeros(nx, ny, nz);\r\n\t\t\t\tP    .= [z_g(iz,dz,P)*1e2 + y_g(iy,dy,P)*1e1 + x_g(ix,dx,P) for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\tP_ref = copy(P);\r\n\t\t\t\tP[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tP[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tP[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tP     = Array(P);\r\n\t\t\t\tP_ref = Array(P_ref);\r\n\t\t\t\t@require !all(P .== P_ref)\r\n\t\t\t\tupdate_halo!(P);\r\n\t\t\t\t@test all(P .== P_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D (not periodic)\" begin\r\n\t\t\t\tme, dims, nprocs, coords = init_global_grid(nx, ny, nz, quiet=true, init_MPI=false);\r\n\t\t\t\tP     = zeros(nx, ny, nz);\r\n\t\t\t\tP    .= [z_g(iz,dz,P)*1e2 + y_g(iy,dy,P)*1e1 + x_g(ix,dx,P) for ix=1:size(P,1), iy=1:size(P,2), iz=1:size(P,3)];\r\n\t\t\t\tP_ref = copy(P);\r\n\t\t\t\tP[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tP[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tP[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tP     = Array(P);\r\n\t\t\t\tP_ref = Array(P_ref);\r\n\t\t\t\t@require !all(P .== P_ref)\r\n\t\t\t\tupdate_halo!(P);\r\n\t\t\t\t@test all(P[2:end-1,2:end-1,2:end-1] .== P_ref[2:end-1,2:end-1,2:end-1])\r\n\t\t\t\tif (coords[1] ==         0) @test all(P[  1,  :,  :] .== 0.0); else @test all(P[      1,2:end-1,2:end-1] .== P_ref[      1,2:end-1,2:end-1]); end  # Verifcation of corner values would be cumbersome here; it is already sufficiently covered in the periodic tests.\r\n\t\t\t\tif (coords[1] == dims[1]-1) @test all(P[end,  :,  :] .== 0.0); else @test all(P[    end,2:end-1,2:end-1] .== P_ref[    end,2:end-1,2:end-1]); end\r\n\t\t\t\tif (coords[2] ==         0) @test all(P[  :,  1,  :] .== 0.0); else @test all(P[2:end-1,      1,2:end-1] .== P_ref[2:end-1,      1,2:end-1]); end\r\n\t\t\t\tif (coords[2] == dims[2]-1) @test all(P[  :,end,  :] .== 0.0); else @test all(P[2:end-1,    end,2:end-1] .== P_ref[2:end-1,    end,2:end-1]); end\r\n\t\t\t\tif (coords[3] ==         0) @test all(P[  :,  :,  1] .== 0.0); else @test all(P[2:end-1,2:end-1,      1] .== P_ref[2:end-1,2:end-1,      1]); end\r\n\t\t\t\tif (coords[3] == dims[3]-1) @test all(P[  :,  :,end] .== 0.0); else @test all(P[2:end-1,2:end-1,    end] .== P_ref[2:end-1,2:end-1,    end]); end\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\tend;\r\n\t\t@testset \"staggered grid (default: periodic)\" begin\r\n\t\t\t@testset \"1D\" begin\r\n\t\t\t\tinit_global_grid(nx, 1, 1, periodx=1, quiet=true, init_MPI=false);\r\n\t\t\t\tVx     = zeros(nx+1);\r\n\t\t\t\tVx    .= [x_g(ix,dx,Vx) for ix=1:size(Vx,1)];\r\n\t\t\t\tVx_ref = copy(Vx);\r\n\t\t\t\tVx[[1, end]] .= 0.0;\r\n\t\t\t\tVx     = Array(Vx);\r\n\t\t\t\tVx_ref = Array(Vx_ref);\r\n\t\t\t\t@require !all(Vx .== Vx_ref)\r\n\t\t\t\tupdate_halo!(Vx);\r\n\t\t\t\t@test all(Vx .== Vx_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"2D\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, 1, periodx=1, periody=1, quiet=true, init_MPI=false);\r\n\t\t\t\tVy     = zeros(nx,ny+1);\r\n\t\t\t\tVy    .= [y_g(iy,dy,Vy)*1e1 + x_g(ix,dx,Vy) for ix=1:size(Vy,1), iy=1:size(Vy,2)];\r\n\t\t\t\tVy_ref = copy(Vy);\r\n\t\t\t\tVy[[1, end],       :] .= 0.0;\r\n\t\t\t\tVy[       :,[1, end]] .= 0.0;\r\n\t\t\t\tVy     = Array(Vy);\r\n\t\t\t\tVy_ref = Array(Vy_ref);\r\n\t\t\t\t@require !all(Vy .== Vy_ref)\r\n\t\t\t\tupdate_halo!(Vy);\r\n\t\t\t\t@test all(Vy .== Vy_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\t\t\tVz     = zeros(nx,ny,nz+1);\r\n\t\t\t\tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\r\n\t\t\t\tVz_ref = copy(Vz);\r\n\t\t\t\tVz[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tVz[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tVz[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tVz     = Array(Vz);\r\n\t\t\t\tVz_ref = Array(Vz_ref);\r\n\t\t\t\t@require !all(Vz .== Vz_ref)\r\n\t\t\t\tupdate_halo!(Vz);\r\n\t\t\t\t@test all(Vz .== Vz_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D (non-default overlap)\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, overlapx=3, overlapz=3, quiet=true, init_MPI=false);\r\n\t\t\t\tVx     = zeros(nx+1,ny,nz);\r\n\t\t\t\tVx    .= [z_g(iz,dz,Vx)*1e2 + y_g(iy,dy,Vx)*1e1 + x_g(ix,dx,Vx) for ix=1:size(Vx,1), iy=1:size(Vx,2), iz=1:size(Vx,3)];\r\n\t\t\t\tVx_ref = copy(Vx);\r\n\t\t\t\tVx[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tVx[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tVx[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tVx     = Array(Vx);\r\n\t\t\t\tVx_ref = Array(Vx_ref);\r\n\t\t\t\t@require !all(Vx .== Vx_ref)\r\n\t\t\t\tupdate_halo!(Vx);\r\n\t\t\t\t@test all(Vx .== Vx_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D (not periodic)\" begin\r\n\t\t\t\tme, dims, nprocs, coords = init_global_grid(nx, ny, nz, quiet=true, init_MPI=false);\r\n\t\t\t\tVz     = zeros(nx,ny,nz+1);\r\n\t\t\t\tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\r\n\t\t\t\tVz_ref = copy(Vz);\r\n\t\t\t\tVz[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tVz[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tVz[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tVz     = Array(Vz);\r\n\t\t\t\tVz_ref = Array(Vz_ref);\r\n\t\t\t\t@require !all(Vz .== Vz_ref)\r\n\t\t\t\tupdate_halo!(Vz);\r\n\t\t\t\t@test all(Vz[2:end-1,2:end-1,2:end-1] .== Vz_ref[2:end-1,2:end-1,2:end-1])\r\n\t\t\t\tif (coords[1] ==         0) @test all(Vz[  1,  :,  :] .== 0.0); else @test all(Vz[      1,2:end-1,2:end-1] .== Vz_ref[      1,2:end-1,2:end-1]); end  # Verifcation of corner values would be cumbersome here; it is already sufficiently covered in the periodic tests.\r\n\t\t\t\tif (coords[1] == dims[1]-1) @test all(Vz[end,  :,  :] .== 0.0); else @test all(Vz[    end,2:end-1,2:end-1] .== Vz_ref[    end,2:end-1,2:end-1]); end\r\n\t\t\t\tif (coords[2] ==         0) @test all(Vz[  :,  1,  :] .== 0.0); else @test all(Vz[2:end-1,      1,2:end-1] .== Vz_ref[2:end-1,      1,2:end-1]); end\r\n\t\t\t\tif (coords[2] == dims[2]-1) @test all(Vz[  :,end,  :] .== 0.0); else @test all(Vz[2:end-1,    end,2:end-1] .== Vz_ref[2:end-1,    end,2:end-1]); end\r\n\t\t\t\tif (coords[3] ==         0) @test all(Vz[  :,  :,  1] .== 0.0); else @test all(Vz[2:end-1,2:end-1,      1] .== Vz_ref[2:end-1,2:end-1,      1]); end\r\n\t\t\t\tif (coords[3] == dims[3]-1) @test all(Vz[  :,  :,end] .== 0.0); else @test all(Vz[2:end-1,2:end-1,    end] .== Vz_ref[2:end-1,2:end-1,    end]); end\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"2D (no halo in one dim)\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, 1, periodx=1, periody=1, quiet=true, init_MPI=false);\r\n\t\t\t\tA     = zeros(nx-1,ny+2);\r\n\t\t\t\tA    .= [y_g(iy,dy,A)*1e1 + x_g(ix,dx,A) for ix=1:size(A,1), iy=1:size(A,2)];\r\n\t\t\t\tA_ref = copy(A);\r\n\t\t\t\tA[[1, end],       :] .= 0.0;\r\n\t\t\t\tA[       :,[1, end]] .= 0.0;\r\n\t\t\t\tA     = Array(A);\r\n\t\t\t\tA_ref = Array(A_ref);\r\n\t\t\t\t@require !all(A .== A_ref)\r\n\t\t\t\tupdate_halo!(A);\r\n\t\t\t\t@test all(A[2:end-1,:] .== A_ref[2:end-1,:])\r\n\t\t\t\t@test all(A[[1, end],:] .== 0.0)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\t\t@testset \"3D (no halo in one dim)\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\t\t\tA     = zeros(nx+2,ny-1,nz+1);\r\n\t\t\t\tA    .= [z_g(iz,dz,A)*1e2 + y_g(iy,dy,A)*1e1 + x_g(ix,dx,A) for ix=1:size(A,1), iy=1:size(A,2), iz=1:size(A,3)];\r\n\t\t\t\tA_ref = copy(A);\r\n\t\t\t\tA[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tA[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tA[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tA     = Array(A);\r\n\t\t\t\tA_ref = Array(A_ref);\r\n\t\t\t\t@require !all(A .== A_ref)\r\n\t\t\t\tupdate_halo!(A);\r\n\t\t\t\t@test all(A[:,2:end-1,:] .== A_ref[:,2:end-1,:])\r\n\t\t\t\t@test all(A[:,[1, end],:] .== 0.0)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\n\t\t\t@testset \"3D (Complex)\" begin\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\n\t\t\t\tVz     = zeros(ComplexF16,nx,ny,nz+1);\n\t\t\t\tVz    .= [(1+im)*(z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz)) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\n\t\t\t\tVz_ref = copy(Vz);\n\t\t\t\tVz[[1, end],       :,       :] .= 0.0;\n\t\t\t\tVz[       :,[1, end],       :] .= 0.0;\n\t\t\t\tVz[       :,       :,[1, end]] .= 0.0;\n\t\t\t\tVz     = Array(Vz);\n\t\t\t\tVz_ref = Array(Vz_ref);\n\t\t\t\t@require !all(Vz .== Vz_ref)\n\t\t\t\tupdate_halo!(Vz);\n\t\t\t\t@test all(Vz .== Vz_ref)\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\n\t\t\tend;\r\n\t\t\t# @testset \"3D (changing datatype)\" begin\r\n\t\t\t# \tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\t\t# \tVz     = zeros(nx,ny,nz+1);\r\n\t\t\t# \tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\r\n\t\t\t# \tVz_ref = copy(Vz);\r\n\t\t\t# \tVx     = zeros(Float32,nx+1,ny,nz);\r\n\t\t\t# \tVx    .= [z_g(iz,dz,Vx)*1e2 + y_g(iy,dy,Vx)*1e1 + x_g(ix,dx,Vx) for ix=1:size(Vx,1), iy=1:size(Vx,2), iz=1:size(Vx,3)];\r\n\t\t\t# \tVx_ref = copy(Vx);\r\n\t\t\t# \tVz[[1, end],       :,       :] .= 0.0;\r\n\t\t\t# \tVz[       :,[1, end],       :] .= 0.0;\r\n\t\t\t# \tVz[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t# \tVz     = Array(Vz);\r\n\t\t\t# \tVz_ref = Array(Vz_ref);\r\n\t\t\t# \t@require !all(Vz .== Vz_ref)\r\n\t\t\t# \tupdate_halo!(Vz);\r\n\t\t\t# \t@test all(Vz .== Vz_ref)\r\n\t\t\t# \tVx[[1, end],       :,       :] .= 0.0;\r\n\t\t\t# \tVx[       :,[1, end],       :] .= 0.0;\r\n\t\t\t# \tVx[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t# \tVx     = Array(Vx);\r\n\t\t\t# \tVx_ref = Array(Vx_ref);\r\n\t\t\t# \t@require !all(Vx .== Vx_ref)\r\n\t\t\t# \tupdate_halo!(Vx);\r\n\t\t\t# \t@test all(Vx .== Vx_ref)\r\n\t\t\t# \t#TODO: added for GPU - quick fix:\r\n\t\t\t# \tVz     = zeros(nx,ny,nz+1);\r\n\t\t\t# \tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\r\n\t\t\t# \tVz_ref = copy(Vz);\r\n\t\t\t# \tVz[[1, end],       :,       :] .= 0.0;\r\n\t\t\t# \tVz[       :,[1, end],       :] .= 0.0;\r\n\t\t\t# \tVz[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t# \tVz     = Array(Vz);\r\n\t\t\t# \tVz_ref = Array(Vz_ref);\r\n\t\t\t# \t@require !all(Vz .== Vz_ref)\r\n\t\t\t# \tupdate_halo!(Vz);\r\n\t\t\t# \t@test all(Vz .== Vz_ref)\r\n\t\t\t# \tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\t# end;\n\t\t\t# @testset \"3D (changing datatype) (Complex)\" begin\n\t\t\t# \tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\n\t\t\t# \tVz     = zeros(nx,ny,nz+1);\n\t\t\t# \tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\n\t\t\t# \tVz_ref = copy(Vz);\n\t\t\t# \tVx     = zeros(ComplexF64,nx+1,ny,nz);\n\t\t\t# \tVx    .= [(1+im)*(z_g(iz,dz,Vx)*1e2 + y_g(iy,dy,Vx)*1e1 + x_g(ix,dx,Vx)) for ix=1:size(Vx,1), iy=1:size(Vx,2), iz=1:size(Vx,3)];\n\t\t\t# \tVx_ref = copy(Vx);\n\t\t\t# \tVz[[1, end],       :,       :] .= 0.0;\n\t\t\t# \tVz[       :,[1, end],       :] .= 0.0;\n\t\t\t# \tVz[       :,       :,[1, end]] .= 0.0;\n\t\t\t# \tVz     = Array(Vz);\n\t\t\t# \tVz_ref = Array(Vz_ref);\n\t\t\t# \t@require !all(Vz .== Vz_ref)\n\t\t\t# \tupdate_halo!(Vz);\n\t\t\t# \t@test all(Vz .== Vz_ref)\n\t\t\t# \tVx[[1, end],       :,       :] .= 0.0;\n\t\t\t# \tVx[       :,[1, end],       :] .= 0.0;\n\t\t\t# \tVx[       :,       :,[1, end]] .= 0.0;\n\t\t\t# \tVx     = Array(Vx);\n\t\t\t# \tVx_ref = Array(Vx_ref);\n\t\t\t# \t@require !all(Vx .== Vx_ref)\n\t\t\t# \tupdate_halo!(Vx);\n\t\t\t# \t@test all(Vx .== Vx_ref)\n\t\t\t# \t#TODO: added for GPU - quick fix:\n\t\t\t# \tVz     = zeros(nx,ny,nz+1);\n\t\t\t# \tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\n\t\t\t# \tVz_ref = copy(Vz);\n\t\t\t# \tVz[[1, end],       :,       :] .= 0.0;\n\t\t\t# \tVz[       :,[1, end],       :] .= 0.0;\n\t\t\t# \tVz[       :,       :,[1, end]] .= 0.0;\n\t\t\t# \tVz     = Array(Vz);\n\t\t\t# \tVz_ref = Array(Vz_ref);\n\t\t\t# \t@require !all(Vz .== Vz_ref)\n\t\t\t# \tupdate_halo!(Vz);\n\t\t\t# \t@test all(Vz .== Vz_ref)\n\t\t\t# \tfinalize_global_grid(finalize_MPI=false);\n\t\t\t# end;\r\n\t\t\t@testset \"3D (two fields simultaneously)\" begin\r\n\t\t\t\tinit_global_grid(nx, ny, nz, periodx=1, periody=1, periodz=1, quiet=true, init_MPI=false);\r\n\t\t\t\tVz     = zeros(nx,ny,nz+1);\r\n\t\t\t\tVz    .= [z_g(iz,dz,Vz)*1e2 + y_g(iy,dy,Vz)*1e1 + x_g(ix,dx,Vz) for ix=1:size(Vz,1), iy=1:size(Vz,2), iz=1:size(Vz,3)];\r\n\t\t\t\tVz_ref = copy(Vz);\r\n\t\t\t\tVx     = zeros(nx+1,ny,nz);\r\n\t\t\t\tVx    .= [z_g(iz,dz,Vx)*1e2 + y_g(iy,dy,Vx)*1e1 + x_g(ix,dx,Vx) for ix=1:size(Vx,1), iy=1:size(Vx,2), iz=1:size(Vx,3)];\r\n\t\t\t\tVx_ref = copy(Vx);\r\n\t\t\t\tVz[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tVz[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tVz[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tVx[[1, end],       :,       :] .= 0.0;\r\n\t\t\t\tVx[       :,[1, end],       :] .= 0.0;\r\n\t\t\t\tVx[       :,       :,[1, end]] .= 0.0;\r\n\t\t\t\tVz     = Array(Vz);\r\n\t\t\t\tVz_ref = Array(Vz_ref);\r\n\t\t\t\tVx     = Array(Vx);\r\n\t\t\t\tVx_ref = Array(Vx_ref);\r\n\t\t\t\t@require !all(Vz .== Vz_ref)\r\n\t\t\t\t@require !all(Vx .== Vx_ref)\r\n\t\t\t\tupdate_halo!(Vz, Vx);\r\n\t\t\t\t@test all(Vz .== Vz_ref)\r\n\t\t\t\t@test all(Vx .== Vx_ref)\r\n\t\t\t\tfinalize_global_grid(finalize_MPI=false);\r\n\t\t\tend;\r\n\t\tend;\r\n\tend;\r\nend;\r\n\r\n## Test tear down\r\nMPI.Finalize()\r\n", "meta": {"hexsha": "80f01c6f8374d40aa50a728fab533a9243df7e73", "size": 46804, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_update_halo.jl", "max_stars_repo_name": "luraess/ImplicitGlobalGrid.jl", "max_stars_repo_head_hexsha": "fc3c5be93b1280a9286d90a03fc8ffe892f9ec72", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 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NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.1847675084034608, "lm_q1q2_score": 0.0794772538445611}}
{"text": "x = 1; y = 2\n\nif (x < y)\n  println(\"x($x) is less than y($y)\")\nelseif (x > y)\n  println(\"x($x) is greater than y($y)\")\nelse\n  println(\"x($x) is equal to y($y)\")\nend\n\n# Leaky Variable Scope\n\nif (x < y)\n  relation = \"x($x) is less than y($y)\"\nelseif (x > y)\n  relation = \"x($x) is greater than y($y)\"\nelse\n  relation = \"x($x) is equal to y($y)\"\nend\n\nprintln(relation)\n\n# Ternary Operator\n# - can be nested\n\nz = x == y ? \"x($x) is equal to y($y)\" : \"x($x) is not equal to y($y)\"", "meta": {"hexsha": "b121062de541ec2a83f98c5f4f7833e30ba03bcf", "size": 475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-programming/6-control-flow/conditional-evaluation.jl", "max_stars_repo_name": "cadamsmith/julia-programming", "max_stars_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia-programming/6-control-flow/conditional-evaluation.jl", "max_issues_repo_name": "cadamsmith/julia-programming", "max_issues_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-programming/6-control-flow/conditional-evaluation.jl", "max_forks_repo_name": "cadamsmith/julia-programming", "max_forks_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.2692307692, "max_line_length": 70, "alphanum_fraction": 0.56, "num_tokens": 183, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3345894279828469, "lm_q2_score": 0.23651622568252118, "lm_q1q2_score": 0.07913582865977668}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.7\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 3f9f4948-74c3-11ec-2daf-51719517f805\n# Import packages\nbegin\n\t# Parse HTML\n\tusing Gumbo\n\n\t# CSS selector for parsed HTML\n\tusing Cascadia\n\n\t# Pluto Notebook utilities\n\tusing PlutoUI\n\n\t# Work with tabular data (similar to pandas in Python)\n\tusing DataFrames\n\n\t# General purpose linear algebra\n\tusing LinearAlgebra\n\n\t# Working with Dates and DateTimes\n\tusing Dates\n\n\t# Make plots for data visualization\n\tusing Plots\nend\n\n# \u2554\u2550\u2561 2398b4e5-5920-442e-980b-f75359ba73ae\nmd\"\"\"\n# Assignment 1\n\n- Name: Eric Nguyen\n- TUID: 915786865\n\"\"\"\n\n# \u2554\u2550\u2561 97d595d3-3c44-4112-a7a8-4efe4c1ba943\nmd\"\"\"\n# Problem 1\n\nThe goal is to provide better data about the top 50 solar flares recorded so far than those shown by [SpaceWeatherLive.com](https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares).\nUse [this messy NASA data](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html) to add more features for the top 50 solar flares.\nYou need to scrape this information directly from each HTML page.\nYou can read here more about [Solar Flares](https://en.wikipedia.org/wiki/Solar_flare), [coronal mass ejections](https://www.spaceweatherlive.com/en/help/what-is-a-coronal-mass-ejection-cme), and [the solar flare alphabet soup](http://spaceweather.com/glossary/flareclasses.html).\n\"\"\"\n\n# \u2554\u2550\u2561 62ba405d-4d1a-42b7-bacc-1350bf90564e\nmd\"## Part 1: Data scraping and preparation\"\n\n# \u2554\u2550\u2561 d6c88a68-c221-417b-bb78-06938fe97407\nmd\"\"\"\n### Task 1: Scrape your competitor's data\n\nScrape data for the top 50 solar flares shown in [SpaceWeatherLive.com](https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares).\n\"\"\"\n\n# \u2554\u2550\u2561 151282f9-0a35-435c-9cba-e7736efccb3f\nswldf() = let\n\t# Make request to website for HTML data using cookies to gain authorization for web scraping\n\tdata = read(`curl 'https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares.html' \\\n  -H 'authority: www.spaceweatherlive.com' \\\n  -H 'cache-control: max-age=0' \\\n  -H 'sec-ch-ua: \" Not;A Brand\";v=\"99\", \"Google Chrome\";v=\"97\", \"Chromium\";v=\"97\"' \\\n  -H 'sec-ch-ua-mobile: ?0' \\\n  -H 'sec-ch-ua-platform: \"macOS\"' \\\n  -H 'upgrade-insecure-requests: 1' \\\n  -H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36' \\\n  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9' \\\n  -H 'sec-fetch-site: same-origin' \\\n  -H 'sec-fetch-mode: navigate' \\\n  -H 'sec-fetch-user: ?1' \\\n  -H 'sec-fetch-dest: document' \\\n  -H 'referer: https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares.html' \\\n  -H 'accept-language: en-US,en;q=0.9' \\\n  -H 'cookie: CookieScriptConsent={\"action\":\"accept\",\"categories\":\"[\\\"targeting\\\",\\\"performance\\\"]\",\"CMP\":\"CPSyPX3PSyPX3F2ADBENBdCsAP_AAH_AAAAAH6Nf_X__bX9j-_59__t0eY1f9_7_v-wzjhfdt-8N2P_X_L0X42E7PF36pq4KuR4Eu3LBIQNlHOHUTUmw6okVrTPsak2Mr7NKJ7LEmnMZe2dYGHtfn91TuZKY7_78_9fz3_-v_v___9f3r-3_3__59X---_e_V399zLv9_____9nA_YAkw1L4ALsSxwZJo0qhRAhCsJDoBQAUUAwtE1hAwuCnZWAR6ggYAITUBGBECDEFGLAIABAIAkIiAkAPBAIgCIBAACAFSAhAARMAgsALAwCAAUA0LECKAIQJCDI4KjlMCAiRaKCeysASi72NMIQyiwAoFH9FRgIlCCBYGQkLBzGAAAAA.YAAAAAAAAAAA\",\"key\":\"cd200ab5-5ddb-4df2-8057-ad9de673f1e6\"}; _gid=GA1.2.1779020148.1642128786; SPSI=e3d46e0ee55ab21c983b674967abeb21; SPSE=QtZcE7RE5wJ43mJgik/6IiEWjaVvJvUBWREba0C084quUcv8c2WF9rgKyHChd+zcp+4hVlkLs6x5OJPlBXX6HA==; spcsrf=2db0574ff9f3e8b9fbc45b8804c9f061; sp_lit=/BKwhMbFX5iEuAmhsZlBwA==; UTGv2=h47aacd40fcd612bc33976e2edcd25116129; adOtr=643eeedS055; PRLST=ua/Ry/Ai; _ga_25HHLX0QL3=GS1.1.1642218626.6.1.1642219616.0; _ga=GA1.2.737209106.1642128785' \\\n  --compressed`, String)\n\n\t# Parse HTML data using CSS selectors\n\tpage = parsehtml(data)\n\ttable = first(eachmatch(sel\".table-responsive-md > table\", page.root))\n\tthead = first(eachmatch(sel\"thead\", table))\n\ttbody = first(eachmatch(sel\"tbody\", table))\n\n\t# Use reshape() to convert list of <td> to 1x8-Matrix\n\t# Vertically concatenate each <td> Matrix to form 50x8 Matrix\n\tdatarows = reduce(vcat, [reshape([Gumbo.text(td.children[1]) for td \u2208 tr.children], (1, :)) for tr \u2208 eachmatch(sel\"tr\", tbody)])\n\n\t# Put rows of data into DataFrame\n\tdf = DataFrame(datarows, [:rank, :x_class, :date, :region, :start_time, :max_time, :end_time, :movie])\nend\n\n# \u2554\u2550\u2561 8f922beb-d685-4790-a08b-0d24810c417d\nmd\"#### DataFrame for top 50 solar flares from SpaceWeatherLive.com\"\n\n# \u2554\u2550\u2561 c93284d7-0cb2-4991-a44e-a30e53c29a93\nswldf()\n\n# \u2554\u2550\u2561 99a21f98-bd1a-4bc4-a619-668e824bb788\nmd\"### Task 2: Tidy the top 50 solar flare data\"\n\n# \u2554\u2550\u2561 6fa4452d-ddcd-4df7-996a-ba75ae21ba6e\ntidy_swldf() = let\n\tdf = swldf()\n\n\t# Remove '+' from x_class\n\tdf[!,:x_class][last.(df[!,:x_class]) .== '+'] = chop.(df[!,:x_class][last.(df[!,:x_class]) .== '+'])\n\n\t# Drop last column of DataFrame\n\tselect!(df, Not(:movie))\n\n\t# Set missing values to missing (if they exist)\n\tallowmissing!(df, :region)\n\tdf[!,:region][df[!,:region] .== \"-\"] .= missing\n\t\n\t# Convert rank to integer\n\tdf.rank = parse.(Int, df.rank)\n\n\t# Convert dates and times to DateTime\n\tdf[!,:date] = Date.(df[:, :date], \"yyyy/mm/dd\")\n\tdf[!,:start_time] = DateTime.(df[:, :date], Time.(df[:, :start_time]))\n\tdf[!,:max_time] = DateTime.(df[:, :date], Time.(df[:, :max_time]))\n\tdf[!,:end_time] = DateTime.(df[:, :date], Time.(df[:, :end_time]))\n\n\t# Drop date columns\n\tselect!(df, Not(:date))\n\n\t# Rename columns\n\trename!(df, :start_time => :start_datetime)\n\trename!(df, :max_time => :max_datetime)\n\trename!(df, :end_time => :end_datetime)\n\n\t# Reorder columns\n\tdf[!, [:rank, :x_class, :start_datetime, :max_datetime, :end_datetime, :region]]\nend\n\n# \u2554\u2550\u2561 e2c62cf7-9c2e-48d7-97a3-0990328e6ec5\nmd\"#### Tidied DataFrame for top 50 solar flares from SpaceWeatherLive.com\"\n\n# \u2554\u2550\u2561 ff744877-012a-4edf-bdf9-a9e29744cff3\ntidy_swldf()\n\n# \u2554\u2550\u2561 d5bf8c5a-229e-4a2f-bbff-6d1ef6fcf9c6\nmd\"\"\"\n### Task 3: Scrape the NASA data\n\nScrape the [NASA data](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html) to get additional features about the solar flares using the [table format description](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2_description.htm).\n\"\"\"\n\n# \u2554\u2550\u2561 467168a3-33d0-46e4-acdc-3aea85fd2ff5\nnasadf() = let\n\t# Make request to website for HTML data\n\tdata = read(`curl https://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html`, String)\n\n\t# Parse HTML data using CSS selectors\n\tpage = parsehtml(data)\n\tpre = Gumbo.text(first(eachmatch(sel\"pre\", page.root)))\n\tdata = reduce(vcat, [reshape(split(row)[1:14], (1,:)) for row \u2208 split(pre, '\\n')[12:end-1]])\n\n\t# Put data into DataFrame\n\tdf = DataFrame(data, [:start_date, :start_time, :end_date, :end_time, :start_frequency, :end_frequency, :flare_location, :flare_region, :flare_classification, :cme_date, :cme_time, :cme_angle, :cme_width, :cme_speed])\nend\n\n# \u2554\u2550\u2561 c7e4ee98-8c09-4eb5-aa9d-df7a8e2b51d7\nmd\"#### DataFrame of solar flare data from NASA\"\n\n# \u2554\u2550\u2561 9f986c37-8f68-4348-a587-1cc18f924fee\nnasadf()\n\n# \u2554\u2550\u2561 fd3f32e1-29b6-4766-b1a4-60ce09c9e8f9\nmd\"### Task 4: Tidy the NASA table\"\n\n# \u2554\u2550\u2561 cc46809e-3436-4748-a29a-2a82cc197d66\ntidy_nasadf() = let\n\tdf = nasadf()\n\tallowmissing!(df)\n\n\t# Replace missing data with 'missing'\n\tdf[!,:start_frequency][df[!,:start_frequency] .== \"????\"] .= missing\n\tdf[!,:end_frequency][df[!,:end_frequency] .== \"????\"] .= missing\n\tdf[!,:flare_location][df[!,:flare_location] .== \"------\"] .= missing\n\tdf[!,:flare_region][df[!,:flare_region] .== \"-----\"] .= missing\n\tdf[!,:flare_classification][df[!,:flare_classification] .== \"----\"] .= missing\n\tdf[!,:cme_date][df[!,:cme_date] .== \"--/--\"] .= missing\n\tdf[!,:cme_time][df[!,:cme_time] .== \"--:--\"] .= missing\n\tdf[!,:cme_angle][df[!,:cme_angle] .== \"----\"] .= missing\n\tdf[!,:cme_width][df[!,:cme_width] .== \"----\" .|| df[!,:cme_width] .== \"---\"] .= missing\n\tdf[!,:cme_speed][df[!,:cme_speed] .== \"----\"] .= missing\n\n\t# Create is_halo column and set Halo angles to missing\n\tdf.is_halo = df[!,:cme_angle] .== \"Halo\"\n\tdf[!,:cme_angle][coalesce.(df.is_halo, false)] .= missing\n\n\t# Indicate lower bound and strip non-numeric parts of width\n\tdf.width_lower_bound = occursin.(\">\", coalesce.(df[!,:cme_width], \"\"))\n\tdf[!,:cme_width][df.width_lower_bound] = replace.(df[!,:cme_width][df.width_lower_bound], \">\" => \"\")\n\tdf[!,:cme_width] = replace.(coalesce.(df[!,:cme_width], \"-1\"), \"h\" => \"\")\n\t\n\t# Convert dates and times to DateTime\n\tdf[!,:start_date] = Date.(df[:, :start_date], \"yyyy/mm/dd\")\n\tdf[!,:end_date] = Date.(string.(year.(df[!,:start_date])) .* \"/\" .* df[:, :end_date], \"yyyy/mm/dd\")\n\t\n\tdf[!,:start_time] = DateTime.(df[:, :start_date], Time.(df[:, :start_time]))\n\tdf[!,:end_time][df[!,:end_time] .== \"24:00\"] .= \"23:59\" # Fix invalid time format\n\tdf[!,:end_time] = DateTime.(df[:, :end_date], Time.(df[:, :end_time]))\n\t\n\tdf[!,:cme_date] = Date.(coalesce.(string.(year.(df[!,:start_date])) .* \"/\" .* df[!,:cme_date], \"0\"), \"yyyy/mm/dd\")\n\tallowmissing!(df, :cme_date)\n\tdf[!,:cme_date][year.(df[!,:cme_date]) .== 0] .= missing\n\t\n\tdf[!,:cme_time] = DateTime.(coalesce.(df[:, :cme_date], Date(0)), Time.(coalesce.(df[:, :cme_time], \"0\")))\n\tallowmissing!(df, :cme_time)\n\tdf[!,:cme_time][year.(df[!,:cme_time]) .== 0] .= missing\n\n\t# Parse int columns\n\tfor col \u2208 [:start_frequency, :end_frequency, :cme_angle, :cme_width, :cme_speed]\n\t\tdf[!,col] .= parse.(Int, coalesce.(df[!,col], \"-1\"))\n\t\tallowmissing!(df, col)\n\t\tdf[!,col][df[!,col] .== -1] .= missing\n\tend\n\t\n\t# Drop date columns\n\tselect!(df, Not(:start_date))\n\tselect!(df, Not(:end_date))\n\tselect!(df, Not(:cme_date))\n\t\n\t# Rename columns\n\trename!(df, :start_time => :start_datetime)\n\trename!(df, :end_time => :end_datetime)\n\trename!(df, :cme_time => :cme_datetime)\n\trename!(df, :cme_angle => :cpa)\n\trename!(df, :cme_width => :width)\n\trename!(df, :cme_speed => :speed)\n\n\tdf\nend\n\n# \u2554\u2550\u2561 8710210a-2dcf-4d63-a274-4ab44445a17f\nmd\"#### Tidied DataFrame of solar flare data from NASA\"\n\n# \u2554\u2550\u2561 12328375-5e07-49c1-83da-bdae88fcc8d4\ntidy_nasadf()\n\n# \u2554\u2550\u2561 1e97263b-5fa9-497b-b610-7f3ad30b5520\nmd\"## Part 2: Analysis\"\n\n# \u2554\u2550\u2561 238002f4-c32b-41bc-8342-ac238828f74a\nmd\"### Task 5: Replication\"\n\n# \u2554\u2550\u2561 3156a7d1-d1a3-414e-8794-c895ed08f3b0\nreplicate_nasadf() = let\n\tdf = tidy_nasadf()\n\n\t# Sort DataFrame by flare classification in descending order\n\tdf = sort(subset(dropmissing(df, :flare_classification), :flare_classification => x -> occursin.('X', x)), order(:flare_classification, by = x -> parse(Float64, x[2:end])), rev=true)\n\t\n\t# Rename columns\n\trename!(df, :cme_datetime => :max_datetime)\n\trename!(df, :flare_classification => :x_class)\n\trename!(df, :flare_region => :region)\n\n\t# Truncate regions\n\tdf[!,:region][length.(coalesce.(df[!,:region], \"\")) .> 4] .= chop.(df[!,:region][length.(coalesce.(df[!,:region], \"\")) .> 4], head = 1, tail = 0)\n\n\t# Remove trailing dots from x_class\n\tdf[!,:x_class][last.(df[!,:x_class]) .== '.'] .= chop.(df[!,:x_class][last.(df[!,:x_class]) .== '.'])\n\n\t# Reorder the columns\n\tdf[!, [:x_class, :start_datetime, :max_datetime, :end_datetime, :region]]\nend\n\n# \u2554\u2550\u2561 b35a3d34-628e-470d-96b2-076a1e3d5ba5\nmd\"#### DataFrame replication for top 50 solar flares using NASA data\"\n\n# \u2554\u2550\u2561 6048f590-30e0-41ea-bcb9-a283d700668b\nreplicate_nasadf()[1:50,:]\n\n# \u2554\u2550\u2561 ec522eda-7c21-4691-863e-4b9adf1414b3\nmd\"\"\"\n**Task 5 result.** Using the NASA table, I cannot produce an exact replicate due to discrepancies between the tables, e.g., some of the dates/times are different between the tables, and there are some records in the tables that don't exist in the other table.\n\"\"\"\n\n# \u2554\u2550\u2561 0509b052-02c7-4cf5-9c1f-fa5cf584360c\nmd\"### Task 6: Integration\"\n\n# \u2554\u2550\u2561 9a30c019-fc18-492c-b147-14c234c8ff12\nintegrated_nasadf() = let\n\tdf = replicate_nasadf()\n\tdf_t50 = tidy_swldf()\n\n\t# Find most similar row from SpaceWeatherLive.com\n\tsimilarities = [\n\t\tfindmin([\n\t\t\tsqrt.(\n\t\t\t\t# Compare x_class\n\t\t\t\t(parse.(Float64, chop.(row[:x_class], head = 1, tail = 0)) - parse.(Float64, chop.(t50row[:x_class], head = 1, tail = 0))).^2 +\n\n\t\t\t\t# Compare start_datetime\n\t\t\t\tDates.value.(Dates.Minute.(row[:start_datetime] - t50row[:start_datetime])).^2 +\n\n\t\t\t\t# Compare max_datetime\n\t\t\t\tDates.value.(Dates.Minute.(coalesce.(row[:max_datetime], DateTime(0)) - t50row[:max_datetime])).^2 .*\n\n\t\t\t\t# Ignore missing max_datetimes\n\t\t\t\tDates.value.(Dates.Minute.(coalesce.(row[:max_datetime], DateTime(0)))) +\n\n\t\t\t\t# Compare end_datetime\n\t\t\t\tDates.value.(Dates.Minute.(row[:end_datetime] - t50row[:end_datetime])).^2 +\n\n\t\t\t\t# Compare region\n\t\t\t\t(parse.(Int, coalesce.(row[:region], \"0\")) - parse.(Int, t50row[:region])).^2 .*\n\n\t\t\t\t# Ignore missing regions\n\t\t\t\tparse.(Int, coalesce.(row[:region], \"0\"))\n\t\t\t) for t50row \u2208 eachrow(df_t50)]) for row \u2208 eachrow(df)\n\t]\n\n\t# Add rank column\n\tinsertcols!(df, 1, :rank => [similarities[x][2] for x \u2208 1:length(similarities)])\n\tallowmissing!(df, :rank)\n\tbuf = Set()\n\tmin_xclass = minimum(parse.(Float64, chop.(df_t50[!,:x_class], head = 1, tail = 0)))\n\tfor row \u2208 eachrow(df)\n\t\tif row[:rank] \u2208 buf || parse(Float64, chop(row[:x_class], head = 1, tail = 0)) < min_xclass\n\t\t\trow[:rank] = missing\n\t\telse\n\t\t\tpush!(buf, row[:rank])\n\t\tend\n\tend\n\n\t# Remove rows with missing rank\n\tdf = df[(!ismissing).(df[!,:rank]), :]\n\t\n\t# Sort rows by rank\n\tsort(df)\nend\n\n# \u2554\u2550\u2561 074eb18b-05eb-456e-89c7-39b7b122e14e\nmd\"#### Integrated DataFrame of top 50 solar flares from NASA\"\n\n# \u2554\u2550\u2561 53a2adeb-4d89-41e5-8a38-bb0abb2466eb\nintegrated_nasadf()\n\n# \u2554\u2550\u2561 f9ac8b88-2383-4ef2-a9b9-01435654ebf8\nmd\"### Task 7: Attributes visualization\"\n\n# \u2554\u2550\u2561 e389aaa7-82c8-4569-ace4-d13e2e3fb3ec\ndataviz(feature) = let\n\tdf = tidy_nasadf()\n\tdf_t50 = integrated_nasadf()\n\tt50 = filter(row -> row[:start_datetime] \u2208 df_t50[!,:start_datetime], df)\n\tnott50 = filter(row -> row[:start_datetime] \u2209 df_t50[!,:start_datetime], df)\n\tscatter(nott50[!,:start_datetime], nott50[!,feature], label=\"Other\")\n\tscatter!(t50[!,:start_datetime], t50[!,feature], label=\"Top 50\", marker=:star, markersize=8)\n\txlabel!(\"Starting DateTime\")\nend\n\n# \u2554\u2550\u2561 c4311bd3-6cd0-4bf9-8210-8b6555d3175f\nlet\n\tdataviz(:start_frequency)\n\tylabel!(\"Starting frequencies (kHz)\")\n\ttitle!(\"Starting frequencies over time\")\nend\n\n# \u2554\u2550\u2561 8f8db2f8-bf75-4828-862c-2b361eb33692\nlet\n\tdataviz(:end_frequency)\n\tylabel!(\"Ending frequencies (kHz)\")\n\ttitle!(\"Ending frequencies over time\")\nend\n\n# \u2554\u2550\u2561 8e6d61eb-923d-47c3-9306-237529a60672\nlet\n\tdataviz(:width)\n\tylabel!(\"CME width in the sky plane (degrees)\")\n\ttitle!(\"CME width over time\")\nend\n\n# \u2554\u2550\u2561 b5eb2437-a13e-4b69-a378-e977ecf0fba7\nlet\n\tdataviz(:speed)\n\txlabel!(\"Starting DateTime\")\n\tylabel!(\"CME Speed in the sky plane (km/s)\")\n\ttitle!(\"CME Speeds over time\")\nend\n\n# \u2554\u2550\u2561 55ec5071-e9a5-4acf-9e15-9947cc9be2b5\nmd\"### Task 8: Attributes comparison\"\n\n# \u2554\u2550\u2561 046bd1e5-e6c8-46b4-bace-11fe8ef95b50\nlet\n\tdf = tidy_nasadf()\n\tdf_t50 = integrated_nasadf()\n\tt50 = filter(row -> row[:start_datetime] \u2208 df_t50[!,:start_datetime], df)\n\tnott50 = filter(row -> row[:start_datetime] \u2209 df_t50[!,:start_datetime], df)\n\tt50ratio = sum(coalesce.(t50[!,:is_halo], false)) / length(t50[!,:is_halo])\n\tnott50ratio = sum(coalesce.(nott50[!,:is_halo], false)) / length(nott50[!,:is_halo])\n\tbar([\"Top 50\", \"Other\"], [t50ratio, nott50ratio], color = [theme_palette(:auto)[1], theme_palette(:auto)[2]], legend = false)\n\tylabel!(\"Ratio of Halo CMEs vs. Number of CMEs\")\n\ttitle!(\"Comparision of ratios of Halo CMEs between the\\n top 50 solar flares and the rest of the solar flares\")\nend\n\n# \u2554\u2550\u2561 e6c5c88c-1d13-4785-bdea-6048cc905dcc\nmd\"### Task 9: Events distribution\"\n\n# \u2554\u2550\u2561 d1ffbd03-3454-4e95-8c8f-3466a0d3ecbc\nlet\n\tdf = tidy_nasadf()\n\tdf_t50 = integrated_nasadf()\n\tt50 = filter(row -> row[:start_datetime] \u2208 df_t50[!,:start_datetime], df)\n\tnott50 = filter(row -> row[:start_datetime] \u2209 df_t50[!,:start_datetime], df)\n\n\t# Plot the number of years instead of months to make it easier to visualize\n\tnumyears = length(df[!,:start_datetime][1]:Year(1):df[!,:start_datetime][end])\n\th = histogram(nott50[!,:start_datetime], label=\"Other\", nbins = numyears)\n\thistogram!(h, t50[!,:start_datetime], label=\"Top 50\", nbins = numyears)\n\tplot(h, xticks = [df[!,:start_datetime][1], df[!,:start_datetime][end\u00f72], df[!,:start_datetime][end]], xformatter = x -> Date(Dates.epochms2datetime(x)))\n\ttitle!(\"Number of solar flares per month\")\nend\n\n# \u2554\u2550\u2561 bd510480-984e-44ef-ac45-2fbfa909eb9e\nmd\"There appears to be a cluster of strong flares between the years 2001-2003 however there are no other clusters apparent in the data.\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCascadia = \"54eefc05-d75b-58de-a785-1a3403f0919f\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nDates = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\nGumbo = \"708ec375-b3d6-5a57-a7ce-8257bf98657a\"\nLinearAlgebra = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\nPlots = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\n\n[compat]\nCascadia = \"~1.0.1\"\nDataFrames = \"~1.3.1\"\nGumbo = \"~0.8.0\"\nPlots = \"~1.25.6\"\nPlutoUI = \"~0.7.29\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.1\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.AbstractTrees]]\ngit-tree-sha1 = \"03e0550477d86222521d254b741d470ba17ea0b5\"\nuuid = \"1520ce14-60c1-5f80-bbc7-55ef81b5835c\"\nversion = \"0.3.4\"\n\n[[deps.Adapt]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"af92965fb30777147966f58acb05da51c5616b5f\"\nuuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\nversion = \"3.3.3\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.Bzip2_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"19a35467a82e236ff51bc17a3a44b69ef35185a2\"\nuuid = \"6e34b625-4abd-537c-b88f-471c36dfa7a0\"\nversion = \"1.0.8+0\"\n\n[[deps.Cairo_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"Fontconfig_jll\", \"FreeType2_jll\", \"Glib_jll\", \"JLLWrappers\", \"LZO_jll\", \"Libdl\", \"Pixman_jll\", \"Pkg\", \"Xorg_libXext_jll\", \"Xorg_libXrender_jll\", \"Zlib_jll\", \"libpng_jll\"]\ngit-tree-sha1 = \"4b859a208b2397a7a623a03449e4636bdb17bcf2\"\nuuid = \"83423d85-b0ee-5818-9007-b63ccbeb887a\"\nversion = \"1.16.1+1\"\n\n[[deps.Cascadia]]\ndeps = [\"AbstractTrees\", \"Gumbo\"]\ngit-tree-sha1 = \"95629728197821d21a41778d0e0a49bc2d58ab9b\"\nuuid = \"54eefc05-d75b-58de-a785-1a3403f0919f\"\nversion = \"1.0.1\"\n\n[[deps.ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"926870acb6cbcf029396f2f2de030282b6bc1941\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.11.4\"\n\n[[deps.ChangesOfVariables]]\ndeps = [\"ChainRulesCore\", \"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"bf98fa45a0a4cee295de98d4c1462be26345b9a1\"\nuuid = \"9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0\"\nversion = \"0.1.2\"\n\n[[deps.ColorSchemes]]\ndeps = [\"ColorTypes\", \"Colors\", \"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"6b6f04f93710c71550ec7e16b650c1b9a612d0b6\"\nuuid = \"35d6a980-a343-548e-a6ea-1d62b119f2f4\"\nversion = \"3.16.0\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.Colors]]\ndeps = [\"ColorTypes\", \"FixedPointNumbers\", \"Reexport\"]\ngit-tree-sha1 = \"417b0ed7b8b838aa6ca0a87aadf1bb9eb111ce40\"\nuuid = \"5ae59095-9a9b-59fe-a467-6f913c188581\"\nversion = \"0.12.8\"\n\n[[deps.Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"44c37b4636bc54afac5c574d2d02b625349d6582\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.41.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.Contour]]\ndeps = [\"StaticArrays\"]\ngit-tree-sha1 = \"9f02045d934dc030edad45944ea80dbd1f0ebea7\"\nuuid = \"d38c429a-6771-53c6-b99e-75d170b6e991\"\nversion = \"0.5.7\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"b618084b49e78985ffa8422f32b9838e397b9fc2\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.0\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"cfdfef912b7f93e4b848e80b9befdf9e331bc05a\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.3.1\"\n\n[[deps.DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"3daef5523dd2e769dad2365274f760ff5f282c7d\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.11\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[deps.Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[deps.DocStringExtensions]]\ndeps = [\"LibGit2\"]\ngit-tree-sha1 = \"b19534d1895d702889b219c382a6e18010797f0b\"\nuuid = \"ffbed154-4ef7-542d-bbb7-c09d3a79fcae\"\nversion = \"0.8.6\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.EarCut_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"3f3a2501fa7236e9b911e0f7a588c657e822bb6d\"\nuuid = \"5ae413db-bbd1-5e63-b57d-d24a61df00f5\"\nversion = \"2.2.3+0\"\n\n[[deps.Expat_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"b3bfd02e98aedfa5cf885665493c5598c350cd2f\"\nuuid = \"2e619515-83b5-522b-bb60-26c02a35a201\"\nversion = \"2.2.10+0\"\n\n[[deps.FFMPEG]]\ndeps = [\"FFMPEG_jll\"]\ngit-tree-sha1 = \"b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8\"\nuuid = \"c87230d0-a227-11e9-1b43-d7ebe4e7570a\"\nversion = \"0.4.1\"\n\n[[deps.FFMPEG_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"FreeType2_jll\", \"FriBidi_jll\", \"JLLWrappers\", \"LAME_jll\", \"Libdl\", \"Ogg_jll\", \"OpenSSL_jll\", \"Opus_jll\", \"Pkg\", \"Zlib_jll\", \"libass_jll\", \"libfdk_aac_jll\", \"libvorbis_jll\", \"x264_jll\", \"x265_jll\"]\ngit-tree-sha1 = \"d8a578692e3077ac998b50c0217dfd67f21d1e5f\"\nuuid = \"b22a6f82-2f65-5046-a5b2-351ab43fb4e5\"\nversion = \"4.4.0+0\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Fontconfig_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"Expat_jll\", \"FreeType2_jll\", \"JLLWrappers\", \"Libdl\", \"Libuuid_jll\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"21efd19106a55620a188615da6d3d06cd7f6ee03\"\nuuid = \"a3f928ae-7b40-5064-980b-68af3947d34b\"\nversion = \"2.13.93+0\"\n\n[[deps.Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[deps.FreeType2_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"87eb71354d8ec1a96d4a7636bd57a7347dde3ef9\"\nuuid = \"d7e528f0-a631-5988-bf34-fe36492bcfd7\"\nversion = \"2.10.4+0\"\n\n[[deps.FriBidi_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"aa31987c2ba8704e23c6c8ba8a4f769d5d7e4f91\"\nuuid = \"559328eb-81f9-559d-9380-de523a88c83c\"\nversion = \"1.0.10+0\"\n\n[[deps.Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[deps.GLFW_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Libglvnd_jll\", \"Pkg\", \"Xorg_libXcursor_jll\", \"Xorg_libXi_jll\", \"Xorg_libXinerama_jll\", \"Xorg_libXrandr_jll\"]\ngit-tree-sha1 = \"0c603255764a1fa0b61752d2bec14cfbd18f7fe8\"\nuuid = \"0656b61e-2033-5cc2-a64a-77c0f6c09b89\"\nversion = \"3.3.5+1\"\n\n[[deps.GR]]\ndeps = [\"Base64\", \"DelimitedFiles\", \"GR_jll\", \"HTTP\", \"JSON\", \"Libdl\", \"LinearAlgebra\", \"Pkg\", \"Printf\", \"Random\", \"RelocatableFolders\", \"Serialization\", \"Sockets\", \"Test\", \"UUIDs\"]\ngit-tree-sha1 = 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\"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.3.0\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[deps.Scratch]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"0b4b7f1393cff97c33891da2a0bf69c6ed241fda\"\nuuid = \"6c6a2e73-6563-6170-7368-637461726353\"\nversion = \"1.1.0\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[deps.Showoff]]\ndeps = [\"Dates\", \"Grisu\"]\ngit-tree-sha1 = \"91eddf657aca81df9ae6ceb20b959ae5653ad1de\"\nuuid = \"992d4aef-0814-514b-bc4d-f2e9a6c4116f\"\nversion = \"1.0.3\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.StaticArrays]]\ndeps = [\"LinearAlgebra\", \"Random\", \"Statistics\"]\ngit-tree-sha1 = \"2ae4fe21e97cd13efd857462c1869b73c9f61be3\"\nuuid = \"90137ffa-7385-5640-81b9-e52037218182\"\nversion = \"1.3.2\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.StatsAPI]]\ngit-tree-sha1 = \"d88665adc9bcf45903013af0982e2fd05ae3d0a6\"\nuuid = \"82ae8749-77ed-4fe6-ae5f-f523153014b0\"\nversion = \"1.2.0\"\n\n[[deps.StatsBase]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"LogExpFunctions\", \"Missings\", \"Printf\", \"Random\", \"SortingAlgorithms\", \"SparseArrays\", \"Statistics\", \"StatsAPI\"]\ngit-tree-sha1 = \"51383f2d367eb3b444c961d485c565e4c0cf4ba0\"\nuuid = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nversion = \"0.33.14\"\n\n[[deps.StructArrays]]\ndeps = [\"Adapt\", \"DataAPI\", \"StaticArrays\", \"Tables\"]\ngit-tree-sha1 = \"2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3\"\nuuid = \"09ab397b-f2b6-538f-b94a-2f83cf4a842a\"\nversion = \"0.6.3\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[deps.TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[deps.Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"bb1064c9a84c52e277f1096cf41434b675cd368b\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.1\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.URIs]]\ngit-tree-sha1 = \"97bbe755a53fe859669cd907f2d96aee8d2c1355\"\nuuid = \"5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4\"\nversion = \"1.3.0\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.UnicodeFun]]\ndeps = [\"REPL\"]\ngit-tree-sha1 = \"53915e50200959667e78a92a418594b428dffddf\"\nuuid = \"1cfade01-22cf-5700-b092-accc4b62d6e1\"\nversion = \"0.4.1\"\n\n[[deps.Unzip]]\ngit-tree-sha1 = \"34db80951901073501137bdbc3d5a8e7bbd06670\"\nuuid = \"41fe7b60-77ed-43a1-b4f0-825fd5a5650d\"\nversion = \"0.1.2\"\n\n[[deps.Wayland_jll]]\ndeps = [\"Artifacts\", \"Expat_jll\", \"JLLWrappers\", \"Libdl\", \"Libffi_jll\", \"Pkg\", \"XML2_jll\"]\ngit-tree-sha1 = \"3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23\"\nuuid = \"a2964d1f-97da-50d4-b82a-358c7fce9d89\"\nversion = \"1.19.0+0\"\n\n[[deps.Wayland_protocols_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"66d72dc6fcc86352f01676e8f0f698562e60510f\"\nuuid = \"2381bf8a-dfd0-557d-9999-79630e7b1b91\"\nversion = \"1.23.0+0\"\n\n[[deps.XML2_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Libiconv_jll\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"1acf5bdf07aa0907e0a37d3718bb88d4b687b74a\"\nuuid = \"02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a\"\nversion = \"2.9.12+0\"\n\n[[deps.XSLT_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Libgcrypt_jll\", \"Libgpg_error_jll\", \"Libiconv_jll\", \"Pkg\", \"XML2_jll\", \"Zlib_jll\"]\ngit-tree-sha1 = \"91844873c4085240b95e795f692c4cec4d805f8a\"\nuuid = \"aed1982a-8fda-507f-9586-7b0439959a61\"\nversion = \"1.1.34+0\"\n\n[[deps.Xorg_libX11_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libxcb_jll\", \"Xorg_xtrans_jll\"]\ngit-tree-sha1 = \"5be649d550f3f4b95308bf0183b82e2582876527\"\nuuid = \"4f6342f7-b3d2-589e-9d20-edeb45f2b2bc\"\nversion = \"1.6.9+4\"\n\n[[deps.Xorg_libXau_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4e490d5c960c314f33885790ed410ff3a94ce67e\"\nuuid = \"0c0b7dd1-d40b-584c-a123-a41640f87eec\"\nversion = \"1.0.9+4\"\n\n[[deps.Xorg_libXcursor_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libXfixes_jll\", \"Xorg_libXrender_jll\"]\ngit-tree-sha1 = \"12e0eb3bc634fa2080c1c37fccf56f7c22989afd\"\nuuid = \"935fb764-8cf2-53bf-bb30-45bb1f8bf724\"\nversion = \"1.2.0+4\"\n\n[[deps.Xorg_libXdmcp_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4fe47bd2247248125c428978740e18a681372dd4\"\nuuid = \"a3789734-cfe1-5b06-b2d0-1dd0d9d62d05\"\nversion = \"1.1.3+4\"\n\n[[deps.Xorg_libXext_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libX11_jll\"]\ngit-tree-sha1 = \"b7c0aa8c376b31e4852b360222848637f481f8c3\"\nuuid = \"1082639a-0dae-5f34-9b06-72781eeb8cb3\"\nversion = \"1.3.4+4\"\n\n[[deps.Xorg_libXfixes_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libX11_jll\"]\ngit-tree-sha1 = \"0e0dc7431e7a0587559f9294aeec269471c991a4\"\nuuid = \"d091e8ba-531a-589c-9de9-94069b037ed8\"\nversion = \"5.0.3+4\"\n\n[[deps.Xorg_libXi_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libXext_jll\", \"Xorg_libXfixes_jll\"]\ngit-tree-sha1 = \"89b52bc2160aadc84d707093930ef0bffa641246\"\nuuid = \"a51aa0fd-4e3c-5386-b890-e753decda492\"\nversion = \"1.7.10+4\"\n\n[[deps.Xorg_libXinerama_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libXext_jll\"]\ngit-tree-sha1 = \"26be8b1c342929259317d8b9f7b53bf2bb73b123\"\nuuid = \"d1454406-59df-5ea1-beac-c340f2130bc3\"\nversion = \"1.1.4+4\"\n\n[[deps.Xorg_libXrandr_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libXext_jll\", \"Xorg_libXrender_jll\"]\ngit-tree-sha1 = \"34cea83cb726fb58f325887bf0612c6b3fb17631\"\nuuid = \"ec84b674-ba8e-5d96-8ba1-2a689ba10484\"\nversion = \"1.5.2+4\"\n\n[[deps.Xorg_libXrender_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libX11_jll\"]\ngit-tree-sha1 = \"19560f30fd49f4d4efbe7002a1037f8c43d43b96\"\nuuid = \"ea2f1a96-1ddc-540d-b46f-429655e07cfa\"\nversion = \"0.9.10+4\"\n\n[[deps.Xorg_libpthread_stubs_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"6783737e45d3c59a4a4c4091f5f88cdcf0908cbb\"\nuuid = \"14d82f49-176c-5ed1-bb49-ad3f5cbd8c74\"\nversion = \"0.1.0+3\"\n\n[[deps.Xorg_libxcb_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"XSLT_jll\", \"Xorg_libXau_jll\", \"Xorg_libXdmcp_jll\", \"Xorg_libpthread_stubs_jll\"]\ngit-tree-sha1 = \"daf17f441228e7a3833846cd048892861cff16d6\"\nuuid = \"c7cfdc94-dc32-55de-ac96-5a1b8d977c5b\"\nversion = \"1.13.0+3\"\n\n[[deps.Xorg_libxkbfile_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libX11_jll\"]\ngit-tree-sha1 = \"926af861744212db0eb001d9e40b5d16292080b2\"\nuuid = \"cc61e674-0454-545c-8b26-ed2c68acab7a\"\nversion = \"1.1.0+4\"\n\n[[deps.Xorg_xcb_util_image_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"0fab0a40349ba1cba2c1da699243396ff8e94b97\"\nuuid = \"12413925-8142-5f55-bb0e-6d7ca50bb09b\"\nversion = \"0.4.0+1\"\n\n[[deps.Xorg_xcb_util_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libxcb_jll\"]\ngit-tree-sha1 = \"e7fd7b2881fa2eaa72717420894d3938177862d1\"\nuuid = \"2def613f-5ad1-5310-b15b-b15d46f528f5\"\nversion = \"0.4.0+1\"\n\n[[deps.Xorg_xcb_util_keysyms_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"d1151e2c45a544f32441a567d1690e701ec89b00\"\nuuid = \"975044d2-76e6-5fbe-bf08-97ce7c6574c7\"\nversion = \"0.4.0+1\"\n\n[[deps.Xorg_xcb_util_renderutil_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"dfd7a8f38d4613b6a575253b3174dd991ca6183e\"\nuuid = \"0d47668e-0667-5a69-a72c-f761630bfb7e\"\nversion = \"0.3.9+1\"\n\n[[deps.Xorg_xcb_util_wm_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"e78d10aab01a4a154142c5006ed44fd9e8e31b67\"\nuuid = \"c22f9ab0-d5fe-5066-847c-f4bb1cd4e361\"\nversion = \"0.4.1+1\"\n\n[[deps.Xorg_xkbcomp_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libxkbfile_jll\"]\ngit-tree-sha1 = \"4bcbf660f6c2e714f87e960a171b119d06ee163b\"\nuuid = \"35661453-b289-5fab-8a00-3d9160c6a3a4\"\nversion = \"1.4.2+4\"\n\n[[deps.Xorg_xkeyboard_config_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xkbcomp_jll\"]\ngit-tree-sha1 = \"5c8424f8a67c3f2209646d4425f3d415fee5931d\"\nuuid = \"33bec58e-1273-512f-9401-5d533626f822\"\nversion = \"2.27.0+4\"\n\n[[deps.Xorg_xtrans_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"79c31e7844f6ecf779705fbc12146eb190b7d845\"\nuuid = \"c5fb5394-a638-5e4d-96e5-b29de1b5cf10\"\nversion = \"1.4.0+3\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[deps.Zstd_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6\"\nuuid = \"3161d3a3-bdf6-5164-811a-617609db77b4\"\nversion = \"1.5.0+0\"\n\n[[deps.libass_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"FreeType2_jll\", \"FriBidi_jll\", \"HarfBuzz_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"5982a94fcba20f02f42ace44b9894ee2b140fe47\"\nuuid = \"0ac62f75-1d6f-5e53-bd7c-93b484bb37c0\"\nversion = \"0.15.1+0\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.libfdk_aac_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"daacc84a041563f965be61859a36e17c4e4fcd55\"\nuuid = \"f638f0a6-7fb0-5443-88ba-1cc74229b280\"\nversion = \"2.0.2+0\"\n\n[[deps.libpng_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"94d180a6d2b5e55e447e2d27a29ed04fe79eb30c\"\nuuid = \"b53b4c65-9356-5827-b1ea-8c7a1a84506f\"\nversion = \"1.6.38+0\"\n\n[[deps.libvorbis_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Ogg_jll\", \"Pkg\"]\ngit-tree-sha1 = \"b910cb81ef3fe6e78bf6acee440bda86fd6ae00c\"\nuuid = \"f27f6e37-5d2b-51aa-960f-b287f2bc3b7a\"\nversion = \"1.3.7+1\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\n[[deps.x264_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4fea590b89e6ec504593146bf8b988b2c00922b2\"\nuuid = \"1270edf5-f2f9-52d2-97e9-ab00b5d0237a\"\nversion = \"2021.5.5+0\"\n\n[[deps.x265_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"ee567a171cce03570d77ad3a43e90218e38937a9\"\nuuid = \"dfaa095f-4041-5dcd-9319-2fabd8486b76\"\nversion = \"3.5.0+0\"\n\n[[deps.xkbcommon_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"ece2350174195bb31de1a63bea3a41ae1aa593b6\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"0.9.1+5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25002398b4e5-5920-442e-980b-f75359ba73ae\n# \u2560\u25503f9f4948-74c3-11ec-2daf-51719517f805\n# \u255f\u250097d595d3-3c44-4112-a7a8-4efe4c1ba943\n# \u255f\u250062ba405d-4d1a-42b7-bacc-1350bf90564e\n# \u255f\u2500d6c88a68-c221-417b-bb78-06938fe97407\n# \u2560\u2550151282f9-0a35-435c-9cba-e7736efccb3f\n# \u255f\u25008f922beb-d685-4790-a08b-0d24810c417d\n# \u2560\u2550c93284d7-0cb2-4991-a44e-a30e53c29a93\n# \u255f\u250099a21f98-bd1a-4bc4-a619-668e824bb788\n# \u2560\u25506fa4452d-ddcd-4df7-996a-ba75ae21ba6e\n# \u255f\u2500e2c62cf7-9c2e-48d7-97a3-0990328e6ec5\n# \u2560\u2550ff744877-012a-4edf-bdf9-a9e29744cff3\n# \u255f\u2500d5bf8c5a-229e-4a2f-bbff-6d1ef6fcf9c6\n# \u2560\u2550467168a3-33d0-46e4-acdc-3aea85fd2ff5\n# \u255f\u2500c7e4ee98-8c09-4eb5-aa9d-df7a8e2b51d7\n# \u2560\u25509f986c37-8f68-4348-a587-1cc18f924fee\n# \u255f\u2500fd3f32e1-29b6-4766-b1a4-60ce09c9e8f9\n# \u2560\u2550cc46809e-3436-4748-a29a-2a82cc197d66\n# \u255f\u25008710210a-2dcf-4d63-a274-4ab44445a17f\n# \u2560\u255012328375-5e07-49c1-83da-bdae88fcc8d4\n# \u255f\u25001e97263b-5fa9-497b-b610-7f3ad30b5520\n# \u255f\u2500238002f4-c32b-41bc-8342-ac238828f74a\n# \u2560\u25503156a7d1-d1a3-414e-8794-c895ed08f3b0\n# \u255f\u2500b35a3d34-628e-470d-96b2-076a1e3d5ba5\n# \u2560\u25506048f590-30e0-41ea-bcb9-a283d700668b\n# \u255f\u2500ec522eda-7c21-4691-863e-4b9adf1414b3\n# \u255f\u25000509b052-02c7-4cf5-9c1f-fa5cf584360c\n# \u2560\u25509a30c019-fc18-492c-b147-14c234c8ff12\n# \u255f\u2500074eb18b-05eb-456e-89c7-39b7b122e14e\n# \u2560\u255053a2adeb-4d89-41e5-8a38-bb0abb2466eb\n# \u255f\u2500f9ac8b88-2383-4ef2-a9b9-01435654ebf8\n# \u2560\u2550e389aaa7-82c8-4569-ace4-d13e2e3fb3ec\n# \u2560\u2550c4311bd3-6cd0-4bf9-8210-8b6555d3175f\n# \u2560\u25508f8db2f8-bf75-4828-862c-2b361eb33692\n# \u2560\u25508e6d61eb-923d-47c3-9306-237529a60672\n# \u2560\u2550b5eb2437-a13e-4b69-a378-e977ecf0fba7\n# \u255f\u250055ec5071-e9a5-4acf-9e15-9947cc9be2b5\n# \u2560\u2550046bd1e5-e6c8-46b4-bace-11fe8ef95b50\n# \u255f\u2500e6c5c88c-1d13-4785-bdea-6048cc905dcc\n# \u2560\u2550d1ffbd03-3454-4e95-8c8f-3466a0d3ecbc\n# \u255f\u2500bd510480-984e-44ef-ac45-2fbfa909eb9e\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "4a50b24f90ddbe57e42d8e0169ced6fe502f1010", "size": 51663, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "assignment-01.jl", 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{"text": "### A Pluto.jl notebook ###\n# v0.12.21\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 b0dff76e-8726-11eb-31c8-ab5878dd9ed4\nbegin\n\tusing PlutoUI\n\tusing NativeSVG\nend\n\n# \u2554\u2550\u2561 bef082a6-8726-11eb-088f-5ffda6b048e7\nmd\"\"\"# Tuples\n\nThis chapter presents one more built-in type, the tuple, and then shows how arrays, dictionaries, and tuples work together. It also introduces a useful feature for variable-length argument arrays, the gather and scatter operators.\"\"\"\n\n# \u2554\u2550\u2561 d10a4cb8-8726-11eb-27e2-b55de2487e54\nmd\"\"\"## Tuples Are Immutable\n\nA *tuple* is a sequence of values. The values can be of any type, and they are indexed by integers, so in that respect tuples are a lot like arrays. The important difference is that tuples are immutable and that each element can have its own type.\n\nSyntactically, a tuple is a comma-separated list of values:\n\n```julia\njulia> t = 'a', 'b', 'c', 'd', 'e' \n('a', 'b', 'c', 'd', 'e')\n```\n\nAlthough it is not necessary, it is common to enclose tuples in parentheses:\n\n```julia\njulia> t = ('a', 'b', 'c', 'd', 'e') \n('a', 'b', 'c', 'd', 'e')\n```\n\nTo create a tuple with a single element, you have to include a final comma:\n\n```julia\njulia> t1 = ('a',) \n('a',)\njulia> typeof(t1) \nTuple{Char}\n```\n\n!!! danger\n    A value in parentheses without comma is not a tuple:\n    \n    ```julia\n    julia> t2 = ('a')\n    'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase) \n    julia> typeof(t2) \n    Char\n    ```\n\nAnother way to create a tuple is using the built-in function `tuple`. With no argument, it creates an empty tuple:\n\n```julia\njulia> tuple() \n()\n```\n\nIf multiple arguments are provided, the result is a tuple with the given arguments:\n\n```julia\njulia> t3 = tuple(1, 'a', pi) \n(1, 'a', \u03c0 = 3.1415926535897...)\n```\n\nBecause `tuple` is the name of a built-in function, you should avoid using it as a variable name.\n\nMost array operators also work on tuples. The bracket operator indexes an element:\n\n```julia\njulia> t = ('a', 'b', 'c', 'd', 'e'); julia> t[1]\n'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)\n```\n\nAnd the slice operator selects a range of elements:\n\n```julia\njulia> t[2:4] \n('b', 'c', 'd')\n```\n\nBut if you try to modify one of the elements of the tuple, you get an error:\n\n```julia\njulia> t[1] = 'A'\nERROR: MethodError: no method matching setindex!(::NTuple{5,Char},::Char, ::Int64)\n```\n\nBecause tuples are immutable, you can\u2019t modify the elements.\n\nThe relational operators work with tuples and other sequences. Julia starts by comparing the first element from each sequence. If they are equal, it goes on to the next elements, and so on until it finds elements that differ. Subsequent elements are not considered (even if they are really big):\n\n```julia\njulia>(0, 1, 2) < (0, 3, 4)\ntrue\njulia> (0, 1, 2000000) < (0, 3, 4) \ntrue\n```\n\"\"\"\n\n# \u2554\u2550\u2561 c7ffd72e-8727-11eb-0f6d-fb8e9395741c\nmd\"\"\"## Tuple Assignment\n\nIt is often useful to swap the values of two variables. With conventional assignments, you have to use a temporary variable. For example, to swap `a` and `b`:\n\n```julia\ntemp=a \na=b \nb=temp\n```\n\nThis solution is cumbersome; *tuple assignment* is more elegant:\n\n```julia\na, b = b, a\n```\n\nThe left side is a tuple of variables; the right side is a tuple of expressions. Each value is assigned to its respective variable. All the expressions on the right side are evaluated before any of the assignments.\n\nThe number of variables on the left has to be fewer than the number of values on the right:\n\n```julia\njulia> (a, b) = (1, 2, 3)\n(1, 2, 3)\njulia>a, b, c= 1, 2\nERROR: BoundsError: attempt to access (1, 2) at index [3]\n```\n\nMore generally, the right side can be any kind of sequence (string, array, or tuple). For example, to split an email address into a username and a domain, you could write:\n\n```julia\njulia> addr = \"julius.caesar@rome\" \n\"julius.caesar@rome\"\njulia> uname, domain = split(addr, '@')\n2-element Array{SubString{String},1}:\n \"julius.caesar\"\n \"rome\"\n```\n\nThe return value from `split` is an array with two elements; the first element is assigned to `uname`, the second to `domain`:\n\n```julia\njulia> uname \n\"julius.caesar\" \njulia> domain \n\"rome\"\n```\n\"\"\"\n\n# \u2554\u2550\u2561 574b6fb0-8728-11eb-0eca-51b778a97602\nmd\"\"\"## Tuples as Return Values\n\nStrictly speaking, a function can only return one value, but if the value is a tuple, the effect is the same as returning multiple values. For example, if you want to divide two integers and compute the quotient and remainder, it is inefficient to compute `x \u00f7 y` and then `x % y`. It is better to compute them both at the same time.\nThe built-in function divrem takes two arguments and returns a tuple of two values, the quotient and remainder. You can store the result as a tuple:\n\n```julia\njulia> t = divrem(7, 3) \n(2, 1)\n```\n\nOr use tuple assignment to store the elements separately:\n\n```julia\njulia> q, r = divrem(7, 3);\njulia> @show q r; \nq = 2\nr = 1\n```\n\nHere is an example of a function that returns a tuple:\n\n```julia\nminmax(t) = minimum(t), maximum(t)\n```\n\n`maximum` and `minimum` are built-in functions that find the largest and smallest ele\u2010 ments of a sequence. `minmax` computes both and returns a tuple of two values. The built-in function `extrema` is more efficient.\n\"\"\"\n\n# \u2554\u2550\u2561 a5a354be-8728-11eb-2ab3-4f994c1939c1\nmd\"\"\"## Variable-Length Argument Tuples\n\nFunctions can take a variable number of arguments. A parameter name that ends with `...` *gathers* arguments into a tuple. For example, `printall` takes any number of arguments and prints them:\n\n```julia\nprintall(args...) = println(args)\n```\n\nThe gather parameter can have any name you like, but `args` is conventional. Here\u2019s how the function works:\n\n```julia\njulia> printall(1, 2.0, '3') \n(1, 2.0, '3')\n```\n\nThe complement of gather is *scatter*. If you have a sequence of values and you want to pass it to a function as multiple arguments, you can use the `...` operator. For example, `divrem` takes exactly two arguments; it doesn\u2019t work with a tuple:\n\n```julia\njulia> t = (7, 3); \n\njulia> divrem(t)\nERROR: MethodError: no method matching divrem(::Tuple{Int64,Int64})\n```\n\nBut if you scatter the tuple, it works:\n\n```julia\njulia> divrem(t...) \n(2, 1)\n```\n\nMany of the built-in functions use variable-length argument tuples. For example, `max` and `min` can take any number of arguments:\n\n```julia\njulia> max(1, 2, 3) \n3\n```\n\nBut sum does not: \n\n```julia\njulia> sum(1, 2, 3)\nERROR: MethodError: no method matching sum(::Int64, ::Int64, ::Int64)\n```\n\nIn the Julia world, gather is often called \u201cslurp\u201d and scatter \u201csplat.\u201d\n\"\"\"\n\n# \u2554\u2550\u2561 19ee0db4-8729-11eb-3ca7-737de22181ff\nmd\"\"\"#### Exercise 12-1\n\nWrite a function called `sumall` that takes any number of arguments and returns their sum.\"\"\"\n\n# \u2554\u2550\u2561 3d55da4a-8729-11eb-3d2f-37fb30c62793\nmd\"\"\"## Arrays and Tuples\n\n`zip` is a built-in function that takes two or more sequences and returns a collection of tuples where each tuple contains one element from each sequence. The name of the function refers to a zipper, which joins and interleaves two rows of teeth.\n\nThis example zips a string and an array:\n\n```julia\njulia> s = \"abc\"; \n\njulia> t = [1, 2, 3];\njulia> zip(s, t) \nBase.Iterators.Zip{Tuple{String,Array{Int64,1}}}((\"abc\", [1, 2, 3]))\n```\n\nThe result is a *zip object* that knows how to iterate through the pairs. The most common use of `zip` is in a `for` loop:\n\n```julia\njulia> for pair in zip(s, t) \n\t   \t   println(pair)\n\t   end\n('a', 1)\n('b', 2)\n('c', 3)\n```\n\nA zip object is a kind of *iterator*, which is any object that iterates through a sequence. Iterators are similar to arrays in some ways, but unlike arrays, you can\u2019t use an index to select an element from an iterator.\n\nIf you want to use array operators and functions, you can use a zip object to make an array:\n\n```julia\njulia> collect(zip(s, t))\n3-element Array{Tuple{Char,Int64},1}:\n ('a', 1)\n ('b', 2)\n ('c', 3)\n```\n\nThe result is an array of tuples; in this example, each tuple contains a character from the string and the corresponding element from the array.\n\nIf the sequences are not the same length, the result has the length of the shorter one:\n\n```julia\njulia> collect(zip(\"Anne\", \"Elk\")) \n3-element Array{Tuple{Char,Char},1}:\n ('A', 'E')\n ('n', 'l')\n ('n', 'k')\n```\n\nYou can use tuple assignment in a `for` loop to traverse an array of tuples: \n\n```julia\njulia> t = [('a', 1), ('b', 2), ('c', 3)];\n\njulia> for (letter, number) in t \n           println(number, \" \", letter)\n       end\n1a 2b 3c\n```\n\nEach time through the loop, Julia selects the next tuple in the array and assigns the elements to `letter` and `number`. The parentheses around (letter, number) are compulsory.\n\nIf you combine `zip`, `for`, and tuple assignment, you get a useful idiom for traversing two (or more) sequences at the same time. For example, `hasmatch` takes two sequences, `t1` and `t2`, and returns true if there is an index `i` such that `t1[i] == t2[i]`:\n\n```julia\nfunction hasmatch(t1, t2) \n\tfor (x, y) in zip(t1, t2)\n\t\tif x == y \n\t\t\treturn true\n\t\tend \n\tend\n\tfalse \nend\n```\n\nIf you need to traverse the elements of a sequence and their indices, you can use the built-in function `enumerate`:\n\n```julia\njulia> for (index, element) in enumerate(\"abc\") \n           println(index, \" \", element)\n       end\n1 a \n2 b \n3 c\n```\n\nThe result from `enumerate` is an enumerate object, which iterates a sequence of pairs; each pair contains an index (starting from 1) and an element from the given sequence.\n\"\"\"\n\n# \u2554\u2550\u2561 221a3360-872a-11eb-2ead-8d1358efe435\nmd\"\"\"## Dictionaries and Tuples\n\nDictionaries can be used as iterators that iterate the key-value pairs. You can use a dictionary in a `for` loop like this:\n\n```julia\njulia> d = Dict('a'=>1, 'b'=>2, 'c'=>3);\n\njulia> for (key, value) in d \n           println(key, \" \", value)\n       end\na 1 \nc 3 \nb 2\n```\n\nAs you should expect from a dictionary, the items are in no particular order.\n\nGoing in the other direction, you can use an array of tuples to initialize a new dictionary:\n\n```julia\njulia> t = [('a', 1), ('c', 3), ('b', 2)];\n\njulia> d = Dict(t) \nDict{Char,Int64} with 3 entries:\n  'a' => 1\n  'c' => 3\n  'b' => 2\n```\n\nCombining `Dict` with `zip` yields a concise way to create a dictionary:\n\n```julia\njulia> d = Dict(zip(\"abc\", 1:3)) \nDict{Char,Int64} with 3 entries:\n  'a' => 1\n  'c' => 3\n  'b' => 2\n```\n\nIt is common to use tuples as keys in dictionaries. For example, a telephone directory might map from *last-name*, *first-name* pairs to telephone numbers. Assuming that we have defined `last`, `first`, and `number`, we could write:\n\n```julia\ndirectory[last, first] = number\n```\n\nThe expression in brackets is a tuple. We could use tuple assignment to traverse this\ndictionary:\n\n```julia\nfor ((last, first), number) in directory \n\tprintln(first, \" \", last, \" \", number)\nend\n```\n\nThis loop traverses the key-value pairs in `directory`, which are tuples. It assigns the elements of the key in each tuple to `last` and `first`, and the value to `number`, then prints the name and corresponding telephone number.\n\nThere are two ways to represent tuples in a state diagram. The more detailed version shows the indices and elements just as they appear in an array. For example, the tuple `(\"Cleese\", \"John\")` would appear as in Figure 12-1.\n\"\"\"\n\n# \u2554\u2550\u2561 ce6beec2-872a-11eb-3b39-5531bfc637cb\nDrawing(width=720, height=70) do\n\tdefs() do\n        marker(id=\"arrow\", markerWidth=\"10\", markerHeight=\"10\", refX=\"0\", refY=\"3\", orient=\"auto\", markerUnits=\"strokeWidth\") do\n      \t\tpath(d=\"M0,0 L0,6 L9,3 z\", fill=\"black\")\n\t\tend\n\tend\n\trect(x=285, y=10, width=150, height=50, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=295, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"1\") \n\tend\n\tline(x1=315, y1=25, x2=355, y2=25, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=375, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"Cleese\") \n\tend\n\ttext(x=295, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"2\") \n\tend\n\tline(x1=315, y1=45, x2=355, y2=45, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=375, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"John\") \n\tend\nend\n\n# \u2554\u2550\u2561 bf393bf4-872b-11eb-0233-cbb084e3ad3f\nmd\"*Figure 12-1. State diagram.*\"\n\n# \u2554\u2550\u2561 c580c4b4-872b-11eb-1aaf-b51e08f9f26c\nmd\"\"\"But in a larger diagram you might want to leave out the details. For example, a diagram of the telephone directory might appear as in Figure 12-2.\"\"\"\n\n# \u2554\u2550\u2561 c608335e-872b-11eb-278e-51296de15fbc\nDrawing(width=720, height=150) do\n\tdefs() do\n        marker(id=\"arrow\", markerWidth=\"10\", markerHeight=\"10\", refX=\"0\", refY=\"3\", orient=\"auto\", markerUnits=\"strokeWidth\") do\n      \t\tpath(d=\"M0,0 L0,6 L9,3 z\", fill=\"black\")\n\t\tend\n\tend\n\trect(x=175, y=10, width=370, height=130, fill=\"rgb(242, 242, 242)\", stroke=\"black\")\n\ttext(x=355, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Cleese\\\", \\\"John\\\")\") \n\tend\n\tline(x1=365, y1=25, x2=405, y2=25, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=30, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\n\ttext(x=355, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Chapman\\\", \\\"Graham\\\")\") \n\tend\n\tline(x1=365, y1=45, x2=405, y2=45, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=50, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\n\ttext(x=355, y=70, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Idle\\\", \\\"Eric\\\")\") \n\tend\n\tline(x1=365, y1=65, x2=405, y2=65, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=70, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\n\ttext(x=355, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Gilliam\\\", \\\"Terry\\\")\") \n\tend\n\tline(x1=365, y1=85, x2=405, y2=85, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=90, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\n\ttext(x=355, y=110, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Jones\\\", \\\"Terry\\\")\") \n\tend\n\tline(x1=365, y1=105, x2=405, y2=105, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=110, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\n\ttext(x=355, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600, text_anchor=\"end\") do \n\t\tstr(\"(\\\"Palin\\\", \\\"Michael\\\")\") \n\tend\n\tline(x1=365, y1=125, x2=405, y2=125, stroke=\"black\", marker_end=\"url(#arrow)\")\n\ttext(x=425, y=130, font_family=\"JuliaMono, monospace\", font_size=\"0.85rem\", font_weight=600) do \n\t\tstr(\"08700 100 222\") \n\tend\nend\n\n# \u2554\u2550\u2561 1f4b8a1e-872d-11eb-20b1-014ddea498de\nmd\"*Figure 12-2. State diagram.*\"\n\n# \u2554\u2550\u2561 286fe504-872d-11eb-06d3-e1c2682eff9c\nmd\"\"\"Here the tuples are shown using Julia syntax as a graphical shorthand. The telephone number in the diagram is the complaints line for the BBC, so please don\u2019t call it.\"\"\"\n\n# \u2554\u2550\u2561 3260e720-872d-11eb-065c-93bbb15621b9\nmd\"\"\"## Sequences of Sequences\n\nI have focused on arrays of tuples, but almost all of the examples in this chapter also work with arrays of arrays, tuples of tuples, and tuples of arrays. To avoid enumerating the possible combinations, it is sometimes easier to talk about sequences of sequences.\n\nIn many contexts, the different kinds of sequences (strings, arrays, and tuples) can be used interchangeably. So how should you choose one over the others?\n\nTo start with the obvious, strings are more limited than other sequences because the elements have to be characters. They are also immutable. If you need the ability to change the characters in a string (as opposed to creating a new string), you might want to use an array of characters instead.\n\nArrays are more common than tuples, mostly because they are mutable. But there are a few cases where you might prefer tuples:\n\n* In some contexts, like a `return` statement, it is syntactically simpler to create a tuple than an array.\n* If you are passing a sequence as an argument to a function, using tuples reduces the potential for unexpected behavior due to aliasing.\n* For performance reasons. The compiler can specialize on the type.\n\nBecause tuples are immutable, they don\u2019t provide functions like `sort!` and `reverse!`, which modify existing arrays. But Julia provides the built-in functions `sort`, which takes an array and returns a new array with the same elements in sorted order, and `reverse`, which takes any sequence and returns a sequence of the same type in reverse order.\n\"\"\"\n\n# \u2554\u2550\u2561 6cd09676-872d-11eb-0ffd-3fd772eb7db4\nmd\"\"\"## Debugging\n\nArrays, dictionaries, and tuples are examples of *data structures*; in this chapter we are starting to see compound data structures, like arrays of tuples, or dictionaries that contain tuples as keys and arrays as values. Compound data structures are useful, but they are prone to what I call *shape errors*; that is, errors caused when a data structure has the wrong type, size, or structure. For example, if you are expecting an array with one integer and I give you a plain old integer (not in an array), it won\u2019t work.\n\nJulia allows you to attach a type to elements of a sequence. How this is done is detailed in Lecture 17. Specifying the type eliminates a lot of shape errors.\n\"\"\"\n\n# \u2554\u2550\u2561 96a213d2-872d-11eb-25e4-fdd44a16150d\nmd\"\"\"## Glossary\n\n*tuple*:\nAn immutable sequence of elements where every element can have its own type.\n\n*tuple assignment*:\nAn assignment with a sequence on the right side and a tuple of variables on the left. The right side is evaluated and then its elements are assigned to the variables on the left.\n\n*gather*:\nThe operation of assembling a variable-length argument tuple.\n\n*scatter*:\nThe operation of treating a sequence as a list of arguments.\n\n*zip object*:\nThe result of calling a built-in function zip, an object that iterates through a sequence of tuples.\n\n*iterator*:\nAn object that can iterate through a sequence, but that does not provide array operators and functions.\n\n*data structure*:\nA collection of related values, often organized in arrays, dictionaries, tuples, etc.\n\n*shape error*:\nAn error caused because a value has the wrong shape; that is, the wrong type or size.\n\"\"\"\n\n# \u2554\u2550\u2561 e7f038d6-872d-11eb-28a3-6307d3e72e85\nmd\"\"\"## Exercises \n\n#### Exercise 12-2\n\nWrite a function called `mostfrequent` that takes a string and prints the letters in decreasing order of frequency. Find text samples from several different languages and see how letter frequency varies between languages. Compare your results with the tables at [https://en.wikipedia.org/wiki/Letter_frequencies](https://en.wikipedia.org/wiki/Letter_frequencies).\n\"\"\"\n\n# \u2554\u2550\u2561 05a7a222-872e-11eb-3de9-c73ab5da92a0\nmd\"\"\"#### Exercise 12-3\n\nMore anagrams!\n\n1. Write a program that reads a word list from a file (see \u201cReading Word Lists\u201d) and prints all the sets of words that are anagrams. Here is an example of what the output might look like:\n   \n   ```julia\n   [\"deltas\", \"desalt\", \"lasted\", \"salted\", \"slated\", \"staled\"]\n   [\"retainers\", \"ternaries\"]\n   [\"generating\", \"greatening\"]\n   [\"resmelts\", \"smelters\", \"termless\"]\n   ```\n   \n   !!! tip\n       You might want to build a dictionary that maps from a collection of letters to an array of words that can be spelled with those letters. The question is, how can you represent the col\u2010 lection of letters in a way that can be used as a key?\n\n2. Modify the previous program so that it prints the longest array of anagrams first, followed by the second longest, and so on.\n\n3. In Scrabble a \u201cbingo\u201d is when you play all seven tiles in your rack, along with a letter on the board, to form an eight-letter word. What collection of eight letters forms the most possible bingos?\n\"\"\"\n\n# \u2554\u2550\u2561 586908a2-872e-11eb-3919-f96ba66ee674\nmd\"\"\"#### Exercise 12-4\n\nTwo words form a \u201cmetathesis pair\u201d if you can transform one into the other by swap\u2010 ping two letters; for example, \u201cconverse\u201d and \u201cconserve.\u201d Write a program that finds all of the metathesis pairs in *words.txt*.\n\n!!! tip\n    Don\u2019t test all pairs of words, and don\u2019t test all possible swaps.\n\n*Credit*: This exercise is inspired by an example at [http://puzzlers.org](http://puzzlers.org).\n\"\"\"\n\n# \u2554\u2550\u2561 8a7b57dc-872e-11eb-189c-7f2d7cb2e695\nmd\"\"\"#### Exercise 12-5\n\nHere\u2019s another Car Talk Puzzler:\n\n> What is the longest English word, that remains a valid English word, as you remove its letters one at a time?\n> \n> Now, letters can be removed from either end, or the middle, but you can\u2019t rearrange any of the letters. Every time you drop a letter, you wind up with another English word. If you do that, you\u2019re eventually going to wind up with one letter and that too is going to be an English word\u2014one that\u2019s found in the dictionary. I want to know what\u2019s the longest word. What\u2019s the word, and how many letters does it have?\n> \n> I\u2019m going to give you a little modest example: Sprite. Ok? You start off with sprite, you take a letter off, one from the interior of the word, take the r away, and we\u2019re left with the word spite, then we take the e off the end, we\u2019re left with spit, we take the s off, we\u2019re left with pit, it, and I.\n\nWrite a program to find all words that can be reduced in this way, and then find the longest one.\n\n!!! tip\n    This exercise is a little more challenging than most, so here are some suggestions:\n    \n    1. You might want to write a function that takes a word and com\u2010 putes an array of all the words that can be formed by removing one letter. These are the \u201cchildren\u201d of the word.\n    \n    2. Recursively, a word is reducible if any of its children are reducible. As a base case, you can consider the empty string reducible.\n    \n    3. The word list I provided, *words.txt*, doesn\u2019t contain singleletter words. So, you might want to add \u201cI,\u201d \u201ca,\u201d and the empty string.\n    \n    4. To improve the performance of your program, you might want to memoize the words that are known to be reducible.\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500b0dff76e-8726-11eb-31c8-ab5878dd9ed4\n# \u255f\u2500bef082a6-8726-11eb-088f-5ffda6b048e7\n# \u255f\u2500d10a4cb8-8726-11eb-27e2-b55de2487e54\n# \u255f\u2500c7ffd72e-8727-11eb-0f6d-fb8e9395741c\n# \u255f\u2500574b6fb0-8728-11eb-0eca-51b778a97602\n# \u255f\u2500a5a354be-8728-11eb-2ab3-4f994c1939c1\n# \u255f\u250019ee0db4-8729-11eb-3ca7-737de22181ff\n# \u255f\u25003d55da4a-8729-11eb-3d2f-37fb30c62793\n# \u255f\u2500221a3360-872a-11eb-2ead-8d1358efe435\n# \u255f\u2500ce6beec2-872a-11eb-3b39-5531bfc637cb\n# \u255f\u2500bf393bf4-872b-11eb-0233-cbb084e3ad3f\n# \u255f\u2500c580c4b4-872b-11eb-1aaf-b51e08f9f26c\n# \u255f\u2500c608335e-872b-11eb-278e-51296de15fbc\n# \u255f\u25001f4b8a1e-872d-11eb-20b1-014ddea498de\n# \u255f\u2500286fe504-872d-11eb-06d3-e1c2682eff9c\n# \u255f\u25003260e720-872d-11eb-065c-93bbb15621b9\n# \u255f\u25006cd09676-872d-11eb-0ffd-3fd772eb7db4\n# \u255f\u250096a213d2-872d-11eb-25e4-fdd44a16150d\n# \u255f\u2500e7f038d6-872d-11eb-28a3-6307d3e72e85\n# \u255f\u250005a7a222-872e-11eb-3de9-c73ab5da92a0\n# \u255f\u2500586908a2-872e-11eb-3919-f96ba66ee674\n# \u255f\u25008a7b57dc-872e-11eb-189c-7f2d7cb2e695\n", "meta": {"hexsha": "892cdcbcd468c70d0181886bc0613eddffff9466", "size": 23195, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lectures/Lecture12.jl", "max_stars_repo_name": "BenLauwens/ES123", "max_stars_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2018-03-07T18:04:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-10T15:07:23.000Z", "max_issues_repo_path": "Lectures/Lecture12.jl", "max_issues_repo_name": "BenLauwens/ES123", "max_issues_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lectures/Lecture12.jl", "max_forks_repo_name": "BenLauwens/ES123", "max_forks_repo_head_hexsha": "8531db895a8d4555e63219d79d4dcc527a685458", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-08-12T05:29:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-02T21:38:27.000Z", "avg_line_length": 37.5323624595, "max_line_length": 522, "alphanum_fraction": 0.7102392757, "num_tokens": 7269, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3007455664065234, "lm_q2_score": 0.26284183159693775, "lm_q1q2_score": 0.07904851551894908}}
{"text": "\n#=\nCreated on Saturday 26 December 2020\nLast update: Monday 27 September 2021\n\n@author: Bram De Jaegher\nbram.de.jaegher@gmail.com\n=#\n\n# --- Types --- #\n\nabstract type AbstractQuestion end\nabstract type AbstractDifficulty end\nabstract type AbstractQuestionBlock end\nstruct NoDiff <: AbstractDifficulty end\nstruct Easy <: AbstractDifficulty end\nstruct Intermediate <: AbstractDifficulty end\nstruct Hard <: AbstractDifficulty end\n\n\"\"\"\nDefines a question.\n\n# Arguments\n\n- `description` (md_str): a markdown string to be displayed above the validators\n- `validators` (Array{Bool}): an array of booleans with the tests the answer to the question should solve.\n- `status` (md_str): a markdown string used to change/display the question state (correct, missing, incorrect).\n\tthe default value is missing and should probably not be changed.\n\nDifficulty types\n------\n- `T \u2208 [NoDiff, Easy, Intermediate, Hard]` \n- At this point purely easthetic since the difficulty is only used as display\n\"\"\"\nmutable struct Question{T<:AbstractDifficulty}  <: AbstractQuestion \n\tdescription::Markdown.MD\n\tvalidators::Any\n\tstatus::Markdown.MD\n\tdifficulty::T\n\n\tQuestion{T}(;description=md\"\", \n\t\t\t\t\t\tvalidators=[], \n\t\t\t\t\t\tstatus=md\"\") where {T<:AbstractDifficulty} = new{T}(description, validators, status, T())\nend\n\n\"\"\"\nDefines a Question block.\n\n# Arguments\n- `title` (md_str): a markdown string to be displayed as title\n- `description` (md_str): the general descriptions, this is diplayed directly below the title.\n- `hints` (Array{md_str}): an array of markdown strings for hint admonitions but this can me any markdown and will be displayed in the \\\"hints\\\" section.\n- `questions` (Array{AbstractQuestion}): an array of questions. Currently exactly one mandatory question is expected but 0-\u221e optional questions can be defined.\n\n# Examples\n```julia\nq\u2081 = Question(;\n\tdescription=md\\\"\\\"\\\"\n\tComplete the function `myclamp(x)` that clamps a number `x` between 0 and 1.\n\n\tOpen assignments always return `missing`. \n\t\\\"\\\"\\\",\n\tvalidators= @safe[myclamp(-1)==0, myclamp(0.3)==0.3, myclamp(1.1)==1.0]\n)\n\nq\u2082 = Question{Easy}(;\n\tdescription=md\\\"\\\"\\\"\n\tTry to make the clamping also work for arrays.\n\t\\\"\\\"\\\",\n\tvalidators= @safe[myclamp([2.0, 0.3])==[1.0, 0.3]]\n)\n\nq\u2083 = Question{Intermediate}(;\n\tdescription=md\\\"\\\"\\\"\n\tThis is an intermediate question. Surely you can complete this\n\t\\\"\\\"\\\",\n\tvalidators= @safe[true]\n)\n\nq\u2084 = Question{Hard}(;\n\tdescription=md\\\"\\\"\\\"\n\tI admit, this one is definitely harder\n\t\\\"\\\"\\\",\n\tvalidators= @safe[false]\n)\n\nqb = QuestionBlock(;\ntitle=md\"### Question 1.0: What a crazy exercise\",\ndescription=md\\\"\\\"\\\"\n\tSome additional general kind off description and all.\n\tAnything markdowny. Just make sure to use the triple accolades.\n\t\n\t\\\"\\\"\\\",\nquestions = [q\u2081, q\u2082, q\u2083, q\u2084],\nhints=[\thint(md\"Have you tried this?\"),\n\t\thint(md\"Have you tried switching it on and off again?\")]\n);\n```\n\"\"\"\nmutable struct QuestionBlock <: AbstractQuestionBlock\n\ttitle::Markdown.MD\n\tdescription::Markdown.MD\n\thints::Array{Markdown.MD}\n\tquestions::Array{T} where {T<:AbstractQuestion}\n\n\tQuestionBlock(;title=md\"\",\n\t\t\t\t\t\t\t\t\tdescription=md\"\",\n\t\t\t\t\t\t\t\t\thints = Markdown.MD[],\n\t\t\t\t\t\t\t\t\tquestions = [Question()]) = new(title, description, hints, questions) \nend\n\n# --- Rendering --- # \nBase.show(io::IO, ::MIME\"text/html\", q::AbstractQuestionBlock) = print(io::IO, tohtml(q))\n\nfunction tohtml(q::QuestionBlock)\n\t\n\thint_string = \"\"\n\tif length(q.hints) > 0\n\t\thint_string = \"<br> <p><b>Hints:</b></p>\"\n\t\tfor hint in q.hints\n\t\t\thint_string *= \"<p>\" * html(hint) * \"</p>\"\n\t\tend\n\tend\n\t\n\tN_mandatory = sum(isa.(q.questions,Question))\n\tstate_string = \"\"\n\tfor index in 1:N_mandatory\n\t\tif q.questions[index].description !== \"\"\n\t\t\tstate_string *= \"<p> $(html(q.questions[index].description)) </p>\"\n\t\tend\n\t\tstate_string *= \"<p> $(html(q.questions[index].status)) </p>\"\n\tend\n\n\tout = \"\"\"\n\t\t<div class=\"question\">\n\t\t\t$(html(q.title))\n\t\t\t<p> $(html(q.description)) </p>\n\t\t\t$state_string\n\t\t\t\n\t\t\t$hint_string\n\t\t</div>\n\t\t$question_css\t\t\n\t\"\"\"\n\treturn out\nend\n\n\n# --- Validation --- #\nfunction check_answer(q::AbstractQuestion)\n\tvalidators = q.validators\n\tall_valid = all(validators)\n\tsome_valid = any(validators)\n\n\tif length(validators) < 1 && return md\"\"\n\telseif ismissing(all_valid) \n\t\tstatus = still_missing()\n\telseif some_valid && !all_valid\n\t\tstatus = keep_working()\n\telseif !all_valid\n\t\tstatus = keep_working()\n\telseif all_valid \n\t\tstatus = correct()\n\tend\n\treturn status\nend\n\nfunction validate(q::QuestionBlock)\n\tfor (index, question) in enumerate(q.questions)\n\t\tq.questions[index].status = check_answer(question)\n\tend\n\treturn q\nend\n", "meta": {"hexsha": "c7dff4dc10d927f887abc939d487f67a9e6212a0", "size": 4571, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/question.jl", "max_stars_repo_name": "Beramos/PlutoTeaching", "max_stars_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/question.jl", "max_issues_repo_name": "Beramos/PlutoTeaching", "max_issues_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/question.jl", "max_forks_repo_name": "Beramos/PlutoTeaching", "max_forks_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.4219653179, "max_line_length": 159, "alphanum_fraction": 0.6987530081, "num_tokens": 1217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.37022540649291935, "lm_q2_score": 0.21206881431678098, "lm_q1q2_score": 0.07851326298490167}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 000c1c07-fd7a-4e08-9e36-3f0aa6069309\nbegin\n\tusing PlutoUI\n\t\n\tusing CSV, DataFrames, Pipe, TabularDisplay\n\tusing Plots, StatsPlots\nend\n\n# \u2554\u2550\u2561 578530b0-9b50-11eb-07e1-5db7ceb1b1c7\nmd\"\"\"\n## Data wrangling in Julia\n\nBased on presentations by Tom Kwong:\n  - [Data Wrangling Techniques in Julia - Part 1](https://www.youtube.com/watch?v=txme9o0EdLk) \n  - [Data Wrangling Techniques in Julia - Part 2](https://www.youtube.com/watch?v=NbqQZq42gLc)\n\n\n$(html\"<div><sub>&copy; Pascal, April 2021</sub></div>\")\n\"\"\"\n\n# \u2554\u2550\u2561 6bcfba84-98ab-4d26-b98c-d86cb65dc277\nPlutoUI.TableOfContents(indent=true, depth=4, aside=true)\n\n# \u2554\u2550\u2561 f88a1737-7a84-4103-82c8-1920bae6b2e8\nmd\"\"\"\n### What is data wrangling?\n\nTake raw data (and/or unorganized data) and turn them (data as plural of datum) into something useful.\n\nA possible data wrangling is as follows:\n\n\n read | $\\Rightarrow$ | clean |  $\\Rightarrow$ | Tidy \n  --- | --- | --- | --- | --- \n      |     |         $\\Uparrow$ |           | $\\Downarrow$ \n      |              | **Visualize** | $\\Leftarrow$ | **Analyze** \n\n\n**Julia ecosystem**:\n\nRead | Clean, Tidy, Analyze | Visualize\n--- | --- | --- |\nCSV | DataFrames| Plots, StatsPlots\nParquet | DataFramesMeta | Makie, StatsMakie\nSASLib | Query| Gadfly\nReadStat | JuliaDB, JuliaDBMeta | Gaston\nJSON, JSON2/3 | | VegaLite\nLightXML, EzXML | | UnicodePlots\nXLSX, ExcelReader, Taro | | \nPDFIO | |\n\"\"\"\n\n# \u2554\u2550\u2561 02ac758d-3706-4bf4-8a36-83aff4422d0e\nmd\"\"\"\n### Read\n\nUtilize automated tools to read data into memory as cleanly as possible.\n\n`CSV.jl`:\n  - Skip leading/trailing rows\n  - Parse missing values e.g \"NA\"\n  - Parse date/time values\n  - Normalize columns\n  - Select/drop columns on read\n  - User specified column types\n  - Auto-delimiter detection\n  - Automatic pooled string columns\n  - Parse booleans\n  - Transpose data\n\"\"\"\n\n# \u2554\u2550\u2561 fb1f4efc-6b89-48a6-95d5-8e3661edf53d\nmd\"\"\"\n### Clean\n\n  - Delete unwanted columns\n  - Delete rows with missing data\n  - Rename columns\n  - Fix column data type\n  - ...\n\nExamples\n```julia\ndescribe(df)          # take a quick peek\n\nselect!(df, 1:5)      # keep \"good\" columns / delete unwanted ones \nselect!(df, Not([\"garbaggecol1\", \"garbaggecol2\"]))\n\n\nrename!(df, \"Region Name\" => :region_name)\n\ndropmissing!(df)      # Remove rows with missing data\ndropmissing!(df, Between(:x3, :x5))\n```\n\n\"\"\"\n\n# \u2554\u2550\u2561 f22c3d88-e0e6-4d1d-92d4-c99f80aa4733\nmd\"\"\"\n### Tidy Data\n\n  - Each variable forms a *column*\n  - Each observation forms a *row*\n  - Each type of observational unit forms a *table*\n\n\n**Tidy Recipes, examples:**\n\n```julia\n# Stack all date columns into rows\n# The column names are stored ion a new column \"Date\"\nsdf = stack(df, Not(:Region_name); variable_name=:Date)\nsdf = stack(df, Not(1); variable_name=:Date)\nsdf = stack(df, 2:5; variable_name=:Date)\n\n# Date column is a CategoricalArray type. Make it Date type\nsdf.Date = [Date(get(x)) for x \u2208 sdf.Date]\n# or maybe using broadcast:\nsdf.Date .= get.(sdf.Data) .|> Date\n\n# Hoe to turn it back to a wide format?\ndf2 = unstack(sdf, :date, :value)\n\n# It may introduce Union{Missing, T} column type. Let's fix it.\ndisallowmissing!(df2, 2:5)\n```\n\"\"\"\n\n# \u2554\u2550\u2561 2dc07426-ad94-46a0-8da2-68d687ad87c2\nmd\"\"\"\n### Analyze\n\nGet some insights from data. \n\n**Analuyze Recipe**:\n\n```julia\nselct(df, :region_name, 4:5)                   # select by column\n\nfilter(:region_name => ==(\"Abilene, TX\"), df)  # note df is the 2nd arg here\nfilter(\"2020-01-31\" => >(400_000), df)\n\nsort(df, :region_name)\nsort(df, :region_name, rev=true)\n\n# Transform means adding new columns - many more ways of using Transform\ntransform(df, :region_name => ByRow(length) => :region_name_len)\n\n# Group\ngroupby(sdf, :Date)\n\n# Summarize the grouped data:\ncombine(groupby(sdf, :Date), :value => mean => :avg)\n\n# Using Pipe.jl to build a transformation pipeline\n@pipe sdf |>\n    groupby(_, :Date) |>\n\tcombine(_, :value => mean => :avg)\n\ncountry = DataFrame(Name=[\"United States\"])\n\n# Joining data is easy\ninnerjoin(df, country, on=[:region_name => :name])\nleftjoin(df, country, on=[:region_name => :name])\nrightjoin(df, country, on=[:region_name => :name])\nouterjoin(df, country, on=[:region_name => :name])\n\n# Also\nsemijoin(df, country, on=[:region_name => :name])\nantijoin(df, country, on=[:region_name => :name])\n```\n\"\"\"\n\n# \u2554\u2550\u2561 cb11e3e6-f385-44db-b739-cf056a7f8841\nmd\"\"\"\n### Visualize\n\nGain more insight by looking at the data in a graphical form.\n\n\n**Visualize Recipes:**\n\n`Plots.jl` | `StatsPlots.jl`\n--- | ---\n`plot` | `groupedbar`\n`scatter` | `corrplot`\n`histogram` | `marginhist`\n`heatmap` | `boxplot`\n`bar` | `violin`\n... | ...\n\n\"\"\"\n\n# \u2554\u2550\u2561 944f1843-1802-45b7-a505-8bb32ad09902\nmd\"\"\"\n## Let's practice\n\n### Tidying\n\"\"\"\n\n# \u2554\u2550\u2561 f9d0761d-a859-404a-812f-f2a424c7b191\nbegin\n\tdf = CSV.File(\"data/youth_suicide.csv\", header=true) |> DataFrame;\n\tsize(df)\nend\n\n# \u2554\u2550\u2561 6790c878-d494-4247-ab1a-d5122d3d845b\nfirst(df, 5)\n\n# \u2554\u2550\u2561 ff4cc1c3-5967-4614-82eb-cc8771e962dc\nnames(df)\n\n# \u2554\u2550\u2561 5315e6b5-344e-42e7-a9a7-b53cfaf2b5b5\nwith_terminal() do\n\tdisplaytable(names(df), index=true)\nend\n\n# \u2554\u2550\u2561 8083c3d3-51f6-437e-ba12-38f8b5814e1e\ndescribe(df, :eltype, :nmissing, :first => first)\n\n# \u2554\u2550\u2561 e70b9da8-34f9-4193-a86e-5e7dc9ed23aa\n## turn colums 2 to 19 to row (observations)\nsdf = stack(df, 2:19; variable_name=:type_year);\n\n# \u2554\u2550\u2561 e783b82c-052e-49d3-87c8-70142b990d5f\nsize(sdf)\n\n# \u2554\u2550\u2561 e9d507d7-df67-4bd4-b80b-02385b2f9be5\nbegin\n\t## Now spread content of column `:type_year` into 2 separate columns\n\tsdf[!, :ctype] .= split.(sdf.type_year, \" \") .|> a -> getindex(a, 1)\n\tsdf[!, :year] .= split.(sdf.type_year, \" \") .|> a -> getindex(a, 2)\n\tselect!(sdf, Not(:type_year))\n\tfirst(sdf, 3)\nend\n\n# \u2554\u2550\u2561 723dc0d9-96c4-43c0-81fd-611209e10106\n## Let's check if the Total type really contains the total\n## 1 - Pick a county\nsdf[sdf.County .== \"Yakima\", :]\n\n# \u2554\u2550\u2561 0031eb09-8e5f-47fe-b04d-1600fad8f994\n## Remove the total row which is redundant\n\nsdf\u2081 = @pipe sdf |>\n\tfilter(:ctype => \u2260(\"Total\"), _) |>\n\tfilter(:year => \u2260(\"(2008-2012)\"), _);\n\n# \u2554\u2550\u2561 f8ee2398-2b69-4133-b2d6-11a2c12b9d27\nsdf\u2081[sdf\u2081.County .== \"Yakima\", :]\n\n# \u2554\u2550\u2561 c52c8ce7-c68b-4b7b-b507-5a813d32cf93\ndescribe(sdf\u2081)\n\n# \u2554\u2550\u2561 4b14a6ad-6dc5-4319-95bf-007e84ae6877\nunique(sdf\u2081.year)\n## Now we want integer instead of string and we want to get rid of the parentheses.\n\n# \u2554\u2550\u2561 54a8cfd0-5daa-40e3-8a0e-12413cc36e83\n## mutate column year from String to Int\nsdf\u2081[!, :year] .= replace.(sdf\u2081.year, r\"[\\(\\)]\" => \"\") |> a -> parse.(Int, a)\n\n# \u2554\u2550\u2561 3378787b-c9cf-4d47-a119-2a35abc3b0b4\ndescribe(sdf\u2081, :eltype, :min, :max, :nunique, :nmissing)\n\n# \u2554\u2550\u2561 53aa180c-7260-46cf-a2a3-4a49e0c30844\nbegin\n\t# Male and Female variables can be put back as columns now\n\tdf\u2081 = unstack(sdf\u2081, :ctype, :value)\n\tdf\u2081[df\u2081.County .== \"Yakima\", :]\nend\n\n# \u2554\u2550\u2561 56d8dcc1-8b76-4332-bbe4-f7081e178300\nmd\"\"\"\n### Visualize\n\"\"\"\n\n# \u2554\u2550\u2561 73a99bae-e8a6-44d1-af74-9778e17df7b9\n# Plot King county's yearly rate\nfilter(:County => ==(\"King\"), df\u2081)\n\n# \u2554\u2550\u2561 5eaa88b0-95e4-4410-bd99-cb9ad40fdb79\n# bar chart\n@pipe df\u2081 |>\n\tfilter(:County => ==(\"King\"), _) |>\n\tbar(_.year, _.Male, \n        title = \"King County Youth Suicides (Male)\",\n        legend = :none,\n        size = (450, 300))\n\n# \u2554\u2550\u2561 5548eaac-d390-4cb1-bf49-0b8985c1f0f0\nbegin\n\t# Prepare to plot both Male and Female together\n\tdf_stacked = stack(df\u2081, 3:4; variable_name = \"gender\");\n\tnames(df_stacked)\nend\n\n# \u2554\u2550\u2561 bb947d94-62f3-45f6-a0d1-9ed596e289c8\n@pipe df_stacked |>\n\tfilter(:County => ==(\"King\"), _) |>\n\t groupedbar(    # StatsPlots.jl\n\t\t_.year,     # x-axis \n\t    _.value;    # y-axis\n        group = _.gender,\n        bar_position = :stack,\n        bar_width = 0.7,\n        title = \"King County Suicides\",\n        size = (450, 300),\n        legend = :topleft)\n\n# \u2554\u2550\u2561 5fadd975-cbcf-4a0e-b493-0382875354ba\n@pipe df\u2081 |>\n    groupby(_, :County)\n\n# \u2554\u2550\u2561 a92c1c18-39bd-4dcf-81a7-f2f62577e156\n# 5 year totals\n@pipe df\u2081 |>\n    groupby(_, :County) |>\n    combine(_, :Female => sum, :Male => sum)\n\n# \u2554\u2550\u2561 2c21d894-7e57-4f92-b08d-a4d527f5c25a\n# 5 year totals with male/female combined\n@pipe df\u2081 |>\n    groupby(_, :County) |>\n    combine(_, :Female => sum, :Male => sum) |>\n    select(_, :County, [:Female_sum, :Male_sum] => ByRow(+) => :Total)\n\n# \u2554\u2550\u2561 72aeaa39-6529-4fc3-ba86-f2b113a5a075\nlet \n    data\u2081 = @pipe df\u2081 |>\n        groupby(_, :year) |>\n        combine(_, :Female => sum => :Female, :Male => sum => :Male) |>\n        stack(_, 2:3; variable_name = :gender, value_name = :suicides) \n\t\n    plot(data\u2081.year, data\u2081.suicides; groups = data\u2081.gender,\n        title = \"Youth Suicides Trend\",\n        legend = :topleft,\n        linewidth = 3,\n        size = (450, 300))\nend\n\n# \u2554\u2550\u2561 a7802df0-6819-4650-a539-ccbb99333e92\nlet\n    data\u2082 = @pipe df\u2081 |>\n        select(_, :County, :year, [:Female, :Male] => ByRow(+) => :total) |>\n        unstack(_, :year, :total)\n\t\n    values = Matrix(data\u2082[:, 2:end])\n\t\n    StatsPlots.heatmap(2008:2012, data\u2082.County, values;\n        title = \"Youth Suicide Heatmap\",\n        xticks = :all,\n        yticks = :all,\n        size = (400, 600))\nend\n\n# \u2554\u2550\u2561 8018394c-a123-43c4-aaaf-3d942284e182\nhtml\"\"\"\n<style>\n  main {\n        max-width: calc(800px + 25px + 6px);\n  }\n  .plutoui-toc.aside {\n    background: linen;\n  }\n  h3, h4 {\n        background: wheat;\n  }\n</style>\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500578530b0-9b50-11eb-07e1-5db7ceb1b1c7\n# \u2560\u2550000c1c07-fd7a-4e08-9e36-3f0aa6069309\n# \u255f\u25006bcfba84-98ab-4d26-b98c-d86cb65dc277\n# \u255f\u2500f88a1737-7a84-4103-82c8-1920bae6b2e8\n# \u255f\u250002ac758d-3706-4bf4-8a36-83aff4422d0e\n# \u255f\u2500fb1f4efc-6b89-48a6-95d5-8e3661edf53d\n# \u255f\u2500f22c3d88-e0e6-4d1d-92d4-c99f80aa4733\n# \u255f\u25002dc07426-ad94-46a0-8da2-68d687ad87c2\n# \u255f\u2500cb11e3e6-f385-44db-b739-cf056a7f8841\n# \u255f\u2500944f1843-1802-45b7-a505-8bb32ad09902\n# \u2560\u2550f9d0761d-a859-404a-812f-f2a424c7b191\n# \u2560\u25506790c878-d494-4247-ab1a-d5122d3d845b\n# \u2560\u2550ff4cc1c3-5967-4614-82eb-cc8771e962dc\n# \u2560\u25505315e6b5-344e-42e7-a9a7-b53cfaf2b5b5\n# \u2560\u25508083c3d3-51f6-437e-ba12-38f8b5814e1e\n# \u2560\u2550e70b9da8-34f9-4193-a86e-5e7dc9ed23aa\n# \u2560\u2550e783b82c-052e-49d3-87c8-70142b990d5f\n# \u2560\u2550e9d507d7-df67-4bd4-b80b-02385b2f9be5\n# \u2560\u2550723dc0d9-96c4-43c0-81fd-611209e10106\n# \u2560\u25500031eb09-8e5f-47fe-b04d-1600fad8f994\n# \u2560\u2550f8ee2398-2b69-4133-b2d6-11a2c12b9d27\n# \u2560\u2550c52c8ce7-c68b-4b7b-b507-5a813d32cf93\n# \u2560\u25504b14a6ad-6dc5-4319-95bf-007e84ae6877\n# \u2560\u255054a8cfd0-5daa-40e3-8a0e-12413cc36e83\n# \u2560\u25503378787b-c9cf-4d47-a119-2a35abc3b0b4\n# \u2560\u255053aa180c-7260-46cf-a2a3-4a49e0c30844\n# \u255f\u250056d8dcc1-8b76-4332-bbe4-f7081e178300\n# \u2560\u255073a99bae-e8a6-44d1-af74-9778e17df7b9\n# \u2560\u25505eaa88b0-95e4-4410-bd99-cb9ad40fdb79\n# \u2560\u25505548eaac-d390-4cb1-bf49-0b8985c1f0f0\n# \u2560\u2550bb947d94-62f3-45f6-a0d1-9ed596e289c8\n# \u2560\u25505fadd975-cbcf-4a0e-b493-0382875354ba\n# \u2560\u2550a92c1c18-39bd-4dcf-81a7-f2f62577e156\n# \u2560\u25502c21d894-7e57-4f92-b08d-a4d527f5c25a\n# \u2560\u255072aeaa39-6529-4fc3-ba86-f2b113a5a075\n# \u2560\u2550a7802df0-6819-4650-a539-ccbb99333e92\n# \u255f\u25008018394c-a123-43c4-aaaf-3d942284e182\n", "meta": {"hexsha": "8d03f9af8158aa606c21b62885711debbd32118d", "size": 10747, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia_Investigation/data_wrangling_with_julia.jl", "max_stars_repo_name": "pascal-p/julia-notebooks", "max_stars_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-01T20:34:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-01T20:34:56.000Z", "max_issues_repo_path": "Julia_Investigation/data_wrangling_with_julia.jl", "max_issues_repo_name": "pascal-p/julia-notebooks", "max_issues_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Julia_Investigation/data_wrangling_with_julia.jl", "max_forks_repo_name": "pascal-p/julia-notebooks", "max_forks_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-10T09:03:18.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-10T09:03:18.000Z", "avg_line_length": 25.8341346154, "max_line_length": 102, "alphanum_fraction": 0.6655810924, "num_tokens": 4524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.403566839388498, "lm_q2_score": 0.19436782035217448, "lm_q1q2_score": 0.07844040693835844}}
{"text": "using HTTP, JSON, DataFrames, CSV\n\n\n\"\"\"\nReturns total num of escaped chars and lines for any cell, including any outputs\n\"\"\"\nfunction get_cell_counts_shared(cell)\n    char_total, lines_total = 0, 0\n    for line in cell[\"source\"]\n        char_total += length(String(line))\n    end\n\n    # Count outputs if they exist\n    if haskey(cell, \"outputs\")\n        char_outputs, lines_outputs = get_counts_outputs(cell[\"outputs\"])\n        char_total += char_outputs\n        lines_total += lines_outputs\n    end\n\n    lines_total += length(cell[\"source\"])\n    return char_total, lines_total\nend\n\n\n\"\"\"\nReturns number of escaped chars and lines from an array of outputs. It considers only the following:\n - The `text` portion of an `stdout` output\n - A `text/plain` portion of a `data` output\n\"\"\"\nfunction get_counts_outputs(outputs)\n    char_total, line_total = 0, 0\n\n    # Loops through lines, checks if keys exist, add to counts if they do\n    for output in outputs\n        if haskey(output, \"name\") && output[\"name\"] == \"stdout\"\n            for line in output[\"text\"]\n                char_total += length(line)\n            end\n            line_total += length(output[\"text\"])\n        elseif haskey(output, \"data\") && haskey(output[\"data\"], \"text/plain\")\n            for line in output[\"data\"][\"text/plain\"]\n                char_total += length(line)\n            end\n            line_total += length(output[\"data\"][\"text/plain\"])\n        end\n    end\n    return char_total, line_total\nend\n\n\"\"\"\nReturns array of counts for #, ##, ###, and #### (respectively) in a cell.\n\"\"\"\nfunction get_cell_counts_markdown(cell)\n    count = zeros(Int32, 4)\n    for line in cell[\"source\"]\n        # Loops through each index of the above array, also using it to count the number of # in a row\n        for i in eachindex(count)\n            # Considers a # surrounded by non-#es a match. Also takes into account BoL and EoL.\n            count[i] += length(collect(m.match for m in eachmatch(Regex(\"^#{$i}[^#]|[^#]#{$i}[^#]|[^#]#{$i}^\"), String(line))))\n        end\n    end\n    return count\nend\n\n\"\"\"\nReturns array of counts for `return`, `for`, `if`, and `using` (respectively) in a cell\n\"\"\"\nfunction get_cell_counts_code(cell)\n    matches = [\"return\", \"for\", \"if\", \"using\"]\n    count = zeros(Int32, 4)\n    for line in cell[\"source\"]\n        # Loop through array of matches, add each match to the count\n        for i in eachindex(matches)\n            count[i] += length(collect(m.match for m in eachmatch(Regex(matches[i]), String(line))))\n        end\n    end\n    return count\nend\n\n\"\"\"\nReturns a formatted, processed row representing a markdown cell\n\"\"\"\nfunction process_cell_markdown(cell_number, markdown_cell)\n    return [cell_number, get_cell_counts_shared(markdown_cell)..., get_cell_counts_markdown(markdown_cell)...]\nend\n\n\"\"\"\nReturns a formatted, processed row representing a code cell\n\"\"\"\nfunction process_cell_code(cell_number, code_cell)\n    return [cell_number, get_cell_counts_shared(code_cell)..., get_cell_counts_code(code_cell)...]\nend\n\n\"\"\"\nIterates through all given cells, formats the markdown and code cells into two separate DataFrames, returns them.\n\"\"\"\nfunction process_cells_all(all_cells)\n    markdown_df = DataFrame(cell_number = Int[], character_count = Int[], line_count = Int[], one_hash = Int[], two_hash = Int[], three_hash = Int[], four_hash = Int[])\n    code_df = DataFrame(cell_number = Int[], character_count = Int[], line_count = Int[], returns = Int[], fors = Int[], ifs = Int[], usings = Int[])\n    for i in 1:lastindex(all_cells)\n        cell = all_cells[i]\n        if cell[\"cell_type\"] == \"markdown\"\n            push!(markdown_df, process_cell_markdown(i, cell))\n        elseif cell[\"cell_type\"] == \"code\"\n            push!(code_df, process_cell_code(i, cell))\n        end\n    end\n    return markdown_df, code_df\nend\n\n\n# Basic request - no need to pull out into a function\n\nr = HTTP.request(\"GET\", \"https://raw.githubusercontent.com/yoninazarathy/ProgrammingCourse-with-Julia-SimulationAnalysisAndLearningSystems/main/practicals_jupyter/practical_B_julia_essentials.ipynb\")\nnotebook_json = JSON.parse(String(r.body))\n\n\n# Print the summary (with the help of an occasional inline)\n\nprintln(\"Total number of cells: \" * string(length(notebook_json[\"cells\"])))\n\ncode_cells_count = count(cell->(cell[\"cell_type\"] == \"code\"), notebook_json[\"cells\"])\nprintln(\"Number of code cells: \" * string(code_cells_count))\n\nprintln(\"Number of markdown cells: \" * string(count(cell->(cell[\"cell_type\"] == \"markdown\"), notebook_json[\"cells\"])))\n\ncode_cells_nooutput_count = count(cell->(cell[\"cell_type\"] == \"code\" && length(cell[\"outputs\"]) == 0), notebook_json[\"cells\"])\nprintln(\"Number of code cells w/o output: \" * string(code_cells_nooutput_count))\nprintln(\"Number of code cells w/ output: \" * string(code_cells_count - code_cells_nooutput_count))\n\n\n\"\"\"\nQuick function that takes a list of cells and iterates through their outputs, returning a somewhat comprehensive list of keys\n\"\"\"\nfunction total_keys_in_outputs(cells)\n    unique_keys = Set()\n    for cell in cells\n        if haskey(cell, \"outputs\")\n            for output in cell[\"outputs\"]\n                union!(unique_keys, Set(keys(output)))\n                for key in keys(output)\n                    if isa(output[key], Dict)\n                        union!(unique_keys, Set(keys(output[key])))\n                    end\n                end\n            end\n        end\n    end\n    return unique_keys\nend\n\n\n# It always worried me whether I missed an edge case in the outputs - so, this is my chosen 'extra summary'\n\nprintln(\"Number of unique keys present in output fields: \" * string(length(total_keys_in_outputs(notebook_json[\"cells\"]))))\n\n# Process cells, chuck em into the csvs with proper formatting\n\nmarkdown_df, code_df = process_cells_all(notebook_json[\"cells\"])\n\nCSV.write(\"markdown_summary.csv\", markdown_df; header = [\"cell_number\", \"character_count\", \"line_count\", \"#\", \"##\", \"###\", \"####\"])\n\nCSV.write(\"code_summary.csv\", code_df; header = [\"cell_number\", \"character_count\", \"line_count\", \"return\", \"for\", \"if\", \"using\"])\n\n\n# Read the files back, then print them out with decent formatting\nmarkdown_csv, code_csv = CSV.File(\"markdown_summary.csv\"; limit=4, header=0) |> DataFrame, CSV.File(\"code_summary.csv\"; limit=4, header=0) |> DataFrame\nprintln(markdown_csv)\nprintln(code_csv)", "meta": {"hexsha": "00de7977c4f1ff5280392e0df4073cedb2eece1d", "size": 6337, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "q2/q2_solution.jl", "max_stars_repo_name": "XxX-Daniil-underscore-Zaikin-XxX/Daniil-Zaikin-2504-2021-HW2", "max_stars_repo_head_hexsha": "bda2cc752f2dd1df1d4982865fe090e05e846dd6", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "q2/q2_solution.jl", "max_issues_repo_name": "XxX-Daniil-underscore-Zaikin-XxX/Daniil-Zaikin-2504-2021-HW2", "max_issues_repo_head_hexsha": "bda2cc752f2dd1df1d4982865fe090e05e846dd6", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "q2/q2_solution.jl", "max_forks_repo_name": "XxX-Daniil-underscore-Zaikin-XxX/Daniil-Zaikin-2504-2021-HW2", "max_forks_repo_head_hexsha": "bda2cc752f2dd1df1d4982865fe090e05e846dd6", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.4970414201, "max_line_length": 199, "alphanum_fraction": 0.6684551049, "num_tokens": 1501, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.28140560742914383, "lm_q2_score": 0.27825678771720325, "lm_q1q2_score": 0.0783030203688419}}
{"text": "## formatting conveniences\n\n\"markdown can leave wrapping p's\"\nfunction strip_p(txt)\n    if occursin(r\"^<p>\", txt)\n        txt = replace(replace(txt, r\"^<p>\" => \"\"), r\"</p>$\" => \"\")\n    end\n    txt\nend\n\nfunction md(x)\n    out = sprint(io ->  show(io, \"text/html\", Markdown.parse(string(x))))\n    strip_p(out)\nend\n\nmarkdown_to_latex(x) = sprint(io -> show(io, \"text/latex\", Markdown.parse(x)))\n\n\"\"\"\nHide output and input, but execute cell.\n\nExamples\n```\n2 + 2\nInvisible()\n```\n\"\"\"\nmutable struct Invisible\nend\n\n\"\"\"\nShow output as HTML\n\nExamples\n```\nHTMLoutput(\"<em>em</em>\")\n```\n\n\"\"\"\nmutable struct HTMLoutput\n    x\nend\nBase.show(io::IO, ::MIME\"text/plain\", x::HTMLoutput) = print(io, \"\"\"<div>$(x.x)</div>\"\"\")\nBase.show(io::IO, ::MIME\"text/html\", x::HTMLoutput) = print(io, x.x)\nBase.show(io::IO, ::MIME\"text/latex\", x::HTMLoutput) = println(io, \"...unable to display raw html...\")\n\n\n\n\"\"\"\nShow as input, but do not execute.\nExamples:\n```\nVerbatim(\"This will print, but not be executed\")\n```\n\"\"\"\nmutable struct Verbatim\n    x\nend\nBase.show(io::IO, ::MIME\"text/plain\", x::Verbatim) = print(io, \"\"\"<pre class=\"sourceCode julia\">$(x.x)</pre>\"\"\")\nBase.show(io::IO, ::MIME\"text/html\", x::Verbatim) = print(io, x.x)\nBase.show(io::IO, ::MIME\"text/latex\", x::Verbatim) = print(io, \"\\verb@$(markdown_to_latex(x.x))@\")\n\n\n\"\"\"\nHide input, but show output\n\nExamples\n```\nx = 2 + 2\nOutputonly(x)\n```\n\"\"\"\nmutable struct Outputonly\n    x\nend\n\n\n\"\"\"\n    JSXGraph(f; [ID], [CLASS], [WIDTH], [HEIGHT]\n\nShow jsxgraph commands contained in file `f`.\n\"\"\"\nfunction JSXGraph(f, caption=\"JSXGraph Demo\"; ID=\"jsxgraph\", CLASS=\"jsxgraph\", WIDTH=600, HEIGHT=400)\n    JSXGRAPH(read(f, String),\n             markdown(caption),\n             ID, CLASS, WIDTH, HEIGHT)\nend\n\nmutable struct JSXGRAPH\n    FILE_CONTENTS\n    CAPTION\n    ID\n    CLASS\n    WIDTH\n    HEIGHT\nend\n\n#<div class=\"card\">\n#   <div class=\"card-header\">{{{CAPTION}}}</div>\n\n#  <div class=\"card-body\">\n#    <div id=\"{{ID}}\" class='{{CLASS}}' style='width:{{WIDTH}}px; height:{{HEIGHT}}px'></div>\n#  </div>\n#</div>\n\n\n## XXX Put in centered\njsxgraph_tpl = Mustache.mt\"\"\"\n<div class=\"d-flex justify-content-center\">\n<div class=\"card border-light mx-3 px-3 my-3 py-3\" style=\"width:{{WIDTH}}px\">\n   <div id=\"{{ID}}\" class='{{CLASS}}' style='width:{{WIDTH}}px; height:{{HEIGHT}}px'></div>\n  <div class=\"card-footer text-muted\">\n    <span class=\"card-text\">\n      <small class=\"text-muted\">\n      {{{CAPTION}}}\n      </small>\n    </span>\n  </div>\n</div>\n</div>\n\n\n<script src='https://cdnjs.cloudflare.com/ajax/libs/jsxgraph/1.2.2/jsxgraphcore.js' type='text/javascript'>\n</script>\n<script src='https://cdnjs.cloudflare.com/ajax/libs/jsxgraph/1.2.2/geogebra.min.js' type='text/javascript'>\n</script>\n\n<script>\n{{{FILE_CONTENTS}}}\n</script>\n\n\"\"\"\n\nBase.show(io::IO, ::MIME\"text/html\", x::JSXGRAPH) = Mustache.render(io, jsxgraph_tpl, x)\nBase.show(io::IO, x::JSXGRAPH) = print(io, \"JSXGraph unavailable\")\n\n## Bootstrap things\nabstract type Bootstrap end\nBase.show(io::IO, ::MIME\"text/html\", x::Bootstrap) = print(io, \"\"\"$(x.x)\"\"\")\nBase.show(io::IO, ::MIME\"text/latex\", x::Bootstrap) = print(io, \"\"\"XXX BOOTSTRAP $(markdown_to_latex(x.x))\"\"\")\n\nmutable struct Alert <: Bootstrap\n    x\n    d::Dict\nend\n\n### An alert\nfunction alert(txt; kwargs...)\n    d = Dict()\n    for (k,v) in kwargs\n        d[k] = v\n    end\n    Alert(txt, d)\nend\n\nwarning(txt; kwargs...) = alert(txt; class=\"warning\", kwargs...)\nnote(txt; kwargs...) = alert(txt; class=\"info\", kwargs...)\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Alert)\n    cls = haskey(x.d,:class) ? x.d[:class] : \"success\"\n    txt = sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    tpl = \"\"\"<div class=\"alert alert-$cls\" role=\"alert\">$txt</div>\"\"\"\n\n    print(io, tpl)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Alert)\n    println(io, \"\"\"\n\\\\begin{mdframed}\n$(markdown_to_latex(x.x))\n\\\\end{mdframed}\n\"\"\")\nend\n\n\n\n\nmutable struct Example <: Bootstrap\n    x\n    d::Dict\nend\n\n## use nm=\"name\" to pass along name\nfunction example(txt; kwargs...)\n d = Dict()\n    for (k,v) in kwargs\n        d[k] = v\n    end\n    Example(txt, d)\nend\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Example)\n    nm = haskey(x.d,:nm) ? \" <small>$(x.d[:nm])</small>\" : \"\"\n    txt = sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    tpl = \"\"\"\n<div class=\"alert alert-danger\" role=\"alert\">\n\n<i class=\"bi bi-snow\"></i>\n\n<span class=\"text-uppercase\">example:</span>$nm$txt\n\n</div>\n\n\"\"\"\n\n    print(io, tpl)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Example)\n    println(io, \"\"\"Example $(markdown_to_latex(x.x))\"\"\")\nend\n\n\nmutable struct Popup <: Bootstrap\n    x\n    title\n    icon\n    label\nend\n\n\"\"\"\n\nCreate a button to toggle the display of more detail.\n\nCan modify text, title, icon and label (for the button)\n\nThe text, title, and label can use Markdown.\n\nLaTeX markup does not work, as MathJax rendering is not supported in the popup.\n\n\"\"\"\npopup(x; title=\" \", icon=\"star\", label=\"click me\") = Popup(x, title, icon, label)\n\n\npopup_html_tpl = mt\"\"\"\n<button type='button'\n class='btn btn btn-secondary'\n data-bs-toggle='popover'\n title='{{{title}}}'\n data-bs-placement='right'\n data-bs-content='{{{body}}}'\n>\n<i class='bi bi-{{icon}}'></i>\n{{{button_label}}}\n</button>\n\"\"\"\n# issue with formatted content\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Popup)\n    d = Dict()\n    d[\"title\"] = x.title #sprint(io -> show(io, \"text/html\", Markdown.parse(x.title)))\n    d[\"icon\"] = x.icon\n    label = x.label #sprint(io -> show(io, \"text/html\", Markdown.parse(x.label)))\n    d[\"button_label\"] = label\n    d[\"body\"] = x.x #sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    Mustache.render(io, popup_html_tpl, d)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Popup)\n    println(io, \"\"\"\n\\\\begin{quotation}\n$(markdown_to_latex(x.x))\n\\\\end{quotation}\n\"\"\")\nend\n\n\"\"\"\n\nWay to convert rectangular gird of values into a table\n\n\"\"\"\nmutable struct Table <: Bootstrap\n    x\nend\ntable(x) = Table(x)\n\ntable_html_tpl=mt\"\"\"\n\n<div class=\"table-responsive\">\n<table class=\"table table-hover\">\n{{{:nms}}}\n{{{:body}}}\n</table>\n</div>\n\n\"\"\"\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Table)\n    vals = Base.invokelatest(names, x.x)\n    d = Dict()\n    d[:nms] = \"<tr><th>$(join(map(string, vals), \"</th><th>\"))</th></tr>\\n\"\n    bdy = \"\"\n    m,n = Base.invokelatest(size, x.x)\n    for i in 1:m\n        bdy = bdy * \"<tr>\"\n        for j in 1:n\n            val = Base.invokelatest(getindex, x.x, i, j)\n            if ismissing(val)\n                val = \".\"\n            end\n            bdy = bdy * \"<td>$(md(val))</td>\"\n        end\n        bdy = bdy * \"</tr>\\n\"\n    end\n    d[:body] = bdy\n    Mustache.render(io, table_html_tpl, d)\nend\n\nfunction df_to_table(df, label=\"label\", caption=\"caption\")\n    nc = size(df, 2)\n    perc = string(round(1/nc, digits=2))\n    fmt = \"l\" * repeat(\"p{$perc\\\\textwidth}\", nc-1)\n    header = join(string.(names(df)), \" & \")\n    row = join([\"{{{:$x}}}\" for x in map(string, names(df))], \" & \")\n\ntpl=\"\"\"\n\\\\begin{table}[!ht]\n  \\\\centering\n  \\\\begin{tabular}{$fmt}\n  $header\\\\\\\\\n  \\\\midrule\\\\\\\\\n{{#:DF}}    $row\\\\\\\\\n{{/:DF}}\n  \\\\bottomrule\n  \\\\end{tabular}\n  \\\\label{tab:$label}\n\n\\\\end{table}\n\"\"\"\n\n    Mustache.render(tpl, DF=df)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Table)\n    d = markdown_to_latex.(x.x)\n    println(io, df_to_table(d))\nend\n\n\n\nmutable struct NamedTable <: Bootstrap\ndata\nrownames\ncolnames\nend\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::NamedTable)\n    vals = x.data\n    cnames = x.colnames\n    rnames = x.rownames\n    d = Dict()\n    d[:nms] = \"<tr><th></th><th>$(join(map(string, cnames), \"</th><th>\"))</th></tr>\\n\"\n    m,n = Base.invokelatest(size, x.data)\n    buf = IOBuffer()\n    for i in 1:m\n        print(buf, \"<tr><td>\", rnames[i],\"</td>\")\n        for j in 1:n\n            val = Base.invokelatest(getindex, x.data, i, j)\n            print(buf, \"<td>\", md(val), \"</td>\")\n        end\n        println(buf, \"</tr>\")\n    end\n    d[:body] = String(take!(buf))\n    Mustache.render(io, table_html_tpl, d)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::NamedTable)\n    d = markdown_to_latex.(x.x)\n    println(io, df_to_table(d))\nend\n\n\n# show footer via\n#=\n```julia; echo=false\nCalculusWithJulia.WeaveSupport.footer(@__FILE__, @__DIR__)\n```\n=#\nstruct Footer\n    f\n    d\nend\n\n# compute from URL\nfunction previous_current_next(foot::Footer)\n    f\u2080 = Symbol(last(split(foot.f, \"/\"))[1:end-4])\n    d\u2080 = Symbol(split(foot.d, \"/\")[end])\n\n    toc_url = \"../misc/toc.html\"\n    suggest_url = \"https://github.com/jverzani/CalculusWithJulia.jl/edit/master/CwJ/$(d\u2080)/$(f\u2080).jmd\"\n\n    prev_url = \"https://calculuswithjulia.github.io\"\n    next_url = \"https://calculuswithjulia.github.io\"\n\n    prev,nxt = prev_next(d\u2080, f\u2080)\n\n    if prev != nothing\n        d,f = prev\n        prev_url = \"../$(d)/$(f).html\"\n    end\n\n    if nxt != nothing\n        d,f = nxt\n        next_url = \"../$(d)/$(f).html\"\n    end\n\n    (base_url=\"https://calculuswithjulia.github.io\",\n     toc_url=toc_url,\n     prev_url=prev_url,\n     next_url = next_url,\n     suggest_edit_url = suggest_url\n     )\nend\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Footer)\n    Mustache.render(io, footer_html_tpl, previous_current_next(x))\nend\n\n# add suggest edit\n# <div class=\"container d-flex justify-content-end\">\n#==\n==#\nfooter_html_tpl = \"\"\"\n<div class=\"card\" style=\"\">\n  <div class=\"card-header float-end text-muted\">\n    <span class=\"text-muted  float-end align-middle\">\n\n<a href=\"{{{:prev_url}}}\"\ndata-bs-toggle=\"tooltip\"\ndata-bs-placement=\"top\"\ntitle=\"Previous section\"\naria-label=\"Previous section\"\nclass=\"bi bi-arrow-left-circle-fill\">\n</a>\n\n&nbsp;\n\n<a href=\"{{{:next_url}}}\"\ndata-bs-toggle=\"tooltip\"\ndata-bs-placement=\"top\"\ntitle=\"Next section\"\naria-label=\"Next section\"\nclass=\"bi bi-arrow-right-circle-fill\">\n</a>\n\n&nbsp;\n\n<a href=\"{{{:suggest_edit_url}}}\"\ndata-bs-toggle=\"tooltip\"\ndata-bs-placement=\"top\"\ntitle=\"Suggest an edit\"\naria-label=\"Suggest an edit\"\nclass=\"bi bi-pencil-square\">\n</a>\n\n&nbsp;\n\n<a href=\"{{{:toc_url}}}\"\ndata-bs-toggle=\"tooltip\"\ndata-bs-placement=\"top\"\ntitle=\"Table of contents\"\naria-label=\"Table of contents\"\nclass=\"bi bi-card-list\">\n</a>\n\n    </span>\n  </div>\n</div>\n\"\"\"\n", "meta": {"hexsha": "07bebc6157182568c5e39bfbad2b6255d409014b", "size": 10199, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/WeaveSupport/formatting.jl", "max_stars_repo_name": "jverzani/CalculusWithJulia.jl", "max_stars_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31, "max_stars_repo_stars_event_min_datetime": "2019-08-29T02:00:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-20T11:15:12.000Z", "max_issues_repo_path": "src/WeaveSupport/formatting.jl", "max_issues_repo_name": "jverzani/CalculusWithJulia.jl", "max_issues_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2020-12-03T15:00:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T00:57:57.000Z", "max_forks_repo_path": "src/WeaveSupport/formatting.jl", "max_forks_repo_name": "jverzani/CalculusWithJulia.jl", "max_forks_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 10, "max_forks_repo_forks_event_min_datetime": "2020-01-07T10:53:24.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-15T06:08:38.000Z", "avg_line_length": 21.6539278132, "max_line_length": 112, "alphanum_fraction": 0.6101578586, "num_tokens": 3056, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2942149597859341, "lm_q2_score": 0.2658804847339313, "lm_q1q2_score": 0.07822601612385825}}
{"text": "# ------------------------------------------------------------\n# We can use a barrier function\nusing BenchmarkTools\n\nfunction double_sum(data)\n    total = 0\n    for v in data\n        total += 2 * v\n    end\n    return total\nend\n\nfunction double_sum_of_random_data(n)\n    data = random_data(n)\n    return double_sum(data)\nend\n\n@btime double_sum_of_random_data(100000);\n@btime double_sum_of_random_data(100001);\n\n#=\njulia> @btime double_sum_of_random_data(100000);\n  245.044 \u03bcs (2 allocations: 781.33 KiB)\n\njulia> @btime double_sum_of_random_data(100001);\n  180.454 \u03bcs (2 allocations: 781.39 KiB)\n\n=#\n\n# Another potential issue is the accumulator.\n\n@code_warntype double_sum(rand(Int, 3));\n@code_warntype double_sum(rand(Float64, 3));\n# -- red text for the Float64 call against the `total` variable\n#=\njulia> @code_warntype double_sum(rand(Int, 3));\nVariables\n  #self#::Core.Compiler.Const(double_sum, false)\n  data::Array{Int64,1}\n  total::Int64\n  @_4::Union{Nothing, Tuple{Int64,Int64}}\n  v::Int64\n\nBody::Int64\n1 \u2500       (total = 0)\n\u2502   %2  = data::Array{Int64,1}\n\u2502         (@_4 = Base.iterate(%2))\n\u2502   %4  = (@_4 === nothing)::Bool\n\u2502   %5  = Base.not_int(%4)::Bool\n\u2514\u2500\u2500       goto #4 if not %5\n2 \u2504 %7  = @_4::Tuple{Int64,Int64}::Tuple{Int64,Int64}\n\u2502         (v = Core.getfield(%7, 1))\n\u2502   %9  = Core.getfield(%7, 2)::Int64\n\u2502   %10 = total::Int64\n\u2502   %11 = (2 * v)::Int64\n\u2502         (total = %10 + %11)\n\u2502         (@_4 = Base.iterate(%2, %9))\n\u2502   %14 = (@_4 === nothing)::Bool\n\u2502   %15 = Base.not_int(%14)::Bool\n\u2514\u2500\u2500       goto #4 if not %15\n3 \u2500       goto #2\n4 \u2504       return total\n\njulia> @code_warntype double_sum(rand(Float64, 3));\nVariables\n  #self#::Core.Compiler.Const(double_sum, false)\n  data::Array{Float64,1}\n  total::Union{Float64, Int64}\n  @_4::Union{Nothing, Tuple{Float64,Int64}}\n  v::Float64\n\nBody::Union{Float64, Int64}\n1 \u2500       (total = 0)\n\u2502   %2  = data::Array{Float64,1}\n\u2502         (@_4 = Base.iterate(%2))\n\u2502   %4  = (@_4 === nothing)::Bool\n\u2502   %5  = Base.not_int(%4)::Bool\n\u2514\u2500\u2500       goto #4 if not %5\n2 \u2504 %7  = @_4::Tuple{Float64,Int64}::Tuple{Float64,Int64}\n\u2502         (v = Core.getfield(%7, 1))\n\u2502   %9  = Core.getfield(%7, 2)::Int64\n\u2502   %10 = total::Union{Float64, Int64}\n\u2502   %11 = (2 * v)::Float64\n\u2502         (total = %10 + %11)\n\u2502         (@_4 = Base.iterate(%2, %9))\n\u2502   %14 = (@_4 === nothing)::Bool\n\u2502   %15 = Base.not_int(%14)::Bool\n\u2514\u2500\u2500       goto #4 if not %15\n3 \u2500       goto #2\n4 \u2504       return total\n\n=#\n\n# There are better ways to write it.\n\n# Fix 1 - use zero function to match type\nfunction double_sum(data)\n    total = zero(eltype(data))\n    for v in data\n        total += 2 * v\n    end\n    return total\nend\n\n# check again - no more red text in the output\n@code_warntype double_sum(rand(Int, 3));\n@code_warntype double_sum(rand(Float64, 3));\n\n# Fix 2 - leverage type parameter, more flexible\nfunction double_sum(data::AbstractVector{T}) where {T <: Number}\n    total = zero(T)\n    for v in data\n        total += v\n    end\n    return total\nend\n\n# check again - no more red text in the output\n@code_warntype double_sum(rand(Int, 3));\n@code_warntype double_sum(rand(Float64, 3));\n\n# -----\n# using zero(s), one(s), and similar\n\n#=\njulia> zero(Int)\n0\n\njulia> zeros(Float64, 5)\n5-element Array{Float64,1}:\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n\njulia> zeros(Float64, 5)^C\n\njulia> one(UInt8)\n0x01\n\njulia> ones(UInt8, 5)\n5-element Array{UInt8,1}:\n 0x01\n 0x01\n 0x01\n 0x01\n 0x01\n\njulia> A = rand(3,4)\n3\u00d74 Array{Float64,2}:\n 0.275294  0.333407   0.380679  0.680078\n 0.795881  0.297336   0.478146  0.377958\n 0.534937  0.0791554  0.916814  0.210469\n\njulia> B = similar(A)\n3\u00d74 Array{Float64,2}:\n 9.88131e-324  4.94066e-324  4.94066e-324  4.94066e-324\n 9.88131e-324  4.94066e-324  4.94066e-324  0.0         \n 4.94066e-324  4.94066e-324  4.94066e-324  2.32833e-314\n\njulia> zeros(axes(A))\n3\u00d74 Array{Float64,2}:\n 0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  0.0\n\n=#", "meta": {"hexsha": "edb17e4b47ebcf90ebae58183f749416ac3c453a", "size": 3870, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter06/BarrierFunctionPattern/2_barrier_function.jl", "max_stars_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_stars_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 167, "max_stars_repo_stars_event_min_datetime": "2020-02-07T14:38:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T09:33:00.000Z", "max_issues_repo_path": "Chapter06/BarrierFunctionPattern/2_barrier_function.jl", "max_issues_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_issues_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter06/BarrierFunctionPattern/2_barrier_function.jl", "max_forks_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_forks_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 33, "max_forks_repo_forks_event_min_datetime": "2020-02-14T05:17:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-24T07:38:59.000Z", "avg_line_length": 23.0357142857, "max_line_length": 64, "alphanum_fraction": 0.6072351421, "num_tokens": 1485, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4339814648038986, "lm_q2_score": 0.1801066618860355, "lm_q1q2_score": 0.07816295294624218}}
{"text": "### A Pluto.jl notebook ###\n# v0.16.4\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 e4dca908-1016-11ec-0b7f-e9d01bd4e1b2\nmd\"\"\"\n# Solution Explorer\nThis is an interactive visualization of by-county solutions. Solutions\nare taken from the `out` directory in the repository root, and can be\nproduced by executing `m-core/sweep.m` in MatLab.\n\n*Note:* currently, only county-level solutions are supported. Solutions aggregated\nto a different level will not be displayed.\n\n---\nAuthors: Kara Ignatenko, 2021;\n\t\t Yaroslav Salii, 2021\n\n\nChangelog:\n- 2021-10-09  v.1.0: First complete version\n- 2021-10-24  v.1.1: Add control effort choropleth \n\n\"\"\"\n\n# \u2554\u2550\u2561 3389dc98-b90f-4c6a-8c96-31840b0516ea\n# a workaround to allow dynamically reloaded includes\n# --refresh this cell to reload the included files\nmodule includes\n\tinclude(\"viz-ui.jl\")\n\tinclude(\"vega-specs.jl\")\n\tinclude(\"exporter.jl\")\nend\n\n# \u2554\u2550\u2561 e598c897-61bf-4bc2-91db-e225362f5606\n# Start off by importing all the stuff\nbegin\n\tusing VegaLite,VegaDatasets\n\tusing DataFrames, CSV, Statistics, Revise\n\tusing PlutoUI, HypertextLiteral\n\t\n\tUI = includes.VizUI\n\tSpecs = includes.VegaSpecs\n\tExp = includes.Exporter\n\t\t\n\tthisPath = splitpath(@__DIR__)\n\tprojRoot = thisPath[1:findfirst(isequal(\"epi-net-m\"), thisPath)]\n\t\n\tconst iDir = joinpath(projRoot...,\"out\") #here be the simulator's outputs\n\t\nend\n\n# \u2554\u2550\u2561 0d909765-a87d-4a3c-bb9f-ff80e463992a\n#TODO: file selector based on the contents of /out\nbegin\n\t#get the list of csv files in ../out\n\toutDir = readdir(iDir)\n\tcsvs = filter(endswith(\".csv\"), outDir)\n\t#get the list of unique solution names (prefixes)\n\tsolNames = map(fname -> split(fname, \"-\")[1], csvs) |> unique\n\tfilter!(contains(\"cty\"), solNames)\n\t\n\t#create a <select> to pick from the prefixes \n\tpicker = @bind slnName Select(solNames)\n\t\n\tmd\"\"\"\n\tSelect solution: $(picker)\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 ced90367-407c-4b67-8276-68edc17933d3\n\"\"\"\nRead the solutions into dataframes and stuff them into a named tuple \n:f,:f0 are *fractional* per-node per-day s/z/r\n:a,:a0 are *absolute* per-node per-day S/Z/R \n:avgc are per-day average infected fraction z, zNull, and average optimal control effort u\n\"\"\"\nfunction rdSolutions(iName, slnDir)\n\tslnSuffs = [\"-frac.csv\",\"-frac0.csv\",\"-abs.csv\",\"-abs0.csv\",\"-avg.csv\"]\n    slns= map( p -> CSV.read(p,DataFrame), (joinpath(iDir,slnName * suff) for suff in slnSuffs))\n    return NamedTuple([:f,:f0,:a,:a0, :avgc] .=> slns) \nend\n\n# \u2554\u2550\u2561 6a2ca31f-716c-48f4-8e1a-70e3c033358b\ntry\n\tss = rdSolutions(slnName, iDir)\n\t\n\tglobal a0Median = ss.a0.Z180 |> median\n\tglobal a0Max = ss.a0.Z180 |> maximum\n\t\n\t#get rid of irrelevant (for now) data by only selecting the Z values\n\ta_clean = select(ss.a, \"id\", r\"^Z\")\n\ta0_clean = select(ss.a0, \"id\", r\"^Z\")\n\tf_clean = select(ss.f, \"id\", r\"^u\")\n\t\n\t#toss everything into one dataframe to simplify rendering\n\t#for the null case, add _NULL to the column names\n\tglobal sol = innerjoin(a_clean, a0_clean, on=\"id\", renamecols=(\"\" => \"_NULL\"))\n\t#add *control effort* data; f_clean should be fine too\n\tglobal sol2 = innerjoin(sol, f_clean, on=\"id\") \n\t\n\t\n\t#\"-avg.csv\" has 3 columns: z_avg; zNull_avg; u_avg, and per-day rows. \n\t#insert day numbers\n\tinsertcols!(ss.avgc,1,:day => 0:(nrow(ss.avgc)-1))\n\tglobal long_avgc = stack(ss.avgc,[:z_avg,:zNull_avg,:u_avg],variable_name=:symbol)\n\t\t\t\n\tmd\"Solution files read successfully!\"\ncatch err\n\tmd\"**Error while reading solution files:** $(err)\"\nend\n\n# \u2554\u2550\u2561 dcabd381-1316-449b-9ebf-68410ee6e3fd\n@bind day UI.dayPicker(0, 180, [1, 10])\n\n# \u2554\u2550\u2561 cc533197-dbe3-41b6-8478-64f82da52ba8\nmd\"Day selected: $day\"\n\n# \u2554\u2550\u2561 8398bb71-aa8e-42b6-b3e3-c08ca57ee5b0\nbegin\n\tplt_u = Specs.pltCtrlByCty(sol2,day)\n\tUI.vegaEmbed(plt_u)\nend\n\n# \u2554\u2550\u2561 a6a64f62-316a-48f5-bd5c-0eebff53022d\nbegin\n\tpltZ = Specs.pltZOptVsNullByCty(sol, day, a0Median)\n\tUI.vegaEmbed(pltZ)\nend\n\n# \u2554\u2550\u2561 f4bf1143-c315-4521-b19b-06bf0373828a\nbegin\n\tpltAvgZandU = Specs.pltAvgInfdCtrl(long_avgc)\n\tUI.vegaEmbed(pltAvgZandU)\nend\n\n# \u2554\u2550\u2561 86ddda30-351d-41f6-94b2-4c84dea08448\nbegin \n\tsel = @bind format Select([\"svg\", \"pdf\"])\n\tcbx = @bind exportMap UI.booleanButton(\"Export map\")\n\tcbx2 = @bind exportPlot UI.booleanButton(\"Export avg. plot\")\n\tfigDir = Exp.figDir \n\t@htl(\"\"\"Select export format: $sel $cbx $cbx2 \u2013 Will be saved to $figDir\"\"\")\nend\n\n# \u2554\u2550\u2561 bfd9753c-323a-4a82-b5e6-057e7519ed6c\nif exportMap\n\tExp.savePlot(slnName * \"_day$(day)_\", pltZ, format)\nend\n\n# \u2554\u2550\u2561 ff5a5927-6bc6-4b05-b0e8-e0bfdc623440\nif exportPlot\n\tExp.savePlot(slnName * \"_avgZ_\", pltAvgZandU, format)\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nHypertextLiteral = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nRevise = \"295af30f-e4ad-537b-8983-00126c2a3abe\"\nStatistics = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\nVegaDatasets = \"0ae4a718-28b7-58ec-9efb-cded64d6d5b4\"\nVegaLite = \"112f6efa-9a02-5b7d-90c0-432ed331239a\"\n\n[compat]\nCSV = \"~0.8.5\"\nDataFrames = \"~1.2.2\"\nHypertextLiteral = \"~0.9.0\"\nPlutoUI = \"~0.7.9\"\nRevise = \"~3.1.19\"\nVegaDatasets = \"~2.1.1\"\nVegaLite = \"~2.6.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[CSV]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"b83aa3f513be680454437a0eee21001607e5d983\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.8.5\"\n\n[[ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"30ee06de5ff870b45c78f529a6b093b3323256a3\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.3.1\"\n\n[[CodeTracking]]\ndeps = [\"InteractiveUtils\", \"UUIDs\"]\ngit-tree-sha1 = \"9aa8a5ebb6b5bf469a7e0e2b5202cf6f8c291104\"\nuuid = \"da1fd8a2-8d9e-5ec2-8556-3022fb5608a2\"\nversion = \"1.0.6\"\n\n[[CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"727e463cfebd0c7b999bbf3e9e7e16f254b94193\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.34.0\"\n\n[[CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[ConstructionBase]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"f74e9d5388b8620b4cee35d4c5a618dd4dc547f4\"\nuuid = \"187b0558-2788-49d3-abe0-74a17ed4e7c9\"\nversion = \"1.3.0\"\n\n[[Crayons]]\ngit-tree-sha1 = \"3f71217b538d7aaee0b69ab47d9b7724ca8afa0d\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.0.4\"\n\n[[DataAPI]]\ngit-tree-sha1 = \"bec2532f8adb82005476c141ec23e921fc20971b\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.8.0\"\n\n[[DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"d785f42445b63fc86caa08bb9a9351008be9b765\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.2.2\"\n\n[[DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"7d9d316f04214f7efdbb6398d545446e246eff02\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.10\"\n\n[[DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[DataValues]]\ndeps = [\"DataValueInterfaces\", \"Dates\"]\ngit-tree-sha1 = \"d88a19299eba280a6d062e135a43f00323ae70bf\"\nuuid = \"e7dc6d0d-1eca-5fa6-8ad6-5aecde8b7ea5\"\nversion = \"0.4.13\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[DocStringExtensions]]\ndeps = [\"LibGit2\"]\ngit-tree-sha1 = \"a32185f5428d3986f47c2ab78b1f216d5e6cc96f\"\nuuid = \"ffbed154-4ef7-542d-bbb7-c09d3a79fcae\"\nversion = \"0.8.5\"\n\n[[DoubleFloats]]\ndeps = [\"GenericLinearAlgebra\", \"LinearAlgebra\", \"Polynomials\", \"Printf\", \"Quadmath\", \"Random\", \"Requires\", \"SpecialFunctions\"]\ngit-tree-sha1 = \"1c962cf7e75c09a5f1fbf504df7d6a06447a1129\"\nuuid = \"497a8b3b-efae-58df-a0af-a86822472b78\"\nversion = \"1.1.23\"\n\n[[Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[ExprTools]]\ngit-tree-sha1 = \"b7e3d17636b348f005f11040025ae8c6f645fe92\"\nuuid = \"e2ba6199-217a-4e67-a87a-7c52f15ade04\"\nversion = \"0.1.6\"\n\n[[FileIO]]\ndeps = [\"Pkg\", \"Requires\", \"UUIDs\"]\ngit-tree-sha1 = \"937c29268e405b6808d958a9ac41bfe1a31b08e7\"\nuuid = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nversion = \"1.11.0\"\n\n[[FilePaths]]\ndeps = [\"FilePathsBase\", \"MacroTools\", \"Reexport\", \"Requires\"]\ngit-tree-sha1 = \"919d9412dbf53a2e6fe74af62a73ceed0bce0629\"\nuuid = \"8fc22ac5-c921-52a6-82fd-178b2807b824\"\nversion = \"0.8.3\"\n\n[[FilePathsBase]]\ndeps = [\"Dates\", \"Mmap\", \"Printf\", \"Test\", \"UUIDs\"]\ngit-tree-sha1 = \"0f5e8d0cb91a6386ba47bd1527b240bd5725fbae\"\nuuid = \"48062228-2e41-5def-b9a4-89aafe57970f\"\nversion = \"0.9.10\"\n\n[[FileWatching]]\nuuid = \"7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee\"\n\n[[Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[GenericLinearAlgebra]]\ndeps = [\"LinearAlgebra\", \"Printf\", \"Random\"]\ngit-tree-sha1 = \"ff291c1827030ffaacaf53e3c83ed92d4d5e6fb6\"\nuuid = \"14197337-ba66-59df-a3e3-ca00e7dcff7a\"\nversion = \"0.2.5\"\n\n[[HTTP]]\ndeps = [\"Base64\", \"Dates\", \"IniFile\", \"MbedTLS\", \"Sockets\"]\ngit-tree-sha1 = \"c7ec02c4c6a039a98a15f955462cd7aea5df4508\"\nuuid = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\nversion = \"0.8.19\"\n\n[[HypertextLiteral]]\ngit-tree-sha1 = \"72053798e1be56026b81d4e2682dbe58922e5ec9\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.0\"\n\n[[IniFile]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"098e4d2c533924c921f9f9847274f2ad89e018b8\"\nuuid = \"83e8ac13-25f8-5344-8a64-a9f2b223428f\"\nversion = \"0.5.0\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[Intervals]]\ndeps = [\"Dates\", \"Printf\", \"RecipesBase\", \"Serialization\", \"TimeZones\"]\ngit-tree-sha1 = \"323a38ed1952d30586d0fe03412cde9399d3618b\"\nuuid = \"d8418881-c3e1-53bb-8760-2df7ec849ed5\"\nversion = \"1.5.0\"\n\n[[InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[IrrationalConstants]]\ngit-tree-sha1 = \"f76424439413893a832026ca355fe273e93bce94\"\nuuid = \"92d709cd-6900-40b7-9082-c6be49f344b6\"\nversion = \"0.1.0\"\n\n[[IterableTables]]\ndeps = [\"DataValues\", \"IteratorInterfaceExtensions\", \"Requires\", \"TableTraits\", \"TableTraitsUtils\"]\ngit-tree-sha1 = \"70300b876b2cebde43ebc0df42bc8c94a144e1b4\"\nuuid = \"1c8ee90f-4401-5389-894e-7a04a3dc0f4d\"\nversion = \"1.0.0\"\n\n[[IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[JLLWrappers]]\ndeps = [\"Preferences\"]\ngit-tree-sha1 = \"642a199af8b68253517b80bd3bfd17eb4e84df6e\"\nuuid = \"692b3bcd-3c85-4b1f-b108-f13ce0eb3210\"\nversion = \"1.3.0\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[JSONSchema]]\ndeps = [\"HTTP\", \"JSON\", \"ZipFile\"]\ngit-tree-sha1 = \"b84ab8139afde82c7c65ba2b792fe12e01dd7307\"\nuuid = \"7d188eb4-7ad8-530c-ae41-71a32a6d4692\"\nversion = \"0.3.3\"\n\n[[JuliaInterpreter]]\ndeps = [\"CodeTracking\", \"InteractiveUtils\", \"Random\", \"UUIDs\"]\ngit-tree-sha1 = \"e273807f38074f033d94207a201e6e827d8417db\"\nuuid = \"aa1ae85d-cabe-5617-a682-6adf51b2e16a\"\nversion = \"0.8.21\"\n\n[[LazyArtifacts]]\ndeps = [\"Artifacts\", \"Pkg\"]\nuuid = \"4af54fe1-eca0-43a8-85a7-787d91b784e3\"\n\n[[LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[LinearAlgebra]]\ndeps = [\"Libdl\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[LogExpFunctions]]\ndeps = [\"ChainRulesCore\", \"DocStringExtensions\", \"IrrationalConstants\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"86197a8ecb06e222d66797b0c2d2f0cc7b69e42b\"\nuuid = \"2ab3a3ac-af41-5b50-aa03-7779005ae688\"\nversion = \"0.3.2\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[LoweredCodeUtils]]\ndeps = [\"JuliaInterpreter\"]\ngit-tree-sha1 = \"491a883c4fef1103077a7f648961adbf9c8dd933\"\nuuid = \"6f1432cf-f94c-5a45-995e-cdbf5db27b0b\"\nversion = \"2.1.2\"\n\n[[MacroTools]]\ndeps = [\"Markdown\", \"Random\"]\ngit-tree-sha1 = \"0fb723cd8c45858c22169b2e42269e53271a6df7\"\nuuid = \"1914dd2f-81c6-5fcd-8719-6d5c9610ff09\"\nversion = \"0.5.7\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[MbedTLS]]\ndeps = [\"Dates\", \"MbedTLS_jll\", \"Random\", \"Sockets\"]\ngit-tree-sha1 = \"1c38e51c3d08ef2278062ebceade0e46cefc96fe\"\nuuid = \"739be429-bea8-5141-9913-cc70e7f3736d\"\nversion = \"1.0.3\"\n\n[[MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"2ca267b08821e86c5ef4376cffed98a46c2cb205\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.1\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[Mocking]]\ndeps = [\"ExprTools\"]\ngit-tree-sha1 = \"748f6e1e4de814b101911e64cc12d83a6af66782\"\nuuid = \"78c3b35d-d492-501b-9361-3d52fe80e533\"\nversion = \"0.7.2\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[MutableArithmetics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\", \"Test\"]\ngit-tree-sha1 = \"3927848ccebcc165952dc0d9ac9aa274a87bfe01\"\nuuid = \"d8a4904e-b15c-11e9-3269-09a3773c0cb0\"\nversion = \"0.2.20\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[NodeJS]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"905224bbdd4b555c69bb964514cfa387616f0d3a\"\nuuid = \"2bd173c7-0d6d-553b-b6af-13a54713934c\"\nversion = \"1.3.0\"\n\n[[Nullables]]\ngit-tree-sha1 = \"8f87854cc8f3685a60689d8edecaa29d2251979b\"\nuuid = \"4d1e1d77-625e-5b40-9113-a560ec7a8ecd\"\nversion = \"1.0.0\"\n\n[[OpenSpecFun_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"13652491f6856acfd2db29360e1bbcd4565d04f1\"\nuuid = \"efe28fd5-8261-553b-a9e1-b2916fc3738e\"\nversion = \"0.5.5+0\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"bfd7d8c7fd87f04543810d9cbd3995972236ba1b\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"1.1.2\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"Suppressor\"]\ngit-tree-sha1 = \"44e225d5837e2a2345e69a1d1e01ac2443ff9fcb\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.9\"\n\n[[Polynomials]]\ndeps = [\"Intervals\", \"LinearAlgebra\", \"MutableArithmetics\", \"RecipesBase\"]\ngit-tree-sha1 = \"0bbfdcd8cda81b8144de4be8a67f5717e959a005\"\nuuid = \"f27b6e38-b328-58d1-80ce-0feddd5e7a45\"\nversion = \"2.0.14\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"a193d6ad9c45ada72c14b731a318bedd3c2f00cf\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.3.0\"\n\n[[Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"00cfd92944ca9c760982747e9a1d0d5d86ab1e5a\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.2\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"0d1245a357cc61c8cd61934c07447aa569ff22e6\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.1.0\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[Quadmath]]\ndeps = [\"Printf\", \"Random\", \"Requires\"]\ngit-tree-sha1 = \"5a8f74af8eae654086a1d058b4ec94ff192e3de0\"\nuuid = \"be4d8f0f-7fa4-5f49-b795-2f01399ab2dd\"\nversion = \"0.5.5\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[RecipesBase]]\ngit-tree-sha1 = \"44a75aa7a527910ee3d1751d1f0e4148698add9e\"\nuuid = \"3cdcf5f2-1ef4-517c-9805-6587b60abb01\"\nversion = \"1.1.2\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"4036a3bd08ac7e968e27c203d45f5fff15020621\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.1.3\"\n\n[[Revise]]\ndeps = [\"CodeTracking\", \"Distributed\", \"FileWatching\", \"JuliaInterpreter\", \"LibGit2\", \"LoweredCodeUtils\", \"OrderedCollections\", \"Pkg\", \"REPL\", \"Requires\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"1947d2d75463bd86d87eaba7265b0721598dd803\"\nuuid = \"295af30f-e4ad-537b-8983-00126c2a3abe\"\nversion = \"3.1.19\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"54f37736d8934a12a200edea2f9206b03bdf3159\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.7\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[Setfield]]\ndeps = [\"ConstructionBase\", \"Future\", \"MacroTools\", \"Requires\"]\ngit-tree-sha1 = \"fca29e68c5062722b5b4435594c3d1ba557072a3\"\nuuid = \"efcf1570-3423-57d1-acb7-fd33fddbac46\"\nversion = \"0.7.1\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[SpecialFunctions]]\ndeps = [\"ChainRulesCore\", \"LogExpFunctions\", \"OpenSpecFun_jll\"]\ngit-tree-sha1 = \"a322a9493e49c5f3a10b50df3aedaf1cdb3244b7\"\nuuid = \"276daf66-3868-5448-9aa4-cd146d93841b\"\nversion = \"1.6.1\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[Suppressor]]\ngit-tree-sha1 = \"a819d77f31f83e5792a76081eee1ea6342ab8787\"\nuuid = \"fd094767-a336-5f1f-9728-57cf17d0bbfb\"\nversion = \"0.2.0\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableShowUtils]]\ndeps = [\"DataValues\", \"Dates\", \"JSON\", \"Markdown\", \"Test\"]\ngit-tree-sha1 = \"14c54e1e96431fb87f0d2f5983f090f1b9d06457\"\nuuid = \"5e66a065-1f0a-5976-b372-e0b8c017ca10\"\nversion = \"0.2.5\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[TableTraitsUtils]]\ndeps = [\"DataValues\", \"IteratorInterfaceExtensions\", \"Missings\", \"TableTraits\"]\ngit-tree-sha1 = \"78fecfe140d7abb480b53a44f3f85b6aa373c293\"\nuuid = \"382cd787-c1b6-5bf2-a167-d5b971a19bda\"\nversion = \"1.0.2\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"d0c690d37c73aeb5ca063056283fde5585a41710\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.5.0\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TextParse]]\ndeps = [\"CodecZlib\", \"DataStructures\", \"Dates\", \"DoubleFloats\", \"Mmap\", \"Nullables\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"eb1f4fb185c8644faa2d18d14c72f2c24412415f\"\nuuid = \"e0df1984-e451-5cb5-8b61-797a481e67e3\"\nversion = \"1.0.2\"\n\n[[TimeZones]]\ndeps = [\"Dates\", \"Future\", \"LazyArtifacts\", \"Mocking\", \"Pkg\", \"Printf\", \"RecipesBase\", \"Serialization\", \"Unicode\"]\ngit-tree-sha1 = \"6c9040665b2da00d30143261aea22c7427aada1c\"\nuuid = \"f269a46b-ccf7-5d73-abea-4c690281aa53\"\nversion = \"1.5.7\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[URIParser]]\ndeps = [\"Unicode\"]\ngit-tree-sha1 = \"53a9f49546b8d2dd2e688d216421d050c9a31d0d\"\nuuid = \"30578b45-9adc-5946-b283-645ec420af67\"\nversion = \"0.4.1\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[Vega]]\ndeps = [\"DataStructures\", \"DataValues\", \"Dates\", \"FileIO\", \"FilePaths\", \"IteratorInterfaceExtensions\", \"JSON\", \"JSONSchema\", \"MacroTools\", \"NodeJS\", \"Pkg\", \"REPL\", \"Random\", \"Setfield\", \"TableTraits\", \"TableTraitsUtils\", \"URIParser\"]\ngit-tree-sha1 = \"43f83d3119a868874d18da6bca0f4b5b6aae53f7\"\nuuid = \"239c3e63-733f-47ad-beb7-a12fde22c578\"\nversion = \"2.3.0\"\n\n[[VegaDatasets]]\ndeps = [\"DataStructures\", \"DataValues\", \"FilePaths\", \"IterableTables\", \"IteratorInterfaceExtensions\", \"JSON\", \"TableShowUtils\", \"TableTraits\", \"TableTraitsUtils\", \"TextParse\"]\ngit-tree-sha1 = \"c997c7217f37205c5795de8c797f8f8531890f1d\"\nuuid = \"0ae4a718-28b7-58ec-9efb-cded64d6d5b4\"\nversion = \"2.1.1\"\n\n[[VegaLite]]\ndeps = [\"Base64\", \"DataStructures\", \"DataValues\", \"Dates\", \"FileIO\", \"FilePaths\", \"IteratorInterfaceExtensions\", \"JSON\", \"MacroTools\", \"NodeJS\", \"Pkg\", \"REPL\", \"Random\", \"TableTraits\", \"TableTraitsUtils\", \"URIParser\", \"Vega\"]\ngit-tree-sha1 = \"3e23f28af36da21bfb4acef08b144f92ad205660\"\nuuid = \"112f6efa-9a02-5b7d-90c0-432ed331239a\"\nversion = \"2.6.0\"\n\n[[WeakRefStrings]]\ndeps = [\"DataAPI\", \"Parsers\", \"Random\", \"Test\"]\ngit-tree-sha1 = \"9ef95db08bf767499a74586bcbd4b5df30c19b9f\"\nuuid = \"ea10d353-3f73-51f8-a26c-33c1cb351aa5\"\nversion = \"1.1.0\"\n\n[[ZipFile]]\ndeps = [\"Libdl\", \"Printf\", \"Zlib_jll\"]\ngit-tree-sha1 = \"c3a5637e27e914a7a445b8d0ad063d701931e9f7\"\nuuid = \"a5390f91-8eb1-5f08-bee0-b1d1ffed6cea\"\nversion = \"0.9.3\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500e4dca908-1016-11ec-0b7f-e9d01bd4e1b2\n# \u2560\u25503389dc98-b90f-4c6a-8c96-31840b0516ea\n# \u255f\u2500e598c897-61bf-4bc2-91db-e225362f5606\n# \u2560\u25500d909765-a87d-4a3c-bb9f-ff80e463992a\n# \u2560\u2550ced90367-407c-4b67-8276-68edc17933d3\n# \u2560\u25506a2ca31f-716c-48f4-8e1a-70e3c033358b\n# \u2560\u2550cc533197-dbe3-41b6-8478-64f82da52ba8\n# \u2560\u2550dcabd381-1316-449b-9ebf-68410ee6e3fd\n# \u2560\u25508398bb71-aa8e-42b6-b3e3-c08ca57ee5b0\n# \u2560\u2550a6a64f62-316a-48f5-bd5c-0eebff53022d\n# \u255f\u2500f4bf1143-c315-4521-b19b-06bf0373828a\n# \u2560\u255086ddda30-351d-41f6-94b2-4c84dea08448\n# \u2560\u2550bfd9753c-323a-4a82-b5e6-057e7519ed6c\n# \u2560\u2550ff5a5927-6bc6-4b05-b0e8-e0bfdc623440\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "ac94c4dd1580397e6550538249b13f75b899dc95", "size": 24188, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tools/viz/solution-explorer.jl", "max_stars_repo_name": "yvs314/epi-net-m", "max_stars_repo_head_hexsha": "d4ddc68612247e420ae5d6c1bcd2cf488ce3d8bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tools/viz/solution-explorer.jl", "max_issues_repo_name": "yvs314/epi-net-m", "max_issues_repo_head_hexsha": "d4ddc68612247e420ae5d6c1bcd2cf488ce3d8bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2021-08-30T22:55:22.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-18T20:01:24.000Z", "max_forks_repo_path": "tools/viz/solution-explorer.jl", "max_forks_repo_name": "yvs314/epi-net-m", "max_forks_repo_head_hexsha": "d4ddc68612247e420ae5d6c1bcd2cf488ce3d8bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8127388535, "max_line_length": 280, "alphanum_fraction": 0.7327187035, "num_tokens": 10545, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.16667540468797667, "lm_q1q2_score": 0.07813586740685935}}
{"text": "# The following test cases and comments are adapted from http://brainfuck.org/tests.b\n\nusing Test\nif !@isdefined(BrainFuck)\n    include(joinpath(@__DIR__, \"..\", \"src\", \"BrainFuck.jl\"))\nend\noutput = IOBuffer()\n\n# Here are some little programs for testing brainfuck implementations.\n\n@testset \"IO test\" begin\n    # This is for testing i/o; give it a return followed by an EOF. (Try it both\n    # with file input \u2014 a file consisting only of one blank line \u2014 and with\n    # keyboard input, i.e. hit return and then ctrl-d (Unix) or ctrl-z (Windows).)\n    # It should give two lines of output; the two lines should be identical, and\n    # should be lined up one over the other. If that doesn't happen, ten is not\n    # coming through as newline on output.\n    # The content of the lines tells how input is being processed; each line\n    # should be two uppercase letters.\n    # Anything with O in it means newline is not coming through as ten on input.\n    # LK means newline input is working fine, and EOF leaves the cell unchanged\n    # (which I recommend).\n    # LB means newline input is working fine, and EOF translates as 0.\n    # LA means newline input is working fine, and EOF translates as -1.\n    # Anything else is fairly unexpected.\n    iotest = \">,>+++++++++,>+++++++++++[<++++++<++++++<+>>>-]<<.>.<<-.>.>.<<.\"\n\n    BrainFuck.interpret(iotest, input = \"\\n\", output = output)\n    @test String(take!(output)) == \"LK\\nLK\\n\"\n\n    BrainFuck.interpret(iotest, input = open(joinpath(@__DIR__, \"empty.txt\")), output = output)\n    @test String(take!(output)) == \"LK\\nLK\\n\"\nend\n\n@testset \"Memory size\" begin\n    memorysizetest = \"++++[>++++++<-]>[>+++++>+++++++<<-]>>++++<[[>[[>>+<<-]<]>>>-]>-[>+>+<<-]>]+++++[>+++++++<<++>-]>.<<.\"\n    # Goes to cell 30000 and reports from there with a #. (Verifies that the\n    # array is big enough.)\n    BrainFuck.interpret(memorysizetest, output = output)\n    @test String(take!(output)) == \"#\\n\"\n\n    BrainFuck.compile(memorysizetest)(output = output)\n    @test String(take!(output)) == \"#\\n\"\nend\n\n@testset \"Several obscure tests\" begin\n    # Tests for several obscure problems. Should output an H.\n    obscuretest = \"\"\"[]++++++++++[>>+>+>++++++[<<+<+++>>>-]<<<<-]\n    \"A*\\$\";?@![#>>+<<]>[>>]<<<<[>++<[-]]>.>.\"\"\"\n    @test BrainFuck.interpret(obscuretest, output = String) == \"H\\n\"\n    @test BrainFuck.compile(obscuretest)(output = String) == \"H\\n\"\nend\n\n@testset \"Unmatched brackets\" begin\n    # Should ideally give error message \"unmatched [\" or the like, and not give\n    # any output. Not essential.\n    unmatchedleft = \"+++++[>+++++++>++<<-]>.>.[\"\n    @test_throws ArgumentError BrainFuck.interpret(unmatchedleft)\n    @test_throws ArgumentError BrainFuck.compile(unmatchedleft)\n\n    # Should ideally give error message \"unmatched ]\" or the like, and not give\n    # any output. Not essential.\n    unmatchedright = \"+++++[>+++++++>++<<-]>.>.][\"\n    @test_throws ArgumentError BrainFuck.interpret(unmatchedright)\n    @test_throws ArgumentError BrainFuck.compile(unmatchedright)\nend\n\n@testset \"Deep brackets\" begin\n    # [Daniel Cristofani's] pathological program rot13.b is good for testing the\n    # response to deep brackets; the input \"~mlk zyx\" should produce the output \"~zyx mlk\".\n    rot13 = \"\"\"\n        ,\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>++++++++++++++<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>>+++++[<----->-]<<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>++++++++++++++<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-\n        [>++++++++++++++<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>>+++++[<----->-]<<-\n        [>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-[>+<-\n        [>++++++++++++++<-\n        [>+<-]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]\n        ]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]>.[-]<,]\n        \"\"\"\n\n    @test BrainFuck.interpret(rot13, input = \"~mlk zyx\", output = String) == \"~zyx mlk\"\n    @test BrainFuck.compile(rot13)(input = \"~mlk zyx\", output = String) == \"~zyx mlk\"\nend\n", "meta": {"hexsha": "82dbb8e681b2c78f361e7cc513c8dac81dbfcbb5", "size": 4430, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Brainfuck/test/BrainFuck.jl", "max_stars_repo_name": "stellartux/Esolangs", "max_stars_repo_head_hexsha": "5ea59ad63c3d4089f39166ab71ef1d768eeefad2", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Brainfuck/test/BrainFuck.jl", "max_issues_repo_name": "stellartux/Esolangs", "max_issues_repo_head_hexsha": "5ea59ad63c3d4089f39166ab71ef1d768eeefad2", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Brainfuck/test/BrainFuck.jl", "max_forks_repo_name": "stellartux/Esolangs", "max_forks_repo_head_hexsha": "5ea59ad63c3d4089f39166ab71ef1d768eeefad2", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.6315789474, "max_line_length": 123, "alphanum_fraction": 0.4909706546, "num_tokens": 1456, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "# Additional tests of the to_dot function\n\n# This adds tests where:\n#   1) there are isolated vertices\n#   2) there are vertex attributes to be shown in .dot\n#   3) the graph verifies implements_edge_list(.) == true\n#      and                implements_vertex_map(.) == true\n\n\n# These functions help with dealing with the fact that in two equivalent\n# renderings of the same graph one may obtain equivalent .dots with permuted\n# lines. This results of the fact that no ordering of vertices is implied by\n# the code\n#    ```for vtx in  vertices(graph) .... end```. For similar reasons,\n#    attributes are extracted of a Dict in arbitrary order.\n#\n# So:\n# 1) lines are sorted (result is independent of order in vertices and edges)\n# 2) attributes are checksummed (with an order independent checksum (not a good\n#    one)). This is not a precise verification of attributes, but good enough\n#           for testing\n\n# NOTE: if to_dot is modified to emit lines with .dot comments, these tests\n#       mail fail erroneously....\n\n\ncomRX = Base.compile(r\"^[^\\[]+\\[([^\\[]+)\\]\\h*$\"x)\nfunction   rewriteAttrs(a::AbstractString)\n     m = match(comRX,a)\n     if m!=nothing\n         attrs = m.captures[1]\n         offset= m.offsets[1]\n         chksum= mod(reduce(+,\n                            map(x->convert(Int,x),collect(attrs)), init=0  ), 25)\n         ch = convert(Char, convert(Int,'a') - 1 + chksum)\n         a[1:offset-1] * \"$ch\" * a[ offset+length(attrs) : end ]\n     else\n         return a\n     end\nend\n\nfunction  check_same_dot(a::AbstractString,b::AbstractString)\n    sa=sort( map( rewriteAttrs, split( a, \"\\n\")))\n    sb=sort( map( rewriteAttrs, split( b, \"\\n\")))\n    la = map(rewriteAttrs,sa)\n    lb = map(rewriteAttrs,sb)\n    return   la==lb\nend\n\n\n\nmodule testDOT1\n\nusing Graphs\nusing Test\n\n\n###########\n#     test dot output for graphs for which\n#         true == implements_edge_list && implements_vertex_map\n#     and no vertex attributes\n###########\n\n### 1) graph without node attributes\nsgd = simple_graph(3)\n\n@test @show implements_edge_list(sgd)==true\n@test @show implements_vertex_map(sgd)==true\n\nadd_vertex!(sgd)\n\ndot1=to_dot(sgd)\nprintln(dot1)\n@test Main.check_same_dot(dot1,\"digraph graphname {\\n1\\n2\\n3\\n4\\n}\\n\")\n\n### 2) graph without node attributes but with some edges\nadd_edge!(sgd,1,3)\nadd_edge!(sgd,3,1)\nadd_edge!(sgd,2,3)\n\ndot2=to_dot(sgd)\nprintln(dot2)\n@test Main.check_same_dot(dot2,\n                 \"digraph graphname {\\n1\\n2\\n3\\n4\\n1 -> 3\\n3 -> 1\\n2 -> 3\\n}\\n\")\n\n\nend # module testDOT1\n\nmodule testDOT2\n\nusing Graphs\nusing Test\n\n###########\n#     test dot output for graphs for which\n#         true == implements_edge_list && implements_vertex_map\n#     and vertex attributes\n###########\n\nstruct MyVtxType\n    name::AbstractString\nend\n\nimport Graphs.attributes\nfunction Graphs.attributes(vtx::MyVtxType,g::G) where {G<:AbstractGraph}\n     rd = Graphs.AttributeDict()\n     rd[\"label\"]=vtx.name\n     rd[\"color\"]=\"bisque\"\n     rd\nend\n\n### 3) directed graph with node attributes and some disconnected vertices\n\nag = Graphs.graph( map( MyVtxType,[ \"a\", \"b\", \"c\",\"d\"]), Graphs.Edge{MyVtxType}[],\n                      is_directed=true)\n\nvl = ag.vertices\n\nadd_edge!(ag,  vl[1], vl[3] )\nadd_edge!(ag,  vl[3], vl[1] )\nadd_edge!(ag,  vl[2], vl[3])\n\n@test @show implements_edge_list(ag)==true\n@test @show implements_vertex_map(ag)==true\n\ndot3 = to_dot( ag )\nprintln(dot3)\n\ncompDot3 =  \"digraph graphname {\\n1\\t[\\\"label\\\"=\\\"a\\\",\\\"color\\\"=\\\"bisque\\\"]\\n2\\t[\\\"label\\\"=\\\"b\\\",\\\"color\\\"=\\\"bisque\\\"]\\n3\\t[\\\"label\\\"=\\\"c\\\",\\\"color\\\"=\\\"bisque\\\"]\\n4\\t[\\\"label\\\"=\\\"d\\\",\\\"color\\\"=\\\"bisque\\\"]\\n1 -> 3\\n3 -> 1\\n2 -> 3\\n}\\n\"\n\n@test Main.check_same_dot(dot3,compDot3)\n\n### 4) undirected graph with node attributes and some disconnected vertices\n\n\nagu = Graphs.graph( map( MyVtxType,[ \"a\", \"b\", \"c\",\"d\"]), Graphs.Edge{MyVtxType}[],\n                      is_directed=false)\n\nvl = agu.vertices\n\nadd_edge!(agu, vl[1], vl[3] )\nadd_edge!(agu,  vl[2], vl[3])\n\ndot4=to_dot(agu)\nprintln(dot4)\n\n@test @show implements_edge_list(agu)==true\n@test @show implements_vertex_map(agu)==true\n\n@test Main.check_same_dot(dot4,\"graph graphname {\\n1\\t[\\\"label\\\"=\\\"a\\\",\\\"color\\\"=\\\"bisque\\\"]\\n2\\t[\\\"label\\\"=\\\"b\\\",\\\"color\\\"=\\\"bisque\\\"]\\n3\\t[\\\"label\\\"=\\\"c\\\",\\\"color\\\"=\\\"bisque\\\"]\\n4\\t[\\\"label\\\"=\\\"d\\\",\\\"color\\\"=\\\"bisque\\\"]\\n1 -- 3\\n2 -- 3\\n}\\n\"\n)\n\nend # module testDOT2\n", "meta": {"hexsha": "86a7e9cdcdf0b22072efd1e8742a5ff41079a4cb", "size": 4339, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/dot2.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Graphs.jl-86223c79-3864-5bf0-83f7-82e725a168b6", "max_stars_repo_head_hexsha": "b76b9914178a417085681167643af510547e8a0a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 41, "max_stars_repo_stars_event_min_datetime": "2018-08-19T13:26:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-07T08:17:35.000Z", "max_issues_repo_path": "test/dot2.jl", "max_issues_repo_name": "JuliaAttic/OldGraphs.jl", "max_issues_repo_head_hexsha": "39a9c6efacc190ac79db47dd4dd951657058d2c0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-02-20T22:05:43.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-24T15:20:41.000Z", "max_forks_repo_path": "test/dot2.jl", "max_forks_repo_name": "JuliaAttic/OldGraphs.jl", "max_forks_repo_head_hexsha": "39a9c6efacc190ac79db47dd4dd951657058d2c0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2019-02-12T18:37:11.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-03T14:09:36.000Z", "avg_line_length": 28.1753246753, "max_line_length": 243, "alphanum_fraction": 0.6383959438, "num_tokens": 1281, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "#%%\nprintln(\"hello\")\nprintln(\"Julia\")\n#%%\n\n#%%\nnum1=22\nnum2=22.35\nprintln(\"n1: \",num1,\" \",typeof(num1))\nprintln(\"n2: \",num2,\" \",typeof(num2))\nd=num1/num2\nprintln(d)\nstrA=\"myString\"\nprintln(typeof(strA))\n\nnum3=convert(Float64,num1)\nprintln(\"n3: \",num3,\" \",typeof(num1))\n\n#%%\n\n#%%\nfunction funcA(x)\n    return x*5\nend\nprintln(funcA(2))\nprintln(funcA([2,22]))\n#%%\n", "meta": {"hexsha": "dedd1582f44c7c1d701d5e8923b1a8526db8268a", "size": 361, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "first.jl", "max_stars_repo_name": "urmi-21/hello-julia", "max_stars_repo_head_hexsha": "08b635acc2f09e0225fefd62f05cf0db70ab5945", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "first.jl", "max_issues_repo_name": "urmi-21/hello-julia", "max_issues_repo_head_hexsha": "08b635acc2f09e0225fefd62f05cf0db70ab5945", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "first.jl", "max_forks_repo_name": "urmi-21/hello-julia", "max_forks_repo_head_hexsha": "08b635acc2f09e0225fefd62f05cf0db70ab5945", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.8928571429, "max_line_length": 37, "alphanum_fraction": 0.6315789474, "num_tokens": 132, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.16667541089705434, "lm_q1q2_score": 0.07684016112231687}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Strings\n#\n# Topics:\n# 1. How to get a string\n# 2. String interpolation\n# 3. String concatenation\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## How to get a string\n#\n# Enclose your characters in \" \" or \"\"\" \"\"\"!\n# ------------------------------------------------------------------------------------------\n\ns1 = \"I am a string.\"\n\ns2 = \"\"\"I am also a string. \"\"\"\n\n# ------------------------------------------------------------------------------------------\n# There are a couple functional differences between strings enclosed in single and triple\n# quotes. <br>\n# One difference is that, in the latter case, you can use quotation marks within your\n# string.\n# ------------------------------------------------------------------------------------------\n\n\"Here, we get an \"error\" because it's ambiguous where this string ends \"\n\n\"\"\"Look, Mom, no \"errors\"!!! \"\"\"\n\n# ------------------------------------------------------------------------------------------\n# Note that ' ' define a character, but NOT a string!\n# ------------------------------------------------------------------------------------------\n\ntypeof('a')\n\n'We will get an error here'\n\n# ------------------------------------------------------------------------------------------\n# ## String interpolation\n#\n# We can use the $ sign to insert existing variables into a string and to evaluate\n# expressions within a string. <br>\n# Below is an example that contains some highly sensitive personal information.\n# ------------------------------------------------------------------------------------------\n\nname = \"Jane\"\nnum_fingers = 10\nnum_toes = 10\n\nprintln(\"Hello, my name is $name.\")\nprintln(\"I have $num_fingers fingers and $num_toes toes.\")\n\n println(\"That is $(num_fingers + num_toes) digits in all!!\")\n\n# ------------------------------------------------------------------------------------------\n# ## String concatenation\n#\n# Below are three ways we can concatenate strings! <br><br>\n# The first way is to use the `string()` function. <br>\n# `string()` converts non-string inputs to strings.\n# ------------------------------------------------------------------------------------------\n\ns3 = \"How many cats \";\ns4 = \"is too many cats?\";\n\ud83d\ude3a = 10\n\nstring(s3, s4)\n\nstring(\"I don't know, but \", \ud83d\ude3a, \" is too few.\")\n\n# ------------------------------------------------------------------------------------------\n# We can also use `*` for concatenation!\n# ------------------------------------------------------------------------------------------\n\ns3*s4\n\n# ------------------------------------------------------------------------------------------\n# ### Exercises\n#\n# #### 2.1\n# Create a string that says \"hi\" 1000 times, first with `repeat` and then with the\n# exponentiation operator, which can call `*` under the hood. Assign it the variable `hi`\n# below.\n# ------------------------------------------------------------------------------------------\n\n\n\n@assert hi == \"hihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihihi\"\n\n# ------------------------------------------------------------------------------------------\n# #### 2.2\n# Declare two variables\n#\n# ```julia\n# a = 3\n# b = 4\n# ```\n# and use them to create two strings:\n# ```julia\n# \"3 + 4\"\n# \"7\"\n# ```\n# and store the results in `c` and `d` respectively\n# ------------------------------------------------------------------------------------------\n\n\n\n@assert c == \"3 + 4\"\n@assert d == \"7\"\n\n# ------------------------------------------------------------------------------------------\n# Please click on `Validate` on the top, once you are done with the exercises.\n# ------------------------------------------------------------------------------------------\n", "meta": {"hexsha": "8a00cf3a0d387a9127699ad9b104fccceb0716cb", "size": 5845, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/02. 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{"text": "#=\nAuthor:= Shreeda Bhat\nLicense:= MIT \nUTF-8 \n=#\n\n\n#################################### Inbuilt functions #######################################\n#= \n\ncos(),\nsin(),\nlog(),\netc ....... =#\n\n\nprintln(\"#################################### Inbuilt functions #######################################\")\na = cos(90)\nprintln(a)\n\n\n#################################### User Defined functions #######################################\n\nprintln(\"#################################### User Defined functions #######################################\")\n\nmyfunction(a,b) = println(a+b)\n\nmyfunction(10,20)\n\n\nmethods(myfunction)\n\nprintln(\"*************************************** 2nd Example *******************************************\")\n\nmymul(a,b) = println(a*b)\n\nmymul(10,20)\n\n\nprintln(\"*************************************** 3rd Example *******************************************\")\n\n\nfunction f(a,b)\n    println(a+b)\nend\n\nf(10,20)\n\n\n\nprintln(\"*************************************** 4th Example *******************************************\")\n\nfunction fun(a,b)\n    println(\"type of a : $(typeof(a)) and $(typeof(b)), Value of A is $a and B is $b \")\n    return a+b\nend\n\nfun(10,20)\n\n\nprintln(\"*************************************** 5th Example *******************************************\")\n# Let's write anonymous function\n\nfriend = name -> println(\"hi $name\")\n\nfriend(\"a\")\n", "meta": {"hexsha": "1d6c14aaf0cbd414898d304b4d734b18dc865421", "size": 1354, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Basics/functions.jl", "max_stars_repo_name": "memetics19/My_julia_practice-", "max_stars_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-16T09:58:59.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-16T09:58:59.000Z", "max_issues_repo_path": "Basics/functions.jl", "max_issues_repo_name": "memetics19/My_julia_practice-", "max_issues_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Basics/functions.jl", "max_forks_repo_name": "memetics19/My_julia_practice-", "max_forks_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-16T05:51:26.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-16T05:51:26.000Z", "avg_line_length": 20.2089552239, "max_line_length": 110, "alphanum_fraction": 0.3338257016, "num_tokens": 247, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2782567937024021, "lm_q2_score": 0.2751297297667525, "lm_q1q2_score": 0.07655671645710488}}
{"text": "#========================================================================================#\n#\tLaboratory 7\n#\n# Encapsulation and software design.\n#\n# Author: Niall Palfreyman, 06/02/2022\n#========================================================================================#\n[\n\tActivity(\n\t\t\"\"\"\n\t\tIn this laboratory we look at the issue of encapsulation that we saw at the end of the\n\t\tprevious laboratory. There, we found that the value of the GLOBAL variable fig had been\n\t\toverwritten by the code inside the function myheatmap() ...\n\n\t\t\tTHIS IS SOMETHING THAT SHOULD _NEVER_EVER_ HAPPEN!\n\n\t\tWe are sometimes tempted to use global variables to pass data between functions, but\n\t\tthis ALWAYS carries the danger that someone might change the value of these variables\n\t\tby accident - particularly since it is usually extremely difficult for users of our\n\t\tprogram to notice that we are using global variables to pass the data!\n\n\t\tA large part of the science of informatics concerns how to pass data in ways that\n\t\tprotect that data from being changed by accident, and that is what we will investigate\n\t\tin this laboratory.\n\n\t\tIn our first experiment, enter the following code, then tell me the value of paula:\n\n\t\tlinus = [5,4,3,2,1]; paula = 5\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow enter the following function:\n\n\t\tfunction change_paula()\n\t\t\tpaula = paula + 2\n\t\t\tpaula\n\t\tend\n\n\t\tThen call the function change_paula() and tell me the value you get back:\n\t\t\"\"\",\n\t\t\"Your result may (not) surprise you, depending on how you think about scoping rules\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tYour result shows us two things: a) The value of the GLOBAL variable paula is available\n\t\tinside the LOCAL scope of the function change_paula(); b) We can change the value of\n\t\tpaula within this LOCAL scope.\n\n\t\tNow tell me the current value of paula:\n\t\t\"\"\",\n\t\t\"Ask Julia for the value of paula\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tAha! So although we can change the value of paula locally within change_paula(), this\n\t\tdoes not change the GLOBAL value of paula. In fact, there exist two different variables\n\t\tnamed paula: the GLOBAL variable containing the value 5, and a LOCAL variable containing\n\t\tthe value 7. When change_paula() ends, the variables in its local scope are thrown away,\n\t\tand the LOCAL paula disappears.\n\n\t\tIf we REALLY want to change the global value of paula, we can do so by redefining the\n\t\tfunction change_paula():\n\n\t\tfunction change_paula()\n\t\t\tglobal paula = paula + 2\n\t\t\tpaula\n\t\tend\n\n\t\tThis is what I did in the function myheatmap of laboratory 6. It is NOT a good idea! Tell\n\t\tme the value of paula now:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tSo Julia does allow us to make use of global values inside a local scope, but it forces us\n\t\tto announce this by using the keyword \"global\".\n\n\t\tThere is a further issue here. Enter this code:\n\n\t\tfunction change_linus()\n\t\t\tlinus[3] = 7\n\t\t\tlinus\n\t\tend\n\n\t\tAgain, the return value tells us that we are able to change the value of a local variable\n\t\tnamed linus. But now tell me the value of the GLOBAL variable named linus:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==[5,4,7,2,1]\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tThe problem is that linus is a Vector that refers to its contents (5,4,3,2,1). We are not\n\t\tallowed to change the value of linus, but we ARE allowed to change the contents that it\n\t\trefers to. So global variables are still unsafe! What are we to do? The solution is this:\n\n\t\tALWAYS encapsulate (i.e.: wrap/hide) EVERYTHING you do inside a MODULE!\n\n\t\tLet's see how to do this. Enter the following code:\n\n\t\tmodule MyModule\n\t\t\tfunction change_paula1()\n\t\t\t\tglobal paula = 9\n\t\t\t\tpaula\n\t\t\tend\n\t\tend\n\n\t\tNow call MyModule.change_paula1(), then tell me the value of paula afterwards:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tOK, so putting change_paula1() inside the module MyModule means it cannot interfere\n\t\twith the value of our global variable paula. But wait! We know that we can load\n\t\tmodules into global scope by means of the keyword \"using\": will that make it possible\n\t\tfor users change paula's value by accident? Load the module MyModule now:\n\n\t\tusing .MyModule\n\n\t\tRepeat the previous experiment - what is the value of paula afterwards?\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tGreat! Now that we know how to hide variables and functions inside a module, we can do\n\t\tsome real live software development! In the next laboratory, we will develop a genetic\n\t\talgorithm (GA), and GAs need to work with arrays of random values. However, generating\n\t\trandom numbers is very time-expensive, so in this laboratory we develop a Casino module\n\t\tthat can generate arrays of random values very quickly.\n\n\t\tThere is just one thing we need to do first. Our modules can get quite complex, so we\n\t\twill build them up step-by-step within the Ingolstadt filesystem. Please create now a\n\t\tsubfolder of Ingolstadt\\\\src named \"Development\", then create within the Development\n\t\tfolder another subfolder named \"Casinos\". Finally, create with\u00efn the Casinos folder an\n\t\tempty file named \"Casinos.jl\" that will contain our wonderful new module ...\n\t\t\"\"\",\n\t\t\"Create this file structure now, before continuing\",\n\t\tx -> isfile(\"Development/Casinos/Casinos.jl\")\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tIf you study the file Ingolstadt.jl, you will see that it contains my source code. It's\n\t\tall packed into a module named Ingolstadt, and this module is prefixed by a triple-quoted\n\t\tmulti-line string that describes its purpose. Use my source code as a template to create\n\t\tin the file Casinos.jl a module Casinos prefixed by an explanatory help-string, and which\n\t\tcontains the following test code:\n\n\t\tmodule Casinos\n\t\ttest = 5\n\t\tend\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow test your new module by entering this line at the Julia prompt:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\n\t\tThis will read and parse the Casino module, and you can test it by telling me the\n\t\tanswer to the following line:\n\n\t\tCasinos.test\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow we can develop our Casinos module in the file Casinos.jl. We start by setting up a\n\t\tuse-case for the module - that is, we sketch out how we will want to use the module when it\n\t\tis finished. Replace your module definition in the file Casinos.jl by the following code,\n\t\tthen move on to the next activity:\n\n\t\tmodule Casinos\n\n\t\t#-----------------------------------------------------------------------------------------\n\t\t\\\"\"\"\n\t\t\tunittest()\n\n\t\tUnit-test the Casinos module.\n\t\t\\\"\"\"\n\t\tfunction unittest()\n\t\t\tprintln(\"\\\\n============ Unit test Casinos: ===============\")\n\t\t\tprintln(\"Casino vault of randomness 2 for matrix withdrawals up to size (2x3):\")\n\t\t\tcasino = Casino(2,3,2)\n\t\t\tdisplay( casino.vault)\n\t\t\tprintln()\n\t\t\n\t\t\tprintln(\"Draw several (2x3) matrices from the casino:\")\n\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\n\t\t\tprintln(\"Finally, reshuffle the casino and redisplay its vault:\")\n\t\t\tshuffle!(casino)\n\t\t\tdisplay(casino.vault)\n\t\tend\n\n\t\tend # of Casinos\n\t\t\"\"\",\n\t\t\"Note: you can't compile or run Casinos yet - just enter this code and move on\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tYou can test your new module by reincluding Casinos.jl:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\n\t\tIf you enter Casinos.unittest() at the Julia prompt, you should see that the command\n\t\truns, but throws various errors (exceptions). We will now start to fix those errors...\n\n\t\tFirst let's get comment out the lines of unittest() that are causing problems. Insert the\n\t\tmulti-line comment marker #= at the beginning of the third line of unittest() so that\n\t\tit looks like this:\n\t\t\t\n\t\t#=\tcasino = Casino(2,3,2)\n\n\t\tNext, close this multi-line comment by inserting the marker =# to the right of the final\n\t\tline of unittest():\n\n\t\t\tdisplay(casino.vault) =#\n\n\t\tReinclude Casinos.jl. Now you should be able to run Casino.unittest() without errors.\n\t\tTell me the last six characters that you see in the output:\n\t\t\"\"\",\n\t\t\"You should get two lines of output, and the final characters specify a matrix size\",\n\t\tx -> x == \"(2x3):\"\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tTo start developing our new module, we'll define the type Casino. Insert the following\n\t\tcode AFTER the \"module\" line and BEFORE the comment box before unittest(), and check that\n\t\teverything still includes and runs properly:\n\n\t\tusing Random\n\n\t\t#-----------------------------------------------------------------------------------------\n\t\t# Module types:\n\t\t\n\t\t\\\"\"\"\n\t\t\tCasino\n\t\t\n\t\tA Casino can return arrays of random numbers in the range [0,1), up to a maximum number of rows\n\t\t(maxrows), and a maximum number of columns (maxcols). It also contains a vault of prepared\n\t\trandom numbers from which it draws the arrays.\n\t\t\\\"\"\"\n\t\tstruct Casino\n\t\t\tmaxrows::Int\t\t\t\t\t\t\t# Maximum number of drawable rows\n\t\t\tmaxcols::Int\t\t\t\t\t\t\t# Maximum number of drawable columns\n\t\t\trandomness::Int\t\t\t\t\t\t\t# How randomised will our withdrawals be?\n\t\t\tvault::Matrix\t\t\t\t\t\t\t# Repository of random numbers in [0,1)\n\t\t\n\t\t\t\"The one and only constructor\"\n\t\t\tfunction Casino(maxrows::Int,maxcols::Int,randomness::Int=5)\n\t\t\t\tnew(\n\t\t\t\t\tmaxrows, maxcols, randomness,\n\t\t\t\t\trand((maxrows+1)*randomness,(maxcols+1)*randomness)\n\t\t\t\t)\n\t\t\tend\n\t\tend\n\t\t\n\t\t#-----------------------------------------------------------------------------------------\n\t\t# Module methods:\n\t\t\"\"\",\n\t\t\"Remember your code won't do anything new yet: just getting it to run is your first goal!\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tRight, now let's activate the new code. Remove the open-comment marker from the third line\n\t\tof unittest() and insert it instead at the beginning of the 7th line:\n\n\t\t#=\tprintln(\"Draw several (2x3) matrices from the casino:\")\n\n\t\tThis reveals the lines 3-6. Test that your program can now correctly create and display a\n\t\tCasino. Now tell me the size of the Casino vault, and think about why I have designed this\n\t\tsize to depend on the constructor argument randomness:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x == (6,8)\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tDid you work out why the size of the vault depends on randomness? The idea is that we\n\t\twant to draw random matricess from the Casino like drawing cards from a shuffled deck of\n\t\tcards, and that is only possible if the deck contains more cards than we actually need.\n\n\t\tNow we'll implement the draw() method. First reveal the next four lines of unittest() and\n\t\tinsert the following dummy code immediately after the \"Module methods:\" comment. Now test\n\t\tthis dummy version of draw to make sure it is robust before moving on:\n\n\t\t\\\"\"\"\n\t\t\tdraw( casino, nrows, ncols)\n\t\t\n\t\tDraw the required number of rows and columns from the casino vault, first ensuring that the\n\t\tvault is large enough to support the withdrawal.\n\t\t\\\"\"\"\n\t\tfunction draw( casino::Casino, nrows::Int, ncols::Int)\n\t\t\tif nrows > casino.maxrows || ncols > casino.maxcols\n\t\t\t\t# Repository is too small - throw exception:\n\t\t\t\terror( \"Requested withdrawal is too large\")\n\t\t\tend\n\t\t\n\t\t\t# Choose random offsets and strides for drawing a matrix of size (nrows x ncols) from\n\t\t\t# the vault, assuming that it is big enough to support the withdrawal:\n\t\t\tones(nrows,ncols)\n\t\tend\n\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow comes the cool part of the code. Notice how we have made absolutely sure that by the\n\t\ttime we get to the dummy line \"ones(nrows,ncols)\", we can rely on the values nrows and\n\t\tncols being small enough to be able to draw our new random matrix. Now replace this\n\t\tdummy line by the following code and test it:\n\n\t\t\tvaultrows, vaultcols = size(casino.vault)\n\t\t\toffset_r = rand( 1 : (vaultrows-nrows))\n\t\t\tstride_r = (nrows <= 1) ? 1 :\n\t\t\t\t\t\t\trand( 1 : (vaultrows-offset_r) \u00f7 (nrows-1))\n\t\t\toffset_c = rand( 1 : (vaultcols-ncols))\n\t\t\tstride_c = (ncols <= 1) ? 1 :\n\t\t\t\t\t\t\trand( 1 : (vaultcols-offset_c) \u00f7 (ncols-1))\n\t\t\n\t\t\t# Return a randomly chosen table of slices from the vault:\n\t\t\tcasino.vault[\n\t\t\t\t(offset_r : stride_r : (offset_r + (nrows-1)*stride_r)),\n\t\t\t\t(offset_c : stride_c : (offset_c + (ncols-1)*stride_c))\n\t\t\t]\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tFinally, we'll now give Casinos the functionality of shuffling. We want users to be\n\t\table to shuffle the contents of the vault to give new random values. Reveal all lines\n\t\tof code in unittest(), insert the command \"using Random\" at the beginning of the Casinos\n\t\tmodule - immediately after the line \"module Casinos\", then insert the following code after\n\t\tdraw() in Casinos. Then test the module again:\n\n\t\t#-----------------------------------------------------------------------------------------\n\t\t\\\"\"\"\n\t\t\tshuffle!( casino)\n\n\t\tReassign random values in the vault.\n\t\t\\\"\"\"\n\t\tfunction shuffle!( casino::Casino)\n\t\t\trand!( casino.vault)\n\t\tend\n\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tCongratulations! You have written your first Julia module! You can test its functionality\n\t\tfor yourself by doing something like this at the Julia prompt:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\t\tcasinos = Casinos.Casino(3,3,5)\n\t\tCasinos.draw(casino,3,3)\n\n\t\tMake sure you also test error cases like this:\n\n\t\tCasinos.draw(casino,7,9)\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tThere is just one small thing we can do to make life easier for users of the Casinos\n\t\tmodule: We shall expose the new functionality. After all, it's a pain to have to write\n\t\t\"Casinos.\" in front of every command. To avoid this, insert the following lines between\n\t\tthe line \"module Casinos\" and the line \"using Random\":\n\n\t\t# Externally callable methods of Casinos\n\t\texport Casino, draw, shuffle!\n\t\t\n\t\tNow reinclude Casinos.jl and repeat your tests from the previous activity, but this time\n\t\tfirst enter \"using .Casinos\" at the Julia prompt. Can you now call all the Casinos\n\t\tfunctionality without typing \"Casinos.\" in front of everything?\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> occursin('y',lowercase(x))\n\t),\n]", "meta": {"hexsha": "8fb9c3f7de13714476b2d970238be6c3fe9b3503", "size": 13831, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Labs/INLab007.jl", "max_stars_repo_name": "cspsych/Ingolstadt.jl", "max_stars_repo_head_hexsha": "204cf94397b83994ae70a1ce4984fe648512bb6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Labs/INLab007.jl", "max_issues_repo_name": "cspsych/Ingolstadt.jl", "max_issues_repo_head_hexsha": "204cf94397b83994ae70a1ce4984fe648512bb6c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Labs/INLab007.jl", "max_forks_repo_name": "cspsych/Ingolstadt.jl", "max_forks_repo_head_hexsha": "204cf94397b83994ae70a1ce4984fe648512bb6c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0665024631, "max_line_length": 97, "alphanum_fraction": 0.6754392307, "num_tokens": 3637, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2689414213699951, "lm_q2_score": 0.28457600421652673, "lm_q1q2_score": 0.07653427506178642}}
{"text": "module SVHN2_Tests\nusing Test\nusing ColorTypes\nusing ImageCore\nusing FixedPointNumbers\nusing MLDatasets\nusing DataDeps\nusing MAT\n\n@testset \"Constants\" begin\n    @test SVHN2.classnames() isa Vector{Int}\n    @test SVHN2.classnames() == [1,2,3,4,5,6,7,8,9,0]\n    @test length(SVHN2.classnames()) == 10\n    @test length(unique(SVHN2.classnames())) == 10\n\n    @test DataDeps.registry[\"SVHN2\"] isa DataDeps.DataDep\nend\n\n@testset \"convert2images\" begin\n    @test_throws DimensionMismatch SVHN2.convert2image(rand(100))\n    @test_throws DimensionMismatch SVHN2.convert2image(rand(228,1))\n    @test_throws DimensionMismatch SVHN2.convert2image(rand(32,32,4))\n\n    data = rand(N0f8,32,32,3)\n    A = SVHN2.convert2image(data)\n    @test size(A) == (32,32)\n    @test eltype(A) == RGB{N0f8}\n    @test SVHN2.convert2image(vec(data)) == A\n    @test permutedims(channelview(A), (3, 2, 1)) == data\n    @test SVHN2.convert2image(reinterpret(UInt8, data)) == A\n\n    data = rand(N0f8,32,32,3,2)\n    A = SVHN2.convert2image(data)\n    @test size(A) == (32,32,2)\n    @test eltype(A) == RGB{N0f8}\n    @test SVHN2.convert2image(vec(data)) == A\n    @test SVHN2.convert2image(reinterpret(UInt8, data)) == A\nend\n\n# NOT executed on CI. only executed locally.\n# This involves dataset download etc.\nif parse(Bool, get(ENV, \"CI\", \"false\"))\n    @info \"CI detected: skipping dataset download\"\nelse\n    data_dir = withenv(\"DATADEPS_ALWAY_ACCEPT\"=>\"true\") do\n        datadep\"SVHN2\"\n    end\n\n    @testset \"Images\" begin\n        X_train = @inferred SVHN2.traintensor()\n        X_test  = @inferred SVHN2.testtensor()\n        X_extra = @inferred SVHN2.extratensor()\n        @test size(X_train, 4) == 73_257\n        @test size(X_test,  4) == 26_032\n        @test size(X_extra, 4) == 531_131\n\n        # Sanity check that the first trainimage is not the\n        # first testimage nor extra image\n        @test X_train[:,:,:,1] != X_test[:,:,:,1]\n        @test X_train[:,:,:,1] != X_extra[:,:,:,1]\n        @test X_test[:,:,:,1]  != X_extra[:,:,:,1]\n        # Make sure other integer types work as indicies\n        @test SVHN2.testtensor(0xBAE) == SVHN2.testtensor(2990)\n\n        # These tests check if the functions return internaly\n        # consistent results for different parameters (e.g. index\n        # as int or as vector). That means no matter how you\n        # specify an index, you will always get the same result\n        # for a specific index.\n        for (image_fun, T, nimages) in (\n                (SVHN2.testtensor, UInt8,   26_032),\n                (SVHN2.testtensor, Int,     26_032),\n                (SVHN2.testtensor, Float64, 26_032),\n                (SVHN2.testtensor, Float32, 26_032),\n                (SVHN2.testtensor, N0f8,    26_032),\n            )\n            @testset \"$image_fun with T=$T\" begin\n                # whole image set\n                A = @inferred image_fun(T)\n                @test typeof(A) <: Union{Array{T,4},Base.ReinterpretArray{T,4}}\n                @test size(A) == (32,32,3,nimages)\n\n                @test_throws BoundsError image_fun(T,-1)\n                @test_throws BoundsError image_fun(T,0)\n                @test_throws BoundsError image_fun(T,nimages+1)\n\n                @testset \"load single images\" begin\n                    # Sample a few random images to compare\n                    for i = rand(1:nimages, 3)\n                        A_i = @inferred image_fun(T,i)\n                        @test typeof(A_i) <: Union{Array{T,3},Base.ReinterpretArray{T,3}}\n                        @test size(A_i) == (32,32,3)\n                        @test A_i == A[:,:,:,i]\n                    end\n                end\n\n                @testset \"load multiple images\" begin\n                    A_5_10 = @inferred image_fun(T,5:10)\n                    @test typeof(A_5_10) <: Union{Array{T,4},Base.ReinterpretArray{T,4}}\n                    @test size(A_5_10) == (32,32,3,6)\n                    for i = 1:6\n                        @test A_5_10[:,:,:,i] == A[:,:,:,i+4]\n                    end\n\n                    # also test edge cases `1`, `nimages`\n                    indices = [10,3,9,1,nimages]\n                    A_vec   = image_fun(T,indices)\n                    A_vec_f = image_fun(T,Vector{Int32}(indices))\n                    @test typeof(A_vec) <: Union{Array{T,4},Base.ReinterpretArray{T,4}}\n                    @test typeof(A_vec_f) <: Union{Array{T,4},Base.ReinterpretArray{T,4}}\n                    @test size(A_vec)   == (32,32,3,5)\n                    @test size(A_vec_f) == (32,32,3,5)\n                    for i in 1:5\n                        @test A_vec[:,:,:,i] == A[:,:,:,indices[i]]\n                        @test A_vec[:,:,:,i] == A_vec_f[:,:,:,i]\n                    end\n                end\n            end\n        end\n    end\n\n    @testset \"Labels\" begin\n        # Sanity check that the first trainlabel is not also\n        # the first testlabel\n        @test SVHN2.trainlabels(1) != SVHN2.testlabels(1)\n        @test SVHN2.trainlabels(1) != SVHN2.extralabels(1)\n        @test SVHN2.testlabels(1) != SVHN2.extralabels(1)\n\n        # Check a few hand picked examples. I looked at both the\n        # pictures and the native output to make sure these\n        # values are correspond to the image at the same index.\n        @test SVHN2.trainlabels(1) === 1\n        @test SVHN2.trainlabels(2) === 9\n        @test SVHN2.trainlabels(1337) === 2\n        @test SVHN2.trainlabels(0xCAF) === 3\n        @test SVHN2.trainlabels(73_257) === 9\n        @test SVHN2.testlabels(1) === 5\n        @test SVHN2.testlabels(4) === 10\n        @test SVHN2.testlabels(0xDAD) === 4\n        @test SVHN2.testlabels(26_032) === 7\n        @test SVHN2.extralabels(1) === 4\n        @test SVHN2.extralabels(3) === 8\n        @test SVHN2.extralabels(531_131) === 4\n\n        # These tests check if the functions return internaly\n        # consistent results for different parameters (e.g. index\n        # as int or as vector). That means no matter how you\n        # specify an index, you will always get the same result\n        # for a specific index.\n        # -- However, technically these tests do not check if\n        #    these are the actual SVHN labels of that index!\n        for (label_fun, nlabels) in\n                    ((SVHN2.trainlabels, 73_257),\n                     (SVHN2.testlabels,  26_032),\n                     (SVHN2.extralabels, 531_131))\n            @testset \"$label_fun\" begin\n                # whole label set\n                A = @inferred label_fun()\n                @test typeof(A) <: Vector{Int64}\n                @test size(A) == (nlabels,)\n\n                @testset \"load single label\" begin\n                    # Sample a few random labels to compare\n                    for i = rand(1:nlabels, 10)\n                        A_i = @inferred label_fun(i)\n                        @test typeof(A_i) <: Int64\n                        @test A_i == A[i]\n                    end\n                end\n\n                @testset \"load multiple labels\" begin\n                    A_5_10 = @inferred label_fun(5:10)\n                    @test typeof(A_5_10) <: Vector{Int64}\n                    @test size(A_5_10) == (6,)\n                    for i = 1:6\n                        @test A_5_10[i] == A[i+4]\n                    end\n\n                    # also test edge cases `1`, `nlabels`\n                    indices = [10,3,9,1,nlabels]\n                    A_vec   = @inferred label_fun(indices)\n                    A_vec_f = @inferred label_fun(Vector{Int32}(indices))\n                    @test typeof(A_vec)   <: Vector{Int64}\n                    @test typeof(A_vec_f) <: Vector{Int64}\n                    @test size(A_vec)   == (5,)\n                    @test size(A_vec_f) == (5,)\n                    for i in 1:5\n                        @test A_vec[i] == A[indices[i]]\n                        @test A_vec[i] == A_vec_f[i]\n                    end\n                end\n            end\n        end\n    end\n\n    # Check against the already tested tensor and labels functions\n    @testset \"Data\" begin\n        for (data_fun, feature_fun, label_fun, nobs) in\n                 ((SVHN2.testdata,  SVHN2.testtensor,  SVHN2.testlabels,  26_032),)\n            @testset \"check $data_fun against $feature_fun and $label_fun\" begin\n                data, labels = @inferred data_fun()\n                @test data == @inferred feature_fun()\n                @test labels == @inferred label_fun()\n\n                for i = rand(1:nobs, 10)\n                    d_i, l_i = @inferred data_fun(i)\n                    @test d_i == @inferred feature_fun(i)\n                    @test l_i == @inferred label_fun(i)\n                end\n\n                data, labels = @inferred data_fun(5:10)\n                @test data == @inferred feature_fun(5:10)\n                @test labels == @inferred label_fun(5:10)\n\n                data, labels = @inferred data_fun(Int, 5:10)\n                @test data == @inferred feature_fun(Int, 5:10)\n                @test labels == @inferred label_fun(5:10)\n\n                indices = [10,3,9,1,nobs]\n                data, labels = @inferred data_fun(indices)\n                @test data == @inferred feature_fun(indices)\n                @test labels == @inferred label_fun(indices)\n            end\n        end\n    end\nend\n\nend\n", "meta": {"hexsha": "da0e92ce0f6fe1198dcf78f973f656215fc8c14e", "size": 9232, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/tst_svhn2.jl", "max_stars_repo_name": "andrew-saydjari/MLDatasets.jl", "max_stars_repo_head_hexsha": "f6ac2c2e33d45e0da821a0d4987a826d1ea00d57", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 171, "max_stars_repo_stars_event_min_datetime": "2016-11-15T03:58:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-17T19:00:31.000Z", "max_issues_repo_path": "test/tst_svhn2.jl", "max_issues_repo_name": "andrew-saydjari/MLDatasets.jl", "max_issues_repo_head_hexsha": "f6ac2c2e33d45e0da821a0d4987a826d1ea00d57", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 79, "max_issues_repo_issues_event_min_datetime": "2016-09-30T16:24:29.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-27T19:24:37.000Z", "max_forks_repo_path": "test/tst_svhn2.jl", "max_forks_repo_name": "andrew-saydjari/MLDatasets.jl", "max_forks_repo_head_hexsha": "f6ac2c2e33d45e0da821a0d4987a826d1ea00d57", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 46, "max_forks_repo_forks_event_min_datetime": "2016-10-22T00:55:07.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T18:57:22.000Z", "avg_line_length": 40.3144104803, "max_line_length": 89, "alphanum_fraction": 0.5240467938, "num_tokens": 2533, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39233683016710835, "lm_q2_score": 0.19436782035217448, "lm_q1q2_score": 0.0762576545234621}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.7\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 845202ce-23d9-11eb-085e-ad2c26fd531c\nbegin\n\tusing TextAnalysis\n\tusing CSV\n\tusing DataFrames\nend\n\n# \u2554\u2550\u2561 cac12010-23d8-11eb-0321-3fc66858fc84\nmd\"# Authorship Analysis using TextAnalysis.jl\n\nIn this notebook we will implement an authorship analysis technique that uses n-gram frequencies for identifying authors. This technique is described in [Grieve 2018](https://research.birmingham.ac.uk/portal/files/53402456/Bixby_PREPRINT.pdf) The technique counts token and character n-grams and compares them to a reference corpus. It counts the number of overlapping n-grams in the reference corpus and identifies the author based on which reference corpus contains more of the n-grams from the text.\"\n\n# \u2554\u2550\u2561 f400d820-23d9-11eb-091a-898d693456ad\nmd\"First off, the necessary imports are made.\"\n\n# \u2554\u2550\u2561 c9d140dc-2463-11eb-06a2-2d0dcb8dbed2\nmd\"Our data will be from [Spooky Author Identification](https://www.kaggle.com/c/spooky-author-identification/overview) Kaggle contest. The data comes in the form of a CSV file which contains sentences attributed to one of three authors. The data was manualy cleaned to remove rows that were corropted as well as making sure that each author had approximately equal word counts.\n\nThe data is read into a dataframe after which we iterate over the df and turn each sentence into a StringDocument which is then accumalted into a Corpus for each author.\"\n\n# \u2554\u2550\u2561 68255d02-23da-11eb-3232-613c0e1b0ee2\nreferenceData = CSV.read(\"train.csv\", DataFrame);\n\n# \u2554\u2550\u2561 c8b1e874-23e0-11eb-1aad-995c03f040ef\nbegin\n\tMWS = GenericDocument[]\n\tEAP = GenericDocument[]\n\tHPL = GenericDocument[]\n\t\n\tfor line in eachrow(referenceData)\n\t\tif line.author == \"MWS\"\n\t\t\tpush!(MWS, StringDocument(line.text))\n\t\telseif line.author == \"EAP\"\n\t\t\tpush!(EAP, StringDocument(line.text))\n\t\telse\n\t\t\tpush!(HPL, StringDocument(line.text))\n\t\tend\n\tend\n\t\n\tMWS_corpus = Corpus(MWS)\n\tEAP_corpus = Corpus(EAP)\n\tHPL_corpus = Corpus(HPL);\nend\n\n# \u2554\u2550\u2561 60390844-2472-11eb-010b-d155abe74997\nmd\"The following code snippet allows one to retrieve the word count of a corpus. The word counts are as follows:\n- MWS_corpus: 147914\n- EAP_corpus: 147911\n- HPL_corpus: 147916\"\n\n# \u2554\u2550\u2561 7b9ce77e-23e4-11eb-39d4-03e26b33cad7\nbegin\n\twordCount = 0\n\tfor doc in HPL_corpus\n\t\tglobal wordCount += length(ngrams(doc, 1))\n\tend\nend\n\n# \u2554\u2550\u2561 43a58408-246f-11eb-29ab-91cc00373c2a\nwordCount\n\n# \u2554\u2550\u2561 96f5d60a-248b-11eb-2795-a51a9fbf3fc0\nmd\"Now that we have our corpora it is time to extract the ngrams that are needed for the analysis. The original paper used both character ngrams and token ngrams. Due to TextAnalysis.jl's functionality I will be restricting this analysis to only using token ngrams.\"\n\n# \u2554\u2550\u2561 95691ad4-2472-11eb-346e-07a9f5dc8f6c\nbegin\n\tMWS_ngrams = []\n\tHPL_ngrams = []\n\tEAP_ngrams = []\n\tfor i in 1:4\n\t\tpush!(MWS_ngrams, map(x -> ngrams(x,i), MWS_corpus))\n\t\tpush!(HPL_ngrams, map(x -> ngrams(x,i), HPL_corpus))\n\t\tpush!(EAP_ngrams, map(x -> ngrams(x,i), EAP_corpus))\n\tend\nend\n\n# \u2554\u2550\u2561 e207949e-2526-11eb-3ae4-0712b7082681\nmd\"We also retrieve the token ngrams from the files we want to analyze and classify.\"\n\n# \u2554\u2550\u2561 62443a4c-2525-11eb-0e70-393860da7b4f\nbegin\n\t# first we load our files from memory\n\ttext1 = FileDocument(\"./MWS.txt\")\n\ttext2 = FileDocument(\"./HPL.txt\")\n\ttext3 = FileDocument(\"./EAP.txt\")\n\t\n\t# this is the same as retrieveing ngrams for the reference corpora\n\ttext1_ngrams = []\n\ttext2_ngrams = []\n\ttext3_ngrams = []\n\tfor i in 1:4\n\t\tpush!(text1_ngrams, ngrams(text1, i))\n\t\tpush!(text2_ngrams, ngrams(text2, i))\n\t\tpush!(text3_ngrams, ngrams(text3, i))\n\tend\nend\n\n# \u2554\u2550\u2561 32909f1e-2527-11eb-0efd-6d81943df28e\nmd\"Finally we will a script that calculates the intersection of the ngrams from the text and the three reference corpora. To try different files edit the second for loop.\"\n\n# \u2554\u2550\u2561 5d1320ee-2479-11eb-3664-c33f5ace1ba7\nbegin\n\tMWS_count = 0\n\tHPL_count = 0\n\tEAP_count = 0\n\tfor i in 1:4\n\t\tfor j in text1_ngrams[i]\n\t\t\tfor k in MWS_ngrams[i]\n\t\t\t\tif haskey(k, j[1])\n\t\t\t\t\tglobal MWS_count += 1\n\t\t\t\t\tbreak\n\t\t\t\tend\n\t\t\tend\n\t\t\tfor k in HPL_ngrams[i]\n\t\t\t\tif haskey(k, j[1])\n\t\t\t\t\tglobal HPL_count += 1\n\t\t\t\t\tbreak\n\t\t\t\tend\n\t\t\tend\n\t\t\tfor k in EAP_ngrams[i]\n\t\t\t\tif haskey(k, j[1])\n\t\t\t\t\tglobal EAP_count += 1\n\t\t\t\t\tbreak\n\t\t\t\tend\n\t\t\tend\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 16a5c100-252a-11eb-1258-b769d50904d3\nmd\"The following block will output the author as identified by this script.\nThe correct authors should be as follows:\n- text1 = MWS\n- text2 = HPL\n- text3 = EAP\"\n\n# \u2554\u2550\u2561 df84c6ee-247a-11eb-24f9-e92653c334bb\nbegin\n\tif MWS_count > HPL_count && MWS_count > EAP_count\n\t\t\"MWS\"\n\telseif HPL_count > MWS_count && HPL_count > EAP_count\n\t\t\"HPL\"\n\telse\n\t\t\"EAP\"\n\tend\nend\n\n# \u2554\u2550\u2561 34de486c-252b-11eb-2071-f12f0f445d9f\nmd\"## Conclusion\nWhile this is a very watered down analysis of what the paper presents it is a good starting point and shows that this technique is viable as it correctly predicts the authors of our random test data. One way to improve the current analysis is to add character ngrams as the paper does.\n\nAll in all this was a fun project to learn more about Julia and its NLP cabalities. This project only scratches the surface of what Julia has to offer given that the TextAnalysis.jl package offers things such as part-of-speech(POS) tagging, tf-idf measures, a sentiment analysis model, and much more.\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500cac12010-23d8-11eb-0321-3fc66858fc84\n# \u255f\u2500f400d820-23d9-11eb-091a-898d693456ad\n# \u2560\u2550845202ce-23d9-11eb-085e-ad2c26fd531c\n# \u255f\u2500c9d140dc-2463-11eb-06a2-2d0dcb8dbed2\n# \u2560\u255068255d02-23da-11eb-3232-613c0e1b0ee2\n# \u2560\u2550c8b1e874-23e0-11eb-1aad-995c03f040ef\n# \u255f\u250060390844-2472-11eb-010b-d155abe74997\n# \u2560\u25507b9ce77e-23e4-11eb-39d4-03e26b33cad7\n# \u2560\u255043a58408-246f-11eb-29ab-91cc00373c2a\n# \u255f\u250096f5d60a-248b-11eb-2795-a51a9fbf3fc0\n# \u2560\u255095691ad4-2472-11eb-346e-07a9f5dc8f6c\n# \u255f\u2500e207949e-2526-11eb-3ae4-0712b7082681\n# \u2560\u255062443a4c-2525-11eb-0e70-393860da7b4f\n# \u255f\u250032909f1e-2527-11eb-0efd-6d81943df28e\n# \u2560\u25505d1320ee-2479-11eb-3664-c33f5ace1ba7\n# \u255f\u250016a5c100-252a-11eb-1258-b769d50904d3\n# \u2560\u2550df84c6ee-247a-11eb-24f9-e92653c334bb\n# \u255f\u250034de486c-252b-11eb-2071-f12f0f445d9f\n", "meta": {"hexsha": "17ee0f4c4eaed59de18824e270fed658999bcd44", "size": 6092, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Week2-TextAnalysis.jl/laurens777/notebook.jl", "max_stars_repo_name": "Humans-of-Julia/Challenges", "max_stars_repo_head_hexsha": "9efd04b90110e4c19456a6a75c3f137d613c92fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-11-15T04:08:32.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-15T08:01:32.000Z", "max_issues_repo_path": "Week2-TextAnalysis.jl/laurens777/notebook.jl", "max_issues_repo_name": "Humans-of-Julia/WeeklyContest", "max_issues_repo_head_hexsha": "9efd04b90110e4c19456a6a75c3f137d613c92fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Week2-TextAnalysis.jl/laurens777/notebook.jl", "max_forks_repo_name": "Humans-of-Julia/WeeklyContest", "max_forks_repo_head_hexsha": "9efd04b90110e4c19456a6a75c3f137d613c92fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-05-09T18:10:26.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-09T18:10:26.000Z", "avg_line_length": 34.0335195531, "max_line_length": 503, "alphanum_fraction": 0.7465528562, "num_tokens": 2241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.15203224354320868, "lm_q1q2_score": 0.07601612177160434}}
{"text": "export MultiStart, n_points, n_params, optimize\n\n\"\"\"\n\t$(SIGNATURES)\n\nContains information to run a multistart optimization.\n\n# Fields\n\n- optFct: \n    Function to be optimized. Signature:\n    `solnV, fVal, exitFlag = optFct(guessV)`\n- guessesV:\n    Vector of guesses. Each guess is a Vector{Float64}\n- historyFn:\n    Full path where optimization history will be stored.\n- fTol, fTolN:\n    Stop if fVal improves by less than `fTol` over the last `fTolN` points\n    This can terminate early.\n\"\"\"\nmutable struct MultiStart{F, U}\n    optFct :: F\n    updateRule :: U\n    guessesV :: Vector{Vector{Float64}}\n    historyFn :: String\n    maxHours :: Float64\n    fTol :: Float64\n    fTolN :: Integer\nend\n\n\"\"\"\n\t$(SIGNATURES)\n\nNumber of points to be evaluated.\n\"\"\"\nn_points(m :: MultiStart{F, U}) where {F, U} = length(m.guessesV);\n\n\"\"\"\n\t$(SIGNATURES)\n\nNumber of calibrated parameters in each point.\n\"\"\"\nn_params(m :: MultiStart{F, U}) where {F, U} = \n    length(first(m.guessesV));\n\n\n\"\"\"\n\t$(SIGNATURES)\n\nRun the multistart optimization.\n\"\"\"\nfunction optimize(m :: MultiStart{F, U}, \n        io = stdout) where {F, U}\n\n    log_msg(1, io, start_header(m));\n    startTime = Dates.now();\n    h = init_history(m.historyFn, m.guessesV);\n\n    done = false;\n    j = 0;\n    while !done\n        j += 1;\n        newGuessV = new_guess(m.updateRule, h, j);\n        log_msg(2, io, point_start_header(j));\n        # startTime = Dates.now();\n        newPoint = m.optFct(newGuessV);\n        # endTime = Dates.now();\n        log_msg(2, io, finish_point_header(j, newPoint));\n        add_to_history!(h, j, newPoint);\n        save_history(h);\n        continue_run(m, h, j, startTime; io)  ||  (done = true);\n    end\n    bestPoint = best_point(h);\n    log_msg(1, io, finish_header(bestPoint, startTime, n_points(m)));\n    return bestPoint.solnV, bestPoint.fVal, bestPoint.exitFlag\nend\n\n\n\"\"\"\n\t$(SIGNATURES)\n\nDecide whether to continue running at the end of iteration `j`.\nReport reason for termination.\n\"\"\"\nfunction continue_run(m :: MultiStart{F,U}, h :: History,\n            j, startTime; io = stdout) where {F,U}\n    cont = true;\n    if reached_max_time(startTime, m.maxHours)\n        log_msg(3, io, \"Max time reached.\");\n        cont = false;\n    end\n    if j >= n_points(m)\n        log_msg(3, io, \"All points evaluated.\");\n        cont = false;\n    end\n    if j > m.fTolN\n        fDiff = fval_improvement(h, j, m.fTolN);\n        if fDiff < m.fTol\n            fDiffStr = round(fDiff; digits = 2);\n            log_msg(3, io, \"Difference in fVal below fTol: $fDiffStr < $(m.fTol)\");\n            cont = false;\n        end\n    end\n    return cont\nend\n\n# function fval_improvement_too_small(m :: MultiStart{F,U}, h :: History, \n#         j) where {F,U}\n#     if j > m.\n#         return fval_improvement(h, j, n) < m.fTol;\n#     else\n#         return false\n#     end\n# end\n\n# Improvement during last `n` points relative to best point of previous history.\nfunction fval_improvement(h :: History, j :: Integer, n :: Integer)\n    @assert (j > n)  \"Do not have past $n points with history of length $j\";\n    fValV = f_values(h);\n    fPast = minimum(fValV[1 : (j-n)]);\n    fNew = minimum(fValV[(j-n+1) : j]);\n    fDiff = fPast - fNew;\n    return fDiff\nend\n\n\nreached_max_time(startTime, maxHours :: Float64) = \n    seconds_elapsed(startTime, Dates.now()) / 3600 >= maxHours;\n\nseconds_elapsed(startTime, endTime) = Dates.value(endTime - startTime) / 1000;\n\n\n# +++++ move to general purpose pkg\nfunction split_duration(dSeconds :: Integer)\n    dHours, hRem = divrem(dSeconds, 3600);\n    dMin, mRem = divrem(hRem, 60);\n    dSec = round(Int, mRem);\n    return dHours, dMin, dSec\nend\n\nfunction format_duration(dSeconds :: Integer)\n    dHours, dMin, dSec = split_duration(dSeconds);\n    d = DateTime(2020,1,1,dHours,dMin,dSec);\n    if dHours > 0\n        return Dates.format(d, \"HH:MM\") * \" hr\";\n    elseif dMin > 0\n        return Dates.format(d, \"MM:SS\") * \" min\";\n    else\n        return Dates.format(d, \"SS\") * \" sec\";\n    end\nend\n\n\n## -----------  Headers to display\n\nstart_header(m :: MultiStart{F, U}) where {F,U} = \n    \"Multistart with $(n_points(m)) points begins at $(format_time())\";\n\npoint_start_header(j :: Integer) = \n    \"Starting point $j at $(format_time())\";\n\nfinish_point_header(j :: Integer, p :: Point{F}) where F = \n    \"Point $j completed at $(format_time()) with [fVal $(round(p.fVal, digits = 3))]  and  [exitFlag $(p.exitFlag)]\";\n\nfunction finish_header(bestPoint :: Point{F}, startTime, nPoints) where F\n    avgTime = round(Int, average_time(startTime, Dates.now(), nPoints));\n    avgTimeStr = format_duration(avgTime);\n    return \"MultiStart completed at $(format_time()) \\n  fVal $(round(bestPoint.fVal, digits = 3)),  exitFlag $(bestPoint.exitFlag) \\n  Average time per point: $(avgTimeStr).\"\nend\n\nformat_time(d = Dates.now()) = Dates.format(d, \"u-d, HH:MM\");\n\naverage_time(dStart, dStop, n) = seconds_elapsed(dStart, dStop) / n;\n\n\n# -------------", "meta": {"hexsha": "6cfc45c6d0e33dc98d8c24adbd33d8042044b86a", "size": 4929, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/multistart.jl", "max_stars_repo_name": "hendri54/MultiStartLH", "max_stars_repo_head_hexsha": "456f015cac19617c8b5d962d6e13b53fc3875b1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/multistart.jl", "max_issues_repo_name": "hendri54/MultiStartLH", "max_issues_repo_head_hexsha": "456f015cac19617c8b5d962d6e13b53fc3875b1b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/multistart.jl", "max_forks_repo_name": "hendri54/MultiStartLH", "max_forks_repo_head_hexsha": "456f015cac19617c8b5d962d6e13b53fc3875b1b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0056818182, "max_line_length": 175, "alphanum_fraction": 0.6275106512, "num_tokens": 1439, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4493926344647597, "lm_q2_score": 0.16885695841556153, "lm_q1q2_score": 0.07588307339007556}}
{"text": "#=\nCreated on Friday 4 December 2020\nLast update: Monday 27 September 2021\n\n@author: Bram De Jaegher\nbram.de.jaegher@gmail.com\n\nTemplates heavily based on the MIT course \"Computational Thinking\"\n\nhttps://computationalthinking.mit.edu/\n=#\n\n\ncorrect(text=rand(yays)) = MD(Admonition(\"correct\", \"Got it!\", [text]))\nkeep_working(text=md\"The answer is not quite right.\") = MD(Admonition(\"danger\", \"Keep working on it!\", [text]))\t\t\t\nnot_defined(variable_name) = MD(Admonition(\"danger\", \"Oopsie!\", [md\"Make sure that you define a variable called **$(Markdown.Code(string(variable_name)))**\"]))\n\nyays = [md\"Great!\", md\"Yay \u2764\", md\"Great! \ud83c\udf89\", md\"Well done!\",\n        md\"Keep it up!\", md\"Good job!\", md\"Awesome!\", md\"You got the right answer!\",\n\t\t\t\tmd\"Let's move on to the next section.\"]\n\t\t\t\t\nhint(text) = Markdown.MD(Markdown.Admonition(\"hint\", \"Hint\", [text]))\nstill_missing(text=md\"Replace `missing` with your answer.\") = MD(Admonition(\"warning\", \"Here we go!\", [text]))\n\nfyi(text) = Markdown.MD(\n\tMarkdown.Admonition(\"info\",\n\t\t\"Additional info\",\n\t\t[fyi_css,\n\t\t\ttext\n\t\t]\n\t)\n)\n", "meta": {"hexsha": "11bf6814e92d16139423fe33a3189fd68eea08c1", "size": 1069, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/admonition.jl", "max_stars_repo_name": "Beramos/PlutoTeaching", "max_stars_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/admonition.jl", "max_issues_repo_name": "Beramos/PlutoTeaching", "max_issues_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/admonition.jl", "max_forks_repo_name": "Beramos/PlutoTeaching", "max_forks_repo_head_hexsha": "1258726411d5043dfc9ede78edaaf5fb08be52d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3939393939, "max_line_length": 159, "alphanum_fraction": 0.6838166511, "num_tokens": 309, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4378234844434674, "lm_q2_score": 0.17328821019878957, "lm_q1q2_score": 0.07586964800220605}}
{"text": "# # Using custom structs\n#\n# This tutorial shows how to use custom structs on the GPU. Our example will be a one dimensional\n# interpolation. Lets start with the CPU version:\nusing CUDA\n\nstruct Interpolate{A}\n    xs::A\n    ys::A\nend\n\nfunction (itp::Interpolate)(x)\n    i = searchsortedfirst(itp.xs, x)\n    i = clamp(i, firstindex(itp.ys), lastindex(itp.ys))\n    @inbounds itp.ys[i]\nend\n\nxs_cpu = [1.0, 2.0, 3.0]\nys_cpu = [10.0,20.0,30.0]\nitp_cpu = Interpolate(xs_cpu, ys_cpu)\npts_cpu = [1.1,2.3]\nresult_cpu = itp_cpu.(pts_cpu)\n\n# Ok the CPU code works, let's move our data to the GPU:\nitp = Interpolate(CuArray(xs_cpu), CuArray(ys_cpu))\npts = CuArray(pts_cpu);\n# If we try to call our interpolate `itp.(pts)`, we get an error however:\n# ```\n# ...\n# KernelError: passing and using non-bitstype argument\n# ...\n# ```\n# Why does it throw an error? Our calculation involves\n# a custom type `Interpolate{CuArray{Float64, 1}}`.\n# At the end of the day all arguments of a CUDA kernel need to\n# be bitstypes. However we have\nisbitstype(typeof(itp))\n# How to fix this? The answer is, that there is a conversion mechanism, which adapts objects into\n# CUDA compatible bitstypes.\n# It is based on the [Adapt.jl](https://github.com/JuliaGPU/Adapt.jl) package and basic types like `CuArray` already participate in this mechanism. For custom types,\n# we just need to add a conversion rule like so:\nimport Adapt\nfunction Adapt.adapt_structure(to, itp::Interpolate)\n    xs = Adapt.adapt_structure(to, itp.xs)\n    ys = Adapt.adapt_structure(to, itp.ys)\n    Interpolate(xs, ys)\nend\n# Now our struct plays nicely with CUDA.jl:\nresult = itp.(pts)\n# It works, we get the same result as on the CPU.\n@assert CuArray(result_cpu) == result\n# Alternatively instead of defining `Adapt.adapt_structure` explictly, we could have done\n# ```julia\n# Adapt.@adapt_structure Interpolate\n# ```\n# which expands to the same code that we wrote manually.\n", "meta": {"hexsha": "0646aaf75db374b6f51f3214a76d671cd4d6eed7", "size": 1914, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/src/tutorials/custom_structs.jl", "max_stars_repo_name": "margaeor/CUDA.jl", "max_stars_repo_head_hexsha": "4e484350ef50543aa5e7be84c197c9c98fddf682", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 784, "max_stars_repo_stars_event_min_datetime": "2015-01-14T19:08:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T15:24:05.000Z", "max_issues_repo_path": "docs/src/tutorials/custom_structs.jl", "max_issues_repo_name": "margaeor/CUDA.jl", "max_issues_repo_head_hexsha": "4e484350ef50543aa5e7be84c197c9c98fddf682", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1328, "max_issues_repo_issues_event_min_datetime": "2015-08-18T00:54:41.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:25:06.000Z", "max_forks_repo_path": "docs/src/tutorials/custom_structs.jl", "max_forks_repo_name": "margaeor/CUDA.jl", "max_forks_repo_head_hexsha": "4e484350ef50543aa5e7be84c197c9c98fddf682", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 185, "max_forks_repo_forks_event_min_datetime": "2015-01-05T00:41:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T08:31:28.000Z", "avg_line_length": 33.5789473684, "max_line_length": 165, "alphanum_fraction": 0.7225705329, "num_tokens": 556, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1500288262426238, "lm_q1q2_score": 0.0750144131213119}}
{"text": "\n# ------------------------------------------------------------------------------------------\n# # \u6570\u636e\u7ed3\u6784\n#\n# \u4e00\u65e6\u6211\u4eec\u5f00\u59cb\u4e00\u6b21\u6027\u5904\u7406\u5927\u91cf\u6570\u636e\uff0c\u91c7\u7528\u6570\u7ec4\uff08array\uff09\u6216\u8005\u5b57\u5178\uff08dictionaries\uff09\u8fd9\u6837\u7684\u6570\u636e\u7ed3\u6784\u4f1a\u6bd4\u5355\u7eaf\u4f9d\u8d56\u666e\u901a\u53d8\u91cf\u8981\u65b9\u4fbf\u5f97\u591a\u3002\n#\n# \u8981\u8bb2\u5230\u7684\u6570\u636e\u7ed3\u6784\uff1a\n# 1. \u5143\u7ec4 Tuples\n# 2. \u5b57\u5178 Dictionaries\n# 3. \u6570\u7ec4 Arrays\n#\n# \u603b\u7684\u6765\u8bf4\uff0c\u5143\u7ec4\u548c\u6570\u7ec4\u90fd\u662f\u5143\u7d20\u7684\u6709\u5e8f\u5e8f\u5217\uff08\u6240\u4ee5\u53ef\u4ee5\u76f4\u63a5\u7d22\u5f15\u5143\u7d20\uff09\u3002\u5b57\u5178\u548c\u6570\u7ec4\u90fd\u662f\u53ef\u53d8\u7c7b\u578b\u3002\n# \u4e0b\u9762\u8fdb\u884c\u8be6\u7ec6\u4e00\u70b9\u7684\u4ecb\u7ecd\uff01\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## \u5143\u7ec4 Tuples\n#\n# \u901a\u8fc7\u7528\u5c0f\u62ec\u53f7 `( )` \u5c06\u4e00\u4e2a\u6709\u5e8f\u96c6\u5408\u62ec\u8d77\u6765\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5143\u7ec4\u3002\n#\n# \u8bed\u6cd5\uff1a\n# ```julia\n# (item1, item2, ...)\n# ```\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals = (\"penguins\", \"cats\", \"sugargliders\")\n\n# ------------------------------------------------------------------------------------------\n# \u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u4e2a\u5143\u7ec4\u91cc\u8fdb\u884c\u7d22\u5f15\uff0c\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals[1]\n\n# ------------------------------------------------------------------------------------------\n# \u4f46\u56e0\u4e3a\u5143\u7ec4\u662f\u4e0d\u53ef\u53d8\u5bf9\u8c61\uff0c\u6211\u4eec\u4e0d\u53ef\u4ee5\u6539\u53d8\u5b83\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals[1] = \"otters\"\n\n# ------------------------------------------------------------------------------------------\n# ## \u547d\u540d\u5143\u7ec4\uff08Named Tuples\uff09\n#\n# \u987e\u540d\u601d\u4e49\uff0c\u547d\u540d\u5143\u7ec4 `NamedTuple` \u5e94\u8be5\u548c\u5143\u7ec4 `Tuple` \u5dee\u4e0d\u591a\uff0c\n# \u53ea\u4e0d\u8fc7\u6bcf\u4e2a\u5143\u7d20\u90fd\u6709\u540d\u5b57\uff01\u547d\u540d\u5143\u7ec4\u7684\u521b\u5efa\u8bed\u6cd5\u662f\u5728\u5143\u7ec4\u91cc\u4f7f\u7528 `=`\uff1a\n#\n# ```julia\n# (name1 = item1, name2 = item2, ...)\n# ```\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals = (bird = \"penguins\", mammal = \"cats\", marsupial = \"sugargliders\")\n\n# ------------------------------------------------------------------------------------------\n# \u5c31\u50cf\u5143\u7ec4 `Tuples`\uff0c\u547d\u540d\u5143\u7ec4 `NamedTuples` \u662f\u6709\u5e8f\u7684\uff0c\u6240\u4ee5\u4e5f\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u83b7\u5f97\u5143\u7d20\uff1a\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals[1]\n\n# ------------------------------------------------------------------------------------------\n# \u547d\u540d\u5143\u7ec4\u8fd8\u6709\u7279\u6b8a\u7684\u901a\u8fc7\u5143\u7d20\u540d\u6765\u8bbf\u95ee\u5143\u7d20\u7684\u65b9\u6cd5\uff1a\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals.bird\n\n# ------------------------------------------------------------------------------------------\n# ## \u5b57\u5178 Dictionaries\n#\n# \u5982\u679c\u4f60\u7684\u6570\u636e\u96c6\u6709\u6620\u5c04\u5173\u7cfb\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u5b57\u5178\u6765\u5b58\u50a8\u6570\u636e\u3002\u901a\u8fc7 `Dict()` \u51fd\u6570\u521d\u59cb\u5316\u5b57\u5178\uff0c\u53ef\u4ee5\u4e3a\u7a7a\u6216\u8005\u6709\u952e\u503c\u5bf9\u3002\n#\n# \u8bed\u6cd5\uff1a\n# ```julia\n# Dict(key1 => value1, key2 => value2, ...)\n# ```\n#\n# \u5178\u578b\u7684\u4f8b\u5b50\u5982\u901a\u8baf\u5217\u8868\uff0c\u5b83\u5c31\u662f\u76f8\u5173\u8054\u7684\u59d3\u540d\u548c\u7535\u8bdd\u53f7\u7801\u3002\n# ------------------------------------------------------------------------------------------\n\nmyphonebook = Dict(\"Jenny\" => \"867-5309\", \"Ghostbusters\" => \"555-2368\")\n\n# ------------------------------------------------------------------------------------------\n# \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6bcf\u5bf9\u59d3\u540d\u548c\u53f7\u7801\u5c31\u662f\u4e00\u4e2a\u952e\u503c\u5bf9\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5bf9\u5e94\u7684\u952e\u6765\u83b7\u53d6Jenny\u7684\u7535\u8bdd\u53f7\u7801\uff08\u503c\uff09\n# ------------------------------------------------------------------------------------------\n\nmyphonebook[\"Jenny\"]\n\n# ------------------------------------------------------------------------------------------\n# \u6211\u4eec\u53ef\u4ee5\u5728\u8fd9\u5b57\u5178\u4e2d\u7ee7\u7eed\u589e\u52a0\u6761\u76ee\n# ------------------------------------------------------------------------------------------\n\nmyphonebook[\"Kramer\"] = \"555-FILK\"\n\n# ------------------------------------------------------------------------------------------\n# \u6765\u77a7\u77a7\u73b0\u5728\u8fd9\u4e2a\u7535\u8bdd\u672c\u957f\u4ec0\u4e48\u6837\uff1a\n# ------------------------------------------------------------------------------------------\n\nmyphonebook\n\n# ------------------------------------------------------------------------------------------\n# \u4f7f\u7528 `pop!` \u51fd\u6570\u53ef\u4ee5\u4ece\u901a\u8baf\u5217\u8868\u4e2d\u5220\u9664 Kramer\uff0c\u5e76\u540c\u65f6\u83b7\u5f97\u4ed6\u7684\u7535\u8bdd\u53f7\u7801\n# ------------------------------------------------------------------------------------------\n\npop!(myphonebook, \"Kramer\")\n\nmyphonebook\n\n# ------------------------------------------------------------------------------------------\n# \u4e0e\u5143\u7ec4\u548c\u6570\u7ec4\u4e0d\u540c\uff0c\u5b57\u5178\u662f\u65e0\u5e8f\u7684\u3002\u6240\u4ee5\u4e0d\u53ef\u901a\u8fc7\u7d22\u5f15\u83b7\u5f97\u5143\u7d20\u3002\n# ------------------------------------------------------------------------------------------\n\nmyphonebook[1]\n\n# ------------------------------------------------------------------------------------------\n# \u4e0a\u9762\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cJulia \u4f1a\u8ba4\u4e3a\u4f60\u60f3\u83b7\u5f97\u952e\u4e3a `1` \u7684\u503c\u3002\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## \u6570\u7ec4 Arrays\n#\n# \u4e0d\u540c\u4e8e\u5143\u7ec4\uff0c\u6570\u7ec4\u662f\u53ef\u53d8\u7684\u3002\u4e0d\u540c\u4e8e\u5b57\u5178\uff0c\u6570\u7ec4\u662f\u6709\u5e8f\u7684\u3002\n#\n# \u4f7f\u7528\u65b9\u62ec\u53f7 `[ ]` \u62ec\u8d77\u5143\u7d20\u96c6\u5408\u6765\u521b\u5efa\u6570\u7ec4\u3002\n#\n# \u8bed\u6cd5\uff1a\n# ```julia\n# [item1, item2, ...]\n# ```\n#\n# \u4e3e\u4e2a\u4f8b\u5b50\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\u6765\u8bb0\u5f55\u6211\u7684\u597d\u53cb\n# ------------------------------------------------------------------------------------------\n\nmyfriends = [\"Ted\", \"Robyn\", \"Barney\", \"Lily\", \"Marshall\"]\n\n# ------------------------------------------------------------------------------------------\n# `Array{String,1}` \u4e2d\u7684 `1` \u8868\u793a\u8fd9\u662f\u4e00\u4e2a\u4e00\u7ef4\u7684\u5411\u91cf\u3002\n# \u4e00\u4e2a `Array{String,2}` \u662f\u4e2a\u4e8c\u7ef4\u77e9\u9635\uff0c\u7b49\u7b49\u3002\n#\n# `Array{String,1}` \u4e2d\u7684 `String` \u662f\u6bcf\u4e2a\u5143\u7d20\u7684\u7c7b\u578b\u3002\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# \u8981\u5b58\u50a8\u4e00\u7cfb\u5217\u6570\u5b57\u7684\u8bdd\n# ------------------------------------------------------------------------------------------\n\nfibonacci = [1, 1, 2, 3, 5, 8, 13]\n\nmixture = [1, 1, 2, 3, \"Ted\", \"Robyn\"]\n\n# ------------------------------------------------------------------------------------------\n# \u83b7\u5f97\u6570\u7ec4\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u6765\u83b7\u5f97\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\u3002\u5982\uff0c\u60f3\u83b7\u5f97 `myfriends` \u4e2d\u7684\u7b2c\u4e09\u4e2a\u6210\u5458\uff0c\u5c31\u53ef\u4ee5\u5199\u6210\n# ------------------------------------------------------------------------------------------\n\nmyfriends[3]\n\n# ------------------------------------------------------------------------------------------\n# \u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u6765\u4fee\u4e2a\u6570\u7ec4\u4e2d\u7684\u5143\u7d20\n# ------------------------------------------------------------------------------------------\n\nmyfriends[3] = \"Baby Bop\"\n\n# ------------------------------------------------------------------------------------------\n#  \u662f\u7684\uff0cJulia \u7684\u7d22\u5f15\u662f\u4ece 1 \u5f00\u59cb\u7684\uff0c\u800c\u4e0d\u50cf Python \u662f\u4ece 0 \u5f00\u59cb\u7684\u3002\n#  \u8fd9\u79cd\u5c0f\u95ee\u9898\u5df2\u7ecf\u6709\u5f88\u591a\u4e89\u8bba\u4e86\u3002\n#\n#  \u6211\u6709\u4e2a\u62e5\u6709 Solomon \u667a\u6167\u7684\u670b\u53cb\u5efa\u8bae\u4f7f\u7528\u4ece \u00bd \u5f00\u59cb\u8ba1\u6570\u6765\u4e00\u52b3\u6c38\u9038\u5730\u89e3\u51b3\u8fd9\u4e2a\u4e89\u7aef\u3002\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# \u51fd\u6570 `push!` \u548c `pop!` \u4e5f\u53ef\u4ee5\u6539\u53d8\u6570\u7ec4\u3002\n# `push!` \u5728\u6570\u7ec4\u6700\u540e\u6dfb\u52a0\u5143\u7d20\uff0c\u800c `pop!` \u5220\u9664\u6570\u7ec4\u7684\u6700\u540e\u4e00\u4e2a\u5143\u7d20\u3002\n#\n# \u7ed9 `fibonnaci` \u6dfb\u52a0\u4e00\u4e2a\u6570\n# ------------------------------------------------------------------------------------------\n\npush!(fibonacci, 21)\n\n# ------------------------------------------------------------------------------------------\n# \u518d\u5220\u6389\u5b83\n# ------------------------------------------------------------------------------------------\n\npop!(fibonacci)\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# \u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u6211\u7ed9\u7684\u4f8b\u5b50\u90fd\u662f\u4e00\u7ef4\u7684\u6807\u91cf\uff0c\u4f46\u5176\u5b9e\u6570\u7ec4\u53ef\u4ee5\u6709\u4efb\u610f\u4e2a\u7ef4\u5ea6\u3001\u53ef\u4ee5\u5305\u542b\u5176\u4ed6\u6570\u7ec4\u3002\n# <br><br>\n# \u4e3e\u4e2a\u4f8b\u5b50\uff0c\u4e0b\u9762\u8fd9\u4e2a\u6570\u7ec4\u5c31\u5305\u542b\u4e86\u6570\u7ec4\uff1a\n# ------------------------------------------------------------------------------------------\n\nfavorites = [[\"koobideh\", \"chocolate\", \"eggs\"],[\"penguins\", \"cats\", \"sugargliders\"]]\n\nnumbers = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]\n\n# ------------------------------------------------------------------------------------------\n# \u4e0b\u9762\u7684\u4f8b\u5b50\u662f\u5305\u542b\u968f\u673a\u6570\u7684\u4e8c\u7ef4\u548c\u4e09\u7ef4\u6570\u7ec4\u3002\n# ------------------------------------------------------------------------------------------\n\nrand(4, 3)\n\nrand(4, 3, 2)\n\n# ------------------------------------------------------------------------------------------\n# \u5f53\u4f60\u590d\u5236\u6570\u7ec4\u7684\u65f6\u5019\u5343\u4e07\u8981\u5c0f\u5fc3\uff01\n# ------------------------------------------------------------------------------------------\n\nfibonacci\n\nsomenumbers = fibonacci\n\nsomenumbers[1] = 404\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# \u4fee\u6539 `somenumbers` \u540c\u65f6\u4e5f\u4fee\u6539\u4e86 `fibonacci`\uff01\n#\n# \u5728\u4e0a\u9762\u90a3\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u5b9e\u9645\u4e0a\u6211\u4eec\u5e76\u6ca1\u6709\u83b7\u5f97 `fibonacci` \u7684\u526f\u672c\u3002\n# \u6211\u4eec\u53ea\u662f\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u7684\u83b7\u53d6 `fibonacci` \u6240\u7ed1\u5b9a\u6570\u7ec4\u7684\u5143\u7d20\u7684\u9014\u5f84\u3002\n#\n# \u5982\u679c\u60f3\u83b7\u5f97 `fibonacci` \u6240\u7ed1\u5b9a\u6570\u7ec4\u7684\u526f\u672c\uff0c\u53ef\u4ee5\u4f7f\u7528 `copy` \u51fd\u6570\u3002\n# ------------------------------------------------------------------------------------------\n\n# \u5148\u590d\u539f fibonacci\nfibonacci[1] = 1\nfibonacci\n\nsomemorenumbers = copy(fibonacci)\n\nsomemorenumbers[1] = 404\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# \u4e0a\u9762\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cfibonacci \u5c31\u6ca1\u6709\u53d7\u5f71\u54cd\u3002\n# \u56e0\u6b64\u53ef\u4ee5\u5224\u65ad `somemorenumbers` \u548c `fibonacci` \u6240\u7ed1\u5b9a\u7684\u6570\u7ec4\u662f\u4e0d\u540c\u7684\u3002\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## \u7ec3\u4e60\n#\n# ### 3.1\n# \u7528\u4e0b\u9762\u7684\u4ee3\u7801\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4 `a_ray`\uff1a\n#\n# ```julia\n# a_ray = [1, 2, 3]\n# ```\n#\n# \u5728\u8fd9\u4e2a\u6570\u7ec4\u7684\u672b\u5c3e\u6dfb\u52a0\u6570\u5b57 `4` \u518d\u5220\u6389\u5b83\u3002\n# ------------------------------------------------------------------------------------------\n\n# \u5728\u6b64\u4f5c\u7b54\n\n\n@assert a_ray == [1, 2, 3]\n\n# ------------------------------------------------------------------------------------------\n# ### 3.2\n# \u8bd5\u7740\u901a\u8fc7\u4e0b\u9762\u7684\u4ee3\u7801\u7ed9 `myphonebook` \u6dfb\u52a0\u4e00\u4e2a\u952e\u4e3a \"Emergency\" \u503c\u4e3a `string(911)` \u6761\u76ee\n# ```julia\n# myphonebook[\"Emergency\"] = 911\n# ```\n#\n# \u4e3a\u5565\u4e0d\u597d\u4f7f\u5462\uff1f\n# ------------------------------------------------------------------------------------------\n\n# \u5728\u6b64\u4f5c\u7b54\u3002\u66f4\u6b63\u4ee3\u7801\u4ee5\u6d88\u9664\u9519\u8bef\nmyphonebook[\"Emergency\"] = 911\n\nans = myphonebook[\"Emergency\"]\n@assert  ans == \"$ans\"\n\n# ------------------------------------------------------------------------------------------\n# ### 3.3\n# \u521b\u5efa\u4e00\u4e2a\u540d\u4e3a `flexible_phonebook` \u7684\u5b57\u5178\uff0c\u5176\u4e2d Jenny \u7684\u53f7\u7801\u4e3a\u6574\u578b\uff0c\u800c Ghostbusters \u7684\u53f7\u7801\u4e3a\u5b57\u7b26\u4e32\n#\n# ```julia\n# (Jenny = 8675309, Ghostbusters = 555-2368)\n# ```\n# ------------------------------------------------------------------------------------------\n\nflexible_phonebook = Dict()\n\n@assert flexible_phonebook == Dict(\"Jenny\" => 8675309, \"Ghostbusters\" => \"555-2368\")\n\n# ------------------------------------------------------------------------------------------\n# ### 3.4\n# \u7ed9`flexible_phonebook`\u6dfb\u52a0\u4e00\u4e2a\u952e\u4e3a\"Emergency\"\u503c\u4e3a\u6574\u6570`911`\u7684\u6761\u76ee\u3002\n# ------------------------------------------------------------------------------------------\n\n# \u5728\u6b64\u4f5c\u7b54\n\n\n@assert haskey(flexible_phonebook, \"Emergency\")\n\n@assert flexible_phonebook[\"Emergency\"] == 911\n\n# ------------------------------------------------------------------------------------------\n# ### 3.5\n# \u601d\u8003\u9898\uff1a\u4e3a\u4ec0\u4e48`flexible_phonebook`\u53ef\u4ee5\u6dfb\u52a0\u503c\u4e3a\u6574\u578b\u7684\u6761\u76ee\u800c`myphonebook`\u4e0d\u884c\uff1f\n# \u8be5\u5982\u4f55\u521d\u59cb\u5316`myphonebook`\u624d\u80fd\u6dfb\u52a0\u503c\u4e3a\u6574\u578b\u7684\u6761\u76ee\u5462\uff1f\n# ------------------------------------------------------------------------------------------\n\n\n\n# ------------------------------------------------------------------------------------------\n# \u8bf7\u5728\u5b8c\u6210\u7ec3\u4e60\u8fd0\u884c `@assert` \u5757\u4ee5\u68c0\u6d4b\u7ed3\u679c\u662f\u5426\u6b63\u786e\uff0c\u82e5\u65e0\u62a5\u9519\u5373\u4e3a\u6b63\u786e\u3002\n# ------------------------------------------------------------------------------------------\n", "meta": {"hexsha": "8a996fb805c59aabac42814f87bb2fbf7bd7976c", "size": 10074, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/03.\u6570\u636e\u7ed3\u6784.jl", "max_stars_repo_name": "NahsiN/JuliaTutorials", "max_stars_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/03.\u6570\u636e\u7ed3\u6784.jl", 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{"text": "# Copyright (c) 2019 Arpit Bhatia and contributors                               #src\n#                                                                                #src\n# Permission is hereby granted, free of charge, to any person obtaining a copy   #src\n# of this software and associated documentation files (the \"Software\"), to deal  #src\n# in the Software without restriction, including without limitation the rights   #src\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #src\n# copies of the Software, and to permit persons to whom the Software is          #src\n# furnished to do so, subject to the following conditions:                       #src\n#                                                                                #src\n# The above copyright notice and this permission notice shall be included in all #src\n# copies or substantial portions of the Software.                                #src\n#                                                                                #src\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #src\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #src\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #src\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #src\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #src\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #src\n# SOFTWARE.                                                                      #src\n\n# # Getting started with Julia\n\n# Since JuMP is embedded in Julia, knowing some basic Julia is important\n# for learning JuMP. This tutorial is designed to provide a minimalist\n# crash course in the basics of Julia. You can find resources that provide\n# a more comprehensive introduction to Julia [here](https://julialang.org/learning/).\n\n# ## Installing Julia\n\n# To install Julia, [download the latest stable release](https://julialang.org/downloads/),\n# then follow the [platform specific install instructions](https://julialang.org/downloads/platform/).\n\n# !!! tip\n#     Unless you know otherwise, you probably want the 64-bit version.\n\n# Next, you need an IDE to develop in. VS Code is a popular choice, so follow\n# [these install instructions](https://www.julia-vscode.org/docs/stable/gettingstarted/).\n\n# ## Numbers and arithmetic\n\n# Since we want to solve optimization problems, we're going to be using a lot of\n# math. Luckily, Julia is great for math, with all the usual operators:\n\n@show 1 + 1\n@show 1 - 2\n@show 2 * 2\n@show 4 / 5\n@show 3^2\nnothing #hide\n\n# !!! info\n#     The `@` in front of something indicates that it is a macro, which is just\n#     a special type of function. In this case, `@show` prints the expression as\n#     typed (e.g., `1 - 2`), as well as the evaluation of the expression (`-1`).\n\n# Did you notice how Julia didn't print `.0` after some of the numbers? Julia is\n# a dynamic language, which means you never have to explictly declare the type\n# of a variable. However, in the background, Julia is giving each variable a\n# type. Check the type of something using the `typeof` function:\n\n@show typeof(1)\n@show typeof(1.0)\nnothing #hide\n\n# Here `1` is an `Int64`, which is an integer with 64 bits of precision, and\n# `1.0` is a `Float64`, which is a floating point number with 64-bits of\n# precision.\n\n# !!! tip\n#     If you aren't familiar with floating point numbers, make sure to read\n#     the [Floating point numbers](@ref) section.\n\n# We create complex numbers using `im`:\n\nx = 2 + 1im\n@show real(x)\n@show imag(x)\n@show typeof(x)\n@show x * (1 - 2im)\nnothing #hide\n\n# !!! info\n#     The curly brackets surround what we call the _parameters_ of a type. You\n#     can read `Complex{Int64}`  as \"a complex number, where the real and\n#     imaginary parts are represented by `Int64`.\" If we call\n#     `typeof(1.0 + 2.0im)` it will be `Complex{Float64}`, which a complex\n#     number with the parts represented by `Float64`.\n\n# There are also some cool things like an irrational representation of \u03c0.\n\n\u03c0\n\n# !!! tip\n#     To make \u03c0 (and most other greek letters), type `\\pi` and then press\n#     `[TAB]`.\n\ntypeof(\u03c0)\n\n# Athough if we do math with irrational numbers, they get converted to\n# `Float64`:\n\ntypeof(2\u03c0 / 3)\n\n# ### Floating point numbers\n\n# !!! warning\n#     If you aren't familiar with floating point numbers, make sure to read this\n#     section carefully.\n\n# A `Float64` is a [floating point](https://en.wikipedia.org/wiki/Floating-point_arithmetic)\n# approximation of a real number using 64-bits of information.\n\n# Because it is an approximation, things we know hold true in mathematics don't\n# hold true in a computer! For example:\n\n0.1 * 3 == 0.3\n\n#- A more complicated example is:\n\nsin(2\u03c0 / 3) == \u221a3 / 2\n\n# !!! tip\n#     Get `\u221a` by typing `\\sqrt` then press `[TAB]`.\n\n# Let's see what the differences are:\n\n0.1 * 3 - 0.3\n\n#-\n\nsin(2\u03c0 / 3) - \u221a3 / 2\n\n# They are small, but not zero!\n\n# One way of explaining this difference is to consider how we would write\n# `1 / 3` and `2 / 3` using only four digits after the decimal point. We would\n# write `1 / 3` as `0.3333`, and `2 / 3` as `0.6667`. So, depiste the fact that\n# `2 * (1 / 3) == 2 / 3`, `2 * 0.3333 == 0.6666 != 0.6667`.\n\n# Let's try that again using \u2248 (`\\approx + [TAB]`) instead of `==`:\n\n0.1 * 3 \u2248 0.3\n\n#-\n\nsin(2\u03c0 / 3) \u2248 \u221a3 / 2\n\n# `\u2248` is just a clever way of calling the `isapprox` function:\n\nisapprox(sin(2\u03c0 / 3), \u221a3 / 2; atol = 1e-8)\n\n# !!! warning\n#     Floating point is the reason solvers use tolerances when they solve\n#     optimization models. A common mistake you're likely to make is checking\n#     whether a binary variable is 0 using `value(z) == 0`. Always remember to\n#     use something like `isapprox` when comparing floating point numbers.\n#\n#     Gurobi has a [good series of articles](https://www.gurobi.com/documentation/9.0/refman/num_grb_guidelines_for_num.html)\n#     on the implications of floating point in optimization if you want to read\n#     more.\n\n# If you aren't careful, floating point arithmetic can throw up all manner of\n# issues. For example:\n\n1 + 1e-16 == 1\n\n# It even turns out that floating point numbers aren't associative!\n\n(1 + 1e-16) - 1e-16 == 1 + (1e-16 - 1e-16)\n\n# It's important to note that this issue isn't Julia-specific. It happens in\n# every programming language (try it out in Python).\n\n# ## Vectors, matrices and arrays\n\n# Similar to Matlab, Julia has native support for vectors, matrices and tensors;\n# all of which are represented by arrays of different dimensions. Vectors are\n# constructed by comma-separated elements surrounded by square brackets:\n\nb = [5, 6]\n\n# !!! info\n#     `Array{Int64, 1}` means that this is an `Array`, with `Int64` elements,\n#     and it has `1` dimension.\n\n# Matrices can by constructed with spaces separating the columns, and semicolons\n# separating the rows:\n\nA = [1.0 2.0; 3.0 4.0]\n\n# Note how this time the type is `Array{Float64, 2}`; the elements are `Float64`\n# and there are `2` dimenions.\n\n# We can do linear algebra:\n\nx = A \\ b\n\n# !!! info\n#     Here is floating point at work again! `x` is approximately `[-4, 4.5]`.\n\n#-\n\nA * x\n\n#-\n\nA * x \u2248 b\n\n# Note that when multiplying vectors and matrices, dimensions matter. For\n# example, you can't multiply a vector by a vector:\n\ntry  #hide\nb * b\ncatch err; showerror(stderr, err); end  #hide\n\n# But multiplying transposes works:\n\nb' * b\n\n#-\n\nb * b'\n\n# ## Other common types\n\n# ### Strings\n\n# Double quotes are used for strings:\n\ntypeof(\"This is Julia\")\n\n# Unicode is fine in strings:\n\ntypeof(\"\u03c0 is about 3.1415\")\n\n# Use [`println`](https://docs.julialang.org/en/v1/base/io-network/#Base.println)\n# to print a string:\n\nprintln(\"Hello, World!\")\n\n# We can use `$()` to interpolate values into a string:\n\nx = 123\nprintln(\"The value of x is: $(x)\")\n\n# ### Symbols\n\n# Julia `Symbol`s provide a way to make human readable unique identifiers:\n\n:my_id\n\n#-\n\ntypeof(:my_id)\n\n# You can think of a `Symbol` as a `String` that takes up less memory, and that\n# can't be modified.\n\n# ### Tuples\n\n# Julia makes extensive use of a simple data structure called Tuples. Tuples are\n# immutable collections of values. For example:\n\nt = (\"hello\", 1.2, :foo)\n\n#-\n\ntypeof(t)\n\n# Tuples can be accessed by index, similar to arrays:\n\nt[2]\n\n# And they be \"unpacked\" like so:\n\na, b, c = t\nb\n\n# The values can also be given names, which is a convenient way of making\n# light-weight data structures.\n\nt = (word = \"hello\", num = 1.2, sym = :foo)\n\n# Values can be accessed using dot syntax:\n\nt.word\n\n# ## Dictionaries\n\n# Similar to Python, Julia has native support for dictionaries. Dictionaries\n# provide a very generic way of mapping keys to values.  For example, a map of\n# integers to strings:\n\nd1 = Dict(1 => \"A\", 2 => \"B\", 4 => \"D\")\n\n# !!! info\n#     Type-stuff again: `Dict{Int64,String}` is a dictionary with `Int64` keys\n#     and `String` values.\n\n# Looking up a values uses the bracket syntax:\n\nd1[2]\n\n# Dictionaries support non-integer keys and can mix data types:\n\nDict(\"A\" => 1, \"B\" => 2.5, \"D\" => 2 - 3im)\n\n# !!! info\n#     Julia types form a hierarchy. Here the value type of the dictionary is\n#     `Number`, which is a generalization of `Int64`, `Float64`, and `Complex{Int}`.\n#     In general, having variables with \"Abstract\" types like `Number` can lead\n#     to slower code, so you should try to make sure every element in a\n#     dictionary or vector is the same type. For example, in this case we could\n#     represent every element as a `Complex{Float64}`:\n\nDict(\"A\" => 1.0 + 0.0im, \"B\" => 2.5 + 0.0im, \"D\" => 2.0 - 3.0im)\n\n# Dictionaries can be nested:\n\nd2 = Dict(\"A\" => 1, \"B\" => 2, \"D\" => Dict(:foo => 3, :bar => 4))\n\n#-\n\nd2[\"B\"]\n\n#-\n\nd2[\"D\"][:foo]\n\n# ## Loops\n\n# Julia has native support for for-each style loops with the syntax\n# `for <value> in <collection> end`:\n\nfor i in 1:5\n    println(i)\nend\n\n\n# !!! info\n#     Ranges are constructed as `start:stop`, or `start:step:stop`.\n\n#-\n\nfor i in [1.2, 2.3, 3.4, 4.5, 5.6]\n    println(i)\nend\n\n# This for-each loop also works with dictionaries:\n\nfor (key, value) in Dict(\"A\" => 1, \"B\" => 2.5, \"D\" => 2 - 3im)\n    println(\"$(key): $(value)\")\nend\n\n# Note that in contrast to vector languages like Matlab and R, loops do not\n# result in a significant performance degradation in Julia.\n\n# ## Control Flow\n\n# Julia control flow is similar to Matlab, using the keywords\n# `if-elseif-else-end`, and the logical operators `||` and `&&` for **or** and\n# **and** respectively:\n\nfor i in 0:3:15\n    if i < 5\n        println(\"$(i) is less than 5\")\n    elseif i < 10\n        println(\"$(i) is less than 10\")\n    else\n        if i == 10\n            println(\"the value is 10\")\n        else\n            println(\"$(i) is bigger than 10\")\n        end\n    end\nend\n\n# ## Comprehensions\n\n# Similar to languages like Haskell and Python, Julia supports the use of simple\n# loops in the construction of arrays and dictionaries, called comprehenions.\n#\n# A list of increasing integers:\n\n[i for i in 1:5]\n\n# Matrices can be built by including multiple indices:\n\n[i * j for i in 1:5, j in 5:10]\n\n# Conditional statements can be used to filter out some values:\n\n[i for i in 1:10 if i % 2 == 1]\n\n# A similar syntax can be used for building dictionaries:\n\nDict(\"$(i)\" => i for i in 1:10 if i % 2 == 1)\n\n# ## Functions\n\n# A simple function is defined as follows:\n\nfunction print_hello()\n    println(\"hello\")\nend\nprint_hello()\n\n# Arguments can be added to a function:\n\nfunction print_it(x)\n    println(x)\nend\nprint_it(\"hello\")\nprint_it(1.234)\nprint_it(:my_id)\n\n# Optional keyword arguments are also possible:\n\nfunction print_it(x; prefix = \"value:\")\n    println(\"$(prefix) $(x)\")\nend\nprint_it(1.234)\nprint_it(1.234, prefix = \"val:\")\n\n# The keyword `return` is used to specify the return values of a function:\n\nfunction mult(x; y = 2.0)\n    return x * y\nend\n\nmult(4.0)\n\n#-\n\nmult(4.0, y = 5.0)\n\n# ### Anonymous functions\n\n# The syntax `input -> output` creates an anonymous function. These are most\n# useful when passed to other functions. For example:\n\nf = x -> x^2\nf(2)\n\n#-\n\nmap(x -> x^2, 1:4)\n\n# ### Type parameters\n\n# We can constrain the inputs to a function using type parameters, which are\n# `::` followed by the type of the input we want. For example:\n\nfunction foo(x::Int)\n    return x^2\nend\n\nfunction foo(x::Float64)\n    return exp(x)\nend\n\nfunction foo(x::Number)\n    return x + 1\nend\n\n@show foo(2)\n@show foo(2.0)\n@show foo(1 + 1im)\nnothing #hide\n\n# But what happens if we call `foo` with something we haven't defined it for?\n\ntry  #hide\nfoo([1, 2, 3])\ncatch err; showerror(stdout, err) end  #hide\n\n# We get a dreaded `MethodError`! A `MethodError` means that you passed a\n# function something that didn't match the type that it was expecting. In this\n# case, the error message says that it doesn't know how to handle an\n# `Array{Int64, 1}`, but it does know how to handle `Float64`, `Int64`, and\n# `Number`.\n#\n# !!! tip\n#     Read the \"Closest candidates\" part of the error message carefully to get a\n#     hint as to what was expected.\n\n# ### Broadcasting\n\n# In the example above, we didn't define what to do if `f` was passed an\n# `Array`. Luckily, Julia provides a convienient syntax for mapping `f`\n# element-wise over arrays! Just add a `.` between the name of the function and\n# the opening `(`. This works for _any_ function, including functions with\n# multiple arguments. For example:\n\nf.([1, 2, 3])\n\n# !!! tip\n#     Get a `MethodError` when calling a function that takes an `Array`? Try\n#     broadcasting it!\n\n# ## Mutable vs immutable objects\n\n# Some types in Julia are *mutable*, which means you can change the values\n# inside them. A good example is an array. You can modify the contents of an\n# array without having to make a new array.\n\n# In contrast, types like `Float64` are *immutable*. You can't modify the\n# contents of a `Float64`.\n\n# This is something to be aware of when passing types into functions. For\n# example:\n\nfunction mutability_example(mutable_type::Vector{Int}, immutable_type::Int)\n    mutable_type[1] += 1\n    immutable_type += 1\n    return\nend\n\nmutable_type = [1, 2, 3]\nimmutable_type = 1\n\nmutability_example(mutable_type, immutable_type)\n\nprintln(\"mutable_type: $(mutable_type)\")\nprintln(\"immutable_type: $(immutable_type)\")\n\n# Because `Vector{Int}` is a mutable type, modifying the variable inside the\n# function changed the value outside of the function. In constrast, the change\n# to `immutable_type` didn't modify the value outside the function.\n\n# You can check mutability with the `isimmutable` function:\n\nisimmutable([1, 2, 3])\n\n#-\n\nisimmutable(1)\n\n# ## The package manager\n\n# ### Installing packages\n\n# No matter how wonderful Julia's base language is, at some point you will want\n# to use an extension package.  Some of these are built-in, for example random\n# number generation is available in the `Random` package in the standard\n# library. These packages are loaded with the commands `using` and `import`.\n\nusing Random  # The equivalent of Python's `from Random import *`\nimport Random  # The equivalent of Python's `import Random`\n\nRandom.seed!(33)\n\n[rand() for i in 1:10]\n\n# The Package Manager is used to install packages that are not part of Julia's\n# standard library.\n\n# For example the following can be used to install JuMP,\n# ```julia\n# using Pkg\n# Pkg.add(\"JuMP\")\n# ```\n\n# For a complete list of registed Julia packages see the package listing at\n# [JuliaHub](https://juliahub.com).\n\n# From time to you may wish to use a Julia package that is not registered.  In\n# this case a git repository URL can be used to install the package.\n# ```julia\n# using Pkg\n# Pkg.add(\"https://github.com/user-name/MyPackage.jl.git\")\n# ```\n\n# ### Package environments\n\n# By default, `Pkg.add` will add packages to Julia's global environment.\n# However, Julia also has built-in support for virtual environments.\n\n# Activate a virtual environment with:\n# ```julia\n# import Pkg; Pkg.activate(\"/path/to/environment\")\n# ```\n\n# You can see what packages are installed in the current environment with\n# `Pkg.status()`.\n\n# !!! tip\n#     We _strongly_ recommend you create a Pkg environment for each project\n#     that you create in Julia, and add only the packages that you need, instead\n#     of adding lots of packages to the global environment. The [Pkg manager documentation](https://julialang.github.io/Pkg.jl/v1/environments/)\n#     has more information on this topic.\n", "meta": {"hexsha": "7af139a58f5a649eaa7dc30aabf6341a649b5b5d", "size": 16499, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/an_introduction_to_julia.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lang/Julia/an_introduction_to_julia.jl", "max_issues_repo_name": "ethansaxenian/RosettaDecode", "max_issues_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lang/Julia/an_introduction_to_julia.jl", "max_forks_repo_name": "ethansaxenian/RosettaDecode", "max_forks_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8699324324, "max_line_length": 144, "alphanum_fraction": 0.672889266, "num_tokens": 4510, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.16667539847889923, "lm_q1q2_score": 0.07490269642289611}}
{"text": "# Type Hierarchy Exercise\n#\n# Demonstration of multiple dispatch as opposed to conditional formatting\n\n#module LJtypeHierarchy\n\nabstract type AbstractPerson end\n\nstruct Person <: AbstractPerson\n    name::String\nend\n\nstruct Student <: AbstractPerson\n    name::String\n    grade::Int\nend\n\nstruct GroupLeader <: AbstractPerson\n    name::String\n    group::String\nend\n\n#end\n\nprintln(\"Hello World\")\n\nadam = Person(\"adam\")\nprintln(\"$(adam.name) is a Person\")\ncameron = Student(\"cameron\", 95)\njohn = GroupLeader(\"John\", \"CDAS\")\n\nperson_info(p::Person) = println(\"$(p.name) is a Person\")\nperson_info(p::Student) = println(\"$(p.name) is a Student with grade $(p.grade)\")\nperson_info(p::GroupLeader) = println(\"$(p.name) is a GroupLeader of $(p.group)\")\n\npeople = (adam, cameron, john)\n\n\nfor person in people\n    person_info(person)\nend\n", "meta": {"hexsha": "d6245ac5690a1406c696e70f4cad2e7253dd9311", "size": 825, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "TypeHierarchy.jl", "max_stars_repo_name": "AdamVStephen/learning-julia", "max_stars_repo_head_hexsha": "442e0dfab5c3e224927e1ca96516af87d8e81319", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "TypeHierarchy.jl", "max_issues_repo_name": "AdamVStephen/learning-julia", "max_issues_repo_head_hexsha": "442e0dfab5c3e224927e1ca96516af87d8e81319", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "TypeHierarchy.jl", "max_forks_repo_name": "AdamVStephen/learning-julia", "max_forks_repo_head_hexsha": "442e0dfab5c3e224927e1ca96516af87d8e81319", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6428571429, "max_line_length": 81, "alphanum_fraction": 0.7151515152, "num_tokens": 211, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.15405755880753266, "lm_q1q2_score": 0.07462241331869914}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.1\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 75fe8fd0-2563-11ec-30ef-d15cdc3d4e63\nusing PlutoUI\n\n# \u2554\u2550\u2561 d4b5618f-f1a7-4483-b738-fb94e1e2bbbe\nhtml\"\"\"\n<p style=\"background-color:lightgrey\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dct=\"http://purl.org/dc/terms/\"><span property=\"dct:title\">&nbsp&nbsp\ud83d\udcbb&nbsp<b>L\u00f3gica de Programa\u00e7\u00e3o</b></span> por <span property=\"cc:attributionName\">Franco Naghetini</span> \u00e9 licenciado sob <a href=\"http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1\" target=\"_blank\" rel=\"license noopener noreferrer\" style=\"display:inline-block;\">CC BY 4.0<img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1\"><img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1\"></a></p>\n\"\"\"\n\n# \u2554\u2550\u2561 deee1735-37ee-4c97-a4df-8fcb61e95d6a\nPlutoUI.TableOfContents(aside=true, title=\"Sum\u00e1rio\",\n\t\t\t\t\t\tindent=true, depth=2)\n\n# \u2554\u2550\u2561 488dcacd-109d-41f2-b904-3d17193e6190\nmd\"\"\"\n![ufmg-logo](https://logodownload.org/wp-content/uploads/2015/02/ufmg-logo-2.png)\n\"\"\"\n\n# \u2554\u2550\u2561 6ca3e113-02db-4cef-ad9e-3941ac7d7a6d\nmd\"\"\"\n# \ud83d\udcbb L\u00f3gica de Programa\u00e7\u00e3o\n\nO ato de **programar** pode ser entendido como uma forma de se comunicar com as m\u00e1quinas a partir de um conjunto de instru\u00e7\u00f5es n\u00e3o amb\u00edguas com o intuito de se realizar uma determinada tarefa. Essa comunica\u00e7\u00e3o com as m\u00e1quinas \u00e9 realizada por meio das **linguagens de programa\u00e7\u00e3o**, como Python, R, C e Julia.\n\nNesse contexto, a **l\u00f3gica de programa\u00e7\u00e3o** \u00e9 de suma import\u00e2ncia, uma vez que ela define a estrutura das instru\u00e7\u00f5es dadas \u00e0s m\u00e1quinas, ou seja, define a forma que voc\u00ea se comunica com as m\u00e1quinas. Se quer aprender mais sobre a import\u00e2ncia da l\u00f3gica de programa\u00e7\u00e3o assista [este v\u00eddeo](https://www.youtube.com/watch?v=l7lIPXij85I) do canal Programa\u00e7\u00e3o Din\u00e2mica.\n\nNeste m\u00f3dulo, estudaremos alguns conceitos b\u00e1sicos de l\u00f3gica de programa\u00e7\u00e3o. Para isso, utilizaremos [Julia](https://julialang.org/), uma linguagem de programa\u00e7\u00e3o moderna e de prop\u00f3sito geral que possui diversos recursos favor\u00e1veis ao desenvolvimento de rotinas geoestat\u00edsticas.\n\n> A linguagem \u00e9 *simples* como Python e *r\u00e1pida* como C \ud83d\ude80\n\nA seguir, ser\u00e3o apresentados alguns conceitos b\u00e1sicos de l\u00f3gica de programa\u00e7\u00e3o, al\u00e9m de uma introdu\u00e7\u00e3o sobre os recursos interativos da linguagem Julia.\n\"\"\"\n\n# \u2554\u2550\u2561 14db5525-7c0a-433b-a23c-088db728f46b\nmd\"\"\"\n>##### \ud83d\udcda Sobre\n>- Voc\u00ea pode exportar este notebook como PDF ou HTML est\u00e1tico. Para isso, clique no \u00edcone \ud83d\udd3a\ud83d\udd34, localizado no canto superior direito da pagina. Entretanto, ambos os formatos n\u00e3o s\u00e3o compat\u00edveis com os recursos interativos do notebook.\n>- Caso deseje executar alguma c\u00e9lula do notebook, clique no \u00edcone \u25b6\ufe0f, localizado no canto inferior direito da c\u00e9lula.\n>- Algumas c\u00e9lulas encontram-se ocultadas (e.g. c\u00e9lulas que geram os plots). Voc\u00ea pode clicar no \u00edcone \ud83d\udc41\ufe0f, localizado no canto superior esquerdo da c\u00e9lula, para ocult\u00e1-la ou exib\u00ed-la.\n>- A explica\u00e7\u00e3o das c\u00e9lulas que geram os plots est\u00e1 fora do escopo deste notebook. Entretanto, a sintaxe \u00e9 bem intuitiva e pode ser facilmente compreendida!\n>- Voc\u00ea pode ainda clicar no \u00edcone `...`, no canto superior direito de uma c\u00e9lula, para exclu\u00ed-la do notebook.\n>- Algumas c\u00e9lulas deste notebook encontram-se encapsuladas pela express\u00e3o `md\"...\"` (e.g. esta c\u00e9lula). Essas s\u00e3o c\u00e9lulas de texto chamadas de *markdown*. Caso deseje aprender um pouco mais sobre a linguagem *markdown*, clique [aqui](https://docs.pipz.com/central-de-ajuda/learning-center/guia-basico-de-markdown#open).\n>- No Pluto, todos os pacotes devem ser importados/baixados na primeira c\u00e9lula do notebook. Clique no \u00edcone \ud83d\udc41\ufe0f para exibir essa c\u00e9lula ou consulte a se\u00e7\u00e3o *Pacotes utilizados* deste notebook para saber mais informa\u00e7\u00f5es sobre os pacotes.\n>- Utilize a macro ` @which` para verificar a qual pacote uma determinada fun\u00e7\u00e3o pertence.\n>- Voc\u00ea pode utilizar este notebook da forma que quiser, basta referenciar [este link](https://github.com/fnaghetini/intro-to-geostats). Consulte a [licen\u00e7a]  (https://github.com/fnaghetini/intro-to-geostats/blob/main/LICENSE) para saber mais detalhes.\n>- Para mais informa\u00e7\u00f5es acesse o [README](https://github.com/fnaghetini/intro-to-geostats/blob/main/README.md) do projeto \ud83d\ude80\n\"\"\"\n\n# \u2554\u2550\u2561 c111da75-d294-4def-93fb-56953c3585ad\nmd\"\"\"\n\n## 1. Vari\u00e1veis\n\nAs **vari\u00e1veis** podem ser entendidas como partes da mem\u00f3ria (ou \"caixas\") onde se armazena **valores** de diferentes **tipos** para serem posteriormente processados. Cada vari\u00e1vel \u00e9 identificada/rotulada por um **nome**.\n\nAs vari\u00e1veis podem ser **num\u00e9ricas**...\n\n\"\"\"\n\n# \u2554\u2550\u2561 7ceb9c27-0310-4f97-8349-91286c1f9235\nidade = 24\n\n# \u2554\u2550\u2561 218bb8c3-729b-43c7-9a53-d3b579cf2d21\naltura = 1.58\n\n# \u2554\u2550\u2561 3b3e6955-1573-4620-8109-d80b71cf0708\nmd\" As vari\u00e1veis podem armazenar **cadeias de caracteres**... \"\n\n# \u2554\u2550\u2561 f2e6adcd-9095-496f-a8e3-88069537015c\nnome = \"Camila\"\n\n# \u2554\u2550\u2561 870cbb76-b0c3-4d14-be22-86e0d34ebe58\nfrase = \"Ol\u00e1, mundo! \ud83c\udf0e\"\n\n# \u2554\u2550\u2561 55a2c622-0f9f-45db-b534-55873c0759d4\nmd\"\"\"\n> \u26a0\ufe0f As cadeias de caracteres (**strings**) devem ser encapsuladas por \u00e1spas duplas. Emojis tamb\u00e9m s\u00e3o considerados cadeias de caracteres! Clique [aqui](https://getemoji.com/) para copiar emojis e colar nas c\u00e9lulas deste notebook.\n\"\"\"\n\n# \u2554\u2550\u2561 449c91c3-7f03-4ffd-95e7-02cdb58323fd\nmd\"\"\"\nUm recurso muito utilizado e presente em diversas linguagens de programa\u00e7\u00e3o \u00e9 a **interpola\u00e7\u00e3o de strings**. Esse procedimento utiliza o valor de uma vari\u00e1vel dentro de uma string, de modo que a string interpolada atua como um \"template\". Em Julia, usamos o s\u00edmbolo `$` para interpolar strings.\n\nA c\u00e9lula abaixo mostra um exemplo de interpola\u00e7\u00e3o de string, utilizando as vari\u00e1veis j\u00e1 criadas `nome`, `idade` e `altura`...\n\"\"\"\n\n# \u2554\u2550\u2561 8d463f50-d694-41eb-bbad-c40f93471852\n\"Ol\u00e1! Meu nome \u00e9 $nome, tenho $idade anos e tenho $altura m de altura.\"\n\n# \u2554\u2550\u2561 4d31b989-7a7d-43ca-a1f8-831db58f1c8e\nmd\"\"\"\n\nPodemos utilizar *s\u00edmbolos matem\u00e1ticos* como nomes das vari\u00e1veis. Para inserir o s\u00edmbolo $\\alpha$, por exemplo, digita-se `\\alpha` + `TAB`. No caso do s\u00edmbolo $\\gamma$, digita-se `\\gamma` + `TAB`.\n\nCique [aqui](https://docs.julialang.org/en/v1/manual/unicode-input/) para acessar a lista completa de s\u00edmbolos dispon\u00edveis.\n\n\"\"\"\n\n# \u2554\u2550\u2561 b0d01238-1213-45c3-9bef-a443afc6b87c\n\u03b2 = 2.0\n\n# \u2554\u2550\u2561 2cce4c5e-7890-4c6c-bd7c-eecc8437bef3\n\u03f5 = 0.5\n\n# \u2554\u2550\u2561 1b454935-c858-4817-928d-da116011d7b0\n\u03b2 + \u03f5\n\n# \u2554\u2550\u2561 96ced3bd-69e7-490c-b8be-b06a30049a31\n\u03c0\n\n# \u2554\u2550\u2561 3de2d16f-8849-4462-9ef3-0cc1830285c6\nmd\"\"\"\n> \u26a0\ufe0f Perceba que o s\u00edmbolo $\u03c0$ j\u00e1 possui um valor associado na linguagem Julia.\n\"\"\"\n\n# \u2554\u2550\u2561 d28119c3-6800-430d-9ffe-d6f25e8e5c2a\nmd\"\"\"\n## 2. Fun\u00e7\u00f5es\n\nAs **fun\u00e7\u00f5es**, no mundo da computa\u00e7\u00e3o, s\u00e3o similares \u00e0s fun\u00e7\u00f5es matem\u00e1ticas. De forma geral, uma fun\u00e7\u00e3o recebe valores de **entrada**, processa-os e retorna valores de **sa\u00edda**.\n\nEm Julia, existem, essencialmente, tr\u00eas maneiras distintas de se construir fun\u00e7\u00f5es:\n\n\"\"\"\n\n# \u2554\u2550\u2561 bb495116-8ff9-4a66-9b66-6f6fe990c5d9\nf(\u03c7) = 5\u03c7 + 25\n\n# \u2554\u2550\u2561 a63d4a04-0ceb-4e4f-90ba-30dc798e1b48\nfunction areatriangulo(b,h)\n\tA = (b * h) / 2\n\treturn A\nend\n\n# \u2554\u2550\u2561 9cf1d023-1421-4adb-af35-64aa6a8ddb53\nmd\"\"\"\n> \u26a0\ufe0f O comando `return` foi utilizado aqui apenas por fins did\u00e1ticos. Entretanto, poder\u00edamos omiti-lo.\n\"\"\"\n\n# \u2554\u2550\u2561 5b168a45-b5a2-4415-80f4-e2347c21730a\ndiagonal = l -> l * \u221a2\n\n# \u2554\u2550\u2561 1a1ac9a0-b8f0-46d1-9d89-21b6bc0e6aec\nf(2) + areatriangulo(10,4) / diagonal(1)\n\n# \u2554\u2550\u2561 784426f8-ac63-4555-adb2-cbfb9b37fdda\nmd\"\"\"\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nCrie uma fun\u00e7\u00e3o `recurso` que retorne a tonelagem em metal contido de um dep\u00f3sito de volume `V`, densidade m\u00e9dia `\u03c1` e teor m\u00e9dio `T` (em %).\n\n\"\"\"\n\n# \u2554\u2550\u2561 ffc5e222-8ae8-4314-a616-e4588d7808fe\nrecurso(V, \u03c1, T) = missing\n\n# \u2554\u2550\u2561 5dc76f3b-badf-4951-9eb4-f61ca66dfb98\nmd\"\"\"\n## 3. Cole\u00e7\u00f5es\n\nAs **cole\u00e7\u00f5es** s\u00e3o conjuntos de elementos, normalmente do mesmo tipo, mas n\u00e3o necessariamente. S\u00e3o exemplos *vetores*, *matrizes* e *tensores*.\n\nOs **vetores** podem ser escritos como...\n\n\"\"\"\n\n# \u2554\u2550\u2561 d349ac4d-44bb-4505-a6c4-3096d8c48ba0\nvetor1 = [1,2,3,4,5,6,7,8,9,10]\n\n# \u2554\u2550\u2561 9e181c4a-a759-4f92-8444-6a23387a20b9\nvetor_2 = collect(10:20)\n\n# \u2554\u2550\u2561 d39f503a-158e-4774-851e-d786014b8f2b\nmd\" As **matrizes** podem ser escritas como... \"\n\n# \u2554\u2550\u2561 b5387324-df28-422d-8e74-4fefa8054e7f\nA = [1 2\n\t 3 4]\n\n# \u2554\u2550\u2561 570f62db-edd7-429d-a084-2686f4b6923c\nB = [1 2 3; 4 5 6; 7 8 9]\n\n# \u2554\u2550\u2561 7e8a4e33-0e9c-45f5-b8bc-e2adece0806d\nmd\" Os **tensores** podem ser escritos como... \"\n\n# \u2554\u2550\u2561 204bcedc-4123-4845-bcac-493ce3544582\ntensor_de_zeros = zeros(5,5,2)\n\n# \u2554\u2550\u2561 92b0cb5f-41a1-420f-8b81-3b967db8138f\ntensor_de_uns = ones(2,2,3)\n\n# \u2554\u2550\u2561 7471120d-6e00-46be-be11-649c8cdbb32c\nmd\"\"\"\nUma opera\u00e7\u00e3o muito utilizada \u00e9 o **fatiamento**, que visa obter apenas certo(s) elemento(s) de uma cole\u00e7\u00e3o. O fatiamento de vetores, por exemplo, \u00e9 realizado a partir da seguinte sintaxe:\n\n```julia\nvetor[\u00edndice inicial:\u00edndice final]\n```\n\nObserve o vetor `v\u2081`...\n\n\"\"\"\n\n# \u2554\u2550\u2561 b31b883d-5a69-4e04-b8b5-cedca81dcf9a\nv\u2081 = [10,20,30,40,50,60,70,80,90,100]\n\n# \u2554\u2550\u2561 c7f94dcd-64a2-4e97-b38d-363dc6f9f757\nmd\" O primeiro elemento pode ser obtido a partir do fatiamento de `v\u2081`...\"\n\n# \u2554\u2550\u2561 2a25a884-44cf-4460-ab23-02e728201f90\nv\u2081[1]\n\n# \u2554\u2550\u2561 e127bdf8-2a53-4534-a628-1463a5735f41\nmd\"\"\"\n\n> \u26a0\ufe0f Assim como R e diferentemente de Python ou JavaScript, a numera\u00e7\u00e3o do \u00edndice se inicia em **1** na linguagem Julia.\n\nOs tr\u00eas primeiros elementos de `v\u2081` podem ser obtidos como...\n\n\"\"\"\n\n# \u2554\u2550\u2561 0806feb3-33eb-402c-92d2-af0b153834ba\nv\u2081[1:3]\n\n# \u2554\u2550\u2561 1a5f467b-7244-4587-b01c-61fd22bfbb9b\nv\u2081[begin:3]\n\n# \u2554\u2550\u2561 4b1ec558-5f94-417a-aa3c-d13a0039a6a0\nmd\" Os dois \u00faltimos elementos de `v\u2081` podem ser obtidos como...\"\n\n# \u2554\u2550\u2561 6bee210a-11cc-40c4-bfb0-d14a6b2495b4\nv\u2081[9:10]\n\n# \u2554\u2550\u2561 10cc97dd-a390-4d4a-9644-20b654ff66bb\nv\u2081[end-1:end]\n\n# \u2554\u2550\u2561 04738501-16fa-43e1-b640-47682c155bcb\nmd\"\"\"\n\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nFatie o vetor `v\u2082`, de modo que apenas os elementos da terceira posi\u00e7\u00e3o em diante sejam retornados. A fatia resultante deve ser armazenada na vari\u00e1vel `v\u2083`.\n\n\"\"\"\n\n# \u2554\u2550\u2561 f9317c0c-2389-43a8-8b91-f8b2f278e258\nbegin\n\tv\u2082 = collect(10:15)\n\tv\u2083 = missing\nend\n\n# \u2554\u2550\u2561 8253149c-e1b0-4e3f-9f5b-909621dba86e\nmd\"\"\"\n## 4. Condicionais\n\nAs **estruturas condicionais** s\u00e3o utilizadas em situa\u00e7\u00f5es em que se deseja executar algum trecho de c\u00f3digo apenas quando uma **condi\u00e7\u00e3o** \u00e9 satisfeita:\n\n```julia\nif condi\u00e7\u00e3o\n\tcomando_1\nelse\n\tcomando_2\nend\n```\n\nO exemplo abaixo mostra uma fun\u00e7\u00e3o `\u00e9granitoide` que utiliza estruturas condicionais. **Se** o conte\u00fado em quartzo for menor que 20%, a fun\u00e7\u00e3o retorna a frase \"N\u00e3o \u00e9 granitoide \ud83d\ude10\". **Caso contr\u00e1rio**, a frase \"\u00c9 granitoide!\" \u00e9 retornada.\n\n\"\"\"\n\n# \u2554\u2550\u2561 4a778055-7ade-4275-aa87-95c121b31232\nfunction \u00e9granitoide(quartzo)\n\tif quartzo < 20\n   \t\trochaignea = \"N\u00e3o \u00e9 granitoide \ud83d\ude10\"\n\telse\n   \t\trochaignea = \"\u00c9 granitoide!\"\n\tend\n\t\n\treturn rochaignea\nend\n\n# \u2554\u2550\u2561 ca5453f5-1768-4f59-b09d-e40bb4981622\n\u00e9granitoide(30)\n\n# \u2554\u2550\u2561 217945a3-919b-4c5f-afb0-6eaadf4e9022\n\u00e9granitoide(5)\n\n# \u2554\u2550\u2561 1f0abfbe-49cd-4529-8f4d-65f8286b6f0e\nmd\"\"\"\n\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nAgora, crie uma fun\u00e7\u00e3o `tiporocha` que recebe o nome de uma rocha e retorna o seu tipo:\n\n* \"gabro\" \u2192 \"\u00edgnea\"\n* \"gnaisse\" \u2192 \"metam\u00f3rfica\"\n* \"ritmito\" \u2192 \"sedimentar\"\n\n\"\"\"\n\n# \u2554\u2550\u2561 6fcc1433-c348-4825-8503-782304ed508b\nfunction tiporocha(rocha)\n\tmissing\nend\n\n# \u2554\u2550\u2561 3cfbddfb-be59-4019-965f-13bdbac2295a\nmd\"\"\"\n\n## 5. La\u00e7os de repeti\u00e7\u00e3o\n\nOs **la\u00e7os de repeti\u00e7\u00e3o** s\u00e3o utilizados quando se deseja repetir determinado trecho do algoritmo m\u00faltiplas vezes. O n\u00famero de repeti\u00e7\u00f5es pode ser indeterminado, mas necessariamente finito (*Dauricio, 2015*).\n\nUma das repeti\u00e7\u00f5es mais utilizadas \u00e9 o `for`, que apresenta a seguinte sintaxe:\n\n```julia\nfor elemento in vetor\n\tcomando\nend\n```\n\nO comando `for` tamb\u00e9m pode ser utilizado para criar vetores/listas, a partir das chamadas **list comprehensions**. A sintaxe \u00e9 definida da seguinte maneira:\n\n```julia\n[comando for elemento in vetor]\n```\n\nO exemplo abaixo mostra a utiliza\u00e7\u00e3o do recurso *list comprehension* para a cria\u00e7\u00e3o de um vetor constitu\u00eddo por pot\u00eancias de 2:\n\n\"\"\"\n\n# \u2554\u2550\u2561 abc428c6-b762-47bc-b5ac-7abedd1a7021\n[2 ^ num for num in 1:10]\n\n# \u2554\u2550\u2561 bc8fa2b9-0803-40e7-af91-2196a11aae44\nmd\"\"\"\n\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nEscreva a fun\u00e7\u00e3o `raiz` que retorna a ra\u00edz quadrada de todos os elementos do vetor `v\u2084`.\n\n\"\"\"\n\n# \u2554\u2550\u2561 729eaff4-7aa1-4db1-a04f-57216175b029\nraiz(v\u2084) = missing\n\n# \u2554\u2550\u2561 5eb8ff96-2adf-465d-bf8c-bd4e64fa6342\nmd\"\"\"\nPodemos ainda utilizar a sintaxe *list comprehension* para criarmos uma tabuada em apenas uma linha!\n\"\"\"\n\n# \u2554\u2550\u2561 0d420048-c4f9-4149-b25b-149ef77f3264\n[linha * coluna for linha in 1:10, coluna in 1:10]\n\n# \u2554\u2550\u2561 f8f272e0-17ca-47e5-8dd0-577eb6322c90\nmd\"\"\"\n\n## 6. Interatividade\n\nO ambiente [Pluto](https://github.com/fonsp/Pluto.jl) e o pacote [PlutoUI](https://github.com/fonsp/PlutoUI.jl) proporciona uma s\u00e9rie de recursos interativos a partir da macro `@bind`. Esses recursos permitem que o valor de uma vari\u00e1vel seja alterado de acordo com a intera\u00e7\u00e3o entre o usu\u00e1rio e o notebook.\n\nO recurso **Slider** funciona como uma barra de deslizamento para vari\u00e1veis que assumem valores cont\u00ednuos. A sua sintaxe \u00e9 dada por:\n\n```julia\n@bind nome_var Slider(inicio:passo:fim)\n```\n\nO *slider* abaixo mostra o teor de Cu (%) em uma amostra...\n\n\"\"\"\n\n# \u2554\u2550\u2561 e137bf5e-d795-49ac-b641-b5f7364cfac9\n@bind CU Slider(0.05:0.01:3.00, default=1.0, show_value=true)\n\n# \u2554\u2550\u2561 fae5e284-66e7-4599-9905-f0d5e64387ea\nmd\" A amostra apresenta $CU % de Cu\"\n\n# \u2554\u2550\u2561 9ee680a7-2866-4f1b-84af-0fdb737246ad\nmd\"\"\"\nUm outro elemento interativo \u00e9 o **TextField**, que se refere a uma caixa de texto. Sua sintaxe \u00e9 definida como:\n\n```julia\n@bind nome_var TextField()\n```\n\n\"\"\"\n\n# \u2554\u2550\u2561 6c7e7d3e-d544-4b59-9504-e28fe2cc3619\nmd\"\"\"\n\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nCom seus conhecimentos geol\u00f3gicos, tente identificar qual rocha \u00e9 exibida na Figura 01. Escreva o nome da rocha (apenas com letras min\u00fasculas) na caixa de texto abaixo.\n\n\"\"\"\n\n# \u2554\u2550\u2561 2fd0708e-b225-4f8c-8f4f-b46305e06366\nmd\"\"\"\n![rocha](http://lh3.googleusercontent.com/-4dxg2gAuqgk/VeqttO0BqSI/AAAAAAAAKq0/j_G5leYIfIg/s720/01465%252520IMG_20150809_104742%252520mylonite.jpg)\n\n_**Figura 01:** Identifique a rocha acima. Extra\u00eddo de [sandatlas.org](https://www.sandatlas.org/mylonite/)._\n\"\"\"\n\n# \u2554\u2550\u2561 98a32d2d-7173-406d-92cf-dfa847d47c49\nmd\" Rocha: $(@bind rocha TextField())\"\n\n# \u2554\u2550\u2561 df1f77ac-bbf5-4d42-91bf-e634ebee4bb9\nmd\"\"\" Podemos tamb\u00e9m utilizar o recurso interativo **Select** que, por sua vez, atua como uma lista suspensa. Esse comando possui a seguinte sintaxe:\n\n```julia\n@bind nome_var Select([\"elem_1\", \"elem_2\", ..., \"elem_n\"])\n```\n\n\"\"\"\n\n# \u2554\u2550\u2561 ba7ce05c-cae6-4eec-9760-e4e253986061\nmd\"\"\"\n\n#### \ud83d\udd8a\ufe0f Exerc\u00edcio\n\nNa lista suspensa seguir, selecione a op\u00e7\u00e3o que corresponda \u00e0 correta subclasse do silicato exibido na Figura 02.\n\n\"\"\"\n\n# \u2554\u2550\u2561 b844b981-2922-4056-811d-8c61b904d996\nmd\"\"\"\n![silicato](https://geology.com/minerals/photos/titanite-crystal.jpg)\n\n_**Figura 02:** Identifique a subclasse do silicato acima. Extra\u00eddo de: [geology.com](https://geology.com/minerals/titanite.shtml)._\n\"\"\"\n\n# \u2554\u2550\u2561 87832d6c-525d-4060-a1e8-d06a611b5dfe\nmd\"\"\" Subclasse: $(@bind silicato Select([\"Tectossilicato\",\"Filossilicato\",\n\t\t\t\t\t\t\t\t\t\t  \"Inossilicato\",\"Ciclossilicato\",\n\t\t\t\t\t\t\t\t\t\t  \"Sorossilicato\",\"Nesossilicato\"]))\n\"\"\"\n\n# \u2554\u2550\u2561 a93dd751-a6b3-454d-9f70-b864fdbbd968\nmd\"\"\"\n## Refer\u00eancias\n\n*Dauricio, J. S. **Algoritmos e L\u00f3gica de Programa\u00e7\u00e3o**. Londrina: Editora e Distribuidora Educacional S.A., 2015.*\n\"\"\"\n\n# \u2554\u2550\u2561 8f866ee4-ce44-4d42-be56-7764168c1c71\nmd\"\"\"\n## Recursos adicionais\n\nAbaixo, s\u00e3o listados alguns recursos complementares a este notebook:\n\n> [Documenta\u00e7\u00e3o da Linguagem Julia](https://docs.julialang.org/en/v1/)\n\n> [YouTube - Julia Language](https://www.youtube.com/c/TheJuliaLanguage)\n\n> [YouTube - Programa\u00e7\u00e3o Din\u00e2mica](https://www.youtube.com/playlist?list=PL5TJqBvpXQv4TO4Y_JZExBCBzNXDLYUCj)\n\"\"\"\n\n# \u2554\u2550\u2561 ce463e41-8cb2-46cb-9656-1503997a883e\nmd\"\"\"\n## Pacotes utilizados\n\nOs seguintes pacotes foram utilizados neste notebook:\n\n|                    Pacote                     |      Descri\u00e7\u00e3o      |\n|:---------------------------------------------:|:-------------------:|\n|[PlutoUI](https://github.com/fonsp/PlutoUI.jl) | Widgets interativos |\n\n\"\"\"\n\n# \u2554\u2550\u2561 bb899ab7-b3d8-493f-aaa6-85a4710a6690\nbegin\n\thint(text) = Markdown.MD(Markdown.Admonition(\"hint\", \"Dica\", [text]))\n\n\talmost(text) = Markdown.MD(Markdown.Admonition(\"warning\", \"Quase l\u00e1!\", [text]))\n\n\tstill_missing(text=md\"Troque `missing` pela sua resposta.\") = Markdown.MD(Markdown.Admonition(\"warning\", \"Aqui vamos n\u00f3s!\", [text]))\n\n\tkeep_working(text=md\"A resposta n\u00e3o est\u00e1 correta \ud83d\ude14\") = Markdown.MD(Markdown.Admonition(\"danger\", \"N\u00e3o desanime, voc\u00ea est\u00e1 quase l\u00e1!\", [text]))\n\n\tyays = [md\"Fant\u00e1stico!\", md\"\u00d3timo!\", md\"Yay \u2764\", md\"Legal! \ud83c\udf89\", md\"Muito bem!\", md\"Bom trabalho!\", md\"Voc\u00ea conseguiu a resposta certa!\", md\"Vamos seguir para pr\u00f3xima se\u00e7\u00e3o.\"]\n\n\tcorrect(text=rand(yays)) = Markdown.MD(Markdown.Admonition(\"correct\", \"Certa resposta!\", [text]))\n\n\tnot_defined(variable_name) = Markdown.MD(Markdown.Admonition(\"danger\", \"Opa!\", [md\"Tenha certeza que definiu uma vari\u00e1vel chamada **$(Markdown.Code(string(variable_name)))**\"]))\nend;\n\n# \u2554\u2550\u2561 a4f3b825-6806-4c64-bbe0-6d7383015d68\nbegin\n\ts1 = false\n\t_rec = recurso(12500000,2.7,5)\n\tif ismissing(_rec)\n\t\tstill_missing()\n\telseif _rec \u2248 (12500000 * 2.7 * 5) / 100\n\t\ts1 = true\n\t\tcorrect()\n\telseif _rec isa Number\n\t\talmost(md\"A f\u00f3rmula n\u00e3o est\u00e1 certa...\")\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 3ccac0bc-9f78-4ece-988b-ba832811e538\nhint(md\"Algu\u00e9m me contou que a f\u00f3rmula \u00e9 $\\frac{V\u03c1T}{100}$...\")\n\n# \u2554\u2550\u2561 a84b90c1-fa85-499a-9fa4-ac1f33ed8469\nbegin\n\ts2 = false\n\tif ismissing(v\u2083)\n\t\tstill_missing()\n\telseif v\u2083 == v\u2082[3:end]\n\t\ts2 = true\n\t\tcorrect()\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 e2c42c9e-abbb-4a33-844d-ef0f0c9b4939\nhint(md\"Utilize `end` como \u00edndice final.\")\n\n# \u2554\u2550\u2561 33b81b24-ca33-4dd3-a85e-0371ca8623e8\nbegin\n\ts3 = false\n\t_rcktype = tiporocha.([\"gabro\",\"gnaisse\",\"ritmito\"])\n\tif all(ismissing.(_rcktype))\n\t\tstill_missing()\n\telseif all(_rcktype .== [\"\u00edgnea\",\"metam\u00f3rfica\",\"sedimentar\"])\n\t\ts3 = true\n\t\tcorrect()\n\telseif _rcktype \u2286 [\"\u00edgnea\",\"metam\u00f3rfica\",\"sedimentar\"]\n\t\talmost(md\"A resposta n\u00e3o est\u00e1 100% correta...\")\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 e462ca14-b38a-46b0-8faa-2261a2cfc150\nhint(md\"Basta escrever uma sequ\u00eancia de `if rocha == \\\"gabro\\\" return \\\"\u00edgnea\\\" end`\")\n\n# \u2554\u2550\u2561 7d12857d-d767-49e5-b7de-11e696f1d84a\nbegin\n\ts4 = false\n\t_sqrt = raiz([1 4; 9 16])\n\tif ismissing(_sqrt)\n\t\tstill_missing()\n\telseif _sqrt == [1 2; 3 4]\n\t\ts4 = true\n\t\tcorrect()\n\telseif _sqrt == [1, 2, 3, 4]\n\t\talmost(md\"Tente usar a dica!\")\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 a9f386e1-8c6e-40e7-ba67-b3792c50f87e\nhint(md\"A nota\u00e7\u00e3o list comprehension `[f(x) for v in v\u2084]` pode ser bem \u00fatil!\")\n\n# \u2554\u2550\u2561 f1080b76-7564-4419-884f-70a49bfe9d57\nbegin\n\ts5 = false\n\tif rocha == \"\"\n\t\talmost(md\"Identifique a rocha!\")\n\telseif rocha == \"milonito\"\n\t\ts5 = true\n\t\tcorrect()\n\telseif rocha == \"protomilonito\" || rocha == \"ultramilonito\"\n\t\talmost(md\"\"\"A porcentagem de matriz \"triturada\" \u00e9 de 50% a 90%.\"\"\")\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 844171ee-dfc3-4985-a6a1-7aa7986d1ac4\nhint(md\"Essa rocha \u00e9 um t\u00edpico produto de recristaliza\u00e7\u00e3o din\u00e2mica!\")\n\n# \u2554\u2550\u2561 8b0df84d-a112-49ec-9f7b-c6e5efd45c48\nbegin\n\ts6 = false\n\tif silicato == \"Nesossilicato\"\n\t\ts6 = true\n\t\tcorrect()\n\telse\n\t\tkeep_working()\n\tend\nend\n\n# \u2554\u2550\u2561 55298aee-618b-4704-98ff-e81fdbddd9a7\nhint(md\"A f\u00f3rmula qu\u00edmica deste silicato \u00e9 `CaTiO(SiO\u2084)`.\")\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\n\n[compat]\nPlutoUI = \"~0.7.14\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[HypertextLiteral]]\ngit-tree-sha1 = \"72053798e1be56026b81d4e2682dbe58922e5ec9\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.0\"\n\n[[IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"a8709b968a1ea6abc2dc1967cb1db6ac9a00dfb6\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.0.5\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"d1fb76655a95bf6ea4348d7197b22e889a4375f4\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.14\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250075fe8fd0-2563-11ec-30ef-d15cdc3d4e63\n# \u255f\u2500d4b5618f-f1a7-4483-b738-fb94e1e2bbbe\n# \u255f\u2500deee1735-37ee-4c97-a4df-8fcb61e95d6a\n# \u255f\u2500488dcacd-109d-41f2-b904-3d17193e6190\n# \u255f\u25006ca3e113-02db-4cef-ad9e-3941ac7d7a6d\n# \u255f\u250014db5525-7c0a-433b-a23c-088db728f46b\n# \u255f\u2500c111da75-d294-4def-93fb-56953c3585ad\n# \u2560\u25507ceb9c27-0310-4f97-8349-91286c1f9235\n# \u2560\u2550218bb8c3-729b-43c7-9a53-d3b579cf2d21\n# \u255f\u25003b3e6955-1573-4620-8109-d80b71cf0708\n# \u2560\u2550f2e6adcd-9095-496f-a8e3-88069537015c\n# \u2560\u2550870cbb76-b0c3-4d14-be22-86e0d34ebe58\n# \u255f\u250055a2c622-0f9f-45db-b534-55873c0759d4\n# \u255f\u2500449c91c3-7f03-4ffd-95e7-02cdb58323fd\n# \u2560\u25508d463f50-d694-41eb-bbad-c40f93471852\n# \u255f\u25004d31b989-7a7d-43ca-a1f8-831db58f1c8e\n# \u2560\u2550b0d01238-1213-45c3-9bef-a443afc6b87c\n# \u2560\u25502cce4c5e-7890-4c6c-bd7c-eecc8437bef3\n# \u2560\u25501b454935-c858-4817-928d-da116011d7b0\n# \u2560\u255096ced3bd-69e7-490c-b8be-b06a30049a31\n# \u255f\u25003de2d16f-8849-4462-9ef3-0cc1830285c6\n# \u255f\u2500d28119c3-6800-430d-9ffe-d6f25e8e5c2a\n# \u2560\u2550bb495116-8ff9-4a66-9b66-6f6fe990c5d9\n# \u2560\u2550a63d4a04-0ceb-4e4f-90ba-30dc798e1b48\n# \u255f\u25009cf1d023-1421-4adb-af35-64aa6a8ddb53\n# \u2560\u25505b168a45-b5a2-4415-80f4-e2347c21730a\n# \u2560\u25501a1ac9a0-b8f0-46d1-9d89-21b6bc0e6aec\n# \u255f\u2500784426f8-ac63-4555-adb2-cbfb9b37fdda\n# \u2560\u2550ffc5e222-8ae8-4314-a616-e4588d7808fe\n# \u255f\u2500a4f3b825-6806-4c64-bbe0-6d7383015d68\n# \u255f\u25003ccac0bc-9f78-4ece-988b-ba832811e538\n# \u255f\u25005dc76f3b-badf-4951-9eb4-f61ca66dfb98\n# \u2560\u2550d349ac4d-44bb-4505-a6c4-3096d8c48ba0\n# \u2560\u25509e181c4a-a759-4f92-8444-6a23387a20b9\n# \u255f\u2500d39f503a-158e-4774-851e-d786014b8f2b\n# \u2560\u2550b5387324-df28-422d-8e74-4fefa8054e7f\n# \u2560\u2550570f62db-edd7-429d-a084-2686f4b6923c\n# \u255f\u25007e8a4e33-0e9c-45f5-b8bc-e2adece0806d\n# \u2560\u2550204bcedc-4123-4845-bcac-493ce3544582\n# \u2560\u255092b0cb5f-41a1-420f-8b81-3b967db8138f\n# \u255f\u25007471120d-6e00-46be-be11-649c8cdbb32c\n# \u2560\u2550b31b883d-5a69-4e04-b8b5-cedca81dcf9a\n# \u255f\u2500c7f94dcd-64a2-4e97-b38d-363dc6f9f757\n# \u2560\u25502a25a884-44cf-4460-ab23-02e728201f90\n# \u255f\u2500e127bdf8-2a53-4534-a628-1463a5735f41\n# \u2560\u25500806feb3-33eb-402c-92d2-af0b153834ba\n# \u2560\u25501a5f467b-7244-4587-b01c-61fd22bfbb9b\n# \u255f\u25004b1ec558-5f94-417a-aa3c-d13a0039a6a0\n# \u2560\u25506bee210a-11cc-40c4-bfb0-d14a6b2495b4\n# \u2560\u255010cc97dd-a390-4d4a-9644-20b654ff66bb\n# \u255f\u250004738501-16fa-43e1-b640-47682c155bcb\n# \u2560\u2550f9317c0c-2389-43a8-8b91-f8b2f278e258\n# \u255f\u2500a84b90c1-fa85-499a-9fa4-ac1f33ed8469\n# \u255f\u2500e2c42c9e-abbb-4a33-844d-ef0f0c9b4939\n# \u255f\u25008253149c-e1b0-4e3f-9f5b-909621dba86e\n# \u2560\u25504a778055-7ade-4275-aa87-95c121b31232\n# \u2560\u2550ca5453f5-1768-4f59-b09d-e40bb4981622\n# \u2560\u2550217945a3-919b-4c5f-afb0-6eaadf4e9022\n# \u255f\u25001f0abfbe-49cd-4529-8f4d-65f8286b6f0e\n# \u2560\u25506fcc1433-c348-4825-8503-782304ed508b\n# \u255f\u250033b81b24-ca33-4dd3-a85e-0371ca8623e8\n# \u255f\u2500e462ca14-b38a-46b0-8faa-2261a2cfc150\n# \u255f\u25003cfbddfb-be59-4019-965f-13bdbac2295a\n# \u2560\u2550abc428c6-b762-47bc-b5ac-7abedd1a7021\n# \u255f\u2500bc8fa2b9-0803-40e7-af91-2196a11aae44\n# \u2560\u2550729eaff4-7aa1-4db1-a04f-57216175b029\n# \u255f\u25007d12857d-d767-49e5-b7de-11e696f1d84a\n# \u255f\u2500a9f386e1-8c6e-40e7-ba67-b3792c50f87e\n# \u255f\u25005eb8ff96-2adf-465d-bf8c-bd4e64fa6342\n# \u2560\u25500d420048-c4f9-4149-b25b-149ef77f3264\n# \u255f\u2500f8f272e0-17ca-47e5-8dd0-577eb6322c90\n# \u255f\u2500e137bf5e-d795-49ac-b641-b5f7364cfac9\n# \u255f\u2500fae5e284-66e7-4599-9905-f0d5e64387ea\n# \u255f\u25009ee680a7-2866-4f1b-84af-0fdb737246ad\n# \u255f\u25006c7e7d3e-d544-4b59-9504-e28fe2cc3619\n# \u255f\u25002fd0708e-b225-4f8c-8f4f-b46305e06366\n# \u255f\u250098a32d2d-7173-406d-92cf-dfa847d47c49\n# \u255f\u2500f1080b76-7564-4419-884f-70a49bfe9d57\n# \u255f\u2500844171ee-dfc3-4985-a6a1-7aa7986d1ac4\n# \u255f\u2500df1f77ac-bbf5-4d42-91bf-e634ebee4bb9\n# \u255f\u2500ba7ce05c-cae6-4eec-9760-e4e253986061\n# \u255f\u2500b844b981-2922-4056-811d-8c61b904d996\n# \u255f\u250087832d6c-525d-4060-a1e8-d06a611b5dfe\n# \u255f\u25008b0df84d-a112-49ec-9f7b-c6e5efd45c48\n# \u255f\u250055298aee-618b-4704-98ff-e81fdbddd9a7\n# \u255f\u2500a93dd751-a6b3-454d-9f70-b864fdbbd968\n# \u255f\u25008f866ee4-ce44-4d42-be56-7764168c1c71\n# \u255f\u2500ce463e41-8cb2-46cb-9656-1503997a883e\n# \u255f\u2500bb899ab7-b3d8-493f-aaa6-85a4710a6690\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "b2e5507a77a43c502d703a7b340757e08e7d1331", "size": 25804, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "1-logica_de_programacao.jl", "max_stars_repo_name": "fnaghetini/intro-to-geostats", "max_stars_repo_head_hexsha": "6f82ddb50f6b0aa6c86d0ef2095b1ca3791d0420", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2021-10-31T12:49:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-24T11:39:45.000Z", "max_issues_repo_path": "1-logica_de_programacao.jl", "max_issues_repo_name": 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NO\n2. NO\n\n", "lm_q1_score": 0.22270014914315836, "lm_q2_score": 0.334589441253186, "lm_q1q2_score": 0.07451311846881055}}
{"text": "# ---\n# title: 139. Word Break\n# id: problem139\n# author: zhwang\n# date: 2022-02-18\n# difficulty: Medium\n# categories: Dynamic Programming\n# link: <https://leetcode.com/problems/word-break/description/>\n# hidden: true\n# ---\n# \n# Given a **non-empty** string _s_ and a dictionary _wordDict_ containing a list\n# of **non-empty** words, determine if _s_ can be segmented into a space-\n# separated sequence of one or more dictionary words.\n# \n# **Note:**\n# \n#   * The same word in the dictionary may be reused multiple times in the segmentation.\n#   * You may assume the dictionary does not contain duplicate words.\n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: s = \"leetcode\", wordDict = [\"leet\", \"code\"]\n#     Output: true\n#     Explanation: Return true because \"leetcode\" can be segmented as \"leet code\".\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: s = \"applepenapple\", wordDict = [\"apple\", \"pen\"]\n#     Output: true\n#     Explanation: Return true because \"applepenapple\" can be segmented as \"apple pen apple\".\n#                  Note that you are allowed to reuse a dictionary word.\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: s = \"catsandog\", wordDict = [\"cats\", \"dog\", \"sand\", \"and\", \"cat\"]\n#     Output: false\n#     \n# \n# \n## @lc code=start\nusing LeetCode\n\n## BFS\nfunction word_break_bfs(s::String, word_dict::Vector{String})::Bool\n    n, word_dict = length(s), sort!(word_dict; by=x -> length(x))\n    valids, valid_pos = fill(false, n), [0]\n    while !isempty(valid_pos)\n        pos = popfirst!(valid_pos)\n        for word in word_dict\n            (new_pos = pos + length(word)) > n && break\n            new_pos == n && s[(pos + 1):new_pos] == word && return true\n            if !valids[new_pos] && s[(pos + 1):new_pos] == word\n                valids[new_pos] = true\n                push!(valid_pos, new_pos)\n            end\n        end\n    end\n    return false\nend\n\n## Dynamic Programming\nfunction word_break(s::String, word_dict::Vector{String})::Bool\n    lens = sort!(unique!(length.(word_dict)))\n    dp = append!([true], fill(false, length(s)))\n    for i in eachindex(dp)\n        for len in lens\n            i > len || break\n            dp[i - len] && s[(i - len):(i - 1)] \u2208 word_dict && (dp[i] = true)\n        end\n    end\n    return last(dp)\nend\n## @lc code=end\n## @lc test=start\n@testset \"139.word-break.jl\" begin\n    tcase1 = (\"leetcode\", [\"leet\", \"code\"])\n    tcase2 = (\"applepenapple\", [\"apple\", \"pen\"])\n    tcase3 = (\"bb\", [\"a\", \"b\", \"bbb\", \"bbbb\"])\n    fcase1 = (\"catsandog\", [\"cats\", \"dog\", \"sand\", \"and\", \"cat\"])\n    fcase2 = (\"a\", [\"b\"])\n    fcase3 = (\"a\"^150 * \"b\", [\"a\"^i for i in 1:10])\n    @test word_break(tcase1...) && word_break_bfs(tcase1...)\n    @test word_break(tcase2...) && word_break_bfs(tcase2...)\n    @test word_break(tcase3...) && word_break_bfs(tcase3...)\n    @test !word_break(fcase1...) && !word_break_bfs(fcase1...)\n    @test !word_break(fcase2...) && !word_break_bfs(fcase2...)\n    @test !word_break(fcase3...) && !word_break_bfs(fcase3...)\nend\n## @lc test=end\n\n## @lc info=start\n# link: [solution to 139](https://leetcode-cn.com/problems/word-break/solution/139-dan-ci-chai-fen-bfs-sou-suo-by-rex-r-jgjc/)\n## @lc info=end\n\n\n\n\n\n\n\n", "meta": {"hexsha": "34887b9c0b4d61a419ace34e25c65ccae0c8693e", "size": 3199, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-03/139.word-break.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/submited/date-03/139.word-break.jl", "max_issues_repo_name": "RexWzh/leetcode_note.jl", "max_issues_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/submited/date-03/139.word-break.jl", "max_forks_repo_name": "RexWzh/leetcode_note.jl", "max_forks_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0818181818, "max_line_length": 126, "alphanum_fraction": 0.5876836511, "num_tokens": 958, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.16026602831693942, "lm_q1q2_score": 0.07450792840231711}}
{"text": "export Lens, set, get, modify\nexport @lens\nexport set, get, modify\nusing ConstructionBase\nexport setproperties\nexport constructorof\n\n\nimport Base: get\nusing Base: setindex, getproperty\n\n\"\"\"\n    Lens\n\nA `Lens` allows to access or replace deeply nested parts of complicated objects.\n\n# Example\n```jldoctest\njulia> using Setfield\n\njulia> struct T;a;b; end\n\njulia> obj = T(\"AA\", \"BB\")\nT(\"AA\", \"BB\")\n\njulia> lens = @lens _.a\n(@lens _.a)\n\njulia> get(obj, lens)\n\"AA\"\n\njulia> set(obj, lens, 2)\nT(2, \"BB\")\n\njulia> obj\nT(\"AA\", \"BB\")\n\njulia> modify(lowercase, obj, lens)\nT(\"aa\", \"BB\")\n```\n\n# Interface\nConcrete subtypes of `Lens` have to implement\n* `set(obj, lens, val)`\n* `get(obj, lens)`\n\nThese must be pure functions, that satisfy the three lens laws:\n\n```jldoctest; output = false, setup = :(using Setfield; obj = (a=\"A\", b=\"B\"); lens = @lens _.a; val = 2; val1 = 10; val2 = 20)\n@assert get(set(obj, lens, val), lens) == val\n        # You get what you set.\n@assert set(obj, lens, get(obj, lens)) == obj\n        # Setting what was already there changes nothing.\n@assert set(set(obj, lens, val1), lens, val2) == set(obj, lens, val2)\n        # The last set wins.\n\n# output\n\n```\n\nSee also [`@lens`](@ref), [`set`](@ref), [`get`](@ref), [`modify`](@ref).\n\"\"\"\nabstract type Lens end\n\n\"\"\"\n    modify(f, obj, l::Lens)\n\nReplace a deeply nested part `x` of `obj` by `f(x)`. See also [`Lens`](@ref).\n\"\"\"\nfunction modify end\n\n\n\"\"\"\n    get(obj, l::Lens)\n\nAccess a deeply nested part of `obj`. See also [`Lens`](@ref).\n\"\"\"\nfunction get end\n\n\"\"\"\n    set(obj, l::Lens, val)\n\nReplace a deeply nested part of `obj` by `val`. See also [`Lens`](@ref).\n\"\"\"\nfunction set end\n\n@inline function modify(f, obj, l::Lens)\n    old_val = get(obj, l)\n    new_val = f(old_val)\n    set(obj, l, new_val)\nend\n\nstruct IdentityLens <: Lens end\nget(obj, ::IdentityLens) = obj\nset(obj, ::IdentityLens, val) = val\n\nstruct PropertyLens{fieldname} <: Lens end\n\nfunction get(obj, l::PropertyLens{field}) where {field}\n    getproperty(obj, field)\nend\n\n@generated function set(obj, l::PropertyLens{field}, val) where {field}\n    Expr(:block,\n         Expr(:meta, :inline),\n        :(setproperties(obj, ($field=val,)))\n       )\nend\n\nstruct ComposedLens{LO, LI} <: Lens\n    outer::LO\n    inner::LI\nend\n\ncompose() = IdentityLens()\ncompose(l::Lens) = l\ncompose(::IdentityLens, ::IdentityLens) = IdentityLens()\ncompose(::IdentityLens, l::Lens) = l\ncompose(l::Lens, ::IdentityLens) = l\ncompose(outer::Lens, inner::Lens) = ComposedLens(outer, inner)\nfunction compose(l1::Lens, ls::Lens...)\n    # We can build _.a.b.c as (_.a.b).c or _.a.(b.c)\n    # The compiler prefers (_.a.b).c\n    compose(l1, compose(ls...))\nend\n\n\"\"\"\n    lens\u2081 \u2218 lens\u2082\n    compose([lens\u2081, [lens\u2082, [lens\u2083, ...]]])\n\nCompose lenses `lens\u2081`, `lens\u2082`, ..., `lens\u2099` to access nested objects.\n\n# Example\n```jldoctest\njulia> using Setfield\n\njulia> obj = (a = (b = (c = 1,),),);\n\njulia> la = @lens _.a\n       lb = @lens _.b\n       lc = @lens _.c\n       lens = la \u2218 lb \u2218 lc\n(@lens _.a.b.c)\n\njulia> get(obj, lens)\n1\n```\n\"\"\"\nBase.:\u2218(l1::Lens, l2::Lens) = compose(l1, l2)\n\nfunction get(obj, l::ComposedLens)\n    inner_obj = get(obj, l.outer)\n    get(inner_obj, l.inner)\nend\n\nfunction set(obj,l::ComposedLens, val)\n    inner_obj = get(obj, l.outer)\n    inner_val = set(inner_obj, l.inner, val)\n    set(obj, l.outer, inner_val)\nend\n\nstruct IndexLens{I <: Tuple} <: Lens\n    indices::I\nend\n\nBase.@propagate_inbounds function get(obj, l::IndexLens)\n    getindex(obj, l.indices...)\nend\nBase.@propagate_inbounds function set(obj, l::IndexLens, val)\n    setindex(obj, val, l.indices...)\nend\n\n\"\"\"\n    ConstIndexLens{I}\n\nLens with index stored in type parameter.  This is useful for type-stable\n[`get`](@ref) and [`set`](@ref) operations on tuples and named tuples.\n\nThis lens can be constructed by, e.g., `@lens _[\\$1]`.  Complex expression\nmust be wrapped with `\\$(...)` like `@lens _[\\$(length(xs))]`.\n\n# Examples\n```jldoctest\njulia> using Setfield\n\njulia> get((1, 2.0), @lens _[\\$1])\n1\n\njulia> Base.promote_op(get, typeof.(((1, 2.0), @lens _[\\$1]))...)\nInt64\n\njulia> Base.promote_op(get, typeof.(((1, 2.0), @lens _[1]))...) !== Int\ntrue\n```\n\"\"\"\nstruct ConstIndexLens{I} <: Lens end\n\nBase.@propagate_inbounds get(obj, ::ConstIndexLens{I}) where I = obj[I...]\n\nBase.@propagate_inbounds set(obj, ::ConstIndexLens{I}, val) where I =\n    setindex(obj, val, I...)\n\n@generated function set(obj::Union{Tuple, NamedTuple},\n                        ::ConstIndexLens{I},\n                        val) where I\n    if length(I) == 1\n        n, = I\n        args = map(1:length(obj.types)) do i\n            i == n ? :val : :(obj[$i])\n        end\n        quote\n            $(Expr(:meta, :inline))\n            ($(args...),)\n        end\n    else\n        quote\n            throw(ArgumentError($(string(\n                \"A `Tuple` and `NamedTuple` can only be indexed with one \",\n                \"integer.  Given: $I\"))))\n        end\n    end\nend\n\nstruct DynamicIndexLens{F} <: Lens\n    f::F\nend\n\nBase.@propagate_inbounds get(obj, I::DynamicIndexLens) = obj[I.f(obj)...]\n\nBase.@propagate_inbounds set(obj, I::DynamicIndexLens, val) =\n    setindex(obj, val, I.f(obj)...)\n\n\"\"\"\n    FunctionLens(f)\n    @lens f(_)\n\nLens with [`get`](@ref) method definition that simply calls `f`.\n[`set`](@ref) method for each function `f` must be implemented manually.\nUse `methods(set, (Any, Setfield.FunctionLens, Any))` to get a list of\nsupported functions.\n\nNote that `FunctionLens` flips the order of composition; i.e.,\n`(@lens f(_)) \u2218 (@lens g(_)) == @lens g(f(_))`.\n\n# Example\n```jldoctest\njulia> using Setfield\n\njulia> obj = ((1, 2), (3, 4));\n\njulia> lens = (@lens first(_)) \u2218 (@lens last(_))\n(@lens last(first(_)))\n\njulia> get(obj, lens)\n2\n\njulia> set(obj, lens, '2')\n((1, '2'), (3, 4))\n```\n\n# Implementation\n\nTo use `myfunction` as a lens, define a `set` method with the following\nsignature:\n\n```julia\nSetfield.set(obj, ::typeof(@lens myfunction(_)), val) = ...\n```\n\n`typeof` is used above instead of `FunctionLens` because how actual\ntype of `@lens myfunction(_)` is implemented is not the part of stable\nAPI.\n\"\"\"\nstruct FunctionLens{f} <: Lens end\nFunctionLens(f) = FunctionLens{f}()\n\nget(obj, ::FunctionLens{f}) where f = f(obj)\n\nBase.@deprecate constructor_of(T) constructorof(T)\nBase.@deprecate get(lens::Lens, obj)       get(obj, lens)\nBase.@deprecate set(lens::Lens, obj, val)  set(obj, lens, val)\nBase.@deprecate modify(f, lens::Lens, obj) modify(f, obj, lens)\n", "meta": {"hexsha": "147415fd82539db6ad40bb941d55fe835da96a62", "size": 6431, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lens.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Setfield.jl-efcf1570-3423-57d1-acb7-fd33fddbac46", "max_stars_repo_head_hexsha": "4536fae5c60fcea2cda4016b770381cfd4e6a774", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/lens.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/Setfield.jl-efcf1570-3423-57d1-acb7-fd33fddbac46", "max_issues_repo_head_hexsha": "4536fae5c60fcea2cda4016b770381cfd4e6a774", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/lens.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/Setfield.jl-efcf1570-3423-57d1-acb7-fd33fddbac46", "max_forks_repo_head_hexsha": "4536fae5c60fcea2cda4016b770381cfd4e6a774", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.7243816254, "max_line_length": 126, "alphanum_fraction": 0.6229202301, "num_tokens": 2006, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4186969093556867, "lm_q2_score": 0.17781086947804958, "lm_q1q2_score": 0.07444886150030677}}
{"text": "# usage example for package developer:\r\n#\r\n#     import ThresholdAPI: AbstractThresholdAlgorithm,\r\n#                          find_threshold\r\n\r\n\"\"\"\r\n    AbstractThresholdAlgorithm <: AbstractImageFilter\r\n\r\nThe root type for `HistogramThreshold` package.\r\n\r\nAny concrete threshold algorithm shall subtype it to support\r\n[`find_threshold`](@ref) API.\r\n\r\n# Examples\r\n\r\nAll algorithms in HistogramThreshold are called in the following pattern:\r\n\r\n```julia\r\n# Generate an algorithm instance.\r\nf = Otsu()\r\n\r\n# If you already have a histogram and concomitant edges at hand\r\n# you can supply those directly.\r\nt = find_threshold(histogram, edges, f)\r\n\r\n# Alternatively, you can supply the raw data and specify histogram \r\n# construction details.\r\nt = find_threshold(data, f; nbins = 64) \r\n```\r\n\r\nFor more examples, please check [`find_threshold`](@ref) and concret\r\nalgorithms.\r\n\"\"\"\r\nabstract type AbstractThresholdAlgorithm <: AbstractImageFilter end\r\n\r\n\r\nfunction find_threshold(histogram::AbstractArray, edges::AbstractArray, f::AbstractThresholdAlgorithm)\r\n    return f(histogram, edges)\r\nend\r\n\r\nfunction find_threshold(data::AbstractArray, f::AbstractThresholdAlgorithm ; nbins::Union{Int,Nothing} = 256)\r\n    edges, counts  = build_histogram(data, nbins) \r\n    #=\r\n        The `counts` array stores at index 0 the frequencies that were below the\r\n        first bin edge. Since we are seeking a threshold over the interval\r\n        partitioned by `edges` we need to discard the first bin in `counts`\r\n        so that the dimensions of `edges` and `counts` match.\r\n    =#   \r\n    return f(view(counts, 1:lastindex(counts)), edges)\r\nend\r\n\r\n\r\n### Docstrings\r\n\r\n\"\"\"\r\n    find_threshold(data::AbstractArray, f::AbstractThresholdAlgorithm; nbins)\r\n    find_threshold(histogram::AbstractArray, edges::AbstractArray, f::AbstractThresholdAlgorithm)\r\n\r\nFind a suitable threshold in `data` using algorithm `f` upon constructing a histogram with `nbins`.\r\nInstead of specifing the raw `data`, you can specify a histogram and accompanying edges directly. \r\n\r\n# Output\r\n\r\nA real number representing a threshold that can be used to split data into two parts. \r\n\r\n# Examples\r\n\r\nJust simply pass an algorithm to `find_threshold`:\r\n\r\n```julia\r\nusing TestImages, HistogramThreshold\r\nimg = testimage(\"cameraman\")\r\nt = find_threshold(img, f ; nbins = 64)\r\n```\r\n\r\n```julia\r\nusing StatsBase, HistogramThreshold\r\ndata = vcat(ones(50,), zeros(50,))\r\nh = fit(Histogram, data)\r\nt = find_threshold(data, f ; nbins = 2)\r\n```\r\n\r\n\"\"\"\r\nfind_threshold", "meta": {"hexsha": "b36c9217bdd299cfeb5f4bc172cf949df0a2c221", "size": 2514, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ThresholdAPI/find_threshold.jl", "max_stars_repo_name": "zygmuntszpak/HistogramThresholding", "max_stars_repo_head_hexsha": "152a08a54e59dfc626446ed8ccf0b1f4a3e6c6c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2021-05-10T01:08:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-15T19:42:59.000Z", "max_issues_repo_path": "src/ThresholdAPI/find_threshold.jl", "max_issues_repo_name": "JuliaImages/HistogramThresholding.jl", "max_issues_repo_head_hexsha": "152a08a54e59dfc626446ed8ccf0b1f4a3e6c6c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-08-03T11:39:14.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-11T04:13:12.000Z", "max_forks_repo_path": "src/ThresholdAPI/find_threshold.jl", "max_forks_repo_name": "zygmuntszpak/HistogramThresholding", "max_forks_repo_head_hexsha": "152a08a54e59dfc626446ed8ccf0b1f4a3e6c6c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-12-20T01:39:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-20T04:21:52.000Z", "avg_line_length": 29.9285714286, "max_line_length": 110, "alphanum_fraction": 0.715592681, "num_tokens": 564, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.15203224546424318, "lm_q1q2_score": 0.07423482100973043}}
{"text": "\"\"\"\n    dataset(name::String)::DataFrame\n\nLoad the dataset associated with `name`.\n\nSome datasets available:\n - `50408`: Gauging (water levels and corresponding discharges) of the Sainte-Anne river in the province of Quebec (Canada).\n\n\n# Examples\n```julia-repl\njulia> RatingCurves.dataset(\"50408\")\n```\n\"\"\"\nfunction dataset(name::String)::DataFrame\n\n    filename = joinpath(dirname(@__FILE__), \"..\", \"data\", string(name, \".csv\"))\n    if isfile(filename)\n        # return DataFrame!(CSV.File(filename))\n        return CSV.read(filename, DataFrame)\n    end\n    error(\"There is no dataset with the name '$name'\")\n\nend", "meta": {"hexsha": "7654db12fadfdba3bc12f32ea852c4c87c2632ca", "size": 613, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data.jl", "max_stars_repo_name": "JuliaExtremes/RatingCurves.jl", "max_stars_repo_head_hexsha": "82512c19f89624443892e24e8fbde3f5b7c00c12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-30T11:25:59.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-30T11:25:59.000Z", "max_issues_repo_path": "src/data.jl", "max_issues_repo_name": "JuliaExtremes/RatingCurves.jl", "max_issues_repo_head_hexsha": "82512c19f89624443892e24e8fbde3f5b7c00c12", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data.jl", "max_forks_repo_name": "JuliaExtremes/RatingCurves.jl", "max_forks_repo_head_hexsha": "82512c19f89624443892e24e8fbde3f5b7c00c12", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5416666667, "max_line_length": 124, "alphanum_fraction": 0.6867862969, "num_tokens": 145, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.15817434679592926, "lm_q1q2_score": 0.07415065115066047}}
{"text": "# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,jl:light\n#     text_representation:\n#       extension: .jl\n#       format_name: light\n#       format_version: '1.4'\n#       jupytext_version: 1.2.4\n#   kernelspec:\n#     display_name: Julia 1.3.1\n#     language: julia\n#     name: julia-1.3\n# ---\n\n# + {\"slideshow\": {\"slide_type\": \"slide\"}, \"cell_type\": \"markdown\"}\n# # `ClimateTools.jl` and `ClimatePlots.jl`\n#\n# [ClimateTools.jl](https://juliaclimate.github.io/ClimateTools.jl/dev/) is a collection of commonly-used tools aimed to ease the typical steps in (1) analyzing climate models outputs from netCDF files that follow CF-conventions and (2) creating climate scenarios. [ClimatePlots.jl](https://juliaclimate.github.io/ClimatePlots.jl/dev/) is the associated plotting library.\n\n# + {\"slideshow\": {\"slide_type\": \"subslide\"}}\nif false #set to true if you have not yet installed packages listed below\n    using Pkg\n    Pkg.add(PackageSpec(name=\"ClimateTools\", rev=\"master\"))\n\n    Pkg.add(\"PyCall\")\n    ENV[\"PYTHON\"]=\"\"\n    Pkg.build(\"PyCall\")\n    Pkg.add(PackageSpec(name=\"ClimatePlots\", rev=\"master\"))\n\n    run(`wget http://esgf-data1.diasjp.net/thredds/fileServer/esg_dataroot/cmip5/output1/MIROC/MIROC5/piControl/day/atmos/day/r1i1p1/v20161012/tas/tas_day_MIROC5_piControl_r1i1p1_20000101-20091231.nc`)\n    run(`mv tas_day_MIROC5_piControl_r1i1p1_20000101-20091231.nc ../inputs/`)\nend\n\n# + {\"slideshow\": {\"slide_type\": \"-\"}, \"cell_type\": \"markdown\"}\n# _Note: `tas_day_MIROC5_piControl_*.nc` was downloaded [here](http://esgf-data1.diasjp.net/thredds/fileServer/esg_dataroot/cmip5/output1/MIROC/MIROC5/piControl/day/atmos/day/r1i1p1/v20161012/tas/tas_day_MIROC5_piControl_r1i1p1_20000101-20091231.nc) by selecting `piControl,day,tas,MIROC5` in [the search engine](https://esgf-node.llnl.gov/search/cmip5/)_\n#\n# ```\n# project=CMIP5, model=MIROC5, Atmosphere and Ocean Research Institute (The University of Tokyo), \n# experiment=pre-industrial control, time_frequency=day, modeling realm=atmos, ensemble=r1i1p1,\n# Description: MIROC5 model output prepared for CMIP5 pre-industrial control \n# ```\n\n# + {\"slideshow\": {\"slide_type\": \"slide\"}, \"cell_type\": \"markdown\"}\n# ## Get Meta-Data From File\n#\n# _Note: uncomment the final line to display the file meta data_\n\n# + {\"slideshow\": {\"slide_type\": \"-\"}}\nusing ClimateTools, ClimatePlots\n\np=\"../inputs\"\nfil=\"$p/tas_day_MIROC5_piControl_r1i1p1_20000101-20091231.nc\"\n#fil=\"$p/clt_day_MIROC5_historical_r4i1p1_19500101-19591231.nc\"\nd=Dataset(fil);\n\n# + {\"slideshow\": {\"slide_type\": \"slide\"}, \"cell_type\": \"markdown\"}\n# ## Read & Plot A Variable\n\n# + {\"slideshow\": {\"slide_type\": \"fragment\"}}\np1=joinpath(dirname(pathof(ClimateTools)),\"../test/data\")\n\n#fil1=\"$p1/orog_fx_GFDL-ESM2G_historicalMisc_r0i0p0.nc\"\nfil1=\"$p1/sresa1b_ncar_ccsm3-example.nc\"\nmodel1 = load(fil1, \"pr\", data_units=\"mm\")\ncontourf(model1, region = \"Mollweide\");\n\n# + {\"slideshow\": {\"slide_type\": \"slide\"}, \"cell_type\": \"markdown\"}\n# ## Extract Subset And Plot Region\n#\n# _Note : see `ClimatePlots.jl/src/maps_definition.jl`_\n\n# + {\"slideshow\": {\"slide_type\": \"fragment\"}}\npoly_reg = [[NaN -65 -80 -80 -65 -65];[NaN 42 42 52 52 42]]\nmodel2 = load(fil, \"tas\", poly=poly_reg)\ncontourf(model2, region = \"Quebec\");\n\n# + {\"slideshow\": {\"slide_type\": \"slide\"}, \"cell_type\": \"markdown\"}\n# ## Inspect Meta Data\n\n# + {\"slideshow\": {\"slide_type\": \"fragment\"}}\n#d\n#model1\nmodel2\n# -\n# ## Read & Plot A Variable (bis)\n\np3=\"../outputs/nctiles-newfiles/interp\"\ntst=sum(occursin.(\"ETAN.nc\",readdir(p3)))>0\nif tst\n    model3 = load(\"$p3/ETAN.nc\", \"ETAN\")\n    contourf(model3, region = \"Mollweide\")\nend\n\n", "meta": {"hexsha": "7ed8b83ea2e914677ebfeb0d72fce2ff25025cbd", "size": 3603, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "DataStructures/02_ClimateTools.jl", "max_stars_repo_name": "abelolivelanna/GlobalOceanNotebooks", "max_stars_repo_head_hexsha": "41230e078c06f30b3124b952118b1599f2dfaf5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DataStructures/02_ClimateTools.jl", "max_issues_repo_name": "abelolivelanna/GlobalOceanNotebooks", "max_issues_repo_head_hexsha": "41230e078c06f30b3124b952118b1599f2dfaf5c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "DataStructures/02_ClimateTools.jl", "max_forks_repo_name": "abelolivelanna/GlobalOceanNotebooks", "max_forks_repo_head_hexsha": "41230e078c06f30b3124b952118b1599f2dfaf5c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.9263157895, "max_line_length": 371, "alphanum_fraction": 0.7063558146, "num_tokens": 1162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1480472036701053, "lm_q1q2_score": 0.07402360183505265}}
{"text": "#------------------------------------------------------------------------------\n\"\"\"\n    printmat([fh::IO],x...;colNames=[],rowNames=[],\n             width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\")\n\nPrint all elements of a matrix (or several) with predefined formatting. It can also handle\nOffsetArrays. StringFmt = \"csv\" prints using a csv format.\n\n# Input\n- `fh::IO`:            (optional) file handle. If not supplied, prints to screen\n- `x::Array(s)`:       (of numbers, dates, strings, ...) to print\n- `colNames::Array`:   of strings with column headers\n- `rowNames::Array`:   of strings with row labels\n- `width::Int`:        (keyword) scalar, minimum width of printed cells\n- `prec::Int`:         (keyword) scalar, precision of printed cells\n- `NoPrinting::Bool`:  (keyword) bool, true: no printing, just return formatted string [false]\n- `StringFmt::String`: (keyword) string, \"\", \"csv\"\n- `cell00::String`:    (keyword) string, for row 0, column 0\n\n# Output\n- str         (if NoPrinting) string, (otherwise nothing)\n\n# Examples\n```\nx = [11 12;21 22]\nprintmat(x)\n```\n```\nx = [1 \"ab\"; Date(2018,10,7) 3.14]\nprintmat(x,width=20,colNames=[\"col 1\",\"col 2\"])\n```\n```\nprintmat([11,12],[21,22])\n```\nCan also call as\n```\nopt = Dict(:rowNames=>[\"1\";\"4\"],:width=>10,:prec=>3,:NoPrinting=>false,:StringFmt=>\"\")\nprintmat(x;colNames=[\"a\",\"b\"],opt...)     #notice ; and ...\n```\n(not all keywords are needed)\n\n# Requires\n- fmtNumPs\n\n# Notice\n- The prefixN and suffixN could potentially be made function inputs. This would allow\na fairly flexible way to format tables.\n\n\nPaul.Soderlind@unisg.ch\n\n\"\"\"\nfunction printmat(fh::IO,x...;colNames=[],rowNames=[],\n                  width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\")\n\n  isempty(x) && return nothing                         #do nothing is isempty(x)\n\n  typeTestQ = any(!=(eltype(x[1])),[eltype(z) for z in x])  #test if eltype(x[i]) differs\n  if typeTestQ                                      #create matrix from tuple created by x...\n    x = hcat(Matrix{Any}(hcat(x[1])),x[2:end]...)   #preserving types of x[i]\n  else\n    x = hcat(x...)\n  end\n\n  (m,n) = (size(x,1),size(x,2))\n\n  (length(rowNames) == 1 < m) && (rowNames = [string(rowNames[1],i) for i = 1:m])  #\"ri\"\n  (length(colNames) == 1 < n) && (colNames = [string(colNames[1],i) for i = 1:n])  #\"ci\"\n\n  if StringFmt == \"csv\"\n    (prefixN,suffixN)   = (fill(\"\",n),vcat(fill(\",\",n-1),\"\"))  #prefix and suffix for column 1:n\n    (prefixC0,suffixC0) = (\"\",\",\")                             #prefix and suffix for column 0\n  else\n    (prefixN,suffixN) = (fill(\"\",n),fill(\"\",n))\n    (prefixC0,suffixC0) = (\"\",\"\")\n  end\n\n  if length(rowNames) == 0                         #width of column 0 (cell00 and rowNames)\n    col0Width = 0\n  else\n    col0Width = maximum(length,vcat(cell00,rowNames)) + length(prefixC0) + length(suffixC0)\n  end\n\n  colWidth = [width + length(prefixN[j]) + length(suffixN[j]) for j=1:n]  #widths of column 1:n\n\n  iob = IOBuffer()\n\n  if !isempty(colNames)                                #print (cell00,colNames), if any\n    !isempty(cell00) ?  txt0 = string(prefixC0,cell00,suffixC0) : txt0 = \"\"\n    print(iob,rpad(txt0,col0Width))\n    for j = 1:n                                #loop over columns\n      print(iob,lpad(string(prefixN[j],colNames[j],suffixN[j]),colWidth[j]))\n    end\n    print(iob,\"\\n\")\n  end\n                                                       #print rowNames and x\n  (i0,j0) = (1 - first(axes(x,1)),1 - first(axes(x,2)))   #i+i0,j+j0 give traditional indices\n  for i in axes(x,1)                           #loop over rows\n    !isempty(rowNames) && print(iob,rpad(string(prefixC0,rowNames[i+i0],suffixC0),col0Width))\n    for j in axes(x,2)                         #loop over columns\n      print(iob,fmtNumPs(x[i,j],width,prec,\"right\",prefix=prefixN[j+j0],suffix=suffixN[j+j0]))\n    end\n    print(iob,\"\\n\")\n  end\n  str = String(take!(iob))\n\n  if NoPrinting                              #no printing, just return str\n    return str\n  else                                       #print, return nothing\n    print(fh,str,\"\\n\")\n    return nothing\n  end\n\nend\n                        #when fh is not supplied: printing to screen\nprintmat(x...;colNames=[],rowNames=[],width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\") =\n    printmat(stdout::IO,x...;colNames,rowNames,width,prec,NoPrinting,StringFmt,cell00)\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    printlnPs([fh::IO],z...;width=10,prec=3)\n\nSubsitute for println, with predefined formatting.\n\n\n# Input\n- `fh::IO`:    (optional) file handle. If not supplied, prints to screen\n- `z::String`: string, numbers and arrays to print\n\nPaul.Soderlind@unisg.ch\n\n\"\"\"\nfunction printlnPs(fh::IO,z...;width=10,prec=3)\n\n  for x in z                              #loop over inputs in z...\n    if isa(x,AbstractArray)\n      iob = IOBuffer()\n      for i = 1:length(x)\n        print(iob,fmtNumPs(x[i],width,prec,\"right\"))\n      end\n      print(fh,String(take!(iob)))\n    else\n      print(fh,fmtNumPs(x,width,prec,\"right\"))\n    end\n  end\n\n  print(fh,\"\\n\")\n\nend\n                      #when fh is not supplied: printing to screen\nprintlnPs(z...;width=10,prec=3) = printlnPs(stdout::IO,z...;width,prec)\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    fmtNumPs(z,width=10,prec=2,justify=\"right\";prefix=\"\",suffix=\"\")\n\nCreate a formatted string of a float (eg, \"%10.4f\"), nothing (\"\"),\nwhile other values are passed through. Strings are right (or left) justified\nand can optionally be given prefix and suffix (eg, \",\")\n\n# Notice\n- With prec > 0 and isa(z,Integer), then the string is padded with 1+prec spaces\nto align with the printing of floats with the same prec.\n\n# Requires\n- Printf (for 1.6-), fmtNumPsC (for < 1.6)\n\n\"\"\"\nfunction fmtNumPs(z,width=10,prec=2,justify=\"right\";prefix=\"\",suffix=\"\")\n\n  isa(z,Bool) && (z = convert(Int,z))             #Bool -> Int\n\n  if isa(z,AbstractFloat)                         #example: 101.0234, prec=3\n    if VERSION < v\"1.6-\"\n      fmt    = \"%$(width).$(prec)f\"\n      zRound = round(z,digits=prec)\n      strLR  = fmtNumPsC(fmt,zRound)                #C fallback solution\n    else\n      fmt   = Printf.Format(\"%$(width).$(prec)f\")\n      strLR = Printf.format(fmt,z)\n    end\n  elseif isa(z,Nothing)\n    strLR = \"\"\n  elseif isa(z,Integer) && prec > 0               #integer followed by (1+prec spaces)\n    strLR = string(z,\" \"^(1+prec))\n  else                                            #Int, String, Date, Missing, etc\n    strLR = string(z)\n  end\n\n  strLR = string(prefix,strLR,suffix)\n\n  if justify == \"left\"                            #justification\n    strLR = rpad(strLR,width+length(prefix)+length(suffix))\n  else\n    strLR = lpad(strLR,width+length(prefix)+length(suffix))\n  end\n\n  return strLR\n\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    fmtNumPsC(fmt,z)\n\nc fallback solution for formatting of floating point number. Used if VERSION < v\"1.6-\"\n\"\"\"\nfunction fmtNumPsC(fmt,z)                           #c fallback solution\n  if ismissing(z) || isnan(z) || isinf(z)    #asprintf does not work for these cases\n    str = string(z)\n  else\n    strp = Ref{Ptr{Cchar}}(0)\n    len = ccall(:asprintf,Cint,(Ptr{Ptr{Cchar}},Cstring,Cdouble...),strp,fmt,z)\n    str = unsafe_string(strp[],len)\n    Libc.free(strp[])\n  end\n  return str\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\nfunction printblue(x...)\n  foreach(z->printstyled(z,color=:blue,bold=true),x)\n  print(\"\\n\")\nend\nfunction printred(x...)\n  foreach(z->printstyled(z,color=:red,bold=true),x)\n  print(\"\\n\")\nend\nfunction printmagenta(x...)\n  foreach(z->printstyled(z,color=:magenta,bold=true),x)\n  print(\"\\n\")\nend\nfunction printyellow(x...)\n  foreach(z->printstyled(z,color=:yellow,bold=true),x)\n  print(\"\\n\")\nend\n#------------------------------------------------------------------------------\n\n", "meta": {"hexsha": "18df2baf18ff0a7996502e208aeab90d42cba1b1", "size": 8258, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "jlFiles/printmat.jl", "max_stars_repo_name": "Japhiolite/JuliaTutorial", "max_stars_repo_head_hexsha": "3222ce1b55361c43a25ea76bb01990d0723a9adf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 86, "max_stars_repo_stars_event_min_datetime": "2017-05-03T06:12:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T19:55:15.000Z", "max_issues_repo_path": "jlFiles/printmat.jl", "max_issues_repo_name": "Japhiolite/JuliaTutorial", "max_issues_repo_head_hexsha": "3222ce1b55361c43a25ea76bb01990d0723a9adf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-03-01T12:26:58.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-09T18:20:07.000Z", "max_forks_repo_path": "jlFiles/printmat.jl", "max_forks_repo_name": "Japhiolite/JuliaTutorial", "max_forks_repo_head_hexsha": "3222ce1b55361c43a25ea76bb01990d0723a9adf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 54, "max_forks_repo_forks_event_min_datetime": "2017-05-03T06:12:58.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-15T07:49:55.000Z", "avg_line_length": 33.4331983806, "max_line_length": 96, "alphanum_fraction": 0.5341487043, "num_tokens": 2216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3522017820478897, "lm_q2_score": 0.20946968626535548, "lm_q1q2_score": 0.07377559678767057}}
{"text": "true # true\nfalse # false\n\n1 # 1\ntypeof(1) # Int64\n\n1.0 # 1.0\ntypeof(1.0) # Float64\n\n1 == 1.0 # true\n1 === 1.0 # false\n\nisa(1, Int64) # true\n\nisa(1, Int) # true\n\ntypeof(\"a\") # ASCIIString\ntypeof('a') # Char\ntypeof(\"\u03bb\") # UTF8String\n\n# Vector (i.e. 1-d array)\n\na1 = [\"a\", \"b\", \"c\"]\ntypeof(a1) # Array{ASCIIString,1}\n\npush!(a1, \"d\")\na1 # [\"a\", \"b\", \"c\", \"d\"]\n\na1[0] # BoundsError (Julia arrays are 1-indexed)\na1[1] # \"a\"\na1[end] # \"d\"\na1[endof(a1)] # \"d\"\n\na1[1:2] # First two elements\na1[end-1:end] # Last two elements\n\na2 = [\"e\", \"f\", \"g\", \"h\"]\n\nvcat(a1, a2) # [\"a\", \"b\", ..., \"g\", \"h\"]\n\n# Tuple\n\nt1 = (\"a\", \"b\", \"c\")\ntypeof(t1) # Tuple{ASCIIString,ASCIIString,ASCIIString}\nt2 = (\"a\", 1.0, :hello)\ntypeof(t2) # Tuple{ASCIIString,Float64,Symbol}\n\n# Dictionary\n\nd1 = Dict(\"a\"=>1, \"b\"=>2)\ntypeof(d1) # Dict{Symbol,Int64}\nd1[\"c\"] = 3\n\n# Set\ns1 = Set([1, 2, 2, 1, 3, 2])\ns1 # e.g. Set([2,3,1])\n", "meta": {"hexsha": "6362735021975c69c12b9f0f46456e838765626d", "size": 888, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/julia/Basics.jl", "max_stars_repo_name": "microamp/j4p", "max_stars_repo_head_hexsha": "900c71925d2e47c3163826526b9132e2882abb6c", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/julia/Basics.jl", "max_issues_repo_name": "microamp/j4p", "max_issues_repo_head_hexsha": "900c71925d2e47c3163826526b9132e2882abb6c", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/julia/Basics.jl", "max_forks_repo_name": "microamp/j4p", "max_forks_repo_head_hexsha": "900c71925d2e47c3163826526b9132e2882abb6c", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.5789473684, "max_line_length": 55, "alphanum_fraction": 0.5472972973, "num_tokens": 403, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.1581743507642642, "lm_q1q2_score": 0.07353550422127195}}
{"text": "\"\"\"\nImplemented of a Trie data structure.  \nThis is an associative data structure with keys of type `NTuple{N,K} where N` and values of type `V`.\n\"\"\"\nmodule Tries\n\nimport Base: get!, show, get, isempty, haskey, setindex!, getindex, pairs, keys, values, keytype, eltype, valtype\nimport AbstractTrees\nimport AbstractTrees: children, printnode, PreOrderDFS, print_tree\nexport children\n##using VectorDicts\n\nexport AbstractTrie, Trie, SubTrie, nodes, subtrie\n\nabstract type AbstractTrie{K,T} end\n\nexport SortedTrie\n\"\"\"\n    SortedTrie{T,S<:Function}\n\n    tree::T\n    by::S\n\"\"\"\nstruct SortedTrie{K,V,T,S<:Function} <: AbstractTrie{K,V}\n    tree::T\n    by::S\n    SortedTrie(tree,by) =\n        new{eltype(keytype(tree)),valtype(tree),typeof(tree),typeof(by)}(tree,by)\nend\nfunction Base.show(io::IO, x::SortedTrie)\n    print_tree(IOContext(io, :compact=>false),x)\nend\nAbstractTrees.children(x::SortedTrie) =\n    map(s->SortedTrie(s,x.by), sort(children(x.tree); by=x.by))\n\nfunction AbstractTrees.printnode(io::IO, x::SortedTrie)\n    printnode(io,x.tree)\nend\n\n\"\"\"\n    Base.length(x::Tries.AbstractTrie)\n\nCumulative count of all nodes.\n\"\"\"\nBase.length(x::AbstractTrie) =\n    1+_length(x)\n\nfunction _length(x::AbstractTrie)\n    direct = isempty(nodes(x)) ? 0 : (0+length(nodes(x)) + (sum)(_length.(values(nodes(x)))))::Int\n    if get(x) isa AbstractTrie\n        direct + length(get(x))\n    else\n        direct\n    end\nend\n\n\"\"\"\n    Base.iterate(x::Tries.AbstractTrie, a...)\n\n`iterate(pairs(x), a...)`.\n\"\"\"\nBase.iterate(x::AbstractTrie, a...) =\n    iterate(pairs(x), a...)\n\nstruct Trie{K,T} <: AbstractTrie{K,T}\n    value::Union{Missing,T}\n    nodes::Dict{K,Trie{K,T}}\nend\n\n\"\"\"\n    nodes(x::AbstractTrie{K,T})\n\nGetter for node dictionary.\n\"\"\"\nnodes(x::Trie) = x.nodes\n\"\"\"\nA Trie with a path.\n\"\"\"\nstruct SubTrie{K,T} <: AbstractTrie{K,T}\n    path::Tuple\n    value::Trie{K,T}\n    SubTrie(path::Tuple, t::Trie{K,V}) where {K,V} =\n        new{K,V}(path, t)\n    SubTrie(path::Tuple, st::SubTrie{K,V}) where {K,V} =\n        new{K,V}(path, st.value)\n    SubTrie{K,V}(path::Tuple, x) where {K,V} =\n        new{K,V}(path, Trie{K,V}(x))\n    SubTrie{K,V}(path::Tuple, x::AbstractTrie{K,V}) where {K,V} =\n        new{K,V}(path, x)\nend\nnodes(x::SubTrie) = x.value.nodes\n\n\n\n\n\"\"\"\n    Trie{K,T}()\n\nConstruct an empty `Trie{K,T}` with root value `missing`.\n\"\"\"\nTrie{K,T}() where {K,T} = Trie{K,T}(missing, Dict{K,Trie{K,T}}())\n\n\"\"\"\n    Trie{K,T}(value)\n\nConstruct an empty `Trie{K,T}` with root value is `value`.\n\"\"\"\nTrie{K,T}(value) where {K,T} = Trie{K,T}(value, Dict{K,Trie{K,T}}())\n\n\"\"\"\n    Trie(values::Vararg{Pair{NTuple{N,K},T} where N}) where {K,T}\n    Trie(values::Vararg{Pair{Vector{K},T}}) where {K,T}\n    Trie(values::Vararg{Pair{NTuple{N,K},<:Any} where N}) where {K}\n    Trie(values::Base.Generator)\n\nConstruct a `Trie{K,T}` and populate it with `r[k...]=v`.\n\n```jldoctest\njulia> Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>1)\nTrie{Symbol,Any}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => 1\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\n\njulia> Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>\"y\")\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => \"y\"\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\n```\n\nSee also [`setindex!`](@ref).\n\"\"\"\nfunction Trie(values::Vararg{Pair{NTuple{N,K},T} where N}) where {K,T}\n    r = Trie{K,T}(missing, Dict{K,Trie{K,T}}())\n    for (k,v) in values\n        r[k...]=v\n    end\n    r\nend\n\nfunction Trie(values::Vararg{Pair{Vector{K},T}}) where {K,T}\n    r = Trie{K,T}(missing, Dict{K,Trie{K,T}}())\n    for (k,v) in values\n        r[k...]=v\n    end\n    r\nend\n\nfunction Trie(values::Vararg{Pair{NTuple{N,K},<:Any} where N}) where {K}\n    r = Trie{K,Any}(missing, Dict{K,Trie{K,Any}}())\n    for (k,v) in values\n        r[k...]=v\n    end\n    r\nend\n\nTrie(values::Base.Generator) = Trie(values...)\n\n# struct ETrie{K,T,D<:AbstractDict}\n#     value::Union{Missing,T}\n#     nodes::D{K,ETrie{K,T,D}}\n# end\n# ETrie{K,T}() where {K,T} =\n#     ETrie{K,T,}(missing, @show VectorDict{K,ETrie{K,T,VectorDict}}())    \n\"\"\"\n    Base.get(x::Trie)\n    Base.get(x::SubTrie)\n\nReturn `value::Union{Missing,valtype(x)}` of `x`.\n\"\"\"\nfunction Base.get(x::Trie)\n    x.value\nend\npath(::Trie) = tuple()\npath(x::SubTrie) = x.path\n\nBase.get(x::SubTrie) = get(x.value)\n\n\n\"\"\"\n    Base.show(x::Trie)\n    Base.show(x::SubTrie)\n\nDisplay `x` with `AbstractTrees.print_tree`.\n\"\"\"\nfunction Base.show(io::IO, x::AbstractTrie)\n    print(io,\"Trie{$(eltype(keytype(x))),$(valtype(x))}\") ## error(\"should print key\")\n    if length(path(x))>1\n        print(io,\"@\")\n        for p in path(x)[1:end-1]\n            show(io,p)\n            print(io,\", \")\n        end\n    end\n    print_tree(io,x,20)\nend\n\n\nAbstractTrees.children(x::AbstractTrie) =\n    _children(x, get(x))\n\n_children(x::AbstractTrie, value) =\n    sort( [ SubTrie(tuple(path(x)...,k), v)\n            for (k,v) in pairs(nodes(x)) ],\n          by=e->path(e)[end] )\n\n_children(x::AbstractTrie, value::AbstractTrie) =\n    vcat(_children(x,nothing),\n         children(SubTrie(path(x),value)))\n\nfunction AbstractTrees.printnode(io::IO, x::AbstractTrie)\n    !isempty(path(x)) && show(io,path(x)[end])\n    v = get(x)\n    if v !== missing\n        print(io, \" => \")\n        if v isa AbstractTrie ## do not print children \n            printnode(io, get(v))\n        else\n            printnode(io, v)\n        end\n    end\nend\n\n\n\n\"\"\"\n    Base.keytype(::Type{Trie{K,V}}) where {K,V}\n    Base.keytype(::Trie{K,V}) where {K,V}\n\nReturns `NTuple{N,K} where N`.\n!!! note\n    `eltype(keytype(Trie{K,V})) == K`\n\"\"\"\nBase.keytype(::Type{<:AbstractTrie{K}}) where {K} = NTuple{N,K} where N\nBase.keytype(x::AbstractTrie) = keytype(typeof(x))\n\n\n\"\"\"\n    Base.eltype(::Type{<:AbstractTrie{K,V}}) where {K,V}\n    Base.etype(::AbstractTrie{K,V}) where {K,V}\n\nReturns `Pair{Tuple{Vararg{K,N} where N},Union{Missing,V}}` for `iterate` and `collect`.\n\"\"\"\nBase.eltype(::Type{<:AbstractTrie{K,V}}) where {K,V} = Pair{Tuple{Vararg{K,N} where N},Union{Missing,V}}\nBase.eltype(x::AbstractTrie) = eltype(typeof(x))\n\n\n\"\"\"\n    Base.valtype(::Type{AbstractTrie{K,V}}) where {K,V}\n    Base.valtype(::AbstractTrie{K,V}) where {K,V}\n\nReturns `V`.\n\"\"\"\nBase.valtype(::Type{<:AbstractTrie{K,V}}) where {K,V} = V\nBase.valtype(x::AbstractTrie) = valtype(typeof(x))\n\n\n\"\"\"\n    Base.get!(x::Trie,k)\n\nReturns `subtrie!(x,k).value`.\n\nSee also [`subtrie!`](@ref)\n\"\"\"\nBase.get!(x::Trie{K,T},k) where {K,T} =\n    get(subtrie!(x, k...))\n\n\n\"\"\"\n    Base.get(x::Trie,k)\n\nReturns `subtrie(x,k).value`.\n\nSee also [`subtrie`](@ref)\n\"\"\"\nBase.get(x::Trie{K,T}, k) where {K,T} =\n    get(subtrie(x, k...))\n\n\"\"\"\n    Base.get!(x::Trie,k)\n\nReturns `subtrie!(x,k).value`.\n\nSee also [`subtrie!`](@ref)\n\"\"\"\nBase.get!(f::Function, x::Trie{K,T}, k) where {K,T} =\n    get(subtrie!(f, x, k...))\n\n\"\"\"\n    Base.isempty(x::Trie)\n\nReturns `true` iif x has no nodes.\n\"\"\"\nBase.isempty(x::Trie) =\n    isempty(x.nodes)\n\nexport hasnextkey\nfunction hasnextkey(x::AbstractTrie,pathel)\n    haskey(nodes(x),pathel) && return true\n    get(x) isa AbstractTrie && return hasnextkey(get(x), pathel)\n    false\nend\n\n\"\"\"\n    Base.haskey(x::AbstractTrie,path)\n\nReturns `true` iif x has nodes along `path`.\n\"\"\"\nBase.haskey(x::AbstractTrie, path) =\n    isempty(path) || ( hasnextkey(x,path[1]) && ( length(path)==1 || haskey(x[path[1]],path[2:end]) ) )\n\nexport subtrie!\n\"\"\"\n    subtrie!(x::Trie,path...)\n\nReturn a subtree at `path`.\nNodes missing in `x` along path are created and populated with values `missing`.\n\"\"\"\nsubtrie!(x::AbstractTrie{K,V},path::K...) where {K,V} =\n    subtrie!((_,_)->missing, x,path...)\n\n\"\"\"\n    subtrie!(f::Function,x::Trie,path...)\n\nReturn a subtree at `path`.\nNodes missing in `x` along path are created and populated with values `f(path, key)`.\n\n```jldoctest\njulia> a = Trie{Int,Int}(0)\nTrie{Int64,Int64} => 0\n\njulia> subtrie!((path,key)->length(p)+1, a, 4,3,2,1)\nSubTrie{Int64,Int64} @ 4, 3, 2, 1 => 4\n\njulia> a\nTrie{Int64,Int64} => 0\n\u2514\u2500 4 => 1\n   \u2514\u2500 3 => 2\n      \u2514\u2500 2 => 3\n         \u2514\u2500 1 => 4\n\n```\n\n\"\"\"\nfunction subtrie!(f::Function, x::AbstractTrie{K,T}, p1::K, p...) where {K,T}\n    st = SubTrie((path(x)...,p1),\n                 get!(() -> Trie{K,T}(f(path(x),p1)),\n                      nodes(x), p1))\n    if isempty(p)\n        st\n    else\n        subtrie!(f, st, p...)\n    end\nend\n\nfunction subtrie!(f::Function, x::AbstractTrie, k::K, p...) where K\n    if get(x) isa AbstractTrie && K <: eltype(keytype(get(x)))\n        subtrie!(f,SubTrie(path(x),get(x)), k, p...)\n    else\n        @show typeof(k) eltype(keytype(x))\n        error(\"key error \")\n    end\nend\n\n\n\"\"\"\n    subtrie(x::Trie{K,T},path...)\n\nReturn a subtree at `path`.\n\n```jldoctest\njulia> a = Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>\"y\")\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => \"y\"\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\njulia> subtrie(a, :a, :b)\nSubTrie{Symbol,String} @ :a, :b => \"c\"\n\u2514\u2500 :d => \"y\"\n\njulia> subtrie(a, :a, :d, :b)\nERROR: KeyError: key (:d, :b) not found\nStacktrace:\n [1] (::Tries.var\"#41#42\")(::Tuple{Symbol,Symbol,Symbol}, ::Int64) at /home/gregor/dev/julia/Tries/src/Tries.jl:334\n [2] subtrie(::Tries.var\"#41#42\", ::Trie{Symbol,String}, ::Symbol, ::Vararg{Symbol,N} where N) at /home/gregor/dev/julia/Tries/src/Tries.jl:386\n [3] subtrie(::Trie{Symbol,String}, ::Symbol, ::Symbol, ::Vararg{Symbol,N} where N) at /home/gregor/dev/julia/Tries/src/Tries.jl:334\n [4] top-level scope at REPL[12]:1\n\n```\n\"\"\"\nfunction subtrie(x::AbstractTrie,path...)\n    subtrie((path,i)->throw(KeyError(path[i:end])),x,path...)\nend\n\n\"\"\"\n    subtrie(::Nothing,x::Trie{K,T},path...)\n\nReturn a subtree at `path`, or `nothing`, if `path` does not exist in `x`.\nDoes not modify `x`.\n\n```jldoctest\njulia> a = Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>\"y\")\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => \"y\"\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\njulia> subtrie(nothing, a, :a, :d)\n\n```\n\"\"\"\nfunction subtrie(::Nothing,x::AbstractTrie{K,T},path...) where {K,T}\n    subtrie((p,i)->nothing,x,path...)\nend\n\n\"\"\"\n    subtrie(notfound::Function,x::Trie{K,T},path...)\n\nReturn a subtree at `path`, or `notfound(path,error_index)`, if `path` does not exist in `x`\n(default `(path,i)->throw(KeyError(path[i:end]))`).\nDoes not modify `x`.\n\n```jldoctest\njulia> a = Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>\"y\")\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => \"y\"\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\n\njulia> subtrie((x...) -> x, a, :a, :d)\n((:a, :d), 2)\n\n```\n\n!!! note\n    check\n\n\"\"\"\nfunction subtrie(f::Function, x::AbstractTrie{K,T}, p...) where {K,T}\n    x_ = x\n    for (i,k) in enumerate(p)\n        if !(haskey(nodes(x_),k))\n            if get(x_) isa AbstractTrie\n                inner=subtrie(f,get(x_),p[i:end]...)\n                return SubTrie((path(x)...,p...), inner)\n                ##x_ = get(x_)[k]\n            else\n                return f(p,i)\n            end\n        end\n        # &&  @warn \"no key $k\" collect(keys(x_.nodes)) # k haskey(x_.nodes,k) x_.nodes\n        x_ = nodes(x_)[k]\n    end\n    SubTrie((path(x)...,p...), x_)\nend\n\n\nimport Base.setindex!\n\"\"\"\n    Base.setindex!(x::Trie{K,T}, v::T, path...) where {K,T}\n\nSet value at `path` to `v and return previous value or missing.\n\n!!! note\n    To retrieve last value you need to call `setindex!` explicitly.\n\n\n```jldoctest\njulia> x = Trie((:a,)=>\"a\", (:a,:b)=>\"c\", (:a,:c,:d)=>\"z\", (:a,:b,:d)=>\"y\")\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u2514\u2500 :d => \"y\"\n   \u2514\u2500 :c\n      \u2514\u2500 :d => \"z\"\n\njulia> x[:a,:b,:z]=\"node added\"\n\"node added\"\n\njulia> setindex!(x,\"value set\",:a,:c)\nTrie{Symbol,String}\n\u2514\u2500 :d => \"z\"\n\n\njulia> x\nTrie{Symbol,String}\n\u2514\u2500 :a => \"a\"\n   \u251c\u2500 :b => \"c\"\n   \u2502  \u251c\u2500 :d => \"y\"\n   \u2502  \u2514\u2500 :z => \"node added\"\n   \u2514\u2500 :c => \"value set\"\n      \u2514\u2500 :d => \"z\"\n\n```\n\nSee also [`subtrie!`](@ref)\n\"\"\"\nfunction Base.setindex!(x::AbstractTrie{K,T}, v, k::K, path...) where {K,T}\n    if isempty(path)\n        leaf=subtrie!(x,k)\n        nodes(x)[k] = Trie{K,T}(v,nodes(leaf))\n        leaf.value\n    else\n        subtrie!(x,k)[path...] = v\n    end\nend\n\nfunction Base.setindex!(x::AbstractTrie{K,T}, v, k, path...) where {K,T}\n    if get(x) isa AbstractTrie && typeof(k) <: eltype(keytype(get(x)))\n        get(x)[k,path...] = v\n    else\n        @show typeof(k) eltype(keytype(x))\n        error(\"key error \")\n    end\nend\n\nfunction Base.setindex!(x::AbstractTrie{K,T}, v, k::Function, path...) where {K,T}\n    x[k(x)...,path...] = v\nend\n\nfunction Base.setindex!(x::AbstractTrie{K,T}, v::AbstractTrie{K,T}, k::K) where {K,T}\n    nodes(x)[k] = v\nend\n\nfunction Base.delete!(x::AbstractTrie{K}, p1::K, p...) where K\n    if isempty(p)\n        delete!(nodes(x), p1)\n    else\n        delete!(nodes(x)[p1], p...)\n    end\nend\n\nfunction Base.delete!(x::AbstractTrie{K}, k, p...) where K\n    if get(x) isa AbstractTrie && typeof(k) <: eltype(keytype(get(x)))\n        if isempty(p)\n            delete!(nodes(get(x)), k)\n        else\n            delete!(nodes(get(x))[k], p...)\n        end\n    else\n        @show typeof(k) eltype(keytype(x))\n        error(\"key error \")\n    end\nend\n\n\"\"\"\n    Base.getindex(x::Trie{K,T}, path...) where {K,T}\n\nGet `SubTrie` at `path`.\n\nSee also [`SubTrie`](@ref).\n\"\"\"\nfunction Base.getindex(x::Trie{K,T}, path...) where {K,T}\n    subtrie(x,path...)\nend\n\n\"\"\"\n    Base.getindex(x::SubTrie, path...)\n\nGet `SubTrie` at `(x.path...,path...)`.\n\nSee also [`SubTrie`](@ref).\n\"\"\"\nfunction Base.getindex(x::SubTrie{K,V}, path...) where {K,V}\n    SubTrie(tuple(x.path...,path...),subtrie(x,path...))\nend\n\n\"\"\"\n    Base.pairs(x::Trie{K,V}) where {K,V}\n    Base.pairs(x::SubTrie)\n\nGenerator returning `path => value` pairs.\n\nSee also [`AbstractTrees.PreOrderDFS`](https://juliacollections.github.io/AbstractTrees.jl/stable/api/#AbstractTrees.PreOrderDFS)\n\"\"\"\nBase.pairs(x::Trie) =\n    pairs(SubTrie(tuple(),x))\n\nfunction Base.pairs(x::SubTrie{K,V}) where {K,V}\n    ( path(x) => get(x)\n      for x in PreOrderDFS(x) )\nend\n\n\n\"\"\"\n    Base.keys(x::AbstractTrie)\n\nGenerator returning `path`s as `first` fields from `pairs(x)`.\n\nSee also [`pairs`](@ref)\n\"\"\"\nBase.keys(x::AbstractTrie) =\n    (  kv.first for kv in pairs(x) )\n\n\n\"\"\"\n    Base.values(x::Union{Trie,SubTrie})\n\nGenerator returning `value`s as `second` fields from `pairs(x)`.\n\nSee also [`pairs`](@ref)\n\"\"\"\nBase.values(x::AbstractTrie) =\n    ( kv.second for kv in pairs(x) )\n\nimport Base: convert\nBase.convert(::Type{Trie{K,V}}, x::SubTrie{K,V}) where {K,V} =\n    x.value\n\nend # module\n", "meta": {"hexsha": "f80b1ae5f19c713234a489d97adcaaea554c2d7e", "size": 14390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Tries.jl", "max_stars_repo_name": "gkappler/Tries", "max_stars_repo_head_hexsha": "ac859555f3236d02d7ba8a54966a8776b163997d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-08-04T09:18:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-09T07:48:01.000Z", "max_issues_repo_path": "src/Tries.jl", "max_issues_repo_name": "gkappler/Tries", "max_issues_repo_head_hexsha": "ac859555f3236d02d7ba8a54966a8776b163997d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Tries.jl", "max_forks_repo_name": "gkappler/Tries", "max_forks_repo_head_hexsha": "ac859555f3236d02d7ba8a54966a8776b163997d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9872204473, "max_line_length": 143, "alphanum_fraction": 0.5677553857, "num_tokens": 5006, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.17781086729958678, "lm_q1q2_score": 0.07309998021445879}}
{"text": "#=\nDoubly linked list\n=#\n\nusing SimpleDataStructures\n\nsll = SimpleLinkedList{Int}()\npush!(sll, 1)\npush!(sll, 2)\npush!(sll, 3)\nfirst(sll)\nlast(sll)\n\nsll[9]\n\ninsert!(sll, 3, 10)\ninsert!(sll, 4, 100)\ncollect(sll)\ndeleteat!(sll, 3)\ndeleteat!(sll, 3)\npop!(sll)\npopfirst!(sll)\ncollect(sll)\n", "meta": {"hexsha": "88a070e1c394164080c64a038d14fe150610fcd1", "size": 284, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/doubly_linked_list.jl", "max_stars_repo_name": "harryscholes/SimpleDataStructures.jl", "max_stars_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/doubly_linked_list.jl", "max_issues_repo_name": "harryscholes/SimpleDataStructures.jl", "max_issues_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/doubly_linked_list.jl", "max_forks_repo_name": "harryscholes/SimpleDataStructures.jl", "max_forks_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.8333333333, "max_line_length": 29, "alphanum_fraction": 0.676056338, "num_tokens": 120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4921881357207956, "lm_q2_score": 0.14804719615221756, "lm_q1q2_score": 0.07286707347285092}}
{"text": "#=\n    A port of my Perl/Java/Python MakeRegex in Julia.\n\n    make_regex(words)\n    generates a regex for words. It use a simple approach which \n    combined common prefixes in generating the regex. \n\n    Some examples:\n    * words = [\"a\", \"al\", \"all\", \"alla\", \"an\", \"ann\", \"anna\", \"annas\", \"ananas\"]\n      regex: a(l(la?)?|n(anas|n(as?)?)?)?\n\n    * words: [\"and\", \"at\", \"do\", \"end\", \"for\", \"in\", \"is\", \"not\", \"of\", \"or\", \"use\"]\n      regex: (a(nd|t)|do|end|for|i[ns]|not|o[fr]|use)\n\n    There is a simple way of handling character classes\n    * words: [\"price1\", \"price2\", \"price3\", \"price4\"]\n      regex: price[1234]\n\n\n    If there is no common prefix then it just put '|' between the words \n    * words: [\"this\", \"is\", \"a\", \"very\", \"boring\", \"example\", \"with\", \"no\", \"common\", \"prefix\"]\n      regex: (a|boring|common|example|is|no|prefix|this|very|with)\n\n\n    Also, see the (very old) page for my Perl package MakeRegex: http://hakank.org/makeregex/index.html\n    The REAME file in that package states:\n    \"\"\"\n    The Perl package MakeRegex composes a regex-expression from a list of\n    words. It had been inspired by the emacs elisp module make-regex.el,\n    by Simon Marshall.\n    \"\"\"\n    \n    This Julia program was created by Hakan Kjellerstrand, hakank@gmail.com\n    See also my Julia page: http://www.hakank.org/julia/\n\n=#\n\n# \n# common_prefix(p,  list)\n# \n# Here is where the main work is done. It's somewhat magically ported from my\n# Perl/Java/Python versions...\n# \nfunction common_prefix(p, list) \n    list_len = length(list)\n    if list_len == 0 \n        return \"\" \n    end\n    if list_len == 1 \n        return p * join(list,\"\")\n    end\n    \n    #\n    # fix for some - as of now - unknown bug. To fix in later version!\n    # \n    if p == \"\" && list[1] == \"\" && list[2] == \"\"\n        return \"\"\n    end\n\n    #\n    # * Collect all the strings with the same prefix-char\n    #\n    hash = Dict()\n    for word in sort(list)\n        prefix = suffixed_word = \"\"\n        if length(word) > 0\n            prefix, suffixed_word... = word \n        end\n        # put the suffix in the list of other suffixes for\n        # the this prefix\n        hash[prefix] = push!(get(hash,prefix,[]),suffixed_word)\n    end\n    \n    # \n    # And recurse this list\n    # \n    all = []\n    for key in keys(hash)\n        comm = \"\"\n        values = hash[key]\n        if length(key) > 0\n            sort!(values)\n            comm = common_prefix(key, values)\n        end\n        \n        # hack to be able to use the '?' char . Should be re-written!\n        if comm == \"\"\n            comm = \" \"\n        end\n        push!(all,comm)\n    end \n    sort!(all)\n    \n    # paren: what to put in parenthesis ('()' or '[]') if anything\n    paren = \"\"\n    all_len = length(all)\n    if  all_len == 1\n        paren = join(all,\"\")\n    else\n        len = maximum(length.(all))\n        joinChar = len != 1 ? '|' : \"\"\n            \n        # joins all entries except for \" \"\n        join_str = mark = \"\"\n        count = 0\n        for w in all\n            got_hack_mark = w == \" \" ? true : false # This is a hack for handling '?'\n            if length(w) > 0 && w != \" \"\n                join_str *= w\n                if count < all_len-1\n                    join_str *=  joinChar\n                end\n            end\n\n            if got_hack_mark\n                mark = '?'\n            end\n                    \n            count = count + 1\n        end\n\n        paren = \"\"\n        if length(join_str) === 1\n            paren = join_str * mark\n        else\n            if len == 1\n                paren = '[' * join_str * ']' * mark\n            else\n                paren = '(' * join_str * ')' * mark\n            end \n        end\n    end\n    return p * paren\n\nend\n\nfunction make_regex(words) \n    replace.(words,r\"([*?+])\"=>s\"\\\\\\1\") # replace meta characters\n    # We sort the words to induce more common prefixes\n    return common_prefix(\"\", sort(words))\n\nend\n\n#\n# check_regex(regex, words)\n# \n# Checks the regex againts a list of words.\n# \nfunction check_regex(regex, words)\n    p = Regex(regex)\n    for word in words\n        println(word, \" matches\", !occursin(p,word) ? \" NOT!\" : \"\")\n    end \nend\n\n\ntests = [\n    [\"all\",\"alla\"],\n\n    # A lot of Swedish words\n    [ \"all\", \"alla\", \"alle\", \"alls\", \"palle\", \"palla\", \"pelle\", \"perkele\",\n      \"ann\", \"anna\", \"annas\", \"anders\", \"h\u00e5kan\", \"\u00e5ngest\", \"\u00e4rlig\", \n      \"solsken\", \"sture\", \"stina\", \"h\u00f6rapparat\", \"h\u00f6rsel\", \"h\u00e5rig\"],\n\n    [\"alla\", \"palla\", \"balla\", \"kalla\", \"all\", \"pall\", \"ball\", \"kall\"],\n\n    # \"ananas\" is the Swedish word for pineapple\n    [\"a\", \"al\", \"all\", \"alla\", \"an\", \"ann\", \"anna\", \"annas\", \"ananas\"],\n\n    [\"a\", \"an\", \"ann\", \"anna\", \"annan\", \"annas\", \"annans\", \"ananas\", \"ananasens\"],\n\n    [\"a\", \"ab\", \"abc\", \"abcd\", \"abcde\", \"abcdef\", \"b\", \"bc\", \"bcd\", \"bcde\", \"bcdef\", \n     \"bcdefg\", \"abb\", \"abbc\", \"abbcc\", \"abbccdd\"],\n\n    [\"this\", \"is\", \"a\", \"very\", \"boring\", \"example\", \"with\", \"no\", \"common\", \"prefix\"],\n\n    [\"price1\",\"price2\",\"price3\",\"price4\"],\n    \n\n    # This is from Marshall's make-regex.el\n    [\"and\", \"at\", \"do\", \"end\", \"for\", \"in\", \"is\", \"not\", \"of\", \"or\", \"use\"],    \n\n    # This is from Marshall's make-regex.el\n    [\"cond\", \"if\", \"while\", \"let*?\", \"prog1\", \"prog2\", \"progn\",\n     \"catch\", \"throw\", \"save-restriction\", \"save-excursion\", \n     \"save-window-excursion\", \"save-match-data\", \"unwind-protect\", \n     \"condition-case\", \"track-mouse\"],\n  \n    # This is from Marshall's make-regex.el\n    [\"abort\", \"abs\", \"accept\", \"access\", \"array\",\n      \"begin\", \"body\", \"case\", \"constant\", \"declare\",\n      \"delay\", \"delta\", \"digits\", \"else\", \"elsif\", \"entry\",\n      \"exception\", \"exit\", \"function\", \"generic\", \"goto\",\n      \"if\", \"others\", \"limited\", \"loop\", \"mod\", \"new\",\n      \"null\", \"out\", \"subtype\", \"package\", \"pragma\",\n      \"private\", \"procedure\", \"raise\", \"range\", \"record\",\n      \"rem\", \"renames\", \"return\", \"reverse\", \"select\",\n      \"separate\", \"task\", \"terminate\", \"then\", \"type\",\n      \"when\", \"while\", \"with\", \"xor\"]\n]\n\nfor t in tests \n    println(\"testing $t\")\n    println(make_regex(t))\n    println()\nend\n\n#=\nwords = last(tests)\nrx = make_regex(words)\nprintln(words)\nprintln(\"regex:$rx\")\ncheck_regex(rx,words)\n=#", "meta": {"hexsha": "f5c7ac9c57bf0dfcd4baa48335bee706a061b3e9", "size": 6177, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/make_regex.jl", "max_stars_repo_name": "tias/hakank", "max_stars_repo_head_hexsha": "87b7f180c9393afce440864eb9e5fb119bdec1a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 279, "max_stars_repo_stars_event_min_datetime": "2015-01-10T09:55:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T02:34:03.000Z", "max_issues_repo_path": "julia/make_regex.jl", "max_issues_repo_name": "tias/hakank", "max_issues_repo_head_hexsha": "87b7f180c9393afce440864eb9e5fb119bdec1a4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2017-10-05T15:48:50.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-20T12:06:52.000Z", "max_forks_repo_path": "julia/make_regex.jl", "max_forks_repo_name": "tias/hakank", "max_forks_repo_head_hexsha": "87b7f180c9393afce440864eb9e5fb119bdec1a4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 83, "max_forks_repo_forks_event_min_datetime": "2015-01-20T03:44:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-13T23:53:06.000Z", "avg_line_length": 28.8644859813, "max_line_length": 103, "alphanum_fraction": 0.5258215962, "num_tokens": 1828, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "# Uncomment to debug:\n# macro dbgutil(x); esc(:(println(_dbg($x)))); end\nmacro dbgutil(x); end\n\n### @primitive and @zerograd macros:\n\n# I would like to make these type signatures as specific as possible.\n# The following are not allowed yet, see https://github.com/JuliaLang/julia/issues/3766\n# f{T<:Number,A<:AbstractArray{T}}(x::Value{A})\n# f{T<:Number,A<:AbstractArray}(x::Value{A{T}})\n\n\"\"\"\n\n`@primitive fx g1 g2...` can be used to define a new primitive\nand (optionally) its gradients.\n\nJulia supports multiple dispatch, i.e. a single function can have\nmultiple methods with different arg types.  AutoGrad supports\nmultiple dispatch for primitives and gradients.  Thus fx is a\ntyped method declaration such as:\n\n* @primitive sin(x::Number)\n* @primitive hypot(x1::Array,x2::Array),dy,y\n\nThe second example specifies variable names for the output gradient\n`dy` and the output `y` after the method declaration which can be used\nin gradient expressions.  Untyped, ellipsis and keyword arguments are\nok as in `f(a::Int,b,c...;d=1)`.  Parametric methods such as\n`f{T<:Number}(x::T)` cannot be used.\n\nThe @primitive macro turns the first example into:\n\n    local sin_r = recorder(sin)\n    sin{T<:Number}(x::Value{T}) = sin_r(x)\n\nThis will cause any call to `sin` with a Value{T<:Number} argument\nto be recorded.  With multiple arguments things are a bit more\ncomplicated.  Here is what happens with the second example:\n\n    local hypot_r = recorder(hypot)\n    hypot{T<:Array,S<:Array}(x1::Value{T},x2::Value{S})=hypot_r(x1,x2)\n    hypot{T<:Array,S<:Array}(x1::Value{T},x2::S)=hypot_r(x1,x2)\n    hypot{T<:Array,S<:Array}(x1::T,x2::Value{S})=hypot_r(x1,x2)\n\nWe want the recorder version to be called if any one of the arguments\nis a boxed Value.  There is no easy way to specify this in Julia, so\nthe macro generates all 2^N-1 boxed/unboxed argument combinations.\n\nThe method declaration can optionally be followed by gradient\nexpressions.  Here are the same examples with gradients:\n\n* @primitive sin(x::Number),dy (dy*cos(x))\n* @primitive hypot(x1::Array,x2::Array),dy,y  `(dy.*x1./y)`  `(dy.*x2./y)`\n\nNote that the parameters, the return variable and the output gradient\nof the original function can be used in the gradient expressions.\n\nIn AutoGrad, gradients are defined using gradient methods that have\nthe following signature:\n\n    f(Grad{i},dy,y,x...) => dx[i]\n\nFor the first example here is the generated gradient method:\n\n`sin{T<:Number}(::Type{Grad{1}}, dy, y, x::Value{T})=(dy*cos(x))`\n\nFor the second example a different gradient method is generated for\neach argument:\n\n`hypot{T<:Array,S<:Array}(::Type{Grad{1}},dy,y,x1::Value{T},x2::Value{S})=(dy.*x1./y)`\n`hypot{T<:Array,S<:Array}(::Type{Grad{2}},dy,y,x1::Value{T},x2::Value{S})=(dy.*x2./y)`\n\nIn fact @primitive generates four more definitions for the other\nboxed/unboxed argument combinations.\n\nNon-differentiable functions such as `sign`, and non-numeric functions\nsuch as `size` should be defined using the @zerograd macro instead.\n\n\"\"\"\nmacro primitive(f,g...)\n    isa(f,Expr) || error(\"'$f' not a method signature\")\n    if f.head == :tuple # Using f(x),dy,y to indicate return variable for gradients\n        if length(f.args) == 3\n            (f,dy,y) = f.args\n        elseif length(f.args) == 2\n            (f,dy) = f.args; y = gensym()\n        else\n            error(\"The first arg '$f' should have the format f(x),dy,y\")\n        end\n    else\n        dy = gensym(); y = gensym()\n    end\n    f.head == :call || error(\"'$f' not a method signature\")\n    isa(dy,Symbol) || error(\"Output gradient '$dy' not a symbol\")\n    isa(y,Symbol) || error(\"Return variable '$y' not a symbol\")\n    b = Expr(:block)\n    r = gensym()\n    push!(b.args, esc(:(local $r = recorder($(fname(f))))))\n    rx = rcall(r,f)\n    for fx in fsigs(f)\n        push!(b.args, esc(:($fx = $rx)))\n        for i=1:length(g)\n            gx = gsig(fx,dy,y,i)\n            push!(b.args, esc(:($gx = $(g[i]))))\n        end\n    end\n    return b\nend\n\n\"\"\"\n`@zerograd f(args...; kwargs...)` allows f to handle its Value inputs\nby unboxing them like @primitive, but unlike @primitive it does not\nrecord its actions or return a Value result.  Some functions, like\nsign(), have zero gradient.  Others, like length() have discrete or\nconstant outputs.  These need to handle Value inputs, but do not need\nto record anything and can return regular values.  Their output can be\ntreated like a constant in the program.  Use the @zerograd macro for\nthose.  Note that kwargs are NOT unboxed.\n\"\"\"\nmacro zerograd(f)\n    b = Expr(:block)\n    f.head == :(::) && (f=f.args[1])\n    for fx in fsigs(f)\n        zx = zcall(fx)\n        push!(b.args, esc(:($fx = $zx)))\n    end\n    return b\nend\n\nfunction zcall(f)\n    z = copy(f)\n    z1 = z.args[1]\n    isa(z1,Expr) && z1.head==:curly && (z.args[1]=z1.args[1])\n    for i=2:length(z.args)\n        zi = z.args[i]\n        if isa(zi,Symbol)\n            # all done\n        elseif !isa(zi,Expr)\n            error(\"Unrecognized argtype '$zi'\")\n        elseif zi.head==:(::)\n            (v,t) = zi.args\n            if t==:Value || (isa(t,Expr) && t.head==:curly && t.args[1]==:Value)\n                z.args[i] = :($v.value)\n            else\n                z.args[i] = v\n            end\n        elseif zi.head==:(...)  # done\n        elseif zi.head==:parameters # done\n        else\n            error(\"Unrecognized argtype '$zi'\")\n        end\n    end\n    return z\nend\n\n# get name out of function declaration\nfunction fname(f)\n    n = f.args[1]\n    isa(n,Expr) && n.head==:curly && error(\"parametric methods not currently supported\")\n    if isa(n,Symbol)\n        return n\n    else\n        error(\"$n not a symbol\")\n    end\nend\n\n# create call to r using typeless argument of f\nfunction rcall(r,f)\n    rx = notypes(f)\n    rx.args[1]=r\n    # Need to fix kwargs\n    r2 = rx.args[2]\n    if isa(r2,Expr) && r2.head == :parameters\n        for i in 1:length(r2.args)\n            k = r2.args[i]\n            if !isa(k,Expr); error(\"Bad kwarg '$k'\")\n            elseif k.head == :(...); continue\n            elseif k.head != :kw; error(\"Bad kwarg '$k'\")\n            elseif !isa(k.args[1],Symbol); error(\"Bad kwarg '$k'\")\n            else; k.args[2]=k.args[1]; end\n        end\n    end\n    return rx\nend\n\n# eliminate type declarations from a function call\nfunction notypes(ex)\n    if isa(ex, Expr)\n        if (ex.head == :(::) || ex.head == :curly)\n            return notypes(ex.args[1])\n        else\n            return Expr(ex.head, map(notypes, ex.args)...)\n        end\n    else\n        return ex\n    end\nend\n\n# create type signatures for f where one or more args are Nodes.\nfunction fsigs(f)\n    f1 = copy(f)\n    a1 = f1.args[1] = Expr(:curly,fname(f1))\n    nargs = 0\n    for i=2:length(f1.args)\n        ai = f1.args[i]\n        if isa(ai,Symbol)\n            nargs+=1\n            ti = gensym()\n            push!(a1.args, Expr(:<:, ti, Any))\n            f1.args[i] = Expr(:(::),ai,ti)\n        elseif !isa(ai,Expr)\n            error(\"Neither Symbol nor Expr: $ai\")\n        elseif in(ai.head, (:parameters, :(...)))\n            continue\n        elseif ai.head == :(::)\n            nargs+=1\n            ti = gensym()\n            push!(a1.args, Expr(:<:,ti,ai.args[2]))\n            ai.args[2] = ti\n        else\n            error(\"Argtype not supported: '$ai'\")\n        end\n    end\n    flist = []\n    for nodes=0:(1<<nargs-2)\n        fn = copy(f1)\n        iargs = 0\n        for i=2:length(fn.args)\n            ai = fn.args[i]\n            in(ai.head, (:parameters, :(...))) && continue\n            ai.head == :(::) || error(\"Bad arg '$ai'\")\n            if nodes & (1<<iargs) == 0\n                ai.args[2] = Expr(:curly,:Value,ai.args[2])\n            end\n            iargs += 1\n        end\n        push!(flist, fn)\n    end\n    return flist\nend\n\nfunction gsig(f,dy,y,i)\n    g = copy(f)\n    if g.args[2].head == :parameters; a = 3; else; a = 2; end\n    insert!(g.args, a, :(::Type{Grad{$i}}))\n    insert!(g.args, a+1, dy)\n    insert!(g.args, a+2, y)\n    return g\nend\n\n\n\n### Testing Utilities:\n\nif !isdefined(:runtests)\nlet tests=[]\n    global addtest,runtests,alltests\n    alltests()=tests\n    addtest(t...)=push!(tests,t)\n    function runtests(a=tests)\n        for fx in a\n            try \n                tx = fixtest(fx...)\n                check_grads(tx...; fname=fx[1])\n            catch e\n                warn((fx...,\"$e\"))\n            end\n        end\n    end\nend\nend\n\nfunction fixtest(f, x...)\n    f = eval(f)\n    y = f(x...)\n    # detect and prevent testing of zero / undefined grads\n    plist = Any[]               # define fnew(plist)\n    alist = Any[x...]           # to return f(alist)\n    fargs = Any[]               # call fnew(fargs...)\n    gargs = (Value(y), Value(y), map(Value,x)...)\n    for i=1:length(alist)\n        g = nothing\n        try\n            g = f(Grad{i},gargs...)\n        catch e\n            if isa(e,MethodError) && e.f === f && e.args[1] === Grad{i}\n                continue        # warn(\"No grad $i for $f: $e\")\n            else\n                error(\"Error during $f$((Grad{i},gargs...)): $e\")\n            end\n        end\n        g == nothing && continue # zero grads\n        push!(fargs, alist[i])\n        alist[i] = Symbol(\"x$i\")\n        push!(plist, alist[i])\n    end\n    isempty(fargs) && error(\"$f has no differentiable arguments.\")\n    f1=f; f = eval(Expr(:->, Expr(:tuple, plist...), Expr(:call, f1, alist...)))\n    # if f has non-scalar output, sum it\n    isbits(y) || (f2=f; f=(x...)->toscalar(f2(x...)))\n    return (f,fargs...)\nend\n\nfunction randin(range, dims...; eps=EPS)\n    if isa(range, UnitRange{Int64})\n        rand(range, dims...)\n    elseif range==(-Inf,Inf)\n        randn(dims...)\n    elseif range==(0,Inf)\n        eps-log(rand(dims...))\n    elseif range==(1,Inf)\n        eps+1-log(rand(dims...))\n    elseif range==(-1,Inf)\n        eps-1-log(rand(dims...))\n    elseif range==(-1,1)\n        (1-eps)*(2rand(dims...)-1)\n    elseif range==(0,1)\n        eps+(1-2eps)*rand(dims...)\n    elseif range==(0,2)\n        eps+2*(1-eps)*rand(dims...)\n    elseif range==(-Inf,-1,1,Inf)\n        x = sec(randn(dims...))\n        sign(x)*eps + x\n    else\n        error(\"Unknown range $range\")\n    end\nend\n\nfunction addtest1(f,r)          # unary\n    addtest(f,randin(r))\n    addtest(f,randin(r,2))\nend\n\nfunction addtest2(f,r1,r2=r1)   # binary\n    addtest(f,randin(r1),randin(r2))\n    addtest(f,randin(r1),randin(r2,2))\n    addtest(f,randin(r1,2),randin(r2))\n    addtest(f,randin(r1,2),randin(r2,2))\nend\n\nfunction addtest3(f,r1,r2=r1)   # broadcasting\n    addtest2(f,r1,r2)\n    addtest(f,randin(r1,2),randin(r2,2,2))\n    addtest(f,randin(r1,2,2),randin(r2,2))\n    addtest(f,randin(r1,1,2),randin(r2,2,2))\n    addtest(f,randin(r1,2,2),randin(r2,1,2))\nend\n\n\n# EPS, RTOL, ATOL = 1e-4, 1e-4, 1e-6\nEPS, RTOL, ATOL = 1e-4, 1e-2, 1e-4\n\n# TODO: do sampling or random direction for large args\n\"\"\"\ncheck_grads(fun, args...) checks the computed gradients for fun(args)\ncomparing them with numeric approximations.\n\"\"\"\nfunction check_grads(fun, args...; eps=EPS, rtol=RTOL, atol=ATOL, fname=fun)\n    @dbgutil((:check_grads,fname,:args,args...))\n    isempty(args) && error(\"No args given\")\n    exact = ntuple(i->grad(fun,i)(args...), length(args))\n    numeric = nd(fun, args...; eps=eps)\n    @dbgutil((:check_grads,fname,:exact,exact,:numeric,numeric))\n    same = isequivalent(exact, numeric; rtol=rtol, atol=atol)\n    same || warn((:check_grads,fname,:args,args,:exact,exact,:numeric,numeric))\n    return same\nend\n\nfunction nd(f, args...; eps=EPS)\n    @dbgutil((:nd,f,args..., :eps, eps))\n    unary_f = x->f(x...)\n    unary_nd(unary_f, args, eps)\nend\n\nunary_nd(f, x::Tuple, eps)         = ntuple(i->unary_nd(indexed_function(f, x, i), x[i], eps), length(x))\nunary_nd(f, x::Associative, eps)   = (a=similar(x); for(k,v) in x; a[k] = unary_nd(indexed_function(f, x, k), v, eps); end; a)\nunary_nd(f, x::AbstractArray, eps) = reshape(eltype(x)[unary_nd(indexed_function(f, x, i), v, eps) for (i,v) in enumerate(x)], size(x))\nunary_nd(f, x::Complex, eps)       = ((f(x + eps/2) - f(x - eps/2)) / eps - im*(f(x + im*eps/2) - f(x - im*eps/2)) / eps)\nunary_nd(f, x::Real, eps)          = ((f(x + eps/2) - f(x - eps/2)) / eps)\n\nfunction indexed_function(fun, arg, index)\n    function partial_function(x)\n        if isa(arg, Tuple)\n            local_arg = (arg[1:index-1]..., x, arg[index+1:end]...)\n        else\n            local_arg = copy(arg); local_arg[index] = x\n        end\n        return fun(local_arg)\n    end\n    return partial_function\nend\n\n# isequivalent uses isapprox for Number and AbstractArray{T<:Number}\nisequivalent(x::Number,y::Number; o...)=isapprox(x,y;o...)\nisequivalent{T<:Number,S<:Number}(x::AbstractArray{T},y::AbstractArray{S}; o...)=isapprox(x,y;o...)\n\n# isequivalent extends to Tuple, Associative, and other Arrays, comparing elementwise\nisequivalent(x::Tuple, y::Tuple; o...)=(length(x)==length(y) && all(i->isequivalent(x[i],y[i];o...), 1:length(x)))\nisequivalent(x::AbstractArray, y::AbstractArray; o...)=(length(x)==length(y) && all(i->isequivalent(x[i],y[i];o...), 1:length(x)))\nisequivalent(x::Associative, y::Associative; o...)=all(k->isequivalent(get(x,k,nothing),get(y,k,nothing);o...), unique([keys(x)...,keys(y)...]))\n\n# isequivalent treats `nothing` as equivalent to zero or zero array.\nisequivalent(x::Number,z::Void; o...)=isequivalent(z,x;o...)\nisequivalent{T<:Number}(x::AbstractArray{T},z::Void; o...)=isequivalent(z,x;o...)\nisequivalent(z::Void,x::Number; o...)=isapprox(zero(x),x;o...)\nisequivalent{T<:Number}(z::Void,x::AbstractArray{T}; rtol::Real=Base.rtoldefault(T), atol::Real=0, norm::Function=vecnorm) = (norm(x) <= atol/(1-rtol)) # Modified from: linalg/generic.jl:522\n\n# The way broadcasting works in Julia:\n# y = f(x...) where f is a broadcasting operation.\n# size(y) = broadcast_shape(x...)\n# ndims(y) = max ndims(x)\n# size(y,i) = max size(x,i)\n# size(x,i) = 1 or size(y,i) for all x and i<=ndims(x)\n# if ndims(x) < ndims(y) the extra dimensions of x are treated as 1\n\nfunction unbroadcast(x, dx)\n    if size(x)==size(dx)\n        return dx\n    elseif isa(getval(x),Number)\n        return sum(dx)\n    else\n        d = []\n        for i=1:ndims(dx)\n            size(x,i) == size(dx,i) && continue\n            size(x,i) != 1 && throw(DimensionMismatch())\n            push!(d,i)\n        end\n        length(d)==1 && (d=d[1])\n        return sum(dx, d)\n    end\nend\n\nfunction toscalar(xv; rng=MersenneTwister())\n    x = getval(xv)\n    isa(x,Number) && return xv\n    isa(x,OneHot) && (x = full(x))\n    idx = isa(x,Tuple) ? (1:length(x)) : eachindex(x)\n    s = 0\n    for i in idx\n        s += xv[i] * rand(rng)\n    end\n    return s\nend\n\n# sumvalues sums values of dictionaries, otherwise acts like sum:\n\nsumvalues(x)=sum(x)\nsumvalues(x::Associative)=sum(values(x))\n@primitive sumvalues(x::Associative),ds fillvalues(ds,x)\nfillvalues(v,x)=(y=similar(x);for k in keys(x); y[k]=v; end; y)\n@primitive fillvalues(v,x),dxv sumvalues(dxv) nothing\naddtest(sumvalues, Dict(1=>1.,2=>2.))\naddtest(fillvalues, 0., Dict(1=>1.,2=>2.,3=>3.))\n\n# This needs more work:\n# @primitive values(x),dy Dict(map((a,b)->(a=>b), keys(x), dy))\n\n# It gets tiresome to write `Type{Grad{1}}` after a while, here are\n# some convenient aliases:\n\ntypealias D1 Type{Grad{1}}\ntypealias D2 Type{Grad{2}}\nif !isdefined(:Dn)\ntypealias Dn{N} Type{Grad{N}}\nend\n\n# Pretty print for debugging:\n_dbg(x)=x # extend to define short printable representations\n_dbg(x::Tuple)=map(_dbg,x)\n_dbg(x::Node)=\"N$(id2(x))_$(id2(x.value))\"\n_dbg(x::Value)=\"V$(id2(x))_$(_dbg(x.value))\"\n_dbg(x::Tape)=\"T$(join([id2(x),map(id2,x)...],'_'))\"\n_dbg(x::AbstractArray)=\"A$(join([id2(x),size(x)...],'_'))\"\nid2(x)=Int(object_id(x)%1000)\n\nBase.show(io::IO, n::Value) = print(io, _dbg(n))\nBase.show(io::IO, n::Node) = print(io, _dbg(n))\nBase.show(io::IO, n::Tape) = print(io, _dbg(n))\n\n", "meta": {"hexsha": "0435f0b644a12776798e13cc91373690f8e95114", "size": 15794, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "JuliaPackageMirrors/AutoGrad.jl", "max_stars_repo_head_hexsha": "3775730f69e07a37a998cd4489ee7c7bcbc3c4a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/util.jl", "max_issues_repo_name": "JuliaPackageMirrors/AutoGrad.jl", "max_issues_repo_head_hexsha": "3775730f69e07a37a998cd4489ee7c7bcbc3c4a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/util.jl", "max_forks_repo_name": "JuliaPackageMirrors/AutoGrad.jl", "max_forks_repo_head_hexsha": "3775730f69e07a37a998cd4489ee7c7bcbc3c4a5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.9041666667, "max_line_length": 190, "alphanum_fraction": 0.5919336457, "num_tokens": 4864, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.14608725262486594, "lm_q1q2_score": 0.07247298459155538}}
{"text": "# read_samples\n\n\"\"\"\n\nRead sample output files created by StanSample.jl.\n\nThis method is added to StanRun's read_sample function.\n\n### Method\n```julia\nread_samples(model::SampleModel; start=1)\n```\n\n### Required arguments\n```julia\n* `model`                    : SampleModel\n```\n\n### Optional arguments\n```julia\n* `start=1`                  : First draw saved in MCMCHains.Chains object\n```\n\n\"\"\"\nfunction read_samples(model::SampleModel;  start=1)\n\n  local a3d, monitors, index, idx, indvec, ftype, noofsamples\n  \n  output_base = model.output_base\n  name_base =\"_chain\"\n  n_samples = model.method. num_samples  \n  n_chains = StanBase.get_n_chains(model)\n  \n  # Handle save_warmup\n  start = model.method.save_warmup ? model.method.num_warmup+1 : start\n  \n  # a3d will contain the samples such that a3d[s, i, c] where\n\n  #   s: num_samples\n  #   i: variables (from cmdstan .csv file)\n  #   c: n_chains\n\n  # Read .csv files created by each chain\n  \n  for i in 1:n_chains\n    if isfile(output_base*name_base*\"_$(i).csv\")\n      #noofsamples = 0\n      instream = open(output_base*name_base*\"_$(i).csv\")\n      #\n      # Skip initial set of commented lines, e.g. containing cmdstan version info, etc.\n      #\n      skipchars(isspace, instream, linecomment='#')\n      #\n      # First non-comment line contains names of variables\n      #\n      line = Unicode.normalize(readline(instream), newline2lf=true)\n      idx = split(strip(line), \",\")\n      index = [idx[k] for k in 1:length(idx)]      \n      indvec = 1:length(index)\n      \n      if i == 1\n        a3d = fill(0.0, n_samples, length(indvec), n_chains)\n      end\n      \n      #println(size(a3d))\n      skipchars(isspace, instream, linecomment='#')\n      for j in 1:n_samples\n        skipchars(isspace, instream, linecomment='#')\n        line = Unicode.normalize(readline(instream), newline2lf=true)\n        if eof(instream) && length(line) < 2\n          close(instream)\n          break\n        else\n          flds = parse.(Float64, split(strip(line), \",\"))\n          flds = reshape(flds[indvec], 1, length(indvec))\n          a3d[j,:,i] = flds\n        end\n      end   # read in samples\n    end   # read in next file\n  end   # read in file for each chain\n  \n  cnames = convert.(String, idx[indvec])\n  chns = convert_a3d(a3d, cnames, Val(:mcmcchains); start=start)\n\nend   # end of read_samples\n\nfunction read_samples(model::StanModel; chain=1)\n  read_samples(default_output_base(model)*\"_chain_$(chain).csv\")\nend\n\n\"\"\"\n\n# convert_a3d\n\nConvert the output file created by cmdstan to the shape of choice.\n\n### Method\n```julia\nconvert_a3d(a3d_array, cnames, ::Val{Symbol}; start=1)\n```\n### Required arguments\n```julia\n* `a3d_array::Array{Float64, 3},`      : Read in from output files created by cmdstan                                   \n* `cnames::Vector{AbstractString}`     : Monitored variable names\n```\n\n### Optional arguments\n```julia\n* `::Val{Symbol}`                      : Output format\n* `::start=1`                          : First draw for MCMCChains.Chains\n```\nMethod called is based on the output_format defined in the stanmodel, e.g.:\n\n   stanmodel = Stanmodel(`num_samples`=1200, thin=2, name=\"bernoulli\", \n     model=bernoullimodel, `output_format`=:mcmcchains);\n\nCurrent formats supported are:\n\n1. :array (a3d_array format, the default for CmdStan)\n2. :namedarray (NamedArrays object)\n3. :dataframe (DataFrames object)\n4. :mambachains (Mamba.Chains object)\n5. :mcmcchains (TuringLang/MCMCChains.Chains object)\n\nOptions 3 through 5 are respectively provided by the packages StanDataFrames, \nStanMamba, StanMCMCChains and StanMCMCChains.\n```\n\n### Return values\n```julia\n* `res`                       : Draws converted to the specified format.\n```\n\"\"\"\nconvert_a3d(a3d_array, cnames, ::Val{:array}; start=1) = a3d_array\n\nconvert_a3d(a3d_array, cnames, ::Val{:namedarray}; start=1) = \n  [NamedArray(a3d_array[:,:,i], (collect(1:size(a3d_array, 1)), Symbol.(cnames))) \n    for i in 1:size(a3d_array, 3)]\n\nfunction convert_a3d(a3d_array, cnames, ::Val{:mcmcchains}; start=1)\n  pi = filter(p -> length(p) > 2 && p[end-1:end] == \"__\", cnames)\n  p = filter(p -> !(p in  pi), cnames)\n\n  MCMCChains.Chains(a3d_array,\n    cnames,\n    Dict(\n      :parameters => p,\n      :internals => pi\n    );\n    start=start\n  )\nend\n", "meta": {"hexsha": "a980c7a09e180a4579d9fd47f0a7f5fd3a7f5e26", "size": 4259, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stansamples/read_samples.jl", "max_stars_repo_name": "UnofficialJuliaMirror/StanSample.jl-c1514b29-d3a0-5178-b312-660c88baa699", "max_stars_repo_head_hexsha": "768894f98284a1840f01fd9c6c51c5247bae2ad5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/stansamples/read_samples.jl", "max_issues_repo_name": "UnofficialJuliaMirror/StanSample.jl-c1514b29-d3a0-5178-b312-660c88baa699", "max_issues_repo_head_hexsha": "768894f98284a1840f01fd9c6c51c5247bae2ad5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/stansamples/read_samples.jl", "max_forks_repo_name": "UnofficialJuliaMirror/StanSample.jl-c1514b29-d3a0-5178-b312-660c88baa699", "max_forks_repo_head_hexsha": "768894f98284a1840f01fd9c6c51c5247bae2ad5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8366013072, "max_line_length": 120, "alphanum_fraction": 0.6386475699, "num_tokens": 1231, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.15203223778010538, "lm_q1q2_score": 0.07245547083167243}}
{"text": "function truncate(y, negtrunc, postrunc)\r\n\r\n# Truncate input if its too big or too small\r\n    if (y < negtrunc) # If y is less than the value negtrunc\r\n       truncy = negtrunc;\r\n    elseif (y > postrunc)\r\n       truncy= postrunc; # If y is greater than the value postrunc\r\n    else\r\n       truncy = y;\r\n    end  \r\n\r\n    return truncy                              \r\n\r\nend\r\n\r\n\r\n                 ", "meta": {"hexsha": "88bee2b3323d52096f5cb6f597b689c0d35d52ec", "size": 394, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "LifecycleCostaDias/v5_julia/code_numericTools/truncate.jl", "max_stars_repo_name": "floswald/ucl-econ-julia", "max_stars_repo_head_hexsha": "c0b9077382d4245fb1276ae2f517cc9372259c25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-08-18T00:50:53.000Z", "max_stars_repo_stars_event_max_datetime": "2017-08-18T00:50:53.000Z", "max_issues_repo_path": "LifecycleCostaDias/v5_julia/code_numericTools/truncate.jl", "max_issues_repo_name": "floswald/ucl-econ-julia", "max_issues_repo_head_hexsha": "c0b9077382d4245fb1276ae2f517cc9372259c25", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-05-01T13:10:23.000Z", "max_issues_repo_issues_event_max_datetime": "2015-05-14T08:44:31.000Z", "max_forks_repo_path": "LifecycleCostaDias/v5_julia/code_numericTools/truncate.jl", "max_forks_repo_name": "floswald/ucl-econ-julia", "max_forks_repo_head_hexsha": "c0b9077382d4245fb1276ae2f517cc9372259c25", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2015-04-25T11:54:42.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T01:23:04.000Z", "avg_line_length": 23.1764705882, "max_line_length": 67, "alphanum_fraction": 0.5355329949, "num_tokens": 103, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.40733340004593027, "lm_q2_score": 0.17781086729958678, "lm_q1q2_score": 0.0724283051422564}}
{"text": "## Could tidy up this HTML to make it look nicer\nhtml_templates = Dict()\n\n# thumbs up/down don't show in my editor\ngrading_partial = \"\"\"\n  if(correct) {\n    msgBox.innerHTML = \"<div class='pluto-output admonition note alert alert-success'><span> \ud83d\udc4d&nbsp; {{#:CORRECT}}{{{:CORRECT}}}{{/:CORRECT}}{{^:CORRECT}}Correct{{/:CORRECT}} </span></div>\";\n    var explanation = document.getElementById(\"explanation_{{:ID}}\")\n    if (explanation != null) {\n       explanation.style.display = \"none\";\n    }\n  } else {\n    msgBox.innerHTML = \"<div class='pluto-output admonition alert alert-danger'><span>\ud83d\udc4e&nbsp; {{#:INCORRECT}}{{{:INCORRECT}}}{{/:INCORRECT}}{{^:INCORRECT}}Incorrect{{/:INCORRECT}} </span></div>\";\n    var explanation = document.getElementById(\"explanation_{{:ID}}\")\n    if (explanation != null) {\n       explanation.style.display = \"block\";\n    }\n  }\n\"\"\"\n\n## Basic question\n## has label and hint option.\n## Hint is put with label when present; otherwise, it appears at bottom of form.\n## this is overridden with input widget in how show method is called\nhtml_templates[\"question_tpl\"] = mt\"\"\"\n<form class=\"mx-2 my-3\" name='WeaveQuestion' data-id='{{:ID}}' data-controltype='{{:TYPE}}'>\n  <div class='form-group {{:STATUS}}'>\n    <div class='controls'>\n      <div class=\"form-floating input-group\" id=\"controls_{{:ID}}\">\n    {{#:LABEL}}\n        <label for=\"controls_{{:ID}}\">{{{:LABEL}}}{{#:HINT}}<span href=\"#\" title='{{{:HINT}}}'>&nbsp;\ud83c\udf81</span>{{/:HINT}}\n</label>\n    {{/:LABEL}}\n        <div style=\"padding-top: 5px\">\n    {{{:FORM}}}\n    {{^:LABEL}}{{#:HINT}}<label for=\"controls_{{:ID}}\"><span href=\"#\" title='{{{:HINT}}}'>&nbsp;\ud83c\udf81</span></label>{{/:HINT}}{{/:LABEL}}\n        </div>\n      </div>\n      <div id='{{:ID}}_message' style=\"padding-bottom: 15px\"></div>\n      {{#:EXPLANATION}}\n      <div id=\"explanation_{{:ID}}\" class='pluto-output admonition alert alert-danger' style=\"display:none;;background-color:#D3D3D366;\">{{{:EXPLANATION}}}</div>\n      {{/:EXPLANATION}}\n    </div>\n  </div>\n</form>\n\n<script text='text/javascript'>\n{{{:GRADING_SCRIPT}}\n</script>\n\"\"\"\n\nhtml_templates[\"input_grading_script\"] = jmt\"\"\"\ndocument.getElementById(\"{{:ID}}\").addEventListener(\"change\", function() {\n  var correct = {{{:CORRECT_ANSWER}}};\n  var msgBox = document.getElementById('{{:ID}}_message');\n  $(grading_partial)\n});\n\"\"\"\n\n##\nhtml_templates[\"inputq_form\"] = mt\"\"\"\n<div class=\"row\">\n  <span style=\"width:90%\">\n    <input id=\"{{:ID}}\" type=\"{{:TYPE}}\" class=\"form-control\" placeholder=\"{{:PLACEHOLDER}}\">\n  </span>\n  <span style=\"width:10%\">{{#:UNITS}}{{{:UNITS}}}{{/:UNITS}}{{#:HINT}}<span href=\"#\" title='{{{:HINT}}}'>&nbsp;\ud83c\udf81</span>{{/:HINT}}\n  </span>\n</div>\n\"\"\"\n\n## Multiple choice (one of many)\n## XXX add {{INLINE}}\nhtml_templates[\"Radioq\"] = mt\"\"\"\n{{#:ITEMS}}\n<div class=\"form-check\">\n  <label>\n    <input class=\"form-check-input\" type=\"radio\" name=\"radio_{{:ID}}\"\n              id=\"radio_{{:ID}}_{{:VALUE}}\" value=\"{{:VALUE}}\">\n      <span = class=\"label-body\">\n        {{{:LABEL}}}\n      </span>\n    </input>\n  </label>\n</div>\n{{/:ITEMS}}\n\"\"\"\n\nhtml_templates[\"radio_grading_script\"] = \"\"\"\ndocument.querySelectorAll('input[name=\"radio_{{:ID}}\"]').forEach(function(rb) {\nrb.addEventListener(\"change\", function() {\n    var correct = rb.value == {{:CORRECT_ANSWER}};\n    var msgBox = document.getElementById('{{:ID}}_message');\n    $(grading_partial)\n})});\n\"\"\"\n## ----\n\nhtml_templates[\"Buttonq\"] = mt\"\"\"\n<div id=\"buttongroup_{{:ID}}\" class=\"btn-group\">\n  {{#:BUTTONS}}\n  <button class=\"toggle-btn\" aria-pressed=\"false\" id=\"button_{{:ID}}_{{:i}}\" value=\"{{:ANSWER}}\" style=\"width:100%;text-align:left; padding-left:10px; {{#:BLUE}}background:{{{:BLUE}}}{{/:BLUE}}\" onclick=\"return false;\">\n    {{{:TEXT}}\n  </button>\n  {{/:BUTTONS}}\n</div>\n<script>\ndocument.querySelectorAll('[id^=\"button_{{:ID}}_\"]').forEach(function(btn) {\n    btn.addEventListener(\"click\", function(btn) {\n\tvar correct = this.value == \"correct\";\n\tvar id = this.id;\n\tif (!correct) {\n\t    {{#:GREEN}}this.style.background = \"{{{:GREEN}}}\";{{/:GREEN}}\n\t    var text = this.innerHTML;\n\t    this.innerHTML = \"<em>{{{:INCORRECT}}</em>&nbsp;\" + text ;\n            var explanation = document.getElementById(\"explanation_{{:ID}}\")\n            if (explanation != null) {\n               explanation.style.display = \"block\";\n            }\n\t}\n\tdocument.querySelectorAll('[id^=\"button_{{:ID}}_\"]').forEach(function(btn) {\n\t    btn.disabled = true;\n            btn.setAttribute(\"aria-pressed\", \"true\");\n\t    if (btn.value == \"correct\") {\n                {{#:RED}}btn.style.background = \"{{{:RED}}}\";{{/:RED}}\n\t\tvar text = btn.innerHTML;\n\t\tbtn.innerHTML =  \" <em>{{{:CORRECT}}}</em>&nbsp;\" + text ;\n\t\tbtn.style.fontSize = \"1.1rem\";\n\t\tbtn.style.color = \"black\";\n                btn.style.borderRadius = \"12px\";\n\t    }\n\t});\n    });\n})\n</script>\n\"\"\"\n\n\n\nhtml_templates[\"Multiq\"] = mt\"\"\"\n{{#:ITEMS}}\n<div class=\"form-check\">\n  <label>\n    <input class=\"form-check-input\" type=\"checkbox\" name=\"check_{{:ID}}\"\n              id=\"check_{{:ID}}_{{:VALUE}}\" value=\"{{:VALUE}}\">\n      <span = class=\"label-body\">\n        {{{:LABEL}}}\n      </span>\n    </input>\n  </label>\n</div>\n{{/:ITEMS}}\n\"\"\"\n\nhtml_templates[\"multi_grading_script\"] = \"\"\"\ndocument.querySelectorAll('input[name=\"check_{{:ID}}\"]').forEach(function(rb) {\nrb.addEventListener(\"change\", function() {\n    var choice_buttons = document.getElementsByName(\"check_{{:ID}}\");\n    var selected = [];\n    for (var i=0; i < choice_buttons.length; i++) {\n        if (choice_buttons[i].checked) {\n           selected.push(i+1)\n        }\n    }\n    var a = selected;\n    var b = {{{:CORRECT_ANSWER}}};\n    // https://stackoverflow.com/questions/7837456/how-to-compare-arrays-in-javascript\n    var  correct =  (a.length === b.length && a.find((v,i) => v !== b[i]) === undefined)\n    var msgBox = document.getElementById('{{:ID}}_message');\n    $(grading_partial)\n})});\n\"\"\"\n\nhtml_templates[\"MultiButtonq\"] = mt\"\"\"\n<div id=\"buttongroup_{{:ID}}\" class=\"btn-group\">\n  {{#:BUTTONS}}\n  <button class=\"toggle-btn\" aria-pressed=\"false\" id=\"button_{{:ID}}_{{:i}}\" name=\"{{:i}}\" value=\"unclicked\" style=\"width:100%;text-align:left; padding-left:10px; {{#:BLUE}}background:{{{:BLUE}}}{{/:BLUE}}\" onclick=\"return false;\">\n      {{{:TEXT}}\n  </button>\n  {{/:BUTTONS}}\n\n  <button id=\"button_{{:ID}}-done\"\n     style=\"display:block; margin:auto;text-align:center;{{#:BLUE}}background:{{{:BLUE}}}{{/:BLUE}}\" onclick=\"return false;\">\n      DONE\n  </button>\n\n\n\"\"\"\n\nhtml_templates[\"multi_button_grading_script\"] = \"\"\"\n\n// toggle button select\ndocument.querySelectorAll('[id^=\"button_{{:ID}}_\"]').forEach(function(btn) {\n    btn.addEventListener(\"click\", function(btn) {\n\tvar unclicked = (this.value == \"unclicked\")\n\tif (unclicked) {\n            this.style.background = \"{{{:SELECTED_COLOR}}}\";\n\t    this.value = \"clicked\";\n            this.setAttribute(\"aria-pressed\", \"true\");\n\t} else {\n\t    this.style.background = null;\n\t    this.value=\"unclicked\";\n            this.setAttribute(\"aria-pressed\", \"false\");\n\t}\n    });\n})\n\n// grade question\ndocument.querySelector('[id^=\"button_{{:ID}}-done\"]').addEventListener(\"click\", function() {\n                this.disabled = true;\n    var multi_choice_buttons = document.querySelectorAll('[id^=\"button_{{:ID}}_\"]');\n    var selected = [];\n    var correct = {{{:CORRECT_ANSWER}}};\n    for (var i=0; i < multi_choice_buttons.length; i++) {\n\tvar btn = multi_choice_buttons[i];\n        btn.disabled = true;\n\tvar btn_txt = btn.innerHTML;\n\tif (multi_choice_buttons[i].value == \"clicked\") {\n\t    selected.push(i+1)\n\t    if (correct.indexOf(i+1) < 0) {\n\t\tbtn.innerHTML = \"{{{:INCORRECT_flag}}}\" + btn_txt\n\t    } else {\n\t\tbtn.innerHTML =  \"{{{:CORRECT_flag}}}\" + btn_txt\n                btn.style.fontSize = \"1.1rem\";\n                btn.style.borderRadius = \"12px\";\n            }\n\t} else {\n\t    if (correct.indexOf(i+1) < 0) {\n\t\tbtn.innerHTML =  \"{{{:INCORRECT_flag}}}\" + btn_txt\n\t    } else {\n\t\tbtn.innerHTML =  \"{{{:CORRECT_flag}}}\" + btn_txt\n                btn.style.fontSize = \"1.1rem\";\n                btn.style.borderRadius = \"12px\";\n\t    }\n\t}\n      }\n      var a = selected;\n      var b = {{{:CORRECT_ANSWER}}}\n          // https://stackoverflow.com/questions/7837456/how-to-compare-arrays-in-javascript\n      var  correct =  (a.length === b.length && a.find((v,i) => v !== b[i]) === undefined)\n      var msgBox = document.getElementById('{{:ID}}_message');\n      $(grading_partial)\n})\n\"\"\"\n\n## ----\n\nhtml_templates[\"Matchq\"] = mt\"\"\"\n<table>\n{{#:ITEMS}}\n<tr>\n  <td>\n   <label for \"select_{{:ID}}\">{{{:QUESTION}}}</label>\n  </td>\n  <td>\n  <select name = \"select_{{:ID}}\" id=\"select_{{:ID}}\">\n     <option value=0 selected=\"selected\">{{{:BLANK}}}</option>\n     {{#:ANSWER_CHOICES}}\n     <option value=\"{{:INDEX}}\">{{{:LABEL}}}</option>\n    {{/:ANSWER_CHOICES}}\n  </select>\n  </td>\n<tr>\n{{/:ITEMS}}\n</table>\n\"\"\"\n\nhtml_templates[\"matchq_grading_script\"] = \"\"\"\n      function callback(element, iterator) {\n\t  element.addEventListener(\"change\", function() {\n\t      var a = [];\n\t      var selectors = document.querySelectorAll('[id ^= \"select_{{:ID}}\"]');\n\t      Array.prototype.forEach.call(selectors, function (element, iterator) {\n\t\t a.push(element.value);\n\t      })\n              var b = {{{:CORRECT_ANSWER}}};\n              // https://stackoverflow.com/questions/7837456/how-to-compare-arrays-in-javascript\n              var correct =  (a.length === b.length && a.find((v,i) => v !== b[i]) === undefined)\n              var msgBox = document.getElementById('{{:ID}}_message');\n              $(grading_partial)\n\t  })\n      }\n      Array.prototype.forEach.call(document.querySelectorAll('[id ^= \"select_{{:ID}}\"]'), callback);\n\"\"\"\n\n\n## ----\n\nhtml_templates[\"fill_in_blank_select\"] = \"\"\"\n  <select name = \"select_{{:ID}}\" id=\"select_{{:ID}}\">\n     <option value=0 selected=\"selected\">{{{:BLANK}}}</option>\n     {{#:ANSWER_CHOICES}}\n     <option value=\"{{:INDEX}}\">{{{:LABEL}}}</option>\n    {{/:ANSWER_CHOICES}}\n  </select>\n\"\"\"\n\nhtml_templates[\"fill_in_blank_input\"] = \"\"\"\n<input id=\"{{:ID}}\" type=\"{{:TYPE}}\" class=\"form-control\" placeholder=\"{{:PLACEHOLDER}}\">\n\"\"\"\n\n## -------\nhtml_templates[\"hotspot\"] = \"\"\"\n<div>\n<img  id=\"hotspot_{{{:ID}}}\" src=\"data:image/gif;base64,{{{:IMG}}}\"/>\n</div>\n\"\"\"\n\nhtml_templates[\"hotspot_grading_script\"] = \"\"\"\n  document.getElementById(\"hotspot_{{{:ID}}}\").addEventListener(\"click\", function(e) {\n      var u = e.offsetX;\n      var v = e.offsetY;\n      var w = this.offsetWidth;\n      var h = this.offsetHeight\n\n      var x = u/w;\n      var y = (h-v)/h\n\n      var correct = {{{:CORRECT_ANSWER}}}\n      var msgBox = document.getElementById('{{:ID}}_message');\n      $(grading_partial)\n\n  })\n\"\"\"\n\n## -----\n## -------\nhtml_templates[\"plotlylight_grading_script\"] = \"\"\"\ndocument.getElementById(\"{{{:ID}}}\").on(\"plotly_click\", function(e) {\n      var x = e.points[0].x\n      var y = e.points[0].y\n\n      var correct = {{{:CORRECT_ANSWER}}}\n      var msgBox = document.getElementById('{{:ID}}_message');\n      $(grading_partial)\n\n  })\n\"\"\"\n", "meta": {"hexsha": "da0602fac88335ed15367bcaabdd18a43a2db9eb", 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"max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4881656805, "max_line_length": 231, "alphanum_fraction": 0.5840998088, "num_tokens": 2970, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2598256379609837, "lm_q2_score": 0.2782567937024021, "lm_q1q2_score": 0.07229824894070444}}
{"text": "## Exercise 9-3\n# Write a function named avoids that takes a word and a string of forbidden letters, and that returns true if the word doesn\u2019t use any of the forbidden letters.\nprintln(\"Ans:\\n[Part 1]\")\n\nfunction avoids(string, forbidden_letters)\n    for letter in forbidden_letters\n        if letter \u2208 string\n            return false\n        end\n    end\n    return true\nend\n\nprintln(avoids(\"hello world\", \"hwe\"))\nprintln(avoids(\"printf\", \"xyz\"))\n\n# Modify your program to prompt the user to enter a string of forbidden letters and then print the number of words that don\u2019t contain any of them. Can you find a combination of 5 forbidden letters that excludes the smallest number of words?\nprintln(\"[Part 2]\")\n\nprint(\"Please Enter forbidden letters: \")\nforbidden_letters = strip(readline())\n\ncount = 0\nfor line in eachline(\"words.txt\")\n    if avoids(line, forbidden_letters)\n        global count += 1\n    end\nend\n\nprintln(\"There are total of $count letters which avoids the forbidden letters.\")\n\nprintln(\"End.\")\n", "meta": {"hexsha": "36c64c82f1c795d791809b4f3add4df7fa7e6644", "size": 1011, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter9/ex3.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter9/ex3.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter9/ex3.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6363636364, "max_line_length": 240, "alphanum_fraction": 0.7210682493, "num_tokens": 231, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.34510525748676846, "lm_q2_score": 0.2094696714602651, "lm_q1q2_score": 0.07228908490496358}}
{"text": "\n## Page(\"SOme blurb\",  (q1,q2,q3, ...);\n#DBSubject=\"Calculus\",\n#KEYWORDS=\"limits\",\n#AuthorText=\"John  Verzani\"\n#AuthorText2=\"Joseph Maher\"\n#)\nraw\"\"\"\n    Page(intro, questions; context=\"\",  meta...)\n\n\nCreate a page  which prints as a `pg`  file.\n\n* `intro` may be marked  up using modified markdown \n* `questions` is a tuple of questions or `QUESTIONS` object\n* `context` optional value to create page context. Typical  usage: `context=\"Interval\"`\n* `answer_context`: Dictionary of context for all answers on the page, e.g \n```\nanswer_context=Dict(:operators=>Dict(:undefine=>\"'+','-','*','/','^'\"),\n                    :functions=>Dict(:disable=>\"'All'\"),\n                    :constants=>Dict(:remove=>\"\\\"e\\\"\")\n                   )\n```\nThe context dictionary above, is aliased as `numbers_only`.\n\n* `meta` for page meta data.\n\nIf `ENV[\"BRANDING\"]` is set, it will be printed on each page generated.\n\nExample:\n\n```\nusing JuliaWeBWorK\nmeta=(AuthorText=\"Julia\", Institution=\"JuliaAcademy\", Question=\"1\")\nintro  = raw\"# Problem 1\"\nq1 = numericq(\"What is  ``{{:a1}} + {{:a2}}``?\",  (x,y)->x+y,  (1:5, 1:5))\np =  Page(intro, (q1,); meta...) \n# open(\"mynew.pg\",\"w\") do io\n#    print(io, p)\n# end\n```\n\"\"\"\nstruct Page\n    intro\n    questions\n    meta_information\n    context\n    answer_context\n    function Page(intro, questions; context=\"\",answer_context=\"\", kwargs...)\n        new(intro,  questions, Dict(kwargs...), context, answer_context)\n    end\nend\n\n# utility function\nfunction kv(io::IO, v,d, ops=[])\n    if isa(d, Dict)\n        if(v != nothing)\n            push!(ops, v)\n            for (vv, dd) in d\n                kv(io, vv, dd, ops)\n            end\n        else\n            for (kk,vv) in d\n                kv(io, kk, vv)\n            end\n        end\n    else\n        println(io, \"\"\"Context()->$(join(string.(ops), \"->\"))->$(v)($(d));\\n\"\"\")\n    end\nend\n\n\n\"\"\"\n    numbers_only\n\nDictionary to pass to `answer_context` to turn off WeBWorK's simplification pass.\nThere is no means to turn this off per problem, only per page.\n\"\"\"\nnumbers_only = Dict(:operators=>Dict(:undefine=>\"'+','-','*','/','^'\"),\n                           :functions=>Dict(:disable=>\"'All'\"),\n                           :constants=>Dict(:remove=>\"\\\"e\\\"\")\n                           )\n\nfunction Base.show(io::IO, p::Page)\n\n    for (k,v) in p.meta_information\n        println(io, \"## $k('$v')\")\n    end\n        \n    \n    println(io, raw\"\"\"\nDOCUMENT();\n\nloadMacros(\"PG.pl\",\"PGbasicmacros.pl\",\"PGanswermacros.pl\");\nloadMacros(\"PGstandard.pl\");\nloadMacros(\"MathObjects.pl\");\nloadMacros(\"Parser.pl\");\nloadMacros(\"AnswerFormatHelp.pl\");\nloadMacros(\"parserRadioButtons.pl\");\nloadMacros(\"PGchoicemacros.pl\");\nloadMacros(\"PGessaymacros.pl\");\n##loadMacros(\"PGML.pl\");\n##loadMacros(\"PGcourse.pl\");\n\nContext()->{format}{number} = \"%.16g\";\nContext()->variables->add(y=>'Real', z=>'Real', t=>'Real', u=>'Real', m=>'Real', n=>'Real');\nContext()->flags->set(ignoreEndpointTypes=> 1);\n\nmy %seen;  # hat tip to https://perlmaven.com/unique-values-in-an-array-in-perl;  filter Context->strings->add\n$seen{\"yes\"} = 1; $seen{\"no\"}=1;$seen{\"true\"} = 1; $seen{\"false\"}=1;\nContext()->strings->add(qq(yes)=>{},qq(no)=>{},qq(true)=>{},qq(false)=>{});\n$ATSYMS  = qw\"@syms\"; \n\n\"\"\")\n\n    println(io, \"TEXT(beginproblem());\")\n\n    ## add in space  for popup  (called  by imagelink)\n    println(io, raw\"\"\"\nHEADER_TEXT(<<EOF);\n  <script type=\"text/javascript\" language=\"javascript\">\n  <!-- //\n  function windowpopup(url) {\n     var opt = \"height=625,width=600,location=no,menubar=no,\" +\n              \"status=no,resizable=yes,scrollbars=yes,\" +\n              \"toolbar=no,\";\n\n    window.open(url,'newwindow',opt).focus();\n  }\n  // -->\n  </script>\n\"\"\")\n\n    ## print javascript headers, as needed\n    for T in unique(typeof.(p.questions))\n        print(io, javascript_headers(T))\n    end\n    \n    println(io, raw\"\"\"            \nEOF\n\"\"\")\n    ## add in somee missing formatting styles\n    println(io,\"\"\"\n\\$BBLOCKQUOTE =  MODES(\nHTML=>\"<BlockQuote>\",\nTeX =>\"\"\n);\n\n\\$EBLOCKQUOTE  = MODES(\nHTML=>\"</BlockQuote>\",\nTeX=>\"\"\n);\n\n\\$ADMONITION = MODES(\nHTML=>\"&#9734;&nbsp;\",\nTeX=>\"\\\\(\\\\bigwhitestar)\");\n\"\"\")\n\n\n    if length(p.context) > 0\n        println(io,  \"Context(\\\"$(p.context)\\\");\")\n    end\n\n    println(io, \"\\n## ---------- create answer values ----------\\n\")\n    for q  in p.questions\n        print(io, create_answer(q))\n        println(io, \"\")\n    end\n\n    println(io, \"\\n## ---------- show  questions  ----------\\n\")\n    \n\n    println(io, raw\"\"\"$branding_ = <<\"END_BRANDING\";\"\"\")\n    println(io, get(ENV, \"BRANDING\",  \"\"))\n    println(io, \"\"\"END_BRANDING\"\"\")\n    println(io, raw\"\"\"$branding = MODES(HTML=>$branding_, TeX=>\"[nothing to see]\");\"\"\")\n \n    println(io, \"BEGIN_TEXT\\n\")\n    println(io, raw\"\"\"$branding\"\"\")\n\n    #intro = replace(p.intro, \"\\\\\" => \"\\\\\\\\\") # had this, replaced with\n    intro = Mustache.render(p.intro)\n    print(io, escape_string(intro))\n\n    println(io, \"\\n\\n\\$HR\\$PAR\\n\")\n    \n    for q in  p.questions\n        print(io,  show_question(q))\n        println(io,\"\\$PAR\\n\")\n    end\n\n    println(io,  \"END_TEXT\")\n\n    println(io, \"\\n## ---------- show  answers  ----------\\n\")\n\n    ops = []\n    if length(p.answer_context) > 0\n        kv(io, nothing, p.answer_context)\n    end\n\n    for q in p.questions\n        println(io, show_answer(q))\n        println(io,\"\")\n    end\n\n    ## Solutions go at end (when added)\n    soln_io = IOBuffer()\n    for q in  p.questions\n         print(soln_io,  show_solution(q))\n    end\n    solns = String(take!(soln_io))\n\n    if length(solns) > 0\n    \n        println(io, \"#***************************************** Solution: \")\n        println(io, \"\"\"\nContext()->texStrings;\nSOLUTION(EV3(<<\"END_SOLUTION\"));\n\"\"\")\n        println(io, escape_string(solns))         \n\n        println(io, \"\"\"\nEND_SOLUTION\nContext()->normalStrings;\n\"\"\")\n    end\n\n\n   println(io, \"ENDDOCUMENT();\")\n\nend\n\n\"\"\"\n    PAGE(SCRIPTNAME)\n\nWrite a page to a file name based on the value of `SCRIPTNAME`. Returns an anonymous function \nwhich can be called repeatedly to write a page with a filename based on `SCRIPTNAME`.\n\nThis is designed to be used as `PAGE = JuliaWeBWorK.PAGE(@__FILE__)`. Then from one script file\nseveral related `pg` files can be generated. This might be useful for authoring exams\nwhere it is a good practice to have many separate problems and not one big one with many\nparts.\n\n```\nusing JuliaWeBWorK\nPAGE = write_page(@__FILE__)\n\nq = numericq(raw\"What is \\\\({{:a1}} + {{:a2}}\\\\)?\", (a,b) -> a+b, (1:4, 2:5))\nPAGE(\"Addition\", (q,))  # writes to SCRIPT_BASE_NAME-1.pg\n\nq = numericq(raw\"What is \\\\({{:a1}} - {{:a2}}\\\\)?\", (a,b) -> a-b, (1:4, 2:5))\nPAGE(\"subtraction\", (q,))  # writes to SCRIPT_BASE_NAME-2.pg\n\nq = numericq(raw\"What is \\\\({{:a1}} * {{:a2}}\\\\)?\", (a,b) -> a*b, (1:4, 2:5))\nPAGE(\"multiplication\", (q,))  # writes to SCRIPT_BASE_NAME-3.pg\n```\n\n\n\"\"\"\nfunction PAGE(SCRIPTNAME)\n    base_nm = replace(SCRIPTNAME, r\".jl$\" => \"\")\n    ctr = Ref(1)\n    # return an anonymous function for printing a page\n    # to the numbered .pg file.\n    (args...;kwargs...) -> begin\n        fname = base_nm * \"-$(ctr[]).pg\"\n        ctr[] += 1\n        open(fname, \"w\") do io\n            show(io, Page(args..., kwargs...))\n        end\n    end\nend\n\n\n### ------ CONVENIENCES ------------\n\n\n# simple struct to hold questions\n# * can be passed to `page`\n# * the call method calls `push!(q, x)` so that questions can be piped into t\n#   these objects, as in `numericq(...) |> qs`\nstruct Questions\n    qs\nend\nQUESTIONS() = Questions(Any[])\nBase.iterate(q::Questions) = iterate(q.qs)\nBase.iterate(q::Questions, st) = iterate(q.qs, st)\nBase.length(q::Questions) = length(q.qs)\nBase.push!(q::Questions, x) = push!(q.qs, x)\n## returning `x` allows use like `u = randomizer(...) |> qs`\n(q::Questions)(x) = (push!(q, x); x)\n\n\n## ----\n\"return iterator over the letters `(a)`, `(b)`, ... Calling function increments letters\"\nfunction LETTERS()\n    letters = [\"(a)\", \"(b)\", \"(c)\", \"(d)\", \"(e)\", \"(f)\", \"(g)\", \"(h)\", \"(i)\", \"(j)\", \"(k)\", \"(l)\", \"(m)\", \"(n)\", \"(o)\", \"(p)\", \"(q)\", \"(r)\", \"(s)\", \"(t)\", \"(u)\", \"(v)\", \"(w)\", \"(x)\", \"(y)\", \"(z)\"]\n    idx = Base.Ref(1)\n    () -> begin\n        idx[] = idx[] + 1\n        letters[idx[]-1]\n    end\nend\n\n### ------ HACKS ------------\n\n## A total hack to print `@syms` in a block\n## First \\{\\} are expanded, then $... and @... are substituted\n## so we can't generalize through the \\{...\\} phase\nATSYMS = \"\\$ATSYMS\"; export ATSYMS\n\n# https://github.com/JuliaLang/julia/blob/master/stdlib/InteractiveUtils/src/clipboard.jl\nfunction mac_clipboard(p)\n    open(pipeline(`pbcopy`, stderr=stderr), \"w\") do io\n        show(io, p)\n    end\nend\nexport mac_clipboard\n\n    \n", "meta": {"hexsha": "ecea2b03d19993496e4e559eaddb918f3d1d87c3", "size": 8652, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/page.jl", "max_stars_repo_name": "mth229/JuliaWeBWorK.jl", "max_stars_repo_head_hexsha": "10a04eeaa45c29158e8a6e6613a1b4d6472c8913", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-25T01:43:39.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-25T01:43:39.000Z", "max_issues_repo_path": "src/page.jl", "max_issues_repo_name": "mth229/JuliaWeBWorK.jl", "max_issues_repo_head_hexsha": "10a04eeaa45c29158e8a6e6613a1b4d6472c8913", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/page.jl", "max_forks_repo_name": "mth229/JuliaWeBWorK.jl", "max_forks_repo_head_hexsha": "10a04eeaa45c29158e8a6e6613a1b4d6472c8913", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.953271028, "max_line_length": 196, "alphanum_fraction": 0.5670365233, "num_tokens": 2506, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3486451488696663, "lm_q2_score": 0.20689403903542758, "lm_q1q2_score": 0.0721326030397532}}
{"text": "# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,jl:hydrogen\n#     text_representation:\n#       extension: .jl\n#       format_name: hydrogen\n#       format_version: '1.3'\n#       jupytext_version: 1.11.2\n#   kernelspec:\n#     display_name: Julia 1.6.2\n#     language: julia\n#     name: julia-1.6\n# ---\n\n# %% [markdown]\n# # Attempt to use ConcreteStructs.jl with Parameters.jl\n#\n# * Gen Kuroki\n# * 2021-09-06\n# * https://github.com/jonniedie/ConcreteStructs.jl/issues/4#issuecomment-913998030\n#\n# I have tried to make ConcreteStructs.jl and Parameters.jl work together well.\n#\n# __Conclusion:__ It is possible to do so by making the following two changes.\n#\n# * Change `@concrete` not creating the inner constructor, so that the `Foo{__T_a, __T_b, __T_c}(a, b, c)`-type default constructor will be defined.\n# * Change `@with_kw` expanding macros in the argument, [like `Base.@kwdef`](https://github.com/JuliaLang/julia/blob/4931faa34a8a1c98b39fb52ed4eb277729120128/base/util.jl#L455).\n#\n# Then `@concrete` works well with `@with_kw` and more completely with `Base.@kwdef`.\n#\n# See below for details.\n\n# %%\nVERSION\n\n# %%\nusing ConcreteStructs\nusing Parameters\n\nmacro macroexpand_rmln(code)\n    :(macroexpand($__module__, $(QuoteNode(code)), recursive=true) |>\n        Base.remove_linenums!)\nend\n\n# %% [markdown]\n# ## Plain struct\n\n# %%\nstruct Foo{A, B, C}\n    a::A\n    b::B\n    c::C\nend\n\n# %%\nmethods(Foo)\n\n# %%\nmethods(Foo{1,2,3})\n\n# %% [markdown]\n# The default constructor `Foo{A, B, C}(a, b, c)` is defined.\n\n# %% [markdown]\n# ## @concrete struct\n#\n# `@concrete` removes the `Foo_concrete{__T_a, __T_b, __T_c}(a, b, c)`-type default constructor.\n\n# %%\n@concrete struct Foo_concrete\n    a\n    b\n    c\nend\n\n# %%\nmethods(Foo_concrete)\n\n# %%\nmethods(Foo_concrete{1,2,3})\n\n# %% [markdown]\n# `Foo_concrete{__T_a, __T_b, __T_c}(a, b, c)` is not defined because only the inner constructor `Foo_concrete(a::__T_a, b::__T_b, c::__T_c)` is defined.\n\n# %%\n@macroexpand_rmln @concrete struct Foo_concrete\n    a\n    b\n    c\nend\n\n# %% [markdown]\n# ## Base.@kwdef concrete struct\n\n# %%\nBase.@kwdef @concrete struct Foo_kwdef_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %%\nFoo_kwdef_concrete()\n\n# %%\nFoo_kwdef_concrete{Int, Float64, String}(a = 4, b = 5.0, c = \"six\")\n\n# %% [markdown]\n# The reason for this error is that `Foo_kwdef_concrete{__T_a, __T_b, __T_c}(a, b, c)` is not defined.\n\n# %%\nmethods(Foo_kwdef_concrete)\n\n# %%\nmethods(Foo_kwdef_concrete{1,2,3})\n\n# %%\n@macroexpand_rmln Base.@kwdef @concrete struct Foo_kwdef_concrete\n    a\n    b\n    c\nend\n\n# %% [markdown]\n# ## @with_kw @concrete struct causes error\n\n# %%\n@with_kw @concrete struct Foo_with_kw_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %% [markdown]\n# [The first line of `Base.@kwdef`](https://github.com/JuliaLang/julia/blob/4931faa34a8a1c98b39fb52ed4eb277729120128/base/util.jl#L455) expands macros in the argument expression:\n#\n# ```julia\n#     expr = macroexpand(__module__, expr) # to expand @static\n# ```\n#\n# This is what makes `Base.@kwdef @concrete struct` possible.  Make a similar change to `Parameters.@with_kw`.\n\n# %% [markdown]\n# ## Change @with_kw expanding macros in the argument\n\n# %%\n@eval Parameters macro with_kw(typedef)\n    typedef = macroexpand(__module__, typedef) # inserted\n    return esc(with_kw(typedef, __module__, true))\nend\n\n# %%\n@with_kw @concrete struct Foo_with_kw_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %% [markdown]\n# Okay, it seems to have worked.  But...\n\n# %%\nmethods(Foo_with_kw_concrete)\n\n# %%\nmethods(Foo_with_kw_concrete{1,2,3})\n\n# %%\nFoo_with_kw_concrete()\n\n# %% [markdown]\n# The reason for this error is that `Foo_with_kw_concrete{__T_a, __T_b, __T_c}(a, b, c)` is not defined again.\n\n# %%\n@macroexpand_rmln @with_kw @concrete struct Foo_with_kw_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %% [markdown]\n# ## Change `@concrete` not creating the inner constructor\n#\n# Change `ConcreteStructs._concretize(expr)` not creating the inner constructor.\n\n# %%\n@eval ConcreteStructs function _concretize(expr)\n    expr isa Expr && expr.head == :struct || error(\"Invalid usage of @concrete\")\n    \n    is_mutable = expr.args[1]\n    struct_name, type_params, super = _parse_head(expr.args[2])\n    line_tuples = _parse_line.(expr.args[3].args)\n    lines = first.(line_tuples)\n    type_params_full = (type_params..., filter(x -> x!==nothing, last.(line_tuples))...)\n\n    struct_type = if length(type_params_full) == 0\n        struct_name\n    else\n        Expr(:curly, struct_name, type_params_full...)\n    end\n\n    head = Expr(:(<:), struct_type, super)\n    # constructor_expr = _make_constructor(struct_name, type_params, type_params_full, lines)\n    # body = Expr(:block, lines..., constructor_expr)\n    body = Expr(:block, lines...)\n    struct_expr = Expr(:struct, is_mutable, head, body)\n    \n    return struct_expr, struct_name, type_params\nend\n\n# %%\n@macroexpand_rmln @concrete struct Bar_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %% [markdown]\n# The inner constructor has been deleted.\n\n# %% [markdown]\n# ## @concrete works well with @with_kw\n\n# %%\n@with_kw @concrete struct Bar_with_kw_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %%\nmethods(Bar_with_kw_concrete)\n\n# %%\nmethods(Bar_with_kw_concrete{1,2,3})\n\n# %% [markdown]\n# The default constructor `Bar_with_kw_concrete{__T_a, __T_b, __T_c}(a, b, c)` is implicitly defined.\n\n# %%\nBar_with_kw_concrete()\n\n# %%\nBar_with_kw_concrete(c = '3')\n\n# %%\nBar_with_kw_concrete{Int, Float64, String}(a = 4, b = 5.0, c = \"six\")\n\n# %%\nBar_with_kw_concrete(4, 5.0, \"six\")\n\n# %% [markdown]\n# `ConcreteStructs.@concrete` works well with `Parameters.@with_kw`!\n\n# %%\n@macroexpand_rmln @with_kw @concrete struct Bar_with_kw_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %% [markdown]\n# ## @concrete works well more completely with Base.@kwdef\n\n# %%\nBase.@kwdef @concrete struct Bar_kwdef_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %%\nBar_kwdef_concrete()\n\n# %%\nBar_kwdef_concrete(c = '3')\n\n# %%\nBar_kwdef_concrete{Int, Float64, String}(a = 4, b = 5.0, c = \"six\") # not error\n\n# %%\nBar_kwdef_concrete(4, 5.0, \"six\")\n\n# %% [markdown]\n# `ConcreteStructs.@concrete` works more completely well with `Base.@kwdef`!\n\n# %%\n@macroexpand_rmln Base.@kwdef @concrete struct Bar_kwdef_concrete\n    a = 1\n    b = 2.0\n    c = \"three\"\nend\n\n# %%\n", "meta": {"hexsha": "f5761427bf96370df735019b53680e567ae8bf25", "size": 6342, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "0019/ConcreteStructs.jl with Parameters.jl.jl", "max_stars_repo_name": "genkuroki/public", "max_stars_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2021-06-06T00:33:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T06:56:08.000Z", "max_issues_repo_path": "0019/ConcreteStructs.jl with Parameters.jl.jl", "max_issues_repo_name": "genkuroki/public", "max_issues_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "0019/ConcreteStructs.jl with Parameters.jl.jl", "max_forks_repo_name": "genkuroki/public", "max_forks_repo_head_hexsha": "339ea5dfd424492a6b21d1df299e52d48902de18", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-08-02T11:58:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-11T11:46:05.000Z", "avg_line_length": 21.4256756757, "max_line_length": 178, "alphanum_fraction": 0.6641438032, "num_tokens": 2040, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3849121585956185, "lm_q2_score": 0.18713267989575072, "lm_q1q2_score": 0.07202964376245631}}
{"text": "# This file is a part of AstroLib.jl. License is MIT \"Expat\".\n# Copyright (C) 2016 Mos\u00e8 Giordano.\n\n\"\"\"\n    ymd2dn(date) -> number_of_days\n\n### Purpose ###\n\nConvert from a date to day of the year.\n\n### Explanation ###\n\nReturns the day of the year for `date` with January 1st being day 1.\n\n### Arguments ###\n\n* `date`: the date with `Date` type.  Can be a single date or an array of dates.\n\n### Output ###\n\nThe day of the year for the given `date`.  If `date` is an array, returns an\narray of days.\n\n### Example ###\n\nFind the days of the year for March 5 in the years 2015 and 2016 (this is a leap\nyear).\n\n``` julia\nymd2dn([Date(2015, 3, 5), Date(2016, 3, 5)])\n# => 2-element Array{Int64,1}:\n#     64\n#     65\n```\n\n### Note ###\n\n`ydn2md` converts from year and day number of year to a date.\n\"\"\"\nconst ymd2dn = Dates.dayofyear\n", "meta": {"hexsha": "f45ee60992da8e48da35a2a919d3fce0aa5696d9", "size": 824, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ymd2dn.jl", "max_stars_repo_name": "JuliaPackageMirrors/AstroLib.jl", "max_stars_repo_head_hexsha": "d56c7307efa7e784554c1ee806664af51b82a052", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/ymd2dn.jl", "max_issues_repo_name": "JuliaPackageMirrors/AstroLib.jl", "max_issues_repo_head_hexsha": "d56c7307efa7e784554c1ee806664af51b82a052", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/ymd2dn.jl", "max_forks_repo_name": "JuliaPackageMirrors/AstroLib.jl", "max_forks_repo_head_hexsha": "d56c7307efa7e784554c1ee806664af51b82a052", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.0975609756, "max_line_length": 80, "alphanum_fraction": 0.6444174757, "num_tokens": 265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4378234844434674, "lm_q2_score": 0.1645164608483867, "lm_q1q2_score": 0.07202917013694794}}
{"text": "\"\"\"\nFunction replaces the negative values in a datacube with missing. \n\"\"\"\nfunction replace_negative(datacube)\n    for i in 1:prod(size(datacube))\n        if ismissing(datacube[i]) \n            continue\n        elseif datacube[i] < 0\n                datacube[i] = missing\n        else\n            continue\n        end\n    end\n    return datacube\nend\n", "meta": {"hexsha": "43845804e2d12d5570257c894a36b18f34e0f5a1", "size": 350, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data_cleaning/replace_negative.jl", "max_stars_repo_name": "JuliaPlanet/NighttimeLights.jl", "max_stars_repo_head_hexsha": "100641b875e7af3c4eaa9e9daacd8e06e8940458", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2021-08-01T02:22:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-16T03:19:42.000Z", "max_issues_repo_path": "src/data_cleaning/replace_negative.jl", "max_issues_repo_name": "xKDR/NighttimeLights.jl", "max_issues_repo_head_hexsha": "3b3015c8c8d45372cde9d5a9bd76feae6a1588c1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data_cleaning/replace_negative.jl", "max_forks_repo_name": "xKDR/NighttimeLights.jl", "max_forks_repo_head_hexsha": "3b3015c8c8d45372cde9d5a9bd76feae6a1588c1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-09T10:57:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-09T10:57:53.000Z", "avg_line_length": 21.875, "max_line_length": 66, "alphanum_fraction": 0.5857142857, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.16026603633858202, "lm_q1q2_score": 0.07202237628542027}}
{"text": "#=\nP04 (*) Find the number of elements of a list.\n=#\n\nfunction my_length(coll)\n    # With built-in `length` function\n    length(coll)\nend\n\nfunction len_with_count(coll, count)\n    # With tail recursion\n    if isempty(coll)\n        count\n    else\n        len_with_count(coll[2:end], count + 1)\n    end\nend\n\nfunction my_length_2(coll)\n    # Counting from 0\n    len_with_count(coll, 0)\nend\n\n@assert my_length(()) == 0\n@assert my_length((:a, :b, :c)) == 3\n@assert my_length((1, 2, 3, 4)) == 4\n@assert my_length(((:a, :b), (:c, :d), (:e, :f))) == 3\n\n@assert my_length_2(()) == 0\n@assert my_length_2((:a, :b, :c)) == 3\n@assert my_length_2((1, 2, 3, 4)) == 4\n@assert my_length_2(((:a, :b), (:c, :d), (:e, :f))) == 3\n\nprintln(\"Tests passed: JL-04.jl\")\n", "meta": {"hexsha": "e9ab1f5491ee08672660e2b46fb81c8949d06ea2", "size": 744, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JL-04.jl", "max_stars_repo_name": "microamp/jl-99", "max_stars_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "JL-04.jl", "max_issues_repo_name": "microamp/jl-99", "max_issues_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "JL-04.jl", "max_forks_repo_name": "microamp/jl-99", "max_forks_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.2571428571, "max_line_length": 56, "alphanum_fraction": 0.5913978495, "num_tokens": 276, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4493926492132671, "lm_q2_score": 0.16026602831693942, "lm_q1q2_score": 0.07202237504423789}}
{"text": "# a() is some operation\na() = sum(i for i in 1:100000)\n\n# b is the Task of \nb = Task(a)\n\n# shows the current task\ncurrent_task()\n\n# Query if the specified is done\nprintln(\"Is the task done yet?:\", istaskdone(b))\n\nschedule(b)\n\nyield()\n\nresult = istaskdone(b)\nprintln(\"Is the task done?:\", result)", "meta": {"hexsha": "bfd5072bbb44202708de6f697c9571854cae2583", "size": 295, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "intro_to_tasks.jl", "max_stars_repo_name": "ryanorsinger/JuliaPlayground", "max_stars_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "intro_to_tasks.jl", "max_issues_repo_name": "ryanorsinger/JuliaPlayground", "max_issues_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intro_to_tasks.jl", "max_forks_repo_name": "ryanorsinger/JuliaPlayground", "max_forks_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.3888888889, "max_line_length": 48, "alphanum_fraction": 0.6745762712, "num_tokens": 86, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.16026603032235007, "lm_q1q2_score": 0.07202237358176995}}
{"text": "#========================================================================================#\n#\tLaboratory 7\n#\n# Encapsulation and software design.\n#\n# Author: Niall Palfreyman, 06/02/2022\n#========================================================================================#\n[\n\tActivity(\n\t\t\"\"\"\n\t\tIn this laboratory we look at the issue of encapsulation - a super-important topic in\n\t\tmodern software engineering. The point is that if everyone is able to change the\n\t\tvalue of important variables, our code will be EXTREMELY hard for people to understand\n\t\tand debug. I have seen one firm come to bankruptcy because of this problem. How does it\n\t\tcome about?\n\n\t\tIn a moment, you will discover that the variables inside a module or function have LOCAL\n\t\tSCOPE - that is, they are only visible and available inside that function, and not in\n\t\tthe GLOBAL SCOPE outside the function.\n\n\t\tSometimes, we are tempted to pass data from one function to another by storing that data\n\t\tin global variables, but this ALWAYS brings with it the danger that someone might change\n\t\tthat data by accident - particularly because it is often very difficult for users of our\n\t\tprogram to notice that we are using global variables to pass the data!\n\n\t\tA large part of the science of informatics concerns how to pass data in ways that\n\t\tprotect that data from being changed by accident, so that is what we will investigate\n\t\tin this laboratory.\n\n\t\tIn our first experiment, enter the following code, then tell me the value of paula:\n\n\t\tlinus = [5,4,3,2,1]; paula = 5\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow enter the following function:\n\n\t\tfunction change_paula()\n\t\t\tpaula = 7\n\t\t\tpaula\n\t\tend\n\n\t\tThen call the function change_paula() and tell me the value you get back:\n\t\t\"\"\",\n\t\t\"Your result may (not) surprise you, depending on how you think about scoping rules\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tYour result shows us that the variable paula has two different meanings: the meaning in\n\t\tthe GLOBAL scope outside the function change_paula, and the meaning inside the LOCAL\n\t\tscope of change_paula.\n\n\t\tNow tell me the current value of paula:\n\t\t\"\"\",\n\t\t\"Ask Julia for the value of paula\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tAha! So although we can change the value of LOCAL paula inside the function change_paula(),\n\t\tthis does not affect the value of GLOBAL paula. In fact, there exist two different variables\n\t\tnamed paula: the GLOBAL variable containing the value 5, and a LOCAL variable containing\n\t\tthe value 7. When change_paula() ends, the variables in its local scope are thrown away,\n\t\tand the LOCAL paula disappears.\n\n\t\tIf we REALLY want to change the global value of paula, we can do so by redefining the\n\t\tfunction change_paula():\n\n\t\tfunction change_paula()\n\t\t\tglobal paula = 7\n\t\t\tpaula\n\t\tend\n\n\t\tNOTE: Doing this is a Very Bad Idea! Tell me the value of paula now:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tSo Julia does allow us to make use of global values inside a local scope, but it forces us\n\t\tto announce this by using the keyword \"global\".\n\n\t\tThere is a further issue here. Enter this code:\n\n\t\tfunction change_linus()\n\t\t\tlinus[3] = 7\n\t\t\tlinus\n\t\tend\n\n\t\tAgain, the return value tells us that we are able to change the value of a local variable\n\t\tnamed linus. But now tell me the value of the GLOBAL variable named linus:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==[5,4,7,2,1]\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tThe point here is that linus is a Vector that refers to its contents (5,4,3,2,1). Julia\n\t\tdoes not allow us to change the value of linus, however it DOES allow us to change the\n\t\tCONTENTS that linus refers to. So global variables are still unsafe! What are we to do?\n\t\tThe solution is this:\n\n\t\t\tALWAYS encapsulate (i.e.: wrap/hide) EVERYTHING you do inside a MODULE!\n\n\t\tModules offer a very effective of preventing our variables and code from being changed by\n\t\tother programmers. Let's see how to do this. Enter the following code:\n\n\t\tmodule MyModule\n\t\t\tfunction change_paula1()\n\t\t\t\tglobal paula = 9\n\t\t\t\tpaula\n\t\t\tend\n\t\tend\n\n\t\tNow call MyModule.change_paula1(), then tell me the value of paula afterwards:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tOK, so putting change_paula1() inside the module MyModule means it cannot interfere\n\t\twith the value of our global variable paula. But wait! We know that we can load\n\t\tmodules into global scope by means of the keyword \"using\": will that make it possible\n\t\tfor users change paula's value by accident? Load the module MyModule now:\n\n\t\tusing .MyModule\n\n\t\tRepeat the previous experiment - what is the value of paula afterwards?\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==7\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tGreat! Now that we know how to hide variables and functions inside a module, we can do\n\t\tsome real live software development! In the next laboratory, we will develop a genetic\n\t\talgorithm (GA), and GAs need to work with arrays of random values. However, generating\n\t\trandom numbers is very time-expensive, so in this laboratory we will develop a software\n\t\ttool called a Casino that will later help us generate arrays of random values very quickly.\n\n\t\tThere is just one thing we need to do first. Our modules can get quite complex, so we\n\t\twill build them up step-by-step within the Ingolstadt filesystem. If you look in the\n\t\tdirectory Ingolstadt/Development/Casinos, you will find a file named Casinos.jl. At\n\t\tpresent, this is just a dummy file that doesn't do anything except create a module named\n\t\tCasinos that contains the single variable test. Notice that the module Casinos is prefixed\n\t\tby a triple-quoted multi-line string that describes its purpose.\n\t\t\n\t\tWhenever you start writing a new module, always start the same way I have here - creating\n\t\ta very simple file that you can gradually expand into a complex program. We call this\n\t\tstyle of programming AGILE programming, because it is a good way to get started quickly\n\t\ton the job of programming a new module.\n\n\t\tTell me the value that I have assigned to test inside the Casinos module:\n\t\t\"\"\",\n\t\t\"The program statement is in line 11 of Casinos.jl\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow we'll test the Casinos module by reading and parsing the file Casinos.jl. Enter the\n\t\tfollowing line from inside the Ingolstadt folder:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\n\t\tNow you can test the Casinos module by telling me the answer to the following line of code:\n\n\t\tCasinos.test\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x==5\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tBingo! Our new module works! Now we can develop our Casinos module in the file Casinos.jl.\n\t\tWe start by setting up a USE-CASE for the module - that is, we sketch out how we will want\n\t\tto use the module when it is finished. Replace your module definition in the file Casinos.jl\n\t\tby the following code, then move on to the next activity:\n\n\t\t\tmodule Casinos\n\n\t\t\t#--------------------------------------------------------------------------------------\n\t\t\t\\\"\"\"\n\t\t\t\tunittest()\n\n\t\t\tUnit-test the Casinos module.\n\t\t\t\\\"\"\"\n\t\t\tfunction unittest()\n\t\t\t\tprintln(\"\\\\n============ Unit test Casinos: ===============\")\n\t\t\t\tprintln(\"Casino deck of randomness 2 for matrix withdrawals up to size (2x3):\")\n\t\t\t\tcasino = Casino(2,3,2)\n\t\t\t\tdisplay( casino.deck)\n\t\t\t\tprintln()\n\t\t\t\n\t\t\t\tprintln(\"Draw several (2x3) matrices from the casino:\")\n\t\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\t\tdisplay( draw( casino,2,3)); println()\n\t\t\t\n\t\t\t\tprintln(\"Finally, reshuffle the casino and redisplay its deck:\")\n\t\t\t\tshuffle!(casino)\n\t\t\t\tdisplay(casino.deck)\n\t\t\tend\n\n\t\t\tend # of Casinos\n\t\t\"\"\",\n\t\t\"Note: you can't compile or run Casinos yet - just enter this code and move on\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tYou can test your new module by reincluding Casinos.jl:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\n\t\tIf you enter Casinos.unittest() at the Julia prompt, you will see that the command\n\t\truns, but throws lots of errors (exceptions). We'll now start fixing those errors...\n\n\t\tFirst, comment out the lines of unittest() that are causing problems. Insert the\n\t\tmulti-line comment marker #= at the beginning of the third line of unittest() so that\n\t\tit looks like this:\n\t\t\t\n\t\t#=\tcasino = Casino(2,3,2)\n\n\t\tNext, close this multi-line comment by inserting the marker =# to the right of the final\n\t\tline of unittest():\n\n\t\t\tdisplay(casino.deck) =#\n\n\t\tReinclude Casinos.jl. Now you should be able to run Casino.unittest() without errors.\n\t\tTell me the last six characters that you see in the output:\n\t\t\"\"\",\n\t\t\"You should get two lines of output, and the final characters specify a matrix size\",\n\t\tx -> x == \"(2x3):\"\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tTo start developing our new module, we'll define the type Casino. Insert the following\n\t\tcode AFTER the \"module\" line and BEFORE the comment box before unittest(), and check that\n\t\teverything still includes and runs properly:\n\n\t\tusing Random\n\n\t\t#-----------------------------------------------------------------------------------------\n\t\t# Module types:\n\t\t\n\t\t\\\"\"\"\n\t\t\tCasino\n\t\t\n\t\tA Casino can return arrays of random numbers in the range [0,1), up to a maximum number of rows\n\t\t(maxrows), and a maximum number of columns (maxcols). It also contains a deck of prepared\n\t\trandom numbers from which it draws the arrays.\n\t\t\\\"\"\"\n\t\tstruct Casino\n\t\t\tmaxrows::Int\t\t\t\t\t\t\t# Maximum number of drawable rows\n\t\t\tmaxcols::Int\t\t\t\t\t\t\t# Maximum number of drawable columns\n\t\t\trandomness::Int\t\t\t\t\t\t\t# How randomised will our withdrawals be?\n\t\t\tdeck::Matrix\t\t\t\t\t\t\t# Repository of random numbers in [0,1)\n\t\t\n\t\t\t\"The one and only constructor\"\n\t\t\tfunction Casino(maxrows::Int,maxcols::Int,randomness::Int=5)\n\t\t\t\tnew(\n\t\t\t\t\tmaxrows, maxcols, randomness,\n\t\t\t\t\trand((maxrows+1)*randomness,(maxcols+1)*randomness)\n\t\t\t\t)\n\t\t\tend\n\t\tend\n\t\t\n\t\t#-----------------------------------------------------------------------------------------\n\t\t# Module methods:\n\t\t\"\"\",\n\t\t\"Remember your code won't do anything new yet: just getting it to run is your first goal!\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tRight, now let's activate the new code. Remove the open-comment marker from the third line\n\t\tof unittest() and insert it instead at the beginning of the 7th line:\n\n\t\t#=\tprintln(\"Draw several (2x3) matrices from the casino:\")\n\n\t\tThis reveals the lines 3-6. Test that your program can now correctly create and display a\n\t\tCasino. Now tell me the size of the Casino deck, and think about why I have designed this\n\t\tsize to depend on the constructor argument randomness:\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> x == (6,8)\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tDid you work out why the size of the deck depends on randomness? The idea is that we\n\t\twant to draw random matricess from the Casino like drawing cards from a shuffled deck of\n\t\tcards, and that is only possible if the deck contains more cards than we actually need.\n\n\t\tNow we'll implement the draw() method. First reveal the next four lines of unittest() and\n\t\tinsert the following dummy code immediately after the \"Module methods:\" comment. Now test\n\t\tthis dummy version of draw to make sure it is robust before moving on:\n\n\t\t\\\"\"\"\n\t\t\tdraw( casino, nrows, ncols)\n\t\t\n\t\tDraw the required number of rows and columns from the casino deck, first ensuring that the\n\t\tdeck is large enough to support the withdrawal.\n\t\t\\\"\"\"\n\t\tfunction draw( casino::Casino, nrows::Int, ncols::Int)\n\t\t\tif nrows > casino.maxrows || ncols > casino.maxcols\n\t\t\t\t# Repository is too small - throw exception:\n\t\t\t\terror( \"Requested withdrawal is too large\")\n\t\t\tend\n\t\t\n\t\t\t# Choose random offsets and strides for drawing a matrix of size (nrows x ncols) from\n\t\t\t# the deck, assuming that it is big enough to support the withdrawal:\n\t\t\tones(nrows,ncols)\n\t\tend\n\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tNow comes the cool part of the code. Notice how we have made absolutely sure that by the\n\t\ttime we get to the dummy line \"ones(nrows,ncols)\", we can rely on the values nrows and\n\t\tncols being small enough to be able to draw our new random matrix. Now replace this\n\t\tdummy line by the following code and test it:\n\n\t\t\tdeckrows, deckcols = size(casino.deck)\n\t\t\toffset_r = rand( 1 : (deckrows-nrows))\n\t\t\tstride_r = (nrows <= 1) ? 1 :\n\t\t\t\t\t\t\trand( 1 : (deckrows-offset_r) \u00f7 (nrows-1))\n\t\t\toffset_c = rand( 1 : (deckcols-ncols))\n\t\t\tstride_c = (ncols <= 1) ? 1 :\n\t\t\t\t\t\t\trand( 1 : (deckcols-offset_c) \u00f7 (ncols-1))\n\t\t\n\t\t\t# Return a randomly chosen table of slices from the deck:\n\t\t\tcasino.deck[\n\t\t\t\t(offset_r : stride_r : (offset_r + (nrows-1)*stride_r)),\n\t\t\t\t(offset_c : stride_c : (offset_c + (ncols-1)*stride_c))\n\t\t\t]\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tFinally, we'll now give Casinos the functionality of shuffling. We want users to be\n\t\table to shuffle the contents of the deck to give new random values. Reveal all lines\n\t\tof code in unittest(), insert the command \"using Random\" at the beginning of the Casinos\n\t\tmodule - immediately after the line \"module Casinos\", then insert the following code after\n\t\tdraw() in Casinos. Then test the module again:\n\n\t\t#-----------------------------------------------------------------------------------------\n\t\t\\\"\"\"\n\t\t\tshuffle!( casino)\n\n\t\tReassign random values in the deck.\n\t\t\\\"\"\"\n\t\tfunction shuffle!( casino::Casino)\n\t\t\trand!( casino.deck)\n\t\tend\n\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tCongratulations! You have written your first Julia module! You can test its functionality\n\t\tfor yourself by doing something like this at the Julia prompt:\n\n\t\tinclude(\"src/Development/Casinos/Casinos.jl\")\n\t\tcasino = Casinos.Casino(3,3,5)\n\t\tCasinos.draw(casino,3,3)\n\n\t\tMake sure you also test error cases like this:\n\n\t\tCasinos.draw(casino,7,9)\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n\tActivity(\n\t\t\"\"\"\n\t\tThere is just one small thing we can do to make life easier for users of the Casinos\n\t\tmodule: We shall expose the new functionality. After all, it's a pain to have to write\n\t\t\"Casinos.\" in front of every command. To avoid this, insert the following lines between\n\t\tthe line \"module Casinos\" and the line \"using Random\":\n\n\t\t# Externally callable methods of Casinos\n\t\texport Casino, draw, shuffle!\n\t\t\n\t\tNow reinclude Casinos.jl and repeat your tests from the previous activity, but this time\n\t\tfirst enter \"using .Casinos\" at the Julia prompt. You should now be able to call all the\n\t\tCasinos functionality without typing \"Casinos.\" in front of everything. :)\n\t\t\"\"\",\n\t\t\"\",\n\t\tx -> true\n\t),\n]", "meta": {"hexsha": "19bd363b4e63f4e27506a2cfa9687aab4f4c4da4", "size": 14246, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Labs/INLab007.jl", "max_stars_repo_name": "NiallPalfreyman/Ingolstadt.jl", "max_stars_repo_head_hexsha": "c8b92b4b2bd8659d2584f153efe70dd7e99c5e9c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-25T09:00:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T09:00:42.000Z", "max_issues_repo_path": "Labs/INLab007.jl", "max_issues_repo_name": "NiallPalfreyman/Ingolstadt.jl", "max_issues_repo_head_hexsha": "c8b92b4b2bd8659d2584f153efe70dd7e99c5e9c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Labs/INLab007.jl", "max_forks_repo_name": "NiallPalfreyman/Ingolstadt.jl", "max_forks_repo_head_hexsha": "c8b92b4b2bd8659d2584f153efe70dd7e99c5e9c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2022-03-18T14:20:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T09:00:45.000Z", "avg_line_length": 35.2623762376, "max_line_length": 97, "alphanum_fraction": 0.6796995648, "num_tokens": 3697, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3311197264277872, "lm_q2_score": 0.21733752104706247, "lm_q1q2_score": 0.07196474051159676}}
{"text": "##Scoping Exercises\n\n# Scoping Example\n\nx=5; y=7; #Defined globally\nfunction scopeTest(z)\n  x += z #Changes global value\n  y = Vector{Float64}(1) #Declares a variable, local scope\n  y[1] = 2\n  return x + y + z\nend\n\n# Caution Example\n\nfunction f1()\n  @parallel for i = 1:100\n    var = 10\n    if var < 100\n      var = var + 1\n    end\n  end\n  var = 100 + 10\nend\nf1()\nfunction f2()\n  @parallel for i = 1:100\n    var = 10\n    if var < 100\n      var = var + 1\n    end\n  end\nend\nf2()\n", "meta": {"hexsha": "cd9b7a3659d72f8b04e632f8327ede40aa630373", "size": 477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ExampleCode/scoping.jl", "max_stars_repo_name": "jla524/IntroToJulia", "max_stars_repo_head_hexsha": "2301ed94f1459893dcc67f67fc9b65df8d45d0ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 251, "max_stars_repo_stars_event_min_datetime": "2016-05-17T06:47:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-30T16:07:03.000Z", "max_issues_repo_path": "ExampleCode/scoping.jl", "max_issues_repo_name": "jla524/IntroToJulia", "max_issues_repo_head_hexsha": "2301ed94f1459893dcc67f67fc9b65df8d45d0ee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 50, "max_issues_repo_issues_event_min_datetime": "2016-10-25T16:11:42.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-02T12:08:06.000Z", "max_forks_repo_path": "ExampleCode/scoping.jl", "max_forks_repo_name": "jla524/IntroToJulia", "max_forks_repo_head_hexsha": "2301ed94f1459893dcc67f67fc9b65df8d45d0ee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 98, "max_forks_repo_forks_event_min_datetime": "2016-05-24T16:44:39.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-19T18:13:08.000Z", "avg_line_length": 14.0294117647, "max_line_length": 58, "alphanum_fraction": 0.5870020964, "num_tokens": 182, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4416730056646256, "lm_q2_score": 0.16238003058646674, "lm_q1q2_score": 0.0717188761690386}}
{"text": "using Pkg\nPkg.add(\"JuMP\")\nPkg.add(\"StructArrays\")\nPkg.add(\"DataFrames\")\nPkg.add(\"XLSX\")\nPkg.add(\"StatsBase\")\nPkg.add(\"Query\")\nPkg.add(\"Gadfly\")\nPkg.add(\"Cairo\")\nPkg.add(\"Fontconfig\")\nPkg.add(\"CSV\")\nPkg.add(\"JLD\")\n\n# these packages need their software installed at the following locations\nENV[\"CPLEX_STUDIO_BINARIES\"] = \"/opt/ibm/ILOG/CPLEX_Studio1210/cplex/bin/x86-64_linux/\"\nPkg.add(\"CPLEX\")\nENV[\"GUROBI_HOME\"] = \"/opt/gurobi911/linux64/\"\nPkg.add(\"Gurobi\")\n", "meta": {"hexsha": "2906614077cc552f3ad47fe7c228ae1f6b41df7d", "size": 458, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "setup.jl", "max_stars_repo_name": "Dibillilia/JuliaOptHeuristics", "max_stars_repo_head_hexsha": "e7acc803d8037969b98534cccba87b99028a0f5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "setup.jl", "max_issues_repo_name": "Dibillilia/JuliaOptHeuristics", "max_issues_repo_head_hexsha": "e7acc803d8037969b98534cccba87b99028a0f5b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "setup.jl", "max_forks_repo_name": "Dibillilia/JuliaOptHeuristics", "max_forks_repo_head_hexsha": "e7acc803d8037969b98534cccba87b99028a0f5b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.1052631579, "max_line_length": 87, "alphanum_fraction": 0.7248908297, "num_tokens": 159, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.14804719051380194, "lm_q1q2_score": 0.07171111061804614}}
{"text": "using Pkg\n\nPkg.update()\n\nPkg.add(\"Plots\")\nPkg.add(\"DataFrames\")\nPkg.add(\"CSV\")\n\nPkg.build(\"Plots\")\nPkg.build(\"DataFrames\")\nPkg.build(\"CSV\")", "meta": {"hexsha": "df26774360cfc184b622cb33d98882202f7593c1", "size": 139, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "setup.jl", "max_stars_repo_name": "yaozhenghangma/Julia-notebook", "max_stars_repo_head_hexsha": "e659053295e0b6b780405057b7e387a0d47379d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "setup.jl", "max_issues_repo_name": "yaozhenghangma/Julia-notebook", "max_issues_repo_head_hexsha": "e659053295e0b6b780405057b7e387a0d47379d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "setup.jl", "max_forks_repo_name": "yaozhenghangma/Julia-notebook", "max_forks_repo_head_hexsha": "e659053295e0b6b780405057b7e387a0d47379d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.6363636364, "max_line_length": 23, "alphanum_fraction": 0.6834532374, "num_tokens": 48, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.16026603433317135, "lm_q1q2_score": 0.07140325175909402}}
{"text": "using BenchmarkTools\r\n\r\nfunction my_append()\r\n\r\n    v = []\r\n\r\n    for i in 1:10000\r\n        push!(v, 5)\r\n    end\r\nend\r\n\r\nfunction my_prealloc()\r\n\r\n    v = Vector{Int64}(undef, 10000)\r\n\r\n    for i in 1:10000\r\n        v[i] = 5\r\n    end\r\nend\r\n\r\n# @benchmark my_append()\r\n@benchmark my_prealloc()", "meta": {"hexsha": "e74711d740444af080635ab5a7df4204f7d5f831", "size": 292, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-learning-src/advance/02c-benchmarking.jl", "max_stars_repo_name": "nunesmelo/djs-office-hours", "max_stars_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2021-03-27T14:23:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T14:50:37.000Z", "max_issues_repo_path": "julia-learning-src/advance/02c-benchmarking.jl", "max_issues_repo_name": "nunesmelo/djs-office-hours", "max_issues_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-learning-src/advance/02c-benchmarking.jl", "max_forks_repo_name": "nunesmelo/djs-office-hours", "max_forks_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2021-03-29T20:01:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T14:33:47.000Z", "avg_line_length": 13.2727272727, "max_line_length": 36, "alphanum_fraction": 0.551369863, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1602660323277607, "lm_q1q2_score": 0.07140325086562435}}
{"text": "\"\"\"\n    BostonHousing(; as_df = true, dir = nothing)\n\nThe classical Boston Housing tabular dataset.\n\nSources:\n   (a) Origin:  This dataset was taken from the StatLib library which is\n                maintained at Carnegie Mellon University.\n   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the \n                 demand for clean air', J. Environ. Economics & Management,\n                 vol.5, 81-102, 1978.\n   (c) Date: July 7, 1993\n\nNumber of Instances: 506\n\nNumber of Attributes: 13 continuous attributes (including target\n                            attribute \"MEDV\"), 1 binary-valued attribute.\n\n# Arguments\n\n$ARGUMENTS_SUPERVISED_TABLE\n\n# Fields\n\n$FIELDS_SUPERVISED_TABLE\n\n# Methods\n\n$METHODS_SUPERVISED_TABLE\n\n# Examples\n    \n```julia-repl\njulia> using MLDatasets: BostonHousing\n\njulia> dataset = BostonHousing()\nBostonHousing:\n  metadata => Dict{String, Any} with 5 entries\n  features => 506\u00d713 DataFrame\n  targets => 506\u00d71 DataFrame\n  dataframe => 506\u00d714 DataFrame\n\n\njulia> dataset[1:5][1]\n5\u00d713 DataFrame\n Row \u2502 CRIM     ZN       INDUS    CHAS   NOX      RM       AGE      DIS      RAD    TAX    PTRATIO  B        LSTAT   \n     \u2502 Float64  Float64  Float64  Int64  Float64  Float64  Float64  Float64  Int64  Int64  Float64  Float64  Float64 \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 0.00632     18.0     2.31      0    0.538    6.575     65.2   4.09        1    296     15.3   396.9      4.98\n   2 \u2502 0.02731      0.0     7.07      0    0.469    6.421     78.9   4.9671      2    242     17.8   396.9      9.14\n   3 \u2502 0.02729      0.0     7.07      0    0.469    7.185     61.1   4.9671      2    242     17.8   392.83     4.03\n   4 \u2502 0.03237      0.0     2.18      0    0.458    6.998     45.8   6.0622      3    222     18.7   394.63     2.94\n   5 \u2502 0.06905      0.0     2.18      0    0.458    7.147     54.2   6.0622      3    222     18.7   396.9      5.33\n\njulia> dataset[1:5][2]\n5\u00d71 DataFrame\nRow \u2502 MEDV    \n    \u2502 Float64 \n\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n  1 \u2502    24.0\n  2 \u2502    21.6\n  3 \u2502    34.7\n  4 \u2502    33.4\n  5 \u2502    36.2  \n\njulia> X, y = BostonHousing(as_df=false)[]\n([0.00632 0.02731 \u2026 0.10959 0.04741; 18.0 0.0 \u2026 0.0 0.0; \u2026 ; 396.9 396.9 \u2026 393.45 396.9; 4.98 9.14 \u2026 6.48 7.88], [24.0 21.6 \u2026 22.0 11.9])\n```\n\"\"\"\nstruct BostonHousing <: SupervisedDataset\n    metadata::Dict{String, Any}\n    features\n    targets\n    dataframe\nend\n\nfunction BostonHousing(; as_df = true, dir = nothing)\n    @assert dir === nothing \"custom `dir` is not supported at the moment.\"\n    path = joinpath(@__DIR__, \"..\", \"..\", \"..\", \"data\", \"boston_housing.csv\")\n    df = read_csv(path)\n    features = df[!, Not(:MEDV)]\n    targets = df[!, [:MEDV]]\n    \n    metadata = Dict{String, Any}()\n    metadata[\"path\"] = path\n    metadata[\"feature_names\"] = names(features)\n    metadata[\"target_names\"] = names(targets)\n    metadata[\"n_observations\"] = size(targets, 1)\n    metadata[\"description\"] = BOSTONHOUSING_DESCR\n    \n    if !as_df \n        features = df_to_matrix(features) \n        targets = df_to_matrix(targets)\n        df = nothing\n    end\n    \n    return BostonHousing(metadata, features, targets, df)\nend\n\nconst BOSTONHOUSING_DESCR = \"\"\"\nThe Boston Housing Dataset.\n\nSources:\n   (a) Origin:  This dataset was taken from the StatLib library which is\n                maintained at Carnegie Mellon University.\n   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the \n                 demand for clean air', J. Environ. Economics & Management,\n                 vol.5, 81-102, 1978.\n   (c) Date: July 7, 1993\n\nNumber of Instances: 506\n\nNumber of Attributes: 13 continuous attributes (including target\n                            attribute \"MEDV\"), 1 binary-valued attribute.\n\nFeatures:\n\n    1. CRIM      per capita crime rate by town\n    2. ZN        proportion of residential land zoned for lots over 25,000 sq.ft.\n    3. INDUS     proportion of non-retail business acres per town\n    4. CHAS      Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n    5. NOX       nitric oxides concentration (parts per 10 million)\n    6. RM        average number of rooms per dwelling\n    7. AGE       proportion of owner-occupied units built prior to 1940\n    8. DIS       weighted distances to five Boston employment centres\n    9. RAD       index of accessibility to radial highways\n    10. TAX      full-value property-tax rate per 10,000 dollars\n    11. PTRATIO  pupil-teacher ratio by town\n    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n    13. LSTAT    % lower status of the population\n        \nTarget:\n    \n    14. MEDV     Median value of owner-occupied homes in 1000's of dollars   \n\nNote: Variable #14 seems to be censored at 50.00 (corresponding to a median price of \\\\\\$50,000); \nCensoring is suggested by the fact that the highest median price of exactly \\\\\\$50,000 is reported in 16 cases, \nwhile 15 cases have prices between \\\\\\$40,000 and \\\\\\$50,000, with prices rounded to the nearest hundred. \nHarrison and Rubinfeld do not mention any censoring.\n\nThe data file stored in this repo is a copy of the UCI ML housing dataset. \nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n\"\"\"\n\n# Deprecated in v0.6,\nfunction Base.getproperty(::Type{BostonHousing}, s::Symbol)\n    if s == :features\n        @warn \"BostonHousing.features() is deprecated, use `BostonHousing().features` instead.\"\n        return () -> BostonHousing(as_df=false).features\n    elseif s == :targets\n        @warn \"BostonHousing.targets() is deprecated, use `BostonHousing().targets` instead.\"\n        return () -> BostonHousing(as_df=false).targets\n    elseif s == :feature_names\n        @warn \"BostonHousing.feature_names() is deprecated, use `BostonHousing().feature_names` instead.\"\n        return () -> lowercase.(BostonHousing().metadata[\"feature_names\"])\n    else\n        return getfield(BostonHousing, s)\n    end\nend\n", "meta": {"hexsha": "811db30b0cb7fd4eb7451158d97def591e190a6d", "size": 5961, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/misc/boston_housing.jl", "max_stars_repo_name": "soham-chitnis10/MLDatasets.jl", "max_stars_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/datasets/misc/boston_housing.jl", "max_issues_repo_name": "soham-chitnis10/MLDatasets.jl", "max_issues_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datasets/misc/boston_housing.jl", "max_forks_repo_name": "soham-chitnis10/MLDatasets.jl", "max_forks_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.25625, "max_line_length": 137, "alphanum_fraction": 0.6129843986, "num_tokens": 1835, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.16026602631152884, "lm_q1q2_score": 0.07140324818521537}}
{"text": "using Pkg\nPkg.activate(pwd())\n\n# # Tuples\n\nt = (1, 2.0, \"3\")\nt = 1, 2.0, \"3\"\n\n#+\n\ntypeof(t)\n\n#+\n\nt[1] # the first element\nt[end] # the last element\nt[1:2] # the first two elements\n\n#+\n\na, b, c = t\nprintln(\"The values stored in the tuple are: $a, $b and $c\")\n\n# ### Exercise:\n# Create a tuple that contains the first four letters of the alphabet (these letters should\n# be of type `String`). Then unpack this tuple into four variables `a`, `b`, `c` and `d`.\n# \n# ---\n# ### Solution:\n\n\n\n# ---\n# \n# # Named Tuples\n\nt = (a = 1, b = 2.0, c = \"3\")\n\n#+\n\na = 1;\nb = 2.0;\nc = \"3\";\n\nt = (; a, b, c)\n\n#+\n\nt[1] # the first element\nt[end] # the last element\nt[1:2] # error\n\n#+\n\nt.a\nt.c\na, b, c = t\n\nprintln(\"The values stored in the tuple are: a = $a, b = $b\")", "meta": {"hexsha": "cc9a94e7d34b23fc1d5f13a348ae4b4b36bee7b4", "size": 747, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lecture_02/02-tuples.jl", "max_stars_repo_name": "JuliaTeachingCTU/Julia-for-Optimization-and-Learning-Scripts", "max_stars_repo_head_hexsha": "8e00299449736e4ccf47c247aa9d80f99a7e5b92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lecture_02/02-tuples.jl", "max_issues_repo_name": "JuliaTeachingCTU/Julia-for-Optimization-and-Learning-Scripts", "max_issues_repo_head_hexsha": "8e00299449736e4ccf47c247aa9d80f99a7e5b92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lecture_02/02-tuples.jl", "max_forks_repo_name": "JuliaTeachingCTU/Julia-for-Optimization-and-Learning-Scripts", "max_forks_repo_head_hexsha": "8e00299449736e4ccf47c247aa9d80f99a7e5b92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.6610169492, "max_line_length": 91, "alphanum_fraction": 0.5542168675, "num_tokens": 284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1602660263115288, "lm_q1q2_score": 0.07140324818521536}}
{"text": "# Code for Video 1.6: \r\n\r\nt1 = (108, 3.14,\"Julia\",'\u03b1') # (108,3.14,\"Julia\",'\u03b1')\r\ntypeof(t1) # Tuple{Int64,Float64,ASCIIString,Char}\r\n\r\na, b, c, d = t1 # (108,3.14,\"Julia\",'\u03b1')\r\n\r\na # 108\r\nb # 3.14\r\nc # \"Julia\"\r\nd # '\u03b1'\r\n\r\na, b = b, a # (3.14,108)\r\nprintln(\"a is now $a, b is now $b\") \r\n# a is now 3.14, b is now 108\r\n\r\nt1[3] # \"Julia\"\r\n\r\nfor e in t1\r\n    print(e, \" - \")\r\nend\r\n# 108 - 3.14 - Julia - \u03b1 -\r\n\r\n# t1[4] = '\u03b2'\r\n# ERROR: MethodError: `setindex!` has no method matching setindex!(::Tuple{Int64,F\r\n# loat64,ASCIIString,Char}, ::Char, ::Int64)\r\n\r\ns = Set(Int64[11, 14, 13, 7, 14, 11])\r\n# Set([7,14,13,11])\r\ns2 = Set(Any[11, 3.14, \"Julia\", '\u03b1'])\r\n# Set(Any[3.14,'\u03b1',\"Julia\",11])\r\nintersect(s, s2) # Set([11])\r\n\r\ndiris = Dict(\"I. setosa\" => 50,\r\n             \"I. versicolor\" => 50,\r\n             \"I. virginica\" => 50)\r\n# Dict{ASCIIString,Int64} with 3 entries:\r\n#   \"I. virginica\"  => 50\r\n#   \"I. versicolor\" => 50\r\n#   \"I. setosa\"     => 50\r\n\r\ndiriss = Dict( :setosa => 50,\r\n               :versicolor => 50,\r\n               :virginica => 50)\r\n# Dict{Symbol,Int64} with 3 entries:\r\n#   :versicolor => 50\r\n#   :virginica  => 50\r\n#   :setosa     => 50\r\n\r\ndiris[\"I. setosa\"] # 50\r\ndiriss[:setosa] # 50\r\n\r\ndiriss[:setosa] = 53\r\ndiriss\r\n# Dict{Symbol,Int64} with 3 entries:\r\n#   :versicolor => 50\r\n#   :virginica  => 50\r\n#   :setosa     => 53\r\n\r\nhaskey(diris, \"I. setosa\") # true\r\n\r\nkeys(diris)\r\n# Base.KeyIterator for a Dict{ASCIIString,Int64} with 3 entries. Keys:\r\n#   \"I. virginica\"\r\n#   \"I. versicolor\"\r\n#   \"I. setosa\"\r\n\r\nvalues(diris)\r\n# Base.ValueIterator for a Dict{ASCIIString,Int64} with 3 entries. Values:\r\n#   50\r\n#   50\r\n#   50\r\n\r\nfor k in keys(diris)\r\n    print(k, \" - \")\r\nend\r\n# I. virginica - I. versicolor - I. setosa -\r\n\r\nfor (k, v) in diris\r\n    println(\"$k has $v measurements\")\r\nend\r\n# I. virginica has 50 measurements\r\n# I. versicolor has 50 measurements\r\n# I. setosa has 50 measurements", "meta": {"hexsha": "3786d7813247652dc46717975623b444804bb4e0", "size": 1911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Section 1/1_6.jl", "max_stars_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_stars_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-09-01T15:05:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-14T14:09:53.000Z", "max_issues_repo_path": "Section 1/1_6.jl", "max_issues_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_issues_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Section 1/1_6.jl", "max_forks_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_forks_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-09-01T15:05:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-08T15:00:22.000Z", "avg_line_length": 22.75, "max_line_length": 83, "alphanum_fraction": 0.5510204082, "num_tokens": 745, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295203152604, "lm_q2_score": 0.16026603032235004, "lm_q1q2_score": 0.07140324761234759}}
{"text": "using LibFoo\nusing Test\n\n@test 1 == 1", "meta": {"hexsha": "2b474685fb611353f31efb7afad6160515ecaa70", "size": 37, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "stdlib/Pkg/test/test_packages/BigProject/test/LibFoo.jl/test/runtests.jl", "max_stars_repo_name": "ninjin/julia", "max_stars_repo_head_hexsha": "2d589cca94c502a696fc5e234835560e28b9efd3", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "stdlib/Pkg/test/test_packages/BigProject/test/LibFoo.jl/test/runtests.jl", "max_issues_repo_name": "ninjin/julia", "max_issues_repo_head_hexsha": "2d589cca94c502a696fc5e234835560e28b9efd3", "max_issues_repo_licenses": ["Zlib"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stdlib/Pkg/test/test_packages/BigProject/test/LibFoo.jl/test/runtests.jl", "max_forks_repo_name": "ninjin/julia", "max_forks_repo_head_hexsha": "2d589cca94c502a696fc5e234835560e28b9efd3", "max_forks_repo_licenses": ["Zlib"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.25, "max_line_length": 12, "alphanum_fraction": 0.7027027027, "num_tokens": 13, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681520167196, "lm_q2_score": 0.18010665968560707, "lm_q1q2_score": 0.0713345118675826}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.3\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 c730d000-5ed6-11ec-36b6-39741ccad6c7\nbegin\n\tusing Pkg; Pkg.activate(@__DIR__); Pkg.instantiate()\n\tPkg.precompile()\n\n\tusing PlutoUI\n\tusing BenchmarkTools\n\tusing DataFrames\n\tusing DelimitedFiles\n\tusing CSV\n\tusing XLSX\n\tusing Downloads\n\tusing JLD, NPZ, MAT, RData\n\tusing RCall\nend;\n\n# \u2554\u2550\u2561 62c5d4f2-9776-43ae-8125-0d429d6cde42\nPlutoUI.TableOfContents(aside=true, indent=true, depth=3)\n\n# \u2554\u2550\u2561 34f92fb1-6c1e-4db6-b795-8a526007ea92\nhtml\"\"\"\n<p align=\"center\">\n\t<img src=\"https://github.com/JuliaAcademy/DataScience/blob/main/datascience.png?raw=true\" alt=\"Course-Logo\" width=\"450px\">\n</p>\n\"\"\"\n\n# \u2554\u2550\u2561 13a10dac-8c7e-4e72-9fa9-d58ca8dd0202\nmd\"\"\"\n# \ud83d\udcbe Data\n\nBeing able to easily load and process data is a crucial task that can make any data science more pleasant. In this notebook, we will cover most common types often encountered in data science tasks, and we will be using this data throughout the rest of this tutorial.\n\n[YouTube link](https://www.youtube.com/watch?v=iG1dZBaxS-U)\n\"\"\"\n\n# \u2554\u2550\u2561 5100df3f-c5d3-4346-bcd9-8afc92bc559c\nmd\"\"\"\n## Get some data\n\nIn Julia, it's pretty easy to dowload a file from the web using the `download` function. But also, you can use your favorite command line commad to download files by easily switching from Julia via the `;` key. Let's try both.\n\n**Note:** `download` depends on external tools such as curl, wget or fetch. So you must have one of these.\n\"\"\"\n\n# \u2554\u2550\u2561 abe62085-b630-4816-af11-b39133892849\ndata\u2081 = Downloads.download(\"https://raw.githubusercontent.com/nassarhuda/easy_data/master/programming_languages.csv\", \"programminglanguages.csv\")\n\n# \u2554\u2550\u2561 51c909b1-1d37-458f-acd6-68f4041bc92f\nmd\"\"\"\n## Read your data from text files\n\nThe key question here is to load data from files such as `csv` files, `xlsx` files, or just raw text files. We will go over some Julia packages that will allow us to read such files very easily.\n\"\"\"\n\n# \u2554\u2550\u2561 6483858c-2e58-4930-b4e9-76b47032eac4\nmd\"\"\"\n### DelimitedFiles.jl\n\nLet's start with the package `DelimitedFiles` which is in the standard library. When loaded, the file is stored as a `Matrix`.\n\n> **readdlm**(source, \n> delim::AbstractChar, \n> T::Type, \n> eol::AbstractChar; \n> header=false, \n> skipstart=0, \n> skipblanks=true, \n> use_mmap, \n> quotes=true, \n> dims, \n> comments=false, \n> comment_char='#')\n\"\"\"\n\n# \u2554\u2550\u2561 89a5a316-5058-4ff2-8d71-fc832a84c58d\nbegin\n\tdata\u2082, header = readdlm(\"programming_languages.csv\",',';header=true)\n\theader\nend\n\n# \u2554\u2550\u2561 853a4e90-ac39-4d73-9953-c7b70e8f78ef\ndata\u2082\n\n# \u2554\u2550\u2561 3a22c018-2ca7-41df-9ee4-91dc8b64d940\nmd\"\"\"\n> **Note:** the output table is a Matrix (2D Array).\n\nWe can use the function `writedlm`, from `DelimitedFiles.jl` package, to write to a text file...\n\"\"\"\n\n# \u2554\u2550\u2561 b4fcdcf9-793f-4d4a-a5fb-950108cb8188\nwritedlm(\"output/programminglanguages_dlm.txt\", data\u2082, '-')\n\n# \u2554\u2550\u2561 cfa37c7c-810c-4095-8887-262dee4a9d04\nmd\"\"\"\n### CSV.jl\n\nA more powerful package to use here is the `CSV.jl` package. By default, this package imports the data to a `DataFrame`, which can have several advantages as we will see below.\n\n_In general, `CSV.jl` is the recommended way to load CSVs in Julia_. Only use `DelimitedFiles.jl` when you have a more complicated file where you want to specify several things.\n\"\"\"\n\n# \u2554\u2550\u2561 4b651b5a-e562-46ef-a3b5-8ee85b5a61ed\ndf = CSV.read(\"programming_languages.csv\", DataFrame)\n\n# \u2554\u2550\u2561 11471bbf-b7b4-4b6e-90cb-9e9ff47bbe4a\nmd\"\"\"\nWe can use the `typeof` function to make sure that the output is a DataFrame.\n\"\"\"\n\n# \u2554\u2550\u2561 2652dc01-d80d-46d9-ae7a-9afa534e88cb\ntypeof(df)\n\n# \u2554\u2550\u2561 b2f35e6b-8c19-4729-87a3-292858bfbcb5\nmd\"\"\"\nWe can use the macro `@btime`, from `BenchMarkTools.jl`, to prove that `CSV.jl` is faster than `DelimitedFiles.jl`...\n\n**Note:** Check Julia REPL to see the processing time.\n\"\"\"\n\n# \u2554\u2550\u2561 e1210609-a216-4d54-be63-04f10c5c15bc\n@btime df\u2081, h\u2081 = readdlm(\"programming_languages.csv\",',';header=true);\n\n# \u2554\u2550\u2561 008b635f-c4e5-475a-83af-4bc3fc722344\n@btime df\u2082 = CSV.read(\"programming_languages.csv\", DataFrame);\n\n# \u2554\u2550\u2561 934c8549-c86c-45e6-a9f5-56297c73513d\nmd\"\"\"\nWe can use the function `write`, from `CSV.jl` package, to write to a text file...\n\n**Note:** if we want to write a Matrix, we could first convert it into a DataFrame (using the `DataFrame` function) and, then, write it using the `write` function.\n\"\"\"\n\n# \u2554\u2550\u2561 92708ece-3620-4cf5-b09d-098002044623\nCSV.write(\"output/programminglanguages_CSV.csv\", df)\n\n# \u2554\u2550\u2561 cac4f1b5-f9e3-469e-9576-553a1cf1b363\nmd\"\"\"\n### Some DataFrame Operations\n\nThere are different ways to slice a DataFrame. Suppose that we want just the first 10 rows of a DataFrame...\n\"\"\"\n\n# \u2554\u2550\u2561 3d378ec4-659f-4032-a5b7-6d6b6d402c2f\ndf[1:10,:]\n\n# \u2554\u2550\u2561 e50ae64c-ff64-4597-bf75-d7c921d23225\nmd\"\"\"\nSuppose that we want all the instances of a given feature (column)...\n\"\"\"\n\n# \u2554\u2550\u2561 d9ea3574-2ffb-4fdb-9ced-db1dc669ecd9\ndf[!,\"year\"]\n\n# \u2554\u2550\u2561 09ff6d8b-15f2-4d8e-a775-dee357a417e4\ndf.year\n\n# \u2554\u2550\u2561 843a668f-7b08-4bce-b02a-3153d0eb7d0d\ndf[!,\"year\"] == df.year\n\n# \u2554\u2550\u2561 159a35f4-09fd-47bd-9935-88668a7e7f27\ntypeof(df.year)\n\n# \u2554\u2550\u2561 494ebede-80d0-430e-91aa-0a9050a1bb29\nmd\"\"\"\nWe can use the `names` function to get an array of all column names...\n\"\"\"\n\n# \u2554\u2550\u2561 00d12274-3057-4a52-8400-0c8c3c358e6b\nnames(df)\n\n# \u2554\u2550\u2561 fede3ce2-da03-4ff9-a13e-7ce55ec05bf6\nmd\"\"\"\nWe can use the `describe` function to check some data stats...\n\"\"\"\n\n# \u2554\u2550\u2561 74783bbb-3c9d-4e15-bc54-b3d8e7c2d230\ndescribe(df)\n\n# \u2554\u2550\u2561 e4b3a444-c487-4fe8-8048-873b8a3affb7\nmd\"\"\"\nLet's create two DataFrames...\n\"\"\"\n\n# \u2554\u2550\u2561 0becba29-d5ff-4595-86ab-6b0575674047\nbegin\n\tfoods = [\"Apple\",\"Cucumber\",\"Tomato\",\"Banana\"]\n\tcalories = [105,47,22,105]\n\n\tdf_cal = DataFrame(Item=foods, Calories=calories)\nend\n\n# \u2554\u2550\u2561 9d522bdc-d066-40ac-84c4-47287d9e07e0\nbegin\n\tprices = [0.85,1.6,0.8,0.6]\n\n\tdf_price = DataFrame(Item=foods, Price=prices)\nend\n\n# \u2554\u2550\u2561 79de9741-c913-4c00-8833-4f1b68f80ea5\nmd\"\"\"\nNow, we can use the `innerjoin` function to join both DataFrames on `Item` column...\n\"\"\"\n\n# \u2554\u2550\u2561 fd724d83-41bb-422c-860e-2bbeff17e0dc\ndf_comb = innerjoin(df_cal, df_price, on=:Item)\n\n# \u2554\u2550\u2561 51301eeb-709a-4ea9-a1a5-d7967f8dd55d\nmd\"\"\"\n### XLSX.jl\n\nAnother type of files that we may often need to read is XLSX files. Let's try to read this type of file using the `XLSX.jl` package...\n\n> **readdata**(filepath, sheetname, cellrange)\n\"\"\"\n\n# \u2554\u2550\u2561 64e7e90b-cd7d-4569-a715-4f27f1787e40\nxlsx\u2081 = XLSX.readdata(\"data/zillow_data_download_april2020.xlsx\",\n    \t\t\t  \t  \"Sale_counts_city\",\n    \t\t\t  \t  \"A1:F9\")\n\n# \u2554\u2550\u2561 e5f2c69b-fbd8-4048-aaed-792a64a4d66c\nmd\"\"\"\nIf you don't want to specify cell ranges, you can use the `readtable` function. However, this will take a little longer...\n\n> **readtable**(filepath, sheetname)\n\"\"\"\n\n# \u2554\u2550\u2561 26047637-1b77-4203-b705-e34f74137295\nxlsx\u2082 = XLSX.readtable(\"data/zillow_data_download_april2020.xlsx\",\n\t\t\t\t\t   \"Sale_counts_city\")\n\n# \u2554\u2550\u2561 0f3d505c-a575-419f-8155-fd071f510714\ntypeof(xlsx\u2082)\n\n# \u2554\u2550\u2561 c2f1eab2-862a-4eb8-b233-71567cdc9963\nmd\"\"\"\nHere, `xlsx\u2082` is a tuple of two items. The first item is an vector of vectors where each vector corresponds to a column in the excel file...\n\"\"\"\n\n# \u2554\u2550\u2561 3a924b3e-3dbe-49b6-9e5e-5a48b0ffd385\nxlsx\u2082[1]\n\n# \u2554\u2550\u2561 58bad348-0586-4d0f-8d54-7c3e1a7a2c40\nmd\"\"\"\nWe can access the first column by typing...\n\"\"\"\n\n# \u2554\u2550\u2561 036de982-36ad-431a-8445-517178626911\nxlsx\u2082[1][1]\n\n# \u2554\u2550\u2561 4ead9039-f4c2-4f82-bf1e-2146718014a3\nmd\"\"\"\nWe can access the third column first 10 elements...\n\"\"\"\n\n# \u2554\u2550\u2561 bb04dc6c-e62e-4b2c-a247-ebe4836d8013\nxlsx\u2082[1][3][begin:10]\n\n# \u2554\u2550\u2561 d9550872-fa7c-4ebd-9a4d-828182c8e49d\nmd\"\"\"\nOn the other hand, the second is the header with the column names...\n\"\"\"\n\n# \u2554\u2550\u2561 e01a65ab-394a-4718-b9ae-ca2bb30e20b5\nxlsx\u2082[2]\n\n# \u2554\u2550\u2561 1eb26b3c-8a5a-4648-bb73-f8291b656edc\nmd\"\"\"\nWe could check the names of the last 10 columns...\n\"\"\"\n\n# \u2554\u2550\u2561 cb027055-2cf6-4f74-b265-9d01c23c2967\nxlsx\u2082[2][end-9:end]\n\n# \u2554\u2550\u2561 12dccfc3-e976-46e6-81b0-dc51b48309f4\nmd\"\"\"\nAnd we can easily store this data in a `DataFrame`. We can use the \"splat\" operator to unwrap these arrays and pass them to the DataFrame constructor...\n\"\"\"\n\n# \u2554\u2550\u2561 403ac783-9c02-4fd1-8506-afe0af36a14b\ndf_xlsx = DataFrame(xlsx\u2082...)\n# df_xlsx = DataFrame(xlsx\u2082[1], xlsx\u2082[2])\n\n# \u2554\u2550\u2561 36d8898e-980c-4f54-bbff-277ded2229ea\nmd\"\"\"\n**Note:** the first argument is the actual data and the second one comprises the column names.\n\nWe can also easily write data to an XLSX file (takes several amount of time)...\n\"\"\"\n\n# \u2554\u2550\u2561 8d769d53-1902-4a09-8228-ce60174b22bb\n# XLSX.writetable(\"output/writefile_using_XLSX.xlsx\",xlsx\u2082[1],xlsx\u2082[2])\n\n# \u2554\u2550\u2561 bc29d2b5-e6c2-4b45-8b79-cbb012659be1\nmd\"\"\"\n## Importing your data\n\nOften, the data you want to import is not stored in plain text, and you might want to import different kinds of types. Here we will go over importing `jld`, `npz`, `rda`, and `mat` files. Hopefully, these four will capture the types from four common programming languages used in Data Science (Julia, Python, R and Matlab).\n\nWe will use a toy example here of a very small matrix. But the same syntax will hold for bigger files.\n\"\"\"\n\n# \u2554\u2550\u2561 7c2fef9c-4e9a-4637-bc8e-6636af5f98ae\nmd\"\"\"\n### Julia Data (JLD)\n\nJLD, for which files conventionally have the extension `.jld`, is a widely-used format for data storage with the Julia programming language.\n\nJLD is a specific \"dialect\" of HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data. By comparison with \"plain\" HDF5, JLD files automatically add attributes and naming conventions to preserve type information for each object.\n\"\"\"\n\n# \u2554\u2550\u2561 8f51aeb5-3e75-4e5a-8c8f-ed0201eb8ce0\n# load JLD file\njld_data = JLD.load(\"data/mytempdata.jld\")\n\n# \u2554\u2550\u2561 879ac394-8598-4399-957f-1280ecd99478\n# see the data\njld_data[\"tempdata\"]\n\n# \u2554\u2550\u2561 0d27e3b3-79e3-424d-bc6b-94252900f7fb\n# save JLD file\nsave(\"output/mywrite.jld\", \"A\", jld_data)\n\n# \u2554\u2550\u2561 13053d54-8680-41e2-b416-8a7bc50f6c92\n# JLD type\ntypeof(jld_data)\n\n# \u2554\u2550\u2561 e0f49c77-0c0d-4740-9227-5619d14748b6\nmd\"\"\"\n### Numpy Data (NPZ)\n\nSeveral arrays into a single file in uncompressed Numpy `.npz` format.\n\"\"\"\n\n# \u2554\u2550\u2561 f4ddf1bf-0cbf-47c5-bb5f-f8abf0fb8ac2\n# load NPZ file\nnpz_data = npzread(\"data/mytempdata.npz\")\n\n# \u2554\u2550\u2561 a979107e-f2cf-42da-aefa-5cb249bc7073\n# save NPZ file\nnpzwrite(\"output/mywrite.npz\", npz_data)\n\n# \u2554\u2550\u2561 edd2093e-f84a-4563-acb7-219f2bd07399\n# NPZ type\ntypeof(npz_data)\n\n# \u2554\u2550\u2561 be91882d-fe73-40d0-8e75-30c2e8ddd564\nmd\"\"\"\n### R Data (RDA)\n\nThe RData format (usually with extension `.rdata` or `.rda`) is a format designed for use with R, a system for statistical computation and related graphics, for storing a complete R workspace or selected \"objects\" from a workspace in a form that can be loaded back by R.\n\"\"\"\n\n# \u2554\u2550\u2561 1f3092e8-3f3a-4d29-a9f5-e64ef96c13a8\n# load RDA file\nR_data = RData.load(\"data/mytempdata.rda\")\n\n# \u2554\u2550\u2561 cf9a1b00-79dd-4305-93ad-69f229319ce2\n# see the data\nR_data[\"tempdata\"]\n\n# \u2554\u2550\u2561 1b49419d-18cd-4617-9442-15080e3ef588\nbegin\n\t# save RDA file\n\t@rput R_data\n\tR\"save(R_data, file=\\\"output/mywrite.rda\\\")\"\nend\n\n# \u2554\u2550\u2561 c3bce6db-d503-4a68-b83f-278c6d603f07\nmd\"\"\"\n**Note:** we must use the `RCall.jl` package to save `.rda` files.\n\"\"\"\n\n# \u2554\u2550\u2561 7131ac1c-984e-4ad9-a1cd-b1d46a4f240e\n# RDA type\ntypeof(R_data)\n\n# \u2554\u2550\u2561 4f70b5df-0026-43cd-b769-d4ca50145fbd\nmd\"\"\"\n### MATLAB\u00ae Data (MAT)\n\nMAT-files are binary MATLAB\u00ae `.mat` files that store workspace variables.\n\"\"\"\n\n# \u2554\u2550\u2561 0c1201b9-6acb-4206-a806-e09e04c6a65e\n# load MAT file\nmatlab_data = matread(\"data/mytempdata.mat\")\n\n# \u2554\u2550\u2561 cc555ffc-0eea-4e35-b0d1-36d3a734f9f2\n# see the data\nmatlab_data[\"tempdata\"]\n\n# \u2554\u2550\u2561 5516dd98-3a87-4801-a62e-e91a95e4923a\n# save MAT file\nmatwrite(\"output/mywrite.mat\", matlab_data)\n\n# \u2554\u2550\u2561 53be8986-1f06-45a5-9303-5018a3ddf06f\n# MAT type\ntypeof(matlab_data)\n\n# \u2554\u2550\u2561 ecefa934-49a8-4abf-880d-d91d3f9f9d05\nmd\"\"\"\nAll files, when loaded, are `Dict`. The only exception is NPZ data, which is loaded as an `Matrix`.\n\"\"\"\n\n# \u2554\u2550\u2561 01be19ba-2aaa-4746-af5d-8ce7f1cf3a55\nmd\"\"\"\n## Processing different types of data\n\nWe will mainly cover `Vector` (`Matrix` included), `DataFrame`, and `Dict`. Let's bring back our programming languages dataset `data\u2082` and start playing it the matrix it's stored in.\n\"\"\"\n\n# \u2554\u2550\u2561 3a6f9dc0-2186-440a-9d78-5da6acabcdae\nmd\"\"\"\n### Matrix\n\"\"\"\n\n# \u2554\u2550\u2561 4f9dca90-9f1a-4c0e-a510-fa640d109b0d\ndata\u2082\n\n# \u2554\u2550\u2561 57b8265f-be5c-4f05-91c6-6c4fec4f0a6b\nmd\"\"\"\nHere are some quick questions we might want to ask about this simple data.\n\n> **Q1:** Which year was was a given language invented?\n\"\"\"\n\n# \u2554\u2550\u2561 157e9e3d-2e10-418e-997e-0178c14d7b3d\nfunction year_created_mtx(data, language::String)\n    loc = findfirst(data[:,2] .== language)\n    !isnothing(loc) && return data[loc,1]\n\terror(\"Error: Language not found!\")\nend\n\n# \u2554\u2550\u2561 e7bd695e-c7d6-4b38-8520-7d915e740015\nmd\"\"\"\n**Note:** this function return the year just when `loc` local variable is not `nothing`. Otherwise, it returns an error message.\n\"\"\"\n\n# \u2554\u2550\u2561 c9ead4e0-aa01-43bc-8107-72d8ec4c073b\nbegin\n\tjulia_year1 = year_created_mtx(data\u2082, \"Julia\")\n\n\t\"Julia was created in $julia_year1.\"\nend\n\n# \u2554\u2550\u2561 92b4a2d9-b271-42b3-97d1-647300a9439a\nbegin\n\tcobol_year1 = year_created_mtx(data\u2082, \"COBOL\")\n\n\t\"COBOL was created in $cobol_year1.\"\nend\n\n# \u2554\u2550\u2561 01a73335-69b9-4297-9910-a441848fe2c5\nmd\"\"\"\n> **Q2:** How many languages were created in a given year?\n\"\"\"\n\n# \u2554\u2550\u2561 fbf69873-af41-428c-9bba-d63cdb20149b\nfunction langs_per_year_mtx(data, year::Int64)\n\tcount = length(findall(data[:,1] .== year))\n\n\treturn count\nend\n\n# \u2554\u2550\u2561 1ea19d7e-5748-4559-b2d2-fd4f4d449516\nbegin\n\tlangs_1988\u2081 = langs_per_year_mtx(data\u2082, 1988)\n\n\t\"In 1988, $langs_1988\u2081 language(s) was/were created.\"\nend\n\n# \u2554\u2550\u2561 2592f7cd-95ec-493c-93e1-bbedf433367f\nbegin\n\tlangs_2006\u2081 = langs_per_year_mtx(data\u2082, 2006)\n\n\t\"In 2006, $langs_2006\u2081 language(s) was/were created.\"\nend\n\n# \u2554\u2550\u2561 12121f45-8a7b-4709-bc85-b2fde7313a21\nmd\"\"\"\n### DataFrame\n\nNow let's try to store this data in a DataFrame...\n\"\"\"\n\n# \u2554\u2550\u2561 d455f80e-c5b5-4b1a-9667-86fade5ee3d3\n# anonymous column names\ndata\u2083 = DataFrame(data\u2082, :auto)\n\n# \u2554\u2550\u2561 42c72fde-8331-4e40-b761-c50d0e2e8486\n# specifying column names\ndata\u2084 = DataFrame(Year = data\u2082[:,1], Language = data\u2082[:,2])\n\n# \u2554\u2550\u2561 201a8534-91ee-4ac9-a120-51b29f196c23\n# specifying both column names and data types\ndata\u2085 = DataFrame(Year = Int.(data\u2082[:,1]), Language = string.(data\u2082[:,2]))\n\n# \u2554\u2550\u2561 0b14e9fc-62e6-4ad7-ab67-eb36e340749c\nmd\"\"\"\n> **Q1:** Which year was was a given language invented?\n\"\"\"\n\n# \u2554\u2550\u2561 679b2b83-07f9-40bd-bdd1-89e7233bfade\nfunction year_created_df(df,language::String)\n    loc = findfirst(df.Language .== language)\n\t!isnothing(loc) && return df.Year[loc]\n\terror(\"Error: Language not found!\")\nend\n\n# \u2554\u2550\u2561 f4738476-8b05-4dbc-a15e-6b3d59c90c50\nmd\"\"\"\n**Note:** this function return the year just when `loc` local variable is not `nothing`. Otherwise, it returns an error message.\n\"\"\"\n\n# \u2554\u2550\u2561 c6b170be-6576-480e-b2ba-65d3b853871a\nbegin\n\tjulia_year2 = year_created_df(data\u2085, \"Julia\")\n\n\t\"Julia was created in $julia_year2.\"\nend\n\n# \u2554\u2550\u2561 9977fdaa-db30-43a5-bd58-14087c2163e4\nbegin\n\tcobol_year2 = year_created_df(data\u2085, \"COBOL\")\n\n\t\"COBOL was created in $cobol_year2.\"\nend\n\n# \u2554\u2550\u2561 a137ebd7-9db4-4c29-a694-b1acad5214a3\nmd\"\"\"\n> **Q2:** How many languages were created in a given year?\n\"\"\"\n\n# \u2554\u2550\u2561 2cc2938b-58d5-4c27-bdab-dddf22231bc8\nfunction langs_per_year_df(df, year::Int64)\n    count = length(findall(df.Year.==year))\n    return count\nend\n\n# \u2554\u2550\u2561 ae7a1714-2245-4d5a-89bb-10b3adff787a\nbegin\n\tlangs_1988\u2082 = langs_per_year_mtx(data\u2085, 1988)\n\n\t\"In 1988, $langs_1988\u2082 language(s) was/were created.\"\nend\n\n# \u2554\u2550\u2561 ce4ac86d-5e06-4c3e-93d6-ebdee46439bd\nbegin\n\tlangs_2006\u2082 = langs_per_year_mtx(data\u2085, 2006)\n\n\t\"In 2006, $langs_2006\u2082 language(s) was/were created.\"\nend\n\n# \u2554\u2550\u2561 515b7479-97d8-464b-8245-1034f3cd8bdc\nmd\"\"\"\n### Dictionary\n\nNext, we'll use dictionaries. A quick way to create a dictionary is with the `Dict()` command. But this creates a dictionary without types. Here, we will specify the types of this dictionary.\n\"\"\"\n\n# \u2554\u2550\u2561 3b08a2c8-546b-4a6c-8ae0-2a1e64578af8\n# dict from a list of tuples\nDict([(\"A\", 1), (\"B\", 2)])\n\n# \u2554\u2550\u2561 21551a38-57fc-42c2-81a2-b314bb06250b\nmd\"\"\"\n**Note:** the key type is `String`, while the value type is `Int64`.\n\"\"\"\n\n# \u2554\u2550\u2561 6da1f5a7-56a2-4a7a-abfc-25ec3f44f892\n# dictionary from a list of tuples\nDict([(\"A\", 1), (\"B\", 2), (\"C\", [1,2,3])])\n\n# \u2554\u2550\u2561 43ad8297-d94c-45a0-941f-ab0d5bfd2ddb\nmd\"\"\"\n**Note:** the key type is `String`, while the value type is `Any`, since we have integers and arrays as values.\n\"\"\"\n\n# \u2554\u2550\u2561 9390d98f-94eb-48bb-ba9c-e6b3f67f762f\n# empty dict\nDict()\n\n# \u2554\u2550\u2561 30718df7-5085-4d0d-8a86-7b4a08ff20b2\n# empty dict, specifying that keys are Integers and values are Vectors of Strings\ndict\u2081 = Dict{Integer, Vector{String}}()\n\n# \u2554\u2550\u2561 f77a5189-19b4-4f99-b394-72190253912f\n# appending a key-value pair to dict\ndict\u2081[2012] = [\"Julia\", \"Programming\", \"Language\"]\n\n# \u2554\u2550\u2561 50ba8f3b-5104-4d4f-85ea-831d4afe9164\n# updated dict\ndict\u2081\n\n# \u2554\u2550\u2561 bb814af7-62e3-4744-955f-31b557540057\nmd\"\"\"\nNow, let's populate the dictionary with years as keys and vectors that hold all the programming languages created in each year as their values. Even though this looks like more work, we often need to do it just once.\n\"\"\"\n\n# \u2554\u2550\u2561 9193df28-7ed1-4853-a97b-aa5a1232e07b\nbegin\n\tlang_dict = Dict{Integer, Vector{String}}()\n\n\tfor i in 1:size(data\u2082,1)\n\t\tyear, lang = data\u2082[i,:]\n\t\tif year \u2208 keys(lang_dict)\n\t\t\tlang_dict[year] = push!(lang_dict[year], lang)\n\t\telse\n\t\t\tlang_dict[year] = [lang]\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 c5aca233-9a26-4800-9b8d-7f0f03852626\nlang_dict\n\n# \u2554\u2550\u2561 7b2d5919-9629-4ca9-8195-f26183734044\nmd\"\"\"\n> **Note:** there is an `enumerate()` method in Julia which is useful when you need not only the values `x` over which you are iterating, but also the number of iterations (index) so far.\n**Syntax:**\n```julia\nfor (index, value) in enumerate(df.column)\n```\n\"\"\"\n\n# \u2554\u2550\u2561 d8054919-1554-4c73-a4a2-09383ee09224\nmd\"\"\"\nWe can check the size of the dictionary...\n\"\"\"\n\n# \u2554\u2550\u2561 7d57f6cc-ab44-4658-a73e-92c873111155\nlength(keys(lang_dict))\n\n# \u2554\u2550\u2561 a322a0e7-3984-4c00-95a5-7b23fc6ec06f\nmd\"\"\"\nOr we can check the size of the vector made up by unique `Year` values...\n\"\"\"\n\n# \u2554\u2550\u2561 e0a28251-a7d6-4c17-b49e-84ffc91c60b6\nlength(unique(data\u2082[:,1]))\n\n# \u2554\u2550\u2561 9073e71f-b922-44a4-a470-5cb081588fcd\nmd\"\"\"\n> **Q1:** Which year was was a given language invented?\n\"\"\"\n\n# \u2554\u2550\u2561 f9325e7b-8df8-448a-98b2-9ea8add93da6\nfunction year_created_dict(dict,language::String)\n    keys_vec = collect(keys(dict))\n    lookup = map(keyid -> findfirst(dict[keyid] .== language), keys_vec)\n    \n    return keys_vec[findfirst((!isnothing).(lookup))]\nend\n\n# \u2554\u2550\u2561 c33df74d-dd81-4efa-81d6-a5deb4685f1d\nbegin\n\tjulia_year3 = year_created_dict(lang_dict, \"Julia\")\n\n\t\"Julia was created in $julia_year3.\"\nend\n\n# \u2554\u2550\u2561 a08ad2fd-5d3c-4e76-a059-0608de3e36aa\nmd\"\"\"\n> **Q2:** How many languages were created in a given year?\n\"\"\"\n\n# \u2554\u2550\u2561 fb6df54d-0765-4493-b889-17707ff0cf14\nlangs_per_year_dict(dict, year::Int64) = length(dict[year])\n\n# \u2554\u2550\u2561 d5e60574-5afd-498c-aedd-1f5ffc4c6bd7\nbegin\n\tlangs_1988\u2083 = langs_per_year_dict(lang_dict, 1988)\n\n\t\"In 1988, $langs_1988\u2083 language(s) was/were created.\"\nend\n\n# \u2554\u2550\u2561 ee8bcf97-9efa-47f0-a82e-cfaa2efd68e6\nmd\"\"\"\n## Missing data\n\nLet's remove the first year value from our data (Matrix) and, after that, create a DataFrame...\n\"\"\"\n\n# \u2554\u2550\u2561 b3927e97-7516-4a80-bcc1-49614cf67166\nbegin\n\tdata\u2082[1,1] = missing\n\n\tdata\u2086 = DataFrame(Year=data\u2082[:,1], Language=data\u2082[:,2])\nend\n\n# \u2554\u2550\u2561 dc7ed277-b044-464e-8ad3-998a83b00d1b\nmd\"\"\"\nWe can now use the `dropmissing!` function to remove (inplace) the line with missing value...\n\"\"\"\n\n# \u2554\u2550\u2561 e2b6b920-2e05-4168-af16-07e9611d895d\ndropmissing!(data\u2086)\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500c730d000-5ed6-11ec-36b6-39741ccad6c7\n# \u255f\u250062c5d4f2-9776-43ae-8125-0d429d6cde42\n# \u255f\u250034f92fb1-6c1e-4db6-b795-8a526007ea92\n# \u255f\u250013a10dac-8c7e-4e72-9fa9-d58ca8dd0202\n# \u255f\u25005100df3f-c5d3-4346-bcd9-8afc92bc559c\n# \u2560\u2550abe62085-b630-4816-af11-b39133892849\n# \u255f\u250051c909b1-1d37-458f-acd6-68f4041bc92f\n# \u255f\u25006483858c-2e58-4930-b4e9-76b47032eac4\n# \u2560\u255089a5a316-5058-4ff2-8d71-fc832a84c58d\n# \u2560\u2550853a4e90-ac39-4d73-9953-c7b70e8f78ef\n# \u255f\u25003a22c018-2ca7-41df-9ee4-91dc8b64d940\n# \u2560\u2550b4fcdcf9-793f-4d4a-a5fb-950108cb8188\n# \u255f\u2500cfa37c7c-810c-4095-8887-262dee4a9d04\n# \u2560\u25504b651b5a-e562-46ef-a3b5-8ee85b5a61ed\n# \u255f\u250011471bbf-b7b4-4b6e-90cb-9e9ff47bbe4a\n# \u2560\u25502652dc01-d80d-46d9-ae7a-9afa534e88cb\n# \u255f\u2500b2f35e6b-8c19-4729-87a3-292858bfbcb5\n# \u2560\u2550e1210609-a216-4d54-be63-04f10c5c15bc\n# \u2560\u2550008b635f-c4e5-475a-83af-4bc3fc722344\n# \u255f\u2500934c8549-c86c-45e6-a9f5-56297c73513d\n# \u2560\u255092708ece-3620-4cf5-b09d-098002044623\n# \u255f\u2500cac4f1b5-f9e3-469e-9576-553a1cf1b363\n# \u2560\u25503d378ec4-659f-4032-a5b7-6d6b6d402c2f\n# \u255f\u2500e50ae64c-ff64-4597-bf75-d7c921d23225\n# \u2560\u2550d9ea3574-2ffb-4fdb-9ced-db1dc669ecd9\n# \u2560\u255009ff6d8b-15f2-4d8e-a775-dee357a417e4\n# \u2560\u2550843a668f-7b08-4bce-b02a-3153d0eb7d0d\n# \u2560\u2550159a35f4-09fd-47bd-9935-88668a7e7f27\n# \u255f\u2500494ebede-80d0-430e-91aa-0a9050a1bb29\n# \u2560\u255000d12274-3057-4a52-8400-0c8c3c358e6b\n# \u255f\u2500fede3ce2-da03-4ff9-a13e-7ce55ec05bf6\n# \u2560\u255074783bbb-3c9d-4e15-bc54-b3d8e7c2d230\n# \u255f\u2500e4b3a444-c487-4fe8-8048-873b8a3affb7\n# \u2560\u25500becba29-d5ff-4595-86ab-6b0575674047\n# \u2560\u25509d522bdc-d066-40ac-84c4-47287d9e07e0\n# \u255f\u250079de9741-c913-4c00-8833-4f1b68f80ea5\n# \u2560\u2550fd724d83-41bb-422c-860e-2bbeff17e0dc\n# \u255f\u250051301eeb-709a-4ea9-a1a5-d7967f8dd55d\n# \u2560\u255064e7e90b-cd7d-4569-a715-4f27f1787e40\n# \u255f\u2500e5f2c69b-fbd8-4048-aaed-792a64a4d66c\n# \u2560\u255026047637-1b77-4203-b705-e34f74137295\n# \u2560\u25500f3d505c-a575-419f-8155-fd071f510714\n# \u255f\u2500c2f1eab2-862a-4eb8-b233-71567cdc9963\n# \u2560\u25503a924b3e-3dbe-49b6-9e5e-5a48b0ffd385\n# \u255f\u250058bad348-0586-4d0f-8d54-7c3e1a7a2c40\n# \u2560\u2550036de982-36ad-431a-8445-517178626911\n# \u255f\u25004ead9039-f4c2-4f82-bf1e-2146718014a3\n# \u2560\u2550bb04dc6c-e62e-4b2c-a247-ebe4836d8013\n# \u255f\u2500d9550872-fa7c-4ebd-9a4d-828182c8e49d\n# \u2560\u2550e01a65ab-394a-4718-b9ae-ca2bb30e20b5\n# \u255f\u25001eb26b3c-8a5a-4648-bb73-f8291b656edc\n# \u2560\u2550cb027055-2cf6-4f74-b265-9d01c23c2967\n# \u255f\u250012dccfc3-e976-46e6-81b0-dc51b48309f4\n# \u2560\u2550403ac783-9c02-4fd1-8506-afe0af36a14b\n# \u255f\u250036d8898e-980c-4f54-bbff-277ded2229ea\n# \u2560\u25508d769d53-1902-4a09-8228-ce60174b22bb\n# \u255f\u2500bc29d2b5-e6c2-4b45-8b79-cbb012659be1\n# \u255f\u25007c2fef9c-4e9a-4637-bc8e-6636af5f98ae\n# \u2560\u25508f51aeb5-3e75-4e5a-8c8f-ed0201eb8ce0\n# \u2560\u2550879ac394-8598-4399-957f-1280ecd99478\n# \u2560\u25500d27e3b3-79e3-424d-bc6b-94252900f7fb\n# \u2560\u255013053d54-8680-41e2-b416-8a7bc50f6c92\n# \u255f\u2500e0f49c77-0c0d-4740-9227-5619d14748b6\n# \u2560\u2550f4ddf1bf-0cbf-47c5-bb5f-f8abf0fb8ac2\n# \u2560\u2550a979107e-f2cf-42da-aefa-5cb249bc7073\n# \u2560\u2550edd2093e-f84a-4563-acb7-219f2bd07399\n# \u255f\u2500be91882d-fe73-40d0-8e75-30c2e8ddd564\n# \u2560\u25501f3092e8-3f3a-4d29-a9f5-e64ef96c13a8\n# \u2560\u2550cf9a1b00-79dd-4305-93ad-69f229319ce2\n# \u2560\u25501b49419d-18cd-4617-9442-15080e3ef588\n# \u255f\u2500c3bce6db-d503-4a68-b83f-278c6d603f07\n# \u2560\u25507131ac1c-984e-4ad9-a1cd-b1d46a4f240e\n# \u255f\u25004f70b5df-0026-43cd-b769-d4ca50145fbd\n# \u2560\u25500c1201b9-6acb-4206-a806-e09e04c6a65e\n# \u2560\u2550cc555ffc-0eea-4e35-b0d1-36d3a734f9f2\n# \u2560\u25505516dd98-3a87-4801-a62e-e91a95e4923a\n# \u2560\u255053be8986-1f06-45a5-9303-5018a3ddf06f\n# \u255f\u2500ecefa934-49a8-4abf-880d-d91d3f9f9d05\n# \u255f\u250001be19ba-2aaa-4746-af5d-8ce7f1cf3a55\n# \u255f\u25003a6f9dc0-2186-440a-9d78-5da6acabcdae\n# \u2560\u25504f9dca90-9f1a-4c0e-a510-fa640d109b0d\n# \u255f\u250057b8265f-be5c-4f05-91c6-6c4fec4f0a6b\n# \u2560\u2550157e9e3d-2e10-418e-997e-0178c14d7b3d\n# \u255f\u2500e7bd695e-c7d6-4b38-8520-7d915e740015\n# \u2560\u2550c9ead4e0-aa01-43bc-8107-72d8ec4c073b\n# \u2560\u255092b4a2d9-b271-42b3-97d1-647300a9439a\n# \u255f\u250001a73335-69b9-4297-9910-a441848fe2c5\n# \u2560\u2550fbf69873-af41-428c-9bba-d63cdb20149b\n# \u2560\u25501ea19d7e-5748-4559-b2d2-fd4f4d449516\n# \u2560\u25502592f7cd-95ec-493c-93e1-bbedf433367f\n# \u255f\u250012121f45-8a7b-4709-bc85-b2fde7313a21\n# \u2560\u2550d455f80e-c5b5-4b1a-9667-86fade5ee3d3\n# \u2560\u255042c72fde-8331-4e40-b761-c50d0e2e8486\n# \u2560\u2550201a8534-91ee-4ac9-a120-51b29f196c23\n# \u255f\u25000b14e9fc-62e6-4ad7-ab67-eb36e340749c\n# \u2560\u2550679b2b83-07f9-40bd-bdd1-89e7233bfade\n# \u255f\u2500f4738476-8b05-4dbc-a15e-6b3d59c90c50\n# \u2560\u2550c6b170be-6576-480e-b2ba-65d3b853871a\n# \u2560\u25509977fdaa-db30-43a5-bd58-14087c2163e4\n# \u255f\u2500a137ebd7-9db4-4c29-a694-b1acad5214a3\n# \u2560\u25502cc2938b-58d5-4c27-bdab-dddf22231bc8\n# \u2560\u2550ae7a1714-2245-4d5a-89bb-10b3adff787a\n# \u2560\u2550ce4ac86d-5e06-4c3e-93d6-ebdee46439bd\n# \u255f\u2500515b7479-97d8-464b-8245-1034f3cd8bdc\n# \u2560\u25503b08a2c8-546b-4a6c-8ae0-2a1e64578af8\n# \u2560\u255021551a38-57fc-42c2-81a2-b314bb06250b\n# \u2560\u25506da1f5a7-56a2-4a7a-abfc-25ec3f44f892\n# \u255f\u250043ad8297-d94c-45a0-941f-ab0d5bfd2ddb\n# \u2560\u25509390d98f-94eb-48bb-ba9c-e6b3f67f762f\n# \u2560\u255030718df7-5085-4d0d-8a86-7b4a08ff20b2\n# \u2560\u2550f77a5189-19b4-4f99-b394-72190253912f\n# \u2560\u255050ba8f3b-5104-4d4f-85ea-831d4afe9164\n# \u255f\u2500bb814af7-62e3-4744-955f-31b557540057\n# \u2560\u25509193df28-7ed1-4853-a97b-aa5a1232e07b\n# \u2560\u2550c5aca233-9a26-4800-9b8d-7f0f03852626\n# \u255f\u25007b2d5919-9629-4ca9-8195-f26183734044\n# \u255f\u2500d8054919-1554-4c73-a4a2-09383ee09224\n# \u2560\u25507d57f6cc-ab44-4658-a73e-92c873111155\n# \u255f\u2500a322a0e7-3984-4c00-95a5-7b23fc6ec06f\n# \u2560\u2550e0a28251-a7d6-4c17-b49e-84ffc91c60b6\n# \u255f\u25009073e71f-b922-44a4-a470-5cb081588fcd\n# \u2560\u2550f9325e7b-8df8-448a-98b2-9ea8add93da6\n# \u2560\u2550c33df74d-dd81-4efa-81d6-a5deb4685f1d\n# \u255f\u2500a08ad2fd-5d3c-4e76-a059-0608de3e36aa\n# \u2560\u2550fb6df54d-0765-4493-b889-17707ff0cf14\n# \u2560\u2550d5e60574-5afd-498c-aedd-1f5ffc4c6bd7\n# \u255f\u2500ee8bcf97-9efa-47f0-a82e-cfaa2efd68e6\n# \u2560\u2550b3927e97-7516-4a80-bcc1-49614cf67166\n# \u255f\u2500dc7ed277-b044-464e-8ad3-998a83b00d1b\n# \u2560\u2550e2b6b920-2e05-4168-af16-07e9611d895d\n", "meta": {"hexsha": "c46a5ac323833890ed7e7d1aaf24c7030bd7d04e", 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NO\n2. NO", "lm_q1_score": 0.32423541204073586, "lm_q2_score": 0.22000708951749934, "lm_q1q2_score": 0.07133408932158945}}
{"text": "module MINLPTests\n\nusing JuMP\nusing Test\n\n###\n### Default tolerances that are used in the tests.\n###\n\n# Absolute tolerance when checking the objective value.\nconst OPT_TOL = 1e-6\n\n# Absolute tolerance when checking the primal solution value.\nconst PRIMAL_TOL = 1e-6\n\n# Absolue tolerance when checking the dual solution value.\nconst DUAL_TOL = 1e-6\n\n###\n### Default expected status codes for different types of problems and solvers.\n###\n\n# We only distinguish between feasible and infeasible problems now.\n@enum ProblemTypeCode FEASIBLE_PROBLEM INFEASIBLE_PROBLEM\n\n# Target status codes for local solvers:\nconst TERMINATION_TARGET_LOCAL = Dict(\n    FEASIBLE_PROBLEM => JuMP.MOI.LOCALLY_SOLVED,\n    INFEASIBLE_PROBLEM => JuMP.MOI.LOCALLY_INFEASIBLE,\n)\nconst PRIMAL_TARGET_LOCAL = Dict(\n    FEASIBLE_PROBLEM => JuMP.MOI.FEASIBLE_POINT,\n    INFEASIBLE_PROBLEM => JuMP.MOI.INFEASIBLE_POINT,\n)\n\n# Target status codes for global solvers:\nconst TERMINATION_TARGET_GLOBAL = Dict(\n    FEASIBLE_PROBLEM => JuMP.MOI.OPTIMAL,\n    INFEASIBLE_PROBLEM => JuMP.MOI.INFEASIBLE,\n)\nconst PRIMAL_TARGET_GLOBAL = Dict(\n    FEASIBLE_PROBLEM => JuMP.MOI.FEASIBLE_POINT,\n    INFEASIBLE_PROBLEM => JuMP.MOI.NO_SOLUTION,\n)\n\n###\n### Helper functions for the tests.\n###\n\nfunction check_status(\n    model,\n    problem_type::ProblemTypeCode,\n    termination_target = TERMINATION_TARGET_LOCAL,\n    primal_target = PRIMAL_TARGET_LOCAL,\n)\n    @test JuMP.termination_status(model) == termination_target[problem_type]\n    @test JuMP.primal_status(model) == primal_target[problem_type]\nend\n\nfunction check_objective(model, solution; tol = OPT_TOL)\n    if !isnan(tol)\n        @test isapprox(JuMP.objective_value(model), solution, atol = tol)\n    end\nend\n\nfunction check_solution(variables, solutions; tol = PRIMAL_TOL)\n    if !isnan(tol)\n        @assert length(variables) == length(solutions)\n        for (variable, solution) in zip(variables, solutions)\n            @test isapprox(JuMP.value(variable), solution, atol = tol)\n        end\n    end\nend\n\nfunction check_dual(constraints, solutions; tol = DUAL_TOL)\n    if !isnan(tol)\n        @assert length(constraints) == length(solutions)\n        for (constraint, solution) in zip(constraints, solutions)\n            @test isapprox(JuMP.dual(constraint), solution, atol = tol)\n        end\n    end\nend\n\n###\n### Loop through and include every model function.\n###\n\nfor directory in [\"nlp\", \"nlp-cvx\", \"nlp-mi\"]\n    files = readdir(joinpath(@__DIR__, directory))\n    for file_name in filter(f -> endswith(f, \".jl\"), files)\n        include(joinpath(@__DIR__, directory, file_name))\n    end\nend\n\n\"\"\"\n    test_directory(\n        directory,\n        optimizer;\n        debug::Bool = false,\n        exclude = String[],\n        include = String[],\n        objective_tol = OPT_TOL,\n        primal_tol = PRIMAL_TOL,\n        dual_tol = DUAL_TOL,\n        termination_target = TERMINATION_TARGET_LOCAL,\n        primal_target = PRIMAL_TARGET_LOCAL,\n    )\n\nTest all of the files in `directory` using `optimizer`.\n\nIf `debug`, print the name of the file befor running it.\n\nUse `exclude` and `include` to run a subset of the files in a directory.\n\nUse the remaining args to control tolerances and status targets.\n\n## Example\n\nTest all but nlp_001_010:\n```julia\ntest_directory(\"nlp\", optimizer; exclude = [\"001_010\"])\n```\n\nTest only nlp_001_010:\n```julia\ntest_directory(\"nlp\", optimizer; include = [\"001_010\"])\n```\n\"\"\"\nfunction test_directory(\n    directory,\n    optimizer;\n    debug::Bool = false,\n    exclude = String[],\n    include = String[],\n    objective_tol = OPT_TOL,\n    primal_tol = PRIMAL_TOL,\n    dual_tol = DUAL_TOL,\n    termination_target = TERMINATION_TARGET_LOCAL,\n    primal_target = PRIMAL_TARGET_LOCAL,\n)\n    @testset \"$(directory)\" begin\n        models = _list_of_models(directory, exclude, include)\n        @testset \"$(model_name)\" for model_name in models\n            if debug\n                println(\"Running $(model_name)\")\n            end\n            getfield(MINLPTests, model_name)(\n                optimizer,\n                objective_tol,\n                primal_tol,\n                dual_tol,\n                termination_target,\n                primal_target,\n            )\n        end\n    end\nend\n\nfunction _list_of_models(\n    directory,\n    exclude::Vector{String},\n    include::Vector{String},\n)\n    dir = replace(directory, \"-\" => \"_\")\n    if length(include) > 0\n        return [Symbol(\"$(dir)_$(i)\") for i in include]\n    else\n        models = Symbol[]\n        for file in readdir(joinpath(@__DIR__, directory))\n            if !endswith(file, \".jl\")\n                continue\n            end\n            file = replace(file, \".jl\" => \"\")\n            if file in exclude\n                continue\n            end\n            push!(models, Symbol(\"$(dir)_$(file)\"))\n        end\n        return models\n    end\nend\n\n###\n### Helper functions to test a subset of models.\n###\n\nfunction test_nlp(\n    optimizer;\n    debug::Bool = false,\n    exclude = String[],\n    objective_tol = OPT_TOL,\n    primal_tol = PRIMAL_TOL,\n    dual_tol = DUAL_TOL,\n    termination_target = TERMINATION_TARGET_LOCAL,\n    primal_target = PRIMAL_TARGET_LOCAL,\n)\n    return test_directory(\n        \"nlp\",\n        optimizer;\n        debug = debug,\n        exclude = exclude,\n        objective_tol = objective_tol,\n        primal_tol = primal_tol,\n        dual_tol = dual_tol,\n        termination_target = termination_target,\n        primal_target = primal_target,\n    )\nend\n\nfunction test_nlp_cvx(\n    optimizer;\n    debug::Bool = false,\n    exclude = String[],\n    objective_tol = OPT_TOL,\n    primal_tol = PRIMAL_TOL,\n    dual_tol = DUAL_TOL,\n    termination_target = TERMINATION_TARGET_LOCAL,\n    primal_target = PRIMAL_TARGET_LOCAL,\n)\n    return test_directory(\n        \"nlp-cvx\",\n        optimizer;\n        debug = debug,\n        exclude = exclude,\n        objective_tol = objective_tol,\n        primal_tol = primal_tol,\n        dual_tol = dual_tol,\n        termination_target = termination_target,\n        primal_target = primal_target,\n    )\nend\n\nfunction test_nlp_mi(\n    optimizer;\n    debug::Bool = false,\n    exclude = String[],\n    objective_tol = OPT_TOL,\n    primal_tol = PRIMAL_TOL,\n    dual_tol = DUAL_TOL,\n    termination_target = TERMINATION_TARGET_LOCAL,\n    primal_target = PRIMAL_TARGET_LOCAL,\n)\n    return test_directory(\n        \"nlp-mi\",\n        optimizer;\n        debug = debug,\n        exclude = exclude,\n        objective_tol = objective_tol,\n        primal_tol = primal_tol,\n        dual_tol = dual_tol,\n        termination_target = termination_target,\n        primal_target = primal_target,\n    )\nend\n\n### Tests that haven't been updated.\n\ninclude(\"nlp-mi-cvx/tests.jl\")\ninclude(\"poly/tests.jl\")\ninclude(\"poly-cvx/tests.jl\")\ninclude(\"poly-mi/tests.jl\")\ninclude(\"poly-mi-cvx/tests.jl\")\n\nend\n", "meta": {"hexsha": "f74d5b33bbb3f7745f0f41c3edfe083921144872", "size": 6805, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MINLPTests.jl", "max_stars_repo_name": "jump-dev/MINLPTests.jl", "max_stars_repo_head_hexsha": "9dc2b751de7470dcb4d9a726e11339a8e0bf745c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-04T22:02:43.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-16T15:49:34.000Z", "max_issues_repo_path": "src/MINLPTests.jl", "max_issues_repo_name": "jump-dev/MINLPTests.jl", "max_issues_repo_head_hexsha": "9dc2b751de7470dcb4d9a726e11339a8e0bf745c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2020-06-14T16:44:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-27T01:32:27.000Z", "max_forks_repo_path": "src/MINLPTests.jl", "max_forks_repo_name": "jump-dev/MINLPTests.jl", "max_forks_repo_head_hexsha": "9dc2b751de7470dcb4d9a726e11339a8e0bf745c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-07-24T16:22:09.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-25T02:23:53.000Z", "avg_line_length": 25.679245283, "max_line_length": 78, "alphanum_fraction": 0.6537839824, "num_tokens": 1640, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.14223189864583877, "lm_q1q2_score": 0.07111594932291938}}
{"text": "\"\"\"\r\n# Get the categories for an economic data series in the FRED database.\r\n\r\n## Arguments\r\n\r\n    - `symbol` : String specifying the name (id) of the time series.\r\n\r\n## Examples\r\n\r\n```jldoctests\r\njulia> get_category(\"GDPC1\")\r\njulia> get_category(\"FEDFUNDS\")\r\njulia> get_category(\"T10Y2Y\")\r\n```\r\n\"\"\"\r\nfunction get_category(symbol::String)\r\n\r\n    url = \"https://api.stlouisfed.org/fred/series/categories\"\r\n    parameters = Dict(\"api_key\" => ENV[\"API_FRED\"],\r\n                      \"file_type\" => \"json\",\r\n                      \"series_id\" => symbol)\r\n\r\n    response = HTTP.request(\"GET\", url; query = parameters)\r\n    body = JSON.parse(String(response.body))[\"categories\"][1][\"name\"]\r\n\r\n    return(body)\r\nend\r\n", "meta": {"hexsha": "250b9bf60c0b1327313caec24cc085e731c16a88", "size": 709, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/get_category.jl", "max_stars_repo_name": "JuliaTagBot/FredApi.jl", "max_stars_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-02-15T19:18:40.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-16T07:37:12.000Z", "max_issues_repo_path": "src/get_category.jl", "max_issues_repo_name": "JuliaTagBot/FredApi.jl", "max_issues_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-08-25T21:43:03.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-31T03:54:17.000Z", "max_forks_repo_path": "src/get_category.jl", "max_forks_repo_name": "JuliaTagBot/FredApi.jl", "max_forks_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:43:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-07T10:14:34.000Z", "avg_line_length": 25.3214285714, "max_line_length": 71, "alphanum_fraction": 0.6121297602, "num_tokens": 176, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.1581743527484317, "lm_q1q2_score": 0.07108238908637592}}
{"text": "const ColKey = Union{Symbol,AbstractString}\n\n\"\"\"\n    coerce(A, specs...; tight=false, verbosity=1)\n\nGiven a table `A`, return a copy of `A`, ensuring that the element\nscitypes of the columns match the new specification, `specs`. There\nare three valid specifiations:\n\n(i) one or more `column_name=>Scitype` pairs:\n\n    coerce(X, col1=>Sciyype1, col2=>Scitype2, ... ; verbosity=1)\n\n(ii) one or more `OldScitype=>NewScitype` pairs (`OldScitype` covering\nboth the `OldScitype` and `Union{Missing,OldScitype}` cases):\n\n    coerce(X, OldScitype1=>NewSciyype1, OldScitype2=>NewScitype2, ... ; verbosity=1)\n\n(iii) a dictionary of scientific types keyed on column names:\n\n    coerce(X, d::AbstractDict{<:ColKey, <:Type}; verbosity=1)\n\nwhere `ColKey = Union{Symbol,AbstractString}`.\n\n### Examples\n\nSpecifiying  `column_name=>Scitype` pairs:\n\n```\nusing CategoricalArrays, DataFrames, Tables\nX = DataFrame(name=[\"Siri\", \"Robo\", \"Alexa\", \"Cortana\"],\n              height=[152, missing, 148, 163],\n              rating=[1, 5, 2, 1])\nXc = coerce(X, :name=>Multiclass, :height=>Continuous, :rating=>OrderedFactor)\nschema(Xc).scitypes # (Multiclass, Continuous, OrderedFactor)\n```\n\nSpecifying `OldScitype=>NewScitype` pairs:\n\n```\nX  = (x = [1, 2, 3],\n      y = rand(3),\n      z = [10, 20, 30])\nXc = coerce(X, Count=>Continuous)\nschema(Xfixed).scitypes # (Continuous, Continuous, Continuous)\n```\n\"\"\"\ncoerce(X, a...; kw...) = coerce(Val(ST.trait(X)), X, a...; kw...)\n\n# Non tabular data is not supported\ncoerce(::Val{:other}, X, a...; kw...) =\n    throw(CoercionError(\"`coerce` is undefined for non-tabular data.\"))\n\n\n_bad_dictionary() = throw(ArgumentError(\n    \"A dictionary specifying a scitype conversion \"*\n    \"must have type `AbstractDict{<:ColKey, <:Type}`. It's keys must \"*\n    \"be column names and its values be scientific types. \"*\n    \"E.g., `Dict(:cats=>Continuous, :dogs=>Textual`. \"))\ncoerce(::Val{:table}, X, types_dict::AbstractDict; kw...) =\n    _bad_dictionary()\n\n_bad_specs() =\n        throw(ArgumentError(\n        \"Invalid `specs` in `coerce(X, specs...;  kwargs...)`. \"*\n        \"Valid `specs` are: (i) one or more pairs of \"*\n        \"the form `column_name=>Scitype`; (ii) one or more pairs \"*\n        \"of the from `OldScitype=>NewScitype`; or (iii) a \"*\n        \"dictionary of scientific \"*\n        \"types keyed on column names. \"))\ncoerce(::Val{:table}, X, specs...; kw...) = _bad_specs()\n\nfunction coerce(::Val{:table},\n                X,\n                types_dict::AbstractDict{<:ColKey, <:Type};\n                kw...)\n    isempty(types_dict) && return X\n    names  = schema(X).names\n    X_ct   = Tables.columntable(X)\n    ct_new = (_coerce_col(X_ct, col, types_dict; kw...) for col in names)\n    return Tables.materializer(X)(NamedTuple{names}(ct_new))\nend\n\n# -------------------------------------------------------------\n# utilities for coerce\n\nstruct CoercionError <: Exception\n    m::String\nend\n\nfunction _coerce_col(Xcol,\n                     name,\n                     types_dict::AbstractDict{Symbol, <:Type};\n                     kw...)\n    y = Tables.getcolumn(Xcol, name)\n    if haskey(types_dict, name)\n        coerce_type = types_dict[name]\n        return coerce(y, coerce_type; kw...)\n    end\n    return y\nend\n\n# -------------------------------------------------------------\n# alternative ways to do coercion, both for coerce and coerce!\n\n# The following extends the two methods so that a mixture of\n# symbol=>type and type=>type pairs can be specified in place of a\n# dictionary:\n\nfeature_scitype_pairs(p::Pair{<:ColKey,<:Type}, X) = [Symbol(first(p)) => last(p), ]\nfunction feature_scitype_pairs(p::Pair{<:Type,<:Type}, X)\n    from_scitype = first(p)\n    to_scitype = last(p)\n    sch = schema(X)\n    ret = Pair{Symbol,Type}[]\n    for j in eachindex(sch.names)\n        if sch.scitypes[j] <: Union{Missing,from_scitype}\n            push!(ret, Pair(sch.names[j], to_scitype))\n        end\n    end\n    return ret\nend\n\nfor c in (:coerce, :coerce!)\n    ex = quote\n        function $c(::Val{:table},\n                    X,\n                    mixed_pairs::Pair{<:Union{<:ColKey,<:Type},<:Type}...;\n                    kw...)\n            components = map(p -> feature_scitype_pairs(p, X), mixed_pairs)\n            pairs = vcat(components...)\n\n            # must construct dictionary by hand to check no conflicts:\n            scitype_given_feature = Dict{Symbol,Type}()\n            for p in pairs\n                feature = first(p)\n                if haskey(scitype_given_feature, feature)\n                    throw(ArgumentError(\"`coerce` argments cannot be \"*\n                                        \"resolved to determined a \"*\n                                        \"*unique* scitype for each \"*\n                                        \"feature. \"))\n                else\n                    scitype_given_feature[feature] = last(p)\n                end\n            end\n\n            return $c(X, scitype_given_feature; kw...)\n        end\n    end\n    eval(ex)\nend\n\n# -------------------------------------------------------------\n# In place coercion\n\n\"\"\"\ncoerce!(X, ...)\n\nSame as [`ScientificTypes.coerce`](@ref) except it does the modification in\nplace provided `X` supports in-place modification (at the moment, only the\nDataFrame! does). An error is thrown otherwise. The arguments are the same as\n`coerce`.\n\n\"\"\"\ncoerce!(X, a...;  kw...) = begin\n    coerce!(Val(ST.trait(X)), X, a...; kw...)\nend\n\ncoerce!(::Val{:other}, X, a...; kw...) =\n    throw(CoercionError(\"`coerce!` is undefined for non-tabular data.\"))\n\ncoerce!(::Val{:table}, X, types_dict::AbstractDict; kw...) =\n    _bad_dictionary()\n\ncoerce!(::Val{:table}, X, specs...; kw...) = _bad_specs()\n\nfunction coerce!(::Val{:table},\n                 X,\n                 types_dict::AbstractDict{<:ColKey, <:Type};\n                 kw...)\n    # DataFrame --> coerce_df!\n    if is_type(X, :DataFrames, :DataFrame)\n        return coerce_df!(X, types_dict; kw...)\n    end\n    # Everything else\n    throw(ArgumentError(\"In place coercion not supported for $(typeof(X)).\" *\n                        \"Try `coerce` instead.\"))\nend\n\n# -------------------------------------------------------------\n# utilities for coerce!\n\n\"\"\"\n    coerce_df!(df, pairs...; kw...)\n\nIn place coercion for a dataframe.(Unexported method)\n\"\"\"\nfunction coerce_df!(df, tdict::AbstractDict{<:ColKey, <:Type}; kw...)\n    names = schema(df).names\n    for name in names\n        name in keys(tdict) || continue\n        coerce_type = tdict[name]\n        df[!, name] = coerce(df[!, name], coerce_type; kw...)\n    end\n    return df\nend\n\n\n\"\"\"\n    is_type(X, spkg, stype)\n\nCheck that an object `X` is of a given type that may be defined in a package\nthat is not loaded in the current environment.\nAs an example say `DataFrames` is not loaded in the current environment, a\nfunction from some package could still return a DataFrame in which case it\ncan be checked with\n\n```\nis_type(X, :DataFrames, :DataFrame)\n```\n\"\"\"\nfunction is_type(X, spkg::Symbol, stype::Symbol)\n    # If the package is loaded, then it will just be `stype`\n    # otherwise it will be `spkg.stype`\n    rx = Regex(\"^($spkg\\\\.)?$stype\")\n    return ifelse(match(rx, \"$(typeof(X))\") === nothing, false, true)\nend\n", "meta": {"hexsha": "00935d6ae3ec02fc3ade14d9f04904036bc08eee", "size": 7208, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/coerce.jl", "max_stars_repo_name": "alan-turing-institute/ScientificTypes.jl", "max_stars_repo_head_hexsha": "cecd1eb3bc0c22c25e7777073ce1347cf20871ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 43, "max_stars_repo_stars_event_min_datetime": "2019-08-02T05:52:08.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-18T08:27:13.000Z", "max_issues_repo_path": "src/coerce.jl", "max_issues_repo_name": "alan-turing-institute/ScientificTypes.jl", "max_issues_repo_head_hexsha": "cecd1eb3bc0c22c25e7777073ce1347cf20871ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 110, "max_issues_repo_issues_event_min_datetime": "2019-08-01T21:50:19.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-12T13:05:19.000Z", "max_forks_repo_path": "src/coerce.jl", "max_forks_repo_name": "alan-turing-institute/ScientificTypes.jl", "max_forks_repo_head_hexsha": "cecd1eb3bc0c22c25e7777073ce1347cf20871ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-01-01T23:25:02.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-29T01:39:33.000Z", "avg_line_length": 31.6140350877, "max_line_length": 84, "alphanum_fraction": 0.5828246393, "num_tokens": 1900, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489884579676883, "lm_q2_score": 0.17106119379155804, "lm_q1q2_score": 0.07097309186473483}}
{"text": "# # Stuff I Like About The Julia Programming Language\n\n# `git_club 2020-06-19` - Hannes\n\n# [View this notebook online](https://nbviewer.jupyter.org/github/Hasnep/stuff-i-like-about-julia/blob/master/stuff-i-like-about-julia.ipynb)\n\n# > Julia's unofficial tagline is \"Looks like Python, feels like Lisp, runs like Fortran.\"\n# > I've been learning Julia for slightly less than a year now, and I'd like to share some of the things I've enjoyed about it.\n# > I'll give an overview of the language's main features with code examples and discuss whether it really is the future scientific computing.\n\n# Technically:\n# > Julia is a high level, JIT compiled, dynamic language designed with multiple dispatch, automatic differentiation and metaprogramming.\n\n# In practice:\n# > Julia finds a balance between being fast and easy to use with lots of features you'll miss when you use another language.\n\n# ## History\n\n# - Julia was started in 2009 by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman\n# - Announced in 2012\n# - Version 1.0 was released in 2018\n# - Now on version 1.4, heading for 1.5 soon\n\n# ## Julia is easy to use\n\n# Let's write a function from scratch to sum an array of numbers, first in Python:\n# ```python\n# def my_sum(array):\n#     \"\"\"\n#     Sum a list.\n#     \"\"\"\n#     total = 0\n#     for x in array:\n#         total += x\n#     return total\n# ```\n\n# Then in Julia:\n\n\"\"\"\nSum an array.\n\"\"\"\nfunction my_sum(array)\n    total = 0\n    for x in array\n        total += x\n    end\n    return total\nend\n\n# This syntax will be familliar if you've used Python.\n    \nmy_sum(1:10)\n\n# Benchmarks for this function in different languages:\n# - C: ~10ms\n# - Python: ~500ms\n# - Julia: ~10ms\n# > Source: [An Introduction to Julia (Beginner Level) | SciPy 2018 Tutorial | Jane Herriman, Sacha Verweij](https://www.youtube.com/watch?v=b5xvVyzUnXI)\n\n# ## It's fast\n\n# Julia was designed to solve the \"two language problem\", where researchers use a slower, high-level language for research and then have to use a slower, low-level language once they hit a bottleneck or for production.\n# Julia is both languages at the same time!\n\n# Some benchmarks (from the Julia website) of different languages relative to C.\n# ![Benchmarks of different languages relative to C](https://julialang.org/assets/benchmarks/benchmarks.svg)\n# > Source: https://julialang.org/benchmarks/\n\n# These benchmarks try to compare implementations of the same algorithm.\n# Julia is comparable to compiled languages like Rust, Go, Fortran, etc. and is sometimes faster than C.\n# Python is sometimes 100x slower than C.\n# R is a bit slower than that.\n\n# How is it so fast?\n\n# Generally languages fall into two categories:\n\n# - Compiled languages like C or Rust compile all the code before you run\n# - Interpreted languages like Python or R don't compile\n\n# Julia uses a _Just-In-Time_ (JIT) compiler which compiles a function the first time it is called.\n\n# In my opinion, speed doesn't matter all that much\n\n# - JIT compilation takes a short while\n# - My time is more valuable than the computer's time\n\n# Now, back to some code!\n\n# ## Dynamically typed\n\n# Let's write a function to calculate the $n$th Fibonacci number.\n# We want the input `n` to only allow integers and we want to specify the output should only be integers.\n\n\"\"\"\nCalculate the nth Fibonacci number.\n\"\"\"\nfunction fib(n::Integer)::Integer\n    if n < 2\n        return n\n    else\n        return fib(n - 1) + fib(n - 2)\n    end\nend\n\n# If we try to run the function with a non-integer input\n\n# ```julia\n# julia> fib(0.5)\n# ```\n\n# Julia will throw an error:\n\n# ```\n# ERROR: MethodError: no method matching fib(::Float64)\n# Closest candidates are:\n#   fib(::Integer)\n# ```\n\nfib(20)\n\n# There is also a one-line function notation that is useful for short functions\n\nshort_fib(n::Integer)::Integer = n < 2 ? n : short_fib(n - 1) + short_fib(n - 2)\n\nshort_fib(20)\n\n# ## It's free!\n\n# Julia is MIT licenced.\n\n# ## Broadcasting\n\n# In Python, most functions accept one element.\n# Running this code:\n# ```python\n# >>> import math\n# >>> math.sin([1, 2, 3])\n# ```\n# will give this error:\n# ```\n# TypeError: must be real number, not list\n# ```\n# Pythonistas would probably use a list comprehension:\n# ```python\n# >>> [math.sin(x) for x in [1, 2, 3]]\n# ```\n# or a map:\n# ```python\n# >>> map(math.sin, [1, 2, 3])\n# ```\n# ```\n# [0.8414709848078965, 0.9092974268256817, 0.1411200080598672]\n# ```\n\n# In R, most functions are vectorised\n# ```R\n# > sin(c(1, 2, 3))\n# ```\n# ```\n# [1] 0.8414710 0.9092974 0.1411200\n# ```\n\n# In Julia, functions are not vectorised, for example, this line:\n# ```julia\n# julia> sin([1, 2, 3])\n# ```\n# gives this error:\n# ```\n# ERROR: MethodError: no method matching sin(::Array{Int64,1})\n# ```\n\n# Using the broadcast operator, the funciton is applied elementwise!\n\nsin.([1, 2, 3])\n\n# Broadcasting even works for user functions\n    \nfib.(1:10)\n\n# And for operators:\n\n## What are the first 10 square numbers?\n(1:10).^2\n\n# This is powerful, but sometimes tricky syntax.\n# For example, adding a dot makes the length function broadcast over the array:\n\nlength(split(\"How many words are in this sentence?\"))\n# +\nlength.(split(\"How many characters are each of these words?\"))\n\n# ## Useful Unicode characters\n\n# Julia lets you type lots of symbols using LaTeX-y abbreviations, e.g. type `\\alpha` and press tab for \u03b1 or `\\sqrt` and tab for \u221a.\n\n\u03b1 = 37\n\n# The square root symbol is an abbreviation for the `sqrt()` function:\n\n\u221a\u03b1\n\n# Some constants like \u03c0 for pi and \u212f for Euler's constant are predefined:\n\n## Use the approximate equals sign \u2248 because this calculation is not exact\n\u212f^(im * \u03c0) \u2248 -1\n    \n# You can define your own operators using either built in functions:\n\nconst \u2282 = issubset\n[2, 5] \u2282 [1, 2, 3, 4, 5]\n\n# or user functions:\n\nconst \u2211 = my_sum\n\u2211(1:10)\n\n# Because Julia supports almost any symbol you can type as a variable name, that includes emojis!\n\n\ud83d\udd25 = 10\n\ud83d\udc36 = 20\n\ud83c\udf2d = 30\n\ud83d\udd25 + \ud83d\udc36 == \ud83c\udf2d\n\n# ## Julia is (mostly) written in Julia\n\n# If you can read Julia code, you can also read Julia's source code to understand what it does.\n# I looked at the most recent PR as an example:\n\ntensor(A::AbstractArray, B::AbstractArray) = [a * b for a in A, b in B]\nconst \u2297 = tensor\n#-\n[1, 2] \u2297 [3, 4, 5]\n\n# Python's numpy is fast because it's mostly written in C/C++ (51.4%), but if you want to do something that numpy can't do, you need to either use C++ or write slower Python code.\n\n# This bridges the gap between a Julia user and a Julia developer.\n# For every user of Julia, there's another possible contributer.\n\n# ## Multiple dispatch\n\n# As an example, let's build a small Julia package based on [Measurements.jl](https://github.com/JuliaPhysics/Measurements.jl/).\n\n\"\"\"\nA number with some error.\n\"\"\"\nstruct Uncertain <: Real\n    val::Real\n    err::Real\nend\n\n# Define the standard gravity on earth.\n\ng = Uncertain(9.8, 0.1)\n\n# Wouldn't it be nicer to show an uncertain number with the plus/minus symbol?\n# Let's write a show function that dispatches on the Uncertain type:\n\nBase.show(io::IO, x::Uncertain) = print(io, \"$(x.val) \u2213 $(x.err)\")\n\ng\n\n# Let's define the plus/minus operator to create `Uncertain` numbers:\n\n\u2213(a::Real, b::Real) = Uncertain(a, b)\nmy_height = 190 \u2213 1\n\n# How do you add two uncertain measurements?\n\nmy_brothers_height = 175 \u2213 2\n\n# ```julia\n# julia> my_height + my_brothers_height\n# ```\n# ```\n# ERROR: + not defined for Uncertain\n# ```\n\n# $$\n# Q = a + b \\\\\n# {\\delta Q} = \\sqrt{(\\delta a)^2 + (\\delta b)^2}\n# $$\n\nBase.:+(a::Uncertain, b::Uncertain) = (a.val + b.val) \u2213 sqrt(a.err^2 + b.err^2)\nmy_height + my_brothers_height\n\n# Similar for subtraction.\n\nBase.:-(a::Uncertain, b::Uncertain) = (a.val - b.val) \u2213 sqrt(a.err^2 + b.err^2)\nmy_height - my_brothers_height\n\n# Slightly more complicated for multiplcation and division.\n\n\nfunction Base.:*(a::Uncertain, b::Uncertain) \n    total_value = a.val * b.val\n    total_error = total_value * sqrt((a.err / a.val)^2 + (b.err / b.val)^2)\n    return Uncertain(total_value, total_error)\nend\n\nfunction Base.:/(a::Uncertain, b::Uncertain) \n    total_value = a.val / b.val\n    total_error = total_value * sqrt((a.err / a.val)^2 + (b.err / b.val)^2)\n    return Uncertain(total_value, total_error)\nend\n#-\nmy_height * my_brothers_height\n#-\nmy_height / my_brothers_height\n\n# Finally powers, again the exact formula is not important.\n\nBase.:^(a::Uncertain, b::Real) = (a.val^b) \u2213 (abs(b) * a.val^(b - 1) * a.err)\nmy_height^2.0\n\n# Finally, we need to tell Julia what to do if we give it an `Uncertain` number and some other number in the same operation.\n\n# ```julia\n# julia> 1 + g\n# ```\n# ```\n# ERROR: promotion of types Int64 and Uncertain failed to change any arguments\n# ```\n\n# We want to convert both numbers to our `Uncertain` type:\n\nBase.promote_rule(::Type{Uncertain}, ::Type{T}) where T <: Real = Uncertain\n\n# Coverting a real number to our Uncertain type just means we add an error of 0: \n\nBase.convert(::Type{Uncertain}, x::Real) = Uncertain(x, 0)\n\n# When Julia wants to convert an Uncertain number it doesn't need to do anything :\n\nBase.convert(::Type{Uncertain}, x::Uncertain) = x\n#-\n1 + g\n\n# Now we can solve a simple problem, how much time would it take if I dropped a ball from my height and my brother caught it at his height?\n\n# Solve for t:\n# $$\n# t = \\frac{\\sqrt{2 a s + u^2} - u}{a} = \\frac{\\sqrt{2 g (h_1 - h_2)}}{g}\n# $$\n\nt = ((2 * g * (my_height - my_brothers_height))^0.5) / g\n\n# Let's use the actual Measurements.jl package\n\nusing Measurements: Measurement, \u00b1\n\n# Let's solve the same problem as before and make sure we get the same result:\n\ng = 9.8 \u00b1 0.1\nmy_height = 190 \u00b1 1\nmy_brothers_height = 175 \u00b1 2\n\nt = (2 * g * (my_height - my_brothers_height))^0.5 / g\n\n# This next part is inspired by a JuliaCon talk called [_The Unreasonable Effectiveness of Multiple Dispatch_](https://www.youtube.com/watch?v=kc9HwsxE1OY) by one of the Julia co-founders, Stefan Karpinski.\n\n# All the \"methods\" are outside the class definition, that can include being in a completely different package.\n\n# Let's solve the same problem using differential equations!\n\n# Calculate the position of the ball between time $t = 0$ and $t = 3$\n# $$\n# v = \\frac{ds}{dt}\n# $$\n# $$\n# f(t) = -g t = - (9.8 \\pm 0.1) t\n# $$\n# With initial conditions $s_0 = 190 \\pm 1$\n\nusing DifferentialEquations\n#-\nf(s, p, t) = -g * t\ns\u2080 = my_height\ntime_span = (0.0, 3.0)\nproblem = ODEProblem(f, s\u2080, time_span)\n#-\nsolution = solve(problem, Tsit5(),  saveat=0.1)\n#-\nusing Plots\n#-\nplot(\n    solution.t,\n    solution.u,\n    title=\"Solution to the ODE\",\n    xaxis=\"Time (t) in seconds\",\n    yaxis=\"Displacement s(t) in metres\"\n)\n\n# ## Interoperability\n\n# We can import any python module using the PyCall package:\n\nusing PyCall\n#-\nmath = pyimport(\"math\")\n#-\nmath.sqrt(100)\n\n# We can mix Python functions and variables with Julia code:\n\nmath.sin.([\u03c0,  math.pi, 2\u03c0,    2 * math.pi])\n\n# We can define Python functions that call Julia funcitons\n\npy\"\"\"\ndef pyfib(n, fun):\n    if n < 2:\n        return n\n    else:\n        return fun(n - 1, pyfib) + fun(n - 2, pyfib)\n\"\"\"\n#-\nfunction jlfib(n, fun)\n    if n < 2\n        return n\n    else\n        return fun(n - 1, jlfib) + fun(n - 2, jlfib)\n    end\nend\n#-\njlfib(20, py\"pyfib\")\n\n# If you have some code written in another language (like Python), there can be a smooth transition to using Julia.\n\n# # Summary\n\n# - Julia looks a bit like Python\n# - It's fast (with some caveats)\n# - It's free!\n# - It's written in Julia\n# - It has powerful features like:\n#     - Broadcasting syntax\n#     - Unicode variables\n#     - Multiple dispatch\n#     - Good interoperability\n\n# Other stuff I haven't mentioned:\n# - Automatic differentiation (Swift also has this now)\n# - Metaprogramming\n# - Data science stuff (The first two letters of Jupyter Notebooks are named after Julia!)\n", "meta": {"hexsha": "f7192404a272cba43f68d01a4b9a00041950a72e", "size": 11755, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stuff-i-like-about-julia.jl", "max_stars_repo_name": "Hasnep/stuff-i-like-about-julia", "max_stars_repo_head_hexsha": "47e92d4889ac9a9e18213066f66dcb84d7476b41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/stuff-i-like-about-julia.jl", "max_issues_repo_name": "Hasnep/stuff-i-like-about-julia", "max_issues_repo_head_hexsha": "47e92d4889ac9a9e18213066f66dcb84d7476b41", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/stuff-i-like-about-julia.jl", "max_forks_repo_name": "Hasnep/stuff-i-like-about-julia", "max_forks_repo_head_hexsha": "47e92d4889ac9a9e18213066f66dcb84d7476b41", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2975391499, "max_line_length": 218, "alphanum_fraction": 0.6801361123, "num_tokens": 3432, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4186969238628498, "lm_q2_score": 0.1688569500503904, "lm_q1q2_score": 0.07069988555896134}}
{"text": "# Copyright (c) 2019 Arpit Bhatia and contributors                               #src\n#                                                                                #src\n# Permission is hereby granted, free of charge, to any person obtaining a copy   #src\n# of this software and associated documentation files (the \"Software\"), to deal  #src\n# in the Software without restriction, including without limitation the rights   #src\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #src\n# copies of the Software, and to permit persons to whom the Software is          #src\n# furnished to do so, subject to the following conditions:                       #src\n#                                                                                #src\n# The above copyright notice and this permission notice shall be included in all #src\n# copies or substantial portions of the Software.                                #src\n#                                                                                #src\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #src\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #src\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #src\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #src\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #src\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #src\n# SOFTWARE.                                                                      #src\n\n# # Solvers and Solutions\n\n# **Originally Contributed by**: Arpit Bhatia\n\n# The purpose of this part of the tutorial is to introduce you to solvers and\n# how to use them with JuMP. We'll also learn what to do with a problem after\n# the solver has finished optimizing it.\n\n# ## What is a Solver?\n\n# A solver is a software package that incorporates algorithms for finding\n# solutions to one or more classes of problem. For example, GLPK, which we used\n# in the previous tutorials is a solver for linear programming (LP) and mixed\n# integer programming (MIP) problems. It incorporates algorithms such as the\n# simplex method, interior-point method etc. JuMP currently supports a number of\n# open-source and commercial solvers which can be viewed in the [Supported-solvers](@ref)\n# table.\n\n# ## What is MathOptInterface?\n\n# Each mathematical optimization solver API has its own concepts and data\n# structures for representing optimization models and obtaining results.\n# However, it is often desirable to represent an instance of an optimization\n# problem at a higher level so that it is easy to try using different solvers.\n\n# MathOptInterface (MOI) is an abstraction layer designed to provide an\n# interface to mathematical optimization solvers so that users do not need to\n# understand multiple solver-specific APIs. MOI can be used directly, or through\n# a higher-level modeling interface like JuMP.\n\n# Note that JuMP re-exports MathOptInterface and you can use the shortcut `MOI`\n# to refer to MathOptInterface in your code.\n\n# ## Constructing a model\n\n# JuMP models can be created in three different modes: `AUTOMATIC`, `MANUAL` and\n# `DIRECT`. We'll use the following LP to illustrate them.\n\n# ```math\n# \\begin{aligned}\n# & \\max_{x,y} & x + 2y \\\\\n# & \\;\\;\\text{s.t.} & x + y &\\leq 1 \\\\\n# & & 0\\leq x, y &\\leq 1 \\\\\n# \\end{aligned}\n# ```\n\nusing JuMP\nusing GLPK\n\n# ### `AUTOMATIC` Mode\n\n# #### With Optimizer\n\n# This is the easiest method to use a solver in JuMP. In order to do so, we\n# simply set the solver inside the Model constructor.\n\nmodel_auto = Model(GLPK.Optimizer)\n@variable(model_auto, 0 <= x <= 1)\n@variable(model_auto, 0 <= y <= 1)\n@constraint(model_auto, x + y <= 1)\n@objective(model_auto, Max, x + 2y)\noptimize!(model_auto)\nobjective_value(model_auto)\n\n# #### No Optimizer (at first)\n\n# It is also possible to create a JuMP model with no optimizer attached. After\n# the model object is initialized empty and all its variables, constraints and\n# objective are set, then we can attach the solver at `optimize!` time.\n\nmodel_auto_no = Model()\n@variable(model_auto_no, 0 <= x <= 1)\n@variable(model_auto_no, 0 <= y <= 1)\n@constraint(model_auto_no, x + y <= 1)\n@objective(model_auto_no, Max, x + 2y)\nset_optimizer(model_auto_no, GLPK.Optimizer)\noptimize!(model_auto_no)\nobjective_value(model_auto_no)\n\n# Note that we can also enforce the automatic mode by passing\n# `caching_mode = MOIU.AUTOMATIC` in the Model function call.\n\n# ### `MANUAL` Mode\n\n# This mode is similar to the `AUTOMATIC` mode, but there are less protections\n# from the user getting errors from the solver API. On the other side, nothing\n# happens silently, which might give the user more control. It requires\n# attaching the solver before the solve step using the `MOIU.attach_optimizer()`\n# function.\n\nmodel_manual = Model(GLPK.Optimizer, caching_mode = MOIU.MANUAL)\n@variable(model_manual, 0 <= x <= 1)\n@variable(model_manual, 0 <= y <= 1)\n@constraint(model_manual, x + y <= 1)\n@objective(model_manual, Max, x + 2y)\nMOIU.attach_optimizer(model_manual)\noptimize!(model_manual)\nobjective_value(model_manual)\n\n# ### `DIRECT` Mode\n\n# Some solvers are able to handle the problem data directly. This is common for\n# LP/MIP solver but not very common for open-source conic solvers. In this case\n# we do not set a optimizer, we set a backend which is more generic and is able\n# to hold data and not only solving a model.\n\nmodel_direct = direct_model(GLPK.Optimizer())\n@variable(model_direct, 0 <= x <= 1)\n@variable(model_direct, 0 <= y <= 1)\n@constraint(model_direct, x + y <= 1)\n@objective(model_direct, Max, x + 2y)\noptimize!(model_direct)\nobjective_value(model_direct)\n\n# ### Solver Options\n\n# Many of the solvers also allow options to be passed in. However, these options\n# are solver-specific. To find out the various options available, please check\n# out the individual solver packages. Some examples for the GLPK solver are\n# given below.\n\nusing GLPK\n\n# To turn off printing (i.e. silence the solver),\n\nmodel = Model(optimizer_with_attributes(GLPK.Optimizer, \"msg_lev\" => 0));\n\n# To increase the maximum number of simplex iterations:\n\nmodel = Model(optimizer_with_attributes(GLPK.Optimizer, \"it_lim\" => 10_000));\n\n# To set the solution timeout limit (in milliseconds):\n\nmodel = Model(optimizer_with_attributes(GLPK.Optimizer, \"tm_lim\" => 5_000));\n\n# ## How to querying the solution\n\n# So far we have seen all the elements and constructs related to writing a JuMP\n# optimization model. In this section we reach the point of what to do with a\n# solved problem. JuMP follows closely the concepts defined in MathOptInterface\n# to answer user questions about a finished call to `optimize!(model)`. The\n# three main steps in querying a solution are given below. We'll use the model\n# we created in `AUTOMATIC` mode with an optimizer attached in this section.\n\n# ### The termination status\n\n# Termination statuses are meant to explain the reason why the optimizer stopped\n# executing in the most recent call to `optimize!`.\n\ntermination_status(model_auto)\n\n# You can view the different termination status codes by referring to the docs\n# or though checking the possible types using the below command.\n\ndisplay(typeof(MOI.OPTIMAL))\n\n# ### The primal and dual status\n\n# These statuses indicate what kind of result is available to be queried from\n# the model. It's possible that no result is available to be queried. We shall\n# discuss more on the dual status and solutions in the Duality tutorial.\n\nprimal_status(model_auto)\n\n#-\n\ndual_status(model_auto)\n\n# As we saw before, the result (solution) status codes can be viewed directly\n# from Julia.\n\ndisplay(typeof(MOI.FEASIBLE_POINT))\n\n# ### Getting the primal solution\n\n# Provided the primal status is not `MOI.NO_SOLUTION`, we can inspect the\n# solution values and optimal cost.\n\nvalue(x)\n\n#-\n\nvalue(y)\n\n#-\n\nobjective_value(model_auto)\n\n# Since it is possible that no solution is available to be queried from the\n# model, calls to [`value`](@ref) may throw errors. Hence, it is recommended to\n# check for the presence of solutions.\n\nmodel_no_solution = Model(GLPK.Optimizer)\n@variable(model_no_solution, 0 <= x <= 1)\n@variable(model_no_solution, 0 <= y <= 1)\n@constraint(model_no_solution, x + y >= 3)\n@objective(model_no_solution, Max, x + 2y)\n\noptimize!(model_no_solution)\n\ntry #hide\nif termination_status(model_no_solution) == MOI.OPTIMAL\n    optimal_solution = value(x)\n    optimal_objective = objective_value(model_no_solution)\nelseif termination_status(model_no_solution) == MOI.TIME_LIMIT && has_values(model_no_solution)\n    suboptimal_solution = value(x)\n    suboptimal_objective = objective_value(model_no_solution)\nelse\n    error(\"The model was not solved correctly.\")\nend\ncatch err; showerror(stderr, err); end  #hide\n", "meta": {"hexsha": "0bec0f5928d0847cdf694a9ae0cdcfa3c870d383", "size": 8887, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/solvers_and_solutions.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lang/Julia/solvers_and_solutions.jl", "max_issues_repo_name": "ethansaxenian/RosettaDecode", "max_issues_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lang/Julia/solvers_and_solutions.jl", "max_forks_repo_name": "ethansaxenian/RosettaDecode", "max_forks_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.3060344828, "max_line_length": 95, "alphanum_fraction": 0.7168898391, "num_tokens": 2092, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4186969093556867, "lm_q2_score": 0.1688569500503904, "lm_q1q2_score": 0.07069988310932603}}
{"text": "## Exercise 5-1\n## As an exercise, draw a stack diagram for printn called with s = \"Hello\" and n = 2. Then write a function called do_n that takes a function object and a number, n, as arguments, and that calls the given function n times.\nprintln(\"Ans: \")\n\nprintln(\" n --> 2\")\nprintln(\" n --> 1\")\nprintln(\" n --> 0\")\n\nfunction printhello()\n    println(\"Hello\")\nend \n\nfunction do_n(func, n)\n    if n <= 0\n        return\n    end\n\n    func()\n    do_n(func, n - 1)\nend \n\ndo_n(printhello, 3)\n\nprintln(\"End.\")\n", "meta": {"hexsha": "ebae24f20e36268239abd25aac9620ac9a3e9c23", "size": 504, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter5/ex1.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter5/ex1.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter5/ex1.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.16, "max_line_length": 222, "alphanum_fraction": 0.6329365079, "num_tokens": 148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.27825680567280014, "lm_q2_score": 0.25386101261427363, "lm_q1q2_score": 0.0706385544549102}}
{"text": "\"\"\"\n\tternary(cmd0::String=\"\", arg1=[]; kwargs...)\n\nreads (x,y) pairs and generates PostScript code that will plot lines,\npolygons, or symbols at those locations on a map.\n\nFull option list at [`ternary`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html)\n\nParameters\n----------\n\n- **A** : **straight_lines** : -- Str --  \n\n    By default, geographic line segments are drawn as great circle arcs. To draw them as straight\n    lines, use the -A flag.\n    [`-A`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#a)\n- $(GMT.opt_J)\n- $(GMT.opt_R)\n- $(GMT.opt_B)\n- $(GMT.opt_C)\n- **G** : **fill** : **markerfacecolor** : **MarkerFaceColor** : -- Str --\n\n    Select color or pattern for filling of symbols or polygons. BUT WARN: the alias 'fill' will set the\n    color of polygons OR symbols but not the two together. If your plot has polygons and symbols, use\n    'fill' for the polygons and 'markerfacecolor' for filling the symbols. Same applyies for W bellow\n    [`-G`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#g)\n- **L** : **labels** : -- Str --            Flags = a/b/c\n\n    Set the labels for the three diagram vertices [none]. \n    [`-L`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#l)\n- **M** : **no_plot** : -- Bool or [] --\n\n    Do no plotting. Instead, convert the input (a,b,c[,*z*]) records to Cartesian (x,y,[,*z*]) records,\n    where x, y are normalized coordinates on the triangle (i.e., 0-1 in xand 0-sqrt(3)/2 in y).\n    [`-M`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#m)\n- **N** : **no_clip** : -- Bool or [] --\n\n    Do NOT clip symbols that fall outside map border \n    [`-N`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#n)\n- $(GMT.opt_P)\n- **S** : **symbol** : **marker** : **Marker** : -- Str --\n\n    Plot symbols (including vectors, pie slices, fronts, decorated or quoted lines). \n    [`-S`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#s)\n    Alternatively select a sub-set of symbols using the aliases: **marker** or **Marker** and values:\n\n    + **-**, **x_dash**\n    + **+**, **plus**\n    + **a**, *, **star**\n    + **c**, **circle**\n    + **d**, **diamond**\n    + **g**, **octagon**\n    + **h**, **hexagon**\n    + **i**, **v**, **inverted_tri**\n    + **n**, **pentagon**\n    + **p**, **.**, **point**\n    + **r**, **rectangle**\n    + **s**, **square**\n    + **t**, **^**, **triangle**\n    + **x**, **cross**\n    + **y**, **y_dash**\n- **W** : **line_attrib** : **markeredgecolor** : **MarkerEdgeColor** : -- Str --\n\n    Set pen attributes for lines or the outline of symbols\n    [`-W`](http://gmt.soest.hawaii.edu/doc/latest/ternary.html#w)\n    WARNING: the pen attributes will set the pen of polygons OR symbols but not the two together.\n    If your plot has polygons and symbols, use **W** or **line_attribs** for the polygons and\n    **markeredgecolor** or **MarkerEdgeColor** for filling the symbols. Similar to S above.\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- **axis** : **aspect** : -- Str --\n    When equal to \"equal\" makes a square plot.\n- $(GMT.opt_a)\n- $(GMT.opt_bi)\n- $(GMT.opt_di)\n- $(GMT.opt_e)\n- $(GMT.opt_f)\n- $(GMT.opt_g)\n- $(GMT.opt_h)\n- $(GMT.opt_i)\n- $(GMT.opt_p)\n- $(GMT.opt_t)\n- $(GMT.opt_swap_xy)\n\"\"\"\nfunction ternary(cmd0::String=\"\", arg1=[]; caller=[], K=false, O=false, first=true, kwargs...)\n\n\targ2 = []\t\t# May be needed if GMTcpt type is sent in via C\n\tN_args = isempty_(arg1) ? 0 : 1\n\n\t((isempty(cmd0) && isempty_(arg1) || occursin(\" -\", cmd0)) && return monolitic(\"ternary\", cmd0, arg1)\t# Monolitic mode\n\n\td = KW(kwargs)\n\toutput, opt_T, fname_ext = fname_out(d)\t\t# OUTPUT may have been an extension only\n\n\topt_J = \" -JX12c/10.4c\"                     # Equilateral triangle\n\tfor sym in [:axis :aspect]\n\t\tif (haskey(d, sym))\n\t\t\tif (d[sym] == \"equal\")\t\t\t\t# Need also a 'tight' option\n\t\t\t\topt_J = \" -JX12c\"\n\t\t\tend\n\t\t\tbreak\n\t\tend\n\tend\n\tcmd, opt_B, opt_J, opt_R = parse_BJR(d, \"\", caller, O, opt_J)\n\tcmd = parse_UVXY(cmd, d)\n\tcmd, = parse_a(cmd, d)\n\tcmd, opt_bi = parse_bi(cmd, d)\n\tcmd, opt_di = parse_di(cmd, d)\n\tcmd, = parse_e(cmd, d)\n\tcmd, = parse_f(cmd, d)\n\tcmd, = parse_g(cmd, d)\n\tcmd, = parse_h(cmd, d)\n\tcmd, opt_i = parse_i(cmd, d)\n\tcmd, = parse_p(cmd, d)\n\tcmd, = parse_t(cmd, d)\n\tcmd, = parse_swap_xy(cmd, d)\n\tcmd = parse_params(cmd, d)\n\n\tcmd, K, O, opt_B = set_KO(cmd, opt_B, first, K, O)\t\t# Set the K O dance\n\n\t# If file name sent in, read it and compute a tight -R if this was not provided \n\tcmd, arg1, = read_data(d, cmd0, cmd, arg1, opt_R, opt_i, opt_bi, opt_di, false)\n\n\tcmd, arg1, arg2, = add_opt_cpt(d, cmd, [:C :color :cmap], 'C', N_args, arg1, arg2)\n\n\tcmd = add_opt(cmd, 'L', d, [:L :straight_lines])\n\tcmd = add_opt(cmd, 'M', d, [:M :offset])\n\tcmd = add_opt(cmd, 'N', d, [:N :error_bars])\n\n\tcmd = add_opt(cmd, 'G', d, [:G :fill])\n\topt_Gsymb = \"\"\t\t\t# Filling color for symbols\n\tfor sym in [:G :markerfacecolor :MarkerFaceColor]\n\t\tif (haskey(d, sym))\n\t\t\topt_Gsymb = \" -G\" * arg2str(d[sym])\n\t\t\tbreak\n\t\tend\n\tend\n\n\topt_Wmarker = \"\"\n\tfor sym in [:markeredgecolor :MarkerEdgeColor]\n\t\tif (haskey(d, sym))\n\t\t\topt_Wmarker = \"0.5p,\" * arg2str(d[sym])\t\t# 0.25p is so thin\n\t\t\tbreak\n\t\tend\n\tend\n\n\tcmd = add_opt(cmd, 'L', d, [:L :labels])\n\tcmd = add_opt(cmd, 'M', d, [:M :no_plot])\n\tcmd = add_opt(cmd, 'N', d, [:N :no_clip])\n\n\topt_W = \"\"\n\tpen = build_pen(d)\t\t\t\t\t\t# Either a full pen string or empty (\"\")\n\tif (!isempty(pen))\n\t\topt_W = \" -W\" * pen\n\telse\n\t\tfor sym in [:W :line_attrib]\n\t\t\tif (haskey(d, sym))\n\t\t\t\tif (isa(d[sym], Tuple))\t\t# Like this it can hold the pen, not extended atts\n\t\t\t\t\topt_W = \" -W\" * parse_pen(d[sym])\n\t\t\t\telse\n\t\t\t\t\topt_W = \" -W\" * arg2str(d[sym])\n\t\t\t\tend\n\t\t\t\tbreak\n\t\t\tend\n\t\tend\n\tend\n\n\topt_S = \"\"\n\tfor sym in [:S :symbol]\n\t\tif (haskey(d, sym))\n\t\t\topt_S = \" -S\" * arg2str(d[sym])\n\t\t\tbreak\n\t\tend\n\tend\n\tif (isempty(opt_S))\t\t\t# OK, no symbol given via the -S option. So fish in aliases\n\t\tmarca = \"\"\n\t\tfor sym in [:marker :Marker]\n\t\t\tif (haskey(d, sym))\n\t\t\t\tt = d[sym]\n\t\t\t\tif (isa(t, Symbol))\tt = string(t)\tend\n\t\t\t\tif (t == \"-\"     || t == \"x-dash\")   marca = \"-\"\n\t\t\t\telseif (t == \"+\" || t == \"plus\")     marca = \"+\"\n\t\t\t\telseif (t == \"a\" || t == \"*\" || t == \"star\")     marca = \"a\"\n\t\t\t\telseif (t == \"c\" || t == \"circle\")   marca = \"c\"\n\t\t\t\telseif (t == \"d\" || t == \"diamond\")  marca = \"d\"\n\t\t\t\telseif (t == \"g\" || t == \"octagon\")  marca = \"g\"\n\t\t\t\telseif (t == \"h\" || t == \"hexagon\")  marca = \"h\"\n\t\t\t\telseif (t == \"i\" || t == \"v\" || t == \"inverted_tri\")  marca = \"i\"\n\t\t\t\telseif (t == \"n\" || t == \"pentagon\")  marca = \"n\"\n\t\t\t\telseif (t == \"p\" || t == \".\" || t == \"point\")     marca = \"p\"\n\t\t\t\telseif (t == \"r\" || t == \"rectangle\") marca = \"r\"\n\t\t\t\telseif (t == \"s\" || t == \"square\")    marca = \"s\"\n\t\t\t\telseif (t == \"t\" || t == \"^\" || t == \"triangle\")  marca = \"t\"\n\t\t\t\telseif (t == \"x\" || t == \"cross\")     marca = \"x\"\n\t\t\t\telseif (is3D && (t == \"u\" || t == \"cube\"))  marca = \"u\"\n\t\t\t\telseif (t == \"y\" || t == \"y-dash\")    marca = \"y\"\n\t\t\t\tend\n\t\t\t\tbreak\n\t\t\tend\n\t\tend\n\t\tif (!isempty(marca))\n\t\t\tdone = false\n\t\t\tfor sym in [:markersize :MarkerSize :size]\n\t\t\t\tif (haskey(d, sym))\n\t\t\t\t\tmarca = marca * arg2str(d[sym])\n\t\t\t\t\tdone = true\n\t\t\t\t\tbreak\n\t\t\t\tend\n\t\t\tend\n\t\t\tif (!done)  marca = marca * \"8p\"  end\t\t\t# Default to 8p\n\t\tend\n\t\tif (!isempty(marca))  opt_S = \" -S\" * marca  end\n\tend\n\n\tif (!isempty(opt_S))\t\t\t# \n\t\topt_ML = \"\"\n\t\tfor sym in [:markerline :MarkerLine]\n\t\t\tif (haskey(d, sym))\n\t\t\t\tif (isa(d[sym], Tuple))\t# Like this it can hold the pen, not extended atts\n\t\t\t\t\topt_ML = \" -W\" * parse_pen(d[sym])\n\t\t\t\telse\n\t\t\t\t\topt_ML = \" -W\" * arg2str(d[sym])\n\t\t\t\tend\n\t\t\t\tif (!isempty(opt_Wmarker))\n\t\t\t\t\topt_Wmarker = \"\"\n\t\t\t\t\t@warn(\"markerline overrides markeredgecolor\")\n\t\t\t\tend\n\t\t\t\tbreak\n\t\t\tend\n\t\tend\n\t\tif (!isempty(opt_W) && !isempty(opt_ML))\n\t\t\t@warn(\"You cannot use both markeredgecolor and W or line_attrib keys.\")\n\t\tend\n\tend\n\n\tif (!isempty(opt_W) && isempty(opt_S)) \t\t\t# We have a line/polygon request\n\t\tcmd = [finish_PS(d, cmd * opt_W, output, K, O)]\n\telseif (isempty(opt_W) && !isempty(opt_S))\t\t# We have a symbol request\n\t\tif (!isempty(opt_Wmarker) && isempty(opt_W))\n\t\t\topt_Gsymb = opt_Gsymb * \" -W\" * opt_Wmarker\t# Piggy back in this option string\n\t\tend\n\t\tif (!isempty(opt_ML))\t\t\t\t\t\t# If we have a symbol outline pen\n\t\t\tcmd = cmd * opt_ML\n\t\tend\n\t\tcmd = [finish_PS(d, cmd * opt_S * opt_Gsymb, output, K, O)]\n\telseif (!isempty(opt_W) && !isempty(opt_S))\t\t# We have both line/polygon and a symbol\n\t\t# that is not a vector (because Vector width is set by -W)\n\t\tif (opt_S[4] == 'v' || opt_S[4] == 'V' || opt_S[4] == '=')\n\t\t\tcmd = [finish_PS(d, cmd * opt_W * opt_S * opt_Gsymb, output, K, O)]\n\t\telse\n\t\t\tif (!isempty(opt_Wmarker))\n\t\t\t\topt_Wmarker = \" -W\" * opt_Wmarker\t# Set Symbol edge color \n\t\t\tend\n\t\t\tcmd1 = cmd * opt_W\n\t\t\tcmd2 = replace(cmd, opt_B => \"\") * opt_S * opt_Gsymb * opt_Wmarker\t# Don't repeat option -B\n\t\t\tif (!isempty(opt_ML))\t\t\t\t\t# If we have a symbol outline pen\n\t\t\t\tcmd1 = cmd1 * opt_ML\n\t\t\tend\n\t\t\tcmd = [finish_PS(d, cmd1, output, true, O)\n\t\t\t       finish_PS(d, cmd2, output, K, true)]\n\t\tend\n\telseif (!isempty(opt_S) && !isempty(opt_ML))\t\t# We have a symbol outline pen\n\t\tcmd = [finish_PS(d, cmd * opt_ML * opt_S * opt_Gsymb, output, K, O)]\n\telse\n\t\tcmd = [finish_PS(d, cmd, output, K, O)]\n\tend\n\n\treturn finish_PS_module(d, cmd, \"\", output, fname_ext, opt_T, K, \"ternary\", arg1, arg2)\nend\n\n# ---------------------------------------------------------------------------------------------------\nternary!(cmd0::String=\"\", arg1=[]; caller=[], K=true, O=true,  first=false, kw...) =\n\tternary(cmd0, arg1; caller=caller, K=K, O=O,  first=first, kw...)\nternary!(arg1=[]; caller=[], K=true, O=true,  first=false, kw...) =\n\tternary(\"\", arg1; caller=caller, K=K, O=O,  first=first, kw...)\n\npsternary  = ternary            # Aliases\npsternary! = ternary!           # Aliases", "meta": {"hexsha": "3c343a658485d0287b78d0a4a0cad992066425a3", "size": 9699, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/psternary.jl", "max_stars_repo_name": "JuliaDocsForks/GMT.jl", "max_stars_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/psternary.jl", "max_issues_repo_name": "JuliaDocsForks/GMT.jl", "max_issues_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/psternary.jl", "max_forks_repo_name": "JuliaDocsForks/GMT.jl", "max_forks_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6392857143, "max_line_length": 119, "alphanum_fraction": 0.5732549747, "num_tokens": 3209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.14223190046381004, "lm_q1q2_score": 0.07056036817401817}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 c50a52e2-30fa-11ec-00e9-4f4a81ed30ed\nusing CSV, DataFrames\n\n# \u2554\u2550\u2561 9ba9e100-6929-4312-8cd8-75de0cb05a3c\niris = CSV.File(\"iris_dataset.csv\") |> DataFrame\n\n# \u2554\u2550\u2561 b1a1e7fc-58de-45ef-b6c4-355f5e30b7ef\ndescribe(iris)\n\n# \u2554\u2550\u2561 63668d67-5d58-415d-9fc9-6a891e26ddbd\nnrow(iris)\n\n# \u2554\u2550\u2561 5346265b-2829-4e49-afc7-a016db72db17\nunique(iris.Species)\n\n# \u2554\u2550\u2561 148805ea-c9d6-4bdf-ac79-b9bea8d24f4c\nspecies = groupby(iris, :Species)\n\n# \u2554\u2550\u2561 7b9ee6d2-95b1-4400-a21d-313b0a97b229\ncombine(species, nrow)\n\n# \u2554\u2550\u2561 9f30a14b-eded-4829-bd2a-6b85548ba6b8\ncombine(species, :SepalLength => minimum => :MinSepalLength)\n\n# \u2554\u2550\u2561 6e73a20f-860e-4b2e-8dc0-64f1b93f6fa1\ncolnames = names(iris)\n\n# \u2554\u2550\u2561 27013be8-56eb-4455-9d2a-53c475d6147a\nnumeric_columns = colnames[1:4]\n\n# \u2554\u2550\u2561 4f193ca1-f0a6-472d-a9bf-6deb725c7bb6\ntransform(iris, numeric_columns .=> (x -> 10x) .=> numeric_columns)\n\n# \u2554\u2550\u2561 432548a4-67b2-45c9-a0bd-fb3099568f15\nbegin\n\tiris[!, :Sample] = 1:nrow(iris)\n\tlong = stack(iris, Not([:Species, :Sample]))\nend\n\n# \u2554\u2550\u2561 b94f9bc0-2e63-4cd5-b4b5-b64c41d79270\nbegin\n\tlong.Dimension = [\n\t\treplace(x, r\"Sepal|Petal\" => \"\") \n\t\tfor x in long.variable\n\t\t]\n\tlong.Structure = [\n\t\treplace(x, r\"Length|Width\" => \"\") \n\t\tfor x in long.variable\n\t\t]\n\tselect!(long, Not(:variable))\n\tunstack(long, :Dimension, :value)\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\n\n[compat]\nCSV = \"~0.9.10\"\nDataFrames = \"~1.2.2\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[CSV]]\ndeps = [\"CodecZlib\", \"Dates\", \"FilePathsBase\", \"InlineStrings\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"74147e877531d7c172f70b492995bc2b5ca3a843\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.9.10\"\n\n[[CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"dce3e3fea680869eaa0b774b2e8343e9ff442313\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.40.0\"\n\n[[Crayons]]\ngit-tree-sha1 = \"3f71217b538d7aaee0b69ab47d9b7724ca8afa0d\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.0.4\"\n\n[[DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"d785f42445b63fc86caa08bb9a9351008be9b765\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.2.2\"\n\n[[DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"7d9d316f04214f7efdbb6398d545446e246eff02\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.10\"\n\n[[DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[FilePathsBase]]\ndeps = [\"Dates\", \"Mmap\", \"Printf\", \"Test\", \"UUIDs\"]\ngit-tree-sha1 = \"d962b5a47b6d191dbcd8ae0db841bc70a05a3f5b\"\nuuid = \"48062228-2e41-5def-b9a4-89aafe57970f\"\nversion = \"0.9.13\"\n\n[[Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[InlineStrings]]\ndeps = [\"Parsers\"]\ngit-tree-sha1 = \"19cb49649f8c41de7fea32d089d37de917b553da\"\nuuid = \"842dd82b-1e85-43dc-bf29-5d0ee9dffc48\"\nversion = \"1.0.1\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[LinearAlgebra]]\ndeps = [\"Libdl\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"bf210ce90b6c9eed32d25dbcae1ebc565df2687f\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.2\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"ae4bbcadb2906ccc085cf52ac286dc1377dceccc\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.1.2\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"a193d6ad9c45ada72c14b731a318bedd3c2f00cf\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.3.0\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"d940010be611ee9d67064fe559edbb305f8cc0eb\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.2.3\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"f45b34656397a1f6e729901dc9ef679610bd12b5\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.8\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"fed34d0e71b91734bf0a7e10eb1bb05296ddbcd0\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.0\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[WeakRefStrings]]\ndeps = [\"DataAPI\", \"InlineStrings\", \"Parsers\"]\ngit-tree-sha1 = \"c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa\"\nuuid = \"ea10d353-3f73-51f8-a26c-33c1cb351aa5\"\nversion = \"1.4.1\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550c50a52e2-30fa-11ec-00e9-4f4a81ed30ed\n# \u2560\u25509ba9e100-6929-4312-8cd8-75de0cb05a3c\n# \u2560\u2550b1a1e7fc-58de-45ef-b6c4-355f5e30b7ef\n# \u2560\u255063668d67-5d58-415d-9fc9-6a891e26ddbd\n# \u2560\u25505346265b-2829-4e49-afc7-a016db72db17\n# \u2560\u2550148805ea-c9d6-4bdf-ac79-b9bea8d24f4c\n# \u2560\u25507b9ee6d2-95b1-4400-a21d-313b0a97b229\n# \u2560\u25509f30a14b-eded-4829-bd2a-6b85548ba6b8\n# \u2560\u25506e73a20f-860e-4b2e-8dc0-64f1b93f6fa1\n# \u2560\u255027013be8-56eb-4455-9d2a-53c475d6147a\n# \u2560\u25504f193ca1-f0a6-472d-a9bf-6deb725c7bb6\n# \u2560\u2550432548a4-67b2-45c9-a0bd-fb3099568f15\n# \u2560\u2550b94f9bc0-2e63-4cd5-b4b5-b64c41d79270\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "0d4e1459b4fb6742ab7029d552a9b14fd57011de", "size": 10638, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter05/describe_iris_dataframe.jl", "max_stars_repo_name": "PacktPublishing/Interactive-Visualization-with-Julia", "max_stars_repo_head_hexsha": "6f9b2f0798784892b7d6965dd56fb2ef42d1012a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-10-04T16:28:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-04T16:28:23.000Z", "max_issues_repo_path": "Chapter05/describe_iris_dataframe.jl", "max_issues_repo_name": "PacktPublishing/Interactive-Visualization-with-Julia", "max_issues_repo_head_hexsha": "6f9b2f0798784892b7d6965dd56fb2ef42d1012a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter05/describe_iris_dataframe.jl", "max_forks_repo_name": "PacktPublishing/Interactive-Visualization-with-Julia", "max_forks_repo_head_hexsha": "6f9b2f0798784892b7d6965dd56fb2ef42d1012a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0686015831, "max_line_length": 280, "alphanum_fraction": 0.7280503854, "num_tokens": 4967, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782349911420193, "lm_q2_score": 0.16026603032235007, "lm_q1q2_score": 0.0701682341848741}}
{"text": "@testset \"Basic Type coercion tests\" begin\n    X = (x=10:10:44, y=1:4, z=collect(\"abcd\"))\n\n    @test_throws ScientificTypes.CoercionError coerce(X, :x=>Float64)\n    @test_throws ScientificTypes.CoercionError coerce(X, :x=>Textual)\n\n    types = Dict(:x => Continuous, :z => Multiclass)\n    X_coerced = coerce(X, types)\n    @test X_coerced ==  coerce(X, :x => Continuous, :z => Multiclass)\n    @test scitype_union(X_coerced.x) === Continuous\n    @test scitype_union(X_coerced.z) <: Multiclass\n    @test !X_coerced.z.pool.ordered\n    @test_throws MethodError coerce([\"a\", \"b\", \"c\"], Count)\n\n    y = collect(Float64, 1:5)\n    y_coerced = coerce(y, Count)\n    @test scitype_union(y_coerced) === Count\n    @test y_coerced == y\n    y = [1//2, 3//4, 6//5]\n    y_coerced = coerce(y, Continuous)\n    @test scitype_union(y_coerced) === Continuous\n    @test y_coerced \u2248 y\n    X_coerced = coerce(X, Dict(:z => OrderedFactor))\n    @test X_coerced == coerce(X, :z => OrderedFactor)\n    @test X_coerced.x === X.x\n    @test scitype_union(X_coerced.z) <: OrderedFactor\n    @test X_coerced.z.pool.ordered\n\n    # Check no-op coercion\n    y = rand(Float64, 5)\n    @test coerce(y, Continuous) === y\n    y = rand(Float32, 5)\n    @test coerce(y, Continuous) === y\n    y = rand(BigFloat, 5)\n    @test coerce(y, Continuous) === y\n    y = rand(Int, 5)\n    @test coerce(y, Count) === y\n    y = big.(y)\n    @test coerce(y, Count) === y\n    y = rand(UInt32, 5)\n    @test coerce(y, Count) === y\n    X_coerced = coerce(X, Dict{Symbol, Type}())\n    @test X_coerced.x === X.x\n    @test X_coerced.z === X.z\n    z = categorical(X.z)\n    @test coerce(z, Multiclass) === z\n    z = categorical(X.z, compress=true, ordered=false)\n    @test coerce(z, Multiclass) === z\n    z = categorical(X.z, compress=true, ordered=true)\n    @test coerce(z, OrderedFactor) === z\n\n    # missing values\n    y_coerced = @test_logs(\n        (:info, r\"Trying to coerce from `Union{Missing,\"),\n        coerce([4, 7, missing], Continuous))\n    @test ismissing(y_coerced == [4.0, 7.0, missing])\n    @test scitype_union(y_coerced) === Union{Missing,Continuous}\n    y_coerced = @test_logs(\n        (:info, r\"Trying to coerce from `Any\"),\n        coerce(Any[4, 7.0, missing], Continuous))\n    @test ismissing(y_coerced == [4.0, 7.0, missing])\n    @test scitype_union(y_coerced) === Union{Missing,Continuous}\n    y_coerced = @test_logs(\n        (:info, r\"Trying to coerce from `Union{Missing,\"),\n        coerce([4.0, 7.0, missing], Count))\n    @test ismissing(y_coerced == [4, 7, missing])\n    @test scitype_union(y_coerced) === Union{Missing,Count}\n    y_coerced = @test_logs(\n        (:info, r\"Trying to coerce from `Any\"),\n        coerce(Any[4, 7.0, missing], Count))\n    @test ismissing(y_coerced == [4, 7, missing])\n    @test scitype_union(y_coerced) === Union{Missing,Count}\n    @test scitype_union(@test_logs(\n        (:info, r\"Trying to coerce from `Union{Missing,\"),\n        coerce(['x', 'y', missing], Multiclass))) ===\n            Union{Missing, Multiclass{2}}\n    @test scitype_union(@test_logs(\n        (:info, r\"Trying to coerce from `Union{Missing,\"),\n        coerce(['x', 'y', missing], OrderedFactor))) ===\n            Union{Missing, OrderedFactor{2}}\n    # non-missing Any vectors\n    @test coerce(Any[4, 7],     Continuous) == [4.0, 7.0]\n    @test coerce(Any[4.0, 7.0], Continuous) == [4, 7]\n\n    # Finite conversions:\n    @test scitype_union(coerce(['x', 'y'], Finite)) === Multiclass{2}\n    @test scitype_union(@test_logs(\n        (:info, r\"Trying to coerce from `Union{Missing,\"),\n        coerce(['x', 'y', missing], Finite))) === Union{Missing, Multiclass{2}}\n\n    # More finite conversions (to check resolution of #48):\n    y = categorical([1, 2, 3, missing]) # unordered\n    yc = coerce(y, Union{Missing,OrderedFactor})\n    @test isordered(yc)\n    @test yc[1].pool.ordered\n    @test scitype(yc) == Vec{Union{Missing, OrderedFactor{3}}}\n    @test scitype_union(yc) == Union{Missing, OrderedFactor{3}}\n    @test scitype_union(y) == Union{Missing, Multiclass{3}}\n\n    # tests fix for issue https://github.com/JuliaAI/ScientificTypes.jl/issues/161\n    X = (x=10:10:44, y=1:4, z=collect(\"abcd\"))\n    Xc = coerce(X, :x => Continuous, \"y\" => Continuous)\n    @test scitype_union(Xc.x) === Continuous\n    @test scitype_union(Xc.y) === Continuous\nend\n\n# issue #62 (ScientficTypes)\n@testset \"coersion of Type=>Type\" begin\n    X = (x=[1,2,1,2,5,1,0,7],\n         y=[0,1,0,1,0,1,0,1],\n         z=['a','b','a','b','a','a',missing,missing])\n    Xc = coerce(X, :y=>OrderedFactor)\n    Xc = coerce(Xc, Count=>Continuous)\n    @test elscitype(Xc.x) == Continuous\n    @test elscitype(Xc.y) == OrderedFactor{2}\n    Xc = coerce(Xc, OrderedFactor=>Count)\n    @test elscitype(Xc.y) == Count\n    Xc = coerce(Xc, :z=>Multiclass, verbosity=0)\n    Xc = coerce(Xc, Multiclass=>OrderedFactor, verbosity=0)\n    @test elscitype(Xc.z) == Union{Missing,OrderedFactor{2}}\n    Xc = coerce(X, Count=>Continuous, Unknown=>Multiclass, verbosity=0)\n    @test elscitype(Xc.x) == Continuous\n    @test elscitype(Xc.y) == Continuous\n    @test elscitype(Xc.z) == Union{Missing, Multiclass{2}}\nend\n\n# issue #13\n@testset \"coersion of mixture of Type=>Type and Symbol=>Type\" begin\n    X = (x=10:10:44, y=1:4, z=collect(\"abcd\"), w=[\"a\", \"b\", \"c\", missing])\n    @test ScientificTypes.feature_scitype_pairs(:x => Continuous, X) ==\n        [:x => Continuous, ]\n    @test ScientificTypes.feature_scitype_pairs(Count => Continuous, X) ==\n        [:x => Union{Continuous}, :y=> Union{Continuous}]\n    X1 = coerce(X, :z => Multiclass, Count=>Continuous)\n    @test schema(X1).scitypes ==\n        (Continuous, Continuous, Multiclass{4}, Union{Missing,Textual})\n    X2 = coerce(X, :z => Multiclass, :w=>Multiclass, Count=>Continuous,\n                verbosity=0)\n    @test schema(X2).scitypes ==\n        (Continuous, Continuous, Multiclass{4}, Union{Missing,Multiclass{3}})\n    @test_throws ArgumentError coerce(X, Count => Continuous, :x=>Multiclass)\nend\n\n@testset \"coerce!\" begin\n    df = DataFrame((x=ones(Int,5), y=ones(5)))\n     @test scitype(df) == Table{Union{AbstractArray{Continuous,1},\n                                      AbstractArray{Count,1}}}\n    coerce!(df, :x=>Continuous)\n    @test scitype(df) == Table{AbstractArray{Continuous,1}}\n \n    df = DataFrame((\n          x=ones(Int, 50),\n          y=ones(50),\n          z=collect(\"abbabaacbcabbabaacbbcccbccbbabaaacacbcabcbccaabaaa\")\n          ))\n     @test scitype(df) == Table{Union{AbstractArray{Continuous,1},\n                                      AbstractArray{Count,1},\n                                      AbstractArray{Unknown,1}}}\n \n    coerce!(df, autotype(df, :few_to_finite))\n     @test scitype(df) == Table{Union{AbstractArray{Multiclass{3},1},\n                                      AbstractArray{OrderedFactor{1},1}}}\n \n    @test_throws ScientificTypes.CoercionError coerce!(randn(5, 5))\n\n    df2 = DataFrame(x=[1,2,3,4],\n    y=[\"a\",\"b\",\"c\",\"a\"],\n    z = Union{Missing,Int}[10, 20, 30, 40])\n    coerce!(df2, Textual=>Finite)\n    coerce!(df2, Union{Missing, Count}=>Count, tight=true) # issue #166\n    @test scitype(df2) == Table{Union{AbstractArray{Count,1},\n                      AbstractArray{Multiclass{3},1} }}\n    \n    # issue #9 (coerce Text => Num)\n    df3 = DataFrame(x=[\"1\",\"2\",\"3\"], y=[2,3,4])\n    coerce!(df3, Textual=>Count)\n    @test scitype(df3) == Table{AbstractArray{Count,1}}\n    \n    x = [1,2,3,4]\n    @test_throws ScientificTypes.CoercionError coerce!(x, Continuous)\n\nend\n", "meta": {"hexsha": "15e7ec4dc7a09adae3fdef58304e3bcfce7520ae", "size": 7465, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/coerce.jl", "max_stars_repo_name": "felixcremer/ScientificTypes.jl", "max_stars_repo_head_hexsha": "593c73f5a7c47a52d474bbbe08ea8865bf0dd625", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 45, "max_stars_repo_stars_event_min_datetime": "2021-06-17T16:58:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T16:24:37.000Z", "max_issues_repo_path": "test/coerce.jl", "max_issues_repo_name": "felixcremer/ScientificTypes.jl", "max_issues_repo_head_hexsha": "593c73f5a7c47a52d474bbbe08ea8865bf0dd625", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 46, "max_issues_repo_issues_event_min_datetime": "2021-06-14T00:19:06.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T01:47:27.000Z", "max_forks_repo_path": "test/coerce.jl", "max_forks_repo_name": "felixcremer/ScientificTypes.jl", "max_forks_repo_head_hexsha": "593c73f5a7c47a52d474bbbe08ea8865bf0dd625", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2021-06-17T16:26:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-09T15:57:25.000Z", "avg_line_length": 40.7923497268, "max_line_length": 82, "alphanum_fraction": 0.609912927, "num_tokens": 2372, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.14033624589467186, "lm_q1q2_score": 0.07016812294733593}}
{"text": "using Random\nexport customop,\nxavier_init,\nload_op_and_grad,\nload_op,\nuse_gpu,\ninstall,\nload_system_op,\nregister,\ndebug,\ndoctor,\nnnuq,\ncompile,\nlist_physical_devices,\nMCMCSimple,\nsimulate,\ndiagnose,\nget_placement,\ntimestamp,\nload_library,\nsleep_for,\nget_library_symbols\n\n\"\"\"\n    xavier_init(size, dtype=Float64)\n\nReturns a matrix of size `size` and its values are from Xavier initialization. \n\"\"\"\nfunction xavier_init(size, dtype=Float64)\n    in_dim = size[1]\n    xavier_stddev = 1. / sqrt(in_dim / 2.)\n    return randn(dtype, size...)*xavier_stddev\nend\n\n############### custom operators ##################\n\"\"\"\n    cmake(DIR::String=\"..\"; CMAKE_ARGS::Union{Array{String}, String} = \"\")\n\nThe built-in Cmake command for building C/C++ libraries. If extra Cmake arguments are needed, please specify it through `CMAKE_ARGS`.\n\n# Example \n```\nADCME.cmake(CMAKE_ARGS=[\"SHARED=YES\", \"STAITC=NO\"])\n```\n\nThe executed command might be:\n```\n/home/darve/kailaix/.julia/adcme/bin/cmake -G Ninja -DCMAKE_MAKE_PROGRAM=/home/darve/kailaix/.julia/adcme/bin/ninja -DJULIA=/home/darve/kailaix/julia-1.3.1/bin/julia -DCMAKE_C_COMPILER=/home/darve/kailaix/.julia/adcme/bin/x86_64-conda_cos6-linux-gnu-gcc -DCMAKE_CXX_COMPILER=/home/darve/kailaix/.julia/adcme/bin/x86_64-conda_cos6-linux-gnu-g++ SHARED=YES STATIC=NO ..\n```\n\"\"\"\nfunction cmake(DIR::String=\"..\"; CMAKE_ARGS::Union{Array{String}, String} = \"\")\n    DIR = abspath(DIR)\n    ENV_ = copy(ENV)\n    LD_PATH = Sys.iswindows() ? \"PATH\" : \"LD_LIBRARY_PATH\"\n    if haskey(ENV_, LD_PATH)\n        ENV_[LD_PATH] = ENV[LD_PATH]*\":$LIBDIR\"\n    else\n        ENV_[LD_PATH] = LIBDIR\n    end\n\n    if has_mpi(false)\n        ENV_[\"MPI_INCLUDE_PATH\"], ENV_[\"MPI_C_LIBRARIES\"] = get_mpi()\n    end\n\n    if Sys.iswindows()\n        if !haskey(ENV_, \"VS150COMNTOOLS\")\n            # @warn \"VS150COMNTOOLS is not set, default to /c/Program Files (x86)/Microsoft Visual Studio/2017/Community/Common7/Tools\" maxlog=1\n            ENV_[\"VS150COMNTOOLS\"] = \"/c/Program Files (x86)/Microsoft Visual Studio/2017/Community/Common7/Tools\"\n        end\n        # @info \"Do remember to add ADD_DEFINITIONS(-DNOMINMAX) to your CMakeLists.txt\" maxlog=1\n        run(setenv(`$CMAKE -G\"Visual Studio 15\" -DJULIA=\"$(joinpath(Sys.BINDIR, \"julia\"))\" -A x64 $CMAKE_ARGS $DIR`, ENV_)) # very important, x64\n    else\n        run(setenv(`$CMAKE -G Ninja -DCMAKE_MAKE_PROGRAM=$NINJA -DJULIA=\"$(joinpath(Sys.BINDIR, \"julia\"))\" -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX $CMAKE_ARGS $DIR`, ENV_))\n    end\nend\n\nfunction make()\n    ENV_ = copy(ENV)\n    LD_PATH = Sys.iswindows() ? \"PATH\" : \"LD_LIBRARY_PATH\"\n    if haskey(ENV_, LD_PATH)\n        ENV_[LD_PATH] = ENV[LD_PATH]*\":$LIBDIR\"\n    else\n        ENV_[LD_PATH] = LIBDIR\n    end\n    if Sys.iswindows()\n        sln_file = filter(x->endswith(x, \".sln\"), readdir())\n        if length(sln_file)==0\n            error(\"No .sln file found. Did you run `ADCME.cmake()`?\")\n        elseif length(sln_file)>1\n            error(\"More than 1 .sln file found. Check your program.\")\n        else\n            sln_file = sln_file[1]\n        end \n        run(`cmd /c $CMAKE  --build . -j --target ALL_BUILD --config Release`)\n    else\n        if haskey(ENV, \"TRAVIS_BRANCH\")\n            run(setenv(`$NINJA -j1`, ENV_))\n        else\n            run(setenv(`$NINJA -j20`, ENV_))\n        end\n    end\nend\n\n\"\"\"\n    make_library(Libdir::String)\n\nMake shared library in `Libdir`. The structure of the source codes files are \n\n```\n- Libdir \n  - *.cpp \n  - *.h \n  - CMakeLists\n  - build (Optional)\n```\n\"\"\"\nfunction make_library(Libdir::String)\n   if !isdir(Libdir)\n    error(\"$Libdir is not a valid directory.\")\n   end\n   PWD = pwd()\n   cd(Libdir)\n   if !isdir(\"build\")\n    mkdir(\"build\")\n   end\n   cd(\"build\")\n   if !isfile(\"Makefile\")\n    ADCME.cmake()\n   end\n   ADCME.make()\n   cd(PWD)\nend\n\nload_op_dict = Dict{Tuple{String, String}, PyObject}()\nload_op_grad_dict = Dict{Tuple{String, String}, PyObject}()\n\n@doc \"\"\"\n    load_op(oplibpath::Union{PyObject, String}, opname::String; verbose::Union{Missing, Bool} = missing)\n\nLoads the operator `opname` from library `oplibpath`.\n\"\"\"\nfunction load_op(oplibpath::Union{PyObject, String}, opname::String; verbose::Union{Missing, Bool} = missing)\n    verbose = coalesce(verbose, options.customop.verbose)\n    oplibpath = get_library(oplibpath)\n    if haskey(load_op_dict, (oplibpath,opname))\n        return load_op_dict[(oplibpath,opname)]\n    end\n    s = getproperty(load_library(oplibpath), opname)\n    load_op_dict[(oplibpath,opname)] = s\n    verbose && printstyled(\"Load library operator: $oplibpath ==> $opname\\n\", color=:green)\n    return s\nend\n\n@doc \"\"\"\n    load_op_and_grad(oplibpath::Union{PyObject, String}, opname::String; multiple::Bool=false)\n\nLoads the operator `opname` from library `oplibpath`; gradients are also imported. \nIf `multiple` is true, the operator is assumed to have multiple outputs. \n\"\"\"\nfunction load_op_and_grad(oplibpath::Union{PyObject, String}, opname::String; \n        multiple::Bool=false, verbose::Union{Missing, Bool} = missing)\n    verbose = coalesce(verbose, options.customop.verbose)\n    is_system_op = oplibpath == LIBADCME\n\n    if isa(oplibpath, String)\n        if Sys.iswindows()\n            a, b = splitdir(oplibpath)\n            if length(b) >=3 && b[1:3]==\"lib\"\n                b = b[4:end]\n            end\n            oplibpath = joinpath(a, b)\n        end\n        if splitext(oplibpath)[2]==\"\"\n            oplibpath = oplibpath * (Sys.islinux() ? \n                            \".so\" : Sys.isapple() ? \".dylib\" : \".dll\")\n        end\n        oplibpath = abspath(oplibpath)\n        if haskey(load_op_grad_dict, (oplibpath,opname))\n            return load_op_grad_dict[(oplibpath,opname)]\n        end\n        if !isfile(oplibpath)\n            error(\"Library $oplibpath does not exist.\")\n        end\n    end\n\n    opname_grad = opname*\"_grad\"\n    fn_name = opname*randstring(8)\n    try\n        if is_system_op\npy\"\"\"\nimport tensorflow as tf\nlib$$fn_name = $libadcme\n\"\"\"\n        elseif isa(oplibpath, String)\npy\"\"\"\nimport tensorflow as tf\nlib$$fn_name = tf.load_op_library($oplibpath)\n\"\"\"\n        else \npy\"\"\"\nimport tensorflow as tf\nlib$$fn_name = $oplibpath\n\"\"\"\n        end\nif !multiple\npy\"\"\"\n@tf.custom_gradient\ndef $$fn_name(*args):\n    u = lib$$fn_name.$$opname(*args)\n    def grad(dy):\n        return lib$$fn_name.$$opname_grad(dy, u, *args)\n    return u, grad\n\"\"\"\nelse\npy\"\"\"\n@tf.custom_gradient\ndef $$fn_name(*args):\n    u = lib$$fn_name.$$opname(*args)\n    def grad(*dy):\n        dy = [y for y in dy if y is not None and y.dtype in [tf.float64, tf.float32]] # only float64 and float32 can backpropagate gradients\n        return lib$$fn_name.$$opname_grad(*dy, *u, *args)\n    return u, grad\n\"\"\"\nend\ncatch(e)\n    printstyled(\"Failed load $oplibpath or its symbols. Error Message from the TensorFlow backend\\n$(string(e))\\n\", color=:red)\n    Libdl.dlopen(oplibpath)\nend\n        s = py\"$$fn_name\"\n        if isa(oplibpath, String)\n            load_op_grad_dict[(oplibpath,opname)] = s\n            verbose && printstyled(\"Load library operator (with gradient, multiple outputs = $multiple): $oplibpath ==> $opname\\n\", color=:green)\n        end\n        return s\nend\n\n\n\"\"\"\n    load_system_op(opname::String, grad::Bool=true; multiple::Bool=false)\n\nLoads custom operator from CustomOps directory (shipped with ADCME instead of TensorFlow)\nFor example \n```\ns = \"SparseOperator\"\noplib = \"libSO\"\ngrad = true\n```\nthis will direct Julia to find library `CustomOps/SparseOperator/libSO.dylib` on MACOSX\n\"\"\"\nfunction load_system_op(opname::String, grad::Bool=true; multiple::Bool=false)\n    if !isfile(LIBADCME)\n        @info \"$LIBADCME does not exist. Precompiling...\"\n        ADCME.precompile()\n    end\n    if grad\n        load_op_and_grad(LIBADCME, opname; multiple=multiple, verbose=false)\n    else\n        load_op(LIBADCME, opname, verbose=false)\n    end\nend\n\n\"\"\"\n    compile(s::String; force::Bool=false)\n\nCompiles the library given by path `deps/s`. If `force` is false, `compile` first check whether \nthe binary product exists. If the binary product exists, return 2. Otherwise, `compile` tries to \ncompile the binary product, and returns 0 if successful; it return 1 otherwise. \n\"\"\"\nfunction compile(s::String; force::Bool=false, customdir::Bool = false)\n    PWD = pwd()\n    dir = s \n    if !customdir\n        dir = joinpath(joinpath(\"$(@__DIR__)\", \"../deps/CustomOps\"), s)\n    end\n    if !isdir(dir)\n        @warn(\"Folder for the operator $s does not exist: $dir\")\n        return 1\n    end\n    cd(dir)\n    \n    local surfix \n    if Sys.isapple()\n        surfix = \".dylib\"\n    elseif Sys.islinux()\n        surfix = \".so\"\n    elseif Sys.iswindows()\n        surfix = \".dll\"\n    end\n    if !force && isdir(\"build\") # check if product exists \n        files = readdir(\"build\")\n        if any([endswith(x, surfix) for x in files])\n            @warn(\"The binary product exists.\")\n            cd(PWD)\n            return 2\n        end\n    end\n    rm(\"build\",force=true,recursive=true)\n    mkdir(\"build\")\n    cd(\"build\")\n    try\n        cmake()\n        make()\n        cd(PWD)\n        return 0\n    catch e \n        error(\"Compilation error: $e\")\n        cd(PWD)\n        return 1\n    end\nend\n\n\"\"\"\n    precompile(force::Bool=false)\n\nPrecompile the built-in custom operators. \n\"\"\"\nfunction Base.:precompile(force::Bool=false)\n    PWD = pwd()\n    cd(\"$(@__DIR__)/../deps/CustomOps\")\n    if force\n        try\n            rm(\"build\", force=true, recursive=true)\n        catch \n            error(\"\"\"Failed to remove build directory. Follow the following steps and try again:\n1. Quit ALL julia processes that use ADCME;\n2. Remove $(joinpath(pwd(), \"build\")) manually.\"\"\")\n        end\n    end\n    change_directory(\"build\")\n    require_cmakecache() do \n        if Sys.isapple()\n            try\n                ADCME.cmake()\n            catch \n                @info \"Use system clang...\"\n                mv(joinpath(BINDIR, \"clang\"), joinpath(BINDIR, \"clang_original\"))\n                mv(joinpath(BINDIR, \"clang++\"), joinpath(BINDIR, \"clang++_original\"))\n                symlink(\"/usr/bin/clang\",joinpath(BINDIR, \"clang\"))\n                symlink(\"/usr/bin/clang++\",joinpath(BINDIR, \"clang++\"))\n                ADCME.cmake()\n            end\n        else\n            ADCME.cmake()\n        end\n    end\n    require_library(\"adcme\") do \n        ADCME.make()\n    end\n    cd(PWD)\n    global libadcme = tf.load_op_library(LIBADCME)\nend\n\n\"\"\"\n    compile()\n\nCompile a custom operator in the current directory. A `CMakeLists.txt` must be present. \n\"\"\"\nfunction compile()\n    PWD = pwd()\n    if !isfile(\"CMakeLists.txt\")\n        error(SystemError(\"No CMakeLists.txt in the current directory found.\"))\n    end\n    if !isdir(\"build\")\n        mkdir(\"build\")\n    end\n    cd(\"build\")\n    try \n        cmake()\n        make()\n    catch e\n        @warn \"Compiling failed: $e\"\n    finally\n        cd(PWD)\n    end\nend\n\n\n\"\"\"\n    customop(;with_mpi::Bool = false)\n\nCreate a new custom operator. Typically users call `customop` twice: the first call generates a `customop.txt`, \nusers edit the content in the file; the second all generates C++ source code, CMakeLists.txt, and gradtest.jl from `customop.txt`.\n\n# Example\n```julia-repl\njulia> customop() # create an editable `customop.txt` file\n[ Info: Edit custom_op.txt for custom operators\njulia> customop() # after editing `customop.txt`, call it again to generate interface files.\n```\n\n# Options \n- `with_mpi`: Whether the custom operator uses MPI\n\"\"\"\nfunction customop(;with_mpi::Bool = false)\n    # install_custom_op_dependency()\n    py_dir = \"$(@__DIR__)/../deps/CustomOpsTemplate\"\n    if !(\"custom_op.txt\" in readdir(\".\"))\n        cp(\"$(py_dir)/custom_op.example\", \"custom_op.txt\")\n        @info \"Edit custom_op.txt for custom operators\"\n        return\n    else\n        python = PyCall.python\n        with_mpi = with_mpi ? 1 : 0\n        run(`$python $(py_dir)/customop.py custom_op.txt $py_dir $with_mpi`)\n    end\n    nothing\nend\n\n\n\nfunction use_gpu(i::Union{Nothing,Int64}=nothing)\n    if length(CUDA_INC)==0\n        error(\"\"\"ADCME is not built against GPU. Set ENV[\"GPU\"]=1 and rebuild GPU.\"\"\")\n    end\n    dl = pyimport(\"tensorflow.python.client.device_lib\")\n    if !isnothing(i) && i>=1\n        i = join(collect(0:i-1),',') \n        ENV[\"CUDA_VISIBLE_DEVICES\"] = i \n    elseif !isnothing(i) && i==0\n        ENV[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n    end\n    local_device_protos = dl.list_local_devices()\n    return [x.name for x in local_device_protos if x.device_type == \"GPU\"]\nend\n\nfunction list_physical_devices(cpu_or_gpu::String = \"all\")\n    dl = pyimport(\"tensorflow.python.client.device_lib\")\n    local_device_protos = dl.list_local_devices()\n    CPU = [x.name for x in local_device_protos if x.device_type == \"CPU\"]\n    GPU = [x.name for x in local_device_protos if x.device_type == \"GPU\"]\n    if cpu_or_gpu == \"all\"\n        return [CPU;GPU]\n    elseif cpu_or_gpu == \"GPU\"\n        return GPU \n    elseif cpu_or_gpu == \"CPU\"\n        return CPU \n    else\n        error(ArgumentError(\"$cpu_or_gpu is not a valid input. Expected: all, CPU, or GPU\"))\n    end\nend\n\n\n\"\"\"\n    install(s::String; force::Bool = false, islocal::Bool = false)\n\nInstall a custom operator from a URL, a directory (when `islocal` is true), or a string. In any of the three case, \n`install` copy the folder to $(abspath(joinpath(LIBADCME, \"../../Plugin\"))). \nWhen `s` is a string, `s` is converted to \n\nhttps://github.com/ADCMEMarket/<s>\n\"\"\"\nfunction install(s::String; force::Bool = false, islocal::Bool = false)\n    if !islocal && !startswith(s, \"http\")\n        s = \"https://github.com/ADCMEMarket/\"*s\n    end \n    _, name = splitdir(s)\n    if force \n        rm(joinpath(LIBPLUGIN, name), force=true, recursive=true)\n    elseif isdir(joinpath(LIBPLUGIN, name)) && (\"build\" in readdir(joinpath(LIBPLUGIN, name)))\n        return _plugin_lib(joinpath(LIBPLUGIN, name))\n    end\n    try\n        LibGit2.clone(s, joinpath(LIBPLUGIN, name))\n    catch\n        LibGit2.clone(\"git://$(s[9:end]).git\", joinpath(LIBPLUGIN, name))\n    end\n    PWD = pwd()\n    cd(joinpath(LIBPLUGIN, name))\n    if isfile(joinpath(LIBPLUGIN, name, \"build.jl\"))\n        include(joinpath(LIBPLUGIN, name, \"build.jl\"))\n    end\n    cmakelists = String(read(\"CMakeLists.txt\"))\n    if !occursin(\"cmake_minimum_required\", cmakelists)\n        cmakelists = replace(String(read(joinpath(LIBPLUGIN, \"CMakeLists.txt\"))), \"[INSTRUCTION]\"=>cmakelists)\n    end\n    open(\"CMakeLists.txt\", \"w\") do io \n        write(io, cmakelists)\n    end\n    mkdir(\"build\")\n    cd(\"build\")\n    ADCME.cmake()\n    ADCME.make()\n    cd(PWD)    \n    return _plugin_lib(joinpath(LIBPLUGIN, name))\nend\n\nfunction _plugin_lib(D)\n    files = readdir(joinpath(D, \"build\"))\n    dylib = filter(x->endswith(x, \".$dlext\"), files)\n    if length(dylib)==0\n        error(SystemError(\"No dynamic library found.\"))\n    elseif length(dylib)>1\n        error(SystemError(\"More then one dynamic libraries found.\"))\n    else\n        return joinpath(D, \"build\", dylib[1])\n    end\nend\n\n@doc raw\"\"\"\n    register(forward::Function, backward::Function; multiple::Bool=false)\n\nRegister a function `forward` with back-propagated gradients rule `backward` to the backward. \n\u2218 `forward`: it takes $n$ inputs and outputs $m$ tensors. When $m>1$, the keyword `multiple` must be true. \n\u2218 `backward`: it takes $\\tilde m$ top gradients from float/double output tensors of `forward`, $m$ outputs of the `forward`, \n   and $n$ inputs of the `forward`. `backward` outputs $n$ gradients for each input of `forward`. When input $i$ of\n   `forward` is not float/double, `backward` should return `nothing` for the corresponding gradients. \n   \n# Example \n```julia\nforward = x->log(1+exp(x))\nbackward = (dy, y, x)->dy*(1-1/(1+y))\nf = register(forward, backward)\n```\n\"\"\"\nfunction register(forward::Function, backward::Function; multiple::Bool=false)\n    fn_name = \"customgrad_\"*randstring(8)\n    if !multiple\npy\"\"\"\nimport tensorflow as tf\n@tf.custom_gradient\ndef $$fn_name(*args):\n    u = $forward(*args)\n    def grad(dy):\n        return $backward(dy, u, *args)\n    return u, grad\n\"\"\"\n    else\npy\"\"\"\nimport tensorflow as tf\n@tf.custom_gradient\ndef $$fn_name(*args):\n    u = forward_$$fn_name(*args)\n    def grad(*dy):\n        dy = [y for y in dy if y is not None and y.dtype in [tf.float64, tf.float32]] # only float64 and float32 can backpropagate gradients\n        return backward_$$fn_name(*dy, *u, *args)\n    return u, grad\n\"\"\"\n    end\n    return py\"$$fn_name\"\nend\n\n\"\"\"\n    debug(sess::PyObject, o::PyObject)\n\nIn the case a session run yields an error from the TensorFlow backend, this function can help print the exact error. \nFor example, you might encounter  `InvalidArgumentError()` with no detailed error information, and this function can be useful for debugging.\n\"\"\"\nfunction debug(sess::PyObject, o::PyObject)\npy\"\"\"\nimport tensorflow as tf\nimport traceback\ntry:\n    $sess.run($o)\nexcept Exception:\n    print(traceback.format_exc())\n\"\"\"\nend\n\n\"\"\"\n    debug(libfile::String = \"\")\n\nLoading custom operator shared library. If the loading fails, detailed error message is printed.\n\"\"\"\nfunction debug(libfile::String = \"\")\n    if libfile==\"\"\n        libfile = LIBADCME\n    end\npy\"\"\"\nimport tensorflow as tf\nimport traceback\ntry:\n    tf.load_op_library($libfile)\nexcept Exception:\n    print(traceback.format_exc())\n\"\"\"\nend\n\n\"\"\"\n    doctor()\n\nReports health of the current installed ADCME package. If some components are broken, possible fix is proposed.\n\"\"\"\nfunction doctor()\n    function yes(name)\n        printstyled(\"[\u2714\ufe0f] $name\\n\", color=:green, bold=true)\n    end\n    function no(name, diagnose, instruction)\n        printstyled(\"[\u2718] $name\\n\", color=:red, bold=true)\n        printstyled(\"\\n[Reason]\\n\", color=:magenta)\n        printstyled(\"$diagnose\\n\\n\", color=:blue)\n        printstyled(\"\\n[Instruction]\\n\", color=:magenta)\n        printstyled(\"$instruction\\n\\n\", color=:blue)\n    end\n\n    c = true \n    if VERSION>=v\"1.4\" && VERSION<v\"1.5\" && Sys.isapple() \n        c = false\n    end\n\n    if c \n        yes(\"Julia version\")\n    else\n        no(\"Julia version\", \n\"\"\"Your Julia version is $VERSION, and your system is MACOSX. This combination may have a compatibility issue.\"\"\",\n\"\"\"If `using ADCME` freezes, consider install another version of Julia.\"\"\")\n    end \n    \n    c = (length(tf.__version__)>=6) && (tf.__version__[1:6]==\"1.15.0\")\n    if c \n        yes(\"TensorFlow version\")\n    else\n        no(\"TensorFlow version\", \n\"\"\"Your TensorFlow version is $(tf.__version__). The TensorFlow version shipped with ADCME is 1.15.0.\"\"\",\n\"\"\"Set ENV[\"FORCE_REINSTALL_ADCME\"] = 1 and rebuild ADCME\njulia> ENV[\"FORCE_REINSTALL_ADCME\"] = 1\njulia> ]\npkg> build ADCME\"\"\")\n    end \n\n\n\n    c = (PyCall.python==ADCME.PYTHON)\n    if c \n        yes(\"Python executable file\")\n    else\n        no(\"Python executable file\", \n\"\"\"PyCall Python path $(PyCall.python) does not match the ADCME-compatible Python $(ADCME.PYTHON)\"\"\",\n\"\"\"Rebuild PyCall with a compatible Python version:\n\nusing Pkg\nENV[\"PYTHON\"] = \"$(ADCME.PYTHON)\"\nPkg.build(\"PyCall\")\n\"\"\")\n    end \n\n    c = true \n    try \n        if Sys.iswindows()\n            run(`cmd /c where julia`)\n        else \n            read(`which julia`)\n        end\n    catch\n        c = false\n    end\n\n    if c \n        yes(\"Julia path\")\n    else\n        no(\"Julia path (Optional)\", \n\"\"\"`julia` outputs nothing. This will break custom operator compilation.\"\"\",\n\"\"\"Add your julia binary path to your environment path, e.g. (Unix systems) \n\nexport PATH=$(Sys.BINDIR):\\$PATH\n\nFor convenience, you can add the above line to your `~/.bashrc` (Linux) or `~/.bash_profile` (Apple).\nFor Windows, you need to add it to system environment.\"\"\")\n    end\n\n    c = Sys.iswindows() ?\n        haskey(ENV, \"PATH\") && occursin(ADCME.LIBDIR, ENV[\"PATH\"]) :\n        haskey(ENV, \"LD_LIBRARY_PATH\") && occursin(ADCME.LIBDIR, ENV[\"LD_LIBRARY_PATH\"])\n    if c \n        yes(\"Dynamic library path\")\n    else\n        no(\"Dynamic library path (Optional)\", \n\"\"\"$(ADCME.LIBDIR) is not in LD_LIBRARY_PATH. This MAY break custom operator compilation. However, in most cases, ADCME automatic fixes this problem for you.\"\"\",\n\"\"\"Add your dynamic library path path to your environment path, e.g. (Unix systems) \n\nexport LD_LIBRARY_PATH=$(ADCME.LIBDIR):\\$LD_LIBRARY_PATH\n\nFor convenience, you can add the above line to your `~/.bashrc` (Linux or Apple).\nFor Windows, you need to add it to PATH instead of LD_LIBRARY_PATH.\"\"\")\n    end\n\n    c  = Sys.WORD_SIZE==64\n    if c \n        yes(\"Memory Address Length =  64\")\n    else \n        no(\"Memory Address Length\",\n\"\"\"Your memory address length is $(Sys.WORD_SIZE). ADCME is only tested against 64-bit machine.\"\"\",\n\"\"\"If you do not need custom operators, then it's fine. Otherwise you need to switch to a 64-bit machine\"\"\")\n    end\n\n    if Sys.iswindows()\n        c = isfile(ADCME.MAKE*\".exe\") && occursin(\"15\", (ADCME.MAKE)) && occursin(\"2017\", ADCME.MAKE)\n        if c \n            yes(\"C Compiler\")\n        else\n            no(\"C Compiler\", \n\"\"\"You specified that the C compiler for custom operators is \n$(ADCME.MAKE)\nHowever, one of the following requirements is not met: \n1*. The file you specified $(ADCME.MAKE*\".exe\") does not exist.\n2**. (Optional) For compatibility, we suggest you use Microsoft Visual Studio 2017 (Version number: 15).\n\n* The path is actually not needed in compilation, but we raise such an issue here in case you obtain some compilation errors in the future.\n\n* We check the version by looking for \"15\" and \"2017\" in the path specification. If you are sure your compiler is correct, you can ignore this message. \"\"\",\n\"\"\"Manually edit $(abspath(joinpath(splitdir(pathof(ADCME))[1], \"../deps/deps.jl\"))) and modify `MAKE` to be the correct compiler.\"\"\")\n        end \n\n    end\n    \n    \n\n    c = haskey(ENV, \"PATH\") && occursin(ADCME.BINDIR, ENV[\"PATH\"])\n    if c \n        yes(\"Binaries path\")\n    else\n        no(\"Binaries path\", \n\"\"\"$(ADCME.BINDIR) is not in PATH. This path contains compatible tools such as a GCC compiler, `cmake`, `make`, or any other tools you want to use directly from terminal.\nHowever, setting the path is NOT a requirement, and ADCME works totally fine without any action.\"\"\",\n\"\"\"(Optional) Add your binary path to your environment path, e.g. (Unix systems) \n\nexport PATH=$(ADCME.BINDIR):\\$PATH\n\nFor convenience, you can add the above line to your `~/.bashrc` (Linux) or `~/.bash_profile` (Apple).\nFor Windows, you need to add it to system environment.\"\"\")\n    end\n\n    if length(ADCME.CUDA_INC)>0\n        c = Sys.iswindows() ?\n                haskey(ENV, \"PATH\") && occursin(ADCME.LIBCUDA, ENV[\"PATH\"]) :\n                haskey(ENV, \"LD_LIBRARY_PATH\") && occursin(ADCME.LIBCUDA, ENV[\"LD_LIBRARY_PATH\"])\n        if c \n            yes(\"CUDA LD_LIBRARY_PATH\")\n        else\n            no(\"CUDA LD_LIBRARY_PATH\", \n    \"\"\"$(ADCME.LIBCUDA) is not in LD_LIBRARY_PATH. This path contains compatible tools such as a GCC compiler, `cmake`, `make`, etc.\"\"\",\n    \"\"\"The fix is OPTIONAL.\n    Add your binary path to your environment path, e.g. (Unix systems) \n    \n    export LD_LIBRARY_PATH=$(ADCME.LIBCUDA):\\$LD_LIBRARY_PATH\n    \n    For convenience, you can add the above line to your `~/.bashrc` (Linux or Apple).\n    For Windows, you need to add it to PATH instead of LD_LIBRARY_PATH.\"\"\")\n        end\n\n        try \n            if !Sys.iswindows()\n                Libdl.dlpath(\"libcuda\")\n                Libdl.dlpath(\"libcudnn\")\n                Libdl.dlpath(\"libcublas\")\n            else\n                Libdl.dlpath(\"cudart64_100\")\n                Libdl.dlpath(\"cudnn64_7\")\n                Libdl.dlpath(\"cublas64_100\")\n            end\n            yes(\"CUDA Shared Library\")\n        catch\n            no(\"CUDA Shared Library\", \n    \"\"\"libcuda, libcudnn, and (or) libcublas can not be loaded.\"\"\",\n    \"\"\"If you intend to use GPU, this fix is mandatory. Make sure cudatoolkit and cudnn libraries can be found in\n$(ADCME.LIBCUDA)\nand `nvcc` is in your path.\"\"\")\n        end\n\n        c = isdir(ADCME.CUDA_INC) && \"cuda.h\" in readdir(ADCME.CUDA_INC)\n        if c \n            yes(\"CUDA Header Files\")\n\n            if !isfile(joinpath(ADCME.TF_INC, \"third_party/gpus/cuda/include/cuda_fp16.h\"))\n                println(\"Fixing third_party/gpus/cuda/include...\")\n                if !ispath(joinpath(ADCME.TF_INC, \"third_party/gpus/cuda/\"))\n                    mkpath(joinpath(ADCME.TF_INC, \"third_party/gpus/cuda/\"))\n                end\n                rm(joinpath(ADCME.TF_INC, \"third_party/gpus/cuda/include/\"), force=true, recursive=true)\n                symlink(ADCME.CUDA_INC, joinpath(ADCME.TF_INC, \"third_party/gpus/cuda/include\"))\n            end\n\n        else \n            no(\"CUDA Header Files\",\n            \"\"\"Cuda include library does not exist or `cuda.h` is missing.\"\"\",\n        \"\"\"It might be possible that your cuda include library is located somewhere else other than $(ADCME.CUDA_INC). Fix the dependency file.\"\"\")\n        end\n    else\n        no(\"GPU Support (Optional)\", \n    \"\"\"ADCME is not compiled against GPU.\"\"\",\n    \"\"\"If you intend to use GPU, set ENV[\"GPU\"] = 1 and then rebuild ADCME.\"\"\")\n    end\n\n    \n    depsfile = abspath(joinpath(@__DIR__, \"../deps/deps.jl\"))\n    println(\"============================================================\\n$(depsfile)\\n============================================================\")\n    println(read(depsfile, String))\n\n    @assert isdir(ADCME.BINDIR)\n    @assert isdir(ADCME.LIBDIR)\n    @assert isdir(ADCME.TF_INC)\n    @assert isdir(ADCME.PREFIXDIR)\n    if !Sys.iswindows()\n        @assert isfile(ADCME.CC) || islink(ADCME.CC)\n        @assert isfile(ADCME.CXX) || islink(ADCME.CXX)\n        @assert isfile(ADCME.NINJA)\n    else \n        @assert isfile(ADCME.MAKE)\n    end\n    @assert isfile(ADCME.CMAKE)\n    @assert isfile(ADCME.TF_LIB_FILE) || islink(ADCME.TF_LIB_FILE)\n    @assert isdir(ADCME.INCDIR)\nend\n\n\n\"\"\"\n    test_gpu()\n\nTests the GPU ultilities\n\"\"\"\nfunction test_gpu()\n    PWD = pwd()\n    run(`which nvcc`)\n    cd(\"$(@__DIR__)/../examples/gpu_custom_op\")\n    mkdir(\"build\")\n    cd(\"build\")\n    ADCME.cmake()\n    ADCME.make()\n    cd(\"..\")\n    include(\"gputest.jl\")\n    cd(PWD)\nend\n\n\n@doc raw\"\"\"\n    nnuq(H::Array{Float64,2}, invR::Union{Float64, Array{Float64,2}}, invQ::Union{Float64, Array{Float64,2}})\n\nReturns the variance matrix for the Baysian inversion. \n\nThe negative log likelihood function is\n\n$$l(s) =\\frac{1}{2} (y-h(s))^T R^{-1} (y-h(s)) + \\frac{1}{2} s^T Q^{-1} s$$\n\nThe covariance matrix is computed by first linearizing $h(s)$\n\n$$h(s)\\approx h(s_0) + \\nabla h(s_0) (s-s_0)$$\n\nand then computing the second order derivative\n\n$$V = \\left(\\frac{\\partial^2 l}{\\partial s^T\\partial s}\\right)^{-1} = (H^T R^{-1} H + Q^{-1})^{-1}$$\n\nNote the result is independent of $s_0$, $y_0$, and only depends on $\\nabla h(s_0)$\n\"\"\"\nfunction nnuq(H::Array{Float64,2}, invR::Union{Float64, Array{Float64,2}}, invQ::Union{Float64, Array{Float64,2}})\n    if isa(invQ, Float64)\n        invQ = invQ * I \n    end\n    \u03a3 = inv(H' * invR * H + invQ)\n    (\u03a3 + \u03a3')/2\nend\n\n\n\nmutable struct MCMCSimple\n    logf::Function\n    proposal::Function\n    \u03b80::Array{Float64, 1}\n    ub::Float64\n    lb::Float64 \n    L::Array{Float64, 1}\n    AC::Array{Float64, 1}\nend\n\n@doc raw\"\"\"\n    MCMCSimple(obs::Array{Float64, 1}, h::Function, \n    \u03c3::Float64, \u03b80::Array{Float64,1}, lb::Float64, ub::Float64)\n\nA very simple yet useful interface for MCMC simulation in many scientific computing problems. \n\n- `obs`: Observations\n- `h`: Forward computation function\n- `\u03c3`: Noise standard deviation for the observed data \n- `ub`, `lb`: upper and lower bound\n- `\u03b80`: Initial guess \n\nThe mathematical model is \n\n```math\ny_{obs} = h(\\theta)\n```\n\nand we have a hard constraint `lb\\leq \\theta \\leq ub`. \n\"\"\"\nfunction MCMCSimple(obs::Array{Float64, 1}, h::Function, \n        \u03c3::Float64, \u03b80::Array{Float64,1}, lb::Float64, ub::Float64,\n        \u03b4::Union{Missing, Float64}=missing)\n    \u03c4 = (ub-lb)/6\n    \u03b4 = coalesce(\u03b4, (ub-lb)/100)\n    function logf(x)\n        -sum((obs - h(x)).^2/2\u03c3^2) - sum((x-\u03b80).^2)/2\u03c4^2\n    end\n    function proposal(x)\n        x + (rand(length(x)) .- 0.5)*2 * \u03b4\n    end\n    MCMCSimple(logf, proposal, \u03b80, ub, lb, zeros(0), zeros(0))\nend\n\nfunction simulate(ms::MCMCSimple, N::Int64, burnin::Union{Int64, Missing} = missing)\n    burnin = coalesce(burnin, Int64(round(N*0.2)))\n    sim = zeros(N, length(ms.\u03b80))\n    sim[1,:] = ms.\u03b80\n    L = zeros(N)\n    AC = ones(N)\n    AC[1] = NaN\n    L[1] = ms.logf(ms.\u03b80)\n    k = 1\n    for i = 2:N \n        sim[i,:], L[i], k_ = _MCMCSimple_simulate(ms, sim[i-1,:])\n        k += k_ \n        AC[i] = k/i\n    end\n    ms.AC = AC\n    ms.L = L \n    return sim\nend\n\nfunction diagnose(ms::MCMCSimple)\n    if !isdefined(Main, :PyPlot)\n        error(\"Package PyPlot.jl must be imported in the main module using `import PyPlot` or `using PyPlot`\")\n    end\n    Main.PyPlot.figure(figsize = (10, 4))\n    Main.PyPlot.subplot(121)\n    Main.PyPlot.title(\"Acceptance Rate\")\n    Main.PyPlot.plot(ms.AC)\n    Main.PyPlot.ylim(0,1.05)\n    Main.PyPlot.subplot(122)\n    Main.PyPlot.title(\"Log Likelihood\")\n    Main.PyPlot.plot(ms.L)\nend\n\nfunction _MCMCSimple_simulate(ms::MCMCSimple, x::Array{Float64})\n    local x_star\n    while true\n        x_star = ms.proposal(x)\n        if all(x_star.<=ms.ub) && all(x_star.>=ms.lb)\n            break\n        end\n    end\n    \u0394 =  ms.logf(x_star) - ms.logf(x)\n    if log(rand())<\u0394\n        return x_star, ms.logf(x_star), 1\n    else \n        return x, ms.logf(x), 0\n    end\nend\n\n\n\n\"\"\"\n    get_placement()\n\nReturns the operation placements.\n\"\"\"\nfunction get_placement()\n    sess = Session(config=tf.ConfigProto(log_device_placement=true))\n    originalSTDOUT = stdout\n    (outRead, outWrite) = redirect_stdout()\n    init(sess)\n    close(outWrite)\n    data = readavailable(outRead)\n    close(outRead)\n    redirect_stdout(originalSTDOUT)\n    lines = split(String(data), '\\n')[1:end-1]\nend\n\n\"\"\"\n    sleep_for(t::Union{PyObject, <:Real})\n\nSleeps for `t` seconds. \n\"\"\"\nfunction sleep_for(t::Union{PyObject, <:Real})\n    sleep_for_ = load_system_op(\"sleep_for\", false)\n    sleep_for_(convert_to_tensor(t, dtype=Float64))\nend\n\n\"\"\"\n    timestamp(deps::Union{PyObject, <:Real, Missing}=missing)\n\nThese functions are usually used with [`bind`](@ref) for profiling. \nNote the timing is not very accurate in a multithreaded environment.\n\n- `deps`: `deps` is always executed before returning the timestamp.\n# Example \n```julia\na = constant(3.0)\nt0 = timestamp(a)\nsleep_time = sleep_for(a)\nt1 = timestamp(sleep_time)\nsess = Session(); init(sess)\nt0_, t1_ = run(sess, [t0, t1])\ntime = t1_ - t0_\n```\n\"\"\"\nfunction timestamp(deps::Union{PyObject, <:Real, Missing}=missing)\n    deps = coalesce(deps, 0.0)\n    deps = convert_to_tensor(deps, dtype = Float64)\n    timer_ = load_system_op(\"timer\", false)\n    timer_(deps)\nend\n\n\"\"\"\n    load_library(filename::String)\n\nLoad custom operator libraries. If used with \n\"\"\"\nfunction load_library(filename::String)\n    filename = get_library(filename)\n    keyname = \"custom_op_library_\"*filename\n    if haskey(STORAGE, keyname)\n        return STORAGE[keyname]\n    end\n    if !isfile(filename)\n        error(\"File $filename not found. If you intend to load a library by absolute path, try `tf.load_op_library(filename)`.\")\n    end\n    try\n        STORAGE[keyname] =  tf.load_op_library(filename)\n    catch e \n        error(\"Failed to load library: $filename. Original error message:\\n$e\")\n    end\n    return STORAGE[keyname] \nend\n\n\"\"\"\n    get_library_symbols(file::Union{String, PyObject})\n\nReturns the symbols in the custom op library `file`.\n\"\"\"\nfunction get_library_symbols(files::Union{String, PyObject})\n    if isa(files, String)\n        files = load_library(files)\n    end\n    files = keys(files)\n    filter(x->islowercase(String(x)[1]) && String(x)!=\"tf_export\" && !(occursin(\"fallback\", String(x))) && String(x)!=\"deprecated_endpoints\", files)\nend\n\n\nfunction Base.:NamedTuple(df::PyObject)\n    names = tuple(Symbol.(df.index.tolist())...)\n    vals = tuple(df.values...)\n    NamedTuple{names}(vals)\nend", "meta": {"hexsha": "f0edd25210b4f9549c64c558720ade3ee2fa3dbc", "size": 31973, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/extra.jl", "max_stars_repo_name": "ziyiyin97/ADCME.jl", "max_stars_repo_head_hexsha": "1c9b2c1ae63059d79a5a6a7b86eee64796868755", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/extra.jl", "max_issues_repo_name": "ziyiyin97/ADCME.jl", "max_issues_repo_head_hexsha": "1c9b2c1ae63059d79a5a6a7b86eee64796868755", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/extra.jl", "max_forks_repo_name": "ziyiyin97/ADCME.jl", "max_forks_repo_head_hexsha": "1c9b2c1ae63059d79a5a6a7b86eee64796868755", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.7432692308, "max_line_length": 367, "alphanum_fraction": 0.6344102837, "num_tokens": 8986, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4263215925474903, "lm_q2_score": 0.16451645470385745, "lm_q1q2_score": 0.07013691696961556}}
{"text": "module errors\ninclude(\"_errors.jl\")\n\nimport Base: InterpreterIP, show_method_candidates, ExceptionStack\n\nimport Term:\n    theme, highlight, reshape_text, read_file_lines, load_code_and_highlight, split_lines\nimport Term.style: apply_style\nimport ..panel: Panel, TextBox\nimport ..renderables: RenderableText\nimport ..layout: hLine, Spacer\nimport ..consoles: Console\nimport ..measure: Measure\n\nexport install_stacktrace\n\nconst ErrorsExplanations = Dict(\n    ArgumentError => \"The parameters to a function call do not match a valid signature.\",\n    AssertionError => \"comes up when an assertion's check fails (e.g., `@assert 1==2`)\",\n    BoundsError => \"comes up when trying to acces a container at invalid position (e.g., a string a='abcd' with 4 characters cannot be accessed as a[5]).\",\n    DimensionMismatch => \"comes up when trying to perform an operation on objects which don't have matching dimensionality (e.g., summing matrixes of different size).\",\n    DivideError => \"comes up when attempting integer division with 0 as denominator. [blue]2/0=Inf[/blue] is okay, but [orange1]div(2, )[/orange1] will give an error\",\n    DomainError => \"comes up when the argument to a function is outside its domain (e.g., \u221a(-1))\",\n    ErrorException => \"is a generic error type\",\n    KeyError => \"comes up when attempting to access a non-existing [blue]Dict[/blue] key.\",\n    InexactError => \"comes up when a type cannot exactly be converted to another (e.g. Int(2.5) cannot convert Float64 to Int64, but Int(round(2.5)) will work)\",\n    LoadError => \"occurs when another comes up while evaluating 'include', 'require' or 'using' statements\",\n    MethodError => \"comes up when to method can be found with a given name and for a given set of argument types.\",\n    StackOverflowError => \"usually comes up when functions call each other recursively.\",\n    TypeError => \"is a type assertion failure, or calling an intrinsic function with an incorrect argument type.\",\n    UndefKeywordError => \"comes up when a function has a keyword argument with no default value and no value is passed to a function call\",\n    UndefVarError => \"comes up when a variable is used which is either not defined, or, which is not visible in the current variables scope (e.g.: variable defined in function A and used in function B)\",\n)\n\n_width() = min(Console(stderr).width, 100)\n\n# ----------------------- error type specific messages ----------------------- #\n\n# ! ARGUMENT ERROR\nfunction error_message(io::IO, er::ArgumentError)\n    return er.msg, \"\"\nend\n\n# ! ASSERTION ERROR\nfunction error_message(io::IO, er::AssertionError)\n    return er.msg, \"\"\nend\n\n# ! BOUNDS ERROR\nfunction error_message(io::IO, er::BoundsError)\n    # @info \"bounds error\" er fieldnames(typeof(er))\n    main_msg = \"Attempted to access $(_highlight_with_type(er.a)) at index $(_highlight_with_type(er.i))\"\n\n    additional_msg = \"\"\n\n    if isdefined(er, :a)\n        if er.a isa AbstractString\n            nunits = ncodeunits(er.a)\n            additional_msg = \"S\\ntring has $nunits codeunits, $(length(er.a)) characters.\"\n        end\n    else\n        additional_msg = \"\\n[red]Variable is not defined!.[/red]\"\n    end\n    return main_msg, additional_msg\nend\n\n# ! Domain ERROR\nfunction error_message(io::IO, er::DomainError)\n    # @info \"err exceprion\" er fieldnames(DomainError) er.val\n    # msg = split(er.msg, \" around \")[1]\n    return er.msg, \"\\nThe invalid value is: $(_highlight_with_type(er.val)).\"\nend\n\n# ! DimensionMismatch\nfunction error_message(io::IO, er::DimensionMismatch)\n    return _highlight_numbers(er.msg), \"\"\nend\n\n# ! DivideError\nfunction error_message(io::IO, er::DivideError)\n    return \"Attempted integer division by [blue]0[/blue]\", \"\"\nend\n\n# ! EXCEPTION ERROR\nfunction error_message(io::IO, er::ErrorException)\n    # @info \"err exceprion\" er fieldnames(ErrorException) er.msg\n    msg = split(er.msg, \" around \")[1]\n    return msg, \"\"\nend\n\n# !  KeyError\nfunction error_message(io::IO, er::KeyError)\n    # @info \"err KeyError\" er fieldnames(KeyError)\n    msg = \"Key $(_highlight_with_type(er.key)) not found!\"\n    return msg, \"\"\nend\n\n# ! InexactError\nfunction error_message(io::IO, er::InexactError)\n    # @info \"load error message\"  fieldnames(InexactError)\n    msg = \"Cannot convert $(_highlight_with_type(er.val)) to type [$(theme.type)]$(er.T)[/$(theme.type)]\"\n    subm = \"\\nConversion error in function: $(_highlight(er.func))\"\n    return msg, subm\nend\n\n# ! LoadError\nfunction error_message(io::IO, er::LoadError)\n    # @info \"load error message\"  fieldnames(LoadError)\n    msg = \"At [grey62 underline]$(er.file)[/grey62 underline] line [bold]$(er.line)\"\n    subm = \"The cause is an error of type: [bright_red]$(string(typeof(er.error)))\"\n    return msg, subm\nend\n\n# ! METHOD ERROR\n_method_regexes = [r\"!Matched+[:a-zA-Z]*\\{+[a-zA-Z\\s \\,]*\\}\", r\"!Matched+[:a-zA-Z]*\"]\nfunction error_message(io::IO, er::MethodError; kwargs...)\n    # get main error message\n    _args = join([string(ar) * _highlight(typeof(ar)) for ar in er.args], \"\\n      \")\n    fn_name = \"$(_highlight(string(er.f)))\"\n    main_line = \"No method matching $fn_name with aguments:\\n      \" * _args\n\n    # get recomended candidates\n    _candidates = split(sprint(show_method_candidates, er; context = io), \"\\n\")\n    candidates::Vector{String} = []\n\n    for can in _candidates[3:(end - 1)]\n        fun, file = split(can, \" at \")\n        name, args = split(fun, \"(\"; limit = 2)\n        # name = \"[red]$name[/red]\"\n\n        for regex in _method_regexes\n            for match in collect(eachmatch(regex, args))\n                args = replace(\n                    args, match.match => \"[dim red]$(match.match[9:end])[/dim red]\"\n                )\n            end\n        end\n\n        file, lineno = split(file, \":\")\n\n        # println(RenderableText(name, \"red\"))\n        push!(candidates, fn_name * \"(\" * args)\n        push!(candidates, \"[dim]$file [bold dim](line: $lineno)[/bold dim][/dim]\\n\")\n    end\n    candidates =\n        length(candidates) == 0 ? [\"[dim]no candidate method found[/dim]\"] : candidates\n\n    return main_line * \"\\n\",\n    Panel(\n        \"\\n\" * join(candidates, \"\\n\");\n        width = _width() - 10,\n        title = \"closest candidates\",\n        title_style = \"yellow\",\n        style = \"blue dim\",\n    )\nend\n\n# ! StackOverflowError\nfunction error_message(io::IO, er::StackOverflowError)\n    return \"Stack overflow error: too many function calls.\", \"\"\nend\n\n# ! TYPE ERROR\nfunction error_message(io::IO, er::TypeError)\n    # @info \"type err\" er fieldnames(typeof(er)) er.func er.context er.expected er.got\n    # var = string(er.var)\n    msg = \"In `[$(theme.emphasis_light) italic]$(er.func)` > `$(er.context)[/$(theme.emphasis_light) italic]` got\"\n    msg *= \" [orange1 bold]$(er.got)[/orange1 bold][$(theme.type)](::$(typeof(er.got)))[/$(theme.type)] but expected argument of type\"\n    msg *= \" [$(theme.type)]::$(er.expected)[/$(theme.type)]\"\n    return msg, \"\"\nend\n\n# ! UndefKeywordError\nfunction error_message(io::IO, er::UndefKeywordError)\n    # @info \"UndefKeywordError\" er er.var typeof(er.var) fieldnames(typeof(er.var))\n    var = string(er.var)\n    return \"Undefined function keyword argument: '$(_highlight(er.var))'.\", \"\"\nend\n\n# ! UNDEFVAR ERROR\nfunction error_message(io::IO, er::UndefVarError)\n    # @info \"undef var error\" er er.var typeof(er.var)\n    var = string(er.var)\n    return \"Undefined variable '$(_highlight(er.var))'.\", \"\"\nend\n\n# ! STRING INDEX ERROR\nfunction error_message(io::IO, er::StringIndexError)\n    # @info er typeof(er) fieldnames(typeof(er)) \n    m1 = \"attempted to access a String at index $(er.index)\\n\"\n    return m1, \"\"\nend\n\n# ! catch all other errors\nfunction error_message(io::IO, er)\n    @debug \"Error message type doesnt have a specialized method!\" er typeof(er) fieldnames(\n        typeof(er)\n    )\n    if hasfield(typeof(er), :error)\n        # @info \"nested error\" typeof(er.error)\n        m1, m2 = error_message(io, er.error)\n        msg = \"\\n[bold red]LoadError:[/bold red]\\n\" * m1\n    else\n        msg = if hasfield(typeof(er), :msg)\n            er.msg\n        else\n            \"no message for error of type $(typeof(er)), sorry.\"\n        end\n        m2 = \"\"\n    end\n    return msg, m2\nend\n\n# ---------------------------------------------------------------------------- #\n#                              INSTALL STACKTRACE                              #\n# ---------------------------------------------------------------------------- #\nfunction install_stacktrace()\n    @eval begin\n\n        # ---------------------------- handle load errors ---------------------------- #\n\n        function Base.showerror(io::IO, er::LoadError, bt; backtrace = true)\n            print(\"\\n\")\n            println(hLine(_width(), \"[bold red]LoadError[/bold red]\"; style = \"dim red\"))\n            Base.display_error(io, er, bt)\n\n            return Base.showerror(io, er.error, bt; backtrace = true)\n        end\n\n        \"\"\"\n        prints a line to mark te start of the error followed\n        by the error's stack trace\n        \"\"\"\n\n        function Base.showerror(io::IO, er, bt; backtrace = true)\n            ename = string(typeof(er))\n            print(hLine(_width(), \"[bold red]$ename[/bold red]\"; style = \"dim red\"))\n\n            try\n                stack = style_backtrace(io, bt)\n                print(stack)\n            catch stack_error\n                @warn \"failed to generate stack trace\" er stack_error\n                println.(style_stacktrace_simple(bt))\n            end\n        end\n\n        # # ------------------ handle all other errors (no backtrace) ------------------ #\n        \"\"\"\n        Re-define Base module function. Prints a nicely formatted error message.\n        \"\"\"\n        \n        function Base.display_error(io::IO, er, bt)\n            try\n                err, err_msg = style_error(io, er)\n                println(err / err_msg)\n            catch styling_error\n                @error \"Failed to generate styled error message\" styling_error stacktrace()\n\n                println(apply_style(\"Original error: [bright_red]$(string(er))\"))\n            end\n        end\n    end\nend\nend\n", "meta": {"hexsha": "adaaec683262f1c53eb255ee8e3e403586868703", "size": 10083, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/errors.jl", "max_stars_repo_name": "thazhemadam/Term.jl", "max_stars_repo_head_hexsha": "f69284b823f8952e445f96066413807c79743925", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/errors.jl", "max_issues_repo_name": "thazhemadam/Term.jl", "max_issues_repo_head_hexsha": "f69284b823f8952e445f96066413807c79743925", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/errors.jl", "max_forks_repo_name": "thazhemadam/Term.jl", "max_forks_repo_head_hexsha": "f69284b823f8952e445f96066413807c79743925", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.9060150376, "max_line_length": 203, "alphanum_fraction": 0.619756025, "num_tokens": 2505, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4610167941228965, "lm_q2_score": 0.15203223778010544, "lm_q1q2_score": 0.07008941486471411}}
{"text": "function fix(a)\r\n    if a>0\r\n        return floor(a)\r\n    elseif a<0\r\n        return ceil(a)\r\n    else\r\n        return a\r\n    end\r\nend \r\n", "meta": {"hexsha": "87f4cb76918d410ee5e52666a72d3858da2182eb", "size": 137, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "fix.jl", "max_stars_repo_name": "umbertosaetti/J-F16", "max_stars_repo_head_hexsha": "d17c15ea94f149494094ba76f27b640c8a200202", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2022-01-03T21:16:03.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T07:54:32.000Z", "max_issues_repo_path": "fix.jl", "max_issues_repo_name": "umbertosaetti/J-F16", "max_issues_repo_head_hexsha": "d17c15ea94f149494094ba76f27b640c8a200202", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fix.jl", "max_forks_repo_name": "umbertosaetti/J-F16", "max_forks_repo_head_hexsha": "d17c15ea94f149494094ba76f27b640c8a200202", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.7, "max_line_length": 24, "alphanum_fraction": 0.4671532847, "num_tokens": 38, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.1441488530327406, "lm_q1q2_score": 0.06982283358042952}}
{"text": "## formatting conveniences\n\n\"markdown can leave wrapping p's\"\nfunction strip_p(txt)\n    if occursin(r\"^<p>\", txt)\n        txt = replace(replace(txt, r\"^<p>\" => \"\"), r\"</p>$\" => \"\")\n    end\n    txt\nend\n\nfunction md(x)\n    out = sprint(io ->  show(io, \"text/html\", Markdown.parse(string(x))))\n    strip_p(out)\nend\n\nmarkdown_to_latex(x) = sprint(io -> show(io, \"text/latex\", Markdown.parse(x)))\n\n\"\"\"\nHide output and input, but execute cell.\n\nExamples\n```\n2 + 2\nInvisible()\n```\n\"\"\"\nmutable struct Invisible\nend\n\n\"\"\"\nShow output as HTML\n\nExamples\n```\nHTMLoutput(\"<em>em</em>\")\n```\n\n\"\"\"\nmutable struct HTMLoutput\n    x\nend\nBase.show(io::IO, ::MIME\"text/plain\", x::HTMLoutput) = print(io, \"\"\"<div>$(x.x)</div>\"\"\")\nBase.show(io::IO, ::MIME\"text/html\", x::HTMLoutput) = print(io, x.x)\nBase.show(io::IO, ::MIME\"text/latex\", x::HTMLoutput) = println(io, \"...unable to display raw html...\")\n\n\n\n\"\"\"\nShow as input, but do not execute.\nExamples:\n```\nVerbatim(\"This will print, but not be executed\")\n```\n\"\"\"\nmutable struct Verbatim\n    x\nend\nBase.show(io::IO, ::MIME\"text/plain\", x::Verbatim) = print(io, \"\"\"<pre class=\"sourceCode julia\">$(x.x)</pre>\"\"\")\nBase.show(io::IO, ::MIME\"text/html\", x::Verbatim) = print(io, x.x)\nBase.show(io::IO, ::MIME\"text/latex\", x::Verbatim) = print(io, \"\\verb@$(markdown_to_latex(x.x))@\")\n\n\n\"\"\"\nHide input, but show output\n\nExamples\n```\nx = 2 + 2\nOutputonly(x)\n```\n\"\"\"\nmutable struct Outputonly\n    x\nend\n\n## Bootstrap things\nabstract type Bootstrap end\nBase.show(io::IO, ::MIME\"text/html\", x::Bootstrap) = print(io, \"\"\"$(x.x)\"\"\")\nBase.show(io::IO, ::MIME\"text/latex\", x::Bootstrap) = print(io, \"\"\"XXX BOOTSTRAP $(markdown_to_latex(x.x))\"\"\")\n\nmutable struct Alert <: Bootstrap\n    x\n    d::Dict\nend\n\n### An alert\nfunction alert(txt; kwargs...)\n    d = Dict()\n    for (k,v) in kwargs\n        d[k] = v\n    end\n    Alert(txt, d)\nend\n\nwarning(txt; kwargs...) = alert(txt; class=\"warning\", kwargs...)\nnote(txt; kwargs...) = alert(txt; class=\"info\", kwargs...)\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Alert)\n    cls = haskey(x.d,:class) ? x.d[:class] : \"success\"\n    txt = sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    tpl = \"\"\"\n<div class=\"alert alert-$cls\" role=\"alert\">\n\n$txt\n\n</div>\n\"\"\"\n\n    print(io, tpl)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Alert)\n    println(io, \"\"\"\n\\\\begin{mdframed}\n$(markdown_to_latex(x.x))\n\\\\end{mdframed}\n\"\"\")\nend\n    \n\n\n\nmutable struct Example <: Bootstrap\n    x\n    d::Dict\nend\n\n## use nm=\"name\" to pass along name\nfunction example(txt; kwargs...)\n d = Dict()\n    for (k,v) in kwargs\n        d[k] = v\n    end\n    Example(txt, d)\nend\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Example)\n    nm = haskey(x.d,:nm) ? \" <small>$(x.d[:nm])</small>\" : \"\"\n    txt = sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    tpl = \"\"\"\n<div class=\"alert alert-danger\" role=\"alert\">\n\n<span class=\"glyphicon glyphicon-th\" aria-hidden=\"true\"></span>\n\n<span class=\"text-uppercase\">example:</span>$nm$txt\n\n</div>\n\n\"\"\"\n\n    print(io, tpl)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Example)\n    println(io, \"\"\"Example $(markdown_to_latex(x.x))\"\"\")\nend\n\n\nmutable struct Popup <: Bootstrap\n    x\n    title\n    icon\n    label\nend\n\n\"\"\"\n\nCreate a button to toggle the display of more detail.\n\nCan modify text, title, icon and label (for the button)\n\nThe text, title, and label can use Markdown.\n\nLaTeX markup does not work, as MathJax rendering is not supported in the popup.\n\n\"\"\"\npopup(x; title=\" \", icon=\"share-alt\", label=\" \") = Popup(x, title, icon, label)\n\npopup_html_tpl=mt\"\"\"\n<button type=\"button\" class=\"btn btn-sm\" aria-label=\"Left Align\"\\\ndata-toggle=\"popover\"\\\ntitle='{{{title}}}'\\\ndata-html=true\\\ndata-content='{{{body}}}'\\\n>\\\n<span class=\"glyphicon glyphicon-{{icon}}\" aria-hidden=\"true\"></span>{{#button_label}} {{{button_label}}}{{/button_label}}\\\n</button>\\\n\"\"\"\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Popup)\n    d = Dict()\n    d[\"title\"] = sprint(io -> show(io, \"text/html\", Markdown.parse(x.title)))\n    d[\"icon\"] = x.icon\n    label = sprint(io -> show(io, \"text/html\", Markdown.parse(x.label)))\n    d[\"button_label\"] = strip_p(label)\n    d[\"body\"] = sprint(io -> show(io, \"text/html\", Markdown.parse(x.x)))\n    println(d)\n    Mustache.render(io, popup_html_tpl, d)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Popup)\n    println(io, \"\"\"\n\\\\begin{quotation}\n$(markdown_to_latex(x.x))\n\\\\end{quotation}\n\"\"\")\nend\n\n\"\"\"\n\nWay to convert rectangular gird of values into a table\n\n\"\"\"\nmutable struct Table <: Bootstrap\n    x\nend\ntable(x) = Table(x)\n\ntable_html_tpl=mt\"\"\"\n\n<div class=\"table-responsive\">\n<table class=\"table table-hover\">\n{{{:nms}}}\n{{{:body}}}\n</table>\n</div>\n\n\"\"\"\n\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::Table)\n    vals = Base.invokelatest(names, x.x)\n    d = Dict()\n    d[:nms] = \"<tr><th>$(join(map(string, vals), \"</th><th>\"))</th></tr>\\n\"\n    bdy = \"\"\n    m,n = Base.invokelatest(size, x.x)\n    for i in 1:m\n        bdy = bdy * \"<tr>\"\n        for j in 1:n\n            val = Base.invokelatest(getindex, x.x, i, j)\n            if ismissing(val)\n                val = \".\"\n            end\n            bdy = bdy * \"<td>$(md(val))</td>\"\n        end\n        bdy = bdy * \"</tr>\\n\"\n    end\n    d[:body] = bdy\n    Mustache.render(io, table_html_tpl, d)\nend\n\nfunction df_to_table(df, label=\"label\", caption=\"caption\")\n    nc = size(df, 2)\n    perc = string(round(1/nc, digits=2))\n    fmt = \"l\" * repeat(\"p{$perc\\\\textwidth}\", nc-1)\n    header = join(string.(names(df)), \" & \")\n    row = join([\"{{{:$x}}}\" for x in map(string, names(df))], \" & \")\n\ntpl=\"\"\"\n\\\\begin{table}[!ht]\n  \\\\centering\n  \\\\begin{tabular}{$fmt}\n  $header\\\\\\\\\n  \\\\midrule\\\\\\\\\n{{#:DF}}    $row\\\\\\\\\n{{/:DF}}  \n  \\\\bottomrule\n  \\\\end{tabular}\n  \\\\label{tab:$label}\n\n\\\\end{table}\n\"\"\"\n\n    Mustache.render(tpl, DF=df)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::Table)\n    d = markdown_to_latex.(x.x)\n    println(io, df_to_table(d))\nend\n  \n\n\nmutable struct NamedTable <: Bootstrap\ndata\nrownames\ncolnames\nend\n\nfunction Base.show(io::IO, ::MIME\"text/html\", x::NamedTable)\n    vals = x.data\n    cnames = x.colnames\n    rnames = x.rownames\n    d = Dict()\n    d[:nms] = \"<tr><th></th><th>$(join(map(string, cnames), \"</th><th>\"))</th></tr>\\n\"\n    m,n = Base.invokelatest(size, x.data)\n    buf = IOBuffer()\n    for i in 1:m\n        print(buf, \"<tr><td>\", rnames[i],\"</td>\")\n        for j in 1:n\n            val = Base.invokelatest(getindex, x.data, i, j)\n            print(buf, \"<td>\", md(val), \"</td>\")\n        end\n        println(buf, \"</tr>\")\n    end\n    d[:body] = String(take!(buf))\n    Mustache.render(io, table_html_tpl, d)\nend\n\nfunction Base.show(io::IO, ::MIME\"text/latex\", x::NamedTable)\n    d = markdown_to_latex.(x.x)\n    println(io, df_to_table(d))\nend\n  \n", "meta": {"hexsha": "ac660f65f982c20b9c54ab00a76f290bf97e584e", "size": 6773, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/WeaveSupport/formatting.jl", "max_stars_repo_name": "BryceStevenWilley/CalculusWithJulia.jl", "max_stars_repo_head_hexsha": "801773a3651b2e8f03a8e6eb2f427ab1527e3844", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/WeaveSupport/formatting.jl", "max_issues_repo_name": "BryceStevenWilley/CalculusWithJulia.jl", "max_issues_repo_head_hexsha": "801773a3651b2e8f03a8e6eb2f427ab1527e3844", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/WeaveSupport/formatting.jl", "max_forks_repo_name": "BryceStevenWilley/CalculusWithJulia.jl", "max_forks_repo_head_hexsha": "801773a3651b2e8f03a8e6eb2f427ab1527e3844", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-17T02:26:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-17T02:26:35.000Z", "avg_line_length": 21.3659305994, "max_line_length": 123, "alphanum_fraction": 0.595157242, "num_tokens": 2047, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.25091278688527247, "lm_q2_score": 0.27825680567280014, "lm_q1q2_score": 0.06981819058115597}}
{"text": "# =================================#\n# Dictionaries store mappings      #\n# key => value pairs               #\n# key is indexed and unique        #\n# =================================#\nd = Dict() \n\n# append elements\nd['a']=1; d['b']=4; d['c']=8\nprintln(\"d = \",d)\n\n# Create a dictionary using a literal\nnumbers = Dict( \"one\"   => 1, \n                \"two\"   => 2, \n                \"three\" => 3)\nprintln(\"numbers = \", numbers)\n\n# Access elements\nprintln(\"first =\", numbers[\"one\"])\nprintln(\"last  =\", numbers[\"three\"])", "meta": {"hexsha": "362a7d95be35459bf2bccdf385f6a4721758dfb6", "size": 515, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "demo/dictionary.jl", "max_stars_repo_name": "elucian/julia", "max_stars_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "demo/dictionary.jl", "max_issues_repo_name": "elucian/julia", "max_issues_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/dictionary.jl", "max_forks_repo_name": "elucian/julia", "max_forks_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.75, "max_line_length": 37, "alphanum_fraction": 0.4485436893, "num_tokens": 129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4186969093556867, "lm_q2_score": 0.16667540261828417, "lm_q1q2_score": 0.06978647594189032}}
{"text": "\"\"\"\n    test_objective_ObjectiveSense_MAX_SENSE(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest setting objective sense to MAX_SENSE.\n\"\"\"\nfunction test_objective_ObjectiveSense_MAX_SENSE(\n    model::MOI.ModelLike,\n    ::Config{T},\n) where {T}\n    @requires MOI.supports(model, MOI.ObjectiveSense())\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveSense_MIN_SENSE(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest setting objective sense to MIN_SENSE.\n\"\"\"\nfunction test_objective_ObjectiveSense_MIN_SENSE(\n    model::MOI.ModelLike,\n    ::Config{T},\n) where {T}\n    @requires MOI.supports(model, MOI.ObjectiveSense())\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MIN_SENSE\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveSense_FEASIBILITY_SENSE(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest setting objective sense to FEASIBILITY_SENSE.\n\"\"\"\nfunction test_objective_ObjectiveSense_FEASIBILITY_SENSE(\n    model::MOI.ModelLike,\n    ::Config{T},\n) where {T}\n    @requires MOI.supports(model, MOI.ObjectiveSense())\n    MOI.set(model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE)\n    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.FEASIBILITY_SENSE\nend\n\n\"\"\"\n    test_objective_FEASIBILITY_SENSE_clears_objective(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest setting objective sense to FEASIBILITY_SENSE  clears previous objective.\n\"\"\"\nfunction test_objective_FEASIBILITY_SENSE_clears_objective(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    @requires _supports(config, MOI.optimize!)\n    x = MOI.add_variable(model)\n    MOI.add_constraint(model, x, MOI.GreaterThan(T(1)))\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    f = MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(T(1), x)], T(0))\n    MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)\n    MOI.optimize!(model)\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), T(1), config)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE)\n    MOI.optimize!(model)\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), T(0), config)\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_FEASIBILITY_SENSE_clears_objective),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) ->\n            MOIU.mock_optimize!(mock, MOI.OPTIMAL, (MOI.FEASIBLE_POINT, T[1])),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_get_ObjectiveFunction_ScalarAffineFunction(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest get objective function.\n\"\"\"\nfunction test_objective_get_ObjectiveFunction_ScalarAffineFunction(\n    model::MOI.ModelLike,\n    ::Config{T},\n) where {T}\n    obj_attr = MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}()\n    @requires MOI.supports(model, obj_attr)\n    x = MOI.add_variable(model)\n    f = MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(T(2), x)], T(1))\n    MOI.set(model, obj_attr, f)\n    @test_throws(\n        InexactError,\n        MOI.get(model, MOI.ObjectiveFunction{MOI.VariableIndex}()),\n    )\n    obj_fun = MOI.get(model, obj_attr)\n    @test obj_fun \u2248 f\n    quad_obj_fun =\n        MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{T}}())\n    @test convert(MOI.ScalarAffineFunction{T}, quad_obj_fun) \u2248 f\n    _test_attribute_value_type(model, MOI.ObjectiveFunctionType())\n    _test_attribute_value_type(model, obj_attr)\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveFunction_constant(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest constant in linear objective,  if `config.solve=true` confirm that it\nsolves correctly, and if `config.duals=true`, check that the duals are computed\ncorrectly.\n\"\"\"\nfunction test_objective_ObjectiveFunction_constant(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    obj_attr = MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}()\n    @requires MOI.supports(model, obj_attr)\n    x = MOI.add_variable(model)\n    f = MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(T(2), x)], T(1))\n    MOI.set(model, obj_attr, f)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    c = MOI.add_constraint(model, x, MOI.GreaterThan(T(1)))\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(3),\n        variable_primal = [(x, T(1))],\n        constraint_primal = [(c, T(1))],\n        constraint_dual = [(c, T(2))],\n    )\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_ObjectiveFunction_constant),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1]),\n            MOI.FEASIBLE_POINT,\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveFunction_blank(\n        model::MOI.ModelLike,\n        config::Config{T},\n    ) where {T}\n\nTest blank linear objective,  if `config.solve=true` confirm that it\nsolves correctly, and if `config.duals=true`, check that the duals are computed\ncorrectly.\n\"\"\"\nfunction test_objective_ObjectiveFunction_blank(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    @requires _supports(config, MOI.optimize!)\n    obj_attr = MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}()\n    @requires MOI.supports(model, obj_attr)\n    x = MOI.add_variable(model)\n    f = MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(T(0), x)], T(0))\n    MOI.set(model, obj_attr, f)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), T(0), config)\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_ObjectiveFunction_blank),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[6//5]),\n            MOI.FEASIBLE_POINT,\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveFunction_VariableIndex(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest VariableIndex objective,  if `config.solve=true` confirm that it\nsolves correctly, and if `config.duals=true`, check that the duals are computed\ncorrectly.\n\"\"\"\nfunction test_objective_ObjectiveFunction_VariableIndex(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    @requires MOI.supports(model, MOI.ObjectiveFunction{MOI.VariableIndex}())\n    x = MOI.add_variable(model)\n    MOI.set(model, MOI.ObjectiveFunction{MOI.VariableIndex}(), x)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    c = MOI.add_constraint(model, x, MOI.GreaterThan(T(1)))\n    _test_attribute_value_type(model, MOI.ObjectiveFunctionType())\n    _test_attribute_value_type(\n        model,\n        MOI.ObjectiveFunction{MOI.VariableIndex}(),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(1),\n        variable_primal = [(x, T(1))],\n        constraint_primal = [(c, T(1))],\n        constraint_dual = [(c, T(1))],\n    )\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_ObjectiveFunction_VariableIndex),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1]),\n            MOI.FEASIBLE_POINT,\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_qp_ObjectiveFunction_edge_cases(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest various edge cases relating to quadratic programs (i.e., with a quadratic\nobjective function).\n\nIf `config.solve=true` confirm that it solves correctly.\n\"\"\"\nfunction test_objective_qp_ObjectiveFunction_edge_cases(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    obj_attr = MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{T}}()\n    @requires MOI.supports(model, obj_attr)\n    x = MOI.add_variables(model, 2)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    vc1 = MOI.add_constraint(model, x[1], MOI.GreaterThan(T(1)))\n    @test vc1.value == x[1].value\n    vc2 = MOI.add_constraint(model, x[2], MOI.GreaterThan(T(2)))\n    @test vc2.value == x[2].value\n    # Basic model\n    # min x^2 + y^2 | x>=1, y>=2\n    MOI.set(\n        model,\n        obj_attr,\n        MOI.ScalarQuadraticFunction(\n            MOI.ScalarQuadraticTerm.(T(2), x, x),  # quad\n            MOI.ScalarAffineTerm{T}[],  # affine terms\n            T(0),  # constant\n        ),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(5),\n        variable_primal = [(x[1], T(1)), (x[2], T(2))],\n    )\n    # Duplicate linear terms\n    # min x + x + x^2 + y^2 | x>=1, y>=2\n    MOI.set(\n        model,\n        obj_attr,\n        MOI.ScalarQuadraticFunction(\n            MOI.ScalarQuadraticTerm.(T(2), x, x),  # quad\n            MOI.ScalarAffineTerm.(T(1), [x[1], x[1]]),  # affine terms\n            T(0),  # constant\n        ),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(7),\n        variable_primal = [(x[1], T(1)), (x[2], T(2))],\n    )\n    # Duplicate diagonal terms\n    # min x^2 + x^2 | x>=1, y>=2\n    MOI.set(\n        model,\n        obj_attr,\n        MOI.ScalarQuadraticFunction(\n            MOI.ScalarQuadraticTerm.(T(2), [x[1], x[1]], [x[1], x[1]]),  # quad\n            MOI.ScalarAffineTerm{T}[],  # affine terms\n            T(0),  # constant\n        ),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(2),\n        variable_primal = [(x[1], T(1))],\n    )\n    # Duplicate off-diagonal terms\" begin\n    # min x^2 + 0.25x*y + 0.25y*x + 0.5x*y + y^2 | x>=1, y>=2\n    MOI.set(\n        model,\n        obj_attr,\n        MOI.ScalarQuadraticFunction(\n            MOI.ScalarQuadraticTerm.(\n                T[2, 1//4, 1//4, 1//2, 2],\n                [x[1], x[1], x[2], x[1], x[2]],\n                [x[1], x[2], x[1], x[2], x[2]],\n            ),  # quad\n            MOI.ScalarAffineTerm{T}[],  # affine terms\n            T(0),  # constant\n        ),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(7),\n        variable_primal = [(x[1], T(1)), (x[2], T(2))],\n    )\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_qp_ObjectiveFunction_edge_cases),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1, 2]),\n        ),\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1, 2]),\n        ),\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1, 2]),\n        ),\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1, 2]),\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_qp_ObjectiveFunction_zero_ofdiag(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest quadratic program with a zero off-diagonal term.\n\nIf `config.solve=true` confirm that it solves correctly.\n\"\"\"\nfunction test_objective_qp_ObjectiveFunction_zero_ofdiag(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    obj_attr = MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{T}}()\n    @requires MOI.supports(model, obj_attr)\n    x = MOI.add_variables(model, 2)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    vc1 = MOI.add_constraint(model, x[1], MOI.GreaterThan(T(1)))\n    @test vc1.value == x[1].value\n    vc2 = MOI.add_constraint(model, x[2], MOI.GreaterThan(T(2)))\n    @test vc2.value == x[2].value\n    MOI.set(\n        model,\n        obj_attr,\n        MOI.ScalarQuadraticFunction(\n            MOI.ScalarQuadraticTerm.(\n                T[2, 0, 2],\n                [x[1], x[1], x[2]],\n                [x[1], x[2], x[2]],\n            ),  # quad\n            MOI.ScalarAffineTerm{T}[],  # affine terms\n            T(0),  # constant\n        ),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(5),\n        variable_primal = [(x[1], T(1)), (x[2], T(2))],\n    )\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_qp_ObjectiveFunction_zero_ofdiag),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1, 2]),\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_ObjectiveFunction_duplicate_terms(\n        model::MOI.ModelLike,\n        config::Config,\n    )\n\nTest duplicate terms in linear objective, if `config.solve=true` confirm that it\nsolves correctly.\n\"\"\"\nfunction test_objective_ObjectiveFunction_duplicate_terms(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    x = MOI.add_variable(model)\n    c = MOI.add_constraint(model, x, MOI.GreaterThan(T(1)))\n    @test c.value == x.value\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}(),\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(T[2, 1], x), T(0)),\n    )\n    _test_model_solution(\n        model,\n        config;\n        objective_value = T(3),\n        variable_primal = [(x, T(1))],\n        constraint_primal = [(c, T(1))],\n        constraint_dual = [(c, T(3))],\n    )\n    return\nend\n\nfunction setup_test(\n    ::typeof(test_objective_ObjectiveFunction_duplicate_terms),\n    model::MOIU.MockOptimizer,\n    ::Config{T},\n) where {T}\n    MOIU.set_mock_optimize!(\n        model,\n        (mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(\n            mock,\n            MOI.OPTIMAL,\n            (MOI.FEASIBLE_POINT, T[1]),\n            MOI.FEASIBLE_POINT,\n        ),\n    )\n    return\nend\n\n\"\"\"\n    test_objective_set_via_modify(\n        model::MOI.ModelLike,\n        config::Config{T},\n    ) where {T}\n\nTest that a ScalarAffineFunction can be set via modification without setting an\nobjective prior.\n\"\"\"\nfunction test_objective_set_via_modify(\n    model::MOI.ModelLike,\n    config::Config{T},\n) where {T}\n    attr = MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}()\n    @requires MOI.supports(model, attr)\n    @requires _supports(config, MOI.modify)\n    @requires _supports(config, MOI.ScalarCoefficientChange)\n    @test MOI.get(model, MOI.ListOfModelAttributesSet()) == []\n    x = MOI.add_variable(model)\n    MOI.modify(model, attr, MOI.ScalarCoefficientChange(x, T(1)))\n    @test MOI.get(model, MOI.ListOfModelAttributesSet()) == [attr]\n    return\nend\n", "meta": {"hexsha": "f384babc8b0a5e8ee68c6e4a4a0b541252f99b94", "size": 15174, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Test/test_objective.jl", "max_stars_repo_name": "guilhermebodin/MathOptInterface.jl", "max_stars_repo_head_hexsha": "6b50fa0f9e31ef97a06facaad60af7d0b6ae3d2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-29T16:27:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-29T16:27:46.000Z", "max_issues_repo_path": "src/Test/test_objective.jl", "max_issues_repo_name": "guilhermebodin/MathOptInterface.jl", "max_issues_repo_head_hexsha": "6b50fa0f9e31ef97a06facaad60af7d0b6ae3d2a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-02-24T21:11:26.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-24T21:11:26.000Z", "max_forks_repo_path": "src/Test/test_objective.jl", "max_forks_repo_name": "guilhermebodin/MathOptInterface.jl", "max_forks_repo_head_hexsha": "6b50fa0f9e31ef97a06facaad60af7d0b6ae3d2a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6301886792, "max_line_length": 80, "alphanum_fraction": 0.6227758007, "num_tokens": 4312, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.1460872396128689, "lm_q1q2_score": 0.06962220567955668}}
{"text": "list_test_str = \"light red bags contain 1 bright white bag, 2 muted yellow bags.\ndark orange bags contain 3 bright white bags, 4 muted yellow bags.\nbright white bags contain 1 shiny gold bag.\nmuted yellow bags contain 2 shiny gold bags, 9 faded blue bags.\nshiny gold bags contain 1 dark olive bag, 2 vibrant plum bags.\ndark olive bags contain 3 faded blue bags, 4 dotted black bags.\nvibrant plum bags contain 5 faded blue bags, 6 dotted black bags.\nfaded blue bags contain no other bags.\ndotted black bags contain no other bags.\"\n\nlist_test = split(list_test_str, '\\n')\n\nlist = readlines(\"day7_input.txt\")\n\nbag = \"shiny gold\"\n\n#regxrule = r\"( bags contain | bag\\, | bags\\, | bag\\.| bags\\.)\"\n\nregxrule = r\"( bags contain | bags?, | bags?.)\"\n\nregxrule = r\" bags contain | bags?[,.] ?\"\n\nregxrule = r\" bags contain | bags?(, |\\.)\"\n\nfunction get_outer_bag_rules(list)\n    outer = Dict()\n    for line in list\n        parts = split(line, regxrule, keepempty=false)\n            for part in parts[2:end]\n                if part \u2260 \"no other\"\n                    num, bag = split(part, ' ', limit=2)\n                    if bag in keys(outer)\n                        push!(outer[bag], parts[1])\n                    else\n                        push!(outer, bag => Set([parts[1]]))\n                    end\n                end\n            end\n    end\n    return outer\nend\n\nfunction get_outer_bags(bag, outer_bag_rules, outer_set = Set())\n    if bag in keys(outer_bag_rules)\n        outermost_bags = outer_bag_rules[bag]\n        if outermost_bags \u2209 outer_set\n            union!(outer_set,outermost_bags)\n            for outer_bag \u2208 outermost_bags\n                union!(outer_set, get_outer_bags(outer_bag, outer_bag_rules, outer_set))\n            end\n        end\n    end\n    return outer_set\nend\n\nget_number_outer_bags(bag, list) = length(get_outer_bags(bag, get_outer_bag_rules(list)))\n\nprintln(\"Number of outer bags containing `$bag` bag in test list is $(get_number_outer_bags(bag, list_test))\\n\")\nprintln(\"Number of outer bags containing `$bag` bag in problem list is $(get_number_outer_bags(bag, list))\\n\")\n\n#= \n```julia\njulia> @btime get_number_outer_bags(\"shiny gold\", list)\n  5.744 ms (99019 allocations: 3.22 MiB)\n185\n\njulia> @btime get_outer_bag_rules(list);\n  1.970 ms (17606 allocations: 1.25 MiB)\n\njulia> outer_bag_rules = get_outer_bag_rules(list);\n\njulia> @btime length(get_outer_bags(bag, outer_bag_rules));\n  3.742 ms (81414 allocations: 1.97 MiB)\n```\n=#\n\n# Outra vers\u00e3o com mais regex, mas s\u00f3 um pouco mais r\u00e1pida.\n\n# A vers\u00e3o regex_web foi testada em https://regexr.com/5hutn\nregexrule_web = r\"(.+)(?: bags contain )((\\d+) ([^,\\n]*) (bag,|bags,) )?((\\d+) ([^,\\n]*) (bag,|bags,) )?((\\d+) ([^,\\n]*) (bag,|bags,) )?((\\d+) ([^,\\n]*) (bag,|bags,))?((\\d+|no) ([^,\\n]*) ((bag\\.|bags\\.)))\\n?\"\nregexrule = r\"(.+)(?: bags contain )((\\d+) ([^,]*) (bag,|bags,) )?((\\d+) ([^,]*) (bag,|bags,) )?((\\d+) ([^,]*) (bag,|bags,) )?((\\d+) ([^,]*) (bag,|bags,))?((\\d+|no) ([^,]*) ((bag\\.|bags\\.)))\"\n#regexrule = r\"(.+)(?: bags contain )((\\d+) ([^,]*) (bag,|bags,) )?{4}((\\d+|no) ([^,]*) ((bag\\.|bags\\.)))\"\n\nfunction get_outer_bag_rules2(list)\n    outer = Dict()\n    for line in list\n        m = match(regexrule, line)\n            for j in 3:4:length(m.captures)\n                if m[j] \u2209 (\"\", \"no\") && !(m[j+1] === nothing)\n                    num = m[j]\n                    bag = m[j+1]\n                    if bag in keys(outer)\n                        push!(outer[bag], m[1])\n                    else\n                        push!(outer, bag => Set([m[1]]))\n                    end\n                end\n            end\n    end\n    return outer\nend\n\n\n# part 2\n\nlist_test2_str = \"shiny gold bags contain 2 dark red bags.\ndark red bags contain 2 dark orange bags.\ndark orange bags contain 2 dark yellow bags.\ndark yellow bags contain 2 dark green bags.\ndark green bags contain 2 dark blue bags.\ndark blue bags contain 2 dark violet bags.\ndark violet bags contain no other bags.\"\n\nlist_test2 = split(list_test2_str, '\\n')\n\nfunction get_inner_bag_rules(list)\n    inner = Dict()\n    for line in list\n        parts = split(line, regxrule, keepempty=false)\n            for part in parts[2:end]\n                if part \u2260 \"no other\"\n                    num, bag = split(part, ' ', limit=2)\n                    if parts[1] in keys(inner)\n                        push!(inner[parts[1]], (bag, parse(Int, num)))\n                    else\n                        push!(inner, parts[1] => [(bag, parse(Int, num))])\n                    end\n                end\n            end\n    end\n    return inner\nend\n\nfunction get_next_inner_bags(bag, inner_bag_rules)\n    if bag in keys(inner_bag_rules)\n        innermost_stuff = inner_bag_rules[bag]\n        inner_sum = 1 # start with containing bag\n        for (innermost_bag, innermost_num) in innermost_stuff\n            # recursively count inner bags\n            inner_sum += innermost_num * get_next_inner_bags(innermost_bag, inner_bag_rules)\n        end\n        return inner_sum\n    else\n        return 1\n    end\nend\n\nget_number_inner_bags(bag, list) = get_next_inner_bags(bag, get_inner_bag_rules(list)) - 1 # discount starting bag\n\nprintln(\"Number of bags contained in `$bag` bag in test list is $(get_number_inner_bags(bag, list_test))\\n\")\nprintln(\"Number of bags contained in `$bag` bag in second test list is $(get_number_inner_bags(bag, list_test2))\\n\")\nprintln(\"Number of bags contained in `$bag` bag in problem list is $(get_number_inner_bags(bag, list))\\n\")\n\n#= \n# In this case, most of the time is taken extracting the rules.\n# We don't need to keep growing with the set of color bags, just the number\n```julia\njulia> @btime get_number_inner_bags(\"shiny gold\", list)\n  1.970 ms (17252 allocations: 1.10 MiB)\n89084\n\njulia> inner_bag_rules = get_inner_bag_rules(list);\n\njulia> @btime get_inner_bag_rules(list);\n  1.906 ms (16404 allocations: 1.07 MiB)\n\njulia> @btime get_next_inner_bags(bag, inner_bag_rules) - 1;\n  61.513 \u03bcs (848 allocations: 33.66 KiB)\n\n```\n=#\n", "meta": {"hexsha": "8eeb27ce86dbb0afb2b17292b4c51e6fcc2bbffa", "size": 5983, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "aoc2020/day07_bags.jl", "max_stars_repo_name": "rmsrosa/adventofcode2020", "max_stars_repo_head_hexsha": "7a14a4c08eb33c9d02b97b1fbe63a150c81b2ade", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "aoc2020/day07_bags.jl", "max_issues_repo_name": "rmsrosa/adventofcode2020", "max_issues_repo_head_hexsha": "7a14a4c08eb33c9d02b97b1fbe63a150c81b2ade", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "aoc2020/day07_bags.jl", "max_forks_repo_name": "rmsrosa/adventofcode2020", "max_forks_repo_head_hexsha": "7a14a4c08eb33c9d02b97b1fbe63a150c81b2ade", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7848837209, "max_line_length": 208, "alphanum_fraction": 0.6052147752, "num_tokens": 1707, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1561049013715009, "lm_q1q2_score": 0.06954934412544315}}
{"text": "\"\"\"\nStanDump --- a package for writing data in the CmdStan dump data format.\n\nThe single exported function is [`stan_dump`](@ref).\n\"\"\"\nmodule StanDump\n\nusing ArgCheck: @argcheck\nusing DocStringExtensions: SIGNATURES\n\nexport stan_dump\n\n####\n#### Representation of formatting\n####\n\n\"\"\"\nWrapper for an IO stream for writing data for use by Stan. See constructor for documentation\nof slots.\n\nPart of the API, but not exported.\n\"\"\"\nstruct StanDumpIO{T <: IO}\n    io::T\n    def_arrow::Bool\n    def_newline::Bool\n    compact::Bool\nend\n\n\"\"\"\n    StanDumpIO(io; def_arrow = false, def_newline = false, compact = false)\n\nWrap an IO stream `io` for writing data to be read by Stan.\n\n# Arguments\n\n- `def_arrow::Bool`: when `true` use `<-`, otherwise `=` for variable definitions.\n\n- `def_newline::Bool`: when `true`, each `=` or `<-` is followed by a newline.\n\n- `compact::Bool`: when `true`, drop whitespace when possible.\n\"\"\"\nfunction StanDumpIO(io; def_arrow = true, def_newline = false, compact = false)\n    StanDumpIO(io, def_arrow, def_newline, compact)\nend\n\n####\n#### Internals\n####\n\n\"\"\"\n    dump(sd, xs...)\n\nWrite arguments `xs...` as data for Stan into `sd`. For internal use.\n\nNOTE: Define methods only for valid Stan objects, using `_dump` for everything else.\n\"\"\"\ndump(sd::StanDumpIO, x) = throw(ArgumentError(\"Can't represent $(x) as data for Stan.\"))\n\ndump(sd::StanDumpIO, x::Float64) = print(sd.io, x)\n\ndump(sd::StanDumpIO, x::Real) = dump(sd, Float64(x))\n\nfunction dump(sd::StanDumpIO, x::Integer)\n    if typemin(Int32) \u2264 x \u2264 typemax(Int32)\n        print(sd.io, x)\n    elseif typemin(Int64) \u2264 x \u2264 typemax(Int64)\n        print(sd.io, x, \"L\")\n    else\n        throw(ArgumentError(\"Integer too large to represent in Stan.\"))\n    end\nend\n\n\"\"\"\n$(SIGNATURES)\n\nWrite arguments arguments as data for Stan into `sd`, passing through strings and\ncharacters, and allowing other special objects which are not valid data.\n\nFor internal implementation. also defined for objects which are not valid in Stan.\n\"\"\"\n_dump(sd::StanDumpIO, xs...) = for x in xs _dump(sd, x) end\n\n_dump(sd::StanDumpIO, x) = dump(sd, x)\n\n_dump(sd::StanDumpIO, x::Union{Char,String}) = print(sd.io, x)\n\n\"Write a space unless output is requested to be compact.\"\nstruct Space end\n\nconst SPACE = Space()\n\n_dump(sd::StanDumpIO, ::Space) = sd.compact || print(sd.io, \" \")\n\n\"\"\"\n$(SIGNATURES)\n\nTest if the argument is valid as a Stan variable name.\n\nNOTE: only basic checks, does not test conflicts with reserved names.\n\"\"\"\nfunction is_valid_varname(name::String)\n    isvalid(c) = isascii(c) && (isdigit(c) || isletter(c) || c == '_')\n    all(isvalid, name) && isletter(name[1]) && !endswith(name, \"__\")\nend\n\nfunction _dump(sd::StanDumpIO, x::Symbol)\n    v = string(x)\n    @argcheck is_valid_stan_varname(v) \"Invalid variable name $(v).\"\n    print(sd.io, v)\nend\n\nfunction _dump(sd::StanDumpIO, x::Pair)\n    name, value = x\n    @argcheck name isa Union{AbstractString, Symbol} \"Use symbols or strings for variable names.\"\n    varname = string(name)\n    @argcheck is_valid_varname(varname)\n    _dump(sd, varname, SPACE, sd.def_arrow ? \"<-\" : '=',\n          sd.def_newline ? \"\\n\" : SPACE, value, \"\\n\")\nend\n\n\"\"\"\n$(SIGNATURES)\n\nDump elements of a vector (or iterable, considered as a vector).\n\nWhen the element type is `<:Integer`, dump as `Int`s, otherwise as `Float64`; all values are\nconverted for consistency.\n\"\"\"\nfunction _dump_vector(sd::StanDumpIO, x)\n    S = eltype(x) <: Integer ? Int : Float64\n    if isempty(x)\n        _dump(sd, S \u2261 Int ? \"integer\" : \"double\", \"(0)\")\n    else\n        _dump(sd, \"c(\")\n        for (i, x) in enumerate(x)\n            i > 1 && _dump(sd, \",\", SPACE)\n            dump(sd, S(x))      # convert value for consistency\n        end\n        _dump(sd, \")\")\n    end\nend\n\ndump(sd::StanDumpIO, x::AbstractVector) = _dump_vector(sd, x)\n\nfunction dump(sd::StanDumpIO, r::UnitRange{<: Integer})\n    isempty(r) ? _dump_vector(sd, r) : _dump(sd, minimum(r), \":\", maximum(r))\nend\n\nfunction dump(sd::StanDumpIO, A::AbstractArray)\n    _dump(sd, \"structure(\", view(A, :), \",\", SPACE, \".Dim\", SPACE, \"=\",\n          SPACE, collect(size(A)), \")\")\nend\n\n####\n#### Interface\n####\n\n\"\"\"\n    stan_dump(filename, data; force = false, kwargs...)\n    stan_dump(io, data; kwargs...)\n    stan_dump(StanDump.StanDumpIO(io; kwargs...), data)\n\nWrite `data`, which is a value that supports `pairs` (eg a `NamedTuple` or a `Dict`) to\n`filename` or `io`.\n\nUsing a `filename`, it will not be overwritten unless `force = true` is specified.\n\nKeyword arguments are passed to `StanDumpIO` to govern formatting (most users should not\ncare about this, except for debugging purposes).\n\n# Example\n\n```jldoctest\njulia> stan_dump(stdout, (N = 1, a = 1:5, b = ones(2, 2)))\nN <- 1\na <- 1:5\nb <- structure(c(1.0, 1.0, 1.0, 1.0), .Dim = c(2, 2))\n```\n\"\"\"\nstan_dump(sd::StanDumpIO, data) = foreach(p -> _dump(sd, p), pairs(data))\n\nstan_dump(io::IO, data; kwargs...) = stan_dump(StanDumpIO(io; kwargs...), data)\n\nfunction stan_dump(filename::AbstractString, data; force::Bool = false, kwargs...)\n    @argcheck force || !ispath(filename) \"$(filename) already exists\"\n    open(io -> stan_dump(StanDumpIO(io; kwargs...), data), filename, \"w\")\nend\n\nend # module\n", "meta": {"hexsha": "0fd5022aad64338b843680ea4d52953dc068d961", "size": 5191, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/StanDump.jl", "max_stars_repo_name": "UnofficialJuliaMirror/StanDump.jl-9713c8f3-0168-54b5-986e-22c526958f39", "max_stars_repo_head_hexsha": "5deab0c727a2d47934e03c5bc34cac36d8afa40c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-06-07T13:42:04.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-05T10:20:27.000Z", "max_issues_repo_path": "src/StanDump.jl", "max_issues_repo_name": "UnofficialJuliaMirror/StanDump.jl-9713c8f3-0168-54b5-986e-22c526958f39", "max_issues_repo_head_hexsha": "5deab0c727a2d47934e03c5bc34cac36d8afa40c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-03T13:14:30.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-11T09:33:33.000Z", "max_forks_repo_path": "src/StanDump.jl", "max_forks_repo_name": "UnofficialJuliaMirror/StanDump.jl-9713c8f3-0168-54b5-986e-22c526958f39", "max_forks_repo_head_hexsha": "5deab0c727a2d47934e03c5bc34cac36d8afa40c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:49:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:49:10.000Z", "avg_line_length": 27.1780104712, "max_line_length": 97, "alphanum_fraction": 0.6569061838, "num_tokens": 1458, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4225046348141882, "lm_q2_score": 0.16451646494473965, "lm_q1q2_score": 0.06950896894239843}}
{"text": "#=\nrun natvsext benchmarks\nsee natvsext/README.md\n=#\n\nusing Test\nusing Printf\nimport DataFrames\nimport CSV\nimport Hypatia\nusing MosekTools\nusing ECOS\n\ninclude(joinpath(@__DIR__, \"../../examples/Examples.jl\"))\nusing Main.Examples\n\nusing Distributed\ninclude(joinpath(@__DIR__, \"spawn.jl\"))\n\n# path to write results DataFrame to CSV\nresults_path = joinpath(mkpath(joinpath(@__DIR__, \"raw\")), \"bench.csv\")\n\n# option to keep setting up larger models, only if solver is Hypatia,\n# even if last solve was killed\n# setup_model_anyway = true\nsetup_model_anyway = false\n\nverbose = true # make solvers print output\n# verbose = false\n\niter_limit = 250\nnum_threads = 16 # number of threads for BLAS and Julia processes running instances\nfree_memory_limit = 8 * 2^30 # keep at least X GB of RAM available\noptimizer_time_limit = 1800\nsetup_time_limit = 1.2 * optimizer_time_limit\ncheck_time_limit = 1.2 * optimizer_time_limit\ntol_loose = 1e-7\ntol_tight = 1e-3 * tol_loose\n\nhyp_solver = (\"Hypatia\", Hypatia.Optimizer, (\n    verbose = verbose,\n    iter_limit = iter_limit,\n    time_limit = optimizer_time_limit,\n    tol_abs_opt = tol_tight,\n    tol_rel_opt = tol_loose,\n    tol_feas = tol_loose,\n    tol_infeas = tol_tight,\n    init_use_indirect = true, # skips dual equalities preprocessing\n    use_dense_model = true,\n    ))\n\nmosek_solver = (\"Mosek\", Mosek.Optimizer, (\n    QUIET = !verbose,\n    MSK_IPAR_INTPNT_MAX_ITERATIONS = iter_limit,\n    MSK_IPAR_NUM_THREADS = num_threads,\n    MSK_IPAR_OPTIMIZER = Mosek.MSK_OPTIMIZER_CONIC,\n    MSK_IPAR_INTPNT_BASIS = Mosek.MSK_BI_NEVER, # no basis identification for LP\n    MSK_DPAR_OPTIMIZER_MAX_TIME = optimizer_time_limit,\n    MSK_DPAR_INTPNT_CO_TOL_REL_GAP = tol_loose,\n    MSK_DPAR_INTPNT_CO_TOL_PFEAS = tol_loose,\n    MSK_DPAR_INTPNT_CO_TOL_DFEAS = tol_loose,\n    MSK_DPAR_INTPNT_CO_TOL_INFEAS = tol_tight,\n    ))\n\necos_solver = (\"ECOS\", ECOS.Optimizer, (\n    verbose = 2 * verbose,\n    maxit = iter_limit, # no time limit option available\n    abstol = tol_tight,\n    reltol = tol_loose,\n    feastol = tol_loose,\n    ))\n\n# instance sets and solvers to run\ninst_sets = [\n    #= natural formulations paper =#\n    # (\"nat\", hyp_solver),\n    # (\"ext\", hyp_solver),\n    # (\"ext\", mosek_solver),\n    # (\"extEP\", hyp_solver), # ExpPSD extender\n    # (\"extSEP\", hyp_solver), # SOCExpPSD extender\n    # (\"extEP\", mosek_solver), # ExpPSD extender\n    # (\"extSEP\", mosek_solver), # SOCExpPSD extender\n    #= spectral function cones paper =#\n    (\"nat\", hyp_solver),\n    (\"ext\", hyp_solver),\n    (\"ext\", mosek_solver),\n    # for nonparametricdistr:\n    (\"vecext\", hyp_solver),\n    (\"vecext\", mosek_solver),\n    (\"vecext\", ecos_solver),\n    # for covarianceest\n    (\"logdet\", hyp_solver),\n    (\"sepspec\", hyp_solver),\n    (\"direct\", hyp_solver),\n    (\"direct\", mosek_solver),\n    ]\n\n# models to run\nJuMP_examples = [\n    #= natural formulations paper =#\n    # \"densityest\",\n    # \"doptimaldesign\",\n    # \"matrixcompletion\",\n    # \"matrixregression\",\n    # \"polymin\",\n    # \"portfolio\",\n    # \"shapeconregr\",\n    #= WSOS cones paper =#\n    # \"nearestpolymat\",\n    # \"polynorm\",\n    #= spectral function cones paper =#\n    \"centralpolymat\",\n    \"classicalquantum\",\n    \"covarianceest\",\n    \"experimentdesign\",\n    \"nonparametricdistr\",\n    ]\n\ninterrupt()\n@assert nprocs() == 1\nprintln()\n\nprint_memory() = println(\"free memory (GB): \", Float64(Sys.free_memory()) / 2^30)\nprint_memory()\n\nextender_name(ext::Nothing) = missing\nextender_name(ext::Symbol) = string(ext)\n\nprintln(\"\\nstarting benchmark runs\")\ntime_all = time()\n\n@testset \"examples tests\" begin\nperf = Examples.setup_benchmark_dataframe()\nCSV.write(results_path, perf)\n\n@testset \"$ex\" for ex in JuMP_examples\n(ex_type, ex_insts) = Examples.get_benchmark_instances(\"JuMP\", ex)\n@testset \"$inst_set, $(solver[1])\" for (inst_set, solver) in inst_sets\n    haskey(ex_insts, inst_set) || continue\n    (extender, inst_subsets) = ex_insts[inst_set]\n    isempty(inst_subsets) && continue\n\n    info_perf = (; inst_set, :example => ex, :model_type => \"JuMP\",\n        :real_T => Float64, :solver => solver[1],\n        :extender => extender_name(extender))\n    str = \"$ex $inst_set $(solver[1])\"\n    println(\"\\nstarting $str\")\n\n    for inst_subset in inst_subsets\n        solve = true\n        # first instance is only used for compilation\n        compile_inst = inst_subset[1]\n        for (inst_num, inst_data) in enumerate(inst_subset[2:end])\n            println(\"\\nstarting $str $inst_num $inst_data\")\n            flush(stdout); flush(stderr)\n\n            total_time = @elapsed (setup_killed, check_killed, run_perf) =\n                spawn_instance(ex, ex_type{Float64}, compile_inst,\n                inst_data, extender, solver, solve, num_threads)\n\n            new_perf = (; info_perf..., run_perf..., total_time,\n                inst_num, inst_data)\n            Examples.write_perf(perf, results_path, new_perf)\n            @printf(\"%8.2e seconds\\n\", total_time)\n            flush(stdout); flush(stderr)\n\n            setup_killed && break\n            if check_killed\n                if setup_model_anyway && (solver[1] == \"Hypatia\")\n                    solve = false\n                else\n                    break\n                end\n            end\n        end\n    end\nend\nend\n\nflush(stdout); flush(stderr)\nprintln(\"\\n\")\nDataFrames.show(perf, allrows = true, allcols = true)\nprintln(\"\\n\")\nflush(stdout); flush(stderr)\nend\n\ninterrupt()\n@printf(\"\\nbenchmarks total time: %8.2e seconds\\n\\n\", time() - time_all)\n;\n", "meta": {"hexsha": "683434b7420115c8bfcec56467b34fade1baa52c", "size": 5482, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmarks/natvsext/run.jl", "max_stars_repo_name": "matbesancon/Hypatia.jl", "max_stars_repo_head_hexsha": "9e1487850b5db87ea23545bd07348e5a36ac8bf3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "benchmarks/natvsext/run.jl", "max_issues_repo_name": "matbesancon/Hypatia.jl", "max_issues_repo_head_hexsha": "9e1487850b5db87ea23545bd07348e5a36ac8bf3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmarks/natvsext/run.jl", "max_forks_repo_name": "matbesancon/Hypatia.jl", "max_forks_repo_head_hexsha": "9e1487850b5db87ea23545bd07348e5a36ac8bf3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8526315789, "max_line_length": 83, "alphanum_fraction": 0.6630791682, "num_tokens": 1536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.1422318931919251, "lm_q1q2_score": 0.0694494717271077}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.20\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 88af7dc6-70fd-11eb-3af2-f7a194340290\nbegin\n\tusing PlutoUI, Logging\n\t# set global loglevel\n\tLogging.global_logger(ConsoleLogger(stderr, Logging.LogLevel(-5000)))\n\tLogging.disable_logging(Logging.LogLevel(-1001))  \nend\n\n# \u2554\u2550\u2561 bb6ed424-70fc-11eb-2a2a-ff0556bb7cda\nmd\"\"\"\n# Rational agents\n\n## Our first agent\nConsider the following example from the book: a simple vacuum-cleaner agent that cleans a square if it is dirty and moves to the other square if not. We consider the assume the following:\n* Performance measure: we awards one point for each clean square at each time step, over a \u201clifetime\u201d of 1000 time steps.\n* Environment: the \u201cgeography\u201d of the environment is known a priori but the dirt distribution and the initial location of the agent are not. Clean squares stay clean and sucking cleans the current square. The Left and Right actions move the agent left and right except when this would take the agent outside the environment, in which case the agent remains where it is.\n* Actuators: the only available actions are Left, Right, and Suck.\n* Sensors: the agent correctly perceives its location and whether that location contains dirt.\n\n$(PlutoUI.LocalResource(\"./img/vacuum.png\", :width => 500, :align => \"middle\"))\n\nQuestions:\n\n1. How many world states are there?\n2. Identify the goal states amongst those from q1.\n3. Implement a performance-measuring environment simulator for the vacuum-cleaner world. Thinks about a modular implementation so that the sensors, actuators, and environment characteristics (size, shape, dirt placement, etc.) can be changed easily. \n4. What is the value of the performance measure for each world state? Confirm your simulation returns the expected value.\n\"\"\"\n\n# \u2554\u2550\u2561 9fd46218-70fe-11eb-1ddd-132f13e0611c\nbegin\n\t# build the world\n\tstruct world\n\t\tlocations::Dict{Tuple{Int64,Int64}, Bool}\n\tend\n\n\tfunction world(locations::Array{Tuple{Int64,Int64}})\n\t\treturn world(Dict((location => rand(Bool) for location in locations)))\n\tend\n\n\tfunction world(locations::Array{Tuple{Int64,Int64}}, dirt::Array{Bool,1})\n\t\tif length(locations) == length(dirt)\n\t\t\treturn world(Dict(zip(locations, dirt)))\n\t\tend\n\tend\n\n\tmutable struct reflex_vacuum\n\t\tw::world\n\t\tpercept::Tuple{Tuple{Int64, Int64}, Bool}\n\tend\n\n\t# include the actions\n\tfunction update!(v::reflex_vacuum)\n\t\tpos = v.percept[1]\n\t\tstatus = v.percept[2]\n\n\t\t@debug \"current pos: $(pos), status: $(status)\"\n\t\tif status == true # if dirty => clean\n\t\t\t@debug \"status is dirty, cleaning...\"\n\t\t\tv.w.locations[pos] = false\n\t\t\tv.percept = (pos, false)\n\t\telse # if clean => move\n\t\t\tpos = rules[pos]\n\t\t\tv.percept = (pos, v.w.locations[pos]) # update sensor\n\t\tend\n\tend\n\n\tfunction runprog(v::reflex_vacuum, n::Int=1000)\n\t\tscore = 0\n\t\tfor _ in 1:n\n\t\t\tscore += sum(.!(values(v.w.locations)))\n\t\t\tupdate!(v)\n\t\tend\n\t\treturn score\n\tend\n\n\n\t# __main__\n\n\t# set the rules\n\tconst rules = Dict((0,0)=>(1,0), (1,0)=>(0,0))\n\n\t@info \"Starting...\"\n\tfor layout in Base.Iterators.product([true, false],[true, false])\n\t\tfor startpos in [(0,0),(1,0)]\n\t\t\t# initiate the world\n\t\t\tw = world([(0,0),(1,0)],[i for i in layout])\n\t\t\t# initiate reflex_vacuum\n\t\t\tv = reflex_vacuum(w, (startpos, w.locations[startpos]))\n\t\t\t# scoring\n\t\t\tscore = runprog(v)\n\t\t\t# output msg\n\t\t\t@info \"layout: $(layout), startpos: $(startpos), score: $(score)\"\n\t\tend\n\tend\n\t@info \"Finished\"\nend\n\n# \u2554\u2550\u2561 ab564aec-70ff-11eb-0d36-cd95fb8def23\nmd\"\"\"\n## Modified agent\nConsider a modified version of the vacuum environment, in which the agent is penalized one point for each movement.\n\n1. Can a simple reflex agent be perfectly rational for this environment? Explain.\n2. What about a reflex agent with state?\n3. How do your answers to a and b change if the agent\u2019s percepts give it the clean/dirty status of every square in the environment?\n\"\"\"\n\n# \u2554\u2550\u2561 f2915910-70ff-11eb-2f60-e9963db3aaeb\nmd\"\"\"\n## Another modified agent\nConsider a modified version of the simple vacuum environment in which the geography of the environment \u2014 its extent, boundaries, and obstacles \u2014 is unknown, as is the initial dirt configuration. (The agent can go Up and Down as well as Left and Right.)\n\n1. Can a simple reflex agent be perfectly rational for this environment? Explain.\n2. Can a simple reflex agent with a randomized agent function outperform a simple reflex agent? Design such an agent (principle) and measure its performance on several environments (if you have time).    \n3. Can you design an environment in which your randomized agent will perform poorly? Show your results.\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u255088af7dc6-70fd-11eb-3af2-f7a194340290\n# \u2560\u2550bb6ed424-70fc-11eb-2a2a-ff0556bb7cda\n# \u2560\u25509fd46218-70fe-11eb-1ddd-132f13e0611c\n# \u255f\u2500ab564aec-70ff-11eb-0d36-cd95fb8def23\n# \u255f\u2500f2915910-70ff-11eb-2f60-e9963db3aaeb\n", "meta": {"hexsha": "1d89aa55472b21da126d5a1489ad241deaa64098", "size": 4791, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "PS/PS01 - rational agents.jl", "max_stars_repo_name": "B4rtDC/DS425-PS", "max_stars_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-07-07T21:55:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T14:29:23.000Z", "max_issues_repo_path": "PS/PS01 - rational agents.jl", "max_issues_repo_name": "Goodwill-Khoa/DS425-PS", "max_issues_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "PS/PS01 - rational agents.jl", "max_forks_repo_name": "Goodwill-Khoa/DS425-PS", "max_forks_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-07-07T21:55:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T13:52:37.000Z", "avg_line_length": 36.572519084, "max_line_length": 369, "alphanum_fraction": 0.7347109163, "num_tokens": 1382, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.14223189137395395, "lm_q1q2_score": 0.06944947083942257}}
{"text": "md\"\"\"\n\u041b\u0435\u043a\u0446\u0438\u044f 8\n\n## \u041f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u0430\u043e\u0432\u0430\u043d\u0438\u0435 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u0438\u0435\u0440\u0430\u0440\u0445\u0438\u0438 \u0442\u0438\u043f\u043e\u0432, \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0442\u0438\u043f\u044b\n\n\n\u0412 \u043b\u0435\u043a\u0446\u0438\u0438 7 \u0431\u044b\u043b\u0430 \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0435\u043d\u0430 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0430\u044f \u0438\u0435\u0440\u0430\u0440\u0445\u0438\u044e \u0442\u0438\u043f\u043e\u0432, \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0432\u0448\u0430\u044f \u043d\u0430\u043c \u043f\u0438\u0441\u0430\u0442\u044c \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u0439 \u043a\u043e\u0434:\n\nAbstractRobot:\n|                        move!(::AbstractCoordRobot, ::HorizonSide), \n|                        isborder(::AbstractCoordRobot, ::HorizonSide), \n|                        putmarker!(::AbstractCoordRobot), \n|                        ismarker(::AbstractCoordRobot), \n|                        temperature(::AbstractCoordRobot)\n|                        \n|                        snake(::AbstractCoordRobot, NTuple(2,HorizonSide))\n|\n|--AbsctractBorderRobot: movements!(::AbsctractBorderRobot, ::HorisonSide),\n|  |                     movements!(::AbsctractBorderRobot, ::HorisonSide, ::Integer), \n|  |                          (\u043f\u0440\u0435\u0434\u043f\u043e\u043b\u0430\u0433\u0430\u0435\u0442\u0441\u044f \u043d\u0430\u043b\u0438\u0447\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 try_move!(::Robot, ::HorizonSide))\n|  |\n|  |--PutmarkersBorderRobot: try_move!(::PutmarkersBorderRobot, ::HorizonSide), get(::PutmarkerRobot)\n|  |--CountmarkerBorderRobot: try_move!(::PutmarkersBorderRobot, ::HorizonSide), get(::CountmarkerRobot)\n|  |--....\n|\n|-- AbstractCoordRobot: \n|  |                    get_coords(::AbstractCoordRobot), \n|  |                    move!(::AbstractCoordRobot, ::HorizonSide)\n|  |\n|  |- CoordRobot: \n|  |              get(::AbstractCoordRobot)\n|  |              get_coords(::AbstractCoordRobot), move!(::AbstractCoordRobot, ::HorizonSide)\n|\n|--....\n\n\n\u0414\u043e\u043f\u0443\u0441\u0442\u0438\u043c \u0442\u0435\u043f\u0435\u0440\u044c, \u0447\u0442\u043e \u043f\u0440\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0438 \u0440\u043e\u0431\u043e\u0442\u0430 \u043f\u043e \u043f\u043e\u043b\u044e \u0441 \u043e\u0431\u0445\u043e\u0434\u043e\u043c \u043f\u0435\u0440\u0435\u0433\u043e\u0440\u043e\u0434\u043e\u043a \u043f\u043e\u043d\u0430\u0434\u043e\u0431\u0438\u043b\u043e\u0441\u044c \u0435\u0449\u0451 \u0438 \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0442\u044c \u0442\u0435\u043a\u0443\u0449\u0438\u0435\n\u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0440\u043e\u0431\u043e\u0442\u0430. \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043c, \u0447\u0442\u043e \u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f \u043d\u0435 \u043f\u043e\u0441\u0442\u0447\u0438\u0442\u0430\u0442\u044c \u0447\u0438\u0441\u043b\u043e \u043c\u0430\u0440\u043a\u0435\u0440\u043e\u0432 \u043d\u0430 \u043f\u043e\u043b\u0435, \u0430 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0438\u0445 \n\u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0437\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0432 \u043c\u0430\u0441\u0441\u0438\u0432).\n\n\u041a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u043f\u043e\u0441\u0442\u0443\u043f\u0438\u0442\u044c \u0432 \u0442\u0430\u043a\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435?\n\n\u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0441\u043f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0442\u0438\u043f \u0434\u0430\u043d\u043d\u044b\u0445.\n\n\"\"\"\nstruct CoordmarkersBorderRobot <: AbstractBorderRobot\n    robot::Robot\n    coord::Coord\n    marker_coord::Vector{NTuple{2,Int}}\nend\n\nmd\"\"\"\n\u041f\u0440\u0438\u043c\u0435\u0440 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u0442\u0438\u043f\u0430 CoormarkersBorderRobot:\n\"\"\"\nr=Robot(animate=true)\n\nr = CoormarkersBorderRobot(r, Coord(), Int[])\n\nmd\"\"\"\n\n\u041f\u0440\u0438 \u044d\u0442\u043e\u043c, \u043e\u0434\u043d\u0430\u043a\u043e, \u043f\u0440\u0438\u0448\u043b\u043e\u0441\u044c \u0431\u044b \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043f\u043e\u0432\u0442\u043e\u0440\u0438\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \n    get_coords(::AbstractCoordRobot), move!(::AbstractCoordRobot, ::HorizonSide),\n(\u0443\u0436\u0435 \u0438\u043c\u0435\u044e\u0449\u0438\u0445\u0441\u044f \u0434\u043b\u044f \u0442\u0438\u043f\u0430 AbstractCoordRobot) \u0442.\u0435.\n\"\"\"\nget_coord(robot::CoordmarkersBorderRobot) = get_coord(robot.coord) #...get_coord -???\nfunction move!(robot::CoordmarkersBorderRobot, side::HorizonSide)\n    move!(robot,side)\n    move!(robot.coord, side)\nend\n\nmd\"\"\"\n\u0418, \u043a\u043e\u043d\u0435\u0447\u043d\u043e, \u043f\u0440\u0438 \u044d\u0442\u043e\u043c \u043f\u043e\u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f \u0435\u0449\u0451 \u043f\u0435\u0440\u0435\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u044e try_move! \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c.\n\"\"\"\n\nfunction try_move!(robot::CoordmarkersBorderRobot, side::HorizonSide)\n    try_move!(robot.robot, side)\n    if ismarker(robot)\n        push!(robot.marker_coord, get_coord(robot))\n    end\nend\n\nmd\"\"\"\n\n\u041e\u0434\u043d\u0430\u043a\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u043f\u0438\u0441\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \n    get_coords(::CoordmarkersBorderRobot), move!(::CoordmarkersBorderRobot, ::HorizonSide),\n(\u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044f \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043e\u043f\u0440\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043e\u0434\u043d\u043e\u0438\u043c\u0435\u043d\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0434\u043b\u044f \u0442\u0438\u043f\u0430 AbstractCoordRobot)    \n\u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0438\u0437\u0431\u0435\u0436\u0430\u0442\u044c, \u0435\u0441\u043b\u0438 \u0431\u044b \u0432\u043c\u0435\u0441\u0442\u043e \u0442\u0438\u043f\u0430 AbsctractBorderRobot \u043d\u0430\u043c\u0438 \u0431\u044b\u043b \u0431\u044b \u0441\u043f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0439\n\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0442\u0438\u043f.\n\n\u0412\u043e\u0442 \u043a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0433\u043b\u043e \u0431\u044b \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u0442\u044c.\n\n#------------------------------------------------------\n\nAbstractRobot:\n|                        move!(::AbstractRobot, ::HorizonSide), \n|                        isborder(::AbstractRobot, ::HorizonSide), \n|                        putmarker!(::AbstractRobot), \n|                        ismarker(::AbstractRobot), \n|                        temperature(::AbstractRobot)\n|\n|--AbsctractBorderRobot{TypeRobot}: # - \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0442\u0438\u043f, \u0433\u0434\u0435 TypeRobot - \u044d\u0442\u043e \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440 \u044d\u0442\u043e\u0433\u043e \u0442\u0438\u043f\u0430\n|  |                     movements!(::AbsctractBorderRobot{TypeRobot},::HorisonSide),\n|  |                     movements!(::AbsctractBorderRobot{TypeRobot},::HorisonSide,Integer), \n|  |                     try_move!(::AbstractBorderRobot{TypeRobot}, ::HorizonSide)\n|  |                     get(robot::AbsctractBorderRobot{TypeRobot}) = robot.robot # get(robot::Robot) = robot\n|  |\n|  |--PutmarkersBorderRobot{TypeRobot}: # - \u0442\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u043e \u043e\u0441\u043e\u0431\u0430\u044f \u0440\u0430\u0437\u043d\u043e\u0432\u0438\u0434\u043d\u043e\u0441\u0442\u044c \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u044b\u0445 \u0442\u0438\u043f\u043e\u0432 (\u0432\u0441\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0442\u0438\u043f\u044b - \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u044b\u0435, \u0445\u043e\u0442\u044f \u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u044e\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430)\n|  |                    try_move!(::PutmarkersBorderRobot{TypeRobot}, ::HorizonSide), \n|  |\n|  |--CountmarkerBorderRobot: \n|  |                    try_move!(::CountmarkerBorderRobot, ::HorizonSide), \n|  |                    \n|  |--....\n|\n|--AbstractCoordRobot \n|  |\n|  |-CoordRobot{TypeRobot}:\n|  |                    get(robot::CoordRobot{TypeRobot}) = robot.robot  #::Robot\n|\n|--PutmarkerRobot\n|--CountmarkersRobot\n|--....\n\n\nget(robot::Robot) = robot # - \u0442\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0434\u043e\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c\n#------------------------------------------------------\n\n\"\"\"\n\nabstract type AbstracrBorderRobot{TypeRobot} <: AbstractRobot end\n\nfunction movements!(robot::AbsctractBorderRobot{TypeRobot},side::HorisonSide)\n    n=0\n    while try_move!(get(robot), side)\n        n+=1\n    end\n    return n\nend\n\nfunction movements!(robot::AbstractBorderRobot{TypeRobot}, side::HorizonSide, num_steps::Integer)\n    for _ in 1:num_steps\n        try_move!(get(robot), side)\n    end\nend\n\n#----------------------------------------------\nget(robot::Robot) = robot\n\n\n#--------------------------------------------------------\nstruct CoordmarkersBorderRobot{TypeRobot} <: AbstractBorderRobot{TypeRobot}\n    robot::TypeRobot\n    markers_coord::Vector{NTuple{2,Int}}\nend\n\nget(robot::CoordmarkersBorderRobot{CoordRobot})=get(robot.robot)\n\nfunction try_move!(robot::CoordmarkersBorderRobot{CoordRobot}, side::HorizonSide)\n    try_move!(get(robot), side)\n    if ismarker(robot)\n        push!(robot.marker_coord, get_coord(robot))\n    end\nend\n\nmd\"\"\"\n\u0417\u0410\u041c\u0415\u0427\u0410\u041d\u0418\u0415\n\n\u0415\u0441\u043b\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d \u0442\u0438\u043f CoordRobot\n\u0442\u043e \u0442\u0438\u043f CoordmarkersBorderRobot{CoordRobot} \u0431\u0443\u0434\u0435\u0442 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u043c\u0438\n\n\u041d\u043e \u0442\u0438\u043f CoordmarkersBorderRobot{TypeRobot} \u0431\u0443\u0434\u0435\u0442 \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u044b\u043c \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u0442\u0438\u043f\u043e\u043c (\u0435\u0441\u043b\u0438 TypeRobot - \u044d\u0442\u043e \u043d\u0435\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0439 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440).\n\n\u0412 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 get \u0438 try_move! \u0431\u044b\u043b\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u044b \u0434\u043b\u044f \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u0442\u0438\u043f\u0430 CoordmarkersBorderRobot{CoordRobot}\n(\u0442.\u0435. \u044d\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0443\u0437\u043a\u043e \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u043c\u0438, \u043d\u0435 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u043c\u0438)\n#-----------------------------------------------------\n\n\u041f\u0420\u0418\u041c\u0415\u0420\u042b \u0421\u041e\u0417\u0414\u0410\u041d\u0418\u042f \u041e\u0411\u042a\u0415\u041a\u0422\u041e\u0412\n\"\"\"\nr = Robot()\n\nr1 = CoordmarkersBorderRobot{Robot}(r, Int[]) \n# - r1 - \u043d\u0430 \u0441\u0430\u043c\u043e\u043c \u0434\u0435\u043b\u0435 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u043d\u0435 \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u0435\u0442\n#        \u0438 \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0434\u043b\u044f \u0442\u0438\u043f\u0430 CoordmarkersBorderRobot{Robot} \u043d\u0430\u043c\u0438 \u043d\u0435 \u0431\u044b\u043b\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0444\u0443\u043d\u043a\u0446\u0438\u044f try_move\n#        (\u043a\u0430\u043a \u0438 \u0444\u0443\u043d\u043a\u0446\u0438\u044f get)\n#   \u041d\u043e \u0442\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435 \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u043e\u0431\u044a\u0435\u043a\u0442 r1 \u043c\u043e\u0436\u043d\u043e, \u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0435\u0433\u043e \u0434\u043b\u044f \u043d\u0430\u0448\u0438\u0445 \u0446\u0435\u043b\u0435\u0439 \u043d\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0441\u044f\n\nr2 = CoordRobot(r) # \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u0443\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u043d\u0443\u043b\u0435\u0432\u044b\u0435 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0435 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b\n\nget(r2) #--> r2.robot (::Robot)\n\nr3 = CoordmarkersBorderRobot{CoordRobot}(r2, Int[]) # - \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u0435\u0442 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b\n\nget(r3) #--> get(r3.robot) --> r3.robot.robot (::Robot)\n\n#--------------------------------------\n\nmd\"\"\"\n\u0412 \u0432\u0438\u0434\u0443 \u0431\u0435\u0441\u043f\u043e\u043b\u0435\u0437\u043d\u043e\u0441\u0442\u0438 \u0442\u0438\u043f\u0430 CoordmarkersBorderRobot{Robot}, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0431\u0435\u0441\u043f\u043e\u043b\u0435\u0437\u043d\u043e\u0441\u0442\u0438 \u0432\u0441\u0435\u0445 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u043f\u043e\u0434\u0442\u0438\u043f\u043e\u0432\n\u0442\u0438\u043f\u0430 CoordmarkersBorderRobot{TypeRobot} \u0437\u0430 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\u043c \u0442\u0438\u043f\u0430\nCoordmarkersBorderRobot{CoordRobot}\n\u0442\u0438\u043f CoordmarkersBorderRobot \u043d\u0435 \u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043b\u043e \u0434\u0435\u043b\u0430\u0442\u044c \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u043c.\n\n\u041f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0442\u0430\u043a.\n\"\"\"\n\nstruct CoordmarkersBorderRobot <: AbstractBorderRobot{CoordRobot}\n    robot::CoordRobot\n    markers_coord::Vector{NTuple{2,Int}}\nend\n\nget(robot::CoordmarkersBorderRobot) = get(robot.robot)\n\nfunction try_move!(robot::CoordmarkersBorderRobot, side::HorizonSide)\n    try_move!(get(robot), side)\n    if ismarker(robot)\n        push!(robot.marker_coord, get_coord(robot))\n    end\nend\n\nmd\"\"\"\n\u0417\u0410\u0414\u0410\u0427\u0410\n\u0414\u0410\u041d\u041e: \u0420\u043e\u0431\u043e\u0442 - \u0432 \u044e\u0433\u043e-\u0437\u0430\u043f\u0430\u0434\u043d\u043e\u043c \u0443\u0433\u043b\u0443 \u043f\u043e\u044f\u043b\u044f, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0438\u043c\u0435\u044e\u0442\u0441\u044f \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u0438\u0435 \u043f\u0440\u044f\u043c\u043e\u0443\u0433\u043e\u043b\u044c\u043d\u044b\u0435 \u0438\u043b\u0438 \u043f\u0440\u044f\u043c\u043e\u043b\u0438\u043d\u0435\u0439\u043d\u044b\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0440\u043e\u0434\u043a\u0438\n\u0420\u0415\u0417\u0423\u041b\u042c\u0422\u0410\u0422: \u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0432\u0435\u0440\u043d\u0443\u043b\u0430 \u043c\u0430\u0441\u0441\u0438\u0432 \u0441 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u0430\u043c\u0438 \u0437\u0430\u043c\u0430\u0440\u043a\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043a\u043b\u0435\u0442\u043e\u043a\n\"\"\"\n\nfunction markers_coord(r::Robot)\n    coord_roobot = CoordRobot(r)\n    robot = CoordmarkersBorderRobot(coord_roobot, Int[])\n    snake!(robot)\n    return robot.markers_coord\nend\n\nmd\"\"\"\n\u0415\u0441\u043b\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0435\u0449\u0451 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0443\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u044e\n\"\"\"\n\nget_markers_coord(robot::CoordmarkersBorderRobot) = robot.markers_coord\n\nmd\"\"\"\n\n\u0438 \u0435\u0441\u043b\u0438 \u043f\u0440\u0438 \u044d\u0442\u043e\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u044f snake! \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0441\u0441\u044b\u043b\u043a\u0443 \u043d\u0430 \u0440\u043e\u0431\u043e\u0442\u0430 (\u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u0442 \u0435\u0451 \u0438 \u0435\u0451 \u0436\u0435 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442),\n\u0442\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 markers_coord(::Robot) \u043c\u043e\u0433\u043b\u043e \u0431\u044b \u0431\u044b\u0442\u044c \u0441\u043e\u0432\u0441\u0435\u043c \u043a\u0440\u0430\u0442\u043a\u0438\u043c:\n\"\"\"\n\nmarkers_coord(r::Robot) = get_markers_coord(snake!(CoordmarkersBorderRobot(CoordRobot(r), Int[])))\n\nmd\"\"\"\n\u0414\u043b\u044f \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u0430 \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0442\u0430\u043a\u0436\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0435\u0449\u0451 \u043e\u0434\u0438\u043d \u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0442\u043e\u0440:\nCoordmarkersBorderRobot(r::Robot) = CoordmarkersBorderRobot(CoordRobot(r), Int[]))\n\n\u0442\u043e\u0433\u0434\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 markers_coord(::Robot) \u043c\u043e\u0433\u043b\u043e \u0431\u044b \u0441\u0442\u0430\u0442\u044c \u0435\u0449\u0435 \u0431\u043e\u043b\u0435\u0435 \u043b\u0430\u043a\u043e\u043d\u0438\u0447\u043d\u044b\u043c:\n\"\"\"\nmarkers_coord(r::Robot) = get_markers_coord(snake!(CoordmarkersBorderRobot(r))\n\nmd\"\"\"\n\u041f\u0440\u0438\u043c\u0435\u0440 \u043f\u0440\u043e\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0435\u0449\u0451 \u043e\u0434\u043d\u043e\u0433\u043e \u0442\u0438\u043f\u0430\n\"\"\"\n\nstruct PutmarkersBorderRobot{TypeRobot} <: AbstractBorderRobot{TypeRobot}\n    robot::TypeRobot\nend\n\nmd\"\"\"\n\u0417\u0434\u0435\u0441\u044c, \u0432 \u043e\u0442\u043b\u0438\u0447\u0438\u0435 \u043e\u0442 CoormarkersBorderRobot \u0442\u0438\u043f PutmarkersBorderRobot{TypeRobot} \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d \u043a\u0430\u043a \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0439.\n\n\u042d\u0442\u043e \u043d\u0435 \u043b\u0438\u0448\u0435\u043d\u043e \u0441\u043c\u044b\u0441\u043b\u0430, \u0442.\u043a. \u0440\u043e\u0431\u043e\u0442-\u043f\u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0449\u0438\u043a \u043c\u0430\u0440\u043a\u0435\u0440\u043e\u0432 \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u0442\u044c \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d \u043a\u0430\u043a \u043d\u0430 \u0431\u0430\u0437\u0435 \u0442\u0438\u043f\u0430 Robot, \u0442\u0430\u043a \u0438 \n\u043d\u0430 \u0431\u0430\u0437\u0435 \u043a\u0430\u043a\u043e\u0433\u043e-\u043d\u0438\u0431\u0443\u0434\u044c \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u0442\u0438\u043f\u0430, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, - CoordRobot\n\n\u041f\u0440\u0438 \u044d\u0442\u043e\u043c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0444\u0443\u043d\u043a\u0446\u0438\u0438 get \u0438 try_move \u043c\u043e\u0433\u0443\u0442 \u0431\u044b\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u044b \u043a\u0430\u043a \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438, \u0442\u0430\u043a \u0438 \u043a\u0430\u043a \u0443\u0437\u043a\u043e\u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \n\"\"\"\nget(robot::PutmarkersBorderRobot{TypeRobot}) where TypeRobot = get(robot.robot)\n\nfunction try_move!(robot::PutmarkersBorderRobot{TypeRobot}, side::HorizonSide) where TypeRobot\n    if try_move!(get(robot), side)\n        putmarker!(robot)\n    end\nend\n\n\u0422\u0430\u043a \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0431\u0443\u0434\u0443\u0442 \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u043c\u0438, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0442\u0438\u043f \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430 PutmarkersBorderRobot{TypeRobot} \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f\n \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u044b\u043c: \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 TypeRobot. \u0422\u043e, \u0447\u0442\u043e TypeRobot - \u044d\u0442\u043e \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440, \u0430 \u043d\u0435 \u0438\u043c\u044f \u043a\u0430\u043a\u043e\u0433\u043e-\u0442\u043e \u0440\u0430\u043d\u0435\u0435 \u043e\u043f\u0440\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0433\u043e\n \u0442\u0438\u043f\u0430 (\u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u043e\u0433\u043e \u0438\u043b\u0438 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e) \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442\u0441\u044f \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0433\u043e \u0441\u043b\u043e\u0432\u0430 where.\n\n\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u043d\u0438\u044f \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f\u043c\u0438 \u0443\u0437\u043a\u043e\u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 (\u043d\u0435 \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u0445)\n\nget(robot::PutmarkersBorderRobot{Robot}) = get(robot.robot)\n\nfunction try_move!(robot::PutmarkersBorderRobot{Robot}, side::HorizonSide)\n    if try_move!(get(robot), side)\n        putmarker!(robot)\n    end\nend\n\n\u0412 \u043e\u0442\u043b\u0438\u0447\u0438\u0435 \u043e\u0442 \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0432\u044b\u0448\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0439 \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 get, try_move!, \u0442\u0430\u043a\u0438\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043f\u043e\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043b\u0438\u0441\u044c \n\u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 TypeRobot.\n\nmd\"\"\"\n# \u042d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f\n\n## \u0424\u0443\u043d\u043a\u0446\u0438\u0438 - \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u043a\u0442\u044b 1-\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0430\n\"\"\"\nx = 0\n\ns = sin  # <: Function \n\nmd\"\"\"\n\u041f\u0443\u0441\u0442\u044c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u044b \u0445\u043e\u0442\u0438\u043c \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0442\u044c \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440, \u043f\u043e\u043a\u0430 \u043d\u0435 \u043d\u0430\u0441\u0442\u0443\u043f\u0438\u0442 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0441\u043e\u0431\u044b\u0442\u0438\u0435,\n\u0432\u044b\u0440\u0430\u0436\u0435\u043d\u043d\u043e\u0435 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c \u0443\u0441\u043b\u043e\u0432\u0438\u0435\u043c.\n\n\u042d\u0442\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u0442\u0441\u044f \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u0435\u0439:\n\"\"\"\n\nfunction movements!(condition::Function, r, side)\n    n=0\n    while !condition(r)\n        move!(r,side)\n        n+=1\n    end\n    return n\nend\n\n#\u0422\u0435\u043f\u0435\u0440\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043c\u043e\u0436\u043d\u043e \u0432\u044b\u0437\u044b\u0432\u0430\u0442\u044c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0442\u0430\u043a:\n\nmovements!(isborder, r, side)\n\nmd\"\"\"\n## \u0410\u043d\u043e\u043d\u0438\u043c\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438\n\u0410\u043d\u043e\u043d\u0438\u043c\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0442 \u0441\u043e\u0431\u043e\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f (<: Function) \u0431\u0435\u0437 \u0438\u043c\u0435\u043d\u0438\n (\u043f\u043e\u0434\u043e\u0431\u043d\u043e \u0442\u043e\u043c\u0443, \u043a\u0430\u043a \u0431\u044b\u0432\u0430\u044e\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0438\u043f\u0430 Int \u0431\u0435\u0437 \u0438\u043c\u0435\u043d\u0438)\n\n\u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440:\n\ncondition = robot -> !isborder(robot) \n\n\u0417\u0434\u0435\u0441\u044c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 condition \u043f\u0440\u0438\u0441\u0432\u043e\u0435\u043d\u043e \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 (\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0430\u043d\u043e\u043d\u0438\u043c\u043d\u043e\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u0438) \n\"\"\"\n\n", "meta": {"hexsha": "b2f9e5995f1f31d570d8145057e867e5c14c6683", "size": 11105, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lectures/Lecture_8.jl", "max_stars_repo_name": "Droideka501/mirea-progs", "max_stars_repo_head_hexsha": "5f96fda66e296f33b1cd9018a11467e4c568c709", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Lectures/Lecture_8.jl", "max_issues_repo_name": "Droideka501/mirea-progs", "max_issues_repo_head_hexsha": "5f96fda66e296f33b1cd9018a11467e4c568c709", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lectures/Lecture_8.jl", "max_forks_repo_name": "Droideka501/mirea-progs", "max_forks_repo_head_hexsha": "5f96fda66e296f33b1cd9018a11467e4c568c709", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-12-15T13:40:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-15T13:40:21.000Z", "avg_line_length": 32.9525222552, "max_line_length": 169, "alphanum_fraction": 0.699234579, "num_tokens": 3356, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42632160712508727, "lm_q2_score": 0.16238002855971875, "lm_q1q2_score": 0.06922611474059687}}
{"text": "ENV[\"PLOTS_TEST\"] = \"true\"\nENV[\"GKSwstype\"] = \"100\"\n\n\nusing Documenter\nusing SymPy\n\nmakedocs(\nsitename = \"SymPy\",\nformat = Documenter.HTML(),\nmodules = [SymPy],\npages = [\n    \"Home\" => \"index.md\",\n    \"Examples\"  =>  \"introduction.md\",\n    \"SymPy tutorial\"=> [\n        \"About\" =>  \"Tutorial/index.md\",\n        \"Introduction\" => \"Tutorial/intro.md\",\n        \"Gotchas\" => \"Tutorial/gotchas.md\",\n        \"Basic operations\" => \"Tutorial/basic_operations.md\",\n        \"Simplification\" => \"Tutorial/simplification.md\",\n        \"Calculus\" => \"Tutorial/calculus.md\",\n        \"Solvers\" => \"Tutorial/solvers.md\",\n        \"Matrices\" => \"Tutorial/matrices.md\",\n        \"Advanced expression  manipulation\" => \"Tutorial/manipulation.md\"\n    ],\n    \"Reference/API\" => \"reference.md\"\n    ],\n)\n\n# Documenter can also automatically deploy documentation to gh-pages.\n# See \"Hosting Documentation\" and deploydocs() in the Documenter manual\n# for more information.\n#=deploydocs(\n    repo = \"<repository url>\"\n)=#\n", "meta": {"hexsha": "1d701970f87b181d5989da757ae803f34ba50cb5", "size": 992, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "fatteneder/SymPy.jl", "max_stars_repo_head_hexsha": "8b25bb9d0941443404de1b3a79205fee32080f6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/make.jl", "max_issues_repo_name": "fatteneder/SymPy.jl", "max_issues_repo_head_hexsha": "8b25bb9d0941443404de1b3a79205fee32080f6c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "docs/make.jl", "max_forks_repo_name": "fatteneder/SymPy.jl", "max_forks_repo_head_hexsha": "8b25bb9d0941443404de1b3a79205fee32080f6c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5555555556, "max_line_length": 73, "alphanum_fraction": 0.627016129, "num_tokens": 256, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.15610489744545744, "lm_q1q2_score": 0.06894731925370332}}
{"text": "# -----------------------------------------------------------------\n# scitype function (generic)\n\n\"\"\"\n    scitype(X)\n\nThe scientific type (interpretation) of `X`, as distinct from its\nmachine type, as specified by the active convention.\n\n### Examples from the MLJ convention\n\n```\njulia> using ScientificTypes # or `using MLJ`\njulia> scitype(3.14)\nContinuous\n\njulia> scitype([1, 2, 3, missing])\nAbstractArray{Union{Missing, Count},1}\n\njulia> scitype((5, \"beige\"))\nTuple{Count, Textual}\n\njulia> using CategoricalArrays\njulia> X = (gender = categorical(['M', 'M', 'F', 'M', 'F']),\n            ndevices = [1, 3, 2, 3, 2])\njulia> scitype(X)\nTable{Union{AbstractArray{Count,1}, AbstractArray{Multiclass{2},1}}}\n```\n\nThe specific behavior of `scitype` is governed by the active\nconvention, as returned by `ScientificTypesBase.convention()`. The\n[ScientificTypes.jl\ndocumentation](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/)\ndetails the convention demonstrated above.\n\n\"\"\"\nscitype(X;    kw...) = scitype(X, convention();     kw...)\nscitype(X, C; kw...) = scitype(X, C, Val(trait(X)); kw...)\n\nscitype(X, C, ::Val{:other}; kw...) = Unknown\nscitype(::Missing;           kw...) = Missing\nscitype(::Nothing;           kw...) = Nothing\n\nscitype(t::Tuple, ::Convention; kw...) = Tuple{scitype.(t; kw...)...}\n\n# -----------------------------------------------------------------\n# convenience methods for scitype over unions\n\n\"\"\"\n    scitype_union(A)\n\nReturn the type union, over all elements `x` generated by the iterable `A`,\nof `scitype(x)`. See also [`scitype`](@ref).\n\"\"\"\nfunction scitype_union(A)\n    isempty(A) && return scitype(eltype(A))\n    reduce((a,b)->Union{a,b}, (scitype(el) for el in A))\nend\n\n\n# -----------------------------------------------------------------\n# Scitype for arrays\n\n\"\"\"\n    Scitype(T, C)\n\nMethod for implementers of a convention `C` to enable speed-up of\nscitype evaluations for arrays.\n\nIn general, one cannot infer the scitype of an object of type\n`AbstractArray{T, N}` from the machine type `T` alone.\n\nNevertheless, for some *restricted* machine types `U`, the statement\n`type(X) == AbstractArray{T, N}` for some `T<:U` already allows one\ndeduce that `scitype(X) = AbstractArray{S,N}`, where `S` is determined\nby `U` alone. This is the case in the *MLJ* convention, for example,\nif `U = Integer`, in which case `S = Count`.\n\nSuch shortcuts are specified as follows:\n\n```\nScitype(::Type{<:U}, ::C) = S\n```\n\nwhich incurs a considerable speed-up in the computation of `scitype(X)`.\nThere is also a speed-up for the case that `T <: Union{U, Missing}`.\n\nFor example, in the *MLJ* convention, one has\n\n```\nScitype(::Type{<:Integer}, ::MLJ) = Count\n```\n\"\"\"\nfunction Scitype end\n\nScitype(::Type, ::Convention) = Unknown\n\n# to distinguish between Any type and Union{T,Missing} for some more\n# specialised `T`, we define the Any case explicitly\nScitype(::Type{Any}, ::Convention) = Unknown\n\n# for the case Union{T,Missing} we return Union{S,Missing} with S\n# the scientific type corresponding to T\nScitype(::Type{Union{T,Missing}}, C::Convention) where T =\n    Union{Missing,Scitype(T, C)}\n\n# for the case Missing, we return Missing\nScitype(::Type{Missing}, C::Convention) = Missing\n\nScitype(::Type{Nothing}, C::Convention) = Nothing\n\n# Broadcasting over arrays\n\nscitype(A::Arr{T}, C::Convention, ::Val{:other}; kw...) where T =\n    arr_scitype(A, C, Scitype(T, C); kw...)\n\n\"\"\"\n    arr_scitype(A, C, S; tight)\n\nReturn the scitype associated with an  array `A` of type `{T,N}` assuming an\nexplicit `Scitype` correspondance exist mapping `T` to `S`.\nIf `tight=true` and `T>:Missing` then the function checks whether there are\n\"true missing values\", otherwise it constructs a \"tight copy\" of the array\nwithout a `Union{Missing,S}` type.\n\"\"\"\nfunction arr_scitype(A::Arr{T,N}, C::Convention, S::Type;\n                     tight::Bool=false) where {T,N}\n    # no explicit scitype available\n    S === Unknown && return Arr{scitype_union(A),N}\n    # otherwise return `Arr{S,N}` or `Arr{Union{Missing,S},N}`\n    if T >: Missing\n        if tight\n            has_missings = findfirst(ismissing, A) !== nothing\n            !has_missings && return Arr{nonmissing(S),N}\n        end\n        return Arr{Union{S,Missing},N}\n    end\n    return Arr{S,N}\nend\n\n\"\"\"\n    elscitype(A)\n\nReturn the element scientific type of an abstract array `A`. By definition, if\n`scitype(A) = AbstractArray{S,N}`, then `elscitype(A) = S`.\n\"\"\"\nelscitype(X::Arr; kw...) = scitype(X; kw...) |> _get_elst\n\n_get_elst(st::Type{Arr{T,N}}) where {T,N} = T\n", "meta": {"hexsha": "84400d56b75640e16ffdcf9bb5d25db404c554ea", "size": 4542, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/scitype.jl", "max_stars_repo_name": "juliohm/ScientificTypesBase.jl", "max_stars_repo_head_hexsha": "f52047c09da2b0e437515bdd58f0e865c32e06c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scitype.jl", "max_issues_repo_name": "juliohm/ScientificTypesBase.jl", "max_issues_repo_head_hexsha": "f52047c09da2b0e437515bdd58f0e865c32e06c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scitype.jl", "max_forks_repo_name": "juliohm/ScientificTypesBase.jl", "max_forks_repo_head_hexsha": "f52047c09da2b0e437515bdd58f0e865c32e06c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.28, "max_line_length": 79, "alphanum_fraction": 0.6402465874, "num_tokens": 1298, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618627863437, "lm_q2_score": 0.15203223970113983, "lm_q1q2_score": 0.06891041617051856}}
{"text": "module NumberSuffix\n\n    export SuffixIt\n\n    function SuffixIt(num)::String\n        if (typeof(num)!=Int64)\n            throw(TypeError(num,\"cannot suffix this type of argument\",Int64))\n        end\n        if (num<0)\n            throw(DomainError(x, \"argument must be nonnegative\"))\n        end\n        suffixedNumber=string(num)\n        lastDigit=num%10\n        if (lastDigit==0 || lastDigit>=4)\n            suffixedNumber=suffixedNumber*\"th\"\n        elseif (lastDigit==1)\n            suffixedNumber=suffixedNumber*\"st\"\n        elseif (lastDigit==2)\n            suffixedNumber=suffixedNumber*\"nd\"\n        else\n            suffixedNumber=suffixedNumber*\"rd\"\n        end\n        return suffixedNumber\n    end\n\nend", "meta": {"hexsha": "938b293ba0704c46cb4174fac1b58eb76204b259", "size": 713, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NumberSuffix.jl", "max_stars_repo_name": "eulerkochy/NumberSuffix.jl", "max_stars_repo_head_hexsha": "5ed924ae59701c9dfcec6e70d1829ea3448df286", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/NumberSuffix.jl", "max_issues_repo_name": "eulerkochy/NumberSuffix.jl", "max_issues_repo_head_hexsha": "5ed924ae59701c9dfcec6e70d1829ea3448df286", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/NumberSuffix.jl", "max_forks_repo_name": "eulerkochy/NumberSuffix.jl", "max_forks_repo_head_hexsha": "5ed924ae59701c9dfcec6e70d1829ea3448df286", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.4230769231, "max_line_length": 77, "alphanum_fraction": 0.5932678822, "num_tokens": 174, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.14223188773801163, "lm_q1q2_score": 0.06889429376941265}}
{"text": "\"\"\"\n    Imputor\n\nAn imputor stores information about imputing values in `AbstractArray`s and `Tables.table`s.\nNew imputation methods are expected to subtype `Imputor` and, at minimum,\nimplement the `_impute!(data::AbstractArrays, imp::<MyImputor>)` method.\n\nWhile fallback `impute` and `impute!` methods are provided to extend your `_impute!` methods to\nn-dimensional arrays and tables, you can always override these methods to change the\nbehaviour as necessary.\n\"\"\"\nabstract type Imputor end\n\n#=\nThese default methods are required because @auto_hash_equals doesn't\nplay nice with Base.@kwdef\n=#\nfunction Base.hash(imp::T, h::UInt) where T <: Imputor\n    h = hash(Symbol(T), h)\n\n    for f in fieldnames(T)\n        h = hash(getfield(imp, f), h)\n    end\n\n    return h\nend\n\nfunction Base.:(==)(a::T, b::T) where T <: Imputor\n    result = true\n\n    for f in fieldnames(T)\n        if !isequal(getfield(a, f), getfield(b, f))\n            result = false\n            break\n        end\n    end\n\n    return result\nend\n\n\"\"\"\n    impute(data::T, imp; kwargs...) -> T\n\nReturns a new copy of the `data` with the missing data imputed by the imputor `imp`.\nFor matrices and tables, data is imputed one variable/column at a time.\nIf this is not the desired behaviour then you should overload this method or specify a different `dims` value.\n\n# Arguments\n* `data`: the data to be impute\n* `imp::Imputor`: the Imputor method to use\n\n# Returns\n* the input `data` with values imputed\n\n# Example\n```jldoctest\njulia> using Impute: Interpolate, impute\n\njulia> v = [1.0, 2.0, missing, missing, 5.0]\n5-element Vector{Union{Missing, Float64}}:\n 1.0\n 2.0\n  missing\n  missing\n 5.0\n\njulia> impute(v, Interpolate())\n5-element Vector{Union{Missing, Float64}}:\n 1.0\n 2.0\n 3.0\n 4.0\n 5.0\n```\n\"\"\"\nfunction impute(data, imp::Imputor; kwargs...)\n    # NOTE: We don't use a return type declaration here because `trycopy` isn't guaranteed\n    # to return the same type passed in. For example, subarrays and subdataframes will\n    # return a regular array or dataframe.\n    return impute!(trycopy(data), imp; kwargs...)\nend\n\n\"\"\"\n    impute!(data::A, imp; dims=:, kwargs...) -> A\n\nImpute the `missing` values in the array `data` using the imputor `imp`.\nOptionally, you can specify the dimension to impute along.\n\n# Arguments\n* `data::AbstractArray{Union{T, Missing}}`: the data to be impute along dimensions `dims`\n* `imp::Imputor`: the Imputor method to use\n\n# Keyword Arguments\n* `dims=:`: The dimension to impute along. `:rows` and `:cols` are also supported for matrices.\n\n# Returns\n* `AbstractArray{Union{T, Missing}}`: the input `data` with values imputed\n\n# NOTES\n1. Matrices have a deprecated `dims=2` special case as `dims=:` is a breaking change\n2. Mutation isn't guaranteed for all array types, hence we return the result\n3. `eachslice` is used internally which requires Julia 1.1\n\n# Example\n```jldoctest\njulia> using Impute: Interpolate, impute!\n\njulia> M = [1.0 2.0 missing missing 5.0; 1.1 2.2 3.3 missing 5.5]\n2\u00d75 Matrix{Union{Missing, Float64}}:\n 1.0  2.0   missing  missing  5.0\n 1.1  2.2  3.3       missing  5.5\n\njulia> impute!(M, Interpolate(); dims=1)\n2\u00d75 Matrix{Union{Missing, Float64}}:\n 1.0  2.0  3.0  4.0  5.0\n 1.1  2.2  3.3  4.4  5.5\n\njulia> M\n2\u00d75 Matrix{Union{Missing, Float64}}:\n 1.0  2.0  3.0  4.0  5.0\n 1.1  2.2  3.3  4.4  5.5\n```\n\"\"\"\nfunction impute!(\n    data::A, imp::Imputor; dims=:, kwargs...\n)::A where A <: AbstractArray{Union{T, Missing}} where T\n    dims === Colon() && return _impute!(data, imp; kwargs...)\n\n    for x in eachslice(data; dims=dims)\n        _impute!(x, imp; kwargs...)\n    end\n\n    return data\nend\n\n\nfunction impute!(\n    data::M, imp::Imputor; dims=nothing, kwargs...\n)::M where M <: AbstractMatrix{Union{T, Missing}} where T\n    dims === Colon() && return _impute!(data, imp; kwargs...)\n    # We're calling our `dim` function to throw a depwarn if `dims === nothing`\n    d = dim(data, dims)\n\n    for x in eachslice(data; dims=d)\n        _impute!(x, imp; kwargs...)\n    end\n\n    return data\nend\n\nimpute!(data::AbstractMatrix{Missing}, imp::Imputor; kwargs...) = data\n\n\"\"\"\n    impute!(data::T, imp; kwargs...) -> T where T <: AbstractVector{<:NamedTuple}\n\nSpecial case rowtables which are arrays, but we want to fallback to the tables method.\n\"\"\"\nfunction impute!(data::T, imp::Imputor)::T where T <: AbstractVector{<:NamedTuple}\n    return materializer(data)(impute!(Tables.columns(data), imp))\nend\n\n\"\"\"\n    impute!(data::AbstractArray, imp) -> data\n\n\nJust returns the `data` when the array doesn't contain `missing`s\n\"\"\"\nimpute!(data::AbstractArray, imp::Imputor; kwargs...) = disallowmissing(data)\n\n\"\"\"\n    impute!(data::AbstractArray{Missing}, imp) -> data\n\nJust return the `data` when the array only contains `missing`s\n\"\"\"\nimpute!(data::AbstractArray{Missing}, imp::Imputor; kwargs...) = data\n\n\n\"\"\"\n    impute!(table, imp; cols=nothing) -> table\n\nImputes the data in a table by imputing the values 1 column at a time;\nif this is not the desired behaviour custom imputor methods should overload this method.\n\n# Arguments\n* `imp::Imputor`: the Imputor method to use\n* `table`: the data to impute\n\n# Keyword Arguments\n* `cols`: The columns to impute along (default is to impute all columns)\n\n# Returns\n* the input `data` with values imputed\n\n# Example\n```jldoctest\njulia> using DataFrames; using Impute: Interpolate, impute\n\n\njulia> df = DataFrame(:a => [1.0, 2.0, missing, missing, 5.0], :b => [1.1, 2.2, 3.3, missing, 5.5])\n5\u00d72 DataFrame\n Row \u2502 a          b\n     \u2502 Float64?   Float64?\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502       1.0        1.1\n   2 \u2502       2.0        2.2\n   3 \u2502 missing          3.3\n   4 \u2502 missing    missing\n   5 \u2502       5.0        5.5\n\njulia> impute(df, Interpolate())\n5\u00d72 DataFrame\n Row \u2502 a         b\n     \u2502 Float64?  Float64?\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502      1.0       1.1\n   2 \u2502      2.0       2.2\n   3 \u2502      3.0       3.3\n   4 \u2502      4.0       4.4\n   5 \u2502      5.0       5.5\n```\n\"\"\"\nfunction impute!(table::T, imp::Imputor; cols=nothing)::T where T\n    # TODO: We could probably handle iterators of tables here\n    istable(table) || throw(MethodError(impute!, (table, imp)))\n\n    # Extract a columns iterator that we should be able to use to mutate the data.\n    # NOTE: Mutation is not guaranteed for all table types, but it avoid copying the data\n    columntable = Tables.columns(table)\n\n    cnames = cols === nothing ? propertynames(columntable) : cols\n    for cname in cnames\n        impute!(getproperty(columntable, cname), imp)\n    end\n\n    return table\nend\n\nfiles = [\n    \"interp.jl\",\n    \"knn.jl\",\n    \"locf.jl\",\n    \"nocb.jl\",\n    \"replace.jl\",\n    \"srs.jl\",\n    \"substitute.jl\",\n    \"svd.jl\",\n]\n\nfor file in files\n    include(joinpath(\"imputors\", file))\nend\n", "meta": {"hexsha": "838cd9703ae7515a1c036140cb5f6b4ce96da1d2", "size": 6712, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/imputors.jl", "max_stars_repo_name": "pitmonticone/Impute.jl", "max_stars_repo_head_hexsha": "bd2e1f2c62a7b9d29cf25cb0bd2d5290cc569d07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 57, "max_stars_repo_stars_event_min_datetime": "2017-05-18T22:52:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T17:09:14.000Z", "max_issues_repo_path": "src/imputors.jl", "max_issues_repo_name": "pitmonticone/Impute.jl", "max_issues_repo_head_hexsha": "bd2e1f2c62a7b9d29cf25cb0bd2d5290cc569d07", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 103, "max_issues_repo_issues_event_min_datetime": "2017-04-11T23:08:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-23T00:04:56.000Z", "max_forks_repo_path": "src/imputors.jl", "max_forks_repo_name": "pitmonticone/Impute.jl", "max_forks_repo_head_hexsha": "bd2e1f2c62a7b9d29cf25cb0bd2d5290cc569d07", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 10, "max_forks_repo_forks_event_min_datetime": "2017-12-21T17:12:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-12T07:20:56.000Z", "avg_line_length": 26.6349206349, "max_line_length": 110, "alphanum_fraction": 0.647794994, "num_tokens": 2129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.13846179412408713, "lm_q1q2_score": 0.06869004168243244}}
{"text": "Pkg.update()\n\n# Install required Julia packages\nPkg.add(\"SpecialFunctions\")\nPkg.add(\"FastGaussQuadrature\")\nPkg.add(\"GSL\")\nPkg.add(\"IterativeSolvers\")\nPkg.add(\"LinearMaps\")\nPkg.add(\"PyPlot\")\nPkg.add(\"SymPy\")\nPkg.add(\"PyCall\")\nPkg.add(\"LowRankApprox\")\nPkg.add(\"MATLAB\")\nPkg.add(\"NullableArrays\")\nPkg.add(\"Revise\")\nPkg.add(\"FMMLIB2D\")\nPkg.add(\"FINUFFT\")\nPkg.add(\"LaTeXStrings\")\nPkg.add(\"JLD\")\nPkg.add(\"Glob\")\nPkg.add(\"HDF5\")\nPkg.add(\"FileIO\")\nPkg.add(\"Humanize\")\nPkg.add(\"Interpolations\")\n", "meta": {"hexsha": "3ba48d43274fd40673211784425bc21298a924fa", "size": 486, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/setup.jl", "max_stars_repo_name": "askhamwhat/inse-fiem-2d", "max_stars_repo_head_hexsha": "06e1f8610b35da67900e1acef1004a0456a696c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:53:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-16T00:15:10.000Z", "max_issues_repo_path": "julia/setup.jl", "max_issues_repo_name": "askhamwhat/inse-fiem-2d", "max_issues_repo_head_hexsha": "06e1f8610b35da67900e1acef1004a0456a696c1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/setup.jl", "max_forks_repo_name": "askhamwhat/inse-fiem-2d", "max_forks_repo_head_hexsha": "06e1f8610b35da67900e1acef1004a0456a696c1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-28T18:44:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-14T04:17:59.000Z", "avg_line_length": 19.44, "max_line_length": 33, "alphanum_fraction": 0.7181069959, "num_tokens": 171, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713671682749485, "lm_q2_score": 0.1500288186418291, "lm_q1q2_score": 0.0685836815834334}}
{"text": "using Test\nusing MPI\n\nif get(ENV,\"JULIA_MPI_TEST_ARRAYTYPE\",\"\") == \"CuArray\"\n    import CUDA\n    ArrayType = CUDA.CuArray\nelse\n    ArrayType = Array\nend\n\nMPI.Init()\n\ncomm = MPI.COMM_WORLD\nsize = MPI.Comm_size(comm)\nrank = MPI.Comm_rank(comm)\n\ndst = mod(rank+1, size)\nsrc = mod(rank-1, size)\n\nN = 32\n\nsend_mesg = ArrayType{Float64}(undef,N)\nrecv_mesg = ArrayType{Float64}(undef,N)\nrecv_mesg_expected = ArrayType{Float64}(undef,N)\n\nfill!(send_mesg, Float64(rank))\nfill!(recv_mesg_expected, Float64(src))\n\nrreq = MPI.Irecv!(recv_mesg, src,  src+32, comm)\nsreq = MPI.Isend(send_mesg, dst, rank+32, comm)\n\nstats = MPI.Waitall!([sreq, rreq])\n@test rreq isa MPI.Request\n@test sreq isa MPI.Request\n@test MPI.Get_source(stats[2]) == src\n@test MPI.Get_tag(stats[2]) == src+32\n@test recv_mesg == recv_mesg_expected\n\n(done, stats) = MPI.Testall!([sreq, rreq])\n@test done\n\nif rank == 0\n    MPI.send(send_mesg, dst, rank+32, comm)\n    recv_mesg = recv_mesg_expected\nelseif rank == size-1\n    (recv_mesg, _) = MPI.recv(src, src+32, comm)\nelse\n    (recv_mesg, _) = MPI.recv(src, src+32, comm)\n    MPI.send(send_mesg, dst, rank+32, comm)\nend\n@test recv_mesg == recv_mesg_expected\n\n\nif rank == 0\n    MPI.Send(Float64(rank), dst, rank+32, comm)\n    recv_val = Float64(src)\nelseif rank == size-1\n    (recv_val, _) = MPI.Recv(Float64, src, src+32, comm)\nelse\n    (recv_val, _) = MPI.Recv(Float64, src, src+32, comm)\n    MPI.Send(Float64(rank), dst, rank+32, comm)\nend\n@test recv_val == Float64(src)\n\n\nrreq = MPI.Irecv!(recv_mesg, src,  src+32, comm)\nsreq = MPI.Isend(send_mesg, dst, rank+32, comm)\n\nreq_arr = [sreq,rreq]\ninds = MPI.Waitsome(req_arr)\nfor i in inds\n    @test MPI.Test(req_arr[i])\nend\n\nrreq = MPI.Irecv!(recv_mesg, src,  src+32, comm)\nMPI.Cancel!(rreq)\nMPI.Wait(rreq)\n@test rreq.buffer == nothing\n\nGC.gc()\n\n# ---------------------\n# MPI_Sendrecv function\n# ---------------------\n#\n# send datatype\n# ---------------------\n# We test this function by executing a left shift of the leftmost element in a 1D\n# cartesian topology with periodic boundary conditions.\n#\n# Assume we have two processors, the data will look like this\n# proc 0 | proc 1\n#  0 0 0 |  1 1 1\n#\n# After the shift the data will contain\n# proc 0 | proc 1\n#  0 0 1 |  1 1 0\n#\n# init data\ncomm_rank = MPI.Comm_rank(comm)\ncomm_size = MPI.Comm_size(comm)\na = Float64[comm_rank, comm_rank, comm_rank]\n\n# construct cartesian communicator with 1D topology\ncomm_cart = MPI.Cart_create(comm, 1, Cint[comm_size], Cint[1], false)\n\n# get source and dest ranks using Cart_shift\nsrc_rank, dest_rank = MPI.Cart_shift(comm_cart, 0, -1)\n\n# execute left shift using subarrays\nMPI.Sendrecv!(@view(a[1]), dest_rank, 0,\n              @view(a[3]), src_rank,  0, comm_cart)\n\n@test a == [comm_rank, comm_rank, (comm_rank+1) % comm_size]\n\n# send elements from a buffer\n# ---------------------------\na = Float64[comm_rank, comm_rank, comm_rank]\nb = Float64[       -1,        -1,        -1]\nMPI.Sendrecv!(@view(a[1:2]), dest_rank, 1,\n              @view(b[1:2]), src_rank,  1, comm_cart)\n\n@test b == [(comm_rank+1) % comm_size, (comm_rank+1) % comm_size, -1]\n\n# send entire buffer\n# ---------------------------\na = Float64[comm_rank, comm_rank, comm_rank]\nb = Float64[       -1,        -1,        -1]\nMPI.Sendrecv!(a, dest_rank, 2,\n              b,  src_rank, 2, comm_cart)\n\n@test b == [(comm_rank+1) % comm_size, (comm_rank+1) % comm_size, (comm_rank+1) % comm_size]\n\n@test MPI.Waitall!(MPI.Request[]) == MPI.Status[]\n\nMPI.Finalize()\n# @test MPI.Finalized()\n", "meta": {"hexsha": "21971cd66de5ab04caed24d45d68b38a928034bd", "size": 3489, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_sendrecv.jl", "max_stars_repo_name": "mikegros/MPI.jl", "max_stars_repo_head_hexsha": "3941405967b16884de159c69dd5168e205373535", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 255, "max_stars_repo_stars_event_min_datetime": "2015-01-08T03:24:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T18:27:43.000Z", "max_issues_repo_path": "test/test_sendrecv.jl", "max_issues_repo_name": "mikegros/MPI.jl", "max_issues_repo_head_hexsha": "3941405967b16884de159c69dd5168e205373535", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 397, "max_issues_repo_issues_event_min_datetime": "2015-01-06T06:50:36.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-13T23:24:49.000Z", "max_forks_repo_path": "test/test_sendrecv.jl", "max_forks_repo_name": "mikegros/MPI.jl", "max_forks_repo_head_hexsha": "3941405967b16884de159c69dd5168e205373535", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 118, "max_forks_repo_forks_event_min_datetime": "2015-02-07T04:16:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-13T14:58:55.000Z", "avg_line_length": 25.2826086957, "max_line_length": 92, "alphanum_fraction": 0.6494697621, "num_tokens": 1106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.1403362476923775, "lm_q1q2_score": 0.06852385950631133}}
{"text": "module MVDA\n\nusing DataFrames: copy, copyto!\nusing DataDeps, CSV, DataFrames, CodecZlib\nusing Parameters, Printf, MLDataUtils, ProgressMeter\nusing LinearAlgebra, Random, Statistics, StatsBase, StableRNGs\nusing KernelFunctions\nusing DelimitedFiles, Plots\nusing Polyester, MLDataUtils\n\nimport Base: show, iterate\n\n##### DATA #####\n\n#=\nUses DataDeps to download data as needed.\nInspired by UCIData.jl: https://github.com/JackDunnNZ/UCIData.jl\n=#\n\nconst DATA_DIR = joinpath(@__DIR__, \"data\")\n\n\"\"\"\n`list_datasets()`\n\nList available datasets in MVDA.\n\"\"\"\nlist_datasets() = map(x -> splitext(x)[1], readdir(DATA_DIR))\n\nfunction __init__()\n    for dataset in list_datasets()\n        include(joinpath(DATA_DIR, dataset * \".jl\"))\n    end\nend\n\n\"\"\"\n`dataset(str)`\n\nLoad a dataset named `str`, if available. Returns data as a `DataFrame` where\nthe first column contains labels/targets and the remaining columns correspond to\ndistinct features.\n\"\"\"\nfunction dataset(str)\n    # Locate dataset file.\n    dataset_path = @datadep_str str\n    file = readdir(dataset_path)\n    index = findfirst(x -> occursin(\"data.\", x), file)\n    if index isa Int\n        dataset_file = joinpath(dataset_path, file[index])\n    else # is this unreachable?\n        error(\"Failed to locate a data.* file in $(dataset_path)\")\n    end\n    \n    # Read dataset file as a DataFrame.\n    df = if splitext(dataset_file)[2] == \".csv\"\n        CSV.read(dataset_file, DataFrame)\n    else # assume .csv.gz\n        open(GzipDecompressorStream, dataset_file, \"r\") do stream\n            CSV.read(stream, DataFrame)\n        end\n    end\n    return df\nend\n\n\"\"\"\nProcess the dataset located at the given `path`.\n\nThis is an extra step to give fine-grain control in generating files with DataDeps.jl.\n\"\"\"\nfunction process_dataset(path::AbstractString; header=false, missingstrings=\"\", kwargs...)\n    input_df = CSV.read(path, DataFrame, header=header, missingstrings=missingstrings)\n    process_dataset(input_df; kwargs...)\n    rm(path)\nend\n\n\"\"\"\nFinal step in processing the given dataset `input_df`.\n\nThis standardizes cached files that live in ~/.julia/datadeps so that labels/targets appear in first column\nfollowed by features in the remaining columns.\nWe also check for uniqueness in features.\n\"\"\"\nfunction process_dataset(input_df::DataFrame;\n    target_index=-1,\n    feature_indices=1:0,\n    ext=\".csv\")\n    # Build output DataFrame.\n    output_df = DataFrame()\n    output_df.target = input_df[!, target_index]\n    output_df = hcat(output_df, input_df[!, feature_indices], makeunique=true)\n    output_cols = [ :target; [Symbol(\"x\", n) for n in eachindex(feature_indices)] ]\n    rename!(output_df, output_cols)\n    dropmissing!(output_df)\n    \n    # Write to disk.\n    output_path = \"data\" * ext\n    if ext == \".csv\"\n        CSV.write(output_path, output_df, delim=',', writeheader=true)\n    elseif ext == \".csv.gz\"\n        open(GzipCompressorStream, output_path, \"w\") do stream\n            CSV.write(stream, output_df, delim=\",\", writeheader=true)\n        end\n    else\n        error(\"Unknown file extension option '$(ext)'\")\n    end\nend\n\n##### IMPLEMENTATION #####\n\ninclude(\"problem.jl\")\ninclude(\"utilities.jl\")\ninclude(\"projections.jl\")\ninclude(\"simulation.jl\")\n\nabstract type AbstractMMAlg end\n\ninclude(joinpath(\"algorithms\", \"SD.jl\"))\ninclude(joinpath(\"algorithms\", \"MMSVD.jl\"))\ninclude(joinpath(\"algorithms\", \"CyclicVDA.jl\"))\n\nconst DEFAULT_ANNEALING = geometric_progression\nconst DEFAULT_CALLBACK = __do_nothing_callback__\nconst DEFAULT_SCORE_FUNCTION = prediction_error\n\n\"\"\"\n    fit(algorithm, problem, \u03f5, s; kwargs...)\n\nSolve optimization problem at sparsity level `s` using a deadzone of size `\u03f5`.\n\nThe solution is obtained via a proximal distance `algorithm` that gradually anneals parameter estimates\ntoward the target sparsity set.\n\"\"\"\nfunction fit(algorithm::AbstractMMAlg, problem::MVDAProblem, \u03f5::Real, s::Real; kwargs...)\n    extras = __mm_init__(algorithm, problem, nothing) # initialize extra data structures\n    MVDA.fit!(algorithm, problem, \u03f5, s, extras, (true,false,); kwargs...)\nend\n\n\"\"\"\n    fit!(algorithm, problem, \u03f5, s, [extras], [update_extras]; kwargs...)\n\nSame as `fit_MVDA(algorithm, problem, \u03f5, s)`, but with preallocated data structures in `extras`.\n\n!!! Note\n    The caller should specify whether to update data structures depending on `s` and `\u03c1` using `update_extras[1]` and `update_extras[2]`, respectively.\n\n    Convergence is determined based on the rule `dist < dtol || abs(dist - old) < rtol * (1 + old)`, where `dist` is the squared distance and `dtol` and `rtol` are tolerance parameters.\n\n!!! Tip\n    The `extras` argument can be constructed using `extras = __mm_init__(algorithm, problem, nothing)`.\n\n# Keyword Arguments\n\n- `nouter`: The number of outer iterations; i.e. the maximum number of `\u03c1` values to use in annealing (default=`100`).\n- `dtol`: An absolute tolerance parameter for the squared distance (default=`1e-6`).\n- `rtol`: A relative tolerance parameter for the squared distance (default=`1e-6`).\n- `rho_init`: The initial value for `\u03c1` (default=1.0).\n- `rho_max`: The maximum value for `\u03c1` (default=1e8).\n- `rhof`: A function `rhof(\u03c1, iter, rho_max)` used to determine the next value for `\u03c1` in the annealing sequence. The default multiplies `\u03c1` by `1.2`.\n- `verbose`: Print convergence information (default=`false`).\n- `cb`: A callback function for extending functionality.\n\nSee also: [`MVDA.anneal!`](@ref) for additional keyword arguments applied at the annealing step.\n\"\"\"\nfunction fit!(algorithm::AbstractMMAlg, problem::MVDAProblem, \u03f5::Real, s::Real,\n    extras=nothing,\n    update_extras::NTuple{2,Bool}=(true,false,);\n    nouter::Int=100,\n    dtol::Real=1e-6,\n    rtol::Real=1e-6,\n    rho_init::Real=1.0,\n    rho_max::Real=1e8,\n    rhof::Function=DEFAULT_ANNEALING,\n    verbose::Bool=false,\n    cb::Function=DEFAULT_CALLBACK,\n    kwargs...)\n    # Check for missing data structures.\n    if extras isa Nothing\n        error(\"Detected missing data structures for algorithm $(algorithm).\")\n    end\n\n    # Get problem info and extra data structures.\n    @unpack intercept, coeff, coeff_prev, proj = problem\n    @unpack projection = extras\n    \n    # Fix model size(s).\n    k = sparsity_to_k(problem, s)\n\n    # Initialize \u03c1 and iteration count.\n    \u03c1, iters = rho_init, 0\n\n    # Update data structures due to hyperparameters.\n    update_extras[1] && __mm_update_sparsity__(algorithm, problem, \u03f5, \u03c1, k, extras)\n    update_extras[2] && __mm_update_rho__(algorithm, problem, \u03f5, \u03c1, k, extras)\n\n    # Check initial values for loss, objective, distance, and norm of gradient.\n    apply_projection(projection, problem, k)\n    init_result = __evaluate_objective__(problem, \u03f5, \u03c1, extras)\n    result = SubproblemResult(0, init_result)\n    cb(0, problem, \u03f5, \u03c1, k, result)\n    old = sqrt(result.distance)\n\n    for iter in 1:nouter\n        # Solve minimization problem for fixed rho.\n        verbose && print(\"\\n\",iter,\"  \u03c1 = \",\u03c1)\n        result = MVDA.anneal!(algorithm, problem, \u03f5, \u03c1, s, extras, (false,true,); verbose=verbose, cb=cb, kwargs...)\n\n        # Update total iteration count.\n        iters += result.iters\n\n        cb(iter, problem, \u03f5, \u03c1, k, result)\n\n        # Check for convergence to constrained solution.\n        dist = sqrt(result.distance)\n        if dist < dtol || abs(dist - old) < rtol * (1 + old)\n            break\n        else\n          old = dist\n        end\n                \n        # Update according to annealing schedule.\n        \u03c1 = ifelse(iter < nouter, rhof(\u03c1, iter, rho_max), \u03c1)\n    end\n    \n    # Project solution to the constraint set.\n    apply_projection(projection, problem, k)\n    loss, obj, dist, gradsq = __evaluate_objective__(problem, \u03f5, \u03c1, extras)\n\n    if verbose\n        print(\"\\n\\niters = \", iters)\n        print(\"\\n\u2211\u1d62 max{0, |y\u1d62-B\u1d40x\u1d62|\u2082-\u03f5}\u00b2 = \", loss)\n        print(\"\\nobjective  = \", obj)\n        print(\"\\ndistance   = \", sqrt(dist))\n        println(\"\\n|gradient| = \", sqrt(gradsq))\n    end\n\n    return SubproblemResult(iters, loss, obj, dist, gradsq)\nend\n\n\"\"\"\n    anneal(algorithm, problem, \u03f5, \u03c1, s; kwargs...)\n\nSolve the `\u03c1`-penalized optimization problem at sparsity level `s` with deadzone `\u03f5`.\n\"\"\"\nfunction anneal(algorithm::AbstractMMAlg, problem::MVDAProblem, \u03f5::Real, \u03c1::Real, s::Real; kwargs...)\n    extras = __mm_init__(algorithm, problem, nothing)\n    MVDA.anneal!(algorithm, problem, \u03f5, \u03c1, s, extras, (true,true,); kwargs...)\nend\n\n\"\"\"\n    anneal!(algorithm, problem, \u03f5, \u03c1, s, [extras], [update_extras]; kwargs...)\n\nSame as `anneal(algorithm, problem, \u03f5, \u03c1, s)`, but with preallocated data structures in `extras`.\n\n!!! Note\n    The caller should specify whether to update data structures depending on `s` and `\u03c1` using `update_extras[1]` and `update_extras[2]`, respectively.\n\n    Convergence is determined based on the rule `gradsq < gtol`, where `gradsq` is squared Euclidean norm of the gradient and `gtol` is a tolerance parameter.\n\n!!! Tip\n    The `extras` argument can be constructed using `extras = __mm_init__(algorithm, problem, nothing)`.\n\n# Keyword Arguments\n\n- `ninner`: The maximum number of iterations (default=`10^4`).\n- `gtol`: An absoluate tolerance parameter on the squared Euclidean norm of the gradient (default=`1e-6`).\n- `nesterov_threshold`: The number of early iterations before applying Nesterov acceleration (default=`10`).\n- `verbose`: Print convergence information (default=`false`).\n- `cb`: A callback function for extending functionality.\n\"\"\"\nfunction anneal!(algorithm::AbstractMMAlg, problem::MVDAProblem, \u03f5::Real, \u03c1::Real, s::Real,\n    extras=nothing,\n    update_extras::NTuple{2,Bool}=(true,true);\n    ninner::Int=10^4,\n    gtol::Real=1e-6,\n    nesterov_threshold::Int=10,\n    verbose::Bool=false,\n    cb::Function=DEFAULT_CALLBACK,\n    kwargs...\n    )\n    # Check for missing data structures.\n    if extras isa Nothing\n        error(\"Detected missing data structures for algorithm $(algorithm).\")\n    end\n\n    # Get problem info and extra data structures.\n    @unpack intercept, coeff, coeff_prev, proj = problem\n    @unpack projection = extras\n\n    # Fix model size(s).\n    k = sparsity_to_k(problem, s)\n\n    # Update data structures due to hyperparameters.\n    update_extras[1] && __mm_update_sparsity__(algorithm, problem, \u03f5, \u03c1, k, extras)\n    update_extras[2] && __mm_update_rho__(algorithm, problem, \u03f5, \u03c1, k, extras)\n\n    # Check initial values for loss, objective, distance, and norm of gradient.\n    apply_projection(projection, problem, k)\n    result = __evaluate_objective__(problem, \u03f5, \u03c1, extras)\n    cb(0, problem, \u03f5, \u03c1, k, result)\n    old = result.objective\n\n    if sqrt(result.gradient) < gtol\n        return SubproblemResult(0, result)\n    end\n\n    # Use previous estimates in case of warm start.\n    copyto!(coeff.all, coeff_prev.all)\n\n    # Initialize iteration counts.\n    iters = 0\n    nesterov_iter = 1\n    verbose && @printf(\"\\n%-5s\\t%-8s\\t%-8s\\t%-8s\\t%-8s\", \"iter.\", \"loss\", \"objective\", \"distance\", \"|gradient|\")\n    for iter in 1:ninner\n        iters += 1\n\n        # Apply the algorithm map to minimize the quadratic surrogate.\n        __mm_iterate__(algorithm, problem, \u03f5, \u03c1, k, extras)\n\n        # Update loss, objective, distance, and gradient.\n        apply_projection(projection, problem, k)\n        result = __evaluate_objective__(problem, \u03f5, \u03c1, extras)\n\n        cb(iter, problem, \u03f5, \u03c1, k, result)\n\n        if verbose\n            @printf(\"\\n%4d\\t%4.3e\\t%4.3e\\t%4.3e\\t%4.3e\", iter, result.loss, result.objective, sqrt(result.distance), sqrt(result.gradient))\n        end\n\n        # Assess convergence.\n        obj = result.objective\n        gradsq = sqrt(result.gradient)\n        if gradsq < gtol\n            break\n        elseif iter < ninner\n            needs_reset = iter < nesterov_threshold || obj > old\n            nesterov_iter = __apply_nesterov__!(coeff.all, coeff_prev.all, nesterov_iter, needs_reset)\n            old = obj\n        end\n    end\n    # Save parameter estimates in case of warm start.\n    copyto!(coeff_prev.all, coeff.all)\n\n    return SubproblemResult(iters, result)\nend\n\n\"\"\"\n    cv(algorithm, problem, grids; [at], [kwargs...])\n\nSplit data in `problem` into cross-validation and a test sets, then run cross-validation over the `grids`.\n\n# Keyword Arguments\n\n- `at`: A value between `0` and `1` indicating the proportion of samples/instances used for cross-validation, with remaining samples used for a test set (default=`0.8`).\n\nSee also: [`MVDA.cv(algorithm::AbstractMMAlg, problem::MVDAProblem, grids::Tuple{E,S}, dataset_split::Tuple{Any,Any})`](@ref)\n\"\"\"\nfunction cv(algorithm::AbstractMMAlg, problem::MVDAProblem, grids::Tuple{E,S}; at::Real=0.8, kwargs...) where {E,S}\n    # Split data into cross-validation and test sets.\n    @unpack p, Y, X, intercept = problem\n    dataset_split = splitobs((Y, view(X, :, 1:p)), at=at, obsdim=1)\n    MVDA.cv(algorithm, problem, grids, dataset_split; kwargs...)\nend\n\n\"\"\"\n    cv(algorithm, problem, grids, dataset_split; [kwargs...])\n\nRun k-fold cross-validation over hyperparameters `(\u03f5, s)` for deadzone radius and sparsity level, respectively.\n\nThe given `problem` should enter with initial model parameters in `problem.coeff.all`.\nHyperparameters are specified in `grids = (\u03f5_grid, s_grid)`, and data subsets are given as `dataset_split = (cv_set, test_set)`.\n\n# Keyword Arguments\n\n- `nfolds`: The number of folds to run in cross-validation.\n- `scoref`: A function that evaluates a classifier over training, validation, and testing sets (default uses misclassification error).\n- `show_progress`: Toggles progress bar.\n\nAdditional arguments are propagated to `fit` and `anneal`. See also [`MVDA.fit`](@ref) and [`MVDA.anneal`](@ref).\n\"\"\"\nfunction cv(algorithm::AbstractMMAlg, problem::MVDAProblem, grids::Tuple{E,S}, dataset_split::Tuple{Any,Any};\n    lambda::Real=1e-3,\n    maxiter::Int=10^4,\n    tol::Real=1e-4,\n    nfolds::Int=5,\n    scoref::Function=DEFAULT_SCORE_FUNCTION,\n    cb::Function=DEFAULT_CALLBACK,\n    show_progress::Bool=true,\n    kwargs...) where {E,S}\n    # Initialize the output.\n    cv_set, test_set = dataset_split\n    \u03f5_grid, s_grid = grids\n    n\u03f5, ns = length(\u03f5_grid), length(s_grid)\n    alloc_score_arrays(a, b, c) = [Matrix{Float64}(undef, a, b) for _ in 1:c]\n    result = (;\n        train=alloc_score_arrays(ns, n\u03f5, nfolds),\n        validation=alloc_score_arrays(ns, n\u03f5, nfolds),\n        test=alloc_score_arrays(ns, n\u03f5, nfolds),\n        time=alloc_score_arrays(ns, n\u03f5, nfolds),\n    )\n\n    # Run cross-validation.\n    if show_progress\n        progress_bar = Progress(nfolds * n\u03f5 * ns, 1, \"Running CV w/ $(algorithm)... \")\n    end\n\n    for (k, fold) in enumerate(kfolds(cv_set, k=nfolds, obsdim=1))\n        # Retrieve the training set and validation set.\n        # TODO: Does this guarantee copies?\n        train_set, validation_set = fold\n        train_Y, train_X = getobs(train_set, obsdim=1)\n        val_Y, val_X = getobs(validation_set, obsdim=1)\n        test_Y, test_X = getobs(test_set, obsdim=1)\n        \n        # Standardize ALL data based on the training set.\n        F = StatsBase.fit(ZScoreTransform, train_X, dims=1)\n        has_nan = any(isnan, F.scale) || any(isnan, F.mean)\n        has_inf = any(isinf, F.scale) || any(isinf, F.mean)\n        has_zero = any(iszero, F.scale)\n        if has_nan\n            error(\"Detected NaN in z-score.\")\n        elseif has_inf\n            error(\"Detected Inf in z-score.\")\n        elseif has_zero\n            for idx in eachindex(F.scale)\n                x = F.scale[idx]\n                F.scale[idx] = ifelse(iszero(x), one(x), x)\n            end\n        end\n\n        foreach(X -> StatsBase.transform!(F, X), (train_X, val_X, test_X))\n        \n        # Create a problem object for the training set.\n        train_idx, _ = parentindices(train_set[1])\n        train_problem = change_data(problem, train_Y, train_X)\n        extras = __mm_init__(algorithm, train_problem, nothing)\n\n        for (j, \u03f5) in enumerate(\u03f5_grid)\n            # Set initial model parameters.\n            set_initial_coefficients!(train_problem, problem, train_idx)\n            \n            for (i, s) in enumerate(s_grid)\n                # Obtain solution as function of (\u03f5, s).\n                if s != 0.0\n                    result.time[k][i,j] = @elapsed MVDA.fit!(algorithm, train_problem, \u03f5, s, extras, (true, false,);\n                        cb=cb, kwargs...\n                    )\n                else# s == 0\n                    result.time[k][i,j] = @elapsed MVDA.init!(algorithm, train_problem, \u03f5, lambda, extras;\n                        maxiter=maxiter, gtol=tol, nesterov_threshold=0,\n                    )\n                end\n                copyto!(train_problem.coeff.all, train_problem.proj.all)\n\n                # Evaluate the solution.\n                r = scoref(train_problem, (train_Y, train_X), (val_Y, val_X), (test_Y, test_X))\n                for (arr, val) in zip(result, r)\n                    arr[k][i,j] = val\n                end\n\n                # Update the progress bar.\n                if show_progress\n                    spercent = string(round(100*s, digits=6), '%')\n                    next!(progress_bar, showvalues=[(:fold, k), (:sparsity, spercent), (:\u03f5, \u03f5)])\n                end\n            end\n        end\n    end\n\n    return result\nend\n\nfunction cv_estimation(algorithm::AbstractMMAlg, problem::MVDAProblem, grids::Tuple{E,S}; at::Real=0.8, kwargs...) where {E,S}\n    # Split data into cross-validation and test sets.\n    @unpack p, Y, X, intercept = problem\n    dataset_split = splitobs((Y, view(X, :, 1:p)), at=at, obsdim=1)\n    MVDA.cv_estimation(algorithm, problem, grids, dataset_split; kwargs...)\nend\n\nfunction cv_estimation(algorithm::AbstractMMAlg, problem::MVDAProblem, grids::Tuple{E,S}, dataset_split::Tuple{Any,Any};\n    nreplicates::Int=10,\n    show_progress::Bool=true,\n    rng::AbstractRNG=StableRNG(1903),\n    kwargs...) where {E,S}\n    # Retrieve subsets and create index set into cross-validation set.\n    cv_set, test_set = dataset_split\n\n    if show_progress\n        progress_bar = Progress(nreplicates, 1, \"Running CV w/ $(algorithm)... \")\n    end\n\n    # Replicate CV procedure several times.\n    replicate = NamedTuple[]\n    for r in 1:nreplicates\n        # Shuffle cross-validation data.\n        cv_shuffled = shuffleobs(cv_set, obsdim=1, rng=rng)\n\n        # Run k-fold cross-validation and store results.\n        result = MVDA.cv(algorithm, problem, grids, (cv_shuffled, test_set); show_progress=false, kwargs...)\n        push!(replicate, result)\n\n        # Update the progress bar.\n        if show_progress\n            next!(progress_bar, showvalues=[(:replicate, r),])\n        end\n    end\n\n    return replicate\nend\n\n\"\"\"\n```init!(algorithm, problem, \u03f5, \u03bb, [_extras_]; [maxiter=10^3], [gtol=1e-6], [nesterov_threshold=10], [verbose=false])```\n\nInitialize a `problem` with its `\u03bb`-regularized solution.\n\"\"\"\nfunction init!(algorithm::AbstractMMAlg, problem::MVDAProblem, \u03f5, \u03bb, _extras_=nothing;\n    maxiter::Int=10^3,\n    gtol::Real=1e-6,\n    nesterov_threshold::Int=10,\n    verbose::Bool=false,\n    )\n    # Check for missing data structures.\n    extras = __mm_init__(algorithm, problem, _extras_)\n\n    # Get problem info and extra data structures.\n    @unpack coeff, coeff_prev, proj = problem\n\n    # Update data structures due to hyperparameters.\n    __mm_update_lambda__(algorithm, problem, \u03f5, \u03bb, extras)\n\n    # Initialize coefficients.\n    randn!(coeff.all)\n    copyto!(coeff_prev.all, coeff.all)\n\n    # Check initial values for loss, objective, distance, and norm of gradient.\n    result = __evaluate_reg_objective__(problem, \u03f5, \u03bb, extras)\n    old = result.objective\n\n    if sqrt(result.gradient) < gtol\n        return SubproblemResult(0, result)\n    end\n\n    # Initialize iteration counts.\n    iters = 0\n    nesterov_iter = 1\n    verbose && @printf(\"\\n%-5s\\t%-8s\\t%-8s\\t%-8s\", \"iter.\", \"loss\", \"objective\", \"|gradient|\")\n    for iter in 1:maxiter\n        iters += 1\n\n        # Apply the algorithm map to minimize the quadratic surrogate.\n        __reg_iterate__(algorithm, problem, \u03f5, \u03bb, extras)\n\n        # Update loss, objective, and gradient.\n        result = __evaluate_reg_objective__(problem, \u03f5, \u03bb, extras)\n\n        if verbose\n            @printf(\"\\n%4d\\t%4.3e\\t%4.3e\\t%4.3e\", iter, result.loss, result.objective, sqrt(result.gradient))\n        end\n\n        # Assess convergence.\n        obj = result.objective\n        gradsq = sqrt(result.gradient)\n        if gradsq < gtol\n            break\n        elseif iter < maxiter\n            needs_reset = iter < nesterov_threshold || obj > old\n            nesterov_iter = __apply_nesterov__!(coeff.all, coeff_prev.all, nesterov_iter, needs_reset)\n            old = obj\n        end\n    end\n    # Save parameter estimates in case of warm start.\n    copyto!(coeff_prev.all, coeff.all)\n    copyto!(proj.all, coeff.all)\n\n    return SubproblemResult(iters, result)\nend\n\n# function fit_MVDA(algorithm::CyclicVDA, problem, \u03f5, \u03b4, \u03bb\u2081, \u03bb\u2082;\n#         niter::Int=10^3,\n#         atol=1e-4,\n#     )\n#     @unpack Y, X, res, coeff = problem\n#     # \u03b4 = 1 / 20\n#     # \u03f5 = 1//2 * sqrt(2*c/(c-1))\n#     n, p, c = probdims(problem)\n#     \u03bc\u2081 = n * \u03bb\u2081\n#     \u03bc\u2082 = n * \u03bb\u2082\n\n#     # initialize residuals\n#     mul!(res.main.all, X, coeff.all)\n#     axpby!(1.0, Y, -1.0, res.main.all)\n#     extras = nothing\n\n#     full_objective, _, _ = fetch_objective(problem, p+1, 1, \u03f5, \u03b4, \u03bb\u2081, \u03bb\u2082)\n#     penalty1 = 0.0\n#     penalty2 = 0.0\n#     for j in 1:p # does not include intercept here\n#         \u03b2 = view(problem.coeff.all, j, :)\n#         penalty1 = penalty1 + \u03bc\u2081 * norm(\u03b2, 1)\n#         penalty2 = penalty2 + \u03bc\u2082 * norm(\u03b2, 2)\n#     end\n#     full_objective = full_objective + penalty2 + penalty1\n\n#     iters = 0\n#     for iter in 1:niter\n#         iters += 1\n\n#         __mm_iterate__(algorithm, problem, \u03f5, \u03b4, \u03bb\u2081, \u03bb\u2082, extras)\n#         loss, _, _ = fetch_objective(problem, p+1, 1, \u03f5, \u03b4, \u03bb\u2081, \u03bb\u2082)\n#         penalty1 = 0.0\n#         penalty2 = 0.0\n#         for j in 1:p # does not include intercept here\n#             \u03b2 = view(problem.coeff.all, j, :)\n#             penalty1 = penalty1 + \u03bc\u2081 * norm(\u03b2, 1)\n#             penalty2 = penalty2 + \u03bc\u2082 * norm(\u03b2, 2)\n#         end\n#         objective = loss + penalty2 + penalty1\n\n#         if objective > full_objective error(\"Descent failure\") end\n\n#         if full_objective - objective < atol break end\n#         full_objective = objective\n#     end\n\n#     copyto!(problem.proj.all, coeff.all)\n\n#     return full_objective, penalty1, penalty2\n# end\n\nexport IterationResult, SubproblemResult\nexport MVDAProblem, SD, MMSVD # CyclicVDA\n\nend\n", "meta": {"hexsha": "4f8e89d8e42c605c000876c71d5943d53ad34b93", "size": 22496, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MVDA.jl", "max_stars_repo_name": "alanderos91/SparseMVDA", "max_stars_repo_head_hexsha": "a2ade5627b3d05fb8346ee8f7f342b312011f6b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/MVDA.jl", "max_issues_repo_name": "alanderos91/SparseMVDA", "max_issues_repo_head_hexsha": "a2ade5627b3d05fb8346ee8f7f342b312011f6b4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/MVDA.jl", "max_forks_repo_name": "alanderos91/SparseMVDA", "max_forks_repo_head_hexsha": "a2ade5627b3d05fb8346ee8f7f342b312011f6b4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.5949367089, "max_line_length": 185, "alphanum_fraction": 0.650737909, "num_tokens": 6107, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.14033624589467186, "lm_q1q2_score": 0.06852385862852152}}
{"text": "# As with the first half of this assignment, `learning_julia1_exercises.jl`,\n# fill in the missing implementation and pass the `@test`s.\n#\n# If you're writing your code within the `@testset`, the functions will not be available outside it.\n# (A `@testset` defines an enclosed scope that doesn't \"pollute\" the outside.)\n# You may find this makes it harder to develop and debug your code.\n# Remember that in VS Code you can select a range of lines and hit Ctrl-Enter to evaluate it,\n# and that may make it easier to experiment in VS Code's Julia REPL.\n#\n# We're also extending your usage of Test by introducing `@test_throws`.\n# Read its help to see what it does. When you pass argument(s) to the exception type, you check that, e.g.,\n# a particular message will be passed to the user.\n# Starting in Julia 1.8 it will be possible to check the error message without being concerned about\n# the exception type. (https://github.com/JuliaLang/julia/pull/41888)\n\nusing Test\n\n@testset \"Learning Julia 2\" begin\n    # Problem 1 [Adapted from ThinkJulia, exercise 3-2]:\n    # Create a function `rightjustify` that prints a string with the right number of leading spaces\n    # so that the final character aligns with a chosen column.\n    # I've given you the signature of one method; also create another method that supplies `stdout`\n    # as the default `io` choice. See the style guide for more info about recommended best\n    # practices for ordering arguments for Julia functions:\n    # https://docs.julialang.org/en/v1/manual/style-guide/#Write-functions-with-argument-ordering-similar-to-Julia-Base\n    function rightjustify(io::IO, str::AbstractString, col::Integer = 70)\n        # fill me in!\n    end\n    # also define a method that doesn't have `io` supplied\n\n    # here are my tests (don't modify this code)\n    checkstr(ostr, refstr, len) = @test length(ostr) == len && endswith(ostr, refstr) && strip(ostr) == refstr\n    for str in (\"short\", \"kind of medium\", \"getting longer, but not too huge\")\n        checkstr(sprint(rightjustify, str),     str, 70)\n        checkstr(sprint(rightjustify, str, 80), str, 80)\n    end\n    mktemp() do fname, io\n        str = \"send me to stdout!\"\n        redirect_stdout(io) do\n            rightjustify(str)\n        end\n        seek(io, 0)\n        checkstr(chomp(read(io, String)), str, 70)\n    end\n\n\n    # Problem 2: adapted from https://exercism.org/tracks/julia/exercises/robot-simulator\n    # (Exercism is a site offering programming exercises in many different languages, with a nice\n    #  framework for in-browser coding, automated testing, progress-monitoring, and even mentoring.)\n    # The version of this problem on Exercism encourages you to develop a custom type, which is great,\n    # but here we're going to treat it as an exercise in control-flow.\n    # If you wish, you can put everything in a single function.\n    #\n    # A robot starts at a location `(x, y)`, where both `x` and `y` are integers, facing in a specified direction\n    # ('N'=North, 'S'=South, 'E'=East, 'W'=West). `x` is the East-West axis and `y` the North-South axis.\n    # You also supply a string, where each character of the string is one of the following:\n    # - 'A': advance one step in the direction it's facing\n    # - 'R': turn right\n    # - 'L': turn left\n    # When the robot turns, it pivots without changing its location. For example, \"RAALAL\" means:\n    # 1. turn right\n    # 2. advance two spaces\n    # 3. turn left\n    # 4. advance one space\n    # 5. turn left\n    #\n    # Your code should return both the final position and the direction the robot is facing.\n    # See if you can come up with a more efficient approach than a huge nest of `if...else...end`\n    # statements. My solution is less than 20 lines of code.\n    # Hint: some of the better solutions might use `%` or `mod1` (read their help strings).\n\n    function robot_command(cmd, (x, y), dir)  # argument destructuring syntax: https://docs.julialang.org/en/v1/manual/functions/#Argument-destructuring\n        # you write the body\n    end\n\n    # Single steps (these are deliberately written out in long form to avoid giving too much of the solution away)\n    @test robot_command(\"R\", (0, 0), 'N') == ((0, 0), 'E')\n    @test robot_command(\"R\", (0, 0), 'E') == ((0, 0), 'S')\n    @test robot_command(\"R\", (0, 0), 'S') == ((0, 0), 'W')\n    @test robot_command(\"R\", (0, 0), 'W') == ((0, 0), 'N')\n    @test robot_command(\"L\", (0, 0), 'N') == ((0, 0), 'W')\n    @test robot_command(\"L\", (0, 0), 'E') == ((0, 0), 'N')\n    @test robot_command(\"L\", (0, 0), 'S') == ((0, 0), 'E')\n    @test robot_command(\"L\", (0, 0), 'W') == ((0, 0), 'S')\n    @test robot_command(\"A\", (0, 0), 'N') == ((0, 1), 'N')\n    @test robot_command(\"A\", (0, 0), 'E') == ((1, 0), 'E')\n    @test robot_command(\"A\", (0, 0), 'S') == ((0, -1), 'S')\n    @test robot_command(\"A\", (0, 0), 'W') == ((-1, 0), 'W')\n    @test_throws ErrorException(\"robot command K not recognized\") robot_command(\"K\", (0, 0), 'W')\n    # Sequences\n    for x in (7, 22), y in (3, -7)\n        @test robot_command(\"RAALAL\", (x, y), 'N') == ((x+2, y+1), 'W')\n        @test robot_command(\"ARAAALA\", (x, y), 'W') == ((x-2, y+3), 'W')\n        for dir in ('N', 'E', 'S', 'W')\n            @test robot_command(\"ALALALAL\", (x, y), dir) == ((x, y), dir)\n        end\n    end\n\n\n    # Problem 3: introduction to structures\n    # Note that when you redefine structures, you may have to shut down your Julia session and restart it.\n    # (Julia won't let you change an existing `struct` definition.)\n    # a) create a new subtype of `Integer` called `MyInt`, one that stores an `Int` internally\n\n    @test MyInt(3) isa Integer\n    @test !ismutable(MyInt(3))\n\n    # b) extend `Base`'s `==` so that you can compare `MyInt` against any other `Integer`s\n    #    You can either `import Base: ==` and write `==(a, b)` methods, or write your new\n    #    methods using module qualification, `Base.:(==)(a, b)`. The `:` is needed only for operators;\n    #    when extending other functions, you don't need `:`.\n\n    @test MyInt(3) == 3\n    @test 2 == MyInt(2)\n    @test MyInt(1) != MyInt(2)   # this will follow automatically if you define `==`\n\n    # c) extend `+` so that `MyInt` \"wraps around\" at 5: `MyInt(5) + 1` return `MyInt(1)` and then it starts counting up again.\n\n    @test MyInt(1) + 1  === MyInt(2)\n    @test MyInt(2) + 1  === MyInt(3)\n    @test MyInt(3) + 1  === MyInt(4)\n    @test MyInt(4) + 1  === MyInt(5)\n    @test MyInt(5) + 1  === MyInt(1)\n    @test MyInt(4) + 2  === MyInt(1)\n    @test MyInt(3) + 10 === MyInt(3)\n\n    # d) also extend the `typemax` \"trait\" for `MyInt`. Read the docs for `typemax`, and then do `@edit typemax(UInt8)`\n    #    to see how such methods are implemented. Mimic that definition.\n\n    @test typemax(MyInt)    === MyInt(5)\n    @test typemax(MyInt(2)) === MyInt(5)   # note you didn't have to write a method specifically for this test\n    @test typemax(UInt8)    === 0xff       # just to make sure it's not broken\n\n    # Problem 4: encapsulating data in objects & functions\n    # a) Create a parametric `struct` representing a unit Gaussian in 1d: if `g isa Gaussian`, then\n    #    - `g.\u03bc` should return the mean (center) of the Gaussian, a scalar of type `T`\n    #    - `g.\u03c3` should return the standard deviation, a scalar also of type `T`\n    #    Also, `T` should only be allowed to be a subtype of `Real`\n\n    g = Gaussian(1.0, 0.5)\n    @test g isa Gaussian{Float64}\n    @test g.\u03bc === 1.0\n    @test g.\u03c3 === 0.5\n    g = Gaussian(2, 5)\n    @test g isa Gaussian{Int}\n    @test g.\u03bc === 2\n    @test g.\u03c3 === 5\n    @test_throws TypeError Gaussian{String}\n\n    # b) make objects of type Gaussian callable (review https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects\n    #    as needed), so that `g(x)` returns `exp(-(x - \u03bc)^2/(2*\u03c3^2))`.\n\n    g = Gaussian(1.0, 0.5)\n    @test g(2) \u2248 0.1353352832366127\n    @test g(2.0) == g(2)\n    @test g(1.25) \u2248 0.8824969025845955\n    g = Gaussian(2, 5)\n    @test g(-3) \u2248 0.6065306597126334\n\n    # c) now do the same thing with a *closure*, a function that wraps data.  Here's a demo:\n    \"\"\"\n        f = adder(x)\n\n    Return a function `f` that adds `x` to any number.\n    \"\"\"\n    function adder(x)\n        return y::Number -> x+y\n    end\n    f = adder(3)\n    @test f isa Function\n    @test f(2) == 5\n    @test_throws MethodError f(\"hello\")   # f isn't defined on strings because of the `y::Number`\n\n    # Another way you could have written the above is\n    # function adder(x)\n    #     f = function(y::Number)\n    #         return x + y\n    #     end\n    #     return f\n    # end\n    # If that's clearer to you, feel free to use that form.\n\n    # Now you write one: `f = gaussian_closure(\u03bc, \u03c3)` should return a function `f` such that\n    # `f(x) == exp(-(x - \u03bc)^2/(2*\u03c3^2))`. Enforce the fact that both `\u03bc` and `\u03c3` must be `Real`,\n    # as must `x`, but don't require them to be the same type.\n\n    f = gaussian_closure(2, 5)\n    @test f isa Function\n    @test f(-3) \u2248 0.6065306597126334\n    @test_throws MethodError f(\"hello\")\n    @test_throws MethodError gaussian_closure(\"hello\", 5)\n    @test_throws MethodError gaussian_closure(2, [5])\nend\n", "meta": {"hexsha": "9fac243edbf66bba40a15d945bc8a262ff97fcc7", "size": 9145, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "homeworks/learning_julia2_exercises.jl", "max_stars_repo_name": "HsupoLeng/AdvancedScientificComputing", "max_stars_repo_head_hexsha": "035f1e08a606bc514e07a6adfcde465b755bf588", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "homeworks/learning_julia2_exercises.jl", "max_issues_repo_name": "HsupoLeng/AdvancedScientificComputing", "max_issues_repo_head_hexsha": "035f1e08a606bc514e07a6adfcde465b755bf588", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "homeworks/learning_julia2_exercises.jl", "max_forks_repo_name": "HsupoLeng/AdvancedScientificComputing", "max_forks_repo_head_hexsha": "035f1e08a606bc514e07a6adfcde465b755bf588", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.1391752577, "max_line_length": 152, "alphanum_fraction": 0.6276653909, "num_tokens": 2821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3522017956470284, "lm_q2_score": 0.1943678016853152, "lm_q1q2_score": 0.06845668876953354}}
{"text": "# Note: Functions surrounded by a comment blocks are there because `Vararg` is still allocating.\n# When Vararg is fast enough, they can simply be removed.\n\n\n#######################\n# UndefBlocksInitializer #\n#######################\n\n\"\"\"\n    UndefBlocksInitializer\n\nSingleton type used in block array initialization, indicating the\narray-constructor-caller would like an uninitialized block array. See also\nundef_blocks (@ref), an alias for UndefBlocksInitializer().\n\nExamples\n\n\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\n```julia\njulia> BlockArray(undef_blocks, Matrix{Float32}, [1,2], [3,2])\n2\u00d72-blocked 3\u00d75 BlockArray{Float32,2}:\n #undef  #undef  #undef  \u2502  #undef  #undef\n \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n #undef  #undef  #undef  \u2502  #undef  #undef\n #undef  #undef  #undef  \u2502  #undef  #undef\n ```\n\"\"\"\nstruct UndefBlocksInitializer end\n\n\"\"\"\n    undef_blocks\n\nAlias for UndefBlocksInitializer(), which constructs an instance of the singleton\ntype UndefBlocksInitializer (@ref), used in block array initialization to indicate the\narray-constructor-caller would like an uninitialized block array.\n\nExamples\n\n\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\u2261\n```julia\njulia> BlockArray(undef_blocks, Matrix{Float32}, [1,2], [3,2])\n2\u00d72-blocked 3\u00d75 BlockArray{Float32,2}:\n #undef  #undef  #undef  \u2502  #undef  #undef\n ------------------------\u253c----------------\n #undef  #undef  #undef  \u2502  #undef  #undef\n #undef  #undef  #undef  \u2502  #undef  #undef\n ```\n\"\"\"\nconst undef_blocks = UndefBlocksInitializer()\n\n##############\n# BlockArray #\n##############\n\nfunction _BlockArray end\n\n\"\"\"\n    BlockArray{T, N, R<:AbstractArray{<:AbstractArray{T,N},N}, BS<:AbstractBlockSizes{N}} <: AbstractBlockArray{T, N}\n\nA `BlockArray` is an array where each block is stored contiguously. This means that insertions and retrieval of blocks\ncan be very fast and non allocating since no copying of data is needed.\n\nIn the type definition, `R` defines the array type that holds the blocks, for example `Matrix{Matrix{Float64}}`.\n\"\"\"\nstruct BlockArray{T, N, R <: AbstractArray{<:AbstractArray{T,N},N}, BS<:AbstractBlockSizes{N}} <: AbstractBlockArray{T, N}\n    blocks::R\n    block_sizes::BS\n\n    global function _BlockArray(blocks::R, block_sizes::BS) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}, BS<:AbstractBlockSizes{N}}\n        new{T, N, R, BS}(blocks, block_sizes)\n    end\nend\n\n# Auxilary outer constructors\nfunction _BlockArray(blocks::R, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}}\n    return _BlockArray(blocks, BlockSizes(block_sizes...))\nend\n\nfunction _BlockArray(blocks::R, block_sizes::BS) where {T, N, R<:AbstractArray{<:AbstractArray{V,N} where V,N}, BS<:AbstractBlockSizes{N}}\n    _BlockArray(convert(AbstractArray{AbstractArray{mapreduce(eltype,promote_type,blocks),N},N}, blocks), block_sizes)\nend\n\nconst BlockMatrix{T, R <: AbstractMatrix{<:AbstractMatrix{T}}} = BlockArray{T, 2, R}\nconst BlockVector{T, R <: AbstractVector{<:AbstractVector{T}}} = BlockArray{T, 1, R}\nconst BlockVecOrMat{T, R} = Union{BlockMatrix{T, R}, BlockVector{T, R}}\n\n################\n# Constructors #\n################\n\n@inline _BlockArray(::Type{R}, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    _BlockArray(R, BlockSizes(block_sizes...))\n\nfunction _BlockArray(::Type{R}, block_sizes::BlockSizes{N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}}\n    n_blocks = nblocks(block_sizes)\n    blocks = R(undef, n_blocks)\n    _BlockArray(blocks, block_sizes)\nend\n\n@inline undef_blocks_BlockArray(::Type{R}, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    _BlockArray(R, block_sizes...)\n\n\"\"\"\nConstructs a `BlockArray` with uninitialized blocks from a block type `R` with sizes defind by `block_sizes`.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> BlockArray(undef_blocks, Matrix{Float64}, [1,3], [2,2])\n2\u00d72-blocked 4\u00d74 BlockArray{Float64,2}:\n #undef  \u2502  #undef  #undef  #undef  \u2502\n --------\u253c--------------------------\u253c\n #undef  \u2502  #undef  #undef  #undef  \u2502\n #undef  \u2502  #undef  #undef  #undef  \u2502\n --------\u253c--------------------------\u253c\n #undef  \u2502  #undef  #undef  #undef  \u2502\n```\n\"\"\"\n@inline BlockArray(::UndefBlocksInitializer, ::Type{R}, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{T,N}} =\n    undef_blocks_BlockArray(Array{R,N}, block_sizes...)\n\n@inline BlockArray{T}(::UndefBlocksInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N} =\n    BlockArray(undef_blocks, Array{T,N}, block_sizes...)\n\n@inline BlockArray{T,N}(::UndefBlocksInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N} =\n    BlockArray(undef_blocks, Array{T,N}, block_sizes...)\n\n@inline BlockArray{T,N,R}(::UndefBlocksInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    undef_blocks_BlockArray(R, block_sizes...)\n\n\n@generated function initialized_blocks_BlockArray(::Type{R}, block_sizes::BlockSizes{N}) where R<:AbstractArray{V,N} where {T,N,V<:AbstractArray{T,N}}\n    return quote\n        block_arr = _BlockArray(R, block_sizes)\n        @nloops $N i i->(1:nblocks(block_sizes, i)) begin\n            block_index = @ntuple $N i\n            setblock!(block_arr, similar(V, blocksize(block_sizes, block_index)), block_index...)\n        end\n\n        return block_arr\n    end\nend\n\n\ninitialized_blocks_BlockArray(::Type{R}, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    initialized_blocks_BlockArray(R, BlockSizes(block_sizes...))\n\n@inline BlockArray{T}(::UndefInitializer, block_sizes::BlockSizes{N}) where {T, N} =\n    initialized_blocks_BlockArray(Array{Array{T,N},N}, block_sizes)\n\n@inline BlockArray{T, N}(::UndefInitializer, block_sizes::BlockSizes{N}) where {T, N} =\n    initialized_blocks_BlockArray(Array{Array{T,N},N}, block_sizes)\n\n@inline BlockArray{T, N, R}(::UndefInitializer, block_sizes::BlockSizes{N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    initialized_blocks_BlockArray(R, block_sizes)\n\n@inline BlockArray{T}(::UndefInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N} =\n    initialized_blocks_BlockArray(Array{Array{T,N},N}, block_sizes...)\n\n@inline BlockArray{T, N}(::UndefInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N} =\n    initialized_blocks_BlockArray(Array{Array{T,N},N}, block_sizes...)\n\n@inline BlockArray{T, N, R}(::UndefInitializer, block_sizes::Vararg{AbstractVector{Int}, N}) where {T, N, R<:AbstractArray{<:AbstractArray{T,N},N}} =\n    initialized_blocks_BlockArray(R, block_sizes...)\n\nfunction BlockArray(arr::AbstractArray{T, N}, block_sizes::Vararg{AbstractVector{Int}, N}) where {T,N}\n    for i in 1:N\n        if sum(block_sizes[i]) != size(arr, i)\n            throw(DimensionMismatch(\"block size for dimension $i: $(block_sizes[i]) does not sum to the array size: $(size(arr, i))\"))\n        end\n    end\n    BlockArray(arr, BlockSizes(block_sizes...))\nend\n\n@generated function BlockArray(arr::AbstractArray{T, N}, block_sizes::BlockSizes{N}) where {T,N}\n    return quote\n        block_arr = _BlockArray(Array{typeof(arr),N}, block_sizes)\n        @nloops $N i i->(1:nblocks(block_sizes, i)) begin\n            block_index = @ntuple $N i\n            indices = globalrange(block_sizes, block_index)\n            setblock!(block_arr, arr[indices...], block_index...)\n        end\n\n        return block_arr\n    end\nend\n\nBlockVector(blocks::AbstractVector, block_sizes::AbstractBlockSizes{1}) = BlockArray(blocks, block_sizes)\nBlockVector(blocks::AbstractVector, block_sizes::AbstractVector{Int}) = BlockArray(blocks, block_sizes)\nBlockMatrix(blocks::AbstractMatrix, block_sizes::AbstractBlockSizes{2}) = BlockArray(blocks, block_sizes)\nBlockMatrix(blocks::AbstractMatrix, block_sizes::Vararg{AbstractVector{Int},2}) = BlockArray(blocks, block_sizes...)\n\n\"\"\"\n    mortar(blocks::AbstractArray)\n    mortar(blocks::AbstractArray{R, N}, sizes_1, sizes_2, ..., sizes_N)\n    mortar(blocks::AbstractArray{R, N}, block_sizes::BlockSizes{N})\n\nConstruct a `BlockArray` from `blocks`.  `block_sizes` is computed from\n`blocks` if it is not given.\n\n# Examples\n```jldoctest; setup = quote using BlockArrays end\njulia> blocks = permutedims(reshape([\n                  1ones(1, 3), 2ones(1, 2),\n                  3ones(2, 3), 4ones(2, 2),\n              ], (2, 2)))\n2\u00d72 Array{Array{Float64,2},2}:\n [1.0 1.0 1.0]               [2.0 2.0]\n [3.0 3.0 3.0; 3.0 3.0 3.0]  [4.0 4.0; 4.0 4.0]\n\njulia> mortar(blocks)\n2\u00d72-blocked 3\u00d75 BlockArray{Float64,2}:\n 1.0  1.0  1.0  \u2502  2.0  2.0\n \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 3.0  3.0  3.0  \u2502  4.0  4.0\n 3.0  3.0  3.0  \u2502  4.0  4.0\n\njulia> ans == mortar(\n                  (1ones(1, 3), 2ones(1, 2)),\n                  (3ones(2, 3), 4ones(2, 2)),\n              )\ntrue\n```\n\"\"\"\nmortar(blocks::AbstractArray{R, N}, block_sizes::AbstractBlockSizes{N}) where {R, N} =\n    _BlockArray(blocks, block_sizes)\n\nmortar(blocks::AbstractArray{R, N}, block_sizes::Vararg{AbstractVector{Int}, N}) where {R, N} =\n    _BlockArray(blocks, block_sizes...)\n\nmortar(blocks::AbstractArray) = mortar(blocks, sizes_from_blocks(blocks))\n\nsizes_from_blocks(blocks) = sizes_from_blocks(blocks, axes(blocks)) #\u00a0allow overriding on axes\n\nfunction sizes_from_blocks(blocks::AbstractArray{<:Any, N}, _) where N\n    if length(blocks) == 0\n        return zeros.(Int, size(blocks))\n    end\n    if !all(b -> ndims(b) == N, blocks)\n        error(\"All blocks must have ndims consistent with ndims = $N of `blocks` array.\")\n    end\n    fullsizes = map!(size, Array{NTuple{N,Int}, N}(undef, size(blocks)), blocks)\n    block_sizes = ntuple(ndims(blocks)) do i\n        [s[i] for s in view(fullsizes, ntuple(j -> j == i ? (:) : 1, ndims(blocks))...)]\n    end\n    checksizes(fullsizes, block_sizes)\n    return BlockSizes(block_sizes...)\nend\n\ngetsizes(block_sizes, block_index) = getindex.(block_sizes, block_index)\n\n@generated function checksizes(fullsizes::Array{NTuple{N,Int}, N}, block_sizes::NTuple{N,Vector{Int}}) where N\n    quote\n        @nloops $N i fullsizes begin\n            block_index = @ntuple $N i\n            if fullsizes[block_index...] != getsizes(block_sizes, block_index)\n                error(\"size(blocks[\", strip(repr(block_index), ['(', ')']),\n                      \"]) (= \", fullsizes[block_index...],\n                      \") is incompatible with expected size: \",\n                      getsizes(block_sizes, block_index))\n            end\n        end\n        return fullsizes\n    end\nend\n\n\"\"\"\n    mortar((block_11, ..., block_1m), ... (block_n1, ..., block_nm))\n\nConstruct a `BlockMatrix` with `n * m`  blocks.  Each `block_ij` must be an\n`AbstractMatrix`.\n\"\"\"\nmortar(rows::Vararg{NTuple{M, AbstractMatrix}}) where M =\n    mortar(permutedims(reshape(\n        foldl(append!, rows, init=eltype(eltype(rows))[]),\n        M, length(rows))))\n\n# Convert AbstractArrays that conform to block array interface\nconvert(::Type{BlockArray{T,N,R}}, A::BlockArray{T,N,R}) where {T,N,R} = A\nconvert(::Type{BlockArray{T,N}}, A::BlockArray{T,N}) where {T,N} = A\nconvert(::Type{BlockArray{T}}, A::BlockArray{T}) where {T} = A\nconvert(::Type{BlockArray}, A::BlockArray) = A\n\nBlockArray{T, N}(A::AbstractArray{T2, N}) where {T,T2,N} =\n    BlockArray(Array{T, N}(A), blocksizes(A))\nBlockArray{T1}(A::AbstractArray{T2, N}) where {T1,T2,N} = BlockArray{T1, N}(A)\nBlockArray(A::AbstractArray{T, N}) where {T,N} = BlockArray{T, N}(A)\n\nconvert(::Type{BlockArray{T, N}}, A::AbstractArray{T2, N}) where {T,T2,N} =\n    BlockArray(convert(Array{T, N}, A), blocksizes(A))\nconvert(::Type{BlockArray{T1}}, A::AbstractArray{T2, N}) where {T1,T2,N} =\n    convert(BlockArray{T1, N}, A)\nconvert(::Type{BlockArray}, A::AbstractArray{T, N}) where {T,N} =\n    convert(BlockArray{T, N}, A)\n\ncopy(A::BlockArray) = _BlockArray(copy.(A.blocks), copy(A.block_sizes))\n\n################################\n# AbstractBlockArray Interface #\n################################\n@inline blocksizes(block_array::BlockArray) = block_array.block_sizes\n\n@inline function getblock(block_arr::BlockArray{T,N}, block::Vararg{Integer, N}) where {T,N}\n    @boundscheck blockcheckbounds(block_arr, block...)\n    block_arr.blocks[block...]\nend\n\n@inline function Base.getindex(block_arr::BlockArray{T,N}, blockindex::BlockIndex{N}) where {T,N}\n    @boundscheck blockcheckbounds(block_arr, Block(blockindex.I))\n    @inbounds block = getblock(block_arr, blockindex.I...)\n    @boundscheck checkbounds(block, blockindex.\u03b1...)\n    @inbounds v = block[blockindex.\u03b1...]\n    return v\nend\n\n\n###########################\n# AbstractArray Interface #\n###########################\n\n@inline function Base.similar(block_array::BlockArray{T,N}, ::Type{T2}) where {T,N,T2}\n    _BlockArray(similar(block_array.blocks, Array{T2, N}), copy(blocksizes(block_array)))\nend\n\n@inline function Base.getindex(block_arr::BlockArray{T, N}, i::Vararg{Integer, N}) where {T,N}\n    @boundscheck checkbounds(block_arr, i...)\n    @inbounds v = block_arr[global2blockindex(blocksizes(block_arr), i)]\n    return v\nend\n\n@inline function Base.setindex!(block_arr::BlockArray{T, N}, v, i::Vararg{Integer, N}) where {T,N}\n    @boundscheck checkbounds(block_arr, i...)\n    @inbounds block_arr[global2blockindex(blocksizes(block_arr), i)] = v\n    return block_arr\nend\n\n############\n# Indexing #\n############\n\nfunction _check_setblock!(block_arr::BlockArray{T, N}, v, block::NTuple{N, Integer}) where {T,N}\n    for i in 1:N\n        if size(v, i) != blocksize(block_arr, i, block[i])\n            throw(DimensionMismatch(string(\"tried to assign $(size(v)) array to \", blocksize(block_arr, block), \" block\")))\n        end\n    end\nend\n\n\n@inline function setblock!(block_arr::BlockArray{T, N}, v, block::Vararg{Integer, N}) where {T,N}\n    @boundscheck blockcheckbounds(block_arr, block...)\n    @boundscheck _check_setblock!(block_arr, v, block)\n    @inbounds block_arr.blocks[block...] = v\n    return block_arr\nend\n\n@propagate_inbounds function Base.setindex!(block_array::BlockArray{T, N}, v, block_index::BlockIndex{N}) where {T,N}\n    getblock(block_array, block_index.I...)[block_index.\u03b1...] = v\nend\n\nBase.dataids(arr::BlockArray) = (dataids(arr.blocks)..., dataids(arr.block_sizes)...)\n# This is not entirely valid.  In principle, we have to concatenate\n# all dataids of all blocks.  However, it makes `dataids` non-inferable.\n\n########\n# Misc #\n########\n\n@generated function Base.Array(block_array::BlockArray{T, N, R}) where {T,N,R}\n    # TODO: This will fail for empty block array\n    return quote\n        block_sizes = blocksizes(block_array)\n        arr = similar(block_array.blocks[1], size(block_array)...)\n        @nloops $N i i->(1:nblocks(block_sizes, i)) begin\n            block_index = @ntuple $N i\n            indices = globalrange(block_sizes, block_index)\n            arr[indices...] = getblock(block_array, block_index...)\n        end\n\n        return arr\n    end\nend\n\n@generated function copyto!(block_array::BlockArray{T, N, R}, arr::R) where {T,N,R <: AbstractArray}\n    return quote\n        block_sizes = blocksizes(block_array)\n\n        @nloops $N i i->(1:nblocks(block_sizes, i)) begin\n            block_index = @ntuple $N i\n            indices = globalrange(block_sizes, block_index)\n            copyto!(getblock(block_array, block_index...), arr[indices...])\n        end\n\n        return block_array\n    end\nend\n\nfunction Base.fill!(block_array::BlockArray, v)\n    for block in block_array.blocks\n        fill!(block, v)\n    end\n    block_array\nend\n\nfunction lmul!(\u03b1::Number, block_array::BlockArray)\n    for block in block_array.blocks\n        lmul!(\u03b1, block)\n    end\n    block_array\nend\n\nfunction rmul!(block_array::BlockArray, \u03b1::Number)\n    for block in block_array.blocks\n        rmul!(block, \u03b1)\n    end\n    block_array\nend\n", "meta": {"hexsha": "a728dde7f6d8d732dab9f96f5492d15811a9b8a1", "size": 15764, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/blockarray.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/BlockArrays.jl-8e7c35d0-a365-5155-bbbb-fb81a777f24e", "max_stars_repo_head_hexsha": "5812573bf3ee4b5797ba631e6b7a8b02c0f9d3d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-12-30T10:45:16.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-30T10:45:16.000Z", "max_issues_repo_path": "src/blockarray.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/BlockArrays.jl-8e7c35d0-a365-5155-bbbb-fb81a777f24e", "max_issues_repo_head_hexsha": "5812573bf3ee4b5797ba631e6b7a8b02c0f9d3d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/blockarray.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/BlockArrays.jl-8e7c35d0-a365-5155-bbbb-fb81a777f24e", "max_forks_repo_head_hexsha": "5812573bf3ee4b5797ba631e6b7a8b02c0f9d3d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8942307692, "max_line_length": 153, "alphanum_fraction": 0.6609997463, "num_tokens": 4526, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "for p in (\"Knet\",\"AutoGrad\",\"ArgParse\",\"Compat\")\n    Pkg.installed(p) == nothing && Pkg.add(p)\nend\n\n\"\"\"\ncharlm.jl: Knet8 version (c) Emre Yolcu, Deniz Yuret, 2016\n\nThis example implements an LSTM network for training and testing\ncharacter-level language models inspired by [\"The Unreasonable\nEffectiveness of Recurrent Neural\nNetworks\"](http://karpathy.github.io/2015/05/21/rnn-effectiveness) from\nthe Andrej Karpathy blog.  The model can be trained with different\ngenres of text, and can be used to generate original text in the same\nstyle.\n\nExample usage:\n\n* `julia charlm.jl`: trains a model using its own code.\n\n* `julia charlm.jl --data foo.txt`: uses foo.txt to train instead.\n\n* `julia charlm.jl --data foo.txt bar.txt`: uses foo.txt for training\n  and bar.txt for validation.  Any number of files can be specified,\n  the first two will be used for training and validation, the rest for\n  testing.\n\n* `julia charlm.jl --best foo.jld --save bar.jld`: saves the best\n  model (according to validation set) to foo.jld, last model to\n  bar.jld.\n\n* `julia charlm.jl --load foo.jld --generate 1000`: generates 1000\n  characters from the model in foo.jld.\n\n* `julia charlm.jl --help`: describes all available options.\n    \n\"\"\"\nmodule CharLM\n\nusing Knet,AutoGrad,ArgParse,Compat\n\nfunction main(args=ARGS)\n    s = ArgParseSettings()\n    s.description=\"charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\"\n    s.exc_handler=ArgParse.debug_handler\n    @add_arg_table s begin\n        (\"--datafiles\"; nargs='+'; help=\"If provided, use first file for training, second for dev, others for test.\")\n        (\"--loadfile\"; help=\"Initialize model from file\")\n        (\"--savefile\"; help=\"Save final model to file\")\n        (\"--bestfile\"; help=\"Save best model to file\")\n        (\"--generate\"; arg_type=Int; default=0; help=\"If non-zero generate given number of characters.\")\n        (\"--hidden\"; nargs='+'; arg_type=Int; default=[256]; help=\"Sizes of one or more LSTM layers.\")\n        (\"--embed\"; arg_type=Int; default=256; help=\"Size of the embedding vector.\")\n        (\"--epochs\"; arg_type=Int; default=3; help=\"Number of epochs for training.\")\n        (\"--batchsize\"; arg_type=Int; default=128; help=\"Number of sequences to train on in parallel.\")\n        (\"--seqlength\"; arg_type=Int; default=100; help=\"Number of steps to unroll the network for.\")\n        (\"--decay\"; arg_type=Float64; default=0.9; help=\"Learning rate decay.\")\n        (\"--lr\"; arg_type=Float64; default=4.0; help=\"Initial learning rate.\")\n        (\"--gclip\"; arg_type=Float64; default=3.0; help=\"Value to clip the gradient norm at.\")\n        (\"--winit\"; arg_type=Float64; default=0.3; help=\"Initial weights set to winit*randn().\")\n        (\"--gcheck\"; arg_type=Int; default=0; help=\"Check N random gradients.\")\n        (\"--seed\"; arg_type=Int; default=-1; help=\"Random number seed.\")\n        (\"--atype\"; default=(gpu()>=0 ? \"KnetArray{Float32}\" : \"Array{Float32}\"); help=\"array type: Array for cpu, KnetArray for gpu\")\n        (\"--fast\"; action=:store_true; help=\"skip loss printing for faster run\")\n        #TODO (\"--dropout\"; arg_type=Float64; default=0.0; help=\"Dropout probability.\")\n    end\n    println(s.description)\n    isa(args, AbstractString) && (args=split(args))\n    o = parse_args(args, s; as_symbols=true)\n    println(\"opts=\",[(k,v) for (k,v) in o]...)\n    o[:seed] > 0 && srand(o[:seed])\n    o[:atype] = eval(parse(o[:atype]))\n    if any(f->(o[f]!=nothing), (:loadfile, :savefile, :bestfile))\n        Pkg.installed(\"JLD\")==nothing && Pkg.add(\"JLD\") # error(\"Please Pkg.add(\\\"JLD\\\") to load or save files.\")\n        eval(Expr(:using,:JLD))\n    end\n\n    # we initialize a model from loadfile, train using datafiles (both optional).\n    # if the user specifies neither, train a model using the charlm.jl source code.\n    isempty(o[:datafiles]) && o[:loadfile]==nothing && push!(o[:datafiles],@__FILE__) # shakespeare()\n\n    # read text and report lengths\n    text = map((@compat readstring), o[:datafiles])\n    !isempty(text) && info(\"Chars read: $(map((f,c)->(basename(f),length(c)),o[:datafiles],text))\")\n\n    # vocab (char_to_index) comes from the initial model if there is one, otherwise from the datafiles.\n    # if there is an initial model make sure the data has no new vocab\n    if o[:loadfile]==nothing\n        vocab = Dict{Char,Int}()\n        for t in text, c in t; get!(vocab, c, 1+length(vocab)); end\n        model = initweights(o[:atype], o[:hidden], length(vocab), o[:embed], o[:winit])\n    else\n        info(\"Loading model from $(o[:loadfile])\")\n        vocab = load(o[:loadfile], \"vocab\") \n        for t in text, c in t; haskey(vocab, c) || error(\"Unknown char $c\"); end\n        model = map(p->convert(o[:atype],p), load(o[:loadfile], \"model\"))\n    end\n    info(\"$(length(vocab)) unique chars.\")\n    if !isempty(text)\n        train!(model, text, vocab, o)\n    end\n    if o[:savefile] != nothing\n        info(\"Saving last model to $(o[:savefile])\")\n        save(o[:savefile], \"model\", model, \"vocab\", vocab)\n    end\n    if o[:generate] > 0\n        state = initstate(o[:atype],o[:hidden],1)\n        generate(model, state, vocab, o[:generate])\n    end\nend\n\n\nfunction train!(model, text, vocab, o)\n    s0 = initstate(o[:atype], o[:hidden], o[:batchsize])\n    data = map(t->minibatch(t, vocab, o[:batchsize]), text)\n    lr = o[:lr]\n    if o[:fast]\n        @time (for epoch=1:o[:epochs]\n               train1(model, copy(s0), data[1]; slen=o[:seqlength], lr=lr, gclip=o[:gclip])\n               end; gpu()>=0 && Knet.cudaDeviceSynchronize())\n        return\n    end\n    losses = map(d->loss(model,copy(s0),d), data)\n    println((:epoch,0,:loss,losses...))\n    devset = ifelse(length(data) > 1, 2, 1)\n    devlast = devbest = losses[devset]\n    for epoch=1:o[:epochs]\n        @time train1(model, copy(s0), data[1]; slen=o[:seqlength], lr=lr, gclip=o[:gclip])\n        @time losses = map(d->loss(model,copy(s0),d), data)\n        println((:epoch,epoch,:loss,losses...))\n        if o[:gcheck] > 0\n            gradcheck(loss, model, copy(s0), data[1], 1:o[:seqlength]; gcheck=o[:gcheck])\n        end\n        devloss = losses[devset]\n        if devloss < devbest\n            devbest = devloss\n            if o[:bestfile] != nothing\n                info(\"Saving best model to $(o[:bestfile])\")\n                save(o[:bestfile], \"model\", model, \"vocab\", vocab)\n            end\n        end\n        if devloss > devlast\n            lr *= o[:decay]\n            info(\"New learning rate: $lr\")\n        end\n        devlast = devloss\n    end\nend    \n\n\n# sequence[t]: input token at time t\n# state is modified in place\nfunction train1(param, state, sequence; slen=100, lr=1.0, gclip=0.0)\n    for t = 1:slen:length(sequence)-slen\n        range = t:t+slen-1\n        gloss = lossgradient(param, state, sequence, range)\n        gscale = lr\n        if gclip > 0\n            gnorm = sqrt(mapreduce(sumabs2, +, 0, gloss))\n            if gnorm > gclip\n                gscale *= gclip / gnorm\n            end\n        end\n        for k in 1:length(param)\n            # param[k] -= gscale * gloss[k]\n            axpy!(-gscale, gloss[k], param[k])\n        end\n        isa(state,Vector{Any}) || error(\"State should not be Boxed.\")\n        # The following is needed in case AutoGrad boxes state values during gradient calculation\n        for i = 1:length(state)\n            state[i] = AutoGrad.getval(state[i])\n        end\n    end\nend\n\n# param[2k-1,2k]: weight and bias for the k'th lstm layer\n# param[end-2]: embedding matrix\n# param[end-1,end]: weight and bias for final prediction\nfunction initweights(atype, hidden, vocab, embed, winit)\n    param = Array(Any, 2*length(hidden)+3)\n    input = embed\n    for k = 1:length(hidden)\n        param[2k-1] = winit*randn(input+hidden[k], 4*hidden[k])\n        param[2k]   = zeros(1, 4*hidden[k])\n        param[2k][1:hidden[k]] = 1 # forget gate bias\n        input = hidden[k]\n    end\n    param[end-2] = winit*randn(vocab,embed)\n    param[end-1] = winit*randn(hidden[end],vocab)\n    param[end] = zeros(1,vocab)\n    return map(p->convert(atype,p), param)\nend\n\n# state[2k-1,2k]: hidden and cell for the k'th lstm layer\nfunction initstate(atype, hidden, batchsize)\n    state = Array(Any, 2*length(hidden))\n    for k = 1:length(hidden)\n        state[2k-1] = zeros(batchsize,hidden[k])\n        state[2k] = zeros(batchsize,hidden[k])\n    end\n    return map(s->convert(atype,s), state)\nend\n\nfunction lstm(weight,bias,hidden,cell,input)\n    gates   = hcat(input,hidden) * weight .+ bias\n    hsize   = size(hidden,2)\n    forget  = sigm(gates[:,1:hsize])\n    ingate  = sigm(gates[:,1+hsize:2hsize])\n    outgate = sigm(gates[:,1+2hsize:3hsize])\n    change  = tanh(gates[:,1+3hsize:end])\n    cell    = cell .* forget + ingate .* change\n    hidden  = outgate .* tanh(cell)\n    return (hidden,cell)\nend\n\n# s[2k-1,2k]: hidden and cell for the k'th lstm layer\n# w[2k-1,2k]: weight and bias for k'th lstm layer\n# w[end-2]: embedding matrix\n# w[end-1,end]: weight and bias for final prediction\n# state is modified in place\nfunction predict(w, s, x)\n    x = x * w[end-2]\n    for i = 1:2:length(s)\n        (s[i],s[i+1]) = lstm(w[i],w[i+1],s[i],s[i+1],x)\n        x = s[i]\n    end\n    return x * w[end-1] .+ w[end]\nend\n\n# sequence[t]: input token at time t\n# state is modified in place\nfunction loss(param,state,sequence,range=1:length(sequence)-1)\n    total = 0.0; count = 0\n    atype = typeof(AutoGrad.getval(param[1]))\n    input = convert(atype,sequence[first(range)])\n    for t in range\n        ypred = predict(param,state,input)\n        ynorm = logp(ypred,2) # ypred .- log(sum(exp(ypred),2))\n        ygold = convert(atype,sequence[t+1])\n        total += sum(ygold .* ynorm)\n        count += size(ygold,1)\n        input = ygold\n    end\n    return -total / count\nend\n\nlossgradient = grad(loss)\n\nfunction generate(param, state, vocab, nchar)\n    index_to_char = Array(Char, length(vocab))\n    for (k,v) in vocab; index_to_char[v] = k; end\n    input = oftype(param[1], zeros(1,length(vocab)))\n    index = 1\n    for t in 1:nchar\n        ypred = predict(param,state,input)\n        input[index] = 0\n        index = sample(exp(logp(ypred)))\n        print(index_to_char[index])\n        input[index] = 1\n    end\n    println()\nend\n\nfunction sample(p)\n    p = convert(Array,p)\n    r = rand()\n    for c = 1:length(p)\n        r -= p[c]\n        r < 0 && return c\n    end\nend\n\nfunction shakespeare()\n    file = Knet.dir(\"data\",\"100.txt\")\n    if !isfile(file)\n        info(\"Downloading 'The Complete Works of William Shakespeare'\")\n        url = \"http://www.gutenberg.org/files/100/100.txt\"\n        download(url,file)\n    end\n    return file\nend\n\nfunction minibatch(chars, char_to_index, batch_size)\n    nbatch = div(length(chars), batch_size)\n    vocab_size = length(char_to_index)\n    data = [ falses(batch_size, vocab_size) for i=1:nbatch ] # using BitArrays\n    cidx = 0\n    for c in chars            # safest way to iterate over utf-8 text\n        idata = 1 + cidx % nbatch\n        row = 1 + div(cidx, nbatch)\n        row > batch_size && break\n        col = char_to_index[c]\n        data[idata][row,col] = 1\n        cidx += 1\n    end\n    return data\nend\n\n# To be able to load/save KnetArrays:\nif Pkg.installed(\"JLD\") != nothing\n    import JLD: writeas, readas\n    type KnetJLD; a::Array; end\n    writeas(c::KnetArray) = KnetJLD(Array(c))\n    readas(d::KnetJLD) = KnetArray(d.a)\nend\n\n# This allows both non-interactive (shell command) and interactive calls like:\n# $ julia charlm.jl --epochs 10\n# julia> CharLM.main(\"--epochs 10\")\n!isinteractive() && !isdefined(Core.Main,:load_only) && main(ARGS)\n\nend  # module\n\n# Note: 10.txt used in the sample runs below was generated using\n#   head -10000 100.txt > 10.txt\n# where 100.txt is the file downloaded by shakespeare().\n\n\n\n### SAMPLE RUN 74a2e6c+ Mon Sep 19 14:03:10 EEST 2016\n### Implemented multi-layer.  Removed the keepstate option fixing it to true.\n### Note that winit default changed so I specify it below for comparison.\n### The slight difference is due to keepstate.\n\n# julia> CharLM.main(\"--data 10.txt --winit 0.3 --fast\")\n# charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\n# opts=(:lr,4.0)(:atype,\"KnetArray{Float32}\")(:winit,0.3)(:savefile,nothing)(:loadfile,nothing)(:generate,0)(:bestfile,nothing)(:gclip,3.0)(:hidden,[256])(:epochs,3)(:decay,0.9)(:gcheck,0)(:seqlength,100)(:seed,42)(:embed,256)(:batchsize,128)(:datafiles,Any[\"10.txt\"])(:fast,true)\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n#   1.406687 seconds (1.74 M allocations: 196.799 MB, 2.49% gc time)\n# (:epoch,0,:loss,6.1075509900258)\n#   4.002638 seconds (6.12 M allocations: 374.188 MB, 2.41% gc time)\n#   3.990772 seconds (6.11 M allocations: 374.129 MB, 2.44% gc time)\n#   4.006249 seconds (6.12 M allocations: 374.240 MB, 2.53% gc time)\n#   1.405878 seconds (1.75 M allocations: 197.059 MB, 2.56% gc time)\n# (:epoch,3,:loss,1.8713183968407767)\n\n\n\n### SAMPLE RUN 4ce58d1+ Fri Sep 16 12:24:00 EEST 2016\n### Transposed everything so getindex does not need to copy\n\n# charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\n# opts=(:keepstate,false)(:lr,4.0)(:atype,\"KnetArray{Float32}\")(:winit,0.3)(:savefile,nothing)(:loadfile,nothing)(:generate,0)(:bestfile,nothing)(:gclip,3.0)(:embedding,256)(:hidden,256)(:epochs,3)(:decay,0.9)(:gcheck,0)(:seqlength,100)(:seed,42)(:batchsize,128)(:datafiles,Any[\"data/10.txt\"])(:fast,true)\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n#   1.388652 seconds (1.73 M allocations: 196.686 MB, 2.04% gc time)\n# (:epoch,0,:loss,6.1075509900258)\n#   3.940298 seconds (6.06 M allocations: 373.166 MB, 2.06% gc time)\n#   3.935995 seconds (6.06 M allocations: 373.244 MB, 2.07% gc time)\n#   3.934983 seconds (6.06 M allocations: 373.245 MB, 2.08% gc time)\n#   1.390374 seconds (1.73 M allocations: 196.820 MB, 2.11% gc time)\n# (:epoch,3,:loss,1.860654126576015)\n\n\n\n### SAMPLE RUN 31136d5+ Wed Sep 14 17:51:44 EEST 2016: using vcat(x,h) and vcat(w...)\n### optimized learning parameters: winit=0.3, lr=4.0, gclip=3.0\n\n# charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\n# opts=(:keepstate,false)(:lr,4.0)(:atype,\"KnetArray{Float32}\")(:winit,0.3)(:savefile,nothing)(:loadfile,nothing)(:generate,0)(:bestfile,nothing)(:gclip,3.0)(:embedding,256)(:hidden,256)(:epochs,3)(:decay,0.9)(:gcheck,0)(:seqlength,100)(:seed,42)(:batchsize,128)(:datafiles,Any[\"10.txt\"])(:fast,true)\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n#   1.596959 seconds (1.79 M allocations: 197.432 MB, 2.56% gc time)\n# (:epoch,0,:loss,5.541976199042528)\n#   4.421566 seconds (6.23 M allocations: 375.843 MB, 2.54% gc time)\n#   4.418540 seconds (6.25 M allocations: 376.058 MB, 2.51% gc time)\n#   4.402317 seconds (6.26 M allocations: 376.297 MB, 2.66% gc time)\n#   1.594677 seconds (1.81 M allocations: 197.737 MB, 2.64% gc time)\n# (:epoch,3,:loss,1.8484550957572192)\n\n\n### SAMPLE RUN 80503e7+ Wed Sep 14 17:35:36 EEST 2016: using vcat(x,h)\n#\n# charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\n# opts=(:keepstate,false)(:lr,1.0)(:atype,\"KnetArray{Float32}\")(:savefile,nothing)(:loadfile,nothing)(:generate,0)(:bestfile,nothing)(:embedding,256)(:gclip,5.0)(:hidden,256)(:epochs,3)(:decay,0.9)(:gcheck,0)(:seqlength,100)(:seed,42)(:batchsize,128)(:datafiles,Any[\"10.txt\"])(:fast,true)\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n#   1.930180 seconds (1.95 M allocations: 213.741 MB, 1.82% gc time)\n# (:epoch,0,:loss,4.462641664662756)\n#   4.968101 seconds (7.47 M allocations: 454.259 MB, 2.24% gc time)\n#   4.963733 seconds (7.47 M allocations: 454.363 MB, 2.26% gc time)\n#   4.967413 seconds (7.45 M allocations: 454.024 MB, 2.14% gc time)\n#   1.945658 seconds (1.98 M allocations: 214.183 MB, 2.02% gc time)\n# (:epoch,3,:loss,3.2389672966290237)\n\n\n### SAMPLE RUN 65f57ff+ Wed Sep 14 10:02:30 EEST 2016: separate x, h, w, b\n#\n# charlm.jl (c) Emre Yolcu, Deniz Yuret, 2016. Character level language model based on http://karpathy.github.io/2015/05/21/rnn-effectiveness.\n# opts=(:keepstate,false)(:lr,1.0)(:atype,\"KnetArray{Float32}\")(:savefile,nothing)(:loadfile,nothing)(:generate,0)(:bestfile,nothing)(:embedding,256)(:gclip,5.0)(:hidden,256)(:epochs,3)(:decay,0.9)(:gcheck,0)(:seqlength,100)(:seed,42)(:batchsize,128)(:datafiles,Any[\"10.txt\"])(:fast,true)\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n#   2.156358 seconds (2.31 M allocations: 237.913 MB, 2.30% gc time)\n# (:epoch,0,:loss,4.465127425659868)\n#   6.287736 seconds (9.54 M allocations: 574.703 MB, 2.84% gc time)\n#   6.272144 seconds (9.54 M allocations: 574.633 MB, 2.80% gc time)\n#   6.277462 seconds (9.54 M allocations: 574.637 MB, 2.86% gc time)\n#   2.165516 seconds (2.34 M allocations: 238.323 MB, 2.56% gc time)\n# (:epoch,3,:loss,3.226540256084356)\n\n\n### SAMPLE OUTPUT (with head -10000 100.txt): first version\n# julia> CharLM.main(\"--gpu --data 10.txt\")\n# opts=(:lr,1.0)(:savefile,nothing)(:loadfile,nothing)(:dropout,0.0)(:generate,0)(:bestfile,nothing)(:embedding,256)(:gclip,5.0)(:hidden,256)(:epochs,10)(:nlayer,1)(:decay,0.9)(:gpu,true)(:seqlength,100)(:seed,42)(:batchsize,128)(:datafiles,Any[\"10.txt\"])\n# INFO: Chars read: [(\"10.txt\",425808)]\n# INFO: 87 unique chars.\n# (0,4.465127425659868)\n#   2.182693 seconds (2.36 M allocations: 240.394 MB, 1.74% gc time)\n#   7.861079 seconds (10.12 M allocations: 601.311 MB, 1.84% gc time)\n# (1,3.3244698245543285)\n#   2.159062 seconds (2.35 M allocations: 239.217 MB, 1.80% gc time)\n#   6.200085 seconds (9.55 M allocations: 575.573 MB, 2.28% gc time)\n# (2,3.24593908969621)\n#   2.352389 seconds (2.34 M allocations: 239.381 MB, 1.51% gc time)\n#   6.211946 seconds (9.55 M allocations: 575.568 MB, 2.21% gc time)\n# (3,3.226540256084356)\n\n", "meta": {"hexsha": "27c71fa1752b535324012bb0bf877522f1b15adf", "size": 18100, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/charlm.jl", "max_stars_repo_name": "enzotarta/Knet.jl", "max_stars_repo_head_hexsha": "4330f3fec812ccbbfeabbcf6aa63450ca743f9c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/charlm.jl", "max_issues_repo_name": "enzotarta/Knet.jl", "max_issues_repo_head_hexsha": "4330f3fec812ccbbfeabbcf6aa63450ca743f9c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/charlm.jl", "max_forks_repo_name": "enzotarta/Knet.jl", "max_forks_repo_head_hexsha": "4330f3fec812ccbbfeabbcf6aa63450ca743f9c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.3014354067, "max_line_length": 305, "alphanum_fraction": 0.6443093923, "num_tokens": 5976, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.13660839354130014, "lm_q1q2_score": 0.06830419677065007}}
{"text": "# ---\n# title: 236. Lowest Common Ancestor of a Binary Tree\n# id: problem236\n# author: zhwang\n# date: 2022-02-10\n# difficulty: Medium\n# categories: Tree\n# link: <https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree/description/>\n# hidden: true\n# ---\n# \n# Given a binary tree, find the lowest common ancestor (LCA) of two given nodes\n# in the tree.\n# \n# According to the [definition of LCA on\n# Wikipedia](https://en.wikipedia.org/wiki/Lowest_common_ancestor): \"The lowest\n# common ancestor is defined between two nodes p and q as the lowest node in T\n# that has both p and q as descendants (where we allow **a node to be a\n# descendant of itself** ).\"\n# \n# \n# \n# **Example 1:**\n# \n# ![](https://assets.leetcode.com/uploads/2018/12/14/binarytree.png)\n# \n#     \n#     \n#     Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 1\n#     Output: 3\n#     Explanation: The LCA of nodes 5 and 1 is 3.\n#     \n# \n# **Example 2:**\n# \n# ![](https://assets.leetcode.com/uploads/2018/12/14/binarytree.png)\n# \n#     \n#     \n#     Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 4\n#     Output: 5\n#     Explanation: The LCA of nodes 5 and 4 is 5, since a node can be a descendant of itself according to the LCA definition.\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: root = [1,2], p = 1, q = 2\n#     Output: 1\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * The number of nodes in the tree is in the range `[2, 105]`.\n#   * `-109 <= Node.val <= 109`\n#   * All `Node.val` are **unique**.\n#   * `p != q`\n#   * `p` and `q` will exist in the tree.\n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction lowest_common_ancestor_236(\n    root::TreeNode{Int}, p::TreeNode{Int}, q::TreeNode{Int}\n)::TreeNode{Int}\n    function dfs(node::Union{TreeNode{Int},Nothing})\n        (isnothing(node) || node == p || node == q) && return node\n        left, right = dfs(node.left), dfs(node.right)\n        isnothing(left) && isnothing(right) && return nothing\n        !isnothing(left) && !isnothing(right) && return node\n        return !isnothing(left) ? left : right\n    end\n    return dfs(root)\nend\n## @lc code=end\n", "meta": {"hexsha": "878c9540e5448babfc8dff83a912d227628f399a", "size": 2113, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/236.lowest-common-ancestor-of-a-binary-tree.jl", "max_stars_repo_name": "RexWzh/LeetCode.jl", "max_stars_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/236.lowest-common-ancestor-of-a-binary-tree.jl", "max_issues_repo_name": "RexWzh/LeetCode.jl", "max_issues_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/236.lowest-common-ancestor-of-a-binary-tree.jl", "max_forks_repo_name": "RexWzh/LeetCode.jl", "max_forks_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.4125, "max_line_length": 125, "alphanum_fraction": 0.6062470421, "num_tokens": 687, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.1384617959016485, "lm_q1q2_score": 0.06814925319338568}}
{"text": "# Code for Video 1.2:\r\n\r\nprintln(\"I love crunching data\")\r\n\r\na = 2\r\nb = 106\r\nc = a + b\r\nprintln(c)\r\n\r\n# in terminal:\r\n# julia 1_2.jl\r\n# julia -h\r\n\r\n# in REPL:\r\n# ?mean\r\n\r\n# Pkg.add(\"IJulia\")\r\n\r\n# if cond\r\n#     #then\r\n# else\r\n#     #default\r\n# end", "meta": {"hexsha": "40d260953a4b738c98e20cd2e79ef4e9382743a7", "size": 247, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Section 1/1_2.jl", "max_stars_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_stars_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-09-01T15:05:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-14T14:09:53.000Z", "max_issues_repo_path": "Section 1/1_2.jl", "max_issues_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_issues_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Section 1/1_2.jl", "max_forks_repo_name": "lytemar/Julia-for-Data-Science-Video", "max_forks_repo_head_hexsha": "e7cb2427b10979d4be0f1e00be1cc1090f4da736", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-09-01T15:05:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-08T15:00:22.000Z", "avg_line_length": 10.7391304348, "max_line_length": 33, "alphanum_fraction": 0.5182186235, "num_tokens": 92, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.15817435076426423, "lm_q1q2_score": 0.06803827759553534}}
{"text": "# Built-in Exceptions\n\n# sqrt(-1)\n\n# Throwing Exceptions\n\na = 1; b = 0\n# b != 0 ? a / b : throw(DivideError())\n\n# Errors\n\n# b != 0 ? a / b : error(\"Divide by zero not allowed!\")\n\n# Try-Catch-Finally\n\ntry\n  sqrt(-1)\ncatch e\n  println(\"Square root of negative number not allowed!\")\nend\n\ntry\n  sqrt(-1)\ncatch e\n  println(\"Square root of negative number not allowed!\")\nfinally\n  println(\"This is always executed!\")\nend", "meta": {"hexsha": "aeee3969215c6eb823de8396f79fe41e5468771e", "size": 414, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-programming/6-control-flow/exception-handling.jl", "max_stars_repo_name": "cadamsmith/julia-programming", "max_stars_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia-programming/6-control-flow/exception-handling.jl", "max_issues_repo_name": "cadamsmith/julia-programming", "max_issues_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-programming/6-control-flow/exception-handling.jl", "max_forks_repo_name": "cadamsmith/julia-programming", "max_forks_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.7857142857, "max_line_length": 56, "alphanum_fraction": 0.652173913, "num_tokens": 125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3923368443773709, "lm_q2_score": 0.1732882037945951, "lm_q1q2_score": 0.06798734704459419}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.0\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 6b1ad54f-61e4-490d-9032-7a557e8dc82f\nmd\"\"\"\n## CHEME 5440/7770: Structural Analysis of the Urea Cycle Network (PS2)\n\"\"\"\n\n# \u2554\u2550\u2561 7057c8e4-9e94-4a28-a885-07f5c96ebe39\nhtml\"\"\"\n<p style=\"font-size:20px;\">Student name, Student name, Student name ... Student name</br>\nSmith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca NY 14850</p>\n\"\"\"\n\n# \u2554\u2550\u2561 87a183bc-3857-4189-8103-18c46ff3245d\nmd\"\"\"\n#### Build the stoichiometric array\n\"\"\"\n\n# \u2554\u2550\u2561 5338451e-3c4b-4030-bbbb-42eaf4209a89\nbegin\n\n\t# fill me in\n\t# ...\n\t\nend\n\n# \u2554\u2550\u2561 6970dab5-16bd-4898-b88d-723cb1b3d89e\nmd\"\"\"\n#### Convex analysis: extreme pathways\n\"\"\"\n\n# \u2554\u2550\u2561 97b0763d-dcab-4afa-b660-52e18b3d523f\nbegin\n\n\t# fill me in\n\t# ...\n\t\nend\n\n# \u2554\u2550\u2561 b473b17e-3bf5-4b6c-af24-fe57b5a7e7e9\nmd\"\"\"\n#### Metabolite connectivity array (MCA)\n\"\"\"\n\n# \u2554\u2550\u2561 999ae1fd-5341-4f66-9db2-dec53fa0cd49\n\n\n# \u2554\u2550\u2561 b7e5d1a6-57ed-4d09-a039-a4bd12386367\nmd\"\"\"\n#### Reaction connectivity array (RCA)\n\"\"\"\n\n# \u2554\u2550\u2561 4520fc6e-7305-487e-924d-af22406e6d45\nbegin\n\n\t# fill me in ...\nend\n\n# \u2554\u2550\u2561 267865de-1b5c-4579-861b-c6c46beb4739\nfunction ingredients(path::String)\n\t\n\t# this is from the Julia source code (evalfile in base/loading.jl)\n\t# but with the modification that it returns the module instead of the last object\n\tname = Symbol(\"lib\")\n\tm = Module(name)\n\tCore.eval(m,\n        Expr(:toplevel,\n             :(eval(x) = $(Expr(:core, :eval))($name, x)),\n             :(include(x) = $(Expr(:top, :include))($name, x)),\n             :(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)),\n             :(include($path))))\n\tm\nend\n\n# \u2554\u2550\u2561 67f5db98-88d0-11ec-27ac-b57538a166f4\nbegin\n\t# import some packages -\n\tusing PlutoUI\n\tusing PrettyTables\n\tusing LinearAlgebra\n\t\n\t# setup paths -\n\tconst _PATH_TO_NOTEBOOK = pwd()\n\tconst _PATH_TO_SRC = joinpath(_PATH_TO_NOTEBOOK,\"src\")\n\n\t# load the PS2 code lib -\n\tlib = ingredients(joinpath(_PATH_TO_SRC, \"Include.jl\"));\n\n\t# return -\n\tnothing\nend\n\n# \u2554\u2550\u2561 ab2bcfd5-3ba7-4388-8a3c-2cb95fba989a\nhtml\"\"\"\n<style>\nmain {\n    max-width: 900px;\n    width: 75%;\n    margin: auto;\n    font-family: \"Roboto, monospace\";\n}\n\na {\n    color: blue;\n    text-decoration: none;\n}\n</style>\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nLinearAlgebra = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPrettyTables = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\n\n[compat]\nPlutoUI = \"~0.7.34\"\nPrettyTables = \"~1.3.1\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.1\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[deps.Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[deps.HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[deps.IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"0b5cfbb704034b5b4c1869e36634438a047df065\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.1\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"8979e9802b4ac3d58c503a20f2824ad67f9074dd\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.34\"\n\n[[deps.PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"dfb54c4e414caa595a1f2ed759b160f5a3ddcba5\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.3.1\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[deps.Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[deps.TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[deps.Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"bb1064c9a84c52e277f1096cf41434b675cd368b\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.1\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25006b1ad54f-61e4-490d-9032-7a557e8dc82f\n# \u255f\u25007057c8e4-9e94-4a28-a885-07f5c96ebe39\n# \u255f\u250087a183bc-3857-4189-8103-18c46ff3245d\n# \u2560\u25505338451e-3c4b-4030-bbbb-42eaf4209a89\n# \u2560\u25506970dab5-16bd-4898-b88d-723cb1b3d89e\n# \u2560\u255097b0763d-dcab-4afa-b660-52e18b3d523f\n# \u255f\u2500b473b17e-3bf5-4b6c-af24-fe57b5a7e7e9\n# \u2560\u2550999ae1fd-5341-4f66-9db2-dec53fa0cd49\n# \u255f\u2500b7e5d1a6-57ed-4d09-a039-a4bd12386367\n# \u2560\u25504520fc6e-7305-487e-924d-af22406e6d45\n# \u2560\u255067f5db98-88d0-11ec-27ac-b57538a166f4\n# \u2560\u2550267865de-1b5c-4579-861b-c6c46beb4739\n# \u255f\u2500ab2bcfd5-3ba7-4388-8a3c-2cb95fba989a\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "f2abe9e5e66d014f9fab15b2f157a6d780dbb3a1", "size": 10245, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "problem_sets/PS2/PS2-5440-7770-Template.jl", "max_stars_repo_name": "za-gao/CHEME-5440-7770-Cornell-Spring-2022", "max_stars_repo_head_hexsha": "ad8c9a10c28bf893c9fa11f23561d37ed4883831", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "problem_sets/PS2/PS2-5440-7770-Template.jl", "max_issues_repo_name": "za-gao/CHEME-5440-7770-Cornell-Spring-2022", "max_issues_repo_head_hexsha": "ad8c9a10c28bf893c9fa11f23561d37ed4883831", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "problem_sets/PS2/PS2-5440-7770-Template.jl", "max_forks_repo_name": "za-gao/CHEME-5440-7770-Cornell-Spring-2022", "max_forks_repo_head_hexsha": "ad8c9a10c28bf893c9fa11f23561d37ed4883831", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2020460358, "max_line_length": 196, "alphanum_fraction": 0.7190824793, "num_tokens": 4570, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39233681595684605, "lm_q2_score": 0.1732882080640581, "lm_q1q2_score": 0.06798734379472}}
{"text": "abstract type Question end\n\nmutable struct Stringq <: Question\n    re::Regex\n    label\n    hint\nend\n\n\"\"\"\n    stringq(re::Regex; label=\"\", hint=\"\")\n\nMatch string answer with regular expression\n\nArguments:\n\n* `re`: a regular expression for grading\n* `label`: optional label for the form element\n* `hint`: optional plain-text hint that can be seen on hover\n\n\"\"\"\nstringq(re::Regex; label=\"\", hint=\"\") = Stringq(re, label, hint)\n\n\n##\nmutable struct Numericq <: Question\n    val\n    tol\n    units\n    label\n    hint\nend\n\n\"\"\"\n    numericq(value, tol=1e-3; label=\"\", hint=\"\", units=\"\")\n\nMatch a numeric answer\n\nArguments:\n\n* `value`: the numeric answer\n* `tol`: ``|answer - value| \\\\le tol`` is used to determine correctness\n* `label`: optional label for the form element\n* `hint`: optional plain-text hint that can be seen on hover\n* `units`: a string indicating expected units.\n\n\"\"\"\nfunction numericq(val, tol=1e-3, args...;\n                  label=\"\",\n                  hint=\"\", units::AbstractString=\"\")\n\n    Numericq(val, tol,  units, label, hint)\nend\n\nnumericq(val::Int; kwargs...) = numericq(val, 0; kwargs...)\n\n\n\n##\nmutable struct Radioq <: Question\n    choices\n    answer\n    values\n    labels\n    label\n    hint\n    inline\nend\n\n\"\"\"\n    radioq(choices, answer; label=\"\", hint=\"\", keep_order=false)\n\nMultiple choice question (one of several)\n\nArguments:\n\n* `choices`: indexable collection of choices. As seen in the example, choices can be formatted with markdown.\n\n* `answer::Int`: index of correct choice\n\n* `keep_order::Boolean`: if `true` keeps display order of choices, otherwise they are shuffled.\n\n* `inline::Bool`: hint to render inline (or not) if supported\n\n* `label`: optional label for the form element\n\n* `hint`: optional plain-text hint that can be seen on hover\n\nExample:\n\n```\nchoices = [\"beta\", raw\"``\\\\beta``\", \"`beta`\"]\nanswer = 2\nradioq(choices, answer; hint=\"Which is the Greek symbol?\")\n```\n\n\"\"\"\nfunction radioq(choices, answer::Integer;\n                label=\"\", hint=\"\", inline::Bool=(hint!=\"\"),\n                keep_order::Bool=false)\n    inds = collect(1:length(choices))\n    values = copy(inds)\n    labels = choices\n    !keep_order && shuffle!(inds)\n\n    Radioq(choices[inds], findfirst(isequal(answer), inds),\n           values, labels[inds], label, hint, inline)\nend\n\n\n\n\n##\nmutable struct Multiq <: Question\n    choices\n    answer\n    values\n    labels\n    label\n    hint\n    inline\nend\n\n\"\"\"\n    multiq(choices, answers; label=\"\", hint=\"\", keep_order=false)\n\nMultiple choice question (one *or more* of several)\n\nArguments:\n\n* `choices`: indexable collection of choices. As seen in the example, choices can be formatted with markdown.\n\n* `answers::Vector{Int}`: index of correct choice(s)\n\n* `keep_order::Boolean`: if `true` keeps display order of choices, otherwise they are shuffled.\n\n* `inline::Bool`: hint to render inline (or not) if supported\n\n* `label`: optional label for the form element\n\n* `hint`: optional plain-text hint that can be seen on hover\n\nExample:\n\n```\nchoices = [\"pear\", \"tomato\", \"banana\"]\nanswers = [1,3]\nmultiplecq(choices, answers; hint=\"not the red one!\")\n```\n\n\"\"\"\nfunction multiq(choices, answers;\n                label=\"\", hint=\"\", inline::Bool=(hint!=\"\"),\n                keep_order::Bool=false)\n    inds = collect(1:length(choices))\n    values = copy(inds)\n    labels = choices\n    !keep_order && shuffle!(inds)\n\n    Multiq(choices[inds], findall(in(answers), inds),\n           values, labels[inds], label, hint, inline)\nend\n\n\n\"\"\"\n    booleanq(ans; [label, hint])\nTrue of false questions:\n\nExample:\n\n```\nbooleanq(true; label=\"Does it hurt...\")\n```\n\n\"\"\"\nfunction booleanq(ans::Bool;\n                  labels::Vector=[\"true\", \"false\"],\n                  label=\"\", hint::AbstractString=\"\",\n                  inline::Bool=true)\n    choices = labels[1:2]\n    ans = 2 - ans\n    radioq(choices, ans;\n           label=label, hint=hint, inline=inline, keep_order=true)\nend\n\n\"\"\"\n    yesnoq(ans)\n\nBoolean question with `yes` or `no` labels.\n\nExamples:\n\n```\nyesnoq(\"yes\")\nyesnoq(true)\n```\n\n\"\"\"\nyesnoq(ans::AbstractString, args...; kwargs...) = radioq([\"Yes\", \"No\"], ans == \"yes\" ? 1 : 2, args...; keep_order=true, kwargs...)\nyesnoq(ans::Bool, args...; kwargs...) = yesnoq(ans ? \"yes\" : \"no\", args...;kwargs...)\n", "meta": {"hexsha": "e9e18f326d46cc7c73746c878fc597893db289f6", "size": 4257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/question_types.jl", "max_stars_repo_name": "sylvaticus/QuizQuestions.jl", "max_stars_repo_head_hexsha": "27b35af704429264b44c1fe2a653ab7fb0ffa9c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/question_types.jl", "max_issues_repo_name": "sylvaticus/QuizQuestions.jl", "max_issues_repo_head_hexsha": "27b35af704429264b44c1fe2a653ab7fb0ffa9c5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/question_types.jl", "max_forks_repo_name": "sylvaticus/QuizQuestions.jl", "max_forks_repo_head_hexsha": "27b35af704429264b44c1fe2a653ab7fb0ffa9c5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.285, "max_line_length": 130, "alphanum_fraction": 0.6328400282, "num_tokens": 1076, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.14223189137395395, "lm_q1q2_score": 0.06778482516112565}}
{"text": "module JuliennedArrays\n\nimport Base: axes, getindex, setindex!, size\nusing Base: promote_op, @pure, @propagate_inbounds, tail\n\nmap_unrolled(call, variables::Tuple{}) = ()\nmap_unrolled(call, variables) =\n    call(first(variables)), map_unrolled(call, tail(variables))...\n\nmap_unrolled(call, variables1::Tuple{}, variables2::Tuple{}) = ()\nmap_unrolled(call, variables1, variables2) =\n    call(first(variables1), first(variables2)),\n    map_unrolled(call, tail(variables1), tail(variables2))...\n\nis_in(needle::Needle, straw1::Needle, straws...) where {Needle} = True()\nis_in(needle, straw1, straws...) = is_in(needle, straws...)\nis_in(needle) = False()\n\nin_unrolled(straws, needle1, needles...) =\n    is_in(needle1, straws...),\n    in_unrolled(straws, needles...)...\nin_unrolled(straws) = ()\n\n@pure as_vals(them::Int...) = map(Val, them)\n\nabstract type TypedBool end\n\"\"\"\n    struct True\n\nTyped `true`\n\"\"\"\nstruct True <: TypedBool end\n\"\"\"\n    struct False\n\nTyped `false`\n\"\"\"\nstruct False <: TypedBool end\n\n@inline untyped(::True) = true\n@inline untyped(::False) = false\n\nnot(::False) = True()\nnot(::True) = False()\n\nexport True\nexport False\n\ngetindex_unrolled(into::Tuple{}, switches::Tuple{}) = ()\nfunction getindex_unrolled(into, switches)\n    next = getindex_unrolled(tail(into), tail(switches))\n    if untyped(first(switches))\n        (first(into), next...)\n    else\n        next\n    end\nend\n\nsetindex_unrolled(old::Tuple{}, something, ::Tuple{}) = ()\nsetindex_unrolled(old, new, switches) =\n    if untyped(first(switches))\n        first(new),\n        setindex_unrolled(tail(old), tail(new), tail(switches))...\n    else\n        first(old),\n        setindex_unrolled(tail(old), new, tail(switches))...\n    end\n\nstruct Slices{Item, Dimensions, Whole, Alongs} <: AbstractArray{Item, Dimensions}\n    whole::Whole\n    alongs::Alongs\nend\nSlices{Item, Dimensions}(whole::Whole, alongs::Alongs) where {Item, Dimensions, Whole, Alongs} =\n    Slices{Item, Dimensions, Whole, Alongs}(whole, alongs)\n\naxes(slices::Slices) =\n    getindex_unrolled(axes(slices.whole), map_unrolled(not, slices.alongs))\nsize(slices::Slices) = map_unrolled(length, axes(slices))\n\nslice_index(slices, indices) = setindex_unrolled(\n    axes(slices.whole),\n    indices,\n    map_unrolled(not, slices.alongs)\n)\n@propagate_inbounds getindex(slices::Slices, indices::Int...) =\n    view(slices.whole, slice_index(slices, indices)...)\n@propagate_inbounds setindex!(slices::Slices, value, indices::Int...) =\n    slices.whole[slice_index(slices, indices)...] = value\n\naxis_or_1(switch, axis) =\n    if untyped(switch)\n        axis\n    else\n        1\n    end\n\"\"\"\n    Slices(whole, alongs::TypedBool...)\n\nSlice `whole` into `view`s.\n\n`alongs`, made of [`True`](@ref) and [`False`](@ref) objects, shows which dimensions will be replaced with `:` when slicing.\n\n```jldoctest\njulia> using JuliennedArrays\n\njulia> whole = [1 2; 3 4];\n\njulia> slices = Slices(whole, False(), True())\n2-element Slices{SubArray{Int64,1,Array{Int64,2},Tuple{Int64,Base.OneTo{Int64}},true},1,Array{Int64,2},Tuple{False,True}}:\n [1, 2]\n [3, 4]\n\njulia> slices[1] == whole[1, :]\ntrue\n\njulia> slices[1] = [2, 1];\n\njulia> whole\n2\u00d72 Array{Int64,2}:\n 2  1\n 3  4\n\njulia> larger = rand(5, 5, 5);\n\njulia> larger_slices = Slices(larger, True(), False(), False());\n\njulia> size(first(larger_slices))\n(5,)\n```\n\"\"\"\nSlices(whole::AbstractArray, alongs::TypedBool...) =\n    Slices{\n        typeof(@inbounds view(\n            whole,\n            map_unrolled(axis_or_1, alongs, axes(whole))...\n        )),\n        length(getindex_unrolled(alongs, map_unrolled(not, alongs)))\n    }(whole, alongs)\n\n\"\"\"\n    Slices(whole, alongs::Int...)\n\nAlternative syntax: `alongs` is which dimensions will be replaced with `:` when slicing.\n\n```jldoctest\njulia> using JuliennedArrays\n\njulia> input = reshape(1:8, 2, 2, 2)\n2\u00d72\u00d72 reshape(::UnitRange{Int64}, 2, 2, 2) with eltype Int64:\n[:, :, 1] =\n 1  3\n 2  4\n\n[:, :, 2] =\n 5  7\n 6  8\n\njulia> Slices(input, 1, 3)\n2-element Slices{SubArray{Int64,2,Base.ReshapedArray{Int64,3,UnitRange{Int64},Tuple{}},Tuple{Base.OneTo{Int64},Int64,Base.OneTo{Int64}},false},1,Base.ReshapedArray{Int64,3,UnitRange{Int64},Tuple{}},Tuple{True,False,True}}:\n [1 5; 2 6]\n [3 7; 4 8]\n```\n\"\"\"\nSlices(whole::AbstractArray{Item, NumberOfDimensions}, alongs::Int...) where {Item, NumberOfDimensions} =\n    Slices(whole, in_unrolled(\n        as_vals(alongs...),\n        ntuple(Val, NumberOfDimensions)...\n    )...)\nexport Slices\n\nstruct Align{Item, Dimensions, Sliced, Alongs} <: AbstractArray{Item, Dimensions}\n    slices::Sliced\n    alongs::Alongs\nend\nAlign{Item, Dimensions}(slices::Sliced, alongs::Alongs) where {Item, Dimensions, Sliced, Alongs} =\n    Align{Item, Dimensions, Sliced, Alongs}(slices, alongs)\n\naxes(aligned::Align) = setindex_unrolled(\n    setindex_unrolled(\n        aligned.alongs,\n        axes(aligned.slices),\n        map_unrolled(not, aligned.alongs)\n    ),\n    axes(first(aligned.slices)),\n    aligned.alongs\n)\nsize(aligned::Align) = map_unrolled(length, axes(aligned))\n\nsplit_indices(aligned, indices) =\n    getindex_unrolled(indices, map_unrolled(not, aligned.alongs)),\n    getindex_unrolled(indices, aligned.alongs)\n@propagate_inbounds function getindex(aligned::Align, indices::Int...)\n    outer, inner = split_indices(aligned, indices)\n    aligned.slices[outer...][inner...]\nend\n@propagate_inbounds function setindex!(aligned::Align, value, indices::Int...)\n    outer, inner = split_indices(aligned, indices)\n    aligned.slices[outer...][inner...] = value\nend\n\n\"\"\"\n    Align(slices, alongs::TypedBool...)\n\n`Align` an array of arrays, all with the same size.\n\n`alongs`, made of [`True`](@ref) and [`False`](@ref) objects, shows which dimensions will be taken up by the inner arrays. Inverse of [`Slices`](@ref).\n\n```jldoctest\njulia> using JuliennedArrays\n\njulia> slices = [[1, 2], [3, 4]];\n\njulia> aligned = Align(slices, False(), True())\n2\u00d72 Align{Int64,2,Array{Array{Int64,1},1},Tuple{False,True}}:\n 1  2\n 3  4\n\njulia> aligned[1, :] == slices[1]\ntrue\n\njulia> aligned[1, 1] = 0;\n\njulia> slices\n2-element Array{Array{Int64,1},1}:\n [0, 2]\n [3, 4]\n```\n\"\"\"\nAlign(slices::AbstractArray{<:AbstractArray{Item, InnerDimensions}, OuterDimensions}, alongs::TypedBool...) where {Item, InnerDimensions, OuterDimensions} =\n    Align{Item, OuterDimensions + InnerDimensions}(slices, alongs)\nexport Align\n\n\"\"\"\n    Along(slices, alongs::Int...)\n\nAlternative syntax: `alongs` is which dimensions will be taken up by the inner arrays.\n\n```jldoctest\njulia> using JuliennedArrays\n\njulia> input = reshape(1:8, 2, 2, 2)\n2\u00d72\u00d72 reshape(::UnitRange{Int64}, 2, 2, 2) with eltype Int64:\n[:, :, 1] =\n 1  3\n 2  4\n\n[:, :, 2] =\n 5  7\n 6  8\n\njulia> slices = collect(Slices(input, 1, 3))\n2-element Array{SubArray{Int64,2,Base.ReshapedArray{Int64,3,UnitRange{Int64},Tuple{}},Tuple{Base.OneTo{Int64},Int64,Base.OneTo{Int64}},false},1}:\n [1 5; 2 6]\n [3 7; 4 8]\n\njulia> Align(slices, 1, 3)\n2\u00d72\u00d72 Align{Int64,3,Array{SubArray{Int64,2,Base.ReshapedArray{Int64,3,UnitRange{Int64},Tuple{}},Tuple{Base.OneTo{Int64},Int64,Base.OneTo{Int64}},false},1},Tuple{True,False,True}}:\n[:, :, 1] =\n 1  3\n 2  4\n\n[:, :, 2] =\n 5  7\n 6  8\n```\n\"\"\"\nAlign(slices::AbstractArray{<:AbstractArray{Item, InnerDimensions}, OuterDimensions}, alongs::Int...) where {Item, InnerDimensions, OuterDimensions} =\n    Align(slices, in_unrolled(\n        as_vals(alongs...),\n        ntuple(Val, InnerDimensions + OuterDimensions)...\n    )...)\n\nend\n", "meta": {"hexsha": "3b5b7981449f889d47f078a2e26ffe119e6f3b6b", "size": 7415, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/JuliennedArrays.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/JuliennedArrays.jl-5cadff95-7770-533d-a838-a1bf817ee6e0", "max_stars_repo_head_hexsha": "8dc49be38ce5d85c0adb92e31917ccd6c5ac3827", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/JuliennedArrays.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/JuliennedArrays.jl-5cadff95-7770-533d-a838-a1bf817ee6e0", "max_issues_repo_head_hexsha": "8dc49be38ce5d85c0adb92e31917ccd6c5ac3827", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/JuliennedArrays.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/JuliennedArrays.jl-5cadff95-7770-533d-a838-a1bf817ee6e0", "max_forks_repo_head_hexsha": "8dc49be38ce5d85c0adb92e31917ccd6c5ac3827", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0620437956, "max_line_length": 222, "alphanum_fraction": 0.6737693864, "num_tokens": 2349, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.14033624949008322, "lm_q1q2_score": 0.06742857563719645}}
{"text": "a = let\n    i=3\n    i+=5\n    i # the value returned from the computation\nend\n\na\n\nb = let i=5\n    i+=42\n    i\nend\n\nc = let i=10\n    i+=42\n    i\nend\n\nc\n\ni\n\nd = begin\n    i=41\n    i+=1\n    i\nend\n\n\ni\nd\n\nconst C = 299792458 # m / s, this is an Int\n\nC = 300000000 # change the value of C\n\nC = 2.998 * 1e8 #change the type of C, not permitted\n\n#%%\nmodule ScopeTestModule\nexport a1\na1 = 25\nb1 = 42\nend # end of module\n#%%\nusing .ScopeTestModule\n\na1\nb1\n\nScopeTestModule.b1=26\n", "meta": {"hexsha": "2d0b12a274f923167272e3d53434cbe8bae27bb0", "size": 467, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lesson-scope/scope.jl", "max_stars_repo_name": "JeffreySarnoff/techytok-examples", "max_stars_repo_head_hexsha": "5c34c9fc0660da1a69f9e3959465ed80659c30cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2019-10-28T11:21:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-14T09:06:49.000Z", "max_issues_repo_path": "lesson-scope/scope.jl", "max_issues_repo_name": "JeffreySarnoff/techytok-examples", "max_issues_repo_head_hexsha": "5c34c9fc0660da1a69f9e3959465ed80659c30cd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lesson-scope/scope.jl", "max_forks_repo_name": "JeffreySarnoff/techytok-examples", "max_forks_repo_head_hexsha": "5c34c9fc0660da1a69f9e3959465ed80659c30cd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2020-01-20T23:53:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-10T20:51:24.000Z", "avg_line_length": 8.9807692308, "max_line_length": 52, "alphanum_fraction": 0.5952890792, "num_tokens": 191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.13477592958647097, "lm_q1q2_score": 0.06738796479323549}}
{"text": "#------------------------------------------------------------------------------\n\"\"\"\n    printmat([fh::IO],x;width=10,prec=3,NoPrinting=false,StringFmt=\"\")\n\nPrint all elements of matrix with predefined formatting.\n\n# Input\n- `fh::IO`:            (optional) file handle. If not supplied, prints to screen\n- `x::Array`:          string, date or array to print\n- `width::Int`:        (keyword) scalar, minimum width of printed cells\n- `prec::Int`:         (keyword) scalar, precision of printed cells\n- `NoPrinting::Bool`:  (keyword) bool, true: no printing, just return formatted string\n- `StringFmt::String`: (keyword) string, \"\", \"html\", \"csv\"\n\n# Output\n- str         (if NoPrinting) string, (otherwise nothing)\n\n# Examples\n```\nx = [11 12;21 22]\nprintmat(x)\n```\n```\nx = Any[1 \"ab\"; Date(2018,10,7) 3.14]\nprintmat(x,width=20)\n```\nCan also call as\n```\nopt = Dict(:width=>10,:prec=>3,:NoPrinting=>false,:StringFmt=>\"\")\nprintmat(x;opt...)     #notice , and ...\n```\n(not all keywords are needed)\n\n# Requires\n- Dates\n- fmtNumPs\n\n# To do\n\n\nPaul.Soderlind@unisg.ch\n\n\"\"\"\nfunction printmat(fh::IO,x;width=10,prec=3,NoPrinting=false,StringFmt=\"\")\n\n  if isa(x,Union{String,Date,DateTime,Missing})  #eg. a single Date\n    str = string(lpad(x,width),\"\\n\")\n    if NoPrinting\n      return str\n    else\n      print(fh,str,\"\\n\")\n      return nothing\n    end\n  elseif isa(x,Nothing)\n    return nothing\n  end\n\n  if ndims(x) > 2\n    @warn(\"more than 2 dimensions\")\n    return nothing\n  end\n\n  (m,n) = (size(x,1),size(x,2))\n\n  iob = IOBuffer()\n  for i = 1:m                #loop over lines\n    for j = 1:n-1                #loop over columns 1:n-1\n      writeElementPs(iob,x,i,j,width,prec,StringFmt)\n    end\n    if StringFmt == \"csv\"                         #last (n) column\n      writeElementPs(iob,x,i,n,width,prec,\"\")     #no , at end of line\n    else\n      writeElementPs(iob,x,i,n,width,prec,StringFmt)\n    end\n    write(iob,\"\\n\")            #newline\n  end\n  str = String(take!(iob))\n\n  if NoPrinting                              #no printing, just return str\n    return str\n  else                                       #print, return nothing\n    print(fh,str,\"\\n\")\n    return nothing\n  end\n\nend\n                  #when fh is not supplied: printing to screen\nprintmat(x;width=10,prec=3,NoPrinting=false,StringFmt=\"\") = printmat(stdout::IO,\n         x,width=width,prec=prec,NoPrinting=NoPrinting,StringFmt=StringFmt)\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    printTable([fh::IO],x,colNames=[],rowNames=[];\n               width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\")\n\nPrint formatted table with row names (1st column) column names (1st row),\nand data matrix (the rest).\n\n\n# Input\n- `fh::IO`:            (optional) file handle. If not supplied, prints to screen\n- `x::Array`:          (of numbers, dates, strings, ...) to print\n- `colNames::Array`:   of strings with column headers\n- `rowNames::Array`:   of strings with row labels\n- `width::Int`:        (keyword) scalar, minimum width of printed cells. [10]\n- `prec::Int`:         (keyword) scalar, precision of printed cells. [3]\n- `NoPrinting::Bool`:  (keyword) bool, true: no printing, just return formatted string [false]\n- `StringFmt::String`: (keyword) string, \"\", \"html\", \"csv\"\n- `cell00::String`:    (keyword) string, for row 0, column 0\n\n# Output\n- `str::String`:      (if NoPrinting) string, (otherwise nothing)\n\n# Example\n```\nxA = [1 \"ab\" \"abc\"; \"ccc\" 3.14 missing]\nprintTable(xA,colNames,[\"1\";\"4\"],width=12,prec=2)\n```\nCan also call as\n```\nopt = Dict(:width=>10,:prec=>3,:NoPrinting=>false,:StringFmt=>\"\",:cell00=>\"\")\nprintTable(x;opt...)     #notice , and ...\n```\n(not all keywords are needed)\n\n# Requires\n- Dates\n- printmat\n\n\"\"\"\nfunction printTable(fh::IO,x,colNames=[],rowNames=[];\n                    width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\")\n\n  isempty(x) && return nothing                        #do nothing is isempty(x)\n\n  (m,n) = (size(x,1),size(x,2))\n\n  if isempty(rowNames)                                 #create row names \"r1\"\n    rowNames = [string(\"r\",i) for i = 1:m]\n  end\n  if isempty(colNames)                                 #create column names \"c1\"\n    colNames = [string(\"c\",i) for i = 1:n]\n  end\n\n  rNamesWidth = maximum([length(rowNames[i]) for i = 1:length(rowNames)])  #max length of rowNames\n  rNamesWidth = max(rNamesWidth,length(cell00))\n\n  iob = IOBuffer()\n  if StringFmt == \"html\"                                             #print column names\n    write(iob,\"<tr><th>\",lpad(cell00,rNamesWidth),\"</th>\")\n    for i = 1:n\n      write(iob,\"<th>\",lpad(colNames[i],width),\"</th>\")\n    end\n    write(iob,\"</tr>\")\n  elseif StringFmt == \"csv\"\n    write(iob,lpad(string(cell00,\",\"),rNamesWidth))\n    for i = 1:n-1\n      write(iob,lpad(colNames[i],width),\",\")\n    end\n    write(iob,lpad(colNames[n],width))       #no , at line end\n  else\n    write(iob,lpad(cell00,rNamesWidth))                  #cell 0,0\n    for i = 1:n                                          #create string\n      write(iob,lpad(colNames[i],width))\n    end\n  end\n  write(iob,\"\\n\")\n\n  xStr  = printmat(fh,x,width=width,prec=prec,NoPrinting=true,StringFmt=StringFmt)   #body of table, one long string\n  xStrV = split(xStr,\"\\n\")                       #vector of strings (one per row of x)\n\n  for i = 1:m                           #loop over rows in x, print rowNames[i] and x[i,:]\n    if StringFmt == \"html\"\n      write(iob,\"<tr><td><b>\",rpad(rowNames[i],rNamesWidth),\"</td></b>\",xStrV[i],\"</tr> \\n\")\n    elseif StringFmt == \"csv\"\n      write(iob,rpad(string(rowNames[i],\",\"),rNamesWidth),xStrV[i],\"\\n\")\n    else\n      write(iob,rpad(rowNames[i],rNamesWidth),xStrV[i],\"\\n\")\n    end\n  end\n  str = String(take!(iob))\n\n  if NoPrinting                              #no printing, just return str\n    return str\n  else                                       #print, return nothing\n    print(fh,str,\"\\n\")\n    return nothing\n  end\n\nend\n                        #when fh is not supplied: printing to screen\nprintTable(x,colNames=[],rowNames=[];width=10,prec=3,NoPrinting=false,StringFmt=\"\",cell00=\"\") =\nprintTable(stdout::IO,x,colNames,rowNames,width=width,prec=prec,NoPrinting=NoPrinting,\n                      StringFmt=StringFmt,cell00=cell00)\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    printlnPs([fh::IO],z...;width=10,prec=3)\n\nSubsitute for println, with predefined formatting.\n\n\n# Input\n- `fh::IO`:    (optional) file handle. If not supplied, prints to screen\n- `z::String`: string, numbers and arrays to print\n\nPaul.Soderlind@unisg.ch\n\n\"\"\"\nfunction printlnPs(fh::IO,z...;width=10,prec=3)\n\n  for x in z                              #loop over inputs in z...\n    if isa(x,Union{String,Date,DateTime,Missing})\n      print(fh,lpad(x,width))\n    elseif isa(x,Nothing)\n      print(fh,\"\")\n    else                                         #other types\n      iob = IOBuffer()\n      for i = 1:length(x)\n        if isa(x[i],AbstractFloat)               #Float\n          write(iob,fmtNumPs(x[i],width,prec,\"right\"))\n        elseif isa(x[i],Nothing)                 #Nothing\n          write(iob,lpad(\"\",width))\n        else                                     #Integer, etc\n          write(iob,lpad(x[i],width))\n        end\n      end\n      print(fh,String(take!(iob)))\n    end\n  end\n\n  print(fh,\"\\n\")\n\nend\n                      #when fh is not supplied: printing to screen\nprintlnPs(z...;width=10,prec=3) = printlnPs(stdout::IO,z...,width=width,prec=prec)\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    printmat2\n\nCall on printmat twice: to print to screen and then to an open file (IOStream)\n\"\"\"\nfunction printmat2(fh,x;width=10,prec=3,NoPrinting=false,StringFmt=\"\")\n  printmat(x,width=width,prec=prec,NoPrinting=NoPrinting,StringFmt=StringFmt)       #to screen\n  if isa(fh,IOStream) && isopen(fh)\n    printmat(fh,x,width=width,prec=prec,NoPrinting=NoPrinting,StringFmt=StringFmt)  #to file\n  end\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    printTable2\n\nCall on printTable2 twice: to print to screen and then to an open file (IOStream)\n\"\"\"\nfunction printTable2(fh,x,colNames=[],rowNames=[];width=10,prec=3,NoPrinting=false,\n                     StringFmt=\"\",cell00=\"\")\n  printTable(x,colNames,rowNames,width=width,prec=prec,NoPrinting=NoPrinting,\n             StringFmt=StringFmt,cell00=cell00)      #to screen\n  if isa(fh,IOStream) && isopen(fh)\n    printTable(fh,x,colNames,rowNames,width=width,prec=prec,NoPrinting=NoPrinting,\n               StringFmt=StringFmt,cell00=cell00) #to file\n  end\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    println2Ps\n\nCall on printlnPs twice: to print to screen and then to an open file (IOStream)\n\"\"\"\nfunction println2Ps(fh::IO,z...;width=10,prec=3)\n  printlnPs(z...,width=width,prec=prec)              #to screen\n  if isa(fh,IOStream) && isopen(fh)\n    printlnPs(fh::IO,z...,width=width,prec=prec)     #to file\n  end\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    writeElementPs(iob,x,i,j,width,prec,StringFmt)\n\nWrites one element to iob, formatting depends on type\n\"\"\"\nfunction writeElementPs(iob,x,i,j,width,prec,StringFmt)\n\n  if isa(x[i,j],AbstractFloat)         #Float\n    write(iob,fmtNumPs(x[i,j],width,prec,\"right\",StringFmt))\n  elseif isa(x[i,j],Union{Int,String}) #Int, String\n    write(iob,fmtNumPs(x[i,j],width,0,\"right\",StringFmt))\n  elseif isa(x[i,j],Bool)              #Bool, BitArrays, as 0/1, left\n    write(iob,fmtNumPs(x[i,j]+0,width,0,\"right\",StringFmt))\n  elseif isa(x[i,j],Nothing)           #Nothing, as \"\"\n    write(iob,fmtNumPs(\"\",width,0,\"right\",StringFmt))\n  else                                 #other types (Missing,Date,...), right\n    write(iob,fmtNumPs(x[i,j],width,0,\"right\",StringFmt))\n  end\n\n  return nothing\n end\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\n#One of the two subsequent functions is needed for handling cases like \"1.5e-6\"\n#This should not be necessary after Julia 1.6\n\n#fmtNumPsX(fmt,z) = @eval Printf.@sprintf($fmt,$z)   #slow fallback solution\n\nfunction fmtNumPsC(fmt,z)                           #c fallback solution\n  if ismissing(z) || isnan(z) || isinf(z)    #asprintf does not work for these cases\n    str = string(z)\n  else\n    strp = Ref{Ptr{Cchar}}(0)\n    len = ccall(:asprintf,Cint,(Ptr{Ptr{Cchar}},Cstring,Cdouble...),strp,fmt,z)\n    str = unsafe_string(strp[],len)\n    Libc.free(strp[])\n  end\n  return str\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\n\"\"\"\n    fmtNumPs(z,width=10,prec=2,justify=\"right\",StringFmt=\"\")\n\nCreate a formatted string of a number. With prec=0, it can be used Bools and Strings\n\n\n\n# Remark\n- with prec=0, the function can be used for non-floats (incl. Bools and Strings)\n- The Formatting.jl package provides more elegant solutions:\n  fmt  = FormatSpec(string(\">\",width,\".\",prec,\"f\"))   #right justified, else \"<\"\n  fmt  = FormatSpec(string(\">\",wid,\"d\"))              #for Int\n  str  = Formatting.fmt(fmt1,z))\n\n# Requires\n- Dates\n\n\"\"\"\nfunction fmtNumPs(z,width=10,prec=2,justify=\"right\",StringFmt=\"\")\n\n  if (prec > 0) && !ismissing(z) && !isnan(z)  && !isinf(z)  #example: 101.0234, prec=3\n    zRound = round(z,digits=prec)\n    str = split(string(zRound),'.')\n    if length(str) > 1 && !occursin(\"e\",str[2])   #skip \"1.5e-6\"\n      strR  = string(\".\",rpad(str[2],prec,\"0\"))   #.23\n      strLR = string(str[1],strR)                 #\"101\" * \".23\"\n    elseif occursin(\"e\",str[2])                   #\"1.5e-6\" -> \" 0.0000015\" if prec=7\n      fmt = \"%$(width).$(prec)f\"\n      #strLR = fmtNumPsX(fmt,zRound)               #slow fallback solution\n      strLR = fmtNumPsC(fmt,zRound)                #C fallback solution\n    else\n      strLR = string(zRound)\n    end\n  else\n    (isa(z,AbstractFloat) && !isnan(z) && !isinf(z)) && (z = round(Int,z))  #Float -> Int\n    strLR = string(z)\n  end\n\n  if justify == \"left\"                            #justification\n    strLR = rpad(strLR,width)\n  else\n    strLR = lpad(strLR,width)\n  end\n\n  if StringFmt == \"html\"                          #html or csv formatting\n    strLR = string(\"<td>\",strLR,\"</td>\")\n  elseif StringFmt == \"csv\"\n    strLR = string(strLR,\",\")\n  end\n\n  return strLR\n\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\nfunction printblue(x...)\n  foreach(z->printstyled(z,color=:blue,bold=true),x)\n  print(\"\\n\")\nend\nfunction printred(x...)\n  foreach(z->printstyled(z,color=:red,bold=true),x)\n  print(\"\\n\")\nend\nfunction printmagenta(x...)\n  foreach(z->printstyled(z,color=:magenta,bold=true),x)\n  print(\"\\n\")\nend\nfunction printyellow(x...)\n  foreach(z->printstyled(z,color=:yellow,bold=true),x)\n  print(\"\\n\")\nend\n#------------------------------------------------------------------------------\n\n\n#------------------------------------------------------------------------------\nfunction printwhere(txt)\n  println(@__FILE__,\" \",@__LINE__,\" \",txt)\nend\n#------------------------------------------------------------------------------\n\n", "meta": {"hexsha": "d051d982cde474679b6f3d6bdaa7a41caf1ae812", "size": 13859, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "printmat.jl", "max_stars_repo_name": "snowdj/JuliaTutorial", "max_stars_repo_head_hexsha": "35a0c370b248101a4f05e2bccdffdcbe9f3ea9ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2016-01-15T21:13:48.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-18T21:21:18.000Z", "max_issues_repo_path": "printmat.jl", "max_issues_repo_name": "fmyilmaz/JuliaTutorial", "max_issues_repo_head_hexsha": "b77a113835f47f430bd6b458731d2cec06da69b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-03-05T13:07:59.000Z", "max_issues_repo_issues_event_max_datetime": "2016-03-07T11:11:21.000Z", "max_forks_repo_path": "printmat.jl", "max_forks_repo_name": "fmyilmaz/JuliaTutorial", "max_forks_repo_head_hexsha": "b77a113835f47f430bd6b458731d2cec06da69b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2016-03-05T13:01:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T19:44:57.000Z", "avg_line_length": 32.919239905, "max_line_length": 116, "alphanum_fraction": 0.5212497294, "num_tokens": 3582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.15610489155639232, "lm_q1q2_score": 0.06714810520426269}}
{"text": "using PkgBenchmark\r\nresults = benchmarkpkg(\"TaylorSeries\")\r\nshow(results)\r\n\r\n#=\r\n# specify tag and uncommit to benchmark versus prior tagged version\r\ntag =\r\nresults = judge(\"TaylorSeries\", tag)\r\nshow(results)\r\n=#\r\n\r\nexport_markdown(\"results.md\", results)\r\n", "meta": {"hexsha": "c074a5399bf95d5a4b4ec103631e2d20aa0b475c", "size": 256, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/run_benchmark.jl", "max_stars_repo_name": "mewilhel/TaylorSeries.jl", "max_stars_repo_head_hexsha": "e169d1f20217d345b8ff24ec9124b01b95b32953", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "benchmark/run_benchmark.jl", "max_issues_repo_name": "mewilhel/TaylorSeries.jl", "max_issues_repo_head_hexsha": "e169d1f20217d345b8ff24ec9124b01b95b32953", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmark/run_benchmark.jl", "max_forks_repo_name": "mewilhel/TaylorSeries.jl", "max_forks_repo_head_hexsha": "e169d1f20217d345b8ff24ec9124b01b95b32953", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6923076923, "max_line_length": 68, "alphanum_fraction": 0.73046875, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.14804719615221756, "lm_q1q2_score": 0.06710414572145049}}
{"text": "## Exercise 7-1\n## Rewrite the function printn from Recursion using iteration instead of recursion.\nprintln(\"Ans: \")\n\nfunction printn(s, n::Int)\n    while n > 0\n        println(s)\n        n -= 1\n    end\nend \n\nprintn(\"Hello iteration!\", 4)\n\nprintln(\"End.\")\n", "meta": {"hexsha": "d04f5565fff29320737666650a2e1e5f6dcb2ace", "size": 256, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter7/ex1.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter7/ex1.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter7/ex1.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.0666666667, "max_line_length": 83, "alphanum_fraction": 0.6328125, "num_tokens": 71, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.22270013882530884, "lm_q2_score": 0.30074559147596, "lm_q1q2_score": 0.0669760849727959}}
{"text": "### A Pluto.jl notebook ###\n# v0.19.8\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 2efa05e7-b130-4a95-9eab-d8bb9d16a29b\n# \u2560\u2550\u2561 show_logs = false\nbegin\n\tusing Logging\n\tglobal_logger(NullLogger())\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 c3e429e4-e7e9-4db6-852c-906630f909a4\n# \u2560\u2550\u2561 show_logs = false\n#Set-up packages\nbegin\n\t\n\tusing DataFrames, HTTP, CSV, Dates, Plots, PlutoUI, Printf, LaTeXStrings, HypertextLiteral, XLSX\n\t\n\tgr();\n\tPlots.GRBackend()\n\n\n\t#Define html elements\n\tnbsp = html\"&nbsp\" #non-breaking space\n\tvspace = html\"\"\"<div style=\"margin-bottom:0.05cm;\"></div>\"\"\"\n\tbr = html\"<br>\"\n\n\t#Sets the width of cells, caps the cell width by 90% of screen width\n\t#(setting overwritten by cell below)\n\t# @bind screenWidth @htl(\"\"\"\n\t# \t<div>\n\t# \t<script>\n\t# \t\tvar div = currentScript.parentElement\n\t# \t\tdiv.value = screen.width\n\t# \t</script>\n\t# \t</div>\n\t# \"\"\")\n\n\t\n\t# cellWidth= min(1000, screenWidth*0.9)\n\t# @htl(\"\"\"\n\t# \t<style>\n\t# \t\tpluto-notebook {\n\t# \t\t\tmargin: auto;\n\t# \t\t\twidth: $(cellWidth)px;\n\t# \t\t}\n\t# \t</style>\n\t# \"\"\")\n\t\n\n\t#Sets the width of the cells\n\t#begin\n\t#\thtml\"\"\"<style>\n\t#\tmain {\n\t#\t\tmax-width: 900px;\n\t#\t}\n\t#\t\"\"\"\n\t#end\n\n\n\t#Sets the height of displayed tables\n\thtml\"\"\"<style>\n\t\tpluto-output.scroll_y {\n\t\t\tmax-height: 550px; /* changed this from 400 to 550 */\n\t\t}\n\t\t\"\"\"\n\t\n\n\t#Two-column cell\n\tstruct TwoColumn{A, B}\n\t\tleft::A\n\t\tright::B\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", tc::TwoColumn)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t<div style=\"display: flex;\">\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.left)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.right)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t</div>\n\t\t\"\"\")\n\tend\n\n\t#Creates a foldable cell\n\tstruct Foldable{C}\n\t\ttitle::String\n\t\tcontent::C\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", fld::Foldable)\n\t\twrite(io,\"<details><summary>$(fld.title)</summary><p>\")\n\t\tshow(io, mime, fld.content)\n\t\twrite(io,\"</p></details>\")\n\tend\n\t\n\t\n\t#helper functions\n\t#round to digits, e.g. 6 digits then prec=1e-6\n\troundmult(val, prec) = (inv_prec = 1 / prec; round(val * inv_prec) / inv_prec); \n\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 41d7b190-2a14-11ec-2469-7977eac40f12\n#add button to trigger presentation mode\nhtml\"<button onclick='present()'>present</button>\"\n\n# \u2554\u2550\u2561 a5de5746-3df0-45b4-a62c-3daf36f015a5\nbegin \n\thtml\"\"\"\n\t<p align=left style=\"font-size:25px; font-family:family:Georgia\"> <b> UD/ISCTE-IUL Trading and Bloomberg Program</b> <p>\n\t\"\"\"\nend\n\n\n\n# \u2554\u2550\u2561 8fb7775c-5c42-4598-8e97-f71ea2a4f59c\nbegin \n\thtml\"\"\"\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:25px; font-family:family:Georgia\">Fixed Income Securities </div>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> <b> U.S. Treasury Securities Market Conventions</b> <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> Summer 2022 <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> Prof. Matt Fleckenstein </div>\n\t<p style=\"padding-bottom:0.0cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> University of Delaware, \n\tLerner College of Business and Economics </div>\n\t<p style=\"padding-bottom:0.5cm\"> </p>\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 3e995602-7a5e-45ce-a31d-449951af1aea\nTableOfContents(aside=true, depth=1)\n\n# \u2554\u2550\u2561 6498b10d-bece-42bf-a32b-631224857753\nmd\"\"\"\n# Overview\n\"\"\"\n\n# \u2554\u2550\u2561 95db374b-b10d-4877-a38d-1d0ac45877c4\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Our goals for today</legend>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\">Overview of Treasury Securities Price Quoting Conventions.<br><br>\n\t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 5b0395a0-4287-41bf-8e06-d7080f996b58\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 93db6880-429c-4b9c-a807-eba600e03df1\nmd\"\"\"\n# Price Quotes of for Treasury Notes/Bonds\n\"\"\"\n\n# \u2554\u2550\u2561 1fa89db5-8185-4c32-81ad-4cc7e4ec44c4\nmd\"\"\"\n## Example: 5-year Treasury Note in Bloomberg\n- On the Bloomberg Terminal type `91282CCZ2` and click on the security in the search result. \n- Next, type `DES` and `Enter`.\n\"\"\"\n\n# \u2554\u2550\u2561 aac27a3c-e90a-437f-a563-f81d41c8d3f7\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasuryNoteDescrExampleBloomberg.png\",:width => 1200) \n\n# \u2554\u2550\u2561 1d267c4d-5687-47a7-8b86-7b147b1c066b\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 39af52c6-ddb1-41ec-be5c-c0e31a2693bb\nmd\"\"\"\n## Price Quotes for 5-year Treasury Note\n- On the Bloomberg Terminal click on `ALLQ` under _Quick Links_\n\"\"\"\n\n# \u2554\u2550\u2561 6561b7a0-368c-43c6-ada9-36b83dc4a749\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasuryNotePriceQuoteBloomberg.png\",:width => 1200) \n\n# \u2554\u2550\u2561 15cb9e60-6e54-4f0e-abb2-9249bdc49677\nmd\"\"\"\n>- How to get there on the Bloomberg terminal?\n>  - On the keyboard type `T Govt`\n>  - Click on `United States Treasury Note/Bond (Multiple Matches) Govt`\n>  - On the next page, click on one of the Treasury notes/bonds.\n>  - Next, type `DES` on the keyboard and press enter.\n>  - The page you now see is the `Description` page for the Treasury you selected.\n>  - Next, type `ALLQ` and press enter.\n>  - This will display a page with quoted prices for the Treasury note/bond you selected.\n\"\"\"\n\n# \u2554\u2550\u2561 c986f826-bb10-4bb7-ba83-03679bb59855\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 30ce6f74-1d7e-465d-abf1-245881fec53b\nmd\"\"\"\n## Price Quotes for Treasury Coupon Securities\n- Expressed as a percent of face value (often called \u201cpoints\u201d) and numbers after the hyphens denote 32nds (often called \u201cticks\u201d).\n\"\"\"\n\n# \u2554\u2550\u2561 5bfbddd7-c014-45c1-871d-ae41d5c02c82\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 4ad79093-2e8b-4fd7-bc1d-87388947ffde\nmd\"\"\"\n- Points: $(@bind p1 Slider(90:1:120, default=98, show_value=true))\n- 32nd: $(@bind p2 Slider(10:1:31, default=26, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 395335f3-3f6c-4fbc-bf0b-9a238c8b6864\nMarkdown.parse(\"\n**Example**:\n- Suppose the price of a Treasury note is quoted as **$(p1)**-**$(p2)**.\n- For \\$100 par value, the quoted price of **$(p1)**-**$(p2)** refers to a dollar price of $p1 dollars plus $p2 ``32^{\\\\textrm{nd}}`` of a dollar.\n- In short, the price is calculated as \n\t\n\\$$p1 + \\\\frac{$p2}{32} = $(p1+p2/32.0)\\$\t\n \n- This means that the price per \\$ 100 par value is \\$$(p1+p2/32.0).\n\")\n\n# \u2554\u2550\u2561 46566086-d518-49d9-b173-f66f2ea0e131\nmd\"\"\"\nThe 32nd are themselves often split by the addition of a **plus** sign or a **third** digit. \n- ``+`` sign means 1/64$^{\\textrm{th}}$\n- A _third_ digit means that this third digit is multiplied by 1/256.\n\"\"\"\n\n# \u2554\u2550\u2561 4d4f241f-83c4-4a78-87e9-6ecfde56b860\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 f01f940c-233c-4b90-882b-bd9d33c6b841\nmd\"\"\"\n- Points: $(@bind p3 Slider(90:1:120, default=98, show_value=true))\n- 32$^{\\textrm{nd}}$: $(@bind p4 Slider(10:1:31, default=25, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 2d37c722-c5bb-4462-a48d-f9374bed4449\nMarkdown.parse(\"\n**Example**:\n- Suppose the price of a Treasury note is quoted as **$(p3)**-**$(p4)+**.\n- For \\$100 par value, the quoted price of **$(p3)**-**$(p4)+** refers to a dollar price of $p3 dollars plus $p4 ``32^{\\\\textrm{nd}}`` plus 1 ``64^{\\\\textrm{th}}``of a dollar.\n- In short, the price is calculated as \n\t\n\\$$p3 + \\\\frac{$p4}{32} + \\\\frac{1}{64}= $(p3+p4/32.0+1/64.0)\\$\t\n \n- This means that the price per \\$ 100 par value is \\$$(p3+p4/32.0+1/64.0)\\$.\n\")\n\n# \u2554\u2550\u2561 b541d16f-59da-48dd-b7f6-a0cebd451256\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 b294a20b-8710-49bb-a069-75dbc3f19ba6\nmd\"\"\"\n- Points: $(@bind p5 Slider(90:1:120, default=98, show_value=true))\n- 32$^{\\textrm{nd}}$: $(@bind p6 Slider(10:1:31, default=25, show_value=true))\n- 256$^{\\textrm{th}}$: $(@bind p7 Slider(1:1:9, default=2, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 64adcfde-8ade-4778-bb01-9d46ee836a55\nMarkdown.parse(\"\n**Example**:\n- Suppose the price of a Treasury note is quoted as **$(p5)**-**$(p6)$(p7)**.\n- For \\$100 par value, the quoted price of **$(p5)**-**$(p6)$(p7)** refers to a dollar price of $p5 dollars plus $p6 ``32^{\\\\textrm{nd}}`` plus $(p7) ``256^{\\\\textrm{th}}``of a dollar.\n- In short, the price is calculated as \n\t\n\\$$p5 + \\\\frac{$p6}{32} + \\\\frac{$p7}{256}= $(p5+p6/32.0+p7/256.0)\\$\t\n \n- This means that the price per \\$ 100 par value is \\$$(p5+p6/32.0+p7/256.0)\\$.\n\")\n\n# \u2554\u2550\u2561 c3f25830-c6d2-4d77-bbd6-91024ee3363b\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 f72ab26d-f986-4ab3-9f15-11cb33b65c69\nmd\"\"\"\n- Points: $(@bind p11 Slider(90:1:120, default=98, show_value=true))\n- 32$^{\\textrm{nd}}$: $(@bind p12 Slider(10:1:31, default=25, show_value=true))\n- Quarters of a 32$^{\\textrm{nd}}$: $(@bind p13 Slider(1:1:3, default=1, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 e68723a6-a1d8-4a3f-8ba0-9acab5d764db\nMarkdown.parse(\"\n**Example**:\n- Often, trading system such as Bloomberg display _fractions_ after the ``32^{\\\\textrm{nd}}``.\n- Suppose the price of a Treasury note is quoted as ``\\\\mathbf{$p11 - $p12 \\\\frac{$p13}{4}}``.\n- For \\$100 par value, the quoted price of ``\\\\mathbf{$p11 - $p12 \\\\frac{$p13}{4}}`` refers to a dollar price of $p11 dollars plus ``($p12 + \\\\frac{$p13}{4})`` ``32^{\\\\textrm{nd}}`` of a dollar.\n- In short, the price is calculated as \n\t\n\\$$p11 + \\\\frac{$p12+\\\\frac{$p13}{4}}{32} = $(p11+(p12+0.25*p13)/32.0)\\$\t\n \n- This means that the price per \\$ 100 par value is \\$$(p11+(p12+0.25*p13)/32.0)\\$.\n\")\n\n# \u2554\u2550\u2561 bbf515d0-e8a2-4391-8e86-41e814e3240a\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 b5ed2793-df92-4f41-97e9-7533b35db4c0\nmd\"\"\"\n- Points: $(@bind p14 Slider(90:1:120, default=98, show_value=true))\n- 32$^{\\textrm{nd}}$: $(@bind p15 Slider(10:1:31, default=25, show_value=true))\n- Eights of a 32$^{\\textrm{nd}}$: $(@bind p16 Slider(1:1:7, default=1, show_value=true))\n\"\"\"\n\n# \u2554\u2550\u2561 b65c747b-f08a-408d-8e01-e921dcbd9056\nMarkdown.parse(\"\n**Example**:\n- One last example. Suppose the price of a Treasury note is quoted as ``\\\\mathbf{$p14 - $p15 \\\\frac{$p16}{8}}``.\n- For \\$100 par value, the quoted price of ``\\\\mathbf{$p14 - $p15 \\\\frac{$p16}{8}}`` refers to a dollar price of $p14 dollars plus ``($p15 + \\\\frac{$p16}{8})`` ``32^{\\\\textrm{nd}}`` of a dollar.\n- In short, the price is calculated as \n\t\n\\$$p14 + \\\\frac{$p15+\\\\frac{$p16}{8}}{32} = $(p14+(p15+1/8*p16)/32.0)\\$\t\n \n- This means that the price per \\$ 100 par value is \\$$(p14+(p15+1/8*p16)/32.0)\\$.\n\")\n\n# \u2554\u2550\u2561 2200d50b-f23f-4764-85e1-d908b5aeeb1e\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 4576f508-91bd-4fdc-a62d-833d8428f78f\nmd\"\"\"\n## Practice Exercise\nFor each quoted price shown below, enter the price per \\$100 par value in decimals.\n\"\"\"\n\n# \u2554\u2550\u2561 6c57bd92-7d1d-41d3-88d5-0b2c191b7693\n# \u2560\u2550\u2561 show_logs = false\nbegin\n\tp161=100\n\tp162=27\n\t\n\tp171=108\n\tp172=31\n\t\t\n\tp181=102\n\tp182=12\n\tp183=3\n\t\n\tp191=102\n\tp192=18\n\tp193=1\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 9a1aa162-d274-4af7-8f13-5a9d6bab98b0\nMarkdown.parse(\"\"\"\n- Quoted Price: ``$p161 - $p162`` \n\"\"\")\n\n# \u2554\u2550\u2561 4bccc40c-953a-4969-b47b-aea4b234919b\nMarkdown.parse(\"\"\"\n!!! hint\n    Price in Decimals: ``$(p161+p162/32)``\n\"\"\")\n\n# \u2554\u2550\u2561 176d61b1-02d9-4da6-a893-72aca3541f2c\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 729198d1-5795-4b70-85f8-f25717edc244\nMarkdown.parse(\"\"\"\n- Quoted Price: ``$p171 - $p172 +`` \n\"\"\")\n\n# \u2554\u2550\u2561 301c4a13-814e-4853-aa5b-aac611cc40f0\nMarkdown.parse(\"\"\"\n!!! hint\n    Price in Decimals: ``$(p171+p172/32+1/64)``\n\"\"\")\n\n# \u2554\u2550\u2561 feb4fbca-0a01-445a-b4e1-d50b3f3525a3\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 9aa0dec2-c1e1-41c5-9ad5-35b51e41128a\nMarkdown.parse(\"\"\"\n- Quoted Price: ``$p181 - $p182 \\\\frac{$p183}{4}`` \n\"\"\")\n\n# \u2554\u2550\u2561 3a2a5dea-4a07-4368-89c9-cde995b9964b\nMarkdown.parse(\"\"\"\n!!! hint\n    Price in Decimals: ``$(p181+(p182+p183/4)/32)``\n\"\"\")\n\n# \u2554\u2550\u2561 d79b4d61-8211-4fca-a83c-46db3959d51e\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 a4670db5-20e5-41cd-a2af-8dd15ce119f6\nMarkdown.parse(\"\"\"\n- Quoted Price: ``$p191 - $p192 \\\\frac{$p193}{8}`` \n\"\"\")\n\n# \u2554\u2550\u2561 16d95a82-743e-478c-a50d-36e800910883\nMarkdown.parse(\"\"\"\n!!! hint\n    Price in Decimals: ``$(p191+(p192+p193/8)/32)``\n\"\"\")\n\n# \u2554\u2550\u2561 be1e2ae2-8b05-42c7-bc54-18c9ff111854\nmd\"\"\"\n# Accrued Interest\n\"\"\"\n\n# \u2554\u2550\u2561 8464e17a-2f95-4bde-8c37-502359bb2dd8\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasuryNoteDescrExampleBloomberg_2.png\",:width => 1200) \n\n# \u2554\u2550\u2561 59023ef2-e4fb-4e41-9652-67f6a107e1b1\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 a7c9120b-ee24-48c2-904a-e40ef95fcffa\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasuryNoteDescrExampleBloombergDirtyPrice_2.png\",:width => 1200) \n\n# \u2554\u2550\u2561 148712ae-b638-45de-9ab1-96492f083439\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 8f9498b5-a1cf-4be7-b0dd-41aad76c959b\nmd\"\"\"\n- U.S. Treasury securities pay coupon interest every six months.\n- At any date between two coupon payments, we must take the **accrued interest** into account.\n- The convention in the Treasury market is that _in addition to_ the **quoted price**, the bond buyer must _pay_ the bond seller the **accrued interest**.\n- Thus, the amount that the buyer pays the seller is the **quoted price** plus **accrued interest**.\n  - This is the actual purchase price and it is called the **dirty price.**\n  - The **quoted price** of a bond (_without accrued interest_) is called the **clean price.**\n- The **clean price** is also called the **flat** price and the **dirty price** is also called the **full** price.\n\"\"\"\n\n# \u2554\u2550\u2561 6ce18679-b433-4110-8ef4-0fd9135737f4\nmd\"\"\"\n> - How to get there on the Bloomberg terminal?\n>   - On the keyboard type `T Govt`\n>   - Click on `United States Treasury Note/Bond (Multiple Matches) Govt`\n>   - On the next page, click on one of the Treasury notes/bonds.\n>   - Next, type `DES` on the keyboard and press enter.\n>   - The page you now see is the `Description` page for the Treasury you selected.\n>   - Next, type `HP` and press enter.\n>   - This will display a page with past prices for the Treasury note/bond you selected.\n>   - Next, at the top, in the row labeled `Market` type in __Mid Line__ in the first field and __Dirty Mid Price__ in the second field. Press enter.\n>   - The price table will now show the *clean* mid price and the *dirty* mid price.\n>     - Recall that the *mid* price is the midpoint between the bid and the ask price.  \n\"\"\"\n\n# \u2554\u2550\u2561 cf464f2d-a329-42d1-b7ea-a4fb5eba421f\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 cedc6044-5eb2-4e95-98c7-e5831597a258\nmd\"\"\"\n## Calculating Accrued Interest for Treasury notes and bonds\n\"\"\"\n\n# \u2554\u2550\u2561 d43ce5da-dee3-4d44-a202-4f5f4770772b\nmd\"\"\"\n- To calculate accrued interest, the following are needed: \n  - the number of days in the accrued interest period (represents the number of days over which the investor has earned interest)\n  - the number of days in the coupon period (representes the number of days between the last and the next coupon payment)\n  - the dollar amount of the coupon payment\n\"\"\"\n\n# \u2554\u2550\u2561 a4a0a069-80d6-4a13-a19d-56b200ca8545\nmd\"\"\"\n- Accrued Interest is the calculated as\n$$\\textrm{Accrued Interest} = \\textrm{Coupon Interest Cash Flow} \\times \\left( \\frac{ \\textrm{Days in Accrued Interest Period}}{\\textrm{Days in Coupon Period}} \\right)$$\n\"\"\"\n\n# \u2554\u2550\u2561 b71c4b72-7004-4600-82d4-651179178a03\nmd\"\"\"\n## 1. Calculate the **Days in Accrued Interest Period**\n\n- We need three key dates: \n- **Trade date**\n  - The trade date (also referred to as the transaction date) is the date on which the transaction is executed (referred to as \u201cT\u201d).\n- **Settlement date** \n  - The settlement date is the date a transaction is deemed to be completed and the seller must transfer the ownership of the bond to the buyer in exchange for the payment.\n  - Treasury securities settle on the next business day after the trade date. This is referred to as *T+1 Settlement*.\n- **Date of previous coupon cash flow**\n- Days in Accrued Interest Period are then calculated as\n\n$$\\textrm{Days in Accrued Interest Period=}$$ \n$$\\textrm{Days from and \\textbf{including} the previous coupon date up to \\textbf{excluding} the settlement date}$$\n\"\"\"\n\n# \u2554\u2550\u2561 a5e630a2-4489-4e6f-b3b6-97abef6b1d66\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 fd4aead4-bf25-4125-b094-6edce0e77b1e\nmd\"\"\"\n## 2. Calculate the **Days in Coupon Period**\n- Simply the number of days between the previous coupon date and the next coupon date.\n\"\"\"\n\n# \u2554\u2550\u2561 fcaff09a-c014-4fe9-81f8-f0fb72d99829\nmd\"\"\"\n## Example\n\"\"\"\n\n# \u2554\u2550\u2561 8323c2cc-cc29-416b-aca4-798f7cc844ed\nmd\"\"\"\n- Consider a Treasury note whose previous coupon payment was May 15. Assume that the coupon rate is 8% p.a. (paid semi-annually) and par value of \\$100.\n  - Thus, the coupon interest cash flows are $\\frac{8\\%}{2}\\times 100 =$ \\$4\n- Suppose you buy Treasury security with a settlement date of September 10.\n- Since Treasury notes pay semi-annual coupon interest, the next coupon payment is on November 15. \n\"\"\"\n\n# \u2554\u2550\u2561 9119860b-2ada-411a-95e7-e1e56ae573c0\nmd\"\"\"\n##\n- **Step 1**: *Days in Accrued Interest Period*\n  - We count the actual number of days between May 15 (the previous coupon date) and September 10 (the settlement date). \n    - May: **17** days (we count the day of the previous coupon cash flow)\n    - June: 30 days\n    - July: 31 days\n    - August: 31 days\n    - September: **9** days (we do not count the settlement date)\n   - Thus, *Days in Accrued Interest Period* = 118\n\"\"\"\n\n# \u2554\u2550\u2561 8dc2cccb-a682-4c42-99fe-ccc91d3823d1\nmd\"\"\"\n##\n- **Step 2**: *Days in Coupon Period*\n  - We count the actual number of days between May 15 (the previous coupon date) and Nov 15 (the next coupon date)\n    - May: **16 days**\n    - June: 30 days\n    - July: 31 days\n    - September: 30 days\n    - October: 31 days\n    - November: **15 days**\n  - Thus, *Days in Coupon Period*=184\n\"\"\"\n\n# \u2554\u2550\u2561 c6df152f-9f62-4eb4-997e-afd9a0868c9e\nmd\"\"\"\n##\n- **Step 3**: *Accrued Interest*\n$$\\textrm{Accrued Interest} = \\textrm{Coupon Interest Cash Flow} \\times \\left( \\frac{ \\textrm{Days in Accrued Interest Period}}{\\textrm{Days in Coupon Period}} \\right)$$\n$$\\textrm{Accrued Interest} = \\textrm{\\$4} \\times \\left( \\frac{ \\textrm{118}}{\\textrm{184}} \\right) = \\textrm{\\$4} \\times 0.641304 = \\textrm{\\$2.565217}$$\n\"\"\"\n\n# \u2554\u2550\u2561 41b91a85-ac56-4b36-87e1-b121c756417e\nmd\"\"\"\n## Accrued Interest Example 2\n\"\"\"\n\n# \u2554\u2550\u2561 4d039efd-c682-4abe-a2a4-8536ed97a3c7\nmd\"\"\"\n- Coupon rate [% p.a.]: $(@bind cpnAI_3 Slider(0:0.25:10, default=2.5, show_value=true))\n- Par value [\\$]: $(@bind par_3 Slider(100:100000, default=100, show_value=true))\n- Previous coupon cash flow date: $(@bind prev_3 DateField(default=Date(2015,7,15)))\n- Next coupon cash flow date: $(@bind next_3 DateField(default=Date(2016,01,15)))\n- Settlement date: $(@bind settle_3 DateField(default=Date(2015,10,10)))\n\"\"\"\n\n# \u2554\u2550\u2561 5752193f-de1f-4832-ab93-a0fc8c4d9c4d\nbegin \n\tdaysAIPeriod = Date(settle_3) - Date(prev_3)\n\tdaysCpnPeriod = Date(next_3) - Date(prev_3)\n\taccrInt_3 = Dates.value(daysAIPeriod)/Dates.value(daysCpnPeriod)*cpnAI_3/(200)*\npar_3\n\tmd\"\"\"\n\t- Coupon interest cash flow:  $(roundmult(par_3 * (cpnAI_3/200),1e-6))\n\t- Number of Days in Accrued Interest Period: $(daysAIPeriod)\n\t- Number of Days in Coupon Period: $(daysCpnPeriod)\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 7b376a7e-215e-40af-82af-6be2762aa7eb\nMarkdown.parse(\"\"\"\n``\\\\begin{align}\n\\\\textrm{Accrued Interest} &= \\\\textrm{Coupon Interest Cash Flow} \\\\times \\\\left( \\\\frac{ \\\\textrm{Days in Accrued Interest Period}}{\\\\textrm{Days in Coupon Period}} \\\\right)\\\\\\\\ \n&= \\\\\\$ $(roundmult(cpnAI_3/(200)*par_3,1e-6)) \\\\times \\\\frac{$(Dates.value(daysAIPeriod)) \\\\textrm{ days}}{$(Dates.value(daysCpnPeriod)) \\\\textrm{ days}} =\\\\\\$ $(roundmult(accrInt_3,1e-6))\n\\\\end{align}``\n\"\"\")\n\n# \u2554\u2550\u2561 cd5ece1a-bd73-4652-bd3f-c2a762b04491\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 70661dd7-0acf-4b6c-b7dd-f4ad71c1cee9\nmd\"\"\"\n# Daycount Conventions\n- In calculating the number of days between two dates, the actual number of days is **not** always the same as the number of days that should be used in the accrued interest formula.\n- The number of days used depends on the **day count convention** for the particular security.\n- For Treasury notes/bonds, the day count convention is to use the **actual** number of days between two dates.\n  - This is referred to as the **actual/actual** day count convention.\n\"\"\"\n\n# \u2554\u2550\u2561 6e1be79b-bfc7-444e-b660-e0d24a2cf5dd\nmd\"\"\"\n##\n- For coupon-bearing agency, municipal, and corporate bonds, a different day count convention is used.\n- It is assumed that every month has **30 days**, that any 6-month period has **180 days**, and that there are **360 days** in a year.\n- This day count convention is referred to as **30/360.**\n- The calculations are analogous to the examples we covered, except that 30 days are used for each month.\n\"\"\"\n\n# \u2554\u2550\u2561 e4c4606f-bb51-43c6-98b7-73e1b133b251\nmd\"\"\"\n##\n- To illustrate the \u201c30/360\u201d convention, suppose the **settlement date** is *July 17* and the *previous coupon cash flow* was on *May 15*.\n- The number of days between May 15 and July 17 is 62 days, \n  - May is assumed to have 30 days. Thus we count 16 days in May (remember to include the date of the previous coupon cash flow).\n  - The month of June is assumed to have 30 days, so we add 30 days.\n  - In July, we count 16 days (remember to exclude the settlement date)\n  - So the total number of days is 16 days + 30 days + 16 day = 62 days.\n\"\"\"\n\n# \u2554\u2550\u2561 16515e3c-52d3-44a8-88cd-6976aab9ecc7\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 371a326e-f13b-44ce-91e8-50d43b7ae59a\nmd\"\"\"\n# Treasury STRIPS\n\"\"\"\n\n# \u2554\u2550\u2561 b7bdc144-7648-403f-bce7-2b6df6a8dd2f\nmd\"\"\"\n- The Treasury does not issue **zero**-coupon notes or bonds. \n- However, by \u201cstripping\u201d coupon payments from Treasury bonds, zero coupon bonds are created synthetically. \n- The process of separating the interest on a bond from the underlying principal is called coupon stripping.\n\n\"\"\"\n\n# \u2554\u2550\u2561 31c17b01-eb86-4fd4-82cc-3c31cf3020d8\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 fe72e3e8-a2b4-43b2-811d-5f4fe2c8dd7a\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasurySTRIPS_1.png\",:width => 1200) \n\n# \u2554\u2550\u2561 3222f82c-b513-403b-b4c5-22d89144f894\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 02748d79-5707-4130-9aae-0c6141e4f760\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasurySTRIPS_2.png\",:width => 600) \n\n# \u2554\u2550\u2561 d01a85dd-1fd5-4b4e-9ff6-6de1f94d1752\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 1217e6ec-8479-4a85-b0e7-088eee30bc63\nmd\"\"\"\n- Zero-coupon Treasury securities were first created in August 1982 by large dealer firms on Wall Street. \n- Today, all Treasury notes and bonds (fixed-principal and inflation-indexed) are eligible for stripping. \n- The zero-coupon Treasury securities created under the STRIPS program are direct obligations of the U.S. government.\n- Strips created from the Treasury coupons are called **coupon STRIPS** and those from the principal are called **principal STRIPS**. \n\"\"\"\n\n# \u2554\u2550\u2561 33ac7bdb-ada2-420b-b793-dc51fbc689bb\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 53c77ef1-899d-47c8-8a30-ea38380d1614\nmd\"\"\"\n## Wrap-Up\n\"\"\"\n\n# \u2554\u2550\u2561 670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Our goals for today</legend>      \n\t\t<br>\n        <input type=\"checkbox\" value=\"\" checked>Overview of Treasury Securities Price Quoting Conventions.<br><br>\n\t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 2ee2c328-5ebe-488e-94a9-2fce2200484c\nmd\"\"\"\n## Reading\nFabozzi, Fabozzi, 2021, Bond Markets, Analysis, and Strategies, 10th Edition\\\nChapter 7 and Chapter 2\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nDates = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\nHTTP = 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\"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"5982a94fcba20f02f42ace44b9894ee2b140fe47\"\nuuid = \"0ac62f75-1d6f-5e53-bd7c-93b484bb37c0\"\nversion = \"0.15.1+0\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.libfdk_aac_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"daacc84a041563f965be61859a36e17c4e4fcd55\"\nuuid = \"f638f0a6-7fb0-5443-88ba-1cc74229b280\"\nversion = \"2.0.2+0\"\n\n[[deps.libpng_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"94d180a6d2b5e55e447e2d27a29ed04fe79eb30c\"\nuuid = \"b53b4c65-9356-5827-b1ea-8c7a1a84506f\"\nversion = \"1.6.38+0\"\n\n[[deps.libvorbis_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Ogg_jll\", \"Pkg\"]\ngit-tree-sha1 = \"b910cb81ef3fe6e78bf6acee440bda86fd6ae00c\"\nuuid = \"f27f6e37-5d2b-51aa-960f-b287f2bc3b7a\"\nversion = \"1.3.7+1\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\n[[deps.x264_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4fea590b89e6ec504593146bf8b988b2c00922b2\"\nuuid = \"1270edf5-f2f9-52d2-97e9-ab00b5d0237a\"\nversion = \"2021.5.5+0\"\n\n[[deps.x265_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"ee567a171cce03570d77ad3a43e90218e38937a9\"\nuuid = \"dfaa095f-4041-5dcd-9319-2fabd8486b76\"\nversion = \"3.5.0+0\"\n\n[[deps.xkbcommon_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"ece2350174195bb31de1a63bea3a41ae1aa593b6\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"0.9.1+5\"\n\"\"\"\n\n# 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\u255f\u250016515e3c-52d3-44a8-88cd-6976aab9ecc7\n# \u255f\u2500371a326e-f13b-44ce-91e8-50d43b7ae59a\n# \u255f\u2500b7bdc144-7648-403f-bce7-2b6df6a8dd2f\n# \u255f\u250031c17b01-eb86-4fd4-82cc-3c31cf3020d8\n# \u255f\u2500fe72e3e8-a2b4-43b2-811d-5f4fe2c8dd7a\n# \u255f\u25003222f82c-b513-403b-b4c5-22d89144f894\n# \u255f\u250002748d79-5707-4130-9aae-0c6141e4f760\n# \u255f\u2500d01a85dd-1fd5-4b4e-9ff6-6de1f94d1752\n# \u255f\u25001217e6ec-8479-4a85-b0e7-088eee30bc63\n# \u255f\u250033ac7bdb-ada2-420b-b793-dc51fbc689bb\n# \u255f\u250053c77ef1-899d-47c8-8a30-ea38380d1614\n# \u255f\u2500670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\n# \u255f\u25002ee2c328-5ebe-488e-94a9-2fce2200484c\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "aaaefc34fe3a19f1e220a81e9f525954192d7422", "size": 60916, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/Lecture_03_TreasuryMarketConventions.jl", "max_stars_repo_name": 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NO\n2. NO", "lm_q1_score": 0.3960681520167196, "lm_q2_score": 0.16885695423297592, "lm_q1q2_score": 0.06687886181822657}}
{"text": "\"\"\"\nA collection of abstract types representing problems and methods for their solution\n\"\"\"\nmodule AbstractProblems\n\nexport Problem, Algorithm, IterativeAlgorithm, solve\n\n\"\"\"\nAbstract type representing a problem to solve\n\"\"\"\nabstract type Problem end\n\n\"\"\"\nAbstract type containing any algorithm\n\"\"\"\nabstract type Algorithm end\n\n\n\n\"\"\"\nIterative methods form an important class of algorithms with iteratively adjust solution.\n\nConcrete types of the `IterativeAlgorithm` should contain the initial value, tolerance and maximum number of iterations and \nare used more for convenience. These can be obtained by fucntions `initial`, `tolerance`, `maxit` fuctions. If applied the\nabstract types, these fucntions return `missing` and can trigger using of the default values.\n\nIn addition, the concrete types contain instructions on whether or not to keeep the history of the convergence and \nsome snapshots of the inner state of an iterator. These values are given by functions `keephistory` and `snapshots`.\n\n\"\"\"\nabstract type IterativeAlgorithm <: Algorithm end\n\ninitial(alg::IterativeAlgorithm) = missing\ntolerance(alg::IterativeAlgorithm) = missing\nmaxit(alg::IterativeAlgorithm) = missing\nkeephistory(alg::IterativeAlgorithm) = false\nsnapshots(alg::IterativeAlgorithm) = Int64[]\n\n\n\"\"\"\n    solve(p::Problem,x\u2070,alg::Algorithm)\n\n    solve(p::Problem,alg::IterativeAlgorithm; x\u2070, \u03f5, maxit, keephistory, snapshshots)\n\n\nSolve problem `p`, using method `alg`. For iterative algorithms the arguments may be specified separately.\nOptionally keep the error history and the iteration snapshots.\n\n\"\"\"\nsolve(p::Problem, alg::Algorithm) = error(\"Don't know how to solve \", typeof(p), \" with method \", typeof(alg))\n\n#unpack the values of iterative algorithm\nsolve(p::Problem, alg::IterativeAlgorithm; x\u2070 = initial(alg), \u03f5 =tolerance(alg), maxit = maxit(alg), keephistory = keephistory(alg), snapshots = snapshots(alg)) = solve(p, alg, x\u2070, \u03f5, maxit, keephistory, snapshots)\n# solve(p::Problem, alg::IterativeAlgorithm) = solve(p, alg, initial(alg), tolerance(alg), maxit(alg), keephistory(alg), snapshots(alg))\n\n# Now proceed with the unpacked default or specified by the user values\nfunction solve(p::Problem, alg::IterativeAlgorithm,  args...) \n    error(\"Don't know how to solve \", typeof(p), \" with method \", typeof(alg))\nend\n    \nend\n\n# these types and fucntions will be modified by including file, so we import them.\nimport .AbstractProblems: Problem, Algorithm, IterativeAlgorithm, solve, initial, tolerance, maxit, keephistory, snapshots \n", "meta": {"hexsha": "bb2ab7c6daf9c4d5b0f9d19c127a1eb5d68fdaae", "size": 2528, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AbstractProblems.jl", "max_stars_repo_name": "olejorik/AlternatingProjections.jl", "max_stars_repo_head_hexsha": "8e6835bfe882b026ab8c93d2e5e7f9c82dd8a254", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/AbstractProblems.jl", "max_issues_repo_name": "olejorik/AlternatingProjections.jl", "max_issues_repo_head_hexsha": "8e6835bfe882b026ab8c93d2e5e7f9c82dd8a254", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/AbstractProblems.jl", "max_forks_repo_name": "olejorik/AlternatingProjections.jl", "max_forks_repo_head_hexsha": "8e6835bfe882b026ab8c93d2e5e7f9c82dd8a254", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.8923076923, "max_line_length": 214, "alphanum_fraction": 0.7654272152, "num_tokens": 602, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4225046493573919, "lm_q2_score": 0.1581743507642642, "lm_q1q2_score": 0.06682939860698855}}
{"text": "using IntroAlgoCh2\nusing Test\nusing Random, Plots\n\n@testset \"IntroAlgoCh2.jl\" begin\n    r = 1:1000 |> collect |> shuffle!\n    @test issorted(insertionsort!(r)) == true\n    @test issorted(mergesort!(r)) == true\nend\n", "meta": {"hexsha": "3b5495e42d44b32e10e82322b8119e083fc3d224", "size": 214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "Dejan-Ilic/IntroAlgoCh2", "max_stars_repo_head_hexsha": "dcc7a8f83f399e1fcfe55f475e3765f124313e90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "Dejan-Ilic/IntroAlgoCh2", "max_issues_repo_head_hexsha": "dcc7a8f83f399e1fcfe55f475e3765f124313e90", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "Dejan-Ilic/IntroAlgoCh2", "max_forks_repo_head_hexsha": "dcc7a8f83f399e1fcfe55f475e3765f124313e90", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.4, "max_line_length": 45, "alphanum_fraction": 0.6869158879, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.136608397056381, "lm_q1q2_score": 0.06670361194061122}}
{"text": "### A Pluto.jl notebook ###\n# v0.19.8\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 4712d6fd-efb3-4f46-9160-c82797a3b5e4\n# \u2560\u2550\u2561 show_logs = false\nbegin\n\tusing Logging\n\tglobal_logger(NullLogger())\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 f0545c67-5cfd-438f-a9ef-92c35ebaefa4\n# \u2560\u2550\u2561 show_logs = false\n#Set-up packages\nbegin\n\t\n\tusing DataFrames, HTTP, CSV, Dates, Plots, PlutoUI, Printf, LaTeXStrings, HypertextLiteral, ShortCodes\n\t\n\tgr();\n\tPlots.GRBackend()\n\n\n\t#Define html elements\n\tnbsp = html\"&nbsp\" #non-breaking space\n\tvspace = html\"\"\"<div style=\"margin-bottom:0.05cm;\"></div>\"\"\"\n\tbr = html\"<br>\"\n\n\t#Sets the width of cells, caps the cell width by 90% of screen width\n\t#(setting overwritten by cell below)\n\t# @bind screenWidth @htl(\"\"\"\n\t# \t<div>\n\t# \t<script>\n\t# \t\tvar div = currentScript.parentElement\n\t# \t\tdiv.value = screen.width\n\t# \t</script>\n\t# \t</div>\n\t# \"\"\")\n\n\t\n\t# cellWidth= min(1000, screenWidth*0.9)\n\t# @htl(\"\"\"\n\t# \t<style>\n\t# \t\tpluto-notebook {\n\t# \t\t\tmargin: auto;\n\t# \t\t\twidth: $(cellWidth)px;\n\t# \t\t}\n\t# \t</style>\n\t# \"\"\")\n\t\n\n\t#Sets the width of the cells\n\t#begin\n\t#\thtml\"\"\"<style>\n\t#\tmain {\n\t#\t\tmax-width: 900px;\n\t#\t}\n\t#\t\"\"\"\n\t#end\n\n\n\t#Sets the height of displayed tables\n\thtml\"\"\"<style>\n\t\tpluto-output.scroll_y {\n\t\t\tmax-height: 550px; /* changed this from 400 to 550 */\n\t\t}\n\t\t\"\"\"\n\t\n\n\t#Two-column cell\n\tstruct TwoColumn{A, B}\n\t\tleft::A\n\t\tright::B\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", tc::TwoColumn)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t<div style=\"display: flex;\">\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.left)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t\t<div style=\"flex: 50%;\">\n\t\t\t\"\"\")\n\t\tshow(io, mime, tc.right)\n\t\twrite(io,\n\t\t\t\"\"\"\n\t\t\t\t</div>\n\t\t\t</div>\n\t\t\"\"\")\n\tend\n\n\t#Creates a foldable cell\n\tstruct Foldable{C}\n\t\ttitle::String\n\t\tcontent::C\n\tend\n\t\n\tfunction Base.show(io, mime::MIME\"text/html\", fld::Foldable)\n\t\twrite(io,\"<details><summary>$(fld.title)</summary><p>\")\n\t\tshow(io, mime, fld.content)\n\t\twrite(io,\"</p></details>\")\n\tend\n\t\n\t\n\t#helper functions\n\t#round to digits, e.g. 6 digits then prec=1e-6\n\troundmult(val, prec) = (inv_prec = 1 / prec; round(val * inv_prec) / inv_prec); \n\n\t\n\tdisplay(\"\")\nend\n\n# \u2554\u2550\u2561 41d7b190-2a14-11ec-2469-7977eac40f12\n#add button to trigger presentation mode\nhtml\"<button onclick='present()'>present</button>\"\n\n# \u2554\u2550\u2561 a2959181-2de9-47b9-a4c5-2439681999ac\nbegin \n\thtml\"\"\"\n\t<p align=left style=\"font-size:25px; font-family:family:Georgia\"> <b> UD/ISCTE-IUL Trading and Bloomberg Program</b> <p>\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 a5de5746-3df0-45b4-a62c-3daf36f015a5\nbegin \n\thtml\"\"\"\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:25px; font-family:family:Georgia\">Fixed Income Securities </div>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> <b> The TIPS-Treasury Bond Puzzle</b> <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<p align=center style=\"font-size:25px; font-family:family:Georgia\"> Summer 2022 <p>\n\t<p style=\"padding-bottom:1cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> Prof. Matt Fleckenstein </div>\n\t<p style=\"padding-bottom:0.0cm\"> </p>\n\t<div align=center style=\"font-size:20px; font-family:family:Georgia\"> University of Delaware, \n\tLerner College of Business and Economics </div>\n\t<p style=\"padding-bottom:0.5cm\"> </p>\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 b44fbfab-03e6-48fe-b9b0-b2c1b0d456d5\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 6498b10d-bece-42bf-a32b-631224857753\nmd\"\"\"\n# Overview\n\"\"\"\n\n# \u2554\u2550\u2561 95db374b-b10d-4877-a38d-1d0ac45877c4\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Outline</legend>      \n\t\t<br>    \n\t\t<input type=\"checkbox\" value=\"\">The U.S. Treasury Bond Puzzle<br><br>\n\t\t<input type=\"checkbox\" value=\"\">TIPS-Treasury Trading Strategy<br><br>\n\t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 c3363f03-fb14-4349-a2a6-5dd3b7913556\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 886da2d4-c1ec-4bb4-8733-e3b46c95dd36\nmd\"\"\"\n# The TIPS-Treasury Bond Puzzle\n\"\"\"\n\n# \u2554\u2550\u2561 bd7b639c-f16a-4627-baeb-c7548f104f02\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 6334202e-2047-4e7c-beca-020768340f08\nmd\"\"\"\n# The Largest Arbitrage Ever Documented\n\n> - _\u201c... you can forget about the concept of picking up pennies in front of a steamroller because ... this **arbitrage** can run to as much **$20 per $100 notional amount**.\u201d_\\\n> - _\u201cThe trade was Barnegat\u2019s most profitable and saw the fund make an impressive **132 per cent return that year**, outpacing almost every other fund in the industry.\u201d_\n\nSources: [The largest arbitrage ever documented](https://www.ft.com/content/3ec205b7-4351-3c0b-a46c-e23ab86c3a44); [Hedge funds reap rewards from Treasuries](https://www.ft.com/content/a5aa3c38-c111-11df-afe0-00144feab49a)\n\"\"\"\n\n# \u2554\u2550\u2561 98f202ba-7720-40fd-b618-87bd30de840c\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 410b6e69-0ebc-4e2f-9b07-48b7f112ab83\nmd\"\"\"\n> - _**\u201cFor Barnegat the opportunity was clear: the fund bought TIPS bonds and went short on regular Treasury bonds of a matched maturity, hedging out the effect of inflation along the way with a swap contract.\u201d**_ \\\n> - _\u201cThe result was a trade that would make money if the divergent prices between the two securities converged. The difference in price between the two securities narrowed sharply through 2009.\u201d_\\\n> - _\u201c 'If 2009 was an excellent year, then 2010 is still a very good year. The opportunities are huge in some cases,' says Mr Treue. Indeed, Barnegat is up 15.75 per cent so far this year.\u201d_\n\nSource: [Hedge funds reap rewards from Treasuries](https://www.ft.com/content/a5aa3c38-c111-11df-afe0-00144feab49a)\n\"\"\"\n\n# \u2554\u2550\u2561 8f8c2c95-25ef-4da0-afe1-b0a204911aeb\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 70e71241-e939-4b06-bcbe-ff7cf611df7a\nmd\"\"\"\n[Presentation by Bob Treue, Fixed Income Arbitrage, Barnegat Fund Management](https://youtu.be/V-ssGaTnl8o)\n\"\"\"\n\n# \u2554\u2550\u2561 3e8baccf-799d-4694-99b0-9612bc59928f\nYouTube(\"V-ssGaTnl8o\")\n\n# \u2554\u2550\u2561 c986f65d-8031-490d-a0a9-e2e25a8b1310\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 355989df-4aab-4c82-9e43-67123e9aecee\nmd\"\"\"\n# The TIPS-Treasury Bond Puzzle in the Journal of Finance\n\"\"\"\n\n# \u2554\u2550\u2561 9185f93e-3503-4dff-8133-8afd36fe148b\nmd\"\"\"\n> _\"It\u2019s contained in a great little paper published earlier this month and it isn\u2019t a fancy, schmancy accessible to high frequency traders only type of trade.\"_\n\nSource: [Kaminska (2010), FT.com](https://www.ft.com/content/3ec205b7-4351-3c0b-a46c-e23ab86c3a44)\n\"\"\"\n\n# \u2554\u2550\u2561 fce5cf58-d66c-4fde-b6bb-854f745d1935\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 8fa726b4-7064-4bd3-a2fa-54d875593009\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/FleckensteinLongstaffLustig2014_Abstract.svg\",:width => 900)\n\n# \u2554\u2550\u2561 6c30ca52-f64e-400a-9a18-1639465c190e\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 921402f8-a70c-4b45-b134-7fd70f0c699a\nmd\"\"\"\n# Combining TIPS and Inflation Swap\n\"\"\"\n\n# \u2554\u2550\u2561 1993f8d1-5e59-4b62-adbd-5cbe3fe6a358\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 3bb9c9a8-c9f7-4850-a201-430c6c145188\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TIPS_Cashflows.png\",:width => 900)\n\n# \u2554\u2550\u2561 375e90f6-dc10-4760-bde1-fe8f0736dfb7\nmd\"\"\"\n##\n##\n\"\"\"\n\n# \u2554\u2550\u2561 41135cf4-cb1a-445c-9be0-7678c097362f\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TIPSInflationswap_Cashflows.png\",:width => 900)\n\n# \u2554\u2550\u2561 a021d092-6180-4690-9041-4ce935f656a9\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 3845bb92-8437-4f27-9fb3-67ecf0a062ca\n# \u2560\u2550\u2561 show_logs = false\nLocalResource(\"./Assets/TreasuryNoteCashflowExample.png\",:width => 900)\n\n\n# \u2554\u2550\u2561 02fb2509-7270-4d31-9e49-75055e18e84f\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 70620b8f-1d3a-4c6e-9a09-e9fa0a0d447c\nmd\"\"\"\n## Numerical Example\n\"\"\"\n\n# \u2554\u2550\u2561 5b3f716a-95e0-425d-84ac-bcefc796b7e6\nmd\"\"\"\n- Suppose we invest in a TIPS with 6 months to maturity, coupon rate of 4%, and  (inflation-adjusted) principal of $100 .\n- Suppose the market price of the TIPS is 100, and that the market price of a Treasury note with six months to maturity and coupon rate of 5% is 102.\n\"\"\"\n\n# \u2554\u2550\u2561 034818e7-caae-495c-b159-e28549acd23a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 780b61fc-903e-4483-80d6-3cd927676589\nmd\"\"\"\n- Thus, we receive one final cash flow consisting of\n  - Inflation-adjusted principal: $$100\\times I_{0.5}$$  \n    - For simplicity, we ignore the deflation option for now.\n  - Inflation-adjusted coupon cash flow: $(c/2) \\times I_{0.5} \\times 100 = 0.02 \\times I_{0.5} \\times 100$\n- The total cash flow from the TIPS is:\n$$102 \\times I_{0.5}$$\n\"\"\"\n\n# \u2554\u2550\u2561 41d9d4e4-a4f0-425f-bb39-80a3c18d0289\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 824dc5be-d6ab-4d43-8795-745dc4e10c8e\nmd\"\"\"\n- Next, suppose we enter into an inflation swap with notional amount of $102 as inflation seller. Let the fixed rate on the inflation swap be 0.9828%, i.e. $$f=0.009828$$.\n- The net cash flow on the inflation swap is\n$$102 \\times [(1+f)^{0.5}-1] - 102 \\times [I_{0.5}-1]$$\n\"\"\"\n\n# \u2554\u2550\u2561 b6f4724c-ed17-4edc-a565-6768fccde65f\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 faaaae87-46e9-4fbd-82e0-6ac979f332ee\nmd\"\"\"\n- The total cash flow of the TIPS and the inflation swap is:\n$$102 \\times I_{0.5} + \\left(102 \\times [(1+f)^{0.5}-1] - 102 \\times [I_{0.5}-1] \\right)$$\n\n- Simplifying\n$$102 \\times (1+f)^{0.5} = 102 \\times (1+0.9828\\%)^{0.5} = 102.50$$\n- This is a deterministic cash flow that does not depend on future inflation.\n\"\"\"\n\n# \u2554\u2550\u2561 69b9a76e-622c-4b69-bcdc-a0aae02a93eb\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 3e23d630-4e11-4a52-8748-d66b02d1bfd4\nmd\"\"\"\n- Next, we take a short position in the Treasury note and buy the TIPS. \n- We pay $100 for the TIPS and get $102 in cash from shorting the Treasury notes.\n- Our net cash flow is 102 - 100 = 2.\n\"\"\"\n\n# \u2554\u2550\u2561 262590e2-745d-49ee-8c25-9e6f71a7b5bb\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 7275809f-4e12-44aa-b7e1-ec9a2b33f870\nmd\"\"\"\n- What is our obligation to pay in six months?\n\"\"\"\n\n# \u2554\u2550\u2561 bb406003-2eb8-4e25-897e-7651fc1e719a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 caa6aae8-c3ab-40e2-a74b-a7241fabfc82\nmd\"\"\"\n- First, we need to close out the short in the Treasury note and pay $102.50. Thus, we have a cash outflow of -102.50.\n- Second, we have a cash flow from the long TIPS position and the inflation swap of $102.5. This is a cash inflow of 102.50.\n- Our net cash flow is -102.5 + 102.5 = 0. Thus, we have zero cash flow in six months.\n\"\"\"\n\n# \u2554\u2550\u2561 36b43def-2c99-47a5-99c6-5d9b75884340\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 d6441d19-d3d4-4d5e-9808-d93a258204f8\nmd\"\"\"\n- However, we pocketed $2 upfront. \n- This is an arbitrage since we collect $2 now and have no future obligation in six months.\n- This is the TIPS-Treasury arbitrage trading strategy in simplified form.\n\"\"\"\n\n# \u2554\u2550\u2561 c24ef65a-7dde-4f06-bfd0-c15e2f769822\nmd\"\"\"\n# Combining TIPS, STRIPS and Inflation Swap\n\n- Suppose we invest in a TIPS with 6 months to maturity, coupon rate of 4%, and  (inflation-adjusted) principal of $100 .\n- Suppose the market price of the TIPS is 100, and that the market price of a Treasury note with six months to maturity and coupon rate of 6% is 102.\n\n\"\"\"\n\n# \u2554\u2550\u2561 e014cbe2-51e1-4437-b6b1-4adc041e9e1a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 cc9f23dc-eaa0-4d19-b673-d147fbb57057\nmd\"\"\"\n- From the TIPS we receive one final cash flow consisting of\n  - Inflation-adjusted principal: $$100\\times I_{0.5}$$  \n    - For simplicity, we ignore the deflation option for now.\n  - Inflation-adjusted coupon cash flow: $(c/2) \\times I_{0.5} \\times 100 = 0.02 \\times I_{0.5} \\times 100$\n- The total cash flow from the TIPS is:\n$$102 \\times I_{0.5}$$\n\"\"\"\n\n# \u2554\u2550\u2561 2a7ce1ef-35e8-4553-85c3-15c98821bde6\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 361360cb-bdf4-4f99-8ccc-3fee22847dd2\nmd\"\"\"\n- Next, suppose we enter into an inflation swap with notional amount of $102 as inflation seller. Let the fixed rate on the inflation swap be 0.9828%, i.e. $$f=0.009828$$.\n- The net cash flow on the inflation swap is\n$$102 \\times [(1+f)^{0.5}-1] - 102 \\times  [I_{0.5}-1]$$\n\"\"\"\n\n# \u2554\u2550\u2561 3f716232-20c4-42fa-847d-e2bad3440e84\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 3f4cdf58-95df-407d-a5f1-d4077d4b9188\nmd\"\"\"\n- The total cash flow of the TIPS and the inflation swap is:\n$$102 \\times I_{0.5} + \\left(102 \\times [(1+f)^{0.5}-1] - 102 \\times [I_{0.5}-1] \\right)$$\n\"\"\"\n\n# \u2554\u2550\u2561 5fea7742-70a3-4a8e-892d-c70a953b0fcf\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 66a6a32b-9639-4591-aca1-56742edca665\nmd\"\"\"\n- Simplifying\n$$102 \\times (1+f)^{0.5} = 102 \\times (1+0.9828\\%)^{0.5} = 102.50$$\n- This is a deterministic cash flow that does not depend on future inflation.\n\"\"\"\n\n# \u2554\u2550\u2561 7aa49db4-82df-4d28-81ae-7cf65fd3acc2\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 23e7092b-6006-4ddd-97ff-454128a88e31\nmd\"\"\"\n- Next, we take a short position in the Treasury note and buy the TIPS. \n- We pay $100 for the TIPS and get $102 in cash from shorting the Treasury notes.\n- Our net cash flow is 102 - 100 = 2.\n\n\"\"\"\n\n# \u2554\u2550\u2561 1771917d-3f40-495d-ad52-50bf674ff0b6\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 76d70fe8-358f-4f2f-9a00-69a147935282\nmd\"\"\"\n- What is our obligation to pay in six months?\n\"\"\"\n\n# \u2554\u2550\u2561 ff21805e-7466-4780-939b-72eeeee17e57\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 e9cef70a-fbf9-47a7-b5c0-aa6bb2ec5f6c\nmd\"\"\"\n- First, we need to close out the short in the Treasury note and pay $103. Thus, we have a cash outflow of -103.00.\n- Second, we have a cash flow from the long TIPS position and the inflation swap of $102.5. This is a cash inflow of 102.50.\n- Our net cash flow is -103 + 102.5 = -0.50. \n\"\"\"\n\n# \u2554\u2550\u2561 55c40cff-0639-46b5-a8dc-9f8cb3738a6f\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 8953ae0b-ab82-4173-bda8-9980ae1ef550\nmd\"\"\"\n- Thus, we have non-zero cash outflow of fifty cents in six months.\n- We want to have zero cash flows in six months. How can we achive this?\n\"\"\"\n\n# \u2554\u2550\u2561 88bf0e46-17c9-4a61-9e82-9c113c1dbadd\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 4e89c4d7-d4e8-4f68-ac5c-7b20d0c446e7\nmd\"\"\"\n- Recall that there are STRIPS which are zero coupon bonds with just one cash flow at maturity.\n- All we need is an inflow of fifty cents from a 6-month STRIPS.\n\"\"\"\n\n# \u2554\u2550\u2561 06b01ce1-94dd-4ddd-ad17-2bdf3aa950ed\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 2423e58f-2ff0-4c80-9666-ccb45243ae19\nmd\"\"\"\n- Suppose the price of a 6-month Treasury STRIPS is 99.50 (per 100 par value). The STRIPS will give us a cash inflow of 100 in 6 months.\n- Since we need \\$0.50, we buy \\$0.50 par value of this STRIPS.\n- This costs us 0.50/100 * 99.50 = $0.4975.\n\"\"\"\n\n# \u2554\u2550\u2561 e72cde3c-ad81-4242-8222-5d6bf86f22f4\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 81e531a9-3afb-4c05-a138-133467882086\nmd\"\"\"\n- Thus, our cash flow today is now the cash inflow of 2 (from the long TIPS and short Treasury note) and the cash outflow of 0.4975 from the STRIPS.\n- This is a net cash flow today of 2 - 0.4975 = 1.5025.\n- We now have zero cash flow in six months and a positive cash flow of 1.5025 today.\n- This is an arbitrage since we collect $1.5025 now and have no future obligation in six months.\n\"\"\"\n\n# \u2554\u2550\u2561 ed5c3bc3-e514-4529-992c-ee1a4dc7ed7c\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 65ace0e1-ccb4-4b49-8149-7230a7b5877e\nmd\"\"\"\n# The TIPS-Treasury Arbitrage Trade in a Nutshell\n\"\"\"\n\n# \u2554\u2550\u2561 309e11f5-5ddb-4b54-b0a9-aaf596bf6ec2\nmd\"\"\"\n- Suppose a Treasury bond with maturity $T$ and coupon rate of $c$ and has a market price of $P$.\n- Suppose the price of a TIPS with the same maturity $T$ and a coupon rate of $s$ has a market price of $V$.\n\n __1.__ Buy the TIPS.\n\n __2.__ Convert inflation-indexed TIPS coupon cash flows into fixed cash flows by enter into an inflation swap for each coupon payment date with a notional amount of $s$ (or $s + 100$ for the final principal payment date).\n\n__3.__ Match fixed cash fows: To match exactly the cash flows $c$ from the Treasury bond, go long or short a small amount of Treasury STRIPS for each coupon payment date.\n\n- You've created a synthetic Treasury\n- By the law of one price Cost(synthetic) should be Price(actual).\n- In reality, Cost(synthetic) < Price(actual).   \n\n__4.__ Short the Treasury bond and buy the synthetic Treasury.\n\"\"\"\n\n# \u2554\u2550\u2561 5abf0785-8384-49ed-8d05-6b4966a91354\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 201a91a8-4154-414e-86b1-ca578fa105c2\nmd\"\"\"\n# TIPS-Treasury Mispricing on December 30, 2008\n\"\"\"\n\n# \u2554\u2550\u2561 680fabca-fc6f-4aa8-8154-49087e019089\nmd\"\"\"\n- Treasury bond with maturity date on Feb 15 2025 and coupon rate of 7.625% and has market price \\$169.47.\n- TIPS with a coupon rate of 2.375% and the same maturity date as the Treasury bond has market price of \\$101.22.\n\n- Executing steps __1.__, __2.__, and __3.__ costs \\$146.379.\n- Since \\$146.379 < \\$169.47, buy the synthetic Treasury and sell the Treasury bond.\n- Pocket ~\\$23 profit (transaction costs would not exceed $1.50).\n\n\"\"\"\n\n# \u2554\u2550\u2561 be2e9fbb-9fe2-4e21-8c43-05b1cf3144c9\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 27038fd7-4dc4-4dd5-87dd-0032478d0622\nmd\"\"\"\n- This table uses real market data on 12/30/2008 and shows the cash flows associated with \n  - the 7.625% Treasury bond with maturity date January 15, 2025, and \n  - the cash flows from the replicating strategy using the 2.375% TIPS issue with the same maturity date that replicates the cash flows of the Treasury bond. \n\"\"\"\n\n# \u2554\u2550\u2561 62ec696b-021f-4756-aff8-f62a122345ae\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 1480c3c4-7ef1-413b-9cd2-5bbb9f61a2e2\nmd\"\"\"\n- Note: \n  - Cash flows are in dollars per $100 notional. \n  - The $I_t$ denotes the realized percentage change in the CPI index from the inception of the strategy to the cash flow date. \n  - Date refers to the number of the semiannual period in which the corresponding cash flows are paid.\n\"\"\"\n\n# \u2554\u2550\u2561 2430b2d2-0343-4316-ae84-d7cc59759f00\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 14e6f7ed-2a81-4101-a1b5-af627df7c805\nmd\"\"\"\n# Cash Flow Table\n\"\"\"\n\n# \u2554\u2550\u2561 c6df6e6e-1c72-4fbb-b73c-7c084bc96b69\nmd\"\"\"\n- The table shows the actual cash flows that would result from applying the arbitrage strategy on December 30, 2008, to replicate the 7.625% coupon Treasury bond maturing on February 15, 2025. \n\"\"\"\n\n# \u2554\u2550\u2561 7c923c8c-9067-42af-b103-391c05bbeb98\nFoldable(\"Cash Flow Table\", \nmd\"\"\"\n| Date | Treasury  | TIPS         | Inflation Swaps        | STRIPS   | Total    |\n|------|-----------|--------------|------------------------|----------|----------|\n| 0    | \u2212169.4793 | -101.2249    | 0                      | -45.6367 | -146.379 |\n| 1    | 3.8125    | 1.1875 I$_1$    | 1.1856 \u2212 1.1875 I$_{1}$     | 2.6269   | 3.8125   |\n| 2    | 3.8125    | 1.1875 I$_2$    | 1.1638 \u2212 1.1875 I$_{2}$     | 2.6487   | 3.8125   |\n| 3    | 3.8125    | 1.1875 I$_3$    | 1.1480 \u2212 1.1875 I$_{3}$     | 2.6645   | 3.8125   |\n| 4    | 3.8125    | 1.1875 I$_4$    | 1.1467 \u2212 1.1875 I$_{4}$     | 2.6658   | 3.8125   |\n| 5    | 3.8125    | 1.1875 I$_5$    | 1.1307 \u2212 1.1875 I$_{5}$     | 2.6818   | 3.8125   |\n| 6    | 3.8125    | 1.1875 I$_6$    | 1.1376 \u2212 1.1875 I$_{6}$     | 2.6749   | 3.8125   |\n| 7    | 3.8125    | 1.1875 I$_7$    | 1.1566 \u2212 1.1875 I$_{7}$     | 2.6559   | 3.8125   |\n| 8    | 3.8125    | 1.1875 I$_8$    | 1.1616 \u2212 1.1875 I$_{8}$     | 2.6509   | 3.8125   |\n| 9    | 3.8125    | 1.1875 I$_9$    | 1.1630 \u2212 1.1875 I$_{9}$     | 2.6495   | 3.8125   |\n| 10   | 3.8125    | 1.1875 I$_{10}$   | 1.1773 \u2212 1.1875 I$_{10}$    | 2.6352   | 3.8125   |\n| 11   | 3.8125    | 1.1875 I$_{11}$   | 1.1967 \u2212 1.1875 I$_{11}$    | 2.6128   | 3.8125   |\n| 12   | 3.8125    | 1.1875 I$_{12}$   | 1.2095 \u2212 1.1875 I$_{12}$    | 2.6030   | 3.8125   |\n| 13   | 3.8125    | 1.1875 I$_{13}$   | 1.2248 \u2212 1.1875 I$_{13}$    | 2.5877   | 3.8125   |\n| 14   | 3.8125    | 1.1875 I$_{14}$   | 1.2466 \u2212 1.1875 I$_{14}$    | 2.5659   | 3.8125   |\n| 15   | 3.8125    | 1.1875 I$_{15}$   | 1.2683 \u2212 1.1875 I$_{15}$    | 2.5442   | 3.8125   |\n| 16   | 3.8125    | 1.1875 I$_{16}$   | 1.2866 \u2212 1.1875 I$_{16}$    | 2.5259   | 3.8125   |\n| 17   | 3.8125    | 1.1875 I$_{17}$   | 1.3058 \u2212 1.1875 I$_{17}$    | 2.5067   | 3.8125   |\n| 18   | 3.8125    | 1.1875 I$_{18}$   | 1.3304 \u2212 1.1875 I$_{18}$    | 2.4821   | 3.8125   |\n| 19   | 3.8125    | 1.1875 I$_{19}$   | 1.3556 \u2212 1.1875 I$_{19}$    | 2.4569   | 3.8125   |\n| 20   | 3.8125    | 1.1875 I$_{20}$   | 1.3792 \u2212 1.1875 I$_{20}$    | 2.4333   | 3.8125   |\n| 21   | 3.8125    | 1.1875 I$_{21}$   | 1.4009 \u2212 1.1875 I$_{21}$    | 2.4116   | 3.8125   |\n| 22   | 3.8125    | 1.1875 I$_{22}$   | 1.4225 \u2212 1.1875 I$_{22}$    | 2.3900   | 3.8125   |\n| 23   | 3.8125    | 1.1875 I$_{23}$   | 1.4427 \u2212 1.1875 I$_{23}$    | 2.3698   | 3.8125   |\n| 24   | 3.8125    | 1.1875 I$_{24}$   | 1.4635 \u2212 1.1875 I$_{24}$    | 2.3490   | 3.8125   |\n| 25   | 3.8125    | 1.1875 I$_{25}$   | 1.4806 \u2212 1.1875 I$_{25}$    | 2.3319   | 3.8125   |\n| 26   | 3.8125    | 1.1875 I$_{26}$   | 1.4979 \u2212 1.1875 I$_{26}$    | 2.3146   | 3.8125   |\n| 27   | 3.8125    | 1.1875 I$_{27}$   | 1.5126 \u2212 1.1875 I$_{27}$    | 2.2999   | 3.8125   |\n| 28   | 3.8125    | 1.1875 I$_{28}$   | 1.5277 \u2212 1.1875 I$_{28}$    | 2.2848   | 3.8125   |\n| 29   | 3.8125    | 1.1875 I$_{29}$   | 1.5407 \u2212 1.1875 I$_{29}$    | 2.2718   | 3.8125   |\n| 30   | 3.8125    | 1.1875 I$_{30}$   | 1.5548 \u2212 1.1875 I$_{30}$    | 2.2577   | 3.8125   |\n| 31   | 3.8125    | 1.1875 I$_{31}$   | 1.5676 \u2212 1.1875 I$_{31}$    | 2.2449   | 3.8125   |\n| 32   | 3.8125    | 1.1875 I$_{32}$   | 1.5823 \u2212 1.1875 I$_{32}$    | 2.2302   | 3.8125   |\n| 33   | 103.8125  | 101.1875 I$_{33}$ | 135.9861 \u2212101.1875 I$_{33}$ | -32.1736 | 103.8125 |\n\"\"\")\n\n# \u2554\u2550\u2561 ed31f60a-8b09-4222-8b81-333eccb33c02\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 004efec5-23d3-4940-abff-9024820daf65\nmd\"\"\"\n\n- As shown, the price of the Treasury bond is **$169.479.** \n- To replicate the Treasury bond\u2019s cash flows, the arbitrageur buys a 2.375% coupon TIPS issue with the same maturity date for a price of $101.225. \n- Since there are 33 semiannual coupon payment dates, 33 inflation swaps are executed with the indicated notional amounts.\n- Finally, positions in Treasury STRIPS of varying small notional amounts are also taken by the arbitrageur. \n\"\"\"\n\n# \u2554\u2550\u2561 fb7ad01e-1a12-4f06-a1a3-d1003cfea04a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 06b6deff-fb0a-4ed2-80ee-aee2cee7e0bf\nmd\"\"\"\n- The net cash flows from the replicating strategy exactly match those from the Treasury bond, but at a cost of only **$146.379.**\n- Thus, the cash flows from the Treasury bond can be replicated at a cost that is **\\$23.10** less than that of the Treasury bond.\n\"\"\"\n\n# \u2554\u2550\u2561 6edf15e1-fcad-42ed-a777-ab5be39e4a41\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 cded3cca-c9ac-4d7d-b9dd-4c5497aa955e\nmd\"\"\"\n# Case Study\n- Date: October 5, 2006\n- TIPS CUSIP: 912828EA\n- Treasury Note CUSIP: 912828EE\n\"\"\"\n\n# \u2554\u2550\u2561 d4dab771-87f1-4fa2-b6a2-900a51af4586\nmd\"\"\"\n# TIPS: 912828EA\n\"\"\"\n\n# \u2554\u2550\u2561 8f3a465e-af54-4b0e-9210-87c140629f2f\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/TIPS_912828EA_Des.png\",:width => 800)\n\n# \u2554\u2550\u2561 d61da00e-9d8c-43e8-89c5-e70220dc761a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 049debc7-b1d5-4908-9b27-70344995a4c4\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/TIPS_912828EA_PxGraph.png\",:width => 800)\n\n# \u2554\u2550\u2561 abda6c84-f124-4d61-bb40-0517e28fbcf6\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 72721c2c-f227-469a-91d9-dbec158c2fa7\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/TIPS_912828EA_PxQuotes.png\",:width => 800)\n\n# \u2554\u2550\u2561 312e0229-445e-42d4-8a0d-709c590c1add\nmd\"\"\"\n# Treasury Note: 912828EA\n\"\"\"\n\n# \u2554\u2550\u2561 97d9ba4b-880f-464e-bd94-7b72a86094b7\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/Treasury_912828EE_Des.png\",:width => 800)\n\n# \u2554\u2550\u2561 bf6e7d0a-7e33-47b1-9240-a0af661057f5\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 d16e82d8-faa9-444f-8db7-6f442c5e5fd4\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/Treasury_912828EE_PxQuotes.png\",:width => 800)\n\n# \u2554\u2550\u2561 dc265679-85cd-4ff1-a728-6353f36e3a9c\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 e36dc8d3-3b4e-46c4-9259-007f75068591\nResource(\"https://raw.githubusercontent.com/fleckenstein-m/TIPS_Treas_W2022/main/notebooks/Assets/Treasury_912828EE_PxGraph_2.png\",:width => 800)\n\n# \u2554\u2550\u2561 4ac1725a-d9e5-4587-9b03-ae8b7379ed56\nmd\"\"\"\n# Inflation Swap Rates\n- Date:\t10/5/2006\t\n\n| Tenor\t  | SwapRate| Bloomberg| Ticker      | \n|-------- |---------|--------- | ---------   |\n|1 Year\t  |\t0.01850\t| USSWIT1  | CMPN CURNCY | \n|2 Years  |\t0.02133\t| USSWIT2  | CMPN CURNCY |\n|3 Years  | 0.02313\t| USSWIT3  | CMPN CURNCY |\n|4 Years  |\t0.02425\t| USSWIT4  | CMPN CURNCY |\n|5 Years  |\t0.02503\t| USSWIT5  | CMPN CURNCY |\n|6 Years  |\t0.02543\t| USSWIT6  | CMPN CURNCY |\n|7 Years  |\t0.02580\t| USSWIT7  | CMPN CURNCY |\n|8 Years  |\t0.02623\t| USSWIT8  | CMPN CURNCY |\n|9 Years  |\t0.02648\t| USSWIT9  | CMPN CURNCY |\n|10 Years |\t0.02683\t| USSWIT10 | CMPN CURNCY |\n|12 Years |\t0.02733\t| USSWIT12 | CMPN CURNCY |\n|15 Years |\t0.02798\t| USSWIT15 | CMPN CURNCY |\n|20 Years |\t0.02960\t| USSWIT20 | CMPN CURNCY |\n|25 Years |\t0.03070\t| USSWIT25 | CMPN CURNCY |\n|30 Years |\t0.03170\t| USSWIT30 | CMPN CURNCY |\n\"\"\"\n\n# \u2554\u2550\u2561 00c3ea64-bbe5-4d7a-a1b2-1197b1455bec\nmd\"\"\"\n# STRIPS Prices\n\"\"\"\n\n# \u2554\u2550\u2561 432dae1b-89b1-4e8a-b59a-98e00391b368\nFoldable(\"Treasury STRIPS Prices\", \nmd\"\"\"\n#### STRIPS Prices\n- Date:\t10/5/2006\n\n| BB ID            | Maturity   | 10/5/2006 |\n|------------------|------------|-----------|\n| 9128333P3 Govt   | 11/30/2006 | 99.483    |\n| 9128333U2   Govt | 2/28/2007  | 98.308    |\n| 9128333W8 Govt   | 3/31/2007  | 97.813    |\n| 9128333Y4   Govt | 4/30/2007  | 97.414    |\n| 9128333Z1 Govt   | 5/31/2007  | 97.103    |\n| 9128334E7   Govt | 8/31/2007  | 96.008    |\n| 9128334G2 Govt   | 9/30/2007  | 95.626    |\n| 912833GB0   Govt | 11/15/2007 | 94.937    |\n| 9128334K3 Govt   | 11/30/2007 | 94.885    |\n| 912833C57   Govt | 1/15/2008  | 94.324    |\n| 9128334R8 Govt   | 1/31/2008  | 94.166    |\n| 912833CT5   Govt | 2/15/2008  | 93.973    |\n| 9128335C0 Govt   | 2/29/2008  | 93.877    |\n| 9128335K2   Govt | 3/31/2008  | 93.265    |\n| 9128335Z9 Govt   | 5/31/2008  | 92.808    |\n| 912833C65   Govt | 7/15/2008  | 92.106    |\n| 9128336P0 Govt   | 7/31/2008  | 91.974    |\n| 912833CU2   Govt | 8/15/2008  | 91.959    |\n| 9128335D8 Govt   | 8/31/2008  | 91.848    |\n| 912833ZY9   Govt | 9/15/2008  | 91.626    |\n| 9128335L0 Govt   | 9/30/2008  | 91.243    |\n| 9128335T3   Govt | 10/31/2008 | 91.086    |\n| 912833GD6 Govt   | 11/15/2008 | 90.914    |\n| 9128336A3   Govt | 11/30/2008 | 90.84     |\n| 912833CV0 Govt   | 2/15/2009  | 89.985    |\n| 9128335E6   Govt | 2/28/2009  | 89.88     |\n| 9128332B5 Govt   | 3/15/2009  | 89.773    |\n| 9128335M8   Govt | 3/31/2009  | 89.713    |\n| 9128335U0 Govt   | 4/30/2009  | 88.682    |\n| 912833GE4   Govt | 5/15/2009  | 88.717    |\n| 9128336B1 Govt   | 5/31/2009  | 88.869    |\n| 9128332J8   Govt | 7/15/2009  | 87.956    |\n| 9128336R6 Govt   | 7/31/2009  | 87.681    |\n| 9128335F3   Govt | 8/31/2009  | 87.87     |\n| 9128333K4 Govt   | 9/15/2009  | 87.666    |\n| 9128335N6   Govt | 9/30/2009  | 86.913    |\n| 9128335V8 Govt   | 10/31/2009 | 87.278    |\n| 912833GF1   Govt | 11/15/2009 | 87.055    |\n| 9128336C9 Govt   | 11/30/2009 | 86.911    |\n| 9128333S7   Govt | 1/15/2010  | 86.419    |\n| 912833CX6 Govt   | 2/15/2010  | 86.161    |\n| 9128335G1   Govt | 2/28/2010  | 86.125    |\n| 9128333V0 Govt   | 3/15/2010  | 85.948    |\n| 9128335P1   Govt | 3/31/2010  | 85.789    |\n| 9128336D7 Govt   | 5/31/2010  | 85.119    |\n| 9128335H9   Govt | 8/31/2010  | 84.304    |\n| 9128334F4 Govt   | 9/15/2010  | 84.145    |\n| 9128336E5   Govt | 11/30/2010 | 83.434    |\n| 9128334N7 Govt   | 1/15/2011  | 82.742    |\n| 9128335J5   Govt | 2/28/2011  | 82.509    |\n| 912834BA5 Govt   | 3/15/2011  | 82.311    |\n| 9128336F2   Govt | 5/31/2011  | 81.984    |\n| 912833DA5 Govt   | 8/15/2011  | 80.721    |\n| 9128336W5   Govt | 8/31/2011  | 80.652    |\n| 912834BB3 Govt   | 9/15/2011  | 80.541    |\n| 9128336Z8   Govt | 11/30/2011 | 80.731    |\n| 9128337G9 Govt   | 2/29/2012  | 78.767    |\n| 9128337K0   Govt | 5/31/2012  | 78.302    |\n| 912833Y53 Govt   | 8/31/2012  | 77.112    |\n| 912833Y87   Govt | 11/30/2012 | 76.043    |\n| 912833Z78 Govt   | 2/28/2013  | 75.172    |\n| 912834AA6   Govt | 5/31/2013  | 74.184    |\n| 912834AF5 Govt   | 8/31/2013  | 73.578    |\n| 912834AJ7   Govt | 11/30/2013 | 72.472    |\n| 912834AV0 Govt   | 2/28/2014  | 71.589    |\n| 912834DW5   Govt | 5/31/2014  | 70.67     |\n| 912834AW8 Govt   | 8/31/2014  | 69.906    |\n| 912834DX3   Govt | 11/30/2014 | 69.049    |\n| 912834AX6 Govt   | 2/28/2015  | 68.153    |\n| 912834DY1   Govt | 5/31/2015  | 67.41     |\n| 912834AY4 Govt   | 8/31/2015  | 66.51     |\n| 912834DZ8   Govt | 11/30/2015 | 65.644    |\n| 912834AZ1 Govt   | 2/29/2016  | 64.789    |\n| 912834EA2   Govt | 5/31/2016  | 64.018    |\n| 912834EQ7 Govt   | 8/31/2016  | 63.176    |\n| 912834EW4   Govt | 11/30/2016 | 62.346    |\n| 912834FC7 Govt   | 2/28/2017  | 61.659    |\n\"\"\")\n\n# \u2554\u2550\u2561 d1eb23f3-7142-493d-b2e6-d4f844e2bfc6\nmd\"\"\"\n# Calculations\n\"\"\"\n\n# \u2554\u2550\u2561 398086e4-4a4c-45f6-a32d-cb5d8847233c\nmd\"\"\"\n|\t\t\t\t             | TIPS\t\t\t    | Tbond     | \n|----------------------------|------------------|-----------|\n| CUSIP\t\t\t             | 912828EA \t\t| 912828EE  |\n| Maturity\t\t             | 7/15/2015\t\t| 8/15/2015 |  \n| Issue Date\t             | 7/15/2005\t\t| 8/15/2005 |\n| Coupon\t\t             | 1.875%\t\t    | \t4.250%  |\n| First Coupon Date\t         | 1/15/2006\t\t| 2/15/2006 |\n| Second Coupon Date         | 7/15/2006\t\t| 8/15/2006 |\n| Reference CPI on issue date| 194.50968\t\t| $-$       |    \n| **Price Quote**\t             | 96-20\t\t    | 97-15+    |\n\"\"\"\n\n# \u2554\u2550\u2561 7c15360a-9f7f-45d0-8059-0420dc46b231\nmd\"\"\"\n# Step 1.1: Calculate Full Prices of the Treasury\n\"\"\"\n\n# \u2554\u2550\u2561 cf85e64a-d5c4-4d04-b6ba-04ac003f7289\nMarkdown.parse(\"\n- **Treasury 912727EE**\n  - Quoted Price: 97-15+ per \\$100 notional.\n> \\$97 + \\\\frac{15}{32} + \\\\frac{1}{64} = $(97+15/32+1/64)\\$\n\")\n\n# \u2554\u2550\u2561 a5a0c420-f28f-4ec4-a9e2-092a02cec9a8\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 448fa11f-4bcb-4b62-9d1b-703d9a12786b\nMarkdown.parse(\"\n- Accrued Interest: \n  - Current Date: 10/05/2006\n  - Last Coupon Date: 8/15/2006\n  - Next Coupon Date: 2/15/2007\n\")\n\n# \u2554\u2550\u2561 d5ebca67-32b9-461d-9d4f-3285517c3cd2\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 d5df1ae7-9cc5-453b-b889-cd57dcc671c4\nMarkdown.parse(\"\n- Days since last coupon = 17 days in August *(daycount includes the date of the previous coupon, i.e. 16+1=17)* + 30 days in September + 4 days October *(daycount excludes the settlement date, thus 4 days)*\n> \\$17 + 30 + 4 = $(17+30+4)\\$\n\")\n\n# \u2554\u2550\u2561 d9aa27ee-eba3-4dee-abeb-7fce49536793\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 3d5a3bc4-f290-4d68-a5d4-ecdafc967537\nMarkdown.parse(\"\n- Days in the coupon period = (31-15) days in August + 30 days September + 31 days in October + 30 days in November + 31 days in December + 31 days in January + 15 days in February\n> \\$(31-15) + 30 + 31 + 30 + 31 + 31 + 15 = $(31-15+30+31+30+31+31+15)\\$\n\")\n\n# \u2554\u2550\u2561 afc4f565-7171-4b47-93e0-ffa66386dedf\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 7e50530e-a0ad-4709-9e89-bc7cc8575328\nMarkdown.parse(\"\n- Calculate accrued interest on October 5, 2006.\n> Accrued Interest = \\$\\\\frac{0.0425}{2} \\\\times 100 \\\\times \\\\frac{51}{184} = $(roundmult((31-15+30+5)/(31-15+30+31+30+31+31+15)*0.0425/2*100,1e-6))\\$\n\")\n\n# \u2554\u2550\u2561 5d53718b-a542-47ea-a4bf-7d94320d1fde\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 6245fadd-8dda-4e59-b619-3b0cfe8fbafe\nMarkdown.parse(\"\n- Calculate the full price of the Treasury note per \\$100 notional. \n  - The full price is the quoted price plus accrued interest.\n> Full Price = \\$$(roundmult((31-15+30+5)/(31-15+30+31+30+31+31+15)*0.0425/2*100+(97+15/32+1/64),1e-6))\\$\n\")\n\n# \u2554\u2550\u2561 bb34c7c8-4d9a-46c8-a1e4-38e01c3c404a\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 31ef4b01-b5ed-47cc-9ff1-6d92ff7bd2b4\nmd\"\"\"\n# Step 1.2: Calculate Full Prices of the TIPS\n\"\"\"\n\n# \u2554\u2550\u2561 d670b261-f427-4498-a8df-fcdacdb14d3e\nMarkdown.parse(\"\n- **TIPS 912828EA**\n  - Quoted Price: 96-20 per \\$100 notional.\n> \\$96 + \\\\frac{20}{32}=$(96+20/32)\\$\n\")\n\n# \u2554\u2550\u2561 cba8fcb7-970e-47ef-9b0f-d4396e89aa66\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 c6ae22ec-78cd-4a00-b36d-806439f9d550\nMarkdown.parse(\"\n- Accrued Interest:\n  - Current Date: 10/05/2006\n  - Last Coupon Date: 7/15/2006\n  - Next Coupon Date: 1/15/2007\n\")\n\n# \u2554\u2550\u2561 fde6eaee-dd08-483a-87c6-6778300c311d\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 3936d198-5f63-4192-b33b-b1ef9941964d\nMarkdown.parse(\"\n- Days since last coupon = 17 days in July *(daycount includes the date of the previous coupon, i.e. 16+1=17)* + 31 days in August + 30 days in September + 4 days October *(daycount excludes the settlement date, thus 4 days)*\n> \\$17 + 31 + 30 + 4 = $(17+31+30+4)\\$\n\")\n\n# \u2554\u2550\u2561 b415d609-0528-4e5f-9d43-bf8cd0e25688\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 2bbcaee7-34a7-4596-ad38-5c6239dd08e6\nMarkdown.parse(\"\n- Days in the coupon period = (31-15) days in July + 31 days in August + 30 days September + 31 days in October + 30 days in November + 31 days in December + 15 days in January \n> \\$(31-15) + 31 + 30 + 31 + 30 + 31 + 15 = $(31-15+31+30+31+30+31+15)\\$\n\")\n\n# \u2554\u2550\u2561 89516578-5de4-47b3-8629-89b590b15b19\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 fbb7288e-806e-4fec-a9f6-cd79b66d33fd\nMarkdown.parse(\"\n- Calculate accrued interest on October 5, 2006.\n> Accrued Interest = \\$\\\\frac{0.01875}{2} \\\\times 100 \\\\times \\\\frac{82}{184} = $(roundmult(0.01875/2*100*(82/184),1e-6))\\$\n\")\n\n# \u2554\u2550\u2561 9cd0acc2-6e2b-42ac-9f92-1692d8b55d80\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 60913481-91ed-4959-9b01-9fe363916045\nMarkdown.parse(\"\n- Calculate the full price of the Treasury TIPS per \\$100 notional. \n  - The full price is the quoted price plus accrued interest.\n> Full Price = \\$$(roundmult((0.01875/2*100*(82/184))+(96+20/32),1e-6))\\$\n\")\n\n# \u2554\u2550\u2561 4e4f2c67-c86e-4e44-9eae-2c39f025c66d\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 083a0cb1-a597-46ca-84c0-e5b7ce23b53a\nmd\"\"\"\n# Step 2: Set-up Cash Flows of the TIPS, Inflation Swaps and STRIPS\n- *Note, initially we assume that the TIPS and Treasury note have identical maturity and coupon cash flow dates. The Time column lists the coupon payment dates of the TIPS.*\n- The (real) coupon cash flows of the TIPS are $\\frac{0.01875}{2}\\times 100=0.9375$\n- The coupon cash flows of the Treasury note are are $\\frac{0.0425}{2}\\times 100=2.125$\n \n\"\"\"\n\n# \u2554\u2550\u2561 ad56ba09-a69c-40db-8caf-53efdd93719a\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 e871ef6e-f4da-4cef-aecf-4103335ebead\nmd\"\"\"\n- *Notation*:\n  - Let $P_{STR}(t,T)$ be the time-$t$ price of Treasury STRIPS with maturity in $T$ years. In the table below, we omit the $T$ to designate the coupon cash flow dates of the TIPS. \n  - Let $x_T$ denote the notional amount of the Treasury STRIPS.\n  - Let $I_t/I_0$ denote the inflation adjustment on the TIPS at time $t$.\n  - Let $P_{\\textrm{Swap}}(t)$ denote the cash flow on the fixed leg of the inflation swap with notional of \\$1, i.e. $$P_{\\textrm{Swap}}(t)=(1+f_t)^t-1$$ \n  - The floating leg of the inflation swap has a time-$t$ cash flow of $\\frac{I_t}{I_0} - 1$\n\n\"\"\"\n\n# \u2554\u2550\u2561 80176747-6460-4840-992f-394e1c1112b2\nmd\"\"\"\n# 2.1. Start with TIPS\n\"\"\"\n\n# \u2554\u2550\u2561 b0b36b4c-9d44-42cf-a1c2-a97fad25b84e\nmd\"\"\"\n| Time\t\t| TIPS \t\t\t| \n|-----------|---------------|\n| 10-05-06\t| $-97.0428$\t    |\n| 1/15/2007 | $0.9375 \\times \\frac{I_t}{I_0}$ |  \n|  $\\vdots$ | $\\vdots$      | \n| 7/15/2015 | $(0.9375+100) \\times \\frac{I_T}{I_0}$ | \n\"\"\"\n\n# \u2554\u2550\u2561 903a6516-2919-4078-89cc-264a726e07ca\nmd\"\"\"\n# 2.2. Add Inflation Swaps\n- The notional amount of the inflation swap is equal to the real coupon cash flow of the TIPS \n  - In this example, $N=0.9375$.\n\"\"\"\n\n# \u2554\u2550\u2561 14623ccd-d970-4046-8e1c-89d1652e3bcf\nmd\"\"\"\n| Time\t\t| TIPS \t\t\t| Inflation Swaps | \n|-----------|---------------|-----------------|\n| 10-05-06\t| $-97.0428$\t    | 0               |\n| 1/15/2007 | $0.9375 \\times \\frac{I_t}{I_0}$ |  $-0.9375 \\times \\left(\\frac{I_t}{I_0}-1\\right) + 0.9375 \\times P_{\\textrm{Swap}}(t)$  | \n| $\\vdots$\t| $\\vdots$\t    | $\\vdots$        |\n| 7/15/2015 | $(0.9375+100) \\times \\frac{I_T}{I_0}$ | $-(0.9375+100) \\times \\left( \\frac{I_T}{I_0} -1\\right) + (0.9375+100) \\times P_{\\textrm{Swap}}(T)$ |\n\"\"\"\n\n# \u2554\u2550\u2561 932c04a6-64e3-4d73-a083-c836fbafdbb3\nmd\"\"\"\n# 2.3. Net TIPS and Inflation Swaps Cash Flows\n\"\"\"\n\n# \u2554\u2550\u2561 fb2d12f6-ebd2-4be5-9506-cdaa52cfaa1e\nmd\"\"\"\n| Time\t\t| TIPS \t+ Inflation Swaps | \n|-----------|-------------------------|\n| 10-05-06\t| $-97.0428$\t              |\n| 1/15/2007 | $0.9375  + 0.9375 \\times P_{\\textrm{Swap}}(t)$  | \n| $\\vdots$\t| $\\vdots$              |\n| 7/15/2015 | $(0.9375+100) + (0.9375+100) \\times P_{\\textrm{Swap}}(T)$ |\n\"\"\"\n\n# \u2554\u2550\u2561 d7e38a5e-2c88-4728-b1cc-d98a5e1a6ec1\nmd\"\"\"\n# 2.4. Add Treasury Note\n\"\"\"\n\n# \u2554\u2550\u2561 4950323f-1c91-4d63-b333-410c69650352\nmd\"\"\"\n| Time\t\t| TIPS + Inflation Swaps | Treasury Note \t|\n|-----------|------------------------|------------------|\n| 10-05-06\t| $-97.0428$\t         | $-98.07337$      | \n| 1/15/2007 | $0.9375 \\times (1+ P_{\\textrm{Swap}}(t))$  | $2.125$ |\n| $\\vdots$\t| $\\vdots$             | $\\vdots$\t| \n| 7/15/2015 | $100.9375 \\times (1+P_{\\textrm{Swap}}(T))$ | $102.125$ |\n\"\"\"\n\n# \u2554\u2550\u2561 8d63c259-14d0-45db-bd85-93b71e963dbb\nmd\"\"\"\n# 2.5. Add Treasury STRIPS\n\"\"\"\n\n# \u2554\u2550\u2561 92383772-0e20-4302-89ca-ccb914a05a33\nmd\"\"\"\n| Time\t\t| TIPS + Inflation Swaps | Treasury Note \t| STRIPS \t\t   |\n|-----------|------------------------|-----------------|-------------------|\n| 10-05-06\t| $-97.0428$\t          | $-98.07337$          |   $-\\sum_{T_i} P_{\\textrm{STR}}(0,T_i)$\t|\n| 1/15/2007 | $0.9375 + 0.9375 \\times P_{\\textrm{Swap}}(t)$  | $2.125$ | $x_t \\times 100$ | \n| $\\vdots$  | $\\vdots$               | $\\vdots$        | \n| 7/15/2015 | $(0.9375+100) + (0.9375+100) \\times P_{\\textrm{Swap}}(T)$ | $102.125$ | $x_T \\times 100$ |\n\"\"\"\n\n# \u2554\u2550\u2561 03f0f3c5-cd48-4dec-b379-0d4245235f25\nmd\"\"\"\n# 2.6. Calculate the STRIPS Positions\n\"\"\"\n\n# \u2554\u2550\u2561 0e8e476f-9933-4fd0-9ad6-b01f7918a67b\nmd\"\"\"\n- Next, we calculate the positions in Treasury STRIPS to match the cash flows from the Treasury note exactly.\n\"\"\"\n\n# \u2554\u2550\u2561 7848436a-46e6-4456-8402-f34e3d16126b\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 5cc070bc-e0f6-4158-a3e2-800acac61361\nmd\"\"\"\n- For each coupon date $t$\n  - Set the notional of the Treasury STRIPS to enter into such that the total cash flow from the TIPS, inflation swap, and STRIPS matches exactly the fixed coupon cash flow of the Treasury note.\n\"\"\"\n\n# \u2554\u2550\u2561 9d03e1f6-4ad5-4b99-9ab8-cc92bff5f224\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 a59495a8-a940-47b1-b240-685afbd62d23\nmd\"\"\"\n  - To keep the notation general, let the fixed real TIPS cash flow be denoted by $c_{\\textrm{TIPS}}= 0.9375$ and let the Treasury note cash flow be $c_{\\textrm{Tnote}}=2.125$.\n${c_{\\textrm{TIPS}}} \\times (1 + {P_{\\textrm{Swap}}(t)}) + x_t \\cdot 100 = {c_{\\textrm{Tnote}}}$\n$\\to {x_t} = \\frac{{{c_{\\textrm{Tnote}}} - {c_{\\textrm{TIPS}}} ( 1+ \\cdot {P_{\\textrm{Swap}}(t)}})}{{100}}$\n$\\to {x_t} = \\frac{{{2.125} - ({c_{\\textrm{TIPS}}} + {c_{\\textrm{TIPS}}} \\cdot {P_{\\textrm{Swap}}(t)}})}{{100}}$\n\"\"\"\n\n# \u2554\u2550\u2561 db457538-d9a1-4acd-be2a-d89e71c43864\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 d72909ff-0908-4759-91be-ad420d7c47e3\nmd\"\"\"\n- For the maturity date $T$\n- Set the notional of the Treasury STRIPS to enter into such that the total cash flow from the TIPS, inflation swap, and STRIPS matches exactly the fixed coupon and principal cash flow of the Treasury note.\n$\\left( {100 + {c_{\\textrm{TIPS}}}} \\right) \\times (1+ {P_{\\textrm{Swap}}(T)}) + x \\cdot 100 = 100 + {c_{\\textrm{Tnote}}}$\n$\\to {x_T} = \\frac{({100+{c_{Tnote}}) - (100+{c_{\\textrm{TIPS}}}) \\times (1 +{P_{\\textrm{Swap}}(T)})}}{{100}}$\n\"\"\"\n\n# \u2554\u2550\u2561 5ec83302-d399-49a5-86fe-514f30d76c7b\nmd\"\"\"\n# Using Market Data\n\n- Use actual market data in Steps 2.3 to 2.6 above.\n\"\"\"\n\n# \u2554\u2550\u2561 9860adbf-82c4-4961-a0e4-dd5c51037430\nmd\"\"\"\n# 2.3. Net TIPS and Inflation Swaps Cash Flows\n\"\"\"\n\n# \u2554\u2550\u2561 68cdb801-d6ca-4fe1-8510-5d66ac78e95e\nbegin\n\tcfDates2=Dates.Date.([\"01/15/2007\",\"07/15/2007\",\"01/15/2008\",\"07/15/2008\",\"01/15/2009\",\"07/15/2009\",\"01/15/2010\",\"07/15/2010\",\"01/15/2011\",\"07/15/2011\",\"01/15/2012\",\"07/15/2012\",\"01/15/2013\",\"07/15/2013\",\"01/15/2014\",\"07/15/2014\",\"01/15/2015\",\"07/15/2015\"],\"mm/dd/yyyy\")\ncfSwaps=[0.014123,0.017091,0.019628,0.020925,0.021960,0.022847,0.023544,0.024078,0.024534,0.024914,0.025180,0.025364,0.025533,0.025768,0.025953,0.026201,0.026308,0.026458]\ncfTenors=[0.279500,0.775300,1.279500,1.778100,2.282200,2.778100,3.282200,3.778100,4.282200,4.778100,5.282200,5.780800,6.284900,6.780800,7.284900,7.780800,8.284900,8.780800]\ndf2 = DataFrame(Date=cfDates2, InflationSwapRates=cfSwaps,SwapTenors=cfTenors)\nend\n\n# \u2554\u2550\u2561 f4dc2755-9f58-4598-8216-e682f59f0632\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 9b1b0269-d975-446a-a168-3db32d0fdb95\nmd\"\"\"\n- The column *Inflation Swap Rates* shows the inflation swap rates from the Bloomberg system on 10/5/2006.\n- The column SwapTenors shows the swap tenors as a fraction of a year.\n- The cash flow dates of the inflation swaps are on the coupon cash flow dates of the TIPS.\n- For example, the first tenor as a fraction of a year is 0.2795.\n    - (31-5) days in October + 30 days in November + 31 days in December + 15 days in January = 102. Thus, 102/365=0.2795. Similarly for the other cash flow dates.\n\"\"\"\n\n# \u2554\u2550\u2561 43e04f32-6175-4a07-b128-427566259646\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 294dde1d-2ce0-40d6-b309-b50f93b15c70\nmd\"\"\"\n- Note: To get inflation swap rates with tenors exactly matching the cash flow dates of the TIPS, we make two adjustments. \n- First, we need to account for seasonalities in inflation swap rates (e.g. inflation rates for certain months during the year are higher/lower than in other months). \n- Second, swap rates for short tenors (e.g. 1 month) are known because the reference CPI index values for near-term months are already known in the current month due to the 3-month indexaction lag). \n- For details, see Fleckenstein, Longstaff, and Lustig (2014).\n\"\"\"\n\n# \u2554\u2550\u2561 59bdffc6-b6fd-45f7-98dc-aec5aab8b9a7\nMarkdown.parse(\"\n# Calculate Cash Flows on the Fixed-Leg of the Inflation Swap\n- We calculate the cash flows on the fixed leg of the inflation swap from the inflation swap rates.\n- Recall that the fixed leg of the inflation swap has cash flows \\$(1+f)^T\\$ where \\$f\\$ is the inflation swap rate and \\$T\\$ is the swap tenor.\n- Swap Fixed Leg = \\$\\\\left( 1 + f(T)\\\\right)^{T} -1\\$\n  - For example, for the swap expiring on 2008-01-15, we have \n\\$(1+0.019628)^{1.2795}-1 = $(roundmult((1+0.019628)^1.2795-1,1e-6))\\$\n\")\n\n# \u2554\u2550\u2561 342d06f0-bc3f-4da4-bfdc-7314fa927e9f\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 4ca9e7a6-fa37-4df5-8941-4aa65af2822f\nbegin\n\tdf3 = copy(df2)\n\tdf3.SwapFixedLeg = ((1 .+ df2.InflationSwapRates).^(df2.SwapTenors) .-1)\n\tdf3\nend\n\n# \u2554\u2550\u2561 0ae821cb-0914-42ac-95a2-b8f4d99e154a\nmd\"\"\"\n# Combine TIPS and Inflation Swap\n\"\"\"\n\n# \u2554\u2550\u2561 9b5dafa0-1ea6-482f-98a8-f2b8bfb7e9f0\nbegin\n\tdf4 = copy(df3)\n\tdf4.TIPSAndInflationSwap = (0.01875/2*100).* (1 .+ df4.SwapFixedLeg)\n\tdf4.TIPSAndInflationSwap[end] = (100 + (0.01875/2*100)) .* (1+df4.SwapFixedLeg[end])  \n\tdf4\nend\n\n# \u2554\u2550\u2561 77b7030e-0cad-4d86-a70e-7896e0e45da7\nmd\"\"\"\n# 2.4. Add Treasury Note\n\"\"\"\n\n# \u2554\u2550\u2561 b00301af-7ee3-4266-9878-44616cba7ecd\nbegin\n\tdf5 = copy(df4)\n\tselect!(df5,:Date,:TIPSAndInflationSwap)\n\tdf5.TNote = 2.125*ones(length(df5.Date))\n\tdf5.TNote[end] = df5.TNote[end]+100\n\tdf5\nend\n\n# \u2554\u2550\u2561 a89a9f56-8c94-47e0-9588-48ce9dcd97a4\nmd\"\"\"\n# 2.5. Add Treasury STRIPS\n\"\"\"\n\n# \u2554\u2550\u2561 81506749-c1b6-4dac-beeb-d551698afb34\nbegin\n\tdf6 = copy(df5)\n\tdf6.STRIPS = NaN.*ones(length(df6.Date))\n\tselect!(df6, :Date, :TIPSAndInflationSwap, :STRIPS, :TNote)\n\t#df5.STRIPS = [98.6650,96.3040,94.1340,92.0890,90.1020,88.1370,86.2470,84.4300,82.6180,80.9500,78.6780,76.8630,75.0760,73.3200,71.5890,69.9060,68.1530,66.5100]\n\tdf6\nend\n\n# \u2554\u2550\u2561 9c8fe1c2-b148-4ded-b5ae-d9c512479a50\nmd\"\"\"\n# 2.6. Calculate the STRIPS Positions\n\"\"\"\n\n# \u2554\u2550\u2561 932d7811-ebb5-4807-8270-afc8905e7b58\nbegin\n\tdf6.STRIPS = df6.TNote .- df6.TIPSAndInflationSwap\n\tdf6\nend\n\n# \u2554\u2550\u2561 3115e3a5-886e-4315-9c05-36cf9fe7692e\nmd\"\"\"\n# 2.7. Calculate the Market Price fo the STRIPS Positions\n\"\"\"\n\n# \u2554\u2550\u2561 73acdb08-b4d2-4e29-8004-1a936e72982f\nmd\"\"\"\n- The column STRIPS shows the market prices of Treasury STRIPS from the Bloomberg system on 10/5/2006.\n  - _Note: To match exactly the dates of the TIPS coupon cash flows, we interpolate market prices of the Treasury STRIPS. For details, see Fleckenstein, Longstaff, and Lustig (2014)._\n\"\"\"\n\n# \u2554\u2550\u2561 89d87124-23a2-4241-ad68-73b5be83106b\nbegin\n\tdf7 = copy(df6)\n\tselect!(df7,:Date,)\n\tdf7.STRIPS = [98.6650,96.3040,94.1340,92.0890,90.1020,88.1370,86.2470,84.4300,82.6180,80.9500,78.6780,76.8630,75.0760,73.3200,71.5890,69.9060,68.1530,66.5100]\n\tdf7\nend\n\n# \u2554\u2550\u2561 803017a7-afa2-439a-96c5-ff42b49e25a3\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 2c8ba6e2-83a9-4c4d-98a5-a1325fba7736\nbegin\n\tdf8 = copy(df6)\n\tdf8.STRIPSPrices = df8.STRIPS .* df7.STRIPS./100\n\tselect!(df8,:Date, :STRIPS, :STRIPSPrices)\n\tPxSTRIPS = sum(df8.STRIPSPrices)\n\tdf8\nend\n\n# \u2554\u2550\u2561 138a92e6-98bc-49d9-8ac1-2d75f41daaeb\nMarkdown.parse(\"\n- The total price of entering the STRIPS positions is $(roundmult(PxSTRIPS,1e-6)).\n\")\n\n# \u2554\u2550\u2561 29568bae-ed4f-4486-9c1f-6dbb234b5842\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 26534a5e-a19b-4ef3-b4b3-1ab10700c601\nmd\"\"\"\n- Is there an arbitrage?\n- The TIPS has a market price of $97.0428. \n- A long position in the TIPS, inflation swaps and STRIPS costs \\$97.0428 - \\$1.1887 \n$P_{\\text{TIPS+Swap}} = $95.8541$\n\n\"\"\"\n\n# \u2554\u2550\u2561 d95020e1-680f-44e5-9049-05202d247399\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 c998015a-5254-48f3-b6f7-630c2e098656\nmd\"\"\"\n- The Treasury note has a market price of \n$P_{\\text{Treasury}}= $98.0734$\n\n- This is a puzzle because the price of two securities with exactly the same cash flows are different. \n- The Treasury note is __\\$2.22__ more expensive than its equivalent TIPS.\n- Thus, by buying the TIPS and entering into the inflation swaps and STRIPS and by taking a short we have an arbitrage.\n\"\"\"\n\n# \u2554\u2550\u2561 569d7b82-ee46-42e2-b24e-e22272769452\nmd\"\"\"\n# Details\n- Note that the TIPS and the Treasury have slightly different maturity dates.\n  - The TIPS has maturity date on July 15, 2015.\n  - The Treasury note has maturity date on August 15, 2015.\n- To adjust for this small difference, we take the following approach.\n  - Step 1: We calculate the yield to maturity of the TIPS.\n  - Step 2: We calculate the price of the Treasury note by discounting the Treasury note cash flows by the yield computed in the previous step.\n\"\"\"\n\n# \u2554\u2550\u2561 b92d4665-551a-4824-8314-a59edc73fbad\nmd\"\"\"\n#\n\"\"\"\n\n# \u2554\u2550\u2561 c87e3c57-f976-44b0-af02-c2f188a35db9\nmd\"\"\"\n- The price calculated in step 2 is the price of the TIPS that can be compared to the price of the Treasury note because it accounts for the difference in th timing of cash flows.\n- The yield of the TIPS turns out to be 4.97% and the market price of the Treasury note if its yield is 4.97% turns out to be 95.4583.\n- Thus the Treasury note is \\$98.07337 - \\$95.4583 = \\$2.61507 more expensive than the equivalent TIPS. The arbitrage mispricing is \\$2.61507.\n\"\"\"\n\n# \u2554\u2550\u2561 53c77ef1-899d-47c8-8a30-ea38380d1614\nmd\"\"\"\n# Wrap-Up\n\"\"\"\n\n# \u2554\u2550\u2561 670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\nbegin\n\thtml\"\"\"\n\t<fieldset>      \n        <legend>Outline</legend>      \n\t\t<br>    \n\t\t<input type=\"checkbox\" value=\"\" checked>The U.S. Treasury Bond Puzzle<br><br>\n\t\t<input type=\"checkbox\" value=\"\" checked>TIPS-Treasury Trading Strategy<br><br>\n\t</fieldset>      \n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 7c16c6a5-170d-4e4b-b18b-30db69eb9f6c\nmd\"\"\"\n##\n\"\"\"\n\n# \u2554\u2550\u2561 2ee2c328-5ebe-488e-94a9-2fce2200484c\nmd\"\"\"\n# Reading: \nFleckenstein, Matthias, Francis A. Longstaff, and Hanno Lustig, 2014, The TIPS\u2013Treasury Bond Puzzle, Journal of Finance, Volume 69, Issue 5, 2014, 2151\u20132197.\n\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nDates = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\nHTTP = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\nHypertextLiteral = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nLaTeXStrings = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\nLogging = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\nPlots = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPrintf = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\nShortCodes = \"f62ebe17-55c5-4640-972f-b59c0dd11ccf\"\n\n[compat]\nCSV = \"~0.9.11\"\nDataFrames = \"~1.2.2\"\nHTTP = \"~0.9.17\"\nHypertextLiteral = \"~0.9.3\"\nLaTeXStrings = \"~1.3.0\"\nPlots = \"~1.22.4\"\nPlutoUI = \"~0.7.15\"\nShortCodes = \"~0.3.2\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[Adapt]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"84918055d15b3114ede17ac6a7182f68870c16f7\"\nuuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\nversion = \"3.3.1\"\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[Bzip2_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"19a35467a82e236ff51bc17a3a44b69ef35185a2\"\nuuid = \"6e34b625-4abd-537c-b88f-471c36dfa7a0\"\nversion = \"1.0.8+0\"\n\n[[CSV]]\ndeps = [\"CodecZlib\", \"Dates\", \"FilePathsBase\", \"InlineStrings\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"49f14b6c56a2da47608fe30aed711b5882264d7a\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.9.11\"\n\n[[Cairo_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"Fontconfig_jll\", \"FreeType2_jll\", \"Glib_jll\", \"JLLWrappers\", \"LZO_jll\", \"Libdl\", \"Pixman_jll\", \"Pkg\", \"Xorg_libXext_jll\", \"Xorg_libXrender_jll\", \"Zlib_jll\", \"libpng_jll\"]\ngit-tree-sha1 = \"4b859a208b2397a7a623a03449e4636bdb17bcf2\"\nuuid = \"83423d85-b0ee-5818-9007-b63ccbeb887a\"\nversion = \"1.16.1+1\"\n\n[[CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[ColorSchemes]]\ndeps = [\"ColorTypes\", \"Colors\", \"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"a851fec56cb73cfdf43762999ec72eff5b86882a\"\nuuid = \"35d6a980-a343-548e-a6ea-1d62b119f2f4\"\nversion = \"3.15.0\"\n\n[[ColorTypes]]\ndeps = [\"FixedPointNumbers\", 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\"Xorg_libpthread_stubs_jll\"]\ngit-tree-sha1 = \"daf17f441228e7a3833846cd048892861cff16d6\"\nuuid = \"c7cfdc94-dc32-55de-ac96-5a1b8d977c5b\"\nversion = \"1.13.0+3\"\n\n[[Xorg_libxkbfile_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libX11_jll\"]\ngit-tree-sha1 = \"926af861744212db0eb001d9e40b5d16292080b2\"\nuuid = \"cc61e674-0454-545c-8b26-ed2c68acab7a\"\nversion = \"1.1.0+4\"\n\n[[Xorg_xcb_util_image_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"0fab0a40349ba1cba2c1da699243396ff8e94b97\"\nuuid = \"12413925-8142-5f55-bb0e-6d7ca50bb09b\"\nversion = \"0.4.0+1\"\n\n[[Xorg_xcb_util_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libxcb_jll\"]\ngit-tree-sha1 = \"e7fd7b2881fa2eaa72717420894d3938177862d1\"\nuuid = \"2def613f-5ad1-5310-b15b-b15d46f528f5\"\nversion = \"0.4.0+1\"\n\n[[Xorg_xcb_util_keysyms_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"d1151e2c45a544f32441a567d1690e701ec89b00\"\nuuid = \"975044d2-76e6-5fbe-bf08-97ce7c6574c7\"\nversion = \"0.4.0+1\"\n\n[[Xorg_xcb_util_renderutil_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"dfd7a8f38d4613b6a575253b3174dd991ca6183e\"\nuuid = \"0d47668e-0667-5a69-a72c-f761630bfb7e\"\nversion = \"0.3.9+1\"\n\n[[Xorg_xcb_util_wm_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xcb_util_jll\"]\ngit-tree-sha1 = \"e78d10aab01a4a154142c5006ed44fd9e8e31b67\"\nuuid = \"c22f9ab0-d5fe-5066-847c-f4bb1cd4e361\"\nversion = \"0.4.1+1\"\n\n[[Xorg_xkbcomp_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_libxkbfile_jll\"]\ngit-tree-sha1 = \"4bcbf660f6c2e714f87e960a171b119d06ee163b\"\nuuid = \"35661453-b289-5fab-8a00-3d9160c6a3a4\"\nversion = \"1.4.2+4\"\n\n[[Xorg_xkeyboard_config_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Xorg_xkbcomp_jll\"]\ngit-tree-sha1 = \"5c8424f8a67c3f2209646d4425f3d415fee5931d\"\nuuid = \"33bec58e-1273-512f-9401-5d533626f822\"\nversion = \"2.27.0+4\"\n\n[[Xorg_xtrans_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"79c31e7844f6ecf779705fbc12146eb190b7d845\"\nuuid = \"c5fb5394-a638-5e4d-96e5-b29de1b5cf10\"\nversion = \"1.4.0+3\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[Zstd_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6\"\nuuid = \"3161d3a3-bdf6-5164-811a-617609db77b4\"\nversion = \"1.5.0+0\"\n\n[[libass_jll]]\ndeps = [\"Artifacts\", \"Bzip2_jll\", \"FreeType2_jll\", \"FriBidi_jll\", \"HarfBuzz_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"5982a94fcba20f02f42ace44b9894ee2b140fe47\"\nuuid = \"0ac62f75-1d6f-5e53-bd7c-93b484bb37c0\"\nversion = \"0.15.1+0\"\n\n[[libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[libfdk_aac_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"daacc84a041563f965be61859a36e17c4e4fcd55\"\nuuid = \"f638f0a6-7fb0-5443-88ba-1cc74229b280\"\nversion = \"2.0.2+0\"\n\n[[libpng_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Zlib_jll\"]\ngit-tree-sha1 = \"94d180a6d2b5e55e447e2d27a29ed04fe79eb30c\"\nuuid = \"b53b4c65-9356-5827-b1ea-8c7a1a84506f\"\nversion = \"1.6.38+0\"\n\n[[libvorbis_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Ogg_jll\", \"Pkg\"]\ngit-tree-sha1 = \"b910cb81ef3fe6e78bf6acee440bda86fd6ae00c\"\nuuid = \"f27f6e37-5d2b-51aa-960f-b287f2bc3b7a\"\nversion = \"1.3.7+1\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\n[[x264_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"4fea590b89e6ec504593146bf8b988b2c00922b2\"\nuuid = \"1270edf5-f2f9-52d2-97e9-ab00b5d0237a\"\nversion = \"2021.5.5+0\"\n\n[[x265_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"ee567a171cce03570d77ad3a43e90218e38937a9\"\nuuid = \"dfaa095f-4041-5dcd-9319-2fabd8486b76\"\nversion = \"3.5.0+0\"\n\n[[xkbcommon_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"ece2350174195bb31de1a63bea3a41ae1aa593b6\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"0.9.1+5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25004712d6fd-efb3-4f46-9160-c82797a3b5e4\n# \u255f\u250041d7b190-2a14-11ec-2469-7977eac40f12\n# \u255f\u2500f0545c67-5cfd-438f-a9ef-92c35ebaefa4\n# 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\u255f\u25001480c3c4-7ef1-413b-9cd2-5bbb9f61a2e2\n# \u255f\u25002430b2d2-0343-4316-ae84-d7cc59759f00\n# \u255f\u250014e6f7ed-2a81-4101-a1b5-af627df7c805\n# \u255f\u2500c6df6e6e-1c72-4fbb-b73c-7c084bc96b69\n# \u255f\u25007c923c8c-9067-42af-b103-391c05bbeb98\n# \u255f\u2500ed31f60a-8b09-4222-8b81-333eccb33c02\n# \u255f\u2500004efec5-23d3-4940-abff-9024820daf65\n# \u255f\u2500fb7ad01e-1a12-4f06-a1a3-d1003cfea04a\n# \u255f\u250006b6deff-fb0a-4ed2-80ee-aee2cee7e0bf\n# \u255f\u25006edf15e1-fcad-42ed-a777-ab5be39e4a41\n# \u255f\u2500cded3cca-c9ac-4d7d-b9dd-4c5497aa955e\n# \u255f\u2500d4dab771-87f1-4fa2-b6a2-900a51af4586\n# \u255f\u25008f3a465e-af54-4b0e-9210-87c140629f2f\n# \u255f\u2500d61da00e-9d8c-43e8-89c5-e70220dc761a\n# \u255f\u2500049debc7-b1d5-4908-9b27-70344995a4c4\n# \u255f\u2500abda6c84-f124-4d61-bb40-0517e28fbcf6\n# \u255f\u250072721c2c-f227-469a-91d9-dbec158c2fa7\n# \u255f\u2500312e0229-445e-42d4-8a0d-709c590c1add\n# \u255f\u250097d9ba4b-880f-464e-bd94-7b72a86094b7\n# \u255f\u2500bf6e7d0a-7e33-47b1-9240-a0af661057f5\n# \u255f\u2500d16e82d8-faa9-444f-8db7-6f442c5e5fd4\n# \u255f\u2500dc265679-85cd-4ff1-a728-6353f36e3a9c\n# \u255f\u2500e36dc8d3-3b4e-46c4-9259-007f75068591\n# \u255f\u25004ac1725a-d9e5-4587-9b03-ae8b7379ed56\n# \u255f\u250000c3ea64-bbe5-4d7a-a1b2-1197b1455bec\n# \u255f\u2500432dae1b-89b1-4e8a-b59a-98e00391b368\n# \u255f\u2500d1eb23f3-7142-493d-b2e6-d4f844e2bfc6\n# \u255f\u2500398086e4-4a4c-45f6-a32d-cb5d8847233c\n# \u255f\u25007c15360a-9f7f-45d0-8059-0420dc46b231\n# \u255f\u2500cf85e64a-d5c4-4d04-b6ba-04ac003f7289\n# \u255f\u2500a5a0c420-f28f-4ec4-a9e2-092a02cec9a8\n# \u255f\u2500448fa11f-4bcb-4b62-9d1b-703d9a12786b\n# \u255f\u2500d5ebca67-32b9-461d-9d4f-3285517c3cd2\n# \u255f\u2500d5df1ae7-9cc5-453b-b889-cd57dcc671c4\n# \u255f\u2500d9aa27ee-eba3-4dee-abeb-7fce49536793\n# \u255f\u25003d5a3bc4-f290-4d68-a5d4-ecdafc967537\n# \u255f\u2500afc4f565-7171-4b47-93e0-ffa66386dedf\n# \u255f\u25007e50530e-a0ad-4709-9e89-bc7cc8575328\n# \u255f\u25005d53718b-a542-47ea-a4bf-7d94320d1fde\n# \u255f\u25006245fadd-8dda-4e59-b619-3b0cfe8fbafe\n# \u255f\u2500bb34c7c8-4d9a-46c8-a1e4-38e01c3c404a\n# \u255f\u250031ef4b01-b5ed-47cc-9ff1-6d92ff7bd2b4\n# \u255f\u2500d670b261-f427-4498-a8df-fcdacdb14d3e\n# \u255f\u2500cba8fcb7-970e-47ef-9b0f-d4396e89aa66\n# \u255f\u2500c6ae22ec-78cd-4a00-b36d-806439f9d550\n# \u255f\u2500fde6eaee-dd08-483a-87c6-6778300c311d\n# \u255f\u25003936d198-5f63-4192-b33b-b1ef9941964d\n# \u255f\u2500b415d609-0528-4e5f-9d43-bf8cd0e25688\n# \u255f\u25002bbcaee7-34a7-4596-ad38-5c6239dd08e6\n# \u255f\u250089516578-5de4-47b3-8629-89b590b15b19\n# \u255f\u2500fbb7288e-806e-4fec-a9f6-cd79b66d33fd\n# \u255f\u25009cd0acc2-6e2b-42ac-9f92-1692d8b55d80\n# \u255f\u250060913481-91ed-4959-9b01-9fe363916045\n# \u255f\u25004e4f2c67-c86e-4e44-9eae-2c39f025c66d\n# \u255f\u2500083a0cb1-a597-46ca-84c0-e5b7ce23b53a\n# \u255f\u2500ad56ba09-a69c-40db-8caf-53efdd93719a\n# \u255f\u2500e871ef6e-f4da-4cef-aecf-4103335ebead\n# \u255f\u250080176747-6460-4840-992f-394e1c1112b2\n# \u255f\u2500b0b36b4c-9d44-42cf-a1c2-a97fad25b84e\n# \u255f\u2500903a6516-2919-4078-89cc-264a726e07ca\n# \u255f\u250014623ccd-d970-4046-8e1c-89d1652e3bcf\n# \u255f\u2500932c04a6-64e3-4d73-a083-c836fbafdbb3\n# \u255f\u2500fb2d12f6-ebd2-4be5-9506-cdaa52cfaa1e\n# \u255f\u2500d7e38a5e-2c88-4728-b1cc-d98a5e1a6ec1\n# \u255f\u25004950323f-1c91-4d63-b333-410c69650352\n# \u255f\u25008d63c259-14d0-45db-bd85-93b71e963dbb\n# \u255f\u250092383772-0e20-4302-89ca-ccb914a05a33\n# \u255f\u250003f0f3c5-cd48-4dec-b379-0d4245235f25\n# \u255f\u25000e8e476f-9933-4fd0-9ad6-b01f7918a67b\n# \u255f\u25007848436a-46e6-4456-8402-f34e3d16126b\n# \u255f\u25005cc070bc-e0f6-4158-a3e2-800acac61361\n# \u255f\u25009d03e1f6-4ad5-4b99-9ab8-cc92bff5f224\n# \u255f\u2500a59495a8-a940-47b1-b240-685afbd62d23\n# \u255f\u2500db457538-d9a1-4acd-be2a-d89e71c43864\n# \u255f\u2500d72909ff-0908-4759-91be-ad420d7c47e3\n# \u255f\u25005ec83302-d399-49a5-86fe-514f30d76c7b\n# \u255f\u25009860adbf-82c4-4961-a0e4-dd5c51037430\n# \u255f\u250068cdb801-d6ca-4fe1-8510-5d66ac78e95e\n# \u255f\u2500f4dc2755-9f58-4598-8216-e682f59f0632\n# \u255f\u25009b1b0269-d975-446a-a168-3db32d0fdb95\n# \u255f\u250043e04f32-6175-4a07-b128-427566259646\n# \u255f\u2500294dde1d-2ce0-40d6-b309-b50f93b15c70\n# \u255f\u250059bdffc6-b6fd-45f7-98dc-aec5aab8b9a7\n# \u255f\u2500342d06f0-bc3f-4da4-bfdc-7314fa927e9f\n# \u255f\u25004ca9e7a6-fa37-4df5-8941-4aa65af2822f\n# \u255f\u25000ae821cb-0914-42ac-95a2-b8f4d99e154a\n# \u255f\u25009b5dafa0-1ea6-482f-98a8-f2b8bfb7e9f0\n# \u255f\u250077b7030e-0cad-4d86-a70e-7896e0e45da7\n# \u255f\u2500b00301af-7ee3-4266-9878-44616cba7ecd\n# \u255f\u2500a89a9f56-8c94-47e0-9588-48ce9dcd97a4\n# \u255f\u250081506749-c1b6-4dac-beeb-d551698afb34\n# \u255f\u25009c8fe1c2-b148-4ded-b5ae-d9c512479a50\n# \u255f\u2500932d7811-ebb5-4807-8270-afc8905e7b58\n# \u255f\u25003115e3a5-886e-4315-9c05-36cf9fe7692e\n# \u255f\u250073acdb08-b4d2-4e29-8004-1a936e72982f\n# \u255f\u250089d87124-23a2-4241-ad68-73b5be83106b\n# \u255f\u2500803017a7-afa2-439a-96c5-ff42b49e25a3\n# \u255f\u2500138a92e6-98bc-49d9-8ac1-2d75f41daaeb\n# \u255f\u25002c8ba6e2-83a9-4c4d-98a5-a1325fba7736\n# \u255f\u250029568bae-ed4f-4486-9c1f-6dbb234b5842\n# \u255f\u250026534a5e-a19b-4ef3-b4b3-1ab10700c601\n# \u255f\u2500d95020e1-680f-44e5-9049-05202d247399\n# \u255f\u2500c998015a-5254-48f3-b6f7-630c2e098656\n# \u255f\u2500569d7b82-ee46-42e2-b24e-e22272769452\n# \u255f\u2500b92d4665-551a-4824-8314-a59edc73fbad\n# \u255f\u2500c87e3c57-f976-44b0-af02-c2f188a35db9\n# \u255f\u250053c77ef1-899d-47c8-8a30-ea38380d1614\n# \u255f\u2500670e45a3-9d28-47ae-a6b6-a1b1c67a0a4c\n# \u255f\u25007c16c6a5-170d-4e4b-b18b-30db69eb9f6c\n# \u255f\u25002ee2c328-5ebe-488e-94a9-2fce2200484c\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "d0d0ac198eb725821ed09b20b3f24d12ad479e63", "size": 86875, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/Lecture_04_TIPSTreasury.jl", "max_stars_repo_name": "fleckenstein-m/TIPS_Treas_W2022", "max_stars_repo_head_hexsha": "3c713cc4e652ad5de4708672b258eb450140d9be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "notebooks/Lecture_04_TIPSTreasury.jl", "max_issues_repo_name": "fleckenstein-m/TIPS_Treas_W2022", "max_issues_repo_head_hexsha": "3c713cc4e652ad5de4708672b258eb450140d9be", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "notebooks/Lecture_04_TIPSTreasury.jl", "max_forks_repo_name": "fleckenstein-m/TIPS_Treas_W2022", "max_forks_repo_head_hexsha": "3c713cc4e652ad5de4708672b258eb450140d9be", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4649460709, "max_line_length": 352, "alphanum_fraction": 0.6895309353, "num_tokens": 40447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.14033625308549463, "lm_q1q2_score": 0.06633462800433655}}
{"text": "\"\"\"\n    count_nucleotides(strand)\n\nThe count of each nucleotide within `strand` as a dictionary.\n\nInvalid strands raise a `DomainError`.\n\n\"\"\"\nfunction count_nucleotides(strand)\n    res = Dict('A' => 0, 'C' => 0, 'G' => 0, 'T' => 0)\n    for c in strand\n        if c in ['A','C','G','T']\n            res[c] = get(res,c,0)+1\n        else \n            throw(DomainError(c, \"expects A,C,G, or T\"))\n        end\n    end\n    return res\nend\n", "meta": {"hexsha": "221d6bc3a4c2c457328cc21d4295d7ccf50995bb", "size": 432, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "nucleocount/nucleotide-count.jl", "max_stars_repo_name": "stepinski/julia-in", "max_stars_repo_head_hexsha": "08c82e2de236ead3b1eb356d60a04c61e275d4fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-01T11:27:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-01T11:27:13.000Z", "max_issues_repo_path": "nucleocount/nucleotide-count.jl", "max_issues_repo_name": "stepinski/julia-init", "max_issues_repo_head_hexsha": "e70b08c72c4db3677483480e72a9f79827a41fff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "nucleocount/nucleotide-count.jl", "max_forks_repo_name": "stepinski/julia-init", "max_forks_repo_head_hexsha": "e70b08c72c4db3677483480e72a9f79827a41fff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.6, "max_line_length": 61, "alphanum_fraction": 0.5439814815, "num_tokens": 137, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.1403362512877889, "lm_q1q2_score": 0.06633462715459075}}
{"text": "\"\"\"\nThe LittleManComputer module used to assemble, disassamble and run code\nwritten for the imaginary little man computer. This is a computer used\nfor educational purposed, to teach beginner assembly code programming.\n\n# Example\n    code = assemble(\"counter.lmc\")\n    inputs = [4, 3]\n    outputs = simulate!(code, inputs)\n       \nRuns code stored in \"counter.lmc\" file with input `[4, 3]`.\n\"\"\"\nmodule LittleManComputer\n\n\nconst opcodes = Dict(\n    \"ADD\" => 100,\n    \"SUB\" => 200,\n    \"STA\" => 300,\n    \"LDA\" => 500,\n    \"BRA\" => 600,\n    \"BRZ\" => 700,\n    \"BRP\" => 800,\n    \"INP\" => 901,\n    \"OUT\" => 902,\n    \"HLT\" => 000\n)\n\nconst mnemonics = Dict(value => key for (key, value) in opcodes)\n\ninclude(\"assembler.jl\")\ninclude(\"disassembler.jl\")\ninclude(\"simulator.jl\")\n\nend # module\n", "meta": {"hexsha": "d53f6f51f5439b2e681249d484312034036354b8", "size": 780, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LittleManComputer.jl", "max_stars_repo_name": "FourMInfo/LittleManComputer.jl", "max_stars_repo_head_hexsha": "602f21bcfb7cc8bda6f3868d4603672ceab59afd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-03-28T16:28:44.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-19T02:11:56.000Z", "max_issues_repo_path": "src/LittleManComputer.jl", "max_issues_repo_name": "FourMInfo/LittleManComputer.jl", "max_issues_repo_head_hexsha": "602f21bcfb7cc8bda6f3868d4603672ceab59afd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-08-01T20:55:02.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-10T17:44:58.000Z", "max_forks_repo_path": "src/LittleManComputer.jl", "max_forks_repo_name": "FourMInfo/LittleManComputer.jl", "max_forks_repo_head_hexsha": "602f21bcfb7cc8bda6f3868d4603672ceab59afd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-01-11T15:50:56.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-15T07:29:03.000Z", "avg_line_length": 21.6666666667, "max_line_length": 71, "alphanum_fraction": 0.6320512821, "num_tokens": 236, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.1540575665754383, "lm_q1q2_score": 0.06626745379201175}}
{"text": "#!/usr/local/bin/julia\n\nfunction linear_search(array::Array{Int64,1}, key::Int64)\n    for i in array\n        if i == key\n            return true\n    return false\n        end\n    end\nend\n\n# Benchmarks\n# julia> @timev linear_search([1:100000000...], 10000000)\n#  11.617622 seconds (100.00 M allocations: 3.033 GiB, 54.06% gc time)\n# elapsed time (ns): 11617622167\n# gc time (ns):      6280118240\n# bytes allocated:   3256270960\n# pool allocs:       99999500\n# non-pool GC allocs:9\n# malloc() calls:    2\n# realloc() calls:   15\n# GC pauses:         6\n# full collections:  4\n# true                          ", "meta": {"hexsha": "be70af1eb3efce61788fbfb3f4697fd6bf373fd5", "size": 604, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "searching/linear-search/linear-search.jl", "max_stars_repo_name": "rahulkp220/Algorithms-Datastructure-Refresher", "max_stars_repo_head_hexsha": "0c42f523832687db9cbabca4e3fa0a8d4c8401d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-10-14T15:49:14.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-16T18:21:57.000Z", "max_issues_repo_path": "searching/linear-search/linear-search.jl", "max_issues_repo_name": "rahulkp220/Algorithms-Datastructure-Refresher", "max_issues_repo_head_hexsha": "0c42f523832687db9cbabca4e3fa0a8d4c8401d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "searching/linear-search/linear-search.jl", "max_forks_repo_name": "rahulkp220/Algorithms-Datastructure-Refresher", "max_forks_repo_head_hexsha": "0c42f523832687db9cbabca4e3fa0a8d4c8401d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-03-29T23:21:41.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-29T23:21:41.000Z", "avg_line_length": 25.1666666667, "max_line_length": 70, "alphanum_fraction": 0.6009933775, "num_tokens": 202, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41869690935568665, "lm_q2_score": 0.15817434481176185, "lm_q1q2_score": 0.06622710931204538}}
{"text": "ex = :(a+b*c+1)\n\ntypeof(ex)\n\n:(a + b*c + 1) ==\n    Meta.parse(\"a + b*c + 1\") ==\n    Expr(:call, :+, :a, Expr(:call, :*, :b, :c), 1)\n\nex = quote\n    x = 1\n    y = 2\n    x + y\nend\n\ntypeof(ex)\n\n:(1 + 2)\n\neval(ans)\n\nex = :(a + b)\n\neval(ex)\n\na = 1; b = 2;\n\neval(ex)\n\nex = :(x = 1)\n\nx\n\neval(ex)\n\nx\n\na = 1;\n\nex = Expr(:call, :+, a, :b)\n\na = 0; b = 2;\n\neval(ex)\n", "meta": {"hexsha": "5d13ddee453eb7cdb454ed8b0236493d207fabf6", "size": 354, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lectures/org/julia_macros/expressions.jl", "max_stars_repo_name": "phrb/PDP", "max_stars_repo_head_hexsha": "ac4abb13c20c91b075f55dde80b8919a8eee0748", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-05-24T15:43:40.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-10T23:18:24.000Z", "max_issues_repo_path": "lectures/org/julia_macros/expressions.jl", "max_issues_repo_name": "phrb/PDP", "max_issues_repo_head_hexsha": "ac4abb13c20c91b075f55dde80b8919a8eee0748", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lectures/org/julia_macros/expressions.jl", "max_forks_repo_name": "phrb/PDP", "max_forks_repo_head_hexsha": "ac4abb13c20c91b075f55dde80b8919a8eee0748", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-05-29T19:52:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-04T15:39:13.000Z", "avg_line_length": 8.0454545455, "max_line_length": 51, "alphanum_fraction": 0.395480226, "num_tokens": 164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.14608724518943897, "lm_q1q2_score": 0.06621577472604194}}
{"text": "\"\"\"\nThis module provides an implementation of NetMSA algorithm in Julia, which\ncan be used for multiple sequence alignment.\n\"\"\"\nmodule NetMSA\n\nusing StatsBase\n\nexport createPeerMatrix, matrixalignment\n\n@doc raw\"\"\"\nStore the position of a given particle. Position ``x_{s_{i}}(r)`` of the particle\n``p_{s_{i}}`` is defined by using the row ``r`` that contains the symbol ``s_i`` as\nwell as locations of the symbol ``s_i`` in the different columns (indexes of the\ncolumns that contain ``s_i`` ) in the row ``r``.\n\"\"\"\nmutable struct Position\n    row::Int64\n    indexes::Vector{Int64}\nend\n\n\"\"\"\nA particle that is used for creating swarms.\n\n# Fields\n- value::Char : Value of the particle, e.g. 'b' or 'c'\n- updated::Int64 : Number of turns till last updated\n- pos::Position : The original position of the particle\n- best::Position : The best local position of the particle\n- bestvalue::Float64 : Best local score\n\"\"\"\nmutable struct Particle\n    value::Char\n    updated::Int64\n    pos::Position\n    best::Position\n    bestvalue::Float64\n\n    \"\"\"\n        Particle(value::Char, pos::Position)\n\n    Initializes the Particle, where the best position is intialized\n    to be the current position.\n    \"\"\"\n    function Particle(value::Char, pos::Position)\n        return new(value, 0, pos, pos, 0.0)\n    end\nend\n\n\"\"\"\n    createPeerMatrix(inputStrings::Vector{String})::Matrix{Union{Missing,Char}}\n\nCreate and return a Peer matrix, containing charachters as elements, where each input sequence,\nprovided in the inputStrings, is represented as a column. Missing values are represented in the\nmatrix by the `missing` keyword.\n\n# Examples\n```jldoctest\njulia> NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n```\n\"\"\"\nfunction createPeerMatrix(inputStrings::Vector{String})::Matrix{Union{Missing,Char}}\n\t\tcol_size = size(inputStrings, 1);\n\t\trow_size = max([length(s) for s in inputStrings]...);\n\t\tpeer_matrix = Matrix{Union{Missing,Char}}(missing, row_size, col_size);\n\t\tfor (i, s) in enumerate(inputStrings)\n\t\t\tpeer_matrix[1:length(s), i] = collect(s);\n\t\tend\n\t\treturn peer_matrix;\nend\n\n\n\"\"\"\n    getposition(value, rowindex, matrix)\n\nReturn the Position (rowindex, [colindex1, colindex2, ...]) of the Particle\nrepresented by `value`, at the `rowindex` in the `matrix`.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.getposition('b', 2, M)\n NetMSA.Position(2, [1, 3, 4])\n```\n\"\"\"\nfunction getposition(value, rowindex, matrix)\n  indexes = findall(i -> i == value, skipmissing(matrix[rowindex, :]))\n  return Position(rowindex, indexes);\nend\n\n\"\"\"\n    mostfrequent(row)\n\nReturn a tuple containing the most frequent element occuring in the `row`, along\nwith its frequency.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.mostfrequent(M[2, :])\n (3, 'b')\n```\n\"\"\"\nfunction mostfrequent(row)\n  counts = countmap(row);\n  delete!(counts, '-');\n  max = isempty(counts) ? 0 : findmax(counts);\n  return max;\nend\n\n\"\"\"\n    aligned(row)::Bool\n\nReturn whether a row is aligned or not.\n\nA row is *aligned* if it only contains different occurrences of the same symbol.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.aligned(M[1, :])\n true\n\n juila> NetMSA.aligned(M[2, :])\n false\n```\n\"\"\"\nfunction aligned(row)::Bool\n  row = Set(row)\n  return (length(row) == 1 && !(missing in row)) || (length(row) == 2 && ('-' in row || missing in row))\nend\n\n\"\"\"\n    aligned(row)::Bool\n\nReturn whether a row is full or not.\n\nAn aligned row r is called full if no gaps (\u2014) are added in the row r .\nThat is, the number of occurrences of the symbol in the row is equal to\nthe number of columns in the matrix.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.full(M[1, :])\n true\n\n juila> NetMSA.full(M[2, :])\n false\n```\n\"\"\"\nfunction full(row)::Bool\n  return length(Set(row)) == 1 && !(missing in Set(row))\nend\n\n@doc raw\"\"\"\n    weight(row; w1=0.25, w2=0.5, w3=1.0)\n\nReturn the weight of the row, calculated as:\n\n```math\nw(r) = \\begin{cases}\n  w_1  \\times  \\frac{x}{c}; & \\text{ if r is not aligned}   \\\\\n  w_2  \\times  \\frac{n_s}{c}; & \\text{ if r is aligned}   \\\\\n  w_3; & \\text{ if r is full}   \\\\\n\\end{cases}\n```\nwhere ``n_s`` is the number of occurrences of the symbol ``s`` in the aligned row ``r``,\nand ``c`` is the total number of columns in the row. The value of ``x`` is equal to zero\nif every symbol in the row ``r`` occurred at most once, otherwise ``x`` is equal to the\nmax number of occurrences (matches) of some symbol in ``r``.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.weight(M[1, :])\n 1.0\n\n juila> NetMSA.weight(M[2, :])\n 0.1875\n```\n\"\"\"\nfunction weight(row; w1=0.25, w2=0.5, w3=1.0)\n  if full(row)\n    return w3;\n    end\n\n  c = length(row);\n  max = mostfrequent(row)[1];\n\n  if aligned(row)\n    return w2 * max / c;\n  else\n    x = max <= 1 ? 0 : max;\n    return w1 * x / c;\n  end\nend\n\n@doc raw\"\"\"\n    objective(M, rowindex; endindex=0)\n\nReturn the objective score of the row, calculated as follows:\n\n```math\nf(x_s(r)) = \\frac{A(r) \\times C(r)}{1 + Gaps(r)} \\times  \\sum_{j=r}^{k} w(j)\n```\nwhere ``A(r)`` is the number of aligned rows in M from ``r`` to the last row,\n``C(r)`` is the maximum number of matched charachters in the current row,\n``Gaps(r)`` is the number of gaps added to the matrix M from row ``r`` to the\nlast row, and ``w(r)`` is the weight of the row ``r``.\n\n\n`endindex` is used to reduce the search area for Gaps, and if it is not provided,\nit would default to `size(M)[1]`.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\n juila> NetMSA.objective(M, 2)\n 2.625\n```\n\"\"\"\nfunction objective(M, rowindex; endindex=0)\n  weights = sum(weight.(eachrow(M[rowindex:end, :])))\n  C = mostfrequent(M[rowindex, :])[1];\n  A = sum(aligned.(eachrow(M))[rowindex:end])\n\n  endindex = endindex == 0 ? size(M)[1] : endindex;\n  if endindex > size(M)[1]\n    throw(ArgumentError(\"endindex exceeds the matrix size\"));\n    end\n\n  counts = countmap(M[rowindex:endindex, :]);\n  Gaps = get(counts, '-', 0);\n\n  return weights * (A * C) / (1 + Gaps)\nend\n\n\"\"\"\n    createswarm(rowindex::Int64, M)\n\nCreate a swarm containing unique Particles in the current row.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njuila> NetMSA.createswarm(2, M)\n2-element Array{NetMSA.Particle,1}:\n NetMSA.Particle('c', 0, NetMSA.Position(2, [2]), NetMSA.Position(2, [2]), 0.0)\n NetMSA.Particle('b', 0, NetMSA.Position(2, [1, 3, 4]), NetMSA.Position(2, [1, 3, 4]), 0.0)\n```\n\"\"\"\nfunction createswarm(rowindex::Int64, M)\n  unique = Set(skipmissing(M[rowindex, :]))\n  swarm = Vector{Particle}(undef, length(unique))\n  for (i, c) in enumerate(unique)\n    swarm[i] = Particle(c, getposition(c, rowindex, M))\n  end\n  return swarm\nend\n\nfunction criteria3(p::Particle, newindex::Int, M)\n  return length(p.pos.indexes) != length(getposition(p.value, newindex, M).indexes)\nend\n\nfunction criteria2(p::Particle; threshold::Int=5)\nreturn p.updated > threshold;\nend\n\n\"\"\"\n    stopcriteria(p::Particle, newindex, M; threshold::Int=5, debug=false)\n\nCheck whether a stopping criteria has been met. Two stopping criteria\nare checked in this function:\n  - Criteria 2: If a particle hasn't updated its best score in the last `threshold` turns.\n  - Criteria 3: If a particle moves to a new row which already contains the same symbol as that of the particle.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njuila> p = NetMSA.Particle('b', NetMSA.getposition('b', 2, M));\nNetMSA.Particle('b', 0, Main.NetMSA.Position(2, [1, 3, 4]), Main.NetMSA.Position(2, [1, 3, 4]), 0.0)\n\njulia> NetMSA.stopcriteria(p, 3, M; debug=true)\n\"Terminating because of criteria 3\"\ntrue\n```\n\"\"\"\nfunction stopcriteria(p::Particle, newindex, M; threshold::Int=5, debug=false)\n  c3 = criteria3(p, newindex, M);\n  c2 = criteria2(p; threshold=threshold);\n  if debug && c3\n    display(\"Terminating because of criteria 3\")\n  elseif debug && c2\n    display(\"Terminating because of criteria 2\")\n  end\n  return c3 || c2;\nend\n\nfunction remove_missing_rows(M)\n  return M[[length(Set(skipmissing(r))) != 0 for r in eachrow(M)], :]\nend\n\n\"\"\"\n    flydown(p, M; stride=1)\n\nFly down the given particle by `stride`.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njuila> p = NetMSA.Particle('b', NetMSA.getposition('b', 2, M));\nNetMSA.Particle('b', 0, Main.NetMSA.Position(2, [1, 3, 4]), Main.NetMSA.Position(2, [1, 3, 4]), 0.0)\n\njulia> NetMSA.flydown(p, M)\n9\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n '-'  'c'      '-'      '-'\n 'b'  'b'      'b'      'b'\n 'c'  'c'      'c'      'c'\n 'b'  'f'      'h'      'b'\n 'c'  'g'      'i'      'c'\n 'd'  missing  'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njulia> NetMSA.flydown(p, M; stride=3)\n11\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n '-'  'c'      '-'      '-'\n '-'  'b'      '-'      '-'\n '-'  'c'      '-'      '-'\n 'b'  'f'      'b'      'b'\n 'c'  'g'      'c'      'c'\n 'b'  missing  'h'      'b'\n 'c'  missing  'i'      'c'\n 'd'  missing  'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n```\n\"\"\"\nfunction flydown(p, M; stride=1)\n  notpcols = setdiff(collect(1:size(M, 2)), p.pos.indexes)\n  colsize = size(M, 2)\n  pos = p.pos\n  newrows = fill('-', (stride, colsize))\n  M = vcat(M[1:pos.row - 1, :], reshape(newrows, stride, colsize), M[pos.row:end, :])\n  for i in collect(pos.row + stride:size(M, 1))\n    M[i - stride,notpcols] = M[i, notpcols]\n    M[i, notpcols] .= missing\n  end\n  M = remove_missing_rows(M)\nend\n\n\"\"\"\n    rowalignment(rowindex, M)\n\nReturn Particle with best position that aligns (maximizes the objective score)\nthe given row in the matrix.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njuila> p = NetMSA.Particle('b', NetMSA.getposition('b', 2, M));\nNetMSA.Particle('b', 0, Main.NetMSA.Position(2, [1, 3, 4]), Main.NetMSA.Position(2, [1, 3, 4]), 0.0)\n\njulia> NetMSA.rowalignment(2, M)\nNetMSA.Particle('c', 0, Main.NetMSA.Position(2, [2]), Main.NetMSA.Position(3, [1]), 9.0)\n```\n\"\"\"\nfunction rowalignment(rowindex, M)\n  row = M[rowindex, :];\n  if aligned(row)\n    return nothing;\n  end\n\n  swarm = createswarm(rowindex, M);\n\n  g\u2092 = g = swarm[1];\n  g\u2092value = gvalue = objective(M, rowindex, endindex=rowindex);\n  cols = size(row, 1)\n\n  for p in swarm\n\n    t = rowindex;\n    N = copy(M);\n\n    p.bestvalue = objective(M, rowindex, endindex=t)\n\n    missingp = setdiff(collect(1:size(N, 2)), p.pos.indexes)\n    maxlen = maximum([length(collect(skipmissing(col))) for col in eachcol(N[:, missingp])])\n    criteria1 = maxlen;\n\n\n    while stopcriteria(p, t, N) != true && t < criteria1\n      t += 1;\n      p.updated += 1;\n\n      N = flydown(p, N);\n      score = objective(N, rowindex);\n      if score > p.bestvalue\n        p.bestvalue = score;\n        p.updated = 0;\n      end\n\n      if score > gvalue\n        gvalue = score;\n        g = deepcopy(p);\n        g.best = getposition(p.value, t, N);\n        g.bestvalue = score;\n    end\n\n    end\n  end\n\n  if gvalue == g\u2092value\n    return nothing;\n  end\n\n  return g;\nend\n\n\n\"\"\"\n    matrixalignment(M)\n\nAlign the matrix using NetMSA algorithm.\n\n# Examples\n```jldoctest\njulia> M = NetMSA.createPeerMatrix([\"abcbcdem\", \"acbcfg\", \"abchimn\", \"abcbcjkm\"])\n8\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'      'a'      'a'\n 'b'  'c'      'b'      'b'\n 'c'  'b'      'c'      'c'\n 'b'  'c'      'h'      'b'\n 'c'  'f'      'i'      'c'\n 'd'  'g'      'm'      'j'\n 'e'  missing  'n'      'k'\n 'm'  missing  missing  'm'\n\njuila> NetMSA.matrixalignment(M)\n9\u00d74 Array{Union{Missing, Char},2}:\n 'a'  'a'  'a'  'a'\n 'b'  '-'  'b'  'b'\n 'c'  'c'  'c'  'c'\n 'b'  'b'  '-'  'b'\n 'c'  'c'  '-'  'c'\n 'd'  'f'  'h'  'j'\n 'e'  'g'  'i'  'k'\n 'm'  '-'  'm'  'm'\n '-'  '-'  'n'  '-'\n```\n\"\"\"\nfunction matrixalignment(M)\n  for (index, row) in enumerate(eachrow(M))\n    g = rowalignment(index, M)\n    if !isnothing(g)\n      M = flydown(g, M, stride=g.best.row - g.pos.row)\n    end\n  end\n  replace!(M, missing => '-')\nend\n\nend\n", "meta": {"hexsha": "09b656539bacf17c2fbe4316c44cfa0726e73833", "size": 15293, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NetMSA.jl", "max_stars_repo_name": "aadimator/NetMSA.jl", "max_stars_repo_head_hexsha": "e2a9282010e7b451254133512708654df7b8714f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-08-09T20:30:35.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-09T20:30:35.000Z", "max_issues_repo_path": "src/NetMSA.jl", "max_issues_repo_name": "aadimator/NetMSA.jl", "max_issues_repo_head_hexsha": "e2a9282010e7b451254133512708654df7b8714f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/NetMSA.jl", "max_forks_repo_name": "aadimator/NetMSA.jl", "max_forks_repo_head_hexsha": "e2a9282010e7b451254133512708654df7b8714f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5965217391, "max_line_length": 112, "alphanum_fraction": 0.5639181325, "num_tokens": 5582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.14223188773801163, "lm_q1q2_score": 0.06612382810880499}}
{"text": "module RunMyNotes\n\nexport folder, package\n\nusing Literate, Conda\n\n\"\"\"\n    folder(in, [out])\nRuns all files `*.jl` found in folder `in`, generating a Jupyter `.ipynb` notebook for each,\nsaved in the same folder or in `out` if provided.\n\nKeywords: \n* `html=true` then converts this notebook to HTML too, using `jupyter nbconvert`.\n* `throw=true` exits with an error if any errors were encountered .\n* `all=true` runs everything, `all=false` will skip any `.jl` files which are older than their `.ipynb` versions.\n* 'verbose=true` adds details about time & computer at the end.\n* `credit=true` adds links.\n\"\"\"\nfunction folder(indir::String, outdir::String=indir;\n        html::Bool=true, throw::Bool=true, all::Bool=true, credit::Bool=true, verbose::Bool=true)\n\n    fulldir = normpath(expanduser(indir))\n    fullout = normpath(expanduser(outdir))\n    @assert isdir(fulldir) \"can't find input folder $fulldir\"\n    @assert isdir(fullout) \"can't find output folder $fullout\"\n    \n    olddir = pwd() # should just fix run command instead\n\n    pre = verbose ? appendinfo : identity\n    post = credit ? appendcredit : identity\n\n    list = filter(s->endswith(s,\".jl\"), readdir(fulldir))\n\n    errs = []\n\n    for name in list\n        jlname = joinpath(fulldir, name)\n        nbname = joinpath(fullout, name)[1:end-2] * \"ipynb\"\n        \n        if isfile(nbname) && mtime(nbname) > mtime(jlname)\n            all || continue # all=false means skip files for which .jl hasn't changed\n        end\n        \n        ran = true\n        nb = \"\"\n        try\n            nb = Literate.notebook(jlname, fullout; credit=credit, preprocess=pre, postprocess=post)\n        catch err\n            @error \"error in creating or running notebook from $jlname \\n\" err\n            push!(errs, err)\n            ran = false\n        end\n\n        if html && ran\n            try\n                cd(Conda.PYTHONDIR)\n                run(`./jupyter nbconvert $nb`) # writes to same folder\n            catch err\n                @error \"error in converting notebook $nb \\n\" err\n                push!(errs, err)\n            end\n        end\n    end\n    \n    cd(olddir)\n\n    if throw && length(errs)>0\n        error(\"encountered $(length(errs)) errors executing & converting notebooks, details printed above\")\n    end\n\n    return length(errs)==0 # for @test\nend\n\n\n\"\"\"\n    package(ModuleName)\nThis essentially runs `RunMyNotes.foler(\"~/.julia/dev/ModuleName/notes/\")`.\nChange keyword `sub=\"notes\"` to select a different folder.\n\"\"\"\nfunction package(mod::Module, outdir=nothing; sub::String=\"notes\", kw...)\n\n    indir = joinpath(dirname(pathof(mod)), \"..\", sub) # pathof gives \"...dev/src/RunMyNotes.jl\"\n    if outdir==nothing\n        outdir = indir # these will get expanduser |> normpath\n    end\n\n    return folder(indir, outdir; kw...)\nend\n\nusing Dates, InteractiveUtils\n\nfunction infostring()\n    io = IOBuffer();\n    InteractiveUtils.versioninfo(io)\n    s = String(take!(io))\n    vinfo = join(split(s, \"\\n\")[[1,4,5]],\",\") # Julia Version, OS, CPU\n    return string(DateTime(now()), \",  \", vinfo)\nend\n\nappendinfo(str) = str * string(\"\\n # ----- \\n # *\", infostring(), \"* \") # not entirely happy with \\n here\n\nfunction appendcredit(dict::Dict)\n    str = dict[\"cells\"][end][\"source\"][end]\n    rep = replace(str, \"/fredrikekre/Literate.jl)\" =>\n        \"/fredrikekre/Literate.jl), called by [RunMyNotes](http://github.com/mcabbott/RunMyNotes.jl)\")\n    dict[\"cells\"][end][\"source\"][end] = rep\n    return dict\nend\n\nend # module\n", "meta": {"hexsha": "d00ac871ee67d5463a79cc1aa6be4be8289fa057", "size": 3477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RunMyNotes.jl", "max_stars_repo_name": "UnofficialJuliaMirror/RunMyNotes.jl-8d3555be-c6c7-539f-8b4e-403d0158b766", "max_stars_repo_head_hexsha": "661205a02aa99046195c1bd164da6da92cc060ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/RunMyNotes.jl", "max_issues_repo_name": "UnofficialJuliaMirror/RunMyNotes.jl-8d3555be-c6c7-539f-8b4e-403d0158b766", "max_issues_repo_head_hexsha": "661205a02aa99046195c1bd164da6da92cc060ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/RunMyNotes.jl", "max_forks_repo_name": "UnofficialJuliaMirror/RunMyNotes.jl-8d3555be-c6c7-539f-8b4e-403d0158b766", "max_forks_repo_head_hexsha": "661205a02aa99046195c1bd164da6da92cc060ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:25:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:25:17.000Z", "avg_line_length": 31.3243243243, "max_line_length": 113, "alphanum_fraction": 0.6252516537, "num_tokens": 927, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.34864512179822543, "lm_q2_score": 0.18952109590739918, "lm_q1q2_score": 0.06607560556596835}}
{"text": "function reverseWord(s::String)\n    result::Array{Char} = []\n    cache::Array{Char} = []\n    for c in s\n        c == ' ' && begin\n            append!(result,cache);\n            cache = []\n            push!(result,c);\n            continue\n        end\n\n        cache = append!([c],cache)\n    end\n\n    append!(result,cache);\n    String(result)\nend\n", "meta": {"hexsha": "04342c786e3e2ec5d78a92bab20235839f934502", "size": 345, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "reverse-word/rw.jl", "max_stars_repo_name": "ccqpein/Arithmetic-Exercises", "max_stars_repo_head_hexsha": "748d7ac1313892d47eb0a66a0b7705e6d33b43ad", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2017-07-11T03:07:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-09T06:46:10.000Z", "max_issues_repo_path": "reverse-word/rw.jl", "max_issues_repo_name": "ccqpein/Arithmetic-Exercises", "max_issues_repo_head_hexsha": "748d7ac1313892d47eb0a66a0b7705e6d33b43ad", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "reverse-word/rw.jl", "max_forks_repo_name": "ccqpein/Arithmetic-Exercises", "max_forks_repo_head_hexsha": "748d7ac1313892d47eb0a66a0b7705e6d33b43ad", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-12-06T22:19:33.000Z", "max_forks_repo_forks_event_max_datetime": "2017-12-06T22:19:33.000Z", "avg_line_length": 19.1666666667, "max_line_length": 34, "alphanum_fraction": 0.4753623188, "num_tokens": 88, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.1441488585478077, "lm_q1q2_score": 0.06589573593097578}}
{"text": "#nb # %% slideshow [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n#md # # [Lecture 2 - Types and Functions](@id lecture2)\n#!md # # [Lecture 2 - Types and Functions]\n#\n# **Essential Skills For Computational Biology**  \n#\n# *2020-06-08*  \n#\n# Kevin Bonham, PhD\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## A program is data and operations on data\n\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\nchromosomes = 23 # this is data\n\nfunction diploid(n) \n    return n * 2 # this is an operation\nend\n#-\n#nb %% A slide [code] {\"slideshow\": {\"slide_type\": \"fragment\"}}\ndiploid(chromosomes) # this is a program\n\n##nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## How to run a julia program\n#\n# - From the REPL\n# - From a script (Live demo)\n# - From a file inside VS Code (Live demo)\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# [Placeholder for video link](#)\n", "meta": {"hexsha": "708178d7d33aca1a59e5c1b5fd8c8056a5fceb4e", "size": 949, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/literate/lectures/lecture2.jl", "max_stars_repo_name": "danielle-peterson/BISC195.jl", "max_stars_repo_head_hexsha": "c58e638d7cd816ef3ef900ea26349408b3d9b25d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-12-16T22:45:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T23:23:35.000Z", "max_issues_repo_path": "docs/literate/lectures/lecture2.jl", "max_issues_repo_name": "danielle-peterson/BISC195.jl", "max_issues_repo_head_hexsha": "c58e638d7cd816ef3ef900ea26349408b3d9b25d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-06-05T15:30:36.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-13T18:25:22.000Z", "max_forks_repo_path": "docs/literate/lectures/lecture2.jl", "max_forks_repo_name": "danielle-peterson/BISC195.jl", "max_forks_repo_head_hexsha": "c58e638d7cd816ef3ef900ea26349408b3d9b25d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-06-05T14:59:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-10T00:45:02.000Z", "avg_line_length": 28.7575757576, "max_line_length": 70, "alphanum_fraction": 0.6385669125, "num_tokens": 284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367316191467, "lm_q2_score": 0.14414884935602928, "lm_q1q2_score": 0.06589573386127597}}
{"text": "# This file was generated, do not modify it. # hide\nfigure(figsize=(8,6))\ncm = countmap(y)\nbar([1, 2], [cm[\"Down\"], cm[\"Up\"]])\nxticks([1, 2], [\"Down\", \"Up\"], fontsize=12)\nyticks(fontsize=12)\nylabel(\"Number of occurences\", fontsize=14)\n\nsavefig(joinpath(@OUTPUT, \"ISL-lab-4-bal.svg\")) # hide", "meta": {"hexsha": "4df80b7d1fb23a3849a43d7d71db46b0f6db2cc4", "size": 290, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/isl/lab-4/code/ex8.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "__site/assets/isl/lab-4/code/ex8.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "__site/assets/isl/lab-4/code/ex8.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2222222222, "max_line_length": 54, "alphanum_fraction": 0.6551724138, "num_tokens": 100, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.13477590525944358, "lm_q1q2_score": 0.06580883662415085}}
{"text": "# Simple macro\nmacro hello()\n    return :(\n    for i in 1:3\n        println(\"hello world\")\n    end\n    )\nend\n#=\njulia> macro hello()\n           return :(\n           for i in 1:3\n               println(\"hello world\")\n           end\n           )\n       end\n\njulia> @hello()\nhello world\nhello world\nhello world\n\njulia> @hello\nhello world\nhello world\nhello world\n=#\n\n# -----------------------------------------------------------------------------\n# passing literal argument\nmacro hello(n)\n    return :(\n    for i in 1:$n\n        println(\"hello world\")\n    end\n    )\nend\n#=\njulia> macro hello(n)\n           return :(\n           for i in 1:$n\n               println(\"hello world\")\n           end\n           )\n       end\n@hello (macro with 2 methods)\n\njulia> @hello(2)\nhello world\nhello world\n\njulia> @hello 2\nhello world\nhello world\n=#\n\n# -----------------------------------------------------------------------------\n# Passing expression arguments\nfunction showme(x)\n    @show x\nend\n#=\njulia> a = 1; b = \"hello\"; c = :hello;\n\njulia> function showme(x)\n           @show x\n       end\n\njulia> showme(a);\nx = 1\n\njulia> showme(b);\nx = \"hello\"\n\njulia> showme(c);\nx = :hello\n=#\n\nmacro showme(x)\n    @show x\nend\n#=\njulia> macro showme(x)\n           @show x\n       end\n\njulia> @showme(a);\nx = :a\n\njulia> @showme(b);\nx = :b\n\njulia> @showme(c);\nx = :c\n=#\n\n# -----------------------------------------------------------------------------\n\n# macro expansion\n#=\njulia> @macroexpand @hello 2\n:(for #4#i = 1:2\n      #= REPL[3]:4 =#\n      Main.println(\"hello world\")\n  end)\n=#\n\n# what happens?\nmacro identity(ex)\n    dump(ex)\n    return ex\nend\n#=\njulia> macro identity(ex)\n           dump(ex)\n           return ex\n       end\n=#\n\nfunction foo()\n    return @identity 1 + 2 + 3\nend\n\n#=\njulia> function foo()\n           return @identity 1 + 2 + 3\n       end\nExpr\n  head: Symbol call\n  args: Array{Any}((4,))\n    1: Symbol +\n    2: Int64 1\n    3: Int64 2\n    4: Int64 3\nfoo (generic function with 1 method)\n=#\n\n@code_lowered foo()\n#=\njulia> @code_lowered foo()\nCodeInfo(\n1 \u2500 %1 = 1 + 2 + 3\n\u2514\u2500\u2500      return %1\n)\n=#\n\n# -----------------------------------------------------------------------------\n# manipulating expressions\n\n# example 1\nmacro squared(ex)\n    return :($(ex) * $(ex))\nend\n#=\njulia> macro squared(ex)\n           return :($(ex) * $(ex))\n       end\n@squared (macro with 1 method)\n\njulia> @squared 3\n9\n=#\n\n# does not quite work properly\nfunction foo()\n    x = 2\n    return @squared x\nend\n#=\njulia> function foo()\n           x = 2\n           return @squared x\n       end\n\njulia> foo()\nERROR: UndefVarError: x not defined\n=#\n\n# why?\n@code_lowered foo()\n\n#=\njulia> @code_lowered foo()\nCodeInfo(\n1 \u2500      x = 2\n\u2502   %2 = Main.x * Main.x\n\u2514\u2500\u2500      return %2\n)\n=#\n\n# let's fix it\nmacro squared(ex)\n    return :($(esc(ex)) * $(esc(ex)))\nend\n#=\njulia> macro squared(ex)\n           return :($(esc(ex)) * $(esc(ex)))\n       end\n=#\n\nfunction foo()\n    x = 2\n    return @squared x\nend\n\n#=\njulia> function foo()\n           x = 2\n           return @squared x\n       end\n\njulia> foo()\n4\n=#\n\n# example 2\n#=\njulia> :(sin(x)) |> dump\nExpr\n  head: Symbol call\n  args: Array{Any}((2,))\n    1: Symbol sin\n    2: Symbol x\n\njulia> :(sin(sin(x))) |> dump\nExpr\n  head: Symbol call\n  args: Array{Any}((2,))\n    1: Symbol sin\n    2: Expr\n      head: Symbol call\n      args: Array{Any}((2,))\n        1: Symbol sin\n        2: Symbol x\n=#\n\nmacro compose_twice(ex)\n    @assert ex.head == :call\n    @assert length(ex.args) == 2\n    me = copy(ex)\n    ex.args[2] = me\n    return ex\nend\n@compose_twice(sin(1)) == sin(sin(1))\n\n#=\njulia> macro compose_twice(ex)\n           @assert ex.head == :call\n           @assert length(ex.args) == 2\n           me = copy(ex)\n           ex.args[2] = me\n           return ex\n       end\n\njulia> @compose_twice(sin(1)) == sin(sin(1))\ntrue\n=#\n\n# -----------------------------------------------------------------------------\n\n# macro hygiene\nmacro ntimes(n, ex)\n    quote\n        times = $(esc(n))\n        for i in 1:times\n            $(esc(ex))\n        end\n    end\nend\n#=\njulia> macro ntimes(n, ex)\n           quote\n               times = $(esc(n))\n               for i in 1:times\n                   $(esc(ex))\n               end\n           end\n       end\n=#\n\nfunction foo()\n    times = 0\n    @ntimes 3 println(\"hello world\")\n    println(\"times = \", times)\nend\n#=\njulia> function foo()\n           times = 0\n           @ntimes 3 println(\"hello world\")\n           println(\"times = \", times)\n       end\n\njulia> foo()\nhello world\nhello world\nhello world\ntimes = 0\n=#\n\n# let's expand the macro and see what happens\n@macroexpand(@ntimes 3 println(\"hello world\"))\n\n#=\njulia> @macroexpand(@ntimes 3 println(\"hello world\"))\nquote\n    #= REPL[73]:3 =#\n    #118#times = 3\n    #= REPL[73]:4 =#\n    for #119#i = 1:#118#times\n        #= REPL[73]:5 =#\n        println(\"hello world\")\n    end\nend\n=#\n\n# -----------------------------------------------------------------------------\n# non standard string literal\n\nusing DataFrames\n\n# regex\n#=\njulia> typeof(r\"^hello\")\nRegex\n=#\n\n# our own\n#=\njulia> DataFrame(x1 = rand(Float64, 100000), x2 = rand(Int16, 100000))\n=#\n\n# nmeric data frame non-standard string literal macro\nmacro ndf_str(s)\n    nstr, spec = split(s, \":\")\n    n = parse(Int, nstr) # number of rows\n    types = split(spec, \",\") # column type specifications\n    \n    num_columns = length(types)\n    \n    mappings = Dict(\n    \"f64\"=>Float64, \"f32\"=>Float32,\n    \"i64\"=>Int64, \"i32\"=>Int32, \"i16\"=>Int16, \"i8\"=>Int8)\n    \n    column_types = [mappings[t] for t in types]\n    column_names = [Symbol(\"x$i\") for i in 1:num_columns]\n    \n    return DataFrame([column_names[i] => rand(column_types[i], n)\n        for i in 1:num_columns]...) \nend\n    \nndf\"100000:f64,f32,i16,i8\"\n#=\njulia> ndf\"100000:f64,f32,i16,i8\"\n100000\u00d74 DataFrame\n\u2502 Row \u2502 x1       \u2502 x2        \u2502 x3    \u2502 x4   \u2502\n\u2502     \u2502 Float64  \u2502 Float32   \u2502 Int16 \u2502 Int8 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 1   \u2502 0.160496 \u2502 0.404637  \u2502 6805  \u2502 96   \u2502\n\u2502 2   \u2502 0.994738 \u2502 0.636778  \u2502 23910 \u2502 -59  \u2502\n\u2502 3   \u2502 0.59626  \u2502 0.520906  \u2502 11857 \u2502 -71  \u2502\n\u2502 4   \u2502 0.637577 \u2502 0.152872  \u2502 -3842 \u2502 48   \u2502\n\u2502 5   \u2502 0.396112 \u2502 0.0520195 \u2502 -2919 \u2502 -111 \u2502\n=#\n", "meta": {"hexsha": "065ef96146c8ed5edfdeb4c5caa5139956891624", "size": 6162, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter04/2_macro.jl", "max_stars_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_stars_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 167, "max_stars_repo_stars_event_min_datetime": "2020-02-07T14:38:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T09:33:00.000Z", "max_issues_repo_path": "Chapter04/2_macro.jl", "max_issues_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_issues_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter04/2_macro.jl", "max_forks_repo_name": "Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia", "max_forks_repo_head_hexsha": "ee95448d05c63db8de3a5e9e27c9351cb6cc0e88", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 33, "max_forks_repo_forks_event_min_datetime": "2020-02-14T05:17:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-24T07:38:59.000Z", "avg_line_length": 16.9285714286, "max_line_length": 79, "alphanum_fraction": 0.4920480364, "num_tokens": 1939, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2877678157610531, "lm_q2_score": 0.22815650216092534, "lm_q1q2_score": 0.06565609827853147}}
{"text": "#####################################################\n#\n#         STRUCTURES FOR THE OPTIONS\n#\n#####################################################\nmutable struct Options\n    x_tol::Float64\n    f_tol::Float64\n    g_tol::Float64\n    h_tol::Float64\n    f_calls_limit::Float64\n    g_calls_limit::Float64\n    h_calls_limit::Float64\n    time_limit::Float64\n\n    iterations::Int\n    store_convergence::Bool\n    show_results::Bool\n    debug::Bool\n    search_type::Symbol\n    seed::UInt\nend\n\n\"\"\"\n    Options(;\n        x_tol::Real = 0.0,\n        f_tol::Real = 0.0,\n        g_tol::Real = 0.0,\n        h_tol::Real = 0.0,\n        f_calls_limit::Real = 0,\n        g_calls_limit::Real = 0,\n        h_calls_limit::Real = 0,\n        time_limit::Real = Inf,\n        iterations::Int = 1,\n        store_convergence::Bool = false,\n        debug::Bool = false,\n        seed = rand(UInt)\n    )\n\n\n`Options` stores common settings for metaheuristics such as the maximum number of iterations\ndebug options, maximum number of function evaluations, etc.\n\nMain properties:\n\n- `x_tol` tolerance to the true minimizer if specified in `Information`.\n- `f_tol` tolerance to the true minimum if specified in `Information`.\n- `f_calls_limit` is the maximum number of function evaluations limit.\n- `time_limit` is the maximum time that `optimize` can spend in seconds.\n- `iterations` is the maximum number iterationn permited.\n- `store_convergence` if `true`, then push the current `State` in `State.convergence` at each generation/iteration\n- `debug` if `true`, then `optimize` function reports the current `State` (and interest information) for each iterations.\n- `seed` non-negative integer for the random generator seed.\n\n# Example\n\n```jldoctest\njulia> options = Options(f_calls_limit = 1000, debug=true, seed=1)\nOptions(0.0, 0.0, 0.0, 0.0, 1000.0, 0.0, 0.0, 0, false, true, true, :minimize, 0x0000000000000001)\n\njulia> f(x) = sum(x.^2)\nf (generic function with 1 method)\n\njulia> bounds = [  -10.0 -10 -10; # lower bounds\n                    10.0  10 10 ] # upper bounds\n2\u00d73 Array{Float64,2}:\n -10.0  -10.0  -10.0\n  10.0   10.0   10.0\n\njulia> state = optimize(f, bounds, ECA(options=options))\n[ Info: Initializing population...\n[ Info: Starting main loop...\n+=========== RESULT ==========+\n| Iter.: 1\n| f(x) = 6.97287\n| solution.x = [-2.3628796262231875, -0.6781207370770752, -0.9642728360479853]\n| f calls: 42\n| Total time: 0.0004 s\n+============================+\n\n...\n\n[ Info: Stopped since call_limit was met.\n+=========== RESULT ==========+\n| Iter.: 47\n| f(x) = 1.56768e-08\n| solution.x = [-2.2626761322304715e-5, -9.838697194048792e-5, 7.405966506272336e-5]\n| f calls: 1000\n| Total time: 0.0313 s\n+============================+\n```\n\n\"\"\"\nfunction Options(;\n    x_tol = 0.0,\n    f_tol = 0.0,\n    g_tol = 0.0,\n    h_tol = 0.0,\n    f_calls_limit = 0.0,\n    g_calls_limit = 0.0,\n    h_calls_limit = 0.0,\n    iterations::Int = 0,\n    time_limit::Float64 = Inf,\n    store_convergence = false,\n    show_results = true,\n    debug = false,\n    search_type = :minimize,\n    seed = rand(UInt)\n)\n\n\n    Options(\n        promote(x_tol, f_tol, g_tol, h_tol)...,\n        promote(f_calls_limit, g_calls_limit, h_calls_limit, time_limit)...,\n        promote(iterations)...,\n\n        # Results options\n        promote(store_convergence, show_results, debug)...,\n        Symbol(search_type),\n        UInt(seed)\n    )\n\nend\n\n", "meta": {"hexsha": "8096d41e60c720dcdf4055136849f4f09bba3d37", "size": 3377, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core/options.jl", "max_stars_repo_name": "pitmonticone/Metaheuristics.jl", "max_stars_repo_head_hexsha": "d429ff51cc921e7166d44e1c15d69f9041b8a0cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 89, "max_stars_repo_stars_event_min_datetime": "2018-03-07T07:11:08.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T12:57:16.000Z", "max_issues_repo_path": "src/core/options.jl", "max_issues_repo_name": "pitmonticone/Metaheuristics.jl", "max_issues_repo_head_hexsha": "d429ff51cc921e7166d44e1c15d69f9041b8a0cd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2021-01-30T23:03:41.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T15:45:15.000Z", "max_forks_repo_path": "src/core/options.jl", "max_forks_repo_name": "pitmonticone/Metaheuristics.jl", "max_forks_repo_head_hexsha": "d429ff51cc921e7166d44e1c15d69f9041b8a0cd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2021-06-08T10:06:13.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T05:15:35.000Z", "avg_line_length": 27.016, "max_line_length": 121, "alphanum_fraction": 0.6082321587, "num_tokens": 1004, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.14608725262486594, "lm_q1q2_score": 0.06565053531880738}}
{"text": "\"\"\"\n$(SIGNATURES)\nSets the number of threads that will be used by BLAS library. \n!Notes: Set the thread number with env JULIA_NUM_THREADS to your desired number of threads. \nFor example: `JULIA_NUM_THREADS=16 julia`\n\n# Arguments\n- `nthread` : desired number of threads \n\n# Output: \nShows number of threads set. \n\n\"\"\"\nfunction set_blas_threads(nthread)\n    LinearAlgebra.BLAS.set_num_threads(nthread)\n    core_nums = ccall((:openblas_get_num_threads64_, Base.libblas_name), Cint, ())\n    println(\"Number of threads using: $core_nums\")\nend\n", "meta": {"hexsha": "c6c7567b912465044b8cf5517e939c7807899525", "size": 538, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "GregFa/LiteQTL.jl", "max_stars_repo_head_hexsha": "11533b519ba8266c9b4f72a0316f236e580e26ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-14T10:39:18.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-14T10:39:18.000Z", "max_issues_repo_path": "src/util.jl", "max_issues_repo_name": "GregFa/LiteQTL.jl", "max_issues_repo_head_hexsha": "11533b519ba8266c9b4f72a0316f236e580e26ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2021-02-14T05:02:09.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-26T18:27:44.000Z", "max_forks_repo_path": "src/util.jl", "max_forks_repo_name": "GregFa/LiteQTL.jl", "max_forks_repo_head_hexsha": "11533b519ba8266c9b4f72a0316f236e580e26ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-19T15:56:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-19T15:56:12.000Z", "avg_line_length": 28.3157894737, "max_line_length": 92, "alphanum_fraction": 0.7527881041, "num_tokens": 140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.13117323564948066, "lm_q1q2_score": 0.06558661782474033}}
{"text": "\"\"\"\nFunctions to run quantum error correction simulations and merge/read/write output data.\n\"\"\"\nmodule App\n\n# imports\nusing ..Model\nusing ..PauliTools:bsp, pack, weight\nusing JSON\nusing Random:AbstractRNG, GLOBAL_RNG, MersenneTwister\nusing Statistics:var\n\n# exports\nexport RunResult, qec_merge, qec_read, qec_run, qec_run_once, qec_write\n\n@doc raw\"\"\"\n    qec_run_once(\n        code, error_model, decoder, p::Real, rng::AbstractRNG=GLOBAL_RNG\n    ) -> RunResult\n\nExecute a stabilizer code error-decode-recovery (ideal) simulation and return run result.\n\nThe parameters `code`, `error_model` and `decoder` should be concrete subtypes or duck-typed\nimplementations of [`StabilizerCode`](@ref), [`ErrorModel`](@ref) and [`Decoder`](@ref),\nrespectively.\n\nThe simulation algorithm is as follows:\n\n1. ``S \u2190`` `stabilizers(code)`\n2. ``L \u2190`` `logicals(code)`\n3. ``e \u2190`` `generate(error_model, code, p, rng)`\n4. ``y \u2190 S \u2299 e``\n5. `decode_result` ``\u2190`` `decode(decoder, code,` ``y```; kwargs...)`\n6. ``r \u2190`` `decode_result.recovery`\n7. sanity check: ``S \u2299 (r \u2295 e) = 0``\n8. `logical_commutations` ``\u2190 L \u2299 (r \u2295 e)``\n8. `success` ``\u2190 L \u2299 (r \u2295 e) = 0``\n\nwhere ``\u2295`` denotes element-wise exclusive-or, and ``\u2299`` is defined in\n[`PauliTools.bsp`](@ref bsp).\n\nThe `kwargs` passed to [`decode`](@ref) include `error_model`, `p` and `error`; most\ndecoders will ignore these parameters. The [`decode`](@ref) method returns a\n[`DecodeResult`](@ref). If `decode_result.success` and/or\n`decode_result.logical_commutations` are specified, they override the values of `success`\nand `logical_commutations`, irrespective of whether `decode_result.recovery` is specified or\nnot. The value `decode_result.custom_values` is passed through in the run result.\n\nSee also [`RunResult`](@ref).\n\n# Examples\n```jldoctest\njulia> using Qecsim.BasicModels, Qecsim.GenericModels, Random\n\njulia> rng = MersenneTwister(6);  # use random seed for reproducible result\n\njulia> qec_run_once(FiveQubitCode(), DepolarizingErrorModel(), NaiveDecoder(), 0.2, rng)\nRunResult{Nothing}(false, 2, Bool[1, 0], nothing)\n```\n\"\"\"\nfunction qec_run_once(code, error_model, decoder, p::Real, rng::AbstractRNG=GLOBAL_RNG)\n    error = generate(error_model, code, p, rng)\n    @debug \"qec_run_once: error=$(error)\" error\n    syndrome = bsp(stabilizers(code), error)\n    @debug \"qec_run_once: syndrome=$(syndrome)\" syndrome\n    ctx = Dict(:error_model => error_model, :p => p, :error => error)\n    result = decode(decoder, code, syndrome; ctx...)\n    @debug \"qec_run_once: result=$(result)\" result\n    success, l_commutations = _resolve_decoding(result.recovery, result.success,\n        result.logical_commutations, code, error)\n    @debug \"qec_run_once: success=$(success)\"\n    @debug \"qec_run_once: logical_commutations=$(l_commutations)\"\n    return RunResult(success, weight(error), l_commutations, result.custom_values)\nend\n\n# resolve tuple (success, logical_commutations)\nfunction _resolve_decoding(::Nothing, success, logical_commutations, code, error)\n    # recovery is null, so just return overrides\n    return (success, logical_commutations)\nend\nfunction _resolve_decoding(recovery, success, logical_commutations, code, error)\n    # recovery is not null, so evaluate success and logical_commutations\n    recovered = xor.(error, recovery)\n    s_comms = bsp(stabilizers(code), recovered)\n    l_comms = bsp(logicals(code), recovered)\n    s_commutes = !any(s_comms)\n    l_commutes = !any(l_comms)\n    if !s_commutes\n        @warn \"RECOVERY DOES NOT RETURN TO CODESPACE\" pack(error) pack(recovery)\n    end\n    # derived overrides\n    dsuccess = isnothing(success) ? s_commutes && l_commutes : success\n    dl_comms = isnothing(logical_commutations) ? l_comms : logical_commutations\n    return (dsuccess, dl_comms)\nend\n\n\"\"\"\n    RunResult(\n        success::Bool,\n        error_weight::Int,\n        logical_commutations::Union{Nothing,BitVector}\n        custom_values::Union{Nothing,Vector}\n    )\n\nConstruct a run result as returned by [`qec_run_once`](@ref).\n\n# Examples\n```jldoctest\njulia> r = RunResult(false, 2, BitVector([0, 1]), [1.2, 3.1])\nRunResult{Vector{Float64}}(false, 2, Bool[0, 1], [1.2, 3.1])\n\njulia> r.success, r.error_weight, r.logical_commutations, r.custom_values\n(false, 2, Bool[0, 1], [1.2, 3.1])\n```\n\"\"\"\nstruct RunResult{T <: Union{Nothing,Vector}}\n    success::Bool\n    error_weight::Int\n    logical_commutations::Union{Nothing,BitVector}\n    custom_values::T\nend\n# equality methods TODO: write equality macro\nfunction Base.hash(a::RunResult, h::UInt)\n    h = hash(typeof(a), h)\n    h = hash(a.success, h)\n    h = hash(a.error_weight, h)\n    h = hash(a.logical_commutations, h)\n    h = hash(a.custom_values, h)\n    return h\nend\nfunction Base.:(==)(a::RunResult, b::RunResult)\n    return (a.success == b.success\n        && a.error_weight == b.error_weight\n        && a.logical_commutations == b.logical_commutations\n        && a.custom_values == b.custom_values)\nend\nfunction Base.isequal(a::RunResult, b::RunResult)\n    return (isequal(a.success, b.success)\n        && isequal(a.error_weight, b.error_weight)\n        && isequal(a.logical_commutations, b.logical_commutations)\n        && isequal(a.custom_values, b.custom_values))\nend\n\n\n@doc raw\"\"\"\n    qec_run(\n        code, error_model, decoder, p::Real, random_seed=nothing;\n        max_runs::Union{Integer,Nothing}=nothing,\n        max_failures::Union{Integer,Nothing}=nothing\n    ) -> Dict\n\nExecute stabilizer code error-decode-recovery (ideal) simulations many times and return\naggregated run data, see [`qec_run_once`](@ref) for details of a single run.\n\nThe parameters `code`, `error_model` and `decoder` should be concrete subtypes or duck-typed\nimplementations of [`StabilizerCode`](@ref), [`ErrorModel`](@ref) and [`Decoder`](@ref),\nrespectively.\n\nSimulations are run one or more times as determined by `max_runs` and `max_failures`. If\n`max_runs` and/or `max_failures` are specified, stop after `max_runs` runs or `max_failures`\nfailures, whichever happens first. If neither is specified, stop after one run.\n\nThe returned aggregated data has the following format:\n\n    Dict(\n        :code => \"5-qubit\"              # label(code)\n        :n_k_d => (5, 1, 3)             # nkd(code)\n        :time_steps => 1                # always 1 for ideal simulations\n        :error_model => \"Depolarizing\"  # label(error_model)\n        :decoder => \"Naive\"             # label(decoder)\n        :error_probability => 0.1       # p\n        :measurement_error_probability => 0.0   # always 0.0 for ideal simulations\n        :n_run => 100                   # count of runs\n        :n_success => 92                # count of successful recoveries\n        :n_fail => 8                    # count of failed recoveries\n        :n_logical_commutations => [5, 6]   # count of logical_commutations\n        :custom_totals => nothing       # sum of custom_values\n        :error_weight_total => 55       # sum of error_weight over n_run runs\n        :error_weight_pvar => 0.4075    # pvariance of error_weight over n_run runs\n        :logical_failure_rate => 0.08   # n_fail / n_run\n        :physical_error_rate => 0.11    # error_weight_total / n_k_d[1] / time_steps / n_run\n        :wall_time => 0.00253906        # wall-time for run in fractional seconds\n    )\n\n# Examples\n```jldoctest; filter = r\":wall_time +=> \\d*\\.?\\d*\"\njulia> using Qecsim.BasicModels, Qecsim.GenericModels\n\njulia> seed = 7;\n\njulia> data = qec_run(FiveQubitCode(), DepolarizingErrorModel(), NaiveDecoder(), 0.1, seed;\n           max_runs=100);\n\u250c Info: qec_run: starting\n\u2502   code = Qecsim.BasicModels.BasicCode([\"XZZXI\", \"IXZZX\", \"XIXZZ\", \"ZXIXZ\"], [\"XXXXX\"], [\"ZZZZZ\"], (5, 1, 3), \"5-qubit\")\n\u2502   error_model = Qecsim.GenericModels.DepolarizingErrorModel()\n\u2502   decoder = Qecsim.GenericModels.NaiveDecoder(10)\n\u2502   p = 0.1\n\u2502   random_seed = 7\n\u2502   max_runs = 100\n\u2514   max_failures = nothing\n[ Info: qec_run: rng=MersenneTwister(7)\n[ Info: qec_run: complete: data=Dict{Symbol, Any}(:error_weight_pvar => 0.4075000000000001, :time_steps => 1, :n_logical_commutations => [5, 6], :error_weight_total => 55, :wall_time => 0.002539058, :n_k_d => (5, 1, 3), :error_model => \"Depolarizing\", :physical_error_rate => 0.11, :measurement_error_probability => 0.0, :error_probability => 0.1, :n_success => 92, :logical_failure_rate => 0.08, :custom_totals => nothing, :code => \"5-qubit\", :decoder => \"Naive\", :n_fail => 8, :n_run => 100)\n\njulia> data\nDict{Symbol, Any} with 17 entries:\n  :error_weight_pvar             => 0.4075\n  :time_steps                    => 1\n  :n_logical_commutations        => [5, 6]\n  :error_weight_total            => 55\n  :wall_time                     => 0.00253906\n  :n_k_d                         => (5, 1, 3)\n  :error_model                   => \"Depolarizing\"\n  :physical_error_rate           => 0.11\n  :measurement_error_probability => 0.0\n  :error_probability             => 0.1\n  :n_success                     => 92\n  :logical_failure_rate          => 0.08\n  :custom_totals                 => nothing\n  :code                          => \"5-qubit\"\n  :decoder                       => \"Naive\"\n  :n_fail                        => 8\n  :n_run                         => 100\n```\n\"\"\"\nfunction qec_run(\n    code, error_model, decoder, p::Real, random_seed=nothing;\n    max_runs::Union{Integer,Nothing}=nothing, max_failures::Union{Integer,Nothing}=nothing,\n)\n    # derived defaults\n    dmax_runs = isnothing(max_runs) && isnothing(max_failures) ? 1 : max_runs\n\n    @info \"qec_run: starting\" code = code error_model = error_model decoder = decoder #=\n        =# p = p random_seed = random_seed max_runs = dmax_runs max_failures = max_failures\n    wall_time_start = time_ns()\n\n    rng = MersenneTwister(random_seed)\n    @info \"qec_run: rng=$(rng)\"\n\n    # run counters\n    n_run = n_success = 0\n    error_weights = Int[]\n    if !isnothing(dmax_runs) sizehint!(error_weights, dmax_runs) end\n    n_logical_commutations::Union{Nothing,Vector{Int}} = nothing  # promote to Int[]\n    custom_totals = nothing\n\n    # do runs\n    while ((isnothing(dmax_runs) || n_run < dmax_runs)\n        && (isnothing(max_failures) || (n_run - n_success) < max_failures)\n    )\n        data = qec_run_once(code, error_model, decoder, p, rng)\n        n_run += 1\n        n_success += data.success ? 1 : 0\n        push!(error_weights, data.error_weight)\n        if n_run == 1  # initialize vector sums\n            n_logical_commutations = _null_vec_copy(data.logical_commutations)\n            custom_totals = _null_vec_copy(data.custom_values)\n        else  # update vector sums\n            _null_vec_add!(n_logical_commutations, data.logical_commutations)\n            _null_vec_add!(custom_totals, data.custom_values)\n        end\n    end\n\n    # prepare data\n    runs_data = Dict(\n        :code => label(code),\n        :n_k_d => nkd(code),\n        :time_steps => 1,\n        :error_model => label(error_model),\n        :decoder => label(decoder),\n        :error_probability => p,\n        :measurement_error_probability => 0.0,\n        :n_run => n_run,\n        :n_success => n_success,\n        :n_fail => n_run - n_success,\n        :n_logical_commutations => n_logical_commutations,\n        :custom_totals => custom_totals,\n        :error_weight_total => sum(error_weights),\n        :error_weight_pvar => var(error_weights; corrected=false),\n        :logical_failure_rate => 0.0,  # set below\n        :physical_error_rate => 0.0,  # set below\n        :wall_time => 0.0,  # set below\n    )\n    # set rate statistics\n    _rate_statistics!(runs_data)\n\n    runs_data[:wall_time] = (time_ns() - wall_time_start) / 10^9\n    @info \"qec_run: complete: data=$(runs_data)\"\n\n    return runs_data\nend\n\n# return deepcopy of val (or nothing if null)\n_null_vec_copy(::Nothing) = nothing\n_null_vec_copy(vec::AbstractVector) = deepcopy(vec)\n# sum scalars or vcat arrays\n_sum_vec_el(el1, el2) = el1 + el2\n_sum_vec_el(el1::AbstractArray, el2::AbstractArray) = vcat(el1, el2)\n# update vec1 with vec2 (sum scalars / vcat arrays) (or nothing if both null)\n_null_vec_add!(::Nothing, ::Nothing) = nothing\n_null_vec_add!(vec1::AbstractVector, vec2::AbstractVector) = vec1 .= _sum_vec_el.(vec1,vec2)\n\n# set :logical_failure_rate and :physical_error_rate as defined in qec_run\nfunction _rate_statistics!(runs_data)\n    # extract data\n    time_steps = runs_data[:time_steps]\n    n_run = runs_data[:n_run]\n    n_fail = runs_data[:n_fail]\n    error_weight_total = runs_data[:error_weight_total]\n    nkd = runs_data[:n_k_d][1]\n    # add rate statistics\n    runs_data[:logical_failure_rate] = n_fail / n_run\n    runs_data[:physical_error_rate] = error_weight_total / nkd[1] / time_steps / n_run\n    return nothing\nend\n\n@doc raw\"\"\"\n    qec_merge(data...) -> Vector{Dict}\n\nMerge simulation run data.\n\nRun data is expected in the format specified by [`qec_run`](@ref). Merged data is grouped\nby: `(:code, :n_k_d, :error_model, :decoder, :error_probability, :time_steps,\n:measurement_error_probability)`. The scalar values: `:n_run`, `:n_success`,\n`:n_fail`, `:error_weight_total` and `:wall_time` are summed. The vector value:\n`:n_logical_commutations` is summed element-wise. The vector value: `:custom_totals` is\nsummed element-wise for scalar elements and concatenated along dimension 1 (`vcat`) for\narray elements. The values: `:logical_failure_rate` and `:physical_error_rate` are\nrecalculated. The value `:error_weight_pvar` is *not* currently recalculated and is\ntherefore omitted.\n\n# Examples\n```jldoctest; filter = r\":wall_time +=> \\d*\\.?\\d*\"\njulia> using Qecsim.BasicModels, Qecsim.GenericModels, Logging\n\njulia> code, error_model, decoder = FiveQubitCode(), BitFlipErrorModel(), NaiveDecoder();\n\njulia> data = Dict[];\n\njulia> with_logger(NullLogger()) do # disable logging for brevity in doctest\n           push!(data, qec_run(code, error_model, decoder, 0.08, 19; max_runs=100))\n           push!(data, qec_run(code, error_model, decoder, 0.08, 23; max_runs=100))\n       end;\n\njulia> qec_merge(data...)\n1-element Vector{Dict{Symbol, Any}}:\n Dict(:measurement_error_probability => 0.0, :error_probability => 0.08, :time_steps => 1, :error_weight_total => 83, :n_logical_commutations => [11, 3], :wall_time => 0.0028351720000000004, :n_k_d => (5, 1, 3), :error_model => \"Bit-flip\", :n_success => 189, :logical_failure_rate => 0.055\u2026)\n```\n\"\"\"\nfunction qec_merge(data...)\n    # define group keys, value keys and zero values\n    grp_keys = (:code, :n_k_d, :error_model, :decoder, :error_probability, :time_steps,\n                :measurement_error_probability)\n    scalar_val_keys = (:n_run, :n_fail, :n_success, :error_weight_total, :wall_time)\n    scalar_zero_vals = (0, 0, 0, 0, 0.0)\n    vector_val_keys = (:n_logical_commutations, :custom_totals,)\n    # map of groups to sums\n    grps_to_scalar_sums = Dict()\n    grps_to_vector_sums = Dict()\n    # iterate through single list from given data lists\n    for runs_data in data\n        # define defaults, create new data with defaults overwritten by data\n        # support for 0.10 and 0.15 files:\n        defaults_0_16 = Dict(:time_steps => 1, :measurement_error_probability => 0.0)\n        # support for pre-1.0b6 files:\n        defaults_1_0b6 = Dict(:n_logical_commutations => nothing, :custom_totals => nothing)\n        # runs_data = Dict(defaults_0_16..., defaults_1_0b6..., runs_data...)\n        runs_data = merge(defaults_0_16, defaults_1_0b6, runs_data)\n        # extract group from data\n        grp_id = Tuple(runs_data[k] for k in grp_keys)\n        # scalars: e.g. (10, 6, 4, 256, 10.34) extracted from data\n        scalar_vals = Tuple(runs_data[k] for k in scalar_val_keys)\n        scalar_sums = get(grps_to_scalar_sums, grp_id, scalar_zero_vals)  # get sums\n        scalar_sums = Tuple(sum(x) for x in zip(scalar_sums, scalar_vals))  # update sums\n        grps_to_scalar_sums[grp_id] = scalar_sums  # put sums\n        # vectors: e.g. ([2, 5], [3, 8, 2], nothing) extracted from data\n        vector_vals = Tuple(_null_vec_copy(runs_data[k]) for k in vector_val_keys)\n        if haskey(grps_to_vector_sums, grp_id)  # sums already in map\n            vector_sums = grps_to_vector_sums[grp_id]  # get sums\n            vector_sums = Tuple(_null_vec_add!(s, v)  # update sums\n                                for (s, v) in zip(vector_sums, vector_vals))\n        else  # sums not in map yet\n            vector_sums = vector_vals  # update sums\n        end\n        grps_to_vector_sums[grp_id] = vector_sums  # put sums\n    end\n    # flatten grps_to_scalar_sums and grps_to_vector_sums\n    merged_data_list = [Dict(\n        zip((grp_keys..., scalar_val_keys..., vector_val_keys...),\n            (grp_id..., scalar_sums..., grps_to_vector_sums[grp_id]...))\n        ) for (grp_id, scalar_sums) in grps_to_scalar_sums]\n    # update rate statistics\n    for runs_data in merged_data_list\n        _rate_statistics!(runs_data)\n    end\n    return merged_data_list\nend\n\n@doc raw\"\"\"\n    qec_write(io::IO, data...)\n    qec_write(filename::AbstractString, data...)\n\nWrite simulation run data to the given I/O stream or file.\n\nRun data is expected in the format specified by [`qec_run`](@ref) and written as a JSON\narray of objects using default JSON encoding. No checking of the format of the given data is\nperformed. The file version of this method will refuse to overwrite an existing file,\ninstead attempting to log the unwritten data and throwing an exception.\n\nThe JSON format is compatible with the format used by the Python package\n[qecsim](https://github.com/qecsim/qecsim).\n\nSee also [`qec_read`](@ref).\n\"\"\"\nfunction qec_write(io::IO, data...)\n    JSON.print(io, data)\nend\nfunction qec_write(filename::AbstractString, data...)\n    try\n        # throw error if file exists\n        !ispath(filename) || error(\"file \\\"$filename\\\" exists; refusing to overwrite\")\n        open(io -> qec_write(io, data...), filename, \"w\")\n    catch\n        @error \"Recovered data: $(JSON.json(data))\"\n        rethrow()\n    end\nend\n\n@doc raw\"\"\"\n    qec_read(io::IO) -> Vector{Dict{Symbol,Any}}\n    qec_read(filename::AbstractString) -> Vector{Dict{Symbol,Any}}\n\nRead simulation run data from the given I/O stream or file.\n\nRun data is expected in the format written by [`qec_write`](@ref) (i.e. a JSON\narray of objects using default JSON encoding of the format specified by [`qec_run`](@ref)).\nThe read data is converted to the specified return type as follows: dictionary keys are\nconverted to symbols, `:n_k_d` entries are converted to tuples, and vector types are\ninferred from their elements. If this conversion fails, an exception is thrown.\n\nThe JSON format is compatible with the format used by the Python package\n[qecsim](https://github.com/qecsim/qecsim).\n\nSee also [`qec_write`](@ref).\n\"\"\"\nfunction qec_read(io::IO)::Vector{Dict{Symbol,Any}}\n    # load into expected raw format\n    raw_data::Vector{Dict{String,Any}} = JSON.parse(io)\n    # convert type of keys to Symbol\n    data = [Dict(Symbol(k) => v for (k, v) in d) for d in raw_data]\n    # convert type of values\n    for d in data\n        if haskey(d, :n_k_d)  # convert type of :n_k_d to Tuple\n            d[:n_k_d] = Tuple(d[:n_k_d])\n        end\n        for (k, v) in d  # narrow type of vector values from Any\n            if isa(v, AbstractVector)\n                d[k] = [v...]\n            end\n        end\n    end\n    return data\nend\nfunction qec_read(filename::AbstractString)::Vector{Dict{Symbol,Any}}\n    return open(io -> qec_read(io), filename, \"r\")\nend\n\nend\n", "meta": {"hexsha": "390cfaf2b4063aa2d81bb388b1a5498daff0909a", "size": 19369, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/App.jl", "max_stars_repo_name": "dkt29/Qecsim.jl", "max_stars_repo_head_hexsha": "8670ea0be7ab8e543d4293fc43473b36b7495756", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/App.jl", "max_issues_repo_name": "dkt29/Qecsim.jl", "max_issues_repo_head_hexsha": "8670ea0be7ab8e543d4293fc43473b36b7495756", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-07-28T01:07:50.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-28T01:07:50.000Z", "max_forks_repo_path": "src/App.jl", "max_forks_repo_name": "dkt29/Qecsim.jl", "max_forks_repo_head_hexsha": "8670ea0be7ab8e543d4293fc43473b36b7495756", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3867521368, "max_line_length": 493, "alphanum_fraction": 0.6705560432, "num_tokens": 5409, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.13117322546005347, "lm_q1q2_score": 0.06558661273002674}}
{"text": "\nPkg.update()\nPkg.add(\"PyPlot\")\nPkg.add(\"StatsFuns\")\nPkg.add(\"SpecialFunctions\")\nPkg.add(\"Distributions\")\nPkg.add(\"PDMats\")\nPkg.add(\"ProgressMeter\")\nPkg.add(\"DataFrames\")\nPkg.add(\"HDF5\")\nPkg.add(\"JLD\")\nPkg.add(\"IJulia\")\n", "meta": {"hexsha": "1b835174b91290a9c5964bbe3ef75f22642e3737", "size": 220, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docker/add_packages.jl", "max_stars_repo_name": "triwave33/BayesBook", "max_stars_repo_head_hexsha": "86967e67381b01181ae3b3ad28d46011140dcc5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 158, "max_stars_repo_stars_event_min_datetime": "2017-10-19T13:33:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T01:59:21.000Z", "max_issues_repo_path": "docker/add_packages.jl", "max_issues_repo_name": "triwave33/BayesBook", "max_issues_repo_head_hexsha": "86967e67381b01181ae3b3ad28d46011140dcc5b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2018-01-29T08:36:11.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-01T14:32:34.000Z", "max_forks_repo_path": "docker/add_packages.jl", "max_forks_repo_name": "triwave33/BayesBook", "max_forks_repo_head_hexsha": "86967e67381b01181ae3b3ad28d46011140dcc5b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 31, "max_forks_repo_forks_event_min_datetime": "2017-10-20T00:12:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-06T20:34:22.000Z", "avg_line_length": 16.9230769231, "max_line_length": 27, "alphanum_fraction": 0.7045454545, "num_tokens": 79, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3923368301671084, "lm_q2_score": 0.16667541296674696, "lm_q1q2_score": 0.06539290319016726}}
{"text": "# ---\n# title: 203. Remove Linked List Elements\n# id: problem203\n# author: zhwang\n# date: 2022-02-16\n# difficulty: Easy\n# categories: Linked List\n# link: <https://leetcode.com/problems/remove-linked-list-elements/description/>\n# hidden: true\n# ---\n# \n# Remove all elements from a linked list of integers that have value **_val_**.\n# \n# **Example:**\n# \n#     \n#     \n#     Input:  1->2->6->3->4->5->6, _**val**_ = 6\n#     Output: 1->2->3->4->5\n#     \n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction remove_elements!(head::Union{ListNode, Nothing}, value::Int)::Union{ListNode, Nothing}\n    cur = fake_head = ListNode()\n    fake_head.next = head\n    while !isnothing(cur.next)\n        if cur.next.val == value\n            cur.next = cur.next.next\n        else\n            cur = cur.next\n        end\n    end\n    fake_head.next\nend\n## @lc code=end\n## @lc test=start\n@testset \"203.remove-linked-list-elements.jl\" begin\n    @test remove_elements!(ListNode{Int}([1,2,6,3,4,5,6]), 6) == ListNode{Int}([1,2,3,4,5])\n    @test remove_elements!(nothing, 1) === nothing\n    @test remove_elements!(ListNode{Int}([7,7,7,7]), 7) === nothing \nend\n## @lc test=end\n\n\n## @lc info=start\n# link: [solution to 203](https://leetcode-cn.com/problems/remove-linked-list-elements/solution/203-yi-chu-lian-biao-yuan-su-python-juli-awfr/)\n## @lc info=end", "meta": {"hexsha": "9e3b0d446b1ef00b0284ca35ed9dd3775c741295", "size": 1326, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-02/203.remove-linked-list-elements.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/submited/date-02/203.remove-linked-list-elements.jl", "max_issues_repo_name": "RexWzh/leetcode_note.jl", "max_issues_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/submited/date-02/203.remove-linked-list-elements.jl", "max_forks_repo_name": "RexWzh/leetcode_note.jl", "max_forks_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.52, "max_line_length": 143, "alphanum_fraction": 0.6282051282, "num_tokens": 416, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39233683016710835, "lm_q2_score": 0.1666754067576692, "lm_q1q2_score": 0.06539290075411737}}
{"text": "# ---\n# title: 83. Remove Duplicates from Sorted List\n# id: problem83\n# author: zhwang\n# date: 2022-02-17\n# difficulty: Easy\n# categories: Linked List\n# link: <https://leetcode.com/problems/remove-duplicates-from-sorted-list/description/>\n# hidden: true\n# ---\n# \n# Given a sorted linked list, delete all duplicates such that each element\n# appear only _once_.\n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: 1->1->2\n#     Output: 1->2\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: 1->1->2->3->3\n#     Output: 1->2->3\n#     \n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction delete_duplicates!(head::Union{ListNode, Nothing})::Union{ListNode, Nothing}\n    cur = head\n    while !isnothing(cur)\n        node, value = next(cur), val(cur)\n        while !isnothing(node) && val(node) == value\n            cur.next = node = next(node)\n        end\n        cur = next(cur)\n    end\n    return head\nend\n## @lc code=end\n## @lc test=start\n@testset \"83.remove-duplicates-from-sorted-list.jl\" begin\n    @test delete_duplicates!(ListNode{Int}([1,1,2])) == ListNode{Int}([1,2])\n    @test delete_duplicates!(ListNode{Int}([1,1,2,3,3])) == ListNode{Int}([1,2,3])\n    @test delete_duplicates!(ListNode{Int}([1,1,1])) == ListNode(1)\nend\n## @lc test=end\n\n\n## @lc info=start\n# link: [solution to 83](https://leetcode-cn.com/problems/remove-duplicates-from-sorted-list/solution/83-shan-chu-pai-xu-lian-biao-zhong-de-zh-hk85/)\n## @lc info=end", "meta": {"hexsha": "1c66c2707e4c1905f12a7abca6a401f3d131f607", "size": 1429, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-02/83.remove-duplicates-from-sorted-list.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/submited/date-02/83.remove-duplicates-from-sorted-list.jl", "max_issues_repo_name": "RexWzh/leetcode_note.jl", "max_issues_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/submited/date-02/83.remove-duplicates-from-sorted-list.jl", "max_forks_repo_name": "RexWzh/leetcode_note.jl", "max_forks_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.6379310345, "max_line_length": 149, "alphanum_fraction": 0.6193142057, "num_tokens": 454, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4073334000459302, "lm_q2_score": 0.16026602831693942, "lm_q1q2_score": 0.06528170622619626}}
{"text": "#=\nAuthor:= Shreeda Bhat\nLicense:= MIT \nUTF-8 =#\n\n#=\nIn Julia the if condition is simple, Unlike python, matlab it won't use vectorized code. \n\n    `if condition.....end`\n\n    `if condition....else.....end`\n\n=#\n\nprintln(\"***********************1st Example*************************\")\n\na = rand()\nprintln(\"lets check the value of a $a\")\nif a>0.5 \n    println(\"wow!! great\")\n\nend\n\nprintln(\"***********************2nd Example*************************\")\n\na = rand()\nprintln(\"lets check the value of a $a\")\nif a>0.5 \n    println(\"wow!! great\")\nelse \n    println(\"normal\")\n\nend\n\n\nprintln(\"***********************3rd Example*************************\")\na = rand()\nb = rand()\n\nc=((a>b) ? println(\"a is greater\") : (\"b is greater\"))\nprint(c)\n\nprintln(\"***********************4th Example*************************\")\na = rand()\nb = rand()\n\nc=((a>b) && println(\"a is greater\"))\nprint(c)", "meta": {"hexsha": "90415b8a4c92c3370eb69321baa7c1ba281ffa4e", "size": 871, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Basics/if-condition.jl", "max_stars_repo_name": "memetics19/My_julia_practice-", "max_stars_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-16T09:58:59.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-16T09:58:59.000Z", "max_issues_repo_path": "Basics/if-condition.jl", "max_issues_repo_name": "memetics19/My_julia_practice-", "max_issues_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Basics/if-condition.jl", "max_forks_repo_name": "memetics19/My_julia_practice-", "max_forks_repo_head_hexsha": "60b043babc4dec05bef55264d4452beaaa646aeb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-16T05:51:26.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-16T05:51:26.000Z", "avg_line_length": 18.1458333333, "max_line_length": 89, "alphanum_fraction": 0.4730195178, "num_tokens": 221, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.2689414213699951, "lm_q2_score": 0.24220562872535945, "lm_q1q2_score": 0.06513912605321148}}
{"text": "# ---\n# title: 912. Sort an Array\n# id: problem912\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: \n# link: <https://leetcode.com/problems/sort-an-array/description/>\n# hidden: true\n# ---\n# \n# Given an array of integers `nums`, sort the array in ascending order.\n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: nums = [5,2,3,1]\n#     Output: [1,2,3,5]\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: nums = [5,1,1,2,0,0]\n#     Output: [0,0,1,1,2,5]\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `1 <= nums.length <= 50000`\n#   * `-50000 <= nums[i] <= 50000`\n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "d23fdf21b4528e4f7d11cab93fc39ae292dabdb6", "size": 677, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/912.sort-an-array.jl", "max_stars_repo_name": "noob-data-analaysis/LeetCode.jl", "max_stars_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/unresolved/912.sort-an-array.jl", "max_issues_repo_name": "noob-data-analaysis/LeetCode.jl", "max_issues_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-10T02:19:49.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-05T05:00:12.000Z", "max_forks_repo_path": "src/unresolved/912.sort-an-array.jl", "max_forks_repo_name": "noob-data-analaysis/LeetCode.jl", "max_forks_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.0444444444, "max_line_length": 71, "alphanum_fraction": 0.5199409158, "num_tokens": 254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.13660840057146195, "lm_q1q2_score": 0.06510478387671421}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Introduction to DataFrames\n# **[Bogumi\u0142 Kami\u0144ski](http://bogumilkaminski.pl/about/), May 23, 2018**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames # load package\n\n# ------------------------------------------------------------------------------------------\n# ## Handling missing values\n#\n# A singelton type `Missings.Missing` allows us to deal with missing values.\n# ------------------------------------------------------------------------------------------\n\nmissing, typeof(missing)\n\n# ------------------------------------------------------------------------------------------\n# Arrays automatically create an appropriate union type.\n# ------------------------------------------------------------------------------------------\n\nx = [1, 2, missing, 3]\n\n# ------------------------------------------------------------------------------------------\n# `ismissing` checks if passed value is missing.\n# ------------------------------------------------------------------------------------------\n\nismissing(1), ismissing(missing), ismissing(x), ismissing.(x)\n\n# ------------------------------------------------------------------------------------------\n# We can extract the type combined with Missing from a `Union` via\n#\n# (This is useful for arrays!)\n# ------------------------------------------------------------------------------------------\n\neltype(x), Missings.T(eltype(x))\n\n# ------------------------------------------------------------------------------------------\n# `missing` comparisons produce `missing`.\n# ------------------------------------------------------------------------------------------\n\nmissing == missing, missing != missing, missing < missing\n\n# ------------------------------------------------------------------------------------------\n# This is also true when `missing`s are compared with values of other types.\n# ------------------------------------------------------------------------------------------\n\n1 == missing, 1 != missing, 1 < missing\n\n# ------------------------------------------------------------------------------------------\n# `isequal`, `isless`, and `===` produce results of type `Bool`.\n# ------------------------------------------------------------------------------------------\n\nisequal(missing, missing), missing === missing, isequal(1, missing), isless(1, missing)\n\n# ------------------------------------------------------------------------------------------\n# In the next few examples, we see that many (not all) functions handle `missing`.\n# ------------------------------------------------------------------------------------------\n\nmap(x -> x(missing), [sin, cos, zero, sqrt]) # part 1\n\nmap(x -> x(missing, 1), [+, - , *, /, div]) # part 2 \n\nmap(x -> x([1,2,missing]), [minimum, maximum, extrema, mean, any, float]) # part 3\n\n# ------------------------------------------------------------------------------------------\n# `skipmissing` returns iterator skipping missing values. We can use `collect` and\n# `skipmissing` to create an array that excludes these missing values.\n# ------------------------------------------------------------------------------------------\n\ncollect(skipmissing([1, missing, 2, missing]))\n\n# ------------------------------------------------------------------------------------------\n# Similarly, here we combine `collect` and `Missings.replace` to create an array that\n# replaces all missing values with some value (`NaN` in this case).\n# ------------------------------------------------------------------------------------------\n\ncollect(Missings.replace([1.0, missing, 2.0, missing], NaN))\n\n# ------------------------------------------------------------------------------------------\n# Another way to do this:\n# ------------------------------------------------------------------------------------------\n\ncoalesce.([1.0, missing, 2.0, missing], NaN)\n\n# ------------------------------------------------------------------------------------------\n# Caution: `nothing` would also be replaced here (for Julia 0.7 a more sophisticated\n# behavior of `coalesce` that allows to avoid this problem is planned).\n# ------------------------------------------------------------------------------------------\n\ncoalesce.([1.0, missing, nothing, missing], NaN)\n\n# ------------------------------------------------------------------------------------------\n# You can use `recode` if you have homogenous output types.\n# ------------------------------------------------------------------------------------------\n\nrecode([1.0, missing, 2.0, missing], missing=>NaN)\n\n# ------------------------------------------------------------------------------------------\n# You can use `unique` or `levels` to get unique values with or without missings,\n# respectively.\n# ------------------------------------------------------------------------------------------\n\nunique([1, missing, 2, missing]), levels([1, missing, 2, missing])\n\n# ------------------------------------------------------------------------------------------\n# In this next example, we convert `x` to `y` with `allowmissing`, where `y` has a type that\n# accepts missings.\n# ------------------------------------------------------------------------------------------\n\nx = [1,2,3]\ny = allowmissing(x)\n\n# ------------------------------------------------------------------------------------------\n# Then, we convert back with `disallowmissing`. This would fail if `y` contained missing\n# values!\n# ------------------------------------------------------------------------------------------\n\nz = disallowmissing(y)\nx,y,z\n\n# ------------------------------------------------------------------------------------------\n# In this next example, we show that the type of each column in `x` is initially `Int64`.\n# After using `allowmissing!` to accept missing values in columns 1 and 3, the types of\n# those columns become `Union`s of `Int64` and `Missings.Missing`.\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(Int, 2, 3)\nprintln(\"Before: \", eltypes(x))\nallowmissing!(x, 1) # make first column accept missings\nallowmissing!(x, :x3) # make :x3 column accept missings\nprintln(\"After: \", eltypes(x))\n\n# ------------------------------------------------------------------------------------------\n# In this next example, we'll use `completecases` to find all the rows of a `DataFrame` that\n# have complete data.\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(A=[1, missing, 3, 4], B=[\"A\", \"B\", missing, \"C\"])\nprintln(x)\nprintln(\"Complete cases:\\n\", completecases(x))\n\n# ------------------------------------------------------------------------------------------\n# We can use `dropmissing` or `dropmissing!` to remove the rows with incomplete data from a\n# `DataFrame` and either create a new `DataFrame` or mutate the original in-place.\n# ------------------------------------------------------------------------------------------\n\ny = dropmissing(x)\ndropmissing!(x)\n[x, y]\n\n# ------------------------------------------------------------------------------------------\n# When we call `showcols` on a `DataFrame` with dropped missing values, the columns still\n# allow missing values.\n# ------------------------------------------------------------------------------------------\n\nshowcols(x)\n\n# ------------------------------------------------------------------------------------------\n# Since we've excluded missing values, we can safely use `disallowmissing!` so that the\n# columns will no longer accept missing values.\n# ------------------------------------------------------------------------------------------\n\ndisallowmissing!(x)\nshowcols(x)\n", "meta": {"hexsha": "c835492615ecf0f7a994611cf3838a6c5df4aa11", "size": 7810, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/03_missingvalues.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/03_missingvalues.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/03_missingvalues.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 47.6219512195, "max_line_length": 92, "alphanum_fraction": 0.3371318822, "num_tokens": 1175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.13117322206357784, "lm_q1q2_score": 0.06507422605755965}}
{"text": "# This file was generated, do not modify it. # hide\ndata = dataset(\"ISLR\", \"OJ\")\n\nX = select(data, [:PriceCH, :PriceMM, :DiscCH, :DiscMM, :SalePriceMM,\n                  :SalePriceCH, :PriceDiff, :PctDiscMM, :PctDiscCH]);", "meta": {"hexsha": "cab9072816bcae26f83f49ea8d4c71a4caf073f6", "size": 221, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/isl/lab-10/code/ex7.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/isl/lab-10/code/ex7.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/isl/lab-10/code/ex7.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 44.2, "max_line_length": 69, "alphanum_fraction": 0.6334841629, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45713671682749485, "lm_q2_score": 0.14223189682786758, "lm_q1q2_score": 0.06501942234403836}}
{"text": "### A Pluto.jl notebook ###\n# v0.15.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 539bb62b-e4e3-4d72-837c-302ac9335e2e\nbegin\n\tusing PlutoUI\n\tTableOfContents()\nend\n\n# \u2554\u2550\u2561 27130015-4f45-4ebd-9094-07ec00261e5f\nmd\"\"\"\n# Useful Packages\n\nUnlike R, Julia does not immediately expose a huge number of functions, but instead requires loading packages (whether from the standard library or from the broader package ecosystem) for a lot of relevant functionality for statistical analysis. There are technical reasons for this, but one further motivation is that Julia is at a broader \"technical computing\" audience (like MATLAB or perhaps Python) and less at a \"statistical analysis\" audience. \n\nThis has two important implications:\n1. Even relatively simple programs will often load several packages.\n2. Packages are often focused on adding a relatively narrow set of functionality, which means that \"basic\" functionality (e.g. reading a CSV file and manipuating it as a DataFrame) is often split across multiple packages. In other words, **see the the first point!**\n\nThis notebook is not intended to be an exhaustive list of packages, but rather to highlight a few packages that I suspect will be particularly useful.\nBefore getting onto the packages, I have one final hint: take advantage of how easy and first-class package management in Julia is. Having good package management makes reproducible analyses much easier and avoids breaking old analyses when you start a new one. Pluto helpfully installs and manages for you, but the package-manager REPL mode (activated by typing `]` at the `julia>` prompt) is very useful.\n\"\"\"\n\n# \u2554\u2550\u2561 cd73a70f-4ce0-4ef8-87be-d3b319066602\nmd\"\"\"\n\n## Data wrangling\n\n### Reading data\n\n- [Arrow.jl](https://arrow.juliadata.org/dev/manual/) a high performance format for data storage, accessible in R via the [`arrow` package](https://arrow.apache.org/docs/r/) and in Python via `pyarrow`. (Confusingly, the function for reading and writing Arrow format files in R is called `read_feather` and `write_feather`, but the modern Arrow format is distinct from the older Feather format provided by the `feather` package.) This is the format that we store the example and test datasets in for MixedModels.jl.\n\n- [CSV.jl](https://csv.juliadata.org/stable/index.html) useful for reading comma-separated values, tab-separated values and basically everything handled by the `read.csv` and `read.table` family of functions in R. \n\nNote that by default both Arrow.jl and CSV.jl do not return a DataFrame, but rather \"column tables\" -- named tuples of column vectors.\n\n### DataFrames\n\nUnlike in R, DataFrames are not part of the base language, nor the standard library. \n\n[DataFrames.jl](https://dataframes.juliadata.org/stable/) provides the basic infrastructure around DataFrames, as well as its own [mini language](https://bkamins.github.io/julialang/2020/12/24/minilanguage.html) for doing the split-apply-combine approach that underlies R's `dplyr` and much of the tidyverse.  The DataFrames.jl documentation is the place to for looking at how to e.g. read in a [CSV or Arrow file as a DataFrame](https://dataframes.juliadata.org/stable/man/importing_and_exporting/). Note that DataFrames.jl by default depends on [CategoricalArrays.jl](https://categoricalarrays.juliadata.org/stable/) to handle the equivalent of `factor` in the R world, but there is an alternative package for `factor`-like array type in Julia, [PooledArrays.jl](https://github.com/JuliaData/PooledArrays.jl/). PooledArrays are simpler, but more limited than CategoricalArrays and we (Phillip and Doug) sometimes use them in our examples and simulations.\n\nDataFrame.jl's mini language can be a bit daunting, if you're used to manipulations in the style of base R or the tidyverse. For that, there are several options; recently, we'e had particularly nice experiences with [DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl) and [Chain.jl](https://github.com/jkrumbiegel/Chain.jl) for a convenient syntax to connect or \"pipe\" together successive operations. It's your choice whether and which of these add-ons you want to use! Phillip tends to write his code using raw DataFrames.jl, but Doug really enjoys DataFrameMacros.jl. \n\"\"\"\n\n# \u2554\u2550\u2561 9143e623-5457-48b5-9304-ce2a757b18b5\nmd\"\"\"\n## Regression\n\nUnlike in R, neither formula processing nor basic regression are part of the base language or the standard library. \n\nThe formula syntax and basic contrast-coding schemes in Julia is provided by [StatsModels.jl](https://juliastats.org/StatsModels.jl/v0.6/). By default, MixedModels.jl re-exports the `@formula` macro and most commonly used contrast schemes from StatsModels.jl, so you often don't have to worry about loading StatsModels.jl directly. The same is true for [GLM.jl](https://juliastats.org/GLM.jl/dev/manual/), which provides basic linear and generalized linear models, such as ordinary least squares (OLS) regression and logistic regression, i.e. the classical, non mixed regression models.\n\nThe basic functionality looks quite similar to R, e.g.\n\n```julia\njulia> lm(@formula(y ~ 1 + x), data)\njulia> glm(@formula(y ~ 1 + x), data, Binomial(), LogitLink())\n```\n\nbut the more general modelling API (also used by MixedModels.jl) is also supported:\n\n```julia\njulia> fit(LinearModel, @formula(y ~ 1 + x), mydata)\njulia> fit(GeneralizedLinearModel, @formula(y ~ 1 + x), data, Binomial(), LogitLink())\n```\n\n(You can also specify your model matrices directly and skip the formula interface, but we don't recommend this as it's easy to mess up in really subtle but very probelmatic ways.)\n\n\n### `@formula`, macros and domain-specific languages\n\nAs a sidebar: why is `@formula` a macro and not a normal function? Well, that's because formulas are essentially their own domain-specific language (a variant of [Wilkinson-Roger notation](https://www.jstor.org/stable/2346786)) and macros are used for manipulating the language itself -- or in this case, handling an entirely new, embedded language! This is also why macros are used by packages like [Turing.jl](https://turing.ml/) and [Soss.jl](https://cscherrer.github.io/Soss.jl/stable/) that define a language for Bayesian probabilistic programming like [PyMC3](https://docs.pymc.io/) or [Stan](https://mc-stan.org/).\n\n### Extensions to the formula syntax\n\nThere are several ongoing efforts to extend the formula syntax to include some of the \"extras\" available in R, e.g. [RegressionFormulae.jl](https://github.com/kleinschmidt/RegressionFormulae.jl) to use the caret (`^`) notation to limit interactions to a certain order (`(a+b+c)^2` generates `a + b + c + a&b + a&c + b&c`, but not `a&b&c`). \nNote also that Julia uses `&` to express interactions, not `:` like in R.\n\n### Standardizing Predictors\n\nAlthough function calls such as `log` can be used within Julia formulae, they must act on a rowwise basis, i.e. on observations. Transformations such as z-scoring or centering (often done with `scale` in R) require knowledge of the entire column. [StandardizedPredictors.jl](https://beacon-biosignals.github.io/StandardizedPredictors.jl/stable/) provides functions for centering, scaling, and z-scoring within the formula. These are treated as pseudo-contrasts and computed on demand, meaning that `predict` and `effects` (see next) computations will handle these transformations on new data (e.g. centering new data *around the mean computed during fitting the original data*) correctly and automatically.\n\n### Effects\n\nJohn Fox's `effects` package in R (and the related `ggeffects` package for plotting these using `ggplot2`) provides a nice way to visualize a model's overall view of the data. This functionality is provided by [`Effects.jl`](https://beacon-biosignals.github.io/Effects.jl/stable/) and works out-of-the-box with most regression model packages in Julia (including MixedModels.jl). Support for formulae with embedded functions (such as `log`) is not yet complete, but we're working on it!\n\n### Estimated Marginal / Least Square Means\n\nTo our knowledge, There is currently nothing like the R package `emmeans` package in Julia. However, it is often better to use sensible, hypothesis-driven contrast coding than to compute all pairwise comparisons after the fact. \ud83d\ude03\n\"\"\"\n\n# \u2554\u2550\u2561 32c08c8a-68ec-4bcb-bd4b-3fd76ffca0f0\nmd\"\"\"\n## Hypothesis Testing\n\nClassical statistical tests such as the t-test can be found in the package [HypothesisTests.jl](https://github.com/JuliaStats/HypothesisTests.jl/).\n\"\"\"\n\n# \u2554\u2550\u2561 4df36c00-be80-4fa5-83ca-c9648352fc42\nmd\"\"\"\n## Plotting ecosystem\n\nThroughtout this course, we have used the Makie ecosystem for plotting, but there are several alternatives in Julia.\n\n### Makie\n\nThe [Makie ecosystem](https://makie.juliaplots.org/stable/) is a relatively new take on graphics that aims to be both powerful and easy to use. Makie.jl itself only provides abstract definitions for many components (and is used in e.g. MixedModelsMakie.jl to define plot types for MixedModels.jl). The actual plotting and rendering is handled by a backend package such as CairoMakie.jl (good for Pluto notebooks or rending static 2D images) and GLMakie.jl (good for dynamic, interactive visuals and 3D images). AlgebraOfGraphics.jl builds a grammar of graphics upon the Makie framework. It's a great way to get good plots very quickly, but extensive customization is still best achieved by using Makie directly.\n\n### Plots.jl\n\n[Plots.jl](https://docs.juliaplots.org/latest/) is the original plotting package in Julia, but we often find it difficult to work with compared to some of the other alternatives. [StatsPlots.jl](https://github.com/JuliaPlots/StatsPlots.jl) builds on this, adding common statistical plots, while [UnicodePlots.jl](https://github.com/Evizero/UnicodePlots.jl) renders plots as Unicode characters directly in the REPL.\n\n[PGFPlotsX.jl](https://kristofferc.github.io/PGFPlotsX.jl/stable/) is a very new package that writes directly to PGF (the format used by LaTeX's tikz framework) and can stand alone or be used as a rendering backend for the Plots.jl ecosystem.\n\n### Gadfly\n\n[Gadfly.jl](https://gadflyjl.org/stable/) was the original attempt to create a plotting system in Julia based on the grammar of graphics (the \"gg\" in `ggplot2`). Development has largely stalled, but some functionality still exceeds AlgebraOfGraphics.jl, which has taken up the grammar of graphics mantle. Notably, the MixedModels.jl documentation still uses Gadfly as of this writing (early September 2021).\n\n### Others\n\nThere are many [other graphics packages available in Julia](https://juliapackages.com/c/graphics), often wrapping well-established frameworks such as [VegaLite](https://www.queryverse.org/VegaLite.jl/stable/).\n\"\"\"\n\n# \u2554\u2550\u2561 919a3d3c-1375-46ab-8507-ae884bb52cfb\nmd\"\"\"\n## Connecting to Other Languages\n\nUsing Julia doesn't mean you have to leave all the packages you knew in other languages behind. In Julia, it's often possible to even easily and quickly invoke code from other languages *from within Julia*. \n\n\n[RCall.jl](https://juliainterop.github.io/RCall.jl/stable/gettingstarted/) provides a very convenient interface for interacting with R. [JellyMe4.jl](https://github.com/palday/JellyMe4.jl/) add support for moving MixedModels.jl and `lme4` models back and forth between the languages (which means that you can use `emmeans`, `sjtools`, `DHARMa`, `car`, etc. to examine MixedModels.jl models!). [RData.jl](https://github.com/JuliaData/RData.jl) provides support for reading `.rds` and `.rda` files from Julia, while [RDatasets.jl](https://github.com/JuliaStats/RDatasets.jl) provides convenient access to many of the standard datasets provided by R and various R packages.\n\n[PyCall.jl](https://github.com/JuliaPy/PyCall.jl/) provides a very convenient way for interacting with Python code and packages. [PyPlot.jl](https://github.com/JuliaPy/PyPlot.jl) builds upon this foundation to provide support for Python's `matplotlib`. Similarly, [PyMNE.jl](\nhttps://github.com/beacon-biosignals/PyMNE.jl) and [PyFOOOF.jl](https://github.com/beacon-biosignals/pyfooof.jl) provide some additional functionality to make interacting with MNE-Python and FOOOF from within Julia even easier than with vanilla PyCall. \n\nFor MATLAB users, there is also [MATLAB.jl](https://github.com/JuliaInterop/MATLAB.jl)\n\n\n[Cxx.jl](https://juliainterop.github.io/Cxx.jl/stable/) provides interoperability with C++. It also provides a C++ REPL mode, making it possible to treating C++ much more like a dynamic language than the traditional compiler toolchain would allow.\n\nSupport for calling C and Fortran is [part of the Julia standard library](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/).\n\"\"\"\n\n# \u2554\u2550\u2561 35c0e59d-23ce-4b60-b80c-e619a50f990d\nmd\"\"\"\n\n## Notebooks and Webpages\n\nWe haved [Pluto.jl](https://github.com/fonsp/Pluto.jl/) throughout this course because it provides a nice, easy way to interact with Julia, including automatic package management. [PlutoUI.jl](https://github.com/fonsp/PlutoUI.jl) provides some nice tools for interacting with Pluto notebooks and making them more dynamic (including the `TableOfContents()` in this one!). We also like that Pluto notebooks are essentially normal Julia code stored in plain text under the hood, which means that they play quite nice with version control systems such as Git. Sometimes, however, the reactive nature of Pluto (update any one cell and the changes propogate to all impacted cells) is not desireable.\n\nAn alternative notebook interface is provide by [Jupyter](https://jupyter.org/), formerly IPython. Indeed, the \"Ju\" actually stands for Julia. Jupyter support is provided by the package [IJulia.jl](https://julialang.github.io/IJulia.jl/stable/).\n\nThe [Weave.jl](https://weavejl.mpastell.com/stable/) package provides \"Julia markdown\", which is similar to knitr/RMarkdown or the the Sweave formats in R. Notably, Weave also provides support for converting between `jmd` files and Jupyter notebooks.\n\nSimilarly, [Literate.jl](https://fredrikekre.github.io/Literate.jl/v2/) is a simple package for literate programming (i.e. programming where documentation and code are \"woven\" together) and can generate Markdown, plain code and Jupyter notebook output. It is designed for simplicity and speed and is less extensive than Weave.jl and Documenter.jl.\n\n[Documenter.jl](https://juliadocs.github.io/Documenter.jl/stable/) is the standard tool for building webpages from Julia documentation\n\n[Books.jl](https://rikhuijzer.github.io/Books.jl/) is a package designed to offer somewhat similar functionality to the `bookdown` package in R.\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\n\n[compat]\nPlutoUI = \"~0.7.9\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"438d35d2d95ae2c5e8780b330592b6de8494e779\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.0.3\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"Suppressor\"]\ngit-tree-sha1 = \"44e225d5837e2a2345e69a1d1e01ac2443ff9fcb\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.9\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[Suppressor]]\ngit-tree-sha1 = \"a819d77f31f83e5792a76081eee1ea6342ab8787\"\nuuid = \"fd094767-a336-5f1f-9728-57cf17d0bbfb\"\nversion = \"0.2.0\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250027130015-4f45-4ebd-9094-07ec00261e5f\n# \u255f\u2500cd73a70f-4ce0-4ef8-87be-d3b319066602\n# \u255f\u25009143e623-5457-48b5-9304-ce2a757b18b5\n# \u255f\u250032c08c8a-68ec-4bcb-bd4b-3fd76ffca0f0\n# \u255f\u25004df36c00-be80-4fa5-83ca-c9648352fc42\n# \u255f\u2500919a3d3c-1375-46ab-8507-ae884bb52cfb\n# \u255f\u250035c0e59d-23ce-4b60-b80c-e619a50f990d\n# \u2560\u2550539bb62b-e4e3-4d72-837c-302ac9335e2e\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "c215e18d864981f03d8b472e7c59667e5581bfb4", "size": 16903, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/useful_packages.jl", "max_stars_repo_name": "jkrumbiegel/SMLP2021", "max_stars_repo_head_hexsha": "c9f7e9277abfb9637feed1af9475f8be5d88354b", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2021-03-29T13:48:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T00:21:02.000Z", "max_issues_repo_path": "notebooks/useful_packages.jl", "max_issues_repo_name": "jkrumbiegel/SMLP2021", "max_issues_repo_head_hexsha": "c9f7e9277abfb9637feed1af9475f8be5d88354b", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2021-04-10T07:56:42.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-30T19:39:27.000Z", "max_forks_repo_path": "notebooks/useful_packages.jl", "max_forks_repo_name": "jkrumbiegel/SMLP2021", "max_forks_repo_head_hexsha": "c9f7e9277abfb9637feed1af9475f8be5d88354b", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-09-06T07:37:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-08T19:11:01.000Z", "avg_line_length": 66.5472440945, "max_line_length": 956, "alphanum_fraction": 0.7720522984, "num_tokens": 4806, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.31405054499180746, "lm_q2_score": 0.20689405370611846, "lm_q1q2_score": 0.06497519032197079}}
{"text": "\"\"\"\n    type_complexity(T::Type) -> c\n\nReturn an integer `c` representing a measure of the \"complexity\" of\ntype `T`. For unnested types, `c = n+1` where `n` is the number of\nparameters in type `T`. However, `type_complexity` calls itself\nrecursively on the parameters, so if the parameters have their own\nparameters then the complexity of the type will be (potentially much)\nhigher than this.\n\n# Examples\n\n    # Array{Float64,2}\n    julia> a = rand(3,5);\n\n    julia> type_complexity(typeof(a))\n    3\n\n    julia> length(typeof(a).parameters)+1\n    3\n\n    # Create an object that has type:\n    #    SubArray{Int64,2,Base.ReshapedArray{Int64,2,UnitRange{Int64},Tuple{}},Tuple{StepRange{Int64,Int64},Colon},false}\n    julia> r = reshape(1:9, 3, 3);\n\n    julia> v = view(r, 1:2:3, :);\n\n    julia> type_complexity(typeof(v))\n    15\n\n    julia> length(typeof(v).parameters)+1\n    6\n\nThe second example indicates that the total complexity of `v`'s type\nis considerably higher than the complexity of just its \"outer\"\nSubArray type.\n\"\"\"\nfunction type_complexity(::Type{T}) where T\n    if isa(T, Union)\n        1 + type_complexity(T.b)\n    else\n        isempty(T.parameters) ? 1 : sum(type_complexity, T.parameters)+1\n    end\nend\ntype_complexity(::Type{Union{}}) = 1\ntype_complexity(x)               = 1\n\n# Fallback definitions\n\"\"\"\n    showarg(stream::IO, x)\n\nShow `x` as if it were an argument to a function. This function is\nused in the printing of \"type summaries\" in terms of sequences of\nfunction calls on objects.\n\nThe fallback definition is to print `x` as `::\\$(typeof(x))`,\nrepresenting argument `x` in terms of its type. However, you can\nspecialize this function for specific types to customize printing.\n\n# Example\n\nA SubArray created as `view(a, :, 3, 2:5)`, where `a` is a\n3-dimensional Float64 array, has type\n\n    SubArray{Float64,2,Array{Float64,3},Tuple{Colon,Int64,UnitRange{Int64}},false}\n\nand this type will be printed in the summary. To change the printing of this object to\n\n    view(::Array{Float64,3}, Colon(), 3, 2:5)\n\nyou could define\n\n    function ShowItLikeYouBuildIt.showarg(io::IO, v::SubArray)\n        print(io, \"view(\")\n        showarg(io, parent(v))\n        print(io, \", \", join(v.indexes, \", \"))\n        print(io, ')')\n    end\n\nNote that we're calling `showarg` recursively for the parent array\ntype.  Printing the parent as `::Array{Float64,3}` is the fallback\nbehavior, assuming no specialized method for `Array` has been defined.\nMore generally, this would display as\n\n    view(<a>, Colon(), 3, 2:5)\n\nwhere `<a>` is the output of `showarg` for `a`.\n\nThis printing might be activated any time `v` is a field in some other\ncontainer, or if you specialize `Base.summary` for `SubArray` to call\n`summary_build`.\n\nSee also: summary_build.\n\"\"\"\nshowarg(io::IO, ::Type{T}) where {T} = print(io, \"::Type{\", T, \"}\")\nshowarg(io::IO, x) = print(io, \"::\", typeof(x))\n\nfunction summary_build(io::IO, A::AbstractArray, cthresh=default_cthresh(A))\n    type_complexity(typeof(A)) <= cthresh && return show_summary_type(io, A)\n    print(io, dimstring(indices(A)), ' ')\n    showarg(io, A)\n    print(io, \" with element type \", eltype(A))\nend\n\n\"\"\"\n    summary_build(A::AbstractArray, [cthresh])\n\nReturn a string representing `A` in terms of the sequence of function\ncalls that might be used to create `A`, along with information about\n`A`'s size or indices and element type. This function should never be\ncalled directly, but instead used to specialize `Base.summary` for\nspecific `AbstractArray` subtypes. For example, if you want to change\nthe summary of SubArray, you might define\n\n    Base.summary(v::SubArray) = summary_build(v)\n\nThis function goes hand-in-hand with `showarg`. If you have defined a\n`showarg` method for `SubArray` as in the documentation for `showarg`,\nthen the summary of a SubArray might look like this:\n\n    3\u00d74 view(::Array{Float64,3}, :, 3, 2:5) with element type Float64\n\ninstead of this:\n\n    3\u00d74 SubArray{Float64,2,Array{Float64,3},Tuple{Colon,Int64,UnitRange{Int64}},false}\n\nThe optional argument `cthresh` is a \"complexity threshold\"; objects\nwith type descriptions that are less complex than the specified\nthreshold will be printed using the traditional type-based\nsummary. The default value is `n+1`, where `n` is the number of\nparameters in `typeof(A)`.  The complexity is calculated with\n`type_complexity`. You can choose a `cthresh` of 0 if you want to\nensure that your `showarg` version is always used.\n\nSee also: showarg, type_complexity.\n\"\"\"\nfunction summary_build(A::AbstractArray, cthresh=default_cthresh(A))\n    io = IOBuffer()\n    summary_build(io, A, cthresh)\n    String(io)\nend\n\n# This is effectively the \"original summary\" as defined in Base. We\n# can't just use that function directly because `summary_build`\n# should only be called by `summary` method that has been specialized\n# for the type, so `summary` isn't available as a fallback.\nshow_summary_type(io::IO, A::AbstractArray) = print(io, dimstring(indices(A)), ' ', typeof(A))\n\ndefault_cthresh(x) = default_cthresh(typeof(x))\ndefault_cthresh(::Type{T}) where {T} = length(T.parameters)+1\n", "meta": {"hexsha": "114d48692f856fa39ef9e08d3d0d4e3491ff147a", "size": 5099, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/code.jl", "max_stars_repo_name": "timholy/ShowItLikeYouBuildIt.jl", "max_stars_repo_head_hexsha": "7dd238d7c559ebb95ffe9fc852df550310cbd775", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2017-01-08T15:47:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T23:23:28.000Z", "max_issues_repo_path": "src/code.jl", "max_issues_repo_name": "timholy/ShowItLikeYouBuildIt.jl", "max_issues_repo_head_hexsha": "7dd238d7c559ebb95ffe9fc852df550310cbd775", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2016-10-15T12:37:22.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-09T08:26:26.000Z", "max_forks_repo_path": "src/code.jl", "max_forks_repo_name": "timholy/ShowItLikeYouBuildIt.jl", "max_forks_repo_head_hexsha": "7dd238d7c559ebb95ffe9fc852df550310cbd775", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2016-10-17T11:10:42.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:54:15.000Z", "avg_line_length": 33.7682119205, "max_line_length": 121, "alphanum_fraction": 0.7085703079, "num_tokens": 1409, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.13296422989056966, "lm_q1q2_score": 0.06492422562386327}}
{"text": "VERSION < v\"0.7.0-beta2.199\" && __precompile__()\n\n\"\"\"\nThe LaTeXStrings module exists mainly to make LaTeX equations easier to type as\nliteral strings, and so that the resulting strings display as formatted equations\nin supporting environments like IJulia.\n\nSee in particular the `LaTeXString` type and the `L\"...\"` constructor macro.\n\"\"\"\nmodule LaTeXStrings\nexport LaTeXString, latexstring, @L_str, @L_mstr\nusing Compat\n\n# IJulia supports LaTeX output for any object with a text/latex\n# writemime method, but these are annoying to type as string literals\n# in Julia because of all the escaping required, e.g. \"\\$\\\\alpha +\n# \\\\beta\\$\".  To simplify this, we add a new string type with a macro\n# constructor, so that one can simply do L\"$\\alpha + \\beta$\".\n\n@doc raw\"\"\"\nA `LaTeXString` is a string type whose contents represent a fragment of LaTeX code,\ntypically containing an equation (`$...$`).   In certain environments (e.g. IJulia)\nthis will display with LaTeX-like formatting.   For the most part, you can use\na `LaTeXString` object in any context that expects an `AbstractString` object.\n\nThe `L\"...\"` macro is convenient for constructing `LaTeXString` objects, because\nit eliminates the need to escape backslashes and dollar signs, and implicitly inserts\ndollar signs around the string if none are present.  For example, `L\"$\\alpha$\"`, `L\"\\alpha\"`,\nand `LaTeXString(\"\\$\\\\alpha\\$\")` are all equivalent.\n\"\"\"\nstruct LaTeXString <: AbstractString\n    s::String\nend\n\n\"\"\"\n    latexstring(args...)\n\nSimilar to `string(args...)`, but generates a `LaTeXString` instead of a `String`.\n\"\"\"\nlatexstring(args...) = latexstring(string(args...))\nfunction latexstring(s::String)\n    # the only point of using LaTeXString to represent equations, since\n    # IJulia doesn't support LaTeX output other than equations, so add $'s\n    # around the string if they aren't there (ignoring \\$)\n    return occursin(r\"[^\\\\]\\$|^\\$\", s) ? LaTeXString(s) :  LaTeXString(string('\\$', s, '\\$'))\nend\nlatexstring(s::AbstractString) = latexstring(String(s))\n\nmacro L_str(s, flags...) latexstring(s) end\nmacro L_mstr(s, flags...) latexstring(s) end\n\nBase.write(io::IO, s::LaTeXString) = write(io, s.s)\nBase.show(io::IO, ::MIME\"application/x-latex\", s::LaTeXString) = write(io, s)\nBase.show(io::IO, ::MIME\"text/latex\", s::LaTeXString) = write(io, s)\nfunction Base.show(io::IO, s::LaTeXString)\n    print(io, \"L\")\n    Base.print_quoted_literal(io, s.s)\nend\n\nCompat.firstindex(s::LaTeXString) = Compat.firstindex(s.s)\nCompat.lastindex(s::LaTeXString) = Compat.lastindex(s.s)\n@static if isdefined(Base, :iterate)\n    Base.iterate(s::LaTeXString, i::Int) = iterate(s.s, i)\nelse\n    Base.start(s::LaTeXString) = start(s.s)\n    Base.next(s::LaTeXString, i) = next(s.s, i)\n    Base.done(s::LaTeXString, i) = done(s.s, i)\nend\nBase.nextind(s::LaTeXString, i::Int) = nextind(s.s, i)\nBase.prevind(s::LaTeXString, i::Int) = prevind(s.s, i)\nBase.eachindex(s::LaTeXString) = eachindex(s.s)\nBase.length(s::LaTeXString) = length(s.s)\nBase.getindex(s::LaTeXString, i::Integer) = getindex(s.s, i)\nBase.getindex(s::LaTeXString, i::Int) = getindex(s.s, i) # for method ambig in Julia 0.6\nBase.getindex(s::LaTeXString, i::UnitRange{Int}) = getindex(s.s, i)\nBase.getindex(s::LaTeXString, i::UnitRange{<:Integer}) = getindex(s.s, i)\nBase.getindex(s::LaTeXString, i::AbstractVector{<:Integer}) = getindex(s.s, i)\nCompat.codeunit(s::LaTeXString, i::Integer) = codeunit(s.s, i)\nCompat.codeunit(s::LaTeXString) = codeunit(s.s)\nCompat.ncodeunits(s::LaTeXString) = ncodeunits(s.s)\nCompat.codeunits(s::LaTeXString) = codeunits(s.s)\nBase.sizeof(s::LaTeXString) = sizeof(s.s)\nBase.isvalid(s::LaTeXString, i::Integer) = isvalid(s.s, i)\nBase.pointer(s::LaTeXString) = pointer(s.s)\nBase.IOBuffer(s::LaTeXString) = IOBuffer(s.s)\nBase.unsafe_convert(T::Union{Type{Ptr{UInt8}},Type{Ptr{Int8}},Cstring}, s::LaTeXString) = Base.unsafe_convert(T, s.s)\n\nend # module\n", "meta": {"hexsha": "0f26c44ee5d70ed61a6fd65915e9980c99125043", "size": 3893, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LaTeXStrings.jl", "max_stars_repo_name": "crstnbr/LaTeXStrings.jl", "max_stars_repo_head_hexsha": "4c9ad10fcfc58b79c00f0a6d1cac0dd4ed3b53a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/LaTeXStrings.jl", "max_issues_repo_name": "crstnbr/LaTeXStrings.jl", "max_issues_repo_head_hexsha": "4c9ad10fcfc58b79c00f0a6d1cac0dd4ed3b53a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/LaTeXStrings.jl", "max_forks_repo_name": "crstnbr/LaTeXStrings.jl", "max_forks_repo_head_hexsha": "4c9ad10fcfc58b79c00f0a6d1cac0dd4ed3b53a5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.7415730337, "max_line_length": 117, "alphanum_fraction": 0.7135884922, "num_tokens": 1135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.35577489351363034, "lm_q2_score": 0.18242552602881168, "lm_q1q2_score": 0.06490242209706848}}
{"text": "# =============================================================================\n# Draw christmas tree\n# Luis Lopez Diaz - llopeza@uninorte.edu.co\n# =============================================================================\n# Input data\nl = 28;                         # Number of lines for drawing\n# =============================================================================\n# cheking data\nif l < 0\n    error(\"Number of line needs to be positive.\");\n    return;\nend\nif ~(typeof(l) == Int)\n    error(\"Number of line needs to be integer\");\n    return;\nend\n# =============================================================================\n# main code\nendw = \"*\";\nform = \"*o\";\nfor i in 1:l\n    for j in i+1:l print(\" \"); end  # Print blank space\n    for j in 1:i-1 print(form); end # Print form\n    print(endw * \"\\n\")              # Print ending\nend\n", "meta": {"hexsha": "6166e46bc38a74827fd3948c57e5d4d6b328ed83", "size": 850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercises/drawXmasTree.jl", "max_stars_repo_name": "llopeza/scholar_codes", "max_stars_repo_head_hexsha": "ee44a61efa9c9e7f2be57723c8aa7e7ff31d769c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercises/drawXmasTree.jl", "max_issues_repo_name": "llopeza/scholar_codes", "max_issues_repo_head_hexsha": "ee44a61efa9c9e7f2be57723c8aa7e7ff31d769c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exercises/drawXmasTree.jl", "max_forks_repo_name": "llopeza/scholar_codes", "max_forks_repo_head_hexsha": "ee44a61efa9c9e7f2be57723c8aa7e7ff31d769c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6923076923, "max_line_length": 79, "alphanum_fraction": 0.3623529412, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.1347759313241159, "lm_q1q2_score": 0.06475696131528672}}
{"text": "module GnuplotRecipes\n\nusing Gnuplot, Tables, Measurements, InvertedIndices\n\nexport bars\n\n\"Helper function returning the number of columns in the table.\"\nncol(table) = length(Tables.columnnames(table))\n\n\"Helper function returning the number of rows in the table.\"\nnrow(table) = Tables.rowcount(table)\n\n\"\"\"\n    bars(table; kwargs...)::Vector{Gnuplot.PlotElement}\n\nExplicit recipe to plot bar graphs from tabular data.\n`Measurement`-type data are plot with errorbars style.\n\n# Arguments\n\n- `table`: data to plot. The first column should contains text labels,\n  the other columns the plotted values. If the values are of\n  `Measurement` type, they will be plotted with errorbars style,\n  unless overridden with `hist_style`.\n- `box_width=0.8`: the width of the boxes. One means that the boxes\n  touch each other.\n- `gap::Union{Int64,Nothing}=nothing`: The gap between bar clusters.\n  If `nothing`, it is set automatically depending on the number of\n  bars in the cluster; to zero for one bar in the cluster, to 1 for\n  multiple bars.\n- `hist_style=nothing`: histogram style \u2014 see Gnuplot documentation.\n- `fill_style=\"solid 1 border -1\"`: fill style \u2014 see Gnuplot documentation.\n- `errorbars=\"\"`: errorbars style \u2014 see Gnuplot documentation.\n- `label_rot=-30`: label rotation angle; if > 0, align label to right.\n- `label_enhanced=false`: whether to apply Gnuplot enhanced formatting to labels.\n- `key_enhanced=false`: whether to apply Gnuplot enhanced formatting to data keys.\n- `y2cols=[]`: Columns (specified as symbols) which should be plot against *y2* axis.\n- `linetypes=1:ncol(table)-1`: Line types (colors) used for different bars\n- `xlabelcol=1`: Index of columns containing labels.\n\n# Example\n\n```jldoctest\njulia> using Measurements, Gnuplot, DataFrames\n\njulia> table = DataFrame(names=[\"very long label\", \"b\", \"c\"],\n                         temp=10:12,\n                         speed=collect(4:-1:2) .\u00b1 1)\n3\u00d73 DataFrame\n Row \u2502 names            temp   speed\n     \u2502 String           Int64  Measurem\u2026\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 very long label     10    4.0\u00b11.0\n   2 \u2502 b                   11    3.0\u00b11.0\n   3 \u2502 c                   12    2.0\u00b11.0\n\njulia> @gp bars(table)\n\n```\n\"\"\"\nfunction bars(table;\n              box_width = 0.8,\n              gap::Union{Int64,Nothing} = nothing,\n              hist_style = nothing,\n              fill_style = \"solid 1 border -1\",\n              errorbars = \"\",\n              label_rot = -30,\n              label_enhanced = false,\n              key_enhanced = false,\n              y2cols = [],\n              linetypes = 1:ncol(table)-1,\n              xlabelcol = 1,\n              )::Gnuplot.PlotElement\n    nr = nrow(table)\n\n    foreach(y2cols) do col\n        @assert col in propertynames(table)\n    end\n\n    axes(group) = (group in y2cols) ? \"axes x1y2 \" : \"\"\n\n    if hist_style === nothing\n        hist_style = (any(map(eltype.(Tables.columns(table))) do type type <: Measurement end)\n                      ? \"errorbars\" : \"clustered\")\n    end\n    eb = hist_style == \"errorbars\"\n\n    if gap === nothing\n        gap = ncol(table) == 2 ? 0 : 1\n    end\n    gap_str = (hist_style \u2208 [\"clustered\", \"errorbars\"]) ? \"gap $gap\" : \"\"\n    hist_style == \"columnstacked\" && @warn \"columnstacked is not well supported\"\n\n    function gpusing(i)\n        cols = [i+1, \"xticlabels(1)\"]\n        eb && insert!(cols, 2, i+ncol(table))\n        join(cols, \":\")\n    end\n\n    label_column = Tables.columns(table)[xlabelcol]\n    value_columns = Tables.columns(table)[Not(xlabelcol)]\n    group_names = Tables.columnnames(value_columns)\n\n    data=Gnuplot.DatasetText(\n        String.(label_column), # labels\n        [Measurements.value.(column) for column in value_columns]...,\n        [eltype(column) <: Measurement ?\n            Measurements.uncertainty.(column) :\n            fill(0.0, nr)\n         for column in value_columns if eb]...,\n    )\n    Gnuplot.PlotElement(\n        xr=[-0.5, nr - 0.5],\n        cmds=vcat([\"set grid ytics y2tics\",\n                   \"set style data histogram\",\n                   \"set style histogram $hist_style $gap_str\",\n                   \"set boxwidth $box_width\",\n                   \"set style fill $fill_style\",\n                   \"\"\"set xtics rotate by $label_rot $(label_rot > 0 ? \"right\" : \"\") $(label_enhanced ? \"\" : \"no\")enhanced\"\"\",\n                   ],\n                  !isempty(errorbars) ? [\"set errorbars $errorbars\"] : String[],\n                  !isempty(y2cols) ? [\"set ytics nomirror\", \"set y2tics\"] : String[],\n                  ),\n        data=data,\n        plot=[ \"using $(gpusing(i)) $(axes(group_name))\" *\n            \"\"\"title '$(String(group_name))' $(key_enhanced ? \"\" : \"no\")enhanced lt $(linetypes[i])\"\"\"\n               for (i, group_name) in enumerate(group_names)]\n    )\nend\n\nend\n", "meta": {"hexsha": "d4f382f1ead7f7e63bc3ad4e49967071771d7a9a", "size": 4778, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GnuplotRecipes.jl", "max_stars_repo_name": "wentasah/GnuplotRecipes.jl", "max_stars_repo_head_hexsha": "70092410dbb339ced3ce8669ab5916806caa0b56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/GnuplotRecipes.jl", "max_issues_repo_name": "wentasah/GnuplotRecipes.jl", "max_issues_repo_head_hexsha": "70092410dbb339ced3ce8669ab5916806caa0b56", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-12T07:30:38.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-12T07:30:38.000Z", "max_forks_repo_path": "src/GnuplotRecipes.jl", "max_forks_repo_name": "wentasah/GnuplotRecipes.jl", "max_forks_repo_head_hexsha": "70092410dbb339ced3ce8669ab5916806caa0b56", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.196969697, "max_line_length": 126, "alphanum_fraction": 0.5958560067, "num_tokens": 1250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047866316946014, "lm_q2_score": 0.1347759243735362, "lm_q1q2_score": 0.06475695597042493}}
{"text": "\nload(\"debug.jl\")\n\nmodule TestDebugEval\nimport Base.*\nimport Debug.*\n\n\n@debug function f(n)\n    x = 0       # line 10\n    for k=1:n   # line 11\n        x += k  # line 12\n    end         # line 13\n    x = x*x     # line 14\nend\n\nfunction debug_hook(line::Int, file, scope::Scope)\n    print(line, \":\")\n\n    if (line == 11) debug_eval(scope, :(x += 1)) end\n\n    if (line >  10) debug_eval(scope, :(print(\"\\tx = \", x))) end\n    if (line == 12) debug_eval(scope, :(print(\"\\tk = \", k))) end\n    println()\nend\n\nf(3)\n\nend\n", "meta": {"hexsha": "1e0b9ac46ea743a043222acd2ea3d4d49fb493d3", "size": 513, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/attic/1/test/test.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Debug.jl-c4541016-4bc8-5661-83c1-830810448607", "max_stars_repo_head_hexsha": "c9f37a7f40319b455889eea9364662f1232ee157", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 75, "max_stars_repo_stars_event_min_datetime": "2015-01-13T12:19:22.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-16T11:51:14.000Z", "max_issues_repo_path": "src/attic/1/test/test.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/Debug.jl-c4541016-4bc8-5661-83c1-830810448607", "max_issues_repo_head_hexsha": "c9f37a7f40319b455889eea9364662f1232ee157", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2015-01-28T15:31:12.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-09T08:34:32.000Z", "max_forks_repo_path": "src/attic/1/test/test.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/Debug.jl-c4541016-4bc8-5661-83c1-830810448607", "max_forks_repo_head_hexsha": "c9f37a7f40319b455889eea9364662f1232ee157", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2015-04-25T18:57:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T19:35:09.000Z", "avg_line_length": 17.1, "max_line_length": 64, "alphanum_fraction": 0.5341130604, "num_tokens": 174, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4035668680822513, "lm_q2_score": 0.16026603633858205, "lm_q1q2_score": 0.06467806234511783}}
{"text": "\nimport Base: ==\n==(x,y,z,ws...) = x==y && ==(y,z,ws...)\n\n\n\"\"\" \nReturn the type's fields as a tuple\n\"\"\"\n@generated fieldvalues(x) = Expr(:tuple, (:(x.$f) for f=fieldnames(x))...)\n@generated fields(x) = Expr(:tuple, (:($f=x.$f) for f=fieldnames(x))...)\nfirstfield(x) = first(fieldvalues(x))\n\n\n\n\"\"\"\nRewrites `@! x = f(args...)` to `x = f!(x,args...)`\n\nSpecial cases for `*` and `\\\\` forward to `mul!` and `ldiv!`, respectively.\n\"\"\"\nmacro !(ex)\n    if @capture(ex, x_ = f_(args__; kwargs_...))\n        esc(:($(Symbol(string(f,\"!\")))($x,$(args...); $kwargs...)))\n    elseif @capture(ex, x_ = f_(args__))\n        if f == :*\n            f = :mul\n        elseif f==:\\\n            f = :ldiv\n        end\n        esc(:($x = $(Symbol(string(f,\"!\")))($x,$(args...))::typeof($x))) # ::typeof part helps inference sometimes\n    else\n        error(\"Usage: @! x = f(...)\")\n    end\nend\n\n\nnan2zero(x::T) where {T} = !isfinite(x) ? zero(T) : x\nnan2zero(x::Diagonal{T}) where {T} = Diagonal{T}(nan2zero.(x.diag))\nnan2inf(x::T) where {T} = !isfinite(x) ? T(Inf) : x\n\n\n\"\"\" Return a tuple with the expression repeated n times \"\"\"\nmacro repeated(ex,n)\n    :(tuple($(repeated(esc(ex),n)...)))\nend\n\n\"\"\" \nPack some variables in a dictionary \n\n```julia\n> x = 3\n> y = 4\n> @dict x y z=>5\nDict(:x=>3,:y=>4,:z=>5)\n```\n\"\"\"\nmacro dict(exs...)\n    kv(ex::Symbol) = :($(QuoteNode(ex))=>$(esc(ex)))\n    kv(ex) = isexpr(ex,:call) && ex.args[1]==:(=>) ? :($(QuoteNode(ex.args[2]))=>$(esc(ex.args[3]))) : error()\n    :(Dict($((kv(ex) for ex=exs)...)))\nend\n\n\"\"\" \nPack some variables into a NamedTuple. E.g.:\n\n```julia\n> x = 3\n> y = 4\n> @namedtuple(x, y, z=5)\n(x=3,y=4,z=5)\n```\n\"\"\"\nmacro namedtuple(exs...)\n    if length(exs)==1 && isexpr(exs[1],:tuple)\n        exs = exs[1].args\n    end\n    kv(ex::Symbol) = :($(esc(ex))=$(esc(ex)))\n    kv(ex) = isexpr(ex,:(=)) ? :($(esc(ex.args[1]))=$(esc(ex.args[2]))) : error()\n    Expr(:tuple, (kv(ex) for ex=exs)...)\nend\n\n\n\n# these allow pinv and sqrt of SMatrices of Diagonals to work correctly, which\n# we use for the T-E block of the covariance. hopefully some of this can be cut\n# down on in the futue with some PRs into StaticArrays.\npermutedims(A::SMatrix{2,2}) = @SMatrix[A[1] A[3]; A[2] A[4]]\nfunction sqrt(A::SMatrix{2,2,<:Diagonal})\n    a,b,c,d = A\n    s = @. sqrt(a*d-b*c)\n    t = pinv(@. sqrt(a+d+2s))\n    @SMatrix[t*(a+s) t*b; t*c t*(d+s)]\nend\nfunction pinv(A::SMatrix{2,2,<:Diagonal})\n    a,b,c,d = A\n    idet = pinv(@. a*d-b*c)\n    @SMatrix[d*idet -(b*idet); -(c*idet) a*idet]\nend\n\n\n# some usefule tuple manipulation functions:\n\n# see: https://discourse.julialang.org/t/efficient-tuple-concatenation/5398/10\n# and https://github.com/JuliaLang/julia/issues/27988\n@inline tuplejoin(x) = x\n@inline tuplejoin(x, y) = (x..., y...)\n@inline tuplejoin(x, y, z...) = (x..., tuplejoin(y, z...)...)\n\n# see https://discourse.julialang.org/t/any-way-to-make-this-one-liner-type-stable/10636/2\nusing Base: tuple_type_cons, tuple_type_head, tuple_type_tail, first, tail\nmap_tupleargs(f,::Type{T}) where {T<:Tuple} = \n    (f(tuple_type_head(T)), map_tupleargs(f,tuple_type_tail(T))...)\nmap_tupleargs(f,::Type{T},::Type{S}) where {T<:Tuple,S<:Tuple} = \n    (f(tuple_type_head(T),tuple_type_head(S)), map_tupleargs(f,tuple_type_tail(T),tuple_type_tail(S))...)\nmap_tupleargs(f,::Type{T},s::Tuple) where {T<:Tuple} = \n    (f(tuple_type_head(T),first(s)), map_tupleargs(f,tuple_type_tail(T),tail(s))...)\nmap_tupleargs(f,::Type{<:Tuple{}}...) = ()\nmap_tupleargs(f,::Type{<:Tuple{}},::Tuple) = ()\n\n\n# returns the base parametric type with all type parameters stripped out\nbasetype(::Type{T}) where {T} = T.name.wrapper\n@generated function basetype(t::UnionAll)\n    unwrap_expr(s::UnionAll, t=:t) = unwrap_expr(s.body, :($t.body))\n    unwrap_expr(::DataType, t) = t\n    :($(unwrap_expr(t.parameters[1])).name.wrapper)\nend\n\n\nfunction ensuresame(args...)\n    @assert all(args .== Ref(args[1]))\n    args[1]\nend\n\n\ntuple_type_len(::Type{<:NTuple{N,Any}}) where {N} = N\n\n\nensure1d(x::Union{Tuple,AbstractArray}) = x\nensure1d(x) = (x,)\n\n\n# see https://discourse.julialang.org/t/dispatching-on-the-result-of-unwrap-unionall-seems-weird/25677\n# for why we need this\n# to use, just decorate the custom show_datatype with it, and make sure the args\n# are named `io` and `t`.\nmacro show_datatype(ex)\n    def = splitdef(ex)\n    def[:body] = quote\n        isconcretetype(t) ? $(def[:body]) : invoke(Base.show_datatype, Tuple{IO,DataType}, io, t)\n    end\n    esc(combinedef(def))\nend\n\n\n\n\"\"\"\n    # symmetric in any of its final arguments except for bar:\n    @sym_memo foo(bar, @sym(args...)) = <body> \n    # symmetric in (i,j), but not baz\n    @sym_memo foo(baz, @sym(i, j)) = <body> \n    \nThe `@sym_memo` macro should be applied to a definition of a function\nwhich is symmetric in some of its arguments. The arguments in which its\nsymmetric are specified by being wrapping them in @sym, and they must come at\nthe very end. The resulting function will be memoized and permutations of the\narguments which are equal due to symmetry will only be computed once.\n\"\"\"\nmacro sym_memo(funcdef)\n    \n    \n    sfuncdef = splitdef(funcdef)\n    \n    asymargs = sfuncdef[:args][1:end-1]\n    symargs = collect(@match sfuncdef[:args][end] begin\n        Expr(:macrocall, [head, _, ex...]), if head==Symbol(\"@sym\") end => ex\n        _ => error(\"final argument(s) should be marked @sym\")\n    end)\n    sfuncdef[:args] = [asymargs..., symargs...]\n    \n    sfuncdef[:body] = quote\n        symargs = [$(symargs...)]\n        sorted_symargs = sort(symargs)\n        if symargs==sorted_symargs\n            $((sfuncdef[:body]))\n        else\n            $(sfuncdef[:name])($(asymargs...), sorted_symargs...)\n        end\n    end\n    \n    esc(:(@memoize $(combinedef(sfuncdef))))\n    \nend\n\n\n@doc doc\"\"\"\n```\n@subst sum(x*$(y+1) for x=1:2)\n```\n    \nbecomes\n\n```\nlet tmp=(y+1)\n    sum(x*tmp for x=1:2)\nend\n```\n\nto aid in writing clear/succinct code that doesn't recompute things\nunnecessarily.\n\"\"\"\nmacro subst(ex)\n    \n    subs = []\n    ex = postwalk(ex) do x\n        if isexpr(x, Symbol(raw\"$\"))\n            var = gensym()\n            push!(subs, :($(esc(var))=$(esc(x.args[1]))))\n            var\n        else\n            x\n        end\n    end\n    \n    quote\n        let $(subs...)\n            $(esc(ex))\n        end\n    end\n\nend\n\n\n\"\"\"\n    @invokelatest expr...\n    \nRewrites all non-broadcasted function calls anywhere within an expression to use\n`Base.invokelatest`. This means functions can be called that have a newer world\nage, at the price of making things non-inferrable.\n\"\"\"\nmacro invokelatest(ex)\n    function walk(x)\n        if isdef(x)\n            x.args[2:end] .= map(walk, x.args[2:end])\n            x\n        elseif @capture(x, f_(args__; kwargs__)) && !startswith(string(f),'.')\n            :(Base.invokelatest($f, $(map(walk,args)...); $(map(walk,kwargs)...)))\n        elseif @capture(x, f_(args__)) && !startswith(string(f),'.')\n            :(Base.invokelatest($f, $(map(walk,args)...)))\n        elseif isexpr(x)\n            x.args .= map(walk, x.args)\n            x\n        else\n            x\n        end\n    end\n    esc(walk(ex))\nend\n\n\n\"\"\"\n    @ondemand(Package.function)(args...; kwargs...)\n    @ondemand(Package.Submodule.function)(args...; kwargs...)\n\nJust like calling `Package.function` or `Package.Submodule.function`, but\n`Package` will be loaded on-demand if it is not already loaded. The call is no\nlonger inferrable.\n\"\"\"\nmacro ondemand(ex)\n    get_root_package(x) = @capture(x, a_.b_) ? get_root_package(a) : x\n    quote\n        @eval import $(get_root_package(ex))\n        (args...; kwargs...) -> Base.invokelatest($(esc(ex)), args...; kwargs...)\n    end\nend\n\nfunction tmap(f, args...)\n    @static if nthreads()==1 || VERSION<v\"1.3\"\n        map(f, args...)\n    else\n        map(fetch,map((args...)->Threads.@spawn(f(args...)),args...))\n    end\nend\n\n\nget_kwarg_names(func::Function) = Vector{Symbol}(kwarg_decl(first(methods(func)), typeof(methods(func).mt.kwsorter)))\nkwarg_decl(m::Method,kw::DataType) = VERSION<=v\"1.3.999\" ? Base.kwarg_decl(m,kw) : Base.kwarg_decl(m)\n\n# maps a function recursively across all arguments of a Broadcasted expression,\n# using the function `broadcasted` to reconstruct the `Broadcasted` object at\n# each point.\nmap_bc_args(f, bc::Broadcasted) = broadcasted(bc.f, map(arg->map_bc_args(f, arg), bc.args)...)\nmap_bc_args(f, arg) = f(arg)\n\n\n# adapting a closure adapts the captured variables\n# this could probably be a PR into Adapt.jl\n@generated function adapt_structure(to, f::F) where {F<:Function}\n    if fieldcount(F) == 0\n        :f\n    else\n        quote\n            captured_vars = $(Expr(:tuple, (:(adapt(to, f.$x)) for x=fieldnames(F))...))\n            $(Expr(:new, :($(F.name.wrapper){map(typeof,captured_vars)...}), (:(captured_vars[$i]) for i=1:fieldcount(F))...))\n        end\n    end\nend\n\nadapt_structure(to, d::Dict) = Dict(k => adapt(to, v) for (k,v) in d)\n\n@doc doc\"\"\"\n\n    cpu(xs)\n\nRecursively move an object to CPU memory (i.e. the opposite of `cu`)\n\"\"\"\ncpu(xs) = adapt_structure(Array, xs)\n\n\nfunction corrify(H)\n    \u03c3 = sqrt.(abs.(diag(H)))\n    for i=1:checksquare(H)\n        H[i,:] ./= \u03c3\n        H[:,i] ./= \u03c3\n    end\n    H\nend\n\n\n\nstruct FailedPyimport\n    err\nend\ngetproperty(p::FailedPyimport, ::Symbol) = throw(getfield(p,:err))\n\n@doc doc\"\"\"\n\n    safe_pyimport(s)\n\nLike `pyimport`, but if `s` fails to import, instead of an error right away, the\nerror will be thrown the first time the user tries to access the contents of the\nmodule.\n\"\"\"\nfunction safe_pyimport(s)\n    try\n        @ondemand(PyCall.pyimport)(s)\n    catch err\n        FailedPyimport(err)\n    end\nend\n\n\n@doc doc\"\"\"\n    @ismain()\n    \nReturn true if the current file is being run as a script.\n\"\"\"\nmacro ismain()\n    (__source__ != nothing) && (String(__source__.file) == abspath(PROGRAM_FILE))\nend\n\n\n@doc doc\"\"\"\n    seed_for_storage!(storage[, seed])\n    seed_for_storage!((storage1, storage2, ...)[, seed])\n    \nSet the global random seed for the RNG which controls `storage`-type. \n\"\"\"\nseed_for_storage!(::Type{<:Array}, seed=nothing) = \n    Random.seed!((seed == nothing ? () : (seed,))...)\nseed_for_storage!(storage::Any, seed=nothing) = \n    error(\"Don't know how to set seed for storage=$storage\")\nseed_for_storage!(storages::Tuple, seed=nothing) = \n    seed_for_storage!.(storages, seed)\n\n\n\n@init @require MPIClusterManagers=\"e7922434-ae4b-11e9-05c5-9780451d2c66\" begin\n\n    using .MPIClusterManagers: MPI, start_main_loop, TCP_TRANSPORT_ALL, MPI_TRANSPORT_ALL\n\n    \"\"\"\n    init_MPI_workers()\n\n    Initialize MPI processes as Julia workers. Should be called from all MPI\n    processes, and will only return on the master process. \n\n    `transport` should be `\"MPI\"` or `\"TCP\"`, which is by default read from the\n    environment variable `JULIA_MPI_TRANSPORT`, and otherwise defaults to `\"TCP\"`.\n\n    If CUDA is loaded and functional in the Main module, additionally calls\n    [`assign_GPU_workers()`](@ref)\n    \"\"\"\n    function init_MPI_workers(;\n        stdout_to_master = false, \n        stderr_to_master = false,\n        transport = get(ENV,\"JULIA_MPI_TRANSPORT\",\"TCP\")\n    )\n        \n        if !MPI.Initialized()\n            MPI.Init()\n        end\n        size = MPI.Comm_size(MPI.COMM_WORLD)\n        rank = MPI.Comm_rank(MPI.COMM_WORLD)\n\n        if size>1\n            # workers don't return from this call:\n            start_main_loop(\n                Dict(\"TCP\"=>TCP_TRANSPORT_ALL,\"MPI\"=>MPI_TRANSPORT_ALL)[transport],\n                stdout_to_master=stdout_to_master,\n                stderr_to_master=stderr_to_master\n            )\n            \n            if @isdefined(CUDA) && CUDA.functional()\n                assign_GPU_workers()\n            end\n            @everywhere begin\n                typ = (myid()==1) ? \"(master)\" : \"(worker)\"\n                dev = (@isdefined(CUDA) && CUDA.functional()) ? device() : \"CPU\"\n                @info \"MPI process $(myid()) $typ is running on $(gethostname())::$dev\"\n            end\n        end\n\n    end\n\n    \nend\n\n\nfirsthalf(x) = x[1:end\u00f72]\nlasthalf(x) = x[end\u00f72:end]\n\n\nfunction sum_kbn(A::AbstractArray{T,N}; dims=:) where {T,N}\n    if (dims == (:)) || (N == length(dims))\n        KahanSummation.sum_kbn(adapt(Array,A))\n    else\n        dropdims(mapslices(sum_kbn, adapt(Array,A), dims=dims), dims=dims) :: Array{T,N-length(dims)}\n    end\nend\n@adjoint sum_kbn(A) = sum_kbn(A), \u0394 -> (fill!(similar(A),\u0394),)\n\n\n# courtesy of Takafumi Arakaki\nversionof(pkg::Module) = versionof(Base.PkgId(pkg))\nif isdefined(Pkg, :dependencies)\n    # can drop this branch once I drop support for 1.3\n    versionof(pkg::Base.PkgId) = Pkg.dependencies()[pkg.uuid].version\nelse\n    versionof(pkg::Base.PkgId) = Pkg.installed()[pkg.name]\nend\n\n\n# for mixed eltype, which Loess stupidly does not support\nLoess.loess(x::AbstractVector, y::AbstractVector; kwargs...) = \n    loess(collect.(zip(promote.(x,y)...))...; kwargs...)\n\n\nexpnorm(x) = exp.(x .- maximum(x))", "meta": {"hexsha": "738e4795479ebde2eee5cef03428520c7eb66a57", "size": 12879, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "RafaelArutjunjan/CMBLensing.jl", "max_stars_repo_head_hexsha": "475b34c75e59dc9bdcfd8acb76541dec278b066e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/util.jl", "max_issues_repo_name": "RafaelArutjunjan/CMBLensing.jl", "max_issues_repo_head_hexsha": "475b34c75e59dc9bdcfd8acb76541dec278b066e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/util.jl", "max_forks_repo_name": "RafaelArutjunjan/CMBLensing.jl", "max_forks_repo_head_hexsha": "475b34c75e59dc9bdcfd8acb76541dec278b066e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.1200873362, "max_line_length": 126, "alphanum_fraction": 0.60975231, "num_tokens": 3829, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.13660839881392145, "lm_q1q2_score": 0.064572532886915}}
{"text": "# ---\n# title: 5. Longest Palindromic Substring\n# id: problem5\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: String, Dynamic Programming\n# link: <https://leetcode.com/problems/longest-palindromic-substring/description/>\n# hidden: true\n# ---\n# \n# Given a string `s`, return  _the longest palindromic substring_ in `s`.\n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: s = \"babad\"\n#     Output: \"bab\"\n#     **Note:** \"aba\" is also a valid answer.\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: s = \"cbbd\"\n#     Output: \"bb\"\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: s = \"a\"\n#     Output: \"a\"\n#     \n# \n# **Example 4:**\n# \n#     \n#     \n#     Input: s = \"ac\"\n#     Output: \"a\"\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `1 <= s.length <= 1000`\n#   * `s` consist of only digits and English letters (lower-case and/or upper-case),\n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction longest_palindrome(s::String)::AbstractString\n    res = \"\"\n    for i in 1:length(s)\n        s_odd = _longest_palindrome(s, i, i)\n        if length(s_odd) > length(res)\n            res = s_odd\n        end\n        s_even = _longest_palindrome(s, i, i + 1)\n        if length(s_even) > length(res)\n            res = s_even\n        end\n    end\n    return res\nend\n\nfunction _longest_palindrome(s, l, r)\n    while l >= 1 && r <= length(s) && s[l] == s[r]\n        l -= 1\n        r += 1\n    end\n    return SubString(s, l + 1, r - 1)\nend\n## @lc code=end\n", "meta": {"hexsha": "ffab65f441cd8ab656aed7585e06f1bd33abbc6b", "size": 1479, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/5.longest-palindromic-substring.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/problems/5.longest-palindromic-substring.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/problems/5.longest-palindromic-substring.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 17.8192771084, "max_line_length": 84, "alphanum_fraction": 0.5206220419, "num_tokens": 495, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.13117322206357784, "lm_q1q2_score": 0.0645619036239623}}
{"text": "\"\"\"\n    NestedSamplers.Proposals\n\nThis module contains the different algorithms for proposing new points within a bounding volume in unit space.\n\nThe available implementations are\n* [`Proposals.Rejection`](@ref) - samples uniformly within the bounding volume\n* [`Proposals.RWalk`](@ref) - random walks to a new point given an existing one\n* [`Proposals.RStagger`](@ref) - random staggering away to a new point given an existing one\n* [`Proposals.Slice`](@ref) - slicing away to a new point given an existing one\n* [`Proposals.RSlice`](@ref) - random slicing away to a new point given an existing one\n\"\"\"\nmodule Proposals\n\nusing ..NestedSamplers: prior_transform_and_loglikelihood\nusing ..Bounds\n\nusing Random\nusing LinearAlgebra\nusing Parameters\n\nexport AbstractProposal\n\n\"\"\"\n    NestedSamplers.AbstractProposal\n\nThe abstract type for live point proposal algorithms.\n\n# Interface\n\nEach `AbstractProposal` must have this function, \n```julia\n(::AbstractProposal)(::AbstractRNG, point, loglstar, bounds, loglikelihood, prior_transform)\n```\nwhich, given the input `point` with loglikelihood `loglstar` inside a `bounds`, returns a new point in unit space, prior space, the loglikelihood, and the number of function calls.\n\"\"\"\nabstract type AbstractProposal end\n\n# ----------------------------------------\n\n# Helper for checking unit-space bounds\nunitcheck(us) = all(u -> 0 < u < 1, us)\n\n\"\"\"\n    Proposals.Rejection(;maxiter=100_000)\n\nPropose a new live point by uniformly sampling within the bounding volume and rejecting samples that do not meet the likelihood constraints. This follows the original nested sampling algorithm proposed in Skilling (2004)[^1]\n\n[^1]: John Skilling, 2004, AIP 735, 395  [\"Nested Sampling\"](https://aip.scitation.org/doi/abs/10.1063/1.1835238)\n\n## Parameters\n- `maxiter` is the maximum number of samples that can be rejected before giving up and throwing an error.\n\"\"\"\nBase.@kwdef struct Rejection <: AbstractProposal\n    maxiter::Int = 100_000\nend\n\n@deprecate Uniform() Rejection()\n\nfunction (prop::Rejection)(\n    rng::AbstractRNG,\n    point::AbstractVector,\n    logl_star,\n    bounds::AbstractBoundingSpace,\n    model\n)\n    \n    ncall = 0\n    for _ in 1:prop.maxiter\n        u = rand(rng, bounds)\n        unitcheck(u) || continue\n        v, logl = prior_transform_and_loglikelihood(model, u)\n        ncall += 1\n        logl \u2265 logl_star && return u, v, logl, ncall\n    end\n    throw(ErrorException(\"Couldn't generate a proper point after $(prop.maxiter) attempts including $ncall likelihood calls. Bounds=$bounds, logl_star=$logl_star.\"))\nend\n\nBase.show(io::IO, p::Rejection) = print(io, \"NestedSamplers.Proposals.Rejection\")\n\n\"\"\"\n    Proposals.RWalk(;ratio=0.5, walks=25, scale=1)\n\nPropose a new live point by random walking away from an existing live point. This follows the algorithm outlined in Skilling (2006).[^5]\n\n[^5]: Skilling, 2006, Bayesian Anal. 1(4), [\"Nested sampling for general Bayesian computation\"](https://projecteuclid.org/journals/bayesian-analysis/volume-1/issue-4/Nested-sampling-for-general-Bayesian-computation/10.1214/06-BA127.full)\n\n## Parameters\n- `ratio` is the target acceptance ratio\n- `walks` is the minimum number of steps to take\n- `scale` is the proposal distribution scale, which will update _between_ proposals.\n\"\"\"\n@with_kw mutable struct RWalk <: AbstractProposal\n    ratio = 0.5\n    walks = 25\n    scale = 1.0\n\n    @assert 1 / walks \u2264 ratio \u2264 1 \"Target acceptance ratio must be between 1/`walks` and 1\"\n    @assert walks > 1 \"Number of steps must be greater than 1\"\n    @assert scale \u2265 0 \"Proposal scale must be non-negative\"\nend\n\nfunction (prop::RWalk)(\n    rng::AbstractRNG,\n    point::AbstractVector,\n    logl_star,\n    bounds::AbstractBoundingSpace,\n    model;\n    kwargs...\n)\n    # setup\n    n = length(point)\n    scale_init = prop.scale\n    accept = reject = fail = nfail = nc = ncall = 0\n    local du, u_prop, logl_prop, u, v, logl\n\n    while nc < prop.walks || iszero(accept)\n        # get proposed point\n        while true\n            # check scale factor to avoid over-shrinking\n            prop.scale < 1e-5 * scale_init && error(\"Random walk sampling appears to be stuck.\")\n            # transform to proposal distribution\n            du = randoffset(rng, bounds)\n            u_prop = @. point + prop.scale * du\n            # inside unit-cube\n            unitcheck(u_prop) && break\n            \n            fail += 1\n            nfail += 1\n            # check if stuck generating bad numbers\n            if fail > 100 * prop.walks\n                @warn \"Random number generation appears extremely inefficient. Adjusting the scale-factor accordingly\"\n                fail = 0\n                prop.scale *= exp(-1/n)\n            end\n        end\n        # check proposed point\n        v_prop, logl_prop = prior_transform_and_loglikelihood(model, u_prop)\n        if logl_prop \u2265 logl_star\n            u = u_prop\n            v = v_prop\n            logl = logl_prop\n            accept += 1\n        else\n            reject += 1\n        end\n        nc += 1\n        ncall += 1\n        \n        # check if stuck generating bad points\n        if nc > 50 * prop.walks\n            @warn \"Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly\"\n            prop.scale *= exp(-1/n)\n            nc = accept = reject = 0\n        end\n    end\n    \n    # update proposal scale using acceptance ratio\n    update_scale!(prop, accept, reject, n)\n\n    return u, v, logl, ncall\nend\n\n# update proposal scale using target acceptance ratio\nfunction update_scale!(prop, accept, reject, n)\n    ratio = accept / (accept + reject)\n    norm = max(prop.ratio, 1 - prop.ratio) * n\n    scale = prop.scale * exp((ratio - prop.ratio) / norm)\n    prop.scale = min(scale, sqrt(n))\n    return prop\nend\n\n\"\"\"\n    Proposals.RStagger(;ratio=0.5, walks=25, scale=1)\n\nPropose a new live point by random staggering away from an existing live point. \nThis differs from the random walk proposal in that the step size here is exponentially adjusted\nto reach a target acceptance rate _during_ each proposal, in addition to _between_\nproposals. This follows the algorithm outlined in Skilling (2006).[^5]\n\n[^5]: Skilling, 2006, Bayesian Anal. 1(4), [\"Nested sampling for general Bayesian computation\"](https://projecteuclid.org/journals/bayesian-analysis/volume-1/issue-4/Nested-sampling-for-general-Bayesian-computation/10.1214/06-BA127.full)\n\n## Parameters\n- `ratio` is the target acceptance ratio\n- `walks` is the minimum number of steps to take\n- `scale` is the proposal distribution scale, which will update _between_ proposals.\n\"\"\"\n@with_kw mutable struct RStagger <: AbstractProposal\n    ratio = 0.5\n    walks = 25\n    scale = 1.0\n\n    @assert 1 / walks \u2264 ratio \u2264 1 \"Target acceptance ratio must be between 1/`walks` and 1\"\n    @assert walks > 1 \"Number of steps must be greater than 1\"\n    @assert scale \u2265 0 \"Proposal scale must be non-negative\"\nend\n\nfunction (prop::RStagger)(\n    rng::AbstractRNG,\n    point::AbstractVector,\n    logl_star,\n    bounds::AbstractBoundingSpace,\n    model;\n    kwargs...\n)\n    #setup\n    n = length(point)\n    scale_init = prop.scale\n    accept = reject = fail = nfail = nc = ncall = 0\n    stagger = 1\n    local du, u_prop, logl_prop, u, v, logl\n    \n    while nc < prop.walks || iszero(accept)\n        # get proposed point\n        while true\n            # check scale factor to avoid over-shrinking\n            prop.scale < 1e-5 * scale_init && error(\"Random walk sampling appears to be stuck.\")\n            # transform to proposal distribution\n            du = randoffset(rng, bounds)\n            u_prop = @. point + prop.scale * stagger * du\n            # inside unit-cube\n            unitcheck(u_prop) && break\n            \n            fail += 1\n            nfail += 1\n            # check if stuck generating bad numbers\n            if fail > 100 * prop.walks\n                @warn \"Random number generation appears extremely inefficient. Adjusting the scale-factor accordingly\"\n                fail = 0\n                prop.scale *= exp(-1/n)\n            end\n        end \n        # check proposed point\n        v_prop, logl_prop = prior_transform_and_loglikelihood(model, u_prop)\n        if logl_prop \u2265 logl_star\n            u = u_prop\n            v = v_prop\n            logl = logl_prop\n            accept += 1\n        else\n            reject += 1\n        end\n        nc += 1\n        ncall += 1\n        \n        # adjust _stagger_ to target an acceptance ratio of `prop.ratio`\n        ratio = accept / (accept + reject)\n        if ratio > prop.ratio\n            stagger *= exp(1 / accept)\n        elseif ratio < prop.ratio\n            stagger /= exp(1 / reject)\n        end\n        \n        # check if stuck generating bad points\n        if nc > 50 * prop.walks\n            @warn \"Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly\"\n            prop.scale *= exp(-1 / n)\n            nc = accept = reject = 0\n        end\n    end\n            \n    # update proposal scale using acceptance ratio\n    update_scale!(prop, accept, reject, n)\n            \n    return u, v, logl, ncall\nend\n      \n\"\"\"\n    Proposals.Slice(;slices=5, scale=1)\n\nPropose a new live point by a series of random slices away from an existing live point.\nThis is a standard _Gibbs-like_ implementation where a single multivariate slice is a combination of `slices` univariate slices through each axis. This follows the algorithm outlined in Neal (2003).[^6]\n\n[^6]: Neal, 2003, Ann. Statist. 31(3), [\"Slice Sampling\"](https://projecteuclid.org/journals/annals-of-statistics/volume-31/issue-3/Slice-sampling/10.1214/aos/1056562461.full)\n\n## Parameters\n- `slices` is the minimum number of slices\n- `scale` is the proposal distribution scale, which will update _between_ proposals.\n\"\"\"\n@with_kw mutable struct Slice <: AbstractProposal\n    slices = 5\n    scale = 1.0\n    \n    @assert slices \u2265 1 \"Number of slices must be greater than or equal to 1\"\n    @assert scale \u2265 0 \"Proposal scale must be non-negative\"\nend\n\nfunction (prop::Slice)(\n    rng::AbstractRNG,\n    point::AbstractVector,\n    logl_star,\n    bounds::AbstractBoundingSpace,\n    model;\n    kwargs...\n)\n    # setup\n    n = length(point)\n    nc = nexpand = ncontract = 0\n    local u, v, logl \n    \n    # modifying axes and computing lengths\n    axes = Bounds.axes(bounds)\n    axes = prop.scale .* axes'\n    # slice sampling loop\n    for it in 1:prop.slices\n        \n        # shuffle axis update order\n        idxs = shuffle!(rng, collect(Base.axes(axes, 1)))\n        \n        # slice sample along a random direction\n        for idx in idxs\n            \n            # select axis\n            axis = axes[idx, :]\n            \n            u, v, logl, nc, nexpand, ncontract = sample_slice(\n                rng, axis, point, logl_star,\n                model,\n                nc, nexpand, ncontract\n            )\n        end # end of slice sample along a random direction             \n    end # end of slice sampling loop    \n    \n    # update slice proposal scale based on the relative size of the slices compared to the initial guess\n    prop.scale = prop.scale * nexpand / (2.0 * ncontract)\n         \n    return u, v, logl, nc\nend   # end of function Slice  \n\n\"\"\"\n    Proposals.RSlice(;slices=5, scale=1)\n\nPropose a new live point by a series of random slices away from an existing live point. This is a standard _random_ implementation where each slice is along a random direction based on the provided axes. This more closely matches the PolyChord implementation outlined in Handley et al. (2015a,b).[^7][^8]\n\n[^7]: Handley, et al., 2015a, MNRAS 450(1), [\"polychord: nested sampling for cosmology\"](https://academic.oup.com/mnrasl/article/450/1/L61/986122)\n[^8]: Handley, et al., 2015b, MNRAS 453(4), [\"polychord: next-generation nested sampling\"](https://academic.oup.com/mnras/article/453/4/4384/2593718)\n\n## Parameters\n- `slices` is the minimum number of slices\n- `scale` is the proposal distribution scale, which will update _between_ proposals.\n\"\"\"\n@with_kw mutable struct RSlice <: AbstractProposal\n    slices = 5\n    scale = 1.0\n    \n    @assert slices \u2265 1 \"Number of slices must be greater than or equal to 1\"\n    @assert scale \u2265 0 \"Proposal scale must be non-negative\"\nend\n\nfunction (prop::RSlice)(\n    rng::AbstractRNG,\n    point::AbstractVector,\n    logl_star,\n    bounds::AbstractBoundingSpace,\n    model;\n    kwargs...\n)\n    # setup\n    n = length(point)\n    nc = nexpand = ncontract = 0\n    local u, v, logl\n    # random slice sampling loop\n    for it in 1:prop.slices\n    \n        # propose a direction on the unit n-sphere \n        drhat = randn(rng, n)\n        drhat /= norm(drhat)\n        \n        # transform and scale into parameter space\n        axis = prop.scale .* (Bounds.axes(bounds) * drhat)\n        u, v, logl, nc, nexpand, ncontract = sample_slice(\n            rng, axis, point, logl_star,\n            model,\n            nc, nexpand, ncontract\n        )\n    end # end of random slice sampling loop\n    \n    # update random slice proposal scale based on the relative size of the slices compared to the initial guess\n    prop.scale = prop.scale * nexpand / (2.0 * ncontract)\n    \n    return u, v, logl, nc\nend # end of function RSlice\n\n# Method for slice sampling\nfunction sample_slice(rng, axis, u, logl_star, model, nc, nexpand, ncontract)\n    # define starting window\n    r = rand(rng)  # initial scale/offset\n    u_l = @. u - r * axis  # left bound\n    if unitcheck(u_l)\n        v_l, logl_l = prior_transform_and_loglikelihood(model, u_l)\n    else\n        logl_l = -Inf\n    end\n    nc += 1\n    nexpand += 1 \n\n    u_r = u_l .+ axis # right bound\n    if unitcheck(u_r)\n        v_r, logl_r = prior_transform_and_loglikelihood(model, u_r)\n    else\n        logl_r = -Inf\n    end    \n    nc += 1\n    nexpand += 1 \n\n    # stepping out left and right bounds\n    while logl_l \u2265 logl_star\n        u_l .-= axis\n        if unitcheck(u_l)\n            v_l, logl_l = prior_transform_and_loglikelihood(model, u_l)\n        else\n            logl_l = -Inf\n        end\n        nc += 1\n        nexpand += 1 \n    end\n\n    while logl_r \u2265 logl_star\n        u_r .+= axis\n        if unitcheck(u_r)\n            v_r, logl_r = prior_transform_and_loglikelihood(model, u_r)\n        else\n            logl_r = -Inf\n        end\n        nc += 1\n        nexpand += 1 \n    end\n\n    # sample within limits. If the sample is not valid, shrink the limits until the `logl_star` bound is hit\n    window_init = norm(u_r - u_l)  # initial window size\n    while true \n        # define slice and window\n        u_hat = u_r - u_l\n        window = norm(u_hat)\n\n        # check if the slice has shrunk to be ridiculously small\n        window < 1e-5 * window_init && error(\"Slice sampling appears to be stuck.\")\n\n        # propose a new position\n        r = rand(rng)\n        u_prop = @. u_l + r * u_hat\n        if unitcheck(u_prop)\n            v_prop, logl_prop = prior_transform_and_loglikelihood(model, u_prop)\n        else\n            logl_prop = -Inf\n        end\n        nc += 1\n        ncontract += 1\n\n        # if success, then move to the new position\n        if logl_prop \u2265 logl_star\n            return u_prop, v_prop, logl_prop, nc, nexpand, ncontract\n        # if fail, then check if the new point is to the left/right of the original point along the proposal axis and update the bounds accordingly\n        else\n            s = dot(u_prop - u, u_hat) # check sign (+/-)\n            if s < 0   # left\n                u_l = u_prop\n            elseif s > 0  # right\n                u_r = u_prop\n            else # if `s = 0` something has gone wrong\n                error(\"Slice sampler has failed to find a valid point.\")\n            end\n        end\n    end # end of sample within limits while    \nend    \n    \nend # module Proposals\n", "meta": {"hexsha": "15478fe20ec7ea4bae683a6780f20b508c70350e", "size": 15806, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/proposals/Proposals.jl", "max_stars_repo_name": "aplavin/NestedSamplers.jl", "max_stars_repo_head_hexsha": "3e6b6eae4620a1bdef46e81ac1a9c9801a364327", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-12-12T22:42:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-25T13:17:34.000Z", "max_issues_repo_path": "src/proposals/Proposals.jl", "max_issues_repo_name": "aplavin/NestedSamplers.jl", "max_issues_repo_head_hexsha": "3e6b6eae4620a1bdef46e81ac1a9c9801a364327", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/proposals/Proposals.jl", "max_forks_repo_name": "aplavin/NestedSamplers.jl", "max_forks_repo_head_hexsha": "3e6b6eae4620a1bdef46e81ac1a9c9801a364327", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-09T05:03:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-09T05:03:24.000Z", "avg_line_length": 33.7735042735, "max_line_length": 304, "alphanum_fraction": 0.6331772745, "num_tokens": 4059, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4301473485858429, "lm_q2_score": 0.15002883004302128, "lm_q1q2_score": 0.06453450345444166}}
{"text": "using Printf\n\nfunction dictionary_examples()\n    println(\"--------------------------------------\")\n    println(\"Dictionary examples\")\n    \n    println()\n\n    println(\"Dictionaries are key/value pairs where the key must be unique\")\n    println(\"REMEMBER TO CHECK THE SYMBOLS.JL FILE! SYMBOLS ARE FREQUENTLY USED AS KEYS FOR DICTIONARIES!!!\")\n    \n    dictionary_1 = Dict(1 => \"one\", 2 => \"two\")\n    \n    println(\"We can display the whole dictionary\")\n    println(dictionary_1)\n    println(\"Or we can display a particular value corresponding to the key\")\n    println(dictionary_1[2])\n\n    println(\"Dictionary keys can be any time, for example strings\")    \n    dictionary_2 = Dict(\"pi\" => 3.1415, \"e\" => 2.718)\n    println(dictionary_2)\n    println(\"And we can still get items by key\")\n    println(dictionary_2[\"pi\"])\n    \n    println(\"We can also add new items\")\n    dictionary_2[\"foobar\"] = 123.456\n    println(dictionary_2)\n    \n    println(\"And we can delete items\")\n    delete!(dictionary_2, \"pi\")\n    println(dictionary_2)\n    \n    println(\"We can check to see if a key exists, in this case 'foobar'\")\n    println(haskey(dictionary_2, \"foobar\"))\n    \n    println(\"We can display all keys\")\n    println(keys(dictionary_2))\n    println(\"We can display all values\")\n    println(values(dictionary_2))\n    \n    println(\"We can also check to see if a particular key/value pair exists in the dictionary\")\n    println(in(\"foobar\"=>123.456, dictionary_2))\n    \n    println(\"We can loop through all the key/value pairs\")\n    for key_value in dictionary_2\n        println(key_value)\n    end\n    \n    println(\"We can also loop through getting the key/value individually\")\n    for(key, value) in dictionary_2\n        println(key, \" : \", value)\n    end\n    \nend\n\n", "meta": {"hexsha": "9edf4bfe0ce131a51016bb8068daad12de75edfb", "size": 1753, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dictionaries.jl", "max_stars_repo_name": "James-P-D/JuliaDump", "max_stars_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/dictionaries.jl", "max_issues_repo_name": "James-P-D/JuliaDump", "max_issues_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/dictionaries.jl", "max_forks_repo_name": "James-P-D/JuliaDump", "max_forks_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3035714286, "max_line_length": 109, "alphanum_fraction": 0.6503137479, "num_tokens": 421, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3812195803163617, "lm_q2_score": 0.16885695214168314, "lm_q1q2_score": 0.06437157642895243}}
{"text": "\"\"\"\n    count_nucleotides(strand)\n\nThe frequency of each nucleotide within `strand` as a dictionary.\n\nInvalid strands raise a `DomainError`.\n\n\"\"\"\nfunction count_nucleotides(strand)\n    d = Dict('A' => 0, 'C' => 0, 'G' => 0, 'T' => 0)\n    for c in strand\n        try d[c] += 1 catch; throw(DomainError(\"Invalid strand\")) end\n    end\n    return d\nend\n", "meta": {"hexsha": "505db447e8e5210c04149cb2010486203edb1c8a", "size": 349, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_stars_repo_name": "aadimator/exercism", "max_stars_repo_head_hexsha": "191d979166258e508230d3b652eeb581ff1077e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_issues_repo_name": "aadimator/exercism", "max_issues_repo_head_hexsha": "191d979166258e508230d3b652eeb581ff1077e1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_forks_repo_name": "aadimator/exercism", "max_forks_repo_head_hexsha": "191d979166258e508230d3b652eeb581ff1077e1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.8125, "max_line_length": 69, "alphanum_fraction": 0.6275071633, "num_tokens": 109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.1347759243735362, "lm_q1q2_score": 0.06423146300972425}}
{"text": "\n\n######################### Basic BED file tools #########################\n\n## .bed file format ##################################################\n#\n# See http://pngu.mgh.harvard.edu/~purcell/plink/binary.shtml\n# http://zzz.bwh.harvard.edu/plink/binary.shtml\n# and https://www.cog-genomics.org/plink2/formats#bed\n#\n######################################################################\n\n\"\"\"\n    rawformat(snp::Integer, quartermap=quarterstohuman)\n\nReturns RAW format from the BED format quarter-byte:\n\n| BED    | RAW (default) | general         | Meaning                 |\n|:------ |:------------- |:--------------- |:----------------------- |\n| `0b00` | `0b10`        | `quartermap[1]` | homozygous minor allele |\n| `0b11` | `0b00`        | `quartermap[2]` | homozygous major allele |\n| `0b01` | `NA_byte`     | `quartermap[3]` | missing value           |\n| `0b10` | `0b01`        | `quartermap[4]` | heterozygous            |\n\n\"\"\"\n@inline function rawformat(snp::Integer, quartermap=Consts.quarterstohuman)\n    @inbounds return quartermap[snp + 1]\nend\n\n\"\"\"\n    breakbyte(byte::UInt8, bytemap=Consts.defaultbytemap)\n\nReturn a `Tuple{Vararg{UInt8, 4}}` of the RAW-formatted SNP quarters, as\ndetermined by `bytemap`, in `byte`.\n\n\"\"\"\n@inline function breakbyte(byte::UInt8, bytemap=Consts.defaultbytemap, flip::Bool=false)\n    @inbounds return bytemap[byte + 1, flip + 1]\nend\n\n@inline function breakbyte(byte::UInt8, bytemap, snpind::Int, flip::Bool=false)\n    @inbounds return breakbyte(byte, bytemap, flip)[snpind]\nend\n\n# Approach from Quantgen/BEDMatrix: shift by 2(n - 1) and then mask\n# result.\n#\n# Performance-wise, the lookup table approach of breakbyte is a bit\n# faster.\n\"\"\"\n    quarter(byte::UInt8, n::Integer, quartermap=quarterstohuman)\n\nReturns the RAW-formatted `n`th quarter of `byte`, equivalent to\n`breakbyte(byte)[n]`.\n\n\"\"\"\nfunction quarter(byte::UInt8, n::Integer, quartermap=Consts.quarterstohuman)\n    @inbounds return quartermap[((byte >> 2(n - 1)) & 0b00000011) + 1]\nend\n\n@inline function flipBEDbyte(byte::UInt8)\n    # 0b00 -> 0b11\n    # 0b11 -> 0b00\n    # 0b10 -> 0b10\n    # 0b01 -> 0b01\n\n    @inbounds return Consts.flipBEDbytes[byte + 1]\nend\n\n\"\"\"\n    BEDsize(filebase::AbstractString)\n\nReturns the number of rows and columns (as a tuple) of the BED matrix\nin `filebase*\".bed\"` as determined by linecounts of `filebase*\".fam\"`\nand `filebase*\".bim\"`.\n\n\"\"\"\nfunction BEDsize(filebase::AbstractString)\n    famfile, bimfile = filebase*\".fam\", filebase*\".bim\"\n    isfile(famfile) || error(\"missing FAM file \\\"$famfile\\\"\")\n    isfile(bimfile) || error(\"missing BIM file \\\"$bimfile\\\"\")\n\n    n = countlines(famfile)\n    p = countlines(bimfile)\n    return n, p\nend\n\n\"\"\"\n    checkmagic(bytes)\n\nDetermine if first two \"magic\" bytes match plink format. If not throws\nan error, else returns `true`.\n\nIf bytes are UInt16, then it should be in big endian format.\n\n\"\"\"\n# Puts first two bytes into big endian format\ncheckmagic(bytes::Vector{UInt8}) = checkmagic((convert(UInt16, bytes[1]) << 8) + bytes[2])\n\ncheckmagic(twobytes::UInt16) = (twobytes == Consts.plinkmagic ||\n                                error(\"Bad magic: not a plink bed file\"))\n\nfunction checkmagic(bedstream::IO)\n    seekstart(bedstream)\n\n    # Note that I don't just read(bedstream, UInt16) to avoid system\n    # endian dependence, although I believe julia currently only runs\n    # on little endian machines.\n    return checkmagic((convert(UInt16, read(bedstream, UInt8)) << 8) + read(bedstream, UInt8))\nend\n\n\n\"\"\"\n    BEDmode(byte)\n\nReturns the BED mode as determined by the third byte of the file:\neither the current standard, `:SNPmajor`, or older plink formats,\n`:SNPminor`.\n\n\"\"\"\nBEDmode(byte::UInt8) = Consts.modes[byte]\n\nfunction BEDmode(bedstream::IO)\n    seek(bedstream, 2)\n    return BEDmode(read(bedstream, UInt8))\nend\n\nBEDmode(bytevector::Vector{UInt8}) = BEDmode(bytevector[3])\n\n\ngetbytemap{T}(navalue::T) = getbytemap((convert(T, 0b10), navalue,\n                                        convert(T, 0b01), convert(T, 0b00)))\nfunction getbytemap{T}(quartermap::Tuple{T, T, T, T})\n    bytemap = hcat([(rawformat(byte & 0b00_00_00_11, quartermap),\n                     rawformat((byte & 0b00_00_11_00) >> 2, quartermap),\n                     rawformat((byte & 0b00_11_00_00) >> 4, quartermap),\n                     rawformat((byte & 0b11_00_00_00) >> 6, quartermap)) for\n                    byte in 0x00:0xff],\n                   [(rawformat(byte & 0b00_00_00_11, quartermap),\n                     rawformat((byte & 0b00_00_11_00) >> 2, quartermap),\n                     rawformat((byte & 0b00_11_00_00) >> 4, quartermap),\n                     rawformat((byte & 0b11_00_00_00) >> 6, quartermap)) for\n                    byte in Consts.flipBEDbytes])\n\n   bytemap\nend\n\n\n\"\"\"\n    unsafe_breakbyte!(vector::AbstractArray, byte::UInt8,\n                      bytemap=bytetoquarters, vecstart=1,\n                      quarterstart=1, quarterstop=4,\n                      flip::Bool=false)\n\nFills `vector` starting at `vecstart` with the `quarterstart`th snp\nthrough the `quarterstop`th snp in `byte`, as determined by `bytemap`.\n\n## (Unchecked) Constraints:\n* 1 <= `quarterstart` <= `quarterstop` <= 4\n* 1 <= `vecstart` <= `end - (quarterstop - quarterstart)`\n\n\"\"\"\nfunction unsafe_breakbyte!(vector::AbstractArray, byte::UInt8,\n                           bytemap=Consts.bytetoquarters, flip::Bool=false,\n                           vecstart::Integer=1, quarterstart::Integer=1, quarterstop::Integer=4)\n\n    @inbounds copy!(vector, vecstart,\n                    breakbyte(byte, bytemap, flip), quarterstart,\n                    quarterstop - quarterstart + 1)\n    vector\nend\n\n\n\"\"\"\n    unsafe_copybytestosnps!(snparray::AbstractArray, bytearray::AbstractArray{UInt8},\n                            bytestart::Integer, quarterstart::Integer,\n                            bytestop::Integer, quarterstop::Integer,\n                            deststart::Integer=1, bytemap=bytetoquarters,\n                            flip::Bool=false)\n\nFills `snparray[deststart:(deststart + num_snps)]` with snps, where\n\n    num_snps = 4*(bytestop - bytestart) + (quarterstop - quarterstart) + 1\n\nThe SNPs start with the `quarterstart`th snp in the `bytestart`th\nbyte, and end with `quarterstop` and `bytestop`.  The SNP\nrepresentation is determined by the `bytemap`. Currently only supports\nunit stride for both `snparray` and `bytearray`, since at present\nthere is no compelling use-case involving non-unit strides.\n\nUtility function used by more front-facing functions.\n\nIf `flip` is `true`, then minor and major allele are flipped when copying.\n\n\"\"\"\nfunction unsafe_copybytestosnps!(snparray::AbstractArray, bytearray::AbstractArray{UInt8},\n                                 bytestart::Integer, quarterstart::Integer,\n                                 bytestop::Integer, quarterstop::Integer,\n                                 deststart::Integer=1, bytemap=Consts.bytetoquarters,\n                                 flip::Bool=false)\n\n    # First byte\n    if quarterstart != 1\n        stop = ifelse(bytestart == bytestop, quarterstop, 4)\n        @inbounds unsafe_breakbyte!(snparray, bytearray[bytestart], bytemap, flip,\n                                    deststart, quarterstart, stop)\n        deststart += stop - quarterstart + 1\n        bytestart += 1\n    end\n\n    if bytestart <= bytestop\n        # Last byte\n        if quarterstop != 4\n            @inbounds unsafe_breakbyte!(snparray, bytearray[bytestop], bytemap, flip,\n                                        deststart + 4*(bytestop - bytestart), 1, quarterstop)\n            bytestop -= 1\n        end\n\n        # Main course\n        @simd for bytej in bytestart:bytestop\n            @inbounds unsafe_breakbyte!(snparray, bytearray[bytej], bytemap, flip,\n                                        deststart + 4*(bytej - bytestart))\n        end\n    end\n\n    return snparray\nend\n\n\n######################### Reading .bed into a Matrix #########################\n\n\"\"\"\n    BEDintomatrix{T<:Real}(bedfilename::AbstractString; datatype::Type{T}=UInt8,\n                           navalue=NA_byte, n::Integer=0, p::Integer=0, use_mmap=true)\n\nReturns a `Matrix{T}` representation of the BED file `bedfilename`. If\nthe number of rows or columns, `n` and `p`, are not provided, then\nthey will attempt to be determined from corresponding .fam and .bim\nfiles. `use_mmap` determines whether to memory map the bedfile and\nthen read into the matrix for potential speedups. Missing values are\nset to `navalue`.\n\n\"\"\"\nfunction BEDintomatrix{T<:Real}(bedfilename::AbstractString;\n                                datatype::Type{T}=UInt8,\n                                navalue=NA_byte,\n                                n::Integer=0,\n                                p::Integer=0,\n                                use_mmap::Bool=true)\n    if !isfile(bedfilename)\n        isfile(bedfilename*\".bed\") || error(\"Cannot find file \\\"$bedfilename\\\"\")\n        bedfilename = bedfilename*\".bed\"\n    end\n    filebase = splitext(bedfilename)[1]\n\n    if n == p == 0\n        n, p = BEDsize(filebase)\n    end\n\n    A = Matrix{T}(n, p)\n\n    fsz = filesize(bedfilename)\n    if use_mmap && 0 < fsz < typemax(Int)\n        bedfilevector = open(bedfilename, \"r\") do bedfile\n            bvec = Mmap.mmap(bedfile, Vector{UInt8}, (Int(fsz),))\n            checkmagic(bvec)\n            BEDmode(bvec) == :SNPmajor || error(\"SNPminor mode not supported\")\n            bvec\n        end\n        BEDintomatrix!(A, bedfilevector, convert(T, navalue))\n    else\n        open(bedfilename, \"r\") do bedfin\n            checkmagic(bedfin)\n            BEDmode(bedfin) == :SNPmajor || error(\"SNPminor mode not supported\")\n\n            BEDintomatrix!(A, bedfin, convert(T, navalue))\n        end\n    end\n\n    return A::Matrix{T}\nend\n\nfunction BEDintomatrix!{T<:Real}(A::AbstractMatrix{T}, bedvector::Vector{UInt8}, navalue=NA_byte)\n    n, p = size(A)\n    byteheight = ceil(Int, n/4)\n    quarterstop = n - 4*(byteheight - 1)\n\n    bytemap = getbytemap(convert(T, navalue))\n\n    @inbounds for col in 1:p\n        unsafe_copybytestosnps!(A, bedvector, byteheight*(col - 1) + 3 + 1, 1, byteheight*col + 3, quarterstop, n*(col - 1) + 1, bytemap)\n    end\n\n    return A\nend\n\n\"\"\"\n    BEDintomatrix!{T<:Real}(A::AbstractMatrix{T}, bedstream::IO, navalue=NA_byte)\n\nFills `A` with type `T` representation of `bedstream` that corresponds\nto .bed file format. `A` must have correct dimensions, as determined\nvia `BEDMatrices.BEDsize`, for example.\n\n\"\"\"\nfunction BEDintomatrix!{T<:Real}(A::AbstractMatrix{T}, bedstream::IO, navalue=NA_byte)\n    n, p = size(A)\n    bytestop = ceil(Int, n/4)\n    quarterstop = n - 4*(bytestop - 1)\n\n    bytemap = getbytemap(convert(T, navalue))\n\n    bytecol = Vector{UInt8}(bytestop)\n    @inbounds for col in 1:p\n        read!(bedstream, bytecol)\n        unsafe_copybytestosnps!(A, bytecol, 1, 1, bytestop, quarterstop, n*(col - 1) + 1, bytemap)\n    end\n\n    return A\nend\n\n\n######################### BEDMatrix type #########################\n# See implementation of BitArray for inspiration julia/base/bitarray.jl\n# in /usr/share/julia\n\n## Things we get for \"free\", but may want to overload for better performance, etc.:\n# * checkbounds(B, i...)\n# * eltype(B)\n# * length(B)\n# * ndims(B)\n# * strides(B)\n# * view(B, i...) -- not sure if it is memory and computationally efficient...\n# * matrix operations: v * B and B * v, norm(B), etc.\n\n\n# Allowing for `X` to be a general subtype `S` of `AbstractMatrix`\n# requires exposing the type as below. See discussion at\n# http://docs.julialang.org/en/stable/manual/performance-tips/#type-declarations\n\nimmutable BEDMatrix{T, S<:AbstractMatrix} <: DenseArray{T, 2}\n    n::Int                               # number of samples (rows)\n    p::Int                               # number of SNPs (columns)\n    X::S                                 # Matrix{UInt8} bed file\n    navalue::T                           # representation of missing\n\n    path::String                         # file location\n    colnames::Vector{String}             # SNP labels, can be used for indexing\n    rownames::Vector{String}             # sample labels, can be used for indexing\n    colnamemap::Dict{String, Int}        # for quick name indexing by column\n    rownamemap::Dict{String, Int}        # for quick name indexing by row\n\n    _byteheight::Int                     # number of bytes in each column\n    _lastrowSNPheight::Int               # number of SNPs in last byte of each column\n\n    _bytemap::Matrix{Tuple{T, T, T, T}}  # quarters and flipped quarters for 0x00:0xff\n\n    _flip::BitVector                     # whether to flip SNP major--minor\n                                         # allele encoding for each SNP\n\n    function BEDMatrix{T, S}(n::Integer, p::Integer, X::S, navalue::T,\n                       path::AbstractString, colnames::AbstractVector, rownames::AbstractVector,\n                       bytemap, flip::BitVector) where {T, S<:AbstractMatrix{UInt8}}\n        byteheight = ceil(Int, n/4)\n        lastrowheight = n - 4*(byteheight - 1)\n\n        size(X) == (byteheight, p) || throw(DimensionMismatch(\"Matrix dimensions $(size(X)) do not agree with supplied BED dimensions (n = $n, p = $p)\"))\n        length(colnames) == p || throw(DimensionMismatch(\"colnames has incorrect length\"))\n        length(rownames) == n || throw(DimensionMismatch(\"rownames has incorrect length\"))\n        size(bytemap) == (256, 2) || error(\"bytemap must be of size (256, 2)\")\n        length(flip) == p || throw(DimensionMismatch(\"flip has length $(length(flip)); expected length of p = $p\"))\n\n        colnamemap = Dict{String, Int}()\n        for (cidx, colname) in enumerate(colnames)\n            colnamemap[colname] = cidx\n        end\n\n        rownamemap = Dict{String, Int}()\n        for (ridx, rowname) in enumerate(rownames)\n            rownamemap[rowname] = ridx\n        end\n\n\n        return new(n, p, X, navalue, path, colnames, rownames, colnamemap, rownamemap,\n                   byteheight, lastrowheight, bytemap, flip)\n    end\nend\n\n\"\"\"\n    BEDMatrix(bedfilename::AbstractString;\n              datatype::DataType=Int8, nsamples::Integer=0, nSNPs::Integer=0, navalue=NA_byte\n              famfile::AbstractString=\"\", bimfile::AbstractString=\"\",\n              quartermap::Tuple=Consts.quarterstohuman, flip::AbstractVector=[])\n\nCreate a `BEDMatrix` of type `datatype` using memory mapping of BED\nfile, `bedfilename`. Use `navalue` for missing values. `famfile` and\n`bimfile` are only required if the .fam and .bim file are not in same\nlocation as or do not share the same base name as the\n`bedfilename`. `nsamples` and `nSNPs` are only used if .bim and .fam\nfile cannot be found and are not provided; in which case the\nrownames/colnames will be generic.\n\n`convert(datatype, x)` must work for `x` in `[0b00, 0b01, 0b10, navalue]`.\n\n## Optional arguments\n - `datatype`: the return type for indexing.\n - `nsamples`: number of samples if .fam file cannot be found\n - `nSNPs`: the number of SNPs if .bim file cannot be found\n - `navalue`: the representation of missing values; this\n   should not be used with quartermap.\n - `famfile`: the .fam file, only needed if the .fam file does\n   not have the same base name as `bedfilename`.\n - `bimfile`: the .bim file, only needed if the .bim file does\n   not have the same base name as `bedfilename`.\n - `quartermap`: a `Tuple` of the respective representation of\n   homozygous minor, homozygous major, missing, and heterozygous\n   values. Useful if you don't want to use standard RAW format\n   encoding.\n - `flip`: a `Vector` of indices that should have the major--minor\n   representation flipped. `flip` may either be a list of indices, a\n   logical vector with `false` representing no flipping, or a list of\n   columnnames. This may be recovered or changed later using\n   `getflips` and `setflips!`, respectively.\n\n## Examples\n```julia\njulia> bed = BEDMatrix(\"test/data/example.bed\");\n\njulia> bed[1:5, 1:5]\n5\u00d75 Array{Int8,2}:\n 0  1  1  1  0\n 1  1  1  1  3\n 1  0  0  2  0\n 2  0  0  0  1\n 0  1  0  0  0\n\njulia> bed = BEDMatrix(\"test/data/example\", datatype=Nullable{Int}, navalue=Nullable{Int}());\n\njulia> bed[1:5, 1:5]\n5\u00d75 Array{Nullable{Int64},2}:\n 0  1  1  1  0    \n 1  1  1  1  #NULL\n 1  0  0  2  0    \n 2  0  0  0  1    \n 0  1  0  0  0    \n\njulia> bed = BEDMatrix(\"test/data/example.bed\", datatype=Float64, navalue=NaN);\n\njulia> bed[1:5, 1:5]\n5\u00d75 Array{Float64,2}:\n 0.0  1.0  1.0  1.0    0.0\n 1.0  1.0  1.0  1.0  NaN  \n 1.0  0.0  0.0  2.0    0.0\n 2.0  0.0  0.0  0.0    1.0\n 0.0  1.0  0.0  0.0    0.0\n\njulia> bed = BEDMatrix(\"test/data/example\", famfile=\"badfilename\", nsamples=50);\n\njulia> rownames(bed)[1:5]\n5-element Array{String,1}:\n \"sample_1\"\n \"sample_2\"\n \"sample_3\"\n \"sample_4\"\n \"sample_5\"\n```\n\n\"\"\"\nfunction BEDMatrix(bedfilename::AbstractString;\n                   datatype::DataType=Int8,\n                   nsamples::Integer=0, nSNPs::Integer=0, navalue=NA_byte,\n                   famfile::AbstractString=\"\", bimfile::AbstractString=\"\",\n                   quartermap::Tuple=Consts.quarterstohuman, flip::AbstractVector=Int[])\n\n    if !isfile(bedfilename)\n        isfile(bedfilename*\".bed\") || error(\"Cannot find file \\\"$bedfilename\\\"\")\n        bedfilename = bedfilename*\".bed\"\n    end\n    filebase = splitext(bedfilename)[1]\n\n    rownames = create_rownames(filebase, famfile, nsamples)\n    colnames = create_colnames(filebase, bimfile, nSNPs)\n    n, p = length(rownames), length(colnames)\n\n    X = open(filebase*\".bed\", \"r\") do bedfile\n        checkmagic(bedfile)\n        BEDmode(bedfile) == :SNPmajor || error(\"Old-style SNP-minor mode bed\"*\n                                               \" files not supported. Use plink\"*\n                                               \" to convert to SNP-major format\")\n\n        Mmap.mmap(bedfile, Matrix{UInt8}, (ceil(Int, n/4), p))\n    end\n\n    # validate quartermap and navalue\n    if quartermap == Consts.quarterstohuman\n        bytemap = getbytemap(convert(datatype, navalue))\n    else\n        bytemap = getbytemap(quartermap)\n        navalue = quartermap[2]\n    end\n\n    # Process and validate flip\n    flip = process_flip(flip, colnames)\n\n    return BEDMatrix{datatype, typeof(X)}(n, p, X, convert(datatype, navalue),\n                                          abspath(bedfilename), colnames, rownames, bytemap, flip)\nend\n\nfunction parsefamline(line)\n    fields = split(line)\n\n    (fields[1], fields[2], parse(Int, fields[3]), parse(Int, fields[4]), parse(Int, fields[5]), parse(Int, fields[6]))\nend\n\nfamrowname(line) = join(split(line)[1:2], '_')\n\nreadrownames(famfile::AbstractString) = map(famrowname, readlines(famfile))\n\nfunction create_rownames(filebase::AbstractString, famfile::AbstractString=\"\", nsamples::Integer=0)\n    famfile = famfile == \"\" ? filebase*\".fam\" : famfile\n    if isfile(famfile)\n        rownames = readrownames(famfile)\n    elseif nsamples > 0\n        rownames = [string(\"sample_\", j) for j in 1:nsamples]\n    else\n        error(\"Cannot find FAM file \\\"$famfile\\\" and nsamples not provided\")\n    end\n\n    return rownames\nend\n\nfunction bimcolname(line)\n    fields = split(line)\n    string(fields[2], '_', fields[5])\nend\n\nreadcolnames(bimfile::AbstractString) = map(bimcolname, readlines(bimfile))\n\nfunction create_colnames(filebase::AbstractString, bimfile::AbstractString=\"\", nSNPs::Integer=0)\n    bimfile = bimfile == \"\" ? filebase*\".bim\" : bimfile\n\n    if isfile(bimfile)\n        colnames = readcolnames(bimfile)\n    elseif nSNPs > 0\n        colnames = [string(\"SNP_\", j) for j in 1:nSNPs]\n    else\n        error(\"Cannot find BIM file \\\"$bimfile\\\" and nSNPs not provided\")\n    end\nend\n\nfunction process_flip(flip, colnames)\n    p = length(colnames)\n\n    if eltype(flip) == Bool\n        length(flip) == p || throw(DimensionMismatch(\"flip has length $(length(flip)) expected length of p = $p\"))\n        flip = BitVector(flip)\n    elseif eltype(flip) <: Integer\n        checkbounds(Bool, 1:p, flip) || error(\"Invalid index in flip vector\")\n        flip = BitVector(map(x -> x in flip, 1:p))\n    elseif eltype(flip) <: AbstractString\n        for name in flip\n            name in colnames || error(\"\\\"$name\\\" in flip is not a column_name\")\n        end\n        flip = BitVector(map(x -> x in flip, colnames))\n    else\n        error(\"Invalid flip vector: expected logical, index, or column name vector\")\n    end\n\n    return flip\nend\n\n\n######################## Getters, Properties, and Setters ########################\n\nBase.size(B::BEDMatrix) = (B.n, B.p)\nfunction Base.size(B::BEDMatrix, k::Integer)\n    k > 0 || Base.throw_boundserror(size(B), k)\n    # This is the same behavior as existing Base.size methods\n    return k == 1 ? B.n : ifelse(k == 2, B.p, 1)\nend\n\nBase.IndexStyle{T<:BEDMatrix}(::Type{T}) = Base.IndexCartesian()\n\n# not sure if this is the right definition; see\n# https://github.com/JuliaLang/julia/issues/16614.\n# This is the size of `B[:, :]`.\nBase.sizeof{T, S}(B::BEDMatrix{T, S}) = B.n*B.p*sizeof(T)\n\n\"\"\"\n    path(B::BEDMatrix)\n\nReturns the location of the .bed file used to create `X`.\n\n\"\"\"\npath(B::BEDMatrix) = B.path\n\n\"\"\"\n    rownames(B::BEDMatrix)\n\nReturns `Vector{String}` of row names. These may be used for indexing.\n\n\"\"\"\nrownames(B::BEDMatrix) = B.rownames\n\n\"\"\"\n    colnames(B::BEDMatrix)\n\nReturns `Vector{String}` of column names. These may be used for indexing.\n\n\"\"\"\ncolnames(B::BEDMatrix) = B.colnames\n\n\"\"\"\n    NArep(B::BEDMatrix)\n\nReturns value used for missing entries.\n\n\"\"\"\nNArep(B::BEDMatrix) = B.navalue\n\ngetrow(B::BEDMatrix, rowname::AbstractString) = B.rownamemap[rowname]\ngetcol(B::BEDMatrix, colname::AbstractString) = B.colnamemap[colname]\n\nfunction _get_quartermap_from_bytemap(bytemap::Matrix)\n    # 0b11_10_01_00 is the BED encoding for (homozygous minor,\n    # homozygous major, missing, heterozygous). Recall the reverse\n    # (little endian-like) order of the SNPs.\n    bytemap[0b11_10_01_00 + 1, false + 1]\nend\n\n\"\"\"\n    getquartermap(bed::BEDMatrix)\n\nReturns the `quartermap` for `bed`. This is a `Tuple` of four values\ngiving the representation of respectively (homozygous minor,\nhomozygous major, missing value, heterozygous).\n\n\"\"\"\nfunction getquartermap(bed::BEDMatrix)\n    _get_quartermap_from_bytemap(bed._bytemap)\nend\n\n\"\"\"\n    getflips(B::BEDMatrix)\n\nReturns a `Vector{Int}` of the columns which have a flipped major--minor allele\nrepresentation. Use `setflips!` to change this.\n\n\"\"\"\ngetflips(B::BEDMatrix) = find(B._flip)\n\n\n\"\"\"\n    setflips!(B::BEDMatrix, indices)\n\n(Re)sets which columns of the BEDMatrix use a flipped major--minor\nrepresentation. For example, `setflips!(bed, [])` would make all columns use the\nstandard encoding and `setflips!(bed, indices(bed, 2))` would make all columns\nuse a flipped representation.\n\n\"\"\"\nfunction setflips!(B::BEDMatrix, flip_logical::AbstractVector{Bool})\n    length(flip_logical) == size(B, 2) || throw(DimensionsMismatch(\"flip had incorrect length\"))\n    copy!(B._flip, flip_logical)\n    B._flip\nend\n\nfunction setflips!{K<:Integer}(B::BEDMatrix, flip_indices::AbstractVector{K})\n    checkbounds(1:B.p, flip_indices)\n\n    fill!(B._flip, false)\n\n    @inbounds for col in flip_indices\n        B._flip[col] = true\n    end\n\n    B._flip\nend\n\nfunction setflips!{S<:AbstractString}(B::BEDMatrix, flip_names::AbstractVector{S})\n    # I don't do this in the same loop as below, since I want an\n    # atomic operation.\n    for name in flip\n        name in colnames || error(\"\\\"$name\\\" in flip is not a column_name\")\n    end\n    fill!(B._flip, false)\n\n    for name in flip\n        B._flip[getcol(B, name)] = true\n    end\n\n    B._flip\nend\n\n\n#################### Indexing ####################\n\nBase.getindex{T<:AbstractString}(B::BEDMatrix, rownames::AbstractVector{T}, col) = B[[getrow(B, rowname) for rowname in rownames], col]\nBase.getindex{T<:AbstractString}(B::BEDMatrix, row, colnames::AbstractVector{T}) = B[row, [getcol(B, colname) for colname in colnames]]\nBase.getindex{T<:AbstractString, S<:AbstractString}(B::BEDMatrix, rownames::AbstractVector{S}, colnames::AbstractVector{T}) = [B[row, col] for row in rownames, col in colnames]\n\nBase.getindex(B::BEDMatrix, rowname::AbstractString, colname::AbstractString) = B[getrow(B, rowname), getcol(B, colname)]\nBase.getindex(B::BEDMatrix, rowname::AbstractString, col) = B[getrow(B, rowname), col]\nBase.getindex(B::BEDMatrix, row, colname::AbstractString) = B[row, getcol(B, colname)]\n\nBase.getindex{T<:AbstractString}(B::BEDMatrix, rownames::AbstractVector{T}, colname::AbstractString) = [B[row, colname] for row in rownames]\nBase.getindex{T<:AbstractString}(B::BEDMatrix, rowname::AbstractString, colnames::AbstractVector{T}) = [B[rowname, col] for col in colnames]\n\n# This is is the only getindex method we _need_, the other methods are\n# provided for better performance, or convenience.\nfunction Base.getindex(B::BEDMatrix, row::Integer, col::Integer)\n    @boundscheck checkbounds(B, row, col)\n\n    unsafe_getindex(B, row, col)\nend\n\nfunction Base.getindex(B::BEDMatrix, ::Colon, col::Integer)\n    @boundscheck checkbounds(B, :, col)\n\n    unsafe_getcol(B, col)\nend\n\nfunction Base.getindex{T, S, K<:Integer}(B::BEDMatrix{T, S}, ::Colon, cols::AbstractVector{K})\n    @boundscheck checkbounds(B, :, cols)\n\n    matrix = Matrix{T}(B.n, length(cols))\n\n    for (cidx, col) in enumerate(cols)\n        unsafe_getcol!(matrix, B, col, (cidx - 1)*B.n + 1)\n    end\n    return matrix\nend\n\nfunction Base.getindex(B::BEDMatrix, rrange::AbstractUnitRange, col::Integer)\n    @boundscheck checkbounds(B, rrange, col)\n\n    unsafe_getrowrange(B, rrange, col)\nend\n\nfunction Base.getindex{T, S, K <: Integer}(B::BEDMatrix{T, S}, rrange::AbstractUnitRange, cols::AbstractVector{K})\n    @boundscheck checkbounds(B, rrange, cols)\n\n    matrix = Matrix{T}(length(rrange), length(cols))\n    for (cidx, col) in enumerate(cols)\n        unsafe_getrowrange!(matrix, B, rrange, col, (cidx - 1)*length(rrange) + 1)\n    end\n    return matrix\nend\n\nfunction unsafe_getindex{T, S}(B::BEDMatrix{T, S}, row::Integer, col::Integer)\n    byterow, snpind = rowtobytequarter(row)\n\n    @inbounds return breakbyte(B.X[byterow, col], B._bytemap, snpind, B._flip[col])\nend\n\nfunction unsafe_getcol{T, S}(B::BEDMatrix{T, S}, col::Integer)\n    column = Vector{T}(B.n)\n\n    unsafe_getcol!(column, B, col)\n    return column\nend\n\nfunction unsafe_getcol!{T, S}(column::AbstractArray{T}, B::BEDMatrix{T, S}, col::Integer, deststart=1)\n    @inbounds unsafe_copybytestosnps!(column, B.X, B._byteheight*(col - 1) + 1, 1, B._byteheight*col,\n                                      B._lastrowSNPheight, deststart, B._bytemap, B._flip[col])\n    return column\nend\n\nfunction unsafe_getrowrange{T, S}(B::BEDMatrix{T, S}, rrange::AbstractUnitRange, col::Integer)\n    vector = Vector{T}(length(rrange))\n    unsafe_getrowrange!(vector, B, rrange, col)\n    return vector\nend\n\nfunction unsafe_getrowrange!{T, S}(vector::AbstractArray{T}, B::BEDMatrix{T, S}, rrange::AbstractUnitRange, col::Integer, deststart=1)\n    bytestart, quarterstart = rowtobytequarter(first(rrange))\n    bytestart += B._byteheight*(col - 1)\n\n    bytestop, quarterstop = rowtobytequarter(last(rrange))\n    bytestop += B._byteheight*(col - 1)\n\n    @inbounds unsafe_copybytestosnps!(vector, B.X, bytestart, quarterstart, bytestop, quarterstop, deststart, B._bytemap, B._flip[col])\n    return vector\nend\n\nfunction rowtobytequarter(row::Integer)\n    # Curse you julia for your foolish 1-indexing!\n    return (((row - 1) >> 2) + 1, ((row - 1) & 3) + 1)\nend\n\n\"\"\"\n    getquarterblock(B::BEDMatrix, row::Integer, col::Integer)\n\nReturns `(fourquarters, snpind)`, where `fourquarters` is the block of\nquarters that `B[row, col]` belongs to: `B[row, col] =\nfourquarters[snpind]`.\n\n\"\"\"\nfunction getquarterblock(B::BEDMatrix, row::Integer, col::Integer)\n    byterow, snpind = rowtobytequarter(row)\n\n    @inbounds return (breakbyte(B.X[byterow, col], B._bytemap, B._flip[col]), byterow, snpind)\nend\n", "meta": {"hexsha": "67370d8ed1717ddb555dc43ff94f158eefd042b0", "size": 27824, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/bedmatrix.jl", "max_stars_repo_name": "traben/BEDMatricesv10.jl", "max_stars_repo_head_hexsha": "a6435aced47dc0adcc26e4f1282be56ae0f1d276", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/bedmatrix.jl", "max_issues_repo_name": "traben/BEDMatricesv10.jl", "max_issues_repo_head_hexsha": "a6435aced47dc0adcc26e4f1282be56ae0f1d276", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/bedmatrix.jl", "max_forks_repo_name": "traben/BEDMatricesv10.jl", "max_forks_repo_head_hexsha": "a6435aced47dc0adcc26e4f1282be56ae0f1d276", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6500622665, "max_line_length": 176, "alphanum_fraction": 0.6363211616, "num_tokens": 8075, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12940273494909915, "lm_q1q2_score": 0.06419589832490899}}
{"text": "\"\"\"\n    linear_search(array, key)\nA simple search of `array`, element per element until `key` is found.\n\"\"\"\nfunction linear_search(array, key)\n    for i in 1:length(array)\n        if array[i] == key\n            return i\n        end\n    end\n    return -1\nend\n", "meta": {"hexsha": "e0468cc255082c610d478e91131624d12ab9a5b9", "size": 258, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/searches/linear_search.jl", "max_stars_repo_name": "KohRongSoon/Julia", "max_stars_repo_head_hexsha": "e0276ab9224e191452f7932343f8b24ea8625eea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 190, "max_stars_repo_stars_event_min_datetime": "2021-06-06T10:00:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T17:00:33.000Z", "max_issues_repo_path": "src/searches/linear_search.jl", "max_issues_repo_name": "KohRongSoon/Julia", "max_issues_repo_head_hexsha": "e0276ab9224e191452f7932343f8b24ea8625eea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 82, "max_issues_repo_issues_event_min_datetime": "2021-06-09T14:08:27.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:04.000Z", "max_forks_repo_path": "src/searches/linear_search.jl", "max_forks_repo_name": "KohRongSoon/Julia", "max_forks_repo_head_hexsha": "e0276ab9224e191452f7932343f8b24ea8625eea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 70, "max_forks_repo_forks_event_min_datetime": "2021-06-06T15:14:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T09:19:17.000Z", "avg_line_length": 19.8461538462, "max_line_length": 69, "alphanum_fraction": 0.5968992248, "num_tokens": 67, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.33807711081162, "lm_q2_score": 0.18952109132967757, "lm_q1q2_score": 0.06407274299460256}}
{"text": "module MatchingTests\nusing  ExpressionPatterns.Matching\nusing  ExpressionPatterns.Helper\nusing  Base.Test\nimport Base.Meta.quot\n\nmatchval(exval) = :(@test match($(exval[1])) == $(exval[2]))\n\nmacro testmatch(pattern, block)\n\n  arg_statements = (x->esc.(x.args[2:end])).(linesof(block))\n  res_statements = matchval.(arg_statements)\n\n  :(let match = $matcher($(esc(pattern)))\n      $(res_statements...)\n    end)\nend\n\n\n# anything\n\n@testmatch (:x) begin\n   1000   => true\n  :(x,y)  => true\n  :(x+10) => true\nend\n\n# tuple\n\n@testmatch :(x,y,z) begin\n  :(a,b,c) => true\n  :(1,2,3) => true\n  :[a,b,c] => false\n  :(1,2)   => false\nend\n\n# vector / literal\n\n@testmatch :[1,a] begin\n  :[1,2]        => true\n  :[1,:(a,b,c)] => true\n  :[1,b,c]      => false\n  :[a,a]        => false\n  :[[1],a]      => false\n  :(1,2)        => false\nend\n\n# call\n\n@testmatch :(f(a,b)) begin\n  :(1+2)                  => true\n  :(reduce(*, [1,2,3,4])) => true\n  :(g[1,2])               => false\n  :(t{a,b})               => false\nend\n\n# ref\n\n@testmatch :((:T{Symbol})[:T{Integer}]) begin\n  :(A[1])      => true\n  :(arr[1000]) => true\n  :(A[1,2])    => false\n  :(1[1])      => false\n  :(A[a])      => false\nend\n\n# curly\n\n@testmatch (:(T{*{a}})) begin\n  :(Vector{Integer}) => true\n  :(A{1,2,3,4})      => true\n  :(T{})             => true\nend\n\n# block\n\n@testmatch (quote *{:T{:P{s->all(islower,s)}, AbstractString}} end) begin\n  (quote \"abc\"; \"onetwothree\"; \"solami\" end) => true\n  (quote \"abc\";     \"123\";     \"solami\" end) => false\n  (quote \"abc\"; \"onetwothree\"; \"solami\" end) => true\n  (quote \"aBc\"; \"onetwothree\"; \"solami\" end) => false\n  (quote \"abc\";      123;      \"solami\" end) => false\nend\n\n# forloop / nested slurps\n\n@testmatch :(for i in [*{[*{1,2}]}]; *{body} end) begin\n  :(for x in [[1,2],[],[1,2,1,2]]; print(x) end) => true\n  :(for x in [[1,2],[],[1,2,1]];   print(x) end) => false\n  :(for x in [[1,2],[3],[1,2,1]];  print(x) end) => false\nend\n\n# while loop / lazy slurps\n\n@testmatch :(while x < t; :?{p(:?{args})}; x+=1 end) begin\n\n  :(while i <   10; println(io, i);             i+=1 end) => true\n  :(while k < t-10; splice!(A,2,k); push!(B,k); k+=1 end) => true\n  :(while x < g(x); println(x);     push!(B,x); x+=1 end) => true\n  :(while x < g(x); println(i);     push!(B,x); x+=1 end) => true\n  :(while x < g(x); println(x,y);   push!(B,x); i+=1 end) => false\n  :(while x > g(x); println(x);     push!(B,x); x+=2 end) => false\n\nend\n\n# macrocall / quote\n\n@testmatch :(@m :x y) begin\n  :(@transport :car   (john, peter))    => true\n  :(@transport plane  (maria, isabela)) => false\nend\n\n# binding\n\n@testmatch (:(:B{+}(1+2))) begin\n  :(f(1+2)) => true\n  :(f(1-2)) => false\nend\n\n# literal\n\n@testmatch :(:L{x+y}) begin\n  :(x+y) => true\n  :(1+2) => false\nend\n\n# autobinding\n\n@testmatch :(:L{:A{x+y}}) begin\n  :(1+2) => true\n  :(x+y) => true\n  :(x-y) => false\nend\n\n# consistency\n\n@testmatch :(x, x) begin\n  :(1,1) => true\n  :(1,2) => false\nend\n\n# equality\n\n@testmatch :(:EQ{x, 10}) begin\n  10 => true\n  11 => false\nend\n\n# type check\n\n@testmatch :(:T{x, Symbol}) begin\n  :x => true\n  10 => false\nend\n\n# predicate check\n\n@testmatch :(:P{x, iseven}) begin\n  2 => true\n  1 => false\nend\n\n# iterable\n\n@testmatch :(:I{x,y,z}) begin\n  (1,2,3) => true\n  [4,5,6] => true\n :(7,8,9) => false\n :[a,b,c] => false\nend\n\n# raw\n\n@testmatch :(:R{:export, x, y, z}) begin\n  :(export a,b,c) => true\n  :(import a.b.c) => false\nend\n\n# line number nodes\n\n@testmatch LineNumberNode(1) begin\n  LineNumberNode(1) => true\n  LineNumberNode(2) => true\n  Expr(:line, 10)   => true\nend\n\nend\n", "meta": {"hexsha": "dc14d128e23ca9037c7b08ef18c13fcbc5a90fb1", "size": 3535, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/matching.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ExpressionPatterns.jl-df068d8e-6bdd-5cd0-b77b-69c5de8ae8a3", "max_stars_repo_head_hexsha": "1db13b2188962533435251396357979412b1db2c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2015-08-08T19:56:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-27T14:27:00.000Z", "max_issues_repo_path": "test/matching.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/ExpressionPatterns.jl-df068d8e-6bdd-5cd0-b77b-69c5de8ae8a3", "max_issues_repo_head_hexsha": "1db13b2188962533435251396357979412b1db2c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 44, "max_issues_repo_issues_event_min_datetime": "2015-07-30T10:33:58.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-31T01:16:40.000Z", "max_forks_repo_path": "test/matching.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/ExpressionPatterns.jl-df068d8e-6bdd-5cd0-b77b-69c5de8ae8a3", "max_forks_repo_head_hexsha": "1db13b2188962533435251396357979412b1db2c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2015-10-24T18:17:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:50:18.000Z", "avg_line_length": 18.8031914894, "max_line_length": 73, "alphanum_fraction": 0.5233380481, "num_tokens": 1346, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.13117322376181564, "lm_q1q2_score": 0.06404970707031961}}
{"text": "\"\"\"\n    NamedColor(name::String,color::RGB)\n\nA named `ColorTypes.RGB`\n\n# Examples\n\n```julia-repl\njulia> NamedColor(\"DarkGreen\")\nNamedColor(\"DarkGreen\",(0.0, 0.39, 0.0))\n```\n\"\"\"\nstruct NamedColor\n    name::String\n    color::ColorTypes.RGB\nend\n\nNamedColor(;name=\"\",color=ColorTypes.RGB(0,0,0)) =\n                                NamedColor(name,color)\n\nNamedColor(S::String) = NamedColor(name=S,color=parse(Colors.Colorant,S))\nNamedColor(S::Symbol) = NamedColor(string(S))\nNamedColor(C::ColorTypes.RGB) = NamedColor(color=C)\nfunction NamedColor(S::String,C::Tuple{<:Real,<:Real,<:Real})\n    NamedColor(S,ColorTypes.RGB(C...))\nend\n\nfunction NamedColor(C::Tuple{<:Real,<:Real,<:Real})\n    NamedColor(\"\",ColorTypes.RGB(C...))\nend\n\nNamedColor(r::Real,g::Real,b::Real) = NamedColor((r,g,b))\n\nsoftblue = NamedColor(0.92,0.95,0.99);\nsoftred = NamedColor(0.99, 0.92, 0.91);\nsoftyellow = NamedColor(0.98, 0.98, 0.9);\nsoftgreen = NamedColor(0.96, 0.995, 0.98);\n\nfunction *(k::Real,C::NamedColor)\n    NamedColor(\"\",ColorTypes.RGB(k*C.color.r,k*C.color.g,k*C.color.b))\nend\ndot(k::Real,C::NamedColor) = k*C\nzero(::Type{NamedColor}) = NamedColor(0,0,0)\n/(C::NamedColor,r::Real) = 1/r*C\nfunction +(C::NamedColor,D::NamedColor)\n    NamedColor(\"\",ColorTypes.RGB(C.color.r + D.color.r,\n                                 C.color.g + D.color.g,\n                                 C.color.b + D.color.b))\nend\n\nfunction ==(C::NamedColor,D::NamedColor)\n    C.name == D.name && C.color == D.color\nend\n\nfunction Base.show(io::IO,NC::NamedColor)\n    t = map(x->round(Float64(x);digits=2),(NC.color.r,NC.color.g,NC.color.b))\n    print(io,\"\"\"NamedColor(\"$(NC.name)\",$t)\"\"\")\nend\n\ndefaultcolor() = NamedColor(name=\"default\")\n\n\"\"\"\n    Pen(color::NamedColor,\n        opacity::Real,\n        linewidth::Real,\n        other::String)\n\nStore drawing properties\n\n# Examples\n\n```julia-repl\njulia> Pen(color=\"DarkGreen\",opacity=0.5)\nPen(color=DarkGreen,opacity=0.5)\n```\n\"\"\"\nstruct Pen\n    color::NamedColor\n    opacity::Real\n    linewidth::Real\n    fontsize::Real\n    other::String\nend\n\nconst _DEFAULT_PEN_ARGS =\n    OrderedDict(\n        :color => defaultcolor(),\n        :opacity => 1,\n        :linewidth => 0,\n        :fontsize => 12,\n        :other => \"\"\n    )\n\nPen(;kwargs...) = Pen(updatedvals(_DEFAULT_PEN_ARGS,kwargs)...)\n\nNoPen() = Pen(other=\"NoPen\")\n\n==(P::Pen,Q::Pen) = all(getfield(P,k)==getfield(Q,k)\n                                for k in keys(_DEFAULT_PEN_ARGS))\n\nconvert(::Type{NamedColor},name::Union{Symbol,AbstractString}) = NamedColor(name)\nconvert(::Type{NamedColor},color::ColorTypes.RGB) = NamedColor(;color=color)\nconvert(::Type{Pen},string::AbstractString) = Pen(;other=string)\nconvert(::Type{Pen},color::NamedColor) = Pen(color=color)\n\nfunction Base.show(io::IO,P::Pen)\n    kwargs = kwargstring(P,_DEFAULT_PEN_ARGS,semicolon=false)\n    print(io,\"Pen($kwargs)\")\nend\n\nisdefault(P::Pen) = (P == Pen())\nisdefault(S::AbstractString) = (S == \"\")\n\n\"\"\"\n    Arrow(name::AbstractString,\n          size::Real,\n          position::Real)\n\nStore instructions for drawing an arrow\n\n`position` is an element of [0,1] which indicates\nhow far along the path the arrow should be drawn\n\nNoArrow() returns a no-arrow instruction, while\nArrow3() gives an arrow suitable for 3D paths\n\"\"\"\nstruct Arrow\n    name::AbstractString\n    size::Real\n    position::Real\nend\n\nconst _DEFAULT_ARROW_KWARGS =\n    OrderedDict(\n        :name => \"Arrow\",\n        :size => 7.5,\n        :position => 1\n    )\n\nfunction Arrow(;kwargs...)\n    return Arrow(updatedvals(_DEFAULT_ARROW_KWARGS,kwargs)...)\nend\n\nfunction Arrow3(;kwargs...)\n    D = deepcopy(_DEFAULT_ARROW_KWARGS)\n    merge!(D,Dict(kwargs))\n    merge!(D,Dict(:name => \"Arrow3\"))\n    return Arrow(values(D)...)\nend\n\nNoArrow() = Arrow(;name=\"None\")\nArrow(n) = Arrow(size=n)\nArrow3(n) = Arrow3(size=n)\nArrows(;kwargs...) = Arrow(name=\"Arrows\";kwargs...)\n\n==(A::Arrow,B::Arrow) = all(getfield(A,k) == getfield(B,k)\n                                    for k in fieldnames(Arrow))\n\nfunction AsyString(A::Arrow)\n    position = A.position == 1 ? \"\" : \"position=$(A.position)\"\n    if A.name == \"None\"\n        return \"None\"\n    elseif A.size \u2260 7.5\n        return \"$(A.name)($(filterjoin(A.size,position)))\"\n    else\n        return \"$(A.name)($position)\"\n    end\nend\n\nfunction string(A::Arrow)\n    if A.name == \"None\"\n        return \"NoArrow()\"\n    else\n        return AsyString(A)\n    end\nend\nprint(io::IO,A::Arrow) = print(io,string(A))\nshow(io::IO,A::Arrow) = print(io,string(A))\n\nfunction string(C::NamedColor)\n    if C.name \u2260 \"default\" && C.name \u2260 \"\"\n        return C.name\n    elseif C.name == \"default\"\n        return \"\"\n    else\n        colorvals = [Float64(getfield(C.color,s)) for s in [:r,:g,:b]]\n        return \"\"\"rgb($(join(map(string,colorvals),\",\")))\"\"\"\n    end\nend\nprint(io::IO,C::NamedColor) = print(io,string(C))\n\nfunction string(P::Pen)\n    color = string(P.color)\n    opacity = P.opacity == 1 ? \"\" : \"opacity($(P.opacity))\"\n    linewidth = P.linewidth == 0 ? \"\" : \"linewidth($(P.linewidth))\"\n    fontsize = P.fontsize == 12 ? \"\" : \"fontsize($(P.fontsize))\"\n    other = P.other\n    join(filter(x->x\u2260\"\",[color,opacity,linewidth,fontsize,other]),\"+\")\nend\nprint(io::IO,P::Pen) = print(io,string(P))\n\nfunction Ticks(major::Union{Vector,AbstractRange},minor::Union{Vector,AbstractRange,Nothing};beginlabel=true,endlabel=true)\n    majorticks = \"Ticks=new real[] {$(join(major,\",\"))}\"\n    minorticks = isa(minor,Nothing) ? \"\" : \"ticks=new real[] {$(join(minor,\",\"))}\"\n    bl = beginlabel ? \"\" : \"beginlabel=false\"\n    el = endlabel ? \"\" : \"endlabel=false\"\n    args = filterjoin(majorticks,minorticks,bl,el)\n    \"Ticks($args)\"\nend\n\nfunction Ticks(major::Union{Vector,AbstractRange};kwargs...)\n    Ticks(major,nothing;kwargs...)\nend\n", "meta": {"hexsha": "ea65c39f198bfe80624686546bcf96a2ccaf6b49", "size": 5728, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pens_and_arrows.jl", "max_stars_repo_name": "JuliaTagBot/AsyPlots.jl", "max_stars_repo_head_hexsha": "b3bd9789b61fad985369871af8918de2e777c980", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24, "max_stars_repo_stars_event_min_datetime": "2018-02-28T20:31:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-11T03:48:36.000Z", "max_issues_repo_path": "src/pens_and_arrows.jl", "max_issues_repo_name": "JuliaTagBot/AsyPlots.jl", "max_issues_repo_head_hexsha": "b3bd9789b61fad985369871af8918de2e777c980", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2018-08-30T16:18:03.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-08T19:48:21.000Z", "max_forks_repo_path": "src/pens_and_arrows.jl", "max_forks_repo_name": "JuliaTagBot/AsyPlots.jl", "max_forks_repo_head_hexsha": "b3bd9789b61fad985369871af8918de2e777c980", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2018-12-27T00:56:10.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-20T23:08:37.000Z", "avg_line_length": 26.7663551402, "max_line_length": 123, "alphanum_fraction": 0.6218575419, "num_tokens": 1640, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4687906266262437, "lm_q2_score": 0.136608375965897, "lm_q1q2_score": 0.06404072617144635}}
{"text": "__precompile__()\n\nmodule cme257demo\n\nusing Distributions.Exponential\n\nexport hello, demofun, exprand\n\nfunction hello()\n  print(\"hello from cme257demo!\")\nend\n\nfunction demofun(x::Int, y::Int)\n  x + y\nend\n\nfunction exprand()\n  d = Exponential()\n  rand(d)\nend\n\nend # module\n", "meta": {"hexsha": "e2d6f8ca063547580f98997c8c3c8531f5a3537a", "size": 271, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cme257demo.jl", "max_stars_repo_name": "bnels/cme257demo.jl", "max_stars_repo_head_hexsha": "b4f6b1639dfb514b7f9d37bd58017ada213b27e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/cme257demo.jl", "max_issues_repo_name": "bnels/cme257demo.jl", "max_issues_repo_head_hexsha": "b4f6b1639dfb514b7f9d37bd58017ada213b27e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cme257demo.jl", "max_forks_repo_name": "bnels/cme257demo.jl", "max_forks_repo_head_hexsha": "b4f6b1639dfb514b7f9d37bd58017ada213b27e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.7826086957, "max_line_length": 33, "alphanum_fraction": 0.7232472325, "num_tokens": 80, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.41489884579676883, "lm_q2_score": 0.1540575665754383, "lm_q1q2_score": 0.06391830655840822}}
{"text": "# Starting string processing example for Learning Julia\n\n# simple string operations - length, size\nmystr = \"\u00c5ngstr\u00f6m\"\nteststr = \"j\u03bc\u039bI\u03b1\" # test string with some Greek characters\nprintln(\"String length of mystr: \", length(mystr))\nprintln(\"Size of mystr: \", sizeof(mystr))\nprintln(\"String length of teststr: \", length(teststr))\nprintln(\"Size of teststr: \", sizeof(teststr), \"\\n\") # non-English unicode characters take up \n                                                    # more than one bit in storage\n\n# concatenation and repetition operators\nteststr = \"Hello\" * \", world!\"\nprintln(teststr)\n\nteststr = \"ABCD\" ^ 3 # repeat ABCD 3 times\nprintln(teststr, \"\\n\")\n\n# search for substrings\nteststr = \"Julia programming is awesome\"\nprintln(findnext(\"Julia\", teststr, 0)) # find test range where Julia appears\nprintln(occursin(\"some\", teststr), \"\\n\")\n\n# pad strings either left or right\nteststr = lpad(\"Test String\", 20) # left padded by 20 spaces\nprintln(teststr)\nteststr = rpad(\"Test String\", 20, '*') # right padded by 20 '*' characters\nprintln(teststr, \"\\n\")\n\n# create a string from an array\narr = [\"Lions\", \"Tigers\", \"Bears\"]\nteststr = join(arr, \", \", \", and \") # last argument specifies what to insert between the last two\nprintln(teststr)\n", "meta": {"hexsha": "ff402e0ca1734e266ea5a5c7b94f5152a57bf9c2", "size": 1238, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "6_packages-and-builtin-fns/02_strproc.jl", "max_stars_repo_name": "scaotravis/Julia-Course", "max_stars_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "6_packages-and-builtin-fns/02_strproc.jl", "max_issues_repo_name": "scaotravis/Julia-Course", "max_issues_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "6_packages-and-builtin-fns/02_strproc.jl", "max_forks_repo_name": "scaotravis/Julia-Course", "max_forks_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4117647059, "max_line_length": 97, "alphanum_fraction": 0.6865912763, "num_tokens": 326, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.14223189682786755, "lm_q1q2_score": 0.06391796682039529}}
{"text": "# TODO: for now, many documentation strings do not wrap at 79 characters\n# because this break list items, discover where is the problem (incorrect use\n# of markdown? documenter.jl is broken?).\n\"\"\"\nGuillotineModels is a collection of mathematical models for 2D Guillotine\nCutting problems, all implemented using Julia+JuMP.\n\nIt was developed as part of the PhD thesis of [Henrique Becker]\n(https://orcid.org/0000-0003-3879-2691).\n\nThe main features are:\n1. The implementation of distinct models using the same technology (Julia+JuMP) which is solver-agnostic.\n2. The implementation of a CommandLine interface that make it easy to call the implemented models from the command-line to be solved by an specified solver, and also is extendable for new models and solvers.\n\"\"\"\nmodule GuillotineModels\n\nimport Dates\nimport TimerOutputs\nconst TIMER = TimerOutputs.TimerOutput()\n\n# Just include all submodules. Except by the specializations of build_model,\n# all methods are part of some submodule and not of the top-level.\n\nexport build_model, get_cut_pattern, CutPattern, to_pretty_str, simplify!\nexport cut_pattern_profit\nexport TimeoutError\nusing DocStringExtensions # for TYPEDFIELDS\nusing JuMP # for JuMP.Model parameter reference\n\n\"\"\"\n    TimeoutError <: Exception\n\nAn exception subtype representing a time limit not respected.\n\"\"\"\nstruct TimeoutError <: Exception\n\t\"The moment the timing started (in seconds since epoch).\"\n\tstart :: Float64\n\t\"The amount of seconds the procedure had to finish.\"\n\tlimit :: Float64\n\t\"The moment the code noticed the timeout (in seconds since epoch)\"\n\tnow :: Float64\nend\n\nfunction Base.showerror(io :: IO, e :: TimeoutError)\n\tstart, limit, now = round.((e.start, e.limit, e.now); digits = 3)\n\tsince_start = round(now - start; digits = 3)\n\tsince_limit = round(since_start - limit; digits = 3)\n\tprint(io,\n\t\t\"TimeoutError: A timer started at $(Dates.unix2datetime(start)) with\" *\n\t\t\" limit of $limit seconds (i.e., until $(Dates.unix2datetime(start +\n\t\tlimit))) was discovered running at $(Dates.unix2datetime(now)), this is,\" *\n\t\t\" $(since_start) seconds since start or $(since_limit) seconds\" *\n\t\t\" since it should have stopped.\\n\"\n\t)\nend\n\n\"\"\"\n    throw_if_timeout(start, limit, now)\n\nThrows a TimeoutError if `start - now > limit`.\n\"\"\"\nfunction throw_if_timeout(start, limit, now)\n\t(now - start) > limit && throw(TimeoutError(start, limit, now))\n\treturn\nend\n\n\"\"\"\n    throw_if_timeout_now(start, limit)\n\nThrows a TimeoutError if `start - time() > limit`.\n\"\"\"\nfunction throw_if_timeout_now(start, limit)\n\tthrow_if_timeout(start, limit, time())\n\treturn\nend\n\n@enum BuildStopReason BUILT_MODEL FOUND_OPTIMUM\n\n\"\"\"\n    build_model(problem, formulation, instance, model[, options])\n\nGiven an `instance` of `problem`, build `formulation` inside `model`.\n\nA dict of options that are `problem`- and `formulation`-specific may\nbe also provided (otherwise defaults are used).\n\nShould always return two values: the first is always an element of\n`BuildStopReason`; the second is `problem`- and `formulation`-specific.\n\n# Arguments\n\n1. `problem`: An object that identifies the problem being solved, e.g., `Val(:G2KP)`. Each problem often requires a different `instance` type but sometimes an instance of a more general problem (that is not using the extra generality) may be passed to a more specific problem.\n2. `formulation`: An object that identifies the formulation used to model the `problem`, e.g., `Val(:PPG2KP)`. Often same formulation can be adapted to multiple different `problem`s with minimal changes.\n3. `instance`: An object containing the `problem` data, e.g., `G2KP` or `SLOPP`.\n4. `model`: An object that behaves as a JuMP model, it will have variables and constraints added to it.\n5. `options::Dict{String, Any}`: Problem- and formulation-specific options.\n\n# Returns\n\n1. `::BuildStopReason`: The reason for which the method returned. It can be: BUILT_MODEL (the model was built successfully) or FOUND_OPTIMUM (in the process of building the model, the optimal solution was already found, so the building process was abandoned). See [`get_cut_pattern`](@ref) for more information.\n2. `::Any`: the specific type of the second returned value depends on the `problem` and `formulation`, however it should always exist (even if it is `nothing`). It is passed as an argument to `get_cut_pattern` in order to assemble a solution (CutPattern) from the variables values of a model.\n\"\"\"\nfunction build_model end\n\n#=\nThe list item below was removed because for now there seems to be no value\nin having a field indicating the piece was rotated or not.\n * `piece_is_rotated` is only relevant if `piece_idx` is not zero; it\n  should always be `false` if `piece_idx` is zero. If `true` then the piece\n\tdimensions are considered swapped in the context of the pattern.\n=#\n\"\"\"\nA guillotine stage-unlimited unrestricted cutting pattern.\n\n$(TYPEDFIELDS)\n\n# Notes\n\n* `cuts_are_vertical` is only relevant if `subpatterns` has length two or more.\n* If `subpatterns` is non-empty, and `cuts_are_vertical == true`, then the sum\n  of the `width` of the patterns in `subpatterns` cannot be greater than\n  `width`, and each individual `length` of a pattern in `subpatterns` cannot be\n  greater than `length`. If `cuts_are_vertical == false` the same applies\n  except `length` and `width` switch roles.\n* It is assumed that zero is not a valid piece index, as it is used in\n  `piece_idx` to mark that a pattern is not sold as a piece (in this case,\n  it is either a cut pattern or waste).\n* If `piece_idx` has a value different from zero, then `subpatterns` must be\n  empty. In other words, a piece cannot be subdivided.\n* Waste may be represented explicitly, by having plates with `piece_idx == 0`\n  but no elements in `subpatterns`, or implicitly, by having elements in\n  `subpatterns` that do not fill all the area defined by `length` and `width`.\n\"\"\"\nstruct CutPattern{D, S}\n\t\"The length of the pattern.\"\n\tlength :: S\n\t\"The width of the pattern.\"\n\twidth :: S\n\t\"If the pattern represents a piece, then the piece index; otherwise zero.\"\n\tpiece_idx :: D\n\t#\"If the piece the pattern represents was rotated (90 degrees).\"\n\t#piece_is_rotated :: Bool\n\t\"The common orientation of the cuts between the plates in `subpatterns`.\"\n\tcuts_are_vertical :: Bool\n\t\"The subpatterns that constitute the pattern.\"\n\tsubpatterns :: Vector{CutPattern{D, S}}\n\n\t\"Inner constructor.\"\n\tfunction CutPattern(\n\t\tL :: S, W :: S, piece_idx :: D, cuts_are_vertical :: Bool,\n\t\tsubpatterns :: Vector{CutPattern{D, S}}\n\t) :: CutPattern{D, S} where {D, S}\n\t\t!iszero(piece_idx) && !isempty(subpatterns) && @error(\n\t\t\t\"A piece cannot be subdivided; \" *\n\t\t\t\"failed: !iszero(piece_idx) && isempty(subpatterns).\"\n\t\t)\n\t\tL <= zero(L) && @error(\n\t\t\t\"The length of a pattern must be positive.\"\n\t\t)\n\t\tW <= zero(W) && @error(\n\t\t\t\"The width of a pattern must be positive.\"\n\t\t)\n\t\tif cuts_are_vertical\n\t\t\tsum_width = reduce(+, getfield.(subpatterns, :width); init = zero(S))\n\t\t\tsum_width > W && @error(\n\t\t\t\t\"The sum of the subpatterns width is $sum_width but the width\" *\n\t\t\t\t\" of the pattern is $W.\"\n\t\t\t)\n\t\t\tmax_length = reduce(max, getfield.(subpatterns, :length); init = zero(S))\n\t\t\tmax_length > L && @error(\n\t\t\t\t\"The largest subpattern length is $max_length but the length\" *\n\t\t\t\t\" of the pattern is $L.\"\n\t\t\t)\n\t\telse\n\t\t\tsum_length = reduce(+, getfield.(subpatterns, :length); init = zero(S))\n\t\t\tsum_length > L && @error(\n\t\t\t\t\"The sum of the subpatterns length is $sum_length but the length\" *\n\t\t\t\t\" of the pattern is $L.\"\n\t\t\t)\n\t\t\tmax_width = reduce(max, getfield.(subpatterns, :width); init = zero(S))\n\t\t\tmax_width > W && @error(\n\t\t\t\t\"The largest subpattern width is $max_width but the width\" *\n\t\t\t\t\" of the pattern is $W.\"\n\t\t\t)\n\t\tend\n\n\t\tnew{D, S}(L, W, piece_idx, cuts_are_vertical, subpatterns)\n\tend\nend\n\n\"\"\"\n    CutPattern(length, width, piece_idx)\n\nSimplified constructor for pieces and waste.\n\"\"\"\nfunction CutPattern(\n\tL :: S, W :: S, piece_idx :: D = 0\n) :: CutPattern{D, S} where {D, S}\n\tCutPattern(L, W, piece_idx, false, CutPattern{D, S}[])\nend\n\n\"\"\"\n    CutPattern(length, width, cuts_are_vertical, subpatterns)\n\nSimplified constructor for intermediary plates.\n\"\"\"\nfunction CutPattern(\n\tL :: S, W :: S, cuts_are_vertical :: Bool,\n\tsubpatterns :: Vector{CutPattern{D, S}}\n) :: CutPattern{D, S} where {D, S}\n\tCutPattern(L, W, zero(D), cuts_are_vertical, subpatterns)\nend\n\n\"\"\"\n    solution_value(problem, instance, solution)\n\nReturn the value of `solution` for `problem` and `instance`.\n\nThe returned value is often an `Int`. If the solution (i) was extracted\nfrom a model with a valid primal solution and (ii) the model correctly\ncomputed the objective function, then the value returned by this function\nshould match the value of the objective function in the model from which\nthe solution was extracted. Basically, this function serves as a\nformulation-agnostic double-check of the obtained solution value.\n\n`solution` is something returned by `get_cut_pattern` so often it is\neither a `Vector{CutPattern{D, S}}` or a single `CutPattern{D, S}`.\n\n!!! This function does not check the validity of the given solution,\nit assumes a valid solution and just compute the value it would have\nif it was valid.\n\"\"\"\nfunction solution_value end\n\n# INTERNAL USE. See `remove_waste!`.\nfunction inner_rec_remove_waste!(p :: CutPattern{D, S}) :: Bool where {D, S}\n\tfilter!(inner_rec_remove_waste!, p.subpatterns)\n\treturn !iszero(p.piece_idx) || !isempty(p.subpatterns)\nend\n\n# INTERNAL USE.\n# Removal every non-root pattern that has no piece among its descendants.\nfunction remove_waste!(p :: CutPattern{D, S}) :: CutPattern{D, S} where {D, S}\n\tinner_rec_remove_waste!(p)\n\treturn p\nend\n\n# INTERNAL USE.\n# Remove every non-root pattern that has no piece among its descendants.\nfunction promote_single_childs!(\n\tp :: CutPattern{D, S}\n) :: CutPattern{D, S} where {D, S}\n\tfor (index, value) in pairs(p.subpatterns)\n\t\tp.subpatterns[index] = promote_single_childs!(value)\n\tend\n\tisone(length(p.subpatterns)) && return first(p.subpatterns)\n\treturn p\nend\n\n# INTERNAL USE.\n# For every subpattern that have itself subpatterns and shares the value of\n# `cuts_are_vertical` with its parent pattern, replace the subpattern with\n# their immediate children.\nfunction flatten!(p :: CutPattern{D, S}) :: CutPattern{D, S} where {D, S}\n\tisempty(p.subpatterns) && return p\n\t# Check beforehand if there is any children to be flattened save it.\n\tto_flatten = @. (\n\t\t(p.cuts_are_vertical === getfield(p.subpatterns, :cuts_are_vertical)) &\n\t\tiszero(getfield(p.subpatterns, :piece_idx))\n\t)\n\tneed_flattening = any(to_flatten)\n\tneed_flattening && (aux_vector = Vector{CutPattern{D, S}}())\n\tfor (i, subpatt) in pairs(p.subpatterns)\n\t\tflatten!(subpatt)\n\t\tif need_flattening\n\t\t\tif to_flatten[i]\n\t\t\t\tappend!(aux_vector, subpatt.subpatterns)\n\t\t\telse\n\t\t\t\tpush!(aux_vector, subpatt)\n\t\t\tend\n\t\tend\n\tend\n\tif need_flattening\n\t\t# The CutPattern object is immutable so we cannot just attribute the new\n\t\t# vector to `subpatterns` field. The trick below becomes necessary.\n\t\tempty!(p.subpatterns)\n\t\tappend!(p.subpatterns, aux_vector)\n\tend\n\treturn p\nend\n\n\"\"\"\n    simplify!(p :: CutPattern{D, S}) :: CutPattern{D, S} where {D, S}\n\nThis method is free to make any changes while keeping all the packed pieces and\nthe the validity of the solution, its purpose is to make the structure easier\nto understand by human beings. If you are trying to debug a model you will\nprobably want to see both the simplified and not simplified output. The\nsimplified will make it easier to understand the solution itself, and the\nnon-simplified will show peculiarities that given insight on how the model\nworks/'see things' internally.\n\nNOTE: the returned pattern is not always the given parameter, in some rare\ncases (a root plate with a single piece inside) the pattern returned may\nbe another object.\n\nNOTE: this method does not create any new `CutPattern` objects but instead\nprunes and rearranges the pattern tree (i.e., throws away some objects and\nchanges the position of others in the tree).\n\"\"\"\nfunction simplify!(p :: CutPattern{D, S}) :: CutPattern{D, S} where {D, S}\n\t# The order these subroutines are called is relevant. For maximum\n\t# simplification we need to remove waste before promoting single childs\n\t# (or some childs that will have no siblings after waste removal will not\n\t# be promoted), and to promote single childs before flattening (or an\n\t# alternating pattern H-V-H-'2 pieces' will end up like H-H-'2 pieces'\n\t# and not like H-'2 pieces' as it should).\n\tp = remove_waste!(p)\n\tp = promote_single_childs!(p)\n\tp = flatten!(p)\n\treturn p\nend\n\n\"\"\"\n    to_pretty_str(p :: CutPattern{D, S}; kwargs...) :: String\n\nCreates a simplified and indented string representing the CutPattern.\n\n# Format\n\nThe format represents pieces as \"`piece_idx`p`length`x`width`\" (e.g., \"1p10x20\"\nis a copy of piece 1 which have length 10 and width 20); non-piece patterns are\nrepresented by \"P`length`x`width`\" (note the `P` is uppercase); if the\nnon-piece pattern has subpatterns (i.e., is not waste) then it starts a set of\nvertical (horizontal) cuts with `[` (`{`) and close it with `]` (`}`). There is\nalways whitespace between the elements of such sets but, for conciseness and\nease of reading, if all the elements of a subpattern have no children they are\nseparated by single spaces (no matter how long the list), otherwise they are\nseparated by newlines.\n\n# Keyword Arguments\n\n* `lvl :: Int64`: The current level of indentation.\n* `indent_str`: The string or character to be repeated as indentation.\n\n\"\"\"\nfunction to_pretty_str(\n\tp :: CutPattern{D, S};\n\tlvl = 0,\n\tindent_str = \"  \" # i.e., two spaces\n) :: String where {D, S}\n\tindent = indent_str^lvl\n\t!iszero(p.piece_idx) && return \"$indent$(p.piece_idx)p$(p.length)x$(p.width)\"\n\tob = p.cuts_are_vertical ? '[' : '{'\n\tcb = p.cuts_are_vertical ? ']' : '}'\n\tbase = \"$(indent)P$(p.length)x$(p.width)\"\n\tisempty(p.subpatterns) && return base\n\tif any(!isempty, getfield.(p.subpatterns, :subpatterns))\n\t\tjoined_sub = mapreduce(*, p.subpatterns) do subpatt\n\t\t\tto_pretty_str(subpatt; lvl = lvl + 1, indent_str = indent_str) * '\\n'\n\t\tend\n\t\treturn \"$base$ob\\n$joined_sub$indent$cb\"\n\telse\n\t\tpiece_strs = map(p.subpatterns) do piece\n\t\t\tto_pretty_str(piece; lvl = 0)\n\t\tend\n\t\tjoined_pieces = join(piece_strs, \" \")\n\t\treturn \"$base$ob $joined_pieces $cb\"\n\tend\nend\n\n\"\"\"\n    get_cut_pattern(problem, formulation, model, build_model_return)\n\nGiven a `model` built with `build_model(problem, formulation, ...)` and its\nrespective `build_model_return` (i.e., the second return of `build_model`),\nreturns a CutPattern representing the optimal solution found. If the first\nreturn of `build_model` is `BUILT_MODEL`, then the model needs to be solved\nbefore calling this method; however, if it is `FOUND_OPTIMUM` then the model\ndoes not need to be solved.\n\nThe implementation of this method is responsability of\nwhoever implemented the corresponding `build_model` method.\n\nThe `D` and `S` type parameters from `CutPattern{D, S}` are inferred from\n`build_model_return` as of now.\n\nIf `build_model` returns `nothing` then `model` somehow needs to contain\nall information needed to build the CutPattern. Note that the problem instance\n(`l`, `w`, `L`, `W`, ...) is not passed to this method, consequently, a\ncommon pattern is to have `build_model` return a struct containing\nthe problem instance and the auxiliary tables used to build the model\n(especially the ones associating variables with dimensions or piece indexes).\n\nThe solutions of some problems are not a single `CutPattern` but instead\nmultiple `CutPatterns` inside an iterable collection of them (often a\n`Vector`).\n\nSee also: [`build_model`](@ref)\n\"\"\"\nfunction get_cut_pattern end\n\ninclude(\"parse_pattern.jl\")\ninclude(\"to_tikz.jl\")\n\ninclude(\"Utilities/Utilities.jl\")\ninclude(\"Data/Data.jl\")\n#include(\"KnapsackPlates.jl\") # not working, and no plans to fix it for now\ninclude(\"Flow/Flow.jl\")\ninclude(\"PPG2KP/PPG2KP.jl\")\ninclude(\"SaveModel.jl\")\ninclude(\"CommandLine.jl\")\n\nend # module\n", "meta": {"hexsha": "76452adf3951b9b288e036bd79a1a01964e12df8", "size": 15959, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GuillotineModels.jl", "max_stars_repo_name": "henriquebecker91/GuillotineModels.jl", "max_stars_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-11T07:08:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-12T13:19:40.000Z", "max_issues_repo_path": "src/GuillotineModels.jl", "max_issues_repo_name": "henriquebecker91/GuillotineModels.jl", "max_issues_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/GuillotineModels.jl", "max_forks_repo_name": "henriquebecker91/GuillotineModels.jl", "max_forks_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4554216867, "max_line_length": 311, "alphanum_fraction": 0.7347578169, "num_tokens": 4131, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204329, "lm_q2_score": 0.1347759035217989, "lm_q1q2_score": 0.06370634264134396}}
{"text": "\"\"\"\n    LinkedList\n\nLinked list abstract supertype.\n\"\"\"\nabstract type LinkedList{T} end\n\n\n\"\"\"\n    LLNode\n\nLinked list node supertype.\n\"\"\"\nabstract type LLNode{T} end\n\n\n\"\"\"\n    NValueType\n\nNode value type.\n\"\"\"\nNValueType = Union{T, Nothing} where T\n\n\n\"\"\"\nSLLNode\n\nSingly linked list node. Holds pointer to next node and some value.\n\"\"\"\nmutable struct SLLNode{T} <: LLNode{T}\n    next::SLLNode{T}\n    value::NValueType{T}\n    SLLNode{T}() where T = (x = new(); x.next = x; x.value = nothing; x)\n    SLLNode{T}(y::T) where T = (x = new(); x.next = x; x.value = y; x)\n    SLLNode{T}(n::SLLNode{T}, y::T) where T = new(n, y)\nend\n\n\n\"\"\"\n    DLLNode\n\nDouble linked list node. Holds pointers to previous and next nodes and some value.\n\"\"\"\nmutable struct DLLNode{T} <: LLNode{T}\n    prev::DLLNode{T}\n    next::DLLNode{T}\n    value::NValueType{T}\n    DLLNode{T}() where T = (x = new(); x.prev = x; x.next = x; x.value = nothing; x)\n    DLLNode{T}(y::T) where T = (x = new(); x.prev = x; x.next = x; x.value = y; x)\n    DLLNode{T}(p::DLLNode{T}, n::DLLNode{T}, y::T) where T = new(p, n, y)\nend\n\n\n\"\"\"\n    SLinkedList\n\nA singly linked list implementation.\n\"\"\"\nmutable struct SLinkedList{T} <: LinkedList{T}\n    head::SLLNode{T}\n    tail::SLLNode{T}\n    size::Int\n    #SLinkedList{T}() where T = (x = new(); x.size = 0; x)\n    SLinkedList{T}() where T = (x = SLLNode{T}(); new(x, x, 0))\nend\n\n\n\"\"\"\n    DLinkedList\n\nA double linked list implementation.\n\"\"\"\nmutable struct DLinkedList{T} <: LinkedList{T}\n    head::DLLNode{T}\n    tail::DLLNode{T}\n    size::Int\n    #DLinkedList{T}() where T = (x = new(); x.size = 0; x)\n    DLinkedList{T}() where T = (x = DLLNode{T}(); new(x, x, 0))\nend\n\n\n\"\"\"\n    addlast!(l::SLinkedList{T}, x::T)\n\nAdd node to last position in linked list. Time complexity O(1).\n\"\"\"\nfunction addlast!(l::SLinkedList{T}, x::T) where T\n    l.tail.next.value = x\n    l.tail = l.tail.next\n    l.tail.next = SLLNode{T}()\n    l.size += 1\n\n    return nothing\nend\n\n\n\"\"\"\n    addlast!(l::DLinkedList{T}, x::T)\n\nAdd node to last position in linked list. Time complexity O(1).\n\"\"\"\nfunction addlast!(l::DLinkedList{T}, x::T) where T\n    l.tail.next.value = x\n    l.tail = l.tail.next\n    l.tail.next = DLLNode{T}()\n    l.tail.next.prev = l.tail\n    l.size += 1\n\n    return nothing\nend\n\n\n\"\"\"\n    addfirst!(l::SLinkedList{T}, x::T)\n\nAdd node to first position in linked list. Time complexity O(1).\n\"\"\"\nfunction addfirst!(l::SLinkedList{T}, x::T) where T\n    n = l.head\n    l.head = SLLNode{T}(x)\n    l.head.next = n\n    if l.size == 0 # do this only if called first on empty list\n        l.tail = l.head\n    end\n    l.size += 1\n\n    return nothing\nend\n\n\n\"\"\"\n    addfirst!(l::DLinkedList{T}, x::T)\n\nAdd node to first position in linked list. Time complexity O(1).\n\"\"\"\nfunction addfirst!(l::DLinkedList{T}, x::T) where T\n    n = l.head\n    l.head = DLLNode{T}(x)\n    l.head.next = n\n    l.head.next.prev = l.head\n    if l.size == 0 # do this only if called first on empty list\n        l.tail = l.head\n    end\n    l.size += 1\n\n    return nothing\nend\n\n\n\"\"\"\n    add!(l::SLinkedList{T}, x::T, pos::Int)\n\nAdd node to linked list at some position. Time complexity O(n).\n\"\"\"\nfunction add!(l::SLinkedList{T}, x::T, pos::Int) where T\n    if pos <= 1\n        addfirst!(l, x)\n    elseif pos > l.size\n        addlast!(l, x)\n    else\n        n = l.head\n        i = 1\n        while i < pos\n            n = n.next\n            i += 1\n        end\n        n.next = SLLNode{T}(n.next, n.value) \n        n.value = x\n        l.size += 1\n    end\n    \n    return nothing\nend\n\n\n\"\"\"\n    add!(l::DLinkedList{T}, x::T, pos::Int)\n\nAdd node to linked list at some position. Time complexity O(n).\n\"\"\"\nfunction add!(l::DLinkedList{T}, x::T, pos::Int) where T\n    if pos <= 1\n        addfirst!(l, x)\n    elseif pos > l.size\n        addlast!(l, x)\n    else\n        n = l.head\n        i = 1\n        while i < pos\n            n = n.next\n            i += 1\n        end\n        n.next = DLLNode{T}(n, n.next, n.value)\n        n.value = x\n        n.next.next.prev = n.next\n        l.size += 1\n    end\n    \n    return nothing\nend\n\n\n\"\"\"\n    removelast!(l::SLinkedList)\n\nRemove last node from linked list. Time complexity O(n).\n\"\"\"\nfunction removelast!(l::SLinkedList)\n    x = l.tail.value\n    n = l.head\n\n    i = 1\n    while i < l.size - 1\n        n = n.next\n        i += 1\n    end\n    empty = l.tail.next\n\n    if l.size == 1\n        # all this necessary?\n        l.head = empty\n        l.head.next = l.head\n        l.tail = empty\n        l.tail.next = l.tail\n    else\n        l.tail = n\n        l.tail.next = empty\n    end\n    l.size -= 1\n\n    return x\nend\n\n\n\"\"\"\n    removelast!(l::DLinkedList)\n\nRemove last node from linked list. Time complexity O(1).\n\"\"\"\nfunction removelast!(l::DLinkedList)\n    x = l.tail.value\n    empty = l.tail.next\n\n    if l.size == 1\n        # all this necessary?\n        l.head = empty\n        l.head.prev = l.head\n        l.head.next = l.head\n        l.tail = empty\n        l.tail.prev = l.tail\n        l.tail.next = l.tail\n    else\n        l.tail = l.tail.prev\n        l.tail.next = empty\n        l.tail.next.prev = l.tail\n    end\n    l.size -= 1\n\n    return x\nend\n\n\n\"\"\"\n    removefirst!(l::SLinkedList)\n\nRemove first node from linked list. Time complexity O(1).\n\"\"\"\nfunction removefirst!(l::SLinkedList)\n    x = l.head.value\n    l.head = l.head.next\n    if l.size == 1\n        l.tail = l.head\n    end\n    l.size -= 1\n\n    return x\nend\n\n\n\"\"\"\n    removefirst!(l::DLinkedList)\n\nRemove first node from linked list. Time complexity O(1).\n\"\"\"\nfunction removefirst!(l::DLinkedList)\n    x = l.head.value\n    l.head = l.head.next\n    l.head.prev = l.head\n    if l.size == 1\n        l.tail = l.head\n    end\n    l.size -= 1\n\n    return x\nend\n\n\n\"\"\"\n    remove!(l::SLinkedList, pos::Int)\n\nRemove node from linked list. Time complexity O(n).\n\"\"\"\nfunction remove!(l::SLinkedList, pos::Int)\n    x = 0\n\n    if pos <= 1\n        x = removefirst!(l)\n    elseif pos > l.size\n        x = removelast!(l)\n    else\n        n = l.head\n\n        i = 1\n        while i < l.size-2\n            n = n.next\n            i += 1\n        end\n        x = n.next.value\n        n.next = n.next.next\n\n        l.size -= 1\n    end\n    \n    return x\nend\n\n\n\"\"\"\n    remove!(l::DLinkedList, pos::Int)\n\nRemove node from linked list. Time complexity O(n).\n\"\"\"\nfunction remove!(l::DLinkedList, pos::Int)\n    x = 0\n\n    if pos <= 1\n        x = removefirst!(l)\n    elseif pos > l.size\n        x = removelast!(l)\n    else\n        n = l.head\n\n        i = 1\n        while i < l.size - 1\n            n = n.next\n            i += 1\n        end\n        x = n.value\n        n.prev.next = n.next\n        n.next.prev = n.prev\n\n        l.size -= 1\n    end\n    \n    return x\nend\n\n\n\"\"\"\n    add!(l::LinkedList{T}, x::T)\n\nAdd node to last position in linked list. Time complexity O(1).\n\"\"\"\nadd!(l::LinkedList{T}, x::T) where T = addlast!(l, x)\n\n\n\"\"\"\n    remove!(l::LinkedList)\n\n Remove first node from linked list. Time complexity O(1).\n\"\"\"\nremove!(l::LinkedList) = removefirst!(l)\n\n\n\"\"\"\n    peekfirst(l::LinkedList)\n\nLookup first element in list.\n\"\"\"\npeekfirst(l::LinkedList) = l.head.value\n\n\n\"\"\"\n    peeklast(l::LinkedList)\n\nLookup last element in list.\n\"\"\"\npeeklast(l::LinkedList) = l.tail.value\n\n\n\"\"\"\n    length(l)\n\nGet length of linked list.\n\"\"\"\nlength(l::LinkedList) = l.size\n\n\n\"\"\"\n    size(l)\n\nGet length of linked list.\n\"\"\"\nsize(l::LinkedList) = length(l)\n\n\n\"\"\"\n    :(==)\n\nTest list equality. O(n)\n\"\"\"\nfunction (==)(x::LinkedList, y::LinkedList)\n    collect(x) == collect(y)\nend\n# (==)(x::LinkedList, y::LinkedList) = (x.head == y.head) && (x.tail == y.tail) && (x.size == y.size) # this is not complete\n\n\n\"\"\"\n    show(l)\n\nDisplay linked list types.\n\"\"\"\nfunction show(io::IO, l::SLinkedList{T}) where T\n    println(io, \"$(typeof(l))\")\n    print(io, \" Head: \")\n    \n    n = l.head\n    while n != n.next\n        print(io, \" $(n.value)  -> \")\n        n = n.next\n    end\n    println(io, \" $(n.value)\")\n    println(io, \" Tail:  $(l.tail.value)\")\n    print(io, \" Size:  $(l.size)\")\nend\n\n\nfunction show(io::IO, l::DLinkedList{T}) where T\n    println(io, \"$(typeof(l))\")\n    print(io, \" Head: \")\n\n    n = l.head\n    while n != n.next\n        print(io, \" $(n.value)  <-> \")\n        n = n.next\n    end\n    println(io, \" $(n.value)\")\n    println(io, \" Tail:  $(l.tail.value)\")\n    print(io, \" Size:  $(l.size)\")\nend\n\n\n\"\"\"\n    iterate(l, n)\n\nIterate over a linked list.\n\"\"\"\niterate(l::LinkedList, n = l.head) = n == n.next ? nothing : (n.value, n.next)\n\n\n\"\"\"\n    eltype(type)\n\nType of elements generated by iteration over a linked list.\n\"\"\"\neltype(::LinkedList{T}) where T = T", "meta": {"hexsha": "f6b246ca3d0279531ddab9d91d795d4ad327cff4", "size": 8578, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/list.jl", "max_stars_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_stars_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/list.jl", "max_issues_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_issues_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/list.jl", "max_forks_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_forks_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.4077253219, "max_line_length": 124, "alphanum_fraction": 0.5525763581, "num_tokens": 2577, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.1403362494900832, "lm_q1q2_score": 0.0636090677874234}}
{"text": "# Functions are typically designated by the function name and parentheses, with an argument in the parentheses \r\nprintln(\"println is a function and this is the argument\")\r\n\r\n# If there is no input, a blank line is printed\r\nprintln()\r\n\r\n# Operators are exceptions to the parentheses rule\r\na = 12 + 14\r\nprintln(a)\r\n\r\n# Creating a basic function\r\nfunction add_numbers(x, y)\r\n    x + y\r\n    end\r\n\r\n# Using the function\r\nprintln(add_numbers(1, 2))\r\nprintln(add_numbers(2, 3))\r\nprintln(add_numbers(3, 4))", "meta": {"hexsha": "b754ef052550b8b0863cd7f24b8f8f02ea9d72cb", "size": 498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Viki/functions.jl", "max_stars_repo_name": "KorfLab/julie", "max_stars_repo_head_hexsha": "9a87778621d94fe2be6a578a59f3900007b5b186", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-08T22:18:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-08T22:18:24.000Z", "max_issues_repo_path": "Viki/functions.jl", "max_issues_repo_name": "KorfLab/julie", "max_issues_repo_head_hexsha": "9a87778621d94fe2be6a578a59f3900007b5b186", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Viki/functions.jl", "max_forks_repo_name": "KorfLab/julie", "max_forks_repo_head_hexsha": "9a87778621d94fe2be6a578a59f3900007b5b186", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-07-08T23:31:44.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-08T23:31:44.000Z", "avg_line_length": 26.2105263158, "max_line_length": 112, "alphanum_fraction": 0.718875502, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4073334000459302, "lm_q2_score": 0.15610489548243567, "lm_q1q2_score": 0.06358673784067509}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.7\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 22b67cb0-6687-11ec-1868-bb216a9703f4\nbegin\n\tusing CondaPkg, PlutoUI\n\tCondaPkg.add.((\"matplotlib\", \"numpy\")); CondaPkg.resolve()\n    using PythonCall\n    import MarkdownLiteral: @mdx\nend\n\n# \u2554\u2550\u2561 f150770d-cbcd-4eda-a0a5-b759a21f5b9b\n@mdx \"\"\"\n# Fun with \ud83d\udc0d\n\nIn this notebook we will showing some useage examples for the new package `PythonCall.jl`. It behaves similarly to the older package `PyCall.jl`, but with a few key differences that make interacting with Python quite nice:\n\n* automatic, project-specific environments for easy reproducibility\n* package management via [micromamba](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) for blazing fast Python package management \ud83d\udd25\n* easy plot display support\n\nLet's try some things out!\n\"\"\"\n\n# \u2554\u2550\u2561 800e21bc-28e3-496d-8871-4c81de6713ca\nTableOfContents()\n\n# \u2554\u2550\u2561 30181f53-97e2-4d21-83d2-9394ef64aba2\n@mdx \"\"\"\n## `pyeval`\nTo see how we can start using this package, let's start by creating some simple Python objects:\n\"\"\"\n\n# \u2554\u2550\u2561 b1f1706d-da3b-48ae-bed1-4f5fb9e95726\npy_list = @pyeval \"[1, 2, 3]\"\n\n# \u2554\u2550\u2561 fa184670-718c-4e68-8e23-fe6e72a651c5\npy_dict = @pyeval \"{'one': 1, 'two': 2, 'three': 3}\"\n\n# \u2554\u2550\u2561 53b89008-8ac0-4f20-955d-1d875530f121\npy_range = @pyeval \"range(0, 10, 2)\"\n\n# \u2554\u2550\u2561 81a0a61d-0ff8-4610-915b-3f32a2742f5d\n@mdx \"\"\"\nThe `@pyeval` macro behaves like Python's [`eval`](https://docs.python.org/3/library/functions.html#eval) and evaluates whatever Python expression (written as a string literal or `cmd`) is passed to it. The returned objects can participate in the usual Python methods now:\n\"\"\"\n\n# \u2554\u2550\u2561 15b2d0ab-bc1a-435a-9557-99537d117d91\npy_list.append(4); py_list\n\n# \u2554\u2550\u2561 ffa07e57-7797-4000-8be4-a166895bcc54\npy_dict[\"three\"]\n\n# \u2554\u2550\u2561 5e0919d8-0a02-46fc-92fe-0a5e40d1708c\npy_range[0] # Python's zero-based indexing is automatically understood\n\n# \u2554\u2550\u2561 0b3322b5-cd15-49d9-a446-35224a4b7637\n@mdx \"\"\"\n!!! tip\n\tWe recommend passing strings (\"\") instead of cmds (\\`\\`) to play nice with Pluto's `ExpressionExplorer`\n\n\tRelated issue? [#961](https://github.com/fonsp/Pluto.jl/issues/961)\n\"\"\"\n\n# \u2554\u2550\u2561 3359adff-8eb5-4911-a914-047ecf3663fe\n@mdx \"\"\"\n`PythonCall.jl` also provides a function version of `@pyeval` if we would like to do things like string interpolation first:\n\"\"\"\n\n# \u2554\u2550\u2561 6be1822a-d905-4de9-8345-4e160e53ea64\nlet\n\tfour = 4\n\tpyeval(\"[1, 2, 3, $four]\", Main)\nend\n\n# \u2554\u2550\u2561 ed066bda-07b4-4c52-9302-15c66cb4e1d8\n@mdx \"\"\"\nMore info about the usage for each version is available in the Live docs.\n\"\"\"\n\n# \u2554\u2550\u2561 a7e24a65-cfa8-4e03-9664-f8f424324917\n@mdx \"\"\"\nTo a certain extent, Julia's functions can also operate on these objects automatically:\n\"\"\"\n\n# \u2554\u2550\u2561 1e94e185-af8c-4796-9358-6a2c15c7fd43\npy_range |> sum\n\n# \u2554\u2550\u2561 b810a02b-8329-4f51-8cfc-c6920ad1cf5d\npy_range |> collect\n\n# \u2554\u2550\u2561 40cb6860-7ac6-40ca-8b1f-30360fd6aef7\n@mdx \"\"\"\nAs an alternative, `PythonCall` makes it very easy to convert these objects to their native Julia counterparts with the `@pyconvert`/`pyconvert` macro/function:\n\"\"\"\n\n# \u2554\u2550\u2561 57686014-99e8-4733-8f1d-ba80306b9da9\n(@pyconvert StepRange py_range) |> collect\n\n# \u2554\u2550\u2561 1360029e-76f3-468a-9b2f-d27482bbc525\npyconvert(Dict, py_dict)\n\n# \u2554\u2550\u2561 fde4db08-e5e1-4e92-b1c7-9a41edb04476\n@mdx \"\"\"\nNext, we will take a look at running Python statements.\n\"\"\"\n\n# \u2554\u2550\u2561 71ba808a-ebde-43ad-b264-ccf8e676891a\n@mdx \"\"\"\n## `pyexec`\n\nWe saw how to run simple Python expressions with `pyeval`. Now we will turn to executing Python statements, which can be used to perform more complex tasks like storing values in variables and defining functions. This is accomplished with `PythonCalls.jl's` `@pyexec`/`pyexec` macro/function, which behave's like Python's [`exec`](https://docs.python.org/3/library/functions.html#exec):\n\"\"\"\n\n# \u2554\u2550\u2561 193e65ae-c344-4dda-b92b-e0b136eb7581\n@pyexec (x=1, y=2) => \"ans = x + y\" => ans::Int\n\n# \u2554\u2550\u2561 0c8e14ef-782d-420b-8906-3a139bacf331\npyexec(@NamedTuple{ans}, \"ans = 1 + 2\", Main)\n\n# \u2554\u2550\u2561 1eccfc11-4ea1-4b79-a688-2644d6d1d0fd\n@mdx \"\"\"\n## Combining `pyexec` and `pyeval`\n\nWe can now compose these ideas to start interacting with full blocks of Python code:\n\"\"\"\n\n# \u2554\u2550\u2561 5b597d2a-2483-4b80-860b-839fa3ddeaec\nbegin\n\t@pyexec \"\"\"\n\tglobal greetings\n\tdef greetings(name):\n\t\treturn f\"Hi {name} \ud83d\udc4b\"\n\t\"\"\"\n\tgreetings(name) = @pyeval(\"greetings\")(name)\nend\n\n# \u2554\u2550\u2561 5aa1ba97-d55c-4794-a839-e90effb84bbe\ngreetings(\"Pluto citizen\")\n\n# \u2554\u2550\u2561 53dd2eb5-ee4c-4f4c-a9ca-6f234a376a78\n@mdx \"\"\"\n!!! note\n\tBy default, this is executed in local scope. To make it accessible to other parts of the notebook, it is required to explicitly state this with `global`.\n\"\"\"\n\n# \u2554\u2550\u2561 e9e6c4e0-6834-4b6b-ac20-ff722f9a5cd9\n@mdx \"\"\"\n## Using packages\n`PythonCall.jl` has a nice companion package named `CondaPkg.jl`, which we can use to easily install Python packages into an environment in the same directory as this notebook:\n\"\"\"\n\n# \u2554\u2550\u2561 0bf45621-7776-403e-b3da-5311a5c30e20\n@mdx \"\"\"\n```julia\nCondaPkg.add.((\"matplotlib\", \"numpy\"))\nCondaPkg.resolve()\n```\n\"\"\"\n\n# \u2554\u2550\u2561 f5d2228c-b45e-4dce-99fb-668f6c50df30\n@mdx \"\"\"\nWe now use the `@py` macro to import and interact with these packages:\n\"\"\"\n\n# \u2554\u2550\u2561 a159c36c-83c8-460e-a7e2-e85c7df8d9da\n@py begin\n\timport matplotlib.pyplot as plt\n\timport numpy as np\nend\n\n# \u2554\u2550\u2561 c83fdb79-b0d9-4830-b6cf-74d2a669ceed\nlet\n\txs = np.random.rand(10, 4, 4)\n\n\tfig, axes = plt.subplots(2, 2, sharex=true, sharey=true)\n\n\tfor (ax, x) \u2208 zip(axes.flat, xs)\n\t\tax.plot(x)\n\tend\n\n\tfig.tight_layout()\n\n\tplt.gcf()\nend\n\n# \u2554\u2550\u2561 681ece7a-6800-4779-8118-28c3179cd43a\n@mdx \"\"\"\n## Environment details\n\nWhere is all this stuff being downloaded/run? Let's see!\n\"\"\"\n\n# \u2554\u2550\u2561 655674de-56c1-4386-8fda-aa5c95b6271f\n@with_terminal CondaPkg.status() # Will show directly in notebook in next Pluto release\n\n# \u2554\u2550\u2561 d407d19b-40ee-496b-b585-ff685232b48b\n@with_terminal run(CondaPkg.MicroMamba.cmd(`list`)) # Will ask for a better API for this\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCondaPkg = \"992eb4ea-22a4-4c89-a5bb-47a3300528ab\"\nMarkdownLiteral = \"736d6165-7244-6769-4267-6b50796e6954\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPythonCall = \"6099a3de-0909-46bc-b1f4-468b9a2dfc0d\"\n\n[compat]\nCondaPkg = \"~0.2.6\"\nMarkdownLiteral = \"~0.1.1\"\nPlutoUI = \"~0.7.32\"\nPythonCall = \"~0.5.1\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.1\"\nmanifest_format = \"2.0\"\n\n[[deps.AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"8eaf9f1b4921132a4cff3f36a1d9ba923b14a481\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.4\"\n\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[deps.Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[deps.Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[deps.ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"024fe24d83e4a5bf5fc80501a314ce0d1aa35597\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.11.0\"\n\n[[deps.CommonMark]]\ndeps = [\"Crayons\", \"JSON\", \"URIs\"]\ngit-tree-sha1 = \"4aff51293dbdbd268df314827b7f409ea57f5b70\"\nuuid = \"a80b9123-70ca-4bc0-993e-6e3bcb318db6\"\nversion = \"0.8.5\"\n\n[[deps.CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[deps.CondaPkg]]\ndeps = [\"JSON3\", \"Markdown\", \"MicroMamba\", \"Pkg\", \"TOML\"]\ngit-tree-sha1 = \"49589b0a9284fd8ea84c64a42e96739d1fe06751\"\nuuid = \"992eb4ea-22a4-4c89-a5bb-47a3300528ab\"\nversion = \"0.2.6\"\n\n[[deps.Crayons]]\ngit-tree-sha1 = \"249fe38abf76d48563e2f4556bebd215aa317e15\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.1.1\"\n\n[[deps.DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[deps.DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[deps.Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[deps.Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[deps.FixedPointNumbers]]\ndeps = [\"Statistics\"]\ngit-tree-sha1 = \"335bfdceacc84c5cdf16aadc768aa5ddfc5383cc\"\nuuid = \"53c48c17-4a7d-5ca2-90c5-79b7896eea93\"\nversion = \"0.8.4\"\n\n[[deps.Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[deps.HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[deps.IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[deps.JSON3]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"StructTypes\", \"UUIDs\"]\ngit-tree-sha1 = \"7d58534ffb62cd947950b3aa9b993e63307a6125\"\nuuid = \"0f8b85d8-7281-11e9-16c2-39a750bddbf1\"\nversion = \"1.9.2\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.MacroTools]]\ndeps = [\"Markdown\", \"Random\"]\ngit-tree-sha1 = \"3d3e902b31198a27340d0bf00d6ac452866021cf\"\nuuid = \"1914dd2f-81c6-5fcd-8719-6d5c9610ff09\"\nversion = \"0.5.9\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MarkdownLiteral]]\ndeps = [\"CommonMark\", \"HypertextLiteral\"]\ngit-tree-sha1 = \"0d3fa2dd374934b62ee16a4721fe68c418b92899\"\nuuid = \"736d6165-7244-6769-4267-6b50796e6954\"\nversion = \"0.1.1\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[deps.MicroMamba]]\ndeps = [\"Pkg\", \"Scratch\"]\ngit-tree-sha1 = \"e61632c723f4d0fafa5419a506f08baafe973391\"\nuuid = \"0b3b1443-0f03-428d-bdfb-f27f9c1191ea\"\nversion = \"0.1.4\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"0b5cfbb704034b5b4c1869e36634438a047df065\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.1\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"ae6145ca68947569058866e443df69587acc1806\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.32\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.PythonCall]]\ndeps = [\"CondaPkg\", \"Dates\", \"Libdl\", \"MacroTools\", \"Markdown\", \"Pkg\", \"Requires\", \"Serialization\", \"Tables\", \"UnsafePointers\"]\ngit-tree-sha1 = \"402a4d004a9e7c9656758f479f456205f7f6a3ce\"\nuuid = \"6099a3de-0909-46bc-b1f4-468b9a2dfc0d\"\nversion = \"0.5.1\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[deps.Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[deps.Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.3.0\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[deps.Scratch]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"0b4b7f1393cff97c33891da2a0bf69c6ed241fda\"\nuuid = \"6c6a2e73-6563-6170-7368-637461726353\"\nversion = \"1.1.0\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.StructTypes]]\ndeps = [\"Dates\", \"UUIDs\"]\ngit-tree-sha1 = \"d24a825a95a6d98c385001212dc9020d609f2d4f\"\nuuid = \"856f2bd8-1eba-4b0a-8007-ebc267875bd4\"\nversion = \"1.8.1\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[deps.TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[deps.Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"bb1064c9a84c52e277f1096cf41434b675cd368b\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.1\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.URIs]]\ngit-tree-sha1 = \"97bbe755a53fe859669cd907f2d96aee8d2c1355\"\nuuid = \"5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4\"\nversion = \"1.3.0\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.UnsafePointers]]\ngit-tree-sha1 = \"c81331b3b2e60a982be57c046ec91f599ede674a\"\nuuid = \"e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39\"\nversion = \"1.0.0\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500f150770d-cbcd-4eda-a0a5-b759a21f5b9b\n# \u2560\u2550800e21bc-28e3-496d-8871-4c81de6713ca\n# \u255f\u250030181f53-97e2-4d21-83d2-9394ef64aba2\n# \u2560\u2550b1f1706d-da3b-48ae-bed1-4f5fb9e95726\n# \u2560\u2550fa184670-718c-4e68-8e23-fe6e72a651c5\n# \u2560\u255053b89008-8ac0-4f20-955d-1d875530f121\n# \u255f\u250081a0a61d-0ff8-4610-915b-3f32a2742f5d\n# \u2560\u255015b2d0ab-bc1a-435a-9557-99537d117d91\n# \u2560\u2550ffa07e57-7797-4000-8be4-a166895bcc54\n# \u2560\u25505e0919d8-0a02-46fc-92fe-0a5e40d1708c\n# \u255f\u25000b3322b5-cd15-49d9-a446-35224a4b7637\n# \u255f\u25003359adff-8eb5-4911-a914-047ecf3663fe\n# \u2560\u25506be1822a-d905-4de9-8345-4e160e53ea64\n# \u255f\u2500ed066bda-07b4-4c52-9302-15c66cb4e1d8\n# \u255f\u2500a7e24a65-cfa8-4e03-9664-f8f424324917\n# \u2560\u25501e94e185-af8c-4796-9358-6a2c15c7fd43\n# \u2560\u2550b810a02b-8329-4f51-8cfc-c6920ad1cf5d\n# \u255f\u250040cb6860-7ac6-40ca-8b1f-30360fd6aef7\n# \u2560\u255057686014-99e8-4733-8f1d-ba80306b9da9\n# \u2560\u25501360029e-76f3-468a-9b2f-d27482bbc525\n# \u255f\u2500fde4db08-e5e1-4e92-b1c7-9a41edb04476\n# \u255f\u250071ba808a-ebde-43ad-b264-ccf8e676891a\n# \u2560\u2550193e65ae-c344-4dda-b92b-e0b136eb7581\n# \u2560\u25500c8e14ef-782d-420b-8906-3a139bacf331\n# \u255f\u25001eccfc11-4ea1-4b79-a688-2644d6d1d0fd\n# \u2560\u25505b597d2a-2483-4b80-860b-839fa3ddeaec\n# \u2560\u25505aa1ba97-d55c-4794-a839-e90effb84bbe\n# \u255f\u250053dd2eb5-ee4c-4f4c-a9ca-6f234a376a78\n# \u255f\u2500e9e6c4e0-6834-4b6b-ac20-ff722f9a5cd9\n# \u255f\u25000bf45621-7776-403e-b3da-5311a5c30e20\n# \u255f\u2500f5d2228c-b45e-4dce-99fb-668f6c50df30\n# \u2560\u2550a159c36c-83c8-460e-a7e2-e85c7df8d9da\n# \u2560\u2550c83fdb79-b0d9-4830-b6cf-74d2a669ceed\n# \u255f\u2500681ece7a-6800-4779-8118-28c3179cd43a\n# \u2560\u2550655674de-56c1-4386-8fda-aa5c95b6271f\n# \u2560\u2550d407d19b-40ee-496b-b585-ff685232b48b\n# \u2560\u255022b67cb0-6687-11ec-1868-bb216a9703f4\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "fa09fdfe30cb0662dc3d9556a2b3fac64429943d", "size": 16717, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "fun_with_python.jl", "max_stars_repo_name": "icweaver/Pluto_sample_notebooks", "max_stars_repo_head_hexsha": "37d5869eecd748ddcf2e7e5600cc730a689af721", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fun_with_python.jl", "max_issues_repo_name": "icweaver/Pluto_sample_notebooks", "max_issues_repo_head_hexsha": "37d5869eecd748ddcf2e7e5600cc730a689af721", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fun_with_python.jl", "max_forks_repo_name": "icweaver/Pluto_sample_notebooks", "max_forks_repo_head_hexsha": "37d5869eecd748ddcf2e7e5600cc730a689af721", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.535335689, "max_line_length": 386, "alphanum_fraction": 0.7333851768, "num_tokens": 7528, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.38121956625614994, "lm_q2_score": 0.16667540261828415, "lm_q1q2_score": 0.06353992469171144}}
{"text": "\n\"\"\"parse a single line of space separated integers\"\"\"\nspace_sep_ints(s::AbstractString; base=10) = parse.(Int, split(s); base)\n\n\nfunction file_extension(path::String)\n    if contains(path, \".\")\n        return path[findlast(isequal('.'),path):end]\n    else\n        return \"\"\n    end\nend\n", "meta": {"hexsha": "1be67d4d745c431b967bcbbd1c4c00e759bffc9b", "size": 287, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "adomasbaliuka/LDPCStorage.jl", "max_stars_repo_head_hexsha": "a79f1c6b9448f35899b5a0f813ca0740a9dbd8c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-07T11:32:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-07T11:32:52.000Z", "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "adomasbaliuka/LDPCStorage.jl", "max_issues_repo_head_hexsha": "a79f1c6b9448f35899b5a0f813ca0740a9dbd8c7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-21T12:41:12.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-21T12:41:12.000Z", "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "adomasbaliuka/LDPCStorage.jl", "max_forks_repo_head_hexsha": "a79f1c6b9448f35899b5a0f813ca0740a9dbd8c7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.0769230769, "max_line_length": 72, "alphanum_fraction": 0.6515679443, "num_tokens": 74, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.13660838299605804, "lm_q1q2_score": 0.06350945191764507}}
{"text": "# Simple Parsers\n# ==============\n\n\"\"\"\n    EP.SatisfiesPredicate(predicate_func[, errormessage])\n\nConstruct an ExprParser which checks whether the given `predicate_func` returns true.\nIf so, the to-be-parsed value is returned as such, otherwise an [`ParseError`](@ref) is thrown as usual.\nIf `errormessage` is given, it will be appended to the default error message.\n\n# Example\n```jldoctest\njulia> using ExprParsers;\n\njulia> parser = EP.SatisfiesPredicate(isodd);\n\njulia> parse_expr(parser, 3)\n3\njulia> parse_expr(parser, 4)\nERROR: ParseError: Predicate `isodd` returned false on expr `4`.\njulia> is44(x) = x==44;\n\njulia> parser2 = EP.SatisfiesPredicate(is44, \"It should be 44.\");\n\njulia> parse_expr(parser2, 44)\n44\njulia> parse_expr(parser2, 4)\nERROR: ParseError: Predicate `is44` returned false on expr `4`. It should be 44.\n```\n\"\"\"\nstruct SatisfiesPredicate{Func} <: ExprParser\n  predicate::Func\n  errormessage\nend\nSatisfiesPredicate(func) = SatisfiesPredicate(func, \"\")\nfunction parse_expr(parser::SatisfiesPredicate, expr)\n  parser.predicate(expr) ? expr : throw(ParseError(\n    \"Predicate `$(parser.predicate)` returned false on expr `$expr`. $(parser.errormessage)\"\n  ))\nend\n\n\"\"\"\n    EP.Isa(T::Type)\n\nConstructs an ExprParser which checks whether a value is of the given type.\n\n# Example\n```jldoctest\njulia> using ExprParsers\n\njulia> parser = EP.Isa(Symbol);\n\njulia> parse_expr(parser, :thisisasymbol)\n:thisisasymbol\njulia> parse_expr(parser, 42)\nERROR: ParseError: Expected type `Symbol`, got `42` of type `Int64`.\n```\n\n`EP.Isa(Symbol)` is so common that there is a special constant for it [`anysymbol`](@ref).\n\"\"\"\nstruct Isa{T} <: ExprParser\n  # this way we cannot forget to instantiate the Type\n  Isa(::Base.Type{T}) where T = new{T}()\nend\nparse_expr(parser::Isa{T}, expr::T) where T = expr\nparse_expr(parser::Isa{T}, other::S) where {T, S} = throw(ParseError(\"Expected type `$T`, got `$other` of type `$S`.\"))\n\n\"\"\"\n    EP.anything = Isa(Any)\n\nSpecial constant ExprParser which matches literally anything.\n\n# Examples\n```jldoctest\njulia> using ExprParsers\n\njulia> parse_expr(EP.anything, 42)\n42\njulia> parse_expr(EP.anything, :whatever)\n:whatever\n```\n\"\"\"\nconst anything = Isa(Any)\n\n\"\"\"\n    EP.anysymbol = Isa(Symbol)\n\nSpecial constant ExprParser which matches Symbols, and only Symbols.\n\n# Examples\n```jldoctest\njulia> using ExprParsers\n\njulia> parse_expr(EP.anysymbol, :asymbol)\n:asymbol\njulia> parse_expr(EP.anysymbol, 42)\nERROR: ParseError: Expected type `Symbol`, got `42` of type `Int64`.\n```\n\"\"\"\nconst anysymbol = Isa(Symbol)\n\n\"\"\"\n    EP.AnyOf(parser1, parser2, parser3, ...; errormessage = \"\")\n\nConstructs an ExprParser from multiple given parsers. When given a value it first tries to match\n`parser1`, and if that fails with a ParseError, then `parser2`, and so forth.\nThe result from the first parser which matches will be returned.\nIf no parser matches, a dedicated ParseError is raised.\n\nIf `errormessage` is given, it will be appended to the default error message in case of ParseError.\n\n# Examples\n```jldoctest\njulia> using ExprParsers\n\njulia> parser = EP.AnyOf(EP.anysymbol, EP.Isa(String), EP.SatisfiesPredicate(isodd),\n                         errormessage=\"My error message.\");\n\njulia> parse_expr(parser, :hi)\n:hi\njulia> parse_expr(parser, 3)\n3\njulia> parse_expr(parser, \"something\")\n\"something\"\njulia> parse_expr(parser, 4)\nERROR: ParseError: AnyOf could not parse expr `4` with any of the parsers `(ExprParsers.Isa{Symbol}(), ExprParsers.Isa{String}(), ExprParsers.SatisfiesPredicate{typeof(isodd)}(isodd, \"\"))`. My error message.\n```\n\"\"\"\nstruct AnyOf{T} <: ExprParser\n  several::T\n  errormessage\n  AnyOf(several...; errormessage = \"\") = new{typeof(several)}(several, errormessage)\nend\n\nfunction parse_expr(parser::AnyOf, expr)\n  for subparser in parser.several\n    try\n      return parse_expr(subparser, expr)\n    catch e\n      # only continue if ParseError\n      e isa ParseError || rethrow()\n    end\n  end\n  throw(ParseError(\"AnyOf could not parse expr `$(expr)` with any of the parsers `$(parser.several)`. $(parser.errormessage)\"))\nend\n\n\"\"\"\n    EP.AllOf(parser1, parser2, parser3, ...)\n\nConstructs an ExprParser from multiple given parsers. When to match a value, all parsers actually need to\nparse correctly, otherwise the `ParseError` from the first non-matching parser is rethrown.\nIf all parsers match, then the return value from the last parser is returned.\n\n# Examples\n```jldoctest\njulia> using ExprParsers\n\njulia> parser = EP.AllOf(EP.Isa(Number), EP.SatisfiesPredicate(isodd), 3);\n\njulia> parse_expr(parser, 3)\n3\njulia> parse_expr(parser, \"something\")\nERROR: ParseError: Expected type `Number`, got `something` of type `String`.\njulia> parse_expr(parser, 4)\nERROR: ParseError: Predicate `isodd` returned false on expr `4`.\njulia> parse_expr(parser, 5)\nERROR: ParseError: Using default `==` comparison, but parser `3` \u2260 value `5`.\n```\n\"\"\"\nstruct AllOf{T} <: ExprParser\n  several::T\n  AllOf(several...) = new{typeof(several)}(several)\nend\n\nfunction parse_expr(parser::AllOf, expr)\n  for subparser in parser.several[1:end-1]\n    parse_expr(subparser, expr)\n  end\n  # return last match, if all else matched\n  parse_expr(parser.several[end], expr)\nend\n", "meta": {"hexsha": "882988dda42ab662944f8acc8034618ae3f8a12e", "size": 5189, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/expr_parsers_core.jl", "max_stars_repo_name": "schlichtanders/ExprParsers.jl", "max_stars_repo_head_hexsha": "d8f48ba434fa14d7505a78526f660246d3a57b97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-22T00:14:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-22T00:14:30.000Z", "max_issues_repo_path": "src/expr_parsers_core.jl", "max_issues_repo_name": "schlichtanders/ExprParsers.jl", "max_issues_repo_head_hexsha": "d8f48ba434fa14d7505a78526f660246d3a57b97", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-08-09T19:24:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-17T08:31:45.000Z", "max_forks_repo_path": "src/expr_parsers_core.jl", "max_forks_repo_name": "schlichtanders/ExprParsers.jl", "max_forks_repo_head_hexsha": "d8f48ba434fa14d7505a78526f660246d3a57b97", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-01T22:21:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-01T22:21:35.000Z", "avg_line_length": 28.510989011, "max_line_length": 207, "alphanum_fraction": 0.7230680285, "num_tokens": 1449, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43398146480389854, "lm_q2_score": 0.14608724704829568, "lm_q1q2_score": 0.06339915746318836}}
{"text": "## Setup the Gurobi software outside of Julia\n#=\n 1. Download Gurobi from Gurobi.com\n 2. Get an account and request a university license\n 3. Setup with Grb\n=# \n\n## Install Gurobi.jl and test\nPkg.add(\"Gurobi\")\n\n## Test that Gurobi works\nPkg.test(\"Gurobi\")\n", "meta": {"hexsha": "e1e32034a3f2872c58aed2de85fa69a94d87be2a", "size": 255, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "install_gurobi.jl", "max_stars_repo_name": "dgleich/CS520-2017", "max_stars_repo_head_hexsha": "520148fb52dcf034561b302c6e2f1e9341c9e883", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-10-23T17:28:01.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-23T17:28:01.000Z", "max_issues_repo_path": "install_gurobi.jl", "max_issues_repo_name": "dgleich/CS520-2017", "max_issues_repo_head_hexsha": "520148fb52dcf034561b302c6e2f1e9341c9e883", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "install_gurobi.jl", "max_forks_repo_name": "dgleich/CS520-2017", "max_forks_repo_head_hexsha": "520148fb52dcf034561b302c6e2f1e9341c9e883", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2017-01-12T19:45:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-02T16:02:04.000Z", "avg_line_length": 19.6153846154, "max_line_length": 51, "alphanum_fraction": 0.7215686275, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.15002882624262381, "lm_q1q2_score": 0.06338787444324108}}
{"text": "module LogicalKoans\n\nfunction not(x)\nend\n\nfunction equals(x, y)\nend\n\nfunction not_equals(x, y)\nend\n\nfunction less_than(x, y) \nend\n\nfunction greater_than(x, y) \nend\n\nfunction less_than_or_equal_to(x, y) \nend\n\nfunction greater_than_or_equal_to(x, y) \nend\n\nend", "meta": {"hexsha": "aec6ca96ab325f66ee59c4b88599cb61733bb973", "size": 257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "koans/LogicalKoans.jl", "max_stars_repo_name": "akielbowicz/julia_koans", "max_stars_repo_head_hexsha": "70b9006f638b9e238630825836b835230c5cbf77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2020-01-10T02:09:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-08T04:21:02.000Z", "max_issues_repo_path": "koans/LogicalKoans.jl", "max_issues_repo_name": "akielbowicz/julia_koans", "max_issues_repo_head_hexsha": "70b9006f638b9e238630825836b835230c5cbf77", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 12, "max_issues_repo_issues_event_min_datetime": "2020-01-14T13:52:51.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-13T21:31:52.000Z", "max_forks_repo_path": "koans/LogicalKoans.jl", "max_forks_repo_name": "akielbowicz/julia_koans", "max_forks_repo_head_hexsha": "70b9006f638b9e238630825836b835230c5cbf77", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-06-10T10:09:26.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-20T20:23:14.000Z", "avg_line_length": 10.7083333333, "max_line_length": 40, "alphanum_fraction": 0.7626459144, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.1329642281726931, "lm_q1q2_score": 0.06336804546649306}}
{"text": "using InstantiateFromURL\nactivate_github(\"QuantEcon/QuantEconLecturePackages\", tag = \"v0.9.5\");\n", "meta": {"hexsha": "19057235bee5102fa9e438b7790a939f0af74add", "size": 96, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "rst_files/_static/includes/deps_no_using.jl", "max_stars_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_stars_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "rst_files/_static/includes/deps_no_using.jl", "max_issues_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_issues_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rst_files/_static/includes/deps_no_using.jl", "max_forks_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_forks_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0, "max_line_length": 70, "alphanum_fraction": 0.8125, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.37022539259558657, "lm_q2_score": 0.17106119590453167, "lm_q1q2_score": 0.06333119841162578}}
{"text": "\"\"\"\n    @check cond msg\n\nIf `cond` evaluates false throw `ArgumentError` and print evaluation of `cond`.\n\"\"\"\nmacro check(cond, msg)\n    str = _chkmsg(cond, msg)\n    quote\n        if !$(esc(cond))\n            throw(ArgumentError($(str)))\n        end\n    end\nend\n\n\"\"\"\n    @checkwarn [showwarn] cond msg\n\nIf `cond` evaluates false warn and print evaluation of `cond`.\nIf optional argument `showwarn=false` hide warning.\n\"\"\"\nmacro checkwarn(cond, msg)\n    str = _chkmsg(cond, msg)\n    quote\n        if !$(esc(cond))\n            @warn $(str)\n        end\n    end\nend\n\nmacro checkwarn(showarn, cond, msg)\n    str = _chkmsg(cond, msg)\n    quote\n        if $(esc(showarn)) && !$(esc(cond))\n            @warn $(str)\n        end\n    end\nend\n\n\"\"\"\n    _chkmsg(cond, msg)\n\nCreate an expression which evaluates as a error string which contrains the original msg and\nsome debug information based on the condition.\n\"\"\"\nfunction _chkmsg(cond, msg)\n    head = lstrip(repr(cond), ':')\n    if head[begin] == '(' && head[end] == ')'\n        head = head[begin+1:end-1]\n    end\n    head = head * \" evaluated false\"\n\n    args = Expr[]\n\n    if cond.args[1] == :(!)\n        _expr_repr_list!(args, cond.args[2].args[2:end])\n    elseif cond.args[1] == :(\u2286)\n        push!(args, :(\"\\n   \u251c set diff = \" * _shortrepr(setdiff($(esc(cond.args[2])), $(esc(cond.args[3]))))))\n        _expr_repr_list!(args, cond.args[2:end])\n    elseif cond.args[1] == :(\u2287)\n        push!(args, :(\"\\n   \u251c set diff = \" * _shortrepr(setdiff($(esc(cond.args[3])), $(esc(cond.args[2]))))))\n        _expr_repr_list!(args, cond.args[2:end])\n    elseif cond.args[1] == :(uniquenames) || cond.args[1] == :(allunique)\n        push!(args, :(\"\\n   \u251c duplicates = \" * _shortrepr(duplicates($(esc(cond.args[2]))))))\n        _expr_repr_list!(args, cond.args[2:end])\n    else\n        _expr_repr_list!(args, cond.args[2:end])\n    end\n    return :($(esc(msg)) * \"\\n  \" * $head * $(args...))\nend\n\nfunction _expr_repr_list!(list, expressions)\n    for (i,a) in enumerate(expressions)\n        lhs = lstrip(repr(a), ':') # remove leading : on symbols\n        if lhs[begin] == '(' && lhs[end] == ')'\n            lhs = lhs[begin+1:end-1]\n        end\n        symbol = (i == length(expressions)) ? \"\u2514 \" : \"\u251c \"\n        push!(list, :(\"\\n   \" * $symbol * $lhs * \" = \" * _shortrepr($(esc(a)))))\n    end\nend\n\n_shortrepr(x) = repr(x)\nfunction _shortrepr(@nospecialize x::Set)\n    isempty(x) && return \"(empty)\"\n    m = match(r\"\\[(.*)\\]\\)$\", repr(x)::String)\n    return m[1]\nend\nfunction _shortrepr(@nospecialize x::AbstractArray)\n    isempty(x) && return \"(empty)\"\n    m = match(r\"\\[(.*)\\]$\", repr(x)::String)\n    return m[1]\nend\n\n\"\"\"\n    remove_namespace(namespace, x)\n\nIf first namespace of `x` is `namespace` then remove it.\n```\nremove_namespace(A, A\u208aB\u208ax) -> B\u208ax\nremove_namespace(B, A\u208aB\u208ax) -> A\u208aB\u208ax\n````\n\"\"\"\nremove_namespace(namespace, name::T) where T = T(replace(String(name), Regex(\"^$(namespace)\u208a\") => \"\"))\nremove_namespace(namespace, x::Sym) = rename(x, remove_namespace(namespace, x.name))\nremove_namespace(namespace, x::Term) = rename(x, remove_namespace(namespace, operation(x).name))\n\n\"\"\"\n    remove_namespace(x)\n\nStrips `x` of its first namespace. `A\u208aB\u208ax -> B\u208ax`\n\"\"\"\nremove_namespace(name::T) where T = T(replace(String(name), Regex(\"^(.+?)\u208a\") => \"\"))\nremove_namespace(x::Sym) = rename(x, remove_namespace(x.name))\nremove_namespace(x::Term) = rename(x, remove_namespace(operation(x).name))\n\neqsubstitute(eq::Equation, rules) = substitute(eq.lhs, rules) ~ substitute(eq.rhs, rules)\n\nuniquenames(syms) = allunique(getname.(syms))\n\nfunction duplicates(X)\n    X = collect(X)\n    dups = Set{eltype(X)}()\n    for (i, x) in enumerate(X)\n        for j in i+1:lastindex(X)\n            if isequal(x, X[j])\n                push!(dups, x)\n                break\n            end\n        end\n    end\n    return dups\nend\n\n\"\"\"\n    eq_type(eq::Equation)\n\nChecks the type of the equation. Returns:\n- `(:diffeq, lhs_variable)` for differential equations\n- `(:explicit_algebraic, lhs_variable)` for explicit algebraic equations\n- `(:implicit_algebraic, lhs_variable)` for implicit algebraic equations\n\"\"\"\nfunction eq_type(eq::Equation)\n    if eq.lhs isa Term && operation(eq.lhs) isa Differential\n        vars = get_variables(eq.lhs)\n        @check length(vars) == 1 \"Diff. eq $eq has more than on variable in lhs!\"\n        return (:diffeq, vars[1])\n    elseif eq.lhs isa Sym || eq.lhs isa Term\n        vars = get_variables(eq.lhs)\n        @check length(vars) == 1 \"Algebraic eq $eq has more than on variable in lhs!\"\n        if vars[1] \u2208 Set(get_variables(eq.rhs))\n            return (:implicit_algebraic, vars[1])\n        else\n            return (:explicit_algebraic, vars[1])\n        end\n    elseif isequal(eq.lhs, 0)\n        return (:implicit_algebraic, nothing)\n    else\n        throw(ArgumentError(\"Uknnown equation type $eq\"))\n    end\nend\n\n\"\"\"\n    lhs_var(eq::Equation)\n\nReturns the variable on the lhs of the equation for equations.\n\"\"\"\nlhs_var(eq::Equation) = eq_type(eq)[2]\n\n\n\"\"\"\n    recusive_substitute(term, rules::Dict)\n\nApply substitutions until there is no more change in `term`.\n\"\"\"\nfunction recursive_substitute(term, rules::Dict)\n    new_term = substitute(term, rules)\n    while !isequal(new_term, term)\n        term = new_term\n        new_term = substitute(term, rules)\n    end\n    return new_term\nend\n\n\"\"\"\n    rhs_differentials(iob::IOBlock)\n\nReturn Set of all differentials which are present in the rhs of the system.\n\"\"\"\nfunction rhs_differentials(iob)\n    diffs = Set{SymbolicUtils.Symbolic}()\n    for eq in equations(iob)\n        _collect_differentials!(diffs, eq.rhs)\n    end\n    return diffs\nend\n\n_collect_differentials(ex) = _collect_differentials!(Set{SymbolicUtils.Symbolic}(), ex)\n\nfunction _collect_differentials!(found, ex)\n    if SymbolicUtils.istree(ex)\n        if operation(ex) isa Differential\n            push!(found, ex)\n        else\n            for arg in arguments(ex)\n                _collect_differentials!(found, arg)\n            end\n        end\n    end\n    return found\nend\n", "meta": {"hexsha": "cc0d7b3d37e8209cb6ae00fcc9ead6df2fb0ff26", "size": 6023, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "hexaeder/BlockSystems.jl", "max_stars_repo_head_hexsha": "2dabd797f51067b19882976705bf49614f09ef33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2021-04-12T14:36:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-19T07:10:17.000Z", "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "hexaeder/BlockSystems.jl", "max_issues_repo_head_hexsha": "2dabd797f51067b19882976705bf49614f09ef33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2021-02-05T18:50:49.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-16T13:15:27.000Z", "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "hexaeder/BlockSystems.jl", "max_forks_repo_head_hexsha": "2dabd797f51067b19882976705bf49614f09ef33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-04-14T13:47:44.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-06T14:19:40.000Z", "avg_line_length": 28.5450236967, "max_line_length": 110, "alphanum_fraction": 0.612983563, "num_tokens": 1678, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3886180408675584, "lm_q2_score": 0.16238003058646674, "lm_q1q2_score": 0.06310380936252692}}
{"text": "# # Splitting a string into words and counting them in parallel\n#\n# We start from the parallel algorithm presented in Guy Steele's 2009\n# ICFP talk [(video)](https://vimeo.com/6624203).  It splits a\n# space-separated string into list of strings (words).  The repeating\n# theme in the talk was to build \"singleton solutions\" and then merge\n# them together using an associative function.  We will follow this\n# guideline and slightly extend the algorithm.\n#\n# It is highly recommended to just watch the talk for understanding\n# the algorithm.  However, we briefly describe how it works.\n#\n# When a certain contiguous region of a string is processed, we either\n# already have seen at least one space or not.  These two states are\n# tracked using following two types.  If there is no space so far, we\n# only have a chunk of a possibly larger word (see example below):\n\nstruct Chunk\n    s::String\nend\n\n# If there are one or more spaces, (possibly zero) words that are\n# already determined and left/right \"chunks\" have to be tracked\n# separately:\n\nstruct Segment\n    l::String\n    A::Vector{String}\n    r::String\nend\n\n# Here is an example taken from the talk:\n#\n# ```\n# Segment(\"Here\", [\"is\", \"a\"], \"\")\n#    |\n#    |       Segment(\"lian\", [], \"string\")\n#  __|_____            _|______\n# |        |          |        |\n# Here is a sesquipedalian string of words\n#           |________|          |________|\n#    Chunk(\"sesquipeda\")        Segment(\"g\", [\"of\"], \"words\")\n# ```\n\n# We then need a way to merge two results which can independently in\n# one of the above two states.\n\n\u2295(x::Chunk, y::Chunk) = Chunk(x.s * y.s)\n\u2295(x::Chunk, y::Segment) = Segment(x.s * y.l, y.A, y.r)\n\u2295(x::Segment, y::Chunk) = Segment(x.l, x.A, x.r * y.s)\n\u2295(x::Segment, y::Segment) =\n    Segment(x.l,\n            append!(append!(x.A, maybewordv(x.r * y.l)), y.A),\n            y.r)\n\nmaybewordv(s::String) = isempty(s) ? String[] : [s]\nnothing  # hide\n\n# Input is a sequence of `Char`s.  Each of them has to be converted\n# into a \"singleton solution\" which can be merged with already\n# aggregated (or another singleton) solution with `\u2295`:\n\nsegmentorchunk(c::Char) = c == ' ' ? Segment(\"\", [], \"\") : Chunk(string(c))\nnothing  # hide\n\n# Putting them together, we get:\n\nfunction collectwords(s::String)\n    g = mapfoldl(segmentorchunk, \u2295, s; init=Segment(\"\", [], \"\"))\n    if g isa Char\n        return maybewordv(g.s)\n    else\n        return append!(append!(maybewordv(g.l), g.A), maybewordv(g.r))\n    end\nend\nnothing  # hide\n\n# Let's run a few tests covering some edge cases:\n\nusing Test\n@testset begin\n    @test collectwords(\"This is a sample\") == [\"This\", \"is\", \"a\", \"sample\"]\n    @test collectwords(\" Here is another sample \") == [\"Here\", \"is\", \"another\", \"sample\"]\n    @test collectwords(\"JustOneWord\") == [\"JustOneWord\"]\n    @test collectwords(\" \") == []\n    @test collectwords(\"\") == []\nend\nnothing  # hide\n\n# ## String-splitting transducer\n#\n# Let's try to make it re-usable by packaging it into transducers.\n\nusing Transducers\n\n# Rather than accumulating words into a vector, we are going to write\n# a transducer that \"emits\" a word as soon as it is ready.  The\n# downstream transducer may choose to record everything or only\n# aggregate, e.g., reduced statistics.  To this end, we replace\n# `Segment` in the original algorithm to\n\nstruct Vacant\n    l::String\n    r::String\nend\n\n# and output the words in the \"middle\" without accumulating it.  We\n# use [`ScanEmit`](@ref) which requires an operator/function like `\u2295`\n# above but returning a pair of output and next state.  This function\n# (`extract` below) must have the signature `(S, S) -> (O, S)` where\n# `S` is the type for accumulated state and input and `O` is the\n# output type.\n\nextract(x::Chunk, y::Chunk) = (), Chunk(x.s * y.s)\nextract(x::Chunk, y::Vacant) = (), Vacant(x.s * y.l, y.r)\nextract(x::Vacant, y::Chunk) = (), Vacant(x.l, x.r * y.s)\nextract(x::Vacant, y::Vacant) = maybewordt(x.r * y.l), Vacant(x.l, y.r)\n\nmaybewordt(s) = isempty(s) ? () : (s,)\nnothing  # hide\n\n# `maybewordt(x.r * y.l)` in `extract(x::Vacant, y::Vacant)` is the\n# \"emission\".\n#\n# The words at the beginning and/or the end are not handled by\n# `extract`.  This must be handled separately:\n\nlastword(x::Chunk) = maybewordt(x.s)\nlastword(x::Vacant) = (maybewordt(x.r)..., maybewordt(x.l)...)\n\nvacantorchunk(c::Char) = c == ' ' ? Vacant(\"\", \"\") : Chunk(string(c))\n\nwordsxf = Map(vacantorchunk) |> ScanEmit(extract, Chunk(\"\"), lastword) |> Cat()\n\n# Test:\n\n@testset begin\n    @test collect(wordsxf, \"This is a sample\") == [\"is\", \"a\", \"sample\", \"This\"]\n    @test collect(wordsxf, \" Here is another sample \") == [\"Here\", \"is\", \"another\", \"sample\"]\n    @test collect(wordsxf, \"JustOneWord\") == [\"JustOneWord\"]\n    @test collect(wordsxf, \" \") == []\n    @test collect(wordsxf, \"\") == []\nend\nnothing  # hide\n\n# Side note: In the first example, the first word `This` comes last.\n# This is actually expected since both `.l` and `.r` are flushed in\n# `lastword` which is called at the very end.  Here, `This` is stored\n# in `.l` field.  If the order of the words is important, there are\n# many possible fixes.  For example, `extract` and `lastword` can\n# bundle information about the origin of the word (left vs\n# middle-or-right).  Alternatively, perhaps the easiest solution is to\n# insert a space in the beginning of input data.\n#\n# ## Word-counting transducer\n#\n# We can pipe the resulting words into various transducers.\n\nprocesscount(word) = Base.ImmutableDict(word => 1)\ncountxf = wordsxf |> Map(processcount)\n\n# Transducer `countxf` constructs a \"singleton solution\" as a\n# dictionary which then accumulated with the associative reducing step\n# function `mergecont!`:\n\nmergecont!(a, b) = merge!(+, a, b)\nnothing  # hide\n\n# Putting the transducer and reducing function together, we get:\n\ncountwords(s; kwargs...) =\n    reduce(mergecont!,\n           countxf,\n           collect(s);\n           init = CopyInit(Dict{String,Int}()),\n           kwargs...)\nnothing  # hide\n\n# Side note: Since [`reduce`](@ref) does not support string, the input\n# string is converted to a `Vector{Char}` first by `collect`.\n#\n# Side note 2: We use [`CopyInit`](@ref) to create a fresh initial\n# state for each sub-reduce to avoid overwriting mutable data between\n# threads.\n#\n# Side note 3: [`reduce`](@ref) wraps `mergecont!` automatically with\n# [`Completing`](@ref).  This is why `mergecont!` does not have to\n# have the unary method.\n\n# Let's run some tests with different `basesize` (`length(s) /\n# basesize` corresponds to number of tasks to be used):\n\n@testset for basesize in [1, 2, 4]\n    @test countwords(\"This is a sample\", basesize=basesize) ==\n        Dict(\"This\" => 1, \"is\" => 1, \"a\" => 1, \"sample\" => 1)\n    @test countwords(\" Here is another sample \", basesize=basesize) ==\n        Dict(\"Here\" => 1, \"is\" => 1, \"another\" => 1, \"sample\" => 1)\n    @test countwords(\"JustOneWord\", basesize=basesize) ==\n        Dict(\"JustOneWord\" => 1)\n    @test countwords(\" \", basesize=basesize) == Dict()\n    @test countwords(\"\", basesize=basesize) == Dict()\n    @test countwords(\"aaa bb aaa aaa bb bb aaa\", basesize=basesize) ==\n        Dict(\"aaa\" => 4, \"bb\" => 3)\n    @test countwords(\"\u3042\u3042\u3042 \u3044\u3044 \u3042\u3042\u3042 \u3042\u3042\u3042 \u3044\u3044 \u3044\u3044 \u3042\u3042\u3042\", basesize=basesize) ==\n        Dict(\"\u3042\u3042\u3042\" => 4, \"\u3044\u3044\" => 3)\nend\nnothing  # hide\n", "meta": {"hexsha": "32bcedc8626df850bd55e0aadbf64c6d9e6c50b8", "size": 7266, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/words.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Transducers.jl-28d57a85-8fef-5791-bfe6-a80928e7c999", "max_stars_repo_head_hexsha": "abfa98490d7f1af655dc2d276dca31964a153c9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/words.jl", "max_issues_repo_name": "UnofficialJuliaMirror/Transducers.jl-28d57a85-8fef-5791-bfe6-a80928e7c999", "max_issues_repo_head_hexsha": "abfa98490d7f1af655dc2d276dca31964a153c9a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/words.jl", "max_forks_repo_name": "UnofficialJuliaMirror/Transducers.jl-28d57a85-8fef-5791-bfe6-a80928e7c999", "max_forks_repo_head_hexsha": "abfa98490d7f1af655dc2d276dca31964a153c9a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.9326923077, "max_line_length": 93, "alphanum_fraction": 0.6530415634, "num_tokens": 2104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.13117322376181567, "lm_q1q2_score": 0.0630259371483595}}
{"text": "__precompile__(true)\n\nmodule Learn\n\nusing Reexport\n@reexport using LearnBase\n@reexport using LossFunctions\n@reexport using Transformations\n@reexport using ObjectiveFunctions\n@reexport using PenaltyFunctions\n@reexport using StochasticOptimization\n# @reexport using MLDataUtils\n@reexport using MLMetrics\n\nend # module\n", "meta": {"hexsha": "64e60abe89724092b85c8fd0155ad77399f421f4", "size": 316, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Learn.jl", "max_stars_repo_name": "JuliaTagBot/Learn.jl", "max_stars_repo_head_hexsha": "d3cb0112e19bc28da65335779026b37b05e386b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 65, "max_stars_repo_stars_event_min_datetime": "2016-06-30T19:54:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-12T01:27:58.000Z", "max_issues_repo_path": "src/Learn.jl", "max_issues_repo_name": "JuliaTagBot/Learn.jl", "max_issues_repo_head_hexsha": "d3cb0112e19bc28da65335779026b37b05e386b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2016-09-03T12:38:56.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-03T22:12:37.000Z", "max_forks_repo_path": "src/Learn.jl", "max_forks_repo_name": "JuliaTagBot/Learn.jl", "max_forks_repo_head_hexsha": "d3cb0112e19bc28da65335779026b37b05e386b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2017-03-06T10:38:47.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T10:57:44.000Z", "avg_line_length": 19.75, "max_line_length": 38, "alphanum_fraction": 0.8481012658, "num_tokens": 77, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.12592276483513232, "lm_q1q2_score": 0.06296138241756616}}
{"text": "# ---\n# title: 226. Invert Binary Tree\n# id: problem226\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Easy\n# categories: Tree\n# link: <https://leetcode.com/problems/invert-binary-tree/description/>\n# hidden: true\n# ---\n# \n# Invert a binary tree.\n# \n# **Example:**\n# \n# Input:\n# \n#     \n#     \n#          4\n#        /   \\\n#       2     7\n#      / \\   / \\\n#     1   3 6   9\n# \n# Output:\n# \n#     \n#     \n#          4\n#        /   \\\n#       7     2\n#      / \\   / \\\n#     9   6 3   1\n# \n# **Trivia:**  \n# This problem was inspired by [this original\n# tweet](https://twitter.com/mxcl/status/608682016205344768) by [Max\n# Howell](https://twitter.com/mxcl):\n# \n# > Google: 90% of our engineers use the software you wrote (Homebrew), but you\n# > can't invert a binary tree on a whiteboard so f*** off.\n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction invert_tree!(root::Union{TreeNode{Int}, Nothing})::Union{TreeNode, Nothing}\n    isnothing(root) && return nothing\n    queue = [root]\n    while !isempty(queue)\n        node = popfirst!(queue)\n        if !isnothing(node.left)\n            push!(queue, node.left)\n        end\n        if !isnothing(node.right)\n            push!(queue, node.right)\n        end\n        node.left, node.right = node.right, node.left\n    end\n    return root\nend\n## @lc code=end\n\n## @lc test=start\n@testset \"226.invert-binary-tree.jl\" begin\n    @test invert_tree!(TreeNode{Int}([4,2,7,1,3,6,9])) == TreeNode{Int}([4,7,2,9,6,3,1])\n    @test invert_tree!(TreeNode{Int}([2,1,3])) == TreeNode{Int}([2,3,1])\n    @test invert_tree!(nothing) === nothing\nend\n## @lc test=end\n\n\n## @lc info=start\n# link: [solution to 226](https://leetcode-cn.com/problems/invert-binary-tree/solution/226-fan-zhuan-er-cha-shu-python-julia-by-sbu2/)\n## @lc info=end", "meta": {"hexsha": "dd1244ef17e1fd220f1a65b7f9eb4de425a3ba06", "size": 1761, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/submited/date-02/226.invert-binary-tree.jl", "max_stars_repo_name": "RexWzh/leetcode_note.jl", "max_stars_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/submited/date-02/226.invert-binary-tree.jl", "max_issues_repo_name": "RexWzh/leetcode_note.jl", "max_issues_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/submited/date-02/226.invert-binary-tree.jl", "max_forks_repo_name": "RexWzh/leetcode_note.jl", "max_forks_repo_head_hexsha": "eae55703e771485d5eff37010f34967694a4158b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.1710526316, "max_line_length": 134, "alphanum_fraction": 0.5797842135, "num_tokens": 570, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834914771175, "lm_q2_score": 0.13296424019782924, "lm_q1q2_score": 0.06285000129831202}}
{"text": "using Weave;\n\nlist_out_formats()\n\nweave(\"Operations Research\\\\01_Introduciton.jmd\", doctype=:\"md2pdf\", out_path=:pwd)\nweave(\"02_SimpleLinearOptimization.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"03_Julia-Basics.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"03_Gadfly.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"04_Numerical-Methods.jmd\", doctype=:\"github\", out_path=:pwd)\n", "meta": {"hexsha": "47c0e066641d79d96200331fefcbc4156a788e1d", "size": 381, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Operations Research/build.jl", "max_stars_repo_name": "bmoretz/Statistical-Computing", "max_stars_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-27T08:18:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-09T09:20:49.000Z", "max_issues_repo_path": "Operations Research/build.jl", "max_issues_repo_name": "bmoretz/Statistical-Computing", "max_issues_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Operations Research/build.jl", "max_forks_repo_name": "bmoretz/Statistical-Computing", "max_forks_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-07-27T08:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-02T11:31:27.000Z", "avg_line_length": 38.1, "max_line_length": 83, "alphanum_fraction": 0.7559055118, "num_tokens": 123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.34864513533394575, "lm_q2_score": 0.18010666408646395, "lm_q1q2_score": 0.06279331227497073}}
{"text": "# # Parallel Computing with Julia\n#\n# This course will cover:\n#\n# * Introduction to parallelism\n#     * What is happening to our computers?\n#\n# * Parallelism strategies\n#     * SIMD\n#     * Multi-threading\n#     * Tasks\n#     * Multi-process\n#         * Shared memory\n#         * Distributed memory\n#     * GPU programming\n#\n# * Challenges of parallel computing\n#     * Order of execution\n#         * execution of out order of Possibility\n#         * simultaneous access and mutation\n#     * Data access and movement\n#     * Code access and movement\n#     * Appropriately matching the parallelism strategy to your machine capabilities\n#     * Appropriately matching the parallelism strategy with the problem at hand\n\n#-\n\n# ## What is happening to our computers!?\n#\n# ![](https://raw.githubusercontent.com/JuliaComputing/JuliaAcademyData.jl/master/courses/Parallel_Computing/images/40-years-processor-trend.png)\n#\n# Not only have we gained multiple cores, but processors have become extremely\n# complex, with multiple levels of caches, pipelines, predictions, speculations...\n#\n# ## What is hard about parallel computing\n#   * We don't think in parallel\n#   * We learn to write and reason about programs serially\n#   * The desire for parallelism often comes _after_ you've written your algorithm (and found it too slow!)\n#\n# ## Summary:\n#   * Current computer archetectures push us towards parallel programming for peak performance \u2014 even if we're not on a cluster!\n#   * But it's hard to design good parallel algorithms\n#   * And it's hard to express and reason about those algorithms\n\n", "meta": {"hexsha": "d2713ba6d2190af63d14d2800d93359604d93ec7", "size": 1586, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Courses/Parallel_Computing/010 Introduction.jl", "max_stars_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_stars_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 45, "max_stars_repo_stars_event_min_datetime": "2020-02-13T00:50:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-05T07:57:22.000Z", "max_issues_repo_path": "Courses/Parallel_Computing/010 Introduction.jl", "max_issues_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_issues_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 52, "max_issues_repo_issues_event_min_datetime": "2019-10-30T16:22:28.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-26T20:02:43.000Z", "max_forks_repo_path": "Courses/Parallel_Computing/010 Introduction.jl", "max_forks_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_forks_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2020-02-26T11:33:28.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-25T22:34:53.000Z", "avg_line_length": 35.2444444444, "max_line_length": 145, "alphanum_fraction": 0.7131147541, "num_tokens": 364, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.13846179412408713, "lm_q1q2_score": 0.06275944868420967}}
{"text": "# Stricker Klaus 2020 - License is MIT: http://julialang.org/license\n# ReadWriteDlm2 - rwd2_read.jl - https://github.com/strickek/ReadWriteDlm2.jl\n\n\"\"\"\n\n    readdlm2(source; options...)\n    readdlm2(source, T::Type; options...)\n    readdlm2(source, delim::AbstractChar; options...)\n    readdlm2(source, delim::AbstractChar, T::Type; options...)\n    readdlm2(source, delim::AbstractChar, eol::AbstractChar; options...)\n    readdlm2(source, delim::AbstractChar, T::Type, eol::AbstractChar; options...)\n\nRead a matrix from `source`. The `source` can be a text file, stream or byte\narray. Each line (separated by `eol`, this is `'\\\\n'` by default) gives one row.\nThe columns are separated by `';'`, another `delim` can be defined.\n\nPre-processing of `source` with regex substitution changes the decimal marks\nfrom `d,d` to `d.d`. For default `rs` the keyword argument `decimal=','` sets\nthe decimal Char in the `r`-string of `rs`. When a special regex substitution\ntuple `rs=(r.., s..)` is defined, the argument `decimal` is not used.\nPre-processing can be switched off with: `rs=()`.\n\nIn addition to stdlib readdlm(), data is also parsed for `Dates` formats,\nthe `Time` format `\\\"HH:MM[:SS[.s{1,9}]]\\\"` and for complex and rational numbers.\nTo deactivate parsing dates/time set: `dfs=\\\"\\\", dtfs=\\\"\\\"`.\n`locale` defines the language of day (`E`, `e`) and month (`U`, `u`) names.\n\nThe result will be a (heterogeneous) array of default element type `Any`. If\n`header=true` it will be a tuple containing the data array and a vector for\nthe columnnames. Other (abstract) types for the data array elements could be\ndefined. If data is empty, a `0\u00d70 Array{T,2}` is returned.\n\nWith `tables=true`[, `header=true`] option[s] a `Tables` interface compatible\n`MatrixTable` with individual column types is returned, which for example\ncan be used as Argument for `DataFrame()`.\n\n# Additional Keyword Arguments\n\n* `decimal=','`: Decimal mark Char used by default `rs`, irrelevant if `rs`-tuple is not the default one\n* `rs=(r\\\"(\\\\d),(\\\\d)\\\", s\\\"\\\\1.\\\\2\\\")`: Regex (r,s)-tuple, the default change d,d to d.d if `decimal=','`\n* `dtfs=\\\"yyyy-mm-ddTHH:MM:SS.s\\\"`: Format string for DateTime parsing\n* `dfs=\\\"yyyy-mm-dd\\\"`: Format string for Date parsing\n* `locale=\\\"english\\\"`: Language for parsing dates names\n* `tables=false`: Return `Tables` interface compatible MatrixTable if `true`\n* `dfheader=false`: 'dfheader=true' is shortform for `tables=true, header=true`\n* `missingstring=\\\"na\\\"`: How missing values are represented\n\nFind more information about `readdlm()` functionality and (keyword) arguments -\nwhich are also supported by `readdlm2()` - in `help` for `readdlm()`.\n\n# Code Example\n```jldoctest\njulia> using ReadWriteDlm2\n\njulia> A = Any[1 1.2; \"text\" missing];\n\njulia> writedlm2(\"test.csv\", A)\n\njulia> readdlm2(\"test.csv\")\n2\u00d72 Array{Any,2}:\n 1        1.2\n  \"text\"   missing\n```\n\"\"\"\nreaddlm2(input; opts...) =\n    readdlm2auto(input, ';', Nothing, '\\n', true; opts...)\n\nreaddlm2(input, T::Type; opts...) =\n    readdlm2auto(input, ';', T, '\\n', false; opts...)\n\nreaddlm2(input, dlm::AbstractChar; opts...) =\n    readdlm2auto(input, dlm, Nothing, '\\n', true; opts...)\n\nreaddlm2(input, dlm::AbstractChar, T::Type; opts...) =\n    readdlm2auto(input, dlm, T, '\\n', false; opts...)\n\nreaddlm2(input, dlm::AbstractChar, eol::AbstractChar; opts...) =\n    readdlm2auto(input, dlm, Nothing, eol, true; opts...)\n\nreaddlm2(input, dlm::AbstractChar, T::Type, eol::AbstractChar; opts...) =\n    readdlm2auto(input, dlm, T, eol, false; opts...)\n\nfunction readdlm2auto(input, dlm, T, eol, auto;\n    decimal::AbstractChar=',',\n    rs::Tuple=(r\"(\\d),(\\d)\", s\"\\1.\\2\"),\n    dtfs::AbstractString=\"yyyy-mm-ddTHH:MM:SS.s\",\n    dfs::AbstractString=\"yyyy-mm-dd\",\n    locale::AbstractString=\"english\",\n    tables::Bool=false,\n    dfheader::Bool=false,\n    missingstring::AbstractString=\"na\",\n    opts...)\n\n    if dfheader == true\n        tables = true\n        opts = [opts...]\n        push!(opts, :header => true)\n    end\n\n    ((!isempty(dtfs) && !occursin(Regex(\"[^YymdHMSs]\"), dtfs)) ||\n    (!isempty(dfs) && !occursin(Regex(\"[^YymdHMSs]\"), dfs))) && info(\n        \"\"\"\n        Format string for DateTime(`$dtfs`) or Date(`$dfs`)\n        contains numeric code elements only. At least one non-numeric\n        code element or character is needed for parsing dates.\n        \"\"\")\n\n    # \"parsing-logic\" - defaults\n    doparsedatetime = !isempty(dtfs)\n    doparsedate = !isempty(dfs)\n    doparsetime = false\n    doparsecomplex = true\n    doparserational = true\n    doparsemissing = false\n    doparsenothing = false\n    convertarray = true\n    T2 = Any\n\n    if T == Nothing            # to know wether T ist Any by default\n            T = Any\n            anybydefault = true\n        else\n            anybydefault = false\n        end\n\n    # \"parsing-logic\" for different T::Types\n    ((typeintersect(DateTime, T)) == Union{}) && (doparsedatetime = false)\n    ((typeintersect(Date, T)) == Union{}) && (doparsedate = false)\n    doparsetime =\n        ((doparsedatetime && doparsedate) || ((Time <: T) && !(Any <:T)))\n    T <: Union{AbstractFloat, AbstractString, Char} &&\n        (T2 = T; convertarray = false)\n    ((typeintersect(Complex, T)) == Union{}) && (doparsecomplex = false)\n    ((typeintersect(Rational, T)) == Union{}) && (doparserational = false)\n    Missing <: T && (doparsemissing = true)\n    Nothing <: T && (doparsenothing = true)\n    (Any <: T) && (convertarray = false)\n    if tables == true\n        T2 = Any\n        convertarray = false\n        !anybydefault && (convertarray = true)\n        T <: Union{AbstractString, Char} &&\n            (T2 = T; convertarray = false)\n    end\n\n    s = read(input, String)\n\n    # empty input data - return empty array\n    if (isempty(s) || (s == string(eol)))\n        return Array{T2}(undef, 0, 0)\n    end\n\n    if (!isempty(rs) && (decimal != '.')) # do pre-processing of decimal mark\n\n        # adopt dfs if decimal between two digits of formatstring\n        if (rs == (r\"(\\d),(\\d)\", s\"\\1.\\2\")) && doparsedate\n            drs = (Regex(\"([YymdHM])$decimal([YymdHM])\"), s\"\\1.\\2\")\n            dfs = replace(dfs, drs[1] => drs[2])\n        end\n\n        # adopt dtfs if decimal between two digits of formatstring\n        if (rs == (r\"(\\d),(\\d)\", s\"\\1.\\2\")) && doparsedatetime\n            drs = (Regex(\"([YymdHMSs])$decimal([YymdHMSs])\"), s\"\\1.\\2\")\n            dtfs = replace(dtfs, drs[1] => drs[2])\n        end\n\n        # Change default regex substitution Tuple if decimal != ','\n        if ((rs == (r\"(\\d),(\\d)\", s\"\\1.\\2\")) && (decimal != ','))\n            rs = (Regex(\"(\\\\d)$decimal(\\\\d)\"), s\"\\1.\\2\")\n        end\n\n        # Error if decimal mark to replace is also the delim Char\n        \"1\"*string(dlm)*\"1\" != replace(\"1\"*string(dlm)*\"1\", rs[1] => rs[2]) &&\n        error(\n            \"\"\"\n            Error: Decimal mark to replace is also the delim Char.\n            Pre-processing with decimal mark Regex substitution for\n            `$(dlm)` (= delim!!) is not allowed - change rs/decimal or delim!\n            \"\"\")\n\n        # Regex substitution decimal\n        s = replace(s, rs[1] => rs[2])\n\n    end\n\n    # Using stdlib DelimitedFiles internal function to read dlm-string\n    z = readdlm_string(s, dlm, T2, eol, auto, val_opts(opts))\n\n    # Formats, Regex for Date/DateTime parsing\n    doparsedatetime &&\n        (dtdf = DateFormat(dtfs, locale); rdt = dfregex(dtfs, locale))\n    doparsedate &&\n        (ddf = DateFormat(dfs, locale); rd = dfregex(dfs, locale))\n\n    if tables == true  # return MatrixTable for Tables Interface\n        if isa(z, Tuple)\n             y, h = z\n             headerexist = true\n             cn = Symbol.(reshape(h, :))\n        else\n             y = z\n             headerexist = false\n        end\n\n        rows, cols = size(y)\n        coltypes = Array{Type, 1}(undef, cols)\n        Tn = typeintersect(T, Number)\n        for c in 1:cols # iterate columns\n            colcontainmissing = false\n            colcontainnothing = false\n            coltype = Union{}\n        for r in 1:rows # iterate rows in columns\n\n            if isa(y[r,c], AbstractString)\n                if doparsedatetime && occursin(rdt, y[r,c]) # parse DateTime\n                    try y[r,c] = DateTime(y[r,c], dtdf) catch; end\n                elseif doparsedate && occursin(rd, y[r,c]) # parse Date\n                    try y[r,c] = Date(y[r,c], ddf) catch; end\n                elseif doparsemissing && y[r,c] == missingstring # parse Missing\n                    try y[r,c] = missing catch; end\n                elseif doparsenothing && y[r,c] == \"nothing\" # parse Nothing\n                    try y[r,c] = nothing catch; end\n                else # parse Time, Complex and Rational\n                    try y[r,c] = parseothers(y[r,c], doparsetime, doparsecomplex, doparserational) catch; end\n                    if isa(y[r,c], AbstractString)\n                        #Substring to String\n                        try y[r,c] = String.(split(y[r,c]))[1] catch; end\n                    end\n                end\n            elseif convertarray && !(typeof(y[r,c]) <: Tn)\n                try y[r,c] = convert(Tn, y[r,c]) catch; end\n            end\n            if typeof(y[r,c]) == Missing\n                colcontainmissing = true\n            elseif typeof(y[r,c]) == Nothing\n                colcontainnothing = true\n            else\n                coltype = typejoin(coltype, typeof(y[r,c]))\n            end\n\n        end # end for - iterate rows in columns\n\n        if colcontainmissing && colcontainnothing\n            coltypes[c] = Union{Missing, Nothing, coltype}\n        elseif colcontainmissing\n            coltypes[c] = Union{Missing, coltype}\n        elseif colcontainnothing\n            coltypes[c] = Union{Nothing, coltype}\n        else\n            coltypes[c] = coltype\n        end\n\n        if convertarray && (coltypes[c] <: T)\n            coltypes[c] = T\n        end\n\n        end # end for - iterate columns\n\n        vdata = vecofvec(y, coltypes)\n\n        if headerexist\n            return Tables.table(vdata, header=cn)\n        else\n            return Tables.table(vdata)\n        end\n\n    else     # return standard Format (Arry or Tuple(data, header))\n\n        if isa(z, Tuple)\n             y, h = z\n        else\n             y = z\n        end\n        for i in eachindex(y)\n            if isa(y[i], AbstractString)\n                if doparsedatetime && occursin(rdt, y[i]) # parse DateTime\n                    try y[i] = DateTime(y[i], dtdf) catch; end\n                elseif doparsedate && occursin(rd, y[i]) # parse Date\n                    try y[i] = Date(y[i], ddf) catch; end\n                elseif doparsemissing && y[i] == missingstring # parse Missing\n                    try y[i] = missing catch; end\n                elseif doparsenothing && y[i] == \"nothing\" # parse Nothing\n                    try y[i] = nothing catch; end\n                else # parse Time, Complex and Rational\n                    try y[i] = parseothers(y[i], doparsetime, doparsecomplex, doparserational) catch; end\n                    end\n                end\n            end\n        end\n\n        if convertarray\n            isa(z, Tuple) ? z = (convert(Array{T}, y), h) : z = convert(Array{T}, z)\n        end\n\n        return z\n\nend # End function readdlm2auto()\n", "meta": {"hexsha": "ea56c4e8c64c795af0a8ed6c783df15811d1a208", "size": 11260, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rwd2_read.jl", "max_stars_repo_name": "quinnj/ReadWriteDlm2.jl", "max_stars_repo_head_hexsha": "13a364587109f4b61069b24aa105d519614eff55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2017-04-20T01:54:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-03T15:20:38.000Z", "max_issues_repo_path": "src/rwd2_read.jl", "max_issues_repo_name": "quinnj/ReadWriteDlm2.jl", "max_issues_repo_head_hexsha": "13a364587109f4b61069b24aa105d519614eff55", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2017-04-15T13:42:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-12T23:29:25.000Z", "max_forks_repo_path": "src/rwd2_read.jl", "max_forks_repo_name": "quinnj/ReadWriteDlm2.jl", "max_forks_repo_head_hexsha": "13a364587109f4b61069b24aa105d519614eff55", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2017-04-19T22:28:02.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-03T05:09:46.000Z", "avg_line_length": 37.7852348993, "max_line_length": 109, "alphanum_fraction": 0.5768206039, "num_tokens": 3097, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3311197264277872, "lm_q2_score": 0.18952109590739918, "lm_q1q2_score": 0.06275417342915243}}
{"text": "\"\"\"\nThis module provides a set of helper function to safely infer return types of functions.\n\nIn Gridap, we rely as less as possible in type inference. But, when needed, we adopt\nthe following mechanism in order to compute returned types. We do not rely on\nthe `Base._return_type` function.\n\nThis module exports following functions:\n$(EXPORTS)\n\n\"\"\"\nmodule Inference\n\nusing DocStringExtensions\nusing FillArrays\n\nexport testvalue\nexport testvalues\nexport testargs\nexport testargs_broadcast\nexport return_type\nexport return_type_broadcast\n\n\"\"\"\n$(TYPEDSIGNATURES)\n\nReturns the type returned by function `f` when called with arguments\nof the types in `Ts`.\n\nThe underlying implementation uses the function [`testargs`](@ref) to generate\nsome test values in order to call the function and determine the returned type.\nThis mechanism does not use `Base._return_type`. One of the advantages is\nthat the given function `f` is called, and thus, meaningful error messages\nwill be displayed if there is any error in `f`. \n    \n\"\"\"\nfunction return_type(f::Function,Ts...)\n  args = testargs(f,Ts...)\n  #try\n    typeof(f(args...))\n  #catch e\n  #  if isa(e,DomainError)\n  #    s = \"Function $(nameof(f)) cannot be evaluated at $args, its not in the domain.\\n\"\n  #    s *= \" Define function `testargs(::typeof{$(nameof(f))},Ts...)`\\n\"\n  #    s *= \" which sould return an argument tuple in the function domain.\"\n  #    error(s)\n  #  else\n  #    throw(e)\n  #  end\n  #end\n  # TODO try - catch block makes function very in-efficient when\n  # called multiple times\nend\n\n\"\"\"\n    return_type_broadcast(f::Function,Ts::DataType...) -> DataType\n\nLike [`return_type`](@ref), but when function `f` is used in a broadcast operation.\n\"\"\"\nfunction return_type_broadcast(f::Function,Ts...)\n  args = testargs_broadcast(f,Ts...)\n  r = broadcast(f,args...)\n  typeof(r)\nend\n\nfunction return_type_broadcast(f::Function,Ts::Type{<:Number}...)\n  return_type(f,Ts...)\nend\n\n\"\"\"\n$(SIGNATURES)\n\"\"\"\nfunction testargs_broadcast(f::Function,Ts...)\n  testvalues(Ts...)\nend\n\nfunction testargs_broadcast(f::Function,Ts::Type{<:Number}...)\n  testargs(f,Ts...)\nend\n\n\"\"\"\n    testargs(f::Function,Ts::DataType...) -> Tuple\n\nReturns a tuple with valid arguments of the types in `Ts` in order to call\nfunction `f`. It defaults to `testvalues(Ts...)`, see the [`testvalues`](@ref)\nfunction.\nThe user can overload the `testargs`\nfunction for particular functions if the default test arguments are not in the domain\nof the function and a `DomainError` is raised.\n\n# Examples\n\nFor the following function, the default test argument (which is a zero)\nis not in the domain. We can overload the `testargs` function to provide\na valid test argument.\n\n```jldoctests\nusing InplaceArrays.Inference\nimport InplaceArrays.Inference: testargs\nfoo(x) = sqrt(x-1)\ntestargs(::typeof(foo),T::DataType) = (one(T),)\nreturn_type(foo, Int)\n# output\nFloat64\n```\n\n\"\"\"\ntestargs(f::Function,Ts...) = testvalues(Ts...)\n\n\"\"\"\n    testvalue(::Type{T}) where T\n\nReturns an arbitrary instance of type `T`. It defaults to `zero(T)` for\nnon-array types and to an empty array for array types.\nThis function is used to compute the default test arguments in\n[`testargs`](@ref).\nIt can be overloaded for new types `T` if `zero(T)` does not makes sense. \n\"\"\"\nfunction testvalue end\n\ntestvalue(::Type{T}) where T = zero(T)\n\nfunction testvalue(::Type{T}) where T<:AbstractArray{E,N} where {E,N}\n   similar(T,fill(0,N)...)\nend\n\nfunction testvalue(::Type{T}) where T<:Fill{E,N} where {E,N}\n  Fill(zero(E),fill(0,N)...)\nend\n\n\"\"\"\n    testvalues(Ts::DataType...) -> Tuple\n\nReturns a tuple with test values for each of the types in `Ts`.\nEquivalent to `map(testvalue,Ts)`.\n\"\"\"\nfunction testvalues(a,b...)\n  ta = testvalue(a)\n  tb = testvalues(b...)\n  (ta,tb...)\nend\n\nfunction testvalues(a)\n  ta = testvalue(a)\n  (ta,)\nend\n\nend # module\n", "meta": {"hexsha": "ef08a983e76ee2059ffea074967a25266c221034", "size": 3832, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Inference/Inference.jl", "max_stars_repo_name": "JuliaTagBot/InplaceArrays.jl", "max_stars_repo_head_hexsha": "c81eee76402d667eae8a752da21bc98e9bd26812", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-11-08T10:10:32.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-08T10:10:32.000Z", "max_issues_repo_path": "src/Inference/Inference.jl", "max_issues_repo_name": "JuliaTagBot/InplaceArrays.jl", "max_issues_repo_head_hexsha": "c81eee76402d667eae8a752da21bc98e9bd26812", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-10-18T12:41:58.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-08T15:39:49.000Z", "max_forks_repo_path": "src/Inference/Inference.jl", "max_forks_repo_name": "JuliaTagBot/InplaceArrays.jl", "max_forks_repo_head_hexsha": "c81eee76402d667eae8a752da21bc98e9bd26812", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:02:35.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:02:35.000Z", "avg_line_length": 25.7181208054, "max_line_length": 89, "alphanum_fraction": 0.7082463466, "num_tokens": 998, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.36658973632215985, "lm_q2_score": 0.17106118956561076, "lm_q1q2_score": 0.06270927637781225}}
{"text": "using Documenter\nusing GeometricProblems\n\n\nmakedocs(\n    sitename = \"GeometricProblems.jl\",\n    format = Documenter.HTML(prettyurls = get(ENV, \"CI\", nothing) == \"true\"),\n    pages = [\"Home\" => \"index.md\",\n             \"Diagnostics\"               => \"diagnostics.md\",\n             \"Exponential Growth\"        => \"exponential_growth.md\",\n             \"He\u0301non-Heiles System\"       => \"henon_heiles.md\",\n             \"Kepler Problem\"            => \"kepler_problem.md\",\n             \"Lorenz Attractor\"          => \"lorenz_attractor.md\",\n             \"Lotka-Volterra 2d\"         => \"lotka_volterra_2d.md\",\n             \"Lotka-Volterra 3d\"         => \"lotka_volterra_3d.md\",\n             \"Lotka-Volterra 4d\"         => \"lotka_volterra_4d.md\",\n             \"Massless Charged Particle\" => \"massless_charged_particle.md\",\n             \"Harmonic Oscillator\"       => \"harmonic_oscillator.md\",\n             \"Nonlinear Oscillators\"     => \"nonlinear_oscillators.md\",\n             \"Mathematical Pendulum\"     => \"pendulum.md\",\n             \"Double Pendulum\"           => \"double_pendulum.md\",\n             \"Point Vortices\"            => \"point_vortices.md\",\n             \"Inner Solar System\"        => \"inner_solar_system.md\",\n             \"Outer Solar System\"        => \"outer_solar_system.md\",\n            ]\n)\n\ndeploydocs(\n    repo   = \"github.com/JuliaGNI/GeometricProblems.jl\",\n)\n", "meta": {"hexsha": "d260784dc69ee2cb3c1ed0a879cf20f7df0f106c", "size": 1370, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "DDMGNI/GeometricProblems.jl", "max_stars_repo_head_hexsha": "367f4cf63ff614c3051c7760b835889fdb7a040f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/make.jl", "max_issues_repo_name": "DDMGNI/GeometricProblems.jl", "max_issues_repo_head_hexsha": "367f4cf63ff614c3051c7760b835889fdb7a040f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2020-02-11T01:23:56.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-24T11:06:23.000Z", "max_forks_repo_path": "docs/make.jl", "max_forks_repo_name": "DDMGNI/GeometricProblems.jl", "max_forks_repo_head_hexsha": "367f4cf63ff614c3051c7760b835889fdb7a040f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.1935483871, "max_line_length": 77, "alphanum_fraction": 0.5379562044, "num_tokens": 362, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.12940273159163906, "lm_q1q2_score": 0.06268010603403011}}
{"text": "\"\"\"\nAuthors : Adrien BLASSIAU Corentin JUVIGNY\nTP RODD - 2019-2020\n\"\"\"\n\n@doc \"\"\"\nModule contenant les types relatifs aux instances et aux solutions du probl\u00e8me de lot sizing avec contraintes environnementales \"Green Lot Sizing\", que l'on note G_ULS.\nIl contient les types et les fonctions suivants:\n\n- Instance\n- Solution\n\nUtilisez using .G_ULS pour utiliser le module et son contenu dans votre code.\n\nUtiliser ?NOM dans l'interface en ligne de commande Julia pour avoir de la documentation sur NOM.\nPar exemple ?Instance affiche la documentation sur le type Instance\n\n\n\"\"\" ->\nmodule G_ULS\n\n  export Instance, Solution\n\n  @doc \"\"\"\n  Type repr\u00e9sentant une entr\u00e9e du probl\u00e8me G_ULS.\n\n  Il n'est pas n\u00e9cessaire de construire vous m\u00eame les entr\u00e9es.\n  Utilisez le fichier generate.jl pour cela.\n\n  \"\"\"\n  mutable struct Instance\n    T::Int # horizon\n    M::Int # nombre de modes\n    R::Int # taille de l'intervalle glissant\n    Emax::Array{Int} # impact environnementale max\n    f::Array{Array{Int}} # cout d'approvisionnement fixe\n    e::Array{Array{Int}} # impact environnementale\n    h::Array{Int} # cout de stockage unitaire\n    p::Array{Array{Int}} # cout d'approvisionnement unitaire\n    d::Array{Int} # demande\n  end\n  Instance() = Instance(0, 0, 0, [], [], [], [], [], []) # Constructeur par d\u00e9faut\n\n  @doc \"\"\"\n  Type repr\u00e9sentant une solution du probl\u00e8me G_ULS.\n\n  Vous n'avez pas \u00e0 construire une solution \u00e0 la main. Vous pouvez utiliser :\n  - Solution(inst::Instance)\n\n  \"\"\"\n  mutable struct Solution\n    inst\n    x\n    y\n    s\n  end\n  Solution(inst) = Solution(inst,[],[],[]) # Constructeur par d\u00e9faut\n\n\nend\n\n\n\n\n", "meta": {"hexsha": "35bcfd228881ed1225ceb4dd8f607f01df96667c", "size": 1618, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tp7/Projet_7/julia/helpers/problem.jl", "max_stars_repo_name": "AdrienBlassiau/RODD", "max_stars_repo_head_hexsha": "2cbe43071e12a75dcd9ea6effa8bee1a1533450b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tp7/Projet_7/julia/helpers/problem.jl", "max_issues_repo_name": "AdrienBlassiau/RODD", "max_issues_repo_head_hexsha": "2cbe43071e12a75dcd9ea6effa8bee1a1533450b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tp7/Projet_7/julia/helpers/problem.jl", "max_forks_repo_name": "AdrienBlassiau/RODD", "max_forks_repo_head_hexsha": "2cbe43071e12a75dcd9ea6effa8bee1a1533450b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.8923076923, "max_line_length": 168, "alphanum_fraction": 0.7027194067, "num_tokens": 443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.1294027231979892, "lm_q1q2_score": 0.06268010196831329}}
{"text": "# ---\n# title: 1604. Alert Using Same Key-Card Three or More Times in a One Hour Period\n# id: problem1604\n# author: AquaIndigo\n# date: 2020-11-08\n# difficulty: Medium\n# categories: String, Ordered Map\n# link: <https://leetcode.com/problems/alert-using-same-key-card-three-or-more-times-in-a-one-hour-period/description/>\n# hidden: true\n# ---\n# \n# LeetCode company workers use key-cards to unlock office doors. Each time a\n# worker uses their key-card, the security system saves the worker's name and\n# the time when it was used. The system emits an **alert** if any worker uses\n# the key-card **three or more times** in a one-hour period.\n# \n# You are given a list of strings `keyName` and `keyTime` where `[keyName[i],\n# keyTime[i]]` corresponds to a person's name and the time when their key-card\n# was used **in a** **single day**.\n# \n# Access times are given in the **24-hour time format \"HH:MM\"**, such as\n# `\"23:51\"` and `\"09:49\"`.\n# \n# Return a _list of unique worker names who received an alert for frequent\n# keycard use_. Sort the names in **ascending order alphabetically**.\n# \n# Notice that `\"10:00\"` \\- `\"11:00\"` is considered to be within a one-hour\n# period, while `\"22:51\"` \\- `\"23:52\"` is not considered to be within a one-hour\n# period.\n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: keyName = [\"daniel\",\"daniel\",\"daniel\",\"luis\",\"luis\",\"luis\",\"luis\"], keyTime = [\"10:00\",\"10:40\",\"11:00\",\"09:00\",\"11:00\",\"13:00\",\"15:00\"]\n#     Output: [\"daniel\"]\n#     Explanation: \"daniel\" used the keycard 3 times in a one-hour period (\"10:00\",\"10:40\", \"11:00\").\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: keyName = [\"alice\",\"alice\",\"alice\",\"bob\",\"bob\",\"bob\",\"bob\"], keyTime = [\"12:01\",\"12:00\",\"18:00\",\"21:00\",\"21:20\",\"21:30\",\"23:00\"]\n#     Output: [\"bob\"]\n#     Explanation: \"bob\" used the keycard 3 times in a one-hour period (\"21:00\",\"21:20\", \"21:30\").\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: keyName = [\"john\",\"john\",\"john\"], keyTime = [\"23:58\",\"23:59\",\"00:01\"]\n#     Output: []\n#     \n# \n# **Example 4:**\n# \n#     \n#     \n#     Input: keyName = [\"leslie\",\"leslie\",\"leslie\",\"clare\",\"clare\",\"clare\",\"clare\"], keyTime = [\"13:00\",\"13:20\",\"14:00\",\"18:00\",\"18:51\",\"19:30\",\"19:49\"]\n#     Output: [\"clare\",\"leslie\"]\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `1 <= keyName.length, keyTime.length <= 105`\n#   * `keyName.length == keyTime.length`\n#   * `keyTime[i]` is in the format **\" HH:MM\"**.\n#   * `[keyName[i], keyTime[i]]` is **unique**.\n#   * `1 <= keyName[i].length <= 10`\n#   * `keyName[i] contains only lowercase English letters.`\n# \n# \n## @lc code=start\nusing LeetCode\nusing Dates\n\nfunction alert_names(key_name::Vector{String}, key_time::Vector{String})\n    res = Vector{String}()\n    mp = Dict{String,Vector{Time}}()\n    for (idx, name) in enumerate(key_name)\n        if !haskey(mp, name)\n            mp[name] = String[]\n        end\n        push!(mp[name], Time(key_time[idx]))\n    end\n    for (name, time) in mp\n        sort!(time)\n        for idx in 1:(length(time) - 2)\n            time1h = time[idx] + Hour(1)\n            if time[idx + 1] <= time1h && time[idx + 2] <= time1h\n                push!(res, name)\n                break\n            end\n        end\n    end\n    return sort!(res)\nend\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "7d6cfd2ea19f97d6ed86d95c063feb0349c993c4", "size": 3289, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/1604.alert-using-same-key-card-three-or-more-times-in-a-one-hour-period.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/problems/1604.alert-using-same-key-card-three-or-more-times-in-a-one-hour-period.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/problems/1604.alert-using-same-key-card-three-or-more-times-in-a-one-hour-period.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 30.738317757, "max_line_length": 152, "alphanum_fraction": 0.5819397993, "num_tokens": 1037, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.14804720179063333, "lm_q1q2_score": 0.06255062892781398}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.1\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 ab02837b-79ec-40d7-bff1-c1d2dd7362ef\nmd\"\"\"\n# PlutoTest.jl\n_Visual, reactive testing library for Julia_\n\nA macro `@test` that you can use to verify your code's correctness. \n\n**But instead of just saying _\"Pass\"_ or _\"Fail\"_, let's try to show you _why_ a test failed.**\n- \u2728 _time travel_ \u2728 to replay the execution step-by-step\n- \u2b50\ufe0f _multimedia display_ \u2b50\ufe0f to make results easy to read\n\"\"\"\n\n# \u2554\u2550\u2561 56347b7e-5007-45f8-8f6d-8ac8cc719637\nmd\"\"\"\nTests have _time-travel_ functionality built in! **Click on the test results above.**\n\"\"\"\n\n# \u2554\u2550\u2561 191f1f04-18d4-485b-af8b-a2f073b7043b\nmd\"\"\"\n## Installation and more info\n\n> [github.com/JuliaPluto/PlutoTest.jl](https://github.com/JuliaPluto/PlutoTest.jl)\n\"\"\"\n\n# \u2554\u2550\u2561 ec1fd70a-d92a-4688-98b2-135879f07141\nmd\"\"\"\n### (You need `Pluto \u2265 0.14.5` to run this notebook)\n\"\"\"\n\n# \u2554\u2550\u2561 78704300-0531-4f8e-8aa5-3f588fbdd190\nimport Test: Test, @test_warn, @test_nowarn, @test_logs, @test_skip, @test_broken, @test_throws, @test_deprecated\n\n# \u2554\u2550\u2561 9129342b-f560-4901-81a2-56e3f8641521\nexport @test_nowarn, @test_warn, @test_logs, @test_skip, @test_broken, @test_throws, @test_deprecated\n\n# \u2554\u2550\u2561 0d70962a-3880-4dee-a439-35068d019f5a\nmd\"\"\"\n# Type definitions\n\"\"\"\n\n# \u2554\u2550\u2561 113cc425-e224-4f77-bfbd-ef4eb1d1ed70\nabstract type TestResult end\n\n# \u2554\u2550\u2561 6188f559-bcab-4da6-84b2-a3fe522a5c3c\nabstract type Fail <: TestResult end\n\n# \u2554\u2550\u2561 c24b46ce-bcbb-4dc9-8a59-b5b1bd2cd617\nabstract type Pass <: TestResult end\n\n# \u2554\u2550\u2561 5041085e-a406-4ed4-ab82-84d8f126cf0f\nconst Code = Any\n\n# \u2554\u2550\u2561 03ccd498-83c3-41bb-84d7-625adabd7aee\nstruct CorrectCall <: Pass\n\texpr::Code\n\tsteps::Vector\nend\n\n# \u2554\u2550\u2561 1bcf8bd1-c8a3-49a1-9791-d813aa856399\nBase.@kwdef struct ErrorCall <: Fail\n\texpr::Code\n\tsteps::Vector\n\terror::CapturedException\nend\n\n# \u2554\u2550\u2561 14c525a1-eca1-466b-8e63-3a90d7d7111c\nstruct WrongCall <: Fail\n\texpr::Code\n\tsteps::Vector\nend\n\n# \u2554\u2550\u2561 a2efc968-246c-40c2-b285-2ec94b185a44\nmd\"\"\"\n# Test macro\n\"\"\"\n\n# \u2554\u2550\u2561 dbfbcc16-c740-436c-bbf0-fee16b0a20c5\nmd\"\"\"\n# $(html\"<img src='https://cdn.jsdelivr.net/gh/ionic-team/ionicons@5.5.1/src/svg/time-outline.svg' style='height: .75em; margin-bottom: -.1em'>\") _Time travel_ evaluation\n\nIn Julia, expressions are objects! This means that, before evaluation, code is expressed as a Julia object:\n\"\"\"\n\n# \u2554\u2550\u2561 7c2bab29-8609-4575-b2ca-7feb34915645\nmd\"\"\"\nYou can use `Core.eval` to evaluate expressions at runtime:\n\"\"\"\n\n# \u2554\u2550\u2561 838b5904-1de2-4d9f-8f3c-a93ec224d40e\nmd\"\"\"\nBut _did you know_ that you can also **partially evaluate** expressions? \n\"\"\"\n\n# \u2554\u2550\u2561 b056a99d-5b13-47ba-a199-d788410e3c99\nmd\"\"\"\nHere, `ex2` is not a raw `Expr` \u2014 it _contains_ an evaluated array!\n\"\"\"\n\n# \u2554\u2550\u2561 5b093e83-78c1-4187-b406-56e79800e1be\nmd\"\"\"\n## `Computed` struct\n\nOur time travel mechanism will be based on the partial evaluation principle introduced above. To differentiate between computed results and the original expression, we will wrap all computed results in a `struct`.\n\"\"\"\n\n# \u2554\u2550\u2561 a461f1fd-b5a5-4ae3-a47c-067a6081fb24\nstruct Computed\n\tx\nend\n\n# \u2554\u2550\u2561 f9c81ab1-556c-4d81-bee8-2897c20e324d\nmd\"\"\"\nWe also add a function to recursively _unwrap_ an expression with `Computed` entries:\n\"\"\"\n\n# \u2554\u2550\u2561 a392d2d6-5a16-4383-b0ef-5003aa2de9fa\nunwrap_computed(x) = x\n\n# \u2554\u2550\u2561 ae95b691-f54b-4bf5-b17b-3e5bd1edf75e\nunwrap_computed(c::Computed) = c.x\n\n# \u2554\u2550\u2561 12119016-fa61-4d38-8c58-821ea435df7d\nunwrap_computed(e::Expr) = Expr(e.head, unwrap_computed.(e.args)...)\n\n# \u2554\u2550\u2561 74929fa6-d1f7-41cd-ab55-48f35d5fbf28\nmd\"\"\"\n## `code_loweredish_with_lenses`\n\"\"\"\n\n# \u2554\u2550\u2561 f1ede628-d158-4296-befd-3eaa87cdad27\nmd\"\"\"\n`ERROR_ON_UNKNOWN_EXPRESSION_TYPE` is set so when you view this notebook, you get explicit errors whenever we reach things we can't parse now but can work around.\n\nExamples like this are `try ... catch ... end`, which we can't parse yet (control flow stuff is complex >_>) but we can still just evaluate that whole thing and call it a day.\n\"\"\"\n\n# \u2554\u2550\u2561 5e66e59b-fdb8-4373-b231-097b0227dc5c\nbegin\n\tstruct ExprWithLens\n\t\texpr\n\t\tlens\n\t\texpr_to_show\n\tend\n\tExprWithLens(; expr, lens=[], expr_to_show=expr) = ExprWithLens(expr, lens, expr_to_show)\nend\n\n# \u2554\u2550\u2561 17dea9e5-84ea-4476-a318-cc475043c83b\nFrames = Vector{ExprWithLens}\n\n# \u2554\u2550\u2561 c47252b9-8869-4878-b9bf-7eeb7ed17c9a\nstruct CantStepifyThisYetException <: Exception\n\texpr\nend\n\n# \u2554\u2550\u2561 0a3f5c6c-6e1b-458c-bf91-523a0b639b41\nmd\"\"\"\n### `code_loweredish_with_lenses` for all non-Expr types\n\"\"\"\n\n# \u2554\u2550\u2561 43fe89d7-f33e-4dfa-853e-327e981feb1e\nfunction code_loweredish_with_lenses(x::Symbol)\n\t# Should we replace variables with their value as a step?\n\t[ExprWithLens(expr=x, lens=[])]\n\t# For now I'm assuming people know what their variables are...\n\t# return ExprWithLens[]\nend\n\n# \u2554\u2550\u2561 fc000550-3053-483e-bc41-6aed22c3999c\ncode_loweredish_with_lenses(x::QuoteNode) = ExprWithLens[]\n\n# \u2554\u2550\u2561 3f11ca4c-dd06-47c9-92e2-cb97c18a06db\ncode_loweredish_with_lenses(x::Number) = ExprWithLens[]\n\n# \u2554\u2550\u2561 b155d336-f746-4c82-8206-ab1a49cedea8\ncode_loweredish_with_lenses(x::String) = ExprWithLens[]\n\n# \u2554\u2550\u2561 f9b2a11d-8c4e-47a5-9d93-38025fae9a95\ncode_loweredish_with_lenses(::Char) = ExprWithLens[]\n\n# \u2554\u2550\u2561 221aa13b-aa25-4145-8076-da77432364bb\ncode_loweredish_with_lenses(x::LineNumberNode) = ExprWithLens[]\n\n# \u2554\u2550\u2561 2a514f2f-79c8-4b0d-be8a-170f3386d5d5\ncode_loweredish_with_lenses(x) = error(\"Type of expression not supported ($(typeof(x)))\")\n# code_loweredish_with-lenses(x) = [ExprWithLens(expr=x, lens=[])]\n\n# \u2554\u2550\u2561 9fb4d52d-77f2-4032-a769-6d5e60be43bf\nmd\"\"\"\n### `expr_lenses_for_quoted`\nWhich will ignore everything except `:$` expressions, and then call `code_loweredish_with_lenses` for those.\n\"\"\"\n\n# \u2554\u2550\u2561 cade56ad-312e-40cf-bcda-11480ce27852\nexpr_lenses_for_quoted(x, _) = ExprWithLens[]\n\n# \u2554\u2550\u2561 810470b8-0a6c-48b8-aba2-a2058b8d9f59\nmd\"## `build_step_by_step_blocks`\" \n\n# \u2554\u2550\u2561 a29d5277-e97a-4cca-8e31-8037f9cfdd80\n\"\"\"\n    build_check_for_computed(lens::Expr, default::Expr)\n\nIdeally, this function doesn't exist.\n\nIt will wrap a normal expression into an expression that first checks if the `lens` refers to a `Computed` value, if so use the value it contains, if not, use the original expression.\n\nThis is fine for now, but it isn't beautiful, and I didn't want to make this output even bigger expressions (as these are put inline into existing expressions, making them already quite unreadable), so there isn't any error handling. (If executing `lens` gives an error, you're out).\n\nIdeally we don't have to check for `Computed`, because we know very well which references will be `Computed` and which won't. But that's for later \ud83d\ude43\n\"\"\"\nfunction build_check_for_computed(lens::Expr, default::Expr)\n\tif Meta.isexpr(default, :..., 1) && Meta.isexpr(default.args[1], :call)\n\t\t# Can't have `x...` as a standalone expression, so need to \n\t\t# dive in to replace the `x` instead of the whole thing\n\t\t# e.g. `(x[1] isa Computed ? x[1].x : (1:7))...` (fine)\n\t\t# instead of `(x isa Computed ? x.x : (1:7)...)` (not fine)\n\t\t# Subtle, but important difference.\n\t\t:((\n\t\t\t$(lens).args[1] isa Computed ?\n\t\t\t$(lens).args[1].x :\n\t\t\t$(esc(default.args[1]))\n\t\t)...)\n\telseif Meta.isexpr(default, :call)\n\t\t# We can assume that a `call` will be done before\n\t\t:($(lens) isa Computed ? $(lens).x : $(esc(default)))\n\telse\n\t\tesc(default)\n\tend\n\nend\n\n# \u2554\u2550\u2561 4f7aac13-9e49-4b2b-8d78-53f583f6130a\nfunction build_check_for_computed(lens::Expr, default::Any)\n\tesc(default)\nend\n\n# \u2554\u2550\u2561 cc7102e1-6af0-43bb-8cf0-43e2cec210e3\nBase.@kwdef struct PartialEvaluatedException <: Exception\n\texpr_with_lens::ExprWithLens\n\tsteps::Vector\n\terror::CapturedException\nend\n\n# \u2554\u2550\u2561 ec6f1b07-d026-45ca-996d-be7693664cd7\ndeepcopy_expr(e::Expr) = Expr(e.head, (deepcopy_expr(sub_e) for sub_e in e.args)...)\n\n# \u2554\u2550\u2561 dadf1c50-6588-4345-a240-69a72336c7cd\ndeepcopy_expr(e) = e\n\n# \u2554\u2550\u2561 d384e3fc-b207-48ce-bc7b-1b47a14b1581\nfunction apply_lens_to_frames(lens, frames)\n\tmap(frames) do frame\n\t\tExprWithLens(\n\t\t\texpr=frame.expr,\n\t\t\tlens=[lens..., frame.lens...]\n\t\t)\n\tend\nend\n\n# \u2554\u2550\u2561 a6e8c835-f209-445a-9f43-cdf2ecfd1b57\nmd\"\"\"\n# Tests for all expression types\n\"\"\"\n\n# \u2554\u2550\u2561 5759b2cc-1e96-4069-ae42-bc159c7cf5fb\nmd\"## Basic\"\n\n# \u2554\u2550\u2561 716d9ddc-18dc-4973-924e-e5ebf9161ff6\nmd\"## Edge cases\"\n\n# \u2554\u2550\u2561 bc08755d-721f-403e-af95-36494b8fb7bc\nmd\"## Things we can't parse yet\"\n\n# \u2554\u2550\u2561 de94f2b5-96ae-4936-870f-7639a39fd40d\nmd\"\"\"\n> TODO  \n>\n> Functions that aren't just coming from simple references (so say, Higher-order functions and such) should actually get a mention in the time travel. Right now it is still as this odd `#X#123` thingy (which is even worse when it is anonymous..), but this we could make prettier later.\n\"\"\"\n\n# \u2554\u2550\u2561 21d4560e-721f-4ed4-9db7-86a8151ab22c\nmd\"\"\"\n# UI\n\"\"\"\n\n# \u2554\u2550\u2561 99afc7f4-727c-4277-8311-f2ffa94830ae\nmd\"\"\"\n#### Slotting\n\nWe walk through the expression tree. Whenever we find a `Computed` object, we generate a random key (e.g. `iosjddfo`), we add it to our dictionary (`found`). In the expression, we replace the `Computed` object with a placeholder symbol `__slotiosjddfo__`. We will later be able to match the object to this slot.\n\"\"\"\n\n# \u2554\u2550\u2561 4956526a-daf9-43c9-bff3-ff2446016e2e\nslot!(found, c::Computed) = let\n\tk = Symbol(\"__slot\", join(rand('a':'z', 16)), \"__\")\n\tfound[k] = c\n\tk\nend\n\n# \u2554\u2550\u2561 84ff6a23-c134-4910-b630-a7ad45f3bf29\nslot!(found, x) = x\n\n# \u2554\u2550\u2561 318363d0-6d9e-4144-b478-b775f437edaf\nslot!(found, e::Expr) = Expr(e.head, slot!.([found], e.args)...)\n\n# \u2554\u2550\u2561 67fd07b7-340b-4e24-bc06-e4c85b186872\nslot(e) = let\n\td = Dict{Symbol,Any}()\n\tnew_e = slot!(d, e)\n\td, new_e\nend\n\n# \u2554\u2550\u2561 c6d5597c-d505-4125-88c4-10415934d2a4\nmd\"\"\"\n## SlottedDisplay\n\nWe use `print` to turn the expression into source code.\n\nFor each line, we regex-search for slot variables, and we split the line around those. The code segments around slots are rendered inside `<pre-ish>` tags (like `<pre>` but inline), and the slots are replaced by [embedded displays](https://github.com/fonsp/Pluto.jl/pull/1126) of the objects.\n\"\"\"\n\n# \u2554\u2550\u2561 c877c109-db16-468c-8f3c-8294db859d6d\nbegin\n\tstruct SlottedDisplay\n\t\td\n\t\te\n\tend\n\tSlottedDisplay(expr) = SlottedDisplay(slot(expr)...)\nend\n\n# \u2554\u2550\u2561 b5763c10-e11c-4389-b6fc-421d2c9682f1\nmd\"\"\"\n## Frame viewer\n\nA widget that takes a series of elements and displays them as 'video frames' with a timeline scrubber.\n\"\"\"\n\n# \u2554\u2550\u2561 3d5abd58-02ab-4b91-a7a3-d9068d4df017\nmd\"\"\"\n## @visual_debug (awesome)\n\"\"\"\n\n# \u2554\u2550\u2561 f9ed2487-a7f6-4ce9-b673-f8a298cd5fc3\nmd\"\"\"\n# Appendix\n\"\"\"\n\n# \u2554\u2550\u2561 20166ec9-7084-4d58-8b19-3aa51cc8f2c6\nmd\"\"\"\n## Macro Lenses\n\nI need stuff like Accessors.jl, but I didn't feel like another dependency and also *felt* like I should be macro-ish. So that's that this is...\n\"\"\"\n\n# \u2554\u2550\u2561 1633fe05-cb51-4032-b6b6-f23db72bbd49\nstruct FieldLens property::Symbol end\n\n# \u2554\u2550\u2561 7c312943-c48b-40e7-a499-227f7ff8aa59\nstruct PropertyLens property end\n\n# \u2554\u2550\u2561 a0207e25-0398-4104-8c0f-a8fbd9fe1d53\nstruct EmptyPropertyLens end\n\n# \u2554\u2550\u2561 7d14b79c-74e5-4986-80b7-de7cd7d48670\nquote_if_needed(x::Union{Expr, Symbol, QuoteNode, LineNumberNode}) = QuoteNode(x)\n\n# \u2554\u2550\u2561 5950488e-2008-48d8-9095-7f9421df191e\nquote_if_needed(x) = x\n\n# \u2554\u2550\u2561 77cc33a3-c2bc-4f2d-ba88-e3693ec79b0c\nfunction lens_to_setter(subject, lens::Vector, value)\n\tif length(lens) === 0\n\t\tthrow(ArgumentError(\"lens needs at least one element in path\"))\n\telseif length(lens) === 1\n\t\tproperty = lens[1]\n\t\tif property isa FieldLens\n\t\t\t:($subject.$(property.property) = $value)\n\t\telseif property isa PropertyLens\n\t\t\t:($subject[$(quote_if_needed(property.property))] = $value)\n\t\telseif property isa EmptyPropertyLens\n\t\t\t:($subject[] = $value)\n\t\telse\n\t\t\terror(\"Unknown lens type\")\n\t\tend\n\telse\n\t\tproperty, path... = lens\n\t\tnext_subject = if property isa FieldLens\n\t\t\t:($subject.$(property.property))\n\t\telseif property isa PropertyLens\n\t\t\t:($subject[$(quote_if_needed(property.property))])\n\t\telseif property isa EmptyPropertyLens\n\t\t\t:($subject[])\n\t\telse\n\t\t\terror(\"Unknown lens type\")\n\t\tend\n\n\t\tlens_to_setter(next_subject, path, value)\n\tend\nend\n\n# \u2554\u2550\u2561 5a3a0f63-dcce-49c9-84fd-a6317184820f\nfunction lens_to_getter(subject, lens::Vector)\n\tif length(lens) == 0\n\t\treturn subject\n\tend\n\t\n\tproperty, path... = lens\n\tnext_subject = if property isa FieldLens\n\t\t:($subject.$(property.property))\n\telseif property isa PropertyLens\n\t\t:($subject[$(quote_if_needed(property.property))])\n\telseif property isa EmptyPropertyLens\n\t\t:($subject[])\n\telse\n\t\terror(\"Unknown lens type\")\n\tend\n\n\tlens_to_getter(next_subject, path)\nend\n\n# \u2554\u2550\u2561 f5d9a4c5-300f-4dae-8507-346ec0b74632\n\"\"\"\n    function build_step_by_step_blocks(lowered::Vector{ExprWithLens};\n      expr_ref_lens,\n      steps_lens,\n     )::Vector{Expr}\n\nTransforms an `ExprWithLens`-list (created by `code_loweredish_with_lenses`), into a list of expressions that will mutate whatever `expr_ref_lens` points to.\n\n- `expr_ref_lens` needs to point to a `Ref{Expr}`\n- `steps_lens` needs to point to a `Vector{Expr}`\n\"\"\"\nfunction build_step_by_step_blocks(\n\tlowered::Vector{ExprWithLens};\n\texpr_ref_lens=:EXPR_REF_LENS_DEFAULT,\n\tsteps_lens=:STEPS_LENS_DEFAULT,\n)::Vector{Expr}\n\tmap(lowered) do frame\n\t\t# This whoooooole thing is to not execute parts of the code multiple times.\n\t\t# It replaces, when possible, the arguments in the expression with a reference\n\t\t# to the argument in the latest version of the expression\n\t\t# (where some nodes are Computed, which we will use)\n\t\tframe_lens = lens_to_getter(expr_ref_lens, [\n\t\t\tEmptyPropertyLens(),\n\t\t\tframe.lens...,\n\t\t])\n\t\t# TODO Use the same check here as we use in `code_loweredish_with_lenses`..\n\t\t# .... Right now it only de-dupes inside calls, but not inside blocks\n\t\t# .... (which we do split up in code_loweredish_with_lenses)\n\t\texpr_with_arguments_as_references = if Meta.isexpr(frame.expr, :call) \n\t\t\tExpr(\n\t\t\t\tframe.expr.head,\n\t\t\t\tmap(enumerate(frame.expr.args)) do (i, arg)\n\t\t\t\t\targ_lens = :($(frame_lens).args[$(i)])\n\t\t\t\t\tbuild_check_for_computed(arg_lens, arg)\n\t\t\t\tend...\n\t\t\t)\n\t\telse\n\t\t\tesc(frame.expr)\n\t\tend\n\t\t\n\t\t# If you feel like \"I don't need that optimised shit\",\n\t\t# and want to see an easier to digest result, uncomment this line\n\t\t#expr_with_arguments_as_references = esc(frame.expr)\n\n\t\t\n\t\tquote\n\t\t\ttry\n\t\t\t\t$(expr_ref_lens == :EXPR_REF_LENS_DEFAULT ? :(error(\"expr_ref_lens wasn't assigned, so this code will not work, but you can see and inspect it\")) : nothing)\n\t\t\t\t$(steps_lens == :STEPS_LENS_DEFAULT ? :(error(\"steps_lens wasn't assigned, so this code will not work, but you can see and inspect it\")) : nothing)\n\t\n\t\t\t\tval = $(expr_with_arguments_as_references)\n\t\t\t\t\n\t\t\t\t# Could use Accessors.jl to make this a lot less expensive... \ud83e\udd37\u200d\u2640\ufe0f\n\t\t\t\t$expr_ref_lens[] = $(deepcopy_expr)($expr_ref_lens[])\n\t\t\t\t$(lens_to_setter(\n\t\t\t\t\texpr_ref_lens,\n\t\t\t\t\t[EmptyPropertyLens(), frame.lens...],\n\t\t\t\t\t:(Computed(val))\n\t\t\t\t))\n\t\t\t\t# TODO Ideally we even render the step here directly,\n\t\t\t\t# .... so any side-effects on mutable objects will\n\t\t\t\t# .... show up in time-travel.\n\t\t\t\tpush!($steps_lens, $expr_ref_lens[])\n\t\t\tcatch error\n\t\t\t\tcaptured_error = CapturedException(\n\t\t\t\t\terror,\n\t\t\t\t\tstacktrace(catch_backtrace())\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\t$expr_ref_lens[] = $(deepcopy_expr)($expr_ref_lens[])\n\t\t\t\t$(lens_to_setter(\n\t\t\t\t\texpr_ref_lens,\n\t\t\t\t\t[EmptyPropertyLens(), frame.lens...],\n\t\t\t\t\t:(:(throw($(Computed(error)))))\n\t\t\t\t))\n\t\t\t\tpush!($steps_lens, $expr_ref_lens[])\n\t\t\t\tpush!($steps_lens, :(throw($(Computed(error)))))\n\n\t\t\t\tthrow(PartialEvaluatedException(\n\t\t\t\t\texpr_with_lens=$(frame),\n\t\t\t\t\tsteps=$steps_lens,\n\t\t\t\t\terror=captured_error,\n\t\t\t\t))\n\t\t\tend\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 35f63c4e-3583-4ea8-a057-31f18f8a09d6\nmd\"\"\"\n## `@skip_as_script`\n\"\"\"\n\n# \u2554\u2550\u2561 ef59d0f0-0f02-4089-a49d-53fb0427c3a0\nembed_display(x) = if isdefined(Main, :PlutoRunner) && isdefined(Main.PlutoRunner, :embed_display)\n\t# if this package is used inside Pluto, and Pluto is new enough\n\tMain.PlutoRunner.embed_display(x)\nelse\n\tidentity(x)\nend\n\n# \u2554\u2550\u2561 35b2770e-1db6-4327-bf86-c27a4b61dbd3\nfunction is_inside_pluto(m::Module)::Bool\n\tif isdefined(m, :PlutoForceDisplay)\n\t\treturn m.PlutoForceDisplay\n\telse\n\t\tisdefined(m, :PlutoRunner) && parentmodule(m) === Main\n\tend\nend\n\n# \u2554\u2550\u2561 cf314b21-3f4f-4637-b1ce-ec1d5d5af966\nbegin\n\tif is_inside_pluto(@__MODULE__)\n\t\timport Pkg\n\t\tPkg.activate(\"..\")\n\t\tPkg.instantiate()\n\tend\n\timport HypertextLiteral: @htl\t\t\nend\n\n# \u2554\u2550\u2561 872b4877-30dd-4a92-a3c8-69eb50675dcb\npreish(x) = @htl(\"<pre-ish>$(x)</pre-ish>\")\n\n# \u2554\u2550\u2561 2f6e353d-2cdc-46d6-9727-01b0a6167ca0\nERROR_ON_UNKNOWN_EXPRESSION_TYPE = is_inside_pluto(@__MODULE__)\n\n# \u2554\u2550\u2561 22640a2f-ea38-4517-a4f3-7a65e60ffebe\n\"\"\"\n\t@displayonly expression\n\nMarks a expression as Pluto-only, which means that it won't be executed when running outside Pluto. Do not use this for your own projects.\n\"\"\"\nmacro skip_as_script(ex) is_inside_pluto(__module__) ? esc(ex) : nothing end\n\n# \u2554\u2550\u2561 fd8428a3-9fa3-471a-8b2d-5bbb8fdb3137\n@skip_as_script begin\n\tis_good_boy(x) = true\n\tis_bad_boy(x) = false\nend\n\n# \u2554\u2550\u2561 d97987a0-bdc0-46ed-a6a5-f35c1ce961dc\nex1 = @skip_as_script :(first([56,sqrt(9)]))\n\n# \u2554\u2550\u2561 69bfb438-7ecf-4f9b-8bc4-51e07aa46ef1\n@skip_as_script Core.eval(Module(), ex1)\n\n# \u2554\u2550\u2561 a3c41025-2f4a-4f9c-8577-72e4b7abbb98\nex2_inner = @skip_as_script ex1.args[2]\n\n# \u2554\u2550\u2561 3e79ff61-6532-4879-9402-86473aa7d960\nex2_inner_result = @skip_as_script Core.eval(Module(), ex2_inner)\n\n# \u2554\u2550\u2561 38e54516-cdf4-4c1d-815b-68e1e7a7f6f7\nex3 = Expr(:call, :first, Computed(ex2_inner_result))\n\n# \u2554\u2550\u2561 9bed78b6-5a8f-44ce-ab66-cab685daf264\nunwrap_computed(ex3)\n\n# \u2554\u2550\u2561 275c5f57-623d-439f-b09d-f7c745e0bed6\nex2 = @skip_as_script Expr(:call, :first, ex2_inner_result)\n\n# \u2554\u2550\u2561 94ebb761-21fb-4015-acb3-26310b19b0dc\n@skip_as_script macro return_one()\n\treturn 1\nend\n\n# \u2554\u2550\u2561 586826a5-d667-4035-9796-bd2db61498d6\n@skip_as_script macro expand_at_runtime(expr)\n\tquote\n\t\tmacroexpand($(__module__), $(QuoteNode(expr)))\n\tend\nend\n\n# \u2554\u2550\u2561 a8fd09d1-c5ca-47f3-8fb3-32d8aeef3e59\n@skip_as_script macro return_error(expr)\n\tquote\n\t\ttry\n\t\t\t$(esc(expr))\n\t\t\terror(\"Expected an error\")\n\t\tcatch error\n\t\t\terror\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 d414f840-4952-4de5-a565-7fdc81a94817\n\"The opposite of `@skip_as_script`\"\nmacro only_as_script(ex) is_inside_pluto(__module__) ? nothing : esc(ex) end\n\n# \u2554\u2550\u2561 64bf02a4-4fe3-424d-ae6e-5906c3395278\nmd\"\"\"\n## PlutoUI favourites\n\"\"\"\n\n# \u2554\u2550\u2561 f3916810-1911-48bd-936b-776206fcad54\nconst toc_css = \"\"\"\n@media screen and (min-width: 1081px) {\n\t.plutoui-toc.aside {\n\t\tposition:fixed; \n\t\tright: 1rem;\n\t\ttop: 5rem; \n\t\twidth:25%; \n\t\tpadding: 10px;\n\t\tborder: 3px solid rgba(0, 0, 0, 0.15);\n\t\tborder-radius: 10px;\n\t\tbox-shadow: 0 0 11px 0px #00000010;\n\t\t/* That is, viewport minus top minus Live Docs */\n\t\tmax-height: calc(100vh - 5rem - 56px);\n\t\toverflow: auto;\n\t\tz-index: 50;\n\t\tbackground: white;\n\t}\n}\n\n.plutoui-toc header {\n\tdisplay: block;\n\tfont-size: 1.5em;\n\tmargin-top: 0.67em;\n\tmargin-bottom: 0.67em;\n\tmargin-left: 0;\n\tmargin-right: 0;\n\tfont-weight: bold;\n\tborder-bottom: 2px solid rgba(0, 0, 0, 0.15);\n}\n\n.plutoui-toc section .toc-row {\n\twhite-space: nowrap;\n\toverflow: hidden;\n\ttext-overflow: ellipsis;\n\tpadding-bottom: 2px;\n}\n\n.highlight-pluto-cell-shoulder {\n\tbackground: rgba(0, 0, 0, 0.05);\n\tbackground-clip: padding-box;\n}\n\n.plutoui-toc section a {\n\ttext-decoration: none;\n\tfont-weight: normal;\n\tcolor: gray;\n}\n.plutoui-toc section a:hover {\n\tcolor: black;\n}\n\n.plutoui-toc.indent section a.H1 {\n\tfont-weight: 700;\n\tline-height: 1em;\n}\n\n.plutoui-toc.indent section a.H1 {\n\tpadding-left: 0px;\n}\n.plutoui-toc.indent section a.H2 {\n\tpadding-left: 10px;\n}\n.plutoui-toc.indent section a.H3 {\n\tpadding-left: 20px;\n}\n.plutoui-toc.indent section a.H4 {\n\tpadding-left: 30px;\n}\n.plutoui-toc.indent section a.H5 {\n\tpadding-left: 40px;\n}\n.plutoui-toc.indent section a.H6 {\n\tpadding-left: 50px;\n}\n\"\"\"\n\n# \u2554\u2550\u2561 122c27a5-a6e8-45ef-a968-b9b4b3f9ad09\nconst toc_js = toc -> \"\"\"\nconst getParentCell = el => el.closest(\"pluto-cell\")\n\nconst getHeaders = () => {\n\tconst depth = Math.max(1, Math.min(6, $(toc.depth))) // should be in range 1:6\n\tconst range = Array.from({length: depth}, (x, i) => i+1) // [1, ..., depth]\n\t\n\tconst selector = range.map(i => `pluto-notebook pluto-cell h\\${i}`).join(\",\")\n\treturn Array.from(document.querySelectorAll(selector))\n}\n\nconst indent = $(repr(toc.indent))\nconst aside = $(repr(toc.aside))\n\nconst render = (el) => html`\\${el.map(h => {\n\tconst parent_cell = getParentCell(h)\n\n\tconst a = html`<a \n\t\tclass=\"\\${h.nodeName}\" \n\t\thref=\"#\\${parent_cell.id}\"\n\t>\\${h.innerText}</a>`\n\t/* a.onmouseover=()=>{\n\t\tparent_cell.firstElementChild.classList.add(\n\t\t\t'highlight-pluto-cell-shoulder'\n\t\t)\n\t}\n\ta.onmouseout=() => {\n\t\tparent_cell.firstElementChild.classList.remove(\n\t\t\t'highlight-pluto-cell-shoulder'\n\t\t)\n\t} */\n\ta.onclick=(e) => {\n\t\te.preventDefault();\n\t\th.scrollIntoView({\n\t\t\tbehavior: 'smooth', \n\t\t\tblock: 'center'\n\t\t})\n\t}\n\n\treturn html`<div class=\"toc-row\">\\${a}</div>`\n})}`\n\nconst tocNode = html`<nav class=\"plutoui-toc\">\n\t<header>
  <meta name="description" content="AI快站提供HuggingFace模型高速下载服务，超过万个AI模型、数据集，下载速度10MB/s+，支持aria2、huggingface-cli等各种下载工具，永久免费。" />
  <meta name="keywords" content="HuggingFace镜像,HuggingFace加速,AI模型下载,大模型下载加速,huggingface.co镜像,人工智能模型,AI快站,ai 快站,ai快站,  huggingface高速下载站,Hugging Face加速,ai模型下载,huggingface国内加速,ai模型下载站" />
  <meta charset="utf-8">$(toc.title)</header>\n\t<section></section>\n</nav>`\ntocNode.classList.toggle(\"aside\", aside)\ntocNode.classList.toggle(\"indent\", aside)\n\nconst updateCallback = () => {\n\ttocNode.querySelector(\"section\").replaceWith(\n\t\thtml`<section>\\${render(getHeaders())}</section>`\n\t)\n}\nupdateCallback()\n\n\nconst notebook = document.querySelector(\"pluto-notebook\")\n\n\n// We have a mutationobserver for each cell:\nconst observers = {\n\tcurrent: [],\n}\n\nconst createCellObservers = () => {\n\tobservers.current.forEach((o) => o.disconnect())\n\tobservers.current = Array.from(notebook.querySelectorAll(\"pluto-cell\")).map(el => {\n\t\tconst o = new MutationObserver(updateCallback)\n\t\to.observe(el, {attributeFilter: [\"class\"]})\n\t\treturn o\n\t})\n}\ncreateCellObservers()\n\n// And one for the notebook's child list, which updates our cell observers:\nconst notebookObserver = new MutationObserver(() => {\n\tupdateCallback()\n\tcreateCellObservers()\n})\nnotebookObserver.observe(notebook, {childList: true})\n\n// And finally, an observer for the document.body classList, to make sure that the toc also works when if is loaded during notebook initialization\nconst bodyClassObserver = new MutationObserver(updateCallback)\nbodyClassObserver.observe(document.body, {attributeFilter: [\"class\"]})\n\ninvalidation.then(() => {\n\tnotebookObserver.disconnect()\n\tbodyClassObserver.disconnect()\n\tobservers.current.forEach((o) => o.disconnect())\n})\n\nreturn tocNode\n\"\"\"\n\n# \u2554\u2550\u2561 9126f47d-cbc7-411f-93bd-8684ba06c9e9\ntoc() = HTML(\"\"\"\n\t<script>\n\t$(toc_js((;title=\"Table of Contents\", indent=true, depth=3, aside=true)))\n\t</script>\n\t<style>\n\t$(toc_css)\n\t</style>\n\t\"\"\")\n\n# \u2554\u2550\u2561 c763ed72-82c9-445c-a8f7-a0c40982e4d9\n@skip_as_script toc()\n\n# \u2554\u2550\u2561 955705f9-c90d-495d-86b4-5f3b5bc9fc8e\nbegin\n\tstruct Slider\n\t\txs\n\tend\n\t\n\tBase.get(s::Slider) = first(s.xs)\n\t\n\tBase.show(io::IO, m::MIME\"text/html\", s::Slider) = show(io, m, @htl(\"<input type=range min=$(minimum(s.xs)) max=$(maximum(s.xs)) value=$(first(s.xs))>\"))\n\t\n\tSlider\nend\n\n# \u2554\u2550\u2561 187c3005-cd43-45a0-8cbd-bc96b9cb39da\nDump(x; maxdepth=8) = sprint(io -> dump(io, x; maxdepth=maxdepth)) |> Text\n\n# \u2554\u2550\u2561 a6709e08-964d-46ea-9813-2c70a834824b\n@skip_as_script Dump(ex1)\n\n# \u2554\u2550\u2561 10803c0d-d0a5-45c5-b7ef-9659e441df69\n@skip_as_script Dump(ex2)\n\n# \u2554\u2550\u2561 411271a6-4236-45e2-ab34-f26410108821\nDump(ex3)\n\n# \u2554\u2550\u2561 6c0156a9-7281-4326-9e1f-989efa73bb7b\nbegin\n\tstruct Show{M <: MIME}\n\t\tmime::M\n\t\tdata\n\tend\n\n\tBase.show(io::IO, ::M, x::Show{M}) where M <: MIME = write(io, x.data)\n\t\n\tShow\nend\n\n# \u2554\u2550\u2561 e46cf3e0-aa15-4c17-a925-3e9fc5109d54\n@skip_as_script Hannes = let\n\turl = \"https://user-images.githubusercontent.com/6933510/116753174-fa40ab80-aa06-11eb-94d7-88f4171970b2.jpeg\"\n\tdata = read(download(url))\n\tShow(MIME\"image/jpg\"(), data)\nend;\n\n# \u2554\u2550\u2561 6f5ba692-4b6a-405a-8cd3-1a8f9cc06611\nplot(args...; kwargs...) = Hannes\n\n# \u2554\u2550\u2561 5b70aaf1-9623-4f55-b055-4263ed8be31d\n@skip_as_script Floep = let\n\turl = \"https://user-images.githubusercontent.com/6933510/116753861-142ebe00-aa08-11eb-8ce8-684af1098935.jpeg\"\n\tdata = read(download(url))\n\tShow(MIME\"image/jpg\"(), data)\nend;\n\n# \u2554\u2550\u2561 bf2abe01-6ae0-4066-8704-12f64e04511b\n@skip_as_script friends = Any[Hannes, Floep];\n\n# \u2554\u2550\u2561 8d3df0c0-eb48-4dae-97a8-8c01f0b0a34b\nmd\"## Pretty printing code\"\n\n# \u2554\u2550\u2561 dbd41240-9fc4-4e25-8b25-2b68afa679f2\nbegin\n\tstruct EscapeExpr\n\t\texpr\n\tend\n\tfunction Base.show(io::IO, val::EscapeExpr)\n\t\tprint(io, \"\\$(esc(\")\n\t\tprint(io, val.expr)\n\t\tprint(io, \"))\")\n\tend\n\tEscapeExpr\nend\n\n# \u2554\u2550\u2561 7cc07d1b-7757-4428-8028-dc892bf05f2f\nmove_escape_calls_up(e::Expr) = begin\n\t\n\targs = move_escape_calls_up.(e.args)\n\tif all(x -> Meta.isexpr(x, :escape, 1), args)\n\t\tExpr(:escape, Expr(e.head, (arg.args[1] for arg in args)...))\n\telse\n\t\tExpr(e.head, args...)\n\tend\nend\n\n# \u2554\u2550\u2561 e0837338-e657-4bdc-ae91-1de9224da78d\nmove_escape_calls_up(x) = x\n\n# \u2554\u2550\u2561 64df4678-0721-4911-8289-fb18f55e6657\nescape_syntax_to_esc_call(e::Expr) = if e.head === :escape\n\tEscapeExpr(e.args[1])\nelse\n\tExpr(e.head, (escape_syntax_to_esc_call(x) for x in e.args)...)\nend\n\n# \u2554\u2550\u2561 58845ff9-821b-45d4-b5ec-96e1949bb277\nescape_syntax_to_esc_call(x) = x\n\n# \u2554\u2550\u2561 4d5f44e4-85e9-4985-9b76-73be5e097186\nremove_linenums(e::Expr) = if e.head === :macrocall\n\tExpr(\n\t\te.head,\n\t\t(\n\t\t\tx isa LineNumberNode ?\n\t\t\tLineNumberNode(0, nothing) :\n\t\t\tremove_linenums(x)\n\t\t\tfor x\n\t\t\tin e.args\n\t\t)...,\n\t)\nelse\n\tExpr(e.head, (remove_linenums(x) for x in e.args if !(x isa LineNumberNode))...)\nend\n\n# \u2554\u2550\u2561 dd495e00-d74d-47d4-a5d5-422fb147ec3b\nremove_linenums(x) = x\n\n# \u2554\u2550\u2561 e414c28a-8111-4821-ab25-21aff8289d26\nfunction remove_singleline_blocks(e::Expr)\n\tif Meta.isexpr(e, :quote) || Meta.isexpr(e, :macrocall)\n\t\te\n\telseif Meta.isexpr(e, :block, 1)\n\t\tremove_singleline_blocks(e.args[1])\n\telse\n\t\tExpr(\n\t\t\te.head, \n\t\t\t(remove_singleline_blocks(a) for a in e.args)...\n\t\t)\n\tend\nend\n\n# \u2554\u2550\u2561 c13e0f00-d3c4-4f1d-9531-84ed480c81f3\nremove_singleline_blocks(x) = x\n\n# \u2554\u2550\u2561 b765dbfe-4e58-4bb9-b1d6-aa4378d4e9c9\nexpr_to_str(e; mod=@__MODULE__(), context::IO=devnull) = let\n\tComputed;\n\t\n\tprinted = sprint() do io\n\t\t\n\t\tBase.print(\n\t\t\tIOContext(IOContext(io, :module => mod), context), \n\t\t\tremove_singleline_blocks(escape_syntax_to_esc_call(move_escape_calls_up(remove_linenums(e))))\n\t\t)\n\tend\n\treplace(printed, r\"#= line 0 =# ?\" => \"\")\nend\n\n# \u2554\u2550\u2561 227129bc-4415-4240-ad55-815bde65a5a1\nfunction Base.showerror(io::IO, error::CantStepifyThisYetException)\n\tprint(io, \"CantStepifyThisYetException: Can't make `$(expr_to_str(error.expr))` into separate steps yet\")\nend\n\n# \u2554\u2550\u2561 ab0a19b8-cf7c-4c4f-802a-f85eef81fc02\nfunction Base.show(io::IO, m::MIME\"text/html\", sd::SlottedDisplay)\n\n\td, e = sd.d, sd.e\n\t\n\ts = expr_to_str(e; context=io)\n\t\n\tlines = split(replace(s, r\"#= line 0 =# ?\" => \"\"), \"\\n\")\n\t\n\tr = r\"\\_\\_slot[a-z]{16}\\_\\_\"\n\tembed_display\n\th = @htl(\"\"\"<slotted-code>\n\t\t$(\n\tmap(lines) do l\n\t\tkeys = [Symbol(m.match) for m in eachmatch(r, l)]\n\t\trest = split(l, r; keepempty=true)\n\t\t\n\t\tresult = vcat((\n\t\t\t[(isempty(r) ? @htl(\"\") : preish(r)), embed_display(d[k].x)]\n\t\t\tfor (r,k) in zip(rest, keys)\n\t\t\t)...)\n\t\t\n\t\tpush!(result, preish(last(rest)))\n\t\t\n\t\t@htl(\"<line-like>$(result)</line-like>\")\n\tend\n\t)\n\t\t</slotted-code>\"\"\")\n\tshow(io, m, h)\nend\n\n# \u2554\u2550\u2561 ef6fc423-f1b1-4dcb-a059-276121391bc6\nprettycolors(e) = Markdown.MD([Markdown.Code(\"julia\", expr_to_str(e))])\n\n# \u2554\u2550\u2561 0f31dd2e-0331-4d4c-8db5-9ce188cd3730\n@skip_as_script [lens_to_getter(:source, [FieldLens(:x), PropertyLens(:y)])] .|> prettycolors\n\n# \u2554\u2550\u2561 cecba3e6-98e8-408a-97dd-96b67c4f42cf\n@skip_as_script [lens_to_setter(:dest, [FieldLens(:x), PropertyLens(:y)], :value)] .|> prettycolors\n\n# \u2554\u2550\u2561 7e6c2162-97e9-4835-b650-52c9723c327f\nmd\"## Utils\"\n\n# \u2554\u2550\u2561 1ac164c8-88fc-4a87-a194-60ef616fb399\nflatmap(args...) = vcat(map(args...)...)\n\n# \u2554\u2550\u2561 1c1b64b1-107e-4d43-9ce2-569c3034017e\nfunction expr_lenses_for_quoted(e::Expr, code_loweredish_with_lenses)::Frames\n\tif e.head == :$\n\t\tframes = code_loweredish_with_lenses(e.args[1])\n\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(1)], frames)\n\telse\n\t\targument_frames = flatmap(enumerate(e.args)) do (i, arg)\n\t\t\tframes = expr_lenses_for_quoted(arg, code_loweredish_with_lenses)\n\t\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(i)], frames)\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 ce90612e-ffc1-4e30-9d89-531f11fd75eb\n\"\"\"\n    code_loweredish_with_lenses(e::Expr)::Vector{ExprWithLens}\n\nTransforms an expression into a set of list of expressions that would be executed one after eachother. It gives every expression a lens referencing where it is inside the original expression. This way you can execute each expr, and then put the result in the expression to create the step-by-step execution.\n\nIt doesn't \"dedupe\" the expressions, so when you run the last expression in the list (which will just be the original expression), it doesn't take advantage of any previously run expressions in the same list. You'll have to do that later, manually.\n\nIn PlutoTest this is done with [TODO](@ref)\n\"\"\"\nfunction code_loweredish_with_lenses(e::Expr)::Frames\n\tif e.head == :kw\n\t\tframes = code_loweredish_with_lenses(e.args[2])\n\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(2)], frames)\n\telseif e.head == :(=)\n\t\t# This was getting closer, but the whole thing still is quite hard...\n\t\t# so for now any assignment just throws :D\n\t\t# We need quite some smartness in build_step_by_step_blocks.\n\t\tthrow(CantStepifyThisYetException(e))\n\t\t\n\t\tframes = code_loweredish_with_lenses(e.args[2])\n\t\tlens = [FieldLens(:args), PropertyLens(2)]\n\t\t[\n\t\t\tapply_lens_to_frames(lens, frames)...,\n\t\t\tExprWithLens(expr=e.args[2], lens=lens, expr_to_show=e),\n\t\t\t# It now adds the whole `x = ...` expression as well,\n\t\t\t# which doesn't look that good in the output...\n\t\t\t# But we'll have to live with it for now\n\t\t]\n\telseif e.head == :parameters\n\t\tflatmap(enumerate(e.args)) do (i, arg)\n\t\t\tframes = code_loweredish_with_lenses(arg)\n\t\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(i)], frames)\n\t\tend\n\telseif e.head == :quote\n\t\tframes = expr_lenses_for_quoted(e.args[1], code_loweredish_with_lenses)\n\t\targument_frames = apply_lens_to_frames(\n\t\t\t[FieldLens(:args), PropertyLens(1)],\n\t\t\tframes\n\t\t)\n\t\t[argument_frames..., ExprWithLens(expr=e, lens=[])]\n\telseif e.head == :...\n\t\tframes = code_loweredish_with_lenses(e.args[1])\n\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(1)], frames)\n\telseif e.head == :macrocall || e.head == :ref\n\t\t[ExprWithLens(expr=e, lens=[])]\n\telseif e.head == :call\n\t\t# With calls we don't want to dive into the callee if it is just a symbol\n\t\t# (This would expand everything like x == y to #function(:==)(x,y) which\n\t\t# is definitely not what we want)\n\t\tpossibly_callee_frames = if e.args[begin] isa Symbol\n\t\t\t[]\n\t\telse\n\t\t\tframes = code_loweredish_with_lenses(e.args[begin])\n\t\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(firstindex(e.args))], frames)\n\t\tend\n\t\t\n\t\targument_frames = flatmap(enumerate(e.args[begin+1:end])) do (i, arg)\n\t\t\tframes = code_loweredish_with_lenses(arg)\n\t\t\t# @info \"III\" i arg\n\t\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(i+1)], frames)\n\t\tend\n\t\t\n\t\t[possibly_callee_frames..., argument_frames..., ExprWithLens(expr=e, lens=[])]\n\n\telseif (\n\t\te.head == :begin ||\n\t\te.head == :block ||\n\t\te.head == :vect ||\n\t\te.head == :string ||\n\t\te.head == :. ||\n\t\te.head == :tuple ||\n\t\te.head == :let\n\t)\n\t\targument_frames = flatmap(enumerate(e.args)) do (i, arg)\n\t\t\tframes = code_loweredish_with_lenses(arg)\n\t\t\tapply_lens_to_frames([FieldLens(:args), PropertyLens(i)], frames)\n\t\tend\n\t\t\n\t\t[argument_frames..., ExprWithLens(expr=e, lens=[])]\n\telseif (\n\t\te.head == :if ||\n\t\te.head == :elseif ||\n\t\te.head == :else ||\n\t\te.head == :&& ||\n\t\te.head == :|| ||\n\t\te.head == :try ||\n\t\te.head == :catch ||\n\t\te.head == :finally\n\t)\n\t\tif ERROR_ON_UNKNOWN_EXPRESSION_TYPE\n\t\t\tthrow(CantStepifyThisYetException(e))\n\t\telse\n\t\t\t[ExprWithLens(expr=e, lens=[])]\n\t\tend\n\telse\n\t\tif ERROR_ON_UNKNOWN_EXPRESSION_TYPE\n\t\t\tthrow(CantStepifyThisYetException(e))\n\t\telse\n\t\t\t[ExprWithLens(expr=e, lens=[])]\n\t\tend\n\tend\nend;\n\n# \u2554\u2550\u2561 e1c306e3-0a47-4149-a9fb-ec7ab380fa11\n\"\"\"\n\tstep_by_step(expr::Expr)\n\nThe preparing for step-by-step testing happens in two steps itself.\n\nFirst there is [`code_loweredish_with_lenses`](@ref) which takes an expression and splits it up in [`ExprWithLens`](@ref)s. These are separate expressions with a lens specifying where in the original expression the result should be placed.\n\nSecond part is combining all those expressions into a block that gradually executes those separate parts, and at each step saves the whole expression to be shown. That's what [`build_step_by_step_blocks`](@ref) is for.\n\nThis functions combines these two. This is the main function used inside the test macro. The reason it is a separate function and not a macro on its own, is because macro hygiene is weird... \n\"\"\"\nfunction step_by_step(expr)\t\n\tlowered = code_loweredish_with_lenses(expr)\n\texpr_ref_lens = gensym(\"expr_ref\")\n\tsteps_lens = gensym(\"steps\")\n\t\n\tquote\n\t\tbegin\n\t\t\ttry\n\t\t\t\texpr = $(QuoteNode(expr))\n\t\t\t\t$steps_lens = Any[expr]\n\t\t\t\t$expr_ref_lens = Ref{Any}(expr)\n\t\t\t\t$(build_step_by_step_blocks(lowered;\n\t\t\t\t\texpr_ref_lens=expr_ref_lens,\n\t\t\t\t\tsteps_lens=steps_lens,\n\t\t\t\t)...)\n\t\t\t\t$steps_lens\n\t\t\tcatch error\n\t\t\t\terror.steps\n\t\t\tend\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 b6e8a170-12cc-4d97-905d-274e2609bfd8\nfunction test(expr, extra_args...)\n\tstep_by_step\n\tTest.test_expr!(\"\", expr, extra_args...)\n\t\t\n\tquote\n\t\tlocal expr_raw = $(QuoteNode(expr))\n\t\ttry\t\t\t\n\t\t\tsteps = $(step_by_step(expr))\n\n\t\t\tresult = unwrap_computed(last(steps))\n\t\t\t\n\t\t\tif result === true\n\t\t\t\tCorrectCall(expr_raw, steps)\n\t\t\telse\n\t\t\t\tWrongCall(expr_raw, steps)\n\t\t\tend\n\t\tcatch error\n\t\t\tif error isa PartialEvaluatedException\n\t\t\t\tErrorCall(\n\t\t\t\t\texpr=expr_raw,\n\t\t\t\t\tsteps=error.steps,\n\t\t\t\t\terror=error.error,\n\t\t\t\t)\n\t\t\telse\n\t\t\t\trethrow(error)\n\t\t\tend\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 d7dc79e6-1f58-4414-aeef-667bdb0dd200\nmacro pretty_step_by_step(e)\n\tquote\n\t\tresulting_expressions = try\n\t\t\t$(step_by_step(e))\n\t\tcatch error\n\t\t\tif error isa PartialEvaluatedException\n\t\t\t\terror.steps\n\t\t\telse\n\t\t\t\trethrow(error)\n\t\t\tend\n\t\tend \n\t\t\n\t\tresulting_expressions .|> prettycolors\n\tend\nend\n\n# \u2554\u2550\u2561 ba4a5762-33da-40e6-94fa-cca9befc6d5a\nexample_equals = @skip_as_script let\n\t@pretty_step_by_step sqrt(sqrt(16)) == 4\nend\n\n# \u2554\u2550\u2561 9101631b-81ca-4c7c-94da-81d9e106df78\nexample_call_spread = @skip_as_script let\n\t@pretty_step_by_step max([1,2,3]...) != min([1,2,3]...)\nend\n\n# \u2554\u2550\u2561 3f0e5a49-5eec-42cd-bd2c-254b277840bf\nexample_show_variable_value = @skip_as_script let\n\tx = [1,2,3]\n\t@pretty_step_by_step x == [1,2,3]\nend\n\n# \u2554\u2550\u2561 fc26d26a-a9a5-4646-b85b-12eac66d96cb\nexample_show_thrown_error = @skip_as_script let\n\t@pretty_step_by_step sqrt(sqrt(16) - 5)\nend\n\n# \u2554\u2550\u2561 312ef6a6-55aa-4913-9416-15e79b4e3362\nexample_with_nested_macro = @skip_as_script let\n\t@pretty_step_by_step @return_one() + 2 == 3\nend\n\n# \u2554\u2550\u2561 1aa319c8-5e1d-4dd9-ae22-ad99e46e7b4d\nexample_keyword_arguments = @skip_as_script let\n\t@pretty_step_by_step round(sqrt(2), digits=Int(sqrt(16)))\nend\n\n# \u2554\u2550\u2561 605d2481-23be-4ad9-82c9-e375b7be8669\n# Seems very similar to `example_keyword_arguments`, but this one\n# has a `;`, which makes a liiiitle bit different AST\nexample_keyword_arguments_explicit = @skip_as_script let\n\t@pretty_step_by_step round(sqrt(2); digits=Int(sqrt(16)))\nend\n\n# \u2554\u2550\u2561 60a398c9-9fe8-4b90-b863-1568183641d9\nexample_returned_function = @skip_as_script let\n\tfunction_that_returns_function = () -> function X() 10 end\n\t@pretty_step_by_step function_that_returns_function()() == 10\nend\n\n# \u2554\u2550\u2561 a661e172-6afb-42ff-bd43-bb5b787ee5ed\nmacro eval_step_by_step(e)\n\tstep_by_step(e)\nend\n\n# \u2554\u2550\u2561 b4b317d7-bed1-489c-9650-8d336e330689\nrs = @skip_as_script @eval_step_by_step(begin\n\t\t(1+2) + (7-6)\n\t\tplot(2000 .+ 30 .* rand(2+2))\n\t\t4+5\n\t\tsqrt(sqrt(sqrt(5)))\n\tend) .|> SlottedDisplay\n\n# \u2554\u2550\u2561 93ed973f-daf6-408b-9d4b-d53495418610\n@skip_as_script @bind rindex Slider(eachindex(rs))\n\n# \u2554\u2550\u2561 dea898a0-1904-4d09-ad0b-6915008fe946\n@skip_as_script rs[rindex]\n\n# \u2554\u2550\u2561 b0ab9327-8240-4d34-bdd9-3f8f5117bb29\nstruct PlutoStylesheet\n\tcode\nend\n\n# \u2554\u2550\u2561 8a2e8348-49cf-4855-b5b3-cdee33e5ed67\n# const pluto_test_css = PlutoStylesheet(\"\"\"\npluto_test_css = PlutoStylesheet(\"\"\"\npt-dot {\n\tflex: 0 0 auto;\n\tbackground: grey;\n\twidth: 1em;\n\theight: 1em;\n\tbottom: -.1em;\n\tborder-radius: 100%;\n\tmargin-right: .7em;\n\tdisplay: block;\n\tposition: relative;\n\tcursor: pointer;\n}\n\npt-dot.floating {\n\tposition: fixed;\n\tz-index: 60;\n\tvisibility: hidden;\n\ttransition: transform linear 240ms;\n\topacity: .8;\n}\n.show-top-float > pt-dot.floating.top,\n.show-bottom-float > pt-dot.floating.bottom {\n\tvisibility: visible;\n}\n\npt-dot.floating.top {\n\ttop: 5px;\n}\npt-dot.floating.bottom {\n\tbottom: 5px;\n}\n\n\n.fail > pt-dot {\n\tbackground: #f75d5d;\n\n}\n.pass > pt-dot {\n\tbackground: #56a038;\n}\n\n@keyframes fadeout {\n    0% { opacity: 1;}\n    100% { opacity: 0; pointer-events: none;}\n}\n\n\n.pass > pt-dot.floating {\n    animation: fadeout 2s;\n\n\tanimation-fill-mode: both;\n\tanimation-delay: 2s;\n\n\t/*opacity: 0.4;*/\n}\n\n\n.pluto-test {\n\tfont-family: \"JuliaMono\", monospace;\n\tfont-size: 0.75rem;\n\twhite-space: normal;\n\tpadding: 4px;\n\n\tmin-height: 25px;\n}\n\n\n.pluto-test.pass {\n\tcolor: rgba(0, 0, 0, 0.5);\n}\n\n@media (prefers-color-scheme: dark) {\n\t.pluto-test.pass {\n\t\tcolor: rgba(200, 200, 200, 0.5);\n\t}\n}\n\n.pluto-test.fail {\nbackground: linear-gradient(90deg, #ff2e2e14, transparent);\nborder-radius: 7px;\n}\n\n\n.pluto-test>.arg_result {\n\tflex: 0 0 auto;\n}\n\n.pluto-test>.arg_result>div,\n.pluto-test>.arg_result>div>pluto-display>div {\n\tdisplay: inline-flex;\n}\n\n\n.pluto-test>.comma {\n\tmargin-right: .5em;\n}\n\n.pluto-test.call>code {\n\tpadding: 0px;\n}\n\n.pluto-test.call.infix-operator>div {\n\toverflow-x: auto;\n}\n\n.pluto-test {\n\tdisplay: flex;\n\talign-items: baseline;\n}\n\n.pluto-test.call.infix-operator>.fname {\n\tmargin: 0px .6em;\n\t/*color: darkred;*/\n}\n\n\n/* expanding */\n\n\n.pluto-test:not(.expanded) {\n\tcursor: pointer;\n}\n\n.pluto-test:not(.expanded) > p-frame-viewer > p-frame-controls {\n\tdisplay: none;\n\t\n}\n\n.pluto-test.expanded > p-frame-viewer {\n    max-width: 100%;\n}\n.pluto-test.expanded > p-frame-viewer > p-frames > slotted-code > line-like {\n\tflex-wrap: wrap;\n}\n.pluto-test.expanded > p-frame-viewer > p-frames > slotted-code > line-like > pluto-display[mime=\"application/vnd.pluto.tree+object\"] {\n\t/*flex-basis: 100%;*/\n}\n\"\"\")\n\n# \u2554\u2550\u2561 42671258-07a0-4015-8f47-4b3032595f08\n# const frames_css = PlutoStylesheet(\"\"\"\nframes_css = PlutoStylesheet(\"\"\"\np-frame-viewer {\n\tdisplay: inline-flex;\n\tflex-direction: column;\n}\np-frames,\np-frame-controls {\n\tdisplay: inline-flex;\n}\n\"\"\")\n\n# \u2554\u2550\u2561 e968fc57-d850-4e2d-9410-8777d03b7b3c\nfunction frames(fs::Vector; startframe::Union{Nothing,Int}=nothing)\n\tl = length(fs)\n\t\n\tstartframe = if isnothing(startframe)\n\t\tl > 2 ? l - 1 : l\n\telse\n\t\tstartframe\n\tend\n\t\n\t@htl(\"\"\"<p-frame-viewer>\n\t\t<p-frames>\n\t\t$(fs)\n\t\t</p-frames>\n\t\t\n\t\t<p-frame-controls>\n\t\t\t<img src=\"https://cdn.jsdelivr.net/gh/ionic-team/ionicons@5.5.1/src/svg/time-outline.svg\" style=\"width: 1em; height: 1em; transform: scale(-1,1); opacity: .5; margin-left: 2em;\">\n\t\t\t<input class=\"timescrub\" style=\"filter: hue-rotate(149deg) grayscale(.9);\" type=range min=1 max=$(l) value=$(startframe)>\n\t\t</p-frame-controls>\n\t\t\n\t\t\n\t\t<script>\n\t\tconst div = currentScript.parentElement\n\t\t\n\t\tconst input = div.querySelector(\":scope > p-frame-controls > input.timescrub\")\n\t\tconst frames = div.querySelector(\":scope > p-frames\")\n\t\t\n\t\tconst setviz = () => {\n\t\t\tArray.from(frames.children).forEach((f,i) => {\n\t\t\t\tf.style.display = i + 1 === input.valueAsNumber ? \"inherit\" : \"none\"\n\t\t\t})\n\t\t}\n\t\t\n\t\tsetviz()\n\t\t\n\t\tinput.addEventListener(\"input\", setviz)\n\t\t</script>\n\t\n\t\t<style>\n\t\t$(frames_css)\n\t\t</style>\n\t</p-frame-viewer>\"\"\")\nend\n\n# \u2554\u2550\u2561 74c19786-1ba7-4865-a993-590a779ae564\n@skip_as_script frames(rs)\n\n# \u2554\u2550\u2561 8480d0d7-bdf7-468d-9344-5b789e33921c\n# const slotted_code_css = PlutoStylesheet(\"\"\"\nslotted_code_css = PlutoStylesheet(\"\"\"\nslotted-code {\n\tfont-family: \"JuliaMono\", monospace;\n\tfont-size: .75rem;\n\tdisplay: flex;\n\tflex-direction: column;\n}\npre-ish {\n\twhite-space: pre;\n}\n\nline-like {\n\tdisplay: flex;\n\talign-items: baseline;\n}\n\"\"\")\n\n# \u2554\u2550\u2561 b273d3d3-648f-4d34-94e7-e49277d4ba29\nwith_slotted_css(x) = @htl(\"\"\"\n\t$(x)\n\t<style>\n\t$(slotted_code_css)\n\t</style>\n\t\"\"\")\n\n# \u2554\u2550\u2561 326f7661-3482-4bf2-a97b-57cc7ac60ee2\nmacro visual_debug(expr)\n\tframes\n\tSlottedDisplay\n\tvar\"@eval_step_by_step\"\n\twith_slotted_css\n\tquote\n\t\t@eval_step_by_step($(expr)) .|> SlottedDisplay |> frames |> with_slotted_css\n\tend\nend\n\n# \u2554\u2550\u2561 a2cbb0c3-23b9-4091-9ca7-5ba96e85e3a3\n@skip_as_script @visual_debug begin\n\t(1+2) + (7-6)\n\tplot(2000 .+ 30 .* rand(2+2))\n\t4+5\n\tsqrt(sqrt(sqrt(5)))\n\tmd\"#### Wow\"\nend\n\n# \u2554\u2550\u2561 1e619ca9-e00f-46d0-b327-85b33929787f\nfunction Base.show(io::IO, mime::MIME\"text/html\", stylesheet::PlutoStylesheet)\n\t# show(io, mime, md\"`<style>...`\")\n\tprint(io, \"Stylesheet\")\nend\n\n# \u2554\u2550\u2561 9c3f6eab-b1c3-4607-add8-d6d7e468c11a\nbegin\n\tshow_for_test_result_should_be_defined_before_test_macro = true\n\t\n\tfunction Base.show(io::IO, m::MIME\"text/html\", call::Union{WrongCall,CorrectCall,ErrorCall})\n\t\t\n\n\t\tinfix = if Meta.isexpr(call.expr, :call)\n\t\t\tfname = call.expr.args[1]\n\t\t\tif fname isa Symbol\n\t\t\t\tMeta.isbinaryoperator(fname)\n\t\t\telse\n\t\t\t\tfalse\n\t\t\tend\n\t\telse\n\t\t\tfalse\n\t\tend\n\t\t\n\t\tclasses = [\n\t\t\t\"pluto-test\", \n\t\t\t\"call\",\n\t\t\t(isa(call,CorrectCall) ? \"correct\" : \"wrong\"),\n\t\t\t(isa(call,Pass) ? \"pass\" : \"fail\"),\n\t\t\tinfix ? \"infix-operator\" : \"prefix-operator\",\n\t\t]\n\t\t\n\t\tresult = @htl(\"\"\"\n\t\t\n\t\t<div class=$(classes)>\n\t\t\t<script>\n\t\t\t\n\t\t\tconst div = currentScript.parentElement\n\t\t\tdiv.addEventListener(\"click\", (e) => {\n\t\t\t\tif(!div.classList.contains(\"expanded\") || e.target.closest(\"pt-dot:not(.floating)\") != null){\n\t\t\t\t\tdiv.classList.toggle(\"expanded\")\n\t\t\t\t\te.stopPropagation()\n\t\t\t\t}\n\t\t\t})\n\t\t\t\n\t\t\tconst throttled = (f, delay) => {\n\t\t\t\tconst waiting = { current: false }\n\t\t\t\treturn () => {\n\t\t\t\t\tif (!waiting.current) {\n\t\t\t\t\t\tf()\n\t\t\t\t\t\twaiting.current = true\n\t\t\t\t\t\tsetTimeout(() => {\n\t\t\t\t\t\t\tf()\n\t\t\t\t\t\t\twaiting.current = false\n\t\t\t\t\t\t}, delay)\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tconst dot = div.querySelector(\"pt-dot\")\n\t\t\tconst dot_top = div.querySelector(\"pt-dot.top\")\n\t\t\tconst dot_bot = div.querySelector(\"pt-dot.bottom\")\n\t\t\t\n\t\t\tconst is_chrome = /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)\n\t\t\tconst is_firefox = /Firefox/.test(navigator.userAgent) && /Mozilla/.test(navigator.userAgent)\n\t\t\t\n\t\t\t// safari is too slow\n\t\t\t\n\t\t\tif(is_chrome || is_firefox){\n\n\t\t\tconst intersect = (r) => {\n\t\t\t\tconst topdistance = r.top\n\t\t\t\tconst botdistance = window.innerHeight - r.bottom\n\t\t\t\n\t\t\t\t\n\t\t\t\tconst t = (x) => `translate(\\${2*Math.sqrt(Math.max(0,-50-x))}px,0)`\n\t\t\t\tdot_top.style.transform = t(topdistance)\n\t\t\t\tdot_bot.style.transform = t(botdistance)\n\n\t\t\t\tdiv.classList.toggle(\"show-top-float\", topdistance < 4)\n\t\t\t\tdiv.classList.toggle(\"show-bottom-float\", botdistance < 4)\n\t\t\t}\n\t\t\t\n\t\t\tintersect(dot.getBoundingClientRect())\n\t\t\t\n\t\t\tconst scroll_listener = throttled(() => {\n\t\t\t\tintersect(dot.getBoundingClientRect())\n\t\t\t}, 200)\n\t\t\t\n\t\t\twindow.addEventListener(\"scroll\", scroll_listener)\n\n\t\t\tlet observer = new IntersectionObserver((es) => {\n\t\t\t\tconst e = es[0]\n\t\t\t\tintersect(e.boundingClientRect)\n\t\t\t},  {\n\t\t\t  rootMargin: '-4px',\n\t\t\t  threshold: 1.0\n\t\t\t});\n\n\t\t\tobserver.observe(dot)\n\t\t\tinvalidation.then(() => {\n\t\t\t\twindow.removeEventListener(\"scroll\", scroll_listener)\n\t\t\t\tobserver.unobserve(dot)\n\t\t\t})\n\t\t\t\n\t\t\tArray.from(div.querySelectorAll(\"pt-dot.floating\")).forEach(e => {\n\t\t\t\te.addEventListener(\"click\", () => dot.scrollIntoView({behavior: \"smooth\", block: \"center\", inline: \"nearest\"}))\n\t\t\t})\n\t\t\t\n\t\t\t}\n\t\t\t\n\t\t\t</script>\n\t\t\t<pt-dot></pt-dot>\n\t\t\t<pt-dot class=\"floating top\"></pt-dot>\n\t\t\t<pt-dot class=\"floating bottom\"></pt-dot>\n\t\t\n\t\t\t$(frames(\n\t\t\t\tSlottedDisplay.( call.steps);\n\t\t\t\tstartframe=isa(call,Pass) ? 1 : nothing,\n\t\t\t))\n\t\t</div>\n\t\t<style>\n\t\t$(pluto_test_css.code)\n\t\t$(slotted_code_css.code)\n\t\t</style>\n\t\t\n\t\t\n\t\t\"\"\")\n\t\tBase.show(io, m, result)\n\tend\n\n\tmd\"\"\"\n\t```julia\n\tfunction Base.show(io::IO, m::MIME\"text/html\", call::Union{WrongCall,CorrectCall,ErrorCall})\n\t```\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 a4a067b5-8b4b-4846-b986-0417d83cba48\nmacro test(main_expr, expr...)\n\tshow_for_test_result_should_be_defined_before_test_macro\n\t\n\tsource = QuoteNode(__source__)\n\torig_expr = QuoteNode(main_expr)\n\t\n\tquote\n\t\tpluto_result, julia_test_result = try\n\t\t\tresult = $(test(main_expr, expr...))\n\t\t\t(result, Test.Returned(result isa CorrectCall, \"\", $(source)))\n\t\tcatch err\t\n\t\t\trethrow(err)\n\t\t\t(err, Test.Threw(err, Base.catch_stack(), $(source)))\n\t\tend\n\n\t\ttry\n\t\t\tTest.do_test(julia_test_result, $(orig_expr))\n\t\tcatch; end\n\t\t\n\t\tpluto_result\n\tend\nend\n\n# \u2554\u2550\u2561 73d74146-8f60-4388-aaba-0dfe4215cb5d\n@skip_as_script @test sqrt(20-11) == 3\n\n# \u2554\u2550\u2561 71b22e76-2b50-4d16-85f6-9dad0415630e\n@skip_as_script @test iseven(123 + 7^3)\n\n# \u2554\u2550\u2561 6762ed72-f422-43a9-a782-de78f739c0ae\n@skip_as_script @test 4+4 \u2208 [1:7...]\n\n# \u2554\u2550\u2561 f77275b9-90aa-4e07-a608-981b5df727af\n@skip_as_script @test is_good_boy(first(friends))\n\n# \u2554\u2550\u2561 de7e71e2-e5e9-4f05-a61d-658f01a3e937\n@skip_as_script @test is_bad_boy(first(friends))\n\n# \u2554\u2550\u2561 37529063-8ee9-46a6-85cc-94db292da541\n@skip_as_script @test sqrt(sqrt(16)) == sqrt(2)\n\n# \u2554\u2550\u2561 89f78031-1c54-468b-9ab8-7410c51df10e\nexport @test\n\n# \u2554\u2550\u2561 97eb4444-a22c-47f2-9247-3bce6d7e179e\nexample_longer_fn_name =  @skip_as_script begin\n\t@test Test.Pass(:symbol, 10, 10, 10) isa Test.Pass\nend\n\n# \u2554\u2550\u2561 24f2eb92-5fd7-429b-b2ea-a987195c6edb\nexample_cant_stepify_assignments = @skip_as_script let\n\t@test @return_error(begin\n\t\t@expand_at_runtime @test begin\n\t\t\tx = 1 + 3\n\t\t\tx\n\t\tend\n\tend).error isa CantStepifyThisYetException\nend\n\n# \u2554\u2550\u2561 bedc3586-6b85-4de2-9ea1-79d842db6b56\nexample_cant_stepify_try_catch = @skip_as_script let\n\t@test @return_error(begin\n\t\t@expand_at_runtime @test try\n\t\t\t0 / 0\n\t\tcatch error\n\t\t\t\"Oops\"\n\t\tend\n\tend).error isa CantStepifyThisYetException\nend\n\n# \u2554\u2550\u2561 42f34453-9935-4e50-a62b-9dcf31d72601\nexample_cant_stepify_if_else = @skip_as_script let\n\t@test @return_error(begin\n\t\t@expand_at_runtime @test if 4 > 5\n\t\t\t\"Yeahhh\"\n\t\telse\n\t\t\t\"Nohhhh\"\n\t\tend\n\tend).error isa CantStepifyThisYetException\nend\n\n# \u2554\u2550\u2561 1b869c21-b8cd-4aaa-91a9-370c1b7a3d32\n@skip_as_script @test quote\n\tbegin begin begin 123 end end end\nend |> remove_linenums |> remove_singleline_blocks === 123\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500ab02837b-79ec-40d7-bff1-c1d2dd7362ef\n# \u2560\u255073d74146-8f60-4388-aaba-0dfe4215cb5d\n# \u2560\u255071b22e76-2b50-4d16-85f6-9dad0415630e\n# \u2560\u25506762ed72-f422-43a9-a782-de78f739c0ae\n# \u2560\u2550f77275b9-90aa-4e07-a608-981b5df727af\n# \u2560\u2550de7e71e2-e5e9-4f05-a61d-658f01a3e937\n# \u2560\u255037529063-8ee9-46a6-85cc-94db292da541\n# \u255f\u250056347b7e-5007-45f8-8f6d-8ac8cc719637\n# \u255f\u2500bf2abe01-6ae0-4066-8704-12f64e04511b\n# \u255f\u2500e46cf3e0-aa15-4c17-a925-3e9fc5109d54\n# \u255f\u25005b70aaf1-9623-4f55-b055-4263ed8be31d\n# \u255f\u2500fd8428a3-9fa3-471a-8b2d-5bbb8fdb3137\n# \u255f\u2500191f1f04-18d4-485b-af8b-a2f073b7043b\n# \u255f\u2500ec1fd70a-d92a-4688-98b2-135879f07141\n# \u2560\u255089f78031-1c54-468b-9ab8-7410c51df10e\n# \u2560\u2550cf314b21-3f4f-4637-b1ce-ec1d5d5af966\n# \u2560\u255078704300-0531-4f8e-8aa5-3f588fbdd190\n# \u2560\u25509129342b-f560-4901-81a2-56e3f8641521\n# \u2560\u2550c763ed72-82c9-445c-a8f7-a0c40982e4d9\n# \u255f\u25008a2e8348-49cf-4855-b5b3-cdee33e5ed67\n# \u255f\u250042671258-07a0-4015-8f47-4b3032595f08\n# \u255f\u25000d70962a-3880-4dee-a439-35068d019f5a\n# \u2560\u2550113cc425-e224-4f77-bfbd-ef4eb1d1ed70\n# \u2560\u25506188f559-bcab-4da6-84b2-a3fe522a5c3c\n# \u2560\u2550c24b46ce-bcbb-4dc9-8a59-b5b1bd2cd617\n# \u2560\u25505041085e-a406-4ed4-ab82-84d8f126cf0f\n# \u2560\u255003ccd498-83c3-41bb-84d7-625adabd7aee\n# \u2560\u25501bcf8bd1-c8a3-49a1-9791-d813aa856399\n# \u2560\u255014c525a1-eca1-466b-8e63-3a90d7d7111c\n# \u255f\u2500a2efc968-246c-40c2-b285-2ec94b185a44\n# \u255f\u2500e1c306e3-0a47-4149-a9fb-ec7ab380fa11\n# \u2560\u2550b6e8a170-12cc-4d97-905d-274e2609bfd8\n# \u2560\u2550a4a067b5-8b4b-4846-b986-0417d83cba48\n# \u255f\u25009c3f6eab-b1c3-4607-add8-d6d7e468c11a\n# \u255f\u2500dbfbcc16-c740-436c-bbf0-fee16b0a20c5\n# \u2560\u2550d97987a0-bdc0-46ed-a6a5-f35c1ce961dc\n# \u2560\u2550a6709e08-964d-46ea-9813-2c70a834824b\n# \u255f\u25007c2bab29-8609-4575-b2ca-7feb34915645\n# \u2560\u255069bfb438-7ecf-4f9b-8bc4-51e07aa46ef1\n# \u255f\u2500838b5904-1de2-4d9f-8f3c-a93ec224d40e\n# \u2560\u2550a3c41025-2f4a-4f9c-8577-72e4b7abbb98\n# \u2560\u25503e79ff61-6532-4879-9402-86473aa7d960\n# \u2560\u2550275c5f57-623d-439f-b09d-f7c745e0bed6\n# \u2560\u255010803c0d-d0a5-45c5-b7ef-9659e441df69\n# \u255f\u2500b056a99d-5b13-47ba-a199-d788410e3c99\n# \u255f\u25005b093e83-78c1-4187-b406-56e79800e1be\n# \u2560\u2550a461f1fd-b5a5-4ae3-a47c-067a6081fb24\n# \u2560\u255038e54516-cdf4-4c1d-815b-68e1e7a7f6f7\n# \u2560\u2550411271a6-4236-45e2-ab34-f26410108821\n# \u255f\u2500f9c81ab1-556c-4d81-bee8-2897c20e324d\n# \u255f\u2500a392d2d6-5a16-4383-b0ef-5003aa2de9fa\n# \u255f\u2500ae95b691-f54b-4bf5-b17b-3e5bd1edf75e\n# \u255f\u250012119016-fa61-4d38-8c58-821ea435df7d\n# \u2560\u25509bed78b6-5a8f-44ce-ab66-cab685daf264\n# \u255f\u250074929fa6-d1f7-41cd-ab55-48f35d5fbf28\n# \u255f\u2500f1ede628-d158-4296-befd-3eaa87cdad27\n# \u2560\u25502f6e353d-2cdc-46d6-9727-01b0a6167ca0\n# \u2560\u255017dea9e5-84ea-4476-a318-cc475043c83b\n# \u255f\u25005e66e59b-fdb8-4373-b231-097b0227dc5c\n# \u2560\u2550c47252b9-8869-4878-b9bf-7eeb7ed17c9a\n# \u2560\u2550227129bc-4415-4240-ad55-815bde65a5a1\n# \u2560\u2550ce90612e-ffc1-4e30-9d89-531f11fd75eb\n# \u255f\u25000a3f5c6c-6e1b-458c-bf91-523a0b639b41\n# \u255f\u250043fe89d7-f33e-4dfa-853e-327e981feb1e\n# \u255f\u2500fc000550-3053-483e-bc41-6aed22c3999c\n# \u255f\u25003f11ca4c-dd06-47c9-92e2-cb97c18a06db\n# \u255f\u2500b155d336-f746-4c82-8206-ab1a49cedea8\n# \u255f\u2500f9b2a11d-8c4e-47a5-9d93-38025fae9a95\n# \u255f\u2500221aa13b-aa25-4145-8076-da77432364bb\n# \u255f\u25002a514f2f-79c8-4b0d-be8a-170f3386d5d5\n# \u255f\u25009fb4d52d-77f2-4032-a769-6d5e60be43bf\n# \u255f\u25001c1b64b1-107e-4d43-9ce2-569c3034017e\n# \u255f\u2500cade56ad-312e-40cf-bcda-11480ce27852\n# \u255f\u2500810470b8-0a6c-48b8-aba2-a2058b8d9f59\n# \u255f\u2500a29d5277-e97a-4cca-8e31-8037f9cfdd80\n# \u255f\u25004f7aac13-9e49-4b2b-8d78-53f583f6130a\n# \u255f\u2500cc7102e1-6af0-43bb-8cf0-43e2cec210e3\n# \u255f\u2500f5d9a4c5-300f-4dae-8507-346ec0b74632\n# \u255f\u2500ec6f1b07-d026-45ca-996d-be7693664cd7\n# \u255f\u2500dadf1c50-6588-4345-a240-69a72336c7cd\n# \u255f\u2500d384e3fc-b207-48ce-bc7b-1b47a14b1581\n# \u255f\u2500d7dc79e6-1f58-4414-aeef-667bdb0dd200\n# \u255f\u2500a661e172-6afb-42ff-bd43-bb5b787ee5ed\n# \u255f\u2500a6e8c835-f209-445a-9f43-cdf2ecfd1b57\n# \u255f\u25005759b2cc-1e96-4069-ae42-bc159c7cf5fb\n# \u255f\u2500ba4a5762-33da-40e6-94fa-cca9befc6d5a\n# \u255f\u25009101631b-81ca-4c7c-94da-81d9e106df78\n# \u255f\u25003f0e5a49-5eec-42cd-bd2c-254b277840bf\n# \u255f\u2500fc26d26a-a9a5-4646-b85b-12eac66d96cb\n# \u255f\u250094ebb761-21fb-4015-acb3-26310b19b0dc\n# \u255f\u2500312ef6a6-55aa-4913-9416-15e79b4e3362\n# \u2560\u255097eb4444-a22c-47f2-9247-3bce6d7e179e\n# \u255f\u2500716d9ddc-18dc-4973-924e-e5ebf9161ff6\n# \u255f\u25001aa319c8-5e1d-4dd9-ae22-ad99e46e7b4d\n# \u255f\u2500605d2481-23be-4ad9-82c9-e375b7be8669\n# \u255f\u2500bc08755d-721f-403e-af95-36494b8fb7bc\n# \u255f\u2500586826a5-d667-4035-9796-bd2db61498d6\n# \u255f\u2500a8fd09d1-c5ca-47f3-8fb3-32d8aeef3e59\n# \u255f\u250024f2eb92-5fd7-429b-b2ea-a987195c6edb\n# \u2560\u2550bedc3586-6b85-4de2-9ea1-79d842db6b56\n# \u2560\u255042f34453-9935-4e50-a62b-9dcf31d72601\n# \u255f\u2500de94f2b5-96ae-4936-870f-7639a39fd40d\n# \u255f\u250060a398c9-9fe8-4b90-b863-1568183641d9\n# \u255f\u250021d4560e-721f-4ed4-9db7-86a8151ab22c\n# \u255f\u250099afc7f4-727c-4277-8311-f2ffa94830ae\n# \u255f\u25004956526a-daf9-43c9-bff3-ff2446016e2e\n# \u255f\u250084ff6a23-c134-4910-b630-a7ad45f3bf29\n# \u255f\u2500318363d0-6d9e-4144-b478-b775f437edaf\n# \u255f\u250067fd07b7-340b-4e24-bc06-e4c85b186872\n# \u255f\u2500c6d5597c-d505-4125-88c4-10415934d2a4\n# \u255f\u2500872b4877-30dd-4a92-a3c8-69eb50675dcb\n# \u255f\u2500c877c109-db16-468c-8f3c-8294db859d6d\n# \u2560\u2550ab0a19b8-cf7c-4c4f-802a-f85eef81fc02\n# \u255f\u25008480d0d7-bdf7-468d-9344-5b789e33921c\n# \u2560\u25506f5ba692-4b6a-405a-8cd3-1a8f9cc06611\n# \u255f\u2500b4b317d7-bed1-489c-9650-8d336e330689\n# \u2560\u255093ed973f-daf6-408b-9d4b-d53495418610\n# \u2560\u2550dea898a0-1904-4d09-ad0b-6915008fe946\n# \u255f\u2500b5763c10-e11c-4389-b6fc-421d2c9682f1\n# \u255f\u250074c19786-1ba7-4865-a993-590a779ae564\n# \u2560\u2550e968fc57-d850-4e2d-9410-8777d03b7b3c\n# \u255f\u25003d5abd58-02ab-4b91-a7a3-d9068d4df017\n# \u255f\u2500326f7661-3482-4bf2-a97b-57cc7ac60ee2\n# \u255f\u2500b273d3d3-648f-4d34-94e7-e49277d4ba29\n# \u2560\u2550a2cbb0c3-23b9-4091-9ca7-5ba96e85e3a3\n# \u255f\u2500f9ed2487-a7f6-4ce9-b673-f8a298cd5fc3\n# \u255f\u250020166ec9-7084-4d58-8b19-3aa51cc8f2c6\n# \u255f\u25000f31dd2e-0331-4d4c-8db5-9ce188cd3730\n# \u255f\u2500cecba3e6-98e8-408a-97dd-96b67c4f42cf\n# \u2560\u25501633fe05-cb51-4032-b6b6-f23db72bbd49\n# \u2560\u25507c312943-c48b-40e7-a499-227f7ff8aa59\n# \u2560\u2550a0207e25-0398-4104-8c0f-a8fbd9fe1d53\n# \u255f\u25007d14b79c-74e5-4986-80b7-de7cd7d48670\n# \u255f\u25005950488e-2008-48d8-9095-7f9421df191e\n# \u255f\u250077cc33a3-c2bc-4f2d-ba88-e3693ec79b0c\n# \u255f\u25005a3a0f63-dcce-49c9-84fd-a6317184820f\n# \u255f\u250035f63c4e-3583-4ea8-a057-31f18f8a09d6\n# \u255f\u2500ef59d0f0-0f02-4089-a49d-53fb0427c3a0\n# \u255f\u250035b2770e-1db6-4327-bf86-c27a4b61dbd3\n# \u255f\u250022640a2f-ea38-4517-a4f3-7a65e60ffebe\n# \u255f\u2500d414f840-4952-4de5-a565-7fdc81a94817\n# \u255f\u250064bf02a4-4fe3-424d-ae6e-5906c3395278\n# \u255f\u2500f3916810-1911-48bd-936b-776206fcad54\n# \u255f\u2500122c27a5-a6e8-45ef-a968-b9b4b3f9ad09\n# \u255f\u25009126f47d-cbc7-411f-93bd-8684ba06c9e9\n# \u255f\u2500955705f9-c90d-495d-86b4-5f3b5bc9fc8e\n# \u255f\u2500187c3005-cd43-45a0-8cbd-bc96b9cb39da\n# \u255f\u25006c0156a9-7281-4326-9e1f-989efa73bb7b\n# \u255f\u25008d3df0c0-eb48-4dae-97a8-8c01f0b0a34b\n# \u255f\u2500ef6fc423-f1b1-4dcb-a059-276121391bc6\n# \u255f\u2500b765dbfe-4e58-4bb9-b1d6-aa4378d4e9c9\n# \u255f\u25001b869c21-b8cd-4aaa-91a9-370c1b7a3d32\n# \u255f\u2500dbd41240-9fc4-4e25-8b25-2b68afa679f2\n# 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{"text": "### A Pluto.jl notebook ###\n# v0.16.0\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 42e620aa-5f4c-11eb-2ebf-85814cf720e7\nbegin \n\tusing Pkg; Pkg.activate(\".\") # using the env in /notebooks/day2\n\tusing PlutoUI\n\tusing DSJulia\nend\n\n# \u2554\u2550\u2561 122cffca-5fdc-11eb-3555-b39b818f1116\nlet \n\tusing JuMP\n\tusing GLPK\nend\n\n# \u2554\u2550\u2561 0963185c-5fdc-11eb-0eed-89d514850353\nusing BioSequences\n\n# \u2554\u2550\u2561 24806108-5fdc-11eb-2f19-bb09f836f893\nusing Sockets\n\n# \u2554\u2550\u2561 4621c212-5fc7-11eb-2c9d-ad577506420e\nusing Plots\n\n# \u2554\u2550\u2561 b2c1cef8-5fe5-11eb-20c7-134432196893\nusing Distributed\n\n# \u2554\u2550\u2561 31c1e25e-5e53-11eb-2467-9153d30962d5\nmd\"\"\"\n# Metaprogramming\n\nThe strongest legacy of [Lisp](https://en.wikipedia.org/wiki/Lisp_(programming_language)) in the Julia language is its metaprogramming support. Like Lisp,\nJulia represents its own code as a data structure of the language itself. Since code is represented\nby objects that can be created and manipulated from within the language, it is possible for a\nprogram to transform and generate its own code. This allows sophisticated code generation without\nextra build steps, and also allows true Lisp-style macros operating at the level of abstract syntax trees.\nBecause all data types and\ncode in Julia are represented by Julia data structures, powerful reflection\ncapabilities are available to explore the internals of a program and its types just like any other\ndata.\n\"\"\"\n\n# \u2554\u2550\u2561 4ab33c0e-5e53-11eb-2e63-2dd6f06de3ba\nmd\"\"\"\n## Program representation\n\nEvery Julia program starts its life as a string:\n\"\"\"\n\n# \u2554\u2550\u2561 e76d8f04-5e53-11eb-26df-db496622642d\nprog = \"1 + 1\"\n\n# \u2554\u2550\u2561 f361d734-5e53-11eb-3957-61bd0a370b5a\nmd\"\"\"\n**What happens next?**\n\nThe next step is to parse each string\ninto an object called an expression, represented by the Julia type `Expr`. \nParsing means taking the input (in this case, a string) and building a data structure \u2013 often some kind of parse tree, [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree) or other hierarchical structure, giving a structural representation of the input while checking for correct syntax. \n\"\"\"\n\n# \u2554\u2550\u2561 15aaa5b6-5e54-11eb-067f-9dcd096940b6\nex1 = Meta.parse(prog)\n\n# \u2554\u2550\u2561 34b6a71e-5e54-11eb-0324-4bffbe85c813\ntypeof(ex1)\n\n# \u2554\u2550\u2561 22770a30-5e54-11eb-28db-1359ab1f402f\nmd\"\"\"\n`Expr` objects contains two parts:\n\n  * a `Symbol` identifying the kind of expression. A symbol is an [interned string](https://en.wikipedia.org/wiki/String_interning)\n    identifier (string interning is a method of storing only one copy of each distinct string value, which must be immutable).\n\n\"\"\"\n\n# \u2554\u2550\u2561 41f9633c-5e54-11eb-323b-e9703c674b0f\nex1.head\n\n# \u2554\u2550\u2561 48104478-5e54-11eb-2f23-c98c4ccbb763\nmd\"\"\"\n  * the expression arguments, which may be symbols, other expressions, or literal values:\n\"\"\"\n\n# \u2554\u2550\u2561 538e2054-5e54-11eb-2b9c-451637e1d8ee\nex1.args\n\n# \u2554\u2550\u2561 6e3beaf8-5e54-11eb-154b-95f5887efcb0\nmd\"\"\"\nExpressions may also be constructed directly in [prefix notation](https://en.wikipedia.org/wiki/Polish_notation) (= Polish notation, a mathematical notation in which operators precede their operands, i.e. `+ 1 1` instead of `1 + 1` with infix notation):\n\"\"\"\n\n# \u2554\u2550\u2561 7de376a8-5e54-11eb-0356-9b74225b671e\nex2 = Expr(:call, :+, 1, 1)\n\n# \u2554\u2550\u2561 825b45a6-5e54-11eb-27e6-f3cca8a2f67e\nmd\"\"\"\nThe two expressions constructed above \u2013 by parsing and by direct construction \u2013 are equivalent:\n\"\"\"\n\n# \u2554\u2550\u2561 8b23cac8-5e54-11eb-2424-5972ea3d2e21\nex1 == ex2\n\n# \u2554\u2550\u2561 8ecd49ba-5e54-11eb-24b6-a328043b7a07\nmd\"\"\"\n**The key point here is that Julia code is internally represented as a data structure that is accessible\nfrom the language itself.**\n\nThe `dump` function provides indented and annotated display of `Expr` objects:\n\"\"\"\n\n# \u2554\u2550\u2561 985a57f2-5e54-11eb-1f2f-bf23014c6171\n@terminal dump(ex2)\n\n# \u2554\u2550\u2561 d66ea502-5e54-11eb-2694-4766a05aed38\nmd\"\"\"\n### Symbols\n\nThe `:` character has two syntactic purposes in Julia. The first form creates a [`Symbol`](@ref),\nan [interned string](https://en.wikipedia.org/wiki/String_interning) used as one building-block\nof expressions:\n\"\"\"\n\n# \u2554\u2550\u2561 db1e9936-5e54-11eb-376f-8ddea65d976c\ns = :foo\n\n# \u2554\u2550\u2561 e4f50fda-5e54-11eb-2210-6b8eb531d898\ntypeof(s)\n\n# \u2554\u2550\u2561 ea891b6e-5e54-11eb-24c9-357a8a65d1c0\nmd\"\"\"\nThe `Symbol` constructor takes any number of arguments and creates a new symbol by concatenating\ntheir string representations together:\n\"\"\"\n\n\n# \u2554\u2550\u2561 efe79c46-5e54-11eb-23c9-3107ea94e5b9\n:foo == Symbol(\"foo\")\n\n# \u2554\u2550\u2561 f5fac50e-5e54-11eb-2ff9-cf7e30a4a8ad\nSymbol(\"func\", 10)\n\n# \u2554\u2550\u2561 fe35b31e-5e54-11eb-258e-29913f1e8723\nSymbol(:var, '_', \"sym\")\n\n# \u2554\u2550\u2561 0464f1be-5e55-11eb-34df-ef1217012663\nmd\"\"\"Note that to use `:` syntax, the symbol's name must be a valid identifier.\nOtherwise the `Symbol(str)` constructor must be used.\n\nIn the context of an expression, symbols are used to indicate access to variables; when an expression\nis evaluated, a symbol is replaced with the value bound to that symbol in the appropriate **scope**.\n\"\"\"\n\n# \u2554\u2550\u2561 abfc3392-5e5a-11eb-149d-ad3dd2755524\nmd\"\"\"\n### Quoting\n\nThe second syntactic purpose of the `:` character is to create expression objects without using\nthe explicit `Expr` constructor. This is referred to as *quoting*. The `:` character, followed\nby paired parentheses around a single statement of Julia code, produces an `Expr` object based\non the enclosed code. Here is an example of the short form used to quote an arithmetic expression:\n\"\"\"\n\n# \u2554\u2550\u2561 917a579c-5e5a-11eb-3fe6-cb0dcad5e4e6\nex = :(a + b * c + 1)\n\n# \u2554\u2550\u2561 82d3220a-5e5a-11eb-3c49-d5e7afb1af2a\ntypeof(ex)\n\n# \u2554\u2550\u2561 67954702-5e5a-11eb-1ab1-e9a285ee208d\nex.args\n\n# \u2554\u2550\u2561 2ae6ac9c-5e5a-11eb-060c-b3eae1df0493\nmd\"\"\"\n### Interpolation\n\nDirect construction of `Expr` objects with value arguments is powerful, but `Expr` constructors\ncan be tedious compared to \"normal\" Julia syntax. As an alternative, Julia allows *interpolation* of\nliterals or expressions into quoted expressions. Interpolation is indicated by a prefix `$`.\n\nIn this example, the value of variable `a` is interpolated:\n\"\"\"\n\n# \u2554\u2550\u2561 5ef3d532-5e5a-11eb-30b3-a569e0bd736e\na = 1\n\n# \u2554\u2550\u2561 3bb1f892-5e5a-11eb-0946-e5e977a4e5b2\n:($a + b)\n\n# \u2554\u2550\u2561 5763cca0-5e5a-11eb-1341-3d9bfb47fb58\nmd\"\"\"\nInterpolating into an unquoted expression is not supported and will cause a compile-time error:\n\"\"\"\n\n# \u2554\u2550\u2561 e6a3902a-5e58-11eb-2bd2-4bc779bcebe7\n$a + b\n\n# \u2554\u2550\u2561 11991248-5e55-11eb-2748-992f6fe48620\nmd\"\"\"\nThe use of `$` for expression interpolation is intentionally reminiscent of string interpolation and command interpolation. Expression interpolation allows convenient, readable programmatic construction of complex Julia expressions.\n\"\"\"\n\n# \u2554\u2550\u2561 1c016bec-5e5d-11eb-3687-931da82f8c04\nmd\"\"\"\n\n### Splatting interpolation\n\nNotice that the `$` interpolation syntax allows inserting only a single expression into an\nenclosing expression.\nOccasionally, you have an array of expressions and need them all to become arguments of\nthe surrounding expression.\nThis can be done with the syntax `$(args...)`.\n\nFor example, the following code generates a function call where the number of arguments is\ndetermined programmatically:\n\"\"\"\n\n# \u2554\u2550\u2561 552ea3bc-5e5d-11eb-0c97-e1e6db526df3\nargs = [:x, :y, :z]\n\n# \u2554\u2550\u2561 629c4f9a-5e5d-11eb-2731-bf7457755829\n:(f(1, $(args...)))\n\n# \u2554\u2550\u2561 66c113d0-5e5d-11eb-0aab-b3fa7f98eae5\nmd\"\"\"\n### Evaluating expressions\n\nGiven an expression object, one can cause Julia to evaluate (execute) it at global scope using `eval`\n\"\"\"\n\n# \u2554\u2550\u2561 6fc2fb9c-5e5d-11eb-0f21-6524555a9c42\n:(1 + 2)\n\n# \u2554\u2550\u2561 6dab1d58-5e5d-11eb-3381-a1796f8d14c5\neval(:(1 + 2))\n\n# \u2554\u2550\u2561 b833d8ba-5e5d-11eb-3311-899efb15c1b0\nmd\"\"\"\n## Macros\n\nNow that we have an understanding of the basic concepts of code representation in Julia, we can introduce the core concept of this notebook: macros. \nMacros provide a method to include generated code in the final body of a program. A macro maps\na tuple of arguments to a returned *expression*, and the resulting expression is compiled directly\nrather than requiring a runtime `eval` call. Macro arguments may include expressions,\nliteral values, and symbols. \n\nIn the following examples, we will show that macros allow us to\n1. modify code before it runs\n2. elegantly add new features or syntax\n3. process strings at compile time instead of runtime\n\n### Basics\n\nHere is an extraordinarily simple macro:\n\"\"\"\n\n# \u2554\u2550\u2561 169c6aa2-5e5e-11eb-1d84-e7e3b4641e10\nmacro sayhello()\n\treturn :( println(\"Hello, world!\") )\nend\n\n# \u2554\u2550\u2561 ae1525ea-5e5e-11eb-3906-593143776559\n@terminal @sayhello\n\n# \u2554\u2550\u2561 c40cebe6-5e5e-11eb-1f7f-37134b7a449f\nmd\"\"\"\nMacros have a dedicated character in Julia's syntax: the `@` (at-sign), followed by the unique\nname declared in a `macro NAME ... end` block. In this example, the compiler will replace all\ninstances of `@sayhello` with:\n\"\"\"\n\n# \u2554\u2550\u2561 f5ab41f4-5e5f-11eb-02f7-efcb3e05e616\n:( println(\"Hello, world!\") )\n\n# \u2554\u2550\u2561 0001a53a-5e60-11eb-18df-d7e9e7dcf43f\nmd\"\"\"\nWhen `@sayhello` is entered in the REPL, the expression executes immediately, thus we only see the evaluation result:\n\"\"\"\n\n# \u2554\u2550\u2561 bda7fd66-5e5e-11eb-1e06-eb67a0178e14\n@terminal @sayhello()\n\n# \u2554\u2550\u2561 1707320e-5e60-11eb-3f33-798c81663488\nmd\"\"\"Now, consider a slightly more complex macro:\"\"\"\n\n# \u2554\u2550\u2561 1fdf9600-5e60-11eb-30d3-b167811ae47f\nmacro sayhello(name)\n\treturn :( println(\"Hello, \", $name) )\nend\n\n# \u2554\u2550\u2561 2dcacdfc-5e60-11eb-3d04-9f2ffb46d0fe\nmd\"\"\"\nThis macro takes one argument: `name`. When `@sayhello` is encountered, the quoted expression is *expanded* to interpolate the value of the argument into the final expression:\n\"\"\"\n\n# \u2554\u2550\u2561 36fc5076-5e60-11eb-04be-bd9f542b1ff3\n@terminal @sayhello(\"Mr. Bond\")\n\n# \u2554\u2550\u2561 0c787198-61a0-11eb-1e30-fbcb5a624076\n@terminal @sayhello \"Mr. Bond\"\n\n# \u2554\u2550\u2561 4a732eb8-5e60-11eb-197b-0729d0f1be15\nmd\"\"\"\nWe can view the quoted return expression using the function `macroexpand` or the macro `@macroexpand` (**important note:**\nthis is an extremely useful tool for debugging macros).\nWe can see that the `\"Mr. Bond\"` literal has been interpolated into the expression.\"\"\"\n\n# \u2554\u2550\u2561 536addec-5e61-11eb-1a9d-d7f2c190d6c2\n@macroexpand @sayhello(\"Mr. Bond\")\n\n# \u2554\u2550\u2561 855412b2-5e61-11eb-3aba-bb862489414e\nmd\"\"\"\n\n### Hold on: why macros?\n\nWe have already seen a function `f(::Expr...) -> Expr` in a previous section. In fact, `macroexpand` is also such a function. So, why do macros exist?\n\nMacros are necessary because they execute when code is parsed, therefore, macros allow the programmer\nto generate and include fragments of customized code *before* the full program is run. To illustrate\nthe difference, consider the following example.\n\nNote that in this case, you need to look at your REPL to see the output. A macro definition is not allowed in the local scope, so we cannot wrap this with our `@terminal` macro.\n\"\"\"\n\n# \u2554\u2550\u2561 cabb3cdc-5e62-11eb-2479-bb8337c05292\nmacro twostep(arg)\n\tprintln(\"I execute at parse time. The argument is: \", arg)\n\tstr1 = \"I execute at runtime. \"\n\tstr2 = \"The argument is: \"\n\tmessage = str1 * str2\n\treturn :(println($message, $arg))\nend\n\n# \u2554\u2550\u2561 f33f0cfe-5f52-11eb-30e0-93c4b899aaa7\nmd\"\"\"Note that the computation of message is compiled away if we expand the macro!\"\"\"\n\n# \u2554\u2550\u2561 01feeca0-5e63-11eb-11a0-1b66a92127d1\nex_twostep = @macroexpand @twostep :(1, 2, 3)\n\n# \u2554\u2550\u2561 f1969888-5e65-11eb-18a3-879d2f87b447\n@terminal dump(ex_twostep)\n\n# \u2554\u2550\u2561 0613f620-5e66-11eb-08d9-01dd3a403321\nmd\"\"\"\n### Macro invocation\n\nMacros are invoked with the following general syntax:\n\n```julia\n@name expr1 expr2 ...\n@name(expr1, expr2, ...)\n```\n\n\"\"\"\n\n# \u2554\u2550\u2561 29a2c0ee-5e66-11eb-2194-6f1c3cef563a\nmd\"\"\"\nNote the distinguishing `@` before the macro name and the lack of commas between the argument\nexpressions in the first form, and the lack of whitespace after `@name` in the second form. The\ntwo styles should not be mixed. For example, the following syntax is different from the examples\nabove; it passes the tuple `(expr1, expr2, ...)` as one argument to the macro:\n\n```julia\n@name (expr1, expr2, ...)\n```\n\"\"\"\n\n# \u2554\u2550\u2561 a1e86478-5e66-11eb-2ef9-6d460e707013\nmd\"\"\"\n### Building an advanced macro\n\nHere is a simplified definition of Julia's `@assert` macro, which checks if an expression is true:\n\n(`... ? ... : ...` is the ternary if-else operator seen in `01-basics.jl` )\n\"\"\"\n\n# \u2554\u2550\u2561 e61381aa-5e66-11eb-3347-5d26b61e6c17\nmacro assert(ex)\n\treturn :( $ex ? nothing : throw(AssertionError($(string(ex)))) )\nend\n\n# \u2554\u2550\u2561 a43490de-5e67-11eb-253e-f7455ade3ffe\nmd\"\"\"This macro can be used like this:\"\"\"\n\n# \u2554\u2550\u2561 c79ad04c-5e67-11eb-1fd9-bf7ff40f0862\nmd\"\"\"\nIn place of the written syntax, the macro call is expanded at parse time to its returned result. This is equivalent to writing:\"\"\"\n\n# \u2554\u2550\u2561 f34e9020-5e67-11eb-1987-e3e6a0969705\n1 == 1.0 ? nothing : throw(AssertionError(\"1 == 1.0\"))\n\n# \u2554\u2550\u2561 f81955fe-5e67-11eb-11b9-5f9fd0096a09\n1 == 0 ? nothing : throw(AssertionError(\"1 == 0\"))\n\n# \u2554\u2550\u2561 fb772ef4-5e67-11eb-218e-330ba087bcc8\nmd\"\"\"\nThat is, in the first call, the expression `:(1 == 1.0)` is spliced into the test condition slot,\nwhile the value of `string(:(1 == 1.0))` is spliced into the assertion message slot. The entire\nexpression, thus constructed, is placed into the syntax tree where the `@assert` macro call occurs.\nThen at execution time, if the test expression evaluates to true, then `nothing` is returned,\nwhereas if the test is false, an error is raised indicating the asserted expression that was false.\nNotice that it would not be possible to write this as a function, since only the *value* of the\ncondition is available and it would be impossible to display the expression that computed it in\nthe error message.\n\nThe actual definition of `@assert` in Julia Base is more complicated. It allows the\nuser to optionally specify their own error message, instead of just printing the failed expression.\nJust like in functions with a variable number of arguments, this is specified with an ellipses\nfollowing the last argument:\n\"\"\"\n\n# \u2554\u2550\u2561 2df2baa8-5e68-11eb-2f28-9968b7ecbcd5\nmacro assert(ex, msgs...)\n\tmsg_body = isempty(msgs) ? ex : msgs[1]\n\tmsg = string(msg_body)\n\treturn :($ex ? nothing : throw(AssertionError($msg)))\nend\n\n# \u2554\u2550\u2561 b0dfd0dc-5e67-11eb-19cf-5970a2e80bfa\n@assert 1 == 1.0\n\n# \u2554\u2550\u2561 c42d2d6a-5e67-11eb-1564-2dcb0e91fb19\n@assert 1 == 0\n\n# \u2554\u2550\u2561 376fe8e4-5e68-11eb-1d7d-c9b1945b133f\nmd\"\"\"\nNow `@assert` has two modes of operation depending upon the number of arguments it receives!\nIf there is only one argument, the tuple of expressions captured by `msgs` will be empty and it\nwill behave the same as the simpler definition above with only one argument. But now if the user specifies a second argument,\nit is printed in the message body instead of the failing expression. You can inspect the result\nof a macro expansion with `@macroexpand`.\n\"\"\"\n\n# \u2554\u2550\u2561 4750ce04-5e68-11eb-237e-3fed9eb1f4c5\n@macroexpand @assert a == b\n\n# \u2554\u2550\u2561 4dce26dc-5e68-11eb-1ff7-8974f93e3cb8\n@macroexpand @assert a==b \"a should equal b!\"\n\n# \u2554\u2550\u2561 558920ca-5e68-11eb-3533-d5d596e3884d\nmd\"\"\"\n\nThere is yet another case that the actual `@assert` macro handles: what if, in addition to printing\n\"a should equal b,\" we wanted to print their values? One might naively try to use string interpolation\nin the custom message, e.g., `@assert a==b \"a ($a) should equal b ($b)!\"`, but this won't work\nas expected with the above macro. Can you see why? Recall from string interpolation that an interpolated string is rewritten to a call to `string`. Compare:\n\"\"\"\n\n# \u2554\u2550\u2561 6d69106a-5e68-11eb-0d50-e17b4db5db74\ntypeof(:(\"a should equal b\"))\n\n# \u2554\u2550\u2561 715464cc-5e68-11eb-3e1a-977db04a7db6\ntypeof(:(\"a ($a) should equal b ($b)!\"))\n\n# \u2554\u2550\u2561 7588018e-5e68-11eb-3be2-b97ce124d6bf\n@terminal dump(:(\"a ($a) should equal b ($b)!\"))\n\n# \u2554\u2550\u2561 84771eaa-5e68-11eb-0684-cf1a3118d10b\nmd\"\"\"\nSo now instead of getting a plain string in `msg_body`, the macro is receiving a full expression that will need to be evaluated in order to display as expected. This can be spliced directly into the returned expression as an argument to the `string` call; see `error.jl` for the complete implementation.\n\nThe `@assert` macro makes great use of splicing into quoted expressions to simplify the manipulation of expressions inside the macro body.\n\"\"\"\n\n# \u2554\u2550\u2561 43a19ace-5e6b-11eb-005b-b7d2d9a46878\nmd\"\"\"\n### Macros and dispatch\n\nMacros, just like Julia functions, are generic. This means they can also have multiple method definitions, thanks to multiple dispatch:\n\"\"\"\n\n# \u2554\u2550\u2561 508167c4-5e6b-11eb-2587-b967b79cf74c\nmacro m end\n\n# \u2554\u2550\u2561 59f86d98-5e6b-11eb-3259-7312501b70bf\nmacro m(args...)\n\t\"$(length(args)) arguments\"\nend\n\n# \u2554\u2550\u2561 5edf55e2-5e6b-11eb-383e-5965ae694c4c\nmacro m(x,y)\n   \t\"Two arguments\"\nend\n\n# \u2554\u2550\u2561 3dc3aa10-5e6c-11eb-3338-7777b8cb97a5\nmd\"\"\"\nHowever one should keep in mind, that macro dispatch is based on the types of AST\nthat are handed to the macro, not the types that the AST evaluates to at runtime:\"\"\"\n\n# \u2554\u2550\u2561 34565ba8-5e6c-11eb-2a27-2ff13394bcc8\nmacro m(::Int)\n\t\"An Integer\"\nend\n\n# \u2554\u2550\u2561 65c94e9c-5e6b-11eb-38a8-4d60c692222b\n@m \"asdl\"\n\n# \u2554\u2550\u2561 42fc5284-5e6c-11eb-0edb-9555a320ec55\n@m 1 2\n\n# \u2554\u2550\u2561 02e4bf64-5e6d-11eb-36b5-fb57c87bff81\n@m 3\n\n# \u2554\u2550\u2561 07f8cc34-5e6d-11eb-36e4-b56b2b92a2e2\nx = 2\n\n# \u2554\u2550\u2561 0bbfce80-5e6d-11eb-07f6-b71086357384\n@m x\n\n# \u2554\u2550\u2561 0e00bff6-5e6d-11eb-342d-bfded7ef8a96\nmd\"\"\"\n## Code Generation\n\nWhen a significant amount of repetitive boilerplate code is required, it is common to generate\nit programmatically to avoid redundancy. In most languages, this requires an extra build step,\nand a separate program to generate the repetitive code. In Julia, expression interpolation and `eval` allow such code generation to take place in the normal course of program execution.\nFor example, consider the following custom type\n\"\"\"\n\n# \u2554\u2550\u2561 36805374-5e6d-11eb-205c-0524096bdf27\nstruct MyNumber\n    x::Float64\nend\n\n# \u2554\u2550\u2561 4056c9bc-5e6d-11eb-377e-238ef8633c74\nmd\"\"\"for which we want to add a number of methods to. We can do this programmatically in the following loop:\"\"\"\n\n# \u2554\u2550\u2561 49807d8c-5e6d-11eb-0170-f1419837d958\nfor op = (:sin, :cos, :tan, :log, :exp, :log)\n    @eval Base.$op(a::MyNumber) = MyNumber($op(a.x))\nend\n\n# \u2554\u2550\u2561 4ef3f280-5e6d-11eb-2020-b7cd9e6b9c03\nmd\"\"\"and we can now use those functions with our custom type:\"\"\"\n\n# \u2554\u2550\u2561 53f36f36-5e6d-11eb-3f06-6de0eff4d173\ny = MyNumber(\u03c0)\n\n# \u2554\u2550\u2561 3428d7b8-5e6d-11eb-32bc-af1df0579e60\nsin(y)\n\n# \u2554\u2550\u2561 18e3753a-5f55-11eb-0a11-47ca6abe9186\nmd\"This will not work since we only have defined `log` and not `log10`.\"\n\n# \u2554\u2550\u2561 2f4fe6ce-5e6c-11eb-1744-2f9534c8b6ba\nlog10(y)\n\n# \u2554\u2550\u2561 cae2546a-5e6b-11eb-3fc2-69f9a15107cf\nmd\"\"\"In this manner, Julia acts as its own preprocessor, and allows code generation from inside the language.\"\"\"\n\n# \u2554\u2550\u2561 0ac5096e-61a1-11eb-24ba-13bc0a3377c6\n\n\n# \u2554\u2550\u2561 efc1357a-61a0-11eb-20f5-a15338080e4c\nmd\"### Example: domain specific languages\"\n\n# \u2554\u2550\u2561 4d9de330-5f55-11eb-0d89-eb81e6c9ffab\nmd\"\nCode generation can for instance be used to simplify the creation of a mathematical optimisation problem. In this case, we we'll use the `JuMP` package.\n`JuMP` ('Julia for Mathematical Programming') is an open-source modeling language that is embedded in Julia. It allows users to formulate various classes of optimization problems (linear, mixed-integer, quadratic, conic quadratic, semidefinite, and nonlinear) with easy-to-read code. `JuMP` also makes advanced optimization techniques easily accessible from a high-level language. \n\nAs a dummy example, let us consider the following linear programming problem:\n\n``\\max_{x,y}\\,\\,x + 2y``\n\n``\\text{s.t.}``\n\n``x + y \\leq 1``\n\n``0\\leq x, y \\leq 1``\n\nWhich can be transcribed into a `JuMP` model as:\n\"\n\n# \u2554\u2550\u2561 92458f54-5f57-11eb-179f-c749ce06f7e2\nlet\n\tmodel = Model(GLPK.Optimizer)\n\t@variable(model, 0 <= x <= 1)\n\t@variable(model, 0 <= y <= 1)\n\t@constraint(model, x + y <= 1)\n\t@objective(model, Max, x + 2y)\n\toptimize!(model)\n\tvalue(x), value(y), objective_value(model)\nend\n\n# \u2554\u2550\u2561 d1222dcc-5f57-11eb-2c87-77a16ea8e65d\nmd\"\nWithout the macros, the code would be more difficult to read. As an example, check the `macroexpansion` of `@constraint` to see the bunch of code that is generated behind the scenes:\n\"\n\n# \u2554\u2550\u2561 0b1f3542-5f58-11eb-1610-75df3e2a4a76\nlet\n\tmodel = Model(GLPK.Optimizer)\n\t@macroexpand @constraint(model, x + y <= 1)\nend\n\n# \u2554\u2550\u2561 57f243ec-5fe3-11eb-04d9-d776ccba4c0f\nmd\"Finally, this section on code generation ends with two more examples:\n1. a macro to repeat a certain expression n-times\n2. a macro to repeat a certain expression until a condition is met\n\nAre these macros the best way to tackle the problems at hand? Maybe not, but they do give a nice illustration of how the code generation works.\n\"\n\n# \u2554\u2550\u2561 211cf9ca-5fe3-11eb-2596-71b1848753d7\nmacro dotimes(n, body)\n    quote\n        for i = 1:$(esc(n))\n            $(esc(body))\n        end\n    end\nend\n\n# \u2554\u2550\u2561 35ccaee2-5fe3-11eb-3bd0-e355b0fd3f71\n@macroexpand @dotimes 3 println(\"hi there\")\n\n# \u2554\u2550\u2561 434669d2-5fe3-11eb-1f4a-27edf1f9e7f6\nmacro until(condition, block)\n    quote\n        while true\n            $(esc(block))\n            if $(esc(condition))\n                break\n            end\n        end\n    end\nend\n\n# \u2554\u2550\u2561 9612965e-5fe3-11eb-172a-59cc73a26ab6\nPlutoUI.with_terminal() do\n\ti = 0\n\t@until i == 10 begin\n\t\ti += 1\n\t\tprintln(i) \n\tend\nend\n\n# \u2554\u2550\u2561 3c2cce20-5fe3-11eb-32e3-f542f9a94a6e\n@macroexpand @until j == 10 begin\n\tj += 1\n\tprintln(j) \nend\n\n# \u2554\u2550\u2561 10a8532a-5e60-11eb-3331-974e708cb39d\nmd\"\"\"\n## Non-Standard String Literals\n\nString literals prefixed by an identifier are called non-standard string literals, and can have different semantics than un-prefixed string literals. For example:\n\n\n  * `r\"^\\s*(?:#|$)\"` produces a regular expression object rather than a string\n  * `b\"DATA\\xff\\u2200\"` is a byte array literal for `[68,65,84,65,255,226,136,128]`.\n\nPerhaps surprisingly, these behaviors are not hard-coded into the Julia parser or compiler. Instead,\nthey are custom behaviors provided by a general mechanism that anyone can use: prefixed string\nliterals are parsed as calls to specially-named macros. For example, the regular expression macro\nis just the following:\n```julia\nmacro r_str(p)\n    Regex(p)\nend\n```\n\"\"\"\n\n# \u2554\u2550\u2561 d8a9ad46-5e7a-11eb-1cce-3d4bc49cd332\nmd\"\"\"\nThat's all. This macro says that the literal contents of the string literal `r\"^\\s*(?:#|$)\"` should\nbe passed to the `@r_str` macro and the result of that expansion should be placed in the syntax\ntree where the string literal occurs. In other words, the expression `r\"^\\s*(?:#|$)\"` is equivalent\nto placing the following object directly into the syntax tree:\n\"\"\"\n\n# \u2554\u2550\u2561 e85b2d3c-5e7a-11eb-3b89-e11bc274f7cf\nRegex(\"^\\\\s*(?:#|\\$)\")\n\n# \u2554\u2550\u2561 ec73cd70-5e7a-11eb-0285-6b1fabd1289d\nmd\"\"\"\nNot only is the string literal form shorter and far more convenient, but it is also more efficient:\nsince the regular expression is compiled, which takes time, and the `Regex` object is actually created *when the code is compiled*,\nthe compilation occurs only once, rather than every time the code is executed. Consider if the\nregular expression occurs in a loop:\n\"\"\"\n\n# \u2554\u2550\u2561 f1d24ba2-5e7a-11eb-010a-f543cfc306b5\nPlutoUI.with_terminal() do\n\tfor line \u2208 [\"first\", \"sec#nd\", \"third\"]\n\t\tm = match(r\"#\", line)\n\t\tif m === nothing\n\t\t\tprintln(\"nothing found\")\n\t\telse\n\t\t\tprintln(\"found something\")\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 f838762c-5e7a-11eb-000a-e963fab5e97d\nmd\"\"\"\nSince the regular expression `r\"^\\s*(?:#|$)\"` is compiled and inserted into the syntax tree when\nthis code is parsed, the expression is only compiled once instead of each time the loop is executed.\nIn order to accomplish this without macros, one would have to write this loop like this:\n\"\"\"\n\n# \u2554\u2550\u2561 149c1c12-5e7b-11eb-297d-6da57f45891b\nPlutoUI.with_terminal() do\n\tre = Regex(\"#\")\n\tfor line \u2208 [\"first\", \"sec#nd\", \"third\"]\n\t\tm = match(re, line)\n\t\tif m === nothing\n\t\t\tprintln(\"nothing found\")\n\t\telse\n\t\t\tprintln(\"found something\")\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 780da60e-5e7c-11eb-0c1a-55be73da188f\nmd\"\"\"\nMoreover, if the compiler could not determine that the regex object was constant over all loops,\ncertain optimizations might not be possible, making this version still less efficient than the\nmore convenient literal form above. Of course, there are still situations where the non-literal\nform is more convenient: if one needs to interpolate a variable into the regular expression, one\nmust take this more verbose approach. In the vast majority of use cases, however, regular expressions\nare not constructed based on run-time data. In this majority of cases, the ability to write regular\nexpressions as compile-time values is invaluable.\n\"\"\"\n\n# \u2554\u2550\u2561 90a672d0-5f5c-11eb-3a62-1b034cd41e67\nmd\"\nDesigning your own custom string literals can be done as such:\n\"\n\n# \u2554\u2550\u2561 b4104d12-5e7c-11eb-3e22-a78b74526129\nmacro foo_str(str, flag)\n    # do stuff\n\tstr, flag\nend\n\n# \u2554\u2550\u2561 20393e86-5e7d-11eb-18e3-613890472903\nfoo\"this is the string\"theflag\n\n# \u2554\u2550\u2561 5069df16-5e7d-11eb-217d-6b740e9b3559\nmd\"\"\"\nThe first example of a custom macro can be found in every notebook in this course! It is the markdown string literal, which allows the usage of Markdown markup language to prettify these lectures!\n\n```julia\nmd\"I am a Markdown string with glorious **formatting capabilities**.\"\n```\nI am a Markdown string with glorious *formatting* **capabilities**.\n\"\"\"\n\n# \u2554\u2550\u2561 1a50ff0e-5e7d-11eb-2fc4-cd5c12015751\nmd\"The second example is from the `BioSequences.jl` package. In that bioinformatics package, you can define sequences e.g. DNA and RNA as string literals:\n\"\n\n# \u2554\u2550\u2561 0664eb22-5e7d-11eb-07a6-ed35143bf03f\ndna\"ACGT\"\n\n# \u2554\u2550\u2561 a965bd70-5e7c-11eb-13dd-5fe83950af11\nmd\"Repetition and concatenation can be performed pretty straightforward:\"\n\n# \u2554\u2550\u2561 fb0c59fa-5f61-11eb-1ab7-bf7b80746aa2\nrepeat(dna\"TTAGGG\", 10)\n\n# \u2554\u2550\u2561 a157dafc-5e7c-11eb-105d-4db3ee9dbec6\ndna = dna\"ACGT\" * dna\"TGCAA\"\n\n# \u2554\u2550\u2561 0ec9887c-5f5e-11eb-29e5-931881dcb222\nmd\"Other typical string operations such as pushing new values work as you would expect\"\n\n# \u2554\u2550\u2561 1d4aca5a-5f5e-11eb-23c3-d1ddef4e6ee6\npush!(dna, DNA_A)\n\n# \u2554\u2550\u2561 c4c2137a-5f5d-11eb-1e06-8f5e2b1f1c0e\nmd\"There exist methods to convert a DNA sequence to its RNA equivalent:\"\n\n# \u2554\u2550\u2561 e13fcbbe-5f5d-11eb-2074-fdbc945861e1\nrna = convert(LongRNASeq, dna)\n\n# \u2554\u2550\u2561 2c1308f4-5f5e-11eb-058f-79cbf7dd23c6\nmd\"Note that altough the printout of the DNA and RNA object is different because of different nucleotides. The information content is the same, and as such this statement is true:\"\n\n# \u2554\u2550\u2561 fdf46c92-5f5d-11eb-345b-e145c9c2874c\ndna.data === rna.data\n\n# \u2554\u2550\u2561 37da420a-5f61-11eb-3688-09e6188165e3\nmd\"Remember before that we mentioned that you can add a flag to a string literal? In `BioSequences.jl` this has a use case. \n\nIf you have a function that generates a sequence, and you want it to create a new sequence each time it is called, then you can add a flag to the end of the sequence literal to dictate behaviour: A flag of 's' means 'static': the sequence will be allocated before code is run, as is the default behaviour. However providing 'd' flag changes the behaviour: 'd' means 'dynamic': the sequence will be allocated whilst the code is running, and not before. So to change foo so as it creates a new sequence each time it is called, simply add the 'd' flag to the sequence literal:\"\n\n# \u2554\u2550\u2561 d9f18616-5e8d-11eb-1b1a-3bcc749fb467\nfunction getdna_dynamic()\n\ts = dna\"CTT\"d     # 'd' flag appended to the string literal.\n\tpush!(s, DNA_A)\nend\n\n# \u2554\u2550\u2561 ea3da4dc-5e8d-11eb-0910-091b926aea58\ngetdna_dynamic()\n\n# \u2554\u2550\u2561 f13dd22a-5e8d-11eb-1def-137de66123ba\ngetdna_dynamic()\n\n# \u2554\u2550\u2561 be11ec9a-5f61-11eb-374a-c96489ea582d\nmd\"Output of `getdna_dynamic()` stays the same!\"\n\n# \u2554\u2550\u2561 f44d757e-5e8d-11eb-18f1-719d3c7b8688\nfunction getdna_static()\n\ts = dna\"CTT\"s     # 's' flag appended to the string literal.\n\tpush!(s, DNA_A)\nend\n\n# \u2554\u2550\u2561 fa310fdc-5e8d-11eb-024b-7ded3e213112\ngetdna_static()\n\n# \u2554\u2550\u2561 1acb02d4-5e8e-11eb-232c-f756e92c3f97\ngetdna_static()\n\n# \u2554\u2550\u2561 2dcb4600-5e8e-11eb-093e-59eb79f0cf20\nmd\"\"\"\nBe careful when you are using sequence literals inside of functions, and inside the bodies of things like for loops. And if you use them and are unsure, use the 's' and 'd' flags to ensure the behaviour you get is the behaviour you intend.\n\"\"\"\n\n# \u2554\u2550\u2561 2e2601b6-5e94-11eb-3613-eb5fee19b6b7\nmd\"\n## Overview of some interesting macros\n\"\n\n# \u2554\u2550\u2561 311a4666-5fc5-11eb-2cc0-ef66e5be96e3\nmd\"check if an expression is true\"\n\n# \u2554\u2550\u2561 4622422a-5fc5-11eb-20f2-9bfe466f8f30\n@assert true == true\n\n# \u2554\u2550\u2561 5ba5d6b6-5fc5-11eb-256b-73a954a5db68\nmd\"Integers and floating point numbers with arbitrary precision. This macro exists because promoting a floating point number to a `BigFloat` will keep the approximation error of `Float64`.\"\n\n# \u2554\u2550\u2561 a47ab60e-5fc5-11eb-368a-d76fc6ee640d\nbig\"0.1\"\n\n# \u2554\u2550\u2561 a99546c2-5fc5-11eb-0902-71cb283543c8\n@big_str \"0.1\"\n\n# \u2554\u2550\u2561 af2c7d4e-5fc5-11eb-30d4-a367d2c0db23\nbig(0.1)\n\n# \u2554\u2550\u2561 4a335466-5fc6-11eb-2273-312fd3928616\nmd\"There exists a lot more unique string literals than we have shown here, such as html strings, ip address literals, etc.\"\n\n# \u2554\u2550\u2561 17f771dc-5fc6-11eb-3b83-ed51f87194e0\nhtml\"\"\"<!-- HTML generated using hilite.me --><div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><table><tr><td><pre style=\"margin: 0; line-height: 125%\">1</pre></td><td><pre style=\"margin: 0; line-height: 125%\">println(<span style=\"color: #a31515\">&quot;hello world!&quot;</span>)\n</pre></td></tr></table></div>\n\"\"\"\n\n# \u2554\u2550\u2561 92836b48-5fc6-11eb-19b6-1d7a5c310052\n@terminal println(\"There is nothing like \", ip\"127.0.0.1\")\n\n# \u2554\u2550\u2561 f26e65a8-5fc6-11eb-3cd7-3f40c12a6abb\nmd\"Find out what function is exactly used in multiple dipatching.\"\n\n# \u2554\u2550\u2561 154e2392-5fc7-11eb-250b-e726ab7710e7\n@which sin(2.2)\n\n# \u2554\u2550\u2561 2cfa3952-5fc7-11eb-2e79-95f86aafbf2c\n@which sin(2)\n\n# \u2554\u2550\u2561 4aece42a-5fc7-11eb-291f-99120483339f\nmd\"\nThere exist some handy macros to analyse execution time and memory allocation.\n\n```julia\nusing BenchmarkTools\n@btime sin(2)\n@benchmark sin(2)\n@elapsed sin(2)\n```\n\"\n\n# \u2554\u2550\u2561 295e0028-5fd6-11eb-39e4-b392de1c0de2\nmd\"\"\"\nWhen developing modules, scripts or julia packages, you can use the `@info`, `@warn`, `@error` and `@debug` as logging macros. They are mostly useful in packages, not in notebooks like this.\n\nAn example usage would be a warning thrown by an optimisation algorithm to tell you that e.g. the predefined accuracy or tolerance was not reached.\n\nNote, to make `@debug` you need to set an environment variable to mark that you are in debug mode: `ENV[\"JULIA_DEBUG\"] = \"all\"`\n\"\"\"\n\n# \u2554\u2550\u2561 4a9d85ba-5fc7-11eb-1b6e-59ef2259dd24\nPlutoUI.with_terminal() do\n\t@info \"Information comes here\"\n\t@error \"Error has been found at this exact location\"\n\t@warn \"Same. but for a warning\"\nend\n\n# \u2554\u2550\u2561 34829ada-5fdc-11eb-263a-cf60a9650556\nmd\"Plot recipes are a nifty thing to make plots for your custom data type:\"\n\n# \u2554\u2550\u2561 5c33798c-5fda-11eb-186f-79450fb59e3b\nstruct TemperatureMeas\n\tt1\n\tt2\n\tt3\n\tTemperatureMeas(t1, t2, t3) = new(t1, t2, t3)\nend\n\n# \u2554\u2550\u2561 1ca80d42-5fdb-11eb-153f-9ddb25a4fb6b\n@recipe function f(tempmeas::TemperatureMeas)\n    xguide --> \"time (seconds)\"\n    yguide --> \"temperature (Celsius)\"\n    [tempmeas.t1, tempmeas.t2, tempmeas.t3] # return the arguments (input data) for the next recipe\nend\n\n# \u2554\u2550\u2561 1a793992-5fdb-11eb-3b38-05544f268a65\nplot(TemperatureMeas(Plots.fakedata(50), Plots.fakedata(50), Plots.fakedata(50)))\n\n# \u2554\u2550\u2561 d418dca4-5fe5-11eb-3f1a-a31e5dc5f7e9\nmd\"\nUsing the `Distributed` module, one can easily transform certain types of code to run distributed (i.e. on multiple cores). Below is an example of how you can transform a for-loop into its distributed version.\n\"\n\n# \u2554\u2550\u2561 f1b70998-5fdb-11eb-292b-0776f1b03816\nPlutoUI.with_terminal() do\n\t@sync @distributed for i \u2208 1:5\n\t\tprintln(i)\n\tend\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u255042e620aa-5f4c-11eb-2ebf-85814cf720e7\n# \u255f\u250031c1e25e-5e53-11eb-2467-9153d30962d5\n# \u255f\u25004ab33c0e-5e53-11eb-2e63-2dd6f06de3ba\n# \u2560\u2550e76d8f04-5e53-11eb-26df-db496622642d\n# \u255f\u2500f361d734-5e53-11eb-3957-61bd0a370b5a\n# \u2560\u255015aaa5b6-5e54-11eb-067f-9dcd096940b6\n# \u2560\u255034b6a71e-5e54-11eb-0324-4bffbe85c813\n# \u255f\u250022770a30-5e54-11eb-28db-1359ab1f402f\n# \u2560\u255041f9633c-5e54-11eb-323b-e9703c674b0f\n# \u255f\u250048104478-5e54-11eb-2f23-c98c4ccbb763\n# \u2560\u2550538e2054-5e54-11eb-2b9c-451637e1d8ee\n# \u255f\u25006e3beaf8-5e54-11eb-154b-95f5887efcb0\n# \u2560\u25507de376a8-5e54-11eb-0356-9b74225b671e\n# \u255f\u2500825b45a6-5e54-11eb-27e6-f3cca8a2f67e\n# \u2560\u25508b23cac8-5e54-11eb-2424-5972ea3d2e21\n# \u255f\u25008ecd49ba-5e54-11eb-24b6-a328043b7a07\n# \u2560\u2550985a57f2-5e54-11eb-1f2f-bf23014c6171\n# \u255f\u2500d66ea502-5e54-11eb-2694-4766a05aed38\n# \u2560\u2550db1e9936-5e54-11eb-376f-8ddea65d976c\n# \u2560\u2550e4f50fda-5e54-11eb-2210-6b8eb531d898\n# \u255f\u2500ea891b6e-5e54-11eb-24c9-357a8a65d1c0\n# \u2560\u2550efe79c46-5e54-11eb-23c9-3107ea94e5b9\n# \u2560\u2550f5fac50e-5e54-11eb-2ff9-cf7e30a4a8ad\n# \u2560\u2550fe35b31e-5e54-11eb-258e-29913f1e8723\n# \u255f\u25000464f1be-5e55-11eb-34df-ef1217012663\n# \u255f\u2500abfc3392-5e5a-11eb-149d-ad3dd2755524\n# \u2560\u2550917a579c-5e5a-11eb-3fe6-cb0dcad5e4e6\n# \u2560\u255082d3220a-5e5a-11eb-3c49-d5e7afb1af2a\n# \u2560\u255067954702-5e5a-11eb-1ab1-e9a285ee208d\n# \u255f\u25002ae6ac9c-5e5a-11eb-060c-b3eae1df0493\n# \u2560\u25505ef3d532-5e5a-11eb-30b3-a569e0bd736e\n# \u2560\u25503bb1f892-5e5a-11eb-0946-e5e977a4e5b2\n# \u255f\u25005763cca0-5e5a-11eb-1341-3d9bfb47fb58\n# \u2560\u2550e6a3902a-5e58-11eb-2bd2-4bc779bcebe7\n# \u255f\u250011991248-5e55-11eb-2748-992f6fe48620\n# \u255f\u25001c016bec-5e5d-11eb-3687-931da82f8c04\n# \u2560\u2550552ea3bc-5e5d-11eb-0c97-e1e6db526df3\n# \u2560\u2550629c4f9a-5e5d-11eb-2731-bf7457755829\n# \u255f\u250066c113d0-5e5d-11eb-0aab-b3fa7f98eae5\n# \u2560\u25506fc2fb9c-5e5d-11eb-0f21-6524555a9c42\n# \u2560\u25506dab1d58-5e5d-11eb-3381-a1796f8d14c5\n# \u255f\u2500b833d8ba-5e5d-11eb-3311-899efb15c1b0\n# \u2560\u2550169c6aa2-5e5e-11eb-1d84-e7e3b4641e10\n# \u2560\u2550ae1525ea-5e5e-11eb-3906-593143776559\n# \u255f\u2500c40cebe6-5e5e-11eb-1f7f-37134b7a449f\n# \u2560\u2550f5ab41f4-5e5f-11eb-02f7-efcb3e05e616\n# \u255f\u25000001a53a-5e60-11eb-18df-d7e9e7dcf43f\n# \u2560\u2550bda7fd66-5e5e-11eb-1e06-eb67a0178e14\n# \u255f\u25001707320e-5e60-11eb-3f33-798c81663488\n# \u2560\u25501fdf9600-5e60-11eb-30d3-b167811ae47f\n# \u255f\u25002dcacdfc-5e60-11eb-3d04-9f2ffb46d0fe\n# 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{"text": "\n#################### Post order traversal ####################\n\"\"\"\n    post_order(root::T, traversal::Vector{T})::Vector{T} where T<:AbstractNode\n\nThis function performs a post order traversal through the tree. It is assumed that `root` is the\nroot of the tree. Thus, if `root` is not the root, the subtree defined by the root `root` is\nused for the post order traversal.\n\nReturns vector of Nodes.\n\n* `root` : root Node of tree.\n\n* `traversal` : vector of Nodes; nodes are pushed to this vector as the tree is traversed.\n\"\"\"\nfunction post_order(root::T, traversal::Vector{T}) where T<:AbstractNode\n   if root.nchild != 0\n        for child in root.children\n            post_order(child, traversal)\n        end\n   end # if\n   push!(traversal, root)\n   return traversal\nend # function post_order_trav\n\n\n\"\"\"\n    post_order(root::T)::Vector{T} where T<:AbstractNode\n\nThis function does post order traversal. Only the root node needs to be supplied.\n\nReturns vector of Nodes.\n\n* `root` : root Node of tree.\n\"\"\"\nfunction post_order(root::T)::Vector{T} where T<:AbstractNode\n    t::Vector{T} = []\n    post_order(root, t)\n    return t\nend # function post_order\n\n\"\"\"\n    get_leaves(root::T, traversal::Vector{T})::Vector{T} where T<:AbstractNode\n\nThis function returns leaf nodes of a tree. It is assumed that `root` is the\nroot of the tree. Thus, if `root` is not the root, the subtree defined by the root `root` is\nused.\n\nReturns a vector of leaf Nodes.\n\n* `root` : root Node of tree to traverse.\n\n* `traversal` : vector of Nodes; leaf Nodes, once found, are pushed to this vector.\n\n\"\"\"\nfunction get_leaves(root::T, traversal::Vector{T})::Vector{T} where T<:AbstractNode\n   if root.nchild != 0\n        for child in root.children\n            get_leaves(child, traversal)\n        end\n   else\n       push!(traversal, root)\n   end # if\n\n   return traversal\nend # function post_order_trav\n\n\n\"\"\"\n    get_leaves(root::T)::Vector{T} where T<:AbstractNode\n\nThis function returns the leaves of a tree. Only the root node needs to be supplied.\n\nReturns vector of leaf Nodes.\n\n* `root` : root Node of tree.\n\"\"\"\nfunction get_leaves(root::T)::Vector{T} where T<:AbstractNode\n    t::Vector{T} = []\n    get_leaves(root, t)\n    return t\nend # function post_order\n\n\n\n\n#################### Pre order traversal ####################\n\n\"\"\"\n    pre_order(root::T, traversal::Vector{T})::Vector{T} where T<:AbstractNode\n\nThis function performs a pre order traversal through the tree. It is assumed that `root` is the\nroot of the tree. Thus, if `root` is not the root, the subtree defined by the root `root` is\nused for the pre order traversal.\n\nReturns vector of Nodes.\n\n* `root` : root Node of tree.\n\n* `traversal` : vector of Nodes; nodes are pushed to this vector as the tree is traversed.\n\"\"\"\nfunction pre_order(root::T, traversal::Vector{T})::Vector{T} where T<:AbstractNode\n    push!(traversal, root)\n    if root.nchild != 0\n        for child in root.children\n            pre_order(child, traversal)\n        end\n    end # if\n    return traversal\nend # function pre_order!\n\n\n\"\"\"\n    pre_order(root::T)::Vector{T} where T<:AbstractNode\n\nThis function does pre order traversal. Only the root node needs to be supplied.\n\nReturns vector of Nodes.\n\n* `root` : root Node of tree.\n\"\"\"\nfunction pre_order(root::T)::Vector{T} where T<:AbstractNode\n    t::Vector{T} = []\n    pre_order(root, t)\n    return t\nend # function pre_order\n\n#################### Level order traversal ####################\n\n\"\"\"\n    level_order(node::T)::Array{T} where T<:AbstractNode\n\nThis function does level order traversal. Only the root node needs to be supplied.\n\nReturns Array of Nodes.\n\n* `node` : root Node of tree.\n\"\"\"\nfunction level_order(node::T)::Array{T} where T<:AbstractNode\n    level = 1\n    stack::Array{T} = []\n    while level_traverse(node, level, stack)\n        level += 1\n    end # while\n    stack\nend # function level_order\n\n\"\"\"\n    level_traverse(node::T, level::Int64, stack::Array{T})::Bool where T <:AbstractNode\n\nThis function traverses a level of the tree specified through `node`. The level\nis specified via the `level` argument and the nodes visited are stored in the\n`stack`.\nThis function is intended as the internal worker for the level_order function.\n\"\"\"\nfunction level_traverse(node::T, level::Int64, stack::Array{T})::Bool where T <:AbstractNode\n\n    if level == 1\n        # level which needs to be traversed right now\n        push!(stack, node)\n        return true\n    else\n        # move down the tree to the correct level\n        boolqueue = [false] # this is used to look for the correct level\n        for child in node.children\n            push!(boolqueue, level_traverse(child, level-1, stack))\n        end # for\n        return reduce(|, boolqueue)\n    end # if\nend # function level_traverse\n", "meta": {"hexsha": "f381a1c7a09a339203ff1c00e53fdb11b7cf7b94", "size": 4773, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Basics/Tree_Traversal.jl", "max_stars_repo_name": "erathorn/MCPhyloTree.jl", "max_stars_repo_head_hexsha": "028a5c43b229902cc67977d75adbc55bb2506842", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-12-28T10:34:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T10:34:45.000Z", "max_issues_repo_path": "src/Basics/Tree_Traversal.jl", "max_issues_repo_name": "erathorn/MCPhyloTree.jl", "max_issues_repo_head_hexsha": "028a5c43b229902cc67977d75adbc55bb2506842", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2021-07-20T10:58:46.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T14:37:57.000Z", "max_forks_repo_path": "src/Basics/Tree_Traversal.jl", "max_forks_repo_name": "erathorn/MCPhyloTree.jl", "max_forks_repo_head_hexsha": "028a5c43b229902cc67977d75adbc55bb2506842", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-10T13:36:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-23T08:15:36.000Z", "avg_line_length": 27.9122807018, "max_line_length": 96, "alphanum_fraction": 0.6681332495, "num_tokens": 1206, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.12592277467581975, "lm_q1q2_score": 0.062469511506531135}}
{"text": "## Exercise 4-3\n## Write a function call that passes t as an argument to square, and then run the macro again.\nusing ThinkJulia\n\nprintln(\"Ans: \")\n\nfunction square(turtle::Turtle, distance::Int = 100)\n    @svg begin\n        for i in 1:3\n            forward(turtle, distance)\n            turn(turtle, -90)\n        end\n        \n        forward(turtle, distance)\n    end\nend\n\nfunction drawSquare()\n    turtle = Turtle()\n    square(turtle)\nend\n\ndrawSquare()\n\nprintln(\"End.\")\n", "meta": {"hexsha": "8fa5b100ab29a08c9d1e48d25678499f63c58006", "size": 470, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter4/ex3.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter4/ex3.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter4/ex3.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.0769230769, "max_line_length": 94, "alphanum_fraction": 0.6191489362, "num_tokens": 120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3886180125441397, "lm_q2_score": 0.16026603032235004, "lm_q1q2_score": 0.0622822661822105}}
{"text": "### A Pluto.jl notebook ###\n# v0.16.0\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 5cd8d60a-de87-429d-a851-a35c4a656b50\nbegin\n\tusing Arrow\n\tusing Chain\n\tusing CSV\n\tusing DataFrameMacros\n\tusing DataFrames\n\tusing PyCall\n\tusing RCall\nend\n\n# \u2554\u2550\u2561 3eb610ba-1470-11ec-3679-5d2c0f46c6a5\nmd\"\"\"\n# Creating Arrow files in Julia\n\nData frames stored in [Arrow](https://arrow.apache.org) can be read using many different languages.\nJulia provides very good facilities for creating Arrow files.\n\nThis notebook illustrates creating Arrow files from a CSV source and from RData or RDS files.\n\nThe conversion itself is straightforward.\nIt can be worthwhile doing some data cleaning and transformation during these conversion steps.\n\n## Simple conversion\n\nConsider the conversion of the CSV file `full_axb.csv` provided by Patricia Amillos.\n\nFirst, load the packages to be used.\n\"\"\"\n\n# \u2554\u2550\u2561 de931048-9d8a-4752-a170-2b6179470dec\nmd\"\"\"\nRead the CSV file and convert to a DataFrame.\n\"\"\"\n\n# \u2554\u2550\u2561 96d01ab2-7b43-41c8-9780-bbd3bd6053e5\naxb_data = CSV.read(\"../Patricia_Amillos/full_axb.csv\", DataFrame)\n\n# \u2554\u2550\u2561 df69d7d5-ea2d-4b37-804b-7c4eb5e3c91c\ndescribe(axb_data)\n\n# \u2554\u2550\u2561 4432e088-5daa-46c5-b344-e5b28fe8df52\ntypeof.(eachcol(axb_data))\n\n# \u2554\u2550\u2561 35ba4c00-9962-46c2-9ad4-8d78e3b2c3b2\nmd\"\"\"\nThis DataFrame can be written to an Arrow file without modification.\n\"\"\"\n\n# \u2554\u2550\u2561 b3cfca7a-dd18-4fa9-b010-705458ccafa6\naxb_full_arrow = Arrow.write(\"./data/full_axb.arrow\", axb_data; compress=:zstd);\n\n# \u2554\u2550\u2561 867462e3-11ea-41b8-bc7a-4aa2fc4afe5b\nmd\"\"\"\n[Zstandard](https://en.wikipedia.org/wiki/Zstandard) compression is quite effective in this case in reducing the file size.\nThe Arrow file is about 1/10 the size of the CSV file.\n\"\"\"\n\n# \u2554\u2550\u2561 4e954cf5-ba20-497b-8bbc-3d84d5fb624d\nfilesize(axb_full_arrow)\n\n# \u2554\u2550\u2561 2983ed07-8790-412d-8ab7-52d1edf913cb\nfilesize(\"../Patricia_Amillos/full_axb.csv\")\n\n# \u2554\u2550\u2561 beebe630-2c4e-4c4b-9fbf-e97044dc4916\nmd\"\"\"\n## Restoring the data frame from the arrow file\n\nThe function to read an Arrow file is `Arrow.Table` which produces a column table.\nOften this table is converted to a `DataFrame` for further manipulation.\n\"\"\"\n\n# \u2554\u2550\u2561 b7269fc0-6e8e-4a6d-955e-a35ba2ab3406\naxb_from_arrow = Arrow.Table(axb_full_arrow)\n\n# \u2554\u2550\u2561 3e9cda79-3843-4a8c-af4e-9334d63a982e\nmd\"\"\"\nAlso, behind the scenes, the `Arrow.write` function has converted some of the categorical covariates to more compact storage formats.\nIn Arrow terminology the storage type for a factor or categorical variable is called `DictEncoded`.\n\"\"\"\n\n# \u2554\u2550\u2561 8429788b-f1a5-4e30-a913-80831c16ebe7\ntypeof.(axb_from_arrow)\n\n# \u2554\u2550\u2561 9b5dd88a-e51d-4892-b79e-9a12dd8750c8\nmd\"\"\"\n## Conversion from RData or RDS\n\nA `.RData` or `.rda` file can be loaded into R producing a new environment from which we extract the names of the objects that have been loaded.\n\nThe `somdemo.Rda` file was contributed by Athanassios Protopapas.\n\"\"\"\n\n# \u2554\u2550\u2561 e44aec27-78ad-47ce-b071-d1ec698808d5\nR\"\"\"load(\"../Athanassios_Protopapas/somdemo.Rda\")\"\"\";\n\n# \u2554\u2550\u2561 9a0e4476-df20-4ba3-80dd-d2f202083936\nR\"ls()\"   # list the contents of the environment created in the previous call\n\n# \u2554\u2550\u2561 fa931a48-eec5-4d48-bc38-733585fa6f98\n@rget som # import this data frame into Julia under the same name\n\n# \u2554\u2550\u2561 973007a0-fddc-4aa2-a88d-c5967f3d77b4\nsom_arrow = Arrow.write(\"./data/somdemo.arrow\", som; compress=:zstd);\n\n# \u2554\u2550\u2561 c9027bc8-87bd-48c6-958e-f9e6f7d4d741\nsom_from_arrow = DataFrame(Arrow.Table(som_arrow))\n\n# \u2554\u2550\u2561 81d362bf-589c-4aa6-abab-2097b9a5f60c\nmd\"\"\"\n## Basic writing and reading of arrow files\n\nWriting and reading an arrow file in Julia is straightforward.\nTypically the file is written as a compressed file and the Zstandard compression is quite effective on such files.\nAn uncompressed file gives maximal performance when reading an arrow file, because it can be [memory-mapped](https://en.wikipedia.org/wiki/Memory-mapped_file).\nHowever, the uncompressed file requires more storage and more information to be transmitted when download the file, for example.\n\nThe structure of these files can be checked in R and in Python.\n\"\"\"\n\n# \u2554\u2550\u2561 16418fe6-7fc2-47d2-92fb-2e4949c552fd\nfeather = pyimport(\"pyarrow.feather\"); \n\n# \u2554\u2550\u2561 62df14a1-52c5-4bcc-93ab-a8646dc67d7c\nfeather.read_table(som_arrow)\n\n# \u2554\u2550\u2561 44c58069-8c0f-469c-ad29-263b1174d5ca\nmd\"\"\"\nThis shows that the `orthaccu` column is being stored as a column of 64-bit integers when, in fact, it is a boolean value.\nIt would be better to convert it to `Bool` before storing it.\n\nFor R, first load the `arrow` and `tibble` packages then read the file.\nFor historical reasons this format is called \"feather\" in R.\n\"\"\"\n\n# \u2554\u2550\u2561 4098bead-379d-42a4-b906-cc44967e9796\nR\"\"\"\nlibrary(\"arrow\")\nlibrary(\"tibble\")\nsom <- read_feather($som_arrow)  # interpolate the file name from Julia\nglimpse(som)\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nArrow = \"69666777-d1a9-59fb-9406-91d4454c9d45\"\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nChain = \"8be319e6-bccf-4806-a6f7-6fae938471bc\"\nDataFrameMacros = \"75880514-38bc-4a95-a458-c2aea5a3a702\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nPyCall = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\nRCall = \"6f49c342-dc21-5d91-9882-a32aef131414\"\n\n[compat]\nArrow = \"~1.6.2\"\nCSV = \"~0.9.1\"\nChain = \"~0.4.8\"\nDataFrameMacros = \"~0.1.0\"\nDataFrames = \"~1.2.2\"\nPyCall = \"~1.92.3\"\nRCall = \"~0.13.12\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Arrow]]\ndeps = [\"ArrowTypes\", \"BitIntegers\", \"CodecLz4\", \"CodecZstd\", \"DataAPI\", \"Dates\", \"Mmap\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"TimeZones\", \"UUIDs\"]\ngit-tree-sha1 = \"b00e6eaba895683867728e73af78a00218f0db10\"\nuuid = \"69666777-d1a9-59fb-9406-91d4454c9d45\"\nversion = \"1.6.2\"\n\n[[ArrowTypes]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"a0633b6d6efabf3f76dacd6eb1b3ec6c42ab0552\"\nuuid = \"31f734f8-188a-4ce0-8406-c8a06bd891cd\"\nversion = \"1.2.1\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[BitIntegers]]\ndeps = [\"Random\"]\ngit-tree-sha1 = \"f50b5a99aa6ff9db7bf51255b5c21c8bc871ad54\"\nuuid = \"c3b6d118-76ef-56ca-8cc7-ebb389d030a1\"\nversion = \"0.2.5\"\n\n[[CSV]]\ndeps = [\"CodecZlib\", \"Dates\", \"FilePathsBase\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"c907e91e253751f5840135f4c9deb1308273338d\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.9.1\"\n\n[[CategoricalArrays]]\ndeps = [\"DataAPI\", \"Future\", \"JSON\", \"Missings\", \"Printf\", \"RecipesBase\", \"Statistics\", \"StructTypes\", \"Unicode\"]\ngit-tree-sha1 = \"1562002780515d2573a4fb0c3715e4e57481075e\"\nuuid = \"324d7699-5711-5eae-9e2f-1d82baa6b597\"\nversion = \"0.10.0\"\n\n[[Chain]]\ngit-tree-sha1 = \"cac464e71767e8a04ceee82a889ca56502795705\"\nuuid = \"8be319e6-bccf-4806-a6f7-6fae938471bc\"\nversion = \"0.4.8\"\n\n[[ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"4ce9393e871aca86cc457d9f66976c3da6902ea7\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.4.0\"\n\n[[CodecLz4]]\ndeps = [\"Lz4_jll\", \"TranscodingStreams\"]\ngit-tree-sha1 = \"59fe0cb37784288d6b9f1baebddbf75457395d40\"\nuuid = \"5ba52731-8f18-5e0d-9241-30f10d1ec561\"\nversion = \"0.4.0\"\n\n[[CodecZlib]]\ndeps = [\"TranscodingStreams\", \"Zlib_jll\"]\ngit-tree-sha1 = \"ded953804d019afa9a3f98981d99b33e3db7b6da\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.7.0\"\n\n[[CodecZstd]]\ndeps = [\"TranscodingStreams\", \"Zstd_jll\"]\ngit-tree-sha1 = \"d19cd9ae79ef31774151637492291d75194fc5fa\"\nuuid = \"6b39b394-51ab-5f42-8807-6242bab2b4c2\"\nversion = \"0.7.0\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"4866e381721b30fac8dda4c8cb1d9db45c8d2994\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.37.0\"\n\n[[CompilerSupportLibraries_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"e66e0078-7015-5450-92f7-15fbd957f2ae\"\n\n[[Conda]]\ndeps = [\"JSON\", \"VersionParsing\"]\ngit-tree-sha1 = \"299304989a5e6473d985212c28928899c74e9421\"\nuuid = \"8f4d0f93-b110-5947-807f-2305c1781a2d\"\nversion = \"1.5.2\"\n\n[[Crayons]]\ngit-tree-sha1 = \"3f71217b538d7aaee0b69ab47d9b7724ca8afa0d\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.0.4\"\n\n[[DataAPI]]\ngit-tree-sha1 = \"bec2532f8adb82005476c141ec23e921fc20971b\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.8.0\"\n\n[[DataFrameMacros]]\ndeps = [\"DataFrames\"]\ngit-tree-sha1 = \"508d57ef7b78551cf69c2837d80af5017ce57217\"\nuuid = \"75880514-38bc-4a95-a458-c2aea5a3a702\"\nversion = \"0.1.0\"\n\n[[DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"d785f42445b63fc86caa08bb9a9351008be9b765\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.2.2\"\n\n[[DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"7d9d316f04214f7efdbb6398d545446e246eff02\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.10\"\n\n[[DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[DocStringExtensions]]\ndeps = [\"LibGit2\"]\ngit-tree-sha1 = \"a32185f5428d3986f47c2ab78b1f216d5e6cc96f\"\nuuid = \"ffbed154-4ef7-542d-bbb7-c09d3a79fcae\"\nversion = \"0.8.5\"\n\n[[Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[ExprTools]]\ngit-tree-sha1 = \"b7e3d17636b348f005f11040025ae8c6f645fe92\"\nuuid = \"e2ba6199-217a-4e67-a87a-7c52f15ade04\"\nversion = \"0.1.6\"\n\n[[FilePathsBase]]\ndeps = [\"Dates\", \"Mmap\", \"Printf\", \"Test\", \"UUIDs\"]\ngit-tree-sha1 = \"0f5e8d0cb91a6386ba47bd1527b240bd5725fbae\"\nuuid = \"48062228-2e41-5def-b9a4-89aafe57970f\"\nversion = \"0.9.10\"\n\n[[Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[IrrationalConstants]]\ngit-tree-sha1 = \"f76424439413893a832026ca355fe273e93bce94\"\nuuid = \"92d709cd-6900-40b7-9082-c6be49f344b6\"\nversion = \"0.1.0\"\n\n[[IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[JLLWrappers]]\ndeps = [\"Preferences\"]\ngit-tree-sha1 = \"642a199af8b68253517b80bd3bfd17eb4e84df6e\"\nuuid = \"692b3bcd-3c85-4b1f-b108-f13ce0eb3210\"\nversion = \"1.3.0\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[LazyArtifacts]]\ndeps = [\"Artifacts\", \"Pkg\"]\nuuid = \"4af54fe1-eca0-43a8-85a7-787d91b784e3\"\n\n[[LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[LinearAlgebra]]\ndeps = [\"Libdl\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[LogExpFunctions]]\ndeps = [\"ChainRulesCore\", \"DocStringExtensions\", \"IrrationalConstants\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"86197a8ecb06e222d66797b0c2d2f0cc7b69e42b\"\nuuid = \"2ab3a3ac-af41-5b50-aa03-7779005ae688\"\nversion = \"0.3.2\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[Lz4_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"5d494bc6e85c4c9b626ee0cab05daa4085486ab1\"\nuuid = \"5ced341a-0733-55b8-9ab6-a4889d929147\"\nversion = \"1.9.3+0\"\n\n[[MacroTools]]\ndeps = [\"Markdown\", \"Random\"]\ngit-tree-sha1 = \"5a5bc6bf062f0f95e62d0fe0a2d99699fed82dd9\"\nuuid = \"1914dd2f-81c6-5fcd-8719-6d5c9610ff09\"\nversion = \"0.5.8\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"2ca267b08821e86c5ef4376cffed98a46c2cb205\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.1\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[Mocking]]\ndeps = [\"ExprTools\"]\ngit-tree-sha1 = \"748f6e1e4de814b101911e64cc12d83a6af66782\"\nuuid = \"78c3b35d-d492-501b-9361-3d52fe80e533\"\nversion = \"0.7.2\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[OpenSpecFun_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"13652491f6856acfd2db29360e1bbcd4565d04f1\"\nuuid = \"efe28fd5-8261-553b-a9e1-b2916fc3738e\"\nversion = \"0.5.5+0\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"438d35d2d95ae2c5e8780b330592b6de8494e779\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.0.3\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"a193d6ad9c45ada72c14b731a318bedd3c2f00cf\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.3.0\"\n\n[[Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"00cfd92944ca9c760982747e9a1d0d5d86ab1e5a\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.2\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"0d1245a357cc61c8cd61934c07447aa569ff22e6\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.1.0\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[PyCall]]\ndeps = [\"Conda\", \"Dates\", \"Libdl\", \"LinearAlgebra\", \"MacroTools\", \"Serialization\", \"VersionParsing\"]\ngit-tree-sha1 = \"169bb8ea6b1b143c5cf57df6d34d022a7b60c6db\"\nuuid = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\nversion = \"1.92.3\"\n\n[[RCall]]\ndeps = [\"CategoricalArrays\", \"Conda\", \"DataFrames\", \"DataStructures\", \"Dates\", \"Libdl\", \"Missings\", \"REPL\", \"Random\", \"Requires\", \"StatsModels\", \"WinReg\"]\ngit-tree-sha1 = \"80a056277142a340e646beea0e213f9aecb99caa\"\nuuid = \"6f49c342-dc21-5d91-9882-a32aef131414\"\nversion = \"0.13.12\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[RecipesBase]]\ngit-tree-sha1 = \"44a75aa7a527910ee3d1751d1f0e4148698add9e\"\nuuid = \"3cdcf5f2-1ef4-517c-9805-6587b60abb01\"\nversion = \"1.1.2\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"4036a3bd08ac7e968e27c203d45f5fff15020621\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.1.3\"\n\n[[Rmath]]\ndeps = [\"Random\", \"Rmath_jll\"]\ngit-tree-sha1 = \"bf3188feca147ce108c76ad82c2792c57abe7b1f\"\nuuid = \"79098fc4-a85e-5d69-aa6a-4863f24498fa\"\nversion = \"0.7.0\"\n\n[[Rmath_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"68db32dff12bb6127bac73c209881191bf0efbb7\"\nuuid = \"f50d1b31-88e8-58de-be2c-1cc44531875f\"\nversion = \"0.3.0+0\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"54f37736d8934a12a200edea2f9206b03bdf3159\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.7\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[ShiftedArrays]]\ngit-tree-sha1 = \"22395afdcf37d6709a5a0766cc4a5ca52cb85ea0\"\nuuid = \"1277b4bf-5013-50f5-be3d-901d8477a67a\"\nversion = \"1.0.0\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[SpecialFunctions]]\ndeps = [\"ChainRulesCore\", \"LogExpFunctions\", \"OpenSpecFun_jll\"]\ngit-tree-sha1 = \"a322a9493e49c5f3a10b50df3aedaf1cdb3244b7\"\nuuid = \"276daf66-3868-5448-9aa4-cd146d93841b\"\nversion = \"1.6.1\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[StatsAPI]]\ngit-tree-sha1 = \"1958272568dc176a1d881acb797beb909c785510\"\nuuid = \"82ae8749-77ed-4fe6-ae5f-f523153014b0\"\nversion = \"1.0.0\"\n\n[[StatsBase]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"Missings\", \"Printf\", \"Random\", \"SortingAlgorithms\", \"SparseArrays\", \"Statistics\", \"StatsAPI\"]\ngit-tree-sha1 = \"8cbbc098554648c84f79a463c9ff0fd277144b6c\"\nuuid = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nversion = \"0.33.10\"\n\n[[StatsFuns]]\ndeps = [\"ChainRulesCore\", \"IrrationalConstants\", \"LogExpFunctions\", \"Reexport\", \"Rmath\", \"SpecialFunctions\"]\ngit-tree-sha1 = \"46d7ccc7104860c38b11966dd1f72ff042f382e4\"\nuuid = \"4c63d2b9-4356-54db-8cca-17b64c39e42c\"\nversion = \"0.9.10\"\n\n[[StatsModels]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"Printf\", \"ShiftedArrays\", \"SparseArrays\", \"StatsBase\", \"StatsFuns\", \"Tables\"]\ngit-tree-sha1 = \"3fa15c1f8be168e76d59097f66970adc86bfeb95\"\nuuid = \"3eaba693-59b7-5ba5-a881-562e759f1c8d\"\nversion = \"0.6.25\"\n\n[[StructTypes]]\ndeps = [\"Dates\", \"UUIDs\"]\ngit-tree-sha1 = \"8445bf99a36d703a09c601f9a57e2f83000ef2ae\"\nuuid = \"856f2bd8-1eba-4b0a-8007-ebc267875bd4\"\nversion = \"1.7.3\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"368d04a820fe069f9080ff1b432147a6203c3c89\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.5.1\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TimeZones]]\ndeps = [\"Dates\", \"Future\", \"LazyArtifacts\", \"Mocking\", \"Pkg\", \"Printf\", \"RecipesBase\", \"Serialization\", \"Unicode\"]\ngit-tree-sha1 = \"6c9040665b2da00d30143261aea22c7427aada1c\"\nuuid = \"f269a46b-ccf7-5d73-abea-4c690281aa53\"\nversion = \"1.5.7\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[VersionParsing]]\ngit-tree-sha1 = \"80229be1f670524750d905f8fc8148e5a8c4537f\"\nuuid = \"81def892-9a0e-5fdd-b105-ffc91e053289\"\nversion = \"1.2.0\"\n\n[[WeakRefStrings]]\ndeps = [\"DataAPI\", \"Parsers\"]\ngit-tree-sha1 = \"4a4cfb1ae5f26202db4f0320ac9344b3372136b0\"\nuuid = \"ea10d353-3f73-51f8-a26c-33c1cb351aa5\"\nversion = \"1.3.0\"\n\n[[WinReg]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"808380e0a0483e134081cc54150be4177959b5f4\"\nuuid = \"1b915085-20d7-51cf-bf83-8f477d6f5128\"\nversion = \"0.3.1\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[Zstd_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6\"\nuuid = \"3161d3a3-bdf6-5164-811a-617609db77b4\"\nversion = \"1.5.0+0\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25003eb610ba-1470-11ec-3679-5d2c0f46c6a5\n# \u2560\u25505cd8d60a-de87-429d-a851-a35c4a656b50\n# \u2560\u2550de931048-9d8a-4752-a170-2b6179470dec\n# \u2560\u255096d01ab2-7b43-41c8-9780-bbd3bd6053e5\n# \u2560\u2550df69d7d5-ea2d-4b37-804b-7c4eb5e3c91c\n# \u2560\u25504432e088-5daa-46c5-b344-e5b28fe8df52\n# \u255f\u250035ba4c00-9962-46c2-9ad4-8d78e3b2c3b2\n# \u2560\u2550b3cfca7a-dd18-4fa9-b010-705458ccafa6\n# \u255f\u2500867462e3-11ea-41b8-bc7a-4aa2fc4afe5b\n# \u2560\u25504e954cf5-ba20-497b-8bbc-3d84d5fb624d\n# \u2560\u25502983ed07-8790-412d-8ab7-52d1edf913cb\n# \u255f\u2500beebe630-2c4e-4c4b-9fbf-e97044dc4916\n# \u2560\u2550b7269fc0-6e8e-4a6d-955e-a35ba2ab3406\n# \u255f\u25003e9cda79-3843-4a8c-af4e-9334d63a982e\n# \u2560\u25508429788b-f1a5-4e30-a913-80831c16ebe7\n# \u255f\u25009b5dd88a-e51d-4892-b79e-9a12dd8750c8\n# \u2560\u2550e44aec27-78ad-47ce-b071-d1ec698808d5\n# \u2560\u25509a0e4476-df20-4ba3-80dd-d2f202083936\n# \u2560\u2550fa931a48-eec5-4d48-bc38-733585fa6f98\n# \u2560\u2550973007a0-fddc-4aa2-a88d-c5967f3d77b4\n# \u2560\u2550c9027bc8-87bd-48c6-958e-f9e6f7d4d741\n# \u255f\u250081d362bf-589c-4aa6-abab-2097b9a5f60c\n# \u2560\u255016418fe6-7fc2-47d2-92fb-2e4949c552fd\n# \u2560\u255062df14a1-52c5-4bcc-93ab-a8646dc67d7c\n# \u255f\u250044c58069-8c0f-469c-ad29-263b1174d5ca\n# \u2560\u25504098bead-379d-42a4-b906-cc44967e9796\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "4037494b4ef3366c9b8498533d71ea43e2aa0d5c", "size": 22103, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "instructors/writingarrow.jl", "max_stars_repo_name": "RePsychLing/SMLP2021datasets", "max_stars_repo_head_hexsha": "afe55e62a9b9c631afd77f0fc86484e4dc832cb9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-09-07T13:43:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T14:18:48.000Z", "max_issues_repo_path": "instructors/writingarrow.jl", "max_issues_repo_name": "RePsychLing/SMLP2021datasets", "max_issues_repo_head_hexsha": "afe55e62a9b9c631afd77f0fc86484e4dc832cb9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "instructors/writingarrow.jl", "max_forks_repo_name": "RePsychLing/SMLP2021datasets", "max_forks_repo_head_hexsha": "afe55e62a9b9c631afd77f0fc86484e4dc832cb9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.9132867133, "max_line_length": 280, "alphanum_fraction": 0.7419807266, "num_tokens": 9762, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42632159254749036, "lm_q2_score": 0.14608724518943897, "lm_q1q2_score": 0.06228014702003732}}
{"text": "mutable struct LinkedStack{T} <: AbstractStack{T}\n    top::LinkNode{T}\n    size::Int\n    LinkedStack{T}() where T = new(LinkNode{T}(), 0) \nend\n\nisempty(l::LinkedStack) = l.size == 0\nlength(l::LinkedStack) = l.size\ntopvalue(l::LinkedStack) = isempty(l) ? throw(ArgumentError(\"stack must be non-empty\")) : l.top.data\n\nfunction push!(l::LinkedStack, item)\n    l.top = LinkNode(l.top, item)\n    l.size += 1\nend\n\nfunction pop!(l::LinkedStack)\n    (l.size == 0) && throw(ArgumentError(\"stack must be non-empty\"))\n    item = l.top.data\n    l.top = l.top.next\n    l.size -= 1\n    return item\nend\n\n\n\n", "meta": {"hexsha": "36baad9cf6f84e8cd883739340473bde4797550a", "size": 591, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/list/linked_stack.jl", "max_stars_repo_name": "hesseltuinhof/DataStructures.jl", "max_stars_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/list/linked_stack.jl", "max_issues_repo_name": "hesseltuinhof/DataStructures.jl", "max_issues_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/list/linked_stack.jl", "max_forks_repo_name": "hesseltuinhof/DataStructures.jl", "max_forks_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.7307692308, "max_line_length": 100, "alphanum_fraction": 0.641285956, "num_tokens": 181, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42632157796989345, "lm_q2_score": 0.14608724518943894, "lm_q1q2_score": 0.06228014489043634}}
{"text": "using Test\nusing Suppressor\n\nfunction countdown(i)\n    println(i)\n    if i <= 0\n        return\n    end\n    countdown(i-1)\nend\n\nresult = @capture_out(countdown(10)) # return stdout result\n@test result == \"10\n9\n8\n7\n6\n5\n4\n3\n2\n1\n0\n\"\n", "meta": {"hexsha": "2d47ceb20bee97458ba538d25eff31d39966cf25", "size": 229, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "03_recursion/julia/01_countdown.jl", "max_stars_repo_name": "filchyboy/grokking_algorithms_work", "max_stars_repo_head_hexsha": "16dace97610e2cb0938704e2b8cfd6e92d6b024d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2021-03-11T00:25:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T00:19:23.000Z", "max_issues_repo_path": "book04grokkingAlgo/03_recursion/julia/01_countdown.jl", "max_issues_repo_name": "mcuallen/CodeLrn2019", "max_issues_repo_head_hexsha": "adc727d92904c5c5d445a2621813dfa99474206d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 160, "max_issues_repo_issues_event_min_datetime": "2021-04-26T19:04:15.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-26T20:18:37.000Z", "max_forks_repo_path": "book04grokkingAlgo/03_recursion/julia/01_countdown.jl", "max_forks_repo_name": "mcuallen/CodeLrn2019", "max_forks_repo_head_hexsha": "adc727d92904c5c5d445a2621813dfa99474206d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2021-04-26T19:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T08:36:29.000Z", "avg_line_length": 9.16, "max_line_length": 59, "alphanum_fraction": 0.6331877729, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.35936414516010196, "lm_q2_score": 0.17328820806405806, "lm_q1q2_score": 0.06227356875726611}}
{"text": "module TestTransformer\n\nusing Test, MLJModels\nusing Tables, CategoricalArrays, Random\nusing ScientificTypes\nusing StatsBase\nusing Statistics\nusing StableRNGs\nstable_rng = StableRNGs.StableRNG(123)\nusing Dates: DateTime, Date, Time, Day, Hour\n\nimport MLJBase\n\n\n#### FEATURE SELECTOR ####\n\n@testset \"Feat Selector\" begin\n    N = 100\n    X = (Zn   = rand(N),\n         Crim = rand(N),\n         x3   = categorical(rand(\"YN\", N)),\n         x4   = categorical(rand(\"YN\", N)))\n\n    # Test feature selection with `features=Symbol[]`\n    namesX   = Tables.schema(X).names |> collect\n    selector = FeatureSelector()\n    f,       = MLJBase.fit(selector, 1, X)\n    @test f == namesX\n    Xt = MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2))\n    @test Set(Tables.schema(Xt).names) == Set(namesX)\n    @test length(Xt.Zn) == 2\n\n    # Test on selecting features if `features` keyword is defined\n    selector = FeatureSelector(features=[:Zn, :Crim])\n    f,       = MLJBase.fit(selector, 1, X)\n    @test MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2)) ==\n            MLJBase.select(X, 1:2, [:Zn, :Crim])\n\n    # test on ignoring a feature, even if it's listed in the `features`\n    selector.ignore = true\n    f,   = MLJBase.fit(selector, 1, X)\n    Xnew = MLJBase.transform(selector, f, X)\n    @test MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2)) ==\n         MLJBase.select(X, 1:2, [:x3, :x4])\n\n    # test error about features selected or excluded in fit.\n    selector = FeatureSelector(features=[:x1, :mickey_mouse])\n    @test_throws(\n        ArgumentError,\n        MLJBase.fit(selector, 1, X)\n    )\n    selector.ignore = true\n    @test_logs(\n        (:warn, r\"Excluding non-existent\"),\n        MLJBase.fit(selector, 1, X)\n    )\n\n    # features must be specified if ignore=true\n    @test_throws ArgumentError FeatureSelector(ignore=true)\n     \n    # test logs for no features selected when using Bool-Callable function interface:\n    selector = FeatureSelector(features= x-> x == (:x1))\n   @test_throws(\n        ArgumentError,\n        MLJBase.fit(selector, 1, X)\n    )\n    selector.ignore = true\n    selector.features = x-> x in [:Zn, :Crim, :x3, :x4]\n     @test_throws(\n        ArgumentError,\n        MLJBase.fit(selector, 1, X)\n    )\n    \n    # Test model Metadata\n    infos = MLJBase.info_dict(selector)\n    @test infos[:input_scitype]  == MLJBase.Table\n    @test infos[:output_scitype] == MLJBase.Table\nend\n\n\n#  To be added with FeatureSelectorRule X = (n1=[\"a\", \"b\", \"a\"], n2=[\"g\", \"g\", \"g\"], n3=[7, 8, 9],\n#               n4 =UInt8[3,5,10],  o1=[4.5, 3.6, 4.0], )\n# MLJBase.schema(X)\n# Xc = coerce(X,  :n1=>Multiclass, :n2=>Multiclass)\n\n# t = Discretizer(features=[:o1, :n3, :n2, :n1])\n# @test Xt.features == [:o1, :n3, :n2, :n1]\n# @test Xt.is_ordinal == [true, false, false, false]\n# @test Xt.A == [512 1 1 1; 1 2 1 2; 256 3 1 1]\n\n\n#### UNIVARIATE DISCRETIZATION ####\n\n@testset \"U-Discr\" begin\n    v = randn(10000)\n    t = UnivariateDiscretizer(n_classes=100);\n    result, = MLJBase.fit(t, 1, v)\n    w = MLJBase.transform(t, result, v)\n    bad_values = filter(v - MLJBase.inverse_transform(t, result, w)) do x\n        abs(x) > 0.05\n    end\n    @test length(bad_values)/length(v) < 0.06\n\n    # scalars:\n    @test MLJBase.transform(t, result, v[42]) == w[42]\n    r =  MLJBase.inverse_transform(t, result, w)[43]\n    @test MLJBase.inverse_transform(t, result, w[43]) \u2248 r\n\n    # test of permitted abuses of argument:\n    @test MLJBase.inverse_transform(t, result, get(w[43])) \u2248 r\n    @test MLJBase.inverse_transform(t, result, map(get, w)) \u2248\n        MLJBase.inverse_transform(t, result, w)\n\n    # all transformed vectors should have an identical pool (determined in\n    # call to fit):\n    v2 = v[1:3]\n    w2 = MLJBase.transform(t, result, v2)\n    @test levels(w2) == levels(w)\n\nend\n\n#### STANDARDIZER ####\n\n@testset begin \"standardization\"\n\n    # UnivariateStandardizer:\n    stand = UnivariateStandardizer()\n    f,    = MLJBase.fit(stand, 1, [0, 2, 4])\n    @test round.(Int, MLJBase.transform(stand, f, [0,4,8])) == [-1.0,1.0,3.0]\n    @test round.(Int, MLJBase.inverse_transform(stand, f, [-1, 1, 3])) ==\n        [0, 4, 8]\n    infos = MLJBase.info_dict(stand)\n\n    N = 5\n    rand_char = rand(\"abcefgh\", N)\n    while length(unique(rand_char)) < 2\n        rand_char = rand(\"abcefgh\", N)\n    end\n    X = (OverallQual  = rand(UInt8, N),\n         GrLivArea    = rand(N),\n         Neighborhood = categorical(rand_char, ordered=true),\n         x1stFlrSF    = sample(1:10, N, replace=false),\n         TotalBsmtSF  = rand(N))\n\n    # introduce a field of type `Char`:\n    x1 = categorical(map(Char, (X.OverallQual |> collect)))\n\n    X = (x1=x1, x2=X[2], x3=X[3], x4=X[4], x5=X[5])\n\n    stand = Standardizer()\n    f,    = MLJBase.fit(stand, 1, X)\n    Xnew  = MLJBase.transform(stand, f, X)\n\n    # test inverse:\n    XX = MLJBase.inverse_transform(stand, f, Xnew)\n    @test MLJBase.schema(X) == MLJBase.schema(XX)\n    @test XX.x1 == X.x1\n    @test XX.x2 \u2248 X.x2\n    @test XX.x3 == X.x3\n    @test XX.x4 == X.x4\n    @test XX.x5 \u2248 X.x5\n\n    # test transformation:\n    @test Xnew[1] == X[1]\n    @test MLJBase.std(Xnew[2]) \u2248 1.0\n    @test Xnew[3] == X[3]\n    @test Xnew[4] == X[4]\n    @test MLJBase.std(Xnew[5]) \u2248 1.0\n\n    # test feature specification (ignore=false):\n    stand.features = [:x1, :x5]\n    f,   = MLJBase.fit(stand, 1, X)\n    Xnew = MLJBase.transform(stand, f, X)\n    @test issubset(Set(keys(f[3])), Set(Tables.schema(X).names[[5,]]))\n    Xt = MLJBase.transform(stand, f, X)\n    @test Xnew[1] == X[1]\n    @test Xnew[2] == X[2]\n    @test Xnew[3] == X[3]\n    @test Xnew[4] == X[4]\n    @test MLJBase.std(Xnew[5]) \u2248 1.0\n\n    # test on ignoring a feature, even if it's listed in the `features`\n    stand.ignore = true\n    f,   = MLJBase.fit(stand, 1, X)\n    Xnew = MLJBase.transform(stand, f, X)\n    @test issubset(Set(keys(f[3])), Set(Tables.schema(X).names[[2,]]))\n    Xt = MLJBase.transform(stand, f, X)\n    @test Xnew[1] == X[1]\n    @test MLJBase.std(Xnew[2]) \u2248 1.0\n    @test Xnew[3] == X[3]\n    @test Xnew[4] == X[4]\n    @test Xnew[5] == X[5]\n\n    # test warnings about features not encountered in fit or no\n    # features need transforming:\n    stand = Standardizer(features=[:x1, :mickey_mouse])\n    @test_logs(\n        (:warn, r\"Some specified\"),\n        (:warn, r\"No features\"),\n        MLJBase.fit(stand, 1, X)\n    )\n    stand.ignore = true\n    @test_logs (:warn, r\"Some specified\") MLJBase.fit(stand, 1, X)\n\n    # features must be specified if ignore=true\n    @test_throws ArgumentError Standardizer(ignore=true)\n\n    # test count, ordered_factor options:\n    stand = Standardizer(features=[:x3, :x4], count=true, ordered_factor=true)\n    f,   = MLJBase.fit(stand, 1, X)\n    Xnew = MLJBase.transform(stand, f, X)\n    @test issubset(Set(keys(f[3])), Set(Tables.schema(X).names[3:4,]))\n    Xt = MLJBase.transform(stand, f, X)\n    @test_throws Exception MLJBase.inverse_transform(stand, f, Xt)\n\n    @test Xnew[1] == X[1]\n    @test Xnew[2] == X[2]\n    @test elscitype(X[3]) <: OrderedFactor\n    @test elscitype(Xnew[3]) <: Continuous\n    @test MLJBase.std(Xnew[3]) \u2248 1.0\n    @test elscitype(X[4]) == Count\n    @test elscitype(Xnew[4]) <: Continuous\n    @test MLJBase.std(Xnew[4]) \u2248 1.0\n    @test Xnew[5] == X[5]\n\n    stand = Standardizer(features= x-> x == (:x2))\n    f,    = MLJBase.fit(stand, 1, X)\n    Xnew  = MLJBase.transform(stand, f, X)\n\n    @test Xnew[1] == X[1]\n    @test MLJBase.std(Xnew[2]) \u2248 1.0\n    @test Xnew[3] == X[3]\n    @test Xnew[4] == X[4]\n    @test Xnew[5] == X[5]\n\n    infos = MLJBase.info_dict(stand)\n\n    @test infos[:name] == \"Standardizer\"\n    @test infos[:input_scitype] ==\n        Union{MLJBase.Table, AbstractVector{<:Continuous}}\n    @test infos[:output_scitype] ==\n        Union{MLJBase.Table, AbstractVector{<:Continuous}}\n\n    # univariate case\n    stand = Standardizer()\n    f, _, _   = MLJBase.fit(stand, 1, [0, 2, 4])\n    @test round.(Int, MLJBase.transform(stand, f, [0,4,8])) == [-1.0,1.0,3.0]\n    @test [(MLJBase.fitted_params(stand, f).mean_and_std)...] \u2248\n        [2, MLJBase.std([0, 2, 4])]\n\nend\n\n### TIMETYPE TO CONTINUOUS\n\n@testset \"TimeTypeToContinuous\" begin\n    let dt = [Date(2018, 6, 15) + Day(i) for i=0:10],\n        transformer = UnivariateTimeTypeToContinuous(; step=Day(1))\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n        @test fr == (Date(2018, 6, 15), Day(1))\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        @test all(dt_continuous .== Float64.(0:10))\n    end\n\n    let dt = [Date(2018, 6, 15) + Day(i) for i=0:10],\n        transformer = UnivariateTimeTypeToContinuous()\n        @test_logs(\n            (:warn, \"Cannot add `TimePeriod` `step` to `Date` `zero_time`. Converting `step` to `Day`.\"),\n            MLJBase.fit(transformer, 1, dt)\n        )\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n        @test fr == (Date(2018, 6, 15), Day(1))\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        @test all(dt_continuous .== Float64.(0:10))\n    end\n\n    let dt = [Time(0, 0, 0) + Hour(i) for i=0:3:30],\n        transformer = UnivariateTimeTypeToContinuous(;\n            step = Hour(1),\n            zero_time = Time(7, 0, 0),\n        )\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n        @test fr == (Time(7, 0, 0), Hour(1))\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        ex = collect(0:3:30) .% 24 .- 7.0\n        diff = map(dt_continuous .- ex) do d\n            mod(d, 24.0)\n        end\n        @test all(diff .\u2248 0.0)\n    end\n\n    let dt = [Time(0, 0, 0) + Hour(i) for i=0:3:30],\n        transformer = UnivariateTimeTypeToContinuous()\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n        @test fr == (Time(0, 0, 0), Hour(24))\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        ex = collect(0:3:30) .% 24 ./ 24\n        diff = map(dt_continuous .- ex) do d\n            mod(d, 1.0)\n        end\n        @test all(diff .\u2248 0.0)\n    end\n\n    # test log messages\n    let dt = [DateTime(2018, 6, 15) + Day(i) for i=0:10],\n        step=Hour(1),\n        zero_time=Date(2018, 6, 15),\n        transformer = UnivariateTimeTypeToContinuous(;\n            step=step,\n            zero_time=zero_time,\n        )\n        @test_logs(\n            (:warn, \"Cannot add `TimePeriod` `step` to `Date` `zero_time`. Converting `zero_time` to `DateTime`.\"),\n            UnivariateTimeTypeToContinuous(;\n                step=step,\n                zero_time=zero_time,\n            )\n        )\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n\n        @test fr == (zero_time, step)\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        @test all(dt_continuous .== Float64.(0:10).*24)\n    end\n\n    let dt = [Time(0, 0, 0) + Hour(i) for i=0:3:30],\n        zero_time=Time(0, 0, 0),\n        step=Day(1),\n        transformer = UnivariateTimeTypeToContinuous(;\n            step=step,\n            zero_time=zero_time,\n        )\n        @test_logs(\n            (:warn, \"Cannot add `DatePeriod` `step` to `Time` `zero_time`. Converting `step` to `Hour`.\"),\n            UnivariateTimeTypeToContinuous(;\n                step=step,\n                zero_time=zero_time,\n            )\n        )\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n\n        @test fr == (zero_time, convert(Hour, step))\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        ex = Float64.((0:3:30) .% 24)./24\n        diff = map(dt_continuous .- ex) do d\n            mod(d, 1.0)\n        end\n        @test all(diff .\u2248 0.0)\n    end\n\n    let dt = [DateTime(2018, 6, 15) + Day(i) for i=0:10],\n        step=Day(1),\n        zero_time=Date(2018, 6, 15),\n        transformer = UnivariateTimeTypeToContinuous(;\n            step=step,\n            zero_time=zero_time,\n        )\n        @test_logs(\n            (:warn, \"`Dates.Date` `zero_time` is not compatible with `Dates.DateTime` vector. Attempting to convert `zero_time`.\"),\n            MLJBase.fit(transformer, 1, dt)\n        )\n        fr, _, _ = MLJBase.fit(transformer, 1, dt)\n\n        @test fr == (zero_time, step)\n        dt_continuous = MLJBase.transform(transformer, fr, dt)\n        @test all(dt_continuous .== Float64.(0:10))\n    end\nend\n\n\n#### UNIVARIATE BOX COX TRANSFORMER ####\n\n@testset \"U-boxcox\" begin\n    # create skewed non-negative vector with a zero value:\n    Random.seed!(1551)\n    v = abs.(randn(1000))\n    v = v .- minimum(v)\n\n    t  = UnivariateBoxCoxTransformer(shift=true)\n    f, = MLJBase.fit(t, 2, v)\n\n    e = v - MLJBase.inverse_transform(t, f, MLJBase.transform(t, f, v))\n    @test sum(abs, e) <= 5000*eps()\n\n    infos = MLJBase.info_dict(t)\n\n    @test infos[:name] == \"UnivariateBoxCoxTransformer\"\n    @test infos[:input_scitype] == AbstractVector{MLJBase.Continuous}\n    @test infos[:output_scitype] == AbstractVector{MLJBase.Continuous}\nend\n\n\n#### ONE HOT ENCODER ####\n\n@testset \"One-Hot\" begin\n    X = (name   = categorical([\"Ben\", \"John\", \"Mary\", \"John\"], ordered=true),\n         height = [1.85, 1.67, 1.5, 1.67],\n         favourite_number = categorical([7, 5, 10, 5]),\n         age    = [23, 23, 14, 23])\n\n    t  = OneHotEncoder()\n    f, _, report = @test_logs((:info, r\"Spawning 3\"),\n                    (:info, r\"Spawning 3\"), MLJBase.fit(t, 1, X))\n\n    Xt = MLJBase.transform(t, f, X)\n\n    @test Xt.name__John == float.([false, true, false, true])\n    @test Xt.height == X.height\n    @test Xt.favourite_number__10 == float.([false, false, true, false])\n    @test Xt.age == X.age\n    @test MLJBase.schema(Xt).names == (:name__Ben, :name__John, :name__Mary,\n                               :height, :favourite_number__5,\n                               :favourite_number__7, :favourite_number__10,\n                               :age)\n\n    @test report.new_features == collect(MLJBase.schema(Xt).names)\n\n    # test that *entire* pool of categoricals is used in fit, including\n    # unseen levels:\n    f, = @test_logs((:info, r\"Spawning 3\"), (:info, r\"Spawning 3\"),\n                          MLJBase.fit(t, 1, MLJBase.selectrows(X,1:2)))\n    Xtsmall = MLJBase.transform(t, f, X)\n    @test Xt == Xtsmall\n\n    # test that transform can be applied to subset of the data:\n    @test MLJBase.transform(t, f, MLJBase.selectcols(X, [:name, :age])) ==\n        MLJBase.selectcols(MLJBase.transform(t, f, X),\n                           [:name__Ben, :name__John, :name__Mary, :age])\n\n    # test ignore\n    t = OneHotEncoder(features=[:name,], ignore=true)\n    f, = MLJBase.fit(t, 0, X)\n    Xt = MLJBase.transform(t, f, X)\n\n    # test exclusion of ordered factors:\n    t  = OneHotEncoder(ordered_factor=false)\n    f, = MLJBase.fit(t, 0, X)\n    Xt = MLJBase.transform(t, f, X)\n    @test keys(Xt) == (:name, :height, :favourite_number__5,\n                       :favourite_number__7, :favourite_number__10, :age)\n\n    @test :name in Tables.schema(Xt).names\n    @test :favourite_number__5 in Tables.schema(Xt).names\n    @test MLJBase.schema(Xt).scitypes == (OrderedFactor{3}, Continuous,\n                                          Continuous, Continuous,\n                                          Continuous, Count)\n\n    # test that one may not add new columns:\n    X = (name = categorical([\"Ben\", \"John\", \"Mary\", \"John\"], ordered=true),\n         height     = [1.85, 1.67, 1.5, 1.67],\n         favourite_number = categorical([7, 5, 10, 5]),\n         age        = [23, 23, 14, 23],\n         gender     = categorical(['M', 'M', 'F', 'M']))\n    @test_throws Exception MLJBase.transform(t, f, X)\n\n    infos = MLJBase.info_dict(t)\n\n    @test infos[:name] == \"OneHotEncoder\"\n    @test infos[:input_scitype] == MLJBase.Table\n    @test infos[:output_scitype] == MLJBase.Table\nend\n\n\n#### FILL IMPUTER ####'\n\n@testset \"UnivariateFillImputer\" begin\n    vpure = rand(stable_rng, 10)\n    v = vcat([missing, ], vpure)\n    filler = median(vpure)\n    imp = MLJModels.UnivariateFillImputer()\n    f, = MLJBase.fit(imp, 1, v)\n    vnew = [missing, 1.0, missing, 2.0, 3.0]\n    @test MLJBase.transform(imp, f, vnew) \u2248 [filler, 1.0, filler, 2.0, 3.0]\n\n    vpure = MLJBase.coerce(rand(stable_rng, \"abc\", 100), OrderedFactor);\n    v = vcat([missing, ], vpure)\n    filler = mode(vpure)\n    imp = MLJModels.UnivariateFillImputer()\n    f, = MLJBase.fit(imp, 1, v)\n    vnew = vcat([missing, ], vpure[end-10:end], [missing, ])\n    @test MLJBase.transform(imp, f, vnew) ==\n        vcat([filler, ], vpure[end-10:end], [filler, ])\n\n    vpure = rand(stable_rng, Int, 10)\n    v = vcat([missing, ], vpure)\n    filler = round(Int, median(vpure))\n    imp = MLJModels.UnivariateFillImputer()\n    f, = MLJBase.fit(imp, 1, v)\n    vnew = [missing, 1, missing, 2, 3]\n    @test MLJBase.transform(imp, f, vnew) == [filler, 1, filler, 2, 3]\n\n    @test_throws Exception MLJBase.transform(imp, f, [missing, \"1\", \"2\"])\n\n    @test_throws ArgumentError MLJBase.fit(imp, 1, [missing, \"1\", \"2\"])\n\nend\n\n@testset \"FillImputer\" begin\n    X = (\n        x = [missing,ones(10)...],\n        y = [missing,ones(10)...],\n        z = [missing,ones(10)...]\n        )\n\n    imp = FillImputer()\n    f,  = MLJBase.fit(imp, 1, X)\n\n    fp = MLJBase.fitted_params(imp, f)\n    @test fp.features_seen_in_fit == [:x, :y, :z]\n    @test fp.univariate_transformer == MLJModels.UnivariateFillImputer()\n    @test fp.filler_given_feature[:x] \u2248 1.0\n    @test fp.filler_given_feature[:x] \u2248 1.0\n    @test fp.filler_given_feature[:x] \u2248 1.0\n\n    Xnew = MLJBase.selectrows(X, 1:5)\n    Xt  = MLJBase.transform(imp, f, Xnew)\n    @test all(.!ismissing.(Xt.x))\n    @test Xt.x isa Vector{Float64} # no missing\n    @test all(Xt.x .== 1.0)\n\n    imp = FillImputer(features=[:x,:y])\n    f,  = MLJBase.fit(imp, 1, X)\n    Xt = MLJBase.transform(imp, f, Xnew)\n    @test all(Xt.x .== 1.0)\n    @test all(Xt.y .== 1.0)\n    @test ismissing(Xt.z[1])\n\n    # adding a new feature not seen in fit:\n    Xnew = (x = X.x, y=X.y, a=X.x)\n    @test_throws ArgumentError  MLJBase.transform(imp, f, Xnew)\n\n    # mixture of features:\n    X = (x = categorical([missing, missing, missing, missing,\n                          \"Old\", \"Young\", \"Middle\", \"Young\",\n                          \"Old\", \"Young\", \"Middle\", \"Young\"]),\n         y = [missing, ones(11)...],\n         z = [missing, missing, 1,1,1,1,1,5,1,1,1,1],\n         a = rand(\"abc\", 12))\n\n    imp = FillImputer()\n    f, = MLJBase.fit(imp, 1, X)\n    Xnew = MLJBase.selectrows(X, 1:4)\n    Xt = MLJBase.transform(imp, f, Xnew)\n\n    @test all(.!ismissing.(Xt.x))\n    @test all(.!ismissing.(Xt.y))\n    @test all(.!ismissing.(Xt.z))\n    @test all(.!ismissing.(Xt.a))\n\n    @test Xt.x[1] == mode(skipmissing(X.x))\n    @test Xt.y[1] == 1\n    @test Xt.z[1] == 1\n\n    # user specifies a feature explicitly that's not supported:\n    imp = FillImputer(features=[:x, :a]) # :a of Unknown scitype not supported\n    @test_logs (:info, r\"Feature a will not\") MLJBase.fit(imp, 1, X)\n\nend\n\n\n#### CONTINUOUS ENCODER ####\n\n@testset \"Continuous encoder\" begin\n\n    X = (name  = categorical([\"Ben\", \"John\", \"Mary\", \"John\"], ordered=true),\n         height = [1.85, 1.67, 1.5, 1.67],\n         rubbish = [\"a\", \"b\", \"c\", \"a\"],\n         favourite_number = categorical([7, 5, 10, 5]),\n         age    = [23, 23, 14, 23])\n\n    t  = ContinuousEncoder()\n    f, _, _ = @test_logs((:info, r\"Some.*dropped\\:.*\\:rubbish\\]\"),\n                              MLJBase.fit(t, 1, X))\n\n    Xt = MLJBase.transform(t, f, X)\n    @test scitype(Xt) <: MLJBase.Table(MLJBase.Continuous)\n    s = MLJBase.schema(Xt)\n    @test s.names == (:name, :height, :favourite_number__5,\n                      :favourite_number__7, :favourite_number__10, :age)\n\n    t  = ContinuousEncoder(drop_last=true, one_hot_ordered_factors=true)\n    f, _, r = MLJBase.fit(t, 0, X)\n    Xt = MLJBase.transform(t, f, X)\n    @test scitype(Xt) <: MLJBase.Table(MLJBase.Continuous)\n    s = MLJBase.schema(Xt)\n    @test s.names == (:name__Ben, :name__John, :height, :favourite_number__5,\n                      :favourite_number__7, :age)\n\nend\n\nend\ntrue\n", "meta": {"hexsha": "6aa143871dd14a2118d515f4fc0c1481b2e701da", "size": 19820, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/builtins/Transformers.jl", "max_stars_repo_name": "juliohm/MLJModels.jl", "max_stars_repo_head_hexsha": "67e775aace89be6af6ed3a17cf0c989c3d10c0ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/builtins/Transformers.jl", "max_issues_repo_name": "juliohm/MLJModels.jl", "max_issues_repo_head_hexsha": "67e775aace89be6af6ed3a17cf0c989c3d10c0ca", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/builtins/Transformers.jl", "max_forks_repo_name": "juliohm/MLJModels.jl", "max_forks_repo_head_hexsha": "67e775aace89be6af6ed3a17cf0c989c3d10c0ca", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4797297297, "max_line_length": 131, "alphanum_fraction": 0.5803229062, "num_tokens": 6517, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.12421299538397429, "lm_q1q2_score": 0.062106497691987145}}
{"text": "#Write a function that checks whether an element occurs in a list.\n\nlist = [1,2,45,\"A\",\"B\"]\n\nfunction occ(y::Any,x::Vector{Any})\n    y \u2208 x\nend\n\nocc(1,list)\n", "meta": {"hexsha": "4199efe47ad53b690d4d603dd986486e1152177e", "size": 156, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lists_Strings/LS3_Largest_In_List.jl", "max_stars_repo_name": "atott/Simple-Programming-Problems-Julia", "max_stars_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Lists_Strings/LS3_Largest_In_List.jl", "max_issues_repo_name": "atott/Simple-Programming-Problems-Julia", "max_issues_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lists_Strings/LS3_Largest_In_List.jl", "max_forks_repo_name": "atott/Simple-Programming-Problems-Julia", "max_forks_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.6, "max_line_length": 66, "alphanum_fraction": 0.6474358974, "num_tokens": 50, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.13117323225300484, "lm_q1q2_score": 0.06200341946088982}}
{"text": "#This files is for runtest.jl\nusing JuliaName\nusing Base.Test\n\n#testfiles= (\n#    \"bubble_test.jl\",\n#    \"insertion_test.jl\",\n#    \"selection_test.jl\"\n#)\n\n#for file in testfiles\n#    @testset \"Output String Tests\" begin include(testfiles) end\n#end\n\n@testset \"Sort O(n^2)\" begin\ninclude(\"bubble_test.jl\")\ninclude(\"insertion_test.jl\")\ninclude(\"selection_test.jl\")\nend\n#@testset \"InsertionSort Test\" begin include(\"insertion_test.jl\") end\n#@testset \"SelectionSort Test\" begin include(\"selection_test.jl\") end\n\n\n#using JuliaName\n#using Base.Test\n\n# write your own tests here\n#@test 1 == 2\n\n# The testing setup\n# Here we split different categories into tests\n# Each category is a separate file\n# By placing `@testset` on the file, it will run all of the tests there,\n# and report back when the entire file is complete\n\n# Optional: If you @testset begin ... end around the entire testing setup\n# then all of your tests will run, and the failures will be counted\n# and printed at the end. There is a balance between the completeness of\n# the test results and the time it takes to run tests!\n\n# Optional: tic() ... toc() around the tests as a quick way to keep track of\n# large performance regressions.\n\n# @testset begin\n#@testset \"Output String Tests\" begin include(\"output_string_tests.jl\") end\n\n#@testset \"\"\n\n# end\n", "meta": {"hexsha": "8467113fe796e59fd64f558e4aba14809e42f4f1", "size": 1310, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "fratava/JuliaName.jl", "max_stars_repo_head_hexsha": "1189d5c9f7347983c149ee67204733d43bbb1aab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "fratava/JuliaName.jl", "max_issues_repo_head_hexsha": "1189d5c9f7347983c149ee67204733d43bbb1aab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "fratava/JuliaName.jl", "max_forks_repo_head_hexsha": "1189d5c9f7347983c149ee67204733d43bbb1aab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2, "max_line_length": 76, "alphanum_fraction": 0.7358778626, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4416730056646256, "lm_q2_score": 0.14033625308549463, "lm_q1q2_score": 0.061982734703982}}
{"text": "using ImageCore, Colors, ColorVectorSpace\nusing Test, Statistics\n\n# Different access patterns (getindex)\nfunction mysum_elt_boundscheck(A)\n    s = zero(eltype(A))\n    for a in A\n        s += a\n    end\n    s\nend\nfunction mysum_index_boundscheck(A)\n    s = zero(eltype(A))\n    for I in eachindex(A)\n        s += A[I]\n    end\n    s\nend\nfunction mysum_elt_inbounds(A)\n    s = zero(eltype(A))\n    @inbounds for a in A\n        s += a\n    end\n    s\nend\nfunction mysum_index_inbounds_simd(A)\n    s = zero(eltype(A))\n    @inbounds @simd for I in eachindex(A)\n        s += A[I]\n    end\n    s\nend\n# setindex!\nfunction myfill1!(A, val)\n    f = convert(eltype(A), val)\n    for I in eachindex(A)\n        A[I] = f\n    end\n    A\nend\nfunction myfill2!(A, val)\n    f = convert(eltype(A), val)\n    @inbounds @simd for I in eachindex(A)\n        A[I] = f\n    end\n    A\nend\n\n# Rather than using BenchmarkTools (and thus run one test repeatedly,\n# accumulating timings), we run the same test interleaving the two\n# array types. This is designed to reduce the risk of spurious\n# failure, particularly on shared machines like Travis where they may\n# get \"distracted\" by other tasks\nfunction test_getindex(f, ar, cv, n)\n    t_ar = Array{Float64}(undef, n)\n    t_cv = Array{Float64}(undef, n)\n    # Store the results to prevent the compiler from eliding the call\n    f_ar = Ref(f(ar))\n    f_cv = Ref(f(cv))\n    @test f_ar[] \u2248 f_cv[]  # but this also gives us a chance to test correctness\n    for i = 1:n\n        t_ar[i] = (tstart = time(); f_ar[] = f(ar); time()-tstart)\n        t_cv[i] = (tstart = time(); f_cv[] = f(cv); time()-tstart)\n    end\n    median(t_ar), median(t_cv)\nend\nfunction test_setindex(f, ar, cv, n)\n    t_ar = Array{Float64}(undef, n)\n    t_cv = Array{Float64}(undef, n)\n    for i = 1:n\n        t_ar[i] = @elapsed f(ar, zero(eltype(ar)))\n        t_cv[i] = @elapsed f(cv, zero(eltype(cv)))\n    end\n    median(t_ar), median(t_cv)\nend\n\ncc_getindex_funcs = (mysum_elt_boundscheck,\n                     mysum_index_boundscheck,\n                     mysum_elt_inbounds,\n                     mysum_index_inbounds_simd)\ncc_setindex_funcs = (myfill1!,\n                     myfill2!)\n\n# Performance tolerances\nisfast = VERSION >= v\"1.6.0-DEV.1083\"\nchanvtol = Dict{Any,Int}(mysum_index_inbounds_simd => isfast ? 3 : 20,\n                         mysum_elt_boundscheck => isfast ? 3 : 20,\n                         myfill1! => 20,\n                         myfill2! => isfast ? 3 : 20)\nchanvdefault = isfast ? 3 : 10\ncolvtol = Dict{Any,Int}(mysum_elt_boundscheck=>isfast ? 3 : 5,\n                        mysum_index_boundscheck=>isfast ? 3 : 5)\ncolvdefault = 3\n\nssz = (1000,300)\n\n@info \"Benchmark tests are warnings for now\"\n# @testset \"benchmarks\" begin\nfor T in (Float32, Float64)\n    c = rand(RGB{T}, ssz...)\n    a = copy(reinterpretc(T, c))\n    vchan = channelview(c)\n    vcol = colorview(RGB, a)\n\n    # view versions\n    rview = 2:ssz[1]-1\n    csub = view(c, rview, :)\n    asub = view(a, :, rview, :)\n    vchansub = channelview(csub)\n    vcolsub = colorview(RGB, asub)\n\n    for (suite, testf) in ((cc_getindex_funcs, test_getindex),\n                           (cc_setindex_funcs, test_setindex))\n        for f in suite\n            # channelview\n            t_ar, t_cv = testf(f, a, vchan, 30)\n            tol = haskey(chanvtol, f) ? chanvtol[f] : chanvdefault\n            if t_cv >= tol*t_ar\n                @warn \"channelview1: failed on $f with eltype $T, time ratio $(t_cv/t_ar), tol $tol\"\n            end\n\n            t_ar, t_cv = testf(f, asub, vchansub, 30)\n            tol = haskey(chanvtol, f) ? chanvtol[f] : chanvdefault\n            if t_cv >= tol*t_ar\n                @warn \"channelview2: failed on $f with eltype $T, time ratio $(t_cv/t_ar), tol $tol\"\n            end\n\n            # colorview\n            t_ar, t_cv = testf(f, c, vcol, 30)\n            tol = haskey(colvtol, f) ? colvtol[f] : colvdefault\n            if t_cv >= tol*t_ar\n                @warn \"colorview1: failed on $f with eltype $T, time ratio $(t_cv/t_ar), tol $tol\"\n            end\n\n            t_ar, t_cv = testf(f, csub, vcolsub, 30)\n            tol = haskey(colvtol, f) ? colvtol[f] : colvdefault\n            if t_cv >= tol*t_ar\n                @warn \"colorview2: failed on $f with eltype $T, time ratio $(t_cv/t_ar), tol $tol\"\n            end\n        end\n    end\nend\n# end\n", "meta": {"hexsha": "dd32ceb09c14b6aca50a1be56fddc9ec43f0fc68", "size": 4340, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/benchmarks.jl", "max_stars_repo_name": "Mechachleopteryx/ImageCore.jl", "max_stars_repo_head_hexsha": "ff7afc048c5832d8f3e6269f40ed06c4399cec7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "max_stars_repo_stars_event_min_datetime": "2016-09-14T18:33:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-06T17:59:15.000Z", "max_issues_repo_path": "test/benchmarks.jl", "max_issues_repo_name": "Mechachleopteryx/ImageCore.jl", "max_issues_repo_head_hexsha": "ff7afc048c5832d8f3e6269f40ed06c4399cec7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 155, "max_issues_repo_issues_event_min_datetime": "2016-09-12T17:39:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-01T18:23:46.000Z", "max_forks_repo_path": "test/benchmarks.jl", "max_forks_repo_name": "Mechachleopteryx/ImageCore.jl", "max_forks_repo_head_hexsha": "ff7afc048c5832d8f3e6269f40ed06c4399cec7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 26, "max_forks_repo_forks_event_min_datetime": "2016-09-24T04:35:32.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-17T23:56:27.000Z", "avg_line_length": 30.1388888889, "max_line_length": 100, "alphanum_fraction": 0.5774193548, "num_tokens": 1333, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618627863437, "lm_q2_score": 0.13660840232900245, "lm_q1q2_score": 0.06191937891190995}}
{"text": "module MyFirstPkg\n\nmy_f(x) = 2x + 3\n\nexport my_f\n\nend # module\n", "meta": {"hexsha": "18eb7836488892586ccf595a9c823b396e3125f1", "size": 63, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MyFirstPkg.jl", "max_stars_repo_name": "caiprozect/MyFirstPkg.jl", "max_stars_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/MyFirstPkg.jl", "max_issues_repo_name": "caiprozect/MyFirstPkg.jl", "max_issues_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/MyFirstPkg.jl", "max_forks_repo_name": "caiprozect/MyFirstPkg.jl", "max_forks_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 7.875, "max_line_length": 17, "alphanum_fraction": 0.6825396825, "num_tokens": 26, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.1276526203437256, "lm_q1q2_score": 0.061832387000271695}}
{"text": "# MIT License\n\n# Copyright (c) 2021 Fraunhofer-Chalmers Centre\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n########################################\n# Reads the results from the \"result\" directory (assuming that the CPLEX results from http://soa.iti.es/files/RCmax.7z has been formatted and placed there also)\n# output results the data profiles and the result_table.md\n# The data tables are generated from the results in results.7z\n########################################\nusing DelimitedFiles, Plots, Statistics, Latexify, Printf\ncd(@__DIR__ )\nfolders = [name  for  name in readdir(\"results\") if isnothing(findfirst('.',name))]\n##\nfunction data_profile(n_task=nothing)\n    T_lr = []\n    T_gur = []\n    for folder in folders\n        res_path = \"results/\"*folder*\"/res.txt\"\n        res,header = readdlm(res_path,',',header=true)\n        t = res[:,(header[:].==\"time\")]\n        if !isnothing(n_task)\n            t = t[res[:,(header[:].==\"n\")].<=n_task]\n        end\n        T_lr = vcat(T_lr,t)\n\n        res_path = \"results/\"*folder*\"/res_gur_cut.txt\"\n        res,header = readdlm(res_path,',',header=true)\n        t = res[:,(header[:].==\"time\")]\n        if !isnothing(n_task)\n            t = t[res[:,(header[:].==\"n\")].<=n_task]\n        end\n        T_gur = vcat(T_gur,t)\n    end\n    \n    T = [T_lr T_gur]\n\n    \u03c4 = 10 .^ range(-2.1, log10(120), length = 100) \n    n, n_solv = size(T)     \n    \u03c1 =  [[count(T[:,i] .< t) for t in \u03c4] ./ n for i in 1:n_solv ]\n    plotly()\n    title_label = \"Data profiles\"\n    if !isnothing(n_task)\n        title_label *= \", n \u2264 \"*string(n_task)\n    end\n    plot(\u03c4, 100*\u03c1, ylims=[0,100], title=title_label, label = [\"LR\" \"Gur\"], xlabel=\"CPU time [s]\", ylabel =\"Solved [%]\", xaxis=:log, xticks = 10.0 .^(-2:2),legend=:bottomright)\nend\nplt = data_profile()\nsavefig(plt, \"data_profile.png\")\ndisplay(plt)\n\nplt = data_profile(200)\nsavefig(plt, \"data_profile200.png\")\ndisplay(plt)\n##\nfunction getvalue(res,header,instance,label)\n    row = findfirst(res[:,1].==instance)\n    if isnothing(row)\n        return NaN\n    end\n    return res[row, header[:].==label]    \nend\nfunction markbest(z,zmin)\n    if z == zmin \n        return \"**\"*string(z)*\"**\"\n    else \n        return z\n    end\nend\nfunction calcmean(res, header, m, n, label)\n    rows = (res[:,(header[:].==\"m\")] .== m) .& (res[:,(header[:].==\"n\")] .==n)\n    if !any(rows)\n        return 0\n    end\n    return  mean(res[rows[:],(header[:].==label)])    \nend\nfunction n_unsolved(res, header, m, n)\n    rows = (res[:,(header[:].==\"m\")] .== m) .& (res[:,(header[:].==\"n\")] .==n)\n    if !any(rows)\n        return 0\n    end\n    return Int(sum(1 .-res[rows[:],(header[:].==\"solved\")])  ) \nend\n\nfunction data_tables()\n    # for each size compute a mean time, iter, nodes, solved, obj \n    machines = [10, 20,30,40,50]\n    tasks = [100, 200,500,1000]\n    outfile = \"result_tables.md\"\n    io = open(outfile,\"w\")\n    write(io, \"#Result summary tables\\n\")\n    write(io, \"f <- solution not proved to be optimal\\n\")\n    for folder in folders\n        res_path = \"results/\"*folder*\"/res.txt\"\n\n        out_table =[\"m\" \"n\" \"LR [s]\" \"LR #f\" \"LR iter\" \"LR nodes\" \"LR z\" \"LR root gap [%]\" \"Gur [s]\" \"Gur #f\" \"Gur iter\" \"Gur nodes\" \"Gur z\" \"Gur root gap [%]\" \"Gur gap [%]\" \"Cplex z\"]\n\n        LRres, LRheader = readdlm(res_path,',',header=true)      \n        res_path = \"results/\"*folder*\"/res_gur_cut.txt\"  \n        GURres, GURheader = readdlm(res_path,',',header=true)\n        CplexRes, CplexHeader = readdlm(\"results/\"*folder*\"/Cplex2horas\"*folder*\".csv\",';',header=true)\n        CplexHeader = [CplexHeader \"n\" \"m\"]\n        CplexRes = Any[CplexRes Int.(floor.(CplexRes[:,1] .-1,sigdigits=1)) Int.(floor.(CplexRes[:,1].-1;digits=-1)) .% 100]\n\n        for m in machines\n            for n in tasks\n                LRtime = @sprintf(\"%0.2f\", calcmean(LRres, LRheader, m, n, \"time\"))\n                LRunsolved = n_unsolved(LRres, LRheader, m, n)\n                LRiter = round(Int,calcmean(LRres, LRheader, m, n, \"iter\"))\n                LRnodes = round(Int,calcmean(LRres, LRheader, m, n, \"nodes\"))\n                LRz = calcmean(LRres, LRheader, m, n, \"z\")\n                LRrootgap = @sprintf(\"%0.2f\", calcmean(LRres, LRheader, m, n, \"lbgaproot\")*100)\n                Gurtime =  @sprintf(\"%0.2f\",calcmean(GURres, GURheader, m, n, \"time\"))\n                Gurunsolved = n_unsolved(GURres, GURheader, m, n)\n                Guriter =round(Int, calcmean(GURres, GURheader, m, n, \"iter\"))\n                Gurnodes =round(Int, calcmean(GURres, GURheader, m, n, \"nodes\"))\n                Gurz = round(calcmean(GURres, GURheader, m, n, \"z\"), digits=1)\n                Gurrootgap = @sprintf(\"%0.2f\", calcmean(GURres, GURheader, m, n, \"lbrootgap\")*100)\n                Gurrgap =@sprintf(\"%0.2f\",  calcmean(GURres, GURheader, m, n, \"gap\")*100  )\n                Cplexz = calcmean(CplexRes, CplexHeader, m, n, \"Objective\")\n                out_table = vcat(out_table, [m n LRtime LRunsolved LRiter LRnodes LRz LRrootgap Gurtime Gurunsolved Guriter Gurnodes Gurz Gurrootgap Gurrgap Cplexz])       \n            end\n        end\n        write(io, \"## \"*folder*\"\\n\\n\")\n        write(io,  string(md(out_table, latex=false)))\n        write(io, \"\\n\")\n    end\n\n    out_table =[\"type\" \"instance\" \"LR value\" \"LR opt\" \"Gurobi value\" \"Gurobi opt\" \"Cplex value\" \"Cplex opt\"]\n    for folder in folders\n        res_path = \"results/\"*folder*\"/res.txt\"\n        LRres, LRheader = readdlm(res_path,',',header=true)     \n        res_path = \"results/\"*folder*\"/res_gur_cut.txt\"  \n        GURres, GURheader = readdlm(res_path,',',header=true)\n        GURres = round.(Int,GURres)\n        CplexRes, CplexHeader = readdlm(\"results/\"*folder*\"/Cplex2horas\"*folder*\".csv\",';',header=true)\n        CplexRes = round.(Int,CplexRes)\n        for instance in LRres[:,1]\n            LRz = getvalue(LRres,LRheader,instance,\"z\")\n            LRopt = getvalue(LRres,LRheader,instance,\"solved\")\n            GURz = getvalue(GURres,GURheader,instance,\"z\")\n            Guropt = getvalue(GURres,GURheader,instance,\"solved\")\n            Cplexz = getvalue(CplexRes,CplexHeader,instance,\"Objective\")\n            Cplexopt = getvalue(CplexRes,CplexHeader,instance,\"Proven opt.\")\n            out_table = vcat(out_table,[folder instance LRz LRopt GURz Guropt Cplexz Cplexopt] )\n        end\n    end\n    \n    write(io, \"## All instances summary \\n\\n\")\n    A = copy(out_table[2:end,3:end])\n    A[isnan.(A)].=Inf\n    z = A[:,1:2:end]\n    best_z = Int.(minimum(z, dims=2))\n    has_best =  z .<= best_z\n    has_strict_best = reduce(hcat,[z[:,j] .< minimum(z[:,setdiff(1:3,j)],dims=2) for j in 1:3])\n    has_opt = A[:,2:2:end] .== 1\n    unique_opt = [has_opt[i,j] && sum(has_opt[i,:])==1  for i in 1:size(has_opt,1),j in 1:3]\n\n    write(io,  string(md( [[\"\" \"LR\" \"Gurobi\" \"Cplex\"];\n     hcat(\"Best sol [%]\", round.(100*sum(has_best,dims=1)/size(has_best,1),digits=2));\n     hcat(\"Strict best sol [%]\", round.(100*sum(has_strict_best,dims=1)/size(has_best,1),digits=2));\n     hcat(\"Proved opt [%]\",round.(100*sum(has_opt,dims=1)/size(has_opt,1),digits=2));\n     hcat(\"Uniquely proved opt [%]\",round.(100*sum(unique_opt,dims=1)/size(has_opt,1),digits=2))], latex=false)))\n    \n    writedlm(\"res_all_instances.csv\", out_table,\",\")\n\n    write(io, \"## All instances \\n\\n\")\n    for (i, j) in Tuple.(findall(has_best))\n        out_table[i+1,1+2*j] = \"**\"*string( out_table[i+1,1+2*j] )*\"**\"\n\n    end\n\n    write(io,  string(md(out_table, latex=false)))\n\n    close(io)\n\nend\ndata_tables()", "meta": {"hexsha": "5010c6c62ddb7830547369ae688853d4e0cd1f60", "size": 8468, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Fanjul_Ruiz_instances/interp_res.jl", "max_stars_repo_name": "Fraunhofer-Chalmers-Centre/RCmax", "max_stars_repo_head_hexsha": "0da830cdd904359582e5662c5c3dfb5c98fea392", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Fanjul_Ruiz_instances/interp_res.jl", "max_issues_repo_name": "Fraunhofer-Chalmers-Centre/RCmax", "max_issues_repo_head_hexsha": "0da830cdd904359582e5662c5c3dfb5c98fea392", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Fanjul_Ruiz_instances/interp_res.jl", 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NO\n2. NO", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.12765261370630573, "lm_q1q2_score": 0.06183238378523769}}
{"text": "\"\"\"\n    grdview(cmd0::String=\"\", arg1=nothing, arg2=nothing, arg3=nothing; kwargs...)\n\nReads a 2-D grid and produces a 3-D perspective plot by drawing a mesh, painting a\ncolored/grayshaded surface made up of polygons, or by scanline conversion of these polygons\nto a raster image.\n\nFull option list at [`grdview`]($(GMTdoc)grdview.html)\n\n- $(GMT.opt_J)\n- $(GMT.opt_Jz)\n- $(GMT.opt_R)\n- $(GMT.opt_B)\n- $(GMT.opt_C)\n- **G** | **drapefile** :: [Type => Str | GMTgrid | a Tuple with 3 GMTgrid types]\n\n    Drape the image in drapefile on top of the relief provided by relief_file.\n    ($(GMTdoc)grdview.html#g)\n- **I** | **shade** | **shading** | **intensity** :: [Type => Str | GMTgrid]\t\t``Arg = GMTgrid | filename``\n\n    Gives the name of a grid file or GMTgrid with intensities in the (-1,+1) range,\n    or a grdgradient shading flags.\n    ($(GMTdoc)grdview.html#i)\n- **N** | **plane** :: [Type => Str | Int]\t\t``Arg = (level [,fill])``\n\n    Draws a plane at this z-level.\n    ($(GMTdoc)grdview.html#n)\n- $(GMT.opt_P)\n- **Q** | **surftype** | **surf** :: [Type => Str | Int] ``Arg = mesh=Bool, surface=Bool, image=Bool, wterfall=(:rows|cols,[fill])``\n\n    Specify **m** for mesh plot, **s** for surface, **i** for image.\n    ($(GMTdoc)grdview.html#q)\n- **S** | **smoothfactor** :: [Type => Number]\n\n    Used to resample the contour lines at roughly every (gridbox_size/smoothfactor) interval..\n    ($(GMTdoc)grdview.html#s)\n- **T** | **tiles** | **no_interp** :: [Type => Str | NT]\t``Arg = (skip|skip_nan=Bool, outlines=Bool|pen)``\n\n    Plot image without any interpolation.\n    ($(GMTdoc)grdview.html#t)\n- **W** | **pens** | **pen** :: [Type => Str]\t``Arg = (contour=Bool|pen, mesh=Bool|pen, facade=Bool|pen)``\n\n    Draw contour, mesh or facade. Append pen attributes.\n    ($(GMTdoc)grdview.html#w)\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- $(GMT.opt_f)\n- $(GMT.opt_n)\n- $(GMT.opt_p)\n- $(GMT.opt_t)\n\"\"\"\nfunction grdview(cmd0::String=\"\", arg1=nothing; first=true, kwargs...)\n\n\tlength(kwargs) == 0 && occursin(\" -\", cmd0) && return monolitic(\"grdview\", cmd0, arg1)\n\targ2 = nothing;\targ3 = nothing;\targ4 = nothing;\targ5 = nothing;\n\n\td, K, O = init_module(first, kwargs...)\t\t# Also checks if the user wants ONLY the HELP mode\n\tcommon_insert_R!(d, O, cmd0, arg1)\t\t\t# Set -R in 'd' out of grid/images (with coords) if limits was not used\n\n\thas_opt_B = (find_in_dict(d, [:B :frame :axis :axes], false)[1] !== nothing)\n\tcmd, opt_B, _, opt_R = parse_BJR(d, \"\", \"grdview\", O, \" -JX\" * split(def_fig_size, '/')[1] * \"/0\")\n\t(!has_opt_B && isa(arg1, GMTimage) && (isimgsize(arg1) || CTRL.limits[1:4] == zeros(4)) && opt_B == def_fig_axes) &&\n\t\t(cmd = replace(cmd, opt_B => \"\"))\t# Dont plot axes for plain images if that was not required\n\n\tcmd, = parse_common_opts(d, cmd, [:UVXY :c :f :n :p :t :params], first)\n\tcmd  = add_opt(d, cmd, 'S', [:S :smooth])\n\tif ((val = find_in_dict(d, [:N :plane])[1]) !== nothing)\n\t\tcmd *= \" -N\" * parse_arg_and_pen(val, \"+g\", false)\n\tend\n\tcmd = add_opt(d, cmd, 'Q', [:Q :surf :surftype],\n\t\t\t\t  (mesh=(\"m\", add_opt_fill), surface=\"_s\", surf=\"_s\", img=(\"i\",arg2str), image=\"i\", nan_alpha=\"_c\", monochrome=\"_+m\", waterfall=(rows=\"my\", cols=\"mx\", fill=add_opt_fill)))\n\tcmd = add_opt(d, cmd, 'W', [:W :pens :pen], (contour=(\"c\", add_opt_pen),\n\t              mesh=(\"m\", add_opt_pen), facade=(\"f\", add_opt_pen)) )\n\tcmd = add_opt(d, cmd, 'T', [:T :no_interp :tiles], (skip=\"_+s\", skip_nan=\"_+s\", outlines=(\"+o\", add_opt_pen)) )\n\t(!occursin(\" -T\", cmd)) ? cmd = parse_JZ(d, cmd)[1] : del_from_dict(d, [:JZ])\t# Means, even if we had one, ignore silently\n\tcmd = add_opt(d, cmd, \"%\", [:layout :mem_layout], nothing)\n\n\tcmd, got_fname, arg1 = find_data(d, cmd0, cmd, arg1)\t\t# Find how data was transmitted\n\n\t(isa(arg1, Array{<:Real})) && (arg1 = mat2grid(arg1))\n\n\tcmd, N_used, arg1, arg2, arg3 = common_get_R_cpt(d, cmd0, cmd, opt_R, got_fname, arg1, arg2, arg3, \"grdview\")\n\tcmd, arg1, arg2, arg3, arg4 = common_shade(d, cmd, arg1, arg2, arg3, arg4, \"grdview\")\n\tcmd, arg1, arg2, arg3, arg4, arg5 = parse_G_grdview(d, [:G :drapefile], cmd, arg1, arg2, arg3, arg4, arg5)\n\n\t_cmd, K = finish_PS_nested(d, [\"grdview \" * cmd], K)\n    return finish_PS_module(d, _cmd, \"\", K, O, true, arg1, arg2, arg3, arg4, arg5)\nend\n\n# ---------------------------------------------------------------------------------------------------\nfunction parse_G_grdview(d::Dict, symbs::Array{<:Symbol}, cmd::String, arg1, arg2, arg3, arg4, arg5)\n\t(show_kwargs[1]) && return print_kwarg_opts(symbs, \"GMTgrid | Tuple | String\"), arg1, arg2, arg3, arg4, arg5\n\tif ((val = find_in_dict(d, [:G :drapefile])[1]) !== nothing)\n\t\tif (isa(val, String))\t\t\t\t# Uff, simple. Either a file name or a -A type modifier\n\t\t\tcmd *= \" -G\" * val\n\t\telseif (isa(val, GMTgrid))\t\t\t# A single drape grid (arg1-3 may be used already)\n\t\t\tcmd, N_used = put_in_slot(cmd, val, 'G', [arg1, arg2, arg3, arg4])\n\t\t\tif     (N_used == 1)  arg1 = val\n\t\t\telseif (N_used == 2)  arg2 = val\n\t\t\telseif (N_used == 3)  arg3 = val\n\t\t\telseif (N_used == 4)  arg4 = val\n\t\t\tend\n\t\telseif (isa(val, Tuple) && length(val) == 3)\n\t\t\tcmd, N_used = put_in_slot(cmd, val[1], 'G', [arg1, arg2, arg3, arg4, arg5])\n\t\t\tcmd *= \" -G -G\"\t\t# Because the above only set one -G and we need 3\n\t\t\tif     (N_used == 1)  arg1 = val[1];\targ2 = val[2];\t\targ3 = val[3]\n\t\t\telseif (N_used == 2)  arg2 = val[1];\targ3 = val[2];\t\targ4 = val[3]\n\t\t\telseif (N_used == 3)  arg3 = val[1];\targ4 = val[2];\t\targ5 = val[3]\n\t\t\tend\n\t\telse\n\t\t\terror(\"Wrong way of setting the drape (G) option.\")\n\t\tend\n\tend\n\treturn cmd, arg1, arg2, arg3, arg4, arg5\nend\n\n# ---------------------------------------------------------------------------------------------------\ngrdview!(cmd0::String=\"\", arg1=nothing; first=false, kw...) = grdview(cmd0, arg1; first=first, kw...)\ngrdview(arg1; first=true, kw...) = grdview(\"\", arg1; first=first, kw...)\ngrdview!(arg1; first=false, kw...) = grdview(\"\", arg1; first=first, kw...)", "meta": {"hexsha": "6f185e396b319a27868a98faaeba64b3c08315a5", "size": 5902, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grdview.jl", "max_stars_repo_name": "jmkuhn/GMT.jl", "max_stars_repo_head_hexsha": "c6bc7031cf668db2507be206158c04c3ec8074d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/grdview.jl", "max_issues_repo_name": "jmkuhn/GMT.jl", "max_issues_repo_head_hexsha": "c6bc7031cf668db2507be206158c04c3ec8074d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/grdview.jl", "max_forks_repo_name": "jmkuhn/GMT.jl", "max_forks_repo_head_hexsha": "c6bc7031cf668db2507be206158c04c3ec8074d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.3770491803, "max_line_length": 175, "alphanum_fraction": 0.595899695, "num_tokens": 2007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.12940272487671914, "lm_q1q2_score": 0.06167070546843566}}
{"text": "using Pkg\nPkg.add(\"Images\")\nPkg.add(\"ImageMagick\")\nPkg.add(\"LinearAlgebra\")\nPkg.add(\"Printf\")\nPkg.add(\"FileIO\")\nPkg.add(\"NLopt\")\nPkg.add(\"SpecialFunctions\")\nPkg.add(\"Gadfly\")", "meta": {"hexsha": "07a1aece3c7e0b1f94dd9cb2ad33f4c1bffc85ec", "size": 174, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Packages.jl", "max_stars_repo_name": "WishIWasBornInTheCreteaceousEra/SparseInverseProblems.jl", "max_stars_repo_head_hexsha": "0b8595419561b791929ea367f0a65378caf59d34", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Packages.jl", "max_issues_repo_name": "WishIWasBornInTheCreteaceousEra/SparseInverseProblems.jl", "max_issues_repo_head_hexsha": "0b8595419561b791929ea367f0a65378caf59d34", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Packages.jl", "max_forks_repo_name": "WishIWasBornInTheCreteaceousEra/SparseInverseProblems.jl", "max_forks_repo_head_hexsha": "0b8595419561b791929ea367f0a65378caf59d34", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.3333333333, "max_line_length": 27, "alphanum_fraction": 0.7183908046, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12421301807811347, "lm_q1q2_score": 0.06162131180851187}}
{"text": "using Base.Test\nusing JLab\n\n# #############################################################################\n#Test significant figures\nprintln(\"########## Testing \\\"to_sf\\\" ##########\")\n\n#Non-positive sigfig error\nprintln(\"Testing for error on non-positive argument num_sf ...\")\n@test_throws to_sf(1,0)\n@test_throws to_sf(1,-1)\n\n#Input types\nprintln(\"Testing input types ...\")\n@test to_sf(1,3) == \"1.00\"\n@test to_sf(1.0,3) == \"1.00\"\n@test to_sf(1e0,3) == \"1.00\"\n@test to_sf(pi,3) == \"3.14\"\n\n#Zero\nprintln(\"Testing 0 as input ...\")\n@test to_sf(0,3) == \"0\"\n\n#Rounding\nprintln(\"Testing rounding ...\")\n@test to_sf(3.141593,6) == \"3.14159\"\n@test to_sf(3.141593,5) == \"3.1416\"\n@test to_sf(3.141593,4) == \"3.142\"\n@test to_sf(3.141593,3) == \"3.14\"\n@test to_sf(3.141593,2) == \"3.1\"\n@test to_sf(3.141593,1) == \"3\"\n\n#Exponents\nprintln(\"Testing exponent notation ...\")\n@test to_sf(3.142e4,5) == \"3.1420e4\"\n@test to_sf(3.142e4,4) == \"3.142e4\"\n\n@test to_sf(3.142e3,5) == \"3.1420e3\"\n@test to_sf(3.142e3,4) == \"3142\"\n@test to_sf(3.142e3,3) == \"3140\"\n@test to_sf(3.142e3,2) == \"3100\"\n@test to_sf(3.142e3,1) == \"3000\"\n@test to_sf(3.142e3,4,force=true) == \"3.142e3\"\n@test to_sf(3.142e3,3,force=true) == \"3.14e3\"\n@test to_sf(3.142e3,2,force=true) == \"3.1e3\"\n@test to_sf(3.142e3,1,force=true) == \"3e3\"\n\n@test to_sf(3.142e2,4) == \"3.142e2\"\n@test to_sf(3.142e2,3) == \"314\"\n@test to_sf(3.142e2,2) == \"310\"\n@test to_sf(3.142e2,1) == \"300\"\n@test to_sf(3.142e2,3,force=true) == \"3.14e2\"\n@test to_sf(3.142e2,2,force=true) == \"3.1e2\"\n@test to_sf(3.142e2,1,force=true) == \"3e2\"\n\n@test to_sf(3.142e1,3) == \"3.14e1\"\n@test to_sf(3.142e1,2) == \"31\"\n@test to_sf(3.142e1,1) == \"30\"\n@test to_sf(3.142e1,2,force=true) == \"3.1e1\"\n@test to_sf(3.142e1,1,force=true) == \"3e1\"\n\n@test to_sf(3.142,2) == \"3.1\"\n@test to_sf(3.142,1,force=true) == \"3\"\n@test to_sf(3.142,1) == \"3\"\n@test to_sf(3.142,1,force=true) == \"3\"\n\n@test to_sf(3.142e-1,3) == \"0.314\"\n@test to_sf(3.142e-1,3,force=true) == \"3.14e-1\"\n\n@test to_sf(3.142e-2,3) == \"3.14e-2\"\n\n#Exponent format\nprintln(\"Testing exponent format ...\")\n@test to_sf(3.14e-2,3,expformat=\"\\\\times10^{%}\") == \"3.14\\\\times10^{-2}\"\n\nprintln(\"Function \\\"to_sf\\\" passes tests.\")\n\n# #############################################################################\n#Test decimal places\nprintln(\"########## Testing \\\"to_dp\\\" ##########\")\n\n#Input types\nprintln(\"Testing input types ...\")\n@test to_dp(1,2) == \"1.00\"\n@test to_dp(1.0,2) == \"1.00\"\n@test to_dp(1e0,2) == \"1.00\"\n@test to_dp(pi,2) == \"3.14\"\n\n#Rounding\nprintln(\"Testing rounding ...\")\n@test to_dp(3.141593,7) == \"3.1415930\"\n@test to_dp(3.141593,6) == \"3.141593\"\n@test to_dp(3.141593,5) == \"3.14159\"\n@test to_dp(3.141593,4) == \"3.1416\"\n@test to_dp(3.141593,3) == \"3.142\"\n\n#Decimal places\nprintln(\"Testing decimal places ...\")\n@test to_dp(3.142e4,2) == \"31420.00\"\n@test to_dp(3.142e3,2) == \"3142.00\"\n@test to_dp(3.142e3,0) == \"3142\"\n@test to_dp(3.142e3,-1) == \"3140\"\n@test to_dp(3.142e3,-3) == \"3000\"\n@test to_dp(3.142e3,-4) == \"0\"\n\nprintln(\"Function \\\"to_dp\\\" passes tests.\")\n", "meta": {"hexsha": "a848fff019369696a50ee859dae4b8f56f6d2582", "size": 3015, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/sfdp.jl", "max_stars_repo_name": "amyascwk/JuniorLab.jl", "max_stars_repo_head_hexsha": "201dc56f3a6d673a021042c007be5fcd90947dfb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/sfdp.jl", "max_issues_repo_name": "amyascwk/JuniorLab.jl", "max_issues_repo_head_hexsha": "201dc56f3a6d673a021042c007be5fcd90947dfb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/sfdp.jl", "max_forks_repo_name": "amyascwk/JuniorLab.jl", "max_forks_repo_head_hexsha": "201dc56f3a6d673a021042c007be5fcd90947dfb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-10-01T12:18:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T12:18:05.000Z", "avg_line_length": 28.1775700935, "max_line_length": 79, "alphanum_fraction": 0.5880597015, "num_tokens": 1237, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12421301645710342, "lm_q1q2_score": 0.061621311004338784}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.3\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 55593255-a4ca-49df-9f6e-12411e9fe9d2\nusing PlutoUI\n\n# \u2554\u2550\u2561 f46bcfa1-445d-4928-abd0-a551cef1e36f\nusing Plots\n\n# \u2554\u2550\u2561 361b5f40-6c24-46b2-a389-f819e3481f71\nmd\"# Interactivity:  \n\nTry changing `x` to a different number.\"\n\n# \u2554\u2550\u2561 dd18192a-a85c-11eb-202d-914944b9ef89\nx = 4\n\n# \u2554\u2550\u2561 d88098cf-aa9a-424b-8682-77aad6901795\ny = 2\n\n# \u2554\u2550\u2561 71eac437-61c2-42b4-86a1-4a8b73b67f85\nx + y\n\n# \u2554\u2550\u2561 798d0410-8615-4164-935b-1419e1ee61b9\nmd\"# Rendering as HTML\"\n\n# \u2554\u2550\u2561 c77a4e1e-a8be-41c8-aaff-9b066184c24c\nmd\"\"\"\n### This is an h3 tag written via Markdown\n\n- I'm a bullet\n- Me too!\n  - I'm a nested bullet\n\n| I'm  | a  | table |\n|------|----|-------|\n| this | is | neat  |\n\"\"\"\n\n# \u2554\u2550\u2561 13b03cc2-f2cf-40d6-8b08-bedc7a0acc95\nmd\"# Using `@bind`\"\n\n# \u2554\u2550\u2561 e762440f-bfc4-411e-a63b-d61fc9ffc4ae\n@bind my_input html\"<input></input>\"\n\n# \u2554\u2550\u2561 94e52639-6072-4649-a7b9-33efa2791918\nmy_input\n\n# \u2554\u2550\u2561 ccac4170-071a-4dd8-b87c-c2cb23f61819\nmd\"# Using PlutoUI\"\n\n# \u2554\u2550\u2561 55518562-76b8-4969-bc03-efe3bd138e68\n@bind thing Slider(1:10)\n\n# \u2554\u2550\u2561 6d676090-70e7-4ef8-b888-ea0b7a3c0491\nthing\n\n# \u2554\u2550\u2561 b7aa9aa5-49d6-4baa-962b-f65dc748b9f2\nmd\"# Custom Display Methods\"\n\n# \u2554\u2550\u2561 39af1a40-21e9-4e02-8193-b33ffd77ec98\nbegin\n\tstruct Thing end \n\t\n\tfunction Base.show(io::IO, ::MIME\"text/html\", o::Thing) \n\t\tprint(io, \"<div style='background-color:blue;height:20px;width: 100%'>\")\n\t\tprint(io, \"</div>\")\n\tend\nend\n\n# \u2554\u2550\u2561 353b54bf-2cd7-4ece-840c-01d9b121dbbd\nThing()\n\n# \u2554\u2550\u2561 f268819d-e75b-4818-8779-b719625b5b4c\nmd\"# Putting this all Together\"\n\n# \u2554\u2550\u2561 5b5e3de9-8413-4ab9-854d-69832e677c1d\nbegin\n\tdata = randn(100)\n\tnobs_ui = @bind nobs Slider(10:10:100)\n\tseries_ui = @bind series Select([\"scatter\", \"line\", \"bar\"])\n\t\n\tmd\"\"\"\n\tThis is a small UI for editing the elements of a plot.\n\n\t- It's neat!\n\n\t$nobs_ui  $series_ui\n\t\"\"\"\nend\n\n# \u2554\u2550\u2561 784f192c-0830-44fa-bf09-bcc0752961c0\nplot(data[1:nobs], seriestype=Symbol(series), title=\"Nobs: $nobs\")\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500361b5f40-6c24-46b2-a389-f819e3481f71\n# \u2560\u2550dd18192a-a85c-11eb-202d-914944b9ef89\n# \u2560\u2550d88098cf-aa9a-424b-8682-77aad6901795\n# \u2560\u255071eac437-61c2-42b4-86a1-4a8b73b67f85\n# \u255f\u2500798d0410-8615-4164-935b-1419e1ee61b9\n# \u255f\u2500c77a4e1e-a8be-41c8-aaff-9b066184c24c\n# \u255f\u250013b03cc2-f2cf-40d6-8b08-bedc7a0acc95\n# \u2560\u2550e762440f-bfc4-411e-a63b-d61fc9ffc4ae\n# \u2560\u255094e52639-6072-4649-a7b9-33efa2791918\n# \u255f\u2500ccac4170-071a-4dd8-b87c-c2cb23f61819\n# \u2560\u255055593255-a4ca-49df-9f6e-12411e9fe9d2\n# \u2560\u255055518562-76b8-4969-bc03-efe3bd138e68\n# \u2560\u25506d676090-70e7-4ef8-b888-ea0b7a3c0491\n# \u255f\u2500b7aa9aa5-49d6-4baa-962b-f65dc748b9f2\n# \u2560\u255039af1a40-21e9-4e02-8193-b33ffd77ec98\n# \u2560\u2550353b54bf-2cd7-4ece-840c-01d9b121dbbd\n# \u255f\u2500f268819d-e75b-4818-8779-b719625b5b4c\n# \u2560\u2550f46bcfa1-445d-4928-abd0-a551cef1e36f\n# \u2560\u25505b5e3de9-8413-4ab9-854d-69832e677c1d\n# \u2560\u2550784f192c-0830-44fa-bf09-bcc0752961c0\n", "meta": {"hexsha": "8d948daa63c3a8d33e725a0e5d377c57134bdb1b", "size": 3154, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "pluto_notebooks/first-steps-5-pluto.jl", "max_stars_repo_name": "joshday/JuliaForDataScience.jl", "max_stars_repo_head_hexsha": "0c330d65ac43733e6820e5c698cb882b28fe4db5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2018-08-22T15:21:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T04:19:53.000Z", "max_issues_repo_path": "pluto_notebooks/first-steps-5-pluto.jl", "max_issues_repo_name": "joshday/JuliaForDataScience.jl", "max_issues_repo_head_hexsha": "0c330d65ac43733e6820e5c698cb882b28fe4db5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-09-29T01:11:07.000Z", "max_issues_repo_issues_event_max_datetime": "2018-10-03T02:00:22.000Z", "max_forks_repo_path": "pluto_notebooks/first-steps-5-pluto.jl", "max_forks_repo_name": "joshday/JuliaForDataScience.jl", "max_forks_repo_head_hexsha": "0c330d65ac43733e6820e5c698cb882b28fe4db5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-11-21T00:52:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:05:43.000Z", "avg_line_length": 24.640625, "max_line_length": 195, "alphanum_fraction": 0.7067216233, "num_tokens": 1566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42632159254749036, "lm_q2_score": 0.1441488511943849, "lm_q1q2_score": 0.061453767805081376}}
{"text": "\n@inline function _to_cartesian(a, i::CanonicalInt)\n    @inbounds(CartesianIndices(ntuple(dim -> indices(a, dim), Val(ndims(a))))[i])\nend\n@inline function _to_linear(a, i::Tuple{CanonicalInt,Vararg{CanonicalInt}})\n    _strides2int(offsets(a), size_to_strides(size(a), static(1)), i) + static(1)\nend\n\n\"\"\"\n    is_splat_index(::Type{T}) -> StaticBool\n    \nReturns `static(true)` if `T` is a type that splats across multiple dimensions. \n\"\"\"\nis_splat_index(@nospecialize(x)) = is_splat_index(typeof(x))\nis_splat_index(::Type{T}) where {T} = static(false)\n_is_splat(::Type{I}, i::StaticInt) where {I} = is_splat_index(_get_tuple(I, i))\n\n\"\"\"\n    ndims_index(::Type{I}) -> StaticInt\n\nReturns the number of dimension that an instance of `I` maps to when indexing. For example,\n`CartesianIndex{3}` maps to 3 dimensions. If this method is not explicitly defined, then `1`\nis returned.\n\n\"\"\"\nndims_index(@nospecialize(i)) = ndims_index(typeof(i))\nndims_index(::Type{I}) where {I} = static(1)\nndims_index(::Type{<:AbstractCartesianIndex{N}}) where {N} = static(N)\nndims_index(::Type{<:AbstractArray{T}}) where {T} = ndims_index(T)\nndims_index(::Type{<:AbstractArray{Bool,N}}) where {N} = static(N)\nndims_index(::Type{<:LogicalIndex{<:Any,<:AbstractArray{Bool,N}}}) where {N} = static(N)\n_ndims_index(::Type{I}, i::StaticInt) where {I} = ndims_index(_get_tuple(I, i))\n\n\"\"\"\n    to_indices(A, I::Tuple) -> Tuple\n\nConverts the tuple of indexing arguments, `I`, into an appropriate form for indexing into `A`.\nTypically, each index should be an `Int`, `StaticInt`, a collection with values of `Int`, or a collection with values of `CartesianIndex`\nThis is accomplished in three steps after the initial call to `to_indices`:\n\n# Extended help\n\nThis implementation differs from that of `Base.to_indices` in the following ways:\n\n*  `to_indices(A, I)` never results in recursive processing of `I` through\n  `to_indices(A, axes(A), I)`. This is avoided through the use of an internal `@generated`\n  method that aligns calls of `to_indices` and `to_index` based on the return values of\n  `ndims_index`. This is beneficial because the compiler currently does not optimize away\n  the increased time spent recursing through\n    each additional argument that needs converting. For example:\n    ```julia\n    julia> x = rand(4,4,4,4,4,4,4,4,4,4);\n\n    julia> inds1 = (1, 2, 1, 2, 1, 2, 1, 2, 1, 2);\n\n    julia> inds2 = (1, CartesianIndex(1, 2), 1, CartesianIndex(1, 2), 1, CartesianIndex(1, 2), 1);\n\n    julia> inds3 = (fill(true, 4, 4), 2, fill(true, 4, 4), 2, 1, fill(true, 4, 4), 1);\n\n    julia> @btime Base.to_indices(\\$x, \\$inds2)\n    1.105 \u03bcs (12 allocations: 672 bytes)\n    (1, 1, 2, 1, 1, 2, 1, 1, 2, 1)\n\n    julia> @btime ArrayInterface.to_indices(\\$x, \\$inds2)\n    0.041 ns (0 allocations: 0 bytes)\n    (1, 1, 2, 1, 1, 2, 1, 1, 2, 1)\n\n    julia> @btime Base.to_indices(\\$x, \\$inds3);\n    340.629 ns (14 allocations: 768 bytes)\n\n    julia> @btime ArrayInterface.to_indices(\\$x, \\$inds3);\n    11.614 ns (0 allocations: 0 bytes)\n\n    ```\n* Recursing through `to_indices(A, axes, I::Tuple{I1,Vararg{Any}})` is intended to provide\n  context for processing `I1`. However, this doesn't tell use how many dimensions are\n  consumed by what is in `Vararg{Any}`. Using `ndims_index` to directly align the axes of\n  `A` with each value in `I` ensures that a `CartesiaIndex{3}` at the tail of `I` isn't\n  incorrectly assumed to only consume one dimension.\n* `Base.to_indices` may fail to infer the returned type. This is the case for `inds2` and\n  `inds3` in the first bullet on Julia 1.6.4.\n* Specializing by dispatch through method definitions like this:\n  `to_indices(::ArrayType, ::Tuple{AxisType,Vararg{Any}}, ::Tuple{::IndexType,Vararg{Any}})`\n  require an excessive number of hand written methods to avoid ambiguities. Furthermore, if\n  `AxisType` is wrapping another axis that should have unique behavior, then unique parametric \n  types need to also be explicitly defined.\n* `to_index(axes(A, dim), index)` is called, as opposed to `Base.to_index(A, index)`. The\n  `IndexStyle` of the resulting axis is used to allow indirect dispatch on nested axis types\n  within `to_index`.\n\"\"\"\nto_indices(A, ::Tuple{}) = ()\n@inline function to_indices(a::A, inds::I) where {A,I}\n    _to_indices(\n        a,\n        inds,\n        IndexStyle(A),\n        static(ndims(A)),\n        eachop(_ndims_index, nstatic(Val(known_length(I))), I),\n        eachop(_is_splat, nstatic(Val(known_length(I))), I)\n    )\nend\n@generated function _to_indices(A, inds::I, ::S, ::StaticInt{N}, ::NDI, ::IS) where {I,S,N,NDI,IS}\n    cnt = zeros(Int, known_length(NDI))\n    splat_position = 0\n    remaining = N\n    for i in 1:known_length(NDI)\n        ndi = known(NDI.parameters[i])\n        splat = known(IS.parameters[i])\n        if splat && splat_position === 0\n            splat_position = i\n        else\n            remaining -= ndi\n            cnt[i] = ndi\n        end\n    end\n    if splat_position !== 0\n        cnt[splat_position] = max(0, remaining)\n    else\n        # if there are additional trailing dimensions not consumed by the index then we have\n        # to assume it's linear indexing or that these are trailing dimensions.\n        cnt[end] += max(0, remaining)\n    end\n\n    t = Expr(:tuple)\n    dim = 0\n    for i in 1:known_length(NDI)\n        if i === known_length(NDI) && S <: IndexLinear\n            ICall = :LinearIndices\n        else\n            ICall = :CartesianIndices\n        end\n        c = cnt[i]\n        iexpr = :(@inbounds(getfield(inds, $i))::$(I.parameters[i]))\n        if dim === N\n            push!(t.args, :(to_index($(ICall)(()), $iexpr)))\n        elseif c === 1\n            dim += 1\n            push!(t.args, :(to_index(@inbounds(getfield(axs, $dim)), $iexpr)))\n        else\n            subaxs = Expr(:tuple)\n            for _ in 1:c\n                if dim < N\n                    dim += 1\n                    push!(subaxs.args, :(@inbounds(getfield(axs, $dim))))\n                end\n            end\n            push!(t.args, :(to_index($(ICall)($subaxs), $iexpr)))\n        end\n    end\n    Expr(:block,\n        Expr(:meta, :inline),\n        Expr(:(=), :axs, :(lazy_axes(A))),\n        :(_flatten_tuples($t))\n    )\nend\n@generated function _flatten_tuples(inds::I) where {I}\n    t = Expr(:tuple)\n    for i in 1:known_length(I)\n        p = I.parameters[i]\n        if p <: Tuple\n            for j in 1:known_length(p)\n                push!(t.args, :(@inbounds(getfield(getfield(inds, $i), $j))))\n            end\n        else\n            push!(t.args, :(@inbounds(getfield(inds, $i))))\n        end\n    end\n    t\nend\n\n\"\"\"\n    to_index([::IndexStyle, ]axis, arg) -> index\n\nConvert the argument `arg` that was originally passed to `getindex` for the dimension\ncorresponding to `axis` into a form for native indexing (`Int`, Vector{Int}, etc.). New\naxis types with unique behavior should use an `IndexStyle` trait:\n\n```julia\nto_index(axis::MyAxisType, arg) = to_index(IndexStyle(axis), axis, arg)\nto_index(::MyIndexStyle, axis, arg) = ...\n```\n\"\"\"\nto_index(x, i::Slice) = i\nto_index(x, ::Colon) = indices(x)\n# logical indexing\nto_index(x, i::AbstractArray{Bool}) = LogicalIndex(i)\nto_index(x::LinearIndices, i::AbstractArray{Bool}) = LogicalIndex{Int}(i)\n# cartesian indexing\n@inline to_index(x, i::CartesianIndices{0}) = i\n@inline to_index(x, i::CartesianIndices) = axes(i)\n@inline to_index(x, i::CartesianIndex) = Tuple(i)\n@inline to_index(x, i::NDIndex) = Tuple(i)\n@inline to_index(x, i::AbstractArray{<:AbstractCartesianIndex}) = i\n# integer indexing\nto_index(x, i::AbstractArray{<:Integer}) = i\nto_index(x, @nospecialize(i::StaticInt)) = i\nto_index(x, i::Integer) = Int(i)\n@inline to_index(x, i) = to_index(IndexStyle(x), x, i)\nfunction to_index(S::IndexStyle, x, i)\n    throw(ArgumentError(\n        \"invalid index: $S does not support indices of type $(typeof(i)) for instances of type $(typeof(x)).\"\n    ))\nend\n\n\"\"\"\n    unsafe_reconstruct(A, data; kwargs...)\n\nReconstruct `A` given the values in `data`. New methods using `unsafe_reconstruct`\nshould only dispatch on `A`.\n\"\"\"\nfunction unsafe_reconstruct(axis::OneTo, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return OneTo(data)\n    end\nend\nfunction unsafe_reconstruct(axis::UnitRange, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return UnitRange(first(data), last(data))\n    end\nend\nfunction unsafe_reconstruct(axis::OptionallyStaticUnitRange, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return OptionallyStaticUnitRange(static_first(data), static_last(data))\n    end\nend\nfunction unsafe_reconstruct(A::AbstractUnitRange, data; kwargs...)\n    return static_first(data):static_last(data)\nend\n\n\"\"\"\n    to_axes(A, inds) -> Tuple\n\nConstruct new axes given the corresponding `inds` constructed after\n`to_indices(A, args) -> inds`. This method iterates through each pair of axes and\nindices calling [`to_axis`](@ref).\n\"\"\"\n@inline function to_axes(A, inds::Tuple)\n    if ndims(A) === 1\n        return (to_axis(axes(A, 1), first(inds)),)\n    elseif length(inds) === 1\n        return (to_axis(eachindex(IndexLinear(), A), first(inds)),)\n    else\n        return to_axes(A, axes(A), inds)\n    end\nend\n# drop this dimension\nto_axes(A, a::Tuple, i::Tuple{<:Integer,Vararg{Any}}) = to_axes(A, tail(a), tail(i))\nto_axes(A, a::Tuple, i::Tuple{I,Vararg{Any}}) where {I} = _to_axes(ndims_index(I), A, a, i)\nfunction _to_axes(::StaticInt{1}, A, axs::Tuple, inds::Tuple)\n    return (to_axis(first(axs), first(inds)), to_axes(A, tail(axs), tail(inds))...)\nend\n@propagate_inbounds function _to_axes(::StaticInt{N}, A, axs::Tuple, inds::Tuple) where {N}\n    axes_front, axes_tail = Base.IteratorsMD.split(axs, Val(N))\n    if IndexStyle(A) === IndexLinear()\n        axis = to_axis(LinearIndices(axes_front), getfield(inds, 1))\n    else\n        axis = to_axis(CartesianIndices(axes_front), getfield(inds, 1))\n    end\n    return (axis, to_axes(A, axes_tail, tail(inds))...)\nend\nto_axes(A, ::Tuple{Ax,Vararg{Any}}, ::Tuple{}) where {Ax} = ()\nto_axes(A, ::Tuple{}, ::Tuple{}) = ()\n\n\"\"\"\n    to_axis(old_axis, index) -> new_axis\n\nConstruct an `new_axis` for a newly constructed array that corresponds to the\npreviously executed `to_index(old_axis, arg) -> index`. `to_axis` assumes that\n`index` has already been confirmed to be in bounds. The underlying indices of\n`new_axis` begins at one and extends the length of `index` (i.e., one-based indexing).\n\"\"\"\n@inline function to_axis(axis, inds)\n    if !can_change_size(axis) &&\n       (known_length(inds) !== nothing && known_length(axis) === known_length(inds))\n        return axis\n    else\n        return to_axis(IndexStyle(axis), axis, inds)\n    end\nend\n\n# don't need to check size b/c slice means it's the entire axis\n@inline function to_axis(axis, inds::Slice)\n    if can_change_size(axis)\n        return copy(axis)\n    else\n        return axis\n    end\nend\nto_axis(S::IndexLinear, axis, inds) = StaticInt(1):static_length(inds)\n\n\"\"\"\n    ArrayInterface.getindex(A, args...)\n\nRetrieve the value(s) stored at the given key or index within a collection. Creating\nanother instance of `ArrayInterface.getindex` should only be done by overloading `A`.\nChanging indexing based on a given argument from `args` should be done through,\n[`to_index`](@ref), or [`to_axis`](@ref).\n\"\"\"\nfunction getindex(A, args...)\n    inds = to_indices(A, args)\n    @boundscheck checkbounds(A, inds...)\n    unsafe_getindex(A, inds...)\nend\nfunction getindex(A; kwargs...)\n    inds = to_indices(A, order_named_inds(dimnames(A), values(kwargs)))\n    @boundscheck checkbounds(A, inds...)\n    unsafe_getindex(A, inds...)\nend\n@propagate_inbounds getindex(x::Tuple, i::Int) = getfield(x, i)\n@propagate_inbounds getindex(x::Tuple, ::StaticInt{i}) where {i} = getfield(x, i)\n\n## unsafe_getindex ##\nfunction unsafe_getindex(a::A) where {A}\n    parent_type(A) <: A && throw(MethodError(unsafe_getindex, (A,)))\n    unsafe_getindex(parent(a))\nend\n\n# TODO Need to manage index transformations between nested layers of arrays\nfunction unsafe_getindex(a::A, i::CanonicalInt) where {A}\n    if IndexStyle(A) === IndexLinear()\n        parent_type(A) <: A && throw(MethodError(unsafe_getindex, (A, i)))\n        return unsafe_getindex(parent(a), i)\n    else\n        return unsafe_getindex(a, _to_cartesian(a, i)...)\n    end\nend\nfunction unsafe_getindex(a::A, i::CanonicalInt, ii::Vararg{CanonicalInt}) where {A}\n    if IndexStyle(A) === IndexLinear()\n        return unsafe_getindex(a, _to_linear(a, (i, ii...)))\n    else\n        parent_type(A) <: A && throw(MethodError(unsafe_getindex, (A, i)))\n        return unsafe_getindex(parent(a), i, ii...)\n    end\nend\n\nunsafe_getindex(a, i::Vararg{Any}) = unsafe_get_collection(a, i)\n\nunsafe_getindex(A::Array) = Base.arrayref(false, A, 1)\nunsafe_getindex(A::Array, i::CanonicalInt) = Base.arrayref(false, A, Int(i))\n@inline function unsafe_getindex(A::Array, i::CanonicalInt, ii::Vararg{CanonicalInt})\n    unsafe_getindex(A, _to_linear(A, (i, ii...)))\nend\n\nunsafe_getindex(A::LinearIndices, i::CanonicalInt) = Int(i)\nunsafe_getindex(A::CartesianIndices, i::CanonicalInt, ii::Vararg{CanonicalInt}) = CartesianIndex(i, ii...)\nunsafe_getindex(A::CartesianIndices, i::CanonicalInt) = @inbounds(A[i])\n\nunsafe_getindex(A::ReshapedArray, i::CanonicalInt) = @inbounds(parent(A)[i])\nfunction unsafe_getindex(A::ReshapedArray, i::CanonicalInt, ii::Vararg{CanonicalInt})\n    @inbounds(parent(A)[_to_linear(A, (i, ii...))])\nend\n\nunsafe_getindex(A::SubArray, i::CanonicalInt) = @inbounds(A[i])\nunsafe_getindex(A::SubArray, i::CanonicalInt, ii::Vararg{CanonicalInt}) = @inbounds(A[i, ii...])\n\n# This is based on Base._unsafe_getindex from https://github.com/JuliaLang/julia/blob/c5ede45829bf8eb09f2145bfd6f089459d77b2b1/base/multidimensional.jl#L755.\n#=\n    unsafe_get_collection(A, inds)\n\nReturns a collection of `A` given `inds`. `inds` is assumed to have been bounds-checked.\n=#\nfunction unsafe_get_collection(A, inds)\n    axs = to_axes(A, inds)\n    dest = similar(A, axs)\n    if map(Base.unsafe_length, axes(dest)) == map(Base.unsafe_length, axs)\n        Base._unsafe_getindex!(dest, A, inds...)\n    else\n        Base.throw_checksize_error(dest, axs)\n    end\n    return dest\nend\n_ints2range(x::Integer) = x:x\n_ints2range(x::AbstractRange) = x\n@inline function unsafe_get_collection(A::CartesianIndices{N}, inds) where {N}\n    if (length(inds) === 1 && N > 1) || stride_preserving_index(typeof(inds)) === False()\n        return Base._getindex(IndexStyle(A), A, inds...)\n    else\n        return CartesianIndices(to_axes(A, _ints2range.(inds)))\n    end\nend\n@inline function unsafe_get_collection(A::LinearIndices{N}, inds) where {N}\n    if length(inds) === 1 && isone(_ndims_index(typeof(inds), static(1)))\n        return @inbounds(eachindex(A)[first(inds)])\n    elseif stride_preserving_index(typeof(inds)) === True()\n        return LinearIndices(to_axes(A, _ints2range.(inds)))\n    else\n        return Base._getindex(IndexStyle(A), A, inds...)\n    end\nend\n\n\"\"\"\n    ArrayInterface.setindex!(A, args...)\n\nStore the given values at the given key or index within a collection.\n\"\"\"\n@propagate_inbounds function setindex!(A, val, args...)\n    can_setindex(A) || error(\"Instance of type $(typeof(A)) are not mutable and cannot change elements after construction.\")\n    inds = to_indices(A, args)\n    @boundscheck checkbounds(A, inds...)\n    unsafe_setindex!(A, val, inds...)\nend\n@propagate_inbounds function setindex!(A, val; kwargs...)\n    can_setindex(A) || error(\"Instance of type $(typeof(A)) are not mutable and cannot change elements after construction.\")\n    inds = to_indices(A, order_named_inds(dimnames(A), values(kwargs)))\n    @boundscheck checkbounds(A, inds...)\n    unsafe_setindex!(A, val, inds...)\nend\n\n## unsafe_setindex! ##\nfunction unsafe_setindex!(a::A, v) where {A}\n    parent_type(A) <: A && throw(MethodError(unsafe_setindex!, (A, v)))\n    return unsafe_setindex!(parent(a), v)\nend\n# TODO Need to manage index transformations between nested layers of arrays\nfunction unsafe_setindex!(a::A, v, i::CanonicalInt) where {A}\n    if IndexStyle(A) === IndexLinear()\n        parent_type(A) <: A && throw(MethodError(unsafe_setindex!, (A, v, i)))\n        return unsafe_setindex!(parent(a), v, i)\n    else\n        return unsafe_setindex!(a, v, _to_cartesian(a, i)...)\n    end\nend\nfunction unsafe_setindex!(a::A, v, i::CanonicalInt, ii::Vararg{CanonicalInt}) where {A}\n    if IndexStyle(A) === IndexLinear()\n        return unsafe_setindex!(a, v, _to_linear(a, (i, ii...)))\n    else\n        parent_type(A) <: A && throw(MethodError(unsafe_setindex!, (A, v, i, ii...)))\n        return unsafe_setindex!(parent(a), v, i, ii...)\n    end\nend\n\n\nfunction unsafe_setindex!(A::Array{T}, v) where {T}\n    Base.arrayset(false, A, convert(T, v)::T, 1)\nend\nfunction unsafe_setindex!(A::Array{T}, v, i::CanonicalInt) where {T}\n    return Base.arrayset(false, A, convert(T, v)::T, Int(i))\nend\n\nunsafe_setindex!(a, v, i::Vararg{Any}) = unsafe_set_collection!(a, v, i)\n\n# This is based on Base._unsafe_setindex!.\n#=\n    unsafe_set_collection!(A, val, inds)\n\nSets `inds` of `A` to `val`. `inds` is assumed to have been bounds-checked.\n=#\nunsafe_set_collection!(A, v, i) = Base._unsafe_setindex!(IndexStyle(A), A, v, i...)\n", "meta": {"hexsha": "3e29b2c98bcb78c8df34caf532905d9a2115a7aa", "size": 17224, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/indexing.jl", "max_stars_repo_name": "N5N3/ArrayInterface.jl", "max_stars_repo_head_hexsha": "fedb318e9d55fba85f0e9c08b0180a44ae4540a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 55, "max_stars_repo_stars_event_min_datetime": "2021-03-09T23:44:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T04:54:06.000Z", "max_issues_repo_path": "src/indexing.jl", "max_issues_repo_name": "N5N3/ArrayInterface.jl", "max_issues_repo_head_hexsha": "fedb318e9d55fba85f0e9c08b0180a44ae4540a1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 129, "max_issues_repo_issues_event_min_datetime": "2021-03-02T07:24:09.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T12:42:14.000Z", "max_forks_repo_path": "src/indexing.jl", "max_forks_repo_name": "N5N3/ArrayInterface.jl", "max_forks_repo_head_hexsha": "fedb318e9d55fba85f0e9c08b0180a44ae4540a1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2021-05-17T04:20:13.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-23T10:32:42.000Z", "avg_line_length": 37.8549450549, "max_line_length": 157, "alphanum_fraction": 0.6655829076, "num_tokens": 4948, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.12252322052837361, "lm_q1q2_score": 0.061261610264186805}}
{"text": "\nimport Base: ==\n==(x,y,z,ws...) = x==y && ==(y,z,ws...)\n\n\n\"\"\" \nReturn the type's fields as a tuple\n\"\"\"\n@generated fieldvalues(x) = Expr(:tuple, (:(x.$f) for f=fieldnames(x))...)\n@generated fields(x) = Expr(:tuple, (:($f=x.$f) for f=fieldnames(x))...)\nfirstfield(x) = first(fieldvalues(x))\n\n\n\n\"\"\"\nRewrites `@! x = f(args...)` to `x = f!(x,args...)`\n\nSpecial cases for `*` and `\\\\` forward to `mul!` and `ldiv!`, respectively.\n\"\"\"\nmacro !(ex)\n    if @capture(ex, x_ = f_(args__; kwargs_...))\n        esc(:($(Symbol(string(f,\"!\")))($x,$(args...); $kwargs...)))\n    elseif @capture(ex, x_ = f_(args__))\n        if f == :*\n            f = :mul\n        elseif f==:\\\n            f = :ldiv\n        end\n        esc(:($x = $(Symbol(string(f,\"!\")))($x,$(args...))::typeof($x))) # ::typeof part helps inference sometimes\n    else\n        error(\"Usage: @! x = f(...)\")\n    end\nend\n\n\nnan2zero(x::T) where {T} = !isfinite(x) ? zero(T) : x\nnan2zero(x::Diagonal{T}) where {T} = Diagonal{T}(nan2zero.(x.diag))\nnan2inf(x::T) where {T} = !isfinite(x) ? T(Inf) : x\n\n\n\"\"\" Return a tuple with the expression repeated n times \"\"\"\nmacro repeated(ex,n)\n    :(tuple($(repeated(esc(ex),n)...)))\nend\n\n\"\"\" \nPack some variables in a dictionary \n\n```julia\n> x = 3\n> y = 4\n> @dictpack x y z=>5\nDict(:x=>3,:y=>4,:z=>5)\n```\n\"\"\"\nmacro dictpack(exs...)\n    kv(ex::Symbol) = :($(QuoteNode(ex))=>$(esc(ex)))\n    kv(ex) = isexpr(ex,:call) && ex.args[1]==:(=>) ? :($(QuoteNode(ex.args[2]))=>$(esc(ex.args[3]))) : error()\n    :(Dict($((kv(ex) for ex=exs)...)))\nend\n\n\"\"\" \nPack some variables into a NamedTuple. E.g.:\n\n```julia\n> x = 3\n> y = 4\n> @namedtuple(x, y, z=5)\n(x=3,y=4,z=5)\n```\n\"\"\"\nmacro namedtuple(exs...)\n    if length(exs)==1 && isexpr(exs[1],:tuple)\n        exs = exs[1].args\n    end\n    kv(ex::Symbol) = :($(esc(ex))=$(esc(ex)))\n    kv(ex) = isexpr(ex,:(=)) ? :($(esc(ex.args[1]))=$(esc(ex.args[2]))) : error()\n    Expr(:tuple, (kv(ex) for ex=exs)...)\nend\n\n\n\n# these allow pinv and sqrt of SMatrices of Diagonals to work correctly, which\n# we use for the T-E block of the covariance. hopefully some of this can be cut\n# down on in the futue with some PRs into StaticArrays.\npermutedims(A::SMatrix{2,2}) = @SMatrix[A[1] A[3]; A[2] A[4]]\nfunction sqrt(A::SMatrix{2,2,<:Diagonal})\n    a,b,c,d = A\n    s = @. sqrt(a*d-b*c)\n    t = pinv(@. sqrt(a+d+2s))\n    @SMatrix[t*(a+s) t*b; t*c t*(d+s)]\nend\nfunction pinv(A::SMatrix{2,2,<:Diagonal})\n    a,b,c,d = A\n    idet = pinv(@. a*d-b*c)\n    @SMatrix[d*idet -(b*idet); -(c*idet) a*idet]\nend\n\n\n# some usefule tuple manipulation functions:\n\n# see: https://discourse.julialang.org/t/efficient-tuple-concatenation/5398/10\n# and https://github.com/JuliaLang/julia/issues/27988\n@inline tuplejoin(x) = x\n@inline tuplejoin(x, y) = (x..., y...)\n@inline tuplejoin(x, y, z...) = (x..., tuplejoin(y, z...)...)\n\n# see https://discourse.julialang.org/t/any-way-to-make-this-one-liner-type-stable/10636/2\nusing Base: tuple_type_cons, tuple_type_head, tuple_type_tail, first, tail\nmap_tupleargs(f,::Type{T}) where {T<:Tuple} = \n    (f(tuple_type_head(T)), map_tupleargs(f,tuple_type_tail(T))...)\nmap_tupleargs(f,::Type{T},::Type{S}) where {T<:Tuple,S<:Tuple} = \n    (f(tuple_type_head(T),tuple_type_head(S)), map_tupleargs(f,tuple_type_tail(T),tuple_type_tail(S))...)\nmap_tupleargs(f,::Type{T},s::Tuple) where {T<:Tuple} = \n    (f(tuple_type_head(T),first(s)), map_tupleargs(f,tuple_type_tail(T),tail(s))...)\nmap_tupleargs(f,::Type{<:Tuple{}}...) = ()\nmap_tupleargs(f,::Type{<:Tuple{}},::Tuple) = ()\n\n\n# returns the base parametric type with all type parameters stripped out\nbasetype(::Type{T}) where {T} = T.name.wrapper\n@generated function basetype(t::UnionAll)\n    unwrap_expr(s::UnionAll, t=:t) = unwrap_expr(s.body, :($t.body))\n    unwrap_expr(::DataType, t) = t\n    :($(unwrap_expr(t.parameters[1])).name.wrapper)\nend\n\n\nfunction ensuresame(args...)\n    @assert all(args .== Ref(args[1]))\n    args[1]\nend\n\n\ntuple_type_len(::Type{<:NTuple{N,Any}}) where {N} = N\n\n\nensure1d(x::Union{Tuple,AbstractArray}) = x\nensure1d(x) = (x,)\n\n\n# see https://discourse.julialang.org/t/dispatching-on-the-result-of-unwrap-unionall-seems-weird/25677\n# for why we need this\n# to use, just decorate the custom show_datatype with it, and make sure the args\n# are named `io` and `t`.\nmacro show_datatype(ex)\n    def = splitdef(ex)\n    def[:body] = quote\n        isconcretetype(t) ? $(def[:body]) : invoke(Base.show_datatype, Tuple{IO,DataType}, io, t)\n    end\n    esc(combinedef(def))\nend\n\n\n\n\"\"\"\n    # symmetric in any of its final arguments except for bar:\n    @sym_memo foo(bar, @sym(args...)) = <body> \n    # symmetric in (i,j), but not baz\n    @sym_memo foo(baz, @sym(i, j)) = <body> \n    \nThe `@sym_memo` macro should be applied to a definition of a function\nwhich is symmetric in some of its arguments. The arguments in which its\nsymmetric are specified by being wrapping them in @sym, and they must come at\nthe very end. The resulting function will be memoized and permutations of the\narguments which are equal due to symmetry will only be computed once.\n\"\"\"\nmacro sym_memo(funcdef)\n    \n    \n    sfuncdef = splitdef(funcdef)\n    \n    asymargs = sfuncdef[:args][1:end-1]\n    symargs = collect(@match sfuncdef[:args][end] begin\n        Expr(:macrocall, [head, _, ex...]), if head==Symbol(\"@sym\") end => ex\n        _ => error(\"final argument(s) should be marked @sym\")\n    end)\n    sfuncdef[:args] = [asymargs..., symargs...]\n    \n    sfuncdef[:body] = quote\n        symargs = [$(symargs...)]\n        sorted_symargs = sort(symargs)\n        if symargs==sorted_symargs\n            $((sfuncdef[:body]))\n        else\n            $(sfuncdef[:name])($(asymargs...), sorted_symargs...)\n        end\n    end\n    \n    esc(:(@memoize $(combinedef(sfuncdef))))\n    \nend\n\n\n@doc doc\"\"\"\n```\n@subst sum(x*$(y+1) for x=1:2)\n```\n    \nbecomes\n\n```\nlet tmp=(y+1)\n    sum(x*tmp for x=1:2)\nend\n```\n\nto aid in writing clear/succinct code that doesn't recompute things\nunnecessarily.\n\"\"\"\nmacro subst(ex)\n    \n    subs = []\n    ex = postwalk(ex) do x\n        if isexpr(x, Symbol(raw\"$\"))\n            var = gensym()\n            push!(subs, :($(esc(var))=$(esc(x.args[1]))))\n            var\n        else\n            x\n        end\n    end\n    \n    quote\n        let $(subs...)\n            $(esc(ex))\n        end\n    end\n\nend\n\n\n\"\"\"\n    @invokelatest expr...\n    \nRewrites all non-broadcasted function calls anywhere within an expression to use\n`Base.invokelatest`. This means functions can be called that have a newer world\nage, at the price of making things non-inferrable.\n\"\"\"\nmacro invokelatest(ex)\n    function walk(x)\n        if isdef(x)\n            x.args[2:end] .= map(walk, x.args[2:end])\n            x\n        elseif @capture(x, f_(args__; kwargs__)) && !startswith(string(f),'.')\n            :(Base.invokelatest($f, $(map(walk,args)...); $(map(walk,kwargs)...)))\n        elseif @capture(x, f_(args__)) && !startswith(string(f),'.')\n            :(Base.invokelatest($f, $(map(walk,args)...)))\n        elseif isexpr(x)\n            x.args .= map(walk, x.args)\n            x\n        else\n            x\n        end\n    end\n    esc(walk(ex))\nend\n\n\n\"\"\"\n    @ondemand(Package.function)(args...; kwargs...)\n    @ondemand(Package.Submodule.function)(args...; kwargs...)\n\nJust like calling `Package.function` or `Package.Submodule.function`, but\n`Package` will be loaded on-demand if it is not already loaded. The call is no\nlonger inferrable.\n\"\"\"\nmacro ondemand(ex)\n    get_root_package(x) = @capture(x, a_.b_) ? get_root_package(a) : x\n    quote\n        @eval import $(get_root_package(ex))\n        (args...; kwargs...) -> Base.invokelatest($(esc(ex)), args...; kwargs...)\n    end\nend\n\nfunction tmap(f, args...)\n    @static if nthreads()==1 || VERSION<v\"1.3\"\n        map(f, args...)\n    else\n        map(fetch,map((args...)->Threads.@spawn(f(args...)),args...))\n    end\nend\n\n\nget_kwarg_names(func::Function) = Vector{Symbol}(kwarg_decl(first(methods(func)), typeof(methods(func).mt.kwsorter)))\nkwarg_decl(m::Method,kw::DataType) = VERSION<=v\"1.3.999\" ? Base.kwarg_decl(m,kw) : Base.kwarg_decl(m)\n\n# maps a function recursively across all arguments of a Broadcasted expression,\n# using the function `broadcasted` to reconstruct the `Broadcasted` object at\n# each point.\nmap_bc_args(f, bc::Broadcasted) = broadcasted(bc.f, map(arg->map_bc_args(f, arg), bc.args)...)\nmap_bc_args(f, arg) = f(arg)\n\n\n# adapting a closure adapts the captured variables\n# this could probably be a PR into Adapt.jl\n@generated function adapt_structure(to, f::F) where {F<:Function}\n    if fieldcount(F) == 0\n        :f\n    else\n        quote\n            captured_vars = $(Expr(:tuple, (:(adapt(to, f.$x)) for x=fieldnames(F))...))\n            $(Expr(:new, :($(F.name.wrapper){map(typeof,captured_vars)...}), (:(captured_vars[$i]) for i=1:fieldcount(F))...))\n        end\n    end\nend\n\nadapt_structure(to, d::Dict) = Dict(k => adapt(to, v) for (k,v) in d)\n", "meta": {"hexsha": "6686a32728ee89a3fe09be8a04006b3f724c5513", "size": 8880, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util.jl", "max_stars_repo_name": "JuliaTagBot/CMBLensing.jl", "max_stars_repo_head_hexsha": "59913ff7a889587e706f3cf634c1e6b379bc1574", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/util.jl", "max_issues_repo_name": "JuliaTagBot/CMBLensing.jl", "max_issues_repo_head_hexsha": "59913ff7a889587e706f3cf634c1e6b379bc1574", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/util.jl", "max_forks_repo_name": "JuliaTagBot/CMBLensing.jl", "max_forks_repo_head_hexsha": "59913ff7a889587e706f3cf634c1e6b379bc1574", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7378640777, "max_line_length": 126, "alphanum_fraction": 0.6025900901, "num_tokens": 2744, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.12252320290590249, "lm_q1q2_score": 0.061261601452951245}}
{"text": "import Pkg; Pkg.add(Pkg.PackageSpec(url=\"https://github.com/JuliaComputing/JuliaAcademyData.jl\"))\nusing JuliaAcademyData; activate(\"Parallel_Computing\")\n\n# # GPUs\n#\n# The graphics processor in your computer is _itself_ like a mini-computer highly\n# tailored for massively and embarassingly parallel operations (like computing how light will bounce\n# off of every point on a 3D mesh of triangles).\n#\n# Of course, recently their utility in other applications has become more clear\n# and thus the GPGPU was born.\n#\n# Just like how we needed to send data to other processes, we need to send our\n# data to the GPU to do computations there.\n\n#-\n\n# ## How is a GPU different from a CPU?\n#\n# This is what a typical consumer CPU looks like:\n#\n# ![](https://raw.githubusercontent.com/JuliaComputing/JuliaAcademyData.jl/master/courses/Parallel_Computing/images/i7.jpg)\n#\n# And this is what a GPU looks like:\n#\n# ![](https://raw.githubusercontent.com/JuliaComputing/JuliaAcademyData.jl/master/courses/Parallel_Computing/images/GK110.jpg)\n#\n# Each SMX isn't just one \"core\", each is a _streaming multiprocessor_ capable of running hundreds of threads simultaneously itself.  There are so many threads, in fact, that you reason about them in groups of 32 \u2014\u00a0called a \"warp.\"  No, no [that warp](https://www.google.com/search?tbm=isch&q=warp&tbs=imgo:1), [this one](https://www.google.com/search?tbm=isch&q=warp%20weaving&tbs=imgo:1).\n#\n# The card above supports up to 6 warps per multiprocessor, with 32 threads each, times 15 multiprocessors... 2880 threads at a time!\n#\n# Also note the memory interfaces.\n#\n# --------------\n#\n# Each thread is relatively limited \u2014\u00a0and a warp is almost like a SIMD unit that supports branching. Except it's still only executing one instruction even after a branch:\n#\n# ![](https://raw.githubusercontent.com/JuliaComputing/JuliaAcademyData.jl/master/courses/Parallel_Computing/images/warp-branch.png)\n\n#-\n\n# You can inspect the installed GPUs with nvidia-smi:\n\n#nb ;nvidia-smi\n#jl run(`nvidia-smi`)\n\n# ## Example\n#\n# The deep learning MNIST example: https://fluxml.ai/experiments/mnist/\n#\n# This is how it looks on the CPU:\n\nusing Flux, Flux.Data.MNIST, Statistics\nusing Flux: onehotbatch, onecold, crossentropy, throttle\nusing Base.Iterators: repeated, partition\n\nimgs = MNIST.images()\nlabels = onehotbatch(MNIST.labels(), 0:9)\n\n## Partition into batches of size 32\ntrain = [(cat(float.(imgs[i])..., dims = 4), labels[:,i])\n         for i in partition(1:60_000, 32)]\n## Prepare test set (first 1,000 images)\ntX = cat(float.(MNIST.images(:test)[1:1000])..., dims = 4)\ntY = onehotbatch(MNIST.labels(:test)[1:1000], 0:9)\n\nm = Chain(\n        Conv((3, 3), 1=>32, relu),\n        Conv((3, 3), 32=>32, relu),\n        x -> maxpool(x, (2,2)),\n        Conv((3, 3), 32=>16, relu),\n        x -> maxpool(x, (2,2)),\n        Conv((3, 3), 16=>10, relu),\n        x -> reshape(x, :, size(x, 4)),\n        Dense(90, 10), softmax)\n\nloss(x, y) = crossentropy(m(x), y)\naccuracy(x, y) = mean(onecold(m(x)) .== onecold(y))\n## opt = ADAM() # <-- Move Flux.params(m) here!\n## @time Flux.train!(loss, Flux.params(m), train[1:10], opt, cb = () -> @show(accuracy(tX, tY)))\nopt = ADAM(Flux.params(m), ) # <-- Move Flux.params(m) here!\nFlux.train!(loss, train[1:1], opt, cb = () -> @show(accuracy(tX, tY)))\n@time Flux.train!(loss, train[1:10], opt, cb = () -> @show(accuracy(tX, tY)))\n\n# Now let's re-do it on a GPU. \"All\" it takes is moving the data there with `gpu`!\n\ninclude(datapath(\"scripts/fixupCUDNN.jl\")) # JuliaBox uses an old version of CuArrays; this backports a fix for it\ngputrain = gpu.(train[1:10])\ngpum = gpu(m)\ngputX = gpu(tX)\ngputY = gpu(tY)\ngpuloss(x, y) = crossentropy(gpum(x), y)\ngpuaccuracy(x, y) = mean(onecold(gpum(x)) .== onecold(y))\ngpuopt = ADAM(Flux.params(gpum), )\nFlux.train!(gpuloss, gpu.(train[1:1]), gpuopt, cb = () -> @show(gpuaccuracy(gputX, gputY)))\n@time Flux.train!(gpuloss, gputrain, gpuopt, cb = () -> @show(gpuaccuracy(gputX, gputY)))\n\n# ## Defining your own GPU kernels\n#\n# So that's leveraging Flux's ability to work with GPU arrays \u2014 which is magical\n# and awesome \u2014\u00a0but you don't always have a library to lean on like that.\n# How might you define your own GPU kernel?\n#\n# Recall the monte carlo pi example:\n\nfunction serialpi(n)\n    inside = 0\n    for i in 1:n\n        x, y = rand(), rand()\n        inside += (x^2 + y^2 <= 1)\n    end\n    return 4 * inside / n\nend\n\n# How could we express this on the GPU?\n\nusing CuArrays.CURAND\nfunction findpi_gpu(n)\n    4 * sum(curand(Float64, n).^2 .+ curand(Float64, n).^2 .<= 1) / n\nend\nfindpi_gpu(10_000_000)\n\n#-\n\nusing BenchmarkTools\n@btime findpi_gpu(10_000_000)\n@btime serialpi(10_000_000)\n\n# That leans on broadcast to build the GPU kernel \u2014 and is creating three arrays\n# in the process \u2014 but it's still much faster than our serial pi from before.\n\n#-\n\n# In general, using CuArrays and broadcast is one of the best ways to just\n# get everything to work. If you really want to get your hands dirty, you\n# can use [CUDAnative.jl](https://github.com/JuliaGPU/CUDAnative.jl) to manually specify exactly how everything works,\n# but be forewarned, it's not for the [faint at heart](https://github.com/JuliaGPU/CUDAnative.jl/blob/master/examples/reduce/reduce.jl)! (If you've done CUDA\n# programming in C or C++, it's very similar.)\n\n", "meta": {"hexsha": "5ae17cf07eefd7dda6aa6e793aca1034f8612f84", "size": 5302, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Courses/Parallel_Computing/080 GPUs.jl", "max_stars_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_stars_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 45, "max_stars_repo_stars_event_min_datetime": "2020-02-13T00:50:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-05T07:57:22.000Z", "max_issues_repo_path": "Courses/Parallel_Computing/080 GPUs.jl", "max_issues_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_issues_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 52, "max_issues_repo_issues_event_min_datetime": "2019-10-30T16:22:28.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-26T20:02:43.000Z", "max_forks_repo_path": "Courses/Parallel_Computing/080 GPUs.jl", "max_forks_repo_name": "fercarozzi/JuliaAcademyMaterials", "max_forks_repo_head_hexsha": "4c7501d42e698379050fd6e6d469f3f84428cdcd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2020-02-26T11:33:28.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-25T22:34:53.000Z", "avg_line_length": 37.8714285714, "max_line_length": 390, "alphanum_fraction": 0.6938890985, "num_tokens": 1602, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.12421300997306337, "lm_q1q2_score": 0.06113616981091064}}
{"text": "#=\nExample of using a breakpoint (in this case an infiltration point) to debug a\nfunction contained in this file.\n=#\n\nusing Infiltrator\n\nfunction calcs(x, y)\n    a = x + y^2\n    @infiltrate\n    b = (a + 3) / x^2\n    return b\nend\n\nz = calcs(2, 9)\nprintln(\"z is $z\")\n", "meta": {"hexsha": "05ab004eaa9d16d6a1a17a0c170ef0abb7f395d1", "size": 265, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/debugging.jl", "max_stars_repo_name": "wigging/julia-computing", "max_stars_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/debugging.jl", "max_issues_repo_name": "wigging/julia-computing", "max_issues_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/debugging.jl", "max_forks_repo_name": "wigging/julia-computing", "max_forks_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.5882352941, "max_line_length": 77, "alphanum_fraction": 0.6339622642, "num_tokens": 91, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.40733340004593027, "lm_q2_score": 0.1500288243424251, "lm_q1q2_score": 0.061111751124293644}}
{"text": "mutable struct Person\n    age::Int64\n\n    function Person(age::Int64)  \n        this = new()\n        if age < 0\n            println(\"Age is not valid\")\n            this.age = 0\n            return this\n        else\n            this.age = age\n            return this \n        end\n    end\nend\n\nfunction yearPasses(self::Person)\n    self.age += 1\nend\n\nfunction amIOld(self::Person)\n    if self.age < 13\n        println(\"You are young.\")\n    elseif self.age < 18\n        println(\"You are a teenager.\")\n    else\n        println(\"You are old.\")\n    end\nend\n\n# a = Person(111)\n# yearPasses(a)\n# println(a.age)\n# amIOld(a)\n", "meta": {"hexsha": "09f5098e3aa8d82d285790d3ddff67fadba9e594", "size": 614, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Hackerrank/30 Days of Code/Julia/day 04.jl", "max_stars_repo_name": "Next-Gen-UI/Code-Dynamics", "max_stars_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Hackerrank/30 Days of Code/Julia/day 04.jl", "max_issues_repo_name": "Next-Gen-UI/Code-Dynamics", "max_issues_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Hackerrank/30 Days of Code/Julia/day 04.jl", "max_forks_repo_name": "Next-Gen-UI/Code-Dynamics", "max_forks_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.5428571429, "max_line_length": 39, "alphanum_fraction": 0.5325732899, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.134775915685312, "lm_q1q2_score": 0.06108878061149038}}
{"text": "export Queue\n\nmutable struct Queue{T}\n   data::Vector{T}\n   Queue{T}(d) where T = new(T[d])\nend\n\nfunction Base.show(io::IO, q::Queue)\n   print(io, \"Queue($(q.data))\")\nend\n\nfunction Base.pop!(q::Queue)\n   popfirst!(q.data)\nend\n\nfunction Base.push!(q::Queue, d)\n   push!(q.data, d)\nend\n", "meta": {"hexsha": "fea62a22d3ec5a1c64afce75a859e62f2515df57", "size": 284, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/queue.jl", "max_stars_repo_name": "yuehhua/DataStructure101", "max_stars_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-04-16T17:39:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-05T12:30:13.000Z", "max_issues_repo_path": "src/queue.jl", "max_issues_repo_name": "yuehhua/DataStructure101", "max_issues_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2020-03-02T07:54:58.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-12T06:17:55.000Z", "max_forks_repo_path": "src/queue.jl", "max_forks_repo_name": "yuehhua/DataStructure101", "max_forks_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T09:05:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T09:05:07.000Z", "avg_line_length": 14.9473684211, "max_line_length": 36, "alphanum_fraction": 0.6373239437, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3522017956470284, "lm_q2_score": 0.1732882037945951, "lm_q1q2_score": 0.061032416540904605}}
{"text": "# ---\n# title: 144. Binary Tree Preorder Traversal\n# id: problem144\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: Stack, Tree\n# link: <https://leetcode.com/problems/binary-tree-preorder-traversal/description/>\n# hidden: true\n# ---\n# \n# Given the `root` of a binary tree, return _the preorder traversal of its nodes\n# ' values_.\n# \n# \n# \n# **Example 1:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_1.jpg)\n# \n#     \n#     \n#     Input: root = [1,null,2,3]\n#     Output: [1,2,3]\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: root = []\n#     Output: []\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: root = [1]\n#     Output: [1]\n#     \n# \n# **Example 4:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_5.jpg)\n# \n#     \n#     \n#     Input: root = [1,2]\n#     Output: [1,2]\n#     \n# \n# **Example 5:**\n# \n# ![](https://assets.leetcode.com/uploads/2020/09/15/inorder_4.jpg)\n# \n#     \n#     \n#     Input: root = [1,null,2]\n#     Output: [1,2]\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * The number of nodes in the tree is in the range `[0, 100]`.\n#   * `-100 <= Node.val <= 100`\n# \n# \n# \n# **Follow up:**\n# \n# Recursive solution is trivial, could you do it iteratively?\n# \n# \n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "c983e6dc097345fcbd84f7441033821a411e8b51", "size": 1333, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/144.binary-tree-preorder-traversal.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/unresolved/144.binary-tree-preorder-traversal.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/unresolved/144.binary-tree-preorder-traversal.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 15.869047619, "max_line_length": 83, "alphanum_fraction": 0.5311327832, "num_tokens": 464, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.13117322546005347, "lm_q1q2_score": 0.060982638638494624}}
{"text": "# Small tools for debugging\n\nexport catch_NaN\n\n\n#nan checker\nfunction catch_NaN(M)\n    l = length(M)\n    for i = 1:l\n        if isnan(M[i])\n            error(\"Found NaN in $i\")\n        end\n        if M[i] < 0.0\n            error(\"Found negative value at $i\")\n        end\n#        if M[i] == 0.0\n#            warn(\"Found zero value at $i\")\n#        end\n    end\n    return false\nend", "meta": {"hexsha": "c16b9c3976e303602d2b44cbf7a92b7beb3ca5de", "size": 380, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/debug.jl", "max_stars_repo_name": "thjsal/toolbox", "max_stars_repo_head_hexsha": "b774ad29f505372cb8f7af5d01bded5ecc9f453e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-10-09T23:28:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-09T23:28:58.000Z", "max_issues_repo_path": "src/debug.jl", "max_issues_repo_name": "thjsal/toolbox", "max_issues_repo_head_hexsha": "b774ad29f505372cb8f7af5d01bded5ecc9f453e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-10-24T20:15:40.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-23T15:05:44.000Z", "max_forks_repo_path": "src/debug.jl", "max_forks_repo_name": "thjsal/toolbox", "max_forks_repo_head_hexsha": "b774ad29f505372cb8f7af5d01bded5ecc9f453e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-10-15T07:40:19.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T12:27:37.000Z", "avg_line_length": 18.0952380952, "max_line_length": 47, "alphanum_fraction": 0.4973684211, "num_tokens": 113, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.13117322376181564, "lm_q1q2_score": 0.06098263784898119}}
{"text": "\"\"\"\n    frule([::RuleConfig,] (\u0394f, \u0394x...), f, x...)\n\nExpressing the output of `f(x...)` as `\u03a9`, return the tuple:\n\n    (\u03a9, \u0394\u03a9)\n\nThe second return value is the tangent w.r.t. the output.\n\nIf no method matching `frule((\u0394f, \u0394x...), f, x...)` has been defined, then return `nothing`.\n\nExamples:\n\nunary input, unary output scalar function:\n\n```jldoctest frule\njulia> dself = NoTangent();\n\njulia> x = rand()\n0.8236475079774124\n\njulia> sinx, \u0394sinx = frule((dself, 1), sin, x)\n(0.7336293678134624, 0.6795498147167869)\n\njulia> sinx == sin(x)\ntrue\n\njulia> \u0394sinx == cos(x)\ntrue\n```\n\nUnary input, binary output scalar function:\n\n```jldoctest frule\njulia> sincosx, \u0394sincosx = frule((dself, 1), sincos, x);\n\njulia> sincosx == sincos(x)\ntrue\n\njulia> \u0394sincosx[1] == cos(x)\ntrue\n\njulia> \u0394sincosx[2] == -sin(x)\ntrue\n```\n\nNote that techically speaking julia does not have multiple output functions, just functions\nthat return a single output that is iterable, like a `Tuple`.\nSo this is actually a [`Tangent`](@ref):\n```jldoctest frule\njulia> \u0394sincosx\nTangent{Tuple{Float64, Float64}}(0.6795498147167869, -0.7336293678134624)\n```\n\nThe optional [`RuleConfig`](@ref) option allows specifying frules only for AD systems that\nsupport given features. If not needed, then it can be omitted and the `frule` without it\nwill be hit as a fallback. This is the case for most rules.\n\nSee also: [`rrule`](@ref), [`@scalar_rule`](@ref), [`RuleConfig`](@ref)\n\"\"\"\nfrule(\u0227rgs, f, ::Vararg{Any}) = nothing\n\n# if no config is present then fallback to config-less rules\nfrule(::RuleConfig, args...) = frule(args...)\n\n# Manual fallback for keyword arguments. Usually this would be generated by\n#\n#   frule(::Any, ::Vararg{Any}; kwargs...) = nothing\n#\n# However - the fallback method is so hot that we want to avoid any extra code\n# that would be required to have the automatically generated method package up\n# the keyword arguments (which the optimizer will throw away, but the compiler\n# still has to manually analyze). Manually declare this method with an\n# explicitly empty body to save the compiler that work.\nconst frule_kwfunc = Core.kwftype(typeof(frule)).instance\n(::typeof(frule_kwfunc))(::Any, ::typeof(frule), \u0227rgs, f, ::Vararg{Any}) = nothing\nfunction (::typeof(frule_kwfunc))(kws::Any, ::typeof(frule), ::RuleConfig, args...)\n    return frule_kwfunc(kws, frule, args...)\nend\n\n\"\"\"\n    rrule([::RuleConfig,] f, x...)\n\nExpressing `x` as the tuple `(x\u2081, x\u2082, ...)` and the output tuple of `f(x...)`\nas `\u03a9`, return the tuple:\n\n    (\u03a9, (\u03a9\u0304\u2081, \u03a9\u0304\u2082, ...) -> (s\u0304elf, x\u0304\u2081, x\u0304\u2082, ...))\n\nWhere the second return value is the the propagation rule or pullback.\nIt takes in cotangents corresponding to the outputs (`x\u0304\u2081, x\u0304\u2082, ...`),\nand `s\u0304elf`, the internal values of the function itself (for closures)\n\nIf no method matching `rrule(f, xs...)` has been defined, then return `nothing`.\n\nExamples:\n\nunary input, unary output scalar function:\n\n```jldoctest\njulia> x = rand();\n\njulia> sinx, sin_pullback = rrule(sin, x);\n\njulia> sinx == sin(x)\ntrue\n\njulia> sin_pullback(1) == (NoTangent(), cos(x))\ntrue\n```\n\nbinary input, unary output scalar function:\n\n```jldoctest\njulia> x, y = rand(2);\n\njulia> hypotxy, hypot_pullback = rrule(hypot, x, y);\n\njulia> hypotxy == hypot(x, y)\ntrue\n\njulia> hypot_pullback(1) == (NoTangent(), (x / hypot(x, y)), (y / hypot(x, y)))\ntrue\n```\n\nThe optional [`RuleConfig`](@ref) option allows specifying rrules only for AD systems that\nsupport given features. If not needed, then it can be omitted and the `rrule` without it\nwill be hit as a fallback. This is the case for most rules.\n\nSee also: [`frule`](@ref), [`@scalar_rule`](@ref), [`RuleConfig`](@ref)\n\"\"\"\nrrule(::Any, ::Vararg{Any}) = nothing\n\n# if no config is present then fallback to config-less rules\nrrule(::RuleConfig, args...) = rrule(args...)\n\n# Manual fallback for keyword arguments. See above\nconst rrule_kwfunc = Core.kwftype(typeof(rrule)).instance\n(::typeof(rrule_kwfunc))(::Any, ::typeof(rrule), ::Any, ::Vararg{Any}) = nothing\nfunction (::typeof(rrule_kwfunc))(kws::Any, ::typeof(rrule), ::RuleConfig, args...)\n    return rrule_kwfunc(kws, rrule, args...)\nend\n\n##############################################################\n### Opt out functionality\n\nconst NO_RRULE_DOC = \"\"\"\n    no_rrule\n\nThis is an piece of infastructure supporting opting out of [`rrule`](@ref).\nIt follows the signature for `rrule` exactly.\nA collection of type-tuples is stored in its method-table.\nIf something has this defined, it means that it must having a must also have a `rrule`, \ndefined that returns `nothing`.\n\n!!! warning \"Do not overload no_rrule directly\"\n    It is fine and intended to query the method table of `no_rrule`.\n    It is not safe to add to that directly, as corresponding changes also need to be made to\n    `rrule`.\n    The [`@opt_out`](@ref) macro does both these things, and so should almost always be used\n    rather than defining a method of `no_rrule` directly.\n\n### Mechanics\nnote: when the text below says methods `==` it actually means:\n`parameters(m.sig)[2:end]` (i.e. the signature type tuple) rather than the method object `m` itself.\n\nTo decide if should opt-out using this mechanism.\n - find the most specific method of `rrule` and `no_rule` e.g with `Base.which`\n  - if the method of `no_rrule` `==` the method of `rrule`, then should opt-out\n\nTo just ignore the fact that rules can be opted-out from, and that some rules thus return\n`nothing`, then filter the list of methods of `rrule` to remove those that are `==` to ones\nthat occur in the method table of `no_rrule`.\n\nNote also when doing this you must still also handle falling back from rule with config, to\nrule without config.\n\nOn the other-hand if your AD can work with `rrule`s that return `nothing`, then it is\nsimpler to just use that mechanism for opting out; and you don't need to worry about this\nat all.\n\nFor more information see the [documentation on opting out of rules](@ref opt_out)\n\"\"\"\n\n\"\"\"\n$NO_RRULE_DOC\n\nSee also [`ChainRulesCore.no_frule`](@ref).\n\"\"\"\nfunction no_rrule end\nno_rrule(::Any, ::Vararg{Any}) = nothing\n\n\"\"\"\n$(replace(NO_RRULE_DOC, \"rrule\"=>\"frule\"))\n\nSee also [`ChainRulesCore.no_rrule`](@ref).\n\"\"\"\nfunction no_frule end\nno_frule(\u0227rgs, f, ::Vararg{Any}) = nothing\n", "meta": {"hexsha": "d99e54a011076987432e6847934bc973eb857bd2", "size": 6215, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rules.jl", "max_stars_repo_name": "mschauer/ChainRulesCore.jl", "max_stars_repo_head_hexsha": "699e61f1539fdba362fff5a1b438fbccf32370f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-08-02T02:52:02.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-02T11:00:26.000Z", "max_issues_repo_path": "src/rules.jl", "max_issues_repo_name": "mschauer/ChainRulesCore.jl", "max_issues_repo_head_hexsha": "699e61f1539fdba362fff5a1b438fbccf32370f0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-07-31T16:08:38.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-01T22:09:30.000Z", "max_forks_repo_path": "src/rules.jl", "max_forks_repo_name": "mschauer/ChainRulesCore.jl", "max_forks_repo_head_hexsha": "699e61f1539fdba362fff5a1b438fbccf32370f0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-08-02T19:31:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-02T19:31:01.000Z", "avg_line_length": 31.2311557789, "max_line_length": 100, "alphanum_fraction": 0.6970233307, "num_tokens": 1802, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4225046348141882, "lm_q2_score": 0.14414886038616345, "lm_q1q2_score": 0.06090356161633739}}
{"text": "\"\"\"\n  nn.jl File\n\nNeural Network implementation (Module BetaML.Nn)\n\n`?BetaML.Nn` for documentation\n\n- [Importable source code (most up-to-date version)](https://github.com/sylvaticus/BetaML.jl/blob/master/src/Nn.jl) - [Julia Package](https://github.com/sylvaticus/BetaML.jl)\n- [Demonstrative static notebook](https://github.com/sylvaticus/BetaML.jl/blob/master/notebooks/Nn.ipynb)\n- [Demonstrative live notebook](https://mybinder.org/v2/gh/sylvaticus/BetaML.jl/master?filepath=notebooks%2FNn.ipynb) (temporary personal online computational environment on myBinder) - it can takes minutes to start with!\n- Theory based on [MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning](https://github.com/sylvaticus/MITx_6.86x) ([Unit 3](https://github.com/sylvaticus/MITx_6.86x/blob/master/Unit%2003%20-%20Neural%20networks/Unit%2003%20-%20Neural%20networks.md))\n- New to Julia? [A concise Julia tutorial](https://github.com/sylvaticus/juliatutorial) - [Julia Quick Syntax Reference book](https://julia-book.com)\n\n\"\"\"\n\n\n\"\"\"\n    BetaML.Nn module\n\nImplement the functionality required to define an artificial Neural Network, train it with data, forecast data and assess its performances.\n\nCommon type of layers and optimisation algorithms are already provided, but you can define your own ones subclassing respectively the `AbstractLayer` and `OptimisationAlgorithm` abstract types.\n\nThe module provide the following type or functions. Use `?[type or function]` to access their full signature and detailed documentation:\n\n# Model definition:\n\n- `DenseLayer`: Classical feed-forward layer with user-defined activation function\n- `DenseNoBiasLayer`: Classical layer without the bias parameter\n- `VectorFunctionLayer`: Parameterless layer whose activation function run over the ensable of its nodes rather than on each one individually\n- `buildNetwork`: Build the chained network and define a cost function\n- `getParams(nn)`: Retrieve current weigthts\n- `getGradient(nn)`: Retrieve the current gradient of the weights\n- `setParams!(nn)`: Update the weigths of the network\n- `show(nn)`: Print a representation of the Neural Network\n\nEach layer can use a default activation function, one of the functions provided in the `Utils` module (`relu`, `tanh`, `softmax`,...) or you can specify your own function. The derivative of the activation function can be optionally be provided, in such case training will be quicker, altought this difference tends to vanish with bigger datasets.\nYou can alternativly implement your own layer defining a new type as subtype of the abstract type `AbstractLayer`. Each user-implemented layer must define the following methods:\n\n- A suitable constructor\n- `forward(layer,x)`\n- `backward(layer,x,nextGradient)`\n- `getParams(layer)`\n- `getGradient(layer,x,nextGradient)`\n- `setParams!(layer,w)`\n- `size(layer)`\n\n# Model training:\n\n- `trainingInfo(nn)`: Default callback function during training\n- `train!(nn)`:  Training function\n- `singleUpdate!(\u03b8,\u25bd;optAlg)`: The parameter update made by the specific optimisation algorithm\n- `SGD`: The default optimisation algorithm\n- `ADAM`: A faster moment-based optimisation algorithm (added in v0.2.2)\n\nTo define your own optimisation algorithm define a subtype of `OptimisationAlgorithm` and implement the function `singleUpdate!(\u03b8,\u25bd;optAlg)` and eventually `initOptAlg(\u22c5)` specific for it.\n\n# Model predictions and assessment:\n\n- `predict(nn)`: Return the output given the data\n- `loss(nn)`: Compute avg. network loss on a test set\n- `Utils.accuracy(y\u0302,y)`: Categorical output accuracy\n\nWhile high-level functions operating on the dataset expect it to be in the standard format (nRecords \u00d7 nDimensions matrices) it is custom to represent the chain of a neural network as a flow of column vectors, so all low-level operations (operating on a single datapoint) expect both the input and the output as a column vector.\n\"\"\"\nmodule Nn\n\n#import Base.Threads.@spawn\n\nusing Random, Zygote, ProgressMeter, Reexport\nimport Distributions: Uniform\n\nusing ForceImport\n@force using ..Api\n@force using ..Utils\n\nimport Base.size\nimport Base: +, -, *, /, sum, sqrt\n\n# module own functions\nexport AbstractLayer, forward, backward, getParams, getNParams, getGradient, setParams!, size, NN,\n       buildNetwork, predict, loss, train!, getindex, initOptAlg!, singleUpdate!,\n       DenseLayer, DenseNoBiasLayer, VectorFunctionLayer,\n       Learnable,\n       show\n\n# for working on gradient as e.g [([1.0 2.0; 3.0 4.0], [1.0,2.0,3.0]),([1.0,2.0,3.0],1.0)]\n\"\"\"\nLearnable(data)\n\nStructure representing the learnable parameters of a layer or its gradient.\n\nThe learnable parameters of a layers are given in the form of a N-tuple of Array{Float64,N2} where N2 can change (e.g. we can have a layer with the first parameter being a matrix, and the second one being a scalar).\nWe wrap the tuple on its own structure a bit for some efficiency gain, but above all to define standard mathematic operations on the gradients without doing \"type pyracy\" with respect to Base tuples.\n\"\"\"\nmutable struct Learnable\n    data::Tuple{Vararg{Array{Float64,N} where N}}\n    function Learnable(data)\n        return new(data)\n    end\nend\nfunction +(items::Learnable...)\n  values = collect(items[1].data)\n  N = length(values)\n  @inbounds for item in items[2:end]\n       @inbounds  @simd for n in 1:N # @inbounds  @simd\n          values[n] += item.data[n]\n      end\n  end\n  return Learnable(Tuple(values))\nend\nsum(items::Learnable...)  = +(items...)\nfunction -(items::Learnable...)\n  values = collect(items[1].data)\n  N = length(values)\n @inbounds for item in items[2:end]\n       @inbounds @simd for n in 1:N # @simd\n          values[n] -= item.data[n]\n      end\n  end\n  return Learnable(Tuple(values))\nend\nfunction *(items::Learnable...)\n  values = collect(items[1].data)\n  N = length(values)\n  @inbounds for item in items[2:end]\n      @inbounds @simd for n in 1:N # @simd\n          values[n] = values[n] .* item.data[n]\n      end\n  end\n  return Learnable(Tuple(values))\nend\n+(item::Learnable,sc::Number) =  Learnable(Tuple([item.data[i] .+ sc for i in 1:length(item.data)]))\n+(sc::Number, item::Learnable) = +(item,sc)\n-(item::Learnable,sc::Number) = Learnable(Tuple([item.data[i] .- sc for i in 1:length(item.data)]))\n-(sc::Number, item::Learnable) = (-(item,sc)) * -1\n*(item::Learnable,sc::Number) = Learnable(item.data .* sc)\n*(sc::Number, item::Learnable) = Learnable(sc .* item.data)\n/(item::Learnable,sc::Number) = Learnable(item.data ./ sc)\n/(sc::Number,item::Learnable,) = Learnable(Tuple([sc ./ item.data[i] for i in 1:length(item.data)]))\nsqrt(item::Learnable) = Learnable(Tuple([sqrt.(item.data[i]) for i in 1:length(item.data)]))\n/(item1::Learnable,item2::Learnable) = Learnable(Tuple([item1.data[i] ./ item2.data[i] for i in 1:length(item1.data)]))\n\n\n\n#=\n# not needed ??\nfunction Base.iterate(iter::Learnable, state=(iter.data[1], 1))\n           element, count = state\n           if count > length(iter)\n               return nothing\n           elseif count == length(iter)\n               return (element, (iter.data[count], count + 1))\n           end\n           return (element, (iter.data[count+1], count + 1))\nend\nBase.length(iter::Learnable) = length(iter.data)\n#Base.eltype(iter::Learnable) = Int\n=#\n\n## Sckeleton for the layer functionality.\n# See nn_default_layers.jl for actual implementations\n\nabstract type AbstractLayer end\n\ninclude(\"Nn_default_layers.jl\")\n\n\"\"\"\n   forward(layer,x)\n\nPredict the output of the layer given the input\n\n# Parameters:\n* `layer`:  Worker layer\n* `x`:      Input to the layer\n\n# Return:\n- An Array{T,1} of the prediction (even for a scalar)\n\"\"\"\nfunction forward(layer::AbstractLayer,x)\n error(\"Not implemented for this kind of layer. Please implement `forward(layer,x)`.\")\nend\n\n\"\"\"\n   backward(layer,x,nextGradient)\n\nCompute backpropagation for this layer\n\n# Parameters:\n* `layer`:        Worker layer\n* `x`:            Input to the layer\n* `nextGradient`: Derivative of the overaall loss with respect to the input of the next layer (output of this layer)\n\n# Return:\n* The evaluated gradient of the loss with respect to this layer inputs\n\n\"\"\"\nfunction backward(layer::AbstractLayer,x,nextGradient)\n    error(\"Not implemented for this kind of layer. Please implement `backward(layer,x,nextGradient)`.\")\nend\n\n\"\"\"\n   getParams(layer)\n\nGet the layers current value of its trainable parameters\n\n# Parameters:\n* `layer`:  Worker layer\n\n# Return:\n* The current value of the layer's trainable parameters as tuple of matrices. It is up to you to decide how to organise this tuple, as long you are consistent with the `getGradient()` and `setParams()` functions. Note that starting from BetaML 0.2.2 this tuple needs to be wrapped in its `Learnable` type.\n\"\"\"\nfunction getParams(layer::AbstractLayer)\n  error(\"Not implemented for this kind of layer. Please implement `getParams(layer)`.\")\nend\n\n\"\"\"\n   getGradient(layer,x,nextGradient)\n\nCompute backpropagation for this layer\n\n# Parameters:\n* `layer`:        Worker layer\n* `x`:            Input to the layer\n* `nextGradient`: Derivative of the overaall loss with respect to the input of the next layer (output of this layer)\n\n# Return:\n* The evaluated gradient of the loss with respect to this layer's trainable parameters as tuple of matrices. It is up to you to decide how to organise this tuple, as long you are consistent with the `getParams()` and `setParams()` functions. Note that starting from BetaML 0.2.2 this tuple needs to be wrapped in its `Learnable` type.\n\"\"\"\nfunction getGradient(layer::AbstractLayer,x,nextGradient)\n    error(\"Not implemented for this kind of layer. Please implement `getGradient(layer,x,nextGradient)`.\")\n  end\n\n\"\"\"\n     setParams!(layer,w)\n\nSet the trainable parameters of the layer with the given values\n\n# Parameters:\n* `layer`: Worker layer\n* `w`:     The new parameters to set (Learnable)\n\n# Notes:\n*  The format of the tuple wrapped by Learnable must be consistent with those of the `getParams()` and `getGradient()` functions.\n\"\"\"\nfunction setParams!(layer::AbstractLayer,w)\n    error(\"Not implemented for this kind of layer. Please implement `setParams!(layer,w)`.\")\nend\n\n\n\"\"\"\n    size(layer)\n\nGet the dimensions of the layers in terms of (dimensions in input , dimensions in output)\n\n# Notes:\n* You need to use `import Base.size` before defining this function for your layer\n\"\"\"\nfunction size(layer::AbstractLayer)\n    error(\"Not implemented for this kind of layer. Please implement `size(layer)`.\")\nend\n\n\"\"\"getNParams(layer)\n\nReturn the number of parameters of a layer.\n\nIt doesn't need to be implemented by each layer type, as it uses getParams().\n\"\"\"\nfunction getNParams(layer::AbstractLayer)\n    pars = getParams(layer)\n    nP = 0\n    for p in pars.data\n        nP += *(size(p)...)\n    end\n    return nP\nend\n\n# ------------------------------------------------------------------------------\n# NN-related functions\n\"\"\"\n   NN\n\nRepresentation of a Neural Network\n\n# Fields:\n* `layers`:  Array of layers objects\n* `cf`:      Cost function\n* `dcf`:     Derivative of the cost function\n* `trained`: Control flag for trained networks\n\"\"\"\nmutable struct NN\n    layers::Array{AbstractLayer,1}\n    cf::Function\n    dcf::Union{Function,Nothing}\n    trained::Bool\n    name::String\nend\n\n\"\"\"\n   buildNetwork(layers,cf;dcf,name)\n\nInstantiate a new Feedforward Neural Network\n\nParameters:\n* `layers`: Array of layers objects\n* `cf`:     Cost function\n* `dcf`:    Derivative of the cost function [def: `nothing`]\n* `name`:   Name of the network [def: \"Neural Network\"]\n\n# Notes:\n* Even if the network ends with a single output note, the cost function and its derivative should always expect y and y\u0302 as column vectors.\n\"\"\"\nfunction buildNetwork(layers,cf;dcf=nothing,name=\"Neural Network\")\n    return NN(layers,cf,dcf,false,name)\nend\n\n\n\"\"\"\n   predict(nn,x)\n\nNetwork predictions\n\n# Parameters:\n* `nn`:  Worker network\n* `x`:   Input to the network (n \u00d7 d)\n\"\"\"\n#=\nfunction predict(nn::NN,x)\n    makeColVector(x)\n    values = x\n    for l in nn.layers\n        values = forward(l,values)\n    end\n    return values\nend\n=#\n\nfunction predict(nn::NN,x)\n    x = makeMatrix(x)\n    # get the output dimensions\n    n = size(x)[1]\n    d = size(nn.layers[end])[2]\n    out = zeros(n,d)\n    for i in 1:size(x)[1]\n        values = x[i,:]\n        for l in nn.layers\n            values = forward(l,values)\n        end\n        out[i,:] = values\n    end\n    return out\nend\n\n\"\"\"\n   loss(fnn,x,y)\n\nCompute avg. network loss on a test set (or a single (1 \u00d7 d) data point)\n\n# Parameters:\n* `fnn`: Worker network\n* `x`:   Input to the network (n) or (n x d)\n* `y`:   Label input (n) or (n x d)\n\"\"\"\nfunction loss(nn::NN,x,y)\n    x = makeMatrix(x)\n    y = makeMatrix(y)\n    (n,d) = size(x)\n    #(nn.trained || n == 1) ? \"\" : @warn \"Seems you are trying to test a neural network that has not been tested. Use first `train!(nn,x,y)`\"\n    \u03f5 = 0.0\n    for i in 1:n\n        y\u0302 = predict(nn,x[i,:]')[1,:]\n        \u03f5 += nn.cf(y\u0302,y[i,:])\n    end\n    return \u03f5/n\nend\n\n\"\"\"\n   getParams(nn)\n\nRetrieve current weigthts\n\n# Parameters:\n* `nn`: Worker network\n\n# Notes:\n* The output is a vector of tuples of each layer's input weigths and bias weigths\n\"\"\"\n@inline function getParams(nn::NN)\n  return [getParams(l) for l in nn.layers]\nend\n\n\n\"\"\"\n   getGradient(nn,x,y)\n\nRetrieve the current gradient of the weigthts (i.e. derivative of the cost with respect to the weigths)\n\n# Parameters:\n* `nn`: Worker network\n* `x`:   Input to the network (d,1)\n* `y`:   Label input (d,1)\n\n#Notes:\n* The output is a vector of tuples of each layer's input weigths and bias weigths\n\"\"\"\nfunction getGradient(nn::NN,x::Union{T,AbstractArray{T,1}},y::Union{T2,AbstractArray{T2,1}}) where { T <: Number, T2 <: Number}\n\n  x = makeColVector(x)\n  y = makeColVector(y)\n\n  nLayers = length(nn.layers)\n\n  # Stap 1: Forward pass\n  forwardStack = Vector{Vector{Float64}}(undef,nLayers+1)\n\n  forwardStack[1] = x\n  @inbounds for (i,l) in enumerate(nn.layers)\n      forwardStack[i+1] = forward(l,forwardStack[i])\n  end\n\n  # Step 2: Backpropagation pass\n  backwardStack = Vector{Vector{Float64}}(undef,nLayers+1)\n  if nn.dcf != nothing\n    backwardStack[end] = nn.dcf(forwardStack[end],y) # adding d\u03f5_dHatY\n  else\n    backwardStack[end] = gradient(nn.cf,forwardStack[end],y)[1] # using AD from Zygote\n  end\n  @inbounds for lidx in nLayers:-1:1\n     l = nn.layers[lidx]\n     d\u03f5_do = backward(l,forwardStack[lidx],backwardStack[lidx+1])\n     backwardStack[lidx] = d\u03f5_do\n  end\n\n  # Step 3: Computing gradient of weigths\n  dWs = Array{Learnable,1}(undef,nLayers)\n  @inbounds for lidx in 1:nLayers\n     dWs[lidx] = getGradient(nn.layers[lidx],forwardStack[lidx],backwardStack[lidx+1])\n  end\n\n  return dWs\nend\n\n\"\"\"\n   getGradient(nn,xbatch,ybatch)\n\nRetrieve the current gradient of the weigthts (i.e. derivative of the cost with respect to the weigths)\n\n# Parameters:\n* `nn`:      Worker network\n* `xbatch`:  Input to the network (n,d)\n* `ybatch`:  Label input (n,d)\n\n#Notes:\n* The output is a vector of tuples of each layer's input weigths and bias weigths\n\"\"\"\nfunction getGradient(nn,xbatch::AbstractArray{T,2},ybatch::AbstractArray{T2,2}) where {T <: Number, T2 <: Number}\n    #return [getGradient(nn,xbatch[j,:],ybatch[j,:]) for j in 1:size(xbatch,1)]\n    bSize = size(xbatch,1)\n    gradients = Array{Vector{Learnable},1}(undef,bSize)\n    # Note: in Julia 1.6 somehow the multithreading is less efficient than in Julia 1.5\n    # Using @inbounds @simd result faster than using 4 threads, so reverting to it.\n    # But to keep following the evolution, as there seems to be some issues on performances\n    # in Julia 1.6: https://discourse.julialang.org/t/drop-of-performances-with-julia-1-6-0-for-interpolationkernels/58085\n    # Maybe when that's solved it will be again more convenient to use multi-threading\n    #Threads.@threads\n    @inbounds  for j in 1:bSize # @simd\n       gradients[j] =  getGradient(nn,xbatch[j,:],ybatch[j,:])\n    end\n    return gradients\nend\n\n\"\"\"\n   setParams!(nn,w)\n\nUpdate weigths of the network\n\n# Parameters:\n* `nn`: Worker network\n* `w`:  The new weights to set\n\"\"\"\nfunction setParams!(nn::NN,w)\n    for lidx in 1:length(nn.layers)\n        setParams!(nn.layers[lidx],w[lidx])\n    end\nend\n\n\n\n\n\"\"\"\n  show(nn)\n\nPrint a representation of the Neural Network (layers, dimensions..)\n\n# Parameters:\n* `nn`: Worker network\n\"\"\"\nfunction show(nn::NN)\n  trainedString = nn.trained == true ? \"trained\" : \"non trained\"\n  println(\"*** $(nn.name) ($(length(nn.layers)) layers, $(trainedString))\\n\")\n  println(\"#\\t # In \\t # Out \\t Type\")\n  for (i,l) in enumerate(nn.layers)\n    shapes = size(l)\n    println(\"$i \\t $(shapes[1]) \\t\\t $(shapes[2]) \\t\\t $(typeof(l)) \")\n  end\nend\n\n\"getNParams(nn) - Return the number of trainable parameters of the neural network.\"\nfunction getNParams(nn::NN)\n    nP = 0\n    for l in nn.layers\n        nP += getNParams(l)\n    end\n    return nP\nend\n\n\nBase.getindex(n::NN, i::AbstractArray) = NN(n.layers[i]...)\n\n# ------------------------------------------------------------------------------\n# Optimisation-related functions\n\n\"\"\"\n    OptimisationAlgorithm\n\nAbstract type representing an Optimisation algorithm.\n\nCurrently supported algorithms:\n- `SGD` (Stochastic) Gradient Descent\n\nSee `?[Name OF THE ALGORITHM]` for their details\n\nYou can implement your own optimisation algorithm using a subtype of `OptimisationAlgorithm` and implementing its constructor and the update function `singleUpdate(\u22c5)` (type `?singleUpdate` for details).\n\n\"\"\"\nabstract type OptimisationAlgorithm end\n\ninclude(\"Nn_default_optalgs.jl\")\n\n\"\"\"\n   trainingInfo(nn,x,y;n,batchSize,epochs,verbosity,nEpoch,nBatch)\n\nDefault callback funtion to display information during training, depending on the verbosity level\n\n# Parameters:\n* `nn`: Worker network\n* `x`:  Batch input to the network (batchSize,d)\n* `y`:  Batch label input (batchSize,d)\n* `n`: Size of the full training set\n* `nBatches` : Number of baches per epoch\n* `epochs`: Number of epochs defined for the training\n* `verbosity`: Verbosity level defined for the training (NONE,LOW,STD,HIGH,FULL)\n* `nEpoch`: Counter of the current epoch\n* `nBatch`: Counter of the current batch\n\n#Notes:\n* Reporting of the error (loss of the network) is expensive. Use `verbosity=NONE` for better performances\n\"\"\"\nfunction trainingInfo(nn,x,y;n,nBatches,epochs,verbosity,nEpoch,nBatch)\n   if verbosity == NONE\n       return false # doesn't stop the training\n   end\n\n   nMsgDict = Dict(LOW => 0, STD => 10,HIGH => 100, FULL => n)\n   nMsgs = nMsgDict[verbosity]\n   batchSize = size(x,1)\n\n   if verbosity == FULL || ( nBatch == nBatches && ( nEpoch == 1  || nEpoch % ceil(epochs/nMsgs) == 0))\n\n      \u03f5 = loss(nn,x,y)\n      println(\"Training.. \\t avg \u03f5 on (Epoch $nEpoch Batch $nBatch): \\t $(\u03f5)\")\n   end\n   return false\nend\n\n\"\"\"\n   train!(nn,x,y;epochs,batchSize,sequential,optAlg,verbosity,cb)\n\nTrain a neural network with the given x,y data\n\n# Parameters:\n* `nn`:         Worker network\n* `x`:          Training input to the network (records x dimensions)\n* `y`:          Label input (records x dimensions)\n* `epochs`:     Number of passages over the training set [def: `100`]\n* `batchSize`:  Size of each individual batch [def: `min(size(x,1),32)`]\n* `sequential`: Wether to run all data sequentially instead of random [def: `false`]\n* `optAlg`:     The optimisation algorithm to update the gradient at each batch [def: `ADAM()`]\n* `verbosity`:  A verbosity parameter for the trade off information / efficiency [def: `STD`]\n* `cb`:         A callback to provide information. [def: `trainingInfo`]\n* `rng`:        Random Number Generator (see [`FIXEDSEED`](@ref)) [deafult: `Random.GLOBAL_RNG`]\n\n# Return:\n- A named tuple with the following information\n  - `epochs`: Number of epochs actually ran\n  - `\u03f5_epochs`: The average error on each epoch (if `verbosity > LOW`)\n  - `\u03b8_epochs`: The parameters at each epoch (if `verbosity > STD`)\n\n# Notes:\n- Currently supported algorithms:\n    - `SGD`, the classical (Stochastic) Gradient Descent optimiser\n    - `ADAM`,  an adaptive moment estimation optimiser\n- Look at the individual optimisation algorithm (`?[Name OF THE ALGORITHM]`) for info on its parameter, e.g. [`?SGD`](@ref SGD) for the Stochastic Gradient Descent.\n- You can implement your own optimisation algorithm using a subtype of `OptimisationAlgorithm` and implementing its constructor and the update function `singleUpdate!(\u22c5)` (type `?singleUpdate!` for details).\n- You can implement your own callback function, altought the one provided by default is already pretty generic (its output depends on the `verbosity` parameter). See [`trainingInfo`](@ref) for informations on the cb parameters.\n- Both the callback function and the [`singleUpdate!`](@ref) function of the optimisation algorithm can be used to stop the training algorithm, respectively returning `true` or `stop=true`.\n- The verbosity can be set to any of `NONE`,`LOW`,`STD`,`HIGH`,`FULL`.\n- The update is done computing the average gradient for each batch and then calling `singleUpdate!` to let the optimisation algorithm perform the parameters update\n\"\"\"\nfunction train!(nn::NN,x,y; epochs=100, batchSize=min(size(x,1),32), sequential=false, verbosity::Verbosity=STD, cb=trainingInfo, optAlg::OptimisationAlgorithm=ADAM(),rng = Random.GLOBAL_RNG)#,   \u03b7=t -> 1/(1+t), \u03bb=1, rShuffle=true, nMsgs=10, tol=0optAlg::SD=SD())\n    if verbosity > STD\n        @codeLocation\n    end\n    x = makeMatrix(x)\n    y = makeMatrix(y)\n    (n,d)     = size(x)\n    batchSize = min(size(x,1),batchSize)\n    if verbosity > NONE # Note that are two \"Verbosity type\" objects. To compare with numbers use Int(NONE) > 1\n        println(\"***\\n*** Training $(nn.name) for $epochs epochs with algorithm $(typeof(optAlg)).\")\n    end\n    \u03f5_epoch_l = Inf\n    \u03b8_epoch_l = getParams(nn)\n    \u03f5_epoch   = loss(nn,x,y)\n    \u03b8_epoch   = getParams(nn)\n    \u03f5_epochs  = Float64[]\n    \u03b8_epochs  = []\n\n    initOptAlg!(optAlg::OptimisationAlgorithm;\u03b8=getParams(nn),batchSize=batchSize,x=x,y=y)\n\n    timetoShowProgress = verbosity > NONE ? 1 : typemax(Int64)\n    @showprogress timetoShowProgress \"Training the Neural Network...\"    for t in 1:epochs\n       batches = batch(n,batchSize,sequential=sequential,rng=rng)\n       nBatches = length(batches)\n       if t == 1\n           if (verbosity >= STD) push!(\u03f5_epochs,\u03f5_epoch); end\n           if (verbosity > STD) push!(\u03b8_epochs,\u03b8_epoch); end\n       end\n       for (i,batch) in enumerate(batches)\n           xbatch = x[batch, :]\n           ybatch = y[batch, :]\n           \u03b8   = getParams(nn)\n           # remove @spawn and fetch (on next row) to get single thread code\n           # note that there is no random number issue here..\n           #gradients   = @spawn getGradient(nn,xbatch,ybatch)\n           #sumGradient = sum(fetch(gradients))\n           gradients   = getGradient(nn,xbatch,ybatch)\n           sumGradient = sum(gradients)\n\n           \u25bd   = sumGradient / length(batch)\n           #\u25bd   = gradDiv.(gradSum([getGradient(nn,xbatch[j,:],ybatch[j,:]) for j in 1:batchSize]), batchSize)\n           res = singleUpdate!(\u03b8,\u25bd;nEpoch=t,nBatch=i,nBatches=nBatches,xbatch=xbatch,ybatch=ybatch,optAlg=optAlg)\n           setParams!(nn,res.\u03b8)\n           cbOut = cb(nn,xbatch,ybatch,n=d,nBatches=nBatches,epochs=epochs,verbosity=verbosity,nEpoch=t,nBatch=i)\n           if(res.stop==true || cbOut==true)\n               nn.trained = true\n               return (epochs=t,\u03f5_epochs=\u03f5_epochs,\u03b8_epochs=\u03b8_epochs)\n           end\n       end\n       if (verbosity >= STD)\n           \u03f5_epoch_l = \u03f5_epoch\n           \u03f5_epoch = loss(nn,x,y)\n           push!(\u03f5_epochs,\u03f5_epoch);\n       end\n       if (verbosity > STD)\n           \u03b8_epoch_l = \u03b8_epoch\n           \u03b8_epoch = getParams(nn)\n           push!(\u03b8_epochs,\u03b8_epoch); end\n    end\n\n    if (verbosity > NONE)\n        if verbosity == LOW\n            \u03f5_epoch = loss(nn,x,y)\n        end\n        println(\"Training of $epochs epoch completed. Final epoch error: $(\u03f5_epoch).\");\n     end\n    nn.trained = true\n    return (epochs=epochs,\u03f5_epochs=\u03f5_epochs,\u03b8_epochs=\u03b8_epochs)\nend\n\n\"\"\"\n   singleUpdate!(\u03b8,\u25bd;nEpoch,nBatch,batchSize,xbatch,ybatch,optAlg)\n\nPerform the parameters update based on the average batch gradient.\n\n# Parameters:\n- `\u03b8`:         Current parameters\n- `\u25bd`:         Average gradient of the batch\n- `nEpoch`:    Count of current epoch\n- `nBatch`:    Count of current batch\n- `nBatches`:  Number of batches per epoch\n- `xbatch`:    Data associated to the current batch\n- `ybatch`:    Labels associated to the current batch\n- `optAlg`:    The Optimisation algorithm to use for the update\n\n# Notes:\n- This function is overridden so that each optimisation algorithm implement their\nown version\n- Most parameters are not used by any optimisation algorithm. They are provided\nto support the largest possible class of optimisation algorithms\n- Some optimisation algorithms may change their internal structure in this function\n\"\"\"\nfunction singleUpdate!(\u03b8,\u25bd;nEpoch,nBatch,nBatches,xbatch,ybatch,optAlg::OptimisationAlgorithm)\n   return singleUpdate!(\u03b8,\u25bd,optAlg;nEpoch=nEpoch,nBatch=nBatch,nBatches=nBatches,xbatch=xbatch,ybatch=ybatch)\nend\n\nfunction singleUpdate!(\u03b8,\u25bd,optAlg::OptimisationAlgorithm;nEpoch,nBatch,nBatches,xbatch,ybatch)\n    error(\"singleUpdate() not implemented for this optimisation algorithm\")\nend\n\n\"\"\"\n   initOptAlg!(optAlg;\u03b8,batchSize,x,y)\n\nInitialize the optimisation algorithm\n\n# Parameters:\n- `optAlg`:    The Optimisation algorithm to use\n- `\u03b8`:         Current parameters\n- `batchSize`:    The size of the batch\n- `x`:   The training (input) data\n- `y`:   The training \"labels\" to match\n* `rng`: Random Number Generator (see [`FIXEDSEED`](@ref)) [deafult: `Random.GLOBAL_RNG`]\n\n# Notes:\n- Only a few optimizers need this function and consequently ovverride it. By default it does nothing, so if you want write your own optimizer and don't need to initialise it, you don't have to override this method\n\"\"\"\ninitOptAlg!(optAlg::OptimisationAlgorithm;\u03b8,batchSize,x,y,rng = Random.GLOBAL_RNG) = nothing\n\n#=\n        if rShuffle\n           # random shuffle x and y\n           ridx = shuffle(1:size(x)[1])\n           x = x[ridx, :]\n           y = y[ridx , :]\n        end\n        \u03f5 = 0\n        #\u03b7 = dyn_\u03b7 ? 1/(1+t) : \u03b7\n        \u03b7\u209c = \u03b7(t)*\u03bb\n        for i in 1:size(x)[1]\n            x\u1d62 = x[i,:]'\n            y\u1d62 = y[i,:]'\n            W  = getParams(nn)\n            dW = getGradient(nn,x\u1d62,y\u1d62)\n            newW = gradientDescentSingleUpdate(W,dW,\u03b7\u209c)\n            setParams!(nn,newW)\n            \u03f5 += loss(nn,x\u1d62,y\u1d62)\n        end\n        if nMsgs != 0 && (t % ceil(maxEpochs/nMsgs) == 0 || t == 1 || t == maxEpochs)\n          println(\"Avg. error after epoch $t : $(\u03f5/size(x)[1])\")\n        end\n\n        if abs(\u03f5l/size(x)[1] - \u03f5/size(x)[1]) < (tol * abs(\u03f5l/size(x)[1]))\n            if nMsgs != 0\n                println((tol * abs(\u03f5l/size(x)[1])))\n                println(\"*** Avg. error after epoch $t : $(\u03f5/size(x)[1]) (convergence reached\")\n            end\n            converged = true\n            break\n        else\n            \u03f5l = \u03f5\n        end\n    end\n    if nMsgs != 0 && converged == false\n        println(\"*** Avg. error after epoch $maxEpochs : $(\u03f5/size(x)[1]) (convergence not reached)\")\n    end\n    nn.trained = true\nend\n\n =#\n\nend # end module\n", "meta": {"hexsha": "f37aa8b54decb5dfc03951ae89af0ccfff52fb58", "size": 27196, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Nn/Nn.jl", "max_stars_repo_name": "arfon/BetaML.jl", "max_stars_repo_head_hexsha": "6a02de0223931a9cc3053c13fc42c7b62352603e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Nn/Nn.jl", "max_issues_repo_name": "arfon/BetaML.jl", "max_issues_repo_head_hexsha": "6a02de0223931a9cc3053c13fc42c7b62352603e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Nn/Nn.jl", "max_forks_repo_name": "arfon/BetaML.jl", "max_forks_repo_head_hexsha": "6a02de0223931a9cc3053c13fc42c7b62352603e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2736705577, "max_line_length": 346, "alphanum_fraction": 0.6806147963, "num_tokens": 7340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4339814794452761, "lm_q2_score": 0.1403362494900832, "lm_q1q2_score": 0.06090333317350768}}
{"text": "using Printf\n\nfunction looping_examples()\n    println(\"--------------------------------------\")\n    println(\"Looping examples\")\n    \n    println()\n    \n    println(\"We can use while loops to count to 20, displaying even numbers, and aborting when we get past 10\")\n    i = 1\n    \n    while i < 20\n        if (i % 2) == 0\n            println(i)\n            i += 1\n            continue # Skip the rest of the code in the loop\n        end\n \n        # Make the code use the global i\n        i += 1\n \n        if i > 10\n            break # Jump out of the loop\n        end\n    end\n    \n    println(\"We can use for loops to go from one number to another, e.g. 1-5\")\n    for i = 1:5\n        println(i)\n    end\n\n    println(\"We can use for loops to go from one number to another, e.g. 1-100, stepping at 10 using 1:10:100\")\n    for i = 1:10:100\n        println(i)\n    end\n \n    println(\"We can use for loops to 'for-each' value in an array\")\n    for i in [2,4,6]\n        println(i)\n    end\n \n    println(\"We can use for loops with multiple variables (a for-loop within a for-loop)\")\n    for i = 1:5, j = 1:10\n        println((i, j))\n    end\nend", "meta": {"hexsha": "39cca7e76f0b59665127ba67b21ebc3ba2f9def3", "size": 1134, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/looping.jl", "max_stars_repo_name": "James-P-D/JuliaDump", "max_stars_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/looping.jl", "max_issues_repo_name": "James-P-D/JuliaDump", "max_issues_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/looping.jl", "max_forks_repo_name": "James-P-D/JuliaDump", "max_forks_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.652173913, "max_line_length": 111, "alphanum_fraction": 0.5317460317, "num_tokens": 319, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.13660838475359835, "lm_q1q2_score": 0.06086307014177773}}
{"text": "using DiffEqSensitivity, Test\n\n@test DiffEqSensitivity.hasbranching(1, 2) do x, y\n   (x < 0 ? -x : x) + exp(y)\nend\n\n@test !DiffEqSensitivity.hasbranching(1, 2) do x, y\n   ifelse(x < 0, -x, x) + exp(y)\nend\n", "meta": {"hexsha": "dccb2636a986938e23e5e245b2bcaacd84d1ce9e", "size": 205, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hasbranching.jl", "max_stars_repo_name": "stjordanis/DiffEqSensitivity.jl", "max_stars_repo_head_hexsha": "8f8753b9fda0300b76a68a22b7f86ee0b7eda635", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 136, "max_stars_repo_stars_event_min_datetime": "2020-04-06T13:59:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T10:57:27.000Z", "max_issues_repo_path": "test/hasbranching.jl", "max_issues_repo_name": "stjordanis/DiffEqSensitivity.jl", "max_issues_repo_head_hexsha": "8f8753b9fda0300b76a68a22b7f86ee0b7eda635", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 212, "max_issues_repo_issues_event_min_datetime": "2020-03-26T16:26:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T11:16:41.000Z", "max_forks_repo_path": "test/hasbranching.jl", "max_forks_repo_name": "stjordanis/DiffEqSensitivity.jl", "max_forks_repo_head_hexsha": "8f8753b9fda0300b76a68a22b7f86ee0b7eda635", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 26, "max_forks_repo_forks_event_min_datetime": "2016-11-04T12:18:32.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-21T05:46:04.000Z", "avg_line_length": 20.5, "max_line_length": 51, "alphanum_fraction": 0.643902439, "num_tokens": 84, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.1276526269811457, "lm_q1q2_score": 0.06083664442403602}}
{"text": "#!/usr/bin/env julia\n\nusing Pkg\nPkg.update()\nPkg.add(\"IJulia\")\nPkg.add(\"Plots\")\nPkg.add(\"Distributions\")\nPkg.add(\"LaTeXStrings\")\nPkg.add(\"StatsPlots\")\nPkg.add(\"Measures\")\nPkg.add(\"JLD2\")\nPkg.add(\"SimJulia\")\nPkg.add(PackageSpec(url=\"https://github.com/BenLauwens/NativeSVG.jl\"))\n\n\nusing IJulia\njupyterlab()", "meta": {"hexsha": "42cdaa715c2d37dcc19c6af2e01feaf0568f0dfe", "size": 305, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Setup/configES313.jl", "max_stars_repo_name": "JuliaTagBot/ES313.jl", "max_stars_repo_head_hexsha": "3601743ca05bdb2562a26efd8b809c1a4f78c7b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Setup/configES313.jl", "max_issues_repo_name": "JuliaTagBot/ES313.jl", "max_issues_repo_head_hexsha": "3601743ca05bdb2562a26efd8b809c1a4f78c7b1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Setup/configES313.jl", "max_forks_repo_name": "JuliaTagBot/ES313.jl", "max_forks_repo_head_hexsha": "3601743ca05bdb2562a26efd8b809c1a4f78c7b1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.9411764706, "max_line_length": 70, "alphanum_fraction": 0.7245901639, "num_tokens": 110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.399811640739795, "lm_q2_score": 0.15203224162217424, "lm_q1q2_score": 0.06078425996831044}}
{"text": "\"\"\"\n    Dimension \n\nAbstract supertype of all dimension types.\n\nExample concrete implementations are [`X`](@ref), [`Y`](@ref), [`Z`](@ref),\n[`Ti`](@ref) (Time), and the custom [`Dim`]@ref) dimension.\n\n`Dimension`s label the axes of an [`AbstractDimArray`](@ref),\nor other dimensional objects, and are used to index into the array.\n\nThey may also provide an alternate index to lookup for each array axis.\nThis may be any `AbstractVector` matching the array axis length, or a `Val`\nholding a tuple for compile-time index lookups.\n\n`Dimension`s also have `mode` and `metadata` fields.\n\n`mode` gives more details about the dimension, such as that it is\n[`Categorical`](@ref) or [`Sampled`](@ref) as [`Points`](@ref) or\n[`Intervals`](@ref) along some transect. DimensionalData will\nattempt to guess the mode from the passed-in index value.\n\n`metadata` can hold any metadata object adding more information about\nthe array axis - useful for extending DimensionalData for specific\ncontexts, like geospatial data in GeoData.jl. By default it is `NoMetadata()`.\n\nExample:\n\n```jldoctest Dimension\nusing DimensionalData, Dates\n\nx = X(2:2:10)\ny = Y(['a', 'b', 'c'])\nti = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))\n\nA = DimArray(zeros(3, 5, 12), (y, x, ti))\n\n# output\n\n3\u00d75\u00d712 DimArray{Float64,3} with dimensions:\n  Y: Char[a, b, c] (Categorical - Unordered)\n  X: 2:2:10 (Sampled - Ordered Regular Points)\n  Ti (Time): DateTime(\"2021-01-01T00:00:00\"):Month(1):DateTime(\"2021-12-01T00:00:00\") (Sampled - Ordered Regular Points)\n[:, :, 1]\n 0.0  0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  0.0  0.0\n[and 11 more slices...]\n```\n\nFor simplicity, the same `Dimension` types are also used as wrappers\nin `getindex`, like:\n\n```jldoctest Dimension\nx = A[X(2), Y(3)]\n\n# output\n\n12-element DimArray{Float64,1} with dimensions:\n  Ti (Time): DateTime(\"2021-01-01T00:00:00\"):Month(1):DateTime(\"2021-12-01T00:00:00\") (Sampled - Ordered Regular Points)\nand reference dimensions:\n  Y: c (Categorical - Unordered)\n  X: 4 (Sampled - Ordered Regular Points)\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n 0.0\n```\n\nA `Dimension` can also wrap [`Selector`](@ref).\n\n```jldoctest Dimension\nx = A[X(Between(3, 4)), Y(At('b'))]\n\n# output\n\n1\u00d712 DimArray{Float64,2} with dimensions:\n  X: 4:2:4 (Sampled - Ordered Regular Points)\n  Ti (Time): DateTime(\"2021-01-01T00:00:00\"):Month(1):DateTime(\"2021-12-01T00:00:00\") (Sampled - Ordered Regular Points)\nand reference dimensions:\n  Y: b (Categorical - Unordered)\n 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n```\n\n`Dimension` objects may have [`mode`](@ref) and [`metadata`](@ref) fields\nto track additional information about the data and the index, and their relationship.\n\"\"\"\nabstract type Dimension{T,Mo,Me} end\n\n\"\"\"\n    IndependentDim <: Dimension\n\nAbstract supertype for independent dimensions. Thise will plot on the X axis.\n\"\"\"\nabstract type IndependentDim{T,Mo,Me} <: Dimension{T,Mo,Me} end\n\n\"\"\"\n    DependentDim <: Dimension\n\nAbstract supertype for Dependent dimensions. These will plot on the Y axis.\n\"\"\"\nabstract type DependentDim{T,Mo,Me} <: Dimension{T,Mo,Me} end\n\n\"\"\"\n    XDim <: IndependentDim\n\nAbstract supertype for all X dimensions.\n\"\"\"\nabstract type XDim{T,Mo,Me} <: IndependentDim{T,Mo,Me} end\n\n\"\"\"\n    YDim <: DependentDim\n\nAbstract supertype for all Y dimensions.\n\"\"\"\nabstract type YDim{T,Mo,Me} <: DependentDim{T,Mo,Me} end\n\n\"\"\"\n    ZDim <: DependentDim\n\nAbstract supertype for all Z dimensions.\n\"\"\"\nabstract type ZDim{T,Mo,Me} <: DependentDim{T,Mo,Me} end\n\n\"\"\"\n    TimeDim <: IndependentDim\n\nAbstract supertype for all time dimensions.\n\nIn a `TimeDime` with `Interval` sampling the locus will automatically\nbe set to `Start()`. Dates and times generally refer to the start of a\nmonth, hour, second etc., not the central point as is more common with spatial data.\n`\"\"\"\nabstract type TimeDim{T,Mo,Me} <: IndependentDim{T,Mo,Me} end\n\nConstructionBase.constructorof(d::Type{<:Dimension}) = basetypeof(d)\nAdapt.adapt_structure(to, dim::Dimension) =\n    rebuild(dim, adapt(to, val(dim)), adapt(to, mode(dim)), adapt(to, metadata(dim)))\n\nconst DimType = Type{<:Dimension}\nconst DimTuple = Tuple{<:Dimension,Vararg{<:Dimension}}\nconst DimTypeTuple = Tuple{<:DimType,Vararg{<:DimType}}\nconst VectorOfDim = Vector{<:Dimension}\nconst DimOrDimType = Union{Dimension,DimType}\nconst AllDims = Union{Dimension,DimTuple,DimType,DimTypeTuple,VectorOfDim}\n\n# DimensionalData interface methods\n\n\"\"\"\n    rebuild(dim::Dimension, val, mode=mode(dim), metadata=metadata(dim)) => Dimension\n    rebuild(dim::Dimension; val=val(dim), mode=mode(dim), metadata=metadata(dim)) => Dimension\n\nRebuild dim with fields from `dim`, and new fields passed in.\n\"\"\"\nfunction rebuild(\n    dim::D, val, mode::IndexMode=mode(dim), metadata=metadata(dim)\n) where D <: Dimension\n    constructorof(D)(val, mode, metadata)\nend\n\ndims(dim::Union{Dimension,DimType,Val{<:Dimension}}) = dim\ndims(dims::DimTuple) = dims\ndims(x) = nothing\ndims(::Nothing) = error(\"No dims found\")\n\nrefdims(x) = ()\n\nval(dim::Dimension) = dim.val\nmode(dim::Dimension) = dim.mode\nmode(dim::Union{DimType,Val{<:Dimension}}) = NoIndex()\nmetadata(dim::Dimension) = dim.metadata\n\nindex(dim::Dimension{<:AbstractArray}) = val(dim)\nindex(dim::Dimension{<:Val}) = unwrap(val(dim))\n\nname(dim::Dimension) = name(typeof(dim))\nname(dim::Val{D}) where D = name(D)\n\nbounds(dim::Dimension) = _bounds(mode(dim), dim)\n\nmodetype(dim::Dimension) = typeof(mode(dim))\nmodetype(::Type{<:Dimension{<:Any,Mo}}) where Mo = Mo\nmodetype(x) = NoIndex\n\n\nfor func in (:order, :span, :sampling, :locus)\n    @eval ($func)(dim::Dimension) = ($func)(mode(dim))\nend\n\n# Dipatch on Tuple{<:Dimension}, and map to single dim methods\nfor f in (:val, :index, :mode, :metadata, :order, :sampling, :span, :bounds, :locus,\n             :name, :label, :units)\n    @eval begin\n        $f(dims::DimTuple) = map($f, dims)\n        $f(dims::Tuple{}) = ()\n        $f(dims::DimTuple, l1, lookup...) = $f(dims, (l1, lookup...))\n        $f(dims::DimTuple, lookup) = $f(DD.dims(dims, key2dim(lookup)))\n    end\nend\n\norder(ot::Type{<:SubOrder}, dims::DimTuple) = map(d -> order(ot, d), dims)\norder(ot::Type{<:SubOrder}, dims::Tuple{}) = ()\norder(ot::Type{<:SubOrder}, dims_::DimTuple, l1, l2, ls...) = \n    order(ot, dims_, (l1, l2, ls...))\norder(ot::Type{<:SubOrder}, dims_::DimTuple, lookup::Tuple) =\n    map(d -> order(ot, d), dims(dims_, key2dim(lookup)))\norder(ot::Type{<:SubOrder}, dims_::DimTuple, lookup) =\n    order(ot, dims(dims_, key2dim(lookup)))\norder(ot::Type{<:SubOrder}, dim::Dimension) = order(ot, mode(dim))\n\n\n# Base methods\nconst ArrayOrVal = Union{AbstractArray,Val}\n\nBase.eltype(d::Type{<:Dimension{T}}) where T = T\nBase.eltype(d::Type{<:Dimension{A}}) where A<:AbstractArray{T} where T = T\n# TODO Use a vector wrapper instead of Val\nBase.eltype(d::Type{<:Dimension{<:Val{Index}}}) where Index = typeof(first(Index))\nBase.size(d::Dimension) = size(val(d))\nBase.size(d::Dimension{<:Val}) = (length(index(d)),)\nBase.axes(d::Dimension{<:AbstractArray}) = axes(index(d))\nBase.axes(d::Dimension{<:Val}) = (Base.OneTo(length(d)),)\nBase.axes(d::Dimension, i) = axes(index(d), i)\nBase.eachindex(d::Dimension{<:Number}) = eachindex(val(d))\nBase.eachindex(d::Dimension{<:ArrayOrVal}) = eachindex(index(d))\nBase.length(d::Dimension{<:Number}) = length(val(d))\nBase.length(d::Dimension{<:ArrayOrVal}) = length(index(d))\nBase.ndims(d::Dimension) = 0\nBase.ndims(d::Dimension{<:AbstractArray}) = ndims(val(d))\nBase.ndims(d::Dimension{<:Val}) = 1\n@inline Base.getindex(d::Dimension{<:Number}) = val(d)\n@propagate_inbounds Base.getindex(d::Dimension{<:AbstractArray}, i) =\n    getindex(index(d), sel2indices(d, i))\n@propagate_inbounds Base.getindex(d::Dimension{<:Val{Index}}, i) where Index =\n    getindex(Index, sel2indices(d, i))\n\n\nBase.iterate(d::Dimension{<:ArrayOrVal}, args...) = iterate(index(d), args...)\nBase.first(d::Dimension) = val(d)\nBase.first(d::Dimension{<:ArrayOrVal}) = first(index(d))\nBase.last(d::Dimension) = val(d)\nBase.last(d::Dimension{<:ArrayOrVal}) = last(index(d))\nBase.firstindex(d::Dimension) = 1\nBase.lastindex(d::Dimension) = 1\nBase.firstindex(d::Dimension{<:ArrayOrVal}) = firstindex(index(d))\nBase.lastindex(d::Dimension{<:ArrayOrVal}) = lastindex(index(d))\nBase.step(d::Dimension) = step(mode(d), d)\nBase.Array(d::Dimension{<:ArrayOrVal}) = collect(index(d))\nfunction Base.:(==)(d1::Dimension, d2::Dimension)\n    typeof(d1) == typeof(d2) &&\n    val(d1) == val(d2) &&\n    mode(d1) == mode(d2) &&\n    metadata(d1) == metadata(d2)\nend\n\n\"\"\"\nAbstract supertype for Dimensions with user-set type paremeters\n\"\"\"\nabstract type ParametricDimension{X,T,Mo,Me} <: Dimension{T,Mo,Me} end\n\n\"\"\"\n    Dim{S}()\n    Dim{S}(val=:; mode=AutoMode(), metadata=NoMetadata())\n    Dim{S}(val, mode, metadata=NoMetadata())\n\nA generic dimension. For use when custom dims are required when loading\ndata from a file. Can be used as keyword arguments for indexing.\n\nDimension types take precedence over same named `Dim` types when indexing\nwith symbols, or e.g. creating Tables.jl keys.\n\n```jldoctest\nusing DimensionalData\n\ndim = Dim{:custom}(['a', 'b', 'c'])\n\n# output\n\nDim{:custom}:\n  val: Char[a, b, c]\nDim{:custom, Vector{Char}, AutoMode{AutoOrder}, NoMetadata}\n```\n\"\"\"\nstruct Dim{S,T,Mo<:Mode,Me<:AllMetadata} <: ParametricDimension{S,T,Mo,Me}\n    val::T\n    mode::Mo\n    metadata::Me\n    Dim{S}(val::T, mode::Mo, metadata::Me=NoMetadata()) where {S,T,Mo<:Mode,Me} =\n        new{S,T,Mo,Me}(val, mode, metadata)\nend\nDim{S}(val=:; mode::Mode=AutoMode(), metadata=NoMetadata()) where S =\n    Dim{S}(val, mode, metadata)\n\nname(::Type{<:Dim{S}}) where S = S\nbasetypeof(::Type{<:Dim{S}}) where S = Dim{S}\nkey2dim(s::Val{S}) where S = Dim{S}()\ndim2key(::Type{D}) where D<:Dim{S} where S = S\n\n\"\"\"\n    AnonDim <: Dimension\n\n    AnonDim()\n\nAnonymous dimension. Used when extra dimensions are created,\nsuch as during transpose of a vector.\n\"\"\"\nstruct AnonDim{T} <: Dimension{T,NoIndex,NoMetadata}\n    val::T\nend\nAnonDim() = AnonDim(Colon())\nAnonDim(val, arg1, args...) = AnonDim(val)\n\nmode(::AnonDim) = NoIndex()\nmetadata(::AnonDim) = NoMetadata()\nname(::AnonDim) = :Anon\n\n\"\"\"\n    @dim typ [supertype=Dimension] [name::String=string(typ)]\n\nMacro to easily define new dimensions. The supertype will be inserted\ninto the type of the dim. The default is simply `YourDim <: Dimension`. Making\na Dimesion inherit from `XDim`, `YDim`, `ZDim` or `TimeDim` will affect\nautomatic plot layout and other methods that dispatch on these types. `<: YDim`\nare plotted on the Y axis, `<: XDim` on the X axis, etc.\n\nExample:\n```julia\n@dim Lat YDim \"latitude\"\n@dim Lon XDim \"Longitude\"\n```\n\"\"\"\nmacro dim end\nmacro dim(typ::Symbol, args...)\n    dimmacro(typ::Symbol, :(DimensionalData.Dimension), args...)\nend\nmacro dim(typ::Symbol, supertyp::Symbol, args...)\n    dimmacro(typ, supertyp, args...)\nend\n\nfunction dimmacro(typ, supertype, name::String=string(typ))\n    quote\n        Base.@__doc__ struct $typ{T,Mo<:DimensionalData.Mode,Me<:DimensionalData.AllMetadata} <: $supertype{T,Mo,Me}\n            val::T\n            mode::Mo\n            metadata::Me\n        end\n        function $typ(\n             val=:; mode::DimensionalData.Mode=DimensionalData.AutoMode(),\n             metadata=DimensionalData.NoMetadata()\n        )\n            $typ(val, mode, metadata)\n        end\n        function $typ(val::V, mode::Mo) where {V,Mo<:DimensionalData.Mode}\n            $typ{V,Mo,NoMetadata}(val, mode, DimensionalData.NoMetadata())\n        end\n        DimensionalData.name(::Type{<:$typ}) = $(QuoteNode(Symbol(name)))\n        DimensionalData.key2dim(::Val{$(QuoteNode(typ))}) = $typ()\n    end |> esc\nend\n\n# Define some common dimensions.\n\n\"\"\"\n    X <: XDim\n\n    X(val=:; mode=AutoMode(), metadata=NoMetadata())\n\nX [`Dimension`](@ref). `X <: XDim <: IndependentDim`\n\n## Example:\n```julia\nxdim = X(2:2:10)\n# Or\nval = A[X(1)]\n# Or\nmean(A; dims=X)\n```\n\"\"\"\n@dim X XDim\n\n\"\"\"\n    Y <: YDim\n\n    Y(val=:; mode=AutoMode(), metadata=NoMetadata())\n\nY [`Dimension`](@ref). `Y <: YDim <: DependentDim`\n\n## Example:\n```julia\nydim = Y(['a', 'b', 'c'])\n# Or\nval = A[Y(1)]\n# Or\nmean(A; dims=Y)\n```\n\"\"\"\n@dim Y YDim\n\n\"\"\"\n    Z <: ZDim\n\n    Z(val=:; mode=AutoMode(), metadata=NoMetadata())\n\nZ [`Dimension`](@ref). `Z <: ZDim <: Dimension`\n\n## Example:\n```julia\nzdim = Z(10:10:100)\n# Or\nval = A[Z(1)]\n# Or\nmean(A; dims=Z)\n```\n\"\"\"\n@dim Z ZDim\n\n\"\"\"m\n    Ti <: TimeDim\n    \n    Ti(val=:; mode=AutoMode(), metadata=NoMetadata())\n\nTime [`Dimension`](@ref). `Ti <: TimeDim <: IndependentDim`\n\n`Time` is already used by Dates, and `T` is a common type parameter,\nWe use `Ti` to avoid clashes.\n\n## Example:\n```julia\ntimedim = Ti(DateTime(2021, 1):Month(1):DateTime(2021, 12))\n# Or\nval = A[Ti(1)]\n# Or\nmean(A; dims=Ti)\n```\n\"\"\"\n@dim Ti TimeDim \"Time\"\n\n# Time dimensions need to default to the Start() locus, as that is\n# nearly always the format and Center intervals are difficult to\n# calculate with DateTime step values.\nidentify(locus::AutoLocus, dimtype::Type{<:TimeDim}, index) = Start()\n\nconst Time = Ti # For some backwards compat\n\n\n\"\"\"\n    formatdims(A, dims) => Tuple{Vararg{<:Dimension,N}}\n\nFormat the passed-in dimension(s) `dims` to match the array `A`.\n\nThis means converting indexes of `Tuple` to `LinRange`, and running\n`identify`. Errors are also thrown if dims don't match the array dims or size.\n\nIf a [`IndexMode`](@ref) hasn't been specified, an mode is chosen\nbased on the type and element type of the index:\n\"\"\"\nformatdims(A::AbstractArray, dims) = formatdims(A, (dims,))\nformatdims(A::AbstractArray, dims::NamedTuple) = begin\n    dims = map((k, v) -> Dim{k}(v), keys(dims), values(dims))\n    formatdims(axes(A), dims)\nend\nformatdims(A::AbstractArray{<:Any,N}, dims::Tuple{Vararg{<:Any,N}}) where N =\n    formatdims(axes(A), dims)\n@noinline formatdims(A::AbstractArray{<:Any,N}, dims::Tuple{Vararg{<:Any,M}}) where {N,M} =\n    throw(DimensionMismatch(\"Array A has $N axes, while the number of dims is $M\"))\nformatdims(axes::Tuple, dims::Tuple) = _formatdims(axes, dims)\n\n_formatdims(axes::Tuple{Vararg{<:AbstractRange}}, dims::Tuple) =\n    map(_formatdims, axes, dims)\n_formatdims(axis::AbstractRange, dimname::Symbol) = Dim{dimname}(axis, NoIndex(), NoMetadata())\n_formatdims(axis::AbstractRange, T::Type{<:Dimension}) = T(axis, NoIndex(), NoMetadata())\n_formatdims(axis::AbstractRange, dim::Dimension) = begin\n    checkaxis(dim, axis)\n    rebuild(dim, val(dim), identify(mode(dim), basetypeof(dim), val(dim)))\nend\n_formatdims(axis::AbstractRange, dim::Dimension{<:NTuple{2}}) = begin\n    start, stop = val(dim)\n    range = LinRange(start, stop, length(axis))\n    _formatdims(axis, rebuild(dim, range))\nend\n# Dimensions holding colon dispatch on mode\n_formatdims(axis::AbstractRange, dim::Dimension{Colon}) = _formatdims(mode(dim), axis, dim)\n_formatdims(mode::Union{AutoMode,NoIndex}, axis::AbstractRange, dim::Dimension{Colon}) =\n    rebuild(dim, axis, NoIndex())\n_formatdims(mode::IndexMode, axis::AbstractRange, dim::Dimension{Colon}) =\n    rebuild(dim, axis, identify(mode, basetypeof(dim), axis))\n\nfunction checkaxis(dim, axis)\n    if !(first(axes(dim)) == axis)\n        throw(DimensionMismatch(\n            \"axes of $(basetypeof(dim)) of $(first(axes(dim))) do not match array axis of $axis\"\n        ))\n    end\nend\n", "meta": {"hexsha": "9db031ea36094873cea0afcb0491c79f2860954d", "size": 15238, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dimension.jl", "max_stars_repo_name": "ali-ramadhan/DimensionalData.jl", "max_stars_repo_head_hexsha": "8a55c87127893d5916f167cccbc53ad77fa0dd64", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/dimension.jl", "max_issues_repo_name": "ali-ramadhan/DimensionalData.jl", "max_issues_repo_head_hexsha": "8a55c87127893d5916f167cccbc53ad77fa0dd64", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/dimension.jl", "max_forks_repo_name": "ali-ramadhan/DimensionalData.jl", "max_forks_repo_head_hexsha": "8a55c87127893d5916f167cccbc53ad77fa0dd64", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-26T02:17:01.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-26T02:17:01.000Z", "avg_line_length": 30.2942345924, "max_line_length": 120, "alphanum_fraction": 0.6720698254, "num_tokens": 4690, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "### A Pluto.jl notebook ###\n# v0.14.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 e4d3550a-72dd-11eb-06bc-fb27a9d9cd72\nmd\"\"\"##### SUBJECT:$~~$ YourId;ClassId;NotebookID;Notebook-Version mm/dd/yyyy\nMyIdDavid1234;\nClass-MissSmith123;\nNotbook#2-MathGrade6;\n02/15/2021\n\"\"\"\n\n# \u2554\u2550\u2561 61919858-72df-11eb-1745-67ab5e72244b\nmd\"\"\"##### EMAIL:$~~$Self@email.com;Teacher@email.com;Friend@email.com\nmySelf@email.com;\nmyTeacher@email.com;\nmyFriend@email.com\n\"\"\"\n\n# \u2554\u2550\u2561 63a1b84a-7324-11eb-0682-1fb702507e32\nmd\"\"\"# $~~~~~~~~~~~\\mathbb Demo~~~\\mathbb Lesson~~~\\mathbb Expanded$\n##### Based On; Version; ServerToken; Sources(put a blank line before each)\nDemo Lesson;\nversion 02/15/2021;\nToken12345\n\nhttps://github.com/paradocs/pluto-community/blob/main/Demo%20Lesson.jl\n\n[ParaDoc.Care] (https://www.paradocs.care/computing)  Copied from this web-page.\n\nCopied from e-mail sent to student.\n\"\"\"\n\n# \u2554\u2550\u2561 7fe2d7b6-72e1-11eb-1a1a-afdd77020baa\nmd\"\"\"##### SAVE/SEND:\n1) Your work will be lost if not sent by e-mail.\n2) Place your ID Name on line 2 of cell 1$~~$ End with a semicolon.             \n3) Send youself copies as you work. for backups.  \n4) Answers must be written in md-cells for your teacher to see.\n5) Click top right \u25b3Export and then \u25b3Notebook file.\n5) Open the new tab, Ctrl a$~~~$ to select all, Ctrl c$~~~$ to copy.\n7) Open a new e-mail and use Ctrl v$~~$ paste in to the body.\n8) Select and past the subject lines ending with semicolons.\n9) Select and past one or more of the e-mail lines.\n10) Check your ID and that you have copied correctly and SEND.\n\"\"\"\n\n# \u2554\u2550\u2561 a30fd548-72f0-11eb-0180-f1902eece57a\nmd\"\"\"##### OPEN NEW NOTEBOOK: (-- work in progress --)\n1) Start a web Pluto Notebook or use a Notebook that is already open.\n2) Open a web page, e-mail, or file in an editor with the new notebook code.\n3) Carefully select just the code with the mouse, holding the left button.\n4) For a code only page use Crtl a$~~$.  Holding shift may help with selecting.\n4) Be sure you have all of the code.  Then copy with Ctrl c.\n5) Click at the start of the top address bar and delete anything there.\n6) With Ctrl v paste the new code.\n7) Click the button to open the new notebook.\n\"\"\"\n\n# \u2554\u2550\u2561 7dd2db00-7300-11eb-20a9-ef06117f61e2\nmd\"\"\"##### HELP/HINT$~~$ EMAIL SYSTEM\n###### If your teacher or assistents are available to scan e-mail for hint requests,\n\n###### you may write a request in a md-cell starting with helpyyyy/mm/dd $~~~$ and\n\n###### place an * in front of your ID.$~~$  E-mail it to your teacher or assistent.\n\n###### Remove the * for the final notebook you send.\n\n!!! start\n\"\"\"\n\n# \u2554\u2550\u2561 c3b85594-72f5-11eb-3717-a1c384543220\nmd\"\"\"\n###### Problem(1)\n\nWrite an expresstion to add 3 plus 5 plus one-half,\n\nClick the Answer(1) cell and type in your answer.\n\n\"\"\"\n\n# \u2554\u2550\u2561 9b1f5900-72f5-11eb-24ac-2b31323430fd\na = 3 + 5 + .5\n\n\n# \u2554\u2550\u2561 aae018b4-74af-11eb-2bd7-f58e6e737e0b\nmd\"\"\"\nAnswer(1)\na = 8.5\n\n\"\"\"\n\n# \u2554\u2550\u2561 ff44b926-78d5-11eb-3ea6-e16e11b73242\nmd\"\"\"\n!!! study\n##### ``~~~~~~~``Use a zoom enabled white board for Hints, Help, and Teaching.\n##### ``~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~``To move and zoom use:\n##### ``~~~~~~~~~~~``arrow keys -or- left click and drag -or- the mousewheel\n##### ``~~~~~~~``You may open to write and edit, but nothing will be saved.\n##### ``~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~``Run the cell to start over.\n##### ``~~~~~~~~~~~~~~``Click (n)ext or the \"n\" key twice the first time.\n##### ``~~~~~~~~~~~``The \"HOME\" key goes home with the stardard zoom.\n''\n\"\"\"\n\n# \u2554\u2550\u2561 5844acaa-78d3-11eb-1582-717d7015401c\nhtml\"\"\"\n<div style=\"width:680px;height:300px;border: 1px solid black;\"><div style=\"position:relative;z-index:10;height:40px;padding-left:4px;width:150px;\"><a style=\"text-decoration:none;color:#CCC;font-size:20px;font-family:Dosis;\" href=\"https://ziteboard.com\" target=\"_blank\">Zoom & Move</a></div><iframe seamless=\"seamless\" style=\"position:relative;width: 100%; height: 100%;top:-40px;\" src=\"https://view.ziteboard.com/shared/07449027541615\" frameborder=\"0\" allowfullscreen></iframe></div>\n\"\"\"\n\n# \u2554\u2550\u2561 592e7fb2-8182-11eb-2066-376bc42c6d1e\n\n\n# \u2554\u2550\u2561 98f1eb34-8187-11eb-05b7-c79cd8da3a7f\n\n\n# \u2554\u2550\u2561 a81ca4de-8182-11eb-2a7a-03d27b8d1e46\n\n\n# \u2554\u2550\u2561 c6021c2c-818a-11eb-0a03-e529ec84eeba\n\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550e4d3550a-72dd-11eb-06bc-fb27a9d9cd72\n# \u2560\u255061919858-72df-11eb-1745-67ab5e72244b\n# \u255f\u250063a1b84a-7324-11eb-0682-1fb702507e32\n# \u255f\u25007fe2d7b6-72e1-11eb-1a1a-afdd77020baa\n# \u255f\u2500a30fd548-72f0-11eb-0180-f1902eece57a\n# \u2560\u25507dd2db00-7300-11eb-20a9-ef06117f61e2\n# \u255f\u2500c3b85594-72f5-11eb-3717-a1c384543220\n# \u2560\u25509b1f5900-72f5-11eb-24ac-2b31323430fd\n# \u2560\u2550aae018b4-74af-11eb-2bd7-f58e6e737e0b\n# \u2560\u2550ff44b926-78d5-11eb-3ea6-e16e11b73242\n# \u255f\u25005844acaa-78d3-11eb-1582-717d7015401c\n# \u255f\u2500592e7fb2-8182-11eb-2066-376bc42c6d1e\n# \u255f\u250098f1eb34-8187-11eb-05b7-c79cd8da3a7f\n# \u255f\u2500a81ca4de-8182-11eb-2a7a-03d27b8d1e46\n# \u255f\u2500c6021c2c-818a-11eb-0a03-e529ec84eeba\n", "meta": {"hexsha": "5a52a73d6f7471fefaeb49dcf0fe17fb384ba019", "size": 4816, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Demo Lesson.jl", "max_stars_repo_name": "paradocs/pluto-community", "max_stars_repo_head_hexsha": "c81b247a1a2407e94270089acb935214cf3aaa2d", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-25T00:16:16.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-25T00:16:16.000Z", "max_issues_repo_path": "Demo Lesson.jl", "max_issues_repo_name": "paradocs/pluto-e-mail", "max_issues_repo_head_hexsha": "c81b247a1a2407e94270089acb935214cf3aaa2d", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demo Lesson.jl", "max_forks_repo_name": "paradocs/pluto-e-mail", "max_forks_repo_head_hexsha": "c81b247a1a2407e94270089acb935214cf3aaa2d", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9154929577, "max_line_length": 483, "alphanum_fraction": 0.6877076412, "num_tokens": 1861, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12252320290590248, "lm_q1q2_score": 0.06078300492863165}}
{"text": "#\n# Utilities\n#\n\n\"\"\"\n```\ntau(sim::Clock, check::Symbol, x::Union{Number,Symbol}; m::Module=Main)\ntau(check::Symbol, x::Union{Number,Symbol}; m::Module=Main)\n```\nCompare the current simulation time against a number or a variable.\n\n# Arguments\n- `sim::Clock`: clock variable, if not given, it is \ud835\udc36.\n- `check::Symbol`: a comparison operator as a symbol like `:>`,\n- `x::Union{Number,Symbol}`: a number or a symbolic variable like `:a`,\n- `m::Module=Main`: the evaluation scope, if a symbolic variable is given.\n\n# Examples\n```jldoctest\njulia> using Simulate\n\njulia> tau(:>=, 1)\nfalse\njulia> tau(:<, 1)\ntrue\njulia> a = 1\n1\njulia> tau(:<=, :a)\ntrue\n```\n\"\"\"\ntau(sim::Clock, check::Symbol, x::Union{Number,Symbol}, m::Module=Main) =\n        x isa Number ? eval(check)(tau(sim), x) : eval(check)(tau(sim), Core.eval(m, x))\ntau(check::Symbol, x::Union{Number,Symbol}, m::Module=Main) = tau(\ud835\udc36, check, x, m)\n\n\"\"\"\n```\nval(a::Union{Number, Symbol}, check::Symbol, x::Union{Number, Symbol}, m::Module=Main)\n```\nCompare two variables or numbers.\n\n# Examples\n```jldoctest\njulia> using Simulate\n\njulia> m = @__MODULE__;  # necessary for doctest\n\njulia> val(1, :<=, 2)\ntrue\njulia> a = 1\n1\njulia> val(:a, :<=, 2, m)\ntrue\n```\n\"\"\"\nfunction val(a::Union{Number, Symbol}, check::Symbol, x::Union{Number, Symbol},\n             m::Module=Main)\n    a = a isa Number ? a : Core.eval(m, a)\n    x = x isa Number ? x : Core.eval(m, x)\n    eval(check)(a, x)\nend\n\n\"\"\"\n```\n@SF(f::Symbol, arg...)\n@SF f arg...\n```\ncreate a `SimFunction` from arguments f, arg...\n\n# Arguments\n- `f::Symbol`: a function given as a symbol, e.g. `:f` if f() is your function,\n- `arg...`: further arguments to your function\n\n!!! note\n    1. keyword arguments don't work with this macro, use SF instead.\n    2. if you give @SF as argument(s) to a function, you must enclose it/them\n        in parentheses ( @SF ... ) or ( (@SF ...), (@SF ...) )\n\n# Examples\n```@jldoctest\njulia> using Simulate\n\njulia> @SF :sin pi\nSimFunction(sin, (\u03c0,), Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}())\njulia> a = 1\n1\njulia> incra() = global a += 1             # create a simple increment function\nincra (generic function with 1 method)\njulia> event!((@SF :incra), after, 3)      # schedule an increment after 3 time units\n3.0\njulia> a\n1\njulia> run!(\ud835\udc36, 5)\n\"run! finished with 1 clock events, 0 sample steps, simulation time: 5.0\"\njulia> a\n2\njulia> event!((@SF :incra), (@tau :>= 8))  # schedule a conditional increment\n5.0\njulia> run!(\ud835\udc36, 5)\n\"run! finished with 0 clock events, 500 sample steps, simulation time: 10.0\"\njulia> a\n3\njulia> event!(((@SF :incra), (@SF :incra)), ((@tau :>= 12), (@val :a :<= 3)))\n10.0\njulia> run!(\ud835\udc36, 5)\n\"run! finished with 0 clock events, 500 sample steps, simulation time: 15.0\"\njulia> a\n5\n```\n\"\"\"\nmacro SF(f::QuoteNode, arg...)\n    return :( SimFunction( Core.eval(@__MODULE__, $f), $(arg...) ) )\nend\n\n\"\"\"\n```\n@SP(id, f::Symbol, input::Channel, output::Channel, arg...)\n@SP id f input output arg...\n```\ncreate a `SimProcess` from arguments f, arg...\n\n!!! note\n    keyword arguments don't work with this macro, use SP instead.\n\"\"\"\nmacro SP(id, f::Symbol, input::Channel, output::Channel, arg...)\n    return :( SimProcess($id, $f, $input, $output, $(arg...) ) )\nend\n\n\"\"\"\n```\n@tau(sim::Clock)\n@tau sim\n@tau()\n@tau\n```\nreturn the current simulation time.\n\n# Arguments\n- `sim::Clock`: if no clock argument is given, it returns \ud835\udc36's time.\n\"\"\"\nmacro tau(sim)\n    return :( tau($sim) )\nend\nmacro tau()\n    return :( tau(\ud835\udc36) )\nend\n\n\"\"\"\n```\n@tau(sim::Clock, check::Symbol, val)\n@tau sim check val\n@tau(check::Symbol, val)\n@tau check val\n```\ncreate a `SimFunction` comparing current simulation time with a given value or\nvariable.\n\n# Arguments\n- `sim::Clock`: if no clock is given, it compares with \ud835\udc36's time,\n- `check::Symbol`: the check operator must be given as a symbol e.g. `:<`,\n- `val::Union{Number, QuoteNode}`: a value or a symbolic variable,\n\n!!! note\n    If you give @tau as argument(s) to a function, you must enclose it/them\n    in parentheses ( @tau ... ) or ( (@tau ...), (@tau ...) )!\n\n# Examples\n```jldoctest\njulia> using Simulate\n\njulia> reset!(\ud835\udc36)\n\"clock reset to t\u2080=0.0, sampling rate \u0394t=0.0.\"\njulia> s = @tau :\u2265 100\nSimFunction(Simulate.tau, (:\u2265, 100, Main), Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}())\njulia> Simulate.simExec(s)\nfalse\njulia> Simulate.simExec(@tau < 100)                ### wrong !!\nERROR: syntax: \"<\" is not a unary operator\njulia> Simulate.simExec(@tau :< 100)               ### correct\ntrue\njulia> a = 1\n1\njulia> Simulate.simExec(@tau :< :a)\ntrue\njulia> event!(SF(()->global a+=1), (@tau :>= 3))   ### create a conditional event\n0.0\njulia> a\n1\njulia> run!(\ud835\udc36, 5)                                  ### run\n\"run! finished with 0 clock events, 500 sample steps, simulation time: 5.0\"\njulia> a\n2\n```\n\"\"\"\nmacro tau(sim, check::Symbol, val::Union{Number, QuoteNode})\n    return :( SimFunction(tau, $sim, $check, $val, @__MODULE__) )\nend\nmacro tau(check::QuoteNode, val::Union{Number, QuoteNode})\n    return :( SimFunction(tau, $check, $val, @__MODULE__) )\nend\n\n\"\"\"\n```\n@val(a, check::QuoteNode, x)\n@val a check b\n```\nCreate a Simfunction comparing two values a and b or two symbolic variables\n:a and :b. The comparison operator must be given symbolically, e.g. `:\u2264`.\n\n# Arguments\n- `a, b::: a number, expression or symbol\n- `check::QuoteNode`: a comparison operator as a symbol like `:\u2264`\n- `, m::Module=Main`: a module scope for evaluation of given symbolic variables\n\n!!! note\n    If you give @val as argument(s) to a function, you must enclose it/them\n    in parentheses ( @val ... ) or e.g. ( (@tau ...), (@val ...) )!\n\n# Examples\n```jldoctest\njulia> using Simulate\n\njulia> reset!(\ud835\udc36)\n\"clock reset to t\u2080=0.0, sampling rate \u0394t=0.0.\"\njulia> @val 1 :\u2264 2\nSimFunction(Simulate.val, (1, :\u2264, 2, Main), Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}())\njulia> Simulate.simExec(@val 1 :\u2264 2)\ntrue\njulia> a = 1\n1\njulia> Simulate.simExec(@val :a :\u2264 2)\ntrue\njulia> event!(SF(()->global a+=1), ((@tau :>= 3), (@val :a :<= 3))) ### a conditional event\n0.0\njulia> run!(\ud835\udc36, 5)\n\"run! finished with 0 clock events, 500 sample steps, simulation time: 5.0\"\njulia> a\n2\n```\n\"\"\"\nmacro val(a, check::QuoteNode, x)\n    return :( SimFunction(val, $a, $check, $x, @__MODULE__) )\nend\n", "meta": {"hexsha": "fea78664ad0837ffd753c5bd25d41febf469311c", "size": 6301, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Simulate.jl-7fe0908f-881e-4672-99a3-35ccdc95dcfc", "max_stars_repo_head_hexsha": "903e226dcb3b8b84f5919570df456611d8ead479", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "UnofficialJuliaMirror/Simulate.jl-7fe0908f-881e-4672-99a3-35ccdc95dcfc", "max_issues_repo_head_hexsha": "903e226dcb3b8b84f5919570df456611d8ead479", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "UnofficialJuliaMirror/Simulate.jl-7fe0908f-881e-4672-99a3-35ccdc95dcfc", "max_forks_repo_head_hexsha": "903e226dcb3b8b84f5919570df456611d8ead479", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8237704918, "max_line_length": 115, "alphanum_fraction": 0.63418505, "num_tokens": 2085, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4571367020358429, "lm_q2_score": 0.13296425222296632, "lm_q1q2_score": 0.06078283974986881}}
{"text": "# # Machine Learning in Julia (continued)\n\n# An introduction to the\n# [MLJ](https://alan-turing-institute.github.io/MLJ.jl/stable/)\n# toolbox.\n\n\n# ### Set-up\n\n# Inspect Julia version:\n\nVERSION\n\n# The following instantiates a package environment.\n\n# The package environment has been created using **Julia 1.6** and may not\n# instantiate properly for other Julia versions.\n\nusing Pkg\nPkg.activate(\"env\")\nPkg.instantiate()\n\n\n# ## General resources\n\n# - [MLJ Cheatsheet](https://alan-turing-institute.github.io/MLJ.jl/dev/mlj_cheatsheet/)\n# - [Common MLJ Workflows](https://alan-turing-institute.github.io/MLJ.jl/dev/common_mlj_workflows/)\n# - [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/)\n# - [Data Science Tutorials in Julia](https://juliaai.github.io/DataScienceTutorials.jl/)\n\n\n# ## Part 4 - Tuning Hyper-parameters\n\n# ### Naive tuning of a single parameter\n\n# The most naive way to tune a single hyper-parameter is to use\n# `learning_curve`, which we already saw in Part 2. Let's see this in\n# the Horse Colic classification problem, a case where the parameter\n# to be tuned is *nested* (because the model is a pipeline).\n\n# Here is the Horse Colic data again, with the type coercions we\n# already discussed in Part 1:\n\nusing MLJ\nusing UrlDownload, CSV, DataFrames\ncsv_file = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                   \"MachineLearningInJulia2020/\"*\n                   \"for-MLJ-version-0.16/data/horse.csv\");\nhorse = DataFrames.DataFrame(csv_file); # convert to data frame\ncoerce!(horse, autotype(horse));\ncoerce!(horse, Count => Continuous);\ncoerce!(horse,\n        :surgery               => Multiclass,\n        :age                   => Multiclass,\n        :mucous_membranes      => Multiclass,\n        :capillary_refill_time => Multiclass,\n        :outcome               => Multiclass,\n        :cp_data               => Multiclass);\n\ny, X = unpack(horse, ==(:outcome));\nschema(X)\n\n# Now for a pipeline model:\n\nLogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels\nmodel = Standardizer |> ContinuousEncoder |> LogisticClassifier\nmach = machine(model, X, y)\n\n#-\n\nr = range(model, :(logistic_classifier.lambda), lower = 1e-2, upper=100, scale=:log10)\n\n# If you're curious, you can see what `lambda` values this range will\n# generate for a given resolution:\n\niterator(r, 5)\n\n#-\n\nusing Plots\ngr(size=(490,300))\n_, _, lambdas, losses = learning_curve(mach,\n                                       range=r,\n                                       resampling=CV(nfolds=6),\n                                       resolution=30, # default\n                                       measure=cross_entropy)\nplt=plot(lambdas, losses, xscale=:log10)\nxlabel!(plt, \"lambda\")\nylabel!(plt, \"cross entropy using 6-fold CV\")\nsavefig(\"learning_curve2.png\")\nplt #!md\n\n# ![](learning_curve2.png) #md\n\nbest_lambda = lambdas[argmin(losses)]\n\n\n# ### Self tuning models\n\n# A more sophisticated way to view hyper-parameter tuning (inspired by\n# MLR) is as a model *wrapper*. The wrapped model is a new model in\n# its own right and when you fit it, it tunes specified\n# hyper-parameters of the model being wrapped, before training on all\n# supplied data. Calling `predict` on the wrapped model is like\n# calling `predict` on the original model, but with the\n# hyper-parameters already optimized.\n\n# In other words, we can think of the wrapped model as a \"self-tuning\"\n# version of the original.\n\n# We now create a self-tuning version of the pipeline above, adding a\n# parameter from the `ContinuousEncoder` to the parameters we want\n# optimized.\n\n# First, let's choose a tuning strategy (from [these\n# options](https://github.com/juliaai/MLJTuning.jl#what-is-provided-here)). MLJ\n# supports ordinary `Grid` search (query `?Grid` for\n# details). However, as the utility of `Grid` search is limited to a\n# small number of parameters, and as `Grid` searches are demonstrated\n# elsewhere (see the [resources below](#resources-for-part-4)) we'll\n# demonstrate `RandomSearch` here:\n\ntuning = RandomSearch(rng=123)\n\n# In this strategy each parameter is sampled according to a\n# pre-specified prior distribution that is fit to the one-dimensional\n# range object constructed using `range` as before. While one has a\n# lot of control over the specification of the priors (run\n# `?RandomSearch` for details) we'll let the algorithm generate these\n# priors automatically.\n\n\n# #### Unbounded ranges and sampling\n\n# In MLJ a range does not have to be bounded. In a `RandomSearch` a\n# positive unbounded range is sampled using a `Gamma` distribution, by\n# default:\n\nr = range(model,\n          :(logistic_classifier.lambda),\n          lower=0,\n          origin=6,\n          unit=5,\n          scale=:log10)\n\n# The `scale` in a range is ignored in a `RandomSearch`, unless it is a\n# function. (It *is* relevant in a `Grid` search, not demonstrated\n# here.) Note however, the choice of scale *does* effect how later plots\n# will look.\n\n# Let's see what sampling using a Gamma distribution is going to mean\n# for this range:\n\nimport Distributions\nsampler_r = sampler(r, Distributions.Gamma)\nplt = histogram(rand(sampler_r, 10000), nbins=50)\nsavefig(\"gamma_sampler.png\")\nplt #!md\n\n# ![](gamma_sampler.png) #md\n\n# The second parameter that we'll add to this is *nominal* (finite) and, by\n# default, will be sampled uniformly. Since it is nominal, we specify\n# `values` instead of `upper` and `lower` bounds:\n\ns  = range(model, :(continuous_encoder.one_hot_ordered_factors),\n           values = [true, false])\n\n\n# #### The tuning wrapper\n\n# Now for the wrapper, which is an instance of `TunedModel`:\n\ntuned_model = TunedModel(model=model,\n                         ranges=[r, s],\n                         resampling=CV(nfolds=6),\n                         measures=cross_entropy,\n                         tuning=tuning,\n                         n=15)\n\n# We can apply the `fit!/predict` work-flow to `tuned_model` just as\n# for any other model:\n\ntuned_mach = machine(tuned_model, X, y);\nfit!(tuned_mach);\npredict(tuned_mach, rows=1:3)\n\n# The outcomes of the tuning can be inspected from a detailed\n# report. For example, we have:\n\nrep = report(tuned_mach);\nrep.best_model\n\n# In the special case of two-parameters, you can also plot the results:\n\nplt = plot(tuned_mach)\nsavefig(\"tuning.png\")\nplt #!md\n\n# ![](tuning.png) #md\n\n# Finally, let's compare cross-validation estimate of the performance\n# of the self-tuning model with that of the original model (an example\n# of [*nested\n# resampling*]((https://mlr.mlr-org.com/articles/tutorial/nested_resampling.html)\n# here):\n\nerr = evaluate!(mach, resampling=CV(nfolds=3), measure=cross_entropy)\n\n#-\n\ntuned_err = evaluate!(tuned_mach, resampling=CV(nfolds=3), measure=cross_entropy)\n\n# <a id='resources-for-part-4'></a>\n\n\n# ### Resources for Part 4\n#\n# - From the MLJ manual:\n#    - [Learning Curves](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)\n#    - [Tuning Models](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/)\n# - The [MLJTuning repo](https://github.com/juliaai/MLJTuning.jl#who-is-this-repo-for) - mostly for developers\n#\n# - From Data Science Tutorials:\n#     - [Tuning a model](https://juliaai.github.io/DataScienceTutorials.jl/getting-started/model-tuning/)\n#     - [Crabs with XGBoost](https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/crabs-xgb/) `Grid` tuning in stages for a tree-boosting model with many parameters\n#     - [Boston with LightGBM](https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/boston-lgbm/) -  `Grid` tuning for another popular tree-booster\n#     - [Boston with Flux](https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/boston-flux/) - optimizing batch size in a simple neural network regressor\n# - [UCI Horse Colic Data Set](http://archive.ics.uci.edu/ml/datasets/Horse+Colic)\n\n\n# ### Exercises for Part 4\n\n# #### Exercise 8\n\n# This exercise continues our analysis of the King County House price\n# prediction problem (Part 1, Exercise 3 and Part 2):\n\nhouse_csv = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                        \"MachineLearningInJulia2020/for-MLJ-version-0.16/\"*\n                        \"data/house.csv\");\nhouse = DataFrames.DataFrame(house_csv)\ncoerce!(house, autotype(house_csv));\ncoerce!(house, Count => Continuous, :zipcode => Multiclass);\ny, X = unpack(house, ==(:price), rng=123);\nschema(X)\n\n# Your task will be to tune the following pipeline regression model,\n# which includes a gradient tree boosting component:\n\nEvoTreeRegressor = @load EvoTreeRegressor\ntree_booster = EvoTreeRegressor(nrounds = 70)\nmodel = ContinuousEncoder |> tree_booster\n\n# (a) Construct a bounded range `r1` for the `evo_tree_booster`\n# parameter `max_depth`, varying between 1 and 12.\n\n# \\star&(b) For the `nbins` parameter of the `EvoTreeRegressor`, define the range\n\nr2 = range(model,\n           :(evo_tree_regressor.nbins),\n           lower = 2.5,\n           upper= 7.5, scale=x->2^round(Int, x))\n\n# Notice that in this case we've specified a *function* instead of a\n# canned scale, like `:log10`. In this case the `scale` function is\n# applied after sampling (uniformly) between the limits of `lower` and\n# `upper`. Perhaps you can guess the outputs of the following lines of\n# code?\n\nr2_sampler = sampler(r2, Distributions.Uniform)\nsamples = rand(r2_sampler, 1000);\nplt = histogram(samples, nbins=50)\nsavefig(\"uniform_sampler.png\")\n\nplt #!md\n\n# ![](uniform_sampler.png)\n\nsort(unique(samples))\n\n# (c) Optimize `model` over these the parameter ranges `r1` and `r2`\n# using a random search with uniform priors (the default). Use\n# `Holdout()` resampling, and implement your search by first\n# constructing a \"self-tuning\" wrap of `model`, as described\n# above. Make `mae` (mean absolute error) the loss function that you\n# optimize, and search over a total of 40 combinations of\n# hyper-parameters.  If you have time, plot the results of your\n# search. Feel free to use all available data.\n\n# (d) Evaluate the best model found in the search using 3-fold\n# cross-validation and compare with that of the self-tuning model\n# (which is different!). Setting data hygiene concerns aside, feel\n# free to use all available data.\n\n# <a id='part-5-advanced-model-composition'>\n", "meta": {"hexsha": "d8dc606c487b3c94a5635d5399fb5e29f660c0f5", "size": 10245, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/04_tuning/notebook.jl", "max_stars_repo_name": "roland-KA/MLJTutorial.jl", "max_stars_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2021-11-02T10:46:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:36:21.000Z", "max_issues_repo_path": "notebooks/04_tuning/notebook.jl", "max_issues_repo_name": "roland-KA/MLJTutorial.jl", "max_issues_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2021-11-11T14:36:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-14T04:31:54.000Z", "max_forks_repo_path": "notebooks/04_tuning/notebook.jl", "max_forks_repo_name": "roland-KA/MLJTutorial.jl", "max_forks_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2021-11-11T12:08:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-30T14:27:30.000Z", "avg_line_length": 34.4949494949, "max_line_length": 172, "alphanum_fraction": 0.6979014153, "num_tokens": 2654, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3738758227716966, "lm_q2_score": 0.16238003666671086, "lm_q1q2_score": 0.06070996981046479}}
{"text": "using InstantiateFromURL\nactivate_github(\"QuantEcon/QuantEconLectureAllPackages\", tag = \"v0.9.5\");\n", "meta": {"hexsha": "840593ddcb997a5bb1c968b5a6c81eb1a872635e", "size": 99, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "rst_files/_static/includes/alldeps_no_using.jl", "max_stars_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_stars_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "rst_files/_static/includes/alldeps_no_using.jl", "max_issues_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_issues_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rst_files/_static/includes/alldeps_no_using.jl", "max_forks_repo_name": "aderdzyan/QuantEcon-lecture-source-jl", "max_forks_repo_head_hexsha": "2a2b79ac6544d0de58abcb588026ff35a022246b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0, "max_line_length": 73, "alphanum_fraction": 0.8181818182, "num_tokens": 31, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.35936414516010196, "lm_q2_score": 0.16885695841556153, "lm_q1q2_score": 0.060681136515343154}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Julia for Data Science - Data\n# Prepared by [@nassarhuda](https://github.com/nassarhuda)! \ud83d\ude03\n#\n# In the next few notebooks, we will discuss why *Julia* is the tool you want to use for\n# your data science applications.\n#\n# We will cover the following:\n# 1. Reading and writing files\n# 1. DataFrames\n# 1. RDatasets\n# 1. FileIO\n# 1. File types\n#\n#\n# ### Data: Build a strong relationship with your data.\n# Every data science task has one main ingredient, the _data_! Most likely, you want to use\n# your data to learn something new. But before the _new_ part, what about the data you\n# already have? Let's make sure you can **read** it, **store** it, and **understand** it\n# before you start using it.\n#\n# Julia makes this step really easy with data structures and packages to process the data,\n# as well as existing functions that are readily usable on your data.\n#\n# The goal of this first part is get you acquainted with some Julia's tools to manage your\n# data.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# **Reading and writing to files is really easy in Julia.** <br>\n#\n# To see this, let's download a csv file from github that we can work with.\n#\n# Note: `download` depends on external tools such as curl, wget or fetch. So you must have\n# one of these.\n# ------------------------------------------------------------------------------------------\n\nP = download(\"https://raw.githubusercontent.com/nassarhuda/easy_data/master/programming_languages.csv\",\"programminglanguages.csv\")\n\n# ------------------------------------------------------------------------------------------\n# We can use shell commands like `ls` in Julia by preceding them with a semicolon.\n# ------------------------------------------------------------------------------------------\n\n;ls\n\n# ------------------------------------------------------------------------------------------\n# And there's the *.csv file we downloaded!\n#\n# By default, `readdlm` will fill an array with the data stored in the input .csv file. If\n# we set the keyword argument `header` to `true`, we'll get a second output array for the\n# headers.\n# ------------------------------------------------------------------------------------------\n\nusing DelimitedFiles\nP,H = readdlm(\"programminglanguages.csv\",header=true)\n\nP\n\n# ------------------------------------------------------------------------------------------\n# You can use different delimiters with the function `readdlm`.\n#\n# To write to files, we can use `writedlm`. <br>\n#\n# Let's write this same data to a file with a different delimiter.\n# ------------------------------------------------------------------------------------------\n\nwritedlm(\"programming_languages_data.txt\", P, '-')\n\n# ------------------------------------------------------------------------------------------\n# We can now check that this worked using a shell command to glance at the file,\n# ------------------------------------------------------------------------------------------\n\n;head -10 programming_languages_data.txt\n\n# ------------------------------------------------------------------------------------------\n# and also check that we can use `readdlm` to read our new text file correctly.\n# ------------------------------------------------------------------------------------------\n\nP_new_delim = readdlm(\"programming_languages_data.txt\", '-');\nP == P_new_delim\n\n# ------------------------------------------------------------------------------------------\n# ### DataFrames!\n# *Shout out to R fans!*\n# One other way to play around with data in Julia is to use a DataFrame.\n#\n# This requires loading the `DataFrames` package.\n#\n# Run this command to install all the packages used in the \"Julia for Data Science\" project\n# -- (those packages are listed in this file: [`Project.toml`](/edit/introductory-\n# tutorials/broader-topics-and-ecosystem/intro-to-julia-for-data-science/Project.toml)):\n# ------------------------------------------------------------------------------------------\n\n] instantiate\n\nusing DataFrames\ndf = DataFrame(year = P[:,1], language = P[:,2])\n\n# ------------------------------------------------------------------------------------------\n# You can access columns by header name, or column index.\n#\n# In this case, `df[1]` is equivalent to `df.year` or `df[!, :year]`.\n#\n# Note that if we want to index columns by header name, we precede the header name with a\n# colon. In Julia, this means that the header names are treated as *symbols*.\n# ------------------------------------------------------------------------------------------\n\ndf.year\n\n# ------------------------------------------------------------------------------------------\n# **`DataFrames` provides some handy features when dealing with data**\n#\n# First, it uses julia's \"missing\" type.\n# ------------------------------------------------------------------------------------------\n\na = missing\ntypeof(a)\n\n# ------------------------------------------------------------------------------------------\n# Let's see what happens when we try to add a \"missing\" type to a number\n# ------------------------------------------------------------------------------------------\n\na + 1\n\n# ------------------------------------------------------------------------------------------\n# ### RDatasets\n#\n# We can use RDatasets to play around with pre-existing datasets\n# ------------------------------------------------------------------------------------------\n\nusing RDatasets\niris = dataset(\"datasets\", \"iris\")\n\n# ------------------------------------------------------------------------------------------\n# Note that data loaded with `dataset` is stored as a DataFrame. \ud83d\ude03\n# ------------------------------------------------------------------------------------------\n\ntypeof(iris) \n\n# ------------------------------------------------------------------------------------------\n# `DataFrames` provides the `describe` can give you quick statistics about each column in\n# your dataframe\n# ------------------------------------------------------------------------------------------\n\ndescribe(iris)\n\n# ------------------------------------------------------------------------------------------\n# You can create your own dataframe quickly as follows\n# ------------------------------------------------------------------------------------------\n\nfoods = [\"apple\", \"cucumber\", \"tomato\", \"banana\"]\ncalories = [missing,47,22,105]\ntypeof(calories)\n\nusing Statistics\nmean(calories)\n\n# ------------------------------------------------------------------------------------------\n# `missing` ruins everything! \ud83d\ude11\n# ------------------------------------------------------------------------------------------\n\nmean(skipmissing(calories))\n\n# ------------------------------------------------------------------------------------------\n# In fact, `describe' will drop these values too\n# ------------------------------------------------------------------------------------------\n\ndescribe(DataFrame(c=calories))\n\n# ------------------------------------------------------------------------------------------\n# Note that `typeof(calories)` is `Vector{Union{Missing, Int64},1}`\n#\n# If we want to replace all `missing` values a default value, we can do it like this:\n# ------------------------------------------------------------------------------------------\n\nnewcalories = replace(calories, missing => 0)\n\n# ------------------------------------------------------------------------------------------\n# Now let's create a `DataFrame` that shows foods and their calories from two `DataArray`s!\n# ------------------------------------------------------------------------------------------\n\ndataframe_calories = DataFrame(item=foods,calories=calories)\n\n# ------------------------------------------------------------------------------------------\n# Let's generate a second `DataFrame` that shows foods and their prices.\n# ------------------------------------------------------------------------------------------\n\nprices = [0.85,1.6,0.8,0.6,]\n\ndataframe_prices = DataFrame(item=foods,price=prices)\n\n# ------------------------------------------------------------------------------------------\n# We can also `join` these two dataframes together because they share a common column,\n# `item`.\n# ------------------------------------------------------------------------------------------\n\nDF = join(dataframe_calories,dataframe_prices,on=:item)\n\n# ------------------------------------------------------------------------------------------\n# Note that we used the keyword argument `on` to say that we wanted to join these dataframes\n# together based on the `item` column.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### FileIO\n# ------------------------------------------------------------------------------------------\n\nusing FileIO\njulialogo = download(\"https://avatars0.githubusercontent.com/u/743164?s=200&v=4\",\"julialogo.png\")\n\n# ------------------------------------------------------------------------------------------\n# Again, let's check that this download worked!\n# ------------------------------------------------------------------------------------------\n\n;ls\n\n# ------------------------------------------------------------------------------------------\n# Next, let's load the Julia logo, stored as a .png file\n# ------------------------------------------------------------------------------------------\n\nX1 = load(\"julialogo.png\")\n\n# ------------------------------------------------------------------------------------------\n# We see below that Julia stores this logo as an array of colors.\n# ------------------------------------------------------------------------------------------\n\n@show typeof(X1);\n@show size(X1);\n\n# ------------------------------------------------------------------------------------------\n# ### File types\n# In Julia, many file types are supported so you do not have to transfer a file you got from\n# another language to a text file before you read it.\n#\n# *Some packages that achieve this:*\n# MAT CSV NPZ JLD FASTAIO\n#\n#\n# Let's try using MAT to write a file that stores a matrix.\n# ------------------------------------------------------------------------------------------\n\nusing MAT\n\nA = rand(5,5)\nmatfile = matopen(\"densematrix.mat\", \"w\") \nwrite(matfile, \"A\", A)\nclose(matfile)\n\n# ------------------------------------------------------------------------------------------\n# Now try opening densematrix.mat with MATLAB!\n# ------------------------------------------------------------------------------------------\n\nnewfile = matopen(\"densematrix.mat\")\nread(newfile,\"A\")\n\nnames(newfile)\n\nclose(newfile)\n\n\n", "meta": {"hexsha": "f224018eebf6c0d2d5458c7f96704959aefe9584", "size": 10922, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-for-data-science/short-version/01.Julia_for_data_science-Data.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", 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"max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 41.846743295, "max_line_length": 130, "alphanum_fraction": 0.3808826222, "num_tokens": 1684, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782351378493645, "lm_q2_score": 0.1384617870138417, "lm_q1q2_score": 0.06062182611534166}}
{"text": "using HorizonSideRobots\n\n#--------- \u0418\u0421\u041f\u041e\u041b\u042c\u0417\u0423\u0415\u041c\u042b\u0415 \"\u0411\u0418\u0411\u041b\u0418\u041e\u0422\u0415\u0427\u041d\u042b\u0415\" \u0424\u0423\u041d\u041a\u0426\u0418\u0418 \u0414\u041b\u042f \u0422\u0418\u041f\u0410 HorizonSideRobots.HorizonSide:\n\n\ninverse(side::HorizonSide) = HorizonSide(mod(Int(side)+2, 4))\nleft(side::HorizonSide) = HorizonSide(mod(Int(side)+1, 4))\nright(side::HorizonSide) = HorizonSide(mod(Int(side)-1, 4))\n\n#---------------- \u0412\u0421\u041f\u041e\u041c\u041e\u0413\u0410\u0422\u0415\u041b\u042c\u041d\u042b\u0419 \u041a\u041e\u041d\u041a\u0420\u0415\u0422\u041d\u042b\u0419 \u0422\u0418\u041f Coord: -----------------------------\n\n\nmutable struct Coord   # supertype(Coord) == Any\n    x::Int\n    y::Int \nend\n\nCoord() = Coord(0,0) \n# - \u044d\u0442\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0432\u043d\u0435\u0448\u043d\u0435\u0433\u043e (\u0432\u043d\u0435 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b) \u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0442\u043e\u0440\u0430 \u0442\u0438\u043f\u0430 CoordRobot (\u0434\u043b\u044f \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f), \n#   \u0433\u0434\u0435 Coord(0,0) - \u044d\u0442\u043e \u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0442\u043e\u0440 \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e\n\nimport HorizonSideRobots: move! \n# !!! \u044d\u0442\u043e\u0442 \u0438\u043c\u043f\u043e\u0440\u0442 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c \u0434\u043b\u044f \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u044f \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u043f\u0435\u0440\u0435\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0438 move!, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0443\u0436\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0432 \u043f\u0430\u043a\u0435\u0442\u0435 HorizonSideRobots,\n#   \u0438 \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u044b \u0441\u043e\u0431\u0438\u0440\u0430\u0435\u043c\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c, \u0434\u043b\u044f \u043d\u043e\u0432\u043e\u0433\u043e \u0442\u0438\u043f\u0430 (Coord)\n#   \u0415\u0441\u043b\u0438 \u044d\u0442\u043e\u0433\u043e \u043d\u0435 \u0441\u0434\u0435\u043b\u0430\u0442\u044c, \u0442\u043e \u043f\u0440\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0438 \u043f\u0430\u043a\u0435\u0442\u0430 HorizonSideRobots \u0432\u043e\u0437\u043d\u0438\u043a\u043d\u0435\u0442 \u043a\u043e\u043d\u0444\u043b\u0438\u043a\u0442 \u0438\u043c\u0451\u043d.\n#   \n\nfunction move!(coord::Coord, side::HorizonSide)\n    if side==Nord\n        coord.y += 1\n    elseif side==Sud\n        coord.y -= 1\n    elseif side==Ost\n        coord.x += 1\n    elseif side==West\n        coord.x -= 1\n    end\n    nothing\nend\n\nget_coord(coord::Coord) = (coord.x, coord.y)\n\n#----------------------- \u0418\u0415\u0420\u0410\u0420\u0425\u0418\u042f \u041f\u0420\u041e\u0415\u041a\u0422\u0418\u0420\u0423\u0415\u041c\u042b\u0425 \u0422\u0418\u041f\u041e\u0412: ------------------------------\n#=\nAny:\n|           try_move! \n|           movements! - \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 (4 \u0440\u0430\u0437\u043d\u044b\u0445 \u043c\u0435\u0442\u043e\u0434\u0430)\n|           shuttle! - \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \"\u0447\u0435\u043b\u043d\u043e\u043a\u043e\u043c\" \u0441 \u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u0435\u043c \u0430\u043c\u043f\u043b\u0438\u0442\u0443\u0434\u044b \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440, \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0441\u044f \u043d\u0435\u043a\u043e\u0440\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u0435 \u0443\u0441\u043b\u043e\u0432\u0438\u0435\n|           spiral! - \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u043f\u043e \u0440\u0430\u0441\u043a\u0440\u0443\u0447\u0438\u0432\u0430\u044e\u0449\u0435\u0439\u0441\u044f \u0441\u043f\u0438\u0440\u0430\u043b\u0438 \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440, \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0441\u044f \u043d\u0435\u043a\u043e\u0440\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u0435 \u0443\u0441\u043b\u043e\u0432\u0438\u0435\n|           snake!  - (2 \u043c\u0435\u0442\u043e\u0434\u0430) \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \"\u0437\u043c\u0435\u0439\u043a\u043e\u0439\" (\u043f\u043e \u0432\u0435\u0440\u0438\u043a\u0430\u043b\u044c\u043d\u044b\u043c \u0438\u043b\u0438 \u043f\u043e \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u043c \u0440\u044f\u0434\u0430\u043c) \n|                     \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440, \u043f\u043e\u043a\u0430 \u043d\u0435 \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u043e\u0439\u0434\u0435\u043d\u043e \u0432\u0441\u0435 \u043f\u043e\u043b\u0435, \u0438\u043b\u0438 \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0441\u044f \u043d\u0435\u043a\u043e\u0440\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u0435 \u0443\u0441\u043b\u043e\u0432\u0438\u0435\n|\n| # \u041a\u041e\u041d\u041a\u0420\u0415\u0422\u041d\u042b\u0415 \u0422\u0418\u041f\u042b:\n|           \n|-- CoordRobot (\u0442\u0438\u043f \u0440\u043e\u0431\u043e\u0442\u0430, \u043f\u0440\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u044f\u0445 \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u044e\u0449\u0435\u0433\u043e \u0441\u0432\u043e\u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b):\n|           move!, isborder, putmarker!, ismarker, temperature - \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u0439 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441 \u0440\u043e\u0431\u043e\u0442\u0430\n|           get_coord - \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0442\u0435\u043a\u0443\u0449\u0438\u0435 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0440\u043e\u0431\u043e\u0442\u0430\n|        \n| # \u041a\u041e\u041d\u041a\u0420\u0415\u0422\u041d\u042b\u0415 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0415 \u0422\u0418\u041f\u042b:\n|\n|-- BorderRobot{TypeRobot}\n|       (\u0437\u0434\u0435\u0441\u044c TypeRobot - \u044d\u0442\u043e \u0444\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440;\n|        c\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0439 \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0440\u0430\u0432\u0435\u043d \u0438\u043b\u0438 SimpleRobot, \u0438\u043b\u0438 CoordRobot)\n|           \n|           try_move! - \u043f\u0435\u0440\u0435\u043e\u043f\u0440\u0435\u0434\u044f\u043b\u044f\u0435\u043c\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f, \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u0432\u0430\u044e\u0449\u0430\u044f \u043e\u0431\u0445\u043e\u0434 \u043f\u0440\u043e\u0441\u0442\u044b\u0445 (\u043f\u0440\u044f\u043c\u043e\u043b\u0438\u043d\u0435\u0439\u043d\u044b\u0445) \u0438\u043b\u0438 \u043f\u0440\u044f\u043c\u043e\u0443\u0433\u043e\u043b\u044c\u043d\u044b\u0445 \u043f\u0435\u0440\u0435\u0433\u043e\u0440\u043e\u0434\u043e\u043a\n|           (\u043f\u043e\u0441\u043b\u0435 \u043a\u0430\u0436\u0434\u043e\u0439 \u0443\u0434\u0430\u0447\u043d\u043e\u0439 \u043f\u043e\u043f\u044b\u0442\u043a\u0438 \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0449\u0430\u0433 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438, \u0432\u044b\u0437\u044b\u0432\u0430\u044e\u0449\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044e action!(), )\n|       \n|--   \n|       (\u0437\u0434\u0435\u0441\u044c TypeRobot - \u044d\u0442\u043e \u0444\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440;\n|       c\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u0439 \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0440\u0430\u0432\u0435\u043d \u0438\u043b\u0438 SimpleRobot, \u0438\u043b\u0438 CoordRobot, \u0438\u043b\u0438 BorderRobot{SimpleRobot}, \n|       \u0438\u043b\u0438 BorderRobot{CoordRobot})\n|\n|-- CountmarkersRobot{TypeRobot}\n|\n|-- MarkersCoordsRobot{TypeRobot}\n\n=#\n\n#------------------- \u041e\u0411\u041e\u0411\u0429\u0415\u041d\u041d\u042b\u0415 \u0424\u0423\u041d\u041a\u0426\u0418\u0418: ------------------------\n\"\"\"\ntry_move!(robot, side)::Bool\n\n-- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u043d\u0430 1 \u0448\u0430\u0433 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438, \u0435\u0441\u043b\u0438 \u044d\u0442\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u0432 \u044d\u0442\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 true, \u0432 \u043f\u0440\u043e\u0442\u0438\u0432\u043d\u043e\u043c - false \n\"\"\"\nfunction try_move!(robot, side)::Bool\n    if isborder(robot, side) # - \u044d\u0442\u043e \u0432\u044b\u0437\u043e\u0432 \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 (\u043a\u0442\u043e\u0440\u0430\u044f \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0432 \u043a\u0430\u043a\u043e\u043c-\u0442\u043e \u0438\u0437 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u043d\u044b\u0445 \u0442\u0438\u043f\u043e\u0432)\n        false\n    else\n        move!(robot, side) # - \u044d\u0442\u043e \u0432\u044b\u0437\u043e\u0432 \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 (\u043a\u0442\u043e\u0440\u0430\u044f \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0432 \u043a\u0430\u043a\u043e\u043c-\u0442\u043e \u0438\u0437 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u043d\u044b\u0445 \u0442\u0438\u043f\u043e\u0432)\n        true\n    end\nend\n\n\n\"\"\"\nmovements!(robot, side)\n\n-- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \"\u0434\u043e \u0443\u043f\u043e\u0440\u0430\" (\u043f\u043e\u043a\u0430 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e) \u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0447\u0438\u0441\u043b\u043e \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432\n\"\"\"\nfunction movements!(robot, side)\n    n=0\n\n    while try_move!(robot, side)\n        n += 1\n    end\n\n    return n\nend\n\n\"\"\"\nmovements!(robot, side)\n\n-- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0435 \u0431\u043e\u043b\u0435\u0435 \u0447\u0435\u043c \u043d\u0430 max_num_steps \u0448\u0430\u0433\u043e\u0432, \u043f\u043e\u043a\u0430 \u044d\u0442\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0447\u0438\u0441\u043b\u043e \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432\n\"\"\"\nfunction movements!(robot, side, max_num_steps::Integer)\n    n=0\n    while n < max_num_steps && try_move!(robot, side) # - \u0432 \u044d\u0442\u043e\u043c \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u043f\u043e\u0440\u044f\u0434\u043e\u043a \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u0432\u0430\u0436\u0435\u043d!\n        n += 1\n    end\n    return n\nend\n\n\n\"\"\"\nmovements!(stop_condition::Function, robot, side, max_num_steps) \n\n-- \u0434\u0435\u043b\u0430\u0435\u0442 \u043d\u0435 \u0431\u043e\u043b\u0435\u0435 \u0447\u0435\u043c max_num_steps \u0448\u0430\u0433\u043e\u0432 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438, \u043f\u043e\u043a\u0430 \u0432\u044b\u043f\u043e\u043d\u044f\u0435\u0442\u0441\u044f \u0443\u0441\u043b\u043e\u0432\u0438\u0435 !stop_condition()==true\", \n\u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0447\u0438\u0441\u043b\u043e \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432\n\n-- stop_condition - \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0431\u0435\u0437 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u043e\u0432, \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u044e\u0449\u0430\u044f \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u0443\u0441\u043b\u043e\u0432\u0438\u0435 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0430\n\"\"\"\nfunction movements!(stop_condition::Function, robot, side, max_num_steps)\n    n = 0\n    while n < max_num_steps && !stop_condition() && try_move!(robot, side) # - \u0432 \u044d\u0442\u043e\u043c \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u043f\u043e\u0440\u044f\u0434\u043e\u043a \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u043e\u0432 \u0432\u0430\u0436\u0435\u043d!\n        n += 1\n    end  \n    return n \nend\n\n\n\"\"\"\nmovements!(stop_condition::Function, robot, side) \n\n-- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 \u043f\u043e\u043a\u0430 \u0432\u043e\u0437\u043c\u043e\u0436\u0435\u043d try_move! \u0438 \u043f\u043e\u043a\u0430 \u0432\u044b\u043f\u043e\u043d\u044f\u0435\u0442\u0441\u044f \u0443\u0441\u043b\u043e\u0432\u0438\u0435 !stop_condition()==true\", \n\u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0447\u0438\u0441\u043b\u043e \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0448\u0430\u0433\u043e\u0432\n\n-- stop_condition - \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0431\u0435\u0437 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u043e\u0432, \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u044e\u0449\u0430\u044f \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\n\"\"\"\nfunction movements!(stop_condition::Function, robot, side) \n    n=0\n    while !stop_condition() && try_move!(robot,side)\n        n += 1\n    end\n    return n\nend\n\n\n\"\"\"\nshuttle!(stop_condition::Function, robot, side)\n\n- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \"\u0442\u0443\u0434\u0430-\u0441\u044e\u0434\u0430\" (\u0447\u0435\u043b\u043d\u043e\u043a\u043e\u043c), \u0441 \u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u0435\u043c \u0430\u043c\u043f\u043b\u0438\u0442\u0443\u0434\u044b, \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440 \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0441\u044f \u0443\u0441\u043b\u043e\u0432\u0438\u0435 condition()\n- side - \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0439\n\"\"\"\nfunction  shuttle!(stop_condition::Function, robot, side)\n    n=0 # \u0447\u0438\u0441\u043b\u043e \u0448\u0430\u0433\u043e\u0432 \u043e\u0442 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u044f\n    while !stop_condition()  # condition(robot) - \u043a\u0430\u043a \u0432\u0430\u0440\u0438\u0430\u043d\u0442\n        n += 1\n        movements!(() -> !stop_condition(), robot, side, n)\n        side = inverse(side)\n    end\nend\n\n\n\"\"\"\nspiral!(condition::Function, robot, side=Nord)\n\n- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u043e\u0442\u0430 \u043f\u043e \u0440\u0430\u0441\u043a\u0440\u0443\u0447\u0438\u0432\u0430\u044e\u0449\u0435\u0439\u0441\u044f \u0441\u043f\u0438\u043f\u0430\u043b\u0438 (\u0432\u043b\u0435\u0432\u043e), \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440 \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u0441\u044f \u0443\u0441\u043b\u043e\u0432\u0438\u0435 condition()\n\n- condition - \u044d\u0442\u043e condition(::HorizonSide)::Bool \n\n- side - \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0439\n\n\"\"\"\nfunction spiral!(condition::Function, robot, side = Nord)\n    n=1\n    while true\n        movements!(() -> !condition(side), robot, side, n)\n        if condition(side)\n            return\n        end        \n        side = left(side)\n        movements!(() -> !condition(side), robot, side, n)\n        if condition(side)\n            return\n        end        \n        side = left(side)\n        n += 1\n    end\nend\n\n\n\"\"\"\nsnake!(stop_condition::Function, robot, (next_row_side, move_side)::NTuple{2,HorizonSide} = (Nord, Ost))\n\n-- \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435 \u0437\u0438\u0435\u0439\u043a\u043e\u0439 \u043f\u043e\u043a\u0430 \u043f\u043e\u043a\u0430 \u043d\u0435 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u043e \u0443\u0441\u043b\u043e\u0432\u0438\u0435 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0430 (\u0438\u043b\u0438 \u043d\u0435 \u043f\u0440\u043e\u0439\u0434\u0435\u043d\u043d\u044b \u0432\u0441\u0435 \u0440\u044f\u0434\u044b \u0432 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 next_row_side)\n\nstop_condition - \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0431\u0435\u0437 \u0443\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430, \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u044e\u0449\u0430\u044f \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u0442 \u0443\u0441\u043b\u043e\u0432\u0438\u0435 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0430 \u043f\u0440\u0438 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0438 \u0437\u043c\u0435\u0439\u043a\u043e\u0439\n\n\"\"\"\nfunction snake!(stop_condition::Function, robot, (next_row_side, move_side)::NTuple{2,HorizonSide} = (Nord, Ost)) # - \u044d\u0442\u043e \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f\n    # \u0420\u043e\u0431\u043e\u0442 - \u0432 (inverse(next_row_side), inverse(move_side)) - \u0443\u0433\u043b\u0443 \u043f\u043e\u043b\u044f\n    movements!(stop_condition, robot, move_side)\n    while !stop_condition() && try_move!(robot, next_row_side)\n        move_side = inverse(move_side)\n        movements!(stop_condition, robot, move_side)\n    end\nend\n\n\"\"\"\nsnake!(robot, (next_row_side, move_side)::NTuple{2,HorizonSide} = (Nord, Ost))\n\n-- \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0430\u0435\u0442 \u0440\u043e\u0431\u0442\u0430 \"\u0437\u0438\u0435\u0439\u043a\u043e\u0439\" \u043f\u043e\u043a\u0430 \u043d\u0435 \u0431\u0443\u0434\u0443\u0442 \u043f\u0440\u043e\u0439\u0434\u0435\u043d\u043d\u044b \u0432\u0441\u0435 \u0440\u044f\u0434\u044b \u0432 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438 next_row_side\n\"\"\"\nfunction snake!(robot, (next_row_side, move_side)::NTuple{2,HorizonSide}=(Nord, Ost)) # - \u044d\u0442\u043e \u043e\u0431\u043e\u0431\u0449\u0435\u043d\u043d\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f\n    # \u0420\u043e\u0431\u043e\u0442 - \u0432 (inverse(next_row_side), inverse(move_side)) - \u0443\u0433\u043b\u0443 \u043f\u043e\u043b\u044f\n    snake!(() -> false, robot, (next_row_side, move_side))\nend\n#----------------------- \u041a\u041e\u041d\u041a\u0420\u0415\u0422\u041d\u042b\u0419 \u0422\u0418\u041f CoordRobot: ----------------------\n\nstruct CoordRobot\n    robot::Robot\n    coord::Coord\nend\n\n# \u0422\u0438\u043f CoordRobot \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u041a\u041e\u041c\u041f\u041e\u0417\u0418\u0426\u0418\u042e \u0442\u0438\u043f\u043e\u0432.\n# \u042d\u0442\u043e \u0435\u0441\u0442\u044c \u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u0430 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c\u0443 \u043d\u0430\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044e, \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0432 \u044f\u0437\u044b\u043a\u0435 Julia \u043d\u0435\u0442.\n\nimport HorizonSideRobots: move!, isborder, putmarker!, ismarker, temperature\n\nfunction move!(robot::CoordRobot, side)\n    move!(robot.robot, side)\n    move!(robot.coord, side)\nend\n\nisborder(robot::CoordRobot, side) = isborder(robot.robot, side)\nputmarker!(robot::CoordRobot) = putmarker!(robot.robot)\nismarker(robot::CoordRobot) = ismarker(robot.robot)\ntemperature(robot::CoordRobot) = temperature(robot.robot)\n\nget_coord(robot::CoordRobot) = robot.coord\n\n#-------------------- \u041a\u041e\u041d\u041a\u0420\u0415\u0422\u041d\u042b\u0415 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0415 \u0422\u0418\u041f\u042b: -----------------------------\n\n\n# \u041a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0439 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0419 \u0442\u0438\u043f BorderRobot{TypeRobot}:\n\nimport HorizonSideRobots: move!, isborder, putmarker!, ismarker, temperature\n\nstruct BorderRobot{TypeRobot}  # TypeRobot = Robot | CoordRobot | ...?\n    robot::TypeRobot\nend\n# robot= BorderRobot{TypeRobot}(robot_of_TypeRobot)\n\n#get(robot::BorderRobot) = robot.robot\n\n\"\"\"\ntry_move!(robot::BorderRobot, side::HorizonSide)::Bool \n\n-- \u0434\u0435\u043b\u0430\u0435\u0442 \u043f\u043e\u043f\u044b\u0442\u043a\u0443 \u043f\u0440\u044f\u043c\u043e\u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0433\u043e \u043f\u0435\u0440\u0435\u043c\u0435\u0449\u0435\u043d\u0438\u0435 \u0440\u043e\u0431\u043e\u0442\u0430 \u0432 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u043c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0438, \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u043f\u044b\u0442\u0430\u044f\u0441\u044c \u043e\u0431\u043e\u0439\u0442\u0438 \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u044e\u044e \u043f\u0440\u044f\u043c\u043e\u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \n\u0438\u043b\u0438 \u043f\u0440\u044f\u043c\u043e\u0443\u0433\u043e\u043b\u044c\u043d\u0443\u044e \u043f\u0435\u0440\u0435\u0433\u043e\u0440\u043e\u0434\u043a\u0443, \u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442, \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0443\u0441\u043f\u0435\u0445\u0430, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 true, \u0438\u043b\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 - false, \u0435\u0441\u043b\u0438 \u0440\u043e\u0431\u043e\u0442 \u0443\u043f\u0438\u0440\u0430\u0435\u0442\u0441\u044f \u0432\u043e \u0432\u043d\u0435\u0448\u043d\u044e\u044e \u0440\u0430\u043c\u043a\u0443 \n\"\"\"\nfunction try_move!(robot::BorderRobot, side::HorizonSide)::Bool\n    ort_side = left(side)\n    n = movements!(() -> !isborder(robot.robot, side), robot.robot, ort_side)\n    if isborder(r, side)\n        movements!(robot.robot, inverse(ort_side), n)\n        return false\n    end\n    move!(robot.robot, side)\n    if n > 0 \n        movements!(() -> !isborder(robot.robot, inverse(ort_side)), robot.robot, side)\n        movements!(robot.robot, inverse(ort_side), n)\n    end\n    return true\nend\n\nimport HorizonSideRobots: move!, isborder, putmarker!, ismarker, temperature\n\nmove!(robot::BorderRobot,side) = move!(robot.robot,side)\nisborder(robot::BorderRobot, side) = isborder(robot.robot, side)\nputmarker!(robot::BorderRobot) = putmarker!(robot.robot)\nismarker(robot::BorderRobot) = ismarker(robot.robot)\ntemperature(robot::BorderRobot) = temperature(robot.robot)\n\n#=\n\u041f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043e\u0442 \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 TypeRobot \u0442\u0438\u043f\u0430 BorderRobot \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0438 try_move! \u0432 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043d\u0435 \u0437\u0430\u0432\u0438\u0441\u0438\u0442, \u0442\u043e\n\u0432 \u0430\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u0438 \u0442\u0438\u043f\u0430 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430 robot \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 TypeRobot \u043d\u0435 \u0443\u043a\u0430\u0437\u0430\u043d\u043e.\n\n\u0417\u0410\u041c\u0415\u0427\u0410\u041d\u0418\u0415. \n\n\u0412 \u043f\u0440\u0438\u043d\u0446\u0438\u043f\u0435, \u043f\u0440\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0438 \u0434\u0430\u043d\u043d\u043e\u0439 \u0444\u0443\u043d\u043a\u0446\u0438\u0438, \u0443\u043a\u0430\u0437\u0430\u0442\u044c \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u0444\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 \u0442\u0438\u043f\u0430 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043b\u044e\u0431\u044b\u043c, \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0435\u0449\u0451 \u0438 \n\u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u044f \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0435 \u0441\u043b\u043e\u0432\u043e where:\n\nfunction try_move!(robot::BorderRobot{TypeRobot}, side::HorizonSide)::Bool where TypeRobot\n    ...\nend\n\n\u0418 \u044d\u0442\u043e \u0431\u044b\u043b\u043e \u0431\u044b \u0432 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0442\u043e \u0436\u0435 \u0441\u0430\u043c\u043e\u0435, \u043d\u043e \u0432 \u0434\u0430\u043d\u043d\u043e\u043c \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043b\u0443\u0447\u0448\u0435 \u043e\u0431\u043e\u0439\u0442\u0438\u0441\u044c \u043f\u0440\u043e\u0441\u0442\u043e \u0431\u0435\u0437 \u0443\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430.\n\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0436\u0435 \u043a\u043b\u044e\u0447\u0435\u0432\u043e\u0433\u043e \u0441\u043b\u043e\u0432\u0430 where \u0431\u044b \u043e\u043f\u0440\u0430\u0432\u0434\u0430\u043d\u043e, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0432 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u0438, \u043a\u043e\u0433\u0434\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0435\u043c\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0438\u043c\u0435\u0435\u0442 2 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430 \n\u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0442\u0438\u043f\u0430, \u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0443\u043a\u0430\u0437\u0430\u0442\u044c, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 \u0442\u0438\u043f\u0430 \u0438 \u0442\u0430\u043c \u0438 \u0442\u0430\u043c \u0434\u043e\u043b\u0436\u043d\u043e \u0431\u044b\u0442\u044c \u043e\u0434\u043d\u043e \u0438 \u0442\u043e\u0436\u0435 (\u0445\u043e\u0442\u044f \u0438 \u043d\u0435 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u043c):\n\nfunction f(x::Rational{T}, y::Rational{T}) where {T <: Unsigned}\n    ...\nend\n=#\n\n#--------------------- \u041a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0439 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0419 \u0442\u0438\u043f PutmarkerRobot: ---------------------\n\nstruct PutmarkersRobot{TypeRobot} # TypeRobot = Robot | CoordRobot | ...?\n    robot::TypeRobot\nend\n\nget(robot::PutmarkersRobot) = robot.robot\n\nfunction try_move!(robot::PutmarkersRobot, side)::Bool\n    result = try_move!(robot.robot, side)\n    if result\n        putmarker!(robot.robot) # \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u0430\u0433\u0430\u0435\u0442\u0441\u044f, \u0447\u0442\u043e \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0433\u043e \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u0442\u0438\u043f\u0430 TypeRobot \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d \u043c\u0435\u0442\u043e\u0434 putmarker!\n    end\n    return result\nend\n\nimport HorizonSideRobots: move!, isborder, putmarker!, ismarker, temperature\n\nmove!(robot::PutmarkersRobot,side) = move!(robot.robot,side)\nisborder(robot::PutmarkersRobot, side) = isborder(robot.robot, side)\nputmarker!(robot::PutmarkersRobot) = putmarker!(robot.robot)\nismarker(robot::PutmarkersRobot) = ismarker(robot.robot)\ntemperature(robot::PutmarkersRobot) = temperature(robot.robot)\n\n#------------------ \u041a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0439 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0419 \u0442\u0438\u043f CountmarkersRobot: ------------------------------\n\nmutable struct CountmarkersRobot{TypeRobot}\n    robot::TypeRobot\n    count::Int\nend\n# TypeRobot = Robot | CoordRobot | BorderRobot{Robot} | BorderRobot{CoordRobot} | ...?\n\nCountmarkersRobot{TypeRobot}(r::Robot) where TypeRobot =  CountmarkersRobot{TypeRobot}(TypeRobot(r))\n# \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0442\u044c \u044d\u0442\u043e\u0442 \u0432\u043d\u0435\u0448\u043d\u0438\u0439 \u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0442\u043e\u0440 \u043d\u0435 \u043e\u0431\u044f\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u043e, \u044d\u0442\u043e \u0441\u0434\u0435\u043b\u0430\u043d\u043e \u043f\u0440\u043e\u0441\u0442\u043e \u0434\u043b\u044f \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f, \u0441\u043c. \u0442\u0430\u043a\u0436\u0435 \u0437\u0430\u043c\u0435\u0447\u0430\u043d\u0438\u0435 \u043d\u0438\u0436\u0435.\n\nfunction move!(robot::CountmarkersRobot, side)\n    if try_move!(robot.robot, side)\n        robot.count += 1\n    end\nend\n\nget_num_markers(robot::CountmarkersRobot) = robot.count\n\n\n#--------------- \u041a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0439 \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0418\u0427\u0415\u0421\u041a\u0418\u0419 \u0442\u0438\u043f MarkersCoordsRobot: ------------------------------\n\nmutable struct MarkersCoordsRobot{TypeRobot}\n    robot::TypeRobot\n    coord::Vector{NTuple{2,Int}}\nend\n# TypeRobot = CoordRobot | BorderRobot{CoordRobot} | ...?\n\n# \u041f\u0440\u0435\u0434\u043f\u043e\u043b\u0430\u0433\u0430\u0435\u0442\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u043a\u043e\u043d\u0441\u0442\u0440\u0443\u043a\u0442\u043e\u0440 \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e\n\nfunction move!(robot::MarkersCoordsRobot, side)\n    if try_move!(robot.robot, side) && ismarker(robot.robot)\n        push!(robot.count, get_coord(robot.robot))\n    end\nend\n\nget_coord(robot::MarkersCoordsRobot) = robot.coord\n\n#-----------------------------------", 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{"text": "module OffsetArrays\n\nusing Base: tail, @propagate_inbounds\n@static if !isdefined(Base, :IdentityUnitRange)\n    const IdentityUnitRange = Base.Slice\nelse\n    using Base: IdentityUnitRange\nend\n\nexport OffsetArray, OffsetMatrix, OffsetVector\n\nconst IIUR = IdentityUnitRange{<:AbstractUnitRange{<:Integer}}\n\ninclude(\"axes.jl\")\ninclude(\"utils.jl\")\ninclude(\"origin.jl\")\n\n# Technically we know the length of CartesianIndices but we need to convert it first, so here we\n# don't put it in OffsetAxisKnownLength.\nconst OffsetAxisKnownLength = Union{Integer, AbstractUnitRange}\nconst OffsetAxis = Union{OffsetAxisKnownLength, Colon}\nconst ArrayInitializer = Union{UndefInitializer, Missing, Nothing}\n\n## OffsetArray\n\"\"\"\n    OffsetArray(A, indices...)\n\nReturn an `AbstractArray` that shares element type and size with the first argument but\nuses the supplied `indices` to infer its axes. If all the indices are `AbstractUnitRange`s then\nthese are directly used as the axis span along each dimension. Refer to the examples below for other\npermissible types.\n\nAlternatively it's possible to specify the coordinates of one corner of the array\nand have the axes be computed automatically from the size of `A`.\nThis constructor makes it convenient to shift to\nan arbitrary starting index along each axis, for example to a zero-based indexing scheme followed by\narrays in languages such as C and Python.\nSee [`Origin`](@ref) and the examples below for this usage.\n\n# Example: offsets\n\nThere are two types of `indices`: integers and ranges-like types.\n\nIntegers are recognized as offsets, where `0` means no offsets are applied:\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> A = OffsetArray(reshape(1:6, 2, 3), -1, -2)\n2\u00d73 OffsetArray(reshape(::UnitRange{$Int}, 2, 3), 0:1, -1:1) with eltype $Int with indices 0:1\u00d7-1:1:\n 1  3  5\n 2  4  6\n\njulia> A[0, 1]\n5\n```\n\nExamples of range-like types are: `UnitRange` (e.g, `-1:2`), `CartesianIndices`,\nand `Colon()` (or concisely `:`). A `UnitRange` specifies the axis span along one particular dimension,\n`CartesianIndices` specify the axis spans along multiple dimensions, and a `Colon` is a placeholder\nthat specifies that the `OffsetArray` shares its axis with its parent along that dimension.\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> OffsetArray(reshape(1:6, 2, 3), 0:1, -1:1)\n2\u00d73 OffsetArray(reshape(::UnitRange{$Int}, 2, 3), 0:1, -1:1) with eltype $Int with indices 0:1\u00d7-1:1:\n 1  3  5\n 2  4  6\n\njulia> OffsetArray(reshape(1:6, 2, 3), :, -1:1) # : as a placeholder to indicate that no offset is to be applied to the first dimension\n2\u00d73 OffsetArray(reshape(::UnitRange{$Int}, 2, 3), 1:2, -1:1) with eltype $Int with indices 1:2\u00d7-1:1:\n 1  3  5\n 2  4  6\n```\n\nUse `CartesianIndices` to specify the coordinates of two diagonally opposite corners:\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> OffsetArray(reshape(1:6, 2, 3), CartesianIndex(0, -1):CartesianIndex(1, 1))\n2\u00d73 OffsetArray(reshape(::UnitRange{$Int}, 2, 3), 0:1, -1:1) with eltype $Int with indices 0:1\u00d7-1:1:\n 1  3  5\n 2  4  6\n```\n\nIntegers and range-like types may not be combined in the same call:\n\n```julia\njulia> OffsetArray(reshape(1:6, 2, 3), 0, -1:1)\nERROR: [...]\n```\n\n# Example: origin\n\n[`OffsetArrays.Origin`](@ref) may be used to specify the origin of the OffsetArray. The term origin here\nrefers to the corner with the lowest values of coordinates, such as the left edge for an `AbstractVector`,\nthe bottom left corner for an `AbstractMatrix` and so on. The coordinates of the origin sets the starting\nindex of the array along each dimension.\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> a = [1 2; 3 4];\n\njulia> OffsetArray(a, OffsetArrays.Origin(0, 1))\n2\u00d72 OffsetArray(::$(Array{Int,2}), 0:1, 1:2) with eltype $Int with indices 0:1\u00d71:2:\n 1  2\n 3  4\n\njulia> OffsetArray(a, OffsetArrays.Origin(0)) # set the origin to zero along each dimension\n2\u00d72 OffsetArray(::$(Array{Int, 2}), 0:1, 0:1) with eltype $Int with indices 0:1\u00d70:1:\n 1  2\n 3  4\n```\n\n\n\"\"\"\nstruct OffsetArray{T,N,AA<:AbstractArray{T,N}} <: AbstractArray{T,N}\n    parent::AA\n    offsets::NTuple{N,Int}\n    @inline function OffsetArray{T, N, AA}(parent::AA, offsets::NTuple{N, Int}; checkoverflow = true) where {T, N, AA<:AbstractArray{T,N}}\n        # allocation of `map` on tuple is optimized away\n        checkoverflow && map(overflow_check, axes(parent), offsets)\n        new{T, N, AA}(parent, offsets)\n    end\nend\n\n\"\"\"\n    OffsetVector(v, index)\n\nType alias and convenience constructor for one-dimensional [`OffsetArray`](@ref)s.\n\"\"\"\nconst OffsetVector{T,AA<:AbstractVector{T}} = OffsetArray{T,1,AA}\n\n\"\"\"\n    OffsetMatrix(A, index1, index2)\n\nType alias and convenience constructor for two-dimensional [`OffsetArray`](@ref)s.\n\"\"\"\nconst OffsetMatrix{T,AA<:AbstractMatrix{T}} = OffsetArray{T,2,AA}\n\n# checks if the offset may be added to the range without overflowing\nfunction overflow_check(r::AbstractUnitRange, offset::Integer)\n    Base.hastypemax(eltype(r)) || return nothing\n    # This gives some performance boost https://github.com/JuliaLang/julia/issues/33273\n    throw_upper_overflow_error(val) = throw(OverflowError(\"offset should be <= $(typemax(Int) - val) corresponding to the axis $r, received an offset $offset\"))\n    throw_lower_overflow_error(val) = throw(OverflowError(\"offset should be >= $(typemin(Int) - val) corresponding to the axis $r, received an offset $offset\"))\n\n    # With ranges in the picture, first(r) might not necessarily be < last(r)\n    # we therefore use the min and max of first(r) and last(r) to check for overflow\n    firstlast_min, firstlast_max = minmax(first(r), last(r))\n\n    if offset > 0 && firstlast_max > typemax(Int) - offset\n        throw_upper_overflow_error(firstlast_max)\n    elseif offset < 0 && firstlast_min < typemin(Int) - offset\n        throw_lower_overflow_error(firstlast_min)\n    end\n    return nothing\nend\n\n# Tuples of integers are treated as offsets\n# Empty Tuples are handled here\n@inline function OffsetArray(A::AbstractArray, offsets::Tuple{Vararg{Integer}}; kw...)\n    _checkindices(A, offsets, \"offsets\")\n    OffsetArray{eltype(A), ndims(A), typeof(A)}(A, offsets; kw...)\nend\n\n# These methods are necessary to disallow incompatible dimensions for\n# the OffsetVector and the OffsetMatrix constructors\nfor (FT, ND) in ((:OffsetVector, :1), (:OffsetMatrix, :2))\n    @eval @inline function $FT(A::AbstractArray{<:Any,$ND}, offsets::Tuple{Vararg{Integer}}; kw...)\n        _checkindices(A, offsets, \"offsets\")\n        OffsetArray{eltype(A), $ND, typeof(A)}(A, offsets; kw...)\n    end\n    FTstr = string(FT)\n    @eval @inline function $FT(A::AbstractArray, offsets::Tuple{Vararg{Integer}}; kw...)\n        throw(ArgumentError($FTstr*\" requires a \"*string($ND)*\"D array\"))\n    end\nend\n\n## OffsetArray constructors\nfor FT in (:OffsetArray, :OffsetVector, :OffsetMatrix)\n    # Nested OffsetArrays may strip off the wrapper and collate the offsets\n    # empty tuples are handled here\n    @eval @inline function $FT(A::OffsetArray, offsets::Tuple{Vararg{Int}}; checkoverflow = true)\n        _checkindices(A, offsets, \"offsets\")\n        # ensure that the offsets may be added together without an overflow\n        checkoverflow && map(overflow_check, axes(A), offsets)\n        I = map(+, _offsets(A, parent(A)), offsets)\n        $FT(parent(A), I, checkoverflow = false)\n    end\n    @eval @inline function $FT(A::OffsetArray, offsets::Tuple{Integer,Vararg{Integer}}; kw...)\n        $FT(A, map(Int, offsets); kw...)\n    end\n\n    # In general, indices get converted to AbstractUnitRanges.\n    # CartesianIndices{N} get converted to N ranges\n    @eval @inline function $FT(A::AbstractArray, inds::Tuple{Any,Vararg{Any}}; kw...)\n        $FT(A, _toAbstractUnitRanges(to_indices(A, axes(A), inds)); kw...)\n    end\n\n    # convert ranges to offsets\n    @eval @inline function $FT(A::AbstractArray, inds::Tuple{AbstractUnitRange,Vararg{AbstractUnitRange}}; kw...)\n        _checkindices(A, inds, \"indices\")\n        # Performance gain by wrapping the error in a function: see https://github.com/JuliaLang/julia/issues/37558\n        throw_dimerr(lA, lI) = throw(DimensionMismatch(\"supplied axes do not agree with the size of the array (got size $lA for the array and $lI for the indices\"))\n        lA = size(A)\n        lI = map(length, inds)\n        lA == lI || throw_dimerr(lA, lI)\n        $FT(A, map(_offset, axes(A), inds); kw...)\n    end\n\n    @eval @inline $FT(A::AbstractArray, inds::Vararg; kw...) = $FT(A, inds; kw...)\n    @eval @inline $FT(A::AbstractArray; checkoverflow = false) = $FT(A, ntuple(zero, Val(ndims(A))), checkoverflow = checkoverflow)\n\n    @eval @inline $FT(A::AbstractArray, origin::Origin; checkoverflow = true) = $FT(A, origin(A); checkoverflow = checkoverflow)\nend\n\n# conversion-related methods\n@inline OffsetArray{T}(M::AbstractArray, I...; kw...) where {T} = OffsetArray{T,ndims(M)}(M, I...; kw...)\n\n@inline function OffsetArray{T,N}(M::AbstractArray{<:Any,N}, I...; kw...) where {T,N}\n    M2 = _of_eltype(T, M)\n    OffsetArray{T,N}(M2, I...; kw...)\nend\n@inline OffsetArray{T,N}(M::OffsetArray{T,N}, I...; kw...) where {T,N} = OffsetArray(M, I...; kw...)\n@inline OffsetArray{T,N}(M::AbstractArray{T,N}, I...; kw...) where {T,N} = OffsetArray{T,N,typeof(M)}(M, I...; kw...)\n\n@inline OffsetArray{T,N,A}(M::AbstractArray{<:Any,N}, I::Vararg; kw...) where {T,N,A<:AbstractArray{T,N}} = OffsetArray{T,N,A}(M, I; kw...)\n@inline function OffsetArray{T,N,A}(M::AbstractArray{<:Any,N}, I::NTuple{N,Int}; checkoverflow = true) where {T,N,A<:AbstractArray{T,N}}\n    checkoverflow && map(overflow_check, axes(M), I)\n    Mv = no_offset_view(M)\n    MvA = convert(A, Mv)::A\n    Iof = map(+, _offsets(M), I)\n    OffsetArray{T,N,A}(MvA, Iof, checkoverflow = false)\nend\n@inline function OffsetArray{T, N, AA}(parent::AbstractArray{<:Any,N}, offsets::NTuple{N, Integer}; kw...) where {T, N, AA<:AbstractArray{T,N}}\n    OffsetArray{T, N, AA}(parent, map(Int, offsets)::NTuple{N,Int}; kw...)\nend\n@inline function OffsetArray{T,N,A}(M::AbstractArray{<:Any,N}, I::Tuple{AbstractUnitRange,Vararg{AbstractUnitRange}}; kw...) where {T,N,A<:AbstractArray{T,N}}\n    _checkindices(M, I, \"indices\")\n    # Performance gain by wrapping the error in a function: see https://github.com/JuliaLang/julia/issues/37558\n    throw_dimerr(lA, lI) = throw(DimensionMismatch(\"supplied axes do not agree with the size of the array (got size $lA for the array and $lI for the indices\"))\n    lM = size(M)\n    lI = map(length, I)\n    lM == lI || throw_dimerr(lM, lI)\n    OffsetArray{T,N,A}(M, map(_offset, axes(M), I); kw...)\nend\n@inline function OffsetArray{T,N,A}(M::AbstractArray{<:Any,N}, I::Tuple; kw...) where {T,N,A<:AbstractArray{T,N}}\n    OffsetArray{T,N,A}(M, _toAbstractUnitRanges(to_indices(M, axes(M), I)); kw...)\nend\n@inline function OffsetArray{T,N,A}(M::AbstractArray{<:Any,N}; kw...) where {T,N,A<:AbstractArray{T,N}}\n    Mv = no_offset_view(M)\n    MvA = convert(A, Mv)::A\n    OffsetArray{T,N,A}(MvA, _offsets(M); kw...)\nend\n@inline OffsetArray{T,N,A}(M::A; checkoverflow = false) where {T,N,A<:AbstractArray{T,N}} = OffsetArray{T,N,A}(M, ntuple(zero, Val(N)); checkoverflow = checkoverflow)\n\nBase.convert(::Type{T}, M::AbstractArray) where {T<:OffsetArray} = M isa T ? M : T(M)\n\n@inline AbstractArray{T,N}(M::OffsetArray{S,N}) where {T,S,N} = OffsetArray{T}(M)\n\n# array initialization\n@inline function OffsetArray{T,N}(init::ArrayInitializer, inds::Tuple{Vararg{OffsetAxisKnownLength}}; kw...) where {T,N}\n    _checkindices(N, inds, \"indices\")\n    AA = Array{T,N}(init, map(_indexlength, inds))\n    OffsetArray{T, N, typeof(AA)}(AA, map(_indexoffset, inds); kw...)\nend\n@inline function OffsetArray{T, N}(init::ArrayInitializer, inds::Tuple; kw...) where {T, N}\n    OffsetArray{T, N}(init, _toAbstractUnitRanges(inds); kw...)\nend\n@inline OffsetArray{T,N}(init::ArrayInitializer, inds::Vararg; kw...) where {T,N} = OffsetArray{T,N}(init, inds; kw...)\n\n@inline OffsetArray{T}(init::ArrayInitializer, inds::NTuple{N, OffsetAxisKnownLength}; kw...) where {T,N} = OffsetArray{T,N}(init, inds; kw...)\n@inline function OffsetArray{T}(init::ArrayInitializer, inds::Tuple; kw...) where {T}\n    OffsetArray{T}(init, _toAbstractUnitRanges(inds); kw...)\nend\n@inline OffsetArray{T}(init::ArrayInitializer, inds::Vararg; kw...) where {T} = OffsetArray{T}(init, inds; kw...)\n\nBase.IndexStyle(::Type{OA}) where {OA<:OffsetArray} = IndexStyle(parenttype(OA))\nparenttype(::Type{OffsetArray{T,N,AA}}) where {T,N,AA} = AA\nparenttype(A::OffsetArray) = parenttype(typeof(A))\n\nBase.parent(A::OffsetArray) = A.parent\n\n# TODO: Ideally we would delegate to the parent's broadcasting implementation, but that\n#       is currently broken in sufficiently many implementation, namely RecursiveArrayTools, DistributedArrays\n#       and StaticArrays, that it will take concentrated effort to get this working across the ecosystem.\n#       The goal would be to have `OffsetArray(CuArray) .+ 1 == OffsetArray{CuArray}`.\n# Base.Broadcast.BroadcastStyle(::Type{<:OffsetArray{<:Any, <:Any, AA}}) where AA = Base.Broadcast.BroadcastStyle(AA)\n\n@inline Base.size(A::OffsetArray) = size(parent(A))\n\n@inline Base.axes(A::OffsetArray) = map(IdOffsetRange, axes(parent(A)), A.offsets)\n@inline Base.axes(A::OffsetArray, d) = d <= ndims(A) ? IdOffsetRange(axes(parent(A), d), A.offsets[d]) : IdOffsetRange(axes(parent(A), d))\n@inline Base.axes1(A::OffsetArray{T,0}) where {T} = IdOffsetRange(axes(parent(A), 1))  # we only need to specialize this one\n\n# Issue 128\n# See https://github.com/JuliaLang/julia/issues/37274 for the issue reported in Base\n# The fix https://github.com/JuliaLang/julia/pull/39404 should be available on v1.6\n# The following method is added on older Julia versions to ensure correct behavior for OffsetVectors\nif VERSION <\u00a0v\"1.6\"\n    @inline function Base.compute_linindex(A::OffsetVector, I::NTuple{N,Any}) where N\n        IP = Base.fill_to_length(axes(A), Base.OneTo(1), Val(N))\n        Base.compute_linindex(first(LinearIndices(A)), 1, IP, I)\n    end\nend\n\n# Utils to translate a function to the parent while preserving offsets\nunwrap(x) = x, identity\nunwrap(x::OffsetArray) = parent(x), data -> OffsetArray(data, x.offsets, checkoverflow = false)\nfunction parent_call(f, x)\n    parent, wrap_offset = unwrap(x)\n    wrap_offset(f(parent))\nend\n\nBase.similar(A::OffsetArray, ::Type{T}, dims::Dims) where T =\n    similar(parent(A), T, dims)\nfunction Base.similar(A::AbstractArray, ::Type{T}, shape::Tuple{OffsetAxisKnownLength,Vararg{OffsetAxisKnownLength}}) where T\n    # strip IdOffsetRanges to extract the parent range and use it to generate the array\n    new_shape = map(_strip_IdOffsetRange, shape)\n    # route through _similar_axes_or_length to avoid a stack overflow if map(_strip_IdOffsetRange, shape) === shape\n    # This tries to use new_shape directly in similar if similar(A, T, ::typeof(new_shape)) is defined\n    # If this fails, it calls similar(A, T, map(_indexlength, new_shape)) to use the size along each axis\n    # to generate the new array\n    P = _similar_axes_or_length(A, T, new_shape, shape)\n    return OffsetArray(P, map(_offset, axes(P), shape))\nend\nfunction Base.similar(::Type{T}, shape::Tuple{OffsetAxisKnownLength,Vararg{OffsetAxisKnownLength}}) where {T<:AbstractArray}\n    new_shape = map(_strip_IdOffsetRange, shape)\n    P = _similar_axes_or_length(T, new_shape, shape)\n    OffsetArray(P, map(_offset, axes(P), shape))\nend\n# Try to use the axes to generate the parent array type\n# This is useful if the axes have special meanings, such as with static arrays\n# This method is hit if at least one axis provided to similar(A, T, axes) is an IdOffsetRange\n# For example this is hit when similar(A::OffsetArray) is called,\n# which expands to similar(A, eltype(A), axes(A))\n_similar_axes_or_length(A, T, ax, ::Any) = similar(A, T, ax)\n_similar_axes_or_length(AT, ax, ::Any) = similar(AT, ax)\n# Handle the general case by resorting to lengths along each axis\n# This is hit if none of the axes provided to similar(A, T, axes) are IdOffsetRanges,\n# and if similar(A, T, axes::AX) is not defined for the type AX.\n# In this case the best that we can do is to create a mutable array of the correct size\n_similar_axes_or_length(A, T, ax::I, ::I) where {I} = similar(A, T, map(_indexlength, ax))\n_similar_axes_or_length(AT, ax::I, ::I) where {I} = similar(AT, map(_indexlength, ax))\n\n# reshape accepts a single colon\nBase.reshape(A::AbstractArray, inds::OffsetAxis...) = reshape(A, inds)\nfunction Base.reshape(A::AbstractArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}})\n    AR = reshape(no_offset_view(A), map(_indexlength, inds))\n    O = OffsetArray(AR, map(_offset, axes(AR), inds))\n    return _popreshape(O, axes(AR), _filterreshapeinds(inds))\nend\n\n# Reshaping OffsetArrays can \"pop\" the original OffsetArray wrapper and return\n# an OffsetArray(reshape(...)) instead of an OffsetArray(reshape(OffsetArray(...)))\n# Short-circuit for AbstractVectors if the axes are compatible to get around the Base restriction\n# to 1-based vectors\nfunction _reshape(A::AbstractVector, inds::Tuple{OffsetAxis})\n    @noinline throw_dimerr(ind::Integer) = throw(\n        DimensionMismatch(\"parent has $(size(A,1)) elements, which is incompatible with length $ind\"))\n    @noinline throw_dimerr(ind) = throw(\n        DimensionMismatch(\"parent has $(size(A,1)) elements, which is incompatible with indices $ind\"))\n    _checksize(first(inds), size(A,1)) || throw_dimerr(first(inds))\n    A\nend\n_reshape(A, inds) = _reshape2(A, inds)\n_reshape2(A, inds) = reshape(A, inds)\n# avoid a stackoverflow by relegating to the parent if no_offset_view returns an offsetarray\n_reshape2(A::OffsetArray, inds) = reshape(parent(A), inds)\n_reshape_nov(A, inds) = _reshape(no_offset_view(A), inds)\n\nBase.reshape(A::OffsetArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) =\n    OffsetArray(_reshape(parent(A), inds), map(_toaxis, inds))\n# And for non-offset axes, we can just return a reshape of the parent directly\nBase.reshape(A::OffsetArray, inds::Tuple{Union{Integer,Base.OneTo},Vararg{Union{Integer,Base.OneTo}}}) = _reshape_nov(A, inds)\nBase.reshape(A::OffsetArray, inds::Dims) = _reshape_nov(A, inds)\nBase.reshape(A::OffsetVector, ::Colon) = A\nBase.reshape(A::OffsetVector, ::Tuple{Colon}) = A\nBase.reshape(A::OffsetArray, ::Colon) = reshape(A, (Colon(),))\nBase.reshape(A::OffsetArray, inds::Union{Int,Colon}...) = reshape(A, inds)\nBase.reshape(A::OffsetArray, inds::Tuple{Vararg{Union{Int,Colon}}}) = _reshape_nov(A, inds)\n\n# permutedims in Base does not preserve axes, and can not be fixed in a non-breaking way\n# This is a stopgap solution\nBase.permutedims(v::OffsetVector) = reshape(v, (1, axes(v, 1)))\n\nBase.fill(v, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(similar(Array{typeof(v)}, inds), v)\nBase.zeros(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =\n    fill!(similar(Array{T}, inds), zero(T))\nBase.ones(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =\n    fill!(similar(Array{T}, inds), one(T))\nBase.trues(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(similar(BitArray, inds), true)\nBase.falses(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(similar(BitArray, inds), false)\n\nBase.zero(A::OffsetArray) = parent_call(zero, A)\nBase.fill!(A::OffsetArray, x) = parent_call(Ap -> fill!(Ap, x), A)\n\n## Indexing\n\n# Note this gets the index of the parent *array*, not the index of the parent *range*\n# Here's how one can think about this:\n#   \u0394i = i - first(r)\n#   i\u2032 = first(r.parent) + \u0394i\n# and one obtains the result below.\nparentindex(r::IdOffsetRange, i) = i - r.offset\n\n@propagate_inbounds Base.getindex(A::OffsetArray{<:Any,0})  = A.parent[]\n\n@inline function Base.getindex(A::OffsetArray{<:Any,N}, I::Vararg{Int,N}) where N\n    @boundscheck checkbounds(A, I...)\n    J = map(parentindex, axes(A), I)\n    @inbounds parent(A)[J...]\nend\n\n@propagate_inbounds Base.getindex(A::OffsetArray{<:Any,N}, c::Vararg{Colon,N}) where N =\n    parent_call(x -> getindex(x, c...), A)\n\n# With one Colon we use linear indexing.\n# In this case we may forward the index to the parent, as the information about the axes is lost\n# The exception to this is with OffsetVectors where the axis information is preserved,\n# but that case is handled by getindex(::OffsetArray{<:Any,N}, ::Vararg{Colon,N})\n@propagate_inbounds Base.getindex(A::OffsetArray, c::Colon) = A.parent[:]\n\n@inline function Base.getindex(A::OffsetVector, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds parent(A)[parentindex(Base.axes1(A), i)]\nend\n@propagate_inbounds Base.getindex(A::OffsetArray, i::Int)  = parent(A)[i]\n\n@inline function Base.setindex!(A::OffsetArray{T,N}, val, I::Vararg{Int,N}) where {T,N}\n    @boundscheck checkbounds(A, I...)\n    J = map(parentindex, axes(A), I)\n    @inbounds parent(A)[J...] = val\n    A\nend\n\n@inline function Base.setindex!(A::OffsetVector, val, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds parent(A)[parentindex(Base.axes1(A), i)] = val\n    A\nend\n@propagate_inbounds function Base.setindex!(A::OffsetArray, val, i::Int)\n    parent(A)[i] = val\n    A\nend\n\n@inline Base.iterate(a::OffsetArray, i...) = iterate(parent(a), i...)\n\nBase.in(x, A::OffsetArray) = in(x, parent(A))\nBase.copy(A::OffsetArray) = parent_call(copy, A)\n\nBase.strides(A::OffsetArray) = strides(parent(A))\nBase.elsize(::Type{OffsetArray{T,N,A}}) where {T,N,A} = Base.elsize(A)\n@inline Base.unsafe_convert(::Type{Ptr{T}}, A::OffsetArray{T}) where {T} = Base.unsafe_convert(Ptr{T}, parent(A))\n\n# For fast broadcasting: ref https://discourse.julialang.org/t/why-is-there-a-performance-hit-on-broadcasting-with-offsetarrays/32194\nBase.dataids(A::OffsetArray) = Base.dataids(parent(A))\nBroadcast.broadcast_unalias(dest::OffsetArray, src::OffsetArray) = parent(dest) === parent(src) ? src : Broadcast.unalias(dest, src)\n\n### Special handling for AbstractRange\nconst OffsetRange{T} = OffsetVector{T,<:AbstractRange{T}}\nconst OffsetUnitRange{T} = OffsetVector{T,<:AbstractUnitRange{T}}\n\nBase.step(a::OffsetRange) = step(parent(a))\n\nBase.checkindex(::Type{Bool}, inds::AbstractUnitRange, or::OffsetRange) = Base.checkindex(Bool, inds, parent(or))\n\n# Certain special methods for linear indexing with integer ranges (or OffsetRanges)\n# These may bypass the default getindex(A, I...) pathway if the parent types permit this\n# For example AbstractUnitRanges and Arrays have special linear indexing behavior defined\n\n# If both the arguments are offset, we may unwrap the indices to call (::OffsetArray)[::AbstractRange{Int}]\n@propagate_inbounds function Base.getindex(A::OffsetArray, r::OffsetRange{Int})\n    _indexedby(A[parent(r)], axes(r))\nend\n# If the indices are offset, we may unwrap them and pass the parent to getindex\n@propagate_inbounds function Base.getindex(A::AbstractRange, r::OffsetRange{Int})\n    _indexedby(A[parent(r)], axes(r))\nend\n\n# An OffsetUnitRange might use the rapid getindex(::Array, ::AbstractUnitRange{Int}) for contiguous indexing\n@propagate_inbounds function Base.getindex(A::Array, r::OffsetUnitRange{Int})\n    B = A[_contiguousindexingtype(parent(r))]\n    OffsetArray(B, axes(r), checkoverflow = false)\nend\n\n# avoid hitting the slow method getindex(::Array, ::AbstractRange{Int})\n# instead use the faster getindex(::Array, ::UnitRange{Int})\nif VERSION <= v\"1.7.0-DEV.1039\"\n    @propagate_inbounds function Base.getindex(A::Array, r::Union{IdOffsetRange, IIUR})\n        B = A[_contiguousindexingtype(r)]\n        _indexedby(B, axes(r))\n    end\nend\n\n# Linear Indexing of OffsetArrays with AbstractUnitRanges may use the faster contiguous indexing methods\n@inline function Base.getindex(A::OffsetArray, r::AbstractUnitRange{Int})\n    @boundscheck checkbounds(A, r)\n    # nD OffsetArrays do not have their linear indices shifted, so we may forward the indices provided to the parent\n    @inbounds B = parent(A)[_contiguousindexingtype(r)]\n    _indexedby(B, axes(r))\nend\n@inline function Base.getindex(A::OffsetVector, r::AbstractUnitRange{Int})\n    @boundscheck checkbounds(A, r)\n    # OffsetVectors may have their linear indices shifted, so we subtract the offset from the indices provided\n    @inbounds B = parent(A)[_subtractoffset(r, A.offsets[1])]\n    _indexedby(B, axes(r))\nend\n\n# This method added mainly to index an OffsetRange with another range\n@inline function Base.getindex(A::OffsetVector, r::AbstractRange{Int})\n    @boundscheck checkbounds(A, r)\n    @inbounds B = parent(A)[_subtractoffset(r, A.offsets[1])]\n    _indexedby(B, axes(r))\nend\n\n# In general we would pass through getindex(A, I...) which calls to_indices(A, I) and finally to_index(I)\n# An OffsetUnitRange{Int} has an equivalent IdOffsetRange with the same values and axes,\n# something similar also holds for OffsetUnitRange{BigInt}\n# We may replace the former with the latter in an indexing operation to obtain a performance boost\n@inline function Base.to_index(r::OffsetUnitRange{<:Union{Int,BigInt}})\n    of = first(axes(r,1)) - 1\n    IdOffsetRange(_subtractoffset(parent(r), of), of)\nend\n\n@inline function _boundscheck_index_retaining_axes(r, s)\n    @boundscheck checkbounds(r, s)\n    @inbounds pr = r[UnitRange(s)]\n    _indexedby(pr, axes(s))\nend\n@inline _boundscheck_return(r, s) = (@boundscheck checkbounds(r, s); s)\n\nfor OR in [:IIUR, :IdOffsetRange]\n    for R in [:StepRange, :StepRangeLen, :LinRange, :UnitRange]\n        @eval @inline Base.getindex(r::$R, s::$OR) = _boundscheck_index_retaining_axes(r, s)\n    end\n\n    # this method is needed for ambiguity resolution\n    @eval @inline function Base.getindex(r::StepRangeLen{T,<:Base.TwicePrecision,<:Base.TwicePrecision}, s::$OR) where T\n        _boundscheck_index_retaining_axes(r, s)\n    end\nend\nBase.getindex(r::Base.OneTo, s::IdOffsetRange) = _boundscheck_index_retaining_axes(r, s)\nif VERSION < v\"1.7.0-beta2\"\n    Base.getindex(r::Base.OneTo, s::IIUR) = _boundscheck_index_retaining_axes(r, s)\nend\n\n# These methods are added to avoid ambiguities with Base.\n# The ones involving Base types should be ported to Base and version-limited here\n@inline Base.getindex(r::IdentityUnitRange, s::IIUR) = _boundscheck_return(r, s)\n@inline Base.getindex(r::IdentityUnitRange, s::IdOffsetRange) = _boundscheck_return(r, s)\nif IdentityUnitRange !== Base.Slice\n    @inline Base.getindex(r::Base.Slice, s::IIUR) = _boundscheck_return(r, s)\n    @inline Base.getindex(r::Base.Slice, s::IdOffsetRange) = _boundscheck_return(r, s)\nend\n\n# eltype conversion\n# This may use specialized map methods for the parent\nBase.map(::Type{T}, O::OffsetArray) where {T} = parent_call(x -> map(T, x), O)\nBase.map(::Type{T}, r::IdOffsetRange) where {T<:Real} = _indexedby(map(T, UnitRange(r)), axes(r))\nif eltype(IIUR) === Int\n    # This is type-piracy, but there is no way to convert an IdentityUnitRange to a non-Int type in Base\n    Base.map(::Type{T}, r::IdentityUnitRange) where {T<:Real} = _indexedby(map(T, UnitRange(r)), axes(r))\nend\n\n# mapreduce is faster with an IdOffsetRange than with an OffsetUnitRange\n# We therefore convert OffsetUnitRanges to IdOffsetRanges with the same values and axes\nfunction Base.mapreduce(f, op, As::OffsetUnitRange{<:Integer}...; kw...)\n    ofs = map(A -> first(axes(A,1)) - 1, As)\n    AIds = map((A, of) -> IdOffsetRange(_subtractoffset(parent(A), of), of), As, ofs)\n    mapreduce(f, op, AIds...; kw...)\nend\n\n# Optimize certain reductions that treat an OffsetVector as a list\nfor f in [:minimum, :maximum, :extrema, :sum]\n    @eval Base.$f(r::OffsetRange) = $f(parent(r))\nend\n\nfunction Base.show(io::IO, r::OffsetRange)\n    show(io, r.parent)\n    print(io, \" with indices \", UnitRange(axes(r, 1)))\nend\nBase.show(io::IO, ::MIME\"text/plain\", r::OffsetRange) = show(io, r)\n\n\n### Some mutating functions defined only for OffsetVector ###\n\nBase.resize!(A::OffsetVector, nl::Integer) = (resize!(A.parent, nl); A)\nBase.push!(A::OffsetVector, x...) = (push!(A.parent, x...); A)\nBase.pop!(A::OffsetVector) = pop!(A.parent)\nBase.append!(A::OffsetVector, items) = (append!(A.parent, items); A)\nBase.empty!(A::OffsetVector) = (empty!(A.parent); A)\n\n# These functions keep the summary compact\nfunction Base.inds2string(inds::Tuple{Vararg{Union{IdOffsetRange, IdentityUnitRange{<:IdOffsetRange}}}})\n    Base.inds2string(map(UnitRange, inds))\nend\nBase.showindices(io::IO, ind1::IdOffsetRange, inds::IdOffsetRange...) = Base.showindices(io, map(UnitRange, (ind1, inds...))...)\n\nfunction Base.showarg(io::IO, @nospecialize(a::OffsetArray), toplevel)\n    print(io, \"OffsetArray(\")\n    Base.showarg(io, parent(a), false)\n    Base.showindices(io, axes(a)...)\n    print(io, ')')\n    if toplevel\n        print(io, \" with eltype \", eltype(a))\n    end\nend\n\nfunction Base.replace_in_print_matrix(A::OffsetArray{<:Any,2}, i::Integer, j::Integer, s::AbstractString)\n    J = map(parentindex, axes(A), (i,j))\n    Base.replace_in_print_matrix(parent(A), J..., s)\nend\nfunction Base.replace_in_print_matrix(A::OffsetArray{<:Any,1}, i::Integer, j::Integer, s::AbstractString)\n    ip = parentindex(axes(A,1), i)\n    Base.replace_in_print_matrix(parent(A), ip, j, s)\nend\n\n\"\"\"\n    no_offset_view(A)\n\nReturn an `AbstractArray` that shares structure and underlying data with the argument,\nbut uses 1-based indexing. May just return the argument when applicable.\nNot exported.\n\nThe default implementation uses `OffsetArrays`, but other types should use something more\nspecific to remove a level of indirection when applicable.\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> A = [1 3 5; 2 4 6];\n\njulia> O = OffsetArray(A, 0:1, -1:1)\n2\u00d73 OffsetArray(::$(Matrix{Int}), 0:1, -1:1) with eltype $Int with indices 0:1\u00d7-1:1:\n 1  3  5\n 2  4  6\n\njulia> OffsetArrays.no_offset_view(O)[1,1] = -9\n-9\n\njulia> A\n2\u00d73 $(Matrix{Int}):\n -9  3  5\n  2  4  6\n```\n\"\"\"\nno_offset_view(A::OffsetArray) = no_offset_view(parent(A))\nif isdefined(Base, :IdentityUnitRange)\n    # valid only if Slice is distinguished from IdentityUnitRange\n    no_offset_view(a::Base.Slice{<:Base.OneTo}) = a\n    no_offset_view(a::Base.Slice) = Base.Slice(UnitRange(a))\n    no_offset_view(S::SubArray) = view(parent(S), map(no_offset_view, parentindices(S))...)\nend\nno_offset_view(a::Array) = a\nno_offset_view(i::Number) = i\nno_offset_view(A::AbstractArray) = _no_offset_view(axes(A), A)\n_no_offset_view(::Tuple{}, A::AbstractArray{T,0}) where T = A\n_no_offset_view(::Tuple{Base.OneTo, Vararg{Base.OneTo}}, A::AbstractArray) = A\n# the following method is needed for ambiguity resolution\n_no_offset_view(::Tuple{Base.OneTo, Vararg{Base.OneTo}}, A::AbstractUnitRange) = A\n_no_offset_view(::Any, A::AbstractArray) = OffsetArray(A, Origin(1))\n_no_offset_view(::Any, A::AbstractUnitRange) = UnitRange(A)\n\n#####\n# center/centered\n# These two helpers are deliberately not exported; their meaning can be very different in\n# other scenarios and will be very likely to cause name conflicts if exported.\n#####\n\"\"\"\n    center(A, [r::RoundingMode=RoundDown])::Dims\n\nReturn the center coordinate of given array `A`. If `size(A, k)` is even,\na rounding procedure will be applied with mode `r`.\n\n!!! compat \"OffsetArrays 1.9\"\n    This method requires at least OffsetArrays 1.9.\n\n# Examples\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> A = reshape(collect(1:9), 3, 3)\n3\u00d73 $(Matrix{Int}):\n 1  4  7\n 2  5  8\n 3  6  9\n\njulia> c = OffsetArrays.center(A)\n(2, 2)\n\njulia> A[c...]\n5\n\njulia> Ao = OffsetArray(A, -2, -2); # axes (-1:1, -1:1)\n\njulia> c = OffsetArrays.center(Ao)\n(0, 0)\n\njulia> Ao[c...]\n5\n```\n\nTo shift the center coordinate of the given array to `(0, 0, ...)`, you\ncan use [`centered`](@ref OffsetArrays.centered).\n\"\"\"\nfunction center(A::AbstractArray, r::RoundingMode=RoundDown)\n    map(axes(A)) do inds\n        round(Int, (length(inds)-1)/2, r) + first(inds)\n    end\nend\n\n\"\"\"\n    centered(A, cp=center(A)) -> Ao\n\nShift the center coordinate/point `cp` of array `A` to `(0, 0, ..., 0)`. Internally, this is\nequivalent to `OffsetArray(A, .-cp)`.\n\n!!! compat \"OffsetArrays 1.9\"\n    This method requires at least OffsetArrays 1.9.\n\n# Examples\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> A = reshape(collect(1:9), 3, 3)\n3\u00d73 $(Matrix{Int}):\n 1  4  7\n 2  5  8\n 3  6  9\n\njulia> Ao = OffsetArrays.centered(A); # axes (-1:1, -1:1)\n\njulia> Ao[0, 0]\n5\n\njulia> Ao = OffsetArray(A, OffsetArrays.Origin(0)); # axes (0:2, 0:2)\n\njulia> Aoo = OffsetArrays.centered(Ao); # axes (-1:1, -1:1)\n\njulia> Aoo[0, 0]\n5\n```\n\nUsers are allowed to pass `cp` to change how \"center point\" is interpreted, but the meaning of the\noutput array should be reinterpreted as well. For instance, if `cp = map(last, axes(A))` then this\nfunction no longer shifts the center point but instead the bottom-right point to `(0, 0, ..., 0)`.\nA commonly usage of `cp` is to change the rounding behavior when the array is of even size at some\ndimension:\n\n```jldoctest; setup=:(using OffsetArrays)\njulia> A = reshape(collect(1:4), 2, 2) # Ideally the center should be (1.5, 1.5) but OffsetArrays only support integer offsets\n2\u00d72 $(Matrix{Int}):\n 1  3\n 2  4\n\njulia> OffsetArrays.centered(A, OffsetArrays.center(A, RoundUp)) # set (2, 2) as the center point\n2\u00d72 OffsetArray(::$(Matrix{Int}), -1:0, -1:0) with eltype $(Int) with indices -1:0\u00d7-1:0:\n 1  3\n 2  4\n\njulia> OffsetArrays.centered(A, OffsetArrays.center(A, RoundDown)) # set (1, 1) as the center point\n2\u00d72 OffsetArray(::$(Matrix{Int}), 0:1, 0:1) with eltype $(Int) with indices 0:1\u00d70:1:\n 1  3\n 2  4\n```\n\nSee also [`center`](@ref OffsetArrays.center).\n\"\"\"\ncentered(A::AbstractArray, cp::Dims=center(A)) = OffsetArray(A, .-cp)\n\ncentered(A::AbstractArray, i::CartesianIndex) = centered(A, Tuple(i))\n\n####\n# work around for segfault in searchsorted*\n#  https://github.com/JuliaLang/julia/issues/33977\n####\n\nfunction _safe_searchsorted(v::OffsetArray, x, ilo::T, ihi::T, o::Base.Ordering) where T<:Integer\n    u = T(1)\n    lo = ilo - u\n    hi = ihi + u\n    @inbounds while lo < hi - u\n        m = (lo + hi) \u00f7 2\n        if Base.lt(o, v[m], x)\n            lo = m\n        elseif Base.lt(o, x, v[m])\n            hi = m\n        else\n            a = searchsortedfirst(v, x, max(lo,ilo), m, o)\n            b = searchsortedlast(v, x, m, min(hi,ihi), o)\n            return a : b\n        end\n    end\n    return (lo + 1) : (hi - 1)\nend\nfunction _safe_searchsortedfirst(v::OffsetArray, x, lo::T, hi::T, o::Base.Ordering) where T<:Integer\n    u = T(1)\n    lo = lo - u\n    hi = hi + u\n    @inbounds while lo < hi - u\n        m = (lo + hi) \u00f7 2\n        if Base.lt(o, v[m], x)\n            lo = m\n        else\n            hi = m\n        end\n    end\n    return hi\nend\nfunction _safe_searchsortedlast(v::OffsetArray, x, lo::T, hi::T, o::Base.Ordering) where T<:Integer\n    u = T(1)\n    lo = lo - u\n    hi = hi + u\n    @inbounds while lo < hi - u\n        m = (lo + hi) \u00f7 2\n        if Base.lt(o, x, v[m])\n            hi = m\n        else\n            lo = m\n        end\n    end\n    return lo\nend\n\nif VERSION \u2264\u00a0v\"1.2\"\n    # ambiguity warnings in earlier versions\n    Base.searchsorted(v::OffsetArray, x, ilo::Int, ihi::Int, o::Base.Ordering) =\n        _safe_searchsorted(v, x, ilo, ihi, o)\n    Base.searchsortedfirst(v::OffsetArray, x, lo::Int, hi::Int, o::Base.Ordering) =\n        _safe_searchsortedfirst(v, x, lo, hi, o)\n    Base.searchsortedlast(v::OffsetArray, x, lo::Int, hi::Int, o::Base.Ordering) =\n        _safe_searchsortedlast(v, x, lo, hi, o)\nend\n\nBase.searchsorted(v::OffsetArray, x, ilo::T, ihi::T, o::Base.Ordering) where T<:Integer =\n    _safe_searchsorted(v, x, ilo, ihi, o)\nBase.searchsortedfirst(v::OffsetArray, x, lo::T, hi::T, o::Base.Ordering) where T<:Integer =\n    _safe_searchsortedfirst(v, x, lo, hi, o)\nBase.searchsortedlast(v::OffsetArray, x, lo::T, hi::T, o::Base.Ordering) where T<:Integer =\n    _safe_searchsortedlast(v, x, lo, hi, o)\n\nif VERSION < v\"1.1.0-DEV.783\"\n    Base.copyfirst!(dest::OffsetArray, src::OffsetArray) = (maximum!(parent(dest), parent(src)); return dest)\nend\n\nif VERSION <= v\"1.7.0-DEV.400\"\n    # https://github.com/JuliaLang/julia/pull/39393\n    # index for zero-argument getindex should be first linear index instead of 1 (#194)\n    Base._to_linear_index(A::OffsetArray) = first(LinearIndices(A))\nend\n\n##\n# Adapt allows for automatic conversion of CPU OffsetArrays to GPU OffsetArrays\n##\nimport Adapt\nAdapt.adapt_structure(to, O::OffsetArray) = parent_call(x -> Adapt.adapt(to, x), O)\n\nif Base.VERSION >= v\"1.4.2\"\n    include(\"precompile.jl\")\n    _precompile_()\nend\n\n\n##\n# Deprecations\n##\n\n# This is a bad API design as it introduces counter intuitive results (#250)\n@deprecate centered(A::AbstractArray, r::RoundingMode) OffsetArray(A, .-center(A, r)) false\n\nend # module\n", "meta": {"hexsha": "4336914fa5a2309a840d32e459ca74441bee2a8f", "size": 36485, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/OffsetArrays.jl", "max_stars_repo_name": "yakir12/OffsetArrays.jl", "max_stars_repo_head_hexsha": "7bc6918ec72b7dab0331264626c7b9d68eb134c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/OffsetArrays.jl", "max_issues_repo_name": "yakir12/OffsetArrays.jl", "max_issues_repo_head_hexsha": "7bc6918ec72b7dab0331264626c7b9d68eb134c1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/OffsetArrays.jl", "max_forks_repo_name": "yakir12/OffsetArrays.jl", "max_forks_repo_head_hexsha": "7bc6918ec72b7dab0331264626c7b9d68eb134c1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.0334101382, "max_line_length": 166, "alphanum_fraction": 0.6982595587, "num_tokens": 10756, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.12592276647524683, "lm_q1q2_score": 0.0605032043721619}}
{"text": "# Copyright (c) 2019 Arpit Bhatia and contributors                               #src\n#                                                                                #src\n# Permission is hereby granted, free of charge, to any person obtaining a copy   #src\n# of this software and associated documentation files (the \"Software\"), to deal  #src\n# in the Software without restriction, including without limitation the rights   #src\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #src\n# copies of the Software, and to permit persons to whom the Software is          #src\n# furnished to do so, subject to the following conditions:                       #src\n#                                                                                #src\n# The above copyright notice and this permission notice shall be included in all #src\n# copies or substantial portions of the Software.                                #src\n#                                                                                #src\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #src\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #src\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #src\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #src\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #src\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #src\n# SOFTWARE.                                                                      #src\n\n# # Getting started with JuMP\n\n# This tutorial is aimed at providing a quick introduction to writing JuMP code.\n# If you're new to Julia, you should start with [Getting started with Julia](@ref).\n\n# ## What is JuMP?\n\n# JuMP (\"Julia for Mathematical Programming\") is an open-source modeling\n# language that is embedded in Julia. It allows users to users formulate various\n# classes of optimization problems (linear, mixed-integer, quadratic, conic\n# quadratic, semidefinite, and nonlinear) with easy-to-read code. These problems\n# can then be solved using state-of-the-art open-source and commercial solvers.\n\n# JuMP also makes advanced optimization techniques easily accessible from a\n# high-level language.\n\n# ## Installation\n\n# JuMP is a package for Julia. From Julia, JuMP is installed by using the\n# built-in package manager.\n\n# ```julia\n# import Pkg\n# Pkg.add(\"JuMP\")\n# ```\n\n# You also need to include a Julia package which provides an appropriate solver.\n# One such solver is `GLPK.Optimizer`, which is provided by the\n# [GLPK.jl package](https://github.com/JuliaOpt/GLPK.jl).\n# ```julia\n# import Pkg\n# Pkg.add(\"GLPK\")\n# ```\n# See [Installation Guide](@ref) for a list of other solvers you can use.\n\n# ## An example\n\n# Let's try to solve the following linear programming problem by using JuMP and\n# GLPK. We will first look at the complete code to solve the problem and then go\n# through it step by step.\n\n# ```math\n# \\begin{aligned}\n# & \\min & 12x + 20y \\\\\n# & \\;\\;\\text{s.t.} & 6x + 8y \\geq 100 \\\\\n# & & 7x + 12y \\geq 120 \\\\\n# & & x \\geq 0 \\\\\n# & & y \\in [0, 3] \\\\\n# \\end{aligned}\n# ```\n\nusing JuMP\nusing GLPK\nmodel = Model(GLPK.Optimizer)\n@variable(model, x >= 0)\n@variable(model, 0 <= y <= 3)\n@objective(model, Min, 12x + 20y)\n@constraint(model, c1, 6x + 8y >= 100)\n@constraint(model, c2, 7x + 12y >= 120)\nprint(model)\noptimize!(model)\n@show termination_status(model)\n@show primal_status(model)\n@show dual_status(model)\n@show objective_value(model)\n@show value(x)\n@show value(y)\n@show shadow_price(c1)\n@show shadow_price(c2)\nnothing #hide\n\n# ## Step-by-step\n\n# Once JuMP is installed, to use JuMP in your programs, we just need to write:\n\nusing JuMP\n\n# We also need to include a Julia package which provides an appropriate solver.\n# We want to use `GLPK.Optimizer` here which is provided by the `GLPK.jl`\n# package.\n\nusing GLPK\n\n# A model object is a container for variables, constraints, solver options, etc.\n# Models are created with the [`Model`](@ref) function. The model can be created\n# with an optimizer attached with default arguments by calling the constructor\n# with the optimizer type, as follows:\n\nmodel = Model(GLPK.Optimizer)\n\n# Variables are modelled using [`@variable`](@ref):\n\n@variable(model, x >= 0)\n\n# They can have lower and upper bounds.\n\n@variable(model, 0 <= y <= 30)\n\n# The objective is set using [`@objective`](@ref):\n\n@objective(model, Min, 12x + 20y)\n\n# Constraints are modelled using [`@constraint`](@ref). Here `c1` and `c2` are\n# the names of our constraint.\n\n@constraint(model, c1, 6x + 8y >= 100)\n\n#-\n\n@constraint(model, c2, 7x + 12y >= 120)\n\n#- Call `print` to display the model:\n\nprint(model)\n\n# To solve the optimization problem, call the [`optimize!`] function.\n\noptimize!(model)\n\n# !!! info\n#     The `!` after optimize is just part of the name. It's nothing special.\n#     Julia has a convention that functions which mutate their arguments should\n#     end in `!`. A common example is `push!`.\n\n# Now let's see what information we can query about the solution.\n\n# [`termination_status`](@ref) tells us why the solver stopped:\n\ntermination_status(model)\n\n# In this case, the solver found an optimal solution. We should also check\n# [`primal_status`](@ref) to see if the solver found a primal feasible point:\n\nprimal_status(model)\n\n# and [`dual_status`](@ref) to see if the solver found a dual feasible point:\n\ndual_status(model)\n\n# Now we know that our solver found an optimal solution, and has a primal and a\n# dual solution to query.\n\n# Query the objective value using [`objective_value`](@ref):\n\nobjective_value(model)\n\n# The primal solution using [`value`](@ref):\n\nvalue(x)\n\n#-\n\nvalue(y)\n\n# and the dual solution using [`shadow_price`](@ref):\n\nshadow_price(c1)\n\n#-\n\nshadow_price(c2)\n", "meta": {"hexsha": "828f8d7c90e17be112cea3fd648160d901119878", "size": 5870, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/getting_started_with_JuMP.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lang/Julia/getting_started_with_JuMP.jl", "max_issues_repo_name": "ethansaxenian/RosettaDecode", "max_issues_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lang/Julia/getting_started_with_JuMP.jl", "max_forks_repo_name": "ethansaxenian/RosettaDecode", "max_forks_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2527472527, "max_line_length": 85, "alphanum_fraction": 0.6689948893, "num_tokens": 1441, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.1259227582746744, "lm_q1q2_score": 0.0605032004319617}}
{"text": "\"\"\"\n    ClimaCore.DataLayouts\n\nNotation:\n- `i,j` are horizontal node indices within an element\n- `k` is the vertical node index within an element\n- `f` is the field index\n- `v` is the vertical element index in a stack\n- `h` is the element stack index\n\nData layout is specified by the order in which they appear, e.g. `IJKFVH`\nindexes the underlying array as `[i,j,k,f,v,h]`\n\n\"\"\"\nmodule DataLayouts\n\nimport StaticArrays: SOneTo, MArray\n\n# TODO:\n#  - doc strings for each type\n#  - printing\n#  - should some of these be subtypes of AbstractArray?\n\nimport ..slab, ..column\nexport slab, column, IJFH, IJF, IFH, IF, VF, VIFH\n\ninclude(\"struct.jl\")\n\nabstract type AbstractData{S} end\n\nBase.size(data::AbstractData, i::Integer) = size(data)[i]\n\nfunction Base.show(io::IO, data::AbstractData)\n    indent_width = 2\n    (rows, cols) = displaysize(io)\n    println(io, summary(data))\n    print(io, \" \"^indent_width)\n    print(\n        IOContext(\n            io,\n            :compact => true,\n            :limit => true,\n            :displaysize => (rows, cols - indent_width),\n        ),\n        vec(parent(data)),\n    )\n    return io\nend\n\n\n\"\"\"\n    DataColumn{S}\n\nAbstract type for data storage for a column. Objects `data` should define a\n`data[k,v]`, returning a value of type `S`.\n\"\"\"\nabstract type DataColumn{S} <: AbstractData{S} end\n\n\"\"\"\n    DataSlab1D{S,Ni}\n\nAbstract type for data storage for a slab of `Ni` values of type `S`.\nObjects `data` should define a `data[i]`, returning a value of type `S`.\n\"\"\"\nabstract type DataSlab1D{S, Nij} <: AbstractData{S} end\n\n\"\"\"\n    DataSlab2D{S,Nij}\n\nAbstract type for data storage for a slab of `Nij \u00d7 Nij` values of type `S`.\nObjects `data` should define a `data[i,j]`, returning a value of type `S`.\n\"\"\"\nabstract type DataSlab2D{S, Nij} <: AbstractData{S} end\n\n\"\"\"\n    Data1D{S,Ni}\n\nAbstract type for data storage for a 1D field made up of `Ni` values of type `S`.\n\nObjects `data` should define `slab(data, h)` to return a `DataSlab2D{S,Nij}` object.\n\"\"\"\nabstract type Data1D{S, Ni} <: AbstractData{S} end\n\n\"\"\"\n    Data2D{S,Nij}\n\nAbstract type for data storage for a 2D field made up of `Nij \u00d7 Nij` values of type `S`.\n\nObjects `data` should define `slab(data, h)` to return a `DataSlab2D{S,Nij}` object.\n\"\"\"\nabstract type Data2D{S, Nij} <: AbstractData{S} end\n\n\"\"\"\n    Data1DX{S,Ni}\n\nAbstract type for data storage for a 1D field with extruded columns.\nThe horizontal is made up of `Ni` values of type `S`.\n\nObjects `data` should define `slab(data, v, h)` to return a\n`DataSlab1D{S,Ni}` object, and a `column(data, i, h)` to return a `DataColumn`.\n\"\"\"\nabstract type Data1DX{S, Ni} <: AbstractData{S} end\n\n\"\"\"\n    Data2DX{S,Nij}\n\nAbstract type for data storage for a 2D field with extruded columns.\nThe horizontal is made  is made up of `Nij \u00d7 Nij` values of type `S`.\n\n\nObjects `data` should define `slab(data, v, h)` to return a\n`DataSlab2D{S,Nij}` object, and a `column(data, i, j, h)` to return a `DataColumn`.\n\"\"\"\nabstract type Data2DX{S, Nij} <: AbstractData{S} end\n\n\"\"\"\n    Data3D{S,Nij,Nk}\n\nAbstract type for data storage for a 3D field made up of `Nij \u00d7 Nij \u00d7 Nk` values of type `S`.\n\"\"\"\nabstract type Data3D{S, Nij, Nk} <: AbstractData{S} end\n\nBase.eltype(::AbstractData{S}) where {S} = S\n@inline function Base.propertynames(::AbstractData{S}) where {S}\n    filter(name -> sizeof(fieldtype(S, name)) > 0, fieldnames(S))\nend\nBase.parent(data::AbstractData) = getfield(data, :array)\n\nBase.similar(data::AbstractData{S}) where {S} = similar(data, S)\n\nfunction Base.copyto!(dest::D, src::D) where {D <: AbstractData}\n    copyto!(parent(dest), parent(src))\n    return dest\nend\n\nfunction nfields(data::AbstractData{S}) where {S}\n    typesize(eltype(parent(data)), S)\nend\n\n# TODO: if this gets used inside kernels, move to a generated function?\n\n@generated function _getproperty(\n    data::AbstractData{S},\n    ::Val{Name},\n) where {S, Name}\n    errorstring = \"Invalid field name $(Name) for type $(S)\"\n    i = findfirst(isequal(Name), fieldnames(S))\n    if i === nothing\n        return :(error($errorstring))\n    end\n    static_idx = Val{i}()\n    return :(Base.@_inline_meta; DataLayouts._property_view(data, $static_idx))\nend\n\n@inline function Base.getproperty(data::AbstractData{S}, name::Symbol) where {S}\n    _getproperty(data, Val{name}())\nend\n\nstruct IJKFVH{S, Nij, Nk, A} <: Data3D{S, Nij, Nk}\n    array::A\nend\n\nfunction IJKFVH{S, Nij, Nk}(array::AbstractArray{T, 6}) where {S, Nij, Nk, T}\n    @assert size(array, 1) == Nij\n    @assert size(array, 2) == Nij\n    @assert size(array, 3) == Nk\n    IJKFVH{S, Nij, Nk, typeof(array)}(array)\nend\n\n@generated function _property_view(\n    data::IJKFVH{S, Nij, Nk, A},\n    idx::Val{Idx},\n) where {S, Nij, Nk, A, Idx}\n    SS = fieldtype(S, Idx)\n    FT = eltype(A)\n    offset = fieldtypeoffset(FT, S, Idx)\n    nbytes = typesize(FT, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(IJKFVH{$SS, $Nij, $Nk}(\n        view(parent(data), :, :, :, $field_byterange, :, :),\n    ))\nend\n\n@inline function Base.getproperty(\n    data::IJKFVH{S, Nij, Nk},\n    i::Integer,\n) where {S, Nij, Nk}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    IJKFVH{SS, Nij, Nk}(view(array, :, :, :, (offset + 1):(offset + len), :, :))\nend\n\nfunction Base.size(data::IJKFVH{S, Nij, Nk}) where {S, Nij, Nk}\n    Nv = size(parent(data), 5)\n    Nh = size(parent(data), 6)\n    return (Nij, Nij, Nk, Nv, Nh)\nend\n\nstruct IJFH{S, Nij, A} <: Data2D{S, Nij}\n    array::A\nend\n\nfunction IJFH{S, Nij}(array::AbstractArray{T, 4}) where {S, Nij, T}\n    @assert size(array, 1) == Nij\n    @assert size(array, 2) == Nij\n    IJFH{S, Nij, typeof(array)}(array)\nend\n\nrebuild(data::IJFH{S, Nij}, array) where {S, Nij} = IJFH{S, Nij}(array)\nBase.copy(data::IJFH{S, Nij}) where {S, Nij} = IJFH{S, Nij}(copy(parent(data)))\nfunction Base.size(data::IJFH{S, Nij}) where {S, Nij}\n    Nv = 1\n    Nh = size(parent(data), 4)\n    (Nij, Nij, 1, Nv, Nh)\nend\n\"\"\"\n    IJFH{S,Nij}(ArrayType, nelements)\n\nConstruct an IJFH structure given the backing `ArrayType`,\nquadrature degrees of freedom `Nij`,\nand the number of mesh elements `nelements`.\n\"\"\"\nfunction IJFH{S, Nij}(ArrayType, nelements) where {S, Nij}\n    FT = eltype(ArrayType)\n    IJFH{S, Nij}(ArrayType(undef, Nij, Nij, typesize(FT, S), nelements))\nend\n\nBase.length(data::IJFH) = size(parent(data), 4)\n\n@generated function _property_view(\n    data::IJFH{S, Nij, A},\n    idx::Val{Idx},\n) where {S, Nij, A, Idx}\n    SS = fieldtype(S, Idx)\n    FT = eltype(A)\n    offset = fieldtypeoffset(FT, S, Idx)\n    nbytes = typesize(FT, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(IJFH{$SS, $Nij}(view(parent(data), :, :, $field_byterange, :)))\nend\n\n@inline function Base.getproperty(data::IJFH{S, Nij}, i::Integer) where {S, Nij}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    IJFH{SS, Nij}(view(array, :, :, (offset + 1):(offset + len), :))\nend\n\n# 1D Data Layouts\nBase.length(data::Data1D) = size(parent(data), 3)\n\nstruct IFH{S, Ni, A} <: Data1D{S, Ni}\n    array::A\nend\nfunction Base.size(data::IFH{S, Ni}) where {S, Ni}\n    Nv = 1\n    Nh = size(parent(data), 3)\n    (Ni, 1, 1, Nv, Nh)\nend\nfunction IFH{S, Ni}(array::AbstractArray{T, 3}) where {S, Ni, T}\n    @assert size(array, 1) == Ni\n    IFH{S, Ni, typeof(array)}(array)\nend\n\nrebuild(data::IFH{S, Ni}, array::AbstractArray{T, 3}) where {S, Ni, T} =\n    IFH{S, Ni}(array)\n\nBase.copy(data::IFH{S, Ni}) where {S, Ni} = IFH{S, Ni}(copy(parent(data)))\n\nfunction IFH{S, Ni}(ArrayType, nelements) where {S, Ni}\n    FT = eltype(ArrayType)\n    IFH{S, Ni}(ArrayType(undef, Ni, typesize(FT, S), nelements))\nend\n\n@inline function slab(data::IFH{S, Ni}, h::Integer) where {S, Ni}\n    @boundscheck (1 <= h <= length(data)) || throw(BoundsError(data, (h,)))\n    IF{S, Ni}(view(parent(data), :, :, h))\nend\n@inline slab(data::IFH, v::Integer, h::Integer) = slab(data, h)\n\n@generated function _property_view(\n    data::IFH{S, Ni, A},\n    i::Val{Idx},\n) where {S, Ni, A, Idx}\n    SS = fieldtype(S, Idx)\n    FT = eltype(A)\n    offset = fieldtypeoffset(FT, S, Idx)\n    nbytes = typesize(FT, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(IFH{$SS, $Ni}(view(parent(data), :, $field_byterange, :)))\nend\n\n@inline function Base.getproperty(data::IFH{S, Ni}, f::Integer) where {S, Ni}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, f)\n    offset = fieldtypeoffset(T, S, f)\n    len = typesize(T, SS)\n    IFH{SS, Ni}(view(array, :, (offset + 1):(offset + len), :))\nend\n\n\n\"\"\"\n    IH1JH2{S, Nij}(data::AbstractMatrix{S})\n\nStores a 2D field in a matrix using a column-major format.\nThe primary use is for interpolation to a regular grid.\n\"\"\"\nstruct IH1JH2{S, Nij, A} <: Data2D{S, Nij}\n    array::A\nend\nfunction Base.size(data::IH1JH2{S, Nij}) where {S, Nij}\n    Nv = 1\n    Nh = div(length(parent(data)), Nij * Nij)\n    (Nij, Nij, 1, Nv, Nh)\nend\n\nfunction IH1JH2{S, Nij}(array::AbstractMatrix{S}) where {S, Nij}\n    @assert size(array, 1) % Nij == 0\n    @assert size(array, 2) % Nij == 0\n    IH1JH2{S, Nij, typeof(array)}(array)\nend\n\nBase.length(data::IH1JH2{S, Nij}) where {S, Nij} =\n    div(length(parent(data)), Nij * Nij)\n\nfunction Base.similar(\n    data::IH1JH2{S, Nij, A},\n    ::Type{Eltype},\n) where {S, Nij, A, Eltype}\n    array = similar(A, Eltype)\n    return IH1JH2{Eltype, Nij}(array)\nend\n\nBase.copy(data::IH1JH2{S, Nij}) where {S, Nij} =\n    IH1JH2{S, Nij}(copy(parent(data)))\n\n@inline function slab(data::IH1JH2{S, Nij}, h::Integer) where {S, Nij}\n    N1, N2 = size(parent(data))\n    n1 = div(N1, Nij)\n    n2 = div(N2, Nij)\n    z2, z1 = fldmod(h - 1, n1)\n\n    @boundscheck (1 <= h <= n1 * n2) || throw(BoundsError(data, (h,)))\n\n    return view(parent(data), Nij * z1 .+ (1:Nij), Nij * z2 .+ (1:Nij))\nend\n\n\n\n\n#=\nstruct KFV{S,A} <: DataColumn{S}\n  array::A\nend\nfunction KFV{S}(array::AbstractArray{T,3}) where {S,T}\n  KFV{S,typeof(array)}(array)\nend\n=#\n\nstruct IJF{S, Nij, A} <: DataSlab2D{S, Nij}\n    array::A\nend\n\nfunction IJF{S, Nij}(array::AbstractArray{T, 3}) where {S, Nij, T}\n    @assert size(array, 1) == Nij\n    @assert size(array, 2) == Nij\n    IJF{S, Nij, typeof(array)}(array)\nend\n\nfunction Base.size(data::IJF{S, Nij}) where {S, Nij}\n    return (Nij, Nij, 1, 1, 1)\nend\n\n@generated function _property_view(\n    data::IJF{S, Nij, A},\n    i::Val{Idx},\n) where {S, Nij, A, Idx}\n    SS = fieldtype(S, Idx)\n    FT = eltype(A)\n    offset = fieldtypeoffset(FT, S, Idx)\n    nbytes = typesize(FT, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(IJF{$SS, $Nij}(view(parent(data), :, :, $field_byterange)))\nend\n\n\n@inline function Base.getproperty(data::IJF{S, Nij}, i::Integer) where {S, Nij}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    IJF{SS, Nij}(view(array, :, :, (offset + 1):(offset + len)))\nend\n\n\n# TODO: should this return a S or a 0-d box containing S?\n#  - perhaps the latter, as then it is mutable?\n\n# function column(ijfh::IJFH{S}, i::Integer, j::Integer, h) where {S}\n#     get_struct(view(parent(ijfh), i, j, :, h), S)\n# end\n\n@inline function Base.getindex(\n    ijf::IJF{S, Nij},\n    i::Integer,\n    j::Integer,\n) where {S, Nij}\n    @boundscheck (1 <= i <= Nij && 1 <= j <= Nij) ||\n                 throw(BoundsError(ijf, (i, j)))\n    @inbounds get_struct(view(parent(ijf), i, j, :), S)\nend\n\n@inline function Base.setindex!(\n    ijf::IJF{S, Nij},\n    val,\n    i::Integer,\n    j::Integer,\n) where {S, Nij}\n    @boundscheck (1 <= i <= Nij && 1 <= j <= Nij) ||\n                 throw(BoundsError(ijf, (i, j)))\n    set_struct!(view(parent(ijf), i, j, :), convert(S, val))\nend\n\nfunction Base.size(::DataSlab1D{<:Any, Ni}) where {Ni}\n    return (Ni, 1, 1, 1, 1)\nend\n\n@propagate_inbounds function Base.getindex(slab::DataSlab1D, I::CartesianIndex)\n    slab[I[1]]\nend\n\n@propagate_inbounds function Base.setindex!(\n    slab::DataSlab1D,\n    val,\n    I::CartesianIndex,\n)\n    slab[I[1]] = val\nend\n\nBase.lastindex(::DataSlab1D{S, Ni}) where {S, Ni} = Ni\n\nstruct IF{S, Ni, A} <: DataSlab1D{S, Ni}\n    array::A\nend\n\nfunction IF{S, Ni}(array::AbstractArray{T, 2}) where {S, Ni, T}\n    @assert size(array, 1) == Ni\n    IF{S, Ni, typeof(array)}(array)\nend\n\n@generated function _property_view(\n    data::IF{S, Ni},\n    idx::Val{Idx},\n) where {S, Ni, Idx}\n    SS = fieldtype(S, Idx)\n    T = basetype(SS)\n    offset = fieldtypeoffset(T, S, Idx)\n    nbytes = typesize(T, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(IF{$SS, $Ni}(view(parent(data), :, $field_byterange)))\nend\n\n@inline function Base.getproperty(data::IF{S, Ni}, f::Integer) where {S, Ni}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, f)\n    offset = fieldtypeoffset(T, S, f)\n    len = typesize(T, SS)\n    IF{SS, Ni}(view(array, :, (offset + 1):(offset + len)))\nend\n\n@inline function Base.getindex(data::IF{S, Ni}, i::Integer) where {S, Ni}\n    @boundscheck (1 <= i <= Ni) || throw(BoundsError(data, (i,)))\n    @inbounds get_struct(view(parent(data), i, :), S)\nend\n\n@inline function Base.setindex!(data::IF{S, Ni}, val, i::Integer) where {S, Ni}\n    @boundscheck (1 <= i <= Ni) || throw(BoundsError(data, (i,)))\n    set_struct!(view(parent(data), i, :), convert(S, val))\nend\n\n# TODO: should this return a S or a 0-d box containing S?\n#  - perhaps the latter, as then it is mutable?\n#function column(ijfh::IJFH{S}, i::Integer, j::Integer, h) where {S}\n#    get_struct(view(parent(ijfh), i, j, :, h), S)\n#end\n\n@inline function slab(ijfh::IJFH{S, Nij}, h::Integer) where {S, Nij}\n    @boundscheck (1 <= h <= length(ijfh)) || throw(BoundsError(ijfh, (h,)))\n    IJF{S, Nij}(view(parent(ijfh), :, :, :, h))\nend\n\n@inline function slab(ijfh::IJFH{S, Nij}, v::Integer, h::Integer) where {S, Nij}\n    @boundscheck (1 <= h <= length(ijfh)) || throw(BoundsError(ijfh, (h,)))\n    IJF{S, Nij}(view(parent(ijfh), :, :, :, h))\nend\n\n\n@propagate_inbounds function Base.getindex(\n    slab::DataSlab2D{S},\n    I::CartesianIndex,\n) where {S}\n    slab[I[1], I[2]]\nend\n\n@propagate_inbounds function Base.setindex!(\n    slab::DataSlab2D{S},\n    val,\n    I::CartesianIndex,\n) where {S}\n    slab[I[1], I[2]] = val\nend\n\nBase.size(::DataSlab2D{S, Nij}) where {S, Nij} = (Nij, Nij, 1, 1, 1)\nBase.axes(::DataSlab2D{S, Nij}) where {S, Nij} = (SOneTo(Nij), SOneTo(Nij))\n\n# Data column\nBase.length(data::DataColumn) = size(parent(data), 1)\nBase.size(data::DataColumn) = (1, 1, 1, length(data), 1)\n\nstruct VF{S, A} <: DataColumn{S}\n    array::A\nend\n\nfunction VF{S}(array::AbstractArray{T, 2}) where {S, T}\n    VF{S, typeof(array)}(array)\nend\n\nfunction VF{S}(array::AbstractVector{T}) where {S, T}\n    @assert typesize(T, S) == 1\n    VF{S}(reshape(array, (:, 1)))\nend\n\nfunction VF{S}(ArrayType, nelements) where {S}\n    FT = eltype(ArrayType)\n    VF{S}(ArrayType(undef, nelements, typesize(FT, S)))\nend\n\nfunction replace_basetype(data::VF{S}, ::Type{FT}) where {S, FT}\n    SS = replace_basetype(S, FT)\n    VF{SS}(similar(parent(data), FT))\nend\n\n\nBase.copy(data::VF{S}) where {S} = VF{S}(copy(parent(data)))\nBase.lastindex(data::VF) = length(data)\n\n@generated function _property_view(\n    data::VF{S, A},\n    idx::Val{Idx},\n) where {S, A, Idx}\n    SS = fieldtype(S, Idx)\n    FT = eltype(A)\n    offset = fieldtypeoffset(FT, S, Idx)\n    nbytes = typesize(FT, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(VF{$SS}(view(parent(data), :, $field_byterange)))\nend\n\n@inline function Base.getproperty(data::VF{S}, i::Integer) where {S}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    VF{SS}(view(array, :, (offset + 1):(offset + len)))\nend\n\n@propagate_inbounds function Base.getindex(data::VF{S}, i::Integer) where {S}\n    get_struct(view(parent(data), i, :), S)\nend\n\n@propagate_inbounds function Base.getindex(\n    col::DataColumn,\n    I::CartesianIndex{5},\n)\n    col[I[4]]\nend\n\n@propagate_inbounds function Base.setindex!(\n    col::DataColumn,\n    val,\n    I::CartesianIndex{5},\n)\n    col[I[4]] = val\nend\n\n@inline function Base.setindex!(data::VF{S}, val, v::Integer) where {S}\n    set_struct!(view(parent(data), v, :), convert(S, val))\nend\n\ncolumn(data::VF, i, h) = data\ncolumn(data::VF, i, j, h) = column(data, i, h)\n\n\n# combined 1D spectral element + extruded 1D FV column data layout\n\nstruct VIFH{S, Ni, A} <: Data1DX{S, Ni}\n    array::A\nend\n\nfunction VIFH{S, Ni}(array::AbstractArray{T, 4}) where {S, Ni, T}\n    @assert size(array, 2) == Ni\n    VIFH{S, Ni, typeof(array)}(array)\nend\n\nfunction Base.size(data::VIFH{<:Any, Ni}) where {Ni}\n    Nv = size(parent(data), 1)\n    Nh = size(parent(data), 4)\n    return (Ni, 1, 1, Nv, Nh)\nend\n\nfunction Base.length(data::VIFH)\n    size(parent(data), 1) * size(parent(data), 4)\nend\nBase.copy(data::VIFH{S, Ni}) where {S, Ni} = VIFH{S, Ni}(copy(parent(data)))\n\n@generated function _property_view(\n    data::VIFH{S, Ni},\n    idx::Val{Idx},\n) where {S, Ni, Idx}\n    SS = fieldtype(S, Idx)\n    T = basetype(SS)\n    offset = fieldtypeoffset(T, S, Idx)\n    nbytes = typesize(T, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(VIFH{$SS, $Ni}(view(parent(data), :, :, $field_byterange, :)))\nend\n\n@inline function Base.getproperty(data::VIFH{S, Ni}, i::Integer) where {S, Ni}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    VIFH{SS, Ni}(view(array, :, :, (offset + 1):(offset + len), :))\nend\n\nfunction slab(data::VIFH{S, Ni}, v, h) where {S, Ni}\n    IF{S, Ni}(view(parent(data), v, :, :, h))\nend\n\nfunction column(data::VIFH{S}, i, h) where {S}\n    VF{S}(view(parent(data), :, i, :, h))\nend\n\nfunction column(data::VIFH{S}, i, j, h) where {S}\n    @assert j == 1\n    column(data, i, h)\nend\n\n@propagate_inbounds function Base.getindex(data::VIFH, I::CartesianIndex)\n    data[I[1], I[4]]\nend\n\n@propagate_inbounds function Base.setindex!(data::VIFH, val, I::CartesianIndex)\n    data[I[1], I[4]] = val\nend\n\n\n\"\"\"\n    IV1JH2{S, Ni}(data::AbstractMatrix{S})\n\nStores values from an extruded 1D spectral field in a matrix using a column-major format.\nThe primary use is for interpolation to a regular grid.\n\"\"\"\nstruct IV1JH2{S, Ni, A} <: Data1DX{S, Ni}\n    array::A\nend\n\nfunction IV1JH2{S, Ni}(array::AbstractMatrix{S}) where {S, Ni}\n    @assert size(array, 2) % Ni == 0\n    IV1JH2{S, Ni, typeof(array)}(array)\nend\n\nfunction Base.size(data::IV1JH2{S, Ni}) where {S, Ni}\n    Nv = size(parent(data), 1)\n    Nh = div(size(parent(data), 2), Ni)\n    (Ni, 1, 1, Nv, Nh)\nend\n\nBase.length(data::IV1JH2{S, Ni}) where {S, Ni} = div(length(parent(data)), Ni)\n\nfunction Base.similar(\n    data::IV1JH2{S, Ni, A},\n    ::Type{Eltype},\n) where {S, Ni, A, Eltype}\n    array = similar(A, Eltype)\n    return IV1JH2{Eltype, Ni}(array)\nend\n\nBase.copy(data::IV1JH2{S, Ni}) where {S, Ni} = IV1JH2{S, Ni}(copy(parent(data)))\n\n@inline function slab(data::IV1JH2{S, Ni}, v::Integer, h::Integer) where {S, Ni}\n    N1, N2 = size(parent(data))\n    n1 = N1\n    n2 = div(N2, Ni)\n    _, z2 = fldmod(h - 1, n2)\n    @boundscheck (1 <= v <= n1) && (1 <= h <= n2) ||\n                 throw(BoundsError(data, (v, h)))\n    return view(parent(data), v, Ni * z2 .+ (1:Ni))\nend\n\n\n# combined 2D spectral element + extruded 1D FV column data layout\n\nstruct VIJFH{S, Nij, A} <: Data2DX{S, Nij}\n    array::A\nend\n\nfunction VIJFH{S, Nij}(array::AbstractArray{T, 5}) where {S, Nij, T}\n    @assert size(array, 2) == size(array, 3) == Nij\n    VIJFH{S, Nij, typeof(array)}(array)\nend\n\nfunction Base.size(data::VIJFH{<:Any, Nij}) where {Nij}\n    Nv = size(parent(data), 1)\n    Nh = size(parent(data), 5)\n    return (Nij, Nij, 1, Nv, Nh)\nend\n\nfunction Base.length(data::VIJFH)\n    size(parent(data), 1) * size(parent(data), 5)\nend\n\nfunction Base.copy(data::VIJFH{S, Nij}) where {S, Nij}\n    VIJFH{S, Nij}(copy(parent(data)))\nend\n\n@generated function _property_view(\n    data::VIJFH{S, Nij},\n    idx::Val{Idx},\n) where {S, Nij, Idx}\n    SS = fieldtype(S, Idx)\n    T = basetype(SS)\n    offset = fieldtypeoffset(T, S, Idx)\n    nbytes = typesize(T, SS)\n    field_byterange = (offset + 1):(offset + nbytes)\n    return :(VIJFH{$SS, $Nij}(view(parent(data), :, :, :, $field_byterange, :)))\nend\n\n@inline function Base.getproperty(\n    data::VIJFH{S, Nij},\n    i::Integer,\n) where {S, Nij}\n    array = parent(data)\n    T = eltype(array)\n    SS = fieldtype(S, i)\n    offset = fieldtypeoffset(T, S, i)\n    len = typesize(T, SS)\n    VIJFH{SS, Nij}(view(array, :, :, :, (offset + 1):(offset + len), :))\nend\n\nfunction slab(data::VIJFH{S, Nij}, v, h) where {S, Nij}\n    IJF{S, Nij}(view(parent(data), v, :, :, :, h))\nend\n\nfunction column(data::VIJFH{S}, i, j, h) where {S}\n    VF{S}(view(parent(data), :, i, j, :, h))\nend\n\n@propagate_inbounds function Base.getindex(data::VIJFH, I::CartesianIndex{5})\n    data[I[1], I[2], I[4]]\nend\n\n@propagate_inbounds function Base.setindex!(\n    data::VIJFH,\n    val,\n    I::CartesianIndex{5},\n)\n    data[I[1], I[2], I[4]] = val\nend\n\n# broadcast machinery\ninclude(\"broadcast.jl\")\n\n# GPU method specializations\ninclude(\"cuda.jl\")\n\nend # module\n", "meta": {"hexsha": "12682bad3750af0bc75d9f52d4159a4fac406503", "size": 21220, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DataLayouts/DataLayouts.jl", "max_stars_repo_name": "aviatesk/ClimaCore.jl", "max_stars_repo_head_hexsha": "44c9cf6b9d6dccaba648f8084fb2185a2c1fa3ce", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/DataLayouts/DataLayouts.jl", "max_issues_repo_name": "aviatesk/ClimaCore.jl", "max_issues_repo_head_hexsha": "44c9cf6b9d6dccaba648f8084fb2185a2c1fa3ce", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/DataLayouts/DataLayouts.jl", "max_forks_repo_name": "aviatesk/ClimaCore.jl", "max_forks_repo_head_hexsha": "44c9cf6b9d6dccaba648f8084fb2185a2c1fa3ce", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.9289340102, "max_line_length": 93, "alphanum_fraction": 0.6238454288, "num_tokens": 7411, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.12592275335433112, "lm_q1q2_score": 0.060503198067841664}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.4\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 6a87a898-9dfd-11ec-2b38-49b883798d79\nbegin\nCURRENT_DIR = @__DIR__\nROOT_DIR = if basename(CURRENT_DIR) == \"pluto\"\n    dirname(dirname(CURRENT_DIR))\nelse\n    CURRENT_DIR\nend\n\n# Activate the environment\nusing Pkg\nPkg.activate(ROOT_DIR; io = devnull)\n\n# Disable warnings\nusing Logging\ndisable_logging(Logging.Warn)\n\nprintln('\\n', \" \"^4, \"> Loading the packages...\")\n\nusing LaTeXStrings\nusing Plots\nusing PlutoUI\n\n# Use the GR backend for plots\ngr()\n\n# Change some of the default parameters for plots\ndefault(fontfamily = \"Computer Modern\", dpi = 300, legend = nothing)\n\n# Define the paths to output directories\nDATA_DIR = joinpath(ROOT_DIR, \"data\", \"notebooks\", \"maps\")\nPLOTS_DIR = joinpath(ROOT_DIR, \"plots\", \"notebooks\", \"maps\")\n\n# Make sure the needed directories exist\nmkpath(DATA_DIR)\nmkpath(PLOTS_DIR)\n\n# Define the floating point type used across the script\nF = Float64\n\n# Define the integer type used across the script\nI = UInt64\n\nmd\"This is a Pluto notebook for plotting the Poincar\u00e9 (stroboscopic) maps.\"\nend\n\n# \u2554\u2550\u2561 4253f362-f966-45af-ac9c-0016672f5952\nbegin\n# A step for the sliders\nS_STEP = 0.001\n\n# The maximum floating point type number\n# that is reachable with the specified step\nF_MAX = floor(typemax(Int) - 1000) * S_STEP / 2\n\n@bind params confirm(\n    PlutoUI.combine() do Child\n        md\"\"\"\n        Parameters of the model:\\\n        ``e`` $(Child(\"e\", NumberField(\n            0.0:S_STEP:0.999,\n            default = 0.0,\n        )))\n        \u2002``h \\; [\\pi / 2]``: $(Child(\"h\", NumberField(\n            S_STEP:S_STEP:0.1,\n            default = 0.01,\n        )))\n        \u2002``P``: $(Child(\"P\", NumberField(\n            1:1:10000,\n            default = 1000,\n        )))\\\n        ``\\tau`` $(Child(\"\u03c4\", Select(\n                [0 => \"0\", 0.25 => \"\u03c0 / 2\", 0.5 => \"\u03c0\", 0.75 => \"3 \u03c0 / 2\"],\n                default = 0\n        )))\n        \u2002``\\theta_s`` $(Child(\"\u03b8\u209b\", Select(\n                [0 => \"0\", 0.25 => \"\u03c0 / 2\", 0.5 => \"\u03c0\", 0.75 => \"3 \u03c0 / 2\"],\n                default = 0\n        )))\n        \u2002Limit by x? $(Child(\"limit\", CheckBox(default=false)))\n        $(Child(\"xl\", NumberField(\n            -100:S_STEP:0,\n            default = -2.5,\n        )))\n        $(Child(\"xr\", NumberField(\n            0:S_STEP:100,\n            default = 2.5,\n        )))\\\n\n        \"\"\"\n    end\n)\nend\n\n# \u2554\u2550\u2561 4fe5044e-4f67-4bb5-afe9-f5f95ac02c78\nbegin\n\n# Define a tuple of pairs of initial values\nINITIAL_VALUES = ((i, 0.) for i in 0.2:0.1:1.8)\n\n\"Read binary files in the `bincode` format\"\nfunction read_bincode(path::AbstractString)::Tuple{I, Vector{F}}\n    open(path, \"r\") do io\n        n = read(io, I)\n        a = reinterpret(F, read(io, sizeof(F) * n))\n        n, a\n    end\nend\n\n# Prepare an empty plot\np = scatter(size = (400, 400));\n\n# For each pair of initial values\nfor pair in INITIAL_VALUES\n    # Define the path to the data file\n    data_problem_dir = joinpath(DATA_DIR, \"$pair\")\n    plots_problem_dir = joinpath(PLOTS_DIR, \"$pair\")\n    # Make sure the needed directories exist\n    mkpath(data_problem_dir)\n    mkpath(plots_problem_dir)\n    # Define the arguments\n    args = [\n        \"-e\", params.e,\n        \"-h\", params.h,\n        \"-t\", params.\u03c4,\n        \"-P\", params.P,\n        \"-p\", pair[begin],\n        \"-v\", pair[end],\n        \"-o\", data_problem_dir\n    ]\n    # Integrate the problem\n    run(pipeline(`cargo run -r -- $args`, stderr = devnull))\n    # Define the paths to the binary files\n    z_path = joinpath(data_problem_dir, \"z.bin\")\n    z_v_path = joinpath(data_problem_dir, \"z_v.bin\")\n    # Read the data\n    n, z = read_bincode(z_path)\n    _, z_v = read_bincode(z_v_path)\n    # Compute the number of points per period\n    np = UInt((n - 1) / params.P)\n    # Compute the starting index\n    si = UInt(1 + params.\u03b8\u209b * np)\n    # Plot the figure\n    println(\" \"^4, \"> Plotting the Poincar\u00e9 map for the $pair pair...\")\n    s = scatter(\n        z[si:np:end],\n        z_v[si:np:end];\n        label = \"\",\n        title = \"Poincar\u00e9 map\",\n        xlabel = L\"z\",\n        ylabel = L\"\\dot{z}\",\n        size = (400, 400),\n        markersize = 0.5,\n    );\n    # Save the figure as PDF and PNG\n    savefig(s, joinpath(plots_problem_dir, \"Poincar\u00e9 map.pdf\"));\n    savefig(s, joinpath(plots_problem_dir, \"Poincar\u00e9 map.png\"));\n    # Plot the data on the gerenal plot\n    scatter!(\n        p,\n        z[si:np:end],\n        z_v[si:np:end];\n        label = \"\",\n        title = \"Poincar\u00e9 map\",\n        xlabel = L\"z\",\n        ylabel = L\"\\dot{z}\",\n        size = (400, 400),\n        markersize = 0.5,\n    );\n    # Limit by x\n    if params.limit\n        xlims!(params.xl, params.xr)\n    end\nend\n\n# Save the final figure as PDF and PNG\nsavefig(p, joinpath(PLOTS_DIR, \"Poincar\u00e9 map.pdf\"));\nsavefig(p, joinpath(PLOTS_DIR, \"Poincar\u00e9 map.png\"));\n\nprintln()\n\n# Show the plot\np\n\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25006a87a898-9dfd-11ec-2b38-49b883798d79\n# \u255f\u25004253f362-f966-45af-ac9c-0016672f5952\n# \u255f\u25004fe5044e-4f67-4bb5-afe9-f5f95ac02c78\n", "meta": {"hexsha": "385ed78b6768759a24a7ee5fe00806f1b5003f94", "size": 5536, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sitnikov/notebooks/pluto/maps.jl", "max_stars_repo_name": "paveloom-university/Computer-Simulation-S10-2021", "max_stars_repo_head_hexsha": "6271ee6d65ceeb1cec4ac936652dc4d2d6f8abd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sitnikov/notebooks/pluto/maps.jl", "max_issues_repo_name": "paveloom-university/Computer-Simulation-S10-2021", "max_issues_repo_head_hexsha": "6271ee6d65ceeb1cec4ac936652dc4d2d6f8abd6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sitnikov/notebooks/pluto/maps.jl", "max_forks_repo_name": "paveloom-university/Computer-Simulation-S10-2021", "max_forks_repo_head_hexsha": "6271ee6d65ceeb1cec4ac936652dc4d2d6f8abd6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7439613527, "max_line_length": 195, "alphanum_fraction": 0.5964595376, "num_tokens": 1716, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.13117321187415157, "lm_q1q2_score": 0.060473051670273385}}
{"text": "\"\"\"\n    nt(s::Symbol, v = 0.0)\n\nCreates a named tuple `(s=v,)` where `s` is a provided symbol, and `v` is the value. \n\"\"\"\nnt(s::Symbol, v = 0.0) = NamedTuple{(s,)}([v])\n\ninrange(x,r) = r[1] \u2264 x \u2264 r[2]", "meta": {"hexsha": "1fa01ff1f8fcc6247e83c8cfbfe23fe61f891f36", "size": 200, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "mmikhasenko/AlgebraPDF.jl", "max_stars_repo_head_hexsha": "de553eeb11cce76d84392cb82ce04f30d8565c30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2020-12-29T22:41:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T14:23:44.000Z", "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "mmikhasenko/AlgebraPDF.jl", "max_issues_repo_head_hexsha": "de553eeb11cce76d84392cb82ce04f30d8565c30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 22, "max_issues_repo_issues_event_min_datetime": "2021-01-21T13:37:38.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-18T13:16:07.000Z", "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "mmikhasenko/AlgebraPDF.jl", "max_forks_repo_head_hexsha": "de553eeb11cce76d84392cb82ce04f30d8565c30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0, "max_line_length": 85, "alphanum_fraction": 0.535, "num_tokens": 81, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3923368301671084, "lm_q2_score": 0.1540575665754383, "lm_q1q2_score": 0.060442457333465725}}
{"text": "# # Machine Learning in Julia, JuliaCon2020\n\n# A workshop introducing the machine learning toolbox\n# [MLJ](https://alan-turing-institute.github.io/MLJ.jl/stable/)\n# ### Environment instantiation\n\n# The following loads a Julia environment and forces pre-compilation\n# of some packages.\n\n# However, if this the **binder** notebook version of the tutorial, we\n# recommend you *skip* evaluation of this first cell.\n\nDIR = @__DIR__\ninclude(joinpath(DIR, \"setup.jl\"))\n\n\n# ## Contents\n\n# ### Basic\n\n# - [Part 1 - Data Representation](#part-1-data-representation)\n# - [Part 2 - Selecting, Training and Evaluating Models](#part-2-selecting-training-and-evaluating-models)\n# - [Part 3 - Transformers and Pipelines](#part-3-transformers-and-pipelines)\n\n# ### Advanced\n\n# - [Part 4 - Tuning Hyper-parameters](#part-4-tuning-hyper-parameters)\n\n\n# <a id='part-1-data-representation'></a>\n\n\n# ## Part 1 - Data Representation\n\n# > **Goals:**\n# > 1. Learn how MLJ specifies it's data requirements using \"scientific\" types\n# > 2. Understand the options for representing tabular data\n# > 3. Learn how to inspect and fix the representation of data to meet MLJ requirements\n\n\n# ### Scientific types\n\n# To help you focus on the intended *purpose* or *interpretation* of\n# data, MLJ models specify data requirements using *scientific types*,\n# instead of machine types. An example of a scientific type is\n# `OrderedFactor`. The other basic \"scalar\" scientific types are\n# illustrated below:\n\n# ![](assets/scitypes.png)\n\n# A scientific type is an ordinary Julia type (so it can be used for\n# method dispatch, for example) but it usually has no instances. The\n# `scitype` function is used to articulate MLJ's convention about how\n# different machine types will be interpreted by MLJ models:\n\nusing MLJ\nscitype(3.141)\n\n#-\n\ntime = [2.3, 4.5, 4.2, 1.8, 7.1]\nscitype(time)\n\n# To fix data which MLJ is interpreting incorrectly, we use the\n# `coerce` method:\n\nheight = [185, 153, 163, 114, 180]\nscitype(height)\n\n#-\n\nheight = coerce(height, Continuous)\n\n# Here's an example of data we would want interpreted as\n# `OrderedFactor` but isn't:\n\nexam_mark = [\"rotten\", \"great\", \"bla\",  missing, \"great\"]\nscitype(exam_mark)\n\n#-\n\nexam_mark = coerce(exam_mark, OrderedFactor)\n\n#-\n\nlevels(exam_mark)\n\n# Use `levels!` to put the classes in the right order:\n\nlevels!(exam_mark, [\"rotten\", \"bla\", \"great\"])\nexam_mark[1] < exam_mark[2]\n\n# When subsampling, no levels are not lost:\n\nlevels(exam_mark[1:2])\n\n# **Note on binary data.** There is no separate scientific type for binary\n# data. Binary data is `OrderedFactor{2}` if it has an intrinsic\n# \"true\" class (eg, \"pass\"/\"fail\") and `Multiclass{2}` otherwise (eg,\n# \"male\"/\"female\").\n\n\n# ### Two-dimensional data\n\n# Whenever it makes sense, MLJ Models generally expect two-dimensional\n# data to be *tabular*. All the tabular formats implementing the\n# [Tables.jl API](https://juliadata.github.io/Tables.jl/stable/) (see\n# this\n# [list](https://github.com/JuliaData/Tables.jl/blob/master/INTEGRATIONS.md))\n# have a scientific type of `Table` and can be used with such models.\n\n# The simplest example of a table is a the julia native *column\n# table*, which is just a named tuple of equal-length vectors:\n\n\ncolumn_table = (h=height, e=exam_mark, t=time)\n\n#-\n\nscitype(column_table)\n\n#-\n\n# Notice the `Table{K}` type parameter `K` encodes the scientific\n# types of the columns. (This is useful when comparing table scitypes\n# with `<:`). To inspect the individual column scitypes, we use the\n# `schema` method instead:\n\nschema(column_table)\n\n# Here are four other examples of tables:\n\nrow_table = [(a=1, b=3.4),\n             (a=2, b=4.5),\n             (a=3, b=5.6)]\nschema(row_table)\n\n#-\n\nimport DataFrames\ndf = DataFrames.DataFrame(column_table)\n\n#-\n\nschema(df)\n\n#-\n\nusing CSV\nfile = CSV.File(joinpath(DIR, \"data\", \"horse.csv\"));\nschema(file) # (triggers a file read)\n\n\n# Most MLJ models do not accept matrix in lieu of a table, but you can\n# wrap a matrix as a table:\n\nmatrix_table = MLJ.table(rand(2,3))\nschema(matrix_table)\n\n# Under the hood many algorithms convert tabular data to matrices. If\n# your table is a wrapped matrix like the above, then the compiler\n# will generally collapse the conversions to a no-op.\n\n\n# **Manipulating tabular data.** In this workshop we assume\n# familiarity with some kind of tabular data container (although it is\n# possible, in principle, to carry out the exercises without this.)\n# For a quick start introduction to `DataFrames`, see [this\n# tutorial](https://alan-turing-institute.github.io/DataScienceTutorials.jl/data/dataframe/)\n\n# ### Fixing scientific types in tabular data\n\n# To show how we can correct the scientific types of data in tables,\n# we introduce a cleaned up version of the UCI Horse Colic Data Set\n# (the cleaning workflow is described\n# [here](https://alan-turing-institute.github.io/DataScienceTutorials.jl/end-to-end/horse/#dealing_with_missing_values))\n\nusing CSV\nfile = CSV.File(joinpath(DIR, \"data\", \"horse.csv\"));\nhorse = DataFrames.DataFrame(file); # convert to data frame without copying columns\nfirst(horse, 4)\n\n#-\n\n# From [the UCI\n# docs](http://archive.ics.uci.edu/ml/datasets/Horse+Colic) we can\n# surmise how each variable ought to be interpreted (a step in our\n# workflow that cannot reliably be left to the computer):\n\n# variable                    | scientific type (interpretation)\n# ----------------------------|-----------------------------------\n# `:surgery`                  | Multiclass\n# `:age`                      | Multiclass\n# `:rectal_temperature`       | Continuous\n# `:pulse`                    | Continuous\n# `:respiratory_rate`         | Continuous\n# `:temperature_extremities`  | OrderedFactor\n# `:mucous_membranes`         | Multiclass\n# `:capillary_refill_time`    | Multiclass\n# `:pain`                     | OrderedFactor\n# `:peristalsis`              | OrderedFactor\n# `:abdominal_distension`     | OrderedFactor\n# `:packed_cell_volume`       | Continuous\n# `:total_protein`            | Continuous\n# `:outcome`                  | Multiclass\n# `:surgical_lesion`          | OrderedFactor\n# `:cp_data`                  | Multiclass\n\n# Let's see how MLJ will actually interpret the data, as it is\n# currently encoded:\n\nschema(horse)\n\n# As a first correction step, we can get MLJ to \"guess\" the\n# appropriate fix, using the `autotype` method:\n\nautotype(horse)\n\n#-\n\n# Okay, this is not perfect, but a step in the right direction, which\n# we implement like this:\n\ncoerce!(horse, autotype(horse));\nschema(horse)\n\n# All remaining `Count` data should be `Continuous`:\n\ncoerce!(horse, Count => Continuous);\nschema(horse)\n\n# We'll correct the remaining truant entries manually:\n\ncoerce!(horse,\n        :surgery               => Multiclass,\n        :age                   => Multiclass,\n        :mucous_membranes      => Multiclass,\n        :capillary_refill_time => Multiclass,\n        :outcome               => Multiclass,\n        :cp_data               => Multiclass);\nschema(horse)\n\n\n# ### Resources for Part 1\n#\n# - From the MLJ manual:\n#    - [A preview of data type specification in\n#   MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#A-preview-of-data-type-specification-in-MLJ-1)\n#    - [Data containers and scientific types](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Data-containers-and-scientific-types-1)\n#    - [Working with Categorical Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/)\n# - [Summary](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/#Summary-of-the-MLJ-convention-1) of the MLJ convention for representing scientific types\n# - [MLJScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/)\n# - From Data Science Tutorials:\n#     - [Data interpretation: Scientific Types](https://alan-turing-institute.github.io/DataScienceTutorials.jl/data/scitype/)\n#     - [Horse colic data](https://alan-turing-institute.github.io/DataScienceTutorials.jl/end-to-end/horse/)\n# - [UCI Horse Colic Data Set](http://archive.ics.uci.edu/ml/datasets/Horse+Colic)\n\n\n# ### Exercises for Part 1\n\n\n# #### Exercise 1\n\n# Try to guess how each code snippet below will evaluate:\n\nscitype(42)\n\n#-\n\nquestions = [\"who\", \"why\", \"what\", \"when\"]\nscitype(questions)\n\n#-\n\nelscitype(questions)\n\n#-\n\nt = (3.141, 42, \"how\")\nscitype(t)\n\n#-\n\nA = rand(2, 3)\n\n# -\n\nscitype(A)\n\n#-\n\nelscitype(A)\n\n#-\n\nusing SparseArrays\nAsparse = sparse(A)\n\n#-\n\nscitype(Asparse)\n\n#-\n\nusing CategoricalArrays\nC1 = categorical(A)\n\n#-\n\nscitype(C1)\n\n#-\n\nelscitype(C1)\n\n#-\n\nC2 = categorical(A, ordered=true)\nscitype(C2)\n\n#-\n\nv = [1, 2, missing, 4]\nscitype(v)\n\n#-\n\nelscitype(v)\n\n#-\n\nscitype(v[1:2])\n\n# Can you guess at the general behaviour of\n# `scitype` with respect to tuples, abstract arrays and missing\n# values? The answers are\n# [here](https://github.com/alan-turing-institute/ScientificTypes.jl#2-the-scitype-and-scitype-methods)\n# (ignore \"Property 1\").\n\n\n# #### Exercise 2\n\n# Coerce the following vector to make MLJ recognize it as a vector of\n# ordered factors (with an appropriate ordering):\n\nquality = [\"good\", \"poor\", \"poor\", \"excellent\", missing, \"good\", \"excellent\"]\n\n#-\n\n\n# #### Exercise 3 (fixing scitypes in a table)\n\n# Fix the scitypes for the [House Prices in King\n# County](https://mlr3gallery.mlr-org.com/posts/2020-01-30-house-prices-in-king-county/)\n# dataset:\n\nfile = CSV.File(joinpath(DIR, \"data\", \"house.csv\"));\nhouse = DataFrames.DataFrame(file); # convert to data frame without copying columns\nfirst(house, 4)\n\n# (Two features in the original data set have been deemed uninformative\n# and dropped, namely `:id` and `:date`. The original feature\n# `:yr_renovated` has been replaced by the `Bool` feature `is_renovated`.)\n\n# <a id='part-2-selecting-training-and-evaluating-models'></a>\n\n\n# ## Part 2 - Selecting, Training and Evaluating Models\n\n# > **Goals:**\n# > 1. Search MLJ's database of model metadata to identify model candidates for a supervised learning task.\n# > 2. Evaluate the performance of a model on a holdout set using basic `fit!`/`predict` workflow.\n# > 3. Inspect the outcomes of training and save these to a file.\n# > 3. Evaluate performance using other resampling strategies, such as cross-validation, in one line, using `evaluate!`\n# > 4. Plot a \"learning curve\", to inspect performance as a function of some model hyper-parameter, such as an iteration parameter\n\n# The \"Hello World!\" of machine learning is to classify Fisher's\n# famous iris data set. This time, we'll grab the data from\n# [OpenML](https://www.openml.org):\n\niris = OpenML.load(61); # a row table\niris = DataFrames.DataFrame(iris);\nfirst(iris, 4)\n\n\n# **Main goal.** To build and evaluate models for predicting the\n# `:class` variable, given the four remaining measurement variables.\n\n\n# ### Step 1. Inspect and fix scientific types\n\nschema(iris)\n\n#-\n\ncoerce!(iris, :class => Multiclass);\nschema(iris)\n\n\n# ### Step 2. Split data into input and target parts\n\n# Here's how we split the data into target and input features, which\n# is needed for MLJ supervised models. We randomize the data at the\n# same time:\n\ny, X = unpack(iris, ==(:class), name->true; rng=123);\nscitype(y)\n\n# Do `?unpack` to learn more:\n\n@doc unpack\n\n# ### On searching for a model\n\n# Here's how to see *all* models (not immediately useful):\n\nkitchen_sink = models()\n\n# Each entry contains metadata for a model whose defining code is not yet loaded:\n\nmeta = kitchen_sink[3]\n\n#-\n\ntargetscitype = meta.target_scitype\n\n#-\n\nscitype(y) <: targetscitype\n\n# So this model won't do. Let's  find all pure julia classifiers:\n\nfilt(meta) = AbstractVector{Finite} <: meta.target_scitype &&\n        meta.is_pure_julia\nmodels(filt)\n\n\n# Find all models with \"Classifier\" in `name` (or `docstring`):\n\nmodels(\"Classifier\")\n\n\n# Find all (supervised) models that match my data!\n\nmodels(matching(X, y))\n\n\n\n# ### Step 3. Select and instantiate a model\n\nmodel = @load NeuralNetworkClassifier\n\n#-\n\ninfo(model)\n\n# In MLJ a *model* is just a struct containing hyper-parameters, and\n# that's all. A model does not store *learned* parameters. Models are\n# mutable:\n\nmodel.epochs = 12\n\n# And all models have a key-word constructor that works once `@load`\n# has been performed:\n\nNeuralNetworkClassifier(epochs=12) == model\n\n\n# ### On fitting, predicting, and inspecting models\n\n# In MLJ a model and training/validation data are typically bound\n# together in a machine:\n\nmach = machine(model, X, y)\n\n# A machine stores *learned* parameters, among other things. We'll\n# train this machine on 70% of the data and evaluate on a 30% holdout\n# set. Let's start by dividing all row indices into `train` and `test`\n# subsets:\n\ntrain, test = partition(eachindex(y), 0.7)\n\n#-\n\nfit!(mach, rows=train, verbosity=2)\n\n# After training, one can inspect the learned parameters:\n\nfitted_params(mach)\n\n#-\n\n# Everything else the user might be interested in is accessed from the\n# training *report*:\n\nreport(mach)\n\n# You save a machine like this:\n\nMLJ.save(\"neural_net.jlso\", mach)\n\n# And retrieve it like this:\n\nmach2 = machine(\"neural_net.jlso\")\npredict(mach2, X)[1:3]\n\n# If you want to fit a retrieved model, you will need to bind some data to it:\n\nmach3 = machine(\"neural_net.jlso\", X, y)\nfit!(mach3)\n\n# Machines remember the last set of hyper-parameters used during fit,\n# which, in the case of iterative models, allows for a warm restart of\n# computations in the case that only the iteration parameter is\n# increased:\n\nmodel.epochs = model.epochs + 4\nfit!(mach, rows=train, verbosity=2)\n\n# By default (for this particular model) we can also increase\n# `:learning_rate` without triggering a cold restart:\n\nmodel.epochs = model.epochs + 4\nmodel.optimiser.eta = 10*model.optimiser.eta\nfit!(mach, rows=train, verbosity=2)\n\n# However, change any other parameter and training will restart from\n# scratch:\n\nmodel.lambda = 0.001\nfit!(mach, rows=train, verbosity=2)\n\n# Let's train silently for a total of 50 epochs, and look at a prediction:\n\nmodel.epochs = 50\nfit!(mach, rows=train)\nyhat = predict(mach, X[test,:]); # or predict(mach, rows=test)\nyhat[1]\n\n# What's going on here?\n\ninfo(model).prediction_type\n\n# **Important**:\n# - In MLJ, a model that can predict probabilities (and not just point values) will do so by default. (These models have supertype `Proababilistic`, while point-estimate predictors have supertype `Deterministic`.)\n# - For most probabilistic predictors, the predicted object is a `Distributions.Distribution` object, supporting the `Distributions.jl` [API](https://juliastats.org/Distributions.jl/latest/extends/#Create-a-Distribution-1) for such objects. In particular, the methods `rand`,  `pdf`, `mode`, `median` and `mean` will apply, where appropriate.\n\n# So, to obtain the probability of \"Iris-virginica\" in the first test\n# prediction, we do\n\npdf(yhat[1], \"Iris-virginica\")\n\n# To get the most likely observation, we do\n\nmode(yhat[1])\n\n# These can be broadcast over multiple predictions in the usual way:\n\nbroadcast(pdf, yhat[1:4], \"Iris-versicolor\")\n\n#-\n\nmode.(yhat[1:4])\n\n# Or, alternatively, you can use the `predict_mode` operation instead\n# of `predict`:\n\npredict_mode(mach, X[test,:])[1:4] # or predict_mode(mach, rows=test)[1:4]\n\n# For a more conventional matrix of probabilities you can do this:\n\nL = levels(y)\npdf(yhat, L)[1:4, :]\n\n# However, in a typical MLJ workflow, this is not as useful as you\n# might imagine. In particular, all probablistic performance measures\n# in MLJ expect distribution objects in their first slot:\n\ncross_entropy(yhat, y[test]) |> mean\n\n# To apply a deterministic measure, we first need to obtain point-estimates:\n\nmisclassification_rate(mode.(yhat), y[test])\n\n# We note in passing that there is also a search tool for measures\n# analogous to `models`:\n\nmeasures(matching(y))\n\n\n# ### Step 4. Evaluate the model performance\n\n# Naturally, MLJ provides boilerplate code for carrying out a model\n# evaluation with a lot less fuss. Let's repeat the performance\n# evaluation above and add an extra measure, `brier_score`:\n\nevaluate!(mach, resampling=Holdout(fraction_train=0.7),\n          measures=[cross_entropy, brier_score])\n\n# Or applying cross-validation instead:\n\nevaluate!(mach, resampling=CV(nfolds=6),\n          measures=[cross_entropy, brier_score])\n\n# Or, Monte-Carlo cross-validation (cross-validation repeated\n# randomizied folds)\n\ne = evaluate!(mach, resampling=CV(nfolds=6, rng=123),\n                repeats=3,\n              measures=[cross_entropy, brier_score])\n\n# One can access the following properties of the output `e` of an\n# evaluation: `measure`, `measurement`, `per_fold` (measurement for\n# each fold) and `per_observation` (measurement per observation, if\n# reported).\n\n# We finally note that you can restrict the rows of observations from\n# which train and test folds are drawn, by specifying `rows=...`. For\n# example, imagining the last 30% of target observations are `missing`\n# you might have a workflow like this:\n\ntrain, test = partition(eachindex(y), 0.7)\nmach = machine(model, X, y)\nevaluate!(mach, resampling=CV(nfolds=6),\n          measures=[cross_entropy, brier_score],\n          rows=train)     # cv estimate, resampling from `train`\nfit!(mach, rows=train)    # re-train using all of `train` observations\npredict(mach, rows=test); # and predict missing targets\n\n\n# ### On learning curves\n\n# Since our model is an iterative one, we might want to inspect the\n# out-of-sample performance as a function of the iteration\n# parameter. For this we can use the `learning_curve` function (which,\n# incidentally can be applied to any model hyper-parameter). This\n# starts by defining a one-dimensional range object for the parameter\n# (more on this when we discuss tuning in Part 4):\n\nr = range(model, :epochs, lower=1, upper=50, scale=:log)\ncurve = learning_curve(mach,\n                       range=r,\n                       resampling=Holdout(fraction_train=0.7), # (default)\n                       measure=cross_entropy)\n\nusing Plots\npyplot(size=(490,300))\nplt=plot(curve.parameter_values, curve.measurements)\nxlabel!(plt, \"epochs\")\nylabel!(plt, \"cross entropy on holdout set\")\nplt\n\n# We will return to learning curves when we look at tuning in Part 4.\n\n\n# ### Resources for Part 2\n\n# - From the MLJ manual:\n#     - [Getting Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/)\n#     - [Model Search](https://alan-turing-institute.github.io/MLJ.jl/dev/model_search/)\n#     - [Evaluating Performance](https://alan-turing-institute.github.io/MLJ.jl/dev/evaluating_model_performance/) (using `evaluate!`)\n#     - [Learning Curves](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)\n#     - [Performance Measures](https://alan-turing-institute.github.io/MLJ.jl/dev/performance_measures/) (loss functions, scores, etc)\n# - From Data Science Tutorials:\n#     - [Choosing and evaluating a model](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/choosing-a-model/)\n#     - [Fit, predict, transform](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/fit-and-predict/)\n\n\n# ### Exercises for Part 2\n\n\n# #### Exercise 4\n\n# (a) Identify all supervised MLJ models that can be applied (without\n# type coercion or one-hot encoding) to a supervised learning problem\n# with input features `X4` and target `y4` defined below:\n\nimport Distributions\npoisson = Distributions.Poisson\n\nage = 18 .+ 60*rand(10);\nsalary = coerce(rand([:small, :big, :huge], 10), OrderedFactor);\nlevels!(salary, [:small, :big, :huge]);\nX4 = DataFrames.DataFrame(age=age, salary=salary)\n\nn_devices(salary) = salary > :small ? rand(poisson(1.3)) : rand(poisson(2.9))\ny4 = [n_devices(row.salary) for row in eachrow(X4)]\n\n# (b) What models can be applied if you coerce the salary to a\n# `Continuous` scitype?\n\n\n# #### Exercise 5 (unpack)\n\n# After evaluating the following ...\n\ndata = (a = [1, 2, 3, 4],\n     b = rand(4),\n     c = rand(4),\n     d = coerce([\"male\", \"female\", \"female\", \"male\"], OrderedFactor));\npretty(data)\n\nusing Tables\ny, X, w = unpack(data, ==(:a),\n                 name -> elscitype(Tables.getcolumn(data, name)) == Continuous,\n                 name -> true);\n\n# ...attempt to guess the evaluations of the following:\n\ny\n\n#-\n\npretty(X)\n\n#-\n\nw\n\n\n# #### Exercise 6 (first steps in modelling Horse Colic)\n\n# (a) Suppose we want to use predict the `:outcome` variable in the\n# Horse Colic study introduced in Part 1, based on the remaining\n# variables that are `Continuous` (one-hot encoding categorical\n# variables is discussed later in Part 3) *while ignoring the others*.\n# Extract from the `horse` data set (defined in Part 1) appropriate\n# input features `X` and target variable `y`. (Do not, however,\n# randomize the observations.)\n\n# (b) Create a 70:30 `train`/`test` split of the data and train a\n# `LogisticClassifier` model, from the `MLJLinearModels` package, on\n# the `train` rows. Use `lambda=100` and default values for the\n# other hyper-parameters. (Although one would normally standardize\n# (whiten) the continuous features for this model, do not do so here.)\n# After training:\n\n# - (i) Recalling that a logistic classifier (aka logistic regressor) is\n#   a linear-based model learning a *vector* of coefficients for each\n#   feature (one coefficient for each target class), use the\n#   `fitted_params` method to find this vector of coefficients in the\n#   case of the `:pulse` feature. (To convert a vector of pairs `v =\n#   [x1 => y1, x2 => y2, ...]` into a dictionary, do `Dict(v)`.)\n\n# - (ii) Evaluate the `cross_entropy` performance on the `test`\n#   observations.\n\n# - &star;(iii) In how many `test` observations does the predicted\n#   probablility of the observed class exceed 50%?\n\n# - (iv) Find the `misclassification_rate` in the `test`\n#   set. (*Hint.* As this measure is deterministic, you will either\n#   need to broadcast `mode` or use `predict_mode` instead of\n#   `predict`.)\n\n# (c) Instead use a `RandomForestClassifier` model from the\n#     `DecisionTree` package and:\n#\n# - (i) Generate an appropriate learning curve to convince yourself\n#   that out-of-sample estimates of the `cross_entropy` loss do not\n#   substatially improve for `n_trees > 50`. Use default values for\n#   all other hyper-parameters, and feel free to use all available\n#   data to generate the curve.\n\n# - (ii) Fix `n_trees=90` and use `evaluate!` to obtain a 9-fold\n#   cross-validation estimate of the `cross_entropy`, restricting\n#   sub-sampling to the `train` observations.\n\n# - (iii) Now use *all* available data but set\n#   `resampling=Holdout(fraction_train=0.7)` to obtain a score you can\n#   compare with the `KNNClassifier` in part (b)(iii). Which model is\n#   better?\n\n# <a id='part-3-transformers-and-pipelines'></a>\n\n\n# ## Part 3 - Transformers and Pipelines\n\n# ### Transformers\n\n# Unsupervised models, which receive no target `y` during training,\n# always have a `transform` operation. They sometimes also support an\n# `inverse_transform` operation, with obvious meaning, and sometimes\n# support a `predict` operation (see the clustering example discussed\n# [here](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Transformers-that-also-predict-1)).\n# Otherwise, they are handled much like supervised models.\n\n# Here's a simple standardization example:\n\nx = rand(100);\n@show mean(x) std(x);\n\n#-\n\nmodel = Standardizer() # a built-in model\nmach = machine(model, x)\nfit!(mach)\nx\u0302 = transform(mach, x);\n@show mean(x\u0302) std(x\u0302);\n\n# This particular model has an `inverse_transform`:\n\ninverse_transform(mach, x\u0302) \u2248 x\n\n\n# ### Re-encoding the King County House data as continuous\n\n# For further illustrations of tranformers, let's re-encode *all* of the\n# King County House input features (see [Ex\n# 3](#ex-3-fixing-scitypes-in-a-table)) into a set of `Continuous`\n# features. We do this with the `ContinousEncoder` model, which, by\n# default, will:\n\n# - one-hot encode all `Multiclass` features\n# - coerce all `OrderedFactor` features to `Continuous` ones\n# - coerce all `Count` features to `Continuous` ones (there aren't any)\n# - drop any remaining non-Continuous features (none of these either)\n\n# First, we reload the data and fix the scitypes (Exercise 3):\n\nfile = CSV.File(joinpath(DIR, \"data\", \"house.csv\"));\nhouse = DataFrames.DataFrame(file)\ncoerce!(house, autotype(file))\ncoerce!(house, Count => Continuous, :zipcode => Multiclass);\nschema(house)\n\n#-\n\ny, X = unpack(house, ==(:price), name -> true, rng=123);\n\n# Instantiate the unsupervised model (transformer):\n\nencoder = ContinuousEncoder() # a built-in model; no need to @load it\n\n# Bind the model to the data and fit!\n\nmach = machine(encoder, X) |> fit!;\n\n# Transform and inspect the result:\n\nXcont = transform(mach, X);\nschema(Xcont)\n\n\n# ### More transformers\n\n# Here's how to list all of MLJ's unsupervised models:\n\nmodels(m->!m.is_supervised)\n\n# Some commonly used ones are built-in (do not require `@load`ing):\n\n# model type                  | does what?\n# ----------------------------|----------------------------------------------\n# ContinuousEncoder | transform input table to a table of `Continuous` features (see above)\n# FeatureSelector | retain or dump selected features\n# FillImputer | impute missing values\n# OneHotEncoder | one-hot encoder `Multiclass` (and optionally `OrderedFactor`) features\n# Standardizer | standardize (whiten) a vector or all `Continuous` features of a table\n# UnivariateBoxCoxTransformer | apply a learned Box-Cox transformation to a vector\n# UnivariateDiscretizer | discretize a `Continuous` vector, and hence render its elscityp `OrderedFactor`\n\n\n# In addition to \"dynamic\" transformers (ones that learn something\n# from the data and must be `fit!`) users can wrap ordinary functions\n# as transformers, and such *static* transformers can depend on\n# parameters, like the dynamic ones. See\n# [here](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/#Static-transformers-1)\n# for how to define your own static transformers.\n\n\n# ### Pipelines\n\nlength(schema(Xcont).names)\n\n# Let's suppose that additionally we'd like to reduce the dimension of\n# our data.  A model that will do this is `PCA` from\n# `MultivariateStats`:\n\nreducer = @load PCA\n\n# Now, rather simply repeating the workflow above, applying the new\n# transformation to `Xcont`, we can combine both the encoding and the\n# dimension-reducing models into a single model, known as a\n# *pipeline*. While MLJ offers a powerful interface for composing\n# models in a variety of ways, we'll stick to these simplest class of\n# composite models for now. The easiest way to construct them is using\n# the `@pipeline` macro:\n\npipe = @pipeline encoder reducer\n\n# Notice that `pipe` is an *instance* of an automatically generated\n# type (called `Pipeline<some digits>`).\n\n# The new model behaves like any other transformer:\n\nmach = machine(pipe, X) |> fit!;\nXsmall = transform(mach, X)\nschema(Xsmall)\n\n# Want to combine this pre-processing with ridge regression?\n\nrgs = @load RidgeRegressor pkg=MLJLinearModels\npipe2 = @pipeline encoder reducer rgs\n\n# Now our pipeline is a supervised model, instead of a transformer,\n# whose performance we can evaluate:\n\nmach = machine(pipe2, X, y) |> fit!\nevaluate!(mach, measure=mae, resampling=Holdout()) # CV(nfolds=6) is default\n\n\n# ### Training of composite models is \"smart\"\n\n# Now notice what happens if we train on all the data, then change a\n# regressor hyper-parameter and retrain:\n\nfit!(mach)\n\n#-\n\npipe2.ridge_regressor.lambda = 0.1\nfit!(mach)\n\n# Second time only the ridge regressor is retrained!\n\n# Mutate a hyper-parameter of the `PCA` model and every model except\n# the `ContinuousEncoder` (which comes before it will be retrained):\n\npipe2.pca.pratio = 0.9999\nfit!(mach)\n\n\n# ### Inspecting composite models\n\n# The dot syntax used above to change the values of *nested*\n# hyper-parameters is also useful when inspecting the learned\n# parameters and report generated when training a composite model:\n\nfitted_params(mach).ridge_regressor\n\n#-\n\nreport(mach).pca\n\n\n# ### Incorporating target transformations\n\n# Next, suppose that instead of using the raw `:price` as the\n# training target, we want to use the log-price (a common practice in\n# dealing with house price data). However, suppose that we still want\n# to report final *predictions* on the original linear scale (and use\n# these for evaluation purposes). Then we supply appropriate functions\n# to key-word arguments `target` and `inverse`.\n\n# First we'll overload `log` and `exp` for broadcasting:\nBase.log(v::AbstractArray) = log.(v)\nBase.exp(v::AbstractArray) = exp.(v)\n\n# Now for the new pipeline:\n\npipe3 = @pipeline encoder reducer rgs target=log inverse=exp\nmach = machine(pipe3, X, y)\nevaluate!(mach, measure=mae)\n\n# MLJ will also allow you to insert *learned* target\n# transformations. For example, we might want to apply\n# `Standardizer()` to the target, to standarize it, or\n# `UnivariateBoxCoxTransformer()` to make it look Gaussian. Then\n# instead of specifying a *function* for `target`, we specify a\n# unsupervised *model* (or model type). One does not specify `inverse`\n# because only models implementing `inverse_transform` are\n# allowed.\n\n# Let's see which of these two options results in a better outcome:\n\nbox = UnivariateBoxCoxTransformer(n=20)\nstand = Standardizer()\n\npipe4 = @pipeline encoder reducer rgs target=box\nmach = machine(pipe4, X, y)\nevaluate!(mach, measure=mae)\n\n#-\n\npipe4.target = stand\nevaluate!(mach, measure=mae)\n\n\n# ### Resources for Part 3\n\n# - From the MLJ manual:\n#     - [Transformers and other unsupervised models](https://alan-turing-institute.github.io/MLJ.jl/dev/transformers/)\n#     - [Linear pipelines](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Linear-pipelines-1)\n# - From Data Science Tutorials:\n#     - [Composing models](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/composing-models/)\n\n\n# ### Exercises for Part 3\n\n# #### Exercise 7\n\n# Consider again the Horse Colic classification problem considered in\n# Exercise 6, but with all features, `Finite` and `Infinite`:\n\ny, X = unpack(horse, ==(:outcome), name -> true);\nschema(X)\n\n# (a) Define a pipeline that:\n# - uses `Standardizer` to ensure that features that are already\n#   continuous are centred at zero and have unit variance\n# - re-encodes the full set of features as `Continuous`, using\n#   `ContinuousEncoder`\n# - uses the `KMeans` clustering model from `Clustering.jl`\n#   to reduce the dimension of the feature space to `k=10`.\n# - trains a `EvoTreeClassifier` (a gradient tree boosting\n#   algorithm in `EvoTrees.jl`) on the reduced data, using\n#   `nrounds=50` and default values for the other\n#    hyper-parameters\n\n# (b) Evaluate the pipeline on all data, using 6-fold cross-validation\n# and `cross_entropy` loss.\n\n# &star;(c) Plot a learning curve which examines the effect on this loss\n# as the tree booster parameter `max_depth` varies from 2 to 10.\n\n# <a id='part-4-tuning-hyper-parameters'></a>\n\n\n# ## Part 4 - Tuning Hyper-parameters\n\n# ### Naive tuning of a single parameter\n\n# The most naive way to tune a single hyper-parameter is to use\n# `learning_curve`, which we alread saw in Part 2. Let's see this in\n# the Horse Colic classification problem, in a case where the parameter\n# to be tuned is *nested* (because the model is a pipeline):\n\ny, X = unpack(horse, ==(:outcome), name -> true);\n\n@load LogisticClassifier pkg=MLJLinearModels\nmodel = @pipeline Standardizer ContinuousEncoder LogisticClassifier\nmach = machine(model, X, y)\n\n#-\n\nr = range(model, :(logistic_classifier.lambda), lower = 1e-2, upper=100, scale=:log10)\n\n# If you're curious, you can see what `lambda` values this range will\n# generate for a given resolution:\n\niterator(r, 5)\n\n#-\n\n_, _, lambdas, losses = learning_curve(mach,\n                                       range=r,\n                                       resampling=CV(nfolds=6),\n                                       resolution=30, # default\n                                       measure=cross_entropy)\nplt=plot(lambdas, losses, xscale=:log10)\nxlabel!(plt, \"epochs\")\nylabel!(plt, \"cross entropy on holdout set\")\n\n#-\n\nbest_lambda = lambdas[argmin(losses)]\n\n\n# ### Self tuning models\n\n# A more sophisticated way to view hyper-parameter tuning (inspired by\n# MLR) is as a model *wrapper*. The wrapped model is a new model in\n# its own right and when you fit it, it tunes specified\n# hyper-parameters of the model being wrapped, before training on all\n# supplied data. Calling `predict` on the wrapped model is like\n# calling `predict` on the original model, but with the\n# hyper-parameters already optimized.\n\n# In other words, we can think of the wrapped model as a \"self-tuning\"\n# version of the original.\n\n# We now create a self-tuning version of the pipeline above, adding a\n# parameter from the `ContiuousEncoder` to the parameters we want\n# optimized.\n\n# First, let's choose a tuning strategy (from [these\n# options](https://github.com/alan-turing-institute/MLJTuning.jl#what-is-provided-here). MLJ\n# supports ordinary `Grid` search (query `?Grid` for\n# details). However, as the utility of `Grid` search is limited to a\n# small number of parameters, we'll demonstrate `RandomSearch` here:\n\ntuning = RandomSearch(rng=123)\n\n# In this strategy each parameter is sampled according to a\n# pre-specified prior distribution that is fit to the one-dimensional\n# range object constructed using `range` as before. While one has a\n# lot of control over the specification of the priors (run\n# `?RandomSearch` for details) we'll let the algorithm generate these\n# priors automatically.\n\n# In `RandomSearch` the `scale` attribute of a one-dimensional range\n# only plays a role if we specify a *function*, which means we'll need\n# to apply the corresponding inverse transform to our bounds, like\n# this:\n\nr = range(model, :(logistic_classifier.lambda), lower = -2, upper=2, scale=x->10^x)\n\n# By default, a *bounded* range is sampled uniformly (before the\n# `:scale` function is applied). We can see what this means like this:\n\nimport Distributions\nsampler_r = sampler(r, Distributions.Uniform)\nhistogram(rand(sampler_r, 10000), nbins=50)\n\n#-\n\n# Alternatively, we can replace `r` with a positive *unbounded* range\n# which, by default, is sampled using a `Gamma` distribution (which\n# has an infinite decaying tail). An positive unbounded range is specified in\n# this way:\n\nr = range(model, :(logistic_classifier.lambda), lower=0, origin=6, unit=5)\n\n# And we then get this kind of distribution:\n\nsampler_r = sampler(r, Distributions.Gamma)\nhistogram(rand(sampler_r, 10000), nbins=50)\n\n# The second parameter we'll add to this is *nominal* (finite) and, by\n# default, will be sampled uniformly. Since it is nominal, we specify\n# `values` instead of `upper` and `lower` bounds:\n\ns  = range(model, :(continuous_encoder.one_hot_ordered_factors),\n           values = [true, false])\n#-\n\n# Now for the wrapper, which is an instance of `TunedModel`:\n\ntuned_model = TunedModel(model=model,\n                         ranges=[r, s],\n                         resampling=CV(nfolds=6),\n                         measures=cross_entropy,\n                         tuning=tuning,\n                         n=15)\n\n# We can apply the `fit!/predict` workflow to `tuned_model` just as\n# for any other model:\n\ntuned_mach = machine(tuned_model, X, y);\nfit!(tuned_mach);\npredict(tuned_mach, rows=1:3)\n\n# The outcomes of the tuning can be inspected from a detailed\n# report. For example, we have:\n\nrep = report(tuned_mach);\nrep.best_model\n\n# By default, sampling of a bounded range is uniform. Lets\n\n# In the special case of two-parameters, you can also plot the results:\n\nplot(tuned_mach)\n\n# Finally, let's compare cross-validation estimate of the\n# performance of the self-tuning model with that of the model\n# (an example of [*nested\n# resampling*](https://mlr3book.mlr-org.com/nested-resampling.html) here):\n\nerr = evaluate!(mach, resampling=CV(nfolds=3), measure=cross_entropy);\n\n#-\n\ntuned_err = evaluate!(tuned_mach, resampling=CV(nfolds=3), measure=cross_entropy);\n\n# ### Resources for Part 4\n#\n# - From the MLJ manual:\n#    - [Learning Curves]https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)\n#    - [Tuning Models](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/)\n# - The [MLJTuning repo](https://github.com/alan-turing-institute/MLJTuning.jl#who-is-this-repo-for) - mostly for developers\n#\n# - From Data Science Tutorials:\n#     - [Tuning a model](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/model-tuning/)\n#     - [Crabs with XGBoost](https://alan-turing-institute.github.io/DataScienceTutorials.jl/end-to-end/crabs-xgb/) `Grid` tuning in stages for a tree-boosting model with many parameters\n#     - [Boston with LightGBM](https://alan-turing-institute.github.io/DataScienceTutorials.jl/end-to-end/boston-lgbm/) -  `Grid` tuning for another popular tree-booster\n#     - [Boston with Flux](https://alan-turing-institute.github.io/DataScienceTutorials.jl/end-to-end/boston-flux/) - optimizing batch size in a simple neural network regressor\n# - [UCI Horse Colic Data Set](http://archive.ics.uci.edu/ml/datasets/Horse+Colic)\n\n\n# ### Exercises for Part 4\n\n# #### Exercise 8\n\n# This exercise continues our analysis of the King County House price\n# prediction problem:\n\ny, X = unpack(house, ==(:price), name -> true, rng=123);\n\n# Your task will be to tune the following pipeline regression model:\n\n@load(EvoTreeRegressor)\ntree_booster = EvoTreeRegressor(nrounds = 70)\nmodel = @pipeline ContinuousEncoder tree_booster\n\n# (a) Construct a bounded range `r1` for the `evo_tree_booster`\n# parameter `max_depth`, varying between 1 and 12.\n\n# &star;(b) Define the one-dimensional range\n\nr2 = range(model,\n           :(evo_tree_regressor.nbins),\n           lower = 2.5,\n           upper= 7.5, scale=x->2^round(Int, x))\n\n# and try to guess the outcome of evaluating the following two code blocks:\n\nr2_sampler = sampler(r2, Distributions.Uniform)\nsamples = rand(r2_sampler, 1000);\nhistogram(samples, nbins=50)\n\n#-\n\nsort(unique(samples))\n\n# (c) Optimize these two parameters over the ranges `r1` and `r2`\n# using a random search with uniform priors. Use `Holdout()`\n# resampling, and implement your search by first constructing a\n# \"self-tuning\" wrap of `model`, as described above. Make `mae` (mean\n# absolute error) the loss function that you optimize, and search a\n# total 40 models (combinations of hyper-parameters).  Plot the\n# results of your search. Feel free to use all available data.\n\n# (d) Evaluate the best model found in the search using 3-fold\n# cross-validation and compare with that of the self-tuning model\n# (which is different!). Setting data hygiene concerns aside, feel\n# free to use all available data.\n\n\n# ## Solutions to exercises\n\n\n# #### Exercise 2 solution\n\nquality = coerce(quality, OrderedFactor);\nlevels!(quality, [\"poor\", \"good\", \"excellent\"]);\nelscitype(quality)\n\n\n# #### Exercise 3 solution\n\n# First pass:\n\ncoerce!(house, autotype(house));\nschema(house)\n\n#-\n\n# All the \"sqft\" fields refer to \"square feet\" so are\n# really `Continuous`. We'll regard `:yr_built` (the other `Count`\n# variable above) as `Continuous` as well. So:\n\ncoerce!(house, Count => Continuous);\n\n# And `:zipcode` should not be ordered:\n\ncoerce!(house, :zipcode => Multiclass);\nschema(house)\n\n# `:bathrooms` looks like it has a lot of levels, but on further\n# inspection we see why, and `OrderedFactor` remains appropriate:\n\nimport StatsBase.countmap\ncountmap(house.bathrooms)\n\n\n# #### Exercise 4 solution\n\n# 4(a)\n\n# There are *no* models that apply immediately:\n\nmodels(matching(X4, y4))\n\n# 4(b)\n\ny4 = coerce(y4, Continuous);\nmodels(matching(X4, y4))\n\n\n# #### Exercise 6 solution\n\n# 6(a)\n\ny, X = unpack(horse,\n              ==(:outcome),\n              name -> elscitype(Tables.getcolumn(horse, name)) == Continuous);\n\n# 6(b)(i)\n\nmodel = @load LogisticClassifier pkg=MLJLinearModels;\nmodel.lambda = 100\nmach = machine(model, X, y)\nfit!(mach, rows=train)\nfitted_params(mach)\n\n#-\n\ncoefs_given_feature = Dict(fitted_params(mach).coefs)\ncoefs_given_feature[:pulse]\n\n#6(b)(ii)\n\nyhat = predict(mach, rows=test); # or predict(mach, X[test,:])\nerr = cross_entropy(yhat, y[test]) |> mean\n\n# 6(b)(iii)\n\n# The predicted probabilities of the actual observations in the test\n# are given by\n\np = broadcast(pdf, yhat, y[test]);\n\n# The number of times this probability exceeds 50% is:\nn50 = filter(x -> x > 0.5, p) |> length\n\n# Or, as a proportion:\n\nn50/length(test)\n\n# 6(b)(iv)\n\nmisclassification_rate(mode.(yhat), y[test])\n\n# 6(c)(i)\n\nmodel = @load RandomForestClassifier pkg=DecisionTree\nmach = machine(model, X, y)\nevaluate!(mach, resampling=CV(nfolds=6), measure=cross_entropy)\n\nr = range(model, :n_trees, lower=10, upper=70, scale=:log)\n\n# Since random forests are inherently randomized, we generate multiple\n# curves:\n\nplt = plot()\nfor i in 1:4\n    curve = learning_curve(mach,\n                           range=r,\n                           resampling=Holdout(),\n                           measure=cross_entropy)\n    plt=plot!(curve.parameter_values, curve.measurements)\nend\nxlabel!(plt, \"n_trees\")\nylabel!(plt, \"cross entropy\")\nplt\n\n# 6(c)(ii)\n\nevaluate!(mach, resampling=CV(nfolds=9),\n                measure=cross_entropy,\n                rows=train).measurement[1]\n\nmodel.n_trees = 90\n\n# 6(c)(iii)\n\nerr_forest = evaluate!(mach, resampling=Holdout(),\n                       measure=cross_entropy).measurement[1]\n\n# #### Exercise 7\n\n# (a)\n\n@load KMeans pkg=Clustering\n@load EvoTreeClassifier\npipe = @pipeline(Standardizer,\n                 ContinuousEncoder,\n                 KMeans(k=10),\n                 EvoTreeClassifier(nrounds=50))\n\n# (b)\n\nmach = machine(pipe, X, y)\nevaluate!(mach, resampling=CV(nfolds=6), measure=cross_entropy)\n\n# (c)\n\nr = range(pipe, :(evo_tree_classifier.max_depth), lower=1, upper=10)\n\ncurve = learning_curve(mach,\n                       range=r,\n                       resampling=CV(nfolds=6),\n                       measure=cross_entropy)\n\nplt = plot(curve.parameter_values, curve.measurements)\nxlabel!(plt, \"max_depth\")\nylabel!(plt, \"CV estimate of cross entropy\")\nplt\n\n# Here's a second curve using a different random seed for the booster:\n\npipe.evo_tree_classifier.seed = 123\ncurve = learning_curve(mach,\n                       range=r,\n                       resampling=CV(nfolds=6),\n                       measure=cross_entropy)\nplot!(curve.parameter_values, curve.measurements)\n\n\n# #### Exercise 8\n\ny, X = unpack(house, ==(:price), name -> true, rng=123);\n\n@load(EvoTreeRegressor)\ntree_booster = EvoTreeRegressor(nrounds = 70)\nmodel = @pipeline ContinuousEncoder tree_booster\n\n# (a)\n\nr1 = range(model, :(evo_tree_regressor.max_depth), lower=1, upper=12)\n\n# (c)\n\ntuned_model = TunedModel(model=model,\n                         ranges=[r1, r2],\n                         resampling=Holdout(),\n                         measures=mae,\n                         tuning=RandomSearch(rng=123),\n                         n=40)\n\ntuned_mach = machine(tuned_model, X, y) |> fit!\nplot(tuned_mach)\n\n# (d)\n\nbest_model = report(tuned_mach).best_model;\nbest_mach = machine(best_model, X, y);\nbest_err = evaluate!(best_mach, resampling=CV(nfolds=3), measure=mae)\n\n#-\n\ntuned_err = evaluate!(tuned_mach, resampling=CV(nfolds=3), measure=mae)\n\n\nusing Literate #src\nLiterate.markdown(@__FILE__, @__DIR__, execute=true) #src\nLiterate.notebook(@__FILE__, @__DIR__, execute=false) #src\n\n", "meta": {"hexsha": "dcb549c7006bae5297eeed1e8815db6ceb2aaaa1", "size": 43418, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials.jl", "max_stars_repo_name": "darenasc/MachineLearningInJulia2020", "max_stars_repo_head_hexsha": "3a88d7471dcdb4c91983dffd52b00ff117329798", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tutorials.jl", "max_issues_repo_name": "darenasc/MachineLearningInJulia2020", "max_issues_repo_head_hexsha": "3a88d7471dcdb4c91983dffd52b00ff117329798", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tutorials.jl", "max_forks_repo_name": "darenasc/MachineLearningInJulia2020", "max_forks_repo_head_hexsha": "3a88d7471dcdb4c91983dffd52b00ff117329798", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9228118539, "max_line_length": 342, "alphanum_fraction": 0.7054217145, "num_tokens": 11409, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.34864513533394575, "lm_q2_score": 0.1732882144682526, "lm_q1q2_score": 0.060416092985061745}}
{"text": "function goma(xs)\n    return sum(x for x in xs)\nend\n\nfunction goma(x::T, y::T...) where {T<:Integer}\n    return goma((x, y...))\nend\n\n\n@show goma(1, 2, 3)\n@show goma((1, 2, 3))\n@show goma([1, 2, 3])\n", "meta": {"hexsha": "9196d55da11452842d08e34418b313f8a3f21495", "size": 198, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "splatExample/splat.jl", "max_stars_repo_name": "terasakisatoshi/juliaExer", "max_stars_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-02T01:24:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-04T12:03:25.000Z", "max_issues_repo_path": "splatExample/splat.jl", "max_issues_repo_name": "terasakisatoshi/juliaExer", "max_issues_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "splatExample/splat.jl", "max_forks_repo_name": "terasakisatoshi/juliaExer", "max_forks_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.2307692308, "max_line_length": 47, "alphanum_fraction": 0.5656565657, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.1276526369372765, "lm_q1q2_score": 0.060339292227277734}}
{"text": "x = 6\nif x < 10\n    println(\"Too small\")\nend\n\ny = 15\nif y < 10\n    println(\"y is too small\")\nelseif y > 10 && y < 25\n    println(\"y is medium\")\nelse\n    println(\"y is big\")\nend\n\nz = 7\nprintln(z < 10 ? \"z is small\" : \"z is big\")", "meta": {"hexsha": "7081fb2626f9aabe3d29dd79b5d06f45f4c96f6d", "size": 227, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "conditionsl.jl", "max_stars_repo_name": "rpillar/JuliaExamples", "max_stars_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "conditionsl.jl", "max_issues_repo_name": "rpillar/JuliaExamples", "max_issues_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "conditionsl.jl", "max_forks_repo_name": "rpillar/JuliaExamples", "max_forks_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.1875, "max_line_length": 43, "alphanum_fraction": 0.550660793, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.36658973632215985, "lm_q2_score": 0.16451646494473965, "lm_q1q2_score": 0.060310047504745966}}
{"text": "using OnlineStats\nexport groupreduce, groupby, aggregate, aggregate_vec, summarize, ApplyColwise\n\n\"\"\"\n`reduce(f, t::Table; select::Selection)`\n\nReduce `t` by applying `f` pair-wise on values or structs\nselected by `select`.\n\n`f` can be:\n\n1. A function\n2. An OnlineStat\n3. A tuple of functions and/or OnlineStats\n4. A named tuple of functions and/or OnlineStats\n5. A named tuple of (selector => function or OnlineStat) pairs\n\n```jldoctest reduce\njulia> t = table([0.1, 0.5, 0.75], [0,1,2], names=[:t, :x])\nTable with 3 rows, 2 columns:\nt     x\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n0.1   0\n0.5   1\n0.75  2\n```\n\nWhen `f` is a function, it reduces the selection as usual:\n\n```jldoctest reduce\njulia> reduce(+, t, select=:t)\n1.35\n```\n\nIf `select` is omitted, the rows themselves are passed to reduce as tuples.\n\n```jldoctest reduce\njulia> reduce((a, b) -> @NT(t=a.t+b.t, x=a.x+b.x), t)\n(t = 1.35, x = 3)\n```\n\nIf `f` is an OnlineStat object from the [OnlineStats](https://github.com/joshday/OnlineStats.jl) package, the statistic is computed on the selection.\n\n```jldoctest reduce\njulia> using OnlineStats\n\njulia> reduce(Mean(), t, select=:t)\nMean: n=3 | value=0.45\n```\n\n# Reducing with multiple functions\n\nOften one needs many aggregate values from a table. This is when `f` can be passed as a tuple of functions:\n\n```jldoctest reduce\njulia> y = reduce((min, max), t, select=:x)\n(min = 0, max = 2)\n\njulia> y.max\n2\n\njulia> y.min\n0\n```\n\nNote that the return value of invoking reduce with a tuple of functions\nwill be a named tuple which has the function names as the keys. In the example, we reduced using `min` and `max` functions to obtain the minimum and maximum values in column `x`.\n\nIf you want to give a different name to the fields in the output, use a named tuple as `f` instead:\n\n```jldoctest reduce\njulia> y = reduce(@NT(sum=+, prod=*), t, select=:x)\n(sum = 3, prod = 0)\n```\n\nYou can also compute many OnlineStats by passing tuple or named tuple of OnlineStat objects as the reducer.\n\n```jldoctest reduce\njulia> y = reduce((Mean(), Variance()), t, select=:t)\n(Mean = Mean: n=3 | value=0.45, Variance = Variance: n=3 | value=0.1075)\n\njulia> y.Mean\nMean: n=3 | value=0.45\n\njulia> y.Variance\nVariance: n=3 | value=0.1075\n```\n\n# Combining reduction and selection\n\nIn the above section where we computed many reduced values at once, we have been using the same selection for all reducers, that specified by `select`. It's possible to select different inputs for different reducers by using a named tuple of `slector => function` pairs:\n\n```jldoctest reduce\njulia> reduce(@NT(xsum=:x=>+, negtsum=(:t=>-)=>+), t)\n(xsum = 3, negtsum = -1.35)\n\n```\n\nSee [`Selection`](@ref) for more on what selectors can be specified. Here since each output can select its own input, `select` keyword is unsually unnecessary. If specified, the slections in the reducer tuple will be done over the result of selecting with the `select` argument.\n\n\"\"\"\nfunction reduce(f, t::Dataset; select=valuenames(t))\n    fs, input, T = init_inputs(f, rows(t, select), reduced_type, false)\n    acc = init_first(fs, input[1])\n    _reduce(fs, input, acc, 2)\nend\n\nfunction reduce(f, v0, t::Dataset; select=valuenames(t))\n    fs, input, T = init_inputs(f, rows(t, select), reduced_type, false)\n    _reduce(fs, input, v0, 1)\nend\n\n@deprecate reduce(f, t::Dataset, v0; select=valuenames(t)) reduce(f, v0, t::Dataset; select=select)\n\nfunction _reduce(fs, input, acc, start)\n    @inbounds @simd for i=start:length(input)\n        acc = _apply(fs, acc, input[i])\n    end\n    acc\nend\n\n## groupreduce\n\naddname(v, name) = v\naddname(v::Tup, name::Type{<:NamedTuple}) = v\naddname(v, name::Type{<:NamedTuple}) = name(v)\n\nstruct GroupReduce{F, S, T, P, N}\n    f::F\n    key::S\n    data::T\n    perm::P\n    name::N\n    n::Int\n\n    GroupReduce(f::F, key::S, data::T, perm::P; name::N = nothing) where{F, S, T, P, N} =\n        new{F, S, T, P, N}(f, key, data, perm, name, length(key))\nend\n\nBase.iteratorsize(::Type{<:GroupReduce}) = Base.SizeUnknown()\n\nBase.start(iter::GroupReduce) = 1\n\nfunction Base.next(iter::GroupReduce, i1)\n    f, key, data, perm, n, name = iter.f, iter.key, iter.data, iter.perm, iter.n, iter.name\n    val = init_first(f, data[perm[i1]])\n    i = i1+1\n    while i <= n && roweq(key, perm[i], perm[i1])\n        val = _apply(f, val, data[perm[i]])\n        i += 1\n    end\n    (key[perm[i1]] => addname(val, name)), i\nend\n\nBase.done(iter::GroupReduce, state) = state > iter.n\n\n\"\"\"\n`groupreduce(f, t[, by::Selection]; select::Selection)`\n\nGroup rows by `by`, and apply `f` to reduce each group. `f` can be a function, OnlineStat or a struct of these as described in [`reduce`](@ref). Recommended: see documentation for [`reduce`](@ref) first. The result of reducing each group is put in a table keyed by unique `by` values, the names of the output columns are the same as the names of the fields of the reduced tuples.\n\n# Examples\n\n```jldoctest groupreduce\njulia> t=table([1,1,1,2,2,2], [1,1,2,2,1,1], [1,2,3,4,5,6],\n               names=[:x,:y,:z]);\n\njulia> groupreduce(+, t, :x, select=:z)\nTable with 2 rows, 2 columns:\nx  +\n\u2500\u2500\u2500\u2500\u2500\n1  6\n2  15\n\njulia> groupreduce(+, t, (:x, :y), select=:z)\nTable with 4 rows, 3 columns:\nx  y  +\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1  3\n1  2  3\n2  1  11\n2  2  4\n\njulia> groupreduce((+, min, max), t, (:x, :y), select=:z)\nTable with 4 rows, 5 columns:\nx  y  +   min  max\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1  3   1    2\n1  2  3   3    3\n2  1  11  5    6\n2  2  4   4    4\n```\n\nIf `f` is a single function or a tuple of functions, the output columns will be named the same as the functions themselves. To change the name, pass a named tuple:\n\n```jldoctest groupreduce\njulia> groupreduce(@NT(zsum=+, zmin=min, zmax=max), t, (:x, :y), select=:z)\nTable with 4 rows, 5 columns:\nx  y  zsum  zmin  zmax\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1  3     1     2\n1  2  3     3     3\n2  1  11    5     6\n2  2  4     4     4\n```\n\nFinally, it's possible to select different inputs for different reducers by using a named tuple of `slector => function` pairs:\n\n```jldoctest groupreduce\njulia> groupreduce(@NT(xsum=:x=>+, negysum=(:y=>-)=>+), t, :x)\nTable with 2 rows, 3 columns:\nx  xsum  negysum\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  3     -4\n2  6     -4\n\n```\n\n\"\"\"\nfunction groupreduce(f, t::Dataset, by=pkeynames(t);\n                     select = t isa AbstractIndexedTable ? Not(by) : valuenames(t),\n                     cache=false)\n\n    if f isa ApplyColwise\n        if !(f.functions isa Union{Function, Type})\n            error(\"Only functions are supported in ApplyColwise for groupreduce\")\n        end\n        return groupby(grp->colwise_group_fast(f.functions, grp), t, by; select=select)\n    end\n\n    isa(f, Pair) && (f = (f,))\n\n    data = rows(t, select)\n\n    by = lowerselection(t, by)\n\n    if !isa(by, Tuple)\n        by=(by,)\n    end\n    key  = rows(t, by)\n    perm = sortpermby(t, by, cache=cache)\n\n    fs, input, T = init_inputs(f, data, reduced_type, false)\n\n    name = isa(t, NextTable) ? namedtuple(nicename(f)) : nothing\n    iter = GroupReduce(fs, key, input, perm, name=name)\n    convert(collectiontype(t), collect_columns(iter),\n            presorted=true, copy=false)\nend\n\ncolwise_group_fast(f, grp::Union{Columns, Dataset}) = map(c->reduce(f, c), columns(grp))\ncolwise_group_fast(f, grp::AbstractVector) = reduce(f, grp)\n\n## GroupBy\n\n_apply_with_key(f::Tup, data::Tup, process_data) = _apply(f, map(process_data, data))\n_apply_with_key(f::Tup, data, process_data) = _apply_with_key(f, columns(data), process_data)\n_apply_with_key(f, data, process_data) = _apply(f, process_data(data))\n\n_apply_with_key(f::Tup, key, data::Tup, process_data) = _apply(f, map(t->key, data), map(process_data, data))\n_apply_with_key(f::Tup, key, data, process_data) = _apply_with_key(f, key, columns(data), process_data)\n_apply_with_key(f, key, data, process_data) = _apply(f, key, process_data(data))\n\nstruct GroupBy\n    f\n    key\n    data\n    perm\n    usekey::Bool\n    name\n    n::Int\n\n    GroupBy(f, key, data, perm; usekey = false, name = nothing) =\n        new(f, key, data, perm, usekey, name, length(key))\nend\n\nBase.iteratorsize(::Type{<:GroupBy}) = Base.SizeUnknown()\n\nBase.start(::GroupBy) = 1\n\nfunction Base.next(iter::GroupBy, i1)\n    f, key, data, perm, usekey, n, name = iter.f, iter.key, iter.data, iter.perm, iter.usekey, iter.n, iter.name\n    i = i1+1\n    while i <= n && roweq(key, perm[i], perm[i1])\n        i += 1\n    end\n    process_data = t -> view(t, perm[i1:(i-1)])\n    val = usekey ? _apply_with_key(f, key[perm[i1]], data, process_data) :\n                   _apply_with_key(f, data, process_data)\n    (key[perm[i1]] => addname(val, name)), i\nend\n\nBase.done(iter, state) = state > iter.n\n\ncollectiontype(::Type{<:NDSparse}) = NDSparse\ncollectiontype(::Type{<:NextTable}) = NextTable\ncollectiontype(t::Dataset) = collectiontype(typeof(t))\n\n\"\"\"\n`groupby(f, t[, by::Selection]; select::Selection, flatten)`\n\nGroup rows by `by`, and apply `f` to each group. `f` can be a function or a tuple of functions. The result of `f` on each group is put in a table keyed by unique `by` values. `flatten` will flatten the result and can be used when `f` returns a vector instead of a single scalar value.\n\n# Examples\n\n```jldoctest groupby\njulia> t=table([1,1,1,2,2,2], [1,1,2,2,1,1], [1,2,3,4,5,6],\n               names=[:x,:y,:z]);\n\njulia> groupby(mean, t, :x, select=:z)\nTable with 2 rows, 2 columns:\nx  mean\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  2.0\n2  5.0\n\njulia> groupby(identity, t, (:x, :y), select=:z)\nTable with 4 rows, 3 columns:\nx  y  identity\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1  [1, 2]\n1  2  [3]\n2  1  [5, 6]\n2  2  [4]\n\njulia> groupby(mean, t, (:x, :y), select=:z)\nTable with 4 rows, 3 columns:\nx  y  mean\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1  1.5\n1  2  3.0\n2  1  5.5\n2  2  4.0\n```\n\nmultiple aggregates can be computed by passing a tuple of functions:\n\n```jldoctest groupby\njulia> groupby((mean, std, var), t, :y, select=:z)\nTable with 2 rows, 4 columns:\ny  mean  std       var\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  3.5   2.38048   5.66667\n2  3.5   0.707107  0.5\n\njulia> groupby(@NT(q25=z->quantile(z, 0.25), q50=median,\n                   q75=z->quantile(z, 0.75)), t, :y, select=:z)\nTable with 2 rows, 4 columns:\ny  q25   q50  q75\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  1.75  3.5  5.25\n2  3.25  3.5  3.75\n```\n\nFinally, it's possible to select different inputs for different functions by using a named tuple of `slector => function` pairs:\n\n```jldoctest groupby\njulia> groupby(@NT(xmean=:z=>mean, ystd=(:y=>-)=>std), t, :x)\nTable with 2 rows, 3 columns:\nx  xmean  ystd\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  2.0    0.57735\n2  5.0    0.57735\n```\n\nBy default, the result of groupby when `f` returns a vector or iterator of values will not be expanded. Pass the `flatten` option as `true` to flatten the grouped column:\n\n```jldoctest\njulia> t = table([1,1,2,2], [3,4,5,6], names=[:x,:y])\n\njulia> groupby((:normy => x->Iterators.repeated(mean(x), length(x)),),\n                t, :x, select=:y, flatten=true)\nTable with 4 rows, 2 columns:\nx  normy\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  3.5\n1  3.5\n2  5.5\n2  5.5\n```\n\nThe keyword option `usekey = true` allows to use information from the indexing column. `f` will need to accept two\narguments, the first being the key (as a `Tuple` or `NamedTuple`) the second the data (as `Columns`).\n\n```jldoctest\njulia> t = table([1,1,2,2], [3,4,5,6], names=[:x,:y])\n\njulia> groupby((:x_plus_mean_y => (key, d) -> key.x + mean(d),),\n                              t, :x, select=:y, usekey = true)\nTable with 2 rows, 2 columns:\nx  x_plus_mean_y\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n1  4.5\n2  7.5\n```\n\n\"\"\"\nfunction groupby end\n\nfunction groupby(f, t::Dataset, by=pkeynames(t);\n            select = t isa AbstractIndexedTable ? Not(by) : valuenames(t),\n            flatten=false, usekey = false)\n\n    isa(f, Pair) && (f = (f,))\n    data = rows(t, select)\n    f = init_func(f, data)\n    by = lowerselection(t, by)\n    if !(by isa Tuple)\n        by = (by,)\n    end\n\n    key = by == () ? fill((), length(t)) : rows(t, by)\n\n    fs, input, S = init_inputs(f, data, reduced_type, true)\n\n    if by == ()\n        res = usekey ? _apply_with_key(fs, (), input, identity) : _apply_with_key(fs, input, identity)\n        res_tup = addname(res, namedtuple(nicename(f)))\n        return flatten ? res_tup[end] : res_tup\n    end\n\n    perm = sortpermby(t, by)\n    # Note: we're not using S here, we'll let _groupby figure it out\n    name = isa(t, NextTable) ? namedtuple(nicename(f)) : nothing\n    iter = GroupBy(fs, key, input, perm, usekey = usekey, name = name)\n\n    t = convert(collectiontype(t), collect_columns(iter), presorted=true, copy=false)\n    t isa NextTable && flatten ?\n        IndexedTables.flatten(t, length(columns(t))) : t\nend\n\nstruct ApplyColwise{T}\n    functions::T\n    names::Vector{Symbol}\n    stack::Bool\n    variable::Symbol\nend\n\nApplyColwise(f; stack = false, variable = :variable) = ApplyColwise(f, [Symbol(f)], stack, variable)\nApplyColwise(t::Tuple; stack = false, variable = :variable) = ApplyColwise(t, [map(Symbol,t)...], stack, variable)\nApplyColwise(t::NamedTuple; stack = false, variable = :variable) = ApplyColwise(Tuple(values(t)), keys(t), stack, variable)\n\ninit_func(f, t) = f\ninit_func(ac::ApplyColwise{<:Tuple}, t::AbstractVector) =\n    Tuple(Symbol(n) => f for (f, n) in zip(ac.functions, ac.names))\ninit_func(ac::ApplyColwise{<:Tuple}, t::Columns) =\n    ac.stack ? dd -> Columns(colnames(t), ([f(x) for x in columns(dd)] for f in ac.functions)...; names = vcat(ac.variable, ac.names)) :\n        Tuple(Symbol(s, :_, n) => s => f for s in colnames(t), (f, n) in zip(ac.functions, ac.names))\ninit_func(ac::ApplyColwise, t::Columns) =\n    ac.stack ? dd -> Columns(colnames(t), [ac.functions(x) for x in columns(dd)]; names = vcat(ac.variable, ac.names)) :\n        Tuple(s => s => ac.functions for s in colnames(t))\ninit_func(ac::ApplyColwise, t::AbstractVector) = ac.functions\n\n\"\"\"\n`summarize(f, t, by = pkeynames(t); select = excludecols(t, by), stack = false, variable = :variable)`\n\nApply summary functions column-wise to a table. Return a `NamedTuple` in the non-grouped case\nand a table in the grouped case. Use `stack=true` to stack results of the same summary function for different columns.\n\n# Examples\n\n```jldoctest colwise\njulia> t = table([1, 2, 3], [1, 1, 1], names = [:x, :y]);\n\njulia> summarize((mean, std), t)\n(x_mean = 2.0, y_mean = 1.0, x_std = 1.0, y_std = 0.0)\n\njulia> s = table([\"a\",\"a\",\"b\",\"b\"], [1,3,5,7], [2,2,2,2], names = [:x, :y, :z], pkey = :x);\n\njulia> summarize(mean, s)\nTable with 2 rows, 3 columns:\nx    y    z\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\"a\"  2.0  2.0\n\"b\"  6.0  2.0\n\njulia> summarize(mean, s, stack = true)\nTable with 4 rows, 3 columns:\nx    variable  mean\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n\"a\"  :y        2.0\n\"a\"  :z        2.0\n\"b\"  :y        6.0\n\"b\"  :z        2.0\n```\n\nUse a `NamedTuple` to have different names for the summary functions:\n\n```jldoctest colwise\njulia> summarize(@NT(m = mean, s = std), t)\n(x_m = 2.0, y_m = 1.0, x_s = 1.0, y_s = 0.0)\n```\n\nUse `select` to only summarize some columns:\n\n```jldoctest colwise\njulia> summarize(@NT(m = mean, s = std), t, select = :x)\n(m = 2.0, s = 1.0)\n```\n\n\"\"\"\nfunction summarize(f, t, by = pkeynames(t); select = t isa AbstractIndexedTable ? excludecols(t, by) : valuenames(t), stack = false, variable = :variable)\n    flatten = stack && !(select isa Union{Int, Symbol})\n    s = groupby(ApplyColwise(f, stack = stack, variable = variable), t, by, select = select, flatten = flatten)\n    s isa Columns ? table(s, copy = false, presorted = true) : s\nend\n\n\nBase.@deprecate aggregate(f, t;\n                          by=pkeynames(t),\n                          with=valuenames(t)) groupreduce(f, t, by; select=with)\n\n\nBase.@deprecate aggregate_vec(\n    fs::Function, x;\n    names=nothing,\n    by=pkeynames(x),\n    with=valuenames(x)) groupby(names === nothing ? fs : (names => fs,), x; select=with)\n\nBase.@deprecate aggregate_vec(\n    fs::AbstractVector, x;\n    names=nothing,\n    by=pkeynames(x),\n    with=valuenames(x)) groupby(names === nothing ? (fs...) : (map(=>, names, fs)...,), x; select=with)\n\nBase.@deprecate aggregate_vec(t; funs...) groupby(namedtuple(first.(funs)...)(last.(funs)...), t)\n\n\n\"\"\"\n`convertdim(x::NDSparse, d::DimName, xlate; agg::Function, vecagg::Function, name)`\n\nApply function or dictionary `xlate` to each index in the specified dimension.\nIf the mapping is many-to-one, `agg` or `vecagg` is used to aggregate the results.\nIf `agg` is passed, it is used as a 2-argument reduction function over the data.\nIf `vecagg` is passed, it is used as a vector-to-scalar function to aggregate\nthe data.\n`name` optionally specifies a new name for the translated dimension.\n\"\"\"\nfunction convertdim(x::NDSparse, d::DimName, xlat; agg=nothing, vecagg=nothing, name=nothing, select=valuenames(x))\n    ks = setcol(pkeys(x), d, d=>xlat)\n    if name !== nothing\n        ks = renamecol(ks, d, name)\n    end\n\n    if vecagg !== nothing\n        y = convert(NDSparse, ks, rows(x, select))\n        return groupby(vecagg, y)\n    end\n\n    if agg !== nothing\n        return convert(NDSparse, ks, rows(x, select), agg=agg)\n    end\n    convert(NDSparse, ks, rows(x, select))\nend\n\nconvertdim(x::NDSparse, d::Int, xlat::Dict; agg=nothing, vecagg=nothing, name=nothing, select=valuenames(x)) = convertdim(x, d, i->xlat[i], agg=agg, vecagg=vecagg, name=name, select=select)\n\nconvertdim(x::NDSparse, d::Int, xlat, agg) = convertdim(x, d, xlat, agg=agg)\n\nsum(x::NDSparse) = sum(x.data)\n\n\"\"\"\n`reducedim(f, x::NDSparse, dims)`\n\nDrop `dims` dimension(s) and aggregate with `f`.\n\n```jldoctest\njulia> x = ndsparse(@NT(x=[1,1,1,2,2,2],\n                        y=[1,2,2,1,2,2],\n                        z=[1,1,2,1,1,2]), [1,2,3,4,5,6])\n3-d NDSparse with 6 values (Int64):\nx  y  z \u2502\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\n1  1  1 \u2502 1\n1  2  1 \u2502 2\n1  2  2 \u2502 3\n2  1  1 \u2502 4\n2  2  1 \u2502 5\n2  2  2 \u2502 6\n\njulia> reducedim(+, x, 1)\n2-d NDSparse with 3 values (Int64):\ny  z \u2502\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\n1  1 \u2502 5\n2  1 \u2502 7\n2  2 \u2502 9\n\njulia> reducedim(+, x, (1,3))\n1-d NDSparse with 2 values (Int64):\ny \u2502\n\u2500\u2500\u253c\u2500\u2500\u2500\n1 \u2502 5\n2 \u2502 16\n\n```\n\"\"\"\nfunction reducedim(f, x::NDSparse, dims)\n    keep = setdiff([1:ndims(x);], map(d->fieldindex(x.index.columns,d), dims))\n    if isempty(keep)\n        throw(ArgumentError(\"to remove all dimensions, use `reduce(f, A)`\"))\n    end\n    groupreduce(f, x, (keep...))\nend\n\nreducedim(f, x::NDSparse, dims::Symbol) = reducedim(f, x, [dims])\n\n\"\"\"\n`reducedim_vec(f::Function, arr::NDSparse, dims)`\n\nLike `reducedim`, except uses a function mapping a vector of values to a scalar instead\nof a 2-argument scalar function.\n\"\"\"\nfunction reducedim_vec(f, x::NDSparse, dims; with=valuenames(x))\n    keep = setdiff([1:ndims(x);], map(d->fieldindex(x.index.columns,d), dims))\n    if isempty(keep)\n        throw(ArgumentError(\"to remove all dimensions, use `reduce(f, A)`\"))\n    end\n    idxs, d = collect_columns(GroupBy(f, keys(x, (keep...)), rows(x, with), sortpermby(x, (keep...)))).columns\n    NDSparse(idxs, d, presorted=true, copy=false)\nend\n\nreducedim_vec(f, x::NDSparse, dims::Symbol) = reducedim_vec(f, x, [dims])\n", "meta": {"hexsha": "63025292ef7bdfdbb65081f5659cbce41fcff59a", "size": 18803, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/reduce.jl", "max_stars_repo_name": "ScottPJones/IndexedTables.jl", "max_stars_repo_head_hexsha": "7ee8cc5e9055210cf2f48ef272c1635f0299a438", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/reduce.jl", "max_issues_repo_name": "ScottPJones/IndexedTables.jl", "max_issues_repo_head_hexsha": "7ee8cc5e9055210cf2f48ef272c1635f0299a438", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/reduce.jl", "max_forks_repo_name": "ScottPJones/IndexedTables.jl", "max_forks_repo_head_hexsha": "7ee8cc5e9055210cf2f48ef272c1635f0299a438", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1330128205, "max_line_length": 379, "alphanum_fraction": 0.6316013402, "num_tokens": 6252, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207955, "lm_q2_score": 0.12252320771203076, "lm_q1q2_score": 0.06030446918631622}}
{"text": "### A Pluto.jl notebook ###\n# v0.15.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 8b92a6cf-cd6a-4364-9f59-598bcf206ef1\nhtml\"<button onclick=present()>Presentation mode</button>\"\n\n# \u2554\u2550\u2561 5c402564-e0e1-11eb-105b-951cfa19cacd\nmd\"\"\"\n# Julia for Statistics and Data Science\n\n- *Cecile Ane*, *Douglas Bates* & *Claudia Solis-Lemus* all with U. of Wisconsin - Madison\n\n1. What is **Julia**?\n    - How does it differ from other languages, particularly **R** and **Python**, used for data science?\n    - How do I get started?\n2. A deep dive into a shallow function - how does compilation work?\n3. Motivating example -- `MixedModels.jl` compared to R's `lme4`\n4. Some recommended packages for Statistics and Data Science  \n5. `RCall.jl` and `PyCall.jl`: to allow you to build on your current skills\n\"\"\"\n\n# \u2554\u2550\u2561 7e8e3903-a76d-4310-b5df-8508fdd7dba3\nmd\"\"\"\n## What is **Julia**?\n\n- an Open Source language designed for \"technical computing\".  The motivation is best explained in [this blog post](https://julialang.org/blog/2012/02/why-we-created-julia/) by its creators.\n- development of the [language itself](https://github.com/JuliaLang/julia) and most packages, e.g. [DataFrames](https://github.com/JuliaData/DataFrames.jl), takes place on github.\n- a few things to notice about [github.com/JuliaLang/julia](https://github.com/JuliaLang/julia)\n    - nearly 1100 contributors\n    - over 40,000 issues or pull requests\n    - over 50,000 commits\n    - the most common coding language used in the `julia` repository is Julia\n- installation is easiest using binary downloads from [julialang.org](https://julialang.org/downloads)\n\"\"\"\n\n# \u2554\u2550\u2561 dc65b3ed-2acf-4bf5-be2e-77dde963a7f1\nmd\"\"\"\n## Package Ecosystem\n- Julia packages are public git repositories (usually on `github.com` or `gitlab.com`) listed in the [General Registry](https://github.com/JuliaRegistries/General)\n- see the [Packages tab at julialang.org](https://julialang.org/packages) for links to package exploration tools.  I prefer the one at [JuliaHub](https://juliahub.com/ui/packages)\n    + when browsing Julia packages visit the repository and look at the Languages bar on the right hand side.  Most packages are 100% Julia code.  As [Erik Engheim wrote](https://medium.com/codex/is-julia-really-fast-12cd7caef96b), \"it's turtles all the way down\".\n    + [this blog posting](https://julialang.org/blog/2021/08/general-survey/) provides some analysis of the package registry contents\n- setting version numbers according to [semantic versioning](https://semver.org/) is strongly encouraged\n- often packages are maintained by organizations on github.com, e.g. DataFrames.jl belongs to the JuliaData organization.\n\"\"\"\n\n# \u2554\u2550\u2561 bb892d27-63f6-4267-bfcc-758e743c31d2\nmd\"\"\"\n## Ways to use Julia\n1. Start the App and use the Read-Eval-Print-Loop (REPL) **[demo this]**\n    - the REPL has name completion and other conveniences\n    - the REPL has \"modes\"\n        + standard Julia input\n        + help mode\n        + package manager mode\n        + shell mode\n        + some packages, e.g. `RCall`, enable their own modes\n2. An Integrated Development Environment (IDE)\n    - [Visual Studio Code](https://code.visualstudio.com/) (with the Julia extension) is the most widely used\n3. [Jupyter notebooks](https://jupyter.org) using the [IJulia package](https://github.com/JuliaLang/IJulia.jl)\n4. [Pluto](https://github.com/fonsp/Pluto.jl) notebooks, which we are using here.\n    - (+) notebooks are self-contained with regard to packages needed and versions thereof\n    - (+) notebooks are reactive - changes in one place are propagated to others\n    - (-) reactivity sometimes requires awkward code phrasing\n    - (-) output displayed is the result of `show` on the value of a block, which sometimes misses things that would normally be displayed\n\"\"\"\n\n# \u2554\u2550\u2561 864522f6-a214-4397-91cb-8d4d670e7638\nmd\"\"\"\n## Why Julia?\n- allows you to bypass the **two language problem**\n    + high-level, *dynamic* languages (R, Python, Matlab/Octave, etc.) allow for ease of use and high productivity\n    + often code in these languages is unable to give the performance required for high-throughput, production uses\n    + the usual solution is to re-write the performance-critical parts in a low-level, *static* language (C, C++, Fortran) and integrate that with the higher-level code\n- language and support system are carefully crafted to provide **both** high level tools and high performance\n- recently developed language with the benefit of hindsight and modern tools.\n\"\"\"\n\n# \u2554\u2550\u2561 d78b2581-31cd-4a08-9c8f-59aedc5c3086\nmd\"\"\"\n## Why not Julia?\n- recently developed language\n    + ecosystem is not as mature as R, Python, etc.\n    + don't have time to learn a new language and support system because this proposal is due Friday\n\"\"\"\n\n# \u2554\u2550\u2561 0aab07a1-893e-4c8b-ae8d-198146058d2e\nmd\"\"\"\n## In what ways is **Julia** similar to **R**?\n\n- Algorithms are expressed as **functions**\n- Functions are *generic* - different *methods* can be defined for argument *signatures*\n    * in **R** a function must be explicitly designated as a generic function\n    * **R** has different systems of generic functions and methods (S3 and S4)\n        - S3 method dispatch is on the first argument's class only (single dispatch)\n        - S4 allows for **multiple dispatch**\n- In Julia all functions are generic and all functions use multiple dispatch\n\"\"\"\n\n# \u2554\u2550\u2561 85fcacb1-7ad2-4fb8-9c87-28cfaa0f1404\nmd\"\"\"\n## What makes Julia fast?\n- The first time a particular method is invoked, it is compiled using a *Just In Time* compiler based on [**LLVM**](https://llvm.org) - the low-level virtual machine\n    * good news - the actual execution is fast and subsequent calls are fast\n    * bad news - the first call can be slow because of the compilation overhead\n        + known informally as the \"Time To First Plot\" problem\n- Ease of programming and ease of compilation are often antithetical goals\n    * ease of programming emphasizes generality provided by languages with dynamic types\n    * ease of compilation requires very specific, usually static, types b/c that's what the processor works with\n- In Julia methods are often defined for general, abstract argument types but compilation takes place for specific, concrete types.\n\nTime for a deep dive (notebook named `2compilation.jl`)\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25008b92a6cf-cd6a-4364-9f59-598bcf206ef1\n# \u255f\u25005c402564-e0e1-11eb-105b-951cfa19cacd\n# \u255f\u25007e8e3903-a76d-4310-b5df-8508fdd7dba3\n# \u255f\u2500dc65b3ed-2acf-4bf5-be2e-77dde963a7f1\n# \u255f\u2500bb892d27-63f6-4267-bfcc-758e743c31d2\n# \u255f\u2500864522f6-a214-4397-91cb-8d4d670e7638\n# \u255f\u2500d78b2581-31cd-4a08-9c8f-59aedc5c3086\n# \u255f\u25000aab07a1-893e-4c8b-ae8d-198146058d2e\n# \u255f\u250085fcacb1-7ad2-4fb8-9c87-28cfaa0f1404\n", "meta": {"hexsha": "1c0906e155b4b5a027a053f39979ecf4bf342b56", "size": 6667, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/1intro.jl", "max_stars_repo_name": "palday/JSM2021", "max_stars_repo_head_hexsha": "bc7cd1232f10bd23a33686b346643557e2ada91d", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "notebooks/1intro.jl", "max_issues_repo_name": "palday/JSM2021", "max_issues_repo_head_hexsha": "bc7cd1232f10bd23a33686b346643557e2ada91d", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "notebooks/1intro.jl", "max_forks_repo_name": "palday/JSM2021", "max_forks_repo_head_hexsha": "bc7cd1232f10bd23a33686b346643557e2ada91d", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.0859375, "max_line_length": 264, "alphanum_fraction": 0.7364631768, "num_tokens": 1953, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.1919327864472368, "lm_q2_score": 0.3140505385717077, "lm_q1q2_score": 0.060276594953323284}}
{"text": "#**************************************************************************************\n# Input_Interface.jl\n# =============== part of the GeoEfficiency.jl package.\n#\n# all the input either from the console or from the csv files to the package is handled by some function here.\n#\n#**************************************************************************************\n\n#------------------ consts - globals - imports ----------------------------\n\nusing Compat\nusing Compat.MathConstants\nusing Compat.DelimitedFiles\nimport Compat: @info, @warn, @error\n\n@compat isconst(@__MODULE__, :dataFolder ) \t||\tconst dataFolder = string(@__MODULE__)\n@compat isconst(@__MODULE__, :dataDir )\t\t||\tconst  dataDir    = joinpath(homedir(), dataFolder); \t\nmkpath(dataDir)\n\nconst Detectors  = \"Detectors.csv\";\nconst srcHeights = \"srcHeights.csv\";\nconst srcRhos    = \"srcRhos.csv\";\nconst srcRadii   = \"srcRadii.csv\";\nconst srcLengths = \"srcLengths.csv\";\n\n\n@enum SrcType srcUnknown=-1 srcPoint=0 srcLine=1 srcDisk=2 srcVolume=3 srcNotPoint =4\nglobal srcType = srcUnknown\n\n#------------------ typeofSrc --------------------------------------\n\n\"\"\"\n    typeofSrc()::SrcType\n\nreturn the current value of the global `GeoEfficiency.srcType`.\n\n\"\"\"\ntypeofSrc()::SrcType = srcType  # srcType !== SrcType, but\n\n\"\"\"\n    typeofSrc(x::Int)::SrcType\n\nset and return the value of the global `GeoEfficiency.srcType` corresponding to `x`.\n\n  *  srcUnknown = -1 also any negative integer treated as so, \n  *  srcPoint   = 0, \n  *  srcLine    = 1, \n  *  srcDisk    = 2, \n  *  srcVolume  = 3, \n  *  srcNotPoint = 4 also any greater than 4 integer treated as so.\n\n\"\"\"\nfunction typeofSrc(x::Int)::SrcType\n\tglobal srcType = if x < 0\n\t\t\t\t\tSrcType(-1)\n\t\t\t\telseif x > 4\n\t\t\t\t\tSrcType(4)\n\t\t\t\telse\n\t\t\t\t\tSrcType(x)\n\t\t\t\tend\nend #function\n\n\n#------------------------ setSrcToPoint ---------------------------\n\n\"\"\"\n    setSrcToPoint()::Bool\n\nreturn whether the source type is a point or not.\n\"\"\"\nsetSrcToPoint()::Bool = srcType === srcPoint\n\n\"\"\"\n\n    setSrcToPoint(yes::Bool)::Bool\n\nreturn whether the source type is a point or not after setting `srcType` to ``srcPoint`` if \n`yes` = ``true`` else if `yes` = ``false`` setting it to ``srcNotPoint`` if it was not already \nset to other non-point type (``srcDisk``, ``srcLine``, ``srcVolume``).\n\n!!! note\n    *  The user can use this function to change the source type any time.\n    *  The source type is set the fist time asked for source.\n\n**see also:** [`typeofSrc(::Int)`](@ref).\n\n\"\"\"\nfunction setSrcToPoint(yes::Bool)::Bool\n\tglobal srcType = if yes \n\t\t\t\t\t\tsrcPoint\n\t\t\t\t\telseif srcType in [srcUnknown, srcPoint] \n\t\t\t\t\t\tsrcNotPoint\n\t\t\t\t\telse\n\t\t\t\t\t\tsrcType \n\t\t\t\t\tend\n\treturn srcType === srcPoint\nend\n\n\"\"\"\n\tsetSrcToPoint(prompt::AbstractString)::Bool\n\nreturn whether the source type is a point or not. only prompt the user to set the source \ntype if it were not already set before. \n\n**see also:** [`typeofSrc(::Int)`](@ref), [`setSrcToPoint(::Bool)`](@ref).\n\n\"\"\"\nsetSrcToPoint(prompt::AbstractString)::Bool = srcType != srcUnknown ?\tsetSrcToPoint() :\n\t\t\t\t\t\t\t\t\t\t\tsetSrcToPoint(input(prompt) |> lowercase != \"n\" )\n\n\n#---------------------------- input ---------------------------------\n\n\"\"\" # UnExported\n\n    input(prompt::AbstractString = \"? \", incolor::Symbol = :green)\n\nreturn a string delimited by new line excluding the new line. prompt the user with the massage `prompt` defaults to `? `. \n`incolor` specify the prompt text color, default to ``green``.\n\n\"\"\"\nfunction input(prompt::AbstractString = \"? \", incolor::Symbol = :green)\n    printstyled(prompt, color=incolor); chomp(readline())\nend # function\n\n\n#---------------------------- getfloat -----------------------------------\n\n\"\"\"# UnExported\n\n\tgetfloat(prompt::AbstractString = \"? \", from::Real = 0.0, to::Real = Inf; value::AbstractString=\"nothing\")::Float64\n\nprompts the user with the massage `prompt` defaults to `? ` to input a numerical expression \nevaluate to a numerical value and asserts that the value is in the semi open interval [`from`, `to`[\nbefore returning it as a `Float64`.\n\n!!! note\n    *  a blank input (i.e just a return) is considered as being ``0.0``.\n    *  input from the `console` can be numerical expression not just a number.\n    *  All ``5/2``, ``5//2``, ``exp(2)``, ``pi``, ``1E-2``, ``5.2/3``, ``sin(1)``, ``pi/2/3`` \n       are valid mathematical expressions.\n    *  the key word  argument `value` , if provided the function will not ask for input from the \n       `console` and take it as if it where inputted from the  `console` [``for test propose mainly``].\n\n# Examples\n```\njulia> getfloat(\"input a number:\", value=\"3\")\n3.0\n\njulia> getfloat(\"input a number:\", value=\"\")\n0.0\n\njulia> getfloat(\"input a number:\", value=\"5/2\")\n2.5\n\njulia> getfloat(\"input a number:\", value=\"5//2\")\n2.5\n\njulia> getfloat(\"input a number:\", value=\"pi\")\n3.141592653589793\n```\n\n\"\"\"\nfunction getfloat(prompt::AbstractString = \"? \", from::Real = 0.0, to::Real = Inf; value::AbstractString=\"nothing\") ::Float64\n\ttry\n\t\t\"nothing\" == value ? value = input(prompt) : nothing\n\t\t\"\" == value && return 0.0\t\t# just pressing return is interapted as <0.0>\n\t\tval::Float64 =  Meta.parse(value) |> eval |> float\n\t\t@assert from <= val < to\n\t\treturn val\n\n    catch err\n        if isa(err, AssertionError) \n\t\t\t@warn(\"\"\"input `$value` evaluated to be outside the semi open interval [$from, $to[,\n\t\t\t\\n Please: provide an adequate value\"\"\")\n        else   \n\t\t\t@warn(\"\"\"input `$value` cannot be parsed to a valid numerical value!,\n\t\t\t\\n Please: provide a valid expression\"\"\")\n        end #if \n        \n        return getfloat(prompt, from, to)\n\n    end #try\nend\t\n\n\n#---------------------------- detector_info_from_csvFile ------------------------------\n\n\"\"\"# UnExported\n\n\t detector_info_from_csvFile(detectors::AbstractString = Detectors, \n                                      datadir::AbstractString = dataDir)\nreturn a vector{Detector} based on information in the file of name `detectors` found in the \ndirectory `datadir`.\n\n!!! note\n    *  if no path is given the second argument `datadir` is default to ``$(dataDir)`` as set by \n       the constant ``dataDir``. \n    *  if no file name is specified the name of the predefined file ``$Detectors`` as set by \n       the constant ``Detectors``. \n    *  the no argument method is the most useful; other methods are mainly for ``test propose``.\n\n\"\"\"\nfunction detector_info_from_csvFile(detectors::AbstractString = Detectors, \n                                      datadir::AbstractString = dataDir)\n    detector_info_array::Matrix{Float64} = Matrix{Float64}(undef, 0, 0)\n    @info(\"opening '$(detectors)'......\")\n    try\n        detector_info_array = readdlm(joinpath(datadir, detectors), ',', header=true)[1];\n        return getDetectors(detector_info_array)\n\t\t\n    catch err\n        if isa(err, SystemError) \n\t\t    @error(\"detector_info_from_csvFile: Some thing went wrong, may be the file '$(joinpath( datadir, detectors))' can't be found\")\n\t\tend\n        rethrow()\n\n    end #try\n\nend #function\n\n\n#---------------------------- read_from_csvFile --------------------------------\n\n\"\"\"# UnExported\n\n\tread_from_csvFile(csv_data::AbstractString, \n                       datadir::AbstractString = dataDir)::Vector{Float64}\n\nreturn Vector{Float64} based on data in csv file named `csv_data`. directory `datadir` point to   \nwhere the file is located default to ``$(dataDir)`` as set by the constant `dataDir`.\n\n\"\"\"\nfunction read_from_csvFile(csv_data::AbstractString, datadir::AbstractString = datadir)::Vector{Float64}\n\t@info(\"Opening `$(csv_data)`......\")\n\ttry\n\t\tindata = readdlm(joinpath(datadir, csv_data), ',',  header=true)[1][:,1]\n\t\treturn float(indata ) |> sort;\n\n\tcatch err\n\t    if isa(err, SystemError) \n\t\t    @error(\"Some thing went wrong, may be `$(csv_data)` can't be found in `$(datadir)`\")\n\t\t\n\t\telse\n\t\t    #println(err)\n\t\t\n\t\tend\t\t\n\t\treturn Float64[0.0]\n\n\tend #try\nend #function\n\n\n#--------------------------- read_batch_info ------------------------------------\n\n\"\"\"# UnExported\n\n\tread_batch_info()\n\nread `detectors` and `sources` parameters from the predefined csv files.\n\nReturn a tuple\n\t   (detectors_array,\n\t\tsrcHeights_array,\n\t\tsrcRhos_array,\n\t\tsrcRadii_array,\n\t\tsrcLengths_array,\n\t\tGeoEfficiency_isPoint)\n\n\"\"\"\nread_batch_info() = read_batch_info(datadir,\n                                  detectors, \n\t\t\t\t\t\t\t\t srcHeights,\n\t\t\t\t\t\t\t\t    srcRhos,\n\t\t\t\t\t\t\t\t   srcRadii,\n\t\t\t\t\t\t\t\t srcLengths)\n\n\n\"\"\"# UnExported\n\n\tread_batch_info(datadir::AbstractString,\n                  detectors::AbstractString, \n                 srcHeights::AbstractString,\n                    srcRhos::AbstractString,\n                   srcRadii::AbstractString,\n                 srcLengths::AbstractString)\n\nread `detectors` and `sources` parameters from the location given in the argument list.\n\nReturn a tuple\n\n\t   (detectors_array,\n\t\tsrcHeights_array,\n\t\tsrcRhos_array,\n\t\tsrcRadii_array,\n\t\tsrcLengths_array,\n\t\tisPoint)\n\n\"\"\"\t\t\t\t\t\t\t\t \nfunction read_batch_info(datadir::AbstractString,\n                       detectors::AbstractString, \n\t\t\t\t\t  srcHeights::AbstractString,\n\t\t\t\t\t     srcRhos::AbstractString,\n\t\t\t\t\t    srcRadii::AbstractString,\n\t\t\t\t\t  srcLengths::AbstractString)\n\n\t@info(\"Starting the Batch Mode ....\")\n\tisPoint = setSrcToPoint(\"\\n Is it a point source {Y|n} ?\")\n\n\t@info(\"Reading data from `CSV files` at $datadir .....\")\n\tdetectors_array ::Vector{RadiationDetector} = try  detector_info_from_csvFile(detectors, datadir); catch err; getDetectors(); end\n\tsrcHeights_array::Vector{Float64} = read_from_csvFile(srcHeights, datadir)\n\tsrcRhos_array   ::Vector{Float64} = [0.0]\n\tsrcRadii_array  ::Vector{Float64} = [0.0]\n\tsrcLengths_array::Vector{Float64} = [0.0]\n\n\tfunction batchfailure(err::AbstractString)\n\t\t@warn(err, \", transfer to direct data input via the `console`......\")\n\t\tsleep(3); src = source()\n\t\tsrcHeights_array, srcRhos_array, srcRadii_array  , srcLengths_array   = \n\t\t[src[1].Height] , [src[1].Rho] , [src[2]]        , [src[3]]\n\t\treturn nothing\n\tend #function\n\n\tif srcHeights_array == [0.0]\n\t\tbatchfailure(\"`$(srcHeights)` is not found in `$(datadir)`)\")\n\n\telseif isPoint\n\t\tsrcRhos_array =\tread_from_csvFile(srcRhos, datadir)\n\n\telse\n\t\tsrcRadii_array = read_from_csvFile(srcRadii, datadir)\n\t\tif srcRadii_array == [0.0]\n\t\t\tbatchfailure(\"`$(srcRadii)` is not found in `$(datadir)`)\")\n\n\t\telse\n\t\t\tsrcLengths_array = read_from_csvFile(srcLengths, datadir)\n\n\t\tend #if\n\tend #if\n\t#println(\"\\n Results log\\n=============\")\n\treturn (\n\t\tdetectors_array,\n\t\tsrcHeights_array,\n\t\tsrcRhos_array,\n\t\tsrcRadii_array,\n\t\tsrcLengths_array,\n\t\tisPoint,\n\t\t)\nend #function\n\n\n#------------------------- getDetectors -------------------------------------\n\n\"\"\"\n\n    getDetectors(detectors_array::Vector{<:Detector} = Detector[])\n\nreturn the  `detectors_array` extended by the entered detectors and sorted according to the \ndetector volume. \nprompt the user to input detector parameters from the `console`.\n\n!!! note\n    If no array received in the input an empty array will be created to receive the converted detectors.\n\n\"\"\"\nfunction getDetectors(detectors_array::Vector{<:RadiationDetector} = RadiationDetector[])\n\tVector{RadiationDetector}(detectors_array); @info(\"Please, input the detector information via the console\")\n\twhile(true)\n\t\ttry push!(detectors_array, RadiationDetector()); catch err\tprintln(err); @warn(\"Please: Enter a New Detector\"); continue; end\n\t\tlowercase(input(\n\t\t\t\"\"\"\\n\n    \t                - To add a new detector press return\\n\n    \t                - To quit press 'q'|'Q' then return\\n\n\t\t\t\\n\\t your Choice: \"\"\", :blue))  == \"q\" && break\n\tend #while\n\treturn detectors_array |> sort\nend #function\n\n\n\"\"\"\n\n\tgetDetectors(detector_info_array::Matrix{<:Real}, detectors_array::Vector{<:Detector} = Detector[] ;console_FB=true) \n\nreturn `detectors_array`, after extending it with the successfully converted detectors. while, \nattempt to convert detectors from the information in `detector_info_array`. \n\n!!! note\n    if `console_FB` argument is set to true , the function will call `getDetectors()` to take input\n    from the `console` if the `detector_info_array` is empty or contain no numerical element.\n\n\"\"\"\nfunction getDetectors(detector_info_array::Matrix{<:Real}, detectors_array::Vector{<:Detector} = Detector[] ; console_FB=true) \n\t\n\tif isempty(detector_info_array) \n\t\tif console_FB \n\t\t\t@info(\"The new detectors information may entred via the console\")\n\t\t\treturn getDetectors(detectors_array)\n\t\telse\t\n\t\t \terror(\"getDetectors: Empty `detector_info_array`\")\n\t\tend\n\t\t\n\telse\n\t\tVector{RadiationDetector}(detectors_array)\n\t\tfor i_th_line = 1:size(detector_info_array)[1]\n\t\t\ttry push!(detectors_array, RadiationDetector((detector_info_array[i_th_line,:])...)) end #try\n\t\tend #for\n\n\t\treturn detectors_array |> sort\n\tend   #if\nend #function\n", "meta": {"hexsha": "cf4d3bb2116eaf36804beb8709faa3a7f0ea0209", "size": 12725, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Input_Interface.jl", "max_stars_repo_name": "GeoEfficiency/GeoEfficiency.github.io", "max_stars_repo_head_hexsha": "4d87b43cd81685d1589eb23b7d0e73ff5f8ad026", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-18T14:29:17.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-18T14:29:17.000Z", "max_issues_repo_path": "src/Input_Interface.jl", "max_issues_repo_name": "GeoEfficiency/GeoEfficiency.github.io", "max_issues_repo_head_hexsha": "4d87b43cd81685d1589eb23b7d0e73ff5f8ad026", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Input_Interface.jl", "max_forks_repo_name": "GeoEfficiency/GeoEfficiency.github.io", "max_forks_repo_head_hexsha": "4d87b43cd81685d1589eb23b7d0e73ff5f8ad026", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-04-13T20:07:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-13T20:07:53.000Z", "avg_line_length": 30.8859223301, "max_line_length": 132, "alphanum_fraction": 0.6375638507, "num_tokens": 3266, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618627863437, "lm_q2_score": 0.13296423504419935, "lm_q1q2_score": 0.06026761686009504}}
{"text": "#\n# rubberindex.jl -\n#\n# Rubber-index for Julia arrays.\n#\n\n\"\"\"\n    colons(n)\n\nyields a `n`-tuple of colons `:` (a.k.a. `Colon()`).\n\nWhen `n` is known at compile time, it is faster to call:\n\n    colons(Val(n))\n\nThis method is suitable to extract sub-arrays of build views when some kind of\nrubber index is needed.  For instance:\n\n    slice(A::AbstractArray{T,N}, i::Integer) where {T,N} =\n        A[colons(Val(N-1))..., i]\n\ndefines a function that returns the `i`-th slice of `A` assuming index `i`\nrefers the last index of `A`.  Using the rubber-index `..`, a shorter\ndefinition is:\n\n    slice(A::AbstractArray, i) = A[.., i]\n\nwhich is also able to deal with multiple trailing indices if `i` is a\n`CartesianIndex`.\n\nSee also: `..`, [`RubberIndex`](@ref).\n\n\"\"\"\ncolons(n::Integer) =\n    (n ==  0 ? () :\n     n ==  1 ? (:,) :\n     n ==  2 ? (:,:,) :\n     n ==  3 ? (:,:,:,) :\n     n ==  4 ? (:,:,:,:,) :\n     n ==  5 ? (:,:,:,:,:,) :\n     n ==  6 ? (:,:,:,:,:,:,) :\n     n ==  7 ? (:,:,:,:,:,:,:,) :\n     n ==  8 ? (:,:,:,:,:,:,:,:,) :\n     n ==  9 ? (:,:,:,:,:,:,:,:,:,) :\n     n == 10 ? (:,:,:,:,:,:,:,:,:,:,) :\n     _colons(n))\n\nfunction _colons(n::Integer)\n    n \u2265 0 || bad_ndims(n)\n    return ([Colon() for i in 1:n]...,)\nend\n\n@noinline bad_ndims(n::Integer) =\n    throw(ArgumentError(string(\"number of dimensions should be \u2265 0, got \", n)))\n\ncolons(v::Val{N}) where {N} = ntuple(colon, v)\n\n# Just yields a colon whatever the argument.\ncolon(x) = Colon()\n\n\"\"\"\n\n`RubberIndex` is the singleron type that represents any number of indices.  The\nconstant `..` is defined as `RubberIndex()` and can be used in array indexation\nto left and/or right justify the other indices.  For instance, assuming `A` is\na `3\u00d74\u00d75\u00d76` array, then all the following equalities hold:\n\n    A[..]           == A[:,:,:,:]\n    A[..,3]         == A[:,:,:,3]\n    A[2,..]         == A[2,:,:,:]\n    A[..,2:4,5]     == A[:,:,2:4,5]\n    A[2:3,..,1,2:4] == A[2:3,:,1,2:4]\n\nAs you can see, the advantage of the rubber index `..` is that it automatically\nexpands as the number of colons needed to have the correct number of indices.\nThe expressions are also more readable.\n\nThe rubber index may also be used for setting values.  For instance:\n\n    A[..] .= 1         # to fill A with ones\n    A[..,3] = A[..,2]  # to copy A[:,:,:,2] in A[:,:,:,3]\n    A[..,3] .= A[..,2] # idem but faster\n    A[2,..] = A[3,..]  # to copy A[3,:,:,:] in A[2,:,:,:]\n    A[..,2:4,5] .= 7   # to set all elements in A[:,:,2:4,5] to 7\n\nLeading/trailing indices may be specified as Cartesian indices (of type\n`CartesianIndex`).\n\n!!! warning\n    There are two known limitations:\n    1. The `end` reserved word can only be used in intervals specified *before*\n       the rubber index but not *after*.  This limitation is due to the Julia\n       parser cannot be avoided.\n    2. At most 9 indices can be specified before the rubber index.  This\n       can be extended by editing the source code.\n\nSee also: [`colons`](@ref).\n\n\"\"\" RubberIndex\n\nstruct RubberIndex end\n\nconst .. = RubberIndex()\nconst \u2026 = .. # FIXME: should be deprecated\n\n# Quickly get the tuple inside a Cartesian index.\nto_tuple(I::CartesianIndex) = I.I\n\n# Grow tuple of colons until tuple of axes is empty.\n@inline growcolons(colons, inds::Tuple{}) = colons\n@inline growcolons(colons, inds::Tuple) = growcolons((colons..., :), tail(inds))\n\n# Drop as many colons as there are specified indices.\n@inline dropcolons(colons, I::Tuple{}) = colons\n@inline dropcolons(colons::Tuple{}, ::Tuple{}) = ()\n@noinline dropcolons(colons::Tuple{}, I::Tuple) =\n    throw(ArgumentError(\"too many indices specifed\"))\n@inline dropcolons(colons, I::Tuple) =\n    dropcolons(tail(colons), tail(I))\n@inline dropcolons(colons, I::Tuple{CartesianIndex, Vararg}) =\n    dropcolons(dropcolons(colons, to_tuple(I[1])), tail(I))\n@noinline dropcolons(colons, I::Tuple{RubberIndex, Vararg}) =\n    throw(ArgumentError(\"more than one rubber index specified\"))\n\n@inline function to_indices(A, inds, I::Tuple{RubberIndex, Vararg})\n    # Align the remaining indices to the tail of the `inds`.  First\n    # `growcolons` is called to build a tuple of as many as colons as the\n    # number of remaining axes. Second, `dropcolons` is used to drop as\n    # many colons as the number of specified indices (taking into account\n    # Cartesian indices).\n    colons = dropcolons(growcolons((), inds), tail(I))\n    to_indices(A, inds, (colons..., tail(I)...))\nend\n\n# avoid copying if indexing with .. alone, see\n# https://github.com/JuliaDiffEq/OrdinaryDiffEq.jl/issues/214\n@inline Base.getindex(A::AbstractArray, ::RubberIndex) = A\n\n# The following is needed to allow for statements like `A[..] .+= expr` to\n# work properly.\ndotview(A::AbstractArray{T,N}, ::RubberIndex) where {T,N} =\n    dotview(A, ntuple(colon, Val{N}())...)\n", "meta": {"hexsha": "e6a5e428bd5a39a835e3adf29c2b11970aecbfba", "size": 4784, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rubberindex.jl", "max_stars_repo_name": "emmt/ArrayTools.jl", "max_stars_repo_head_hexsha": "f4f8ba53bcd4b5cbada93e314e82ec4a518fcbd2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-10-28T12:12:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-16T06:03:15.000Z", "max_issues_repo_path": "src/rubberindex.jl", "max_issues_repo_name": "emmt/ArrayTools.jl", "max_issues_repo_head_hexsha": "f4f8ba53bcd4b5cbada93e314e82ec4a518fcbd2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-11-08T00:25:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-03T10:15:44.000Z", "max_forks_repo_path": "src/rubberindex.jl", "max_forks_repo_name": "emmt/ArrayTools.jl", "max_forks_repo_head_hexsha": "f4f8ba53bcd4b5cbada93e314e82ec4a518fcbd2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4545454545, "max_line_length": 80, "alphanum_fraction": 0.6151755853, "num_tokens": 1459, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.12421301321508334, "lm_q1q2_score": 0.06016630980942023}}
{"text": "\"\"\"\n    sign_cadlag(x::N)::N where {N<:Real}\n\nThis function works like the sign function but is ``1`` for input ``0``.\n\n### Input\n\n- `x` -- real scalar\n\n### Output\n\n``1`` if ``x \u2265 0``, ``-1`` otherwise.\n\n### Notes\n\nThis is the sign function right-continuous at zero (see\n[c\u00e0dl\u00e0g function](https://en.wikipedia.org/wiki/C%C3%A0dl%C3%A0g)).\nIt can be used with vector-valued arguments via the dot operator.\n\n### Examples\n\n```jldoctest\njulia> LazySets.sign_cadlag.([-0.6, 1.3, 0.0])\n3-element Array{Float64,1}:\n -1.0\n  1.0\n  1.0\n```\n\"\"\"\nfunction sign_cadlag(x::N)::N where {N<:Real}\n    return x < zero(x) ? -one(x) : one(x)\nend\n\n\"\"\"\n    substitute(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n\nApply a substitution to a given vector.\n\n### Input\n\n- `substitution` -- substitution (a mapping from an index to a new value)\n- `x`            -- vector\n\n### Output\n\nA fresh vector corresponding to `x` after `substitution` was applied.\n\"\"\"\nfunction substitute(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n    return substitute!(substitution, copy(x))\nend\n\n\"\"\"\n    substitute!(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n\nApply a substitution to a given vector.\n\n### Input\n\n- `substitution` -- substitution (a mapping from an index to a new value)\n- `x`            -- vector (modified in this function)\n\n### Output\n\nThe same (but see the Notes below) vector `x` but after `substitution` was\napplied.\n\n### Notes\n\nThe vector `x` is modified in-place if it has type `Vector` or `SparseVector`.\nOtherwise, we first create a new `Vector` from it.\n\"\"\"\nfunction substitute!(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n    return substitute!(Vector(x), substitution)\nend\n\nfunction substitute!(substitution::Dict{Int, T},\n                     x::Union{Vector{T}, SparseVector{T}}) where {T}\n    for (index, value) in substitution\n        x[index] = value\n    end\n    return x\nend\n\n\"\"\"\n    reseed(rng::AbstractRNG, seed::Union{Int, Nothing})::AbstractRNG\n\nReset the RNG seed if the seed argument is a number.\n\n### Input\n\n- `rng`  -- random number generator\n- `seed` -- seed for reseeding\n\n### Output\n\nThe input RNG if the seed is `nothing`, and a reseeded RNG otherwise.\n\"\"\"\nfunction reseed(rng::AbstractRNG, seed::Union{Int, Nothing})::AbstractRNG\n    if seed != nothing\n        return Random.seed!(rng, seed)\n    end\n    return rng\nend\n\n\"\"\"\n    StrictlyIncreasingIndices\n\nIterator over the vectors of `m` strictly increasing indices from 1 to `n`.\n\n### Fields\n\n- `n` -- size of the index domain\n- `m` -- number of indices to choose (resp. length of the vectors)\n\n### Notes\n\nThe vectors are modified in-place.\n\nThe iterator ranges over ``\\\\binom{n}{m}`` (`n` choose `m`) possible vectors.\n\nThis implementation results in a lexicographic order with the last index growing\nfirst.\n\n### Examples\n\n```jldoctest\njulia> for v in LazySets.StrictlyIncreasingIndices(4, 2)\n           println(v)\n       end\n[1, 2]\n[1, 3]\n[1, 4]\n[2, 3]\n[2, 4]\n[3, 4]\n```\n\"\"\"\nstruct StrictlyIncreasingIndices\n    n::Int\n    m::Int\n\n    function StrictlyIncreasingIndices(n::Int, m::Int)\n        @assert n >= m > 0 \"require n >= m > 0\"\n        new(n, m)\n    end\nend\n\nBase.eltype(::Type{StrictlyIncreasingIndices}) = Vector{Int}\nBase.length(sii::StrictlyIncreasingIndices) = binomial(sii.n, sii.m)\n\n# initialization\nfunction Base.iterate(sii::StrictlyIncreasingIndices)\n    v = [1:sii.m;]\n    return (v, v)\nend\n\n# normal iteration\nfunction Base.iterate(sii::StrictlyIncreasingIndices, state::AbstractVector{Int})\n    v = state\n    i = sii.m\n    diff = sii.n\n    if i == diff\n        return nothing\n    end\n    while v[i] == diff\n        i -= 1\n        diff -= 1\n    end\n    # update vector\n    v[i] += 1\n    for j in i+1:sii.m\n        v[j] = v[j-1] + 1\n    end\n    # detect termination: first index has maximum value\n    if i == 1 && v[1] == (sii.n - sii.m + 1)\n        return (v, nothing)\n    end\n    return (v, v)\nend\n\n# termination\nfunction Base.iterate(sii::StrictlyIncreasingIndices, state::Nothing)\n    return nothing\nend\n\n\"\"\"\n    subtypes(interface, concrete::Bool)\n\nReturn the concrete subtypes of a given interface.\n\n### Input\n\n- `interface` -- an abstract type, usually a set interface\n- `concrete`  -- if `true`, seek further the inner abstract subtypes of the given\n                 interface, otherwise return only the direct subtypes of `interface`\n\n### Output\n\nA list with the subtypes of the abstract type `interface`, sorted alphabetically.\n\n### Examples\n\nConsider the `AbstractPolytope` interface. If we include the abstract subtypes\nof this interface,\n\n```jldoctest subtypes\njulia> using LazySets: subtypes\n\njulia> subtypes(AbstractPolytope, false)\n4-element Array{Any,1}:\n AbstractCentrallySymmetricPolytope\n AbstractPolygon\n HPolytope\n VPolytope\n```\n\nWe can use this function to obtain the concrete subtypes of\n`AbstractCentrallySymmetricPolytope` and `AbstractPolygon` (further until all\nconcrete types are obtained), using the `concrete` flag:\n\n```jldoctest subtypes\njulia> subtypes(AbstractPolytope, true)\n14-element Array{Type,1}:\n Ball1\n BallInf\n HPolygon\n HPolygonOpt\n HPolytope\n Hyperrectangle\n Interval\n LineSegment\n Singleton\n SymmetricIntervalHull\n VPolygon\n VPolytope\n ZeroSet\n Zonotope\n```\n\"\"\"\nfunction subtypes(interface, concrete::Bool)\n\n    subtypes_to_test = subtypes(interface)\n\n    # do not seek the concrete subtypes further\n    if !concrete\n        return sort(subtypes_to_test, by=string)\n    end\n\n    result = Vector{Type}()\n    i = 0\n    while i < length(subtypes_to_test)\n        i += 1\n        subtype = subtypes_to_test[i]\n        new_subtypes = subtypes(subtype)\n        if isempty(new_subtypes)\n            # base type found\n            push!(result, subtype)\n        else\n            # yet another interface layer\n            append!(subtypes_to_test, new_subtypes)\n        end\n    end\n    return sort(result, by=string)\nend\n", "meta": {"hexsha": "5c7ad124f08a3de1c20226ea3b4816242c30597c", "size": 5887, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/helper_functions.jl", "max_stars_repo_name": "ueliwechsler/LazySets.jl", "max_stars_repo_head_hexsha": "9bc60f06aea88026656a17a07a7ed2c337386033", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/helper_functions.jl", "max_issues_repo_name": "ueliwechsler/LazySets.jl", "max_issues_repo_head_hexsha": "9bc60f06aea88026656a17a07a7ed2c337386033", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/helper_functions.jl", "max_forks_repo_name": "ueliwechsler/LazySets.jl", "max_forks_repo_head_hexsha": "9bc60f06aea88026656a17a07a7ed2c337386033", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.2150943396, "max_line_length": 84, "alphanum_fraction": 0.6675726176, "num_tokens": 1655, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1347759226358913, "lm_q1q2_score": 0.060046654146498075}}
{"text": "# <hide>\nfunction printsum(a)\n    println(summary(a), \": \", repr(a))\nend\n# </hide>\nfor i in 1:5\n    print(i, \", \")\nend\n#> 1, 2, 3, 4, 5,\n# In loop definitions \"in\" is equivilent to \"=\" \n# (AFAIK, the two are interchangable in this context)\nfor i = 1:5\n    print(i, \", \")\nend\nprintln() #> 1, 2, 3, 4, 5,\n\n# arrays can also be looped over directly:\na1 = [1,2,3,4]\nfor i in a1\n    print(i, \", \")\nend\nprintln() #> 1, 2, 3, 4,\n\n# **continue** and **break** work in the same way as python\na2 = collect(1:20)\nfor i in a2\n    if i % 2 != 0\n        continue\n    end\n    print(i, \", \")\n    if i >= 8\n        break\n    end\nend\nprintln() #> 2, 4, 6, 8,\n\n# if the array is being manipulated during evaluation a while loop shoud be used\n# [pop](https://docs.julialang.org/en/v1/base/collections/#Base.pop!-Tuple{Any,Any,Any}) removes the last element from an array\nwhile !isempty(a1)\n    print(pop!(a1), \", \")\nend\nprintln() #> 4, 3, 2, 1,\n\nd1 = Dict(1=>\"one\", 2=>\"two\", 3=>\"three\")\n# dicts may be looped through using the keys function:\nfor k in sort(collect(keys(d1)))\n    print(k, \": \", d1[k], \", \")\nend\nprintln() #> 1: one, 2: two, 3: three,\n\n# like python [enumerate](https://docs.julialang.org/en/v1/base/iterators/#Base.Iterators.enumerate) can be used to get both the index and value in a loop\na3 = [\"one\", \"two\", \"three\"]\nfor (i, v) in enumerate(a3)\n    print(i, \": \", v, \", \")\nend\nprintln() #> 1: one, 2: two, 3: three,\n\n# (note enumerate starts from 1 since Julia arrays are 1 indexed unlike python)\n\n# [map](https://docs.julialang.org/en/v1/base/collections/#Base.map) works as you might expect performing the given function on each member of \n# an array or iter much like comprehensions\na4 = map((x) -> x^2, [1, 2, 3, 7])\nprint(a4) \nprintln() #> [1, 4, 9, 49]\n", "meta": {"hexsha": "742ffbf53af21979bf6a6ec78983b141fda079a9", "size": 1758, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/loops_map.jl", "max_stars_repo_name": "talgatomarov/JuliaByExample", "max_stars_repo_head_hexsha": "9fc5516b7f6d03bd2f340b7a9bb52a06fedb7458", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 281, "max_stars_repo_stars_event_min_datetime": "2015-02-01T01:18:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T19:51:35.000Z", "max_issues_repo_path": "src/loops_map.jl", "max_issues_repo_name": "talgatomarov/JuliaByExample", "max_issues_repo_head_hexsha": "9fc5516b7f6d03bd2f340b7a9bb52a06fedb7458", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2015-04-10T04:23:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-23T12:32:59.000Z", "max_forks_repo_path": "src/loops_map.jl", "max_forks_repo_name": "talgatomarov/JuliaByExample", "max_forks_repo_head_hexsha": "9fc5516b7f6d03bd2f340b7a9bb52a06fedb7458", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 101, "max_forks_repo_forks_event_min_datetime": "2015-03-24T20:45:23.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T23:24:46.000Z", "avg_line_length": 27.0461538462, "max_line_length": 154, "alphanum_fraction": 0.6177474403, "num_tokens": 627, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713670203584295, "lm_q2_score": 0.13117323564948066, "lm_q1q2_score": 0.059964100340174055}}
{"text": "#\n# Using LabelledArrays source code and types to \n# \"pass through\" all properties to an underlying\n# LArray or SLArray. This allows us to write \n# a new abstract type that *functions*\n# like a LabelledArray type!\n#\n# All code in this file was copied, and modified \n# from LabelledArrays.jl source code. Their license is shown below. \n# All credit goes to LabelledArrays.jl developers.\n#\n\nconst __LABELLED_ARRAYS_LICENSE = \"\"\"\nThe LabelledArrays.jl package is licensed under the MIT \"Expat\" License:\n\n> Copyright (c) 2017: Christopher Rackauckas.\n>\n>\n> Permission is hereby granted, free of charge, to any person obtaining a copy\n>\n> of this software and associated documentation files (the \"Software\"), to deal\n>\n> in the Software without restriction, including without limitation the rights\n>\n> to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n>\n> copies of the Software, and to permit persons to whom the Software is\n>\n> furnished to do so, subject to the following conditions:\n>\n>\n>\n> The above copyright notice and this permission notice shall be included in all\n>\n> copies or substantial portions of the Software.\n>\n>\n>\n> THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n>\n> IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n>\n> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n>\n> AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n>\n> LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n>\n> OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n>\n> SOFTWARE.\n>\n>\n\"\"\"\n\nconst __LABELLED_ARRAYS_CREDITS = \"\"\"\nThis source code which provides this functionality was copied directly from LabelledArrays.jl source code. The LabelledArrays.jl license text is provided in Julia's **Extended Help** section (accessible via `@doc`, or `??` in Julia's REPL).\n\n# Extended Help\n\n__LabelledArrays.jl License__\n\n$__LABELLED_ARRAYS_LICENSE\n\"\"\"\n\n\"\"\"\n$(TYPEDEF)\n\nA supertype for types that *function* like \n`LabelledArray.LArray` or `LabelledArray.SLArray` \ninstances, but are under a new type tree. This is\nused in `GeneralAstrodynamics` for parameterizing \nastrodynamics state vectors by physical units.\n\n!!! note\n    All subtypes __must__ have only one field:\n    a `LabelledArrays.LArray` or `LabelledArrays.SLArray`\n    field called `__rawdata`. All methods on this abstract\n    type require this field to be called `__rawdata`!\n\nNearly all code which acts on this type is copied \nand / or modified from LabelledArrays.jl source code.\nAll credit goes to LabelledArrays.jl developers. \nThe LabelledArrays.jl LICENSE file is provided \nin this docstring under Julia's __Extended Help__\ndocstring section.\n\n# Extended help\n\n__LabelledArrays.jl License__\n\n$__LABELLED_ARRAYS_LICENSE\n\"\"\"\nabstract type ParameterizedLabelledArray{F,N,T,L} <: DenseArray{F,N} end\n\n\"\"\"\n$(SIGNATURES)\n\nReturns dot-accessible property names for a `ParameterizedLabelledArray`.\n\"\"\"\nBase.propertynames(::ParameterizedLabelledArray{F,N,T}) where {F,N,T} = T isa NamedTuple ? keys(T) : T\n\n\"\"\"\n$(SIGNATURES)\n\nOverrides `Base.getproperty` for all `ParameterizedLabelledArray` instances.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.@propagate_inbounds function Base.getproperty(x::ParameterizedLabelledArray, s::Symbol)\n    if s \u2208 propertynames(x)\n        return getproperty(getfield(x, :__rawdata), s)\n    end\n    return getfield(x, s) # will throw an error if s is not :__rawdata!\nend\n\n\"\"\"\n$(SIGNATURES)\n\nSets indices of a `ParameterizedLabelledArray` via label.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.@propagate_inbounds function Base.setproperty!(x::ParameterizedLabelledArray, s::Symbol,y)\n    if s \u2208 propertynames(x)\n        return setproperty!(getfield(x, :__rawdata), s, y)\n    end\n    setfield!(x, s, y)\nend\n\n\"\"\"\n$(SIGNATURES)\n\nOverrides `similar` for `ParameterizedLabelledArray` instances.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nfunction Base.similar(x::ParameterizedLabelledArray, ::Type{S}, dims::NTuple{N,Int}) where {S,N}\n    tmp = similar(x.__rawdata, S, dims)\n    return typeof(x)(tmp)\nend\n\n\"\"\"\n$(SIGNATURES)\n\nShallow copies a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.copy(x::ParameterizedLabelledArray) = typeof(x)(copy(getfield(x,:__rawdata)))\n\n\"\"\"\n$(SIGNATURES)\n\nDeep copies a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.deepcopy(x::ParameterizedLabelledArray) = typeof(x)(deepcopy(getfield(x, :__rawdata)))\n\n\"\"\"\n$(SIGNATURES)\n\nCopies one `ParameterizedLabelledArray` to another.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.copyto!(x::C,y::C) where C <: ParameterizedLabelledArray = copyto!(getfield(x,:__rawdata),getfield(y,:__rawdata))\n\n\"\"\"\n$(SIGNATURES)\n\nProvides `unsafe_convert` for `ParameterizedLabelledArray` types for use with LAPACK.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.unsafe_convert(::Type{Ptr{T}}, a::ParameterizedLabelledArray{F}) where {T, F} = Base.unsafe_convert(Ptr{T}, getfield(a,:__rawdata))\n\n\"\"\"\n$(SIGNATURES)\n\nConverts the underlying floating point type for a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.convert(::Type{T}, x) where {T<:ParameterizedLabelledArray} = T(x)\n\n\"\"\"\n$(SIGNATURES)\n\nConverts the underlying floating point type for a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.convert(::Type{T}, x::T) where {T<:ParameterizedLabelledArray} = x\n\n\"\"\"\n$(SIGNATURES)\n\nConverts the underlying floating point type for a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.convert(::Type{<:Array},x::ParameterizedLabelledArray) = convert(Array, getfield(x,:__rawdata))\n\n\"\"\"\n$(SIGNATURES)\n\nReshapes a `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nArrayInterface.restructure(x::ParameterizedLabelledArray{F1}, y::ParameterizedLabelledArray{F2}) where {F1, F2} = reshape(y, size(x)...)\n\n\"\"\"\n$(SIGNATURES)\n\nImplements `dataids` for a `ParameterizedLabelledArray` instance.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.dataids(A::ParameterizedLabelledArray) = Base.dataids(A.__rawdata)\n\n\"\"\"\n$(SIGNATURES)\n\nReturns the index of the `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.getindex(state::ParameterizedLabelledArray, args...) = Base.getindex(state.__rawdata, args...)\n\n\"\"\"\n$(SIGNATURES)\n\nSets the index of the `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.setindex!(state::ParameterizedLabelledArray, args...) = Base.setindex!(state.__rawdata, args...)\n\n\"\"\"\n$(SIGNATURES)\n\nReturns the memory stride for any `ParameterizedLabelledArray`.\n\n$__LABELLED_ARRAYS_CREDITS\n\"\"\"\nBase.elsize(::ParameterizedLabelledArray{F}) where F = sizeof(F)\n", "meta": {"hexsha": "d3c8e662778d7b666d85cb008f8e0a8bac975fb9", "size": 6644, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/States/Common/ParameterizedLabelledArrays.jl", "max_stars_repo_name": "pbouffard/GeneralAstrodynamics.jl", "max_stars_repo_head_hexsha": "80f175a5b3c6dac2140e645b016d39f131ecea05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2021-05-25T00:32:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:31:08.000Z", "max_issues_repo_path": "src/States/Common/ParameterizedLabelledArrays.jl", "max_issues_repo_name": "pbouffard/GeneralAstrodynamics.jl", "max_issues_repo_head_hexsha": "80f175a5b3c6dac2140e645b016d39f131ecea05", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2020-10-11T16:07:53.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-10T12:28:56.000Z", "max_forks_repo_path": "src/States/Common/ParameterizedLabelledArrays.jl", "max_forks_repo_name": "cadojo/UnitfulAstrodynamics.jl", "max_forks_repo_head_hexsha": "c24888c1500450ea420169af4ca54dbd5bc99092", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-28T12:13:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-28T12:13:09.000Z", "avg_line_length": 26.576, "max_line_length": 240, "alphanum_fraction": 0.757525587, "num_tokens": 1727, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12085324357297283, "lm_q1q2_score": 0.059954548408097885}}
{"text": "function pattern(n::Integer)::String\n  if n < 1\n      return \"\"\n  end\n  output = Char[]\n  sizehint!(output,n^2)\n  # only convert one time the integers to strings\n  numbers = Array{String}(undef,n)\n  map!( string, numbers, 1:n )\n  # generate the pattern\n  for last = 1:n\n      for idx = n:-1:last\n          append!(output, numbers[idx] )\n      end\n      push!(output, '\\n' )\n  end\n  # end-1 because no \\n at the end\n  String( output[1:end-1] )\nend", "meta": {"hexsha": "78e934200092175c75c6e219195cf763ef35f847", "size": 446, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "7_kyu/Complete_The_Pattern_2.jl", "max_stars_repo_name": "UlrichBerntien/Codewars-Katas", "max_stars_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "7_kyu/Complete_The_Pattern_2.jl", "max_issues_repo_name": "UlrichBerntien/Codewars-Katas", "max_issues_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "7_kyu/Complete_The_Pattern_2.jl", "max_forks_repo_name": "UlrichBerntien/Codewars-Katas", "max_forks_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.4736842105, "max_line_length": 49, "alphanum_fraction": 0.600896861, "num_tokens": 139, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.12085323249047067, "lm_q1q2_score": 0.059954542910136945}}
{"text": "println(\"Hello, World!\")\n\n# This is a comment\n\n#= This is a multi-line comment\nThe second line of the comment =#\n\n# println (\"this is not allowed\")\nprintln(\"This is allowed.\")\nprintln(\"This is also allowed.\");\n\nprintln(1 + 2)\n1 + 2 # Will not print in script\n\n# Installing, updating and removing packages\nusing Pkg # Alternatively, enter pkg mode in REPL with ]\nPkg.add(\"Example\")\nPkg.status()\nPkg.update(\"Example\") # Pkg.update() updates all packages\nPkg.rm(\"Example\") # removes from environment, does not delete files\n\n# Environments\n# In pkg mode:\n# activate tutorial\n# st\n# add Example\n# activate # goes back to default environment", "meta": {"hexsha": "c73c73fb0f6ba78233bac5248427c754f4937470", "size": 635, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/intro.jl", "max_stars_repo_name": "EnzioKam/learning_julia", "max_stars_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/intro.jl", "max_issues_repo_name": "EnzioKam/learning_julia", "max_issues_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/intro.jl", "max_forks_repo_name": "EnzioKam/learning_julia", "max_forks_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5185185185, "max_line_length": 67, "alphanum_fraction": 0.7228346457, "num_tokens": 157, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.1602660403494035, "lm_q2_score": 0.37387583672470853, "lm_q1q2_score": 0.05991959993418913}}
{"text": "## Exercise 4-10\n## Write an appropriately general set of functions that can draw shapes as in Turtle pies.\n## https://benlauwens.github.io/ThinkJulia.jl/latest/book.html#fig04-3\nprintln(\"Ans: \")\n\nprintln(\"End.\")\n", "meta": {"hexsha": "9561a74f1b512e533cf77f8a3189c92ce76cc484", "size": 213, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter4/ex10.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter4/ex10.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter4/ex10.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.4285714286, "max_line_length": 90, "alphanum_fraction": 0.7417840376, "num_tokens": 58, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4263215925474903, "lm_q2_score": 0.1403362422992606, "lm_q1q2_score": 0.059828370309151245}}
{"text": "import MathOptInterface\nusing Test\n\nconst MOI = MathOptInterface\nconst MOIU = MOI.Utilities\nconst MOF = MOI.FileFormats.MOF\n\nconst TEST_MOF_FILE = \"test.mof.json\"\n\n@test sprint(show, MOF.Model()) == \"A MathOptFormat Model\"\n\ninclude(\"nonlinear.jl\")\n\nstruct UnsupportedSet <: MOI.AbstractSet end\nstruct UnsupportedFunction <: MOI.AbstractFunction end\n\nfunction test_model_equality(model_string, variables, constraints; suffix=\"\")\n    model = MOF.Model(validate = true)\n    MOIU.loadfromstring!(model, model_string)\n    MOI.write_to_file(model, TEST_MOF_FILE * suffix)\n    model_2 = MOF.Model()\n    MOI.read_from_file(model_2, TEST_MOF_FILE * suffix)\n    MOIU.test_models_equal(model, model_2, variables, constraints)\n    MOF.validate(TEST_MOF_FILE * suffix)\nend\n\n@testset \"Error handling: read_from_file\" begin\n    failing_models_dir = joinpath(@__DIR__, \"failing_models\")\n\n    @testset \"Non-empty model\" begin\n        model = MOF.Model(warn=true)\n        MOI.add_variable(model)\n        @test !MOI.is_empty(model)\n        exception = ErrorException(\n            \"Cannot read model from file as destination model is not empty.\")\n        @test_throws exception MOI.read_from_file(\n            model, joinpath(@__DIR__, \"empty_model.mof.json\"))\n        options = MOF.get_options(model)\n        @test options.warn\n        MOI.empty!(model)\n        @test MOI.is_empty(model)\n        MOI.read_from_file(\n            model, joinpath(@__DIR__, \"empty_model.mof.json\"))\n        options2 = MOF.get_options(model)\n        @test options2.warn\n    end\n\n    @testset \"$(filename)\" for filename in filter(\n        f -> endswith(f, \".mof.json\"), readdir(failing_models_dir))\n        @test_throws Exception MOI.read_from_file(MOF.Model(),\n            joinpath(failing_models_dir, filename))\n    end\nend\n@testset \"Names\" begin\n    @testset \"Blank variable name\" begin\n        model = MOF.Model()\n        variable_index = MOI.add_variable(model)\n        @test_throws Exception MOF.moi_to_object(variable_index, model)\n        MOI.FileFormats.create_unique_names(model, warn=true)\n        @test MOF.moi_to_object(variable_index, model) ==\n            MOF.OrderedObject(\"name\" => \"x1\")\n    end\n    @testset \"Duplicate variable name\" begin\n        model = MOF.Model()\n        x = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), x, \"x\")\n        y = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), y, \"x\")\n        @test MOF.moi_to_object(x, model) == MOF.OrderedObject(\"name\" => \"x\")\n        @test MOF.moi_to_object(y, model) == MOF.OrderedObject(\"name\" => \"x\")\n        MOI.FileFormats.create_unique_names(model, warn=true)\n        @test MOF.moi_to_object(x, model) == MOF.OrderedObject(\"name\" => \"x\")\n        @test MOF.moi_to_object(y, model) == MOF.OrderedObject(\"name\" => \"x_1\")\n    end\n    @testset \"Blank constraint name\" begin\n        model = MOF.Model()\n        x = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), x, \"x\")\n        c = MOI.add_constraint(model, MOI.SingleVariable(x), MOI.ZeroOne())\n        name_map = Dict(x => \"x\")\n        MOI.FileFormats.create_unique_names(model, warn=true)\n        @test MOF.moi_to_object(c, model, name_map)[\"name\"] == \"c1\"\n    end\n    @testset \"Duplicate constraint name\" begin\n        model = MOF.Model()\n        x = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), x, \"x\")\n        c1 = MOI.add_constraint(model, MOI.SingleVariable(x), MOI.LessThan(1.0))\n        c2 = MOI.add_constraint(model, MOI.SingleVariable(x), MOI.GreaterThan(0.0))\n        MOI.set(model, MOI.ConstraintName(), c1, \"c\")\n        MOI.set(model, MOI.ConstraintName(), c2, \"c\")\n        name_map = Dict(x => \"x\")\n        @test MOF.moi_to_object(c1, model, name_map)[\"name\"] == \"c\"\n        @test MOF.moi_to_object(c2, model, name_map)[\"name\"] == \"c\"\n        MOI.FileFormats.create_unique_names(model, warn=true)\n        @test MOF.moi_to_object(c1, model, name_map)[\"name\"] == \"c_1\"\n        @test MOF.moi_to_object(c2, model, name_map)[\"name\"] == \"c\"\n    end\nend\n@testset \"round trips\" begin\n    @testset \"Empty model\" begin\n        model = MOF.Model(validate = true)\n        MOI.write_to_file(model, TEST_MOF_FILE)\n        model_2 = MOF.Model(validate = true)\n        MOI.read_from_file(model_2, TEST_MOF_FILE)\n        MOIU.test_models_equal(model, model_2, String[], String[])\n    end\n    @testset \"FEASIBILITY_SENSE\" begin\n        model = MOF.Model(validate = true)\n        x = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), x, \"x\")\n        MOI.set(model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE)\n        MOI.write_to_file(model, TEST_MOF_FILE)\n        model_2 = MOF.Model(validate = true)\n        MOI.read_from_file(model_2, TEST_MOF_FILE)\n        MOIU.test_models_equal(model, model_2, [\"x\"], String[])\n    end\n    @testset \"Empty function term\" begin\n        model = MOF.Model(validate = true)\n        x = MOI.add_variable(model)\n        MOI.set(model, MOI.VariableName(), x, \"x\")\n        c = MOI.add_constraint(model,\n            MOI.ScalarAffineFunction(MOI.ScalarAffineTerm{Float64}[], 0.0),\n            MOI.GreaterThan(1.0)\n        )\n        MOI.set(model, MOI.ConstraintName(), c, \"c\")\n        MOI.write_to_file(model, TEST_MOF_FILE)\n        model_2 = MOF.Model(validate = true)\n        MOI.read_from_file(model_2, TEST_MOF_FILE)\n        MOIU.test_models_equal(model, model_2, [\"x\"], [\"c\"])\n    end\n    @testset \"min objective\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: x\n        \"\"\", [\"x\"], String[])\n    end\n    @testset \"max objective\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            maxobjective: x\n        \"\"\", [\"x\"], String[], suffix=\".gz\")\n    end\n    @testset \"min scalaraffine\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n        \"\"\", [\"x\"], String[])\n    end\n    @testset \"max scalaraffine\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            maxobjective: 1.2x + 0.5\n        \"\"\", [\"x\"], String[], suffix=\".gz\")\n    end\n    @testset \"singlevariable-in-lower\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x >= 1.0\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-upper\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            maxobjective: 1.2x + 0.5\n            c1: x <= 1.0\n        \"\"\", [\"x\"], [\"c1\"], suffix=\".gz\")\n    end\n    @testset \"singlevariable-in-interval\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x in Interval(1.0, 2.0)\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-equalto\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x == 1.0\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-zeroone\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x in ZeroOne()\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-integer\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x in Integer()\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-Semicontinuous\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x in Semicontinuous(1.0, 2.0)\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"singlevariable-in-Semiinteger\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.2x + 0.5\n            c1: x in Semiinteger(1.0, 2.0)\n        \"\"\", [\"x\"], [\"c1\"])\n    end\n    @testset \"scalarquadratic-objective\" begin\n        test_model_equality(\"\"\"\n            variables: x\n            minobjective: 1.0*x*x + -2.0x + 1.0\n        \"\"\", [\"x\"], String[])\n    end\n    @testset \"SOS1\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in SOS1([1.0, 2.0, 3.0])\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"SOS2\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in SOS2([1.0, 2.0, 3.0])\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"Reals\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in Reals(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"Zeros\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in Zeros(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"Nonnegatives\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in Nonnegatives(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"Nonpositives\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in Nonpositives(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"PowerCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in PowerCone(2.0)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"DualPowerCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in DualPowerCone(0.5)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"GeometricMeanCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in GeometricMeanCone(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"vectoraffine-in-zeros\" begin\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [1.0x + -3.0, 2.0y + -4.0] in Zeros(2)\n        \"\"\", [\"x\", \"y\"], [\"c1\"])\n    end\n    @testset \"vectorquadratic-in-nonnegatives\" begin\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [1.0*x*x + -2.0x + 1.0, 2.0y + -4.0] in Nonnegatives(2)\n        \"\"\", [\"x\", \"y\"], [\"c1\"])\n    end\n    @testset \"ExponentialCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in ExponentialCone()\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"DualExponentialCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in DualExponentialCone()\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"SecondOrderCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in SecondOrderCone(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"RotatedSecondOrderCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in RotatedSecondOrderCone(3)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"PositiveSemidefiniteConeTriangle\" begin\n        test_model_equality(\"\"\"\n            variables: x1, x2, x3\n            minobjective: x1\n            c1: [x1, x2, x3] in PositiveSemidefiniteConeTriangle(2)\n        \"\"\", [\"x1\", \"x2\", \"x3\"], [\"c1\"])\n    end\n    @testset \"PositiveSemidefiniteConeSquare\" begin\n        test_model_equality(\"\"\"\n            variables: x1, x2, x3, x4\n            minobjective: x1\n            c1: [x1, x2, x3, x4] in PositiveSemidefiniteConeSquare(2)\n        \"\"\", [\"x1\", \"x2\", \"x3\", \"x4\"], [\"c1\"])\n    end\n    @testset \"LogDetConeTriangle\" begin\n        test_model_equality(\"\"\"\n            variables: t, u, x1, x2, x3\n            minobjective: x1\n            c1: [t, u, x1, x2, x3] in LogDetConeTriangle(2)\n        \"\"\", [\"t\", \"u\", \"x1\", \"x2\", \"x3\"], [\"c1\"])\n    end\n    @testset \"LogDetConeSquare\" begin\n        test_model_equality(\"\"\"\n            variables: t, u, x1, x2, x3, x4\n            minobjective: x1\n            c1: [t, u, x1, x2, x3, x4] in LogDetConeSquare(2)\n        \"\"\", [\"t\", \"u\", \"x1\", \"x2\", \"x3\", \"x4\"], [\"c1\"])\n    end\n    @testset \"RootDetConeTriangle\" begin\n        test_model_equality(\"\"\"\n            variables: t, x1, x2, x3\n            minobjective: x1\n            c1: [t, x1, x2, x3] in RootDetConeTriangle(2)\n        \"\"\", [\"t\", \"x1\", \"x2\", \"x3\"], [\"c1\"])\n    end\n    @testset \"RootDetConeSquare\" begin\n        test_model_equality(\"\"\"\n            variables: t, x1, x2, x3, x4\n            minobjective: x1\n            c1: [t, x1, x2, x3, x4] in RootDetConeSquare(2)\n        \"\"\", [\"t\", \"x1\", \"x2\", \"x3\", \"x4\"], [\"c1\"])\n    end\n    @testset \"IndicatorSet\" begin\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [x, y] in IndicatorSet{ACTIVATE_ON_ONE}(GreaterThan(1.0))\n            c2: x >= 0.0\n        \"\"\", [\"x\", \"y\"], [\"c1\", \"c2\"])\n\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [x, y] in IndicatorSet{ACTIVATE_ON_ZERO}(GreaterThan(1.0))\n            c2: x >= 0.0\n        \"\"\", [\"x\", \"y\"], [\"c1\", \"c2\"])\n    end\n    @testset \"NormOneCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [x, y] in NormOneCone(2)\n            c2: x >= 0.0\n        \"\"\", [\"x\", \"y\"], [\"c1\", \"c2\"])\n    end\n    @testset \"NormInfinityCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y\n            minobjective: x\n            c1: [x, y] in NormInfinityCone(2)\n            c2: x >= 0.0\n        \"\"\", [\"x\", \"y\"], [\"c1\", \"c2\"])\n    end\n    @testset \"RelativeEntropyCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in RelativeEntropyCone(3)\n            c2: x >= 0.0\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\", \"c2\"])\n    end\n    @testset \"NormSpectralCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in NormSpectralCone(1, 2)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    @testset \"NormNuclearCone\" begin\n        test_model_equality(\"\"\"\n            variables: x, y, z\n            minobjective: x\n            c1: [x, y, z] in NormNuclearCone(1, 2)\n        \"\"\", [\"x\", \"y\", \"z\"], [\"c1\"])\n    end\n    # Clean up\n    sleep(1.0)  # allow time for unlink to happen\n    rm(TEST_MOF_FILE, force=true)\n    rm(TEST_MOF_FILE * \".gz\", force=true)\nend\n", "meta": {"hexsha": "f7b0637c15611f38918f57fc9d71790a92320927", "size": 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"max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.756501182, "max_line_length": 83, "alphanum_fraction": 0.5340089784, "num_tokens": 4531, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.12252320771203078, "lm_q1q2_score": 0.05982604786416719}}
{"text": "using Distributed\n\nusing Test\n\n@everywhere begin\n    using MLJBase\n    using MLJTuning\n    using ..Models\n    import ComputationalResources: CPU1, CPUProcesses, CPUThreads\nend\n\nusing Random\nRandom.seed!(1234*myid())\nusing .TestUtilities\n\nN = 30\nx1 = rand(N);\nx2 = rand(N);\nx3 = rand(N);\nX = (x1=x1, x2=x2, x3=x3);\ny = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.4*rand(N);\n\nm(K) = KNNRegressor(K=K)\nr = [m(K) for K in 13:-1:2]\n\n# TODO: replace the above with the line below and post an issue on\n# the failure (a bug in Distributed, I reckon):\n# r = (m(K) for K in 13:-1:2)\n\n@testset \"constructor\" begin\n    @test_throws(MLJTuning.ERR_SPECIFY_RANGE,\n                 TunedModel(model=first(r), tuning=Grid(), measure=rms))\n    @test_throws(MLJTuning.ERR_SPECIFY_RANGE,\n                 TunedModel(model=first(r), measure=rms))\n    @test_throws(MLJTuning.ERR_BOTH_DISALLOWED,\n                 TunedModel(model=first(r),\n                            models=r, tuning=Explicit(), measure=rms))\n    tm = TunedModel(models=r, tuning=Explicit(), measure=rms)\n    @test tm.tuning isa Explicit && tm.range ==r && tm.model == first(r)\n    @test input_scitype(tm) == Unknown\n    @test TunedModel(models=r, measure=rms) == tm\n    @test_logs (:info, r\"No measure\") @test TunedModel(models=r) == tm\n\n    @test_throws(MLJTuning.ERR_SPECIFY_MODEL,\n                 TunedModel(range=r, measure=rms))\n    @test_throws(MLJTuning.ERR_MODEL_TYPE,\n                 TunedModel(model=42, tuning=Grid(),\n                            range=r, measure=rms))\n    @test_logs (:info, MLJTuning.INFO_MODEL_IGNORED) tm =\n        TunedModel(model=42, tuning=Explicit(), range=r, measure=rms)\n    @test_logs (:info, r\"No measure\") tm =\n        TunedModel(model=first(r), range=r)\n    @test_throws(MLJTuning.ERR_SPECIFY_RANGE_OR_MODELS,\n                TunedModel(tuning=Explicit(), measure=rms))\n    @test_throws(MLJTuning.ERR_NEED_EXPLICIT,\n                 TunedModel(models=r, tuning=Grid()))\n    tm = TunedModel(model=first(r), range=r, measure=rms)\n    @test tm.tuning isa Grid\n    @test input_scitype(tm) == Table(Continuous)\nend\n\nresults = [(evaluate(model, X, y,\n                     resampling=CV(nfolds=2),\n                     measure=rms,\n                     verbosity=0,)).measurement[1] for model in r]\n\n@testset \"measure compatibility check\" begin\n    tm = TunedModel(model=first(r), tuning=Explicit(),\n                    range=r, resampling=CV(nfolds=2),\n                    measures=cross_entropy)\n    @test_logs((:error, r\"Problem\"),\n               (:info, r\"\"),\n               (:info, r\"\"),\n               @test_throws ArgumentError fit(tm, 0, X, y))\nend\n\n@testset_accelerated \"basic fit (CPU1)\" accel begin\n    printstyled(\"\\n Testing progressmeter basic fit with $(accel) and CPU1 resampling \\n\", color=:bold)\n    best_index = argmin(results)\n    tm = TunedModel(model=first(r), tuning=Explicit(),\n                    range=r, resampling=CV(nfolds=2),\n                    measures=[rms, l1], acceleration=accel)\n    verbosity = accel isa CPU1 ? 2 : 1\n    fitresult, meta_state, _report = fit(tm, verbosity, X, y);\n    history, _, state = meta_state;\n    results2 = map(event -> event.measurement[1], history)\n    @test results2 \u2248 results\n    @test fitresult.model == collect(r)[best_index]\n    @test _report.best_model == collect(r)[best_index]\n    @test _report.history[5] == MLJTuning.delete(history[5], :metadata)\n\n    # training_losses:\n    losses = training_losses(tm, _report)\n    @test all(eachindex(losses)) do i\n        minimum(results[1:i]) == losses[i]\n    end\n    @test MLJBase.iteration_parameter(tm) == :n\n\nend\n\n@static if VERSION >= v\"1.3.0-DEV.573\"\n@testset_accelerated \"Basic fit (CPUThreads)\" accel begin\n    printstyled(\"\\n Testing progressmeter basic fit with $(accel) and CPUThreads resampling \\n\", color=:bold)\n    tm = TunedModel(model=first(r), tuning=Explicit(),\n                    range=r, resampling=CV(nfolds=2),\n                    measures=[rms, l1], acceleration= CPUThreads(),\n                    acceleration_resampling=accel)\n    fitresult, meta_state, report = fit(tm, 1, X, y);\n    history, _, state = meta_state;\n    results3 = map(event -> event.measurement[1], history)\n    @test results3 \u2248 results\nend\nend\n@testset_accelerated \"Basic fit (CPUProcesses)\" accel begin\n    printstyled(\"\\n Testing progressmeter basic fit with $(accel) and CPUProcesses resampling \\n\", color=:bold)\n    best_index = argmin(results)\n    tm = TunedModel(model=first(r), tuning=Explicit(),\n                    range=r, resampling=CV(nfolds=2),\n                    measures=[rms, l1], acceleration=CPUProcesses(),\n                    acceleration_resampling=accel)\n    fitresult, meta_state, report = fit(tm, 1, X, y);\n    history, _, state = meta_state;\n    results4 = map(event -> event.measurement[1], history)\n    @test results4 \u2248 results\nend\n\n@testset_accelerated(\"under/over supply of models\", accel, begin\n    tm = TunedModel(model=first(r), tuning=Explicit(),\n                    range=r, measures=[rms, l1],\n                    acceleration=accel,\n                    resampling=CV(nfolds=2),\n                    n=4)\n    mach = machine(tm, X, y)\n    fit!(mach, verbosity=0)\n    history = MLJBase.report(mach).history\n    @test map(event -> event.measurement[1], history) \u2248 results[1:4]\n\n    tm.n += 2\n    @test_logs((:info, r\"^Updating\"),\n               (:info, r\"^Attempting to add 2.*to 6\"),\n               fit!(mach, verbosity=1))\n    history = MLJBase.report(mach).history\n    @test map(event -> event.measurement[1], history) \u2248 results[1:6]\n\n    tm.n=100\n    @test_logs (:info, r\"Only 12\") fit!(mach, verbosity=0)\n    history = MLJBase.report(mach).history\n    @test map(event -> event.measurement[1], history) \u2248 results\nend)\n\n@everywhere begin\n\n    # variation of the Explicit strategy that annotates the models\n    # with metadata\n    mutable struct MockExplicit <: MLJTuning.TuningStrategy end\n\n    annotate(model) = (model, params(model)[1])\n\n    _length(x) = length(x)\n    _length(::Nothing) = 0\n    MLJTuning.models(tuning::MockExplicit,\n                     model,\n                     history,\n                     state,\n                     n_remaining,\n                     verbosity) =\n                         annotate.(state)[_length(history) + 1:end], state\n\n    function default_n(tuning::Explicit, range)\n        try\n            length(range)\n        catch MethodError\n            DEFAULT_N\n        end\n\n    end\n\nend\n\n@testset_accelerated(\"passing of model metadata\", accel,\n                  begin\n                     tm = TunedModel(model=first(r), tuning=MockExplicit(),\n                                     range=r, resampling=CV(nfolds=2),\n                                     measures=[rms, l1], acceleration=accel)\n                     fitresult, meta_state, report = fit(tm, 0, X, y);\n                     history, _, state = meta_state;\n                     @test all(history) do event\n                         event.metadata == event.model.K\n                     end\n                     end)\n\n\n\n@testset \"data caching\" begin\n    X = (x1= ones(5),);\n    y = coerce(collect(\"abcaa\"), Multiclass);\n\n    m(b) = ConstantClassifier(testing=true, bogus=b)\n    r = [m(b) for b in 1:5]\n\n    tuned_model = TunedModel(model=first(r), tuning=Explicit(),\n                             range=r, resampling=Holdout(fraction_train=0.8),\n                             measure=log_loss, cache=true)\n\n    # There is reformatting and resampling of X and y for training of\n    # first model, and then resampling on the prediction side only\n    # thereafter. Then, finally, for training on all supplied data, there\n    # is reformatting and resampling again:\n    @test_logs(\n        (:info, \"reformatting X, y\"), # fit 1\n        (:info, \"resampling X, y\"),   # fit 1\n        (:info, \"resampling X\"),      # predict 1\n        (:info, \"resampling X\"),      # predict 2\n        (:info, \"resampling X\"),      # predict 3\n        (:info, \"resampling X\"),      # predict 4\n        (:info, \"resampling X\"),      # predict 5\n        (:info, \"reformatting X, y\"), # fit on all data\n        (:info, \"resampling X, y\"),   # fit one all data\n        fit(tuned_model, 0, X, y)\n    );\n\n    # Otherwise, resampling and reformatting happen for every model\n    # trained, and we get a reformat on the prediction side every\n    # time:\n    tuned_model.cache = false\n    @test_logs(\n        (:info, \"reformatting X, y\"), # fit 1\n        (:info, \"resampling X, y\"),   # fit 1\n        (:info, \"reformatting X\"),    # predict 1\n        (:info, \"reformatting X, y\"), # fit 2\n        (:info, \"resampling X, y\"),   # fit 2\n        (:info, \"reformatting X\"),    # predict 2\n        (:info, \"reformatting X, y\"), # fit 3\n        (:info, \"resampling X, y\"),   # fit 3\n        (:info, \"reformatting X\"),    # predict 3\n        (:info, \"reformatting X, y\"), # fit 4\n        (:info, \"resampling X, y\"),   # fit 4\n        (:info, \"reformatting X\"),    # predict 4\n        (:info, \"reformatting X, y\"), # fit 5\n        (:info, \"resampling X, y\"),   # fit 5\n        (:info, \"reformatting X\"),    # predict 5\n        (:info, \"reformatting X, y\"), # fit on all data\n        (:info, \"resampling X, y\"),   # fit on all data\n        fit(tuned_model, 0, X, y)\n    );\nend\n\n@testset \"issue #128\" begin\n    X, y = make_regression(10, 2)\n    dtc = DecisionTreeRegressor()\n    r   = range(dtc, :max_depth, lower=1, upper=50);\n\n    tmodel = TunedModel(model=dtc, ranges=[r, ],\n                        tuning=Grid(resolution=50),\n                        measure=mae,\n                        n=48);\n    mach = machine(tmodel, X, y)\n    fit!(mach, verbosity=0);\n\n    @test length(report(mach).history) == 48\n\n    tmodel.n = 49\n    fit!(mach, verbosity=0);\n\n    @test length(report(mach).history) == 49\nend\n\n@testset_accelerated \"Resampling reproducibility\" accel begin\n    X, y = make_regression(100, 2)\n    dcr = DeterministicConstantRegressor()\n\n    # Hold out reproducibility\n    homodel = TunedModel(tuning=Explicit(),\n                         models=fill(dcr, 10),\n                         resampling=Holdout(rng=StableRNG(1234)),\n                         acceleration_resampling=accel,\n                         measure=mae)\n    homach = machine(homodel, X, y)\n    fit!(homach, verbosity=0);\n    horep = report(homach)\n    measurements = getproperty.(horep.history, :measurement)\n    @test all(==(measurements[1]), measurements)\n\n    # Cross-validation reproducibility\n    cvmodel = TunedModel(tuning=Explicit(),\n                         models=fill(dcr, 10),\n                         resampling=CV(nfolds=5, rng=StableRNG(1234)),\n                         acceleration_resampling=accel,\n                         measure=mae)\n    cvmach = machine(cvmodel, X, y)\n    fit!(cvmach, verbosity=0);\n    cvrep = report(cvmach)\n    per_folds = getproperty.(cvrep.history, :per_fold)\n    @test all(==(per_folds[1]), per_folds)\nend\n\n@testset \"deterministic metrics for probabilistic models\" begin\n\n    # https://github.com/JuliaAI/MLJBase.jl/pull/599 allows mix of\n    # deterministic and probabilistic metrics:\n    X, y = MLJBase.make_blobs()\n    model = DecisionTreeClassifier()\n    range = MLJBase.range(model, :max_depth, values=[1,2])\n    tmodel = TunedModel(model=model,\n                        range=range,\n                        measures=[MisclassificationRate(),\n                                  LogLoss()])\n    mach = machine(tmodel, X, y)\n    @test_logs fit!(mach, verbosity=0)\n\nend\n\ntrue\n", "meta": {"hexsha": "849329f577cdeb225c55c1db75802e2ed6b97c51", "size": 11452, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/tuned_models.jl", "max_stars_repo_name": "alan-turing-institute/MLJTuning.jl", "max_stars_repo_head_hexsha": "b058e8df63ebbd67ef8df823480a42cfd45462b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 41, "max_stars_repo_stars_event_min_datetime": "2020-01-28T04:27:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-11T05:56:59.000Z", "max_issues_repo_path": "test/tuned_models.jl", "max_issues_repo_name": "alan-turing-institute/MLJTuning.jl", "max_issues_repo_head_hexsha": "b058e8df63ebbd67ef8df823480a42cfd45462b7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 64, "max_issues_repo_issues_event_min_datetime": "2020-02-03T21:26:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-11T03:49:59.000Z", "max_forks_repo_path": "test/tuned_models.jl", "max_forks_repo_name": "alan-turing-institute/MLJTuning.jl", "max_forks_repo_head_hexsha": "b058e8df63ebbd67ef8df823480a42cfd45462b7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-29T10:25:21.000Z", "avg_line_length": 36.4713375796, "max_line_length": 111, "alphanum_fraction": 0.5832169053, "num_tokens": 3081, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.12252320450794524, "lm_q1q2_score": 0.05982604629966542}}
{"text": "module ResizableArraysTests\n\nusing Test\nusing ResizableArrays\nusing ResizableArrays: checkdimension, checkdimensions, _same_elements\nusing Base: unsafe_convert, elsize\n\n# FIXME: used @generated\nslice(A::AbstractArray{<:Any,2}, I) = A[:,I]\nslice(A::AbstractArray{<:Any,3}, I) = A[:,:,I]\nslice(A::AbstractArray{<:Any,4}, I) = A[:,:,:,I]\nslice(A::AbstractArray{<:Any,5}, I) = A[:,:,:,:,I]\n\nsum_v1(iter::AbstractArray) = (s = zero(eltype(iter));\n                               for x in iter; s += x; end;\n                               return s)\nsum_v2(iter::AbstractArray) = (s = zero(eltype(iter));\n                               @inbounds for x in iter; s += x; end;\n                               return s)\nsum_v3(iter::AbstractArray) = (s = zero(eltype(iter));\n                               @inbounds @simd for x in iter; s += x; end;\n                               return s)\n\n@testset \"Basic methods\" begin\n    @testset \"Utilities\" begin\n        @test checkdimension(Bool, \u03c0) == false\n        @test checkdimensions(Bool, ()) == true\n        @test checkdimensions(Bool, (1,)) == true\n        @test checkdimensions(Bool, (1,2,0)) == true\n        @test checkdimensions(Bool, (1,-2,0)) == false\n        @test_throws ErrorException checkdimensions((1,-2,0))\n        @test isgrowable(\u03c0) == false\n        @test isgrowable((1,2,3)) == false\n        @test isgrowable([1,2,3]) == true\n        for T in (Int16, Float32, Tuple{Float64,Float64})\n            @test elsize(ResizableArray{T,3,Vector{T}}) == sizeof(T)\n        end\n\n        # Make sure all variants of _same_elements are tested.\n        A = randn(3,4)\n        indexstyles = (IndexLinear(), IndexCartesian())\n        for indexstyle1 in indexstyles, indexstyle2 in indexstyles\n            @test _same_elements(indexstyle1, A, indexstyle2, A, length(A))\n        end\n\n    end\n    @testset \"Dimensions: $dims\" for dims in ((), (3,), (2,3), (2,3,4))\n        altdims = map(UInt, dims) # used later\n        N = length(dims)\n        if N > 0\n            A = rand(dims...)\n            T = eltype(A)\n        else\n            T = Float64\n            A = Array{T}(undef, dims)\n            A[1] = rand(dims...)\n        end\n        B = ResizableArray{T}(undef, size(A))\n        @test isgrowable(B) == (N > 0)\n        @test IndexStyle(typeof(B)) == IndexLinear()\n        @test eltype(B) == eltype(A)\n        @test elsize(B) == elsize(A)\n        @test sizeof(B) == sizeof(A)\n        @test ndims(B) == ndims(A) == N\n        @test size(B) == size(A)\n        @test all(d -> size(B,d) == size(A,d), 1:(N+2))\n        @test axes(B) == axes(A)\n        @test Base.axes1(B) == axes(B,1)\n        @test length(B) == length(A) == prod(dims)\n        @test maxlength(B) == length(B)\n        @test all(d -> axes(B,d) == axes(A,d), 1:(N+2))\n        @test unsafe_convert(Ptr{T}, B) == unsafe_convert(Ptr{T}, parent(B))\n        @test pointer(B) == pointer(parent(B))\n        @test pointer(B, 2) == pointer(parent(B), 2)\n        copyto!(B, A)\n        @test all(i -> A[i] == B[i], 1:length(A))\n        @test all(i -> A[i] == B[i], CartesianIndices(A))\n        @test all(i -> A[i] == parent(B)[i], 1:length(A))\n        @test A == B\n        for i in eachindex(B)\n            B[i] = rand()\n        end\n        copyto!(A, B)\n        @test A == B\n        if N > 0\n            # Extend array B.\n            tmpdims = collect(size(B))\n            tmpdims[end] += 1\n            resize!(B, tmpdims...)\n            for i in length(A)+1:length(B); B[i] = 0; end\n            @test maxlength(B) == length(B) == prod(tmpdims)\n            @test A != B\n            @test B != A\n            @test all(i -> B[i] == A[i], 1:length(A))\n            C = view(B, axes(A)...)\n            @test C != B && B != C\n            @test C == A && A == C\n            # Shrink array B.\n            oldmaxlen = maxlength(B)\n            resize!(B, dims)\n            @test B == A\n            @test C == B && B == C\n            @test maxlength(B) == oldmaxlen\n            # Use copy to make a fresh resizable copy\n            C = copy(ResizableArray, B)\n            @test C == B\n            @test pointer(C) != pointer(B)\n            @test maxlength(C) == length(C)\n            C = copy(ResizableArray{T}, B)\n            @test C == B\n            @test pointer(C) != pointer(B)\n            @test maxlength(C) == length(C)\n            C = copy(ResizableArray{T,N}, B)\n            @test C == B\n            @test pointer(C) != pointer(B)\n            @test maxlength(C) == length(C)\n            shrink!(B)\n            @test B == A\n            @test maxlength(B) == length(B)\n        end\n\n        # Check errors.\n        @test_throws BoundsError B[0]\n        @test_throws BoundsError B[length(B) + 1]\n        @test_throws ErrorException resize!(B, (dims..., 5))\n        @test_throws DimensionMismatch ResizableArray{T,N+1}(A)\n        @test_throws DimensionMismatch copy(ResizableArray{T,N+1}, A)\n        @test_throws ErrorException ResizableArray{T,N,Vector{Char}}(A)\n        @test_throws ErrorException copy(ResizableArray{T,N,Vector{Char}}, A)\n\n        # Make a copy of A using a resizable array.\n        C = copyto!(similar(ResizableArray{T}, axes(A)), A)\n        @test C == A\n\n        # Check equality for a different list of dimensions.\n        C = rand(7)\n        @test (B == C) == false\n        @test (C == B) == false\n        @test (B == ResizableArray(C)) == false\n        @test (ResizableArray(C) == B) == false\n\n        # Check various constructors and custom buffer\n        # (do not splat dimensions if N=0).\n        buf = Vector{T}(undef, length(A))\n        for arg in (undef, buf), sz in (dims, altdims)\n            if isa(arg, Vector)\n                # No parameters.\n                C = copyto!(ResizableArray(arg, sz), A)\n                @test eltype(C) == eltype(A) && C == A\n                if N > 0\n                    C = copyto!(ResizableArray(arg, sz...), A)\n                    @test eltype(C) == eltype(A) && C == A\n                end\n            end\n            # Parameter {T}.\n            C = copyto!(ResizableArray{T}(arg, sz), A)\n            @test eltype(C) == eltype(A) && C == A\n            if N > 0\n                C = copyto!(ResizableArray{T}(arg, sz...), A)\n                @test eltype(C) == eltype(A) && C == A\n            end\n            # Parameters {T,N}.\n            C = copyto!(ResizableArray{T,N}(arg, sz), A)\n            if N > 0\n                @test eltype(C) == eltype(A) && C == A\n                C = copyto!(ResizableArray{T,N}(arg, sz...), A)\n            end\n        end\n\n        # Use constructor to convert array.\n        C = ResizableArray{T,N}()\n        resize!(C, dims)\n        copyto!(C, A)\n        @test eltype(C) == eltype(A) && C == A\n\n        # Use constructor to convert ordinary array.\n        C = ResizableArray(A)\n        @test eltype(C) == eltype(A) && C == A\n        C = ResizableArray{T}(A)\n        @test eltype(C) == eltype(A) && C == A\n        C = ResizableArray{T,N}(A)\n        @test eltype(C) == eltype(A) && C == A\n\n        # Use convert to convert ordinary array.\n        C = convert(ResizableArray, A)\n        @test eltype(C) == eltype(A) && C == A\n        C = convert(ResizableArray{T}, A)\n        @test eltype(C) == eltype(A) && C == A\n        C = convert(ResizableArray{T,N}, A)\n        @test eltype(C) == eltype(A) && C == A\n\n        # Use convert to convert resizable array, result should be identical.\n        for pass in 1:4\n            C = (pass == 1 ? convert(ResizableArray,             B) :\n                 pass == 2 ? convert(ResizableArray{T},          B) :\n                 pass == 3 ? convert(ResizableArray{T,N},        B) :\n                 pass == 4 ? convert(ResizableArray{T,N,Vector}, B) : nothing)\n            @test eltype(C) == eltype(B)\n            @test C == B\n            @test pointer(C) == pointer(B)\n        end\n        # Use convert to convert resizable array, result should be different.\n        B = ResizableArray{Int16,N}(undef, dims)\n        for i in 1:length(B); B[i] = i; end\n        for pass in 1:3\n            C = (pass == 1 ? convert(ResizableArray{Int32},   B)        :\n                 pass == 2 ? convert(ResizableArray{Int32,N}, B)        :\n                 pass == 3 ? convert(ResizableArray{Int32,N,Vector}, B) :\n                 nothing)\n            @test eltype(C) == Int32\n            @test elsize(C) == sizeof(eltype(C))\n            @test sizeof(C) == elsize(C)*length(C)\n            @test C == B\n            @test pointer(C) != pointer(B)\n        end\n    end\nend\n\n@testset \"Queue methods\" begin\n    T = Float32\n    @testset \"Dimensions: $dims\" for dims in ((3,), (2,3), (2,3,4))\n        N = length(dims)\n        m = 5\n        altdims = map(UInt16, dims) # used later\n        extdims = (dims..., m)\n        extaltdims = (altdims..., Int16(m))\n        A = rand(T, dims..., m)\n        B = rand(T, dims)\n        C = rand(T, dims)\n        R = ResizableArray(A)\n        append!(R, B)\n        @test slice(R, 1:m) == A\n        @test slice(R, m+1) == B\n        prepend!(R, C)\n        @test slice(R, 1) == C\n        @test slice(R, 2:m+1) == A\n        @test slice(R, m+2) == B\n        @test length(extdims) == length(extaltdims) == N+1\n        for hint in (prod(extdims), extdims, extaltdims)\n            R = sizehint!(ResizableArray{T}(undef, dims..., 0), hint...)\n            for k in 1:m\n                if isodd(k)\n                    append!(R, B)\n                else\n                    prepend!(R, C)\n                end\n            end\n            @test slice(R, 1) == C\n            @test slice(R, m) == B\n        end\n    end\nend\n\n@testset \"Iterations\" begin\n    T = Float64\n    @testset \"Dimensions: $dims\" for dims in ((3,), (2,3), (2,3,4))\n        N = length(dims)\n        A = rand(T, dims)\n        B = ResizableArray(A)\n        val = sum(A)\n        @test val \u2248 sum(B)\n        @test val \u2248 sum_v1(B)\n        @test val \u2248 sum_v2(B)\n        @test val \u2248 sum_v3(B)\n    end\nend\n\nend # module\n", "meta": {"hexsha": "fce77deb7d38cb05a32a9e7de939cafa4dffcc61", "size": 9885, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_stars_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_issues_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_forks_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7472118959, "max_line_length": 78, "alphanum_fraction": 0.4838644411, "num_tokens": 2802, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939264921326705, "lm_q2_score": 0.13296422645481662, "lm_q1q2_score": 0.05975314597712281}}
{"text": "using MPI\n\nfunction do_hello()\n    comm = MPI.COMM_WORLD\n    rk = MPI.Comm_rank(comm)\n    sz = (MPI.Comm_size(comm))\n    println(\"Hello world, I'm $(rk) of $(sz)\")\n    MPI.Barrier(comm)\nend\n\nfunction main()\n    MPI.Init()\n    do_hello()\n    MPI.Finalize()\nend\n\nmain()\n", "meta": {"hexsha": "9c88316f6752cec0f64cdd832b8fba2e273194fa", "size": 268, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "messagePassingInterfaceExer/hello.jl", "max_stars_repo_name": "terasakisatoshi/juliaExer", "max_stars_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-02T01:24:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-04T12:03:25.000Z", "max_issues_repo_path": "messagePassingInterfaceExer/hello.jl", "max_issues_repo_name": "terasakisatoshi/juliaExer", "max_issues_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "messagePassingInterfaceExer/hello.jl", "max_forks_repo_name": "terasakisatoshi/juliaExer", "max_forks_repo_head_hexsha": "e3c2195f39de858915a3dcd47684eccbb7ecb552", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.8888888889, "max_line_length": 46, "alphanum_fraction": 0.6119402985, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.40356685373537454, "lm_q2_score": 0.14804720179063333, "lm_q1q2_score": 0.059746943430972}}
{"text": "# Lies!  Lies and Slander!  Supposedly ccache doesn't make things fail, but\n# WHAT DO WE HAVE?! Literal proof that this is not the case.  This whole thing\n# fails to do the proper symbol renaming with `ccache`, which is TERRIBLE.\nENV[\"BINARYBUILDER_USE_CCACHE\"] = \"false\"\nusing BinaryBuilder\n\n# Collection of sources required to build Arpack\nname = \"Arpack\"\nversion = v\"3.7.0\"\nsources = [\n    \"https://github.com/opencollab/arpack-ng.git\" =>\n    \"a580f6fc103c4bba1630ddd957b0b5f58dec284c\",\n]\n\n\n# Bash recipe for building across all platforms\nscript = raw\"\"\"\ncd $WORKSPACE/srcdir\nmkdir arpack-build\ncd arpack-build\n\n# arpack tests require finding libgfortran when linking with C linkers,\n# and gcc doesn't automatically add that search path.  So we do it for it.\nEXE_LINK_FLAGS=\"${LDFLAGS}\"\nif [[ ${target} != *darwin* ]]; then\n    EXE_LINK_FLAGS=\"${EXE_LINK_FLAGS} -Wl,-rpath-link,/opt/${target}/${target}/lib -Wl,-rpath-link,/opt/${target}/${target}/lib64\"\nfi\n\n# Symbols that have float32, float64, complexf32, and complexf64 support\nSDCZ_SYMBOLS=\"axpy copy gemv geqr2 lacpy lahqr lanhs larnv lartg \\\n              lascl laset scal trevc trmm trsen gbmv gbtrf gbtrs \\\n              gttrf gttrs pttrf pttrs\"\n\n# All symbols that have float32/float64 support (including the SDCZ_SYMBOLS above)\nSD_SYMBOLS=\"${SDCZ_SYMBOLS} dot ger labad laev2 lamch lanst lanv2 \\\n            lapy2 larf larfg lasr nrm2 orm2r rot steqr swap\"\n\n# All symbols that have complexf32/complexf64 support (including the SDCZ_SYMBOLS above)\nCZ_SYMBOLS=\"${SDCZ_SYMBOLS} dotc geru unm2r\"\n\n# Add in (s|d)*_64 symbol remappings:\nfor sym in ${SD_SYMBOLS}; do\n    SYMBOL_DEFS=\"${SYMBOL_DEFS} -Ds${sym}=s${sym}_64 -Dd${sym}=d${sym}_64\"\ndone\n\n# Add in (c|z)*_64 symbol remappings:\nfor sym in ${CZ_SYMBOLS}; do\n    SYMBOL_DEFS=\"${SYMBOL_DEFS} -Dc${sym}=c${sym}_64 -Dz${sym}=z${sym}_64\"\ndone\n\n# Add one-off symbol mappings; things that don't fit into any other bucket:\nfor sym in scnrm2 dznrm2 csscal zdscal dgetrf dgetrs; do\n    SYMBOL_DEFS=\"${SYMBOL_DEFS} -D${sym}=${sym}_64\"\ndone\n\n# Set up not only lowercase symbol remappings, but uppercase as well:\nSYMBOL_DEFS=\"${SYMBOL_DEFS} ${SYMBOL_DEFS^^}\"\n\nFFLAGS=\"${FFLAGS} -O2 -fPIC -ffixed-line-length-none -cpp\"\nLIBOPENBLAS=openblas\nif [[ ${nbits} == 64 ]]; then\n    LIBOPENBLAS=openblas64_\n    FFLAGS=\"${FFLAGS} -fdefault-integer-8 ${SYMBOL_DEFS}\"\nfi\n\ncmake ../arpack-ng -DCMAKE_INSTALL_PREFIX=\"$prefix\" -DCMAKE_TOOLCHAIN_FILE=\"/opt/$target/$target.toolchain\" -DBUILD_SHARED_LIBS=ON -DBLAS_LIBRARIES=\"-L$prefix/lib -l${LIBOPENBLAS}\" -DLAPACK_LIBRARIES=\"-L$prefix/lib -l${LIBOPENBLAS}\" -DCMAKE_Fortran_FLAGS=\"${FFLAGS}\" -DCMAKE_EXE_LINKER_FLAGS=\"${EXE_LINK_FLAGS}\"\n\nmake -j${nproc} VERBOSE=1\nmake install VERBOSE=1\n\n# For now, we'll have to adjust the name of the OpenBLAS library on macOS.\n# Eventually, this should be fixed upstream\nif [[ ${target} == \"x86_64-apple-darwin14\" ]]; then\n    echo \"-- Modifying library name for OpenBLAS\"\n    install_name_tool -change libopenblas64_.0.3.0.dev.dylib libopenblas64_.dylib ${prefix}/lib/libarpack.2.1.0.dylib\nfi\n\"\"\"\n\n# These are the platforms we will build for by default, unless further\n# platforms are passed in on the command line.  We enable the full\n# combinatorial explosion of GCC versions because this package most\n# definitely links against libgfortran.\nplatforms = expand_gcc_versions(supported_platforms())\n\n# The products that we will ensure are always built\nproducts(prefix) = [\n    LibraryProduct(prefix, \"libarpack\", :libarpack)\n]\n\n# Dependencies that must be installed before this package can be built\ndependencies = [\n    \"https://github.com/JuliaLinearAlgebra/OpenBLASBuilder/releases/download/v0.3.0-3/build_OpenBLAS.v0.3.0.jl\"\n]\n\n# Build the tarballs, and possibly a `build.jl` as well.\nbuild_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)\n\n", "meta": {"hexsha": "f434774eba67fde3f10ec8c8b6f303931b2d34a8", "size": 3850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "build_tarballs.jl", "max_stars_repo_name": "daviehh/ArpackBuilder", "max_stars_repo_head_hexsha": "e5e96a12af42513f66ffb04066941002c77dc190", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "build_tarballs.jl", "max_issues_repo_name": "daviehh/ArpackBuilder", "max_issues_repo_head_hexsha": "e5e96a12af42513f66ffb04066941002c77dc190", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "build_tarballs.jl", "max_forks_repo_name": "daviehh/ArpackBuilder", "max_forks_repo_head_hexsha": "e5e96a12af42513f66ffb04066941002c77dc190", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2857142857, "max_line_length": 311, "alphanum_fraction": 0.7381818182, "num_tokens": 1173, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11920292202211755, "lm_q1q2_score": 0.05960146101105877}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 88d2a9b8-d8e4-47a2-b1de-369d8fb44c20\nusing PlutoUI; PlutoUI.TableOfContents(title = \"Benchmarking Procesos\")\n\n# \u2554\u2550\u2561 07ddd182-470c-11ec-3704-93171dbc510e\nbegin\n\tusing CSV, DataFrames, DataFramesMeta, Dates, IndexedTables\n\tusing FileIO, JLD2, JuliaDB, JuliaDBMeta, Statistics, StatsBase \n\tusing Plots, StatsPlots\n\t#cd(raw\"\\\\srvfs02\\Departamento de Investigacion Economica\\Departamento\\BD Internacional\\Remesas\")\n\tcd(raw\"D:\\Departamento\\BD Internacional\\Remesas\")\n\t#include(\"functions/fn_all.jl\");\n\tPlots.plotly()\n\t#ENV[\"COLUMNS\"]=1000\n\t#ENV[\"LINES\"] = 60\n\t\n\t## Cargar base desde archivo, como dataframe\n\t#directorio = \"//srvfs02/Departamento de Investigacion Economica/Departamento/BD Internacional/Remesas/\"\n\tdirectorio = \"D:/Departamento/BD Internacional/Remesas/\"\nend\n\n# \u2554\u2550\u2561 d1d0d4b2-92c1-4faa-8e42-7b7241fa1cc2\nbegin\n\t# 801197710224\n\tx = round.(rand(5)*10^12)#00000000000)\n\tusing Formatting\n\tformat.(x)\n\t#x\n\tround.(x./10^11)\nend\n\n# \u2554\u2550\u2561 454637cc-589c-459d-93fd-7da3ba6fd67c\nmd\"# Leer CSVs\"\n\n# \u2554\u2550\u2561 53266a68-82e6-4024-af9d-8cefe89399f9\nglobal function mergeCSV(fecha_proceso)\n\tn1 = Dates.now()\n\tsearchdir(path, key) = Base.filter(\n\t\tx -> Base.contains(x, key), Base.readdir(path))\n\tkey = fecha_proceso * \".csv\"\n\tpath = \"csv/\"\n\tcsv = searchdir(path, key)\n\tNArchivo_csv = Base.size(csv)[1];\n\n\t# Importar archivos, seleccionar y renombrar variables\n\t#path * csv[1]\n\tbases_csv = []\n\n\tfor i in 1:NArchivo_csv\n\t\tBase.push!(bases_csv,\n\t\t\tCSV.read(path * csv[i],\n\t\t\t\tdelim = ',',\n\t\t\t\tDataFrames.DataFrame))\n\t\tDataFrames.rename!(bases_csv[i],\n\t\t\t:Fecha_Contable => :Fecha,\n\t\t\t:Departamento => :DeptoTransacc,\n\t\t\t:Municipio => :MunicTransacc,\n\t\t\t:RTN_Identidad => :ID)\n\t\tbases_csv[i] = DataFramesMeta.select(bases_csv[i],\n\t\t\t:Fecha,\n\t\t\t#:Agente_Cambiario,\n\t\t\t#:Tipo_Transaccion,\n\t\t\t#:Actividad_Economica,\n\t\t\t#:Medio_Pago,\n\t\t\t:DeptoTransacc,\n\t\t\t:MunicTransacc,\n\t\t\t:Tasa_Cambio,\n\t\t\t:Monto,\n\t\t\t:ID,\n\t\t\t:Tipo_Cliente);\n\tend\n\n\t# Unir en una sola tabla\n\tdatabase = Base.copy(bases_csv[1])\n\tfor i in 2:NArchivo_csv\n\t\tdatabase = Base.vcat(\n\t\t\tdatabase, bases_csv[i])\n\tend\n\tdatabase\nend\n\n# \u2554\u2550\u2561 4a5b02b5-83d5-46b9-88ec-41f4d0df54d1\ndatabase = mergeCSV(\"2021\")\n\n# \u2554\u2550\u2561 ae2c7408-7ca2-4166-98be-f9ba3d8476ed\nmd\"# Cambiar Formatos\"\n\n# \u2554\u2550\u2561 38b7bde3-7c34-4930-8582-bfd067a05c09\nmd\"## Fecha\"\n\n# \u2554\u2550\u2561 cd85e83f-f380-480a-9c78-5fdbbba068ab\nbegin\n\tdatabase[!, :Fechas] .= Dates.Date(\"1-1-1\")\n\tdatabase[!, :Days] .= 0\n    database[!, :Weeks] .= 0\n    database[!, :Months] .= 0\n    database[!, :Years] .= 0\n\tdatabase\nend\n\n# \u2554\u2550\u2561 5cb827be-5ad3-49a3-b920-b1df8a8cb861\n\n\n# \u2554\u2550\u2561 df699c60-d076-4319-9d95-13ea2afe955c\n\n\n# \u2554\u2550\u2561 fbd5cb7c-e722-4a2e-84ef-4c6702134234\n# Benchmarking\n#=\n@time database[!, :Fechas] = Dates.Date.(\n\t\tdatabase[!, :Fecha],\n\t\tDates.DateFormat(\"d/m/Y H:M:S\"))\n@time for i in 1:Base.size(database)[1]\n\tdatabase[i, :Fechas] = Dates.Date.(\n\t\tdatabase[i, :Fecha],\n\t\tDates.DateFormat(\"d/m/Y H:M:S\"))\n\tend\n=#\n\n# \u2554\u2550\u2561 e5552459-e2ad-4f44-9760-757ea37fa233\n@time for i in 1:Base.size(database)[1]\n\tdatabase[i, :Fechas] = Dates.Date.(\n\t\tdatabase[i, :Fecha],\n\t\tDates.DateFormat(\"d/m/Y H:M:S\"))\nend\n\n# \u2554\u2550\u2561 50ca420b-2a11-4366-9541-555585eeb5ea\n\n\n# \u2554\u2550\u2561 b45cf1f6-c70c-450b-a8e4-29f7eb44e791\ndatabase\n\n# \u2554\u2550\u2561 e3c415b1-0964-4337-9f9f-c1a9616a8b5d\n\n\n# \u2554\u2550\u2561 029a721d-4a55-4cc0-8898-61bdfb80c10c\nmd\"## Depurar IDs, solo enteros\"\n\n# \u2554\u2550\u2561 7ffa2874-0690-4587-91db-f0e55570bcb1\nglobal function StrToInt(x)\n\ttry\n\t\tBase.parse.(Int64, x)\n\tcatch\n\t\treturn 0\n\tend\nend\n\n# \u2554\u2550\u2561 11a22c40-1da4-4f9c-a650-000230546bfa\nmd\"## Tipo de Cliente\"\n\n# \u2554\u2550\u2561 3e06e476-3c9d-42c4-b167-c2ce926b2c3c\nbegin\n\tdatabase[!, :Tipo_Clientes] .= 0\n\tfor i in 1:Base.size(database)[1]\n\t\tif database[i, :Tipo_Cliente] == \"Natural Residente\"\n\t\t\tdatabase[i, :Tipo_Clientes] = 1\n\t\telse\n\t\t\tdatabase[i, :Tipo_Clientes] = 0\n\t\tend\n\tend\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nDataFramesMeta = \"1313f7d8-7da2-5740-9ea0-a2ca25f37964\"\nDates = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\nFileIO = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nFormatting = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nIndexedTables = \"6deec6e2-d858-57c5-ab9b-e6ca5bd20e43\"\nJLD2 = \"033835bb-8acc-5ee8-8aae-3f567f8a3819\"\nJuliaDB = \"a93385a2-3734-596a-9a66-3cfbb77141e6\"\nJuliaDBMeta = \"2c06ca41-a429-545c-b8f0-5ca7dd64ba19\"\nPlots = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nStatistics = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\nStatsBase = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nStatsPlots = \"f3b207a7-027a-5e70-b257-86293d7955fd\"\n\n[compat]\nCSV = \"~0.8.5\"\nDataFrames = \"~0.22.7\"\nDataFramesMeta = \"~0.8.0\"\nFileIO = \"~1.11.2\"\nFormatting = \"~0.4.2\"\nIndexedTables = \"~0.8.1\"\nJLD2 = \"~0.4.3\"\nJuliaDB = \"~0.9.0\"\nJuliaDBMeta = \"~0.4.0\"\nPlots = \"~0.28.4\"\nPlutoUI = \"~0.7.1\"\nStatsBase = \"~0.32.2\"\nStatsPlots = \"~0.14.5\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[AbstractFFTs]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"485ee0867925449198280d4af84bdb46a2a404d0\"\nuuid = \"621f4979-c628-5d54-868e-fcf4e3e8185c\"\nversion = \"1.0.1\"\n\n[[Adapt]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"84918055d15b3114ede17ac6a7182f68870c16f7\"\nuuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\nversion = \"3.3.1\"\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Arpack]]\ndeps = [\"Arpack_jll\", \"Libdl\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"2ff92b71ba1747c5fdd541f8fc87736d82f40ec9\"\nuuid = \"7d9fca2a-8960-54d3-9f78-7d1dccf2cb97\"\nversion = \"0.4.0\"\n\n[[Arpack_jll]]\ndeps = [\"Libdl\", \"OpenBLAS_jll\", \"Pkg\"]\ngit-tree-sha1 = \"e214a9b9bd1b4e1b4f15b22c0994862b66af7ff7\"\nuuid = \"68821587-b530-5797-8361-c406ea357684\"\nversion = \"3.5.0+3\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[AxisAlgorithms]]\ndeps = [\"LinearAlgebra\", \"Random\", \"SparseArrays\", \"WoodburyMatrices\"]\ngit-tree-sha1 = \"66771c8d21c8ff5e3a93379480a2307ac36863f7\"\nuuid = \"13072b0f-2c55-5437-9ae7-d433b7a33950\"\nversion = \"1.0.1\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[BinaryProvider]]\ndeps = [\"Libdl\", \"Logging\", \"SHA\"]\ngit-tree-sha1 = \"ecdec412a9abc8db54c0efc5548c64dfce072058\"\nuuid = \"b99e7846-7c00-51b0-8f62-c81ae34c0232\"\nversion = \"0.5.10\"\n\n[[CSV]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"b83aa3f513be680454437a0eee21001607e5d983\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.8.5\"\n\n[[Calculus]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"f641eb0a4f00c343bbc32346e1217b86f3ce9dad\"\nuuid = \"49dc2e85-a5d0-5ad3-a950-438e2897f1b9\"\nversion = \"0.5.1\"\n\n[[CategoricalArrays]]\ndeps = [\"DataAPI\", \"Future\", \"JSON\", \"Missings\", \"Printf\", \"Statistics\", \"StructTypes\", \"Unicode\"]\ngit-tree-sha1 = \"18d7f3e82c1a80dd38c16453b8fd3f0a7db92f23\"\nuuid = \"324d7699-5711-5eae-9e2f-1d82baa6b597\"\nversion = \"0.9.7\"\n\n[[Chain]]\ngit-tree-sha1 = \"cac464e71767e8a04ceee82a889ca56502795705\"\nuuid = \"8be319e6-bccf-4806-a6f7-6fae938471bc\"\nversion = \"0.4.8\"\n\n[[ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"f885e7e7c124f8c92650d61b9477b9ac2ee607dd\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.11.1\"\n\n[[ChangesOfVariables]]\ndeps = [\"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"9a1d594397670492219635b35a3d830b04730d62\"\nuuid = \"9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0\"\nversion = \"0.1.1\"\n\n[[Clustering]]\ndeps = [\"Distances\", \"LinearAlgebra\", \"NearestNeighbors\", \"Printf\", \"SparseArrays\", \"Statistics\", \"StatsBase\"]\ngit-tree-sha1 = \"75479b7df4167267d75294d14b58244695beb2ac\"\nuuid = \"aaaa29a8-35af-508c-8bc3-b662a17a0fe5\"\nversion = \"0.14.2\"\n\n[[CodecZlib]]\ndeps = [\"BinaryProvider\", \"Libdl\", \"TranscodingStreams\"]\ngit-tree-sha1 = \"05916673a2627dd91b4969ff8ba6941bc85a960e\"\nuuid = \"944b1d66-785c-5afd-91f1-9de20f533193\"\nversion = \"0.6.0\"\n\n[[ColorTypes]]\ndeps = [\"FixedPointNumbers\", \"Random\"]\ngit-tree-sha1 = \"b9de8dc6106e09c79f3f776c27c62360d30e5eb8\"\nuuid = \"3da002f7-5984-5a60-b8a6-cbb66c0b333f\"\nversion = \"0.9.1\"\n\n[[Colors]]\ndeps = [\"ColorTypes\", \"FixedPointNumbers\", \"InteractiveUtils\", \"Printf\", \"Reexport\"]\ngit-tree-sha1 = \"177d8b959d3c103a6d57574c38ee79c81059c31b\"\nuuid = \"5ae59095-9a9b-59fe-a467-6f913c188581\"\nversion = \"0.11.2\"\n\n[[CommonSubexpressions]]\ndeps = [\"MacroTools\", \"Test\"]\ngit-tree-sha1 = \"7b8a93dba8af7e3b42fecabf646260105ac373f7\"\nuuid = \"bbf7d656-a473-5ed7-a52c-81e309532950\"\nversion = \"0.3.0\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", 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\"4d1e1d77-625e-5b40-9113-a560ec7a8ecd\"\nversion = \"1.0.0\"\n\n[[Observables]]\ngit-tree-sha1 = \"3469ef96607a6b9a1e89e54e6f23401073ed3126\"\nuuid = \"510215fc-4207-5dde-b226-833fc4488ee2\"\nversion = \"0.3.3\"\n\n[[OffsetArrays]]\ndeps = [\"Adapt\"]\ngit-tree-sha1 = \"043017e0bdeff61cfbb7afeb558ab29536bbb5ed\"\nuuid = \"6fe1bfb0-de20-5000-8ca7-80f57d26f881\"\nversion = \"1.10.8\"\n\n[[OnlineStats]]\ndeps = [\"DataStructures\", \"Dates\", \"LearnBase\", \"LinearAlgebra\", \"LossFunctions\", \"OnlineStatsBase\", \"PenaltyFunctions\", \"Random\", \"RecipesBase\", \"Reexport\", \"Statistics\", \"StatsBase\", \"SweepOperator\", \"Test\"]\ngit-tree-sha1 = \"a783fe25075b9d09d6dd2ba67dafd374d835acfa\"\nuuid = \"a15396b6-48d5-5d58-9928-6d29437db91e\"\nversion = \"0.23.0\"\n\n[[OnlineStatsBase]]\ndeps = [\"Dates\", \"LearnBase\", \"Statistics\", \"Test\"]\ngit-tree-sha1 = \"2d62fb8d9b1b407f9f80e99f4a63f32dea90423c\"\nuuid = \"925886fa-5bf2-5e8e-b522-a9147a512338\"\nversion = \"0.10.2\"\n\n[[OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[OpenSpecFun_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"13652491f6856acfd2db29360e1bbcd4565d04f1\"\nuuid = \"efe28fd5-8261-553b-a9e1-b2916fc3738e\"\nversion = \"0.5.5+0\"\n\n[[Optim]]\ndeps = [\"Compat\", \"FillArrays\", \"LineSearches\", \"LinearAlgebra\", \"NLSolversBase\", \"NaNMath\", \"Parameters\", \"PositiveFactorizations\", \"Printf\", \"SparseArrays\", \"StatsBase\"]\ngit-tree-sha1 = \"c05aa6b694d426df87ff493306c1c5b4b215e148\"\nuuid = \"429524aa-4258-5aef-a3af-852621145aeb\"\nversion = \"0.22.0\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[PDMats]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\", \"SuiteSparse\", \"Test\"]\ngit-tree-sha1 = \"95a4038d1011dfdbde7cecd2ad0ac411e53ab1bc\"\nuuid = \"90014a1f-27ba-587c-ab20-58faa44d9150\"\nversion = \"0.10.1\"\n\n[[Parameters]]\ndeps = [\"OrderedCollections\", \"UnPack\"]\ngit-tree-sha1 = \"34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe\"\nuuid = \"d96e819e-fc66-5662-9728-84c9c7592b0a\"\nversion = \"0.12.3\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"bfd7d8c7fd87f04543810d9cbd3995972236ba1b\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"1.1.2\"\n\n[[PenaltyFunctions]]\ndeps = [\"InteractiveUtils\", \"LearnBase\", \"LinearAlgebra\", \"RecipesBase\", \"Reexport\", \"Test\"]\ngit-tree-sha1 = \"b0baaa5218ca0ffd6a8ae37ef0b58e0df688ac8b\"\nuuid = \"06bb1623-fdd5-5ca2-a01c-88eae3ea319e\"\nversion = \"0.1.2\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PlotThemes]]\ndeps = [\"PlotUtils\", \"Requires\", \"Statistics\"]\ngit-tree-sha1 = \"87a4ea7f8c350d87d3a8ca9052663b633c0b2722\"\nuuid = \"ccf2f8ad-2431-5c83-bf29-c5338b663b6a\"\nversion = \"1.0.3\"\n\n[[PlotUtils]]\ndeps = [\"Colors\", \"Dates\", \"Printf\", \"Random\", \"Reexport\"]\ngit-tree-sha1 = \"51e742162c97d35f714f9611619db6975e19384b\"\nuuid = \"995b91a9-d308-5afd-9ec6-746e21dbc043\"\nversion = \"0.6.5\"\n\n[[Plots]]\ndeps = [\"Base64\", \"Contour\", \"Dates\", \"FFMPEG\", \"FixedPointNumbers\", \"GR\", \"GeometryTypes\", \"JSON\", \"LinearAlgebra\", \"Measures\", \"NaNMath\", \"Pkg\", \"PlotThemes\", \"PlotUtils\", \"Printf\", \"REPL\", \"Random\", \"RecipesBase\", \"Reexport\", \"Requires\", \"Showoff\", \"SparseArrays\", \"Statistics\", \"StatsBase\", \"UUIDs\"]\ngit-tree-sha1 = \"efbe466a790d7e8a5c4b5ee1601c0c8edc99780b\"\nuuid = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\nversion = \"0.28.4\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"InteractiveUtils\", \"Logging\", \"Markdown\", \"Random\", \"Suppressor\"]\ngit-tree-sha1 = \"45ce174d36d3931cd4e37a47f93e07d1455f038d\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.1\"\n\n[[Polynomials]]\ndeps = [\"Intervals\", \"LinearAlgebra\", \"MutableArithmetics\", \"RecipesBase\"]\ngit-tree-sha1 = \"79bcbb379205f1c62913fa9ebecb413c7a35f8b0\"\nuuid = \"f27b6e38-b328-58d1-80ce-0feddd5e7a45\"\nversion = \"2.0.18\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"b1333d4eced1826e15adbdf01a4ecaccca9d353c\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"0.5.3\"\n\n[[PositiveFactorizations]]\ndeps = [\"LinearAlgebra\"]\ngit-tree-sha1 = \"17275485f373e6673f7e7f97051f703ed5b15b20\"\nuuid = \"85a6dd25-e78a-55b7-8502-1745935b8125\"\nversion = \"0.2.4\"\n\n[[Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"00cfd92944ca9c760982747e9a1d0d5d86ab1e5a\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.2\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"574a6b3ea95f04e8757c0280bb9c29f1a5e35138\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"0.11.1\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[Profile]]\ndeps = [\"Printf\"]\nuuid = \"9abbd945-dff8-562f-b5e8-e1ebf5ef1b79\"\n\n[[QuadGK]]\ndeps = [\"DataStructures\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"78aadffb3efd2155af139781b8a8df1ef279ea39\"\nuuid = \"1fd47b50-473d-5c70-9696-f719f8f3bcdc\"\nversion = \"2.4.2\"\n\n[[Quadmath]]\ndeps = [\"Printf\", \"Random\", \"Requires\"]\ngit-tree-sha1 = \"5a8f74af8eae654086a1d058b4ec94ff192e3de0\"\nuuid = \"be4d8f0f-7fa4-5f49-b795-2f01399ab2dd\"\nversion = \"0.5.5\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Ratios]]\ndeps = [\"Requires\"]\ngit-tree-sha1 = \"01d341f502250e81f6fec0afe662aa861392a3aa\"\nuuid = \"c84ed2f1-dad5-54f0-aa8e-dbefe2724439\"\nversion = \"0.4.2\"\n\n[[RecipesBase]]\ngit-tree-sha1 = \"7bdce29bc9b2f5660a6e5e64d64d91ec941f6aa2\"\nuuid = \"3cdcf5f2-1ef4-517c-9805-6587b60abb01\"\nversion = \"0.7.0\"\n\n[[Reexport]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"7b1d07f411bc8ddb7977ec7f377b97b158514fe0\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"0.2.0\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"4036a3bd08ac7e968e27c203d45f5fff15020621\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.1.3\"\n\n[[Rmath]]\ndeps = [\"Random\", \"Rmath_jll\"]\ngit-tree-sha1 = \"bf3188feca147ce108c76ad82c2792c57abe7b1f\"\nuuid = \"79098fc4-a85e-5d69-aa6a-4863f24498fa\"\nversion = \"0.7.0\"\n\n[[Rmath_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"68db32dff12bb6127bac73c209881191bf0efbb7\"\nuuid = \"f50d1b31-88e8-58de-be2c-1cc44531875f\"\nversion = \"0.3.0+0\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"f45b34656397a1f6e729901dc9ef679610bd12b5\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.8\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[Showoff]]\ndeps = [\"Dates\", \"Grisu\"]\ngit-tree-sha1 = \"ee010d8f103468309b8afac4abb9be2e18ff1182\"\nuuid = \"992d4aef-0814-514b-bc4d-f2e9a6c4116f\"\nversion = \"0.3.2\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\", \"Random\", \"Test\"]\ngit-tree-sha1 = \"03f5898c9959f8115e30bc7226ada7d0df554ddd\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"0.3.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[SpecialFunctions]]\ndeps = [\"OpenSpecFun_jll\"]\ngit-tree-sha1 = \"d8d8b8a9f4119829410ecd706da4cc8594a1e020\"\nuuid = \"276daf66-3868-5448-9aa4-cd146d93841b\"\nversion = \"0.10.3\"\n\n[[StaticArrays]]\ndeps = [\"LinearAlgebra\", \"Random\", \"Statistics\"]\ngit-tree-sha1 = \"da4cf579416c81994afd6322365d00916c79b8ae\"\nuuid = \"90137ffa-7385-5640-81b9-e52037218182\"\nversion = \"0.12.5\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[StatsAPI]]\ngit-tree-sha1 = \"1958272568dc176a1d881acb797beb909c785510\"\nuuid = \"82ae8749-77ed-4fe6-ae5f-f523153014b0\"\nversion = \"1.0.0\"\n\n[[StatsBase]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"Missings\", \"Printf\", \"Random\", \"SortingAlgorithms\", \"SparseArrays\", \"Statistics\"]\ngit-tree-sha1 = \"19bfcb46245f69ff4013b3df3b977a289852c3a1\"\nuuid = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nversion = \"0.32.2\"\n\n[[StatsFuns]]\ndeps = [\"Rmath\", \"SpecialFunctions\"]\ngit-tree-sha1 = \"ced55fd4bae008a8ea12508314e725df61f0ba45\"\nuuid = \"4c63d2b9-4356-54db-8cca-17b64c39e42c\"\nversion = \"0.9.7\"\n\n[[StatsPlots]]\ndeps = [\"Clustering\", \"DataStructures\", \"DataValues\", \"Distributions\", \"Interpolations\", \"KernelDensity\", \"MultivariateStats\", \"Observables\", \"Plots\", \"RecipesBase\", \"Reexport\", \"StatsBase\", \"TableOperations\", \"Tables\", \"Widgets\"]\ngit-tree-sha1 = \"388e6588a1e09e763b0f38c498aeb350b5f76828\"\nuuid = \"f3b207a7-027a-5e70-b257-86293d7955fd\"\nversion = \"0.14.5\"\n\n[[StructTypes]]\ndeps = [\"Dates\", \"UUIDs\"]\ngit-tree-sha1 = \"d24a825a95a6d98c385001212dc9020d609f2d4f\"\nuuid = \"856f2bd8-1eba-4b0a-8007-ebc267875bd4\"\nversion = \"1.8.1\"\n\n[[SuiteSparse]]\ndeps = [\"Libdl\", \"LinearAlgebra\", \"Serialization\", \"SparseArrays\"]\nuuid = \"4607b0f0-06f3-5cda-b6b1-a6196a1729e9\"\n\n[[Suppressor]]\ngit-tree-sha1 = \"a819d77f31f83e5792a76081eee1ea6342ab8787\"\nuuid = \"fd094767-a336-5f1f-9728-57cf17d0bbfb\"\nversion = \"0.2.0\"\n\n[[SweepOperator]]\ndeps = [\"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"2039aaa96f7b21634a1b3246cb2699dd1d26d473\"\nuuid = \"7522ee7d-7047-56d0-94d9-4bc626e7058d\"\nversion = \"0.2.0\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableOperations]]\ndeps = [\"Tables\", \"Test\"]\ngit-tree-sha1 = \"208630a14884abd110a8f8008b0882f0d0f5632c\"\nuuid = \"ab02a1b2-a7df-11e8-156e-fb1833f50b87\"\nversion = \"0.2.1\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[TableTraitsUtils]]\ndeps = [\"DataValues\", \"IteratorInterfaceExtensions\", \"Missings\", \"TableTraits\"]\ngit-tree-sha1 = \"78fecfe140d7abb480b53a44f3f85b6aa373c293\"\nuuid = \"382cd787-c1b6-5bf2-a167-d5b971a19bda\"\nversion = \"1.0.2\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"fed34d0e71b91734bf0a7e10eb1bb05296ddbcd0\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.0\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TextParse]]\ndeps = [\"CodecZlib\", \"DataStructures\", \"Dates\", \"DoubleFloats\", \"Mmap\", \"Nullables\", \"PooledArrays\", \"WeakRefStrings\"]\ngit-tree-sha1 = \"26b43d6746b52cca13c4cdef90f89652273b413e\"\nuuid = \"e0df1984-e451-5cb5-8b61-797a481e67e3\"\nversion = \"0.9.1\"\n\n[[TimeZones]]\ndeps = [\"Dates\", \"Future\", \"LazyArtifacts\", \"Mocking\", \"Pkg\", \"Printf\", \"RecipesBase\", \"Serialization\", \"Unicode\"]\ngit-tree-sha1 = \"a5688ffdbd849a98503c6650effe79fe89a41252\"\nuuid = \"f269a46b-ccf7-5d73-abea-4c690281aa53\"\nversion = \"1.5.9\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[UnPack]]\ngit-tree-sha1 = \"387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b\"\nuuid = \"3a884ed6-31ef-47d7-9d2a-63182c4928ed\"\nversion = \"1.0.2\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[WeakRefStrings]]\ndeps = [\"Missings\", \"Random\", \"Test\"]\ngit-tree-sha1 = \"960639a12ffd223ee463e93392aeb260fa325566\"\nuuid = \"ea10d353-3f73-51f8-a26c-33c1cb351aa5\"\nversion = \"0.5.8\"\n\n[[Widgets]]\ndeps = [\"Colors\", \"Dates\", \"Observables\", \"OrderedCollections\"]\ngit-tree-sha1 = \"80661f59d28714632132c73779f8becc19a113f2\"\nuuid = \"cc8bc4a8-27d6-5769-a93b-9d913e69aa62\"\nversion = \"0.6.4\"\n\n[[WoodburyMatrices]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"de67fa59e33ad156a590055375a30b23c40299d3\"\nuuid = \"efce3f68-66dc-5838-9240-27a6d6f5f9b6\"\nversion = \"0.5.5\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u255088d2a9b8-d8e4-47a2-b1de-369d8fb44c20\n# \u2560\u255007ddd182-470c-11ec-3704-93171dbc510e\n# \u2560\u2550d1d0d4b2-92c1-4faa-8e42-7b7241fa1cc2\n# \u255f\u2500454637cc-589c-459d-93fd-7da3ba6fd67c\n# \u2560\u255053266a68-82e6-4024-af9d-8cefe89399f9\n# \u2560\u25504a5b02b5-83d5-46b9-88ec-41f4d0df54d1\n# \u255f\u2500ae2c7408-7ca2-4166-98be-f9ba3d8476ed\n# \u255f\u250038b7bde3-7c34-4930-8582-bfd067a05c09\n# \u2560\u2550cd85e83f-f380-480a-9c78-5fdbbba068ab\n# \u2560\u25505cb827be-5ad3-49a3-b920-b1df8a8cb861\n# \u2560\u2550df699c60-d076-4319-9d95-13ea2afe955c\n# \u2560\u2550fbd5cb7c-e722-4a2e-84ef-4c6702134234\n# \u2560\u2550e5552459-e2ad-4f44-9760-757ea37fa233\n# \u2560\u255050ca420b-2a11-4366-9541-555585eeb5ea\n# \u2560\u2550b45cf1f6-c70c-450b-a8e4-29f7eb44e791\n# \u2560\u2550e3c415b1-0964-4337-9f9f-c1a9616a8b5d\n# \u255f\u2500029a721d-4a55-4cc0-8898-61bdfb80c10c\n# \u2560\u25507ffa2874-0690-4587-91db-f0e55570bcb1\n# \u255f\u250011a22c40-1da4-4f9c-a650-000230546bfa\n# \u2560\u25503e06e476-3c9d-42c4-b167-c2ce926b2c3c\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "fa346b9f94c19d229ee0c7f39fb1d336f2a583d2", "size": 36196, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Benchmarking_Julia.jl", "max_stars_repo_name": "ElvisCasco/Remesas", "max_stars_repo_head_hexsha": "511165eaee92d77f976d23ed57e46264df225a3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Benchmarking_Julia.jl", "max_issues_repo_name": "ElvisCasco/Remesas", "max_issues_repo_head_hexsha": "511165eaee92d77f976d23ed57e46264df225a3c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Benchmarking_Julia.jl", "max_forks_repo_name": "ElvisCasco/Remesas", "max_forks_repo_head_hexsha": "511165eaee92d77f976d23ed57e46264df225a3c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3656845754, "max_line_length": 303, "alphanum_fraction": 0.735274616, "num_tokens": 16216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11920292045759122, "lm_q1q2_score": 0.05960146022879561}}
{"text": "using SafeTestsets\n# TODO add description to the tests\n\n## ===-===-===-===-===-===-===-===-\n@safetestset \"MatrixProcessing tests\" begin\n    include(\"matrixProcessing_tests.jl\")\nend\n\n## ===-===-===-===-===-===-===-===-\n@safetestset \"MatrixOrganization tests\" begin\n    include(\"matrixOrganisation_tests.jl\")\nend\n\n## ===-===-===-===-===-===-===-===-\n@safetestset \"BettiCurves tests\" begin\n    include(\"bettiCurves_tests.jl\")\nend\n\n## ===-===-===-===-===-===-===-===-\n", "meta": {"hexsha": "32c6b80f005862807c1ddf0032e7a27c669d42e7", "size": 464, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "edd26/TopologyPreprocessing.jl", "max_stars_repo_head_hexsha": "273497114e8adf84244d3b24b0155b3ae813b84d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-08T02:01:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-08T02:01:57.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "edd26/TopologyPreprocessing", "max_issues_repo_head_hexsha": "12948c0ff3885f8fc2fa6b98d46b8d1c599715fa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-11-30T16:38:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T15:10:02.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "edd26/TopologyPreprocessing", "max_forks_repo_head_hexsha": "12948c0ff3885f8fc2fa6b98d46b8d1c599715fa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.2, "max_line_length": 45, "alphanum_fraction": 0.5431034483, "num_tokens": 152, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.11920291107043361, "lm_q1q2_score": 0.059601455535216806}}
{"text": "\"\"\"\nRead CSV-formatted posterior output.\n\n# API\n\nSee [`StanCSV.read_chain`](@ref) and [`StanCSV.matching_files`], neither is exported.\n\n# File format\n\nPosterior samples are supposed to be available in a CSV file which follows the following\nconventions:\n\n1. Whitespace and content after `#` is ignored. No fields are quoted or escaped. If a line\nhas a `#`, it cannot have content.\n\n2. The first non-ignored line contains a comma-separated list of variable names, of the\nformat `var[.i1.i2.\u2026]` where optional indexes follow dots. Indexes for the same variable are\ncontiguous, and have a column-major layout.\n\n3. Subsequent lines contain the same number of comma-separated floating-point values.\n\n`cmdstan` outputs posterior samples in this format (diagnostics are ignored for now), hence\nthe name.\n\"\"\"\nmodule StanCSV\n\nusing ArgCheck: @argcheck\nusing DocStringExtensions: FUNCTIONNAME, SIGNATURES\nimport ..Chains\n\n####\n#### Header parsing building blocks\n####\n\n\"\"\"\n$(SIGNATURES)\n\nParse a Stan variable name as `name::Symbol => indexes::Tuple`.\n\"\"\"\nfunction parse_variable_name(variable_name::AbstractString)\n    parts = split(variable_name, '.')\n    @argcheck !isempty(first(parts))\n    name = Symbol(strip(first(parts)))\n    index = tuple(parse.(Ref(Int), parts[2:end])...)\n    name => index\nend\n\n\"\"\"\n$(SIGNATURES)\n\nGiven a vector of `name => index` pairs and a starting index `i`, which should be `1`s, find\nthe last index for this variable (ie the dimensions) and check that that intermediate\nindices are contiguous (in column major ordering).\n\nReturn `name`, `dimensions`, and the position for the continuation.\n\"\"\"\nfunction collapse_contiguous_dimensions(names_indexes, i)\n    name, index = names_indexes[i]\n    @argcheck all(index .== 1) \"Indexes for $(name) don't start with ones.\"\n    n = length(names_indexes)\n    j = i + 1\n    while j \u2264 n\n        if names_indexes[j][1] \u2260 name\n            break\n        else\n            j += 1\n        end\n    end\n    dimensions = names_indexes[j - 1][2]\n    indexes = CartesianIndices(dimensions)\n    for (k, ix) in zip(i:(j-1), CartesianIndices(dimensions))\n        index_k = names_indexes[k][2]\n        @argcheck CartesianIndex(index_k) == ix \"Non-contiguous index $(k) for $(name).\"\n    end\n    name, dimensions, j\nend\n\n\"\"\"\n$(SIGNATURES)\n\nParse a vector of `names => indexes` to a column schema.\n\"\"\"\nfunction parse_schema(names_indexes)\n    n = length(names_indexes)\n    i = 1\n    schema = Pair{Symbol,Tuple{Vararg{Int}}}[]\n    while i \u2264 n\n        name, dimensions, next_i = collapse_contiguous_dimensions(names_indexes, i)\n        @argcheck all(s -> first(schema) \u2260 name, schema) \"Duplicate name $(name) in column $(i).\"\n        push!(schema, name => dimensions)\n        i = next_i\n    end\n    Chains.IndexSchema(NamedTuple{Tuple(first.(schema))}(last.(schema)))\nend\n\n####\n#### CSV file reading.\n####\n\n\"\"\"\n$(SIGNATURES)\n\nTest if `line` is a comment line.\n\"\"\"\nis_comment_line(line) = occursin(r\"^ *#\", line)\n\n\"\"\"\n$(SIGNATURES)\n\nFind the first non-comment line and read it as a `Chains.IndexSchema`. When there is no\nsuch line, throw an `EOFError`.\n\"\"\"\nfunction read_schema(io::IO)\n    while true                  # will throw an EOFError if not fount\n        line = readline(io; keep = false)\n        if !is_comment_line(line)\n            return parse_schema(parse_variable_name.(split(line, ',')))\n        end\n    end\nend\n\n\"\"\"\n$(SIGNATURES)\n\nRead `nfields` fields from `io`, parse as `Float64`, and push into `buffer`, returning\n`true`. Fewer fields than `nfields` results in an error.\n\nIf a line starts with a `'#'`, do no parsing, return `false`.\n\"\"\"\nfunction read_csv_line!(buffer, io::IO, nfields::Integer)\n    line = readline(io; keep = false)\n    is_comment_line(line) && return false\n    pos = 1\n    last_pos = lastindex(line)\n    for _ in 1:nfields\n        @argcheck pos \u2264 last_pos \"Fewer than $(nfields) fields in line.\"\n        delim_pos = something(findnext(isequal(','), line, pos), last_pos + 1)\n        push!(buffer, parse(Float64, SubString(line, pos, delim_pos - 1)))\n        pos = delim_pos + 1\n    end\n    true\nend\n\nfunction read_csv_flat_data!(buffer, io, nfields)\n    row_count = 0\n    while !eof(io)\n        row_count += read_csv_line!(buffer, io, nfields)\n    end\n    row_count\nend\n\nfunction csv_buffer_to_array(buffer, row_count, dims)\n    n = length(dims)\n    permutedims(reshape(buffer, dims..., row_count), vcat([1 + n], 1:n))\nend\n\nfunction read_chain(io::IO)\n    sch = read_schema(io)\n    nfields = length(sch)\n    buffer = Vector{Float64}()\n    row_count = read_csv_flat_data!(buffer, io, nfields)\n    sample_matrix = collect(permutedims(reshape(buffer, nfields, row_count)))\n    Chains.Chain(sch, sample_matrix)\nend\n\n\"\"\"\n$(SIGNATURES)\n\nRead data from a CSV file, that uses the output format from Stan.\n\nReturn a vector of `name => array` pairs, where `name::Symbol` is the variable name, and the\n`array` is the data for that variable.\n\nConsecutive columns for the same variable name with `n`-dimensional indexes are assembled\ninto an `n+1` dimensional array, with index `var[row, i1, i2, \u2026]` containing the results\nfor `var.i1.i2.\u2026` in the given `row`.\n\"\"\"\nread_chain(filename::AbstractString) = open(read_chain, filename, \"r\")\n\n\"\"\"\n$(SIGNATURES)\n\nReturn a vector `id::Int => filename` where `filename` is `prefix_<digits>.csv` and the path\nis a file. Prefix should be a path, eg\n\n```julia\n$(FUNCTIONNAME)(\"/tmp/samples_\")\n```\n\nwill return `[1 => \"/tmp/samples_1.csv\", 2 => \"/tmp/samples_2.csv\", \u2026]` if the files exist.\n\nRecommended use: obtaining all the filenames in a directory.\n\"\"\"\nfunction matching_files(prefix::AbstractString)\n    dir = dirname(prefix)\n    base = basename(prefix)\n    pattern = Regex(\"^\" * base * raw\"(\\d+)\\.csv$\")\n    ids_files = Pair{Int,String}[]\n    for filename in readdir(dir)\n        m = match(pattern, filename)\n        p = joinpath(dir, filename)\n        if m \u2262 nothing && isfile(p)\n            push!(ids_files, parse(Int, m.captures[1]) => p)\n        end\n    end\n    @argcheck allunique(first.(ids_files)) \"Non-unique file ids, perhaps because of 0-padding?\"\n    sort!(ids_files, by = first)\n    ids_files\nend\n\nend\n", "meta": {"hexsha": "ede427bc43a19f7c87fcd05a0448470da954a3f9", "size": 6123, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stan_csv.jl", "max_stars_repo_name": "JuliaTagBot/MCMCStorage.jl", "max_stars_repo_head_hexsha": "69497470ed71c00ee975e6a1d243c4f2f83ca49e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-14T01:37:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-14T01:37:19.000Z", "max_issues_repo_path": "src/stan_csv.jl", "max_issues_repo_name": "JuliaTagBot/MCMCStorage.jl", "max_issues_repo_head_hexsha": "69497470ed71c00ee975e6a1d243c4f2f83ca49e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/stan_csv.jl", "max_forks_repo_name": "JuliaTagBot/MCMCStorage.jl", "max_forks_repo_head_hexsha": "69497470ed71c00ee975e6a1d243c4f2f83ca49e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:48:39.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:48:39.000Z", "avg_line_length": 28.8820754717, "max_line_length": 97, "alphanum_fraction": 0.6731994121, "num_tokens": 1604, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.13477591568531197, "lm_q1q2_score": 0.05952688377193389}}
{"text": "# Bogumi\u0142 Kami\u0144ski, 2022\n\n# Codes for chapter 12\n\n# Codes for section 12.1\n\n# Code for listing 12.1\n\nimport Downloads\nusing SHA\ngit_zip = \"git_web_ml.zip\"\nif !isfile(git_zip)\n    Downloads.download(\"https://snap.stanford.edu/data/\" *\n                       \"git_web_ml.zip\",\n                       git_zip)\nend\nisfile(git_zip)\nopen(sha256, git_zip) == [0x56, 0xc0, 0xc1, 0xc2,\n                          0xc4, 0x60, 0xdc, 0x4c,\n                          0x7b, 0xf8, 0x93, 0x57,\n                          0xb1, 0xfe, 0xc0, 0x20,\n                          0xf4, 0x5e, 0x2e, 0xce,\n                          0xba, 0xb8, 0x1d, 0x13,\n                          0x1d, 0x07, 0x3b, 0x10,\n                          0xe2, 0x8e, 0xc0, 0x31]\n\n# Code for opening a zip archive\n\nimport ZipFile\ngit_archive = ZipFile.Reader(git_zip)\n\n# Code for listing 12.2\n\nfunction ingest_to_df(archive::ZipFile.Reader, filename::AbstractString)\n    idx = only(findall(x -> x.name == filename, archive.files))\n    return CSV.read(read(archive.files[idx]), DataFrame)\nend\n\n# Code for working with zip archive\n\ngit_archive.files\n\ngit_archive.files[2].name\n\nfindall(x -> x.name == \"git_web_ml/musae_git_edges.csv\", git_archive.files)\nfindall(x -> x.name == \"\", git_archive.files)\n\nonly(findall(x -> x.name == \"git_web_ml/musae_git_edges.csv\", git_archive.files))\nonly(findall(x -> x.name == \"\", git_archive.files))\n\n# Code for listing 12.3\n\nusing CSV\nusing DataFrames\nedges_df = ingest_to_df(git_archive, \"git_web_ml/musae_git_edges.csv\");\nclasses_df = ingest_to_df(git_archive, \"git_web_ml/musae_git_target.csv\");\nclose(git_archive)\nsummary(edges_df)\ndescribe(edges_df, :min, :max, :mean, :nmissing, :eltype)\nsummary(classes_df)\ndescribe(classes_df, :min, :max, :mean, :nmissing, :eltype)\n\n# Code for updating data frame columns using broadcasting\n\nedges_df .+= 1\nclasses_df.id .+= 1\n\n# Code for examples of data frame broadcasting\n\ndf = DataFrame(a=1:3, b=[4, missing, 5])\ndf .^ 2\ncoalesce.(df, 0)\ndf .+ [10, 11, 12]\n\n# Code for checking the order of :id column in a data frame\n\nclasses_df.id == axes(classes_df, 1)\n\n# Code for the difference between ! and : in broadcasting assignment\n\ndf = DataFrame(a=1:3, b=1:3)\ndf[!, :a] .= \"x\"\ndf[:, :b] .= \"x\"\ndf\n\n# Code for the difference between ! and : in assignment\n\ndf = DataFrame(a=1:3, b=1:3, c=1:3)\ndf[!, :a] = [\"x\", \"y\", \"z\"]\ndf[:, :b] = [\"x\", \"y\", \"z\"]\ndf[:, :c] = [11, 12, 13]\ndf\n\n# Codes for section 12.2\n\n# Code for listing 12.4\n\nusing Graphs\ngh = SimpleGraph(nrow(classes_df))\nfor (src, dst) in eachrow(edges_df)\n    add_edge!(gh, src, dst)\nend\ngh\nne(gh)\nnv(gh)\n\n# Code for iterator destruction in iteration specification\n\nmat = [1 2; 3 4; 5 6]\nfor (x1, x2) in eachrow(mat)\n    @show x1, x2\nend\n\n# Code for getting degrees of nodes in the graph\n\ndegree(gh)\n\n# Code for adding a column to a data frame\n\nclasses_df.deg = degree(gh)\n\n# Code for the difference between ! and : when adding a column\n\ndf = DataFrame()\nx = [1, 2, 3]\ndf[!, :x1] = x\ndf[:, :x2] = x\ndf\ndf.x1 === x\ndf.x2 === x\ndf.x2 == x\n\n# Code for creating a column using broadcasting\n\ndf.x3 .= 1\ndf\n\n# Code for edge iterator of a graph\n\nedges(gh)\n\ne1 = first(edges(gh))\ndump(e1)\ne1.src\ne1.dst\n\n# Code for listing 12.5\n\nfunction deg_class(gh, class)\n    deg_ml = zeros(Int, length(class))\n    deg_web = zeros(Int, length(class))\n    for edge in edges(gh)\n        a, b = edge.src, edge.dst\n        if class[b] == 1\n            deg_ml[a] += 1\n        else\n            deg_web[a] += 1\n        end\n        if class[a] == 1\n            deg_ml[b] += 1\n        else\n            deg_web[b] += 1\n        end\n    end\n    return (deg_ml, deg_web)\nend\n\n# Code for computing machine learning and web neighbors for gh graph\n\nclasses_df.deg_ml, classes_df.deg_web =\ndeg_class(gh, classes_df.ml_target)\n\n# Code for checking type stability of deg_class function\n\n@time deg_class(gh, classes_df.ml_target);\n@code_warntype deg_class(gh, classes_df.ml_target)\n\n# Code for checking the classes_df summary statistics\n\ndescribe(classes_df, :min, :max, :mean, :std)\n\n# Code for average degree of node in the graph\n\n2 * ne(gh) / nv(gh)\n\n# Code for checking correctness of computations\n\nclasses_df.deg_ml + classes_df.deg_web == classes_df.deg\n\n# Code for showing that DataFrames.jl checks consistency of stored objects\n\ndf = DataFrame(a=1, b=11)\npush!(df.a, 2)\ndf\n\n# Codes for section 12.3\n\n# Code for computing groupwise means of columns\n\nusing Statistics\nfor type in [0, 1], col in [\"deg_ml\", \"deg_web\"]\n    println((type, col, mean(classes_df[classes_df.ml_target .== type, col])))\nend\n\ngdf = groupby(classes_df, :ml_target)\n\ncombine(gdf,\n        :deg_ml => mean => :mean_deg_ml,\n        :deg_web => mean => :mean_deg_web)\n\nusing DataFramesMeta\n@combine(gdf,\n         :mean_deg_ml = mean(:deg_ml),\n         :mean_deg_web = mean(:deg_web))\n\n# Code for simple plotting of relationship between developer degree and type\n\nusing Plots\nscatter(classes_df.deg_ml, classes_df.deg_web;\n        color=[x == 1 ? \"black\" : \"gray\" for x in classes_df.ml_target],\n        xlabel=\"degree ml\", ylabel=\"degree web\", labels=false)\n\n# Code for aggregation of degree data\n\nagg_df = combine(groupby(classes_df, [:deg_ml, :deg_web]),\n                 :ml_target => (x -> 1 - mean(x)) => :web_mean)\n\n# Code for comparison how Julia parses expressions\n\n:ml_target => (x -> 1 - mean(x)) => :web_mean\n:ml_target => x -> 1 - mean(x) => :web_mean\n\n# Code for aggregation using DataFramesMeta.jl\n\n@combine(groupby(classes_df, [:deg_ml, :deg_web]),\n         :web_mean = 1 - mean(:ml_target))\n\n# Code for getting summary information about the aggregated data frame\n\ndescribe(agg_df)\n\n# Code for log1p function\n\nlog1p(0)\n\n# Code for listing 12.6\n\nfunction gen_ticks(maxv)\n    max2 = round(Int, log2(maxv))\n    tick = [0; 2 .^ (0:max2)]\n    return (log1p.(tick), tick)\nend\n\nlog1pjitter(x) = log1p(x) - 0.05 + rand() / 10\n\nusing Random\nRandom.seed!(1234);\nscatter(log1pjitter.(agg_df.deg_ml),\n        log1pjitter.(agg_df.deg_web);\n        zcolor=agg_df.web_mean,\n        xlabel=\"degree ml\", ylabel=\"degree web\",\n        markersize=2, markerstrokewidth=0, markeralpha=0.8,\n        legend=:topleft, labels=\"fraction web\",\n        xticks=gen_ticks(maximum(classes_df.deg_ml)),\n        yticks=gen_ticks(maximum(classes_df.deg_web)))\n\n# Code for fitting logistic regression model\n\nusing GLM\nglm(@formula(ml_target~log1p(deg_ml)+log1p(deg_web)), classes_df, Binomial(), LogitLink())\n\n# Code for inspecting @formula result\n\n@formula(ml_target~log1p(deg_ml)+log1p(deg_web))\n\n# Code for inserting columns to a data frame\n\ndf = DataFrame(x=1:2)\ninsertcols!(df, :y => 4:5)\ninsertcols!(df, :y => 4:5)\ninsertcols!(df, :z => 1)\n\ninsertcols!(df, 1, :a => 0)\ninsertcols!(df, :x, :pre_x => 2)\ninsertcols!(df, :x, :post_x => 3, after=true)\n", "meta": {"hexsha": "98878f4c9392367bfe44016f3c66ee99300a3f49", "size": 6745, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ch12.jl", "max_stars_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_stars_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ch12.jl", "max_issues_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_issues_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ch12.jl", "max_forks_repo_name": "Mo-Gul/JuliaForDataAnalysis", "max_forks_repo_head_hexsha": "28af42c8fb30bb56569281c9d2978ad76069e3b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5839160839, "max_line_length": 90, "alphanum_fraction": 0.6532246108, "num_tokens": 2083, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.1259227615549033, "lm_q1q2_score": 0.059521608717417476}}
{"text": "ENV[\"JUPYTER\"]=\"/home/idies/miniconda3/bin/jupyter\"\nusing Pkg\nPkg.add(\"IJulia\")\nPkg.add(\"Plots\")\nPkg.add(\"PyPlot\")\nPkg.add(\"PyCall\")\n", "meta": {"hexsha": "647557dd60b08894d9582dd2b0814a268e7ad9a0", "size": 133, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".refactored/oceanography/2.0/julia-setup.jl", "max_stars_repo_name": "sciserver/sciserver-base-images", "max_stars_repo_head_hexsha": "bde00a4d28b4e7e42e2dd3240c0a2bab5d88bf88", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-28T21:57:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-28T21:57:35.000Z", "max_issues_repo_path": ".refactored/sciserver-julia/julia-setup.jl", "max_issues_repo_name": "sciserver/sciserver-base-images", "max_issues_repo_head_hexsha": "bde00a4d28b4e7e42e2dd3240c0a2bab5d88bf88", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2020-04-01T20:34:53.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-20T17:17:22.000Z", "max_forks_repo_path": ".refactored/oceanography/2.1/julia-setup.jl", "max_forks_repo_name": "sciserver/sciserver-base-images", "max_forks_repo_head_hexsha": "bde00a4d28b4e7e42e2dd3240c0a2bab5d88bf88", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.0, "max_line_length": 51, "alphanum_fraction": 0.7067669173, "num_tokens": 52, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.13117323225300487, "lm_q1q2_score": 0.059455821661148434}}
{"text": "\r\n# ============================================================================ #\r\n# Variable\r\n# ============================================================================ #\r\n\r\n\r\nmutable struct Variable <: AbstractVariable\r\n    name::String\r\n    value::Float64\r\n\r\n    function Variable(name, value = 0.0)\r\n        return new(string(name), value)\r\n    end\r\nend\r\n\r\nmacro variable(varname, value = default_variable_value())\r\n    lhs = esc(varname)\r\n    rhs = :(     Variable( $(QuoteNode(varname)), $value)   )\r\n    ex  = :($(lhs) = $(rhs))\r\n\r\n    return ex\r\nend\r\n\r\nfunction evaluate(x::Variable)\r\n    return x.value\r\nend\r\n\r\nfunction evaluate(x::A) where {A <: AbstractArray{<: Variable}}\r\n    s = similar(x)\r\n    for ii in eachindex(x)\r\n        s[ii] = x[ii].value\r\n    end\r\n    return s\r\nend\r\n\r\nfunction to_string(x::Variable)\r\n    return x.name\r\nend\r\n\r\nfunction clone(x::Variable)\r\n    y = Variable(x.name, x.value)\r\n    return y\r\nend\r\n\r\n\r\n", "meta": {"hexsha": "0055d8991c8a5417a1bbc6268580b80618644682", "size": 943, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "symbolics/src/variable.jl", "max_stars_repo_name": "HomoModelicus/julia", "max_stars_repo_head_hexsha": "26be81348032ccd2728046193ce627c823a3804b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "symbolics/src/variable.jl", "max_issues_repo_name": "HomoModelicus/julia", "max_issues_repo_head_hexsha": "26be81348032ccd2728046193ce627c823a3804b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "symbolics/src/variable.jl", "max_forks_repo_name": "HomoModelicus/julia", "max_forks_repo_head_hexsha": "26be81348032ccd2728046193ce627c823a3804b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.5, "max_line_length": 81, "alphanum_fraction": 0.4899257688, "num_tokens": 210, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769844, "lm_q2_score": 0.12252322533450247, "lm_q1q2_score": 0.05934781021341426}}
{"text": "\nstruct RandomStrategy{G <: Game} <: Strategy end\n\nfunction BoardGames.getmove(board, s::RandomStrategy)\n    rand(getmoves(board))\nend\n\nfunction BoardGames.getvarsnames(s::RandomStrategy)\n    return String[]\nend\n\nfunction BoardGames.getvalues(s::RandomStrategy)\n    return ()\nend\n\nBoardGames.name(::RandomStrategy) = \"Random Selection\"\n\nfunction Base.copy(s::RandomStrategy)\n    return s\nend", "meta": {"hexsha": "86b60f8c5e46c3a87f457ba587c0c2ff5900049c", "size": 391, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RandomStrategy.jl", "max_stars_repo_name": "antcap96/BoardGamesStrategies.jl", "max_stars_repo_head_hexsha": "b7fda322d76726ef778eaf663a759f02c38de78a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-12T09:22:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-12T09:22:10.000Z", "max_issues_repo_path": "src/RandomStrategy.jl", "max_issues_repo_name": "antcap96/BoardGamesStrategies.jl", "max_issues_repo_head_hexsha": "b7fda322d76726ef778eaf663a759f02c38de78a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/RandomStrategy.jl", "max_forks_repo_name": "antcap96/BoardGamesStrategies.jl", "max_forks_repo_head_hexsha": "b7fda322d76726ef778eaf663a759f02c38de78a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.55, "max_line_length": 54, "alphanum_fraction": 0.7570332481, "num_tokens": 91, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4649015713733884, "lm_q2_score": 0.12765263361856616, "lm_q1q2_score": 0.059345909959222835}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 de098278-8e74-11eb-13e3-49c1b6e06e7d\nusing Pkg; Pkg.activate(\"MLJ_env\", shared=true)\n\n# \u2554\u2550\u2561 ac463e7a-8b59-11eb-229e-db560e17c5f5\nbegin\n\tusing Test\n\tusing PlutoUI\nend\n\n# \u2554\u2550\u2561 8c80e072-8b59-11eb-3c21-a18fe43c4536\nmd\"\"\"\n## Databases and SQL\n\nref. from book **\"Data Science from Scratch\"**, Chap 24\n\n$(html\"<div><sub>&copy; Pascal, April 2021</sub></div>\")\n\"\"\"\n\n# \u2554\u2550\u2561 e7373726-8b59-11eb-2a2b-b5138e4f5268\nPlutoUI.TableOfContents(indent=true, depth=4, aside=true)\n\n# \u2554\u2550\u2561 f5ee64b2-8b59-11eb-2751-0778efd589cd\nhtml\"\"\"\n<style>\n  main {\n\tmax-width: calc(800px + 25px + 6px);\n  }\n  .plutoui-toc.aside {\n    background: linen;\n  }\n  h3, h4 {\n\tbackground: wheat;\n  }\n</style>\n\"\"\"\n\n# \u2554\u2550\u2561 81290d1c-8ce2-11eb-3340-337957fd81b7\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 8ff1bb20-8ce2-11eb-1de6-fd84daec8930\nmd\"\"\"\n#### Create table and insert rows\n\n\"\"\"\n\n# \u2554\u2550\u2561 d3ee2138-8ce2-11eb-0b29-659a3be01512\nmd\"\"\"\nWe will represent our table in memory by a dictionary (not really space efficient).   \n\nAlso, we will:\n  - Support the primary key for our table (some table do not and will not have a primary key).\n  - Allow for NULL value for any column of our table (but for the primary key).\n\"\"\"\n\n# \u2554\u2550\u2561 6d759180-928b-11eb-1cc1-0593c7f3b0c2\nbegin\n\tconst Row = Dict{Symbol, Any}\n\tconst GRow = Union{Row, Vector{Pair{Symbol, Any}}, Vector{Any}}  ## Generic Row\n\tconst WhereClause = Function\n\tconst HavingClause = Function\n\tconst S_N = Union{Symbol, Nothing}\n\t# const PK = Pair{S_N, DataType}\n\tconst D_SF = Dict{Symbol, Function}\n\tconst U_IN = Union{Int, Nothing}\n\tconst U_SN = Union{String, Nothing}\n\tconst UDT = Union{Union, DataType, Type};\nend\n\n# \u2554\u2550\u2561 2e8684fc-2ba6-48ec-81b3-f1c31cdde43a\nmd\"\"\"\nLet us define our custom type for a relational table:\n\"\"\"\n\n# \u2554\u2550\u2561 53accaea-92d4-11eb-0bc2-3d2ee10f9bfb\nmutable struct Table\n\tcolumns::Vector{Symbol}\n\ttypes::Vector{UDT}\n\tpkey::Union{Symbol, Nothing}\n\trows::Vector{Row}\n\t\n  function Table(col_types::Vector{Pair{Symbol, DT}};\n               pkey::Pair=(:id => Int)) where DT <: UDT\n    # vector of pairs keeps implicit order\n    @assert length(col_types) \u2265 1\n    #\n    cols = map(((k, _)=p) -> k, col_types)\n    types = map(((_, v)=p) -> v, col_types)\n    cols, types = check_pk(cols, types, pkey)\n    new(cols, types, pkey.first, Vector{Row}[])\n  end\n\n  function Table(cols::Vector{Symbol}, types::Vector{DT};\n               pkey::Pair=(:id => Int)) where DT <: UDT\n    cols, types = check_pk(cols, types, pkey)\n    new(cols, types, pkey.first, Vector{Row}[])\n  end\nend\n\n# \u2554\u2550\u2561 1b1b8d8c-3803-43fa-b3af-7a58d75bb13b\nmd\"\"\"\nAnd let us start developing our API and utilities:\n\"\"\"\n\n# \u2554\u2550\u2561 6f9c58d4-92d1-11eb-2c09-cb1ea5afcd6d\nbegin\n\timport Base: length\n\t##\n\t## API for Table\n\t##\n\n\tid(self::Table) = self.pkey\n\n\tlength(self::Table) = length(self.rows)\n\n\tfunction gen_pkey_value(self::Table)\n\t  \"\"\"\n\t  Assuming pkey is instance of a numeric type\n\t  \"\"\"\n\t  length(self.rows) == 0 && (return 1)\n\t  (map(r -> r[id(self)], self.rows) |> maximum)  + 1\n\tend\n\n\t## utility\n\tfunction check_pk(cols, types, pkey)\n\t  if pkey.first !== nothing\n\t\t@assert pkey[2] <: Integer \"Expecting an Integer, got: $(pkey[2])\"\n\t  end\n\n\t  if pkey.first !== nothing && pkey.first \u2209 cols\n\t\tcols  = [pkey[1], cols...]\n\t\ttypes = [pkey[2], types...]\n\t  end\n\t  (cols, types)\n\tend\nend\n\n# \u2554\u2550\u2561 040a7354-92d2-11eb-2825-7bac34b9fdf9\nYaUsers = Table([:name => String, :num_friends => Int];\n\tpkey=(:user_id => Int32))\n\n# \u2554\u2550\u2561 17d214a7-be15-4236-a614-3127b535ef66\nAltUsers = Table([:name => String, :num_friends => Int]) ## Default pkey\n\n# \u2554\u2550\u2561 85211d8a-be50-454d-a6c5-cd820a20f8d4\n## Default pkey and NULL values allowed\nAltUsers\u2082 = Table([:name, :num_friends], [U_SN, U_IN])\n\n# \u2554\u2550\u2561 adf67244-92d4-11eb-3004-41e62e906e32\nbegin\n\t##\n\t## Convention: by default all tables have a primary key called :id,\n\t## unless explicitly stated otherwise, something like:\n\t##   :pkey => (:user_id, Int)\n\t##\n\tUsers = Table([:name => U_SN, :num_friends => U_IN];\n\t\tpkey=(:user_id => Int))\n\t@assert length(Users) == 0\n\tUsers;\nend\n\n# \u2554\u2550\u2561 650237ee-928e-11eb-0e43-17ab98a0cc11\nbegin\n\t##\n\t## API for Table (cont'ed)\n\t##\n\n\tfunction insert(self::Table, row::Vector{Pair{Symbol, DT}}) where DT <: Any\n\t  \"\"\"\n\t  row is something like [:name => \"FooBar\", :num_friends => 3, id(Users) => 3]\n\t  \"\"\"\n\t  res = filter(p -> p.first == id(self), row)\n\t  has_pkey = length(res) > 0\n\t  ##\n\t  ## NULL values permitted\n\t  #length(row) \u2260 length(self.types) && has_pkey &&\n\t  #  throw(ArgumentError(\"Mismatch with expected number of columns\"))\n\t  ##\n\t  check_value_dtype(self, row)\n\t  ## find the pair <pkey, value> pkey is given by id(self)\n\t  row_ids = filter(p -> p.first == id(self), row)\n\t  if length(row_ids) == 0\n\t\tpk_id =  gen_pkey_value(self)\n\t\trow = [row..., id(self) => pk_id]\n\t  else\n\t\tid_val = row_ids[1][2]  ## value assoc with this pkey\n\t\trow_already_inserted(self, id_val) && (return nothing)\n\t  end\n\t  push!(self.rows, Dict(row...))\n\tend\n\n\tfunction insert(self::Table, row::Vector{Any})\n\t  \"\"\"\n\t  row is something like: [9, \"Foo\", 2 ] - just pure values\n\t  \"\"\"\n\t  length(row) \u2260 length(self.types) && id(self) \u2208 map(p -> p[1], row) &&\n\t\tthrow(ArgumentError(\"Mismatch with expected number of columns \"))\n\t  #\n\t  check_value_dtype(self, row)\n\t  ## row[1] is the value assoc. with pkey\n\t  row_already_inserted(self, row[1]) && (return nothing)\n\t  push!(self.rows, Dict(zip(self.columns, row)))\n\tend\n\n\tfunction insert(self::Table, row::Row)\n\t  \"\"\"\n\t  row is a  Dict, like: insert(Users, Dict(:name => \"Foo\", :num_friends => 5))\n\t  \"\"\"\n\t  length(row) \u2260 length(self.types) && haskey(row, id(self)) &&\n\t\tthrow(ArgumentError(\"Mismatch with expected number of columns\"))\n\t  check_value_dtype(self, row)\n\t  if haskey(row, id(self))\n\t\trow_already_inserted(self, row[id(self)]) && (return nothing)\n\t  else\n\t\trow[id(self)] = gen_pkey_value(self)\n\t  end\n\t  push!(self.rows, row)\n\tend\n\n\tinsert(self::Table, rows::Vector{T}) where T <: GRow = insert.(Ref(self), rows)\n\n\tfunction coltype(self::Table, colname::Symbol)::UDT\n\t  ix = findfirst(col -> col == colname, self.columns)\n\t  ix === nothing && throw(ArgumentError(\"column $(colname) inexistent\"))\n\t  self.types[ix]\n\tend\n\n\t# function Base.show(io::IO, self::Table)\n\t# \ts = \"num. records: $(length(self)):\\n\"\n\t# \tfor rd \u2208 self.rows\n\t# \t\ts\u2081 = []\n\t# \t\tfor (k, v) \u2208 rd\n\t# \t\t\tpush!(s\u2081, \"$(k): $(v)\")\n\t# \t\tend\n\t# \t\ts = string(s, \"  <\", join(s\u2081, \", \"), \">\\n\")\n\t# \tend\n\t# \tprint(io, \"$(s)\")\n\t# end\n\n\n\t##\n\t## Internal checkers\n\t##\n\n\tfunction check_value_dtype(self::Table, values::Vector{DT}) where DT <: Any\n\t  dtypes = self.types\n\t  for (v, dt) \u2208 zip(values, dtypes)\n\t\t!(typeof(v) <: dt) && v !== nothing &&\n\t\t  throw(\"Expected type for <$(v)>: $(dt), got: $(typeof(v))\")\n\t  end\n\tend\n\n\n\tfunction check_value_dtype(self::Table,\n\t\t\t\t\t\t\t   row::Union{Row, Vector{Pair{Symbol, DT}}}) where DT <: Any\n\t  (cols, types) = self.columns, self.types\n\t  col_type = Dict(zip(cols, types))\n\t  for (k, v) \u2208 row\n\t\t(haskey(col_type, k) && typeof(v) <: col_type[k]) ||\n\t\t  throw(\"Expected type/2: $(col_type[k]), got: $(typeof(v))\")\n\t  end\n\tend\n\n\tfunction row_already_inserted(self::Table, id_val::Any)::Bool\n\t\tid(self) === nothing && return false  ## no pkey => ignore check\n\t\t#\n\t\tres = filter(r -> r[id(self)] == id_val, self.rows)\n\t\tlength(res) > 0    ## row already inserted id > 0\n\tend\n\nend\n\n# \u2554\u2550\u2561 d146a8ac-92cc-11eb-29b1-bb065a468477\nmd\"\"\"\nInsert using an array/vector:\n\"\"\"\n\n# \u2554\u2550\u2561 13a0940e-8ce4-11eb-231c-f331a607203c\nbegin\n\tinsert(Users, [0, \"Hero\", 0])\n\t@assert length(Users) \u2265 1\nend\n\n# \u2554\u2550\u2561 e3458d34-92cc-11eb-2426-79f229abd908\nmd\"\"\"\nInsert using a vector of pairs:\n\"\"\"\n\n# \u2554\u2550\u2561 96c2a668-9290-11eb-074d-19ffeb68eba6\nbegin\n\tinsert(Users, [id(Users) => 1, :name => \"Dunn\", :num_friends => 2])\n\t@assert length(Users) \u2265 2\nend\n\n# \u2554\u2550\u2561 f1357972-92cc-11eb-1f73-43aae5a43aa5\nmd\"\"\"\nInsert using a dictionary\n\"\"\"\n\n# \u2554\u2550\u2561 5a3a8fdc-929d-11eb-25e3-e309dffdb455\nbegin\n\t## Insert with a Dict\n\tinsert(Users, Dict(id(Users) => 2, :name => \"Sue\", :num_friends => 3))\n\t@assert length(Users) \u2265 3\nend\n\n# \u2554\u2550\u2561 0292e48e-92cd-11eb-1882-81a2faaad136\nmd\"\"\"\nInsert using a dictionary, no primary key value specified:\n\"\"\"\n\n# \u2554\u2550\u2561 8545d17c-929e-11eb-0989-ffd4749bbb25\nbegin\n\t## Insert with a Dict no pkey/1\n\tinsert(Users, Dict(:name => \"Ayumi\", :num_friends => 5))\n\t@assert length(Users) \u2265 4\nend\n\n# \u2554\u2550\u2561 95d2b19e-92a4-11eb-0f91-e343c9317983\nbegin\n\t## Insert with a Dict no pkey/2\n\tinsert(Users, [:name => \"PasMas\", :num_friends => 5])\n\t@assert length(Users) \u2265 5\nend\n\n# \u2554\u2550\u2561 8a945abc-012f-4ace-a67a-b7e0f520346b\nbegin\n\t## Insert with a Dict no pkey/2 + null value (encoded as nothing)\n\t# insert(Users,\n\t# \t[:name => \"Foobar\", :num_friends => nothing])\n\tinsert(Users,\n\t\t[:name => \"Foobar\", :num_friends => nothing, :user_id => 17])\nend\n\n# \u2554\u2550\u2561 28ba0bf6-92cd-11eb-0669-75eadb768518\nmd\"\"\"\nInsert using a collection (vector of rows):\n\"\"\"\n\n# \u2554\u2550\u2561 d4562f7e-9290-11eb-2d8c-952f2e0edfed\nbegin\n\t## Insert a collection (Vector of) of Records \n\tinsert(Users, [\n\t\t[5, \"Chi\", 3],\n\t\t[6, \"Thor\", 3],\n\t\t[7, \"Clive\", 2],\n\t\t[8, \"Devin\", 2],\n\t\t[9, \"Kate\", 2],\n\t\t[10, \"Kaze\", nothing]\n\t])\n\t@assert length(Users) \u2265 10\nend\n\n# \u2554\u2550\u2561 f877c3e6-929c-11eb-00cd-c15568f99627\nUsers\n\n# \u2554\u2550\u2561 d5657a88-92c4-11eb-2a4f-c7dcaa97bcf6\nbegin\n\t@test_throws ArgumentError  coltype(Users, :foobar)\n\t@test coltype(Users, :num_friends) == U_IN\n\t@test coltype(Users, :name) == U_SN\nend\n\n# \u2554\u2550\u2561 80ddf0f4-8ce2-11eb-3046-331119a0dc9b\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 80a71408-8ce2-11eb-3978-75a4a2df9116\nmd\"\"\"\n#### Update\n\"\"\"\n\n# \u2554\u2550\u2561 d1ed1e36-8ce5-11eb-34ee-4db60fb0db8a\nmd\"\"\"\nThe key features for an update:\n  - what table,\n  - which fields/rows \n  - what their new values will be\n\"\"\"\n\n# \u2554\u2550\u2561 4ccdb1ce-92c2-11eb-18dd-c510e88474c6\n##\n## API (cont'ed)\n##\nfunction update(self::Table, updates::Row, pred::WhereClause=row -> true)\n\t## 1 - Make sure key/columns and values are consistent with column types\n\tfor (col, val) \u2208 updates\n\t\tcol \u2209 self.columns && throw(ArgumentError(\"invalid column $(col)\"))\n\t\tcol_type = coltype(self, col)\n\t\t!(typeof(val) <: col_type) && val !== nothing && \n\t\t\tthrow(ArgumentError(\"Expected $(col_type), got $(typeof(val))\"))\n\tend\n\n\t## 2 - OK, update\n\trows_to_update = filter(((ix, r)=t) -> pred(r), collect(enumerate(self.rows)))\n\tfor (ix, row) \u2208 rows_to_update\n\t\t# self.rows[ix] = Row(row..., updates...)\n\t\tfor (k, v) \u2208 updates\n\t\t\tself.rows[ix][k] = v\n\t\tend\n\tend\n\tnothing\nend\n\n# \u2554\u2550\u2561 2af6dbec-92c6-11eb-1155-596ed44b04f8\nbegin\n\tupdate(Users, Dict{Symbol, Any}(:num_friends => 7),\n\t\trow -> row[:name] == \"Ayumi\")\n\tUsers\nend\n\n# \u2554\u2550\u2561 2abb0114-92c6-11eb-01d5-cd34aa6426b2\nbegin\n\tupdate(Users, Dict{Symbol, Any}(:num_friends => 4), row -> row[:num_friends] == 2)\n\tUsers\nend\n\n# \u2554\u2550\u2561 bc1fb8d2-92ca-11eb-14e0-c9586461bc1e\nbegin\n\tupdate(Users, Dict{Symbol, Any}(:num_friends => 1))\n\tUsers\nend\n\n# \u2554\u2550\u2561 d2b198b6-8ce2-11eb-170e-0f17904c9f2c\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 d37f37b8-8ce8-11eb-2c00-3f98ca407f41\nmd\"\"\"\n#### Delete\n\"\"\"\n\n# \u2554\u2550\u2561 5bf7729e-8dfd-11eb-070f-9b7ec0746bd7\n##\n## API (cont'ed)\n##\nfunction delete(self::Table, pred::WhereClause=row -> true)|\n\tself.rows = [\n\t\trow for row \u2208 self.rows if !pred(row)\n\t]\n\tnothing\nend\n\n# \u2554\u2550\u2561 394093fa-8cea-11eb-0071-eff3045a012b\nbegin\n\tn = length(Users)\n\tdelete(Users, row -> row[id(Users)] == 1)\n\t@assert length(Users) == n - 1\nend\n\n# \u2554\u2550\u2561 435c4444-8dfd-11eb-24e8-5543f905f199\nbegin\n\tdelete(Users)\n\t@assert length(Users) == 0\nend\n\n# \u2554\u2550\u2561 e95983ba-8ceb-11eb-38fd-ed92cdcf754c\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 d3a749a2-8cec-11eb-1f06-b568f244b576\nmd\"\"\"\n#### Select\n\"\"\"\n\n# \u2554\u2550\u2561 31d3226a-8cf4-11eb-0897-39989ba76b58\nmd\"\"\"\nWe will give our Table struct a select method that returns a new Table . The\nmethod accepts two optional arguments:\n  - `keep_cols` which specifies the names of the columns we want to\nkeep in the result. if none supply, the result contains all the columns, and\n\n  - `add_cols` is a dictionary whose keys are new column names and whose values are functions specifying how to compute the values of the new columns. \n\"\"\"\n\n# \u2554\u2550\u2561 3ec9f5d5-6d6d-4586-b68b-84e3ee5aee27\n## Utility function\n##\nfunction create_res_table(self::Table, new_cols, new_types;\n                          pred::Function=_a -> true)\n  ## keep pkey or not...\n  pred(self) ?\n\tTable(new_cols, new_types; pkey=id(self) => coltype(self, id(self))) :\n    Table(new_cols, new_types; pkey=nothing => Nothing)\nend\n\n# \u2554\u2550\u2561 a56253c8-92cd-11eb-31b9-51e1fd5ae026\nfunction select(self::Table;\n\t\tkeep_cols=Vector{Symbol}[], add_cols=D_SF())::Table\n\t##\n\tlength(keep_cols) == 0 && (keep_cols = self.columns)\n\n\t## New column names and types\n\tnew_cols = [keep_cols..., collect(keys(add_cols))...]\n\tkeep_types = [coltype(self, col) for col in keep_cols]\n\n\t## collect the rows for result table\n\tn_rows = Vector{Any}[]\n\tadd_types = Any[]\n\tfor (ix, row) \u2208 enumerate(self.rows)\n\t\tn_row = Any[row[col] for col \u2208 keep_cols]\n\t\t## as we process the first row, we can get the return type...\n\t\t## ...of each function defined in add_cols\n\t\t## What if no row to process => tag with Any\n\t\tfor (_col_name, fn) \u2208 add_cols\n\t\t\tr = fn(row)\n\t\t\tix == 1 && push!(add_types, typeof(r))\n\t\t\tpush!(n_row, r)\n\t\tend\n\t\tpush!(n_rows, n_row)\n\tend\n\n\tif length(add_cols) > 0 && length(add_types) == 0\n\t\t## add as many Any as column in add_cols\n\t\tpush!(add_types, repeat(Any[Any], inner=length(add_cols)))\n\tend\n\n\t## Create result table\n\tnew_types = UDT[keep_types..., add_types...]\n\t@assert(length(new_cols) == length(new_types),\n\t\t\"length(new_cols) == length(new_types)\")\n\n\tn_table = create_res_table(self, new_cols, new_types;\n\t\tpred=s -> id(s) \u2208 keep_cols)\n\n\tinsert(n_table, n_rows)\n\tn_table\nend\n\n# \u2554\u2550\u2561 e6348294-92cb-11eb-3bfd-09d80933b33a\nbegin\n\tdelete(Users)\n\tinsert(Users, [[0, \"Hero\", 0],\n\t\t\t[1, \"Dunn\", 2],\n\t\t\t[2, \"Sue\", 3],\n\t\t\t[3, \"Chi\", 3],\n\t\t\t[4, \"Thor\", 3],\n\t\t\t[5, \"Clive\", 2],\n\t\t\t[6, \"Hicks\", 3],\n\t\t\t[7, \"Devin\", 2],\n\t\t\t[8, \"Kate\", 2]\n\t])\n\tinsert(Users, Dict(:name => \"Ayumi\", :num_friends => 5))\n\tinsert(Users, [:name => \"PasMas\", :num_friends => 5])\nend\n\n# \u2554\u2550\u2561 96386ab8-93ef-44c7-a524-1246379d7103\nTable([:user_id, :name, :num_friends], UDT[Int64, Union{Nothing, String}, Union{Nothing, Int64}])\n\n# \u2554\u2550\u2561 a5441766-92cd-11eb-1c8e-edcf3db0022e\nbegin\n\t## SELECT * FROM Users;\n\tn\u2080 = length(Users)\n\tall_users = select(Users)\n\t@assert length(all_users) == n\u2080\nend\n\n# \u2554\u2550\u2561 3bfad78b-2ebd-4067-b306-b687f5a92a10\nbegin\n\t## SELECT id FROM Users;\n\tuser_ids = select(Users, keep_cols=[:name])\nend\n\n# \u2554\u2550\u2561 cfed19ac-94e2-4c84-85b4-6134765cce0b\nmd\"\"\"\n##### Limit\n\"\"\"\n\n# \u2554\u2550\u2561 386a0591-92ce-4267-b1e6-73e293eb727c\n##\n## API (cont'ed)\n##\nfunction limit(self::Table, num_rows::Int=5)::Table\n\t\"\"\"\n\tOnly the first num_rows are returned\n\t\"\"\"\n\t@assert 1 \u2264 num_rows \u2264 length(self.rows) \"1 \u2264 $(num_rows) \u2264 $(length(self.rows))\"\n\n \tn_table = create_res_table(self, self.columns, self.types;\n\t\tpred=s -> id(s) !== nothing)\n\n\t## NOTE: mark vector as GRow type\n\trows = Vector{Any}[]\n\tfor row \u2208 self.rows[1:num_rows]\n\t\tpush!(rows, Any[v for (_, v) \u2208 row])\n\tend\n\tinsert(n_table, rows)\n\tn_table\nend\n\n# \u2554\u2550\u2561 53c15148-1b04-4d85-aac6-874a6f750a00\nuser_names\u2081 = select(Users, keep_cols=[:name]) |> limit\n\n# \u2554\u2550\u2561 1ac32ed0-6e47-4606-8b70-524159660d93\nuser_names\u2082 = select(Users, keep_cols=[:name]) |>\n  u -> limit(u, 2)\n\n# \u2554\u2550\u2561 be425e03-c7b4-401d-9d29-fa2b07618ee5\nmd\"\"\"\n###### Where\n\"\"\"\n\n# \u2554\u2550\u2561 e99eab12-08d9-4955-ba10-2eb46f74bd85\n##\n## API (cont'ed)\n##\nfunction where(self::Table, pred::WhereClause=row -> true)::Table\n\t\"\"\"\n\tOnly the rows that satisfy pred are returned\n\t\"\"\"\n\tn_table = create_res_table(self, self.columns, self.types;\n\t\tpred=s -> id(s) !== nothing)\n\n\tn_rows = Vector{Any}[]\n\tfor row \u2208 self.rows\n\t\tpred(row) && (push!(n_rows, [row[col] for col \u2208 n_table.columns]))\t\n\tend\n\n\tinsert(n_table, n_rows)\n\tn_table\nend\n\n# \u2554\u2550\u2561 b08b9470-5d53-4420-bef6-ef1c0bb4414c\nbegin\n\tdunn_ids = where(Users, row -> row[:name] == \"Dunn\") |> \n\t\tu -> select(u, keep_cols=[id(Users)])\n\t# @assert length(dunn_ids) == 1\nend\n\n# \u2554\u2550\u2561 955fe2a1-22b3-44db-b23d-d595d58bac3d\nbegin\n\tamp_ids = where(Users, row -> row[:name][1] \u2208 ['A', 'P']) |> \n\t\tu -> select(u, keep_cols=[id(Users), :name])\nend\n\n# \u2554\u2550\u2561 964507de-c48d-4f7c-ab05-9b7739ecd5f3\nbegin\n\tncol = length(Users.columns)\n\tfunction name_len_fn(row)::Int \n\t\tlength(row[:name])\n\tend\n\n\tname_lengths = select(Users;\n\t\tadd_cols=D_SF(:name_length => name_len_fn))\nend\n\n# \u2554\u2550\u2561 13c464eb-2a59-488b-b0ab-922ecda91bbd\nbegin\n\tname_lengths\u2082 = select(Users;\n\t\tadd_cols=Dict{Symbol, Function}(\n\t\t\t:name_ini => row -> string(row[:name][1]),\n\t\t\t:len_name => row -> length(row)\n\t\t)\n\t)\nend\n\n# \u2554\u2550\u2561 95ae9db4-f9ba-4fde-a021-0fc952550627\nmd\"\"\"\n##### Aside on introspection\n\"\"\"\n\n# \u2554\u2550\u2561 17b2e439-4f70-4db6-a01f-638ae2f88b6f\nbegin\n\t## with explicit return type\n\t##\n\tstr_fn = \"\"\"\nfunction name_len_fn(row, bar, vargs...)::UInt16\n\tlength(row[:name])\nend\n\"\"\"\n\texpr = Meta.parse(str_fn)\nend\n\n# \u2554\u2550\u2561 a57b3859-d026-4325-a5b2-7d4e7b4db408\nbegin\n\t## w/o explicit return type\n\t##\n\tstr_fn\u2082 = \"\"\"\nfunction name_len_fn(row1, bar1; foo1=10)\n\tlength(row[:name])\nend\n\"\"\"\n\texpr\u2082 = Meta.parse(str_fn\u2082)\nend\n\n# \u2554\u2550\u2561 8e25eb57-b6f0-4c61-b657-4d815f1eb31f\nwith_terminal() do\n\tdump(expr)\nend\n\n# \u2554\u2550\u2561 8ea2ba50-ad35-40b2-ab2d-1c34c9d90bd9\nexpr.args[1], expr\u2082.args[1]\n\n# \u2554\u2550\u2561 b34d8aaa-7696-452f-9fd7-1002aa771547\n## get return type\nexpr.args[1].args, length(expr.args[1].args), expr.args[1].args[end]\n\n# \u2554\u2550\u2561 4ff7e8e9-354e-4c99-b6f8-aedbf7ce1f3f\nexpr\u2082.args[1].args, length(expr\u2082.args[1].args)\n\n# \u2554\u2550\u2561 3a438295-3d30-41cc-95bd-db3b93e62d12\nfunction get_fn_retype(str_fn::String)::DataType\n\t\"\"\"\n\tReturn output type of a user function if such type is available...\n\t...which is the case iff expr.args[1].args has length of 2\n\tOtherwise fallback to Any\n\t\"\"\"\n\texpr = Meta.parse(str_fn)\n\tlength(expr.args[1].args) == 2 ? eval(expr.args[1].args[end]) : Any\nend\n\n# \u2554\u2550\u2561 9b24fead-ccfd-43ed-afa8-ed5ef6c4cf8f\nget_fn_retype(str_fn), get_fn_retype(str_fn\u2082)\n\n# \u2554\u2550\u2561 6e7e7896-8cf8-11eb-062e-99492ec8cff8\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 70f707c0-8cf6-11eb-11c2-73d7b28f7a0c\nmd\"\"\"\n#### Group by\n\"\"\"\n\n# \u2554\u2550\u2561 70d84470-8cf6-11eb-23b6-c7ba506d8552\nmd\"\"\"\nThis function will take a list of columns we want to group by and a dictionary of aggragation functions plus an optional predicate parameter (having clause) that can operate on multiple rows. \n\"\"\"\n\n# \u2554\u2550\u2561 c451451a-8cf7-11eb-183b-e358c9d618e0\n##\n## API (cont'ed)\n##\n\nfunction group_by(self::Table; group_by_cols::Vector{Symbol}, agg::D_SF,\n\thaving=HavingClause=row_gp -> true)::Table\n\tgrouped_rows = Dict{}()\n\n\t## 1 - Populate groups\n\tfor row \u2208 self.rows\n\t\tkey = Tuple(row[col] for col \u2208 group_by_cols)\n\t\tary = get(grouped_rows, key, Row[])\n\t\tpush!(ary, row)\n\t\tgrouped_rows[key] = ary\n\tend\n\n\t## 2 - populate rows\n\tn_rows = Vector{Any}[]\n\tagg_types = Any[]\n\tfor (ix, (key, rows)) \u2208 enumerate(grouped_rows)\n\t\tagg_row = Any[]\n\t\tif having(rows)\n\t\t\tagg_row = Any[agg_row..., key...]  ## Keep Any[]\n\t\t\tfor (_, agg_fn) \u2208 agg\n\t\t\t\tr = agg_fn(rows)\n\t\t\t\tix == 1 && push!(agg_types, typeof(r))\n\t\t\t\tpush!(agg_row, r)\n\t\t\tend\n\t\tend\n\t\tlength(agg_row) > 0 && (push!(n_rows, agg_row))\n\tend\n\n\t## 3 - res. table consists of group_by columns and aggregates\n\tnew_cols = [group_by_cols..., collect(keys(agg))...] |> unique\n\tgp_by_types = [coltype(self, col) for col \u2208 group_by_cols]\n\tnew_types = [gp_by_types..., agg_types...]\n\n\tn_table = create_res_table(self, new_cols, new_types;\n\t\tpred=s -> id(s) \u2208 new_cols)\n\tinsert(n_table, n_rows)\n\tn_table\nend\n\n# \u2554\u2550\u2561 30bdc788-8d01-11eb-2f8f-9fd79593ddb8\nbegin\n\t\"\"\"\n\t-- find number of users and smallest user_id for each possible name length:\n\tSELECT LENGTH(name) as name_length, MIN(user_id) AS min_user_id,\n\t\tCOUNT(*) AS num_users\n\tFROM users\n\tGROUP BY LENGTH(name);\n\t\"\"\"\n\tmin_user_id = rows -> minimum(row[id(Users)] for row \u2208 rows)\n\tnum_rows = rows -> length(rows)\n\n\tstats_by_len = select(Users, add_cols=D_SF(:name_length => name_len_fn)) |>\n\t\tu -> group_by(u,\n\t\t\t\tgroup_by_cols=[:name_length],\n\t\t\t\tagg=D_SF(:min_user_id => min_user_id, :num_users => num_rows))\n\t#\nend\n\n# \u2554\u2550\u2561 c5bac3ae-c63b-44ba-9b10-75144410f456\nUsers\n\n# \u2554\u2550\u2561 e696c2dd-b250-418c-9161-9fd87bfc378e\nbegin\n\t\"\"\"\n\t-- average number of friends for users whose names start with specific letters\n\t-- but see only the results for letters whose corresponding average is greater\n\t-- than 1\n\tSELECT SUBSTR(name, 1, 1) AS first_letter,\n\t\tAVG(num_friends) AS avg_num_friends\n\tFROM users\n\tGROUP BY SUBSTR(name, 1, 1)\n\tHAVING AVG(num_friends) > 1;\n\t\"\"\"\n\tfirst_letter_fn = row -> row[:name] !== nothing ? string(row[:name][1]) : \"\"\n\n\tavg_num_friends_fn =\n\t\trows -> sum([row[:num_friends] for row in rows]) / length(rows)\n\n\tenough_friends_fn = rows -> avg_num_friends_fn(rows) > 1.\n\n\tavg_friends_by_letter =\n\t\tselect(Users, add_cols=D_SF(:first_letter => first_letter_fn)) |>\n\t\tu -> group_by(u, group_by_cols=[:first_letter],\n\t\t\t\tagg=D_SF(:avg_num_friends => avg_num_friends_fn),\n\t\t\t\thaving=enough_friends_fn)\nend\n\n# \u2554\u2550\u2561 84946000-8d02-11eb-3160-571efee8fb0b\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 d05f1c40-8d17-11eb-2724-4d989c4c6b92\nmd\"\"\"\n#### Order by\n\"\"\"\n\n# \u2554\u2550\u2561 848fd812-8d02-11eb-01d6-75965b08bcc5\n##\n## API (cont'ed)\n##\nfunction order_by(self::Table, order::Function)::Table\n\tn_table = select(self)\n\tsort!(n_table.rows, by=order)\n\tn_table\nend\n\n# \u2554\u2550\u2561 6cde1330-e1bb-419e-87f3-cf73c0e11e3d\nfriendliest_letters =  avg_friends_by_letter |>\n\tu -> order_by(u, row -> -row[:avg_num_friends])\n# NOTE: -row(...) to reverse the sort\n\n# \u2554\u2550\u2561 7d35f85e-8e07-11eb-228c-81d28a444b52\nfriendliest_letters\u2082 =  avg_friends_by_letter |>\n\tu -> order_by(u, row -> -row[:avg_num_friends]) |>\n\tu -> limit(u, 3)\n# NOTE: -row(...) to reverse the sort\n\n# \u2554\u2550\u2561 40d46802-8e0f-11eb-2e47-53096e24dbd8\nhtml\"\"\"\n<hr />\n\"\"\"\n\n# \u2554\u2550\u2561 80dff352-8d02-11eb-06b1-5f5e325046f5\nmd\"\"\"\n#### Join\n\"\"\"\n\n# \u2554\u2550\u2561 f1af4256-8e02-11eb-0238-2515d39a89cd\n##\n## API (cont'ed)\n##\nfunction join(self::Table, otable::Table; left_join=false)::Table\n\tjoin_on_cols = [c for c \u2208 self.columns if c \u2208 otable.columns]\n\n\tadd_cols = [c for c \u2208 otable.columns if c \u2209 join_on_cols && c != id(otable)]\n\tnew_cols = [self.columns..., add_cols...]\n\tnew_types = UDT[self.types..., coltype.(Ref(otable), add_cols)...]\n\n\tn_table = Table(new_cols, new_types; pkey=nothing => Nothing)\n\n\tn_rows = Vector{Any}[]\n\tfor row \u2208 self.rows\n\t\tis_join = orow -> all(orow[c] == row[c] for c \u2208 join_on_cols)\n\n\t\to_rows = where(otable, is_join).rows\n\t\tfor o_row \u2208 o_rows\n\t\t\tpush!(n_rows, Any[[row[c] for c \u2208 self.columns]...,\n\t\t\t\t\t[o_row[c] for c \u2208 add_cols]...])\n\t\tend\n\n\t\tif left_join && length(o_rows) == 0\n\t\t\tpush!(n_rows, Any[[row[c] for c \u2208 self.columns]...,\n\t\t\t\t\t[nothing for _ \u2208 add_cols]...])\n\t\tend\n\tend\n\t@show \"join/4\", n_rows, length(n_rows)\n\tinsert(n_table, n_rows)\n\tn_table\nend\n\n# \u2554\u2550\u2561 26571096-f30c-476a-ad0e-ef133ba2562f\nbegin\n\tUser_Interests = Table([:user_id => Int, :interest => String]) ## Default pkey\n\tinsert(User_Interests, [\n\t\t\t[1, 0, \"SQL\"],\n\t\t\t[2, 0, \"NoSQL\"],\n\t\t\t[3, 2, \"SQL\"],\n\t\t\t[4, 2, \"MySQL\"],\n\t\t\t[5, 7, \"PostgreSQL\"],\n\t\t\t[6, 7, \"SQL\"]\n\t])\nend\n\n# \u2554\u2550\u2561 a1a91e04-ffdb-49b1-a999-e1cf1a730cb4\nbegin\n\tsql_users = join(Users, User_Interests) |>\n\t\tu -> where(u, r -> r[:interest] == \"SQL\") |>\n\t\tu -> select(u, keep_cols=[:name])\nend\n\n# \u2554\u2550\u2561 43a52692-8e10-11eb-2043-8f0be195f58a\nbegin\n\tsql_users\u2082 = join(Users, User_Interests; left_join=true) |>\n\t\tu -> where(u, r -> r[:interest] !== nothing &&\n\t\t\t\t\t\t\toccursin(r\"SQL\\z\", r[:interest])) |>\n\t\tu -> select(u, keep_cols=[:name])\n\t@test length(sql_users\u2082.rows) == 6\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25008c80e072-8b59-11eb-3c21-a18fe43c4536\n# \u2560\u2550de098278-8e74-11eb-13e3-49c1b6e06e7d\n# \u2560\u2550ac463e7a-8b59-11eb-229e-db560e17c5f5\n# \u255f\u2500e7373726-8b59-11eb-2a2b-b5138e4f5268\n# \u255f\u2500f5ee64b2-8b59-11eb-2751-0778efd589cd\n# \u255f\u250081290d1c-8ce2-11eb-3340-337957fd81b7\n# \u255f\u25008ff1bb20-8ce2-11eb-1de6-fd84daec8930\n# \u255f\u2500d3ee2138-8ce2-11eb-0b29-659a3be01512\n# \u2560\u25506d759180-928b-11eb-1cc1-0593c7f3b0c2\n# \u255f\u25002e8684fc-2ba6-48ec-81b3-f1c31cdde43a\n# \u2560\u255053accaea-92d4-11eb-0bc2-3d2ee10f9bfb\n# \u255f\u25001b1b8d8c-3803-43fa-b3af-7a58d75bb13b\n# \u2560\u25506f9c58d4-92d1-11eb-2c09-cb1ea5afcd6d\n# \u2560\u2550040a7354-92d2-11eb-2825-7bac34b9fdf9\n# \u2560\u255017d214a7-be15-4236-a614-3127b535ef66\n# \u2560\u255085211d8a-be50-454d-a6c5-cd820a20f8d4\n# \u2560\u2550adf67244-92d4-11eb-3004-41e62e906e32\n# \u2560\u2550650237ee-928e-11eb-0e43-17ab98a0cc11\n# \u255f\u2500d146a8ac-92cc-11eb-29b1-bb065a468477\n# \u2560\u255013a0940e-8ce4-11eb-231c-f331a607203c\n# \u255f\u2500e3458d34-92cc-11eb-2426-79f229abd908\n# \u2560\u255096c2a668-9290-11eb-074d-19ffeb68eba6\n# \u255f\u2500f1357972-92cc-11eb-1f73-43aae5a43aa5\n# \u2560\u25505a3a8fdc-929d-11eb-25e3-e309dffdb455\n# \u255f\u25000292e48e-92cd-11eb-1882-81a2faaad136\n# \u2560\u25508545d17c-929e-11eb-0989-ffd4749bbb25\n# \u2560\u255095d2b19e-92a4-11eb-0f91-e343c9317983\n# \u2560\u25508a945abc-012f-4ace-a67a-b7e0f520346b\n# \u255f\u250028ba0bf6-92cd-11eb-0669-75eadb768518\n# \u2560\u2550d4562f7e-9290-11eb-2d8c-952f2e0edfed\n# \u2560\u2550f877c3e6-929c-11eb-00cd-c15568f99627\n# \u2560\u2550d5657a88-92c4-11eb-2a4f-c7dcaa97bcf6\n# \u255f\u250080ddf0f4-8ce2-11eb-3046-331119a0dc9b\n# \u255f\u250080a71408-8ce2-11eb-3978-75a4a2df9116\n# \u255f\u2500d1ed1e36-8ce5-11eb-34ee-4db60fb0db8a\n# \u2560\u25504ccdb1ce-92c2-11eb-18dd-c510e88474c6\n# \u2560\u25502af6dbec-92c6-11eb-1155-596ed44b04f8\n# \u2560\u25502abb0114-92c6-11eb-01d5-cd34aa6426b2\n# \u2560\u2550bc1fb8d2-92ca-11eb-14e0-c9586461bc1e\n# \u255f\u2500d2b198b6-8ce2-11eb-170e-0f17904c9f2c\n# \u255f\u2500d37f37b8-8ce8-11eb-2c00-3f98ca407f41\n# \u2560\u25505bf7729e-8dfd-11eb-070f-9b7ec0746bd7\n# \u2560\u2550394093fa-8cea-11eb-0071-eff3045a012b\n# \u2560\u2550435c4444-8dfd-11eb-24e8-5543f905f199\n# \u255f\u2500e95983ba-8ceb-11eb-38fd-ed92cdcf754c\n# \u255f\u2500d3a749a2-8cec-11eb-1f06-b568f244b576\n# \u255f\u250031d3226a-8cf4-11eb-0897-39989ba76b58\n# \u2560\u25503ec9f5d5-6d6d-4586-b68b-84e3ee5aee27\n# \u2560\u2550a56253c8-92cd-11eb-31b9-51e1fd5ae026\n# \u2560\u2550e6348294-92cb-11eb-3bfd-09d80933b33a\n# \u2560\u255096386ab8-93ef-44c7-a524-1246379d7103\n# \u2560\u2550a5441766-92cd-11eb-1c8e-edcf3db0022e\n# \u2560\u25503bfad78b-2ebd-4067-b306-b687f5a92a10\n# \u255f\u2500cfed19ac-94e2-4c84-85b4-6134765cce0b\n# \u2560\u2550386a0591-92ce-4267-b1e6-73e293eb727c\n# \u2560\u255053c15148-1b04-4d85-aac6-874a6f750a00\n# \u2560\u25501ac32ed0-6e47-4606-8b70-524159660d93\n# \u255f\u2500be425e03-c7b4-401d-9d29-fa2b07618ee5\n# \u2560\u2550e99eab12-08d9-4955-ba10-2eb46f74bd85\n# \u2560\u2550b08b9470-5d53-4420-bef6-ef1c0bb4414c\n# \u2560\u2550955fe2a1-22b3-44db-b23d-d595d58bac3d\n# \u2560\u2550964507de-c48d-4f7c-ab05-9b7739ecd5f3\n# \u2560\u255013c464eb-2a59-488b-b0ab-922ecda91bbd\n# \u255f\u250095ae9db4-f9ba-4fde-a021-0fc952550627\n# \u2560\u255017b2e439-4f70-4db6-a01f-638ae2f88b6f\n# \u2560\u2550a57b3859-d026-4325-a5b2-7d4e7b4db408\n# \u2560\u25508e25eb57-b6f0-4c61-b657-4d815f1eb31f\n# \u2560\u25508ea2ba50-ad35-40b2-ab2d-1c34c9d90bd9\n# \u2560\u2550b34d8aaa-7696-452f-9fd7-1002aa771547\n# \u2560\u25504ff7e8e9-354e-4c99-b6f8-aedbf7ce1f3f\n# \u2560\u25503a438295-3d30-41cc-95bd-db3b93e62d12\n# \u2560\u25509b24fead-ccfd-43ed-afa8-ed5ef6c4cf8f\n# \u255f\u25006e7e7896-8cf8-11eb-062e-99492ec8cff8\n# \u255f\u250070f707c0-8cf6-11eb-11c2-73d7b28f7a0c\n# \u255f\u250070d84470-8cf6-11eb-23b6-c7ba506d8552\n# \u2560\u2550c451451a-8cf7-11eb-183b-e358c9d618e0\n# \u2560\u255030bdc788-8d01-11eb-2f8f-9fd79593ddb8\n# \u2560\u2550c5bac3ae-c63b-44ba-9b10-75144410f456\n# \u2560\u2550e696c2dd-b250-418c-9161-9fd87bfc378e\n# \u255f\u250084946000-8d02-11eb-3160-571efee8fb0b\n# \u255f\u2500d05f1c40-8d17-11eb-2724-4d989c4c6b92\n# \u2560\u2550848fd812-8d02-11eb-01d6-75965b08bcc5\n# \u2560\u25506cde1330-e1bb-419e-87f3-cf73c0e11e3d\n# \u2560\u25507d35f85e-8e07-11eb-228c-81d28a444b52\n# \u255f\u250040d46802-8e0f-11eb-2e47-53096e24dbd8\n# \u255f\u250080dff352-8d02-11eb-06b1-5f5e325046f5\n# \u2560\u2550f1af4256-8e02-11eb-0238-2515d39a89cd\n# \u2560\u255026571096-f30c-476a-ad0e-ef133ba2562f\n# \u2560\u2550a1a91e04-ffdb-49b1-a999-e1cf1a730cb4\n# \u2560\u255043a52692-8e10-11eb-2043-8f0be195f58a\n", "meta": {"hexsha": "e54eed33ad8f4538e8781df434a0bebfec2c2fd6", "size": 26862, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Data Science From Scratch/24_DB_SQL.jl", "max_stars_repo_name": "pascal-p/julia-notebooks", "max_stars_repo_head_hexsha": "568c884c8b0de8ce34a84e8d1ce5fb6994cf32b8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-01T20:34:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-01T20:34:56.000Z", "max_issues_repo_path": 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NO\n2. NO", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.14414884935602926, "lm_q1q2_score": 0.059261158757574026}}
{"text": "\"\"\"\n    grdview(cmd0::String=\"\", arg1=nothing, arg2=nothing, arg3=nothing; kwargs...)\n\nReads a 2-D grid and produces a 3-D perspective plot by drawing a mesh, painting a\ncolored/grayshaded surface made up of polygons, or by scanline conversion of these polygons\nto a raster image.\n\nFull option list at [`grdview`]($(GMTdoc)grdview.html)\n\n- $(GMT.opt_J)\n- $(GMT.opt_Jz)\n- $(GMT.opt_R)\n- $(GMT.opt_B)\n- $(GMT.opt_C)\n- **G** | **drape** | **drapefile** :: [Type => Str | GMTgrid | a Tuple with 3 GMTgrid types]\n\n    Drape the image in drapefile on top of the relief provided by relief_file.\n    ($(GMTdoc)grdview.html#g)\n- **I** | **shade** | **shading** | **intensity** :: [Type => Str | GMTgrid]\t\t``Arg = GMTgrid | filename``\n\n    Gives the name of a grid file or GMTgrid with intensities in the (-1,+1) range,\n    or a grdgradient shading flags.\n    ($(GMTdoc)grdview.html#i)\n- **N** | **plane** :: [Type => Str | Int]\t\t``Arg = (level [,fill])``\n\n    Draws a plane at this z-level.\n    ($(GMTdoc)grdview.html#n)\n- $(GMT.opt_P)\n- **Q** | **surftype** | **surf** :: [Type => Str | Int] ``Arg = mesh=Bool, surface=Bool, image=Bool, wterfall=(:rows|cols,[fill])``\n\n    Specify **m** for mesh plot, **s** for surface, **i** for image.\n    ($(GMTdoc)grdview.html#q)\n- **S** | **smoothfactor** :: [Type => Number]\n\n    Used to resample the contour lines at roughly every (gridbox_size/smoothfactor) interval..\n    ($(GMTdoc)grdview.html#s)\n- **T** | **tiles** | **no_interp** :: [Type => Str | NT]\t``Arg = (skip|skip_nan=Bool, outlines=Bool|pen)``\n\n    Plot image without any interpolation.\n    ($(GMTdoc)grdview.html#t)\n- **W** | **pens** | **pen** :: [Type => Str]\t``Arg = (contour=Bool|pen, mesh=Bool|pen, facade=Bool|pen)``\n\n    Draw contour, mesh or facade. Append pen attributes.\n    ($(GMTdoc)grdview.html#w)\n\n- **isgeog** :: [Type => Any]\n\n    When drapping an image that has projection info over a grid that is in geographics but does not carry any\n    information about this fact we may need to use this option to help the program finding the common BoundingBox.\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- $(GMT.opt_f)\n- $(GMT.opt_n)\n- $(GMT.opt_p)\n- $(GMT.opt_t)\n\"\"\"\nfunction grdview(cmd0::String=\"\", arg1=nothing; first=true, kwargs...)\n\n\tlength(kwargs) == 0 && occursin(\" -\", cmd0) && return monolitic(\"grdview\", cmd0, arg1)\n\targ2 = nothing;\targ3 = nothing;\targ4 = nothing;\targ5 = nothing;\n\n\td, K, O = init_module(first, kwargs...)\t\t# Also checks if the user wants ONLY the HELP mode\n\tcommon_insert_R!(d, O, cmd0, arg1)\t\t\t# Set -R in 'd' out of grid/images (with coords) if limits was not used\n\n\thas_opt_B = (find_in_dict(d, [:B :frame :axis :axes], false)[1] !== nothing)\n\tcmd, opt_B, opt_J, opt_R = parse_BJR(d, \"\", \"grdview\", O, \" -JX\" * split(def_fig_size, '/')[1] * \"/0\")\n\t(startswith(opt_J, \" -JX\") && !contains(opt_J, \"/\")) && (cmd = replace(cmd, opt_J => opt_J * \"/0\")) # When sub-regions\n\t(!has_opt_B && isa(arg1, GMTimage) && (isimgsize(arg1) || CTRL.limits[1:4] == zeros(4)) && opt_B == def_fig_axes) &&\n\t\t(cmd = replace(cmd, opt_B => \"\"))\t# Dont plot axes for plain images if that was not required\n\n\tcmd, = parse_common_opts(d, cmd, [:UVXY :c :f :n :p :t :params], first)\n\tcmd  = add_opt(d, cmd, 'S', [:S :smooth])\n\tif ((val = find_in_dict(d, [:N :plane])[1]) !== nothing)\n\t\tcmd *= \" -N\" * parse_arg_and_pen(val, \"+g\", false)\n\tend\n\tcmd = add_opt(d, cmd, 'Q', [:Q :surf :surftype],\n\t\t\t\t  (mesh=(\"m\", add_opt_fill), surface=\"_s\", surf=\"_s\", img=(\"i\",arg2str), image=\"i\", nan_alpha=\"_c\", monochrome=\"_+m\", waterfall=(rows=\"my\", cols=\"mx\", fill=add_opt_fill)))\n\tcmd = add_opt(d, cmd, 'W', [:W :pens :pen], (contour=(\"c\", add_opt_pen),\n\t              mesh=(\"m\", add_opt_pen), facade=(\"f\", add_opt_pen)) )\n\tcmd = add_opt(d, cmd, 'T', [:T :no_interp :tiles], (skip=\"_+s\", skip_nan=\"_+s\", outlines=(\"+o\", add_opt_pen)) )\n\t(!occursin(\" -T\", cmd)) ? cmd = parse_JZ(d, cmd)[1] : del_from_dict(d, [:JZ])\t# Means, even if we had one, ignore silently\n\tcmd = add_opt(d, cmd, \"%\", [:layout :mem_layout], nothing)\n\n\tcmd, got_fname, arg1 = find_data(d, cmd0, cmd, arg1)\t\t# Find how data was transmitted\n\n\t(isa(arg1, Array{<:Real})) && (arg1 = mat2grid(arg1))\n\n\tcmd, N_used, arg1, arg2, arg3 = common_get_R_cpt(d, cmd0, cmd, opt_R, got_fname, arg1, arg2, arg3, \"grdview\")\n\tcmd, arg1, arg2, arg3, arg4 = common_shade(d, cmd, arg1, arg2, arg3, arg4, \"grdview\")\n\tcmd, arg1, arg2, arg3, arg4, arg5 = parse_G_grdview(d, [:G :drape :drapefile], cmd0, cmd, arg1, arg2, arg3, arg4, arg5)\n\n\t_cmd, K = finish_PS_nested(d, [\"grdview \" * cmd], K)\n\tfinish_PS_module(d, _cmd, \"\", K, O, true, arg1, arg2, arg3, arg4, arg5)\nend\n\n# ---------------------------------------------------------------------------------------------------\nfunction parse_G_grdview(d::Dict, symbs::Array{<:Symbol}, cmd0::String, cmd::String, arg1, arg2, arg3, arg4, arg5)\n\t(show_kwargs[1]) && return print_kwarg_opts(symbs, \"GMTgrid | Tuple | String\"), arg1, arg2, arg3, arg4, arg5\n\tif ((val = find_in_dict(d, symbs)[1]) !== nothing)\n\t\tfunction range_it(val)\n\t\t\tcmd, N_used = put_in_slot(cmd, val, 'G', [arg1, arg2, arg3, arg4])\n\t\t\tif     (N_used == 1)  arg1 = val\n\t\t\telseif (N_used == 2)  arg2 = val\n\t\t\telseif (N_used == 3)  arg3 = val\n\t\t\telseif (N_used == 4)  arg4 = val\n\t\t\tend\n\t\t\treturn cmd, arg1, arg2, arg3, arg4\n\t\tend\n\n\t\tif (isa(val, String) || isa(val, GMTimage))\n\t\t\tif (isa(val, String) && guess_T_from_ext(val) != \" -Ti\")\n\t\t\t\tcmd *= \" -G\" * val\n\t\t\telse\n\t\t\t\tif (cmd0 != \"\")\n\t\t\t\t\tprj = (startswith(cmd0, \"@earth_r\")) ? prj4WGS84 : getproj(cmd0, proj4=true)\n\t\t\t\telse\n\t\t\t\t\tprj = (isa(arg1, GMTgrid)) ? getproj(arg1) : \"\"\n\t\t\t\tend\n\t\t\t\tt = split(scan_opt(cmd, \"-R\"), '/')\n\t\t\t\tIname = drape_prepare(d, val, [\"-projwin\", t[1], t[4], t[2], t[3]], prj)\n\t\t\t\tcmd *= \" -G\" * Iname\n\t\t\t\t(!contains(cmd, \" -Qi\")) && (cmd *= \" -Qi300\")\t# Otherwise GMT crashes because grdview goes through the \"MESH\" branch\n\t\t\tend\n\t\telseif (isa(val, GMTgrid))\t\t\t# A single drape grid (arg1-3 may be used already)\n\t\t\tcmd, arg1, arg2, arg3, arg4 = range_it(val)\n\t\telseif (isa(val, Tuple) && length(val) == 3)\n\t\t\tcmd, N_used = put_in_slot(cmd, val[1], 'G', [arg1, arg2, arg3, arg4, arg5])\n\t\t\tcmd *= \" -G -G\"\t\t\t\t\t# Because the above only set one -G and we need 3\n\t\t\tif     (N_used == 1)  arg1 = val[1];\targ2 = val[2];\t\targ3 = val[3]\n\t\t\telseif (N_used == 2)  arg2 = val[1];\targ3 = val[2];\t\targ4 = val[3]\n\t\t\telseif (N_used == 3)  arg3 = val[1];\targ4 = val[2];\t\targ5 = val[3]\n\t\t\tend\n\t\telse\n\t\t\terror(\"Wrong way of setting the drape (G) option.\")\n\t\tend\n\tend\n\treturn cmd, arg1, arg2, arg3, arg4, arg5\nend\n\n# ---------------------------------------------------------------------------------------------------\nfunction drape_prepare(d::Dict, fname, opts::Vector{AbstractString}, prj::String)\n\t# Deal with the option of drapping an image, which can be smaller, larger or with fifferent projection.\n\tprj_img = getproj(fname, proj4=true)\n\t(prj_img == \"\") && return fname\t\t\t# If drape image has no RefSys just return its name and let it all be used\n\n\t(prj == \"\" && find_in_dict(d, [:isgeog])[1] !== nothing) && (prj = prj4WGS84)\n\n\t# Layout is \"TRB\" so all matrices are contrary to Julia order. opts=[-projwin xmin ymax xmax ymin]\n\tG_I = (prj == \"\" || prj == prj_img) ? gdaltranslate(fname, opts) :\n\t                                      gdalwarp(fname, [\"-t_srs\", prj, \"-te\", opts[2], opts[5], opts[4], opts[3]])\n\n\tW = parse(Float64, opts[2]);\tE = parse(Float64, opts[4])\n\tS = parse(Float64, opts[5]);\tN = parse(Float64, opts[3])\n\tWbk, Ebk, Sbk, Nbk = W, E, S, N\n\tdx_W = G_I.range[1] - W;\t\tdx_E = G_I.range[2] - E\n\tdy_S = G_I.range[3] - S;\t\tdy_N = G_I.range[4] - N\n\tpad_W = ceil(Int, abs(dx_W) / G_I.inc[1]);\t\tpad_E = ceil(Int, abs(dx_E) / G_I.inc[1])\n\tpad_S = ceil(Int, abs(dy_S) / G_I.inc[2]);\t\tpad_N = ceil(Int, abs(dy_N) / G_I.inc[2])\n\t# Recompute the WESN such that the increments don't change (original -R was in GRID increment multiples)\n\tW = G_I.range[1] - pad_W * G_I.inc[1];\t\t\tE = G_I.range[2] + pad_E * G_I.inc[1]\n\tS = G_I.range[3] - pad_S * G_I.inc[2];\t\t\tN = G_I.range[4] + pad_N * G_I.inc[2]\n\tif (abs(W - Wbk) > 2*G_I.inc[1] || abs(E - Ebk) > 2*G_I.inc[1] || abs(S - Sbk) > 2*G_I.inc[2] || abs(N - Nbk) > 2*G_I.inc[2])\n\t\timg_is_geo = contains(prj_img, \"longlat\") || contains(prj_img, \"latlong\")\n\t\tmsg = \"Grid and draping image do not carry enough information about their Referencing Systems\"\n\t\t(!img_is_geo) && (msg *= \"\\n\\tDraping image is not in geogs but grid probably is. Please use option `isgeog=true`\")\n\t\terror(msg)\n\tend\n\tif (pad_W > 0 || pad_E > 0 || pad_S > 0 || pad_N > 0)\n\t\timg_new = (size(G_I, 3) == 1) ? fill(UInt8(255), size(G_I,1)+pad_W+pad_E, size(G_I,2)+pad_S+pad_N) :\n\t\t\t\t\t\t\t\t\t\tfill(UInt8(255), size(G_I,1)+pad_W+pad_E, size(G_I,2)+pad_S+pad_N, size(G_I,3))\n\t\tn = 0\n\t\tfor l = 1:size(img_new,3)\n\t\t\t@simd for row = pad_N+1:(size(G_I,2)+pad_N)\n\t\t\t\t@simd for col = pad_W+1:(size(G_I,1)+pad_W)\n\t\t\t\t\t@inbounds img_new[col,row,l] = G_I.image[n += 1]\n\t\t\t\tend\n\t\t\tend\n\t\tend\n\t\tG_I = mat2img(img_new, G_I)\n\t\tG_I.x = linspace(W, E, size(img_new,1)+G_I.registration)\n\t\tG_I.y = linspace(S, N, size(img_new,2)+G_I.registration)\n\t\tG_I.inc = [G_I.x[2]-G_I.x[1], G_I.y[2]-G_I.y[1]]\n\t\tG_I.range[1:4] = [W, E, S, N]\n\tend\n\n\t#def_name = joinpath(tempdir(), \"GMTjl_2grdview.tiff\")\n\tdef_name = \"/vsimem/GMTjl_2grdview.tiff\"\t# I'm amazed that this works\n\tgdalwrite(def_name, G_I)\n\tressurectGDAL()\n\treturn def_name\nend\n\n# ---------------------------------------------------------------------------------------------------\ngrdview!(cmd0::String=\"\", arg1=nothing; first=false, kw...) = grdview(cmd0, arg1; first=first, kw...)\ngrdview(arg1; first=true, kw...) = grdview(\"\", arg1; first=first, kw...)\ngrdview!(arg1; first=false, kw...) = grdview(\"\", arg1; first=first, kw...)\n", "meta": {"hexsha": "ce8941e25242fcdaef676dfb3fd5ae7269e2cbc2", "size": 9710, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grdview.jl", "max_stars_repo_name": "adigitoleo/GMT.jl", "max_stars_repo_head_hexsha": "81cb97ea6916109cb63bd0293984fb922e5f6649", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 137, "max_stars_repo_stars_event_min_datetime": "2017-10-05T23:45:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-16T15:31:15.000Z", "max_issues_repo_path": "src/grdview.jl", "max_issues_repo_name": "adigitoleo/GMT.jl", "max_issues_repo_head_hexsha": "81cb97ea6916109cb63bd0293984fb922e5f6649", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 627, "max_issues_repo_issues_event_min_datetime": "2017-10-09T14:43:36.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:01:16.000Z", "max_forks_repo_path": "src/grdview.jl", "max_forks_repo_name": "adigitoleo/GMT.jl", "max_forks_repo_head_hexsha": "81cb97ea6916109cb63bd0293984fb922e5f6649", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 34, "max_forks_repo_forks_event_min_datetime": "2017-10-10T19:22:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-22T14:59:45.000Z", "avg_line_length": 48.7939698492, "max_line_length": 175, "alphanum_fraction": 0.6001029866, "num_tokens": 3364, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1329642264548166, "lm_q1q2_score": 0.05923948998931089}}
{"text": "\nfunction __init__svhn2()\n    DEPNAME = \"SVHN2\"\n    TRAINDATA = \"train_32x32.mat\"\n    TESTDATA  = \"test_32x32.mat\"\n    EXTRADATA = \"extra_32x32.mat\"\n\n    register(DataDep(\n        DEPNAME,\n        \"\"\"\n        Dataset: The Street View House Numbers (SVHN) Dataset\n        Authors: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng\n        Website: http://ufldl.stanford.edu/housenumbers\n        Format: Cropped Digits (Format 2 on the website)\n        Note: for non-commercial use only\n\n        [Netzer et al., 2011]\n            Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng\n            \"Reading Digits in Natural Images with Unsupervised Feature Learning\"\n            NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011\n\n        The dataset is split up into three subsets: 73257\n        digits for training, 26032 digits for testing, and\n        531131 additional to use as extra training data.\n\n        The files are available for download at the official\n        website linked above. Note that using the data\n        responsibly and respecting copyright remains your\n        responsibility. For example the website mentions that\n        the data is for non-commercial use only. Please read\n        the website to make sure you want to download the\n        dataset.\n        \"\"\",\n        \"http://ufldl.stanford.edu/housenumbers/\" .* [TRAINDATA, TESTDATA, EXTRADATA],\n        \"2fa3b0b79baf39de36ed7579e6947760e6241f4c52b6b406cabc44d654c13a50\"\n    ))\nend\n\n\n\"\"\"\n    SVHN2(; Tx=Float32, split=:train, dir=nothing)\n    SVHN2([Tx, split])\n\nThe Street View House Numbers (SVHN) Dataset.\n\n- Authors: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng\n- Website: http://ufldl.stanford.edu/housenumbers\n\nSVHN was obtained from house numbers in Google Street View\nimages. As such they are quite diverse in terms of orientation\nand image background. Similar to MNIST, SVHN has 10 classes (the\ndigits 0-9), but unlike MNIST there is more data and the images\nare a little bigger (32x32 instead of 28x28) with an additional\nRGB color channel. The dataset is split up into three subsets:\n73257 digits for training, 26032 digits for testing, and 531131\nadditional to use as extra training data.\n\n# Arguments\n\n$ARGUMENTS_SUPERVISED_ARRAY\n- `split`: selects the data partition. Can take the values `:train:`, `:test` or `:extra`. \n\n# Fields\n\n$FIELDS_SUPERVISED_ARRAY\n- `split`.\n\n# Methods\n\n$METHODS_SUPERVISED_ARRAY\n- [`convert2image`](@ref) converts features to `RGB` images.\n\n# Examples\n\n```julia-repl\njulia> using MLDatasets: SVHN2\n\njulia> using MLDatasets: SVHN2\n\njulia> dataset = SVHN2()\nSVHN2:\n  metadata    =>    Dict{String, Any} with 2 entries\n  split       =>    :train\n  features    =>    32\u00d732\u00d73\u00d773257 Array{Float32, 4}\n  targets     =>    73257-element Vector{Int64}\n\njulia> dataset[1:5].targets\n5-element Vector{Int64}:\n 1\n 9\n 2\n 3\n 2\n\njulia> dataset.metadata\nDict{String, Any} with 2 entries:\n  \"n_observations\" => 73257\n  \"class_names\"    => [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"0\"]\n```\n\"\"\"\nstruct SVHN2 <: SupervisedDataset\n    metadata::Dict{String, Any}\n    split::Symbol\n    features::Array{<:Any, 4}\n    targets::Vector{Int}\nend\n\nSVHN2(; split=:train, Tx=Float32, dir=nothing) = SVHN2(Tx, split; dir)\n\nfunction SVHN2(Tx::Type, split::Symbol=:train; dir=nothing)\n    DEPNAME = \"SVHN2\"\n    TRAINDATA = \"train_32x32.mat\"\n    TESTDATA  = \"test_32x32.mat\"\n    EXTRADATA = \"extra_32x32.mat\"\n    CLASSES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]\n    @assert split \u2208 [:train, :test, :extra]\n    if split == :train\n        PATH = TRAINDATA\n    elseif split == :test\n        PATH = TESTDATA\n    else\n        PATH = EXTRADATA\n    end\n\n    path = datafile(DEPNAME, PATH, dir)\n    vars = matread(path)\n    images = vars[\"X\"]::Array{UInt8,4}\n    labels = vars[\"y\"]\n    images = permutedims(images, (2, 1, 3, 4))\n    features = bytes_to_type(Tx, images)\n    targets = Vector{Int}(vec(labels))\n\n    metadata = Dict{String, Any}()\n    metadata[\"n_observations\"] = size(features)[end]\n    metadata[\"class_names\"] = string.(CLASSES)\n\n    return SVHN2(metadata, split, features, targets)\nend\n\nconvert2image(::Type{<:SVHN2}, x) = convert2image(CIFAR10, x)\n\n      \n\n# DEPRECATED INTERFACE, REMOVE IN v0.7 (or 0.6.x)\nfunction Base.getproperty(::Type{SVHN2}, s::Symbol)\n    if s == :traintensor\n        @warn \"SVHN2.traintensor() is deprecated, use `SVHN2(split=:train).features` instead.\" maxlog=2\n        traintensor(T::Type=N0f8; kws...) = traintensor(T, :; kws...)\n        traintensor(i; kws...) = traintensor(N0f8, i; kws...)\n        function traintensor(T::Type, i; dir=nothing)\n            SVHN2(; split=:train, Tx=T, dir)[i][1]\n        end\n        return traintensor\n    elseif s == :testtensor\n        @warn \"SVHN2.testtensor() is deprecated, use `SVHN2(split=:test).features` instead.\"  maxlog=2\n        testtensor(T::Type=N0f8; kws...) = testtensor(T, :; kws...)\n        testtensor(i; kws...) = testtensor(N0f8, i; kws...)\n        function testtensor(T::Type, i; dir=nothing)\n            SVHN2(; split=:test, Tx=T, dir)[i][1]\n        end\n        return testtensor        \n    elseif s == :trainlabels\n        @warn \"SVHN2.trainlabels() is deprecated, use `SVHN2(split=:train).targets` instead.\"  maxlog=2\n        trainlabels(; kws...) = trainlabels(:; kws...)\n        function trainlabels(i; dir=nothing)\n            SVHN2(; split=:train, dir)[i][2]\n        end\n        return trainlabels\n    elseif s == :testlabels\n        @warn \"SVHN2.testlabels() is deprecated, use `SVHN2(split=:test).targets` instead.\" maxlog=2\n        testlabels(; kws...) = testlabels(:; kws...)\n        function testlabels(i; dir=nothing)\n            SVHN2(; split=:test, dir)[i][2]\n        end\n        return testlabels\n    elseif s == :traindata\n        @warn \"SVHN2.traindata() is deprecated, use `SVHN2(split=:train)[]` instead.\" maxlog=2\n        traindata(T::Type=N0f8; kws...) = traindata(T, :; kws...)\n        traindata(i; kws...) = traindata(N0f8, i; kws...)\n        function traindata(T::Type, i; dir=nothing)\n            SVHN2(; split=:train, Tx=T, dir)[i]\n        end\n        return traindata\n    elseif s == :testdata\n        @warn \"SVHN2.testdata() is deprecated, use `SVHN2(split=:test)[]` instead.\"  maxlog=2\n        testdata(T::Type=N0f8; kws...) = testdata(T, :; kws...)\n        testdata(i; kws...) = testdata(N0f8, i; kws...)\n        function testdata(T::Type, i; dir=nothing)\n            SVHN2(; split=:test, Tx=T, dir)[i]\n        end\n        return testdata\n    elseif s == :convert2image\n        @warn \"SVHN2.convert2image(x) is deprecated, use `convert2image(SVHN2, x)` instead\"\n        return x -> convert2image(SVHN2, x)\n    else\n        return getfield(SVHN2, s)\n    end\nend\n", "meta": {"hexsha": "ab5ac002600249832b14a062291c7a5a16fadf21", "size": 6772, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/vision/svhn2.jl", "max_stars_repo_name": "soham-chitnis10/MLDatasets.jl", "max_stars_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/datasets/vision/svhn2.jl", "max_issues_repo_name": "soham-chitnis10/MLDatasets.jl", "max_issues_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datasets/vision/svhn2.jl", "max_forks_repo_name": "soham-chitnis10/MLDatasets.jl", "max_forks_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0301507538, "max_line_length": 103, "alphanum_fraction": 0.6370348494, "num_tokens": 2071, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.12421300673104345, "lm_q1q2_score": 0.05919739140545072}}
{"text": "\nmodule Testing\n\nusing Test, Printf\n\nusing ACEbase.FIO: read_dict, write_dict, save_dict, load_dict\nusing LinearAlgebra: norm\nusing StaticArrays\n\nexport print_tf, test_fio, h0, h1, h2, h3, println_slim \n\n\nfunction h0(str)\n   dashes = \"\u2261\"^(length(str)+4)\n   printstyled(dashes, color=:magenta); println()\n   printstyled(\"  \"*str*\"  \", bold=true, color=:magenta); println()\n   printstyled(dashes, color=:magenta); println()\nend\n\nfunction h1(str)\n   dashes = \"=\"^(length(str)+2)\n   printstyled(dashes, color=:magenta); println()\n   printstyled(\" \" * str * \" \", bold=true, color=:magenta); println()\n   printstyled(dashes, color=:magenta); println()\nend\n\nfunction h2(str)\n   dashes = \"-\"^length(str)\n   printstyled(dashes, color=:magenta); println()\n   printstyled(str, bold=true, color=:magenta); println()\n   printstyled(dashes, color=:magenta); println()\nend\n\nh3(str) = (printstyled(str, bold=true, color=:magenta); println())\n\n\nprint_tf(::Test.Pass) = printstyled(\"+\", bold=true, color=:green)\nprint_tf(::Test.Fail) = printstyled(\"-\", bold=true, color=:red)\nprint_tf(::Tuple{Test.Error,Bool}) = printstyled(\"x\", bold=true, color=:magenta)\n\nprintln_slim(::Test.Pass) = printstyled(\"Test Passed\\n\", bold=true, color=:green)\nprintln_slim(::Test.Fail) = printstyled(\"Test Failed\\n\", bold=true, color=:red)\n\n\"\"\"\n`test_fio(obj): `  performs two tests:\n\n- encodes `obj` as a Dict using `write_dict`, then decodes it using\n`read_dict` and tests whether the two objects are equivalent using `==`\n- writes `Dict` to file then reads it and decodes it and test the result is\nagain equivalent to `obj`\n\nThe two results are returned as Booleans.\n\"\"\"\nfunction test_fio(obj; warntype = true)\n   D = write_dict(obj)\n   test1 = (obj == read_dict(D))\n   if !test1 \n      @warn(\"test_fio (1) fails - read_dict(write_dict(obj)) != obj\")\n   end\n   tmpf = tempname() * \".json\"\n   save_dict(tmpf, D)\n   D2 = load_dict(tmpf)\n   # if D != D2 \n   #    @warn(\"load_dict(save_dict(D)) != D\")\n   # end\n   obj2 = read_dict(load_dict(tmpf))\n   if warntype && (typeof(obj) != typeof(obj2))\n      @warn(\n      \"\"\"test_fio: the loaded object does not have the same type\n             original : $(typeof(obj))\n         deserialised : $(typeof(obj2))\n      \"\"\")\n   end\n   test2 = (obj == obj2)\n   if !test2 \n      @warn(\"test_fio (2) fails - obj2 != obj\")\n   end\n   return test1, test2\nend\n\n_Vec(X::AbstractVector{<: StaticVector{3}}) = \n      collect(reinterpret(Float64, X))\n\n_svecs(x::AbstractVector{T}) where {T} = \n      collect(reinterpret(SVector{3, T}, x))\n\n\nfdtest(F, dF, X::AbstractVector{<: StaticVector{3}}; kwargs...) = \n      fdtest( x -> F(_svecs(x)), \n              x -> _Vec(dF(_svecs(x))), \n              _Vec(X); kwargs... )\n\nfdtest(F, dF, X::Number; kwargs...) = \n      fdtest( x -> F(x[1]), \n              x -> [dF(x[1])], \n              [X]; kwargs... )\n\n\n\"\"\"\nfirst-order finite-difference test for scalar F\n```julia\nfdtest(F, dF, x; h0 = 1.0, verbose=true)\n```\n\"\"\"\nfunction fdtest(F, dF, x::AbstractVector; h0 = 1.0, verbose=true)\n   errors = Float64[]\n   E = F(x)\n   dE = dF(x)\n   # loop through finite-difference step-lengths\n   verbose && @printf(\"---------|----------- \\n\")\n   verbose && @printf(\"    h    | error \\n\")\n   verbose && @printf(\"---------|----------- \\n\")\n   for p = 2:11\n      h = 0.1^p\n      dEh = copy(dE)\n      for n = 1:length(dE)\n         x[n] += h\n         dEh[n] = (F(x) - E) / h\n         x[n] -= h\n      end\n      push!(errors, norm(dE - dEh, Inf))\n      verbose && @printf(\" %1.1e | %4.2e  \\n\", h, errors[end])\n   end\n   verbose && @printf(\"---------|----------- \\n\")\n   if minimum(errors) <= 1e-3 * maximum(errors)\n      verbose && println(\"passed\")\n      return true\n   else\n      @warn(\"\"\"It seems the finite-difference test has failed, which indicates\n      that there is an inconsistency between the function and gradient\n      evaluation. Please double-check this manually / visually. (It is\n      also possible that the function being tested is poorly scaled.)\"\"\")\n      return false\n   end\nend\n\ndirfdtest(F, dF, x, u; kwargs...) =\n      fdtest(t -> F(x + t * u),\n             t -> dF(x + t * u) .* Ref(u),\n             0.0; kwargs...)\n\n\nend\n", "meta": {"hexsha": "9a3c27e5983b3c2fe2064ae445dc467a4b1a6ead", "size": 4162, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/testing.jl", "max_stars_repo_name": "gelzinyte/ACEbase.jl", "max_stars_repo_head_hexsha": "3127b688f41cdd14247f8db5c8e4f531e31dc761", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/testing.jl", "max_issues_repo_name": "gelzinyte/ACEbase.jl", "max_issues_repo_head_hexsha": "3127b688f41cdd14247f8db5c8e4f531e31dc761", "max_issues_repo_licenses": ["RSA-MD"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-09-07T16:33:05.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-07T20:43:35.000Z", "max_forks_repo_path": "src/testing.jl", "max_forks_repo_name": "gelzinyte/ACEbase.jl", "max_forks_repo_head_hexsha": "3127b688f41cdd14247f8db5c8e4f531e31dc761", "max_forks_repo_licenses": ["RSA-MD"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-04-09T16:07:07.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-07T20:33:48.000Z", "avg_line_length": 28.9027777778, "max_line_length": 81, "alphanum_fraction": 0.5973089861, "num_tokens": 1247, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3738758227716966, "lm_q2_score": 0.15817435473259922, "lm_q1q2_score": 0.05913756701703274}}
{"text": "using Pkg\n\nPkg.add(\"PyPlot\")\nPkg.add(\"DelimitedFiles\")\nPkg.add(\"Random\")\nPkg.add(\"StatsBase\")\nPkg.add(\"CSV\")\nPkg.add(\"DataFrames\")\nPkg.add(\"SpecialFunctions\")\nPkg.add(\"StatsFuns\")\nPkg.add(\"Distributions\")", "meta": {"hexsha": "27628a3efa516bf5d179b94c9f6000bc4006af3a", "size": 204, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "demo/dependencies.jl", "max_stars_repo_name": "rodrigcd/PPSeq.jl", "max_stars_repo_head_hexsha": "0212364241163ab5aad5ce49abf94882d5ce654b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "demo/dependencies.jl", "max_issues_repo_name": "rodrigcd/PPSeq.jl", "max_issues_repo_head_hexsha": "0212364241163ab5aad5ce49abf94882d5ce654b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/dependencies.jl", "max_forks_repo_name": "rodrigcd/PPSeq.jl", "max_forks_repo_head_hexsha": "0212364241163ab5aad5ce49abf94882d5ce654b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-05-19T13:47:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-19T13:47:52.000Z", "avg_line_length": 18.5454545455, "max_line_length": 27, "alphanum_fraction": 0.7254901961, "num_tokens": 67, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.11920292828022305, "lm_q1q2_score": 0.059135837174683216}}
{"text": "# InputLayer\n# We adopt the same whitespace convention as in the Mocha code base\nMocha.@defstruct InputLayer Mocha.Layer (\n    data :: AbstractArray = [],\n    name :: AbstractString = \"signal\",\n    (symbols :: Vector{Symbol} = Symbol[], length(symbols) == ndims(data)),\n    tops :: Vector{Symbol} = [:data],\n)\n\nMocha.can_do_bp(::InputLayer) = false\nMocha.has_neuron(::InputLayer) = false\nMocha.has_param(::InputLayer) = false\nMocha.is_source(::InputLayer) = true\n", "meta": {"hexsha": "3015d76fc186b30a920375e3a41646692365d9ae", "size": 463, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/inputlayer.jl", "max_stars_repo_name": "lostanlen/Scattering.jl", "max_stars_repo_head_hexsha": "701cfeabe984d96987641e8269d7fd918be47d0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2015-09-16T20:08:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T05:33:16.000Z", "max_issues_repo_path": "src/inputlayer.jl", "max_issues_repo_name": "lostanlen/Scattering.jl", "max_issues_repo_head_hexsha": "701cfeabe984d96987641e8269d7fd918be47d0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-03-30T14:33:16.000Z", "max_issues_repo_issues_event_max_datetime": "2017-02-17T20:16:34.000Z", "max_forks_repo_path": "src/inputlayer.jl", "max_forks_repo_name": "lostanlen/WaveletScattering.jl", "max_forks_repo_head_hexsha": "701cfeabe984d96987641e8269d7fd918be47d0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0714285714, "max_line_length": 75, "alphanum_fraction": 0.6954643629, "num_tokens": 124, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.11920292045759122, "lm_q1q2_score": 0.05913583329392384}}
{"text": "x = 1\n\nfunction times2()\n  x * 2\nend\n\nprintln(times2())\n\nfor i = 1:1\n  global a = 3\n  println(a)\nend\n\nprintln(a)\nprintln(a += 1)\n\n# Global variables not optimized by Julia compiler - can be a huge performance overhead", "meta": {"hexsha": "06bf2c0427f33c86b85f5a2820a8d19f92a76698", "size": 217, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-programming/8-variable-scope/global-scope.jl", "max_stars_repo_name": "cadamsmith/julia-programming", "max_stars_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia-programming/8-variable-scope/global-scope.jl", "max_issues_repo_name": "cadamsmith/julia-programming", "max_issues_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-programming/8-variable-scope/global-scope.jl", "max_forks_repo_name": "cadamsmith/julia-programming", "max_forks_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.7647058824, "max_line_length": 87, "alphanum_fraction": 0.6774193548, "num_tokens": 66, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3040416749665474, "lm_q2_score": 0.19436781568545952, "lm_q1q2_score": 0.05909591624059627}}
{"text": "#=\nCreated on 20/01/2021 16:45:00\nLast update: -\n\n@author: Michiel Stock\nmichielfmstock@gmail.com\n\nIllustration of the composite and parametric types:\n- composite types\n    - mutable/immuatble\n- unions\n- Parametric types\n=#\n\n#=\nComposite types, sometimes call records, structs or object, can store several values in its *fields*.\n\nWhen defining a new composite type, we can choose them to be mutable or immuatble:\n- mutable types are defined using `mutable struct ... end`, they allow the fields to be changed after the object is created;\n- immuatble types are defined similarly using `mutable struct ... end`, after creating the object its fields cannot be changed.\nMutable types are a bit more flexible, though might be a somewhat less safe and are more difficult to work with. As the compiler\nknows everything in advance, it might better optimize for immutable types. Which one you choose depends on your application, though\ngenerally immuatable types are the better choice!\n\nAs an example, let us define an agent type for an ecological individual-based model (IBM). We create the abstract type `Agent` for which we\ncan then specify several children types.\n=#\n\nabstract type Agent end\n\n#=\nThe concrete types in such an IBM might represent an animal type you want to model, for example preys and predators. Making a concrete type of \na prey animal, we want each to have an unique identifier (represented by an integer) and a position. As we expect the agent to move, hence changing\nits position when our simulation runs, we choose a mutable type.\n=#\n\nmutable struct Prey <: Agent\n    id::Int\n    pos\nend\n# notice the type annotation for `id`, which we choose to always reprsenent by an integer.\n# the position might be represented by (x,y) coordinates, or as a location ID, or a position on a grid. We don't know, so we leave it untyped.\n\n# defining a composite type immediately a constructor available.\ndeer = Prey(1, (0.5, 1.9))\n\n# you can always check which field names are available\nfieldnames(Prey)\n\n# the fields can be accessed simply\ndeer.id, deer.pos\n\n# FYI: This is just syntactic sugar for `getfield`, e.g. `getfield(deer, :id)`\n\n# Similarly, a predator type can be defined. In addition to an id and position, which each agent has, they also have a size, determining its mobility.\n\nmutable struct Predator <: Agent\n    id::Int\n    pos\n    size::Float64\nend\n\nwolf = Predator(2, (0.0, 0.0), 40.0)  # 40 kg wolf\n\n#=\nUsing the `.` syntax for accessing the fields is not very tidy! We should define custom getter functions\nfor the user to access the relevant fields. We could define `id` and `position` methods to get the respective\nfields for the two agents. However, since these fields should be defined for every `Agent` type, we can just create\nthese for the Agent type!\n=#\n\nid(agent::Agent) = agent.id\nposition(agent::Agent) = agent.pos\n\n# Here, we could theoretically have ommited the type annotation in the function. Then the function would accept\n# objects of the non-agent type and likely yield an error because they don't have the `id` or `pos` field. Now,\n# these functions will return a `MethodError` when given a non-`Agent` input.\n\n# A slightly more interesting example is by extending `size`.\n\nBase.size(agent::Predator) = agent.size\n\n# Here, we had to import `size` because we are extending a function from the `Base` library to work with a new type (doing something vastly different\n# than its original function).\n\n# similarly, we can program behaviour between the agents\n\ninteract(agent1::Agent, agent2::Agent) = nothing\ninteract(agent1::Predator, agent2::Prey) = \"eat\"\ninteract(agent1::Prey, agent2::Predator) = \"run\"\n\n# We have chosen the default behaviour that two Agents of unspecified types do not interact at al,\n# this will now be the case when a prey meets other prey, a predator an other predator or a new third type comes into the equation.\n\n# FYI: Since in these simple examples, the `interact` methods do not use their arguments, merely perform type checking, we could have written this as `interact(::Agent,::Agent) = ...` etc.\n\n# PARAMETRIC TYPES\n\n#=\nSometimes we want more flexiblility in defining types. Think of designing a new type of matrix. Here you would like to work them for all\nnumeric datatypes, Int, Int8, Float6, Rational, in addition to new datatypes that might not even be defined yet! To this end, we use\n*parametric types*, types that depend on another type.\n\nFor example, consider a 2-dimensional coordinate:\n=#\n\nstruct Point{T}\n    x::T\n    y::T\nend\n\n# Here, each coordinate of the type `Point` has two attributes, `x` and `y`, of the same type. The specific type of Point can vary.\n\np = Point(1.0, 2.0)\n\nPoint(1, 2)\n\n# note that\n\np isa Point\n\n# But what will happen if you evaluate `Point(1, 2.0)`?\n\nPoint(1, 2.0)\n\n# Parametric types can be used in dispatch. For example, if we want to compute the norm of a Point, this would only make sense if Point is a number.\n\nnorm(p::Point{T} where {T<:Number}) = sqrt(p.x^2 + p.y^2)\n\nnorm(p)\n\n# Constructors\n\n## Outer constructors\n\n# Constructors are functions that create new objects. We have already seen that when creating a new `struc`, this immediately initiates the constructor (e.g., `Point(1.0, 2.0)`). These can also be made explicitly:\n\nPoint(x::T, y::T) where {T<:Real} = Point{T}(x,y)\n\n# Constructors, however, allow us to have custom behavior when initializing types. For example, we have seen that `Point(1, 2.0)` won't work, because the two inputs are of the same type.\n# In this case, we can make the rule that one of the inputs has to be promoted to a more general type.\n\n\nPoint(x::Real, y::Real) = Point(promote(x, y)...)\n\n\nPoint(1, 2.0)\n\n\n# We can write other constructors just like functions. For example, support that when we provide a single `x`, we want to create a point (x, y):\n\nPoint(x) = Point(x, x);\n\nPoint(1)\n\n## Inner constructors\n\n#The above examples show *outer constructors*. These are defined outside the structure. We can also use *inner constructors*, which are declared within the definition of the type. These make use of the keyword `new`. For example, let us define an ordered pair.\n\nstruct OrderedPair\n  x\n  y\n  function OrderedPair(x, y)\n    if x < y\n      new(x, y)\n    else\n      new(y, x)\n    end\n  end\nend\n\nOrderedPair(18, 23)\n\nOrderedPair(8, 2)\n\n# FYI: for parametric types, the `new` keyword should be type annotated. So, for in the `Point` example one would use `new{T}(x,y)`.", "meta": {"hexsha": "65e18e7c2a57151dcee00c67b9062f12b520d203", "size": 6424, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/composite_types.jl", "max_stars_repo_name": "jpgmolina/DS-Julia2925", "max_stars_repo_head_hexsha": "4d96351afb72f4107fa12561a6a460dcd3c617e3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-02-03T14:07:25.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-18T13:27:08.000Z", "max_issues_repo_path": "examples/composite_types.jl", "max_issues_repo_name": "jpgmolina/DS-Julia2925", "max_issues_repo_head_hexsha": "4d96351afb72f4107fa12561a6a460dcd3c617e3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 74, "max_issues_repo_issues_event_min_datetime": "2020-11-23T22:50:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-27T12:49:00.000Z", "max_forks_repo_path": "examples/composite_types.jl", "max_forks_repo_name": "jpgmolina/DS-Julia2925", "max_forks_repo_head_hexsha": "4d96351afb72f4107fa12561a6a460dcd3c617e3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-01-31T14:56:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-19T07:11:30.000Z", "avg_line_length": 36.5, "max_line_length": 260, "alphanum_fraction": 0.7392590286, "num_tokens": 1639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3451052574867685, "lm_q2_score": 0.171061193791558, "lm_q1q2_score": 0.05903411732942963}}
{"text": "export Stack\n\nmutable struct Stack{T}\n    data::Vector{T}\n    Stack{T}(d::T) where T = new(T[d])\nend\n\nfunction Base.show(io::IO, s::Stack)\n    print(io, \"Stack([\")\n    for (i, x) in enumerate(s.data)\n        print(io, \"$(x)\")\n        if i != length(s.data)\n            print(io, \", \")\n        end\n    end\n    print(io, \"])\")\nend\n\nBase.pop!(s::Stack) = deleteat!(s.data, 1)\n\nBase.push!(s::Stack, d) = pushfirst!(s.data, d)\n", "meta": {"hexsha": "ff474bf589668c65675d4c03a78a69a5c9ce4d19", "size": 422, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stack.jl", "max_stars_repo_name": "yuehhua/DataStructure101", "max_stars_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-04-16T17:39:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-05T12:30:13.000Z", "max_issues_repo_path": "src/stack.jl", "max_issues_repo_name": "yuehhua/DataStructure101", "max_issues_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2020-03-02T07:54:58.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-12T06:17:55.000Z", "max_forks_repo_path": "src/stack.jl", "max_forks_repo_name": "yuehhua/DataStructure101", "max_forks_repo_head_hexsha": "64a3de64295c133bb7b7fcf114090c01d49521f3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T09:05:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T09:05:07.000Z", "avg_line_length": 19.1818181818, "max_line_length": 47, "alphanum_fraction": 0.5308056872, "num_tokens": 133, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4263216071250873, "lm_q2_score": 0.13846178879140303, "lm_q1q2_score": 0.05902925232296534}}
{"text": "# constants\nconst Ry2eV = 13.6058         # Ry to eV\nconst ANG2BOHR = 1.889725989  # angstrom to bohr", "meta": {"hexsha": "1d9ef3e8f185ff6ed7c66b7b096a8daa6015f600", "size": 101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "LO_Gaussian/constants.jl", "max_stars_repo_name": "f-fathurrahman/ffr-ElectronicStructure.jl", "max_stars_repo_head_hexsha": "35dca9831bfc6a3e49bb0f3a5872558ffce4b211", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2018-01-03T02:19:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-29T13:30:20.000Z", "max_issues_repo_path": "LO_Gaussian/constants.jl", "max_issues_repo_name": "f-fathurrahman/ffr-ElectronicStructure.jl", "max_issues_repo_head_hexsha": "35dca9831bfc6a3e49bb0f3a5872558ffce4b211", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LO_Gaussian/constants.jl", "max_forks_repo_name": "f-fathurrahman/ffr-ElectronicStructure.jl", "max_forks_repo_head_hexsha": "35dca9831bfc6a3e49bb0f3a5872558ffce4b211", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-03-23T06:58:47.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T00:54:28.000Z", "avg_line_length": 33.6666666667, "max_line_length": 48, "alphanum_fraction": 0.6732673267, "num_tokens": 41, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782349911420193, "lm_q2_score": 0.13477591221002244, "lm_q1q2_score": 0.059008061480100515}}
{"text": "using Printf\n\nfunction exception_examples()\n    println(\"--------------------------------------\")\n    println(\"Exception examples\")\n    \n    println()\n    \n    print(\"Enter a number \")\n    num1 = chomp(readline())\n    print(\"Enter a number \")\n    num2 = chomp(readline())\n\n    try\n        val = (parse(Int32, num1)) / (parse(Int32, num2))\n        if (val == Inf)\n            error(\"Can't divide by zero\")\n        else\n            println(val)\n        end\n    catch e\n        println(e)\n    end    \nend\n\n", "meta": {"hexsha": "c8174e40e3922293b43345e970036947d0027e4d", "size": 503, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/exceptions.jl", "max_stars_repo_name": "James-P-D/JuliaDump", "max_stars_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/exceptions.jl", "max_issues_repo_name": "James-P-D/JuliaDump", "max_issues_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/exceptions.jl", "max_forks_repo_name": "James-P-D/JuliaDump", "max_forks_repo_head_hexsha": "6be0a591aa6372709c1b990b9657a3fa9113591a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.3461538462, "max_line_length": 57, "alphanum_fraction": 0.4831013917, "num_tokens": 117, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4493926344647597, "lm_q2_score": 0.13117323055476698, "lm_q1q2_score": 0.058948283650260044}}
{"text": "using Base.Dates\nusing Base.Test\n\nusing MarketData\n\nusing TimeSeries\n\n\n@testset \"timearray\" begin\n\n\n@testset \"field extraction methods work\" begin\n    @testset \"timestamp, values, colnames and meta\" begin\n        @test typeof(timestamp(cl)) == Array{Date,1}\n        @test typeof(values(cl))    == Array{Float64,1}\n        @test typeof(colnames(cl))  == Array{String,1}\n        @test meta(mdata)           == \"Apple\"\n    end\nend\n\n\n@testset \"type constructors allow views\" begin\n    source_rows = 101:121\n    source_cols = 1:size(AAPL.values)[2]\n    tstamps = view(AAPL.timestamp, source_rows)\n    tvalues = view(AAPL.values, source_rows, source_cols)\n\n    AAPL1 = TimeArray(AAPL.timestamp[source_rows],\n                      AAPL.values[source_rows, source_cols],\n                      AAPL.colnames, AAPL.meta)\n\n    AAPL2 = TimeArray(tstamps, tvalues, AAPL.colnames, AAPL.meta)\n\n    @testset \"match first date\" begin\n        @test AAPL1[1].timestamp == AAPL2[1].timestamp\n    end\n\n    @testset \"match first values\" begin\n        @test AAPL1[1].values == AAPL2[1].values\n    end\n\n    @testset \"match all values\" begin\n        @test AAPL1.values == AAPL2.values\n    end\nend\n\n\n@testset \"type constructors enforce invariants\" begin\n    mangled_stamp = vcat(cl.timestamp[200:end], cl.timestamp[1:199])\n    dupe_stamp    = vcat(cl.timestamp[1:499], cl.timestamp[499])\n    dupe_cnames   = rename(AAPL, [\"a\", \"b\", \"c\", \"a\", \"a\", \"b\", \"d\", \"e\", \"e\", \"e\", \"e\", \"f\"])\n\n    @testset \"unequal length between values and timestamp fails\" begin\n        @test_throws(\n            DimensionMismatch,\n            TimeArray(cl.timestamp, cl.values[2:end], [\"Close\"]))\n    end\n\n    @testset \"unequal length between colnames and array width fails\" begin\n        @test_throws(\n            DimensionMismatch,\n            TimeArray(cl.timestamp, cl.values, [\"Close\", \"Open\"]))\n    end\n\n    @testset \"duplicate timestamp values fails\" begin\n        @test_throws(\n            ArgumentError,\n            TimeArray(dupe_stamp, cl.values, [\"Close\"]))\n    end\n\n    @testset \"mangled order of timestamp values fails\" begin\n        @test_throws(\n            ArgumentError,\n            TimeArray(mangled_stamp, cl.values, [\"Close\"]))\n    end\n\n    @testset \"flipping occurs when needed\" begin\n        @test TimeArray(flipdim(cl.timestamp, 1), flipdim(cl.values, 1),  [\"Close\"]).timestamp[1] == Date(2000,1,3)\n        @test TimeArray(flipdim(cl.timestamp, 1), flipdim(cl.values, 1),  [\"Close\"]).values[1]    == 111.94\n    end\n\n    @testset \"duplicate column names are enumerated by inner constructor\" begin\n        @test dupe_cnames.colnames[1]  == \"a\"\n        @test dupe_cnames.colnames[2]  == \"b\"\n        @test dupe_cnames.colnames[3]  == \"c\"\n        @test dupe_cnames.colnames[4]  == \"a_1\"\n        @test dupe_cnames.colnames[5]  == \"a_2\"\n        @test dupe_cnames.colnames[6]  == \"b_1\"\n        @test dupe_cnames.colnames[7]  == \"d\"\n        @test dupe_cnames.colnames[8]  == \"e\"\n        @test dupe_cnames.colnames[9]  == \"e_1\"\n        @test dupe_cnames.colnames[10] == \"e_2\"\n        @test dupe_cnames.colnames[11] == \"e_3\"\n        @test dupe_cnames.colnames[12] == \"f\"\n    end\nend\n\n\n@testset \"construction without colnames\" begin\n    no_colnames_one   = TimeArray(cl.timestamp, cl.values)\n    no_colnames_multi = TimeArray(AAPL.timestamp, AAPL.values)\n\n    @testset \"default colnames to empty String vector\" begin\n        @test no_colnames_one.colnames   == String[\"\"]\n        @test no_colnames_multi.colnames == String[\"_1\", \"_2\", \"_3\", \"_4\", \"_5\", \"_6\", \"_7\", \"_8\", \"_9\", \"_10\", \"_11\", \"_12\"]\n    end\n\n    @testset \"empty colnames forces meta to nothing\" begin\n        @test no_colnames_one.meta   == nothing\n        @test no_colnames_multi.meta == nothing\n    end\nend\n\n\n@testset \"conversion methods\" begin\n    @testset \"convert works \" begin\n        @test isa(convert(TimeArray{Float64,1}, (cl.>op)), TimeArray{Float64,1})                == true\n        @test isa(convert(TimeArray{Float64,2}, (merge(cl.<op, cl.>op))), TimeArray{Float64,2}) == true\n        @test isa(convert(cl.>op), TimeArray{Float64,1})                                        == true\n        @test isa(convert(merge(cl.<op, cl.>op)), TimeArray{Float64,2})                         == true\n    end\nend\n\n\n@testset \"index by integer works with both 1d and 2d time array\" begin\n    @testset \"1d time array\" begin\n        @test cl[1].timestamp == [Date(2000,1,3)]\n        @test cl[1].values    == [111.94]\n        @test cl[1].colnames  == [\"Close\"]\n        @test cl[1].meta      == \"AAPL\"\n    end\n\n    @testset \"2d time array\" begin\n        @test ohlc[1].timestamp == [Date(2000,1,3)]\n        @test ohlc[1].values    == [104.88 112.5 101.69 111.94]\n        @test ohlc[1].colnames  == [\"Open\", \"High\", \"Low\",\"Close\"]\n        @test ohlc[1].meta      == \"AAPL\"\n    end\nend\n\n\n@testset \"ordered collection methods\" begin\n    @testset \"iterator protocol is valid\" begin\n        @test !isempty(op)\n        @test isempty(op[op .< 0])\n        @test start(op)              == 1\n        @test next(op, 1)            == ((op.timestamp[1], op.values[1,:]), 2)\n        @test done(op, length(op)+1) == true\n    end\n\n    @testset \"end keyword returns correct index\" begin\n        @test ohlc[end].timestamp[1] == ohlc.timestamp[end]\n    end\n\n    @testset \"getindex on single Int and Date\" begin\n        @test ohlc[1].timestamp              == [Date(2000,1,3)]\n        @test ohlc[Date(2000,1,3)].timestamp == [Date(2000,1,3)]\n    end\n\n    @testset \"getindex on array of Int and Date\" begin\n        @test ohlc[[1,10]].timestamp                           == [Date(2000,1,3), Date(2000,1,14)]\n        @test ohlc[[Date(2000,1,3),Date(2000,1,14)]].timestamp == [Date(2000,1,3), Date(2000,1,14)]\n    end\n\n    @testset \"getindex on range of Int and Date\" begin\n        @test ohlc[1:2].timestamp                                  == [Date(2000,1,3), Date(2000,1,4)]\n        @test ohlc[1:2:4].timestamp                                == [Date(2000,1,3), Date(2000,1,5)]\n        @test ohlc[Int8(1):Int8(2):Int8(4)].timestamp              == [Date(2000,1,3), Date(2000,1,5)]\n        @test ohlc[Date(2000,1,3):Day(1):Date(2000,1,4)].timestamp == [Date(2000,1,3), Date(2000,1,4)]\n    end\n\n    @testset \"getindex on range of DateTime when only Date is in timestamp\" begin\n        @test_throws(\n            MethodError,\n            ohlc[DateTime(2000,1,3,0,0,0)])\n        @test_throws(\n            MethodError,\n            ohlc[[DateTime(2000,1,3,0,0,0),DateTime(2000,1,14,0,0,0)]])\n        @test_throws(\n            MethodError,\n            ohlc[DateTime(2000,1,3,0,0,0):Day(1):DateTime(2000,1,4,0,0,0)])\n    end\n\n    @testset \"getindex on range of Date\" begin\n        @test length(cl[Date(2000,1,1):Date(2001,12,31)]) == 500\n    end\n\n    @testset \"getindex on single column name\" begin\n        @test size(ohlc[\"Open\"].values, 2)                                        == 1\n        @test size(ohlc[\"Open\"][Date(2000,1,3):Day(1):Date(2000,1,14)].values, 1) == 10\n    end\n\n    @testset \"getindex on multiple column name\" begin\n        @test ohlc[\"Open\", \"Close\"].values[1]   == 104.88\n        @test ohlc[\"Open\", \"Close\"].values[2]   == 108.25\n        @test ohlc[\"Open\", \"Close\"].values[501] == 111.94\n    end\n\n    @testset \"getindex on 1d returns 1d object\" begin\n        @test isa(cl[1], TimeArray{Float64,1})   == true\n        @test isa(cl[1:2], TimeArray{Float64,1}) == true\n    end\n\n    @testset \"getindex on a 1d Boolean TimeArray returns appropriate rows\" begin\n        @test ohlc[op .> cl][2].values             == ohlc[4].values\n        @test ohlc[op[300:end] .> cl][2].timestamp == ohlc[303].timestamp\n        # MethodError, Bool must be 1D-TimeArray\n        @test_throws MethodError ohlc[merge(op.>cl, op.<cl)]\n    end\nend\n\n\n@testset \"Base.size\" begin\n    @test size(ohlc) == (500, 4)\n    @test size(ohlc, 1) == 500\n    @test size(ohlc, 2) == 4\n\n    @test size(cl) == (500,)\n    @test size(cl, 1) == 500\n    @test size(cl, 2) == 1\nend\n\n\n@testset \"show methods don't throw errors\" begin\n    show(ohlc)\n    show(ohlc[1:4])\n    show(ohlc[1:0])\n    show(lag(cl[1:2], padding=true))\nend\n\n\nend  # @testset \"timearray\"\n", "meta": {"hexsha": "eb674c6db36c8105539f4256395cae1c775c1bfd", "size": 8120, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/timearray.jl", "max_stars_repo_name": "BenjaminBorn/TimeSeries.jl", "max_stars_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/timearray.jl", "max_issues_repo_name": "BenjaminBorn/TimeSeries.jl", "max_issues_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/timearray.jl", "max_forks_repo_name": "BenjaminBorn/TimeSeries.jl", "max_forks_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-16T22:57:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-16T22:57:15.000Z", "avg_line_length": 34.8497854077, "max_line_length": 125, "alphanum_fraction": 0.5814039409, "num_tokens": 2565, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.13117322715829127, "lm_q1q2_score": 0.058948282123908874}}
{"text": "module Benchmarks\n\nusing BenchmarkTools, MathOptInterface\n\nconst MOI = MathOptInterface\nconst BENCHMARKS = Dict{String,Function}()\n\n\"\"\"\n    suite(\n        new_model::Function;\n        exclude::Vector{Regex} = Regex[]\n    )\n\nCreate a suite of benchmarks. `new_model` should be a function that takes no\narguments, and returns a new instance of the optimizer you wish to benchmark.\n\nUse `exclude` to exclude a subset of benchmarks.\n\n### Examples\n\n```julia\nsuite() do\n    GLPK.Optimizer()\nend\nsuite(exclude = [r\"delete\"]) do\n    Gurobi.Optimizer(OutputFlag=0)\nend\n```\n\"\"\"\nfunction suite(new_model::Function; exclude::Vector{Regex} = Regex[])\n    group = BenchmarkGroup()\n    for (name, func) in BENCHMARKS\n        any(occursin.(exclude, Ref(name))) && continue\n        group[name] = @benchmarkable $func($new_model)\n    end\n    return group\nend\n\n\"\"\"\n    create_baseline(suite, name::String; directory::String = \"\"; kwargs...)\n\nRun all benchmarks in `suite` and save to files called `name` in `directory`.\n\nExtra `kwargs` are based to `BenchmarkTools.run`.\n\n### Examples\n\n```julia\nmy_suite = suite(() -> GLPK.Optimizer())\ncreate_baseline(my_suite, \"glpk_master\"; directory = \"/tmp\", verbose = true)\n```\n\"\"\"\nfunction create_baseline(\n    suite::BenchmarkTools.BenchmarkGroup,\n    name::String;\n    directory::String = \"\",\n    kwargs...,\n)\n    tune!(suite)\n    BenchmarkTools.save(\n        joinpath(directory, name * \"_params.json\"),\n        params(suite),\n    )\n    results = run(suite; kwargs...)\n    BenchmarkTools.save(joinpath(directory, name * \"_baseline.json\"), results)\n    return\nend\n\n\"\"\"\n    compare_against_baseline(\n        suite, name::String; directory::String = \"\",\n        report_filename::String = \"report.txt\"\n    )\n\nRun all benchmarks in `suite` and compare against files called `name` in\n`directory` that were created by a call to `create_baseline`.\n\nA report summarizing the comparison is written to `report_filename` in\n`directory`.\n\nExtra `kwargs` are based to `BenchmarkTools.run`.\n\n### Examples\n\n```julia\nmy_suite = suite(() -> GLPK.Optimizer())\ncompare_against_baseline(\n    my_suite, \"glpk_master\"; directory = \"/tmp\", verbose = true\n)\n```\n\"\"\"\nfunction compare_against_baseline(\n    suite::BenchmarkTools.BenchmarkGroup,\n    name::String;\n    directory::String = \"\",\n    report_filename::String = \"report.txt\",\n    kwargs...,\n)\n    params_filename = joinpath(directory, name * \"_params.json\")\n    baseline_filename = joinpath(directory, name * \"_baseline.json\")\n    if !isfile(params_filename) || !isfile(baseline_filename)\n        error(\"You create a baseline with `create_baseline` first.\")\n    end\n    loadparams!(\n        suite,\n        BenchmarkTools.load(params_filename)[1],\n        :evals,\n        :samples,\n    )\n    new_results = run(suite; kwargs...)\n    old_results = BenchmarkTools.load(baseline_filename)[1]\n    open(joinpath(directory, report_filename), \"w\") do io\n        println(stdout, \"\\n========== Results ==========\")\n        println(io, \"\\n========== Results ==========\")\n        for key in keys(new_results)\n            judgement = judge(\n                BenchmarkTools.median(new_results[key]),\n                BenchmarkTools.median(old_results[key]),\n            )\n            println(stdout, \"\\n\", key)\n            println(io, \"\\n\", key)\n            show(stdout, MIME\"text/plain\"(), judgement)\n            show(io, MIME\"text/plain\"(), judgement)\n        end\n    end\n    return\nend\n\n###\n### Benchmarks\n###\n\nmacro add_benchmark(f)\n    name = f.args[1].args[1]\n    return quote\n        $(esc(f))\n        BENCHMARKS[String($(Base.Meta.quot(name)))] = $(esc(name))\n    end\nend\n\n@add_benchmark function add_variable(new_model)\n    model = new_model()\n    for i in 1:10_000\n        MOI.add_variable(model)\n    end\n    return model\nend\n\n@add_benchmark function add_variables(new_model)\n    model = new_model()\n    MOI.add_variables(model, 10_000)\n    return model\nend\n\n@add_benchmark function add_variable_constraint(new_model)\n    model = new_model()\n    x = MOI.add_variables(model, 10_000)\n    for (i, xi) in enumerate(x)\n        MOI.add_constraint(model, MOI.SingleVariable(xi), MOI.LessThan(1.0 * i))\n    end\n    return model\nend\n\n@add_benchmark function add_variable_constraints(new_model)\n    model = new_model()\n    x = MOI.add_variables(model, 10_000)\n    MOI.add_constraints(\n        model,\n        MOI.SingleVariable.(x),\n        MOI.LessThan.(1.0:10_000.0),\n    )\n    return model\nend\n\n@add_benchmark function delete_variable(new_model)\n    model = new_model()\n    x = MOI.add_variables(model, 1_000)\n    MOI.add_constraint.(model, MOI.SingleVariable.(x), Ref(MOI.LessThan(1.0)))\n    MOI.delete.(model, x)\n    return model\nend\n\n@add_benchmark function delete_variable_constraint(new_model)\n    model = new_model()\n    x = MOI.add_variables(model, 1_000)\n    cons =\n        MOI.add_constraint.(\n            model,\n            MOI.SingleVariable.(x),\n            Ref(MOI.LessThan(1.0)),\n        )\n    for con in cons\n        MOI.delete(model, con)\n    end\n    cons =\n        MOI.add_constraint.(\n            model,\n            MOI.SingleVariable.(x),\n            Ref(MOI.LessThan(1.0)),\n        )\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.0),\n    )\n    MOI.optimize!(model)\n    for con in cons\n        MOI.delete(model, con)\n    end\n    return model\nend\n\n@add_benchmark function add_constraint(new_model)\n    model = new_model()\n    index = MOI.add_variables(model, 10_000)\n    for (i, x) in enumerate(index)\n        MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0),\n            MOI.LessThan(1.0 * i),\n        )\n    end\n    return model\nend\n\n@add_benchmark function add_constraints(new_model)\n    model = new_model()\n    x = MOI.add_variables(model, 10_000)\n    MOI.add_constraints(\n        model,\n        [\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, xi)], 0.0) for\n            xi in x\n        ],\n        MOI.LessThan.(1:1.0:10_000),\n    )\n    return model\nend\n\n@add_benchmark function delete_constraint(new_model)\n    model = new_model()\n    index = MOI.add_variables(model, 1_000)\n    cons = Vector{\n        MOI.ConstraintIndex{\n            MOI.ScalarAffineFunction{Float64},\n            MOI.LessThan{Float64},\n        },\n    }(\n        undef,\n        1_000,\n    )\n    for (i, x) in enumerate(index)\n        cons[i] = MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0),\n            MOI.LessThan(1.0 * i),\n        )\n    end\n    for con in cons\n        MOI.delete(model, con)\n    end\n    return model\nend\n\n@add_benchmark function copy_model(new_model)\n    model = new_model()\n    index = MOI.add_variables(model, 1_000)\n    cons = Vector{\n        MOI.ConstraintIndex{\n            MOI.ScalarAffineFunction{Float64},\n            MOI.LessThan{Float64},\n        },\n    }(\n        undef,\n        1_000,\n    )\n    for (i, x) in enumerate(index)\n        cons[i] = MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0),\n            MOI.LessThan(1.0 * i),\n        )\n    end\n\n    model2 = new_model()\n    MOI.copy_to(model2, model)\n    # MOI.copy_to(model2, model, filter_constraints=(x) -> x in cons[1:500])\n\n    return model2\nend\n\nend\n", "meta": {"hexsha": "bb2e7eb178a8363c41582bec5c65c9634323ca19", "size": 7457, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Benchmarks/Benchmarks.jl", "max_stars_repo_name": "guimarqu/MathOptInterface.jl", "max_stars_repo_head_hexsha": "cc1814d8d02d6708a307e4e12abcf81f379d2373", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Benchmarks/Benchmarks.jl", "max_issues_repo_name": "guimarqu/MathOptInterface.jl", "max_issues_repo_head_hexsha": "cc1814d8d02d6708a307e4e12abcf81f379d2373", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Benchmarks/Benchmarks.jl", "max_forks_repo_name": "guimarqu/MathOptInterface.jl", "max_forks_repo_head_hexsha": "cc1814d8d02d6708a307e4e12abcf81f379d2373", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.3639455782, "max_line_length": 80, "alphanum_fraction": 0.6223682446, "num_tokens": 1953, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3106943959796865, "lm_q2_score": 0.18952109819626, "lm_q1q2_score": 0.05888314312949386}}
{"text": "\"\"\"\nRun a coarse thermal bubble simulation and save the output to NetCDF at the\n10th time step. Then read back the output and test that it matches the model's\nstate.\n\"\"\"\nfunction run_thermal_bubble_netcdf_tests(arch)\n    Nx, Ny, Nz = 16, 16, 16\n    Lx, Ly, Lz = 100, 100, 100\n    \u0394t = 6\n\n    grid = RegularCartesianGrid(size=(Nx, Ny, Nz), length=(Lx, Ly, Lz))\n    closure = ConstantIsotropicDiffusivity(\u03bd=4e-2, \u03ba=4e-2)\n    model = Model(architecture=arch, grid=grid, closure=closure)\n\n    # Add a cube-shaped warm temperature anomaly that takes up the middle 50%\n    # of the domain volume.\n    i1, i2 = round(Int, Nx/4), round(Int, 3Nx/4)\n    j1, j2 = round(Int, Ny/4), round(Int, 3Ny/4)\n    k1, k2 = round(Int, Nz/4), round(Int, 3Nz/4)\n    model.tracers.T.data[i1:i2, j1:j2, k1:k2] .+= 0.01\n\n    outputs = Dict(\"v\"=>model.velocities.v,\n                   \"u\"=>model.velocities.u,\n                   \"w\"=>model.velocities.w,\n                   \"T\"=>model.tracers.T,\n                   \"S\"=>model.tracers.S)\n    nc_writer = NetCDFOutputWriter(model, outputs,\n                                   filename=\"dumptest.nc\",\n                                   frequency=10)\n    push!(model.output_writers, nc_writer)\n\n    xC_slice = 1:10\n    xF_slice = 2:11\n    yC_slice = 10:15\n    yF_slice = 1\n    zC_slice = 10\n    zF_slice = 9:11\n    nc_sliced_writer = NetCDFOutputWriter(model, outputs,\n                                          filename=\"dumptestsliced.nc\",\n                                          frequency=10,\n                                          xC=xC_slice, xF=xF_slice, yC=yC_slice,\n                                          yF=yF_slice, zC=zC_slice, zF=zF_slice)\n    push!(model.output_writers, nc_sliced_writer)\n\n    time_step!(model, 10, \u0394t)\n\n    close(nc_writer)\n    close(nc_sliced_writer)\n\n    u = read_output(nc_writer, \"u\")\n    v = read_output(nc_writer, \"v\")\n    w = read_output(nc_writer, \"w\")\n    T = read_output(nc_writer, \"T\")\n    S = read_output(nc_writer, \"S\")\n  \n    @test all(u .\u2248 Array(interiorparent(model.velocities.u)))\n    @test all(v .\u2248 Array(interiorparent(model.velocities.v)))\n    @test all(w .\u2248 Array(interiorparent(model.velocities.w)))\n    @test all(T .\u2248 Array(interiorparent(model.tracers.T)))\n    @test all(S .\u2248 Array(interiorparent(model.tracers.S)))\n\n    u_sliced = read_output(nc_sliced_writer, \"u\")\n    v_sliced = read_output(nc_sliced_writer, \"v\")\n    w_sliced = read_output(nc_sliced_writer, \"w\")\n    T_sliced = read_output(nc_sliced_writer, \"T\")\n    S_sliced = read_output(nc_sliced_writer, \"S\")\n\n    @test all(u_sliced .\u2248 Array(interiorparent(model.velocities.u))[xF_slice, yC_slice, zC_slice])\n    @test all(v_sliced .\u2248 Array(interiorparent(model.velocities.v))[xC_slice, yF_slice, zC_slice])\n    @test all(w_sliced .\u2248 Array(interiorparent(model.velocities.w))[xC_slice, yC_slice, zF_slice])\n    @test all(T_sliced .\u2248 Array(interiorparent(model.tracers.T))[xC_slice, yC_slice, zC_slice])\n    @test all(S_sliced .\u2248 Array(interiorparent(model.tracers.S))[xC_slice, yC_slice, zC_slice])\nend\n\nfunction run_jld2_file_splitting_tests(arch)\n    model = Model(grid=RegularCartesianGrid(size=(16, 16, 16), length=(1, 1, 1)))\n\n    u(model) = Array(model.velocities.u.data.parent)\n    fields = Dict(:u => u)\n\n    function fake_bc_init(file, model)\n        file[\"boundary_conditions/fake\"] = \u03c0\n    end\n\n    ow = JLD2OutputWriter(model, fields; dir=\".\", prefix=\"test\", frequency=1,\n                          init=fake_bc_init, including=[:grid],\n                          max_filesize=200KiB, force=true)\n    push!(model.output_writers, ow)\n\n    # 531 KiB of output will be written which should get split into 3 files.\n    time_step!(model, 10, 1)\n\n    # Test that files has been split according to size as expected.\n    @test filesize(\"test_part1.jld2\") > 200KiB\n    @test filesize(\"test_part2.jld2\") > 200KiB\n    @test filesize(\"test_part3.jld2\") < 200KiB\n\n    for n in string.(1:3)\n        filename = \"test_part\" * n * \".jld2\"\n        jldopen(filename, \"r\") do file\n            # Test to make sure all files contain structs from `including`.\n            @test file[\"grid/Nx\"] == 16\n\n            # Test to make sure all files contain info from `init` function.\n            @test file[\"boundary_conditions/fake\"] == \u03c0\n        end\n\n        # Leave test directory clean.\n        rm(filename)\n    end\nend\n\n\"\"\"\nRun two coarse rising thermal bubble simulations and make sure that when\nrestarting from a checkpoint, the restarted simulation matches the non-restarted\nsimulation numerically.\n\"\"\"\nfunction run_thermal_bubble_checkpointer_tests(arch)\n    Nx, Ny, Nz = 16, 16, 16\n    Lx, Ly, Lz = 100, 100, 100\n    \u0394t = 6\n\n    grid = RegularCartesianGrid(size=(Nx, Ny, Nz), length=(Lx, Ly, Lz))\n    closure = ConstantIsotropicDiffusivity(\u03bd=4e-2, \u03ba=4e-2)\n    true_model = Model(architecture=arch, grid=grid, closure=closure)\n\n    # Add a cube-shaped warm temperature anomaly that takes up the middle 50%\n    # of the domain volume.\n    i1, i2 = round(Int, Nx/4), round(Int, 3Nx/4)\n    j1, j2 = round(Int, Ny/4), round(Int, 3Ny/4)\n    k1, k2 = round(Int, Nz/4), round(Int, 3Nz/4)\n    true_model.tracers.T.data[i1:i2, j1:j2, k1:k2] .+= 0.01\n\n    checkpointed_model = deepcopy(true_model)\n\n    time_step!(true_model, 9, \u0394t)\n\n    checkpointer = Checkpointer(checkpointed_model; frequency=5, force=true)\n    push!(checkpointed_model.output_writers, checkpointer)\n\n    # Checkpoint should be saved as \"checkpoint5.jld\" after the 5th iteration.\n    time_step!(checkpointed_model, 5, \u0394t)\n\n    # Remove all knowledge of the checkpointed model.\n    checkpointed_model = nothing\n\n    restored_model = restore_from_checkpoint(\"checkpoint5.jld2\")\n\n    time_step!(restored_model, 4, \u0394t; init_with_euler=false)\n\n    rm(\"checkpoint0.jld2\", force=true)\n    rm(\"checkpoint5.jld2\", force=true)\n\n    # Now the true_model and restored_model should be identical.\n    @test all(restored_model.velocities.u.data      .\u2248 true_model.velocities.u.data)\n    @test all(restored_model.velocities.v.data      .\u2248 true_model.velocities.v.data)\n    @test all(restored_model.velocities.w.data      .\u2248 true_model.velocities.w.data)\n    @test all(restored_model.tracers.T.data         .\u2248 true_model.tracers.T.data)\n    @test all(restored_model.tracers.S.data         .\u2248 true_model.tracers.S.data)\n    @test all(restored_model.timestepper.G\u207f.u.data .\u2248 true_model.timestepper.G\u207f.u.data)\n    @test all(restored_model.timestepper.G\u207f.v.data .\u2248 true_model.timestepper.G\u207f.v.data)\n    @test all(restored_model.timestepper.G\u207f.w.data .\u2248 true_model.timestepper.G\u207f.w.data)\n    @test all(restored_model.timestepper.G\u207f.T.data .\u2248 true_model.timestepper.G\u207f.T.data)\n    @test all(restored_model.timestepper.G\u207f.S.data .\u2248 true_model.timestepper.G\u207f.S.data)\nend\n\n@testset \"Output writers\" begin\n    println(\"Testing output writers...\")\n\n    for arch in archs\n         @testset \"NetCDF [$(typeof(arch))]\" begin\n             println(\"  Testing NetCDF output writer [$(typeof(arch))]...\")\n             run_thermal_bubble_netcdf_tests(arch)\n         end\n\n        @testset \"JLD2 [$(typeof(arch))]\" begin\n            println(\"  Testing JLD2 output writer [$(typeof(arch))]...\")\n            run_jld2_file_splitting_tests(arch)\n        end\n\n        @testset \"Checkpointer [$(typeof(arch))]\" begin\n            println(\"  Testing Checkpointer [$(typeof(arch))]...\")\n            run_thermal_bubble_checkpointer_tests(arch)\n        end\n    end\nend\n", "meta": {"hexsha": "f962578180514080f3cfc21a27640dec2aacd344", "size": 7406, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_output_writers.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Oceananigans.jl-9e8cae18-63c1-5223-a75c-80ca9d6e9a09", "max_stars_repo_head_hexsha": "d753151ca11649756cd6452faf443306002cac79", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/test_output_writers.jl", "max_issues_repo_name": "UnofficialJuliaMirror/Oceananigans.jl-9e8cae18-63c1-5223-a75c-80ca9d6e9a09", "max_issues_repo_head_hexsha": "d753151ca11649756cd6452faf443306002cac79", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_output_writers.jl", "max_forks_repo_name": "UnofficialJuliaMirror/Oceananigans.jl-9e8cae18-63c1-5223-a75c-80ca9d6e9a09", "max_forks_repo_head_hexsha": "d753151ca11649756cd6452faf443306002cac79", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.6042780749, "max_line_length": 98, "alphanum_fraction": 0.6473129895, "num_tokens": 2139, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.1225232237324595, "lm_q1q2_score": 0.05886979656914009}}
{"text": "function insert_element(values)\n  push!(values, -10)\nend\n\nlist = [1,2,3]\ninsert_element(list)\nprintln(list)\n\nf(x,y) = x^3 + x*y - y\ng(a, b = 5) = a + b\nk(x; a1=1, a2=2) = x * (a1+a2)\n\nprintln(f(3,2))\n\nprintln(\"g(3) + f(1,2) = $( g(3) + f(1,2) )\")\nprintln(\"k(2) = $(k(2))\")\n\ncubes = map(x->x^3, [1,2,3,5])\nprintln(\"$cubes\")\n\nprintln(\"$([1:3])\")\n\na = split(\"A,B,C,D\",\",\")\nprintln(\"typeof(a): $(typeof(a))\")\nprintln(\"a: $(a)\")\n\nstr::String = \"This is a string\"\nprintln(str)\n", "meta": {"hexsha": "e957baa8b33b8920e0411caacefa4c11b568c5ba", "size": 471, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ch3.jl", "max_stars_repo_name": "ykyang/org.allnix.julia", "max_stars_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ch3.jl", "max_issues_repo_name": "ykyang/org.allnix.julia", "max_issues_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ch3.jl", "max_forks_repo_name": "ykyang/org.allnix.julia", "max_forks_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.2413793103, "max_line_length": 45, "alphanum_fraction": 0.5350318471, "num_tokens": 201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.12252320931407355, "lm_q1q2_score": 0.05886978964141307}}
{"text": "\"\"\"\n    ispangram(input)\n\nReturn `true` if `input` contains every alphabetic character (case insensitive).\n\n\"\"\"\nfunction ispangram(input)\n    input_low = lowercase(input)\n    lower_alpha = 'a':'z'\n\n    return all(\n        [(c in input_low) for c in lower_alpha])    \n\nend\n\n", "meta": {"hexsha": "fe6ac098d55b9f2426a1da1f2dfadc4a9745b65a", "size": 273, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/pangram/pangram.jl", "max_stars_repo_name": "jfcliang/jl-exercism-solutions", "max_stars_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/pangram/pangram.jl", "max_issues_repo_name": "jfcliang/jl-exercism-solutions", "max_issues_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/pangram/pangram.jl", "max_forks_repo_name": "jfcliang/jl-exercism-solutions", "max_forks_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.0625, "max_line_length": 80, "alphanum_fraction": 0.6483516484, "num_tokens": 67, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.12252320450794524, "lm_q1q2_score": 0.05886978733217089}}
{"text": "function sum_values(x,y)\n    return x+y\nend", "meta": {"hexsha": "9fb6c616a38e25b77ab16d1ce5082fe211dbe830", "size": 43, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/simple_function.jl", "max_stars_repo_name": "jeff788/JuliaPackageTest", "max_stars_repo_head_hexsha": "bc3e40fa840343fdf9d11239e5e701d4c9bec0b3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/simple_function.jl", "max_issues_repo_name": "jeff788/JuliaPackageTest", "max_issues_repo_head_hexsha": "bc3e40fa840343fdf9d11239e5e701d4c9bec0b3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/simple_function.jl", "max_forks_repo_name": "jeff788/JuliaPackageTest", "max_forks_repo_head_hexsha": "bc3e40fa840343fdf9d11239e5e701d4c9bec0b3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.3333333333, "max_line_length": 24, "alphanum_fraction": 0.7209302326, "num_tokens": 12, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1175721304535499, "lm_q1q2_score": 0.05878606522677495}}
{"text": "#==============================================================================\n    Generate DataFrame of Column Names and Type from a DataFrame\n\n    author:     mahiki@users.noreply.github.com\n=============================================================================#\n\nusing DataFrames, Dates\n\n\"\"\"\n    printschema(df::DataFrame)\n\nReturn a DataFrame of ordered columns and column type for input dataFrame df\n\n# Examples\n```jldoctest\njulia> df = DataFrame(a = 1:2, b = [1.0, \u03c0], c = [Date(\"20191002\", \"yyyymmdd\"), Date(\"21120101\", \"yyyymmdd\")], d = [1//2, missing]);\njulia> printschema(df)\n4\u00d72 DataFrame\n Row \u2502 variable  value\n     \u2502 String    Type\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 a         Int64\n   2 \u2502 b         Float64\n   3 \u2502 c         Date\n   4 \u2502 d         Union{Missing, Rational{Int64}}\n```\n\"\"\"\nfunction printschema(df::DataFrame)\n    dfcols = mapcols(eltype, df)\n    dfcolnames = names(dfcols)\n    stack(dfcols, dfcolnames)\nend\n\n\n", "meta": {"hexsha": "9c85af7ca5c1705ff0d38d793f2940f78f5e0cb1", "size": 965, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/printschema.jl", "max_stars_repo_name": "mahiki/DesertIslandDisk", "max_stars_repo_head_hexsha": "f8e3e31c0123322807863749cdd2bb5198adb889", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-07T04:15:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-07T04:15:35.000Z", "max_issues_repo_path": "src/printschema.jl", "max_issues_repo_name": "mahiki/DesertIslandDisk", "max_issues_repo_head_hexsha": "f8e3e31c0123322807863749cdd2bb5198adb889", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-10-05T01:10:46.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-20T08:04:06.000Z", "max_forks_repo_path": "src/printschema.jl", "max_forks_repo_name": "mahiki/DesertIslandDisk", "max_forks_repo_head_hexsha": "f8e3e31c0123322807863749cdd2bb5198adb889", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5714285714, "max_line_length": 132, "alphanum_fraction": 0.4984455959, "num_tokens": 244, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207956, "lm_q2_score": 0.11920292515117027, "lm_q1q2_score": 0.058670265502620035}}
{"text": "\"Your optional docstring here\"\nfunction distance(a, b)\n    if length(a) != length(b)\n        throw((a, b), ArgumentError(\"Unequal strand lengths!\"))\n    end\n    hamming_distance = 0\n\n    for i in 1:length(a)\n        if a[i] != b[i]\n            hamming_distance += 1\n        end\n    end\n\n    return hamming_distance\nend\n", "meta": {"hexsha": "e1946059c608682072d365c033b16f953075b1de", "size": 319, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/hamming/hamming.jl", "max_stars_repo_name": "jfcliang/jl-exercism-solutions", "max_stars_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/hamming/hamming.jl", "max_issues_repo_name": "jfcliang/jl-exercism-solutions", "max_issues_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/hamming/hamming.jl", "max_forks_repo_name": "jfcliang/jl-exercism-solutions", "max_forks_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.9375, "max_line_length": 63, "alphanum_fraction": 0.5862068966, "num_tokens": 88, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11920291107043361, "lm_q1q2_score": 0.0586702585722485}}
{"text": "module GnuplotSimple\n\nusing Gnuplot\nimport Gnuplot: PlotElement, DatasetText\n\nexport plot\n\n\n\n\n\"\"\"\n`plot(x,Y; kwargs...)`\n\nA simple plot recipe for `Gnuplot.jl`.\n\nRequired: vector-like `x` and vector or matrix-like `Y`.\n\nAll keyword arguments are optional:\n\n`title`, `xlab`, `ylab`, `key` should be strings.\n\n`grid`, `scatter`, `xlog`, `ylog`, `mono` are boolean.\n\n`xr` and `yr` are like `[0,1]`. Use `NaN` for unbounded.\n\n`labels`, `linecolor`, `linetype` should be vectors of strings (at least as many as columns in `Y`).\n\n`linewidth` should be a vector of numbers.\n\n\n\n\n```julia\nusing Gnuplot, GnuplotSimple\nx = 0:0.05:2\ny = x.^(1:3)'\n@gp plot(x,y; title=\"My Title\",xlab=\"My x\",ylab=\"My y\",xr=[0,1],labels=[\"y1\",\"y2\",\"y3\"])\n```\n\"\"\"\nfunction plot(x,Y; grid::Bool=true, scatter::Bool=false, title=nothing, xlab=nothing, ylab=nothing, key=nothing, labels=nothing, xr=[NaN,NaN], yr=[NaN,NaN],\n        linetype=nothing, linewidth=nothing, linecolor=nothing, xlog::Bool=false, ylog::Bool=false, mono::Bool=false)\n    # assertions and input processing\n    @assert !isempty(x)\n    @assert !isempty(Y)\n    Lx = length(x)\n    Ly, Ny = size(Y)\n    dimsx = length(size(x))\n    dimsY = length(size(Y))\n    @assert dimsx == 1 \"x must be 1D\"\n    @assert dimsY <= 2 \"Y must be 1D or 2D\"\n    @assert Lx == Ly \"x and Y (or its columns) must have equal number of observations\"\n    \n    ptype = ifelse(scatter,\"p\",\"l\")\n    \n    # plot setup\n    out = Vector{Gnuplot.PlotElement}()\n    grid && push!(out,PlotElement(cmds=[\"set grid\"]))\n    mono && push!(out,PlotElement(cmds=[\"set monochrome\"]))\n    !isnothing(title) && push!(out, PlotElement(;tit=title))\n    !isnothing(xlab) && push!(out, PlotElement(;xlabel=xlab))\n    !isnothing(ylab) && push!(out, PlotElement(;ylabel=ylab))\n    !isnothing(key) && push!(out, PlotElement(;key=key))\n    !isnothing(xr) && push!(out, PlotElement(;xrange=xr))\n    !isnothing(yr) && push!(out, PlotElement(;yrange=yr))\n    push!(out, PlotElement(;xlog=xlog,ylog=ylog))\n\n    # do plotting\n    for i in 1:Ny\n        plotstr = \"w $ptype\"\n        if !isnothing(labels)\n            if length(labels) >= i\n                thislab = labels[i]\n                plotstr = string(plotstr,\" t '$thislab'\")\n            end\n        end\n        if !isnothing(linetype)\n            if length(linetype) >= i\n                thislt = linetype[i]\n                plotstr = string(plotstr,\" $thislt\")\n            end\n        end\n        if !isnothing(linewidth)\n            if length(linewidth) >= i\n                thislw = linewidth[i]\n                plotstr = string(plotstr,\" lw $thislw\")\n            end\n        end\n        if !isnothing(linecolor)\n            if length(linecolor) >= i\n                thiscolor = linecolor[i]\n                plotstr = string(plotstr,\" lc rgb '$thiscolor'\")\n            end\n        end\n        \n        push!(out, PlotElement(data=DatasetText(x, Y[:,i]), plot=plotstr))\n    end\n    return out\nend\n\n\n\n\nend # module\n", "meta": {"hexsha": "b4e7a8944da099ab93c20e75d9d4602fbbe9ac69", "size": 2950, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GnuplotSimple.jl", "max_stars_repo_name": "tbeason/GnuplotSimple.jl", "max_stars_repo_head_hexsha": "a9a2134de21b0ea5754c95620cd8d6b0ecf7b96c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/GnuplotSimple.jl", "max_issues_repo_name": "tbeason/GnuplotSimple.jl", "max_issues_repo_head_hexsha": "a9a2134de21b0ea5754c95620cd8d6b0ecf7b96c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/GnuplotSimple.jl", "max_forks_repo_name": "tbeason/GnuplotSimple.jl", "max_forks_repo_head_hexsha": "a9a2134de21b0ea5754c95620cd8d6b0ecf7b96c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.3653846154, "max_line_length": 156, "alphanum_fraction": 0.5894915254, "num_tokens": 865, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.12085323249047068, "lm_q1q2_score": 0.05853889893888017}}
{"text": "\nusing Latexify: Latexify\n\n# Recipe to hook into Latexify.jl's `latexify` function for `Sym`s.\n# We do not use SymPy's `latex` function here since it behaves a bit different\n# from Latexify.jl. Thus, we let Latexify.jl handle everything for the sake of\n# consistency.\n# For example, SymPy doesn't print a multiplication symbol by default while\n# Latexify uses a `\"\\\\cdot\"` (unless the keyword argument `cdot=false` is set).\nLatexify.@latexrecipe function _(x::Sym)\n  return string(x)\nend\n", "meta": {"hexsha": "35184a80caf6ac879c6eecea848cf75195712ce8", "size": 488, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/latexify_recipe.jl", "max_stars_repo_name": "simeonschaub/SymPy.jl", "max_stars_repo_head_hexsha": "937f3c209df413b47007413ddce3c750df8a4deb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 179, "max_stars_repo_stars_event_min_datetime": "2016-09-09T07:38:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T23:14:33.000Z", "max_issues_repo_path": "src/latexify_recipe.jl", "max_issues_repo_name": "simeonschaub/SymPy.jl", "max_issues_repo_head_hexsha": "937f3c209df413b47007413ddce3c750df8a4deb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 256, "max_issues_repo_issues_event_min_datetime": "2016-09-08T13:37:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-01T12:26:16.000Z", "max_forks_repo_path": "src/latexify_recipe.jl", "max_forks_repo_name": "simeonschaub/SymPy.jl", "max_forks_repo_head_hexsha": "937f3c209df413b47007413ddce3c750df8a4deb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 68, "max_forks_repo_forks_event_min_datetime": "2016-11-07T20:26:42.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-15T03:16:24.000Z", "avg_line_length": 37.5384615385, "max_line_length": 79, "alphanum_fraction": 0.7520491803, "num_tokens": 135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.12085322774082714, "lm_q1q2_score": 0.058538896638247434}}
{"text": "\n# # Introduction to [PowerSystems.jl](https://github.com/NREL-SIIP/PowerSystems.jl)\n#\n\n# **Originally Contributed by**: Clayton Barrows and Jose Daniel Lara\n\n# ## Introduction\n\n# This notebook is intended to show a power system data specification framework that exploits the\n# capabilities of Julia to improve performance and allow modelers to develop modular software\n# to create problems with different complexities and enable large scale analysis. The\n# [PowerSystems documentation](https://nrel-siip.github.io/PowerSystems.jl/stable/) is also\n# an excellent resource.\n#\n# ### Objective\n# PowerSystems.jl provides a type specification for bulk power system data.\n# The objective is to exploit Julia's integration of dynamic types to enable efficient data\n# handling and enable functional dispatch in modeling and analysis applications\n# As explained in Julia's documentation:\n#\n# \"Julia\u2019s type system is dynamic, but gains some of the advantages of static type systems\n# by making it possible to indicate that certain values are of specific types. This can be\n# of great assistance in generating efficient code, but even more significantly, it allows\n# method dispatch on the types of function arguments to be deeply integrated with the language.\"\n#\n# For more details on Julia types, refer to the [documentation](https://docs.julialang.org/en/v1/)\n#\n#\n# ## Environment and packages\n#\n# PowerSystems.jl relies on a framework for data handling established in\n# [InfrastructureSystems.jl](https://github.com/NREL-SIIP/InfrastructureSystems.jl).\n# Users of PowerSystems.jl should not need to interact directly with InfrastructureSystems.jl.\n# However, it's worth recognizing that InfrastructureSystems provides much of the back end\n# code for managing and accessing data, especially time series data.\n#\n# The examples in this notebook depend upon Julia 1.5 and a specific set of package releases\n# as defined in the `Manifest.toml`.\nusing Pkg\nPkg.status()\n\nusing SIIPExamples;\nusing PowerSystems;\nusing D3TypeTrees;\nIS = PowerSystems.IS\n\n# ## Types in PowerSystems\n# PowerSystems.jl provides a type hierarchy for specifying power system data. Data that\n# describes infrastructure components is held in `struct`s. For example, a `Bus` is defined\n# as follows with fields for the parameters required to describe a bus (along with an\n# `internal` field used by InfrastructureSystems to improve the efficiency of handling data).\n\nprint_struct(Bus)\n\n# ### Type Hierarchy\n# PowerSystems is intended to organize data containers by the behavior of the devices that\n# the data represents. To that end, a type hierarchy has been defined with several levels of\n# abstract types starting with `InfrastructureSystemsType`. There are a bunch of subtypes of\n# `InfrastructureSystemsType`, but the important ones to know about are:\n# - `Component`: includes all elements of power system data\n#   - `Topology`: includes non physical elements describing network connectivity\n#   - `Service`: includes descriptions of system requirements (other than energy balance)\n#   - `Device`: includes descriptions of all the physical devices in a power system\n# - `InfrastructureSystems.DeviceParameter`: includes structs that hold data describing the\n#  dynamic, or economic capabilities of `Device`.\n# - `TimeSeriesData`: Includes all time series types\n#   - `time series`: includes structs to define time series of forecasted data where multiple\n# values can represent each time stamp\n#   - `StaticTimeSeries`: includes structs to define time series with a single value for each\n# time stamp\n# - `System`: collects all of the `Component`s\n#\n# *The following trees are made with [D3TypeTrees](https://github.com/claytonpbarrows/D3TypeTrees.jl),\n# nodes that represent Structs will show the Fields in the hoverover tooltip.*\n\n# TypeTree(InfrastructureSystemsType)\n\n# ### `TimeSeriesData`\n# [_Read the Docs!_](https://nrel-siip.github.io/PowerSystems.jl/stable/modeler_guide/time_series/)\n# Every `Component` has a `time_series_container::InfrastructureSystems.TimeSeriesContainer`\n# field. `TimeSeriesData` are used to hold time series information that describes the\n# temporally dependent data of fields within the same struct. For example, the\n# `ThermalStandard.time_series_container` field can\n# describe other fields in the struct (`available`, `activepower`, `reactivepower`).\n\n# `TimeSeriesData`s themselves can take the form of the following:\nTypeTree(TimeSeriesData)\n\n# In each case, the time series contains fields for `scaling_factor_multiplier` and `data`\n# to identify the details of  th `Component` field that the time series describes, and the\n# time series `data`. For example: we commonly want to use a time series to\n# describe the maximum active power capability of a renewable generator. In this case, we\n# can create a `SingleTimeSeries` with a `TimeArray` and an accessor function to the\n# maximum active power field in the struct describing the generator. In this way, we can\n# store a scaling factor time series that will get multiplied by the maximum active power\n# rather than the magnitudes of the maximum active power time series.\n\nprint_struct(Deterministic)\n\n# Examples of how to create and add time series to system can be found in the\n# [Add Time Series Example](../PowerSystems.jl Examples/add_forecasts.ipynb)\n\n# ### System\n# The `System` object collects all of the individual components into a single struct along\n# with some metadata about the system itself (e.g. `base_power`)\n\nprint_struct(System)\n\n# ## Basic example\n# PowerSystems contains a few basic data files (mostly for testing and demonstration).\n\nBASE_DIR = abspath(joinpath(dirname(Base.find_package(\"PowerSystems\")), \"..\"))\ninclude(joinpath(BASE_DIR, \"test\", \"data_5bus_pu.jl\")) #.jl file containing 5-bus system data\nnodes_5 = nodes5() # function to create 5-bus buses\n\n# ### Create a `System`\n\nsys = System(\n    100.0,\n    nodes_5,\n    vcat(thermal_generators5(nodes_5), renewable_generators5(nodes_5)),\n    loads5(nodes_5),\n    branches5(nodes_5),\n)\n\n# ### Accessing `System` Data\n# PowerSystems provides functional interfaces to all data. The following examples outline\n# the intended approach to accessing data expressed using PowerSystems.\n\n# PowerSystems enforces unique `name` fields between components of a particular concrete type.\n# So, in order to retrieve a specific component, the user must specify the type of the component\n# along with the name and system\n\n# #### Accessing components\n@show get_component(Bus, sys, \"nodeA\")\n@show get_component(Line, sys, \"1\")\n\n# Similarly, you can access all the components of a particular type: *note: the return type\n# of get_components is a `FlattenIteratorWrapper`, so call `collect` to get an `Array`\n\nget_components(Bus, sys) |> collect\n\n# `get_components` also works on abstract types:\n\nget_components(Branch, sys) |> collect\n\n# The fields within a component can be accessed using the `get_*` functions:\n# *It's highly recommended that users avoid using the `.` to access fields since we make no\n# guarantees on the stability field names and locations. We do however promise to keep the\n# accessor functions stable.*\n\nbus1 = get_component(Bus, sys, \"nodeA\")\n@show get_name(bus1);\n@show get_magnitude(bus1);\n\n# #### Accessing `TimeSeries`\n\n# First we need to add some time series to the `System`\nloads = collect(get_components(PowerLoad, sys))\nfor (l, ts) in zip(loads, load_timeseries_DA[2])\n    add_time_series!(\n        sys,\n        l,\n        Deterministic(\n            \"activepower\",\n            Dict(TimeSeries.timestamp(load_timeseries_DA[2][1])[1] => ts),\n        ),\n    )\nend\n\n# If we want to access a specific time series for a specific component, we need to specify:\n#  - time series type\n#  - `component`\n#  - initial_time\n#  - label\n#\n# We can find the initial time of all the time series in the system:\nget_forecast_initial_times(sys)\n\n# We can find the names of all time series attached to a component:\nts_names = get_time_series_names(Deterministic, loads[1])\n\n# We can access a specific time series for a specific component:\nta = get_time_series_array(Deterministic, loads[1], ts_names[1])\n\n# Or, we can just get the values of the time series:\nts = get_time_series_values(Deterministic, loads[1], ts_names[1])\n", "meta": {"hexsha": "127cad9cf8bf519df736e526132e8b3c0e97b30b", "size": 8255, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "script/2_PowerSystems_examples/PowerSystems_intro.jl", "max_stars_repo_name": "raphaelsaavedra/SIIPExamples.jl", "max_stars_repo_head_hexsha": "d7304c84fe5382db2ff4c20f058bc1e5d01cae8c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "script/2_PowerSystems_examples/PowerSystems_intro.jl", "max_issues_repo_name": "raphaelsaavedra/SIIPExamples.jl", "max_issues_repo_head_hexsha": "d7304c84fe5382db2ff4c20f058bc1e5d01cae8c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "script/2_PowerSystems_examples/PowerSystems_intro.jl", "max_forks_repo_name": "raphaelsaavedra/SIIPExamples.jl", "max_forks_repo_head_hexsha": "d7304c84fe5382db2ff4c20f058bc1e5d01cae8c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.9095744681, "max_line_length": 102, "alphanum_fraction": 0.766444579, "num_tokens": 1885, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.1208532261576127, "lm_q1q2_score": 0.058538895871369886}}
{"text": "using Documenter\n\nmakedocs(\n    sitename = \"\ud83c\udfa2 Control Theory\",\n    format = Documenter.LaTeX(),\n    authors = \"Joe Carpinelli\",\n    pages = [\n        \"Frontmatter\" => [\n            \"Welcome!\" => \"index.md\",\n            \"Chapter 1: Introduction\" => \"Chapter 1: Introduction.md\"\n        ],\n        \"Topic 1: Dynamics\" => [\n        \"Chapter 2: General Dynamics\" => \"Chapter 2: General Dynamics.md\",\n        \"Chapter 3: Flight Dynamics\" => \"Chapter 3: Flight Dynamics.md\",\n        \"Chapter 4: Linear Dynamics\" => \"Chapter 4: Linear Dynamics.md\",\n        \"Chapter 5: Equilibrium Points\" => \"Chapter 5: Equilibrium Points.md\",\n        \"Chapter 6: Linearization\" => \"Chapter 6: Linearization.md\"\n        ],\n        \"Topic 2: Systems\" => [],\n        \"Topic 3: Controls\" => [],\n        \"Topic 4: Analysis\" => []\n    ]\n)", "meta": {"hexsha": "28a7a10352924b0a9084368e43ed4652c229026b", "size": 810, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make_pdf.jl", "max_stars_repo_name": "cadojo/controls", "max_stars_repo_head_hexsha": "d128521e5277a2d77323362420bb3401b04159ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-29T01:35:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-29T04:04:56.000Z", "max_issues_repo_path": "docs/make_pdf.jl", "max_issues_repo_name": "cadojo/exploring-control-theory", "max_issues_repo_head_hexsha": "eaa8c4653c3e77c6f3272a18630984f1b33097b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "docs/make_pdf.jl", "max_forks_repo_name": "cadojo/exploring-control-theory", "max_forks_repo_head_hexsha": "eaa8c4653c3e77c6f3272a18630984f1b33097b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2173913043, "max_line_length": 78, "alphanum_fraction": 0.5530864198, "num_tokens": 218, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3557749071749625, "lm_q2_score": 0.16451646904109266, "lm_q1q2_score": 0.05853083150184733}}
{"text": "import Base.BaseDocs: @kw_str\n\n\"\"\"\n**\u6b22\u8fce\u6765\u5230 Julia $(string(VERSION)).** \u5b8c\u6574\u7684\u4e2d\u6587\u624b\u518c\u53ef\u4ee5\u5728\u8fd9\u91cc\u627e\u5230\n\n    https://docs.juliacn.com/\n\n\u66f4\u591a\u4e2d\u6587\u8d44\u6599\u548c\u6559\u7a0b\uff0c\u4e5f\u8bf7\u5173\u6ce8 Julia \u4e2d\u6587\u793e\u533a\n\n    https://cn.julialang.org\n\n\u65b0\u624b\u8bf7\u53c2\u8003\u4e2d\u6587 discourse \u4e0a\u7684\u65b0\u624b\u6307\u5f15\n\n    https://discourse.juliacn.com/t/topic/159\n\n\u8f93\u5165 `?`\uff0c \u7136\u540e\u8f93\u5165\u4f60\u60f3\u8981\u67e5\u770b\u5e2e\u52a9\u6587\u6863\u7684\u51fd\u6570\u6216\u8005\u5b8f\u540d\u79f0\u5c31\u53ef\u4ee5\u67e5\u770b\u5b83\u4eec\u7684\u6587\u6863\u3002\u4f8b\u5982 `?cos`, \u6216\u8005 `?@time` \u7136\u540e\u6309\u56de\u8f66\u952e\u5373\u53ef\u3002\n\n\u5728 REPL \u4e2d\u8f93\u5165 `ENV[\"REPL_LOCALE\"]=\"\"` \u5c06\u6062\u590d\u82f1\u6587\u6a21\u5f0f\u3002\u518d\u6b21\u56de\u5230\u4e2d\u6587\u6a21\u5f0f\u8bf7\u8f93\u5165 `ENV[\"REPL_LOCALE\"]=\"zh_CN\"`\u3002\n\"\"\"\nkw\"help\", kw\"?\", kw\"julia\", kw\"\"\n\n\n\"\"\"\n    using\n\n`using Foo` \u5c06\u4f1a\u52a0\u8f7d\u4e00\u4e2a\u540d\u4e3a `Foo` \u7684\u6a21\u5757\uff08module\uff09\u6216\u8005\u4e00\u4e2a\u5305\uff0c\u7136\u540e\u5176 [`export`](@ref) \u7684\u540d\u79f0\u5c06\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u3002\u4e0d\u8bba\u662f\u5426\u88ab `export`\uff0c\u540d\u79f0\u90fd\u53ef\u4ee5\u901a\u8fc7\u70b9\u6765\u8bbf\u95ee\uff08\u4f8b\u5982\uff0c\u8f93\u5165 `Foo.foo` \u6765\u8bbf\u95ee\u5230 `foo`\uff09\u3002\u67e5\u770b[\u624b\u518c\u4e2d\u5173\u4e8e\u6a21\u5757\u7684\u90e8\u5206](@ref modules)\u4ee5\u83b7\u53d6\u66f4\u591a\u7ec6\u8282\u3002\n\"\"\"\nkw\"using\"\n\n\"\"\"\n    import\n\n`import Foo` \u5c06\u4f1a\u52a0\u8f7d\u4e00\u4e2a\u540d\u4e3a `Foo` \u7684\u6a21\u5757\uff08module\uff09\u6216\u8005\u4e00\u4e2a\u5305\u3002`Foo` \u6a21\u5757\u4e2d\u7684\u540d\u79f0\u53ef\u4ee5\u901a\u8fc7\u70b9\u6765\u8bbf\u95ee\u5230\uff08\u4f8b\u5982\uff0c\u8f93\u5165 `Foo.foo` \u53ef\u4ee5\u83b7\u53d6\u5230 `foo`\uff09\u3002\u67e5\u770b[\u624b\u518c\u4e2d\u5173\u4e8e\u6a21\u5757\u7684\u90e8\u5206](@ref modules)\u4ee5\u83b7\u53d6\u66f4\u591a\u7ec6\u8282\u3002\n\"\"\"\nkw\"import\"\n\n\"\"\"\n    export\n\n`export` \u88ab\u7528\u6765\u5728\u6a21\u5757\u4e2d\u544a\u8bc9Julia\u54ea\u4e9b\u51fd\u6570\u6216\u8005\u540d\u5b57\u53ef\u4ee5\u7531\u7528\u6237\u4f7f\u7528\u3002\u4f8b\u5982 `export foo` \u5c06\u5728 [`using`](@ref) \u8fd9\u4e2a module \u7684\u65f6\u5019\u4f7f\u5f97 `foo`\u53ef\u4ee5\u76f4\u63a5\u88ab\u8bbf\u95ee\u5230\u3002\u67e5\u770b[\u624b\u518c\u4e2d\u5173\u4e8e\u6a21\u5757\u7684\u90e8\u5206](@ref modules)\u4ee5\u83b7\u53d6\u66f4\u591a\u7ec6\u8282\u3002\n\"\"\"\nkw\"export\"\n\n\"\"\"\n    abstract type\n\n`abstract type` \u58f0\u660e\u6765\u4e00\u4e2a\u4e0d\u80fd\u5b9e\u4f8b\u5316\u7684\u7c7b\u578b\uff0c\u5b83\u5c06\u4ec5\u4ec5\u4f5c\u4e3a\u7c7b\u578b\u56fe\u4e2d\u7684\u4e00\u4e2a\u8282\u70b9\u5b58\u5728\uff0c\u4ece\u800c\u80fd\u591f\u63cf\u8ff0\u4e00\u7cfb\u5217\u76f8\u4e92\u5173\u8054\u7684\u5177\u4f53\u7c7b\u578b\uff08concrete type\uff09\uff1a\u8fd9\u4e9b\u5177\u4f53\u7c7b\u578b\u90fd\u662f\u62bd\u8c61\u7c7b\u578b\u7684\u5b50\u8282\u70b9\u3002\u62bd\u8c61\u7c7b\u578b\u5728\u6982\u5ff5\u4e0a\u4f7f\u5f97 Julia \u7684\u7c7b\u578b\u7cfb\u7edf\u4e0d\u4ec5\u4ec5\u662f\u4e00\u7cfb\u5217\u5bf9\u8c61\u7684\u96c6\u5408\u3002\u4f8b\u5982\uff1a\n\n```julia\nabstract type Number end\nabstract type Real <: Number end\n```\n\n[`Number`](@ref) \u6ca1\u6709\u7236\u8282\u70b9\uff08\u7236\u7c7b\u578b\uff09, \u800c [`Real`](@ref) \u662f `Number` \u7684\u4e00\u4e2a\u62bd\u8c61\u5b50\u7c7b\u578b\u3002\n\"\"\"\nkw\"abstract type\"\n\n\"\"\"\n    module\n\n`module` \u4f1a\u58f0\u660e\u4e00\u4e2a `Module` \u7c7b\u578b\u7684\u5b9e\u4f8b\u7528\u4e8e\u63cf\u8ff0\u4e00\u4e2a\u72ec\u7acb\u7684\u53d8\u91cf\u540d\u7a7a\u95f4\u3002\u5728\u4e00\u4e2a\u6a21\u5757\uff08module\uff09\u91cc\uff0c\u4f60\u53ef\u4ee5\u63a7\u5236\u6765\u81ea\u4e8e\u5176\u5b83\u6a21\u5757\u7684\u540d\u5b57\u662f\u5426\u53ef\u89c1\uff08\u901a\u8fc7\u8f7d\u5165\uff0cimport\uff09\uff0c\u4f60\u4e5f\u53ef\u4ee5\u51b3\u5b9a\u4f60\u7684\u540d\u5b57\u6709\u54ea\u4e9b\u662f\u53ef\u4ee5\u516c\u5f00\u7684\uff08\u901a\u8fc7\u66b4\u9732\uff0cexport\uff09\u3002\u6a21\u5757\u4f7f\u5f97\u4f60\u5728\u5728\u521b\u5efa\u4e0a\u5c42\u5b9a\u4e49\u65f6\u65e0\u9700\u62c5\u5fc3\u547d\u540d\u51b2\u7a81\u3002\u67e5\u770b[\u624b\u518c\u4e2d\u5173\u4e8e\u6a21\u5757\u7684\u90e8\u5206](@ref modules)\u4ee5\u83b7\u53d6\u66f4\u591a\u7ec6\u8282\u3002\n\n# \u4f8b\u5b50\n```julia\nmodule Foo\nimport Base.show\nexport MyType, foo\n\nstruct MyType\n    x\nend\n\nbar(x) = 2x\nfoo(a::MyType) = bar(a.x) + 1\nshow(io::IO, a::MyType) = print(io, \"MyType \\$(a.x)\")\nend\n```\n\"\"\"\nkw\"module\"\n\n\"\"\"\n    baremodule\n\n`baremodule` \u5c06\u58f0\u660e\u4e00\u4e2a\u4e0d\u5305\u542b `using Base` \u6216\u8005 `eval` \u5b9a\u4e49\u7684\u6a21\u5757\u3002\u4f46\u662f\u5b83\u5c06\u4ecd\u7136\u8f7d\u5165 `Core` \u6a21\u5757\u3002\n\"\"\"\nkw\"baremodule\"\n\n\"\"\"\n    primitive type\n\n`primitive type` \u58f0\u660e\u4e86\u4e00\u4e2a\u5176\u6570\u636e\u4ec5\u4ec5\u7531\u4e00\u7cfb\u5217\u4e8c\u8fdb\u5236\u6570\u8868\u793a\u7684\u5177\u4f53\u7c7b\u578b\u3002\u6bd4\u8f83\u5e38\u89c1\u7684\u4f8b\u5b50\u662f\u6574\u6570\u7c7b\u578b\u548c\u6d6e\u70b9\u7c7b\u578b\u3002\u4e0b\u9762\u662f\u4e00\u4e9b\u5185\u7f6e\u7684\u539f\u59cb\u7c7b\u578b\uff08primitive type\uff09\uff1a\n\n```julia\nprimitive type Char 32 end\nprimitive type Bool <: Integer 8 end\n```\n\n\u540d\u79f0\u540e\u9762\u7684\u6570\u5b57\u8868\u8fbe\u4e86\u8fd9\u4e2a\u7c7b\u578b\u5b58\u50a8\u6240\u9700\u7684\u6bd4\u7279\u6570\u76ee\u3002\u76ee\u524d\u8fd9\u4e2a\u6570\u5b57\u8981\u6c42\u662f 8 bit \u7684\u500d\u6570\u3002[`Bool`](@ref) \u7c7b\u578b\u7684\u58f0\u660e\u5c55\u793a\u4e86\u4e00\u4e2a\u539f\u59cb\u7c7b\u578b\u5982\u4f55\u9009\u62e9\u6210\u4e3a\u53e6\u4e00\u4e2a\u7c7b\u578b\u7684\u5b50\u7c7b\u578b\u3002\n\"\"\"\nkw\"primitive type\"\n\n\n\"\"\"\n    macro\n\n`macro` \u5b9a\u4e49\u4e86\u4e00\u79cd\u4f1a\u5c06\u751f\u6210\u7684\u4ee3\u7801\u5305\u542b\u5728\u6700\u7ec8\u7a0b\u5e8f\u4f53\u4e2d\u7684\u65b9\u6cd5\uff0c\u8fd9\u79f0\u4e4b\u4e3a\u5b8f\u3002\u4e00\u4e2a\u5b8f\u5c06\u4e00\u7cfb\u5217\u8f93\u5165\u6620\u5c04\u5230\u4e00\u4e2a\u8868\u8fbe\u5f0f\uff0c\u7136\u540e\u6240\u8fd4\u56de\u7684\u8868\u8fbe\u5f0f\u5c06\u4f1a\u88ab\u76f4\u63a5\u8fdb\u884c\u7f16\u8bd1\u800c\u4e0d\u9700\u8981\u5728\u8fd0\u884c\u65f6\u8c03\u7528 `eval` \u51fd\u6570\u3002\u5b8f\u7684\u8f93\u5165\u53ef\u4ee5\u5305\u62ec\u8868\u8fbe\u5f0f\u3001\u5b57\u9762\u91cf\u548c\u7b26\u53f7\u3002\u4f8b\u5982\uff1a\n\n# \u4f8b\u5b50\n\n```jldoctest\njulia> macro sayhello(name)\n           return :( println(\"Hello, \", \\$name, \"!\") )\n       end\n@sayhello (macro with 1 method)\n\njulia> @sayhello \"\u5c0f\u660e\"\nHello, \u5c0f\u660e!\n```\n\"\"\"\nkw\"macro\"\n\n\"\"\"\n    local\n\n`local`\u5c06\u4f1a\u5b9a\u4e49\u4e00\u4e2a\u65b0\u7684\u5c40\u90e8\u53d8\u91cf\u3002\n\n\u67e5\u770b[\u624b\u518c\uff1a\u53d8\u91cf\u4f5c\u7528\u57df](@ref scope-of-variables)\u4ee5\u83b7\u53d6\u66f4\u8be6\u7ec6\u7684\u4fe1\u606f\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> function foo(n)\n           x = 0\n           for i = 1:n\n               local x # introduce a loop-local x\n               x = i\n           end\n           x\n       end\nfoo (generic function with 1 method)\n\njulia> foo(10)\n0\n```\n\"\"\"\nkw\"local\"\n\n\"\"\"\n    global\n\n`global x` \u5c06\u4f1a\u4f7f\u5f97\u5f53\u524d\u4f5c\u7528\u57df\u548c\u5f53\u524d\u4f5c\u7528\u6240\u5305\u542b\u7684\u4f5c\u7528\u57df\u91cc\u7684 `x` \u6307\u5411\u540d\u4e3a `x` \u7684\u5168\u5c40\u53d8\u91cf\u3002\u67e5\u770b[\u624b\u518c\uff1a\u53d8\u91cf\u4f5c\u7528\u57df](@ref scope-of-variables)\u4ee5\u83b7\u53d6\u66f4\u591a\u4fe1\u606f\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> z = 3\n3\n\njulia> function foo()\n           global z = 6 # use the z variable defined outside foo\n       end\nfoo (generic function with 1 method)\n\njulia> foo()\n6\n\njulia> z\n6\n```\n\"\"\"\nkw\"global\"\n\n\"\"\"\n    let\n\n`let` \u4f1a\u5728\u6bcf\u6b21\u88ab\u8fd0\u884c\u65f6\u58f0\u660e\u4e00\u4e2a\u65b0\u7684\u53d8\u91cf\u7ed1\u5b9a\u3002\u8fd9\u4e2a\u65b0\u7684\u53d8\u91cf\u7ed1\u5b9a\u5c06\u62e5\u6709\u4e00\u4e2a\u65b0\u7684\u5730\u5740\u3002\u8fd9\u91cc\u7684\u4e0d\u540c\u53ea\u6709\u5f53\u53d8\u91cf\u901a\u8fc7\u95ed\u5305\u751f\u5b58\u5728\u5b83\u4eec\u7684\u4f5c\u7528\u57df\u5916\u65f6\u624d\u4f1a\u663e\u73b0\u3002`let` \u8bed\u6cd5\u63a5\u53d7\u9017\u53f7\u5206\u5272\u7684\u4e00\u7cfb\u5217\u8d4b\u503c\u8bed\u53e5\u548c\u53d8\u91cf\u540d\uff1a\n\n```julia\nlet var1 = value1, var2, var3 = value3\n    code\nend\n```\n\n\u8fd9\u4e9b\u8d4b\u503c\u8bed\u53e5\u662f\u6309\u7167\u987a\u5e8f\u6c42\u503c\u7684\uff0c\u7b49\u53f7\u53f3\u8fb9\u7684\u8868\u8fbe\u5f0f\u5c06\u4f1a\u9996\u5148\u6c42\u503c\uff0c\u7136\u540e\u624d\u7ed1\u5b9a\u7ed9\u5de6\u8fb9\u7684\u53d8\u91cf\u3002\u56e0\u6b64\u8fd9\u4f7f\u5f97 `let x = x` \u8fd9\u6837\u7684\u8868\u8fbe\u5f0f\u6709\u610f\u4e49\uff0c\u56e0\u4e3a\u8fd9\u4e24\u4e2a `x` \u53d8\u91cf\u5c06\u5177\u6709\u4e0d\u540c\u7684\u5730\u5740\u3002\n\"\"\"\nkw\"let\"\n\n\"\"\"\n    quote\n\n`quote` \u4f1a\u5c06\u5176\u5305\u542b\u7684\u4ee3\u7801\u6269\u53d8\u6210\u4e00\u4e2a\u591a\u91cd\u7684\u8868\u8fbe\u5f0f\u5bf9\u8c61\uff0c\u800c\u65e0\u9700\u663e\u793a\u8c03\u7528 `Expr` \u7684\u6784\u9020\u5668\u3002\u8fd9\u79f0\u4e4b\u4e3a\u5f15\u7528\uff0c\u6bd4\u5982\u8bf4\n\n```julia\nex = quote\n    x = 1\n    y = 2\n    x + y\nend\n```\n\n\u548c\u5176\u5b83\u5f15\u7528\u65b9\u5f0f\u4e0d\u540c\u7684\u662f\uff0c`:( ... )`\u5f62\u5f0f\u7684\u5f15\u7528\uff08\u88ab\u5305\u542b\u65f6\uff09\u5c06\u4f1a\u5728\u8868\u8fbe\u5f0f\u6811\u91cc\u5f15\u5165\u4e00\u4e2a\u5728\u64cd\u4f5c\u8868\u8fbe\u5f0f\u6811\u65f6\u5fc5\u987b\u8981\u8003\u8651\u7684 `QuoteNode` \u5143\u7d20\u3002\u800c\u5728\u5176\u5b83\u573a\u666f\u4e0b\uff0c`:( ... )`\u548c `quote .. end` \u4ee3\u7801\u5757\u662f\u88ab\u540c\u7b49\u5bf9\u5f85\u7684\u3002\n\"\"\"\nkw\"quote\"\n\n\n\"\"\"\n    '\n\n\u5384\u7c73\u7b97\u7b26\uff08\u5171\u8f6d\u8f6c\u7f6e\uff09\uff0c\u53c2\u89c1 [`adjoint`](@ref)\n\n# \u4f8b\u5b50\n```jldoctest\njulia> A = [1.0 -2.0im; 4.0im 2.0]\n2\u00d72 Array{Complex{Float64},2}:\n 1.0+0.0im  -0.0-2.0im\n 0.0+4.0im   2.0+0.0im\n\njulia> A'\n2\u00d72 Array{Complex{Float64},2}:\n  1.0-0.0im  0.0-4.0im\n -0.0+2.0im  2.0-0.0im\n```\n\"\"\"\nkw\"'\"\n\n\"\"\"\n    const\n\n`const` \u88ab\u7528\u6765\u58f0\u660e\u5e38\u6570\u5168\u5c40\u53d8\u91cf\u3002\u5728\u5927\u90e8\u5206\uff08\u5c24\u5176\u662f\u6027\u80fd\u654f\u611f\u7684\u4ee3\u7801\uff09\u5168\u5c40\u53d8\u91cf\u5e94\u5f53\u88ab\u58f0\u660e\u4e3a\u5e38\u6570\u3002\n\n```julia\nconst x = 5\n```\n\n\u53ef\u4ee5\u4f7f\u7528\u5355\u4e2a `const` \u58f0\u660e\u591a\u4e2a\u5e38\u6570\u53d8\u91cf\u3002\n\n```julia\nconst y, z = 7, 11\n```\n\n\u6ce8\u610f `const` \u53ea\u4f1a\u4f5c\u7528\u4e8e\u4e00\u4e2a `=` \u64cd\u4f5c\uff0c\u56e0\u6b64 `const x = y = 1` \u58f0\u660e\u4e86 `x` \u662f\u5e38\u6570\uff0c\u800c `y` \u4e0d\u662f\u3002\u5728\u53e6\u4e00\u65b9\u9762\uff0c`const x = const y = 1`\u58f0\u660e\u4e86 `x` \u548c `y` \u90fd\u662f\u5e38\u6570\u3002\n\n\u6ce8\u610f\u300c\u5e38\u6570\u6027\u8d28\u300d\u5e76\u4e0d\u4f1a\u5f3a\u5236\u5bb9\u5668\u5185\u90e8\u53d8\u6210\u5e38\u6570\uff0c\u6240\u4ee5\u5982\u679c `x` \u662f\u4e00\u4e2a\u6570\u7ec4\u6216\u8005\u5b57\u5178\uff08\u4e3e\u4f8b\u6765\u8bb2\uff09\u4f60\u4ecd\u7136\u53ef\u4ee5\u7ed9\u5b83\u4eec\u6dfb\u52a0\u6216\u8005\u5220\u9664\u5143\u7d20\u3002\n\n\u4e25\u683c\u6765\u8bb2\uff0c\u4f60\u751a\u81f3\u53ef\u4ee5\u91cd\u65b0\u5b9a\u4e49 `const`\uff08\u5e38\u6570\uff09\u53d8\u91cf\uff0c\u5c3d\u7ba1\u8fd9\u5c06\u4f1a\u8ba9\u7f16\u8bd1\u5668\u4ea7\u751f\u4e00\u4e2a\u8b66\u544a\u3002\u552f\u4e00\u4e25\u683c\u7684\u8981\u6c42\u662f\u8fd9\u4e2a\u53d8\u91cf\u7684**\u7c7b\u578b**\u4e0d\u80fd\u6539\u53d8\uff0c\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u5e38\u6570\u53d8\u91cf\u4f1a\u6bd4\u4e00\u822c\u7684\u5168\u5c40\u53d8\u91cf\u66f4\u5feb\u7684\u539f\u56e0\u3002\n\"\"\"\nkw\"const\"\n\n\"\"\"\n    function\n\n\u51fd\u6570\u7531 `function` \u5173\u952e\u8bcd\u5b9a\u4e49\uff1a\n\n```julia\nfunction add(a, b)\n    return a + b\nend\n```\n\n\u6216\u8005\u662f\u66f4\u77ed\u7684\u5f62\u5f0f\uff1a\n\n```julia\nadd(a, b) = a + b\n```\n\n[`return`](@ref) \u5173\u952e\u8bcd\u7684\u4f7f\u7528\u65b9\u6cd5\u548c\u5176\u5b83\u8bed\u8a00\u5b8c\u5168\u4e00\u6837\uff0c\u4f46\u662f\u5e38\u5e38\u662f\u4e0d\u4f7f\u7528\u7684\u3002\u4e00\u4e2a\u6ca1\u6709\u663e\u793a\u58f0\u660e `return` \u7684\u51fd\u6570\u5c06\u8fd4\u56de\u51fd\u6570\u4f53\u6700\u540e\u4e00\u4e2a\u8868\u8fbe\u5f0f\u3002\n\"\"\"\nkw\"function\"\n\n\"\"\"\n    return\n\n`return` \u53ef\u4ee5\u7528\u6765\u5728\u51fd\u6570\u4f53\u4e2d\u7acb\u5373\u9000\u51fa\u5e76\u8fd4\u56de\u7ed9\u5b9a\u503c\uff0c\u4f8b\u5982\n\n```julia\nfunction compare(a, b)\n    a == b && return \"equal to\"\n    a < b ? \"less than\" : \"greater than\"\nend\n```\n\n\u901a\u5e38\uff0c\u4f60\u53ef\u4ee5\u5728\u51fd\u6570\u4f53\u7684\u4efb\u610f\u4f4d\u7f6e\u653e\u7f6e `return` \u8bed\u53e5\uff0c\u5305\u62ec\u5728\u591a\u5c42\u5d4c\u5957\u7684\u5faa\u73af\u548c\u6761\u4ef6\u8868\u8fbe\u5f0f\u4e2d\uff0c\u4f46\u8981\u6ce8\u610f `do` \u5757\u3002\u4f8b\u5982\uff1a\n\n```julia\nfunction test1(xs)\n    for x in xs\n        iseven(x) && return 2x\n    end\nend\n\nfunction test2(xs)\n    map(xs) do x\n        iseven(x) && return 2x\n        x\n    end\nend\n```\n\n\u5728\u7b2c\u4e00\u4e2a\u4f8b\u5b50\u4e2d\uff0creturn \u4e00\u78b0\u5230\u5076\u6570\u5c31\u8df3\u51fa\u5305\u542b\u5b83\u7684\u51fd\u6570\uff0c\u56e0\u6b64 `test1([5,6,7])` \u8fd4\u56de `12`\u3002\n\n\u4f60\u53ef\u80fd\u5e0c\u671b\u7b2c\u4e8c\u4e2a\u4f8b\u5b50\u7684\u884c\u4e3a\u4e0e\u6b64\u76f8\u540c\uff0c\u4f46\u5b9e\u9645\u4e0a\uff0c\u8fd9\u91cc\u7684 `return` \u53ea\u4f1a\u8df3\u51fa\uff08\u5728 `do` \u5757\u4e2d\u7684\uff09*\u5185\u90e8*\u51fd\u6570\u5e76\u628a\u503c\u8fd4\u56de\u7ed9 `map`\u3002\u4e8e\u662f\uff0c`test2([5,6,7])` \u8fd4\u56de `[5,12,7]`\u3002\n\"\"\"\nkw\"return\"\n\n# \u4eff\u7167 https://docs.juliacn.com/latest/manual/control-flow/#man-conditional-evaluation-1\n\"\"\"\n    if/elseif/else\n\n`if`/`elseif`/`else` \u6267\u884c\u6761\u4ef6\u8868\u8fbe\u5f0f\uff08Conditional evaluation\uff09\u53ef\u4ee5\u6839\u636e\u5e03\u5c14\u8868\u8fbe\u5f0f\u7684\u503c\uff0c\u8ba9\u90e8\u5206\u4ee3\u7801\u88ab\u6267\u884c\u6216\u8005\u4e0d\u88ab\u6267\u884c\u3002\u4e0b\u9762\u662f\u5bf9 `if`-`elseif`-`else` \u6761\u4ef6\u8bed\u6cd5\u7684\u5206\u6790\uff1a\n\n```julia\nif x < y\n    println(\"x is less than y\")\nelseif x > y\n    println(\"x is greater than y\")\nelse\n    println(\"x is equal to y\")\nend\n```\n\n\u5982\u679c\u8868\u8fbe\u5f0f `x < y` \u662f `true`\uff0c\u90a3\u4e48\u5bf9\u5e94\u7684\u4ee3\u7801\u5757\u4f1a\u88ab\u6267\u884c\uff1b\u5426\u5219\u5224\u65ad\u6761\u4ef6\u8868\u8fbe\u5f0f `x > y`\uff0c\u5982\u679c\u5b83\u662f `true`\uff0c\u5219\u6267\u884c\u5bf9\u5e94\u7684\u4ee3\u7801\u5757\uff1b\u5982\u679c\u6ca1\u6709\u8868\u8fbe\u5f0f\u662f true\uff0c\u5219\u6267\u884c `else` \u4ee3\u7801\u5757\u3002`elseif` \u548c `else` \u4ee3\u7801\u5757\u662f\u53ef\u9009\u7684\uff0c\u5e76\u4e14\u53ef\u4ee5\u4f7f\u7528\u4efb\u610f\u591a\u4e2a `elseif` \u4ee3\u7801\u5757\u3002\n\"\"\"\nkw\"if\", kw\"elseif\", kw\"else\"\n\n\"\"\"\n    for\n\n`for` \u5faa\u73af\u901a\u8fc7\u8fed\u4ee3\u4e00\u7cfb\u5217\u503c\u6765\u91cd\u590d\u8ba1\u7b97\u5faa\u73af\u4f53\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> for i in [1, 4, 0]\n           println(i)\n       end\n1\n4\n0\n```\n\"\"\"\nkw\"for\"\n\n# \u4eff\u7167 https://docs.juliacn.com/latest/manual/control-flow/#man-loops-1\n\"\"\"\n    while\n\n`while` \u5faa\u73af\u4f1a\u91cd\u590d\u6267\u884c\u6761\u4ef6\u8868\u8fbe\u5f0f\uff0c\u5e76\u5728\u8be5\u8868\u8fbe\u5f0f\u4e3a `true` \u65f6\u7ee7\u7eed\u6267\u884c while \u5faa\u73af\u7684\u4e3b\u4f53\u90e8\u5206\u3002\u5f53 while \u5faa\u73af\u7b2c\u4e00\u6b21\u6267\u884c\u65f6\uff0c\u5982\u679c\u6761\u4ef6\u8868\u8fbe\u5f0f\u4e3a false\uff0c\u90a3\u4e48\u4e3b\u4f53\u4ee3\u7801\u5c31\u4e00\u6b21\u4e5f\u4e0d\u4f1a\u88ab\u6267\u884c\u3002\n\n# \u4f8b\u5b50\njldoctest\njulia> i = 1\n1\n\njulia> while i < 5\n           println(i)\n           global i += 1\n       end\n1\n2\n3\n4\n```\n\"\"\"\nkw\"while\"\n\n\"\"\"\n    end\n\n`end` \u6807\u8bb0\u4e00\u4e2a\u8868\u8fbe\u5f0f\u5757\u7684\u7ed3\u675f\uff0c\u4f8b\u5982 [`module`](@ref)\u3001[`struct`](@ref)\u3001[`mutable struct`](@ref)\u3001[`begin`](@ref)\u3001[`let`](@ref)\u3001[`for`](@ref) \u7b49\u3002`end` \u5728\u7d22\u5f15\u6570\u7ec4\u65f6\u4e5f\u53ef\u4ee5\u7528\u6765\u8868\u793a\u7ef4\u5ea6\u7684\u6700\u540e\u4e00\u4e2a\u7d22\u5f15\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> A = [1 2; 3 4]\n2\u00d72 Array{Int64,2}:\n 1  2\n 3  4\n\njulia> A[end, :]\n2-element Array{Int64,1}:\n 3\n 4\n```\n\"\"\"\nkw\"end\"\n\n# \u4eff\u7167 https://docs.juliacn.com/latest/manual/control-flow/#try/catch-%E8%AF%AD%E5%8F%A5-1\n\"\"\"\n    try/catch\n\n`try/catch` \u8bed\u53e5\u53ef\u4ee5\u7528\u6765\u6355\u83b7 `Exception`\uff0c\u5e76\u8fdb\u884c\u5f02\u5e38\u5904\u7406\u3002\u4f8b\u5982\uff0c\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u5e73\u65b9\u6839\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7 `Exception` \u6765\u5b9e\u73b0\u81ea\u52a8\u6309\u9700\u8c03\u7528\u6c42\u89e3\u5b9e\u6570\u6216\u8005\u590d\u6570\u5e73\u65b9\u6839\u7684\u65b9\u6cd5\uff1a\n\n```julia\nf(x) = try\n    sqrt(x)\ncatch\n    sqrt(complex(x, 0))\nend\n```\n\n`try/catch` \u8bed\u53e5\u5141\u8bb8\u4fdd\u5b58 `Exception` \u5230\u4e00\u4e2a\u53d8\u91cf\u4e2d\uff0c\u4f8b\u5982 `catch y`\u3002\n\n`try/catch` \u7ec4\u4ef6\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u80fd\u591f\u5c06\u9ad8\u5ea6\u5d4c\u5957\u7684\u8ba1\u7b97\u7acb\u523b\u89e3\u8026\u6210\u66f4\u9ad8\u5c42\u6b21\u5730\u8c03\u7528\u51fd\u6570\u3002\n\"\"\"\nkw\"try\", kw\"catch\"\n\n# \u4eff\u7167 https://docs.juliacn.com/latest/manual/control-flow/#finally-%E5%AD%90%E5%8F%A5-1\n\"\"\"\n    finally\n\n\u65e0\u8bba\u4ee3\u7801\u5757\u662f\u5982\u4f55\u9000\u51fa\u7684\uff0c\u90fd\u8ba9\u4ee3\u7801\u5757\u5728\u9000\u51fa\u65f6\u8fd0\u884c\u67d0\u6bb5\u4ee3\u7801\u3002\u8fd9\u91cc\u662f\u4e00\u4e2a\u786e\u4fdd\u4e00\u4e2a\u6253\u5f00\u7684\u6587\u4ef6\u88ab\u5173\u95ed\u7684\u4f8b\u5b50\uff1a\n\n```julia\nf = open(\"file\")\ntry\n    operate_on_file(f)\nfinally\n    close(f)\nend\n```\n\n\u5f53\u63a7\u5236\u6d41\u79bb\u5f00 `try` \u4ee3\u7801\u5757\uff08\u4f8b\u5982\uff0c\u9047\u5230 `return`\uff0c\u6216\u8005\u6b63\u5e38\u7ed3\u675f\uff09\uff0c`close(f)` \u5c31\u4f1a\u88ab\u6267\u884c\u3002\u5982\u679c `try` \u4ee3\u7801\u5757\u7531\u4e8e\u5f02\u5e38\u9000\u51fa\uff0c\u8fd9\u4e2a\u5f02\u5e38\u4f1a\u7ee7\u7eed\u4f20\u9012\u3002`catch` \u4ee3\u7801\u5757\u53ef\u4ee5\u548c `try` \u8fd8\u6709 `finally` \u914d\u5408\u4f7f\u7528\u3002\u8fd9\u65f6 `finally` \u4ee3\u7801\u5757\u4f1a\u5728 `catch` \u5904\u7406\u9519\u8bef\u4e4b\u540e\u624d\u8fd0\u884c\u3002\n\"\"\"\nkw\"finally\"\n\n\"\"\"\n    break\n\n\u7acb\u5373\u8df3\u51fa\u5f53\u524d\u5faa\u73af\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> i = 0\n0\n\njulia> while true\n           global i += 1\n           i > 5 && break\n           println(i)\n       end\n1\n2\n3\n4\n5\n```\n\"\"\"\nkw\"break\"\n\n\"\"\"\n    continue\n\n\u8df3\u8fc7\u5f53\u524d\u5faa\u73af\u8fed\u4ee3\u7684\u5269\u4f59\u90e8\u5206\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> for i = 1:6\n           iseven(i) && continue\n           println(i)\n       end\n1\n3\n5\n```\n\"\"\"\nkw\"continue\"\n\n\"\"\"\n    do\n\n\u521b\u5efa\u4e00\u4e2a\u533f\u540d\u51fd\u6570\u3002\u4f8b\u5982\uff1a\n\n```julia\nmap(1:10) do x\n    2x\nend\n```\n\n\u7b49\u4ef7\u4e8e `map(x->2x, 1:10)`\u3002\n\n\u50cf\u8fd9\u6837\u4fbf\u53ef\u4f7f\u7528\u591a\u4e2a\u53c2\u6570\uff1a\n\n```julia\nmap(1:10, 11:20) do x, y\n    x + y\nend\n```\n\"\"\"\nkw\"do\"\n\n\"\"\"\n    ...\n\n\u300csplat\u300d\u8fd0\u7b97\u7b26 `...` \u8868\u793a\u53c2\u6570\u5e8f\u5217\u3002`...` \u53ef\u4ee5\u5728\u51fd\u6570\u5b9a\u4e49\u4e2d\u7528\u6765\u8868\u793a\u8be5\u51fd\u6570\u63a5\u53d7\u4efb\u610f\u6570\u91cf\u7684\u53c2\u6570\u3002`...` \u4e5f\u53ef\u4ee5\u7528\u6765\u5c06\u51fd\u6570\u4f5c\u7528\u4e8e\u53c2\u6570\u5e8f\u5217\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> add(xs...) = reduce(+, xs)\nadd (generic function with 1 method)\n\njulia> add(1, 2, 3, 4, 5)\n15\n\njulia> add([1, 2, 3]...)\n6\n\njulia> add(7, 1:100..., 1000:1100...)\n111107\n```\n\"\"\"\nkw\"...\"\n\n\"\"\"\n    ;\n\n`;` \u5728 Julia \u4e2d\u5177\u6709\u4e0e\u8bb8\u591a\u7c7b C \u8bed\u8a00\u76f8\u4f3c\u7684\u4f5c\u7528\uff0c\u7528\u4e8e\u5206\u9694\u524d\u4e00\u4e2a\u8bed\u53e5\u7684\u7ed3\u5c3e\u3002`;` \u5728\u6362\u884c\u4e2d\u4e0d\u662f\u5fc5\u8981\u7684\uff0c\u4f46\u53ef\u4ee5\u7528\u4e8e\u5728\u5355\u884c\u4e2d\u5206\u9694\u8bed\u53e5\u6216\u8005\u5c06\u591a\u4e2a\u8868\u8fbe\u5f0f\u8fde\u63a5\u4e3a\u5355\u4e2a\u8868\u8fbe\u5f0f\u3002`;` \u4e5f\u7528\u4e8e\u6291\u5236 REPL \u548c\u7c7b\u4f3c\u754c\u9762\u4e2d\u7684\u8f93\u51fa\u6253\u5370\u3002\n\n# \u4f8b\u5b50\n```julia\njulia> function foo()\n           x = \"Hello, \"; x *= \"World!\"\n           return x\n       end\nfoo (generic function with 1 method)\n\njulia> bar() = (x = \"Hello, Mars!\"; return x)\nbar (generic function with 1 method)\n\njulia> foo();\n\njulia> bar()\n\"Hello, Mars!\"\n```\n\"\"\"\nkw\";\"\n\n\"\"\"\n    x && y\n\n\u77ed\u8def\u5e03\u5c14 AND\u3002\n\"\"\"\nkw\"&&\"\n\n\"\"\"\n    x || y\n\n\u77ed\u8def\u5e03\u5c14 OR\u3002\n\"\"\"\nkw\"||\"\n\n\"\"\"\n    ccall((function_name, library), returntype, (argtype1, ...), argvalue1, ...)\n    ccall(function_name, returntype, (argtype1, ...), argvalue1, ...)\n    ccall(function_pointer, returntype, (argtype1, ...), argvalue1, ...)\n\n\u8c03\u7528\u7531 C \u5bfc\u51fa\u7684\u5171\u4eab\u5e93\u91cc\u7684\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u7531\u5143\u7ec4 `(function_name, library)` \u6307\u5b9a\uff0c\u5176\u4e2d\u7684\u6bcf\u4e2a\u7ec4\u4ef6\u90fd\u662f\u5b57\u7b26\u4e32\u6216\u7b26\u53f7\u3002\u82e5\u4e0d\u6307\u5b9a\u5e93\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528 `function_name` \u7684\u7b26\u53f7\u6216\u5b57\u7b26\u4e32\uff0c\u5b83\u4f1a\u5728\u5f53\u524d\u8fdb\u7a0b\u4e2d\u89e3\u6790\u3002\u53e6\u5916\uff0c`ccall` \u4e5f\u53ef\u7528\u4e8e\u8c03\u7528\u51fd\u6570\u6307\u9488 `function_pointer`\uff0c\u6bd4\u5982 `dlsym` \u8fd4\u56de\u7684\u51fd\u6570\u6307\u9488\u3002\n\n\u8bf7\u6ce8\u610f\u53c2\u6570\u7c7b\u578b\u5143\u7ec4\u5fc5\u987b\u662f\u5b57\u9762\u4e0a\u7684\u5143\u7ec4\uff0c\u800c\u4e0d\u662f\u5143\u7ec4\u7c7b\u578b\u7684\u53d8\u91cf\u6216\u8868\u8fbe\u5f0f\u3002\n\n\u901a\u8fc7\u81ea\u52a8\u63d2\u5165\u5bf9 `unsafe_convert(argtype, cconvert(argtype, argvalue))` \u7684\u8c03\u7528\uff0c\u6bcf\u4e2a\u4f20\u7ed9 `ccall` \u7684 `argvalue` \u5c06\u88ab\u7c7b\u578b\u8f6c\u6362\u4e3a\u5bf9\u5e94\u7684 `argtype`\u3002\uff08\u6709\u5173\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u8bf7\u53c2\u9605 [`unsafe_convert`](@ref Base.unsafe_convert) \u548c [`cconvert`](@ref Base.cconvert) \u7684\u6587\u6863\u3002\uff09\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0c\u8fd9\u53ea\u4f1a\u7b80\u5355\u5730\u8c03\u7528 `convert(argtype, argvalue)`\u3002\n\"\"\"\nkw\"ccall\"\n\n\"\"\"\n    begin\n\n`begin...end` \u8868\u793a\u4e00\u4e2a\u4ee3\u7801\u5757\u3002\n\n```julia\nbegin\n    println(\"Hello, \")\n    println(\"World!\")\nend\n```\n\n\u901a\u5e38\uff0c`begin` \u4e0d\u4f1a\u662f\u5fc5\u9700\u7684\uff0c\u56e0\u4e3a\u8bf8\u5982 [`function`](@ref) and [`let`](@ref) \u4e4b\u7c7b\u7684\u5173\u952e\u5b57\u4f1a\u9690\u5f0f\u5730\u5f00\u59cb\u4ee3\u7801\u5757\u3002\u53e6\u8bf7\u53c2\u9605 [`;`](@ref)\u3002\n\"\"\"\nkw\"begin\"\n\n\"\"\"\n    struct\n\nstruct \u662f Julia \u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u7c7b\u578b\uff0c\u7531\u540d\u79f0\u548c\u4e00\u7ec4\u5b57\u6bb5\u6307\u5b9a\u3002\n\n```julia\nstruct Point\n    x\n    y\nend\n```\n\n\u53ef\u5bf9\u5b57\u6bb5\u65bd\u52a0\u7c7b\u578b\u9650\u5236\uff0c\u8be5\u9650\u5236\u4e5f\u53ef\u88ab\u53c2\u6570\u5316\uff1a\n\n```julia\n    struct Point{X}\n        x::X\n        y::Float64\n    end\n```\n\nstruct \u53ef\u4ee5\u901a\u8fc7 `<:` \u8bed\u6cd5\u58f0\u660e\u4e00\u4e2a\u62bd\u8c61\u8d85\u7c7b\u578b\uff1a\nA struct can also declare an abstract super type via `<:` syntax:\n\n```julia\nstruct Point <: AbstractPoint\n    x\n    y\nend\n```\n\n`struct` \u9ed8\u8ba4\u662f\u4e0d\u53ef\u53d8\u7684\uff1b\u8fd9\u4e9b\u7c7b\u578b\u7684\u5b9e\u4f8b\u5728\u6784\u9020\u540e\u4e0d\u80fd\u88ab\u4fee\u6539\u3002\u5982\u9700\u4fee\u6539\u5b9e\u4f8b\uff0c\u8bf7\u4f7f\u7528 [`mutable struct`](@ref) \u6765\u58f0\u660e\u4e00\u4e2a\u53ef\u4ee5\u4fee\u6539\u5176\u5b9e\u4f8b\u7684\u7c7b\u578b\u3002\n\n\u6709\u5173\u66f4\u591a\u7ec6\u8282\uff0c\u6bd4\u5982\u600e\u4e48\u5b9a\u4e49\u6784\u9020\u51fd\u6570\uff0c\u8bf7\u53c2\u9605\u624b\u518c\u7684 [\u590d\u5408\u7c7b\u578b](@ref) \u7ae0\u8282\u3002\n\"\"\"\nkw\"struct\"\n\n\"\"\"\n    mutable struct\n\n`mutable struct` \u7c7b\u4f3c\u4e8e [`struct`](@ref)\uff0c\u4f46\u53e6\u5916\u5141\u8bb8\u5728\u6784\u9020\u540e\u8bbe\u7f6e\u7c7b\u578b\u7684\u5b57\u6bb5\u3002\u6709\u5173\u8be6\u7ec6\u4fe1\u606f\uff0c\u8bf7\u53c2\u9605 [\u590d\u5408\u7c7b\u578b](@ref)\u3002\n\"\"\"\nkw\"mutable struct\"\n\n\"\"\"\n    new\n\n\u4ec5\u5728\u5185\u90e8\u6784\u9020\u51fd\u6570\u4e2d\u53ef\u7528\u7684\u7279\u6b8a\u51fd\u6570\uff0c\u7528\u6765\u521b\u5efa\u8be5\u7c7b\u578b\u7684\u5bf9\u8c61\u3002\u6709\u5173\u66f4\u591a\u4fe1\u606f\uff0c\u8bf7\u53c2\u9605\u624b\u518c\u7684 [\u5185\u90e8\u6784\u9020\u65b9\u6cd5](@ref) \u7ae0\u8282\u3002\n\"\"\"\nkw\"new\"\n\n\"\"\"\n    where\n\n`where` \u5173\u952e\u5b57\u521b\u5efa\u4e00\u4e2a\u7c7b\u578b\uff0c\u8be5\u7c7b\u578b\u662f\u5176\u4ed6\u7c7b\u578b\u5728\u4e00\u4e9b\u53d8\u91cf\u4e0a\u6240\u6709\u503c\u7684\u8fed\u4ee3\u5e76\u96c6\u3002\u4f8b\u5982 `Vector{T} where T<:Real` \u5305\u542b\u6240\u6709\u5143\u7d20\u7c7b\u578b\u662f\u67d0\u79cd `Real` \u7684 [`Vector`](@ref)\u3002\n\n\u5982\u679c\u7701\u7565\uff0c\u53d8\u91cf\u4e0a\u754c\u9ed8\u8ba4\u4e3a `Any`\uff1a\n\n```julia\nVector{T} where T    # short for `where T<:Any`\n```\n\n\u53d8\u91cf\u4e5f\u53ef\u4ee5\u5177\u6709\u4e0b\u754c\uff1a\n\n```julia\nVector{T} where T>:Int\nVector{T} where Int<:T<:Real\n```\n\n\u5d4c\u5957\u7684 `where` \u4e5f\u6709\u7b80\u6d01\u7684\u8bed\u6cd5\u3002\u4f8b\u5982\uff0c\u8fd9\u884c\u4ee3\u7801\uff1a\n\n```julia\nPair{T, S} where S<:Array{T} where T<:Number\n```\n\n\u53ef\u4ee5\u7f29\u5199\u4e3a\uff1a\n\n```julia\nPair{T, S} where {T<:Number, S<:Array{T}}\n```\n\n\u8fd9\u79cd\u5f62\u5f0f\u5e38\u89c1\u4e8e\u65b9\u6cd5\u7b7e\u540d\uff1a\n\n\u8bf7\u6ce8\u610f\uff0c\u5728\u8fd9\u79cd\u5f62\u5f0f\u4e2d\uff0c\u6700\u5916\u5c42\u53d8\u91cf\u5217\u5728\u6700\u524d\u9762\u3002\u8fd9\u4e0e\u4f7f\u7528\u8bed\u6cd5 `T{p1, p2, ...}` \u5c06\u7c7b\u578b\u300c\u4f5c\u7528\u300d\u4e8e\u53c2\u6570\u503c\u65f6\u6240\u66ff\u6362\u53d8\u91cf\u7684\u6b21\u5e8f\u76f8\u5339\u914d\u3002\n\"\"\"\nkw\"where\"\n\n\"\"\"\n    ans\n\n\u4e00\u4e2a\u5f15\u7528\u6700\u540e\u4e00\u6b21\u8ba1\u7b97\u7ed3\u679c\u7684\u53d8\u91cf\uff0c\u5728\u4ea4\u4e92\u5f0f\u63d0\u793a\u7b26\u4e2d\u4f1a\u81ea\u52a8\u8bbe\u7f6e\u3002\n\"\"\"\nkw\"ans\"\n\n\"\"\"\n    Union{}\n\n`Union{}`\uff0c\u5373\u7a7a\u7684\u7c7b\u578b [`Union`](@ref)\uff0c\u662f\u6ca1\u6709\u503c\u7684\u7c7b\u578b\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u5b83\u5177\u6709\u51b3\u5b9a\u6027\u6027\u8d28\uff1a\u5bf9\u4e8e\u4efb\u4f55 `x`\uff0c`isa(x, Union{}) == false`\u3002`Base.Bottom` \u88ab\u5b9a\u4e49\u4e3a\u5176\u522b\u540d\uff0c`Union{}` \u7684\u7c7b\u578b\u662f `Core.TypeofBottom`\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> isa(nothing, Union{})\nfalse\n```\n\"\"\"\nkw\"Union{}\", Base.Bottom\n\n\"\"\"\n    ::\n\n`::` \u8fd0\u7b97\u7b26\u7528\u4e8e\u7c7b\u578b\u58f0\u660e\uff0c\u5728\u7a0b\u5e8f\u4e2d\u53ef\u88ab\u9644\u52a0\u5230\u8868\u8fbe\u5f0f\u548c\u53d8\u91cf\u540e\u3002\u8be6\u89c1\u624b\u518c\u7684 [\u7c7b\u578b\u58f0\u660e](@ref) \u7ae0\u8282\n\n\u5728\u7c7b\u578b\u58f0\u660e\u5916\uff0c`::` \u7528\u4e8e\u65ad\u8a00\u7a0b\u5e8f\u4e2d\u7684\u8868\u8fbe\u5f0f\u548c\u53d8\u91cf\u5177\u6709\u7ed9\u5b9a\u7c7b\u578b\u3002\n\n# \u4f8b\u5b50\n```jldoctest\njulia> (1+2)::AbstractFloat\nERROR: TypeError: typeassert: expected AbstractFloat, got Int64\n\njulia> (1+2)::Int\n3\n```\n\"\"\"\nkw\"::\"\n\n\"\"\"\nJulia \u7684\u57fa\u7840\u5e93\u3002\n\"\"\"\nkw\"Base\"\n", "meta": {"hexsha": "5b5abdfbfeebcba59678d56501022aa5209333d6", "size": 11505, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/basedocs.jl", "max_stars_repo_name": "UnofficialJuliaMirror/JuliaZH.jl-652e05fd-ed22-5b6c-bf99-44e63a676e5f", "max_stars_repo_head_hexsha": "78c38dadab009de38e34f12af89eff240bb9a724", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 504, "max_stars_repo_stars_event_min_datetime": "2018-07-19T03:38:47.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T04:55:11.000Z", "max_issues_repo_path": "src/basedocs.jl", "max_issues_repo_name": "UnofficialJuliaMirror/JuliaZH.jl-652e05fd-ed22-5b6c-bf99-44e63a676e5f", "max_issues_repo_head_hexsha": "78c38dadab009de38e34f12af89eff240bb9a724", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 78, "max_issues_repo_issues_event_min_datetime": "2018-07-18T16:21:06.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-04T14:44:51.000Z", "max_forks_repo_path": "src/basedocs.jl", "max_forks_repo_name": "UnofficialJuliaMirror/JuliaZH.jl-652e05fd-ed22-5b6c-bf99-44e63a676e5f", "max_forks_repo_head_hexsha": "78c38dadab009de38e34f12af89eff240bb9a724", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 114, "max_forks_repo_forks_event_min_datetime": "2018-07-18T19:24:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-06T08:23:42.000Z", "avg_line_length": 16.3191489362, "max_line_length": 258, "alphanum_fraction": 0.6392003477, "num_tokens": 6183, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.32082130082460697, "lm_q2_score": 0.18242552825126704, "lm_q1q2_score": 0.05852599527718758}}
{"text": "mutable struct Point\n  x::Int64\n  y::Float64\n  meta\nend\np = Point(0, 0.0, \"Origin\")\np.x               # \u8bbf\u95ee\u57df\np.meta = 2        # \u6539\u53d8\u57df\u7684\u503c\np.x = 1.5         # \u9519\u8bef\uff0c\u6570\u636e\u7c7b\u578b\u4e0d\u5339\u914d\np.z = 1           # \u9519\u8bef\uff0c\u6ca1\u6709\u8fd9\u4e2a\u57df\nfieldnames(Point) # \u83b7\u53d6\u6240\u6709\u7684\u57df\u540d\n", "meta": {"hexsha": "5f9ad620262639b6644531df5c35dbffadecb934", "size": 222, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/struct.jl", "max_stars_repo_name": "zhaiyusci/Julia-Notes", "max_stars_repo_head_hexsha": "b8b6992af2a04d9eff5612f3d673f7e1e705a034", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/struct.jl", "max_issues_repo_name": "zhaiyusci/Julia-Notes", "max_issues_repo_head_hexsha": "b8b6992af2a04d9eff5612f3d673f7e1e705a034", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-04-23T13:40:13.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-23T13:40:29.000Z", "max_forks_repo_path": "src/struct.jl", "max_forks_repo_name": "zhaiyusci/Julia-Notes", "max_forks_repo_head_hexsha": "b8b6992af2a04d9eff5612f3d673f7e1e705a034", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5, "max_line_length": 30, "alphanum_fraction": 0.518018018, "num_tokens": 109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.13846179590164853, "lm_q1q2_score": 0.058500750513142685}}
{"text": "\"\"\"\n    grdimage(cmd0::String=\"\", arg1=[], arg2=[], arg3=[]; kwargs...)\n\nProduces a gray-shaded (or colored) map by plotting rectangles centered on each grid node and assigning them a gray-shade (or color) based on the z-value.\n\nFull option list at [`grdimage`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html)\n\nParameters\n----------\n\n- **A** : **img_out** : **image_out** : -- Str --\n\n    Save an image in a raster format instead of PostScript.\n    [`-A`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#a)\n- $(GMT.opt_J)\n- $(GMT.opt_B)\n- $(GMT.opt_C)\n- **D** : **img_in** : **image_in** : -- Str or [] --\n\n    Specifies that the grid supplied is an image file to be read via GDAL.\n    [`-D`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#d)\n- **E** : **dpi** : -- Int or [] --  \n\n    Sets the resolution of the projected grid that will be created.\n    [`-E`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#e)\n- **G** : -- Str or Int --\n\n    [`-G`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#g)\n- **I** : **shade** : **intensity** : **intensfile** : -- Str or GMTgrid --\n\n    Gives the name of a grid file or GMTgrid with intensities in the (-1,+1) range,\n    or a grdgradient shading flags.\n    [`-I`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#i)\n- **M** : **monochrome** : -- Bool or [] --\n\n    Force conversion to monochrome image using the (television) YIQ transformation.\n    [`-M`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#m)\n- **N** : **noclip** : -- Bool or [] --\n\n    Do not clip the image at the map boundary.\n    [`-N`](http://gmt.soest.hawaii.edu/doc/latest/grdimage.html#n)\n- $(GMT.opt_P)\n- **Q** : **nan_t** : **nan_alpha** : -- Bool or [] --\n\n    Make grid nodes with z = NaN transparent, using the colormasking feature in PostScript Level 3.\n- $(GMT.opt_R)\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- $(GMT.opt_f)\n- $(GMT.opt_n)\n- $(GMT.opt_p)\n- $(GMT.opt_t)\n\"\"\"\nfunction grdimage(cmd0::String=\"\", arg1=[], arg2=[], arg3=[], arg4=[]; K=false, O=false, first=true, kwargs...)\n\n\tlength(kwargs) == 0 && occursin(\" -\", cmd0) && return monolitic(\"grdimage\", cmd0, arg1)\t# Speedy mode\n\n\td = KW(kwargs)\n\toutput, opt_T, fname_ext = fname_out(d)\t\t# OUTPUT may have been an extension only\n\n\tcmd, opt_B, = parse_BJR(d, \"\", \"\", O, \" -JX12c/0\")\n\tcmd = parse_UVXY(cmd, d)\n\tcmd, = parse_f(cmd, d)\n\tcmd, = parse_n(cmd, d)\n\tcmd, = parse_p(cmd, d)\n\tcmd, = parse_t(cmd, d)\n\tcmd  = parse_params(cmd, d)\n\n\tcmd, K, O, = set_KO(cmd, opt_B, first, K, O)\t\t\t# Set the K O dance\n\n\tcmd = add_opt(cmd, 'A', d, [:A :img_out :image_out])\n\tcmd = add_opt(cmd, 'D', d, [:D :img_in :image_in])\n\tcmd = add_opt(cmd, 'E', d, [:E :dpi])\n\tcmd = add_opt(cmd, 'G', d, [:G])\n\tcmd = add_opt(cmd, 'M', d, [:M :monochrome])\n\tcmd = add_opt(cmd, 'N', d, [:N :noclip])\n\tcmd = add_opt(cmd, 'Q', d, [:Q :nan_t :nan_alpha])\n\n\tcmd, got_fname, arg1 = find_data(d, cmd0, cmd, 1, arg1)\t\t# Find how data was transmitted\n\tif (got_fname == 0 && isempty_(arg1))\t\t\t# Than it must be using the three r,g,b grids\n\t\tcmd, got_fname, arg1, arg2, arg3 = find_data(d, cmd0, cmd, 3, arg1, arg2, arg3)\n\t\tif (got_fname == 0 && isempty_(arg1))\n\t\t\terror(\"No input data to use in grdimage.\")\n\t\tend\n\tend\n\n\tif (isa(arg1, Array{<:Number}))\n\t\targ1 = mat2grid(arg1)\n\t\tif (!isempty_(arg2) && isa(arg2, Array{<:Number}))  arg2 = mat2grid(arg2)  end\n\t\tif (!isempty_(arg3) && isa(arg3, Array{<:Number}))  arg3 = mat2grid(arg3)  end\n\tend\n\n\tN_used = got_fname == 0 ? 1 : 0\t\t# To know whether a cpt will go to arg1 or arg2\n\tcmd, arg1, arg2, = add_opt_cpt(d, cmd, [:C :color :cmap], 'C', N_used, arg1, arg2)\n\tif (!isempty_(arg3) && occursin(\"-C\", cmd))\t\t# This lieves out the case when the r,g,b were sent as a text.\n\t\terror(\"Cannot use the three R,G,B grids and a color table.\")\n\tend\n\n\tfor sym in [:I :shade :intensity :intensfile]\n\t\tif (haskey(d, sym))\n\t\t\tif (!isa(d[sym], GMTgrid))\t\t# Uff, simple. Either a file name or a -A type modifier\n\t\t\t\tcmd = cmd * \" -I\" * arg2str(d[sym])\n\t\t\telse\n\t\t\t\tcmd, N = put_in_slot(cmd, d[sym], 'I', [arg1, arg2, arg3, arg4])\n\t\t\t\tif (N == 1)     arg1 = d[sym]\n\t\t\t\telseif (N == 2) arg2 = d[sym]\n\t\t\t\telseif (N == 3) arg3 = d[sym]\n\t\t\t\telseif (N == 4) arg4 = d[sym]\n\t\t\t\tend\n\t\t\tend\n\t\t\tbreak\n\t\tend\n\tend\n\n\tcmd = finish_PS(d, cmd, output, K, O)\n    return finish_PS_module(d, cmd, \"\", output, fname_ext, opt_T, K, \"grdimage\", arg1, arg2, arg3, arg4)\nend\n\n# ---------------------------------------------------------------------------------------------------\ngrdimage!(cmd0::String=\"\", arg1=[], arg2=[], arg3=[], arg4=[]; K=true, O=true, first=false, kw...) =\n\tgrdimage(cmd0, arg1, arg2, arg3, arg4; K=true, O=true, first=false, kw...) \n\ngrdimage(arg1, cmd0::String=\"\", arg2=[], arg3=[], arg4=[]; K=false, O=false, first=true, kw...) =\n\tgrdimage(cmd0, arg1, arg2, arg3, arg4; K=K, O=O, first=first, kw...)\n\ngrdimage!(arg1, cmd0::String=\"\", arg2=[], arg3=[], arg4=[]; K=true, O=true, first=false, kw...) =\n\tgrdimage(cmd0, arg1, arg2, arg3, arg4; K=K, O=O, first=first, kw...)", "meta": {"hexsha": "7d25c406b86728a3b3611f2255269d9502c4d558", "size": 5001, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grdimage.jl", "max_stars_repo_name": "JuliaDocsForks/GMT.jl", "max_stars_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/grdimage.jl", "max_issues_repo_name": "JuliaDocsForks/GMT.jl", "max_issues_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/grdimage.jl", "max_forks_repo_name": "JuliaDocsForks/GMT.jl", "max_forks_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.7674418605, "max_line_length": 154, "alphanum_fraction": 0.5970805839, "num_tokens": 1674, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.1422318950098963, "lm_q1q2_score": 0.058473077990056924}}
{"text": "\"\"\"\nThrow an `AssertionError` if conditions like `op(exp1, exp2)` are `false`, where `op` is a conditional infix operator.\n\n# Examples\n\n```\njulia> a = 3; b = 4;\njulia> @assert_op a == b\nERROR: AssertionError: 3 == 4\n\njulia> @assert_op a + 3 > b + 4\nERROR: AssertionError: 6 > 8\n```\n\"\"\"\nmacro assert_op(expr)\n    assert_op(expr)\nend\n\nfunction assert_op(expr::Expr)\n    # Only special case expressions of the form `expr1 == expr2`\n    if length(expr.args) == 3 && expr.head == :call\n        return assert_op(expr.args[1], expr.args[2], expr.args[3])\n    else\n        return :(@assert $(expr))\n    end\nend\n\nfunction assert_op(op, exp1, exp2)\n    return :(\n        if !$op($(esc(exp1)), $(esc(exp2)))\n            val1 = $(esc(exp1))\n            val2 = $(esc(exp2))\n            op_str = $(esc(op))\n            throw(AssertionError(\"$val1 $op_str $val2\"))\n        end\n    )\nend\n", "meta": {"hexsha": "28e5ac96fc4ed9095de1037d170e890a45f4defb", "size": 872, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/assert_op.jl", "max_stars_repo_name": "m-bossart/InfrastructureSystems.jl", "max_stars_repo_head_hexsha": "1879ffa82715c2ec1446d3781818972120d2bfaf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 25, "max_stars_repo_stars_event_min_datetime": "2020-04-25T20:30:52.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-25T01:01:09.000Z", "max_issues_repo_path": "src/utils/assert_op.jl", "max_issues_repo_name": "m-bossart/InfrastructureSystems.jl", "max_issues_repo_head_hexsha": "1879ffa82715c2ec1446d3781818972120d2bfaf", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 196, "max_issues_repo_issues_event_min_datetime": "2020-04-01T21:36:00.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-22T03:22:18.000Z", "max_forks_repo_path": "src/utils/assert_op.jl", "max_forks_repo_name": "m-bossart/InfrastructureSystems.jl", "max_forks_repo_head_hexsha": "1879ffa82715c2ec1446d3781818972120d2bfaf", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2020-07-15T16:32:36.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T16:09:30.000Z", "avg_line_length": 22.9473684211, "max_line_length": 118, "alphanum_fraction": 0.5802752294, "num_tokens": 264, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3998116407397951, "lm_q2_score": 0.14608724704829565, "lm_q1q2_score": 0.05840738193353887}}
{"text": "a = b = 0\n\nn = parse(Int, readline())\nfor i in 1:n\n    sa, sb = split(readline())\n    if sa < sb\n        global b += 3\n    elseif sa > sb\n        global a += 3\n    else\n        a += 1\n        b += 1\n    end\nend\nprintln(a, \" \", b)\n\n\n", "meta": {"hexsha": "5fad9d6618a8637d539806e1df1e072f5eaa9cd4", "size": 232, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ITP1/ITP1_9/ITP1_9_C.jl", "max_stars_repo_name": "goropikari/AizuOnlineJudge_solution_julia", "max_stars_repo_head_hexsha": "4c135f2359b421024800cbabd6efb458345075b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ITP1/ITP1_9/ITP1_9_C.jl", "max_issues_repo_name": "goropikari/AizuOnlineJudge_solution_julia", "max_issues_repo_head_hexsha": "4c135f2359b421024800cbabd6efb458345075b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ITP1/ITP1_9/ITP1_9_C.jl", "max_forks_repo_name": "goropikari/AizuOnlineJudge_solution_julia", "max_forks_repo_head_hexsha": "4c135f2359b421024800cbabd6efb458345075b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.8888888889, "max_line_length": 30, "alphanum_fraction": 0.4310344828, "num_tokens": 83, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.1225232173242878, "lm_q1q2_score": 0.05839207215960802}}
{"text": "# This file was generated, do not modify it. # hide\nusing RDatasets, DataFrames\nboston = dataset(\"MASS\", \"Boston\")\nfirst(boston, 3)", "meta": {"hexsha": "fa011cb368200b648b0dd9f49388b90b3059d730", "size": 131, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/isl/lab-3/code/ex2.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/isl/lab-3/code/ex2.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/isl/lab-3/code/ex2.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 32.75, "max_line_length": 51, "alphanum_fraction": 0.7328244275, "num_tokens": 39, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.12765263527792128, "lm_q1q2_score": 0.05835470658532657}}
{"text": "using Test\nusing Mimi\n\n@defcomp ArgTester begin\n  varA = Variable(index=[time])\n  parA = Parameter()\n   \n  function run_timestep(p, v, d, t)\n    v.varA[t] = p.parA\n  end\nend\n\nm = Model()\n\n# trying to run model with no components\nset_dimension!(m, :time, 1:10)\n@test_throws ErrorException run(m)\n", "meta": {"hexsha": "5b211a924b16c54e2f4cc2c3d0642d85851b5cec", "size": 295, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_num_components.jl", "max_stars_repo_name": "arnavgautam/Mimi.jl", "max_stars_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/test_num_components.jl", "max_issues_repo_name": "arnavgautam/Mimi.jl", "max_issues_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_num_components.jl", "max_forks_repo_name": "arnavgautam/Mimi.jl", "max_forks_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.3888888889, "max_line_length": 40, "alphanum_fraction": 0.6847457627, "num_tokens": 93, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45713671682749474, "lm_q2_score": 0.12765261702501565, "lm_q1q2_score": 0.058354698241253215}}
{"text": "module Example\n\nexport func\n\n\"\"\"\n    func(x)\n\nReturns double the number `x` plus `1`.\n\nHere's an equation:\n\n``\\\\frac{n!}{k!(n - k)!} = \\\\binom{n}{k}``\n\n\"\"\"\nfunc(x) = 2x + 1\n\nend\n", "meta": {"hexsha": "2cba04500b4dc95f0556e1ed0de0d6e84788a6ff", "size": 178, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Example.jl", "max_stars_repo_name": "jorgepz/Julia_Docs_Testbed", "max_stars_repo_head_hexsha": "f1271de3a024e194f5161b28cb9e7e7d11fca238", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Example.jl", "max_issues_repo_name": "jorgepz/Julia_Docs_Testbed", "max_issues_repo_head_hexsha": "f1271de3a024e194f5161b28cb9e7e7d11fca238", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-06-23T14:34:23.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-24T20:35:58.000Z", "max_forks_repo_path": "src/Example.jl", "max_forks_repo_name": "jorgepz/Julia_Docs_Testbed", "max_forks_repo_head_hexsha": "f1271de3a024e194f5161b28cb9e7e7d11fca238", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.8888888889, "max_line_length": 42, "alphanum_fraction": 0.5449438202, "num_tokens": 63, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.11757213663746784, "lm_q1q2_score": 0.05832681150355962}}
{"text": "# # GridapMakie\n#\n# [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://gridap.github.io/GridapMakie.jl/stable)\n# [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://gridap.github.io/GridapMakie.jl/dev)\n# [![Build Status](https://github.com/gridap/GridapMakie.jl/workflows/CI/badge.svg?branch=master)](https://github.com/gridap/GridapMakie.jl/actions)\n# [![Coverage](https://codecov.io/gh/gridap/GridapMakie.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/gridap/GridapMakie.jl)\n\n# ## Overview\n\n# The visualization of numerical results is an important part of finite element (FE) computations. However, before the inception of GridapMakie.jl, the \n# only approach available to data visualization of [Gridap.jl](https://github.com/gridap/Gridap.jl) computations was to write simulation \n# data to data files (e.g., in vtu format) for later visualization with, e.g., Paraview or VisIt. From the idea of visually inspecting \n# data from Julia code directly or to manipulate it with packages of the Julia \n# open-source package ecosystem, [GridapMakie.jl](https://github.com/gridap/GridapMakie.jl) is born. As a part of the Google Summer of \n# Code 2021 program, GridapMakie adopts [Makie.jl](https://github.com/JuliaPlots/Makie.jl) as a second visualization back-end for \n# Gridap.jl simulations. This package is thought as a built-in tool to assess the user in their FE calculations with a smoother workflow \n# in a highly intuitive API.\n#\n# ## Installation\n\n# According to Makie's guidelines, it is enough to install one of its backends, e.g. GLMakie. Additionally, Gridap provides the plot objects\n# to be visualized and `FileIO` allows to save the figures plotted. \n\n# ```julia\n# julia> ] \n# pkg> add Gridap, GridapMakie, GLMakie, FileIO\n# ```\n#\n# ## Examples\n\n# First things first, we shall be using the three packages as well as `FileIO`.\n# We may as well create directories to store downloaded meshes and output files\n\nusing Gridap, GridapMakie, GLMakie\nusing FileIO\nmkdir(\"models\")\nmkdir(\"images\")\n\n# ### 2D Plots\n\n# Then, let us consider a simple, 2D simplexified cartesian triangulation \u03a9\n\ndomain = (0, 1, 0, 1)\ncell_nums = (10, 10)\nmodel = CartesianDiscreteModel(domain, cell_nums) |> simplexify\n\u03a9 = Triangulation(model)\n\n\n# The visualization of the vertices, edges, and faces of \u03a9 can be achieved as follows\n\nfig = plot(\u03a9)\nwireframe!(\u03a9, color=:black, linewidth=2)\nscatter!(\u03a9, marker=:star8, markersize=20, color=:blue)\nsave(\"images/2d_Fig1.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/2d_Fig1.png\" width=\"500\"/>\n# </p>\n\n# We now consider a FE function `uh` constructed with Gridap\n\nreffe = ReferenceFE(lagrangian, Float64, 1)\nV = FESpace(model, reffe)\nuh = interpolate(x->sin(\u03c0*(x[1]+x[2])), V)\n\n# and plot it over \u03a9, adding a colorbar\n\nfig, _ , plt = plot(\u03a9, uh)\nColorbar(fig[1,2], plt)\nsave(\"images/2d_Fig11.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/2d_Fig11.png\" width=\"500\"/>\n# </p>\n\n# On the other hand, we may as well plot cell values\n\ncelldata = \u03c0*rand(num_cells(\u03a9)) .-1\nfig, _ , plt = plot(\u03a9, color=celldata, colormap=:heat)\nColorbar(fig[2,1], plt, vertical=false)\nsave(\"images/2d_Fig13.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/2d_Fig13.png\" width=\"500\"/>\n# </p>\n\n# If we are only interested in the boundary of \u03a9, namely \u0393\n\n\u0393 = BoundaryTriangulation(model)\nfig, _ , plt = plot(\u0393, uh, colormap=:algae, linewidth=10)\nColorbar(fig[1,2], plt)\nsave(\"images/2d_Fig111.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/2d_Fig111.png\" width=\"500\"/>\n# </p>\n\n# ### 3D Plots\n\n# In addition to the 2D plots, GridapMakie is able to handle more complex geometries. For example, \n# take the mesh from the [first Gridap tutorial](https://gridap.github.io/Tutorials/stable/pages/t001_poisson/#Tutorial-1:-Poisson-equation-1),\n# which can be downloaded using\n\nurl = \"https://github.com/gridap/GridapMakie.jl/raw/d5d74190e68bd310483fead8a4154235a61815c5/_readme/model.json\"\ndownload(url,\"models/model.json\")\n\n# Therefore, we may as well visualize such mesh\n\nmodel = DiscreteModelFromFile(\"models/model.json\")\n\u03a9 = Triangulation(model)\n\u2202\u03a9 = BoundaryTriangulation(model)\nfig = plot(\u03a9, shading=true)\nwireframe!(\u2202\u03a9, color=:black)\nsave(\"images/3d_Fig1.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/3d_Fig1.png\" width=\"500\"/>\n# </p>\n\nv(x) = sin(\u03c0*(x[1]+x[2]+x[3]))\nfig, ax, plt = plot(\u03a9, v, shading=true)\nColorbar(fig[1,2], plt)\nsave(\"images/3d_Fig2.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/3d_Fig2.png\" width=\"500\"/>\n# </p>\n\n# we can even plot functions in certain subdomains, e.g.\n\n\u0393 = BoundaryTriangulation(model, tags=[\"square\", \"triangle\", \"circle\"])\nfig = plot(\u0393, v, colormap=:rainbow, shading=true)\nwireframe!(\u2202\u03a9, linewidth=0.5, color=:gray)\nsave(\"images/3d_Fig3.png\", fig)\n# <p align=\"center\">\n# <img src=\"_readme/images/3d_Fig3.png\" width=\"500\"/>\n# </p>\n\n# ### Animations and interactivity\n\n# Finally, by using Makie [Observables](https://makie.juliaplots.org/stable/interaction/nodes.html), we\n# can create animations or interactive plots. For example, if the nodal field has a time dependence\n\nt = Observable(0.0)\nu = lift(t) do t\n    x->sin(\u03c0*(x[1]+x[2]+x[3]))*cos(\u03c0*t)\nend\nfig = plot(\u03a9, u, colormap=:rainbow, shading=true, colorrange=(-1,1))\nwireframe!(\u2202\u03a9, color=:black, linewidth=0.5)\nframerate = 30\ntimestamps = range(0, 2, step=1/framerate)\nrecord(fig, \"images/animation.gif\", timestamps; framerate=framerate) do this_t\n    t[] = this_t\nend\n# <p align=\"center\">\n# <img src=\"_readme/images/animation.gif\" width=\"500\"/>\n# </p>", "meta": {"hexsha": "728a2c53f58777eff1075d7ddf33a2f29539ffe5", "size": 5561, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_readme/README.jl", "max_stars_repo_name": "gridap/GridapMakie.jl", "max_stars_repo_head_hexsha": "524f3a4f8b63a4a9ac9b965865fc4bb3170ae98c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2020-08-11T12:13:28.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T01:22:36.000Z", "max_issues_repo_path": "_readme/README.jl", "max_issues_repo_name": "gridap/GridapMakie.jl", "max_issues_repo_head_hexsha": "524f3a4f8b63a4a9ac9b965865fc4bb3170ae98c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2020-08-12T11:02:12.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-01T11:42:36.000Z", "max_forks_repo_path": "_readme/README.jl", "max_forks_repo_name": "gridap/GridapMakie.jl", "max_forks_repo_head_hexsha": "524f3a4f8b63a4a9ac9b965865fc4bb3170ae98c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8278145695, "max_line_length": 152, "alphanum_fraction": 0.7187556195, "num_tokens": 1767, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.12421300186801369, "lm_q1q2_score": 0.05822989098083293}}
{"text": "f(x, y...) = y\n@assert f(1, 2, 3) == (2, 3)\n\ng(x; y...) = y\n@assert g(1, a=2, b=5) == [(:a,2),(:b,5)]", "meta": {"hexsha": "104f8cb1ac1189ed5bda8a000e89afab8824441b", "size": 101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/parameter_packing_and_unpacking.jl", "max_stars_repo_name": "BBK-DCSIS-Programming-Paradigms-2018-19/code", "max_stars_repo_head_hexsha": "fbf4e45899b7555fc3b12fcb1fefa172ef415060", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-10-17T18:35:39.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-27T13:18:08.000Z", "max_issues_repo_path": "julia/parameter_packing_and_unpacking.jl", "max_issues_repo_name": "BBK-DCSIS-Programming-Paradigms-2018-19/code", "max_issues_repo_head_hexsha": "fbf4e45899b7555fc3b12fcb1fefa172ef415060", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/parameter_packing_and_unpacking.jl", "max_forks_repo_name": "BBK-DCSIS-Programming-Paradigms-2018-19/code", "max_forks_repo_head_hexsha": "fbf4e45899b7555fc3b12fcb1fefa172ef415060", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-28T21:50:43.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-28T21:50:43.000Z", "avg_line_length": 20.2, "max_line_length": 41, "alphanum_fraction": 0.3564356436, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.12421299700498412, "lm_q1q2_score": 0.05822988870109024}}
{"text": "abstract type BinaryNode{T} <: AbstractNode{T} end\n\n### Interface\n\n# Return the node's value.\ngetvalue(n::BinaryNode) = nothing\n\n# Set the node's value.\nsetvalue!(n::BinaryNode) = nothing\n\n# Return node's left child.\nleft(n::BinaryNode) = nothing\n\n# Set the node' left child.\nsetleft!(n::BinaryNode, node) = nothing\n\n# Return node's left child.\nright(n::BinaryNode) = nothing\n\n# Set the node' left child.\nsetright!(n::BinaryNode, node) = nothing\n\n# Check if node is a leaf.\nisleaf(n::BinaryNode) = nothing\n\n# generic traversal and others\n# function traversal() end\n\n# preorder, inorder, postorder, levelorder\n", "meta": {"hexsha": "f271bd4c3da0a0f4136edb847e2e9df54ffb6d68", "size": 609, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tree/abstract_binarynode.jl", "max_stars_repo_name": "hesseltuinhof/DataStructures.jl", "max_stars_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/tree/abstract_binarynode.jl", "max_issues_repo_name": "hesseltuinhof/DataStructures.jl", "max_issues_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/tree/abstract_binarynode.jl", "max_forks_repo_name": "hesseltuinhof/DataStructures.jl", "max_forks_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.3, "max_line_length": 50, "alphanum_fraction": 0.7192118227, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.414898860266261, "lm_q2_score": 0.1403362494900832, "lm_q1q2_score": 0.05822534996747717}}
{"text": "#### Elementwise manipulations (scaling/clamping/type conversion) ####\n\n# This file exists primarily to handle conversions for display and\n# saving to disk. Both of these operations require UFixed-valued\n# elements, but with display we always want to convert to 8-bit\n# whereas saving can handle 16-bit.\n# We also can't trust that user images are clamped properly.\n# Finally, this supports adjustable contrast limits.\n\n# Structure of MapInfo subtype definitions:\n#   - type definition\n#   - constructors for scalars\n#   - constructors for AbstractArrays\n#   - similar  (syntax: similar(mapi, ToType, FromType))\n#   - implementation of map() for scalars\n#   - implementation of map() for AbstractArrays\n# map(mapi::MapInfo{T}, x) should return an object of type T (for x not an array)\n# map1(mapi::MapInfo{T}, x) is designed to allow T<:Color to work on\n#    scalars x::Fractional\n\n\n# Dispatch-based elementwise manipulations\n\"\"\"\n`MapInfo{T}` is an abstract type that encompasses objects designed to\nperform intensity or color transformations on pixels.  For example,\nbefore displaying an image in a window, you might need to adjust the\ncontrast settings; `MapInfo` objects provide a means to describe these\ntransformations without calculating them immediately.  This delayed\nexecution can be useful in many contexts.  For example, if you want to\ndisplay a movie, it would be quite wasteful to have to first transform\nthe entire movie; instead, `MapInfo` objects allow one to specify a\ntransformation to be performed on-the-fly as particular frames are\ndisplayed.\n\nYou can create your own custom `MapInfo` objects. For example, given a\ngrayscale image, you could color \"saturated\" pixels red using\n\n```jl\nimmutable ColorSaturated{C<:AbstractRGB} <: MapInfo{C}\nend\n\nBase.map{C}(::ColorSaturated{C}, val::Union{Number,Gray}) = ifelse(val == 1, C(1,0,0), C(val,val,val))\n\nimgc = map(ColorSaturated{RGB{U8}}(), img)\n```\n\nFor pre-defined types see `MapNone`, `BitShift`, `ClampMinMax`, `ScaleMinMax`,\n`ScaleAutoMinMax`, and `ScaleSigned`.\n\"\"\"\nabstract MapInfo{T}\neltype{T}(mapi::MapInfo{T}) = T\n\n\n## MapNone\n\"`MapNone(T)` is a `MapInfo` object that converts `x` to have type `T`.\"\nimmutable MapNone{T} <: MapInfo{T}; end\n\n# Constructors\nMapNone{T}(::Type{T}) = MapNone{T}()\nMapNone{T}(val::T) = MapNone{T}()\nMapNone{T}(A::AbstractArray{T}) = MapNone{T}()\n\nsimilar{T}(mapi::MapNone, ::Type{T}, ::Type) = MapNone{T}()\n\n# Implementation\nmap{T}(mapi::MapNone{T}, val::Union{Number,Colorant}) = convert(T, val)\nmap1(mapi::Union{MapNone{RGB24}, MapNone{ARGB32}}, b::Bool) = ifelse(b, 0xffuf8, 0x00uf8)\nmap1(mapi::Union{MapNone{RGB24},MapNone{ARGB32}}, val::Fractional) = convert(UFixed8, val)\nmap1{CT<:Colorant}(mapi::MapNone{CT}, val::Fractional) = convert(eltype(CT), val)\n\nmap{T<:Colorant}(mapi::MapNone{T}, img::AbstractImageIndexed{T}) = convert(Image{T}, img)\nmap{C<:Colorant}(mapi::MapNone{C}, img::AbstractImageDirect{C}) = img  # ambiguity resolution\nmap{T}(mapi::MapNone{T}, img::AbstractArray{T}) = img\n\n\n## BitShift\n\"\"\"\n`BitShift{T,N}` performs a \"saturating rightward bit-shift\" operation.\nIt is particularly useful in converting high bit-depth images to 8-bit\nimages for the purpose of display.  For example,\n\n```\nmap(BitShift(UFixed8, 8), 0xa2d5uf16) === 0xa2uf8\n```\n\nconverts a `UFixed16` to the corresponding `UFixed8` by discarding the\nleast significant byte.  However,\n\n```\nmap(BitShift(UFixed8, 7), 0xa2d5uf16) == 0xffuf8\n```\n\nbecause `0xa2d5>>7 == 0x0145 > typemax(UInt8)`.\n\nWhen applicable, the main advantage of using `BitShift` rather than\n`MapNone` or `ScaleMinMax` is speed.\n\"\"\"\nimmutable BitShift{T,N} <: MapInfo{T} end\nBitShift{T}(::Type{T}, n::Int) = BitShift{T,n}()  # note that this is not type-stable\n\nsimilar{S,T,N}(mapi::BitShift{S,N}, ::Type{T}, ::Type) = BitShift{T,N}()\n\n# Implementation\nimmutable BS{N} end\n_map{T<:Unsigned,N}(::Type{T}, ::Type{BS{N}}, val::Unsigned) = (v = val>>>N; tm = oftype(val, typemax(T)); convert(T, ifelse(v > tm, tm, v)))\n_map{T<:UFixed,N}(::Type{T}, ::Type{BS{N}}, val::UFixed) = reinterpret(T, _map(FixedPointNumbers.rawtype(T), BS{N}, reinterpret(val)))\nmap{T<:Real,N}(mapi::BitShift{T,N}, val::Real) = _map(T, BS{N}, val)\nmap{T<:Real,N}(mapi::BitShift{Gray{T},N}, val::Gray) = Gray(_map(T, BS{N}, val.val))\nmap1{N}(mapi::Union{BitShift{RGB24,N},BitShift{ARGB32,N}}, val::Unsigned) = _map(UInt8, BS{N}, val)\nmap1{N}(mapi::Union{BitShift{RGB24,N},BitShift{ARGB32,N}}, val::UFixed) = _map(UFixed8, BS{N}, val)\nmap1{CT<:Colorant,N}(mapi::BitShift{CT,N}, val::UFixed) = _map(eltype(CT), BS{N}, val)\n\n\n## Clamp types\n# The Clamp types just enforce bounds, but do not scale or offset\n\n# Types and constructors\nabstract AbstractClamp{T} <: MapInfo{T}\n\"\"\"\n`ClampMin(T, minvalue)` is a `MapInfo` object that clamps pixel values\nto be greater than or equal to `minvalue` before converting to type `T`.\n\nSee also: `ClampMax`, `ClampMinMax`.\n\"\"\"\nimmutable ClampMin{T,From} <: AbstractClamp{T}\n    min::From\nend\nClampMin{T,From}(::Type{T}, min::From) = ClampMin{T,From}(min)\nClampMin{T}(min::T) = ClampMin{T,T}(min)\n\"\"\"\n`ClampMax(T, maxvalue)` is a `MapInfo` object that clamps pixel values\nto be less than or equal to `maxvalue` before converting to type `T`.\n\nSee also: `ClampMin`, `ClampMinMax`.\n\"\"\"\nimmutable ClampMax{T,From} <: AbstractClamp{T}\n    max::From\nend\nClampMax{T,From}(::Type{T}, max::From) = ClampMax{T,From}(max)\nClampMax{T}(max::T) = ClampMax{T,T}(max)\nimmutable ClampMinMax{T,From} <: AbstractClamp{T}\n    min::From\n    max::From\nend\n\"\"\"\n`ClampMinMax(T, minvalue, maxvalue)` is a `MapInfo` object that clamps\npixel values to be between `minvalue` and `maxvalue` before converting\nto type `T`.\n\nSee also: `ClampMin`, `ClampMax`, and `Clamp`.\n\"\"\"\nClampMinMax{T,From}(::Type{T}, min::From, max::From) = ClampMinMax{T,From}(min,max)\nClampMinMax{T}(min::T, max::T) = ClampMinMax{T,T}(min,max)\n\"\"\"\n`Clamp(C)` is a `MapInfo` object that clamps color values to be within\ngamut.  For example,\n\n```\nmap(Clamp(RGB{U8}), RGB(1.2, -0.4, 0.6)) === RGB{U8}(1, 0, 0.6)\n```\n\"\"\"\nimmutable Clamp{T} <: AbstractClamp{T} end\nClamp{T}(::Type{T}) = Clamp{T}()\n\nsimilar{T,F}(mapi::ClampMin, ::Type{T}, ::Type{F}) = ClampMin{T,F}(convert(F, mapi.min))\nsimilar{T,F}(mapi::ClampMax, ::Type{T}, ::Type{F}) = ClampMax{T,F}(convert(F, mapi.max))\nsimilar{T,F}(mapi::ClampMinMax, ::Type{T}, ::Type{F}) = ClampMin{T,F}(convert(F, mapi.min), convert(F, mapi.max))\nsimilar{T,F}(mapi::Clamp, ::Type{T}, ::Type{F}) = Clamp{T}()\n\n# Implementation\nmap{T<:Real,F<:Real}(mapi::ClampMin{T,F}, val::F) = convert(T, max(val, mapi.min))\nmap{T<:Real,F<:Real}(mapi::ClampMax{T,F}, val::F) = convert(T, min(val, mapi.max))\nmap{T<:Real,F<:Real}(mapi::ClampMinMax{T,F}, val::F) = convert(T,min(max(val, mapi.min), mapi.max))\nmap{T<:Fractional,F<:Real}(mapi::ClampMin{Gray{T},F}, val::F) = convert(Gray{T}, max(val, mapi.min))\nmap{T<:Fractional,F<:Real}(mapi::ClampMax{Gray{T},F}, val::F) = convert(Gray{T}, min(val, mapi.max))\nmap{T<:Fractional,F<:Real}(mapi::ClampMinMax{Gray{T},F}, val::F) = convert(Gray{T},min(max(val, mapi.min), mapi.max))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMin{Gray{T},F}, val::Gray{F}) = convert(Gray{T}, max(val, mapi.min))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMax{Gray{T},F}, val::Gray{F}) = convert(Gray{T}, min(val, mapi.max))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMinMax{Gray{T},F}, val::Gray{F}) = convert(Gray{T},min(max(val, mapi.min), mapi.max))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMin{Gray{T},Gray{F}}, val::Gray{F}) = convert(Gray{T}, max(val, mapi.min))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMax{Gray{T},Gray{F}}, val::Gray{F}) = convert(Gray{T}, min(val, mapi.max))\nmap{T<:Fractional,F<:Fractional}(mapi::ClampMinMax{Gray{T},Gray{F}}, val::Gray{F}) = convert(Gray{T},min(max(val, mapi.min), mapi.max))\nmap1{T<:Union{RGB24,ARGB32},F<:Fractional}(mapi::ClampMin{T,F}, val::F) = convert(UFixed8, max(val, mapi.min))\nmap1{T<:Union{RGB24,ARGB32},F<:Fractional}(mapi::ClampMax{T,F}, val::F) = convert(UFixed8, min(val, mapi.max))\nmap1{T<:Union{RGB24,ARGB32},F<:Fractional}(mapi::ClampMinMax{T,F}, val::F) = convert(UFixed8,min(max(val, mapi.min), mapi.max))\nmap1{CT<:Colorant,F<:Fractional}(mapi::ClampMin{CT,F}, val::F) = convert(eltype(CT), max(val, mapi.min))\nmap1{CT<:Colorant,F<:Fractional}(mapi::ClampMax{CT,F}, val::F) = convert(eltype(CT), min(val, mapi.max))\nmap1{CT<:Colorant,F<:Fractional}(mapi::ClampMinMax{CT,F}, val::F) = convert(eltype(CT), min(max(val, mapi.min), mapi.max))\n\nmap{To<:Real}(::Clamp{To}, val::Real) = clamp01(To, val)\nmap{To<:Real}(::Clamp{Gray{To}}, val::AbstractGray) = Gray(clamp01(To, val.val))\nmap{To<:Real}(::Clamp{Gray{To}}, val::Real) = Gray(clamp01(To, val))\nmap1{CT<:AbstractRGB}(::Clamp{CT}, val::Real) = clamp01(eltype(CT), val)\nmap1{P<:TransparentRGB}(::Clamp{P}, val::Real) = clamp01(eltype(P), val)\n\n# Also available as a stand-alone function\nclamp01{T}(::Type{T}, x::Real) = convert(T, min(max(x, zero(x)), one(x)))\nclamp01(x::Real) = clamp01(typeof(x), x)\nclamp01(x::Colorant) = clamp01(typeof(x), x)\nclamp01{Cdest<:AbstractRGB   }(::Type{Cdest}, x::AbstractRGB)    = (To = eltype(Cdest);\n    Cdest(clamp01(To, red(x)), clamp01(To, green(x)), clamp01(To, blue(x))))\nclamp01{Pdest<:TransparentRGB}(::Type{Pdest}, x::TransparentRGB) = (To = eltype(Pdest);\n    Pdest(clamp01(To, red(x)), clamp01(To, green(x)), clamp01(To, blue(x)), clamp01(To, alpha(x))))\n\n# clamp is generic for any colorspace; this version does the right thing for any RGB type\nclamp(x::Union{AbstractRGB, TransparentRGB}) = clamp01(x)\n\n## ScaleMinMax\n\"\"\"\n`ScaleMinMax(T, min, max, [scalefactor])` is a `MapInfo` object that\nclamps the image at the specified `min`/`max` values, subtracts the\n`min` value, scales the result by multiplying by `scalefactor`, and\nfinally converts to type `T`.  If `scalefactor` is not specified, it\ndefaults to scaling the interval `[min,max]` to `[0,1]`.\n\nAlternative constructors include `ScaleMinMax(T, img)` for which\n`min`, `max`, and `scalefactor` are computed from the minimum and\nmaximum values found in `img`.\n\nSee also: `ScaleMinMaxNaN`, `ScaleAutoMinMax`, `MapNone`, `BitShift`.\n\"\"\"\nimmutable ScaleMinMax{To,From,S<:AbstractFloat} <: MapInfo{To}\n    min::From\n    max::From\n    s::S\n\n    function ScaleMinMax(min, max, s)\n        min >= max && error(\"min must be smaller than max\")\n        new(min, max, s)\n    end\nend\n\nScaleMinMax{To,From}(::Type{To}, min::From, max::From, s::AbstractFloat) = ScaleMinMax{To,From,typeof(s)}(min, max, s)\nScaleMinMax{To<:Union{Fractional,Colorant},From}(::Type{To}, mn::From, mx::From) = ScaleMinMax(To, mn, mx, 1.0f0/(convert(Float32, mx)-convert(Float32, mn)))\n\n# ScaleMinMax constructors that take AbstractArray input\nScaleMinMax{To,From<:Real}(::Type{To}, img::AbstractArray{From}, mn::Real, mx::Real) = ScaleMinMax(To, convert(From,mn), convert(From,mx), 1.0f0/(convert(Float32, convert(From, mx))-convert(Float32,convert(From, mn))))\nScaleMinMax{To,From<:Real}(::Type{To}, img::AbstractArray{Gray{From}}, mn::Real, mx::Real) = ScaleMinMax(To, convert(From,mn), convert(From,mx), 1.0f0/(convert(Float32, convert(From,mx))-convert(Float32, convert(From,mn))))\nScaleMinMax{To,From<:Real,R<:Real}(::Type{To}, img::AbstractArray{From}, mn::Gray{R}, mx::Gray{R}) = ScaleMinMax(To, convert(From,mn.val), convert(From,mx.val), 1.0f0/(convert(Float32, convert(From,mx.val))-convert(Float32, convert(From,mn.val))))\nScaleMinMax{To,From<:Real,R<:Real}(::Type{To}, img::AbstractArray{Gray{From}}, mn::Gray{R}, mx::Gray{R}) = ScaleMinMax(To, convert(From,mn.val), convert(From,mx.val), 1.0f0/(convert(Float32, convert(From,mx.val))-convert(Float32, convert(From,mn.val))))\nScaleMinMax{To}(::Type{To}, img::AbstractArray) = ScaleMinMax(To, img, minfinite(img), maxfinite(img))\nScaleMinMax{To,CV<:AbstractRGB}(::Type{To}, img::AbstractArray{CV}) = (imgr = reinterpret(eltype(CV), img); ScaleMinMax(To, minfinite(imgr), maxfinite(imgr)))\n\nsimilar{T,F,To,From,S}(mapi::ScaleMinMax{To,From,S}, ::Type{T}, ::Type{F}) = ScaleMinMax{T,F,S}(convert(F,mapi.min), convert(F.mapi.max), mapi.s)\n\n# Implementation\nfunction map{To<:Union{Real,AbstractGray},From<:Union{Real,AbstractGray}}(mapi::ScaleMinMax{To,From}, val::From)\n    g = gray(val)\n    t = ifelse(g < mapi.min, zero(From), ifelse(g > mapi.max, mapi.max-mapi.min, g-mapi.min))\n    convert(To, mapi.s*t)\nend\nfunction map{To<:Union{Real,AbstractGray},From<:Union{Real,AbstractGray}}(mapi::ScaleMinMax{To,From}, val::Union{Real,Colorant})\n    map(mapi, convert(From, val))\nend\nfunction map1{To<:Union{RGB24,ARGB32},From<:Real}(mapi::ScaleMinMax{To,From}, val::From)\n    t = ifelse(val < mapi.min, zero(From), ifelse(val > mapi.max, mapi.max-mapi.min, val-mapi.min))\n    convert(UFixed8, mapi.s*t)\nend\nfunction map1{To<:Colorant,From<:Real}(mapi::ScaleMinMax{To,From}, val::From)\n    t = ifelse(val < mapi.min, zero(From), ifelse(val > mapi.max, mapi.max-mapi.min, val-mapi.min))\n    convert(eltype(To), mapi.s*t)\nend\nfunction map1{To<:Union{RGB24,ARGB32},From<:Real}(mapi::ScaleMinMax{To,From}, val::Union{Real,Colorant})\n    map1(mapi, convert(From, val))\nend\nfunction map1{To<:Colorant,From<:Real}(mapi::ScaleMinMax{To,From}, val::Union{Real,Colorant})\n    map1(mapi, convert(From, val))\nend\n\n## ScaleSigned\n\"\"\"\n`ScaleSigned(T, scalefactor)` is a `MapInfo` object designed for\nvisualization of images where the pixel's sign has special meaning.\nIt multiplies the pixel value by `scalefactor`, then clamps to the\ninterval `[-1,1]`. If `T` is a floating-point type, it stays in this\nrepresentation.  If `T` is an `AbstractRGB`, then it is encoded as a\nmagenta (positive)/green (negative) image, with the intensity of the\ncolor proportional to the clamped absolute value.\n\"\"\"\nimmutable ScaleSigned{T, S<:AbstractFloat} <: MapInfo{T}\n    s::S\nend\nScaleSigned{T}(::Type{T}, s::AbstractFloat) = ScaleSigned{T, typeof(s)}(s)\n\nScaleSigned{T}(::Type{T}, img::AbstractArray) = ScaleSigned(T, 1.0f0/maxabsfinite(img))\nScaleSigned(img::AbstractArray) = ScaleSigned(Float32, img)\n\nsimilar{T,To,S}(mapi::ScaleSigned{To,S}, ::Type{T}, ::Type) = ScaleSigned{T,S}(mapi.s)\n\nmap{T}(mapi::ScaleSigned{T}, val::Real) = convert(T, clamppm(mapi.s*val))\nfunction map{C<:AbstractRGB}(mapi::ScaleSigned{C}, val::Real)\n    x = clamppm(mapi.s*val)\n    g = UFixed8(abs(x))\n    ifelse(x >= 0, C(g, zero(UFixed8), g), C(zero(UFixed8), g, zero(UFixed8)))\nend\n\nclamppm(x::Real) = ifelse(x >= 0, min(x, one(x)), max(x, -one(x)))\n\n## ScaleAutoMinMax\n# Works only on whole arrays, not values\n\"\"\"\n`ScaleAutoMinMax(T)` constructs a `MapInfo` object that causes images\nto be dynamically scaled to their specific min/max values, using the\nsame algorithm for `ScaleMinMax`. When displaying a movie, the min/max\nwill be recalculated for each frame, so this can result in\ninconsistent contrast scaling.\n\"\"\"\nimmutable ScaleAutoMinMax{T} <: MapInfo{T} end\nScaleAutoMinMax{T}(::Type{T}) = ScaleAutoMinMax{T}()\nScaleAutoMinMax() = ScaleAutoMinMax{UFixed8}()\n\nsimilar{T}(mapi::ScaleAutoMinMax, ::Type{T}, ::Type) = ScaleAutoMinMax{T}()\n\n## NaN-nulling mapping\n\"\"\"\n`ScaleMinMaxNaN(smm)` constructs a `MapInfo` object from a\n`ScaleMinMax` object `smm`, with the additional property that `NaN`\nvalues map to zero.\n\nSee also: `ScaleMinMax`.\n\"\"\"\nimmutable ScaleMinMaxNaN{To,From,S} <: MapInfo{To}\n    smm::ScaleMinMax{To,From,S}\nend\n\n\"\"\"\n`Clamp01NaN(T)` or `Clamp01NaN(img)` constructs a `MapInfo` object\nthat clamps grayscale or color pixels to the interval `[0,1]`, sending\n`NaN` pixels to zero.\n\"\"\"\nimmutable Clamp01NaN{T} <: MapInfo{T} end\n\nClamp01NaN{T}(A::AbstractArray{T}) = Clamp01NaN{T}()\n\n# Implementation\nsimilar{T,F,To,From,S}(mapi::ScaleMinMaxNaN{To,From,S}, ::Type{T}, ::Type{F}) = ScaleMinMaxNaN{T,F,S}(similar(mapi.smm, T, F))\nsimilar{T}(mapi::Clamp01NaN, ::Type{T}, ::Type) = Clamp01NaN{T}()\n\nBase.map{To}(smmn::ScaleMinMaxNaN{To}, g::Number) = isnan(g) ? zero(To) : map(smmn.smm, g)\nBase.map{To}(smmn::ScaleMinMaxNaN{To}, g::Gray) = isnan(g) ? zero(To) : map(smmn.smm, g)\n\nfunction Base.map{T<:RGB}(::Clamp01NaN{T}, c::AbstractRGB)\n    r, g, b = red(c), green(c), blue(c)\n    if isnan(r) || isnan(g) || isnan(b)\n        return T(0,0,0)\n    end\n    T(clamp(r, 0, 1), clamp(g, 0, 1), clamp(b, 0, 1))\nend\nfunction Base.map{T<:Union{Fractional,Gray}}(::Clamp01NaN{T}, c::Union{Fractional,AbstractGray})\n    g = gray(c)\n    if isnan(g)\n        return T(0)\n    end\n    T(clamp(g, 0, 1))\nend\n\n# Conversions to RGB{T}, RGBA{T}, RGB24, ARGB32,\n# for grayscale, AbstractRGB, and abstract ARGB inputs.\n# This essentially \"vectorizes\" map over a single pixel's color channels using map1\nfor SI in (MapInfo, AbstractClamp)\n    for ST in subtypes(SI)\n        ST.abstract && continue\n        ST == ScaleSigned && continue  # ScaleSigned gives an RGB from a scalar, so don't \"vectorize\" it\n        @eval begin\n            # Grayscale and GrayAlpha inputs\n            map(mapi::$ST{RGB24}, g::Gray) = map(mapi, g.val)\n            map(mapi::$ST{RGB24}, g::Real) = (x = map1(mapi, g); convert(RGB24, RGB{UFixed8}(x,x,x)))\n            function map(mapi::$ST{RGB24}, g::AbstractFloat)\n                if isfinite(g)\n                    x = map1(mapi, g)\n                    convert(RGB24, RGB{UFixed8}(x,x,x))\n                else\n                    RGB24(0)\n                end\n            end\n            map{G<:Gray}(mapi::$ST{RGB24}, g::TransparentColor{G}) = map(mapi, gray(g))\n            map(mapi::$ST{ARGB32}, g::Gray) = map(mapi, g.val)\n            function map(mapi::$ST{ARGB32}, g::Real)\n                x = map1(mapi, g)\n                convert(ARGB32, ARGB{UFixed8}(x,x,x,0xffuf8))\n            end\n            function map{G<:Gray}(mapi::$ST{ARGB32}, g::TransparentColor{G})\n                x = map1(mapi, gray(g))\n                convert(ARGB32, ARGB{UFixed8}(x,x,x,map1(mapi, g.alpha)))\n            end\n        end\n        for O in (:RGB, :BGR)\n            @eval begin\n                map{T}(mapi::$ST{$O{T}}, g::Gray) = map(mapi, g.val)\n                function map{T}(mapi::$ST{$O{T}}, g::Real)\n                    x = map1(mapi, g)\n                    $O{T}(x,x,x)\n                end\n            end\n        end\n        for OA in (:RGBA, :ARGB, :BGRA)\n            exAlphaGray = ST == MapNone ? :nothing : quote\n                function map{T,G<:Gray}(mapi::$ST{$OA{T}}, g::TransparentColor{G})\n                    x = map1(mapi, gray(g))\n                    $OA{T}(x,x,x,map1(mapi, g.alpha))\n                end  # avoids an ambiguity warning with MapNone definitions\n            end\n            @eval begin\n                map{T}(mapi::$ST{$OA{T}}, g::Gray) = map(mapi, g.val)\n                function map{T}(mapi::$ST{$OA{T}}, g::Real)\n                    x = map1(mapi, g)\n                    $OA{T}(x,x,x)\n                end\n                $exAlphaGray\n            end\n        end\n        @eval begin\n            # AbstractRGB and abstract ARGB inputs\n            map(mapi::$ST{RGB24}, rgb::AbstractRGB) =\n                convert(RGB24, RGB{UFixed8}(map1(mapi, red(rgb)), map1(mapi, green(rgb)), map1(mapi, blue(rgb))))\n            map{C<:AbstractRGB, TC}(mapi::$ST{RGB24}, argb::TransparentColor{C,TC}) =\n                convert(RGB24, RGB{UFixed8}(map1(mapi, red(argb)), map1(mapi, green(argb)),\n                                            map1(mapi, blue(argb))))\n            map{C<:AbstractRGB, TC}(mapi::$ST{ARGB32}, argb::TransparentColor{C,TC}) =\n                convert(ARGB32, ARGB{UFixed8}(map1(mapi, red(argb)), map1(mapi, green(argb)),\n                                              map1(mapi, blue(argb)), map1(mapi, alpha(argb))))\n            map(mapi::$ST{ARGB32}, rgb::AbstractRGB) =\n                convert(ARGB32, ARGB{UFixed8}(map1(mapi, red(rgb)), map1(mapi, green(rgb)), map1(mapi, blue(rgb))))\n        end\n        for O in (:RGB, :BGR)\n            @eval begin\n                map{T}(mapi::$ST{$O{T}}, rgb::AbstractRGB) =\n                    $O{T}(map1(mapi, red(rgb)), map1(mapi, green(rgb)), map1(mapi, blue(rgb)))\n                map{T,C<:AbstractRGB, TC}(mapi::$ST{$O{T}}, argb::TransparentColor{C,TC}) =\n                    $O{T}(map1(mapi, red(argb)), map1(mapi, green(argb)), map1(mapi, blue(argb)))\n            end\n        end\n        for OA in (:RGBA, :ARGB, :BGRA)\n            @eval begin\n                map{T, C<:AbstractRGB, TC}(mapi::$ST{$OA{T}}, argb::TransparentColor{C,TC}) =\n                    $OA{T}(map1(mapi, red(argb)), map1(mapi, green(argb)),\n                            map1(mapi, blue(argb)), map1(mapi, alpha(argb)))\n                map{T}(mapi::$ST{$OA{T}}, argb::ARGB32) = map(mapi, convert(RGBA{UFixed8}, argb))\n                map{T}(mapi::$ST{$OA{T}}, rgb::AbstractRGB) =\n                    $OA{T}(map1(mapi, red(rgb)), map1(mapi, green(rgb)), map1(mapi, blue(rgb)))\n                map{T}(mapi::$ST{$OA{T}}, rgb::RGB24) = map(mapi, convert(RGB{UFixed8}, argb))\n            end\n        end\n    end\nend\n\n# # Apply to any Colorant\n# map(f::Callable, x::Color) = f(x)\n# map(mapi, x::Color) = map(mapi, convert(RGB, x))\n# map{C<:Color, TC}(f::Callable, x::TransparentColor{C, TC}) = f(convert(ARGB, x))\n# map{C<:Color, TC}(mapi, x::TransparentColor{C, TC}) = map(mapi, convert(ARGB, x))\n\n## Fallback definitions of map() for array types\n\nfunction map{T}(mapi::MapInfo{T}, img::AbstractArray)\n    out = similar(img, T)\n    map!(mapi, out, img)\nend\n\nmap{C<:Colorant,R<:Real}(mapi::MapNone{C}, img::AbstractImageDirect{R}) = mapcd(mapi, img)  # ambiguity resolution\nmap{C<:Colorant,R<:Real}(mapi::MapInfo{C}, img::AbstractImageDirect{R}) = mapcd(mapi, img)\nfunction mapcd{C<:Colorant,R<:Real}(mapi::MapInfo{C}, img::AbstractImageDirect{R})\n    # For this case we have to check whether color is defined along an array axis\n    cd = colordim(img)\n    if cd > 0\n        dims = setdiff(1:ndims(img), cd)\n        out = similar(img, C, size(img)[dims])\n        map!(mapi, out, img, TypeConst{cd})\n    else\n        out = similar(img, C)\n        map!(mapi, out, img)\n    end\n    out   # note this isn't type-stable\nend\n\nfunction map{T<:Colorant}(mapi::MapInfo{T}, img::AbstractImageIndexed)\n    out = Image(Array(T, size(img)), properties(img))\n    map!(mapi, out, img)\nend\n\nmap!{T,T1,T2,N}(mapi::MapInfo{T1}, out::AbstractArray{T,N}, img::AbstractArray{T2,N}) =\n    _map_a!(mapi, out, img)\nfunction _map_a!{T,T1,T2,N}(mapi::MapInfo{T1}, out::AbstractArray{T,N}, img::AbstractArray{T2,N})\n    mi = take(mapi, img)\n    dimg = data(img)\n    dout = data(out)\n    size(dout) == size(dimg) || throw(DimensionMismatch())\n    for I in eachindex(dout, dimg)\n        @inbounds dout[I] = map(mi, dimg[I])\n    end\n    out\nend\n\ntake(mapi::MapInfo, img::AbstractArray) = mapi\ntake{T}(mapi::ScaleAutoMinMax{T}, img::AbstractArray) = ScaleMinMax(T, img)\n\n# Indexed images (colormaps)\nmap!{T,T1,N}(mapi::MapInfo{T}, out::AbstractArray{T,N}, img::AbstractImageIndexed{T1,N}) =\n    _mapindx!(mapi, out, img)\nfunction _mapindx!{T,T1,N}(mapi::MapInfo{T}, out::AbstractArray{T,N}, img::AbstractImageIndexed{T1,N})\n    dimg = data(img)\n    dout = data(out)\n    cmap = map(mapi, img.cmap)\n    for I in eachindex(dout, dimg)\n        @inbounds dout[I] = cmap[dimg[I]]\n    end\n    out\nend\n\n# For when color is encoded along dimension CD\n# NC is the number of color channels\n# This is a very flexible implementation: color can be stored along any dimension, and it handles conversions to\n# many different colorspace representations.\nfor (CT, NC) in ((Union{AbstractRGB,RGB24}, 3), (Union{RGBA,ARGB,ARGB32}, 4), (Union{AGray,GrayA,AGray32}, 2))\n    for N = 1:4\n        N1 = N+1\n        @eval begin\nfunction map!{T<:$CT,T1,T2,CD}(mapi::MapInfo{T}, out::AbstractArray{T1,$N}, img::AbstractArray{T2,$N1}, ::Type{TypeConst{CD}})\n    mi = take(mapi, img)\n    dimg = data(img)\n    dout = data(out)\n    # Set up the index along the color axis\n    # We really only need dimension CD, but this will suffice\n    @nexprs $NC k->(@nexprs $N1 d->(j_k_d = k))\n    # Loop over all the elements in the output, performing the conversion on each color component\n    @nloops $N i dout d->(d<CD ? (@nexprs $NC k->(j_k_d = i_d)) : (@nexprs $NC k->(j_k_{d+1} = i_d))) begin\n        @inbounds @nref($N, dout, i) = @ncall $NC T k->(map1(mi, @nref($N1, dimg, j_k)))\n    end\n    out\nend\n        end\n    end\nend\n\n\n#### MapInfo defaults\n# Each \"client\" can define its own methods. \"clients\" include UFixed,\n# RGB24/ARGB32, and ImageMagick\n\nconst bitshiftto8 = ((UFixed10, 2), (UFixed12, 4), (UFixed14, 6), (UFixed16, 8))\n\n# typealias GrayType{T<:Fractional} Union{T, Gray{T}}\ntypealias GrayArray{T<:Fractional} Union{AbstractArray{T}, AbstractArray{Gray{T}}}\n# note, though, that we need to override for AbstractImage in case the\n# \"colorspace\" property is defined differently\n\n# mapinfo{T<:Union{Real,Colorant}}(::Type{T}, img::AbstractArray{T}) = MapNone(img)\n\"\"\"\n`mapi = mapinf(T, img)` returns a `MapInfo` object that is deemed\nappropriate for converting pixels of `img` to be of type `T`. `T` can\neither be a specific type (e.g., `RGB24`), or you can specify an\nabstract type like `Clamp` and it will return one of the `Clamp`\nfamily of `MapInfo` objects.\n\nYou can define your own rules for `mapinfo`.  For example, the\n`ImageMagick` package defines methods for how pixels values should be\nconverted before saving images to disk.\n\"\"\"\nmapinfo{T<:UFixed}(::Type{T}, img::AbstractArray{T}) = MapNone(img)\nmapinfo{T<:AbstractFloat}(::Type{T}, img::AbstractArray{T}) = MapNone(img)\n\n# Grayscale methods\nmapinfo(::Type{UFixed8}, img::GrayArray{UFixed8}) = MapNone{UFixed8}()\nmapinfo(::Type{Gray{UFixed8}}, img::GrayArray{UFixed8}) = MapNone{Gray{UFixed8}}()\nmapinfo(::Type{GrayA{UFixed8}}, img::AbstractArray{GrayA{UFixed8}}) = MapNone{GrayA{UFixed8}}()\nfor (T,n) in bitshiftto8\n    @eval mapinfo(::Type{UFixed8}, img::GrayArray{$T}) = BitShift{UFixed8,$n}()\n    @eval mapinfo(::Type{Gray{UFixed8}}, img::GrayArray{$T}) = BitShift{Gray{UFixed8},$n}()\n    @eval mapinfo(::Type{GrayA{UFixed8}}, img::AbstractArray{GrayA{$T}}) = BitShift{GrayA{UFixed8},$n}()\nend\nmapinfo{T<:UFixed,F<:AbstractFloat}(::Type{T}, img::GrayArray{F}) = ClampMinMax(T, zero(F), one(F))\nmapinfo{T<:UFixed,F<:AbstractFloat}(::Type{Gray{T}}, img::GrayArray{F}) = ClampMinMax(Gray{T}, zero(F), one(F))\nmapinfo{T<:AbstractFloat, R<:Real}(::Type{T}, img::AbstractArray{R}) = MapNone(T)\n\nmapinfo(::Type{RGB24}, img::Union{AbstractArray{Bool}, BitArray}) = MapNone{RGB24}()\nmapinfo(::Type{ARGB32}, img::Union{AbstractArray{Bool}, BitArray}) = MapNone{ARGB32}()\nmapinfo{F<:Fractional}(::Type{RGB24}, img::GrayArray{F}) = ClampMinMax(RGB24, zero(F), one(F))\nmapinfo{F<:Fractional}(::Type{ARGB32}, img::AbstractArray{F}) = ClampMinMax(ARGB32, zero(F), one(F))\n\n# Color->Color methods\nmapinfo(::Type{RGB{UFixed8}}, img) = MapNone{RGB{UFixed8}}()\nmapinfo(::Type{RGBA{UFixed8}}, img) = MapNone{RGBA{UFixed8}}()\nfor (T,n) in bitshiftto8\n    @eval mapinfo(::Type{RGB{UFixed8}}, img::AbstractArray{RGB{$T}}) = BitShift{RGB{UFixed8},$n}()\n    @eval mapinfo(::Type{RGBA{UFixed8}}, img::AbstractArray{RGBA{$T}}) = BitShift{RGBA{UFixed8},$n}()\nend\nmapinfo{F<:Fractional}(::Type{RGB{UFixed8}}, img::AbstractArray{RGB{F}}) = Clamp(RGB{UFixed8})\nmapinfo{F<:Fractional}(::Type{RGBA{UFixed8}}, img::AbstractArray{RGBA{F}}) = Clamp(RGBA{UFixed8})\n\n\n\n# Color->RGB24/ARGB32\nmapinfo(::Type{RGB24}, img::AbstractArray{RGB24}) = MapNone{RGB24}()\nmapinfo(::Type{ARGB32}, img::AbstractArray{ARGB32}) = MapNone{ARGB32}()\nfor C in tuple(subtypes(AbstractRGB)..., Gray)\n    C == RGB24 && continue\n    @eval mapinfo(::Type{RGB24}, img::AbstractArray{$C{UFixed8}}) = MapNone{RGB24}()\n    @eval mapinfo(::Type{ARGB32}, img::AbstractArray{$C{UFixed8}}) = MapNone{ARGB32}()\n    for (T, n) in bitshiftto8\n        @eval mapinfo(::Type{RGB24}, img::AbstractArray{$C{$T}}) = BitShift{RGB24, $n}()\n        @eval mapinfo(::Type{ARGB32}, img::AbstractArray{$C{$T}}) = BitShift{ARGB32, $n}()\n    end\n    @eval mapinfo{F<:AbstractFloat}(::Type{RGB24}, img::AbstractArray{$C{F}}) = ClampMinMax(RGB24, zero(F), one(F))\n    @eval mapinfo{F<:AbstractFloat}(::Type{ARGB32}, img::AbstractArray{$C{F}}) = ClampMinMax(ARGB32, zero(F), one(F))\n    for AC in subtypes(TransparentColor)\n        length(AC.parameters) == 2 || continue\n        @eval mapinfo(::Type{ARGB32}, img::AbstractArray{$AC{$C{UFixed8},UFixed8}}) = MapNone{ARGB32}()\n        @eval mapinfo(::Type{RGB24}, img::AbstractArray{$AC{$C{UFixed8},UFixed8}}) = MapNone{RGB24}()\n        for (T, n) in bitshiftto8\n            @eval mapinfo(::Type{ARGB32}, img::AbstractArray{$AC{$C{$T},$T}}) = BitShift{ARGB32, $n}()\n            @eval mapinfo(::Type{RGB24}, img::AbstractArray{$AC{$C{$T},$T}}) = BitShift{RGB24, $n}()\n        end\n        @eval mapinfo{F<:AbstractFloat}(::Type{ARGB32}, img::AbstractArray{$AC{$C{F},F}}) = ClampMinMax(ARGB32, zero(F), one(F))\n        @eval mapinfo{F<:AbstractFloat}(::Type{RGB24}, img::AbstractArray{$AC{$C{F},F}}) = ClampMinMax(RGB24, zero(F), one(F))\n    end\nend\n\nmapinfo{CT<:Colorant}(::Type{RGB24},  img::AbstractArray{CT}) = MapNone{RGB24}()\nmapinfo{CT<:Colorant}(::Type{ARGB32}, img::AbstractArray{CT}) = MapNone{ARGB32}()\n\n\n# UInt32 conversions will use ARGB32 for images that have an alpha channel,\n# and RGB24 when not\nmapinfo{CV<:Union{Fractional,Color,AbstractGray}}(::Type{UInt32}, img::AbstractArray{CV}) = mapinfo(RGB24, img)\nmapinfo{CV<:TransparentColor}(::Type{UInt32}, img::AbstractArray{CV}) = mapinfo(ARGB32, img)\nmapinfo(::Type{UInt32}, img::Union{AbstractArray{Bool},BitArray}) = mapinfo(RGB24, img)\nmapinfo(::Type{UInt32}, img::AbstractArray{UInt32}) = MapNone{UInt32}()\n\n\n# Clamping mapinfo client. Converts to RGB and uses UFixed, clamping\n# floating-point values to [0,1].\nmapinfo{T<:UFixed}(::Type{Clamp}, img::AbstractArray{T}) = MapNone{T}()\nmapinfo{T<:AbstractFloat}(::Type{Clamp}, img::AbstractArray{T}) = ClampMinMax(UFixed8, zero(T), one(T))\nlet handled = Set()\nfor ACV in (Color, AbstractRGB)\n    for CV in subtypes(ACV)\n        (length(CV.parameters) == 1 && !(CV.abstract)) || continue\n        CVnew = CV<:AbstractGray ? Gray : RGB\n        @eval mapinfo{T<:UFixed}(::Type{Clamp}, img::AbstractArray{$CV{T}}) = MapNone{$CVnew{T}}()\n        @eval mapinfo{CV<:$CV}(::Type{Clamp}, img::AbstractArray{CV}) = Clamp{$CVnew{UFixed8}}()\n        CVnew = CV<:AbstractGray ? Gray : BGR\n        AC, CA       = alphacolor(CV), coloralpha(CV)\n        if AC in handled\n            continue\n        end\n        push!(handled, AC)\n        ACnew, CAnew = alphacolor(CVnew), coloralpha(CVnew)\n        @eval begin\n            mapinfo{T<:UFixed}(::Type{Clamp}, img::AbstractArray{$AC{T}}) = MapNone{$ACnew{T}}()\n            mapinfo{P<:$AC}(::Type{Clamp}, img::AbstractArray{P}) = Clamp{$ACnew{UFixed8}}()\n            mapinfo{T<:UFixed}(::Type{Clamp}, img::AbstractArray{$CA{T}}) = MapNone{$CAnew{T}}()\n            mapinfo{P<:$CA}(::Type{Clamp}, img::AbstractArray{P}) = Clamp{$CAnew{UFixed8}}()\n        end\n    end\nend\nend\nmapinfo(::Type{Clamp}, img::AbstractArray{RGB24}) = MapNone{RGB{UFixed8}}()\nmapinfo(::Type{Clamp}, img::AbstractArray{ARGB32}) = MapNone{BGRA{UFixed8}}()\n\n\n# Backwards-compatibility\nuint32color(img) = map(mapinfo(UInt32, img), img)\nuint32color!(buf, img::AbstractArray) = map!(mapinfo(UInt32, img), buf, img)\nuint32color!(buf, img::AbstractArray, mi::MapInfo) = map!(mi, buf, img)\nuint32color!{T,N}(buf::Array{UInt32,N}, img::AbstractImageDirect{T,N}) =\n    map!(mapinfo(UInt32, img), buf, img)\nuint32color!{T,N,N1}(buf::Array{UInt32,N}, img::AbstractImageDirect{T,N1}) =\n    map!(mapinfo(UInt32, img), buf, img, TypeConst{colordim(img)})\nuint32color!{T,N}(buf::Array{UInt32,N}, img::AbstractImageDirect{T,N}, mi::MapInfo) =\n    map!(mi, buf, img)\nuint32color!{T,N,N1}(buf::Array{UInt32,N}, img::AbstractImageDirect{T,N1}, mi::MapInfo) =\n    map!(mi, buf, img, TypeConst{colordim(img)})\n\n\"\"\"\n```\nimgsc = sc(img)\nimgsc = sc(img, min, max)\n```\n\nApplies default or specified `ScaleMinMax` mapping to the image.\n\"\"\"\nsc(img::AbstractArray) = map(ScaleMinMax(UFixed8, img), img)\nsc(img::AbstractArray, mn::Real, mx::Real) = map(ScaleMinMax(UFixed8, img, mn, mx), img)\n\nfor (fn,T) in ((:float32, Float32), (:float64, Float64), (:ufixed8, UFixed8),\n               (:ufixed10, UFixed10), (:ufixed12, UFixed12), (:ufixed14, UFixed14),\n               (:ufixed16, UFixed16))\n    @eval begin\n        function $fn{C<:Colorant}(A::AbstractArray{C})\n            newC = eval(C.name.name){$T}\n            convert(Array{newC}, A)\n        end\n        $fn{C<:Colorant}(img::AbstractImage{C}) = shareproperties(img, $fn(data(img)))\n    end\nend\n\n\nufixedsc{T<:UFixed}(::Type{T}, img::AbstractImageDirect) = map(mapinfo(T, img), img)\nufixed8sc(img::AbstractImageDirect) = ufixedsc(UFixed8, img)\n", "meta": {"hexsha": "1b65412f52fb34d3e52492e151f6e6277d3b9542", "size": 32574, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/map.jl", "max_stars_repo_name": "rsrock/Images.jl", "max_stars_repo_head_hexsha": "8e4192a04c45be0f93f8b13b189249ed93f394c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/map.jl", "max_issues_repo_name": "rsrock/Images.jl", "max_issues_repo_head_hexsha": "8e4192a04c45be0f93f8b13b189249ed93f394c1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/map.jl", "max_forks_repo_name": "rsrock/Images.jl", "max_forks_repo_head_hexsha": "8e4192a04c45be0f93f8b13b189249ed93f394c1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.5342857143, "max_line_length": 253, "alphanum_fraction": 0.6518388899, "num_tokens": 10646, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828341018881344, "lm_q2_score": 0.11920292045759122, "lm_q1q2_score": 0.058204808505498515}}
{"text": "# constructing:\nstart_time = time()\n# long computation\ntime_elapsed = time() - start_time\nprintln(\"Time elapsed: $time_elapsed\") #>  0.0009999275207519531\n\nusing Dates\nd = Date(2018,9,1) #> 2018-09-01\ndt = DateTime(2018,9,1,12,30,59,1) #> 2018-09-01T12:30:59.001\n# accessors:\nyear(d)\nmonth(d)\nweek(d)\nday(d)\n# functions:\nisleapyear(d)\ndayofyear(d)\nmonthname(d)\ndaysinmonth(d)", "meta": {"hexsha": "bafcbbf291048c68729065dc29248880375d8211", "size": 375, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter02/dates.jl", "max_stars_repo_name": "tavoludra1/Julia1.0", "max_stars_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2018-09-29T03:07:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-08T13:12:33.000Z", "max_issues_repo_path": "Chapter02/dates.jl", "max_issues_repo_name": "tavoludra1/Julia1.0", "max_issues_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter02/dates.jl", "max_forks_repo_name": "tavoludra1/Julia1.0", "max_forks_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2018-09-16T05:55:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-08T14:59:54.000Z", "avg_line_length": 19.7368421053, "max_line_length": 64, "alphanum_fraction": 0.7146666667, "num_tokens": 143, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4804786780479071, "lm_q2_score": 0.12085324357297283, "lm_q1q2_score": 0.05806740670974372}}
{"text": "module CytofImg\n\nimport Cytof5\nusing Plots\npyplot()\nusing StatPlots\nusing Distributions # std, mean, quantile\nusing RCall # TODO: GET RID OF THIS DEPENDENCY!\n\nstartupMsg = \"\"\"\nTo scale font size globally for plots (for presentations mostly), do this:\n```\njulia> Plots.scalefontsizes(2) # scales all texts by factor of 2\n\n# To reset\njulia> Plots.reset_defaults()\n```\n\"\"\"\nprintln(startupMsg)\n\nfunction getPosterior(sym::Symbol, monitor)\n  return [ m[sym] for m in monitor]\nend\n\n\n\"\"\"\nturn on legend with `legend=true`\n\nNote that xlabel / ylabel can be supplied by either:\n\n```\njulia> xlabel!(\"my xlab\")\n# OR\njulia> plotZ(Z, xlabel=\"my xlab\")\n```\n\n\"\"\"\nfunction plotZ(Z::Matrix{T}; kw...) where {T <: Number}\n  # FIXME: doesn't work for Z having either 1 row or 1 column!\n  J, K = size(Z)\n  if J < 2 || K < 2\n    println(\"heatmap is not supported for single row / column matrices\")\n    return\n  end\n\n  img = heatmap(Z, c=:Greys, legend=:none, border=true, bordercolor=:lightgrey; kw...)\n  if J > 1\n    hline!((2:J) .- .5, c=:lightgrey)\n  end\n  if K > 1\n    vline!((2:K) .- .5, c=:lightgrey)\n  end\n\n  return img\nend\n\nfunction plotY_heatmap(y::Matrix{T}; clim=(-5, 5), c=:pu_or) where {T <: Number}\n  img = heatmap(y, c=c, clim=clim, background_color_inside=:black, bordercolor=:transparent);\n  return img\nend\n\nfunction plotYZ(y::Matrix{V}, Z::Matrix{T}; ycolor=:balance, clim=(-5, 5), heights=[.7, .3], kw...) where {T <: Number, V <: Number}\n  # TODO: change proportions\n  heatY = plotY_heatmap(y, c=ycolor, clim=clim)\n  heatZ = plotZ(Matrix(Z'))\n  img = plot(heatY, heatZ, layout=grid(2, 1, heights=heights); kw...);\n  return img\nend\n\nfunction annotateHeatmap(mat::Matrix{T}; digits=2, textsize=6) where T\n  rows, cols = size(mat)\n  for c in 1:cols\n    for r in 1:rows\n      annotate!([(float(c), float(r),\n                  text(\"$(round(mat[r,c], digits=digits))\", textsize))])\n    end\n  end\nend\n\nfunction plotPost(x::Vector{T}; a::Float64=0.05, q_digits::Int=3, sd_digits::Int=3, add=false,\n                  trace::Bool=true, useDensity::Bool=true, showAccRate::Bool=true,\n                  parent=1, offset=1, traceFont=font(16), kw...) where {T <: Number}\n  denColor = (0, .99, :steelblue)\n  if useDensity\n    if add\n      img = StatPlots.density!(x, label=\"\", grid=false, yaxis=false,\n                               linecolor=:transparent,\n                               fill=denColor, bordercolor=:transparent; kw...)\n    else\n      img = StatPlots.density(x, label=\"\", grid=false, bordercolor=:transparent, yaxis=false,\n                              fill=denColor, linecolor=:transparent; kw...)\n    end\n  else\n    if add\n      img = histogram!(x, label=\"\", normed=true, grid=false,\n                       bordercolor=:white, yaxis=false,\n                       linecolor=:transparent, c=:steelblue; kw...)\n    else\n      img = histogram(x, label=\"\", normed=true, grid=false,\n                      bordercolor=:white, yaxis=false,\n                      linecolor=:transparent, c=:steelblue; kw...)\n    end\n  end\n\n\n  if trace\n    accRate = length(unique(x)) / length(x)\n\t\ttraceTitle = showAccRate ? \"acc: $(Int(round(accRate*100)))%\" : \"\"\n    #                                x    y   W   H\n    plot!(x, inset_subplots=(parent, bbox(0, .1, .3, .3, :top, :right)), subplot=parent+offset,\n          axis=false, legend=false, title=traceTitle,\n          c=:grey70, grid=false, titlefont=traceFont, linewidths=.5)\n\n  end\n\n  xmean = mean(x)\n  xsd = std(x)\n  q = quantile(x, [a/2, 1-a/2])\n\n  xticks!(round.([q; xmean], digits=q_digits), axiscolor=:transparent; kw...)\n  #vline!(q, c=:red, linewidths=1, line=:dot, label=\"95% CI\", legend=:best; kw...)\n  #vline!([xmean], c=:red, linewidths=2, legend=:best,\n  #       label=\"Mean: $(round(xmean, digits=q_digits))\"; kw...)\n  #hline!([0], label=\"SD: $(round(xsd, digits=sd_digits))\", \n  #       linewidth=0, bgcolor_legend=:transparent; kw...)\n  vline!(q, c=:red, label=\"\", linewidths=1, line=:dot; kw...)\n  vline!([xmean], c=:red, label=\"\", linewidths=1; kw...)\n  hline!([0], linewidth=0, label=\"\", bgcolor_legend=:transparent; kw...)\n\n  # TODO:\n  # Option for traceplot\n\n  return img\nend\n\nfunction plotPosts(X::Matrix{T}; a::Float64=0.05, q_digits::Int=3,\n                   sd_digits::Int=3, cor_digits::Int=3, titles=:none,\n                   detail=false, hist2d::Bool=false, trace::Bool=true,\n                   traceFont=font(16), kw...) where {T <: Number}\n  N = size(X, 2)\n\n  if titles == :none\n    titles = fill(\"\", N)\n  end\n\n  img = plot(;layout=(N, N))\n  counter = 0\n  offset = 0\n  for r in 1:N\n    for c in 1:N\n      counter += 1\n\n      if r == c\n        offset += 1\n        plotPost(X[:, r], a=a, q_digits=q_digits, sd_digits=sd_digits,\n                 subplot=counter, add=true, title=titles[c], trace=trace,\n                 parent=counter, offset=N*N-counter+offset,\n                 traceFont=traceFont; kw...)\n      elseif r < c\n        if hist2d\n          if detail\n            histogram2d!(X[:, c], X[:,r], subplot=counter, colorbar=:none, grid=false)\n          else\n            histogram2d!(X[:, c], X[:,r], subplot=counter, colorbar=:none, grid=false,\n                         axis=false)\n          end\n        else\n          plot!(X[:, c], X[:,r], subplot=counter, c=:grey70, grid=false,\n                axis=false, legend=false, linewidths=.5)\n        end\n      else # r > c\n        corXrc = cor(X[:, r], X[:, c])\n        annotate!([(0.5, 0.5, text(\"r=$(round(corXrc, digits=cor_digits))\", \n                                   Int(ceil(abs(corXrc) * 5))+5, :center))],\n                  subplot=counter, axis=false, grid=false)\n      end\n    end\n  end\n\n  return img\nend\n\nfunction postProbMiss(b0, b1, i::Int;\n                      y::Vector{Float64}=collect(-10:.1:10),\n                      credibility::Float64=.95)\n\n  N, I = size(b0)\n  @assert size(b0) == size(b1)\n  \n  len_y = length(y)\n  alpha = 1 - credibility\n  p_lower = alpha / 2\n  p_upper = 1 - alpha / 2\n\n  pmiss = hcat([Cytof5.Model.prob_miss.(yi, b0[:,i], b1[:,i]) for yi in y]...)\n  pmiss_mean = vec(mean(pmiss, dims=1))\n  pmiss_lower = [ quantile(pmiss[:, col], p_lower) for col in 1:len_y ]\n  pmiss_upper = [ quantile(pmiss[:, col], p_upper) for col in 1:len_y ]\n\n  return (pmiss_mean, pmiss_lower, pmiss_upper, y)\nend\n\nfunction pairwiseAlloc(Z, W, i)\n  J, K = size(Z)\n  A = zeros(J, J)\n  for r in 1:J\n    for c in 1:J\n      for k in 1:K\n        if Z[r, k] == 1 && Z[c, k] == 1\n          A[r, c] += W[i, k]\n          A[c, r] += W[i, k]\n        end\n      end\n    end\n  end\n\n  return A\nend # pairwiseAlloc\n\nfunction estimate_ZWi_index(monitor, i)\n  As = [pairwiseAlloc(m[:Z], m[:W], i) for m in monitor]\n\n  Amean = mean(As)\n  mse = [ mean((A - Amean) .^ 2) for A in As]\n\n  return argmin(mse)\nend\n\nfunction countmap(x::Vector{T}) where {T <: Number}\n  out = Dict{Int, T}()\n  for xi in x\n    if xi in keys(out)\n      out[xi] += 1\n    else\n      out[xi] = 1\n    end\n  end\n  return out\nend\n\nfunction reorder_lam_i(lam_i::Vector{T}, W_i::Vector{V}) where {T <: Number, V <: Number}\n  N = length(lam_i)\n  new_lam_i = Vector{Int}(undef, N)\n  K = length(W_i)\n  ord = sortperm(W_i)\n\n  for k in 1:K\n    new_lam_i[lam_i .== ord[k]] .= k\n  end\n\n  return new_lam_i\nend\n\n# TODO: Implement the threshold\nfunction yZ_inspect(monitor, y::Vector{Matrix{Float64}},\n                    i::Int; thresh=0.7, ycolor=:balance,\n                    addLines::Bool=true, marker_names = missing,\n                    clim=(-5,5), W_digits=1, kw...)\n  idx_best = estimate_ZWi_index(monitor, i)\n  Zi = monitor[idx_best][:Z]\n  Wi = monitor[idx_best][:W][i, :]\n  lami = monitor[idx_best][:lam][i]\n  new_lami = reorder_lam_i(lami, Wi)\n  ord = sortperm(new_lami)\n  K = length(Wi)\n  J = size(y[i], 2)\n\n  W_order = sortperm(Wi)\n\n  cumProb = 0.0; k = K\n  for w in reverse(Wi[W_order])\n    cumProb += w\n    if cumProb > thresh\n      break\n    end\n    k -= 1\n  end\n  W_order = W_order[k:end]\n\n  plotYZ(y[i][ord, :], Zi[:, W_order], clim=clim, ycolor=ycolor)\n\n  # TODO: cell types on left, percentage on right\n  W_val = map(x->\"$(x)%\", round.(Wi[W_order] * 100, digits=W_digits))\n  #plot!(yticks=(1:K, W_order), subplot=2)\n  plot!(yticks=(1:length(W_order), W_val), subplot=2, ymirror=true)\n\n  # add marker names if provided\n  if !ismissing(marker_names)\n    plot!(xticks=(1:J, marker_names), subplot=1, rotation=90)\n  end\n\n  if addLines\n    groups = cumsum(sort(collect(values(countmap(new_lami)))))\n    hline!(groups, c=:yellow, subplot=1, linewidths=1, legend=false)\n  end\nend\n\nfunction postSummary(x::Matrix{T}; alpha::Float64=.05) where {T <: Number}\n  K = size(x, 2)\n  xmean = mean(x, dims=1)\n  xsd = std(x, dims=1)\n  xqLower = [ quantile(x[:, k], alpha / 2) for k in 1:K ]\n  xqLower = reshape(xqLower, 1, K)\n  xqUpper = [ quantile(x[:, k], 1- alpha / 2) for k in 1:K ]\n  xqUpper = reshape(xqUpper, 1, K)\n  xmedian = median(x, dims=1)\n\n  summaryMatrix = Matrix([xmean; xsd; xqLower; xmedian; xqUpper]')\n  println(\"mean   sd  lower  upper  median\")\n  return summaryMatrix\nend\n\n# TODO: GET RID OF THIS!\nR\"\"\"\nari <- function(x, y) {\n  x = as.vector(x)\n  y = as.vector(y)\n  n = length(x)\n  stopifnot(n == length(y))\n  tab = table(x, y)\n  index = sum(choose(tab, 2))\n  rowSumsChoose2.sum = sum(choose(rowSums(tab), 2))\n  colSumsChoose2.sum = sum(choose(colSums(tab), 2))\n  expected.index = rowSumsChoose2.sum * colSumsChoose2.sum/choose(n,\n      2)\n  max.index = mean(c(rowSumsChoose2.sum, colSumsChoose2.sum))\n  (index - expected.index)/(max.index - expected.index)\n}\n\"\"\"\nari = R\"ari\"\n\nfunction confusion(x::Vector{T}, y::Vector{T}) where {T <: Number}\n  @assert length(y) == length(x)\n  n = length(x)\n  confusionMat = zeros(Int, n, n)\n  for i in 1:n\n    for j in 1:n\n      if x[i] == y[j]\n        confusionMat[i, j] += 1\n      end\n    end\n  end\n  return confusionMat\nend\n\n#=\nfunction ari(x::Vector{T}, y::Vector{T}) where {T <: Number}\n  @assert length(y) == length(x)\n  n = length(x)\n  tab = confusion(x, y)\n  # TODO: https://en.wikipedia.org/wiki/Rand_index\nend\n=#\n\n\n\nend\n", "meta": {"hexsha": "3cbd4455640f9599264567e332f935aca157706b", "size": 9970, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sims/sim_study/CytofImg.jl", "max_stars_repo_name": "luiarthur/cytof5", "max_stars_repo_head_hexsha": "6b4df5e9fd94bfd586e96579b8c618fdf6f913ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-01-30T21:56:52.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-30T21:56:52.000Z", "max_issues_repo_path": "sims/sim_study/CytofImg.jl", "max_issues_repo_name": "luiarthur/cytof5", "max_issues_repo_head_hexsha": "6b4df5e9fd94bfd586e96579b8c618fdf6f913ed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2018-12-20T18:22:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-24T03:13:32.000Z", "max_forks_repo_path": "sims/sim_study/CytofImg.jl", "max_forks_repo_name": "luiarthur/cytof5", "max_forks_repo_head_hexsha": "6b4df5e9fd94bfd586e96579b8c618fdf6f913ed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8491620112, "max_line_length": 132, "alphanum_fraction": 0.5870611836, "num_tokens": 3179, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.12085323090725615, "lm_q1q2_score": 0.05806740062413689}}
{"text": "using InteractiveUtils # 0.7 \u4e4b\u540e\u9700\u8981\u8c03\u7528\u8fd9\u4e2a\u6807\u51c6\u5e93\n\nfunction view_tree(T, depth=0)\n    println(\"  \"^depth, T)\n    for each in subtypes(T)\n        view_tree(each, depth+1)\n    end\nend\n\nabstract type AbstractAnimal end\n\nabstract type AbstractBird <: AbstractAnimal end\nabstract type AbstractDog <: AbstractAnimal end\nabstract type AbstractCat <: AbstractAnimal end\n\nstruct Sparrow <: AbstractBird end\nstruct Kitty <: AbstractCat end\nstruct Snoope <: AbstractDog end\n\nview_tree(AbstractAnimal)", "meta": {"hexsha": "fa30f48a6d02b50a556194a59b92b453cf23cf2d", "size": 480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/005_abstrac.jl", "max_stars_repo_name": "liushooter/OneDayOneCommit", "max_stars_repo_head_hexsha": "87dc037fcb21c9cd91723c282d1b618bef3e0414", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/005_abstrac.jl", "max_issues_repo_name": "liushooter/OneDayOneCommit", "max_issues_repo_head_hexsha": "87dc037fcb21c9cd91723c282d1b618bef3e0414", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/005_abstrac.jl", "max_forks_repo_name": "liushooter/OneDayOneCommit", "max_forks_repo_head_hexsha": "87dc037fcb21c9cd91723c282d1b618bef3e0414", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0, "max_line_length": 48, "alphanum_fraction": 0.7625, "num_tokens": 122, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.12085322457439822, "lm_q1q2_score": 0.05806739758133369}}
{"text": "using Interact, Blink, Colors\nexport interactive_orbitdiagram, scaleod\n\n\"\"\"\n    controlwindow(D, n, Ttr, density, i)\nCreate an Electron control window for the orbit diagram interactive application.\n\n```julia\nreturn n, Ttr, density, i, \u25a2update, \u25a2back, \u25a2reset, \u03b1,\n       \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax\n```\n\nAll returned values are `Observable`s.\nTheir value corresponds to the one chosen in the Electron window.\nItems with `\u25a2` are buttons and with `\u2b1c` are the boxes with limits.\n\"\"\"\nfunction controlwindow(D, n, Ttr, density, i)\n    n = Interact.textbox(string(n); value = n, label = \"n\")\n    Ttr = Interact.textbox(string(Ttr); value = Ttr, label = \"Ttr\")\n    density = Interact.textbox(string(density); value = density, label = \"density\")\n\n    i = Interact.dropdown(OrderedDict(string(j) => j for j in 1:D); label = \"variable\")\n    \u25a2update = Interact.button(\"update\")\n    \u25a2back = Interact.button(\"\u2190 back\")\n    \u25a2reset = Interact.button(\"reset\")\n\n    \u03b1 = Interact.slider(0:0.001:1; value = 0.1, label = \"\u03b1 (transparency)\")\n\n    # Limit boxes\n    \u2b1cpmin = Interact.textbox(; value = 0.0, label = \"pmin\")\n    \u2b1cpmax = Interact.textbox(; value = 1.0, label = \"pmax\")\n    \u2b1cumin = Interact.textbox(; value = 0.0, label = \"umin\")\n    \u2b1cumax = Interact.textbox(; value = 1.0, label = \"umax\")\n\n    w = Window(Dict(:height => 400, :title => \"Orbit Diagram controls\"))\n    s = 5em\n    body!(w, Interact.vbox(\n        \u03b1,\n        Interact.hbox(n, Ttr, density),\n        Interact.hbox(i, hskip(s), \u25a2update, hskip(s), \u25a2back, hskip(s), \u25a2reset),\n        Interact.hline(),\n        Interact.hbox(\u2b1cpmin, \u2b1cpmax),\n        Interact.hbox(\u2b1cumin, \u2b1cumax)\n        )\n    )\n\n    return n, Ttr, density, i, \u25a2update, \u25a2back, \u25a2reset, observe(\u03b1),\n           \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax\nend\n\n\n\n\"\"\"\n    interactive_orbitdiagram(ds::DiscreteDynamicalSystem,\n        i::Int, p_index, p_min, p_max;\n        density = 500, u0 = get_state(ds), Ttr = 200, n = 500,\n        parname = \"p\"\n    )\n\nOpen an interactive application for exploring orbit diagrams (ODs) of\ndiscrete systems. The functionality works for _any_ discrete system.\n\nOnce initialized it opens a Makie plot window and an Electron control window.\n\n## Interaction\nBy using the Electron window you are able to update all parameters of the OD\ninteractively (like e.g. `n` or `Ttr`). You have to press `update` after changing\nthese parameters. You can even decide which variable to get the OD for,\nby choosing one of the variables from the wheel (again, press `update` afterwards).\n\nIn the Makie window you can interactively zoom into the OD. Click\nand drag with the left mouse button to select a region in the OD. This region is then\n**re-computed** at a higher resolution (i.e. we don't \"just zoom\").\n\nBack in the Electron window, you can press `reset` to bring the OD in the original\nstate (and variable). Pressing `back` will go back through the history of your exploration\nHistory is stored when any change happens (besides transparency).\n\n## Accessing the data\nWhat is plotted on the application window is a _true_ orbit diagram, not a plotting\nshorthand. This means that all data are obtainable and usable directly.\nInternally we always scale the orbit diagram to [0,1]\u00b2 (to allow `Float64` precision\neven though plotting is `Float32`-based). This however means that it is\nnecessary to transform the data in real scale. This is done through the function\n[`scaleod`](@ref) which accepts the 5 arguments returned from the current function:\n```\nod, pmin, pmax, umin, umax = interactive_orbitdiagram(...)\nps, us = scaleod(od, pmin, pmax, umin, umax)\n```\n\"\"\"\nfunction interactive_orbitdiagram(ds::DiscreteDynamicalSystem,\n    i::Int, p_index, p_min, p_max;\n    density = 1000, u0 = get_state(ds), Ttr = 200, n = 500,\n    parname = \"p\"\n    )\n\n    # UI elements\n    n, Ttr, density, i, \u25a2update, \u25a2back, \u25a2reset, \u03b1, \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax =\n    controlwindow(dimension(ds), n, Ttr, density, i)\n\n    # Initial Orbit diagram data\n    integ = integrator(ds, u0)\n    pmin, pmax = p_min, p_max\n    odinit, xmin, xmax = minimal_normalized_od(integ, i[], p_index, pmin, pmax, density[], n[], Ttr[], u0)\n    od_node = Observable(odinit)\n    densityinit = density[]; ninit = n[]; Ttrinit = Ttr[]\n\n    # History stores the variable index and true diagram limits\n    history = [(i[], pmin, pmax, xmin, xmax, n[], Ttr[], density[])]\n    update_controls!(history[end], i, n, Ttr, density,  \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n\n    color = lift(a -> RGBA(0,0,0,a), \u03b1)\n    scplot = Scene(resolution = (1200, 800))\n    scatter!(scplot, od_node, markersize = 0.008, color = color)\n\n    scplot[Axis][:ticks][:ranges] = (collect(0:0.25:1), collect(0:0.25:1))\n    scplot[Axis][:ticks][:labels] =\n    ([\"pmin\", \" \", \" \", \" \", \"pmax\"], [\"umin\", \" \", \" \", \" \", \"umax\"])\n    scplot[Axis][:names][:axisnames] = (parname, \"u\"*subscript(i[]))\n\n    display(scplot)\n    rect = select_rectangle(scplot)\n\n    # Uppon interactively selecting a rectangle, with value `r` (in [0,1]\u00b2)\n    on(rect) do r\n        spmin, sxmin = r.origin\n        spmax, sxmax = r.origin + r.widths\n        # Convert p,x to true values\n        j, ppmin, ppmax, pxmin, pxmax = history[end]\n        pdif = ppmax - ppmin; xdif = pxmax - pxmin\n        pmin = spmin*pdif + ppmin\n        pmax = spmax*pdif + ppmin\n        xmin = sxmin*xdif + pxmin\n        xmax = sxmax*xdif + pxmin\n\n        od_node[] = minimal_normalized_od(\n            integ, j,  p_index, pmin, pmax,\n            density[], n[], Ttr[], u0, xmin, xmax\n        )\n        # update history and controls\n        push!(history, (j, pmin, pmax, xmin, xmax, n[], Ttr[], density[]))\n        update_controls!(history[end], i, n, Ttr, density,  \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n    end\n\n    # Upon hitting the update button\n    on(\u25a2update) do clicks\n        j, pmin, pmax, xmin, xmax, m, T, d = history[end]\n        # Check if there was any change:\n        if !(\u2b1cpmin[] == pmin && \u2b1cpmax[] == pmax && \u2b1cumin[] == xmin && \u2b1cumax[] == xmax &&\n            j == i[] && m == n[] && T == Ttr[] && d == density[])\n\n            pmin, pmax, xmin, xmax = \u2b1cpmin[], \u2b1cpmax[], \u2b1cumin[], \u2b1cumax[]\n            j, m, T, d = i[], n[], Ttr[], density[]\n\n            od_node[] = minimal_normalized_od(\n            integ, j, p_index, pmin, pmax, density[], n[], Ttr[], u0, xmin, xmax\n            )\n            # Update history and controls\n            push!(history, (j, pmin, pmax, xmin, xmax, m, T, d))\n            update_controls!(history[end], i, n, Ttr, density,  \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n            scplot[Axis][:names][:axisnames] = (parname, \"u\"*subscript(i[]))\n        end\n    end\n\n    # Upon hitting the \"reset\" button\n    on(\u25a2reset) do clicks\n        if length(history) > 1\n            deleteat!(history, 2:length(history))\n            j, pmin, pmax, xmin, xmax, m, T, d = history[end]\n            od_node[] = odinit\n            # Update controls/labels\n            update_controls!(history[end], i, n, Ttr, density,  \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n            scplot[Axis][:names][:axisnames] = (parname, \"u\"*subscript(j))\n        end\n    end\n\n    # Upon hitting the \"back\" button\n    on(\u25a2back) do clicks\n        if length(history) > 1\n            pop!(history)\n            j, pmin, pmax, xmin, xmax, m, T, d = history[end]\n            od_node[] = minimal_normalized_od(\n                integ, j, p_index, pmin, pmax, d, m, T, u0, xmin, xmax\n            )\n            # Update limits in textboxes\n            update_controls!(history[end], i, n, Ttr, density,  \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n            scplot[Axis][:names][:axisnames] = (parname, \"u\"*subscript(j))\n        end\n    end\n\n    display(scplot)\n    return od_node, \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax\nend\n\n\n\"\"\"\n    minimal_normalized_od(integ, i, p_index, pmin, pmax,\n                         density, n, Ttr, u0)\n    minimal_normalized_od(integ, i, p_index, pmin, pmax,\n                         density, n, Ttr, u0, xmin, xmax)\n\nCompute and return a minimal and normalized orbit diagram (OD).\n\nAll points are stored in a single vector of `Point2f0` to ensure fastest possible\nplotting. In addition all numbers are scaled to [0, 1]. This allows us to have\n64-bit precision while display is only 32-bit!\n\nThe version with `xmin, xmax` only keeps points with limits between the\nreal `xmin, xmax` (in the normal units of the dynamical system).\n\"\"\"\nfunction minimal_normalized_od(integ, i, p_index, pmin, pmax,\n                              density::Int, n::Int, Ttr::Int, u0)\n\n    pvalues = range(pmin, stop = pmax, length = density)\n    pdif = pmax - pmin\n    od = Vector{Point2f0}() # make this pre-allocated\n    xmin = eltype(integ.u)(Inf); xmax = eltype(integ.u)(-Inf)\n    #= @inbounds =# for (j, p) in enumerate(pvalues)\n        pp = (p - pmin)/pdif # p to plot, in [0, 1]\n        DynamicalSystemsBase.reinit!(integ, u0)\n        integ.p[p_index] = p\n        DynamicalSystemsBase.step!(integ, Ttr)\n        for z in 1:n\n            DynamicalSystemsBase.step!(integ)\n            x = integ.u[i]\n            push!(od, Point2f0(pp, integ.u[i]))\n            # update limits\n            if x < xmin\n                xmin = x\n            elseif x > xmax\n                xmax = x\n            end\n        end\n    end\n    # normalize x values to [0, 1]\n    xdif = xmax - xmin\n    #= @inbounds =# for j in eachindex(od)\n        x = od[j][2]; p = od[j][1]\n        od[j] = Point2f0(p, (x - xmin)/xdif)\n    end\n    return od, xmin, xmax\nend\n\nfunction minimal_normalized_od(integ, i, p_index, pmin, pmax,\n                              density::Int, n::Int, Ttr::Int, u0, xmin, xmax)\n\n    pvalues = range(pmin, stop = pmax, length = density)\n    pdif = pmax - pmin; xdif = xmax - xmin\n    od = Vector{Point2f0}()\n    #= @inbounds =# for p in pvalues\n        pp = (p - pmin)/pdif # p to plot, in [0, 1]\n        DynamicalSystemsBase.reinit!(integ, u0)\n        integ.p[p_index] = p\n        DynamicalSystemsBase.step!(integ, Ttr)\n        for z in 1:n\n            DynamicalSystemsBase.step!(integ)\n            x = integ.u[i]\n            if xmin \u2264 x \u2264 xmax\n                push!(od, Point2f0(pp, (integ.u[i] - xmin)/xdif))\n            end\n        end\n    end\n    return od\nend\n\nfunction  update_controls!(h, i, n, Ttr, density, \u2b1cpmin, \u2b1cpmax, \u2b1cumin, \u2b1cumax)\n    j, pmin, pmax, xmin, xmax, m, T, d = h\n    i[] = j; n[] = m; Ttr[] = T; density[] = d\n    \u2b1cpmin[] = pmin; \u2b1cpmax[] = pmax\n    \u2b1cumin[] = xmin; \u2b1cumax[] = xmax\n    return\nend\n\n\"\"\"\n    scaleod(od, pmin, pmax, umin, umax) -> ps, us\nGiven the return values of [`interactive_orbitdiagram`](@ref), produce\norbit diagram data scaled correctly in data units. Return the data as a vector of\nparameter values and a vector of corresponding variable values.\n\"\"\"\nfunction scaleod(od, pmin, pmax, umin, umax)\n    oddata = od[]; L = length(oddata);\n    T = promote_type(typeof(umin[]), Float32)\n    ps = zeros(T, L); us = copy(ps)\n    udif = umax[] - umin[]; um = umin[]\n    pdif = pmax[] - pmin[]; pm = pmin[]\n    @inbounds for i \u2208 1:length(oddata)\n        p, u = oddata[i]\n        ps[i] = pm + pdif*p; us[i] = um + udif*u\n    end\n    return ps, us\nend\n\n# TODO: Use `\u03b1` directly after Simon's fix, no need for observe(\u03b1)\n# TODO: Add exit button that closes both windows\n# TODO: Make marker GLMakie.FastPixel()\n# TODO: Allow initial state to be a function of paramter (define function `get_u(f, p)`)\n", "meta": {"hexsha": "c8967a0f1b002ab3ba1a3f499bd0b71a5471f423", "size": 11250, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/orbitdiagram.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/InteractiveChaos.jl-81850811-659e-51ab-a7c2-5dd848ff4aa4", "max_stars_repo_head_hexsha": "e1beca1584272a389408cec2f05655105194bc68", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/orbitdiagram.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/InteractiveChaos.jl-81850811-659e-51ab-a7c2-5dd848ff4aa4", "max_issues_repo_head_hexsha": "e1beca1584272a389408cec2f05655105194bc68", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/orbitdiagram.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/InteractiveChaos.jl-81850811-659e-51ab-a7c2-5dd848ff4aa4", "max_forks_repo_head_hexsha": "e1beca1584272a389408cec2f05655105194bc68", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8787878788, "max_line_length": 106, "alphanum_fraction": 0.6023111111, "num_tokens": 3566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11596071978154186, "lm_q1q2_score": 0.05798035989077093}}
{"text": "module SyntaxTree\n\n#   This file is part of SyntaxTree.jl. It is licensed under the MIT license\n#   Copyright (C) 2018 Michael Reed\n\nexport linefilter!, linefilter, callcount, genfun, @genfun\n\nif VERSION < v\"0.7.0\"\n    linefilter(expr) = linefilter!(expr)\nelse\n    @deprecate linefilter(expr) linefilter!(expr)\nend\n\n\"\"\"\n    linefilter!(::Expr)\n\nRecursively filters out `:LineNumberNode` from `Expr` objects.\n\"\"\"\n@noinline function linefilter!(expr::Expr)\n    total = length(expr.args)\n    i = 0\n    while i < total\n        i += 1\n        if expr.args[i] |> typeof == Expr\n            if expr.args[i].head == :line\n                deleteat!(expr.args,i)\n                total -= 1\n                i -= 1\n            else\n                expr.args[i] = linefilter!(expr.args[i])\n            end\n        elseif expr.args[i] |> typeof == LineNumberNode\n            if expr.head == :macrocall\n                expr.args[i] = nothing\n            else\n                deleteat!(expr.args,i)\n                total -= 1\n                i -= 1\n            end\n        end\n    end\n    return expr\nend\n\n\"\"\"\n    sub(T::DataType,expr::Expr)\n\nMake a substitution to convert numerical values inside an `Expr` to type `T`.\n\"\"\"\n@noinline function sub(T::DataType,expr)\n    if typeof(expr) == Expr\n        ixpr = deepcopy(expr)\n        if ixpr.head == :call && ixpr.args[1] == :^\n            ixpr.args[2] = sub(T,ixpr.args[2])\n            if typeof(ixpr.args[3]) == Expr\n                ixpr.args[3] = sub(T,ixpr.args[3])\n            end\n        elseif ixpr.head == :macrocall &&\n                ixpr.args[1] \u2208 [Symbol(\"@int128_str\"), Symbol(\"@big_str\")]\n            return convert(T,eval(ixpr))\n        else\n            for a \u2208 1:length(ixpr.args)\n                ixpr.args[a] = sub(T,ixpr.args[a])\n            end\n        end\n        return ixpr\n    elseif typeof(expr) <: Number\n        return convert(T,expr)\n    end\n    return expr\nend\n\n\"\"\"\n    SyntaxTree.abs(expr)\n\nApply `abs` to the expression recursively.\n\"\"\"\n@noinline function abs(expr)\n    if typeof(expr) == Expr\n        ixpr = deepcopy(expr)\n        if ixpr.head == :call && ixpr.args[1] == :^\n            ixpr.args[2] = abs(ixpr.args[2])\n            if typeof(ixpr.args[3]) == Expr\n                ixpr.args[3] = abs(ixpr.args[3])\n            end\n        elseif ixpr.head == :macrocall &&\n                ixpr.args[1] \u2208 [Symbol(\"@int128_str\"), Symbol(\"@big_str\")]\n                val = VERSION < v\"0.7\" ? (ixpr.args[1],) : (ixpr.args[1],nothing)\n                rep = ('-',\"\")\n                return Expr(:macrocall,val...,replace(ixpr.args[end],(VERSION < v\"0.7\" ? rep : (Pair(rep...),))...))\n        else\n            ixpr.head == :call && ixpr.args[1] == :- && (ixpr.args[1] = :+)\n            for a \u2208 1:length(ixpr.args)\n                ixpr.args[a] = abs(ixpr.args[a])\n            end\n        end\n        return ixpr\n    elseif typeof(expr) <: Number\n        return Base.abs(expr)\n    end\n    return expr\nend\n\n\"\"\"\n    alg(expr,f=:(1+\u03f5))\n\nRecursively insert a machine epsilon bound (1+\u03f5) per call in `expr`.\n\"\"\"\n@noinline function alg(expr,f=:(1+\u03f5))\n    if typeof(expr) == Expr\n        ixpr = deepcopy(expr)\n        if ixpr.head == :call\n            ixpr.args[2:end] = alg.(ixpr.args[2:end],Ref(f))\n            ixpr = Expr(:call,:*,f,ixpr)\n        end\n        return ixpr\n    else\n        return expr\n    end\nend\n\n\"\"\"\n    @genfun(expr, args)\n\nReturns an anonymous function based on the given `expr` and `args`.\n\n```Julia\njulia> @genfun x^2+y^2 x y\n```\n\"\"\"\nmacro genfun(expr,args...); :(($(args...),)->$expr) end\n\n\"\"\"\n    genfun(expr, args)\n\nReturns an anonymous function based on the given `expr` and `args`.\n\n```Julia\njulia> genfun(:(x^2+y^2),[:x,:y])\njulia> genfun(:(x^2+y^2),(:x,:y))\njulia> genfun(:(x^2+y^2),:x,:y)\n```\n\"\"\"\ngenfun(expr,args::Union{Vector,Tuple}) = eval(:(($(args...),)->$expr))\ngenfun(expr,args::Symbol...) = genfun(expr,args)\n\n\"\"\"\n    @genlatest(expr, args)\n\nReturns an invokelatest function based on the given `expr` and `args`.\n\n```Julia\njulia> @genlatest x^2+y^2 x y\n```\n\"\"\"\nmacro genlatest(expr,args,gs = gensym())\n    eval(Expr(:function,Expr(:call,gs,args.args...),expr))\n    :($(Expr(:tuple,args.args...))->Base.invokelatest($gs,$(args.args...)))\nend\n\n\"\"\"\n    genlatest(expr, args)\n\nReturns an invokelatest function based on the given `expr` and `args`.\n\n```Julia\njulia> genlatest(:(x^2+y^2),[:x,:y])\njulia> genlatest(:(x^2+y^2),(:x,:y))\njulia> genlatest(:(x^2+y^2),:x,:y)\n```\n\"\"\"\nfunction genlatest(expr,args::T,gs=gensym()) where T<:Union{Vector,Tuple}\n    eval(Expr(:function,Expr(:call,gs,args...),expr))\n    if length(args) == 0\n        ()->Base.invokelatest(eval(gs))\n    elseif length(args) == 1\n        (a)->Base.invokelatest(eval(gs),a)\n    elseif length(args) == 2\n        (a,b)->Base.invokelatest(eval(gs),a,b)\n    elseif length(args) == 3\n        (a,b,c)->Base.invokelatest(eval(gs),a,b,c)\n    elseif length(args) == 4\n        (a,b,c,d)->Base.invokelatest(eval(gs),a,b,c,d)\n    elseif length(args) == 5\n        (a,b,c,d,e)->Base.invokelatest(eval(gs),a,b,c,d,e)\n    elseif length(args) == 6\n        (a,b,c,d,e,f)->Base.invokelatest(eval(gs),a,b,c,d,e,f)\n    elseif length(args) == 7\n        (a,b,c,d,e,f,g)->Base.invokelatest(eval(gs),a,b,c,d,e,f,g)\n    end\nend\ngenlatest(expr,arg,gs=gensym()) = genlatest(expr,(arg,),gs)\n\n\"\"\"\n    callcount(expr)\n\nReturns a count of the `call` operations in `expr`.\n\"\"\"\n@noinline function callcount(expr)\n    c = 0\n    if typeof(expr) == Expr\n        expr.head == :call && (c += 1)\n        c += sum(callcount.(expr.args))\n    end\n    return c\nend\n\ninclude(\"exprval.jl\")\n\nend # module\n", "meta": {"hexsha": "b7b26da27a458456163a8b78bb7f4ab45ca61073", "size": 5605, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SyntaxTree.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SyntaxTree.jl-a4af3ec5-f8ac-5fed-a759-c2e80b4d74cb", "max_stars_repo_head_hexsha": "631d8141e9ae509952d6c6b6477082d0b541e47f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2018-10-06T10:07:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T14:12:52.000Z", "max_issues_repo_path": "src/SyntaxTree.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/SyntaxTree.jl-a4af3ec5-f8ac-5fed-a759-c2e80b4d74cb", "max_issues_repo_head_hexsha": "631d8141e9ae509952d6c6b6477082d0b541e47f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/SyntaxTree.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/SyntaxTree.jl-a4af3ec5-f8ac-5fed-a759-c2e80b4d74cb", "max_forks_repo_head_hexsha": "631d8141e9ae509952d6c6b6477082d0b541e47f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-18T19:45:09.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-18T19:45:09.000Z", "avg_line_length": 26.5639810427, "max_line_length": 116, "alphanum_fraction": 0.5482604817, "num_tokens": 1682, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11596070756094144, "lm_q1q2_score": 0.05798035378047072}}
{"text": "using Test\nusing DifferentialAlgebra\n\n@test 1+1==2\n", "meta": {"hexsha": "9d8ddf14ed121accc5164f4c6648357aff5ec58d", "size": 51, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "Stopa42/DifferentialAlgebra.jl", "max_stars_repo_head_hexsha": "4938f848903836867438dba2d70c7a295e67ffde", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "Stopa42/DifferentialAlgebra.jl", "max_issues_repo_head_hexsha": "4938f848903836867438dba2d70c7a295e67ffde", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "Stopa42/DifferentialAlgebra.jl", "max_forks_repo_head_hexsha": "4938f848903836867438dba2d70c7a295e67ffde", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.2, "max_line_length": 25, "alphanum_fraction": 0.7843137255, "num_tokens": 16, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.41869689484852374, "lm_q2_score": 0.13846179412408713, "lm_q1q2_score": 0.05797352325491085}}
{"text": "\"\"\"\nTools for generating Julia packages for _educational_ purposes.\n\n# Extended Help\n\n## Overview \n\nHave you ever wanted to write a note-set, or a textbook, or a \nseries of homework assignments with tools within the Julia \necosystem? Your instincts are correct! Mediums like \n`Documenter.jl` websites, `Pluto.jl` notebooks, and `Markdown.jl` \nand PDF formats each have their own strengths, and all can help \nyou write informative, and educational content. \n\nWouldn't it be nice if there was a tool which allowed you to \nwrite _one_ file, and generate content within all of the following \nmediums automatically? Well, it already exists, and it's called \n`Literate.jl`.\n\nAlright, but wouldn't it be nice if there was a tool to help you \ncreate packages with structures that `Literate.jl` can benefit\nfrom? That's where `EducationalPkg` comes in! The following \nuse cases are a few examples that might benefit from \n`EducationalPkg`.\n\n1. Writing a note-set which is compiled with `Documenter.jl`\n2. Developing a bunch of homework assignments with `Pluto.jl`\n3. Showing several investigations, or experiments that you \nwant accessible in a variety of mediums\n\n## Usage\n\n```julia\njulia> import EducationalPkg\nedu} # enter `}` in the REPL to enter `EducationalPkg` mode!\nedu} generate FutureNobelPrize\n```\n\n\"\"\"\nmodule EducationalPkg\n\nimport Pkg\n\nusing Pluto\nusing Literate\nusing Documenter\nusing DocStringExtensions\n\ninclude(joinpath(\"EduREPL\", \"EduREPL.jl\"))\n\nmodule EduREPLInit\n    using ..EduREPL\n    function __init__()\n        if isdefined(Base, :active_repl)\n            EduREPL.runEduREPL()\n        end\n    end\nend\n\nend # module\n", "meta": {"hexsha": "f9037210ff146ddb27433ced02f36a7f202b0256", "size": 1630, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/EducationalPkg.jl", "max_stars_repo_name": "cadojo/EducationalPkg.jl", "max_stars_repo_head_hexsha": "2c7acd114dd758b42090f17bf396d63f195482da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/EducationalPkg.jl", "max_issues_repo_name": "cadojo/EducationalPkg.jl", "max_issues_repo_head_hexsha": "2c7acd114dd758b42090f17bf396d63f195482da", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/EducationalPkg.jl", "max_forks_repo_name": "cadojo/EducationalPkg.jl", "max_forks_repo_head_hexsha": "2c7acd114dd758b42090f17bf396d63f195482da", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7213114754, "max_line_length": 67, "alphanum_fraction": 0.7558282209, "num_tokens": 413, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.23370634623958195, "lm_q2_score": 0.24798742624020279, "lm_q1q2_score": 0.057956235299955625}}
{"text": "\n# rewrite.jl - expression pattern matching and rewriting.\n#\n# The general idea behind functions in this file is to provide easy means\n# of finding specific pieces of expressions and using them elsewhere.\n# At the time of writing it is used in 2 parts of this package:\n#\n# * for applyging derivatives, e.g. `x^n` ==> `n*x^(n-1)`\n# * for expression simplification, e.g. `1 * x` ==> `x`\n#\n# Pieces of expression are matched to so-called placeholders - symbols that either\n# start with `_` (e.g. `_x`) or are passed via `phs` paraneter, or set globally\n# using `set_default_placeholders(::Set{Symbol})`. Using list of placeholders\n# instead of _-prefixed names is convenient when writing a lot of transformation\n# rules where using underscores creates unnecessary noise.\n\n\nconst Symbolic = Union{Expr, Symbol}\nconst Numeric = Union{Number, Array}\n\n## pattern matching\n\nconst DEFAULT_PHS = [Set{Symbol}()]\nset_default_placeholders(set::Set{Symbol}) = (DEFAULT_PHS[1] = set)\n\nisplaceholder(x, phs) = false\nisplaceholder(x::Symbol, phs) = (startswith(string(x), \"_\")\n                                 || in(x, phs))\n# isplaceholder(ex::Expr, phs) = ex.head == :... && isplaceholder(ex.args[1], phs)\n\nfunction find_key(d::Dict{K, V}, val) where {K,V}\n    r = nothing\n    for (k,v) in d\n        if v == val\n            r = k\n            break\n        end\n    end\n    return r\nend\n\n\nfunction matchex!(m::Dict{Symbol,Any}, p::QuoteNode, x::QuoteNode;\n                  opts...)\n    return matchex!(m, p.value, x.value)\nend\n\n\nfunction matchex!(m::Dict{Symbol,Any}, ps::Vector, xs::Vector; opts...)\n    opts = to_dict(opts)\n    phs = get(opts, :phs, Set([]))\n    length(ps) <= length(xs) || return false\n    for i in eachindex(ps)\n        if isa(ps[i], Expr) && ps[i].head == :... && isplaceholder(ps[i].args[1], phs)\n            p = ps[i].args[1]\n            haskey(m, p) && m[p] != xs[i] && m[p] != xs[i:end] && return false\n            # TODO: add something here?\n            m[p] = xs[i:end]\n            return true\n        else\n            matchex!(m, ps[i], xs[i]; opts...) || return false\n        end\n    end\n    # matched everything, didn't encounter dots expression\n    return length(ps) == length(xs)\nend\n\n\nfunction matchex!(m::Dict{Symbol,Any}, p, x; phs=DEFAULT_PHS[1], allow_ex=true, exact=false)\n    allow_ex = exact ? false : allow_ex  # override allow_ex=false if exact==true\n    if isplaceholder(p, phs)\n        if haskey(m, p) && m[p] != x\n            # different bindings to the same pattern, treat as no match\n            return false\n        elseif !allow_ex && isa(x, Expr)\n            # x is expression, but matching to expression is not allowed, treat as no match\n            return false\n        elseif exact\n            k = find_key(m, x)\n            if k != p\n                return false\n            else\n                m[p] = x\n                return true\n            end\n        else                       \n            m[p] = x\n            return true\n        end\n    elseif isa(p, Expr) && isa(x, Expr)\n        return (matchex!(m, p.head, x.head) &&\n                matchex!(m, p.args, x.args; phs=phs, allow_ex=allow_ex, exact=exact))\n    else\n        return p == x\n    end\nend\n\n\n\"\"\"\nMatch expression `ex` to a pattern `pat`, return nullable dictionary of matched\nsymbols or rpatpressions.\nExample:\n\n```\nex = :(u ^ v)\npat = :(_x ^ _n)\nmatchex(pat, ex)\n# ==> Union{ Dict{Symbol,Any}(:_n=>:v,:_x=>:u), Void }\n```\n\nNOTE: two symbols match if they are equal or symbol in pattern is a placeholder.\nPlaceholder is any symbol that starts with '_'. It's also possible to pass\nlist of placeholder names (not necessarily starting wiht '_') via `phs` parameter:\n\n```\nex = :(u ^ v)\npat = :(x ^ n)\nmatchex(pat, ex; phs=Set([:x, :n]))\n# ==> Union{ Dict{Symbol,Any}(:n=>:v,:x=>:u), Void } \n```\n\nSeveral elements may be matched using `...` expression, e.g.:\n\n```\nex = :(A[i, j, k])\npat = :(x[I...])\nmatchex(pat, ex; phs=Set([:x, :I]))\n# ==> Union{ Dict(:x=>:A, :I=>[:i,:j,:k]), Void }\n```\n\nOptional parameters:\n\n * phs::Set{Symbol} = DEFAULT_PHS[1]\n       A set of placeholder symbols\n * allow_ex::Boolean = true\n       Allow matchinng of symbol pattern to an expression. Example:\n\n           matchex(:(_x + 1), :(a*b + 1); allow_ex=true)  # ==> matches\n           matchex(:(_x + 1), :(a*b + 1); allow_ex=false)  # ==> doesn't match\n * exact::Boolean = false\n       Allow matching of the same expression to different keys\n\n           matchex(:(_x + _y), :(a + a); exact=false) # ==> matches\n           matchex(:(_x = _y), :(a + a); exact=true)  # ==> doesn't match\n\n\"\"\"\nfunction matchex(pat, ex; opts...)\n    m = Dict{Symbol,Any}()\n    res = matchex!(m, pat, ex; opts...)\n    if res\n        return m\n    else\n        return nothing\n    end\nend\n\n\"\"\"\nCheck if expression matches pattern. See `matchex()` for details.\n\"\"\"\nfunction matchingex(pat, ex; opts...)\n    return matchex(pat, ex; opts...) != nothing\nend\n\n\n## find rpatpression\n\nfunction findex!(res::Vector, pat, ex; phs=DEFAULT_PHS[1])\n    if matchingex(pat, ex; phs=phs)\n        push!(res, ex)\n    elseif expr_like(ex)\n        for arg in ex.args\n            findex!(res, pat, arg; phs=phs)\n        end\n    end\nend\n\n\n\"\"\"\nFind sub-expressions matching a pattern. Example:\n\n    ex = :(a * f(x) + b * f(y))\n    pat = :(f(_))\n    findex(pat, ex)   # ==> [:(f(x)), :(f(y))]\n\n\"\"\"\nfunction findex(pat, ex; phs=DEFAULT_PHS[1])\n    res = Any[]\n    findex!(res, pat, ex; phs=phs)\n    return res\nend\n\n\n## substitution\n\n\"\"\"\nGiven a list of expression arguments, flatten the dotted ones. Example:\n\n    args = [:foo, :([a, b, c]...)]\n    flatten_dots(args)\n    # ==> [:foo, :a, :b, :c]\n\"\"\"\nfunction flatten_dots(args::Vector)\n    new_args = Vector{Any}()\n    for arg in args\n        if isa(arg, Expr) && arg.head == :... && isa(arg.args[1], AbstractArray)\n            for x in arg.args[1]\n                push!(new_args, x)\n            end\n        else\n            push!(new_args, arg)\n        end\n    end\n    return new_args\nend\n\n\n\"\"\"\nSubstitute symbols in `ex` according to substitute table `st`.\nExample:\n\n    ex = :(x ^ n)\n    subs(ex, x=2)            # ==> :(2 ^ n)\n\nalternatively:\n\n    subs(ex, Dict(:x => 2))  # ==> :(2 ^ n)\n\nIf `ex` contains a :(xs...) argument and `st` contains an array-valued\nsabstitute for it, the substitute will be flattened:\n\n    ex = :(foo(xs...))\n    subs(ex, Dict(:xs => [:a, :b, :c]))\n    # ==> :(foo(a, b, c))\n\"\"\"\nfunction subs(ex::Expr, st::Dict)\n    if haskey(st, ex)\n        return st[ex]\n        # elseif ex.head == :... && haskey(st, ex.args[1])        \n    else\n        new_args = [subs(arg, st) for arg in ex.args]\n        new_args = flatten_dots(new_args)\n        return Expr(ex.head, new_args...)\n    end\nend\n\n\nfunction subs(s::Symbol, st::Dict)\n    return haskey(st, s) ? st[s] : s\nend\n\nsubs(q::QuoteNode, st::Dict) = QuoteNode(subs(q.value, st))\nsubs(x::Any, st::Dict) = x\nsubs(ex; st...) = subs(ex, to_dict(st))\n\n\n## remove rpatpression\n\n\"\"\"\nRemove rpatpression conforming to a pattern.\nExample:\n\n    ex = :(x * (m == n))\n    pat = :(_i == _j)\n    ex = without(ex, pat)  # ==> :x\n\"\"\"\nfunction without(ex::Expr, pat; phs=DEFAULT_PHS[1])\n    new_args_without = [without(arg, pat; phs=phs) for arg in ex.args]\n    new_args = filter(arg -> !matchingex(pat, arg; phs=phs), new_args_without)\n    if ex.head == :call && length(new_args) == 2 &&\n        (ex.args[1] == :+ || ex.args[1] == :*)\n        # pop argument of now-single-valued operation\n        # TODO: make more general, e.g. handle (x - y) with x removed\n        return new_args[2]\n    elseif ex.head == :call && length(new_args) == 1 && ex.args[1] == :*\n        return 1.0\n    elseif ex.head == :call && length(new_args) == 1 && ex.args[1] == :+\n        return 0.0\n    else\n        return Expr(ex.head, new_args...) |> simplify\n    end\nend\n\nwithout(x, pat; phs=DEFAULT_PHS[1]) = x\n\n\n## rewriting\n\n\"\"\"\nrewrite(ex, pat, rpat)\n\nRewrite expression `ex` according to a transform from pattern `pat`\nto a substituting expression `rpat`.\nExample (derivative of x^n):\n\n    ex = :(u ^ v)\n    pat = :(_x ^ _n)\n    rpat = :(_n * _x ^ (_n - 1))\n    rewrite(ex, pat, rpat) # ==> :(v * u ^ (v - 1))\n\"\"\"\nfunction rewrite(ex::Symbolic, pat::Symbolic, rpat::Any; opts...)\n    st = matchex(pat, ex; opts...)\n    if st == nothing\n        error(\"Expression $ex doesn't match pattern $pat\")\n    else\n        return subs(rpat, st)\n    end\nend\n\n\n\"\"\"\nSame as rewrite, but returns Union{Expr, Void} and doesn't throw an error\nwhen expression doesn't match pattern\n\"\"\"\nfunction tryrewrite(ex::Symbolic, pat::Symbolic, rpat::Any; opts...)\n    st = matchex(pat, ex; opts...)\n    if st == nothing\n        return nothing\n    else\n        return subs(rpat, st)\n    end\nend\n\n\n\n\"\"\"\nrewrite_all(ex, rules)\n\nRecursively rewrite an expression according to a list of rules like [pat => rpat]\nExample:\n\n    ex = :(foo(bar(foo(A))))\n    rules = [:(foo(x)) => :(quux(x)),\n             :(bar(x)) => :(baz(x))]\n    rewrite_all(ex, rules; phs=[:x])\n    # ==> :(quux(baz(quux(A))))\n\"\"\"\nfunction rewrite_all(ex::Symbolic, rules; opts...)\n    new_ex = ex\n    if isa(ex, Expr)\n        new_args = [rewrite_all(arg, rules; opts...)\n                    for arg in ex.args]\n        new_ex = Expr(ex.head, new_args...)\n    end\n    for (pat, rpat) in rules\n        if matchingex(pat, new_ex; opts...)\n            new_ex = rewrite(new_ex, pat, rpat; opts...)\n        end\n    end\n    return new_ex\nend\n\n\n\"\"\"\nrewrite_all(ex, pat, rpat)\n\nRecursively rewrite all occurrences of a pattern in an expression.\nExample:\n\n    ex = :(foo(bar(foo(A))))\n    pat = :(foo(x))\n    rpat = :(quux(x))\n    rewrite_all(ex, pat, rpat; phs=[:x])\n    # ==> :(quux(bar(quux(A))))\n\"\"\"\nfunction rewrite_all(ex::Symbolic, pat::Symbolic, rpat; opts...)\n    return rewrite_all(ex, [pat => rpat]; opts...)\nend\n\nrewrite_all(x, pat, rpat; opts...) = x\nrewrite_all(x, rules; opts...) = x\n", "meta": {"hexsha": "79e84b140423170521c6bd64ac8e1b49bfc2ea0f", "size": 9850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rewrite.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Espresso.jl-6912e4f1-e036-58b0-9138-08d1e6358ea9", "max_stars_repo_head_hexsha": "3fc95cdf87ec8f7c7180723b1047715eb9918605", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 53, "max_stars_repo_stars_event_min_datetime": "2016-08-15T12:25:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T00:19:00.000Z", "max_issues_repo_path": "src/rewrite.jl", "max_issues_repo_name": "dfdx/Hydra.jl", "max_issues_repo_head_hexsha": "5362d8cef68d0f0f91a34834dc7095ad7ab024f6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2016-08-14T15:41:56.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-08T21:02:28.000Z", "max_forks_repo_path": "src/rewrite.jl", "max_forks_repo_name": "dfdx/Hydra.jl", "max_forks_repo_head_hexsha": "5362d8cef68d0f0f91a34834dc7095ad7ab024f6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2017-01-20T07:14:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:32:22.000Z", "avg_line_length": 26.5498652291, "max_line_length": 92, "alphanum_fraction": 0.5726903553, "num_tokens": 2873, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4073334000459302, "lm_q2_score": 0.1422318986458388, "lm_q1q2_score": 0.05793580287039765}}
{"text": "## CPItree.jl - Tipos y m\u00e9todos para operar la estructura jer\u00e1rquica del IPC\n\n##  ----------------------------------------------------------------------------\n#   Main type definitions\n#   ----------------------------------------------------------------------------\n\n\"\"\"\n    Item{T}(code::String, name::String, weight::T<:AbstractFloat)\n\nRepresenta un gasto b\u00e1sico en la estructura de nodos del IPC. Es el nivel m\u00e1s\nbajo de la estructura. Almacena el c\u00f3digo del gasto b\u00e1sico, su nombre o\ndescripci\u00f3n y su ponderaci\u00f3n en el IPC. Los datos de este nodo deben estar\ndisponibles en alg\u00fan [`FullCPIBase`](@ref). \n\nPosee los campos: \n- `code`: que almacena el c\u00f3digo del gasto b\u00e1sico como un `String`.\n- `name`: que almacena el nombre del gasto b\u00e1sico como un `String`.\n- `weight::T`: que almacena la ponderaci\u00f3n del gasto b\u00e1sico como un valor\n  flotante de tipo `T`.\n\nAunque este nodo ser\u00e1 usualmente creado de manera autom\u00e1tica por m\u00e9todos como\n[`CPITree`](@ref), se pueden crear estructuras jer\u00e1rquicas manualmente. Por\nejemplo, para crear un nodo del nivel inferior: \n```julia-repl\njulia> Item(\"_011101\", \"Item A\", 7.352945f0)\nItem{Float32}(\"_011101\", \"Item A\", 7.352945f0)\n```\n\nVer tambi\u00e9n: [`Group`](@ref), [`CPITree`](@ref).\n\"\"\"\nstruct Item{T}\n    code::String\n    name::String\n    weight::T\nend\n\n\"\"\"\n    Group{S,T}\n\n    Group(code, name, children::Vector{S}) where S\n    Group(code, name, children...) \n\nRepresenta un nodo de agrupaci\u00f3n de cualquier nivel en la estructura de nodos\ndel IPC. Puede almacenar gastos b\u00e1sicos u otros grupos de mayor jerarqu\u00eda.\nAlmacena el c\u00f3digo del grupo, su nombre o descripci\u00f3n y su ponderaci\u00f3n en el\nIPC. \n\nPosee los campos: \n- `code`: almacena el c\u00f3digo del grupo como un `String`.\n- `name`: almacena el nombre del grupo como un `String`.\n- `weight::T`: almacena la ponderaci\u00f3n del grupo como un valor flotante de tipo\n  `T`.\n- `children::Vector{S}`: almacena el vector de nodos \"hijos\" de la estructura.\n  Por ejemplo, este vector podr\u00eda ser un vector de elementos `Item` para agrupar\n  un conjunto de gastos b\u00e1sicos.\n\nAunque este nodo ser\u00e1 usualmente creado de manera autom\u00e1tica por m\u00e9todos como\n[`CPITree`](@ref), se pueden crear estructuras jer\u00e1rquicas manualmente. Por\nejemplo, para crear un grupo: \n```julia-repl\njulia> a = Item(\"_011201\", \"Item B\", 6.7442417f0)\nItem{Float32}(\"_011201\", \"Item B\", 6.7442417f0)\n\njulia> b = Item(\"_011202\", \"Item C\", 7.394718f0)\nItem{Float32}(\"_011202\", \"Item C\", 7.394718f0)\n\njulia> g = Group(\"_0112\", \"Subgr._0112\", a, b)\nGroup{Item{Float32}, Float32}\n_0112: Subgr._0112 [14.13896] \n\u251c\u2500 _011201: Item B [6.7442417]\n\u2514\u2500 _011202: Item C [7.394718]\n```\n\nVer tambi\u00e9n: [`Item`](@ref), [`CPITree`](@ref).\n\"\"\"\nstruct Group{S,T}\n    code::String\n    name::String\n    weight::T\n    children::Vector{S}\n\n    function Group(code, name, children...)\n        sum_weights = sum(child.weight for child in children)\n        T = eltype(sum_weights)\n        S = eltype(children)\n        new{S,T}(code, name, sum_weights, S[children...])\n    end\n    Group(code, name, children::Vector{S}) where {S} = Group(code, name, children...)\nend\n\n# Redefine methods for getting children\nchildren(::Item) = ()\nchildren(g::Group) = g.children\n\n# Redefine how to print a node in the print_tree function \nprintnode(io::IO, g::Item) = print(io, g.code * \": \" * g.name * \" [\" * string(g.weight) * \"] \")\nprintnode(io::IO, g::Group) = print(io, g.code * \": \" * g.name * \" [\" * string(g.weight) * \"] \")\n\n# How to show a Group\nfunction Base.show(io::IO, g::Group)\n    println(io, typeof(g)) \n    print_tree(io, g)\nend\n\n\n##  ----------------------------------------------------------------------------\n#   CPI tree functions \n#   ----------------------------------------------------------------------------\n\n\"\"\"\n    cpi_tree_nodes(codes::Vector{<:AbstractString}; \n        characters::(NTuple{N, Int} where N), depth::Int=1, chars::Int=characters[depth], prefix::AbstractString=\"\", \n        base::FullCPIBase, \n        group_names::Vector{<:AbstractString}, \n        group_codes::Vector{<:AbstractString})\n\nConstruye y devuelve una lista de nodos a partir de la lista de c\u00f3digos `codes`\no de la especificaci\u00f3n jer\u00e1rquica de caracteres en `characters`. Los nombres y\nlas ponderaciones del nivel inferior (nivel de gasto b\u00e1sico) son\nobtenidas de la estructura `base`. Se debe proveer el vector de c\u00f3digos y\nnombres de todas las jerarqu\u00edas superiores en la estructura de c\u00f3digos en los\nvectores `group_names` y `group_codes`.\n\nEsta funci\u00f3n permite crear \u00fanicamente la estructura jer\u00e1rquica de nodos. Para construir de manera autom\u00e1tica una estructura del IPC, se recomienda utilizar preferentemente [`CPITree`](@ref).\n\nVea tambi\u00e9n: [`CPITree`](@ref).\n\"\"\"\nfunction cpi_tree_nodes(codes::Vector{<:AbstractString}; \n    characters::(NTuple{N, Int} where N), depth::Int=1, chars::Int=characters[depth], prefix::AbstractString=\"\", \n    base::FullCPIBase, \n    group_names::Vector{<:AbstractString}, \n    group_codes::Vector{<:AbstractString})\n    \n    # Get available starting codes \n    available = filter(code -> startswith(code, prefix), codes)\n    \n    # Base case: \n    # If code length is the last available then we reached a leaf node\n    if chars == last(characters)\n        # With available codes construct leaf CPI nodes\n        children = map(available) do code\n            # Find the code index \n            icode = findfirst(==(code), codes)\n            Item(code, base.names[icode], base.w[icode])\n        end\n        return children\n    end\n\n    # Get possible prefixes values from the available list. Possible prefixes\n    # are the ones from the beginning of the string to the number of chars,\n    # which depends on the depth we are on.\n    possibles = unique(getindex.(available, Ref(1:chars)))\n\n    # For each available code, call the function itself with the next downward hierarchy\n    groups = map(possibles) do prefixcode\n        # Go get the children in the next downward level\n        children = cpi_tree_nodes(codes; \n            characters, depth=depth+1, prefix=prefixcode,\n            base, group_names, group_codes\n        )\n        # Get the group name \n        @debug \"Prefix code:\" prefixcode\n        i = findfirst(==(prefixcode), group_codes)\n        if i === nothing \n            @warn \"C\u00f3digo de grupo para $(prefixcode) no encontrado en `group_codes`. Utilizando nombre gen\u00e9rico.\"\n            gname = \"Group: $prefixcode\"\n        else\n            gname = group_names[i]\n        end\n        # With the children create a group \n        group = Group(prefixcode, gname, children)\n        group\n    end\n\n    # Return the group for the upward parents\n    return groups\nend\n\n\"\"\"\n    get_cpi_tree(; \n        base::FullCPIBase, \n        group_names::Vector{<:AbstractString}, \n        group_codes::Vector{<:AbstractString}, \n        characters::(NTuple{N, Int} where N),\n        upperlevel_code = \"_0\", \n        upperlevel_name = \"IPC\")\n\nFunci\u00f3n superior para obtener estructura jer\u00e1rquica del IPC. Devuelve el nodo\nsuperior del \u00e1rbol jer\u00e1rquico. Utiliza la funci\u00f3n de m\u00e1s bajo nivel\n`cpi_tree_nodes` para construir los nodos del nivel m\u00e1s alto y hacia abajo en la\nestructura jer\u00e1rquica. Se debe proveer el vector de c\u00f3digos y nombres de todas\nlas jerarqu\u00edas superiores en la estructura de c\u00f3digos en los vectores\n`group_names` y `group_codes`. \n\nEsta funci\u00f3n permite crear \u00fanicamente la estructura jer\u00e1rquica de nodos. Para construir de manera autom\u00e1tica una estructura del IPC, se recomienda utilizar preferentemente [`CPITree`](@ref).\n\nVea tambi\u00e9n: [`CPITree`](@ref).\n\"\"\"\nfunction get_cpi_tree(; \n    base::FullCPIBase, \n    group_names::Vector{<:AbstractString}, \n    group_codes::Vector{<:AbstractString}, \n    characters::(NTuple{N, Int} where N),\n    upperlevel_code = \"_0\", \n    upperlevel_name = \"IPC\")\n\n    length(characters) >= 2 || throw(ArgumentError(\"`characters` debe ser una tupla con al menos dos valores\"))\n    for i in 2:length(characters)\n        characters[i] > characters[i-1] || throw(ArgumentError(\"Valores en `characters` deben ser ascendentes hasta el largo de los c\u00f3digos en base\"))\n    end\n    # Get the codes list from the FullCPIBase object\n    codes = base.codes\n    @debug \"Codes:\" codes\n\n    # Call lower level tree building function\n    upper_nodes = cpi_tree_nodes(codes; characters, base, group_names, group_codes)\n    \n    # Build upper level tree node\n    tree = Group(upperlevel_code, upperlevel_name, upper_nodes)\n    tree\nend\n\n\n## Build functions to find the inner tree of a given code\nfunction find_tree(code, tree::Item) \n    code == tree.code && return tree \n    nothing\nend\n\nfunction find_tree(code, tree::Group)\n    # Most basic case: the code is the same as the tree in which to search \n    code == tree.code && return tree\n\n    # Search in the tree's nodes \n    for child in tree.children\n        # If code searched is one of the children, return the child\n        code == child.code && return child\n\n        # Look if the code starts the same as the child's code, i.e the code\n        # contains the child's code. If so, find in the inner tree and break out\n        # of this level's search\n        contains(code, child.code) && return find_tree(code, child)\n    end\n\n    # Code not found at any level\n    nothing\nend\n\n# Redefine getindex to search for specific nodes within the tree\nBase.getindex(tree::Union{Item, Group}, code::AbstractString) = find_tree(code, tree)\n\n\n\n\n##  ----------------------------------------------------------------------------\n#   Functions to compute any code's price index from the lower level data\n#   ----------------------------------------------------------------------------\n\n# Basic case: compute index of an Item, which is stored in the `base` structure\nfunction compute_index(good::Item, base::FullCPIBase)\n    i = findfirst(==(good.code), base.codes)\n    # Maybe add a warning here if code not found\n    base.ipc[:, i]\nend\n\n# Recursive function to compute inner price indexes for groups\nfunction compute_index(group, base::FullCPIBase)\n    # Get the indexes of the children. At the lowest level the dispatch will\n    # select compute_index(::Item, ::FullCPIBase) to return the Goods indices\n    ipcs = mapreduce(c -> compute_index(c, base), hcat, group.children)\n\n    # If there exists only one good in the group, that is the group's index\n    size(ipcs, 2) == 1 && return ipcs\n    \n    # Get the weights\n    weights = map(c -> c.weight, group.children) \n\n    # Return normalized sum product \n    ipcs * weights / sum(weights)\nend\n\n# Edge case called when searching for a node that doest not exist. For example,\n# if called compute_index(tree[\"_0101101\"], base) and node with code \"_0101101\"\n# does not exist, returns nothing and raises a warning\nfunction compute_index(::Nothing, ::FullCPIBase)\n    @warn \"Nodo no disponible en la estructura\"\n    nothing \nend\n\n\n##  ----------------------------------------------------------------------------\n#   In-place functions to compute any code's price index from the lower level data\n#   ----------------------------------------------------------------------------\n\nfunction compute_index!(cache::Dict, good::Item, base::FullCPIBase)\n    i = findfirst(==(good.code), base.codes)\n    cache[good.code] = base.ipc[:, i] # save a copy in the cache\n    cache[good.code]\nend\n\nfunction compute_index!(cache::Dict, group::Group, base::FullCPIBase)\n    # If code is available in cache, just return it\n    group.code in keys(cache) && return cache[group.code]\n\n    # Else, compute the index and store it \n    # Get the indexes of the children. At the lowest level the dispatch will\n    # select compute_index(::Item, ::FullCPIBase) to return the Goods indices\n    ipcs = mapreduce(c -> compute_index!(cache, c, base), hcat, group.children)\n\n    # If there exists only one good in the group, that is the group's index\n    if size(ipcs, 2) == 1 \n        cache[group.code] = ipcs \n        return ipcs\n    end\n    \n    # Get the weights\n    weights = map(c -> c.weight, group.children) \n\n    # Store normalized sum product \n    cache[group.code] = ipcs * weights / sum(weights)\n    cache[group.code]\nend\n\nfunction compute_index!(::Dict, ::Nothing, ::FullCPIBase)\n    @warn \"Nodo no disponible en la estructura\"\n    nothing \nend\n\n\n##  ----------------------------------------------------------------------------\n#   CPITree: estructura contenedora del \u00e1rbol jer\u00e1rquico y los datos necesarios\n#   para computar cualquier nodo en la estructura del IPC.\n#   ----------------------------------------------------------------------------\n\n\"\"\"\n    CPITree\n\n    CPITree(base::FullCPIBase, tree::Union{Group, Item}, group_names::Vector{String}, group_codes::Vector{String})\n    CPITree(; base::FullCPIBase, groupsdf::DataFrame, characters::(NTuple{N, Int} where N), upperlevel_code = \"_0\", upperlevel_name = \"IPC\")\n\nContenedor envolvente de un \u00e1rbol jer\u00e1rquico del IPC y los datos necesarios de\nlos gastos b\u00e1sicos para computar cualquier jerarqu\u00eda dentro del \u00e1rbol. Permite\nvisualizar y explorar la composici\u00f3n del IPC de un pa\u00eds, as\u00ed como computar los\n\u00edndices de precios de las diferentes jerarqu\u00edas de la estructura del IPC. Est\u00e1\ncompuesto por: \n- Un objeto `base`, de tipo [`FullCPIBase`](@ref), el cual almacena las series\n  de tiempo de los \u00edndices de los gastos b\u00e1sicos. \n- Un objeto `tree` que contiene la estructura de nodos del IPC.\n- El vector de nombres `group_names` de los grupos del \u00e1rbol `tree`.\n- El vector de c\u00f3digos `group_codes` de los grupos del \u00e1rbol `tree`.\n\nEl constructor simple requiere una estructura jer\u00e1rquica de nodos como la\ndevuelta por [`get_cpi_tree`](@ref). Al utilizar el constructor con `groupsdf` y\n`characters`, se construye autom\u00e1ticamente un `CPITree` utilizando los c\u00f3digos\ncomo indicadores de la estructura jer\u00e1rquica. Los c\u00f3digos de los gastos b\u00e1sicos\ncontenidos en `base` describen c\u00f3mo se agrupan las jerarqu\u00edas. Por ejemplo, el\nsiguiente `FullCPIBase` contiene 10 gastos b\u00e1sicos: \n```julia-repl\njulia> base\nFullCPIBase{Float32, Float32}: 36 per\u00edodos \u00d7 10 gastos b\u00e1sicos Jan-01-Dec-03\n\u250c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Row \u2502      Dates \u2502 _011101 \u2502 _011201 \u2502 _011202 \u2502 _021101 \u2502 _022101 \u2502 _031101 \u2502 _041101 \u2502 _041201 \u2502 _041202 \u2502 _041301 \u2502\n\u2502     \u2502            \u2502  Item A \u2502  Item B \u2502  Item C \u2502  Item D \u2502  Item E \u2502  Item F \u2502  Item G \u2502  Item H \u2502  Item I \u2502  Item J \u2502\n\u2502     \u2502            \u2502 11.8947 \u2502 2.39931 \u2502 7.80836 \u2502 1.58585 \u2502  20.141 \u2502 12.0526 \u2502 9.56901 \u2502 14.6256 \u2502 15.0276 \u2502 4.89602 \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502   1 \u2502 2001-01-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  101.13 \u2502  100.00 \u2502  100.00 \u2502  100.51 \u2502  100.00 \u2502\n\u2502   2 \u2502 2001-02-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  102.28 \u2502  100.00 \u2502  100.00 \u2502  101.02 \u2502  100.00 \u2502\n\u2502   3 \u2502 2001-03-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  103.44 \u2502  100.00 \u2502  100.00 \u2502  101.53 \u2502  100.00 \u2502\n\u2502   4 \u2502 2001-04-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  104.61 \u2502  100.00 \u2502  100.00 \u2502  102.05 \u2502  100.00 \u2502\n\u2502   5 \u2502 2001-05-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  105.79 \u2502  100.00 \u2502  100.00 \u2502  102.56 \u2502  100.00 \u2502\n\u2502  \u22ee  \u2502     \u22ee      \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502    \u22ee    \u2502\n\u2502  32 \u2502 2003-08-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  143.39 \u2502  100.00 \u2502  100.00 \u2502  117.59 \u2502  100.00 \u2502\n\u2502  33 \u2502 2003-09-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  145.02 \u2502  100.00 \u2502  100.00 \u2502  118.19 \u2502  100.00 \u2502\n\u2502  34 \u2502 2003-10-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  146.66 \u2502  100.00 \u2502  100.00 \u2502  118.79 \u2502  100.00 \u2502\n\u2502  35 \u2502 2003-11-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  148.32 \u2502  100.00 \u2502  100.00 \u2502  119.39 \u2502  100.00 \u2502\n\u2502  36 \u2502 2003-12-01 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  100.00 \u2502  150.00 \u2502  100.00 \u2502  100.00 \u2502  120.00 \u2502  100.00 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n                                                                                                         26 rows omitted\n```\n\nEn este ejemplo, el argumento debe especificarse como `characters = (3, 4, 5,\n7)`, pues los c\u00f3digos indican las jerarqu\u00edas en el IPC de manera siguiente: \n- Los primeros 3 caracteres indican la jerarqu\u00eda de *divisi\u00f3n de gasto*. \n- El siguiente caracter indica la jerarqu\u00eda de *agrupaci\u00f3n de gasto*. \n- El siguiente caracter indica la jerarqu\u00eda de *subgrupo de gasto*. \n- Los siguientes 2 caracteres indican el *n\u00famero de gasto b\u00e1sico dentro de su\n  grupo*. \n\nPor su parte, el DataFrame `groupsdf` debe tener la estructura m\u00ednima siguiente: \n- La primera columna debe ser de tipo `String` y contiene los c\u00f3digos de los\n  grupos disponibles en la estructura del IPC. \n- La segunda columna debe ser de tipo `String` y contiene las descripciones o\nnombres de los grupos disponibles en la estructura del IPC. Por ejemplo, el\nDataFrame `groupsdf` puede verse de esta forma: \n```\n17\u00d72 DataFrame\n Row \u2502 code    description \n     \u2502 String  String      \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 _01     Div._01\n   2 \u2502 _02     Div._02\n   3 \u2502 _03     Div._03\n   4 \u2502 _04     Div._04\n   5 \u2502 _011    Agr._011\n   6 \u2502 _021    Agr._021\n   7 \u2502 _022    Agr._022\n   8 \u2502 _031    Agr._031\n   9 \u2502 _041    Agr._041\n  10 \u2502 _0111   Subgr._0111\n  11 \u2502 _0112   Subgr._0112\n  12 \u2502 _0211   Subgr._0211\n  13 \u2502 _0221   Subgr._0221\n  14 \u2502 _0311   Subgr._0311\n  15 \u2502 _0411   Subgr._0411\n  16 \u2502 _0412   Subgr._0412\n  17 \u2502 _0413   Subgr._0413\n```\n\nPor ejemplo, al construir un `CPITree` con la estructura de c\u00f3digos indicada anteriormente y el DataFrame de ejemplo, el \u00e1rbol del IPC se puede ver de esta forma: \n```julia-repl\njulia> tree = CPITree(; base, groupsdf, characters=(3,4,5,7))\nCPITree{Group{Group{Group{Group{Item{Float32}, Float32}, Float32}, Float32}, Float32}} con datos\n\u2514\u2500\u2192 FullCPIBase{Float32, Float32}: 36 per\u00edodos \u00d7 10 gastos b\u00e1sicos Jan-01-Dec-03\n_0: IPC [100.0]\n\u251c\u2500 _01: Div._01 [21.491905]\n\u2502  \u2514\u2500 _011: Agr._011 [21.491905]\n\u2502     \u251c\u2500 _0111: Subgr._0111 [7.352945]\n\u2502     \u2502  \u2514\u2500 _011101: Item A [7.352945]\n\u2502     \u2514\u2500 _0112: Subgr._0112 [14.13896]\n\u2502        \u251c\u2500 _011201: Item B [6.7442417]\n\u2502        \u2514\u2500 _011202: Item C [7.394718]\n\u251c\u2500 _02: Div._02 [3.0530455]\n\u2502  \u251c\u2500 _021: Agr._021 [1.1036392]\n\u2502  \u2502  \u2514\u2500 _0211: Subgr._0211 [1.1036392]\n\u2502  \u2502     \u2514\u2500 _021101: Item D [1.1036392]\n\u2502  \u2514\u2500 _022: Agr._022 [1.9494063]\n\u2502     \u2514\u2500 _0221: Subgr._0221 [1.9494063]\n\u2502        \u2514\u2500 _022101: Item E [1.9494063]\n\u251c\u2500 _03: Div._03 [11.68543]\n\u2502  \u2514\u2500 _031: Agr._031 [11.68543]\n\u2502     \u2514\u2500 _0311: Subgr._0311 [11.68543]\n\u2502        \u2514\u2500 _031101: Item F [11.68543]\n\u2514\u2500 _04: Div._04 [63.769615]\n   \u2514\u2500 _041: Agr._041 [63.769615]\n      \u251c\u2500 _0411: Subgr._0411 [16.103952]\n      \u2502  \u2514\u2500 _041101: Item G [16.103952]\n      \u251c\u2500 _0412: Subgr._0412 [28.824577]\n      \u2502  \u251c\u2500 _041201: Item H [11.367162]\n      \u2502  \u2514\u2500 _041202: Item I [17.457417]\n      \u2514\u2500 _0413: Subgr._0413 [18.841085]\n         \u2514\u2500 _041301: Item J [18.841085]\n```\n\"\"\"\nstruct CPITree{G}\n    base::FullCPIBase\n    tree::G\n    group_names::Vector{String}\n    group_codes::Vector{String}\n    function CPITree(base::FullCPIBase, tree::Union{Group, Item}, group_names::Vector{String}, group_codes::Vector{String})\n        G = typeof(tree)\n        new{G}(base, tree, group_names, group_codes)\n    end\nend\n\nfunction CPITree(; \n    base::FullCPIBase, \n    groupsdf::DataFrame, \n    characters::(NTuple{N, Int} where N),\n    upperlevel_code = \"_0\", \n    upperlevel_name = \"IPC\")\n\n    # Obtener c\u00f3digos y nombres de los grupos en las jerarqu\u00edas\n    group_codes = convert.(String, groupsdf[!, 1])\n    group_names = groupsdf[!, 2]\n\n    tree = get_cpi_tree(;\n        base, \n        characters,\n        group_names, \n        group_codes,\n        upperlevel_code, \n        upperlevel_name\n    )\n\n    CPITree(base, tree, group_names, group_codes)\nend\n\n\nfunction Base.show(io::IO, cpitree::CPITree)\n    println(io, typeof(cpitree), \" con datos\")\n    println(io, \"\u2514\u2500\u2192 \", sprint(summary, cpitree.base)) \n    print_tree(io, cpitree.tree)\nend\n\n\"\"\"\n    Base.getindex(cpitree::CPITree, code::AbstractString)\n\nEste m\u00e9todo se utiliza para indexar el \u00e1rbol jer\u00e1rquico `cpitree` y obtener una\nestructura similar cuyo nodo superior sea el nodo con el c\u00f3digo provisto `code`. Por ejemplo, si tenemos el siguiente \u00e1rbol: \n```julia-repl\njulia> tree\nCPITree{Group{Group{Group{Group{Item{Float32}, Float32}, Float32}, Float32}, Float32}} con datos\n\u2514\u2500\u2192 FullCPIBase{Float32, Float32}: 36 per\u00edodos \u00d7 10 gastos b\u00e1sicos Jan-01-Dec-03\n_0: IPC [100.0]\n\u251c\u2500 _01: Div._01 [21.491905]\n\u2502  \u2514\u2500 _011: Agr._011 [21.491905]\n\u2502     \u251c\u2500 _0111: Subgr._0111 [7.352945]\n\u2502     \u2502  \u2514\u2500 _011101: Item A [7.352945]\n\u2502     \u2514\u2500 _0112: Subgr._0112 [14.13896]\n\u2502        \u251c\u2500 _011201: Item B [6.7442417]\n\u2502        \u2514\u2500 _011202: Item C [7.394718]\n\u251c\u2500 _02: Div._02 [3.0530455]\n\u2502  \u251c\u2500 _021: Agr._021 [1.1036392] \n\u2502  \u2502  \u2514\u2500 _0211: Subgr._0211 [1.1036392]\n\u2502  \u2502     \u2514\u2500 _021101: Item D [1.1036392]\n\u2502  \u2514\u2500 _022: Agr._022 [1.9494063]\n\u2502     \u2514\u2500 _0221: Subgr._0221 [1.9494063]\n\u2502        \u2514\u2500 _022101: Item E [1.9494063]\n\u251c\u2500 _03: Div._03 [11.68543]\n\u2502  \u2514\u2500 _031: Agr._031 [11.68543]\n\u2502     \u2514\u2500 _0311: Subgr._0311 [11.68543]\n\u2502        \u2514\u2500 _031101: Item F [11.68543]\n\u2514\u2500 _04: Div._04 [63.769615]\n   \u2514\u2500 _041: Agr._041 [63.769615]\n      \u251c\u2500 _0411: Subgr._0411 [16.103952]\n      \u2502  \u2514\u2500 _041101: Item G [16.103952]\n      \u251c\u2500 _0412: Subgr._0412 [28.824577]\n      \u2502  \u251c\u2500 _041201: Item H [11.367162]\n      \u2502  \u2514\u2500 _041202: Item I [17.457417]\n      \u2514\u2500 _0413: Subgr._0413 [18.841085]\n         \u2514\u2500 _041301: Item J [18.841085]\n```\n\nAl indexar por un c\u00f3digo, como `_041`, obtenemos una estructura similar a partir de ese nodo:\n```julia-repl\njulia> tree[\"_041\"]\nCPITree{Group{Group{Item{Float32}, Float32}, Float32}} con datos\n\u2514\u2500\u2192 FullCPIBase{Float32, Float32}: 36 per\u00edodos \u00d7 10 gastos b\u00e1sicos Jan-01-Dec-03\n_041: Agr._041 [63.769615]\n\u251c\u2500 _0411: Subgr._0411 [16.103952]\n\u2502  \u2514\u2500 _041101: Item G [16.103952] \n\u251c\u2500 _0412: Subgr._0412 [28.824577]\n\u2502  \u251c\u2500 _041201: Item H [11.367162]\n\u2502  \u2514\u2500 _041202: Item I [17.457417]\n\u2514\u2500 _0413: Subgr._0413 [18.841085]\n   \u2514\u2500 _041301: Item J [18.841085]\n```\n\"\"\"\nfunction Base.getindex(cpitree::CPITree, code::AbstractString) \n    node = cpitree.tree[code]\n    node === nothing && return nothing\n    CPITree(cpitree.base, node, cpitree.group_names, cpitree.group_codes)\nend\n\n\"\"\"\n    compute_index(cpitree::CPITree [, code::AbstractString])\n\nPermite computar el \u00edndice de precios de la jerarqu\u00eda provista en `code`. Si se\nomite `code`, se computa la jerarqu\u00eda padre de la estructura `cpitree`. Si el\nnodo no se encuentra en el \u00e1rbol, devuelve `nothing`.\n\n```julia-repl\njulia> tree\nCPITree{Group{Group{Group{Group{Item{Float32}, Float32}, Float32}, Float32}, Float32}} con datos\n\u2514\u2500\u2192 FullCPIBase{Float32, Float32}: 36 per\u00edodos \u00d7 10 gastos b\u00e1sicos Jan-01-Dec-03\n_0: IPC [100.0]\n\u251c\u2500 _01: Div._01 [21.491905]\n\u2502  \u2514\u2500 _011: Agr._011 [21.491905]\n\u2502     \u251c\u2500 _0111: Subgr._0111 [7.352945]\n\u2502     \u2502  \u2514\u2500 _011101: Item A [7.352945]\n\u2502     \u2514\u2500 _0112: Subgr._0112 [14.13896]\n\u2502        \u251c\u2500 _011201: Item B [6.7442417]\n\u2502        \u2514\u2500 _011202: Item C [7.394718]\n\u251c\u2500 _02: Div._02 [3.0530455]\n\u2502  \u251c\u2500 _021: Agr._021 [1.1036392]\n\u2502  \u2502  \u2514\u2500 _0211: Subgr._0211 [1.1036392]\n\u2502  \u2502     \u2514\u2500 _021101: Item D [1.1036392]\n\u2502  \u2514\u2500 _022: Agr._022 [1.9494063]\n\u2502     \u2514\u2500 _0221: Subgr._0221 [1.9494063]\n\u2502        \u2514\u2500 _022101: Item E [1.9494063]\n\u251c\u2500 _03: Div._03 [11.68543]\n\u2502  \u2514\u2500 _031: Agr._031 [11.68543]\n\u2502     \u2514\u2500 _0311: Subgr._0311 [11.68543]\n\u2502        \u2514\u2500 _031101: Item F [11.68543]\n\u2514\u2500 _04: Div._04 [63.769615]\n   \u2514\u2500 _041: Agr._041 [63.769615]\n      \u251c\u2500 _0411: Subgr._0411 [16.103952]\n      \u2502  \u2514\u2500 _041101: Item G [16.103952]\n      \u251c\u2500 _0412: Subgr._0412 [28.824577]\n      \u2502  \u251c\u2500 _041201: Item H [11.367162]\n      \u2502  \u2514\u2500 _041202: Item I [17.457417]\n      \u2514\u2500 _0413: Subgr._0413 [18.841085]\n         \u2514\u2500 _041301: Item J [18.841085]\n\njulia> compute_index(tree, \"_041\")\n36-element Vector{Float32}:\n 100.13899\n 100.2787\n 100.41912\n 100.560234\n 100.70207\n 100.84464\n 100.98792\n   \u22ee\n 104.65377\n 104.81638\n 104.97984\n 105.14412\n 105.309235\n 105.47519\n\njulia> compute_index(tree[\"_041\"])\n36-element Vector{Float32}:\n 100.13899\n 100.2787\n 100.41912\n 100.560234\n 100.70207\n 100.84464\n 100.98792\n   \u22ee\n 104.65377\n 104.81638\n 104.97984\n 105.14412\n 105.309235\n 105.47519\n\njulia> a = tree[\"_041302\"]\n\njulia> a === nothing\ntrue\n```\n\"\"\"\nfunction compute_index(cpitree::CPITree, code::AbstractString)\n    node = cpitree.tree[code]\n    node === nothing && return nothing \n    compute_index(node, cpitree.base)\nend\n\n# When single argument, compute the top level node\nfunction compute_index(cpitree::CPITree)\n    node = cpitree.tree\n    compute_index(node, cpitree.base)\nend\n\n\nchildren(cpitree::CPITree) = children(cpitree.tree)\n\n", "meta": {"hexsha": "1f7a7c62d79591819a6c12faf9a4db010b1e4d0b", "size": 24677, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tree/CPItree.jl", "max_stars_repo_name": "DIE-BG/CPIDataBase.jl", "max_stars_repo_head_hexsha": "bc541a4217d4ec30c0959238b38a494548798f39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/tree/CPItree.jl", "max_issues_repo_name": "DIE-BG/CPIDataBase.jl", "max_issues_repo_head_hexsha": "bc541a4217d4ec30c0959238b38a494548798f39", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2022-02-10T03:41:00.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-24T02:04:02.000Z", "max_forks_repo_path": "src/tree/CPItree.jl", "max_forks_repo_name": "DIE-BG/CPIDataBase.jl", "max_forks_repo_head_hexsha": "bc541a4217d4ec30c0959238b38a494548798f39", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.9646153846, "max_line_length": 190, "alphanum_fraction": 0.6274263484, "num_tokens": 8096, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.13117322546005342, "lm_q1q2_score": 0.057935672751665376}}
{"text": "#\n# ResizableArrays.jl --\n#\n# Implement arrays which are resizable.\n#\n\nmodule ResizableArrays\n\nexport\n    ResizableArray,\n    ResizableMatrix,\n    ResizableVector,\n    isgrowable,\n    maxlength,\n    shrink!\n\nusing Base: elsize, tail, OneTo, throw_boundserror, @propagate_inbounds\n\n\"\"\"\n\n```julia\nResizableArray{T}(undef, dims)\n```\n\nyields a resizable array with uninitialized elements of type `T` and dimensions\n`dims`.  Dimensions may be a tuple of integers or a a list of integers.  The\nnumber `N` of dimensions may be explicitly specified:\n\n```julia\nResizableArray{T,N}(undef, dims)\n```\n\nTo create an empty resizable array of given rank and element type, call:\n\n```julia\nResizableArray{T,N}()\n```\n\nThe dimensions of a resizable array `A` may be changed by calling\n`resize!(A,dims)` with `dims` the new dimensions.  The number of dimensions\nmust remain unchanged but the length of the array may change.  Depending on the\ntype of the object backing the storage of the array, it may be possible or not\nto augment the number of elements of the array.  When array elements are stored\nin a regular Julia vector, the number of element can always be augmented.\nChanging only the last dimension of a resizable array preserves its contents.\n\nResizable arrays are designed to re-use storage if possible to avoid calling\nthe garbage collector.  This may be useful for real-time applications.  As a\nconsequence, the storage used by a resizable array `A` can only grow unless\n`skrink!(A)` is called to reduce the storage to the minimum.  The call\n`copy(ResizableArray,A)` yields a copy of `A` which is a resizable array.\n\nTo improve performances, call `sizehint!(A,n)` to indicate the minimum number\nof elements to preallocate for `A` (`n` can be a number of elements or array\ndimensions).\n\nThe `ResizableArray` constructor and the `convert` method can be used to to\nconvert an array `A` to a resizable array:\n\n```julia\nResizableArray(A)\nconvert(ResizableArray, A)\n```\n\nElement type `T` and number of dimensions `N` may be specified:\n\n```julia\nResizableArray{T[,N]}(A)\nconvert(ResizableArray{T[,N]}, A)\n```\n\n`N` must match `ndims(A)` but `T` may be different from `eltype(A)`.  If\npossible, the `convert` method returns the input array while the\n`ResizableArray` constructor always returns a new instance.\n\nThe default storage for the elements of a resizable array is provided by a\nregular Julia vector.  To use an object `buf` to store the elements of a\nresizable array, use one of the following:\n\n```julia\nA = ResizableArray(buf, dims)\nA = ResizableArray{T}(buf, dims)\nA = ResizableArray{T,N}(buf, dims)\n```\n\nThe buffer `buf` must store its elements contiguously using linear indexing\nstyle with 1-based indices and have element type `T`, that is\n`IndexStyle(typeof(buf))` and `eltype(buf)` must yield `IndexLinear()` and `T`\nrespectively.  The methods, `IndexStyle`, `eltype`, `length`, `getindex` and\n`setindex!` must be applicable for the type of `buf`.  If the method `resize!`\nis applicable for `buf`, the number of elements of `A` can be augmented;\notherwise the maximum number of elements of `A` is `length(buf)`.\n\n!!! warning\n    When explictely providing a resizable buffer `buf` for backing the\n    storage of a resizable array `A`, you have the responsibility to make\n    sure that the same buffer is not resized elsewhere.  Otherwise a\n    segmentation fault may occur because `A` might assume a wrong buffer\n    size.  To avoid this, the best is to make sure that only `A` owns `buf`\n    and only `A` manages its size.  In the current implementation, the size\n    of the internal buffer is never reduced so the same buffer may be\n    safely shared by different resizable arrays.\n\n\"\"\"\nmutable struct ResizableArray{T,N,B} <: DenseArray{T,N}\n    len::Int\n    dims::NTuple{N,Int}\n    vals::B\n    # Inner constructor for provided storage buffer.\n    function ResizableArray{T,N}(buf::B,\n                                 dims::NTuple{N,Int}) where {T,N,B}\n        eltype(B) === T || error(\"buffer has a different element type\")\n        IndexStyle(B) === IndexLinear() ||\n            error(\"buffer must have linear indexing style\")\n        checkdimensions(dims)\n        len = prod(dims)\n        length(buf) \u2265 len || error(\"buffer is too small\")\n        return new{T,N,B}(len, dims, buf)\n    end\n    # Inner constructor using regular Julia's vector to store elements.\n    function ResizableArray{T,N}(::UndefInitializer,\n                                 dims::NTuple{N,Int}) where {T,N}\n        checkdimensions(dims)\n        len = prod(dims)\n        buf = Vector{T}(undef, len)\n        return new{T,N,Vector{T}}(len, dims, buf)\n    end\n\nend\n\n# Calling the `ResizableArray` constructor always creates a new instance.\n\nResizableArray(arg, dims::Integer...) =\n    ResizableArray(arg, dims)\nResizableArray(arg, dims::Tuple{Vararg{Integer}}) =\n    ResizableArray(arg, map(Int, dims))\nResizableArray(buf::B, dims::NTuple{N,Int}) where {N,B} =\n    ResizableArray{eltype(B),N}(buf, dims)\n\nResizableArray{T}(arg, dims::Integer...) where {T} =\n    ResizableArray{T}(arg, dims)\nResizableArray{T}(arg, dims::Tuple{Vararg{Integer}}) where {T} =\n    ResizableArray{T}(arg, map(Int, dims))\nResizableArray{T}(arg, dims::NTuple{N,Int}) where {T,N} =\n    ResizableArray{T,N}(arg, dims)\n\nResizableArray{T,N}(arg, dims::Integer...) where {T,N} =\n    ResizableArray{T,N}(arg, dims)\nfunction ResizableArray{T,N}(arg, dims::Tuple{Vararg{Integer}}) where {T,N}\n    length(dims) == N || _throw_mismatching_number_of_dimensions()\n    return ResizableArray{T,N}(arg, map(Int, dims))\nend\n\nResizableArray{T,N,B}(A::AbstractArray) where {T,N,B} =\n    (Vector{T} <: B ? ResizableArray{T,N}(A) :\n     _throw_invalid_buffer_type(B,T))\nResizableArray{T,N}(A::AbstractArray{<:Any,M}) where {T,N,M} =\n    (M == N ? copyto!(ResizableArray{T,N}(undef, size(A)), A) :\n     _throw_mismatching_number_of_dimensions())\nResizableArray{T}(A::AbstractArray) where {T} =\n    copyto!(ResizableArray{T}(undef, size(A)), A)\nResizableArray(A::AbstractArray{T}) where {T} =\n    copyto!(ResizableArray{T}(undef, size(A)), A)\n\n# Constructor for, initially empty, workspace of given rank and element type.\nResizableArray{T,N}() where {T,N} =\n    ResizableArray{T,N}(undef, ntuple(i -> 0, Val(N)))\n\n@noinline _throw_mismatching_number_of_dimensions() =\n    throw(DimensionMismatch(\"mismatching number of dimensions\"))\n\n# TypeError is more appropriate but we want a specific error message.\n@noinline _throw_invalid_buffer_type(::Type{B},::Type{T}) where {B,T} =\n    throw(ErrorException(\"invalid buffer type $B (must be \u2265 Vector{$T})\"))\n\n# Make a resizable copy.\nBase.copy(::Type{ResizableArray}, A::AbstractArray) =\n    ResizableArray(A)\nBase.copy(::Type{ResizableArray{T}}, A::AbstractArray) where {T} =\n    ResizableArray{T}(A)\nBase.copy(::Type{ResizableArray{T,N}}, A::AbstractArray) where {T,N} =\n    ResizableArray{T,N}(A)\nBase.copy(::Type{ResizableArray{T,N,B}}, A::AbstractArray) where {T,N,B} =\n    ResizableArray{T,N,B}(A)\n\n# Unlike the `ResizableArray` constructor, calling the `convert` method avoids\n# creating a new instance if possible.\nBase.convert(::Type{ResizableArray{T,N,B}}, A::ResizableArray{T,N,C}) where {T,N,B,C<:B} = A\nBase.convert(::Type{ResizableArray{T,N}}, A::ResizableArray{T,N}) where {T,N} = A\nBase.convert(::Type{ResizableArray{T}}, A::ResizableArray{T}) where {T} = A\nBase.convert(::Type{ResizableArray}, A::ResizableArray) = A\nBase.convert(::Type{ResizableArray{T,N,B}}, A::AbstractArray) where {T,N,B} =\n    ResizableArray{T,N,B}(A)\nBase.convert(::Type{ResizableArray{T,N}}, A::AbstractArray) where {T,N} =\n    ResizableArray{T,N}(A)\nBase.convert(::Type{ResizableArray{T}}, A::AbstractArray) where {T} =\n    ResizableArray{T}(A)\nBase.convert(::Type{ResizableArray}, A::AbstractArray) =\n    ResizableArray(A)\n\n\"\"\"\n```julia\nResizableVector{T}\n```\n\nSupertype for one-dimensional resizable arrays with elements of type `T`.\nAlias for [`ResizableArray{T,1}`](@ref).\n\n\"\"\"\nconst ResizableVector{T,B} = ResizableArray{T,1,B}\n\n\"\"\"\n```julia\nResizableMatrix{T}\n```\n\nSupertype for two-dimensional resizable arrays with elements of type `T`.\nAlias for [`ResizableArray{T,2}`](@ref).\n\n\"\"\"\nconst ResizableMatrix{T,B} = ResizableArray{T,2,B}\n\n\"\"\"\n```julia\ncheckdimension(Bool, dim) -> boolean\n```\n\nyields whether `dim` is a valid dimension length (that is a nonnegative\ninteger).\n\n\"\"\"\n@inline checkdimension(::Type{Bool}, dim::Integer) = (dim \u2265 0)\n@inline checkdimension(::Type{Bool}, dim) = false\n\n\"\"\"\n```julia\ncheckdimensions(Bool, dims) -> boolean\n```\n\nyields whether `dims` is a valid list of dimensions.\n\n```julia\ncheckdimensions(dims)\n```\nthrows an error if `dims` is not a valid list of dimensions.\n\n\"\"\"\n@inline checkdimensions(::Type{Bool}, dims::Tuple) =\n    checkdimension(Bool, dims[1]) & checkdimensions(Bool, tail(dims))\n@inline checkdimensions(::Type{Bool}, ::Tuple{}) = true\n@inline checkdimensions(dims::Tuple) =\n    checkdimensions(Bool, dims) || throw_invalid_dimensions()\n\n@noinline throw_invalid_dimensions() =\n    error(\"invalid dimension(s)\")\n\n\"\"\"\n```julia\nisgrowable(x) -> boolean\n```\n\nyields whether `x` is a growable object, that is its size can be augmented.\n\n\"\"\"\nisgrowable(A::ResizableArray) = isgrowable(A.vals)\nisgrowable(A::ResizableArray{T,0}) where {T} = false\nisgrowable(::Vector) = true\nisgrowable(::Any) = false\n\n\"\"\"\n```julia\nmaxlength(A)\n```\n\nyields the maximum number of elements which can be stored in resizable\narray `A` without resizing its internal buffer.\n\nSee also: [`ResizableArray`](@ref).\n\n\"\"\"\nmaxlength(A::ResizableArray) = length(A.vals)\n\nBase.length(A::ResizableArray) = A.len\nBase.size(A::ResizableArray) = A.dims\nBase.size(A::ResizableArray{T,N}, d::Integer) where {T,N} =\n    (d > N ? 1 : d \u2265 1 ? A.dims[d] : error(\"out of range dimension\"))\nBase.axes(A::ResizableArray) = map(OneTo, A.dims)\nBase.axes(A::ResizableArray, d::Integer) = Base.OneTo(size(A, d))\n@inline Base.axes1(A::ResizableArray{<:Any,0}) = OneTo(1)\n@inline Base.axes1(A::ResizableArray) = OneTo(A.dims[1])\nBase.IndexStyle(::Type{<:ResizableArray}) = IndexLinear()\nBase.parent(A::ResizableArray) = A.vals\nBase.similar(::Type{ResizableArray{T}}, dims::NTuple{N,Int}) where {T,N} =\n    ResizableArray{T,N}(undef, dims)\n\n# Make sizeof() return the number of bytes of the actual contents.\nBase.elsize(::Type{ResizableArray{T,N,B}}) where {T,N,B} = elsize(B)\nBase.sizeof(A::ResizableArray) = elsize(A)*length(A)\n\n# Make ResizableArray's efficient iterators.\n@inline Base.iterate(A::ResizableArray, i=1) =\n    ((i % UInt) - 1 < length(A) ? (@inbounds A[i], i + 1) : nothing)\n\n# Equality is false if rank is different.\nBase.:(==)(::ResizableArray, ::AbstractArray) = false\nBase.:(==)(::AbstractArray,  ::ResizableArray) = false\nBase.:(==)(::ResizableArray, ::ResizableArray) = false\n\nBase.:(==)(A::ResizableVector{<:Any}, B::ResizableVector{<:Any}) =\n    (length(A) == length(B) && _same_elements(A.vals, B.vals, length(A)))\n\nBase.:(==)(A::ResizableArray{<:Any,N}, B::ResizableArray{<:Any,N}) where {N} =\n    (size(A) == size(B) && _same_elements(A.vals, B.vals, length(A)))\n\nBase.:(==)(A::ResizableVector{<:Any}, B::Vector{<:Any}) =\n    (length(A) == length(B) && _same_elements(A.vals, B, length(A)))\nBase.:(==)(A::Vector{<:Any}, B::ResizableVector{<:Any}) =\n    (length(A) == length(B) && _same_elements(A, B.vals, length(A)))\n\nBase.:(==)(A::ResizableArray{<:Any,N}, B::Array{<:Any,N}) where {N} =\n    (size(A) == size(B) && _same_elements(A.vals, B, length(A)))\nBase.:(==)(A::Array{<:Any,N}, B::ResizableArray{<:Any,N}) where {N} =\n    (size(A) == size(B) && _same_elements(A, B.vals, length(A)))\n\nBase.:(==)(A::ResizableArray{<:Any,N}, B::AbstractArray{<:Any,N}) where {N} =\n    (axes(A) == axes(B) && _same_elements(A.vals, B, length(A)))\nBase.:(==)(A::AbstractArray{<:Any,N}, B::ResizableArray{<:Any,N}) where {N} =\n    (axes(A) == axes(B) && _same_elements(A, B.vals, length(A)))\n\n# Yields whether the `n` first elements of `A` and `B` are identical.  This\n# method is unsafe as it assumes that `A` and `B` have at least `n` elements.\n_same_elements(A, B, n::Integer) =\n    _same_elements(IndexStyle(A), A, IndexStyle(B), B, Int(n))\n\nfunction _same_elements(::IndexLinear, A, ::IndexLinear, B, n::Int)\n    @inbounds for i in Base.OneTo(n)\n        A[i] == B[i] || return false\n    end\n    return true\nend\n\nfunction _same_elements(::IndexLinear, A, ::IndexStyle, B, n::Int)\n    i = 0\n    @inbounds for j in eachindex(B)\n        (i += 1) \u2264 n || return true\n        A[i] == B[j] || return false\n    end\n    return (i \u2265 n)\nend\n\nfunction _same_elements(::IndexStyle, A, ::IndexLinear, B, n::Int)\n    j = 0\n    @inbounds for i in eachindex(A)\n        (j += 1) \u2264 n || return true\n        A[i] == B[j] || return false\n    end\n    return (j \u2265 n)\nend\n\nfunction _same_elements(::IndexStyle, A, ::IndexStyle, B, n::Int)\n    # this case should never occur\n    j = 0\n    @inbounds for i in eachindex(A,B)\n        (j += 1) \u2264 n || return true\n        A[i] == B[i] || return false\n    end\n    return (j \u2265 n)\nend\n\nBase.resize!(A::ResizableArray, dims::Integer...) = resize!(A, dims)\nfunction Base.resize!(A::ResizableArray, dims::Tuple{Vararg{Integer}})\n    length(dims) == ndims(A) ||\n        error(\"changing the number of dimensions is not allowed\")\n    return resize!(A, map(Int, dims))\nend\nfunction Base.resize!(A::ResizableArray{T,N}, dims::NTuple{N,Int}) where {T,N}\n    if dims != size(A)\n        checkdimensions(dims)\n        newlen = prod(dims)\n        newlen > length(A.vals) && resize!(A.vals, newlen)\n        A.dims = dims\n        A.len = newlen\n    end\n    return A\nend\n\nBase.sizehint!(A::ResizableArray, dims::Integer...) = sizehint!(A, dims)\nfunction Base.sizehint!(A::ResizableArray, dims::Tuple{Vararg{Integer}})\n    length(dims) == ndims(A) ||\n        error(\"changing the number of dimensions is not allowed\")\n    checkdimensions(dims)\n    len = prod(dims)\n    len > maxlength(A) && sizehint!(parent(A), len)\n    return A\nend\nfunction Base.sizehint!(A::ResizableArray, len::Integer)\n    len \u2265 0 || throw(ArgumentError(\"number of elements must be nonnegative\"))\n    len > maxlength(A) && sizehint!(parent(A), len)\n    return A\nend\n\n\"\"\"\n\n```julia\nshrink!(A) -> A\n```\n\nshrinks as much as possible the storage of resizable array `A` and returns `A`.\nCall `copy(ResizableArray,A)` to make a copy of `A` which is a resizable array\nwith skrinked storage.\n\n\"\"\"\nfunction shrink!(A::ResizableArray)\n    length(A) < length(A.vals) && resize!(A.vals, length(A))\n    return A\nend\n\n\"\"\"\n\n```julia\ngrow!(A, B, prepend=false) -> A\n```\n\ngrows resizable array `A` with the elements of `B` and returns `A`.  If\n`prepend` is `true`, the elements of `B` are inserted before those of `A`;\notherwise, the elements of `B` are appended after those of `A`.  By default,\n`prepend` is `false`.\n\nAssuming `A` has `N` dimensions, array `B` may have `N` or `N-1` dimensions.\nThe `N-1` leading dimensions of `A` and `B` must be identical and are the\nleading dimensions of the result.  If `B` has the same number of dimensions as\n`A`, the last dimension of the result is the sum of the last dimensions of `A`\nand `B`; otherwise, the last dimension of the result is one plus the last\ndimension of `A`.\n\nDepending on argument `prepend`, calling the `grow!` method is equivalent to\ncalling `append!` or `prepend!` methods.\n\nSee also [`ResizableArray`](@ref).\n\n\"\"\"\nfunction grow!(A::ResizableArray{<:Any,N},\n               B::AbstractArray{<:Any,M}, prepend::Bool=false) where {N,M}\n    N - 1 \u2264 M \u2264 N || throw(DimensionMismatch(\"invalid number of dimensions\"))\n    indA = axes(A)\n    indB = axes(B)\n    @inbounds for d in 1:N-1\n        indA[d] == indB[d] ||\n            throw(DimensionMismatch(\"leading dimensions must be identical\"))\n    end\n    dimN = length(indA[N]) + (M == N ? length(indB[N]) : 1)\n    lenA = length(A)\n    lenB = length(B)\n    minlen = lenA + lenB\n    buf = A.vals\n    length(buf) \u2265 minlen || resize!(buf, minlen)\n    if prepend\n        copyto!(buf, lenB + 1, buf, 1, lenA)\n        copyto!(buf, 1, B, 1, lenB)\n    else\n        copyto!(buf, lenA + 1, B, 1, lenB)\n    end\n    A.len = lenA + lenB\n    A.dims = ntuple(d -> (d < N ? A.dims[d] : dimN), Val(N))\n    return A\nend\n\nBase.append!(dst::ResizableArray, src::AbstractArray) =\n    grow!(dst, src, false)\n\nBase.prepend!(dst::ResizableArray, src::AbstractArray) =\n    grow!(dst, src, true)\n\n@inline @propagate_inbounds Base.getindex(A::ResizableArray, i::Int) =\n    (@boundscheck checkbounds(A, i);\n     @inbounds getindex(A.vals, i))\n\n@inline @propagate_inbounds Base.setindex!(A::ResizableArray, x, i::Int) =\n    (@boundscheck checkbounds(A, i);\n     @inbounds setindex!(A.vals, x, i))\n\n@inline Base.checkbounds(::Type{Bool}, A::ResizableArray, i::Int) =\n    (i % UInt) - 1 < length(A)\n\nBase.copyto!(dst::ResizableArray{T}, src::Array{T}) where {T} =\n    copyto!(dst, 1, src, 1, length(src))\nBase.copyto!(dst::Array{T}, src::ResizableArray{T}) where {T} =\n    copyto!(dst, 1, src, 1, length(src))\nBase.copyto!(dst::ResizableArray{T}, src::ResizableArray{T}) where {T} =\n    copyto!(dst, 1, src, 1, length(src))\n\nfunction Base.copyto!(dst::ResizableArray{T}, doff::Integer,\n                      src::Array{T}, soff::Integer, n::Integer) where {T}\n    if n != 0\n        checkcopyto(length(dst), doff, length(src), soff, n)\n        unsafe_copyto!(dst.vals, doff, src, soff, n)\n    end\n    return dst\nend\n\nfunction Base.copyto!(dst::Array{T}, doff::Integer,\n                      src::ResizableArray{T}, soff::Integer,\n                      n::Integer) where {T}\n    if n != 0\n        checkcopyto(length(dst), doff, length(src), soff, n)\n        unsafe_copyto!(dst, doff, src.vals, soff, n)\n    end\n    return dst\nend\n\nfunction Base.copyto!(dst::ResizableArray{T}, doff::Integer,\n                      src::ResizableArray{T}, soff::Integer,\n                      n::Integer) where {T}\n    if n != 0\n        checkcopyto(length(dst), doff, length(src), soff, n)\n        unsafe_copyto!(dst.vals, doff, src.vals, soff, n)\n    end\n    return dst\nend\n\n@inline function checkcopyto(dlen::Integer, doff::Integer,\n                             slen::Integer, soff::Integer, n::Integer)\n     @noinline throw_invalid_length() =\n        throw(ArgumentError(\"number of elements to copy must be nonnegative\"))\n    n \u2265 0 || throw_invalid_length()\n    (doff > 0 && doff - 1 + n \u2264 dlen &&\n     soff > 0 && soff - 1 + n \u2264 slen) || throw(BoundsError())\nend\n\nBase.unsafe_convert(::Type{Ptr{T}}, A::ResizableArray{T}) where {T} =\n    Base.unsafe_convert(Ptr{T}, A.vals)\n\nBase.pointer(A::ResizableArray) = pointer(A.vals)\nBase.pointer(A::ResizableArray, i::Integer) = pointer(A.vals, i)\n\nend # module\n", "meta": {"hexsha": "1fb640696321114ac4372e3129168b0689d6e82f", "size": 18591, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ResizableArrays.jl", "max_stars_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_stars_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/ResizableArrays.jl", "max_issues_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_issues_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/ResizableArrays.jl", "max_forks_repo_name": "JuliaTagBot/ResizableArrays.jl", "max_forks_repo_head_hexsha": "c2d29fbab36e687dce951d7e7ededc254c4ee783", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6201117318, "max_line_length": 92, "alphanum_fraction": 0.6662363509, "num_tokens": 5321, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.13117321527062692, "lm_q1q2_score": 0.05793566825127075}}
{"text": "\"\"\"\n    scatterplot(x, y; kwargs...)\n\nDescription\n============\n\nDraws the given points on a new canvas.\n\nThe first (optional) vector `x` should contain the horizontal\npositions for all the points. The second vector `y` should then\ncontain the corresponding vertical positions respectively. This\nmeans that the two vectors must be of the same length and\nordering.\n\nUsage\n======\n\n    scatterplot([x], y; name = \"\", marker = :pixel, title = \"\", xlabel = \"\", ylabel = \"\", labels = true, border = :solid, margin = 3, padding = 1, color = :auto, xlim = (0, 0), ylim = (0, 0), canvas = BrailleCanvas, grid = true)\n\nArguments\n==========\n\n- **`x`** : Optional. The horizontal position for each point.\n  If omitted, the axes of `y` will be used as `x`.\n\n- **`y`** : The vertical position for each point.\n\n- **`name`** : Annotation of the current drawing to be displayed on the right\n\n- **`marker`** : Choose a marker from $(keys(MARKERS)), a `Char`, a unit length `String` or a vector of these\n\n$DOC_PLOT_PARAMS\n\n- **`height`** : Number of character rows that should be used for plotting.\n\n- **`xlim`** : Plotting range for the x axis.\n  `(0, 0)` stands for automatic.\n\n- **`ylim`** : Plotting range for the y axis.\n  `(0, 0)` stands for automatic.\n\n- **`canvas`** : The type of canvas that should be used for drawing.\n\n- **`grid`** : If `true`, draws grid-lines at the origin.\n\nReturns\n========\n\nA plot object of type `Plot{T<:Canvas}`\n\nAuthor(s)\n==========\n\n- Christof Stocker (Github: https://github.com/Evizero)\n\nExamples\n=========\n\n```julia-repl\njulia> scatterplot(randn(50), randn(50), title = \"My Scatterplot\")\n                   My Scatterplot\n      \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n    3 \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2846\u2800\u2800\u2800\u2800\u2800\u2880\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2810\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2810\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2820\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2821\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2840\u2801\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2808\u2800\u2800\u2801\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2810\u2800\u2800\u2800\u2800\u2800\u2800\u2802\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2810\u2804\u2800\u2800\u2800\u2800\u2840\u2800\u2802\u2847\u2800\u2800\u2800\u2802\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2824\u2824\u2824\u2824\u2824\u2824\u2834\u2824\u2824\u2824\u282c\u2824\u282c\u2824\u2824\u2824\u2867\u28a5\u2864\u2824\u2824\u2864\u2824\u2824\u2824\u282c\u2824\u2824\u2824\u2824\u2824\u2824\u2824\u2824\u2864\u2824\u2824\u2824\u2824\u2804\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2801\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2857\u2810\u2828\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2810\u2800\u2800\u2800\u2800\u2847\u2800\u2810\u2800\u2828\u2800\u2800\u2802\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2880\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2820\u2800\u2804\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2810\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2801\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2801\u2802\u2830\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n   -3 \u2502\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2847\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2800\u2502\n      \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n      -2                                       3\n```\n\nSee also\n=========\n\n[`Plot`](@ref), [`lineplot`](@ref), [`stairs`](@ref),\n[`BrailleCanvas`](@ref), [`BlockCanvas`](@ref),\n[`AsciiCanvas`](@ref), [`DotCanvas`](@ref)\n\"\"\"\nfunction scatterplot(\n    x::AbstractVector, y::AbstractVector;\n    canvas::Type = BrailleCanvas, color::Union{UserColorType,AbstractVector} = :auto,\n    marker::Union{MarkerType,AbstractVector} = :pixel, name = \"\", kw...\n)\n    new_plot = Plot(x, y, canvas; kw...)\n    scatterplot!(new_plot, x, y; color = color, name = name, marker = marker)\nend\n\nscatterplot(y::AbstractVector; kw...) = scatterplot(axes(y, 1), y; kw...)\n\nfunction scatterplot!(\n    plot::Plot{<:Canvas}, x::AbstractVector, y::AbstractVector;\n    color::Union{UserColorType,AbstractVector} = :auto,\n    marker::Union{MarkerType,AbstractVector} = :pixel, name = \"\"\n)\n    color = (color == :auto) ? next_color!(plot) : color\n    name == \"\" || label!(plot, :r, string(name), color isa AbstractVector ? color[1] : color)\n    if marker \u2208 (:pixel, :auto)\n        points!(plot, x, y, color)\n    else\n        for (xi, yi, mi, ci) in zip(x, y, iterable(marker), iterable(color))\n            annotate!(plot, xi, yi, char_marker(mi); color=ci)\n        end\n    end\n    plot\nend\n\nscatterplot!(plot::Plot{<:Canvas}, y::AbstractVector; kw...) = scatterplot!(\n  plot, axes(y, 1), y; kw...\n)\n", "meta": {"hexsha": "68fd25bd329897078ae559372d794a96f1b3c303", "size": 3840, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/interface/scatterplot.jl", "max_stars_repo_name": "Mechachleopteryx/UnicodePlots.jl", "max_stars_repo_head_hexsha": "6584706d886744f5609d40ad25c4f1c2e507f919", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 844, "max_stars_repo_stars_event_min_datetime": "2015-08-15T05:22:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-01T11:13:32.000Z", "max_issues_repo_path": "src/interface/scatterplot.jl", "max_issues_repo_name": "Mechachleopteryx/UnicodePlots.jl", "max_issues_repo_head_hexsha": "6584706d886744f5609d40ad25c4f1c2e507f919", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 140, "max_issues_repo_issues_event_min_datetime": "2015-08-16T15:45:45.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-01T11:22:54.000Z", "max_forks_repo_path": "src/interface/scatterplot.jl", "max_forks_repo_name": "Mechachleopteryx/UnicodePlots.jl", "max_forks_repo_head_hexsha": "6584706d886744f5609d40ad25c4f1c2e507f919", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 59, "max_forks_repo_forks_event_min_datetime": "2015-08-15T02:31:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T10:30:02.000Z", "avg_line_length": 32.0, "max_line_length": 228, "alphanum_fraction": 0.48671875, "num_tokens": 1452, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204329, "lm_q2_score": 0.12252320450794524, "lm_q1q2_score": 0.05791469427349186}}
{"text": "function is_leap_year(year)\n    # https://docs.julialang.org/en/v1/manual/control-flow/#Short-Circuit-Evaluation\n    return year % 4 == 0 && (year % 100 != 0 || year % 400 == 0)\nend\n", "meta": {"hexsha": "510e4a7ce5da13c94033471a1a1d0e5272056f71", "size": 182, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercism/leap.jl", "max_stars_repo_name": "joaopalmeiro/learning-julia", "max_stars_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercism/leap.jl", "max_issues_repo_name": "joaopalmeiro/learning-julia", "max_issues_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2022-01-07T00:39:37.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-11T08:46:14.000Z", "max_forks_repo_path": "exercism/leap.jl", "max_forks_repo_name": "joaopalmeiro/learning-julia", "max_forks_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4, "max_line_length": 84, "alphanum_fraction": 0.6538461538, "num_tokens": 59, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.12252320450794522, "lm_q1q2_score": 0.057914694273491836}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.2\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 6c1969e0-02f5-11eb-3fa2-09931a63b1ac\nbegin\n    using PoreMatMod, PlutoUI, Bio3DView\n    HOME = joinpath(homedir(), \".PoreMatModGO\")\n\trc[:paths][:crystals] = HOME\n\trc[:paths][:moieties] = HOME\n\tif !isdir(HOME)\n\t\tmkdir(HOME)\n\tend\n\tcd(HOME)\nmd\"\"\"\n# \ud83d\udca0 PoreMatModGO \ud83d\ude80\n\nThis notebook interactively substitutes moieties within a crystal using a modified implementation of Ullmann's algorithm to perform substructure searches and applying singular value decomposition to align fragments of the generated materials. Read the docs [here](SimonEnsemble.github.io/PoreMatMod.jl).\n\nSee the original publication on PoreMatMod.jl here: [(article)](https://doi.org/10.33774/chemrxiv-2021-vx5r3) [(GitHub)](https://github.com/SimonEnsemble/PoreMatMod.jl)\n\"\"\"\nend\n\n# \u2554\u2550\u2561 5dc43a20-10b8-11eb-26dc-7fb98e9aeb1a\nmd\"\"\"\nAdrian Henle, [Simon Ensemble](http://simonensemble.github.io), 2021\n\n$(Resource(\"https://simonensemble.github.io/osu_logo.jpg\", :width => 250))\n\"\"\"\n\n# \u2554\u2550\u2561 90696d20-10b7-11eb-20b5-6174faeaf613\n@bind load_inputs Button(\"Reset\")\n\n# \u2554\u2550\u2561 50269ffe-02ef-11eb-0614-f11975d991fe\nbegin load_inputs\n    # input fields: query, replacement, xtal\n    md\"\"\"\n    ##### Input Files\n\n    Parent Crystal $(@bind parent_crystal FilePicker())\n\n    Search Moiety $(@bind search_moiety FilePicker())\n\n    Replace Moiety $(@bind replace_moiety FilePicker())\n    \"\"\"\nend\n\n# \u2554\u2550\u2561 33b1fb50-0f73-11eb-2ab2-9d2cb6c5a533\n# write file input strings to files in temp directory\nbegin\n    # dict for tracking load status of inputs\n    isloaded = Dict([:replacement => false, :query => false, :parent => false])\n    # replacement loader\n    if !isnothing(replace_moiety)\n        write(\"replacement.xyz\", replace_moiety[\"data\"])\n        replacement = moiety(\"replacement.xyz\")\n        isloaded[:replacement] = true\n    end\n    # query loader\n    if !isnothing(search_moiety)\n        write(\"query.xyz\", search_moiety[\"data\"])\n        query = moiety(\"query.xyz\")\n        isloaded[:query] = true\n    end\n    # xtal loader\n    if !isnothing(parent_crystal)\n        write(\"parent.cif\", parent_crystal[\"data\"])\n        xtal = Crystal(\"parent.cif\", check_overlap=false)\n        Xtals.strip_numbers_from_atom_labels!(xtal)\n        infer_bonds!(xtal, true)\n        isloaded[:parent] = true\n    end\n    # run search and display terminal message\n    if isloaded[:query] && isloaded[:parent]\n        search = query \u2208 xtal\n        with_terminal() do\n            @info \"Search Results\" isomorphisms=nb_isomorphisms(search) locations=nb_locations(search)\n        end\n    end\nend\n\n# \u2554\u2550\u2561 415e9210-0f71-11eb-15c8-e7484b5be309\n# choose replacement type\nif all(values(isloaded))\n    md\"\"\"\n    ### Find/Replace Options\n\n    Mode $(@bind replace_mode Select([\"\", \"random replacement at each location\", \"random replacement at n random locations\", \"random replacement at specific locations\", \"specific replacements\"]))\n    \"\"\"\nend\n\n# \u2554\u2550\u2561 3997c4d0-0f75-11eb-2976-c161879b8d0c\n# options populated w/ conditional logic based on mode selection\nif all(values(isloaded))\n\tlocal output = nothing\n\tx = [\"$(x)\" for x in 1:nb_locations(search)]\n\tif replace_mode == \"random replacement at each location\"\n\t\toutput = nothing\n\telseif replace_mode == \"random replacement at n random locations\"\n\t\toutput = md\"Number of locations $(@bind nb_loc Slider(1:nb_locations(search)))\"\n\telseif replace_mode == \"random replacement at specific locations\"\n\t\toutput = md\"Locations $(@bind loc MultiSelect(x))\"\n\telseif replace_mode == \"specific replacements\"\n\t\toutput = md\"\"\"\n\tLocations $(@bind loc MultiSelect(x))\n\n\tOrientations $(@bind ori TextField())\n\t\"\"\"\n\tend\n\toutput\nend\n\n# \u2554\u2550\u2561 69edca20-0f94-11eb-13ba-334438ca2406\nif all(values(isloaded))\n\tnew_xtal_flag = true\n\tif replace_mode == \"random replacement at each location\"\n\t\tnew_xtal = substructure_replace(search, replacement)\n\telseif replace_mode == \"random replacement at n random locations\" && nb_loc > 0\n\t\tnew_xtal = substructure_replace(search, replacement, nb_loc=nb_loc)\n\telseif replace_mode == \"random replacement at specific locations\" && loc \u2260 []\n\t\tnew_xtal = substructure_replace(search, replacement, loc=[parse(Int, x) for x in loc])\n\telseif replace_mode == \"specific replacements\"\n\t\tif loc \u2260 [] && ori \u2260 \"\" && length(loc) == length(split(ori, \",\"))\n\t\t\tnew_xtal = substructure_replace(search, replacement,\n\t\t\t\tloc=[parse(Int, x) for x in loc],\n\t\t\t\tori=[parse(Int, x) for x in split(ori, \",\")])\n\t\telse\n\t\t\tnew_xtal_flag = false\n\t\tend\n\telse\n\t\tnew_xtal_flag = false\n\tend\n\tif new_xtal_flag\n\t\twith_terminal() do\n\t\t\tif replace_mode == \"random replacement at each location\"\n\t\t\t\t@info replace_mode new_xtal\n\t\t\telseif replace_mode == \"random replacement at n random locations\"\n\t\t\t\t@info replace_mode nb_loc new_xtal\n\t\t\telseif replace_mode == \"random replacement at specific locations\"\n\t\t\t\t@info replace_mode loc new_xtal\n\t\t\telseif replace_mode == \"specific replacements\"\n\t\t\t\t@info replace_mode loc ori new_xtal\n\t\t\tend\n\t\tend\n\tend\nend;\n\n# \u2554\u2550\u2561 5918f770-103d-11eb-0537-81036bd3e675\nif all(values(isloaded)) && new_xtal_flag\n\twrite_cif(new_xtal, \"crystal.cif\")\n\twrite_xyz(new_xtal, \"atoms.xyz\")\n\twrite_vtk(new_xtal.box, \"unit_cell.vtk\")\n\twrite_bond_information(new_xtal, \"bonds.vtk\")\n\tno_pb = deepcopy(new_xtal)\n\tdrop_cross_pb_bonds!(no_pb)\n\twrite_mol2(new_xtal, filename=\"crystal.mol2\")\n\twrite_mol2(no_pb, filename=\"view.mol2\")\n\tviewfile(\"view.mol2\", \"mol2\", vtkcell=\"unit_cell.vtk\", axes=Axes(4, 0.25))\nend\n\n# \u2554\u2550\u2561 31832e30-1054-11eb-24ed-219fd3e236a1\nif all(values(isloaded)) && new_xtal_flag\n    download_cif = DownloadButton(read(\"crystal.cif\"), \"crystal.cif\")\n    download_box = DownloadButton(read(\"unit_cell.vtk\"), \"unit_cell.vtk\")\n    download_xyz = DownloadButton(read(\"atoms.xyz\"), \"atoms.xyz\")\n    download_bonds = DownloadButton(read(\"bonds.vtk\"), \"bonds.vtk\")\n\tdownload_mol2 = DownloadButton(read(\"crystal.mol2\"), \"crystal.mol2\")\nmd\"\"\"\n### Output Files\nComplete Crystal $download_mol2 $download_cif\n\nComponents $download_xyz $download_bonds $download_box\n\"\"\"\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nBio3DView = \"99c8bb3a-9d13-5280-9740-b4880ed9c598\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPoreMatMod = \"2de0d7f0-0963-4438-8bc8-7e7ffe3dc69a\"\n\n[compat]\nBio3DView = \"~0.1.3\"\nPlutoUI = \"~0.7.14\"\nPoreMatMod = \"~0.2.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ANSIColoredPrinters]]\ngit-tree-sha1 = \"574baf8110975760d391c710b6341da1afa48d8c\"\nuuid = \"a4c015fc-c6ff-483c-b24f-f7ea428134e9\"\nversion = \"0.0.1\"\n\n[[AbstractPlutoDingetjes]]\ndeps = [\"Pkg\"]\ngit-tree-sha1 = \"abb72771fd8895a7ebd83d5632dc4b989b022b5b\"\nuuid = \"6e696c72-6542-2067-7265-42206c756150\"\nversion = \"1.1.2\"\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[ArnoldiMethod]]\ndeps = [\"LinearAlgebra\", \"Random\", \"StaticArrays\"]\ngit-tree-sha1 = \"62e51b39331de8911e4a7ff6f5aaf38a5f4cc0ae\"\nuuid = \"ec485272-7323-5ecc-a04f-4719b315124d\"\nversion = \"0.2.0\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[BinaryProvider]]\ndeps = [\"Libdl\", \"Logging\", \"SHA\"]\ngit-tree-sha1 = \"ecdec412a9abc8db54c0efc5548c64dfce072058\"\nuuid = \"b99e7846-7c00-51b0-8f62-c81ae34c0232\"\nversion = \"0.5.10\"\n\n[[Bio3DView]]\ndeps = [\"Requires\"]\ngit-tree-sha1 = \"7f472efd9b6af772307dd017f9deeff2a243754f\"\nuuid = \"99c8bb3a-9d13-5280-9740-b4880ed9c598\"\nversion = \"0.1.3\"\n\n[[CSV]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"b83aa3f513be680454437a0eee21001607e5d983\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.8.5\"\n\n[[CategoricalArrays]]\ndeps = [\"DataAPI\", \"Future\", \"JSON\", \"Missings\", \"Printf\", \"Statistics\", \"StructTypes\", \"Unicode\"]\ngit-tree-sha1 = \"18d7f3e82c1a80dd38c16453b8fd3f0a7db92f23\"\nuuid = \"324d7699-5711-5eae-9e2f-1d82baa6b597\"\nversion = \"0.9.7\"\n\n[[ChainRulesCore]]\ndeps = [\"Compat\", \"LinearAlgebra\", \"SparseArrays\"]\ngit-tree-sha1 = \"f885e7e7c124f8c92650d61b9477b9ac2ee607dd\"\nuuid = \"d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4\"\nversion = \"1.11.1\"\n\n[[ChangesOfVariables]]\ndeps = [\"LinearAlgebra\", \"Test\"]\ngit-tree-sha1 = \"9a1d594397670492219635b35a3d830b04730d62\"\nuuid = \"9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0\"\nversion = \"0.1.1\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"dce3e3fea680869eaa0b774b2e8343e9ff442313\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.40.0\"\n\n[[Conda]]\ndeps = [\"JSON\", \"VersionParsing\"]\ngit-tree-sha1 = \"299304989a5e6473d985212c28928899c74e9421\"\nuuid = \"8f4d0f93-b110-5947-807f-2305c1781a2d\"\nversion = \"1.5.2\"\n\n[[Configurations]]\ndeps = [\"ExproniconLite\", \"OrderedCollections\", \"TOML\"]\ngit-tree-sha1 = \"79e812c535bb9780ba00f3acba526bde5652eb13\"\nuuid = \"5218b696-f38b-4ac9-8b61-a12ec717816d\"\nversion = \"0.16.6\"\n\n[[Crayons]]\ngit-tree-sha1 = \"3f71217b538d7aaee0b69ab47d9b7724ca8afa0d\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.0.4\"\n\n[[DataAPI]]\ngit-tree-sha1 = \"cc70b17275652eb47bc9e5f81635981f13cea5c8\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.9.0\"\n\n[[DataFrames]]\ndeps = [\"CategoricalArrays\", \"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"d50972453ef464ddcebdf489d11885468b7b83a3\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"0.22.7\"\n\n[[DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"7d9d316f04214f7efdbb6398d545446e246eff02\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.10\"\n\n[[DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[DocStringExtensions]]\ndeps = [\"LibGit2\"]\ngit-tree-sha1 = \"b19534d1895d702889b219c382a6e18010797f0b\"\nuuid = \"ffbed154-4ef7-542d-bbb7-c09d3a79fcae\"\nversion = \"0.8.6\"\n\n[[Documenter]]\ndeps = [\"ANSIColoredPrinters\", \"Base64\", \"Dates\", \"DocStringExtensions\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"LibGit2\", \"Logging\", \"Markdown\", \"REPL\", \"Test\", \"Unicode\"]\ngit-tree-sha1 = \"f425293f7e0acaf9144de6d731772de156676233\"\nuuid = \"e30172f5-a6a5-5a46-863b-614d45cd2de4\"\nversion = \"0.27.10\"\n\n[[Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[ExproniconLite]]\ngit-tree-sha1 = \"8b08cc88844e4d01db5a2405a08e9178e19e479e\"\nuuid = \"55351af7-c7e9-48d6-89ff-24e801d99491\"\nversion = \"0.6.13\"\n\n[[FIGlet]]\ndeps = [\"BinaryProvider\", \"Pkg\"]\ngit-tree-sha1 = \"bfc6b52f75b4720581e3e49ae786da6764e65b6a\"\nuuid = \"3064a664-84fe-4d92-92c7-ed492f3d8fae\"\nversion = \"0.2.1\"\n\n[[FileIO]]\ndeps = [\"Pkg\", \"Requires\", \"UUIDs\"]\ngit-tree-sha1 = \"2db648b6712831ecb333eae76dbfd1c156ca13bb\"\nuuid = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nversion = \"1.11.2\"\n\n[[FileWatching]]\nuuid = \"7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee\"\n\n[[Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[FromFile]]\ngit-tree-sha1 = \"81e918d0ed5978fcdacd06b7c64c0c5074c4d55a\"\nuuid = \"ff7dd447-1dcb-4ce3-b8ac-22a812192de7\"\nversion = \"0.1.2\"\n\n[[Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[FuzzyCompletions]]\ndeps = [\"REPL\"]\ngit-tree-sha1 = \"2cc2791b324e8ed387a91d7226d17be754e9de61\"\nuuid = \"fb4132e2-a121-4a70-b8a1-d5b831dcdcc2\"\nversion = \"0.4.3\"\n\n[[GitHubActions]]\ndeps = [\"JSON\", \"Logging\"]\ngit-tree-sha1 = \"56e01ec63d13e1cf015d9ff586156eae3cc7cd6f\"\nuuid = \"6b79fd1a-b13a-48ab-b6b0-aaee1fee41df\"\nversion = \"0.1.4\"\n\n[[Graphs]]\ndeps = [\"ArnoldiMethod\", \"DataStructures\", \"Distributed\", \"Inflate\", \"LinearAlgebra\", \"Random\", \"SharedArrays\", \"SimpleTraits\", \"SparseArrays\", \"Statistics\"]\ngit-tree-sha1 = \"92243c07e786ea3458532e199eb3feee0e7e08eb\"\nuuid = \"86223c79-3864-5bf0-83f7-82e725a168b6\"\nversion = \"1.4.1\"\n\n[[HTTP]]\ndeps = [\"Base64\", \"Dates\", \"IniFile\", \"Logging\", \"MbedTLS\", \"NetworkOptions\", \"Sockets\", \"URIs\"]\ngit-tree-sha1 = \"0fa77022fe4b511826b39c894c90daf5fce3334a\"\nuuid = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\nversion = \"0.9.17\"\n\n[[Hyperscript]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"8d511d5b81240fc8e6802386302675bdf47737b9\"\nuuid = \"47d2ed2b-36de-50cf-bf87-49c2cf4b8b91\"\nversion = \"0.0.4\"\n\n[[HypertextLiteral]]\ngit-tree-sha1 = \"2b078b5a615c6c0396c77810d92ee8c6f470d238\"\nuuid = \"ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2\"\nversion = \"0.9.3\"\n\n[[IOCapture]]\ndeps = [\"Logging\", \"Random\"]\ngit-tree-sha1 = \"f7be53659ab06ddc986428d3a9dcc95f6fa6705a\"\nuuid = \"b5f81e59-6552-4d32-b1f0-c071b021bf89\"\nversion = \"0.2.2\"\n\n[[Inflate]]\ngit-tree-sha1 = \"f5fc07d4e706b84f72d54eedcc1c13d92fb0871c\"\nuuid = \"d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9\"\nversion = \"0.1.2\"\n\n[[IniFile]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"098e4d2c533924c921f9f9847274f2ad89e018b8\"\nuuid = \"83e8ac13-25f8-5344-8a64-a9f2b223428f\"\nversion = \"0.5.0\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[InverseFunctions]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"a7254c0acd8e62f1ac75ad24d5db43f5f19f3c65\"\nuuid = \"3587e190-3f89-42d0-90ee-14403ec27112\"\nversion = \"0.1.2\"\n\n[[InvertedIndices]]\ngit-tree-sha1 = \"bee5f1ef5bf65df56bdd2e40447590b272a5471f\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.1.0\"\n\n[[IrrationalConstants]]\ngit-tree-sha1 = \"7fd44fd4ff43fc60815f8e764c0f352b83c49151\"\nuuid = \"92d709cd-6900-40b7-9082-c6be49f344b6\"\nversion = \"0.1.1\"\n\n[[IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[JLD2]]\ndeps = [\"DataStructures\", \"FileIO\", \"MacroTools\", \"Mmap\", \"Pkg\", \"Printf\", \"Reexport\", \"TranscodingStreams\", \"UUIDs\"]\ngit-tree-sha1 = \"46b7834ec8165c541b0b5d1c8ba63ec940723ffb\"\nuuid = \"033835bb-8acc-5ee8-8aae-3f567f8a3819\"\nversion = \"0.4.15\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[LinearAlgebra]]\ndeps = [\"Libdl\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[LogExpFunctions]]\ndeps = [\"ChainRulesCore\", \"ChangesOfVariables\", \"DocStringExtensions\", \"InverseFunctions\", \"IrrationalConstants\", \"LinearAlgebra\"]\ngit-tree-sha1 = \"be9eef9f9d78cecb6f262f3c10da151a6c5ab827\"\nuuid = \"2ab3a3ac-af41-5b50-aa03-7779005ae688\"\nversion = \"0.3.5\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[MacroTools]]\ndeps = [\"Markdown\", \"Random\"]\ngit-tree-sha1 = \"3d3e902b31198a27340d0bf00d6ac452866021cf\"\nuuid = \"1914dd2f-81c6-5fcd-8719-6d5c9610ff09\"\nversion = \"0.5.9\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[MbedTLS]]\ndeps = [\"Dates\", \"MbedTLS_jll\", \"Random\", \"Sockets\"]\ngit-tree-sha1 = \"1c38e51c3d08ef2278062ebceade0e46cefc96fe\"\nuuid = \"739be429-bea8-5141-9913-cc70e7f3736d\"\nversion = \"1.0.3\"\n\n[[MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[MetaGraphs]]\ndeps = [\"Graphs\", \"JLD2\", \"Random\"]\ngit-tree-sha1 = \"2af69ff3c024d13bde52b34a2a7d6887d4e7b438\"\nuuid = \"626554b9-1ddb-594c-aa3c-2596fe9399a5\"\nversion = \"0.7.1\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"f8c673ccc215eb50fcadb285f522420e29e69e1c\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"0.4.5\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[MsgPack]]\ndeps = [\"Serialization\"]\ngit-tree-sha1 = \"a8cbf066b54d793b9a48c5daa5d586cf2b5bd43d\"\nuuid = \"99f44e22-a591-53d1-9472-aa23ef4bd671\"\nversion = \"1.1.0\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"bfd7d8c7fd87f04543810d9cbd3995972236ba1b\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"1.1.2\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[Pluto]]\ndeps = [\"Base64\", \"Configurations\", \"Dates\", \"Distributed\", \"FileWatching\", \"FuzzyCompletions\", \"HTTP\", \"InteractiveUtils\", \"Logging\", \"Markdown\", \"MsgPack\", \"Pkg\", \"REPL\", \"Sockets\", \"TableIOInterface\", \"Tables\", \"UUIDs\"]\ngit-tree-sha1 = \"a5b3fee95de0c0a324bab53a03911395936d15d9\"\nuuid = \"c3e4b0f8-55cb-11ea-2926-15256bba5781\"\nversion = \"0.17.2\"\n\n[[PlutoSliderServer]]\ndeps = [\"Base64\", \"Configurations\", \"Distributed\", \"FromFile\", \"GitHubActions\", \"HTTP\", \"Logging\", \"Pkg\", \"Pluto\", \"SHA\", \"Sockets\", \"TOML\", \"UUIDs\"]\ngit-tree-sha1 = \"ed9660bb2c9eee9d389601bd80a10cee3dd64f0b\"\nuuid = \"2fc8631c-6f24-4c5b-bca7-cbb509c42db4\"\nversion = \"0.2.7\"\n\n[[PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"b68904528fd538f1cb6a3fbc44d2abdc498f9e8e\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.21\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"db3a23166af8aebf4db5ef87ac5b00d36eb771e2\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.4.0\"\n\n[[PoreMatMod]]\ndeps = [\"Bio3DView\", \"CSV\", \"DataFrames\", \"FIGlet\", \"Graphs\", \"LinearAlgebra\", \"MetaGraphs\", \"PlutoSliderServer\", \"PlutoUI\", \"Reexport\", \"StatsBase\", \"Xtals\"]\ngit-tree-sha1 = \"e4b40bedba7a4aadefe93b52b809de10539bc915\"\nuuid = \"2de0d7f0-0963-4438-8bc8-7e7ffe3dc69a\"\nversion = \"0.2.6\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"574a6b3ea95f04e8757c0280bb9c29f1a5e35138\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"0.11.1\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[PyCall]]\ndeps = [\"Conda\", \"Dates\", \"Libdl\", \"LinearAlgebra\", \"MacroTools\", \"Serialization\", \"VersionParsing\"]\ngit-tree-sha1 = \"4ba3651d33ef76e24fef6a598b63ffd1c5e1cd17\"\nuuid = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\nversion = \"1.92.5\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"4036a3bd08ac7e968e27c203d45f5fff15020621\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.1.3\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"f45b34656397a1f6e729901dc9ef679610bd12b5\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.8\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[SimpleTraits]]\ndeps = [\"InteractiveUtils\", \"MacroTools\"]\ngit-tree-sha1 = \"5d7e3f4e11935503d3ecaf7186eac40602e7d231\"\nuuid = \"699a6c99-e7fa-54fc-8d76-47d257e15c1d\"\nversion = \"0.9.4\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\", \"Random\", \"Test\"]\ngit-tree-sha1 = \"03f5898c9959f8115e30bc7226ada7d0df554ddd\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"0.3.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[StaticArrays]]\ndeps = [\"LinearAlgebra\", \"Random\", \"Statistics\"]\ngit-tree-sha1 = \"3c76dde64d03699e074ac02eb2e8ba8254d428da\"\nuuid = \"90137ffa-7385-5640-81b9-e52037218182\"\nversion = \"1.2.13\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[StatsAPI]]\ngit-tree-sha1 = \"0f2aa8e32d511f758a2ce49208181f7733a0936a\"\nuuid = \"82ae8749-77ed-4fe6-ae5f-f523153014b0\"\nversion = \"1.1.0\"\n\n[[StatsBase]]\ndeps = [\"DataAPI\", \"DataStructures\", \"LinearAlgebra\", \"LogExpFunctions\", \"Missings\", \"Printf\", \"Random\", \"SortingAlgorithms\", \"SparseArrays\", \"Statistics\", \"StatsAPI\"]\ngit-tree-sha1 = \"2bb0cb32026a66037360606510fca5984ccc6b75\"\nuuid = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\nversion = \"0.33.13\"\n\n[[StructTypes]]\ndeps = [\"Dates\", \"UUIDs\"]\ngit-tree-sha1 = \"d24a825a95a6d98c385001212dc9020d609f2d4f\"\nuuid = \"856f2bd8-1eba-4b0a-8007-ebc267875bd4\"\nversion = \"1.8.1\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableIOInterface]]\ngit-tree-sha1 = \"9a0d3ab8afd14f33a35af7391491ff3104401a35\"\nuuid = \"d1efa939-5518-4425-949f-ab857e148477\"\nversion = \"0.1.6\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"fed34d0e71b91734bf0a7e10eb1bb05296ddbcd0\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.6.0\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[URIs]]\ngit-tree-sha1 = \"97bbe755a53fe859669cd907f2d96aee8d2c1355\"\nuuid = \"5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4\"\nversion = \"1.3.0\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[VersionParsing]]\ngit-tree-sha1 = \"e575cf85535c7c3292b4d89d89cc29e8c3098e47\"\nuuid = \"81def892-9a0e-5fdd-b105-ffc91e053289\"\nversion = \"1.2.1\"\n\n[[Xtals]]\ndeps = [\"Bio3DView\", \"CSV\", \"DataFrames\", \"Documenter\", \"FIGlet\", \"Graphs\", \"JLD2\", \"LinearAlgebra\", \"Logging\", \"MetaGraphs\", \"Printf\", \"PyCall\", \"UUIDs\"]\ngit-tree-sha1 = \"3147503cd35c4f2b3744fe36301c7de3efee98c5\"\nuuid = \"ede5f01d-793e-4c47-9885-c447d1f18d6d\"\nversion = \"0.3.9\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25006c1969e0-02f5-11eb-3fa2-09931a63b1ac\n# \u255f\u250050269ffe-02ef-11eb-0614-f11975d991fe\n# \u255f\u250033b1fb50-0f73-11eb-2ab2-9d2cb6c5a533\n# \u255f\u2500415e9210-0f71-11eb-15c8-e7484b5be309\n# \u255f\u25003997c4d0-0f75-11eb-2976-c161879b8d0c\n# \u255f\u250069edca20-0f94-11eb-13ba-334438ca2406\n# \u255f\u25005918f770-103d-11eb-0537-81036bd3e675\n# \u255f\u250031832e30-1054-11eb-24ed-219fd3e236a1\n# \u255f\u25005dc43a20-10b8-11eb-26dc-7fb98e9aeb1a\n# \u255f\u250090696d20-10b7-11eb-20b5-6174faeaf613\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "a4d1c13bea5627c2532e315d6d5ec0edc1ca2a2b", "size": 24746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PoreMatModGO.jl", "max_stars_repo_name": "eahenle/PoreMatMod.jl", "max_stars_repo_head_hexsha": "e23345111708563f82d7e92d771af3f1dc578f8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-09-04T11:47:47.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-19T01:03:18.000Z", "max_issues_repo_path": "src/PoreMatModGO.jl", "max_issues_repo_name": "eahenle/PoreMatMod.jl", "max_issues_repo_head_hexsha": "e23345111708563f82d7e92d771af3f1dc578f8b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41, "max_issues_repo_issues_event_min_datetime": "2021-09-06T18:04:41.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T19:13:39.000Z", "max_forks_repo_path": "src/PoreMatModGO.jl", "max_forks_repo_name": "eahenle/PoreMatMod.jl", "max_forks_repo_head_hexsha": "e23345111708563f82d7e92d771af3f1dc578f8b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-02-08T19:41:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-20T06:08:50.000Z", "avg_line_length": 32.1794538362, "max_line_length": 303, "alphanum_fraction": 0.7291683504, "num_tokens": 10232, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.1276526369372765, "lm_q1q2_score": 0.05786007012222709}}
{"text": "export AbstractPolynomial\n\nconst SymbolLike = Union{AbstractString,Char,Symbol}\n\n\"\"\"\n    AbstractPolynomial{T}\n\nAn abstract container for various polynomials. \n\n# Properties\n- `coeffs` - The coefficients of the polynomial\n- `var` - The indeterminate of the polynomial\n\"\"\"\nabstract type AbstractPolynomial{T} end\n\n# We want  \u27d2(P{\u03b1\u2026,T}) = P{\u03b1\u2026}; this default\n# works for most cases\n\u27d2(P::Type{<:AbstractPolynomial}) = constructorof(P)\n\n# convert `as` into polynomial of type P based on instance, inheriting variable\n# (and for LaurentPolynomial the offset)\n_convert(p::P, as) where {P <: AbstractPolynomial} = \u27d2(P)(as, p.var)\n\n\"\"\"\n    Polynomials.@register(name)\n\nGiven a polynomial with `name`, creates some common convenience constructors and conversions to minimize code required for implementation of a new polynomial type.\n\n# Example\n```julia\nstruct MyPolynomial{T} <: AbstractPolynomial{T} end\n\nPolynomials.@register MyPolynomial\n```\n\n# Implementations\nThis will implement simple self-conversions like `convert(::Type{MyPoly}, p::MyPoly) = p` and creates two promote rules. The first allows promotion between two types (e.g. `promote(Polynomial, ChebyshevT)`) and the second allows promotion between parametrized types (e.g. `promote(Polynomial{T}, Polynomial{S})`). \n\nFor constructors, it implements the shortcut for `MyPoly(...) = MyPoly{T}(...)`, singleton constructor `MyPoly(x::Number, ...)`,  conversion constructor `MyPoly{T}(n::S, ...)`, and `variable` alternative  `MyPoly(var=:x)`.\n\"\"\"\nmacro register(name)\n    poly = esc(name)\n    quote\n        Base.convert(::Type{P}, p::P) where {P<:$poly} = p\n        Base.convert(P::Type{<:$poly}, p::$poly{T}) where {T} = P(coeffs(p), p.var)\n        Base.promote(p::P, q::Q) where {T, P <:$poly{T}, Q <: $poly{T}} = p,q\n        Base.promote_rule(::Type{<:$poly{T}}, ::Type{<:$poly{S}}) where {T,S} =\n            $poly{promote_type(T, S)}\n        Base.promote_rule(::Type{<:$poly{T}}, ::Type{S}) where {T,S<:Number} =\n            $poly{promote_type(T, S)}\n        $poly(coeffs::AbstractVector{T}, var::SymbolLike = :x) where {T} =\n            $poly{T}(coeffs, Symbol(var))\n        $poly{T}(x::AbstractVector{S}, var::SymbolLike = :x) where {T,S<:Number} =\n            $poly(T.(x), Symbol(var))\n        function $poly(coeffs::G, var::SymbolLike=:x) where {G}\n            !Base.isiterable(G) && throw(ArgumentError(\"coeffs is not iterable\"))\n            $poly(collect(coeffs), var)\n        end\n        $poly{T}(n::S, var::SymbolLike = :x) where {T, S<:Number} =\n            n *  one($poly{T}, Symbol(var))\n        $poly(n::S, var::SymbolLike = :x)  where {S  <: Number} = n * one($poly{S}, Symbol(var))\n        $poly{T}(var::SymbolLike=:x) where {T} = variable($poly{T}, Symbol(var))\n        $poly(var::SymbolLike=:x) = variable($poly, Symbol(var))\n    end\nend\n\n\nmacro registerN(name, params...)\n    poly = esc(name)\n    \u03b1s = tuple(esc.(params)...)\n    quote\n        Base.convert(::Type{P}, q::Q) where {$(\u03b1s...),T, P<:$poly{$(\u03b1s...),T}, Q <: $poly{$(\u03b1s...),T}} = q\n        Base.convert(::Type{$poly{$(\u03b1s...)}}, q::Q) where {$(\u03b1s...),T, Q <: $poly{$(\u03b1s...),T}} = q        \n        Base.promote(p::P, q::Q) where {$(\u03b1s...),T, P <:$poly{$(\u03b1s...),T}, Q <: $poly{$(\u03b1s...),T}} = p,q\n        Base.promote_rule(::Type{<:$poly{$(\u03b1s...),T}}, ::Type{<:$poly{$(\u03b1s...),S}}) where {$(\u03b1s...),T,S} =\n            $poly{$(\u03b1s...),promote_type(T, S)}\n        Base.promote_rule(::Type{<:$poly{$(\u03b1s...),T}}, ::Type{S}) where {$(\u03b1s...),T,S<:Number} = \n            $poly{$(\u03b1s...),promote_type(T,S)}\n\n        function $poly{$(\u03b1s...),T}(x::AbstractVector{S}, var::SymbolLike = :x) where {$(\u03b1s...),T,S}\n            $poly{$(\u03b1s...),T}(T.(x), Symbol(var))\n        end\n        $poly{$(\u03b1s...)}(coeffs::AbstractVector{T}, var::SymbolLike=:x) where {$(\u03b1s...),T} =\n            $poly{$(\u03b1s...),T}(coeffs, Symbol(var))\n        $poly{$(\u03b1s...),T}(n::Number, var::SymbolLike = :x) where {$(\u03b1s...),T} = n*one($poly{$(\u03b1s...),T}, Symbol(var))\n        $poly{$(\u03b1s...)}(n::Number, var::SymbolLike = :x) where {$(\u03b1s...)} = n*one($poly{$(\u03b1s...)}, Symbol(var))\n        $poly{$(\u03b1s...),T}(var::SymbolLike=:x) where {$(\u03b1s...), T} = variable($poly{$(\u03b1s...),T}, Symbol(var))\n        $poly{$(\u03b1s...)}(var::SymbolLike=:x) where {$(\u03b1s...)} = variable($poly{$(\u03b1s...)}, Symbol(var))\n    end\nend\n\n\n# deprecated. If desired,  replace with  @registerN  type  parameters... macro\n# Macros to register POLY{\u03b1, T} and POLY{\u03b1, \u03b2, T}\nmacro register1(name)\n    @warn \"@register1 is deprecated use @registerN\"\n    poly = esc(name)\n    quote\n        Base.convert(::Type{P}, p::P) where {P<:$poly} = p\n        Base.promote(p::P, q::Q) where {\u03b1,T, P <:$poly{\u03b1,T}, Q <: $poly{\u03b1,T}} = p,q\n        Base.promote_rule(::Type{<:$poly{\u03b1,T}}, ::Type{<:$poly{\u03b1,S}}) where {\u03b1,T,S} =\n            $poly{\u03b1,promote_type(T, S)}\n        Base.promote_rule(::Type{<:$poly{\u03b1,T}}, ::Type{S}) where {\u03b1,T,S<:Number} = \n            $poly{\u03b1,promote_type(T,S)}\n        function $poly{\u03b1,T}(x::AbstractVector{S}, var::SymbolLike = :x) where {\u03b1,T,S}\n            $poly{\u03b1,T}(T.(x), Symbol(var))\n        end\n        $poly{\u03b1}(coeffs::AbstractVector{T}, var::SymbolLike=:x) where {\u03b1,T} =\n            $poly{\u03b1,T}(coeffs, Symbol(var))\n        $poly{\u03b1,T}(n::Number, var::SymbolLike = :x) where {\u03b1,T} = n*one($poly{\u03b1,T}, Symbol(var))\n        $poly{\u03b1}(n::Number, var::SymbolLike = :x) where {\u03b1} = n*one($poly{\u03b1}, Symbol(var))\n        $poly{\u03b1,T}(var::SymbolLike=:x) where {\u03b1, T} = variable($poly{\u03b1,T}, Symbol(var))\n        $poly{\u03b1}(var::SymbolLike=:x) where {\u03b1} = variable($poly{\u03b1}, Symbol(var))\n    end\nend\n\n\n# Macro to register POLY{\u03b1, \u03b2, T}\nmacro register2(name)\n    @warn \"@register2  is deprecated use @registerN\"\n    poly = esc(name)\n    quote\n        Base.convert(::Type{P}, p::P) where {P<:$poly} = p\n        Base.promote(p::P, q::Q) where {\u03b1,\u03b2,T, P <:$poly{\u03b1,\u03b2,T}, Q <: $poly{\u03b1,\u03b2,T}} = p,q        \n        Base.promote_rule(::Type{<:$poly{\u03b1,\u03b2,T}}, ::Type{<:$poly{\u03b1,\u03b2,S}}) where {\u03b1,\u03b2,T,S} =\n            $poly{\u03b1,\u03b2,promote_type(T, S)}\n        Base.promote_rule(::Type{<:$poly{\u03b1,\u03b2,T}}, ::Type{S}) where {\u03b1,\u03b2,T,S<:Number} =\n            $poly{\u03b1,\u03b2,promote_type(T, S)}\n        $poly{\u03b1,\u03b2}(coeffs::AbstractVector{T}, var::SymbolLike = :x) where {\u03b1,\u03b2,T} =\n            $poly{\u03b1,\u03b2,T}(coeffs, Symbol(var))\n        $poly{\u03b1,\u03b2,T}(x::AbstractVector{S}, var::SymbolLike = :x) where {\u03b1,\u03b2,T,S<:Number} = $poly{\u03b1,\u03b2,T}(T.(x), var)\n        $poly{\u03b1,\u03b2,T}(n::Number, var::SymbolLike = :x) where {\u03b1,\u03b2,T} = n*one($poly{\u03b1,\u03b2,T}, Symbol(var))\n        $poly{\u03b1,\u03b2}(n::Number, va::SymbolLiker = :x) where {\u03b1,\u03b2} = n*one($poly{\u03b1,\u03b2}, Symbol(var))\n        $poly{\u03b1,\u03b2,T}(var::SymbolLike=:x) where {\u03b1,\u03b2, T} = variable($poly{\u03b1,\u03b2,T}, Symbol(var))\n        $poly{\u03b1,\u03b2}(var::SymbolLike=:x) where {\u03b1,\u03b2} = variable($poly{\u03b1,\u03b2}, Symbol(var))\n    end\nend\n\n", "meta": {"hexsha": "4bb0a1007d1f9c215d013ba81ccc25831b17c6ce", "size": 6746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abstract.jl", "max_stars_repo_name": "michakraus/Polynomials.jl", "max_stars_repo_head_hexsha": "8b6a3d282d8f67cfca219ec91aa4076257a16ff0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/abstract.jl", "max_issues_repo_name": "michakraus/Polynomials.jl", "max_issues_repo_head_hexsha": "8b6a3d282d8f67cfca219ec91aa4076257a16ff0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-01-09T18:43:34.000Z", "max_issues_repo_issues_event_max_datetime": "2017-01-11T11:18:49.000Z", "max_forks_repo_path": "src/abstract.jl", "max_forks_repo_name": "michakraus/Polynomials.jl", "max_forks_repo_head_hexsha": "8b6a3d282d8f67cfca219ec91aa4076257a16ff0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.5323741007, "max_line_length": 314, "alphanum_fraction": 0.5677438482, "num_tokens": 2265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3702253925955866, "lm_q2_score": 0.15610489940847916, "lm_q1q2_score": 0.05779399766959875}}
{"text": "\"\"\"\n\tgrdvector(cmd0::String=\"\", arg1=nothing, arg2=nothing, kwargs...)\n\nTakes two 2-D grid files which represents the x- and y-components of a vector field and produces\na vector field plot by drawing vectors with orientation and length according to the information\nin the files. Alternatively, polar coordinate r, theta grids may be given instead.\n\nFull option list at [`grdvector`]($(GMTdoc)grdvector.html)\n\nParameters\n----------\n\n- **A** | **polar** :: [Type => Bool]  \n\n    The grid contain polar (r, theta) components instead of Cartesian (x, y) [Default is Cartesian components].\n    ($(GMTdoc)grdvector.html#a)\n- $(GMT.opt_B)\n- $(GMT.opt_C)\n- **G** | **fill** :: [Type => Str | Number]\n\n    Sets color or shade for vector interiors [Default is no fill].\n    ($(GMTdoc)grdvector.html#g)\n- **I** | **inc** :: [Type => Sytr | Number]\t``Arg=[x]dx[/dy]``\n\n    Only plot vectors at nodes every x_inc, y_inc apart (must be multiples of original grid spacing).\n    ($(GMTdoc)grdvector.html#i)\n- **N** | **noclip** | **no_clip** :: [Type => Bool]\n\n    Do NOT clip symbols that fall outside map border \n    ($(GMTdoc)grdvector.html#n)\n- **Q** | **vec** | **vector** | **arrow** :: [Type => Str]\n\n    Modify vector parameters. For vector heads, append vector head size [Default is 0, i.e., stick-plot].\n    ($(GMTdoc)grdvector.html#q)\n- $(GMT.opt_P)\n- $(GMT.opt_R)\n- **S** | **vec_scale** :: [Type => Str | Number]\t\t``Arg = [i|l]scale[unit]``\n\n    Sets scale for vector plot length in data units per plot distance measurement unit [1].\n    ($(GMTdoc)grdvector.html#s)\n- **T** | **sign_scale** :: [Type => Bool]\n\n    Means the azimuths of Cartesian data sets should be adjusted according to the signs of the\n    scales in the x- and y-directions [Leave alone].\n    ($(GMTdoc)grdvector.html#t)\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- **W** | **pen** :: [Type => Str | Number]\n\n    Sets the attributes for the particular line.\n    ($(GMTdoc)grdvector.html#w)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- **Z** | **azimuth** :: [Type => Bool]\n\n    The theta grid provided contains azimuths rather than directions (implies -A).\n    ($(GMTdoc)grdvector.html#z)\n- $(GMT.opt_V)\n- $(GMT.opt_f)\n\"\"\"\nfunction grdvector(cmd0::String=\"\", arg1=nothing, arg2=nothing; first=true, kwargs...)\n\n\tlength(kwargs) == 0 && return monolitic(\"grdvector\", cmd0, arg1, arg2)\n\n\td, K, O = init_module(first, kwargs...)\t\t# Also checks if the user wants ONLY the HELP mode\n\n\tcmd::String = parse_BJR(d, \"\", \"\", O, \" -JX12c/0\")[1]\n\tcmd = parse_common_opts(d, cmd, [:I :UVXY :f :p :t :params], first)[1]\n\tcmd = parse_these_opts(cmd, d, [[:A :polar], [:N :noclip :no_clip], [:S :vec_scale], [:T :sign_scale], [:Z :azimuth]])\n\n    # Check case in which the two grids were transmitted by name. \n    (cmd0 != \"\" && isa(arg1, String)) && (cmd0 *= \" \" * arg1; arg1 = nothing)\n\n\tcmd, got_fname, arg1 = find_data(d, cmd0, cmd, arg1)\t# Find how data was transmitted\n\n\tN_used = got_fname == 0 ? 1 : 0\t\t# To know whether a cpt will go to arg1 or arg2\n\tcmd, arg1, arg2, = add_opt_cpt(d, cmd, CPTaliases, 'C', N_used, arg1, arg2)\n\topt_Q = parse_Q_grdvec(d, [:Q :vec :vector :arrow])\n\t!occursin(\" -G\", opt_Q) && (cmd = add_opt_fill(cmd, d, [:G :fill], 'G'))\t# If fill not passed in arrow, try from regular option\n\tcmd *= add_opt_pen(d, [:W :pen], \"W\", true)\t\t\t\t\t\t\t\t\t# TRUE to also seek (lw,lc,ls)\n\t(!occursin(\" -C\", cmd) && !occursin(\" -W\", cmd) && !occursin(\" -G\", opt_Q)) && (cmd *= \" -W0.5\")\t# If still nothing, set -W.\n\t(opt_Q != \"\") && (cmd *= opt_Q)\n\n    return finish_PS_module(d, \"grdvector \" * cmd, \"\", K, O, true, arg1, arg2)\nend\n\n# ---------------------------------------------------------------------------------------------------\nfunction parse_Q_grdvec(d::Dict, symbs::Array{<:Symbol})::String\n\t(show_kwargs[1]) && return print_kwarg_opts(symbs, \"NamedTuple | String\")\n\tcmd::String = \"\"\n    if ((val = find_in_dict(d, symbs)[1]) !== nothing)\n\t\tif (isa(val, String))  cmd *= \" -Q\" * val\t\t# An hard core GMT string directly with options\n\t\telse                   cmd *= \" -Q\" * vector_attrib(val)\n\t\tend\n\t\tif ((ind = findfirst(\"+g\", cmd)) !== nothing)   # -Q0.4+e+gred+n0.4+pcyan+h0\n\t\t\tcmd *= \" -G\" * split(cmd[ind[1]+2:end], \"+\")[1]\t# Add a -G (does the same) to have at least one of the -G, -W, -C\n\t\tend\n\tend\n\treturn cmd\nend\n\n# ---------------------------------------------------------------------------------------------------\ngrdvector(arg1, arg2=nothing, cmd0::String=\"\";  kw...) = grdvector(cmd0, arg1, arg2; first=true, kw...)\ngrdvector!(cmd0::String=\"\", arg1=nothing, arg2=nothing; kw...) = grdvector(cmd0, arg1, arg2; first=false, kw...)\ngrdvector!(arg1, arg2=nothing, cmd0::String=\"\"; kw...) = grdvector(cmd0, arg1, arg2; first=false, kw...)\n", "meta": {"hexsha": "35595f5feefa89a8b9f2685659a235840f5c0e4b", "size": 4689, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/grdvector.jl", "max_stars_repo_name": "stevengj/GMT.jl", "max_stars_repo_head_hexsha": "40f8ac4b2e352369adf98c8e2a7864e454599e18", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/grdvector.jl", "max_issues_repo_name": "stevengj/GMT.jl", "max_issues_repo_head_hexsha": "40f8ac4b2e352369adf98c8e2a7864e454599e18", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/grdvector.jl", "max_forks_repo_name": "stevengj/GMT.jl", "max_forks_repo_head_hexsha": "40f8ac4b2e352369adf98c8e2a7864e454599e18", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.2358490566, "max_line_length": 128, "alphanum_fraction": 0.5986351034, "num_tokens": 1442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3849121303722487, "lm_q2_score": 0.15002882814282253, "lm_q1q2_score": 0.0577479158577058}}
{"text": "\"\"\"\n    Clock{T}\n\nManages a simulation's time information.\n\"\"\"\nmutable struct Clock{T}\n    time::T         # current simulation time\n    dt::T           # simulation timestep\n    stop_time::T    # simulation end time\nend\n\ntick!(clock::Clock) = (clock.time += clock.dt)\n\nstop_time_exceeded(clock::Clock) = (clock.time >= clock.stop_time)\n", "meta": {"hexsha": "22f7cdc3630d1db65afa2a360b8998458bdecaad", "size": 337, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CoupledSimulations/clock.jl", "max_stars_repo_name": "CliMA/CouplerMachine", "max_stars_repo_head_hexsha": "34768de8dbca05b8c03c5c24626dea6c14404b03", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-05T07:09:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-05T07:09:01.000Z", "max_issues_repo_path": "src/CoupledSimulations/clock.jl", "max_issues_repo_name": "CliMA/CouplerMachine", "max_issues_repo_head_hexsha": "34768de8dbca05b8c03c5c24626dea6c14404b03", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 20, "max_issues_repo_issues_event_min_datetime": "2021-04-01T13:23:06.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-02T04:15:01.000Z", "max_forks_repo_path": "src/CoupledSimulations/clock.jl", "max_forks_repo_name": "CliMA/ClimaCoupler.jl", "max_forks_repo_head_hexsha": "d21349895a9bdaf9e192b534977deff9bb05385b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-05T16:29:27.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-05T16:29:27.000Z", "avg_line_length": 22.4666666667, "max_line_length": 66, "alphanum_fraction": 0.6439169139, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.11920292358664392, "lm_q1q2_score": 0.05773952217296151}}
{"text": "#=\nMain generic API\n\n@author : Spencer Lyon <spencer.lyon@stern.nyu.edu>\n@date : 2015-04-13 11:29:12\n\n=#\n\n# ----------- #\n# Manager API #\n# ----------- #\n\"\"\"\n`verbose(m::IterationManager) -> Bool`\n\nSpecifies if the iteration manager should be verbose and print status updates.\n\nChecks the `verbose` field of the manager and returns it. If the field\ndoesn't exist, the default is false.\n\n\"\"\"\nverbose(m::IterationManager) = isdefined(m, :verbose) ? m.verbose : false\n\n\"\"\"\n`prefix(m::IterationManager) -> String`\n\nDefines the prefix that should be applied to all printed messages\n\nChecks the `prefix` field of the manager and returns it. If the field\ndoesn't exist, the default is `\"\"`.\n\n\"\"\"\nprefix(m::IterationManager) = isdefined(m, :print_prefix) ? m.print_prefix : \"\"\n\n\"\"\"\n`print_now(m::IterationManager, n::Int) -> Bool`\n\nSpecifies if the iteration manager should print on the `n`th iteration\n\"\"\"\nfunction print_now(mgr::IterationManager, n::Int)\n    if verbose(mgr)\n        n == 1 && return true\n        isdefined(mgr, :print_skip) ? n % mgr.print_skip == 0 : true\n    else\n        false\n    end\nend\n\n# fallback in case x is not an Int\nprint_now(mgr::IterationManager, x::Any) = verbose(mgr)\n\n# --------- #\n# State API #\n# --------- #\n\n# default methods for the `by` argument of `update!`\ndefault_by(x::T, y::T) where T <: AbstractArray = maximum(abs.(x .- y))\ndefault_by(x::T, y::T) where T <: Number = abs(x - y)\n\nfunction default_by(x::S, y::T) where S where T\n    msg = \"default_by not implemented for types x::$S, y::$T\"\n    throw(ArgumentError(msg))\nend\n\nnum_iter(::IterationState) = nothing\nLinearAlgebra.norm(::IterationState) = Inf\n\n\"\"\"\n`default_by(x, y)`\n\nGives the default comparison `by` argument to `managed_iteration` based on the\ntypes of x and y\n\"\"\" default_by\n\ndisplay_iter(istate::IterationState, prefix=\"\") =\n    display_iter(stdout, istate, prefix)\n\ndisplay_iter(io::IO, istate::T, prefix=\"\") where T <: IterationState =\n    error(\"`display_iter` must be implemented directly by type $T\")\n\n\"\"\"\n    display_iter([io::IO=stdout], st::IterationState, [prefix::String])\n\nA `display` method for an iteration state. Must be implemented by all concrete\nsubtypes of `IterationState`\n\"\"\"\ndisplay_iter\n\n# --------------------------- #\n# Combining Manager and State #\n# --------------------------- #\n\n\"\"\"\n    finished(mgr::IterationManager, istate::IterationState)::Bool\n\nGiven a manager and a state, determine if the iterations have finished and\nshould thus be terminated\n\"\"\"\nfinished\n\n# ------------- #\n# default hooks #\n# ------------- #\n\"\"\"\n    pre_hook(mgr::IterationManager, istate::IterationState)\n\nCalled before iterations begin.\n\nFor the default manager and state this is used to print the column headers for\nverbose output printing\n\"\"\"\npre_hook(mgr::IterationManager, istate::IterationState) =\n    verbose(mgr) && display_iter(istate, prefix(mgr))\n\nprint_now(mgr::IterationManager, istate::IterationState) =\n    print_now(mgr, num_iter(istate))\n\n\"\"\"\n    iter_hook(mgr::IterationManager, istate::IterationState)\n\nCalled after every iteration.\n\nFor the default manager and state this is used to print updates on the\niterations if `verbose(mgr) == true`.\n\"\"\"\niter_hook(mgr::IterationManager, istate::IterationState) =\n    print_now(mgr, istate) && display_iter(istate, prefix(mgr))\n\n\"\"\"\n    post_hook(mgr::IterationManager, istate::IterationState)\n\nCalled after iterations have finished\n\nFor the default manager this is used to print a warning if the maximum number\nof iterations was exceeded.\n\"\"\"\nfunction post_hook(mgr::IterationManager, istate::IterationState)\n    if !(isdefined(mgr, :maxiter))\n        return nothing\n    end\n    if num_iter(istate) >= mgr.maxiter\n        @warn \"Maximum iterations exceeded. Algorithm may not have converged\"\n    end\n    nothing\nend\n\nfunction managed_iteration(\n        f::Base.Callable,\n        mgr::IterationManager,\n        istate::IterationState{T};\n        by=default_by\n    ) where T\n    pre_hook(mgr, istate)\n\n    while !(finished(mgr, istate))\n        v = f(istate.prev)::T  # if we don't get a T back it should be an error\n        update!(istate, v; by=by)\n        iter_hook(mgr, istate)\n    end\n\n    post_hook(mgr, istate)\n    istate\nend\n\n\"\"\"\n    managed_iteration!(\n        f!::Base.Callable,\n        mgr::IterationManager,\n        dest::T,\n        istate::IterationState{T};\n        by::Base.Callable=default_by\n    ) where T <: AbstractArray\n\nGiven a function with signature `f!(dest::T, src::T)`, run a non-allocating\nversion of managed_iteration where each step calls the mutating function `f!`\nthat fills its first argument based on the value of the second argument. All\nother behavior is equivalent to other forms of `managed_iteration`\n\n\"\"\"\nfunction managed_iteration!(\n        f!::Base.Callable,\n        mgr::IterationManager,\n        dest::T,\n        istate::IterationState{T};\n        by::Base.Callable=default_by\n    ) where T <: AbstractArray\n    pre_hook(mgr, istate)\n\n    while !(finished(mgr, istate))\n        f!(dest, istate.prev)\n        update!(istate, dest; by=by)\n        iter_hook(mgr, istate)\n    end\n\n    post_hook(mgr, istate)\n    istate\nend\n\n# kwarg version to create default manger/state\nfunction managed_iteration(\n        f::Base.Callable,\n        init;\n        tol::Float64=NaN,\n        maxiter::Int=typemax(Int),\n        by::Base.Callable=default_by,\n        verbose::Bool=true,\n        print_skip::Int=div(maxiter, 5)\n    )\n    mgr = DefaultManager(tol, maxiter, verbose, print_skip)\n    istate = DefaultState(init)\n    managed_iteration(f, mgr, istate; by=by)\nend\n", "meta": {"hexsha": "d3246e4d15ac12d69525c2695087533a0214e5ce", "size": 5564, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/api.jl", "max_stars_repo_name": "UnofficialJuliaMirror/IterationManagers.jl-69680d3f-cf1d-5315-a21f-791b8226f004", "max_stars_repo_head_hexsha": "7d210fbcd7b52b7d987bccee4ce7fe89d2490c01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-04-15T08:04:58.000Z", "max_stars_repo_stars_event_max_datetime": "2016-11-28T09:15:17.000Z", "max_issues_repo_path": "src/api.jl", "max_issues_repo_name": "UnofficialJuliaMirror/IterationManagers.jl-69680d3f-cf1d-5315-a21f-791b8226f004", "max_issues_repo_head_hexsha": "7d210fbcd7b52b7d987bccee4ce7fe89d2490c01", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2015-04-24T21:34:54.000Z", "max_issues_repo_issues_event_max_datetime": "2016-08-20T19:28:19.000Z", "max_forks_repo_path": "src/api.jl", "max_forks_repo_name": "UnofficialJuliaMirror/IterationManagers.jl-69680d3f-cf1d-5315-a21f-791b8226f004", "max_forks_repo_head_hexsha": "7d210fbcd7b52b7d987bccee4ce7fe89d2490c01", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2017-05-16T06:01:20.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-01T10:14:31.000Z", "avg_line_length": 26.2452830189, "max_line_length": 79, "alphanum_fraction": 0.6703810208, "num_tokens": 1413, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43398147944527615, "lm_q2_score": 0.13296425050508953, "lm_q1q2_score": 0.057704022147531064}}
{"text": "### A Pluto.jl notebook ###\n# v0.15.1\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 ac87ced2-986f-4e6c-80b2-104b25c171c2\nbegin\n\tusing CSV, JLD2, FITSIO, FileIO# , HDF5\n\tusing DataFrames #, Query\n\tusing PyCall\n\tusing PlutoUI, PlutoTeachingTools\n\teval(Meta.parse(code_for_check_type_funcs))\nend\n\n# \u2554\u2550\u2561 e0e60e09-3808-4d6b-a773-6ba59c02f517\nmd\"\"\"\n# Astro 528, Lab 3, Exercise 2\n# Benchmarking File I/O &\n# (and calling Python packages)\n\"\"\"\n\n# \u2554\u2550\u2561 8c1d81ab-d215-4972-afeb-7e00bf6063c2\nmd\"\"\"\nFor many applications, its important that we be able to read input data from a file and/or to write our outputes to files so they can be reused later.  Disk access is typically much slower than accessing system memory.  Therefore, disk access can easily become the limiting factor for a project.  In this set of exercises, you'll see examples of how to perform basic file I/O.  In the process, you'll also see how to import Python packages, call python functions and access data computed by python functions.\n\nYou'll be provided with most of the code you need, so that you can focus on comparing how much disk space and time is required by different file formats.  Near the end of the lab, you'll be asked to think about when each type of file format would be a good choice for you to use in your research projects.\n\"\"\"\n\n# \u2554\u2550\u2561 1607eac9-e76f-4d1f-a9ce-981ce3be9bea\nmd\"\"\"\n### Download some data\nFirst, we're going to download some data from the web.  Julia has a built in `download` function that can be handy for this.  It relies on your system having some utilities already installed (e.g., `curl`, `wget` or `fetch`).  If you run this on a local system and run into trouble, then you can leave the cell below, and manually download the file to the data subdirectory.\n\"\"\"\n\n# \u2554\u2550\u2561 f27e1e8f-15eb-4754-a94c-7f37c54b871e\nbegin \n\tpath = basename(pwd())==\"test\" ? \"../data/\" : \"data/\"\n\tif !isdir(path)  mkdir(path)  end  # make sure there's a data directory\n\turl = \"https://exoplanetarchive.ipac.caltech.edu/data/KeplerData/Simulated/kplr_dr25_inj1_plti.txt\"\n\tfilename_ipac = joinpath(path,basename(url)) # extract the filename and prepend \"data/\"\n\ttime_to_download = NaN\n\tif !isfile(filename_ipac)  # skip downloading if file already exists\n\t    time_to_download = @elapsed download(url,filename_ipac)\n\tend\nend\n\n# \u2554\u2550\u2561 80f02c3a-6751-48df-92ec-13f5c7d8c71e\nif !isnan(time_to_download)\n\tmd\"Downloading the file took $time_to_download seconds.\"\nend\n\n# \u2554\u2550\u2561 624c9038-3008-4e78-a149-60796dacf9c0\nmd\"\"\"\nPreviously, everything you needed for an assignment was included in a GitHub repository.  So why did I make you download the file?\n\nNotice the size of the file.  Git is great for tracking source code, but it wasn't really designed for working with large files (especially large _binary_ files).  Since  we're not going to be editing it, we'll simply download it once.  Besides, it's useful to know how to download a file from within a julia script and to compare the time required to download the file from the internet to the time required to read the file from disk.\n\"\"\"\n\n# \u2554\u2550\u2561 2eb255d9-707d-4224-a0ce-fe90a1c69722\nmd\"\"\"\n## Calling AstroPy to read data in unusual formats\n\nHere I've picked a file containing the results of applying the pipeline for NASA's Kepler mission to [simulated data](https://exoplanetarchive.ipac.caltech.edu/docs/KeplerSimulated.html) in which the signals of simulated \"planet's\" have been injected into actual Kepler data.  This data set is the basis for computing the efficiency of the Kepler pipeline at detecting real planets.  This dataset has played an important role in enabling astronomers to estimate the occurrence rates of planets around other stars.  \nFor documentation of its contents, you could read [its documentation](https://exoplanetarchive.ipac.caltech.edu/docs/KSCI-19110-001.pdf).  However, that's not necessary for this lab.  For now, we'll just do some basic manipulations of the file, and the details of its contents aren't important.  That said, it is important to know the _file format_.  \n\nThis data file that we downloaded is in [IPAC format](https://irsa.ipac.caltech.edu/applications/DDGEN/Doc/ipac_tbl.html).  \nIt would be tedious to learn the details of every file format that is used in astronomy, let alone to write our own code to read them.  Fortunately, there are packages that can read the most common file formats.  Sometimes astronomers use specialized/less common file formats which aren't always implemented in native Julia.  Reading such files is an example of something that [astropy](http://docs.astropy.org) is particularly good for.  It provides a function [`astropy.io.ascii.read`](http://docs.astropy.org/en/stable/io/ascii/) that will read a file in IPAC for us.  \nSince astropy is written in Python, we import the [`PyCall`](https://github.com/stevengj/PyCall.jl) package, so we can import python packages and call python functions from Julia.  \nSince reading a file from disk is typically limited by the rate of getting data from disk, rather than compute speed, it's usually not a problem that Python isn't particularly fast, when it's comes to reading files.\n\"\"\"\n\n# \u2554\u2550\u2561 8571ccaf-5fbc-4593-82db-ead073a4074f\nmd\"\"\"\nAt the bottom of this notebook, I've run `using PyCall`.  Here, we'll import the required python module using `pyimport`. \n\"\"\"\n\n# \u2554\u2550\u2561 442604d0-b490-404b-8b1c-7e1c01cebad7\nastropy_io_ascii = pyimport(\"astropy.io.ascii\")\n\n# \u2554\u2550\u2561 70f5fe63-fea1-4afc-b03d-f113b8bd621e\nmd\"\"\"\nNow, we can review the documentation for [`astropy.io.ascii.read`](http://docs.astropy.org/en/stable/io/ascii/) and call that function load the data in our input file.  Let's use `@time`, so we can compare the time required to read various formats.\n\"\"\"\n\n# \u2554\u2550\u2561 8cd5a772-a3aa-4166-92e6-39436d0d2278\nwith_terminal() do \n\t@time astropy_io_ascii.read(filename_ipac, format=\"ipac\", fast_reader=false)\nend\n\n# \u2554\u2550\u2561 074b00ce-dae6-4e4f-8673-49be73085327\nmd\"\"\"\nOn the plus side, the data was read in.  However, what is the type of the data that astropy read for us and stored into `data_from_astropy`?  Let's check.\n\"\"\"\n\n# \u2554\u2550\u2561 ced3ecc9-94ea-408f-bdef-d34802166663\nbegin\n\ttime_to_read_with_astropy = @elapsed data_from_astropy = astropy_io_ascii.read(filename_ipac, format=\"ipac\", fast_reader=false)\n\ttypeof(data_from_astropy)\nend\n\n# \u2554\u2550\u2561 c62adfb6-6ef8-45fd-992f-07bca59f82cd\nmd\"\"\"\nSince Python is a weakly-typed language, it's type is `PyObject`.  That can contain most anything!  \nThat flexibility can be convenient, but it is also one of the reasons that Python is not a good language for high-performance computing.  To enable Julia to work efficiently with the data, we'll want Julia to know what type the data is and store a list of strictly-typed columns into a the data into a `DataFrame`.\nThe PyCall package provides an interface for For accessing data from PyObjects.  Because of the weak typing issue, the syntax and details can be a bit confusing.  If you're not intending to call Python for your class project, then there's no reason to worry about that these details.  \n\"\"\"\n\n# \u2554\u2550\u2561 03edae54-4368-4b81-8713-17f93bbb9ed9\nprotip(md\"\"\"\nThis list is just for students who are curious about accessing Python data and methods from Julia.\n\t\n   - Given o::PyObject, o.attribute in Julia is equivalent to o.attribute in Python, with automatic type conversion. To get an attribute as a PyObject without type conversion, do o.\"attribute\" instead. The keys(o::PyObject) function returns an array of the available attribute symbols.\n\n   - Given o::PyObject, get(o, key) is equivalent to o[key] in Python, with automatic type conversion. To get as a PyObject without type conversion, do get(o, PyObject, key), or more generally get(o, SomeType, key). You can also supply a default value to use if the key is not found by get(o, key, default) or get(o, SomeType, key, default). Similarly, set!(o, key, val) is equivalent to o[key] = val in Python, and delete!(o, key) is equivalent to del o[key] in Python. For one or more integer indices, o[i] in Julia is equivalent to o[i-1] in Python.\n\n   - You can call an o::PyObject via o(args...) just like in Python (assuming that the object is callable in Python). The explicit pycall form is still useful in Julia if you want to specify the return type.\n\n   - pystr(o) and pyrepr(o) are analogous to str and repr in Python, respectively.\n\n   - There's more information about accessing data in PyObjects (and other types to contain Python data) in the [PyCall documentation](https://github.com/JuliaPy/PyCall.jl#types).  (The above info is copied directly from there.)\n\"\"\")\n\n# \u2554\u2550\u2561 e0b82e6c-043c-4e66-a2a4-ae0f3e4e5404\nmd\"\"\"\nFor now, you can get a [`Dict` or \"Dictionary\"](https://docs.julialang.org/en/v1/base/collections/index.html#Dictionaries-1) by using `data_from_astropy.columns`.  The dictionary consists of a set of _keys_ (in this case strings), where each key is associated with a _value_ (in this case a Vector or 1-d array).  \nOften times, the data you want to work with can be represented as a table.  For efficiency's sake, it's usually best to represent these as a bunch of `Vector`'s, each containing one columns of data.  Using a `Dict` allows you to give the columns names (instead of just numbers, so you're less likely to access the wrong column) and allows each column to have a different type (again useful for Julia to optimize your code).  \n\"\"\"\n\n# \u2554\u2550\u2561 aa62f1e5-6af5-43f6-be11-665ed25986c0\ndict_from_astropy = data_from_astropy.columns\n\n# \u2554\u2550\u2561 540468dc-3f9f-457b-9b44-efb3ec036166\nmd\"\"\"\nMost of the columns can be automatically converted to an array with a known Julia type.  For example, `data_from_astropy.columns[\"KIC_ID\"]` returns data as an `Array{Int64,1}`.  However, value associated with `TCE_ID` is some `PyObject` that can't be automatically reinterpretted as a Julia array.  \n\"\"\"\n\n# \u2554\u2550\u2561 21e3adbb-0176-4963-bdfc-0ba2422af4bf\ntypeof(dict_from_astropy[\"KIC_ID\"])\n\n# \u2554\u2550\u2561 d27aef3b-bfe7-466a-9580-93fc53dcef18\ntypeof(dict_from_astropy[\"TCE_ID\"])\n\n# \u2554\u2550\u2561 02f638d9-ec2f-4072-9fb3-e6fabac1b1e6\nmd\"\"\"Since many `KIC_ID`'s don't have an associated `TCE_ID`, there are many missing entries.  Python is trying to store a list with lots of empty entries efficiently, but PyCall doesn't (yet?) know how to deal with this `MaskedColumn` for us.  Working with the data when Julia can't know its type would be very inefficient.  Therefore, we want to create an array of Strings that allows Julia to represent this data more efficiently.  Technically, it will be an array where each element is either a `String` or a `missing`.  It took a little tinkering, but eventually, I figured out how to extract that data into an efficient Julia object.\n\"\"\"\n\n# \u2554\u2550\u2561 481c5b7a-4660-4954-84f3-29d33bd73f3d\nbegin\n\tTCE_ID_list = map(x -> x != nothing ? x : missing, data_from_astropy.columns[\"TCE_ID\"].data.tolist())\n\tTCE_ID_list_type = typeof(TCE_ID_list)\n\tTCE_ID_list\nend\n\n# \u2554\u2550\u2561 aba84742-a927-4ce5-9f7c-6e535db1ee47\nprotip(md\"Note that the type of `TCE_ID_list` is a $TCE_ID_list_type.  Julia is able to store and compute on vectors with missing data very efficiently thanks to its rich type system.\")\n\n# \u2554\u2550\u2561 aa7c4dfe-a3d4-448b-a559-1bc7b338a1dc\nmd\"\"\"\nNow let's replace the value associated with TCE_ID with this list.\n\"\"\"\n\n# \u2554\u2550\u2561 a97f9c8e-dbd3-4613-9c6e-c471340ea2d6\ndict_from_astropy[\"TCE_ID\"] = TCE_ID_list;\n\n# \u2554\u2550\u2561 7c7430a0-d16e-485b-bbd9-7f231fee853a\nmd\"\"\"\nIf we just wantted to access the data, then we could use the data stored in the columns as a dictionary.  \nHowever, a dictionary doesn't guarentee anything about the relationship of the value of different keys.\nFor example, in a table, each column should have the same number of rows.  Therefore, we'll switch from representing the data as a dictionary and start using a `DataFrame`, a type provided by the [DataFrames.jl package](https://github.com/JuliaData/DataFrames.jl).  A `DataFrame` can be thought of as a table, where the data for each column is stored as an array.   A `DataFrame` also provides some additional features to allow easy and efficient access and manipulation of the table that will come in useful later.  \n\"\"\"\n\n# \u2554\u2550\u2561 cebe52b3-386c-4d31-8497-c19b6c742577\nbegin\n\t# First, we'll create and copy a small `DataFrame`, so the functions get compiled before we start timing things.\n\tsmall_dict = Dict(:a=>[1,2],:b=>[3.0,4.0],:c=>[\"hello\",missing])\n\tsmall_df1 = DataFrame(small_dict)\n\tsmall_df2 = DataFrame(small_dict, copycols=false)\nend;\n\n# \u2554\u2550\u2561 3d09d38a-ca72-43f2-bd85-17fc0e05a645\nmd\"\"\"\nWe'll use our existing dictionary to initialize a `DataFrame`.  \n\"\"\"\n\n# \u2554\u2550\u2561 9257c879-d96f-4d2b-994e-d542617b0c65\nwith_terminal() do\n\tsmall_dict  # makes sure already compiled code for creating small DataFrame\n\t@time df_tmp = DataFrame(dict_from_astropy)\nend\n\n# \u2554\u2550\u2561 307a7a29-afa6-4c4d-88ae-4f757aeba892\nmd\"\"\"\n2a.  Look at how much memory was allocated during this line of code.  \nDo you think it involved allocating and copying lots of data from the dictionary?\n\n\"\"\"\n\n# \u2554\u2550\u2561 afdc442a-2931-4129-824e-98431e1d8be2\nresponse_2a = missing\n\n# \u2554\u2550\u2561 c42deea3-acf1-491a-97bd-fdc472917584\ndisplay_msg_if_fail(check_type_isa(:response_2a,response_2a,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 7c1f4a27-dfd7-428a-97ef-eac1784e8d6f\nmd\"\"\"By default `DataFrames` prioritizes _safe operations_ over efficient ones.  However, it also provides options for more efficient operations for times when users want to invest a little more time to get good performance.  For example, we can tell it not to copy the data, using `copycols=false`).\"\"\"\n\n# \u2554\u2550\u2561 97111ccc-8a16-4e7d-adf7-0dcd90524be3\nwith_terminal() do\n\t\t@time df = DataFrame(dict_from_astropy, copycols=false)\nend\n\n# \u2554\u2550\u2561 f45668b1-1cbd-48e6-9224-d469826e093b\nmd\"\"\"\n2b.  Look at how much memory was allocated the second time.  \nDid Julia make a new copy of all of the data?  How did the time required compare to the first attempt?\n\"\"\"\n\n# \u2554\u2550\u2561 52beb148-6214-4a03-a0cf-6c2a6da77d40\nresponse_2b = missing\n\n# \u2554\u2550\u2561 1ecbd400-0157-45ce-b1b8-db764012ba5e\ndisplay_msg_if_fail(check_type_isa(:response_2b,response_2b,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 191ba96e-2573-4bc1-a352-46a66e0a5c4f\nmd\"\"\"\n## Writing a CSV file\n\nThe IPAC format allows for significant metadata, but reading not-so-common file formats can be annoying.  \nLet's say that we'd like to write the data to a [CSV file](https://en.wikipedia.org/wiki/Comma-separated_values), so that it's easier for other programs to read in.  Using the CSV package, we can read a CSV file into a DataFrame (or several other tabular data structures) and write CSV files from a DataFrame with code like the following.\n\"\"\"\n\n# \u2554\u2550\u2561 82a757ad-566d-4c1d-8b3d-366ffd980fb4\nbegin\n\t# Read & write a small test file so compilation time not included below\n\tn_small_df = 1024\n\tsmall_csv_filename = \"random_numbers.csv\"\n\tsmall_df = DataFrame(a=rand(n_small_df),b=rand(n_small_df) ) \n\tCSV.write(joinpath(path,small_csv_filename),small_df)  \n\tsmall_df_from_csv = CSV.read(joinpath(path,small_csv_filename),DataFrame)\nend;\n\n# \u2554\u2550\u2561 3e550b71-4750-460b-be18-911a848a8f49\nmd\"\"\"\nNow, let's write the DataFrame from our IPAC file to a CSV file.  We'll investigate how the filesize and time to read files compares.\n\"\"\"\n\n# \u2554\u2550\u2561 b0c3875b-ef07-4e92-a0a8-55f42b266c6b\nbegin\n\tfilename_csv = replace(filename_ipac, \".txt\" => \".csv\") \n\tdf = DataFrame(dict_from_astropy, copycols=false)\n\twith_terminal() do\n\t\t@time CSV.write(filename_csv,df)\n\tend\nend\n\n# \u2554\u2550\u2561 58e021c6-500f-40d8-a388-a5732bc808b3\nmd\"\"\"Next, we'll compare the filesizes for the IPAC file and the CSV file containing the same data.  \n\n2c.  Which do you expect will be larger?  Why?\nOnce you've made a prediction, mouse over the hint box below to see the sizes of the two files.  If you were suprsised, try to explain what happened.\"\"\"\n\n# \u2554\u2550\u2561 36bfd224-a13b-438c-9106-382c15a6a1d2\nresponse_2c = missing\n\n# \u2554\u2550\u2561 7f4c3ab8-041e-4ef0-bb2e-320f5293cdde\ndisplay_msg_if_fail(check_type_isa(:response_2c,response_2c,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 195c2df4-7e1c-49f8-871f-f76b985dab6d\nbegin\n\tipac_filesize = filesize(filename_ipac) /1024^2\n\tcsv_filesize = filesize(filename_csv) /1024^2\n\thint(md\"The IPAC file size is $ipac_filesize MB versus $csv_filesize MB for the CSV.\")\nend\n\n# \u2554\u2550\u2561 32124f8a-f5cf-41d1-97a9-ce1d05145fde\nmd\"\"\"\n\n## Reading CSV files\n\n2d. Think back to how long it took to read in the file in IPAC format ($time_to_read_with_astropy seconds).  How long do you think it will take to read in the same data once it's been stored in CSV format?\n\"\"\"\n\n# \u2554\u2550\u2561 507e1516-5433-49eb-831d-32426f30895e\nresponse_2d = missing\n\n# \u2554\u2550\u2561 eac67cc9-754b-4f7d-add8-93900a1b5b49\ndisplay_msg_if_fail(check_type_isa(:response_2d,response_2d,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 64224c6b-c5a0-44f2-b2a0-7f77759cb848\nmd\"\"\"\nNow, try reading it in and see.   \n\"\"\"\n\n# \u2554\u2550\u2561 73c06bb1-be49-46bd-b7f1-c45cc56af7b4\nmd\"\"\"\nI'm ready to benchmark reading a CSV file. $(@bind ready_read_csv CheckBox()) \n$(@bind go_read_csv Button(\"Rerun the benchmarks.\"))\n\"\"\"\n\n# \u2554\u2550\u2561 ebfaa677-829b-4bf9-bdbb-19f3c87dd3a4\nif  ready_read_csv\n\tgo_read_csv\n\ttime_read_csv = @elapsed df_csv = CSV.read(filename_csv, DataFrame)\n\tmd\"It took $time_read_csv seconds to read the CSV file.\"\nend\n\n# \u2554\u2550\u2561 945f5a55-3026-4497-9ece-8af878c87788\nmd\"\"\"\n2e.  How did the time required to read the data in CSV format compare to the time to read the data in IPAC format?  If you were suprsised, try to explain what happened.\n\"\"\"\n\n# \u2554\u2550\u2561 57397ee4-9efc-48b3-b640-d2b7a10da633\nresponse_2e = missing\n\n# \u2554\u2550\u2561 8059a6a3-384a-4344-8a23-650ee0be10c2\ndisplay_msg_if_fail(check_type_isa(:response_2e,response_2e,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 f5b93929-2c59-4360-8c41-97a1324ba455\nmd\"2f. How do you think the sizes of the files in the two formats will compare?  Once you've made your prediction, mouse over the hint box.  If you were suprsised, try to explain what happened.\"\n\n# \u2554\u2550\u2561 122196fa-45ca-4031-85eb-4afd4782de9e\nresponse_2f = missing\n\n# \u2554\u2550\u2561 e9dc1456-616b-4e4b-b209-9f6ba4c48607\ndisplay_msg_if_fail(check_type_isa(:response_2f,response_2f,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 f15d37a7-d962-4da0-977f-76729a3313be\nhint(md\"The IPAC file size is $ipac_filesize MB versus $csv_filesize MB for the CSV.\")\n\n# \u2554\u2550\u2561 28f195c4-4f61-4873-85d6-b4e3aaa3660f\nmd\"\"\"\n## Binary formats: HDF5/JLD2\n\nThere are numerous binary file formats that one could use.  Here, we'll try using JLD2 which is a subset of the [HDF5](https://www.hdfgroup.org/solutions/hdf5/) file format.  This means that when [Julia's JLD2 package](https://github.com/JuliaIO/JLD2.jl) writes jld2 files, they can be read by other programs that can read HDF5 files.  However, a generic HDF5 file is not a valid JLD2 file.  If you want to read a HDF5 file, then you can use Julia's [HDF5.jl package](https://github.com/JuliaIO/HDF5.jl).  The [FileIO.jl](https://github.com/JuliaIO/FileIO.jl) package provides a common interface for reading and writing from multiple file formats, including these and several others.\n\nAs before, we'll call each function once using a small DataFrame, just so they get compiled before we benchmark them.\n\"\"\"\n\n# \u2554\u2550\u2561 3837e439-250b-4577-b0d7-93352ec19f6e\nbegin\n\tfilename_jld2_small = replace(small_csv_filename, \".csv\" => \".jld2\")  \n\t@save joinpath(path,filename_jld2_small) small_df \n\tsmall_df_from_jld2 = load(joinpath(path,filename_jld2_small), \"small_df\")\nend;\n\n# \u2554\u2550\u2561 df9b701f-d314-4512-b2ea-1f6ae015166c\nresponse_2g = missing\n\n# \u2554\u2550\u2561 377f7527-a338-4526-bb24-9766c635e719\ndisplay_msg_if_fail(check_type_isa(:response_2g,response_2g,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 25f24754-16d4-4343-b54a-cf8ea1358ce9\nbegin \n\tsmall_jld2_filesize = filesize(joinpath(path,filename_jld2_small))/1024\n\tsmall_csv_filesize = filesize(joinpath(path,small_csv_filename))/1024\n\thint(md\"For the small random data set, the JLD2 file's size is $small_jld2_filesize KB versus $small_csv_filesize KB for the CSV file.\")\nend\n\n# \u2554\u2550\u2561 8255b5a9-cbe6-4604-bedd-0e366f311096\nmd\"\"\"\nLet's check the filesizes for the JLD2 file to the CSV file.  \nThe small CSV file we created is $small_jld2_filesize KB.  \n\n2g.  How large would you guess the JLD2 file will be?  Once you've made your prediction, mouse over the hint box.  If you were suprised, try to explain what happened.\n\"\"\"\n\n# \u2554\u2550\u2561 570fd826-23fd-46ee-bdb4-58fb0c45719a\nmd\"\"\"\nNow let's time how long it takes to save the data from IPAC into a JLD2 file.\n\"\"\"\n\n# \u2554\u2550\u2561 691410bb-0472-4800-a90d-29ddf947de3e\nbegin\n\tfilename_jld2 = replace(filename_ipac, \".txt\" => \".jld2\") \n\twith_terminal() do \n\t\t@time @save filename_jld2 df\n\tend\nend\n\n# \u2554\u2550\u2561 8cbb1c90-bd94-44b5-80b6-81d38f3e6252\nmd\"2h.  How long do you think it will take to load the data from the JLD2 file? \"\n\n# \u2554\u2550\u2561 c3065acf-6205-455f-ba74-ca51f3f6761b\nresponse_2h = missing\n\n# \u2554\u2550\u2561 9c392be9-1505-40bc-a290-68085a1c2700\ndisplay_msg_if_fail(check_type_isa(:response_2h,response_2h,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 d738bdcc-5f83-4dfa-a17f-e9ea23db2986\nmd\"\"\"\nNow time how long it takes to load the data from the JLD2 file.\n\"\"\"\n\n# \u2554\u2550\u2561 d55ce157-099c-4c1b-94db-62918f04e5fe\nmd\"\"\"\nI'm ready to benchmark reading a JLD2 file. $(@bind ready_read_jld2 CheckBox()) \n$(@bind go_read_jld2 Button(\"Rerun the benchmarks.\"))\n\"\"\"\n\n# \u2554\u2550\u2561 206f464b-55fe-46aa-85b7-8f0246a0aaad\nif ready_read_jld2\n\tgo_read_jld2\n\ttime_read_jld2 = @elapsed df_jld2_read_back_in = load(filename_jld2, \"df\")\n\tmd\"It took $time_read_jld2 seconds to read the JLD2 file.\"\nend\n\n# \u2554\u2550\u2561 6f72d1b2-63f6-4301-8272-bb2d6d2d049e\nmd\"\"\"\n2i.  How does the time required to read and write the JLD2 file compare to the time required to read the IPAC and CSV formatted files?  If you were suprised, try to explain what happend.\n\"\"\"\n\n# \u2554\u2550\u2561 01201c37-0b79-46b1-a001-e716f5b3ba67\nresponse_2i = missing\n\n# \u2554\u2550\u2561 e3d37bc1-a119-4add-a111-899ee0caea05\ndisplay_msg_if_fail(check_type_isa(:response_2i,response_2i,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 6dff3f21-fac0-42e1-910a-f969a231374f\nmd\"\"\"\nNext, we'll compare the filesizes for the JLD2 file to the CSV file.  \n\n2j.  How large would you guess the JLD2 file will be?  Once you've made your prediction, mouse over the hint box.  If you were suprised, try to explain what happened.\n\"\"\"\n\n# \u2554\u2550\u2561 b26b8253-e6cd-49f4-81c5-2a3c2963a37c\nresponse_2j = missing\n\n# \u2554\u2550\u2561 e5dc123f-1596-4311-9398-f0cfe80a5342\ndisplay_msg_if_fail(check_type_isa(:response_2j,response_2j,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 7b9d26c2-899e-45e9-b664-39d5f1adfe3f\nbegin \n\tjld2_filesize = filesize(filename_jld2) / 1024^2;\n\thint(md\"The JLD2 file size is $jld2_filesize MB versus $csv_filesize MB for the CSV.\")\nend\n\n# \u2554\u2550\u2561 fc01d57f-c90b-4231-96be-ddd48656d55e\nmd\"\"\"\n## Flexible Format: FITS\n\nAstronomers often use the [FITS file format](https://en.wikipedia.org/wiki/FITS).  Like [HDF\n5](https://www.hdfgroup.org/solutions/hdf5/), it's a very flexible (e.g., it can store both text and binary data) and thus complicated file\n format.  \nTherefore, most languages call a common [FITSIO library written in C](https://heasarc.gsfc.nasa.gov/fitsio/), rather than implementing code themselves.  Indeed, that's what [Julia's FITSIO.jl package](https://github.com/JuliaAstro/FITSIO.jl) does.\n\nUnfortunately, the FITSIO package isn't as polished as the others.  It expects a `Dict` rather than a `DataFrame`, and it can't handle missing values.  So I've provided some helper functions at the bottom of the notebook.  Also, FITS files have complicated headers, so I'll provide a function to read all the tabular data from a simple FITS file.  As usual, we'll use each function once, so that Julia compiles them before we start timing.\n\"\"\"\n\n# \u2554\u2550\u2561 3b232365-f2fe-4edb-a39f-3e37c8cbb666\nmd\"Now we can time how long it takes to write and read the data as FITS files.\"\n\n# \u2554\u2550\u2561 568862b3-6fce-426a-a9e2-e558adf3932a\nmd\"\"\"\nI'm ready to benchmark reading a FITS file. $(@bind ready_read_fits CheckBox()) \n$(@bind go_read_fits Button(\"Rerun the benchmarks.\"))\n\"\"\"\n\n# \u2554\u2550\u2561 ae79ee89-d788-4612-8b1d-fc22d85c7744\nmd\"2k.  How do the read/write times and file sizes for FITS compare to CSV and JLD2?\"\n\n# \u2554\u2550\u2561 3745c237-ba48-4f2c-959e-441484244764\nresponse_2k = missing\n\n# \u2554\u2550\u2561 6d061411-6f19-4119-aefb-cc380198b0ce\ndisplay_msg_if_fail(check_type_isa(:response_2k,response_2k,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 8def87d2-f10b-4a82-b353-a6477eeead9b\nmd\"## Implications for your project\"\n\n# \u2554\u2550\u2561 c74b3105-f480-4688-b85f-3e7dff70da3b\nmd\"\"\"\n2l.  How does the time required to read any of the above formats compare to the time required to download the files ($time_to_download seconds)?  \nWill your project need to transfer large files over the internet?  If so, very roughly how large do you expect they will be?  \n\"\"\"\n\n# \u2554\u2550\u2561 55438c09-1d94-4ff7-90c3-0cc6064a091e\nresponse_2l = missing\n\n# \u2554\u2550\u2561 bcc796c9-db11-4a09-a5f9-215127ac0938\ndisplay_msg_if_fail(check_type_isa(:response_2l,response_2l,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 92b2ecdd-9491-4f16-8cb9-bacbcf180280\nmd\"\"\"\n2m.  Will your project need to read in any input files?  If so, what format are they in?  \n\nI assume everyone's project code will write at least some output files?  Very roughly, how large do you expect that they'll be?   What format(s) would be a good choice for your project?\"\"\"\n\n# \u2554\u2550\u2561 83216915-cdcf-4f0f-829f-a5ff4c4b8da0\nresponse_2m = missing\n\n# \u2554\u2550\u2561 452872f9-2009-4733-b2d3-28f262ae19b7\ndisplay_msg_if_fail(check_type_isa(:response_2m,response_2m,[AbstractString,Markdown.MD]))\n\n# \u2554\u2550\u2561 29415ddc-e002-4f56-a169-95f7b1c36be9\nmd\"# Helper Functions\"\n\n# \u2554\u2550\u2561 fb23d6c6-b812-4fe1-b224-0014bedbd43f\nChooseDisplayMode()\n\n# \u2554\u2550\u2561 1e53aa10-dff6-40d5-89e2-da194ffc2052\nTableOfContents()\n\n# \u2554\u2550\u2561 14cca8ce-cc61-4fae-b871-21c3fd23d0ea\n\"Convert a DataFrame to a Dict, replacing missing values with 0 or an empty string.\"\nfunction convert_dataframe_to_dict_remove_missing(df::DataFrame)\n    d = Dict(map(n->\"$n\"=>               # create a dictionary\n              ( any(ismissing.(df[!,n])) ? # if column contains a missing\n                    map(x-> !ismissing(x) ? # search for missings\n                        x :                 # leave non-missing values alone\n                        ( (eltype(df[!,n]) <: Number) ? zero(eltype(n)) : \"\")\n                        , df[!,n])            # but replace missing with 0 or \"\"\n                : df[!,n] ), # if nothing is missing, just use column as is\n            names(df) ))  \nend\n\n# \u2554\u2550\u2561 28fc8de4-749b-4093-b32f-c398f8d27d3d\n\"Write a DataFrame to a FITS file, replacing missing values with 0 or an empty string.\"\nfunction write_dataframe_as_fits(filename::String, df::DataFrame)\n    try \n       dict = convert_dataframe_to_dict_remove_missing(df) \n       fits_file = FITS(filename,\"w\")\n       write(fits_file, dict )\n       close(fits_file)\n    catch\n        @warn(\"There was a problem writing a dataframe to \" * filename * \".\")\n    end\nend\n\n\n# \u2554\u2550\u2561 57b422e5-0ad0-4674-bdd3-a8358bc7aaeb\n\n\"Read the columns of the first table from a FITS file into a Dict\"\nfunction read_fits_tables(filename::String)\n    dict = Dict{String,Any}()\n    fits_file = FITS(filename,\"r\")\n    # fits_file[1] is image data, we're interested in the table\n    @assert length(fits_file) >= 2\n    header = read_header(fits_file[2])\n    for i in 1:length(header)\n        c = get_comment(header,i)\n        if !occursin(\"label for field\",c)\n            continue\n        end\n        h = header[i]\n        @assert typeof(h) == String\n        try  \n            dict[h] = read(fits_file[2],h)\n        catch\n            @warn \"# Problem reading table column \" * h * \".\"\n        end\n    end\n    close(fits_file)\n    return dict\nend\n\n\n# \u2554\u2550\u2561 e05f16d6-eb50-49a9-bf14-95d63c9da7ff\nbegin\n\tfilename_fits_small = replace(small_csv_filename, \".csv\" => \".fits\")  \n\twrite_dataframe_as_fits(joinpath(path,filename_fits_small),small_df)\n\tread_fits_tables(joinpath(path,filename_fits_small))\nend;\n\n# \u2554\u2550\u2561 1992595f-9976-4b18-bf9c-df8a73d30dc8\nbegin\n\tfilename_fits_small # make sure have already compiled functions\n\tfilename_fits = replace(filename_ipac, \".txt\" => \".fits\") \n\twith_terminal() do \n\t\t@time write_dataframe_as_fits(filename_fits,df)\n\tend\nend\n\n# \u2554\u2550\u2561 90d5244e-17be-4601-b922-8c254f1248bf\nbegin\n\tfits_filesize = filesize(filename_fits) /1024^2\n\thint(md\"The FITS file size is $fits_filesize MB versus $jld2_filesize MB for the JLD2 and $csv_filesize MB for the CSV.\")\nend\n\n# \u2554\u2550\u2561 52f9edd0-79b0-4a9a-9930-3a05d3aa2447\nif ready_read_fits\n\tgo_read_fits\n\ttime_read_fits = @elapsed read_fits_tables(filename_fits)\n\tmd\"It took $time_read_fits seconds to read the FITS file.\"\nend\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nCSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nDataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nFITSIO = \"525bcba6-941b-5504-bd06-fd0dc1a4d2eb\"\nFileIO = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nJLD2 = \"033835bb-8acc-5ee8-8aae-3f567f8a3819\"\nPlutoTeachingTools = \"661c6b06-c737-4d37-b85c-46df65de6f69\"\nPlutoUI = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nPyCall = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\n\n[compat]\nCSV = \"~0.8.5\"\nDataFrames = \"~1.2.2\"\nFITSIO = \"~0.16.7\"\nFileIO = \"~1.10.1\"\nJLD2 = \"~0.4.13\"\nPlutoTeachingTools = \"~0.1.3\"\nPlutoUI = \"~0.7.9\"\nPyCall = \"~1.92.3\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\n[[ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\n\n[[Artifacts]]\nuuid = \"56f22d72-fd6d-98f1-02f0-08ddc0907c33\"\n\n[[Base64]]\nuuid = \"2a0f44e3-6c83-55bd-87e4-b1978d98bd5f\"\n\n[[CFITSIO]]\ndeps = [\"CFITSIO_jll\"]\ngit-tree-sha1 = \"c860f5545064216f86aa3365ec186ce7ced6a935\"\nuuid = \"3b1b4be9-1499-4b22-8d78-7db3344d1961\"\nversion = \"1.3.0\"\n\n[[CFITSIO_jll]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"LibCURL_jll\", \"Libdl\", \"Pkg\"]\ngit-tree-sha1 = \"2fabb5fc48d185d104ca7ed7444b475705993447\"\nuuid = \"b3e40c51-02ae-5482-8a39-3ace5868dcf4\"\nversion = \"3.49.1+0\"\n\n[[CSV]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"PooledArrays\", \"SentinelArrays\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"b83aa3f513be680454437a0eee21001607e5d983\"\nuuid = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\nversion = \"0.8.5\"\n\n[[Compat]]\ndeps = [\"Base64\", \"Dates\", \"DelimitedFiles\", \"Distributed\", \"InteractiveUtils\", \"LibGit2\", \"Libdl\", \"LinearAlgebra\", \"Markdown\", \"Mmap\", \"Pkg\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"SharedArrays\", \"Sockets\", \"SparseArrays\", \"Statistics\", \"Test\", \"UUIDs\", \"Unicode\"]\ngit-tree-sha1 = \"727e463cfebd0c7b999bbf3e9e7e16f254b94193\"\nuuid = \"34da2185-b29b-5c13-b0c7-acf172513d20\"\nversion = \"3.34.0\"\n\n[[Conda]]\ndeps = [\"JSON\", \"VersionParsing\"]\ngit-tree-sha1 = \"299304989a5e6473d985212c28928899c74e9421\"\nuuid = \"8f4d0f93-b110-5947-807f-2305c1781a2d\"\nversion = \"1.5.2\"\n\n[[Crayons]]\ngit-tree-sha1 = \"3f71217b538d7aaee0b69ab47d9b7724ca8afa0d\"\nuuid = \"a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f\"\nversion = \"4.0.4\"\n\n[[DataAPI]]\ngit-tree-sha1 = \"ee400abb2298bd13bfc3df1c412ed228061a2385\"\nuuid = \"9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a\"\nversion = \"1.7.0\"\n\n[[DataFrames]]\ndeps = [\"Compat\", \"DataAPI\", \"Future\", \"InvertedIndices\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"Markdown\", \"Missings\", \"PooledArrays\", \"PrettyTables\", \"Printf\", \"REPL\", \"Reexport\", \"SortingAlgorithms\", \"Statistics\", \"TableTraits\", \"Tables\", \"Unicode\"]\ngit-tree-sha1 = \"d785f42445b63fc86caa08bb9a9351008be9b765\"\nuuid = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\nversion = \"1.2.2\"\n\n[[DataStructures]]\ndeps = [\"Compat\", \"InteractiveUtils\", \"OrderedCollections\"]\ngit-tree-sha1 = \"7d9d316f04214f7efdbb6398d545446e246eff02\"\nuuid = \"864edb3b-99cc-5e75-8d2d-829cb0a9cfe8\"\nversion = \"0.18.10\"\n\n[[DataValueInterfaces]]\ngit-tree-sha1 = \"bfc1187b79289637fa0ef6d4436ebdfe6905cbd6\"\nuuid = \"e2d170a0-9d28-54be-80f0-106bbe20a464\"\nversion = \"1.0.0\"\n\n[[Dates]]\ndeps = [\"Printf\"]\nuuid = \"ade2ca70-3891-5945-98fb-dc099432e06a\"\n\n[[DelimitedFiles]]\ndeps = [\"Mmap\"]\nuuid = \"8bb1440f-4735-579b-a4ab-409b98df4dab\"\n\n[[Distributed]]\ndeps = [\"Random\", \"Serialization\", \"Sockets\"]\nuuid = \"8ba89e20-285c-5b6f-9357-94700520ee1b\"\n\n[[Downloads]]\ndeps = [\"ArgTools\", \"LibCURL\", \"NetworkOptions\"]\nuuid = \"f43a241f-c20a-4ad4-852c-f6b1247861c6\"\n\n[[FITSIO]]\ndeps = [\"CFITSIO\", \"Printf\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"85b66005c9d16d3d27c9d06fd3be5e25074128da\"\nuuid = \"525bcba6-941b-5504-bd06-fd0dc1a4d2eb\"\nversion = \"0.16.7\"\n\n[[FileIO]]\ndeps = [\"Pkg\", \"Requires\", \"UUIDs\"]\ngit-tree-sha1 = \"256d8e6188f3f1ebfa1a5d17e072a0efafa8c5bf\"\nuuid = \"5789e2e9-d7fb-5bc7-8068-2c6fae9b9549\"\nversion = \"1.10.1\"\n\n[[Formatting]]\ndeps = [\"Printf\"]\ngit-tree-sha1 = \"8339d61043228fdd3eb658d86c926cb282ae72a8\"\nuuid = \"59287772-0a20-5a39-b81b-1366585eb4c0\"\nversion = \"0.4.2\"\n\n[[Future]]\ndeps = [\"Random\"]\nuuid = \"9fa8497b-333b-5362-9e8d-4d0656e87820\"\n\n[[InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[InvertedIndices]]\ndeps = [\"Test\"]\ngit-tree-sha1 = \"15732c475062348b0165684ffe28e85ea8396afc\"\nuuid = \"41ab1584-1d38-5bbf-9106-f11c6c58b48f\"\nversion = \"1.0.0\"\n\n[[IteratorInterfaceExtensions]]\ngit-tree-sha1 = \"a3f24677c21f5bbe9d2a714f95dcd58337fb2856\"\nuuid = \"82899510-4779-5014-852e-03e436cf321d\"\nversion = \"1.0.0\"\n\n[[JLD2]]\ndeps = [\"DataStructures\", \"FileIO\", \"MacroTools\", \"Mmap\", \"Pkg\", \"Printf\", \"Reexport\", \"TranscodingStreams\", \"UUIDs\"]\ngit-tree-sha1 = \"59ee430ac5dc87bc3eec833cc2a37853425750b4\"\nuuid = \"033835bb-8acc-5ee8-8aae-3f567f8a3819\"\nversion = \"0.4.13\"\n\n[[JLLWrappers]]\ndeps = [\"Preferences\"]\ngit-tree-sha1 = \"642a199af8b68253517b80bd3bfd17eb4e84df6e\"\nuuid = \"692b3bcd-3c85-4b1f-b108-f13ce0eb3210\"\nversion = \"1.3.0\"\n\n[[JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"8076680b162ada2a031f707ac7b4953e30667a37\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.2\"\n\n[[LaTeXStrings]]\ngit-tree-sha1 = \"c7f1c695e06c01b95a67f0cd1d34994f3e7db104\"\nuuid = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\nversion = \"1.2.1\"\n\n[[LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[LinearAlgebra]]\ndeps = [\"Libdl\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[MacroTools]]\ndeps = [\"Markdown\", \"Random\"]\ngit-tree-sha1 = \"0fb723cd8c45858c22169b2e42269e53271a6df7\"\nuuid = \"1914dd2f-81c6-5fcd-8719-6d5c9610ff09\"\nversion = \"0.5.7\"\n\n[[Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[Missings]]\ndeps = [\"DataAPI\"]\ngit-tree-sha1 = \"2ca267b08821e86c5ef4376cffed98a46c2cb205\"\nuuid = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\nversion = \"1.0.1\"\n\n[[Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[OrderedCollections]]\ngit-tree-sha1 = \"85f8e6578bf1f9ee0d11e7bb1b1456435479d47c\"\nuuid = \"bac558e1-5e72-5ebc-8fee-abe8a469f55d\"\nversion = \"1.4.1\"\n\n[[Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"bfd7d8c7fd87f04543810d9cbd3995972236ba1b\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"1.1.2\"\n\n[[Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[PlutoTeachingTools]]\ndeps = [\"LaTeXStrings\", \"Markdown\", \"PlutoUI\", \"Random\"]\ngit-tree-sha1 = \"e2b63ee022e0b20f43fcd15cda3a9047f449e3b4\"\nuuid = \"661c6b06-c737-4d37-b85c-46df65de6f69\"\nversion = \"0.1.4\"\n\n[[PlutoUI]]\ndeps = [\"Base64\", \"Dates\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"Suppressor\"]\ngit-tree-sha1 = \"44e225d5837e2a2345e69a1d1e01ac2443ff9fcb\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.9\"\n\n[[PooledArrays]]\ndeps = [\"DataAPI\", \"Future\"]\ngit-tree-sha1 = \"cde4ce9d6f33219465b55162811d8de8139c0414\"\nuuid = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\nversion = \"1.2.1\"\n\n[[Preferences]]\ndeps = [\"TOML\"]\ngit-tree-sha1 = \"00cfd92944ca9c760982747e9a1d0d5d86ab1e5a\"\nuuid = \"21216c6a-2e73-6563-6e65-726566657250\"\nversion = \"1.2.2\"\n\n[[PrettyTables]]\ndeps = [\"Crayons\", \"Formatting\", \"Markdown\", \"Reexport\", \"Tables\"]\ngit-tree-sha1 = \"0d1245a357cc61c8cd61934c07447aa569ff22e6\"\nuuid = \"08abe8d2-0d0c-5749-adfa-8a2ac140af0d\"\nversion = \"1.1.0\"\n\n[[Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[PyCall]]\ndeps = [\"Conda\", \"Dates\", \"Libdl\", \"LinearAlgebra\", \"MacroTools\", \"Serialization\", \"VersionParsing\"]\ngit-tree-sha1 = \"169bb8ea6b1b143c5cf57df6d34d022a7b60c6db\"\nuuid = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\nversion = \"1.92.3\"\n\n[[REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[Random]]\ndeps = [\"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[Reexport]]\ngit-tree-sha1 = \"5f6c21241f0f655da3952fd60aa18477cf96c220\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.1.0\"\n\n[[Requires]]\ndeps = [\"UUIDs\"]\ngit-tree-sha1 = \"4036a3bd08ac7e968e27c203d45f5fff15020621\"\nuuid = \"ae029012-a4dd-5104-9daa-d747884805df\"\nversion = \"1.1.3\"\n\n[[SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[SentinelArrays]]\ndeps = [\"Dates\", \"Random\"]\ngit-tree-sha1 = \"54f37736d8934a12a200edea2f9206b03bdf3159\"\nuuid = \"91c51154-3ec4-41a3-a24f-3f23e20d615c\"\nversion = \"1.3.7\"\n\n[[Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[SharedArrays]]\ndeps = [\"Distributed\", \"Mmap\", \"Random\", \"Serialization\"]\nuuid = \"1a1011a3-84de-559e-8e89-a11a2f7dc383\"\n\n[[Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[SortingAlgorithms]]\ndeps = [\"DataStructures\"]\ngit-tree-sha1 = \"b3363d7460f7d098ca0912c69b082f75625d7508\"\nuuid = \"a2af1166-a08f-5f64-846c-94a0d3cef48c\"\nversion = \"1.0.1\"\n\n[[SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[Suppressor]]\ngit-tree-sha1 = \"a819d77f31f83e5792a76081eee1ea6342ab8787\"\nuuid = \"fd094767-a336-5f1f-9728-57cf17d0bbfb\"\nversion = \"0.2.0\"\n\n[[TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[TableTraits]]\ndeps = [\"IteratorInterfaceExtensions\"]\ngit-tree-sha1 = \"c06b2f539df1c6efa794486abfb6ed2022561a39\"\nuuid = \"3783bdb8-4a98-5b6b-af9a-565f29a5fe9c\"\nversion = \"1.0.1\"\n\n[[Tables]]\ndeps = [\"DataAPI\", \"DataValueInterfaces\", \"IteratorInterfaceExtensions\", \"LinearAlgebra\", \"TableTraits\", \"Test\"]\ngit-tree-sha1 = \"d0c690d37c73aeb5ca063056283fde5585a41710\"\nuuid = \"bd369af6-aec1-5ad0-b16a-f7cc5008161c\"\nversion = \"1.5.0\"\n\n[[Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[TranscodingStreams]]\ndeps = [\"Random\", \"Test\"]\ngit-tree-sha1 = \"216b95ea110b5972db65aa90f88d8d89dcb8851c\"\nuuid = \"3bb67fe8-82b1-5028-8e26-92a6c54297fa\"\nversion = \"0.9.6\"\n\n[[UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[VersionParsing]]\ngit-tree-sha1 = \"80229be1f670524750d905f8fc8148e5a8c4537f\"\nuuid = \"81def892-9a0e-5fdd-b105-ffc91e053289\"\nversion = \"1.2.0\"\n\n[[Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500e0e60e09-3808-4d6b-a773-6ba59c02f517\n# \u255f\u25008c1d81ab-d215-4972-afeb-7e00bf6063c2\n# \u255f\u25001607eac9-e76f-4d1f-a9ce-981ce3be9bea\n# \u2560\u2550f27e1e8f-15eb-4754-a94c-7f37c54b871e\n# \u2560\u255080f02c3a-6751-48df-92ec-13f5c7d8c71e\n# \u255f\u2500624c9038-3008-4e78-a149-60796dacf9c0\n# \u255f\u25002eb255d9-707d-4224-a0ce-fe90a1c69722\n# \u255f\u25008571ccaf-5fbc-4593-82db-ead073a4074f\n# \u2560\u2550442604d0-b490-404b-8b1c-7e1c01cebad7\n# 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NO\n2. NO", "lm_q1_score": 0.34158249943831703, "lm_q2_score": 0.16885695214168317, "lm_q1q2_score": 0.05767857976009242}}
{"text": "module IOcdf\n\n\"\"\"\n    module EmpiricalCDFS.IOcdf\n\nBinary IO functions for `EmpiricalCDFs`.\n\nTypes: `CDFfile`\n\nFunctions: `save`, `save`, `readcdf`, `readcdfinfo`, `header`, `version`, `getcdf`, `data`\n\n`CDFfile` contains an `AbstractEmpiricalCDF` as a field. Many functions on the latter are forwarded to\nthe former.\n\"\"\"\nIOcdf\n\nexport CDFfile, save, readcdf, readcdfinfo, header, version, getcdf, data\n\nimport Statistics\nusing ..EmpiricalCDFs: AbstractEmpiricalCDF, EmpiricalCDF, EmpiricalCDFHi\n\nimport ..EmpiricalCDFs: data, linprint, logprint, getindex, getcdfindex, counts\n\ninclude(\"readstring.jl\")\n\n#### CDFfile type\n\n@enum CDFTYPE EmpiricalCDFtype=1 EmpiricalCDFHitype=2\n\nconst CDFfileVersion = v\"0.0.2\"\n\n\"\"\"\n    CDFfile(cdf::AbstractEmpiricalCDF, header=\"\")\n\n    struct CDFfile{T <: AbstractEmpiricalCDF}\n        cdf::T\n        header::String\n    end\n\nBinary data file for `AbstractEmpiricalCDF`\n\nThe file format is\n\n- Identifying string\n- `n::Int64` number of bytes in the header string\n- `s::String` The header string\n- `t::Int64` Type of `AbstractEmpiricalCDF`, 1 or 2. 1 for `EmpiricalCDF`, 2 for `EmpiricalCDFHi`.\n- `lowreject::Float64` the lower cutoff, only for `EmpiricalCDFHi`.\n- `npts::Int64` number of data points in the CDF\n- `npts` data points of type `Float64`\n\"\"\"\nstruct CDFfile{T <: AbstractEmpiricalCDF}\n    cdf::T\n    header::String\n    vn::VersionNumber\nend\n\nCDFfile(cdf) = CDFfile(cdf,\"\",CDFfileVersion)\n\ndata(cdff::CDFfile) = data(cdff.cdf)\n\nfunction Base.show(io::IO, cdff::CDFfile)\n    print(io, string(typeof(cdff)), \"(\")\n    show(io,cdff.cdf)\n    print(io, \",\")\n    print(io, \"v\", cdff.vn)\n    print(io, \")\")\n    nothing\nend\n\n\"\"\"\n    header::String = header(cdff::CDFfile)\n\nReturn the header from `cdff`.\n\"\"\"\nheader(cdff::CDFfile) = cdff.header\n\n\"\"\"\n    cdf::AbstractEmpiricalCDF = getcdf(cdff::CDFfile)\n\nReturn the CDF from `cdff`.\n\"\"\"\ngetcdf(cdff::CDFfile) = cdff.cdf\n\n\"\"\"\n    version(cdff::CDFfile)\n\nReturn the version number of the file format.\n\"\"\"\nversion(cdff::CDFfile) = cdff.vn\n\nfor f in (:sort!, :push!, :append!, :getindex, :length, :size, :firstindex, :lastindex, :rand, :minimum, :maximum)\n    @eval Base.$(f)(cdff::CDFfile, args...) = $(f)(cdff.cdf, args...)\nend\n\nfor f in (:mean, :std, :quantile )\n    @eval Statistics.$(f)(cdff::CDFfile, args...) = $(f)(cdff.cdf, args...)\nend\n\nfor f in (:getcdfindex, :counts)\n#           :mle, :KSstatistic, :mleKS, :scanmle)\n    @eval $(f)(cdff::CDFfile, args...) = $(f)(cdff.cdf, args...)\nend\n\nfor f in (:linprint, :logprint )\n    @eval $(f)(ioorfile, cdff::CDFfile, args...) = $(f)(ioorfile, cdff.cdf, args...)\nend\n\n(cdff::CDFfile)(x::Real) = getcdf(cdff)(x)\n(cdff::CDFfile)(v::AbstractArray) = getcdf(cdff)(v)\n\n####\n\nfunction make_CDFfile_version_string(v)\n    nchars = 100\n    s = \"CDFfile \" * string(v)\n    s = s * \" \"^(nchars-length(s))\nend\n\nmake_CDFfile_version_string() = make_CDFfile_version_string(CDFfileVersion)\n\nfunction read_CDFfile_version_string(io::IO)\n    nchars = 100\n    local s\n    try\n        s = readstring(io,nchars)\n    catch\n        error(\"Failed reading identifying string and version number from CDFfile\")\n    end\n    (filetype,versionstring) = split(s)\n    if filetype != \"CDFfile\"\n        error(\"Failed reading version number from CDFfile\")\n    end\n    local vn\n    try\n        vn = VersionNumber(versionstring)\n    catch\n        error(\"Unable to parse version number string '$versionstring'\")\n    end\n    vn\nend\n\n#### Writing CDFfile and CDF\n\nfunction save(io::IO,cdff::CDFfile)\n    write(io, make_CDFfile_version_string())\n    write(io,sizeof(cdff.header))\n    write(io,cdff.header)\n    save(io,cdff.cdf)\nend\n\nfunction save(io::IO,cdf::EmpiricalCDF)\n    write(io, Int64(EmpiricalCDFtype))\n    _writedata(io,cdf)\nend\n\nfunction save(io::IO,cdf::EmpiricalCDFHi)\n    write(io, Int64(EmpiricalCDFHitype))\n    write(io, convert(Float64, cdf.lowreject))\n    _writedata(io, cdf)\nend\n\nfunction _writedata(io::IO,cdf::AbstractEmpiricalCDF)\n    write(io, convert(Int64, length(cdf)))\n    for x in cdf.xdata\n        write(io,x)\n    end\nend\n\n\"\"\"\n    save(fn::String, cdf::AbstractEmpiricalCDF, header::String=\"\")\n\nwrite `cdf` to file `fn` in a fast binary format.\n\"\"\"\nfunction save(fn::String, cdf::AbstractEmpiricalCDF, header::String=\"\")\n    io = open(fn,\"w\")\n    save(io, CDFfile(cdf,header,CDFfileVersion))\n    close(io)\nend\n\n#save(fn::String, cdf::AbstractEmpiricalCDF) = save(fn,cdf,\"\")\n\n#### Reading CDFfile and CDF\n\nfunction readcdfdata(io::IO, cdf::AbstractEmpiricalCDF)\n    npts = read(io,Int64)\n    resize!(cdf.xdata,npts)\n@inbounds for i in 1:npts\n        x = read(io,Float64)\n        cdf.xdata[i] = x\n    end\nend\n\nfunction readcdf(io::IO)\n    info = readcdfinfo(io)\n    (vn,header) = (info.vn,info.header)\n    cdf = typeof(info) == CDFInfo ?  EmpiricalCDF() : EmpiricalCDFHi(info.lowreject)\n    readcdfdata(io,cdf)\n    CDFfile(cdf,header,vn)\nend\n\nstruct CDFInfo\n    vn::VersionNumber\n    header::String\n    npts::Int\nend\n\nfunction Base.show(io::IO, i::CDFInfo)\n    print(io,typeof(i),\"(\")\n    print(io,\"length(header)=\", length(i.header))\n    print(io,\",vn=v\", i.vn)\n    print(io,\",npts=\",i.npts,\")\")\nend\n\nstruct CDFHiInfo\n    vn::VersionNumber\n    header::String\n    npts::Int\n    lowreject::Float64\nend\n\nfunction Base.show(io::IO, i::CDFHiInfo)\n    print(io,typeof(i),\"(\")\n    print(io,\"length(header)=\", length(i.header))\n    print(io,\",vn=v\", i.vn)\n    print(io,\",npts=\",i.npts)\n    print(io,\",lowreject=\",i.lowreject,\")\")\nend\n\nfunction readcdfinfo(io::IO)\n    vn = read_CDFfile_version_string(io)\n    header = readlengthandstring(io)\n    cdftype = CDFTYPE(read(io,Int64))\n    local lowreject\n    if cdftype == EmpiricalCDFtype\n        npts = peektype(io,Int64)\n        CDFInfo(vn,header,npts)\n    elseif cdftype == EmpiricalCDFHitype\n        lowreject = read(io,Float64)\n        npts = peektype(io,Int64)\n        CDFHiInfo(vn,header,npts,lowreject)\n    else\n        error(\"Uknown cdf type \", cdftype)\n    end\nend\n\n\"\"\"\n    readcdfinfo(fn::String)\n\nReturn an object containing information about the cdf saved in the binary\nfile `fn`. The data itself is not read.\n\"\"\"\nfunction readcdfinfo(fn::String)\n    io = open(fn,\"r\")\n    info = readcdfinfo(io)\n    close(io)\n    info\nend\n\n\"\"\"\n    readcdf(fn::String)\n\nRead an empirical CDF from file `fn`. Return an object\n`cdff` of type `CDFfile`. The header is in field `header`.\nThe cdf is in in field `cdf`.\n\"\"\"\nfunction readcdf(fn::String)\n    io = open(fn,\"r\")\n    cdffile = readcdf(io)\n    close(io)\n    cdffile\nend\n\nend # module IO\n", "meta": {"hexsha": "24eb0382ad7f0b11dc21f939decb82ff4a574f64", "size": 6495, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IOcdf.jl", "max_stars_repo_name": "jlapeyre/EmpiricalCDFs.jl", "max_stars_repo_head_hexsha": "37c68e4f382486c56f67e8e1b1b4f00c907f6ed0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2018-05-09T16:49:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T07:46:20.000Z", "max_issues_repo_path": "src/IOcdf.jl", "max_issues_repo_name": "jlapeyre/EmpiricalCDFs.jl", "max_issues_repo_head_hexsha": "37c68e4f382486c56f67e8e1b1b4f00c907f6ed0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2018-12-13T08:43:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-21T08:46:00.000Z", "max_forks_repo_path": "src/IOcdf.jl", "max_forks_repo_name": "jlapeyre/EmpiricalCDFs.jl", "max_forks_repo_head_hexsha": "37c68e4f382486c56f67e8e1b1b4f00c907f6ed0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-05-21T17:48:03.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:54:17.000Z", "avg_line_length": 23.6181818182, "max_line_length": 114, "alphanum_fraction": 0.6635873749, "num_tokens": 2001, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.120853219824755, "lm_q1q2_score": 0.05759618533400346}}
{"text": "\"\"\"\n    @add_kwonly function_definition\n\nDefine keyword-only version of the `function_definition`.\n\n    @add_kwonly function f(x; y=1)\n        ...\n    end\n\nexpands to:\n\n    function f(x; y=1)\n        ...\n    end\n    function f(; x = error(\"No argument x\"), y=1)\n        ...\n    end\n\"\"\"\nmacro add_kwonly(ex)\n  esc(add_kwonly(ex))\nend\n\nadd_kwonly(ex::Expr) = add_kwonly(Val{ex.head}, ex)\n\nfunction add_kwonly(::Type{<: Val}, ex)\n  error(\"add_only does not work with expression $(ex.head)\")\nend\n\nfunction add_kwonly(::Union{Type{Val{:function}},\n                            Type{Val{:(=)}}}, ex::Expr)\n  body = ex.args[2:end]  # function body\n  default_call = ex.args[1]  # e.g., :(f(a, b=2; c=3))\n  kwonly_call = add_kwonly(default_call)\n  if kwonly_call === nothing\n    return ex\n  end\n\n  return quote\n    begin\n      $ex\n      $(Expr(ex.head, kwonly_call, body...))\n    end\n  end\nend\n\nfunction add_kwonly(::Type{Val{:where}}, ex::Expr)\n  default_call = ex.args[1]\n  rest = ex.args[2:end]\n  kwonly_call = add_kwonly(default_call)\n  if kwonly_call === nothing\n    return nothing\n  end\n  return Expr(:where, kwonly_call, rest...)\nend\n\nfunction add_kwonly(::Type{Val{:call}}, default_call::Expr)\n  # default_call is, e.g., :(f(a, b=2; c=3))\n  funcname = default_call.args[1]  # e.g., :f\n  required = []  # required positional arguments; e.g., [:a]\n  optional = []  # optional positional arguments; e.g., [:(b=2)]\n  default_kwargs = []\n  for arg in default_call.args[2:end]\n    if isa(arg, Symbol)\n      push!(required, arg)\n    elseif arg.head == :(::)\n      push!(required, arg)\n    elseif arg.head == :kw\n      push!(optional, arg)\n    elseif arg.head == :parameters\n      @assert default_kwargs == []  # can I have :parameters twice?\n      default_kwargs = arg.args\n    else\n      error(\"Not expecting to see: $arg\")\n    end\n  end\n  if isempty(required) && isempty(optional)\n    # If the function is already keyword-only, do nothing:\n    return nothing\n  end\n  if isempty(required)\n    # It's not clear what should be done.  Let's not support it at\n    # the moment:\n    error(\"At least one positional mandatory argument is required.\")\n  end\n\n  kwonly_kwargs = Expr(:parameters, [\n    Expr(:kw, pa, :(error($(\"No argument $pa\"))))\n    for pa in required\n  ]..., optional..., default_kwargs...)\n  kwonly_call = Expr(:call, funcname, kwonly_kwargs)\n  # e.g., :(f(; a=error(...), b=error(...), c=1, d=2))\n\n  return kwonly_call\nend\n\nfunction num_types_in_tuple(sig)\n  length(sig.parameters)\nend\n\nfunction num_types_in_tuple(sig::UnionAll)\n  length(Base.unwrap_unionall(sig).parameters)\nend\n\nfunction numargs(f)\n  typ = Tuple{Any, Val{:analytic}, Vararg}\n  typ2 = Tuple{Any, Type{Val{:analytic}}, Vararg} # This one is required for overloaded types\n  typ3 = Tuple{Any, Val{:jac}, Vararg}\n  typ4 = Tuple{Any, Type{Val{:jac}}, Vararg} # This one is required for overloaded types\n  typ5 = Tuple{Any, Val{:tgrad}, Vararg}\n  typ6 = Tuple{Any, Type{Val{:tgrad}}, Vararg} # This one is required for overloaded types\n  numparam = maximum([(m.sig<:typ || m.sig<:typ2 || m.sig<:typ3 || m.sig<:typ4 || m.sig<:typ5 || m.sig<:typ6) ? 0 : num_types_in_tuple(m.sig) for m in methods(f)])\n  return (numparam-1) #-1 in v0.5 since it adds f as the first parameter\nend\n\nfunction isinplace(f,inplace_param_number)\n  numargs(f)>=inplace_param_number\nend\n\n### Default Linsolve\n\n# Try to be as smart as possible\n# lu! if Matrix\n# lu if sparse\n# gmres if operator\n\nmutable struct DefaultLinSolve\n  A\n  iterable\nend\nDefaultLinSolve() = DefaultLinSolve(nothing, nothing)\n\nfunction (p::DefaultLinSolve)(x,A,b,update_matrix=false;tol=nothing, kwargs...)\n  if p.iterable isa Vector && eltype(p.iterable) <: LinearAlgebra.BlasInt # `iterable` here is the pivoting vector\n    F = LU{eltype(A)}(A, p.iterable, zero(LinearAlgebra.BlasInt))\n    ldiv!(x, F, b)\n    return nothing\n  end\n  if update_matrix\n    if typeof(A) <: Matrix\n      blasvendor = BLAS.vendor()\n      # if the user doesn't use OpenBLAS, we assume that is a better BLAS\n      # implementation like MKL\n      #\n      # RecursiveFactorization seems to be consistantly winning below 100\n      # https://discourse.julialang.org/t/ann-recursivefactorization-jl/39213\n      if ArrayInterface.can_setindex(x) && (size(A,1) <= 100 || ((blasvendor === :openblas || blasvendor === :openblas64) && size(A,1) <= 500))\n        p.A = RecursiveFactorization.lu!(A)\n      else\n        p.A = lu!(A)\n      end\n    elseif typeof(A) <: Tridiagonal\n      p.A = lu!(A)\n    elseif typeof(A) <: Union{SymTridiagonal}\n      p.A = ldlt!(A)\n    elseif typeof(A) <: Union{Symmetric,Hermitian}\n      p.A = bunchkaufman!(A)\n    elseif typeof(A) <: SparseMatrixCSC\n      p.A = lu(A)\n    elseif ArrayInterface.isstructured(A)\n      p.A = factorize(A)\n    elseif !(typeof(A) <: AbstractDiffEqOperator)\n      # Most likely QR is the one that is overloaded\n      # Works on things like CuArrays\n      p.A = qr(A)\n    end\n  end\n\n  if typeof(A) <: Union{Matrix,SymTridiagonal,Tridiagonal,Symmetric,Hermitian} # No 2-arg form for SparseArrays!\n    x .= b\n    ldiv!(p.A,x)\n  # Missing a little bit of efficiency in a rare case\n  #elseif typeof(A) <: DiffEqArrayOperator\n  #  ldiv!(x,p.A,b)\n  elseif ArrayInterface.isstructured(A) || A isa SparseMatrixCSC\n    ldiv!(x,p.A,b)\n  elseif typeof(A) <: AbstractDiffEqOperator\n    # No good starting guess, so guess zero\n    if p.iterable === nothing\n      p.iterable = IterativeSolvers.gmres_iterable!(x,A,b;initially_zero=true,restart=5,maxiter=5,tol=1e-16,kwargs...)\n      p.iterable.reltol = tol\n    end\n    x .= false\n    iter = p.iterable\n    purge_history!(iter, x, b)\n\n    for residual in iter\n    end\n  else\n    ldiv!(x,p.A,b)\n  end\n  return nothing\nend\n\nfunction (p::DefaultLinSolve)(::Type{Val{:init}},f,u0_prototype)\n  DefaultLinSolve()\nend\n\nconst DEFAULT_LINSOLVE = DefaultLinSolve()\n\n@inline UNITLESS_ABS2(x) = real(abs2(x))\n@inline DEFAULT_NORM(u::Union{AbstractFloat,Complex}) = @fastmath abs(u)\n@inline DEFAULT_NORM(u::Array{T}) where T<:Union{AbstractFloat,Complex} =\n                                         sqrt(real(sum(abs2,u)) / length(u))\n@inline DEFAULT_NORM(u::StaticArray{T}) where T<:Union{AbstractFloat,Complex} =\n                                            sqrt(real(sum(abs2,u)) / length(u))\n@inline DEFAULT_NORM(u::RecursiveArrayTools.AbstractVectorOfArray) =\n                                        sum(sqrt(real(sum(UNITLESS_ABS2,_u)) / length(_u)) for _u in u.u)\n@inline DEFAULT_NORM(u::AbstractArray) = sqrt(real(sum(UNITLESS_ABS2,u)) / length(u))\n@inline DEFAULT_NORM(u) = norm(u)\n\n\"\"\"\n  prevfloat_tdir(x, x0, x1)\n\nMove `x` one floating point towards x0.\n\"\"\"\nfunction prevfloat_tdir(x, x0, x1)\n  x1 > x0 ? prevfloat(x) : nextfloat(x)\nend\n\nfunction nextfloat_tdir(x, x0, x1)\n  x1 > x0 ? nextfloat(x) : prevfloat(x)\nend\n\nfunction max_tdir(a, b, x0, x1)\n  x1 > x0 ? max(a, b) : min(a, b)\nend\n\nalg_autodiff(alg::AbstractNewtonAlgorithm{CS,AD}) where {CS,AD} = AD\nalg_autodiff(alg) = false\n\n\"\"\"\n  value_derivative(f, x)\n\nCompute `f(x), d/dx f(x)` in the most efficient way.\n\"\"\"\nfunction value_derivative(f::F, x::R) where {F,R}\n    T = typeof(ForwardDiff.Tag(f, R))\n    out = f(ForwardDiff.Dual{T}(x, one(x)))\n    ForwardDiff.value(out), ForwardDiff.extract_derivative(T, out)\nend\n\nvalue(x) = x\nvalue(x::Dual) = ForwardDiff.value(x)\nvalue(x::AbstractArray{<:Dual}) = map(ForwardDiff.value, x)\n", "meta": {"hexsha": "df2ae74b913403957c0244a69ca67087bfdeb94b", "size": 7356, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "utkarsh530/NonlinearSolve.jl", "max_stars_repo_head_hexsha": "f232c8f06a991cbc1849659017ee6daa414d5bbc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "utkarsh530/NonlinearSolve.jl", "max_issues_repo_head_hexsha": "f232c8f06a991cbc1849659017ee6daa414d5bbc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "utkarsh530/NonlinearSolve.jl", "max_forks_repo_head_hexsha": "f232c8f06a991cbc1849659017ee6daa414d5bbc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.0244897959, "max_line_length": 163, "alphanum_fraction": 0.6500815661, "num_tokens": 2196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.34510525748676846, "lm_q2_score": 0.1666754088273618, "lm_q1q2_score": 0.05752055988007909}}
{"text": "\"\"\"\n\thistogram(cmd0::String=\"\", arg1=[]; kwargs...)\n\nReads file and examines the first data column to calculate histogram parameters based on the bin-width provided.\n\nFull option list at [`pshistogram`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html)\n\nParameters\n----------\n\n- $(GMT.opt_J)\n- **W** : **bin** : **width** : -- Number or Str --\n\n\tSets the bin width used for histogram calculations.\n\t[`-W`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#w)\n- **A** : **horizontal** : -- Bool or [] --\n\n\tPlot the histogram horizontally from x = 0 [Default is vertically from y = 0].\n\t[`-A`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#a)\n- $(GMT.opt_B)\n- **C** : **color** : -- Str or GMTcpt --\n\n\tGive a CPT. The mid x-value for each bar is used to look-up the bar color.\n\t[`-C`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#c)\n- **D** : **annot** : **annotate** : -- Str or [] --\n\n\tAnnotate each bar with the count it represents.\n\t[`-D`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#d)\n- **F** : **center** : -- Bool or [] --\n\n\tCenter bin on each value. [Default is left edge].\n\t[`-F`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#f)\n- **G** : **fill** : -- Number or Str --\n\n\tSelect filling of bars [if no G, L or C set G=100].\n\t[`-G`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#g)\n- **I** : **inquire** : -- Bool or [] --\n\n\tInquire about min/max x and y after binning.\n\t[`-I`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#i)\n- **L** : **labels** : -- Str or [] --\n\n\tDraw bar outline using the specified pen thickness [if no G, L or C set L=0.5].\n\t[`-L`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#l)\n- **N** : **normal** : -- Str --\n\n\tDraw the equivalent normal distribution; append desired pen [0.5p,black].\n\t[`-N`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#n)\n- $(GMT.opt_P)\n- **Q** : **alpha** : -- Number or [] --\n\n\tSets the confidence level used to determine if the mean resultant is significant.\n\t[`-Q`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#q)\n- **R** : **region** : -- Str --\n\n\tSpecifies the \u2018region\u2019 of interest in (r,azimuth) space. r0 is 0, r1 is max length in units.\n\t[`-R`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#r)\n- **S** : **stairs** : -- Str or number --\n\n\tDraws a stairs-step diagram which does not include the internal bars of the default histogram.\n\t[`-S`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#s)\n- **Z** : **kind** : -- Number or Str --\n\n\tChoose between 6 types of histograms.\n\t[`-Z`](http://gmt.soest.hawaii.edu/doc/latest/pshistogram.html#z)\n- $(GMT.opt_U)\n- $(GMT.opt_V)\n- $(GMT.opt_X)\n- $(GMT.opt_Y)\n- $(GMT.opt_bi)\n- $(GMT.opt_di)\n- $(GMT.opt_e)\n- $(GMT.opt_h)\n- $(GMT.opt_i)\n- $(GMT.opt_p)\n- $(GMT.opt_t)\n- $(GMT.opt_swap_xy)\n\"\"\"\nfunction histogram(cmd0::String=\"\", arg1=[]; caller=[], K=false, O=false, first=true, kwargs...)\n\n\targ2 = []\t\t# May be needed if GMTcpt type is sent in via C\n\tN_args = isempty_(arg1) ? 0 : 1\n\n\tlength(kwargs) == 0 && return monolitic(\"pshistogram\", cmd0, arg1)\t# Speedy mode\n\n\td = KW(kwargs)\n\toutput, opt_T, fname_ext = fname_out(d)\t\t# OUTPUT may have been an extension only\n\n\tcmd, opt_B, opt_J, opt_R = parse_BJR(d, \"\", caller, O, \" -JX12c/12c\")\n\tcmd, = parse_JZ(cmd, d)\n\tcmd  = parse_UVXY(cmd, d)\n\tcmd, opt_bi = parse_bi(cmd, d)\n\tcmd, opt_di = parse_di(cmd, d)\n\tcmd, = parse_e(cmd, d)\n\tcmd, = parse_h(cmd, d)\n\tcmd, opt_i = parse_i(cmd, d)\n\tcmd, = parse_p(cmd, d)\n\tcmd, = parse_t(cmd, d)\n\tcmd, = parse_swap_xy(cmd, d)\n\tcmd = parse_params(cmd, d)\n\n\tcmd, K, O, opt_B = set_KO(cmd, opt_B, first, K, O)\t\t# Set the K O dance\n\n\t# If file name sent in, read it and compute a tight -R if this was not provided\n\tif (isempty(opt_R))  opt_R = \" \"  end\t\t# So it doesn't try to find the -R in next call\n\tcmd, arg1, opt_R, opt_i = read_data(d, cmd0, cmd, arg1, opt_R, opt_i, opt_bi, opt_di)\n\n\tcmd, arg1, arg2, = add_opt_cpt(d, cmd, [:C :color :cmap], 'C', N_args, arg1, arg2)\n\n\tcmd = add_opt(cmd, 'A', d, [:A :horizontal])\n\tcmd = add_opt(cmd, 'D', d, [:D :annot :annotate])\n\tcmd = add_opt(cmd, 'F', d, [:F :center])\n\tcmd = add_opt(cmd, 'G', d, [:G :fill])\n\tcmd = add_opt(cmd, 'I', d, [:I :inquire])\n\topt_L = opt_pen(d, 'L', [:L :pen])\n\tcmd = add_opt(cmd, 'Q', d, [:Q :cumulative])\n\tcmd = add_opt(cmd, 'S', d, [:S :stairs])\n\tcmd = add_opt(cmd, 'W', d, [:W :bin :width])\n\tcmd = add_opt(cmd, 'Z', d, [:Z :kind])\n\tif (isempty(opt_L) && !occursin(\"-G\", cmd) && !occursin(\"-C\", cmd))\t\t# If no -L, -G or -C set these defaults\n\t\tcmd = cmd * \" -G150\" * \" -L0.5p\"\n\telseif (!isempty(opt_L))\n\t\tcmd = cmd * opt_L\n\tend\n\n\tfor symb in [:N :normal]\n\t\tif (haskey(d, symb))\n\t\t\tif (isa(d[symb], Number))      cmd = @sprintf(\"%s -N%d\", cmd, d[symb])\n\t\t\telseif (isa(d[symb], String))  cmd = cmd * \" -N\" * d[symb]\n\t\t\telseif (isa(d[symb], Tuple))   cmd = cmd * \" -N\" * parse_arg_and_pen(d[symb])\n\t\t\tend\n\t\t\tbreak\n\t\tend\n\tend\n\n\tcmd = finish_PS(d, cmd, output, K, O)\n\treturn finish_PS_module(d, cmd, \"\", output, fname_ext, opt_T, K, \"pshistogram\", arg1, arg2)\nend\n\n# ---------------------------------------------------------------------------------------------------\nhistogram!(cmd0::String=\"\", arg1=[]; caller=[], K=true, O=true, first=false, kw...) =\n\thistogram(cmd0, arg1; caller=caller, K=K, O=O, first=first, kw...)\n\nhistogram(arg1=[]; caller=[], K=false, O=false, first=true, kw...) =\n\thistogram(\"\", arg1; caller=caller, K=K, O=O, first=first, kw...)\n\nhistogram!(arg1=[]; caller=[], K=true, O=true, first=false, kw...) =\n\thistogram(\"\", arg1; caller=caller, K=K, O=O, first=first, kw...)\n\npshistogram  = histogram\t\t\t# Alias\npshistogram! = histogram!\t\t\t# Alias", "meta": {"hexsha": "fc14c550e1440f29fb972931b729d82ec825c71d", "size": 5646, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pshistogram.jl", "max_stars_repo_name": "JuliaDocsForks/GMT.jl", "max_stars_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/pshistogram.jl", "max_issues_repo_name": "JuliaDocsForks/GMT.jl", "max_issues_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pshistogram.jl", "max_forks_repo_name": "JuliaDocsForks/GMT.jl", "max_forks_repo_head_hexsha": "53d9c030559fe3f079f3b662ce262e7ed682d357", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3907284768, "max_line_length": 112, "alphanum_fraction": 0.6126461211, "num_tokens": 1817, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782349911420193, "lm_q2_score": 0.1311732373477186, "lm_q1q2_score": 0.057430725765715875}}
{"text": "# Julia docs: https://docs.julialang.org/en/latest/manual/variables-and-scoping/\n\nx = 42\n\nfunction f()\n    y = x + 3 # equivalent to `local y = x + 3`\n    @show y\nend\n\nf()\n\ny\n\n# x is global whereas y is local (to the function f)\n\n# we can create a global variables from a local scope\n# but we must be explicit about it.\nfunction g()\n    global y = x + 3\n    @show y\nend\n\ng()\n\ny\n\n\n# we ALWAYS need to be explicit when writing to globals from a local scope:\nfunction h()\n    x = x + 3\n    @show x\nend\n\nh()\n\nfunction h2()\n    global x = x + 3\n    @show x\nend\n\nh2()\n\n# This fact can lead to subtle somewhat unintuitive errors:\na = 0\nfor i in 1:10\n    a += 1\nend\n\n\n\n# Note that if we do not use global scope, everything is simple and intuitive:\nfunction k()\n    a = 0\n    for i in 1:10\n        a += 1\n    end\n    a\nend\n\nk()\n\n\n\n# Variables are inherited from non-global scope\nfunction nested()\n    function inner()\n        # Try with and without \"local\"\n        j = 2\n        k = j + 1\n    end\n    j = 0\n    k = 0\n    return inner(), j, k\nend\n\nnested()\n\n\n\nfunction inner()\n    j = 2 # Try with and without \"local\"\n    k = j + 1\nend\n\nfunction nested()\n    j = 0\n    k = 0\n    return inner(), j, k\nend\n\nnested()\n", "meta": {"hexsha": "f8330fd964739db3a16d9739b7854815f0b7e99a", "size": 1204, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "backup/scoping.jl", "max_stars_repo_name": "de-souza/JuliaOulu20", "max_stars_repo_head_hexsha": "f9685a8b769a66d8f1145d7428bb4894918830b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 98, "max_stars_repo_stars_event_min_datetime": "2019-07-26T20:02:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-06T08:12:15.000Z", "max_issues_repo_path": "backup/scoping.jl", "max_issues_repo_name": "de-souza/JuliaOulu20", "max_issues_repo_head_hexsha": "f9685a8b769a66d8f1145d7428bb4894918830b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-07-25T14:24:54.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-25T17:37:37.000Z", "max_forks_repo_path": "backup/scoping.jl", "max_forks_repo_name": "de-souza/JuliaOulu20", "max_forks_repo_head_hexsha": "f9685a8b769a66d8f1145d7428bb4894918830b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 25, "max_forks_repo_forks_event_min_datetime": "2020-02-11T08:28:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-18T06:21:50.000Z", "avg_line_length": 13.0869565217, "max_line_length": 80, "alphanum_fraction": 0.5905315615, "num_tokens": 379, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4378235137849365, "lm_q2_score": 0.13117322546005344, "lm_q1q2_score": 0.057430722485424295}}
{"text": "\"\"\"\natleast2d \\\\\n\nFor formatting the dimensions of the random variables to at least 2 dimensions  \\\\\nArguments:  random_variable \\\\\nReturns:    A 2-d version of the variable \\\\\n\"\"\"\nfunction atleast2d(random_variable::Array)\n  is_one_d = 1 == ndims(random_variable)\n  return is_one_d ? reshape(random_variable, size(random_variable)[1], 1) : random_variable\nend\n", "meta": {"hexsha": "3a2fa21f7a0910553404554a522b64420434f93b", "size": 361, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/atleast2d.jl", "max_stars_repo_name": "thomasjdelaney/Jentropy.jl", "max_stars_repo_head_hexsha": "7d991182ea3deb1643907edda77b02782da8b933", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-05T18:19:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-05T18:19:27.000Z", "max_issues_repo_path": "src/atleast2d.jl", "max_issues_repo_name": "thomasjdelaney/Jentropy.jl", "max_issues_repo_head_hexsha": "7d991182ea3deb1643907edda77b02782da8b933", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/atleast2d.jl", "max_forks_repo_name": "thomasjdelaney/Jentropy.jl", "max_forks_repo_head_hexsha": "7d991182ea3deb1643907edda77b02782da8b933", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.0833333333, "max_line_length": 91, "alphanum_fraction": 0.7479224377, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.11757214127540647, "lm_q1q2_score": 0.05740852433415701}}
{"text": "# # how to print\n# # assigning variables\n# # syntax for basic math\n\n# println(\"Print to Standard output\")\n# 123\n\n# # the \"ans\" variable holds the last expression in memory\n\n# # basic math operations\n# sum = 3 + 8\n# difference = 10 - 3\n# product = 20 * 3\n# power = 10^2\n# println(product)\n\ns1 = \"this is a string\"\ns2 = \"\"\" this is another string\"\"\"\n\n# typeof(string)\n\n# typeof('a')\n# # typeof('aa')\n\n# # String Interpolation\n# # dollar sign to throw variables into a string\n# a = 5\n# print(\"The value of a is $a\")\n# println()\n# b = 3\n# print(\"The sum of a and b is $(a + b)\")\n\n# String concatenation\n# Method 1, use the string function\nstring(\"con\", \"cat\", \"e\", \"nate\")\nstring(\"con\", 3, \"cat\", 3, \"e\", 3, \"nate\")\n\na = 1\nb = 2\n# Method 2 of string concatenation\n\"$a$b\"\n\n\n\n", "meta": {"hexsha": "9a00b5a629779d2955ac810a303d16aab1ebcd23", "size": 770, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "playground.jl", "max_stars_repo_name": "ryanorsinger/JuliaPlayground", "max_stars_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "playground.jl", "max_issues_repo_name": "ryanorsinger/JuliaPlayground", "max_issues_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "playground.jl", "max_forks_repo_name": "ryanorsinger/JuliaPlayground", "max_forks_repo_head_hexsha": "11f67480cf82b42ba05bcd57bdb776dbf95196e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.1111111111, "max_line_length": 58, "alphanum_fraction": 0.6207792208, "num_tokens": 255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4035668537353746, "lm_q2_score": 0.14223189682786755, "lm_q1q2_score": 0.05740007910363691}}
{"text": "# x, y without type, this is a generic function\nfunction f(x, y)\n    x + y\nend\n\nprintln(f(1, 2))\n# 3\ng = f # g is f\nprintln(g(1, 2))\n\nfunction with_return()\n    return \"I have return\"\nend\nprintln(with_return())\n\nfunction hypot(x, y)\n    x = abs(x)\n    y = abs(y)\n    if x > y\n        r = y/x\n        return x*sqrt(1+r*r)\n    end\n    if y == 0\n        return zero(x)\n    end\n    r = x/y\n    return y*sqrt(1+r*r)\nend\n\nprintln(hypot(3, 4))\n# 5\n\nprintln(+(1, 2, 3) == (1 + 2 + 3))\n# operator is function\n", "meta": {"hexsha": "7fe6dfe41b340aaaa3339ebc717189feda5dd2f1", "size": 500, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/basic/function.jl", "max_stars_repo_name": "dannypsnl/languages-learn", "max_stars_repo_head_hexsha": "2351a235bd55e720394111237d41f65482eb89ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-04T00:47:25.000Z", "max_stars_repo_stars_event_max_datetime": "2018-01-12T08:07:50.000Z", "max_issues_repo_path": "julia/basic/function.jl", "max_issues_repo_name": "dannypsnl/languages-learn", "max_issues_repo_head_hexsha": "2351a235bd55e720394111237d41f65482eb89ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-01-08T14:45:55.000Z", "max_issues_repo_issues_event_max_datetime": "2018-01-09T05:02:09.000Z", "max_forks_repo_path": "julia/basic/function.jl", "max_forks_repo_name": "dannypsnl/languages-learn", "max_forks_repo_head_hexsha": "2351a235bd55e720394111237d41f65482eb89ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.2857142857, "max_line_length": 47, "alphanum_fraction": 0.536, "num_tokens": 188, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939264921326716, "lm_q2_score": 0.12765261204695083, "lm_q1q2_score": 0.05736614550677266}}
{"text": "#=\nSet\n=#\n\nusing SimpleDataStructures\n\nss = SimpleSet{Int}()\npush!(ss, 1)\n1 in ss\n2 in ss\npush!(ss, 2)\n2 in ss\n\ndelete!(ss, 1)\ndelete!(ss, 2)\n\nSimpleSet([1,2,3,1,2,3])\n", "meta": {"hexsha": "cdc9e9e580cdc089cec778a133c7c79ec0d8ad3c", "size": 168, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/set.jl", "max_stars_repo_name": "harryscholes/SimpleDataStructures.jl", "max_stars_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/set.jl", "max_issues_repo_name": "harryscholes/SimpleDataStructures.jl", "max_issues_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/set.jl", "max_forks_repo_name": "harryscholes/SimpleDataStructures.jl", "max_forks_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.3333333333, "max_line_length": 26, "alphanum_fraction": 0.619047619, "num_tokens": 74, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.11920291419948609, "lm_q1q2_score": 0.057274458634027164}}
{"text": "module TestMOIWrapper\n\nusing Test\nusing MathOptInterface\nimport Clp\n\nconst MOI = MathOptInterface\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$(name)\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\nend\n\nfunction test_SolverName()\n    @test MOI.get(Clp.Optimizer(), MOI.SolverName()) == \"Clp\"\n    return\nend\n\nfunction test_supports_default_copy_to()\n    @test !MOI.supports_incremental_interface(Clp.Optimizer())\n    return\nend\n\nfunction test_runtests()\n    model = MOI.Bridges.full_bridge_optimizer(\n        MOI.Utilities.CachingOptimizer(\n            MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n            Clp.Optimizer(),\n        ),\n        Float64,\n    )\n    MOI.set(model, MOI.Silent(), true)\n    MOI.Test.runtests(\n        model,\n        MOI.Test.Config(\n            exclude = Any[MOI.DualObjectiveValue, MOI.ObjectiveBound],\n        ),\n        exclude = [\n            # Unable to prove infeasibility\n            \"test_conic_NormInfinityCone_INFEASIBLE\",\n            \"test_conic_NormOneCone_INFEASIBLE\",\n        ],\n    )\n    return\nend\n\nfunction test_Nonexistant_unbounded_ray()\n    model = MOI.Utilities.CachingOptimizer(\n        MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n        Clp.Optimizer(),\n    )\n    MOI.set(model, MOI.Silent(), true)\n    x = MOI.add_variables(model, 5)\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.0),\n    )\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    MOI.optimize!(model)\n    status = MOI.get(model, MOI.TerminationStatus())\n    @test status == MOI.DUAL_INFEASIBLE\n    return\nend\n\nfunction test_RawOptimizerAttribute()\n    model = Clp.Optimizer()\n    MOI.set(model, MOI.RawOptimizerAttribute(\"LogLevel\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"LogLevel\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"LogLevel\"), 2)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"LogLevel\")) == 2\n\n    MOI.set(model, MOI.RawOptimizerAttribute(\"SolveType\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"SolveType\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"SolveType\"), 4)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"SolveType\")) == 4\n\n    MOI.set(model, MOI.RawOptimizerAttribute(\"PresolveType\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"PresolveType\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"PresolveType\"), 0)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"PresolveType\")) == 0\n    return\nend\n\nfunction test_All_parameters()\n    model = Clp.Optimizer()\n    param = MOI.RawOptimizerAttribute(\"NotAnOption\")\n    @test !MOI.supports(model, param)\n    @test_throws MOI.UnsupportedAttribute(param) MOI.get(model, param)\n    @test_throws MOI.UnsupportedAttribute(param) MOI.set(model, param, false)\n    for key in Clp.SUPPORTED_PARAMETERS\n        @test MOI.supports(model, MOI.RawOptimizerAttribute(key))\n        value = MOI.get(model, MOI.RawOptimizerAttribute(key))\n        MOI.set(model, MOI.RawOptimizerAttribute(key), value)\n        @test MOI.get(model, MOI.RawOptimizerAttribute(key)) == value\n    end\n    return\nend\n\nfunction test_copy_to_bug()\n    model = MOI.Utilities.Model{Float64}()\n    x = MOI.add_variable(model)\n    con = [\n        MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0),\n            MOI.EqualTo(1.0),\n        ) for i in 1:2\n    ]\n    clp = Clp.Optimizer()\n    index_map = MOI.copy_to(clp, model)\n    @test index_map[con[1]] != index_map[con[2]]\n    return\nend\n\nfunction test_options_after_empty!()\n    model = Clp.Optimizer()\n    @test MOI.get(model, MOI.Silent()) == false\n    MOI.set(model, MOI.Silent(), true)\n    @test MOI.get(model, MOI.Silent()) == true\n    MOI.empty!(model)\n    @test MOI.get(model, MOI.Silent()) == true\n    return\nend\n\nend  # module TestMOIWrapper\n\nTestMOIWrapper.runtests()\n", "meta": {"hexsha": "52534dab065cd46bd1e709a4161b542d98de1134", "size": 4095, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/MOI_wrapper.jl", "max_stars_repo_name": "jump-dev/Clp.jl", "max_stars_repo_head_hexsha": "eb6ec27e1207593d98671dbdc2b429d3f2961d47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-29T16:56:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T23:36:55.000Z", "max_issues_repo_path": "test/MOI_wrapper.jl", "max_issues_repo_name": "jump-dev/Clp.jl", "max_issues_repo_head_hexsha": "eb6ec27e1207593d98671dbdc2b429d3f2961d47", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2020-06-13T16:30:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-03T03:37:09.000Z", "max_forks_repo_path": "test/MOI_wrapper.jl", "max_forks_repo_name": "jump-dev/Clp.jl", "max_forks_repo_head_hexsha": "eb6ec27e1207593d98671dbdc2b429d3f2961d47", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-10-09T12:10:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-08T02:12:14.000Z", "avg_line_length": 30.5597014925, "max_line_length": 77, "alphanum_fraction": 0.6625152625, "num_tokens": 1148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.12421300348902362, "lm_q1q2_score": 0.05726427881721912}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.4\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 1d460401-7c48-4718-914e-ba3fc9f7950a\nbegin\n    import Pkg\n    # activate a clean environment\n    Pkg.activate(mktempdir())\n\n    Pkg.add([\n        Pkg.PackageSpec(name=\"PlutoUI\"),\n        Pkg.PackageSpec(name=\"Unitful\"),\n        # ... keep adding your packages\n    ])\n\n    using PlutoUI\n\tusing Unitful\n\t\n\tfunction Image(filepath::String)\n\t\t@assert !isempty(filepath)\n\t\tPlutoUI.LocalResource(joinpath(split(@__FILE__, '#')[1] * \".assets\", filepath))\n\tend\nend;\n\n# \u2554\u2550\u2561 baaa0f44-a9d8-11eb-0d0d-53b78cd74315\nmd\"# Physics II Final Exam Review\n\n- PHYS 1062 Section 003 (Spring 2021)\n- Instructor: Zbigniew Dziembowski\n- Temple University\"\n\n# \u2554\u2550\u2561 c565da05-80ca-4b72-af73-d5da2f1c2abc\nmd\"### Information about the exam\n\n- Please print out and bring to the exam two copies of the IRDEA worksheet.\n  The worksheet is available for download in the *Final exam* Module.\n  It includes a one sectence honesty statement which you have to sign.\n\n- The exam will have two parts:\n    - A quiz consisting of 25 multiple choice questions covering **units 5 through 12**.\n      The quiz setting will be such that there will be only one attempt given.\n      You will see one question at a time and the questions will be locked after answering.\n      The quiz will be available on Canvas.\n    - 2 word problems covering **units 9-12**.\n      These problems, where you are expected to show your explanations, must be solved using the IRDEA worksheet.\n      The solutions must be uploaded for grading on Canvas (like the in-class problem solutions).\n\n- Final exam is worth 234 pts (125pts/quiz and 54.5 pts/problem)\n\n- Extra Credit\n    - 4 pts extra credit per problem will be given, if a solution is submitted on time\n      and on the IRDEA worksheet with signed the one sentence honesty statement.\n\n- Penalties\n    - 4 pts penalty per problem will be applied if a solution is uploaded to Canvas less than 10 mins LATE.\n    - 8 pts penalty per problem if uploaded to Canvas 10-20 mins LATE.\n    - Solution is not graded if it is uploaded to Canvas more than 20 mins. LATE.\n\n- Do not wait to the last moment. Upload each solution once you completed it!\n\n- After the exam, we reserve the right to ask anyone to work out a few new problems with us to spot check the exam inconsistency.\n\"\n\n# \u2554\u2550\u2561 a1c99c25-e72e-4c4f-9ee9-855dad795483\nmd\"### How to review and prepare for the final exam\n\n1. Review the lecture notes for Unit 5 through 12.\n   Especially, go through the quick-check questions in the posted lecture notes.\n   Then take the practice quiz to be posted on Canvas by Sunday, April 25th.\n\n\n2. The contents of the two word-problem section of the exam will be limited to the contents of Units 9 through 12.\n\n   To prepare for this section of the exam, redo, using a copy of the RIDEA worksheet, all exercises and problems in the DT and PSW homework for Unit 9 through 12.\n   Use the wording of the assignments as in the back of Chapters 29, 30, 31 and 34, respectively.\n   Check your answers against the answers to those exercises and problems as given in the file posted on Canvas (\\\"DT-PSW list for Unit XX with answers\\\") in the module **Final exam.**\"\n\n# \u2554\u2550\u2561 c5cf2733-8d62-4f97-9c6c-97c6bce90b02\nmd\"### Electricity and Magnetism formulas\"\n\n# \u2554\u2550\u2561 7abb1db1-d637-446c-9e55-3457db3db17e\nmd\"$$\\begin{gather*}\n\\vec{E} = k \\frac{q}{r^2} \\hat{r}; \\qquad \\vec{F} = q\\vec{E} \\\\\nV_B - V_A = -\\int_A^B \\vec{E} \\cdot d\\vec{r} \\\\\nV = k \\frac{q}{r}; \\qquad U = qV = k\\frac{q_1q_2}{r} \\\\\nC \\equiv \\frac{Q}{\\Delta V_C} = \\frac{\\epsilon_0 A}{d} \\\\\nU = \\frac{1}{2} \\frac{Q^2}{C} \\\\\nI = \\frac{dQ}{dt} \\qquad J = v_d e n_e \\\\\nI = \\frac{\\Delta V}{R} \\qquad R = \\rho \\frac{L}{A} \\qquad P = I \\mathcal{E} \\\\\n\\frac{1}{C_{\\text{eq}}} = \\sum \\frac{1}{C_i} \\qquad C_{\\text{eq}} = \\sum C_i \\\\\nR_{\\text{eq}} = \\sum R_i \\qquad \\frac{1}{R_{\\text{eq}}} = \\sum \\frac{1}{R_i} \\\\\n\\vec{F} = I\\vec{L} \\times \\vec{B} \\qquad \\vec{F} = q\\vec{v} \\times \\vec{B} \\\\\nr = \\frac{mv}{qB} \\qquad f = \\frac{qB}{2\\pi m} \\\\\nF = \\frac{\\mu_0}{2\\pi} \\frac{I_1 I_2}{d} L\n\\end{gather*}$$\"\n\n# \u2554\u2550\u2561 be1d2a6e-8c24-4a1e-b84b-aabfad8e6135\nmd\"$$\\begin{gather*}\n\\tau = NIAB \\sin{\\theta} \\\\\nB = \\frac{\\mu_0}{2\\pi} \\frac{I}{r} \\qquad B = \\frac{\\mu_0 I a^2}{2(x^2 + a^2)^{3/2}} \\\\\n\\vec{B} = \\frac{\\mu_0}{2\\pi} \\frac{\\vec{\\mu}}{z^3} \\qquad B = \\frac{\\mu_0 IN}{L} \\\\\n\\Phi_{\\text{m}} = \\int \\vec{B} \\cdot d\\vec{A} \\qquad \\mathcal{E} = \\left|\\frac{d\\Phi_{\\text{m}}}{dt}\\right| \\\\\nE_{\\text{inside}} = \\frac{r}{2} \\left|\\frac{dB}{dt}\\right| \\qquad V_S = \\left(\\frac{N_S}{N_P}\\right) V_P \\\\\nU_L = \\frac{1}{2} LI^2 \\qquad L_{\\text{sol}} = \\frac{\\mu_0 N^2 A}{\\ell} \\\\\n\\mathcal{E} = NB \\omega A \\sin{\\omega t} \\\\\nI = \\frac{P}{A} = S_{\\text{avg}} = \\frac{1}{2c\\mu_0} {E_0}^2 = \\frac{c\\epsilon_0}{2} {E_0}^2 \\\\\nk = \\frac{2\\pi}{\\lambda} \\qquad \\omega = 2\\pi f \\qquad f\\lambda = \\frac{\\omega}{k} = v \\\\\nv = \\frac{\\omega}{k} = \\frac{1}{\\sqrt{\\epsilon_0 \\mu_0}} \\qquad E = \\frac{\\omega}{k} B = cB\n\\end{gather*}$$\"\n\n# \u2554\u2550\u2561 6c9182b4-2bf5-40a8-8a7e-ecfb1f7e59f3\nmd\"#### Relevant constants and conversion factors\n\n$$\\begin{gather*}\nk \\approx 9.0 \\times 10^9 \\text{ N} \\cdot \\text{m}^2 / \\text{C}^2 \\\\\n\\mu_0 = 4\\pi \\times 10^{-7} \\text{ T} \\cdot \\text{m} / \\text{A} \\\\\n\\epsilon_0 = \\frac{1}{4\\pi k} = 8.85 \\times 10^{-12} \\text{ C}^2 / \\text{N}\\cdot\\text{m}^2 \\\\\n1 \\text{ eV} = 1.602 \\times 10^{-19} \\text{ J} \\\\\ne = 1.60 \\times 10^{-19} \\text{ C} \\qquad c = 3.00 \\times 10^8 \\text{ m/c} \\\\\nm_e = 9.11 \\times 10^{-31} \\text{ kg} \\qquad m_p = 1.67 \\times 10^{-27} \\text{ kg}\n\\end{gather*}$$\n\"\n\n# \u2554\u2550\u2561 b670ab02-1fe7-4204-9747-237348707d97\nbegin\n\tk = 9.0e9u\"N*m^2/C^2\"\n\t\u03bc0 = 1.26e-6u\"T*m/A\"\n\t\u03f50 = 8.85e-12u\"C^2/(N*m^2)\"\n\te = 1.60e-19u\"C\"\n\tc = 3.00e8u\"m/s\"\n\tme = 9.11e-31u\"kg\"\n\tmp = 1.67e-27u\"kg\"\n\t\n\t# Other constants\n\tB_earth = 50e-6u\"T\" # Earth's magnetic field\n\tg = 9.8u\"m/s^2\"\nend;\n\n# \u2554\u2550\u2561 3240dda1-8d4c-4551-8878-8c2ddd9f12d1\nmd\"### Ray Optics\n| Medium              |        $n$ |\n| :---                |       :--- |\n| Vacuum              | 1.00 exact |\n| Air (actual)        |     1.0003 |\n| Air (accepted)      |       1.00 |\n| Water               |       1.33 |\n| Ethyl alcohol       |       1.36 |\n| Oil                 |       1.46 |\n| Glass (typical)     |       1.50 |\n| Polystyrene plastic |       1.59 |\n| Cubic zirconia      |       2.18 |\n| Diamond             |       2.41 |\n| Silicon (infrared)  |       3.50 |\n\n$$\\begin{gather*}\nn = \\frac{c}{v} \\qquad n_1 \\sin{\\theta_1} = n_2 \\sin_{\\theta_2} \\\\\n\\frac{1}{s} + \\frac{1}{s'} = \\frac{1}{f} \\\\\nM = \\frac{h'}{h} = -\\frac{s'}{s} \\\\\n|f| = \\frac{R}{2}\n\\end{gather*}$$\n\"\n\n# \u2554\u2550\u2561 8a56ceb5-dbd9-4950-bbfe-50b56c682487\nmd\"# Notebook environment\"\n\n# \u2554\u2550\u2561 98b7f219-578b-4717-8dfa-5f22f86c04b2\nmd\"# Topics covered\n\n- Unit 5: Chapters 22 and 23---Electric Charges and Forces, The Electric Field\n- Unit 6: Chapters 25 and 26---The Electric Potential, Potential and Field\n- Unit 7: Chapter 27---Current and Resistance\n- Unit 8: Chapter 28---Fundamentals of Circuits\n- Unit 9: Chapter 29---The Magnetic Field\n- Unit 10: Chapter 30---Electromagnetic Induction\n- Unit 11: Chapter 31---Electromagnetic Fields and Waves\n- Unit 12: Chapter 34---Ray Optics\"\n\n# \u2554\u2550\u2561 f6b01ecd-9629-422c-800e-a41512fa9933\nmd\"# QuickChecks\"\n\n# \u2554\u2550\u2561 81d41ebd-9c50-49a1-b77d-1f6c600477e7\nmd\"## Unit 5 The Electric Charge, Force and Field\"\n\n# \u2554\u2550\u2561 067e90ac-b370-4c53-b1d8-8ee65189b036\nmd\"### Unit 5 Part 1 QuickCheck #1\n\nWhich is the direction of the net force on the charge at the lower left?\"\n\n# \u2554\u2550\u2561 179020bb-048a-4040-b517-0c081a5b68c0\nImage(\"Unit5/P1QC1.png\")\n\n# \u2554\u2550\u2561 8065e0db-4aa8-447f-a130-49feedcddad7\nmd\"**Answer:** B, down to the left\"\n\n# \u2554\u2550\u2561 5745f4ea-219d-4e60-b269-9e08f010836a\nmd\"### Unit 5 Part 2 QuickCheck #1\n\nWhat is the direction of the net electric field at the dot?\"\n\n# \u2554\u2550\u2561 885d48fa-4a05-4468-8492-0cae4bfb7b26\nImage(\"Unit5/P2QC1.png\")\n\n# \u2554\u2550\u2561 2d690e4b-3d09-4907-99de-5f0ef4662b7e\nmd\"**Answer:** D, to the right\"\n\n# \u2554\u2550\u2561 eae4eef0-1701-406f-966d-736a2b2381f5\nmd\"### Unit 5 Part 2 QuickCheck #2\n\nAn electron is in the plane that bisects a dipole.\nWhat is the direction of the electric force on the electron?\"\n\n# \u2554\u2550\u2561 9ca8f846-5d5b-4b28-85b6-90c5f9c068b6\nImage(\"Unit5/P2QC2.png\")\n\n# \u2554\u2550\u2561 04912a55-5871-4917-8c95-7f03bd4abf1d\nmd\"**Answer:** A, up because the electron goes towards the proton\"\n\n# \u2554\u2550\u2561 42f03b7e-9fec-40bc-8d22-e594a22ab54e\nmd\"### Unit 5 Part 3 QuickCheck #1\n\nThree points inside a parallel-plate capacitor are marked.\nWhich is true?\n\nA. $E_1 > E_2 > E_3$\n\nB. $E_1 < E_2 < E_3$\n\nC. $E_1 = E_2 = E_3$\n\nD. $E_1 = E_3 > E_2$\n\"\n\n# \u2554\u2550\u2561 34b75779-b546-4628-ac0a-1d8fedc231eb\nImage(\"Unit5/P3QC1.png\")\n\n# \u2554\u2550\u2561 fb93dd3c-de36-4087-9870-8365161f7d1f\nmd\"**Answer:**\nC. $E_1 = E_2 = E_3$\"\n\n# \u2554\u2550\u2561 ab703047-2e82-4c13-a9d9-c97ffd42af67\nmd\"## Unit 6 The Electric Potential, and Capacitors\"\n\n# \u2554\u2550\u2561 253d5697-9088-424f-aa2e-c5df2c7675bf\nmd\"### Unit 6 Part 2 QuickCheck #1\n\nAt the midpoint between these two equal but opposite charges,\n\nA. $\\vec{E} = \\vec{0}; \\;V = 0$\n\nB. $\\vec{E} = \\vec{0}; \\;V > 0$\n\nC. $\\vec{E} = \\vec{0}; \\;V < 0$\n\nD. $\\vec{E}$ points right; $V = 0$\n\nE. $\\vec{E}$ points left; $V = 0$\n\"\n\n# \u2554\u2550\u2561 fcdb7ea5-6203-4e74-b746-4470052b5f99\nImage(\"Unit6/P2QC1.png\")\n\n# \u2554\u2550\u2561 5db75338-4b19-4037-942d-979856662d6c\nmd\"**Answer:** D. $\\vec{E}$ points right; $V = 0$\n\n**Explanation:** $V = \\frac{k q_1}{r} + \\frac{k q_2}{r} = 0$\"\n\n# \u2554\u2550\u2561 74378daa-7994-403e-9c41-86bae271a74d\nmd\"### Unit 6 QuickCheck #2 (Exercise 25.6)\nWhat is the electrostatic energy of the group of charges in figure below?\n\nA. positive\n\nB. negative\n\nC. zero\n\"\n\n# \u2554\u2550\u2561 af3ab252-16c6-4e6f-bdc6-9a01fede5a30\nImage(\"Unit6/P2QC2.png\")\n\n# \u2554\u2550\u2561 64bbc192-28dd-4db4-8dab-1cde2a031bc5\nmd\"**Answer:** C. zero\n\n**Explanation:**\n\n$\\begin{gather*}\nU = \\;? \\\\\nU = k \\sum_{i<j} \\frac{q_i q_j}{r_{ij}} = \\frac{kq_1q_2}{r_{12}} + \\frac{kq_1q_3}{r_{13}} + \\frac{kq_2q_3}{r_{23}} \\\\\nq_1 = 1.0 \\text{ nC} \\\\\nq_2 = q_3 = -2.0 \\text{ nC} \\\\\nq_1q_2 = -2.0 \\text{ nC} \\\\\nq_1q_3 = -2.0 \\text{ nC} \\\\\nq_2q_3 = 4.0 \\text{ nC} \\\\\nr = r_{12} = r_{13} = r_{23} \\\\\nU = \\frac{k}{r} \\left(q_1q_2 + q_1q_3 + q_2q_3\\right) = 0\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 185600d3-131d-491d-869d-06a865280a5c\nmd\"### Unit 6 Part 3 QuickCheck #1\n\nWhat is the capacitance of these two electrodes?\n\nA. 8 nF\n\nB. 4 nF\n\nC. 2 nF\n\nD. 1 nF\n\nE. Some other value\n\"\n\n# \u2554\u2550\u2561 5fd137c7-a14e-4a53-9c9f-39f0992b1e43\nImage(\"Unit6/P3QC1.png\")\n\n# \u2554\u2550\u2561 8a9474e5-4b73-4a3e-9573-fa0ca066f9de\nmd\"**Answer:** C. 2 nF\n\n**Explanation:**\n\n$\\begin{gather*}\nC = \\;? \\\\\nQ = -4 \\times 10^{-9} \\text{ C} \\\\\nV = 2.0 \\text{ V} \\\\\nC = \\frac{Q}{V} = 2 \\text{ nF}\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 d8c9b9e2-eaa1-40e5-9899-f60d8fb9dddb\nmd\"### Unit 6 QuickCheck #2\nThe equivalent capacitance is\n\nA. 9 $\\mu$F\n\nB. 6 $\\mu$F\n\nC. 3 $\\mu$F\n\nD. 2 $\\mu$F\n\nE. 1 $\\mu$F\n\"\n\n# \u2554\u2550\u2561 f6000dca-7d5a-4bb6-93ec-3de97124fe51\nImage(\"Unit6/P3QC2.png\")\n\n# \u2554\u2550\u2561 05c12afa-6ce0-4ab6-aac9-bb6a4f7a20e8\nmd\"**Answer:** D. 2 \u03bcF\n\n**Explanation:**\n\n$C = \\;?$\n\n$\\begin{align*}\n\\frac{1}{C_{\\text{series}}} &= \\sum_i \\frac{1}{C_i} = \\frac{1}{C_1} + \\frac{1}{C_2} \\\\\n&= \\frac{1}{3} + \\frac{1}{6} \\\\\n&= \\frac{2}{6} + \\frac{1}{6} \\\\\n&= \\frac{3}{6} \\implies C = 2 \\;\\text{\u03bcF}\n\\end{align*}$\"\n\n# \u2554\u2550\u2561 f30d8566-72fd-4658-a4cb-fcd2ac33b063\nmd\"## Unit 7 Electric Current\"\n\n# \u2554\u2550\u2561 b6605cff-3e3c-40ed-b003-547a908a89f3\nmd\"### Unit 7 Part 1 QuickCheck #1\n\nEvery minute, 120 C of charge flow through this cross section of the wire.\n\nThe wire's current is\n\nA. 240 A\n\nB. 120 A\n\nC. 60 A\n\nD. 2 A\n\nE. Some other value.\n\"\n\n# \u2554\u2550\u2561 c53a0513-5a65-453b-8833-f4be54af3e0c\nImage(\"Unit7/P1QC1.png\")\n\n# \u2554\u2550\u2561 6be1aa76-6cf2-4e57-bde4-7effcc453073\nmd\"**Answer:** D. 2 A\n\n**Explanation:**\n\n$\\begin{gather*}\nI = \\;? \\\\\n\\Delta t = 60 \\text{ s} \\\\\nQ = 120 \\text{ C} \\\\\nI = \\frac{Q}{\\Delta t} = 2 \\text{ A}\n\\end{gather*}$\n\"\n\n# \u2554\u2550\u2561 8acec63d-de21-4698-9e51-579c16d2e398\nmd\"### Unit 7 Part 2 QuickCheck #1\n\nThe current in the fourth wire is\n\nA. 16 A to the right.\n\nB. 4 A to the left.\n\nC. 2 A to the right.\n\nD. 2 A to the left.\n\nE. Not enough information to tell.\"\n\n# \u2554\u2550\u2561 1e73fe74-4f6d-4137-b771-2cfac7cf6332\nImage(\"Unit7/P2QC1.png\")\n\n# \u2554\u2550\u2561 762087fa-ffcf-4691-8c67-6837555bbfb0\nmd\"**Answer:** 2 A to the left.\n\n**Explanation:** Kirchoff's junction law.\nFor a *junction*, the law of conservation of current requires that\n\n$\\sum I_{\\text{in}} = \\sum I_{\\text{out}}$\"\n\n# \u2554\u2550\u2561 d6dba861-4f6e-4aad-9b8c-1e18ee2639af\nmd\"## Unit 8 Fundamentals of Circuits\"\n\n# \u2554\u2550\u2561 6cf18ee6-6b70-466f-9e23-32368cb35865\nmd\"### Unit 8 Part 1 QuickCheck #1\n\nWhich resistor disspates more power?\n\nA. The 9 \u03a9 resistor\n\nB. The 1 \u03a9 resistor\n\nC. They dissipate the same power.\"\n\n# \u2554\u2550\u2561 c5ee0bee-d2db-4daa-bbcf-5ac9f30b2f12\nImage(\"Unit8/P1QC1.png\")\n\n# \u2554\u2550\u2561 24a53a41-96a8-4bab-a8cf-1f3cd6ad30ff\nmd\"**Answer**: 1 \u03a9\n\n**Explanation:** \nRate of energy transfer by a resistor is $P_R = I \\Delta V_R$.\nUse Ohm's law $\\Delta V_R = IR \\implies P_R = \\frac{(\\Delta V_R)^2}{R}$. Since $\\Delta V_R = 9 \\text{ V}$ across both resistors, the 1 \u03a9 resistor dissipates more power $(9 \\text{ W} > 1 \\text{ W})$.\"\n\n# \u2554\u2550\u2561 060fd1a0-5548-4eb4-b4c2-f2db077e0d50\nmd\"### Unit 8 Part 1 QuickCheck #2\n\nWhich bulb is brighter?\n\nA. The 60 W bulb\n\nB. The 100 W bulb\n\nC. Their brightnesses are the same.\n\nD. There's not enough information to tell.\"\n\n# \u2554\u2550\u2561 8eb2caa8-5341-40a7-8ae5-aad8e44c1af1\nImage(\"Unit8/P1QC2.png\")\n\n# \u2554\u2550\u2561 860bbf33-9025-44ee-aa26-7217f0bc7562\nmd\"**Answer:** A. The 60 W bulb\n\n**Explanation:**\n\n$\\begin{gather*}\nV = 120 \\text{ V} \\\\\nP_{60} = 60 \\text{ W} \\\\\nP_{100} = 100 \\text{ W} \\\\\nP_R = \\frac{V^2}{R} \\implies R = \\frac{(\\Delta V_R)^2}{P_R} \\\\\nR_{60} = \\frac{V^2}{60} \\;\\Omega \\\\\nR_{100} = \\frac{V^2}{100} \\;\\Omega \\\\\nV = IR \\\\\nR_{60} > R_{100} \\implies V_{60} > V_{100}\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 28acdfbe-1020-4921-8c6c-0e38d0732d78\nmd\"### Unit 8 Part 2 QuickCheck #1\n\nThe battery current $I$ is\n\nA. 3 A\n\nB. 2 A\n\nC. 1 A\n\nD. 2/3 A\n\nE. 1/2 A\"\n\n# \u2554\u2550\u2561 e06efb86-5366-49c3-acb6-f9b209bf5d81\nImage(\"Unit8/P2QC1.png\")\n\n# \u2554\u2550\u2561 6c9233eb-058a-4e8a-9c9b-22964e210dd1\nmd\"**Answer:** 2/3 A\n\n**Explanation:** Use Ohm's Law $V = IR$\"\n\n# \u2554\u2550\u2561 c83063bd-0bba-47e7-b869-e169d6e9c03e\nmd\"### Unit 8 Part 2 QuickCheck #2\n\nWhat does the (ideal) ammeter read?\n\nA. 6 A\n\nB. 3 A\n\nC. 2 A\n\nD. Some other value\n\nE. Nothing because this will fry the meter.\"\n\n# \u2554\u2550\u2561 c9102584-2196-4483-b79f-60089ad9e1b4\nImage(\"Unit8/P2QC2.png\")\n\n# \u2554\u2550\u2561 b30d503e-ab6c-4515-9a68-81327795aa6c\nmd\"**Answer:** E. Nothing because this will fry the meter.\n\n**Explanation:** Since the ammeter is ideal, its resistance $R = 0 \\implies I = \\lim_{R \\to 0} \\frac{V}{R} = \\infty$\"\n\n# \u2554\u2550\u2561 85e2b232-6a09-4621-ab97-e2345b676999\nmd\"### Unit 8 Part 2 QuickCheck #3\n\nThe battery current $I$ is\n\nA. 3 A\n\nB. 2 A\n\nC. 1 A\n\nD. 2/3 A\n\nE. 1/2 A\"\n\n# \u2554\u2550\u2561 69bdb37e-2f06-4c78-a5bc-c3fce3cf6be8\nImage(\"Unit8/P2QC3.png\")\n\n# \u2554\u2550\u2561 f9ba57aa-4113-40e0-af24-8d7066baef4c\nmd\"**Answer:** 3 A\n\n**Explanation:** The problem involves resistors in parallel and Ohm's Law.\n\n$\\begin{gather*}\nI = \\;? \\\\\nV = 12 \\text{ V} \\\\\n\\frac{1}{R} = \\frac{1}{12} + \\frac{1}{6} = \\frac{3}{12} \\implies R = 4 \\\\\nV = IR \\implies I = \\frac{V}{R} = \\frac{12}{4} = 3 \\text{ A}\n\\end{gather*}$\n\"\n\n# \u2554\u2550\u2561 cdc4c352-a21c-4d88-8a6e-28da5974e893\nmd\"### Unit 8 Part 2 QuickCheck #4\n\nWhat does the (ideal) voltmeter read?\n\nA. 6 V\n\nB. 3 V\n\nC. 2 V\n\nD. Some other value\n\nE. Nothing because this will fry the meter.\n\"\n\n# \u2554\u2550\u2561 bc7ebdc7-3614-43ad-bb1c-695f535c339b\nImage(\"Unit8/P2QC4.png\")\n\n# \u2554\u2550\u2561 3badd910-d0be-435d-8b61-751d2fc46984\nmd\"**Answer:** A. 6 V\n\n**Answer:** An ideal voltmeter will have very high resistance to measure the voltage of the circuit.\"\n\n# \u2554\u2550\u2561 f5871103-8943-4085-8ec8-f7c19155262c\nmd\"## Unit 9 Magnetism: Force and Field\"\n\n# \u2554\u2550\u2561 64256f0e-f61a-41d9-9f07-f53f12b81d9f\nmd\"### Unit 9 Part 1 QuickCheck #1\n\nA long straight wire extends into and out of the screen.\nThe current in the wire is\n\nA. Into the screen.\n\nB. Out of the screen.\n\nC. There is no current in the wire.\n\nD. Not enough info to tell the direction.\"\n\n# \u2554\u2550\u2561 56988b0d-40f0-485c-bfd1-468431e2f906\nImage(\"Unit9/P1QC1.png\")\n\n# \u2554\u2550\u2561 baf581de-7077-4c25-a5df-b5e82490b113\nmd\"**Answer:** Out of the screen.\n\n**Explanation:** Use the right-hand rule along the direction of the magnetic field.\"\n\n# \u2554\u2550\u2561 11c087b0-7823-41ac-8034-e66875bcaec1\nmd\"### Unit 9 Part 1 QuickCheck #2\n\nWhat is the direction of the magnetic field at the position of the dot?\n\nA. Into the screen\n\nB. Out of the screen\n\nC. Up\n\nD. Down\n\nE. Left\"\n\n# \u2554\u2550\u2561 ad23f43f-c8ac-48fd-9b21-901658710684\nImage(\"Unit9/P1QC2.png\")\n\n# \u2554\u2550\u2561 a59c5606-b60e-44c8-a7e3-b84cd89b322c\nmd\"**Answer:** C. Up\n\n**Explanation:** Use the right-hand rule for the cross product $\\vec{v} \\times \\hat{r}$ where $\\hat{r}$ is the distance between the two points and determine the direction of the magnetic field at the given point.\"\n\n# \u2554\u2550\u2561 69065726-b456-4ab1-b8a6-22e638304388\nmd\"### Unit 9 Part 2 QuickCheck #1\n\nCompared to the magnetic field at point A, the magnetic field at point B is\n\nA. Half as strong, same direction.\n\nB. Half as strong, opposite direction.\n\nC. One-quarter as strong, same direction.\n\nD. One-quarter as strong, opposite direction.\n\nE. Can't compare without knowing $I$.\"\n\n# \u2554\u2550\u2561 47e713c1-0741-4135-ad0c-8b7ad003c141\nImage(\"Unit9/P2QC1.png\")\n\n# \u2554\u2550\u2561 45baa2c9-e2a9-44fc-a6a3-9e1b149d3ec6\nmd\"**Answer:** B. Half as strong, opposite direction.\n\n**Explanation:** The magnetic field of a current carrying wire is\n\n$B = \\frac{\\mu_0}{2\\pi} \\frac{I}{r}$\"\n\n# \u2554\u2550\u2561 a44466e8-0954-4738-9836-e7d445d41a91\nmd\"### Unit 9 Part 3 QuickCheck #1\n\nWhat is the current direction in this loop?\nAnd where is the north magnetic pole of this current loop?\n\nA. Current CW; north pole on top\n\nB. Current CW; north pole on bottom\n\nC. Current CCW; north pole on top\n\nD. Current CCW; north pole on bottom\"\n\n# \u2554\u2550\u2561 13f7bece-837b-473e-bb86-df55d3f5dbdf\nImage(\"Unit9/P3QC1.png\")\n\n# \u2554\u2550\u2561 6917fdb5-0538-4995-9ac5-14d7fc92b903\nmd\"**Answer:** B. Current CW; north pole on bottom\n\n**Explanation:** Use the right-hand rule.\"\n\n# \u2554\u2550\u2561 018a2522-f201-44a7-8f63-d67411970a90\nmd\"### Unit 9 Part 5 QuickCheck #1\n\nThe direction of the magnetic force on the proton is\n\nA. To the right.\n\nB. To the left.\n\nC. Into the screen.\n\nD. Out of the screen.\n\nE. The magnetic force is zero.\"\n\n# \u2554\u2550\u2561 ebe19088-8374-441c-9c1b-c00194737814\nImage(\"Unit9/P5QC1.png\")\n\n# \u2554\u2550\u2561 30625a27-4961-4eff-be88-27a351a3db12\nmd\"**Answer:** D. Out of the screen.\n\n**Explanation:** Use the right-hand rule for $\\vec{v} \\times \\vec{B}$\"\n\n# \u2554\u2550\u2561 66bd1832-dd76-43fa-ab08-018c8e726885\nmd\"### Unit 9 Part 5 QuickCheck #2\n\nThe direction of the magnetic force on the electron is\n\nA. Upward.\n\nB. Downward.\n\nC. Into the screen.\n\nD. Out of the screen.\n\nE. The magnetic force is zero.\"\n\n# \u2554\u2550\u2561 08c4b0d3-a431-46dd-a0ae-3bd5a59b81e3\nImage(\"Unit9/P5QC2.png\")\n\n# \u2554\u2550\u2561 eff73afd-3ded-49d2-9411-ec11933fb31f\nmd\"**Answer:** E. The magnetic force is zero.\n\n**Explanation:** Use the right-hand rule for $\\vec{v} \\times \\vec{B}$\"\n\n# \u2554\u2550\u2561 6e2b45e2-67bd-4512-9b3c-6f25dc75824f\nmd\"### Unit 9 Part 6 QuickCheck #1\n\nThe horizontal wire can be levitated---held up against the force of gravity---if the current in the wire is\n\nA. Right to left.\n\nB. Left to right.\n\nC. It can't be done with this magnetic field.\"\n\n# \u2554\u2550\u2561 9cf0faf9-ea42-45da-9788-975cfecdca37\nImage(\"Unit9/P6QC1.png\")\n\n# \u2554\u2550\u2561 67aca9a5-da3f-4a2c-9159-2dfb922217f8\nmd\"**Answer:** B. Left to right\n\n**Explanation:** Use the right-hand rule for $\\vec{v} \\times \\vec{B}$\"\n\n# \u2554\u2550\u2561 37aeddcf-e1cf-40a0-925e-20d600f24845\nmd\"### Unit 9 Part 6 QuickCheck #2\n\nIf released from rest, the current loop will\n\nA. Move upward.\n\nB. Move downward.\n\nC. Rotate clockwise.\n\nD. Rotate counterclockwise.\n\nE. Do something not listed here.\"\n\n# \u2554\u2550\u2561 5fb3d735-8df5-4e69-aa47-ff467dee9047\nImage(\"Unit9/P6QC2.png\")\n\n# \u2554\u2550\u2561 c4f15be9-ba69-4b9f-9b5a-407a975c2fbe\nmd\"**Answer:** D. Rotate counterclockwise.\n\n**Explanation:** Use the right-hand rule for $\\vec{v} \\times \\vec{B}$ on both ends of the current loop to determine the direction of each end.\"\n\n# \u2554\u2550\u2561 2c330d4d-8fa2-4c3f-a5b8-8a3ac19fd075\nmd\"## Unit 10 Electromagnetic Induction\"\n\n# \u2554\u2550\u2561 a2fbf237-3b84-4331-be33-e13c0f726838\nmd\"### Unit 10 Part 2 QuickCheck #1\n\nAn induced current flows clockwise as the metal bar is pushed to the right.\nThe original magnetic field points\n\nA. Up.\n\nB. Down.\n\nC. Into the screen.\n\nD. Out of the screen.\n\nE. To the right.\"\n\n# \u2554\u2550\u2561 84cdc818-8a8c-4888-b3bd-12399e60a739\nImage(\"Unit10/P2QC1.png\")\n\n# \u2554\u2550\u2561 b759a10f-6110-4e66-8121-e1c8bf24efee\nmd\"**Answer:** C. Into the screen\n\n**Explanation:** Apply Lenz's Law for direction of current induced in a loop.\"\n\n# \u2554\u2550\u2561 9c245886-b491-4adf-9ccd-0f8d20326949\nmd\"Unit 10 Part 2 QuickCheck #2\n\nThe current in the straight wire is decreasing.\nWhich is true?\n\nA. There is a clockwise induced current in the loop.\n\nB. There is a counterclockwise induced current in the loop.\n\nC. There is no induced current in the loop.\"\n\n# \u2554\u2550\u2561 c352a601-03e9-4f47-9d8e-2e7e806a7433\nImage(\"Unit10/P2QC2.png\")\n\n# \u2554\u2550\u2561 d085be3a-89ec-4672-a47d-f5f2c020885b\nmd\"**Answer:** A. There is a clockwise induced current in the loop.\n\n**Explanation:** Apply Lenz's law for direction of current induced in a loop for decreasing magnetic flux. To oppose the decrease (pointing out of the screen), the magnetic field of the induced current must point into the screen.\"\n\n# \u2554\u2550\u2561 3d7c7ea7-24a4-4acc-8238-e71118fef4f1\nmd\"## Unit 11 EM Fields and Waves\"\n\n# \u2554\u2550\u2561 27a07162-335c-4d3a-9771-2a3793490631\nmd\"### Unit 11 Part 1 QuickCheck #1\"\n\n# \u2554\u2550\u2561 04beeb35-c9b8-4aca-9afa-6d9cefaacc62\nImage(\"Unit11/P1QC1.png\")\n\n# \u2554\u2550\u2561 2f72f311-4bb9-4459-a1ee-3a3b5339b13f\nmd\"**Answer**: A.\"\n\n# \u2554\u2550\u2561 082f1121-811b-4404-b561-6ce9ca6a05ce\nmd\"### Unit 11 Part 1 QuickCheck #2\n\nIn which direction is this electro-magnetic wave traveling?\n\nA. Up\n\nB. Down\n\nC. Into the screen\n\nD. Out of the screen\n\nE. These are not allowable fields for an electromagnetic wave\"\n\n# \u2554\u2550\u2561 4584596e-c66f-45b3-ad3d-3042f13a1eca\nImage(\"Unit11/P1QC2.png\")\n\n# \u2554\u2550\u2561 99a92b4f-0a27-4681-a372-e6db84db033a\nmd\"**Answer:** A. Up\n\n**Explanation:** Use the right-hand rule for $\\vec{E} \\times \\vec{B}$ to determine the direction of the electro-magnetic wave.\"\n\n# \u2554\u2550\u2561 3a92f0e7-983c-433d-a83d-7c4e1a52a206\nmd\"### Unit 11 Part 2 QuickCheck #1\"\n\n# \u2554\u2550\u2561 ae445aa3-dcd9-4d97-87f7-7be0a2287cf3\nImage(\"Unit11/P2QC1.png\")\n\n# \u2554\u2550\u2561 6c521a07-2a6b-4ce8-a772-68b511bddf50\nmd\"**Answer:** A.\n\n**Explanation:** Use the right-hand rule where $\\vec{E} \\times \\vec{B}$ determines the direction of the wave.\"\n\n# \u2554\u2550\u2561 90a8d78d-cdf9-4485-ba31-1364e2c920ae\nmd\"## Unit 12 Ray Optics\"\n\n# \u2554\u2550\u2561 1f9d99d8-d613-4e62-9d46-5ebc61b029d6\nmd\"### Unit 12 Part 1 QuickCheck #1\n\nA laser beam passing from medium 1 to medium 2 is refracted as shown.\nWhich is true?\n\nA. $n_1 < n_2$\n\nB. $n_1 > n_2$\n\nC. There's not enough information to compare $n_1$ and $n_2$.\"\n\n# \u2554\u2550\u2561 141ea713-8276-4799-8e9c-f46f14e30c44\nImage(\"Unit12/P1QC1.png\")\n\n# \u2554\u2550\u2561 59508d51-2fab-4cab-816c-6d0c88979af8\nmd\"**Answer:** B. $n_1 > n_2$\n\n**Explanation:** The light bends away from the normal.\"\n\n# \u2554\u2550\u2561 685e68c4-2be2-4fc7-b22f-9dc78b231236\nmd\"### Unit 12 Part 2 QuickCheck #1\n\nIn an optical setup consisting of a single mirror and an object we measure the magnification factor $m = +3$.\nWe can conclude that the image is:\n\nA. Inverted and virtual\n\nB. Inverted and real\n\nC. Upright and virtual\n\nD. Upright and real\"\n\n# \u2554\u2550\u2561 f0d975f2-dc73-476a-9dec-6c7939511066\nmd\"**Answer:** Upright and virtual\n\n**Explanation:** The mirror must be concave to produce an enlarged image. The image must be upright since the magnification is positive. The image is only upright with the condition that the object's distance from the mirror is less than the focal length, i.e., $s < f$. The image will form behind the mirror so it will be virtual.\"\n\n# \u2554\u2550\u2561 d1a284e5-e3d9-4d9c-b350-f6a9f9d4bf23\nmd\"### Unit 12 Part 3 QuickCheck #1\n\nIn an optical setup consisting of a single lens and an object we measure the magnification factor $m = -0.5$.\nWe can conclude that object in this setup is:\n\nA. Between lens and focal point\n\nB. Between focal point and point of twice the focal length\n\nC. Exactly at point of twice the focal length\n\nD. At distance greater than twice the focal length\"\n\n# \u2554\u2550\u2561 a76b95fb-bf1e-4fea-ae0b-927017a78d1b\nmd\"**Answer:** D. At distance greater than twice the focal length\n\n**Explanation:** The image must be inverted, so a converging lens must be used. Use two rays to determine the height of the image: one passing through the lens bending towards the focal point and another passing through the center of the lens. Notice that the image will only get smaller for distances larger than $2f$.\"\n\n# \u2554\u2550\u2561 368b2131-d30c-42ae-9550-b51073e7592b\nmd\"# Final Practice Quiz\"\n\n# \u2554\u2550\u2561 0acf5486-3702-4e8a-889e-871916d2ecf3\nmd\"### Question 1\n\nWhich is the direction of the net force on the charge at the lower left?\n\n- A\n\n- D\n\n- B\n\n- C\n\n- E. None of these.\"\n\n# \u2554\u2550\u2561 bfef7588-5c90-4a37-967e-660eb3e5efcd\nImage(\"Unit5/P1QC1.png\")\n\n# \u2554\u2550\u2561 86fa33b9-17da-4322-b195-fdc99687c518\nmd\"**Answer:** B\n\n**Explanation:** Apply the principle of superposition.\"\n\n# \u2554\u2550\u2561 9fdb361f-cb53-4270-a59c-6b967da468ea\nmd\"### Question 2\n\nWhich is the direction of the net force on the charge at the top?\n\n- A\n\n- D\n\n- C\n\n- E. None of these.\n\n- B\"\n\n# \u2554\u2550\u2561 4c54fa63-06a1-4519-8104-aea78a2bbfbe\nImage(\"Unit5/P2QC1.png\")\n\n# \u2554\u2550\u2561 a68a80e3-b74e-4dbf-93ce-2892bc6dafd5\nmd\"**Answer:** D\n\n**Explanation:** The positive charge pushes, the negative charge pulls. Their vertical forces cancel each other out.\"\n\n# \u2554\u2550\u2561 cda191c4-afb2-4512-a930-171f5757a094\nmd\"### Question 3\n\nA uniform electric field, with a magnitude of 500 V/m, is directed parallel to the +x axis.\nIf the potential at x = 5.0 m is 2500 V, what is the potential at x = 2.0 m?\n\n- 1000V\n\n- 2000V\n\n- 4000V\n\n- 500V\"\n\n# \u2554\u2550\u2561 c721d98c-3586-450c-a22f-a4ff048d12c6\nmd\"\n##### Incorrect\n**Answer:** 1000V\"\n\n# \u2554\u2550\u2561 b55d0b05-3713-46ea-819e-d180e662a47e\nmd\"##### Correct answer\n4000V\n\n**Explanation:**\nThe electric field's magnitude $E$ is the rate at which the potential $U$ is decreasing.\nThen the potential at 2.0 m must be greater than the potential at 5.0 m.\n\n$\\begin{gather*}\nE = 500 \\text{ V/m} \\\\\nU_1 = 2500 \\text{ V} \\\\\nx_1 = 5.0 \\text{ m} \\\\\nx_0 = 2.0 \\text{ m} \\\\\nU_2 = U_1 + E(x_1 - x_0) = 2500 + 500(3) = 4000 \\text{ V}\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 16f0dd9e-f5bb-4173-a07c-7feba801837f\nlet\n\tE = 500\n\tU1 = 2500\n\tx1 = 5.0\n\tx0 = 2.0\n\tU2 = U1 + E * (x1 - x0)\nend\n\n# \u2554\u2550\u2561 b87d87a8-da10-4af4-94af-868947f45d02\nmd\"### Question 4\n\nA parallel-plate capacitor is connected to a battery and becomes fully charged.\nThe capacitor is then disconnected, and the separation between the plates is increased in such a way that no charge leaks off.\nThe energy stored in this capacitor has\n\n- increased.\n\n- become zero.\n\n- decreased.\n\n- not changed.\"\n\n# \u2554\u2550\u2561 e664b06c-2924-423c-9f1b-52174fca7cc3\nmd\"**Answer:** increased.\n\n**Explanation:** $C = \\frac{\\epsilon_0 A}{d}$\"\n\n# \u2554\u2550\u2561 7ec6edf3-7b6d-4ea2-8a4f-86dd2178eb91\nmd\"### Question 5\n\nThe plates of a parallel-plate capacitor are maintained with constant voltage by a battery as they are pulled apart.\nWhat happens to the strength of the electric field during this process?\n\n- It remains constant\n\n- cannot be determined from the information given\n\n- It decreases.\n\n- It increases.\n\n- not changed.\"\n\n# \u2554\u2550\u2561 6de0da98-8dfe-496d-adc8-82c5c9af499e\nmd\"**Answer:** It decreases.\n\n**Explanation:** $C = \\frac{\\epsilon_0 A}{d}$\"\n\n# \u2554\u2550\u2561 d518d932-52d5-45de-aaf4-d94dfaf3eb64\nmd\"### Question 6\n\nConsider two copper wires.\nOne has twice the cross-sectional area of the other.\nHow do the resistances of these two wires compare?\n\n- The thicker wire has twice the resistance of the shorter\n\n- Both wires have the same resistance\n\n- none of the given answers\n\n- The thicker wire has half the resistance of the shorter wire.\"\n\n# \u2554\u2550\u2561 570a8d6f-b108-4bba-81a4-8adffe79cea5\nmd\"**Answer:** The thicker wire has half the resistance of the shorter wire.\n\n**Explanation:** $R = \\rho \\frac{L}{A}$\"\n\n# \u2554\u2550\u2561 e275bc47-c73c-4cf5-bbdb-bebee2fb08af\nmd\"### Question 7\n\nConsider two copper wires.\nOne has twice the length of the other.\nHow do the resistances of these two wires compare?\n\n- The longer wire has twice the resistance of the shorter wire.\n\n- The longer wire has half the resistance of the shorter wire.\n\n- Both wires have the same resistance.\n\n- none of the given answers\"\n\n# \u2554\u2550\u2561 07adb0ac-6cb2-40ed-ad5f-659a6f58303b\nmd\"\n##### Incorrect\n**Answer:** Both wires have the same resistance\"\n\n# \u2554\u2550\u2561 276749db-45c7-43b9-a746-e48a6bdede2e\nmd\"##### Correct answer\n\nThe longer wire has twice the resistance of the shorter wire.\n\n**Explanation:**\nRefer to the formula sheet to find that the resistance of a wire is\n\n$R = \\rho \\frac{L}{A}$\n\"\n\n# \u2554\u2550\u2561 4689568d-ba6d-4954-86ba-3fcadb52f957\nmd\"### Question 8\n\nIf the voltage across a circuit of constant resistance is doubled, the power dissipated by that circuit will\n\n- decrease to one fourth.\n\n- decrease to one half.\n\n- double.\n\n- quadruple.\"\n\n# \u2554\u2550\u2561 9a0b5bac-f167-4150-beaa-3c770dbc8cac\nmd\"**Answer:** quadruple.\n\n**Explanation:** $P = \\frac{V^2}{R}$\"\n\n# \u2554\u2550\u2561 7094b76b-519e-4427-91fe-88d33304fef1\nmd\"### Question 9\n\nIf the resistance in a constant voltage circuit is doubled, the power dissipated by that circuit will\n\n- decrease to one-fourth its original value.\n\n- increase by a factor of four.\n\n- decrease to one-half its original value.\n\n- increase by a factor of two.\"\n\n# \u2554\u2550\u2561 82836166-8d43-4c15-a2d8-ac451a1d20ab\nmd\"**Answer:** decrease to one-half its original value.\n\n**Explanation:** $P = \\frac{V^2}{R}$\"\n\n# \u2554\u2550\u2561 06389e81-54a5-46df-b7a6-fd11b44c5406\nmd\"### Question 10\n\nIf the resistance in a circuit with constant current flowing is doubled, the power dissipated by that circuit will\n\n- decrease to one fourth.\n\n- quadruple\n\n- decrease to one half.\n\n- double\"\n\n# \u2554\u2550\u2561 5b87298f-9b97-4f7c-aab5-b3d740f9cab0\nmd\"**Answer:** double\n\n**Explanation:** $P = I^2 R$\"\n\n# \u2554\u2550\u2561 980aa663-98cd-42be-9b92-238a452f537a\nmd\"#### Question 11\n\nConsider three identical resistors, each of resistance R.\nThe maximum power each can dissipate is P.\nThree of the resistors are connected in series, and a fourth is connected in parallel with these three.\nWhat is the maximum power this network can dissipate?\n\n- 2P\n\n- 3P\n\n- 3P/2\n\n- 4P/3\"\n\n# \u2554\u2550\u2561 44d12feb-10cf-48b2-929a-8e6ff39a9496\nmd\"**Answer:** 4P/3\"\n\n# \u2554\u2550\u2561 9f4b96a0-2b5e-47d0-8694-1c1d2144fd01\nmd\"### Question 12\n\nA charged particle is observed traveling in a circular path in a uniform magnetic field.\nIf the particle had been traveling twice as fast, the radius of the circular path would be\n\n- four times the original radius.\n\n- twice the original radius.\n\n- one-fourth the original radius.\n\n- one-half the original radius.\"\n\n# \u2554\u2550\u2561 2434b3b5-2da8-4c1b-8bf2-522fc75b5cd1\nmd\"**Answer:** one-half the original radius.\n\n**Explanation:** $2B = \\frac{\\mu_0}{2\\pi} \\frac{I}{r} \\implies r \\propto \\frac{1}{2}$\"\n\n# \u2554\u2550\u2561 72bd9e28-5048-48f2-a110-f5feaa67caee\nmd\"### Question 13\n\nAt a particular instant, a proton moves eastward at speed V in a uniform magnetic field that is directed straight downward.\nThe magnetic force that acts on it is\n\n- directed to the north.\n\n- directed upward.\n\n- zero\n\n- directed to the south.\"\n\n# \u2554\u2550\u2561 a8dd4343-6913-4e19-bc12-23e89c9e6236\nmd\"**Answer:** directed to the north.\"\n\n# \u2554\u2550\u2561 239d8478-4571-4155-90e3-95ae1deb4939\nmd\"### Question 14\n\nAn electron moving along the +x axis enters a region where there is a uniform magnetic field in the +y direction.\nWhat is the direction of the magnetic force on the electron? (+x to the right, +y up, and +z out of the page.)\n\n- -z direction\n\n- -x direction\n\n- -y direction\n\n- +z direction\"\n\n# \u2554\u2550\u2561 a748ef26-adc8-4fe5-ad2b-8c8b1d79b54d\nmd\"**Answer:** -z direction\"\n\n# \u2554\u2550\u2561 f584bd06-21b6-41c2-b729-84b77acd2e2f\nmd\"### Question 15\n\nWhat is the current direction in this loop?\nAnd where is the north magnetic pole of this current loop?\n\n- Current CW; north pole on top\n\n- Current CW; north pole on bottom\n\n- Current CCW; north pole on bottom\n\n- Current CCW; north pole on top\"\n\n# \u2554\u2550\u2561 0fbb0fa0-6c8e-497f-856f-2f3f8f2ce735\nImage(\"Unit9/P3QC1.png\")\n\n# \u2554\u2550\u2561 4e172839-92b8-411a-ab77-3f8238d3a5e1\nmd\"**Answer:** Current CW; north pole on bottom\"\n\n# \u2554\u2550\u2561 51cf30e3-8fdc-4b62-a1a6-9252ed608dc9\nmd\"### Question 16\n\nA long, straight wire extends into and out of the screen.\nThe current in the wire is\n\n- Into the screen\n\n- Not enough info to tell the direction.\n\n- There is no current in the wire.\n\n- Out of the screen.\"\n\n# \u2554\u2550\u2561 1183767b-3d27-49c0-91ba-fd65e96cf773\nImage(\"Unit9/P1QC1.png\")\n\n# \u2554\u2550\u2561 9086f546-aa48-438e-b682-b0f27b2bff5c\nmd\"**Answer:** Out of the screen.\"\n\n# \u2554\u2550\u2561 d78a8b7b-4ea8-45ad-9d8d-331a090170a4\nmd\"### Question 17\n\nSolenoid 2 has twice the diameter, twice the length, and twice as many turns as solenoid 1.\nHow does the field B\u2082 at the center of solenoid 2 compare to B\u2081 at the center of solenoid 1?\n\n- B2 = 4B1\n\n- B2 = B1\n\n- B2 = B1/2\n\n- B2 = 2B1\n\n- B2 = B1/4\"\n\n# \u2554\u2550\u2561 b7181523-93b0-4865-9c91-9ee965124bfa\nImage(\"Quiz/Q17.png\")\n\n# \u2554\u2550\u2561 fe0fb4f9-6d8f-48e7-9809-71c55035ba75\nmd\"**Answer:** B2 = B1\"\n\n# \u2554\u2550\u2561 39fe00bb-0eb1-4f31-8073-e17186da71e7\nmd\"### Question 18\n\nWhere is the north magnetic pole of this current loop?\n\n- Right side\n\n- Top side\n\n- Left side\n\n- Bottom side\"\n\n# \u2554\u2550\u2561 818d7074-6ce6-4746-a15e-a101ae653ec9\nImage(\"Quiz/Q18.png\")\n\n# \u2554\u2550\u2561 da06ac7d-67f3-483c-aad0-98529cf44a28\nmd\"**Answer:** Bottom side\"\n\n# \u2554\u2550\u2561 eae1f535-37ac-48dd-8f2c-65b88852fa43\nmd\"### Question 19\n\nWhat is the induced current direction in the loop?\n\n- In at the top, out at the bottom.\n\n- Out at the top, in at the bottom.\"\n\n# \u2554\u2550\u2561 d0224ffa-a5ef-465c-89fe-bc293e4d46ad\nImage(\"Quiz/Q19.png\")\n\n# \u2554\u2550\u2561 4ff2da18-3d84-4cc8-9588-9fcc017aa4db\nmd\"\n##### Incorrect\n**Answer:** Out at the top, in at the bottom.\"\n\n# \u2554\u2550\u2561 73709c95-3e75-48b9-9465-b80684920db7\nmd\"##### Correct answer\n\nIn at the top, out at the bottom.\n\n**Explanation:**\nBecome familiar with Lenz's Law.\nThe change in magnetic flux is increasing, so the induced current's direction is switched.\n\"\n\n# \u2554\u2550\u2561 6036b824-7567-444a-8bad-a0472aeb549e\nmd\"### Question 20\n\nThe bar magnet is pushed toward the center of a wire loop.\nWhich is true?\n\n- There is a clockwise induced current in the loop.\n\n- There is no induced current in the loop.\n\n- There is a counterclockwise induced current in the loop.\"\n\n# \u2554\u2550\u2561 9a0f2ce9-6ac6-4c9a-a1dc-dc2e93187247\nImage(\"Quiz/Q20.png\")\n\n# \u2554\u2550\u2561 3bd6fefb-1475-4044-b637-f2358469d38f\nmd\"**Answer:** There is a clockwise induced current in the loop.\"\n\n# \u2554\u2550\u2561 87b9e579-9e52-4d85-8c33-8438c548184a\nmd\"### Question 21\n\nThe current in the straight wire is decreasing.\nWhich is true?\n\n- There is no induced current in the loop.\n\n- There is a counterclockwise induced current in the loop.\n\n- There is a clockwise induced current in the loop.\"\n\n# \u2554\u2550\u2561 8633003f-0de3-466a-974a-9087eaea00ec\nImage(\"Unit10/P2QC2.png\")\n\n# \u2554\u2550\u2561 ffa99665-c461-43ee-9939-e6027e53fad8\nmd\"**Answer:** There is a clockwise induced current in the loop.\"\n\n# \u2554\u2550\u2561 81deea62-431f-404e-9df4-7536baa0cabd\nmd\"### Question 22\n\nThe out of the page magnetic field in the circular region is decreasing.\nWhich is the induced electric field?\n\n- D\n\n- B\n\n- C\n\n- A\"\n\n# \u2554\u2550\u2561 cb6cc92b-2d9a-4ee7-9e82-a175c0d1202f\nImage(\"Quiz/Q22.png\")\n\n# \u2554\u2550\u2561 16e94b95-66bd-4b73-8185-e564eb2d6789\nmd\"\n##### Incorrect\n**Answer:** A.\"\n\n# \u2554\u2550\u2561 6516bc36-7df5-4522-90e1-57ddbf01bd9d\nmd\"##### Correct answer\n\nB.\n\n**Explanation:** Use the right-hand rule into the screen. The electric field is induced to oppose the direction of the increasing magnetic field.\"\n\n# \u2554\u2550\u2561 aab4689d-9e64-486c-8913-99972148d7f5\nmd\"### Question 23\n\nAn electromagnetic wave is traveling to the east.\nAt one instant at a given point its E vector points straight up.\nWhat is the direction of its B vector?\n\n- down\n\n- east\n\n- south\n\n- north\"\n\n# \u2554\u2550\u2561 b7a99f37-aeb3-4123-aa05-dadfca72f06e\nmd\"\n##### Incorrect\n**Answer:** north\"\n\n# \u2554\u2550\u2561 c6563fbe-7118-4ddc-a922-9e9327b46bc5\nmd\"##### Correct answer\n\nsouth\n\n**Explanation:** Apply the right-hand rule where $\\vec{E} \\times \\vec{B}$ determines the direction of the electromagnetic wave.\"\n\n# \u2554\u2550\u2561 79f4dc36-9ea9-4f22-9e0a-23f7e7bc139e\nmd\"### Question 24\n\nA lens creates an image as shown.\nIn this situation, the object distance $s$ is\n\n- Larger than the focal length f\n\n- Larger than 2f\n\n- Equal to the focal length f.\n\n- Larger than the focal length f but smaller than 2f\"\n\n# \u2554\u2550\u2561 80c5e3e4-61bd-46cd-aa7c-1f6b098269a6\nImage(\"Quiz/Q24.png\")\n\n# \u2554\u2550\u2561 cd63ab55-26fe-4015-b171-0a1073eee06d\nmd\"**Answer:** Larger than the focal length f but smaller than 2f\"\n\n# \u2554\u2550\u2561 f08902fd-e2b8-47bc-b6fe-ffee0c0b5267\nmd\"### Question 25\n\nA lens creates an image as shown.\nIn this situation, the image distance $s'$ is\n\n- Larger than the focal length f.\n\n- Smaller than focal length f.\n\n- Equal to the focal length f.\"\n\n# \u2554\u2550\u2561 ed2b1790-224a-442e-9971-dc8f5313dbcc\nImage(\"Quiz/Q24.png\")\n\n# \u2554\u2550\u2561 c32eba5a-fc0e-4eb3-b179-3c4ef4dbc000\nmd\"**Answer:** Larger than the focal length f.\"\n\n# \u2554\u2550\u2561 574b6d09-5147-404b-a0b7-3a7f6c88e251\nmd\"### Question 26\n\nYou see an upright, magnified image of your face when you look into magnifying \\\"cosmetic mirror\\\". The image is located\n\n- Behind the mirror's surface.\n\n- In front of the mirror's surface.\n\n- Only in your mind because it's a virtual image\n\n- On the mirror's surface.\"\n\n# \u2554\u2550\u2561 6a8c8c80-3833-42ef-b5e2-b36385d2a456\nmd\"**Answer:** Behind the mirror's surface\"\n\n# \u2554\u2550\u2561 3d9f7f6a-f6d4-4566-80ca-61b791cde540\nmd\"### Question 27\n\nIn an optical setup consisting of a single concave mirror and an object we measure the magnification factor M=-3.\nWe can conclude that this image is:\n\n- Inverted and virtual\n\n- Upright and virtual\n\n- Upright and real\n\n- Inverted and real\"\n\n# \u2554\u2550\u2561 e25ab1b9-ead0-48cd-8bb4-8611cba660dc\nmd\"**Answer:** Inverted and real\"\n\n# \u2554\u2550\u2561 e18a7261-ef49-4a4d-a961-4d4977554e1d\nmd\"### Question 28\n\nIn an optical setup consisting of a single lens and an object we measure the magnification factor M = -1.0.\nWe can conclude that object in this setup is:\n\n- At distance greater than twice the focal length\n\n- Exactly at point of twice the focal length\n\n- Between focal point and point of twice the focal length\n\n- Between lens and focal point\"\n\n# \u2554\u2550\u2561 2c9665eb-2e88-4405-8eb6-4804253a34dd\nmd\"**Answer:** Exactly at point of twice the focal length\"\n\n# \u2554\u2550\u2561 bb10b8d6-d5e3-415f-bda7-9f26cd52f3a6\nmd\"## Diagnostic Test 5\"\n\n# \u2554\u2550\u2561 ff1c72bf-e271-46df-a288-8619a9f0a2e4\nmd\"### Conceptual Question 22.14\n\nCharges **A** and **B** in the figure are equal. Each charge exerts a force on the other of magnitude $F$. Suppose the charge of **B** is increased by a factor of 4, but everything else is unchanged.\"\n\n# \u2554\u2550\u2561 c40cfee1-3a46-4b05-ad7e-e2a8126f56cd\nImage(\"Unit5/DTP1.png\")\n\n# \u2554\u2550\u2561 c24e62c5-08e4-465c-b8cb-80328a8df16f\nmd\"#### Part A\n\nIn terms of $F$, what is the magnitude of the force on **A**?\n\n**Express your answer in terms of $F$.**\"\n\n# \u2554\u2550\u2561 19d8e693-2fa4-4311-8fab-40c636d19169\nmd\"**Answer:** $F_A = 4F$\"\n\n# \u2554\u2550\u2561 c810aee0-42df-4e43-9a0c-5b3a37e0c892\nmd\"#### Part B\n\nIn terms of $F$, what is the magnitude of the force on **B**?\n\n**Express your answer in terms of $F$.**\"\n\n# \u2554\u2550\u2561 058a4e62-655b-4e86-8ac9-c14c9f303153\nmd\"**Answer:** $F_B = 4F$\"\n\n# \u2554\u2550\u2561 9594aa75-3585-4a4b-8f16-0e23e999268d\nmd\"### Conceptual Question 22.15\n\nThe electric force on a charged particle in an electric field is $F$.\"\n\n# \u2554\u2550\u2561 4379e2f3-d536-40c1-bff6-dbd652c496ff\nmd\"#### Part A\n\nWhat will be the force if the particle's charge is tripled and the electric field strength is halved?\n\n**Give your answer in terms of F**\"\n\n# \u2554\u2550\u2561 746b5d5b-5fde-4270-9e52-129f21ac722b\nmd\"**Answer:** $F' = \\frac{3}{2}F$\n\n**Explanation:** $\\vec{F} = q\\vec{E}$\"\n\n# \u2554\u2550\u2561 40616231-34a0-4308-ac97-05ef93da2c86\nmd\"### Problem 22.17 - Enhanced - with Feedback\n\nYou may want to review (Pages 613 - 616).\"\n\n# \u2554\u2550\u2561 f8c5aaf6-7cbb-4a87-b08c-fbea27e0658a\nImage(\"Unit5/DTP3.png\")\n\n# \u2554\u2550\u2561 65c05232-36bb-4df8-8146-4e8237e11758\nmd\"#### Part A\n\nWhat is the magnitude of the net electric force on charge A in the figure (Figure 1)?\nAssume that $q_1$ = 1.2 nC and $q_2$ = 5.2 nC.\n\n**Express your answer to two significant figures and include the appropriate units.**\"\n\n# \u2554\u2550\u2561 ae4eb3d7-cb85-4655-9e12-fdbb1e99af1b\nmd\"**Answer:**\n\n**Explanation:** Use $F = \\frac{k q_1 q_2}{r}$\n\n$\\begin{gather*}\nq_1 = 1.2 \\text{ nC} = 1.2 \\times 10^{-9} \\text{ C} \\\\\nq_2 = 5.2 \\text{ nC} = 5.2 \\times 10^{-9} \\text{ C} \\\\\nr = 1 \\text{ cm} = 0.01 \\text{ m} \\\\\n|F| = \\left|\\frac{kq_1q_1}{r^2} - \\frac{kq_1q_2}{(2r)^2}\\right| = 1.1 \\times 10^{-5} \\text{ N}\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 9319bfbb-97c1-44f4-b605-16ee995476c3\nlet\n\tq1 = 1.2e-9\n\tq2 = 5.2e-9\n\tr = 1 / 100\n\tk = 9e9\n\tF = k*q1*q1 / r^2 - k*q1*q2/(2r)^2\nend\n\n# \u2554\u2550\u2561 ef5e8506-4a02-42a1-950d-4a53aecd42d7\nmd\"#### Part B\n\nWhat is the direction of the net electric force on charge A?\n\n- right\n\n- left\n\n- up\n\n- down\"\n\n# \u2554\u2550\u2561 9f5e3964-3c67-41c8-b194-3dcdd6d485c2\nmd\"**Answer:** left\n\n**Explanation:** Compare the two forces being exerted on charge A.\"\n\n# \u2554\u2550\u2561 5fe3b6d7-2277-4c43-8e21-7cd4203433cc\nlet\n\tq1 = 1.2e-9\n\tq2 = 5.2e-9\n\tr = 1 / 100\n\tk = 9e9\n\tFB = k*q1*q1 / r^2\n\tFA = -k*q1*q2 / (2r)^2\n\tFB, FA\nend\n\n# \u2554\u2550\u2561 c50bcc01-6ab1-43d6-bdb0-4e9e17b524c0\nmd\"## Diagnostic Test 9 (Chapter 29)\"\n\n# \u2554\u2550\u2561 4830439e-3665-44c1-88c4-2b63da020890\nmd\"### Problem 29.5\"\n\n# \u2554\u2550\u2561 dde0edeb-b85c-4bca-b5e9-a75255dc50af\nImage(\"Unit9/DTP1.png\")\n\n# \u2554\u2550\u2561 1120ad44-3519-4a0e-b464-d3666470ca35\nmd\"#### Part A\n\nWhat is the magnetic field at the position of the dot in the figure (Figure 1)?\nGive your answer as a vector.\n\nExpress your answer in terms of the unit vectors $\\hat{i}$, $\\hat{j}$, and $\\hat{k}$. Use the 'unit vector' button to denote unit vectors in your answer.\"\n\n# \u2554\u2550\u2561 fefd36c1-8607-4825-b72b-4d4f0f357523\nmd\"**Answer:** \n$\\begin{gather*}\nB = \\frac{\\mu_0}{4\\pi} \\frac{qv\\sin{\\theta}}{r^2}\n\\end{gather*}$\"\n\n# \u2554\u2550\u2561 ffb0edda-d3b5-4154-9df0-95758f3f3f12\nlet\n\tv = 2e7u\"m/s\"\n\t\u03b8 = 45u\"\u00b0\"\n\tq = e\n\tx = (2/100)u\"m\"\n\ty = (2/100)u\"m\"\n\t\n\t# B = ?\n\t# Magnetic field of a moving charge\n\t# B = (\u03bc0 / 4\u03c0) * (qvsin(\u03b8) / r^2)\n\t\n\t# r = ?\n\t# Distance formula\n\tr = sqrt(x^2 + y^2)u\"m\"\n\t\n\tB = (\u03bc0 / 4\u03c0) * (q*v*sin(\u03b8))/(r^2)\nend\n\n# \u2554\u2550\u2561 c45dde4e-d60f-4543-a2ee-d061f45bc848\nmd\"### Problem 29.9\n\nAlthough the evidence is weak, there has been concern in recent years over possible health effects from the magnetic fields generated by electric transmission lines. A typical high-voltage transmission line is 20 m above the ground and carries a 200 A current at a potential of 110 kV.\"\n\n# \u2554\u2550\u2561 0bcd7967-9648-4f80-8b8b-7a1459f963b7\nmd\"#### Part A\n\nWhat is the magnetic field strength on the ground directly under such a transmission line?\n\n**Express your answer with the appropriate units.**\"\n\n# \u2554\u2550\u2561 ee17d3a7-a845-4b0d-991f-0d1546bd89c8\nmd\"**Answer:**\n$B = \\frac{\\mu_0}{2\\pi} \\frac{I}{r}$\"\n\n# \u2554\u2550\u2561 0497b136-6780-4e31-9066-5cab808fa9f3\nlet\n\tr = 20u\"m\"\n\tI = 200u\"A\"\n\t\n\t# B = ?\n\t# Magnetic field of a wire\n\tB = (\u03bc0 / 2\u03c0) * (I / r)\nend\n\n# \u2554\u2550\u2561 a57d36a2-c69a-4a4b-b623-e7067527a8d6\nmd\"#### Part B\n\nWhat percentage is this of the earth's magnetic field of 50 \u03bcT?\n\n$\\frac{B_{\\text{wire}}}{B_{\\text{earth}}}$\"\n\n# \u2554\u2550\u2561 42774779-7c00-46c6-af46-29e6bfa5b362\nlet\n\t(r, I) = 20u\"m\", 200u\"A\"\n\tB_wire = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\t(B_wire / B_earth) * 100\nend\n\n# \u2554\u2550\u2561 924acf6e-5ada-4a64-ad51-1ff5ce84bb5e\nmd\"# Notice\n\nFrom this point on, I will no longer write out each problem since it takes too long and my exam is coming up very soon.\"\n\n# \u2554\u2550\u2561 7bb28d6d-6d18-4209-b48e-5e87eedec74b\nmd\"### Problem 29.14\"\n\n# \u2554\u2550\u2561 d878a54c-a6e3-4fa7-81c9-5b045781f353\nlet\n\tI = 12u\"A\"\n\tr = (2/100)u\"m\"\n\t\n\t# B = ?\n\t# Magnetic field of a wire + superposition principle\n\t# B = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\tB1a = (\u03bc0 / 2\u03c0) * (I / r)\n\tB2a = -(\u03bc0 / 2\u03c0) * (I / 3r)\n\tB1b = (\u03bc0 / 2\u03c0) * (I / r)\n\tB2b = (\u03bc0 / 2\u03c0) * (I / r)\n\tB1c = -(\u03bc0 / 2\u03c0) * (I / 3r)\n\tB2c = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\tB1a + B2a, (B1a, B2a), B1b + B2b, B1c + B2c, (B1c, B2c)\nend\n\n# \u2554\u2550\u2561 862df6ac-1091-48b6-93a3-6cf64aa27dde\nmd\"### Problem 29.46\"\n\n# \u2554\u2550\u2561 294246d2-c696-4f46-b746-d5f40234eb59\nlet\n\tI = 4.0u\"A\"\n\tr1 = (1 / 100)u\"m\"\n\tr2 = (2.0 / 100)u\"m\"\n\ts1 = 2\u03c0 * r1\n\ts2 = 2\u03c0 * r2\n\t\n\t# B = ?\n\t# Magnetic field of a loop + superposition principle\n\t# B = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\tB1 = (\u03bc0 / 4\u03c0) * (I / r1)\n\tB2 = (\u03bc0 / 4\u03c0) * (I / r2)\n\t\n\tB1 + B2 # Incorrect!\nend\n\n# \u2554\u2550\u2561 9a3ac337-de34-41cb-96cf-7c2dd7cd47e3\nmd\"### Problem 29.44\"\n\n# \u2554\u2550\u2561 5bfab755-ed80-4318-ba3e-5774c89fff9f\nmd\"$B = \\frac{\\mu_0 IR^2}{2(z^2 + R^2)^{3/2}} = \\frac{\\mu_0 I}{4R}$\n\n$(z^2 + R^2)^{3/2} = 2R^3$\n\n$z^2 + R^2 = 1.59R^2$\n\n$z^2 = 0.59R^2$\n\n$z = 0.77$\"\n\n# \u2554\u2550\u2561 06f1e08c-95b3-430d-86a1-3de041fe1506\nlet\n\t# z = ?\n\t# Magnetic field of a circular loop\n\t# B = (\u03bc0 I R^2) / (2(z^2 + R^2)^(3/2))\n\t\n\t# Compare to center of loop, z = 0\n\t# B = (\u03bc0 I) / 2R\n\t#\n\t# Half it\n\t# B = (\u03bc0 I) / 4R\n\t\n\tsqrt(2^(2/3) - 1)\nend\n\n# \u2554\u2550\u2561 64d28e04-f67f-4b98-afd6-9c16a15f2277\nmd\"### Problem 29.29\"\n\n# \u2554\u2550\u2561 a502e763-da48-4811-ab9b-6d2075c94758\nlet\n\tB = 3.0000u\"T\"\n\tu = 1.6605e-27u\"kg\"\n\tmO2 = 2 * 15.995 * u\n\tmN2 = 2 * 14.003 * u\n\tmCO = 12.000 * u + 15.995 * u\n\tq = 1.6022e-19u\"C\"\n\t\n\t# f = ?\n\t# Frequency of cyclotron\n\t# f = qB / (2\u03c0 * m)\n\t\n\tfO2 = q*B / (2\u03c0 * mO2)\n\tfN2 = q*B / (2\u03c0 * mN2)\n\tfCO = q*B / (2\u03c0 * mCO)\n\tfO2, fN2, fCO # Hz\nend\n\n# \u2554\u2550\u2561 634882f3-baf9-4e98-a7c2-03c08314322d\nmd\"### Problem 29.30\"\n\n# \u2554\u2550\u2561 0a95a636-be2d-43cc-ac71-707c88d5f117\nlet\n\tv = (0.1c)u\"m/s\"\n\td = (42 / 100)u\"m\"\n\tr = (d / 2)u\"m\"\n\tq = e\n\tB = (mp * v) / (q * r)\nend\n\n# \u2554\u2550\u2561 9fa6bc2f-f99f-4d4b-91fa-695d04f0e83b\nmd\"### Problem 29.33\"\n\n# \u2554\u2550\u2561 2b72b700-5b08-4744-b29e-2714ec33580a\nlet\n\tm = (2 / 1000)u\"kg\"\n\tI = 1.6u\"A\"\n\td = (12 / 100)u\"m\"\n\tB = m*g / (I*d)\nend\n\n# \u2554\u2550\u2561 998d6c8b-3af1-4549-b633-13f4977e9afe\nmd\"### Problem 29.34\"\n\n# \u2554\u2550\u2561 a18585ce-d13b-4581-9c49-65c54bc3436f\nlet\n\tL = 10 / 100\n\td = 5 / 1000\n\tF = 7.2e-5\n\tV = 9\n\tR1 = 2\n\tI1 = V / R1\n\tI2 = F * 2\u03c0 * d / (\u03bc0 * I1 * L)\n\tR2 = V / I2\nend\n\n# \u2554\u2550\u2561 d8f53b47-1267-426b-a6d6-15b397dc8d9e\nmd\"### Problem 29.38\"\n\n# \u2554\u2550\u2561 f6840354-2325-470c-8fd1-a3246f236550\nlet\n\ts = (5.50 / 100)u\"m\"\n\tI = (510 / 1000)u\"A\"\n\tB = 1.60u\"T\"\n\t\u03b8 = 30u\"\u00b0\"\n\t\u03c4 = I*s^2*B*sin(\u03b8)\nend\n\n# \u2554\u2550\u2561 dc205f5d-0512-4429-8f26-f60e9f07115b\nmd\"## PSW 9 (Chapter 29)\"\n\n# \u2554\u2550\u2561 d6e4e26e-cb79-4aa1-8d97-1f2f238aeb10\nmd\"### Problem 29.15\"\n\n# \u2554\u2550\u2561 9f8323d5-1480-490a-b088-be9d69bf6e00\nlet\n\td = (6.0 / 1000)u\"m\"\n\t\u2130 = 60u\"V\"\n\tR100 = 100u\"\u03a9\"\n\tR30 = 30u\"\u03a9\"\n\tR20 = 20u\"\u03a9\"\n\tR = (1/R100 + 1/(R30+R20))^-1\n\tI = \u2130 / R\n\tI1 = \u2130 / R100\n\tI2 = \u2130 / (R30 + R20)\n\tB = (\u03bc0 / 2\u03c0) * (I2 / d)\nend\n\n# \u2554\u2550\u2561 4a0ecf24-5fc7-4586-9313-3c2fc33ea91c\nmd\"### Problem 29.37\"\n\n# \u2554\u2550\u2561 c494b302-bf92-44b5-abf2-59956da672d5\nlet\n\tl = 1u\"m\"\n\tm = (58 / 1000)u\"kg\"\n\tr = (4 / 100)u\"m\"\n\t\u03b8 = 60u\"\u00b0\"\n\t# 2F = mg\n\t# \u27f9 2IlB sin(\u03b8) = mg\n\t# \u27f9 \u03bc0 * I^2 sin(\u03b8) / \u03c0r = mg\n\t# \u27f9 I = sqrt(mg * \u03c0r / (\u03bc0 * sin(\u03b8)))\n\tI = sqrt(m*g * \u03c0*r / (\u03bc0 * sin(\u03b8)))\nend\n\n# \u2554\u2550\u2561 c8d2f67e-33ec-4ecd-b7f4-2037c1ac91ec\nmd\"### Problem 29.50\"\n\n# \u2554\u2550\u2561 6bc0b95d-be29-42ba-93ca-bb1bc910256e\nlet\n\td = (2 / 100)u\"m\"\n\tL = (8 / 100)u\"m\"\n\tB = 0.10u\"T\"\n\tI = 1.6u\"A\"\n\tN = B * L / (\u03bc0 * I)\nend\n\n# \u2554\u2550\u2561 971c6622-d701-4142-addf-55c2fb8007bd\nmd\"### Problem 29.52\"\n\n# \u2554\u2550\u2561 beedbeca-0477-4162-9c9e-05aa9cfd907f\nmd\"\n$B = \\frac{\\mu_0 I a^2}{2(x^2 + a^2)^{3/2}}$\"\n\n# \u2554\u2550\u2561 63619364-64ad-44aa-bbc5-1a847358c892\nlet\n\td = (18 / 100)u\"m\"\n\tr = (d / 2)\n\tB = 6e-12u\"T\"\n\tx = r\n\tI = (B * 2 * (x^2 + r^2)^(3/2)) / (\u03bc0 * r^2)\n\tI = (B * 2 * (r^2 + r^2)^(3/2)) / (\u03bc0 * r^2)\n\tI = (B * 2 * (2r^2)^(3/2)) / (\u03bc0 * r^2)\n\tI = (B * 2 * (sqrt(2) * r)^(3)) / (\u03bc0 * r^2)\nend\n\n# \u2554\u2550\u2561 d05ed603-19ce-4c7d-b67d-78e7258378d5\nmd\"### Problem 29.73\"\n\n# \u2554\u2550\u2561 21c3aacf-abb7-4f65-8681-440ee3c13341\nlet\n\tm = 4.0u\"kg\"\n\ts = 4.0u\"m\"\n\tI = 25u\"A\"\n\t\u03b8 = 25u\"\u00b0\"\n\t\n\t# Force acts on center of object\n\t# \u03c4 = Fd cos(\u03b8)\n\t# \u27f9 mg (s / 2) cos(\u03b8) = Is^2B sin(90-\u03b8)\n\t# \u27f9 B = mg cos(\u03b8) / (2Is sin(90-\u03b8))\n\tB = m*g*cos(\u03b8) / (2*I*s*sin(90u\"\u00b0\"-\u03b8))\nend\n\n# \u2554\u2550\u2561 651147e8-f7f8-4b87-9793-21e9b82a7c91\nmd\"### Problem 29.65\"\n\n# \u2554\u2550\u2561 f30306c0-0006-4a6f-873a-07fadcdc5d2d\nlet\n\tB = u\"T\"(29.0u\"mT\")\n\tv = 4.9e6u\"m/s\"\n\t\u03b8 = 30u\"\u00b0\"\n\tvpp = v*cos(\u03b8)\n\tvpl = v*sin(\u03b8)\n\tq = e\n\tr = me*(vpp) / (q*B)\n\t\n\t\u0394t = 2\u03c0*r / vpp\n\tp = vpl * \u0394t\nend\n\n# \u2554\u2550\u2561 bfec2cfd-b400-4e73-8a40-7b3665209a5d\nmd\"## Diagnostic Test 10 (Chapter 30)\"\n\n# \u2554\u2550\u2561 1b65d5b3-ffb4-4c4c-a071-24cf1cfdd0c4\nmd\"### Problem 30.4\"\n\n# \u2554\u2550\u2561 6a010c2a-f8e6-44db-84ff-8e9a9593a29e\nlet\n\ta = u\"m\"(30u\"cm\")\n\tB1 = 2.0u\"T\"\n\tB2 = 1.0u\"T\"\n\t\u03a61 = a^2 * B1\n\t\u03a62 = a^2 * B2\n\t\u03a61 - \u03a62\nend\n\n# \u2554\u2550\u2561 e5254b57-0930-4083-b8d5-b37ae9292052\nmd\"### Problem 30.5\"\n\n# \u2554\u2550\u2561 d7fd0a6d-2df6-4614-abf0-72971f07220b\nlet\n\tA = u\"m^2\"((5*10)u\"cm^2\")\n\tB = 5.50e-2u\"T\"\n\t\u03b8 = 45u\"\u00b0\"\n\t\u03a61 = A*B*cos(\u03b8)\n\t\u03a62 = A*B*cos(\u03b8)\n\t\u03a61 + \u03a62\nend\n\n# \u2554\u2550\u2561 e6375fb6-ae08-4442-b7c5-26953bd1cd95\nmd\"### Problem 30.8\"\n\n# \u2554\u2550\u2561 adcf50c9-8c45-46fa-aca3-10a5c660f80b\nlet\n\tds = u\"m\"(2.6u\"cm\")\n\tdl = u\"m\"(7.0u\"cm\")\n\tA = 1/4 * \u03c0 * ds^2\n\tB = 0.16u\"T\"\n\t\u03a61 = \u03a62 = A*B\nend\n\n# \u2554\u2550\u2561 549d2a7f-d307-4c96-9df5-6f936842cc59\nmd\"### Problem 30.11\"\n\n# \u2554\u2550\u2561 4cd51b45-2e77-41a2-9691-2d5e1fc01d3f\nlet\n\ts = u\"m\"(20u\"cm\")\n\tB = 0.30u\"T\"\n\t\u03b8 = 60u\"\u00b0\"\n\tb = s/2\n\th = s*sin(\u03b8)\n\tA = b*h / 2\n\t\u03a6 = A*B\nend\n\n# \u2554\u2550\u2561 3945a727-df1d-4717-b9d1-3b14f9e07adf\nmd\"### Problem 30.12\n\n- Yes, clockwise. Use Lenz's law.\"\n\n# \u2554\u2550\u2561 9c8a76a8-7caa-4d0d-9608-4e98d3955677\nmd\"### Problem 30.13\"\n\n# \u2554\u2550\u2561 bf938d16-4f96-409a-b3e7-39f6873ca275\nlet\n\tB = 0.50u\"T\"\n\tv = 55u\"m/s\"\n\tR = 0.70u\"\u03a9\"\n\tl = u\"m\"(5.0u\"cm\")\n\t\u2130 = v*l*B\n\tI = \u2130 / R\nend\n\n# \u2554\u2550\u2561 e4ecd474-4d48-4414-93b4-6eb029da45eb\nmd\"### Problem 30.15\"\n\n# \u2554\u2550\u2561 b798ffc4-bc44-4add-885f-96b88cf66960\nlet\n\tR = 0.30u\"\u03a9\"\n\tI = u\"A\"(150u\"mA\")\n\ts = u\"m\"(8.0u\"cm\")\n\tE = I*R\n\tA = s^2\n\t# E = d\u03a6/dt = A |dB/dt|\n\tdBdt = E/A\nend\n\n# \u2554\u2550\u2561 1d2210ec-00a9-4960-8686-85df92dae42d\nmd\"### Problem 30.22\"\n\n# \u2554\u2550\u2561 d2162fdf-dc4b-4138-8cc8-6ac803d82566\nlet\n\tV1 = 13000u\"V\"\n\tf = 60u\"Hz\"\n\tV2 = 120u\"V\"\n\tN2 = 150\n\tN1 = N2*V1/V2\n\t\n\tI2 = 220u\"A\"\n\t# I1V1 = I2V2 \u27f9 I1 = I2V2/V1\n\tI1 = I2*V2/V1\n\t\n\tN1, I1\nend\n\n# \u2554\u2550\u2561 57086fd7-66cb-4a72-b589-dc5fa3c8c8c2\nmd\"### Problem 30.26\"\n\n# \u2554\u2550\u2561 a7972df8-734e-4108-86b6-c0a5d0ede63d\nlet\n\tL = (100 / 1000)u\"H\"\n\tRw = 5.0u\"\u03a9\"\n\t\u2130 = 9u\"V\"\n\tRi = 3.0u\"\u03a9\"\n\tR = Rw + Ri\n\tI = \u2130/R\n\tUL = 1/2 * L * I^2\nend\n\n# \u2554\u2550\u2561 465bc5f9-d40c-4227-8962-da6746b1dcc4\nmd\"### Problem 30.27\"\n\n# \u2554\u2550\u2561 2817d2a7-fb3f-4986-adb9-3fb36a66d1b9\nlet\n\td = u\"m\"(3.00u\"cm\")\n\tA = 1/4 * \u03c0 * d^2\n\t\u2113 = u\"m\"(14.0u\"cm\")\n\tN = 200\n\tI = 0.750u\"A\"\n\tLsol = \u03bc0 * N^2 * A / \u2113\n\tUL = 1/2 * Lsol * I^2\nend\n\n# \u2554\u2550\u2561 6f906af6-1379-42a3-b24d-dd5d257241c9\nmd\"### Problem 30.19\"\n\n# \u2554\u2550\u2561 b94bf1ef-fb85-4e1a-92f8-3f47e143f9e6\nlet\n\t# F = ma = qE \u27f9 a = qE/m\n\tdBdt = 0.7u\"T/s\"\n\tra = u\"m\"(1.0u\"cm\")\n\trb = u\"m\"(0.0u\"cm\")\n\trc = u\"m\"(1.0u\"cm\")\n\trd = u\"m\"(2.0u\"cm\")\n\tq = e\n\tEa = ra/2 * dBdt\n\tEb = rb/2 * dBdt\n\tEc = rc/2 * dBdt\n\tEd = rd/2 * dBdt\n\taa = q*Ea / mp # upwards\n\tab = q*Eb / mp # a = 0\n\tac = q*Ec / mp # downwards\n\tad = q*Ed / mp # downwards\n\taa,ab,ac,ad\nend\n\n# \u2554\u2550\u2561 1568e950-6ae3-4eee-8c28-8db36795a929\nmd\"### Problem 30.20\"\n\n# \u2554\u2550\u2561 66cb890a-45af-4faf-8ecd-02df1bcdf3e6\nlet\n\td = u\"m\"(5.0u\"cm\")\n\tB = 2.0u\"T\"\n\tdBdt = 3.40u\"T/s\"\n\tr = u\"m\"(1.60u\"cm\")\n\tE = r/2 * dBdt\nend\n\n# \u2554\u2550\u2561 22fc3d80-341b-4902-89c1-c1a587c28a08\nmd\"## PSW 10 (Chapter 30)\"\n\n# \u2554\u2550\u2561 63b35422-6697-4a1f-8d60-55c977a87360\nmd\"### Problem 30.39\"\n\n# \u2554\u2550\u2561 d410ae9c-d876-4ac2-9d7d-44154cc24325\nlet\n\tN = 100\n\tdc = u\"m\"(5.0u\"cm\")\n\tdw = u\"m\"(0.50u\"mm\")\n\trc = dc / 2\n\tI = 4.0u\"A\"\n\t\u03c1 = 1.7e-8u\"\u03a9\"\n\n\t# |dB/dt| = ?\n\t# Faraday's Law\n\t# E = N|d\u03a6/dt| = NA |dB/dt| \u27f9 |dB/dt| = E/NA\n\t\n\tAw = 1/4 * \u03c0 * dw^2\n\tL = dw * N\n\tR = N * \u03c1 * (2\u03c0*rc) / Aw\n\tE = I * R\n\t\n\tA = 1/4 * \u03c0 * dc^2\n\t\n\tdBdt = E/(N*A)\nend\n\n# \u2554\u2550\u2561 e0a50b78-31a1-45ee-be74-2c6d8b5f13b8\nmd\"### Problem 30.49\"\n\n# \u2554\u2550\u2561 77c5d223-6de7-454f-b6d3-2895600e2b1a\nlet\n\td = u\"m\"(18u\"cm\")\n\tN = 120\n\tf = 60u\"Hz\"\n\t\u2130 = 350u\"V\"\n\t\n\t# B = ?\n\t# Electromotive force of a generator\n\t# \u2130 = NB\u03c9A sin\u03c9t \u27f9 B = \u2130/(N\u03c9Asin(\u03c9t)) = \u2130/(N\u03c9A) since peak of sin = 1\n\t\n\t\u03c9 = 2\u03c0 * f\n\tA = 1/4 * \u03c0 * d^2\n\tB = \u2130/(N*\u03c9*A)\nend\n\n# \u2554\u2550\u2561 85dee8d0-d364-4744-a5ac-5459f7c1a95c\nmd\"### Problem 30.50\"\n\n# \u2554\u2550\u2561 91d1441c-8d8b-4652-b089-5d06d191d21d\nlet\n\tNc = 40\n\tdc = u\"m\"(4.0u\"cm\")\n\tR = 0.40u\"\u03a9\"\n\t\n\tds = u\"m\"(4.0u\"cm\")\n\tLs = u\"m\"(20u\"cm\")\n\tNs = 200\n\t\n\tf = 60u\"Hz\"\n\tI = 0.20u\"A\"\n\t\n\t# I0 = ?\n\t# Magnetic field of a solenoid\n\t# Bsol = \u03bc0 INs / Ls = \u03bc0 (I0 sin(2\u03c0ft)) Ns / Ls\n\t\n\t# Magnetic flux through coil\n\t# \u03a6m = Nc ABsol = Nc A (\u03bc0 (I0 sin(2\u03c0ft)) Ns / Ls)\n\t\n\t# Induced emf in coil\n\t# \u2130 = -d\u03a6/dt = Nc A (2\u03c0f \u03bc0 (I0 cos(2\u03c0ft)) Ns / Ls)\n\t# I = \u2130/R = Nc A (2\u03c0f \u03bc0 (I0 cos(2\u03c0ft)) Ns / Ls) / R\n\t\n\t# Solve for I0\n\t# I0 = (I * Ls * R) / (Nc A 2\u03c0f \u03bc0 Ns)\n\t\n\tA = 1/4 * \u03c0 * ds^2\n\tI0 = I*Ls*R / (Nc*A*2\u03c0*f*\u03bc0*Ns) # The correct answer is 6.0 A.\nend\n\n# \u2554\u2550\u2561 2e69b5bd-0b56-4bf2-a338-b4d373dde517\nmd\"### Problem 30.65\"\n\n# \u2554\u2550\u2561 e65a8d69-d925-4de2-9edd-c33669b756a8\nlet\n\tA = 6.3e-2u\"m^2\"\n\tB = 5.3u\"T\"\n\t\u2130 = 9e-2u\"V\"\n\t\n\t# \u0394t = ?\n\t# Faraday's Law\n\t# \u2130 = \u0394\u03a6m/\u0394t \u27f9 \u0394t = \u0394\u03a6m/\u2130\n\t\n\t# \u0394\u03a6m = ?\n\t# Magnetic flux equation\n\t\u0394\u03a6m = A*B\n\t\n\t\u0394t = \u0394\u03a6m/\u2130\nend\n\n# \u2554\u2550\u2561 e0d4b7ca-578b-4d25-a9e6-81baafa497b0\nmd\"### Problem 30.80\"\n\n# \u2554\u2550\u2561 f44da9f7-87fa-4a44-864b-11e54553b3b0\nlet\n\tR = 2.4e-2u\"\u03a9\"\n\tI = 15u\"A\"\n\tr = u\"m\"(2.0u\"cm\")\n\tl = u\"m\"(4.0u\"cm\")\n\tx = u\"m\"(1.0u\"cm\")\n\tv = 10u\"m/s\"\n\t\n\t# I_loop = ?\n\t# Ohm's Law\n\t# \u2130 = IR \u27f9 I = \u2130/R\n\t\n\t# \u2130 = ?\n\t# Faraday's Law\n\t# \u2130 = d\u03a6/dt = d/dt(AB) = d/dt(xlB) = vlB = vl(B_r - B_rx)\n\t\n\t# B_r = ?\n\t# Magnetic field of a wire\n\tB_r = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\t# B_rx = ?\n\t# Magnetic field of a wire\n\tB_rx = (\u03bc0 / 2\u03c0) * (I / (r+x))\n\t\n\t\u2130 = v*l*(B_r - B_rx)\n\t\n\tI = \u2130/R\nend\n\n# \u2554\u2550\u2561 ca39044c-f352-4fb9-a8be-355f4a01a17d\nmd\"### Problem 30.85\"\n\n# \u2554\u2550\u2561 092aa2fc-ddc6-4729-911c-aa4e21ad7bd9\nlet\n\td = u\"m\"(2.1u\"cm\")\n\tN = 1200\n\tL = 1u\"m\"\n\tr = u\"m\"(0.60u\"cm\")\n\t\u2130 = 5.4e-4u\"V/m\"\n\t\n\t# dI/dt = ?\n\t# Magnetic field of a solenoid\n\t# dB/dt = (\u03bc0 N / L) (dI/dt) \u27f9 dI/dt = (dB/dt) / (\u03bc0 N / L)\n\t\n\t# dB/dt = ?\n\t# Faraday's Law of induction\n\t# \u2130 = r/2 (dB/dt) \u27f9 dB/dt = 2\u2130 / r\n\t\n\tdBdt = 2\u2130 / r\n\t\n\tdIdt = dBdt / (\u03bc0 * N / L)\nend\n\n# \u2554\u2550\u2561 3ece80ba-1bde-48a9-93ff-d6b9778b43e2\nmd\"## Diagnostic Test 11 (Chapter 31)\"\n\n# \u2554\u2550\u2561 07586a45-e8b7-466d-99b5-5b1fc20b32eb\nmd\"### Conceptual Question 31.7\"\n\n# \u2554\u2550\u2561 88053dfe-70e5-498a-bfd1-cb7eb33fad96\nlet\n\t# Part A\n\t# Out of the page\n\t\n\t# Part B\n\t# Upward\nend\n\n# \u2554\u2550\u2561 4c49f6cf-0f0e-408c-acde-ad77faa9903c\nmd\"### Problem 31.19\"\n\n# \u2554\u2550\u2561 da63cb45-9b93-4423-8fd7-0b6aae9e4a29\nlet\n\td = u\"m\"(2.0u\"mm\")\n\tP = u\"W\"(1.6u\"mW\")\n\t\n\t# E0 = ?\n\t# Intensity of electromagnetic wave\n\t# P/A = 1/2c\u03bc0 E0^2\n\t# \u27f9 E0 = sqrt((P/A) * 2c\u03bc0)\n\t\n\t# A = ?\n\tA = 1/4 * \u03c0 * d^2\n\t\n\tE0 = sqrt((P/A) * 2c*\u03bc0)\n\t\n\t# B0 = ?\n\t# EM Field strength relationship\n\t# E0 = cB0\n\t# \u27f9 B0 = E0/c\n\tB0 = E0/c\n\t\n\tE0, B0\nend\n\n# \u2554\u2550\u2561 472fc923-c527-47e4-9ea2-e2a762c08f62\nmd\"### Problem 31.21\"\n\n# \u2554\u2550\u2561 916addb5-4a54-422d-b9c9-5b24c3f16e3f\nlet\n\tP = u\"W\"(160u\"MW\")\n\td = u\"m\"(1.6u\"\u03bcm\")\n\t\n\t# E0 = ?\n\t# Intensity of electromagnetic wave\n\t# P/A = 1/2c\u03bc0 E0^2\n\t# \u27f9 E0 = sqrt((P/A) * 2c\u03bc0)\n\t\n\t# A = ?\n\tA = 1/4 * \u03c0 * d^2\n\t\n\tE0 = sqrt((P/A) * 2c*\u03bc0)\n\t\n\t# Ehydrogen = ?\n\t# Electric field produced by a point charge\n\tq = e\n\tr = u\"m\"(0.053u\"nm\")\n\tEhydrogen = k*q/r^2\n\t\n\tE0, E0 / Ehydrogen\nend\n\n# \u2554\u2550\u2561 bfafde7f-8d61-41fb-be8c-d3bf12404fab\nmd\"### Problem 31.22\"\n\n# \u2554\u2550\u2561 892661d3-046f-4df9-9269-89f26ca79b4c\nlet\n\tP = 1000u\"W\"\n\t\u03bb = u\"m\"(10u\"\u03bcm\")\n\td = u\"m\"(3.0u\"mm\")\n\t\n\t# F = ?\n\t# Radiation force due to beam of light\n\tF = P / c\nend\n\n# \u2554\u2550\u2561 04898c9a-5eca-4d1f-bd19-aec0d16f51d8\nmd\"### Conceptual Question 31.10\"\n\n# \u2554\u2550\u2561 8f1b5656-c3e6-4490-ba16-fbeb3f9c574e\nlet\n\t\u03b8 = Dict(\n\t\t:a => 90u\"\u00b0\",\n\t\t:b => 45u\"\u00b0\",\n\t\t:c => 30u\"\u00b0\",\n\t\t:d => 0u\"\u00b0\",\n\t\t:e => 30u\"\u00b0\"\n\t)\n\t\n\tsort(collect(\u03b8), by=x->(cos(x[2])^2), rev=true)\nend\n\n# \u2554\u2550\u2561 705a1476-eb5d-4f76-af51-4cf3bb61d36d\nmd\"## PSW 11 (Chapter 31)\"\n\n# \u2554\u2550\u2561 ff9db5b4-9875-4279-8957-4cfd3245d6f7\nmd\"### Problem 31.47\"\n\n# \u2554\u2550\u2561 61c3c8a1-4a00-4c1f-8c82-6594bcdfb006\nlet\n\tr = u\"m\"(4.5e9u\"km\")\n\tP = 21u\"W\"\n\t\n\t# I = ?\n\t# Intensity of point source\n\t# I = P/4\u03c0r^2\n\tI = P/(4\u03c0*r^2)\n\t\n\t# E0 = ?\n\t# Intensity of electromagnetic wave\n\t# I = 1/2c\u03bc0 E0^2\n\t# \u27f9 E0 = sqrt(2Ic\u03bc0)\n\tE0 = sqrt(2I*c*\u03bc0)\n\t\n\tI, E0\nend\n\n# \u2554\u2550\u2561 241d99b8-c672-40f3-8b9d-854413a57630\nmd\"### Problem 31.59\"\n\n# \u2554\u2550\u2561 d40d40e9-e141-4161-8cc7-403d30e74304\nlet\n\ts = u\"m\"(10u\"cm\")\n\tE0 = u\"V/m\"(11u\"kV/m\")\n\t\u0394T = 45\n\t\n\t# t = ?\n\t# 0.85P = E/t\n\t# \u27f9 t = E/0.85P\n\t\n\t# P = ?\n\t# Intensity of electromagnetic wave\n\t# P/A = 1/2c\u03bc0 E0^2\n\t# \u27f9 P = A/2c\u03bc0 E0^2\n\t\n\tbegin\n\t\t# A = ?\n\t\t# Area of one side of the cube\n\t\tA = s^2\n\n\t\tP = A/(2c*\u03bc0) * E0^2\n\tend\n\t\n\t# E = ?\n\t# Specific heat of water\n\t# E = Q = mc\u0394T = \u03c1Vc\u0394T\n\t\n\tbegin\n\t\t# \u03c1 = ?\n\t\t# Density of water\n\t\t\u03c1 = 1000u\"kg/m^3\"\n\n\t\t# V = ?\n\t\t# Volume of water\n\t\tV = s^3\n\n\t\t# c = ?\n\t\t# Specific heat capacity of water\n\t\tcw = 4190u\"J/(kg*K)\"\n\t\n\t\tE = Q = \u03c1*V*cw*\u0394T\n\tend\n\t\n\tt = E/0.85P\nend\n\n# \u2554\u2550\u2561 df7ed866-611b-4b64-94fd-b5876ed39336\nmd\"### Problem 31.60\"\n\n# \u2554\u2550\u2561 c74ce47c-697f-4fa6-9d27-bef932bd2d54\nlet\n\tm = 80u\"kg\"\n\t\u0394x = 5.0u\"m\"\n\tP = 1000u\"W\"\n\tt0 = u\"s\"(1.0u\"hr\")\n\t\n\t# t = ?\n\t# Initial time plus extra time\n\t# t = t0 + t1\n\t\n\tbegin\n\t\t# t1 = ?\n\t\t# Change in position over time\n\t\t# v = \u0394x1/t1 \u27f9 t = \u0394x1/v\n\t\t\n\t\tbegin\n\t\t\t# \u0394x1 = ?\n\t\t\t# Change in position after 1.0hr\n\t\t\t# \u0394x1 = \u0394x - \u0394x1\n\n\t\t\tbegin\n\t\t\t\t# \u0394x0 = ?\n\t\t\t\t# Kinematic equation\n\t\t\t\t# \u0394x0 = 1/2 a(t0)^2\n\t\t\t\t\n\t\t\t\tbegin\n\t\t\t\t\t# a = ?\n\t\t\t\t\t# Newton's second law\n\t\t\t\t\t# F = ma \u27f9 a = F/m\n\t\t\t\t\t\n\t\t\t\t\tbegin\n\t\t\t\t\t\t# F = ?\n\t\t\t\t\t\t# Force due to beam of light\n\t\t\t\t\t\t# F = P/c\n\t\t\t\t\t\t\n\t\t\t\t\t\tF = P/c\n\t\t\t\t\tend\n\t\t\t\t\t\n\t\t\t\t\ta = F/m\n\t\t\t\tend\n\t\t\t\t\n\t\t\t\t\u0394x0 = 1/2 * a * t0^2\n\t\t\tend\n\t\t\t\n\t\t\t\u0394x1 = \u0394x - \u0394x0\n\t\tend\n\t\t\n\t\tbegin\n\t\t\t# v = ?\n\t\t\t# Kinematic equation\n\t\t\t# v = at0\n\t\t\t\n\t\t\tv = a*t0\n\t\tend\n\t\t\n\t\tt1 = \u0394x1/v\n\tend\n\t\n\tt = t0 + t1\n\tu\"hr\"(t)\nend\n\n# \u2554\u2550\u2561 69e73591-3bdf-4e31-9ce9-4c1f44e207a8\nmd\"### Problem 31.37\"\n\n# \u2554\u2550\u2561 3388d0f4-ace3-4322-89ba-a06ba0840adf\nlet\n\tI = 5.0u\"A\"\n\td = u\"m\"(1.0u\"cm\")\n\tR = d / 2\n\tr = u\"m\"(1.8u\"mm\")\n\t\n\t# B = ?\n\t# Magnetic field of a wire\n\tB1 = (\u03bc0 / 2\u03c0) * (I / r)\n\t\n\t# B = ?\n\t# Biot-Savart Law\n\tB2 = (\u03bc0 / 2\u03c0) * (r / R^2) * I\n\t\n\tB1, B2\nend\n\n# \u2554\u2550\u2561 aa2e1f09-bb0e-452d-b277-32ed87f1d805\nmd\"## Diagnostic Test 12 (Chapter 34)\"\n\n# \u2554\u2550\u2561 49522fe9-e9d9-433c-92a2-072d60e1576e\nmd\"### Problem 34.40\"\n\n# \u2554\u2550\u2561 e9661551-68aa-41fc-be08-e88b8444e4f6\nlet\n\ts = 30.0u\"cm\"\n\tf = -22.0u\"cm\"\n\t\n\t# s\u2032 = ?\n\t# Mirror equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 s\u2032(f - s) = -sf\n\t# \u27f9 s\u2032 = -sf/(f - s)\n\ts\u2032 = -s*f/(f - s)\nend\n\n# \u2554\u2550\u2561 8d7d25f5-f27e-4b86-a845-15e9a3dd2f72\nmd\"### Problem 34.41\"\n\n# \u2554\u2550\u2561 3548c0a7-1de7-4534-a930-58d80e9efad7\nlet\n\th = 1.3u\"cm\"\n\ts = 18.0u\"cm\"\n\tf = 56.0u\"cm\"\n\t\n\t# s\u2032 = ?\n\t# Mirror equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 s\u2032(f - s) = -sf\n\t# \u27f9 s\u2032 = -sf/(f - s)\n\ts\u2032 = -s*f/(f - s)\n\t\n\t# h\u2032 = ?\n\t# Magnification equation\n\t# M = h\u2032/h = -s\u2032/s\n\t# \u27f9 h\u2032 = -s\u2032/s h\n\th\u2032 = -s\u2032/s * h\n\t\n\ts\u2032, h\u2032\nend\n\n# \u2554\u2550\u2561 b550aa41-be86-4336-91cd-816565045da5\nmd\"### Problem 34.33\"\n\n# \u2554\u2550\u2561 0fe25f6d-0132-4fa4-a1b8-a03963fd1ee7\nlet\n\th = 2.2u\"cm\"\n\ts = 36.0u\"cm\"\n\tf = 18.0u\"cm\"\n\t\n\t# s\u2032 = ?\n\t# Lens equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 s\u2032(f - s) = -sf\n\t# \u27f9 s\u2032 = -sf/(f - s)\n\ts\u2032 = -s*f/(f - s)\n\t\n\t# h\u2032 = ?\n\t# Magnification equation\n\t# M = h\u2032/h = -s\u2032/s\n\t# \u27f9 h\u2032 = -s\u2032/s h\n\th\u2032 = -s\u2032/s * h\n\t\n\ts\u2032, abs(h\u2032)\nend\n\n# \u2554\u2550\u2561 a6e0b275-276f-4c0b-b548-4a4c283a6330\nmd\"### Problem 34.16\"\n\n# \u2554\u2550\u2561 f0a5c1d8-0de4-4455-b6e7-73b0426cd04c\nlet\n\t# d = ?\n\t# Transparent hemisphere\n\t# d = R / n\nend\n\n# \u2554\u2550\u2561 ceccbe3d-5c9a-47bf-a168-18154f30729b\nmd\"### Problem 34.11\"\n\n# \u2554\u2550\u2561 6f920972-e2ab-4e62-855e-9bac912831df\nlet\n\ty = 1.0u\"cm\"\n\t\u03b81 = 69u\"\u00b0\"\n\tn1 = 1\n\tn2 = 1.33\n\tn3 = 1.50\n\t\n\t# \u03b82 = ?\n\t# Snell's Law\n\t# n1 sin(\u03b81) = n2 sin(\u03b82)\n\t# \u27f9 \u03b82 = asin(n1 sin(\u03b81) / n2)\n\t\u03b82 = asind(n1 * sind(\u03b81) / n2)\n\t\n\t# \u03b83 = ?\n\t# Snell's Law\n\t# n2 sin(\u03b82) = n3 sin(\u03b83)\n\t# \u27f9 \u03b83 = asin(n2 sin(\u03b82) / n3)\n\t\u03b83 = asind(n2 * sind(\u03b82) / n3)\nend\n\n# \u2554\u2550\u2561 3be6b392-ea30-4015-a718-41f43797626e\nmd\"### Problem 34.7\"\n\n# \u2554\u2550\u2561 1c600f48-8f4d-4841-bfcf-14e805e284b8\nlet\n\tb = 5.0u\"m\"\n\th = 3.0u\"m\"\n\tmid = h / 2\n\t\n\t# \u03d5 = ?\n\t# Trigonometry\n\t# \u03d5 = atan(h/x)\n\t\n\tbegin\n\t\t# x = ?\n\t\t# Trigonometry relationship\n\t\t# tan(\u03d5) = h/x = mid / (b - x)\n\t\t# \u27f9 (b - x) h = x mid\n\t\t# \u27f9 bh - xh = x mid\n\t\t# \u27f9 bh = x(mid + h)\n\t\t# \u27f9 x = bh / (mid + h)\n\t\t\n\t\tx = b*h / (mid + h)\n\tend\n\t\n\t\u03d5 = atand(h/x)\nend\n\n# \u2554\u2550\u2561 f4730b14-2902-457d-a5f8-7e9140926b1e\nmd\"### Problem 34.14\"\n\n# \u2554\u2550\u2561 2fbca1ac-29e5-4f5b-8cac-faf6b48045eb\nlet\n\tn1 = 1.60\n\tn2 = 1.45\n\t\n\t# \u03b8c = ?\n\t# Critical angle\n\t\u03b8c = asind(n2/n1)u\"\u00b0\"\n\t\n\t\u03b8max = 90u\"\u00b0\" - \u03b8c\nend\n\n# \u2554\u2550\u2561 c9cae5ed-a959-4c20-a4a6-21b6962f7fc5\nmd\"### Problem 34.37\"\n\n# \u2554\u2550\u2561 53439784-13c7-4f08-949d-e60cd41d4494\nlet\n\th = 2.2u\"cm\"\n\ts = 15u\"cm\"\n\tf = -23u\"cm\"\n\t\n\t# s\u2032 = ?\n\t# Lens equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 s\u2032(f - s) = -sf\n\t# \u27f9 s\u2032 = -sf/(f - s)\n\ts\u2032 = -s*f/(f - s)\n\t\n\t# h\u2032 = ?\n\t# Magnification equation\n\t# M = h\u2032/h = -s\u2032/s\n\t# \u27f9 h\u2032 = -s\u2032/s h\n\th\u2032 = -s\u2032/s * h\n\t\n\ts\u2032, abs(h\u2032)\nend\n\n# \u2554\u2550\u2561 e98b7caa-8df6-4292-93f5-f364ddeda2a3\nmd\"### Problem 34.5\"\n\n# \u2554\u2550\u2561 692de57c-99d1-439c-ab59-945db21dfe47\nlet\n\t(Ax, Ay) = (10u\"cm\", 5u\"cm\")\n\t(Bx, By) = (15u\"cm\", 15u\"cm\")\n\t\n\t# d = ?\n\t# Initial displacement\n\t# d = d0 + y\n\t\n\td0 = Ay\n\t\n\tbegin\n\t\t# y = ?\n\t\t# Use trigonometry relationship\n\t\t# tan(\u03d5) = (By - Ay)/y = By/((By - Ay) - y)\n\t\t# let dy = By - Ay\n\t\t# \u27f9 dy (dy - y) = By * y\n\t\t# \u27f9 dy^2 - dy y = By * y\n\t\t# \u27f9 dy^2 = y * (By + dy)\n\t\t# \u27f9 y = dy^2 / (By + dy)\n\t\t\n\t\tdy = By - Ay\n\t\ty = dy^2 / (By + dy)\n\tend\n\t\n\td = d0 + y\nend\n\n# \u2554\u2550\u2561 0efd04be-af2c-45e4-9aaa-b3fe5fb9c7d0\nmd\"### Problem 34.34\"\n\n# \u2554\u2550\u2561 da59c2ff-871e-4dee-9ec8-467ed823f11a\nlet\n\th = 1.1u\"cm\"\n\ts = 87.0u\"cm\"\n\tf = 31.0u\"cm\"\n\t\n\t# s\u2032 = ?\n\t# Lens equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 s\u2032(f - s) = -sf\n\t# \u27f9 s\u2032 = -sf/(f - s)\n\ts\u2032 = -s*f/(f - s)\n\t\n\t# h\u2032 = ?\n\t# Magnification equation\n\t# M = h\u2032/h = -s\u2032/s\n\t# \u27f9 h\u2032 = -s\u2032/s h\n\th\u2032 = -s\u2032/s * h\n\t\n\ts\u2032, abs(h\u2032)\nend\n\n# \u2554\u2550\u2561 b520e5e2-702d-4131-9d10-3b12b04bda7e\nmd\"## PSW 12 (Chapter 34)\"\n\n# \u2554\u2550\u2561 c843abfd-f681-4850-83c0-f80241b3b122\nmd\"### Problem 34.52\"\n\n# \u2554\u2550\u2561 210917a3-6257-4723-a0ee-5bc04dd87884\nlet\n\tx1 = 2.1u\"m\"\n\ty1 = 1.0u\"m\"\n\ty2 = 3.0u\"m\"\n\tn1 = 1.00\n\tn2 = 1.33\n\t\n\t# d = ?\n\t# d = x1 + x2\n\t\n\tbegin\n\t\t# x2 = ?\n\t\t# Trigonometry\n\t\t# tan(\u03b82) = x2/y2 \u27f9 x2 = y2 tan(\u03b82)\n\t\t\n\t\tbegin\n\t\t\t# \u03b82 = ?\n\t\t\t# Snell's Law\n\t\t\t# n1 sin(\u03b81) = n2 sin(\u03b82)\n\t\t\t# \u27f9 \u03b82 = asin(n1 sin(\u03b81) / n2)\n\n\t\t\tbegin\n\t\t\t\t# \u03b81 = ?\n\t\t\t\t# Trigonometry\n\t\t\t\t# tan(\u03b81) = x1/y1 \u27f9 \u03b81 = atan(x1/y1)\n\t\t\t\t\u03b81 = atand(x1/y1)\n\t\t\tend\n\t\t\t\n\t\t\t\u03b82 = asind(n1 * sind(\u03b81) / n2)\n\t\tend\n\t\t\n\t\tx2 = y2 * tand(\u03b82)\n\tend\n\t\n\td = x1 + x2\nend\n\n# \u2554\u2550\u2561 ad1e49db-25cf-425a-88dd-47422330e85a\nmd\"### Problem 34.53\"\n\n# \u2554\u2550\u2561 b51230db-b12f-4c92-a140-79002eb2c1e0\nlet\n\ty = 1.90u\"m\"\n\tn1 = 1.00\n\ts = u\"m\"(36.0u\"cm\")\n\ts\u2032 = u\"m\"(46.0u\"cm\")\n\tx = s\u2032 - s\n\t\n\t# n2 = ?\n\t# Snell's law\n\t# n1 sin(\u03b81) = n2 sin(\u03b82)\n\t# \u27f9 n1 = n2 sin(\u03b82)\n\t# \u27f9 n2 = n1 / sin(\u03b82)\n\t\n\tbegin\n\t\t# \u03b82 = ?\n\t\t# Trigonometry\n\t\t# tan(\u03b82) = x/y \u27f9 \u03b82 = atan(x/y)\n\t\t\n\t\t\u03b82 = atand(x/y)\n\tend\n\t\n\tn2 = n1 / sind(\u03b82) # Incorrect!\nend\n\n# \u2554\u2550\u2561 9bf8ac4d-5c33-4dd5-a2d2-5ae1b03ebcd2\nmd\"### Problem 34.63\"\n\n# \u2554\u2550\u2561 8799b871-5a4c-4663-afcc-35e58ccf502e\nlet\n\tM = 1.5\n\ts = 1.8u\"cm\"\n\t\n\t# f = ?\n\t# Mirror equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 f = ss\u2032 / (s\u2032 + s)\n\n\tbegin\n\t\t# s\u2032 = ?\n\t\t# Magnification equation\n\t\t# M = -s\u2032/s\n\t\t# \u27f9 s\u2032 = -M * s\n\t\t\n\t\ts\u2032 = -M * s\n\tend\n\t\n\tf = s*s\u2032 / (s\u2032 + s)\nend\n\n# \u2554\u2550\u2561 9e90dab3-6ba7-4298-889d-047bac5b5059\nmd\"### Problem 34.76\"\n\n# \u2554\u2550\u2561 9df87359-662d-46d0-8983-d45c2798a93c\nlet\n\tM = 3\n\tr = 30u\"cm\"\n\tf = r / 2\n\t\n\t# s = ?\n\t# Mirror equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 1/s + 1/3s = 1/f\n\t# \u27f9 3f - f = 3s\n\t# \u27f9 2f = 3s\n\t# \u27f9 s = 2f/3\n\t\n\ts = 2f/3\nend\n\n# \u2554\u2550\u2561 a47cc5f3-9375-4128-bac4-da8bd689b46c\nmd\"### Problem 34.69\"\n\n# \u2554\u2550\u2561 ed90dde8-3230-4fe9-87c2-615aab4269bb\nlet\n\tr = 2.6u\"m\"\n\tM = -2\n\t\n\t# f = ?\n\t# Mirror equation\n\t# 1/s + 1/s\u2032 = 1/f\n\t# \u27f9 s\u2032f + sf = ss\u2032\n\t# \u27f9 f = ss\u2032 / (s\u2032 + s)\n\t\n\t# s = ?\n\t# Magnification equation\n\t# M = -s\u2032/s = -(r - s)/s\n\t# \u27f9 sM = s - r\n\t# \u27f9 s = r / (1 - M)\n\ts = r / (1 - M)\n\t\n\t# s\u2032 = ?\n\t# s + s\u2032 = r\n\t# \u27f9 s\u2032 = r - s\n\ts\u2032 = r - s\n\t\n\tf = s*s\u2032 / (s\u2032 + s)\n\t\n\tf, s\nend\n\n# \u2554\u2550\u2561 14f633eb-43f6-44b3-8558-6871f318d908\nmd\"### Problem 34.73\"\n\n# \u2554\u2550\u2561 bd8ebbf4-7a57-4216-9690-f7c407fcfc58\nlet\n\ts = 13u\"cm\"\n\tM = 2\n\t\n\t# s\u2032 = ?\n\t# Magnification equation\n\t# M = -s\u2032/s \u27f9 s\u2032 = -M*s\n\ts\u2032 = -M * s\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500baaa0f44-a9d8-11eb-0d0d-53b78cd74315\n# \u255f\u2500c565da05-80ca-4b72-af73-d5da2f1c2abc\n# \u255f\u2500a1c99c25-e72e-4c4f-9ee9-855dad795483\n# \u255f\u2500c5cf2733-8d62-4f97-9c6c-97c6bce90b02\n# \u255f\u25007abb1db1-d637-446c-9e55-3457db3db17e\n# \u255f\u2500be1d2a6e-8c24-4a1e-b84b-aabfad8e6135\n# \u255f\u25006c9182b4-2bf5-40a8-8a7e-ecfb1f7e59f3\n# \u2560\u2550b670ab02-1fe7-4204-9747-237348707d97\n# \u255f\u25003240dda1-8d4c-4551-8878-8c2ddd9f12d1\n# \u255f\u25008a56ceb5-dbd9-4950-bbfe-50b56c682487\n# \u2560\u25501d460401-7c48-4718-914e-ba3fc9f7950a\n# \u255f\u250098b7f219-578b-4717-8dfa-5f22f86c04b2\n# \u255f\u2500f6b01ecd-9629-422c-800e-a41512fa9933\n# \u255f\u250081d41ebd-9c50-49a1-b77d-1f6c600477e7\n# \u255f\u2500067e90ac-b370-4c53-b1d8-8ee65189b036\n# \u255f\u2500179020bb-048a-4040-b517-0c081a5b68c0\n# \u255f\u25008065e0db-4aa8-447f-a130-49feedcddad7\n# \u255f\u25005745f4ea-219d-4e60-b269-9e08f010836a\n# \u255f\u2500885d48fa-4a05-4468-8492-0cae4bfb7b26\n# \u255f\u25002d690e4b-3d09-4907-99de-5f0ef4662b7e\n# \u255f\u2500eae4eef0-1701-406f-966d-736a2b2381f5\n# \u255f\u25009ca8f846-5d5b-4b28-85b6-90c5f9c068b6\n# \u255f\u250004912a55-5871-4917-8c95-7f03bd4abf1d\n# \u255f\u250042f03b7e-9fec-40bc-8d22-e594a22ab54e\n# \u255f\u250034b75779-b546-4628-ac0a-1d8fedc231eb\n# \u255f\u2500fb93dd3c-de36-4087-9870-8365161f7d1f\n# \u255f\u2500ab703047-2e82-4c13-a9d9-c97ffd42af67\n# \u255f\u2500253d5697-9088-424f-aa2e-c5df2c7675bf\n# \u255f\u2500fcdb7ea5-6203-4e74-b746-4470052b5f99\n# \u255f\u25005db75338-4b19-4037-942d-979856662d6c\n# \u255f\u250074378daa-7994-403e-9c41-86bae271a74d\n# \u255f\u2500af3ab252-16c6-4e6f-bdc6-9a01fede5a30\n# \u255f\u250064bbc192-28dd-4db4-8dab-1cde2a031bc5\n# \u255f\u2500185600d3-131d-491d-869d-06a865280a5c\n# \u255f\u25005fd137c7-a14e-4a53-9c9f-39f0992b1e43\n# \u255f\u25008a9474e5-4b73-4a3e-9573-fa0ca066f9de\n# \u255f\u2500d8c9b9e2-eaa1-40e5-9899-f60d8fb9dddb\n# \u255f\u2500f6000dca-7d5a-4bb6-93ec-3de97124fe51\n# \u255f\u250005c12afa-6ce0-4ab6-aac9-bb6a4f7a20e8\n# \u255f\u2500f30d8566-72fd-4658-a4cb-fcd2ac33b063\n# \u255f\u2500b6605cff-3e3c-40ed-b003-547a908a89f3\n# \u255f\u2500c53a0513-5a65-453b-8833-f4be54af3e0c\n# \u255f\u25006be1aa76-6cf2-4e57-bde4-7effcc453073\n# \u255f\u25008acec63d-de21-4698-9e51-579c16d2e398\n# \u255f\u25001e73fe74-4f6d-4137-b771-2cfac7cf6332\n# \u255f\u2500762087fa-ffcf-4691-8c67-6837555bbfb0\n# \u255f\u2500d6dba861-4f6e-4aad-9b8c-1e18ee2639af\n# \u255f\u25006cf18ee6-6b70-466f-9e23-32368cb35865\n# \u255f\u2500c5ee0bee-d2db-4daa-bbcf-5ac9f30b2f12\n# \u255f\u250024a53a41-96a8-4bab-a8cf-1f3cd6ad30ff\n# \u255f\u2500060fd1a0-5548-4eb4-b4c2-f2db077e0d50\n# \u255f\u25008eb2caa8-5341-40a7-8ae5-aad8e44c1af1\n# \u255f\u2500860bbf33-9025-44ee-aa26-7217f0bc7562\n# \u255f\u250028acdfbe-1020-4921-8c6c-0e38d0732d78\n# \u255f\u2500e06efb86-5366-49c3-acb6-f9b209bf5d81\n# \u255f\u25006c9233eb-058a-4e8a-9c9b-22964e210dd1\n# \u255f\u2500c83063bd-0bba-47e7-b869-e169d6e9c03e\n# \u255f\u2500c9102584-2196-4483-b79f-60089ad9e1b4\n# \u255f\u2500b30d503e-ab6c-4515-9a68-81327795aa6c\n# \u255f\u250085e2b232-6a09-4621-ab97-e2345b676999\n# \u255f\u250069bdb37e-2f06-4c78-a5bc-c3fce3cf6be8\n# \u255f\u2500f9ba57aa-4113-40e0-af24-8d7066baef4c\n# \u255f\u2500cdc4c352-a21c-4d88-8a6e-28da5974e893\n# \u255f\u2500bc7ebdc7-3614-43ad-bb1c-695f535c339b\n# \u255f\u25003badd910-d0be-435d-8b61-751d2fc46984\n# \u255f\u2500f5871103-8943-4085-8ec8-f7c19155262c\n# \u255f\u250064256f0e-f61a-41d9-9f07-f53f12b81d9f\n# \u255f\u250056988b0d-40f0-485c-bfd1-468431e2f906\n# \u255f\u2500baf581de-7077-4c25-a5df-b5e82490b113\n# \u255f\u250011c087b0-7823-41ac-8034-e66875bcaec1\n# \u255f\u2500ad23f43f-c8ac-48fd-9b21-901658710684\n# \u255f\u2500a59c5606-b60e-44c8-a7e3-b84cd89b322c\n# \u255f\u250069065726-b456-4ab1-b8a6-22e638304388\n# \u255f\u250047e713c1-0741-4135-ad0c-8b7ad003c141\n# \u255f\u250045baa2c9-e2a9-44fc-a6a3-9e1b149d3ec6\n# \u255f\u2500a44466e8-0954-4738-9836-e7d445d41a91\n# \u255f\u250013f7bece-837b-473e-bb86-df55d3f5dbdf\n# \u255f\u25006917fdb5-0538-4995-9ac5-14d7fc92b903\n# \u255f\u2500018a2522-f201-44a7-8f63-d67411970a90\n# \u255f\u2500ebe19088-8374-441c-9c1b-c00194737814\n# \u255f\u250030625a27-4961-4eff-be88-27a351a3db12\n# \u255f\u250066bd1832-dd76-43fa-ab08-018c8e726885\n# \u255f\u250008c4b0d3-a431-46dd-a0ae-3bd5a59b81e3\n# \u255f\u2500eff73afd-3ded-49d2-9411-ec11933fb31f\n# \u255f\u25006e2b45e2-67bd-4512-9b3c-6f25dc75824f\n# \u255f\u25009cf0faf9-ea42-45da-9788-975cfecdca37\n# \u255f\u250067aca9a5-da3f-4a2c-9159-2dfb922217f8\n# \u255f\u250037aeddcf-e1cf-40a0-925e-20d600f24845\n# \u255f\u25005fb3d735-8df5-4e69-aa47-ff467dee9047\n# \u255f\u2500c4f15be9-ba69-4b9f-9b5a-407a975c2fbe\n# \u255f\u25002c330d4d-8fa2-4c3f-a5b8-8a3ac19fd075\n# \u255f\u2500a2fbf237-3b84-4331-be33-e13c0f726838\n# \u255f\u250084cdc818-8a8c-4888-b3bd-12399e60a739\n# \u255f\u2500b759a10f-6110-4e66-8121-e1c8bf24efee\n# \u255f\u25009c245886-b491-4adf-9ccd-0f8d20326949\n# \u255f\u2500c352a601-03e9-4f47-9d8e-2e7e806a7433\n# 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{"text": "# Before running this, please make sure to activate and instantiate the environment\n# corresponding to [this `Project.toml`](https://raw.githubusercontent.com/alan-turing-institute/MLJTutorials/master/Project.toml) and [this `Manifest.toml`](https://raw.githubusercontent.com/alan-turing-institute/MLJTutorials/master/Manifest.toml)\n# so that you get an environment which matches the one used to generate the tutorials:\n#\n# ```julia\n# cd(\"MLJTutorials\") # cd to folder with the *.toml\n# using Pkg; Pkg.activate(\".\"); Pkg.instantiate()\n# ```\n\n# ## Machine type vs Scientific Type## ### Why make a distinction?## When analysing data, it is important to distinguish between## * *how the data is encoded* (e.g. `Int`), and# * *how the data should be interpreted* (e.g. a class label, a count, ...)## How the data is encoded will be referred to  as the **machine type** whereas how the data should be interpreted  will  be referred to   as the **scientific type** (or `scitype`).## In some cases, this may be un-ambiguous, for instance if you have a vector of floating point values, this should usually be interpreted as a continuous feature (e.g.: weights, speeds, temperatures, ...).## In many other cases however, there may be ambiguities, we list a few examples below:## * A vector  of `Int` e.g. `[1, 2, ...]` which should be interpreted as categorical labels,# * A vector of `Int` e.g. `[1, 2,  ...]` which should be interpreted as count data,# * A vector of `String` e.g. `[\"High\", \"Low\", \"High\", ...]` which should  be  interpreted as ordered categorical labels,# * A vector of `String` e.g. `[\"John\", \"Maria\", ...]` which should not interpreted as informative data,# * A vector of floating points `[1.5,  1.5, -2.3, -2.3]` which should be interpreted as categorical data (e.g. the few possible values of some setting), etc.## ### The Scientific Types## The package [ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl) defines a barebone type hierarchy which can be used to indicate how a particular feature should be interpreted; in particular:## ```plaintext# Found# \u251c\u2500 Known# \u2502  \u251c\u2500 Textual# \u2502  \u251c\u2500 Finite# \u2502  \u2502  \u251c\u2500 Multiclass# \u2502  \u2502  \u2514\u2500 OrderedFactor# \u2502  \u2514\u2500 Infinite# \u2502     \u251c\u2500 Continuous# \u2502     \u2514\u2500 Count# \u2514\u2500 Unknown# ```## A *scientific type convention* is a specific implementation indicating how machine types can be related to scientific types. It may also provide helper functions to convert data to a given scitype.## The convention used in MLJ is implemented in [MLJScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl).# This is what we will use throughout; you never need to use ScientificTypes.jl# unless you intend to implement your own scientific type convention.## ### Inspecting the scitype## The `schema` function\nusing RDatasets, MLJScientificTypes\nboston = dataset(\"MASS\", \"Boston\")\nsch = schema(boston)\n\n# In this cases, most of the variables have a (machine) type `Float64` and# their default  interpretation is `Continuous`.# There is also `:Chas`, `:Rad` and `:Tax` that have a (machine) type  `Int64`# and their default interpretation is `Count`.## While the interpretation as `Continuous` is usually fine, the interpretation# as `Count` needs a bit more attention.# For instance note that:\nunique(boston.Chas)\n\n# so even  though it's got a machine type of `Int64` and consequently a# default  interpretation of `Count`, it would be more appropriate to interpret# it as an `OrderedFactor`.## ### Changing the scitype## In order to re-specify the scitype(s) of  feature(s) in a dataset, you can# use the `coerce` function and  specify pairs of variable name and  scientific# type:\nboston2 = coerce(boston, :Chas => OrderedFactor);\n\n# the effect of this is to convert the `:Chas` column to an ordered categorical# vector:\neltype(boston2.Chas)\n\n# corresponding to the `OrderedFactor` scitype:\nelscitype(boston2.Chas)\n\n# You can also specify multiple pairs in one shot with `coerce`:\nboston3 = coerce(boston, :Chas => OrderedFactor, :Rad => OrderedFactor);\n\n# ### String and Unknown## If a feature in  your dataset has String elements, then the  default scitype# is `Textual`; you can either choose to  drop  such columns or to coerce them# to categorical:\nfeature = [\"AA\", \"BB\", \"AA\", \"AA\", \"BB\"]\nelscitype(feature)\n\n# which you can coerce:\nfeature2 = coerce(feature, Multiclass)\nelscitype(feature2)\n\n# ## Tips and tricks## ### Type to Type coercion## In  some cases you will want to reinterpret all features currently# interpreted as some scitype `S1` into some other scitype `S2`.# An example  is if some features are currently interpreted as `Count` because# their original type was `Int` but you  want  to  consider all such as# `Continuous`:\ndata = select(boston, [:Rad, :Tax])\nschema(data)\n\n# let's coerce from `Count` to `Continuous`:\ndata2 = coerce(data, Count => Continuous)\nschema(data2)\n\n# ### Autotype## A last useful tool is `autotype` which allows you to specify *rules* to# define the interpretation of features automatically.# You can code your own rules but there are three useful ones that are pre-# coded:## * the `:few_to_finite` rule which checks how many unique entries are present# in a vector and if there are \"few\" suggests a categorical type,# * the `:discrete_to_continuous` rule converts `Integer` or `Count` to# `Continuous`# * the `:string_to_multiclass` which returns `Multiclass` for any string-like# column.## For instance:\nboston3 = coerce(boston, autotype(boston, :few_to_finite))\nschema(boston3)\n\n# You can also specify multiple rules, see [the docs](https://alan-turing-institute.github.io/MLJScientificTypes.jl/stable/#Automatic-type-conversion-for-tabular-data-1) for more information.\n# This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl\n\n", "meta": {"hexsha": "a4446a680ba3860d051df3f1e5f238579e4f7aa9", "size": 5792, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/generated/scripts/D0-scitype.jl", "max_stars_repo_name": "ven-k/MLJTutorials", "max_stars_repo_head_hexsha": "42151c8a96ad701aeaf763d53c8b7c6689eb6e8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "__site/generated/scripts/D0-scitype.jl", "max_issues_repo_name": "ven-k/MLJTutorials", "max_issues_repo_head_hexsha": "42151c8a96ad701aeaf763d53c8b7c6689eb6e8d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "__site/generated/scripts/D0-scitype.jl", "max_forks_repo_name": "ven-k/MLJTutorials", "max_forks_repo_head_hexsha": "42151c8a96ad701aeaf763d53c8b7c6689eb6e8d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 109.2830188679, "max_line_length": 2245, "alphanum_fraction": 0.7353245856, "num_tokens": 1495, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "module FranklinTheorems\n\nimport Franklin\nexport lx_fakebiblabel, lx_newcounter, lx_stepcounter, lx_arabic\n\n\nmodule ExportConfigPath\n\n\texport css_path, config_path\n\n\t\"\"\"\n\tReturns a path to FranklinTheorem's default CSS file. Copy this file to the Franklin `\\\\_css\\\\` directory.\n\t\"\"\"\n\tfunction css_path()\n\t\treturn normpath(joinpath(@__FILE__, \"..\", \"FranklinTheorems.css\"))\n\tend\n\n\t\"\"\"\n\tReturns a path to FranklinTheorem's markdown file, full of LaTeX-style definitions.\n\tBy default, the theorem, lemma, and definition blocks are defined.\n\tIf a list of strings is given, then the associated counters and environments are created.\n\tSince this is dynamic, because of the optional parameters, this returns a path to a tempfile.\n\t\"\"\"\n\tfunction config_path()\n\t\tconfig_path([])\n\tend\n\tfunction config_path(theorem_class_names)\n\t\tall_class_names = [[\"theorem\", \"lemma\", \"definition\"]; theorem_class_names]\n\t\tbaseMarkdownText = read(normpath(joinpath(@__FILE__, \"..\", \"FranklinTheorems.md\")), String)\n\n\t\tf = tempname()\n\t\twrite(f, baseMarkdownText * \"\\n\" * make_custom_environments(all_class_names))\n\t\treturn f\n\tend\n\n\t\"\"\"\n\tReturns a path to a temporary file that includes Franklin-Markdown code for a custom amstheorem-style environment.\n\t\"\"\"\n\tfunction make_custom_environments(theorem_class_names)\n\t\t\n\t\tenv_string = \"\"\n\t\tenable_string = \"\\\\newcommand{\\\\enabletheorems}{\"\n\n\t\tfor i=1:length(theorem_class_names)\n\t\t\tdefn_name = theorem_class_names[i]\n\t\t\tdefn_titlecase = titlecase(defn_name)\n\t\t\tdefn_body = \"\"\"\n\\\\newcommand{\\\\$(defn_name)ref}[1]{\\\\cite{!#1}}\n\n\\\\newenvironment{$(defn_name)}[2]{\n\t\\\\stepcounter{Num$(defn_name)}\n\t\\\\begin{thmBlock}{$(defn_titlecase) \\\\arabic{Num$(defn_name)}}{#1}{$(defn_titlecase)}{ \\\\fakebiblabel{!#2 @ $(defn_titlecase) \\\\arabic{Num$(defn_name)}} }\n}{\n\t\\\\end{thmBlock}\n}\n\t\t\t\"\"\"\n\t\t\tenv_string = env_string * \"\\n\" * defn_body\n\t\t\tenable_string = enable_string * \"\\n\" * \"\\\\newcounter{Num$(defn_name)}\"\n\t\tend\n\n\t\tenable_string = enable_string * \"\\n}\"\n\n\t\treturn env_string * \"\\n\" * enable_string\n\tend\n\nend\n\nusing .ExportConfigPath\n\n\n\n\"\"\"\nSpecialized code for automatic numbering by abuse of the biblabel function.\n\n- Given an input of form `\\\\fakebiblable{@ Legible Name}`, this returns the string `\"Legible Name\"`\n- Given an input of form `\\\\fakebiblabel{goob @ Legible Name}`, this runs `\\\\biblabel{goob}{Legible Name}`, so that Franklin's referencing system keeps track of `goob` as a name that points to `\"Legible Name\"`, and the function returns the string `\"goob\"`.\n\nThis `@` symbol approach is a hack to allow a single optional input variable to the function.\nThe `@` symbol was chosen because of its relevance to LaTeX.\n\nFurther, the string `\"Legible Name\"` is always parsed by Franklin, which has two notable consequences\n1. Other lx_functions like `\\\\arabic{counter}` can be used in the full name\n2. Markdown syntaxing like `_italics_` will be parsed into `<em>italics<em>`, which breaks the references\n\nSo, while `Legible Name` can reference counters, it should not have any markdown styling.\n\"\"\"\nfunction lx_fakebiblabel(lxc, _)\n\targs = Franklin.content(lxc.braces[1]) |> strip |> split\n\n\tnvars = length(args)\n\n\tif nvars < 2\n\t\terror(\"Bad fakebiblabel input: Less than 2 inputs were given. Input was:\\n\\\\fakebiblablel{\" * join(args, \" \") * \"}\")\n\tend\n\n\tif args[1] == \"@\"\n\t\treturn Franklin.fd2html(join(args[2:end], \" \"), internal=true, nop=true)\n\telseif args[2] == \"@\"\n\t\tref_name = args[1]\n\t\tlegible_name = Franklin.fd2html(join(args[3:end], \" \"), internal=true, nop=true)\n\t\tFranklin.fd2html(\"\\\\biblabel{\" * ref_name * \"}{\" * legible_name * \"}\", internal=true)\n\t\treturn ref_name\n\telse\n\t\terror(\"Bad fakebiblabel input: @ was not the second input. Input was:\\n\\\\fakebiblablel{\" * join(args, \" \") * \"}\")\n\tend\nend\n\n\n# Module for the counters. By making this into a module, this hides\n# access to the counters dictionary. If you need a new way to interact\n# with the system of counters, impliment a new method in this module.\nmodule LxCounters\n\t\n\t# Used for latex-style parsing\n\tusing FranklinUtils\n\n\texport lx_newcounter, lx_stepcounter, lx_arabic\n\n\t# The core dictionary that stores all the things we want to count.\n\tcounters = Dict{String,Int64}()\n\n\t\"\"\"\n\tCall `\\\\newcounter{name}` to create a new counter. Returns an empty string.\n\t\"\"\"\n\tfunction lx_newcounter(lxc, _)\n\t\targs, kwargs = lxargs(lxc)\n\n\t\tname = string(args[1])\n\n\t\tdelete!(counters, name)\n\t\tget!(counters, name, 0)\n\t\treturn \"\"\n\tend\n\n\t\"\"\"\n\tCall `\\\\stepcounter{name}` to incriment the value of a counter by 1. Returns an empty string.\n\t\"\"\"\n\tfunction lx_stepcounter(lxc, _)\n\t\targs, kwargs = lxargs(lxc)\n\n\t\tname = string(args[1])\n\n\t\tcounters[name] = counters[name] + 1\n\t\treturn \"\"\n\tend\n\n\t\"\"\"\n\tCall `\\\\arabic{name}` to return the value of a counter, as an arabic number.\n\t\"\"\"\n\tfunction lx_arabic(lxc, _)\n\t\targs, kwargs = lxargs(lxc)\n\n\t\tname = string(args[1])\n\n\t\treturn string(counters[name])\n\tend\n\nend\n\nusing .LxCounters\n\nend # Module\n", "meta": {"hexsha": "ee13e7c9d446d7bac995e9c099887daec8ee1b35", "size": 4911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FranklinTheorems.jl", "max_stars_repo_name": "Shuvomoy/FranklinTheorems.jl", "max_stars_repo_head_hexsha": "febec5a2e5c980d4912f15ce7fae92f00fad54b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-02-17T15:55:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-20T21:38:40.000Z", "max_issues_repo_path": "src/FranklinTheorems.jl", "max_issues_repo_name": "Shuvomoy/FranklinTheorems.jl", "max_issues_repo_head_hexsha": "febec5a2e5c980d4912f15ce7fae92f00fad54b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/FranklinTheorems.jl", "max_forks_repo_name": "Shuvomoy/FranklinTheorems.jl", "max_forks_repo_head_hexsha": "febec5a2e5c980d4912f15ce7fae92f00fad54b9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-20T21:38:42.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-20T21:38:42.000Z", "avg_line_length": 30.1288343558, "max_line_length": 256, "alphanum_fraction": 0.7151293016, "num_tokens": 1336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014733397551624, "lm_q2_score": 0.13296422989056964, "lm_q1q2_score": 0.05719420900153618}}
{"text": "# conditional loop\ni = 0\nwhile i <= 5    \n    print(i)\n    print(\",\")\n    global i += 1\nend\nprint(i)\n", "meta": {"hexsha": "768550b444204cb0a4b9a16f35632fdfa82d1ad8", "size": 101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "demo/while_loop.jl", "max_stars_repo_name": "elucian/julia", "max_stars_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "demo/while_loop.jl", "max_issues_repo_name": "elucian/julia", "max_issues_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/while_loop.jl", "max_forks_repo_name": "elucian/julia", "max_forks_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.2222222222, "max_line_length": 18, "alphanum_fraction": 0.5148514851, "num_tokens": 34, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4073334000459302, "lm_q2_score": 0.1403362440969662, "lm_q1q2_score": 0.05716363945769285}}
{"text": "module MyFunRep\n\n# Write your package code here.\n\"\"\"\nA simple add method for testing\n    ```julia> my_fun1(2,3)\n    ```\n\"\"\"\nmy_fun1(x,y) = x+y \n\n\"\"\"\nA sample function as an experiment\n    ```julia> my_fun2(1,2,3)\n\"\"\"\nfunction my_fun2(x,y,z)\n    return x+y+z\nend\n\n\"\"\"\n    func(x)\nReturns double the number `x` plus `1`.\n```julia-repl\njulia > func(2)\n```\n\"\"\"\nfunc(x) = 2x + 1\n\n\"\"\"\n    func2(x,y)\nReturns something else double the number `x` plus `1`.\n\"\"\"\nfunc2(x,y) = 2x + 1 + 3y\n\nexport my_fun1, my_fun2, func,func2\n\nend\n", "meta": {"hexsha": "8e22f7465308d54c8a246ec1802424e60da397fe", "size": 520, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MyFunRep.jl", "max_stars_repo_name": "geekymode/MyFunRep.jl", "max_stars_repo_head_hexsha": "a128a6a52ca8b33510c0975ab8aeeb289273b0ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/MyFunRep.jl", "max_issues_repo_name": "geekymode/MyFunRep.jl", "max_issues_repo_head_hexsha": "a128a6a52ca8b33510c0975ab8aeeb289273b0ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/MyFunRep.jl", "max_forks_repo_name": "geekymode/MyFunRep.jl", "max_forks_repo_head_hexsha": "a128a6a52ca8b33510c0975ab8aeeb289273b0ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.0540540541, "max_line_length": 54, "alphanum_fraction": 0.6057692308, "num_tokens": 183, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.1208532340736852, "lm_q1q2_score": 0.05712532684277248}}
{"text": "# saveTable(\"test.txt\", \"<tab:testJulia1>\", [[1 2]; [3 4]])\n# saveTable(\"test.txt\", \"<tab:testJulia2>\", rand(2, 2))\n\nusing DelimitedFiles\n\nfunction saveTable(outfile, tag, outmatrix)\n    open(outfile, \"a\") do file\n        write(file, \"$(tag)\\n\")\n        writedlm(file, outmatrix, \"\\t\")\n    end\nend\n", "meta": {"hexsha": "fc559d5ff3226e2595494f8086284abf38c8649d", "size": 298, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/programs/saveTable.jl", "max_stars_repo_name": "kylebarron/tablefill", "max_stars_repo_head_hexsha": "5f56a54c63939e03538d7530f5a64ede2b2455be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2017-12-25T20:07:26.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T01:28:00.000Z", "max_issues_repo_path": "docs/programs/saveTable.jl", "max_issues_repo_name": "kylebarron/tablefill", "max_issues_repo_head_hexsha": "5f56a54c63939e03538d7530f5a64ede2b2455be", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2018-07-05T21:52:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-23T23:56:36.000Z", "max_forks_repo_path": "docs/programs/saveTable.jl", "max_forks_repo_name": "kylebarron/tablefill", "max_forks_repo_head_hexsha": "5f56a54c63939e03538d7530f5a64ede2b2455be", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-08-06T16:29:52.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-08T22:00:29.000Z", "avg_line_length": 24.8333333333, "max_line_length": 59, "alphanum_fraction": 0.610738255, "num_tokens": 96, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834766204329, "lm_q2_score": 0.12085322140796939, "lm_q1q2_score": 0.0571253208558979}}
{"text": "\"\"\"\nThis is a Julia translation of the schema expected by typescript.\n\nCalling [`to_json`](@ref) on a SemagramSchema should give a schema that\ntypescript can understand.\n\nThere is also a macro here for creating a SemagramSchema: [`@semagramschema`](@ref),\nwhich makes writing down a schema much more convenient.\n\"\"\"\nmodule Schema\n\nexport AttributeType, Numeric, Stringlike,\n  BoxProperties, PortProperties, WireProperties,\n  SemagramSchema, BoxDesc, PortDesc, WireDesc, DataDesc,\n  @semagramschema\n  \nimport ..Muesli: to_json, from_json\nusing ..Muesli\nusing ..SVG\nusing Catlab.Present, Catlab.Theories\nusing MLStyle\n\n@enum AttributeType begin\n  Numeric\n  Stringlike\nend\n\nto_json(x::AttributeType) = @match x begin\n  Numeric => \"Numeric\"\n  Stringlike => \"Stringlike\"\nend\n\nfunction from_json(d::Any, ::Type{AttributeType})\n  @match d begin\n    \"Numeric\" => Numeric\n    \"Stringlike\" => Stringlike\n  end\nend\n\nstruct BoxProperties\n  weights::Vector{Tuple{AttributeType, Symbol}}\n  shape::String\n  label::Union{Symbol,Nothing}\nend\n\nto_json(x::BoxProperties) = generic_to_json(x)\nfrom_json(d::Dict{String,<:Any}, ::Type{BoxProperties}) = generic_from_json(d, BoxProperties)\n\nstruct PortProperties\n  weights::Vector{Tuple{AttributeType, Symbol}}\n  box::Symbol\n  box_map::Symbol\n  style::String\nend\n\nto_json(x::PortProperties) = generic_to_json(x)\nfrom_json(d::Dict{String,<:Any}, ::Type{PortProperties}) = generic_from_json(d, PortProperties)\n\nstruct WireProperties\n  weights::Vector{Tuple{AttributeType, Symbol}}\n  src::Tuple{String, Symbol}\n  src_map::Symbol\n  tgt::Tuple{String, Symbol}\n  tgt_map::Symbol\n  style::String\nend\n\nto_json(x::WireProperties) = generic_to_json(x)\nfrom_json(d::Dict{String,<:Any}, ::Type{WireProperties}) = generic_from_json(d, WireProperties)\n\nstruct SemagramSchema\n  box_types::Dict{Symbol, BoxProperties}\n  port_types::Dict{Symbol, PortProperties}\n  wire_types::Dict{Symbol, WireProperties}\nend\n\nto_json(x::SemagramSchema) = generic_to_json(x)\nfrom_json(d::Dict{String, <:Any}, ::Type{SemagramSchema}) = generic_from_json(d, SemagramSchema)\n\nstruct BoxDesc\n  name::Symbol\n  shape::SVGNode\n  label::Union{Symbol,Nothing}\n  function BoxDesc(name,shape=Circle,label=nothing)\n    new(name,shape, label)\n  end\nend\n\nstruct PortDesc\n  name::Symbol\n  box_map::Symbol\n  style::String\n  function PortDesc(name,box_map,style=\"Circular\")\n    new(name,box_map,style)\n  end\nend\n\nstruct WireDesc\n  name::Symbol\n  src_map::Symbol\n  tgt_map::Symbol\n  style::String\n  function WireDesc(name,src_map,tgt_map,style=\"DefaultWire\")\n    new(name,src_map,tgt_map,style)\n  end\nend\n\nstruct DataDesc\n  name::Symbol\n  type::AttributeType\nend\n\nconst EntityDesc = Union{BoxDesc, PortDesc, WireDesc, DataDesc}\n\nfunction pres_to_semagramschema(p::Presentation, descs::Array)\n  ws = SemagramSchema(Dict(),Dict(),Dict())\n  datadescs = filter(desc -> typeof(desc) <: DataDesc, descs)\n  datatypes = Dict(map(desc -> desc.name => desc.type, datadescs)...)\n  function weights(ob::Symbol)\n    attrs = filter(attr -> nameof(dom(attr)) == ob, p.generators[:Attr])\n    map(attr -> (get(datatypes, nameof(codom(attr)), Stringlike), nameof(attr)), attrs)\n  end\n  for desc in descs\n    ob = desc.name\n    if typeof(desc)<:BoxDesc\n      ws.box_types[ob] = BoxProperties(weights(ob),write_svg(desc.shape),desc.label)\n    elseif typeof(desc)<:PortDesc\n      box = nameof(codom(p[desc.box_map]))\n      ws.port_types[ob] = PortProperties(weights(ob),box,desc.box_map,desc.style)\n    elseif typeof(desc)<:WireDesc\n      src = nameof(codom(p[desc.src_map]))\n      src_type = if src \u2208 keys(ws.box_types)\n        \"Box\"\n      else\n        \"Port\"\n      end\n      tgt = nameof(codom(p[desc.tgt_map]))\n      tgt_type = if src \u2208 keys(ws.box_types)\n        \"Box\"\n      else\n        \"Port\"\n      end\n      ws.wire_types[ob] = WireProperties(\n        weights(ob),\n        (src_type, src), desc.src_map,\n        (tgt_type, tgt), desc.tgt_map,\n        desc.style\n      )\n    end\n  end\n  ws\nend\n\nq(x) = Expr(:quote,x)\n\n\"\"\"\nSee the examples/general documentation for how to use this.\n\"\"\"\nmacro semagramschema(head,body)\n  descs = @match body begin\n    Expr(:block,lines...) => begin\n      map(lines) do line\n        @match line begin\n          Expr(:macrocall, mname, _, desc, args...) => begin\n            (name,morphs) = @match desc begin\n              name::Symbol => (name,[])\n              Expr(:call, name, args...) => (name,args)\n            end\n            constructor = if mname == Symbol(\"@box\")\n              BoxDesc\n            elseif mname == Symbol(\"@port\")\n              PortDesc\n            elseif mname == Symbol(\"@wire\")\n              WireDesc\n            elseif mname == Symbol(\"@data\")\n              DataDesc\n            end\n            Expr(:call, constructor, q(name), q.(morphs)..., args...)\n          end\n          _ => missing\n        end\n      end\n    end\n    _ => error(\"the body must be a block\")\n  end\n  descs = filter(x -> !ismissing(x), descs)\n  name, pres = @match head begin\n    Expr(:call, name, pres) => (name, pres)\n    _ => error(\"the head must be a name and presentation\")\n  end\n  :($(esc(name)) = pres_to_semagramschema($(esc(pres)), $(esc(Expr(:vect, descs...)))))\nend\n\nend\n", "meta": {"hexsha": "dae47028dfc468b21d246d71f843595c48dd1a82", "size": 5172, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Schema.jl", "max_stars_repo_name": "bosonbaas/Semagrams.jl", "max_stars_repo_head_hexsha": "8e2cab4548650ad26bda44290843276927cca14d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Schema.jl", "max_issues_repo_name": "bosonbaas/Semagrams.jl", "max_issues_repo_head_hexsha": "8e2cab4548650ad26bda44290843276927cca14d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Schema.jl", "max_forks_repo_name": "bosonbaas/Semagrams.jl", "max_forks_repo_head_hexsha": "8e2cab4548650ad26bda44290843276927cca14d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5230769231, "max_line_length": 96, "alphanum_fraction": 0.6664733179, "num_tokens": 1405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11596072894699293, "lm_q1q2_score": 0.05707449499724494}}
{"text": "using Documenter, NumericalAnalysis\n\n\nmakedocs(\n    modules = [NumericalAnalysis],\n    clean = false,\n    sitename = \"NumericalAnalysis.jl\",\n    pages = Any[\n        \"Home\" => \"index.md\",\n        \"Functions\" => Any[\n            \"Basic function\" => \"m/basic.md\",\n            \"SEq in one variable\" => \"m/SEq1.md\",\n            \"Interpolation and the Lagrange Polynomial\" => \"m/Polynomial.md\",\n            \"Numerical Differentiation and Integration\" => \"m/NCalculus.md\",\n        ],\n    ],\n)\n", "meta": {"hexsha": "1e19ab3095ca2858acb69e1f3184ab073dfb89cd", "size": 487, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "ZhouZhuofei/NumericalAnalysis.jl", "max_stars_repo_head_hexsha": "1e4926d6968fa72cc6ba102ad04052a77044351a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/make.jl", "max_issues_repo_name": "ZhouZhuofei/NumericalAnalysis.jl", "max_issues_repo_head_hexsha": "1e4926d6968fa72cc6ba102ad04052a77044351a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2020-09-24T17:58:58.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-11T00:37:24.000Z", "max_forks_repo_path": "docs/make.jl", "max_forks_repo_name": "ZhouZhuofei/NumericalAnalysis.jl", "max_forks_repo_head_hexsha": "1e4926d6968fa72cc6ba102ad04052a77044351a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0555555556, "max_line_length": 77, "alphanum_fraction": 0.5667351129, "num_tokens": 125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11596072436426731, "lm_q1q2_score": 0.05707449274168176}}
{"text": "#######################################################################################################################################################################################################\n#\n# Changes to the function\n# General\n#     2021-Dec-24: migrate the function from PkgUtility to NetcdfIO\n#     2022-Jan-28: remove the complicated funtion to create var and dim at the same time\n#     2022-Jan-28: add global attributes to the generated file\n#\n#######################################################################################################################################################################################################\n\"\"\"\nNCDatasets.jl does not have a convenient function (1 line command) to save dataset as a file. Thus, we provide a few methods as supplements:\n\n$(METHODLIST)\n\n\"\"\"\nfunction save_nc! end\n\n\n\"\"\"\nTo save the code and effort to redefine the common attributes like latitude, longitude, and cycle index, we provide a shortcut method that handles these within the function:\n\n    save_nc!(file::String, var_name::String, var_data::Array{T,N}, var_attribute::Dict{String,String}; compress::Int = 4, growable::Bool = false) where {T<:Union{AbstractFloat,Int,String},N}\n\nSave the 1D, 2D, or 3D data as netcdf file, given\n- `file` Path to save the dataset\n- `var_name` Variable name for the data in the NC file\n- `var_data` Data to save\n- `var_attribute` Variable attributes for the data, such as unit and long name\n- `compress` Compression level fro NetCDF, default is 4\n- `growable` If true, make index growable, default is false\n\n---\n# Examples\n```julia\n# generate data to write into NC file\ndata1 = rand(12) .+ 273.15;\ndata2 = rand(36,18) .+ 273.15;\ndata3 = rand(36,18,12) .+ 273.15;\n\n# save data as NC files (2D and 3D)\nsave_nc!(\"data1.nc\", \"data1\", data1, Dict(\"description\" => \"Random temperature\", \"unit\" => \"K\"));\nsave_nc!(\"data2.nc\", \"data2\", data2, Dict(\"description\" => \"Random temperature\", \"unit\" => \"K\"));\nsave_nc!(\"data3.nc\", \"data3\", data3, Dict(\"description\" => \"Random temperature\", \"unit\" => \"K\"));\n```\n\"\"\"\nsave_nc!(file::String, var_name::String, var_data::Array{T,N}, var_attribute::Dict{String,String}; compress::Int = 4, growable::Bool = false) where {T<:Union{AbstractFloat,Int,String},N} = (\n    @assert 1 <= N <= 3 \"Variable must be a 1D, 2D, or 3D dataset!\";\n    @assert 0 <= compress <= 9 \"Compression rate must be within 0 to 9\";\n\n    # create the file\n    _dset = Dataset(file, \"c\");\n\n    # global title attribute\n    for (_title,_notes) in ATTR_ABOUT\n        _dset.attrib[_title] = _notes;\n    end;\n\n    # the case if the dimension is 1D\n    if N==1\n        _n_ind = (growable ? Inf : length(var_data));\n        _inds  = collect(eachindex(var_data));\n        add_nc_dim!(_dset, \"ind\", _n_ind);\n        append_nc!(_dset, \"ind\", _inds, ATTR_CYC, [\"ind\"]; compress=compress);\n        append_nc!(_dset, var_name, var_data, var_attribute, [\"ind\"]; compress=compress);\n\n        close(_dset);\n\n        return nothing\n    end;\n\n    # if the dimension is 2D or 3D\n    _n_lon   = size(var_data, 1);\n    _n_lat   = size(var_data, 2);\n    _res_lon = 360 / _n_lon;\n    _res_lat = 180 / _n_lat;\n    _lons    = collect(_res_lon/2:_res_lon:360) .- 180;\n    _lats    = collect(_res_lat/2:_res_lat:180) .- 90;\n    add_nc_dim!(_dset, \"lon\", _n_lon);\n    add_nc_dim!(_dset, \"lat\", _n_lat);\n    append_nc!(_dset, \"lon\", _lons, ATTR_LON, [\"lon\"]; compress=compress);\n    append_nc!(_dset, \"lat\", _lats, ATTR_LAT, [\"lat\"]; compress=compress);\n\n    if N==2\n        append_nc!(_dset, var_name, var_data, var_attribute, [\"lon\", \"lat\"]; compress=compress);\n    elseif N==3\n        _n_ind = (growable ? Inf : size(var_data,3));\n        _inds  = collect(1:_n_ind);\n        add_nc_dim!(_dset, \"ind\", _n_ind);\n        append_nc!(_dset, \"ind\", _inds, ATTR_CYC, [\"ind\"]; compress=compress);\n        append_nc!(_dset, var_name, var_data, var_attribute, [\"lon\", \"lat\", \"ind\"]; compress=compress);\n    end;\n\n    close(_dset);\n\n    return nothing\n);\n\n\n\"\"\"\nThis method saves DataFrame as a NetCDF file to save more space (compared to a CSV file).\n\n    save_nc!(file::String, df::DataFrame, var_names::Vector{String}, var_attributes::Vector{Dict{String,String}}; compress::Int = 4, growable::Bool = false)\n\nSave DataFrame to NetCDF, given\n- `file` Path to save the data\n- `df` DataFrame to save\n- `var_names` The label of data in DataFrame to save\n- `var_attributes` Variable attributes for the data to save\n- `compress` Compression level fro NetCDF, default is 4\n- `growable` If true, make index growable, default is false\n\n---\n# Examples\n```julia\ndf = DataFrame();\ndf[!,\"A\"] = rand(5);\ndf[!,\"B\"] = rand(5);\ndf[!,\"C\"] = rand(5);\nsave_nc!(\"dataf.nc\", df, [\"A\",\"B\"], [Dict(\"A\" => \"Attribute A\"), Dict(\"B\" => \"Attribute B\")]);\n```\n\"\"\"\nsave_nc!(file::String, df::DataFrame, var_names::Vector{String}, var_attributes::Vector{Dict{String,String}}; compress::Int = 4, growable::Bool = false) = (\n    @assert 0 <= compress <= 9 \"Compression rate must be within 0 to 9\";\n    @assert length(var_names) == length(var_attributes) \"Variable name and attributes lengths must match!\";\n\n    # create the file\n    _dset = Dataset(file, \"c\");\n\n    # global title attribute\n    for (_title,_notes) in ATTR_ABOUT\n        _dset.attrib[_title] = _notes;\n    end;\n\n    # define dimension related variables\n    _n_ind = (growable ? Inf : size(df)[1]);\n    _inds  = collect(1:_n_ind);\n\n    # save the variables\n    add_nc_dim!(_dset, \"ind\", _n_ind);\n    append_nc!(_dset, \"ind\", _inds, ATTR_CYC, [\"ind\"]; compress=compress);\n    for _i in eachindex(var_names)\n        append_nc!(_dset, var_names[_i], df[:, var_names[_i]], var_attributes[_i], [\"ind\"]; compress = compress);\n    end;\n\n    close(_dset);\n\n    return nothing\n);\n\n\n\"\"\"\nThis method is a simplified version of the method above, namely when users do not want to define the attributes.\n\n    save_nc!(file::String, df::DataFrame; compress::Int = 4, growable::Bool = false)\n\nSave DataFrame to NetCDF, given\n- `file` Path to save the data\n- `df` DataFrame to save\n- `notes` Global attributes (notes)\n- `compress` Compression level fro NetCDF, default is 4\n- `growable` If true, make index growable, default is false\n\n---\n# Examples\n```julia\ndf = DataFrame();\ndf[!,\"A\"] = rand(5);\ndf[!,\"B\"] = rand(5);\ndf[!,\"C\"] = rand(5);\nsave_nc!(\"test.nc\", df);\n```\n\"\"\"\nsave_nc!(file::String, df::DataFrame; compress::Int = 4, growable::Bool = false) = (\n    _var_names = names(df);\n    _var_attrs = [Dict{String,String}(_vn => _vn) for _vn in _var_names];\n\n    save_nc!(file, df, _var_names, _var_attrs; compress=compress, growable = growable);\n\n    return nothing\n);\n", "meta": {"hexsha": "d0bb9a8f4769a616395d5e8243c066024c070178", "size": 6626, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/save.jl", "max_stars_repo_name": "Yujie-W/NetcdfIO.jl", "max_stars_repo_head_hexsha": "015b1b8bd26739721a0c6a0fb0fd9dad9b8ea8ca", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-12-30T22:36:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-30T22:36:35.000Z", "max_issues_repo_path": "src/save.jl", "max_issues_repo_name": "Yujie-W/NetcdfIO.jl", "max_issues_repo_head_hexsha": "015b1b8bd26739721a0c6a0fb0fd9dad9b8ea8ca", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2021-12-29T06:59:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-12T00:59:43.000Z", "max_forks_repo_path": "src/save.jl", "max_forks_repo_name": "Yujie-W/NetcdfIO.jl", "max_forks_repo_head_hexsha": "015b1b8bd26739721a0c6a0fb0fd9dad9b8ea8ca", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.6077348066, "max_line_length": 199, "alphanum_fraction": 0.6193782071, "num_tokens": 1894, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207956, "lm_q2_score": 0.11596071978154188, "lm_q1q2_score": 0.057074490486118686}}
{"text": "module WeaveSupport\n## Modified from\n## https://github.com/SciML/SciMLTutorials.jl/blob/master/src/SciMLTutorials.jl\n\nimport Base64: base64encode\nusing Random\n\nusing Weave\n\nusing Mustache\nimport Markdown\nusing JSON\nusing Reexport\n@reexport using LaTeXStrings\n#using SymPy\n#function Base.show(io::IO, ::MIME\"text/latex\", x::SymPy.SymbolicObject)\n#    print(io, SymPy.as_markdown(sympy.latex(x)))\n#end\n\n\nusing Pkg\n\ninclude(\"formatting.jl\")\ninclude(\"bootstrap.jl\")\ninclude(\"questions.jl\")\ninclude(\"show-methods.jl\")\ninclude(\"toc.jl\")\n\n#import Plots\n# just show body, not standalone\n#function Plots._show(io::IO, ::MIME\"text/html\", plt::Plots.Plot{Plots.PlotlyBackend})\n#    write(io, Plots.html_body(plt))\n#end\n\n\n## we have jmd files that convert to html files\n## using a specialized template\n\nconst repo_directory = joinpath(@__DIR__,\"..\", \"..\")\nconst cssfile = joinpath(@__DIR__, \"..\", \"..\", \"templates\", \"skeleton_css.css\")\nconst htmlfile = joinpath(@__DIR__,\"..\", \"..\", \"templates\", \"bootstrap.tpl\")\nconst latexfile = joinpath(@__DIR__, \"..\", \"..\", \"templates\", \"julia_tex.tpl\")\n\nfunction weave_file(folder, file; build_list=(:script,:html,:pdf,:github,:notebook), force=false, kwargs...)\n\n\n    jmddir = joinpath(repo_directory,\"CwJ\",folder)\n    tmp = joinpath(jmddir, file)\n    bnm = replace(basename(tmp), r\".jmd$\" => \"\")\n\n\n    if !force\n        testfile = joinpath(repo_directory, \"html\", folder, bnm*\".html\")\n        if isfile(testfile) && (mtime(testfile) >= mtime(tmp))\n            return\n        end\n    end\n\n    Pkg.activate(dirname(tmp))\n    Pkg.instantiate()\n    args = Dict{Symbol,String}(:folder=>folder,:file=>file)\n\n    if :script \u2208 build_list\n        println(\"Building Script\")\n        dir = joinpath(repo_directory,\"script\",folder)\n        isdir(dir) || mkpath(dir)\n        args[:doctype] = \"script\"\n        tangle(tmp;out_path=dir)\n    end\n\n    if :html \u2208 build_list\n        println(\"Building HTML\")\n        dir = joinpath(repo_directory,\"html\",folder)\n        isdir(dir) || mkpath(dir)\n\n        figdir = joinpath(jmddir,\"figures\")\n        htmlfigdir = joinpath(dir, \"figures\")\n\n        if isdir(figdir)\n            isdir(htmlfigdir) && rm(htmlfigdir, recursive=true)\n            cp(figdir, htmlfigdir)\n        end\n\n        Weave.set_chunk_defaults!(:wrap=>false)\n        args[:doctype] = \"html\"\n        #weave(tmp,doctype = \"md2html\",out_path=dir,args=args; fig_ext=\".svg\", css=cssfile, kwargs...)\n        weave(tmp,doctype = \"md2html\", out_path=dir,args=args; fig_ext=\".svg\",\n              template=htmlfile,\n              fig_path=tempdir(),\n              kwargs...)\n\n        # clean up\n        isdir(htmlfigdir) && rm(htmlfigdir, recursive=true)\n\n    end\n\n    if :pdf \u2208 build_list\n\n        eval(quote using Tectonic end) # load Tectonic; wierd testing error\n\n        println(\"Building PDF\")\n        dir = joinpath(repo_directory,\"pdf\",folder)\n        isdir(dir) || mkpath(dir)\n\n        fig_path = \"_figures_\" * bnm\n        figdir = joinpath(jmddir,\"figures\")\n        texfigdir = joinpath(dir, \"figures\")\n\n        if isdir(figdir)\n            isdir(texfigdir) && rm(texfigdir, recursive=true)\n            cp(figdir, texfigdir)\n        end\n\n        args[:doctype] = \"pdf\"\n        try\n            weave(tmp,doctype=\"md2tex\",out_path=dir,args=args;\n                  template=latexfile,\n                  fig_path=fig_path,\n                  kwargs...)\n\n            texfile = joinpath(dir, bnm * \".tex\")\n            Base.invokelatest(Tectonic.tectonic, bin -> run(`$bin $texfile`))\n\n\n            # clean up\n            for ext in (\".tex\",)\n                f = joinpath(dir, bnm * ext)\n                isfile(f) && rm(f)\n            end\n\n        catch ex\n            @warn \"PDF generation failed\" exception=(ex, catch_backtrace())\n\n        end\n\n        isdir(texfigdir) && rm(texfigdir, recursive=true)\n        isdir(joinpath(dir,fig_path)) && rm(joinpath(dir,fig_path), recursive=true)\n        for ext in (\".aux\", \".log\", \".out\")\n            f = joinpath(dir, bnm * ext)\n            isfile(f) && rm(f)\n        end\n\n    end\n\n    if :github \u2208 build_list\n        println(\"Building Github Markdown\")\n        dir = joinpath(repo_directory,\"markdown\",folder)\n        isdir(dir) || mkpath(dir)\n        args[:doctype] = \"github\"\n        weave(tmp,doctype = \"github\",out_path=dir, args=args;\n              fig_path=tempdir(),\n              kwargs...)\n    end\n\n    if :notebook \u2208 build_list\n        println(\"Building Notebook\")\n        dir = joinpath(repo_directory,\"notebook\",folder)\n        isdir(dir) || mkpath(dir)\n        args[:doctype] = \"notebook\"\n        Weave.convert_doc(tmp,joinpath(dir,file[1:end-4]*\".ipynb\"))\n    end\nend\n\n\"\"\"\n    weave_all(; force=false, build_list=(:script,:html,:pdf,:github,:notebook))\n\nRun `weave` on all source files.\n\n* `force`: by default, only run `weave` on files with `html` file older than the source file in `CwJ`\n* `build_list`: list of output types to be built. The default is all types\n\nThe files will be built as subdirectories in the package directory. This is returned by `pathof(CalculusWithJulia)`.\n\n\"\"\"\nfunction weave_all(;force=false, build_list=(:script,:html,:pdf,:github,:notebook))\n    for folder in readdir(joinpath(repo_directory,\"CwJ\"))\n        folder == \"test.jmd\" && continue\n        weave_folder(folder; force=force, build_list=build_list)\n    end\nend\n\nfunction weave_folder(folder; force=false, build_list=(:script,:html,:pdf,:github,:notebook))\n    !isnothing(match(r\"\\.ico$\", folder)) && return nothing\n    for file in readdir(joinpath(repo_directory,\"CwJ\",folder))\n        !occursin(r\".jmd$\", basename(file)) && continue\n        println(\"Building $(joinpath(folder,file))\")\n        try\n            weave_file(folder,file; force=force, build_list=build_list)\n        catch\n        end\n    end\nend\n\n\n\n\n\nmacro q_str(x)\n    \"`$x`\"\nend\n\nexport mmd\nexport @q_str\nexport ImageFile, Verbatim, Invisible, Outputonly, HTMLonly, JSXGraph\nexport alert, warning, note\nexport example, popup, table\nexport gif_to_data\nexport numericq, radioq, booleanq, yesnoq, shortq, longq, multiq\n\n\nend # module\n", "meta": {"hexsha": "b23a3e896382d7b73523b8dfe9b24f291a21d10f", "size": 6058, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/WeaveSupport/WeaveSupport.jl", "max_stars_repo_name": "jverzani/CalculusWithJulia.jl", "max_stars_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31, "max_stars_repo_stars_event_min_datetime": "2019-08-29T02:00:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-20T11:15:12.000Z", "max_issues_repo_path": "src/WeaveSupport/WeaveSupport.jl", "max_issues_repo_name": "jverzani/CalculusWithJulia.jl", "max_issues_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2020-12-03T15:00:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T00:57:57.000Z", "max_forks_repo_path": "src/WeaveSupport/WeaveSupport.jl", "max_forks_repo_name": "jverzani/CalculusWithJulia.jl", "max_forks_repo_head_hexsha": "6ee5135e82c11a1f83b024556be55ad6cbf2622d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 10, "max_forks_repo_forks_event_min_datetime": "2020-01-07T10:53:24.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-15T06:08:38.000Z", "avg_line_length": 28.5754716981, "max_line_length": 116, "alphanum_fraction": 0.6223175966, "num_tokens": 1617, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.37022540649291935, "lm_q2_score": 0.15405756851741473, "lm_q1q2_score": 0.057036025927670646}}
{"text": "d1 = Dict(1 => 4.2, 2 => 5.3) \n# Dict{Int64,Float64} with 2 entries:\n#   2 => 5.3\n#   1 => 4.2\nd1 = Dict{Int64,Float64}(1 => 4.2, 2 => 5.3)\nd1 = [1 => 4.2, 2 => 5.3]\n# 2-element Array{Pair{Int64,Float64},1}:\n#  1 => 4.2\n#  2 => 5.3\n\nd2 = Dict{Any,Any}(\"a\"=>1, (2,3)=>true) \n\nd3 = Dict(:A => 100, :B => 200)\n# Dict{Symbol,Int64} with 2 entries:\n#   :A => 100\n#   :B => 200\nd3 = Dict{Symbol,Int64}(:A => 100, :B => 200)\nd3[:B] #> 200\n# d3[:Z] #> ERROR: KeyError: key :Z not found\nget(d3, :Z, 999) #> 999\n\nd3[:A] = 150 #> d3 is now [:A => 150, :B => 200]\nd3[:C] = 300 #> d3 is now [:A => 150, :B => 200, :C => 300]\nlength(d3) #> 3\n# d3[\"CVO\"] = 500 #> ERROR: KeyError: key \"CVO\" not found \n# d3[:CVO] = \"Julia\" #> ERROR: KeyError: key \"CVO\" not found\n\ndmus = Dict{Symbol,String}(:first_name => \"Louis\", :surname => \"Armstrong\", :occupation => \"musician\",  \n \t\t :date_of_birth => \"4/8/1901\")\n# Dict{Symbol,String} with 4 entries:\n#   :date_of_birth => \"4/8/1901\"\n#   :occupation    => \"musician\"\n#   :surname       => \"Armstrong\"\n#   :first_name    => \"Louis\"\n\nhaskey(d3, :Z) #> false\nhaskey(d3, :B) #> true\n\nd4 = Dict() #> Dict{Any,Any} with 0 entries\nd4[\"lang\"] = \"Julia\"\n\nd5 = Dict{Float64, Int64}() #> Dict{Float64,Int64} with 0 entries\n# d5[\"c\"] = 6 #> ERROR: MethodError: `convert` has no method matching convert(::Type{Float64}, ::ASCIIString)\n\nd3 = Dict(:A => 100, :B => 200) \ndelete!(d3, :B) #> Dict{Symbol,Int64} with 1 entry:  :A => 100\n\nd3 = Dict(:A => 100, :B => 200) \nki = keys(d3) #> KeyIterator\nfor k in ki\n   println(k)\nend #> A   B\n:A in ki #> true\n:Z in ki #> false\n\ncollect(keys(d3)) #> 2-element Array{Symbol,1}:\n#  :A\n#  :B\nvi = values(d3) #> ValueIterator\nfor v in values(d3)\n    println(v)\nend #> 100  200\n\nkeys1 = [\"J.S. Bach\", \"Woody Allen\", \"Barack Obama\"]\nvalues1 = [ 1685, 1935, 1961]\nd5 = Dict(zip(keys1, values1))\n#>\n# Dict{String,Int64} with 3 entries:\n#   \"J.S. Bach\"    => 1685\n#   \"Woody Allen\"  => 1935\n#   \"Barack Obama\" => 1961\n\nfor (k, v) in d5\n  println(\"$k was born in $v\")\nend\nfor p in d5\n  println(\"$(p[1]) was born in $(p[2])\")\nend\n#J.S. Bach was born in 1685\n#Barack Obama was born in 1961\n#Woody Allen was born in 1935\n\ncapitals = Dict{String, String}(\"France\"=> \"Paris\", \"China\"=>\"Beijing\")\n# Dict{String,String} with 2 entries:\n#   \"China\"  => \"Beijing\"\n#   \"France\" => \"Paris\"\n\n# neat tricks:\ndict = Dict(\"a\" => 1, \"b\" => 2, \"c\" => 3) \narrkey = [key for (key, value) in dict] #> 3-element Array{String,1}: \"a\" \"b\" \"c\"\n# same as collect(keys(dict)) \narrval = [value for (key, value) in dict] #> 3-element Array{Int64,1}: 1 2 3\n# same as collect(values(dict))\n", "meta": {"hexsha": "8af62d133f6617bfa038f46f44d14085e296989c", "size": 2603, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter05/dicts.jl", "max_stars_repo_name": "tavoludra1/Julia1.0", "max_stars_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2018-09-29T03:07:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-08T13:12:33.000Z", "max_issues_repo_path": "Chapter05/dicts.jl", "max_issues_repo_name": "tavoludra1/Julia1.0", "max_issues_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter05/dicts.jl", "max_forks_repo_name": "tavoludra1/Julia1.0", "max_forks_repo_head_hexsha": "92dc1e3955393394f10f538dceded45facd1cff6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2018-09-16T05:55:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-08T14:59:54.000Z", "avg_line_length": 27.6914893617, "max_line_length": 109, "alphanum_fraction": 0.5747214752, "num_tokens": 1106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3849121444839335, "lm_q2_score": 0.1480472055495773, "lm_q1q2_score": 0.05698516737294149}}
{"text": "using Oceananigans.Models: AbstractModel\nusing Oceananigans.Utils: prettykeys\n\nmutable struct NaNChecker{F}\n    fields :: F\n    erroring :: Bool\nend\n\nNaNChecker(fields) = NaNChecker(fields, false) # default\n\nfunction Base.summary(nc::NaNChecker)\n    fieldnames = prettykeys(nc.fields)\n    if nc.erroring\n        return \"Erroring NaNChecker for $fieldnames\"\n    else\n        return \"NaNChecker for $fieldnames\"\n    end\nend\n\nBase.show(io, nc::NaNChecker) = print(io, summary(nc))\n\n\"\"\"\n    NaNChecker(; fields, erroring=false)\n\nReturn a `NaNChecker`, which sets `sim.running=false` if a `NaN` is detected\nin any member of `fields` when `NaNChecker(sim)` is called. `fields` should be\na container with key-value pairs like a dictionary or `NamedTuple`.\n\nIf `erroring=true`, the `NaNChecker` will throw an error on NaN detection.\n\"\"\"\nNaNChecker(; fields, erroring=false) = NaNChecker(fields, erroring)\n\nhasnan(field) = any(isnan, parent(field)) \nhasnan(model::AbstractModel) = hasnan(first(fields(model)))\n\nfunction (nc::NaNChecker)(simulation)\n    for (name, field) in pairs(nc.fields)\n        if hasnan(field)\n            simulation.running = false\n\n            if nc.erroring\n                clock = simulation.model.clock\n                error(\"time = $(clock.time), iteration = $(clock.iteration): NaN found in field $name. Aborting simulation.\")\n            else\n                @info \"NaN found in field $name. Stopping simulation.\"\n            end\n        end\n    end\n    return nothing\nend\n\n\"\"\"\n    erroring_NaNChecker!(simulation)\n\nToggles `simulation`'s `NaNChecker` to throw an error when a `NaN` is detected.\n\"\"\"\nfunction erroring_NaNChecker!(simulation)\n    simulation.callbacks[:nan_checker].func.erroring = true\n    return nothing\nend\n\n", "meta": {"hexsha": "fb120a7cf19eeb7b5fdefde285a649632a04d818", "size": 1747, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Simulations/nan_checker.jl", "max_stars_repo_name": "johnryantaylor/Oceananigans.jl", "max_stars_repo_head_hexsha": "b769cc4f42f2d531133fb6359f197633efc975c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 239, "max_stars_repo_stars_event_min_datetime": "2019-03-05T03:46:44.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-04T21:53:14.000Z", "max_issues_repo_path": "src/Simulations/nan_checker.jl", "max_issues_repo_name": "johnryantaylor/Oceananigans.jl", "max_issues_repo_head_hexsha": "b769cc4f42f2d531133fb6359f197633efc975c7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 654, "max_issues_repo_issues_event_min_datetime": "2019-03-02T02:20:29.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-02T00:13:53.000Z", "max_forks_repo_path": "src/Simulations/nan_checker.jl", "max_forks_repo_name": "ali-ramadhan/OceanDispatch.jl", "max_forks_repo_head_hexsha": "65b8851d37052e90ca4a3e0c4a1c20398b0ee09a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 45, "max_forks_repo_forks_event_min_datetime": "2019-03-05T18:25:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-04T08:04:25.000Z", "avg_line_length": 28.1774193548, "max_line_length": 125, "alphanum_fraction": 0.6823125358, "num_tokens": 421, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.12252322533450248, "lm_q1q2_score": 0.056961239987745965}}
{"text": "LATEXIFY(x) = md\"$(latexify(x))\"\n\nLATEXIFY(s::AbstractString, x) = md\"$(s) $(latexify(x))\"\n\nL = LATEXIFY\n\nexport LATEXIFY, L", "meta": {"hexsha": "0c2f71ddb91277b1f5f582368050e88a0e5f86a1", "size": 124, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pluto_helpers.jl", "max_stars_repo_name": "PtFEM/NumericalMethodsforEngineers.jl", "max_stars_repo_head_hexsha": "e4a997a14adbb86b7efe1586962df39eb9285ebb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-07-23T18:12:52.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-25T03:32:45.000Z", "max_issues_repo_path": "src/pluto_helpers.jl", "max_issues_repo_name": "PtFEM/NumericalMethodsforEngineers.jl", "max_issues_repo_head_hexsha": "e4a997a14adbb86b7efe1586962df39eb9285ebb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2018-07-23T21:46:36.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-27T23:14:46.000Z", "max_forks_repo_path": "src/pluto_helpers.jl", "max_forks_repo_name": "PtFEM/NumericalMethodsforEngineers.jl", "max_forks_repo_head_hexsha": "e4a997a14adbb86b7efe1586962df39eb9285ebb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-10-27T14:13:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-20T18:54:06.000Z", "avg_line_length": 17.7142857143, "max_line_length": 56, "alphanum_fraction": 0.6451612903, "num_tokens": 52, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.12252320450794522, "lm_q1q2_score": 0.056961230305446774}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.2\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 227de887-4284-46fb-83e2-22d33830c9cf\nusing LaTeXStrings\n\n# \u2554\u2550\u2561 1eb8d032-dbeb-4544-8bcd-e512f19d6cd5\n\u03b8 = round(1/3, digits=2)\n\n# \u2554\u2550\u2561 ec80fb74-5aa2-11ec-0e31-bb7b0d8fe4f7\nmd\"\"\"\n``\\theta`` = $(round (\u03b8, digits = 2))\n\"\"\"\n\n# \u2554\u2550\u2561 76f42d5d-7b83-4d11-a52b-261ad8b55567\nmd\"\"\"\n``\\theta`` = $\u03b8\n\"\"\"\n\n# \u2554\u2550\u2561 18fbc708-96ea-4a2a-b631-442295d59b86\nmd\"\"\"\n\u03b8 = $(round (\u03b8, digits = 2))\n\"\"\"\n\n# \u2554\u2550\u2561 147eb4f3-bbf7-49d4-b7db-2c017878ff21\n\u03b8\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nLaTeXStrings = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\n\n[compat]\nLaTeXStrings = \"~1.3.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000002\nPLUTO_MANIFEST_TOML_CONTENTS = \"\"\"\n# This file is machine-generated - editing it directly is not advised\n\njulia_version = \"1.7.0\"\nmanifest_format = \"2.0\"\n\n[[deps.LaTeXStrings]]\ngit-tree-sha1 = \"f2355693d6778a178ade15952b7ac47a4ff97996\"\nuuid = \"b964fa9f-0449-5b57-a5c2-d3ea65f4040f\"\nversion = \"1.3.0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550227de887-4284-46fb-83e2-22d33830c9cf\n# \u2560\u25501eb8d032-dbeb-4544-8bcd-e512f19d6cd5\n# \u2560\u2550ec80fb74-5aa2-11ec-0e31-bb7b0d8fe4f7\n# \u2560\u255076f42d5d-7b83-4d11-a52b-261ad8b55567\n# \u255f\u250018fbc708-96ea-4a2a-b631-442295d59b86\n# \u2560\u2550147eb4f3-bbf7-49d4-b7db-2c017878ff21\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "fd6c3886c1d882deee8730a6497559329e7ef409", "size": 1372, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/intros/latex.jl", "max_stars_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_stars_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-12-17T01:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T00:23:37.000Z", "max_issues_repo_path": "notebooks/intros/latex.jl", "max_issues_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_issues_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "notebooks/intros/latex.jl", "max_forks_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_forks_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-21T21:01:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-21T21:01:50.000Z", "avg_line_length": 22.1290322581, "max_line_length": 69, "alphanum_fraction": 0.7128279883, "num_tokens": 710, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438006939036565, "lm_q2_score": 0.11757213354550883, "lm_q1q2_score": 0.056949598205146904}}
{"text": "\"\"\"\n    TestPackage.jl\n\nHallo!\n\"\"\"\nmodule TestPackage\n\nexport plusTwo\n\n\"\"\"\n    plusTwo(x)\n\nSum the numeric \"2\" to whatever it receives as input\n\nA more detailed explanation can go here, although I guess it is not needed in this case\n\n# Arguments\n* `x`: The amount to which we want to add 2\n\n# Notes\n* Notes can go here\n\n# Examples\n```julia\njulia> five = plusTwo(3)\n5\n```\n\"\"\"\nplusTwo(x) = x + 2\n\nend\n", "meta": {"hexsha": "34ea6e8cc76f32786ec4be5b8ce8892e45baff6e", "size": 399, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TestPackage.jl", "max_stars_repo_name": "matthiasbaitsch/TestPackage.jl", "max_stars_repo_head_hexsha": "e6ac91e1f60e18715a758f7d2eb2e256a90cdaee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/TestPackage.jl", "max_issues_repo_name": "matthiasbaitsch/TestPackage.jl", "max_issues_repo_head_hexsha": "e6ac91e1f60e18715a758f7d2eb2e256a90cdaee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/TestPackage.jl", "max_forks_repo_name": "matthiasbaitsch/TestPackage.jl", "max_forks_repo_head_hexsha": "e6ac91e1f60e18715a758f7d2eb2e256a90cdaee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.46875, "max_line_length": 87, "alphanum_fraction": 0.671679198, "num_tokens": 117, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.134775927848826, "lm_q1q2_score": 0.056943454177511614}}
{"text": "\"\"\"\n\nConstruct a csv file containing data for all instances\n\"\"\"\n\n@everywhere ROOT = pwd()\n@everywhere include(joinpath(ROOT,\"..\",\"src_PowSysMod\", \"PowSysMod_body.jl\"))\n@everywhere include(\"para_fct.jl\"))\n\n\n# NOTE: Launch in console using 'julia -p nbparathreads compute_GOC_all_scenario.jl'\n# where the nb of cores is likely a good value for nbparathreads\n\ninstances_path = joinpath(\"instances\", \"GOC\")\nfolders = readdir(instances_path)\n\n##empty dictionaries which will contain data to export in csv\ninstances_nb_scenario_data = Dict{String,Int64}()\ninstances_nb_contingency_data = Dict{String,Int64}()\ninstances_nb_bus_data = Dict{String,Int64}()\ninstances_nb_links_data = Dict{String,Int64}()\ninstances_nb_violated_constraints = Dict{String, Float64}()\ninstances_nb_cc_Qgen_active = Dict{String, Float64}()\n\n\n##loop on instances to get data\nepsilon = 1e-3\nfor folder in folders\n    instances_nb_scenario_data[folder] = get_nb_scenarios(instances_path,folder)\n    scenario = readdir(joinpath(instances_path, folder))[1]\n    instances_nb_contingency_data[folder] = get_nb_contingencies(folder,scenario)\n    instances_nb_bus_data[folder] = get_nb_bus(folder, scenario)\n    instances_nb_links_data[folder] = get_nb_links(folder,scenario)\n    instances_nb_cc_Qgen_active[folder] = mean(get_nb_cc_reactive_power_active(folder, scenario, epsilon) for scenario in readdir(joinpath(instances_path, folder)) if scenario !=\"scorepara.csv\")\n    #instances_nb_violated_constraints[folder] = mean(get_feasibility(folder, scenario, epsilon) for scenario in readdir(joinpath(instances_path, folder)))\nend\n##create csv file\ntouch(\"GOCdata.csv\")\nf = open(\"GOCdata.csv\", \"w\")\nwrite(f, \"Instance;Nb_scenarios;Nb_contingencies;Nb_bus;Nb_links;Mean nb of violated constraints by GOC solution at $(epsilon);Mean nb of reactive Qgen contingency constraints at $(epsilon)\\n\")\nfor (folder, nb_scenarios) in instances_nb_scenario_data\n    nb_cont = instances_nb_contingency_data[folder]\n    nb_bus = instances_nb_bus_data[folder]\n    nb_links = instances_nb_links_data[folder]\n    # nb_vc = instances_nb_violated_constraints[folder]\n    nb_vc = \"NONE\"\n    nb_cc_act = instances_nb_cc_Qgen_active[folder]\n    write(f, \"$(folder);$(nb_scenarios);$(nb_cont);$(nb_bus);$(nb_links);$(nb_vc);$(nb_cc_act)\\n\")\nend\nclose(f)\n\n\n\n##functions to get specific data\n\"\"\"\n    get_nb_scenarios(instances_path,folder)\n\nReturns the number of scenarios for an instance `folder` in `instances_path`.\n\"\"\"\nfunction get_nb_scenarios(instances_path,folder)\n    folder_path = joinpath(instances_path, folder)\n    scenarios = sort(filter(x->!ismatch(r\"\\.\", x), readdir(folder_path)), by=x->parse(split(x, \"_\")[2]))\n    nb_scenarios = length(scenarios)\n    return nb_scenarios\nend\n\n\"\"\"\n    get_nb_bus(folder, scenario)\n\nReturns the number of bus for a `scenario` in instance `folder`.\n\"\"\"\nfunction get_nb_bus(folder, scenario)\n    power_data = getpowerdata([folder, scenario])\n    try\n        nb_bus =  Int(power_data[\"totalbus\"])\n        return nb_bus\n    catch\n        warn(\"power_data coming from .mat file does not have key totalbus\")\n        return 0.0\n    end\nend\n\n\"\"\"\n    get_nb_contingencies(folder,scenario)\n\nReturns the number of contingencies for a `scenario` in instance `folder`.\n\"\"\"\nfunction get_nb_contingencies(folder,scenario)\n    contingency_data = getcontingencydata([folder, scenario])\n    nb_contingency = size(contingency_data,1)\n    return nb_contingency\nend\n\n\"\"\"\n    get_nb_links(folder, scenario)\n\nReturns the number of links for a `scenario` in instance `folder`.\n\"\"\"\nfunction get_nb_links(folder, scenario)\n    power_data = getpowerdata([folder, scenario])\n    try\n        nb_links =  Int(power_data[\"totalbranch\"])\n        return nb_links\n    catch\n        warn(\"power_data coming from .mat file does not have key totalbranch\")\n        return 0.0\n    end\nend\n\n\"\"\"\n    get_feasibility(folder, scenario, epsilon)\n\nReturns the number of violated constraints at `epsilon` (except balance constraints) for a `scenario` in instance `folder`.\n\"\"\"\nfunction get_feasibility(folder, scenario, epsilon)\n    try\n        violations = test_feasibility_GOC([folder, scenario],epsilon)\n        nb_violated_constraints = 0\n        for (scenario, dict_infeas) in violations\n            nb_violated_constraints += length(dict_infeas)\n        end\n        return nb_violated_constraints\n\n    catch\n        return -1.0\n    end\nend\n\n\n\"\"\"\n    get_nb_cc_reactive_power_active(folder, scenario,epsilon)\n\nReturns the number of active constraints of type contingency Qgen coupling constraints at `epsilon` for a `scenario` in instance `folder`.\n\"\"\"\nfunction get_nb_cc_reactive_power_active(folder, scenario,epsilon)\n    try\n        return nb_active_constraints_in_scenario_cc_Qgen([folder, scenario], epsilon)\n    catch\n        println([folder, scenario])\n        return -1.0\n    end\nend\n", "meta": {"hexsha": "278c30b7b5a80e1d11f0079cd3c3b813147c1cbe", "size": 4835, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "dev/collect_data_instances.jl", "max_stars_repo_name": "Gimaju/GOC", "max_stars_repo_head_hexsha": "daaee3ff9a0c8d21a739597ce0a7172213f43924", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/collect_data_instances.jl", "max_issues_repo_name": "Gimaju/GOC", "max_issues_repo_head_hexsha": "daaee3ff9a0c8d21a739597ce0a7172213f43924", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/collect_data_instances.jl", "max_forks_repo_name": "Gimaju/GOC", "max_forks_repo_head_hexsha": "daaee3ff9a0c8d21a739597ce0a7172213f43924", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.8111888112, "max_line_length": 194, "alphanum_fraction": 0.7420889349, "num_tokens": 1191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.13477592089824647, "lm_q1q2_score": 0.05694345124085955}}
{"text": "\nusing Distributed\nif nprocs() == 1\n    addprocs(3)\nend\n# Number of logical CPU cores available in the system.\n# println(\"CPU cores: \", Sys.CPU_CORES)\nSys.cpu_summary()\n# Number of available processes.\n# println(\"nprocs: \", nprocs())\n# println(\"nworkers: \", nworkers())\nprintln(\"nprocs: \", nprocs())\nprintln(\"nworkers: \", nworkers())\n\nn = 20000000\n# Ordinary for loop.\nlet\n    global sum1\n    sum1 = 0\n    @time for i = 1:n\n         sum1 += Int(rand(Bool));\n    end\nend\n\n# Parallel for loop\n# @time @parallel (+) for i = 1:20000\n@time sum2 = @distributed (+) for i = 1:n\n    Int(rand(Bool));\nend\n\n# Predefined function.\n@time sum3 =sum(rand(0:1, n))\n\n# For a not so small amount of work:\n# n = 200000000;\n# n = 20000;\n# @time @parallel (+) for i = 1:n\n@time sum4 = @distributed (+) for i = 1:n\n    Int(rand(Bool))\nend\n\n# println(sum1)\n# println(sum2)\n# println(sum3)\n# println(sum4)\n\n# DO NOT run sum(rand(0:1, n)) or the ordinary for loop with 'n': You may run\n# out of memory.\n", "meta": {"hexsha": "f313033b9d38bf9d399f2e6989323e9f0c9aa493", "size": 979, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "devel/dev_parallel_example.jl", "max_stars_repo_name": "mjirik/LarSurf.jl", "max_stars_repo_head_hexsha": "de2eaec62dfe8c63e7d621bc973aa01d8de019c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-09-17T22:56:08.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-04T09:50:42.000Z", "max_issues_repo_path": "devel/dev_parallel_example.jl", "max_issues_repo_name": "mjirik/lario3d.jl", "max_issues_repo_head_hexsha": "de2eaec62dfe8c63e7d621bc973aa01d8de019c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-11-16T15:47:22.000Z", "max_issues_repo_issues_event_max_datetime": "2019-11-18T17:43:46.000Z", "max_forks_repo_path": "devel/dev_parallel_example.jl", "max_forks_repo_name": "mjirik/lario3d.jl", "max_forks_repo_head_hexsha": "de2eaec62dfe8c63e7d621bc973aa01d8de019c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-05T15:01:47.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-05T15:01:47.000Z", "avg_line_length": 19.9795918367, "max_line_length": 77, "alphanum_fraction": 0.6353421859, "num_tokens": 320, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4225046348141882, "lm_q2_score": 0.13477591568531197, "lm_q1q2_score": 0.05694344903837056}}
{"text": "\"\"\"\n    count_nucleotides(strand)\n\nThe frequency of each nucleotide within `strand` as a dictionary.\n\nInvalid strands raise a `DomainError`.\n\n\"\"\"\nfunction count_nucleotides(strand)\n  dna = Dict('A'=>0,'C'=>0,'G'=>0,'T'=>0)\n\n  for i in strand\n    try\n      dna[i] += 1\n    catch\n      throw(DomainError(\"Invalid Key\"))\n    end\n  end\n\n  return dna\nend\n", "meta": {"hexsha": "58f163174d5fe042cd900c7aed7938344f93046f", "size": 350, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_stars_repo_name": "res0nat0r/exercism", "max_stars_repo_head_hexsha": "b3f7df7cefee52cecb932af7ca80280060bfdaf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_issues_repo_name": "res0nat0r/exercism", "max_issues_repo_head_hexsha": "b3f7df7cefee52cecb932af7ca80280060bfdaf4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_forks_repo_name": "res0nat0r/exercism", "max_forks_repo_head_hexsha": "b3f7df7cefee52cecb932af7ca80280060bfdaf4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.9090909091, "max_line_length": 65, "alphanum_fraction": 0.6342857143, "num_tokens": 104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.1276526236624356, "lm_q1q2_score": 0.05687301406690649}}
{"text": "struct Config{T<:Real}\n    atol::T # absolute tolerance for ...\n    rtol::T # relative tolerance for ...\n    solve::Bool # optimize and test result\n    query_number_of_constraints::Bool # can get `MOI.NumberOfConstraints` attribute\n    query::Bool # can get objective function, and constraint functions, and constraint sets\n    modify_lhs::Bool # can modify function of a constraint\n    duals::Bool # test dual solutions\n    dual_objective_value::Bool # test `DualObjectiveValue`\n    infeas_certificates::Bool # check for primal or dual infeasibility certificates when appropriate\n    # The expected \"optimal\" status returned by the solver. Either\n    # MOI.OPTIMAL or MOI.LOCALLY_SOLVED.\n    optimal_status::MOI.TerminationStatusCode\n    basis::Bool # can get variable and constraint basis status\n\n    \"\"\"\n        Config{T}(;\n            atol::Real = Base.rtoldefault(T),\n            rtol::Real = Base.rtoldefault(T),\n            solve::Bool = true,\n            query_number_of_constraints::Bool = true,\n            query::Bool = true,\n            modify_lhs::Bool = true,\n            duals::Bool = true,\n            dual_objective_value::Bool = duals,\n            infeas_certificates::Bool = true,\n            optimal_status = MOI.OPTIMAL,\n            basis::Bool = false,\n        )\n\n    Return an object that is used to configure various tests.\n\n    ## Keywords\n\n     * `atol::Real = Base.rtoldefault(T)`: Control the absolute tolerance used\n        when comparing solutions.\n     * `rtol::Real = Base.rtoldefault(T)`: Control the relative tolerance used\n        when comparing solutions.\n     * `solve::Bool = true`: Set to `false` to skip tests requiring a call to\n       [`MOI.optimize!`](@ref)\n     * `query_number_of_constraints::Bool = true`: Set to `false` to skip tests\n       requiring a call to [`MOI.NumberOfConstraints`](@ref).\n     * `query::Bool = true`: Set to `false` to skip tests requiring a call to\n       [`MOI.get`](@ref) for [`MOI.ConstraintFunction`](@ref) and\n       [`MOI.ConstraintSet`](@ref)\n     * `modify_lhs::Bool = true`:\n     * `duals::Bool = true`: Set to `false` to skip tests querying\n       [`MOI.ConstraintDual`](@ref).\n     * `dual_objective_value::Bool = duals`: Set to `false` to skip tests\n       querying [`MOI.DualObjectiveValue`](@ref).\n     * `infeas_certificates::Bool = true`: Set to `false` to skip tests querying\n       primal and dual infeasibility certificates.\n     * `optimal_status = MOI.OPTIMAL`: Set to `MOI.LOCALLY_SOLVED` if the solver\n       cannot prove global optimality.\n     * `basis::Bool = false`: Set to `true` if the solver supports\n       [`MOI.ConstraintBasisStatus`](@ref) and [`MOI.VariableBasisStatus`](@ref).\n    \"\"\"\n    function Config{T}(;\n        atol::Real = Base.rtoldefault(T),\n        rtol::Real = Base.rtoldefault(T),\n        solve::Bool = true,\n        query_number_of_constraints::Bool = true,\n        query::Bool = true,\n        modify_lhs::Bool = true,\n        duals::Bool = true,\n        dual_objective_value::Bool = duals,\n        infeas_certificates::Bool = true,\n        optimal_status = MOI.OPTIMAL,\n        basis::Bool = false,\n    ) where {T<:Real}\n        return new(\n            atol,\n            rtol,\n            solve,\n            query_number_of_constraints,\n            query,\n            modify_lhs,\n            duals,\n            dual_objective_value,\n            infeas_certificates,\n            optimal_status,\n            basis,\n        )\n    end\n    Config(; kwargs...) = Config{Float64}(; kwargs...)\nend\n\n@deprecate TestConfig Config\n\n\"\"\"\n    @moitestset setname subsets\n\nDefines a function `setnametest(model, config, exclude)` that runs the tests defined in the dictionary `setnametests`\nwith the model `model` and config `config` except the tests whose dictionary key is in `exclude`.\nIf `subsets` is `true` then each test runs in fact multiple tests hence the `exclude` argument is passed\nas it can also contains test to be excluded from these subsets of tests.\n\"\"\"\nmacro moitestset(setname, subsets = false)\n    testname = Symbol(string(setname) * \"test\")\n    testdict = Symbol(string(testname) * \"s\")\n    if subsets\n        runtest = :(f(model, config, exclude))\n    else\n        runtest = :(f(model, config))\n    end\n    return esc(\n        :(\n            function $testname(\n                model::$MOI.ModelLike,\n                config::$MOI.DeprecatedTest.Config,\n                exclude::Vector{String} = String[],\n            )\n                for (name, f) in $testdict\n                    if name in exclude\n                        continue\n                    end\n                    @testset \"$name\" begin\n                        $runtest\n                    end\n                end\n            end\n        ),\n    )\nend\n", "meta": {"hexsha": "84d0cfdfb6b03b99566ad1750dcff96be3ea6419", "size": 4745, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DeprecatedTest/config.jl", "max_stars_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_stars_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 132, "max_stars_repo_stars_event_min_datetime": "2020-06-20T00:45:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T22:06:34.000Z", "max_issues_repo_path": "src/DeprecatedTest/config.jl", "max_issues_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_issues_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 460, "max_issues_repo_issues_event_min_datetime": "2020-06-08T14:12:55.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:03:39.000Z", "max_forks_repo_path": "src/DeprecatedTest/config.jl", "max_forks_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_forks_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 28, "max_forks_repo_forks_event_min_datetime": "2020-06-08T01:45:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T10:36:25.000Z", "avg_line_length": 37.96, "max_line_length": 117, "alphanum_fraction": 0.6037934668, "num_tokens": 1133, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3998116264369279, "lm_q2_score": 0.14223190046381004, "lm_q1q2_score": 0.05686596745565113}}
{"text": "using Test\n\n@testset \"Create tests for your project\" begin\n\n@testset \"Testing linear algebra results\" begin\n   @test 1 == 1\nend;\n\nend; # ex1.jl\n\n", "meta": {"hexsha": "d20b338115a8389022dfa2759e8c5d0eca3576cf", "size": 145, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test1.jl", "max_stars_repo_name": "PsuAstro528/project-template", "max_stars_repo_head_hexsha": "2397a484cbb09f60f632d6a725682ca6faaa9fcf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-11-20T16:12:35.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-20T16:12:35.000Z", "max_issues_repo_path": "test/test1.jl", "max_issues_repo_name": "PsuAstro528/project-template", "max_issues_repo_head_hexsha": "2397a484cbb09f60f632d6a725682ca6faaa9fcf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test1.jl", "max_forks_repo_name": "PsuAstro528/project-template", "max_forks_repo_head_hexsha": "2397a484cbb09f60f632d6a725682ca6faaa9fcf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.1818181818, "max_line_length": 47, "alphanum_fraction": 0.7034482759, "num_tokens": 43, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.11920292515117027, "lm_q1q2_score": 0.0568096884743142}}
{"text": "# Bogumi\u0142 Kami\u0144ski, 2019-03-25\n\nurl = \"https://projecteuler.net/project/resources/p096_sudoku.txt\"\nfilename = \"p096_sudoku.txt\"\nif !isfile(filename) # download only if needed\n    println(\"File not found. Fetching from a remote location ...\")\n    try\n        # this uses OS tools and might fail if they are not found\n        download(url, filename)\n    catch\n        # thus we provide a fallback\n        using HTTP\n        r = HTTP.get(url)\n        write(filename, r.body)\n    end\nend\n", "meta": {"hexsha": "6e138bc2f9d21c04b67eb2086fc7cf3bddf78b69", "size": 484, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "7. Sudoku/getproblems.jl", "max_stars_repo_name": "bkamins/UEP-Workshop-20190405", "max_stars_repo_head_hexsha": "7fcbb371c3c2efd862ce58752cb1a6cef4300fea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:10:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-12T01:24:01.000Z", "max_issues_repo_path": "7. Sudoku/getproblems.jl", "max_issues_repo_name": "bkamins/UEP-Workshop-20190405", "max_issues_repo_head_hexsha": "7fcbb371c3c2efd862ce58752cb1a6cef4300fea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "7. Sudoku/getproblems.jl", "max_forks_repo_name": "bkamins/UEP-Workshop-20190405", "max_forks_repo_head_hexsha": "7fcbb371c3c2efd862ce58752cb1a6cef4300fea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-04-03T19:31:29.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-09T06:11:08.000Z", "avg_line_length": 28.4705882353, "max_line_length": 66, "alphanum_fraction": 0.6549586777, "num_tokens": 127, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.1192029188930649, "lm_q1q2_score": 0.05680968549182854}}
{"text": "const MYID = myid()\nconst OTHERIDS = filter(id-> id != MYID, procs())[rand(1:(nprocs()-1))]\n\nfunction check_leaks()\n    if length(DistributedArrays.refs) > 0\n        sleep(0.1)  # allow time for any cleanup to complete and test again\n        length(DistributedArrays.refs) > 0 && warn(\"Probable leak of \", length(DistributedArrays.refs), \" darrays\")\n    end\nend\n\n@testset \"test distribute\" begin\n    A = rand(1:100, (100,100))\n\n    @testset \"test default distribute\" begin\n        DA = distribute(A)\n        @test length(procs(DA)) == nworkers()\n        @test sum(DA) == sum(A)\n        close(DA)\n    end\n\n    @testset \"test distribute with procs arguments\" begin\n        DA = distribute(A, procs = procs())\n        @test length(procs(DA)) == nprocs()\n        @test sum(DA) == sum(A)\n        close(DA)\n    end\n\n    @testset \"test distribute with procs and dist arguments\" begin\n        DA = distribute(A, procs = [1, 2], dist = [1,2])\n        @test size(procs(DA)) == (1,2)\n        @test sum(DA) == sum(A)\n        close(DA)\n    end\n\n    @testset \"Create darray with unconventional distribution and distibute like it\" begin\n        block = 10\n        Y = nworkers() * block\n        X = nworkers() * block\n        remote_parts = map(workers()) do wid\n            remotecall(rand, wid, block, Y)\n        end\n        DA1 = DArray(reshape(remote_parts, (length(remote_parts), 1)))\n        A = rand(X, Y)\n        DA2 = distribute(A, DA1)\n\n        @test size(DA1) == size(DA2)\n\n        close(DA1)\n        close(DA2)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test DArray equality\" begin\n    D = drand((200,200), [MYID, OTHERIDS])\n    DC = copy(D)\n\n    @testset \"test isequal(::DArray, ::DArray)\" begin\n        @test D == DC\n    end\n\n    @testset \"test copy(::DArray) does a copy of each localpart\" begin\n        @spawnat OTHERIDS localpart(DC)[1] = 0\n        @test fetch(@spawnat OTHERIDS localpart(D)[1] != 0)\n    end\n\n    close(D)\n    close(DC)\nend\n\ncheck_leaks()\n\n@testset \"test DArray similar\" begin\n    D = drand((200,200), [MYID, OTHERIDS])\n    DS = similar(D,Float16)\n\n    @testset \"test eltype of a similar\" begin\n        @test eltype(DS) == Float16\n    end\n\n    @testset \"test dims of a similar\" begin\n        @test size(D) == size(DS)\n    end\n    close(D)\n    close(DS)\nend\n\ncheck_leaks()\n\n@testset \"test DArray reshape\" begin\n    D = drand((200,200), [MYID, OTHERIDS])\n\n    @testset \"Test error-throwing in reshape\" begin\n        @test_throws DimensionMismatch reshape(D,(100,100))\n    end\n\n    DR = reshape(D,(100,400))\n    @testset \"Test reshape\" begin\n        @test size(DR) == (100,400)\n    end\n    close(D)\nend\n\ncheck_leaks()\n\n@testset \"test @DArray comprehension constructor\" begin\n\n    @testset \"test valid use of @DArray\" begin\n        D = @DArray [i+j for i=1:10, j=1:10]\n        @test D == [i+j for i=1:10, j=1:10]\n        close(D)\n    end\n\n    @testset \"test invalid use of @DArray\" begin\n        @test_throws ArgumentError eval(:((@DArray [1,2,3,4])))\n    end\nend\n\ncheck_leaks()\n\n@testset \"test DArray / Array conversion\" begin\n    D = drand((200,200), [MYID, OTHERIDS])\n\n    @testset \"test convert(::Array, ::(Sub)DArray)\" begin\n        S = convert(Matrix{Float64}, D[1:150, 1:150])\n        A = convert(Matrix{Float64}, D)\n\n        @test A[1:150,1:150] == S\n        D2 = convert(DArray{Float64,2,Matrix{Float64}}, A)\n        @test D2 == D\n        @test fetch(@spawnat MYID localpart(D)[1,1]) == D[1,1]\n        @test fetch(@spawnat OTHERIDS localpart(D)[1,1]) == D[1,101]\n        close(D2)\n\n        S2 = convert(Vector{Float64}, D[4, 23:176])\n        @test A[4, 23:176] == S2\n\n        S3 = convert(Vector{Float64}, D[23:176, 197])\n        @test A[23:176, 197] == S3\n\n        S4 = zeros(4)\n        setindex!(S4, D[3:4, 99:100], :)\n        @test S4 == vec(D[3:4, 99:100])\n        @test S4 == vec(A[3:4, 99:100])\n\n        S5 = zeros(2,2)\n        setindex!(S5, D[1,1:4], :, 1:2)\n        @test vec(S5) == D[1, 1:4]\n        @test vec(S5) == A[1, 1:4]\n    end\n    close(D)\nend\n\ncheck_leaks()\n\n@testset \"copy!\" begin\n    D1 = dzeros((10,10))\n    r1 = remotecall_wait(() -> randn(3,10), workers()[1])\n    r2 = remotecall_wait(() -> randn(7,10), workers()[2])\n    D2 = DArray(reshape([r1; r2], 2, 1))\n    copy!(D2, D1)\n    @test D1 == D2\n    close(D1)\n    close(D2)\nend\n\ncheck_leaks()\n\n@testset \"test DArray reduce\" begin\n    D = DArray(id->fill(myid(), map(length,id)), (10,10), [MYID, OTHERIDS])\n\n    @testset \"test reduce\" begin\n        @test reduce(+, D) == ((50*MYID) + (50*OTHERIDS))\n    end\n\n    @testset \"test map / reduce\" begin\n        D2 = map(x->1, D)\n        @test reduce(+, D2) == 100\n        close(D2)\n    end\n\n    @testset \"test map! / reduce\" begin\n        map!(x->1, D)\n        @test reduce(+, D) == 100\n    end\n    close(D)\nend\n\ncheck_leaks()\n\n@testset \"test scale\" begin\n    A = randn(100,100)\n    DA = distribute(A)\n    @test scale!(DA, 2) == scale!(A, 2)\n    close(DA)\nend\n\ncheck_leaks()\n\n@testset \"test scale!(b, A)\" begin\n    A = randn(100, 100)\n    b = randn(100)\n    DA = distribute(A)\n    @test scale!(b, A) == scale!(b, DA)\n    close(DA)\n    A = randn(100, 100)\n    b = randn(100)\n    DA = distribute(A)\n    @test scale!(A, b) == scale!(DA, b)\n    close(DA)\nend\n\ncheck_leaks()\n\n@testset \"test mapreduce on DArrays\" begin\n    for _ = 1:25, f = [x -> Int128(2x), x -> Int128(x^2), x -> Int128(x^2 + 2x - 1)], opt = [+, *]\n        A = rand(1:5, rand(2:30))\n        DA = distribute(A)\n        @test mapreduce(f, opt, DA) - mapreduce(f, opt, A) == 0\n        close(DA)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test mapreducedim on DArrays\" begin\n    D = DArray(I->fill(myid(), map(length,I)), (73,73), [MYID, OTHERIDS])\n    D2 = map(x->1, D)\n    @test mapreducedim(t -> t*t, +, D2, 1) == mapreducedim(t -> t*t, +, convert(Array, D2), 1)\n    @test mapreducedim(t -> t*t, +, D2, 2) == mapreducedim(t -> t*t, +, convert(Array, D2), 2)\n    @test mapreducedim(t -> t*t, +, D2, (1,2)) == mapreducedim(t -> t*t, +, convert(Array, D2), (1,2))\n\n    # Test non-regularly chunked DArrays\n    r1 = DistributedArrays.remotecall(() -> sprandn(3, 10, 0.1), workers()[1])\n    r2 = DistributedArrays.remotecall(() -> sprandn(7, 10, 0.1), workers()[2])\n    D = DArray(reshape([r1; r2], (2,1)))\n    @test Array(sum(D, 2)) == sum(Array(D), 2)\n\n    # close(D)\n    # close(D2)\n    darray_closeall()   # temp created by the mapreduce above\nend\n\ncheck_leaks()\n\n@testset \"test mapreducdim, reducedim on DArrays\" begin\n    dims = (20,20,20)\n    DA = drandn(dims)\n    A = convert(Array, DA)\n\n    @testset \"dimension $dms\" for dms in (1, 2, 3, (1,2), (1,3), (2,3), (1,2,3))\n        @test mapreducedim(t -> t*t, +, A, dms) \u2248 mapreducedim(t -> t*t, +, DA, dms)\n        @test mapreducedim(t -> t*t, +, A, dms, 1.0) \u2248 mapreducedim(t -> t*t, +, DA, dms, 1.0)\n        @test reducedim(*, A, dms) \u2248 reducedim(*, DA, dms)\n        @test reducedim(*, A, dms, 2.0) \u2248 reducedim(*, DA, dms, 2.0)\n    end\n    close(DA)\n    darray_closeall()   # temp created by the mapreduce above\nend\n\ncheck_leaks()\n\n@testset \"test statistical functions on DArrays\" begin\n    dims = (20,20,20)\n    DA = drandn(dims)\n    A = convert(Array, DA)\n\n    @testset \"test $f for dimension $dms\" for f in (mean, ), dms in (1, 2, 3, (1,2), (1,3), (2,3), (1,2,3))\n        # std is pending implementation\n        @test f(DA,dms) \u2248 f(A,dms)\n    end\n\n    close(DA)\n    darray_closeall()   # temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test sum on DArrays\" begin\n    A = randn(100,100)\n    DA = distribute(A)\n\n    # sum either throws an ArgumentError or a CompositeException of ArgumentErrors\n    try\n        sum(DA, -1)\n    catch err\n        if isa(err, CompositeException)\n            @test !isempty(err.exceptions)\n            for excep in err.exceptions\n                # Unpack the remote exception\n                orig_err = excep.ex.captured.ex\n                @test isa(orig_err, ArgumentError)\n            end\n        else\n            @test isa(err, ArgumentError)\n        end\n    end\n    try\n        sum(DA, 0)\n    catch err\n        if isa(err, CompositeException)\n            @test !isempty(err.exceptions)\n            for excep in err.exceptions\n                # Unpack the remote exception\n                orig_err = excep.ex.captured.ex\n                @test isa(orig_err, ArgumentError)\n            end\n        else\n            @test isa(err, ArgumentError)\n        end\n    end\n\n    @test sum(DA) \u2248 sum(A)\n    @test sum(DA,1) \u2248 sum(A,1)\n    @test sum(DA,2) \u2248 sum(A,2)\n    @test sum(DA,3) \u2248 sum(A,3)\n    close(DA)\n    darray_closeall()   # temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test size on DArrays\" begin\n\n    A = randn(100,100)\n    DA = distribute(A)\n\n    @test_throws BoundsError size(DA, 0)\n    @test size(DA,1) == size(A,1)\n    @test size(DA,2) == size(A,2)\n    @test size(DA,3) == size(A,3)\n    close(DA)\nend\n\ncheck_leaks()\n\n# test length / endof\n@testset \"test collections API\" begin\n    A = randn(23,23)\n    DA = distribute(A)\n\n    @testset \"test length\" begin\n        @test length(DA) == length(A)\n    end\n\n    @testset \"test endof\" begin\n        @test endof(DA) == endof(A)\n    end\n    close(DA)\nend\n\ncheck_leaks()\n\n@testset \"test max / min / sum\" begin\n    a = map(x->Int(round(rand() * 100)) - 50, Array(Int, 100,1000))\n    d = distribute(a)\n\n    @test sum(d) == sum(a)\n    @test maximum(d) == maximum(a)\n    @test minimum(d) == minimum(a)\n    @test maxabs(d) == maxabs(a)\n    @test minabs(d) == minabs(a)\n    @test sumabs(d) == sumabs(a)\n    @test sumabs2(d) == sumabs2(a)\n    close(d)\nend\n\ncheck_leaks()\n\n@testset \"test all / any\" begin\n    a = map(x->Int(round(rand() * 100)) - 50, Array(Int, 100,1000))\n    a = [true for i in 1:100]\n    d = distribute(a)\n\n    @test all(d)\n    @test any(d)\n\n    close(d)\n\n    a[50] = false\n    d = distribute(a)\n    @test !all(d)\n    @test any(d)\n\n    close(d)\n\n    a = [false for i in 1:100]\n    d = distribute(a)\n    @test !all(d)\n    @test !any(d)\n\n    close(d)\n\n    d = dones(10,10)\n    @test !all(x-> x>1.0, d)\n    @test all(x-> x>0.0, d)\n\n    close(d)\n\n    a = ones(10,10)\n    a[10] = 2.0\n    d = distribute(a)\n    @test any(x-> x == 1.0, d)\n    @test any(x-> x == 2.0, d)\n    @test !any(x-> x == 3.0, d)\n\n    close(d)\nend\n\ncheck_leaks()\n\n@testset \"test count\"  begin\n    a = ones(10,10)\n    a[10] = 2.0\n    d = distribute(a)\n\n    @test count(x-> x == 2.0, d) == 1\n    @test count(x-> x == 1.0, d) == 99\n    @test count(x-> x == 0.0, d) == 0\n\n    close(d)\nend\n\ncheck_leaks()\n\n@testset \"test prod\" begin\n    a = fill(2, 10);\n    d = distribute(a);\n    @test prod(d) == 2^10\n\n    close(d)\nend\n\ncheck_leaks()\n\n@testset \"test zeros\" begin\n    @testset \"1D dzeros default element type\" begin\n        A = dzeros(10)\n        @test A == zeros(10)\n        @test eltype(A) == Float64\n        @test size(A) == (10,)\n        close(A)\n    end\n\n    @testset \"1D dzeros with specified element type\" begin\n        A = dzeros(Int, 10)\n        @test A == zeros(10)\n        @test eltype(A) == Int\n        @test size(A) == (10,)\n        close(A)\n    end\n\n    @testset \"2D dzeros default element type, Dims constuctor\" begin\n        A = dzeros((10,10))\n        @test A == zeros((10,10))\n        @test eltype(A) == Float64\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dzeros specified element type, Dims constructor\" begin\n        A = dzeros(Int, (10,10))\n        @test A == zeros(Int, (10,10))\n        @test eltype(A) == Int\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dzeros, default element type\" begin\n        A = dzeros(10,10)\n        @test A == zeros(10,10)\n        @test eltype(A) == Float64\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dzeros, specified element type\" begin\n        A = dzeros(Int, 10, 10)\n        @test A == zeros(Int, 10, 10)\n        @test eltype(A) == Int\n        @test size(A) == (10,10)\n        close(A)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test dones\" begin\n    @testset \"1D dones default element type\" begin\n        A = dones(10)\n        @test A == ones(10)\n        @test eltype(A) == Float64\n        @test size(A) == (10,)\n        close(A)\n    end\n\n    @testset \"1D dones with specified element type\" begin\n        A = dones(Int, 10)\n        @test eltype(A) == Int\n        @test size(A) == (10,)\n        close(A)\n    end\n\n    @testset \"2D dones default element type, Dims constuctor\" begin\n        A = dones((10,10))\n        @test A == ones((10,10))\n        @test eltype(A) == Float64\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dones specified element type, Dims constructor\" begin\n        A = dones(Int, (10,10))\n        @test A == ones(Int, (10,10))\n        @test eltype(A) == Int\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dones, default element type\" begin\n        A = dones(10,10)\n        @test A == ones(10,10)\n        @test eltype(A) == Float64\n        @test size(A) == (10,10)\n        close(A)\n    end\n\n    @testset \"2D dones, specified element type\" begin\n        A = dones(Int, 10, 10)\n        @test A == ones(Int, 10, 10)\n        @test eltype(A) == Int\n        @test size(A) == (10,10)\n        close(A)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test drand\" begin\n    @testset \"1D drand\" begin\n        A = drand(100)\n        @test eltype(A) == Float64\n        @test size(A) == (100,)\n        @test all(x-> x >= 0.0 && x <= 1.0, A)\n        close(A)\n    end\n\n    @testset \"1D drand, specified element type\" begin\n        A = drand(Int, 100)\n        @test eltype(A) == Int\n        @test size(A) == (100,)\n        close(A)\n    end\n\n    @testset \"2D drand, Dims constructor\" begin\n        A = drand((50,50))\n        @test eltype(A) == Float64\n        @test size(A) == (50,50)\n        @test all(x-> x >= 0.0 && x <= 1.0, A)\n        close(A)\n    end\n\n    @testset \"2D drand\" begin\n        A = drand(100,100)\n        @test eltype(A) == Float64\n        @test size(A) == (100,100)\n        @test all(x-> x >= 0.0 && x <= 1.0, A)\n        close(A)\n    end\n\n    @testset \"2D drand, Dims constructor, specified element type\" begin\n        A = drand(Int, (100,100))\n        @test eltype(A) == Int\n        @test size(A) == (100,100)\n        close(A)\n    end\n\n    @testset \"2D drand, specified element type\" begin\n        A = drand(Int, 100, 100)\n        @test eltype(A) == Int\n        @test size(A) == (100,100)\n        close(A)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test randn\" begin\n    @testset \"1D drandn\" begin\n        A = drandn(100)\n        @test eltype(A) == Float64\n        @test size(A) == (100,)\n        close(A)\n    end\n\n    @testset \"2D drandn, Dims constructor\" begin\n        A = drandn((50,50))\n        @test eltype(A) == Float64\n        @test size(A) == (50,50)\n        close(A)\n    end\n\n    @testset \"2D drandn\" begin\n        A = drandn(100,100)\n        @test eltype(A) == Float64\n        @test size(A) == (100,100)\n        close(A)\n    end\nend\n\ncheck_leaks()\n\n@testset \"test c/transpose\" begin\n    @testset \"test ctranspose real\" begin\n        A = drand(Float64, 100, 200)\n        @test A' == Array(A)'\n        close(A)\n    end\n    @testset \"test ctranspose complex\" begin\n        A = drand(Complex128, 200, 100)\n        @test A' == Array(A)'\n        close(A)\n    end\n    @testset \"test transpose real\" begin\n        A = drand(Float64, 200, 100)\n        @test A.' == Array(A).'\n        close(A)\n    end\n    @testset \"test ctranspose complex\" begin\n        A = drand(Complex128, 100, 200)\n        @test A.' == Array(A).'\n        close(A)\n    end\n\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test convert from subdarray\" begin\n    a = drand(20, 20);\n\n    s = view(a, 1:5, 5:8)\n    @test isa(s, SubDArray)\n    @test s == convert(DArray, s)\n\n    s = view(a, 6:5, 5:8)\n    @test isa(s, SubDArray)\n    @test s == convert(DArray, s)\n    close(a)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test scalar math\" begin\n    a = drand(20, 20);\n    b = convert(Array, a)\n    @testset \"$f\" for f in (-, abs, abs2, acos, acosd, acot,\n              acotd, acsch, angle, asech, asin,\n              asind, asinh, atan, atand, atanh,\n              big, cbrt, ceil, cis, complex, conj,\n              cos, cosc, cosd, cosh, cospi, cot,\n              cotd, coth, csc, cscd, csch, dawson,\n              deg2rad, digamma, erf, erfc, erfcinv,\n              erfcx, erfi, erfinv, exp, exp10, exp2,\n              expm1, exponent, float, floor, gamma, imag,\n              invdigamma, isfinite, isinf, isnan, lfact,\n              lgamma, log, log10, log1p, log2, rad2deg, real,\n              sec, secd, sech, sign, sin, sinc, sind,\n              sinh, sinpi, sqrt, tan, tand, tanh, trigamma)\n        @test f.(a) == f.(b)\n    end\n    a = a + 1\n    b = b + 1\n    @testset \"$f\" for f in (asec, asecd, acosh, acsc, acscd, acoth)\n        @test f.(a) == f.(b)\n    end\n    close(a)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test mapslices\" begin\n    A = randn(5,5,5)\n    D = distribute(A, procs = workers(), dist = [1, 1, min(nworkers(), 5)])\n    @test mapslices(svdvals, D, (1,2)) \u2248 mapslices(svdvals, A, (1,2))\n    @test mapslices(svdvals, D, (1,3)) \u2248 mapslices(svdvals, A, (1,3))\n    @test mapslices(svdvals, D, (2,3)) \u2248 mapslices(svdvals, A, (2,3))\n    @test mapslices(sort, D, (1,)) \u2248 mapslices(sort, A, (1,))\n    @test mapslices(sort, D, (2,)) \u2248 mapslices(sort, A, (2,))\n    @test mapslices(sort, D, (3,)) \u2248 mapslices(sort, A, (3,))\n\n    # issue #3613\n    B = mapslices(sum, dones(Float64, (2,3,4), workers(), [1,1,min(nworkers(),4)]), [1,2])\n    @test size(B) == (1,1,4)\n    @test all(B.==6)\n\n    # issue #5141\n    C1 = mapslices(x-> maximum(-x), D, [])\n    @test C1 == -D\n\n    # issue #5177\n    c = dones(Float64, (2,3,4,5), workers(), [1,1,1,min(nworkers(),5)])\n    m1 = mapslices(x-> ones(2,3), c, [1,2])\n    m2 = mapslices(x-> ones(2,4), c, [1,3])\n    m3 = mapslices(x-> ones(3,4), c, [2,3])\n    @test size(m1) == size(m2) == size(m3) == size(c)\n\n    n1 = mapslices(x-> ones(6), c, [1,2])\n    n2 = mapslices(x-> ones(6), c, [1,3])\n    n3 = mapslices(x-> ones(6), c, [2,3])\n    n1a = mapslices(x-> ones(1,6), c, [1,2])\n    n2a = mapslices(x-> ones(1,6), c, [1,3])\n    n3a = mapslices(x-> ones(1,6), c, [2,3])\n    @test (size(n1a) == (1,6,4,5) && size(n2a) == (1,3,6,5) && size(n3a) == (2,1,6,5))\n    @test (size(n1) == (6,1,4,5) && size(n2) == (6,3,1,5) && size(n3) == (2,6,1,5))\n    close(D)\n    close(c)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test scalar ops\" begin\n    a = drand(20,20)\n    b = convert(Array, a)\n    c = drand(20,20)\n    d = convert(Array, c)\n\n    @testset \"$f\" for f in (+, -, .+, .-, .*, ./, .%, div, mod)\n        x = rand()\n        @test f(a, x) == f(b, x)\n        @test f(x, a) == f(x, b)\n        @test f(a, c) == f(b, d)\n    end\n\n    close(a)\n    close(c)\n\n    a = dones(Int, 20, 20)\n    b = convert(Array, a)\n    @testset \"$f\" for f in (.<<, .>>)\n        @test f(a, 2) == f(b, 2)\n        @test f(2, a) == f(2, b)\n        @test f(a, a) == f(b, b)\n    end\n\n    @testset \"$f\" for f in (rem,)\n        x = rand()\n        @test f(a, x) == f(b, x)\n    end\n    close(a)\n    close(c)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test broadcast ops\" begin\n    wrkrs = workers()\n    nwrkrs = length(wrkrs)\n    nrows = 20 * nwrkrs\n    ncols = 10 * nwrkrs\n    a = drand((nrows,ncols), wrkrs, (1, nwrkrs))\n    m = mean(a, 1)\n    c = a .- m\n    d = convert(Array, a) .- convert(Array, m)\n    @test c == d\n    darray_closeall()\nend\n\ncheck_leaks()\n\n@testset \"test matrix multiplication\" begin\n    A = drandn(20,20)\n    b = drandn(20)\n    B = drandn(20,20)\n\n    @test norm(convert(Array, A*b) - convert(Array, A)*convert(Array, b), Inf) < sqrt(eps())\n    @test norm(convert(Array, A*B) - convert(Array, A)*convert(Array, B), Inf) < sqrt(eps())\n    @test norm(convert(Array, A'*b) - convert(Array, A)'*convert(Array, b), Inf) < sqrt(eps())\n    @test norm(convert(Array, A'*B) - convert(Array, A)'*convert(Array, B), Inf) < sqrt(eps())\n    close(A)\n    close(b)\n    close(B)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test norm\" begin\n    x = drandn(20)\n\n    @test abs(norm(x) - norm(convert(Array, x))) < sqrt(eps())\n    @test abs(norm(x, 1) - norm(convert(Array, x), 1)) < sqrt(eps())\n    @test abs(norm(x, 2) - norm(convert(Array, x), 2)) < sqrt(eps())\n    @test abs(norm(x, Inf) - norm(convert(Array, x), Inf)) < sqrt(eps())\n    close(x)\nend\n\ncheck_leaks()\n\n@testset \"test axpy!\" begin\n    x = drandn(20)\n    y = drandn(20)\n\n    @test norm(convert(Array, LinAlg.axpy!(2.0, x, copy(y))) - LinAlg.axpy!(2.0, convert(Array, x), convert(Array, y))) < sqrt(eps())\n    @test_throws DimensionMismatch LinAlg.axpy!(2.0, x, zeros(length(x) + 1))\n    close(x)\n    close(y)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test ppeval\" begin\n    A = drandn((10, 10, nworkers()), workers(), [1, 1, nworkers()])\n    B = drandn((10, nworkers()), workers(), [1, nworkers()])\n\n    R = zeros(10, nworkers())\n    for i = 1:nworkers()\n        R[:, i] = convert(Array, A)[:, :, i]*convert(Array, B)[:, i]\n    end\n    @test convert(Array, ppeval(*, A, B)) \u2248 R\n    @test sum(ppeval(eigvals, A)) \u2248 sum(ppeval(eigvals, A, eye(10, 10)))\n    close(A)\n    close(B)\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\n@testset \"test nnz\" begin\n    A = sprandn(10, 10, 0.5)\n    @test nnz(distribute(A)) == nnz(A)\nend\n\n@testset \"test matmatmul\" begin\n    A = drandn(30, 30)\n    B = drandn(30, 20)\n    a = convert(Array, A)\n    b = convert(Array, B)\n\n    AB = A * B\n    AtB = A.' * B\n    AcB = A' * B\n\n    ab = a * b\n    atb = a.' * b\n    acb = a' * b\n\n    @test AB \u2248 ab\n    @test AtB \u2248 atb\n    @test AcB \u2248 acb\n    darray_closeall()  # close the temporaries created above\nend\n\n@testset \"sort, T = $T\" for i in 0:6, T in [Int, Float64]\n    d = DistributedArrays.drand(T, 10^i)\n    @testset \"sample = $sample\" for sample in Any[true, false, (minimum(d),maximum(d)), rand(T, 10^i>512 ? 512 : 10^i)]\n        d2 = DistributedArrays.sort(d; sample=sample)\n\n        @test length(d) == length(d2)\n        @test sort(convert(Array, d)) == convert(Array, d2)\n    end\n    darray_closeall()  # close the temporaries created above\nend\n\ncheck_leaks()\n\ndarray_closeall()\n\n@testset \"test for any leaks\" begin\n    sleep(1.0)     # allow time for any cleanup to complete\n    allrefszero = Bool[remotecall_fetch(()->length(DistributedArrays.refs) == 0, p) for p in procs()]\n    @test all(allrefszero)\n\n    allregistrieszero = Bool[remotecall_fetch(()->length(DistributedArrays.registry) == 0, p) for p in procs()]\n    @test all(allregistrieszero)\nend\n", "meta": {"hexsha": "a08ae03481288098475ecaf9596db5106cda8b77", "size": 22763, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/darray.jl", "max_stars_repo_name": "JuliaPackageMirrors/DistributedArrays.jl", "max_stars_repo_head_hexsha": "9018902e7c323b39a02d1e515aa283fbb1f913f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/darray.jl", "max_issues_repo_name": "JuliaPackageMirrors/DistributedArrays.jl", "max_issues_repo_head_hexsha": "9018902e7c323b39a02d1e515aa283fbb1f913f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/darray.jl", "max_forks_repo_name": "JuliaPackageMirrors/DistributedArrays.jl", "max_forks_repo_head_hexsha": 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{"text": "@testset \"Primitives\" begin\n\nusing Base.Test\nusing DataValueArrays\n\nn = rand(1:5)\nsiz = [ rand(2:5) for i in n ]\nA = rand(siz...)\nM = rand(Bool, siz...)\nX = DataValueArray(A, M)\ni = rand(1:length(X))\n@test values(X, i) == X.values[i]\n@test isnull(X, i) == X.isnull[i]\nI = [ rand(1:size(X,i)) for i in 1:n ]\n@test values(X, I...) == X.values[I...]\n@test isnull(X, I...) == X.isnull[I...]\n\n# ----- test Base.similar, Base.size  ----------------------------------------#\n\nx = DataValueArray(Int, (5, 2))\n@test isa(x, DataValueMatrix{Int})\n@test size(x) === (5, 2)\n\ny = similar(x, DataValue{Int}, (3, 3))\n@test isa(y, DataValueMatrix{Int})\n@test size(y) === (3, 3)\n\nz = similar(x, DataValue{Int}, (2,))\n@test isa(z, DataValueVector{Int})\n@test size(z) === (2, )\n\n# test common use-patterns for 'similar'\ndv = DataValueArray(Int, 2)\ndm = DataValueArray(Int, 2, 2)\ndt = DataValueArray(Int, 2, 2, 2)\n\nsimilar(dv)\nsimilar(dm)\nsimilar(dt)\n\nsimilar(dv, 2)\nsimilar(dm, 2, 2)\nsimilar(dt, 2, 2, 2)\n\n# ----- test Base.copy/Base.copy! --------------------------------------------#\n\n#copy\nx = DataValueArray([1, 2, nothing], Int, Void)\ny = DataValueArray([3, nothing, 5], Int, Void)\n@test isequal(copy(x), x)\n@test isequal(copy!(y, x), x)\n\n\n# copy!\nfunction nonbits(dv)\n    ret = similar(dv, Integer)\n    for i = 1:length(dv)\n        if !dv.isnull[i]\n            ret[i] = dv[i]\n        end\n    end\n    ret\nend\nset1 = Any[DataValueArray([1, nothing, 3], Int, Void),\n            DataValueArray([nothing, 5], Int, Void),\n            DataValueArray([1, 2, 3, 4, 5], Int, Void),\n            DataValueArray(Int[]),\n            DataValueArray([nothing, 5, 3], Int, Void),\n            DataValueArray([1, 5, 3], Int, Void)]\nset2 = map(nonbits, set1)\n\nfor (dest, src, bigsrc, emptysrc, res1, res2) in Any[set1, set2]\n    @test isequal(copy!(copy(dest), src), res1)\n    @test isequal(copy!(copy(dest), 1, src), res1)\n\n    @test isequal(copy!(copy(dest), 2, src, 2), res2)\n    @test isequal(copy!(copy(dest), 2, src, 2, 1), res2)\n\n    @test isequal(copy!(copy(dest), 99, src, 99, 0), dest)\n\n    @test isequal(copy!(copy(dest), 1, emptysrc), dest)\n    @test_throws BoundsError copy!(dest, 1, emptysrc, 1)\n\n    for idx in [0, 4]\n        @test_throws BoundsError copy!(dest, idx, src)\n        @test_throws BoundsError copy!(dest, idx, src, 1)\n        @test_throws BoundsError copy!(dest, idx, src, 1, 1)\n        @test_throws BoundsError copy!(dest, 1, src, idx)\n        @test_throws BoundsError copy!(dest, 1, src, idx, 1)\n    end\n\n    @test_throws ArgumentError copy!(dest, 1, src, 1, -1)\n\n    @test_throws BoundsError copy!(dest, bigsrc)\n\n    @test_throws BoundsError copy!(dest, 3, src)\n    @test_throws BoundsError copy!(dest, 3, src, 1)\n    @test_throws BoundsError copy!(dest, 3, src, 1, 2)\n    @test_throws BoundsError copy!(dest, 1, src, 2, 2)\nend\n\n# ----- test Base.fill! ------------------------------------------------------#\n\nX = DataValueArray(Int, 10, 2)\nfill!(X, DataValue(10))\nY = DataValueArray(Float64, 10)\nfill!(Y, rand(Float64))\n\n@test X.values == fill(10, 10, 2)\n@test isequal(X.isnull, fill(false, 10, 2))\n@test isequal(Y.isnull, fill(false, 10))\n\nfill!(X, DataValue())\n@test isequal(X.isnull, fill(true, 10, 2))\n\n# ----- test Base.deepcopy ---------------------------------------------------#\n\nY1 = deepcopy(Y)\n@test isequal(Y1, Y)\n@assert !(Y === Y1)\n\n# ----- test Base.resize! ----------------------------------------------------#\n\nresize!(Y1, 20)\n@test Y1.values[1:10] == Y.values[1:10]\n@test Y1.isnull[1:10] == Y.isnull[1:10]\n@test Y1.isnull[11:20] == fill(true, 10)\n\nresize!(Y1, 5)\n@test Y1.values[1:5] == Y.values[1:5]\n@test Y1.isnull[1:5] == Y.isnull[1:5]\n\n# ----- test Base.reshape ----------------------------------------------------#\n\nY1 = reshape(copy(Y), length(Y), 1)\n@test size(Y1) == (length(Y), 1)\n@test all(i->isequal(Y1[i], Y[i]), 1:length(Y))\nY2 = reshape(Y1, 1, length(Y1))\n@test size(Y2) == (1, length(Y1))\n@test all(i->isequal(Y1[i], Y2[i]), 1:length(Y2))\n# Test that arrays share the same data\nY2.values[1] += 1\nY2.isnull[2] = true\n@test all(i->isequal(Y1[i], Y2[i]), 1:length(Y2))\n\n# ----- test Base.ndims ------------------------------------------------------#\n\nfor n in 1:4\n    @test ndims(DataValueArray(Int, collect(1:n)...)) == n\nend\n\n# ----- test Base.length -----------------------------------------------------#\n\n@test length(DataValueArray(Int, 10)) == 10\n@test length(DataValueArray(Int, 5, 5)) == 25\n@test length(DataValueArray(Int, (3, 3, 3))) == 27\n\n# ----- test Base.endof ------------------------------------------------------#\n\n@test endof(DataValueArray(collect(1:10))) == 10\n@test endof(DataValueArray([1, 2, nothing, 4, nothing])) == 5\n\n# ----- test Base.find -------------------------------------------------------#\n\nz = DataValueArray(rand(Bool, 10))\n@test find(z) == find(z.values)\n\nz = DataValueArray([false, true, false, true, false, true])\n@test isequal(find(z), [2, 4, 6])\n\n# ----- test dropnull --------------------------------------------------------#\n\n# dropnull(X::DataValueVector)\nz = DataValueArray([1, 2, 3, 'a', 5, 'b', 7, 'c'], Int, Char)\n@test dropnull(z) == [1, 2, 3, 5, 7]\n\n# dropnull(X::AbstractVector)\nA = Any[1, 2, 3, DataValue(), 5, DataValue(), 7, DataValue()]\n@test dropnull(A) == [1, 2, 3, 5, 7]\n\n# dropnull(X::AbstractVector{<:DataValue})\nB = [1, 2, 3, DataValue(), 5, DataValue(), 7, DataValue()]\n@test dropnull(B) == [1, 2, 3, 5, 7]\n# assert dropnull returns copy for !(DataValue <: eltype(X))\nnullfree = [1, 2, 3, 4]\nreturned_copy = dropnull(nullfree)\n@test nullfree == returned_copy && !(nullfree === returned_copy)\n\n# ----- test dropnull! -------------------------------------------------------#\n\n# for each, assert returned values are unwrapped and inplace change\n# dropnull!(X::DataValueVector)\n@test dropnull!(z) == [1, 2, 3, 5, 7]\n@test isequal(z, DataValueArray([1, 2, 3, 5, 7]))\n\n# dropnull!(X::AbstractVector)\n@test dropnull!(A) == [1, 2, 3, 5, 7]\n@test isequal(A, Any[1, 2, 3, 5, 7])\n\n# dropnull!(X::AbstractVector{<:DataValue})\n@test dropnull!(B) == [1, 2, 3, 5, 7]\n@test isequal(B, DataValue[1, 2, 3, 5, 7])\n\n# when no nulls present, dropnull! returns input vector\nreturned_view = dropnull!(nullfree)\n@test nullfree == returned_view && nullfree === returned_view\n\n# test that dropnull! returns unwrapped values when DataValues are present\nX = [false, 1, :c, \"string\", DataValue(\"I am not null\"), DataValue()]\n@test !any(x -> isa(x, DataValue), dropnull!(X))\n@test any(x -> isa(x, DataValue), X)\nY = Any[false, 1, :c, \"string\", DataValue(\"I am not null\"), DataValue()]\n@test !any(x -> isa(x, DataValue), dropnull!(Y))\n@test any(x -> isa(x, DataValue), Y)\n\n# ----- test any(isnull, X) --------------------------------------------------#\n\n# any(isnull, X::DataValueArray)\nz = DataValueArray([1, 2, 3, 'a', 5, 'b', 7, 'c'], Int, Char)\n@test any(isnull, z) == true\n@test any(isnull, dropnull(z)) == false\nz = DataValueArray(Int, 10)\n@test any(isnull, z) == true\n\n# any(isnull, A::AbstractArray)\nA2 = [DataValue(1), DataValue(2), DataValue(3)]\n@test any(isnull, A2) == false\npush!(A2, DataValue{Int}())\n@test any(isnull, A2) == true\n\n# any(isnull, xs::NTuple)\n@test any(isnull, (DataValue(1), DataValue(2))) == false\n@test any(isnull, (DataValue{Int}(), DataValue(1), 3, 6)) == true\n\n# any(isnull, S::SubArray{T, N, U<:DataValueArray})\nA = rand(10, 3, 3)\nM = rand(Bool, 10, 3, 3)\nX = DataValueArray(A, M)\ni, j = rand(1:3), rand(1:3)\nS = view(X, :, i, j)\n\n@test any(isnull, S) == any(isnull, X[:, i, j])\nX = DataValueArray(A)\nS = view(X, :, i, j)\n@test any(isnull, S) == false\n\n\n# ----- test all(isnull, X) --------------------------------------------------#\n\n# all(isnull, X::DataValueArray)\nz = DataValueArray(Int, 10)\n@test all(isnull, z) == true\nz[1] = 10\n@test all(isnull, z) == false\n\n# all(isnull, X::AbstractArray{<:DataValue})\n@test all(isnull, DataValue{Int}[DataValue(), DataValue()]) == true\n@test all(isnull, DataValue{Int}[DataValue(1), DataValue()]) == false\n\n# all(isnull, X::Any)\n@test all(isnull, Any[DataValue(), DataValue()]) == true\n@test all(isnull, [1, 2]) == false\n@test all(isnull, 1:3) == false\n\n# ----- test Base.isnan ------------------------------------------------------#\n\nx = DataValueArray([1, 2, NaN, 4, 5, NaN, Inf, nothing], Float64, Void)\n_x = isnan(x)\n@test isequal(_x, DataValueArray([false, false, true, false,\n                                false, true, false, nothing], Bool, Void))\n@test _x.isnull[8] == true\n\n# ----- test Base.isfinite ---------------------------------------------------#\n\n_x = isfinite(x)\n@test isequal(_x, DataValueArray([true, true, false, true,\n                                true, false, false, nothing], Bool, Void))\n@test _x.isnull[8] == true\n\n# ----- test conversion methods ----------------------------------------------#\n\nu = DataValueArray(collect(1:10))\nv = DataValueArray(Int, 4, 4)\nfill!(v, 4)\nw = DataValueArray(['a', 'b', 'c', 'd', 'e', 'f', nothing], Char, Void)\nx = DataValueArray([(i, j, k) for i in 1:10, j in 1:10, k in 1:10])\ny = DataValueArray([2, 4, 6, 8, 10])\nz = DataValueArray([i*j for i in 1:10, j in 1:10])\n_z = DataValueArray(reshape(collect(1:100), 10, 10),\n                    convert(Array{Bool},\n                            reshape([mod(j, 2) for i in 1:10, j in 1:10],\n                                    (10, 10))))\n_x = DataValueArray([false, true, false, nothing, false, true, nothing],\n                    Bool, Void)\na = [i*j*k for i in 1:2, j in 1:2, k in 1:2]\nb = collect(1:10)\nc = [i for i in 1:10, j in 1:10]\n\ne = convert(DataValueArray, a)\nf = convert(DataValueArray{Float64}, b)\ng = convert(DataValueArray, c)\nh = convert(DataValueArray{Float64}, g)\n\n@test_throws NullException convert(Array{Char, 1}, w)\n@test convert(Array{Char, 1},\n                DataValueArray(dropnull(w))) == ['a', 'b', 'c', 'd', 'e', 'f']\n@test_throws NullException convert(Array{Char}, w)\n@test convert(Array{Float64}, u) == Float64[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n@test convert(Vector{Float64}, y) == Float64[2, 4, 6, 8, 10]\n@test convert(Matrix{Float64}, z) == Float64[i*j for i in 1:10, j in 1:10]\n@test_throws NullException convert(Array, w)\n@test convert(Array, v) == [4 4 4 4; 4 4 4 4; 4 4 4 4; 4 4 4 4]\n@test convert(Array, z) == [i*j for i in 1:10, j in 1:10]\n@test convert(Array, u) == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n@test convert(Array, _x, false) == [false, true, false, false,\n                                    false, true, false]\n@test convert(Array{Int, 1}, _x, 0) == [0, 1, 0, 0, 0, 1, 0]\n@test convert(Vector, _x, false) == [false, true, false, false,\n                                    false, true, false]\n@test sum(convert(Matrix, _z, 0)) == 2775\n@test isequal(e[:, :, 1], DataValueArray([1 2; 2 4]))\n@test isequal(f, DataValueArray(Float64[i for i in 1:10]))\n@test isa(g, DataValueArray{Int, 2})\n@test isa(h, DataValueArray{Float64, 2})\n\n# Base.convert{T}(::Type{Vector}, X::DataValueVector{T})\nX = DataValueArray([1, 2, 3, 4, 5])\n@test convert(Vector, X) == [1, 2, 3, 4, 5]\npush!(X, DataValue())\n@test_throws NullException convert(Vector, X)\n\n# Base.convert{T}(::Type{Matrix}, X::DataValueMatrix{T})\nY = DataValueArray([1 2; 3 4; 5 6; 7 8; 9 10])\n@test convert(Matrix, Y) == [1 2; 3 4; 5 6; 7 8; 9 10]\nZ = DataValueArray([1 2; 3 4; 5 6; 7 8; 9 nothing], Int, Void)\n@test_throws NullException convert(Matrix, Z)\n\n# float(X::DataValueArray)\nA = rand(Int, 20)\nM = rand(Bool, 20)\nX = DataValueArray(A, M)\n@test isequal(float(X), DataValueArray(float(A), M))\n\n# ----- test Base.hash (julia/base/hashing.jl:5) -----------------------------#\n\n# Omitted for now, pending investigation into DataValueArray-specific\n# method.\n# TODO: reinstate testing once decision whether or not to implement\n# DataValueArray-specific hash method is reached.\n\n# ----- test unique (julia/base/set.jl:107) ----------------------------------#\n\nx = DataValueArray([1, nothing, -2, 1, nothing, 4], Int, Void)\n@assert isequal(unique(x), DataValueArray([1, nothing, -2, 4], Int, Void))\n@assert isequal(unique(reverse(x)),\n                DataValueArray([4, nothing, 1, -2], Int, Void))\n\n\n\nend\n", "meta": {"hexsha": "c62f6d74c7325c9f428edf3cf199f77bcfae14e8", "size": 12038, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/primitives.jl", "max_stars_repo_name": "davidanthoff/DataArrays2.jl", "max_stars_repo_head_hexsha": "75c0a234076f977f16a931a6c9e1020a82457db0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/primitives.jl", "max_issues_repo_name": "davidanthoff/DataArrays2.jl", "max_issues_repo_head_hexsha": "75c0a234076f977f16a931a6c9e1020a82457db0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/primitives.jl", "max_forks_repo_name": "davidanthoff/DataArrays2.jl", "max_forks_repo_head_hexsha": "75c0a234076f977f16a931a6c9e1020a82457db0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3462603878, "max_line_length": 79, "alphanum_fraction": 0.5647948164, "num_tokens": 3996, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.12085324198975819, "lm_q1q2_score": 0.05665486704219182}}
{"text": "module LP\n\nimport ..FileFormats\nimport VecMathOptInterface\nconst MOI = VecMathOptInterface\n\n# Julia 1.6 removes Grisu from Base. Previously, we went\n#   print_shortest(io, x) = Base.Grisu.print_shortest(io, x)\n# To avoid adding Grisu as a dependency, use the following printing heuristic.\n# TODO(odow): consider printing 1.0 as 1.0 instead of 1, i.e., without the\n# rounding branch.\nfunction print_shortest(io::IO, x::Real)\n    x_int = round(Int, x)\n    if isapprox(x, x_int)\n        print(io, x_int)\n    else\n        print(io, x)\n    end\n    return\nend\n\nMOI.Utilities.@model(\n    Model,\n    (MOI.ZeroOne, MOI.Integer),\n    (MOI.EqualTo, MOI.GreaterThan, MOI.LessThan, MOI.Interval),\n    (),\n    (),\n    (),\n    (MOI.ScalarAffineFunction,),\n    (),\n    ()\n)\nfunction MOI.supports(\n    ::Model{T},\n    ::MOI.ObjectiveFunction{<:MOI.ScalarQuadraticFunction{T}},\n) where {T}\n    return false\nend\n\nstruct Options\n    maximum_length::Int\n    warn::Bool\nend\n\nfunction get_options(m::Model)\n    default_options = Options(255, false)\n    return get(m.ext, :LP_OPTIONS, default_options)\nend\n\n\"\"\"\n    Model(; kwargs...)\n\nCreate an empty instance of FileFormats.LP.Model.\n\nKeyword arguments are:\n\n - `maximum_length::Int=255`: the maximum length for the name of a variable.\n   lp_solve 5.0 allows only 16 characters, while CPLEX 12.5+ allow 255.\n\n - `warn::Bool=false`: print a warning when variables or constraints are renamed.\n\"\"\"\nfunction Model(; maximum_length::Int = 255, warn::Bool = false)\n    model = Model{Float64}()\n    options = Options(maximum_length, warn)\n    model.ext[:LP_OPTIONS] = options\n    return model\nend\n\nfunction Base.show(io::IO, ::Model)\n    print(io, \"A .LP-file model\")\n    return\nend\n\n# ==============================================================================\n#\n#   Base.write\n#\n# ==============================================================================\n\nconst START_REG = r\"^([\\.0-9eE])\"\nconst NAME_REG = r\"([^a-zA-Z0-9\\!\\\"\\#\\$\\%\\&\\(\\)\\/\\,\\.\\;\\?\\@\\_\\`\\'\\{\\}\\|\\~])\"\n\nfunction write_function(\n    io::IO,\n    model::Model,\n    func::MOI.VariableIndex,\n    variable_names::Dict{MOI.VariableIndex,String},\n)\n    print(io, variable_names[func])\n    return\nend\n\nfunction write_function(\n    io::IO,\n    model::Model,\n    func::MOI.ScalarAffineFunction{Float64},\n    variable_names::Dict{MOI.VariableIndex,String},\n)\n    is_first_item = true\n    if !(func.constant \u2248 0.0)\n        print_shortest(io, func.constant)\n        is_first_item = false\n    end\n    for term in func.terms\n        if !(term.coefficient \u2248 0.0)\n            if is_first_item\n                print_shortest(io, term.coefficient)\n                is_first_item = false\n            else\n                print(io, term.coefficient < 0 ? \" - \" : \" + \")\n                print_shortest(io, abs(term.coefficient))\n            end\n\n            print(io, \" \", variable_names[term.variable])\n        end\n    end\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.LessThan)\n    print(io, \" <= \")\n    print_shortest(io, set.upper)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.GreaterThan)\n    print(io, \" >= \")\n    print_shortest(io, set.lower)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.EqualTo)\n    print(io, \" = \")\n    print_shortest(io, set.value)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.Interval)\n    print(io, \" <= \")\n    print_shortest(io, set.upper)\n    println(io)\n    return\nend\n\nfunction write_constraint_prefix(io::IO, set::MOI.Interval)\n    print_shortest(io, set.lower)\n    print(io, \" <= \")\n    return\nend\n\nwrite_constraint_prefix(io::IO, set) = nothing\n\nfunction write_constraint(\n    io::IO,\n    model::Model,\n    index::MOI.ConstraintIndex,\n    variable_names::Dict{MOI.VariableIndex,String};\n    write_name::Bool = true,\n)\n    func = MOI.get(model, MOI.ConstraintFunction(), index)\n    set = MOI.get(model, MOI.ConstraintSet(), index)\n    if write_name\n        print(io, MOI.get(model, MOI.ConstraintName(), index), \": \")\n    end\n    write_constraint_prefix(io, set)\n    write_function(io, model, func, variable_names)\n    return write_constraint_suffix(io, set)\nend\n\nconst SCALAR_SETS = (\n    MOI.LessThan{Float64},\n    MOI.GreaterThan{Float64},\n    MOI.EqualTo{Float64},\n    MOI.Interval{Float64},\n)\n\nfunction write_sense(io::IO, model::Model)\n    if MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE\n        println(io, \"maximize\")\n    else\n        println(io, \"minimize\")\n    end\n    return\nend\n\nfunction write_objective(\n    io::IO,\n    model::Model,\n    variable_names::Dict{MOI.VariableIndex,String},\n)\n    print(io, \"obj: \")\n    obj_func_type = MOI.get(model, MOI.ObjectiveFunctionType())\n    obj_func = MOI.get(model, MOI.ObjectiveFunction{obj_func_type}())\n    write_function(io, model, obj_func, variable_names)\n    println(io)\n    return\nend\n\n\"\"\"\n    Base.write(io::IO, model::FileFormats.LP.Model)\n\nWrite `model` to `io` in the LP file format.\n\"\"\"\nfunction Base.write(io::IO, model::Model)\n    options = get_options(model)\n    FileFormats.create_unique_names(\n        model,\n        warn = options.warn,\n        replacements = [\n            s -> match(START_REG, s) !== nothing ? \"_\" * s : s,\n            s -> replace(s, NAME_REG => \"_\"),\n            s -> s[1:min(length(s), options.maximum_length)],\n        ],\n    )\n    variable_names = Dict{MOI.VariableIndex,String}(\n        index => MOI.get(model, MOI.VariableName(), index) for\n        index in MOI.get(model, MOI.ListOfVariableIndices())\n    )\n    write_sense(io, model)\n    write_objective(io, model, variable_names)\n    println(io, \"subject to\")\n    for S in SCALAR_SETS\n        for index in MOI.get(\n            model,\n            MOI.ListOfConstraintIndices{MOI.ScalarAffineFunction{Float64},S}(),\n        )\n            write_constraint(\n                io,\n                model,\n                index,\n                variable_names;\n                write_name = true,\n            )\n        end\n    end\n    println(io, \"Bounds\")\n    free_variables = Set(keys(variable_names))\n    for S in SCALAR_SETS\n        for index in\n            MOI.get(model, MOI.ListOfConstraintIndices{MOI.VariableIndex,S}())\n            delete!(free_variables, MOI.VariableIndex(index.value))\n            write_constraint(\n                io,\n                model,\n                index,\n                variable_names;\n                write_name = false,\n            )\n        end\n    end\n    for index in MOI.get(\n        model,\n        MOI.ListOfConstraintIndices{MOI.VariableIndex,MOI.ZeroOne}(),\n    )\n        delete!(free_variables, MOI.VariableIndex(index.value))\n    end\n    for variable in sort(collect(free_variables), by = x -> x.value)\n        println(io, variable_names[variable], \" free\")\n    end\n    for (S, str_S) in [(MOI.Integer, \"General\"), (MOI.ZeroOne, \"Binary\")]\n        indices =\n            MOI.get(model, MOI.ListOfConstraintIndices{MOI.VariableIndex,S}())\n        if length(indices) > 0\n            println(io, str_S)\n            for index in indices\n                write_function(\n                    io,\n                    model,\n                    MOI.get(model, MOI.ConstraintFunction(), index),\n                    variable_names,\n                )\n                println(io)\n            end\n        end\n    end\n    println(io, \"End\")\n    return\nend\n\n# ==============================================================================\n#\n#   Base.read!\n#\n# ==============================================================================\n\nfunction Base.read!(io::IO, model::Model)\n    return error(\"read! is not implemented for LP files.\")\nend\n\nend\n", "meta": {"hexsha": "edefc6c13a54e231ae8c822e8eee94460c7c3652", "size": 7668, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FileFormats/LP/LP.jl", "max_stars_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_stars_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/FileFormats/LP/LP.jl", "max_issues_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_issues_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/FileFormats/LP/LP.jl", "max_forks_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_forks_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.0816326531, "max_line_length": 81, "alphanum_fraction": 0.5850286907, "num_tokens": 1917, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4687906266262437, "lm_q2_score": 0.12085324198975818, "lm_q1q2_score": 0.0566548670421918}}
{"text": "module JuliaBlackBoard\n\nusing CommonMark\nusing Mustache\nusing Base64\n\n# for LaTeX\nusing Tectonic\nusing ImageMagick_jll\nusing TtH_jll\n\n\n# Exports\nexport question\nexport MC, MA, TF, ESS, ORD, MAT, FIB, FIB_PLUS, FIL, NUM, SR, OP, JUMBLED_SENTENCE #, QUIZ_BOWL\nexport Plot, File, Tikz, LaTeX, preview, LaTeX\u2032\nexport lquestion, mdquestion # tentative\nexport @ltx_str, @mdltx_str, @MT_str, @mt_str # reexoprt\n\n\ninclude(\"mustache-additions.jl\")\ninclude(\"questions.jl\")\n\nend\n", "meta": {"hexsha": "6e5b91b31c995521f4fb8871f41786c529b003eb", "size": 468, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/JuliaBlackBoard.jl", "max_stars_repo_name": "mth229/JuliaBlackBoard.jl", "max_stars_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-10T04:33:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-10T04:33:36.000Z", "max_issues_repo_path": "src/JuliaBlackBoard.jl", "max_issues_repo_name": "mth229/JuliaBlackBoard.jl", "max_issues_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/JuliaBlackBoard.jl", "max_forks_repo_name": "mth229/JuliaBlackBoard.jl", "max_forks_repo_head_hexsha": "66188b1aca1eb5ab8268a97dc51283019fda542c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-10T04:30:39.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-10T04:30:39.000Z", "avg_line_length": 18.72, "max_line_length": 96, "alphanum_fraction": 0.7670940171, "num_tokens": 153, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.12085322932404165, "lm_q1q2_score": 0.056654861104622624}}
{"text": "module data_loader\nusing CSV, DataFrames, Random\n\nfunction read_csv(file_name)\n    df = CSV.read(\"data_proj_414.csv\";\n    delim=\",\",\n    types=Dict(\"Potter\"=>Bool,\n    \"Weasley\"=>Bool,\n    \"Granger\"=>Bool))\n    df = DataFrame(df)\n    return df\nend\n\nfunction data_split(df, proportion=0.9)\n    n = nrow(df)\n    idx = shuffle(1:n)\n    train_idx = view(idx, 1:floor(Int, proportion*n))\n    test_idx = view(idx, (floor(Int, proportion*n)+1):n)\n    train_df = df[train_idx, :]\n    test_df = df[test_idx, :]\n    return train_df, test_df\nend\n\nfunction save_csv(df, file_name)\n    CSV.write(file_name, df, delim=\"\\t\")\nend\nend  # data_loader\n", "meta": {"hexsha": "c102b1caf489b42aee4797859592ce6568d2757f", "size": 633, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data_loader.jl", "max_stars_repo_name": "JackSnowWolf/VE414_final_project", "max_stars_repo_head_hexsha": "b766b84886f41d79ff9a18e4e0f676037d1124a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/data_loader.jl", "max_issues_repo_name": "JackSnowWolf/VE414_final_project", "max_issues_repo_head_hexsha": "b766b84886f41d79ff9a18e4e0f676037d1124a9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data_loader.jl", "max_forks_repo_name": "JackSnowWolf/VE414_final_project", "max_forks_repo_head_hexsha": "b766b84886f41d79ff9a18e4e0f676037d1124a9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-09-02T00:56:44.000Z", "max_forks_repo_forks_event_max_datetime": "2019-09-02T00:56:44.000Z", "avg_line_length": 22.6071428571, "max_line_length": 56, "alphanum_fraction": 0.6524486572, "num_tokens": 186, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.1259227582746744, "lm_q1q2_score": 0.05658876008012503}}
{"text": "\"\"\"\nCore functionality\n\n## Basic usage\n\nThe primary export of the package is `Mat{T,N}`, which is the Julia type for\n`cv::Mat`. `Mat{T,N}` is designed to be a subtype of `AbstractArray{T,N}`. It\nhas element type (`T`) and dimension (`N`) as type parameters, whereas\n`cv::Mat` doesn't. Note that matrix construction interface is different between\nJulia and C++. `cv::Mat(3,3,CV_8U)` in C++ can be translated in Julia as\n`Mat{UInt8}(3,3)`.\n\n### Create uninitialized matrix:\n\n```julia\nusing CVCore\n\njulia> A = Mat{Float64}(3,3)\n3\u00d73 CVCore.Mat{Float64,2}:\n   3.39519e-313  4.94066e-324   2.122e-314\n NaN             1.72723e-77   -2.32036e77\n   1.97626e-323  0.0            0.0\n```\n\n### Create matrix filled with Scalar (`cv::Scalar`)\n\n```julia\njulia> A = Mat{Float64}(3,3,Scalar(1))\n3\u00d73 CVCore.Mat{Float64,2}:\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n```\n\n### Matirx operations\n\n```julia\njulia> A * A\nCVCore.MatExpr{Float64,2}\n3\u00d73 CVCore.Mat{Float64,2}:\n 3.0  3.0  3.0\n 3.0  3.0  3.0\n 3.0  3.0  3.0\n\njulia> A + A\nCVCore.MatExpr{Float64,2}\n3\u00d73 CVCore.Mat{Float64,2}:\n 2.0  2.0  2.0\n 2.0  2.0  2.0\n 2.0  2.0  2.0\n\njulia> A - A\nCVCore.MatExpr{Float64,2}\n3\u00d73 CVCore.Mat{Float64,2}:\n 0.0  0.0  0.0\n 0.0  0.0  0.0\n 0.0  0.0  0.0\n```\n\n### Create multi-channel matrix\n\n```julia\njulia> A = Mat{Float64}(3,3,3,Scalar(1,2,3))\n3\u00d73\u00d73 CVCore.Mat{Float64,3}:\n[:, :, 1] =\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n\n[:, :, 2] =\n 2.0  2.0  2.0\n 2.0  2.0  2.0\n 2.0  2.0  2.0\n\n[:, :, 3] =\n 3.0  3.0  3.0\n 3.0  3.0  3.0\n 3.0  3.0  3.0\n```\n\n### Conversion between `Mat{T,N}` and `Array{T,N}`\n\n```julia\njulia> B = Array(A)\n3\u00d73 Array{Float64,2}:\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n\njulia> C = Mat(B)\n3\u00d73 CVCore.Mat{Float64,2}:\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n 1.0  1.0  1.0\n```\n\nNote that `Mat(B)` where `B` is an array shares the underlying data.\n\n\"\"\"\nmodule CVCore\n\nexport AbstractCvMat, MatExpr, Mat, UMat, depth, channels, flags, dims, rows,\n    cols, clone, total, isContinuous, elemSize, Scalar, TermCriteria,\n    convertScaleAbs!, convertScaleAbs, addWeighted!, addWeighted\n\n#=\nNaming convention:\n  1. Cxx types should have prefix cv: e.g. cv::Mat -> cvMat\n  2. Julia types should not have prefix cv: e.g. cv::Mat -> Mat\n=#\n\nusing LibOpenCV\nusing Cxx\n\n\"\"\"\nReturns a refrence of the interal structure\n\"\"\"\nfunction handle end\n\ninclude(\"macros.jl\")\ninclude(\"const.jl\")\n\nimport Base: convert, eltype, size\n\n### Cxx types ###\n\ncvScalar_{T} =  cxxt\"cv::Scalar_<$T>\"\n\nconst cvScalar = cxxt\"cv::Scalar\"\ncvScalar(v) = icxx\"return cv::Scalar($v);\"\n\nconst AbstractCvScalar = Union{cvScalar, cvScalar_}\n\ncvPoint_{T} =  cxxt\"cv::Point_<$T>\"\ncvPoint_{T}(x, y) where {T} = icxx\"cv::Point_<$T>($x, $y);\"\neltype(p::cvPoint_{T}) where {T} = T\n\ncvPoint3_{T} =  cxxt\"cv::Point3_<$T>\"\ncvPoint3_{T}(x, y, z) where {T} = icxx\"cv::Point3_<$T>($x, $y, $z);\"\neltype(p::cvPoint3_{T}) where {T} = T\n\nconst cvPoint = cxxt\"cv::Point\"\ncvPoint(x, y) = icxx\"cv::Point($x, $y);\"\n\nconst AbstractCvPoint = Union{cvPoint, cvPoint_}\n\ncvSize_{T} =  cxxt\"cv::Size_<$T>\"\ncvSize_{T}(x, y) where {T} = icxx\"cv::Size_<$T>($x, $y);\"\neltype(s::cvSize_{T}) where {T} = T\n\nconst cvSize = cxxt\"cv::Size\"\ncvSize(x, y) = icxx\"cv::Size($x, $y);\"\n\nconst AbstractCvSize = Union{cvSize, cvSize_}\n\nheight(s::AbstractCvSize) = Int(icxx\"$s.height;\")\nwidth(s::AbstractCvSize) = Int(icxx\"$s.width;\")\narea(s::AbstractCvSize) = Int(icxx\"$s.area();\")\n\n\"\"\"Determine julia type from the depth of cv::Mat\n\"\"\"\nfunction jltype(depth)\n    if depth == CV_8U\n        return UInt8\n    elseif depth == CV_8S\n        return Int8\n    elseif depth == CV_16U\n        return UInt16\n    elseif depth == CV_16S\n        return Int16\n    elseif depth == CV_32S\n        return Int32\n    elseif depth == CV_32F\n        return Float32\n    elseif depth == CV_64F\n        return Float64\n    else\n        error(\"This shouldn't happen\")\n    end\nend\n\n\"\"\"Determine cv::Mat depth from Julia type\n\"\"\"\nfunction cvdepth(T)\n    if T == UInt8\n        return CV_8U\n    elseif T == Int8\n        return CV_8S\n    elseif T == UInt16\n        return CV_16U\n    elseif T == Int16\n        return CV_16S\n    elseif T == Int32\n        return CV_32S\n    elseif T == Float32\n        return CV_32F\n    elseif T == Float64\n        return CV_64F\n    else\n        error(\"$T: not supported in cv::Mat\")\n    end\nend\n\nmat_depth(flags) = flags & CV_MAT_DEPTH_MASK\nmat_channel(flags) = (flags & CV_MAT_CN_MASK) >> CV_CN_SHIFT + 1\nmaketype(depth, cn) = mat_depth(depth) + ((cn-1) << CV_CN_SHIFT)\n\n### Scalar ###\n\n# TODO: need to be subtype of cv::Vec\nconst Scalar = cvScalar_\nScalar{T}(s1=01,s2=0,s3=0,s4=0) where {T} =\n    icxx\"return cv::Scalar_<$T>($s1,$s2,$s3,$s4);\"\nScalar(s1=0,s2=0,s3=0,s4=0) = Scalar{Float64}(s1,s2,s3,s4)\neltype(s::Scalar{T}) where {T} = T\n\n### cv::TermCriteria ###\n\nconst TERM_CRITERIA_COUNT = icxx\"cv::TermCriteria::COUNT;\"\nconst TERM_CRITERIA_MAX_ITER = icxx\"cv::TermCriteria::MAX_ITER;\"\nconst TERM_CRITERIA_EPS = icxx\"cv::TermCriteria::EPS;\"\n\nconst TermCriteria = cxxt\"cv::TermCriteria\"\nTermCriteria(typ, maxCount, epsilon) =\n    icxx\"cv::TermCriteria($typ, $maxCount, $epsilon);\"\n\ninclude(\"mat.jl\")\n\n@gencxxf(convertScaleAbs!(src::AbstractCvMat, dst::AbstractCvMat,\n    alpha=1,beta=0), \"cv::convertScaleAbs\")\nfunction convertScaleAbs(src::AbstractCvMat, alpha=1, beta=0)\n    dst = similar_empty(src)\n    convertScaleAbs!(src, dst, alpha, beta)\n    dst\nend\n\n@gencxxf(addWeighted!(src1::AbstractCvMat, alpha, src2::AbstractCvMat, beta,\n    gamma, dst::AbstractCvMat, dtype=-1), \"cv::addWeighted\")\nfunction addWeighted(src1::AbstractCvMat, alpha, src2::AbstractCvMat, beta,\n    gamma, dtype=-1)\n    dst = similar_empty(src)\n    addWeighted!(src1, alpha, src2, beta, gamma, dst, dtype)\n    dst\nend\n\nend # module\n", "meta": {"hexsha": "51c706d0aed61bdc4d3771ea507d441e0409bd72", "size": 5715, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CVCore.jl", "max_stars_repo_name": "r9y9/CVCore.jl", "max_stars_repo_head_hexsha": "d57d9f0ba5e13a25bdb85ad4bfcacf63437b9234", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-09-04T04:15:15.000Z", "max_stars_repo_stars_event_max_datetime": "2016-09-04T04:15:15.000Z", "max_issues_repo_path": "src/CVCore.jl", "max_issues_repo_name": "r9y9/CVCore.jl", "max_issues_repo_head_hexsha": "d57d9f0ba5e13a25bdb85ad4bfcacf63437b9234", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2016-06-22T08:02:34.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-09T11:11:28.000Z", "max_forks_repo_path": "src/CVCore.jl", "max_forks_repo_name": "r9y9/CVCore.jl", "max_forks_repo_head_hexsha": "d57d9f0ba5e13a25bdb85ad4bfcacf63437b9234", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-04-14T23:56:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T18:04:06.000Z", "avg_line_length": 23.1376518219, "max_line_length": 79, "alphanum_fraction": 0.6365704287, "num_tokens": 2270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.11757212736159103, "lm_q1q2_score": 0.056490900329977424}}
{"text": "\"\"\"\n    ArcEager()\n\nArc-Eager transition system for dependency parsing.\n\n# Transitions\n\n| Transition  | Definition                                    |\n|:----------- |:--------------------------------------------- |\n| LeftArc(l)  | (\u03c3\\\\|s, b\\\\|\u03b2, A) \u2192 (\u03c3, b\\\\|\u03b2, A \u222a (b, l, s)) |\n| RightArc(l) | (\u03c3\\\\|s, b\\\\|\u03b2, A) \u2192 (\u03c3, b\\\\|\u03b2, A \u222a (b, l, s)) |\n| Reduce      | (\u03c3\\\\|s, \u03b2,  A) \u2192 (\u03c3, \u03b2,   A)                  |\n| Shift\t      | (\u03c3,  b\\\\|\u03b2, A) \u2192 (\u03c3\\\\|b, \u03b2, A)                |\n\n# Preconditions\n\n| Transition  | Condition                        |\n|:----------- |:-------------------------------- |\n| LeftArc(l)  | \u00ac[s = 0], \u00ac\u2203k\u2203l'[(k, l', i) \u03f5 A] |\n| RightArc(l) | \u00ac\u2203k\u2203l'[(k, l', j) \u03f5 A]           |\n| Reduce      | \u2203k\u2203l[(k, l, i) \u03f5 A]              |\n\n# References\n\n[Nivre 2003](http://stp.lingfil.uu.se/~nivre/docs/iwpt03.pdf), [Nivre 2008](https://www.aclweb.org/anthology/J08-4003.pdf).\n\"\"\"\nstruct ArcEager <: AbstractTransitionSystem end\n\ninitconfig(::ArcEager, graph::DependencyTree) =\n    ArcEagerConfig(graph)\n\ntransition_space(::ArcEager, labels=[]) =\n    isempty(labels) ? [LeftArc(), RightArc(), Reduce(), Shift()] :\n    [LeftArc.(labels)..., RightArc.(labels)..., Reduce(), Shift()]\n\nprojective_only(::ArcEager) = true\n\nstruct ArcEagerConfig <: AbstractParserConfiguration\n    stack::Vector{Int}\n    buffer::Vector{Int}\n    A::Vector{Token}\nend\n\nArcEagerConfig(sentence) = stack_buffer_config(ArcEagerConfig, sentence)\n\nbuffer(cfg::ArcEagerConfig) = cfg.buffer\nstack(cfg::ArcEagerConfig)  = cfg.stack\ntokens(cfg::ArcEagerConfig) = cfg.A\n\nfunction apply_transition(f, cfg::ArcEagerConfig, a...; k...)\n    \u03c3, \u03b2, A = f(cfg.stack, cfg.buffer, cfg.A, a...; k...)\n    return ArcEagerConfig(\u03c3, \u03b2, A)\nend\n\nleftarc(cfg::ArcEagerConfig, args...; kwargs...) =\n    apply_transition(leftarc_reduce, cfg, args...; kwargs...)\n\nrightarc(cfg::ArcEagerConfig, args...; kwargs...) =\n    apply_transition(rightarc_shift, cfg, args...; kwargs...)\n\nreduce(cfg::ArcEagerConfig) = apply_transition(reduce, cfg)\n\nshift(cfg::ArcEagerConfig) = apply_transition(shift, cfg)\n\nisfinal(cfg::ArcEagerConfig) = all(has_head, cfg.A)\n\nhas_head(cfg::ArcEagerConfig, k) = has_head(token(cfg, k))\n\n\"\"\"\n    static_oracle(cfg::ArcEagerConfig, gold, arc=untyped)\n\nDefault static oracle function for arc-eager dependency parsing.\n\nSee [Goldberg & Nivre 2012](https://www.aclweb.org/anthology/C12-1059.pdf).\n(Also called Arc-Eager-Reduce in [Qi & Manning 2017](https://nlp.stanford.edu/pubs/qi2017arcswift.pdf)).\n\"\"\"\nfunction static_oracle(cfg::ArcEagerConfig, gold, arc=untyped)\n    if stacklength(cfg) >= 1\n        (\u03c3, s) = popstack(cfg)\n        if bufferlength(cfg) >= 1\n            (b, \u03b2) = shiftbuffer(cfg)\n            has_arc(gold, b, s) && return LeftArc(arc(gold[s])...)\n            has_arc(gold, s, b) && return RightArc(arc(gold[b])...)\n        end\n        if all(k -> k > 0 && has_head(cfg, k), [s ; deps(gold, s)])\n            return Reduce()\n        end\n    end\n    return Shift()\nend\n\n\"\"\"\n    static_oracle_prefer_shift(cfg::ArcEagerConfig, tree, arc=untyped)\n\nStatic oracle for arc-eager dependency parsing. Similar to the\n\"regular\" static oracle, but always Shift when ambiguity is present.\n\nSee [Qi & Manning 2017](https://nlp.stanford.edu/pubs/qi2017arcswift.pdf).\n\"\"\"\nfunction static_oracle_prefer_shift(cfg::ArcEagerConfig, tree, arc=untyped)\n    l = i -> arc(token(tree, i))\n    gold_arc = (a, b) -> has_arc(tree, a, b)\n    (\u03c3, s), (b, \u03b2) = popstack(cfg), shiftbuffer(cfg)\n    gold_arc(b, s) && return LeftArc(l(s)...)\n    gold_arc(s, b) && return RightArc(l(b)...)\n    must_reduce = false\n    for k in stack(cfg)\n        if gold_arc(k, b) || gold_arc(b, k)\n            must_reduce = true\n            break\n        elseif has_head(token(cfg, k), -1)\n            break\n        end\n    end\n    has_right_children = any(k -> s in rightdeps(tree, k), buffer(cfg))\n    if !must_reduce || s > 0 && !has_head(cfg, s) || has_right_children\n        return Shift()\n    else\n        return Reduce()\n    end\nend\n\n\"\"\"\n    dynamic_oracle(cfg::ArgEagerConfig, tree, arc=untyped)\n\nDynamic oracle function for arc-eager parsing.\n\nFor details, see [Goldberg & Nivre 2012](https://aclweb.org/anthology/C12-1059).\n\"\"\"\ndynamic_oracle(cfg::ArcEagerConfig, tree, arc=untyped) =\n    filter(t -> cost(t, cfg, tree) == 0, possible_transitions(cfg, tree, arc))\n\n# see figure 2 in goldberg & nivre 2012 \"a dynamic oracle...\"\npossible_transitions(cfg::ArcEagerConfig, graph::DependencyTree, arc=untyped) =\n    possible_transitions(cfg, arc)\n\nfunction possible_transitions(cfg::ArcEagerConfig, arc=untyped)\n    ts = TransitionOperator[]\n    if is_possible(LeftArc(), cfg)\n        s = last(stack(cfg))\n        push!(ts, LeftArc(arc(token(cfg, s))...))\n    end\n    if is_possible(RightArc(), cfg)\n        b = first(buffer(cfg))\n        push!(ts, RightArc(arc(token(cfg, b))...))\n    end\n    is_possible(Reduce(), cfg) && push!(ts, Reduce())\n    is_possible(Shift(), cfg) && push!(ts, Shift())\n    return ts\nend\n\nfunction cost(t::LeftArc, cfg::ArcEagerConfig, gold)\n    # left arc cost: num of arcs (k,l',s), (s,l',k) s.t. k \u03f5 \u03b2\n    \u03c3, s = popstack(cfg)\n    b, \u03b2 = shiftbuffer(cfg)\n    if has_arc(gold, b, s)\n        0\n    else\n        count(k -> has_arc(gold, k, s) || has_arc(gold, s, k), \u03b2)\n    end\nend\n\nfunction cost(t::RightArc, cfg::ArcEagerConfig, gold)\n    # right arc cost: num of gold arcs (k,l',b), s.t. k \u03f5 \u03c3 or k \u03f5 \u03b2,\n    #                 plus num of gold arcs (b,l',k) s.t. k \u03f5 \u03c3\n    \u03c3, s = popstack(cfg)\n    b, \u03b2 = shiftbuffer(cfg)\n    if has_arc(gold, s, b)\n        0\n    else\n        count(k -> has_arc(gold, k, b), [\u03c3 ; \u03b2]) + count(k -> has_arc(gold, b, k), \u03c3)\n    end\nend\n\nfunction cost(t::Reduce, cfg::ArcEagerConfig, gold)\n    # num of gold arcs (s,l',k) s.t. k \u03f5 b|\u03b2\n    \u03c3, s = popstack(cfg)\n    count(k -> has_arc(gold, s, k), buffer(cfg))\nend\n\nfunction cost(t::Shift, cfg::ArcEagerConfig, gold)\n    # num of gold arcs (k,l',b), (b,l',k) s.t. k \u03f5 s|\u03c3\n    b, \u03b2 = shiftbuffer(cfg)\n    count(k -> has_arc(gold, k, b) || has_arc(gold, b, k), stack(cfg))\nend\n\nfunction is_possible(::LeftArc, cfg::ArcEagerConfig)\n    if length(cfg.stack) > 0 && length(cfg.buffer) > 0\n        s = last(stack(cfg))\n        return s != 0 && !has_head(token(cfg, s))\n    end\n    return false\nend\n\nfunction is_possible(::RightArc, cfg::ArcEagerConfig)\n    return length(cfg.stack) > 0 && length(cfg.buffer) > 0 && \n        !has_head(token(cfg, first(buffer(cfg))))\nend\n\nis_possible(::Reduce, cfg::ArcEagerConfig) =\n    stacklength(cfg) > 0 && has_head(token(cfg, last(stack(cfg))))\n\nis_possible(::Shift, cfg::ArcEagerConfig) = bufferlength(cfg) > 0\n\n==(cfg1::ArcEagerConfig, cfg2::ArcEagerConfig) =\n    cfg1.stack == cfg2.stack && cfg1.buffer == cfg2.buffer && cfg1.A == cfg2.A\n\nBase.getindex(cfg::ArcEagerConfig, i) = token(cfg, i)\n", "meta": {"hexsha": "78791e5cc5d79bf5a99f92295f67d32d62526c0a", "size": 6781, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/transition_parsing/systems/arc_eager.jl", "max_stars_repo_name": "dellison/DependencyTrees.jl", "max_stars_repo_head_hexsha": "d5889ac8ce64f00b500bf3485507e7ab94375b3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2019-03-13T02:07:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-07T21:30:21.000Z", "max_issues_repo_path": "src/transition_parsing/systems/arc_eager.jl", "max_issues_repo_name": "dellison/DependencyTrees.jl", "max_issues_repo_head_hexsha": "d5889ac8ce64f00b500bf3485507e7ab94375b3c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2018-09-09T22:43:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-19T17:10:31.000Z", "max_forks_repo_path": "src/transition_parsing/systems/arc_eager.jl", "max_forks_repo_name": "dellison/DependencyTrees.jl", "max_forks_repo_head_hexsha": "d5889ac8ce64f00b500bf3485507e7ab94375b3c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2019-07-25T04:22:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:25:41.000Z", "avg_line_length": 32.4449760766, "max_line_length": 123, "alphanum_fraction": 0.602860935, "num_tokens": 2098, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.1225232125181592, "lm_q1q2_score": 0.056485256826120184}}
{"text": "# !!! this script needs Julia v 0.4 or higher !!!\n# constructing:\nstart_time = time()\n# long computation\ntime_elapsed = time() - start_time\nprintln(\"Time elapsed: $time_elapsed\") #> 0.0010001659393310547\n\nd = Date(2014,9,1) #> 2014-09-01\ndt = DateTime(2014,9,1,12,30,59,1) #> 2014-09-01T12:30:59.001\n# accessors:\nDates.year(d)\nDates.month(d)\nDates.week(d)\nDates.day(d)\n# functions:\nDates.isleapyear(d)\nDates.dayofyear(d)\nDates.monthname(d)\nDates.daysinmonth(d)\n", "meta": {"hexsha": "de9a17600628a8fe09a3ff1d03e32f18743d5448", "size": 461, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Module 1/Chapter02/dates.jl", "max_stars_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_stars_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_stars_repo_stars_event_min_datetime": "2017-02-12T15:36:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T03:30:39.000Z", "max_issues_repo_path": "Module 1/Chapter02/dates.jl", "max_issues_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_issues_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Module 1/Chapter02/dates.jl", "max_forks_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_forks_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2017-02-10T16:19:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T11:46:44.000Z", "avg_line_length": 23.05, "max_line_length": 63, "alphanum_fraction": 0.7136659436, "num_tokens": 163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.12252321251815919, "lm_q1q2_score": 0.05648525682612018}}
{"text": "\nfunction f1(x, y)\n  m = 0\n  n = 2\n\n  if x > 10\n    m = x - 10\n    println(\"OK\")\n  else\n    m = 3 * x\n  end\n\n  while y < 3\n    y += 1\n    n = 2 * y\n  end\n\n  d = m + n\n  return d\nend\n\nres = f1(1, 1) # should be 9\nif res < 10\n  println(\"Here1\")\nelse\n  println(\"Here2\")\nend\n\ns = [1,2,3,4,5,6]\n\nfor num in s\n  k = num + 1\n  println(k)\nend", "meta": {"hexsha": "77a562906e4021d7a8a80e3e3b9fc42ecbaa258c", "size": 334, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/blocks/blocks1.jl", "max_stars_repo_name": "judy-vscode/judy", "max_stars_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_stars_repo_stars_event_min_datetime": "2019-01-17T05:54:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-28T17:46:08.000Z", "max_issues_repo_path": "test/blocks/blocks1.jl", "max_issues_repo_name": "judy-vscode/judy", "max_issues_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2018-12-25T11:20:16.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T18:49:04.000Z", "max_forks_repo_path": "test/blocks/blocks1.jl", "max_forks_repo_name": "judy-vscode/judy", "max_forks_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2019-01-17T05:53:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-28T17:46:09.000Z", "avg_line_length": 9.8235294118, "max_line_length": 28, "alphanum_fraction": 0.4730538922, "num_tokens": 162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101676450173545, "lm_q2_score": 0.1225232029059025, "lm_q1q2_score": 0.056485250580068806}}
{"text": "\"\"\"\n    AbstractMillNode\n\nSupertype for any structure representing a data node.\n\"\"\"\nabstract type AbstractMillNode end\n\n\"\"\"\n    AbstractProductNode <: AbstractMillNode\n\nSupertype for any structure representing a data node implementing a Cartesian product of data in subtrees.\n\"\"\"\nabstract type AbstractProductNode <: AbstractMillNode end\n\n\"\"\"\n    AbstractBagNode <: AbstractMillNode\n\nSupertype for any data node structure representing a multi-instance learning problem.\n\"\"\"\nabstract type AbstractBagNode <: AbstractMillNode end\n\n\"\"\"\n    Mill.data(n::AbstractMillNode)\n\nReturn data stored in node `n`.\n\n# Examples\n```jldoctest\njulia> Mill.data(ArrayNode([1 2; 3 4], \"metadata\"))\n2\u00d72 Matrix{Int64}:\n 1  2\n 3  4\n\njulia> Mill.data(BagNode(ArrayNode([1 2; 3 4]), bags([1:3, 4:4]), \"metadata\"))\n2\u00d72 ArrayNode{Matrix{Int64}, Nothing}:\n 1  2\n 3  4\n```\n\nSee also: [`Mill.metadata`](@ref)\n\"\"\"\ndata(n::AbstractMillNode) = getfield(n, :data)\n\n\"\"\"\n    Mill.metadata(n::AbstractMillNode)\n\nReturn metadata stored in node `n`.\n\n# Examples\n```jldoctest\njulia> Mill.metadata(ArrayNode([1 2; 3 4], \"metadata\"))\n\"metadata\"\n\njulia> Mill.metadata(BagNode(ArrayNode([1 2; 3 4]), bags([1:3, 4:4]), \"metadata\"))\n\"metadata\"\n```\n\nSee also: [`Mill.data`](@ref)\n\"\"\"\nmetadata(x::AbstractMillNode) = getfield(x, :metadata)\n\n\"\"\"\n    catobs(ns...)\n\nMerge multiple nodes storing samples (observations) into one suitably promoting in the process if possible.\n\nSimilar to `Base.cat` but concatenates along the abstract \\\"axis\\\" where samples are stored.\n\nIn case of repeated calls with varying number of arguments or argument types, use `reduce(catobs, [ns...])`\nto save compilation time.\n\n# Examples\n```jldoctest\njulia> catobs(ArrayNode(zeros(2, 2)), ArrayNode([1 2; 3 4]))\n2\u00d74 ArrayNode{Matrix{Float64}, Nothing}:\n 0.0  0.0  1.0  2.0\n 0.0  0.0  3.0  4.0\n\njulia> n = ProductNode((t1=ArrayNode(randn(2, 3)), t2=BagNode(ArrayNode(randn(3, 8)), bags([1:3, 4:5, 6:8]))))\nProductNode with 3 obs\n  \u251c\u2500\u2500 t1: ArrayNode(2\u00d73 Array with Float64 elements)\n  \u2514\u2500\u2500 t2: BagNode with 3 obs\n            \u2514\u2500\u2500 ArrayNode(3\u00d78 Array with Float64 elements)\n\njulia> catobs(n[1], n[3])\nProductNode with 2 obs\n  \u251c\u2500\u2500 t1: ArrayNode(2\u00d72 Array with Float64 elements)\n  \u2514\u2500\u2500 t2: BagNode with 2 obs\n            \u2514\u2500\u2500 ArrayNode(3\u00d76 Array with Float64 elements)\n```\n\nSee also: [`Mill.subset`](@ref).\n\"\"\"\nfunction catobs end\n\n\"\"\"\n    subset(n, i)\n\nExtract a subset `i` of samples (observations) stored in node `n`.\n\nSimilar to `Base.getindex` or `MLDataPattern.getobs` but defined for all `Mill.jl` compatible data as well.\n\n# Examples\n```jldoctest\njulia> Mill.subset(ArrayNode(NGramMatrix([\"Hello\", \"world\"])), 2)\n2053\u00d71 ArrayNode{NGramMatrix{String, Vector{String}, Int64}, Nothing}:\n \"world\"\n\njulia> Mill.subset(BagNode(ArrayNode(randn(2, 8)), [1:2, 3:3, 4:7, 8:8]), 1:3)\nBagNode with 3 obs\n  \u2514\u2500\u2500 ArrayNode(2\u00d77 Array with Float64 elements)\n```\n\nSee also: [`catobs`](@ref).\n\"\"\"\nfunction subset end\n\n\"\"\"\n    removeinstances(n::AbstractBagNode, mask)\n\nRemove instances from `n` using `mask` and remap bag indices accordingly.\n\n# Examples\n```jldoctest\njulia> b1 = BagNode(ArrayNode([1 2 3; 4 5 6]), bags([1:2, 0:-1, 3:3]))\nBagNode with 3 obs\n  \u2514\u2500\u2500 ArrayNode(2\u00d73 Array with Int64 elements)\n\njulia> b2 = removeinstances(b1, [false, true, true])\nBagNode with 3 obs\n  \u2514\u2500\u2500 ArrayNode(2\u00d72 Array with Int64 elements)\n\njulia> b2.data\n2\u00d72 ArrayNode{Matrix{Int64}, Nothing}:\n 2  3\n 5  6\n\njulia> b2.bags\nAlignedBags{Int64}(UnitRange{Int64}[1:1, 0:-1, 2:2])\n```\n\"\"\"\nfunction removeinstances end\n\n\"\"\"\n    dropmeta(n:AbstractMillNode)\n\nDrop metadata stored in data node `n`.\n\n# Examples\n```jldoctest\njulia> n1 = ArrayNode(NGramMatrix([\"foo\", \"bar\"]), [\"metafoo\", \"metabar\"])\n2053\u00d72 ArrayNode{NGramMatrix{String, Vector{String}, Int64}, Vector{String}}:\n \"foo\"\n \"bar\"\n\njulia> n2 = dropmeta(n1)\n2053\u00d72 ArrayNode{NGramMatrix{String, Vector{String}, Int64}, Nothing}:\n \"foo\"\n \"bar\"\n\njulia> isnothing(Mill.metadata(n2))\ntrue\n```\n\nSee also: [`Mill.metadata`](@ref).\n\"\"\"\nfunction dropmeta end\n\n\"\"\"\n    mapdata(f, x)\n\nRecursively apply `f` to data in all leaves of `x`.\n\n# Examples\n```jldoctest\njulia> n1 = ProductNode((a=ArrayNode(zeros(2,2)), b=ArrayNode(ones(2,2))))\nProductNode with 2 obs\n  \u251c\u2500\u2500 a: ArrayNode(2\u00d72 Array with Float64 elements)\n  \u2514\u2500\u2500 b: ArrayNode(2\u00d72 Array with Float64 elements)\n\njulia> n2 = Mill.mapdata(x -> x .+ 1, n1)\nProductNode with 2 obs\n  \u251c\u2500\u2500 a: ArrayNode(2\u00d72 Array with Float64 elements)\n  \u2514\u2500\u2500 b: ArrayNode(2\u00d72 Array with Float64 elements)\n\njulia> Mill.data(n2).a\n2\u00d72 ArrayNode{Matrix{Float64}, Nothing}:\n 1.0  1.0\n 1.0  1.0\n\njulia> Mill.data(n2).b\n2\u00d72 ArrayNode{Matrix{Float64}, Nothing}:\n 2.0  2.0\n 2.0  2.0\n```\n\"\"\"\nmapdata(f, x) = f(x)\n\n# functions to make datanodes compatible with getindex and with MLDataPattern\nBase.getindex(x::T, i::BitArray{1}) where T <: AbstractMillNode = x[findall(i)]\nBase.getindex(x::T, i::Vector{Bool}) where T <: AbstractMillNode = x[findall(i)]\nBase.getindex(x::AbstractMillNode, i::Int) = x[i:i]\nBase.lastindex(ds::AbstractMillNode) = nobs(ds)\nMLDataPattern.getobs(x::AbstractMillNode, i) = x[i]\nMLDataPattern.getobs(x::AbstractMillNode, i, ::ObsDim.Undefined) = x[i]\nMLDataPattern.getobs(x::AbstractMillNode, i, ::ObsDim.Last) = x[i]\n\nsubset(x::AbstractMatrix, i) = x[:, i]\nsubset(x::AbstractVector, i) = x[i]\nsubset(x::AbstractMillNode, i) = x[i]\nsubset(x::DataFrame, i) = x[i, :]\nsubset(::Missing, i) = missing\nsubset(::Nothing, i) = nothing\nsubset(xs::Union{Tuple, NamedTuple}, i) = map(x -> x[i], xs)\n\ninclude(\"arraynode.jl\")\n\nStatsBase.nobs(::Missing) = nothing\n\ninclude(\"bagnode.jl\")\ninclude(\"weighted_bagnode.jl\")\nStatsBase.nobs(a::AbstractBagNode) = length(a.bags)\nStatsBase.nobs(a::AbstractBagNode, ::Type{ObsDim.Last}) = nobs(a)\nBase.ndims(x::AbstractBagNode) = Colon()\n\ninclude(\"productnode.jl\")\n\nBase.ndims(x::AbstractProductNode) = Colon()\nStatsBase.nobs(a::AbstractProductNode) = nobs(a.data[1], ObsDim.Last)\nStatsBase.nobs(a::AbstractProductNode, ::Type{ObsDim.Last}) = nobs(a)\n\ninclude(\"lazynode.jl\")\n\ncatobs(as::Maybe{ArrayNode}...) = reduce(catobs, collect(as))\ncatobs(as::Maybe{BagNode}...) = reduce(catobs, collect(as))\ncatobs(as::Maybe{WeightedBagNode}...) = reduce(catobs, collect(as))\ncatobs(as::Maybe{ProductNode}...) = reduce(catobs, collect(as))\ncatobs(as::Maybe{LazyNode}...) = reduce(catobs, collect(as))\n\nBase.cat(as::AbstractMillNode...; dims=:) = catobs(as...)\n\n# reduction of common datatypes the way we like it\nBase.reduce(::typeof(catobs), as::Vector{<:DataFrame}) = reduce(vcat, as)\nBase.reduce(::typeof(catobs), as::Vector{<:AbstractMatrix}) = reduce(hcat, as)\nBase.reduce(::typeof(catobs), as::Vector{<:AbstractVector}) = reduce(vcat, as)\nBase.reduce(::typeof(catobs), as::Vector{Missing}) = missing\nBase.reduce(::typeof(catobs), as::Vector{Nothing}) = nothing\nBase.reduce(::typeof(catobs), as::Vector{Union{Missing, Nothing}}) = nothing\n\nfunction Base.reduce(::typeof(catobs), as::Vector{Maybe{T}}) where T <: AbstractMillNode\n    reduce(catobs, skipmissing(as) |> collect)\nend\n\nfunction Base.show(io::IO, @nospecialize(n::AbstractMillNode))\n    print(io, nameof(typeof(n)))\n    if !get(io, :compact, false)\n        _show_data(IOContext(io, :compact => true), n)\n        if !(n isa ArrayNode)\n            print(io, \" with \", nobs(n), \" obs\")\n        end\n    end\nend\n\n_show_data(io, n::LazyNode{Name}) where {Name} = print(io, \"{\", Name, \"}\")\n_show_data(io, _) = print(io)\n", "meta": {"hexsha": "967054245812077f1a7b5d35de9a365d02121055", "size": 7373, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datanodes/datanode.jl", "max_stars_repo_name": "tlauli/Mill.jl", "max_stars_repo_head_hexsha": "a76adf8f91c55a12c193be10921085bf4fd44a4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/datanodes/datanode.jl", "max_issues_repo_name": "tlauli/Mill.jl", "max_issues_repo_head_hexsha": "a76adf8f91c55a12c193be10921085bf4fd44a4e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datanodes/datanode.jl", "max_forks_repo_name": "tlauli/Mill.jl", "max_forks_repo_head_hexsha": "a76adf8f91c55a12c193be10921085bf4fd44a4e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7180451128, "max_line_length": 110, "alphanum_fraction": 0.6879153669, "num_tokens": 2367, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4073334000459302, "lm_q2_score": 0.1384617799035966, "lm_q1q2_score": 0.05640010758454325}}
{"text": "function merge_sorted_arrays(x::Vector{T}, y::Vector{T})::Vector{T} where {T}\n    x_len=length(x)\n    y_len=length(y)\n    a,b=1,1\n    i = 1\n    merged_array=Vector{T}(undef, x_len + y_len)\n    @inbounds while (a <= x_len) && (b <= y_len)\n        if x[a] < y[b]\n            merged_array[i] = x[a]\n            a += 1\n            i += 1\n        else\n            merged_array[i] = y[b]\n            b += 1\n            i += 1\n        end\n    end\n    while a <= x_len\n        @inbounds merged_array[i] = x[a]\n        a += 1\n        i += 1\n    end\n    while b <= y_len\n        @inbounds merged_array[i] = y[b]\n        b += 1\n        i += 1\n    end\n    return merged_array\nend", "meta": {"hexsha": "fdb0c15ab9c5ff7314c0aea9c4dc815d595e423d", "size": 667, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/merge_sort.jl", "max_stars_repo_name": "Dinesh-Adhithya-H/ReadCoverageDistributions.jl", "max_stars_repo_head_hexsha": "21b059b08b6ca915fa5cda45c9928c8a17d5eb0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/merge_sort.jl", "max_issues_repo_name": "Dinesh-Adhithya-H/ReadCoverageDistributions.jl", "max_issues_repo_head_hexsha": "21b059b08b6ca915fa5cda45c9928c8a17d5eb0b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2021-07-04T08:46:07.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-17T15:53:29.000Z", "max_forks_repo_path": "src/merge_sort.jl", "max_forks_repo_name": "Dinesh-Adhithya-H/ReadCoverageDistributions.jl", "max_forks_repo_head_hexsha": "21b059b08b6ca915fa5cda45c9928c8a17d5eb0b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.0, "max_line_length": 77, "alphanum_fraction": 0.4527736132, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11279540926528922, "lm_q1q2_score": 0.05639770463264461}}
{"text": "using PlotUtils\nusing Test\nusing Statistics: mean\nusing Dates\nusing Random\nusing StableRNGs\n\nrng = StableRNG(42)\n\n# TODO: real tests\n\n# ----------------------\n# colors\n\nconst C = RGBA{Float64}\nconst C0 = RGBA{PlotUtils.Colors.N0f8}\n\n@testset \"colors\" begin\n\n    @test plot_color(nothing) == C(0, 0, 0, 0)\n    @test plot_color(false) == C(0, 0, 0, 0)\n    @test_throws ErrorException plot_color(true)\n\n    @test plot_color(:red) == parse(C, :red)\n    @test plot_color(\"red\") == parse(C, \"red\")\n    @test_throws ErrorException plot_color(\"notacolor\")\n\n    @test plot_color(colorant\"red\") == C(1, 0, 0, 1)\n\n    grad = cgrad()\n    @test typeof(grad) == PlotUtils.ContinuousColorGradient\n    @test plot_color(grad) === grad\n\n    grad = cgrad([:red, \"blue\"])\n    @test color_list(grad) == C[colorant\"red\", colorant\"blue\"]\n    @test grad.values == collect(range(0, stop = 1, length = 2))\n\n    grad = cgrad([:red, \"blue\"], alpha = 0.5)\n    @test C0.(color_list(grad)) == C0[C(1, 0, 0, 0.5), C(0, 0, 1, 0.5)]\n    @test grad.values == collect(range(0, stop = 1, length = 2))\n\n    grad = cgrad([:red,:blue], [0,0.1,1])\n    @test length(color_list(grad)) == 3\n    @test grad.values == [0,0.1,1]\n\n    cs = plot_color(rand(rng, 10))\n    @test typeof(cs) == Vector{C}\n    @test length(cs) == 10\n\n    cs = plot_color(rand(rng, 4, 4))\n    @test typeof(cs) == Matrix{C}\n    @test length(cs) == 16\n    @test size(cs) == (4, 4)\n\n\n    cs = plot_color(rand(rng, 10), 0.5)\n    @test typeof(cs) == Vector{C}\n    @test length(cs) == 10\n    for c in cs\n        @test alpha(c) == 0.5\n    end\n\n    cs = plot_color(rand(rng, 4, 4), 0.5)\n    @test typeof(cs) == Matrix{C}\n    @test length(cs) == 16\n    @test size(cs) == (4, 4)\n    for c in cs\n        @test alpha(c) == 0.5\n    end\nend\n\n# ----------------------\n# gradients\n\n@testset \"gradients\" begin\n    grad = cgrad(:inferno)\n    @test length(grad) == 256\n    @test RGB(grad.colors[1]) == RGB(0.001462, 0.000466, 0.013866)\n    @test RGB(grad.colors[end]) == RGB(0.988362, 0.998364, 0.644924)\nend\n\n# ----------------------\n# ticks\n\n# Copied from Plots.is_uniformly_spaced to avoid dependency on recent version\n# on Plots which is not used on Travis.\nfunction is_uniformly_spaced(v; tol = 1e-6)\n    dv = diff(v)\n    maximum(dv) - minimum(dv) < tol * mean(abs.(dv))\nend\n\n@testset \"ticks\" begin\n    @test optimize_ticks(-1, 2) == ([-1.0,0.0,1.0,2.0], -1.0, 2.0)\n    dt1, dt2 = Dates.value(DateTime(2000)), Dates.value(DateTime(2100))\n    @test optimize_datetime_ticks(dt1, dt2) == (\n        [63113990400000, 63902908800000, 64691827200000, 65480745600000],\n        [\"2001-01-01\", \"2026-01-01\", \"2051-01-01\", \"2076-01-01\"])\n\n    @testset \"small range\" begin\n        @testset \"small range $x, $(i)\u03f5\" for x in exp10.(-12:12), i in -5:5\n            y = x + i * eps(x)\n            x, y = minmax(x, y)\n            ticks = PlotUtils.optimize_ticks(x, y)[1]\n            @test issorted(ticks)\n            @test all(x .<= ticks .<= y)\n            # Fails:\n            # @test allunique(ticks)\n        end\n    end\n\n    function test_ticks(x, y, ticks)\n        @test issorted(ticks)\n        @test all(x .<= ticks .<= y)\n        if x < y\n            @test length(ticks) >= 2\n            @test is_uniformly_spaced(ticks)\n        end\n    end\n\n    @testset \"fixed ranges\" begin\n        @testset \"fixed range $x..$y\" for (x, y) in [(2, 14),(14, 25),(16, 36),(57, 69)]\n            test_ticks(x, y, optimize_ticks(x, y)[1])\n            test_ticks(-y, -x, optimize_ticks(-y, -x)[1])\n        end\n    end\n\n    @testset \"random ranges\" begin\n        r = [minmax(rand(rng, -100:100, 2)...) .* 10.0^i for _ = 1:10, i = -5:5]\n        @testset \"random range $x..$y\" for (x, y) in r\n            test_ticks(x, y, optimize_ticks(x, y)[1])\n        end\n    end\n\n    # issue 86\n    let x = -1.0, y = 13.0\n        test_ticks(x, y, optimize_ticks(x, y, k_min = 4, k_max = 8)[1])\n    end\n\n    @testset \"digits\" begin\n        @testset \"digits $((10^n) - 1)*10^$i\" for n in 1:9, i in -9:9\n            y0 = 10^n\n            x0 = y0 - 1\n            x, y = (x0, y0) .* 10.0^i\n            ticks = optimize_ticks(x, y)[1]\n            test_ticks(x, y, ticks)\n        end\n    end\nend\n\n# ----------------------\n# adapted grid\n\n@testset \"adapted grid\" begin\n    f = sin\n    int = (0, \u03c0)\n    xs, fs = adapted_grid(f, int)\n    l = length(xs) - 1\n    for i in 1:l\n        for \u03bb in 0:0.1:1\n            # test that `f` is well approximated by a line\n            # in the interval `(xs[i], xs[i+1])`\n            x = \u03bb * xs[i] + (1 - \u03bb) * xs[i + 1]\n            y = \u03bb * fs[i] + (1 - \u03bb) * fs[i + 1]\n            @test y \u2248 f(x) atol = 1e-2\n        end\n    end\n\n    int = (2, 2)\n    xs, fs = adapted_grid(f, int)\n    @test xs == [2]\n    @test fs == [f(2)]\n\n    int = (2, 1)\n    @test_throws ArgumentError adapted_grid(f, int)\nend\n\n@testset \"zscale\" begin\n    bkg = 30 .* randn(rng, 8192) .+ 1000\n    data = bkg .+ 100 .* randn(rng, 8192) .+ 2500\n    defects = rand(rng, CartesianIndices(bkg), 500)\n    data[defects] .= rand(rng, [0, 1e7], 500)\n    cmin, cmax = zscale(data)\n    # values calculated using IRAF\n    @test cmin \u2248 2841.586 atol = 1e-3\n    @test cmax \u2248 4171.648 atol = 1e-3\n    @test cmin > minimum(data)\n    @test cmax < maximum(data)\n\n    data = vcat(1:100)\n    cmin, cmax = zscale(data)\n    @test cmin == 1\n    @test cmax == 100\n\n    # Make sure output is finite\n    data = vcat(0:999, NaN)\n    cmin, cmax = zscale(data)\n    @test cmin == 0\n    @test cmax == 999\nend\n", "meta": {"hexsha": "2a598a9c81e3f5a97ea68877d5928584f71bdbb1", "size": 5440, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "t-bltg/PlotUtils.jl", "max_stars_repo_head_hexsha": "4007370cd3c3b9a6cdc62871687dac21364190ff", "max_stars_repo_licenses": ["Apache-2.0", "CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "t-bltg/PlotUtils.jl", "max_issues_repo_head_hexsha": "4007370cd3c3b9a6cdc62871687dac21364190ff", "max_issues_repo_licenses": ["Apache-2.0", "CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "t-bltg/PlotUtils.jl", "max_forks_repo_head_hexsha": "4007370cd3c3b9a6cdc62871687dac21364190ff", "max_forks_repo_licenses": ["Apache-2.0", "CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.2, "max_line_length": 88, "alphanum_fraction": 0.5380514706, "num_tokens": 1947, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.11279539882690352, "lm_q1q2_score": 0.05639769941345176}}
{"text": "import Base: ==, length, in, eltype;\n\nexport Problem, InstrumentedProblem,\n        actions, get_result, goal_test, path_cost, value,\n        format_instrumented_results,\n        Node, expand, child_node, solution, path, ==,\n        search,\n        GAState, mate, mutate,\n        tree_search, graph_search,\n        breadth_first_tree_search, depth_first_tree_search, depth_first_graph_search,\n        breadth_first_search, best_first_graph_search, uniform_cost_search,\n        recursive_dls, depth_limited_search, iterative_deepening_search,\n        greedy_best_first_graph_search,\n        Graph, make_undirected, connect_nodes, get_linked_nodes, get_nodes,\n        UndirectedGraph, RandomGraph,\n        GraphProblem,\n        astar_search, recursive_best_first_search,\n        hill_climbing, exp_schedule, simulated_annealing,\n        or_search, and_search, and_or_graph_search,\n        OnlineDFSAgentProgram, update_state, execute,\n        OnlineSearchProblem, LRTAStarAgentProgram,\n        learning_realtime_astar_cost,\n        genetic_search, genetic_algorithm,\n        NQueensProblem, conflict, conflicted,\n        random_boggle, print_boggle, boggle_neighbors, int_sqrt,\n        WordList, lookup, length, in,\n        BoggleFinder, set_board, find, words, score,\n        boggle_hill_climbing, mutate_boggle,\n        execute_searcher, compare_searchers, beautify_node;\n\n#=\n\n    Problem is a abstract problem that contains a initial state and goal state.\n\n=#\nmutable struct Problem <: AbstractProblem\n    initial::String\n    goal::Union{Nothing, String}\n\n    function Problem(initial_state::String; goal_state::Union{Nothing, String}=nothing)\n        return new(initial_state, goal_state);\n    end\nend\n\n\"\"\"\n    actions(ap::T, state::String) where {T <: AbstractProblem}\n\nReturn an array of possible actions that can be executed in the given state 'state'.\n\"\"\"\nfunction actions(ap::T, state::String) where {T <: AbstractProblem}\n    println(\"actions() is not implemented yet for \", typeof(ap), \"!\");\n    nothing;\nend\n\n\"\"\"\n    get_result(ap::T, state::String, action::String) where {T <: AbstractProblem}\n\nReturn the resulting state from executing the given action 'action' in the given state 'state'.\n\"\"\"\nfunction get_result(ap::T, state::String, action::String) where {T <: AbstractProblem}\n    println(\"get_result() is not implemented yet for \", typeof(ap), \"!\");\n    nothing;\nend\n\n\"\"\"\n    goal_test(ap::T, state::String) where {T <: AbstractProblem}\n\nReturn a boolean value representing whether the given state 'state' is a goal state in the given\nproblem 'ap'.\n\"\"\"\nfunction goal_test(ap::T, state::String) where {T <: AbstractProblem}\n    return ap.goal == state;\nend\n\n\"\"\"\n    path_cost(ap::T, cost::Float64, state1::String, action::String, state2::String) where {T <: AbstractProblem}\n    path_cost(ap::T, cost::Float64, state1::AbstractVector, action::Int64, state2::AbstractVector) where {T <: AbstractProblem}\n\nReturn the cost of a solution path arriving at 'state2' from 'state1' with the given action 'action' and\ncost 'cost' to arrive at 'state1'. The default path_cost() method costs 1 for every step in a path.\n\"\"\"\nfunction path_cost(ap::T, cost::Float64, state1::String, action::String, state2::String) where {T <: AbstractProblem}\n    return cost + 1;\nend\n\nfunction path_cost(ap::T, cost::Float64, state1::AbstractVector, action::Int64, state2::AbstractVector) where {T <: AbstractProblem}\n    return cost + 1;\nend\n\n\"\"\"\n    value(ap::T, state::String) where {T <: AbstractProblem}\n\nReturn a value for the given state 'state' in the given problem 'ap'.\n\nThis value is used in optimization problems such as hill climbing or simulated annealing.\n\"\"\"\nfunction value(ap::T, state::String) where {T <: AbstractProblem}\n    println(\"value() is not implemented yet for \", typeof(ap), \"!\");\n    nothing;\nend\n\n#=\n\n    InstrumentedProblem is a AbstractProblem implementation that wraps another AbstractProblem\n\n    implementation and tracks the number of function calls made. This problem is used in\n\n    compare_searchers() and execute_searcher().\n\n=#\nmutable struct InstrumentedProblem <: AbstractProblem\n    problem::AbstractProblem\n    actions::Int64\n    results::Int64\n    goal_tests::Int64\n    found   # can be any DataType, but check for Nothing DataType later\n\n    function InstrumentedProblem(ap::T) where {T <: AbstractProblem}\n        return new(ap, Int64(0), Int64(0), Int64(0), nothing);\n    end\nend\n\nfunction actions(ap::InstrumentedProblem, state::AbstractVector)\n    ap.actions = ap.actions + 1;\n    return actions(ap.problem, state);\nend\n\nfunction actions(ap::InstrumentedProblem, state::String)\n    ap.actions = ap.actions + 1;\n    return actions(ap.problem, state);\nend\n\nfunction get_result(ap::InstrumentedProblem, state::String, action::String)\n    ap.results = ap.results + 1;\n    return get_result(ap.problem, state, action);\nend\n\nfunction get_result(ap::InstrumentedProblem, state::AbstractVector, action::Int64)\n    ap.results = ap.results + 1;\n    return get_result(ap.problem, state, action);\nend\n\nfunction goal_test(ap::InstrumentedProblem, state::String)\n    ap.goal_tests = ap.goal_tests + 1;\n    local result::Bool = goal_test(ap.problem, state);\n    if (result)\n        ap.found = state;\n    end\n    return result;\nend\n\nfunction goal_test(ap::InstrumentedProblem, state::AbstractVector)\n    ap.goal_tests = ap.goal_tests + 1;\n    local result::Bool = goal_test(ap.problem, state);\n    if (result)\n        ap.found = state;\n    end\n    return result;\nend\n\nfunction path_cost(ap::InstrumentedProblem, cost::Float64, state1::String, action::String, state2::String)\n    return path_cost(ap.problem, cost, state1, action, state2);\nend\n\nfunction path_cost(ap::InstrumentedProblem, cost::Float64, state1::AbstractVector, action::Int64, state2::AbstractVector)\n    return path_cost(ap.problem, cost, state1, action, state2);\nend\n\nfunction value(ap::InstrumentedProblem, state::String)\n    return value(ap.problem, state);\nend\n\nfunction value(ap::InstrumentedProblem, state::AbstractVector)\n    return value(ap.problem, state);\nend\n\nfunction format_instrumented_results(ap::InstrumentedProblem)\n    return @sprintf(\"<%4d/%4d/%4d/%s>\", ap.actions, ap.goal_tests, ap.results, string(ap.found));\nend\n\n# A node should not exist without a state.\nmutable struct Node{T}\n    state::T\n    path_cost::Float64\n    depth::UInt32\n    action::Union{Nothing, String, Int64, Tuple}\n    parent::Union{Nothing, Node}\n    f::Float64\n\n    function Node{T}(state::T; parent::Union{Nothing, Node}=nothing, action::Union{Nothing, String, Int64, Tuple}=nothing, path_cost::Float64=0.0, f::Union{Nothing, Float64}=nothing) where T\n        nn = new(state, path_cost, UInt32(0), action, parent);\n        if (typeof(parent) <: Node)\n            nn.depth = UInt32(parent.depth + 1);\n        end\n        if (typeof(f) <: Float64)\n            nn.f = f;\n        end\n        return nn;\n    end\nend\n\n\"\"\"\n    expand(n::Node, ap::T) where {T <: AbstractProblem}\n\nReturn an array of nodes reachable by 1 step from the given node 'n' in the problem 'ap'.\n\"\"\"\nfunction expand(n::Node, ap::T) where {T <: AbstractProblem}\n    return collect(child_node(n, ap, act) for act in actions(ap, n.state));\nend\n\n\"\"\"\n    child_node(n::Node, ap::T, action::String) where {T <: AbstractProblem}\n\nReturn a child node for the given node 'n' in problem 'ap' after executing the action 'action' (Fig. 3.10).\n\"\"\"\nfunction child_node(n::Node, ap::T, action::String) where {T <: AbstractProblem}\n    local next_node = get_result(ap, n.state, action);\n    return Node{typeof(next_node)}(next_node, parent=n, action=action, path_cost=path_cost(ap, n.path_cost, n.state, action, next_node));\nend\n\nfunction child_node(n::Node, ap::T, action::Int64) where {T <: AbstractProblem}\n    local next_node = get_result(ap, n.state, action);\n    return Node{typeof(next_node)}(next_node, parent=n, action=action, path_cost=path_cost(ap, n.path_cost, n.state, action, next_node));\nend\n\nfunction child_node(n::Node, ap::T, action::Tuple) where {T <: AbstractProblem}\n    local next_node = get_result(ap, n.state, action);\n    return Node{typeof(next_node)}(next_node, parent=n, action=action, path_cost=path_cost(ap, n.path_cost, n.state, action, next_node));\nend\n\n\"\"\"\n    solution(n::Node)\n\nReturn an array of actions to get from the root node of node 'n' to the given node 'n'.\n\"\"\"\nfunction solution(n::Node)\n    local path_sequence = path(n);\n    return [node.action for node in path_sequence[2:length(path_sequence)]];\nend\n\n\"\"\"\n    path(n::Node)\n\nReturn the path between the root node of node 'n' to the given node 'n' as an array of nodes.\n\"\"\"\nfunction path(n::Node)\n    local node = n;\n    local path_back = [];\n    while true\n        push!(path_back, node);\n        if (!(node.parent === nothing))\n            node = node.parent;\n        else\n            # The root node does not have a parent node.\n            break;\n        end\n    end\n    path_back = reverse(path_back);\n    return path_back;\nend\n\nfunction ==(n1::Node, n2::Node)\n    return (n1.state == n2.state);\nend\n\n#=\n\n    SimpleProblemSolvingAgentProgram is a abstract problem solving agent (Fig. 3.1).\n\n=#\nmutable struct SimpleProblemSolvingAgentProgram <: AgentProgram\n    state::Union{Nothing, String}\n    goal::Union{Nothing, String}\n    seq::Array{String, 1}\n    problem::Union{Nothing, Problem}\n\n    function SimpleProblemSolvingAgentProgram(;initial_state::Union{Nothing, String}=nothing)\n        return new(initial_state, nothing, Array{String, 1}(), nothing);\n    end\nend\n\nfunction execute(spsap::SimpleProblemSolvingAgentProgram, percept::Tuple{Any, Any})\n    spsap.state = update_state(spsap, spsap.state, percept);\n    if (length(spsap.seq) == 0)\n        spsap.goal = formulate_problem(spsap, spsap.state);\n        spsap.problem = forumate_problem(spsap, spsap.state, spsap.goal);\n        spsap.seq = search(spsap, spsap.problem);\n        if (length(spsap.seq) == 0)\n            return Nothing;\n        end\n    end\n    local action = popfirst!(spsap.seq);\n    return action;\nend\n\nfunction update_state(spsap::SimpleProblemSolvingAgentProgram, state::String, percept::Tuple{Any, Any})\n    println(\"update_state() is not implemented yet for \", typeof(spsap), \"!\");\n    nothing;\nend\n\nfunction formulate_goal(spsap::SimpleProblemSolvingAgentProgram, state::String)\n    println(\"formulate_goal() is not implemented yet for \", typeof(spsap), \"!\");\n    nothing;\nend\n\nfunction formulate_problem(spsap::SimpleProblemSolvingAgentProgram, state::String, goal::String)\n    println(\"formulate_problem() is not implemented yet for \", typeof(spsap), \"!\");\n    nothing;\nend\n\nfunction search(spsap::SimpleProblemSolvingAgentProgram, problem::T) where {T <: AbstractProblem}\n    println(\"search() is not implemented yet for \", typeof(spsap), \"!\");\n    nothing;\nend\n\nstruct GAState\n    genes::Array{Any, 1}\n\n    function GAState(genes::Array{Any, 1})\n        return new(Array{Any,1}(deepcopy(genes)));\n    end\nend\n\nfunction mate(ga_state::T, other::T) where {T <: GAState}\n    local c = rand(RandomDeviceInstance, range(1, stop=length(ga_state.genes)));\n    local new_ga_state = deepcopy(ga_state[1:c]);\n    for element in other.genes[(c + 1):length(other.genes)]\n        push!(new_ga_state, element);\n    end\n    return new_ga_state;\nend\n\nfunction mutate(ga_state::T) where {T <: GAState}\n    println(\"mutate() is not implemented yet for \", typeof(ga_state), \"!\");\n    nothing;\nend\n\n\"\"\"\n    tree_search{T1 <: AbstractProblem, T2 <: Queue}(problem::T1, frontier::T2)\n\nSearch the given problem by using the general tree search algorithm (Fig. 3.7) and return the node solution.\n\"\"\"\nfunction tree_search(problem::T1, frontier::T2) where {T1 <: AbstractProblem, T2 <: Queue}\n    push!(frontier, Node{typeof(problem.initial)}(problem.initial));\n    while (length(frontier) != 0)\n        local node = pop!(frontier);\n        if (goal_test(problem, node.state))\n            return node;\n        end\n        extend!(frontier, expand(node, problem));\n    end\n    return nothing;\nend\n\nfunction tree_search(problem::InstrumentedProblem, frontier::T) where {T <: Queue}\n    push!(frontier, Node{typeof(problem.problem.initial)}(problem.problem.initial));\n    while (length(frontier) != 0)\n        local node = pop!(frontier);\n        if (goal_test(problem, node.state))\n            return node;\n        end\n        extend!(frontier, expand(node, problem));\n    end\n    return nothing;\nend\n\n\"\"\"\n    graph_search{T1 <: AbstractProblem, T2 <: Queue}(problem::T1, frontier::T2)\n\nSearch the given problem by using the general graph search algorithm (Fig. 3.7) and return the node solution.\n\nThe uniform cost algorithm (Fig. 3.14) should be used when the frontier is a priority queue.\n\"\"\"\nfunction graph_search(problem::T1, frontier::T2) where {T1 <: AbstractProblem, T2 <: Queue}\n    local explored::Set;\n    if (typeof(problem.initial) <: Tuple)\n        explored = Set{NTuple}();\n    else\n        explored = Set{typeof(problem.initial)}();\n    end\n    push!(frontier, Node{typeof(problem.initial)}(problem.initial));\n    while (length(frontier) != 0)\n        local node = pop!(frontier);\n        if (goal_test(problem, node.state))\n            return node;\n        end\n        push!(explored, node.state);\n        extend!(frontier, collect(child_node for child_node in expand(node, problem)\n                                if (!(child_node.state in explored) && !(child_node in frontier))));\n    end\n    return nothing;\nend\n\nfunction graph_search(problem::InstrumentedProblem, frontier::T) where {T <: Queue}\n    local explored::Set;\n    if (typeof(problem.problem.initial) <: Tuple)\n        explored = Set{NTuple}();\n    else\n        explored = Set{typeof(problem.problem.initial)}();\n    end\n    push!(frontier, Node{typeof(problem.problem.initial)}(problem.problem.initial));\n    while (length(frontier) != 0)\n        local node = pop!(frontier);\n        if (goal_test(problem, node.state))\n            return node;\n        end\n        push!(explored, node.state);\n        extend!(frontier, collect(child_node for child_node in expand(node, problem)\n                                if (!(child_node.state in explored) && !(child_node in frontier))));\n    end\n    return nothing;\nend\n\n\"\"\"\n    breadth_first_tree_search(problem::T) where {T <: AbstractProblem}\n\nSearch the shallowest nodes in the search tree first.\n\"\"\"\nfunction breadth_first_tree_search(problem::T) where {T <: AbstractProblem}\n    return tree_search(problem, FIFOQueue());\nend\n\n\"\"\"\n    depth_first_tree_search(problem::T) where {T <: AbstractProblem}\n\nSearch the deepest nodes in the search tree first.\n\"\"\"\nfunction depth_first_tree_search(problem::T) where {T <: AbstractProblem}\n    return tree_search(problem, Stack());\nend\n\n\"\"\"\n    depth_first_graph_search(problem::T) where {T <: AbstractProblem}\n\nSearch the deepest nodes in the search tree first.\n\"\"\"\nfunction depth_first_graph_search(problem::T) where {T <: AbstractProblem}\n    return graph_search(problem, Stack());\nend\n\n\"\"\"\n    breadth_first_search(problem::T) where {T <: AbstractProblem}\n    breadth_first_search(problem::InstrumentedProblem)\n\nReturn a solution by using the breadth-first search algorithm (Fig. 3.11)\non the given problem 'problem'. Otherwise, return 'nothing' on failure.\n\"\"\"\nfunction breadth_first_search(problem::T) where {T <: AbstractProblem}\n    local node = Node{typeof(problem.initial)}(problem.initial);\n    if (goal_test(problem, node.state))\n        return node;\n    end\n    local frontier = FIFOQueue();\n    push!(frontier, node);\n    local explored = Set{String}();\n    while (length(frontier) != 0)\n        node = pop!(frontier);\n        push!(explored, node.state);\n        for child_node in expand(node, problem)\n            if (!(child_node.state in explored) && !(child_node in frontier))\n                if (goal_test(problem, child_node.state))\n                    return child_node;\n                end\n                push!(frontier, child_node);\n            end\n        end\n    end\n    return nothing;\nend\n\nfunction breadth_first_search(problem::InstrumentedProblem)\n    local node = Node{typeof(problem.problem.initial)}(problem.problem.initial);\n    if (goal_test(problem, node.state))\n        return node;\n    end\n    local frontier = FIFOQueue();\n    push!(frontier, node);\n    local explored = Set{String}();\n    while (length(frontier) != 0)\n        node = pop!(frontier);\n        push!(explored, node.state);\n        for child_node in expand(node, problem)\n            if (!(child_node.state in explored) && !(child_node in frontier))\n                if (goal_test(problem, child_node.state))\n                    return child_node;\n                end\n                push!(frontier, child_node);\n            end\n        end\n    end\n    return nothing;\nend\n\n\"\"\"\n    best_first_graph_search(problem::T, f::Function) where {T <: AbstractProblem}\n\nSearch the nodes in the given problem 'problem' by visiting the nodes with the lowest\nscores returned by f(). If f() is a heuristics estimate function to the goal state, then\nthis function becomes greedy best first search. If f() is a function that gets the node's\ndepth, then this function becomes breadth-first search.\n\nReturns a solution if found, otherwise returns 'nothing' on failure.\n\nThis function uses f as a Function, because using f as an MemoizedFunction exhibits unusual\nbehavior when relying on MemoizedFunction by producing unexpected results.\n\"\"\"\nfunction best_first_graph_search(problem::T, f::Function) where {T <: AbstractProblem}\n    local node = Node{typeof(problem.initial)}(problem.initial);\n    if (goal_test(problem, node.state))\n        return node;\n    end\n    local frontier = PQueue();\n    push!(frontier, node, f);\n    local explored = Set{typeof(problem.initial)}();\n    while (length(frontier) != 0)\n        node = pop!(frontier);\n        if (goal_test(problem, node.state))\n            return node;\n        end\n        push!(explored, node.state);\n        for child_node in expand(node, problem)\n            if (!(child_node.state in explored) &&\n                !(child_node in collect(getindex(x, 2) for x in frontier.array)))\n                push!(frontier, child_node, f);\n            elseif (child_node in [getindex(x, 2) for x in frontier.array])\n                # Recall that Nodes can share the same state and different values for other fields.\n                local existing_node = pop!(collect(getindex(x, 2)\n                                                    for x in frontier.array\n                                                    if (getindex(x, 2) == child_node)));\n                if (f(child_node) < f(existing_node))\n                    delete!(frontier, existing_node);\n                    push!(frontier, child_node, f);\n                end\n            end\n        end\n    end\n    return nothing;\nend\n\n\n\"\"\"\n    uniform_cost_search(problem::T) where {T <: AbstractProblem}\n\nSearch the given problem by using the uniform cost algorithm (Fig. 3.14) and return the node solution.\n\nsolution() can be used on the node solution to reconstruct the path taken to the solution.\n\"\"\"\nfunction uniform_cost_search(problem::T) where {T <: AbstractProblem}\n    return best_first_graph_search(problem, (function(n::Node)return n.path_cost;end));\nend\n\nfunction recursive_dls(node::Node, problem::T, limit::Int64) where {T <: AbstractProblem}\n    if (goal_test(problem, node.state))\n        return node;\n    elseif (node.depth == limit)\n        return \"cutoff\";\n    else\n        local cutoff_occurred = false;\n        for child_node in expand(node, problem)\n            local result = recursive_dls(child_node, problem, limit);\n            if (result == \"cutoff\")\n                cutoff_occurred = true;\n            elseif (!(typeof(result) <: Nothing))\n                return result;\n            end\n        end\n        return if_(cutoff_occurred, \"cutoff\", nothing);\n    end\nend;\n\n\"\"\"\n    depth_limited_search(problem::T; limit::Int64) where {T <: AbstractProblem}\n\nSearch the given problem by using the depth limited tree search algorithm (Fig. 3.17)\nand return the node solution if a solution was found. Otherwise, this function returns 'nothing'.\n\nsolution() can be used on the node solution to reconstruct the path taken to the solution.\n\"\"\"\nfunction depth_limited_search(problem::T; limit::Int64=50) where {T <: AbstractProblem}\n    return recursive_dls(Node{typeof(problem.initial)}(problem.initial), problem, limit);\nend\n\nfunction depth_limited_search(problem::InstrumentedProblem; limit::Int64=50)\n    return recursive_dls(Node{typeof(problem.problem.initial)}(problem.problem.initial), problem, limit);\nend\n\n\"\"\"\n    iterative_deepening_search(problem::T) where {T <: AbstractProblem}\n\nSearch the given problem by using the iterative deepening search algorithm (Fig. 3.18)\nand return the node solution if a solution was found. Otherwise, this function returns 'nothing'.\n\nsolution() can be used on the node solution to reconstruct the path taken to the solution.\n\"\"\"\nfunction iterative_deepening_search(problem::T) where {T <: AbstractProblem}\n    for depth in 1:typemax(Int64)\n        local result = depth_limited_search(problem, limit=depth)\n        if (result != \"cutoff\")\n            return result;\n        end\n    end\n    return nothing;\nend\n\nconst greedy_best_first_graph_search = best_first_graph_search;\n\n#=\n\n    Graph is a graph that consists of nodes (vertices) and edges (links).\n\n    The Graph constructor uses the keyword 'directed' to specify if the graph\n\n    is directed or undirected.\n\n\n    For an example Graph instance:\n\n        Graph(dict=Dict([(\"A\", Dict([(\"B\", 1), (\"C\", 2)]))]))\n\n    The example Graph is a directed graph with 3 vertices (\"A\", \"B\", and \"C\")\n\n    and link \"A\"=>\"B\" (length 1) and \"A\"=>\"C\" (length 2).\n\n=#\nstruct Graph{N}\n    dict::Dict{N, Any}\n    locations::Dict{N, Tuple{Any, Any}}\n    directed::Bool\n\n    function Graph{N}(;dict::Union{Nothing, Dict{N, }}=nothing, locations::Union{Nothing, Dict{N, Tuple{Any, Any}}}=nothing, directed::Bool=true) where N\n        local ng::Graph;\n        if ((typeof(dict) <: Nothing) && (typeof(locations) <: Nothing))\n            ng = new(Dict{Any, Any}(), Dict{Any, Tuple{Any, Any}}(), Bool(directed));\n        elseif (typeof(locations) <: Nothing)\n            ng = new(Dict{eltype(dict.keys), Any}(dict), Dict{Any, Tuple{Any, Any}}(), Bool(directed));\n        else\n            ng = new(Dict{eltype(dict.keys), Any}(dict), Dict{eltype(locations.keys), Tuple{Any, Any}}(locations), Bool(directed));\n        end\n        if (!ng.directed)\n            make_undirected(ng);\n        end\n        return ng;\n    end\n\n    function Graph{N}(graph::Graph{N}) where N\n        return new(Dict{Any, Any}(graph.dict), Dict{String, Tuple{Any, Any}}(graph.locations), Bool(graph.directed));\n    end\nend\n\neltype(::Type{<:Graph{T}}) where {T} = T\n\nfunction make_undirected(graph::Graph)\n    for location_A in keys(graph.dict)\n        for (location_B, d) in graph.dict[location_A]\n            connect_nodes(graph, location_B, location_A, distance=d);\n        end\n    end\nend\n\n\"\"\"\n    connect_nodes(graph::Graph{N}, A::N, B::N; distance::Int64=Int64(1)) where N\n\nAdd a link between Node 'A' to Node 'B'. If the graph is undirected, then add\nthe inverse link from Node 'B' to Node 'A'.\n\"\"\"\nfunction connect_nodes(graph::Graph{N}, A::N, B::N; distance::Int64=Int64(1)) where N\n    get!(graph.dict, A, Dict{String, Int64}())[B]=distance;\n    if (!graph.directed)\n        get!(graph.dict, B, Dict{String, Int64}())[A]=distance;\n    end\n    nothing;\nend\n\n\"\"\"\n    get_linked_nodes(graph::Graph{N}, a::N; b::Union{Nothing, N}=nothing) where N\n\nReturn a dictionary of nodes and their distances if the 'b' keyword is not given.\nOtherwise, return the distance between 'a' and 'b'.\n\"\"\"\nfunction get_linked_nodes(graph::Graph{N}, a::N; b::Union{Nothing, N}=nothing) where N\n    local linked = get!(graph.dict, a, Dict{Any, Any}());\n    if (typeof(b) <: Nothing)\n        return linked;\n    else\n        return get(linked, b, nothing);\n    end\nend\n\nfunction get_nodes(graph::Graph)\n    return collect(keys(graph.dict));\nend\n\n\"\"\"\n    UndirectedGraph(dict::Dict{T, }, locations::Dict{T, Tuple{Any, Any}}) where T\n    UndirectedGraph()\n\nReturn an undirected graph from the given dictionary of links 'dict' and dictionary \nof locations 'locations' if given.\n\"\"\"\nfunction UndirectedGraph(dict::Dict{T, }, locations::Dict{T, Tuple{Any, Any}}) where T\n    return Graph{eltype(dict.keys)}(dict=dict, locations=locations, directed=false);\nend\n\nfunction UndirectedGraph()\n    return Graph{Any}(directed=false);\nend\n\n\"\"\"\n    RandomGraph()\n\nReturn a random graph with the specified nodes and number of links.\n\"\"\"\nfunction RandomGraph(;nodes::UnitRange=1:10,\n                    min_links::Int64=2,\n                    width::Int64=400,\n                    height::Int64=300,\n                    curvature::Function=(function()\n                                            return (0.4*rand(RandomDeviceInstance)) + 1.1;\n                                        end))\n    local g = UndirectedGraph();\n    for node in nodes\n        g.locations[node] = Tuple((rand(RandomDeviceInstance, 1:width), rand(RandomDeviceInstance, 1:height)));\n    end\n    for i in 1:min_link\n        for node in nodes\n            if (get_linked_nodes(g, node) < min_links)\n                local here = g.locations[node];\n                local neighbor = argmin(nodes, (function(n, ; graph::Graph=g, current_node::Node=node, current_location::Tuple=here)\n                                                    if (n == current_node || get_linked_nodes(graph, current_node, n) != nothing)\n                                                        return Inf;\n                                                    end\n                                                    return distance(g.locations[n], current_location);\n                                                end));\n                local d = distance(g.locations[neighbor], here) * curvature();\n                connect(g, node, neighbor, Int64(floor(d)));\n            end\n        end\n    end\n    return g;\nend\n\n#=\n\n    GraphProblem is the problem of searching a graph from one node to another node.\n\n=#\nstruct GraphProblem <: AbstractProblem\n    initial::String\n    goal::String\n    graph::Graph\n    h::MemoizedFunction\n\n\n    function GraphProblem(initial_state::String, goal_state::String, graph::Graph)\n        return new(initial_state, goal_state, Graph{eltype(graph)}(graph), MemoizedFunction(initial_to_goal_distance));\n    end\nend\n\nfunction actions(gp::GraphProblem, loc::String)\n    return collect(keys(get_linked_nodes(gp.graph,loc)));\nend\n\nfunction get_result(gp::GraphProblem, state::String, action::String)\n    return action;\nend\n\nfunction path_cost(gp::GraphProblem, current_cost::Float64, location_A::String, action::String, location_B::String)\n    local AB_distance::Float64;\n    if (haskey(gp.graph.dict, location_A) && haskey(gp.graph.dict[location_A], location_B))\n        AB_distance= Float64(get_linked_nodes(gp.graph,location_A, b=location_B));\n    else\n        AB_distance = Float64(Inf);\n    end\n    return current_cost + AB_distance;\nend\n\n\"\"\"\n    initial_to_goal_distance(gp::GraphProblem, n::Node)\n\nCompute the straight line distance between the initial state and goal state.\n\"\"\"\nfunction initial_to_goal_distance(gp::GraphProblem, n::Node)\n    local locations = gp.graph.locations;\n    if (isempty(locations))\n        return Inf;\n    else\n        return Float64(floor(distance(locations[n.state], locations[gp.goal])));\n    end\nend\n\nfunction initial_to_goal_distance(gp::InstrumentedProblem, n::Node)\n    local locations = gp.problem.graph.locations;\n    if (isempty(locations))\n        return Inf;\n    else\n        return Float64(floor(distance(locations[n.state], locations[gp.problem.goal])));\n    end\nend\n\n\"\"\"\n    astar_search(problem::GraphProblem; h::Union{Nothing, Function}=nothing)\n\nApply the A* search (best-first graph search with f(n)=g(n)+h(n)) to the given problem 'problem'.\nIf the 'h' keyword is not used, this function uses the function problem.h.\n\nThis function uses mh as a Function, because using mh as an MemoizedFunction exhibits unusual\nbehavior when relying on MemoizedFunction by producing unexpected results.\n\"\"\"\nfunction astar_search(problem::GraphProblem; h::Union{Nothing, Function}=nothing)\n    local mh::Function;\n    if (!(typeof(h) <: Nothing))\n        mh = h;\n    else\n        mh = problem.h.f;\n    end\n    return best_first_graph_search(problem,\n                                    (function(node::Node; h::Function=mh, prob::GraphProblem=problem)\n                                        return node.path_cost + h(prob, node);\n                                    end));\nend\n\n\"\"\"\n    RBFS(problem::T1, node::T2, flmt::Float64, h::MemoizedFunction) where {T1 <: AbstractProblem, T2 <: Node}\n\nRecursively calls RBFS() with a new 'flmt' value and returns its solution to recursive_best_first_search().\n\"\"\"\nfunction RBFS(problem::T1, node::T2, flmt::Float64, h::MemoizedFunction) where {T1 <: AbstractProblem, T2 <: Node}\n    if (goal_test(problem, node.state))\n        return node, 0.0;\n    end\n    local successors = expand(node, problem);\n    if (length(successors) == 0);\n        return node, Inf;\n    end\n    for successor in successors\n        successor.f = max(successor.path_cost + eval_memoized_function(h, problem, successor), node.f);\n    end\n    while (true)\n        sort!(successors, lt=(function(n1::Node, n2::Node)return isless(n1.f, n2.f);end));\n        local best::Node = successors[1];\n        if (best.f > flmt)\n            return nothing, best.f;\n        end\n        local alternative::Float64;\n        if (length(successors) > 1)\n            alternative = successors[1].f;\n        else\n            alternative = Inf;\n        end\n        result, best.f = RBFS(problem, best, min(flmt, alternative), h);\n        if (!(result === nothing))\n            return result, best.f;\n        end\n    end\nend\n\n\"\"\"\n    recursive_best_first_search(problem::T; h::Union{Nothing, MemoizedFunction}) where {T <: AbstractProblem}\n\nSearch the given problem by using the recursive best first search algorithm (Fig. 3.26)\nand return the node solution.\n\nsolution() can be used on the node solution to reconstruct the path taken to the solution.\n\"\"\"\nfunction recursive_best_first_search(problem::T; h::Union{Nothing, MemoizedFunction}=nothing) where {T <: AbstractProblem}\n    local mh::MemoizedFunction; #memoized h(n) function\n    if (!(typeof(h) <: Nothing))\n        mh = MemoizedFunction(h);\n    else\n        mh = problem.h;\n    end\n\n    local node = Node{typeof(problem.initial)}(problem.initial);\n    node.f = eval_memoized_function(mh, problem, node);\n    result, bestf = RBFS(problem, node, Inf, mh);\n    return result;\nend\n\nfunction recursive_best_first_search(problem::InstrumentedProblem; h::Union{Nothing, MemoizedFunction}=nothing)\n    local mh::MemoizedFunction; #memoized h(n) function\n    if (!(typeof(h) <: Nothing))\n        mh = MemoizedFunction(h);\n    else\n        mh = problem.problem.h;\n    end\n\n    local node = Node{typeof(problem.problem.initial)}(problem.problem.initial);\n    node.f = eval_memoized_function(mh, problem, node);\n    result, bestf = RBFS(problem, node, Inf, mh);\n    return result;\nend\n\n\"\"\"\n    hill_climbing(problem::T) where {T <: AbstractProblem}\n\nReturn a state that is a local maximum for the given problem 'problem' by using\nthe hill-climbing search algorithm (Fig. 4.2) on the initial state of the problem.\n\"\"\"\nfunction hill_climbing(problem::T) where {T <: AbstractProblem}\n    local current_node = Node{typeof(problem.initial)}(problem.initial);\n    while (true)\n        local neighbors = expand(current_node, problem);\n        if (length(neighbors) == 0)\n            break;\n        end\n        local neighbor = argmax_random_tie(neighbors,\n                                            (function(n::Node,; p::AbstractProblem=problem)\n                                                return value(p, n.state);\n                                            end));\n        if (value(problem, neighbor.state) <= value(problem, current_node.state))\n            break;\n        end\n        current_node = neighbor;\n    end\n    return current_node.state;\nend\n\n\"\"\"\n    exp_schedule(;kvar::Int64=20, delta::Float64=0.005, lmt::Int64=100)\n\nReturn a scheduled time for simulated annealing.\n\"\"\"\nfunction exp_schedule(;kvar::Int64=20, delta::Float64=0.005, lmt::Int64=100)\n    return (function(t::Real; k=kvar, d=delta, limit=lmt)\n                return if_((t < limit), (k * exp(-d * t)), 0);\n            end);\nend\n\n\"\"\"\n    simulated_annealing(problem::T; schedule::Function=exp_schedule()) where {T <: AbstractProblem}\n\nReturn the solution node by applying the simulated annealing algorithm (Fig. 4.5) on the given\nproblem 'problem' and schedule function 'schedule'. If a solution node can't be found,\nthis function returns 'nothing' on failure.\n\"\"\"\nfunction simulated_annealing(problem::T; schedule::Function=exp_schedule()) where {T <: AbstractProblem}\n    local current_node = Node{typeof(problem.initial)}(problem.initial);\n    for t in 0:(typemax(Int64) - 1)\n        local temperature::Float64 = schedule(t);\n        if (temperature == 0)\n            return current_node;\n        end\n        local neighbors = expand(current_node, problem);\n        if (length(neighbors) == 0)\n            return current_node;\n        end\n        local next_node = rand(RandomDeviceInstance, neighbors);\n        delta_e = value(problem, next_node.state) - value(problem, current_node.state);\n        if ((delta_e > 0) || (exp(delta_e/temperature) > rand(RandomDeviceInstance)))\n            current_node = next_node;\n        end\n    end\n    return nothing;\nend\n\n#=\n\n    and_search() and or_search() are used by and_or_graph_search().\n\n=#\nfunction or_search(problem::T, state::AbstractVector, path::AbstractVector) where {T <: AbstractProblem}\n    if (goal_test(problem, state))\n        return [];\n    end\n    if (state in path)\n        return nothing;\n    end\n    for action in actions(problem, state)\n        local plan = and_search(get_result(problem, state, action), vcat(path, [state,]));\n        if (plan != nothing)\n            return [action, plan];\n        end\n    end\n    return nothing;\nend\n\nfunction or_search(problem::T, state::String, path::AbstractVector) where {T <: AbstractProblem}\n    if (goal_test(problem, state))\n        return [];\n    end\n    if (state in path)\n        return nothing;\n    end\n    for action in actions(problem, state)\n        local plan = and_search(problem, get_result(problem, state, action), vcat(path, [state,]));\n        if (plan != nothing)\n            return [action, plan];\n        end\n    end\n    return nothing;\nend\n\nfunction and_search(problem::T, states::AbstractVector, path::AbstractVector) where {T <: AbstractVector}\n    local plan = Dict{Any, Any}();\n    for state in states\n        plan[state] = or_search(problem, state, path);\n        if (plan[state] == nothing)\n            return nothing;\n        end\n    end\n    return plan;\nend\n\n\"\"\"\n    and_or_graph_search(problem::T) where {T <: AbstractProblem}\n\nReturn a conditional plan by using the algorithm for searching and-or graphs (Fig. 4.11)\non the given problem 'problem'. This function returns 'nothing' on failure.\n\"\"\"\nfunction and_or_graph_search(problem::T) where {T <: AbstractProblem}\n    return or_search(problem, problem.initial, []);\nend\n\n#=\n\n    OnlineDFSAgentProgram is a online depth first search agent (Fig. 4.21)\n\n    implementation of AgentProgram.\n\n=#\nmutable struct OnlineDFSAgentProgram <: AgentProgram\n    result::Dict\n    untried::Dict\n    unbacktracked::Dict\n    state::Union{Nothing, String}\n    action::Union{Nothing, String}\n    problem::AbstractProblem\n\n    function OnlineDFSAgentProgram(problem::T) where {T <: AbstractProblem}\n        return new(Dict(), Dict(), Dict(), nothing, nothing, problem);\n    end\nend\n\nfunction update_state(odfsap::OnlineDFSAgentProgram, percept::String)\n    return percept;\nend\n\nfunction execute(odfsap::OnlineDFSAgentProgram, percept::String)\n    local s_prime::String = update_state(odfsap, percept);\n    if (goal_test(odfsap.problem, s_prime))\n        odfsap.action = nothing;\n    else\n        if (!(s_prime in keys(odfsap.untried)))\n            odfsap.untried[s_prime] = actions(odfsap.problem, s_prime);\n        end\n        if (!(odfsap.state === nothing))\n            if (haskey(odfsap.result, (odfsap.state, odfsap.action)))\n                if (s_prime != odfsap.result[(odfsap.state, odfsap.action)])\n                    odfsap.result[(odfsap.state, odfsap.action)] = s_prime;\n                    pushfirst!(odfsap.unbacktracked[s_prime], odfsap.state);\n                end\n            else\n                if (s_prime != [])\n                    odfsap.result[(odfsap.state, odfsap.action)] = s_prime;\n                    pushfirst!(odfsap.unbacktracked[s_prime], odfsap.state);\n                end\n            end\n        end\n        if (length(odfsap.untried[s_prime]) == 0)\n            if (length(odfsap.unbacktracked[s_prime]) == 0)\n                odfsap.action = nothing;\n            else\n                first_item = popfirst!(odfsap.unbacktracked[s_prime]);\n                for (state, b) in keys(odfsap.result)\n                    if (odfsap.result[(state, b)] == first_item)\n                        odfsap.action = b;\n                        break;\n                    end\n                end\n            end\n        else\n            odfsap.action = popfirst!(odfsap.untried[s_prime]);\n        end\n    end\n    odfsap.state = s_prime;\n    return odfsap.action;\nend\n\n#=\n\n    OnlineSearchProblem is a AbstractProblem implementation of a online search problem\n\n    that can be solved by a online search agent.\n\n=#\nstruct OnlineSearchProblem <: AbstractProblem\n    initial::String\n    goal::String\n    graph::Graph\n    least_costs::Dict\n    h::Function\n\n    function OnlineSearchProblem(initial::String, goal::String, graph::Graph, least_costs::Dict)\n        return new(initial, goal, graph, least_costs, online_search_least_cost);\n    end\nend\n\nfunction actions(osp::OnlineSearchProblem, state::String)\n    return collect(keys(osp.graph.dict[state]));\nend\n\nfunction get_result(osp::OnlineSearchProblem, state::String, action::String)\n    return osp.graph.dict[state][action];\nend\n\nfunction online_search_least_cost(osp::OnlineSearchProblem, state::String)\n    return osp.least_costs[state];\nend\n\nfunction path_cost(osp::OnlineSearchProblem, state1::String, action::String, state2::String)\n    return 1;\nend\n\nfunction goal_test(osp::OnlineSearchProblem, state::String)\n    if (state == osp.goal)\n        return true;\n    else\n        return false;\n    end\nend\n\n#=\n\n    LRTAStarAgentProgram is an AgentProgram implementation of LRTA*-Agent (Fig. 4.24).\n\n    The 'result' field is not necessary as the given problem contains the results table.\n\n=#\nmutable struct LRTAStarAgentProgram <: AgentProgram\n    H::Dict\n    state::Union{Nothing, String}\n    action::Union{Nothing, String}\n    problem::AbstractProblem\n\n    function LRTAStarAgentProgram(problem::T) where {T <: AbstractProblem}\n        return new(Dict(), nothing, nothing, problem);\n    end\nend\n\nfunction learning_realtime_astar_cost(lrtaap::LRTAStarAgentProgram, state::String, action::String, s_prime::String, H::Dict)\n    if (haskey(lrtaap.H, s_prime))\n        return path_cost(lrtaap.problem, state, action, s_prime) + lrtaap.H[s_prime];\n    else\n        return path_cost(lrtaap.problem, state, action, s_prime) + lrtaap.problem.h(lrtaap.problem, s_prime);\n    end\nend\n\n\"\"\"\n    execute(lrtaap::LRTAStarAgentProgram, s_prime::String)\n\nReturn an action given the percept 's_prime' and by using the LRTA*-Agent\nprogram (Fig. 4.24). If the current state of the agent is at the goal\nstate, return 'nothing'.\n\"\"\"\nfunction execute(lrtaap::LRTAStarAgentProgram, s_prime::String)\n    if (goal_test(lrtaap.problem, s_prime))\n        lrtaap.action = nothing;\n        return nothing;\n    else\n        if (!haskey(lrtaap.H, s_prime))\n            lrtaap.H[s_prime] = lrtaap.problem.h(lrtaap.problem, s_prime);\n        end\n        if (!(lrtaap.state === nothing))\n            lrtaap.H[lrtaap.state] = reduce(min, learning_realtime_astar_cost(lrtaap,\n                                                                        lrtaap.state,\n                                                                        b,\n                                                                        get_result(lrtaap.problem, lrtaap.state, b),\n                                                                        lrtaap.H)\n                                        for b in actions(lrtaap.problem, lrtaap.state));\n        end\n        lrtaap.action = argmin(actions(lrtaap.problem, s_prime),\n                                (function(b::String)\n                                    return learning_realtime_astar_cost(lrtaap,\n                                                                        s_prime,\n                                                                        b,\n                                                                        get_result(lrtaap.problem, s_prime, b),\n                                                                        lrtaap.H);\n                                end));\n        lrtaap.state = s_prime;\n        return lrtaap.action;\n    end\nend\n\nfunction genetic_search(problem::T; ngen::Int64=1000, pmut::Float64=0.1, n::Int64=20) where {T <: AbstractProblem}\n    local s = problem.initial;\n    local states = [result(s, action) for action in actions(problem, s)];\n    shuffle!(RandomDeviceInstance, states);\n    if (length(states) < n)\n        n = length(states);\n    end\n    return genetic_algorithm(states[1:n], value, ngen=ngen, pmut=pmut);\nend\n\nfunction genetic_algorithm(population::T, fitness::Function; ngen::Int64=1000, pmut::Float64=0.1) where {T <: AbstractVector}\n    for i in 1:ngen\n        local new_population = Array{Any, 1}();\n        for j in 1:length(population)\n            local fitnesses = map(fitness, population);\n            p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2);\n            local child = mate(p1, p2);\n            if (rand(RandomDeviceInstance) < pmut)\n                mutate(child);\n            end\n            push!(new_population, child);\n        end\n        population = new_population;\n    end\n    return argmax(population, fitness);\nend\n\n# Simplified road map of Romania example (Fig. 3.2)\nromania = UndirectedGraph(Dict(\n                            Pair(\"A\", Dict(\"Z\"=>75, \"S\"=>140, \"T\"=>118)),\n                            Pair(\"B\", Dict(\"U\"=>85, \"P\"=>101, \"G\"=>90, \"F\"=>211)),\n                            Pair(\"C\", Dict(\"D\"=>120, \"R\"=>146, \"P\"=>138)),\n                            Pair(\"D\", Dict(\"M\"=>75)),\n                            Pair(\"E\", Dict(\"H\"=>86)),\n                            Pair(\"F\", Dict(\"S\"=>99)),\n                            Pair(\"H\", Dict(\"U\"=>98)),\n                            Pair(\"I\", Dict(\"V\"=>92, \"N\"=>87)),\n                            Pair(\"L\", Dict(\"T\"=>111, \"M\"=>70)),\n                            Pair(\"O\", Dict(\"Z\"=>71, \"S\"=>151)),\n                            Pair(\"P\", Dict(\"R\"=>97)),\n                            Pair(\"R\", Dict(\"S\"=>80)),\n                            Pair(\"U\", Dict(\"V\"=>142)),\n                                ),\n                            Dict{String, Tuple{Any, Any}}(\n                                \"A\"=>( 91, 492), \"B\"=>(400, 327), \"C\"=>(253, 288), \"D\"=>(165, 299),\n                                \"E\"=>(562, 293), \"F\"=>(305, 449), \"G\"=>(375, 270), \"H\"=>(534, 350),\n                                \"I\"=>(473, 506), \"L\"=>(165, 379), \"M\"=>(168, 339), \"N\"=>(406, 537),\n                                \"O\"=>(131, 571), \"P\"=>(320, 368), \"R\"=>(233, 410), \"S\"=>(207, 457),\n                                \"T\"=>( 94, 410), \"U\"=>(456, 350), \"V\"=>(509, 444), \"Z\"=>(108, 531),\n                                )\n                            );\n\n# One-dimensional state space example (Fig. 4.23)\none_dim_state_space = Graph{String}(dict=Dict{String, Dict{String, String}}([Pair(\"State_1\", Dict([Pair(\"Right\", \"State_2\")])),\n                                        Pair(\"State_2\", Dict([Pair(\"Right\", \"State_3\"),\n                                                            Pair(\"Left\", \"State_1\")])),\n                                        Pair(\"State_3\", Dict([Pair(\"Right\", \"State_4\"),\n                                                            Pair(\"Left\", \"State_2\")])),\n                                        Pair(\"State_4\", Dict([Pair(\"Right\", \"State_5\"),\n                                                            Pair(\"Left\", \"State_3\")])),\n                                        Pair(\"State_5\", Dict([Pair(\"Right\", \"State_6\"),\n                                                            Pair(\"Left\", \"State_4\")])),\n                                        Pair(\"State_6\", Dict([Pair(\"Left\", \"State_5\")]))]));\n\none_dim_state_space_least_costs = Dict([Pair(\"State_1\", 8),\n                                        Pair(\"State_2\", 9),\n                                        Pair(\"State_3\", 2),\n                                        Pair(\"State_4\", 2),\n                                        Pair(\"State_5\", 4),\n                                        Pair(\"State_6\", 3)]);\n\n# Principal states and territories of Australia example (Fig. 6.1)\naustralia = UndirectedGraph(Dict(\n                                Pair(\"T\",   Dict()),\n                                Pair(\"SA\",  Dict(\"WA\"=>1, \"NT\"=>1, \"Q\"=>1, \"NSW\"=>1, \"V\"=>1)),\n                                Pair(\"NT\",  Dict(\"WA\"=>1, \"Q\"=>1)),\n                                Pair(\"NSW\", Dict(\"Q\"=>1, \"V\"=>1)),\n                            ),\n                            Dict{String, Tuple{Any, Any}}(\"WA\"=>(120, 24), \"NT\"=>(135, 20), \"SA\"=>(135, 30),\n                                \"Q\"=>(145, 20), \"NSW\"=>(145, 32), \"T\"=>(145, 42), \"V\"=>(145, 37),\n                                )\n                            );\n\n#=\n\n    NQueensProblem is the problem of placing 'N' non-attacking queens on a 'N'x'N' chess board.\n\n    Each state is represented as an 'N' element array where the value of 'r' at index 'c' implies\n\n    that a queen occupies the position at row 'r' and column 'c'. The columns are values are filled\n\n    from left to right.\n\n=#\nstruct NQueensProblem <: AbstractProblem\n    N::Int64\n    initial::Array{Union{Nothing, Int64}, 1}\n\n    function NQueensProblem(n::Int64)\n        return new(n, fill(nothing, n));\n    end\nend\n\nfunction actions(problem::NQueensProblem, state::AbstractVector)\n    if (!(state[length(state)] === nothing))\n        return Array{Any, 1}([]);\n    else\n        local col = utils.null_index(state);\n        return collect(row for row in 1:problem.N if (!conflicted(problem, state, row, col)));\n    end\nend\n\nfunction conflict(problem::NQueensProblem, row1::Int64, col1::Int64, row2::Int64, col2::Int64)\n    return ((row1 == row2) ||\n            (col1 == col2) ||\n            (row1 - col1 == row2 - col2) ||\n            (row1 + col1 == row2 + col2));\nend\n\nfunction conflict(problem::NQueensProblem, row1::Int64, col1::Int64, row2::Nothing, col2::Int64)\n    error(\"conflict(): 'row2' is not initialized!\");\nend\n\nfunction conflicted(problem::NQueensProblem, state::AbstractVector, row::Int64, col::Int64)\n    return any(conflict(problem, row, col, state[i], i) for i in 1:(col-1));\nend\n\nfunction get_result(problem::NQueensProblem, state::AbstractVector, row::Int64)\n    local col = utils.null_index(state);\n    local new_result = deepcopy(state);\n    new_result[col] = row;\n    return new_result;\nend\n\nfunction goal_test(problem::NQueensProblem, state::AbstractVector)\n    if ((state[length(state)] === nothing))\n        return false;\n    end\n    return !any(conflicted(problem, state, state[col], col) for col in 1:length(state));\nend\n\ncapital_case_alphabet = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\";\n\ncubes16 = Array{String, 1}([\"FORIXB\", \"MOQABJ\", \"GURILW\", \"SETUPL\",\n                            \"CMPDAE\", \"ACITAO\", \"SLCRAE\", \"ROMASH\",\n                            \"NODESW\", \"HEFIYE\", \"ONUDTK\", \"TEVIGN\",\n                            \"ANEDVZ\", \"PINESH\", \"ABILYT\", \"GKYLEU\"]);\n\nfunction random_boggle(;n::Int64=4)\n    local cubes = collect(cubes16[(i % 16) + 1] for i in 0:((n * n) - 1));\n    shuffle!(RandomDeviceInstance, cubes);\n    return map((function(array::String)\n                    return rand(RandomDeviceInstance, collect(array));\n                end), cubes);\nend\n\nboyan_best = collect(\"RSTCSDEIAEGNLRPEATESMSSID\");\n\nfunction print_boggle(board::Array{Char, 1})\n    local nn::Int64 = length(board);\n    local n::Int64 = int_sqrt(nn);\n    local board_str::String = \"\";\n    for i in 0:(nn - 1)\n        if ((i % n == 0) && (i > 0))\n            board_str = board_str * \"\\n\";\n        end\n        if (board[i + 1] == 'Q')\n            board_str = board_str * \"Qu \";\n        else\n            board_str = board_str * String([board[i + 1]]) * \"  \";\n        end\n    end\n    print(board_str);\n    nothing;\nend\n\nfunction boggle_neighbors(nn::Int64; cache::Dict=Dict{Any, Any}())\n    if haskey(cache, nn)\n        return cache[nn];\n    end\n    local n::Int64 = int_sqrt(nn)\n    local neighbors::AbstractVector = Array{Any, 1}(undef, nn);\n    for i in 0:(nn - 1)\n        neighbors[i + 1] = Array{Int64, 1}([]);\n        on_top::Bool = (i < n);\n        on_bottom::Bool = (i >= (nn - n));\n        on_left::Bool = (i % n == 0);\n        on_right::Bool = ((i + 1) % n == 0);\n        if (!on_top)\n            push!(neighbors[i + 1], (i + 1) - n);\n            if (!on_left)\n                push!(neighbors[i + 1], (i + 1) - n - 1);\n            end\n            if (!on_right)\n                push!(neighbors[i + 1], (i + 1) - n + 1);\n            end\n        end\n        if (!on_bottom)\n            push!(neighbors[i + 1], (i + 1) + n);\n            if (!on_left)\n                push!(neighbors[i + 1], (i + 1) + n - 1);\n            end\n            if (!on_right)\n                push!(neighbors[i + 1], (i + 1) + n + 1);\n            end\n        end\n        if (!on_left)\n            push!(neighbors[i + 1], (i + 1) - 1);\n        end\n        if (!on_right)\n            push!(neighbors[i + 1], (i + 1) + 1);\n        end\n    end\n    cache[nn] = neighbors;\n    return neighbors;\nend\n\nfunction int_sqrt(n::Number)\n    return Int64(sqrt(n));\nend\n\n#=\n\n    WordList contains an array of words.\n\n=#\nstruct WordList\n    words::Array{String, 1}\n    bounds::Dict{Char, Tuple{Any, Any}}\n\n    function WordList(filename::String; min_len::Int64=3)\n        local wlba = read(filename);\n        local wls = uppercase(String(wlba));\n        local wlsa = sort(map(strip, split(wls, '\\n')));\n        local wlsa_filtered = collect(s for s in wlsa if (length(s) >= min_len));\n        nwl::WordList = new(wlsa_filtered, Dict{Char, Tuple{Any, Any}}());\n        for c in capital_case_alphabet\n            fc::Char = c + 1;   #following character\n            nwl.bounds[c] = (searchsorted(nwl.words, String([c]), 1, length(nwl.words), Base.Order.Forward).stop + 1,\n                        searchsorted(nwl.words, String([fc]), 1, length(nwl.words), Base.Order.Forward).stop + 1);\n        end\n        return nwl;\n    end\nend\n\nfunction lookup(wl::WordList, prefix::String; lo::Int64=1, hi::Union{Nothing, Int64}=nothing)\n    local words = wl.words;\n    if (typeof(hi) <: Nothing)\n        hi = length(words);\n    end\n    local i::Int64 = searchsorted(words, prefix, lo, hi, Base.Order.Forward).start;\n\n    #'i' is only larger than length of words when the returned index is not in WordList.\n    if (i <= length(words) && startswith(words[i], prefix))\n        return i, (words[i] == prefix);\n    else\n        return nothing, false;\n    end\nend\n\nlength(wl::WordList) = length(wl.words);\n\nin(prefix::String, wl::WordList) = getindex(lookup(wl, prefix), 2);\n\n#=\n\n    BoggleFinder contains the words found on the Boggle board\n\n    and the array of possible words for the Boggle board.\n\n=#\nmutable struct BoggleFinder\n    wordlist::WordList\n    scores::AbstractVector\n    found::Dict\n    board::AbstractVector\n    neighbors::AbstractVector\n\n    function BoggleFinder(;board::Union{Nothing, AbstractVector}=nothing, fn::Union{Nothing, String}=nothing)\n        local wlfn::String;\n        if (typeof(fn) <: Nothing)\n            if (is_windows())\n                wlfn = \"..\\\\aima-data\\\\EN-text\\\\wordlist.txt\";\n            elseif (is_apple() || is_unix())\n                wlfn = \"../aima-data/EN-text/wordlist.txt\";\n            end\n        else\n            wlfn = fn;\n        end\n        nbf = new(WordList(wlfn),\n                vcat([0, 0, 0, 0, 1, 2, 3, 5], fill(11, 100)),\n                Dict{Any, Any}())\n        if (!(typeof(board) <: Nothing))\n            set_board(nbf, board=board);\n        end\n        return nbf;\n    end\nend\n\nfunction set_board(bf::BoggleFinder; board::Union{Nothing, AbstractVector}=nothing)\n    if (typeof(board) <: Nothing)\n        board = random_boggle();\n    end\n    bf.board = board;\n    bf.neighbors = boggle_neighbors(length(board));\n    bf.found = Dict{Any, Any}();\n    for i in 1:length(board)\n        lo::Int64, hi::Int64 = bf.wordlist.bounds[board[i]];\n        find(bf, lo, hi, i, [], \"\");\n    end\n    return bf;\nend\n\nfunction find(bf::BoggleFinder, lo::Int64, hi::Int64, i::Int64, visited::AbstractVector, prefix::String)\n    if i in visited\n        return nothing;\n    end\n    wordpos, is_word::Bool = lookup(bf.wordlist, prefix, lo=lo, hi=hi);\n    if (!(typeof(wordpos) <: Nothing))\n        if (is_word)\n            bf.found[prefix] = true;\n        end\n        push!(visited, i);\n        local c = bf.board[i];\n        if (c == 'Q')\n            c = \"QU\";\n        else\n            c = String([c]);\n        end\n        prefix = prefix * c;\n        for j in bf.neighbors[i]\n            find(bf, wordpos, hi, j, visited, prefix);\n        end\n        pop!(visited);\n    end\n    return nothing;\nend\n\nfunction words(bf::BoggleFinder)\n    return collect(keys(bf.found));\nend\n\nfunction score(bf::BoggleFinder)\n    return sum(collect(scores[len(w)] for w in words(bf)));\nend\n\nlength(bf::BoggleFinder) = length(bf.found);\n\n\"\"\"\n    boggle_hill_climbing(;board::Union{Nothing, AbstractVector}=nothing, ntimes::Int64=100, verbose::Bool=true)\n\nSolve the inverse Boggle by using hill climbing (initially use a random Boggle board and changing it).\n\nReturn the best Boggle board and its length.\n\"\"\"\nfunction boggle_hill_climbing(;board::Union{Nothing, AbstractVector}=nothing, ntimes::Int64=100, verbose::Bool=true)\n    finder = BoggleFinder();\n    if (typeof(board) <: Nothing)\n        board = random_boggle();\n    end\n    local best_length::Int64 = length(set_board(finder, board=board));\n    for t in 1:ntimes\n        i, old_char = mutate_boggle(board);\n        local new_length::Int64 = length(set_board(finder, board=board));\n        if (new_length > best_length)\n            best_length = new_length;\n            if (verbose)\n                println(best_length, \" \", t, \" \", board);\n            end\n        else\n            board[i] = old_char;\n        end\n    end\n    if (verbose)\n        print_boggle(board);\n    end\n    return board, best_length;\nend\n\nfunction mutate_boggle(board::AbstractArray)\n    local i::Int64 = rand(RandomDeviceInstance, 1:length(board));\n    local old_char::Char = board[i];\n    board[i] = rand(RandomDeviceInstance, collect(rand(RandomDeviceInstance, cubes16)));\n    return i, old_char;\nend\n\nfunction execute_searcher(searcher::Function, problem::T) where {T <: AbstractProblem}\n    local p = InstrumentedProblem(problem);\n    searcher(p);\n    return p;\nend\n\nfunction compare_searchers(problems::Array{T, 1},\n                            header::Array{String, 1};\n                            searchers::Array{Function, 1}=[breadth_first_tree_search,\n                                                        breadth_first_search,\n                                                        depth_first_graph_search,\n                                                        iterative_deepening_search,\n                                                        depth_limited_search,\n                                                        recursive_best_first_search]) where {T <: AbstractProblem}\n    local table = vcat(permutedims(hcat(header), [2, 1]), \n                        hcat(map(string, searchers), \n                            permutedims(reduce(hcat,\n                                collect(\n                                    collect(format_instrumented_results(execute_searcher(s, p)) for p in problems)\n                                for s in searchers)),\n                            [2,1])));\n    return table;\nend\n\nfunction beautify_node(n::Node)\n    return @sprintf(\"%s%s%s\", \"<Node \", string(n.state), \">\");\nend\n\n", "meta": {"hexsha": "da70a9fa6585ca56f7a10e3135fddbc1a18c2723", "size": 56665, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "search.jl", "max_stars_repo_name": "mikhail-j/aima-julia-aimacode-fork", "max_stars_repo_head_hexsha": "07ea7acd534266fd2b10ee0d4aeb847a88225ac0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 131, "max_stars_repo_stars_event_min_datetime": "2016-07-23T14:31:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T07:49:47.000Z", "max_issues_repo_path": "search.jl", "max_issues_repo_name": "mikhail-j/aima-julia-aimacode-fork", "max_issues_repo_head_hexsha": "07ea7acd534266fd2b10ee0d4aeb847a88225ac0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-03-30T19:04:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-08T16:35:26.000Z", "max_forks_repo_path": "search.jl", "max_forks_repo_name": "mikhail-j/aima-julia-aimacode-fork", "max_forks_repo_head_hexsha": "07ea7acd534266fd2b10ee0d4aeb847a88225ac0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 65, "max_forks_repo_forks_event_min_datetime": "2016-09-24T17:30:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T07:49:50.000Z", "avg_line_length": 35.4599499374, "max_line_length": 190, "alphanum_fraction": 0.6105179564, "num_tokens": 13881, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4416730056646256, "lm_q2_score": 0.1276526269811457, "lm_q1q2_score": 0.05638071943974791}}
{"text": "# test for unify.jl\r\nusing Test\r\n#include(\"utils.jl\") # for isinvar()\r\n#include(\"unify.jl\")\r\n\r\n#==\r\nidea:\r\nunify([:x,:y], \"P(x,y)\", \"P(x,f(x))\")\r\nunify([:x,:y], :(P(x,y)), :(P(x,f(x))))\r\n\r\n:(P(x,y)) == Meta.Meta.parse(\"P(x,y)\")\r\nyoucan readline(from console) and Meta.parse it to get the Expr\r\n==#\r\n\r\n@testset \"unify0 find a pair unifiable\" begin\r\n @test unify0([], :P, :P) ==()\r\n @test unify0([:x], :x, :x) ==()\r\n @test unify0([:x], :x, :a) ==(:x, :a)\r\n @test unify0([:x], :a, :x) ==(:x, :a)\r\n\r\n @test_throws ICMP unify0([:x], :P, :Q)\r\n @test_throws ICMP unify0([], :P, :Q)\r\n\r\n @test unify0([:x], :(P(x)), :(P(a))) == (:x, :a)\r\n @test unify0([:x], :(P(x)), :(P(f(a)))) == (:x,:(f(a)))\r\n @test unify0([:x], :(P(f(a))), :(P(f(x)))) == (:x,:a)\r\n @test unify0([:x], :(P(f(a))), :(P(x))) == (:x,:(f(a)))\r\n\r\n @test_throws ICMP unify0([:x],:(P(g(a))),:(P(f(x))))\r\n @test_throws ICMP unify0([:x],:(P(f(a))),:(P(f(b))))\r\n @test_throws ICMP unify0([:x],:(P(a)), :(P(f(b)))) \r\nend\r\n\r\n@testset \"unify1\" begin\r\n @test unify1([], :(f()), :(f()), []) == []\r\n @test unify1([:x], :x, :x, [:x]) == [:x]\r\n @test unify1([:x], :x, :a, [:x]) == [:a]\r\n @test unify1([:x], :x, :x, [:x]) == [:x]\r\n\r\n @test unify1([:x], :(f(x)), :(f(x)), [:x]) == [:x]\r\n @test unify1([:x], :(f(x)), :(f(a)), [:x]) == [:a]\r\n @test unify1([:x], :(f(x)), :(f(x)), [:b]) == [:b]\r\n\r\n @test_throws ICMP unify1([:x], :(f(x)), :(f(a)), [:b])\r\n\r\n @test unify1([:x,:y], :x , :(f(a)), [:x,:y]) == [:(f(a)), :y]\r\n @test unify1([:x,:y], :x , :(f(a)), [:x,:a]) == [:(f(a)), :a]\r\n\r\n @test unify1([:x,:y,:z], :a , :z, [:y,:z,:z]) == [:a,:a,:a]\r\n @test unify1([:x,:y,:z], :z , :a, [:y,:z,:z]) == [:a,:a,:a]\r\n\r\n @test unify1([:x,:y,:z], :z , :(f(a)), [:y,:z,:z]) == [:(f(a)),:(f(a)),:(f(a))]\r\n\r\n @test unify1([:x,:y], :x , :(f(a)), [:x,:x]) == [:(f(a)), :(f(a))]\r\n\r\n @test unify1([:x,:y,:z],:x,:(f(a)),[:y,:z,:z]) == [:(f(a)),:(f(a)),:(f(a))]\r\n @test unify1([:x,:y,:z],:z,:(f(a)),[:y,:z,:z]) == [:(f(a)),:(f(a)),:(f(a))]\r\n @test unify1([:x,:y,:z,:w,:u],:z,:(f(a)),[:y,:z,:w,:u,:u]) == [:(f(a)),:(f(a)),:(f(a)),:(f(a)),:(f(a))]\r\n\r\n @test unify1([:x,:y], :x , :(f(a)), [:y,:y]) == [:(f(a)), :(f(a))]\r\n\r\n @test unify1([:x,:y], :(f(x)), :(f(x)), [:x,:a]) == [:x,:a]\r\n @test unify1([:x,:y], :(f(x)), :(f(b)), [:x,:a]) == [:b,:a]\r\n\r\n @test unify1([:x,:y], :(f(b)), :(f(x)), [:x,:a]) == [:b,:a]\r\n @test unify1([:x,:y], :(f(g(b))), :(f(x)), [:x,:a]) == [:(g(b)),:a]\r\n\r\n @test unify1([:x,:y], :(f(x)), :(f(y)), [:b,:y]) == [:b,:b]\r\n\r\n @test unify1([:x,:y], :(f(g(b,y))), :(f(x)), [:x,:(h(a))]) == [:(g(b,y)),:(h(a))]\r\n @test unify1([:x,:y], :x, :p, [:y,:y]) == [:p,:p]\r\n\r\n @test unify1([:x,:y], :x, :y, [:p,:y]) == [:p,:p]\r\n\r\nend\r\n\r\n@testset \"unify\" begin\r\n @test_throws ICMP unify([],:(P()),:(Q()))\r\n\r\n @test unify([:x],:(),:())== [:x]\r\n @test unify([:x],:(P()),:(P())) == [:x]\r\n @test_throws ICMP unify([:x],:(P()),:(Q()))\r\n\r\n\r\n @test unify([:x],:(P(x)), :(P(x))) == [:x]\r\n @test unify([:y],:(P(x)), :(P(x))) == [:y]\r\n\r\n @test unify([:x],:(P(x)), :(P(y))) == [:y]\r\n @test unify([:y],:(P(x)), :(P(y))) == [:x]\r\n\r\n @testset  \"with number\" begin\r\n  @test unify([], :(f(2)), :(f(2))) == []\r\n  @test unify([:x], :(f(x)), :(f(2))) == [2]\r\n  @test unify([:x], :(f(3)), :(f(x))) == [3]\r\n  @test unify([:x,:y], :(f(3,y)), :(f(x,23))) == [3,23]\r\n end\r\n# in this case, x and y are constants\r\n @test_throws ICMP unify([:z],:(P(x)), :(P(y)))\r\n\r\n @test unify([],:(P(a)), :(P(a))) == []\r\n @test_throws ICMP unify([],:(P(a)), :(P(b)))\r\n\r\n (\u03c3=unify([:x,:y],:(P(x)),:(P(y))); @test \u03c3==[:x,:x] || \u03c3==[:y,:y])\r\n\r\n @test unify([:x,:y],:(P(x)),:(P(a))) == [:a,:y]\r\n @test unify([:x,:y],:(P(f(x))),:(P(f(a)))) == [:a,:y]\r\n\r\n @test unify([:x,:y],:(P(x)),:(P(f(a)))) == [:(f(a)),:y]\r\n (\u03c3 = unify([:x,:y],:(P(x,f(a,x))),:(P(y,f(a,y)))); @test \u03c3 == [:y,:y] || \u03c3 == [:y,:y])\r\n\r\n @test unify([:x,:y],:(P(x,f(x))),:(P(a,f(y)))) == [:a,:a]\r\n @test unify([:x,:y],:(P(x,y)),:(P(y,f(a)))) == [:(f(a)),:(f(a))]\r\n\r\n @test unify([:x,:y], :(f(g(b,y),h(a))), :(f(x,y))) == [:(g(b,h(a))),:(h(a))]\r\n\r\n @testset \"fixed point of subst\" begin\r\n   @test fp_subst([:x,:y],[:x,:y]) == [:x,:y]\r\n   @test fp_subst([:x,:y],[:a,:x]) == [:a,:a]\r\n\r\n   @test fp_subst([:x,:y],[:x,:x]) == [:x,:x]\r\n\r\n   @test fp_subst([:x,:y,:z],[:a,:(f(x,z)),:b]) == [:a,:(f(a,b)),:b]\r\n   @test fp_subst([:x,:y,:z,:w],[:x,:(f(g(z,w),h(w))),:(k(w)),:(m(a))]) == [:x,:(f(g(k(m(a)),m(a)),h(m(a)))),:(k(m(a))),:(m(a))]\r\n\r\n   @test fp_subst([:x,:y,:z,:u],[:(f(y)),:(g(z)),:(h(u)),:(k(a))]) == [:(f(g(h(k(a))))),:(g(h(k(a)))),:(h(k(a))),:(k(a))]\r\n\r\n   @test unify([:x,:y,:z,:u],:(P(x,y,z,u)),:(P(f(y),g(z),h(u),k(a)))) == [:(f(g(h(k(a))))),:(g(h(k(a)))),:(h(k(a))),:(k(a))]\r\n\r\n\r\n   @testset \"loop check for fixed point of subst\" begin\r\n     @test_throws Loop fp_subst([:x,:y],[:(f(y)),:x])\r\n     @test_throws Loop fp_subst([:x,:y,:z],[:a,:(f(x,z)),:(g(y))]) == [:a,:(f(a,b)),:b]\r\n     @test_throws Loop fp_subst([:x,:y,:z,:w],[:a,:(f(x,z)),:(g(w)),:(h(z))]) == [:a,:(f(a,b)),:b]\r\n   end\r\n end\r\n\r\n @testset \"unify intervention\" begin\r\n  @test unify([:x,:y],:(P(f(x),x)),:(P(y,a))) == [:a,:(f(a))]\r\n\r\n  @test unify([:x,:y,:w],:(P(f(w,x),w,x)),:(P(y,h(b),g(a)))) == [:(g(a)),:(f(h(b),g(a))),:(h(b))]\r\n\r\n  @test unify([:x,:y,:w,:u,:z,:n],:(P(f(w,z),w,g(n),z)),:(P(y,h(b),u,h(u)))) == [:x,:(f(h(b),h(g(n)))),:(h(b)),:(g(n)),:(h(g(n))),:n]\r\n\r\n  @test unify([:x,:y,:z,:w,:u,:n,:v],:((P(f(x),y,h(y,z),z,k(n)))),:((P(w,g(w),u,q(v),v)))) == [:x,:(g(f(x))),:(q(k(n))),:(f(x)),:(h(g(f(x)),q(k(n)))),:n,:(k(n))]\r\n\r\n  @test unify([:x,:y,:z,:w,:u,:n,:v],:((P(f(y,z),y,h(z),z,k(n)))),:((P(w,g(u,v),u,q(v),v)))) == [:x,:(g(h(q(k(n))),k(n))),:(q(k(n))),:(f(g(h(q(k(n))),k(n)),q(k(n)))),:(h(q(k(n)))),:n,:(k(n))]\r\n\r\n end\r\n\r\n @test unify([:x,:y], :(f(a=x,b=x)), :(f(a=p,b=y))) == [:p, :p]\r\n\r\n @testset \"unify loop test fail\" begin\r\n  @test_throws Loop unify([:x,:y],:(P(x)),:(P(f(x))))\r\n  @test_throws Loop unify([:x,:y],:(P(x,y)),:(P(y,f(y))))\r\n end\r\n\r\nend\r\n\r\n", "meta": {"hexsha": "d2312c02de5a56845c86cb6b473c463f0afa5a82", "size": 5897, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Prover/testunify.jl", "max_stars_repo_name": "sazare/cheapViews", "max_stars_repo_head_hexsha": "b235bafa85e5734c0aaa303d480cb7f4f97ab949", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Prover/testunify.jl", "max_issues_repo_name": "sazare/cheapViews", "max_issues_repo_head_hexsha": "b235bafa85e5734c0aaa303d480cb7f4f97ab949", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Prover/testunify.jl", "max_forks_repo_name": "sazare/cheapViews", "max_forks_repo_head_hexsha": "b235bafa85e5734c0aaa303d480cb7f4f97ab949", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.0880503145, "max_line_length": 192, "alphanum_fraction": 0.3823978294, "num_tokens": 2607, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11436853825632723, "lm_q1q2_score": 0.05629083762949418}}
{"text": "using OREnvironment\nusing ORInterface\nusing Test\n\n@testset \"Building Status\" begin\n    numConstraints = 5;\n    status = OREnvironment.constructStatus(numConstraints);\n    # conditions after creation\n    @test OREnvironment.is_feasible(status) == false;\n    @test OREnvironment.is_optimal(status) == false;\n    @test OREnvironment.get_objfunction(status) == 0.0;\n    for i in 1:numConstraints\n        @test OREnvironment.get_constraint_consumption(status, i) == 0.0;\n    end\n    # conditions after modification\n    OREnvironment.set_feasible!(status, true);\n    OREnvironment.set_optimal!(status, true);\n    OREnvironment.set_objfunction!(status, 16.0);\n    OREnvironment.set_constraint_consumption!(status, 1, 15.0);\n    @test OREnvironment.is_feasible(status) == true;\n    @test OREnvironment.is_optimal(status) == true;\n    @test OREnvironment.get_objfunction(status) == 16.0;\n    @test OREnvironment.get_constraint_consumption(status, 1) == 15.0;\nend\n\n@testset \"is_first_status_better\" begin\n    numConstraints = 5;\n    s1 = OREnvironment.constructStatus(numConstraints);\n    s2 = OREnvironment.constructStatus(numConstraints);\n    OREnvironment.set_objfunction!(s1, 34.0);\n    OREnvironment.set_objfunction!(s2, 3.0);\n\n    @testset \"feasibility not required\" begin\n        feasibility = false;\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, true);\n        OREnvironment.set_feasible!(s2, false);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, false);\n        OREnvironment.set_feasible!(s2, true);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, true);\n        OREnvironment.set_feasible!(s2, true);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n    end\n\n    @testset \"feasibility required\" begin\n        feasibility = true;\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0)\n        OREnvironment.set_feasible!(s1, false);\n        OREnvironment.set_feasible!(s2, false);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, true);\n        OREnvironment.set_feasible!(s2, false);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == true;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, false);\n        OREnvironment.set_feasible!(s2, true);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == true;\n\n        OREnvironment.set_objfunction!(s1, 34.0);\n        OREnvironment.set_objfunction!(s2, 3.0);\n        OREnvironment.set_feasible!(s1, true);\n        OREnvironment.set_feasible!(s2, true);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == true;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        OREnvironment.set_objfunction!(s2, 34.0);\n        @test OREnvironment.is_first_status_better(s1,s2,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:max, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s1,s2,:min, feasibility) == false;\n        @test OREnvironment.is_first_status_better(s2,s1,:min, feasibility) == false;\n    end\nend\n\n@testset \"Worst Value\" begin\n    @test OREnvironment.worst_value(:max) == -Inf;\n    @test OREnvironment.worst_value(:min) == Inf;\nend\n\n@testset \"update_status\" begin\n    numConstraints = 5;\n    # first status\n    s1 = OREnvironment.constructStatus(numConstraints);\n    OREnvironment.set_objfunction!(s1, 34.0);\n    OREnvironment.set_feasible!(s1, true);\n    OREnvironment.set_optimal!(s1, true);\n    OREnvironment.set_constraint_consumption!(s1, 1, 1.2);\n    OREnvironment.set_constraint_consumption!(s1, 2, 2.2);\n    OREnvironment.set_constraint_consumption!(s1, 3, 3.2);\n    OREnvironment.set_constraint_consumption!(s1, 4, 4.2);\n    OREnvironment.set_constraint_consumption!(s1, 5, 5.2);\n\n    @test OREnvironment.is_feasible(s1) == true;\n    @test OREnvironment.is_optimal(s1) == true;\n    @test OREnvironment.get_objfunction(s1) == 34.0;\n    @test OREnvironment.get_constraint_consumption(s1, 1) == 1.2;\n    @test OREnvironment.get_constraint_consumption(s1, 2) == 2.2;\n    @test OREnvironment.get_constraint_consumption(s1, 3) == 3.2;\n    @test OREnvironment.get_constraint_consumption(s1, 4) == 4.2;\n    @test OREnvironment.get_constraint_consumption(s1, 5) == 5.2;\n\n    # second status\n    s2 = OREnvironment.constructStatus(numConstraints);\n    @test OREnvironment.is_feasible(s2) == false;\n    @test OREnvironment.is_optimal(s2) == false;\n    @test OREnvironment.get_objfunction(s2) == 0.0;\n    for i in 1:numConstraints\n        @test OREnvironment.get_constraint_consumption(s2, i) == 0.0;\n    end\n\n    # update\n    OREnvironment.update_status!(s2, s1);\n\n    @test OREnvironment.is_feasible(s1) == true;\n    @test OREnvironment.is_optimal(s1) == true;\n    @test OREnvironment.get_objfunction(s1) == 34.0;\n    @test OREnvironment.get_constraint_consumption(s1, 1) == 1.2;\n    @test OREnvironment.get_constraint_consumption(s1, 2) == 2.2;\n    @test OREnvironment.get_constraint_consumption(s1, 3) == 3.2;\n    @test OREnvironment.get_constraint_consumption(s1, 4) == 4.2;\n    @test OREnvironment.get_constraint_consumption(s1, 5) == 5.2;\n\n    @test OREnvironment.is_feasible(s2) == true;\n    @test OREnvironment.is_optimal(s2) == true;\n    @test OREnvironment.get_objfunction(s2) == 34.0;\n    @test OREnvironment.get_constraint_consumption(s2, 1) == 1.2;\n    @test OREnvironment.get_constraint_consumption(s2, 2) == 2.2;\n    @test OREnvironment.get_constraint_consumption(s2, 3) == 3.2;\n    @test OREnvironment.get_constraint_consumption(s2, 4) == 4.2;\n    @test OREnvironment.get_constraint_consumption(s2, 5) == 5.2;\nend\n", "meta": {"hexsha": "7e192c4c669af2b8c3315a68919d34aa3d2ba190", "size": 9798, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Status.jl", "max_stars_repo_name": "DavidGarHeredia/OREnvironment.jl", "max_stars_repo_head_hexsha": "d3bc3c3bdbb0e282ff09ab4bab5a74fcf372b45f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/Status.jl", "max_issues_repo_name": "DavidGarHeredia/OREnvironment.jl", "max_issues_repo_head_hexsha": "d3bc3c3bdbb0e282ff09ab4bab5a74fcf372b45f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2021-07-24T13:31:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-24T17:33:36.000Z", "max_forks_repo_path": "test/Status.jl", "max_forks_repo_name": "DavidGarHeredia/OREnvironment.jl", "max_forks_repo_head_hexsha": "d3bc3c3bdbb0e282ff09ab4bab5a74fcf372b45f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.8412698413, "max_line_length": 85, "alphanum_fraction": 0.7081036946, "num_tokens": 2978, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207955, "lm_q2_score": 0.11436853523769844, "lm_q1q2_score": 0.0562908361437609}}
{"text": "using Pkg\n\n# Pkg.add(\"BinaryBuilder\")\nPkg.add(\"IJulia\")\nPkg.add(\"NamedArrays\")\nPkg.add(\"Feather\")\nPkg.add(\"DataFrames\")\nPkg.add(\"Conda\")\n# Problems with Plots > GR > GR_jll > Qt5Base:\n# - dylib not found\n# - if renamed 'binary' to .dylib >> issue with _NSAppearanceNameDarkAqua not found (this is only avail. on 10.14!))\n# cf. here: https://discourse.julialang.org/t/problem-with-qt-dependency-of-plots-jl/58765\n# cf. here: https://github.com/jheinen/GR.jl/issues/392\n# v1.1.2 is causing the dylib not found error (and subsequently the _NSAppearanceNameDarkAqua when renaming the framework binaries to dylib)\n# v1.1.1 is causing the _NSAppearanceNameDarkAqua not found error \nENV[\"JULIA_GR_PROVIDER\"] = \"GR\" \nPkg.add(Pkg.PackageSpec(;name=\"Plots\", version=\"1.1.0\"))\n\nPkg.add(\"WebIO\")\nPkg.add(\"Interact\")\n\nusing Plots\nusing IJulia\nusing WebIO\n\n# precompile the packages ? \nPkg.precompile()\n\n", "meta": {"hexsha": "63d9a2c7d3721e85516db7f0936c20cd589353bf", "size": 890, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "jlab_server-dist/julia/depot_init.jl", "max_stars_repo_name": "benbenz/jupyterlab-econ-desktop", "max_stars_repo_head_hexsha": "78ed9c8c6d84f520bb7edcfb5b2603f68447eb10", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "jlab_server-dist/julia/depot_init.jl", "max_issues_repo_name": "benbenz/jupyterlab-econ-desktop", "max_issues_repo_head_hexsha": "78ed9c8c6d84f520bb7edcfb5b2603f68447eb10", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "jlab_server-dist/julia/depot_init.jl", "max_forks_repo_name": "benbenz/jupyterlab-econ-desktop", "max_forks_repo_head_hexsha": "78ed9c8c6d84f520bb7edcfb5b2603f68447eb10", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6896551724, "max_line_length": 140, "alphanum_fraction": 0.7382022472, "num_tokens": 286, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207955, "lm_q2_score": 0.11436853372838407, "lm_q1q2_score": 0.056290835400894274}}
{"text": "n = parse(Int64, readline())\n\nfor i in 1:n\n    S = readline()\n\n    len = length(S)\n\n    odd = \"\"\n    even = \"\"\n\n    for j in 1:2:len\n        odd = odd*S[j]\n    end\n    for j in 2:2:len\n        even = even*S[j]\n    end\n    println(odd,\" \",even)\n    # println(even)\nend", "meta": {"hexsha": "0d19299bf11196a18bcd8f379eed13262991562d", "size": 267, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Hackerrank/30 Days of Code/Julia/day 06.jl", "max_stars_repo_name": "Next-Gen-UI/Code-Dynamics", "max_stars_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Hackerrank/30 Days of Code/Julia/day 06.jl", "max_issues_repo_name": "Next-Gen-UI/Code-Dynamics", "max_issues_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Hackerrank/30 Days of Code/Julia/day 06.jl", "max_forks_repo_name": "Next-Gen-UI/Code-Dynamics", "max_forks_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.0526315789, "max_line_length": 28, "alphanum_fraction": 0.4756554307, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.1329642436335826, "lm_q1q2_score": 0.05617800919975158}}
{"text": "# This file was generated, do not modify it. # hide\ndescribe(data)\n\n#\u00a0The describe() function shows that there are several features with missing values.", "meta": {"hexsha": "d432a2da1a5f7cfc9658e43d36a006030a540f3a", "size": 152, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/data/processing/code/ex6.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "__site/assets/data/processing/code/ex6.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "__site/assets/data/processing/code/ex6.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 38.0, "max_line_length": 84, "alphanum_fraction": 0.7697368421, "num_tokens": 33, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42250462027098473, "lm_q2_score": 0.1329642333263228, "lm_q1q2_score": 0.056178002911160624}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.0\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID(\"6e696c72-6542-2067-7265-42206c756150\"), \"AbstractPlutoDingetjes\")].Bonds.initial_value catch; b -> missing; end\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)\n        el\n    end\nend\n\n# \u2554\u2550\u2561 cab5e5da-8609-11ec-2b2c-2d98fe66bb99\nbegin\n    import Pkg\n\tENV[\"JULIA_MARGO_LOAD_PYPLOT\"] = \"no thank you\"\n    Pkg.activate(mktempdir())\n    Pkg.add([\n        Pkg.PackageSpec(name=\"Plots\", version=\"1\"),\n        Pkg.PackageSpec(name=\"ClimateMARGO\", version=v\"0.3.2\"),\n        Pkg.PackageSpec(name=\"PlutoUI\", version=\"0.7\"),\n        Pkg.PackageSpec(name=\"HypertextLiteral\", version=\"0.9\"),\n    ])\n\t\n\tusing Plots\n\tusing Plots.Colors\n\tusing ClimateMARGO\n\tusing ClimateMARGO.Models\n\tusing ClimateMARGO.Optimization\n\tusing ClimateMARGO.Diagnostics\n\tusing PlutoUI\n    using HypertextLiteral\n\t\n\tPlots.default(linewidth=5)\nend;\n\n# \u2554\u2550\u2561 cc8614a3-dd69-42d4-9954-44885bac93eb\nmd\"## Exploring uncertainty in climate forcing, feedbacks, and ocean heat uptake.\n\n*by Henri F. Drake*\n\"\n\n# \u2554\u2550\u2561 84ff8ba3-fd8a-4bc9-ab4e-7f763f98d34d\nmd\"##### Anthropogenic forcing\"\n\n# \u2554\u2550\u2561 8037ac4e-5c70-4aa7-9982-7442baaa83a0\nmd\"##### The climate response\"\n\n# \u2554\u2550\u2561 4575f6fd-d1ec-4fd1-bd56-268fab59a533\n\n\n# \u2554\u2550\u2561 7ea69497-8b83-45f9-93d3-bbce4c3c10b2\nreveal_box = @bind reveal CheckBox(default=false);\n\n# \u2554\u2550\u2561 71975b7f-e8b7-4097-b573-8e383bb83868\nif reveal\nmd\"The analytical scalings are:\n\n$\\text{TCR}\\propto \\frac{a}{\\lambda + \\kappa}, \\quad\\quad\\quad \\text{ECS} \\propto \\frac{a}{\\lambda}, \\quad\\quad\\quad \\tau = \\frac{C_{D}}{\\lambda}\\,\\frac{\\lambda + \\kappa}{\\kappa}.$\"\nelse\nmd\"Reveal solution: $(reveal_box)\"\nend\n\n# \u2554\u2550\u2561 8b17a78f-f3d8-4777-becd-01de58974de3\n\n\n# \u2554\u2550\u2561 56e32b2b-8830-4f2c-97ea-7bc5820dca1b\n\u03c1_slider = @bind \u03c1 Slider(0.:0.5:3, default=3, show_value=true);\n\n# \u2554\u2550\u2561 7e63eaf5-6841-4f2d-9388-427d9905fd2e\ndo_optimize_box = @bind do_optimize CheckBox(default=false);\n\n# \u2554\u2550\u2561 a442349e-f6ba-4841-93a6-d7ea7235371a\nif reveal\nmd\"#### Exercise 2: policy solutions\nExplore the sensivity of ''optimal'' policy solutions. Check the box: $(do_optimize_box)\n\"\nend\n\n# \u2554\u2550\u2561 053db221-7f7f-4358-915f-bcfb9f6b6783\nif do_optimize\n\tmd\"*Note the emissions reductions (and carbon dioxide removal) and corresponding warming reductions above!*\"\nend\n\n# \u2554\u2550\u2561 2f4a79c4-5455-4b8d-9a85-1aac1cdad3f7\nif do_optimize\n\tmd\"#### Exercise 3: Intergenerational (in)equity\n\nYou may have guessed the ''optimal'' level of warming depends strongly on economic preferences, e.g. how much the model values the welfare of future generations.\n  \nHow does the ''optimal'' policy response change as we decrease the ''discount rate'' towards intergenerational equity?\n\n $(\u03c1_slider) % per year\n\t\"\nend\n\n# \u2554\u2550\u2561 42a58da0-bffc-4fd1-ab7b-0a90b0664e4e\nbegin\n\tif do_optimize\n\t\tlast_year = 2800\n\telse\n\t\tlast_year = 4000\n\tend\n\tend_year_slider = @bind end_year Slider(2100:50:last_year, default=2200, show_value=true);\nend;\n\n# \u2554\u2550\u2561 97e66e74-7f38-4811-8d11-f69df3e5ce49\nmd\"\n#### Exercise 1: transient and equilibrium global warming\nLengthen the simulation's time horizon and graphically explore the scaling of the following emergent properties with the four key parameters above:\n\nA) the Transient Climate Response (TCR), i.e. the warming in 2150 when forcing stabilizes\n\nB) the Equilibrium Climate Sensivity (ECS), i.e. the warming when temperatures have equilibrated with\n\nC) the timescale $\\tau_{D}$ of the equilibration\n\n$(end_year_slider)\n\"\n\n# \u2554\u2550\u2561 56b88523-aa8e-42d7-8637-17b0a0c6fd22\n\n\n# \u2554\u2550\u2561 b72a5d5f-1806-4ae0-95e1-d3b1e4b845ce\n\n\n# \u2554\u2550\u2561 4fee292d-9cf1-40ec-b96d-0eacb0135cae\nmd\"# Appendix\"\n\n# \u2554\u2550\u2561 1a3b7318-d0a8-424a-96db-1deb03da716b\ncmip5_std = Dict(\"a\"=>0.9*log(2.), \"\u03bb\"=>0.31, \"\u03ba\"=>0.18, \"Cd\"=>62.)\n\n# \u2554\u2550\u2561 8742ea1c-54af-4a7d-8f41-1566a71c4d94\nblob(el, color = \"blue\") = @htl(\"\"\"<div style=\"\nbackground: $(color);\npadding: 1em 1em 1em 1em;\nborder-radius: 2em;\n\">$(el)</div>\"\"\");\n\n# \u2554\u2550\u2561 24d098eb-a548-4c80-935b-0e9bec229105\nfunction default_parameters(years)::ClimateModelParameters\n\tresult = deepcopy(ClimateMARGO.IO.included_configurations[\"default\"])\n\tresult.domain = years isa Domain ? years : Domain(step(years), first(years), last(years))\n\tresult.economics.baseline_emissions = ramp_emissions(result.domain)\n    result.economics.extra_CO\u2082 = zeros(size(result.economics.baseline_emissions))\n\treturn result\nend;\n\n# \u2554\u2550\u2561 226ae32f-36d4-4563-8af4-1aa9d16702c6\nbegin\n\t# Create default parameter values\n\tyears = 2020:6.0:end_year;\n\tdefault_params = default_parameters(years);\n\tdefault_params.physics.a = 6.9*log(2.);\n\tphys = default_params.physics\nend\n\n# \u2554\u2550\u2561 c834b860-866d-4f21-b51a-4fa301d17348\na_slider = @bind a Slider(\n\tround(phys.a-2*cmip5_std[\"a\"], digits=2)\n\t:round(4*cmip5_std[\"a\"]/100., digits=2)\n\t:round(phys.a+2*cmip5_std[\"a\"], digits=2),\n\tdefault=phys.a, show_value=true\n);\n\n# \u2554\u2550\u2561 27c8bca8-55f9-4289-a839-cebbbf696169\n\u03bb_slider = @bind \u03bb Slider(\n\tround(phys.B-2*cmip5_std[\"\u03bb\"], digits=2)\n\t:round(4*cmip5_std[\"\u03bb\"]/100., digits=2)\n\t:round(phys.B+2*cmip5_std[\"\u03bb\"], digits=2),\n\tdefault=phys.B, show_value=true\n);\n\n# \u2554\u2550\u2561 672287f1-f1bd-4899-9d7e-29a6a5646f10\n\u03ba_slider = @bind \u03ba Slider(\n\tround(phys.\u03ba-2*cmip5_std[\"\u03ba\"], digits=2)\n\t:round(4*cmip5_std[\"\u03ba\"]/100., digits=2)\n\t:round(phys.\u03ba+2*cmip5_std[\"\u03ba\"], digits=2),\n\tdefault=phys.\u03ba, show_value=true\n);\n\n# \u2554\u2550\u2561 58441503-2998-472e-b466-5d4c832f9042\nCd_slider = @bind Cd Slider(\n\tmax(Int(round(phys.Cd-2*cmip5_std[\"Cd\"], digits=0)), 20)\n\t:Int(round(4*cmip5_std[\"Cd\"]/100., digits=0))\n\t:Int(round(phys.Cd+2*cmip5_std[\"Cd\"], digits=0)),\n\tdefault=phys.Cd, show_value=true\n);\n\n# \u2554\u2550\u2561 9c7d7e6c-d2d9-4754-b2e3-72c3b9e0ec06\nlet\n\tblob(\n\t\t@htl(\"\"\"\n\t<h4>Energy Balance Model Parameters</h4>\n\t<h6>Defaults are CMIP5 multi-model mean; ranges are \u00b12\u03c3.</h6>\n\t<style>\n\t.controltable thead th,\n\t.controltable tbody td {\n\t  text-align: center;\n\t}\n\t.controltable input[type=range] {\n\t  width: 10em;\n\t}\n\t</style>\n\t<table class=\"controltable\">\n\t<thead>\n\t<th></th><th>Value</th>\n\t</thead>\n\t<tbody>\n\t\t<tr>\n\n\t\t<th>CO2 forcing parameter, a [W m\u207b\u00b2]</th>\n\t\t<td>$(a_slider)</td>\n\t\t</tr>\t\n\n\t\t\n\t\t<th>Feedback parameter, \u03bb [W m\u207b\u00b2 K\u207b\u00b9]</th>\n\t\t<td>$(\u03bb_slider)</td>\n\t\t</tr>\n\n\t\t<tr>\n\t\t<th> Heat uptake rate, \u03ba [W m\u207b\u00b2 K\u207b\u00b9]</th>\n\t\t<td>$(\u03ba_slider)</td>\n\t\t</tr>\n\t\t\n\t\t<tr>\n\t\t<th> Deep ocean heat capacity, C\u2080 [W yr m\u207b\u00b2 K\u207b\u00b9]</th>\n\t\t<td>$(Cd_slider)</td>\n\t\t</tr>\n\t\t\n\t\t\n\t</tbody>\n\t</table>\n\t\t\"\"\"),\n\t\t\"#c0ecff33\"\n\t)\nend\n\n# \u2554\u2550\u2561 8c1ddae9-a9dd-4556-9027-4f701a064604\nbegin\n\t# Overwrite defaults with slider values\n\tparams = deepcopy(default_params)\n\tparams.economics.\u03c1 = \u03c1/100. + params.economics.\u03b3;\n\tparams.economics.\u03b2 = 0.003;\n\tparams.physics.T0 = 1.0;\n\tparams.physics.a = a;\n\tparams.physics.B = \u03bb;\n\tparams.physics.\u03ba = \u03ba;\n\tparams.physics.Cd = Cd;\n\tm = ClimateModel(params);\n\tmax_deployment = Dict(\"mitigate\"=>1., \"remove\"=>0.5, \"geoeng\"=>0., \"adapt\"=>0.)\n\tif do_optimize\n\t\topt = optimize_controls!(\n\t\t\tm, obj_option=\"net_benefit\", max_deployment=max_deployment\n\t\t);\n\tend\n\tm;\nend\n\n# \u2554\u2550\u2561 1285f653-a4a8-4c5c-b395-2f3ffd24811d\nbegin\n\tp_emit = plot(t(m), effective_emissions(m), color=:grey, linewidth=3, alpha=0.5, label=\"baseline fossil growth\")\n\tif do_optimize\n\t\tplot!(p_emit, t(m), effective_emissions(m, M=true, R=true), linewidth=5, color=:deepskyblue2, label=\"''optimal'' policy\")\n\tend\n\tplot!(p_emit, t(m), 0. *t(m), linestyle=:dash, color=:black, alpha=0.3, linewidth = 2.5, label=\"\")\n\tplot!(p_emit, ylim=(-5., 15.), xlim=(t(m)[1], t(m)[end]))\n\tplot!(p_emit, ylabel=\"emissions [ppm/year]\", xlabel=\"year\")\n\tplot!(p_emit, size=(700, 225), margins=2.75Plots.Measures.mm)\nend\n\n# \u2554\u2550\u2561 13b0c090-ff52-4eca-98a5-1306f859c58f\nbegin\n\tp = plot(t(m), T(m), color=:grey, linewidth=3, alpha=0.5, label=\"baseline fossil growth\")\n\tif do_optimize\n\t\tplot!(t(m), T(m, M=true, R=true), linewidth=5, color=:deepskyblue2, label=\"''optimal'' policy\")\n\tend\n\tif minimum(T(m, M=true, R=true)) < 0.\n\t\tplot!(ylim=(-2, max(8, maximum(T(m, M=true, R=true)))), xlim=(t(m)[1], t(m)[end]))\n\telse\n\t\tplot!(ylim=(0, max(8, maximum(T(m, M=true, R=true)))), xlim=(t(m)[1], t(m)[end]))\n\tend\n\tplot!(ylabel=\"warming (relative to P-I)\", xlabel=\"year\")\n\tplot!(yticks=[-1., 0., 1., 1.5, 2., 2.5, 3., 4., 5., 6., 7., 8., 9, 10.])\n\tplot!(size=(700, 250), margins=2.75Plots.Measures.mm)\n\tplot!(legend=:topleft)\nend\n\n# \u2554\u2550\u2561 10befe5b-9e7c-40a6-9079-1ab5a380888b\nif do_optimize\n\tp_discount = plot(\n\t\tt(m),\n\t\t(1 .- (m.economics.\u03c1 - m.economics.\u03b3)).^((t(m) .- t(m)[1])) * 100., label=\"\"\n\t)\n\tplot!(xlabel=\"year\", ylabel=\"discount factor [%]\", ylim = (0, 100))\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500cc8614a3-dd69-42d4-9954-44885bac93eb\n# \u255f\u250084ff8ba3-fd8a-4bc9-ab4e-7f763f98d34d\n# \u255f\u25001285f653-a4a8-4c5c-b395-2f3ffd24811d\n# \u255f\u25008037ac4e-5c70-4aa7-9982-7442baaa83a0\n# \u255f\u250013b0c090-ff52-4eca-98a5-1306f859c58f\n# \u255f\u25009c7d7e6c-d2d9-4754-b2e3-72c3b9e0ec06\n# \u255f\u250097e66e74-7f38-4811-8d11-f69df3e5ce49\n# \u255f\u25004575f6fd-d1ec-4fd1-bd56-268fab59a533\n# \u255f\u250071975b7f-e8b7-4097-b573-8e383bb83868\n# \u255f\u25007ea69497-8b83-45f9-93d3-bbce4c3c10b2\n# \u255f\u2500a442349e-f6ba-4841-93a6-d7ea7235371a\n# \u255f\u2500053db221-7f7f-4358-915f-bcfb9f6b6783\n# \u255f\u25008b17a78f-f3d8-4777-becd-01de58974de3\n# \u255f\u25002f4a79c4-5455-4b8d-9a85-1aac1cdad3f7\n# \u255f\u250010befe5b-9e7c-40a6-9079-1ab5a380888b\n# \u255f\u250042a58da0-bffc-4fd1-ab7b-0a90b0664e4e\n# \u255f\u250056e32b2b-8830-4f2c-97ea-7bc5820dca1b\n# \u255f\u25007e63eaf5-6841-4f2d-9388-427d9905fd2e\n# \u255f\u250056b88523-aa8e-42d7-8637-17b0a0c6fd22\n# \u255f\u2500b72a5d5f-1806-4ae0-95e1-d3b1e4b845ce\n# \u255f\u25004fee292d-9cf1-40ec-b96d-0eacb0135cae\n# \u2560\u2550cab5e5da-8609-11ec-2b2c-2d98fe66bb99\n# \u2560\u2550226ae32f-36d4-4563-8af4-1aa9d16702c6\n# \u2560\u25508c1ddae9-a9dd-4556-9027-4f701a064604\n# \u2560\u25501a3b7318-d0a8-424a-96db-1deb03da716b\n# \u255f\u25008742ea1c-54af-4a7d-8f41-1566a71c4d94\n# \u255f\u2500c834b860-866d-4f21-b51a-4fa301d17348\n# \u255f\u250027c8bca8-55f9-4289-a839-cebbbf696169\n# \u255f\u2500672287f1-f1bd-4899-9d7e-29a6a5646f10\n# \u255f\u250058441503-2998-472e-b466-5d4c832f9042\n# \u255f\u250024d098eb-a548-4c80-935b-0e9bec229105\n", "meta": {"hexsha": "6a9cd632985e651978f614e002af0ce93fad2413", "size": 10045, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ebm.jl", "max_stars_repo_name": "ClimateMARGO/interactive", "max_stars_repo_head_hexsha": "9351c5d33e4a17f0272bd397f7bb429ab50c1ecd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-09-24T18:15:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-07T21:21:09.000Z", "max_issues_repo_path": "ebm.jl", "max_issues_repo_name": "ClimateMARGO/interactive", "max_issues_repo_head_hexsha": "9351c5d33e4a17f0272bd397f7bb429ab50c1ecd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2022-02-03T13:31:45.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-11T20:47:51.000Z", 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{"text": "## RESAMPLING STRATEGIES\n\nabstract type ResamplingStrategy <: MLJType end\nshow_as_constructed(::Type{<:ResamplingStrategy}) = true\n\n# resampling strategies are `==` if they have the same type and their\n# field values are `==`:\nfunction ==(s1::S, s2::S) where S <: ResamplingStrategy\n    return all(getfield(s1, fld) == getfield(s2, fld) for fld in fieldnames(S))\nend\n\n# fallbacks:\ntrain_test_pairs(s::ResamplingStrategy, rows, X, y, w) =\n    train_test_pairs(s, rows, X, y)\ntrain_test_pairs(s::ResamplingStrategy, rows, X, y) =\n    train_test_pairs(s, rows, y)\ntrain_test_pairs(s::ResamplingStrategy, rows, y) =\n    train_test_pairs(s, rows)\n\n\n# Helper to interpret rng, shuffle in case either is `nothing` or if\n# `rng` is an integer:\nfunction shuffle_and_rng(shuffle, rng)\n    if rng isa Integer\n        rng = MersenneTwister(rng)\n    end\n\n    if shuffle === nothing\n        shuffle = ifelse(rng===nothing, false, true)\n    end\n\n    if rng === nothing\n        rng = Random.GLOBAL_RNG\n    end\n\n    return shuffle, rng\nend\n\n\"\"\"\n    holdout = Holdout(; fraction_train=0.7,\n                         shuffle=nothing,\n                         rng=nothing)\n\nHoldout resampling strategy, for use in `evaluate!`, `evaluate` and in tuning.\n\n    train_test_pairs(holdout, rows)\n\nReturns the pair `[(train, test)]`, where `train` and `test` are\nvectors such that `rows=vcat(train, test)` and\n`length(train)/length(rows)` is approximatey equal to fraction_train`.\n\nPre-shuffling of `rows` is controlled by `rng` and `shuffle`. If `rng`\nis an integer, then the `Holdout` keyword constructor resets it to\n`MersenneTwister(rng)`. Otherwise some `AbstractRNG` object is\nexpected.\n\nIf `rng` is left unspecified, `rng` is reset to `Random.GLOBAL_RNG`,\nin which case rows are only pre-shuffled if `shuffle=true` is\nspecified.\n\n\"\"\"\nstruct Holdout <: ResamplingStrategy\n    fraction_train::Float64\n    shuffle::Bool\n    rng::Union{Int,AbstractRNG}\n\n    function Holdout(fraction_train, shuffle, rng)\n        0 < fraction_train < 1 ||\n            error(\"`fraction_train` must be between 0 and 1.\")\n        return new(fraction_train, shuffle, rng)\n    end\nend\n\n# Keyword Constructor\nHoldout(; fraction_train::Float64=0.7, shuffle=nothing, rng=nothing) =\n    Holdout(fraction_train, shuffle_and_rng(shuffle, rng)...)\n\nfunction train_test_pairs(holdout::Holdout, rows)\n\n    train, test = partition(rows, holdout.fraction_train,\n                          shuffle=holdout.shuffle, rng=holdout.rng)\n    return [(train, test),]\n\nend\n\n\n\"\"\"\n    cv = CV(; nfolds=6,  shuffle=nothing, rng=nothing)\n\nCross-validation resampling strategy, for use in `evaluate!`,\n`evaluate` and tuning.\n\n    train_test_pairs(cv, rows)\n\nReturns an `nfolds`-length iterator of `(train, test)` pairs of\nvectors (row indices), where each `train` and `test` is a sub-vector\nof `rows`. The `test` vectors are mutually exclusive and exhaust\n`rows`. Each `train` vector is the complement of the corresponding\n`test` vector. With no row pre-shuffling, the order of `rows` is\npreserved, in the sense that `rows` coincides precisely with the\nconcatenation of the `test` vectors, in the order they are\ngenerated. All but the last `test` vector have equal length.\n\nPre-shuffling of `rows` is controlled by `rng` and `shuffle`. If `rng`\nis an integer, then the `CV` keyword constructor resets it to\n`MersenneTwister(rng)`. Otherwise some `AbstractRNG` object is\nexpected.\n\nIf `rng` is left unspecified, `rng` is reset to `Random.GLOBAL_RNG`,\nin which case rows are only pre-shuffled if `shuffle=true` is\nexplicitly specified.\n\n\"\"\"\nstruct CV <: ResamplingStrategy\n    nfolds::Int\n    shuffle::Bool\n    rng::Union{Int,AbstractRNG}\n    function CV(nfolds, shuffle, rng)\n        nfolds > 1 || error(\"Must have nfolds > 1. \")\n        return new(nfolds, shuffle, rng)\n    end\nend\n\n# Constructor with keywords\nCV(; nfolds::Int=6,  shuffle=nothing, rng=nothing) =\n    CV(nfolds, shuffle_and_rng(shuffle, rng)...)\n\nfunction train_test_pairs(cv::CV, rows)\n\n    n_observations = length(rows)\n    nfolds = cv.nfolds\n\n    if cv.shuffle\n        rows=shuffle!(cv.rng, collect(rows))\n    end\n\n    # number of observations per fold\n    k = floor(Int, n_observations/nfolds)\n    k > 0 || error(\"Inusufficient data for $nfolds-fold cross-validation.\\n\"*\n                   \"Try reducing nfolds. \")\n\n    # define the (trainrows, testrows) pairs:\n    firsts = 1:k:((nfolds - 1)*k + 1) # itr of first `test` rows index\n    seconds = k:k:(nfolds*k)          # itr of last  `test` rows index\n\n    ret = map(1:nfolds) do k\n        f = firsts[k]\n        s = seconds[k]\n        k < nfolds || (s = n_observations)\n        return (vcat(rows[1:(f - 1)], rows[(s + 1):end]), # trainrows\n                rows[f:s])                                # testrows\n    end\n\n    return ret\nend\n\n\"\"\"\n    stratified_cv = StratifiedCV(; nfolds=6,\n                                   shuffle=false,\n                                   rng=Random.GLOBAL_RNG)\n\nStratified cross-validation resampling strategy, for use in\n`evaluate!`, `evaluate` and in tuning. Applies only to classification\nproblems (`OrderedFactor` or `Multiclass` targets).\n\n    train_test_pairs(stratified_cv, rows, y)\n\nReturns an `nfolds`-length iterator of `(train, test)` pairs of\nvectors (row indices) where each `train` and `test` is a sub-vector of\n`rows`. The `test` vectors are mutually exclusive and exhaust\n`rows`. Each `train` vector is the complement of the corresponding\n`test` vector.\n\nUnlike regular cross-validation, the distribution of the levels of the\ntarget `y` corresponding to each `train` and `test` is constrained, as\nfar as possible, to replicate that of `y[rows]` as a whole.\n\nSpecifically, the data is split into a number of groups on which `y`\nis constant, and each individual group is resampled according to the\nordinary cross-validation strategy `CV(nfolds=nfolds)`. To obtain the\nfinal `(train, test)` pairs of row indices, the per-group pairs are\ncollated in such a way that each collated `train` and `test` respects\nthe original order of `rows` (after shuffling, if `shuffle=true`).\n\nPre-shuffling of `rows` is controlled by `rng` and `shuffle`. If `rng`\nis an integer, then the `StratifedCV` keyword constructor resets it to\n`MersenneTwister(rng)`. Otherwise some `AbstractRNG` object is\nexpected.\n\nIf `rng` is left unspecified, `rng` is reset to `Random.GLOBAL_RNG`,\nin which case rows are only pre-shuffled if `shuffle=true` is\nexplicitly specified.\n\n\"\"\"\nstruct StratifiedCV <: ResamplingStrategy\n    nfolds::Int\n    shuffle::Bool\n    rng::Union{Int,AbstractRNG}\n    function StratifiedCV(nfolds, shuffle, rng)\n        nfolds > 1 || error(\"Must have nfolds > 1. \")\n        return new(nfolds, shuffle, rng)\n    end\nend\n\n# Constructor with keywords\nStratifiedCV(; nfolds::Int=6,  shuffle=nothing, rng=nothing) =\n       StratifiedCV(nfolds, shuffle_and_rng(shuffle, rng)...)\n\nfunction train_test_pairs(stratified_cv::StratifiedCV, rows, X, y)\n\n    n_observations = length(rows)\n    nfolds = stratified_cv.nfolds\n\n    if stratified_cv.shuffle\n        rows=shuffle!(stratified_cv.rng, collect(rows))\n    end\n\n    st = scitype(y)\n    st <: AbstractArray{<:Finite} ||\n        error(\"Supplied target has scitpye $st but stratified \"*\n              \"cross-validation applies only to classification problems. \")\n\n\n    freq_given_level = countmap(y[rows])\n    minimum(values(freq_given_level)) >= nfolds ||\n        error(\"The number of observations for which the target takes on a \"*\n              \"given class must, for each class, exceed `nfolds`. Try \"*\n              \"reducing `nfolds`. \")\n\n    levels_seen = keys(freq_given_level) |> collect\n\n    cv = CV(nfolds=nfolds)\n\n    # the target is constant on each stratum, a subset of `rows`:\n    class_rows = [rows[y[rows] .== c] for c in levels_seen]\n\n    # get the cv train/test pairs for each level:\n    train_test_pairs_per_level = (train_test_pairs(cv, class_rows[m])\n                              for m in eachindex(levels_seen))\n\n    # just the train rows in each level:\n    trains_per_level = map(x -> first.(x),\n                           train_test_pairs_per_level)\n\n    # just the test rows in each level:\n    tests_per_level  = map(x -> last.(x),\n                                train_test_pairs_per_level)\n\n    # for each fold, concatenate the train rows over levels:\n    trains_per_fold = map(x->vcat(x...), zip(trains_per_level...))\n\n    # for each fold, concatenate the test rows over levels:\n    tests_per_fold = map(x->vcat(x...), zip(tests_per_level...))\n\n    # restore ordering specified by rows:\n    trains_per_fold = map(trains_per_fold) do train\n        filter(in(train), rows)\n    end\n    tests_per_fold = map(tests_per_fold) do test\n        filter(in(test), rows)\n    end\n\n    # re-assemble:\n    return zip(trains_per_fold, tests_per_fold) |> collect\n\nend\n\n\n## EVALUATION TYPE\n\nconst PerformanceEvaluation = NamedTuple{(:measure, :measurement,\n                               :per_fold, :per_observation)}\n# pretty printing:\nround3(x) = round(x, sigdigits=3)\n_short(v::Vector{<:Real}) = MLJBase.short_string(v)\n_short(v::Vector) = string(\"[\", join(_short.(v), \", \"), \"]\")\n_short(::Missing) = missing\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", e::PerformanceEvaluation)\n    data = hcat(e.measure, round3.(e.measurement),\n                [round3.(v) for v in e.per_fold])\n    header = [\"_.measure\", \"_.measurement\", \"_.per_fold\"]\n    PrettyTables.pretty_table(io, data, header;\n                              header_crayon=Crayon(bold=false),\n                              alignment=:l)\n    println(io, \"_.per_observation = $(_short(e.per_observation))\")\nend\n\nfunction Base.show(io::IO, e::PerformanceEvaluation)\n    summary = Tuple(round3.(e.measurement))\n    print(io, \"PerformanceEvaluation$summary\")\nend\n\n\n## EVALUATION METHODS\n\nfunction _check_measure(model, measure, y, operation, override)\n\n    override && (return nothing)\n\n    T = scitype(y)\n\n    T == Unknown && (return nothing)\n    target_scitype(measure) == Unknown && (return nothing)\n    prediction_type(measure) == :unknown && (return nothing)\n\n    avoid = \"\\nTo override measure checks, set check_measure=false. \"\n\n    T <: target_scitype(measure) ||\n        throw(ArgumentError(\n            \"\\nscitype of target = $T but target_scitype($measure) = \"*\n            \"$(target_scitype(measure)).\"*avoid))\n\n    if model isa Probabilistic\n        if operation == predict\n            if prediction_type(measure) != :probabilistic\n                suggestion = \"\"\n                if target_scitype(measure) <: Finite\n                    suggestion = \"\\nPerhaps you want to set operation=\"*\n                    \"predict_mode. \"\n                elseif target_scitype(measure) <: Continuous\n                    suggestion = \"\\nPerhaps you want to set operation=\"*\n                    \"predict_mean or operation=predict_median. \"\n                else\n                    suggestion = \"\"\n                end\n                throw(ArgumentError(\n                   \"\\n$model <: Probabilistic but prediction_type($measure) = \"*\n                      \":$(prediction_type(measure)). \"*suggestion*avoid))\n            end\n        end\n    end\n\n    model isa Deterministic && prediction_type(measure) != :deterministic &&\n        throw(ArgumentError(\"$model <: Deterministic but \"*\n                            \"prediction_type($measure) =\"*\n              \":$(prediction_type(measure)).\"*avoid))\n\n    return nothing\n\nend\n\nfunction _process_weights_measures(weights, measures, mach,\n                                   operation, verbosity, check_measure)\n\n    if measures === nothing\n        candidate = default_measure(mach.model)\n        candidate ===  nothing && error(\"You need to specify measure=... \")\n        _measures = [candidate, ]\n    elseif !(measures isa AbstractVector)\n        _measures = [measures, ]\n    else\n        _measures = measures\n    end\n\n    y = mach.args[2]\n\n    [ _check_measure(mach.model, m, y, operation, !check_measure)\n     for m in _measures ]\n\n    if weights != nothing\n        weights isa AbstractVector{<:Real} ||\n            throw(ArgumentError(\"`weights` must be a `Real` vector.\"))\n        length(weights) == nrows(y) ||\n            throw(DimensionMismatch(\"`weights` and target \"*\n                                    \"have different lengths. \"))\n        _weights = weights\n    elseif  length(mach.args) == 3\n        verbosity < 1 ||\n            @info \"Passing machine sample weights to any supported measures. \"\n        _weights = mach.args[3]\n    else\n        _weights = weights\n    end\n\n    return _weights, _measures\n\nend\n\n\"\"\"\n    evaluate!(mach,\n              resampling=CV(),\n              measure=nothing,\n              weights=nothing,\n              operation=predict,\n              n = 1,\n              acceleration=default_resource(),\n              force=false,\n              verbosity=1)\n\nEstimate the performance of a machine `mach` wrapping a supervised\nmodel in data, using the specified `resampling` strategy (defaulting\nto 6-fold cross-validation) and `measure`, which can be a single\nmeasure or vector.\n\nDo `subtypes(MLJ.ResamplingStrategy)` to obtain a list of available\nresampling strategies. If `resampling` is not an object of type\n`MLJ.ResamplingStrategy`, then a vector of pairs (of the form\n`(train_rows, test_rows)` is expected. For example, setting\n\n    resampling = [(1:100), (101:200)),\n                   (101:200), (1:100)]\n\ngives two-fold cross-validation using the first 200 rows of data.\n\nThe resampling strategy is applied repeatedly if `repeats > 1`. For\n`resampling = CV(nfolds=5)`, for example, this generates a total of\n`5n` test folds for evaluation and subsequent aggregation.\n\nIf `resampling isa MLJ.ResamplingStrategy` then one may optionally\nrestrict the data used in evaluation by specifying `rows`.\n\nAn optional `weights` vector may be passed for measures that support\nsample weights (`MLJ.supports_weights(measure) == true`), which is\nignored by those that don't.\n\n*Important:* If `mach` already wraps sample weights `w` (as in `mach =\nmachine(model, X, y, w)`) then these weights, which are used for\n*training*, are automatically passed to the measures for\nevaluation. However, for evaluation purposes, any `weights` specified\nas a keyword argument will take precedence over `w`.\n\nUser-defined measures are supported; see the manual for details.\n\nIf no measure is specified, then `default_measure(mach.model)` is\nused, unless this default is `nothing` and an error is thrown.\n\nThe `acceleration` keyword argument is used to specify the compute resource (a\nsubtype of `ComputationalResources.AbstractResource`) that will be used to\naccelerate/parallelize the resampling operation.\n\nAlthough evaluate! is mutating, `mach.model` and `mach.args` are\nuntouched.\n\n### Return value\n\nA property-accessible object of type `PerformanceEvaluation` with\nthese properties:\n\n- `measure`: the vector of specified measures\n\n- `measurements`: the corresponding measurements, aggregated across the\n  test folds using the aggregation method defined for each measure (do\n  `aggregation(measure)` to inspect)\n\n- `per_fold`: a vector of vectors of individual test fold evaluations\n  (one vector per measure)\n\n- `per_observation`: a vector of vectors of individual observation\n  evaluations of those measures for which\n  `reports_each_observation(measure)` is true, which is otherwise\n  reported `missing`.\n\nSee also [`evaluate`](@ref)\n\n\"\"\"\nfunction evaluate!(mach::Machine{<:Supervised};\n                   resampling=CV(),\n                   measures=nothing, measure=measures, weights=nothing,\n                   operation=predict, acceleration=default_resource(),\n                   rows=nothing, repeats=1, force=false,\n                   check_measure=true, verbosity=1)\n\n    # this method just checks validity of options, preprocess the\n    # weights and measures, and dispatches a strategy-specific\n    # `evaluate!`\n\n    repeats > 0 || error(\"Need n > 0. \")\n\n    if resampling isa TrainTestPairs\n        if rows !== nothing\n            error(\"You cannot specify `rows` unless `resampling \"*\n                  \"isa MLJ.ResamplingStrategy` is true. \")\n        end\n        if repeats != 1 && verbosity > 0\n            @warn \"repeats > 1 not supported unless \"*\n            \"`resampling<:ResamplingStrategy. \"\n        end\n    end\n\n    _weights, _measures =\n        _process_weights_measures(weights, measure, mach,\n                                  operation, verbosity, check_measure)\n\n    if verbosity >= 0 && weights !== nothing\n        unsupported = filter(_measures) do m\n            !supports_weights(m)\n        end\n        if !isempty(unsupported)\n            unsupported_as_string = string(unsupported[1])\n            unsupported_as_string *=\n                reduce(*, [string(\", \", m) for m in unsupported[2:end]])\n                @warn \"Sample weights ignored in evaluations of the following\"*\n            \" measures, as unsupported: \\n$unsupported_as_string \"\n        end\n    end\n\n    evaluate!(mach, resampling, _weights, rows, verbosity, repeats,\n                   _measures, operation, acceleration, force)\n\nend\n\n\"\"\"\n    evaluate(model, X, y; measure=nothing, options...)\n    evaluate(model, X, y, w; measure=nothing, options...)\n\nEvaluate the performance of a supervised model `model` on input data\n`X` and target `y`, optionally specifying sample weights `w` for\ntraining, where supported. The same weights are passed to measures\nthat support sample weights, unless this behaviour is overridden by\nexplicitly specifying the option `weights=...`.\n\nSee the machine version `evaluate!` for the complete list of options.\n\n\"\"\"\nevaluate(model::Supervised, args...; kwargs...) =\n    evaluate!(machine(model, args...); kwargs...)\n\nconst AbstractRow = Union{AbstractVector{<:Integer}, Colon}\nconst TrainTestPair = Tuple{AbstractRow,AbstractRow}\nconst TrainTestPairs = AbstractVector{<:TrainTestPair}\n\nfunction _evaluate!(func::Function, res::CPU1, nfolds, verbosity)\n    p = Progress(nfolds + 1, dt=0, desc=\"Evaluating over $nfolds folds: \",\n                 barglyphs=BarGlyphs(\"[=> ]\"), barlen=25, color=:yellow)\n    verbosity > 0 && next!(p)\n    return reduce(vcat, (func(k, p, verbosity) for k in 1:nfolds))\nend\nfunction _evaluate!(func::Function, res::CPUProcesses, nfolds, verbosity)\n    # TODO: use pmap here ?:\n    return @distributed vcat for k in 1:nfolds\n        func(k)\n    end\nend\n@static if VERSION >= v\"1.3.0-DEV.573\"\n    function _evaluate!(func::Function, res::CPUThreads, nfolds, verbosity)\n        task_vec = [Threads.@spawn func(k) for k in 1:nfolds]\n        return reduce(vcat, fetch.(task_vec))\n    end\nend\n\n# Evaluation when resampling is a TrainTestPairs (core evaluator):\nfunction evaluate!(mach::Machine, resampling, weights,\n                   rows, verbosity, repeats,\n                   measures, operation, acceleration, force)\n\n    # Note: `rows` and `n` are ignored here\n\n    resampling isa TrainTestPairs ||\n        error(\"`resampling` must be an \"*\n              \"`MLJ.ResamplingStrategy` or tuple of pairs \"*\n              \"of the form `(train_rows, test_rows)`\")\n\n    X = mach.args[1]\n    y = mach.args[2]\n\n    nfolds = length(resampling)\n\n    nmeasures = length(measures)\n\n    function get_measurements(k)\n        train, test = resampling[k]\n        fit!(mach; rows=train, verbosity=verbosity-1, force=force)\n        Xtest = selectrows(X, test)\n        ytest = selectrows(y, test)\n        if weights == nothing\n            wtest = nothing\n        else\n            wtest = weights[test]\n        end\n        yhat = operation(mach, Xtest)\n        return [value(m, yhat, Xtest, ytest, wtest)\n                for m in measures]\n    end\n    function get_measurements(k, p, verbosity) # p = progress meter\n        ret = get_measurements(k)\n        verbosity > 0 && next!(p)\n        return ret\n    end\n\n    measurements_flat = if acceleration isa CPUProcesses\n        ## TODO: progress meter for distributed case\n        if verbosity > 0\n            @info \"Distributing cross-validation computation \" *\n                  \"among $(nworkers()) workers.\"\n        end\n    end\n    measurements_flat =\n        _evaluate!(get_measurements, acceleration, nfolds, verbosity)\n\n    # in the following rows=folds, columns=measures:\n    measurements_matrix = permutedims(\n        reshape(measurements_flat, (nmeasures, nfolds)))\n\n    # measurements for each observation:\n    per_observation = map(1:nmeasures) do k\n        m = measures[k]\n        if reports_each_observation(m)\n            [measurements_matrix[:,k]...]\n        else\n            missing\n        end\n    end\n\n    # measurements for each fold:\n    per_fold = map(1:nmeasures) do k\n        m = measures[k]\n        if reports_each_observation(m)\n            broadcast(MLJBase.aggregate, per_observation[k], [m,])\n        else\n            [measurements_matrix[:,k]...]\n        end\n    end\n\n    # overall aggregates:\n    per_measure = map(1:nmeasures) do k\n        m = measures[k]\n        MLJBase.aggregate(per_fold[k], m)\n    end\n\n    ret = (measure=measures,\n           measurement=per_measure,\n           per_fold=per_fold,\n           per_observation=per_observation)\n\n    return ret\n\nend\n\nfunction actual_rows(rows, N, verbosity)\n    unspecified_rows = (rows === nothing)\n    _rows = unspecified_rows ? (1:N) : rows\n    unspecified_rows || @info \"Creating subsamples from a subset of all rows. \"\n    return _rows\nend\n\n# Evaluation when resampling is a ResamplingStrategy:\nfunction evaluate!(mach::Machine, resampling::ResamplingStrategy,\n                   weights, rows, verbosity, repeats, args...)\n\n    y = mach.args[2]\n    _rows = actual_rows(rows, length(y), verbosity)\n\n    repeated_train_test_pairs =\n        vcat([train_test_pairs(resampling, _rows, mach.args...)\n              for i in 1:repeats]...)\n\n    return evaluate!(mach::Machine,\n                     repeated_train_test_pairs,\n                     weights, nothing, verbosity, repeats, args...)\n\nend\n\n\n## RESAMPLER - A MODEL WRAPPER WITH `evaluate` OPERATION\n\n\"\"\"\n    resampler = Resampler(model=ConstantRegressor(),\n                          resampling=CV(),\n                          measure=nothing,\n                          weights=nothing,\n                          operation=predict,\n                          repeats = 1,\n                          acceleration=default_resource(),\n                          check_measure=true)\n\nResampling model wrapper, used internally by the `fit` method of\n`TunedModel` instances. See [`evaluate!](@ref) for options. Not\nintended for general use.\n\nGiven a machine `mach = machine(resampler, args...)` one obtains a\nperformance evaluation of the specified `model`, performed according\nto the prescribed `resampling` strategy and other parameters, using\ndata `args...`, by calling `fit!(mach)` followed by\n`evaluate(mach)`. The advantage over using `evaluate(model, X, y)` is\nthat the latter call always calls `fit` on the `model` but\n`fit!(mach)` only calls `update` after the first call.\n\nThe sample `weights` are passed to the specified performance\nmeasures that support weights for evaluation.\n\n*Important:* If `weights` are left unspecified, then any weight vector\n`w` used in constructing the resampler machine, as in\n`resampler_machine = machine(resampler, X, y, w)` (which is then used\nin *training* the model) will also be used in evaluation.\n\n\"\"\"\nmutable struct Resampler{S,M<:Supervised} <: Supervised\n    model::M\n    resampling::S # resampling strategy\n    measure\n    weights::Union{Nothing,AbstractVector{<:Real}}\n    operation\n    acceleration::AbstractResource\n    check_measure::Bool\n    repeats::Int\nend\n\nMLJBase.is_wrapper(::Type{<:Resampler}) = true\nMLJBase.supports_weights(::Type{<:Resampler{<:Any,M}}) where M =\n    supports_weights(M)\nMLJBase.is_pure_julia(::Type{<:Resampler}) = true\n\nfunction MLJBase.clean!(resampler::Resampler)\n    warning = \"\"\n    if resampler.measure === nothing\n        measure = default_measure(resampler.model)\n        if measure === nothing\n            error(\"No default measure known for $(resampler.model). \"*\n                  \"You must specify measure=... \")\n        else\n            warning *= \"No `measure` specified. \"*\n            \"Setting `measure=$measure`. \"\n        end\n    end\n    return warning\nend\n\nfunction Resampler(; model=ConstantRegressor(), resampling=CV(),\n            measure=nothing, weights=nothing, operation=predict,\n            acceleration=default_resource(), check_measure=true, repeats=1)\n\n    resampler = Resampler(model, resampling, measure, weights, operation,\n                          acceleration, check_measure, repeats)\n    message = MLJBase.clean!(resampler)\n    isempty(message) || @warn message\n\n    return resampler\n\nend\n\nfunction MLJBase.fit(resampler::Resampler, verbosity::Int, args...)\n\n    mach = machine(resampler.model, args...)\n\n    weights, measures =\n        _process_weights_measures(resampler.weights, resampler.measure,\n                                  mach, resampler.operation,\n                                  verbosity, resampler.check_measure)\n\n    fitresult = evaluate!(mach, resampler.resampling,\n                          weights, nothing, verbosity - 1, resampler.repeats,\n                          measures, resampler.operation,\n                          resampler.acceleration, false)\n\n    cache = (mach, deepcopy(resampler.resampling))\n    report = NamedTuple()\n\n    return fitresult, cache, report\n\nend\n\n# in special case of holdout, we can reuse the underlying model's\n# machine, provided the training_fraction has not changed:\nfunction MLJBase.update(resampler::Resampler{Holdout},\n                        verbosity::Int, fitresult, cache, args...)\n\n    old_mach, old_resampling = cache\n\n    if old_resampling.fraction_train == resampler.resampling.fraction_train\n        mach = old_mach\n    else\n        mach = machine(resampler.model, args...)\n        cache = (mach, deepcopy(resampler.resampling))\n    end\n\n    weights, measures =\n        _process_weights_measures(resampler.weights, resampler.measure,\n                                  mach, resampler.operation,\n                                  verbosity, resampler.check_measure)\n    mach.model = resampler.model\n    fitresult = evaluate!(mach, resampler.resampling,\n                          weights, nothing, verbosity - 1, resampler.repeats,\n                          measures, resampler.operation,\n                          resampler.acceleration, false)\n\n\n    report = NamedTuple\n\n    return fitresult, cache, report\n\nend\n\nMLJBase.input_scitype(::Type{<:Resampler{S,M}}) where {S,M} =\n    MLJBase.input_scitype(M)\nMLJBase.target_scitype(::Type{<:Resampler{S,M}}) where {S,M} =\n    MLJBase.target_scitype(M)\nMLJBase.package_name(::Type{<:Resampler}) = \"MLJBase\"\n\nMLJBase.load_path(::Type{<:Resampler}) = \"MLJBase.Resampler\"\n\nevaluate(resampler::Resampler, fitresult) = fitresult\n\nfunction evaluate(machine::AbstractMachine{<:Resampler})\n    if isdefined(machine, :fitresult)\n        return evaluate(machine.model, machine.fitresult)\n    else\n        throw(error(\"$machine has not been trained.\"))\n    end\nend\n\n", "meta": {"hexsha": "7ee7571cf0ecb257dd4d3910d0f8a3f02e202608", "size": 27063, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/resampling.jl", "max_stars_repo_name": "darenasc/MLJBase.jl", "max_stars_repo_head_hexsha": "88d70061e2823528026998c2019d25b885dc37e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/resampling.jl", "max_issues_repo_name": "darenasc/MLJBase.jl", "max_issues_repo_head_hexsha": "88d70061e2823528026998c2019d25b885dc37e4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/resampling.jl", "max_forks_repo_name": "darenasc/MLJBase.jl", "max_forks_repo_head_hexsha": "88d70061e2823528026998c2019d25b885dc37e4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7443890274, "max_line_length": 80, "alphanum_fraction": 0.6503344049, "num_tokens": 6509, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769844, "lm_q2_score": 0.11596071825396675, "lm_q1q2_score": 0.05616906248067606}}
{"text": "module Queues\n\nexport Queue, enqueue!, dequeue!, peek\n\ninclude(\"LinkedLists.jl\")\nusing .LinkedLists\n\nstruct Queue{T}\n  list::LinkedList{T}\nend\n\nQueue(t::Type{T}) where {T} = Queue{T}(LinkedList{T}())\nQueue{T}() where {T} = Queue{T}(LinkedList{T}())\nQueue() = Queue{Any}(LinkedList{Any}())\n\nfunction enqueue!(q::Queue{T}, elem::T) where {T}\n  pushfirst!(q.list, Node(elem))\n  q\nend\n\nfunction dequeue!(q::Queue{T}) where {T}\n  isempty(q) && throw(ArgumentError(\"Queue must not be empty\"))\n  pop!(q.list).data\nend\n\nfunction peek(q::Queue{T}) where {T}\n  isempty(q) && throw(ArgumentError(\"Queue must not be empty\"))\n  (q.list[end]).data\nend\n\nfunction Base.length(q::Queue{T}) where {T}\n  length(q.list)\nend\n\nfunction Base.isempty(q::Queue{T}) where {T}\n  length(q) == 0\nend\n\nfunction Base.empty!(q::Queue{T}) where {T}\n  empty!(q.list)\n\n  q\nend\n\nfunction Base.eltype(q::Queue{T}) where {T}\n  eltype(q.list)\nend\n\nfunction Base.iterate(q::Queue{T}, state::Union{Queue,Nothing} = q) where {T}\n  (state == nothing || isempty(q)) && return nothing\n  dequeue!(q), q\nend\n\nend\n", "meta": {"hexsha": "72a244b9a4d15b0ee283916a2a27c0e9c770e8f5", "size": 1066, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter 6/Queues.jl", "max_stars_repo_name": "lytemar/Hands-On-Data-Structures-and-Algorithms-with-Julia", "max_stars_repo_head_hexsha": "c3a0dfbc6dba61fc8e8d5062a69bdafa62f78543", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2019-08-14T03:23:55.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-04T06:36:56.000Z", "max_issues_repo_path": "Chapter 6/Queues.jl", "max_issues_repo_name": "lytemar/Hands-On-Data-Structures-and-Algorithms-with-Julia", "max_issues_repo_head_hexsha": "c3a0dfbc6dba61fc8e8d5062a69bdafa62f78543", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter 6/Queues.jl", "max_forks_repo_name": "lytemar/Hands-On-Data-Structures-and-Algorithms-with-Julia", "max_forks_repo_head_hexsha": "c3a0dfbc6dba61fc8e8d5062a69bdafa62f78543", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-11-15T16:43:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-06T07:48:52.000Z", "avg_line_length": 19.3818181818, "max_line_length": 77, "alphanum_fraction": 0.6641651032, "num_tokens": 324, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.11757213199952937, "lm_q1q2_score": 0.05603248564314816}}
{"text": "#=\nQueue\n=#\n\nusing SimpleDataStructures\n\nsq = SimpleQueue{Int}()\n\nenqueue!(sq, 1)\nenqueue!(sq, 2)\ndequeue!(sq)\nenqueue!(sq, 3)\nenqueue!(sq, 4)\ndequeue!(sq)\ndequeue!(sq)\ndequeue!(sq)\ndequeue!(sq)\n", "meta": {"hexsha": "589259b07381f15fd381b42c23dd0375ca61b047", "size": 195, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/queue.jl", "max_stars_repo_name": "harryscholes/SimpleDataStructures.jl", "max_stars_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/queue.jl", "max_issues_repo_name": "harryscholes/SimpleDataStructures.jl", "max_issues_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/queue.jl", "max_forks_repo_name": "harryscholes/SimpleDataStructures.jl", "max_forks_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.8333333333, "max_line_length": 26, "alphanum_fraction": 0.6717948718, "num_tokens": 72, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.12252321732428778, "lm_q1q2_score": 0.05600986130276655}}
{"text": "# This file was generated, do not modify it. # hide\nusing ScientificTypes\ndata = coerce(boston, :Tax=>Continuous, :Rad=>Continuous);", "meta": {"hexsha": "84d3d0f6747da8991f07a7abf5d84af4e1f86f27", "size": 132, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "assets/pages/isl/lab-3/code/ex4.jl", "max_stars_repo_name": "vdayanand/MLJTutorials", "max_stars_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "assets/pages/isl/lab-3/code/ex4.jl", "max_issues_repo_name": "vdayanand/MLJTutorials", "max_issues_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assets/pages/isl/lab-3/code/ex4.jl", "max_forks_repo_name": "vdayanand/MLJTutorials", "max_forks_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.0, "max_line_length": 58, "alphanum_fraction": 0.75, "num_tokens": 34, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091957, "lm_q2_score": 0.11279540926528922, "lm_q1q2_score": 0.055957106529156975}}
{"text": "# See learn_Plotly.jl for more examples\r\n# http://juliaplots.org/PlotlyJS.jl/stable/\r\n\r\nusing PlotlyJS, HTTP, CSV\r\nimport JSON\r\nimport DataFrames\r\n#import RDatasets\r\nimport WebIO\r\n#import UUIDs\r\nimport DefaultApplication\r\n\r\n\"\"\"\r\n    dataset(dir, name)\r\n\r\nReplace the dataset function from RDatasets.  It stopped working.\r\n\"\"\"\r\nfunction dataset(dir, name)\r\n    url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/$(dir)/$(name).csv\"\r\n    df = urldownload(url)\r\n\r\n    return df\r\nend\r\n\r\nfunction urldownload(url)\r\n    body = HTTP.get(url).body\r\n    csv = CSV.File(body)\r\n    df = DataFrames.DataFrame(csv)\r\n\r\n    return df\r\nend\r\n\r\n#\r\n# Building Blocks\r\n#\r\n\r\n\"\"\"\r\n\r\nhttp://juliaplots.org/PlotlyJS.jl/stable/building_traces_layouts/\r\n\"\"\"\r\nfunction sec_Traces()\r\n    # http://juliaplots.org/PlotlyJS.jl/stable/building_traces_layouts/\r\n    t = PlotlyJS.scatter(\r\n        x=[1,2,3,4,5],\r\n        y=[1,6,3,6,1],\r\n        mode=\"markers+text\",\r\n        name=\"Team A\",\r\n        text=[\"A1\", \"A2\", \"A3\", \"A4\", \"A5\"],\r\n        textposition=\"top center\",\r\n        textfont_family=\"Raleway, sans-serif\", # _ for nested attributes\r\n        marker_size=12\r\n    )\r\n    #print(JSON.json(t,2))\r\n\r\n    # Access attributes\r\n    @show getindex(t, :text)\r\n    @show t[:text]\r\n    @show t[\"textfont.family\"]\r\n    @show t[\"textfont_family\"]\r\n\r\n    # Setting attributes\r\n    t[\"marker_color\"] =\"red\"\r\n    t[\"line_width\"] = 5\r\n\r\n    return t\r\nend\r\n\r\nfunction sec_Layouts()\r\n    l = Layout(\r\n        title=\"Penguins\",\r\n        xaxis_range=[0, 42.0],\r\n        xaxis_title=\"fish\",\r\n        xaxis_showgrid=true,\r\n        yaxis_title=\"Weight\",\r\n        yaxis_showgrid=true,\r\n        legend_x=0.7,\r\n        legend_y=1.15\r\n    )\r\n    \r\n    # Using attr()\r\n    l = Layout(\r\n        title=\"Penguins\",\r\n        xaxis=attr(range=[0, 42.0], title=\"fish\", showgrid=true),\r\n        yaxis=attr(title=\"Weight\", showgrid=true),\r\n        legend_x=0.7,\r\n        legend_y=1.15\r\n    )\r\n    return l\r\nend\r\n\r\nfunction sec_DataFrames()\r\n    iris = RDatasets.dataset(\"datasets\", \"iris\")\r\n    trace = scatter(\r\n        iris,\r\n        x=:SepalLength,\r\n        y=:SepalWidth,\r\n        marker_color=:red\r\n    )\r\n\r\n    # TODO: group\r\n    return iris\r\nend\r\n\r\n#\r\n# Putting it Together\r\n#\r\n\r\n\"\"\"\r\n\r\nhttp://juliaplots.org/PlotlyJS.jl/stable/syncplots/#Plot\r\n\"\"\"\r\nfunction sec_Plot()\r\n    iris = dataset(\"datasets\", \"iris\")\r\n    layout = Layout(title=\"Iris\", width=800, height=600)\r\n    plot = Plot(iris, x=Symbol(\"Sepal.Length\"), y=Symbol(\"Sepal.Width\"), layout,\r\n        mode=\"markers\", marker_size=8,\r\n        group=:Species\r\n    )\r\n    @info typeof(plot)\r\n\r\n    return plot\r\nend\r\n#display(sec_Plot())\r\n\r\nfunction sec_SyncPlots1()\r\n    p = sec_Plot()\r\n    sp = plot(p)\r\n    display(sp)\r\n\r\n    # Call back\r\n    # only works when launch from console\r\n    obs = WebIO.on(sp[\"hover\"]) do data\r\n        println(\"\\nYou hovered over $data\")\r\n    end\r\nend\r\n\r\nfunction sec_SyncPlots2()\r\n    p = plot(rand(10,4))\r\n    display(p)\r\n    WebIO.on(p[\"hover\"]) do data\r\n        println(\"\\nYou hovered over: $data\")\r\n    end\r\nend\r\n\r\nfunction sec_SyncPlots3()\r\n    colors = [fill(\"red\", 10), fill(\"blue\", 10)]\r\n    symbols = [fill(\"circle\", 10), fill(\"circle\", 10)]\r\n\r\n    ys = [rand(10), rand(10)]\r\n    # list comprehension\r\n    scatters = [\r\n        scatter(y=y, marker=attr(color=c, symbol=s, size=15), line_color=c[1])\r\n        for (y,c,s) in zip(ys, colors, symbols)\r\n    ]\r\n    p = plot(scatters)\r\n    display(p)\r\n   \r\n    # Callback\r\n    WebIO.on(p[\"click\"]) do data\r\n        colors = (fill(\"red\", 10), fill(\"blue\", 10))\r\n        symbols = (fill(\"circle\", 10), fill(\"circle\", 10))\r\n        for point in data[\"points\"]\r\n            # zero-offset -> unit-offset\r\n            colors[point[\"curveNumber\"] + 1][point[\"pointIndex\"] + 1] = \"gold\"\r\n            symbols[point[\"curveNumber\"] + 1][point[\"pointIndex\"] + 1] = \"star\"\r\n        end\r\n        restyle!(p, marker_color=colors, marker_symbol=symbols)\r\n    end\r\n\r\n    nothing\r\nend\r\n\r\n\r\n#\r\n# Styles\r\n#\r\n\r\nfunction sec_Cyclers()\r\n    style = Style(global_trace=attr(\r\n        marker_color=Cycler([\"green\",\"red\"]),\r\n        line_color=\"blue\"\r\n        )\r\n    )\r\n    #p = plot(rand(10,3), style=style)\r\n    p = plot(\r\n    [\r\n        scatter(y=rand(4)),\r\n        scatter(y=rand(4), marker_color=\"black\"),\r\n        scatter(y=rand(4)),\r\n        scatter(y=rand(4)),\r\n    ],\r\n    style=style\r\n    )   \r\n    display(p)\r\nend\r\n\r\n#\r\n# 3D\r\n# \r\nusing PlotlyJS, DataFrames, Colors, Distributions, LinearAlgebra\r\n\r\nfunction random_line()\r\n    n = 400\r\n    rw() = cumsum(randn(n))\r\n    trace1 = scatter3d(;x=rw(),y=rw(), z=rw(), mode=\"lines\",\r\n                        marker=attr(color=\"#1f77b4\", size=12, symbol=\"circle\",\r\n                                    line=attr(color=\"rgb(0,0,0)\", width=0)),\r\n                        line=attr(color=\"#1f77b4\", width=1))\r\n    trace2 = scatter3d(;x=rw(),y=rw(), z=rw(), mode=\"lines\",\r\n                        marker=attr(color=\"#9467bd\", size=12, symbol=\"circle\",\r\n                                    line=attr(color=\"rgb(0,0,0)\", width=0)),\r\n                        line=attr(color=\"rgb(44, 160, 44)\", width=1))\r\n    trace3 = scatter3d(;x=rw(),y=rw(), z=rw(), mode=\"lines\",\r\n                        marker=attr(color=\"#bcbd22\", size=12, symbol=\"circle\",\r\n                                    line=attr(color=\"rgb(0,0,0)\", width=0)),\r\n                        line=attr(color=\"#bcbd22\", width=1))\r\n    layout = Layout(autosize=false, width=500, height=500,\r\n                    margin=attr(l=0, r=0, b=0, t=65))\r\n    plot([trace1, trace2, trace3], layout)\r\nend\r\n#display(random_line())\r\n\r\nfunction topo_surface()\r\n    z = Vector[[27.80985, 49.61936, 83.08067, 116.6632, 130.414, 150.7206, 220.1871,\r\n                156.1536, 148.6416, 203.7845, 206.0386, 107.1618, 68.36975, 45.3359,\r\n                49.96142, 21.89279, 17.02552, 11.74317,   14.75226, 13.6671, 5.677561,\r\n                3.31234, 1.156517, -0.147662],\r\n               [27.71966, 48.55022, 65.21374, 95.27666, 116.9964, 133.9056, 152.3412,\r\n                151.934, 160.1139, 179.5327, 147.6184, 170.3943, 121.8194, 52.58537,\r\n                33.08871, 38.40972, 44.24843, 69.5786, 4.019351, 3.050024, 3.039719,\r\n                2.996142, 2.967954, 1.999594],\r\n               [30.4267, 33.47752, 44.80953, 62.47495, 77.43523, 104.2153, 102.7393, 137.0004,\r\n                186.0706, 219.3173, 181.7615, 120.9154, 143.1835, 82.40501, 48.47132,\r\n                74.71461, 60.0909, 7.073525, 6.089851, 6.53745, 6.666096, 7.306965, 5.73684,\r\n                3.625628],\r\n               [16.66549, 30.1086, 39.96952, 44.12225, 59.57512, 77.56929, 106.8925,\r\n                166.5539, 175.2381, 185.2815, 154.5056, 83.0433, 62.61732, 62.33167,\r\n                60.55916, 55.92124, 15.17284, 8.248324, 36.68087, 61.93413, 20.26867,\r\n                68.58819, 46.49812, 0.2360095],\r\n               [8.815617, 18.3516, 8.658275, 27.5859, 48.62691, 60.18013, 91.3286,\r\n                145.7109, 116.0653, 106.2662, 68.69447, 53.10596, 37.92797, 47.95942,\r\n                47.42691, 69.20731, 44.95468, 29.17197, 17.91674, 16.25515, 14.65559,\r\n                17.26048, 31.22245, 46.71704],\r\n               [6.628881, 10.41339, 24.81939, 26.08952, 30.1605, 52.30802, 64.71007,\r\n                76.30823, 84.63686, 99.4324, 62.52132, 46.81647, 55.76606, 82.4099,\r\n                140.2647, 81.26501, 56.45756, 30.42164, 17.28782, 8.302431, 2.981626,\r\n                2.698536, 5.886086, 5.268358],\r\n               [21.83975, 6.63927, 18.97085, 32.89204, 43.15014, 62.86014, 104.6657,\r\n                130.2294, 114.8494, 106.9873, 61.89647, 55.55682, 86.80986, 89.27802,\r\n                122.4221, 123.9698, 109.0952, 98.41956, 77.61374, 32.49031, 14.67344,\r\n                7.370775, 0.03711011, 0.6423392],\r\n               [53.34303, 26.79797, 6.63927, 10.88787, 17.2044, 56.18116, 79.70141,\r\n                90.8453, 98.27675, 80.87243, 74.7931, 75.54661, 73.4373, 74.11694, 68.1749,\r\n                46.24076, 39.93857, 31.21653, 36.88335, 40.02525, 117.4297, 12.70328,\r\n                1.729771, 0],\r\n               [25.66785, 63.05717, 22.1414, 17.074, 41.74483, 60.27227, 81.42432, 114.444,\r\n                102.3234, 101.7878, 111.031, 119.2309, 114.0777, 110.5296, 59.19355,\r\n                42.47175, 14.63598, 6.944074, 6.944075, 27.74936, 0, 0, 0.09449376, 0.07732264],\r\n               [12.827, 69.20554, 46.76293, 13.96517, 33.88744, 61.82613, 84.74799,\r\n                121.122, 145.2741, 153.1797, 204.786, 227.9242, 236.3038, 228.3655,\r\n                79.34425, 25.93483, 6.944074, 6.944074, 6.944075, 7.553681, 0, 0, 0, 0],\r\n               [0, 68.66396, 59.0435, 33.35762, 47.45282, 57.8355, 78.91689, 107.8275,\r\n                168.0053, 130.9597, 212.5541, 165.8122, 210.2429, 181.1713, 189.7617,\r\n                137.3378, 84.65395, 8.677168, 6.956576, 8.468093, 0, 0, 0, 0],\r\n               [0, 95.17499, 80.03818, 59.89862, 39.58476, 50.28058, 63.81641, 80.61302,\r\n                66.37824, 198.7651, 244.3467, 294.2474, 264.3517, 176.4082, 60.21857,\r\n                77.41475, 53.16981, 56.16393, 6.949235, 7.531059, 3.780177, 0, 0, 0],\r\n               [0, 134.9879, 130.3696, 96.86325, 75.70494, 58.86466, 57.20374, 55.18837,\r\n                78.128, 108.5582, 154.3774, 319.1686, 372.8826, 275.4655, 130.2632, 54.93822,\r\n                25.49719, 8.047439, 8.084393, 5.115252, 5.678269, 0, 0, 0],\r\n               [0, 48.08919, 142.5558, 140.3777, 154.7261, 87.9361, 58.11092, 52.83869,\r\n                67.14822, 83.66798, 118.9242, 150.0681, 272.9709, 341.1366, 238.664, 190.2,\r\n                116.8943, 91.48672, 14.0157, 42.29277, 5.115252, 0, 0, 0],\r\n               [0, 54.1941, 146.3839, 99.48143, 96.19411, 102.9473, 76.14089, 57.7844,\r\n                47.0402, 64.36799, 84.23767, 162.7181, 121.3275, 213.1646, 328.482,\r\n                285.4489, 283.8319, 212.815, 164.549, 92.29631, 7.244015, 1.167, 0, 0],\r\n               [0, 6.919659, 195.1709, 132.5253, 135.2341, 89.85069, 89.45549, 60.29967,\r\n                50.33806, 39.17583, 59.06854, 74.52159, 84.93402, 187.1219, 123.9673,\r\n                103.7027, 128.986, 165.1283, 249.7054, 95.39966, 10.00284, 2.39255, 0, 0],\r\n               [0, 21.73871, 123.1339, 176.7414, 158.2698, 137.235, 105.3089, 86.63255, 53.11591,\r\n                29.03865, 30.40539, 39.04902, 49.23405, 63.27853, 111.4215, 101.1956,\r\n                40.00962, 59.84565, 74.51253, 17.06316, 2.435141, 2.287471, -0.0003636982, 0],\r\n               [0, 0, 62.04672, 136.3122, 201.7952, 168.1343, 95.2046, 58.90624, 46.94091,\r\n                49.27053, 37.10416, 17.97011, 30.93697, 33.39257, 44.03077, 55.64542,\r\n                78.22423, 14.42782, 9.954997, 7.768213, 13.0254, 21.73166, 2.156372,\r\n                0.5317867],\r\n               [0, 0, 79.62993, 139.6978, 173.167, 192.8718, 196.3499, 144.6611, 106.5424,\r\n                57.16653, 41.16107, 32.12764, 13.8566, 10.91772, 12.07177, 22.38254,\r\n                24.72105, 6.803666, 4.200841, 16.46857, 15.70744, 33.96221, 7.575688,\r\n                -0.04880907],\r\n               [0, 0, 33.2664, 57.53643, 167.2241, 196.4833, 194.7966, 182.1884, 119.6961,\r\n                73.02113, 48.36549, 33.74652, 26.2379, 16.3578, 6.811293, 6.63927, 6.639271,\r\n                8.468093, 6.194273, 3.591233, 3.81486, 8.600739, 5.21889, 0],\r\n               [0, 0, 29.77937, 54.97282, 144.7995, 207.4904, 165.3432, 171.4047, 174.9216,\r\n                100.2733, 61.46441, 50.19171, 26.08209, 17.18218, 8.468093, 6.63927,\r\n                6.334467, 6.334467, 5.666687, 4.272203, 0, 0, 0, 0],\r\n               [0, 0, 31.409, 132.7418, 185.5796, 121.8299, 185.3841, 160.6566, 116.1478,\r\n                118.1078, 141.7946, 65.56351, 48.84066, 23.13864, 18.12932, 10.28531,\r\n                6.029663, 6.044627, 5.694764, 3.739085, 3.896037, 0, 0, 0],\r\n               [0, 0, 19.58994, 42.30355, 96.26777, 187.1207, 179.6626, 221.3898, 154.2617,\r\n                142.1604, 148.5737, 67.17937, 40.69044, 39.74512, 26.10166, 14.48469,\r\n                8.65873, 3.896037, 3.571392, 3.896037, 3.896037, 3.896037, 1.077756, 0],\r\n               [0.001229679, 3.008948, 5.909858, 33.50574, 104.3341, 152.2165, 198.1988,\r\n                191.841, 228.7349, 168.1041, 144.2759, 110.7436, 57.65214, 42.63504,\r\n                27.91891, 15.41052, 8.056102, 3.90283, 3.879774, 3.936718, 3.968634,\r\n                0.1236256, 3.985531, -0.1835741],\r\n               [0, 5.626141, 7.676256, 63.16226, 45.99762, 79.56688, 227.311, 203.9287,\r\n                172.5618, 177.1462, 140.4554, 123.9905, 110.346, 65.12319, 34.31887,\r\n                24.5278, 9.561069, 3.334991, 5.590495, 5.487353, 5.909499, 5.868994,\r\n                5.833817, 3.568177]]\r\n    trace = surface(z=z)\r\n    layout = Layout(title=\"Mt. Bruno Elevation\", autosize=false, width=500,\r\n                    height=500, margin=attr(l=65, r=50, b=65, t=90))\r\n    plot(trace, layout)\r\nend\r\n#display(topo_surface())\r\nfunction multiple_surface()\r\n    z1 = Vector[[8.83, 8.89, 8.81, 8.87, 8.9, 8.87],\r\n                [8.89, 8.94, 8.85, 8.94, 8.96, 8.92],\r\n                [8.84, 8.9, 8.82, 8.92, 8.93, 8.91],\r\n                [8.79, 8.85, 8.79, 8.9, 8.94, 8.92],\r\n                [8.79, 8.88, 8.81, 8.9, 8.95, 8.92],\r\n                [8.8, 8.82, 8.78, 8.91, 8.94, 8.92],\r\n                [8.75, 8.78, 8.77, 8.91, 8.95, 8.92],\r\n                [8.8, 8.8, 8.77, 8.91, 8.95, 8.94],\r\n                [8.74, 8.81, 8.76, 8.93, 8.98, 8.99],\r\n                [8.89, 8.99, 8.92, 9.1, 9.13, 9.11],\r\n                [8.97, 8.97, 8.91, 9.09, 9.11, 9.11],\r\n                [9.04, 9.08, 9.05, 9.25, 9.28, 9.27],\r\n                [9, 9.01, 9, 9.2, 9.23, 9.2],\r\n                [8.99, 8.99, 8.98, 9.18, 9.2, 9.19],\r\n                [8.93, 8.97, 8.97, 9.18, 9.2, 9.18]]\r\n    z2 = map(x->x.+1, z1)\r\n    z3 = map(x->x.-1, z1)\r\n    trace1 = surface(z=z1, colorscale=\"Viridis\")\r\n    trace2 = surface(z=z2, showscale=false, opacity=0.9, colorscale=\"Viridis\")\r\n    trace3 = surface(z=z3, showscale=false, opacity=0.9, colorscale=\"Viridis\")\r\n    plot([trace1, trace2, trace3])\r\nend\r\n#display(multiple_surface())\r\n\r\nfunction clustering_alpha_shapes()\r\n    # load data\r\n    iris = dataset(\"datasets\", \"iris\")\r\n    nms = unique(iris[!,:Species])\r\n    colors = [RGB(0.89, 0.1, 0.1), RGB(0.21, 0.50, 0.72), RGB(0.28, 0.68, 0.3)]\r\n\r\n    data = GenericTrace[]\r\n\r\n    for (i, nm) in enumerate(nms)\r\n        df = iris[iris[!,:Species] .== nm, :]\r\n        x=df[!,Symbol(\"Sepal.Length\")]\r\n        y=df[!,Symbol(\"Sepal.Width\")]\r\n        z=df[!,Symbol(\"Petal.Length\")]\r\n        trace = scatter3d(;name=nm, mode=\"markers\",\r\n                           marker_size=3, marker_color=colors[i], marker_line_width=0,\r\n                           x=x, y=y, z=z)\r\n        push!(data, trace)\r\n\r\n        cluster = mesh3d(;color=colors[i], opacity=0.3, x=x, y=y, z=z)\r\n        push!(data, cluster)\r\n    end\r\n\r\n    # notice the nested attrs to create complex JSON objects\r\n    layout = Layout(width=800, height=550, autosize=false, title=\"Iris dataset\",\r\n                    scene=attr(xaxis=attr(gridcolor=\"rgb(255, 255, 255)\",\r\n                                          zerolinecolor=\"rgb(255, 255, 255)\",\r\n                                          showbackground=true,\r\n                                          backgroundcolor=\"rgb(230, 230,230)\"),\r\n                               yaxis=attr(gridcolor=\"rgb(255, 255, 255)\",\r\n                                           zerolinecolor=\"rgb(255, 255, 255)\",\r\n                                           showbackground=true,\r\n                                           backgroundcolor=\"rgb(230, 230,230)\"),\r\n                               zaxis=attr(gridcolor=\"rgb(255, 255, 255)\",\r\n                                           zerolinecolor=\"rgb(255, 255, 255)\",\r\n                                           showbackground=true,\r\n                                           backgroundcolor=\"rgb(230, 230,230)\"),\r\n                               aspectratio=attr(x=1, y=1, z=0.7),\r\n                               aspectmode = \"manual\"))\r\n\r\n    # Need to use Plot to work with Dash                               \r\n    # return plot(data, layout)\r\n    return Plot(data, layout)\r\nend\r\n#display(clustering_alpha_shapes())\r\n\r\nfunction scatter_3d()\r\n    \u03a3 = fill(0.5, 3, 3) + Diagonal([0.5, 0.5, 0.5])\r\n    obs1 = rand(MvNormal(zeros(3), \u03a3), 200)'\r\n    obs2 = rand(MvNormal(zeros(3), 0.5\u03a3), 100)'\r\n\r\n    trace1 = scatter3d(;x=obs1[:, 1], y=obs1[:, 2], z=obs1[:, 3],\r\n                        mode=\"markers\", opacity=0.8,\r\n                        marker_size=12, marker_line_width=0.5,\r\n                        marker_line_color=\"rgba(217, 217, 217, 0.14)\")\r\n\r\n    trace2 = scatter3d(;x=obs2[:, 1], y=obs2[:, 2], z=obs2[:, 3],\r\n                        mode=\"markers\", opacity=0.9,\r\n                        marker=attr(color=\"rgb(127, 127, 127)\",\r\n                                    symbol=\"circle\", line_width=1.0,\r\n                                    line_color=\"rgb(204, 204, 204)\"))\r\n\r\n    layout = Layout(margin=attr(l=0, r=0, t=0, b=0))\r\n\r\n    return Plot([trace1, trace2], layout)\r\nend\r\n#display(scatter_3d())\r\n\r\nfunction trisurf()\r\n    facecolor = repeat([\r\n    \t\"rgb(50, 200, 200)\",\r\n    \t\"rgb(100, 200, 255)\",\r\n    \t\"rgb(150, 200, 115)\",\r\n    \t\"rgb(200, 200, 50)\",\r\n    \t\"rgb(230, 200, 10)\",\r\n    \t\"rgb(255, 140, 0)\"\r\n    ], inner=[2])\r\n\r\n    t = mesh3d(\r\n        x=[0, 0, 1, 1, 0, 0, 1, 1],\r\n        y=[0, 1, 1, 0, 0, 1, 1, 0],\r\n        z=[0, 0, 0, 0, 1, 1, 1, 1],\r\n        i=[7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],\r\n        j=[3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],\r\n        k=[0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],\r\n        facecolor=facecolor)\r\n\r\n    plot(t)\r\nend\r\n#display(trisurf())\r\n\r\nfunction meshcube()\r\n    t = mesh3d(\r\n        x=[0, 0, 1, 1, 0, 0, 1, 1],\r\n        y=[0, 1, 1, 0, 0, 1, 1, 0],\r\n        z=[0, 0, 0, 0, 1, 1, 1, 1],\r\n        i=[7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2],\r\n        j=[3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3],\r\n        k=[0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6],\r\n        intensity=range(0, stop=1, length=8),\r\n        colorscale=[\r\n            [0, \"rgb(255, 0, 255)\"],\r\n            [0.5, \"rgb(0, 255, 0)\"],\r\n            [1, \"rgb(0, 0, 255)\"]\r\n        ]\r\n    )\r\n    plot(t)\r\nend\r\n#display(meshcube())\r\n\r\n#\r\n# Contour\r\n#\r\nfunction contour1()\r\n    \r\nend\r\n\r\n\r\n\r\n#\r\n# Line and Scatter\r\n#\r\nusing PlotlyJS, DataFrames, CSV, Dates\r\nfunction linescatter1()\r\n    l = Layout(\r\n        #title=\"Penguins\",\r\n        #xaxis=attr(range=[0, 42.0], title=\"fish\", showgrid=true),\r\n        #yaxis=attr(title=\"Weight\", showgrid=true),\r\n        legend=attr(x=0.1, y=0.1)\r\n    )\r\n    trace1 = scatter(;x=1:4, y=[10, 15, 13, 17], mode=\"markers\")\r\n    trace2 = scatter(;x=2:5, y=[16, 5, 11, 9], mode=\"lines\")\r\n    trace3 = scatter(;x=1:4, y=[12, 9, 15, 12], mode=\"lines+markers\")\r\n    #p = Plot([trace1, trace2, trace3], l)\r\n    plot([trace1, trace2, trace3], l)\r\nend\r\n#display(linescatter1())\r\n\r\nfunction linescatter2()\r\n    trace1 = scatter(;x=1:5, y=[1, 6, 3, 6, 1],\r\n                      mode=\"markers\", name=\"Team A\",\r\n                      text=[\"A-1\", \"A-2\", \"A-3\", \"A-4\", \"A-5\"],\r\n                      marker_size=12)\r\n\r\n    trace2 = scatter(;x=1:5+0.5, y=[4, 1, 7, 1, 4],\r\n                      mode=\"markers\", name= \"Team B\",\r\n                      text=[\"B-a\", \"B-b\", \"B-c\", \"B-d\", \"B-e\"])\r\n    # setting marker.size this way is _equivalent_ to what we did for trace1\r\n    trace2[\"marker\"] = Dict(:size => 12)\r\n\r\n    data = [trace1, trace2]\r\n    layout = Layout(;title=\"Data Labels Hover\", xaxis_range=[0.75, 5.25],\r\n                     yaxis_range=[0, 8])\r\n    plot(data, layout)\r\nend\r\n#display(linescatter2())\r\n\r\nfunction linescatter3()\r\n    trace1 = scatter(;x=1:5, y=[1, 6, 3, 6, 1],\r\n                      mode=\"markers+text\", name=\"Team A\",\r\n                      textposition=\"top center\",\r\n                      text=[\"A-1\", \"A-2\", \"A-3\", \"A-4\", \"A-5\"],\r\n                      marker_size=12, textfont_family=\"Raleway, sans-serif\")\r\n\r\n    trace2 = scatter(;x=1:5+0.5, y=[4, 1, 7, 1, 4],\r\n                      mode=\"markers+text\", name= \"Team B\",\r\n                      textposition=\"bottom center\",\r\n                      text= [\"B-a\", \"B-b\", \"B-c\", \"B-d\", \"B-e\"],\r\n                      marker_size=12, textfont_family=\"Times New Roman\")\r\n\r\n    data = [trace1, trace2]\r\n\r\n    layout = Layout(;title=\"Data Labels on the Plot\", xaxis_range=[0.75, 5.25],\r\n                     yaxis_range=[0, 8], legend_y=0.5, legend_yref=\"paper\",\r\n                     legend=attr(family=\"Arial, sans-serif\", size=20,\r\n                                 color=\"grey\"))\r\n    plot(data, layout)\r\nend\r\n#display(linescatter3())\r\n\r\nfunction linescatter4()\r\n    trace1 = scatter(;y=fill(5, 40), mode=\"markers\", marker_size=40,\r\n                      marker_color=0:39)\r\n    layout = Layout(title=\"Scatter Plot with a Color Dimension\")\r\n    plot(trace1, layout)\r\nend\r\n#display(linescatter4())\r\n\r\nfunction linescatter5()\r\n\r\n    country = [\"Switzerland (2011)\", \"Chile (2013)\", \"Japan (2014)\",\r\n               \"United States (2012)\", \"Slovenia (2014)\", \"Canada (2011)\",\r\n               \"Poland (2010)\", \"Estonia (2015)\", \"Luxembourg (2013)\",\r\n               \"Portugal (2011)\"]\r\n\r\n    votingPop = [40, 45.7, 52, 53.6, 54.1, 54.2, 54.5, 54.7, 55.1, 56.6]\r\n    regVoters = [49.1, 42, 52.7, 84.3, 51.7, 61.1, 55.3, 64.2, 91.1, 58.9]\r\n\r\n    # notice use of `attr` function to make nested attributes\r\n    trace1 = scatter(;x=votingPop, y=country, mode=\"markers\",\r\n                      name=\"Percent of estimated voting age population\",\r\n                      marker=attr(color=\"rgba(156, 165, 196, 0.95)\",\r\n                                  line_color=\"rgba(156, 165, 196, 1.0)\",\r\n                                  line_width=1, size=16, symbol=\"circle\"))\r\n\r\n    trace2 = scatter(;x=regVoters, y=country, mode=\"markers\",\r\n                      name=\"Percent of estimated registered voters\")\r\n    # also could have set the marker props above by using a dict\r\n    trace2[\"marker\"] = Dict(:color => \"rgba(204, 204, 204, 0.95)\",\r\n                           :line => Dict(:color=> \"rgba(217, 217, 217, 1.0)\",\r\n                                         :width=> 1),\r\n                           :symbol => \"circle\",\r\n                           :size => 16)\r\n\r\n    data = [trace1, trace2]\r\n    layout = Layout(Dict{Symbol,Any}(:paper_bgcolor => \"rgb(254, 247, 234)\",\r\n                                     :plot_bgcolor => \"rgb(254, 247, 234)\");\r\n                    title=\"Votes cast for ten lowest voting age population in OECD countries\",\r\n                    width=600, height=600, hovermode=\"closest\",\r\n                    margin=Dict(:l => 140, :r => 40, :b => 50, :t => 80),\r\n                    xaxis=attr(showgrid=false, \r\n                               showline=true,\r\n                               linecolor=\"rgb(102, 102, 102)\",\r\n                               titlefont_color=\"rgb(204, 204, 204)\",\r\n                               tickfont_color=\"rgb(102, 102, 102)\",\r\n                               autotick=false, dtick=10, ticks=\"outside\",\r\n                               tickcolor=\"rgb(102, 102, 102)\"),\r\n                    legend=attr(font_size=10, yanchor=\"middle\",\r\n                                xanchor=\"right\"),\r\n    )\r\n\r\n    return Plot(data, layout)\r\nend\r\n#display(linescatter5())\r\n\r\nfunction linescatter6()\r\n    trace1 = scatter(;x=[52698, 43117], y=[53, 31],\r\n                      mode=\"markers\",\r\n                      name=\"North America\",\r\n                      text=[\"United States\", \"Canada\"],\r\n                      marker=attr(color=\"rgb(164, 194, 244)\", size=12,\r\n                                  line=attr(color=\"white\", width=0.5))\r\n                      )\r\n\r\n    trace2 = scatter(;x=[39317, 37236, 35650, 30066, 29570, 27159, 23557, 21046, 18007],\r\n                      y=[33, 20, 13, 19, 27, 19, 49, 44, 38],\r\n                      mode=\"markers\", name=\"Europe\",\r\n                      marker_size=12, marker_color=\"rgb(255, 217, 102)\",\r\n                      text=[\"Germany\", \"Britain\", \"France\", \"Spain\", \"Italy\",\r\n                            \"Czech Rep.\", \"Greece\", \"Poland\", \"Portugal\"])\r\n\r\n    trace3 = scatter(;x=[42952, 37037, 33106, 17478, 9813, 5253, 4692, 3899],\r\n                      y=[23, 42, 54, 89, 14, 99, 93, 70],\r\n                      mode=\"markers\",\r\n                      name=\"Asia/Pacific\",\r\n                      marker_size=12, marker_color=\"rgb(234, 153, 153)\",\r\n                      text=[\"Australia\", \"Japan\", \"South Korea\", \"Malaysia\",\r\n                            \"China\", \"Indonesia\", \"Philippines\", \"India\"])\r\n\r\n    trace4 = scatter(;x=[19097, 18601, 15595, 13546, 12026, 7434, 5419],\r\n                      y=[43, 47, 56, 80, 86, 93, 80],\r\n                      mode=\"markers\", name=\"Latin America\",\r\n                      marker_size=12, marker_color=\"rgb(142, 124, 195)\",\r\n                      text=[\"Chile\", \"Argentina\", \"Mexico\", \"Venezuela\",\r\n                            \"Venezuela\", \"El Salvador\", \"Bolivia\"])\r\n\r\n    data = [trace1, trace2, trace3, trace4]\r\n\r\n    layout = Layout(;title=\"Quarter 1 Growth\",\r\n                     xaxis=attr(title=\"GDP per Capital\", showgrid=false, zeroline=false),\r\n                     yaxis=attr(title=\"Percent\", zeroline=false))\r\n\r\n    plot(data, layout)\r\nend\r\n#display(linescatter6())\r\n\r\n\r\nfunction dumbell()\r\n    # reference: https://plot.ly/r/dumbbell-plots/\r\n    # read Data into dataframe\r\n    nm = tempname()\r\n    url = \"https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv\"\r\n    download(url, nm)\r\n    df = CSV.read(nm, DataFrame)\r\n    rm(nm)\r\n\r\n    # sort dataframe by male earnings\r\n    df = sort(df, :Men, rev=false)\r\n\r\n    men = scatter(;y=df[!,:School], x=df[!,:Men], mode=\"markers\", name=\"Men\",\r\n                   marker=attr(color=\"blue\", size=12))\r\n    women = scatter(;y=df[!,:School], x=df[!,:Women], mode=\"markers\", name=\"Women\",\r\n                     marker=attr(color=\"pink\", size=12))\r\n\r\n    lines = map(eachrow(df)) do r\r\n        scatter(y=fill(r[:School], 2), x=[r[:Women], r[:Men]], mode=\"lines\",\r\n                name=r[:School], showlegend=false, line_color=\"gray\")\r\n    end\r\n\r\n    data = Base.typed_vcat(GenericTrace, men, women, lines)\r\n    layout = Layout(width=650, height=650, margin_l=100, yaxis_title=\"School\",\r\n                    xaxis_title=\"Annual Salary (thousands)\",\r\n                    title=\"Gender earnings disparity\")\r\n\r\n    plot(data, layout)\r\nend\r\n#display(dumbell())\r\n\r\nfunction errorbars1()\r\n    trace1 = scatter(;x=vcat(1:10, 10:-1:1),\r\n                     y=vcat(2:11, 9:-1:0),\r\n                     fill=\"tozerox\",\r\n                     fillcolor=\"rgba(0, 100, 80, 0.2)\",\r\n                     line_color=\"transparent\",\r\n                     name=\"Fair\",\r\n                     showlegend=false)\r\n\r\n    trace2 = scatter(;x=vcat(1:10, 10:-1:1),\r\n                     y=[5.5, 3.0, 5.5, 8.0, 6.0, 3.0, 8.0, 5.0, 6.0, 5.5, 4.75,\r\n                        5.0, 4.0, 7.0, 2.0, 4.0, 7.0, 4.4, 2.0, 4.5],\r\n                     fill=\"tozerox\",\r\n                     fillcolor=\"rgba(0, 176, 246, 0.2)\",\r\n                     line_color=\"transparent\",\r\n                     name=\"Premium\",\r\n                     showlegend=false)\r\n\r\n    trace3 = scatter(;x=vcat(1:10, 10:-1:1),\r\n                     y=[11.0, 9.0, 7.0, 5.0, 3.0, 1.0, 3.0, 5.0, 3.0, 1.0,\r\n                        -1.0, 1.0, 3.0, 1.0, -0.5, 1.0, 3.0, 5.0, 7.0, 9.],\r\n                     fill=\"tozerox\",\r\n                     fillcolor=\"rgba(231, 107, 243, 0.2)\",\r\n                     line_color=\"transparent\",\r\n                     name=\"Fair\",\r\n                     showlegend=false)\r\n\r\n    trace4 = scatter(;x=1:10, y=1:10,\r\n                     line_color=\"rgb(00, 100, 80)\",\r\n                     mode=\"lines\",\r\n                     name=\"Fair\")\r\n\r\n    trace5 = scatter(;x=1:10,\r\n                     y=[5.0, 2.5, 5.0, 7.5, 5.0, 2.5, 7.5, 4.5, 5.5, 5.],\r\n                     line_color=\"rgb(0, 176, 246)\",\r\n                     mode=\"lines\",\r\n                     name=\"Premium\")\r\n\r\n    trace6 = scatter(;x=1:10, y=vcat(10:-2:0, [2, 4,2, 0]),\r\n                     line_color=\"rgb(231, 107, 243)\",\r\n                     mode=\"lines\",\r\n                     name=\"Ideal\")\r\n    data = [trace1, trace2, trace3, trace4, trace5, trace6]\r\n    layout = Layout(;paper_bgcolor=\"rgb(255, 255, 255)\",\r\n                    plot_bgcolor=\"rgb(229, 229, 229)\",\r\n\r\n                    xaxis=attr(gridcolor=\"rgb(255, 255, 255)\",\r\n                               range=[1, 10],\r\n                               showgrid=true,\r\n                               showline=false,\r\n                               showticklabels=true,\r\n                               tickcolor=\"rgb(127, 127, 127)\",\r\n                               ticks=\"outside\",\r\n                               zeroline=false),\r\n\r\n                    yaxis=attr(gridcolor=\"rgb(255, 255, 255)\",\r\n                               showgrid=true,\r\n                               showline=false,\r\n                               showticklabels=true,\r\n                               tickcolor=\"rgb(127, 127, 127)\",\r\n                               ticks=\"outside\",\r\n                               zeroline=false))\r\n\r\n    plot(data, layout)\r\nend\r\n#display(errorbars1())\r\n\r\n#t = sec_Traces()\r\n#l = sec_Layouts()\r\n#iris = sec_DataFrames()\r\n#p = sec_Plot()\r\n#display(plot(p))\r\n\r\n# Open in Pane when run from VS Code\r\n# Open in Electron when run from console\r\n#sec_SyncPlots()\r\n\r\n# Open in default browser\r\n#savefig(p, \"plotlyjs.html\")\r\n#savefig(p, \"plotlyjs.png\")\r\n#DefaultApplication.open(\"plotlyjs.html\")\r\n\r\n#sec_Cyclers()\r\n\r\nnothing\r\n", "meta": {"hexsha": "a7bc2380dac83fd0dccdac0c0808b441a932f4d9", "size": 29616, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "learn/learn_PlotlyJS.jl", "max_stars_repo_name": "ykyang/org.allnix.julia", "max_stars_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "learn/learn_PlotlyJS.jl", "max_issues_repo_name": "ykyang/org.allnix.julia", "max_issues_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "learn/learn_PlotlyJS.jl", "max_forks_repo_name": "ykyang/org.allnix.julia", "max_forks_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.4038199181, "max_line_length": 105, "alphanum_fraction": 0.4929092382, "num_tokens": 10343, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42632159254749025, "lm_q2_score": 0.13117321527062692, "lm_q1q2_score": 0.055921974033748434}}
{"text": "module LP\n\nimport ..MathOptFormat\n\nimport MathOptInterface\nconst MOI = MathOptInterface\n\nMOI.Utilities.@model(InnerModel,\n    (MOI.ZeroOne, MOI.Integer),\n    (MOI.EqualTo, MOI.GreaterThan, MOI.LessThan, MOI.Interval),\n    (),\n    (),\n    (),\n    (MOI.ScalarAffineFunction,),\n    (),\n    ()\n)\n\nstruct Options\n    maximum_length::Int\n    warn::Bool\n    warn_once::Bool\n    warned_start::Set{Char}\n    warned_illegal::Set{Char}\nend\n\nfunction get_options(m::InnerModel)\n    default_options = Options(255, false, false, Set{Char}(), Set{Char}())\n    return get(m.ext, :LP_OPTIONS, default_options)\nend\n\n\"\"\"\n    Model(; kwargs...)\n\nCreate an empty instance of MathOptFormat.LP.Model.\n\nKeyword arguments are:\n\n - `maximum_length::Int=255`: the maximum length for the name of a variable.\n   lp_solve 5.0 allows only 16 characters, while CPLEX 12.5+ allow 255.\n\n - `warn::Bool=false`: print a warning when variables or constraints are renamed.\n\n - `warn_once::Bool=false`: print a warning when variables or constraints are\n   renamed, but only once per kind of replacement (e.g., once per illegal\n   character).\n\"\"\"\nfunction Model(;\n    maximum_length::Int = 255, warn::Bool = false, warn_once::Bool = false\n)\n    model = InnerModel{Float64}()\n    options = Options(maximum_length, warn, warn_once, Set{Char}(), Set{Char}())\n    model.ext[:LP_OPTIONS] = options\n    return model\nend\n\nfunction Base.show(io::IO, ::InnerModel)\n    print(io, \"A .LP-file model\")\n    return\nend\n\n# ==============================================================================\n#\n#   MOI.write_to_file\n#\n# ==============================================================================\n\n\nconst START_REG = r\"^([\\.0-9eE])\"\nconst NAME_REG = r\"([^a-zA-Z0-9\\!\\\"\\#\\$\\%\\&\\(\\)\\/\\,\\.\\;\\?\\@\\_\\`\\'\\{\\}\\|\\~])\"\n\nfunction sanitized_name(name::String, options::Options)\n    m = match(START_REG, name)\n    if m !== nothing\n        plural = length(m.match) > 1\n\n        if options.warn || (options.warn_once && !(m.match[1] in options.warned_start))\n            @warn(\"Name $(name) cannot start with a period, a number, e, or E. \" *\n                  \"Prepending an underscore to name.\")\n            push!(options.warned_start, m.match[1])\n        end\n\n        return sanitized_name(\"_\" * name, options)\n    end\n\n    m = match(NAME_REG, name)\n    if m !== nothing\n        plural = length(m.match) > 1\n\n        if options.warn || (options.warn_once && !(m.match[1] in options.warned_illegal))\n            @warn(\"Name $(name) contains $(ifelse(plural, \"\", \"an \"))\" *\n                  \"illegal character$(ifelse(plural, \"s\", \"\")): \" *\n                  \"\\\"$(m.match)\\\". Removing the offending \" *\n                  \"character$(ifelse(plural, \"s\", \"\")) from name.\")\n            push!(options.warned_illegal, m.match[1])\n        end\n\n        return sanitized_name(replace(name, NAME_REG => s\"_\"), options)\n    end\n\n    # Truncate at the end to fit as many characters as possible.\n    if length(name) > options.maximum_length\n        @warn(\"Name $(name) too long (length: $(length(name)); \" *\n              \"maximum: $(options.maximum_length)). Truncating.\")\n        return sanitized_name(String(name[1:options.maximum_length]), options)\n    end\n\n    return name\nend\n\nfunction write_function(io::IO, model::InnerModel, func::MOI.SingleVariable, sanitized_names::Dict{MOI.VariableIndex, String})\n    print(io, sanitized_names[func.variable])\n    return\nend\n\nfunction write_function(io::IO, model::InnerModel, func::MOI.ScalarAffineFunction{Float64}, sanitized_names::Dict{MOI.VariableIndex, String})\n    is_first_item = true\n    if !(func.constant \u2248 0.0)\n        Base.Grisu.print_shortest(io, func.constant)\n        is_first_item = false\n    end\n    for term in func.terms\n        if !(term.coefficient \u2248 0.0)\n            if is_first_item\n                Base.Grisu.print_shortest(io, term.coefficient)\n                is_first_item = false\n            else\n                print(io, term.coefficient < 0 ? \" - \" : \" + \")\n                Base.Grisu.print_shortest(io, abs(term.coefficient))\n            end\n\n            print(io, \" \", sanitized_names[term.variable_index])\n        end\n    end\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.LessThan)\n    print(io, \" <= \", )\n    Base.Grisu.print_shortest(io, set.upper)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.GreaterThan)\n    print(io, \" >= \", )\n    Base.Grisu.print_shortest(io, set.lower)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.EqualTo)\n    print(io, \" = \", )\n    Base.Grisu.print_shortest(io, set.value)\n    println(io)\n    return\nend\n\nfunction write_constraint_suffix(io::IO, set::MOI.Interval)\n    print(io, \" <= \", )\n    Base.Grisu.print_shortest(io, set.upper)\n    println(io)\n    return\nend\n\nfunction write_constraint_prefix(io::IO, set::MOI.Interval)\n    Base.Grisu.print_shortest(io, set.lower)\n    print(io, \" <= \")\n    return\nend\n\nwrite_constraint_prefix(io::IO, set) = nothing\n\nfunction write_constraint(io::IO, model::InnerModel, index, sanitized_names::Dict{MOI.VariableIndex, String}; write_name::Bool = true)\n    func = MOI.get(model, MOI.ConstraintFunction(), index)\n    set = MOI.get(model, MOI.ConstraintSet(), index)\n    if write_name\n        print(io, MOI.get(model, MOI.ConstraintName(), index), \": \")\n    end\n    write_constraint_prefix(io, set)\n    write_function(io, model, func, sanitized_names)\n    write_constraint_suffix(io, set)\nend\n\nconst SCALAR_SETS = (\n    MOI.LessThan{Float64}, MOI.GreaterThan{Float64}, MOI.EqualTo{Float64},\n    MOI.Interval{Float64}\n)\n\nfunction write_sense(io::IO, model::InnerModel)\n    if MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE\n        println(io, \"maximize\")\n    else\n        println(io, \"minimize\")\n    end\n    return\nend\n\nfunction write_objective(io::IO, model::InnerModel, sanitized_names::Dict{MOI.VariableIndex, String})\n    print(io, \"obj: \")\n    obj_func_type = MOI.get(model, MOI.ObjectiveFunctionType())\n    obj_func = MOI.get(model, MOI.ObjectiveFunction{obj_func_type}())\n    write_function(io, model, obj_func, sanitized_names)\n    println(io)\n    return\nend\n\nfunction MOI.write_to_file(model::InnerModel, io::IO)\n    options = get_options(model)\n    max_length = options.maximum_length\n    # Ensure each variable has a unique name that does not infringe LP constraints.\n    MathOptFormat.create_unique_names(model, warn = options.warn)\n    sanitized_names = Dict{MOI.VariableIndex, String}()\n    sanitized_names_set = Set{String}()\n    for v in MOI.get(model, MOI.ListOfVariableIndices())\n        proposed_sanitized_name = sanitized_name(MOI.get(model, MOI.VariableName(), v), options)\n        # In case of duplicate names after sanitization, add a number at the end.\n        if proposed_sanitized_name in sanitized_names_set\n            # If the name is already too long, make some space for the suffix.\n            if length(proposed_sanitized_name) >= max_length\n                proposed_sanitized_name = String(proposed_sanitized_name[1:max_length - 2])\n            end\n            i = 1\n            while proposed_sanitized_name * '_' * string(i) in sanitized_names_set\n                i += 1\n\n                # If the maximum length constraint would be broken with the *next* i,\n                # truncate a bit more the proposed_sanitized_name.\n                if length(proposed_sanitized_name * '_' * string(i)) > max_length\n                    proposed_sanitized_name = String(proposed_sanitized_name[1:length(proposed_sanitized_name) - 1])\n                end\n            end\n            proposed_sanitized_name *= '_' * string(i)\n        end\n        push!(sanitized_names_set, proposed_sanitized_name)\n        sanitized_names[v] = proposed_sanitized_name\n    end\n\n    write_sense(io, model)\n    write_objective(io, model, sanitized_names)\n    println(io, \"subject to\")\n    for S in SCALAR_SETS\n        for index in MOI.get(model, MOI.ListOfConstraintIndices{MOI.ScalarAffineFunction{Float64}, S}())\n            write_constraint(io, model, index, sanitized_names; write_name = true)\n        end\n    end\n\n    println(io, \"Bounds\")\n    for S in SCALAR_SETS\n        for index in MOI.get(model, MOI.ListOfConstraintIndices{MOI.SingleVariable, S}())\n            write_constraint(io, model, index, sanitized_names; write_name = false)\n        end\n    end\n\n    for (S, str_S) in [(MOI.Integer, \"General\"), (MOI.ZeroOne, \"Binary\")]\n        indices = MOI.get(model, MOI.ListOfConstraintIndices{MOI.SingleVariable, S}())\n        if length(indices) > 0\n            println(io, str_S)\n            for index in indices\n                write_function(io, model, MOI.get(model, MOI.ConstraintFunction(), index), sanitized_names)\n                println(io)\n            end\n        end\n    end\n\n    println(io, \"End\")\n\n    return\nend\n\n# ==============================================================================\n#\n#   MOI.read_from_to_file\n#\n# ==============================================================================\n\nfunction MOI.read_from_file(model::InnerModel, io::IO)\n    error(\"Read from file is not implemented for LP files.\")\nend\n\nend\n", "meta": {"hexsha": "f2f01a1e0f4efa35e5f9deb7f4f61b529248bf6e", "size": 9134, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LP/LP.jl", "max_stars_repo_name": "josepalos/MathOptFormat.jl", "max_stars_repo_head_hexsha": "04c41af0a81a867d3cd78beafd2617cdd033456e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/LP/LP.jl", "max_issues_repo_name": "josepalos/MathOptFormat.jl", "max_issues_repo_head_hexsha": "04c41af0a81a867d3cd78beafd2617cdd033456e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/LP/LP.jl", "max_forks_repo_name": "josepalos/MathOptFormat.jl", "max_forks_repo_head_hexsha": "04c41af0a81a867d3cd78beafd2617cdd033456e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.390070922, "max_line_length": 141, "alphanum_fraction": 0.624589446, "num_tokens": 2273, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489883132727684, "lm_q2_score": 0.1347759191606016, "lm_q1q2_score": 0.055918371350793146}}
{"text": "## Exercise 4-9\n## Write an appropriately general set of functions that can draw flowers as in Turtle flowers.\n## https://benlauwens.github.io/ThinkJulia.jl/latest/book.html#fig04-2\nprintln(\"Ans: \")\n\nprintln(\"End.\")\n", "meta": {"hexsha": "15f483bc576e54194d8587511a352e6bdb63108a", "size": 216, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter4/ex9.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter4/ex9.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter4/ex9.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8571428571, "max_line_length": 94, "alphanum_fraction": 0.7453703704, "num_tokens": 58, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4148988457967689, "lm_q2_score": 0.13477590699708825, "lm_q1q2_score": 0.05591836825430458}}
{"text": "\"\"\"\n    NOCB <: Imputor\n\nFills in missing data using the Next Observation Carried Backward (NOCB) approach.\n\"\"\"\nstruct NOCB <: Imputor end\n\n\"\"\"\n    impute!(imp::NOCB, ctx::Context, data::AbstractVector)\n\nIterates backwards through the `data` and fills missing data with the next\nexisting observation.\n\nWARNING: missing elements at the tail of the array may not be imputed if there is no\nexisting observation to carry backward. As a result, this method does not guarantee\nthat all missing values will be imputed.\n\n# Usage\n```\n\n```\n\"\"\"\nfunction impute!(imp::NOCB, ctx::Context, data::AbstractVector)\n    end_idx = findlast(ctx, data) - 1\n    for i in end_idx:-1:1\n        if ismissing(ctx, data[i])\n            data[i] = data[i+1]\n        end\n    end\n\n    return data\nend\n", "meta": {"hexsha": "dd7c914460bffb226a3b098bb8fbb21feab3de04", "size": 770, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/imputors/nocb.jl", "max_stars_repo_name": "nalimilan/Impute.jl", "max_stars_repo_head_hexsha": "73e78d1a18abbad7679745f085aea2fa136f15bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/imputors/nocb.jl", "max_issues_repo_name": "nalimilan/Impute.jl", "max_issues_repo_head_hexsha": "73e78d1a18abbad7679745f085aea2fa136f15bd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/imputors/nocb.jl", "max_forks_repo_name": "nalimilan/Impute.jl", "max_forks_repo_head_hexsha": "73e78d1a18abbad7679745f085aea2fa136f15bd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.3333333333, "max_line_length": 84, "alphanum_fraction": 0.6883116883, "num_tokens": 206, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879064146934857, "lm_q2_score": 0.1192029235866439, "lm_q1q2_score": 0.05588121501320453}}
{"text": "using BasicPOMCP\nusing Test\nusing POMDPs\nusing POMDPModels\nusing NBInclude\nusing D3Trees\nusing Random\nusing POMDPSimulators\nusing POMDPModelTools\nusing POMDPTesting\nusing POMDPLinter: @requirements_info, @show_requirements, requirements_info\nusing ParticleFilters: n_particles, particles, particle, weights, weighted_particles, weight_sum, weight\n\nimport POMDPs:\n\ttransition,\n\tobservation,\n    reward,\n    discount,\n\tinitialstate,\n\tupdater,\n\tstates,\n\tactions,\n\tobservations\n\nstruct ConstObsPOMDP <: POMDP{Bool, Symbol, Bool} end\nupdater(problem::ConstObsPOMDP) = DiscreteUpdater(problem)\ninitialstate(::ConstObsPOMDP) = BoolDistribution(0.0)\ntransition(p::ConstObsPOMDP, s::Bool, a::Symbol) = BoolDistribution(0.0)\nobservation(p::ConstObsPOMDP, a::Symbol, sp::Bool) = BoolDistribution(1.0)\nreward(p::ConstObsPOMDP, s::Bool, a::Symbol, sp::Bool) = 1.\ndiscount(p::ConstObsPOMDP) = 0.9\nstates(p::ConstObsPOMDP) = (true, false)\nactions(p::ConstObsPOMDP) = (:the_only_action,)\nobservations(p::ConstObsPOMDP) = (true, false)\n\n@testset \"POMDPTesting\" begin\n\tpomdp = BabyPOMDP()\n\ttest_solver(POMCPSolver(), BabyPOMDP())\nend;\n\n@testset \"type stability\" begin\n\tpomdp = BabyPOMDP()\n\tsolver = POMCPSolver(rng = MersenneTwister(1))\n\tplanner = solve(solver, pomdp)\n\tb = initialstate(pomdp)\n    tree = BasicPOMCP.POMCPTree(pomdp, b, solver.tree_queries)\n    node = BasicPOMCP.POMCPObsNode(tree, 1)\n\n    r = @inferred BasicPOMCP.simulate(planner, rand(MersenneTwister(1), initialstate(pomdp)), node, 20)\nend;\n\n@testset \"belief dependent actions\" begin\n\tpomdp = ConstObsPOMDP()\n\tfunction POMDPs.actions(m::ConstObsPOMDP, b::LeafNodeBelief)\n\t\t@test currentobs(b) == true\n        @test history(b)[end].o == true\n        @test history(b)[end].a == :the_only_action\n        @test mean(b) == 0.0\n        @test mode(b) == 0.0\n        @test only(support(b)) == false\n        @test pdf(b, false) == 1.0\n        @test pdf(b, true) == 0.0\n        @test rand(b) == false\n        @test n_particles(b) == 1\n        @test only(particles(b)) == false\n        @test only(weights(b)) == 1.0\n        @test only(weighted_particles(b)) == (false => 1.0)\n        @test weight_sum(b) == 1.0\n        @test weight(b, 1) == 1.0 \n        @test particle(b, 1) == false\n\n        # old type name - this can be removed when upgrading versions\n        @test b isa AOHistoryBelief\n        return actions(m)\n\tend\n\n\tsolver = POMCPSolver(rng = MersenneTwister(1))\n\tplanner = solve(solver, pomdp)\n\tb = initialstate(pomdp)\n    tree = BasicPOMCP.POMCPTree(pomdp, b, solver.tree_queries)\n    node = BasicPOMCP.POMCPObsNode(tree, 1)\n\n    @inferred BasicPOMCP.simulate(planner, rand(MersenneTwister(1), initialstate(pomdp)), node, 20)\nend;\n\n@testset \"simulation\" begin\n\tpomdp = BabyPOMDP()\n\tsolver = POMCPSolver(rng = MersenneTwister(1))\n\tplanner = solve(solver, pomdp)\n    solver = POMCPSolver(max_time=0.1, tree_queries=typemax(Int), rng = MersenneTwister(1))\n    planner = solve(solver, pomdp)\n\tb = initialstate(pomdp)\n\n    a, info = action_info(planner, b)\n    println(\"time below should be about 0.1 seconds\")\n    etime = @elapsed a, info = action_info(planner, b)\n    @show etime\n    @test etime < 0.2\n    @show info[:search_time_us]\n\n    sim = HistoryRecorder(max_steps=10)\n    simulate(sim, pomdp, planner, updater(pomdp))\nend;\n\n@testset \"d3t\" begin\n\tpomdp = BabyPOMDP()\n    solver = POMCPSolver(max_time=0.1, tree_queries=typemax(Int), rng = MersenneTwister(1))\n    planner = solve(solver, pomdp)\n\tb = initialstate(pomdp)\n    a, info = action_info(planner, b, tree_in_info=true)\n\n    d3t = D3Tree(info[:tree], title=\"test tree\")\n    # inchrome(d3t)\n    show(stdout, MIME(\"text/plain\"), d3t)\n\n\n    solver = POMCPSolver(max_time=0.1, tree_queries=typemax(Int), rng=MersenneTwister(1), tree_in_info=true)\n    planner = solve(solver, pomdp)\n    a, info = action_info(planner, b)\n\n    d3t = D3Tree(info[:tree], title=\"test tree (tree_in_info solver option)\")\nend;\n\n@testset \"Minimal_Example\" begin\n    @nbinclude(joinpath(dirname(@__FILE__), \"..\", \"notebooks\", \"Minimal_Example.ipynb\"))\nend;\n\n@testset \"consistency\" begin\n    # test consistency when rng is specified\n    pomdp = BabyPOMDP()\n    solver = POMCPSolver(rng = MersenneTwister(1))\n    planner = solve(solver, pomdp)\n    hist1 = simulate(HistoryRecorder(max_steps=1000, rng=MersenneTwister(3)), pomdp, planner)\n\n    solver = POMCPSolver(rng = MersenneTwister(1))\n    planner = solve(solver, pomdp)\n    hist2 = simulate(HistoryRecorder(max_steps=1000, rng=MersenneTwister(3)), pomdp, planner)\n\n    @test discounted_reward(hist1) == discounted_reward(hist2)\nend;\n\n@testset \"requires\" begin\n    # REQUIREMENTS\n    solver = POMCPSolver()\n    pomdp = TigerPOMDP()\n\n    println(\"============== @requirements_info with only solver:\")\n    requirements_info(solver)\n    println(\"============== @requirements_info with solver and pomdp:\")\n    requirements_info(solver, pomdp)\n    @show_requirements POMDPs.solve(solver, pomdp)\nend;\n\n@testset \"errors\" begin\n    struct TerminalPOMDP <: POMDP{Int, Int, Int} end\n    POMDPs.isterminal(::TerminalPOMDP, s) = true\n    POMDPs.actions(::TerminalPOMDP) = [1,2,3]\n\n    solver = POMCPSolver()\n    planner = solve(solver, TerminalPOMDP())\n    @test_throws AllSamplesTerminal action(planner, Deterministic(1))\n    let ex = nothing\n        try\n            action(planner, Deterministic(1))\n        catch ex\n        end\n\n        @test sprint(showerror, ex) == \"\"\"\n        Planner failed to choose an action because all states sampled from the belief were terminal.\n\n        To see the belief, catch this exception as ex and see ex.belief.\n\n        To specify an action for this case, use the default_action solver parameter.\n        \"\"\"\n    end\nend\n", "meta": {"hexsha": "71758530ffa95364dc35ebf5e6d93de9b487e015", "size": 5681, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "WhiffleFish/BasicPOMCP.jl", "max_stars_repo_head_hexsha": "de923f282ccde51335057d78c14c8a61af61d3fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 25, "max_stars_repo_stars_event_min_datetime": "2017-10-09T04:54:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-20T21:24:33.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "WhiffleFish/BasicPOMCP.jl", "max_issues_repo_head_hexsha": "de923f282ccde51335057d78c14c8a61af61d3fd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 24, "max_issues_repo_issues_event_min_datetime": "2017-09-11T19:32:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-11T21:08:21.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "WhiffleFish/BasicPOMCP.jl", "max_forks_repo_head_hexsha": "de923f282ccde51335057d78c14c8a61af61d3fd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2018-02-28T01:50:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-20T21:27:55.000Z", "avg_line_length": 32.0960451977, "max_line_length": 108, "alphanum_fraction": 0.6884351347, "num_tokens": 1730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.11436853372838406, "lm_q1q2_score": 0.05584425596390156}}
{"text": "@testset \"model.jl\" begin\r\n    @testset \"lastVar\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        x = SeaPearl.IntVar(2, 6, \"x\", trailer)\r\n        y = SeaPearl.IntVar(2, 6, \"y\", trailer)\r\n    \r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n\r\n        valueselection = SeaPearl.BasicHeuristic()\r\n        @test isnothing(model.statistics.lastVar)\r\n        v = valueselection(SeaPearl.DecisionPhase, model, x)\r\n        @test model.statistics.lastVar.id==\"x\"\r\n        SeaPearl.assign!(x, v)\r\n        v = valueselection(SeaPearl.DecisionPhase, model, y)\r\n        @test model.statistics.lastVar.id==\"y\"\r\n        SeaPearl.assign!(y, v)\r\n\r\n    end\r\n\r\n    @testset \"addVariable!()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 6, \"x\", trailer)\r\n        y = SeaPearl.IntVar(2, 6, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n\r\n        @test length(model.variables) == 2\r\n        @test SeaPearl.branchable_variables(model) == Dict{String, SeaPearl.AbstractVar}([\"x\" => x, \"y\" => y])\r\n\r\n        z = SeaPearl.IntVar(2, 6, \"y\", trailer)\r\n\r\n        @test_throws AssertionError SeaPearl.addVariable!(model, z)\r\n\r\n        # Not branching\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 6, \"x\", trailer)\r\n        y = SeaPearl.IntVar(2, 6, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addVariable!(model, x; branchable=false)\r\n        SeaPearl.addVariable!(model, y)\r\n\r\n        @test length(model.variables) == 2\r\n        @test SeaPearl.branchable_variables(model) == Dict{String, SeaPearl.AbstractVar}([\"y\" => y])\r\n\r\n        # Trying to branch on Set variable\r\n        trailer = SeaPearl.Trailer()\r\n        y = SeaPearl.IntSetVar(2, 6, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        @test_throws AssertionError SeaPearl.addVariable!(model, y)\r\n    end\r\n\r\n    @testset \"merge!()\" begin\r\n        test1 = SeaPearl.CPModification(\"x\" => [2, 3, 4],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8])\r\n        test2 = SeaPearl.CPModification(\"x\" => [5],\"y\" => [7, 8])\r\n\r\n        SeaPearl.merge!(test1, test2)\r\n\r\n        @test test1 == SeaPearl.CPModification(\"x\" => [2, 3, 4, 5],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8, 7, 8])\r\n    end\r\n\r\n    @testset \"addToPrunedDomains!()\" begin\r\n        test1 = SeaPearl.CPModification(\"x\" => [2, 3, 4],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8])\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 6, \"x\", trailer)\r\n        t = SeaPearl.IntVar(2, 6, \"t\", trailer)\r\n        b = SeaPearl.BoolVar(\"b\", trailer)\r\n\r\n        SeaPearl.addToPrunedDomains!(test1, x, [5, 6])\r\n\r\n        @test test1 == SeaPearl.CPModification(\"x\" => [2, 3, 4, 5, 6],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8])\r\n\r\n        SeaPearl.addToPrunedDomains!(test1, t, [5, 6])\r\n\r\n        @test test1 == SeaPearl.CPModification(\"x\" => [2, 3, 4, 5, 6],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8], \"t\" => [5, 6])\r\n\r\n        SeaPearl.addToPrunedDomains!(test1, b, [true])\r\n\r\n        @test test1 == SeaPearl.CPModification(\"x\" => [2, 3, 4, 5, 6],\"z\" => [11, 12, 13, 14, 15],\"y\" => [7, 8], \"t\" => [5, 6], \"b\" => [true])\r\n\r\n    end\r\n\r\n    @testset \"solutionFound()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 6, \"x\", trailer)\r\n        y = SeaPearl.IntVar(2, 6, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n\r\n        @test !SeaPearl.solutionFound(model)\r\n\r\n        constraint = SeaPearl.EqualConstant(x, 3, trailer)\r\n        constraint2 = SeaPearl.Equal(x, y, trailer)\r\n        SeaPearl.addConstraint!(model, constraint)\r\n        SeaPearl.addConstraint!(model, constraint2)\r\n\r\n        SeaPearl.fixPoint!(model)\r\n\r\n        @test SeaPearl.solutionFound(model)\r\n    end\r\n\r\n    @testset \"triggerFoundSolution!()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 2, \"x\", trailer)\r\n        y = SeaPearl.IntVar(3, 3, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n        SeaPearl.addObjective!(model, y)\r\n\r\n        SeaPearl.triggerFoundSolution!(model)\r\n\r\n        @test length(model.statistics.solutions) == 1\r\n        @test model.statistics.solutions[1] == Dict(\"x\" => 2,\"y\" => 3)\r\n        @test model.statistics.nodevisitedpersolution[1] == 0\r\n        @test model.objectiveBound == 2\r\n        @test model.statistics.numberOfSolutions == 1\r\n        @test model.statistics.objectives[1] == 3\r\n        @test model.statistics.numberOfInfeasibleSolutions == 0\r\n        @test model.statistics.numberOfSolutionsBeforeRestart == 1\r\n        @test model.statistics.numberOfInfeasibleSolutionsBeforeRestart == 0\r\n        @test model.statistics.numberOfNodesBeforeRestart == 0\r\n    end\r\n\r\n    @testset \"tightenObjective!()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        x = SeaPearl.IntVar(2, 2, \"x\", trailer)\r\n        y = SeaPearl.IntVar(3, 3, \"y\", trailer)\r\n\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n        SeaPearl.addObjective!(model, y)\r\n\r\n        @test isnothing(model.objectiveBound)\r\n\r\n\r\n        SeaPearl.tightenObjective!(model)\r\n\r\n        @test model.objectiveBound == 2\r\n    end\r\n\r\n    @testset \"belowLimits()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        model.statistics.numberOfNodes = 1500\r\n        model.statistics.numberOfSolutions = 15\r\n        SeaPearl.tic()\r\n        @test SeaPearl.belowLimits(model)\r\n\r\n        model.limit.numberOfNodes = 1501\r\n        model.limit.numberOfSolutions = 16\r\n        model.limit.searchingTime = 1\r\n        SeaPearl.tic()\r\n        sleep(0.1)\r\n        @test SeaPearl.belowLimits(model)\r\n\r\n        model.statistics.numberOfNodes = 1501\r\n        @test !SeaPearl.belowLimits(model)\r\n\r\n        model.statistics.numberOfNodes = 1500\r\n        model.statistics.numberOfSolutions = 16\r\n        @test !SeaPearl.belowLimits(model)\r\n\r\n        SeaPearl.tic()\r\n        sleep(1)\r\n        @test !SeaPearl.belowLimits(model)\r\n    end\r\n\r\n    @testset \"belowNodeLimit()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        model.statistics.numberOfNodes = 1500\r\n\r\n        @test SeaPearl.belowNodeLimit(model)\r\n\r\n        model.limit.numberOfNodes = 1501\r\n        @test SeaPearl.belowNodeLimit(model)\r\n\r\n        model.statistics.numberOfNodes = 1501\r\n        @test !SeaPearl.belowNodeLimit(model)\r\n    end\r\n\r\n    @testset \"belowSolutionLimits()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        model.statistics.numberOfSolutions = 15\r\n\r\n        @test SeaPearl.belowSolutionLimit(model)\r\n\r\n        model.limit.numberOfSolutions = 16\r\n        @test SeaPearl.belowSolutionLimit(model)\r\n\r\n        model.statistics.numberOfSolutions = 16\r\n        @test !SeaPearl.belowSolutionLimit(model)\r\n    end\r\n\r\n    @testset \"belowTimeLimits()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.tic()\r\n        sleep(0.1)\r\n        @test SeaPearl.belowTimeLimit(model)\r\n\r\n        model.limit.searchingTime = 1\r\n        SeaPearl.tic()\r\n        sleep(0.1)\r\n\r\n        @test SeaPearl.belowTimeLimit(model)\r\n\r\n        model.limit.searchingTime = 0\r\n        SeaPearl.tic()\r\n        sleep(0.1)\r\n        @test !SeaPearl.belowTimeLimit(model)\r\n    end\r\n\r\n    @testset \"Base.isempty()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        @test isempty(model)\r\n\r\n        x = SeaPearl.IntVar(2, 2, \"x\", trailer)\r\n        SeaPearl.addVariable!(model, x)\r\n\r\n        @test !isempty(model)\r\n    end\r\n\r\n    @testset \"Base.empty!()\" begin\r\n\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        x = SeaPearl.IntVar(2, 2, \"x\", trailer)\r\n        SeaPearl.addVariable!(model, x)\r\n\r\n        empty!(model)\r\n\r\n        @test isempty(model)\r\n    end\r\n\r\n    @testset \"reset_model!()\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        x = SeaPearl.IntVar(2, 5, \"x\", trailer)\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addObjective!(model, x)\r\n\r\n        SeaPearl.assign!(x, 3)\r\n        SeaPearl.fixPoint!(model)\r\n        SeaPearl.triggerFoundSolution!(model)\r\n\r\n        @test SeaPearl.length(x.domain) == 1\r\n        @test model.objectiveBound == 2\r\n        SeaPearl.reset_model!(model)\r\n        @test SeaPearl.length(x.domain) == 4\r\n        @test isnothing(model.objectiveBound)\r\n    end\r\n    @testset \"restart_search\" begin\r\n\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        x = SeaPearl.IntVar(1, 2, \"x\", trailer)\r\n        y = SeaPearl.IntVar(1, 2, \"y\", trailer)\r\n        z = SeaPearl.IntVar(1, 2, \"z\", trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n        SeaPearl.addVariable!(model, z)\r\n        SeaPearl.addConstraint!(model, SeaPearl.NotEqual(x, y, trailer))\r\n        SeaPearl.addConstraint!(model, SeaPearl.NotEqual(y, z, trailer))\r\n        SeaPearl.addConstraint!(model, SeaPearl.NotEqual(x, z, trailer))\r\n\r\n        SeaPearl.search!(model, SeaPearl.DFSearch(), SeaPearl.MinDomainVariableSelection(), SeaPearl.BasicHeuristic())\r\n        @test model.statistics.numberOfInfeasibleSolutionsBeforeRestart == 2\r\n        @test model.statistics.numberOfNodesBeforeRestart == 3\r\n        @test model.statistics.numberOfSolutionsBeforeRestart == 0\r\n\r\n        SeaPearl.restart_search!(model)\r\n\r\n        @test model.statistics.numberOfInfeasibleSolutionsBeforeRestart == 0\r\n        @test model.statistics.numberOfNodesBeforeRestart == 0\r\n        @test model.statistics.numberOfSolutionsBeforeRestart == 0\r\n    end\r\n\r\n    @testset \"addKnownObjective!\" begin\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        SeaPearl.addKnownObjective!(model, 1)\r\n        @test model.knownObjective == 1\r\n\r\n    end\r\n    @testset \"triggerInfeasible!\" begin\r\n\r\n        trailer = SeaPearl.Trailer()\r\n        model = SeaPearl.CPModel(trailer)\r\n\r\n        x = SeaPearl.IntVar(1, 2, \"x\", trailer)\r\n        y = SeaPearl.IntVar(1, 2, \"y\", trailer)\r\n        z = SeaPearl.IntVar(1, 2, \"z\", trailer)\r\n\r\n        SeaPearl.addVariable!(model, x)\r\n        SeaPearl.addVariable!(model, y)\r\n        SeaPearl.addVariable!(model, z)\r\n\r\n        SeaPearl.addConstraint!(model, SeaPearl.NotEqual(x, y, trailer))\r\n        SeaPearl.addConstraint!(model, SeaPearl.NotEqual(y, z, trailer))\r\n\r\n\r\n        @test model.statistics.infeasibleStatusPerVariable[\"x\"] == 1\r\n        @test model.statistics.infeasibleStatusPerVariable[\"y\"] == 2\r\n        @test model.statistics.infeasibleStatusPerVariable[\"z\"] == 1\r\n\r\n        constraint = model.constraints[1]\r\n        SeaPearl.triggerInfeasible!(constraint, model)\r\n        @test model.statistics.infeasibleStatusPerVariable[\"x\"] == 2\r\n        @test model.statistics.infeasibleStatusPerVariable[\"y\"] == 3\r\n\r\n    end\r\nend\r\n", "meta": {"hexsha": "609a988b64c22b3483f3c7e61fcad8d3fad80775", "size": 11286, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/CP/core/model.jl", "max_stars_repo_name": "pitmonticone/SeaPearl.jl", "max_stars_repo_head_hexsha": "0c0ca5ec5cce81515acd202ea2d87c985c0c3fea", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 44, "max_stars_repo_stars_event_min_datetime": "2021-04-20T16:29:52.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T07:17:03.000Z", "max_issues_repo_path": "test/CP/core/model.jl", "max_issues_repo_name": "pitmonticone/SeaPearl.jl", "max_issues_repo_head_hexsha": "0c0ca5ec5cce81515acd202ea2d87c985c0c3fea", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 65, "max_issues_repo_issues_event_min_datetime": "2021-04-23T17:20:56.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-22T23:42:24.000Z", "max_forks_repo_path": "test/CP/core/model.jl", "max_forks_repo_name": "pitmonticone/SeaPearl.jl", "max_forks_repo_head_hexsha": "0c0ca5ec5cce81515acd202ea2d87c985c0c3fea", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2021-05-10T23:32:49.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-15T02:44:34.000Z", "avg_line_length": 32.9037900875, "max_line_length": 143, "alphanum_fraction": 0.5972000709, "num_tokens": 3209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.11436853070975538, "lm_q1q2_score": 0.05584425448995529}}
{"text": "##### model_diagnostics.jl\n# Created by Alexander Dowling\n\n###############\n##### Exported functions for model diagnostics\n\nfunction round_to_bounds!(m::Model, epsilon::Float64=1.0E-6, f=STDOUT)\n\t# This function rounds each element of the solution vector to the closest bound if\n\t# it is within epsilon.\n\t# Why is this necessary? Some solvers (e.g., IPOPT) give solutions that are slightly\n\t# infeasible or violate bounds. This is problematic in a meta-algorithm with calls\n\t# to other solvers.\n\t\n\tx = get_x(m, :Unsolved)\n\t\n\tup = m.colUpper\n\tlo = m.colLower\n\t\n\tfor i = 1:length(x)\n\t\n\t\tv = Variable(m, i)\n\t\n\t\tif(up[i] < lo[i])\n\t\t\tprintln(f,\"Warning: Upper bound (\",up[i],\") is smaller than lower bound (\",lo[i],\") for variable \",v)\n\t\tend\n\t\n\t\tx_ = NaN\n\t\n\t\tif(x[i] > up[i])\n\t\t\tx_ = up[i]\n\t\telseif(x[i] < lo[i])\n\t\t\tx_ = lo[i]\n\t\telseif(up[i] - x[i] < epsilon)\n\t\t\tx_ = up[i]\n\t\telseif(x[i] - lo[i] < epsilon)\n\t\t\tx_ = lo[i]\n\t\tend\n\t\t\n\t\tif(!isnan(x_))\n\t\t\tsetvalue(v, x_)\n\t\tend\n\t\n\tend\n\t\n\treturn nothing\n\nend\n\nfunction printVariableDiagnostics(m::Model, epsilon::Float64=1.0E-6, f=STDOUT, status::Symbol=:Unknown)\n\tx = get_x(m, status)\n\n\tprintln(f,\"Uninitialized Variables: \")\n\tfor i = 1:length(x)\n\t\tif(isnan(x[i]))\n\t\t\tv = Variable(m, i)\n\t\t\t#println(\"x[\",i,\"] = \",v)\n\t\t\tprintln(f,v)\n\t\tend\n\tend\n\tprintln(f,\" \")\n\n\tprintln(f,\"Variable Violating Bounds: \")\n\tup = m.colUpper\n\tlo = m.colLower\n\n\tfor i = 1:length(x)\n\t\tviol_up = x[i] - up[i] > epsilon\n\t\tviol_lo = x[i] - lo[i] < -epsilon\n\t\tif(viol_up || viol_lo)\n\n\t\t\tv = Variable(m, i)\n\t\t\tprint(f,v, \" = \",x[i], \", bounds: [ \")\n\n\t\t\tif(viol_lo)\n\t\t\t\tprint_with_color(:red, f, string(lo[i]))\n\t\t\telse\n\t\t\t\tprint(f, string(lo[i]))\n\t\t\tend\n\n\t\t\tprint(f, \" , \")\n\n\t\t\tif(viol_up)\n\t\t\t\tprint_with_color(:green, f, string(up[i]))\n\t\t\telse\n\t\t\t\tprint(f, string(up[i]))\n\t\t\tend\n\n\t\t\tprintln(f, \" ]\")\n\t\tend\n\tend\n\n\treturn nothing\n\nend\n\nfunction printInfeasibleEquations(m2::Model, eqns, f=STDOUT, status::Symbol=:Unknown)\n\n\tdd = DegenData(m2, status)\n\treturn printInfeasibleEquations(m2, dd, eqns, f)\n\nend\n\nfunction printInfeasibleEquations(m2::Model, dd::DegenData, eqns, f=STDOUT)\n\n\tprintln(f,\"Checking infeasibility for specified equations: \")\n\n\tr = reshape(min(dd.g - dd.gLB, 0) + max(dd.g - dd.gUB, 0),length(dd.g))\n\n\tfor i = eqns\n\t\tprint(f, \"r[\",string(i),\"] = \")\n\t\tprint_with_color(:green,f,string(r[i]))\n\t\tprint(f,\"\\n\")\n\n\t\tprintEquation(m2, dd, i, f)\n\n\t\tprintln(f, \" \")\n\n\tend\n\n\treturn nothing\n\nend\n\nfunction printInfeasibleEquations(m2::Model, epsilon::Float64, f=STDOUT, status::Symbol=:Unknown)\n\n\tdd = DegenData(m2, f, status)\n\treturn printInfeasibleEquations(m2, dd, epsilon, f)\nend\n\nfunction printInfeasibleEquations(m2::Model, dd::DegenData, epsilon::Float64, f=STDOUT)\n\n\tprintln(f,\"Infeasible equations: \")\n\n\t#=\n\tprintln(\"size(dd.g) = \",size(dd.g))\n\tprintln(\"size(dd.gLB) = \",size(dd.gLB))\n\tprintln(\"size(dd.gUB) = \",size(dd.gUB))\n\t=#\n\n\tr = reshape(min(dd.g - dd.gLB, 0) + max(dd.g - dd.gUB, 0),length(dd.g))\n\n\tk = sortperm(abs(r))\n\n\tfor j = 1:length(k)\n\n\t\ti = k[j]\n\n\t\tif(abs(r[i]) >= epsilon || isnan(r[i]))\n\t\t\tprint(f,\"r[\",string(i),\"] = \")\n\t\t\tprint_with_color(:green,f,string(r[i]))\n\t\t\tprint(f,\"\\n\")\n\n\t\t\tprintEquation(m2, dd, i,f)\n\n\t\t\tprintVariablesInEquation(m2, dd, i, true, true, f)\n\n\t\t\tprintln(f,\" \")\n\n\t\tend\n\n\tend\n\n\treturn nothing\n\nend\n\nfunction printInactiveEquations(m2::Model, epsilon::Float64=1E-6, f=STDOUT)\n\n\tdd = DegenData(m2, f)\n\treturn printInactiveEquations(m2, dd, epsilon, f)\n\nend\n\nfunction printInactiveEquations(m2::Model, dd::DegenData, epsilon::Float64=1E-6, f=STDOUT)\n\n\tprintln(f,\"Inactive Equations: \")\n\n\tinactive = ((dd.gUB - dd.g) .> epsilon) & ((dd.g - dd.gLB) .> epsilon)\n\n\tfor i = 1:length(dd.g)\n\t\tif(((dd.gUB[i] - dd.g[i]) .> epsilon) & ((dd.g[i] - dd.gLB[i]) .> epsilon))\n\t\t\tprintEquation(m2, dd, i, f)\n\t\tend\n\tend\n\n\treturn nothing\n\nend\n\nfunction checkVarBounds(m::Model, f=STDOUT)\n\n\tlow = m.colLower\n\tup = m.colUpper\n\tval = m.colVal\n\n\tn = length(val)\n\n\tfor i = 1:n\n\t\tif(up[i] < low[i] || val[i] < low[i] || val[i] > up[i])\n\n\t\t\tprint(f,getname(m,i),\":\\t\\t\",\"Lower: \")\n\n\t\t\tif(up[i] < low[i] || val[i] < low[i])\n\t\t\t\tprint_with_color(:red,f,string(low[i]))\n\t\t\telse\n\t\t\t\tprint(f,low[i])\n\t\t\tend\n\n\t\t\tprint(f,\"\\t Upper: \")\n\n\t\t\tif(up[i] < low[i] || val[i] > up[i])\n\t\t\t\tprint_with_color(:blue,f,string(up[i]))\n\t\t\telse\n\t\t\t\tprint(f,up[i])\n\t\t\tend\n\n\t\t\tprint(f,\"\\t Value: \")\n\n\t\t\tif(val[i] < low[i] || val[i] > up[i])\n\t\t\t\tprint_with_color(:green,f,string(val[i]))\n\t\t\telse\n\t\t\t\tprint(f,val[i])\n\t\t\tend\n\n\t\t\tprint(f,\"\\n\")\n\t\tend\n\tend\n\n\treturn nothing\n\nend\n\nfunction printBound(m::Model, i::Int64, epsiActive::Float64, f=STDOUT)\n\n\tv = Variable(m, i)\n\n\tup = m.colUpper[i]\n\tlo = m.colLower[i]\n\tval = m.colVal[i]\n\n\tif(abs(val-lo) < epsiActive)\n\t\tprint(f,string(lo),\" <= \")\n\tend\n\n\tprint(getname(v))\n\n\tif(abs(val-up) < epsiActive)\n\t\tprint(f,\" <= \",string(up))\n\tend\n\n\tprint(f,\"\\n\")\n\n\treturn nothing\n\nend\n\nfunction printRows(m::Model, rows::Int64, f=STDOUT; lite::Bool=false)\n\treturn printRows(m, Array([rows]), f, lite)\nend\n\nfunction printRows(m::Model, rows::Array{Int64,1}, f=STDOUT; lite::Bool=false)\n\n\tdd = DegenData(m, f)\n\treturn printRows(m, dd, rows, f, lite)\n\nend\n\nfunction printRows(m::Model, dd::DegenData, rows::Int64, f=STDOUT; lite::Bool=false)\n\treturn printRows(m, dd, Array([rows]), f, lite=lite)\nend\n\nfunction printRows(m::Model, dd::DegenData, rows::Array{Int64,1}, f=STDOUT; lite::Bool=false)\n\n\tif(lite && length(rows) == 1)\n\t\n\t\tr = rows[1]\n\t\t\n\t\tif(r <= dd.nConstr)\n\t\t\tprintEquation(m, dd, rows[1], f)\n\t\telse\n\t\t\tprintBound(m, r - dd.nConstr, 1E-6, f)\n\t\tend\n\telse\n\n\t\tfor i = 1:length(rows)\n\t\t\tr = rows[i]\n\t\t\t\n\t\t\tprintln(f,\"**********************************\")\n\t\t\t\n\t\t\tif(r <= dd.nConstr)\n\t\t\t\t\t\n\t\t\t\tprintln(f,\"Constraint \",string(r),\"...\")\n\t\t\t\tprintEquation(m, dd, r, f)\n\t\t\t\tprintln(f,string(dd.gLB[r]),\" <= \", string(dd.g[r]), \" <= \",string(dd.gUB[r]))\n\n\t\t\t\tprintln(f,\" \")\n\t\t\t\tprintln(f,\"Involved Variables:\")\n\t\t\t\tk = find(dd.iR .== r)\n\n\t\t\t\tfor j in unique(dd.jC[k])\n\t\t\t\t\tv = Variable(m,j)\n\t\t\t\t\tprint(f,getname(v),\" \\t\")\n\t\t\t\t\tprintln(f,getlowerbound(v),\" <= \",getvalue(v),\" <= \",getupperbound(v))\n\t\t\t\tend\n\t\t\t\n\t\t\telse\n\t\t\t\tv = r - dd.nConstr\n\t\t\t\tprintln(f,\"Bound for variable \",v)\n\t\t\t\tprintBounds(m, v, 1E-6, f)\n\t\t\t\t\n\t\t\tend\n\n\t\t\tprintln(f,\" \")\n\n\t\tend\n\tend\n\n\n\treturn nothing\n\nend\n\nfunction checkEquationScaling(m::Model, status::Symbol=:Unknown, f=STDOUT)\n\n\tdd = DegenData(m, status)\n\t\n\tcheckJacobianScaling(m, dd, f)\n\n\treturn nothing\n\nend", "meta": {"hexsha": "7e8638430307dc46d37877fe3c6e677961767f0c", "size": 6373, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/model_diagnostics.jl", "max_stars_repo_name": "adowling2/DegeneracyHunter", "max_stars_repo_head_hexsha": "44b569c9504819be8f07544c3e5968ddbdf9cc86", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2016-09-06T18:15:26.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-04T14:10:38.000Z", "max_issues_repo_path": "src/model_diagnostics.jl", "max_issues_repo_name": "adowling2/DegeneracyHunter", "max_issues_repo_head_hexsha": "44b569c9504819be8f07544c3e5968ddbdf9cc86", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2016-09-02T00:32:50.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-26T12:12:28.000Z", "max_forks_repo_path": "src/model_diagnostics.jl", "max_forks_repo_name": "adowling2/DegeneracyHunter", "max_forks_repo_head_hexsha": "44b569c9504819be8f07544c3e5968ddbdf9cc86", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2017-04-13T13:07:45.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-27T18:15:06.000Z", "avg_line_length": 19.253776435, "max_line_length": 104, "alphanum_fraction": 0.6107013965, "num_tokens": 2076, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.11436852316318397, "lm_q1q2_score": 0.055844250805089785}}
{"text": "#=\n\n    Reading input\n\n=#\nfunction read_input()\n\n    pass=[]\n    policy=[]\n    letter=[]\n    open(\"./input\", \"r\") do f\n        while ! eof(f)\n            s::String = readline(f)\n            entry = split(s,\" \")\n            push!(pass,entry[3])\n            push!(letter,entry[2][1])\n\n            policy_split = split(entry[1],\"-\")\n            policy_tuple = (parse(Int32,policy_split[1]), \n                            parse(Int32,policy_split[2]))\n            push!(policy,policy_tuple)\n        end\n    end\n\n    return pass, policy, letter\nend\n\ninput = read_input()\npass = input[1]\npolicy = input[2]\nletter = input[3]\n\n\n\n\n#=\n\n--- Day 2: Password Philosophy ---\n\nYour flight departs in a few days from the coastal airport; the easiest way down to \nthe coast from here is via toboggan.\n\nThe shopkeeper at the North Pole Toboggan Rental Shop is having a bad day. \"Something's \nwrong with our computers; we can't log in!\" You ask if you can take a look.\n\nTheir password database seems to be a little corrupted: some of the passwords wouldn't \nhave been allowed by the Official Toboggan Corporate Policy that was in effect when they \nwere chosen.\n\nTo try to debug the problem, they have created a list (your puzzle input) of passwords \n(according to the corrupted database) and the corporate policy when that password was set.\n\nFor example, suppose you have the following list:\n\n1-3 a: abcde\n1-3 b: cdefg\n2-9 c: ccccccccc\n\nEach line gives the password policy and then the password. The password policy indicates \nthe lowest and highest number of times a given letter must appear for the password to be \nvalid. For example, 1-3 a means that the password must contain a at least 1 time and at \nmost 3 times.\n\nIn the above example, 2 passwords are valid. The middle password, cdefg, is not; it contains \nno instances of b, but needs at least 1. The first and third passwords are valid: they contain \none a or nine c, both within the limits of their respective policies.\n\nHow many passwords are valid according to their policies?\n\n=#\n\n\nfunction is_valid(policy, letter, password)\n    letter_count = count(==(letter), password)\n    if letter_count<policy[1]\n        return false \n    elseif letter_count>policy[2]\n        return false\n    else\n        return true\n    end\nend\n\nfunction count_valid_passwords(policy, letter, pass)\n    count_valid = 0\n    for i in 1:size(pass,1)\n        if is_valid(policy[i], letter[i], pass[i])\n            count_valid+=1\n        end\n    end\n    return count_valid\nend\n\nprintln(size(policy,1),\", \",size(letter,1), \", \", size(pass,1))\nprintln(\"Valid passwords found: \", count_valid_passwords(policy, letter, pass))\n\n\n#=\n--- Part Two ---\n\nWhile it appears you validated the passwords correctly, they don't seem to be \nwhat the Official Toboggan Corporate Authentication System is expecting.\n\nThe shopkeeper suddenly realizes that he just accidentally explained the password \npolicy rules from his old job at the sled rental place down the street! The Official \nToboggan Corporate Policy actually works a little differently.\n\nEach policy actually describes two positions in the password, where 1 means the \nfirst character, 2 means the second character, and so on. (Be careful; Toboggan \nCorporate Policies have no concept of \"index zero\"!) Exactly one of these positions \nmust contain the given letter. Other occurrences of the letter are irrelevant for \nthe purposes of policy enforcement.\n\nGiven the same example list from above:\n\n    1-3 a: abcde is valid: position 1 contains a and position 3 does not.\n    1-3 b: cdefg is invalid: neither position 1 nor position 3 contains b.\n    2-9 c: ccccccccc is invalid: both position 2 and position 9 contain c.\n\nHow many passwords are valid according to the new interpretation of the policies?\n=#\n\nfunction is_valid(policy, letter, password)\n    in_fst_pos = (password[policy[1]] == letter)\n    in_snd_pos = (password[policy[2]] == letter)\n\n    if in_fst_pos\n        if in_snd_pos\n            return false\n        else\n            return true\n        end\n    elseif in_snd_pos\n        return true\n    else\n        return false\n    end\nend\n\nfunction count_valid_passwords(policy, letter, pass)\n    count_valid = 0\n    for i in 1:size(pass,1)\n        if is_valid(policy[i], letter[i], pass[i])\n            count_valid+=1\n        end\n    end\n    return count_valid\nend\n\nprintln(\"Valid passwords found with second policy: \", count_valid_passwords(policy, letter, pass))", "meta": {"hexsha": "79dab5720a6ef06ce8e079d844719f8adcb50880", "size": 4432, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Day2/password_philosophy.jl", "max_stars_repo_name": "colombelli/AoC2020-julia", "max_stars_repo_head_hexsha": "ae857741dccad0dc4f7da3636b3c7dd6fe00bd3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Day2/password_philosophy.jl", "max_issues_repo_name": "colombelli/AoC2020-julia", "max_issues_repo_head_hexsha": "ae857741dccad0dc4f7da3636b3c7dd6fe00bd3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Day2/password_philosophy.jl", "max_forks_repo_name": "colombelli/AoC2020-julia", "max_forks_repo_head_hexsha": "ae857741dccad0dc4f7da3636b3c7dd6fe00bd3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5466666667, "max_line_length": 98, "alphanum_fraction": 0.6949458484, "num_tokens": 1065, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958346, "lm_q2_score": 0.11436851561661297, "lm_q1q2_score": 0.05584424712022446}}
{"text": "@testset \"1089.duplicate-zeros.jl\" begin\n    arr = [1,0,2,3,0,4,5,0]\n    duplicate_zeros!(arr)\n    @test arr == [1,0,0,2,3,0,0,4]\n\n    arr2 = [1,2,3]\n    duplicate_zeros!(arr2)\n    @test arr2 == [1,2,3]\nend\n", "meta": {"hexsha": "e273f8b33ffd9bcb6bb3b722bd517ec2c75d612e", "size": 207, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/1089.duplicate-zeros.jl", "max_stars_repo_name": "Moelf/LeetCode.jl", "max_stars_repo_head_hexsha": "c5a37331356060b743bc850c5255d6695d30fb57", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/problems/1089.duplicate-zeros.jl", "max_issues_repo_name": "Moelf/LeetCode.jl", "max_issues_repo_head_hexsha": "c5a37331356060b743bc850c5255d6695d30fb57", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/problems/1089.duplicate-zeros.jl", "max_forks_repo_name": "Moelf/LeetCode.jl", "max_forks_repo_head_hexsha": "c5a37331356060b743bc850c5255d6695d30fb57", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.7, "max_line_length": 40, "alphanum_fraction": 0.5603864734, "num_tokens": 95, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.12085323090725614, "lm_q1q2_score": 0.05571536728235085}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.2\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 c3530026-5a87-4447-88f5-fc744cb48ff2\nbegin\n\tusing PlutoUI \nend\n\n# \u2554\u2550\u2561 2c895e14-e71a-435d-86d4-c1b101877b3c\nmd\"\"\"\n### The End of the Beginning: Whole-Cell Metabolic Engineering\n\nIn the first lecture, we introduced metabolic engineering. Today, we'll close the loop at put together all we learned. Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a desired small molecule or protein product of interest. \n\nMetabolic engineers manipulate the biochemical networks cells use to convert raw materials into molecules necessary for the cell's survival. Metabolic engineering specifically seeks to:\n\n1. Mathematically model biochemical networks, calculate the yield (product divided by substrate) of valuable products and identify parts of the network that constrain the production of these products of interest. \n1. Use genetic engineering techniques to modify the biochemical network to relieve constraints limiting production. Metabolic engineers can then model the modified network to calculate the new product yield and identify new constraints (back to 1).\n\nIn this (the final lecture of Part I of the course), we'll:\n\n* Incorporate the costs of transcription and translation into the flux balance analysis problem\n* Introduce a workaround (often used in practice) to account for the metabolic cost of gene expression\n* Close the loop: using a metabolic model and flux balance to optimize glycan production in _E.coli_. \n\n\"\"\"\n\n# \u2554\u2550\u2561 ce5c8e9c-7652-4c3e-98a7-98e3567dc695\nmd\"\"\"\n### Integrating the Cost and Logic of Gene Expression With Flux Balance Analysis\n\nThe [Allen and Palsson study](https://pubmed.ncbi.nlm.nih.gov/12453446/) gave us a roadmap of integrating the cost of gene expression with the metabolic operation of the cell. Further, [Vilkhovoy et al.](https://pubmed.ncbi.nlm.nih.gov/29944340/) showed how we could do this in a cell-free system. However, how do you account for gene expression in metabolic models at the whole-genome scale?\n\n* [Thiele I, Jamshidi N, Fleming RM, Palsson B\u00d8. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol. 2009;5(3):e1000312. doi:10.1371/journal.pcbi.1000312](https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC2648898/?report=classic)\n* [Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, Schrimpe-Rutledge AC, Smith RD, Adkins JN, Zengler K, Palsson BO. In silico method for modeling metabolism and gene product expression at genome scale. Nat Commun. 2012 Jul 3;3:929. doi: 10.1038/ncomms1928. PMID: 22760628; PMCID: PMC3827721.](https://pubmed.ncbi.nlm.nih.gov/22760628/)\n\"\"\"\n\n# \u2554\u2550\u2561 bfd134fe-b311-4768-a53b-2c5c4c1b121a\nmd\"\"\"\n##### Aside: A question of timescales\nOne open question is whether we need to solve the gene expression and flux estimation problem simultaneously. The time scale of enzyme activity (and hence metabolic flux) is much faster than gene expression. Why? \n\n* The $k_{cat}$ for [phosphofructokinase-2 (pfk) in _E.coli_ is 9240 s$^{-1}$](https://bionumbers.hms.harvard.edu/bionumber.aspx?id=104955&ver=7&trm=kcat+pfk+in+E.+coli&org=). Thus, pfk has a characteristic time scale of $\\tau\\sim{k_{cat}}^{-1}$ or about 1$\\times$10$^{-4}$ s.\n* The $k_{cat}$ for RNAP polymerase is $k_{cat}$=$e_{x}L^{-1}$. If we assume [$e_{x}\\sim{35}$ nt/s](https://bionumbers.hms.harvard.edu/bionumber.aspx?id=111871&ver=1&trm=elongation+rate+RNAP+E.coli+&org=) and average read length [$L\\sim{924}$ nt](https://bionumbers.hms.harvard.edu/bionumber.aspx?id=111922&ver=3&trm=avarge+gene+length+in+E.+coli&org=), this gives $k_{cat}\\sim$ 0.038 s$^{-1}$ or a timescale of $\\tau\\sim$26.4 s.\n\nThus, the time scale of metabolic flux (e.g., the timescale of enzyme activity) is approximately 5-orders of magnitude faster than gene expression. Therefore, gene expression seems constant  from the perspective of metabolic flux; hence, we treat these scales separately in traditional models. \n\"\"\"\n\n# \u2554\u2550\u2561 8041cb6c-b80f-4e81-8d1d-5a2e9942d809\nmd\"\"\"\n##### Aside: How do you account for macromolecular synthesis without the E/ME formulation?\nThe E/ME formulation of Palsson and coworkers is detailed and requires more time investment than perhaps you're willing to give in a metabolic engineering application. How do you account for the energy and resource cost of producing enzymes (and all the other machinery) required to make a cell?\n\nThe traditional way metabolic engineers address this question is to treat cell mass formation as just another reaction, e.g., the growth reaction $\\mu$: \n\n$$\\left\\{precursors\\right\\}~\\rightarrow~cell$$\n\nwhere each cellmass precursor has some stoichiometric coefficient $\\sigma_{i,\\mu}$ which describes how much of precursor $i$ is consumed (or produced) during cellmass formation. This formulation leads to species material balances of the form (assuming cellmass specific units, in a constant volume batch culture):\n\n$$\\frac{dx_{i}}{dt} = \\sum_{j=1}^{\\mathcal{R}}\\sigma_{ij}\\hat{r}_{j} - \\left(x_{i}-\\sigma_{i,\\mu}\\right)\\mu\\qquad{i=1,2,\\dots,\\mathcal{M}}$$\n\nAt steady state, for example in a flux balance analysis problem, these balances form the species constraints:\n\n$$\\sum_{j=1}^{\\mathcal{R}}\\sigma_{ij}\\hat{r}_{j} - \\left(x_{i}-\\sigma_{i,\\mu}\\right)\\mu = 0\\qquad{i=1,2,\\dots,\\mathcal{M}}$$\n\nPalsson and coworkers gave an example cell mass reaction for _E.coli_ in:\n\n* [Orth JD, Fleming RM, Palsson B\u00d8. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide. EcoSal Plus. 2010 Sep;4(1). doi: 10.1128/ecosalplus.10.2.1. PMID: 26443778.](https://pubmed.ncbi.nlm.nih.gov/26443778/)\n\nA recent paper on sampling uncertain biomass stoichiometric coefficients:\n\n* [Dinh et al. (2022) Quantifying the propagation of parametric uncertainty on flux balance analysis. Metabolic Engineering, 69:26-39; https://doi.org/10.1016/j.ymben.2021.10.012](https://www.sciencedirect.com/science/article/pii/S1096717621001634)\n\n\"\"\"\n\n# \u2554\u2550\u2561 06c3eafa-10b3-4a47-9a06-39f6abd29db3\nmd\"\"\"\n### Using flux balance analysis to improve metabolic performance\nUltimately a metabolic engineer uses a model to improve the performance of a production host or cell-free system. There is a huge variety of design approaches to improve metabolic function; let's look at a recent one from Cornell:\n\n* [Wayman JA, Glasscock C, Mansell TJ, DeLisa MP, Varner JD. Improving designer glycan production in Escherichia coli through model-guided metabolic engineering. Metab Eng Commun. 2019;9:e00088. Published 2019 Mar 29. doi:10.1016/j.mec.2019.e00088](https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC6454127/)\n\n\"\"\"\n\n# \u2554\u2550\u2561 b15b540b-8f13-4f21-b2da-510d34143b33\nmd\"\"\"\n### Summary and conclusions\n\nToday, we:\n\n* Incorporated the costs of transcription and translation into the flux balance analysis problem\n* Introduced a workaround (often used in practice) to account for the metabolic cost of gene expression\n* Closed the loop: using a metabolic model and flux balance to optimize glycan production in _E.coli_. \n\"\"\"\n\n# \u2554\u2550\u2561 a818128b-d642-49e0-8c7c-db36b37ea882\nmd\"\"\"\n### Alas, if we just had some more time, it would have been cool to talk about:\n\n##### So many interesting tools:\n\n* [Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012 Feb 27;10(4):291-305. doi: 10.1038/nrmicro2737. PMID: 22367118; PMCID: PMC3536058.](https://pubmed.ncbi.nlm.nih.gov/22367118/)\n\n##### Better constraints give better flux estimates:\n\n* [Buescher JM, Antoniewicz MR, Boros LG, et al. A roadmap for interpreting (13)C metabolite labeling patterns from cells. Curr Opin Biotechnol. 2015;34:189-201. doi:10.1016/j.copbio.2015.02.003](https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC4552607/)\n\n##### Flux balance analysis prediction of mutant response is not correct:\n\n* [Segr\u00e8 D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7. doi: 10.1073/pnas.232349399. Epub 2002 Nov 1. PMID: 12415116; PMCID: PMC137552.](https://pubmed.ncbi.nlm.nih.gov/12415116/)\n\n##### Molecular crowding plays a role in metabolism (non-representative random sample):\n\n\n* [Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barab\u00e1si AL, Oltvai ZN. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A. 2007 Jul 31;104(31):12663-8. doi: 10.1073/pnas.0609845104. Epub 2007 Jul 24. PMID: 17652176; PMCID: PMC1937523.](https://pubmed.ncbi.nlm.nih.gov/17652176/)'\n* [Conrado RJ, Mansell TJ, Varner JD, DeLisa MP. Stochastic reaction-diffusion simulation of enzyme compartmentalization reveals improved catalytic efficiency for a synthetic metabolic pathway. Metab Eng. 2007 Jul;9(4):355-63. doi: 10.1016/j.ymben.2007.05.002. Epub 2007 May 26. PMID: 17601761.](https://pubmed.ncbi.nlm.nih.gov/17601761/)\n* [Klumpp S, Scott M, Pedersen S, Hwa T. Molecular crowding limits translation and cell growth. Proc Natl Acad Sci U S A. 2013 Oct 15;110(42):16754-9. doi: 10.1073/pnas.1310377110. Epub 2013 Sep 30. PMID: 24082144; PMCID: PMC3801028.](https://pubmed.ncbi.nlm.nih.gov/22760628/)\n\n\"\"\"\n\n# \u2554\u2550\u2561 84d8a1c4-483c-4cc2-bdd0-7c88d26fd319\nTableOfContents(title=\"\ud83d\udcda Lecture Outline\", indent=true, depth=5, aside=true)\n\n# \u2554\u2550\u2561 7988263a-a5f5-11ec-3836-150105d04264\nhtml\"\"\"\n<style>\nmain {\n    max-width: 900px;\n    width: 70%;\n    margin: auto;\n    font-family: \"Roboto, monospace\";\n}\n\na {\n    color: blue;\n    text-decoration: none;\n}\n</style>\"\"\"\n\n# \u2554\u2550\u2561 dee24b55-a5c5-4a47-9e1c-88ea0cf1cd61\nhtml\"\"\"\n<script>\n\t// initialize -\n\tvar section = 0;\n\tvar subsection = 0;\n\tvar subsubsection = 0;\n\tvar headers = document.querySelectorAll('h3, h5, h6');\n\t\n\t// main loop -\n\tfor (var i=0; i < headers.length; i++) {\n\t    \n\t\tvar header = headers[i];\n\t    var text = header.innerText;\n\t    var original = header.getAttribute(\"text-original\");\n\t    if (original === null) {\n\t        \n\t\t\t// Save original header text\n\t        header.setAttribute(\"text-original\", text);\n\t    } else {\n\t        \n\t\t\t// Replace with original text before adding section number\n\t        text = header.getAttribute(\"text-original\");\n\t    }\n\t\n\t    var numbering = \"\";\n\t    switch (header.tagName) {\n\t        case 'H3':\n\t            section += 1;\n\t            numbering = section + \".\";\n\t            subsection = 0;\n\t            break;\n\t        case 'H5':\n\t            subsection += 1;\n\t            numbering = section + \".\" + subsection;\n\t            break;\n\t\t\tcase 'H6':\n\t            subsubsection += 1;\n\t            numbering = section + \".\" + subsection + \".\" + subsubsection;\n\t            break;\n\t    }\n\t\t// update the header text \n\t\theader.innerText = numbering + \" \" + text;\n\t};\n</script>\"\"\"\n\n# \u2554\u2550\u2561 00000000-0000-0000-0000-000000000001\nPLUTO_PROJECT_TOML_CONTENTS = \"\"\"\n[deps]\nPlutoUI = 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\"0.2.2\"\n\n[[deps.InteractiveUtils]]\ndeps = [\"Markdown\"]\nuuid = \"b77e0a4c-d291-57a0-90e8-8db25a27a240\"\n\n[[deps.JSON]]\ndeps = [\"Dates\", \"Mmap\", \"Parsers\", \"Unicode\"]\ngit-tree-sha1 = \"3c837543ddb02250ef42f4738347454f95079d4e\"\nuuid = \"682c06a0-de6a-54ab-a142-c8b1cf79cde6\"\nversion = \"0.21.3\"\n\n[[deps.LibCURL]]\ndeps = [\"LibCURL_jll\", \"MozillaCACerts_jll\"]\nuuid = \"b27032c2-a3e7-50c8-80cd-2d36dbcbfd21\"\n\n[[deps.LibCURL_jll]]\ndeps = [\"Artifacts\", \"LibSSH2_jll\", \"Libdl\", \"MbedTLS_jll\", \"Zlib_jll\", \"nghttp2_jll\"]\nuuid = \"deac9b47-8bc7-5906-a0fe-35ac56dc84c0\"\n\n[[deps.LibGit2]]\ndeps = [\"Base64\", \"NetworkOptions\", \"Printf\", \"SHA\"]\nuuid = \"76f85450-5226-5b5a-8eaa-529ad045b433\"\n\n[[deps.LibSSH2_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"MbedTLS_jll\"]\nuuid = \"29816b5a-b9ab-546f-933c-edad1886dfa8\"\n\n[[deps.Libdl]]\nuuid = \"8f399da3-3557-5675-b5ff-fb832c97cbdb\"\n\n[[deps.LinearAlgebra]]\ndeps = [\"Libdl\", \"libblastrampoline_jll\"]\nuuid = \"37e2e46d-f89d-539d-b4ee-838fcccc9c8e\"\n\n[[deps.Logging]]\nuuid = \"56ddb016-857b-54e1-b83d-db4d58db5568\"\n\n[[deps.Markdown]]\ndeps = [\"Base64\"]\nuuid = \"d6f4376e-aef5-505a-96c1-9c027394607a\"\n\n[[deps.MbedTLS_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"c8ffd9c3-330d-5841-b78e-0817d7145fa1\"\n\n[[deps.Mmap]]\nuuid = \"a63ad114-7e13-5084-954f-fe012c677804\"\n\n[[deps.MozillaCACerts_jll]]\nuuid = \"14a3606d-f60d-562e-9121-12d972cd8159\"\n\n[[deps.NetworkOptions]]\nuuid = \"ca575930-c2e3-43a9-ace4-1e988b2c1908\"\n\n[[deps.OpenBLAS_jll]]\ndeps = [\"Artifacts\", \"CompilerSupportLibraries_jll\", \"Libdl\"]\nuuid = \"4536629a-c528-5b80-bd46-f80d51c5b363\"\n\n[[deps.Parsers]]\ndeps = [\"Dates\"]\ngit-tree-sha1 = \"85b5da0fa43588c75bb1ff986493443f821c70b7\"\nuuid = \"69de0a69-1ddd-5017-9359-2bf0b02dc9f0\"\nversion = \"2.2.3\"\n\n[[deps.Pkg]]\ndeps = [\"Artifacts\", \"Dates\", \"Downloads\", \"LibGit2\", \"Libdl\", \"Logging\", \"Markdown\", \"Printf\", \"REPL\", \"Random\", \"SHA\", \"Serialization\", \"TOML\", \"Tar\", \"UUIDs\", \"p7zip_jll\"]\nuuid = \"44cfe95a-1eb2-52ea-b672-e2afdf69b78f\"\n\n[[deps.PlutoUI]]\ndeps = [\"AbstractPlutoDingetjes\", \"Base64\", \"ColorTypes\", \"Dates\", \"Hyperscript\", \"HypertextLiteral\", \"IOCapture\", \"InteractiveUtils\", \"JSON\", \"Logging\", \"Markdown\", \"Random\", \"Reexport\", \"UUIDs\"]\ngit-tree-sha1 = \"bf0a1121af131d9974241ba53f601211e9303a9e\"\nuuid = \"7f904dfe-b85e-4ff6-b463-dae2292396a8\"\nversion = \"0.7.37\"\n\n[[deps.Printf]]\ndeps = [\"Unicode\"]\nuuid = \"de0858da-6303-5e67-8744-51eddeeeb8d7\"\n\n[[deps.REPL]]\ndeps = [\"InteractiveUtils\", \"Markdown\", \"Sockets\", \"Unicode\"]\nuuid = \"3fa0cd96-eef1-5676-8a61-b3b8758bbffb\"\n\n[[deps.Random]]\ndeps = [\"SHA\", \"Serialization\"]\nuuid = \"9a3f8284-a2c9-5f02-9a11-845980a1fd5c\"\n\n[[deps.Reexport]]\ngit-tree-sha1 = \"45e428421666073eab6f2da5c9d310d99bb12f9b\"\nuuid = \"189a3867-3050-52da-a836-e630ba90ab69\"\nversion = \"1.2.2\"\n\n[[deps.SHA]]\nuuid = \"ea8e919c-243c-51af-8825-aaa63cd721ce\"\n\n[[deps.Serialization]]\nuuid = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n\n[[deps.Sockets]]\nuuid = \"6462fe0b-24de-5631-8697-dd941f90decc\"\n\n[[deps.SparseArrays]]\ndeps = [\"LinearAlgebra\", \"Random\"]\nuuid = \"2f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n\n[[deps.Statistics]]\ndeps = [\"LinearAlgebra\", \"SparseArrays\"]\nuuid = \"10745b16-79ce-11e8-11f9-7d13ad32a3b2\"\n\n[[deps.TOML]]\ndeps = [\"Dates\"]\nuuid = \"fa267f1f-6049-4f14-aa54-33bafae1ed76\"\n\n[[deps.Tar]]\ndeps = [\"ArgTools\", \"SHA\"]\nuuid = \"a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e\"\n\n[[deps.Test]]\ndeps = [\"InteractiveUtils\", \"Logging\", \"Random\", \"Serialization\"]\nuuid = \"8dfed614-e22c-5e08-85e1-65c5234f0b40\"\n\n[[deps.UUIDs]]\ndeps = [\"Random\", \"SHA\"]\nuuid = \"cf7118a7-6976-5b1a-9a39-7adc72f591a4\"\n\n[[deps.Unicode]]\nuuid = \"4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5\"\n\n[[deps.Zlib_jll]]\ndeps = [\"Libdl\"]\nuuid = \"83775a58-1f1d-513f-b197-d71354ab007a\"\n\n[[deps.libblastrampoline_jll]]\ndeps = [\"Artifacts\", \"Libdl\", \"OpenBLAS_jll\"]\nuuid = \"8e850b90-86db-534c-a0d3-1478176c7d93\"\n\n[[deps.nghttp2_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"8e850ede-7688-5339-a07c-302acd2aaf8d\"\n\n[[deps.p7zip_jll]]\ndeps = [\"Artifacts\", \"Libdl\"]\nuuid = \"3f19e933-33d8-53b3-aaab-bd5110c3b7a0\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25002c895e14-e71a-435d-86d4-c1b101877b3c\n# \u255f\u2500ce5c8e9c-7652-4c3e-98a7-98e3567dc695\n# \u255f\u2500bfd134fe-b311-4768-a53b-2c5c4c1b121a\n# \u255f\u25008041cb6c-b80f-4e81-8d1d-5a2e9942d809\n# \u255f\u250006c3eafa-10b3-4a47-9a06-39f6abd29db3\n# \u255f\u2500b15b540b-8f13-4f21-b2da-510d34143b33\n# \u255f\u2500a818128b-d642-49e0-8c7c-db36b37ea882\n# \u2560\u255084d8a1c4-483c-4cc2-bdd0-7c88d26fd319\n# \u2560\u2550c3530026-5a87-4447-88f5-fc744cb48ff2\n# \u255f\u25007988263a-a5f5-11ec-3836-150105d04264\n# \u255f\u2500dee24b55-a5c5-4a47-9e1c-88ea0cf1cd61\n# \u255f\u250000000000-0000-0000-0000-000000000001\n# \u255f\u250000000000-0000-0000-0000-000000000002\n", "meta": {"hexsha": "22c3a7a3f1c4f2a529ab0b83f1db20314e2a40a9", "size": 17419, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lectures/Lecture-13-5440-7770-S2022.jl", "max_stars_repo_name": "varnerlab/CHEME-5440-7770-Cornell-Spring-2022", "max_stars_repo_head_hexsha": "f580d12cb9d08657b6e7488280e5bf8391bb8530", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-02-07T23:40:03.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-14T19:06:26.000Z", "max_issues_repo_path": "lectures/Lecture-13-5440-7770-S2022.jl", "max_issues_repo_name": "varnerlab/CHEME-5440-7770-Cornell-Spring-2022", "max_issues_repo_head_hexsha": "f580d12cb9d08657b6e7488280e5bf8391bb8530", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lectures/Lecture-13-5440-7770-S2022.jl", "max_forks_repo_name": "varnerlab/CHEME-5440-7770-Cornell-Spring-2022", "max_forks_repo_head_hexsha": "f580d12cb9d08657b6e7488280e5bf8391bb8530", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2022-02-03T15:15:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T22:46:36.000Z", "avg_line_length": 41.6722488038, "max_line_length": 429, "alphanum_fraction": 0.7331649348, "num_tokens": 6142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3522017684487511, "lm_q2_score": 0.15817435274843167, "lm_q1q2_score": 0.05570928676123421}}
{"text": "using PMI\nusing Test\n\n@test PMI.initialized() == false\n\nPMI.init()\n\n@test PMI.initialized()\n\nnranks = PMI.get_size()\nrank = PMI.get_rank()\n\n@test nranks >= 1\n@test 0 <= rank < nranks\n\nPMI.barrier()\n\n@test PMI.get_clique_size() >= 1\n@test rank \u2208 PMI.get_clique_ranks()\n\nPMI.finalize()", "meta": {"hexsha": "d39a3742e3c2bdef169ffe01b5a4df68e7319b7d", "size": 283, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_setup.jl", "max_stars_repo_name": "JuliaParallel/PMI.jl", "max_stars_repo_head_hexsha": "85c04b987d6f53bedbeb6d63b1b93344199aaae3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-13T11:25:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-13T11:25:10.000Z", "max_issues_repo_path": "test/test_setup.jl", "max_issues_repo_name": "JuliaParallel/PMI.jl", "max_issues_repo_head_hexsha": "85c04b987d6f53bedbeb6d63b1b93344199aaae3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-11-30T02:37:28.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-30T02:37:28.000Z", "max_forks_repo_path": "test/test_setup.jl", "max_forks_repo_name": "JuliaParallel/PMI.jl", "max_forks_repo_head_hexsha": "85c04b987d6f53bedbeb6d63b1b93344199aaae3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.4761904762, "max_line_length": 35, "alphanum_fraction": 0.6855123675, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.12940274166401952, "lm_q1q2_score": 0.055662246226516776}}
{"text": "n = parse(Int64, readline())\nA = map((x) -> parse(Int64,x) , split(readline(), \" \"))\n\nfor i in n:-1:1\n    print(A[i],\" \")\nend\nprintln()", "meta": {"hexsha": "3b86ce39f779f8de1acf5df924d0c7915c3c1e1d", "size": 135, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Hackerrank/30 Days of Code/Julia/day 07.jl", "max_stars_repo_name": "Next-Gen-UI/Code-Dynamics", "max_stars_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Hackerrank/30 Days of Code/Julia/day 07.jl", "max_issues_repo_name": "Next-Gen-UI/Code-Dynamics", "max_issues_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Hackerrank/30 Days of Code/Julia/day 07.jl", "max_forks_repo_name": "Next-Gen-UI/Code-Dynamics", "max_forks_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.2857142857, "max_line_length": 55, "alphanum_fraction": 0.5481481481, "num_tokens": 47, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4301473631961697, "lm_q2_score": 0.12940273494909915, "lm_q1q2_score": 0.055662245228727834}}
{"text": "macro import_basic_huge()\n    :(\n        using ComplexVisual:\n            CV_Error, cv_error,\n            CV_TranslateByOffset, CV_MultiplyByFactor,\n            CV_CyclicValue, cv_set_value!,\n            cv_create_angle_cross_test, cv_half,\n            CV_AttachType, cv_north, cv_south, cv_east, cv_west\n     )\nend\n\n# {{{ Error Handling\nstruct CV_Error <: Exception\n    msg :: String;\nend\ncv_error(messages::String...) = throw(CV_Error(join(messages)))\n\ncv_error(something...) = cv_error(\n    (@sprintf(\"%s\", thing) for thing in something)...)\n# }}}\n\n# {{{ helper functions for showing concrete datatypes with their fields\n\n\"\"\"\nreturn fieldnames of a Datatype. Put the `parent_...` fields at the end.\n\"\"\"\nfunction cv_sorted_fieldnames(t::DataType)\n    fnames = fieldnames(t)\n    return tuple(\n        filter(x -> !contains(string(x), \"parent_\"), fnames)...,\n        filter(x ->  contains(string(x), \"parent_\"), fnames)...)\nend\n\nfunction cv_show_value_replacements(value)\n    if value isa Cairo.CairoSurfaceImage  || value isa Cairo.CairoContext\n        value = value.ptr\n    end\n    if value isa CV_LineSegments\n        value = string(length(value)) * \" CV_LineSegments\"\n    end\n    return value\nend\n\n\"\"\"\nimplementation for `show(io, obj)` for ComplexVisual-objects with\n`typeof(obj) isa DataType`\n\"\"\"\nfunction cv_show_impl(io::IO, obj)\n    t = typeof(obj)::DataType\n    print(io, string(t.name.name), '(')\n    first = true\n    fnames = cv_sorted_fieldnames(t)\n    for name_sym in fnames\n        value = getfield(obj, name_sym)\n        vtype = typeof(value)\n        value = cv_show_value_replacements(value)\n        if !first\n            print(io, \", \")\n        end\n        print(io, string(name_sym), \": \")\n        if contains(string(vtype.name.name), '#')\n            show(io, MIME(\"text/plain\"), value)\n        else\n            show(io, value)\n        end\n        first = false\n    end\n    print(io, ')')\n    return nothing\nend\n\n\"\"\"\nimplementation for `show(io, mime, obj)` for ComplexVisual-objects with\n`typeof(obj) isa DataType` and where `mime` is \"text/plain\".\n\"\"\"\nfunction cv_show_impl(io::IO, m::MIME{Symbol(\"text/plain\")}, obj)\n    t = typeof(obj)\n    outer_indent = (get(io, :cv_indent, \"\")::AbstractString)\n    indent = outer_indent * \"  \"\n    iio = IOContext(io, :cv_indent => indent)\n    println(io, string(t.name.name), '(')\n    fnames = cv_sorted_fieldnames(t)\n    for name_sym in fnames\n        print(io, indent, string(name_sym), \": \")\n        show(iio, m, cv_show_value_replacements(getfield(obj, name_sym)))\n        println(io)\n    end\n    print(io, outer_indent, ')')\n    return nothing\nend\n# }}} \n\n# {{{ Dynamic helpers\n\"\"\"\nA mutable struct with one (mutable) `value` (i.e. degree of freedom).\nThe struct is callable. When called, the input is translated by the\ngiven `value`.\n\"\"\"\nmutable struct CV_TranslateByOffset{N<:Number} # {{{\n    value    :: N\n\n    function CV_TranslateByOffset(::Type{T}) where {T<:Number}\n        return new{T}(zero(T))\n    end\nend\nfunction (tbp::CV_TranslateByOffset{N})(z::N) where {N}\n    return z + tbp.value\nend # }}}\n\n\"\"\"\nA mutable struct with one `factor` as degree of freedom.\nThe struct is callable. When called, the input is multiplied by\nthe given `factor.\n\nIf the factor is a complex number then this can be used to code\na scaling and rotation operator.\n\"\"\"\nmutable struct CV_MultiplyByFactor{N<:Number} # {{{\n    factor   :: N\n    function CV_MultiplyByFactor(::Type{T}) where {T<:Number}\n        return new{T}(one(T))\n    end\nend\nfunction (mbf::CV_MultiplyByFactor{N})(z::N) where {N}\n    return mbf.factor * z\nend\n# }}}\n\n\"\"\"\nA mutable struct with an integer `value` in the interval [1, maxvalue].\nThe struct is callable. When called, the value is increased by 1 or \nset to 1 if maxvalue was reached.\n\"\"\"\nmutable struct CV_CyclicValue{maxV} # {{{\n    value :: Int\n\n    function CV_CyclicValue(maxV::Int)\n        maxV < 1 && cv_error(\"CV_CyclicValue: maxV must be >= 1\")\n        return new{maxV}(1)\n    end\nend\nfunction (cv::CV_CyclicValue{maxV})() where {maxV}\n    cv.value = (cv.value == maxV) ? 1 : cv.value + 1\n    return nothing\nend\n\nfunction cv_set_value!(cv::CV_CyclicValue{maxV}, new_value::Int) where {maxV}\n    if !(1 \u2264 new_value \u2264 maxV)\n        cv_error(\"cv_set_value: new_value must be in [1, \", string(maxV),\n            \"]; but found: \", string(new_value))\n    end\n    cv.value = new_value\n    return nothing\nend\n\n\n# }}}\n\n# }}}\n\n# {{{ Helper functions\n\"\"\"\ncreates a test, which checks if a line segment [z,w] intersects\nthe line segemnt [rmin*exp(i\u03d5), rmax*exp(i\u03d5)].\n\"\"\"\nfunction cv_create_angle_cross_test(\u03d5::Real, rmin::Real, rmax::Real; \u03b4=100eps())\n    em\u03d5, rmin, rmax = exp(-Float64(\u03d5)*1im), Float64(rmin), Float64(rmax)\n\n    return (z, w) -> begin\n        hz = z*em\u03d5\n        rhz, ihz = real(hz), imag(hz)\n        z == w &&  return (rmin \u2264 rhz \u2264 rmax) && (abs(ihz) \u2264 \u03b4)\n\n        hw = w*em\u03d5\n        rhw, ihw = real(hw), imag(hw)\n        if (abs(ihz) \u2264 \u03b4) && (abs(ihw) \u2264 \u03b4)\n            # both are very close to the cross-boundary\n            return (rmin \u2264 rhz \u2264 rmax) || (rmin \u2264 rhw \u2264 rmax)\n        else\n            sign(ihz)*sign(ihw) != -1 && return false\n            r = (rhz - rhw + rhw*ihz - rhz*ihw)/(ihz - ihw)\n            return rmin \u2264 r \u2264 rmax\n        end\n    end\nend\n\ncv_half(x::N) where {N<:Integer}        =  x \u00f7 N(2)\ncv_half(x::N) where {N<:AbstractFloat}  =  x/2\n\n# }}}\n\n# {{{ Directions/Locations \nconst cv_north, CV_northT = Val(:north), Val{:north}\nconst cv_south, CV_southT = Val(:south), Val{:south}\nconst cv_east, CV_eastT = Val(:east), Val{:east}\nconst cv_west, CV_westT = Val(:west), Val{:west}\nconst CV_AttachType = Union{CV_northT, CV_southT, CV_eastT, CV_westT}\n# }}}\n\n# vim:syn=julia:cc=79:fdm=marker:sw=4:ts=4:\n", "meta": {"hexsha": "986677de7e747d7c1378aed2fb2c4710f6b4a221", "size": 5719, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/basic.jl", "max_stars_repo_name": "April-Hannah-Lena/ComplexVisual.jl", "max_stars_repo_head_hexsha": "954defe63191cc507793ffccfb748fb646de4b07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-05-05T12:21:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-19T14:10:13.000Z", "max_issues_repo_path": "src/basic.jl", "max_issues_repo_name": "April-Hannah-Lena/ComplexVisual.jl", "max_issues_repo_head_hexsha": "954defe63191cc507793ffccfb748fb646de4b07", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-05-11T16:17:42.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-11T11:59:11.000Z", "max_forks_repo_path": "src/basic.jl", "max_forks_repo_name": "April-Hannah-Lena/ComplexVisual.jl", "max_forks_repo_head_hexsha": "954defe63191cc507793ffccfb748fb646de4b07", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-05-06T10:16:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-02T15:35:10.000Z", "avg_line_length": 28.4527363184, "max_line_length": 80, "alphanum_fraction": 0.6254589963, "num_tokens": 1672, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4416729909662417, "lm_q2_score": 0.12592275499444552, "lm_q1q2_score": 0.055616679829106}}
{"text": "lines = readlines(\"input.txt\")\nre = r\"(\\w+)\"\n\nfunction main()\n    uniq = 0\n    for line = lines\n        input, output = split(line, '|')\n        signals = [m.match for m = eachmatch(re, output)]\n        for signal = signals\n            if length(signal) == 2 # one\n                uniq += 1\n            elseif length(signal) == 3 # seven\n                uniq += 1\n            elseif length(signal) == 4 # four\n                uniq += 1\n            elseif length(signal) == 7 # eight\n                uniq += 1\n            end\n        end\n    end\n    println(uniq)\nend\n\nmain()\n", "meta": {"hexsha": "0fc53dacd876220913b50681eba3bd97502a40fb", "size": 575, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "08/8a.jl", "max_stars_repo_name": "dominikbayerl/aoc-2021", "max_stars_repo_head_hexsha": "2fb3a61216b6b34255e9bffc86d84cf7ac7f319a", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "08/8a.jl", "max_issues_repo_name": "dominikbayerl/aoc-2021", "max_issues_repo_head_hexsha": "2fb3a61216b6b34255e9bffc86d84cf7ac7f319a", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "08/8a.jl", "max_forks_repo_name": "dominikbayerl/aoc-2021", "max_forks_repo_head_hexsha": "2fb3a61216b6b34255e9bffc86d84cf7ac7f319a", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.0, "max_line_length": 57, "alphanum_fraction": 0.4539130435, "num_tokens": 148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11279541373888335, "lm_q1q2_score": 0.05551656440599681}}
{"text": "using Markdown\nusing Latexify\n\narr = [\"x/(y-1)\", 1.0, 3//2, :(x-y), :symb]\n\nM = vcat(hcat(arr...), hcat(arr...))\nhead = [\"col$i\" for i in 1:size(M, 2)]\nside = [\"row$i\" for i in 1:size(M, 1)]\n\n@test mdtable(arr) == Markdown.md\"\n| $\\frac{x}{y - 1}$ |\n| -----------------:|\n|             $1.0$ |\n|     $\\frac{3}{2}$ |\n|           $x - y$ |\n|            $symb$ |\n\"\n\n@test mdtable(arr; head = [\"head\"]) == Markdown.md\"\n|              head |\n| -----------------:|\n| $\\frac{x}{y - 1}$ |\n|             $1.0$ |\n|     $\\frac{3}{2}$ |\n|           $x - y$ |\n|            $symb$ |\n\"\n\n@test mdtable(arr; head = [\"head\"], side=1:length(arr)) == Markdown.md\"\n|   \u2218 |              head |\n| ---:| -----------------:|\n|   1 | $\\frac{x}{y - 1}$ |\n|   2 |             $1.0$ |\n|   3 |     $\\frac{3}{2}$ |\n|   4 |           $x - y$ |\n|   5 |            $symb$ |\n\"\n\n@test mdtable(arr; head = [\"head\"], side=1:length(arr)+1) == Markdown.md\"\n|   1 |              head |\n| ---:| -----------------:|\n|   2 | $\\frac{x}{y - 1}$ |\n|   3 |             $1.0$ |\n|   4 |     $\\frac{3}{2}$ |\n|   5 |           $x - y$ |\n|   6 |            $symb$ |\n\"\n\n@test mdtable(arr, arr) == Markdown.md\"\n| $\\frac{x}{y - 1}$ | $\\frac{x}{y - 1}$ |\n| -----------------:| -----------------:|\n|             $1.0$ |             $1.0$ |\n|     $\\frac{3}{2}$ |     $\\frac{3}{2}$ |\n|           $x - y$ |           $x - y$ |\n|            $symb$ |            $symb$ |\n\"\n\n@test mdtable(arr, arr; head = [\"col1\", \"col2\"]) == Markdown.md\"\n|              col1 |              col2 |\n| -----------------:| -----------------:|\n| $\\frac{x}{y - 1}$ | $\\frac{x}{y - 1}$ |\n|             $1.0$ |             $1.0$ |\n|     $\\frac{3}{2}$ |     $\\frac{3}{2}$ |\n|           $x - y$ |           $x - y$ |\n|            $symb$ |            $symb$ |\n\"\n\n@test mdtable(M) == Markdown.md\"\n| $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n| -----------------:| -----:| -------------:| -------:| ------:|\n| $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n\"\n\n@test mdtable(M, head=head) == Markdown.md\"\n|              col1 |  col2 |          col3 |    col4 |   col5 |\n| -----------------:| -----:| -------------:| -------:| ------:|\n| $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n| $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n\"\n\n@test mdtable(M, head=head, side=side) == Markdown.md\"\n|    \u2218 |              col1 |  col2 |          col3 |    col4 |   col5 |\n| ----:| -----------------:| -----:| -------------:| -------:| ------:|\n| row1 | $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n| row2 | $\\frac{x}{y - 1}$ | $1.0$ | $\\frac{3}{2}$ | $x - y$ | $symb$ |\n\"\n\n@test mdtable(M, head=side, side=head, transpose=true) == Markdown.md\"\n|    \u2218 |              row1 |              row2 |\n| ----:| -----------------:| -----------------:|\n| col1 | $\\frac{x}{y - 1}$ | $\\frac{x}{y - 1}$ |\n| col2 |             $1.0$ |             $1.0$ |\n| col3 |     $\\frac{3}{2}$ |     $\\frac{3}{2}$ |\n| col4 |           $x - y$ |           $x - y$ |\n| col5 |            $symb$ |            $symb$ |\n\"\n\n\nm = [\"one_two_tree\"; \"four_five_six\"; \"seven_eight\"]\n@test latexify(m; env=:mdtable, latex=false, escape_underscores=true) == Markdown.md\"\n|  one\\_two\\_tree |\n| -------------:|\n| four\\_five\\_six |\n|   seven_eight |\n\"\n\n\nusing DataFrames\nd = DataFrame(A = 11:13, B = [:X, :Y, :Z])\n\n\n@test latexify(d; env=:mdtable, side=1:3) == Markdown.md\"\n|   \u2218 |    A |   B |\n| ---:| ----:| ---:|\n|   1 | $11$ | $X$ |\n|   2 | $12$ | $Y$ |\n|   3 | $13$ | $Z$ |\n\"\n\n@test latexify(d; env=:mdtable) == Markdown.md\"\n|    A |   B |\n| ----:| ---:|\n| $11$ | $X$ |\n| $12$ | $Y$ |\n| $13$ | $Z$ |\n\"\n\n\n\n@test latexify(((1.0, 2), (3, 4)); env=:mdtable) == Markdown.md\"\n| $1.0$ | $3$ |\n| -----:| ---:|\n|   $2$ | $4$ |\n\"\n\n# @test_throws MethodError mdtable(M; bad_kwarg=\"should error\")\n", "meta": {"hexsha": "cc5ed499b636c0794c6d7c3e21d1e611e10d6807", "size": 3829, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/mdtable_test.jl", "max_stars_repo_name": "EuklidAlexandria/Latexify.jl", "max_stars_repo_head_hexsha": "20371f5c891345fbfce4064a0ef226a8bc51f14d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-07-19T01:42:16.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-01T12:38:30.000Z", "max_issues_repo_path": "test/mdtable_test.jl", "max_issues_repo_name": "EuklidAlexandria/Latexify.jl", "max_issues_repo_head_hexsha": "20371f5c891345fbfce4064a0ef226a8bc51f14d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/mdtable_test.jl", "max_forks_repo_name": "EuklidAlexandria/Latexify.jl", "max_forks_repo_head_hexsha": "20371f5c891345fbfce4064a0ef226a8bc51f14d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9489051095, "max_line_length": 85, "alphanum_fraction": 0.3298511361, "num_tokens": 1448, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.11920292045759123, "lm_q1q2_score": 0.055417625033031194}}
{"text": "\"\"\"\n# Description\n\nGiven a tuple of symbols, return a `Dict{Symbol, Int}`` mapping each symbol to\nits index in the input tuple.\n\n# Arguments\n\n1. `column_names::NTuple{N, Symbol} where N`: A tuple of symbols.\n\n# Return Values\n\n1. `index::Dict{Symbol, Int}`: A dictionary mapping symbols to their indices in\n  the input tuple.\n\n# Examples\n\n```\njulia> index_column_names(((:a, false), (:b, false)))\nDict{ColumnName,Int64} with 2 entries:\n  (:a, false) => 1\n  (:b, false) => 2\n```\n\"\"\"\nfunction index_column_names(column_names::NTuple{N, ColumnName}) where N\n    Dict{ColumnName, Int}(column_names .=> 1:length(column_names))\nend\n", "meta": {"hexsha": "6310792f66a5e1412954eb2644bd761f63e275d3", "size": 625, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/query/expression_operations/index_column_names.jl", "max_stars_repo_name": "johnmyleswhite/Volcanito.jl", "max_stars_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2020-08-28T14:55:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-21T09:54:01.000Z", "max_issues_repo_path": "src/query/expression_operations/index_column_names.jl", "max_issues_repo_name": "johnmyleswhite/Volcanito.jl", "max_issues_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 29, "max_issues_repo_issues_event_min_datetime": "2020-08-28T16:24:08.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-29T16:26:01.000Z", "max_forks_repo_path": "src/query/expression_operations/index_column_names.jl", "max_forks_repo_name": "johnmyleswhite/Volcanito.jl", "max_forks_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-09-06T14:21:02.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-12T10:14:07.000Z", "avg_line_length": 22.3214285714, "max_line_length": 79, "alphanum_fraction": 0.6944, "num_tokens": 180, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.11436853221906972, "lm_q1q2_score": 0.05539783927490799}}
{"text": "\"\"\"\n    parserl(T, io)\n\nReads a line from `io` and parses the result to type `T`. Typically used for single-value lines.\n\"\"\"\nparserl(T, io) = parse(T, readline(io))\n\n\n\"\"\"\n    parsesrl(T, io; sep = \",\", headskip = 0, tailskip = 0)\n\nReads a line from `io`, splits the line over `sep`. Use keywords `headskip` and `tailskip` to optionally exclude the head and tail of the string before splitting.\n\n#Usage\njulia> parsesrl(Int, io) # read \"1,0,1\"\n\n\"\"\"\nfunction parsesrl(T, io; sep = \",\", headskip = 0, tailskip = 0)\n    l = readline(io)\n    i = 1 + headskip\n    j = length(l) - tailskip\n    l = l[i:j]\n    parse.(T, split(l, sep))\nend", "meta": {"hexsha": "d8a2f0e095defbe1784630a767a6ab4a6cc5e3de", "size": 629, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/io/utilities.jl", "max_stars_repo_name": "Michiel-VL/GCSPET.jl", "max_stars_repo_head_hexsha": "1b288f878c85e988f8ba8ed7791dc6f60e011a4b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/io/utilities.jl", "max_issues_repo_name": "Michiel-VL/GCSPET.jl", "max_issues_repo_head_hexsha": "1b288f878c85e988f8ba8ed7791dc6f60e011a4b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/io/utilities.jl", "max_forks_repo_name": "Michiel-VL/GCSPET.jl", "max_forks_repo_head_hexsha": "1b288f878c85e988f8ba8ed7791dc6f60e011a4b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2083333333, "max_line_length": 162, "alphanum_fraction": 0.6279809221, "num_tokens": 195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4843800842769844, "lm_q2_score": 0.11436852618181248, "lm_q1q2_score": 0.05539783635058083}}
{"text": "name = \"Jacob\"\nnum = 42\n\nif true\n    println(\"True!\")\nelseif false\n    println(\"False!\")\nelse\n    println(\"WTF?!\")\nend\n\nfor x in 1:10\n    y = x^2\n    println(\"$(x) squared is $(y)\")\nend\n\nfunction area(width, height)\n    return width * height\nend\n\nprintln(area(12, 34))\n", "meta": {"hexsha": "b82a907f82abe227cb4daa092e86abc7dec7b8d5", "size": 269, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia/mail.jl", "max_stars_repo_name": "jgphilpott/babel", "max_stars_repo_head_hexsha": "47d81262ed3fb7a05302a8986782b99762f393a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-12-10T14:53:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-10T14:53:57.000Z", "max_issues_repo_path": "Julia/mail.jl", "max_issues_repo_name": "jgphilpott/babel", "max_issues_repo_head_hexsha": "47d81262ed3fb7a05302a8986782b99762f393a8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Julia/mail.jl", "max_forks_repo_name": "jgphilpott/babel", "max_forks_repo_head_hexsha": "47d81262ed3fb7a05302a8986782b99762f393a8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.2272727273, "max_line_length": 35, "alphanum_fraction": 0.6022304833, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.38861802670584894, "lm_q2_score": 0.1422318931919251, "lm_q1q2_score": 0.055273877666883006}}
{"text": "### A Pluto.jl notebook ###\n# v0.18.0\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 aff9313b-2fd8-46ca-b399-47fa21b324c5\nbegin\n    import Pkg\n    Pkg.activate(Base.current_project())\n    Pkg.instantiate()\nend\n\n# \u2554\u2550\u2561 bcca4c48-89be-11ec-3c89-3189fd524f08\nmd\"\"\"\n# Template-matching in Julia using Pluto.jl\n\n## Introduction\n\nJulia is a general programming language suited for scientific computing applications. Moreover, Julia is a dynamic, high-level language: executing a program written in Julia does not require the user to compile it beforehand, as for C or Fortran, and its syntax is similar to MATLAB or Python. Nonetheless, its performance is close to ones of static languages.\n\nPluto is a Julia library for writing notebooks similar (but better in many respect) to Jupyter notebooks. In this notebook I will work out an example and very briefly introduce Julia and Pluto. The goal is to template match some signals from acoustic measurements of hydraulic fracture growth performed under triaxial confinement in laboratory (more informations [here](https://www.researchgate.net/profile/Seyyedmaalek-Momeni/publication/352780078_Combining_active_and_passive_acoustic_methods_to_image_hydraulic_fracture_growth_in_laboratory_experiments/links/60d87837a6fdccb745ea29dd/Combining-active-and-passive-acoustic-methods-to-image-hydraulic-fracture-growth-in-laboratory-experiments.pdf)). \n\"\"\"\n\n# \u2554\u2550\u2561 11ba1299-05e6-46c1-8716-ac06d5fc46a5\nmd\"\"\"\n## Julia and Pluto.jl\n\n### Pluto.jl\n\nA Pluto notebook is a sequence of cells. Each cell contain some Julia code, which can be executed. In Julia, every statement is an expression and hence evaluates to a value. Pluto converts that value to an HTML element and shows it. What you are reading is the output of a cell containing just a string, which returns the string itself [^1]. Pluto is able to parse the content of cells and detect dependencies between one cell and another. When the user evaluates a cell, for example changing the value of a variable, Pluto will re-evaluate all the cells that depend on that one and in the right order.\n\n\nNotice that Pluto requires one statement per cell, hence if you want to put more than one statement in a single cell you need to wrap them between the keywords `begin` and `end`, which are used to chain statements. The resulting block will evaluate to the value of the last statement.\n\nFor example\n\n```julia\nbegin\n\tx = 1\n\ty = x + 1\n\t\"hello world\"\n\t2 * y\nend\n```\n\nevaluates to 4.\n\n[^1]: Actually, the value that is rendered as the piece of text you are reading is not a string, but an object whose type is defined in the Markdown standard library. You can create these object prefixing the string literals with `md`. In this way, the content will be formatted using Markdown. Otherwise you can use plain strings.\n\"\"\"\n\n# \u2554\u2550\u2561 6dca0719-eeb3-42d5-8585-f89a77e579b3\nmd\"\"\"\n### Defining Functions in Julia\n\nJulia does not have methods attached to objects and accessible using the dot notation like in Python or C++. The main way of organizing code in Julia is by writing functions, and eventually grouping them into modules.\n\nFunction are defined using the `function` keyword. For example, the following will multiply the first two arguments and add the product to the third.\n\n```julia\nfunction f(x, y, z)\n\tx * y + z\nend\n```\n\nTh `return` keyword is implied in the last statement of a function definition, but can be supplyied if one wants to.\n\nNotice that the first time a function is called, Julia compiles on the fly that function. This is called just in time compilation or JIT. Hence the execution time of a function call change drastically between the first and subsequent calls. This is something to keep in mind when writing Julia code. There are some subtleties related to JIT compilation and the types of the arguments of a function, but we will talk about that in the next section.\n\"\"\"\n\n# \u2554\u2550\u2561 364a442a-17ef-455d-9608-aecd3126db59\nmd\"\"\"\n### Arrays and other types in Julia\n\nIn theory, one might write Julia code without thinking or caring about types, like in Python. However, it is practically impossibile to write and debug efficient code without some knowledge of the topic. One of the most important things to know is that the compiler can produce optimized machine code only for type-stable functions, where a function is called type-stabile if the type of its output is determined only from the types of its arguments (and not from their values). \n\nThe most used type in scientific computing is probably the array, i.e. a data type that represent indexed objects of the same type and contiguous in memory. Julia has a rich type system that includes arrays. In particular, arrays are an example of parametric types in Julia, i.e. types that depends on other types. The arguments of a dependent type are surrounded by braces, so for example `Array{Float64, 1}` is the type of one-dimensional arrays of 64-bit floating points numbers. To produce an exemplar of this type, you have to call its constructor. The constructor is a function with the same name of the type. In the case of one-dimensional array (or vector) constructors, they take two arguments, the value with which filling the array and the number of elements, so for example `Array{Float64, 1}(1.0, 10)` will be a ten element vector filled with the number 1.0. Array indexing in Julia is similar to Python with one important exceptions, in Julia one starts to count from 1 instead of 0, as you would do in Fortran or Matlab. Also, the last element is indicated with the keyword `end` and you can apply a function that acts on scalars element-wise on arrays posfixing a dot to its name in the function call, as in the function definition below (really, the dot notation is used for broadcasting, a powerful concept that should be familiar to Numpy user).\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550aff9313b-2fd8-46ca-b399-47fa21b324c5\n# \u255f\u2500bcca4c48-89be-11ec-3c89-3189fd524f08\n# \u255f\u250011ba1299-05e6-46c1-8716-ac06d5fc46a5\n# \u255f\u25006dca0719-eeb3-42d5-8585-f89a77e579b3\n# \u255f\u2500364a442a-17ef-455d-9608-aecd3126db59\n", "meta": {"hexsha": "fd33329fa4a98d644634a1c38b57a988d22b9b82", "size": 6030, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/0_julia_intro.jl", "max_stars_repo_name": "stefanocampanella/TemplateMatching.jl", "max_stars_repo_head_hexsha": "6a016534630a257f97012d8f73586b11d15747ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "notebooks/0_julia_intro.jl", "max_issues_repo_name": "stefanocampanella/TemplateMatching.jl", "max_issues_repo_head_hexsha": "6a016534630a257f97012d8f73586b11d15747ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-08T09:45:59.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-08T09:45:59.000Z", "max_forks_repo_path": "notebooks/0_julia_intro.jl", "max_forks_repo_name": "stefanocampanella/TemplateMatching.jl", "max_forks_repo_head_hexsha": "6a016534630a257f97012d8f73586b11d15747ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 70.1162790698, "max_line_length": 1364, "alphanum_fraction": 0.7842454395, "num_tokens": 1517, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3886180267058489, "lm_q2_score": 0.14223188773801163, "lm_q1q2_score": 0.0552738755473939}}
{"text": "module TestDescribe\n\nusing Test\nusing RLEVectors\n\n@testset begin\n\n    x = RLEVector([4, 5, 6], [3, 6, 9])\n\n    # nrun\n    @test nrun(x) == 3\n\n    # length\n    @test length(x) == 9\n\n    # size\n    @test size(x) == (9,)\n    @test size(x, 1) == 9\n    @test size(x, 2) == (9, 1)\n\n    # isempty\n    @test isempty(x) == false\n    @test isempty(RLEVector(Int[], Int[])) == true\n\n    # ==\n    @test x == x\n\n    # isequal\n    @test isequal(x, x)\n\n    # eltype, endtype\n    @test endtype(x) == Int64\n    @test eltype(x) == Int64\n\n\nend # testset\n\nend # module\n", "meta": {"hexsha": "5f5d6653c1cd9cf12ceed4d3226392b7d4ac9f89", "size": 549, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_describe.jl", "max_stars_repo_name": "phaverty/RleVectors.jl", "max_stars_repo_head_hexsha": "52fffeb9ce6549441771fd62997f387826415963", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2015-10-28T01:45:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-10T07:52:55.000Z", "max_issues_repo_path": "test/test_describe.jl", "max_issues_repo_name": "phaverty/RleVectors.jl", "max_issues_repo_head_hexsha": "52fffeb9ce6549441771fd62997f387826415963", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 43, "max_issues_repo_issues_event_min_datetime": "2016-06-02T08:38:37.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T21:07:27.000Z", "max_forks_repo_path": "test/test_describe.jl", "max_forks_repo_name": "phaverty/RleVectors.jl", "max_forks_repo_head_hexsha": "52fffeb9ce6549441771fd62997f387826415963", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2015-11-20T23:22:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-23T22:45:35.000Z", "avg_line_length": 14.0769230769, "max_line_length": 50, "alphanum_fraction": 0.5173041894, "num_tokens": 204, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.11596071672639166, "lm_q1q2_score": 0.055264517914557505}}
{"text": "using Dates\nusing Statistics\nusing Test\n\nusing MarketData\n\nusing TimeSeries\n\n\n@testset \"meta\" begin\n\n\n@testset \"construction with and without meta field\" begin\n    nometa = TimeArray(timestamp(cl), values(cl), colnames(cl))\n\n    @testset \"default meta field to nothing\" begin\n        @test meta(nometa) == nothing\n    end\n\n    @testset \"allow objects in meta field\" begin\n        @test meta(mdata) == \"Apple\"\n    end\nend\n\n\n@testset \"get index operations preserve meta\" begin\n    @testset \"index by integer row\" begin\n        @test meta(mdata[1]) == \"Apple\"\n    end\n\n    @testset \"index by integer range\" begin\n        @test meta(mdata[1:2]) == \"Apple\"\n    end\n\n    @testset \"index by column name\" begin\n        @test meta(mdata[:Close]) == \"Apple\"\n    end\n\n    @testset \"index by date range\" begin\n        @test meta(mdata[[Date(2000,1,3), Date(2000,1,14)]]) == \"Apple\"\n    end\nend\n\n\n@testset \"split operations preserve meta\" begin\n    @testset \"when\" begin\n        @test meta(when(mdata, dayofweek, 1)) == \"Apple\"\n    end\n\n    @testset \"from\" begin\n        @test meta(from(mdata, Date(2000,1,1))) == \"Apple\"\n    end\n\n    @testset \"to\" begin\n        @test meta(to(mdata, Date(2000,1,1))) == \"Apple\"\n    end\nend\n\n\n@testset \"apply operations preserve meta\" begin\n    @testset \"lag\" begin\n        @test meta(lag(mdata)) == \"Apple\"\n    end\n\n    @testset \"lead\" begin\n        @test meta(lead(mdata)) == \"Apple\"\n    end\n\n    @testset \"percentchange\" begin\n        @test meta(percentchange(mdata)) == \"Apple\"\n    end\n\n    @testset \"moving\" begin\n        @test meta(moving(mean,mdata,10)) == \"Apple\"\n    end\n\n    @testset \"upto\" begin\n        @test meta(upto(sum, mdata)) == \"Apple\"\n    end\nend\n\n\n@testset \"combine operations preserve meta\" begin\n    @testset \"merge when both have identical meta\" begin\n        @test meta(merge(cl, op))         == \"AAPL\"\n        @test meta(merge(cl, op, :left))  == \"AAPL\"\n        @test meta(merge(cl, op, :right)) == \"AAPL\"\n        @test meta(merge(cl, op, :outer)) == \"AAPL\"\n    end\n\n    @testset \"merged meta field value concatenates when both objects' meta field values are strings\" begin\n        @test meta(merge(mdata, cl))         == \"Apple_AAPL\"\n        @test meta(merge(mdata, cl, :left))  == \"Apple_AAPL\"\n        @test meta(merge(mdata, cl, :right)) == \"Apple_AAPL\"\n        @test meta(merge(mdata, cl, :outer)) == \"Apple_AAPL\"\n    end\n\n    @testset \"merge when supplied with meta\" begin\n        @test meta(merge(mdata, mdata, meta=47))         == 47\n        @test meta(merge(mdata, mdata, :left, meta=47))  == 47\n        @test meta(merge(mdata, mdata, :right, meta=47)) == 47\n        @test meta(merge(mdata, mdata, :outer, meta=47)) == 47\n        @test meta(merge(mdata, cl, meta=47))            == 47\n        @test meta(merge(mdata, cl, :left, meta=47))     == 47\n        @test meta(merge(mdata, cl, :right, meta=47))    == 47\n        @test meta(merge(mdata, cl, :outer, meta=47))    == 47\n    end\n\n    @testset \"merged meta field value for disparate types in meta field defaults to Void\" begin\n        @test meta(merge(mdata, merge(cl, op, meta=47)))         == nothing\n        @test meta(merge(mdata, merge(cl, op, meta=47), :left))  == nothing\n        @test meta(merge(mdata, merge(cl, op, meta=47), :right)) == nothing\n        @test meta(merge(mdata, merge(cl, op, meta=47), :outer)) == nothing\n    end\n\n    @testset \"collapse\" begin\n        @test meta(collapse(mdata, week, first)) == \"Apple\"\n    end\nend\n\n\n@testset \"basecall operations preserve meta\" begin\n    @testset \"basecall\" begin\n        @test meta(basecall(mdata, cumsum)) == \"Apple\"\n    end\nend\n\n\n@testset \"mathematical and comparison operations preserve meta\" begin\n    @testset \".+\" begin\n        @test meta(mdata .+ mdata) == \"Apple\"\n        @test meta(mdata .+ cl)    == nothing\n    end\n\n    @testset \".<\" begin\n        @test meta(mdata .< mdata) == \"Apple\"\n        @test meta(mdata .< cl)    == nothing\n    end\nend\n\n\n@testset \"readwrite accepts meta argument\" begin\n    @testset \"Apple is present\" begin\n        @test meta(mdata) == \"Apple\"\n    end\nend\n\n\nend  # @testset \"meta\"\n", "meta": {"hexsha": "9314096f41953b0d6414f1000a6040580da8e9a4", "size": 4086, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/meta.jl", "max_stars_repo_name": "bovine3dom/TimeSeries.jl", "max_stars_repo_head_hexsha": "49859163fcf5ebe46ce0a8a07645a1edf4cf94db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/meta.jl", "max_issues_repo_name": "bovine3dom/TimeSeries.jl", "max_issues_repo_head_hexsha": "49859163fcf5ebe46ce0a8a07645a1edf4cf94db", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/meta.jl", "max_forks_repo_name": "bovine3dom/TimeSeries.jl", "max_forks_repo_head_hexsha": "49859163fcf5ebe46ce0a8a07645a1edf4cf94db", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.24, "max_line_length": 106, "alphanum_fraction": 0.5971610377, "num_tokens": 1245, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.1208532261576127, "lm_q1q2_score": 0.05524644702370178}}
{"text": "# Copyright (c) 2013: Clp.jl contributors\n#\n# Use of this source code is governed by an MIT-style license that can be found\n# in the LICENSE.md file or at https://opensource.org/licenses/MIT.\n\nmodule TestMOIWrapper\n\nusing Test\nusing MathOptInterface\nimport Clp\n\nconst MOI = MathOptInterface\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$(name)\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\nend\n\nfunction test_SolverName()\n    @test MOI.get(Clp.Optimizer(), MOI.SolverName()) == \"Clp\"\n    return\nend\n\nfunction test_supports_default_copy_to()\n    @test !MOI.supports_incremental_interface(Clp.Optimizer())\n    return\nend\n\nfunction test_runtests()\n    # This is what JuMP would construct\n    model = MOI.Utilities.CachingOptimizer(\n        MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n        MOI.instantiate(Clp.Optimizer; with_bridge_type = Float64),\n    )\n    @test model.optimizer.model.model_cache isa\n          MOI.Utilities.UniversalFallback{Clp.OptimizerCache}\n    # `Variable.ZerosBridge` makes dual needed by some tests fail.\n    MOI.Bridges.remove_bridge(\n        model.optimizer,\n        MathOptInterface.Bridges.Variable.ZerosBridge{Float64},\n    )\n    MOI.set(model, MOI.Silent(), true)\n    MOI.Test.runtests(\n        model,\n        MOI.Test.Config(\n            exclude = Any[MOI.DualObjectiveValue, MOI.ObjectiveBound],\n        ),\n        exclude = [\n            # Unable to prove infeasibility\n            \"test_conic_NormInfinityCone_INFEASIBLE\",\n            \"test_conic_NormOneCone_INFEASIBLE\",\n        ],\n    )\n    return\nend\n\nfunction test_Nonexistant_unbounded_ray()\n    inner = Clp.Optimizer()\n    model =\n        MOI.Utilities.CachingOptimizer(MOI.default_cache(inner, Float64), inner)\n    MOI.set(model, MOI.Silent(), true)\n    x = MOI.add_variables(model, 5)\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.0),\n    )\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    MOI.optimize!(model)\n    status = MOI.get(model, MOI.TerminationStatus())\n    @test status == MOI.DUAL_INFEASIBLE\n    return\nend\n\nfunction test_RawOptimizerAttribute()\n    model = Clp.Optimizer()\n    MOI.set(model, MOI.RawOptimizerAttribute(\"LogLevel\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"LogLevel\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"LogLevel\"), 2)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"LogLevel\")) == 2\n\n    MOI.set(model, MOI.RawOptimizerAttribute(\"SolveType\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"SolveType\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"SolveType\"), 4)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"SolveType\")) == 4\n\n    MOI.set(model, MOI.RawOptimizerAttribute(\"PresolveType\"), 1)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"PresolveType\")) == 1\n    MOI.set(model, MOI.RawOptimizerAttribute(\"PresolveType\"), 0)\n    @test MOI.get(model, MOI.RawOptimizerAttribute(\"PresolveType\")) == 0\n    return\nend\n\nfunction test_All_parameters()\n    model = Clp.Optimizer()\n    param = MOI.RawOptimizerAttribute(\"NotAnOption\")\n    @test !MOI.supports(model, param)\n    @test_throws MOI.UnsupportedAttribute(param) MOI.get(model, param)\n    @test_throws MOI.UnsupportedAttribute(param) MOI.set(model, param, false)\n    for key in Clp.SUPPORTED_PARAMETERS\n        @test MOI.supports(model, MOI.RawOptimizerAttribute(key))\n        value = MOI.get(model, MOI.RawOptimizerAttribute(key))\n        MOI.set(model, MOI.RawOptimizerAttribute(key), value)\n        @test MOI.get(model, MOI.RawOptimizerAttribute(key)) == value\n    end\n    return\nend\n\nfunction test_copy_to_bug()\n    model = MOI.Utilities.Model{Float64}()\n    x = MOI.add_variable(model)\n    con = [\n        MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0),\n            MOI.EqualTo(1.0),\n        ) for i in 1:2\n    ]\n    clp = Clp.Optimizer()\n    index_map = MOI.copy_to(clp, model)\n    @test index_map[con[1]] != index_map[con[2]]\n    return\nend\n\nfunction test_options_after_empty!()\n    model = Clp.Optimizer()\n    @test MOI.get(model, MOI.Silent()) == false\n    MOI.set(model, MOI.Silent(), true)\n    @test MOI.get(model, MOI.Silent()) == true\n    MOI.empty!(model)\n    @test MOI.get(model, MOI.Silent()) == true\n    return\nend\n\nend  # module TestMOIWrapper\n\nTestMOIWrapper.runtests()\n", "meta": {"hexsha": "d82c1f57b40b1b2d45cb5a8d4381889780d6a11f", "size": 4568, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/MOI_wrapper.jl", "max_stars_repo_name": "JuliaOpt/Clp.jl", "max_stars_repo_head_hexsha": "8efff608c2b9e2af5cc0d87fd829b16d7e3683d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 27, "max_stars_repo_stars_event_min_datetime": "2016-07-12T14:14:24.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-25T19:29:36.000Z", "max_issues_repo_path": "test/MOI_wrapper.jl", "max_issues_repo_name": "JuliaOpt/Clp.jl", "max_issues_repo_head_hexsha": "8efff608c2b9e2af5cc0d87fd829b16d7e3683d4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 69, "max_issues_repo_issues_event_min_datetime": "2015-06-04T09:36:49.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-05T23:30:28.000Z", "max_forks_repo_path": "test/MOI_wrapper.jl", "max_forks_repo_name": "JuliaOpt/Clp.jl", "max_forks_repo_head_hexsha": "8efff608c2b9e2af5cc0d87fd829b16d7e3683d4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 29, "max_forks_repo_forks_event_min_datetime": "2015-06-04T08:10:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-29T14:06:45.000Z", "avg_line_length": 31.9440559441, "max_line_length": 80, "alphanum_fraction": 0.6757880911, "num_tokens": 1268, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.12085322457439823, "lm_q1q2_score": 0.05524644629995632}}
{"text": "module SearchablePDFs\n\nusing Pkg\nusing Pkg.Artifacts\nusing Random\n\nusing ProgressMeter\nusing Scratch\nusing CSV\nusing Comonicon\n\nusing Poppler_jll\nusing unpaper_jll\nusing Tesseract_jll\n\nexport ocr\n\n#####\n##### Utilities\n#####\n\nfunction argument_error(msg; exception=isinteractive())\n    if exception\n        throw(ArgumentError(msg))\n    else\n        printstyled(\"ERROR:\"; bold=true, color=:red)\n        printstyled(\" \", msg, \"\\n\"; color=:red)\n        exit(1)\n    end\nend\n\nfunction require_extension(path, ext; exception=isinteractive())\n    _ext = splitext(path)[2]\n    _ext == ext ||\n        argument_error(\"Expected $path to have file extension `$ext`; got `$(_ext)`\";\n                       exception)\n    return nothing\nend\n\nfunction require_no_file(path; exception=isinteractive())\n    isfile(path) && argument_error(\"File already exists at `$(path)`!\"; exception)\n    return nothing\nend\n\n# For now we will hardcode this choice of training data\nfunction get_data_path()\n    return artifact\"tessdata_fast\"\nend\n\n# a place to store intermediate files; we could use temporary directories,\n# but I've occasionally run into permissions issues there\n# so I'd prefer to use a more local location. This also means that we are\n# more in charge of the cleanup, which can be good for debugging.\nfunction get_scratch_dir(pdf)\n    return joinpath(@get_scratch!(\"pdf_tmps\"),\n                    splitext(basename(pdf))[1] * \"_\" * string(randstring(10)))\nend\n\n# https://discourse.julialang.org/t/collecting-all-output-from-shell-commands/15592/7\nfunction run_and_collect_logs(cmd::Cmd)\n    out = Pipe()\n    err = Pipe()\n    process = run(pipeline(cmd; stdout=out, stderr=err); wait=false)\n    close(out.in)\n    close(err.in)\n\n    stdout = @async String(read(out))\n    stderr = @async String(read(err))\n    wait(process)\n    return (stdout=fetch(stdout), stderr=fetch(stderr), code=process.exitcode)\nend\n\n# There's gotta be a better way...\nfunction num_pages(pdf)\n    result = read(`$(Poppler_jll.pdfinfo()) $pdf`, String)\n    m = match(r\"Pages\\:\\s*([0-9]*)\", result)\n    return parse(Int, m.captures[1])\nend\n\n#####\n##### Step 1: Extract images\n#####\n\n# Use Poppler to extract the image\nfunction get_images(pdf, page_range::UnitRange{Int}, tmp, total_pages)\n    logs = run_and_collect_logs(`$(Poppler_jll.pdftoppm()) -f $(first(page_range)) -l $(last(page_range)) $pdf -tiff -forcenum $(tmp)/page`)\n    @debug \"`pdftoppm`\" logs\n    paths = [joinpath(tmp, string(\"page-\", lpad(page, ndigits(total_pages), '0'), \".tif\"))\n             for page in page_range]\n    return paths, (; binary=\"pdftoppm\", logs...)\nend\n\n# Clean up an image with unpaper\nfunction unpaper(img)\n    img_base, img_ext = splitext(img)\n    img_unpaper = img_base * \"_unpaper\" * img_ext\n    logs = run_and_collect_logs(`$(unpaper_jll.unpaper()) $img $img_unpaper`)\n    return (; img_unpaper, logs=(; binary=\"unpaper\", logs...))\nend\n\n#####\n##### Step 2: Use tesseract to generate a one-page searchable PDF from an image\n#####\n\nfunction make_pdf(img; tesseract_nthreads)\n    data_path = get_data_path() * \"/\"\n    img_base, img_ext = splitext(img)\n    output = img_base\n    tesseract = addenv(Tesseract_jll.tesseract(), \"OMP_THREAD_LIMIT\" => tesseract_nthreads)\n    cmd = `$tesseract -l eng+equ --tessdata-dir $data_path $img $output -c tessedit_create_pdf=1`\n    @debug \"Tesseracting!\" img\n    logs = run_and_collect_logs(cmd)\n    @debug logs\n    return (; pdf=output * \".pdf\", logs=(; binary=\"tesseract\", logs...))\nend\n\n#####\n##### Step 3: collect all the PDFs into one with `pdfunite`\n#####\n\nfunction unite_pdfs(pdfs, output)\n    logs = run_and_collect_logs(`$(Poppler_jll.pdfunite()) $pdfs $output`)\n    return (; binary=\"pdfunite\", logs...)\nend\n\n# unites all the pdfs in `pdfs`, `max_files` at a time.\n# I ran into \"too many open files\" errors otherwise\n# (which seems weird... maybe <https://github.com/JuliaLang/julia/issues/31126>? It was on MacOS)\nfunction unite_many_pdfs!(unite_progress_meter, all_logs, tmp, pdfs, output_path;\n                          max_files_per_unite=100)\n    isdir(tmp) || mkdir(tmp)\n\n    output_paths = map(enumerate(Iterators.partition(pdfs, max_files_per_unite))) do (i,\n                                                                                      current_pdfs)\n        current_output_path = joinpath(tmp, string(\"section_\", i, \".pdf\"))\n        unite_logs = unite_pdfs(current_pdfs, current_output_path)\n        put!(all_logs, (; page=missing, unite_logs...))\n        next!(unite_progress_meter; step=length(current_pdfs))\n        return current_output_path\n    end\n\n    unite_logs = unite_pdfs(output_paths, output_path)\n    put!(all_logs, (; page=missing, unite_logs...))\n\n    next!(unite_progress_meter; step=length(output_paths))\n    return nothing\nend\n\n#####\n##### Apply steps 1 -- 3\n#####\n\n\"\"\"\n    ocr(pdf, output_path=string(splitext(pdf)[1], \"_OCR\", \".pdf\"); apply_unpaper=false,\n             ntasks=Sys.CPU_THREADS - 1, tesseract_nthreads=1, pages=num_pages(pdf),\n             cleanup_after=true, cleanup_at_exit=true, tmp=get_scratch_dir(pdf),\n             verbose=true)\n             \nReads in a PDF located at `pdf`, uses Tesseract to OCR each page and combines the results into a pdf located `output_path`.\n\nKeyword arguments:\n\n* `ntasks`: how many parallel tasks to use for launching `tesseract` and `pdftoppm`.\n* `tesseract_nthreads`: how many threads to direct Tesseract to use\n* `apply_unpaper`: whether or not to apply `unpaper` to try to improve the image quality\n* `tmp`: a directory to store intermediate files. This directory is deleted at the end of the function if `cleanup_after` is set to `true`, and when the Julia session is ended if `cleanup_at_exit` is set to `true`.\n* `pages=nothing`: the number of pages of the PDF to process; the default of `nothing` indicates all pages in the PDF. It can help in debugging to set this to something small.\n* `verbose`: show a progress bar for each step of the process.\n\nSet `ENV[\"JULIA_DEBUG\"] = SearchablePDFs` to see (many) debug messages.\n\"\"\"\nfunction ocr(pdf, output_path=string(splitext(pdf)[1], \"_OCR\", \".pdf\"); apply_unpaper=false,\n             ntasks=Sys.CPU_THREADS - 1, tesseract_nthreads=1, pages=nothing,\n             cleanup_after=true, cleanup_at_exit=true, tmp=get_scratch_dir(pdf),\n             verbose=true, force=false, max_files_per_unite=100)\n    isfile(pdf) || argument_error(\"Input file not found at `$pdf`\"; exception=true)\n    force || require_no_file(output_path; exception=true)\n    require_extension(pdf, \".pdf\"; exception=true)\n    require_extension(output_path, \".pdf\"; exception=true)\n\n    total_pages = num_pages(pdf)\n\n    if pages === nothing\n        pages = total_pages\n    elseif pages > total_pages\n        argument_error(\"`pages` must be less than the total number of pages ($(total_pages))\";\n                       exception=true)\n    end\n\n    mkpath(tmp)\n    if cleanup_at_exit\n        atexit(() -> rm(tmp; force=true, recursive=true))\n    end\n\n    @debug \"Found file\" pdf pages tmp\n\n    all_logs = Channel{@NamedTuple{page::Union{Int,UnitRange{Int},Missing},binary::String,\n                                   stdout::String,stderr::String,code::Int}}(Inf)\n\n    @debug \"Generating images...\"\n    imag_prog = Progress(pages; desc=\"(1/3) Extracting images: \", enabled=verbose)\n\n    img_paths_grps = asyncmap(Iterators.partition(1:pages, 20); ntasks) do page_range\n        paths, pdftoppm_logs = get_images(pdf, page_range, tmp, total_pages)\n        put!(all_logs, (; page=page_range, pdftoppm_logs...))\n        next!(imag_prog; step=length(page_range))\n        return paths\n    end\n\n    img_paths = reduce(vcat, img_paths_grps)\n\n    @debug \"Finished generating images. Starting tesseracting...\"\n    ocr_prog = Progress(pages; desc=\"(2/3) OCRing: \", enabled=verbose)\n    pdfs = asyncmap(enumerate(img_paths); ntasks) do (page, img)\n        @debug \"img\" page img\n        if apply_unpaper\n            img, unpaper_logs = unpaper(img)\n            put!(all_logs, (; page, unpaper_logs...))\n        end\n        pdf, tesseract_logs = make_pdf(img; tesseract_nthreads)\n        put!(all_logs, (; page, tesseract_logs...))\n        next!(ocr_prog)\n        return pdf\n    end\n    @debug \"Finished processing pages. Uniting...\"\n    unite_dir = joinpath(tmp, \"unite\")\n    unite_progress_meter = Progress(pages + cld(pages, max_files_per_unite) + 1;\n                                    desc=\"(3/3) Collecting pages: \", enabled=verbose)\n    unite_many_pdfs!(unite_progress_meter, all_logs, unite_dir, pdfs, output_path;\n                     max_files_per_unite)\n    @debug \"Done uniting pdfs\"\n    if cleanup_after\n        @debug \"Cleaning up\"\n        rm(tmp; recursive=true, force=true)\n    end\n    @debug \"Done\"\n    if verbose\n        isfile(output_path) || @error \"File was not generated, check the logs!\"\n    end\n    close(all_logs)\n    return (; output_path, logs=collect(all_logs), tmp)\nend\n\n#####\n##### CLI interface\n#####\n\n\"\"\"\nCreate a searchable version of a PDF.\n\"\"\"\n@main function searchable(input_pdf::String,\n                          output_path::String=string(splitext(input_pdf)[1], \"_OCR\",\n                                                     \".pdf\"); apply_unpaper::Bool=false,\n                          ntasks::Int=Sys.CPU_THREADS - 1, tesseract_nthreads::Int=1,\n                          keep_intermediates::Bool=false,\n                          tmp::String=get_scratch_dir(input_pdf), quiet::Bool=false,\n                          logfile::Union{Nothing,String}=nothing, force::Bool=false)\n    # some of these are redundant with checks inside `ocr`; that's because we want to do them before the \"Starting to ocr\" message,\n    # and we want them to exit if they fail in a non-interactive context, instead of printing a stacktracee.\n    isfile(input_pdf) || argument_error(\"Input file not found at `$(input_pdf)`\")\n    force || require_no_file(output_path)\n    require_extension(input_pdf, \".pdf\")\n    require_extension(output_path, \".pdf\")\n    if logfile !== nothing\n        force || require_no_file(logfile)\n        require_extension(logfile, \".csv\")\n    end\n    verbose = !quiet\n    verbose &&\n        println(\"Starting to ocr `$(input_pdf)`; result will be located at `$(output_path)`.\")\n    result = ocr(input_pdf, output_path; apply_unpaper, ntasks, tesseract_nthreads,\n                 cleanup_after=!keep_intermediates, cleanup_at_exit=!keep_intermediates,\n                 tmp, verbose)\n    verbose && println(\"\\nOutput is located at `$(output_path)`.\")\n    if keep_intermediates && verbose\n        println(\"Intermediate files located at `$tmp`.\")\n    end\n    if logfile !== nothing\n        verbose && println(\"Writing logs...\")\n        CSV.write(logfile, result.logs)\n        verbose && println(\"Logs written to `$(logfile)`.\")\n    end\n\n    return result\nend\n\nend # module\n", "meta": {"hexsha": "a35a69f702d1af965132be471a559f7b5adf0011", "size": 10785, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SearchablePDFs.jl", "max_stars_repo_name": "ericphanson/PDFSandwich.jl", "max_stars_repo_head_hexsha": "90ba0ebcb6ceb8e8d599c78ed46b8c3d59c23dd5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2021-05-26T20:41:40.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T20:39:08.000Z", "max_issues_repo_path": "src/SearchablePDFs.jl", "max_issues_repo_name": "ericphanson/PDFSandwich.jl", "max_issues_repo_head_hexsha": "90ba0ebcb6ceb8e8d599c78ed46b8c3d59c23dd5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2021-03-13T03:13:43.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-02T01:01:36.000Z", "max_forks_repo_path": "src/SearchablePDFs.jl", "max_forks_repo_name": "ericphanson/PDFSandwich.jl", "max_forks_repo_head_hexsha": "90ba0ebcb6ceb8e8d599c78ed46b8c3d59c23dd5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-05-26T20:21:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-26T20:21:09.000Z", "avg_line_length": 37.4479166667, "max_line_length": 214, "alphanum_fraction": 0.6577654149, "num_tokens": 2678, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.33111973962899144, "lm_q2_score": 0.16667540675766923, "lm_q1q2_score": 0.05518951728815567}}
{"text": "using Base.Dates\nusing Base.Test\n\nusing MarketData\n\nusing TimeSeries\n\n\n@testset \"meta\" begin\n\n\n@testset \"construction with and without meta field\" begin\n    nometa = TimeArray(cl.timestamp, cl.values, cl.colnames)\n\n    @testset \"default meta field to nothing\" begin\n        @test nometa.meta == nothing\n    end\n\n    @testset \"allow objects in meta field\" begin\n        @test mdata.meta == \"Apple\"\n    end\nend\n\n\n@testset \"get index operations preserve meta\" begin\n    @testset \"index by integer row\" begin\n        @test mdata[1].meta == \"Apple\"\n    end\n\n    @testset \"index by integer range\" begin\n        @test mdata[1:2].meta == \"Apple\"\n    end\n\n    @testset \"index by column name\" begin\n        @test mdata[\"Close\"].meta == \"Apple\"\n    end\n\n    @testset \"index by date range\" begin\n        @test mdata[[Date(2000,1,3), Date(2000,1,14)]].meta == \"Apple\"\n    end\nend\n\n\n@testset \"split operations preserve meta\" begin\n    @testset \"when\" begin\n        @test when(mdata, dayofweek, 1).meta == \"Apple\"\n    end\n\n    @testset \"from\" begin\n        @test from(mdata, Date(2000,1,1)).meta == \"Apple\"\n    end\n\n    @testset \"to\" begin\n        @test to(mdata, Date(2000,1,1)).meta == \"Apple\"\n    end\nend\n\n\n@testset \"apply operations preserve meta\" begin\n    @testset \"lag\" begin\n        @test lag(mdata).meta == \"Apple\"\n    end\n\n    @testset \"lead\" begin\n        @test lead(mdata).meta == \"Apple\"\n    end\n\n    @testset \"percentchange\" begin\n        @test percentchange(mdata).meta == \"Apple\"\n    end\n\n    @testset \"moving\" begin\n        @test moving(mdata,mean,10).meta == \"Apple\"\n    end\n\n    @testset \"upto\" begin\n        @test upto(mdata,sum).meta == \"Apple\"\n    end\nend\n\n\n@testset \"combine operations preserve meta\" begin\n    @testset \"merge when both have identical meta\" begin\n        @test merge(cl, op).meta         == \"AAPL\"\n        @test merge(cl, op, :left).meta  == \"AAPL\"\n        @test merge(cl, op, :right).meta == \"AAPL\"\n        @test merge(cl, op, :outer).meta == \"AAPL\"\n    end\n\n    @testset \"merged meta field value concatenates when both objects' meta field values are strings\" begin\n        @test merge(mdata, cl).meta         == \"Apple_AAPL\"\n        @test merge(mdata, cl, :left).meta  == \"Apple_AAPL\"\n        @test merge(mdata, cl, :right).meta == \"Apple_AAPL\"\n        @test merge(mdata, cl, :outer).meta == \"Apple_AAPL\"\n    end\n\n    @testset \"merge when supplied with meta\" begin\n        @test merge(mdata, mdata, meta=47).meta         == 47\n        @test merge(mdata, mdata, :left, meta=47).meta  == 47\n        @test merge(mdata, mdata, :right, meta=47).meta == 47\n        @test merge(mdata, mdata, :outer, meta=47).meta == 47\n        @test merge(mdata, cl, meta=47).meta            == 47\n        @test merge(mdata, cl, :left, meta=47).meta     == 47\n        @test merge(mdata, cl, :right, meta=47).meta    == 47\n        @test merge(mdata, cl, :outer, meta=47).meta    == 47\n    end\n\n    @testset \"merged meta field value for disparate types in meta field defaults to Void\" begin\n        @test merge(mdata, merge(cl, op, meta=47)).meta         == Void\n        @test merge(mdata, merge(cl, op, meta=47), :left).meta  == Void\n        @test merge(mdata, merge(cl, op, meta=47), :right).meta == Void\n        @test merge(mdata, merge(cl, op, meta=47), :outer).meta == Void\n    end\n\n    @testset \"collapse\" begin\n        @test collapse(mdata, week, first).meta == \"Apple\"\n    end\nend\n\n\n@testset \"basecall operations preserve meta\" begin\n    @testset \"basecall\" begin\n        @test basecall(mdata, cumsum).meta == \"Apple\"\n    end\nend\n\n\n@testset \"mathematical and comparison operations preserve meta\" begin\n    @testset \".+\" begin\n        @test (mdata .+ mdata).meta == \"Apple\"\n        @test (mdata .+ cl).meta == Void\n    end\n\n    @testset \".<\" begin\n        @test (mdata .< mdata).meta == \"Apple\"\n        @test (mdata .< cl).meta == Void\n    end\nend\n\n\n@testset \"readwrite accepts meta argument\" begin\n    @testset \"Apple is present\" begin\n        @test mdata.meta == \"Apple\"\n    end\nend\n\n\nend  # @testset \"meta\"\n", "meta": {"hexsha": "56740d02d9f61d70495e6854af8da5af38b44b39", "size": 4019, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/meta.jl", "max_stars_repo_name": "BenjaminBorn/TimeSeries.jl", "max_stars_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/meta.jl", "max_issues_repo_name": "BenjaminBorn/TimeSeries.jl", "max_issues_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/meta.jl", "max_forks_repo_name": "BenjaminBorn/TimeSeries.jl", "max_forks_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-16T22:57:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-16T22:57:15.000Z", "avg_line_length": 26.9731543624, "max_line_length": 106, "alphanum_fraction": 0.6008957452, "num_tokens": 1216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.11124119472172637, "lm_q1q2_score": 0.055186070284421476}}
{"text": "\"\"\"\n# Summary statistics\n\n```julia\ndescribe(ts::TS)\n```\n\nCompute summary statistics of `ts`. The output is a `DataFrame`\ncontaining standard statistics along with number of missing values and\ndata types of columns.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> using Random;\njulia> random(x) = rand(MersenneTwister(123), x...);\njulia> ts = TS(random(([1, 2, 3, 4, missing], 10)))\njulia> describe(ts)\n2\u00d77 DataFrame\n Row \u2502 variable  mean     min    median   max    nmissing  eltype\n     \u2502 Symbol    Float64  Int64  Float64  Int64  Int64     Type\n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 Index        5.5       1      5.5     10         0  Int64\n   2 \u2502 x1           2.75      2      3.0      4         2  Union{Missing, Int64}\n\n```\n\"\"\"\nfunction describe(io::IO, ts::TS)\n    DataFrames.describe(ts.coredata)\nend\nTSx.describe(ts::TS) = TSx.describe(stdout, ts)\n\n\nfunction Base.show(io::IO, ts::TS)\n    println(\"(\", TSx.nrow(ts), \" x \", TSx.ncol(ts), \") TS with \", eltype(index(ts)), \" Index\")\n    println(\"\")\n    DataFrames.show(ts.coredata, show_row_number=false, summary=false)\n    return nothing\nend\nBase.show(ts::TS) = show(stdout, ts)\n\n\nfunction Base.summary(io::IO, ts::TS)\n    println(\"(\", nr(ts), \" x \", nc(ts), \") TS\")\nend\n\n\"\"\"\n# Size methods\n\n```julia\nnrow(ts::TS)\nnr(ts::TS)\n```\n\nReturn the number of rows of `ts`. `nr` is an alias for `nrow`.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> ts = TS(collect(1:10))\njulia> TSx.nrow(ts)\n10\n```\n\"\"\"\nfunction nrow(ts::TS)\n    DataFrames.size(ts.coredata)[1]\nend\n# alias\nnr = TSx.nrow\n\nfunction Base.lastindex(ts::TS)\n    lastindex(index(ts))\nend\n\nfunction Base.length(ts::TS)\n    TSx.nrow(ts)\nend\n\n# Number of columns\n\"\"\"\n# Size methods\n\n```julia\nncol(ts::TS)\n```\n\nReturn the number of columns of `ts`. `nc` is an alias for `ncol`.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> using Random;\n\njulia> random(x) = rand(MersenneTwister(123), x);\n\njulia> TSx.ncol(TS([random(100) random(100) random(100)]))\n3\n\njulia> nc(TS([random(100) random(100) random(100)]))\n3\n```\n\"\"\"\nfunction ncol(ts::TS)\n    DataFrames.size(ts.coredata)[2] - 1\nend\n# alias\nnc = TSx.ncol\n\n# Size of\n\"\"\"\n# Size methods\n```julia\nsize(ts::TS)\n```\n\nReturn the number of rows and columns of `ts` as a tuple.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> TSx.size(TS([collect(1:100) collect(1:100) collect(1:100)]))\n(100, 3)\n```\n\"\"\"\nfunction size(ts::TS)\n    nr = TSx.nrow(ts)\n    nc = TSx.ncol(ts)\n    (nr, nc)\nend\n\n# Return index column\n\"\"\"\n# Index column\n\n```julia\nindex(ts::TS)\n```\n\nReturn the index vector from the `coredata` DataFrame.\n\n# Examples\n\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> using Random;\n\njulia> random(x) = rand(MersenneTwister(123), x);\n\njulia> ts = TS(random(10), Date(\"2022-02-01\"):Month(1):Date(\"2022-02-01\")+Month(9));\n\n\njulia> show(ts)\n(10 x 1) TS with Dates.Date Index\n\n Index       x1\n Date        Float64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-02-01  0.768448\n 2022-03-01  0.940515\n 2022-04-01  0.673959\n 2022-05-01  0.395453\n 2022-06-01  0.313244\n 2022-07-01  0.662555\n 2022-08-01  0.586022\n 2022-09-01  0.0521332\n 2022-10-01  0.26864\n 2022-11-01  0.108871\n\njulia> index(ts)\n10-element Vector{Date}:\n 2022-02-01\n 2022-03-01\n 2022-04-01\n 2022-05-01\n 2022-06-01\n 2022-07-01\n 2022-08-01\n 2022-09-01\n 2022-10-01\n 2022-11-01\n\njulia>  eltype(index(ts))\nDate\n```\n\"\"\"\nfunction index(ts::TS)\n    ts.coredata[!, :Index]\nend\n\n\"\"\"\n# Column names\n```julia\nnames(ts::TS)\n```\n\nReturn a `Vector{String}` containing column names of the TS object,\nexcludes index.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> names(TS([1:10 11:20]))\n2-element Vector{String}:\n \"x1\"\n \"x2\"\n```\n\"\"\"\n            \nfunction names(ts::TS)\n    names(ts.coredata[!, Not(:Index)])\nend\n\n\n\"\"\"\n# First Row\n```julia\nfirst(ts::TS)\n```\n\nReturns the first row of `ts` as a TS object.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random)\njulia> first(TS(1:10))\n(10 x 1) TS with Dates.Date Index\n\n Index       x1\n Date        Float64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-02-01  0.768448\n\n```\n\"\"\"\nfunction Base.first(ts::TS)\n    TS(Base.first(ts.coredata,1))\nend\n\n\n\"\"\"\n# Head\n```julia\nhead(ts::TS, n::Int = 10)\n```\nReturns the first `n` rows of `ts`.\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random)\njulia> head(TS(1:100))\n(10 x 1) TS with Int64 Index\n\n Index  x1    \n Int64  Int64 \n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n     1      1\n     2      2\n     3      3\n     4      4\n     5      5\n     6      6\n     7      7\n     8      8\n     9      9\n    10     10\n```\n\"\"\"\nfunction head(ts::TS, n::Int = 10)\n    TS(Base.first(ts.coredata, n))\nend\n\n\n\"\"\"\n# Tail\n```julia\ntail(ts::TS, n::Int = 10)\n```\n\nReturns the last `n` rows of `ts`.\n\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random)\njulia> tail(TS(1:100))\n(10 x 1) TS with Int64 Index\n\n Index  x1    \n Int64  Int64 \n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    91     91\n    92     92\n    93     93\n    94     94\n    95     95\n    96     96\n    97     97\n    98     98\n    99     99\n   100    100\n```\n\"\"\"\nfunction tail(ts::TS, n::Int = 10)\n    TS(DataFrames.last(ts.coredata, n))\nend\n", "meta": {"hexsha": "7a951f24de87cc2681ce62d8bfec31f8b0766c13", "size": 5341, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "xKDR/TSx.jl", "max_stars_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "xKDR/TSx.jl", "max_issues_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "xKDR/TSx.jl", "max_forks_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.1050847458, "max_line_length": 94, "alphanum_fraction": 0.5948324284, "num_tokens": 1891, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42632159254749036, "lm_q2_score": 0.1294027332703691, "lm_q1q2_score": 0.05516717932782187}}
{"text": "# script to install required packages\n\nmodule Setup\n    using Pkg\n\n    packages = [\n        :PackageCompiler, :PyCall, :PyPlot,\n        :Plots, :DataFrames, :CSV, :Test,\n        :StatsPlots, :Statistics, :FreqTables, \n        :NamedArrays, :Distributions, :LinearAlgebra,\n        :StatsBase, :PDMats, :Combinatorics, :SpecialFunctions\n    ]\n\n    function install_packages()\n        for pkg in packages\n            Pkg.add(String(pkg))\n        end        \n    end\n\n    function update_packages()\n        for pkg in packages\n            Pkg.update(String(pkg))\n        end        \n    end\nend", "meta": {"hexsha": "1642540b0c57938fd19d6981afc19ebc39754312", "size": 590, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "setup.jl", "max_stars_repo_name": "ShisatoYano/JuliaAutonomy", "max_stars_repo_head_hexsha": "d1643add4ab9625996fafeac23fc03f25eedff12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2021-03-10T12:43:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-01T16:40:19.000Z", "max_issues_repo_path": "setup.jl", "max_issues_repo_name": "ShisatoYano/JuliaAutonomy", "max_issues_repo_head_hexsha": "d1643add4ab9625996fafeac23fc03f25eedff12", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "setup.jl", "max_forks_repo_name": "ShisatoYano/JuliaAutonomy", "max_forks_repo_head_hexsha": "d1643add4ab9625996fafeac23fc03f25eedff12", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-08-14T02:46:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-07T09:19:40.000Z", "avg_line_length": 23.6, "max_line_length": 62, "alphanum_fraction": 0.5847457627, "num_tokens": 146, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.37754066879814546, "lm_q2_score": 0.14608725076600915, "lm_q1q2_score": 0.05515387835708148}}
{"text": "\"\"\"\nDifferenceLists, (c) 2018, Bill Burdick (William R. Burdick Jr.)\nMIT Licensed (see LICENSE file).\n\"\"\"\n\nmodule DifferenceLists\n\nexport DL, dl, concat, push, pushfirst, todl, dlconcat\n\n\"\"\"\n    DL(func)\n\nGiven function `func`, construct a difference list.\n\nDifference lists are highly efficient, immutable, concatenate and prepend in constant time, and iterate in time N.\n\n# Examples\n```jldoctest\njulia> [x for x = dl(1, 2, 3)]\n3-element Array{Int64,1}:\n 1\n 2\n 3\n```\n\"\"\"\nstruct DL\n    func\nend\n\n\"\"\"\n    dl()::DL\n    dl(items...)::DL\n\nConstruct a difference list of `items`.\n\n# Examples\n```jldoctest\njulia> dl()\ndl()\n\njulia> dl(1)\ndl(1)\n\njulia> dl(1, 2, 3)\ndl(1, 2, 3)\n\njulia> dl(1, dl(2, 3), 4)\ndl(1, dl(2, 3), 4)\n```\n\"\"\"\ndl() = DL(last -> last)\ndl(items...) = todl(items)\n\n\"\"\"\n    todl(items)\n\nCreate a difference list from something you can iterate over\n\n# Examples\n```jldoctest\njulia> todl([1, 2, 3])\ndl(1, 2, 3)\n```\n\"\"\"\ntodl(items) = DL(last -> nextFor(items, iterate(items), last))\ntodl(dl::DL) = dl\n\n\"\"\"\n    nextFor(items, state, last)\n\nCompute the next iteration value for an embedded collection.\n\"\"\"\nnextFor(items, ::Nothing, last) = last\nnextFor(items, (item, state), last) = item, (items, state, last)\n\n\"\"\"\n    push(item, dl::DL)\n\nPush an item onto the end of a difference list.\n\n# Examples\n```jldoctest\njulia> push(2, push(1, dl(7, 8, 9)))\ndl(7, 8, 9, 1, 2)\n```\n\"\"\"\npush(dl::DL, items...) = concat(dl, todl(items))\n\n\"\"\"\n    pushfirst(item, dl::DL)\n\nPush an item onto the front of a difference list.\n\n# Examples\n```jldoctest\njulia> pushfirst(1, pushfirst(2, dl(7, 8, 9)))\ndl(1, 2, 7, 8, 9)\n```\n\"\"\"\npushfirst(dl::DL, items...) = concat(todl(items), dl)\n\n\"\"\"\n    concat(lists::DL...)::DL\n\nConcatenate difference lists in constant time\n\nSee also: [`dl`](@ref)\n\n# Examples\n```jldoctest\njulia> concat(dl(1, 2), dl(3, 4))\ndl(1, 2, 3, 4)\n\njulia> concat(dl(1), dl(2))\ndl(1, 2)\n```\n\"\"\"\nconcat(lists::DL...) = DL(last -> foldr((x, y) -> x.func(y), lists, init=last))\ndlconcat(lists...) = DL(last -> foldr((x, y) -> x.func(y), map(todl, lists), init=last))\n\n\"\"\"\n    (a::DL)(lists::DL...)::DL\n\nA difference list itself can be used as shorthand for concat.\n\nSee also: [`dl`](@ref), [`concat`](@ref)\n\n# Examples\n```jldoctest\njulia> dl(1, 2)(dl(3, 4), dl(5, 6, 7))\ndl(1, 2, 3, 4, 5, 6, 7)\n```\n\"\"\"\n(a::DL)(lists...) = concat(a, map(todl, lists)...)\n\n# Iteration support\nBase.iterate(d::DL) = d.func(nothing)\nBase.iterate(::DL, cur::Tuple{Any, Any}) = cur\nBase.iterate(::DL, (items, state, last)::Tuple{Any, Any, Any}) = nextFor(items, iterate(items, state), last)\nBase.iterate(::DL, ::Nothing) = nothing\nBase.IteratorSize(::DL) = Base.SizeUnknown()\n\n# value display support\nBase.show(io::IO, dl::DL) = print(io, \"dl(\", join([sprint(show, x) for x = dl], \", \"), \")\")\n\nend # module\n", "meta": {"hexsha": "47651b107a307e60008a00db576db50c2ce234d4", "size": 2775, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DifferenceLists.jl", "max_stars_repo_name": "UnofficialJuliaMirror/DifferenceLists.jl-6951a811-51c6-5b2e-b13c-b6857b9511c8", "max_stars_repo_head_hexsha": "db485d4c59969dafb2c6cd5b8fcb4d5329c8d1d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/DifferenceLists.jl", "max_issues_repo_name": "UnofficialJuliaMirror/DifferenceLists.jl-6951a811-51c6-5b2e-b13c-b6857b9511c8", "max_issues_repo_head_hexsha": "db485d4c59969dafb2c6cd5b8fcb4d5329c8d1d1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-09-06T14:42:00.000Z", "max_issues_repo_issues_event_max_datetime": "2018-09-07T07:18:01.000Z", "max_forks_repo_path": "src/DifferenceLists.jl", "max_forks_repo_name": "UnofficialJuliaMirror/DifferenceLists.jl-6951a811-51c6-5b2e-b13c-b6857b9511c8", "max_forks_repo_head_hexsha": "db485d4c59969dafb2c6cd5b8fcb4d5329c8d1d1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-03T12:27:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:27:13.000Z", "avg_line_length": 18.8775510204, "max_line_length": 114, "alphanum_fraction": 0.6154954955, "num_tokens": 979, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782351378493656, "lm_q2_score": 0.12592276811536138, "lm_q1q2_score": 0.05513194880179329}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Packages\n#\n# Julia has over 2000 registered packages, making packages a huge part of the Julia\n# ecosystem.\n#\n# Even so, the package ecosystem still has some growing to do. Notably, we have first class\n# function calls  to other languages, providing excellent foreign function interfaces. We\n# can easily call into python or R, for example, with `PyCall` or `Rcall`.\n#\n# This means that you don't have to wait until the Julia ecosystem is fully mature, and that\n# moving to Julia doesn't mean you have to give up your favorite package/library from\n# another language!\n#\n# To see all available packages, check out\n#\n# https://pkg.julialang.org/\n# or\n# https://juliaobserver.com/\n#\n# For now, let's learn how to use a package.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# The first time you use a package on a given Julia installation, you need to use the\n# package manager to explicitly add it:\n# ------------------------------------------------------------------------------------------\n\nusing Pkg\nPkg.add(\"Example\")\n\n# ------------------------------------------------------------------------------------------\n# Every time you use Julia (start a new session at the REPL, or open a notebook for the\n# first time, for example), you load the package with the `using` keyword\n# ------------------------------------------------------------------------------------------\n\nusing Example\n\n# ------------------------------------------------------------------------------------------\n# In the source code of `Example.jl` at\n# https://github.com/JuliaLang/Example.jl/blob/master/src/Example.jl\n# we see the following function declared\n#\n# ```\n# hello(who::String) = \"Hello, $who\"\n# ```\n#\n# Having loaded `Example`, we should now be able to call `hello`\n# ------------------------------------------------------------------------------------------\n\nhello(\"it's me. I was wondering if after all these years you'd like to meet.\")\n\n# ------------------------------------------------------------------------------------------\n# Now let's play with the Colors package\n# ------------------------------------------------------------------------------------------\n\nPkg.add(\"Colors\")\n\nusing Colors\n\n# ------------------------------------------------------------------------------------------\n# Let's create a palette of 100 different colors\n# ------------------------------------------------------------------------------------------\n\npalette = distinguishable_colors(100)\n\n# ------------------------------------------------------------------------------------------\n# and then we can create a randomly checkered matrix using the `rand` command\n# ------------------------------------------------------------------------------------------\n\nrand(palette, 3, 3)\n\n# ------------------------------------------------------------------------------------------\n# In the next notebook, we'll use a new package to plot datasets.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Exercises\n#\n# #### 7.1\n# Load the Primes package (source code at https://github.com/JuliaMath/Primes.jl).\n# ------------------------------------------------------------------------------------------\n\n\n\n@assert @isdefined Primes\n\n# ------------------------------------------------------------------------------------------\n# #### 7.2\n# Verify that you can now use the function `primes` to grab all prime numbers under\n# 1,000,000 and store it in variable `primes_list`\n# ------------------------------------------------------------------------------------------\n\n\n\n@assert primes_list == primes(1000000)\n\n# ------------------------------------------------------------------------------------------\n# Please click on `Validate` on the top, once you are done with the exercises.\n# ------------------------------------------------------------------------------------------\n", "meta": {"hexsha": "b844818af23861d4872053fc4a14c590c698fa0e", "size": 4201, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/long-version/060 Packages.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/long-version/060 Packages.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/intro-to-julia/long-version/060 Packages.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 41.5940594059, "max_line_length": 92, "alphanum_fraction": 0.3741966199, "num_tokens": 627, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4035668680822513, "lm_q2_score": 0.13660839354130014, "lm_q1q2_score": 0.05513062153521014}}
{"text": "using StaticCompiler\nusing Test\nusing Libdl\nusing LinearAlgebra\nusing LoopVectorization\nusing ManualMemory\nusing StrideArraysCore\nusing Distributed\n\naddprocs(1)\n@everywhere using StaticCompiler\n\nremote_load_call(path, args...) = fetch(@spawnat 2 load_function(path)(args...))\n\n@testset \"Basics\" begin\n\n    simple_sum(x) = x + one(typeof(x))\n\n    # This probably needs a macro\n    for T \u2208 (Int, Float64, Int32, Float32, Int16, Float16)\n        _, path, = compile(simple_sum, (T,))\n        @test remote_load_call(path, T(1)) == T(2)\n    end\nend\n\n\nfib(n) = n <= 1 ? n : fib(n - 1) + fib(n - 2) # This needs to be defined globally due to https://github.com/JuliaLang/julia/issues/40990\n\n@testset \"Recursion\" begin\n    _, path = compile(fib, (Int,))\n    @test remote_load_call(path, 10) == fib(10)\n\n    # Trick to work around #40990\n    _fib2(_fib2, n) = n <= 1 ? n : _fib2(_fib2, n-1) + _fib2(_fib2, n-2)\n    fib2(n) = _fib2(_fib2, n)\n    \n    _, path = compile(fib2, (Int,))\n    @test remote_load_call(path, 20) == fib(20)\n    #@test compile(fib2, (Int,))[1](20) == fib(20)    \nend\n\n# Call binaries for testing\n# @testset \"Generate binary\" begin\n#     fib(n) = n <= 1 ? n : fib(n - 1) + fib(n - 2)\n#     libname = tempname() \n#     generate_shlib(fib, (Int,), libname)\n#     ptr = Libdl.dlopen(libname * \".\" * Libdl.dlext, Libdl.RTLD_LOCAL)\n#     fptr = Libdl.dlsym(ptr, \"julia_fib\")\n#     @assert fptr != C_NULL\n#     # This works on REPL\n#     @test_skip ccall(fptr, Int, (Int,), 10) == 55\n# end\n\n\n@testset \"Loops\" begin\n    function sum_first_N_int(N)\n        s = 0\n        for a in 1:N\n            s += a\n        end\n        s\n    end\n    _, path = compile(sum_first_N_int, (Int,))\n    @test remote_load_call(path, 10) == 55\n    \n    function sum_first_N_float64(N)\n        s = Float64(0)\n        for a in 1:N\n            s += Float64(a)\n        end\n        s\n    end\n    _, path = compile(sum_first_N_float64, (Int,))\n    @test remote_load_call(path, 10) == 55.\n\n    function sum_first_N_int_inbounds(N)\n        s = 0\n        @inbounds for a in 1:N\n            s += a\n        end\n        s\n    end\n    _, path = compile(sum_first_N_int_inbounds, (Int,))\n    @test remote_load_call(path, 10) == 55\n\n    function sum_first_N_float64_inbounds(N)\n        s = Float64(0)\n        @inbounds for a in 1:N\n            s += Float64(a)\n        end\n        s\n    end\n    _, path = compile(sum_first_N_float64_inbounds, (Int,))\n    @test remote_load_call(path, 10) == 55.\nend\n\n# Arrays with different input types Int32, Int64, Float32, Float64, Complex?\n@testset \"Arrays\" begin\n    function array_sum(n, A)\n        s = zero(eltype(A))\n        for i in 1:n\n            s += A[i]\n        end\n        s\n    end\n    for T \u2208 (Int, Complex{Float32}, Complex{Float64})\n        _, path = compile(array_sum, (Int, Vector{T}))\n        @test remote_load_call(path, 10, T.(1:10)) == T(55)\n    end\n\n    # @test (10, Int.(1:10)) == 55\n    # @test compile(array_sum, (Int, Vector{Complex{Float32}}))[1](10, Complex{Float32}.(1:10)) == 55f0 + 0f0im\n    # @test compile(array_sum, (Int, Vector{Complex{Float64}}))[1](10, Complex{Float64}.(1:10)) == 55f0 + 0f0im\nend\n\n\n# Julia wants to treat Tuple (and other things like it) as plain bits, but LLVM wants to treat it as something with a pointer.\n# We need to be careful to not send, nor receive an unwrapped Tuple to a compiled function.\n# The interface made in `compile` should handle this fine. \n@testset \"Send and receive Tuple\" begin\n    foo(u::Tuple) = 2 .* reverse(u) .- 1\n    \n    _, path = compile(foo, (NTuple{3, Int},))\n    @test remote_load_call(path, (1, 2, 3)) == (5, 3, 1)\nend\n\n\n# Just to call external libraries\n@testset \"BLAS\" begin\n    function mydot(a::Vector{Float64})\n        N = length(a)\n        BLAS.dot(N, a, 1, a, 1)\n    end\n    a = [1.0, 2.0]\n    mydot_compiled, path = compile(mydot, (Vector{Float64},))\n    @test_skip remote_load_call(path, a) == 5.0 # this needs a relocatable pointer to work\n    @test mydot_compiled(a) == 5.0\nend\n\n\n@testset \"Hello World\" begin\n    function hello(N)\n        println(\"Hello World $N\")\n        N\n    end\n    # How do I test this?\n    # Also ... this segfaults\n    @test_skip ccall(generate_shlib_fptr(hello, (Int,)), Int, (Int,), 1) == 1\nend\n\n# I can't beleive this works.\n@testset \"LoopVectorization\" begin\n    function mul!(C, A, B)\n        # note: @tturbo does NOT work\n        @turbo for n \u2208 indices((C,B), 2), m \u2208 indices((C,A), 1)\n            Cmn = zero(eltype(C))\n            for k \u2208 indices((A,B), (2,1))\n                Cmn += A[m,k] * B[k,n]\n            end\n            C[m,n] = Cmn\n        end\n    end\n\n    C = Array{Float64}(undef, 10, 12)\n    A = rand(10, 11)\n    B = rand(11, 12)\n\n    _, path = compile(mul!, (Matrix{Float64}, Matrix{Float64}, Matrix{Float64},))\n    # remote_load_call(path, C, A, B) This won't work because @spawnat copies C\n    C .= fetch(@spawnat 2 (load_function(path)(C, A, B); C))\n    @test C \u2248 A*B\nend\n\n# This is a trick to get stack allocated arrays inside a function body (so long as they don't escape).\n# This lets us have intermediate, mutable stack allocated arrays inside our \n@testset \"Alloca\" begin\n    function f(N)\n        # this can hold at most 100 Int values, if you use it for more, you'll segfault\n        buf = ManualMemory.MemoryBuffer{100, Int}(undef)\n        GC.@preserve buf begin\n            # wrap the first N values in a PtrArray\n            arr = PtrArray(pointer(buf), (N,))\n            arr .= 1 # mutate the array to be all 1s\n            sum(arr) # compute the sum. It is very imporatant that no references to arr escape the function body\n        end\n    end\n    _, path = compile(f, (Int,))\n    @test remote_load_call(path, 20) == 20\nend \n\n\n# data structures, dictionaries, tuples, named tuples\n", "meta": {"hexsha": "9c1191babd84eba06c2ec7761fb54d4f332461c6", "size": 5744, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "MasonProtter/StaticCompiler.jl", "max_stars_repo_head_hexsha": "4d348e52a430365a3e13aa8da6222a267e3e51d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-02T02:59:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-02T02:59:55.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "MasonProtter/StaticCompiler.jl", "max_issues_repo_head_hexsha": "4d348e52a430365a3e13aa8da6222a267e3e51d2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-04T04:17:41.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-04T04:49:10.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "MasonProtter/StaticCompiler.jl", "max_forks_repo_head_hexsha": "4d348e52a430365a3e13aa8da6222a267e3e51d2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-02-04T02:15:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-04T04:51:07.000Z", "avg_line_length": 29.7616580311, "max_line_length": 136, "alphanum_fraction": 0.5995821727, "num_tokens": 1811, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.11757214127540647, "lm_q1q2_score": 0.05511671778228706}}
{"text": "module TestThis\n\n# package code goes here\nprintln(\"Sherlock! We are loading the module now...\")\nusing PyPlot\n\nfunction greet(name)\n\tprintln(\"Hello, $name\")\nend\n\nfunction addme(a,b)\n\treturn a + b\nend\n\nfunction plotStuff(savehere::String)\n\ta = [1,2,3,4]\n\tb = [5,4,5,3]\n\tplot(a,b)\n\tsavefig(savehere*\".png\")\nend\n\nend # module\n", "meta": {"hexsha": "a1386de03924ee30f24b1933ebf927ce48f56dba", "size": 322, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TestThis.jl", "max_stars_repo_name": "rymanderson/TestThis.jl", "max_stars_repo_head_hexsha": "b6a236ddae2525a8c2c0abd8e94ed3135719c5ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/TestThis.jl", "max_issues_repo_name": "rymanderson/TestThis.jl", "max_issues_repo_head_hexsha": "b6a236ddae2525a8c2c0abd8e94ed3135719c5ef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/TestThis.jl", "max_forks_repo_name": "rymanderson/TestThis.jl", "max_forks_repo_head_hexsha": "b6a236ddae2525a8c2c0abd8e94ed3135719c5ef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.0, "max_line_length": 53, "alphanum_fraction": 0.6894409938, "num_tokens": 101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.11279541523008142, "lm_q1q2_score": 0.05507612832234766}}
{"text": "module Grader\n\nexport Problem, rungolden!, runstudent!, grade!, pl_JSON\n\nusing Parameters\nimport Random\nimport JSON\n\n@with_kw mutable struct Image\n    label::String = \"\"\n    url::String = \"\"\nend\n\n@with_kw mutable struct TestResult\n    name::String = \"\"\n    description::String = \"\"\n    points::Float64 = 0\n    max_points::Float64 = 0\n    message::String = \"\"\n    output::String = \"\"\n    images::Vector{Image} = []\nend\n\n@with_kw mutable struct Problem\n    gradable::Bool = true\n    score::Float64 = 0\n    message::String = \"\"\n    output::String = \"\"\n\n    images::Vector{Image} = []\n\n    tests::Vector{TestResult} = []\nend\n\n# Evaluate the provided code string inside a module,\n# and return the resulting module. May not \n# work if expected if the code string already is a module.\nfunction evalasmodule(code::AbstractString)\n    mod = \"module \" * Random.randstring(Random.MersenneTwister(), \"abcdefghijklmnopqrstuvwxyz\", 20) * \"\\n\" * code * \"\\nend\"\n    expr = Meta.parse(mod)\n    eval(expr)\nend\n\nfunction rungolden!(p::Problem, goldencode::AbstractString)::Module\n    goldenresult = Module()\n    try\n        goldenresult = evalasmodule(goldencode)\n    catch err\n        p.output = p.output * \"error running golden code:\\n\" * sprint(showerror, err, backtrace()) * \"\\n\"\n        p.gradable = false\n        p.message = \"Internal grading error, please notify instructor.\"\n    end\n    return goldenresult\nend\n\nfunction runstudent!(p::Problem, studentcode::AbstractString)::Module\n    studentresult = Module()\n    try\n        studentresult = evalasmodule(studentcode)\n    catch err\n        p.output = p.output * \"error running student code:\\n\" * sprint(showerror, err, backtrace()) * \"\\n\"\n        p.gradable = false\n        p.message = \"There was an error running your code, please see information below.\"\n    end\n    return studentresult\nend\n\n\nfunction grade!(p::Problem, name::String, description::String, points::Real, expr::Expr, msg_if_incorrect::String)\n    if !p.gradable \n        return nothing\n    end\n    \n    t = TestResult(max_points=points, name=name, description=description)\n\n    correct = false\n    try \n        correct = eval(expr)\n    catch err\n        t.output = t.output * \"\\n\" * sprint(showerror, err)\n    end\n\n    if correct === missing\n        t.message = \"your answer contains a 'missing' value\"\n    else\n        try\n            correct ? t.points = points : t.message = msg_if_incorrect\n        catch err\n            t.message = msg_if_incorrect\n            t.output = t.output * \"\\n\" * sprint(showerror, err)\n        end\n    end\n\n    append!(p.tests, [t])\n\n    pts = 0\n    totalpts = 0\n    for t in p.tests\n        pts += t.points\n    totalpts += t.max_points\n    end\n    p.score = float(pts) / float(totalpts)\n    return nothing\nend\n\nfunction pl_JSON(io::IO, p::Problem)\n    JSON.print(io, p)\nend\n\nend", "meta": {"hexsha": "eb5f189bc7b648803bba9b2ae6ae0c0112984e69", "size": 2818, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Grader.jl", "max_stars_repo_name": "ctessum/Grader.jl", "max_stars_repo_head_hexsha": "26b39510625c578f3d7b1bc46f4d5db385810fb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-17T16:58:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T16:58:33.000Z", "max_issues_repo_path": "src/Grader.jl", "max_issues_repo_name": "ctessum/Grader.jl", "max_issues_repo_head_hexsha": "26b39510625c578f3d7b1bc46f4d5db385810fb0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-06-15T00:47:33.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-15T00:47:33.000Z", "max_forks_repo_path": "src/Grader.jl", "max_forks_repo_name": "ctessum/Grader.jl", "max_forks_repo_head_hexsha": "26b39510625c578f3d7b1bc46f4d5db385810fb0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6181818182, "max_line_length": 123, "alphanum_fraction": 0.64158978, "num_tokens": 720, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4493926344647597, "lm_q2_score": 0.12252322533450248, "lm_q1q2_score": 0.05506103501619146}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Introduction to DataFrames\n# **[Bogumi\u0142 Kami\u0144ski](http://bogumilkaminski.pl/about/), Apr 21, 2017**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames # load package\n\n# ------------------------------------------------------------------------------------------\n# ## Joining DataFrames\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Preparing DataFrames for a join\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(ID=[1,2,3,4,missing], name = [\"Alice\", \"Bob\", \"Conor\", \"Dave\",\"Zed\"])\ny = DataFrame(id=[1,2,5,6,missing], age = [21,22,23,24,99])\nx,y\n\nrename!(x, :ID=>:id) # names of columns on which we want to join must be the same\n\n# ------------------------------------------------------------------------------------------\n# ### Standard joins: inner, left, right, outer, semi, anti\n# ------------------------------------------------------------------------------------------\n\njoin(x, y, on=:id) # :inner join by default, missing is joined\n\njoin(x, y, on=:id, kind=:left)\n\njoin(x, y, on=:id, kind=:right)\n\njoin(x, y, on=:id, kind=:outer)\n\njoin(x, y, on=:id, kind=:semi)\n\njoin(x, y, on=:id, kind=:anti)\n\n# ------------------------------------------------------------------------------------------\n# ### Cross join\n# ------------------------------------------------------------------------------------------\n\n# cross-join does not require on argument\n# it produces a Cartesian product or arguments\nfunction expand_grid(;xs...) # a simple replacement for expand.grid in R\n    reduce((x,y) -> join(x, DataFrame(Pair(y...)), kind=:cross),\n           DataFrame(Pair(xs[1]...)), xs[2:end])\nend\n\nexpand_grid(a=[1,2], b=[\"a\",\"b\",\"c\"], c=[true,false])\n\n# ------------------------------------------------------------------------------------------\n# ### Complex cases of joins\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(id1=[1,1,2,2,missing,missing],\n              id2=[1,11,2,21,missing,99],\n              name = [\"Alice\", \"Bob\", \"Conor\", \"Dave\",\"Zed\", \"Zoe\"])\ny = DataFrame(id1=[1,1,3,3,missing,missing],\n              id2=[11,1,31,3,missing,999],\n              age = [21,22,23,24,99, 100])\nx,y\n\njoin(x, y, on=[:id1, :id2]) # joining on two columns\n\njoin(x, y, on=[:id1], makeunique=true) # with duplicates all combinations are produced (here :inner join)\n\njoin(x, y, on=[:id1], kind=:semi) # but not by :semi join (as it would duplicate rows)\n", "meta": {"hexsha": "346e226953f6b20dea68c92780d7ef025aeb3316", "size": 2761, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/08_joins.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/08_joins.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/08_joins.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 40.6029411765, "max_line_length": 105, "alphanum_fraction": 0.367982615, "num_tokens": 583, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.12252321251815919, "lm_q1q2_score": 0.05506102925662117}}
{"text": "#!/usr/bin/env julia\n\nx = begin\n    a = 5\n    b = 6\n    a + b\nend\n\nprintln(\"x = $x, a = $a, b = $b\")\n\nx = begin\n    a = 9\n    b = -9\n    a + b\nend\n\nprintln(\"x = $x, a = $a, b = $b\")\n", "meta": {"hexsha": "37066ed48ea333ef1ba3891423fdb1e322d9b5ab", "size": 182, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "source-code/semantics/compound_expressions.jl", "max_stars_repo_name": "gjbex/Julia_good_bad_ugly", "max_stars_repo_head_hexsha": "0d5d25bb6cc80aa36eae6758d2c86d3106e9bcee", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-07-12T14:32:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-18T08:04:35.000Z", "max_issues_repo_path": "source-code/semantics/compound_expressions.jl", "max_issues_repo_name": "gjbex/Julia_good_bad_ugly", "max_issues_repo_head_hexsha": "0d5d25bb6cc80aa36eae6758d2c86d3106e9bcee", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source-code/semantics/compound_expressions.jl", "max_forks_repo_name": "gjbex/Julia_good_bad_ugly", "max_forks_repo_head_hexsha": "0d5d25bb6cc80aa36eae6758d2c86d3106e9bcee", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-18T08:03:54.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-18T08:03:54.000Z", "avg_line_length": 10.1111111111, "max_line_length": 33, "alphanum_fraction": 0.3846153846, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4493926344647597, "lm_q2_score": 0.12252320931407355, "lm_q1q2_score": 0.0550610278167287}}
{"text": "### A Pluto.jl notebook ###\n# v0.14.0\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 db24490e-7eac-11ea-094e-9d3fc8f22784\nmd\"# Introducing _bound_ variables\n\nWith the new `@bind` macro, Pluto.jl can listen to real-time events from HTML objects!\"\n\n# \u2554\u2550\u2561 bd24d02c-7eac-11ea-14ab-95021678e71e\n@bind x html\"<input type=range>\"\n\n# \u2554\u2550\u2561 cf72c8a2-7ead-11ea-32b7-d31d5b2dacc2\nmd\"This syntax displays the HTML object as the cell's output, and uses its latest value as the definition of `x`. Of course, the variable `x` is _reactive_, and all references to `x` come to life \u2728\n\n_Try it out!_ \ud83d\udc46\" \n\n# \u2554\u2550\u2561 cb1fd532-7eac-11ea-307c-ab16b1977819\nx\n\n# \u2554\u2550\u2561 816ea402-7eae-11ea-2134-fb595cca3068\nmd\"\"\n\n# \u2554\u2550\u2561 ce7bec8c-7eae-11ea-0edb-ad27d2df059d\nmd\"### Combining bonds\n\nThe `@bind` macro returns a `Bond` object, which can be used inside Markdown and HTML literals:\"\n\n# \u2554\u2550\u2561 fc99521c-7eae-11ea-269b-0d124b8cbe48\nbegin\n\t\ud83d\udc36slider = @bind \ud83d\udc36 html\"<input type=range>\"\n\t\ud83d\udc31slider = @bind \ud83d\udc31 html\"<input type=range>\"\n\t\n\tmd\"\"\"**How many pets do you have?**\n\t\n\tDogs: $(\ud83d\udc36slider)\n\n\tCats: $(\ud83d\udc31slider)\"\"\"\nend\n\n# \u2554\u2550\u2561 1cf27d7c-7eaf-11ea-3ee3-456ed1e930ea\nmd\"You have $(\ud83d\udc36) dogs and $(\ud83d\udc31) cats!\"\n\n# \u2554\u2550\u2561 e3204b38-7eae-11ea-32be-39db6cc9faba\nmd\"\"\n\n# \u2554\u2550\u2561 5301eb68-7f14-11ea-3ff6-1f075bf73955\nmd\"### Input types\n\nYou can use _any_ DOM element that fires an `input` event. For example:\"\n\n# \u2554\u2550\u2561 c7203996-7f14-11ea-00a3-8192ccc54bd6\nmd\"\"\"\n`a = ` $(@bind a html\"<input type=range >\")\n\n`b = ` $(@bind b html\"<input type=text >\")\n\n`c = ` $(@bind c html\"<input type=button value='Click'>\")\n\n`d = ` $(@bind d html\"<input type=checkbox >\")\n\n`e = ` $(@bind e html\"<select><option value='one'>First</option><option value='two'>Second</option></select>\")\n\n`f = ` $(@bind f html\"<input type=color >\")\n\n\"\"\"\n\n# \u2554\u2550\u2561 ede8009e-7f15-11ea-192a-a5c6135a9dcf\n(a, b, c, d, e, f)\n\n# \u2554\u2550\u2561 e2168b4c-7f32-11ea-355c-cf5932419a70\nmd\"\"\"**You can also use JavaScript to write more complicated input objects.** The `input` event can be triggered on any object using\n\n```js\nobj.dispatchEvent(new CustomEvent(\"input\"))\n```\n\nTry drawing a rectangle in the canvas below \ud83d\udc47 and notice that the `area` variable updates.\"\"\"\n\n# \u2554\u2550\u2561 7f4b0e1e-7f16-11ea-02d3-7955921a70bd\n@bind dims html\"\"\"\n<canvas width=\"200\" height=\"200\" style=\"position: relative\"></canvas>\n\n<script>\n// \ud83d\udc38 `currentScript` is the current script tag - we use it to select elements \ud83d\udc38 //\nconst canvas = currentScript.parentElement.querySelector(\"canvas\")\nconst ctx = canvas.getContext(\"2d\")\n\nvar startX = 80\nvar startY = 40\n\nfunction onmove(e){\n\t// \ud83d\udc38 We send the value back to Julia \ud83d\udc38 //\n\tcanvas.value = [e.layerX - startX, e.layerY - startY]\n\tcanvas.dispatchEvent(new CustomEvent(\"input\"))\n\n\tctx.fillStyle = '#ffecec'\n\tctx.fillRect(0, 0, 200, 200)\n\tctx.fillStyle = '#3f3d6d'\n\tctx.fillRect(startX, startY, ...canvas.value)\n}\n\ncanvas.onmousedown = e => {\n\tstartX = e.layerX\n\tstartY = e.layerY\n\tcanvas.onmousemove = onmove\n}\n\ncanvas.onmouseup = e => {\n\tcanvas.onmousemove = null\n}\n\n// Fire a fake mousemoveevent to show something\nonmove({layerX: 130, layerY: 160})\n\n</script>\n\"\"\"\n\n# \u2554\u2550\u2561 5876b98e-7f32-11ea-1748-0bb47823cde1\narea = abs(dims[1] * dims[2])\n\n# \u2554\u2550\u2561 72c7f60c-7f48-11ea-33d9-c5ea55a0ad1f\ndims\n\n# \u2554\u2550\u2561 d774fafa-7f34-11ea-290d-37805806e14b\nmd\"\"\n\n# \u2554\u2550\u2561 8db857f8-7eae-11ea-3e53-058a953f2232\nmd\"\"\"## Can I use it?\n\nThe `@bind` macro is **built into Pluto.jl** \u2014 it works without having to install a package. \n\nYou can use the (tiny) package [`PlutoUI`](https://github.com/fonsp/PlutoUI.jl) for some predefined `<input>` HTML codes. For example, you use `PlutoUI` to write\n\n```julia\n@bind x Slider(5:15)\n```\n\ninstead of \n\n```julia\n@bind x html\"<input type=range min=5 max=15>\"\n```\n\nHave a look at the [sample notebook about PlutoUI](./sample/PlutoUI.jl.jl)!\n\n_The `@bind` syntax in not limited to `html\"...\"` objects, but **can be used for any HTML-showable object!**_\n\"\"\"\n\n# \u2554\u2550\u2561 d5b3be4a-7f52-11ea-2fc7-a5835808207d\nmd\"\"\"#### More packages\n\nIn fact, **_any package_ can add bindable values to their objects**. For example, a geoplotting package could add a JS `input` event to their plot that contains the cursor coordinates when it is clicked. You can then use those coordinates inside Julia.\n\nA package _does not need to add `Pluto.jl` as a dependency to do so_: only the `Base.show(io, MIME(\"text/html\"), obj)` function needs to be extended to contain a `<script>` that triggers the `input` event with a value. (It's up to the package creator _when_ and _what_.) This _does not affect_ how the object is displayed outside of Pluto.jl: uncaught events are ignored by your browser.\"\"\"\n\n# \u2554\u2550\u2561 aa8f6a0e-303a-11eb-02b7-5597c167596d\n\n\n# \u2554\u2550\u2561 5c1ececa-303a-11eb-1faf-0f3a6f94ac48\nmd\"\"\"## Separate definition and reference\nInteractivity works through reactivity. If you put a bond and a reference to the same variable together, then setting the bond will trigger the _entire cell_ to re-evaluate, including the bond itself.\n\nSo **do not** write\n```julia\nmd\"\"\\\"$(@bind r html\"<input type=range>\")  $(r^2)\"\"\\\"\n```\nInstead, create two cells:\n```julia\nmd\"\"\\\"$(@bind r html\"<input type=range>\")\"\"\\\"\n```\n```julia\nr^2\n```\n\"\"\"\n\n# \u2554\u2550\u2561 55783466-7eb1-11ea-32d8-a97311229e93\n\n\n# \u2554\u2550\u2561 582769e6-7eb1-11ea-077d-d9b4a3226aac\nmd\"## Behind the scenes\n\n#### What is x?\n\nIt's an **`Int64`**! Not an Observable, not a callback function, but simply _the latest value of the input element_.\n\nThe update mechanism is _lossy_ and _lazy_, which means that it will skip values if your code is still running - and **only send the latest value when your code is ready again**. This is important when changing a slider from `0` to `100`, for example. If it would send all intermediate values, it might take a while for your code to process everything, causing a noticable lag.\"\n\n# \u2554\u2550\u2561 8f829274-7eb1-11ea-3888-13c00b3ba70f\nmd\"\"\"#### What does the macro do?\n\nThe `@bind` macro does not actually contain the interactivity mechanism, this is built into Pluto itself. Still, it does two things: it assigns a _default value_ to the variable (`missing` in most cases), and it wraps the second argument in a `PlutoRunner.Bond` object.\n\nFor example, _expanding_ the `@bind` macro turns this expression:\n\n```julia\n@bind x Slider(5:15)\n```\n\ninto:\n```julia\nbegin\n\tlocal el = Slider(5:15)\n\tglobal x = if applicable(Base.get, el)\n\t\tBase.get(el)\n\telse\n\t\tmissing\n\tend\n\tPlutoRunner.Bond(el, :x)\nend\n```\n\nThe `if` block in the middle assigns an initial value to `x`, which will be `missing`, unless an extension of `Base.get` has been declared for the element. Most objects (like `html\"<input>\"` or `md\"quelque chose\"`) don't have a `Base.get` method defined. In fact, `Base.get` has _no_ single-argument methods by default, but you can write one for your special types!\n\nDeclaring a default value using `Base.get` is **not necessary**, as shown by the examples above, but the default value will be used for `x` if the `notebook.jl` file is _run as a plain julia file_, without Pluto's interactivity. The package [`PlutoUI`](https://github.com/fonsp/PlutoUI.jl) defines default values.\n\n\"\"\"\n\n# \u2554\u2550\u2561 ced18648-7eb2-11ea-2052-07795685f0da\nmd\"#### JavaScript?\n\nYes! We are using `Generator.input` from [`observablehq/stdlib`](https://github.com/observablehq/stdlib#Generators_input) to create a JS _Generator_ (kind of like an Observable) that listens to `onchange`, `onclick` or `oninput` events, [depending on the element type](https://github.com/observablehq/stdlib#Generators_input).\n\nThis makes it super easy to create nice HTML/JS-based interaction elements - a package creator simply has to write a `show` method for MIME type `text/html` that creates a DOM object that triggers the `input` event. In other words, _Pluto's `@bind` will behave exactly like `viewof` in observablehq_.\n\n_If you want to make a cool new UI, go to [observablehq.com/@observablehq/introduction-to-views](https://observablehq.com/@observablehq/introduction-to-views) to learn how._\"\n\n# \u2554\u2550\u2561 dddb9f34-7f37-11ea-0abb-272ef1123d6f\nmd\"\"\n\n# \u2554\u2550\u2561 23db0e90-7f35-11ea-1c05-115773b44afa\nmd\"\"\n\n# \u2554\u2550\u2561 f7555734-7f34-11ea-069a-6bb67e201bdc\nmd\"That's it for now! Let us know what you think using the feedback button below! \ud83d\udc47\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500db24490e-7eac-11ea-094e-9d3fc8f22784\n# \u2560\u2550bd24d02c-7eac-11ea-14ab-95021678e71e\n# \u255f\u2500cf72c8a2-7ead-11ea-32b7-d31d5b2dacc2\n# \u2560\u2550cb1fd532-7eac-11ea-307c-ab16b1977819\n# \u255f\u2500816ea402-7eae-11ea-2134-fb595cca3068\n# \u255f\u2500ce7bec8c-7eae-11ea-0edb-ad27d2df059d\n# \u2560\u2550fc99521c-7eae-11ea-269b-0d124b8cbe48\n# \u2560\u25501cf27d7c-7eaf-11ea-3ee3-456ed1e930ea\n# \u255f\u2500e3204b38-7eae-11ea-32be-39db6cc9faba\n# \u255f\u25005301eb68-7f14-11ea-3ff6-1f075bf73955\n# \u255f\u2500c7203996-7f14-11ea-00a3-8192ccc54bd6\n# \u2560\u2550ede8009e-7f15-11ea-192a-a5c6135a9dcf\n# \u255f\u2500e2168b4c-7f32-11ea-355c-cf5932419a70\n# \u255f\u25007f4b0e1e-7f16-11ea-02d3-7955921a70bd\n# \u2560\u25505876b98e-7f32-11ea-1748-0bb47823cde1\n# \u2560\u255072c7f60c-7f48-11ea-33d9-c5ea55a0ad1f\n# \u255f\u2500d774fafa-7f34-11ea-290d-37805806e14b\n# \u255f\u25008db857f8-7eae-11ea-3e53-058a953f2232\n# \u255f\u2500d5b3be4a-7f52-11ea-2fc7-a5835808207d\n# \u255f\u2500aa8f6a0e-303a-11eb-02b7-5597c167596d\n# \u255f\u25005c1ececa-303a-11eb-1faf-0f3a6f94ac48\n# \u255f\u250055783466-7eb1-11ea-32d8-a97311229e93\n# \u255f\u2500582769e6-7eb1-11ea-077d-d9b4a3226aac\n# \u255f\u25008f829274-7eb1-11ea-3888-13c00b3ba70f\n# \u255f\u2500ced18648-7eb2-11ea-2052-07795685f0da\n# \u255f\u2500dddb9f34-7f37-11ea-0abb-272ef1123d6f\n# \u255f\u250023db0e90-7f35-11ea-1c05-115773b44afa\n# \u255f\u2500f7555734-7f34-11ea-069a-6bb67e201bdc\n", "meta": {"hexsha": "23e3be7571eaa15afc41eb9364afdec3789db6ea", "size": 9645, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "sample/Interactivity.jl", "max_stars_repo_name": "Mechachleopteryx/Pluto.jl", "max_stars_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-09-14T09:48:06.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-14T09:53:35.000Z", "max_issues_repo_path": "sample/Interactivity.jl", "max_issues_repo_name": "Mechachleopteryx/Pluto.jl", "max_issues_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-03T11:28:21.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-05T19:08:15.000Z", "max_forks_repo_path": "sample/Interactivity.jl", "max_forks_repo_name": "Mechachleopteryx/Pluto.jl", "max_forks_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8194945848, "max_line_length": 390, "alphanum_fraction": 0.7212026957, "num_tokens": 3611, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.2942149597859341, "lm_q2_score": 0.18713268442910244, "lm_q1q2_score": 0.05505723522394227}}
{"text": "using RecipesBase\n\nimport Base: getindex, setindex!, push!, keys, show\n\nexport ConvergenceHistory\nexport nprods, niters, nrests\n\n########\n# Type #\n########\n\n\"\"\"\nStore general and in-depth information about an iterative method.\n\n# Fields\n\n`mvps::Int`: number of matrix vector products.\n\n`mtvps::Int`: number of transposed matrix-vector products\n\n`iters::Int`: iterations taken by the method.\n\n`restart::T`: restart relevant information.\n\n* `T == Int`: iterations per restart.\n* `T == Void`: methods without restarts.\n\n`isconverged::Bool`: convergence of the method.\n\n`data::Dict{Symbol,Any}`: Stores all the information stored during the method execution.\nIt stores tolerances, residuals and other information, e.g. Ritz values in [`svdl`](@ref).\n\n# Constructors\n\n    ConvergenceHistory()\n    ConvergenceHistory(restart)\n\nCreate `ConvergenceHistory` with empty fields.\n\n# Arguments\n\n`restart`: number of iterations per restart.\n\n# Plots\n\nSupports plots using the `Plots.jl` package via a type recipe. Vectors are\nploted as series and matrices as scatterplots.\n\n# Implements\n\n`Base`: `getindex`, `setindex!`, `push!`\n\n\"\"\"\nmutable struct ConvergenceHistory{T,K}\n    mvps::Int\n    mtvps::Int\n    iters::Int\n    restart::K\n    isconverged::Bool\n    data::Dict{Symbol, Any}\nend\nfunction ConvergenceHistory(;restart=nothing, partial=false)\n    ConvergenceHistory{!partial,typeof(restart)}(\n        0,0,0,restart,false,Dict{Symbol, Any}()\n        )\nend\n\n\"\"\"\nStores information of the current iteration.\n\"\"\"\nconst PartialHistory = ConvergenceHistory{false}\n\n\"\"\"\nStores the information of all the iterations.\n\"\"\"\nconst CompleteHistory = ConvergenceHistory{true}\n\n\"\"\"\nHistory without resets.\n\"\"\"\nconst UnrestartedHistory{T} = ConvergenceHistory{T, Void}\n\n\"\"\"\nHistory with resets.\n\"\"\"\nconst RestartedHistory{T} = ConvergenceHistory{T, Int}\n\n\n#############\n# Functions #\n#############\n\nfunction show(io::IO, ch::ConvergenceHistory)\n    print(io, ch.isconverged ? \"Converged\" : \"Not converged\",\n        \" after \", ch.iters, \" iterations.\")\nend\n\n\"\"\"\n    getindex(ch, s)\n\nGet collection or tolerance associated with key `s` in `ch::ConvergenceHistory`.\n\n    getindex(ch, s, kwargs...)\n\nAccess elements of the collection associated with key `s` in `ch::ConvergenceHistory`.\n\"\"\"\ngetindex(ch::ConvergenceHistory, s::Symbol) = ch.data[s]\ngetindex(ch::ConvergenceHistory, s::Symbol, kwargs...) = ch.data[s][kwargs...]\n\n\"\"\"\n    setindex!(ch, tol, s)\n\nSet tolerance value associated with `s` in `ch::ConvergenceHistory` to `tol`.\n\n    setindex!(ch, val, s, kwargs...)\n\nSet collection element associated with key `s` in `ch::ConvergenceHistory` to val.\n\"\"\"\nsetindex!(ch::ConvergenceHistory, val, s::Symbol) = ch.data[s] = val\nsetindex!(ch::ConvergenceHistory, val, s::Symbol, kwargs...) = ch.data[s][kwargs...] = val\n\n\"\"\"\n    push!(ch, key, val)\n\nPush contents of `val` to collection associated with `key` in `ch::ConvergenceHistory`.\n\"\"\"\nfunction push!(ch::ConvergenceHistory, key::Symbol, vec::Union{Vector,Tuple})\n    matrix = ch.data[key]\n    width = size(matrix,2)\n    iter = isa(ch,CompleteHistory) ? ch.iters : 1\n    base = (iter-1)*width\n    for i in 1:min(width,length(vec))\n        matrix[base+i] = vec[i]\n    end\nend\npush!(ch::ConvergenceHistory, key::Symbol, data) = push_custom_data!(ch, key, data)\n\npush_custom_data!(ch::PartialHistory, key::Symbol, data) = ch.data[key] = data\npush_custom_data!(ch::CompleteHistory, key::Symbol, data) = ch.data[key][ch.iters] = data\n\n\"\"\"\n    reserve!(ch, key, maxiter)\n    reserve!(typ, key, maxiter)\n    reserve!(ch, key, maxiter, size)\n    reserve!(typ, ch, key, maxiter, size)\n\nReserve space for per iteration data in `ch`. If size is provided, intead of a\nvector it will reserve matrix of dimensions `(maxiter, size)`.\n\n# Arguments\n\n`typ::Type`: Type of the elements to store. Defaults to `Float64` when not given.\n\n`ch::ConvergenceHistory`: convergence history.\n\n`key::Union{Symbol,Vector{Symbol}}`: key used to identify the data.\n\n`maxiter::Int`: number of iterations to save space for.\n\n`size::Int`: number of elements to store with the `key` identifier.\n\n\"\"\"\nfunction reserve!(ch::ConvergenceHistory, keys::Vector{Symbol}, kwargs...)\n    for key in keys\n        reserve!(ch, key, kwargs...)\n    end\nend\nfunction reserve!(ch::ConvergenceHistory, key::Symbol, kwargs...)\n    reserve!(Float64, ch, key, kwargs...)\nend\nfunction reserve!(typ::Type, ch::ConvergenceHistory, key::Symbol, kwargs...)\n    _reserve!(typ, ch, key, kwargs...)\nend\n\n#If partialhistory, theres no need to store a vector or matrix, instead\n#store nothing or store a vector respectively.\n_reserve!(typ::Type, ch::PartialHistory, key::Symbol, ::Int) = nothing\nfunction _reserve!(typ::Type, ch::PartialHistory, key::Symbol, ::Int, size::Int)\n    ch.data[key] = Vector{typ}(size)\nend\nfunction _reserve!(typ::Type, ch::CompleteHistory, key::Symbol, len::Int)\n    ch.data[key] = Vector{typ}(len)\nend\nfunction _reserve!(typ::Type, ch::CompleteHistory, key::Symbol, len::Int, size::Int)\n    ch.data[key] = Matrix{typ}(len, size)\nend\n\n\"\"\"\n    shrink!(ml)\n\nshrinks the reserved space for `ConvergenceHistory` `ch` to the space actually used to log.\n\"\"\"\nshrink!(::PartialHistory) = nothing\nfunction shrink!(ch::CompleteHistory)\n    for key in keys(ch)\n        elem = ch.data[key]\n        if isa(elem, Vector)\n            resize!(elem, ch.iters)\n        elseif isa(elem, Matrix)\n            ch.data[key] = elem[1:ch.iters, :]\n        end\n    end\nend\n\n\"\"\"\n    nextiter!(ml)\n\nAdds one the the number of iterations in [`ConvergenceHistory`](@ref) `ch`. This is\nnecessary to avoid overwriting information with `push!(ml)`. It is also able\nto update other information of the method.\n\"\"\"\nfunction nextiter!(ch::ConvergenceHistory; mvps=0,mtvps=0)\n    ch.iters+=1\n    ch.mvps+=mvps\n    ch.mtvps+=mtvps\nend\n\n\"\"\"\n    keys(ch)\n\nKey iterator of the per iteration data logged in `ConvergenceHistory` `ch`.\n\"\"\"\nkeys(ch::ConvergenceHistory) = keys(ch.data)\n\n\"\"\"\n    setconv(ml, val)\n\nSet `val` as convergence status of the method in [`ConvergenceHistory`](@ref) ch.\n\"\"\"\nsetconv(ch::ConvergenceHistory, val::Bool) = ch.isconverged=val\n\n\"\"\"\n    nprods(ch)\n\nNumber of matrix-vector products plus transposed matrix-vector products\nlogged in `ConvergenceHistory` `ch`.\n\"\"\"\nnprods(ch::ConvergenceHistory) = ch.mvps+ch.mtvps\n\n\"\"\"\n    niters(ch)\n\nNumber of iterations logged in `ConvergenceHistory` `ch`.\n\"\"\"\nniters(ch::ConvergenceHistory) = ch.iters\n\n\"\"\"\n    nrests(ch)\n\nNumber of restarts logged in `ConvergenceHistory` `ch`.\n\"\"\"\nnrests(ch::RestartedHistory) = Int(ceil(ch.iters/ch.restart))\n\n#########\n# Plots #\n#########\n\n\"\"\"\n    plotable(x)\n\nDetermine whether a collection `x` is plotable. Only vectors and matrices are\nsuch objects.\n\"\"\"\nplotable(::VecOrMat{T}) where {T <: Real} = true\nplotable(::Any) = false\n\n\n# Plot entire ConvergenceHistory. `sep` is the color of the restart separator.\n@recipe function chef(ch::CompleteHistory; sep = :white)\n    candidates = collect(values(ch.data))\n    plotables = convert(Vector{Bool}, map(plotable, candidates))\n    n = length(filter(identity, plotables))\n    n > 0 || error(\"No plotables\")\n    frame = 1\n    layout := (n, 1)\n    for (name, draw) in collect(ch.data)[plotables]\n        @series begin\n            isa(draw, Vector) && (seriestype:= :line; label:=\"$name\")\n            isa(draw, Matrix) && (seriestype:= :scatter; title:=\"$name\"; label:=\"\")\n            subplot := frame\n            map(x->isnan(x) ? typeof(x)(0) : x,draw)\n        end\n        if isa(ch, RestartedHistory)\n            label := \"\"\n            linecolor := sep\n\n            left=1\n            maxy = round(maximum(draw),2)\n            miny = round(minimum(draw),2)\n            for restart in 2:nrests(ch)\n                @series begin\n                    left+=ch.restart\n                    subplot := frame\n                    [left,left],[miny,maxy]\n                end\n            end\n        end\n        frame+=1\n    end\nend\n\n# Plot collection `ch[name]`. `sep` is the color of the restart separator.\n@recipe function chef(ch::CompleteHistory, name::Symbol; sep = :white)\n    draw = ch[name]\n    plotable(draw) || error(\"Not plotable\")\n    isa(draw, Vector) && (seriestype-->:line; label-->\"$name\")\n    isa(draw, Matrix) && (seriestype-->:scatter; title-->\"$name\"; label-->\"\")\n    @series begin\n        draw\n    end\n    if isa(ch, RestartedHistory)\n        label := \"\"\n        linecolor := sep\n\n        left=1\n        maxy = round(maximum(draw),2)\n        miny = round(minimum(draw),2)\n        for restart in 2:nrests(ch)\n            @series begin\n                left+=ch.restart\n                [left,left],[miny,maxy]\n            end\n        end\n    end\nend\n", "meta": {"hexsha": "758e5922261de0c493c1bc90f9ddaec554724c7b", "size": 8659, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/history.jl", "max_stars_repo_name": "tkf/IterativeSolvers.jl", "max_stars_repo_head_hexsha": "c1c6dedd8770fe65f307eef4cb92eaacc0535018", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/history.jl", "max_issues_repo_name": "tkf/IterativeSolvers.jl", "max_issues_repo_head_hexsha": "c1c6dedd8770fe65f307eef4cb92eaacc0535018", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/history.jl", "max_forks_repo_name": "tkf/IterativeSolvers.jl", "max_forks_repo_head_hexsha": "c1c6dedd8770fe65f307eef4cb92eaacc0535018", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5613496933, "max_line_length": 91, "alphanum_fraction": 0.6544635639, "num_tokens": 2302, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.11436852920044105, "lm_q1q2_score": 0.05495163972051137}}
{"text": "# ---\n# title: 6. ZigZag Conversion\n# id: problem6\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: String\n# link: <https://leetcode.com/problems/zigzag-conversion/description/>\n# hidden: true\n# ---\n# \n# The string `\"PAYPALISHIRING\"` is written in a zigzag pattern on a given number\n# of rows like this: (you may want to display this pattern in a fixed font for\n# better legibility)\n# \n#     \n#     \n#     P   A   H   N\n#     A P L S I I G\n#     Y   I   R\n#     \n# \n# And then read line by line: `\"PAHNAPLSIIGYIR\"`\n# \n# Write the code that will take a string and make this conversion given a number\n# of rows:\n# \n#     \n#     \n#     string convert(string s, int numRows);\n#     \n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: s = \"PAYPALISHIRING\", numRows = 3\n#     Output: \"PAHNAPLSIIGYIR\"\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: s = \"PAYPALISHIRING\", numRows = 4\n#     Output: \"PINALSIGYAHRPI\"\n#     Explanation:\n#     P     I    N\n#     A   L S  I G\n#     Y A   H R\n#     P     I\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: s = \"A\", numRows = 1\n#     Output: \"A\"\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `1 <= s.length <= 1000`\n#   * `s` consists of English letters (lower-case and upper-case), `','` and `'.'`.\n#   * `1 <= numRows <= 1000`\n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction zig_zag_convert(s::String, num_rows::Int)::String\n    if num_rows == 1 || num_rows >= length(s)\n        s\n    else\n        table = [Char[] for _ in 1:num_rows]\n        i, dir = 1, 1\n        for x in s\n            push!(table[i], x)\n            if i == 1\n                dir = 1\n            elseif i == num_rows\n                dir = -1\n            end\n            i += dir\n        end\n        join(Iterators.flatten(table))\n    end\nend\n## @lc code=end\n", "meta": {"hexsha": "d1e2432d9b8ccaad50ea70b9fe59292d28c5fba1", "size": 1811, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/6.zig-zag-conversion.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/problems/6.zig-zag-conversion.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/problems/6.zig-zag-conversion.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 19.0631578947, "max_line_length": 83, "alphanum_fraction": 0.5069022639, "num_tokens": 597, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.11436852014455551, "lm_q1q2_score": 0.05495163536935146}}
{"text": "function greet()\n    println(\"hello world\")\nend\n# greet (generic function with 1 method)\n\ngreet()\n# hello world\n\n\n\nfunction calculator(x, y, operation)\n    if operation == \"+\"\n        x+y\n    elseif operation == \"-\"\n        x-y\n    elseif operation == \"*\"\n        x*y\n    elseif operation == \"/\"\n        x/y\n    else\n        println(\"Incorrect operation\")\n        return 0\n    end\nend\n# calculator (generic function with 1 method)\n\n\nprintln(calculator(10,20, \"+\"))\n# 30\nprintln(calculator(10,20, \"-\"))\n# -10\nprintln(calculator(10,20, \"*\"))\n# 200\nprintln(calculator(10,20, \"/\"))\n# 0.5\n\n\n# passing arguments\nfunction say_hello(name)\n    println(\"hello $name\")\nend\n# say_hello (generic function with 1 method)\n\nsay_hello(\"rahul\")\n# hello rahul\n\n\n# explicitly defining the type\nfunction say_hello(name::String)\n    println(\"hello $name\")\nend\n# say_hello (generic function with 1 method)\n\nsay_hello(\"rahul\")\n# hello rahul\n\n\n\n# Variable arguments\nfunction letsplay(x,y...)\n    println(x)\n    println(y)\nend\n# letsplay (generic function with 1 method)\n\nletsplay(\"cricket\",\"hockey\",\"tennis\")\n# cricket\n# (\"hockey\",\"tennis\")\n", "meta": {"hexsha": "8f48e057d7a323890f5493c73eb07c509505f608", "size": 1116, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter03/chapter-3.jl", "max_stars_repo_name": "PacktPublishing/Learning-Julia", "max_stars_repo_head_hexsha": "cf2c51f183679dd446acc2ebfe9999c73d600d8a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2018-02-20T19:28:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-05T19:45:39.000Z", "max_issues_repo_path": "Chapter03/chapter-3.jl", "max_issues_repo_name": "PacktPublishing/Learning-Julia", "max_issues_repo_head_hexsha": "cf2c51f183679dd446acc2ebfe9999c73d600d8a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter03/chapter-3.jl", "max_forks_repo_name": "PacktPublishing/Learning-Julia", "max_forks_repo_head_hexsha": "cf2c51f183679dd446acc2ebfe9999c73d600d8a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2018-02-19T16:15:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T11:47:39.000Z", "avg_line_length": 16.1739130435, "max_line_length": 45, "alphanum_fraction": 0.646953405, "num_tokens": 301, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.11436851561661299, "lm_q1q2_score": 0.05495163319377162}}
{"text": "# ------------------------------------------------------------------------------\n# Make SymPy work better\n# -------------------------------------------------------------------------------\nimport SymPy: simplify\n\n# If a number is passed to simplify, then simplify return it. SymPy.simplify now only takes symbolic\n# expressions.\n# Will be removed when SymPy.jl update.\nsimplify(val) = val\n", "meta": {"hexsha": "b87d1d70061cfdf1e1f24f757720eb49e84d5d9a", "size": 388, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/simpy.jl", "max_stars_repo_name": "newptcai/PlaneGeometry.jl", "max_stars_repo_head_hexsha": "fe2644fdf824a364daa1ce362bf5557d40775226", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-05-12T04:22:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T23:20:31.000Z", "max_issues_repo_path": "src/simpy.jl", "max_issues_repo_name": "newptcai/PlaneGeometry.jl", "max_issues_repo_head_hexsha": "fe2644fdf824a364daa1ce362bf5557d40775226", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/simpy.jl", "max_forks_repo_name": "newptcai/PlaneGeometry.jl", "max_forks_repo_head_hexsha": "fe2644fdf824a364daa1ce362bf5557d40775226", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.8, "max_line_length": 100, "alphanum_fraction": 0.4458762887, "num_tokens": 63, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.43014733397551624, "lm_q2_score": 0.12765263195921103, "lm_q1q2_score": 0.054909439312212406}}
{"text": "using BenchmarkTools\r\n\r\nmodule A\r\n\r\nstruct Triangle\r\n    side1\r\n    side2\r\n    side3\r\n    angle1\r\n    angle2\r\n    angle3\r\nend\r\n\r\nend # end of module A\r\n\r\nmodule B\r\n\r\nstruct Triangle\r\n    side1   :: Float64 \r\n    side2   :: Float64 \r\n    side3   :: Float64 \r\n    angle1  :: Float64 \r\n    angle2  :: Float64 \r\n    angle3  :: Float64 \r\nend\r\n\r\nend # end of module B\r\n\r\nmodule C \r\n\r\nstruct Triangle{T}\r\n    side1   ::  T\r\n    side2   ::  T\r\n    side3   ::  T\r\n    angle1  ::  T\r\n    angle2  ::  T\r\n    angle3  ::  T\r\nend\r\n\r\nend # end of module C \r\n\r\n\r\nusing .A\r\nusing .B\r\nusing .C\r\n\r\nfunction main()\r\n\r\n    a::Float64 = 5.0\r\n\r\n    # t = A.Triangle(a, a, a, a, a, a)\r\n    # t = B.Triangle(a, a, a, a, a, a)\r\n    t = C.Triangle{Float64}(a, a, a, a, a, a)\r\n\r\nend\r\n\r\n@benchmark main()", "meta": {"hexsha": "5f6c9328f921e7b615281e71a4234f997878d017", "size": 775, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-learning-src/advance/02a-benchmarking.jl", "max_stars_repo_name": "nunesmelo/djs-office-hours", "max_stars_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2021-03-27T14:23:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T14:50:37.000Z", "max_issues_repo_path": "julia-learning-src/advance/02a-benchmarking.jl", "max_issues_repo_name": "nunesmelo/djs-office-hours", "max_issues_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-learning-src/advance/02a-benchmarking.jl", "max_forks_repo_name": "nunesmelo/djs-office-hours", "max_forks_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2021-03-29T20:01:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T14:33:47.000Z", "avg_line_length": 13.5964912281, "max_line_length": 46, "alphanum_fraction": 0.5096774194, "num_tokens": 263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10970577824417874, "lm_q1q2_score": 0.05485288912208937}}
{"text": "# ---\n# title: 228. Summary Ranges\n# id: problem228\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Easy\n# categories: Array\n# link: <https://leetcode.com/problems/summary-ranges/description/>\n# hidden: true\n# ---\n# \n# You are given a **sorted unique** integer array `nums`.\n# \n# Return _the **smallest sorted** list of ranges that **cover all the numbers in\n# the array exactly**_. That is, each element of `nums` is covered by exactly\n# one of the ranges, and there is no integer `x` such that `x` is in one of the\n# ranges but not in `nums`.\n# \n# Each range `[a,b]` in the list should be output as:\n# \n#   * `\"a->b\"` if `a != b`\n#   * `\"a\"` if `a == b`\n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: nums = [0,1,2,4,5,7]\n#     Output: [\"0->2\",\"4->5\",\"7\"]\n#     Explanation: The ranges are:\n#     [0,2] --> \"0->2\"\n#     [4,5] --> \"4->5\"\n#     [7,7] --> \"7\"\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: nums = [0,2,3,4,6,8,9]\n#     Output: [\"0\",\"2->4\",\"6\",\"8->9\"]\n#     Explanation: The ranges are:\n#     [0,0] --> \"0\"\n#     [2,4] --> \"2->4\"\n#     [6,6] --> \"6\"\n#     [8,9] --> \"8->9\"\n#     \n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: nums = []\n#     Output: []\n#     \n# \n# **Example 4:**\n# \n#     \n#     \n#     Input: nums = [-1]\n#     Output: [\"-1\"]\n#     \n# \n# **Example 5:**\n# \n#     \n#     \n#     Input: nums = [0]\n#     Output: [\"0\"]\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `0 <= nums.length <= 20`\n#   * `-231 <= nums[i] <= 231 - 1`\n#   * All the values of `nums` are **unique**.\n#   * `nums` is sorted in ascending order.\n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "26da5a6aca416019ff2e4a5cac76fcbbe728dfe8", "size": 1646, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/228.summary-ranges.jl", "max_stars_repo_name": "noob-data-analaysis/LeetCode.jl", "max_stars_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/unresolved/228.summary-ranges.jl", "max_issues_repo_name": "noob-data-analaysis/LeetCode.jl", "max_issues_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/unresolved/228.summary-ranges.jl", "max_forks_repo_name": "noob-data-analaysis/LeetCode.jl", "max_forks_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.2888888889, "max_line_length": 80, "alphanum_fraction": 0.4732685298, "num_tokens": 606, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10970576369015278, "lm_q1q2_score": 0.05485288184507639}}
{"text": "# This file was generated, do not modify it. # hide\nmao = dataset(\"gap\", \"mao\")\ndescribe(mao, :nmissing)", "meta": {"hexsha": "c94b01d57134766c38fc78c001bb94950c5a6d20", "size": 104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/data/dataframe/code/ex17.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/data/dataframe/code/ex17.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/data/dataframe/code/ex17.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 34.6666666667, "max_line_length": 51, "alphanum_fraction": 0.6923076923, "num_tokens": 33, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.11596071061609145, "lm_q1q2_score": 0.05481271184539004}}
{"text": "#nb # %% slideshow [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n#md # # [Lecture 6 - Puzzle time! (Regular Expressions)](@id lecture6)\n#!md # # Lecture 6 - Puzzle time! (Regular Expressions)\n#\n# **Essential Skills For Computational Biology**  \n#\n# *2020-06-30*  \n#\n# Kevin Bonham, PhD\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## Strings contain patterns\n# \n# Here's a typical list of files from a DNA sequencing run:\n\nsequences = [\n    \"C0005-3F_L001_R1_001.fastq.gz\",\n    \"C0005-3F_L001_R2_001.fastq.gz\",\n    \"C0005-3F_L002_R1_001.fastq.gz\",\n    \"C0005-3F_L002_R2_001.fastq.gz\",\n    \"C0016-3E_L001_R1_001.fastq.gz\",\n    \"C0016-3E_L001_R2_001.fastq.gz\",\n    \"C0016-3E_L002_R1_001.fastq.gz\",\n    \"C0016-3E_L002_R2_001.fastq.gz\",\n]\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ##Simple Patterns:\n# \n# - `abc` matches literal \"abc\"\n# - `[abc]` matches any one of `a`, `b`, or `c`\n# - `[^abc]` matches any character _except_ `a`, `b`, or `c`\n# - `[a-z0-9]` matches any lowercase letter or digit\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# Puzzle: https://regexr.com/57ijc\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ##Repeating Patterns:\n# \n# - `+` matches 1 or more of a pattern\n#   - eg. `[0-9]+` matches one or more digits\n# - `*` matches 0 or more of a pattern\n# - `?` matches 0 or 1 of a pattern\n# - `{N}` matches `N` of a pattern\n#   - eg. `[abcdr]{4}` matches `abra` and `cada`, but not `abracadabra`\n#   - Can also to ranges; `{2,4}` matches 2 to 4, `{3,}` is 3 or more\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# Puzzle: https://regexr.com/57ik4\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## Special Patterns:\n# \n# - `.` matches any character, except a new line\n# - `\\w` matches any word character (same as `[A-Za-z0-9_]`)\n# - `\\d` matches any digit (same as `[0-9]`)\n# - `\\s` matches any whitespace (space, tab, newline, etc)\n# - If you need to match a character that is otherwise special,\n#   \"escape\" it with `\\`\n#   - eg. to match a literal period, `\\.`\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# Puzzles: \n# - https://regexr.com/57ikd\n# - https://regexr.com/57ikg\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ## More fun with regular Expressions\n# \n# - Use the regexr cheatsheet (or find another one - they're all over)\n#   - There are _a lot_ more patterns that can be used\n# - https://regexcrossword.com/\n# - My most complicated regex to date: \n\n# ```\n# ^([`~]{3,})(?:(?:(?:\\\\{|\\\\{\\\\.|)(julia)(?:;|))|(@(docs|autodocs|ref|meta|index|content|example|repl|eval|setup|raw)))\\\\s*(?:.*?)(\\\\}|)\\\\s*$\n# ```\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"fragment\"}}\n# > Some people, when confronted with a problem, think \"I know, I'll use regular expressions.\" Now they have two problems. \n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"subslide\"}}\n# ![xkcd regex](https://imgs.xkcd.com/comics/regular_expressions.png)\n\n#nb # %% A slide [markdown] {\"slideshow\": {\"slide_type\": \"slide\"}}\n# ## Regular expressions in julia\n\nfor seq in sequences\n    m = match(r\"^([MC]\\d+\\-[0-9][EF])_L00.+fastq\\.gz\", seq)\n    println(m.captures[1])\nend", "meta": {"hexsha": "0e5a2e7c8010b10e7d2a91f2b5bcef2a5c8fc86d", "size": 3299, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/literate/lectures/lecture6.jl", "max_stars_repo_name": "wellesley-bisc195/BISC195.jl", "max_stars_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-12-16T22:45:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T23:23:35.000Z", "max_issues_repo_path": "docs/literate/lectures/lecture6.jl", "max_issues_repo_name": "wellesley-bisc195/BISC195.jl", "max_issues_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-06-05T15:30:36.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-13T18:25:22.000Z", "max_forks_repo_path": "docs/literate/lectures/lecture6.jl", "max_forks_repo_name": "wellesley-bisc195/BISC195.jl", "max_forks_repo_head_hexsha": "726140e16bd49f38d023ec793e30394f852be164", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-06-05T14:59:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-10T00:45:02.000Z", "avg_line_length": 35.8586956522, "max_line_length": 141, "alphanum_fraction": 0.6280691119, "num_tokens": 1109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.35577489351363034, "lm_q2_score": 0.1540575704593912, "lm_q1q2_score": 0.05480981572515851}}
{"text": "\nexport numstring\n\n\"\"\"\n`numstring(x, n=2)`\n\nLeft pads a number (`x`) with zeros to a total of `n` charaters.\n\n## Example\n```julia-repl\njulia> numstring(11, 2)\n\"11\"\n\njulia> numstring(11, 3)\n\"011\"\n\njulia> numstring(11, 4)\n\"0011\"\n``` \n\"\"\"\nnumstring(x, n=2) = string(10^n + x)[2:end]\n", "meta": {"hexsha": "f83e64378f19985f3a34aaf36fe432058c3913d7", "size": 280, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "pjsjipt/AbstractDAQs.jl", "max_stars_repo_head_hexsha": "9fce348643324722d4824862e9889faff08673f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/utils.jl", "max_issues_repo_name": "pjsjipt/AbstractDAQs.jl", "max_issues_repo_head_hexsha": "9fce348643324722d4824862e9889faff08673f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/utils.jl", "max_forks_repo_name": "pjsjipt/AbstractDAQs.jl", "max_forks_repo_head_hexsha": "9fce348643324722d4824862e9889faff08673f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.7272727273, "max_line_length": 64, "alphanum_fraction": 0.6214285714, "num_tokens": 109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.1208532261576127, "lm_q1q2_score": 0.05477815662682093}}
{"text": "# The following simple model has a variable number of agent types,\n# but there is no killing or creating of additional agents.\n# It creates a model that has the same number of agents and does\n# overall the same number of operations, but these operations\n# are split in a varying number of agents. It shows how much of a\n# performance hit is to have many different agent types.\n\nusing Agents, Random\n\nmutable struct Agent1 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent2 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent3 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent4 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent5 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent6 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent7 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent8 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent9 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent10 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent11 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent12 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent13 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent14 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nmutable struct Agent15 <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int}\n    money::Int\nend\n\nfunction initialize_model_1(;n_agents=600,dims=(5,5))\n    space = GridSpace(dims)\n    model = ABM(Agent1, space; scheduler=Schedulers.randomly, warn=false)\n    id = 0\n    for id in 1:n_agents\n        agent = Agent1(id, (0,0), 10)\n        add_agent!(agent, model)\n    end\n    return model\nend\n\nfunction initialize_model(;n_agents=600, n_types=1, dims=(5,5))\n    agent_types = [Agent1,Agent2,Agent3,Agent4,Agent5,Agent6,Agent7,Agent8,\n        Agent9,Agent10,Agent11,Agent12,Agent13,Agent14,Agent15]\n    agents_used = agent_types[1:n_types]\n    space = GridSpace(dims)\n    model = ABM(Union{agents_used...}, space; scheduler=Schedulers.randomly, warn=false)\n    id = 0\n    agents_per_type = div(n_agents, n_types)\n    for A in agents_used\n        for _ in 1:agents_per_type\n            id += 1\n            agent = A(id, (0,0), 10)\n            add_agent!(agent, model)\n        end\n    end\n    return model\nend\n\nfunction agent_step!(agent, model)\n    move!(agent, model)\n    agents = agents_in_position(agent.pos, model)\n    for a in agents; exchange!(agent, a); end\n    return nothing\nend\n\nfunction move!(agent, model)\n    neighbors = nearby_positions(agent, model)\n    cell = rand(collect(neighbors))\n    move_agent!(agent, cell, model)\n    return nothing\nend\n\nfunction exchange!(agent, other_agent)\n    v1 = agent.money\n    v2 = other_agent.money\n    agent.money = v2\n    other_agent.money = v1\n    return nothing\nend\n\nfunction run_simulation_1(n_steps, n_reps)\n    t = @timed for _ in 1:n_reps\n        model = initialize_model_1()\n        Agents.step!(model, agent_step!, n_steps)\n    end\n    return t[2]/n_reps\nend\n\nfunction run_simulation(n_steps, n_reps; n_types)\n    t = @timed for _ in 1:n_reps\n        model = initialize_model(;n_types=n_types)\n        Agents.step!(model, agent_step!, n_steps)\n    end\n    return t[2]/n_reps\nend\n\n# %% Run the simulation, do performance estimate, first with 1, then with many\nRandom.seed!(2514)\nn_steps = 500\nn_reps = 5\nn_types = [2,3,4,5,10,15]\n# compile\nrun_simulation_1(1, 1)\nfor n in n_types; run_simulation(1, 1; n_types=n); end  # compile\n\ntime_1 = run_simulation_1(n_steps, n_reps)\ntimes = Float64[]\nfor n in n_types\n    println(n)\n    t = run_simulation(n_steps, n_reps; n_types=n)\n    push!(times, t/time_1)\nend\n\nimport CairoMakie\nfig, ax, = CairoMakie.lines(n_types, times)\nCairoMakie.scatter!(ax, n_types, times)\nax.xlabel = \"# types\"\nax.ylabel = \"time, relative to 1 type\"\nfig\n", "meta": {"hexsha": "df0e5424656f1cd14e7bb1381a1395c9d937efd1", "size": 4259, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/performance/variable_agent_types_simple_dynamics.jl", "max_stars_repo_name": "Alim-faraji/Agents.jl", "max_stars_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 395, "max_stars_repo_stars_event_min_datetime": "2019-10-19T01:22:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T00:56:36.000Z", "max_issues_repo_path": "test/performance/variable_agent_types_simple_dynamics.jl", "max_issues_repo_name": "Alim-faraji/Agents.jl", "max_issues_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 506, "max_issues_repo_issues_event_min_datetime": "2019-10-09T09:53:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T18:14:17.000Z", "max_forks_repo_path": "test/performance/variable_agent_types_simple_dynamics.jl", "max_forks_repo_name": "Alim-faraji/Agents.jl", "max_forks_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 85, "max_forks_repo_forks_event_min_datetime": "2019-10-26T19:44:36.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-22T09:34:42.000Z", "avg_line_length": 22.4157894737, "max_line_length": 88, "alphanum_fraction": 0.6778586523, "num_tokens": 1263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207955, "lm_q2_score": 0.11124121387367902, "lm_q1q2_score": 0.05475160567180437}}
{"text": "using Pkg\n\ncd(@__DIR__)\nPkg.activate(\".\")\nPkg.instantiate()\n#Pkg.precompile()\n", "meta": {"hexsha": "a2283a6af7ec92ee233ecd8ef98674876ceea6d0", "size": 78, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "init.jl", "max_stars_repo_name": "Yoshinobu-Ishizaki/StatsWithJuliaBook", "max_stars_repo_head_hexsha": "4c704e96d87b91e680122a6b6fa2d2083c70ea88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 988, "max_stars_repo_stars_event_min_datetime": "2018-06-21T00:44:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T01:37:47.000Z", "max_issues_repo_path": "init.jl", "max_issues_repo_name": "Yoshinobu-Ishizaki/StatsWithJuliaBook", "max_issues_repo_head_hexsha": "4c704e96d87b91e680122a6b6fa2d2083c70ea88", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41, "max_issues_repo_issues_event_min_datetime": "2019-02-20T05:06:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-23T16:53:08.000Z", "max_forks_repo_path": "init.jl", "max_forks_repo_name": "Yoshinobu-Ishizaki/StatsWithJuliaBook", "max_forks_repo_head_hexsha": "4c704e96d87b91e680122a6b6fa2d2083c70ea88", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 264, "max_forks_repo_forks_event_min_datetime": "2018-07-31T03:11:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T16:12:13.000Z", "avg_line_length": 11.1428571429, "max_line_length": 17, "alphanum_fraction": 0.7051282051, "num_tokens": 25, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.11124120650754306, "lm_q1q2_score": 0.05475160204627965}}
{"text": "## Exercise 9-2\n# Write a function called hasno_e that returns true if the given word doesn\u2019t have the letter e in it.\nusing Printf\n\nprintln(\"Ans: [Part 1]\")\n\nfunction hasno_e(string)\n    return !('e' in string)\nend\n\nprintln(hasno_e(\"Hello\"))\nprintln(hasno_e(\"print\"))\n\n# Modify your program from the previous section to print only the words that have no e and compute the percentage of the words in the list that have no e.\nprintln(\"[Part 2]\")\n\ntotal = 0\nhave_no_e = 0\n\nfor line in eachline(\"words.txt\")\n    global total += 1\n    if hasno_e(line)\n        global have_no_e += 1\n        println(line)\n    end\nend\n\n@printf(\"%0.2f%% words have no e.\\n\", ((have_no_e)/total)*100)\n\nprintln(\"End.\")\n", "meta": {"hexsha": "174c5e7283f3bde7c76354583335b93796793db5", "size": 693, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter9/ex2.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter9/ex2.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter9/ex2.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.3548387097, "max_line_length": 154, "alphanum_fraction": 0.6854256854, "num_tokens": 195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3849121444839335, "lm_q2_score": 0.14223189500989633, "lm_q1q2_score": 0.05474678372227288}}
{"text": "##### Packages #####\n\n## Import package manager\nimport Pkg\n\n## Activate project\nPkg.activate(\".\")\n\n## Add necessary packages to project\nPkg.add(\"Plots\")\nPkg.add(\"DifferentialEquations\")\nPkg.add(\"SparseArrays\")\nPkg.add(\"DataFrames\")\nPkg.add(\"CSV\")\n\n## check package status\nPkg.status()\n\n## load packages\nusing Plots, DifferentialEquations, SparseArrays, DataFrames, CSV\n", "meta": {"hexsha": "374798bb2ec187c94edc99549631d75c797f1d8f", "size": 369, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "setup.jl", "max_stars_repo_name": "hanamayall/Recreating_Binzer12", "max_stars_repo_head_hexsha": "c5eb237be10a134b489770fe47e6fc2d28d6f3f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "setup.jl", "max_issues_repo_name": "hanamayall/Recreating_Binzer12", "max_issues_repo_head_hexsha": "c5eb237be10a134b489770fe47e6fc2d28d6f3f3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "setup.jl", "max_forks_repo_name": "hanamayall/Recreating_Binzer12", "max_forks_repo_head_hexsha": "c5eb237be10a134b489770fe47e6fc2d28d6f3f3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.5714285714, "max_line_length": 65, "alphanum_fraction": 0.7262872629, "num_tokens": 94, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3849121444839335, "lm_q2_score": 0.1422318931919251, "lm_q1q2_score": 0.05474678302251368}}
{"text": "# Copyright (c) 2020-2021, Bank of Canada\n# All rights reserved.\n\n@testset \"TSeries\" begin\n    # test constructors\n    s = TSeries(20Q1, collect(10.0 .+ (1:12)))\n    @test typeof(s) === TSeries{Quarterly,Float64,Array{Float64,1}}\n    @test size(s) == (12,)\n    @test axes(s) == (20Q1:22Q4,)\n    @test length(s) == 12\n\n    t = TSeries(Int, 5)   # from type and number\n    @test typeof(t) === TSeries{Unit,Int,Vector{Int}} && t.firstdate == 1U && length(t.values) == 5\n    t = TSeries(UInt8, 4 .+ (1:5)) # from type and Int range\n    @test typeof(t) === TSeries{Unit,UInt8,Vector{UInt8}} && t.firstdate == 5U && length(t.values) == 5\n    t = TSeries(Float32, 1:5, undef) # from type, range and undef\n    @test typeof(t) === TSeries{Unit,Float32,Vector{Float32}} && t.firstdate == 1U && length(t.values) == 5\n\n    # constructing with similar()\n    t = similar(ones(Float64, 5), (2Q1:4Q4))\n    @test typeof(t) === TSeries{Quarterly,Float64,Vector{Float64}} && t.firstdate == 2Q1 && length(t.values) == 12\n\n    # indexing\n    @test s[1] == 11.0\n    @test s[12] == 22.0\n    @test s[1:3] == [11.0, 12.0, 13.0]\n    @test s[1:2:12] == collect(10.0 .+ (1:2:12))\n    @test s[s .< 13] == [11.0, 12.0]\n    #\n    @test s[20Q1] == 11.0\n    @test s[begin] == s.values[1]\n    @test s[end] == s.values[end]\n    @test s[begin:begin + 3] isa typeof(s)\n    @test s[begin:begin + 3].values == s.values[begin:begin + 3]\n    @test (@. 13 < s < 16 ) isa TSeries{frequencyof(s),Bool}\n    #\n    @test_throws ArgumentError s[2:end]  # can't mix Int indexing with begin/end\n    @test_throws ArgumentError s[begin:4]\n    #\n    @test_throws ArgumentError s[1U]  # wrong frequency\n    @test_throws ArgumentError s[1Y:3Y]  # wrong frequency\n    @test_throws ArgumentError s[2Y] = 5  # wrong frequency\n\n    q = copy(s)\n    s[19Q1] = 5  # outside range\n    @test s.values \u2248 [5, NaN, NaN, NaN, q.values...] nans=true\n    s[end + 3] = 3  # outside range\n    @test s.values \u2248 [5, NaN, NaN, NaN, q.values..., NaN, NaN, 3] nans=true\n\n    @test_throws ArgumentError s[20Y:21Y] = [2, 3]  # wrong frequency\n\n    i = TSeries(20Y, ones(Int32, 5))\n    i[17Y] = -1\n    @test i.values == [-1, typenan(Int32), typenan(Int32), ones(5)...]\n\n    @test_throws ArgumentError resize!(i, 17U:24U)  # wrong frequency\n    @test_throws ArgumentError copyto!(i, 17U:24U, i)  # wrong frequency\n    @test_throws ArgumentError copyto!(i, s)  # wrong frequency\n\n    # rangeof with drop\n    let myts = TSeries(20Q1:21Q4,1)\n        @test rangeof(myts,drop= 2) == 20Q3:21Q4\n        @test rangeof(myts,drop=-2) == 20Q1:21Q2\n    end\n\nend\n\n@testset \"Bcast\" begin\n    t = TSeries(5U:10U, rand(6))\n\n    # we can broadcast with a singleton\n    r = t .+ 5\n    @test typeof(r) == typeof(t) && eachindex(r) == eachindex(t) && all(r.values .== t.values .+ 5)\n\n    # we can broadcast with another TSeries of identical range\n    r = t .+ TSeries(5U, collect(1:6))\n    @test typeof(r) == typeof(t) && eachindex(r) == eachindex(t) && all(r.values .== t.values .+ (1:6))\n\n    # we can broadcast with another TSeries of different range\n    r = t .+ TSeries(4U, collect(1:6))\n    @test typeof(r) == typeof(t) && eachindex(r) == 5U:9U && all(r.values .== t.values[1:end - 1] .+ (2:6))\n\n    # we can broadcast with a Vector\n    r = t .+ collect(1:6)\n    @test typeof(r) == typeof(t) && eachindex(r) == eachindex(t) && all(r.values .== t.values .+ (1:6))\n\n    # broadcast with a vector of the wrong dimension throws a DimensionMismatch\n    @test_throws DimensionMismatch t .+ collect(1:4)\n    @test_throws DimensionMismatch t .+ collect(1:8)\n\n    # broadcast with a TSeries of another frequency throws a mixed_freq_error\n    @test_throws ArgumentError t .+ TSeries(2020Q1 .+ (0:5), 0.75)\n\n    # we can .= correctly\n    t .= 1.0\n    @test all(t.values .== 1.0)\n\n    t .+= collect(1:6)\n    @test eachindex(t) == (5U:10U) && all(t.values .== 1.0 .+ (1:6))\n\n    s = similar(t, 3U:12U)\n    fill!(s, 0.0)\n    s .= t .- 2\n    @test eachindex(s) === (3U:12U) && all(s[5U:10U].values .== (1:6) .- 1.0)\n    @test all(s[3U:4U].values .== 0.0) && all(s[11U:end] .== 0.0)\n\n    # dot-assign when the rhs is a vector\n    @test_throws DimensionMismatch s .= ones(length(s) + 1)\n    @test_throws DimensionMismatch s .= ones(length(s) - 1)\n    @test (s .= ones(length(s)); rangeof(s) === 3U:12U && all(s.values .== 1.0))\n\n    # dot-assign into a range\n    t[begin + 2:end - 2] .= 2\n    @test t.values == [2, 3, 2, 2, 6, 7]\n    \n    t[end + 2:end + 4] .= 8\n    @test t.values \u2248 [2, 3, 2, 2, 6, 7, NaN, 8, 8, 8] nans = true\n\n    # setindex with BitArray and broadcasting\n    t[t .< 7] .= 0\n    @test t.values \u2248 [0, 0, 0, 0, 0, 7, NaN, 8, 8, 8] nans = true\n    \n    t[2:4] .= 1\n    @test t.values \u2248 [0, 1, 1, 1, 0, 7, NaN, 8, 8, 8] nans = true\n\nend\n\nts_u = TSeries(5)\nts_v = TSeries(3:5)\nts_m = TSeries(mm(2018, 1), collect(1.0:12.0))\nts_q = TSeries(qq(2018, 1):qq(2020, 4), collect(1:12))\nts_y = TSeries(yy(2018), collect(1:12))\n\n@testset \"TSeries 1\" begin\n\n    @test ts_u.firstdate == 1U\n    # @test ts_u.values == 1:5\n\n    @test ts_m.firstdate == mm(2018, 1)\n    @test ts_m.values == collect(1.0:12.0)\n\n    @test ts_q.firstdate == qq(2018, 1)\n    @test ts_q.values == collect(1.0:12.0)\n\n    @test ts_y.firstdate == yy(2018)\n    @test ts_y.values == collect(1.0:12.0)\n\n    # Make sure if lengths are different we get an error\n    @test_throws ArgumentError TSeries(1U:5U, 1:6)\n\n    let t = TSeries(2U, rand(5))\n        @test firstdate(t) == 2U\n        @test lastdate(t) == 6U\n        @test length(t.values) == 5\n        @test length(t) == 5\n    end\n\n    let t = TSeries(1991Q1:1992Q4), s = TSeries(2222Y:2225Y, undef), r = TSeries(1006M3:1009M5, 0.3), e = TSeries(2000Y:1995Y, 7)\n        @test isempty(e)\n        @test length(r) == 10 + 12 + 12 + 5\n        @test length(t) == length(t.values) == 8\n        @test firstindex(t) == firstdate(t) == 1991Q1\n        @test lastindex(t) == lastdate(t) == 1992Q4\n    end\n\nend\n\n@testset \"Int indexing\" begin\n    let t = TSeries(4U:8U, rand(5))\n        @test t.firstdate == 4U && lastdate(t) == 8U\n        # test access\n        @test t[1] isa Number\n        @test t[1] == t.values[1]\n        @test t[2:4] isa Vector{Float64} # TSeries{frequencyof(t),Float64,Vector{Float64}}\n        @test t[2:4] == t.values[2:4]\n        @test t[[1,3,4]] isa Vector{Float64}\n        @test t[[1,3,4]] == t.values[[1,3,4]]\n        # test assignment\n        @test begin\n            t[2:4] .= 2.5\n            t.values[2:4] == fill(2.5, 3)\n        end\n        @test 5 == (t[3] = 5)\n        @test t.values == [first(t), 2.5, 5.0, 2.5, last(t)]\n    end\nend\n\n@testset \"Views\" begin\n    let t = TSeries(2010M1, rand(20))\n        @test axes(t) == (2010M1 - 1 .+ (1:20),)\n        @test Base.axes1(t) == 2010M1 - 1 .+ (1:20)\n        z = similar(t)\n        @test z isa typeof(t)\n        @test z.firstdate == t.firstdate\n        @test z != t\n        z = copy(t)\n        @test z == t\n        z[begin .+ (2:4)] .+= 0.2\n        z[begin .+ (3:4)] = [3,4]\n        @test z != t\n        z = view(t, 2:5)\n        c = view(t, 2010M2:2010M5)\n@test c == z\n        @test z == t[begin .+ (1:4)]\n        z[[1,3]] += [0.5, 0.5]\n        z[[2,4]] = [1,1.5]\n        @test c == z\n        @test z == t[begin .+ (1:4)]\n    end\nend\n\n@testset \"show\" begin\n    for (nrow, fd) = zip([3, 4, 5, 6, 7, 8, 22, 23, 24, 25, 26, 30], \n        Iterators.cycle((qq(2010, 1), mm(2010, 1), yy(2010), 1U)))\n        let io = IOBuffer()\n            t = TSeries(fd, rand(24))\n            show(IOContext(io, :displaysize => (nrow, 80)), MIME\"text/plain\"(), t)\n            @test length(readlines(seek(io, 0))) == max(2, min(length(t) + 1, nrow - 3))\n        end\n    end\n    for fd = (2020Q1, 2020M1, 2020Y, 2020U)\n        let io = IOBuffer()\n            t = TSeries(fd, Float64[])\n            show(IOContext(io, :displaysize => (10, 80)), MIME\"text/plain\"(), t)\n            @test startswith(readlines(seek(io, 0))[1], \"Empty\")\n        end\n    end\nend\n            \n@testset \"math\" begin\n    tq = TSeries(2020Q1, rand(12))\n    tm = TSeries(2020M1, copy(tq.values))\n    tu = TSeries(11U, copy(tq.values))\n\n    tmp = tq .* 5\n    @test 5tq isa TSeries{Quarterly} \n    @test tq * 5 isa TSeries{Quarterly} \n    @test 5tq == tmp\n    @test tq * 5 == tmp\n    @test (5tm).values == (5tq).values && 5tm \u2260 5tq\n\n    @test (tq + tq) == (tq .+ tq)\n\n    # approx works when ranges don't match \n    sq = resize!(copy(tq), 2020Q3:2022Q2)\n    @test sq \u2260 tq && sq \u2248 tq\n\n    # adding two TSeries of same frequency and range works\n    @test tq + 5tq \u2248 6tq\n    # adding TSeries and \n    @test_throws MethodError 5 + tq\n    @test_throws MethodError tq + 5\n    @test 5 .+ tq == tq .+ 5  # broadcasting works\n    @test_throws ArgumentError tq + 5tm   # different frequencies not allowed\n    \nend\n\n@testset \"Monthly\" begin\n    @test ts_m[mm(2018, 1):mm(2018, 12)] == ts_m\n    @test ts_m[mm(2018, 1):mm(2018, 12)].firstdate == mm(2018, 1)\n\n    # access outside of ts boundaries\n    @test_throws BoundsError ts_m[mm(2017, 1):mm(2019, 12)] == ts_m\n    # @test ts_m[mm(2017, 1):mm(2019, 12)].firstdate == ts_m.firstdate\n\n    # partially out of boundary\n    @test_throws BoundsError ts_m[mm(2017, 1):mm(2018, 6)] == ts_m[mm(2018, 1):mm(2018, 6)]\n    @test_throws BoundsError ts_m[mm(2017, 1):mm(2018, 6)] == ts_m[mm(2018, 1):mm(2018, 6)]\n\n    @test_throws BoundsError ts_m[mm(2017, 1):mm(2018, 6)] == ts_m[1:6]\n\n\n    @test_throws BoundsError ts_m[mm(2018, 6):mm(2019, 12)] == ts_m[mm(2018, 6):mm(2018, 12)]\n    @test_throws BoundsError ts_m[mm(2018, 6):mm(2019, 12)] == ts_m[6:12]\n\n    # fully out of boundary\n    @test_throws BoundsError ts_m[mm(2017, 1)] === nothing\n    @test_throws BoundsError ts_m[mm(2017, 1):mm(2017, 3)] === nothing\nend\n\n@testset \"Quarterly\" begin\n    @test ts_q[qq(2018, 1):qq(2020, 4)] == ts_q\n\n    # access outside of ts boundaries\n    @test_throws BoundsError ts_q[qq(2017, 1):qq(2021, 4)] == ts_q\n\n    # partially out of boundary\n    @test_throws BoundsError ts_q[qq(2017, 1):qq(2018, 4)] == ts_q[qq(2018, 1):qq(2018, 4)]\n    @test_throws BoundsError ts_q[qq(2017, 1):qq(2018, 4)] == ts_q[1:4]\n    \n    @test_throws BoundsError ts_q[qq(2018, 4):qq(2021, 4)] == ts_q[qq(2018, 4):qq(2020, 4)]\n    @test_throws BoundsError ts_q[qq(2018, 4):qq(2021, 4)] == ts_q[4:12]\n\n    # fully out of boundary\n    @test_throws BoundsError ts_q[qq(2017, 1)] === nothing\n    @test_throws BoundsError ts_q[qq(2017, 1):qq(2017, 3)] === nothing\nend\n\n@testset \"Yearly\" begin\n    @test ts_y[yy(2018):yy(2029)] == ts_y\n\n    # access outside of ts boundaries\n    @test_throws BoundsError ts_y[yy(2017):yy(2017) + 100] == ts_y\n\n    # partially out of boundary\n    @test_throws BoundsError ts_y[yy(2017):yy(2018)] == ts_y[yy(2018):yy(2018)]\n    @test_throws BoundsError ts_y[yy(2017):yy(2021)] == ts_y[1:4]\n\n    @test_throws BoundsError ts_y[yy(2018):yy(2100)] == ts_y[yy(2018):yy(2029)]\n    @test_throws BoundsError ts_y[yy(2021):yy(2100)] == ts_y[4:12]\n\n    # fully out of boundary\n    @test_throws BoundsError ts_y[yy(2017)] === nothing\n    @test_throws BoundsError ts_y[yy(2010):yy(2017)] === nothing\nend\n\n# ts_m = TSeries(mm(2018, 1), collect(1.0:12.0))\n@testset \"Setting\" begin\n    begin\n        ts_m = TSeries(mm(2018, 1):mm(2018, 12), collect(1.0:12.0))\n        @test_throws BoundsError ts_m[mm(2019, 2):mm(2019, 4)] = 1;\n        ts_m[mm(2019, 2):mm(2019, 4)] .= 1;\n        @test ts_m[mm(2019, 2):mm(2019, 4)].values == [1, 1, 1]\n        @test ts_m.firstdate == mm(2018, 1)\n        @test isequal(ts_m.values, vcat(collect(1.0:12.0), [NaN], [1, 1, 1]))\n    end\n\n    begin\n        ts_m = TSeries(mm(2018, 1):mm(2018, 12), collect(1.0:12.0))\n        @test_throws BoundsError ts_m[mm(2017, 10):mm(2017, 11)] = 1;\n        ts_m[mm(2017, 10):mm(2017, 11)] .= 1;\n        @test ts_m[mm(2017, 10):mm(2017, 11)].values == [1, 1]\n        @test ts_m.firstdate == mm(2017, 10)\n        @test isequal(ts_m.values, vcat([1, 1], [NaN], collect(1.0:12.0)))\n    end\n\n    begin\n        ts_m = TSeries(mm(2018, 1):mm(2018, 12), collect(1.0:12.0))\n        ts_m[mm(2019, 2):mm(2019, 4)] = [9, 10, 11];\n        @test ts_m[mm(2019, 2):mm(2019, 4)].values == [9, 10, 11]\n        @test ts_m.firstdate == mm(2018, 1)\n@test isequal(ts_m.values, vcat(collect(1.0:12.0), [NaN], [9, 10, 11]))\n    end\n\n    begin\n        ts_m = TSeries(mm(2018, 1):mm(2018, 12), collect(1.0:12.0))\n        ts_m[mm(2017, 10):mm(2017, 11)] = [9, 10];\n        @test ts_m[mm(2017, 10):mm(2017, 11)].values == [9, 10]\n        @test ts_m.firstdate == mm(2017, 10)\n        @test isequal(ts_m.values, vcat([9, 10], [NaN], collect(1.0:12.0)))\n    end\nend\n\n@testset \"Addition\" begin\n    x = TSeries(1U, [7, 7, 7])\n    y = TSeries(3U, [2, 4, 5])\n    @test x + y == TSeries(3U, [9])\n\n    x = TSeries(1U, [7, 7, 7])\n    y = TSeries(2U, [2, 4, 5])\n    @test x + y == TSeries(2U, [9, 11])\nend\n\n@testset \"Iris\" begin\n    # IRIS based assignment of values from other TSeries\n    x = TSeries(qq(2020, 1), zeros(3));\n    y = TSeries(qq(2020, 1), ones(3));\n    x[qq(2020, 1):qq(2020, 2)] = y;\n    @test x == TSeries(qq(2020, 1), [1, 1, 0])\n\n\n    let t = TSeries(5U:10U, ones)\n        s = t[6U:8U]\n        @test rangeof(s) === 6U:8U\n        s .= 0.7\n        t[6U:8U] = s\n        @test t.values == [1, 0.7, 0.7, 0.7, 1, 1]\n\n        s .= 0.8\n        t[6U:7U] = s\n        @test t.values == [1, 0.8, 0.8, 0.7, 1, 1]\n\n        t[6U:7U] = [2,3]\n        @test t.values == [1, 2, 3, 0.7, 1, 1]\n\n        @test_throws ArgumentError t[6Y:7Y] = s  # mixed frequency in indexing range\n        @test_throws ArgumentError t[6U:7U] = TSeries(6Y, s.values)  # mixed frequency of src and dest\n    end\n\n        # IRIS related: shift\n    x = TSeries(qq(2020, 1), zeros(3));\n    @test shift(x, 1) == TSeries(qq(2019, 4), zeros(3))\n\n    shift!(x, 1)\n    @test x == TSeries(qq(2019, 4), zeros(3))\n\n    # # IRIS related: nanrm!\n    # x = TSeries(qq(2020, 1), [NaN, 123, NaN]);\n    # nanrm!(x)\n    # @test x == TSeries(qq(2020, 2), [123])\n\n\n    # TODO\n    # - pct\n    # - apct\n\n\n\nend\n\n@testset \"TS.math\" begin\n    let x = TSeries(2000Y:2010Y, ones)\n        # lags and leads\n        y = cumsum(x)\n        @test rangeof(y) === rangeof(x)\n        z = y\n        @test z === y\n        y1 = lag(y)\n        @test rangeof(y1) === 1 .+ (rangeof(y))  && y1.values == y.values\n        lag!(y)\n        @test y1 == y && y1 !== y\n        @test z === y\n        y2 = lead(y, 2)\n        @test rangeof(y2) === -2 .+ (rangeof(y)) && y2.values == y.values\n        lead!(y, 3)\n        @test lead(y2) == y && lead(y2) !== y\n        @test z === y\n        # opeartions\n        @test x + x + 3x == 5x\n        @test_throws ArgumentError x + x.values \n        @test_throws ArgumentError x.values + x\n    end\nend\n\n@testset \"axes.range\" begin\n    @test axes(1U:5U) == axes(1:5)\n    @test Base.axes1(2020Y:2030Y) == Base.OneTo(11)\nend\n\n@testset \"overlay\" begin\n    # from FAME manual\n    A, B = (TSeries(87Y, [1, 2, NaN, 4]), TSeries(87Y, [NaN, 6, 7, 8]))\n    @test overlay(A, B) == TSeries(87Y, [1,2,7,4])\n    @test overlay(B, A) == TSeries(87Y, [1,6,7,8])\n    @test overlay(86Y:92Y, A, B) \u2248 TSeries(86Y, [NaN, 1, 2, 7, 4, NaN, NaN]) nans = true\n    @test (C = overlay(A,B); overlay(C,A).values == C.values)\nend\n\n@testset \"fconvert\" begin\n    t = TSeries(5U, collect(1:10))\n    @test fconvert(Unit, t) === t\n    @test_throws ErrorException fconvert(Quarterly, t) \n    \n    q = TSeries(5Q1, 1.0collect(1:10))\n    @test_throws ErrorException  fconvert(Unit, q)\n    mq = fconvert(Monthly, q)\n    @test typeof(mq) === TSeries{Monthly, Float64, Vector{Float64}}\n    yq = fconvert(Yearly, q)\n    @test typeof(yq) === TSeries{Yearly, Float64, Vector{Float64}}\n\n    for i = 1:11\n        @test rangeof(fconvert(Yearly, TSeries(1M1 .+ (i:50)))) == 2Y:4Y\n        @test rangeof(fconvert(Yearly, TSeries(1M1 .+ (0:47+i)))) == 1Y:4Y\n    end\n    for i = 1:3\n        @test rangeof(fconvert(Yearly, TSeries(1Q1 .+ (i:50)))) == 2Y:12Y\n        @test rangeof(fconvert(Yearly, TSeries(1Q1 .+ (0:47+i)))) == 1Y:12Y\n    end\n    for i = 1:11\n        @test rangeof(fconvert(Quarterly, TSeries(1M1 .+ (i:50)))) == 1Q2+div(i-1,3):5Q1\n        # @test rangeof(fconvert(Quarterly, TSeries(1M1 .+ (0:47+i)))) == 1Y:4Y\n    end\n\nend\n\n@testset \"strip\" begin\n    let rng_x = 2000Y:2010Y, x = TSeries(rng_x, ones)\n        x[2011Y:2015Y] .= NaN\n        x[1995Y:1999Y] .= NaN\n        @test (rangeof(strip(x)) == rng_x)\n        @test (TimeSeriesEcon.strip!(x); rangeof(x) == rng_x)\n    end\nend\n\n@testset \"recursive\" begin\n    ts = TSeries(1U, zeros(0))\n    ts[1U] = ts[2U] = 1.0\n    @rec 3U:10U ts[t] = ts[t-1]+ts[t-2]\n            @test ts.values == [1.0,1,2,3,5,8,13,21,34,55]\n    t = zeros(10,7)\n    r = rand(1, 7)\n    t[1, :] = r\n    @rec s=2:10 t[s,:] = t[s-1,:] .* s\n    @test t \u2248 factorial.(1:10) * r\n    #\n    s = ones(15);\n    @rec i=3:15 s[i] = s[i-1] + s[i-2]\n    @test s == Float64[1,1,2,3,5,8,13,21,34,55,89,144,233,377,610]\n    s = TSeries(2020Q1:2021Q4)\n    s[begin] = 0\n    @test_throws UndefVarError @rec 2020Q1:2021Q2 s[i+1] = 1.0 + s[i]\n    @rec i=firstdate(s):2023Q2 s[i+1] = 1.0 + s[i]\n    @test rangeof(s) == 2020Q1:2023Q3\n    @test values(s) == Float64[0:14...]\n    resize!(s, 2020Q1:2020Q2)\n    @rec firstdate(s)+1:2022Q3 s[t+1] = 2.0 * s[t] + s[t-1]\n    @test rangeof(s) == 2020Q1:2022Q4\n    @test values(s) == Float64[0,1,2,5,12,29,70,169,408,985,2378,5741]\nend\n", "meta": {"hexsha": "0316d0dff86d39cb88d754d13c65747a26911926", "size": 17257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_tseries.jl", "max_stars_repo_name": "bbejanov/TimeSeriesEcon.jl", "max_stars_repo_head_hexsha": "9cff80b556f46587822d89a42d9d3bb65d1dae1c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2020-07-27T17:06:06.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T02:03:20.000Z", "max_issues_repo_path": "test/test_tseries.jl", "max_issues_repo_name": "bbejanov/TimeSeriesEcon.jl", "max_issues_repo_head_hexsha": "9cff80b556f46587822d89a42d9d3bb65d1dae1c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2020-07-28T22:42:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-05T00:16:50.000Z", "max_forks_repo_path": "test/test_tseries.jl", "max_forks_repo_name": "bbejanov/TimeSeriesEcon.jl", "max_forks_repo_head_hexsha": "9cff80b556f46587822d89a42d9d3bb65d1dae1c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-07-27T17:06:10.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-30T14:41:25.000Z", "avg_line_length": 33.837254902, "max_line_length": 129, "alphanum_fraction": 0.5609897433, "num_tokens": 6964, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4225046348141882, "lm_q2_score": 0.12940274166401952, "lm_q1q2_score": 0.054673258110711304}}
{"text": "# To download data from a url, use the below code \n\nurl = \"https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv\"\ndataname = \"iris.csv\" # Here you can give any name you wish instead of iris \ndownload(url, dataname)\n# =================================================\n\n# To load CSV Files from your working directory \nusing CSV # This is a pacakge we use for loading CSV Files.\nusing DataFrames\ndata = CSV.read(\"iris.csv\", DataFrame) # This will load the dataset and convert it into a DataFrame\n# =================================================\n\n# To load it from Packages like RDatasets\nusing RDatasets\ndata = dataset(\"datasets\", \"iris\")\n# =================================================\n", "meta": {"hexsha": "6fd43426cbbaf6455311baaca6f184e101f964d4", "size": 755, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-scripts/data-handling/getting-data.jl", "max_stars_repo_name": "coinslab/ComputationalCognitiveModeling", "max_stars_repo_head_hexsha": "14c761ab8bc6685e7ec7f2bd79e7adae6abbad92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia-scripts/data-handling/getting-data.jl", "max_issues_repo_name": "coinslab/ComputationalCognitiveModeling", "max_issues_repo_head_hexsha": "14c761ab8bc6685e7ec7f2bd79e7adae6abbad92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-scripts/data-handling/getting-data.jl", "max_forks_repo_name": "coinslab/ComputationalCognitiveModeling", "max_forks_repo_head_hexsha": "14c761ab8bc6685e7ec7f2bd79e7adae6abbad92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-06T09:17:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-06T09:17:46.000Z", "avg_line_length": 41.9444444444, "max_line_length": 124, "alphanum_fraction": 0.6132450331, "num_tokens": 184, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.13296424191570594, "lm_q1q2_score": 0.05466304507076213}}
{"text": "# This file was generated, do not modify it. # hide\nusing MLJ, RDatasets, PrettyPrinting, ScientificTypes\n\n@load DecisionTreeClassifier pkg=DecisionTree\n\ncarseats = dataset(\"ISLR\", \"Carseats\")\n\nfirst(carseats, 3) |> pretty", "meta": {"hexsha": "899e9aad92aba31db865cd6a4a609c79a10cacec", "size": 222, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/isl/lab-8/code/ex1.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/isl/lab-8/code/ex1.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/isl/lab-8/code/ex1.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 27.75, "max_line_length": 53, "alphanum_fraction": 0.7702702703, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4843800842769844, "lm_q2_score": 0.11279541672127952, "lm_q1q2_score": 0.05463585345751095}}
{"text": "# Copyright (c) 2017: Miles Lubin and contributors\n# Copyright (c) 2017: Google Inc.\n#\n# Use of this source code is governed by an MIT-style license that can be found\n# in the LICENSE.md file or at https://opensource.org/licenses/MIT.\n\nmodule TestConstraintMap\n\nusing Test\n\nusing MathOptInterface\nconst MOI = MathOptInterface\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$(name)\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\n    return\nend\n\nstruct ConstraintDummyBridge <: MOI.Bridges.Constraint.AbstractBridge\n    id::Int\nend\n\nfunction test_map()\n    map = MOI.Bridges.Constraint.Map()\n\n    @test isempty(map)\n    @test !MOI.Bridges.Constraint.has_bridges(map)\n\n    x = MOI.VariableIndex(1)\n    y = MOI.VariableIndex(2)\n    b1 = ConstraintDummyBridge(1)\n    c1 = MOI.Bridges.Constraint.add_key_for_bridge(map, b1, x, MOI.EqualTo(0.0))\n\n    @test c1.value == x.value\n    @test haskey(map, c1)\n    @test map[c1] == b1\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c1)) == 1\n    @test collect(MOI.Bridges.Constraint.keys_of_type(map, typeof(c1))) == [c1]\n\n    @test length(map) == 1\n    @test !isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    b2 = ConstraintDummyBridge(2)\n    vov = MOI.VectorOfVariables([x, y])\n    c2 = MOI.Bridges.Constraint.add_key_for_bridge(\n        map,\n        b2,\n        vov,\n        MOI.SecondOrderCone(2),\n    )\n    @test c2.value == -1\n    @test haskey(map, c2)\n    @test map[c2] == b2\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c2)) == 1\n    @test collect(MOI.Bridges.Constraint.keys_of_type(map, typeof(c2))) == [c2]\n\n    @test length(map) == 2\n    @test !isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    b3 = ConstraintDummyBridge(3)\n    c3 = MOI.Bridges.Constraint.add_key_for_bridge(\n        map,\n        b3,\n        1.0x + 2.0,\n        MOI.EqualTo(0.0),\n    )\n    @test haskey(map, c3)\n    @test map[c3] == b3\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c3)) == 1\n    @test collect(MOI.Bridges.Constraint.keys_of_type(map, typeof(c3))) == [c3]\n\n    @test length(map) == 3\n    @test !isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    bridges = collect(values(map))\n    @test sort([b.id for b in bridges]) == 1:3\n    elements = sort(collect(map), by = el -> el.second.id)\n    @test elements[1].first == c1\n    @test elements[1].second == b1\n    @test elements[2].first == c2\n    @test elements[2].second == b2\n    @test elements[3].first == c3\n    @test elements[3].second == b3\n\n    @test MOI.Bridges.Constraint.list_of_key_types(map) == Set([\n        (MOI.VectorOfVariables, MOI.SecondOrderCone),\n        (MOI.VariableIndex, MOI.EqualTo{Float64}),\n        (MOI.ScalarAffineFunction{Float64}, MOI.EqualTo{Float64}),\n    ])\n    @test MOI.Bridges.Constraint.variable_constraints(map, x) == [c1]\n    @test isempty(MOI.Bridges.Constraint.variable_constraints(map, y))\n\n    delete!(map, c1)\n    @test length(map) == 2\n    @test sprint(MOI.Bridges.print_num_bridges, map) ==\n          \"\\nwith 2 constraint bridges\"\n    @test !isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    @test MOI.Bridges.Constraint.list_of_key_types(map) == Set([\n        (MOI.VectorOfVariables, MOI.SecondOrderCone),\n        (MOI.ScalarAffineFunction{Float64}, MOI.EqualTo{Float64}),\n    ])\n    @test isempty(MOI.Bridges.Constraint.variable_constraints(map, x))\n    @test isempty(MOI.Bridges.Constraint.variable_constraints(map, y))\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c1)) == 0\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c2)) == 1\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c3)) == 1\n\n    delete!(map, c2)\n    @test length(map) == 1\n    @test sprint(MOI.Bridges.print_num_bridges, map) ==\n          \"\\nwith 1 constraint bridge\"\n    @test !isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    @test MOI.Bridges.Constraint.list_of_key_types(map) ==\n          Set([(MOI.ScalarAffineFunction{Float64}, MOI.EqualTo{Float64})])\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c1)) == 0\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c2)) == 0\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c3)) == 1\n\n    delete!(map, c3)\n    @test length(map) == 0\n    @test isempty(map)\n    @test MOI.Bridges.Constraint.has_bridges(map)\n\n    @test isempty(MOI.Bridges.Constraint.list_of_key_types(map))\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c1)) == 0\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c2)) == 0\n    @test MOI.Bridges.Constraint.number_of_type(map, typeof(c3)) == 0\n\n    @test isempty(collect(map))\n\n    map = MOI.Bridges.Constraint.EmptyMap()\n    empty!(map)\n    @test isempty(map)\n    @test isempty(keys(map))\n    @test isempty(values(map))\n    @test !MOI.Bridges.Constraint.has_bridges(map)\n    @test iszero(MOI.Bridges.Constraint.number_of_type(map, typeof(c1)))\n    @test iszero(MOI.Bridges.Constraint.number_of_type(map, typeof(c2)))\n    @test iszero(MOI.Bridges.Constraint.number_of_type(map, typeof(c3)))\n    @test sprint(MOI.Bridges.print_num_bridges, map) == \"\"\nend\n\nend  # module\n\nTestConstraintMap.runtests()\n", "meta": {"hexsha": "49b9df9566d1f5f1429a126a972c5053d5b03bc1", "size": 5311, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Bridges/Constraint/map.jl", "max_stars_repo_name": "JuliaOpt/MathOptInterface", "max_stars_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 123, "max_stars_repo_stars_event_min_datetime": "2017-06-30T01:25:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-26T12:21:22.000Z", "max_issues_repo_path": "test/Bridges/Constraint/map.jl", "max_issues_repo_name": "JuliaOpt/MathOptInterface", "max_issues_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 918, "max_issues_repo_issues_event_min_datetime": "2017-06-30T05:09:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-03T15:13:02.000Z", "max_forks_repo_path": "test/Bridges/Constraint/map.jl", "max_forks_repo_name": "JuliaOpt/MathOptInterface", "max_forks_repo_head_hexsha": "27f9b622e6c114ef3e5a9cb032162bb436652c56", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 76, "max_forks_repo_forks_event_min_datetime": "2017-06-30T03:18:15.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-26T12:22:28.000Z", "avg_line_length": 33.4025157233, "max_line_length": 80, "alphanum_fraction": 0.6674825833, "num_tokens": 1578, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44552953503957266, "lm_q2_score": 0.12252320931407357, "lm_q1q2_score": 0.054587708477255435}}
{"text": "struct KnotVector{T,P<:AbstractVector{T}} <: AbstractVector{T}\n    parent::P\n    front::Int\n    back::Int\n\n    @inline function KnotVector{T,P}(parent, front, back) where {T,P<:AbstractVector{T}}\n        @boundscheck begin\n            front \u2265 0 || throw(DomainError(front, \"front padding must be non-negative.\"))\n            back  \u2265 0 || throw(DomainError(back,  \"back padding must be non-negative.\"))\n            has_offset_axes(parent) && throw(ArgumentError(\"parent array must not have offset axes.\"))\n            isempty(parent) && throw(ArgumentError(\"parent array must not be empty.\"))\n        end\n        new(parent, front, back)\n    end\nend\n\n\"\"\"\n    KnotVector(parent::AbstractVector, front[, back=front])\n\nCreate a view into `parent` where the first element of `parent` is appended `front` times at\nthe front and the last element `back` times at the back. (not exported)\n\n# Examples \n\n```jldoctest\njulia> BSplines.KnotVector(0:4, 2)\n9-element $(BSplines.KnotVector{Int64, UnitRange{Int64}}):\n 0\n 0\n 0\n 1\n 2\n 3\n 4\n 4\n 4\n\njulia> BSplines.KnotVector([\"first\", \"second\", \"last\"], 2, 3)\n8-element $(BSplines.KnotVector{String, Vector{String}}):\n \"first\"\n \"first\"\n \"first\"\n \"second\"\n \"last\"\n \"last\"\n \"last\"\n \"last\"\n```\n\"\"\"\n@propagate_inbounds KnotVector(parent::AbstractVector, front, back=front) =\n    KnotVector{eltype(parent), typeof(parent)}(parent, front, back)\n@propagate_inbounds KnotVector(p::KnotVector, front, back=front) =\n    KnotVector(parent(p), front + p.front, back + p.back)\n\n@inline @propagate_inbounds function Base.getindex(p::KnotVector, i::Int)\n    @boundscheck checkbounds(p, i)\n    @inbounds parent(p)[clamp(i-p.front, 1, length(parent(p)))]\nend\nBase.getindex(p::KnotVector, ::Colon) = copy(p)\n\nBase.IndexStyle(p::KnotVector) = IndexLinear()\nBase.parent(p::KnotVector) = p.parent\nBase.size(p::KnotVector) = (length(parent(p)) + p.front + p.back,)\n\nBase.copy(p::KnotVector) = @inbounds KnotVector(copy(parent(p)), p.front, p.back)\n\nBase.first(p::KnotVector) = @inbounds first(parent(p))\nBase.last(p::KnotVector) = @inbounds last(parent(p))\nBase.issorted(p::KnotVector) = issorted(parent(p))\n\nBase.allunique(p::KnotVector) = iszero(p.front) && iszero(p.back) && allunique(parent(p))\nBase.unique(p::KnotVector) = unique(parent(p))\n\nfunction intervalindex(p::KnotVector, x)\n    index = intervalindex(parent(p), x)\n    index === nothing && return nothing\n    return index + p.front\nend\n\nfunction intervalindex(p::KnotVector, x, start::Integer)\n    checkbounds(p, start)\n    index = intervalindex(parent(p), x, clamp(start-p.front, 1, length(parent(p))))\n    index === nothing && return nothing\n    return index + p.front\nend\n\nstruct StandardBasisVector{T<:Number} <: AbstractVector{T}\n    length::Int\n    index::Int\n\n    @inline function StandardBasisVector{T}(length, index) where T\n        @boundscheck if !(1 \u2264 index \u2264 length)\n            throw(DomainError(index, \"Index not in range 1:$length.\"))\n        end\n        new(length, index)\n    end\nend\n\n\"\"\"\n    StandardBasisVector([T=Bool,] length, i)\n\nCreate a vector of the specified `length` where the `i`-th element is equal to `1` and all\nother elements are equal to `0`. The elements are of type `T`. (not exported)\n\n# Examples\n\n```jldoctest\njulia> BSplines.StandardBasisVector(5, 3)\n5-element BSplines.StandardBasisVector{Bool}:\n 0\n 0\n 1\n 0\n 0\n\njulia> BSplines.StandardBasisVector(Float64, 6, 2)\n6-element BSplines.StandardBasisVector{Float64}:\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 0.0\n```\n\"\"\"\n@propagate_inbounds StandardBasisVector(length, index) = StandardBasisVector(Bool, length, index)\n@propagate_inbounds StandardBasisVector(T::Type, length, index) =\n    StandardBasisVector{T}(length, index)\n\nBase.size(A::StandardBasisVector) = (A.length,)\n\nBase.copy(A::StandardBasisVector) = A\n\n@inline @propagate_inbounds function Base.getindex(A::StandardBasisVector, i::Int)\n    @boundscheck checkbounds(A, i)\n    convert(eltype(A), i == A.index)\nend\nBase.getindex(A::StandardBasisVector, ::Colon) = A\n\nBase.:(==)(x::StandardBasisVector, y::StandardBasisVector) =\n    (x.length == y.length) & (x.index == y.index)\n", "meta": {"hexsha": "c558c9c213ff65fc32ef832c36921c92a743a724", "size": 4079, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/vectortypes.jl", "max_stars_repo_name": "sostock/BSplines.jl", "max_stars_repo_head_hexsha": "c76eb279f1ea0c309058f49c9eaec438da88ff7a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-04-13T12:31:26.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T07:34:39.000Z", "max_issues_repo_path": "src/vectortypes.jl", "max_issues_repo_name": "sostock/BSplines.jl", "max_issues_repo_head_hexsha": "c76eb279f1ea0c309058f49c9eaec438da88ff7a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2020-04-26T15:47:13.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-01T18:52:24.000Z", "max_forks_repo_path": "src/vectortypes.jl", "max_forks_repo_name": "sostock/BSplines.jl", "max_forks_repo_head_hexsha": "c76eb279f1ea0c309058f49c9eaec438da88ff7a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-04-28T21:08:02.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-02T12:28:11.000Z", "avg_line_length": 28.7253521127, "max_line_length": 102, "alphanum_fraction": 0.6888943368, "num_tokens": 1203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.11436853070975538, "lm_q1q2_score": 0.05450571445832053}}
{"text": "# ---\n# title: 83. Remove Duplicates from Sorted List\n# id: problem83\n# author: zhwang\n# date: 2022-02-17\n# difficulty: Easy\n# categories: Linked List\n# link: <https://leetcode.com/problems/remove-duplicates-from-sorted-list/description/>\n# hidden: true\n# ---\n# \n# Given a sorted linked list, delete all duplicates such that each element\n# appear only _once_.\n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: 1->1->2\n#     Output: 1->2\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: 1->1->2->3->3\n#     Output: 1->2->3\n#     \n# \n# \n## @lc code=start\nusing LeetCode\n\nfunction delete_duplicates!(head::Union{ListNode,Nothing})::Union{ListNode,Nothing}\n    cur = head\n    while !isnothing(cur)\n        node, value = next(cur), val(cur)\n        while !isnothing(node) && val(node) == value\n            cur.next = node = next(node)\n        end\n        cur = next(cur)\n    end\n    return head\nend\n## @lc code=end\n", "meta": {"hexsha": "10feae515a99f114bf70fbff69f0e5de0911a6ee", "size": 918, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/83.remove-duplicates-from-sorted-list.jl", "max_stars_repo_name": "RexWzh/LeetCode.jl", "max_stars_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/problems/83.remove-duplicates-from-sorted-list.jl", "max_issues_repo_name": "RexWzh/LeetCode.jl", "max_issues_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/problems/83.remove-duplicates-from-sorted-list.jl", "max_forks_repo_name": "RexWzh/LeetCode.jl", "max_forks_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.5319148936, "max_line_length": 87, "alphanum_fraction": 0.5860566449, "num_tokens": 283, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10970578261038684, "lm_q1q2_score": 0.05442436181029102}}
{"text": "\"\"\"\n    onehot(x, labels, [default])\n\nReturn a `OneHotVector` which is roughly a sparse representation of `x .== labels`.\n\nInstead of storing say `Vector{Bool}`, it stores the index of the first occurrence \nof `x` in `labels`. If `x` is not found in labels, then it either returns `onehot(default, labels)`,\nor gives an error if no default is given.\n\nSee also [`onehotbatch`](@ref) to apply this to many `x`s, \nand [`onecold`](@ref) to reverse either of these, as well as to generalise `argmax`.\n\n# Examples\n```jldoctest\njulia> \u03b2 = onehot(:b, (:a, :b, :c))\n3-element OneHotVector(::UInt32) with eltype Bool:\n \u22c5\n 1\n \u22c5\n\njulia> \u03b1\u03b2\u03b3 = (onehot(0, 0:2), \u03b2, onehot(:z, [:a, :b, :c], :c))  # uses default\n(Bool[1, 0, 0], Bool[0, 1, 0], Bool[0, 0, 1])\n\njulia> hcat(\u03b1\u03b2\u03b3...)  # preserves sparsity\n3\u00d73 OneHotMatrix(::Vector{UInt32}) with eltype Bool:\n 1  \u22c5  \u22c5\n \u22c5  1  \u22c5\n \u22c5  \u22c5  1\n```\n\"\"\"\nfunction onehot(x, labels)\n  i = _findval(x, labels)\n  isnothing(i) && error(\"Value $x is not in labels\")\n  OneHotVector{UInt32, length(labels)}(i)\nend\n\nfunction onehot(x, labels, default)\n  i = _findval(x, labels)\n  isnothing(i) && return onehot(default, labels)\n  OneHotVector{UInt32, length(labels)}(i)\nend\n\n_findval(val, labels) = findfirst(isequal(val), labels)\n# Fast unrolled method for tuples:\nfunction _findval(val, labels::Tuple, i::Integer=1)\n  ifelse(isequal(val, first(labels)), i, _findval(val, Base.tail(labels), i+1))\nend\n_findval(val, labels::Tuple{}, i::Integer) = nothing\n\n\"\"\"\n    onehotbatch(xs, labels, [default])\n\nReturns a `OneHotMatrix` where `k`th column of the matrix is [`onehot(xs[k], labels)`](@ref onehot).\nThis is a sparse matrix, which stores just a `Vector{UInt32}` containing the indices of the\nnonzero elements.\n\nIf one of the inputs in `xs` is not found in `labels`, that column is `onehot(default, labels)`\nif `default` is given, else an error.\n\nIf `xs` has more dimensions, `N = ndims(xs) > 1`, then the result is an \n`AbstractArray{Bool, N+1}` which is one-hot along the first dimension, \ni.e. `result[:, k...] == onehot(xs[k...], labels)`.\n\nNote that `xs` can be any iterable, such as a string. And that using a tuple\nfor `labels` will often speed up construction, certainly for less than 32 classes.\n\n# Examples\n```jldoctest\njulia> oh = onehotbatch(\"abracadabra\", 'a':'e', 'e')\n5\u00d711 OneHotMatrix(::Vector{UInt32}) with eltype Bool:\n 1  \u22c5  \u22c5  1  \u22c5  1  \u22c5  1  \u22c5  \u22c5  1\n \u22c5  1  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  1  \u22c5  \u22c5\n \u22c5  \u22c5  \u22c5  \u22c5  1  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5\n \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  1  \u22c5  \u22c5  \u22c5  \u22c5\n \u22c5  \u22c5  1  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  \u22c5  1  \u22c5\n\njulia> reshape(1:15, 3, 5) * oh  # this matrix multiplication is done efficiently\n3\u00d711 Matrix{Int64}:\n 1  4  13  1  7  1  10  1  4  13  1\n 2  5  14  2  8  2  11  2  5  14  2\n 3  6  15  3  9  3  12  3  6  15  3\n```\n\"\"\"\nonehotbatch(data, labels, default...) = _onehotbatch(data, length(labels) < 32 ? Tuple(labels) : labels, default...)\n\nfunction _onehotbatch(data, labels)\n  indices = UInt32[something(_findval(i, labels), 0) for i in data]\n  if 0 in indices\n    for x in data\n      isnothing(_findval(x, labels)) && error(\"Value $x not found in labels\")\n    end\n  end\n  return OneHotArray(indices, length(labels))\nend\n\nfunction _onehotbatch(data, labels, default)\n  default_index = _findval(default, labels)\n  isnothing(default_index) && error(\"Default value $default is not in labels\")\n  indices = UInt32[something(_findval(i, labels), default_index) for i in data]\n  return OneHotArray(indices, length(labels))\nend\n\n\"\"\"\n    onecold(y::AbstractArray, labels = 1:size(y,1))\n\nRoughly the inverse operation of [`onehot`](@ref) or [`onehotbatch`](@ref): \nThis finds the index of the largest element of `y`, or each column of `y`, \nand looks them up in `labels`.\n\nIf `labels` are not specified, the default is integers `1:size(y,1)` --\nthe same operation as `argmax(y, dims=1)` but sometimes a different return type.\n\n# Examples\n```jldoctest\njulia> onecold([false, true, false])\n2\n\njulia> onecold([0.3, 0.2, 0.5], (:a, :b, :c))\n:c\n\njulia> onecold([ 1  0  0  1  0  1  0  1  0  0  1\n                 0  1  0  0  0  0  0  0  1  0  0\n                 0  0  0  0  1  0  0  0  0  0  0\n                 0  0  0  0  0  0  1  0  0  0  0\n                 0  0  1  0  0  0  0  0  0  1  0 ], 'a':'e') |> String\n\"abeacadabea\"\n```\n\"\"\"\nonecold(y::AbstractVector, labels = 1:length(y)) = labels[argmax(y)]\nfunction onecold(y::AbstractArray, labels = 1:size(y, 1))\n  indices = _fast_argmax(y)\n  xs = isbits(labels) ? indices : collect(indices) # non-bit type cannot be handled by CUDA\n\n  return map(xi -> labels[xi[1]], xs)\nend\n\n_fast_argmax(x::AbstractArray) = dropdims(argmax(x; dims = 1); dims = 1)\nfunction _fast_argmax(x::OneHotLike)\n  if _isonehot(x)\n    return _indices(x)\n  else\n    return _fast_argmax(convert(_onehot_bool_type(x), x))\n  end\nend\n\nChainRulesCore.@non_differentiable onehot(::Any...)\nChainRulesCore.@non_differentiable onehotbatch(::Any...)\nChainRulesCore.@non_differentiable onecold(::Any...)\n\nChainRulesCore.@non_differentiable (::Type{<:OneHotArray})(indices::Any, L::Integer)\n", "meta": {"hexsha": "6231f3cee39062f54d5ebe58e72840c480c17b3e", "size": 4996, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/onehot.jl", "max_stars_repo_name": "FluxML/OneHotArrays.jl", "max_stars_repo_head_hexsha": "d44a239928bdb14517cdb9c1963d1deeed31c714", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/onehot.jl", "max_issues_repo_name": "FluxML/OneHotArrays.jl", "max_issues_repo_head_hexsha": "d44a239928bdb14517cdb9c1963d1deeed31c714", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2022-03-02T17:44:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-12T16:57:50.000Z", "max_forks_repo_path": "src/onehot.jl", "max_forks_repo_name": "FluxML/OneHotArrays.jl", "max_forks_repo_head_hexsha": "d44a239928bdb14517cdb9c1963d1deeed31c714", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-03-01T20:40:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T04:15:42.000Z", "avg_line_length": 33.0860927152, "max_line_length": 116, "alphanum_fraction": 0.6439151321, "num_tokens": 1880, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10970577242256814, "lm_q1q2_score": 0.054424356756177024}}
{"text": "@testset \"OrderedRobinDict\" begin\n\n    @testset \"Constructors\" begin\n        @test isa(@inferred(OrderedRobinDict()), OrderedRobinDict{Any,Any})\n        @test isa(@inferred(OrderedRobinDict([(1,2.0)])), OrderedRobinDict{Int,Float64})\n        @test isa(@inferred(OrderedRobinDict([(\"a\",1),(\"b\",2)])), OrderedRobinDict{String,Int})\n        @test isa(@inferred(OrderedRobinDict(Pair(1, 1.0))), OrderedRobinDict{Int,Float64})\n        @test isa(@inferred(OrderedRobinDict(Pair(1, 1.0), Pair(2, 2.0))), OrderedRobinDict{Int,Float64})\n        @test isa(@inferred(OrderedRobinDict{Int,Float64}(Pair(1, 1), Pair(2, 2))), OrderedRobinDict{Int,Float64})\n        @test isa(@inferred(OrderedRobinDict(Pair(1, 1.0), Pair(2, 2.0), Pair(3, 3.0))), OrderedRobinDict{Int,Float64})\n        @test OrderedRobinDict(()) == OrderedRobinDict{Any,Any}()\n        @test isa(@inferred(OrderedRobinDict([Pair(1, 1.0), Pair(2, 2.0)])), OrderedRobinDict{Int,Float64})\n        @test_throws ArgumentError OrderedRobinDict([1,2,3,4])\n        iter = Iterators.filter(x->x.first>1, [Pair(1, 1.0), Pair(2, 2.0), Pair(3, 3.0)])\n        @test @inferred(OrderedRobinDict(iter)) == OrderedRobinDict{Int,Float64}(2=>2.0, 3=>3.0)\n        iter = Iterators.drop(1:10, 1)\n        @test_throws ArgumentError OrderedRobinDict(iter)\n    end\n\n    @testset \"empty dictionary\" begin\n        d = OrderedRobinDict{Char, Int}()\n        @test length(d) == 0\n        @test isempty(d)\n        @test_throws KeyError d['c'] == 1\n        d['c'] = 1\n        @test !isempty(d)\n        @test_throws KeyError d[0.01]\n        @test isempty(empty(d))\n        empty!(d)\n        @test isempty(d)\n\n        # access, modification\n        for c in 'a':'z'\n            d[c] = c - 'a' + 1\n        end\n\n        @test (d['a'] += 1) == 2\n        @test 'a' in keys(d)\n        @test haskey(d, 'a')\n        @test get(d, 'B', 0) == 0\n        @test getkey(d, 'b', nothing) == 'b'\n        @test getkey(d, 'B', nothing) == nothing\n        @test !('B' in keys(d))\n        @test !haskey(d, 'B')\n        @test pop!(d, 'a') == 2\n\n        @test collect(keys(d)) == collect('b':'z')\n        @test collect(values(d)) == collect(2:26)\n        @test collect(d) == [Pair(a,i) for (a,i) in zip('b':'z', 2:26)]\n    end\n\n    @testset \"convert\" begin\n        d = OrderedRobinDict{Int,Float32}(i=>Float32(i) for i = 1:10)\n        @test convert(OrderedRobinDict{Int,Float32}, d) === d\n        dc = convert(OrderedRobinDict{Int,Float64}, d)\n        @test dc !== d\n        @test keytype(dc) == Int\n        @test valtype(dc) == Float64\n        @test keys(dc) == keys(d)\n        @test collect(values(dc)) == collect(values(d))\n    end\n\n    @testset \"Issue #60\" begin\n        od60 = OrderedRobinDict{Int,Int}()\n        od60[1] = 2\n\n        ranges = [2:5, 6:9, 10:13]\n        for range in ranges\n            for i = range\n                od60[i] = i+1\n            end\n            for i = range\n                delete!( od60, i )\n            end\n        end\n        od60[14]=15\n\n        @test od60[14] == 15\n    end\n\n\n    # #############################\n    # Copied and modified from Base/test/dict.jl\n\n    # OrderedRobinDict\n\n    @testset \"OrderedRobinDict{Int,Int}\" begin\n        h = OrderedRobinDict{Int,Int}()\n        for i=1:10000\n            h[i] = i+1\n        end\n\n        @test collect(h) == [Pair(x,y) for (x,y) in zip(1:10000, 2:10001)]\n\n        for i=1:2:10000\n            delete!(h, i)\n        end\n        for i=1:2:10000\n            h[i] = i+1\n        end\n\n        for i=1:10000\n            @test h[i]==i+1\n        end\n\n        for i=1:10000\n            delete!(h, i)\n        end\n        @test isempty(h)\n\n        h[77] = 100\n        @test h[77]==100\n\n        for i=1:10000\n            h[i] = i+1\n        end\n\n        for i=1:2:10000\n            delete!(h, i)\n        end\n\n        for i=10001:20000\n            h[i] = i+1\n        end\n\n        for i=2:2:10000\n            @test h[i]==i+1\n        end\n\n        for i=10000:20000\n            @test h[i]==i+1\n        end\n    end\n\n    @testset \"OrderedRobinDict{Any,Any}\" begin\n        h = OrderedRobinDict{Any,Any}([(\"a\", 3)])\n        @test h[\"a\"] == 3\n        h[\"a\",\"b\"] = 4\n        @test h[\"a\",\"b\"] == h[(\"a\",\"b\")] == 4\n        h[\"a\",\"b\",\"c\"] = 4\n        @test h[\"a\",\"b\",\"c\"] == h[(\"a\",\"b\",\"c\")] == 4\n    end\n\n    @testset \"KeyError\" begin\n        z = OrderedRobinDict()\n        get_KeyError = false\n        try\n            z[\"a\"]\n        catch _e123_\n            get_KeyError = isa(_e123_, KeyError)\n        end\n        @test get_KeyError\n    end\n\n    @testset \"filter\" begin\n        _d = OrderedRobinDict([(\"a\", 0)])\n        v = [k for k in filter(x->length(x)==1, collect(keys(_d)))]\n        @test isa(v, Vector{String})\n    end\n\n    @testset \"from tuple/vector/pairs/tuple of pair 1\" begin\n        d = OrderedRobinDict(((1, 2), (3, 4)))\n        d2 = OrderedRobinDict([(1, 2), (3, 4)])\n        d3 = OrderedRobinDict(1 => 2, 3 => 4)\n        d4 = OrderedRobinDict((1 => 2, 3 => 4))\n\n        @test d[1] === 2\n        @test d[3] === 4\n\n        @test d == d2 == d3 == d4\n        @test isa(d, OrderedRobinDict{Int,Int})\n        @test isa(d2, OrderedRobinDict{Int,Int})\n        @test isa(d3, OrderedRobinDict{Int,Int})\n        @test isa(d4, OrderedRobinDict{Int,Int})\n\n        h = OrderedRobinDict{Int, Char}(1=>'a')\n        @test h[1] == 'a'\n    end\n\n    @testset \"from tuple/vector/pairs/tuple of pair 2\" begin\n        d = OrderedRobinDict(((1, 2), (3, \"b\")))\n        d2 = OrderedRobinDict([(1, 2), (3, \"b\")])\n        d3 = OrderedRobinDict(1 => 2, 3 => \"b\")\n        d4 = OrderedRobinDict((1 => 2, 3 => \"b\"))\n\n        @test d2[1] === 2\n        @test d2[3] == \"b\"\n\n        ## TODO: tuple of tuples doesn't work for mixed tuple types\n        # @test d == d2 == d3 == d4\n        # @test isa(d, OrderedRobinDict{Int,Any})\n        @test d2 == d3 == d4\n        @test isa(d2, OrderedRobinDict{Int,Any})\n        @test isa(d3, OrderedRobinDict{Int,Any})\n        @test isa(d4, OrderedRobinDict{Int,Any})\n    end\n\n    @testset \"from tuple/vector/pairs/tuple of pair 3\" begin\n        d = OrderedRobinDict(((1, 2), (\"a\", 4)))\n        d2 = OrderedRobinDict([(1, 2), (\"a\", 4)])\n        d3 = OrderedRobinDict(1 => 2, \"a\" => 4)\n        d4 = OrderedRobinDict((1 => 2, \"a\" => 4))\n\n        @test d2[1] === 2\n        @test d2[\"a\"] === 4\n\n        ## TODO: tuple of tuples doesn't work for mixed tuple types\n        # @test d == d2 == d3 == d4\n        @test d2 == d3 == d4\n        # @test isa(d, OrderedRobinDict{Any,Int})\n        @test isa(d2, OrderedRobinDict{Any,Int})\n        @test isa(d3, OrderedRobinDict{Any,Int})\n        @test isa(d4, OrderedRobinDict{Any,Int})\n    end\n\n    @testset \"from tuple/vector/pairs/tuple of pair 4\" begin\n        d = OrderedRobinDict(((1, 2), (\"a\", \"b\")))\n        d2 = OrderedRobinDict([(1, 2), (\"a\", \"b\")])\n        d3 = OrderedRobinDict(1 => 2, \"a\" => \"b\")\n        d4 = OrderedRobinDict((1 => 2, \"a\" => \"b\"))\n\n        @test d[1] === 2\n        @test d[\"a\"] == \"b\"\n\n        @test d == d2 == d3 == d4\n        @test isa(d, OrderedRobinDict{Any,Any})\n        @test isa(d2, OrderedRobinDict{Any,Any})\n        @test isa(d3, OrderedRobinDict{Any,Any})\n        @test isa(d4, OrderedRobinDict{Any,Any})\n    end\n\n    @testset \"first\" begin\n        @test_throws ArgumentError first(OrderedRobinDict())\n        @test first(OrderedRobinDict([(:f, 2)])) == Pair(:f,2)\n    end\n\n    @testset \"Issue #1821\" begin\n        d = OrderedRobinDict{String, Vector{Int}}()\n        d[\"a\"] = [1, 2]\n        @test_throws MethodError d[\"b\"] = 1\n        @test isa(repr(d), AbstractString)  # check that printable without error\n    end\n\n    @testset \"Issue #2344\" begin\n        bestkey(d, key) = key\n        bestkey(d::AbstractDict{K,V}, key) where {K<:AbstractString,V} = string(key)\n        bar(x) = bestkey(x, :y)\n        @test bar(OrderedRobinDict([(:x, [1,2,5])])) == :y\n        @test bar(OrderedRobinDict([(\"x\", [1,2,5])])) == \"y\"\n    end\n\n    @testset \"isequal\" begin\n        @test  isequal(OrderedRobinDict(), OrderedRobinDict())\n        @test  isequal(OrderedRobinDict([(1, 1)]), OrderedRobinDict([(1, 1)]))\n        @test !isequal(OrderedRobinDict([(1, 1)]), OrderedRobinDict())\n        @test !isequal(OrderedRobinDict([(1, 1)]), OrderedRobinDict([(1, 2)]))\n        @test !isequal(OrderedRobinDict([(1, 1)]), OrderedRobinDict([(2, 1)]))\n\n        @test isequal(OrderedRobinDict(), sizehint!(OrderedRobinDict(),96))\n\n        # Here is what currently happens when dictionaries of different types\n        # are compared. This is not necessarily desirable. These tests are\n        # descriptive rather than proscriptive.\n        @test !isequal(OrderedRobinDict([(1, 2)]), OrderedRobinDict([(\"dog\", \"bone\")]))\n        @test isequal(OrderedRobinDict{Int,Int}(), OrderedRobinDict{AbstractString,AbstractString}())\n    end\n\n    @testset \"data_in\" begin\n        # Generate some data to populate dicts to be compared\n        data_in = [ (rand(1:1000), randstring(2)) for _ in 1:1001 ]\n\n        # Populate the first dict\n        d1 = OrderedRobinDict{Int, String}()\n        for (k,v) in data_in\n            d1[k] = v\n        end\n        data_in = collect(d1)\n        # shuffle the data\n        for i in 1:length(data_in)\n            j = rand(1:length(data_in))\n            data_in[i], data_in[j] = data_in[j], data_in[i]\n        end\n        # Inserting data in different (shuffled) order should result in\n        # equivalent dict.\n        d2 = OrderedRobinDict{Int, AbstractString}()\n        for (k,v) in data_in\n            d2[k] = v\n        end\n\n        @test  isequal(d1, d2)\n        d3 = copy(d2)\n        d4 = copy(d2)\n        # Removing an item gives different dict\n        delete!(d1, data_in[rand(1:length(data_in))][1])\n        @test !isequal(d1, d2)\n        # Changing a value gives different dict\n        d3[data_in[rand(1:length(data_in))][1]] = randstring(3)\n        !isequal(d1, d3)\n        # Adding a pair gives different dict\n        d4[1001] = randstring(3)\n        @test !isequal(d1, d4)\n    end\n\n    @testset \"get!\" begin\n        # get! (get with default values assigned to the given location)\n        f(x) = x^2\n        d = OrderedRobinDict(8 => 19)\n\n        @test get!(d, 8, 5) == 19\n        @test get!(d, 19, 2) == 2\n\n        @test get!(d, 42) do  # d is updated with f(2)\n            f(2)\n        end == 4\n\n        @test get!(d, 42) do  # d is not updated\n            f(200)\n        end == 4\n\n        @test get(d, 13) do   # d is not updated\n            f(4)\n        end == 16\n\n        @test d == OrderedRobinDict(8=>19, 19=>2, 42=>4)\n    end\n\n    @testset \"Issue #5886\" begin\n        d5886 = OrderedRobinDict()\n        for k5886 in 1:11\n            d5886[k5886] = 1\n        end\n        for k5886 in keys(d5886)\n            # undefined ref if not fixed\n            d5886[k5886] += 1\n        end\n    end\n\n    @testset \"Issue #216\" begin\n        @test !isordered(Dict{Int, String})\n        @test isordered(OrderedRobinDict{Int, String})\n    end\n\n    @testset \"Test merging\" begin\n        a = OrderedRobinDict(\"foo\"  => 0.0, \"bar\" => 42.0)\n        b = OrderedRobinDict(\"\u30d5\u30fc\" => 17, \"\u30d0\u30fc\" => 4711)\n        @test isa(merge(a, b), OrderedRobinDict{String,Float64})\n    end\n\n    @testset \"Issue #9295\" begin\n        d = OrderedRobinDict()\n        @test push!(d, 'a'=> 1) === d\n        @test d['a'] == 1\n        @test push!(d, 'b' => 2, 'c' => 3) === d\n        @test d['b'] == 2\n        @test d['c'] == 3\n        @test push!(d, 'd' => 4, 'e' => 5, 'f' => 6) === d\n        @test d['d'] == 4\n        @test d['e'] == 5\n        @test d['f'] == 6\n        @test length(d) == 6\n    end\n\n    @testset \"Serialization\" begin\n        s = IOBuffer()\n        od = OrderedRobinDict{Char,Int64}()\n        for c in 'a':'e'\n            od[c] = c-'a'+1\n        end\n        serialize(s, od)\n        seek(s, 0)\n        dd = deserialize(s)\n        @test isa(dd, OrderedRobinDict{Char,Int64})\n        @test dd == od\n        close(s)\n    end\n\n    @testset \"Issue #148\" begin\n        d148 = OrderedRobinDict(\n                    :gps => [],\n                    :direction => 1:8,\n                    :weather => 1:10\n            )\n\n        d148_2 = OrderedRobinDict(\n            :time => 1:10,\n            :features => OrderedRobinDict(\n                :gps => 1:5,\n                :direction => 1:8,\n                :weather => 1:10\n            )\n        )\n    end\n\n    @testset \"Issue #400\" begin\n        @test filter(p->first(p) > 1, OrderedRobinDict(1=>2, 3=>4)) isa OrderedRobinDict\n    end\n\n    @testset \"Test that OrderedRobinDict merge with combiner returns type OrderedRobinDict\" begin\n        @test merge(+, OrderedRobinDict(:a=>1, :b=>2), OrderedRobinDict(:b=>7, :c=>4)) == OrderedRobinDict(:a=>1, :b=>9, :c=>4)\n        @test merge(+, OrderedRobinDict(:a=>1, :b=>2), Dict(:b=>7, :c=>4)) isa OrderedRobinDict\n    end\n\n    @testset \"pop!\" begin\n        h = OrderedRobinDict{Int, Int}()\n        for i in 1:10\n            h[i] = i+1\n        end\n        for i in 10:-1:1\n            @test pop!(h) == (i => i+1)\n        end\n\n        d = OrderedRobinDict(1=>'a', 2=>'b', 3=>'c')\n        @test pop!(d, 4, 'e') == 'e'\n    end\n\n    @testset \"filter!\" begin\n        h = OrderedRobinDict{Int, Int}()\n        for i in 1:100\n            h[i] = i\n        end\n        filter!(x->isodd(x.first), h)\n        for i in 1:2:100\n            @test h[i] == i\n        end\n    end\n\nend # @testset OrderedRobinDict\n", "meta": {"hexsha": "2a78fc7aac8b18d51af0d4ab65d6ddd5a2abd417", "size": 13317, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/test_ordered_robin_dict.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 477, "max_stars_repo_stars_event_min_datetime": "2017-02-10T02:17:09.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-15T18:26:56.000Z", "max_issues_repo_path": "lang/Julia/test_ordered_robin_dict.jl", "max_issues_repo_name": "ethansaxenian/RosettaDecode", "max_issues_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 512, "max_issues_repo_issues_event_min_datetime": "2017-02-02T04:26:48.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T12:37:01.000Z", "max_forks_repo_path": "lang/Julia/test_ordered_robin_dict.jl", "max_forks_repo_name": "ethansaxenian/RosettaDecode", "max_forks_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 192, "max_forks_repo_forks_event_min_datetime": "2017-02-09T05:22:18.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T04:57:31.000Z", "avg_line_length": 31.1144859813, "max_line_length": 127, "alphanum_fraction": 0.5118269881, "num_tokens": 4350, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3775406687981454, "lm_q2_score": 0.14414884567931802, "lm_q1q2_score": 0.05442205160425038}}
{"text": "## Copyright (c) 2013 Miguel Bazdresch\n##\n## This file is distributed under the 2-clause BSD License.\n\n# Gaston test framework\n#\n# There are two kinds of tests:\n#   - Tests that should error -- things that Gaston should not allow, such as\n#     building a figure with an invalid plotstyle.\n#   - Tests that should succeed -- things that should work without producing\n#     an error.\n\nusing Gaston\nusing Base.Test\n\ntest_success_expected(r::Test.Success) = println(\"Test passed.\")\ntest_success_expected(r) = error(\"Success expected; test failed: $(r.expr).\")\ntest_failure_expected(r::Test.Success) = error(\"Error expected; test failed: $(r.expr)\")\ntest_failure_expected(r) = println(\"Test passed.\")\n\nfunction run_tests_success_expected()\n\tset_filename(tempname())\n\tcloseall()\n\tTest.with_handler(test_success_expected) do\n\t\t@test figure() == 1\n\t\t@test figure() == 2\n\t\t@test figure(4) == 4\n\t\t@test closefigure(4) == 4\n\t\t@test closefigure() == 2\n\t\t@test closeall() == 1\n\t\t@test closeall() == 0\n\t\t@test plot(1:10) == 1\n\t\t@test plot(2,1:10) == 2\n\t\t@test plot(4,1:10) == 4\n\t\t@test closeall() == 3\n\t\t@test begin\n\t\t\tplot(sin(-3:0.01:3),\n\t\t\t\"legend\", \"sine\",\n\t\t\t\"plotstyle\", \"lines\",\n\t\t\t\"color\",\"blue\",\n\t\t\t\"marker\",\"ecircle\",\n\t\t\t\"linewidth\",2,\n\t\t\t\"pointsize\",1.1,\n\t\t\t\"title\",\"test plot 1\",\n\t\t\t\"xlabel\",\"x\",\n\t\t\t\"ylabel\",\"y\",\n\t\t\t\"box\",\"inside horizontal left top\",\n\t\t\t\"axis\",\"loglog\") == 1\n\t\tend\n\t\t@test histogram(rand(1000)) == 1\n\t\t@test begin\n\t\t\thistogram(randn(1000),\n            \"bins\",100,\n            \"norm\",1,\n            \"color\",\"blue\",\n            \"linewidth\",2,\n            \"title\",\"test histogram\",\n            \"xlabel\",\"x\",\n            \"ylabel\",\"y\",\n            \"box\",\"inside horizontal left top\") == 1\n\t\tend\n\t\tz = rand(5,6)\n\t\t@test imagesc(z,\"title\",\"test imagesc 1\",\"xlabel\",\"xx\",\"ylabel\",\"yy\") == 1\n\t\t@test imagesc(1:5,z,\"title\",\"test imagesc 2\",\"xlabel\",\"xx\",\"ylabel\",\"yy\") == 1\n\t\t@test imagesc(1:5,1:6,z,\"title\",\"test imagesc 3\",\"xlabel\",\"xx\",\"ylabel\",\"yy\") == 1\n\t\t@test surf(rand(10,10)) == 1\n\t\t@test surf(2:11,rand(10,10)) == 1\n\t\t@test surf(0:9,2:11,rand(10,10)) == 1\n\t    @test surf(0:9,2:11,(x,y)->x*y) == 1\n\t\t@test surf(0:9,2:11,(x,y)->x*y,\"title\",\"test\",\"plotstyle\",\"pm3d\") == 1\n\t\t@test printfigure() == 1\n\t\t@test begin\n\t\t\tplot(2,1:10)\n\t\t\tprintfigure(2,\"png\")\n\t\t\tclosefigure() == 2\n\t\tend\n\t\t@test printfigure(\"eps\") == 1\n\t\t@test printfigure(\"pdf\") == 1\n\t\t@test begin\n\t\t\tset_print_size(\"640,480\")\n\t\t\tprintfigure(\"svg\") == 1\n\t\tend\n\t\t@test printfigure(\"gif\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[2:3]\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[-1.1:3.4]\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[:3.4]\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[3.4:]\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[3.4:*]\") == 1\n\t\t@test plot(1:10,\"xrange\",\"[*:3.4]\") == 1\n\t\tcloseall()\n\tend\nend\n\nfunction run_tests_failure_expected()\n\tcloseall()\n\tTest.with_handler(test_failure_expected) do\n\t\t# high-level functions: figure\n\t\t@test figure(\"invalid\")\n    \t@test figure(1.0)\n\t    @test figure(1:2)\n\t\t# high-level functions: closefigure\n\t\t@test closefigure(-1)\n\t\t@test closefigure(\"invalid\")\n\t\t@test closefigure(1.0)\n\t\t@test closefigure(1:2)\n\t\t# high-level functions: plot and histogram\n\t\tfor op = (:plot, :histogram)\n\t\t\t@test op(\"linewidth\")\n\t\t\t@test op(0:10,0:11)\n\t\t\t@test op(0:10+im*0:10)\n\t\t\t@test op(0:10,\"legend\",0)\n\t\t\t@test op(0:10,\"plotstyle\",\"invalid\")\n\t\t\t@test op(0:10,\"marker\",\"invalid\")\n\t\t\t@test op(0:10,\"marker\",0)\n\t\t\t@test op(0:10,\"linewidth\",\"b\")\n\t\t\t@test op(0:10,\"linewidth\",im)\n\t\t\t@test op(0:10,\"pointsize\",\"b\")\n\t\t\t@test op(0:10,\"pointsize\",im)\n\t\t\t@test op(0:10,\"title\",0)\n\t\t\t@test op(0:10,\"xlabel\",0)\n\t\t\t@test op(0:10,\"ylabel\",0)\n\t\t\t@test op(0:10,\"zlabel\",\"z\")\n\t\t\t@test op(0:10,\"axis\",\"invalid\")\n\t\tend\n\t\t@test plot(1:10,\"xrange\",\"2:3\")\n\t\t@test plot(1:10,\"xrange\",\"ab\")\n\tend\nend\n\nprintln(\"Running Gaston tests.\")\nrun_tests_success_expected()\nrun_tests_failure_expected()\n", "meta": {"hexsha": "31eb110b7be038603ca90fe1baf4d3db7d82044d", "size": 3838, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaPackageMirrors/Gaston.jl", "max_stars_repo_head_hexsha": "ec5d9748e1bbc17fbe086bf987866200ab0ba908", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "JuliaPackageMirrors/Gaston.jl", "max_issues_repo_head_hexsha": "ec5d9748e1bbc17fbe086bf987866200ab0ba908", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "JuliaPackageMirrors/Gaston.jl", "max_forks_repo_head_hexsha": "ec5d9748e1bbc17fbe086bf987866200ab0ba908", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8571428571, "max_line_length": 88, "alphanum_fraction": 0.6068264721, "num_tokens": 1361, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.11596071061609145, "lm_q1q2_score": 0.05436129419374203}}
{"text": "struct Stack{T}\n    content::Vector{T}\n    Stack{T}() where {T} = new(T[])\nend\n\nst = Stack{Int64}()\nprintln(st)\n\nimport Base: push!, pop!\n\npush!(st::Stack{T}, x::T) where {T} = pushfirst!(st.content, x); st\n\npop!(st::Stack) = popfirst!(st.content)\n\nprintln(push!(st, 5))\nprintln(push!(st, 3))\nprintln(push!(st, 1))\n\nprintln(pop!(st))\n\nfunction Base.show(io::IO, st::Stack{T}) where {T}\n    print(\"Stack{$T} [\")\n    for x = st.content[1:end-1]\n        print(x, \", \")\n    end\n    print(st.content[end], \"]\")\nend\n\nprintln(st)\n", "meta": {"hexsha": "0d55dd74dd2a34d9bec5d181c2f29350ea7c0ef8", "size": 523, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "materials/stack-and-queue/stack.jl", "max_stars_repo_name": "rd8312/advanced-programming-2020", "max_stars_repo_head_hexsha": "b95561019f85048a62098d94e5c55a5bdde610a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "materials/stack-and-queue/stack.jl", "max_issues_repo_name": "rd8312/advanced-programming-2020", "max_issues_repo_head_hexsha": "b95561019f85048a62098d94e5c55a5bdde610a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2020-10-27T08:34:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-23T12:04:19.000Z", "max_forks_repo_path": "materials/stack-and-queue/stack.jl", "max_forks_repo_name": "rd8312/advanced-programming-2020", "max_forks_repo_head_hexsha": "b95561019f85048a62098d94e5c55a5bdde610a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 10, "max_forks_repo_forks_event_min_datetime": "2020-10-21T10:37:20.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-29T07:28:59.000Z", "avg_line_length": 17.4333333333, "max_line_length": 67, "alphanum_fraction": 0.5850860421, "num_tokens": 169, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.39233683016710835, "lm_q2_score": 0.13846179056896438, "lm_q1q2_score": 0.0543236600110895}}
{"text": "# Single branch conditional\nif 1 == 1\n    print(\"Of course 1==1 silly\")\nend", "meta": {"hexsha": "f179936dda5601f5cffb820b1c36582a50a5369a", "size": 75, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "demo/condition.jl", "max_stars_repo_name": "elucian/julia", "max_stars_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "demo/condition.jl", "max_issues_repo_name": "elucian/julia", "max_issues_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/condition.jl", "max_forks_repo_name": "elucian/julia", "max_forks_repo_head_hexsha": "653dc99961b6e27c2a3910c2594ba1a94df84063", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.75, "max_line_length": 33, "alphanum_fraction": 0.6666666667, "num_tokens": 24, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.12085323090725615, "lm_q1q2_score": 0.054310551820989754}}
{"text": "\"\"\"\nGiven an integer n = 4, the output should be\n\n   #\n  ##\n ###\n####\n\nref: https://www.hackerrank.com/challenges/staircase/problem\n\"\"\"\n\nfunction staircase(size = nothing,builder = nothing, pad_char = nothing)\n    \"\"\"\n    The method takes the size, builder and padding character of a staircase\n    and prints the result to console\n    if size is not provided, size is defaulted to 6\n    if builder is not provided, defaulted to #\n    if pad_char is not provided, defaulted to ' ', white space\n    \"\"\"\n    \n    # check if values are not provided and assign appropriate defaults\n    size = (size === nothing) ? 6 : size\n    builder = (builder === nothing) ? \"#\" : builder\n    pad_char = (pad_char === nothing) ? \" \" : pad_char\n\n    # Julia is 1-index based, this means from 1 to size\n    # for example, for size = 6, we get 1,2,3,4,5,6\n    for loop in 1:size\n        # print line to console, pad white spaces and then builder\n        println(join([pad_char for lp in 1:(size-loop)]),join([builder for rl in 1:loop]))\n    end\nend\n\n# printing an empty line\nprintln() # printing an empty line at the end, to look nice\nstaircase(9)\nprintln() # printing an empty line at the end, to look nice", "meta": {"hexsha": "6355a5bfac7d721a1bc9ab4965eb1c1816a097bc", "size": 1185, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/staircase.jl", "max_stars_repo_name": "csendranshi/Hackerrank-Codes", "max_stars_repo_head_hexsha": "cebb70ba3895f7f9e5a9aabcfe05d34bd9f11995", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 70, "max_stars_repo_stars_event_min_datetime": "2020-10-04T09:23:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-01T09:44:39.000Z", "max_issues_repo_path": "julia/staircase.jl", "max_issues_repo_name": "csendranshi/Hackerrank-Codes", "max_issues_repo_head_hexsha": "cebb70ba3895f7f9e5a9aabcfe05d34bd9f11995", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 148, "max_issues_repo_issues_event_min_datetime": "2020-06-05T15:32:12.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-01T08:29:01.000Z", "max_forks_repo_path": "julia/staircase.jl", "max_forks_repo_name": "csendranshi/Hackerrank-Codes", "max_forks_repo_head_hexsha": "cebb70ba3895f7f9e5a9aabcfe05d34bd9f11995", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 298, "max_forks_repo_forks_event_min_datetime": "2020-10-04T04:27:01.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T04:02:59.000Z", "avg_line_length": 32.027027027, "max_line_length": 90, "alphanum_fraction": 0.6599156118, "num_tokens": 316, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3665897363221598, "lm_q2_score": 0.14804719803168948, "lm_q1q2_score": 0.05427258328967162}}
{"text": "function get_module_2_excersise_1_author_name()\n    return \"PUT YOUR NAME HERE\"\nend\n\nfunction get_odd_numbers(number_of_values)\n    odd_numbers = zeros(number_of_values)\n    # implement your loop here\n    return odd_numbers\nend\n\nfunction get_powers_of_three(number_of_values)\n    powers_of_three = zeros(number_of_values)\n    # implement your loop here\n    return powers_of_three\nend\n", "meta": {"hexsha": "a27db1c5b3a6695b460cd3107783a8dadee03e91", "size": 384, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "module2/module-2-exercise-1.jl", "max_stars_repo_name": "j-hayes/chem-324-programming-tutorials", "max_stars_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-15T01:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T01:54:45.000Z", "max_issues_repo_path": "module2/module-2-exercise-1.jl", "max_issues_repo_name": "j-hayes/chem-324-programming-tutorials", "max_issues_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "module2/module-2-exercise-1.jl", "max_forks_repo_name": "j-hayes/chem-324-programming-tutorials", "max_forks_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0, "max_line_length": 47, "alphanum_fraction": 0.7916666667, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.35220178204788966, "lm_q2_score": 0.15405756074950905, "lm_q1q2_score": 0.05425934743392811}}
{"text": "using ArgParse\n\n\nfunction parse_commandline()\n    s = ArgParseSettings()\n\n    @add_arg_table! s begin\n        \"--scaling\"\n            default = 0.002\n            arg_type = Float64\n        \"--nsim\"\n            default = 10\n            arg_type = Int64\n        \"label\"\n            default = \"nothing\"\n    end\n\n    return parse_args(s)\nend\n\nparameters = parse_commandline()\nprintln(parameters)\n\nlabel = parameters[\"label\"]\nscaling = parameters[\"scaling\"]\nnsim = parameters[\"nsim\"]", "meta": {"hexsha": "12c43de99d65c98de81a84aaf9d324eabc42707e", "size": 478, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/nn_fwi/mcmc/readargs.jl", "max_stars_repo_name": "kailaix/ADSeismic.jl", "max_stars_repo_head_hexsha": "c4cd214a6de10cadc1a59b1c302ccfe42d4d25f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 36, "max_stars_repo_stars_event_min_datetime": "2020-03-21T09:56:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T14:05:14.000Z", "max_issues_repo_path": "examples/nn_fwi/mcmc/readargs.jl", "max_issues_repo_name": "kailaix/ADSeismic.jl", "max_issues_repo_head_hexsha": "c4cd214a6de10cadc1a59b1c302ccfe42d4d25f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-08-12T15:40:39.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-15T14:09:40.000Z", "max_forks_repo_path": "examples/nn_fwi/mcmc/readargs.jl", "max_forks_repo_name": "kailaix/ADSeismic.jl", "max_forks_repo_head_hexsha": "c4cd214a6de10cadc1a59b1c302ccfe42d4d25f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2020-04-18T09:25:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T08:06:13.000Z", "avg_line_length": 18.3846153846, "max_line_length": 32, "alphanum_fraction": 0.5878661088, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.11757213199952936, "lm_q1q2_score": 0.0542027256313055}}
{"text": "# -------------------------------\n#  Cyclic data exchange example - Master\n# -------------------------------\n\n# Every worker will have a global variable \"mynumber\" which holds some data (a random number in this example).\n# The goal is to make the workers exchange this data cyclically (forward it to the next worker).\n# We will realize this exchange by multiple channels, one on each worker.\n# Every worker will push his number to the next workers channel and check his own channel to receive a new number.\n\nusing Distributed\n\nN = 4\n\n# Create workers\naddprocs(N)\n\n# Create a channel on every worker for receiving data.\n# Store RemoteChannel handles to those channels in a dictoniary pid => RemoteChannel\nconst channels = Dict(p=>RemoteChannel(() -> Channel{Int}(1), p) for p in workers())\n\n# Make the dictionary a global constant on every worker process\n@everywhere workers() channels=$channels # will also be a constant global on the worker processes\n\n\n# Function for displaying the current data (\"mynumber\") distribution.\nfunction print_numbers()\n    for p in workers()\n        println(\"$p => \", fetch(@spawnat p mynumber))\n    end\nend\n\n# Will perform one cyclic exchange between the workers.\nfunction makestep()\n    # run worker script on every worker\n    @everywhere workers() include(\"worker.jl\")\n    print_numbers()\nend\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# # Tell every worker who his neighbor is (create global constant \"npid\")\n# # We could use ParallelDataTransfer.jl here to make this nicer.\n# w = workers()\n# nw = nworkers()\n# @sync for i in 1:length(w)\n#     pid = w[i]\n#     npid = w[mod1(i+1, nw)]\n#     @spawnat pid eval(:(global const npid = $npid))\n# end", "meta": {"hexsha": "ebcc0efe8e72760400bbdb3ec6d694ac0ea96772", "size": 1657, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "5 Parallel computing/cyclic-data-exchange/master.jl", "max_stars_repo_name": "crstnbr/julia-workshop", "max_stars_repo_head_hexsha": "8c7b40b2303207f98d7402d34dce249f0833d60d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 27, "max_stars_repo_stars_event_min_datetime": "2018-09-20T23:29:13.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-05T03:20:58.000Z", "max_issues_repo_path": "5 Parallel computing/cyclic-data-exchange/master.jl", "max_issues_repo_name": "carstenbauer/JuliaWorkshop18", "max_issues_repo_head_hexsha": "8c7b40b2303207f98d7402d34dce249f0833d60d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-04-30T09:21:36.000Z", "max_issues_repo_issues_event_max_datetime": "2019-04-30T09:21:36.000Z", "max_forks_repo_path": "5 Parallel computing/cyclic-data-exchange/master.jl", "max_forks_repo_name": "carstenbauer/JuliaWorkshop18", "max_forks_repo_head_hexsha": "8c7b40b2303207f98d7402d34dce249f0833d60d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-05-08T22:38:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-03T16:09:44.000Z", "avg_line_length": 24.3676470588, "max_line_length": 114, "alphanum_fraction": 0.6855763428, "num_tokens": 384, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.480478678047907, "lm_q2_score": 0.11279541224768527, "lm_q1q2_score": 0.054195790566636516}}
{"text": "################################################################################\n###  Introduction to Scientific Programming and Machine Learning with Julia  ###\n###                                                                          ###\n### Run each script on a new clean Julia session                             ###\n### GitHub: https://github.com/sylvaticus/IntroSPMLJuliaCourse               ###\n### Licence (apply to all material of the course: scripts, videos, quizes,..)###\n### Creative Commons By Attribution (CC BY 4.0), Antonello Lobianco          ###\n################################################################################\n\n# ## 0104 Control Flow and Functions\n\n# ## Some stuff to set-up the environment..\ncd(@__DIR__)         \nusing Pkg             \nPkg.activate(\".\")     \n## If using a Julia version different than 1.7 please uncomment and run the following line (reproductibility guarantee will hower be lost)\n## Pkg.resolve()   \n## Pkg.instantiate() # run this if you didn't in Segment 01.01\nusing Random\nRandom.seed!(123)\nusing InteractiveUtils # loaded automatically when working... interactively\n\n# ## Variables scope\n\n# The scope of a variable is the region of code where the variable can be accessed directly (without using prefixes).\n# Modules, functions, `for` and other blocks (but notably not \"if\" blocks) introduce an inner scope that hinerit from the scope where the block or function is defined (but not, for function, from the caller's scope).\n# Variables that are defined outside any block or function are _global_ for the module where they are defined (the `Main` module if outside any other module, e.g. on the REPL), the others being _local_.\n# Variables defined in a `for` block that already exists as global behave differently depending if we are working interactively or not:\n\ng  = 2\ng2 = 20\nfor i in 1:2\n    l1 = g2                                       # l1: local, g2: global (read only)\n    l1 += i\n    g = i                                         # REPL/INTERACTIVE: global (from Julia 1.5), FILE MODE: local by default (with a warning `g` being already defined)\n    g += i                                       \n    println(\"i: $i\")\n    println(\"l1: $l1\")\n    println(\"g: $g\")\n    for j in 1:2\n        l1 += j                                   # still the local in outer loop, not a new local one \n        l2 = j\n        g  += j\n        println(\"j: $j\")\n        println(\"l1 inside inner loop: $l1\")\n        println(\"l2 inside inner loop: $l2\")\n        println(\"g inside inner loop: $g\")\n    end\n    ## println(\"l2 post: $l2\")                     # error: l2 not defined in this scope \n    println(\"l1 post: $l1\")\n    println(\"g post: $g\")\nend\n## println(\"l1 global $l1\")                        # error; l1 is not defined in the global scope\nprintln(\"g in global: $g\")                        # REPL/INTERACTIVE: \"7\", FILE MODE: \"2\"\n\nfunction foo(i)\n    l1 = g2                                       # l1: local, g2: global (read only)\n    l1 += i\n    g = i                                         # REPL/INTERACTIVE and FILE MODE: local by default (with a warning `g` being already defined)\n    g += i                                       \n    println(\"i: $i\")\n    println(\"l1: $l1\")\n    println(\"g: $g\")\n    for j in 1:2\n        l1 += j                                   # still the local in outer loop, not a new local one \n        l2 = j\n        g  += j\n        println(\"j: $j\")\n        println(\"l1 inside inner loop: $l1\")\n        println(\"l2 inside inner loop: $l2\")\n        println(\"g inside inner loop: $g\")\n    end\n    ## println(\"l2 post: $l2\")                     # error: l2 not defined in this scope \n    println(\"l1 post: $l1\")\n    println(\"g post: $g\")\nend\n\nprintln(\"Calling foo..\")\nfoo(10)\nprintln(\"g in global: $g\")                        # REPL/INTERACTIVE: \"7\", FILE MODE: \"2\"\n\ng = 2\ninclude(\"010401-varScopeExample.jl.txt\")            # gives a warning !\n\n\n# ## Repeated iterations: `for` and `while` loops, List Comprehension, Maps\n\nfor i in 1:2, j in 3:4             # j is the inner loop\n    println(\"i: $i, j: $j\")\nend\na = 1\nwhile true             # or condition, e.g. while a == 10\n    global a += 1\n    println(\"a: $a\")\n    if a == 10\n        break \n    else \n        continue\n    end\n    println(\"This is never printed\")\nend\n\n# ### List Comprehension\n[ i+j for i in 1:2, j in 3:4 if j >= 4]\n\n# ### Maps\n\n# Apply a (possible anonymous) function to a list of arguments:\n\nmap((name,year) -> println(\"$name is $year year old\"), [\"Marc\",\"Anna\"], [25,22])\n\n# !!! warninng\n#     Don't confuse the single-line arrows used in anonymous functions (`->`) with the double-line arrow used to define a Pair (`=>`)\n\n# We can use maps to substitute values of an array based on a dictionary:\ncountries = [\"US\",\"UK\",\"IT\",\"UK\",\"UK\"]\ncountryNames = Dict(\"IT\" => \"Italy\", \"UK\" => \"United Kngdom\",  \"US\"=>\"United States\")\ncountryLongNames = map(cShortName -> countryNames[cShortName], countries)\n\n# ## Conditional statements: if blocks and ternary operators\n\na = 10\n\nif a < 4                      # use `!`, `&&` and `||` for \"not\", \"and\" and  \"or\" conditions\n    println(\"a < 4\")\nelseif a < 8\n    println(\"a < 8\")\nelse \n    println(\"a is big!\")\nend\n\na = 10\n\nif a < 5\n  b = 100\nelse \n  b = 200\nend\n\nb\n\n# Ternary operators\nb =  a < 5 ? 100 : 200   # ? condition : if true : if false\n\n# Short-circuit evaluation\nb = 100\n(a < 5) || (b = 200)    # replace an if: the second part is executed unless the first part is already true\nb\n(a < 50) || (b = 500)   # here is never executed\nb\n\n# !!! warning \n#     Don't confuse boolean operators `&&` and `||` with their analogous `&` and `|` bitwise operators\n\na = 3  \nb = 2 \nbitstring(a)\nbitstring(b)\n\n##a && b     # error non boolean used in boolean context\na & b       \na | b \n\n\n# ## Functions\n\nfunction foo(x)             # function definition\n    x+2\nend\nfoo(2)                      # function call\ninlineFunction(x) = x+2\nfoo2 = x -> x+2             # anonymous function (aka \"lambda function\") and assignment to the variable foo2\nfoo2(2)\n\n# A nested function:\nfunction f1(x)\n    function f2(x,y)\n        x+y\n    end\n    f2(x,2)\nend\n\nf1(2)\n\n# A recursive function:\nfunction fib(n)                   # This is a naive implementation. Much faster implementations of the Fibonacci numbers exist\n    if n == 0  return 0\n    elseif n == 1 return 1\n    else\n     return fib(n-1) + fib(n-2)\n    end\nend\nfib(4)\n\n# ### Function arguments\n\n# #### Positional vs keyword arguments\nf(a,b=1;c=1) = a+10b+100c  # `a` and `b` are positional arguments (`b` with a default provided), `c` is a keyword argument\nf(2)\nf(2,c=3)\n\nfoo(a, args...;c=1) = a + length(args) + sum(args) + c\nfoo(1,2,3,c=4)\n\n# Rules for positional and keyword arguments:\n# - keyword arguments follow a semicolon `;` in the parameters list of the function definition \n# - a positional argument without a default can not follow a positional argument with a default provided\n# - the splat operator to define variable number of arguments must be the last positional argument \n# - the function call must use positional arguments by position and keyword arguments by name\n\n\n## foo(a::String=\"aaa\",b::Int64) = \"$a \"+string(b) # error! Optional positional argument before a mandatory positional one  \n\n# ### Argument types and multiple dispatch\n\n# Simple to understand the usage, complex to understand the deep implications\nfoo3(a::Int64,b::String) = a + parse(Int64,b)\nfoo3(2,\"3\")\nfoo3(a::String,b::Int64) = parse(Int64,a) + b\nfoo3(\"3\",2)\nmethods(foo3)\n\n# Multiple dispatch allows to compile a _specialised_ version JIT at run time, on the first call with the given parameters type\n# We will see it again when dealing with type inheritance\n# In general, unless we need to write specialised methods, no need to specify the type of the parameters. No influence on performances, this is automatically inferred (and the funciton compiled) based on the run-time type of the argument\n\n# !!! tip Functions performances tip\n#     The most important things for performances are (1) that the function is _type stable_, that is, that conditional to a specific combination of the types of the parameters the function returns the same type. This is a condition necessary to have a working chain of type inference across function calls; (2) that no (non constant) global constants are used in the function and indeed all the required information for the functio ndoing its work is embedded in the function parameters\n\n\n# ### Function templates\nfoo3(a::T,b::String) where {T<: Number} = a + parse(T,b)             # can use T in the function body\nfoo3(2,\"1\")\nfoo3(1.5,\"1.5\")\nfoo4(a::Int64,b::T where T <: Number) = a + b                        # ok not used in functio nbody\nfoo4(a::Int64,b::Array{T} where T <: Number) = a .+ fill(T,b,2)      # wil lerror, can't use T in the function body\n## foo4(2,[1,2])                                                     # run time error, T not defined \n\n# ### Call by reference vs. call by value \n\n# How the variable used as function argument within the function body relates to the variable used in calling the function ?\n# - **call by value**: the value of the argument is copied and the function body works on a copy of the value\n# - **call by reference**: the function works on the same object being referenced by the caller variable and the function argument\n# - **call by sharing** (Julia): the arguments are just new local variables that bind the same object. The effects of \"modifications\" on the local variable on the caller's one depends on the mutability property of the object as we saw in the _Types and objects_ segment:\n#   - immutable objects: we can only have that the argument is rebinded to other objects. No effects on the original caller object\n#   - mutable objects: if the argument is rebinded to an other object, no effects on the caller object. If the object is modified, the caller object (being the same object) is also modified\n\nx = 10\nfoo(y) = y = 1\nfoo(x)\nx\nfoo(x) = x[1] = 10\nx = [1,2]\nfoo(x)\nx\nfoo3(x) = x = [10,20]\nfoo3(x)\nx\n\n\n# !!! info\n#     Functions that modify at least one of their arguments are named, by convention, with an exclamation mark at the end of their name and the argument(s) that is (are) modified set as the first(s) argument(s) \n\nfoo!(x) = x[1] = 10 # to follow the convention\n\n\n# ## `do` blocks\n\n# Functions that accept an other function as their first parameter can be rewritten with the function itself defined in a `do` block:\nusing Statistics\npool(f,x,poolSize=3) = [f(x[i:i+poolSize-1]) for i in 1:length(x)-poolSize+1] # a real case, used in neural networks as pooling layer\npool(mean,[1,2,3,4,5,6])\npool(maximum,[1,2,3,4,5,6])\npool([1,2,3,4,5]) do x      # x is a local variable within the do block. We need as many local variables as the number of parameters of the inner function\n    sum(x)/length(x)\nend\n# Using the `do` block we can call the outer function and define the inner function at the same time.\n# `do` blocks are frequently used in input/output operations\n", "meta": {"hexsha": "1e89f33d5bd7050da50c600471ce776a0b5b4b8e", "size": 11006, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0104_-_Control_flow_and_functions.jl", "max_stars_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_stars_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2022-02-26T15:44:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T13:05:52.000Z", "max_issues_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0104_-_Control_flow_and_functions.jl", "max_issues_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_issues_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lessonsSources/01_-_JULIA1_-_Basic_Julia_programming/0104_-_Control_flow_and_functions.jl", "max_forks_repo_name": "sylvaticus/IntroSPMLJuliaCourse", "max_forks_repo_head_hexsha": "84ba4432548dcfe826353c03616c03e86f46e9eb", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-26T08:06:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T08:06:40.000Z", "avg_line_length": 39.4480286738, "max_line_length": 486, "alphanum_fraction": 0.6212974741, "num_tokens": 2886, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.30074557894124154, "lm_q2_score": 0.1801066574851787, "lm_q1q2_score": 0.05416628097655196}}
{"text": "\"\"\"\n    sign_cadlag(x::N) where {N<:Real}\n\nThis function works like the sign function but is ``1`` for input ``0``.\n\n### Input\n\n- `x` -- real scalar\n\n### Output\n\n``1`` if ``x \u2265 0``, ``-1`` otherwise.\n\n### Notes\n\nThis is the sign function right-continuous at zero (see\n[c\u00e0dl\u00e0g function](https://en.wikipedia.org/wiki/C%C3%A0dl%C3%A0g)).\nIt can be used with vector-valued arguments via the dot operator.\n\n### Examples\n\n```jldoctest\njulia> LazySets.sign_cadlag.([-0.6, 1.3, 0.0])\n3-element Array{Float64,1}:\n -1.0\n  1.0\n  1.0\n```\n\"\"\"\nfunction sign_cadlag(x::N) where {N<:Real}\n    return x < zero(x) ? -one(x) : one(x)\nend\n\n\"\"\"\n    substitute(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n\nApply a substitution to a given vector.\n\n### Input\n\n- `substitution` -- substitution (a mapping from an index to a new value)\n- `x`            -- vector\n\n### Output\n\nA fresh vector corresponding to `x` after `substitution` was applied.\n\"\"\"\nfunction substitute(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n    return substitute!(substitution, copy(x))\nend\n\n\"\"\"\n    substitute!(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n\nApply a substitution to a given vector.\n\n### Input\n\n- `substitution` -- substitution (a mapping from an index to a new value)\n- `x`            -- vector (modified in this function)\n\n### Output\n\nThe same (but see the Notes below) vector `x` but after `substitution` was\napplied.\n\n### Notes\n\nThe vector `x` is modified in-place if it has type `Vector` or `SparseVector`.\nOtherwise, we first create a new `Vector` from it.\n\"\"\"\nfunction substitute!(substitution::Dict{Int, T}, x::AbstractVector{T}) where {T}\n    return substitute!(Vector(x), substitution)\nend\n\nfunction substitute!(substitution::Dict{Int, T},\n                     x::Union{Vector{T}, SparseVector{T}}) where {T}\n    for (index, value) in substitution\n        x[index] = value\n    end\n    return x\nend\n\n\"\"\"\n    reseed(rng::AbstractRNG, seed::Union{Int, Nothing})\n\nReset the RNG seed if the seed argument is a number.\n\n### Input\n\n- `rng`  -- random number generator\n- `seed` -- seed for reseeding\n\n### Output\n\nThe input RNG if the seed is `nothing`, and a reseeded RNG otherwise.\n\"\"\"\nfunction reseed(rng::AbstractRNG, seed::Union{Int, Nothing})\n    if seed != nothing\n        return Random.seed!(rng, seed)\n    end\n    return rng\nend\n\n\"\"\"\n    StrictlyIncreasingIndices\n\nIterator over the vectors of `m` strictly increasing indices from 1 to `n`.\n\n### Fields\n\n- `n` -- size of the index domain\n- `m` -- number of indices to choose (resp. length of the vectors)\n\n### Notes\n\nThe vectors are modified in-place.\n\nThe iterator ranges over ``\\\\binom{n}{m}`` (`n` choose `m`) possible vectors.\n\nThis implementation results in a lexicographic order with the last index growing\nfirst.\n\n### Examples\n\n```jldoctest\njulia> for v in LazySets.StrictlyIncreasingIndices(4, 2)\n           println(v)\n       end\n[1, 2]\n[1, 3]\n[1, 4]\n[2, 3]\n[2, 4]\n[3, 4]\n```\n\"\"\"\nstruct StrictlyIncreasingIndices\n    n::Int\n    m::Int\n\n    function StrictlyIncreasingIndices(n::Int, m::Int)\n        @assert n >= m > 0 \"require n >= m > 0\"\n        new(n, m)\n    end\nend\n\nBase.eltype(::Type{StrictlyIncreasingIndices}) = Vector{Int}\nBase.length(sii::StrictlyIncreasingIndices) = binomial(sii.n, sii.m)\n\n# initialization\nfunction Base.iterate(sii::StrictlyIncreasingIndices)\n    v = [1:sii.m;]\n    return (v, v)\nend\n\n# normal iteration\nfunction Base.iterate(sii::StrictlyIncreasingIndices, state::AbstractVector{Int})\n    v = state\n    i = sii.m\n    diff = sii.n\n    if i == diff\n        return nothing\n    end\n    while v[i] == diff\n        i -= 1\n        diff -= 1\n    end\n    # update vector\n    v[i] += 1\n    for j in i+1:sii.m\n        v[j] = v[j-1] + 1\n    end\n    # detect termination: first index has maximum value\n    if i == 1 && v[1] == (sii.n - sii.m + 1)\n        return (v, nothing)\n    end\n    return (v, v)\nend\n\n# termination\nfunction Base.iterate(sii::StrictlyIncreasingIndices, state::Nothing)\n    return nothing\nend\n\n\"\"\"\n    subtypes(interface, concrete::Bool)\n\nReturn the concrete subtypes of a given interface.\n\n### Input\n\n- `interface` -- an abstract type, usually a set interface\n- `concrete`  -- if `true`, seek further the inner abstract subtypes of the given\n                 interface, otherwise return only the direct subtypes of `interface`\n\n### Output\n\nA list with the subtypes of the abstract type `interface`, sorted alphabetically.\n\n### Examples\n\nConsider the `AbstractPolytope` interface. If we include the abstract subtypes\nof this interface,\n\n```jldoctest subtypes\njulia> using LazySets: subtypes\n\njulia> subtypes(AbstractPolytope, false)\n4-element Array{Any,1}:\n AbstractCentrallySymmetricPolytope\n AbstractPolygon\n HPolytope\n VPolytope\n```\n\nWe can use this function to obtain the concrete subtypes of\n`AbstractCentrallySymmetricPolytope` and `AbstractPolygon` (further until all\nconcrete types are obtained), using the `concrete` flag:\n\n```jldoctest subtypes\njulia> subtypes(AbstractPolytope, true)\n14-element Array{Type,1}:\n Ball1\n BallInf\n HPolygon\n HPolygonOpt\n HPolytope\n Hyperrectangle\n Interval\n LineSegment\n Singleton\n SymmetricIntervalHull\n VPolygon\n VPolytope\n ZeroSet\n Zonotope\n```\n\"\"\"\nfunction subtypes(interface, concrete::Bool)\n\n    subtypes_to_test = subtypes(interface)\n\n    # do not seek the concrete subtypes further\n    if !concrete\n        return sort(subtypes_to_test, by=string)\n    end\n\n    result = Vector{Type}()\n    i = 0\n    while i < length(subtypes_to_test)\n        i += 1\n        subtype = subtypes_to_test[i]\n        new_subtypes = subtypes(subtype)\n        if isempty(new_subtypes)\n            # base type found\n            push!(result, subtype)\n        else\n            # yet another interface layer\n            append!(subtypes_to_test, new_subtypes)\n        end\n    end\n    return sort(result, by=string)\nend\n\n\"\"\"\n    implementing_sets(op::Function;\n                      signature::Tuple{Vector{Type}, Int}=(Type[], 1),\n                      type_args=Float64, binary::Bool=false)\n\nCompute a dictionary containing information about availability of (unary or\nbinary) concrete set operations.\n\n### Input\n\n- `op`        -- set operation (respectively its `Function` object)\n- `signature` -- (optional, default: `Type[]`) the type signature of the\n                 function without the `LazySet` type(s) (see also the `index`\n                 option and the `Examples` section below)\n- `index`     -- (optional, default: `1`) index of the set type in the signature\n                 in the unary case (see the `binary` option)\n- `type_args` -- (optional, default: `Float64`) type arguments added to the\n                 `LazySet`(s) when searching for available methods; valid\n                 inputs are a type or `nothing`, and in the unary case (see the\n                 `binary` option) it can also be a list of types\n- `binary`    -- (optional, default: `false`) flag indicating whether `op` is a\n                 binary function (`true`) or a unary function (`false`)\n\n### Output\n\nA dictionary with three keys each mapping to a list:\n- `\"available\"` -- This list contains all set types such that there exists an\n                   implementation of `op`.\n- `\"missing\"`   -- This list contains all set types such that there does not\n                   exist an implementation of `op`. Note that this is the\n                   complement of the `\"available\"` list.\n- `\"specific\"`  -- This list contains all set types such that there exists a\n                   type-specific implementation. Note that those set types also\n                   occur in the `\"available\"` list.\n\nIn the unary case, the lists contain set types.\nIn the binary case, the lists contain pairs of set types.\n\n### Examples\n\n```jldoctests\njulia> using LazySets: implementing_sets\n\njulia> dict = implementing_sets(tovrep);\n\njulia> dict[\"available\"]  # tovrep is only available for polyhedral set types\n6-element Array{Type,1}:\n HPolygon\n HPolygonOpt\n HPolyhedron\n HPolytope\n VPolygon\n VPolytope\n\njulia> dict = implementing_sets(\u03c3; signature=Type[AbstractVector{Float64}], index=2);\n\njulia> dict[\"missing\"]  # every set type implements function \u03c3\n0-element Array{Type,1}\n\njulia> N = Rational{Int};  # restriction of the number type\n\njulia> dict = implementing_sets(\u03c3; signature=Type[AbstractVector{N}], index=2, type_args=N);\n\njulia> dict[\"missing\"]  # some set types are not available with number type N\n4-element Array{Type,1}:\n Ball2\n Ballp\n Bloating\n Ellipsoid\n\njulia> dict = LazySets.implementing_sets(convex_hull; binary=true);  # binary case\n\njulia> (HPolytope, HPolytope) \u2208 dict[\"available\"]  # dict contains pairs now\ntrue\n```\n\"\"\"\nfunction implementing_sets(op::Function;\n                           signature::Vector{Type}=Type[],\n                           index::Int=1,\n                           type_args=Float64,\n                           binary::Bool=false)\n    function get_unary_dict()\n        return Dict{String, Vector{Type}}(\"available\" => Vector{Type}(),\n                                          \"missing\" => Vector{Type}(),\n                                          \"specific\" => Vector{Type}())\n    end\n\n    if !binary\n        # unary case\n        dict = get_unary_dict()\n        _implementing_sets_unary!(dict, op, signature, index, type_args)\n        return dict\n    end\n\n    # binary case by considering all set types as second argument\n    binary_dict = Dict{String, Vector{Tuple{Type, Type}}}(\n        \"available\" => Vector{Tuple{Type, Type}}(),\n        \"missing\" => Vector{Tuple{Type, Type}}(),\n        \"specific\" => Vector{Tuple{Type, Type}}())\n    signature_copy = copy(signature)\n    insert!(signature_copy, 1, LazySet)  # insert a dummy type\n    for set_type in subtypes(LazySet, true)\n        augmented_set_type = set_type\n        try\n            if type_args isa Type\n                augmented_set_type = set_type{type_args}\n            else\n                @assert type_args == nothing \"for binary functions, \" *\n                    \"`type_args` must not be a list\"\n            end\n        catch e\n            # type combination not available\n            push!(binary_dict[\"missing\"], (set_type, LazySet))\n            continue\n        end\n        signature_copy[1] = augmented_set_type  # replace dummy type\n        unary_dict = get_unary_dict()\n        _implementing_sets_unary!(unary_dict, op, signature_copy, 1, type_args)\n\n        # create pairs\n        for key in [\"available\", \"missing\", \"specific\"]\n            i = 1\n            for other_set_type in unary_dict[key]\n                push!(binary_dict[key], (set_type, other_set_type))\n                i += 1\n            end\n        end\n    end\n    return binary_dict\nend\n\nfunction _implementing_sets_unary!(dict, op, signature, index, type_args)\n    function create_type_tuple(given_type)\n        if isempty(signature)\n            return Tuple{given_type}\n        end\n        # create dynamic tuple\n        new_signature = copy(signature)\n        insert!(new_signature, index, given_type)\n        return Tuple{new_signature...}\n    end\n\n    for set_type in subtypes(LazySet, true)\n        augmented_set_type = set_type\n        try\n            if type_args isa Type\n                augmented_set_type = set_type{type_args}\n            elseif type_args != nothing\n                augmented_set_type = set_type{type_args...}\n            end\n        catch e\n            # type combination not available\n            push!(dict[\"missing\"], set_type)\n            continue\n        end\n        tuple_set_type = create_type_tuple(augmented_set_type)\n        if !hasmethod(op, tuple_set_type)\n            # implementation not available\n            push!(dict[\"missing\"], set_type)\n            continue\n        end\n        # implementation available\n        push!(dict[\"available\"], set_type)\n\n        # check if there is a type-specific method\n        super_type = supertype(augmented_set_type)\n        tuple_super_type = create_type_tuple(super_type)\n        if !hasmethod(op, tuple_super_type)\n            continue\n        end\n        method_set_type = which(op, tuple_set_type)\n        method_super_type = which(op, tuple_super_type)\n        if method_set_type != method_super_type\n            push!(dict[\"specific\"], set_type)\n        end\n    end\nend\n", "meta": {"hexsha": "3a2082cd2270f6f21fb56f8c7ff1cd837e4053be", "size": 12254, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utils/helper_functions.jl", "max_stars_repo_name": "birm/LazySets.jl", "max_stars_repo_head_hexsha": "634d9c0cbbc4acd81fb0e787757409946d6e1550", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Utils/helper_functions.jl", "max_issues_repo_name": "birm/LazySets.jl", "max_issues_repo_head_hexsha": "634d9c0cbbc4acd81fb0e787757409946d6e1550", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Utils/helper_functions.jl", "max_forks_repo_name": "birm/LazySets.jl", "max_forks_repo_head_hexsha": "634d9c0cbbc4acd81fb0e787757409946d6e1550", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.6613995485, "max_line_length": 92, "alphanum_fraction": 0.6384853925, "num_tokens": 3109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681662740417, "lm_q2_score": 0.13660839881392148, "lm_q1q2_score": 0.05410623801586285}}
{"text": "\"\"\"\n# Subsetting based on Index\n\n```julia\nsubset(ts::TS, from::T, to::T) where {T<:Union{Int, TimeType}}\n```\n\nCreate a subset of `ts` based on the `Index` starting `from`\n(inclusive) till `to` (inclusive).\n\n# Examples\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Random, Statistics)\njulia> using Random;\njulia> random(x) = rand(MersenneTwister(123), x);\njulia> dates = Date(\"2022-02-01\"):Week(1):Date(\"2022-02-01\")+Month(9);\njulia> ts = TS(random(length(dates)), dates)\njulia> show(ts)\n(40 x 1) TS with Date Index\n\n Index       x1\n Date        Float64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-02-01  0.768448\n 2022-02-08  0.940515\n 2022-02-15  0.673959\n 2022-02-22  0.395453\n 2022-03-01  0.313244\n 2022-03-08  0.662555\n 2022-03-15  0.586022\n 2022-03-22  0.0521332\n 2022-03-29  0.26864\n 2022-04-05  0.108871\n 2022-04-12  0.163666\n 2022-04-19  0.473017\n 2022-04-26  0.865412\n 2022-05-03  0.617492\n 2022-05-10  0.285698\n 2022-05-17  0.463847\n 2022-05-24  0.275819\n 2022-05-31  0.446568\n 2022-06-07  0.582318\n 2022-06-14  0.255981\n 2022-06-21  0.70586\n 2022-06-28  0.291978\n 2022-07-05  0.281066\n 2022-07-12  0.792931\n 2022-07-19  0.20923\n 2022-07-26  0.918165\n 2022-08-02  0.614255\n 2022-08-09  0.802665\n 2022-08-16  0.555668\n 2022-08-23  0.940782\n 2022-08-30  0.48\n 2022-09-06  0.790201\n 2022-09-13  0.356221\n 2022-09-20  0.900925\n 2022-09-27  0.529253\n 2022-10-04  0.031831\n 2022-10-11  0.900681\n 2022-10-18  0.940299\n 2022-10-25  0.621379\n 2022-11-01  0.348173\n\njulia> subset(ts, Date(2022, 03), Date(2022, 07))\n(18 x 1) TS with Date Index\n\n Index       x1\n Date        Float64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-03-01  0.313244\n 2022-03-08  0.662555\n 2022-03-15  0.586022\n 2022-03-22  0.0521332\n 2022-03-29  0.26864\n 2022-04-05  0.108871\n 2022-04-12  0.163666\n 2022-04-19  0.473017\n 2022-04-26  0.865412\n 2022-05-03  0.617492\n 2022-05-10  0.285698\n 2022-05-17  0.463847\n 2022-05-24  0.275819\n 2022-05-31  0.446568\n 2022-06-07  0.582318\n 2022-06-14  0.255981\n 2022-06-21  0.70586\n 2022-06-28  0.291978\n\njulia> subset(TS(1:20, -9:10), -4, 5)\n(10 x 1) TS with Int64 Index\n\n Index  x1\n Int64  Int64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n    -4      6\n    -3      7\n    -2      8\n    -1      9\n     0     10\n     1     11\n     2     12\n     3     13\n     4     14\n     5     15\n\njulia> subset(ts,:,Date(\"2022-04-12\"))\n(11 x 1) TS with Date Index\n\n Index       x1        \n Date        Float64   \n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-02-01  0.768448\n 2022-02-08  0.940515\n 2022-02-15  0.673959\n 2022-02-22  0.395453\n 2022-03-01  0.313244\n 2022-03-08  0.662555\n 2022-03-15  0.586022\n 2022-03-22  0.0521332\n 2022-03-29  0.26864\n 2022-04-05  0.108871\n 2022-04-12  0.163666\n\njulia> subset(ts,Date(\"2022-9-27\"),:)\n(6 x 1) TS with Date Index\n\n Index       x1       \n Date        Float64  \n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-09-27  0.529253\n 2022-10-04  0.031831\n 2022-10-11  0.900681\n 2022-10-18  0.940299\n 2022-10-25  0.621379\n 2022-11-01  0.348173\n\n\n```\n\n\"\"\"\nfunction subset(ts::TS, from::T, to::T) where {T<:Union{Int, TimeType}}\n    TS(DataFrames.subset(ts.coredata, :Index => x -> x .>= from .&& x .<= to))\nend\n\nfunction subset(ts::TS, ::Colon, to::T) where {T<:Union{Int, TimeType}}\n    TS(DataFrames.subset(ts.coredata, :Index => x -> x .<= to))\nend\n\nfunction subset(ts::TS, from::T, ::Colon) where {T<:Union{Int, TimeType}}\n    TS(DataFrames.subset(ts.coredata, :Index => x -> x .>= from))\nend\n", "meta": {"hexsha": "7ae692f1d22bdb619d8c0e591be22e197b3544f0", "size": 3347, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/subset.jl", "max_stars_repo_name": "xKDR/TSx.jl", "max_stars_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/subset.jl", "max_issues_repo_name": "xKDR/TSx.jl", "max_issues_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/subset.jl", "max_forks_repo_name": "xKDR/TSx.jl", "max_forks_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.0197368421, "max_line_length": 78, "alphanum_fraction": 0.6089034957, "num_tokens": 1647, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1081889639072845, "lm_q1q2_score": 0.05409448195364225}}
{"text": "\nextract_name(line) = join(split(line)[2:end-1], \" \")\n\nstart_testset!(dest, line) =\n    println(dest, \"@testset \\\"\", extract_name(line), \"\\\" begin\")\nfinish_testset!(dest) = println(dest, \"end\\n\")\n\nbegins_testset(line) = startswith(line, \"testcase\")\nfinishes_testset(line) = line == \"}\"\nbegins_decoration_testset(line) = occursin(\"dec\", extract_name(line))\nis_comment(line) = startswith(line, \"//\")\nbegins_multiline_comment(line) = startswith(line, \"/*\")\n\nskip_block(src, stopline) = readuntil(src, \"\\n\" * stopline * \"\\n\")\nskip_multiline_comment(src) = skip_block(src, \"*/\")\nskip_testset(src) = skip_block(src, \"}\")\n\nfunction translate!(dest::IO, src::IO)\n    while !eof(src)\n        line = rstrip(strip(readline(src)), ';')\n        if isempty(line) || is_comment(line)\n            nothing\n        elseif begins_multiline_comment(line)\n            skip_multiline_comment(src)\n        elseif begins_testset(line)\n            if begins_decoration_testset(line)\n                skip_testset(src)\n            else\n                start_testset!(dest, line)\n            end\n        elseif finishes_testset(line)\n            finish_testset!(dest)\n        else\n            itl_test = line\n            translate!(dest, itl_test)\n        end\n    end\nend\n\ntests_decorated_intervals(case) =\n    occursin(\"]_\", case) ||\n    occursin(\"d-numsToInterval\", case) ||\n    occursin(\"setDec\", case) ||\n    occursin(\"intervalPart\", case)\ntests_string_input(case) = occursin(\"textToInterval\", case)\ntests_logging(case) = occursin(\"signal\", case)\n\nskip_testcase(case) =\n    tests_decorated_intervals(case) ||\n    tests_string_input(case)\n\nindent(io) = print(io, repeat(' ', 4))\n\nfunction translate!(dest::IO, itl_test::AbstractString)\n    skip_testcase(itl_test) && return\n    jl_test = tests_logging(itl_test) ?\n        translate_logtest(itl_test) : translate(itl_test)\n    indent(dest)\n    println(dest, jl_test)\nend\n\nfunction translate_logtest(itl_test)\n    itl_test, exc = split(itl_test, \" signal \")\n    exc != \"UndefinedOperation\" && return \"# \" * itl_test\n    lhs, rhs = split(itl_test, \"=\")\n    jl_test = rebuild(rebuild_lhs(lhs), rebuild_rhs(rhs))\n    return \"@test_throws AssertionError \" * jl_test\nend\n\nisbroken(expr) = !eval(Meta.parse(expr))\n\n\"\"\"\n\nThis function parses a line into julia code, e.g.\n\n```\nadd [1, 2] [1, 2] = [2, 4]\n```\n\nis parsed into\n```\n@test +(Interval(1, 2), Interval(1, 2)) === Interval(2, 4)\n```\n\"\"\"\nfunction translate(itl_test)\n    lhs, rhs = split(itl_test, \"=\")\n    jl_test = rebuild(rebuild_lhs(lhs), rebuild_rhs(rhs))\n    try\n        return ifelse(isbroken(jl_test), \"@test_broken \", \"@test \") * jl_test\n    catch\n        @warn \"caused exception: \" * jl_test\n        return \"# \" * itl_test\n    end\nend\n\nconst interval_pattern = r\"\\[([^\\]]+)\\]\"\n\nfunction rebuild_lhs(lhs)\n    lhs = strip(lhs)\n    fname, args = split(lhs, limit = 2)\n\n    # input numbers\n    args = replace(args, \"infinity\" => \"Inf\")\n    args = replace(args, \"X\" => \"x\")\n    if fname == \"b-numsToInterval\"\n        args = replace(args, ' ' => ',')\n        return \"Interval($args)\"\n    end\n\n    # input intervals\n    for m in eachmatch(interval_pattern, args)\n        args = replace(args, m.match => translate_interval(m[1]))\n    end\n    args = replace(args, \" \" => \", \")\n    args = replace(args, \",,\" => \",\")\n    args = replace(args, \"{\" => \"[\")\n    args = replace(args, \"}\" => \"]\")\n    return functions[fname](args)\nend\n\nisintstring(x) = !isnothing(tryparse(Int, x))\nfloatstring(x) = x * ifelse(isintstring(x), \".0\", \"\")\n\nfunction rebuild_rhs(rhs)\n    rhs = strip(rhs)\n    rhs = replace(rhs, \"infinity\" => \"Inf\")\n    rhs = replace(rhs, \"X\" => \"x\")\n    if '[' \u2209 rhs # one or more scalar/boolean values separated by space\n        return map(floatstring, split(rhs))\n    else # one or more intervals\n        intervals = map(\n            m -> translate_interval(m[1]),\n            eachmatch(interval_pattern, rhs)\n        )\n        return intervals\n    end\nend\n\nconst special_intervals = Dict(\n    \"nai\" => \"nai()\",\n    \"entire\" => \"entireinterval()\",\n    \"empty\" => \"emptyinterval()\"\n)\n\ntranslate_interval(ival) =\n    haskey(special_intervals, ival) ?\n    special_intervals[ival] :\n    \"interval($ival)\"\n\nconst relaxed_functions = [\n        \"sinh\",\n        \"cosh\",\n        \"tanh\",\n        \"coth\",\n        \"sech\",\n        \"csch\",\n        \"asinh\",\n        \"acosh\",\n        \"atanh\",\n        \"acoth\",\n        \"atan\",\n        \"cot\",\n        \"acot\",\n        \"sec\",\n        \"csc\",\n        \"exp2\",\n        \"exp10\",\n        \"^\",\n        \"cbrt\"\n    ]\n\nonly_requires_validity(lhs) =\n    any(startswith(lhs, f * \"(\") for f in relaxed_functions)\n\nfunction rebuild(lhs, rhs::AbstractString)\n    rhs == \"nai()\" && return \"isnai($lhs)\"\n    rhs == \"NaN\" && return \"isnan($lhs)\"\n    rhs == \"true\" && return lhs\n    rhs == \"false\" && return lhs[end] == ')' ? \"!\" * lhs : \"!($lhs)\"\n    only_requires_validity(lhs) && return \"$lhs \u2287 $rhs\"\n    return \"$lhs == $rhs\"\nend\n\nfunction rebuild(lhs, rhs::Vector)\n    length(rhs) == 1 && return rebuild(lhs, rhs[1])\n    expr = [rebuild(lhs*\"[$i]\", r) for (i, r) in enumerate(rhs)]\n    return join(expr, \" && \")\nend\n", "meta": {"hexsha": "b60cd054f44cec607581522ea3e924e83a22d29a", "size": 5112, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parse.jl", "max_stars_repo_name": "gwater/ITF1788.jl", "max_stars_repo_head_hexsha": "7b32341b784e72b57d1badcc7fbea09edd29ce30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/parse.jl", "max_issues_repo_name": "gwater/ITF1788.jl", "max_issues_repo_head_hexsha": "7b32341b784e72b57d1badcc7fbea09edd29ce30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/parse.jl", "max_forks_repo_name": "gwater/ITF1788.jl", "max_forks_repo_head_hexsha": "7b32341b784e72b57d1badcc7fbea09edd29ce30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0476190476, "max_line_length": 77, "alphanum_fraction": 0.5907668232, "num_tokens": 1399, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.10818895599979557, "lm_q1q2_score": 0.054094477999897785}}
{"text": "export MontyHallEnv\n\nconst REWARD_OF_GOAT = 10.0\nconst REWARD_OF_CAR = 1_000.0\n\nmutable struct MontyHallEnv <: AbstractEnv\n    doors::Vector{Symbol}\n    rng::AbstractRNG\n    guest_action::Union{Nothing,Int}\n    host_action::Union{Nothing,Int}\n    reward::Union{Nothing,Float64}\nend\n\n\"\"\"\n    MontyHallEnv(;rng=Random.GLOBAL_RNG)\n\nQuoted from [wiki](https://en.wikipedia.org/wiki/Monty_Hall_problem):\n\n> Suppose you're on a game show, and you're given the choice of three doors:\n> Behind one door is a car; behind the others, goats. You pick a door, say No.\n> 1, and the host, who knows what's behind the doors, opens another door, say\n> No. 3, which has a goat. He then says to you, \"Do you want to pick door No.\n> 2?\" Is it to your advantage to switch your choice?\n\nHere we'll introduce the first environment which is of [`FULL_ACTION_SET`](@ref).\n\"\"\"\nfunction MontyHallEnv(; rng = Random.GLOBAL_RNG)\n    doors = fill(:goat, 3)\n    doors[rand(rng, 1:3)] = :car\n    MontyHallEnv(doors, rng, nothing, nothing, nothing)\nend\n\nRandom.seed!(env::MontyHallEnv, s) = Random.seed!(env.rng, s)\n\nRLBase.action_space(::MontyHallEnv) = Base.OneTo(3)\n\n\"\"\"\nIn the first round, the guest has 3 options, in the second round only two\noptions are valid, those different then the host's action.\n\"\"\"\nfunction RLBase.legal_action_space(env::MontyHallEnv)\n    if isnothing(env.host_action)\n        1:3\n    else\n        findall(!=(env.host_action), 1:3)\n    end\nend\n\n\"\"\"\nFor environments of [`FULL_ACTION_SET`], this function must be implemented.\n\"\"\"\nfunction RLBase.legal_action_space_mask(env::MontyHallEnv)\n    mask = BitArray(undef, 3)\n    fill!(mask, true)\n    if !isnothing(env.host_action)\n        mask[env.host_action] = false\n    end\n    mask\nend\n\nfunction RLBase.state(env::MontyHallEnv)\n    if isnothing(env.host_action)\n        1\n    else\n        env.host_action + 1\n    end\nend\n\nRLBase.state_space(env::MontyHallEnv) = 1:4\n\nfunction (env::MontyHallEnv)(action)\n    if isnothing(env.host_action)\n        # first round\n        env.guest_action = action\n        if env.doors[action] == :car\n            env.host_action = findall(!=(action), 1:3)[rand(env.rng, 1:2)]\n        else\n            for i in 1:3\n                if i == action\n                    continue\n                elseif env.doors[i] == :goat\n                    env.host_action = i\n                end\n            end\n        end\n    else\n        # second round\n        if action == env.host_action\n            @error \"Invalid action. Can not select the same door with the host.\"\n        else\n            env.guest_action = action\n            env.reward = env.doors[action] == :goat ? REWARD_OF_GOAT : REWARD_OF_CAR\n        end\n    end\nend\n\nRLBase.reward(env::MontyHallEnv) = isnothing(env.reward) ? 0.0 : env.reward\n\nRLBase.is_terminated(env::MontyHallEnv) = !isnothing(env.reward)\n\nfunction RLBase.reset!(env::MontyHallEnv)\n    env.doors .= :goat\n    env.doors[rand(env.rng, 1:3)] = :car\n    env.guest_action = nothing\n    env.host_action = nothing\n    env.reward = nothing\nend\n\nRLBase.NumAgentStyle(::MontyHallEnv) = SINGLE_AGENT\nRLBase.DynamicStyle(::MontyHallEnv) = SEQUENTIAL\nRLBase.ActionStyle(::MontyHallEnv) = FULL_ACTION_SET\nRLBase.InformationStyle(::MontyHallEnv) = IMPERFECT_INFORMATION  # the distribution of noise and original reward is unknown to the agent\nRLBase.StateStyle(::MontyHallEnv) = Observation{Int}()\nRLBase.RewardStyle(::MontyHallEnv) = TERMINAL_REWARD\nRLBase.UtilityStyle(::MontyHallEnv) = GENERAL_SUM\nRLBase.ChanceStyle(::MontyHallEnv) = STOCHASTIC  # the same action lead to different reward each time.\n", "meta": {"hexsha": "d552ab169a23593c87dee24d4c8b124a52a2aa7f", "size": 3586, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReinforcementLearningEnvironments/src/environments/examples/MontyHallEnv.jl", "max_stars_repo_name": "LaarsOman/ReinforcementLearning.jl", "max_stars_repo_head_hexsha": "b04e3f192e71418dbca496331ada44f65b2822d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 352, "max_stars_repo_stars_event_min_datetime": "2018-08-30T18:41:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T08:41:22.000Z", "max_issues_repo_path": "src/ReinforcementLearningEnvironments/src/environments/examples/MontyHallEnv.jl", "max_issues_repo_name": "LaarsOman/ReinforcementLearning.jl", "max_issues_repo_head_hexsha": "b04e3f192e71418dbca496331ada44f65b2822d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 325, "max_issues_repo_issues_event_min_datetime": "2018-08-24T12:41:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T14:29:23.000Z", "max_forks_repo_path": "src/ReinforcementLearningEnvironments/src/environments/examples/MontyHallEnv.jl", "max_forks_repo_name": "LaarsOman/ReinforcementLearning.jl", "max_forks_repo_head_hexsha": "b04e3f192e71418dbca496331ada44f65b2822d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 62, "max_forks_repo_forks_event_min_datetime": "2018-09-02T03:40:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T12:35:19.000Z", "avg_line_length": 30.6495726496, "max_line_length": 136, "alphanum_fraction": 0.6776352482, "num_tokens": 1007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10818895384320777, "lm_q1q2_score": 0.054094476921603886}}
{"text": "d1 = [1 => 4.2, 2 => 5.3]\nd1 = {1 => 4.2, 2 => 5.3}\nd2 = {\"a\"=>1, (2,3)=>true}\n\nd3 = [:A => 100, :B => 200]\nd3[:B] #> 200\n# d3[:Z] #> ERROR: key not found: :Z\nget(d3, :Z, 999) #> 999\nin( (:Z, 999), d3) #> false\nin( (:A, 100), d3) #> true\nd3[:A] = 150 #> d3 is now [:A => 150, :B => 200]\nd3[:C] = 300 #> d3 is now [:A => 150, :B => 200, :C => 300]\nlength(d3) #> 3\n# d3[\"CVO\"] = 500 #> ERROR: CVO is not a valid key for type Symbol# \n# d3[:CVO] = \"Julia\" #> ERROR: `convert` has no method matching     # convert(::Type{Int64}, ::ASCIIString)\n\nd4 = Dict()\nd4[\"lang\"] = \"Julia\"\n\nd5 = Dict{Float64, Int64}()\n# d5[\"c\"] = 6 #> ERROR: `convert` has no method matching convert(::Type{Float64}, ::ASCIIString)\n\nd3 = [:A => 100, :B => 200]\ndelete!(d3, :B) #> Dict{Symbol,Int64} with 1 entry:  :A => 100\n\nd3 = [:A => 100, :B => 200]\nki = keys(d3) #> KeyIterator\nfor k in ki\n   println(k)\nend #> A   B\n:A in ki #> true\n:Z in ki #> false\nhaskey(d3, :A) #> true\ncollect(keys(d3)) #>\nvi = values(d3) #> ValueIterator\nfor v in values(d3)\n    println(v)\nend #> 100  200\n\nkeys1 = [\"J.S. Bach\", \"Woody Allan\", \"Barak Obama\"]\nvalues1 = [ 1685, 1935, 1961]\nd5 = Dict(keys1, values1)\n#>\nd5 = [ \"J.S. Bach\" => 1685,\n       \"Woody Allan\" => 1935,\n       \"Barak Obama\" => 1961 ]\n#> Dict{ASCIIString,Int64} with 3 entries:\nfor (k, v) in d5\n  println(\"$k was born in $v\")\nend\nfor p in d5\n  println(\"$(p[1]) was born in $(p[2])\")\nend\n#J.S. Bach was born in 1685\n#Barak Obama was born in 1961\n#Woody Allan was born in 1935\n\ndpairs = [\"a\", 1, \"b\", 2, \"c\", 3]\nd6 = [dpairs[i]=>dpairs[i+1] for i in 1:2:length(dpairs)]\nd6 = (String => Int64)[dpairs[i]=>dpairs[i+1] for i in 1:2:length(dpairs)]\n\n# use of a dictionary:\nfunction showfactor(n)\n d = factor(n)\n println(\"factors for $n\")\n for (k, v) in d\n     print(\"$k^$v\\t\")\n end\nend\n\n# neat tricks:\ndict = d6\narrkey = [key for (key, value) in dict] #> 3-element Array{Any,1}: \"c\" \"b\" \"a\"\narrval = [value for (key, value) in dict] #> 3-element Array{Any,1}: 3 2 1\n\ndict = d1\nn = 1\narrn = [dict[i] for i = 1:n] #> 1-element Array{Any,1}: 4.2\narrn = map((i) -> dict[i], [1:n]) #> 1-element Array{Any,1}: 4.2", "meta": {"hexsha": "979d162b1609aff9bdb16a3ba445773fddadf95c", "size": 2119, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Module 1/Chapter05/Old/Old/dicts.jl", "max_stars_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_stars_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_stars_repo_stars_event_min_datetime": "2017-02-12T15:36:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T03:30:39.000Z", "max_issues_repo_path": "Module 1/Chapter05/Old/Old/dicts.jl", "max_issues_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_issues_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Module 1/Chapter05/Old/Old/dicts.jl", "max_forks_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_forks_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2017-02-10T16:19:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T11:46:44.000Z", "avg_line_length": 26.8227848101, "max_line_length": 107, "alphanum_fraction": 0.5630014158, "num_tokens": 924, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.11436853674701282, "lm_q1q2_score": 0.05406011756556974}}
{"text": "using FProfile\nusing Base.Test\nusing NBInclude\n\nnbinclude(\"../Manual.ipynb\")\n", "meta": {"hexsha": "e072bef3f3a0af81937fa43af42f4ad810c0f70c", "size": 77, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/FProfile.jl-480c51c8-11cd-5857-ba64-2c3c1abd9c7c", "max_stars_repo_head_hexsha": "b28f1b1c2d923e0398cc18d40c8b182270a4644c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2017-10-22T16:09:13.000Z", "max_stars_repo_stars_event_max_datetime": "2018-06-16T19:50:17.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "UnofficialJuliaMirror/FProfile.jl-480c51c8-11cd-5857-ba64-2c3c1abd9c7c", "max_issues_repo_head_hexsha": "b28f1b1c2d923e0398cc18d40c8b182270a4644c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2018-03-29T16:34:22.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-09T08:33:31.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "UnofficialJuliaMirror/FProfile.jl-480c51c8-11cd-5857-ba64-2c3c1abd9c7c", "max_forks_repo_head_hexsha": "b28f1b1c2d923e0398cc18d40c8b182270a4644c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-02-26T18:26:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-03T12:43:25.000Z", "avg_line_length": 12.8333333333, "max_line_length": 28, "alphanum_fraction": 0.7792207792, "num_tokens": 20, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.11436852467249821, "lm_q1q2_score": 0.0540601118581462}}
{"text": "# # Recipe Ingredients Example\n# Following example demonstrates prediction of cuisine from set of ingredients.\n\n# ## A gentle introduction to creation of neural networks reflexing structure of JSON documents \n\n# This notebook serves as an introduction to `Mill` and `JsonGrinder` libraries. \n# The former provides support for Multi-instance learning problems, their cascades, and their Cartesian product ([see the paper](https://arxiv.org/abs/2105.09107) for theoretical explanation). \n# The latter `JsonGrinder` simplifies processing of JSON documents. It allows to infer schema of JSON documents from which it suggests an extractor to convert JSON document to a `Mill` structure.\n# `JsonGrinder` defines basic set of \"extractors\" converting values of keys to numeric representation (matrices) or to convert them to corresponding structures in `Mill`. Naturally, this set of extractors can be extended.\n# \n# Below, the intended workflow is demonstrated on a simple problem of guessing type of a cuisine from a list of ingrediences. \n# The whole dataset and problem description can be found [on this Kaggle page](https://www.kaggle.com/kaggle/recipe-ingredients-dataset/home).\n# Note that the goal is not to achieve state of the art, but to demonstrate the workflow.\n\n#md # !!! tip\n#md #     This example is also available as a Jupyter notebook:\n#md #     [`recipes.ipynb`](@__NBVIEWER_ROOT_URL__/examples/recipes.ipynb)\n\n# todo: dod\u011blat, domigrovat z ipynb a ten pak smazat\n# **Caution**\n# To decrease the computational load, we keep locally in the repo only a subset of the whole dataset (39774), size of the minibatch, and size of the validation data. \n# Of course these numbers are useless in practice, and therefore the resulting accuracy is poor. \n# Using all samples (666920), setting minibatch size to 100, and leaving 1000 samples for validation / testing gives you accuracy 0.74 on validation data.\n# \n#nb # We start by installing JsonGrinder and few other packages we need for the example.\n#nb # Julia Ecosystem follows philosophy of many small single-purpose composable packages\n#nb # which may be different from e.g. python where we usually use fewer larger packages.\n#nb using Pkg\n#nb pkg\"add JsonGrinder#master Flux Mill#master MLDataPattern Statistics ChainRulesCore JSON\"\n\nusing JsonGrinder, Flux, Mill, MLDataPattern, Statistics, ChainRulesCore, JSON\n\n# start by loading all samples\n#src magic for resolving paths\ndata_file = \"data/recipes.json\" #src\ndata_file = \"../../../data/recipes.json\" #nb\ndata_file = \"data/recipes.json\" #md\ndata_file = \"data/recipes.json\" #jl\nsamples = open(data_file,\"r\") do fid\n\tVector{Dict}(JSON.parse(read(fid, String)))\nend\nJSON.print(samples[1],2)\n\n# create schema of the JSON\nsch = JsonGrinder.schema(samples)\n\n# create extractor and split it into one for loading targets and\n# one for loading data, using custom function to set conditions for using n-gram representation\ndelete!(sch.childs,:id)\n\nextractor = suggestextractor(sch)\nextract_data = ExtractDict(deepcopy(extractor.dict))\nextract_target = ExtractDict(deepcopy(extractor.dict))\ndelete!(extract_target.dict, :ingredients)\ndelete!(extract_data.dict, :cuisine)\n\nextract_data(samples[1])\nextract_target(samples[1])[:cuisine]\n# we convert JSONs to Datasets\n# advised to use all the samples, this is just speedup to demonstrate functionality\ndata = extract_data.(samples[1:5_000])\ndata = reduce(catobs, data)\ntarget = extract_target.(samples[1:5_000])\ntarget = reduce(catobs, target)[:cuisine].data\n\n# # Create the model\nm = reflectinmodel(sch, extract_data,\n\tlayer -> Dense(layer,20,relu),\n\tbag -> SegmentedMeanMax(bag),\n\tfsm = Dict(\"\" => layer -> Dense(layer, size(target, 1))),\n)\n\n@non_differentiable getobs(x::DataSubset{<:ProductNode})\n\n# # Train the model\nopt = Flux.Optimise.ADAM()\nloss(x, y) = Flux.logitcrossentropy(m(x).data, y)\nloss(x::DataSubset, y) = loss(getobs(x), y)\nloss(xy::Tuple) = loss(xy...)\nvaldata = data[1:1_000],target[:,1:1_000]\ndata, target = data[1_001:5_000], target[:,1001:5_000]\n# for less recourse-chungry training, we use only part of data for trainng, but it is advised to used all, as i following line:\n# data, target = data[1001:nobs(data)], target[:,1001:size(target,2)]\n\ncb = () -> println(\"accuracy = \",mean(Flux.onecold(m(valdata[1]).data) .== Flux.onecold(valdata[2])))\nps = Flux.params(m)\nmean(Flux.onecold(m(data).data) .== Flux.onecold(target))\n\niterations = 20\nminibatchsize = 4000\nminibatches = RandomBatches((data, target), size = minibatchsize, count = iterations)\n\n@info \"testing the gradient\"\nloss(first(minibatches))\ngs = gradient(() -> loss(first(minibatches)), ps)\nFlux.Optimise.update!(opt, ps, gs)\n\n# feel free to train for longer period of time, this example is learns only 20 iterations, so it runs fast\nloss(first(minibatches))\ngs = gradient(() -> loss(first(minibatches)), ps)\nFlux.Optimise.train!(loss, ps, minibatches, opt, cb = Flux.throttle(cb, 2))\n\n# calculate the accuracy\nmean(Flux.onecold(m(data).data) .== Flux.onecold(target))\n", "meta": {"hexsha": "37f81aee85d0d81097512192901fa8f1ebb052be", "size": 5001, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/recipes.jl", "max_stars_repo_name": "CTUAvastLab/JsonGrinder.jl", "max_stars_repo_head_hexsha": "fa5266c614ecbae227c7de1038012f8718d1a48c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2021-02-12T10:36:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-09T07:39:00.000Z", "max_issues_repo_path": "examples/recipes.jl", "max_issues_repo_name": "CTUAvastLab/JsonGrinder.jl", "max_issues_repo_head_hexsha": "fa5266c614ecbae227c7de1038012f8718d1a48c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41, "max_issues_repo_issues_event_min_datetime": "2021-02-18T01:03:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T09:38:32.000Z", "max_forks_repo_path": "examples/recipes.jl", "max_forks_repo_name": "CTUAvastLab/JsonGrinder.jl", "max_forks_repo_head_hexsha": "fa5266c614ecbae227c7de1038012f8718d1a48c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.6285714286, "max_line_length": 221, "alphanum_fraction": 0.7550489902, "num_tokens": 1284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.11920292984474948, "lm_q1q2_score": 0.05403014027027957}}
{"text": "module InterpolationTestUtils\n\nusing Test, Interpolations, ForwardDiff, StaticArrays\nusing Interpolations: degree, itpflag, bounds, lbounds, ubounds\n\nexport check_axes, check_inbounds_values, check_oob, can_eval_near_boundaries,\n       check_gradient, check_hessian\nexport MyPair\n\nconst failstore = Ref{Any}(nothing)   # stash the inputs to failing tests here\n\n## Property accessors\ncoefs(itp) = itp.coefs\n\nboundaryconditions(itp::AbstractInterpolation) = boundaryconditions(itpflag(itp))\nboundaryconditions(bs::BSpline) = degree(bs)\nboundaryconditions(ni::NoInterp) = ni\n\nndaccessor(x, d) = x\nndaccessor(x::Tuple, d) = x[d]\n\nhaspadding(itp) = haspadding(boundaryconditions(itp))\nhaspadding(::Union{Constant,Linear,NoInterp}) = false\nhaspadding(::Quadratic{BC}) where BC = haspadding(BC())\nhaspadding(::Cubic{BC})     where BC = haspadding(BC())\nhaspadding(::BC) where BC<:Interpolations.BoundaryCondition = !(BC <: Union{Periodic,InPlace,InPlaceQ})\nhaspadding(bcs::Tuple) = map(haspadding, bcs)\nhaspadding(::NoInterp) = false\n\ngetindexib(itp, i...) = @inbounds itp[i...]\ncallib(itp, i...)     = @inbounds itp(i...)\n\n\u2282(r1::AbstractRange, r2::AbstractRange) = first(r2) < first(r1) < last(r2) && first(r2) < last(r1) < last(r2)\n\nfunction check_axes(itp, A, isinplace=false)\n    @test ndims(itp) == ndims(A)\n    axsi, axsA = @inferred(axes(itp)), axes(A)\n    szi, szA = @inferred(size(itp)), size(A)\n    haspad = haspadding(itp)\n    for d = 1:ndims(A)\n        if isinplace && ndaccessor(haspad, d)\n            @test axsi[d] != axsA[d] && axsi[d] \u2282 axsA[d]\n            @test szi[d] < szA[d]\n        else\n            @test axsi[d] == axsA[d]\n            @test szi[d] == szA[d]\n        end\n    end\n    nothing\nend\n\nfunction check_inbounds_values(itp, A)\n    i = first(eachindex(itp))\n    @test A[i] \u2248 @inferred(itp[i]) == @inferred(itp[Tuple(i)...]) \u2248 @inferred(itp(i)) \u2248 @inferred(itp(float.(Tuple(i))...))\n    for i in eachindex(itp)\n        @test A[i] \u2248 itp[i] == itp[Tuple(i)...] \u2248 itp(i) \u2248 itp(float.(Tuple(i))...)\n    end\n    cb = Base.JLOptions().check_bounds == 1\n    if ndims(itp) == 1\n        for i in eachindex(itp)\n            @test itp[i,1] \u2248 A[i]   # used in the AbstractArray display infrastructure\n            @test_throws BoundsError itp[i,2]\n            # if cb\n            #     @test_throws BoundsError getindexib(itp, i, 2)\n            #     @test_throws BoundsError callib(itp, i, 2)\n            # else\n            #     @test getindexib(itp, i, 2) \u2248 A[i]\n            #     @test callib(itp, i, 2) \u2248 A[i]\n            # end\n        end\n    end\n    nothing\nend\n\nfunction check_oob(itp)\n    widen(r) = first(r)-1:last(r)+1\n    indsi = axes(itp)\n    indsci = CartesianIndices(indsi)\n    for i in CartesianIndices(widen.(indsi))\n        i \u2208 indsci && continue\n        @test_throws BoundsError itp[i]\n    end\n    nothing\nend\n\nfunction can_eval_near_boundaries(itp::AbstractInterpolation{T,1}) where T\n    l, u = bounds(itp, 1)\n    @test isfinite(itp(l+0.1))\n    @test isfinite(itp(u-0.1))\n    @test_throws BoundsError itp(l-0.1)\n    @test_throws BoundsError itp(u+0.1)\nend\n\nfunction can_eval_near_boundaries(itp::AbstractInterpolation)\n    l, u = Float64.(lbounds(itp)), Float64.(ubounds(itp))\n    for d = 1:ndims(itp)\n        nearl = substitute(l, d, l[d]+0.1)\n        # @show summary(itp) nearl\n        @test isfinite(itp(nearl...))\n        outl = substitute(l, d, l[d]-0.1)\n        @test_throws BoundsError itp(outl...)\n        nearu = substitute(u, d, u[d]-0.1)\n        # @show nearu\n        @test isfinite(itp(nearu...))\n        outu = substitute(u, d, u[d]+0.1)\n        @test_throws BoundsError itp(outu...)\n    end\nend\n\nfunction substitute(default::NTuple{N,T}, d::Integer, val::T) where {T,N}\n    ntuple(i->ifelse(i==d, val, default[i]), Val(N))\nend\n\n\n# Generate a grid of points [1.0, 1.3333, 1.6667, 2.0, 2.3333, ...] along each coordinate\nthirds(axs) = Iterators.product(_thirds(axs...)...)\n\n_thirds(a, axs...) =\n    (sort(Float64[a; (first(a):last(a)-1) .+ 1/3; (first(a)+1:last(a)) .- 1/3]), _thirds(axs...)...)\n_thirds() = ()\n\nfunction check_gradient(itp::AbstractInterpolation, gtmp)\n    val(x) = itp(Tuple(x)...)\n    g!(gstore, x) = ForwardDiff.gradient!(gstore, val, x)\n    gtmp2 = similar(gtmp)\n    i = first(thirds(axes(itp)))\n    @inferred(Interpolations.gradient(itp, i...))\n    for i in thirds(axes(itp))\n        @test Interpolations.gradient(itp, i...) \u2248 g!(gtmp, SVector(i))\n        @test Interpolations.gradient!(gtmp2, itp, i...) \u2248 gtmp\n    end\nend\n\nfunction check_hessian(itp::AbstractInterpolation, htmp)\n    val(x) = itp(Tuple(x)...)\n    h!(hstore, x) = ForwardDiff.hessian!(hstore, val, x)\n    htmp2 = similar(htmp)\n    i = first(thirds(axes(itp)))\n    @inferred(Interpolations.hessian(itp, i...))\n    for i in thirds(axes(itp))\n        @test Interpolations.hessian(itp, i...) \u2248 h!(htmp, SVector(i))\n        @test Interpolations.hessian!(htmp2, itp, i...) \u2248 htmp\n    end\nend\n\n## A type used for multi-valued tests\nimport Base: +, -, *, /, \u2248\n\nstruct MyPair{T}\n    first::T\n    second::T\nend\n\n# Here's the interface your type must define\n(+)(p1::MyPair, p2::MyPair) = MyPair(p1.first+p2.first, p1.second+p2.second)\n(-)(p1::MyPair, p2::MyPair) = MyPair(p1.first-p2.first, p1.second-p2.second)\n(*)(n::Number, p::MyPair) = MyPair(n*p.first, n*p.second)\n(*)(p::MyPair, n::Number) = n*p\n(/)(p::MyPair, n::Number) = MyPair(p.first/n, p.second/n)\nBase.zero(::Type{MyPair{T}}) where {T} = MyPair(zero(T),zero(T))\nBase.promote_rule(::Type{MyPair{T1}}, ::Type{T2}) where {T1,T2<:Number} = MyPair{promote_type(T1,T2)}\n\u2248(p1::MyPair, p2::MyPair) = (p1.first \u2248 p2.first) & (p1.second \u2248 p2.second)\n\nend\n", "meta": {"hexsha": "8232cd3d0c27052675f95903dfcc26a2c59da374", "size": 5627, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/InterpolationTestUtils.jl", "max_stars_repo_name": "mapclyps/Interpolations.jl", "max_stars_repo_head_hexsha": "f9f49614406746510a0dc4d48ca17e897bf51df6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 311, "max_stars_repo_stars_event_min_datetime": "2017-04-25T06:34:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T18:58:47.000Z", "max_issues_repo_path": "test/InterpolationTestUtils.jl", "max_issues_repo_name": "mapclyps/Interpolations.jl", "max_issues_repo_head_hexsha": "f9f49614406746510a0dc4d48ca17e897bf51df6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 328, "max_issues_repo_issues_event_min_datetime": "2017-03-15T08:35:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T04:53:16.000Z", "max_forks_repo_path": "test/InterpolationTestUtils.jl", "max_forks_repo_name": "mapclyps/Interpolations.jl", "max_forks_repo_head_hexsha": "f9f49614406746510a0dc4d48ca17e897bf51df6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 101, "max_forks_repo_forks_event_min_datetime": "2017-03-28T15:02:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T15:27:26.000Z", "avg_line_length": 34.103030303, "max_line_length": 123, "alphanum_fraction": 0.6237782122, "num_tokens": 1886, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.1097057753333734, "lm_q1q2_score": 0.053995881039137494}}
{"text": "@test mdinfo() !== nothing\ngroups = [\"symmetric\", \"inverse\", \"illcond\", \"posdef\", \"eigen\",\"sparse\", \"random\", \"regprob\", \"all\"]\n\nfor group in groups \n    @test !isempty(listnames(Symbol(group)))\nend\n\n@test_throws DataError matrixdepot(\"something\")\n\nn = rand(1:55)\nname = mdlist(builtin(n))\n@test mdinfo(name) !== nothing\n\nnlist = mdlist(builtin([1, 3]) | builtin(4:20))\nm = length(mdlist(\"**\"))\n@test isempty(mdinfo(builtin(m+1)))\n\n@addgroup newlist = mdlist(builtin(3:6) | builtin(20))\n\n@test mdlist(:newlist) == mdlist(builtin(3:6,20))\n\n@rmgroup newlist\n\n# testing the new API\n#\n# it is assumed that all matrices are generated during \"test/download.jl\"\n# and the builtin- and user-defined \"randsym\" but no others are available\n#\n\n@testset \"mdlist\" begin\n\nREM = length(mdlist(\"*/*\"))\n@test REM >= 2500\n#@test REM in [2757, 2856]   # depends on whether UFL or TAMU url has been used\n@test length(mdlist(:builtin)) == 59\n@test length(mdlist(:user)) in [0, 1]\n\n@test mdlist(\"\") == []\n@test mdlist(\"HB/1138_bus\") == [\"HB/1138_bus\"]\n@test mdlist(sp(1)) == [\"HB/1138_bus\"]\n@test mdlist(mm(1)) == [\"Harwell-Boeing/psadmit/1138_bus\"]\n@test sort(mdlist(sp(1:3000))) == mdlist(\"*/*\")\n@test sort(mdlist(mm(1:3000))) == mdlist(\"*/*/*\")\n@test mdlist(builtin(:)) == mdlist(isbuiltin)\n@test mdlist(user(:)) == mdlist(isuser)\n@test mdlist(\"*\") == mdlist(islocal)\n@test length(mdlist(\"HB/*\")) == 292\n@test mdlist(\"HB**\") == mdlist(\"HB/**\")\n@test_throws ArgumentError  listdir(\"*/*\")\n@test listdir(\"Harwell-Boeing//\") == [\"Harwell-Boeing/*/* - (292)\"]\n@test mdlist(\"*/hamm/*\") == [\"misc/hamm/add20\", \"misc/hamm/add32\", \"misc/hamm/memplus\"]\n@test mdlist(\"*/hamm/*a*3?\") == [\"misc/hamm/add32\"]\n@test mdlist(\"*/hamm/*a*3[123]\") == [\"misc/hamm/add32\"]\n@test mdlist(\"*/hamm/*a*3[123]?\") == []\n@test length(mdlist(\"*/*/*\")) == 498\n\n@test listdir(\"*//*/*\") == [\"Harwell-Boeing/*/* - (292)\", \"NEP/*/* - (73)\",\n                           \"SPARSKIT/*/* - (107)\", \"misc/*/* - (26)\"]\n@test listdir(\"//*\") == [\"/* - ($(length(mdlist(:local))))\"]\n@test listdir(\"//*/*\") == [\"/*/* - ($REM)\"]\n@test listdir(\"//*/*/*\") == [\"/*/*/* - (498)\"]\n@test listdir(\"HB/\") == [\"HB/* - (292)\"]\n@test length(mdlist(\"Harwell-Boeing/*/*\")) == 292\n@test mdlist(r\".*ng/ma.*\") == [\"Harwell-Boeing/manteuffel/man_5976\"]\n@test mdlist(sp(2001:2002)) == [\"JGD_Groebner/c8_mat11_I\", \"JGD_Groebner/f855_mat9\"]\n@test length(mdlist(sp(2757:3000))) == REM - 2756\n@test_throws ArgumentError mdlist(:xxx)\n@test length(mdlist(isremote)) == REM + 498\n@test length(mdlist(isloaded)) + length(mdlist(isunloaded)) == length(mdlist(isremote))\n@test length(mdlist(isbuiltin)) + length(mdlist(isuser)) == length(mdlist(islocal))\n@test length(mdlist(islocal)) + length(mdlist(isremote)) == length(mdlist(:all))\n@test length(mdlist(\"**\")) == length(mdlist(\"*\")) + length(mdlist(\"*/*\")) + length(mdlist(\"*/*/*\"))\n@test mdlist(:all) == mdlist(\"**\")\n@test length(mdlist(:symmetric)) == 22\n@test length(mdlist(:illcond)) == 20\n\n# intersections and unions\n@test mdlist((:posdef, :sparse)) == [\"poisson\", \"wathen\"]\n@test length(mdlist([:posdef,:sparse])) + length(mdlist((:posdef,:sparse))) == length(mdlist(:posdef)) + length(mdlist(:sparse))\n\n\n# predicates of remote and local matrices\n@test length(mdlist(issymmetric)) == 30\n@test length(mdlist(:symmetric & \"kahan\")) == 0\n@test length(mdlist(:symmetric & \"hankel\")) == 1\n@test mdlist(:local) == mdlist(islocal)\n@test mdlist(:builtin) == mdlist(isbuiltin)\n@test mdlist(:user) == mdlist(isuser)\n@test mdlist(:symmetric) == mdlist(issymmetric & islocal)\n\n# general metadata\n@test length(mdlist(@pred(m < 10000) & \"HB/*\")) == 284   # items with m < *\n@test length(mdlist(@pred(m < 10000) & isloaded)) == 10   # items with m < *\n@test length(mdlist(@pred(n < 10000) & \"HB/*\")) == 283   # items with n < *\n@test length(mdlist(@pred(n < 10000) &  isloaded)) == 9    # items with n < *\n@test length(mdlist(@pred(nnz < 5000) & \"HB/*\")) == 163    # items with nnz < *\n@test length(mdlist(@pred(nnz < 5000) & isloaded)) == 2    # items with nnz < *\n@test length(mdlist(@pred(m > n) & \"HB/*\")) == 9   # items with m > n\n@test length(mdlist(@pred(m > n) & isloaded)) == 0   # items with m > n\n\n# metadata from header files\n@test length(mdlist(@pred(occursin(\"problem\", kind)) & isloaded)) == 6\n@test length(mdlist(keyword(\"graph\") & isloaded)) == 2\n@test length(mdlist(@pred(0 < date <= 1990) & isloaded)) == 1\n@test length(mdlist(@pred(date >= 2006) & @pred(0 < date <= 2006) & isloaded)) == 2\n@test length(mdlist(@pred(date == 0) & mm(:) & isloaded)) == 3\n\n# metadata from ss_index.mat\n@test length(mdlist(@pred(posdef) & \"HB/*\")) == 60\n@test length(mdlist(isposdef)) >= 60\n@test mdlist(@pred(posdef)) == mdlist(isposdef & isremote)\n@test mdlist(:posdef) == mdlist(isposdef & islocal)\n\n# metadata from svd files\n@test mdlist(@pred(svdok)) == [\"HB/1138_bus\"]\ndata = mdopen(\"HB/1138_bus\").data\n@test length(data.sv) > 0\n@test data.cond > 8.0e6\n@test data.norm > 30000\n@test data.rank == 1138\n@test data.svgap == Inf\n@test data.cholcand\n\nend\n\n@testset \"logical\" begin\n# for the boolean syntax\n@test mdlist(~islocal) == mdlist(isremote)\n@test length(mdlist(isloaded & issymmetric)) == 7\n@test length(mdlist(isloaded & ~issymmetric)) == 4\n@test length(mdlist(isloaded & ~issymmetric | isuser & issymmetric)) == 5\n@test length(mdlist(!islocal & issymmetric | isuser & issymmetric)) == 9\n@test mdlist(islocal & ~isbuiltin) == mdlist(isuser)\n@test mdlist(islocal & ~isbuiltin) == mdlist(isuser)\n@test \"a\" & \"b\" === (\"a\", \"b\")\n@test ~\"a\" & \"b\" === (~\"a\", \"b\")\n@test ~\"a\" * \"b\" === ~\"ab\"\n@test ~\"ab\" === ~'a' * 'b'\n@test ~~islocal === islocal\n@test mdlist(~\"*a*\" & ~ \"*e*\") == mdlist(~[\"*a*\", \"*e*\"])\n@test mdlist(()) == mdlist(:all)\n@test \"a\" & r\"b\" == (\"a\", r\"b\")\n@test \"a\" & r\"b\" & \"c\" == (\"a\", r\"b\", \"c\")\n@test \"a\" | r\"b\" == [\"a\", r\"b\"]\n@test \"a\" | r\"b\" | \"c\" == [\"a\", r\"b\", \"c\"]\n@test mdlist(islocal & ~(\"*a*\" | \"*e*\")) == mdlist(:local & ~\"*a*\" & ~ \"*e*\")\n@test \"a\" & ( \"b\" & \"c\" ) == (\"a\", \"b\", \"c\")\n@test \"a\" | ( \"b\" | \"c\" ) == [\"a\", \"b\", \"c\"]\n@test_throws ArgumentError mdlist([] & :invalid_group_name)\n\n@test MatrixDepot.mdlist(builtin(10, 1:7, 3)) == [\"baart\", \"binomial\", \"blur\", \"cauchy\",\n                                                \"chebspec\", \"chow\", \"circul\", \"deriv2\"] \n\n@test MatrixDepot.fname(sin) == \"unknown-function\"\n@test_throws MethodError mdopen(\"baart\", 10, 11)\nend\n\n@testset \"properties\" begin\n\nmd = mdopen(\"gravity\", 10)\nio = IOBuffer()\n@test show(io, md) === nothing\n@test propertynames(md) == [:A, :m, :n, :nnz, :dnz]\n@test md.A isa AbstractMatrix\n@test md.nnz == count(md.A .!= 0)\n@test md.dnz == 0\n@test_throws DataError md.x\n\nmd = mdopen(\"gravity\", 10, false)\nio = IOBuffer()\n@test show(io, md) === nothing\n@test propertynames(md) == [:A, :b, :x, :m, :n, :nnz, :dnz]\n@test md.A isa AbstractMatrix\n@test md.b isa AbstractVector\n@test md.x isa AbstractVector\n\nmd = mdopen(\"HB/well1033\")\nio = IOBuffer()\n@test show(io, md) === nothing\n@test propertynames(md) == [:A, :b, :m, :n, :nnz, :dnz]\n@test md.A isa AbstractMatrix\n@test md.b isa AbstractMatrix\n@test_throws DataError md.x\n@test_throws DataError md.invalidname\n@test_throws ErrorException md.data.invalidname\n@test md.dnz == md.data.header.nnz\n\n@test length(mdlist(keyword(\"graph\" & (\"butterfly\" | \"network\")) & isloaded)) == 2\n@test length(mdlist(hasdata(:A & (:b | :x)))) == 2\n\nend\n\n", "meta": {"hexsha": "253436a88144cec5db555ff5c247902fbaaa5a57", "size": 7329, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/common.jl", "max_stars_repo_name": "briochemc/MatrixDepot.jl", "max_stars_repo_head_hexsha": "35545d1b71abe84cdb45799bbfc007367169b971", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 46, "max_stars_repo_stars_event_min_datetime": "2018-01-16T15:07:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-27T05:33:08.000Z", "max_issues_repo_path": "test/common.jl", "max_issues_repo_name": "JuliaMatrices/MatrixDepot.jl", "max_issues_repo_head_hexsha": "054d0718872388e2393e428abb664b618035ef0d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 63, "max_issues_repo_issues_event_min_datetime": "2017-12-01T11:03:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-20T19:41:57.000Z", "max_forks_repo_path": "test/common.jl", "max_forks_repo_name": "KlausC/MatrixDepot.jl", "max_forks_repo_head_hexsha": "894d58a20317f784ddf66e37123d6266871ba643", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2018-02-19T22:47:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-07T02:02:00.000Z", "avg_line_length": 37.9740932642, "max_line_length": 128, "alphanum_fraction": 0.615909401, "num_tokens": 2442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.1097057753333734, "lm_q1q2_score": 0.053995881039137494}}
{"text": "\n# ------------------------------------------------------------------------------------------\n# ## \u4e0a\u624b Jupyter notebook\n#\n# ### \u76ee\u5f55\n# - [\u8fd0\u884c\u4ee3\u7801\u5757\uff08cell\uff09](#\u8fd0\u884c\u4ee3\u7801\u5757\uff08cell\uff09)\n# - [\u67e5\u770b Julia \u7684\u5e2e\u52a9\u6587\u6863](#\u4f7f\u7528-shell-\u547d\u4ee4)\n# - [\u4f7f\u7528 shell \u547d\u4ee4](#\u4f7f\u7528-shell-\u547d\u4ee4)\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### \u8fd0\u884c\u4ee3\u7801\u5757\uff08cell\uff09\n# \u9009\u4e2d\u4e00\u4e2a\u4ee3\u7801\u5757\u4e4b\u540e\uff08\u9009\u4e2d\u7684\u4ee3\u7801\u5757\u4f1a\u88ab\u7eff\u8272\u77e9\u5f62\u6846\u8d77\u6765\uff09\uff0c\u6709\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\u53ef\u4ee5\u8fd0\u884c\u5b83\uff1a\n# 1. \u6309\u4e0b\u7ec4\u5408\u952e `Shift` + `Enter`\n# 2. \u70b9\u51fb\u4e0a\u65b9\u5de5\u5177\u680f\u4e2d\u7684 **\u8fd0\u884c\uff08Run\uff09** \u6309\u94ae\n# 3. \u6309\u4e0b\u7ec4\u5408\u952e `Ctrl` + `Enter`\n# ------------------------------------------------------------------------------------------\n\n1 + 1\n2 + 2\n\n# ------------------------------------------------------------------------------------------\n# \u65b0\u624b\u6ce8\u610f\uff01\u6267\u884c\u4e00\u4e2a\u4ee3\u7801\u5757\u65f6\u9ed8\u8ba4\u6253\u5370\u6700\u540e\u4e00\u884c\u3002\u60f3\u8981\u4ec0\u4e48\u4e5f\u4e0d\u8f93\u51fa\uff0c\u5728\u6700\u540e\u4e00\u884c\u7684\u884c\u5c3e\u52a0\u4e0a\u5206\u53f7 `;` \u5c31\u884c\u4e86\u3002\u5982\u4e0b\u6240\u793a\n# ------------------------------------------------------------------------------------------\n\n1 + 1\n2 + 2;\n\n# ------------------------------------------------------------------------------------------\n# ### \u67e5\u770b Julia \u7684\u5e2e\u52a9\u6587\u6863\n#\n# \u9047\u5230\u4e0d\u719f\u6089\u7684 Julia \u51fd\u6570\u6216\u8bed\u6cd5\uff0c\u5728\u51fd\u6570\u540d\u6216\u8868\u8fbe\u5f0f\u524d\u52a0\u4e2a\u95ee\u53f7 `?` \u5c31\u53ef\u4ee5\u67e5\u8be2\u5bf9\u5e94\u7684\u5e2e\u52a9\u6587\u6863\u3002\uff08\u5728Julia\u7684REPL\u4e2d\u4e5f\u597d\u4f7f\u54e6\uff01\uff09\n# ------------------------------------------------------------------------------------------\n\n?println\n\n# ------------------------------------------------------------------------------------------\n# ### \u4f7f\u7528 shell \u547d\u4ee4\n#\n# \u5728 shell \u547d\u4ee4\u524d\u52a0\u4e0a\u5206\u53f7 `;` \u5373\u53ef\uff0c\u50cf\u8fd9\u6837\uff1a\n#\n# > \u3010\u8bd1\u6ce8\u3011\u4ee5\u4e0b\u547d\u4ee4\u4ec5\u5728 macOS \u548c *nix \u7cfb\u7edf\u4e0b\u53ef\u7528\n# ------------------------------------------------------------------------------------------\n\n;ls\n\n;pwd\n\n# ------------------------------------------------------------------------------------------\n# shell \u547d\u4ee4\u5728 Julia \u7684 REPL \u4e2d\u4e5f\u597d\u4f7f\u54e6\uff01\n#\n# > \u3010\u8bd1\u6ce8\u3011\u5bf9\u4e8e Windows \u7cfb\u7edf\uff0c\u4f60\u9700\u8981\u4f7f\u7528 Windows cmd \u547d\u4ee4\uff0c\u50cf\u8fd9\u6837\uff1a\n# ------------------------------------------------------------------------------------------\n\n;cd\n\n# ------------------------------------------------------------------------------------------\n# \u4f46\u5e76\u4e0d\u662f\u6240\u6709\u7684 cmd \u547d\u4ee4\u90fd\u53ef\u4ee5\u4f7f\u7528\u3002\u5982 `dir` \u547d\u4ee4\u4e0d\u53ef\u7528\uff0c\u4f1a\u62a5\u9519\n# ------------------------------------------------------------------------------------------\n\n;dir\n\n# ------------------------------------------------------------------------------------------\n# \u4e2d\u6587\u793e\u533a\u7684\u8ba8\u8bba\u8d34\n# - [Windows \u4e2d Julia \u7684 shell \u6a21\u5f0f\u4e0e `;dir` \u62a5\u9519 - \u7efc\u5408\u8ba8\u8bba\u533a / \u5fc3\u5f97\u4f53\u4f1a -\n# Julia\u4e2d\u6587\u793e\u533a](https://discourse.juliacn.com/t/topic/2890)\n# ------------------------------------------------------------------------------------------\n", "meta": {"hexsha": "f7fcf3e8482b4902504e3a4dd36cd3c69306a03a", "size": 2328, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/00.\u4e0a\u624bJupyter_notebook.jl", "max_stars_repo_name": "NahsiN/JuliaTutorials", "max_stars_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/00.\u4e0a\u624bJupyter_notebook.jl", "max_issues_repo_name": "NahsiN/JuliaTutorials", "max_issues_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/00.\u4e0a\u624bJupyter_notebook.jl", "max_forks_repo_name": "NahsiN/JuliaTutorials", "max_forks_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 34.2352941176, "max_line_length": 92, "alphanum_fraction": 0.2280927835, "num_tokens": 628, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.24220563966531902, "lm_q2_score": 0.22270013366638422, "lm_q1q2_score": 0.05393922832821864}}
{"text": "a_1 = 5\na_2 = -3\n\nif (a_1 == 5) && (a_2 == -3)\na_1 = 10\nend\n\nprintln(a_1)\n", "meta": {"hexsha": "30bc43b2017b1b5f64603066ac7e081f2d3b2984", "size": 74, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tests/test_if_equals_and_10.jl", "max_stars_repo_name": "guigs10mil/CompiladorJulia", "max_stars_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/test_if_equals_and_10.jl", "max_issues_repo_name": "guigs10mil/CompiladorJulia", "max_issues_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/test_if_equals_and_10.jl", "max_forks_repo_name": "guigs10mil/CompiladorJulia", "max_forks_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.2222222222, "max_line_length": 28, "alphanum_fraction": 0.4864864865, "num_tokens": 46, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4225046202709847, "lm_q2_score": 0.12765261204695083, "lm_q1q2_score": 0.05393381837949628}}
{"text": "# The SLEEF.jl package is licensed under the MIT \"Expat\" License:\n\n# > Copyright (c) 2016: Mustafa Mohamad and other contributors:\n# > \n# > https://github.com/musm/SLEEF.jl/graphs/contributors\n# > \n# > Permission is hereby granted, free of charge, to any person obtaining a copy\n# > of this software and associated documentation files (the \"Software\"), to deal\n# > in the Software without restriction, including without limitation the rights\n# > to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# > copies of the Software, and to permit persons to whom the Software is\n# > furnished to do so, subject to the following conditions:\n# > \n# > The above copyright notice and this permission notice shall be included in all\n# > copies or substantial portions of the Software.\n# > \n# > THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# > IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# > FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# > AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# > LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# > OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# > SOFTWARE.\n# > \n\n# SLEEF.jl includes ported code from the following project\n\n#     - [SLEEF](https://github.com/shibatch/SLEEF) [public domain] Author Naoki Shibata\n\n    \n\n\nusing Base.Math: significand_bits\n\nisnzero(x::T) where {T <: AbstractFloat} = signbit(x)\nispzero(x::T) where {T <: AbstractFloat} = !signbit(x)\n\n# function cmpdenorm(x::Tx, y::Ty) where {Tx <: AbstractFloat, Ty <: AbstractFloat}\n#     sizeof(Tx) < sizeof(Ty) ? y = Tx(y) : x = Ty(x) # cast larger type to smaller type\n#     (isnan(x) && isnan(y)) && return true\n#     (isnan(x) || isnan(y)) && return false\n#     (isinf(x) != isinf(y)) && return false\n#     (x == Tx(Inf)  && y == Ty(Inf))  && return true\n#     (x == Tx(-Inf) && y == Ty(-Inf)) && return true\n#     if y == 0\n#         (ispzero(x) && ispzero(y)) && return true\n#         (isnzero(x) && isnzero(y)) && return true\n#         return false\n#     end\n#     (!isnan(x) && !isnan(y) && !isinf(x) && !isinf(y)) && return sign(x) == sign(y)\n#     return false\n# end\n\n# the following compares the ulp between x and y.\n# First it promotes them to the larger of the two types x,y\ninfh(::Type{Float64}) = 1e300\ninfh(::Type{Float32}) = 1e37\nfunction countulp(::Type{T}, __x, __y) where {T}\n    _x, _y = promote(__x, __y)\n    x, y = convert(T, _x), convert(T, _y) # Cast to smaller type\n\n    ulpc = convert(T, abs(_x - _y) / ulp(y))\n    nanulp = VectorizationBase.ifelse(isnan(x) \u22bb isnan(y), T(10000), T(0))\n    infulp = VectorizationBase.ifelse((sign(x) == sign(y)) & (abs(y) > infh(T)), T(0), T(10001))\n    \n    ulpc = VectorizationBase.ifelse(isinf(x), infulp, VectorizationBase.ifelse(isfinite(y), ulpc, T(10003)))\n    ulpc = VectorizationBase.ifelse(isnan(x) | isnan(y), nanulp, ulpc)\n    ulpc = VectorizationBase.ifelse(iszero(y), VectorizationBase.ifelse(iszero(x), T(0), T(10002)), ulpc)\n    return ulpc\nend\n\nDENORMAL_MIN(::Type{Float64}) = 2.0^-1074\nDENORMAL_MIN(::Type{Float32}) = 2f0^-149\n\nfunction ulp(x::Union{<:VectorizationBase.AbstractSIMD{<:Any,T},T}) where {T<:AbstractFloat}\n    e = exponent(x)\n    ulpc = max(VectorizationBase.vscalef(T(1.0), e - significand_bits(T)), DENORMAL_MIN(T))\n    ulpc = VectorizationBase.ifelse(x == T(0.0), DENORMAL_MIN(T), ulpc)\n    return ulpc\nend\n\ncountulp(x::T, y::T) where {T <: AbstractFloat} = countulp(T, x, y)\ncountulp(x::VectorizationBase.AbstractSIMD{W,T}, y::VectorizationBase.AbstractSIMD{W,T}) where {W,T <: AbstractFloat} = countulp(T, x, y)\n\n\n# test the accuracy of a function where fun_table is a Dict mapping the function you want\n# to test to a reference function\n# xx is an array of values (which may be tuples for multiple arugment functions)\n# tol is the acceptable tolerance to test against\nfunction test_acc(f1, f2, T, xx, tol, ::StaticInt{W} = pick_vector_width(T); debug = false, tol_debug = 5) where {W}\n    @testset \"accuracy $(f1)\" begin\n\n        reference = map(f2 \u2218 big, xx)\n        comp = similar(xx)\n        i = 0\n        spc = VectorizationBase.zstridedpointer(comp); spx = VectorizationBase.zstridedpointer(xx);\n        GC.@preserve xx comp begin\n            while i < length(xx)\n                vstore!(spc, f1(vload(spx, (MM{W}(i),))), (MM{W}(i),))\n                i += W\n            end\n        end\n        rmax = 0.0\n        rmean = 0.0\n        xmax = map(zero, first(xx))\n        for i \u2208 eachindex(xx)\n            q = comp[i]\n            c = reference[i]\n            u = countulp(T, q, c)\n            rmax = max(rmax, u)\n            xmax = rmax == u ? xx[i] : xmax\n            rmean += u\n            if debug && u > tol_debug\n                @show f1, q, f2, T(c), x, T(c)\n            end\n        end\n        rmean = rmean / length(xx)\n\n        t = @test trunc(rmax, digits=1) <= tol\n\n        \n        fmtxloc = isa(xmax, Tuple) ? join(xmax, \", \") : string(xmax)\n        println(\n            rpad(f1, 18, \" \"), \": max \", rmax,\n            rpad(\" at x = \" * fmtxloc, 40, \" \"),  \": mean \", rmean\n        )\n\n    end\nend\n\n\n", "meta": {"hexsha": "430a4355fff60f09533e34cfa5c18470f25c5903", "size": 5210, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/accuracy.jl", "max_stars_repo_name": "GiggleLiu/VectorizationBase.jl", "max_stars_repo_head_hexsha": "5c9b5f5b7e278ebbb143ccfc3876626601ca02e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/accuracy.jl", "max_issues_repo_name": "GiggleLiu/VectorizationBase.jl", "max_issues_repo_head_hexsha": "5c9b5f5b7e278ebbb143ccfc3876626601ca02e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/accuracy.jl", "max_forks_repo_name": "GiggleLiu/VectorizationBase.jl", "max_forks_repo_head_hexsha": "5c9b5f5b7e278ebbb143ccfc3876626601ca02e8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4696969697, "max_line_length": 137, "alphanum_fraction": 0.6239923225, "num_tokens": 1564, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.13117322206357784, "lm_q1q2_score": 0.053926737342541685}}
{"text": "# setup path -\n_PATH_TO_PROJECT = pwd()\n\n# import PKG, activate the project and instaintiate -\nimport Pkg\nPkg.activate(_PATH_TO_PROJECT)\nPkg.instantiate()\n\n# include packages -\nusing DataFrames\nusing DifferentialEquations\nusing CSV\nusing Optim\nusing Convex\nusing GLPK\nusing Plots", "meta": {"hexsha": "07f9574a733c24d0bd69034f63dec8d710278bda", "size": 279, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Include.jl", "max_stars_repo_name": "varnerlab/CHEME-5440-7770-PS1-S21", "max_stars_repo_head_hexsha": "9cb6f16d7f3cb4fb8525c6cd32977c5774eb7474", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Include.jl", "max_issues_repo_name": "varnerlab/CHEME-5440-7770-PS1-S21", "max_issues_repo_head_hexsha": "9cb6f16d7f3cb4fb8525c6cd32977c5774eb7474", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Include.jl", "max_forks_repo_name": "varnerlab/CHEME-5440-7770-PS1-S21", "max_forks_repo_head_hexsha": "9cb6f16d7f3cb4fb8525c6cd32977c5774eb7474", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.4375, "max_line_length": 53, "alphanum_fraction": 0.8028673835, "num_tokens": 70, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.11596071519881658, "lm_q1q2_score": 0.0539103187135118}}
{"text": "# This file is a part of AstroLib.jl. License is MIT \"Expat\".\n# Copyright (C) 2016 Mos\u00e8 Giordano.\n\n\"\"\"\n    daycnv(julian_days) -> DateTime\n\n### Purpose ###\n\nConverts Julian days number to Gregorian calendar dates.\n\n### Explanation ###\n\nTakes the number of Julian calendar days since epoch `-4713-11-24T12:00:00` and\nreturns the corresponding proleptic Gregorian Calendar date.\n\n### Argument ###\n\n* `julian_days`: Julian days number.\n\n### Output ###\n\nProleptic Gregorian Calendar date, of type `DateTime`, corresponding to the\ngiven Julian days number.\n\n### Example ###\n\n```jldoctest\njulia> using AstroLib\n\njulia> daycnv(2440000)\n1968-05-23T12:00:00\n```\n\n### Notes ###\n\n`jdcnv` is the inverse of this function.\n\"\"\"\nconst daycnv=Dates.julian2datetime\n", "meta": {"hexsha": "87efaaec0019a86fc222e69c4926cbc1e778642f", "size": 749, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/daycnv.jl", "max_stars_repo_name": "UnofficialJuliaMirror/AstroLib.jl-c7932e45-9af1-51e7-9da9-f004cd3a462b", "max_stars_repo_head_hexsha": "fb2ef587a2ac68a1c864bf251e8d9c3601ec4719", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 62, "max_stars_repo_stars_event_min_datetime": "2016-09-11T14:59:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-23T20:45:36.000Z", "max_issues_repo_path": "src/daycnv.jl", "max_issues_repo_name": "UnofficialJuliaMirror/AstroLib.jl-c7932e45-9af1-51e7-9da9-f004cd3a462b", "max_issues_repo_head_hexsha": "fb2ef587a2ac68a1c864bf251e8d9c3601ec4719", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 64, "max_issues_repo_issues_event_min_datetime": "2017-01-19T21:03:34.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T12:27:46.000Z", "max_forks_repo_path": "src/daycnv.jl", "max_forks_repo_name": "UnofficialJuliaMirror/AstroLib.jl-c7932e45-9af1-51e7-9da9-f004cd3a462b", "max_forks_repo_head_hexsha": "fb2ef587a2ac68a1c864bf251e8d9c3601ec4719", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 26, "max_forks_repo_forks_event_min_datetime": "2016-07-12T02:11:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-30T11:55:21.000Z", "avg_line_length": 19.2051282051, "max_line_length": 79, "alphanum_fraction": 0.7129506008, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4339814501625211, "lm_q2_score": 0.12421301969912356, "lm_q1q2_score": 0.053906146418091445}}
{"text": "i=1\nwhile true\n    i=rand(1:8)\n    if i==3\n        break\n    end\n    if i==7\n        continue\n    end\n    println(i)\nend\n\nfor i in [5,5,5,3,3,3,5,5,5]\n    for j in 1:i\n        print('*')\n    end\n    println()\nend\n", "meta": {"hexsha": "17cc9b770c45478cb2a4d65139e8f7005c83f9ea", "size": 213, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "code/05.jl", "max_stars_repo_name": "Rratic/JuliaGuide", "max_stars_repo_head_hexsha": "c9a81e4b5b04371fc3904d218267275137810cc1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/05.jl", "max_issues_repo_name": "Rratic/JuliaGuide", "max_issues_repo_head_hexsha": "c9a81e4b5b04371fc3904d218267275137810cc1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/05.jl", "max_forks_repo_name": "Rratic/JuliaGuide", "max_forks_repo_head_hexsha": "c9a81e4b5b04371fc3904d218267275137810cc1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.2105263158, "max_line_length": 28, "alphanum_fraction": 0.455399061, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.12085324357297283, "lm_q1q2_score": 0.05384368941709081}}
{"text": "\"\"\"\n    count_nucleotides(strand)\n\nThe frequency of each nucleotide within `strand` as a dictionary.\n\nInvalid strands raise a `DomainError`.\n\n\"\"\"\nfunction count_nucleotides(strand)\n    count = Dict(\n        'A' => 0, 'G' => 0, \n        'C' => 0, 'T' => 0\n    )\n    for x in strand\n        try\n            count[x] += 1\n        catch e\n            throw(DomainError(x, \"Invalid nucleotide\"))\n        end\n    end\n\n    return count\n\nend\n", "meta": {"hexsha": "8958c3c7586655a0d27ec2b88cf1ebc2d9e3f33e", "size": 434, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_stars_repo_name": "jfcliang/jl-exercism-solutions", "max_stars_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_issues_repo_name": "jfcliang/jl-exercism-solutions", "max_issues_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/nucleotide-count/nucleotide-count.jl", "max_forks_repo_name": "jfcliang/jl-exercism-solutions", "max_forks_repo_head_hexsha": "8e8c2ee48dbea3175bcd0d0f2071de945014ce0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.36, "max_line_length": 65, "alphanum_fraction": 0.5460829493, "num_tokens": 125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.12085323249047067, "lm_q1q2_score": 0.053843684479508776}}
{"text": "# Progress_bar function. \r\n# To use as a separate file: Should be added as a progress_bar.jl file in the same folder as your code. In the main code, use: include(\"progress_bar.jl\");\r\n# To use in Jupyter notebook, just include the funciton in a cell and run it. \r\n\r\n# Input arguments: \r\n# current_step = current position in the loop. Use the loop index.\r\n# total_step = total number of steps in your loop. Note that it is assumed that the loop starts from 1 and ends at total_step.\r\n# Nbar = how many blocks you want to include in the progress bar.\r\n\r\n\r\nfunction progress_bar(current_step,total_step,Nblock)\r\n    \r\n    percentage = round(current_step*100/total_step,1);     # Calculate percentage completion\r\n    \r\n    print(\"\\r\") \r\n    if current_step == 1\r\n        print(\"Progress = 0.0\\% \");\r\n        for i = 1:Nblock    \r\n            print(\"\\u2591\");                      # Use the unicode character \"light shade\" to create the empty progress bar.\r\n        end\r\n        print(\"\\r\");\r\n    end\r\n    \r\n    \r\n    print(\"Progress = \",percentage, \"\\% \");         # Print percentage completion\r\n   \r\n    for i = 1:round(percentage*Nblock/100)        # Map the progress percentage to a 1 to Nblock grid.\r\n        print(\"\\u2593\");                          # Use the unicode character \"dark shade\" to fill the progress bar.\r\n    end\r\n\r\nend\r\n", "meta": {"hexsha": "f4b28394b847747c5b18b0e71fbcf217d679c1bf", "size": 1334, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Simulated Annealing code/progress_bar.jl", "max_stars_repo_name": "ConstantineRudenko/Simulated-Annealing-Optimization-Algorithm", "max_stars_repo_head_hexsha": "a1d07375251277bd1999e1f989938fd0bbdcbf2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2018-07-02T20:47:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-04T00:13:16.000Z", "max_issues_repo_path": "Progress_bar_code/progress_bar.jl", "max_issues_repo_name": "zaman13/Progress_bar", "max_issues_repo_head_hexsha": "3d6e32c7efbd6e4a325ba3d2db256f51fa01c64b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Progress_bar_code/progress_bar.jl", "max_forks_repo_name": "zaman13/Progress_bar", "max_forks_repo_head_hexsha": "3d6e32c7efbd6e4a325ba3d2db256f51fa01c64b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-02T20:58:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-02T20:58:06.000Z", "avg_line_length": 41.6875, "max_line_length": 155, "alphanum_fraction": 0.6274362819, "num_tokens": 312, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2909808539178129, "lm_q2_score": 0.18476751289253926, "lm_q1q2_score": 0.05376380867774158}}
{"text": "using Pkg;\nPkg.add(\"PyPlot\");\nPkg.add(\"IJulia\");\nPkg.add(url=\"https://github.com/OpenJAC/JAC.jl\")\n\n", "meta": {"hexsha": "71ecdf0fee1be44c27522847ccb6cd3ae7516f83", "size": 99, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JuliaSetup.jl", "max_stars_repo_name": "Joseph-33/JACFork.jl", "max_stars_repo_head_hexsha": "e866abe256e9ae4dc5293a7efe9a24a5e6213f90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 115, "max_stars_repo_stars_event_min_datetime": "2019-03-11T11:24:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T22:33:28.000Z", "max_issues_repo_path": "JuliaSetup.jl", "max_issues_repo_name": "Joseph-33/JACFork.jl", "max_issues_repo_head_hexsha": "e866abe256e9ae4dc5293a7efe9a24a5e6213f90", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2019-04-02T22:04:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-06T06:21:57.000Z", "max_forks_repo_path": "JuliaSetup.jl", "max_forks_repo_name": "Joseph-33/JACFork.jl", "max_forks_repo_head_hexsha": "e866abe256e9ae4dc5293a7efe9a24a5e6213f90", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2019-03-20T10:28:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-04T12:51:19.000Z", "avg_line_length": 16.5, "max_line_length": 48, "alphanum_fraction": 0.6767676768, "num_tokens": 36, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.11279540777409121, "lm_q1q2_score": 0.053755996078561435}}
{"text": "# Copyright (c) 2013: Cbc.jl contributors\n#\n# Use of this source code is governed by an MIT-style license that can be found\n# in the LICENSE.md file or at https://opensource.org/licenses/MIT.\n\nmodule TestMOIWrapper\n\nusing Test\nusing MathOptInterface\nimport Cbc\n\nconst MOI = MathOptInterface\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$(name)\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\n    return\nend\n\nfunction test_SolverName()\n    @test MOI.get(Cbc.Optimizer(), MOI.SolverName()) ==\n          \"COIN Branch-and-Cut (Cbc)\"\nend\n\nfunction test_supports_incremental_interface()\n    @test !MOI.supports_incremental_interface(Cbc.Optimizer())\n    return\nend\n\nfunction test_runtests()\n    model = MOI.Utilities.CachingOptimizer(\n        MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n        MOI.instantiate(Cbc.Optimizer; with_bridge_type = Float64),\n    )\n    MOI.set(model, MOI.Silent(), true)\n    MOI.Test.runtests(\n        model,\n        MOI.Test.Config(\n            exclude = Any[\n                MOI.ConstraintDual,\n                MOI.DualObjectiveValue,\n                MOI.ConstraintBasisStatus,\n                MOI.VariableBasisStatus,\n            ],\n        ),\n        exclude = [\n            # TODO(odow): upstream bug in Cbc\n            \"test_linear_Indicator_\",\n            \"test_linear_SOS1_integration\",\n            \"test_linear_SOS2_integration\",\n            \"test_solve_SOS2_add_and_delete\",\n            # Can't prove infeasible.\n            \"test_conic_NormInfinityCone_INFEASIBLE\",\n            \"test_conic_NormOneCone_INFEASIBLE\",\n            \"test_solve_TerminationStatus_DUAL_INFEASIBLE\",\n        ],\n    )\n    return\nend\n\nfunction test_params()\n    # Note: we generate a non-trivial problem to ensure that Cbc struggles to\n    # find a solution at the root node.\n    knapsack_model = MOI.Utilities.Model{Float64}()\n    N = 100\n    x = MOI.add_variables(knapsack_model, N)\n    MOI.add_constraint.(knapsack_model, x, MOI.ZeroOne())\n    MOI.add_constraint(\n        knapsack_model,\n        MOI.ScalarAffineFunction(\n            MOI.ScalarAffineTerm.([1 + sin(i) for i in 1:N], x),\n            0.0,\n        ),\n        MOI.LessThan(10.0),\n    )\n    MOI.set(\n        knapsack_model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        MOI.ScalarAffineFunction(\n            MOI.ScalarAffineTerm.([cos(i) for i in 1:N], x),\n            0.0,\n        ),\n    )\n    model = Cbc.Optimizer()\n    MOI.set(model, MOI.RawOptimizerAttribute(\"maxSol\"), 1)\n    MOI.set(model, MOI.RawOptimizerAttribute(\"presolve\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"cuts\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"heur\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"logLevel\"), 0)\n    MOI.copy_to(model, knapsack_model)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.SOLUTION_LIMIT\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    MOI.empty!(model)\n    MOI.copy_to(model, knapsack_model)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.SOLUTION_LIMIT\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    return\nend\n\n\"\"\"\n    test_threads()\n\nTest solving a model with the threads parameter set.\n\nSee issues #112 and #186.\n\"\"\"\nfunction test_threads()\n    model = MOI.Utilities.CachingOptimizer(\n        MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n        MOI.instantiate(Cbc.Optimizer; with_bridge_type = Float64),\n    )\n    MOI.set(model, MOI.RawOptimizerAttribute(\"presolve\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"cuts\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"heur\"), \"off\")\n    MOI.set(model, MOI.RawOptimizerAttribute(\"threads\"), 4)\n    MOI.set(model, MOI.RawOptimizerAttribute(\"logLevel\"), 3)\n    N = 100\n    x = MOI.add_variables(model, N)\n    MOI.add_constraint.(model, x, MOI.ZeroOne())\n    w = [1 + sin(i) for i in 1:N]\n    c = [1 + cos(i) for i in 1:N]\n    MOI.add_constraint(\n        model,\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(w, x), 0.0),\n        MOI.LessThan(10.0),\n    )\n    obj = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(c, x), 0.0)\n    MOI.set(model, MOI.ObjectiveFunction{typeof(obj)}(), obj)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL\n    return\nend\n\nfunction test_PrimalStatus()\n    model = MOI.Utilities.Model{Float64}()\n    x = MOI.add_variable(model)\n    MOI.add_constraint(model, x, MOI.GreaterThan(1.0))\n    MOI.add_constraint(model, x, MOI.LessThan(0.0))\n    cbc = Cbc.Optimizer()\n    MOI.copy_to(cbc, model)\n    MOI.optimize!(cbc)\n    MOI.get(cbc, MOI.PrimalStatus()) == MOI.NO_SOLUTION\n    return\nend\n\nfunction test_issue_187()\n    if Sys.iswindows() || Sys.islinux()\n        # This test segfaults on Windows and Linux\n        @test_broken 1 == 2\n        return\n    end\n    model = MOI.Utilities.CachingOptimizer(\n        MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n        MOI.instantiate(Cbc.Optimizer; with_bridge_type = Float64),\n    )\n    MOI.set(model, MOI.Silent(), true)\n    x = MOI.add_variables(model, 2)\n    MOI.add_constraint.(model, x, MOI.ZeroOne())\n    MOI.set.(model, MOI.VariablePrimalStart(), x, 0.0)\n    y = MOI.add_variables(model, 2)\n    MOI.add_constraint.(model, y, MOI.ZeroOne())\n\n    MOI.add_constraint(\n        model,\n        MOI.Utilities.operate(vcat, Float64, x[1], 1.0 * y[1]),\n        MOI.Indicator{MOI.ACTIVATE_ON_ONE}(MOI.EqualTo(1.0)),\n    )\n    MOI.add_constraint(\n        model,\n        MOI.Utilities.operate(vcat, Float64, x[2], 1.0 * y[1]),\n        MOI.Indicator{MOI.ACTIVATE_ON_ONE}(MOI.EqualTo(0.0)),\n    )\n    MOI.set.(model, MOI.VariablePrimalStart(), y, 0.0)\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    f = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.0)\n    MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL\n    @test \u2248(sum(MOI.get(model, MOI.VariablePrimal(), x)), 1.0, atol = 1e-4)\n    return\nend\n\n# The test_linear_SOS1_integration test with the additional requirement that all\n# variables are integer.\nfunction test_SOS1()\n    model = MOI.Bridges.full_bridge_optimizer(\n        MOI.Utilities.CachingOptimizer(\n            MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n            Cbc.Optimizer(),\n        ),\n        Float64,\n    )\n    config = MOI.Test.Config()\n    MOI.set(model, MOI.Silent(), true)\n    @test MOI.supports_constraint(\n        model,\n        MOI.VectorOfVariables,\n        MOI.SOS1{Float64},\n    )\n    @test MOI.supports_constraint(\n        model,\n        MOI.VariableIndex,\n        MOI.LessThan{Float64},\n    )\n    v = MOI.add_variables(model, 3)\n    MOI.add_constraint.(model, v, MOI.Integer())\n    @test MOI.get(model, MOI.NumberOfVariables()) == 3\n    vc1 = MOI.add_constraint(model, v[1], MOI.LessThan(1.0))\n    @test vc1.value == v[1].value\n    vc2 = MOI.add_constraint(model, v[2], MOI.LessThan(1.0))\n    @test vc2.value == v[2].value\n    vc3 = MOI.add_constraint(model, v[3], MOI.LessThan(2.0))\n    @test vc3.value == v[3].value\n    c1 = MOI.add_constraint(\n        model,\n        MOI.VectorOfVariables([v[1], v[2]]),\n        MOI.SOS1([1.0, 2.0]),\n    )\n    c2 = MOI.add_constraint(\n        model,\n        MOI.VectorOfVariables([v[1], v[3]]),\n        MOI.SOS1([1.0, 2.0]),\n    )\n    @test MOI.get(\n        model,\n        MOI.NumberOfConstraints{MOI.VectorOfVariables,MOI.SOS1{Float64}}(),\n    ) == 2\n    #=\n        To allow for permutations in the sets and variable vectors\n        we're going to sort according to the weights\n    =#\n    cs_sos = MOI.get(model, MOI.ConstraintSet(), c2)\n    cf_sos = MOI.get(model, MOI.ConstraintFunction(), c2)\n    p = sortperm(cs_sos.weights)\n    @test isapprox(cs_sos.weights[p], [1.0, 2.0], config)\n    @test cf_sos.variables[p] == v[[1, 3]]\n    objf =\n        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 1.0, 1.0], v), 0.0)\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        objf,\n    )\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    @test MOI.get(model, MOI.ResultCount()) >= 1\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), 3, config)\n    @test isapprox(MOI.get(model, MOI.VariablePrimal(), v), [0, 1, 2], config)\n    MOI.delete(model, c1)\n    MOI.delete(model, c2)\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    @test MOI.get(model, MOI.ResultCount()) >= 1\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), 5, config)\n    @test isapprox(MOI.get(model, MOI.VariablePrimal(), v), [1, 1, 2], config)\n    return\nend\n\n# The test_linear_SOS2_integration test with the additional requirement that all\n# variables are integer.\nfunction test_SOS2()\n    model = MOI.Bridges.full_bridge_optimizer(\n        MOI.Utilities.CachingOptimizer(\n            MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n            Cbc.Optimizer(),\n        ),\n        Float64,\n    )\n    config = MOI.Test.Config()\n    MOI.set(model, MOI.Silent(), true)\n    @test MOI.supports_constraint(\n        model,\n        MOI.VectorOfVariables,\n        MOI.SOS1{Float64},\n    )\n    @test MOI.supports_constraint(\n        model,\n        MOI.VectorOfVariables,\n        MOI.SOS2{Float64},\n    )\n    v = MOI.add_variables(model, 10)\n    @test MOI.get(model, MOI.NumberOfVariables()) == 10\n    bin_constraints = []\n    for i in 1:8\n        vc = MOI.add_constraint(model, v[i], MOI.Interval(0.0, 2.0))\n        @test vc.value == v[i].value\n        push!(bin_constraints, MOI.add_constraint(model, v[i], MOI.ZeroOne()))\n        @test bin_constraints[i].value == v[i].value\n    end\n    MOI.add_constraint(\n        model,\n        MOI.ScalarAffineFunction(\n            MOI.ScalarAffineTerm.([1.0, 2.0, 3.0, -1.0], v[[1, 2, 3, 9]]),\n            0.0,\n        ),\n        MOI.EqualTo(0.0),\n    )\n    MOI.add_constraint(\n        model,\n        MOI.ScalarAffineFunction(\n            MOI.ScalarAffineTerm.(\n                [5.0, 4.0, 7.0, 2.0, 1.0, -1.0],\n                v[[4, 5, 6, 7, 8, 10]],\n            ),\n            0.0,\n        ),\n        MOI.EqualTo(0.0),\n    )\n    MOI.add_constraint(\n        model,\n        MOI.VectorOfVariables(v[[1, 2, 3]]),\n        MOI.SOS1([1.0, 2.0, 3.0]),\n    )\n    vv = MOI.VectorOfVariables(v[[4, 5, 6, 7, 8]])\n    sos2 = MOI.SOS2([5.0, 4.0, 7.0, 2.0, 1.0])\n    c = MOI.add_constraint(model, vv, sos2)\n    #=\n        To allow for permutations in the sets and variable vectors\n        we're going to sort according to the weights\n    =#\n    cs_sos = MOI.get(model, MOI.ConstraintSet(), c)\n    cf_sos = MOI.get(model, MOI.ConstraintFunction(), c)\n    p = sortperm(cs_sos.weights)\n    @test isapprox(cs_sos.weights[p], [1.0, 2.0, 4.0, 5.0, 7.0], config)\n    @test cf_sos.variables[p] == v[[8, 7, 5, 4, 6]]\n    objf = MOI.ScalarAffineFunction(\n        MOI.ScalarAffineTerm.([1.0, 1.0], [v[9], v[10]]),\n        0.0,\n    )\n    MOI.set(\n        model,\n        MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),\n        objf,\n    )\n    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n    @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE\n    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    @test MOI.get(model, MOI.ResultCount()) >= 1\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), 15.0, config)\n    @test isapprox(\n        MOI.get(model, MOI.VariablePrimal(), v),\n        [0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 3.0, 12.0],\n        config,\n    )\n    for cref in bin_constraints\n        MOI.delete(model, cref)\n    end\n    MOI.add_constraint.(model, v, MOI.Integer())\n    MOI.optimize!(model)\n    @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status\n    @test MOI.get(model, MOI.ResultCount()) >= 1\n    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT\n    @test isapprox(MOI.get(model, MOI.ObjectiveValue()), 30.0, config)\n    @test isapprox(\n        MOI.get(model, MOI.VariablePrimal(), v),\n        [0.0, 0.0, 2.0, 2.0, 0.0, 2.0, 0.0, 0.0, 6.0, 24.0],\n        config,\n    )\n    return\nend\n\n\"\"\"\n    test_VariablePrimalStart()\n\nTesting that VariablePrimalStart is actually applied is a little convoluted.\n\nWe formulate a MIP with various setttings turned off to avoid a trivial solve in\npresolve.\n\nThen we solve and return the optimal primal solution and the number of nodes\nvisited.\n\nFor the second pass, we rebuild the same MIP, but this time we pass the optimal\nsolution as the VariablePrimalStart, and we set maxSol=1 to force Cbc to exit\nafter finding a single solution. Because we passed a primal feasible point, it\nshould return the optimal solution after exploring 0 nodes.\n\"\"\"\nfunction test_VariablePrimalStart()\n    function formulate_and_solve(start)\n        model = MOI.Utilities.CachingOptimizer(\n            MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),\n            MOI.instantiate(Cbc.Optimizer; with_bridge_type = Float64),\n        )\n        MOI.set(model, MOI.RawOptimizerAttribute(\"presolve\"), \"off\")\n        MOI.set(model, MOI.RawOptimizerAttribute(\"cuts\"), \"off\")\n        MOI.set(model, MOI.RawOptimizerAttribute(\"heur\"), \"off\")\n        MOI.set(model, MOI.RawOptimizerAttribute(\"logLevel\"), 0)\n        N = 100\n        x = MOI.add_variables(model, N)\n        MOI.add_constraint.(model, x, MOI.ZeroOne())\n        w = [1 + sin(i) for i in 1:N]\n        c = [1 + cos(i) for i in 1:N]\n        if start !== nothing\n            MOI.set(model, MOI.RawOptimizerAttribute(\"maxSol\"), 1)\n            MOI.set.(model, MOI.VariablePrimalStart(), x, start)\n        end\n        MOI.add_constraint(\n            model,\n            MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(w, x), 0.0),\n            MOI.LessThan(10.0),\n        )\n        obj = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(c, x), 0.0)\n        MOI.set(model, MOI.ObjectiveFunction{typeof(obj)}(), obj)\n        MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)\n        MOI.optimize!(model)\n        sol = MOI.get.(model, MOI.VariablePrimal(), x)\n        return sol, MOI.get(model, MOI.NodeCount())\n    end\n    x_sol, nodes = formulate_and_solve(nothing)\n    y_sol, nodes_start = formulate_and_solve(x_sol)\n    @test x_sol == y_sol\n    @test nodes > 0\n    @test nodes_start == 0\n    return\nend\n\nend\n\nTestMOIWrapper.runtests()\n", "meta": {"hexsha": "bf296e07ab21f7a327a88b0586fc4fe8050a3e08", "size": 15168, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/MOI_wrapper.jl", "max_stars_repo_name": "JuliaOpt/Cbc.jl", "max_stars_repo_head_hexsha": "3db1f16751a18d4f200b15bfd6116925fbdfddb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 38, "max_stars_repo_stars_event_min_datetime": "2015-10-04T11:39:07.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-01T02:05:46.000Z", "max_issues_repo_path": "test/MOI_wrapper.jl", "max_issues_repo_name": "JuliaOpt/Cbc.jl", "max_issues_repo_head_hexsha": "3db1f16751a18d4f200b15bfd6116925fbdfddb0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 82, "max_issues_repo_issues_event_min_datetime": "2015-01-10T18:07:57.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-12T14:45:30.000Z", "max_forks_repo_path": "test/MOI_wrapper.jl", "max_forks_repo_name": "JuliaOpt/Cbc.jl", "max_forks_repo_head_hexsha": "3db1f16751a18d4f200b15bfd6116925fbdfddb0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 35, "max_forks_repo_forks_event_min_datetime": "2015-01-10T18:20:25.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T16:42:21.000Z", "avg_line_length": 34.7889908257, "max_line_length": 80, "alphanum_fraction": 0.6294171941, "num_tokens": 4615, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4765796361952087, "lm_q2_score": 0.11279539882690352, "lm_q1q2_score": 0.05375599013741915}}
{"text": "\"\"\"\nAbstract type representing a function (mapping) that provides a cache and an in-place\nevaluation for performance. This is the type to be used in the [`lazy_map`](@ref) function.\n\nDerived types must implement the following method:\n\n- [`evaluate!(cache,k,x...)`](@ref)\n\nand optionally these ones:\n\n- [`return_cache(k,x...)`](@ref)\n- [`return_type(k,x...)`](@ref)\n\nThe mapping interface can be tested with the [`test_map`](@ref) function.\n\nNote that most of the functionality implemented in terms of this interface\nrelies in duck typing. That is, it is not strictly needed to work with types\nthat inherit from `Map`. This is specially useful in order to accommodate\nexisting types into this framework without the need to implement a wrapper type\nthat inherits from `Map`. For instance, a default implementation is available\nfor `Function` objects.  However, we recommend that new types inherit from `Map`.\n\n\"\"\"\nabstract type Map <: GridapType end\n\n\"\"\"\n    return_cache(f,x...)\n\nReturns the `cache` needed to lazy_map mapping `f` with arguments\nof the same type as the objects in `x`.\nThis function returns `nothing` by default, i.e., no cache.\n\"\"\"\nreturn_cache(f,x...) = nothing\n\n\"\"\"\n    evaluate!(cache,f,x...)\n\nApplies the mapping `f` at the arguments `x...` using\nthe scratch data provided in the given `cache` object. The `cache` object\nis built with the [`return_cache`](@ref) function using arguments of the same type as in `x`.\nIn general, the returned value `y` can share some part of its state with the `cache` object.\nIf the result of two or more calls to this function need to be accessed simultaneously\n(e.g., in multi-threading), create and use several `cache` objects (e.g., one cache\nper thread).\n\"\"\"\n@noinline function evaluate!(cache,f,x...)\n  @abstractmethod \"\"\"\n  Method\n\n      Gridap.Arrays.evaluate!(cache,f,x...)\n\n  has not been defined for the requested types (see stack trace).\n  \"\"\"\nend\n\n\"\"\"\n    return_type(f,x...)\n\nReturns the type of the result of calling mapping `f` with\narguments of the types of the objects `x`.\n\"\"\"\nreturn_type(f,x...) = typeof(return_value(f,x...))\n\nreturn_value(f,x...) = evaluate(f,testargs(f,x...)...)\n\n\"\"\"\n    testargs(f,x...)\n\nThe default implementation of this function is `testargs(f,x...) = x`.\nOne can overload it in order to use `lazy_map` with 0-length array\nand maps with non-trivial domains.\n\"\"\"\ntestargs(f,x...) = x\n\n\"\"\"\n    evaluate(f,x...)\n\nevaluates the mapping `f` at the arguments in `x` by creating a temporary cache\ninternally. This functions is equivalent to\n```jl\ncache = return_cache(f,x...)\nevaluate!(cache,f,x...)\n```\n\"\"\"\nfunction evaluate(f,x...)\n  c = return_cache(f,x...)\n  y = evaluate!(c,f,x...)\nend\n\n(m::Map)(x...) = evaluate(m,x...)\n\n# Default implementation for Function\nevaluate!(cache,f::Function,x...) = f(x...)\n\n# Default implementation for constructors\nevaluate!(cache,::Type{f},x...) where f = f(x...)\n\n\n# Testing the interface\n\"\"\"\n    test_map(y,f,x...;cmp=(==))\n\nFunction used to test if the mapping `f` has been\nimplemented correctly. `f` is a `Map` sub-type, `x` is a tuple in the domain of the\nmapping and `y` is the expected result. Function `cmp` is used to compare\nthe computed result with the expected one. The checks are done with the `@test`\nmacro.\n\"\"\"\nfunction test_map(y,f,x...;cmp=(==))\n  z = evaluate(f,x...)\n  @test cmp(z,y)\n  @test typeof(z) == return_type(f,x...)\n  cache = return_cache(f,x...)\n  z = evaluate!(cache,f,x...)\n  @test cmp(z,y)\n  z = evaluate!(cache,f,x...)\n  @test cmp(z,y)\n  true\nend\n\n# Broadcast Functions\n\n\"\"\"\n    Broadcasting(f)\n\nReturns a mapping that represents the \"broadcasted\" version of the\nfunction `f`.\n\n# Example\n\n```jldoctest\nusing Gridap.Arrays\n\na = [3,2]\nb = [2,1]\n\nbm = Broadcasting(+)\n\nc = evaluate(bm,a,b)\n\nprintln(c)\n\n# output\n[5, 3]\n```\n\"\"\"\nstruct Broadcasting{F} <: Map\n  f::F\nend\n\nreturn_cache(f::Broadcasting,x...) = nothing\n\nevaluate!(cache,f::Broadcasting,x...) = broadcast(f.f,x...)\n\nfunction return_value(f::Broadcasting,x...)\n  broadcast( (y...) -> f.f(testargs(f.f,y...)...), x... )\nend\n\nfunction evaluate!(cache,f::Broadcasting,x::Union{Number,AbstractArray{<:Number}}...)\n  r = _prepare_cache!(cache,x...)\n  a = r.array\n  broadcast!(f.f,a,x...)\n  a\nend\n\nfunction evaluate!(cache,f::Broadcasting,x::AbstractArray{<:Number})\n  setsize!(cache,size(x))\n  a = cache.array\n  @inbounds for i in eachindex(x)\n    a[i] = f.f(x[i])\n  end\n  a\nend\n\nfunction evaluate!(cache,f::Broadcasting,args::Number...)\n  f.f(args...)\nend\n\nfunction return_value(f::Broadcasting,x::Number...)\n  return_value(f.f,x...)\nend\n\nfunction return_cache(f::Broadcasting,x::Number...)\n  nothing\nend\n\nfunction return_value(f::Broadcasting,x::Union{Number,AbstractArray{<:Number}}...)\n  s = map(_size_zero,x)\n  bs = Base.Broadcast.broadcast_shape(s...)\n  T = return_type(f.f,map(testitem,x)...)\n  r = fill(testvalue(T),bs)\n  r\nend\n\nfunction _size_zero(a)\n  s = size(a)\n  if length(a) == 0\n    r = map(i-> (i==0 ? 1 : i) ,s)\n  else\n    r = s\n  end\n  r\nend\n_size_zero(a::Number) = (1,)\n\nfunction return_cache(f::Broadcasting,x::Union{Number,AbstractArray{<:Number}}...)\n  s = map(_size,x)\n  bs = Base.Broadcast.broadcast_shape(s...)\n  T = return_type(f.f,map(testitem,x)...)\n  r = fill(testvalue(T),bs)\n  cache = CachedArray(r)\n  _prepare_cache!(cache,x...)\n  cache\nend\n\nfunction _prepare_cache!(c,x...)\n  s = map(_size,x)\n  bs = Base.Broadcast.broadcast_shape(s...)\n  if bs != size(c)\n    setsize!(c,bs)\n  end\n  c\nend\n\n_size(a) = size(a)\n_size(a::Number) = (1,)\n\n\"\"\"\n    OperationMap(f,args)\n\nReturns a mapping that represents the result of applying the function `f`\nto the arguments in the tuple `args`.\nThat is, `OperationMap(f,args)(x...)` is formally defined as\n`f(map(a->a(x...),args)...)`\n\"\"\"\nstruct OperationMap{K,L} <: Map\n  k::K\n  l::L\n  function OperationMap(k,l)\n    new{typeof(k),typeof(l)}(k,l)\n  end\nend\n\nfunction return_cache(c::OperationMap,x...)\n  cl = map(fi -> return_cache(fi,x...),c.l)\n  lx = map(fi -> return_value(fi,x...),c.l)\n  ck = return_cache(c.k,lx...)\n  ck, cl\nend\n\nfunction evaluate!(cache,c::OperationMap,x...)\n  ck, cf = cache\n  lx = map((ci,fi) -> evaluate!(ci,fi,x...),cf,c.l)\n  evaluate!(ck,c.k,lx...)\nend\n\n# Operations\n\n\"\"\"\n    Operation(op)\n\nRetuns the map that results after applying an operation `f` over a set of map(s) `args`.\nThat is `Operation(f)(args)(x...)` is formally defined as\n`f(map(a->a(x...),args)...)`.\n\n# Example\n\n```jldoctest\nusing Gridap.Arrays\n\nfa(x) = x.*x\nfb(x) = sqrt.(x)\n\nx = collect(0:5)\n\nfab = Operation(fa)(fb)\nc = evaluate(fab,x)\n\nprintln(c)\n\n# output\n[0.0, 1.0, 2.0, 3.0, 4.0, 5.0]\n```\n\"\"\"\nstruct Operation{T} <: Map\n  op::T\nend\n\nevaluate!(cache,op::Operation,args::Map...) = OperationMap(op.op,args)\nevaluate!(cache,op::Operation,args::Function...) = OperationMap(op.op,args)\n\n\"\"\"\n\"\"\"\nfunction inverse_map(f)\n  @unreachable \"\"\"\\n\n  Function inverse_map is not implemented yet for objects of type $(typeof(f))\n  \"\"\"\nend\n", "meta": {"hexsha": "6ef9b15bce9d732520fffb96f7439e6c0f8b8859", "size": 6904, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Arrays/Maps.jl", "max_stars_repo_name": "aerappa/Gridap.jl", "max_stars_repo_head_hexsha": "1fb3dc9abf8c47685637901bd14a74e4355a9492", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-03-18T06:52:56.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-16T11:01:18.000Z", "max_issues_repo_path": "src/Arrays/Maps.jl", "max_issues_repo_name": "aerappa/Gridap.jl", "max_issues_repo_head_hexsha": "1fb3dc9abf8c47685637901bd14a74e4355a9492", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 22, "max_issues_repo_issues_event_min_datetime": "2019-03-17T12:26:02.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-13T14:53:34.000Z", "max_forks_repo_path": "src/Arrays/Maps.jl", "max_forks_repo_name": "aerappa/Gridap.jl", "max_forks_repo_head_hexsha": "1fb3dc9abf8c47685637901bd14a74e4355a9492", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-03-17T12:23:55.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-17T12:23:55.000Z", "avg_line_length": 23.0903010033, "max_line_length": 93, "alphanum_fraction": 0.6655561993, "num_tokens": 1923, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "## Copyright (c) 2013 Miguel Bazdresch\n##\n## This file is distributed under the 2-clause BSD License.\n\n# Gaston test framework\n#\n# There are two kinds of tests:\n#   - Tests that should error -- things that Gaston should not allow, such as\n#     building a figure with an invalid plotstyle.\n#   - Tests that should succeed -- things that should work without producing\n#     an error.\n\nusing Gaston, Test\n\n@testset \"Figure and set commands\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    # figures\n    @test figure() == 1\n    @test figure() == 2\n    @test figure(4) == 4\n    @test closefigure(4) == 2\n    @test closefigure() == 1\n    @test closeall() == 1\n    @test begin\n        closeall()\n        figure()\n        figure()\n        figure(4);\n        closefigure(1,2)\n    end == 4\n    @test begin\n        closeall()\n        figure()\n        figure()\n        figure(4);\n        closefigure(4)\n    end == 2\n    @test begin\n        closeall()\n        figure()\n        figure()\n        figure(4);\n        closefigure(1,2,3,4,5)\n    end== nothing\n    @test begin\n        closeall()\n        figure(3)\n        figure(3)\n    end== 3\n    @test set(plotstyle=\"linespoints\") == nothing\n    @test set(linecolor=\"red\") == nothing\n    @test set(pointtype=\"ecircle\") == nothing\n    @test set(linewidth=\"3\") == nothing\n    @test set(pointsize=\"3\") == nothing\n    @test set(palette=\"gray\") == nothing\n    @test set(fillstyle=\"solid\") == nothing\n    @test set(grid=\"on\") == nothing\n    if !Sys.iswindows()\n        @test set(terminal=\"x11\") == nothing # This test does not pass in Windows\n    end\n    @test set(terminal=\"x11\") == nothing\n    @test set(termopts=\"noenhanced\") == nothing\n    @test set(print_outputfile=\"A\") == nothing\n    @test set(print_font=\"A\") == nothing\n    @test set(print_linewidth=\"3\") == nothing\n    @test set(print_size=\"10,10\") == nothing\nend\n\n@testset \"2-D plots\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    @test closeall() == 0\n    @test begin\n        plot(1:10)\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,handle=2)\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,handle=4) == 4\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test closeall() == 3\n    @test begin\n        set(termopts=\"feed noenhanced ansi\")\n        plot(sin.(-3:0.01:3),\n             legend = \"sine\",\n             title = \"test plot 1\",\n             xlabel = \"x\",\n             ylabel = \"y\",\n             plotstyle = \"lines\",\n             linecolor = \"blue\",\n             linewidth = \"2\",\n             linestyle = \"-.-\",\n             pointtype = \"ecircle\",\n             pointsize = \"1.1\",\n             fillstyle = \"\",\n             keyoptions = \"inside horizontal left top\",\n             axis =\"loglog\",\n             xrange = \"[2:3]\",\n             yrange = \"[2:3]\",\n             xzeroaxis = \"\",\n             yzeroaxis = \"\",\n             font = \"Arial, 12\",\n             size = \"79,24\",\n             background = \"\",\n             gpcom = \"\"\n            )\n        Gaston.gnuplot_state.gp_error\n    end == false\n    set(termopts=\"\")\n    @test begin\n        plot!(cos.(-3:0.01:3),\n              legend = \"cos\",\n              plotstyle = \"linespoints\",\n              linecolor = \"red\",\n              linewidth = \"2\",\n              linestyle = \".\",\n              pointtype = \"fcircle\",\n              pointsize = \"1\"\n             )\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,xrange = \"[:3.4]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,xrange = \"[3.4:]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,xrange = \"[3.4:*]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10,xrange = \"[*:3.4]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(3,4,plotstyle=\"points\",pointsize=\"3\",xrange=\"[2.95:3.05]\",\n             yrange=\"[3.95:4.045]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(rand(10).+im.*rand(10))\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(3+4im,plotstyle=\"points\",pointsize=\"3\",xrange=\"[2.95:3.05]\",\n             yrange=\"[3.95:4.045]\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40))\n        plot(1:40,err=err,plotstyle=\"errorbars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40))\n        plot!(1:40,err=err,plotstyle=\"errorbars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40))\n        plot(1:40,err=err,plotstyle=\"errorlines\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40))\n        plot!(1:40,err=err,plotstyle=\"errorlines\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40),rand(40))\n        plot(1:40,err=err,plotstyle=\"errorbars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40),rand(40))\n        plot!(1:40,err=err,plotstyle=\"errorbars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40),rand(40))\n        plot(1:40,err=err,plotstyle=\"errorlines\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        err = Gaston.ErrorCoords(rand(40),rand(40))\n        plot!(1:40,err=err,plotstyle=\"errorlines\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        fin = Gaston.FinancialCoords(0.1*rand(10),0.1*rand(10),\n                                     0.1*rand(10),0.1*rand(10))\n        plot(1:10,financial=fin,plotstyle=\"financebars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        fin = Gaston.FinancialCoords(0.1*rand(10),0.1*rand(10),\n                                     0.1*rand(10),0.1*rand(10))\n        plot!(1:10,financial=fin,plotstyle=\"financebars\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    # This test is cool, but it fails way too often due to slight\n    # OS, configuration, and gnuplot version differences. Disabled for now.\n    if false\n    #if occursin(\"5.2\",read(`gnuplot --version`, String))\n        @test begin\n            set(reset=true)\n            set(terminal=\"dumb\", size=\"27,13\")\n            x=\"\\f                           \\n  11 +-----------------+   \\n  10 |-+ + + + + + + +*|   \\n   9 |-+          ****-|   \\n   7 |-+        **   +-|   \\n   6 |-+      **     +-|   \\n   5 |-+    **       +-|   \\n   4 |-+  **         +-|   \\n   3 |****           +-|   \\n   1 |-+ + + + + + + +-|   \\n   0 +-----------------+   \\n     1 2 3 4 5 6 7 8 9 10  \\n                           \\n\"\n            a=repr(\"text/plain\", plot(1:10))\n            a == x\n        end == true\n    end\n    @test begin\n        set(reset=true)\n        stem(rand(10))\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        stem(rand(10),onlyimpulses=true)\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        scatter(rand(10),rand(10))\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        scatter(randn(10), randn(10), pointtype=\"\u03bb\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        t = -2:0.06:2\n        plot(t, sin.(2\u03c0*t), plotstyle=\"fillsteps\", fillstyle=\"solid 0.5\", title=\"Fillsteps plot\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        set(reset=true)\n        set(mode=\"ijulia\")\n        a = repr(\"text/plain\", plot(1:10))\n        a[1:35] == \"<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\"\n    end == true\n    # build a multiple-plot figure manually\n    closeall()\n    set(mode=\"null\")\n    @test begin\n        ac = Gaston.AxesConf(title=\"T\")\n        x1, exp_pdf = Gaston.hist(randn(10000),25)\n        exp_pdf .= exp_pdf./(step(x1)*sum(exp_pdf))\n        exp_cconf = Gaston.CurveConf(plotstyle=\"boxes\",\n                                     linecolor=\"blue\",\n                                     legend=\"E\")\n        exp_curve = Gaston.Curve(x1,exp_pdf,exp_cconf)\n        x2 = -5:0.05:5\n        theo_pdf = @. 1/sqrt(2\u03c0)*exp((-x2^2)/2)\n        theo_cconf = Gaston.CurveConf(linecolor=\"black\",legend=\"T\")\n        theo_curve = Gaston.Curve(x2,theo_pdf,theo_cconf)\n        figure(1)\n        Gaston.push_figure!(1,ac,exp_curve,theo_curve)\n        figure(1,redraw=false)\n    end == 1\nend\n\n@testset \"Histograms\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    @test begin\n        histogram(rand(1000))\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        histogram(randn(1000),\n                  bins = 100,\n                  norm = 1,\n                  title = \"test histogram\",\n                  xlabel = \"x\",\n                  ylabel = \"y\",\n                  linecolor = \"blue\",\n                  linewidth = \"2\",\n                  fillstyle = \"solid\",\n                  keyoptions = \"inside horizontal left top\",\n                  xrange = \"[*:*]\",\n                  yrange = \"[*:*]\",\n                  font = \"Arial, 12\",\n                  size = \"79,24\",\n                  background = \"red\"\n                 )\n        Gaston.gnuplot_state.gp_error\n    end == false\nend\n\n@testset \"Images\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    z = rand(5,6)\n    @test begin\n        imagesc(z,\n                title = \"test imagesc 1\",\n                xlabel = \"xx\",\n                ylabel = \"yy\",\n                font = \"Arial, 12\",\n                size = \"79,24\"\n               )\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        imagesc(1:6,1:5,z,title=\"test imagesc 3\",xlabel=\"xx\",ylabel=\"yy\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        R = [x+y for x=0:5:120, y=0:5:120]\n        G = [x+y for x=0:5:120, y=120:-5:0]\n        B = [x+y for x=120:-5:0, y=0:5:120]\n        Z = zeros(25,25,3)\n        Z[:,:,1] = R\n        Z[:,:,2] = G\n        Z[:,:,3] = B\n        imagesc(Z,title=\"RGB Image\",clim=[10,200])\n        Gaston.gnuplot_state.gp_error\n    end == false\nend\n\n@testset \"3-D plots\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    @test begin\n        surf(rand(10,10))\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        x=[0,1,2,3]; y=[0,1,2]; Z=[10 10 10; 10 5 10;10 1 10; 10 0 10]\n        surf(x, y, Z,\n             legend = \"test\",\n             plotstyle = \"lines\",\n             linecolor = \"black\",\n             pointtype = \"ecircle\",\n             title = \"test\",\n             xlabel = \"x\",\n             ylabel = \"y\",\n             zlabel = \"z\",\n             keyoptions = \"inside horizontal left top\",\n             xrange = \"\",\n             yrange = \"\",\n             zrange = \"\",\n             xzeroaxis = \"on\",\n             yzeroaxis = \"on\",\n             zzeroaxis = \"on\",\n             font = \"Arial, 12\",\n             size = \"79,24\",\n             gpcom = \"set view 90,60\"\n            )\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        surf(0:9,2:11,(x,y)->x*y)\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        surf(0:9,2:11,(x,y)->x*y,\n             legend = \"test\",\n             plotstyle=\"pm3d\",\n             title=\"test\",\n            )\n        Gaston.gnuplot_state.gp_error\n    end == false\nend\n\n@testset \"Saving plots\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    set(print_outputfile=tempname())\n    plot(1:10)\n    @test begin\n        printfigure()\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        printfigure(handle=1,term=\"png\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        printfigure(term=\"eps\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        printfigure(term=\"pdf\",\n                    font = \"Arial, 12\",\n                    size = \"5,3\",\n                    linewidth = \"3\"\n                   )\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        set(print_size=\"640,480\")\n        printfigure(term=\"svg\")\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        set(print_size=\"640,480\")\n        printfigure(term=\"gif\")\n        Gaston.gnuplot_state.gp_error\n    end == false\nend\n\n@testset \"Linestyle tests\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    @test begin\n        plot(1:10) # solid\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10, linestyle=\"\") # solid\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10, linestyle=\"-\") # dashed\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10, linestyle=\".\") # dotted\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10, linestyle=\"_\") # em-dashed\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test begin\n        plot(1:10, linestyle=\"- ._. \") # complex pattern\n        Gaston.gnuplot_state.gp_error\n    end == false\n    @test_throws DomainError plot(1:10, linestyle=\" \") # only spaces not allowed\n    @test_throws DomainError plot(1:10, linestyle=\"-=\") # = is not allowed in pattern\n    closeall()\nend\n\n@testset \"async_reader\" begin\n    # Simplest way to test async_reader would be to use plain pipes,\n    # but there seems to be no way to create them in Julia, so we pipe\n    # data through an external process. The advantage of this approach\n    # is that it corresponds to how Gaston communicates with gnuplot.\n    # This testset is only enabled on Unix-like systems.\n    if Sys.isunix()\n        pin = Pipe()\n        pout = Pipe()\n        proc = try\n            run(pipeline(`cat`, stdin = pin, stdout = pout),\n                wait = false)\n        catch err\n            @warn \"Skipping async_reader tests\"\n            return\n        end\n        close(pout.in)\n        close(pin.out)\n\n        @test begin\n            ch = Gaston.async_reader(pout, 1)\n            write(pin, \"GastonBegin\\ncontent\\nGastonDone\\n\")\n            take!(ch)\n        end == \"content\\n\"\n\n        @test begin\n            ch = Gaston.async_reader(pout, 0.001)\n            write(pin, \"GastonBegin\\nmissing end pointtype\\n\")\n            take!(ch)\n        end === :timeout\n\n        @test begin\n            ch = Gaston.async_reader(pout, 1)\n            write(pin, \"no begin pointtype\\nGastonDone\\n\")\n            take!(ch)\n        end == \"no begin pointtype\\n\"\n\n        @test begin\n            ch = Gaston.async_reader(pout, 1)\n            kill(proc)\n            take!(ch)\n        end === :eof\n    end\nend\n\n@testset \"Tests that should fail\" begin\n    closeall()\n    set(reset=true)\n    set(mode=\"null\")\n    # figure-related\n    @test_throws MethodError figure(\"invalid\")\n    @test_throws MethodError figure(1.0)\n    @test_throws MethodError figure(1:2)\n    @test_throws DomainError closefigure(-1)\n    @test_throws MethodError closefigure(\"invalid\")\n    @test_throws MethodError closefigure(1.0)\n    @test_throws MethodError closefigure(1:2)\n    # plot\n    @test_throws DimensionMismatch plot(0:10,0:11)\n    @test_throws DimensionMismatch surf([1,2],[3,4],[5,6])\n    @test_throws TypeError plot(0:10,legend=0)\n    @test_throws DomainError plot(0:10,plotstyle=\"invalid\")\n    @test_throws DomainError plot(0:10,pointtype=\"invalid\")\n    @test_throws TypeError plot(0:10,pointtype=0)\n    @test_throws TypeError plot(0:10,linewidth=im)\n    @test_throws TypeError plot(0:10,pointsize=im)\n    @test_throws TypeError plot(0:10,title=0)\n    @test_throws TypeError plot(0:10,xlabel=0)\n    @test_throws TypeError plot(0:10,ylabel=0)\n    @test_throws DomainError plot(0:10,axis=\"invalid\")\n    @test_throws DomainError plot(1:10,xrange = \"2:3\")\n    @test_throws DomainError plot(1:10,yrange = \"ab\")\n    f = Gaston.FinancialCoords([1,2],[1,2],[1,2],[1,2])\n    @test_throws DimensionMismatch plot(1:10,financial=f)\n    er = Gaston.ErrorCoords([0.1,0.1])\n    @test_throws DimensionMismatch plot(1:10,err=er)\n    # plot!\n    plot(1:10)\n    @test_throws TypeError plot!(0:10,legend=0)\n    @test_throws MethodError plot!(0:10,axis=\"loglog\")\n    # imagesc\n    z = rand(5,6)\n    @test_throws DimensionMismatch imagesc(1:5,1:7,z)\n    # histogram\n    @test_throws MethodError histogram(0:10+im*0:10)\n    # printfigure\n    @test_throws DomainError begin\n        printfigure(handle=2,term=\"png\")\n        Gaston.gnuplot_state.gp_error\n    end\n    @test_throws DomainError begin\n        printfigure(term=\"xyz\")\n        Gaston.gnuplot_state.gp_error\n    end\n    @test_throws ErrorException plot!(rand(10),handle=10)\n    # set\n    set(mode=\"normal\")\n    @test_throws MethodError set(legend=3)\n    @test_throws DomainError set(plotstyle=\"A\")\n    @test_throws MethodError set(color=3)\n    @test_throws DomainError set(pointtype=\"xyz\")\n    @test_throws MethodError set(title=3)\n    @test_throws MethodError set(xlabel=3)\n    @test_throws MethodError set(ylabel=3)\n    @test_throws MethodError set(zlabel=3)\n    @test_throws DomainError set(terminal=\"x12\")\n    @test_throws MethodError set(print_outputfile=3)\n    @test_throws MethodError set(print_color=3)\n    @test_throws MethodError set(print_font=3)\n    @test_throws MethodError set(print_linewidth=3)\n    @test_throws MethodError set(print_size=10)\n    closeall()\nend\n", "meta": {"hexsha": "5df2d80405166e2107f4d7dcd378178648ba30e8", "size": 17586, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Gaston.jl-4b11ee91-296f-5714-9832-002c20994614", "max_stars_repo_head_hexsha": "ba37127392c7dd76fe75068424e6cab3803a7d02", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/Gaston.jl-4b11ee91-296f-5714-9832-002c20994614", "max_issues_repo_head_hexsha": "ba37127392c7dd76fe75068424e6cab3803a7d02", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "UnofficialJuliaMirrorSnapshots/Gaston.jl-4b11ee91-296f-5714-9832-002c20994614", "max_forks_repo_head_hexsha": "ba37127392c7dd76fe75068424e6cab3803a7d02", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2362344583, "max_line_length": 395, "alphanum_fraction": 0.5474240873, "num_tokens": 5163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489884579676883, "lm_q2_score": 0.12940274334274965, "lm_q1q2_score": 0.053689048855842345}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Motivation\n#\n# Hello, and welcome! We're excited to be your gateway into machine learning. ML is a\n# rapidly growing field that's buzzing with opportunity. Why? Because the tools and skills\n# employed by ML specialists are extremely powerful and allow them to draw conclusions from\n# large data sets quickly and with relative ease.\n#\n# Take the Celeste project, for example. This is a project that took 178 **tera**bytes of\n# data on the visible sky and used it to catalogue 188 millions stars and galaxies.\n# \"Cataloguing\" these stars meant identifying characteristics like their locations, colors,\n# sizes, and morphologies. This is an amazing feat, *especially* because this entire\n# calculation took under 15 minutes.\n#\n# <img src=\"data/Celeste.png\" alt=\"Drawing\" style=\"width: 1000px;\"/>\n#\n# How are Celeste's calculations so fast? To achieve performance on this scale, the Celeste\n# team uses the Julia programming language to write their software and supercomputers from\n# Lawrence Berkeley National Lab's NERSC as their hardware. In this course, we unfortunately\n# won't be able to give you access to a top 10 supercomputer, but we will teach you how to\n# use Julia!\n#\n# We're confident that this course will put you on your way to understanding many of the\n# important concepts and \"buzz words\" in ML. To get you started, we'll teach you how to\n# teach a machine to tell the difference between images of apples and bananas, i.e to\n# **classify** images as being one or the other type of fruit.\n#\n# Like Project Celeste, we'll use the [Julia programming language](https://julialang.org/)\n# to do this. In particular, we'll be working in [Jupyter notebooks](http://jupyter.org/)\n# like this one! (Perhaps you already know that the \"ju\" in Jupyter comes from Julia.)\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## What do the images we want to classify look like?\n#\n# We can use the `Images.jl` package in Julia to load sample images from this dataset. Most\n# of the data we will use live in the `data` folder in this repository.\n# ------------------------------------------------------------------------------------------\n\nusing Images  # To execute hit <shift> + enter\n\napple = load(\"data/10_100.jpg\")\n\nbanana = load(\"data/104_100.jpg\")\n\n# ------------------------------------------------------------------------------------------\n# The dataset consists of many images of different fruits, viewed from different positions.\n# These images are [available on GitHub here](https://github.com/Horea94/Fruit-Images-\n# Dataset).\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## What is the goal?\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# The ultimate goal is to feed one of these images to the computer and for it to identify\n# whether the image represents an apple or a banana!  To do so, we will **train** the\n# computer to learn **for itself** how to\n# distinguish the two images.\n#\n# The following notebooks will walk you step by step through the underlying math and machine\n# learning concepts you need to know in order to accomplish this classification.\n#\n# They alternate between two different types of notebooks: those labelled **ML** (Machine\n# Learning), which are designed to give a high-level overview of the concepts we need for\n# machine learning, but which gloss over some of the technical details; and those labelled\n# **Tools**, which dive into the details of coding in Julia that will be key to actually\n# implement the machine learning algorithms ourselves.\n#\n# The notebooks contain many **Exercises**. By doing these exercises in Julia, you will\n# learn the basics of machine learning!\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## Course outline\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# The course notebooks are listed below. We recommend that you follow them in order.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# - [01. Representing data in a computer](01. ML - Representing data in a computer.ipynb)\n# - [02. Using arrays to store data](02. Tools - Using arrays to store data.ipynb)\n#\n# - [03. Representing data with models](03. ML - Representing data with models.ipynb)\n# - [04. Functions](04. Tools - Functions.ipynb)\n# - [05. Building models](05. ML - Building models.ipynb)\n# - [06. Adding a function parameter](06. Tools - Adding a function parameter.ipynb)\n# - [07. Model complexity](07. ML - Model complexity.ipynb)\n# - [08. Multiple function parameters](08. Tools - Multiple function parameters.ipynb)\n# - [09. What is learning](09. ML - What is learning.ipynb)\n# - [10. Minimizing functions - how a computer learns](10. Tools - Minimizing functions -\n# how a computer learns.ipynb)\n# - [11. Intro to Neurons](11. ML - Intro to Neurons.ipynb)\n# - [12. Learning with a single neuron](12. Tools - Learning with a single neuron.ipynb)\n# - [13. Intro to Flux.jl](13. ML - Intro to Flux.jl.ipynb)\n# - [14. Learning with a single neuron using Flux.jl](14. Tools - Learning with a single\n# neuron using Flux.jl.ipynb)\n# - [15. Intro to neural networks](15. ML - Intro to neural networks.ipynb)\n# - [16. Using Flux to build a single layer neural net](16. Tools - Using Flux to build a\n# single layer neural net.ipynb)\n# - [17. Introduction to deep learning](17. ML - Introduction to deep learning.ipynb)\n# - [18. Multi-layer neural networks with Flux](18. Tools - Multi-layer neural networks with\n# Flux.ipynb)\n# - [19. Recognizing handwriting using a neural network](19. Recognizing handwriting using a\n# neural network.ipynb)\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# Let's get started with [Representing data in a computer](01. ML - Representing data in a\n# computer.ipynb\n# )!\n# ------------------------------------------------------------------------------------------\n", "meta": {"hexsha": "4062e14dbcac173ea198a6dc9d809f3fee4be627", "size": 6817, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-ml/00. Motivation - Classifying fruit.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-ml/00. Motivation - Classifying fruit.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-ml/00. Motivation - Classifying fruit.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 58.7672413793, "max_line_length": 92, "alphanum_fraction": 0.5606571806, "num_tokens": 1332, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10818896534500981, "lm_q1q2_score": 0.05367187812450252}}
{"text": "a = 1\n\nwhile a < 10\na = a + 1\nend\n\nprintln(a)\n", "meta": {"hexsha": "b0a95bd77c68a098c161c957c0edc579d1fdf8a9", "size": 46, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tests/test_while_lessthan_10.jl", "max_stars_repo_name": "guigs10mil/CompiladorJulia", "max_stars_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/test_while_lessthan_10.jl", "max_issues_repo_name": "guigs10mil/CompiladorJulia", "max_issues_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/test_while_lessthan_10.jl", "max_forks_repo_name": "guigs10mil/CompiladorJulia", "max_forks_repo_head_hexsha": "70ef9839bbef61543ef58e86b11ac31842a1e981", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 5.75, "max_line_length": 12, "alphanum_fraction": 0.5217391304, "num_tokens": 23, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4378234991142019, "lm_q2_score": 0.12252321572224492, "lm_q1q2_score": 0.05364354303023747}}
{"text": "\"\"\"\n    is_leap_year(year)\n\nReturn `true` if `year` is a leap year in the gregorian calendar.\n\n\"\"\"\nfunction is_leap_year(year)\n\nend\n\n", "meta": {"hexsha": "0d6a565f2335b206d8a3e47af9340b82d8e50b7f", "size": 133, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercises/leap/leap.jl", "max_stars_repo_name": "tomerarnon/julia-1", "max_stars_repo_head_hexsha": "6313e702d82f4fee10efdf29e943df50857cd7b5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 42, "max_stars_repo_stars_event_min_datetime": "2017-06-27T01:08:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T08:34:10.000Z", "max_issues_repo_path": "exercises/leap/leap.jl", "max_issues_repo_name": "tomerarnon/julia-1", "max_issues_repo_head_hexsha": "6313e702d82f4fee10efdf29e943df50857cd7b5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 269, "max_issues_repo_issues_event_min_datetime": "2017-06-19T13:56:09.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T22:15:18.000Z", "max_forks_repo_path": "exercises/leap/leap.jl", "max_forks_repo_name": "tomerarnon/julia-1", "max_forks_repo_head_hexsha": "6313e702d82f4fee10efdf29e943df50857cd7b5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 69, "max_forks_repo_forks_event_min_datetime": "2017-06-20T18:47:38.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-11T22:15:52.000Z", "avg_line_length": 12.0909090909, "max_line_length": 65, "alphanum_fraction": 0.6917293233, "num_tokens": 37, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4378234991142019, "lm_q2_score": 0.12252320450794522, "lm_q1q2_score": 0.05364353812035353}}
{"text": "module CoolPkg\n\nexport add2\n\n\"\"\"\n    add2(a, b)    \n\nAdds two numbers together...\n\n* `a`: A number\n* `b`: Another number\n\"\"\"\nadd2(a, b) = a + b\n\nend\n", "meta": {"hexsha": "bb1d798640a43faf4a5e5102b667975ccc141764", "size": 149, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CoolPkg.jl", "max_stars_repo_name": "danielw2904/CoolPkg.jl", "max_stars_repo_head_hexsha": "c56f6bfa1f5ac1bf722563b8e4b3c885ec3a5f4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/CoolPkg.jl", "max_issues_repo_name": "danielw2904/CoolPkg.jl", "max_issues_repo_head_hexsha": "c56f6bfa1f5ac1bf722563b8e4b3c885ec3a5f4f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/CoolPkg.jl", "max_forks_repo_name": "danielw2904/CoolPkg.jl", "max_forks_repo_head_hexsha": "c56f6bfa1f5ac1bf722563b8e4b3c885ec3a5f4f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.3125, "max_line_length": 28, "alphanum_fraction": 0.5637583893, "num_tokens": 51, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4687906266262437, "lm_q2_score": 0.11436852014455551, "lm_q1q2_score": 0.053614890224882354}}
{"text": "# -----------------------------------------------------------------------------------------\n# This file introduces `scitype`, `Scitype` methods and associated fallbacks methods. \n# It also defines some conveneince methods.\n# -----------------------------------------------------------------------------------------\n\n# -----------------------------------------------------------------------------------------\n# scitype function (generic) with fallbacks.\n\"\"\"\n    scitype(X, C::Convention)\n\nThe scientific type (interpretation) of object `X` (distinct from its\nmachine type) as specified by the convention 'C'.\n\"\"\"\nfunction scitype end\n\nscitype(X, C::Convention) = fallback_scitype(X, C)\nfallback_scitype(X, C) = Unknown\nfallback_scitype(::Missing, C) = Missing\nfallback_scitype(::Nothing, C) = Nothing\n\n# -----------------------------------------------------------------------------------------\n# `Scitype` function (generic) with fallbacks.\n# (When implemented can considerably speed-up computation of `scitype` of abstractarrays.)\n\n\"\"\"\n    Scitype(T::Type, C::Convention)\n\nMethod for implementers of a convention `C` to enable speed-up of\nscitype evaluations for arrays.\n\nIn general, one cannot infer the scitype of an object of type\n`AbstractArray{T, N}` from the machine type `T` alone.\n\nNevertheless, for some *restricted* machine types `U`, the statement\n`type(X) == AbstractArray{T, N}` for some `T<:U` already allows one\ndeduce that `scitype(X, C) = AbstractArray{S, N}`, where `S` is determined\nby `U`, and convention `C` alone. This is the case in the `DefaultConvention` which is \nused by *ScientificTypes.jl* , where for example, if `U = Integer`, then `S = Count`.\n\nSuch shortcuts are specified as follows:\n\n```\nScitype(::Type{<:U}, ::C) = S\n```\n\nwhich incurs a considerable speed-up in the computation of `scitype(X, C)`.\nThere is also a speed-up for the case that `T <: Union{U, Missing}`.\n\nFor example, in the `DefaultConvention`, one has\n\n```\nScitype(::Type{<:Integer}, ::DefaultConvention) = Count\n```\n\"\"\"\nfunction Scitype end\n\nScitype(T, C::Convention) = Fallback_Scitype(T, C)\nFallback_Scitype(::Type, C) = Unknown\n\n# To distinguish between `Any` type and `Union{T, Missing}` for some more\n# specialised `T`, we define the `Any` case explicitly.\nFallback_Scitype(::Type{Any}, C) = Unknown\n\n# For the case `Union{T, Missing}` we return `Union{S, Missing}` with `S`\n# the scientific type corresponding to `T`.\nfunction Fallback_Scitype(::Type{Union{T, Missing}}, C) where T\n    return Union{Scitype(Missing, C), Scitype(T, C)}\nend\n\n# For the case `Missing` and `Nothing`, \n# we return `Missing` and `Nothing` respectively.\nFallback_Scitype(::Type{Missing}, C) = Missing\nFallback_Scitype(::Type{Nothing}, C) = Nothing\n\n# ---------------------------------------------------------------------------------------\n# Fallbacks for `scitype` of tuples and abstractarrays.\n\nfunction fallback_scitype(@nospecialize(t::Tuple), C)\n    if length(t) <= TUPLE_SPECIALIZATION_THRESHOLD\n        return _tuple_scitype(t, C)\n    else\n        return Tuple{(scitype(el, C) for el in t)...}\n    end\nend\n\n@inline function _tuple_scitype(t::T, C) where {T}\n    if @generated\n        stypes = Tuple(\n            :(scitype(getindex(t, $i), C)) for i in 1:fieldcount(T)\n        )\n        return :(Tuple{$(stypes...)})\n    else\n        stypes_ = Tuple(\n            scitype(getindex(t, i), C) for i in 1:fieldcount(T)\n        )\n        return Tuple{(stypes_)...}\n    end\nend\n\nfunction fallback_scitype(A::Arr{T}, C) where T\n    return arr_scitype(A, Scitype(T, C), C)\nend\n\n\"\"\"\n    arr_scitype(A, S, C)\n\nReturn the scitype associated with an  array `A` of type `{T, N}` assuming an\nexplicit `Scitype` correspondence exist mapping `T` to `S`.\n\"\"\"\n@inline function arr_scitype(\n    @nospecialize(A::Arr{T, N}), S::Type, C;\n    tight::Bool = false\n) where {T, N}\n    if S === Unknown\n        return Arr{scitype_union(A, C), N}\n    elseif S === Union{Scitype(Missing, C), Unknown}\n        return Arr{Union{Scitype(Missing, C), scitype_union(A, C)}, N}\n    else    \n        return Arr{S, N}\n    end\nend\n\n# -----------------------------------------------------------------------------------------\n# internal methods.\n\n\"\"\"\n    scitype_union(A, C::convention)\n\nReturn the type union, over all elements `x` generated by the iterable `A`,\nof `scitype(x, C)` for a given convention `C`. See also [`scitype`](@ref).\n\"\"\"\nfunction scitype_union(A, C::Convention)\n    iterate(A) === nothing && return Unknown\n    reduce((a, b)->Union{a, b}, (scitype(el, C) for el in A))\nend\n", "meta": {"hexsha": "acef4fc5646e18601c9ea7e3b262318e43922410", "size": 4546, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/scitype.jl", "max_stars_repo_name": "JuliaAI/ScientificTypesBase.jl", "max_stars_repo_head_hexsha": "94ca98ba7e92f8db8528fd062b8045acb4c08920", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2021-06-16T10:59:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T03:19:04.000Z", "max_issues_repo_path": "src/scitype.jl", "max_issues_repo_name": "JuliaAI/ScientificTypesBase.jl", "max_issues_repo_head_hexsha": "94ca98ba7e92f8db8528fd062b8045acb4c08920", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2021-06-14T01:08:32.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T20:26:58.000Z", "max_forks_repo_path": "src/scitype.jl", "max_forks_repo_name": "JuliaAI/ScientificTypesBase.jl", "max_forks_repo_head_hexsha": "94ca98ba7e92f8db8528fd062b8045acb4c08920", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-08-19T04:58:51.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-29T14:06:14.000Z", "avg_line_length": 33.4264705882, "max_line_length": 91, "alphanum_fraction": 0.5956885174, "num_tokens": 1190, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.10970577824417872, "lm_q1q2_score": 0.05356750988463949}}
{"text": "# Call a function n times and print the average elapsed time.\nfunction benchElapsedTime(n:: Int64, benchfun:: Function)\n  acctime = 0.0\n  for i = 1:n\n    start = time_ns()\n    benchfun()\n    stop = time_ns()\n    acctime += (stop - start) / 1e9\n  end\n  println(\"ELAPSEDTIME: \" * string(acctime/n))\nend\n", "meta": {"hexsha": "e343db236253fc1034ca26f5444881bca8e5f4da", "size": 301, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "include/bench.jl", "max_stars_repo_name": "cau-placc/julia-curry", "max_stars_repo_head_hexsha": "dff85178faf7825e458064f7002fecf7be84b19f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-07-14T18:11:08.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-07T12:30:21.000Z", "max_issues_repo_path": "include/bench.jl", "max_issues_repo_name": "cau-placc/julia-curry", "max_issues_repo_head_hexsha": "dff85178faf7825e458064f7002fecf7be84b19f", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/bench.jl", "max_forks_repo_name": "cau-placc/julia-curry", "max_forks_repo_head_hexsha": "dff85178faf7825e458064f7002fecf7be84b19f", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0833333333, "max_line_length": 61, "alphanum_fraction": 0.6544850498, "num_tokens": 95, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.36658973632215985, "lm_q2_score": 0.14608724518943894, "lm_q1q2_score": 0.053554084694027136}}
{"text": "abstract type AbstractStack{T} end\n\n### Interface\n\n# Push an item onto the top of the stack.\npush!(s::AbstractStack, item) = nothing\n\n# Remove and return top element of the stack.\npop!(s::AbstractStack) = nothing\n\n# Return the top element.\ntopvalue(s::AbstractStack) = nothing\n\n# Return the number of elements in the stack.\nlength(s::AbstractStack) = nothing\n\n# Check if stack is empty.\nisempty(s::AbstractStack) = nothing\n\n### Generic\n\neltype(s::AbstractStack{T}) where T = T\n", "meta": {"hexsha": "1ae87a8926fb55729f6e1a339e30034ffcf03135", "size": 477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/list/abstract_stack.jl", "max_stars_repo_name": "hesseltuinhof/DataStructures.jl", "max_stars_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/list/abstract_stack.jl", "max_issues_repo_name": "hesseltuinhof/DataStructures.jl", "max_issues_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/list/abstract_stack.jl", "max_forks_repo_name": "hesseltuinhof/DataStructures.jl", "max_forks_repo_head_hexsha": "50c630bfb9b0eb43329bfd92148e1ca8b3fec9b1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.7391304348, "max_line_length": 45, "alphanum_fraction": 0.7295597484, "num_tokens": 121, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.32082131381216084, "lm_q2_score": 0.1666754046879767, "lm_q1q2_score": 0.05347302231217028}}
{"text": "@testset \"test_sum\" begin\n\n@test 0 == my_sum(0,0)\n\nend", "meta": {"hexsha": "94c33fb11a2723e429a44697e10b8a17c5c582a9", "size": 54, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_sum.jl", "max_stars_repo_name": "pietrop88/test_julia", "max_stars_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/test_sum.jl", "max_issues_repo_name": "pietrop88/test_julia", "max_issues_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_sum.jl", "max_forks_repo_name": "pietrop88/test_julia", "max_forks_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.8, "max_line_length": 25, "alphanum_fraction": 0.6666666667, "num_tokens": 20, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4804786780479071, "lm_q2_score": 0.11124121387367901, "lm_q1q2_score": 0.053449031386469795}}
{"text": "\n\"\"\"\n    neighbors(vec::AbstractVector)\n\nReturns an iterator over neighboring pairs.\n```julia\njulia> neighbors([1,2,3]) |> collect\n2-element Array{Tuple{Int64,Int64},1}:\n (1, 2)\n (2, 3)\n ```\n \"\"\"\nfunction neighbors(itr::AbstractVector)\n    first  = @view itr[1:end-1]\n    second = @view itr[2:end]\n    zip(first, second)\nend\n", "meta": {"hexsha": "fe86fc73dd3cab659cf9e073bcde91343a0cecbb", "size": 325, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/helpers.jl", "max_stars_repo_name": "jonalm/SparseTimeSeries.jl", "max_stars_repo_head_hexsha": "1dedc8380e6cdd00deda59664b890ebc49f3a714", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-02-04T15:41:52.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-27T23:03:26.000Z", "max_issues_repo_path": "src/helpers.jl", "max_issues_repo_name": "jonalm/SparseTimeSeries.jl", "max_issues_repo_head_hexsha": "1dedc8380e6cdd00deda59664b890ebc49f3a714", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2020-02-03T15:18:56.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-15T17:53:20.000Z", "max_forks_repo_path": "src/helpers.jl", "max_forks_repo_name": "jonalm/SparseTimeSeries.jl", "max_forks_repo_head_hexsha": "1dedc8380e6cdd00deda59664b890ebc49f3a714", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:41:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T10:41:07.000Z", "avg_line_length": 18.0555555556, "max_line_length": 43, "alphanum_fraction": 0.6461538462, "num_tokens": 101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4301473485858429, "lm_q2_score": 0.12421300511003351, "lm_q1q2_score": 0.05342989480796067}}
{"text": "\"\"\"\n    abstract type AbstractBlock\n\nAbstract supertype of all blocks. You should not subtype form this,\nbut instead from [`Block`](#) or [`WrapperBlock`](#).\n\"\"\"\nabstract type AbstractBlock end\n\n\n\n\"\"\"\n    abstract type Block\n\nA block describes the meaning of a piece of data in the context of a learning task.\nFor example, for supervised learning tasks, there is an input and a target\nand we want to learn to predict targets from inputs. Learning to predict a\ncat/dog label from 2D images is a supervised image classification task that can\nbe represented with the `Block`s `Image{2}()` and `Label([\"cat\", \"dog\"])`.\n\n`Block`s are used in virtually every part of the high-level interfaces, from data\nprocessing over model creation to visualization.\n\n## Extending\n\nConsider the following when subtyping `Block`. A block\n\n- Does not hold observation data itself. Instead they are used in conjunction with\n  data to annotate it with some meaning.\n- If it has any fields, they should be metadata that cannot be\n  derived from the data itself and is constant for every sample in\n  the dataset. For example `Label` holds all possible classes which\n  are constant for the learning problem.\n\n### Interfaces\n\nThere are many interfaces that can be implemented for a `Block`. See the docstrings\nof each function for more info about how to implement it.\n\n- [`checkblock`](#)`(block, data)`: check whether a piece of data is a valid block\n- [`mockblock`](#)`(block)`: randomly generate a piece of data\n- [`blocklossfn`](#)`(predblock, yblock)`: loss function for comparing two blocks\n- [`blockmodel`](#)`(inblock, outblock[, backbone])`: construct a task-specific model\n- [`blockbackbone`](#)`(inblock)`: construct a backbone model that takes in specific data\n- [`plotblock!`](#)`(block, data)`: visualize block data\n\n\"\"\"\nabstract type Block <: AbstractBlock end\n\n\n\"\"\"\n    checkblock(block, data)\n    checkblock(blocks, datas)\n\nCheck whether `data` is compatible with `block`, returning a `Bool`.\n\n## Examples\n\n```julia\ncheckblock(Image{2}(), rand(RGB, 16, 16)) == true\n```\n\n```julia\ncheckblock(\n    (Image{2}(),        Label([\"cat\", \"dog\"])),\n    (rand(RGB, 16, 16), \"cat\"                ),\n) == true\n```\n\n## Extending\n\nAn implementation of `checkblock` should be as specific as possible. The\ndefault method returns `false`, so you only need to implement methods for valid types\nand return `true`.\n\"\"\"\ncheckblock(::Block, data) = false\n\nfunction checkblock(blocks::Tuple, datas::Tuple)\n    @assert length(blocks) == length(datas)\n    return all(checkblock(block, data) for (block, data) in zip(blocks, datas))\nend\n\n\n\"\"\"\n    mockblock(block)\n    mockblock(blocks)\n\nRandomly generate an instance of `block`.\n\"\"\"\nmockblock(blocks::Tuple) = map(mockblock, blocks)\n\n\n\"\"\"\n    setup(Block, data)\n\nCreate an instance of block type `Block` from data container `data`.\n\n## Examples\n\n```julia\nsetup(Label, [\"cat\", \"dog\", \"cat\"]) == Label([\"cat\", \"dog\"])\n```\n\n    setup(Encoding, block, data; kwargs...)\n\nCreate an encoding using statistics derived from a data container `data`\nwith observations of block `block`. Used when some arguments of the encoding\nare dependent on the dataset. `data` should be the training dataset. Additional\n`kwargs` are passed through to the regular constructor of `Encoding`.\n\n## Examples\n\n```julia\n(images, labels), blocks = loaddataset(\"imagenette2-160\", (Image, Label))\nsetup(ImagePreprocessing, Image{2}(), images; buffered = false)\n```\n\n```julia\ndata, block = loaddataset(\"adult_sample\", TableRow)\nsetup(TabularPreprocessing, block, data)\n```\n\"\"\"\nfunction setup end\n\n\n# ## Utilities\n\ntypify(T::Type) = T\ntypify(t::Tuple) = Tuple{map(typify, t)...}\ntypify(block::FastAI.AbstractBlock) = typeof(block)\n\n\n# ## Block implementations\n\n\n# Image\n\n\"\"\"\n    Image{N}() <: Block\n\n`Block` for an N-dimensional mask. `data` is valid for `Image{N}()`\nif it is an N-dimensional array with color or number element type.\n\"\"\"\nstruct Image{N} <: Block end\n\ncheckblock(::Image{N}, ::AbstractArray{T,N}) where {T <: Union{Colorant,Number},N} = true\nmockblock(::Image{N}) where N = rand(RGB{N0f8}, ntuple(_ -> 16, N))\n\nsetup(::Type{Image}, data) = Image{ndims(getobs(data, 1))}()\n\n\"\"\"\n    Mask{N, T}(classes) <: Block\n\nBlock for an N-dimensional categorical mask. `data` is valid for\n`Mask{N, T}(classes)`\nif it is an N-dimensional array with every element in `classes`.\n\"\"\"\nstruct Mask{N,T} <: Block\n    classes::AbstractVector{T}\nend\nMask{N}(classes::AbstractVector{T}) where {N,T} = Mask{N,T}(classes)\n\nfunction checkblock(block::Mask{N,T}, a::AbstractArray{T,N}) where {N,T}\n    return all(map(x -> x \u2208 block.classes, a))\nend\n\nmockblock(mask::Mask{N}) where N = rand(mask.classes, ntuple(_ -> 16, N))\n\n# Keypoints\n\n\"\"\"\n\n    Keypoints{N}(sz) <: Block\n\nA block representing an array of size `sz` filled with keypoints of type\n`SVector{N}`.\n\"\"\"\nstruct Keypoints{N, M} <: Block\n    sz::NTuple{M, Int}\nend\nKeypoints{N}(n::Int) where N = Keypoints{N, 1}((n,))\nKeypoints{N}(t::NTuple{M, Int}) where {N, M} = Keypoints{N, M}(t)\n\nfunction checkblock(\n        block::Keypoints{N,M},\n        a::AbstractArray{<:Union{SVector{N,T},Nothing},M}) where {M,N,T}\n    return true\nend\n\nmockblock(block::Keypoints{N}) where N = rand(SVector{N, Float32}, block.sz)\n\n\n# TableRow\n\n\"\"\"\n    TableRow{M, N}(catcols, contcols, categorydict) <: Block\n\n`Block` for table rows with M categorical and N continuous columns. `data`\nis valid if it satisfies the `AbstractRow` interface in Tables.jl, values\npresent in indices for categorical and continuous columns are consistent,\nand `data` is indexable by the elements of `catcols` and `contcols`.\n\"\"\"\nstruct TableRow{M, N, T} <: Block\n    catcols::NTuple{M}\n    contcols::NTuple{N}\n    categorydict::T\nend\n\nfunction TableRow(catcols, contcols, categorydict)\n    TableRow{length(catcols), length(contcols)}(catcols, contcols, categorydict)\nend\n\nfunction checkblock(block::TableRow, x)\n    columns = Tables.columnnames(x)\n    (all(col -> col \u2208 columns, (block.catcols..., block.contcols...)) &&\n    all(col -> haskey(block.categorydict, col) &&\n        (ismissing(x[col]) || x[col] \u2208 block.categorydict[col]), block.catcols) &&\n    all(col -> ismissing(x[col]) || x[col] isa Number, block.contcols))\nend\n\nfunction mockblock(block::TableRow)\n    cols = (block.catcols..., block.contcols...)\n    vals = map(cols) do col\n        col in block.catcols ?\n            rand(block.categorydict[col]) : rand()\n    end\n    return NamedTuple(zip(cols, vals))\nend\n\n\"\"\"\n    setup(TableRow, data[; catcols, contcols])\n\nCreate a `TableRow` block from data container `data::TableDataset`. If the\ncategorical and continuous columns are not specified manually, try to\nguess them from the dataset's column types.\n\"\"\"\nfunction setup(::Type{TableRow}, data; catcols=nothing, contcols=nothing)\n    catcols_, contcols_ = getcoltypes(data)\n    catcols = isnothing(catcols) ? catcols_ : catcols\n    contcols = isnothing(contcols) ? contcols_ : contcols\n\n    return TableRow(\n        catcols,\n        contcols,\n        gettransformdict(data, DataAugmentation.Categorify, catcols))\nend\n\nfunction Base.show(io::IO, block::TableRow)\n    print(io, ShowCase(block, (:catcols, :contcols), show_params=false, new_lines=true))\nend\n\n# Continous\n\n\"\"\"\n    Continuous(size) <: Block\n\n`Block` for collections of numbers. `data` is valid if it's\nlength is `size` and contains `Number`s.\n\"\"\"\n\nstruct Continuous <: Block\n    size::Int\nend\n\nfunction checkblock(block::Continuous, x)\n    block.size == length(x) && eltype(x) <: Number\nend\n\nmockblock(block::Continuous) = rand(block.size)\n", "meta": {"hexsha": "82d1320274cc5ee2480ddb6bbcbbc1ddf1868ac8", "size": 7551, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datablock/block.jl", "max_stars_repo_name": "inferential/FastAI.jl", "max_stars_repo_head_hexsha": "3a017af061a1125231fe7d7a4ec98da0a255d781", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/datablock/block.jl", "max_issues_repo_name": "inferential/FastAI.jl", "max_issues_repo_head_hexsha": "3a017af061a1125231fe7d7a4ec98da0a255d781", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datablock/block.jl", "max_forks_repo_name": "inferential/FastAI.jl", "max_forks_repo_head_hexsha": "3a017af061a1125231fe7d7a4ec98da0a255d781", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0706319703, "max_line_length": 89, "alphanum_fraction": 0.6947424182, "num_tokens": 1993, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681662740417, "lm_q2_score": 0.13477591394766722, "lm_q1q2_score": 0.05338044909516059}}
{"text": "# This file was generated, do not modify it. # hide\n#\u00a0And keep only the corresponding features values.\ny = collect(skipmissing(y))\nX = X[no_miss, :]\n\n#\u00a0Let's now split our dataset into a train and test sets.\ntrain, test = partition(eachindex(y), 0.5, shuffle=true, rng=424);", "meta": {"hexsha": "04de3d293a4f6608672fb47392e6bdbae5bbd5e0", "size": 274, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/isl/lab-6b/code/ex6.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "__site/assets/isl/lab-6b/code/ex6.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "__site/assets/isl/lab-6b/code/ex6.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 39.1428571429, "max_line_length": 66, "alphanum_fraction": 0.7189781022, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.11279541672127952, "lm_q1q2_score": 0.0533165297226649}}
{"text": "#---profiling \n#=NEW - Profiling\nonce code is writting, running \nwe hat to profile it ens then you\n\n1.Start tuning: the performance of code \n\nwhy: once working, figure out where it spends its time\n-Timing the execution is very informative \n\nTakeaway: but as code gets more complicated, \n\n        \"Measuring time become Less & Less Useful\"\n\n2.start guessing: which part of my code is  \n eating up all time (optimize:if you can somehow lower that down)\n\nfor that: encourage you to use a set of Profilers \n\n\nProfiling: what parts of code are Taking part of the time \n\n\n(in very rough terms) there are two types of profilers:\n# instrumenting profiles\nan instrument profiler adds measurement 'instrumentation' to your source code \n\nsay call f(of x) store results in y: y = f(x) #me: calling f(x) a single variable function \n\n\n=#\nusing BenchmarkTools\n\ny = f(x) # this is line\n\nz = g(x, y)\n\n\"\"\"\nwhen you run code under instunmenting profiler \n\nit will actually modify that code (effectively)\n\n& run a code looks like this :\n\"\"\"\n\npush!(timebuffer, ProfileInfo(\"somefile.jl\", 17, time()))\ny = f(x)\npush!(timebuffer, ProfileInfo(\"somefile.jl\", 18, time()))\n\nz = g(x, y)\n\npush!(timebuffer, ProfileInfo(\"somefile.jl\", 19, time()))\n\n\"\"\"basucally it has some buffer that keeps track of timing data\n Right before it runs(first line), it might basically\n Record some information about \n1 .where it is (in your source code)\n2. what the current time is \n\nthen it will make that work \n \"\"\"\n\n\"\"\"\nfirst it knows what time it is \nqueries the system \n\n(me:simulates time advancesment : by passing i as an argument )\ntime would have advanced by the function call \n(On) every execution, it gets called \nthat's a very nice way of measuring time \n\nthere are a Quite a few profilers, work this way \n\nhowever,\nInsturment profiles come with hurdles:\n\n1.instrumentation slows your code: having to store data at every single function call #TODO: that implements as well in metatrader \n2.instumentation can block compiler optimizations: the compiled code of the real version may be quire different from that of the instrumented code \"minus instrumentation\"\n**3.recursion is a bit tricky**: if you also instrument `f`, the added instrumentation inside `f` distorts your measurement of the runtime of `f` itself\n\nso, running code with instrumentation is a very different code, than the code that runs without the instrumentation  \nyou're actually getting a misleading picture of the cirumstances  \n\nanytime you do instrumentation inside a function call \nhas a little instrumentation around it -gets complicated to subtract out ]\ncosts of insturmentation \nworld has moved on to a different stle of profiler\n\"\"\"\ni = 17\npush!(timebuffer, ProfileInfo(\"somefile.jl\", i, time()))\ny = f(x)\npush!(timebuffer, ProfileInfo(\"somefile.jl\", i = i + 1, time()))\n\nz = g(x, y)\npush!(timebuffer, ProfileInfo(\"somefile.jl\", i = i + 1, time()))\n\n\n#=Sampling profilers \n\na sampling profiler ,periodically, \n1.interrupts your code and \n2. collects program-location data (where you are)\n\nAnalogy: \n\n8 hours a day at work \n1 hour a day at the gym \n\nreal word \nwaka-time collecting time intervals  of user code activity plus langauge programmed in \n shortcoming: does this 100s times over a month\n \n aggregates all responses the\n \n =#\nusing Makie \n# using GLMakie\nlocation = [rand(1:9) <= 8 ? \"work\" : \"gym\" for i = 1:1000]\n\nbins = rand(5:16)\n#(values = location, bins = bins, normalization = :none) #TODO: hist equivalent (makie)\n#(\"Counts\")\n\n\nusing PyPlot\n\nlocation = [rand]\nlocation = [rand(1:9) <= 8 ? \"work\" : \"gym\" for i = 1:1000]\n\nbins = rand(5:16)\nhist(values = location; bins = bins, normalization = :none)\nylabels(\"Counts\")\n\n\"\"\"\nDisadvantages of sampling profilers\n1. you don't collect exhuastive information \nspending 5 minutses at bank will only be captured at all \n\n2. it's subject to sampling noise (given `n` counts, you have uncertainty\n of approx. `sqrt(n)` counts)\n\nyou'll get different number \n\nAdvantages of sampling profiler\nyou're running unmodified code \n\nCode has all the real world optimizations (the compiler can do)\n\n\"\"\"\n\n#--deno of profiling \n\n\nfunction busywait(t)\n\n        x = 0\n        for i = 1:round(Int, 2.1e10 * t)\n                x += i % 2\n        end\n        return x # return `x` to prevent the compiler from noticing that this doesn't do real work & eliminating it \nend\n\n@time busywait(0.8) # 8400000000\n\nfunction mydays(n)\n        x = 0\n        for i = 1:n\n                x += work()\n                x += gym()\n        end\n        return x\nend\n\n@noinline work() = busywait(0.08)\n@noinline gym() = busywait(0.01)\n\nmydays(1) # run once to compile it #945000000 \n\n\"\"\"NEW\ngood rule of thumb:\ntakeaway: clear the buffer of the library before use \n\nbullet note: the overhead, when the function itself actually is doiing the work\n\nCount  Overhead File                   Line Function\n =====  ======== ====                   ==== ========\n  1877         0 @Base\\boot.jl           360 eval\n  1877         0 @Base\\essentials.jl     708 invokelatest(::Any, ::Any, ::...  1877         0 @Base\\essentials.jl     708 #invokelatest#2\n  1877         0 @Base\\essentials.jl     706 invokelatest(::Any, ::Any, ::...  1580      1580 @Base\\int.jl             87 +\n  1877         0 @Base\\loading.jl       1116 include_string(mapexpr::typeo...  1877         0 @Base\\logging.jl        603 with_logger\n  1877         0 @Base\\logging.jl        491 with_logstate(f::Function, lo...  1877         0 @Base\\task.jl           411 (::VSCodeServer.var\"#53#54\")()  \n     1         1 ...ile\\src\\Profile.jl   352 stop_timer()\n  1580         0 ...torial\\profiler.jl   157 busywait\n   190        29 ...torial\\profiler.jl   174 gym()\n  1686         0 ...torial\\profiler.jl   167 mydays(n::Int64)\n   190         0 ...torial\\profiler.jl   168 mydays(n::Int64)\n  1686       267 ...torial\\profiler.jl   173 work()\n\nme:  count/overhead ratio : for gym : \n\n 190/29 =  6.551724137931034\n 1686/267 =  6.314606741573034\n\nwork()/gym() ratio:\n 267/29 = 9.206896551724138 (over 8 , overworking (seemingly, undergyming)  )\n 1686/267 = 6.314606741573034 (under 8, underworking (seemingly over gyming))\n  \nfrom looping at the sampling profiler data \n\n\n\"\"\"\n\nusing Profile\nProfile.clear() # clear old results (not really needed on the first usuage)\n@profile mydays(30)\n\nProfile.print(format = :flat) # represents number of snapshots that it's calling\n\n\n\"\"\"another default way prented in t\n Count  Overhead File                   Line Function\n =====  ======== ====                   ==== ========\n  1877         0 @Base\\boot.jl           360 eval\n  1877         0 @Base\\essentials.jl     708 invokelatest(::Any, ::Any, ::...  1877         0 @Base\\essentials.jl     708 #invokelatest#2\n  1877         0 @Base\\essentials.jl     706 invokelatest(::Any, ::Any, ::...  1580      1580 @Base\\int.jl             87 +\n  1877         0 @Base\\loading.jl       1116 include_string(mapexpr::typeo...  1877         0 @Base\\logging.jl        603 with_logger\n  1877         0 @Base\\logging.jl        491 with_logstate(f::Function, lo...  1877         0 @Base\\task.jl           411 (::VSCodeServer.var\"#53#54\")()  \n     1         1 ...ile\\src\\Profile.jl   352 stop_timer()\n  1580         0 ...torial\\profiler.jl   157 busywait\n   190        29 ...torial\\profiler.jl   174 gym()\n  1686         0 ...torial\\profiler.jl   167 mydays(n::Int64)\n   190         0 ...torial\\profiler.jl   168 mydays(n::Int64)\n  1686       267 ...torial\\profiler.jl   173 work()\n  1877         0 ...Server\\src\\eval.jl   201 (::VSCodeServer.var\"#55#59\"{V...  1877         0 ...Server\\src\\eval.jl   124 (::VSCodeServer.var\"#56#60\"{B...  1877         0 ...Server\\src\\eval.jl   153 (::VSCodeServer.var\"#57#61\"{B...  1877         0 ...Server\\src\\eval.jl   155 (::VSCodeServer.var\"#58#62\"{B...  1877         0 ...Server\\src\\eval.jl   211 inlineeval(m::Module, code::S...  1877         0 ...Server\\src\\eval.jl   209 inlineeval##kw\n  1877         0 ...Server\\src\\eval.jl    34 macro expansion\n  1877         0 ...Server\\src\\repl.jl    36 hideprompt(f::VSCodeServer.va...  1877         0 ...Server\\src\\repl.jl   185 withpath(f::VSCodeServer.var\"...Total snapshots: 1877\n\nOverhead \u254e [+additional indent] Count File:Line; Function\n=========================================================\n    \u254e1877 @Base\\task.jl:411; (::VSCodeServer.var\"#53#54\")()\n    \u254e 1877 ...Server\\src\\eval.jl:34; macro expansion\n    \u254e  1877 @Base\\essentials.jl:706; invokelatest(::Any)\n    \u254e   1877 @Base\\essentials.jl:708; #invokelatest#2\n    \u254e    1877 ...erver\\src\\eval.jl:201; (::VSCodeServer.var\"#55#59\"...       \n    \u254e     1877 @Base\\logging.jl:603; with_logger\n    \u254e    \u254e 1877 @Base\\logging.jl:491; with_logstate(f::Function...\n    \u254e    \u254e  1877 ...ver\\src\\eval.jl:124; (::VSCodeServer.var\"#56#6...        \n    \u254e    \u254e   1877 ...ver\\src\\repl.jl:36; hideprompt(f::VSCodeServe...\n    \u254e    \u254e    1877 ...ver\\src\\eval.jl:153; (::VSCodeServer.var\"#57#...       \n    \u254e    \u254e     1877 ...er\\src\\repl.jl:185; withpath(f::VSCodeServe...        \n    \u254e    \u254e    \u254e 1877 ...er\\src\\eval.jl:155; (::VSCodeServer.var\"#5...        \n    \u254e    \u254e    \u254e  1877 ...r\\src\\eval.jl:209; inlineeval##kw\n    \u254e    \u254e    \u254e   1877 ...r\\src\\eval.jl:211; inlineeval(m::Module,...        \n    \u254e    \u254e    \u254e    1877 ...essentials.jl:706; invokelatest(::Any, :...\n    \u254e    \u254e    \u254e     1877 ...ssentials.jl:708; invokelatest(::Any, ...        \n    \u254e    \u254e    \u254e    \u254e 1877 ...\\loading.jl:1116; include_string(mape...        \n    \u254e    \u254e    \u254e    \u254e  1877 @Base\\boot.jl:360; eval\n   1\u254e    \u254e    \u254e    \u254e   1    ...\\Profile.jl:352; stop_timer()\n    \u254e    \u254e    \u254e    \u254e   1686 ...profiler.jl:167; mydays(n::Int64)\n 267\u254e    \u254e    \u254e    \u254e    1686 ...profiler.jl:173; work()\n    \u254e    \u254e    \u254e    \u254e     1419 ...rofiler.jl:157; busywait\n1418\u254e    \u254e    \u254e    \u254e    \u254e 1419 @Base\\int.jl:87; +\n    \u254e    \u254e    \u254e    \u254e   190  ...profiler.jl:168; mydays(n::Int64)\n  29\u254e    \u254e    \u254e    \u254e    190  ...profiler.jl:174; gym()\n    \u254e    \u254e    \u254e    \u254e     161  ...rofiler.jl:157; busywait\n 161\u254e    \u254e    \u254e    \u254e    \u254e 161  @Base\\int.jl:87; +\nTotal snapshots: 1877\n\n1686/ 1877 = 0.89824\n\nTakeaway: in profiler tree: the indentation indicates who called `whom` \n\nInformation is attributed to the entire call sequence \n(aggregated in al time spent in that particular line of code )\n\ndifferent way of checking, it doesn't take a different amount of time \n\n\nTODO: \nQ. what is an overhead, & overcount #Research \n\nu\n\n\"\"\"\nProfile.print(format = :tree)\n\n#--visualization of profile data \n\n\nusing ProfileView # `ProfileView` for real work instead\n\nProfileView.view()\n\n\n#= on jupyter only \nusing ProfileSVG\n\nProfileSVG.view()\n=#\n#--- Performace profiling in action \n#=\nDiscovering an opportunity for an optimization \n by a profile \n\n=#\n\n#Data definition \n\nA = rand(10_000, 2)\nB = rand(2, 8_000)\nx = rand(8_000)\nfunction mult(A, B, x)\n        C = A * B\n        return C * x\nend\n\nmult(A, B, x)\n\n@time mult(A, B, x)  # 0.500144 seconds #2.57 M allocations: 748.353 MiB, 4.44% gc time, 77.02% compilation time\nusing ProfileSVG\n#Error:@profview is undefined - which macro is it from?\n@profview mult(A, B, x)\n\n\"\"\"\n(always) know what to look for!\nit's revealing this 'gemm' where \nmm is matrix multiply\nmv: matrix vectore (multiply)\n\nmost of time spent (lost) multiplying those matrices \n-Going back above, 'C' is a matrix multiplication - \nthus, a bottle is easily spottable, now\n\nwhat if: \n1.we do the matrix multiplication, first \n2. do a Second matrix-vector multiplication \n\"\"\"\nfunction mult2(A, B, x)\n\n        y = B * x\n        return A * y\n\nend\n\n# the use of \\approx or \u2248\nmult2(A, B, x) \u2248 mult(A, B, x) #they are approx. equal \nmult2(A, B, x) == mult(A, B, x) # but they wont ever be equal \nerror = mult2(A, B, x) - mult(A, B, x)\n#= ME:\ncritical side-note:\nthis introduces an unexpected  systematic model error  \n=#\n\"\"\"Tim Holy\nhere the error is small, \nthere's a need for a hight precision  \nso, to a very high-degree of precision , these two are equal\n(to ecach other )\nme: discarding the error, as if it doesn't exist\nlike blinding turning an eye on it \nit won't help much \n\n\n\"\"\"\n\n@time mult2(A, B, x) #0.000718 seconds (3allocations:78.297KiB)\n\n#=we've made a dramatic  Improvement on the matrix multipkucatuib \n\n=#\n#--- Using profiling to detect \"gotchas\"\n\n\n#=\nRecall the addTwo was slopw when passed a `Vector{Any}`\nbut fast for a `Vector{T}` with concrete `T`` \n=#\n#= 0.001 ns (0 allocations: 0 bytes)\n(:\n:var\"#addTwo#5\") (generic function with 1 method)=#\n@btime addTwo(z) = (x = rand(1:5); y = rand(1:5); z = [x, y])\n\n#=  0.001 ns (0 allocations: 0 bytes)\n(::var\"#addTwo#6\") (generic function with 1 method) =#\n@btime addTwo(z) = (x = rand(1:5); y = rand(1:5); z = Any[x, y])\n\n\"\"\"Any type makes  running code longer \nQ. what if i did it on accident, what should I do now?\n\nsometimes you'll be surpised about the code you write that breaks type-Inference\n\n#BenchmarkTools have a macro  @bprofile \nWhy: to collect profile Data on (code)\nme: outputs statistics of  a 10K samples,  1K evaluations \n\"\"\"\n\n@bprofile addTwo(z) = (x = rand(1:5); y = rand(1:5); z = Any[x, y])\n\n\"\"\"\n       \nBenchmarkTools.Trial: 10000 samples with 1000 evaluations.\n Range (min \u2026 max):  0.001 ns \u2026 1.400 ns  \u250a GC (min \u2026 \nmax): 0.00% \u2026 0.00%\n Time  (median):     0.001 ns             \u250a GC (median):    0.00%\n Time  (mean \u00b1 \u03c3):   0.043 ns \u00b1 0.053 ns  \u250a GC (mean \u00b1 \u03c3):  0.00% \u00b1 0.00%\n\n  \u2588                        \n    \u2584\n  \u2588\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2581\u2588 \u2582\n  0.001 ns       Histogram: frequency by time        0.1 ns <\n\n Memory estimate: 0 bytes, allocs estimate: 0.   \n\n involving strings, \n comes from times operation on code\n prints in REPL all type info & method instumentationif right click on it \n it takes you to the code \n\n all its copying going on \n\n the Hint: the copying (hat's going on)\n we're starting of fwith an empty string \n 2. we're going char by char \n 3. the, you add another Whole character to a string \n -it's gonna have to copy the old string, with a little extrra space on the end (then the character)\n \"\"\"\n\nusing Profile, ProfileSVG, ProfileView, BenchmarkTools\nconst charlist = 'a':'z' #Global Variable # define as const # \n\nfunction randomstring(n::Integer)\n        str = \"\"\n        for i = 1:n\n                str *= rand(charlist)\n        end\n        return str\nend\n\n\"\"\"preallocate is  the answer\nInstead, preallocate some Memory\n \nafter applying changes, check if it's working, as expected before \n\"\"\"\n\n\nfunction randomstring2(n::Integer)\n        # preallocate some Memory\n        chars = Vector{UInt8}(undef, n)\n        #preallocate a buffer for this \n        #instead of growing it each time , simply stash the random variable\n        for i = 1:n\n                chars[i] = rand(charlist) #stash the chars (me:1 by 1)\n        end\n        #return str \n        return String(chars)\nend\n\nfunction randomstring3(n::Integer)\n        # preallocate some Memory\n        chars = Vector{UInt8}(undef, n)#local variable #how did he know that, is scope is this function only, isn't that enough to make it local\n        #preallocate a buffer for this \n        #instead of growing it each time , simply stash the random variable\n        for i = 1:n\n                chars[i] = rand(charlist) #stash the chars (me:1 by 1)\n        end\n        #return str \n        return String(chars)\nend\n\nProfileSVG.view() #initializes ProfileView\n\n\n\nrandomstring(8)\n@time randomstring(10^5);#4.639694 seconds (300.00 k allocations: 4.672 GiB, 16.61% gc time)\n\nrandomstring2(8)\n@time randomstring2(10^5); #in REPL #0.008138 seconds (99.49 k allocations: 1.709 MiB)\n\nProfileView.@view()\nwarntype_last #warntype at last thi clickedd onsa\n@time randomstring3(10^5); # global vairable charlist # 0.063154 seconds (155.18 k allocations: 4.939 MiB, 85.53% compilation time)\n #=first page on the ma\n return \n main.charlist is a global variable\n  & not constant \n  since it's not constant - it change the type \n  any moment \n  so, julia can't create an optimized version \n  of this function with Fixed height (length)\n for charlist\n\none point of View: it could be multi-threaded (we could change its type)\ntakeaway: we can change type of code of fuliea \nwhile the code is running \n\n        so, that would be really bad if it would hit\n        hard wired-in Known Type, & that type change \n       that would be really problematic \nif wanna fix  the performance , the way you can do it, declare this list constant \n\nTakeaway: for any project, determine list of constants, for them to be preallocated each time the project loads \nthat will saved from problems & code performace issues  \n        =# \n\n#=\nlooking at the second method \nwhat is that line red ?\n-it's doing dispatch \n profile View's other feature \n it'll show you code Type, or specialized color version\n @code_warn_type (warntype_last()../6) (outputs shows which line) on the last thing you clicked on\n=#\n\n#=\njulia can be fast, but you need to learn enought to avoid some common gotchas \n1. don't use non-`const` global variables\n2. don't use containers like arrays with non-concrete types (unless absolutely necessary)\n3. measure & analyze performance with @time, BenchmarkTools,`@Profile`, `@bProfile`,ProfileView\n=#\n#===end of profiling optimization part 1=====", "meta": {"hexsha": "fd7d6ebd0b3131f6e35b1da0a75609c8daa8c0c1", "size": 17163, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Tutorial/Tim Holy/profiler.jl", "max_stars_repo_name": "adamwillisMastery/DeepLearner", "max_stars_repo_head_hexsha": "f920e93af5ece5c4f8751bf493047770689ecaff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-01-15T00:43:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-17T12:38:14.000Z", "max_issues_repo_path": "Tutorial/Tim Holy/profiler.jl", "max_issues_repo_name": "adamwillisMastery/DeepLearner", "max_issues_repo_head_hexsha": "f920e93af5ece5c4f8751bf493047770689ecaff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-01-23T12:20:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-01T05:33:04.000Z", "max_forks_repo_path": "Tutorial/Tim Holy/profiler.jl", "max_forks_repo_name": "adamwillisXanax/DeepLearner", "max_forks_repo_head_hexsha": "f920e93af5ece5c4f8751bf493047770689ecaff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0057692308, "max_line_length": 444, "alphanum_fraction": 0.643302453, "num_tokens": 5301, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "module TestParameterTypes\n\nusing Mimi\nusing Test\n\nimport Mimi:\n    external_params, external_param, TimestepMatrix, TimestepVector,\n    ArrayModelParameter, ScalarModelParameter, FixedTimestep, import_params!, \n    set_first_last!, _get_param_times\n\n#\n# Test that parameter type mismatches are caught\n#\nexpr = :(\n    @defcomp BadComp1 begin\n        a = Parameter(index=[time, regions], default=[10, 11, 12])  # should be 2D default\n        function run_timestep(p, v, d, t)\n        end\n    end\n)\n@test_throws ErrorException eval(expr)\n\nexpr = :(\n    @defcomp BadComp2 begin\n        a = Parameter(default=[10, 11, 12])  # should be scalar default\n        function run_timestep(p, v, d, t)\n        end\n    end\n)\n@test_throws ErrorException eval(expr)\n\n#\n# Test that the old type parameterization syntax errors\n#\nexpr = :(\n    @defcomp BadComp3 begin\n        a::Int = Parameter()\n        function run_timestep(p, v, d, t)\n        end\n    end\n)\n@test_throws LoadError eval(expr)\n\n\n@defcomp MyComp begin\n    a = Parameter(index=[time, regions], default=ones(101,3))\n    b = Parameter(index=[time], default=1:101)\n    c = Parameter(index=[regions])\n    d = Parameter()\n    e = Parameter(index=[four])\n    f = Parameter{Array{Float64, 2}}()\n    g = Parameter{Int}(default=10.0)    # value should be Int despite Float64 default\n    h = Parameter(default=10)           # should be \"numtype\", despite Int default\n    j = Parameter{Int}(index = [regions])\n\n    function run_timestep(p, v, d, t)\n    end\nend\n\n# Check that explicit number type for model works as expected\nnumtype = Float32\narrtype = Union{Missing, numtype}\n\nm = Model(numtype)\n\nset_dimension!(m, :time, 2000:2100)\nset_dimension!(m, :regions, 3)\nset_dimension!(m, :four, 4)\n\nadd_comp!(m, MyComp)\nset_param!(m, :MyComp, :c, [4,5,6])\nset_param!(m, :MyComp, :d, 0.5)   # 32-bit float constant\nset_param!(m, :MyComp, :e, [1,2,3,4])\nset_param!(m, :MyComp, :f, reshape(1:16, 4, 4))\nset_param!(m, :MyComp, :j, [1,2,3])\n\nMimi.build!(m)    # applies defaults, creating external params in the model instance's copied definition\nextpars = external_params(m.mi.md)\n\n@test isa(extpars[:a], ArrayModelParameter)\n@test isa(extpars[:b], ArrayModelParameter)\n@test _get_param_times(extpars[:a]) == _get_param_times(extpars[:b]) == 2000:2100\n\n@test isa(extpars[:c], ArrayModelParameter)\n@test isa(extpars[:d], ScalarModelParameter)\n@test isa(extpars[:e], ArrayModelParameter)\n@test isa(extpars[:f], ScalarModelParameter) # note that :f is stored as a scalar parameter even though its values are an array\n\n@test typeof(extpars[:a].values) == TimestepMatrix{FixedTimestep{2000, 1, 2100}, arrtype, 1, Array{arrtype, 2}}\n@test typeof(extpars[:b].values) == TimestepVector{FixedTimestep{2000, 1, 2100}, arrtype, Array{arrtype, 1}}\n\n@test typeof(extpars[:c].values) == Array{arrtype, 1}\n@test typeof(extpars[:d].value) == numtype\n@test typeof(extpars[:e].values) == Array{arrtype, 1}\n@test typeof(extpars[:f].value) == Array{Float64, 2}\n@test typeof(extpars[:g].value) <: Int\n@test typeof(extpars[:h].value) == numtype\n\n# test updating parameters\n@test_throws ErrorException update_param!(m, :a, 5) # expects an array\n@test_throws ErrorException update_param!(m, :a, ones(101)) # wrong size\n@test_throws ErrorException update_param!(m, :a, fill(\"hi\", 101, 3)) # wrong type\n\nset_param!(m, :a, Array{Int,2}(zeros(101, 3))) # should be able to convert from Int to Float\n@test_throws ErrorException update_param!(m, :d, ones(5)) # wrong type; should be scalar\nupdate_param!(m, :d, 5) # should work, will convert to float\nnew_extpars = external_params(m)    # Since there are changes since the last build, need to access the updated dictionary in the model definition\n@test extpars[:d].value == 0.5      # The original dictionary still has the old value\n@test new_extpars[:d].value == 5.   # The new dictionary has the updated value\n@test_throws ErrorException update_param!(m, :e, 5) # wrong type; should be array\n@test_throws ErrorException update_param!(m, :e, ones(10)) # wrong size\nupdate_param!(m, :e, [4,5,6,7])\n\n@test length(extpars) == 9          # The old dictionary has the default values that were added during build, so it has more entries\n@test length(new_extpars) == 6\n@test typeof(new_extpars[:a].values) == TimestepMatrix{FixedTimestep{2000, 1, 2100}, arrtype, 1, Array{arrtype, 2}}\n\n@test typeof(new_extpars[:d].value) == numtype\n@test typeof(new_extpars[:e].values) == Array{arrtype, 1}\n\n\n#------------------------------------------------------------------------------\n# Test updating TimestepArrays with update_param!\n#------------------------------------------------------------------------------\n\n@defcomp MyComp2 begin\n    x=Parameter(index=[time])\n    y=Variable(index=[time])\n    function run_timestep(p,v,d,t)\n        v.y[t]=p.x[t]\n    end\nend\n\n# 1. update_param! with Fixed Timesteps\n\nm = Model()\nset_dimension!(m, :time, 2000:2004)\nadd_comp!(m, MyComp2, first=2001, last=2003)\nset_param!(m, :MyComp2, :x, [1, 2, 3, 4, 5])\n# Year      x       Model   MyComp2 \n# 2000      1       first   \n# 2001      2               first\n# 2002      3\n# 2003      4               last\n# 2004      5      last\n\nupdate_param!(m, :x, [2.,3.,4.,5.,6.])\nupdate_param!(m, :x, zeros(5))\nupdate_param!(m, :x, [1,2,3,4,5])\n\nset_dimension!(m, :time, 1999:2001)\n# Year      x       Model   MyComp2 \n# 1999      missing first\n# 2000      1          \n# 2001      2       last    first, last\n\nx = external_param(m.md, :x) \n@test ismissing(x.values.data[1])\n@test x.values.data[2:3] == [1.0, 2.0]\n@test _get_param_times(x) == 1999:2001\nrun(m) # should be runnable\n\nupdate_param!(m, :x, [2, 3, 4]) # change x to match \n# Year      x       Model   MyComp2 \n# 1999      2       first   \n# 2000      3               \n# 2001      4       last    first, last\n\nx = external_param(m.md, :x)\n@test x.values isa Mimi.TimestepArray{Mimi.FixedTimestep{1999, 1, 2001}, Union{Missing,Float64}, 1}\n@test x.values.data == [2., 3., 4.]\nrun(m)\n@test ismissing(m[:MyComp2, :y][1])  # 1999\n@test ismissing(m[:MyComp2, :y][2])  # 2000\n@test m[:MyComp2, :y][3] == 4   # 2001\n\nset_first_last!(m, :MyComp2, first = 1999, last = 2001)\n# Year      x       Model   MyComp2 \n# 1999      2       first   first\n# 2000      3               \n# 2001      4       last    last\n\nrun(m)\n@test m[:MyComp2, :y] == [2, 3, 4]\n\n# 2. Test with Variable Timesteps\n\nm = Model()\nset_dimension!(m, :time, [2000, 2005, 2020])\nadd_comp!(m, MyComp2)\nset_param!(m, :MyComp2, :x, [1, 2, 3])\n# Year      x       Model   MyComp2 \n# 2000      1       first   first\n# 2005      2               \n# 2010      3       last    last\n\nset_dimension!(m, :time, [2000, 2005, 2020, 2100])\n# Year      x       Model   MyComp2 \n# 2000      1       first   first\n# 2005      2               \n# 2020      3               last\n# 2100      missing last\n\nx = external_param(m.md, :x) \n@test ismissing(x.values.data[4])\n@test x.values.data[1:3] == [1.0, 2.0, 3.0]\n\nupdate_param!(m, :x, [2, 3, 4, 5]) # change x to match \n# Year      x       Model   MyComp2 \n# 2000      2       first   first\n# 2005      3               \n# 2020      4               last\n# 2100      5        last\n\nx = external_param(m.md, :x)\n@test x.values isa Mimi.TimestepArray{Mimi.VariableTimestep{(2000, 2005, 2020, 2100)}, Union{Missing,Float64}, 1}\n@test x.values.data == [2., 3., 4., 5.]\nrun(m)\n@test m[:MyComp2, :y][1] == 2   # 2000\n@test m[:MyComp2, :y][2] == 3   # 2005\n@test m[:MyComp2, :y][3] == 4   # 2020\n@test ismissing(m[:MyComp2, :y][4]) # 2100 - past last attribute for component \n\nset_first_last!(m, :MyComp2, first = 2000, last = 2020)\n# Year      x       Model   MyComp2 \n# 2000      1       first   first\n# 2005      2               \n# 2020      3       last    last\n\nrun(m)\n@test m[:MyComp2, :y][1:3] == [2., 3., 4.]\n@test ismissing(m[:MyComp2, :y][4])\n\n# 3. Test updating from a dictionary\n\nm = Model()\nset_dimension!(m, :time, [2000, 2005, 2020])\nadd_comp!(m, MyComp2)\nset_param!(m, :MyComp2, :x, [1, 2, 3])\n\nset_dimension!(m, :time, [2000, 2005, 2020, 2100])\n\nupdate_params!(m, Dict(:x=>[2, 3, 4, 5]))\nx = external_param(m.md, :x)\n@test x.values isa Mimi.TimestepArray{Mimi.VariableTimestep{(2000, 2005, 2020, 2100)}, Union{Missing,Float64}, 1}\n@test x.values.data == [2., 3., 4., 5.]\nrun(m)\n\n@test m[:MyComp2, :y][1] == 2   # 2000\n@test m[:MyComp2, :y][2] == 3   # 2005\n@test m[:MyComp2, :y][3] == 4   # 2020\n@test ismissing(m[:MyComp2, :y][4])   # 2100\n\n# 4. Test updating the time index to a different length\n\nm = Model()\nset_dimension!(m, :time, 2000:2002)     # length 3\nadd_comp!(m, MyComp2)\nset_param!(m, :MyComp2, :x, [1, 2, 3])\n# Year      x       Model   MyComp2 \n# 2000      1       first   first\n# 2001      2               \n# 2002      3       last    last\n\nset_dimension!(m, :time, 1999:2003)     # length 5\nupdate_param!(m, :x, [2, 3, 4, 5, 6])\n# Year      x       Model   MyComp2 \n# 1999      2       first   \n# 2000      3               first\n# 2001      4               \n# 2002      5               last\n# 2003      6       last\n\nx = external_param(m.md, :x)\n@test x.values isa Mimi.TimestepArray{Mimi.FixedTimestep{1999, 1, 2003}, Union{Missing, Float64}, 1, 1}\n@test x.values.data == [2., 3., 4., 5., 6.]\n\nrun(m)\n@test ismissing(m[:MyComp2, :y][1]) \n@test m[:MyComp2, :y][2:4] == [3., 4., 5.]\n@test ismissing(m[:MyComp2, :y][5]) \n\nset_first_last!(m, :MyComp2, first = 1999, last = 2001)\n# Year      x       Model   MyComp2 \n# 1999      2       first   first\n# 2000      3               \n# 2001      4               last\n# 2002      5               \n# 2003      6       last\n\nrun(m)\n@test ismissing(m[:MyComp2, :y][4])\n@test ismissing(m[:MyComp2, :y][5])\n@test m[:MyComp2, :y][1:3] == [2., 3., 4.]\n\n# 5. Test all the warning and error cases\n\n@defcomp MyComp3 begin\n    regions=Index()\n    x=Parameter(index=[time])       # One timestep array parameter\n    y=Parameter(index=[regions])    # One non-timestep array parameter\n    z=Parameter()                   # One scalar parameter\nend\n\nm = Model()                             # Build the model\nset_dimension!(m, :time, 2000:2002)     # Set the time dimension\nset_dimension!(m, :regions, [:A, :B])\nadd_comp!(m, MyComp3)\nset_param!(m, :MyComp3, :x, [1, 2, 3])\nset_param!(m, :MyComp3, :y, [10, 20])\nset_param!(m, :MyComp3, :z, 0)\n\n@test_throws ErrorException update_param!(m, :x, [1, 2, 3, 4]) # Will throw an error because size\nupdate_param!(m, :y, [10, 15])\n@test external_param(m.md, :y).values == [10., 15.]\nupdate_param!(m, :z, 1)\n@test external_param(m.md, :z).value == 1\n\n# Reset the time dimensions\nset_dimension!(m, :time, 1999:2001)\n\nupdate_params!(m, Dict(:x=>[3,4,5], :y=>[10,20], :z=>0)) # Won't error when updating from a dictionary\n\n@test external_param(m.md, :x).values isa Mimi.TimestepArray{Mimi.FixedTimestep{1999,1, 2001},Union{Missing,Float64},1}\n@test external_param(m.md, :x).values.data == [3.,4.,5.]\n@test external_param(m.md, :y).values == [10.,20.]\n@test external_param(m.md, :z).value == 0\n\n#------------------------------------------------------------------------------\n# Test the three different set_param! methods for a Symbol type parameter\n#------------------------------------------------------------------------------\n\n@defcomp A begin\n    p1 = Parameter{Symbol}()\nend\n\nfunction _get_model()\n    m = Model()\n    set_dimension!(m, :time, 10)\n    add_comp!(m, A)\n    return m\nend\n\n# Test the 3-argument version of set_param!\nm = _get_model()\n@test_throws MethodError set_param!(m, :p1, 3)  # Can't set it with an Int\n\nset_param!(m, :p1, :foo)    # Set it with a Symbol\nrun(m)\n@test m[:A, :p1] == :foo\n\n# Test the 4-argument version of set_param!\nm = _get_model()\n@test_throws MethodError set_param!(m, :A, :p1, 3)\n\nset_param!(m, :A, :p1, :foo)\nrun(m)\n@test m[:A, :p1] == :foo\n\n# Test the 5-argument version of set_param!\nm = _get_model()\n@test_throws MethodError set_param!(m, :A, :p1, :A_p1, 3)\n\nset_param!(m, :A, :p1, :A_p1, :foo)\nrun(m)\n@test m[:A, :p1] == :foo\n\n#------------------------------------------------------------------------------\n# Test that if set_param! errors in the connection step, \n#       the created param doesn't remain in the model's list of params\n#------------------------------------------------------------------------------\n\n@defcomp A begin\n    p1 = Parameter(index = [time])\nend\n\n@defcomp B begin\n    p1 = Parameter(index = [time])\nend\n\nm = Model()\nset_dimension!(m, :time, 10)\nadd_comp!(m, A)\nadd_comp!(m, B)\n\n@test_throws ErrorException set_param!(m, :p1, 1:5)     # this will error because the provided data is the wrong size\n@test isempty(m.md.external_params)                     # But it should not be added to the model's dictionary\n\nend #module\n", "meta": {"hexsha": "2156b4950ee8263e0420492951b762579163611c", "size": 12630, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_parametertypes.jl", "max_stars_repo_name": "evildmp/Mimi.jl", "max_stars_repo_head_hexsha": "5af94a222dfa01dd5fa74417d85f8f4e0184be92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/test_parametertypes.jl", "max_issues_repo_name": "evildmp/Mimi.jl", "max_issues_repo_head_hexsha": "5af94a222dfa01dd5fa74417d85f8f4e0184be92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_parametertypes.jl", "max_forks_repo_name": "evildmp/Mimi.jl", "max_forks_repo_head_hexsha": "5af94a222dfa01dd5fa74417d85f8f4e0184be92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3846153846, "max_line_length": 145, "alphanum_fraction": 0.5983372922, "num_tokens": 4164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.11279539733570564, "lm_q1q2_score": 0.05331652055942444}}
{"text": "abstract type SaturationGoal end\n\nreached(g::EGraph, goal::Nothing) = false\nreached(g::EGraph, goal::SaturationGoal) = false\n\n\"\"\"\nThis goal is reached when the `exprs` list of expressions are in the \nsame equivalence class.\n\"\"\"\nstruct EqualityGoal <: SaturationGoal\n    exprs::Vector{Any}\n    ids::Vector{EClassId}\n    function EqualityGoal(exprs, eclasses) \n        @assert length(exprs) == length(eclasses) && length(exprs) != 0 \n        new(exprs, eclasses)\n    end\nend\n\nfunction reached(g::EGraph, goal::EqualityGoal)\n    all(x -> in_same_class(g, goal.ids[1], x), @view goal.ids[2:end])\nend\n\n\"\"\"\nBoolean valued function as an arbitrary saturation goal.\nUser supplied function must take an [`EGraph`](@ref) as the only parameter.\n\"\"\"\nstruct FunctionGoal <: SaturationGoal\n    fun::Function\nend\n\nfunction reached(g::EGraph, goal::FunctionGoal)::Bool\n    fun(g)    \nend\n\nmutable struct Report\n    reason::Union{Symbol, Nothing}\n    egraph::EGraph\n    iterations::Int\n    to::TimerOutput\nend\n\nReport() = Report(nothing, EGraph(), 0, TimerOutput())\nReport(g::EGraph) = Report(nothing, g, 0, TimerOutput())\n\n\n\n# string representation of timedata\nfunction Base.show(io::IO, x::Report)\n    g = x.egraph\n    println(io, \"Equality Saturation Report\")\n    println(io, \"=================\")\n    println(io, \"\\tStop Reason: $(x.reason)\")\n    println(io, \"\\tIterations: $(x.iterations)\")\n    # println(io, \"\\tRules applied: $(g.age)\")\n    println(io, \"\\tEGraph Size: $(g.numclasses) eclasses, $(length(g.memo)) nodes\")\n    print_timer(io, x.to)\nend\n\n\"\"\"\nConfigurable Parameters for the equality saturation process.\n\"\"\"\n@with_kw mutable struct SaturationParams\n    timeout::Int = 8\n    timelimit::Period = Second(-1)\n    # default sizeout. TODO make this bytes\n    # sizeout::Int = 2^14\n    matchlimit::Int = 5000\n    eclasslimit::Int = 5000\n    enodelimit::Int = 15000\n    goal::Union{Nothing, SaturationGoal} = nothing\n    stopwhen::Function = ()->false\n    scheduler::Type{<:AbstractScheduler} = BackoffScheduler\n    schedulerparams::Tuple=()\n    threaded::Bool = false\n    timer::Bool = true\n    printiter::Bool = false\n    simterm::Function = similarterm\nend\n\nstruct Match\n    rule::AbstractRule \n    # the rhs pattern to instantiate \n    pat_to_inst\n    # the substitution\n    sub::Sub \n    # the id the matched the lhs  \n    id::EClassId\nend\n\nconst MatchesBuf = Vector{Match}\n\nfunction cached_ids(g::EGraph, p::AbstractPat)# ::Vector{Int64}\n    if isground(p)\n        id = lookup_pat(g, p)\n        !isnothing(id) && return [id]\n    else\n        return collect(keys(g.classes))\n    end\n    return []\nend\n\nfunction cached_ids(g::EGraph, p) # p is a literal\n    id = lookup(g, ENodeLiteral(p))\n    !isnothing(id) && return [id]\n    return []\nend\n\n# FIXME \nfunction cached_ids(g::EGraph, p::PatTerm)\n    # println(\"pattern $p, $(p.head)\")\n    # println(\"all ids\")\n    # keys(g.classes) |> println\n    # println(\"cached symbols\")\n    # cached = get(g.symcache, p.head, Set{Int64}())\n    # println(\"symbols where $(p.head) appears\")\n    # appears = Set{Int64}() \n    # for (id, class) \u2208 g.classes \n    #     for n \u2208 class \n    #         if n.head == p.head\n    #             push!(appears, id) \n    #         end\n    #     end\n    # end\n    # # println(appears)\n    # if !(cached == appears)\n    #     @show cached \n    #     @show appears\n    # end\n\n    collect(keys(g.classes))\n    # cached\n    # get(g.symcache, p.head, [])\nend\n\n# function cached_ids(g::EGraph, p::PatLiteral)\n#     get(g.symcache, p.val, [])\n# end\n\nfunction (r::SymbolicRule)(g::EGraph, id::EClassId)\n    ematch(g, r.ematch_program, id) .|> sub -> Match(r, r.right, sub, id)\nend\n\nfunction (r::DynamicRule)(g::EGraph, id::EClassId)\n    ematch(g, r.ematch_program, id) .|> sub -> Match(r, nothing, sub, id)\nend\n\nfunction (r::BidirRule)(g::EGraph, id::EClassId)\n    vcat(ematch(g, r.ematch_program_l, id) .|> sub -> Match(r, r.right, sub, id),\n        ematch(g, r.ematch_program_r, id) .|> sub -> Match(r, r.left, sub, id))\nend\n\n\n\"\"\"\nReturns an iterator of `Match`es.\n\"\"\"\nfunction eqsat_search!(egraph::EGraph, theory::Vector{<:AbstractRule},\n    scheduler::AbstractScheduler, report; threaded=false)\n    match_groups = Vector{Match}[]\n    function pmap(f, xs) \n        # const propagation should be able to optimze one of the branch away\n        if threaded\n            # # try to divide the work evenly between threads without adding much overhead\n            # chunks = Threads.nthreads() * 10\n            # basesize = max(length(xs) \u00f7 chunks, 1)\n            # ThreadsX.mapi(f, xs; basesize=basesize) \n            ThreadsX.map(f, xs)\n        else\n            map(f, xs)\n        end\n    end\n\n    inequalities = filter(Base.Fix2(isa, UnequalRule), theory)\n    # never skip contradiction checks\n    append_time = TimerOutput()\n    for rule \u2208 inequalities\n        @timeit report.to repr(rule) begin\n            ids = cached_ids(egraph, rule.left)\n            rule_matches = pmap(i -> rule(egraph, i), ids)\n            @timeit append_time \"appending matches\" begin\n                append!(match_groups, rule_matches)\n            end\n        end\n    end\n\n    other_rules = filter(theory) do rule \n        !(rule isa UnequalRule)\n    end\n    for rule \u2208 other_rules \n        @timeit report.to repr(rule) begin\n            # don't apply banned rules\n            if !cansearch(scheduler, rule)\n                # println(\"skipping banned rule $rule\")\n                continue\n            end\n            ids = cached_ids(egraph, rule.left)\n            rule_matches = pmap(i -> rule(egraph, i), ids)\n\n            n_matches = isempty(rule_matches) ? 0 : sum(length, rule_matches)\n            # @show (rule, n_matches)\n            can_yield = inform!(scheduler, rule, n_matches)\n            if can_yield\n                @timeit append_time \"appending matches\" begin\n                    append!(match_groups, rule_matches)\n                end\n            end\n        end\n    end\n\n    # @timeit append_time \"appending matches\" begin\n    #     result = reduce(vcat, match_groups) # this should be more efficient than multiple appends\n    # end\n    merge!(report.to, append_time, tree_point=[\"Search\"])\n\n    return Iterators.flatten(match_groups)\n    # return result\nend\n    \n\nfunction (rule::UnequalRule)(g::EGraph, match::Match; simterm=similarterm)\n    lc = match.id\n    rinst = instantiate(g, match.pat_to_inst, match.sub, rule; simterm=simterm)\n    rc, node = addexpr!(g, rinst)\n\n    if find(g, lc) == find(g, rc)\n        @log \"Contradiction!\" rule\n        return :contradiction\n    end\n    return nothing\nend\n\nfunction (rule::SymbolicRule)(g::EGraph, match::Match; simterm=similarterm)\n    rinst = instantiate(g, match.pat_to_inst, match.sub, rule; simterm=simterm)\n    rc, node = addexpr!(g, rinst)\n    merge!(g, match.id, rc.id)\n    return nothing\nend\n\n\nfunction (rule::DynamicRule)(g::EGraph, match::Match; simterm=similarterm)\n    f = rule.rhs_fun\n    actual_params = [instantiate(g, PatVar(v, i, alwaystrue), match.sub, rule) for (i, v) in enumerate(rule.patvars)]\n    r = f(g[match.id], match.sub, g, actual_params...)\n    isnothing(r) && return nothing\n    rc, node = addexpr!(g, r)\n    merge!(g, match.id, rc.id)\n    return nothing\nend\n\n\nfunction eqsat_apply!(g::EGraph, matches, rep::Report, params::SaturationParams)\n    i = 0\n    # println.(matches)\n    for match \u2208 matches\n        i += 1\n\n        # if params.eclasslimit > 0 && g.numclasses > params.eclasslimit\n        #     @log \"E-GRAPH SIZEOUT\"\n        #     rep.reason = :eclasslimit\n        #     return\n        # end\n\n        if reached(g, params.goal)\n            @log \"Goal reached\"\n            rep.reason = :goalreached\n            return\n        end\n\n\n        rule = match.rule\n        # println(\"applying $rule\")\n\n        halt_reason = rule(g, match; simterm=params.simterm)\n        if (halt_reason !== nothing)\n            rep.reason = halt_reason\n            return \n        end \n\n        # println(rule)\n        # println(sub)\n        # println(l); println(r)\n        # display(egraph.classes); println()\n    end\nend\n\nimport ..@log\n\n\n\"\"\"\nCore algorithm of the library: the equality saturation step.\n\"\"\"\nfunction eqsat_step!(g::EGraph, theory::Vector{<:AbstractRule}, curr_iter,\n        scheduler::AbstractScheduler, match_hist::MatchesBuf, \n        params::SaturationParams, report)\n\n    instcache = Dict{AbstractRule, Dict{Sub, EClassId}}()\n\n    setiter!(scheduler, curr_iter)\n\n    matches = @timeit report.to \"Search\" eqsat_search!(g, theory, scheduler, report; threaded=params.threaded)\n\n    # matches = setdiff!(matches, match_hist)\n\n    @timeit report.to \"Apply\" eqsat_apply!(g, matches, report, params)\n    \n\n    # union!(match_hist, matches)\n\n    if report.reason === nothing && cansaturate(scheduler) && isempty(g.dirty)\n        report.reason = :saturated\n    end\n    @timeit report.to \"Rebuild\" rebuild!(g)\n   \n    return report, g\nend\n\n\"\"\"\nGiven an [`EGraph`](@ref) and a collection of rewrite rules,\nexecute the equality saturation algorithm.\n\"\"\"\nfunction saturate!(g::EGraph, theory::Vector{<:AbstractRule}, params=SaturationParams())\n    curr_iter = 0\n\n    sched = params.scheduler(g, theory, params.schedulerparams...)\n    match_hist = MatchesBuf()\n    report = Report(g)\n\n    start_time = Dates.now().instant\n\n    !params.timer && disable_timer!(report.to)\n    timelimit = params.timelimit > Second(0)\n    \n\n    while true\n        curr_iter+=1\n\n        params.printiter && @info(\"iteration \", curr_iter)\n\n        report, egraph = eqsat_step!(g, theory, curr_iter, sched, match_hist, params, report)\n\n        elapsed = Dates.now().instant - start_time\n\n        if timelimit && params.timelimit <= elapsed\n            report.reason = :timelimit\n            break\n        end\n\n        # report.reason == :matchlimit && break\n        if !(report.reason isa Nothing)\n            break\n        end\n\n        if curr_iter >= params.timeout \n            report.reason = :timeout\n            break\n        end\n\n        if params.eclasslimit > 0 && g.numclasses > params.eclasslimit \n            # println(params.eclasslimit)\n            report.reason = :eclasslimit\n            break\n        end\n\n        if reached(g, params.goal)\n            report.reason = :goalreached\n            break\n        end\n    end\n    report.iterations = curr_iter\n    @log report\n\n    return report\nend\n\nfunction areequal(theory::Vector, exprs...; params=SaturationParams())\n    g = EGraph(exprs[1])\n    areequal(g, theory, exprs...; params=params)\nend\n\nfunction areequal(g::EGraph, t::Vector{<:AbstractRule}, exprs...; params=SaturationParams())\n    @log \"Checking equality for \" exprs\n    if length(exprs) == 1; return true end\n    # rebuild!(G)\n\n    @log \"starting saturation\"\n\n    n = length(exprs)\n    ids = Vector{EClassId}(undef, n)\n    nodes = Vector{AbstractENode}(undef, n)\n    for i \u2208 1:n\n        ec, node = addexpr!(g, exprs[i])\n        ids[i] = ec.id\n        nodes[i] = node\n    end\n\n    goal = EqualityGoal(collect(exprs), ids)\n    \n    # alleq = () -> (all(x -> in_same_set(G.uf, ids[1], x), ids[2:end]))\n\n    params.goal = goal\n    # params.stopwhen = alleq\n\n    report = saturate!(g, t, params)\n\n    # display(g.classes); println()\n    if !(report.reason === :saturated) && !reached(g, goal)\n        return missing # failed to prove\n    end\n    return reached(g, goal)\nend\n\nmacro areequal(theory, exprs...)\n    esc(:(areequal($theory, $exprs...))) \nend\n\nmacro areequalg(G, theory, exprs...)\n    esc(:(areequal($G, $theory, $exprs...)))\nend\n", "meta": {"hexsha": "c9964dfc585ea8de874b878067bbca649310ebf1", "size": 11469, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/EGraphs/saturation.jl", "max_stars_repo_name": "Wilkenfeld/Metatheory.jl", "max_stars_repo_head_hexsha": "40d535ebe9765eca77658f090f9151ac72788455", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 157, "max_stars_repo_stars_event_min_datetime": "2021-02-12T17:44:12.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-08T03:28:09.000Z", "max_issues_repo_path": "src/EGraphs/saturation.jl", "max_issues_repo_name": "Wilkenfeld/Metatheory.jl", "max_issues_repo_head_hexsha": "40d535ebe9765eca77658f090f9151ac72788455", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 62, "max_issues_repo_issues_event_min_datetime": "2021-02-12T12:39:15.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-11T15:47:19.000Z", "max_forks_repo_path": "src/EGraphs/saturation.jl", "max_forks_repo_name": "Wilkenfeld/Metatheory.jl", "max_forks_repo_head_hexsha": "40d535ebe9765eca77658f090f9151ac72788455", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2021-02-21T13:34:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-29T18:56:22.000Z", "avg_line_length": 27.4377990431, "max_line_length": 117, "alphanum_fraction": 0.6148748801, "num_tokens": 3103, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.38491215859561845, "lm_q2_score": 0.1384618030118943, "lm_q1q2_score": 0.05329563148034954}}
{"text": "function isinplace_jump(p,rj)\n    if p isa DiscreteProblem && p.f === DiffEqBase.DISCRETE_INPLACE_DEFAULT && rj !== nothing\n        # Just a default discrete problem f, so don't use it for iip\n        DiffEqBase.isinplace(rj)\n    else\n        DiffEqBase.isinplace(p)\n    end\nend\n\n\"\"\"\n$(TYPEDEF)\n\nDefines a collection of jump processes to associate with another problem type.\n- [Documentation Page](https://diffeq.sciml.ai/stable/types/jump_types/) \n- [Tutorial\n  Page](https://diffeq.sciml.ai/stable/tutorials/discrete_stochastic_example/)\n- [FAQ\n  Page](https://diffeq.sciml.ai/stable/tutorials/discrete_stochastic_example/#FAQ)\n\n### Constructors\n\n`JumpProblem`s can be constructed by first building another problem type to\nwhich the jumps will be associated. For example, to  simulate a collection of\njump processes for which the transition rates do not explicitly depend on time\n(called `ConstantRateJump`s or `MassActionJump`s), we must first construct a\n`DiscreteProblem`\n```julia\nprob = DiscreteProblem(u0, p, tspan)\n```\nwhere `u0` is the initial condition, `p` the parameters and `tspan` the time\nspan. If we wanted to have the jumps coupled with a system of ODEs, or have\ntransition rates with explicit time dependence, we would use an `ODEProblem`\ninstead that defines the ODE portion of the dynamics. \n\nGiven `prob` we define the jumps via\n- `JumpProblem(prob, aggregator::AbstractAggregatorAlgorithm, jumps::JumpSet ;\n  kwargs...)`\n- `JumpProblem(prob, aggregator::AbstractAggregatorAlgorithm, jumps...;\n  kwargs...)`\n\nHere `aggregator` specifies the underlying algorithm for calculating next jump\ntimes and types, for example `Direct`. The collection of different\n`AbstractJump` types can then be passed within a single `JumpSet` or as\nsubsequent sequential arguments. \n\n### Fields\n\n$(FIELDS)\n\n## Keyword Arguments\n- `rng`, the random number generator to use. On 1.7 and up defaults to Julia's\n  builtin generator, below 1.7 uses RandomNumbers.jl's\n  `Xorshifts.Xoroshiro128Star(rand(UInt64))`.\n- `save_positions=(true,true)`, specifies whether to save the system's state\n  (before,after) the jump occurs. \n- `spatial_system`, for spatial problems the underlying spatial structure.\n- `hopping_constants`, for spatial problems the spatial transition rate\n  coefficients.\n\nPlease see the [tutorial\npage](https://diffeq.sciml.ai/stable/tutorials/discrete_stochastic_example/) in\nthe DifferentialEquations.jl [docs](https://diffeq.sciml.ai/stable/) for usage\nexamples and commonly asked questions.\n\"\"\"\nmutable struct JumpProblem{iip,P,A,C,J<:Union{Nothing,AbstractJumpAggregator},J2,J3,J4,R} <: DiffEqBase.AbstractJumpProblem{P,J}\n  \"\"\"The type of problem to couple the jumps to. For a pure jump process use `DiscreteProblem`, to couple to ODEs, `ODEProblem`, etc.\"\"\"\n  prob::P\n  \"\"\"The aggregator algorithm that determines the next jump times and types for `ConstantRateJump`s and `MassActionJump`s. Examples include `Direct`.\"\"\"\n  aggregator::A\n  \"\"\"The underlying state data associated with the chosen aggregator.\"\"\"\n  discrete_jump_aggregation::J\n  \"\"\"`CallBackSet` with the underlying `ConstantRate` and `VariableRate` jumps.\"\"\"\n  jump_callback::C\n  \"\"\"The `VariableRateJump`s.\"\"\"\n  variable_jumps::J2\n  \"\"\"The `RegularJump`s.\"\"\"\n  regular_jump::J3\n  \"\"\"The `MassActionJump`s.\"\"\"\n  massaction_jump::J4\n  \"\"\"The random number generator to use.\"\"\"\n  rng::R\nend\nfunction JumpProblem(p::P,a::A,dj::J,jc::C,vj::J2,rj::J3,mj::J4,rng::R) where {P,A,J,C,J2,J3,J4,R}\n    iip = isinplace_jump(p,rj)\n    JumpProblem{iip,P,A,C,J,J2,J3,J4,R}(p,a,dj,jc,vj,rj,mj,rng)\nend\n\n# for remaking\nBase.@pure remaker_of(prob::T) where {T <: JumpProblem} = DiffEqBase.parameterless_type(T)\nfunction DiffEqBase.remake(thing::JumpProblem; kwargs...)\n  T = remaker_of(thing)\n\n  errmesg = \"\"\"\n  JumpProblems can currently only be remade with new u0, p, tspan or prob fields. To change other fields create a new JumpProblem. Feel free to open an issue on DiffEqJump to discuss further.\n  \"\"\"\n  !issubset(keys(kwargs),(:u0,:p,:tspan,:prob)) && error(errmesg)\n\n  if :prob \u2209 keys(kwargs)\n    dprob = DiffEqBase.remake(thing.prob; kwargs...)\n\n    # if the parameters were changed we must remake the MassActionJump too\n    if (:p \u2208 keys(kwargs)) && using_params(thing.massaction_jump)\n      update_parameters!(thing.massaction_jump, dprob.p; kwargs...)\n    end \n  else\n    any(k -> k in keys(kwargs), (:u0,:p,:tspan)) && error(\"If remaking a JumpProblem you can not pass both prob and any of u0, p, or tspan.\")\n    dprob = kwargs[:prob]\n\n    # we can't know if p was changed, so we must remake the MassActionJump\n    if using_params(thing.massaction_jump)\n      update_parameters!(thing.massaction_jump, dprob.p; kwargs...)\n    end \n  end\n\n  T(dprob, thing.aggregator, thing.discrete_jump_aggregation, thing.jump_callback,\n     thing.variable_jumps, thing.regular_jump, thing.massaction_jump, thing.rng)\nend\n\nDiffEqBase.isinplace(::JumpProblem{iip}) where {iip} = iip\nJumpProblem(prob::JumpProblem) = prob\n\nJumpProblem(prob,jumps::ConstantRateJump;kwargs...) = JumpProblem(prob,JumpSet(jumps);kwargs...)\nJumpProblem(prob,jumps::VariableRateJump;kwargs...) = JumpProblem(prob,JumpSet(jumps);kwargs...)\nJumpProblem(prob,jumps::RegularJump;kwargs...) = JumpProblem(prob,JumpSet(jumps);kwargs...)\nJumpProblem(prob,jumps::MassActionJump;kwargs...) = JumpProblem(prob,JumpSet(jumps);kwargs...)\nJumpProblem(prob,jumps::AbstractJump...;kwargs...) = JumpProblem(prob,JumpSet(jumps...);kwargs...)\n\nJumpProblem(prob,aggregator::AbstractAggregatorAlgorithm,jumps::ConstantRateJump;kwargs...) = JumpProblem(prob,aggregator,JumpSet(jumps);kwargs...)\nJumpProblem(prob,aggregator::AbstractAggregatorAlgorithm,jumps::VariableRateJump;kwargs...) = JumpProblem(prob,aggregator,JumpSet(jumps);kwargs...)\nJumpProblem(prob,aggregator::AbstractAggregatorAlgorithm,jumps::RegularJump;kwargs...) = JumpProblem(prob,aggregator,JumpSet(jumps);kwargs...)\nJumpProblem(prob,aggregator::AbstractAggregatorAlgorithm,jumps::AbstractMassActionJump;kwargs...) = JumpProblem(prob,aggregator,JumpSet(jumps);kwargs...)\nJumpProblem(prob,aggregator::AbstractAggregatorAlgorithm,jumps::AbstractJump...;kwargs...) = JumpProblem(prob,aggregator,JumpSet(jumps...);kwargs...)\nJumpProblem(prob,jumps::JumpSet;kwargs...) = JumpProblem(prob,NullAggregator(),jumps;kwargs...)\n\nfunction JumpProblem(prob, aggregator::AbstractAggregatorAlgorithm, jumps::JumpSet;\n                     save_positions = typeof(prob) <: DiffEqBase.AbstractDiscreteProblem ? (false,true) : (true,true),\n                     rng = DEFAULT_RNG, scale_rates = true, useiszero = true, spatial_system=nothing, hopping_constants=nothing, kwargs...)\n\n  # initialize the MassActionJump rate constants with the user parameters\n  if using_params(jumps.massaction_jump) \n    rates = jumps.massaction_jump.param_mapper(prob.p)\n    maj = MassActionJump(rates, jumps.massaction_jump.reactant_stoch, jumps.massaction_jump.net_stoch, \n                         jumps.massaction_jump.param_mapper; scale_rates=scale_rates, useiszero=useiszero, \n                         nocopy=true)\n  else\n    maj = jumps.massaction_jump\n  end\n\n  ## Spatial jumps handling\n  if spatial_system !== nothing && hopping_constants !== nothing && !is_spatial(aggregator) # check if need to flatten\n    prob, maj = flatten(maj, prob, spatial_system, hopping_constants; kwargs...)\n  end\n  ## Constant Rate Handling\n  t,end_time,u = prob.tspan[1],prob.tspan[2],prob.u0\n  if (typeof(jumps.constant_jumps) <: Tuple{}) && (maj === nothing) && !is_spatial(aggregator) # check if there are no jumps\n    disc = nothing\n    constant_jump_callback = CallbackSet()\n  else\n    disc = aggregate(aggregator,u,prob.p,t,end_time,jumps.constant_jumps,maj,save_positions,rng; spatial_system = spatial_system, hopping_constants = hopping_constants, kwargs...)\n    constant_jump_callback = DiscreteCallback(disc)\n  end\n\n  iip = isinplace_jump(prob, jumps.regular_jump)\n\n  ## Variable Rate Handling\n  if typeof(jumps.variable_jumps) <: Tuple{}\n    new_prob = prob\n    variable_jump_callback = CallbackSet()\n  else\n    new_prob = extend_problem(prob,jumps; rng=rng)\n    variable_jump_callback = build_variable_callback(CallbackSet(),0,jumps.variable_jumps...; rng=rng)\n  end\n  callbacks = CallbackSet(constant_jump_callback,variable_jump_callback)\n\n  JumpProblem{iip,typeof(new_prob),typeof(aggregator),typeof(callbacks),\n              typeof(disc),typeof(jumps.variable_jumps),\n              typeof(jumps.regular_jump),typeof(maj),typeof(rng)}(\n                        new_prob,aggregator,disc,\n                        callbacks,\n                        jumps.variable_jumps,\n                        jumps.regular_jump, maj, rng)\nend\n\nfunction extend_problem(prob::DiffEqBase.AbstractDiscreteProblem, jumps; rng=DEFAULT_RNG)\n  error(\"VariableRateJumps require a continuous problem, like an ODE/SDE/DDE/DAE problem.\")\nend\n\nfunction extend_problem(prob::DiffEqBase.AbstractODEProblem,jumps; rng=DEFAULT_RNG)\n  function jump_f(du::ExtendedJumpArray,u::ExtendedJumpArray,p,t)\n    prob.f(du.u,u.u,p,t)\n    update_jumps!(du,u,p,t,length(u.u),jumps.variable_jumps...)\n  end  \n  ttype = eltype(prob.tspan)\n  u0 = ExtendedJumpArray(prob.u0,[-randexp(rng,ttype) for i in 1:length(jumps.variable_jumps)])\n  remake(prob,f=ODEFunction{true}(jump_f),u0=u0)\nend\n\nfunction extend_problem(prob::DiffEqBase.AbstractSDEProblem,jumps; rng=DEFAULT_RNG)\n  function jump_f(du,u,p,t)\n    prob.f(du.u,u.u,p,t)\n    update_jumps!(du,u,p,t,length(u.u),jumps.variable_jumps...)\n  end\n\n  if prob.noise_rate_prototype === nothing\n    jump_g = function (du,u,p,t)\n      prob.g(du.u,u.u,p,t)\n    end\n  else\n    jump_g = function (du,u,p,t)\n      prob.g(du,u.u,p,t)\n    end\n  end\n\n  ttype = eltype(prob.tspan)\n  u0 = ExtendedJumpArray(prob.u0,[-randexp(rng,ttype) for i in 1:length(jumps.variable_jumps)])\n  remake(prob,f=SDEFunction{true}(jump_f,jump_g),g=jump_g,u0=u0)\nend\n\nfunction extend_problem(prob::DiffEqBase.AbstractDDEProblem,jumps; rng=DEFAULT_RNG)\n  jump_f = function (du,u,h,p,t)\n    prob.f(du.u,u.u,h,p,t)\n    update_jumps!(du,u,p,t,length(u.u),jumps.variable_jumps...)\n  end\n  ttype = eltype(prob.tspan)\n  u0 = ExtendedJumpArray(prob.u0,[-randexp(rng,ttype) for i in 1:length(jumps.variable_jumps)])\n  ramake(prob,f=DDEFunction{true}(jump_f),u0=u0)\nend\n\n# Not sure if the DAE one is correct: Should be a residual of sorts\nfunction extend_problem(prob::DiffEqBase.AbstractDAEProblem,jumps; rng=DEFAULT_RNG)\n  jump_f = function (out,du,u,p,t)\n    prob.f(out.u,du.u,u.u,t)\n    update_jumps!(du,u,t,length(u.u),jumps.variable_jumps...)\n  end\n  ttype = eltype(prob.tspan)\n  u0 = ExtendedJumpArray(prob.u0,[-randexp(rng,ttype) for i in 1:length(jumps.variable_jumps)])\n  remake(prob,f=DAEFunction{true}(jump_f),u0=u0)\nend\n\nfunction build_variable_callback(cb,idx,jump,jumps...; rng=DEFAULT_RNG)\n  idx += 1\n  condition = function (u,t,integrator)\n    u.jump_u[idx]\n  end\n  affect! = function (integrator)\n    jump.affect!(integrator)\n    integrator.u.jump_u[idx] = -randexp(rng,typeof(integrator.t))   \n  end\n  new_cb = ContinuousCallback(condition,affect!;\n                      idxs = jump.idxs,\n                      rootfind = jump.rootfind,\n                      interp_points = jump.interp_points,\n                      save_positions = jump.save_positions,\n                      abstol = jump.abstol,\n                      reltol = jump.reltol)\n  build_variable_callback(CallbackSet(cb,new_cb),idx,jumps...; rng=DEFAULT_RNG)\nend\n\nfunction build_variable_callback(cb,idx,jump; rng=DEFAULT_RNG)\n  idx += 1\n  condition = function (u,t,integrator)\n    u.jump_u[idx]\n  end\n  affect! = function (integrator)\n    jump.affect!(integrator)\n    integrator.u.jump_u[idx] = -randexp(rng,typeof(integrator.t))\n  end\n  new_cb = ContinuousCallback(condition,affect!;\n                      idxs = jump.idxs,\n                      rootfind = jump.rootfind,\n                      interp_points = jump.interp_points,\n                      save_positions = jump.save_positions,\n                      abstol = jump.abstol,\n                      reltol = jump.reltol)\n  CallbackSet(cb,new_cb)\nend\n\naggregator(jp::JumpProblem{P,A,C,J,J2}) where {P,A,C,J,J2} = A\n\n@inline function extend_tstops!(tstops,jp::JumpProblem{P,A,C,J,J2}) where {P,A,C,J,J2}\n  !(typeof(jp.jump_callback.discrete_callbacks) <: Tuple{}) && push!(tstops,jp.jump_callback.discrete_callbacks[1].condition.next_jump_time)\nend\n\n@inline function update_jumps!(du,u,p,t,idx,jump)\n  idx += 1\n  du[idx] = jump.rate(u.u,p,t)\nend\n\n@inline function update_jumps!(du,u,p,t,idx,jump,jumps...)\n  idx += 1\n  du[idx] = jump.rate(u.u,p,t)\n  update_jumps!(du,u,p,t,idx,jumps...)\nend\n\n\n### Displays\nnum_constant_rate_jumps(aggregator::AbstractSSAJumpAggregator) = length(aggregator.rates)\n\nBase.summary(io::IO, prob::JumpProblem) = string(DiffEqBase.parameterless_type(prob),\" with problem \",DiffEqBase.parameterless_type(prob.prob),\" and aggregator \",typeof(prob.aggregator))\nfunction Base.show(io::IO, mime::MIME\"text/plain\", A::JumpProblem)\n  println(io,summary(A))\n  println(io,\"Number of constant rate jumps: \",A.discrete_jump_aggregation === nothing ? 0 : num_constant_rate_jumps(A.discrete_jump_aggregation))\n  println(io,\"Number of variable rate jumps: \",length(A.variable_jumps))\n  if A.regular_jump !== nothing\n    println(io,\"Have a regular jump\")\n  end\n  if (A.massaction_jump !== nothing) && (get_num_majumps(A.massaction_jump) > 0)\n    println(io,\"Have a mass action jump\")\n  end\nend\n\nTreeViews.hastreeview(x::JumpProblem) = true\n", "meta": {"hexsha": "c34c160a10c006fd1ac7dd098fe568925a8864c1", "size": 13498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problem.jl", "max_stars_repo_name": "JuliaDiffEq/JumpDiffEq.jl", "max_stars_repo_head_hexsha": "d150ace322fbb16b37af9f24988013fafb45d04d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "max_stars_repo_stars_event_min_datetime": "2017-02-18T18:37:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-21T18:44:34.000Z", "max_issues_repo_path": "src/problem.jl", "max_issues_repo_name": "JuliaDiffEq/DiffEqJump.jl", "max_issues_repo_head_hexsha": "d150ace322fbb16b37af9f24988013fafb45d04d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 68, "max_issues_repo_issues_event_min_datetime": "2017-02-09T06:38:26.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-24T17:51:48.000Z", "max_forks_repo_path": "src/problem.jl", "max_forks_repo_name": "JuliaDiffEq/JumpDiffEq.jl", "max_forks_repo_head_hexsha": "d150ace322fbb16b37af9f24988013fafb45d04d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 16, "max_forks_repo_forks_event_min_datetime": "2017-02-08T17:11:59.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:36:36.000Z", "avg_line_length": 43.124600639, "max_line_length": 191, "alphanum_fraction": 0.7190694918, "num_tokens": 3573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.11757212890757046, "lm_q1q2_score": 0.05329096042374859}}
{"text": "using AMDGPU\n\n@show AMDGPU.agents()\n\n# easiest way to program GPUs is using array operations\n\na = ROCArray([1 2 3 4])\n", "meta": {"hexsha": "24761f88ad8d081a7a74b82374bb928ccda48dfc", "size": 118, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "GPU_programming_in_Julia/tutorial.jl", "max_stars_repo_name": "john-waczak/JuliaCon2021", "max_stars_repo_head_hexsha": "48979656e94af4e5707094fc836ed76971593eac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "GPU_programming_in_Julia/tutorial.jl", "max_issues_repo_name": "john-waczak/JuliaCon2021", "max_issues_repo_head_hexsha": "48979656e94af4e5707094fc836ed76971593eac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GPU_programming_in_Julia/tutorial.jl", "max_forks_repo_name": "john-waczak/JuliaCon2021", "max_forks_repo_head_hexsha": "48979656e94af4e5707094fc836ed76971593eac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.75, "max_line_length": 55, "alphanum_fraction": 0.7203389831, "num_tokens": 34, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.11757212581561162, "lm_q1q2_score": 0.05329095902228161}}
{"text": "\"\"\"\r\n# Get information about a time series in the FRED database.\r\n\r\n    get_metadata(symbol)\r\n\r\n## Arguments\r\n\r\n    - `symbol` : String specifying the name (id) of the time series.\r\n\r\n## Examples\r\n\r\n```jldoctest\r\njulia> get_metadata(\"GDPC1\")\r\njulia> get_metadata(\"FEDFUNDS\")\r\njulia> get_metadata(\"T10Y2Y\")\r\n```\r\n\"\"\"\r\nfunction get_metadata(symbol::String)\r\n\r\n    url = \"https://api.stlouisfed.org/fred/series\"\r\n\r\n    parameters = Dict(\"api_key\" => ENV[\"API_FRED\"],\r\n                      \"file_type\" => \"json\",\r\n                      \"series_id\" => symbol)\r\n\r\n    response = HTTP.request(\"GET\", url; query = parameters)\r\n    results = JSON.parse(String(response.body))[\"seriess\"][1]\r\n\r\n    println(\"\")\r\n    println(\"Metadata for: \" * symbol)\r\n    println(\"Title: \" * results[\"title\"])\r\n    println(\"Units: \" * results[\"units\"])\r\n    println(\"Adjustment: \" * results[\"seasonal_adjustment\"])\r\n    println(\"Frequency: \" * results[\"frequency\"])\r\n    println(\"Notes: \" * results[\"notes\"])\r\n\r\n    return(results)\r\nend\r\n", "meta": {"hexsha": "b82e4b591cfc15943e3b54cdecfddf86af2b6719", "size": 1012, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/get_metadata.jl", "max_stars_repo_name": "JuliaTagBot/FredApi.jl", "max_stars_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-02-15T19:18:40.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-16T07:37:12.000Z", "max_issues_repo_path": "src/get_metadata.jl", "max_issues_repo_name": "JuliaTagBot/FredApi.jl", "max_issues_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-08-25T21:43:03.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-31T03:54:17.000Z", "max_forks_repo_path": "src/get_metadata.jl", "max_forks_repo_name": "JuliaTagBot/FredApi.jl", "max_forks_repo_head_hexsha": "6e997585368bb316fe505b5c7ad55c52c6cbb22f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:43:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-07T10:14:34.000Z", "avg_line_length": 25.9487179487, "max_line_length": 69, "alphanum_fraction": 0.6017786561, "num_tokens": 251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45326184801538616, "lm_q2_score": 0.11757212117767353, "lm_q1q2_score": 0.05329095692008123}}
{"text": "# These Symata functions (symbols) translate between Symata and Julia data and expressions\n\n### JVar\n\n@sjdoc JVar \"\"\"\n    JVar(x)\n\nreturn the Julia value of the Symbol that `x` evaluates to.\n\nFor example, if `a = 1` in Julia and `b = a` in Symata, then `JVar(b)` evaluates to `1`.\n\"\"\"\n\n@sjseealso_group(Jxpr, JVar)\napprules(mx::Mxpr{:JVar}) = Core.eval(Main,symname(mx[1]))\n\n### SetJ\n\n@mkapprule SetJ nargs => 2  \"\"\"\n    SetJ(x,val)\n\nsets the Julia symbol `x` to `val`.\n\nVariables and functions in Symata are separate from those in Julia, ie, their table of bindings to symbols are separate.\n\"\"\"\n\nexportj(lhs::SymString) = exportj(lhs,doeval(lhs))\n## FIXME: find out what we want on the next line and do it properly.\nexportj(lhs::SymString, rhs) = Main.eval(Expr(:(=),symname(Symbol(lhs)),rhs))\nexportj(lhs::SymString, rhs::Symbol) = exportj(lhs,QuoteNode(rhs))\n@doap SetJ(lhs::SymString,rhs) = exportj(lhs,rhs)\n\n### ExportJ\n\n@mkapprule ExportJ \"\"\"\n    ExportJ(x,y,...)\n\nsets the Julia symbols `x`,`y`, etc. to the Symata-value of `x`,`y`, etc.\n`ExportJ(x)` is equivalent to `SetJ(x,x)`.\n\"\"\"\n\n@doap ExportJ(vars::SymString...) = foreach(exportj, vars)\n\n### Jxpr\n\n@sjdoc Jxpr \"\"\"\n    Jxpr\n\nallows embedding Julia expressions. A Jxpr is entered like this `J( expr )`.\n\n`expr` is interpreted as a Julia expression and it is wrapped expression with head `Jxpr`, which is then evaluated when\n`Jxpr` is evaluated. You never see the head `Jxpr`. For example,\n `m = J( collect(1:10) )`  creates a Julia array and binds it to the Symata symbol `m`.\n\"\"\"\n\n@sjexamp( Jxpr,\n         \"This creates a Julia Array{Int,1} and \\\"binds\\\" it to the Symata symbol m.\",\n         (\"m = J( collect(1:3) )\",\n          \"3-element Array{Int64,1}:\\n 1\\n 2\\n 3\"))\n\n@sjexamp( Jxpr,\n         \"Call a Julia function\",\n         (\"tf = J( time )\",\"\"),\n         (\"tf()\",\"1.424287593897437e9\"))\n\n# quote, i.e. :( expr ) is parsed as a Julia expression and is wrapped as  < -- we will remove this\n# Mxpr with head Jxpr. It is evaluated here.\n@mkapprule Jxpr :nodefault => true\n\n@doap function Jxpr(args...)\n    local res\n    for x in args\n        res = eval(x)\n    end\n    res\nend\n\n@sjdoc J \"\"\"\n    J(expr1,expr2,...)\n\nEvaluate Julia expressions `expr1`, `expr2`, ... in Julia and return the final result.\n\"\"\"\n\n@sjseealso_group(J, Compile, SymataCall)\n\n#### Unpack\n\n@sjdoc Unpack \"\"\"\n    Unpack(a)\n\nunpack a Julia typed `AbstractArray` into an Symata `List` expression.\n\nOnly `1`-d is supported. If `a` is a Julia `Dict`, then a `List` of `Lists` of\nkey,value pairs is returned.\n\"\"\"\n\n@sjexamp( Unpack,\n         \"This creates a List of three random Float64's.\",\n         (\"Unpack( J(rand(3)) )\", \"[0.5548766917324894,0.034964001133465095,0.9122052258982192]\"))\n\n\n@mkapprule Unpack :nargs => 1\n\n@doap Unpack(a::AbstractArray) = unpacktoList(a)\n\n\"\"\"\n    unpacktoList(obj::AbstractArray{T,1})\n\nConverts a Julia array to a Symata list of type `ListT`. Only one-dimensional\narrays are supported.\n\"\"\"\nfunction unpacktoList(obj)\n    mx = mxpr(:List, do_unpack(obj))\n    setfixed(mx)\n    setcanon(mx)\n    return mx\nend\ndo_unpack(obj) = copyto!(newargs(length(obj)),obj)\n\n## We copy this and change it a bit to tolistoflists in mxpr_utils\nfunction do_unpack(dict::Dict)\n    args = newargs(length(dict))\n    i = 0\n    for (k,v) in dict\n        i += 1\n        args[i] = mxpr(:List,k,v)\n    end\n    return args\nend\n\n#### Pack\n\n@sjdoc Pack \"\"\"\n    Pack(expr)\n\npack the arguments of the Symata expression `expr` into a Julia array,\nwhose element type is the minimum required to holds the arguments of `expr`.\n\"\"\"\n\n@sjexamp( Pack,\n         \"This returns a Julia array of element type Int [1,2,3].\",\n         (\"ClearAll(f)\",\"\"),\n         (\"Pack(f(1,2,3))\",\"3-element Array{Int64,1}: [1,2,3]\"))\n\n@sjseealso_group(Pack,Unpack)\n\n\"\"\"\n    typejoin_array(a::Array)\n\nreturn the typejoin of all elements in `a`.\nI saw this in Base somewhere. Probably more efficient there.\n\"\"\"\nfunction typejoin_array(a)\n    length(a) == 0 && return Any\n    T = typeof(a[1])\n@inbounds  for i in 2:length(a)\n        T = typejoin(T, typeof(a[i]))\n    end\n    T\nend\n\n# 1-d unpack\nfunction apprules(mx::Mxpr{:Pack})\n    a = margs(margs(mx)[1])\n    T = typejoin_array(a)\n    do_pack(T,a)\nend\n\ndo_pack(T,sjobj) = copyto!(Array{T}(undef, length(sjobj)), sjobj)\n\n#### Translate Symata to Julia\n\n##\n# Wrap Expr to prevent Symata from evaluating it.\n\nabstract type AbstractMtoE end\n\nmutable struct MtoECompile <: AbstractMtoE\nend\n\nmutable struct MtoEPlain <: AbstractMtoE\nend\n\nmxpr_to_expr(x, aux::AbstractMtoE) = x\n\n# Don't really want to do this with everything !\nfunction mxpr_to_expr(s::Symbol, aux::AbstractMtoE)\n    s\n#    Symbol(lowercase(string(s)))\nend\n\nfunction mxpr_to_expr(s::Symbol,aux::MtoEPlain)\n    Symbol(lowercase(string(s)))  # only lowercase some\nend\n\n\nconst MTOJSYM_COMPILE = Dict(\n    :Times => :mmul,\n    :Plus => :mplus,\n    :Power => :mpow,\n    :Abs => :mabs,\n    :Exp => :Exp  # Is this necessary ?\n)\n\nmtojsym_compile(s::Symbol) =  get(MTOJSYM_COMPILE, s, mtojsym(s))\n\nconst MTOJSYM_PLAIN = Dict(\n                             :Times => :(*),\n                             :Plus => :(+),\n                             :Power => :(^),\n                             :Abs => :abs,\n                             :E => :e\n                             )\n\nfunction mtojsym_plain(s::Symbol)\n    Symbol(lowercase(string(get(MTOJSYM_PLAIN, s, mtojsym(s)))))\nend\n\nfunction mxpr_to_expr(mx::Mxpr,aux::MtoECompile)\n    head = mtojsym_compile(mhead(mx))\n    mxpr_to_expr_body(head,mx,aux)\nend\n\nfunction mxpr_to_expr(mx::Mxpr{:Power}, aux::MtoECompile)\n    if mx[1] == :E\n        #        return Expr(:call, :Exp, margs(mx)[2:end]...)\n        return Expr(:call, :Exp, [mxpr_to_expr(x,aux) for x in margs(mx)[2:end]]...)\n    else\n#        return Expr(:call, :mpow, margs(mx)[1:end]...)\n        return Expr(:call, :mpow, [mxpr_to_expr(x,aux) for x in margs(mx)]...)\n    end\nend\n\n# convert List to Julia array\nfunction mxpr_to_expr(mx::Mxpr{:List},aux::MtoECompile)\n    Expr(:vect, [mxpr_to_expr(x,aux) for x in margs(mx)]...)\nend\n\nfunction mxpr_to_expr(mx::Mxpr,aux::MtoEPlain)\n    head = mtojsym_plain(mhead(mx))\n    mxpr_to_expr_body(head,mx,aux)\nend\n\nfunction mxpr_to_expr_body(head,mx::Mxpr,aux::AbstractMtoE)\n    isempty(mx) && return :( $(head)() )\n    a = margs(mx)\n    a1 = mxpr_to_expr(a[1],aux)\n    ex = :( $(head)($(a1)) )\n    length(mx) == 1 && return ex\n    for i in 2:length(a)\n        push!(ex.args, mxpr_to_expr(a[i],aux))\n    end\n    ex\nend\n\n#### Compile\n\n\n@mkapprule Compile \"\"\"\n    f = Compile(expr)\n\nconvert `expr` to a compiled function.\n\n    f = Compile( [x,y,...], expr)\n\ncreate an anonymous Julia function `(x,y,...) -> expr` from Symata expression `expr` and bind\nthe result to `f`.\n\n```\nsymata> f = Compile(x^2 + y^2)\nsymata> f(3,4)\n        25\n```\n\nCompile works by creating an anonymous Julia function from the Symata expression `expr`. This anonymous\nfunction is just-in-time compiled for the arguments that it is called with.\nIf no arguments are specified, then the function signature is the sorted list of free symbols found in `expr`. Free symbols are all symbols\nthat are not bound in the Julia. More precisely, in the Symata module scope. This\nexcludes `e`, for instance. Alternatively, the arguments may be specified, for example `Compile( [x,y], expr)`.\n\nBy default `expr` is not evaluated. This works for literal expressions, such as `x^2`. If the expression must be evaluated, wrap it\nin `Evaluate`. For example\n\n```\nsymata 1> expr = x^2 + y^3\nOut(1) = x^2 + y^3\n\nsymata 2> f = Compile(Evaluate(expr))\nOut(2) = (::#1) (generic function with 1 method)\n\nsymata 3> f(2,3)\nOut(3) = 31\n```\n\"\"\"\n@doap function Compile(a::Mxpr{:List}, body)\n    aux = MtoECompile()\n    jexpr = Expr(:function, Expr(:tuple, [mxpr_to_expr(x, aux) for x in margs(a)]...) , mxpr_to_expr(body, aux))\n    Core.eval(Main, jexpr)\nend\n\n# This is not reliable. It is not worth the complexity of fixing it, I think.\n# We require an explicit list of variables, instead.\n# We could look at Attributes, Protected, etc. and just use plain variables.\n# @doap function Compile(body)\n#     aux = MtoECompile()\n#     jbody = mxpr_to_expr(body,aux)\n#     Core.eval(Main, Expr(:function, Expr(:tuple, freesyms(jbody)...), jbody))\n# end\n\n## NOTE: freesyms is unrelated to getfreesyms and setfreesyms\n\"\"\"\n    freesyms(ex::Expr)\n\nreturn a list of Symata-unbound symbols at any depth in expression `ex`, by\nwalking through the expression. This may not be useful anymore.\n\"\"\"\nfunction freesyms(x)\n    syms = Dict()\n    freesyms!(x, syms)\n    sort(collect(keys(syms)))\nend\n\nfunction freesyms!(ex::Expr, syms::Dict)\n    a = ( ex.head == :call ?  view(ex.args,2:length(ex.args)) : ex.args )\n    foreach(x -> freesyms!(x,syms), a)\nend\n\n# We have been using isdefined for a long time,\n# but this is incorrect. We want isbound, which\n# checks for Symata binding.\n# Neither is correct. For instance, E is not Symata bound.\n# E is bound in Julia Main. (we did it). So, the fact\n# that this was worked for several examples, is fortuitous.\nfunction freesyms!(s::Symbol, syms)\n#    if ! isdefined(Symata,s)\n    if ! isbound(s)\n        syms[s] = 1\n    end\nend\n\nfreesyms!(x, syms) = nothing\n\n####  ToJuliaExpression\n\n@mkapprule ToJuliaExpression\n\n@doap function ToJuliaExpression(x)\n    mxpr(:JuliaExpression, mxpr_to_expr(x, MtoEPlain()))\nend\n\n\"\"\"\n    @symExpr(expr)\n\nSymata-evaluates `expr`, translates the result to a Julia expression, and inserts it into the surrounding code. This works\njust like any Julia macro for inserting code, except that the code is generated by evaluating a Symata expression.\n\nThe following evaluates Symata code, translates the result to Julia and uses it as the body of a Julia function.\n```\njulia> f1(z) = @symExpr Together(PolyLog(-1,z) + (1-z))\n```\n\"\"\"\nmacro symExpr(expr)\n    mx = symataevaluate(expr, EvaluateJuliaSyntaxSimple())\n    mx = doeval(mx)\n    local ex\n    if isa(mx,Mxpr{:JuliaExpression})\n       ex = mx[1]\n    else\n       ex = mxpr_to_expr(mx, MtoECompile())\n    end\n    :(($(esc(ex))))  ## We need to escape this so that the code is evaluated in the correct module\nend\n\n### ToJuliaString\n\n@sjdoc ToJuliaString \"\"\"\n    ToJuliaString(expr)\n\ntranslate `expr` to Julia, returning a string.\nIf the option `NoSymata => False` is given, then translated code requires `using Symata`.\nCurrently, only arithemetic, trigonometric functions, and a few other things are translated.\n\"\"\"\n@mkapprule ToJuliaString :nodefault => true  :options => Dict( :NoSymata => true )\n\nprotect(:NoSymata)\n\nfunction do_ToJuliaString(mx, x; NoSymata=true)\n    aux = NoSymata ? MtoEPlain() : MtoECompile()\n    string(mxpr_to_expr(x, aux))\nend\n\n@mkapprule SymataCall  \"\"\"\n    SymataCall(var, body)\n    SymataCall(varlist, body)\n\nreturn a Julia function that substitues `var` or variable in `varlist` into `body` and\nsubmits the result to the Symata evaluation sequence.\n\nThe variables are localized before writing the Julia function.\n\"\"\"\n@doap SymataCall(var::Symbol, ex::Mxpr) = wrap_symata(doeval(ex),var)\n@doap SymataCall(vars::Mxpr{:List}, ex::Mxpr) = wrap_symata(doeval(ex),vars...)\n\n\"\"\"\n    f = wrap_symata(expr::Mxpr,var)\n\nreturn a Julia function that Symata-evaluates an expression when called.\n\nThe function corresponds to `f(var) = expr`.\nThis wraps `expr` in a single-argument Julia function.\n\nFirst creates a lexical scope, that is a new gensym symbol `nvar` is generated and is substituted\neverywhere in `expr` for `var`. Calling `f(x)`  Symata-binds `x` to `nvar` and `expr` is\nSymata-evaluated and the result is returned.\n\n`wrap_symata` is used by `NIntegrate` and `Plot`.\n\"\"\"\nfunction wrap_symata(var0,expr0::Mxpr)   # Why are arguments reversed here ? Is this ever called ?\n    (var,expr) = localize_variable(var0,expr0)\n#    deepunsetfixed(expr)\n    function (x)\n        setsymval(var,x)\n        # deepunsetfixed(expr)\n        # this seems wasteful. When called from NIntegrate, it is not needed. Don't know why.\n        # because we needed HoldAll\n        doeval(expr)\n    end\nend\n\nfunction wrap_symata(expr0::Mxpr,vars0...)\n    vars = Array{Symbol}(undef, length(vars0))\n    expr = expr0\n    for i in 1:length(vars0)\n        (vars[i],expr) = localize_variable(vars0[i],expr)\n    end\n    deepunsetfixed(expr)\n    function (args...)\n        for i in 1:length(args)\n            setsymval(vars[i],args[i])\n        end\n        deepunsetfixed(expr)\n        doeval(expr)\n    end\nend\n\nfunction wrap_symata(expr0::SJSym,var0)\n    var0 == expr0 && return (x) -> x\n    sym = get_localized_symbol(var0)\n    return (sym,expr0)   # this does not appear to be useful\nend\n\n\n#### CodeNative\n\n# This does not yet work. Symata evaluates the symbol `f` to\n# the function object, so we lose its name. We probably need\n# a robust way to associate the symbol with the function.\n# ... Julia numbers anonymous functions and names others.\n# we can get this information somehow\n# ok, it is string(func::Function). Then we can keep a\n# hash table of Symata symbols bound to functions.\n# ... a PITA.\n# Better to define a Julia type symfunction or something\n# that stores the function and the name.\n# we can make it callable and it passes the call to its function\n# can also do this at the Symata level.\n\n# FIXME. implement functions according to the description above.\n\n# @mkapprule CodeNative\n# @doap function CodeNative(f::Symbol, arg)\n#     thetypes = (margs(args)...)\n#     code_native(eval(f), thetypes)\n# end\n", "meta": {"hexsha": "d8563efbc4bab7f177f3fe130836482845938da8", "size": 13414, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/symata_julia_interface.jl", "max_stars_repo_name": "jlapeyre/Symata.jl", "max_stars_repo_head_hexsha": "bfe97c96176853d80e7fbb15f35aa9a3d3a4534f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 149, "max_stars_repo_stars_event_min_datetime": "2016-10-09T07:17:14.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-13T02:18:19.000Z", "max_issues_repo_path": "src/symata_julia_interface.jl", "max_issues_repo_name": "jlapeyre/Symata.jl", "max_issues_repo_head_hexsha": "bfe97c96176853d80e7fbb15f35aa9a3d3a4534f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 183, "max_issues_repo_issues_event_min_datetime": "2016-10-14T15:49:08.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-01T12:20:47.000Z", "max_forks_repo_path": "src/symata_julia_interface.jl", "max_forks_repo_name": "jlapeyre/SJulia.jl", "max_forks_repo_head_hexsha": "bfe97c96176853d80e7fbb15f35aa9a3d3a4534f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 23, "max_forks_repo_forks_event_min_datetime": "2016-10-09T07:08:01.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-29T10:37:49.000Z", "avg_line_length": 28.2995780591, "max_line_length": 139, "alphanum_fraction": 0.6652005368, "num_tokens": 3925, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.30735800417608683, "lm_q2_score": 0.17328821019878957, "lm_q1q2_score": 0.05326151843394618}}
{"text": "function readarray(fileName)\n    f=open(fileName);\n    arr=Float64[];\n    a = readline(f);    # Get the first line\n    while a != \"\"\n        push!(arr,parsefloat(Float64,a));\n        a=readline(f);  # Get subsequent lines\n    end\n    return arr;\nend\n", "meta": {"hexsha": "294f11ca23b14635243faeb1b61893cd08345125", "size": 250, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "set03/q1/readarray.jl", "max_stars_repo_name": "stefco/g6080", "max_stars_repo_head_hexsha": "dd57d90bcdf67f096b35a18533be8660510ffc97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "set03/q1/readarray.jl", "max_issues_repo_name": "stefco/g6080", "max_issues_repo_head_hexsha": "dd57d90bcdf67f096b35a18533be8660510ffc97", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "set03/q1/readarray.jl", "max_forks_repo_name": "stefco/g6080", "max_forks_repo_head_hexsha": "dd57d90bcdf67f096b35a18533be8660510ffc97", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.7272727273, "max_line_length": 46, "alphanum_fraction": 0.588, "num_tokens": 66, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10818896175069655, "lm_q1q2_score": 0.05324932338964379}}
{"text": "using Test\n\ninclude(\"leap.jl\")\n\n@testset \"year not divisible by 4 in common year\" begin\n    @test !isleapyear(2015)\nend\n\n@testset \"year divisible by 2, not divisible by 4 in common year\" begin\n    @test !isleapyear(1970)\nend\n\n@testset \"year divisible by 4, not divisible by 100 in leap year\" begin\n    @test isleapyear(1996)\nend\n\n@testset \"year divisible by 4 and 5 is still a leap year\" begin\n    @test isleapyear(1960)\nend\n\n@testset \"year divisible by 100, not divisible by 400 in common year\" begin\n    @test !isleapyear(2100)\nend\n\n@testset \"year divisible by 100 but not by 3 is still not a leap year\" begin\n    @test !isleapyear(1900)\nend\n\n@testset \"year divisible by 400 in leap year\" begin\n    @test isleapyear(2000)\nend\n\n@testset \"year divisible by 400 but not by 125 is still a leap year\" begin\n    @test isleapyear(2400)\nend\n\n@testset \"year divisible by 200, not divisible by 400 in common year\" begin\n    @test !isleapyear(1800)\nend\n", "meta": {"hexsha": "f1f1b6b3dc086c920b8e72ed5414c65023a482b5", "size": 944, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercises/concept/leap/runtests.jl", "max_stars_repo_name": "ee7/julia", "max_stars_repo_head_hexsha": "8ea1a08503d6ee9480655039d45ca02c43d85dd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 200, "max_stars_repo_stars_event_min_datetime": "2019-12-12T13:50:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-20T22:38:42.000Z", "max_issues_repo_path": "exercises/concept/leap/runtests.jl", "max_issues_repo_name": "ee7/julia", "max_issues_repo_head_hexsha": "8ea1a08503d6ee9480655039d45ca02c43d85dd0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1938, "max_issues_repo_issues_event_min_datetime": "2019-12-12T08:07:10.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T12:56:13.000Z", "max_forks_repo_path": "exercises/concept/leap/runtests.jl", "max_forks_repo_name": "SaschaMann/julia-1", "max_forks_repo_head_hexsha": "c2a07ca4b3d63e5cd4fd58351aa1224bf3f9b71b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 239, "max_forks_repo_forks_event_min_datetime": "2019-12-12T14:09:08.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-18T00:04:07.000Z", "avg_line_length": 23.6, "max_line_length": 76, "alphanum_fraction": 0.7256355932, "num_tokens": 298, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10818895887524602, "lm_q1q2_score": 0.053249321974381154}}
{"text": "function my_sum(a,b)\n    return a + b;\nend", "meta": {"hexsha": "8d57a5d8ea9b8ff850a05094c16e0d80c63377b4", "size": 42, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sum.jl", "max_stars_repo_name": "pietrop88/test_julia", "max_stars_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/sum.jl", "max_issues_repo_name": "pietrop88/test_julia", "max_issues_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/sum.jl", "max_forks_repo_name": "pietrop88/test_julia", "max_forks_repo_head_hexsha": "6fffebeffdd2ce43ada11c85e598f1a9ba7bd6a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.0, "max_line_length": 20, "alphanum_fraction": 0.6428571429, "num_tokens": 13, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10818895599979557, "lm_q1q2_score": 0.05324932055911856}}
{"text": "using Test\n\nfunction fizzbuzz(number::Int)\n    if number < 1\n        throw(DomainError(number, \"number needs to be 1 or higher\"))\n    elseif number % 15 == 0\n        return \"FizzBuzz\"\n    elseif number % 3 == 0\n        return \"Fizz\"\n    elseif number % 5 == 0\n        return \"Buzz\"\n    else\n        return number\n    end\nend\n\n@testset begin\n    expected_result = [1, 2, \"Fizz\", 4, \"Buzz\", \"Fizz\",\n                       7, 8, \"Fizz\", \"Buzz\", 11, \"Fizz\",\n                       13, 14, \"FizzBuzz\", 16, 17, \"Fizz\", 19, \"Buzz\"]\n    obtained_result = [fizzbuzz(i) for i in 1:20]\n\n    @test obtained_result == expected_result\n\n    @test_throws MethodError fizzbuzz(1.5)\n    @test_throws DomainError fizzbuzz(0)\n    @test_throws DomainError fizzbuzz(-5)\n\nend\n\nfor i in 1:20\n    println(fizzbuzz(i))\nend\n", "meta": {"hexsha": "c4b71bb532d0307cf5820587ff0ab1c5f1959fd7", "size": 797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "content/code/julia/fizzbuzz.jl", "max_stars_repo_name": "transferorbit/coderefinery_testing", "max_stars_repo_head_hexsha": "b1011345cd6ed614e702b372bd2d987521bba130", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-03-11T12:36:25.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-30T00:32:11.000Z", "max_issues_repo_path": "content/code/julia/fizzbuzz.jl", "max_issues_repo_name": "transferorbit/coderefinery_testing", "max_issues_repo_head_hexsha": "b1011345cd6ed614e702b372bd2d987521bba130", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 126, "max_issues_repo_issues_event_min_datetime": "2016-12-13T11:12:23.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T20:17:17.000Z", "max_forks_repo_path": "content/code/julia/fizzbuzz.jl", "max_forks_repo_name": "transferorbit/coderefinery_testing", "max_forks_repo_head_hexsha": "b1011345cd6ed614e702b372bd2d987521bba130", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 37, "max_forks_repo_forks_event_min_datetime": "2016-12-13T11:00:28.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-18T13:53:07.000Z", "avg_line_length": 23.4411764706, "max_line_length": 70, "alphanum_fraction": 0.582183187, "num_tokens": 247, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.1081889444979944, "lm_q1q2_score": 0.05324931489806849}}
{"text": "#' # [MNIST: Elastic Distortions](@id elastic)\n\n#' In this example we are going to use Augmentor on the famous\n#' **MNIST database of handwritten digits** [^MNIST1998] to\n#' reproduce the elastic distortions discussed in [^SIMARD2003].\n#' It may be interesting to point out, that the way Augmentor\n#' implements distortions is a little different to how it is\n#' described by the authors of the paper.\n#' This is for a couple of reasons, most notably that we want the\n#' parameters for our deformations to be independent of the size\n#' of image it is applied on. As a consequence the\n#' parameter-numbers specified in the paper are not 1-to-1\n#' transferable to Augmentor.\n\n#' If the effects are sensible for the dataset, then applying\n#' elastic distortions can be a really effective way to improve\n#' the generalization ability of the network.\n#' That said, our implementation of [`ElasticDistortion`](@ref)\n#' has a lot of possible parameters to choose from. To that end,\n#' we will introduce a simple strategy for interactively\n#' exploring the parameter space on our dataset of interest.\n\n#md #' !!! note\n#md #'\n#md #'     This tutorial was designed to be performed in a\n#md #'     [Juypter](https://jupyter.org/) notebook. You can\n#md #'     find a link to the Juypter version of this tutorial\n#md #'     in the top right corner of this page.\n\n#' ## Loading the MNIST Trainingset\n\n#' In order to access and visualize the MNIST images we employ\n#' the help of two additional Julia packages. In the interest of\n#' time and space we will not go into great detail about their\n#' functionality. Feel free to click on their respective names to\n#' find out more information about the utility they can provide.\n#'\n#' - [Images.jl](https://github.com/JuliaImages/Images.jl) will\n#'   provide us with the necessary tools for working with image\n#'   data in Julia.\n#'\n#' - [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl)\n#'   has an MNIST submodule that offers a convenience interface\n#'   to read the MNIST database.\n\n#' The function `MNIST.traintensor` returns the MNIST training\n#' images corresponding to the given indices as a\n#' multi-dimensional array. These images are stored in the native\n#' horizontal-major memory layout as a single floating point\n#' array, where all values are scaled to be between 0.0 and 1.0.\n\nusing Images, MLDatasets\ntrain_tensor = MNIST.traintensor()\n@show summary(train_tensor);\n#md nothing # hide\n\n#' This horizontal-major format is the standard way of utilizing\n#' this dataset for training machine learning models.\n#' In this tutorial, however, we are more interested in working\n#' with the MNIST images as actual Julia images in vertical-major\n#' layout, and as black digits on white background.\n\n#' We can convert the \"tensor\" to a `Colorant` array using the\n#' provided function `MNIST.convert2image`.\n#' This way, Julia knows we are dealing with image data and can\n#' tell programming environments such as Juypter how to visualize\n#' it. If you are working in the terminal you may want to use the\n#' package [ImageInTerminal.jl](https://github.com/JuliaImages/ImageInTerminal.jl)\n\ntrain_images = MNIST.convert2image(train_tensor)\nimg_1 = train_images[:,:,1] # show first image\n#md save(\"mnist_1.png\",repeat(img_1,inner=(4,4))) # hide\n#md nothing # hide\n\n#md #' ![first image](mnist_1.png)\n\n#' ## Visualizing the Effects\n\n#' Before applying an operation (or pipeline of operations) on\n#' some dataset to train a network, we strongly recommend\n#' investing some time in selecting a decent set of hyper\n#' parameters for the operation(s). A useful tool for tasks like\n#' this is the package [Interact.jl](https://github.com/JuliaGizmos/Interact.jl).\n#' We will use this package to define a number of widgets for\n#' controlling the parameters to our operation.\n\n#' Note that while the code below only focuses on configuring\n#' the parameters of a single operation, specifically\n#' [`ElasticDistortion`](@ref), it could also be adapted to tweak\n#' a whole pipeline. Take a look at the corresponding section in\n#' [High-level Interface](@ref pipeline) for more information\n#' on how to define and use a pipeline.\n\n# These two package will provide us with the capabilities\n# to perform interactive visualisations in a jupyter notebook\nusing Augmentor, Interact, Reactive\n\n# The manipulate macro will turn the parameters of the\n# loop into interactive widgets.\n@manipulate for\n        unpaused = true,\n        #ticks = fpswhen(Signal(unpaused), 5.), # non-boolean ... error\n        image_index = 1:100,\n        grid_size = 3:20,\n        scale = .1:.1:.5,\n        sigma = 1:5,\n        iterations = 1:6,\n        free_border = true\n    op = ElasticDistortion(grid_size, grid_size, # equal width & height\n                           sigma = sigma,\n                           scale = scale,\n                           iter = iterations,\n                           border = free_border)\n    augment(train_images[:, :, image_index], op)\nend\n#md nothing # hide\n\n#md #' Executing the code above in a Juypter notebook will result\n#md #' in the following interactive visualisation. You can now\n#md #' use the sliders to investigate the effects that different\n#md #' parameters have on the MNIST training images.\n#md #'\n#md #' !!! tip\n#md #'\n#md #'     You should always use your **training** set to do this\n#md #'     kind of visualisation (not the test test!). Otherwise\n#md #'     you are likely to achieve overly optimistic (i.e. biased)\n#md #'     results during training.\n#md #'\n#md #' ![interact](https://user-images.githubusercontent.com/10854026/30867456-4afe0800-a2dc-11e7-90eb-800b6ea025d0.gif)\n\n#' Congratulations! With just a few simple lines of code, you\n#' created a simple interactive tool to visualize your image\n#' augmentation pipeline. Once you found a set of parameters that\n#' you think are appropriate for your dataset you can go ahead\n#' and train your model.\n\n#' ## References\n#'\n#' [^MNIST1998]: LeCun, Yan, Corinna Cortes, Christopher J.C. Burges. [\"The MNIST database of handwritten digits\"](http://yann.lecun.com/exdb/mnist/) Website. 1998.\n#'\n#' [^SIMARD2003]: Simard, Patrice Y., David Steinkraus, and John C. Platt. [\"Best practices for convolutional neural networks applied to visual document analysis.\"](https://www.microsoft.com/en-us/research/publication/best-practices-for-convolutional-neural-networks-applied-to-visual-document-analysis/) ICDAR. Vol. 3. 2003.\n", "meta": {"hexsha": "d89feb415211a3badfa540c985440de3663ce688", "size": 6424, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/mnist_elastic.jl", "max_stars_repo_name": "baggepinnen/Augmentor.jl", "max_stars_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/mnist_elastic.jl", "max_issues_repo_name": "baggepinnen/Augmentor.jl", "max_issues_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/mnist_elastic.jl", "max_forks_repo_name": "baggepinnen/Augmentor.jl", "max_forks_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9230769231, "max_line_length": 325, "alphanum_fraction": 0.7236924035, "num_tokens": 1575, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.1294027332703691, "lm_q1q2_score": 0.05319887015579983}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Julia for Data Science\n# Prepared by [@nassarhuda](https://github.com/nassarhuda)! \ud83d\ude03\n#\n# `Last updated on 03/Jan/2020`\n#\n# In this tutorial, we will discuss why *Julia* is the tool you want to use for your data\n# science applications.\n#\n# We will cover the following:\n# * **Data**\n# * Data processing\n# * Visualization\n#\n# ### Data: Build a strong relationship with your data.\n# Every data science task has one main ingredient, the _data_! Most likely, you want to use\n# your data to learn something new. But before the _new_ part, what about the data you\n# already have? Let's make sure you can **read** it, **store** it, and **understand** it\n# before you start using it.\n#\n# Julia makes this step really easy with data structures and packages to process the data,\n# as well as, existing functions that are readily usable on your data.\n#\n# The goal of this first part is get you acquainted with some Julia's tools to manage your\n# data.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# First, let's download a csv file from github that we can work with.\n#\n# Note: `download` depends on external tools such as curl, wget or fetch. So you must have\n# one of these.\n# ------------------------------------------------------------------------------------------\n\nP = download(\"https://raw.githubusercontent.com/nassarhuda/easy_data/master/programming_languages.csv\",\"programminglanguages.csv\")\n\n# ------------------------------------------------------------------------------------------\n# We can use shell commands like `ls` in Julia by preceding them with a semicolon.\n# ------------------------------------------------------------------------------------------\n\n;ls\n\n# ------------------------------------------------------------------------------------------\n# And there's the *.csv file we downloaded!\n#\n# Now let's load it into Julia\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# We'll use the `DelimitedFiles` standard library package and its `readdlm()` function as\n# shown\n# below.\n#\n# (Today, the [CSV.jl](https://juliadata.github.io/CSV.jl/stable/) package is the\n# recommended way to load CSVs in Julia. We can install it via `Pkg.add()`, and load .csv\n# files using `CSV.read()`. This tutorial hasn't been updated to use CSV.jl yet.)\n# ------------------------------------------------------------------------------------------\n\n# using Pkg\n# Pkg.add(\"CSV\") # for CSV.read()\n# Pkg.add(\"DelimitedFiles\") # for readdlm\n\n# using CSV\n# P = CSV.read(\"programminglanguages.csv\",header=true)\n# or\nusing DelimitedFiles  # Standard library in Base\n\n# ------------------------------------------------------------------------------------------\n# By default, `readdlm` will fill an array with the data stored in the input .csv file. If\n# we set the keyword argument `header` to `true`, we'll get a second output array for just\n# the headers.\n# ------------------------------------------------------------------------------------------\n\nP,H = readdlm(\"programminglanguages.csv\", ',', header=true)\n\nP # stores the dataset\n\nH # stores the header names\n\n# ------------------------------------------------------------------------------------------\n# Here we write our first small function. <br>\n# Now you can answer questions such as, \"when was language X created?\"\n# ------------------------------------------------------------------------------------------\n\nfunction language_created_year(P,language::String)\n    loc = findfirst(P[:,2].==language)\n    return P[loc,1]\nend\n\nlanguage_created_year(P,\"Julia\")\n\nlanguage_created_year(P,\"julia\")\n\n# ------------------------------------------------------------------------------------------\n# As expected, this will not return what you want, but thankfully, string manipulation is\n# really easy in Julia!\n# ------------------------------------------------------------------------------------------\n\nfunction language_created_year_v2(P,language::String)\n    loc = findfirst(lowercase.(P[:,2]).==lowercase.(language))\n    return P[loc,1]\nend\nlanguage_created_year_v2(P,\"julia\")\n\n# ------------------------------------------------------------------------------------------\n# **Reading and writing to files is really easy in Julia.** <br>\n#\n# You can use different delimiters with the function `readdlm`, from the `DelimitedFiles`\n# package. <br>\n#\n# To write to files, we can use `writedlm`. <br>\n#\n# Let's write this same data to a file with a different delimiter.\n# ------------------------------------------------------------------------------------------\n\nwritedlm(\"programming_languages_data.txt\", P, '-')\n\n# ------------------------------------------------------------------------------------------\n# We can now check that this worked using a shell command to glance at the file,\n# ------------------------------------------------------------------------------------------\n\n;head -10 programming_languages_data.txt\n\n# ------------------------------------------------------------------------------------------\n# and also check that we can use `readdlm` to read our new text file correctly.\n# ------------------------------------------------------------------------------------------\n\nP_new_delim = readdlm(\"programming_languages_data.txt\", '-');\nP == P_new_delim\n\n# ------------------------------------------------------------------------------------------\n# ### Dictionaries\n# Let's try to store the above data in a dictionary format!\n#\n# First, let's initialize an empty dictionary\n# ------------------------------------------------------------------------------------------\n\ndict = Dict{Integer,Vector{String}}()\n\n# ------------------------------------------------------------------------------------------\n# Here we told Julia that we want `dict` to only accept integers as keys and vectors of\n# strings as values.\n#\n# However, we could have initialized an empty dictionary without providing this information\n# (depending on our application).\n# ------------------------------------------------------------------------------------------\n\ndict2 = Dict()\n\n# ------------------------------------------------------------------------------------------\n# This dictionary takes keys and values of any type!\n#\n# Now, let's populate the dictionary with years as keys and vectors that hold all the\n# programming languages created in each year as their values.\n# ------------------------------------------------------------------------------------------\n\nfor i = 1:size(P,1)\n    year,lang = P[i,:]\n    \n    if year in keys(dict)\n        dict[year] = push!(dict[year],lang)\n    else\n        dict[year] = [lang]\n    end\nend\n\n# ------------------------------------------------------------------------------------------\n# Now you can pick whichever year you want and find what programming languages were invented\n# in that year\n# ------------------------------------------------------------------------------------------\n\ndict[2003]\n\n# ------------------------------------------------------------------------------------------\n# ### DataFrames!\n# *Shout out to R fans!*\n# One other way to play around with data in Julia is to use a DataFrame.\n#\n# This requires loading the `DataFrames` package. Thankfully, this tutorial came with a\n# Project.toml file that specifies exactly which version of DataFrames to install...\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# #### Project.toml files\n#\n# For this tutorial (Julia for Data Science), you may have noticed that there are files in\n# this folder called [`Project.toml`](/edit/introductory-tutorials/broader-topics-and-\n# ecosystem/intro-to-julia-for-data-science/Project.toml) and\n# [`Manifest.toml`](/edit/introductory-tutorials/broader-topics-and-ecosystem/intro-to-\n# julia-for-data-science/Manifest.toml). These are files autogenerated by Julia's package\n# manager, `Pkg`, that describe the _exact set of packages_ installed for a julia project,\n# allowing you to share your work in a perfectly reproducible way.\n#\n# Jupyter was able to detect those `.toml` files, and so this notebook was automatically\n# started with _this project activated!_ Note that this means any packages you add or remove\n# inside this notebook will only affect this \"Julia for Data Science\" _project_.\n#\n# To install all of the package dependencies used in the rest of the tutorial, you only need\n# to run this next cell (commands that start with `]` are package repl commands):\n# ------------------------------------------------------------------------------------------\n\n] instantiate\n\n# ------------------------------------------------------------------------------------------\n# You can read more about package manager commands, here:\n# https://docs.julialang.org/en/v1/stdlib/Pkg/index.html\n#\n# **Now back to DataFrames!**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames\ndf = DataFrame(year = P[:,1], language = P[:,2])\n\n# ------------------------------------------------------------------------------------------\n# You can access columns by header name, or column index.\n#\n# In this case, `df[1]` is equivalent to `df.year` or `df[!, :year]`.\n#\n# Note that if we want to index columns by header name, we precede the header name with a\n# colon. In Julia, this means that the header names are treated as *symbols*.\n# ------------------------------------------------------------------------------------------\n\ndf.year\n\n# ------------------------------------------------------------------------------------------\n# **`DataFrames` provides some handy features when dealing with data**\n#\n# First, it uses julia's \"missing\" type.\n# ------------------------------------------------------------------------------------------\n\na = missing\ntypeof(a)\n\n# ------------------------------------------------------------------------------------------\n# Let's see what happens when we try to add a \"missing\" type to a number.\n# ------------------------------------------------------------------------------------------\n\na + 1\n\n# ------------------------------------------------------------------------------------------\n# `DataFrames` provides the `describe` function, which can give you quick statistics about\n# each column in your dataframe\n# ------------------------------------------------------------------------------------------\n\ndescribe(df)\n\n# ------------------------------------------------------------------------------------------\n# ### RDatasets\n#\n# We can use RDatasets to play around with pre-existing datasets\n# ------------------------------------------------------------------------------------------\n\nusing RDatasets\niris = dataset(\"datasets\", \"iris\")\n\n# ------------------------------------------------------------------------------------------\n# Note that data loaded with `dataset` is stored as a DataFrame. \ud83d\ude03\n# ------------------------------------------------------------------------------------------\n\ntypeof(iris) \n\n# ------------------------------------------------------------------------------------------\n# The summary we get from `describe` on `iris` gives us a lot more information than the\n# summary on `df`!\n# ------------------------------------------------------------------------------------------\n\ndescribe(iris)\n\n# ------------------------------------------------------------------------------------------\n# ### More on Missing Values\n#\n# Julia 1.0 and beyond has native support for `missing` values. (Before Julia 1.0, this was\n# done via the DataArrays.jl package.)\n# More information on using arrays with missing values can be found\n# [in the Julia documentation](https://docs.julialang.org/en/v1/manual/missing/#Arrays-With-\n# Missing-Values-1).\n# ------------------------------------------------------------------------------------------\n\nfoods = [\"apple\", \"cucumber\", \"tomato\", \"banana\"]\ncalories = [missing,47,22,105]\ntypeof(calories)\n\nusing Statistics  # julia's standard library for stats\nmean(calories)\n\n# ------------------------------------------------------------------------------------------\n# Missing values ruin everything! \ud83d\ude11\n#\n# Luckily we can ignore them with `skipmissing`!\n# ------------------------------------------------------------------------------------------\n\nmean(skipmissing(calories))\n\n# ------------------------------------------------------------------------------------------\n# Oh WAIT! Detour. How did I get the emoji there?\n#\n# Try this out:\n#\n# ```\n# \\:expressionless: + <TAB>\n# ```\n# ------------------------------------------------------------------------------------------\n\n\ud83d\ude11 = 0 # expressionless\n\ud83d\ude00 = 1\n\ud83d\ude1e = -1\n\n# ------------------------------------------------------------------------------------------\n# *Back to missing values*\n# ------------------------------------------------------------------------------------------\n\nprices = [0.85,1.6,0.8,0.6,]\n\ndataframe_calories = DataFrame(item=foods,calories=calories)\n\ndataframe_prices = DataFrame(item=foods,price=prices)\n\n# ------------------------------------------------------------------------------------------\n# We can also `join` two dataframes together\n# ------------------------------------------------------------------------------------------\n\nDF = join(dataframe_calories,dataframe_prices,on=:item)\n\n# ------------------------------------------------------------------------------------------\n# ### FileIO\n# ------------------------------------------------------------------------------------------\n\nusing FileIO\njulialogo = download(\"https://avatars0.githubusercontent.com/u/743164?s=200&v=4\",\"julialogo.png\")\n\n# ------------------------------------------------------------------------------------------\n# Again, let's check that this download worked!\n# ------------------------------------------------------------------------------------------\n\n;ls\n\n# ------------------------------------------------------------------------------------------\n#\n# Next, let's load the Julia logo, stored as a .png file\n#\n# **NOTE: You may see errors below, that certain Image packages could not be found. If so:**\n#  - This is because these packages are specific to your OS, so aren't installed by default.\n#  - Simply run the suggested commands to install the packages, then try again.\n# ------------------------------------------------------------------------------------------\n\n# These commands may vary depending on your operating system.\n#import Pkg; Pkg.add(\"QuartzImageIO\")\n#import Pkg; Pkg.add(\"ImageMagick\")\n\nX1 = load(\"julialogo.png\")\n\n# ------------------------------------------------------------------------------------------\n# We see here that Julia stores this logo as an array of colors.\n# ------------------------------------------------------------------------------------------\n\n@show typeof(X1);\n@show size(X1);\n\n# ------------------------------------------------------------------------------------------\n# And if we load the Images package, it will display in Jupyter as an image:\n# ------------------------------------------------------------------------------------------\n\nusing Images\n\nX1\n\n# ------------------------------------------------------------------------------------------\n# ### File types\n# In Julia, many file types are supported so you do not have to transfer a file you from\n# another language to a text file before you read it.\n#\n# *Some packages that achieve this:*\n# MAT CSV NPZ JLD FASTAIO\n#\n#\n# Let's try using MAT to write a file that stores a matrix.\n# ------------------------------------------------------------------------------------------\n\nusing MAT\n\nA = rand(5,5)\nmatfile = matopen(\"densematrix.mat\", \"w\") \nwrite(matfile, \"A\", A)\nclose(matfile)\n\n# ------------------------------------------------------------------------------------------\n# Now try opening densematrix.mat with MATLAB!\n# ------------------------------------------------------------------------------------------\n\nnewfile = matopen(\"densematrix.mat\")\nread(newfile,\"A\")\n\nnames(newfile)\n\nclose(newfile)\n\n\n", "meta": {"hexsha": "b975902b08b8e488abb28783e3395cbf0ffad001", "size": 16374, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-for-data-science/1. Julia for Data Science - Data.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-for-data-science/1. Julia for Data Science - Data.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-for-data-science/1. Julia for Data Science - Data.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 41.1407035176, "max_line_length": 130, "alphanum_fraction": 0.4212165628, "num_tokens": 2707, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43398147944527615, "lm_q2_score": 0.12252320771203076, "lm_q1q2_score": 0.05317280294924798}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Introduction to DataFrames\n# **[Bogumi\u0142 Kami\u0144ski](http://bogumilkaminski.pl/about/), Apr 21, 2018**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames # load package\nsrand(1);\n\n# ------------------------------------------------------------------------------------------\n# ## Manipulating rows of DataFrame\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Reordering rows\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(id=1:10, x = rand(10), y = [zeros(5); ones(5)]) # and we hope that x[:x] is not sorted :)\n\nissorted(x), issorted(x, :x) # check if a DataFrame or a subset of its columns is sorted\n\nsort!(x, :x) # sort x in place\n\ny = sort(x, :id) # new DataFrame\n\nsort(x, (:y, :x), rev=(true, false)) # sort by two columns, first is decreasing, second is increasing\n\nsort(x, (order(:y, rev=true), :x)) # the same as above\n\nsort(x, (order(:y, rev=true), order(:x, by=v->-v))) # some more fancy sorting stuff\n\nx[shuffle(1:10), :] # reorder rows (here randomly)\n\nsort!(x, :id)\nx[[1,10],:] = x[[10,1],:] # swap rows\nx\n\nx[1,:], x[10,:] = x[10,:], x[1,:] # and swap again\nx\n\n# ------------------------------------------------------------------------------------------\n# ### Merging/adding rows\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(rand(3, 5))\n\n[x; x] # merge by rows - data frames must have the same column names; the same is vcat\n\ny = x[reverse(names(x))] # get y with other order of names\n\nvcat(x, y) # we get what we want as vcat does column name matching\n\nvcat(x, y[1:3]) # but column names must still match\n\nappend!(x, x) # the same but modifies x\n\nappend!(x, y) # here column names must match exactly\n\npush!(x, 1:5) # add one row to x at the end; must give correct number of values and correct types\nx\n\npush!(x, Dict(:x1=> 11, :x2=> 12, :x3=> 13, :x4=> 14, :x5=> 15)) # also works with dictionaries\nx\n\n# ------------------------------------------------------------------------------------------\n# ### Subsetting/removing rows\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(id=1:10, val='a':'j')\n\nx[1:2, :] # by index\n\nview(x, 1:2) # the same but a view\n\nx[repmat([true, false], 5), :] # by Bool, exact length required\n\nview(x, repmat([true, false], 5), :) # view again\n\ndeleterows!(x, 7) # delete one row\n\ndeleterows!(x, 6:7) # delete a collection of rows\n\nx = DataFrame([1:4, 2:5, 3:6])\n\nfilter(r -> r[:x1] > 2.5, x) # create a new DataFrame where filtering function operates on DataFrameRow\n\n# in place modification of x, an example with do-block syntax\nfilter!(x) do r\n    if r[:x1] > 2.5\n        return r[:x2] < 4.5\n    end\n    r[:x3] < 3.5\nend\n\n# ------------------------------------------------------------------------------------------\n# ### Deduplicating\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(A=[1,2], B=[\"x\",\"y\"])\nappend!(x, x)\nx[:C] = 1:4\nx\n\nunique(x, [1,2]) # get first unique rows for given index\n\nunique(x) # now we look at whole rows\n\nnonunique(x, :A) # get indicators of non-unique rows\n\nunique!(x, :B) # modify x in place\n\n# ------------------------------------------------------------------------------------------\n# ### Extracting one row from `DataFrame` into a vector\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(x=[1,missing,2], y=[\"a\", \"b\", missing], z=[true,false,true])\n\ncols = [:x, :y]\n[x[1, col] for col in cols] # subset of columns\n\n[[x[i, col] for col in names(x)] for i in 1:nrow(x)] # vector of vectors, each entry contains one full row of x\n\nTuple(x[1, col] for col in cols) # similar construct for Tuples, when ported to Julia 0.7 NamedTuples will be added\n", "meta": {"hexsha": "e4071dcb5c6084173f2a534d2de90ea8d717de0a", "size": 4117, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/06_rows.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/06_rows.jl", "max_issues_repo_name": "grenkoca/JuliaTutorials", "max_issues_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/06_rows.jl", "max_forks_repo_name": "grenkoca/JuliaTutorials", "max_forks_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 33.4715447154, "max_line_length": 115, "alphanum_fraction": 0.4374544571, "num_tokens": 1007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.399811640739795, "lm_q2_score": 0.13296424878721277, "lm_q1q2_score": 0.05316065446734984}}
{"text": "# WRITTEN IN THE REPL\n# ] add IJulia InstantiateFromURL # Enter package\n# mode and install link to Jupyter and manager for\n# QE dependencies.\n\n# QuantEcon packages\n# using InstantiateFromURL\n# activate_github(\"QuantEcon/QuantEconLectureAllPackages\", tag = \"v0.9.6\", add_default_environment = true)\n\n# Status and Gadfly plotting package\n# ]\n#status\n# add Gadfly\n", "meta": {"hexsha": "00e943551ddaaafe3f301259447ee136d8569e71", "size": 361, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "practice.jl", "max_stars_repo_name": "MarsFenix23/example_repository", "max_stars_repo_head_hexsha": "7fc50a982e58dc6a8b7d811d4a90a6f490e4a599", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "practice.jl", "max_issues_repo_name": "MarsFenix23/example_repository", "max_issues_repo_head_hexsha": "7fc50a982e58dc6a8b7d811d4a90a6f490e4a599", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "practice.jl", "max_forks_repo_name": "MarsFenix23/example_repository", "max_forks_repo_head_hexsha": "7fc50a982e58dc6a8b7d811d4a90a6f490e4a599", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.7857142857, "max_line_length": 106, "alphanum_fraction": 0.7728531856, "num_tokens": 100, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.39981164073979497, "lm_q2_score": 0.1329642316084462, "lm_q1q2_score": 0.05316064759907898}}
{"text": "using Weave\n\nfilenames = [\"01-basics\", \"02-collections\", \"03-types\" ,\n              \"04-project-1DCA\", \"05-project-errorprop\"]\nnotesDir = \"notes/\"\nnotebookDir = \"notebooks/\"\n\n\nfunction cleanTemps(filename,notesDir;exts=[\".out\", \".log\", \".aux\"])\n  for ext in exts\n    rm(notesDir * filename * ext)\n  end\nend\n\nfor filename in filenames\n  # Basics\n  println(\"Building $(filename)...\")\n  file = filename * \".jmd\"                              # file = filename + extension\n\n  weave(file; doctype=\"md2pdf\",\n        out_path = notesDir)                            # weaving notes\n  cleanTemps(filename,notesDir)                         # removing ugly .tex perifery\n  convert_doc(file, notebookDir * filename * \".ipynb\")  # weaving notebooks\nend\n", "meta": {"hexsha": "f7a9db82576016b58d061dfe2188a16e892b1efa", "size": 739, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "build.jl", "max_stars_repo_name": "MichielStock/Julia925", "max_stars_repo_head_hexsha": "e5ec351749ebbcd6312e14c8e793850c53348afb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-12-19T08:32:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-11T08:28:10.000Z", "max_issues_repo_path": "build.jl", "max_issues_repo_name": "MichielStock/Julia925", "max_issues_repo_head_hexsha": "e5ec351749ebbcd6312e14c8e793850c53348afb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17, "max_issues_repo_issues_event_min_datetime": "2019-12-12T13:07:27.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-07T15:52:20.000Z", "max_forks_repo_path": "build.jl", "max_forks_repo_name": "MichielStock/Julia925", "max_forks_repo_head_hexsha": "e5ec351749ebbcd6312e14c8e793850c53348afb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2019-12-18T09:05:41.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-11T18:30:27.000Z", "avg_line_length": 29.56, "max_line_length": 85, "alphanum_fraction": 0.5967523681, "num_tokens": 193, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.31069438321455395, "lm_q2_score": 0.1710611959045317, "lm_q1q2_score": 0.05314775275350246}}
{"text": "function find_passage(r)\n    side=Ost\n    while isborder(r,Nord)==true # \u043f\u0440\u043e\u0445\u043e\u0434\u0430 \u0441\u0432\u0435\u0440\u0445\u0443 \u043d\u0435\u0442\n        putmarker!(r)\n        move_by_markers!(r,side)\n        side=inverse(side)\n    end\nend\n\nmove_by_markers(r::Robot,side::HorizonSide) = \n    while ismarker(r)==true \n        move!(r,side) \n    end\n\ninverse(side::HorizonSide) = HorizonSide(mod(Int(side)+2,4))", "meta": {"hexsha": "b56a48b6ab0c8291f0d78e744dc805138c9ad4c4", "size": 355, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "osnovi_algoritmiki/TR-8.jl", "max_stars_repo_name": "Vardan03/mirea_TR_prog", "max_stars_repo_head_hexsha": "fd7d54daba2f3b742d2d9d12541f551645d3d69d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "osnovi_algoritmiki/TR-8.jl", "max_issues_repo_name": "Vardan03/mirea_TR_prog", "max_issues_repo_head_hexsha": "fd7d54daba2f3b742d2d9d12541f551645d3d69d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "osnovi_algoritmiki/TR-8.jl", "max_forks_repo_name": "Vardan03/mirea_TR_prog", "max_forks_repo_head_hexsha": "fd7d54daba2f3b742d2d9d12541f551645d3d69d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.6666666667, "max_line_length": 60, "alphanum_fraction": 0.6422535211, "num_tokens": 109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10970577387797077, "lm_q1q2_score": 0.053139291996683276}}
{"text": "#=\nYou run an e-commerce website and want to record the last N order ids in a log. Implement a data structure to accomplish this, with the following API:\n\n    record(order_id): adds the order_id to the log\n    get_last(i): gets the ith last element from the log. i is guaranteed to be smaller than or equal to N.\n\nYou should be as efficient with time and space as possible.\n=#\n\nusing Test\ninclude(\"Solutions/problem16_log_orderID.jl\")\n\nfor i in 1:10\n    record(i)\nend\n\n@test length(log) == N\n@test get_last(2) == 9\n", "meta": {"hexsha": "d392bd087dc9fc1d9db93b8e2b791475dba88c5e", "size": 515, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Tests/problem16_log_orderID_test.jl", "max_stars_repo_name": "DominiqueCaron/daily-coding-problem", "max_stars_repo_head_hexsha": "41234497aa3a2c21c5dff43d86e9153d9582cced", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tests/problem16_log_orderID_test.jl", "max_issues_repo_name": "DominiqueCaron/daily-coding-problem", "max_issues_repo_head_hexsha": "41234497aa3a2c21c5dff43d86e9153d9582cced", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2019-06-17T14:04:18.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-12T20:01:45.000Z", "max_forks_repo_path": "Tests/problem16_log_orderID_test.jl", "max_forks_repo_name": "DominiqueCaron/daily-coding-problem", "max_forks_repo_head_hexsha": "41234497aa3a2c21c5dff43d86e9153d9582cced", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1052631579, "max_line_length": 150, "alphanum_fraction": 0.732038835, "num_tokens": 133, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.11920292984474949, "lm_q1q2_score": 0.05310842590908604}}
{"text": "\"\"\"\n`SaveModel` module purpose is to provide a delegate-to-solver option to\n`MathOptInterface.write_to_file`. Instead of using the current MOI\nfallback (that only works for MPS and is kinda memory-hungry and slow),\n`SaveModel.write_to_file` checks if `SaveModel.natively_supports` returns\n`true` for its arguments, and in such case calls\n`SaveModel.native_write_to_file` over the object returned by the\n`MOI.RawSolver()` property; otherwise it calls the `MOI` fallback.\n\n\"\"\"\nmodule SaveModel\n\nimport JuMP\nimport MathOptInterface\nconst MOI = MathOptInterface\n\nimport Requires.@require\n\nimport TimerOutputs.@timeit\nimport ..TIMER\n\n# Based on: https://discourse.julialang.org/t/trying-to-save-mps-from-raw-solver-and-problems-with-moi-rawsolver/58515/2?u=henrique_becker\n_get_raw_solver(model :: JuMP.Model) = _get_raw_solver(JuMP.backend(model))\nfunction _get_raw_solver(model :: MOI.Utilities.CachingOptimizer)\n\tif model.state == MOI.Utilities.EMPTY_OPTIMIZER\n\t\tMOI.Utilities.attach_optimizer(model)\n\tend\n\treturn _get_raw_solver(model.optimizer)\nend\nfunction _get_raw_solver(model :: MOI.Bridges.LazyBridgeOptimizer)\n\t_get_raw_solver(model.model)\nend\n_get_raw_solver(model) = model # Unrecognized means we arrived were we wanted.\n\n\"\"\"\n\twrite_to_file(model, filename; format = FORMAT_AUTOMATIC\n\nSave the model to filename trying to use the solver mechanism instead of\nthe generic `JuMP`/`MathOptInterface` one.\n\"\"\"\n@timeit TIMER function write_to_file(\n\tmodel :: JuMP.Model,\n\tfilename :: String;\n\tformat :: MOI.FileFormats.FileFormat = MOI.FileFormats.FORMAT_AUTOMATIC\n)\n\traw_solver = _get_raw_solver(model)\n\tif natively_supports(raw_solver, filename; format = format)\n\t\tnative_write_to_file(raw_solver, filename; format = format)\n\telse\n\t\tJuMP.write_to_file(model, filename; format = format)\n\tend\n\n\treturn\nend\nexport write_to_file\n\nfunction natively_supports(\n\traw_solver, filename :: String;\n\tformat :: MOI.FileFormats.FileFormat = MOI.FileFormats.FORMAT_AUTOMATIC\n) :: Bool\n\treturn false\nend\n\nfunction native_write_to_file(\n\traw_solver, filename :: String;\n\tformat :: MOI.FileFormats.FileFormat = MOI.FileFormats.FORMAT_AUTOMATIC\n)\n\tthrow(ArgumentError(\n\t\t\"`native_write_to_file` was called for a solver it does know.\" *\n\t\t\" Check if the the solver package is loaded, and if some package\" *\n\t\t\" provides a solver-specific method for the functions in\" *\n\t\t\" `GuillotineModels.SaveModel`.\"\n\t))\nend\n\nconst MOI_FF = MOI.FileFormats\nconst MOI_FF_FF = MOI_FF.FileFormat\n\nconst _KNOWN_FORMATS = Tuple{String, MOI_FF_FF}[\n\t(\".cbf\", MOI_FF.FORMAT_CBF),\n\t(\".lp\", MOI_FF.FORMAT_LP),\n\t(\".mof.json\", MOI_FF.FORMAT_MOF),\n\t(\".mps\", MOI_FF.FORMAT_MPS),\n\t(\".sdpa\", MOI_FF.FORMAT_SDPA),\n\t(\".dat-s\", MOI_FF.FORMAT_SDPA),\n]\n\nfunction _format_from_ext(filename :: String)\n\tfor (ext, enum) in _KNOWN_FORMATS\n\t\t(endswith(filename, ext) || occursin(\"$ext.\", filename)) && return enum\n\tend\n\terror(\"Unable to automatically detect format of $(filename).\")\nend\n\nfunction _real_format(filename :: String, format :: MOI_FF_FF) :: MOI_FF_FF\n\treturn if format == MOI_FF.FORMAT_AUTOMATIC\n\t\t_format_from_ext(filename) :: MOI_FF_FF\n\telse\n\t\tformat\n\tend\nend\n\nfunction __init__()\n\t@require CPLEX=\"a076750e-1247-5638-91d2-ce28b192dca0\" begin\n\t\tfunction natively_supports(\n\t\t\tmodel :: CPLEX.Optimizer, filename :: String;\n\t\t\tformat :: MOI_FF_FF = MOI_FF.FORMAT_AUTOMATIC\n\t\t)\n\t\t\treal_format = _real_format(filename, format)\n\t\t\treturn real_format in (MOI_FF.FORMAT_MPS, MOI_FF.FORMAT_LP)\n\t\tend\n\n\t\tfunction native_write_to_file(\n\t\t\tmodel :: CPLEX.Optimizer, filename :: String;\n\t\t\tformat :: MOI_FF_FF = MOI_FF.FORMAT_AUTOMATIC\n\t\t)\n\t\t\t@assert format == MOI_FF.FORMAT_AUTOMATIC ||\n\t\t\t\tformat == _format_from_ext(filename)\n\t\t\treturn CPLEX.CPXwriteprob(model.env, model.lp, filename, C_NULL)\n\t\tend\n\tend # @require CPLEX\n\n\t@require Gurobi=\"2e9cd046-0924-5485-92f1-d5272153d98b\" begin\n\t\tfunction natively_supports(\n\t\t\tmodel :: Gurobi.Optimizer, filename :: String;\n\t\t\tformat :: MOI_FF_FF = MOI_FF.FORMAT_AUTOMATIC\n\t\t)\n\t\t\treal_format = _real_format(filename, format)\n\t\t\treturn real_format in (MOI_FF.FORMAT_MPS, MOI_FF.FORMAT_LP)\n\t\tend\n\t\tfunction native_write_to_file(\n\t\t\tmodel :: Gurobi.Optimizer, filename :: String;\n\t\t\tformat :: MOI_FF_FF = MOI_FF.FORMAT_AUTOMATIC\n\t\t)\n\t\t\t@assert format == MOI_FF.FORMAT_AUTOMATIC ||\n\t\t\t\tformat == _format_from_ext(filename)\n\t\t\t# Calling GRBupdatemodel directly here is a recipe for a disaster,\n\t\t\t# we need to use Gurobi.jl internals to avoid any subtle bugs.\n\t\t\tGurobi._update_if_necessary(model)\n\t\t\treturn Gurobi.GRBwrite(model, filename)\n\t\tend\n\tend # @require Gurobi\nend # __init__()\n\nend # module\n", "meta": {"hexsha": "65ffbaa4f2065e6825160fa72a0d33a7624a9816", "size": 4588, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SaveModel.jl", "max_stars_repo_name": "henriquebecker91/GuillotineModels.jl", "max_stars_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-11T07:08:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-12T13:19:40.000Z", "max_issues_repo_path": "src/SaveModel.jl", "max_issues_repo_name": "henriquebecker91/GuillotineModels.jl", "max_issues_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/SaveModel.jl", "max_forks_repo_name": "henriquebecker91/GuillotineModels.jl", "max_forks_repo_head_hexsha": "0aae3b5b5e0ee8be21a0fc9ba34943979aa11e4e", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4246575342, "max_line_length": 138, "alphanum_fraction": 0.7619877942, "num_tokens": 1277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3886180408675583, "lm_q2_score": 0.13660838651113866, "lm_q1q2_score": 0.053088483532036886}}
{"text": "# This file was generated, do not modify it. # hide\niris = dataset(\"datasets\", \"iris\")\nfirst(iris, 3)", "meta": {"hexsha": "d637c50394e5974be8a4a35635805b00bba58905", "size": 101, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/data/dataframe/code/ex20.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/data/dataframe/code/ex20.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/data/dataframe/code/ex20.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 33.6666666667, "max_line_length": 51, "alphanum_fraction": 0.6930693069, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.11124121829336078, "lm_q1q2_score": 0.053015300998148086}}
{"text": "#===============================================================================\n# Practice with constructors of composite types or objects.\n#\n# Author: Carl Cortright\n# Date: 12/30/2016\n#\n===============================================================================#\n# This is the type we will construct\ntype Foo\n  bar\n  baz\nend\n\n# Outer constructors create new instances by calling inner constructors\nFoo(x) = Foo(x,x)\nFoo() = Foo(0)\n\n# Testing our new constructor\nfood = Foo()\n\n# Inner constructors allow us to recurse and provide invariants\ntype OrderedPair\n  x::Real\n  y::Real\n\n  # Note: the new keyword is locally defined and creates a new instance\n  OrderedPair(x, y) = x > y ? error(\"out of order\") : new(x,y)\nend\n\n# Seeing if the new constructor works as a longer function\ntype OrderedPair\n  x::Real\n  y::Real\n\n  function OrderedPair(x,y)\n    if x > y\n      error(\"out of order\")\n    else\n      new(x,y)\n    end\n  end\nend\n\n# Note: once an inner constructor is defined Julia assumes you've defined all the constructors you need\ntype Foo\n  bar\n  baz\n\n  Foo(bar, baz) = new(bar, baz)\nend\n\n# The new function can be called with less parameters than itself to allow for self-referal\ntype SelfReferalials\n  obj::SelfReferalials\n\n  SelfReferalials() = (x = new(); x.obj = x)\nend\n\nx = SelfReferalials()\nis(x,x)\nis(x,x.obj)\nis(x, x.obj.obj.obj)\n\n# Parametric types have new constructors for each type they implement\ntype Point{T <: Real}\n  x::T\n  y::T\nend\n\n# We can also redefine outer constructors to dispatch on unusual arguments\nPoint(x::Int64, y::Float64) = Point(convert(x, Float64), y)\n", "meta": {"hexsha": "41089dec422de1a523c706f3890e87f49423120f", "size": 1594, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia/constructors.jl", "max_stars_repo_name": "ekmixon/Playground-1", "max_stars_repo_head_hexsha": "08266d131735a7bcd72a975c6e402832f379d1cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-26T03:50:44.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-08T10:28:31.000Z", "max_issues_repo_path": "Julia/constructors.jl", "max_issues_repo_name": "carlcortright/Playground", "max_issues_repo_head_hexsha": "08266d131735a7bcd72a975c6e402832f379d1cb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2021-07-15T20:58:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-19T03:37:57.000Z", "max_forks_repo_path": "Julia/constructors.jl", "max_forks_repo_name": "ekmixon/Playground-1", "max_forks_repo_head_hexsha": "08266d131735a7bcd72a975c6e402832f379d1cb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-06-26T03:50:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-26T03:50:46.000Z", "avg_line_length": 22.1388888889, "max_line_length": 103, "alphanum_fraction": 0.6336260979, "num_tokens": 405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.11124120650754304, "lm_q1q2_score": 0.05301529538126719}}
{"text": "# ---\n# title: 1437. Check If All 1's Are at Least Length K Places Away\n# id: problem1437\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: Array\n# link: <https://leetcode.com/problems/check-if-all-1s-are-at-least-length-k-places-away/description/>\n# hidden: true\n# ---\n# \n# Given an array `nums` of 0s and 1s and an integer `k`, return `True` if all\n# 1's are at least `k` places away from each other, otherwise return `False`.\n# \n# \n# \n# **Example 1:**\n# \n# **![](https://assets.leetcode.com/uploads/2020/04/15/sample_1_1791.png)**\n# \n#     \n#     \n#     Input: nums = [1,0,0,0,1,0,0,1], k = 2\n#     Output: true\n#     Explanation: Each of the 1s are at least 2 places away from each other.\n#     \n# \n# **Example 2:**\n# \n# **![](https://assets.leetcode.com/uploads/2020/04/15/sample_2_1791.png)**\n# \n#     \n#     \n#     Input: nums = [1,0,0,1,0,1], k = 2\n#     Output: false\n#     Explanation: The second 1 and third 1 are only one apart from each other.\n# \n# **Example 3:**\n# \n#     \n#     \n#     Input: nums = [1,1,1,1,1], k = 0\n#     Output: true\n#     \n# \n# **Example 4:**\n# \n#     \n#     \n#     Input: nums = [0,1,0,1], k = 1\n#     Output: true\n#     \n# \n# \n# \n# **Constraints:**\n# \n#   * `1 <= nums.length <= 10^5`\n#   * `0 <= k <= nums.length`\n#   * `nums[i]` is `0` or `1`\n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "512148e72f0d27c1b28a6bed5d889e4c119ba014", "size": 1385, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/1437.check-if-all-1s-are-at-least-length-k-places-away.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/unresolved/1437.check-if-all-1s-are-at-least-length-k-places-away.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/unresolved/1437.check-if-all-1s-are-at-least-length-k-places-away.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 20.3676470588, "max_line_length": 102, "alphanum_fraction": 0.5559566787, "num_tokens": 510, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47657966593214324, "lm_q2_score": 0.11124120208786166, "lm_q1q2_score": 0.053015294928923144}}
{"text": "#Write function that reverses a list, preferably in place.\n\nlist = [1,23,3,4, \"A\",\"B\"]\n\nprintln(reverse!(list))\n", "meta": {"hexsha": "dfc932bf702dafe8ed601fa9cf627e3fdf12f303", "size": 112, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Lists_Strings/LS2_Reverse_List.jl", "max_stars_repo_name": "atott/Simple-Programming-Problems-Julia", "max_stars_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Lists_Strings/LS2_Reverse_List.jl", "max_issues_repo_name": "atott/Simple-Programming-Problems-Julia", "max_issues_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lists_Strings/LS2_Reverse_List.jl", "max_forks_repo_name": "atott/Simple-Programming-Problems-Julia", "max_forks_repo_head_hexsha": "a403a756f6312ccddd86eedf646044ba9bfb38b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.6666666667, "max_line_length": 58, "alphanum_fraction": 0.6785714286, "num_tokens": 34, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.1112411961949534, "lm_q1q2_score": 0.0530152904664968}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.7\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 e077ab16-22e5-11eb-15ed-5f0654bf7328\nusing Pkg, DrWatson\n\n# \u2554\u2550\u2561 ca5fbdc8-22e5-11eb-0a60-cb0a127a3ca5\nmd\"## Listing4.01\"\n\n# \u2554\u2550\u2561 e75a9a06-22e5-11eb-2e36-8d4f62b830ed\nbegin\n\t@quickactivate \"StatisticsWithJuliaPlutoNotebooks\"\nend\n\n# \u2554\u2550\u2561 fa68607e-22e5-11eb-0558-c9a4d9f77426\nbegin\n\tf(z::Int) = begin z = 0 end\n\tf(z::Array{Int}) = begin z[1] = 0 end\nend\n\n# \u2554\u2550\u2561 572972b2-22e6-11eb-1ec4-2d53fc807d03\nbegin\n\tx1 = 1\n\ttypeof(x1)\nend\n\n# \u2554\u2550\u2561 5729a994-22e6-11eb-00b0-e525c9e271cb\nisimmutable(x1)\n\n# \u2554\u2550\u2561 572a4444-22e6-11eb-351f-65750fcd0d5e\nText(\"Before call by value: $x1\")\n\n# \u2554\u2550\u2561 57305f50-22e6-11eb-1aef-61c42e0c6363\nf(x1)\n\n# \u2554\u2550\u2561 573d81bc-22e6-11eb-3b89-010224cc8027\nText(\"After call by value: $x1\")\n\n# \u2554\u2550\u2561 573e105a-22e6-11eb-32cb-859417a95d32\nbegin\n\tx2 = [1]\n\ttypeof(x2)\nend\n\n# \u2554\u2550\u2561 574411da-22e6-11eb-2e9e-739f51ea9fc6\nisimmutable(x2)\n\n# \u2554\u2550\u2561 574ff9c8-22e6-11eb-3866-817010612a7e\nText(\"Before call by reference: $x2\")\n\n# \u2554\u2550\u2561 575094b4-22e6-11eb-25b6-fb702eec82a2\nf(x2)\n\n# \u2554\u2550\u2561 575b02be-22e6-11eb-29ad-3f48f1b6eae6\nText(\"After call by reference: $x2\")\n\n# \u2554\u2550\u2561 475ff888-22e6-11eb-2354-09f8f40a8e12\nmd\"## End of listing4.01\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500ca5fbdc8-22e5-11eb-0a60-cb0a127a3ca5\n# \u2560\u2550e077ab16-22e5-11eb-15ed-5f0654bf7328\n# \u2560\u2550e75a9a06-22e5-11eb-2e36-8d4f62b830ed\n# \u2560\u2550fa68607e-22e5-11eb-0558-c9a4d9f77426\n# \u2560\u2550572972b2-22e6-11eb-1ec4-2d53fc807d03\n# \u2560\u25505729a994-22e6-11eb-00b0-e525c9e271cb\n# \u2560\u2550572a4444-22e6-11eb-351f-65750fcd0d5e\n# \u2560\u255057305f50-22e6-11eb-1aef-61c42e0c6363\n# \u2560\u2550573d81bc-22e6-11eb-3b89-010224cc8027\n# \u2560\u2550573e105a-22e6-11eb-32cb-859417a95d32\n# \u2560\u2550574411da-22e6-11eb-2e9e-739f51ea9fc6\n# \u2560\u2550574ff9c8-22e6-11eb-3866-817010612a7e\n# \u2560\u2550575094b4-22e6-11eb-25b6-fb702eec82a2\n# \u2560\u2550575b02be-22e6-11eb-29ad-3f48f1b6eae6\n# \u255f\u2500475ff888-22e6-11eb-2354-09f8f40a8e12\n", "meta": {"hexsha": "e66618960afa3b9b5b69b9228fd1e5b677c5a02e", "size": 1824, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/04/listing4.01.jl", "max_stars_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_stars_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 68, "max_stars_repo_stars_event_min_datetime": "2020-11-08T07:32:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T16:58:01.000Z", "max_issues_repo_path": "notebooks/04/listing4.01.jl", "max_issues_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_issues_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-12-07T08:07:30.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-20T16:16:06.000Z", "max_forks_repo_path": "notebooks/04/listing4.01.jl", "max_forks_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_forks_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2020-11-14T05:07:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T21:05:35.000Z", "avg_line_length": 23.0886075949, "max_line_length": 51, "alphanum_fraction": 0.7319078947, "num_tokens": 1083, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.37754066879814546, "lm_q2_score": 0.1403362494900832, "lm_q1q2_score": 0.05298264148910941}}
{"text": "using MyPackage2\nusing Test\n\n@testset \"MyPackage2.jl\" begin\n\n    x,y=3,4\n    \n    #\u6d4b\u8bd5\u6b63\u5e38\u60c5\u51b5\n    @test add_func(x,y) == 7\n    \n    #\u6d4b\u8bd5\u5f02\u5e38\u60c5\u51b5\n    x = \"blah\"  \n    @test_throws MethodError add_func(x, y)\n\n    #\u6d4b\u8bd5\u9519\u8bef\u60c5\u51b5\n    #@test subtract_func(x,y) == 0\n    \nend\n", "meta": {"hexsha": "2aece6424723ed44719eaee37203ac97a1d91e9b", "size": 254, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "zhanghh0108/MyPackage2", "max_stars_repo_head_hexsha": "6cd3fa5deed6cf16c8b036e91984af61a1e0be1f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "zhanghh0108/MyPackage2", "max_issues_repo_head_hexsha": "6cd3fa5deed6cf16c8b036e91984af61a1e0be1f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "zhanghh0108/MyPackage2", "max_forks_repo_head_hexsha": "6cd3fa5deed6cf16c8b036e91984af61a1e0be1f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.3684210526, "max_line_length": 43, "alphanum_fraction": 0.5708661417, "num_tokens": 98, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.41489884579676883, "lm_q2_score": 0.1276526220030806, "lm_q1q2_score": 0.05296292553200936}}
{"text": "# # Example 3: Pulse Parametrization\n\n#md # !!! tip\n#md #     This example is also available as a Jupyter notebook:\n#md #     [`state_to_state_parametrizations.ipynb`](@__NBVIEWER_ROOT_URL__/examples/state_to_state_parametrizations.ipynb).\n\n#md # ``\\gdef\\op#1{\\hat{#1}}``\n#md # ``\\gdef\\init{\\text{init}}``\n#md # ``\\gdef\\tgt{\\text{tgt}}``\n\n#nb # $\n#nb # \\newcommand{tr}[0]{\\operatorname{tr}}\n#nb # \\newcommand{diag}[0]{\\operatorname{diag}}\n#nb # \\newcommand{abs}[0]{\\operatorname{abs}}\n#nb # \\newcommand{pop}[0]{\\operatorname{pop}}\n#nb # \\newcommand{aux}[0]{\\text{aux}}\n#nb # \\newcommand{opt}[0]{\\text{opt}}\n#nb # \\newcommand{tgt}[0]{\\text{tgt}}\n#nb # \\newcommand{init}[0]{\\text{init}}\n#nb # \\newcommand{lab}[0]{\\text{lab}}\n#nb # \\newcommand{rwa}[0]{\\text{rwa}}\n#nb # \\newcommand{bra}[1]{\\langle#1\\vert}\n#nb # \\newcommand{ket}[1]{\\vert#1\\rangle}\n#nb # \\newcommand{Bra}[1]{\\left\\langle#1\\right\\vert}\n#nb # \\newcommand{Ket}[1]{\\left\\vert#1\\right\\rangle}\n#nb # \\newcommand{Braket}[2]{\\left\\langle #1\\vphantom{#2}\\mid{#2}\\vphantom{#1}\\right\\rangle}\n#nb # \\newcommand{op}[1]{\\hat{#1}}\n#nb # \\newcommand{Op}[1]{\\hat{#1}}\n#nb # \\newcommand{dd}[0]{\\,\\text{d}}\n#nb # \\newcommand{Liouville}[0]{\\mathcal{L}}\n#nb # \\newcommand{DynMap}[0]{\\mathcal{E}}\n#nb # \\newcommand{identity}[0]{\\mathbf{1}}\n#nb # \\newcommand{Norm}[1]{\\lVert#1\\rVert}\n#nb # \\newcommand{Abs}[1]{\\left\\vert#1\\right\\vert}\n#nb # \\newcommand{avg}[1]{\\langle#1\\rangle}\n#nb # \\newcommand{Avg}[1]{\\left\\langle#1\\right\\rangle}\n#nb # \\newcommand{AbsSq}[1]{\\left\\vert#1\\right\\vert^2}\n#nb # \\newcommand{Re}[0]{\\operatorname{Re}}\n#nb # \\newcommand{Im}[0]{\\operatorname{Im}}\n#nb # $\n\n\n# This example illustrates the parametrization of control pulses as a\n# form of amplitude constraint.\n\nusing DrWatson\n@quickactivate \"KrotovTests\"\n#-\nusing QuantumControl\nusing QuantumControl.Shapes: flattop\nusing Krotov:\n    SquareParametrization,\n    TanhParametrization,\n    TanhSqParametrization,\n    LogisticParametrization,\n    LogisticSqParametrization\nusing LinearAlgebra\n\n#-\n#jl using Test; println(\"\")\nusing Plots\nPlots.default(\n    linewidth               = 3,\n    size                    = (550, 300),\n    legend                  = :top,\n    foreground_color_legend = nothing,\n    background_color_legend = RGBA(1, 1, 1, 0.8),\n)\n#-\n\n# ## Parametrizations\n\n# ## Symmetric Bounded Controls\n\n#-\ninclude(joinpath(@__DIR__, \"plots\", \"symmetric_parametrization_comparison.jl\"))  # hide\nfig = plot_symmetric_parametrization_comparison()  # hide\n#jl display(fig)\n\n# ## Positive (Bounded) Controls\n\n#-\ninclude(joinpath(@__DIR__, \"plots\", \"positive_parametrization_comparison.jl\"))  # hide\nfig = plot_positive_parametrization_comparison()  # hide\n#jl display(fig)\n\n# ## Two-level Hamiltonian\n\n# We consider the Hamiltonian $\\op{H}_{0} = - \\frac{\\omega}{2} \\op{\\sigma}_{z}$, representing\n# a simple qubit with energy level splitting $\\omega$ in the basis\n# $\\{\\ket{0},\\ket{1}\\}$. The control field $\\epsilon(t)$ is assumed to couple via\n# the Hamiltonian $\\op{H}_{1}(t) = \\epsilon(t) \\op{\\sigma}_{x}$ to the qubit,\n# i.e., the control field effectively drives transitions between both qubit\n# states.\n#\n# We we will use\n\n\u03f5(t) = 0.2 * flattop(t, T=5, t_rise=0.3, func=:blackman);\n\n\n#-\n\"\"\"Two-level-system Hamiltonian.\"\"\"\nfunction hamiltonian(\u03a9=1.0, \u03f5=\u03f5)\n    \u03c3\u0302_z = ComplexF64[\n        1  0\n        0 -1\n    ]\n    \u03c3\u0302_x = ComplexF64[\n        0  1\n        1  0\n    ]\n    H\u0302\u2080 = -0.5 * \u03a9 * \u03c3\u0302_z\n    H\u0302\u2081 = \u03c3\u0302_x\n    return (H\u0302\u2080, (H\u0302\u2081, \u03f5))\nend;\n#-\n\nH = hamiltonian();\n#jl @test length(H) == 2\n\n# The control field here switches on from zero at $t=0$ to it's maximum amplitude\n# 0.2 within the time period 0.3 (the switch-on shape is half a [Blackman pulse](https://en.wikipedia.org/wiki/Window_function#Blackman_window)).\n# It switches off again in the time period 0.3 before the\n# final time $T=5$). We use a time grid with 500 time steps between 0 and $T$:\n\ntlist = collect(range(0, 5, length=500));\n\n#-\nfunction plot_control(pulse::Vector, tlist)\n    plot(tlist, pulse, xlabel=\"time\", ylabel=\"amplitude\", legend=false)\nend\n\nplot_control(\u03f5::T, tlist) where {T<:Function} = plot_control([\u03f5(t) for t in tlist], tlist)\n\nplot_control(H[2][2], tlist)\n\n# ## Optimization target\n\n# The `krotov` package requires the goal of the optimization to be described by a\n# list of `Objective` instances. In this example, there is only a single\n# objective: the state-to-state transfer from initial state $\\ket{\\Psi_{\\init}} =\n# \\ket{0}$ to the target state $\\ket{\\Psi_{\\tgt}} = \\ket{1}$, under the dynamics\n# of the Hamiltonian $\\op{H}(t)$:\n\nfunction ket(label)\n    result = Dict(\"0\" => Vector{ComplexF64}([1, 0]), \"1\" => Vector{ComplexF64}([0, 1]),)\n    return result[string(label)]\nend;\n\n#-\n#jl @test dot(ket(0), ket(1)) \u2248 0\n#-\n\nobjectives = [Objective(initial_state=ket(0), generator=H, target_state=ket(1))]\n\n#-\n#jl @test length(objectives) == 1\n#-\n\n\n# ## Square-parametrization for positive pulses\n\n\nproblem = ControlProblem(\n    objectives=objectives,\n    pulse_options=IdDict(\n        \u03f5 => Dict(\n            :lambda_a => 5,\n            :update_shape => t -> flattop(t, T=5, t_rise=0.3, func=:blackman),\n            :parametrization => SquareParametrization(),\n        )\n    ),\n    tlist=tlist,\n    iter_stop=50,\n    J_T=QuantumControl.Functionals.J_T_ss,\n    check_convergence=res -> begin\n        ((res.J_T < 1e-3) && (res.converged = true) && (res.message = \"J_T < 10\u207b\u00b3\"))\n    end\n);\n#-\nopt_result_positive, file = @optimize_or_load(\n    datadir(),\n    problem;\n    method=:krotov,\n    filename=\"parametrization#opt_result_positive.jld2\"\n);\n#-\nopt_result_positive\n\n# We can plot the optimized field:\n\n#-\n#!jl plot_control(opt_result_positive.optimized_controls[1], tlist)\n#-\n\n#-\n#jl @test minimum(opt_result_positive.optimized_controls[1]) \u2265 0.0\n#jl @test minimum(opt_result_positive.optimized_controls[1]) < 1e-16\n#jl @test maximum(opt_result_positive.optimized_controls[1]) > 0.0\n#-\n\n# ## Tanh-Square-Parametrization for positive amplitude-constrained pulses\n\n\nproblem_tanhsq = ControlProblem(\n    objectives=objectives,\n    pulse_options=IdDict(\n        \u03f5 => Dict(\n            :lambda_a => 10,\n            :update_shape => t -> flattop(t, T=5, t_rise=0.3, func=:blackman),\n            :parametrization => TanhSqParametrization(3),\n        )\n    ),\n    tlist=tlist,\n    iter_stop=50,\n    J_T=QuantumControl.Functionals.J_T_ss,\n    check_convergence=res -> begin\n        ((res.J_T < 1e-3) && (res.converged = true) && (res.message = \"J_T < 10\u207b\u00b3\"))\n    end\n);\n#-\nopt_result_tanhsq, file = @optimize_or_load(\n    datadir(),\n    problem_tanhsq;\n    method=:krotov,\n    filename=\"parametrization#opt_result_tanhsq.jld2\"\n);\n#-\nopt_result_tanhsq\n\n# We can plot the optimized field:\n\n#-\n#!jl plot_control(opt_result_tanhsq.optimized_controls[1], tlist)\n#-\n\n#-\n#jl @test minimum(opt_result_tanhsq.optimized_controls[1]) \u2265 0.0\n#jl @test minimum(opt_result_tanhsq.optimized_controls[1]) < 1e-16\n#jl @test maximum(opt_result_tanhsq.optimized_controls[1]) > 0.0\n#jl @test maximum(opt_result_tanhsq.optimized_controls[1]) < 3.0\n#-\n\n# ## Logistic-Square-Parametrization for positive amplitude-constrained pulses\n\nproblem_logisticsq = ControlProblem(\n    objectives=objectives,\n    pulse_options=IdDict(\n        \u03f5 => Dict(\n            :lambda_a => 1,\n            :update_shape => t -> flattop(t, T=5, t_rise=0.3, func=:blackman),\n            :parametrization => LogisticSqParametrization(3, k=1.0),\n        )\n    ),\n    tlist=tlist,\n    iter_stop=50,\n    J_T=QuantumControl.Functionals.J_T_ss,\n    check_convergence=res -> begin\n        ((res.J_T < 1e-3) && (res.converged = true) && (res.message = \"J_T < 10\u207b\u00b3\"))\n    end\n);\n#-\nopt_result_logisticsq, file = @optimize_or_load(\n    datadir(),\n    problem_logisticsq;\n    method=:krotov,\n    filename=\"parametrization#opt_result_logisticsq.jld2\"\n);\n# We can plot the optimized field:\n\n#-\n#!jl plot_control(opt_result_logisticsq.optimized_controls[1], tlist)\n#-\n\n#-\n#jl @test minimum(opt_result_logisticsq.optimized_controls[1]) \u2265 0.0\n#jl @test minimum(opt_result_logisticsq.optimized_controls[1]) < 1e-16\n#jl @test maximum(opt_result_logisticsq.optimized_controls[1]) > 0.0\n#jl @test maximum(opt_result_logisticsq.optimized_controls[1]) < 3.0\n#-\n# ## Tanh-parametrization for amplitude-constrained pulses\n\nproblem_tanh = ControlProblem(\n    objectives=objectives,\n    pulse_options=IdDict(\n        \u03f5 => Dict(\n            :lambda_a => 1,\n            :update_shape => t -> flattop(t, T=5, t_rise=0.3, func=:blackman),\n            :parametrization => TanhParametrization(-0.5, 0.5),\n        )\n    ),\n    tlist=tlist,\n    iter_stop=50,\n    J_T=QuantumControl.Functionals.J_T_ss,\n    check_convergence=res -> begin\n        ((res.J_T < 1e-3) && (res.converged = true) && (res.message = \"J_T < 10\u207b\u00b3\"))\n    end\n);\n#-\nopt_result_tanh, file = @optimize_or_load(\n    datadir(),\n    problem_tanh;\n    method=:krotov,\n    filename=\"parametrization#opt_result_tanh.jld2\"\n);\n#-\n#!jl plot_control(opt_result_tanh.optimized_controls[1], tlist)\n#-\n#jl @test minimum(opt_result_tanh.optimized_controls[1]) > -0.5\n#jl @test maximum(opt_result_tanh.optimized_controls[1]) < 0.5\n#-\n", "meta": {"hexsha": "34e4c88f6b1a624c89744159616694ce457cf13a", "size": 9108, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/state_to_state_parametrizations.jl", "max_stars_repo_name": "JuliaQuantumControl/Krotov.jl", "max_stars_repo_head_hexsha": "6eab22b1d2e084210ecaf7fe32fcf28a2183be83", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-11-13T03:01:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-10T00:52:11.000Z", "max_issues_repo_path": "examples/state_to_state_parametrizations.jl", "max_issues_repo_name": "JuliaQuantumControl/Krotov.jl", "max_issues_repo_head_hexsha": "6eab22b1d2e084210ecaf7fe32fcf28a2183be83", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2021-10-02T04:17:36.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T01:52:40.000Z", "max_forks_repo_path": "examples/state_to_state_parametrizations.jl", "max_forks_repo_name": "JuliaQuantumControl/Krotov.jl", "max_forks_repo_head_hexsha": "6eab22b1d2e084210ecaf7fe32fcf28a2183be83", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-10T00:52:12.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T00:52:12.000Z", "avg_line_length": 29.1923076923, "max_line_length": 145, "alphanum_fraction": 0.66523935, "num_tokens": 2960, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10669059749614017, "lm_q1q2_score": 0.05292854708040067}}
{"text": "\"\"\"\ncreating asset tree hierarchy\n\n\"\"\"\n\nabstract type Asset end\n\nabstract type Property <: Asset end\nabstract type Investment <: Asset end\nabstract type Cash <: Asset end\n\nabstract type House <: Property end\nabstract type Apartment <: Property end\n\nabstract type FixedIncome <: Investment end\nabstract type Equity <: Investment end\n\n\n#composite type\n#by default, is immutable\n#can be made mutable by adding mutable in front of struct\nmutable struct Stock <: Equity\n    symbol::String\n    name::String\nend\n\nfunction describe(s::Stock)\n    return s.symbol * \"(\" * s.name * \")\"\nend\n\n\nstruct BasketOfStocks\n    stocks::Vector{Stock}\n    reason::String\nend\n\n\n\n\nabstract type Art end\n\nstruct Painting <: Art\n    artist::String\n    title::String\nend\n\n\n#demonstrating how Union works: enables multiple data types to be allowed in a field\nconst Thing = Union{Painting,Stock}\n\nstruct BasketOfThings\n    thing::Vector{Thing}\n    reason::String\nend\n\n\n# Display the entire type hierarchy starting from the specified `roottype`\nfunction subtypetree(roottype, level = 1, indent = 4)\n    level == 1 && println(roottype)\n    for s in subtypes(roottype)\n       println(join(fill(\" \", level * indent)) * string(s))\n       subtypetree(s, level + 1, indent)\n    end\nend\n\n\n#using a type parameter enables flexible typing - ex: be able to hold int and fractional shares\nstruct StockHolding{T <: Real}\n    stock::Stock\n    quantity::T\nend\n\n#using two type parameters: this enforces price and marketvalue to both be of the same data type\nstruct StockHolding2{T <: Real, P <: AbstractFloat} \n    stock::Stock\n    quantity::T\n    price::P\n    marketvalue::P\nend\n\n#parametric abstract types\nabstract type Holding{P} end\n\n\nstruct StockHolding3{T, P} <: Holding{P}\n\n    stock::Stock\n    quantity::T\n    price::P\n    marketvalue::P\n   end\n   \n   struct CashHolding{P} <: Holding{P}\n    currency::String\n    amount::P\n    marketvalue::P\n   end", "meta": {"hexsha": "955e0082a2595854daad565ff3a54f0e19197e72", "size": 1911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/AssetHierarchy.jl", "max_stars_repo_name": "mnalley95/Calculator", "max_stars_repo_head_hexsha": "0ed2e40861a4ddb224d16b4101272a4ebf91a983", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/AssetHierarchy.jl", "max_issues_repo_name": "mnalley95/Calculator", "max_issues_repo_head_hexsha": "0ed2e40861a4ddb224d16b4101272a4ebf91a983", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/AssetHierarchy.jl", "max_forks_repo_name": "mnalley95/Calculator", "max_forks_repo_head_hexsha": "0ed2e40861a4ddb224d16b4101272a4ebf91a983", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.7010309278, "max_line_length": 96, "alphanum_fraction": 0.7053898482, "num_tokens": 481, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49609382947091957, "lm_q2_score": 0.1066905911052601, "lm_q1q2_score": 0.05292854390992451}}
{"text": "### A Pluto.jl notebook ###\n# v0.11.14\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 cbcf36de-f360-11ea-0c7f-719e93324b27\na = 1\n\n# \u2554\u2550\u2561 d71c5ee2-f360-11ea-2753-a132fa41871a\nb = 2\n\n# \u2554\u2550\u2561 d8f5a4f6-f360-11ea-043d-47667f6a7e76\nc = 3\n\n# \u2554\u2550\u2561 dcd9ebb8-f360-11ea-2050-fd2e11d27c6d\na + b + c\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u2550cbcf36de-f360-11ea-0c7f-719e93324b27\n# \u2560\u2550d71c5ee2-f360-11ea-2753-a132fa41871a\n# \u2560\u2550d8f5a4f6-f360-11ea-043d-47667f6a7e76\n# \u2560\u2550dcd9ebb8-f360-11ea-2050-fd2e11d27c6d\n", "meta": {"hexsha": "cb164af453d4521eb16a375a18f739c7e91c2bb3", "size": 465, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/frontend/fixtures/simple_sum_notebook.jl", "max_stars_repo_name": "Mechachleopteryx/Pluto.jl", "max_stars_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3974, "max_stars_repo_stars_event_min_datetime": "2020-03-06T10:50:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:39:46.000Z", "max_issues_repo_path": "test/frontend/fixtures/simple_sum_notebook.jl", "max_issues_repo_name": "Mechachleopteryx/Pluto.jl", "max_issues_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1585, "max_issues_repo_issues_event_min_datetime": "2020-03-01T23:46:18.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:04:08.000Z", "max_forks_repo_path": "test/frontend/fixtures/simple_sum_notebook.jl", "max_forks_repo_name": "Mechachleopteryx/Pluto.jl", "max_forks_repo_head_hexsha": "78fbd9f00a3323208a6df9a4ff0ecbdcf2d9845a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 280, "max_forks_repo_forks_event_min_datetime": "2020-03-28T10:47:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T19:50:31.000Z", "avg_line_length": 19.375, "max_line_length": 42, "alphanum_fraction": 0.7096774194, "num_tokens": 279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10669058755477132, "lm_q1q2_score": 0.052928542148548925}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.16\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 9dfa6a82-3b9d-11eb-2557-3bb72249910a\nusing PaddedViews\n\n# \u2554\u2550\u2561 97dcb6ca-3b9b-11eb-2c96-0904d9a90046\ns=\"\"\"L.LL.LL.LL\nLLLLLLL.LL\nL.L.L..L..\nLLLL.LL.LL\nL.LL.LL.LL\nL.LLLLL.LL\n..L.L.....\nLLLLLLLLLL\nL.LLLLLL.L\nL.LLLLL.LL\"\"\"\n\n# \u2554\u2550\u2561 1501f7ee-3ba3-11eb-2b57-7b10a8d1d5d4\nsbig=\"\"\"LLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.L.LLLLL.LLLL.LLLLLLLLL..LLL.LLLLLLLLLLLLLL.LLLLLLLLL.LLL.LLLLLL\nLLLLLLLLLL.L.LLLLLLL.LL.LLLLLLL.LLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL.LLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLL.L.LLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLLL.LLLLLL\n.LLLLLLLLLLLLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLL.LLL.LLLLLLLLLL.LLLLLL.LLLLLLLL.LLLLLL.LLLLL.LLLLLL\nLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLL.LLLLL.LL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLL.LLLLLLL.LLLL.LLLLLL\nLLL.LL..L..L.LL.L.L........L.............LLL....LL...L..L.L.....L..L.L.......L..L......LLLL.L.L\nLLLLLLLLLLLLLLLLL..LLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLL.LLLLLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLL\nLLLLLLLLLL.LLLL.LL.LLLL.LLLLLLLLLLLLLLLLLL.L.LLLLLLLLL.LLLL.L.L.LL.LLLLLLLLLLLLL.LL.LLLLLLLLLLL\nLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLLLLLLLLLLL.LL.LLLLLLLLL.LLLLL.\nLLLLLLLLLLLLLLLLL..LLL.LLLLLLLLLLLLLLL.LLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLL.LLLLLLLLLLLLL.LLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLL.LLL.L..LLLLLLLL.LLLL.LLLLLL..LLLLLLLLLLLLLLL.LLLL.LLLLLL\nL.LLLLLLLL..LLLLLL.LLLL.LLLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLL.LLLLLLLLL.LLLLL.LLLLLLL.LLLL.LLLLLL\n.LLLLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLL\nLL.L..LL...LL..L..L.L.L.....L..LL...L.LLL......L.L...L.LL...L.L.L.........L.LL....L.LL.LL...LL.\nLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLL.LLLL.LLLLLL.L.LLLLLLLLLLLLLL.LLL..LLLLLL\nLLLLL.LLLL.LLLL.LLLLLLLLLLLLLLL.LLLLLL.L.LLL.LLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLL.LLLLLLL\nLLL.LLLLLL.LLLLLLL.LLLL.LLL.LLL.LLLL.L.LL.LLLLLLLLLLLL.LLLL.LLL.LL.LLLLLLLLLLLLLLLLLLLLL.LL.LLL\nLLLLLLLLLL.LLL.LLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLL.LLLL.LLLLLL\n...L...L..L.L...L..L......L.......L.L...L...L...L.....L...L...L....L...LL..L..L.............LL.\nLLL..LLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLLLLLLL.LLL.LLLL.LLLLLL.LLLLLLLLLLLLLLLL.LLLLL.LLLLL\nLLLLLLLLLL.LLL..LLLLLLL.L.LLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLLLLLLLLLL.LLL.LLLLLLLLLLLLLLLLLLLLLLLL\nLLLLLL.L.L.L.LLLLLLLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLL.LLLLLL.LLLLLLLL.LLLLLLL.LLLL.LLLLL.\nLLLLLL.LLL.LLLLLLLLLLLL.LLLLLLL.L.LLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLL.LLL.LLLL.LLLLLLL.LLLL.LLLLL.\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLL.LLL.LLLLL.L.LL.LLLLLL.LLLLLLLL..LLLLLLLLLLL.LLLLLL\nLLLLLL.LLL.LLLLLLL.LLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLL.LLLLLLLL.LLLLLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLL..LLLLLLLLL.LL.LLLLLL\nLLLLLLLLLLLLLLLLLLLLLL..LLLLLLL.LLLLLL.LLLLL.LL.LLLLLL.LLLLLLLLLLL.LLLLL.L.LLLLLLLLLLLLL.LLLLLL\nLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLLLL.LLLL.LLLLLLLLLLLLLL.LLLLLLLLLLLLLLL.LL.LLLL.LLLL.LLLLLL\n.L......L..L........LL..LLL...LL.L.....L..L.L..LLL..LLL..LL.LL...LLL.......L...LL...........LLL\nLLLLLLLLLL.LL.LLLL.LLLL.LLLLLLL.LLLLL.LLLLLL.LLLLLLLLL.LLLLLLLLLLL.LLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLL.LLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLLL.LLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLL..LLLLLL.LLLLLLLLLLLLLLL.LL.L.L.LLLLLLLLLLLLL.LLLLLLL.LLL..LLLLLL\nLLLLLL.LLL.LLLLLLL.LLLLLLLLL.LL.LLLLLL.LLLLLLLLLLLLLLL.LLLL.LLLLLL.LLL.LLLL.LLLLLLL.LLLL.LLLLLL\nLLLLLLLLLLLLLLLL.L.LLLL.LLLLLL..LLLLLLLLLLLL.LLLLLLL.L.LLLLLLLLLLL.LLLLLLLL.LLLLLLL.LLLL..LLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLLLL.LL.LLLLL.LL.L.LL.LLLLLLLLLLL\nLLLLLL.LLL.LLLLLLL.LLLLLLL.LLLL.LLLLLLLLLLLL.LLLLLLLLL.LLL.LLLLLLL.LLLLLLLLLLLLLLL..LLLL.LLLLLL\nLL.LLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLL.LLLLLLLL.LLLLLLLLLLLLLL.LLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLL.LLL.LLLLLL.LLLL..LLLLLLLLLLLLLL.LLLLLLLLLLLLLLL.LLLLLLL.LLLL.LLLLLL\n........LL.L..LLLL..L..LLLL..LLL.........L.L......LL.LL..L........L...L..LL..LLLL.L.L.....L.LL.\nLL.LLLLLLL.LLLLLLL.LLLL.LLLL.LL.LLLLLLLLLLLLLLLLLLLLLL.LLLL.LLLLL..LLLLLLLLLLLLLLLL.LLLLL.L..LL\nLLLLLLLLL.LLLLLLLL.LLL..LLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLL.LLLLLLLLLL.LLLL.LLLL.LLLLLLLLLLLLLL\nLL.LLLLLLL.LLLLLLL.LLLL.LLLLLLLLLLLLLL.LLLLLLLL.LLLLLLLLLLL.LLLLLL.LLLLLLL..LLL.LLLLLLLLLL.LLLL\nLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLL.LLL.LLL..LLLLLLL.LLLL.LLLLLL\nLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL..LLL.LLLLLLLLLL.LLLL.LLLLLL.LLLLLLLLLLLLLLLL.LLLL.LLLL.L\nLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLL.LLLLLLLL.LLLLLLLLLLL.LLLLLL.LL.LLLL..LLLLLLLLLLLLLLLLLLL\n..LL..LLL.....LL......L..L.L.LLL........LL......LLL...L......L.LL..L.LL.LL.L.......LL....LL...L\n.LL.LLLLLLLLLLLLLL.L.LL.LLLLLLL.LLL.LL...L.L.LLLLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLLL.LLLL.LLLLLL\nL.LLLLLLLLLLLL.LLL.LLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLLL.LLL.LLLL.LLL.LLL.LLLL.LLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLL..L.LLLL.LLLLLLLL..LLL.LLLLLLL.LLLLLL\n.LLLLLLLLL.LLL.LLL.LLLL.LLL..LLLLLLLLLLLLLLLLLLLLLLLLLL.LLL.LLLLLL.LLLLLLLL.LLLLLLL.LLLL.LLLLLL\nL..L...LL...L.LL....L.LL.L..LL.L.........L.........LLLL..L.L..LL...L.L.L....LLL.....L..L.L...LL\n.LLLLLLLLL.LLLL.LL.LLLL.LLLLLLLLL.LLLL.LLLLL.LLL.LLLLL.LLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL\nLLLLLLLLLL.L..LLLL.LLLL.LLLLLLL..LLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLLLLL.LLLLL.LLLLLLLLLLLL.LLLLLLLL.LLLLLLL.LL.LLLLL.LL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLLLLL.LLLLLLLLLLL\nL.LLLLLLLL.LLLLLLLLLLLL.LLLL.LL..LLLLL.LLLL.LLLLLL.LLLLLLLL.LLL.LLLLLLLLLLLLLLLLL.L.LLLL.LLLLLL\nLLLLLLLLLL..LLLLLL.LLLLLLLLLLLL.L.LLLL.LLLLLLLLLLLLLLL.LLLL.LLLLLL..LL.LLLL.LLLLLLL.LLLL.LLLLLL\nLLL.LLLLLLLLLLLLLL.LLLLLLLLLLLL.LL.LLL.LLLLL.LLLLL.LLL.LLLL.LLLLLL.LL.LLLL..LLLLLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLL..LLLL.LLLLLLLLLLLLLLL.LLLLL.LLLLLL.LLLLLL\nL.L...L...L..L..L....L...L..LLL..L....L..LL...LLL.LL.L...L.L..L.LLL..L....L.L...LL..L..L.L..L.L\nLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLL.LLLLLLL.LLLL.LLLLLL\nLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLL.L\nLLLLLLLLLL.LLLLL.L.LLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLLLLLLL.LLLL.LLLLLLLLL\nL..LLLLLLLLL.LLLLL.L.LLLLLL.LLL.LLLLLL.LLLLLLLLLLLLLLL.LLLL.LLLLLL.LL.LLLLLLLLLLLLL.LLLLLLLLLLL\nLLLLL.LLLL.LLLL.LL.LLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLLLLL.LLLLLL.LL.L.LLL.LLLLLLLLL.LL.LLLL.LLLLLL\n....L....L..L.....L....L..LL....L.L..L..LL.LLL...L.....L.LLL.L.LL.....L....L.L........L.LLL....\nL.L.LL.LLL.LLLLL.L.LLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLL.LLLLLLLL.LLLLLLLLLLL.LL.LLLL.LLLLLL\nLLLLLLLLL.LLLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLL.LL.LLLLLL.LLLL.LLLLLL.LLLLLLL.LLLLLLLL.LLLL.LLLLLL\nLLLLLLLLL.LL.LLLLLLLLLL.LLLL.LLLL.LLLLLLLLLLLLLLLLLLLL..LLL.L..LLL.LLL.L.LL.LLLLLL..LLLL.LLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLLLLLLLLLL\nLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLLLLL.LLLL.LLLLLLLLLLLL.LL.LLLLLLL.LLLL.LLLLLL\n.....LL.LL.L.....LL.LL..L...L...LLLLL......L.L....LL...LLL...LLLLL..LL..L....L......L.......L.L\nLLLLLLLLLL.LLLL.LL.LLLL.L.LLLLL.LLLLLL.LL.LL.LLLLLLLLL.LLLL.LLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLL.LL.LLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLLLLLLLL.L\nLLLL.LLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLLL..LLLLLLL.L.LL..LLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLL.LLL.LL.LLLLLLLL.LLLL.LLLLLLLLLLLLLL\nLLLLLLLLLLLLL.LLLLLLLLL.LLLLLL..LLLLLL.LLLLL.LLLLLLLLLLL.LLLLLLLLLL.LLLLLLL.LLLLLLL.LLLLLLLLLLL\n..LLL..L..L...L..L..L.L.....L............L.....L..L....L.LL......L.....L..LLL..L.L..L..........\nLLL.LLLLLL.LLLLLLLLLLLL.LL.LLLLLLLLLLL.LLLLLL.LLLLLLL..LLLL.LLLL.L.L.LLLL.LLLLLLLLLLLLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLL.LLLLLL.LL.LL.LLLLLLLLLL.LLL.LL.LLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLL\n.LLLLLLLLL.LLLL.LLLLLLLLLLLLL...LL.L.LLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLLLLLLLL.L.LLLLLL\nLLLL.LLLLLLLLLLLLL.LLLL.LLLLLLLLLLLLLL.LLLLL.LLLLL.LLL.LLLL.LLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLLL.LLL.L.LLLLLLLL.LLLLLL..LLLLLLLLLLL\n.LLLLLLLLL.LLLLLLL.LLLL.LLLLLL..LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLLLLL.LLLL.LLLLLL\n.L.....LL......L........L.L....LLL......L...L.LL...L...L..LL................L...L..L...........\nLLLLLLLLLL.LLLLLLL.LLLL.LLLL.LL.LLLLLL.LLLL..LLLLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLL.LL.LLL.LLLLLLL\nLLLLLL.LLL.LLLLLL..LLL..LLLLLL..LLLLLL.LLLL..LLLLLLLLLLLLLLLLLLLLL.LLLLLLLL.LLLLLLL..LLLLLLLLLL\nLLLLLL..LL.LLLLLLL.LLLL.LLLL.LL.LLL.LL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLL.LL.L.LLLLLL.L.LLLLLL.LLLLLLLLLLLLLLLL.LL\nLLLLLLLLLL.LLLLLL.LLLLLLL.LLLLL.LLLLL.LLLLLL.L.LLLLLLL.LL.LLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.LLLLLLLLLLLLLL.LLLL..LLLLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLLL.LL.L.LLLLLL\nLLLLL.LLLL.LLLLLLL..LLLLLLLLLLL.LLLLLL.LLL.L.L.LLLLLLLLLLLL.LLLL.L.LLLLLLLL.LLLLLLL.LLLLLLLLLLL\n....LLL...LLL.....L.L...LL...L..L....LL...L.L.LLLLL....L......LLLL......L..LLLLL.L.LL...LL..LLL\nLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LL.LLL.LLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLL.LLLLLLLLLLLL.LLLLLL\nLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLLLL.LLLL.LLLLLL.LLLLLLLLLLLLLLLL.LLLLLLLLLLL\n.LLLLLLLLL.L..LLLLLLLLLLLLLLLLL.LLLLLL.LLLL..LLLLLLLLL.LLLLLLLLLLL.LLLLLLLLLLLLLLLL.LLLLLLLLLLL\nLLLLLLLLLL.LLLLLLL.LLLL.L.L.LLLLLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLL.LLL.LLLL.LLL.LLLLLL\nLLLLLLLLLLLLLLLLLL.LLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLL.LLLLLL.LLLLLLLL..LLLLLL.LLLL.L.LLLL\"\"\"\n\n# \u2554\u2550\u2561 c06dfe28-3b9b-11eb-0e90-73b965f01cef\nres = collect.(split(sbig))\n\n# \u2554\u2550\u2561 f2078fda-3b9b-11eb-02f6-69faf6d6ce6b\nparsedict = Dict('L'=>0, '.'=>-1, '#'=>1)\n\n# \u2554\u2550\u2561 74450896-3ba2-11eb-27f9-5f052f8bf906\nprintdict = Dict(-1 => '.', 0 => 'L', 1 => '#')\n\n# \u2554\u2550\u2561 39aeaf84-3b9d-11eb-209a-15a19031a709\nstate = ((ch -> parsedict[ch]).(hcat(res...)))'\n\n# \u2554\u2550\u2561 85e5c7d2-3b9d-11eb-2a71-9fff27469aa8\nfunction nextstate(state, countfn, occthreshold)\n\temptyseat = state .== 0\n\toccupied = state .> 0\n\toccneigh = countfn(state)\n\tupdate1 = (emptyseat) .& (occneigh .== 0)  # 0 -> 1\n\tupdate2 = (occupied) .& (occneigh .>= occthreshold)  # 1 -> 0\n\tanyupdate = (sum(update1) + sum(update2)) > 0\n\tstate + update1 - update2, anyupdate\nend\n\n# \u2554\u2550\u2561 716c36ee-3b9d-11eb-02cd-59c13327fbb4\nfunction run(countfn, occthreshold; maxiter=100)\n\tmystate = state\n\tfor i=1:maxiter\n\t\tmystate, anyupdate = nextstate(mystate, countfn, occthreshold)\n\t\tif anyupdate\n\t\t\tprintln(\"Updated\")\n\t\telse\n\t\t\tprintln(\"No change after $i steps\")\n\t\t\tprintln(\"Sum:\", sum(mystate.==1))\n\t\t\tbreak\n\t\tend\n\t\t#printstate(mystate)\n\tend\n\tmystate\nend\n\n# \u2554\u2550\u2561 837a6fae-3ba2-11eb-3dd4-a971f8334552\nfunction printstate(state)\n\tfor col = 1:size(state, 1)\n\t\tfor row = 1:size(state, 2)\n\t\t\tprint(printdict[state[col,row]])\n\t\tend\n\t\tprintln()\n\tend\n\tprintln()\nend\n\n# \u2554\u2550\u2561 eb8e5ce6-3ba6-11eb-34e1-c541043ea307\n\"\"\"\nPart 2: for each seat, count the occupancy of the next seats in each of the eight cardinal directions.\n\"\"\"\nfunction countoccupiedneighbours2(state)\n\taccum = zeros(Int64, size(state))\n\trow, col = size(state)\n\tfor i=1:row\n\t\tfor j=1:col\n\t\t\tif state[i, j] < 0\n\t\t\t\tcontinue\n\t\t\tend\n\t\t\tfor delta in (\n\t\t\t\t\t[0,1], [0,-1], [1,0], [-1,0],\n\t\t\t\t\t[1,1], [1,-1], [-1,1], [-1,-1])\n\t\t\t\tpos = [i,j] + delta\n\t\t\t\twhile ((1 <= pos[1] <= row)\n\t\t\t\t\t\t&& (1 <= pos[2] <= col)\n\t\t\t\t\t\t&& (state[pos...] < 0))\n\t\t\t\t\tpos += delta\n\t\t\t\tend\n\t\t\t\tif ((1 <= pos[1] <= row) && (1 <= pos[2] <= col))\n\t\t\t\t\tif state[pos...] == 1\n\t\t\t\t\t\taccum[i,j] += 1\n\t\t\t\t\tend\n\t\t\t\tend\n\t\t\tend\n\t\tend\n\tend\n\taccum\nend\n\n# \u2554\u2550\u2561 f89e53ea-3c7c-11eb-323b-576f7f77da00\nsum(run(countoccupiedneighbours2, 5) .== 1)\n\n# \u2554\u2550\u2561 19ad6462-3b9f-11eb-056e-1b99dc5b3979\n\"\"\"\nShifts an array left or right and/or up or down and pads the empty row/column.\n\"\"\"\nfunction shiftpad(a, leftright, topbottom; v=0)\n\trow, col = size(a)\n\tif leftright == \"l\"\n\t\tyold = 1:col-1\n\t\tynew = 2:col\n\telseif leftright == \"r\"\n\t\tyold = 2:col\n\t\tynew = 1:col-1\n\telse\n\t\tyold = ynew = 1:col\n\tend\n\tif topbottom == \"t\"\n\t\txold = 1:row-1\n\t\txnew = 2:row\n\telseif topbottom == \"b\"\n\t\txold = 2:row\n\t\txnew = 1:row-1\n\telse\n\t\txold = xnew = 1:row\n\tend\n\t\t\n\tPaddedView(v, convert(Array{Int64,2}, a)[xold, yold], (1:row, 1:col), (xnew, ynew))\nend\n\n# \u2554\u2550\u2561 6100b904-3b9f-11eb-1d2e-d5f8ed748767\n\"\"\"\nSums up the values in the eight neighbouring cells for a 2D array.\n\"\"\"\nfunction neighboursum(a)\n\taccum = zeros(Int64, size(a))\n\tfor lr in (\"\", \"l\", \"r\")\n\t\tfor tb in (\"\", \"t\", \"b\")\n\t\t\tif lr == \"\" && tb == \"\"\n\t\t\t\tcontinue\n\t\t\tend\n\t\t\taccum += shiftpad(a, lr, tb)\n\t\tend\n\tend\n\taccum\nend\n\n# \u2554\u2550\u2561 222c5fc0-3c7c-11eb-3f59-095768dc679b\n\"\"\"\nPart 1: for each seat, count the occupied seats amongst the eight neighbouring spaces.\n\"\"\"\nfunction countoccupiedneighbours(state)\n\tneighboursum(state .> 0)\nend\n\n# \u2554\u2550\u2561 b5b08618-3c7c-11eb-1a95-25275bbf81af\nsum(run(countoccupiedneighbours, 4) .== 1)\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u255097dcb6ca-3b9b-11eb-2c96-0904d9a90046\n# \u255f\u25001501f7ee-3ba3-11eb-2b57-7b10a8d1d5d4\n# \u2560\u2550c06dfe28-3b9b-11eb-0e90-73b965f01cef\n# \u2560\u2550f2078fda-3b9b-11eb-02f6-69faf6d6ce6b\n# \u255f\u250074450896-3ba2-11eb-27f9-5f052f8bf906\n# \u2560\u255039aeaf84-3b9d-11eb-209a-15a19031a709\n# \u2560\u255085e5c7d2-3b9d-11eb-2a71-9fff27469aa8\n# \u2560\u2550716c36ee-3b9d-11eb-02cd-59c13327fbb4\n# \u2560\u2550b5b08618-3c7c-11eb-1a95-25275bbf81af\n# \u2560\u2550f89e53ea-3c7c-11eb-323b-576f7f77da00\n# \u2560\u2550837a6fae-3ba2-11eb-3dd4-a971f8334552\n# \u2560\u2550eb8e5ce6-3ba6-11eb-34e1-c541043ea307\n# \u2560\u2550222c5fc0-3c7c-11eb-3f59-095768dc679b\n# \u2560\u25506100b904-3b9f-11eb-1d2e-d5f8ed748767\n# \u2560\u25509dfa6a82-3b9d-11eb-2557-3bb72249910a\n# \u2560\u255019ad6462-3b9f-11eb-056e-1b99dc5b3979\n", "meta": {"hexsha": "44423066f80d69f7e06f00ae34704464f78be93d", "size": 13816, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2020/11.jl", "max_stars_repo_name": "st--/advent-of-code", "max_stars_repo_head_hexsha": "b01ca4502820c972015e940024dbd482f7bc69c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020/11.jl", "max_issues_repo_name": "st--/advent-of-code", "max_issues_repo_head_hexsha": "b01ca4502820c972015e940024dbd482f7bc69c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020/11.jl", "max_forks_repo_name": "st--/advent-of-code", "max_forks_repo_head_hexsha": "b01ca4502820c972015e940024dbd482f7bc69c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.9929078014, "max_line_length": 103, "alphanum_fraction": 0.7532570932, "num_tokens": 5530, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43782348444346736, "lm_q2_score": 0.12085322299118381, "lm_q1q2_score": 0.05291237919622346}}
{"text": "using Literate\n\n# directory where the markdown files are put\nmd_dir = joinpath(@__DIR__, \"md\")\nLiterate.markdown(\"car_travels.jl\", md_dir, execute=true, documenter=false, credit=false)\n", "meta": {"hexsha": "dad6744c9f49c33ce301bfeaf8e47564f00f9a16", "size": 185, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/literate-script.jl", "max_stars_repo_name": "eth-vaw-glaciology/course-101-0250-00-L8Documentation.jl", "max_stars_repo_head_hexsha": "8dad7cddfcc38e898efbe0b2e2ad74f61dd12865", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/literate-script.jl", "max_issues_repo_name": "eth-vaw-glaciology/course-101-0250-00-L8Documentation.jl", "max_issues_repo_head_hexsha": "8dad7cddfcc38e898efbe0b2e2ad74f61dd12865", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/literate-script.jl", "max_forks_repo_name": "eth-vaw-glaciology/course-101-0250-00-L8Documentation.jl", "max_forks_repo_head_hexsha": "8dad7cddfcc38e898efbe0b2e2ad74f61dd12865", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8333333333, "max_line_length": 89, "alphanum_fraction": 0.7783783784, "num_tokens": 50, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.2974699426047947, "lm_q2_score": 0.17781087383497524, "lm_q1q2_score": 0.052893390434198474}}
{"text": "# Declaring variables and using data types in Julia\n\n# 5 basic types of data:\n# Integers, Floating point numbers, booleans, strings, and characters\n\n# Variable names typically are lowercase. Use underscores if the name\n# is too long to be easily read.\nfirst = 1\nsecond = 2.0\nstringVar = \"This is a string\"\ninitial = 'J'\nboolVal = true\n\nprintln(first)\nprintln(second)\nprintln(stringVar)\nprintln(initial)\nprintln(boolVal)\n\n# Variables can be redefined and change type\nfirst = 1.0\nprintln(first)\n\n# Strings have to be in double quotes. This will error: anotherString = 'hello world'\n\n# Constant values are declared with const and are all uppercase\n# Note: this is only useful in the global scope\nconst CONSTANT = 42\n\n# Constants of the same type *can* be reassigned, but with a warning\nCONSTANT = 43\n\n# Constants of different types cannot be reassigned, this is an error\n# Cannot do: CONSTANT = 43.0\n\n# Type Annotation identifies a variable as a particular type\nfunction testFunc()\n    x::String = \"some string\"    \nend\n\ntestFunc()", "meta": {"hexsha": "9f0a0970707f69223c076b65f9818fb0bf6b4075", "size": 1028, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2_basics/01_variables.jl", "max_stars_repo_name": "scaotravis/Julia-Course", "max_stars_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2_basics/01_variables.jl", "max_issues_repo_name": "scaotravis/Julia-Course", "max_issues_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2_basics/01_variables.jl", "max_forks_repo_name": "scaotravis/Julia-Course", "max_forks_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0731707317, "max_line_length": 85, "alphanum_fraction": 0.7519455253, "num_tokens": 255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2974699426047947, "lm_q2_score": 0.17781086947804958, "lm_q1q2_score": 0.05289338913814405}}
{"text": "# Unit test functions\nfunction unit_test_slinkedlist_empty()\n    l = SLinkedList{Float64}()\n    @test l.head.value === nothing\n    @test l.tail.value === nothing\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_addlast()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    addlast!(l, val1)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n\n    val2 = 2.0\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_addfirst()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    addfirst!(l, val1)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n\n    val2 = 2.0\n    addfirst!(l, val2)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 2\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_add()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    add!(l, val1, 0)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n    # 1\n\n    val2 = 2.0\n    add!(l, val2, 0)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 2\n    # 2 -> 1\n\n    val3 = 3.0\n    add!(l, val3, 5)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 3\n    # 2 -> 1 -> 3\n\n    val4 = 4.0\n    add!(l, val4, 2)\n    @test l.head.value == val2\n    @test l.head.next.value == val4\n    @test l.head.next.next.value == val1\n    @test l.head.next.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 4\n    # 2 -> 4 -> 1 -> 3\n\n    val5 = 5.0\n    add!(l, val5, 3)\n    @test l.head.value == val2\n    @test l.head.next.value == val4\n    @test l.head.next.next.value == val5\n    @test l.head.next.next.next.value == val1\n    @test l.head.next.next.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 5\n    # 2 -> 4 -> 5 -> 1 -> 3\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_add_interface()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    add!(l, val1, 0)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n    # 1\n\n    val2 = 2.0\n    add!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n    # 1 -> 2\nend\n\n\nfunction unit_test_slinkedlist_addlastfirst()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    addfirst!(l, val1)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n\n    val2 = 2.0\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n\n    val3 = 3.0\n    addfirst!(l, val3)\n    @test l.head.value == val3\n    @test l.head.next.value == val1\n    @test l.head.next.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 3\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_removelast()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 3\n    @test removelast!(l) == val3\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n    @test removelast!(l) == val2\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test length(l) == 1\n    @test removelast!(l) == val1\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_removefirst()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 3\n    @test removefirst!(l) == val1\n    @test l.head.value == val2\n    @test l.head.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 2\n    @test removefirst!(l) == val2\n    @test l.head.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 1\n    @test removefirst!(l) == val3\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_remove()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 3\n    @test remove!(l, 2) == val2\n    @test l.head.value == val1\n    @test l.head.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 2\n    @test remove!(l, 0) == val1\n    @test l.head.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 1\n    @test remove!(l, 2) == val3\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_remove_interface()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n    @test remove!(l) == val1\n    @test l.head.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 1\n    @test remove!(l) == val2\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_removelastfirst()\n    l = SLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.tail.value == val3\n    @test length(l) == 3\n    @test removelast!(l) == val3\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 2\n    @test removefirst!(l) == val1\n    @test l.head.value == val2\n    @test l.tail.value == val2\n    @test length(l) == 1\n    @test removelast!(l) == val2\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_peekfirst()\n    val1 = 1.0\n    val2 = 2.0\n    l = SLinkedList{Float64}()\n    addlast!(l, val1)\n    @test peekfirst(l) == val1\n    addlast!(l, val2)\n    @test peekfirst(l) == val1\n\n    return nothing\nend\n\n\nfunction unit_test_slinkedlist_peeklast()\n    val1 = 1.0\n    val2 = 2.0\n    l = SLinkedList{Float64}()\n    addlast!(l, val1)\n    @test peeklast(l) == val1\n    addlast!(l, val2)\n    @test peeklast(l) == val2\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_empty()\n    l = DLinkedList{Float64}()\n    @test l.head.value === nothing\n    @test l.tail.value === nothing\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_addlast()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    addlast!(l, val1)\n    @test l.head.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    @test length(l) == 1\n\n    val2 = 2.0\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 2\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_addfirst()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    addfirst!(l, val1)\n    @test l.head.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n\n    val2 = 2.0\n    addfirst!(l, val2)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.prev.value == val1\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_add()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    add!(l, val1, 0)\n    @test l.head.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    # 1\n\n    val2 = 2.0\n    add!(l, val2, 0)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.prev.value == val1\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    # 2 <-> 1\n\n    val3 = 3.0\n    add!(l, val3, 5)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.value == val3\n    @test l.head.next.next.next.prev.value == val3\n    @test l.head.next.next.prev.value == val1\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    # 2 <-> 1 <-> 3\n\n    val4 = 4.0\n    add!(l, val4, 2)\n    @test l.head.value == val2\n    @test l.head.next.value == val4\n    @test l.head.next.next.value == val1\n    @test l.head.next.next.next.value == val3\n    @test l.head.next.next.next.next.prev.value == val3\n    @test l.head.next.next.next.prev.value == val1\n    @test l.head.next.next.prev.value == val4\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    # 2 <-> 4 <-> 1 <-> 3\n\n    val5 = 5.0\n    add!(l, val5, 3)\n    @test l.head.value == val2\n    @test l.head.next.value == val4\n    @test l.head.next.next.value == val5\n    @test l.head.next.next.next.value == val1\n    @test l.head.next.next.next.next.value == val3\n    @test l.head.next.next.next.next.next.prev.value == val3\n    @test l.head.next.next.next.next.prev.value == val1\n    @test l.head.next.next.next.prev.value == val5\n    @test l.head.next.next.prev.value == val4\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    # 2 <-> 4 <-> 5 <-> 1 <-> 3\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_add_interface()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    add!(l, val1, 0)\n    @test l.head.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    # 1\n\n    val2 = 2.0\n    add!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    # 1 <-> 2\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_addlastfirst()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    addlast!(l, val1)\n    @test l.head.value == val1\n    @test l.tail.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.next.prev.value == val1\n    @test length(l) == 1\n\n    val2 = 2.0\n    addfirst!(l, val2)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.prev.value == val1\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    @test length(l) == 2\n\n    val3 = 3.0\n    addlast!(l, val3)\n    @test l.head.value == val2\n    @test l.head.next.value == val1\n    @test l.head.next.next.value == val3\n    @test l.head.next.next.next.prev.value == val3\n    @test l.head.next.next.prev.value == val1\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    @test length(l) == 3\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_removelast()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 2\n    @test removelast!(l) == val2\n    @test l.head.value == val1\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val1\n    @test l.tail.next.prev.value == val1\n    @test length(l) == 1\n    @test removelast!(l) == val1\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_removefirst()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 2\n    @test removefirst!(l) == val1\n    @test l.head.value == val2\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 1\n    @test removefirst!(l) == val2\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_remove()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.head.next.next.next.prev.value == val3\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    @test length(l) == 3\n    @test remove!(l, 2) == val2\n    @test l.head.value == val1\n    @test l.head.next.value == val3\n    @test l.head.next.next.prev.value == val3\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    @test length(l) == 2\n    @test remove!(l, 0) == val1\n    @test l.head.value == val3\n    @test l.head.next.prev.value == val3\n    @test l.tail.value == val3\n    @test l.tail.next.prev.value == val3\n    @test length(l) == 1\n    @test remove!(l, 2) == val3\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_remove_interface()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 2\n    @test remove!(l) == val1\n    @test l.head.value == val2\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 1\n    @test remove!(l) == val2\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_removelastfirst()\n    l = DLinkedList{Float64}()\n    val1 = 1.0\n    val2 = 2.0\n    val3 = 3.0\n    addlast!(l, val1)\n    addlast!(l, val2)\n    addlast!(l, val3)\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.value == val3\n    @test l.head.next.next.next.prev.value == val3\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test length(l) == 3\n    @test removelast!(l) == val3\n    @test l.head.value == val1\n    @test l.head.next.value == val2\n    @test l.head.next.next.prev.value == val2\n    @test l.head.next.prev.value == val1\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 2\n    @test removefirst!(l) == val1\n    @test l.head.value == val2\n    @test l.head.next.prev.value == val2\n    @test l.tail.value == val2\n    @test l.tail.next.prev.value == val2\n    @test length(l) == 1\n    @test removelast!(l) == val2\n    @test l.head == l.tail\n    @test length(l) == 0\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_peekfirst()\n    val1 = 1.0\n    val2 = 2.0\n    l = DLinkedList{Float64}()\n    addlast!(l, val1)\n    @test peekfirst(l) == val1\n    addlast!(l, val2)\n    @test peekfirst(l) == val1\n\n    return nothing\nend\n\n\nfunction unit_test_dlinkedlist_peeklast()\n    val1 = 1.0\n    val2 = 2.0\n    l = DLinkedList{Float64}()\n    addlast!(l, val1)\n    @test peeklast(l) == val1\n    addlast!(l, val2)\n    @test peeklast(l) == val2\n\n    return nothing\nend\n\n\nfunction unit_test_linkedlist_length()\n    val1 = 1.0\n    val2 = 2.0\n    l = SLinkedList{Float64}()\n    addlast!(l, val1)\n    @test length(l) == 1\n    addlast!(l, val2)\n    @test length(l) == 2\n\n    d = DLinkedList{Float64}()\n    addlast!(d, val1)\n    @test length(d) == 1\n    addlast!(d, val2)\n    @test length(d) == 2\nend\n\n\nfunction unit_test_linkedlist_size()\n    val1 = 1.0\n    val2 = 2.0\n    l = SLinkedList{Float64}()\n    addlast!(l, val1)\n    @test size(l) == 1\n    addlast!(l, val2)\n    @test size(l) == 2\n\n    d = DLinkedList{Float64}()\n    addlast!(d, val1)\n    @test size(d) == 1\n    addlast!(d, val2)\n    @test size(d) == 2\nend\n\n\nfunction unit_test_linkedlist_equality()\n    val1 = 1.0\n    val2 = 2.0\n    l1 = SLinkedList{Float64}()\n    addlast!(l1, val1)\n    addlast!(l1, val2)\n    l2 = SLinkedList{Float64}()\n    addlast!(l2, val1)\n    addlast!(l2, val2)\n    @test l1 == l2\n\n    d1 = DLinkedList{Float64}()\n    addlast!(d1, val1)\n    addlast!(d1, val2)\n    d2 = DLinkedList{Float64}()\n    addlast!(d2, val1)\n    addlast!(d2, val2)\n    @test d1 == d2\nend\n\n\nfunction unit_test_linkedlist_eltype()\n\nend\n\n\nfunction unit_test_linkedlist_iterate()\n    n = 100\n    c = rand((1:n), n)\n    l = SLinkedList{Int64}()\n    for val in c\n        addlast!(l, val)\n    end\n    for (i, v) in enumerate(l)\n        @test v == c[i]\n    end\n\n    d = DLinkedList{Int64}()\n    for val in c\n        addlast!(d, val)\n    end\n    for (i, v) in enumerate(d)\n        @test v == c[i]\n    end\n\n    return nothing\nend\n\n\nfunction unit_test_linkedlist_collect()\n    n = 100\n    c = rand((1:n), n)\n    l = SLinkedList{Int64}()\n    for val in c\n        addlast!(l, val)\n    end\n    @test c == collect(l)\n\n    d = DLinkedList{Int64}()\n    for val in c\n        addlast!(d, val)\n    end\n    @test c == collect(d)\n\n    return nothing\nend\n\n# Run the unit tests\n@testset \"Singly linked list   \" begin\n    unit_test_slinkedlist_empty()\n    unit_test_slinkedlist_addlast()\n    unit_test_slinkedlist_addfirst()\n    unit_test_slinkedlist_add()\n    unit_test_slinkedlist_add_interface()\n    unit_test_slinkedlist_addlastfirst()\n    unit_test_slinkedlist_removelast()\n    unit_test_slinkedlist_removefirst()\n    unit_test_slinkedlist_remove()\n    unit_test_slinkedlist_remove_interface()\n    unit_test_slinkedlist_removelastfirst()\n    unit_test_slinkedlist_peekfirst()\n    unit_test_slinkedlist_peeklast()\nend\n\n@testset \"Doubly linked list   \" begin\n    unit_test_dlinkedlist_empty()\n    unit_test_dlinkedlist_addlast()\n    unit_test_dlinkedlist_addfirst()\n    unit_test_dlinkedlist_add()\n    unit_test_dlinkedlist_add_interface()\n    unit_test_dlinkedlist_addlastfirst()\n    unit_test_dlinkedlist_removelast()\n    unit_test_dlinkedlist_removefirst()\n    unit_test_dlinkedlist_remove()\n    unit_test_dlinkedlist_remove_interface()\n    unit_test_dlinkedlist_removelastfirst()\n    unit_test_dlinkedlist_peekfirst()\n    unit_test_dlinkedlist_peeklast()\nend\n\n@testset \"Linked list interface\" begin\n    unit_test_linkedlist_length()\n    unit_test_linkedlist_size()\n    unit_test_linkedlist_equality()\n    unit_test_linkedlist_eltype()\n    unit_test_linkedlist_iterate()\n    unit_test_linkedlist_collect()\nend", "meta": {"hexsha": "cd93cca5ffebaf173e84c8040e8c24e019d7caab", "size": 19945, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/list.jl", "max_stars_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_stars_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/list.jl", "max_issues_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_issues_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/list.jl", "max_forks_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_forks_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.293544458, "max_line_length": 60, "alphanum_fraction": 0.6033091, "num_tokens": 6629, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.11757214127540647, "lm_q1q2_score": 0.052836054307417814}}
{"text": "ispangram(input::AbstractString) = length(Set(matchall(r\"[a-z]\", lowercase(input)))) == 26\n\n", "meta": {"hexsha": "e297dcc0db694d4bf36a9b8cc20531423ea54a56", "size": 92, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercises/pangram/example.jl", "max_stars_repo_name": "miguelraz/julia-1", "max_stars_repo_head_hexsha": "ef059b088e967251b3180820ac4d2768f1b0ac85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercises/pangram/example.jl", "max_issues_repo_name": "miguelraz/julia-1", "max_issues_repo_head_hexsha": "ef059b088e967251b3180820ac4d2768f1b0ac85", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exercises/pangram/example.jl", "max_forks_repo_name": "miguelraz/julia-1", "max_forks_repo_head_hexsha": "ef059b088e967251b3180820ac4d2768f1b0ac85", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6666666667, "max_line_length": 90, "alphanum_fraction": 0.6956521739, "num_tokens": 25, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44939263446475963, "lm_q2_score": 0.11757213663746784, "lm_q1q2_score": 0.05283605222316236}}
{"text": "# Run this file to add packages to Project.toml\r\n\r\nusing Pkg\r\n\r\n\"\"\"\r\n    addpackage()\r\n\r\nInstall required packages\r\n\"\"\"\r\nfunction addpackage()\r\n    Pkg.add(\"BenchmarkTools\") # adds DataFrames and DelimitedFiles\r\n    Pkg.add(\"CSV\")\r\n    Pkg.add(\"XLSX\")\r\n    Pkg.add(\"JLD\")\r\n    Pkg.add(\"NPZ\")\r\n    Pkg.add(\"RData\")\r\n    Pkg.add(\"MAT\")\r\n    Pkg.add(\"Images\")\r\n    Pkg.add(\"ImageMagick\")\r\n    Pkg.add(\"DataFrames\")\r\n    # Statistics\r\n    Pkg.add(\"Statistics\")\r\n    Pkg.add(\"StatsBase\")\r\n    Pkg.add(\"RDatasets\")\r\n    Pkg.add(\"Plots\")\r\n    Pkg.add(\"Plotly\")\r\n    Pkg.add(\"StatsPlots\")\r\n    Pkg.add(\"KernelDensity\")\r\n    Pkg.add(\"Distributions\")\r\n    Pkg.add(\"HypothesisTests\")\r\n    Pkg.add(\"PyCall\")\r\n    Pkg.add(\"MLBase\")\r\n    # Dimensionality Reduction\r\n    Pkg.add(\"UMAP\")\r\n    Pkg.add(\"Makie\")\r\n    Pkg.add(\"VegaDatasets\")\r\n    Pkg.add(\"MultivariateStats\")\r\n    Pkg.add(\"ScikitLearn\")\r\n    Pkg.add(\"Distances\")\r\nend\r\naddpackage() # Do this only once\r\n", "meta": {"hexsha": "530a60b07f97f267d324e243ee4a7bfefe76026b", "size": 951, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JuliaAcademy/DataScience/package.jl", "max_stars_repo_name": "ykyang/org.allnix.julia", "max_stars_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "JuliaAcademy/DataScience/package.jl", "max_issues_repo_name": "ykyang/org.allnix.julia", "max_issues_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "JuliaAcademy/DataScience/package.jl", "max_forks_repo_name": "ykyang/org.allnix.julia", "max_forks_repo_head_hexsha": "58933a5848dec81c53d591b4163e9a70df62ddd8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.6428571429, "max_line_length": 67, "alphanum_fraction": 0.6004206099, "num_tokens": 282, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3812195662561499, "lm_q2_score": 0.1384617852362804, "lm_q1q2_score": 0.052784341710826986}}
{"text": "using Pkg\n\ndependencies = [\n    \"Distributions\",\n    \"Plots\"\n]\n\nPkg.add(dependencies)\n", "meta": {"hexsha": "8a257ad19122584185fc8f0457daf655745cbde2", "size": 86, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "1_Introduction/1-1_Polynomial_Curve_Fitting/packages.jl", "max_stars_repo_name": "koba-jon/prml_julia", "max_stars_repo_head_hexsha": "9e01357f3ee79cc610b760bab9a1ff3cf604dd9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "1_Introduction/1-1_Polynomial_Curve_Fitting/packages.jl", "max_issues_repo_name": "koba-jon/prml_julia", "max_issues_repo_head_hexsha": "9e01357f3ee79cc610b760bab9a1ff3cf604dd9e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1_Introduction/1-1_Polynomial_Curve_Fitting/packages.jl", "max_forks_repo_name": "koba-jon/prml_julia", "max_forks_repo_head_hexsha": "9e01357f3ee79cc610b760bab9a1ff3cf604dd9e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.5555555556, "max_line_length": 21, "alphanum_fraction": 0.6511627907, "num_tokens": 24, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4610167793123159, "lm_q2_score": 0.11436852014455551, "lm_q1q2_score": 0.0527258068117587}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.20\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 0e4dcd02-71f8-11eb-2ac3-612951cdb3d4\nmd\"\"\"\n# Examen scheduling\nThe purpose of the first project is to create an exam schedule. This is a group work where the groups are in competition with each other. The evaluation is made on the basis of:\n* the correctness of the proposed solution\n* the computational cost\n* the functionality\n* the scalability\n* the generalizability\n\nIf the results meet expectations, your work can also be used effectively in the future for planning exams or class schedules.\n\nThe absolute minimum requirements (or constraints) that must be respected are\n* the availability of professors (and teaching assistants)\n* the non-overlapping of subjects\n\npossible additions are:\n* distinction between an oral or written exam\n* whether or not it is compulsory to conjoin several examination days for the same course if it runs over several days\n* courses that are evaluated in several parts (possibly by several professors)\n* preferences (e.g. certain minimum number of days of preparation for a certain course, taking into account student's input)\n\nYou may consider this a constraint satisfaction problem. Different way of solving it can and may be used (backtracking <> local search etc.). \n\nStart with one year, then extend the functionality to one faculty and finally to both faculties.\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25000e4dcd02-71f8-11eb-2ac3-612951cdb3d4\n", "meta": {"hexsha": "8eadcd32c496495aadcfa3564c700e49e49b712f", "size": 1454, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Projects/Project 1 - schudeling.jl", "max_stars_repo_name": "B4rtDC/DS425-PS", "max_stars_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-07-07T21:55:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T14:29:23.000Z", "max_issues_repo_path": "Projects/Project 1 - schudeling.jl", "max_issues_repo_name": "Goodwill-Khoa/DS425-PS", "max_issues_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Projects/Project 1 - schudeling.jl", "max_forks_repo_name": "Goodwill-Khoa/DS425-PS", "max_forks_repo_head_hexsha": "585f1c69d4abb9801feb9e281d0b1a04b89b7761", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-07-07T21:55:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T13:52:37.000Z", "avg_line_length": 40.3888888889, "max_line_length": 177, "alphanum_fraction": 0.7854195323, "num_tokens": 341, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.461016779312316, "lm_q2_score": 0.11436851561661296, "lm_q1q2_score": 0.052725804724301224}}
{"text": "## operations on Tape\n\n## Input\n\nstruct Input <: AbstractOp\n    var::TAny\n    argid::Int\nend\n\n@inline exec!(tape::Tape, op::Input) = op.var\n\nfunction record!(tape::Tape, ::Type{Input}, val; argid=-1)\n    op = Input(tracked(tape, val), argid)\n    push!(tape.ops, op)\n    op.var.id = length(tape)\n    return op.var\nend\n\n\n## Constant\n\nstruct Constant <: AbstractOp\n    var::TAny\nend\nconstant(tape::Tape, x::TAny) = x\nconstant(tape::Tape, x) = record!(tape, Constant, x)\n\n\n@inline exec!(tape::Tape, op::Constant) = op.var\n\nfunction record!(tape::Tape, ::Type{Constant}, val)\n    op = Constant(tracked(tape, val))\n    push!(tape.ops, op)\n    op.var.id = length(tape)\n    return op.var\nend\n\n\n## Call\n\n\"\"\"\nMethod call\n\"\"\"\nstruct Call{Fn, ARGS <: Tuple} <: AbstractOp\n    var::TAny                  # tracked var, result of the call\n    fn::Fn                     # function to apply to args\n    args::ARGS                 # call arguments\n    kwargs::Dict{Symbol, Any}  # keyword arguments\n    # Call(var::TAny, fn::Fn, args::ARGS; kwargs=Dict()) where {Fn, ARGS} =\n    #     new{Fn, ARGS}(var, fn, args, kwargs)\nend\n\nfunction Base.show(io::IO, op::Call)\n    args_str = join([var isa TAny ? \"%$(var.id)\" : var for var in op.args], \", \")\n    kwargs_str = isempty(op.kwargs) ? \"\" : \"; \" * join([\"$k=$v\" for (k, v) in op.kwargs], \", \")\n    print(io, \"Call(%$(op.var.id) = $(op.fn)($(args_str)$kwargs_str))\")\nend\n\nfunction record!(tape::Tape, ::Type{Call}, fn::Fn, args::ARGS;\n                 kwargs=Dict{Symbol,Any}()) where {Fn, ARGS<:Tuple}\n    arg_vals = map(getvalue, args)\n    val = to_device(tape.device, fn(arg_vals...; kwargs...))\n    var = tracked(tape, val)\n    op = Call(var, fn, args, kwargs)\n    _record!(tape, op)\n    return var\nend\n\n\"\"\"\nExecute operation on a tape, store result to op's var.\n\"\"\"\nfunction exec!(tape::Tape, op::Call)\n    arg_data = map(getvalue, op.args)\n    op.var.val = op.fn(arg_data...; op.kwargs...)\n    return op.var\nend\n\n\n## Bcast\n\n\"\"\"\nBroadcasting\n\"\"\"\nstruct Bcast{Fn, ARGS <: Tuple} <: AbstractOp\n    var::TAny\n    fn::Fn\n    args::ARGS\nend\n\nfunction Base.show(io::IO, op::Bcast)\n    args_str = join([var isa TAny ? \"%$(var.id)\" : var for var in op.args], \", \")\n    print(io, \"Bcast(%$(op.var.id) = $(op.fn).($args_str))\")\nend\n\nfunction record!(tape::Tape, ::Type{Bcast}, fn::Fn, args::ARGS) where {Fn, ARGS<:Tuple}\n    arg_vals = map(getvalue, args)\n    # println(\"**** $(fn). | $(map(typeof, arg_vals)) | $(map(size, arg_vals))\")\n    val = fn.(arg_vals...)\n    var = tracked(tape, val)\n    op = Bcast(var, fn, args)\n    _record!(tape, op)\n    return var\nend\n\n\"\"\"\nExecute operation on a tape, store result to op's var.\n\"\"\"\nfunction exec!(tape::Tape, op::Bcast)\n    arg_data = map(getvalue, op.args)\n    op.var.val = op.fn.(arg_data...)\n    return op.var\nend\n\n\n## Assign\n\nstruct Assign <: AbstractOp\n    var::TAny\n    src::TAny\nend\n\nBase.show(io::IO, op::Assign) = print(io, \"Assign(%$(op.var.id) = %$(op.src.id))\")\n\nfunction exec!(tape::Tape, op::Assign)\n    op.var.val = op.src.val\n    return op.var\nend\n\n\nfunction record!(tape::Tape, ::Type{Assign}, src::TAny)\n    var = tracked(tape, src.val)\n    op = Assign(var, src)\n    _record!(tape, op)\n    exec!(tape, op)\n    return var\nend\n\n\n## COMMON UTILS\n\ngetvar(op::AbstractOp) = op.var\nsetvar!(op::AbstractOp, var::TAny) = (op.var = var)\n\ngetvalue(op::AbstractOp) = op |> getvar |> getvalue\nsetvalue!(op::AbstractOp, val) = setvalue!(getvar(op), val)\n\n@inline Base.getproperty(op::AbstractOp, name::Symbol) = _getproperty(op::AbstractOp, Val(name))\n@inline _getproperty(x::AbstractOp, ::Val{F}) where F = getfield(x, F)\n@inline _getproperty(x::AbstractOp, ::Val{:val}) = getvalue(x)\n\n## mutable structs: writing all trackable fields to the tape\n\n\"\"\"\nTraverse mutable struct and write all trackable fields to the tape,\nkeeping mapping from field paths to tracked vars.\n\"\"\"\nfunction record_struct!(tape::Tape, s, argid::Int; field_path=[])\n    # S = typeof(s)\n    S = Core.eval(Main, Symbol(typeof(s).name))  # parameter-free type of \n    new_fields = []\n    for name in fieldnames(S)\n        full_field_path = vcat(field_path, name)\n        field = getfield(s, name)\n        is_trackable = (field isa Real && !isa(field, Bool)) || field isa AbstractArray\n        is_concrete = fieldtype(S, name) |> isconcretetype\n        if is_trackable && !is_concrete\n            var = record!(tape, Input, field; argid=argid)\n            push!(new_fields, var)\n            # save mapping field_path -> var\n            if !haskey(tape.sfields, argid)\n                tape.sfields[argid] = Dict{Any,Any}()\n            end\n            full_field_path_tuple = (full_field_path...,)\n            tape.sfields[argid][full_field_path_tuple] = var.id\n        elseif isstruct(field)\n            var = record_struct!(tape, field, argid; field_path=full_field_path)\n            push!(new_fields, var)\n        else\n            # if field isn't trackable, take it as it is\n            push!(new_fields, field)\n        end\n    end\n    # assuming default constuctor\n    return S(new_fields...)\nend\n", "meta": {"hexsha": "f2dbdfedb0fc03eb31984d286fbe7c6ae67ac2ba", "size": 5049, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ops.jl", "max_stars_repo_name": "JuliaTagBot/YotaTracked.jl", "max_stars_repo_head_hexsha": "a445367da35b04801078bfefd204ffd1b5c216eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-19T17:18:05.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-19T17:18:05.000Z", "max_issues_repo_path": "src/ops.jl", "max_issues_repo_name": "JuliaTagBot/YotaTracked.jl", "max_issues_repo_head_hexsha": "a445367da35b04801078bfefd204ffd1b5c216eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/ops.jl", "max_forks_repo_name": "JuliaTagBot/YotaTracked.jl", "max_forks_repo_head_hexsha": "a445367da35b04801078bfefd204ffd1b5c216eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-08T11:30:02.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:30:02.000Z", "avg_line_length": 27.1451612903, "max_line_length": 96, "alphanum_fraction": 0.6108140226, "num_tokens": 1486, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43014734858584286, "lm_q2_score": 0.12252320931407355, "lm_q1q2_score": 0.052703033626676984}}
{"text": "lowered Julia code without syntactic sugare \n@code_lowered <expr>\n\ntyped Julia code (types are propagated)\n@code_type <expr>\n\ncode compiled for the computer interface\n@code_llvm <expr>\n\nassembly code compiled for you specific machine\n@code_native <expr> \n\nlist methods of the function with links to its implementations \nmethods(<function_name>)\n\nexamples\n@code_lowered 1 + 2\nmethods(add)\n\n##binary conversion\n\nif you have your binary as a string you can parse it to decimal \njulia> parse(Int, \"1010\", base=2)\n10\n\nand from base 16 \njulia> parse(Int, \"abcd\", base = 16)\n43981\n", "meta": {"hexsha": "b56156a136c8b62267f12cebcb7f0f1c6db6ef12", "size": 574, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "intro.jl", "max_stars_repo_name": "jocrv/julia-lang", "max_stars_repo_head_hexsha": "22f98e630e79b6d1ca3dfb81b8e25da60c081336", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "intro.jl", "max_issues_repo_name": "jocrv/julia-lang", "max_issues_repo_head_hexsha": "22f98e630e79b6d1ca3dfb81b8e25da60c081336", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intro.jl", "max_forks_repo_name": "jocrv/julia-lang", "max_forks_repo_head_hexsha": "22f98e630e79b6d1ca3dfb81b8e25da60c081336", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.7931034483, "max_line_length": 64, "alphanum_fraction": 0.7648083624, "num_tokens": 150, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3557748935136304, "lm_q2_score": 0.1480472055495773, "lm_q1q2_score": 0.052671478789391414}}
{"text": "using RE2\nusing Test\n\nfunction collect_eachmatch(re, str; overlap=false)\n    [m.match for m in collect(eachmatch(re, str, overlap = overlap))]\nend\n\nfor f in [matchall #=, collect_eachmatch =#]\n    @test f(rr\"a?b?\", \"asbd\") == [\"a\",\"\",\"b\",\"\",\"\"] == f(rr\"\"\"a?b?\"\"\", \"asbd\")\n    @test f(rr\"a?b?\", \"asbd\", overlap=true) == [\"a\",\"\",\"b\",\"\",\"\"]\n    @test f(rr\"\\w+\", \"hello\", overlap=true) == [\"hello\",\"ello\",\"llo\",\"lo\",\"o\"]\n    @test f(rr\".\\s\", \"x \\u2200 x \\u2203 y\") == [\"x \", \"\u2200 \", \"x \", \"\u2203 \"]\n    @test f(rr\"(\\w+)(\\s*)\", \"The dark side of the moon\") ==\n          [\"The \", \"dark \", \"side \", \"of \", \"the \", \"moon\"]\n    @test f(rr\"\", \"\") == [\"\"]\n    @test f(rr\"\", \"\", overlap=true) == [\"\"]\n    @test f(rr\"aa\", \"aaaa\") == [\"aa\", \"aa\"]\n    @test f(rr\"aa\", \"aaaa\", overlap=true) == [\"aa\", \"aa\", \"aa\"]\n    @test f(rr\"\", \"aaa\") == [\"\", \"\", \"\", \"\"]\n    @test f(rr\"\", \"aaa\", overlap=true) == [\"\", \"\", \"\", \"\"]\n    @test f(rr\"GCG\",\"GCGCG\") == [\"GCG\"]\n    @test f(rr\"GCG\",\"GCGCG\",overlap=true) == [\"GCG\",\"GCG\"]\nend\n\n# Issue 8278\ntarget = \"\"\"71.163.72.113 - - [30/Jul/2014:16:40:55 -0700] \"GET emptymind.org/thevacantwall/wp-content/uploads/2013/02/DSC_006421.jpg HTTP/1.1\" 200 492513 \"http://images.search.yahoo.com/images/view;_ylt=AwrB8py9gdlTGEwADcSjzbkF;_ylu=X3oDMTI2cGZrZTA5BHNlYwNmcC1leHAEc2xrA2V4cARvaWQDNTA3NTRiMzYzY2E5OTEwNjBiMjc2YWJhMjkxMTEzY2MEZ3BvcwM0BGl0A2Jpbmc-?back=http%3A%2F%2Fus.yhs4.search.yahoo.com%2Fyhs%2Fsearch%3Fei%3DUTF-8%26p%3Dapartheid%2Bwall%2Bin%2Bpalestine%26type%3Dgrvydef%26param1%3D1%26param2%3Dsid%253Db01676f9c26355f014f8a9db87545d61%2526b%253DChrome%2526ip%253D71.163.72.113%2526p%253Dgroovorio%2526x%253DAC811262A746D3CD%2526dt%253DS940%2526f%253D7%2526a%253Dgrv_tuto1_14_30%26hsimp%3Dyhs-fullyhosted_003%26hspart%3Dironsource&w=588&h=387&imgurl=occupiedpalestine.files.wordpress.com%2F2012%2F08%2F5-peeking-through-the-wall.jpg%3Fw%3D588%26h%3D387&rurl=http%3A%2F%2Fwww.stopdebezetting.com%2Fwereldpers%2Fcompare-the-berlin-wall-vs-israel-s-apartheid-wall-in-palestine.html&size=49.0KB&name=...+%3Cb%3EApartheid+wall+in+Palestine%3C%2Fb%3E...+%7C+Or+you+go+peeking+through+the+%3Cb%3Ewall%3C%2Fb%3E&p=apartheid+wall+in+palestine&oid=50754b363ca991060b276aba291113cc&fr2=&fr=&tt=...+%3Cb%3EApartheid+wall+in+Palestine%3C%2Fb%3E...+%7C+Or+you+go+peeking+through+the+%3Cb%3Ewall%3C%2Fb%3E&b=0&ni=21&no=4&ts=&tab=organic&sigr=13evdtqdq&sigb=19k7nsjvb&sigi=12o2la1db&sigt=12lia2m0j&sign=12lia2m0j&.crumb=.yUtKgFI6DE&hsimp=yhs-fullyhosted_003&hspart=ironsource\" \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\"\"\"\npat = rr\"\"\"([\\d\\.]+) ([\\w.-]+) ([\\w.-]+) (\\[.+\\]) \"([^\"\\r\\n]*|[^\"\\r\\n\\[]*\\[.+\\][^\"]+|[^\"\\r\\n]+.[^\"]+)\" (\\d{3}) (\\d+|-) (\"(?:[^\"]|\\\")+)\"? (\"(?:[^\"]|\\\")+)\"?\"\"\"\nrm = match(pat, target)\n@test length(rm.captures) == 9\n@test rm.offsets == [1, 15, 17, 19, 49, 133, 137, 144, 1454]\n\n# Issue 9545 (32 bit)\nbuf = PipeBuffer()\nshow(buf, rr\"\")\n@test read(buf, String) == \"rr\\\"\\\"\"\n\n# see #10994, #11447: RE2 allows NUL chars in the pattern\n@test contains(\"a\\0b\", Regex2(\"^a\\0b\\$\"))\n\n# regex match / search string must be a String\n@test_throws ArgumentError match(rr\"test\", GenericString(\"this is a test\"))\n@test_throws ArgumentError findfirst(rr\"test\", GenericString(\"this is a test\"))\n\n# Named subpatterns\nlet m = match(rr\"(?P<a>.)(.)(?P<b>.)\", \"xyz\")\n    @test (m[:a], m[2], m[\"b\"]) == (\"x\", \"y\", \"z\")\n    @test sprint(show, m) == \"Regex2Match(\\\"xyz\\\", a=\\\"x\\\", 2=\\\"y\\\", b=\\\"z\\\")\"\nend\n\n# Backcapture reference in substitution string\n@test replace(\"xaay\", rr\"x(a+)\" => s\"!\\1!\") == \"!aa!y\"\n@test replace(\"abcde\", rr\"(..)(?P<byname>d)\" => s\"\\g<byname>xy\\\\\\1\") == \"adxy\\\\bce\"\n@test_throws ErrorException replace(\"a\", rr\"(?P<x>)\" => s\"\\g<y>\")\n\n# Proper unicode handling\n@test  match(rr\"\u2200\u2200\", \"\u2200x\u2200\u2200\u2200\").match == \"\u2200\u2200\"\n\n# TODO missing tests for different flags\n# TODO test for error exceptions\n# TODO test different behavior of RE2 compared to PCRE2\n\n", "meta": {"hexsha": "bec4eb79daabd1a22c88104a3ec8b5732084b3ba", "size": 3921, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "KlausC/RE2.jl", "max_stars_repo_head_hexsha": "cbd0e8b470756efff47fa903e06427a34e1fe907", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-01-12T16:50:29.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-12T16:50:29.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "KlausC/RE2.jl", "max_issues_repo_head_hexsha": "cbd0e8b470756efff47fa903e06427a34e1fe907", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "KlausC/RE2.jl", "max_forks_repo_head_hexsha": "cbd0e8b470756efff47fa903e06427a34e1fe907", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 63.2419354839, "max_line_length": 1578, "alphanum_fraction": 0.6383575618, "num_tokens": 1663, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39606818053136394, "lm_q2_score": 0.13296424706933604, "lm_q1q2_score": 0.05266290741247467}}
{"text": "# This file was generated, do not modify it. # hide\nr3(x) = round(x, sigdigits=3)\nr3(pi)", "meta": {"hexsha": "5116c7ffb7bbb9fdf0a36ec06ee6df6491e35d4a", "size": 88, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/isl/lab-4/code/ex3.jl", "max_stars_repo_name": "JuliaAI/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "76b1d3d1a5045c62b104a8358e4c37049f15ccb6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "__site/assets/isl/lab-4/code/ex3.jl", "max_issues_repo_name": "alan-turing-institute/MLJTutorials", "max_issues_repo_head_hexsha": "f1613ac8968fceee0e4fe43d1fdb5b58c8a84bad", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "__site/assets/isl/lab-4/code/ex3.jl", "max_forks_repo_name": "alan-turing-institute/MLJTutorials", "max_forks_repo_head_hexsha": "f1613ac8968fceee0e4fe43d1fdb5b58c8a84bad", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 29.3333333333, "max_line_length": 51, "alphanum_fraction": 0.6818181818, "num_tokens": 31, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681662740417, "lm_q2_score": 0.13296424535145931, "lm_q1q2_score": 0.05266290483636426}}
{"text": "e1 = Expr(:call, *, 3, 4) #> :((*)(3,4))\neval(e1) #> 12\n\n# e1 = expr(:call, *, 3, a) #> ERROR: a not defined\ne2 = Expr(:call, *, 3, :a) #> :((*)(3,a))\n# eval(e2) #> ERROR: a not defined\na = 4 #> 4\neval(e2) #> 12\n\n# b #> ERROR: b not defined\ne3 = :(b = 1)\neval(e3) #> 1\nb #> 1\n\ne4 = :(a + b) #> :(a + b)\ne5 = :($a + b) #> :(4 + b)\neval(e4) #> 5\neval(e5) #> 5", "meta": {"hexsha": "9525bd5c814a3a39fc7f574ff088c50628d60438", "size": 357, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Module 1/Chapter07/eval.jl", "max_stars_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_stars_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "max_stars_repo_stars_event_min_datetime": "2017-02-12T15:36:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T03:30:39.000Z", "max_issues_repo_path": "Module 1/Chapter07/eval.jl", "max_issues_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_issues_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Module 1/Chapter07/eval.jl", "max_forks_repo_name": "PacktPublishing/Julia-High-Performance-Programming", "max_forks_repo_head_hexsha": "861d655d163d8b87bb05478bfd255735b9263d60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2017-02-10T16:19:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-07T11:46:44.000Z", "avg_line_length": 19.8333333333, "max_line_length": 51, "alphanum_fraction": 0.4285714286, "num_tokens": 189, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39606816627404173, "lm_q2_score": 0.1329642281726931, "lm_q1q2_score": 0.05266289803240184}}
{"text": "module TestConstructors\n\nusing Test, InMemoryDatasets, Random, CategoricalArrays, DataStructures, PooledArrays\nusing InMemoryDatasets: Index, _columns, index\nconst \u2245 = isequal\n\n#\n# Dataset\n#\n@testset \"constructors\" begin\n    ds = Dataset()\n    @inferred Dataset()\n\n    @test isempty(_columns(ds))\n    @test _columns(ds) isa Vector{AbstractVector}\n    @test index(ds) == Index()\n    @test size(Dataset(copycols=false)) == (0, 0)\n\n    vecvec = [CategoricalVector{Union{Float64, Missing}}(zeros(3)),\n              CategoricalVector{Union{Float64, Missing}}(ones(3))]\n\n    ds = Dataset(collect(Any, vecvec), Index([:x1, :x2]))\n    @test size(ds, 1) == 3\n    @test size(ds, 2) == 2\n\n    ds2 = Dataset(collect(Any, vecvec), Index([:x1, :x2]), copycols=false)\n    @test size(ds2, 1) == 3\n    @test size(ds2, 2) == 2\n    @test ds2.x1.val === vecvec[1]\n    @test ds2.x2.val === vecvec[2]\n\n    @test_throws ArgumentError Dataset([[1, 2]], :autos)\n    @test_throws ArgumentError Dataset([1 2], :autos)\n\n    for copycolsarg in (true, false)\n        @test ds == Dataset(vecvec, :auto, copycols=copycolsarg)\n        @test ds == Dataset(collect(Any, vecvec), :auto, copycols=copycolsarg)\n        @test ds == Dataset(collect(AbstractVector, vecvec), :auto, copycols=copycolsarg)\n        @test ds == Dataset(x1 = vecvec[1], x2 = vecvec[2], copycols=copycolsarg)\n\n        for cols in ([:x1, :x2], [\"x1\", \"x2\"])\n            @test ds == Dataset(vecvec, cols, copycols=copycolsarg)\n            @test ds == Dataset(collect(Any, vecvec), cols, copycols=copycolsarg)\n            @test ds == Dataset(collect(AbstractVector, vecvec), cols, copycols=copycolsarg)\n            @test ds == Dataset([col=>vect for (col, vect) in zip(cols, vecvec)])\n        end\n    end\n\n    @test Dataset([1:3, 1:3], :auto) == Dataset(Any[1:3, 1:3], :auto) ==\n          Dataset(UnitRange[1:3, 1:3], :auto) == Dataset(AbstractVector[1:3, 1:3], :auto) ==\n          Dataset([[1, 2, 3], [1, 2, 3]], :auto) == Dataset(Any[[1, 2, 3], [1, 2, 3]], :auto) ==\n          Dataset([1:3, [1, 2, 3]], :auto)\n          Dataset([:x1=>1:3, :x2=>[1, 2, 3]]) == Dataset([\"x1\"=>1:3, \"x2\"=>[1, 2, 3]])\n\n    @inferred Dataset([1:3, 1:3], :auto)\n    @inferred Dataset([1:3, 1:3], [:a, :b])\n    @inferred Dataset([1:3, 1:3], [\"a\", \"b\"])\n\n    @inferred Dataset([:x1=>1:3, :x2=>[1, 2, 3]])\n    @inferred Dataset([\"x1\"=>1:3, \"x2\"=>[1, 2, 3]])\n\n    @test ds !== Dataset(ds)\n    @test ds == Dataset(ds)\n\n    @test ds == Dataset(x1 = Union{Float64, Missing}[0.0, 0.0, 0.0],\n                          x2 = Union{Float64, Missing}[1.0, 1.0, 1.0])\n    @test ds == Dataset(x1 = Union{Float64, Missing}[0.0, 0.0, 0.0],\n                          x2 = Union{Float64, Missing}[1.0, 1.0, 1.0],\n                          x3 = Union{Float64, Missing}[2.0, 2.0, 2.0])[:, [:x1, :x2]]\n    @test ds == Dataset(x1 = Union{Float64, Missing}[0.0, 0.0, 0.0],\n                          x2 = Union{Float64, Missing}[1.0, 1.0, 1.0],\n                          x3 = Union{Float64, Missing}[2.0, 2.0, 2.0])[:, [\"x1\", \"x2\"]]\n\n    @test_throws BoundsError SubDataset(Dataset(A=1), 0:0, :)\n    @test_throws ArgumentError SubDataset(Dataset(A=1), 0, :)\n    @test_throws BoundsError Dataset(A=1)[0, :]\n    @test_throws BoundsError Dataset(A=1)[0, 1:1]\n\n    @test Dataset(a=1, b=1:2) == Dataset(a=[1, 1], b=[1, 2])\n\n    @test_throws ArgumentError Dataset(makeunique=true)\n    @test_throws ArgumentError Dataset(a=1, makeunique=true)\n    @test_throws ArgumentError Dataset(a=1, makeunique=true, copycols=false)\nend\n\n@testset \"Dataset keyword argument constructor\" begin\n    x = allowmissing([1, 2, 3])\n    y = allowmissing([4, 5, 6])\n\n    ds = Dataset(x=x, y=y)\n    @test size(ds) == (3, 2)\n    @test propertynames(ds) == [:x, :y]\n    @test ds.x == x\n    @test ds.y == y\n    @test ds.x.val !== x\n    @test ds.y.val !== y\n    ds = Dataset(x=x, y=y, copycols=true)\n    @test size(ds) == (3, 2)\n    @test propertynames(ds) == [:x, :y]\n    @test ds.x == x\n    @test ds.y == y\n    @test ds.x.val !== x\n    @test ds.y.val !== y\n    ds = Dataset(x=x, y=y, copycols=false)\n    @test size(ds) == (3, 2)\n    @test propertynames(ds) == [:x, :y]\n    @test ds.x.val === x\n    @test ds.y.val === y\n    @test_throws ArgumentError Dataset(x=x, y=y, copycols=1)\n\n    ds = Dataset(x=x, y=y, copycols=false)\n    @test size(ds) == (3, 2)\n    @test propertynames(ds) == [:x, :y]\n    @test ds.x.val === x\n    @test ds.y.val === y\nend\n\n@testset \"Dataset constructor\" begin\n    ds1 = Dataset(x=1:3, y=1:3)\n\n    ds2 = Dataset(ds1)\n    ds3 = copy(ds1)\n    @test ds1 == ds2 == ds3\n    @test ds1.x.val !== ds2.x.val\n    @test ds1.x.val !== ds3.x.val\n    @test ds1.y.val !== ds2.y.val\n    @test ds1.y.val !== ds3.y.val\n\n    ds2 = Dataset(ds1)\n    ds3 = copy(ds1)\n    @test ds1 == ds2 == ds3\n    @test ds1.x.val !== ds2.x.val\n    @test ds1.x.val !== ds3.x.val\n    @test ds1.y.val !== ds2.y.val\n    @test ds1.y.val !== ds3.y.val\n\n    ds1 = view(ds1, :, :)\n    ds2 = Dataset(ds1)\n    ds3 = copy(ds1)\n    @test ds1 == ds2 == ds3\n    @test ds1.x.val !== ds2.x.val\n    @test ds1.x.val !== ds3.x.val\n    @test ds1.y.val !== ds2.y.val\n    @test ds1.y.val !== ds3.y.val\n\n    # FIXME\n    # ds2 = Dataset(ds1, copycols=false)\n    # @test ds1 == ds2\n    # @test ds1.x.val === ds2.x.val\n    # @test ds1.y.val === ds2.y.val\nend\n\n@testset \"pair constructor\" begin\n    @test Dataset(:x1 => zeros(3), :x2 => ones(3)) ==\n          Dataset([:x1 => zeros(3), :x2 => ones(3)]) ==\n          Dataset(\"x1\" => zeros(3), \"x2\" => ones(3)) ==\n          Dataset(\"x1\" => zeros(3), \"x2\" => ones(3))\n\n    @inferred Dataset(:x1 => zeros(3), :x2 => ones(3))\n    ds = Dataset([:x1 => zeros(3), :x2 => ones(3)])\n    @test size(ds, 1) == 3\n    @test size(ds, 2) == 2\n    @test isequal(ds, Dataset(x1 = [0.0, 0.0, 0.0], x2 = [1.0, 1.0, 1.0]))\n\n    ds = Dataset(:type => [], :begin => [])\n    @test propertynames(ds) == [:type, :begin]\n\n    a=allowmissing([1, 2, 3])\n    ds = Dataset(:a=>a, :b=>1, :c=>1:3)\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.a.val == a\n    @test ds.a.val !== a\n\n    ds = Dataset(:a=>a, :b=>1, :c=>1:3)\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.a.val == a\n\n    ds = Dataset(\"x1\" => zeros(3), \"x2\" => ones(3))\n    @inferred Dataset(\"x1\" => zeros(3), \"x2\" => ones(3))\n    @test size(ds, 1) == 3\n    @test size(ds, 2) == 2\n    @test isequal(ds, Dataset(x1 = [0.0, 0.0, 0.0], x2 = [1.0, 1.0, 1.0]))\n\n    ds = Dataset(\"type\" => [], \"begin\" => [])\n    @test propertynames(ds) == [:type, :begin]\n\n    a=allowmissing([1, 2, 3])\n    ds = Dataset(\"a\"=>a, \"b\"=>1, \"c\"=>1:3)\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.\"a\" == a\n    @test ds.\"a\".val !== a\n\n    ds = Dataset(\"a\"=>a, \"b\"=>1, \"c\"=>1:3)\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.\"a\".val == a\n\n    @test_throws ArgumentError Dataset([\"type\" => 1, :begin => 2])\nend\n\n@testset \"associative\" begin\n    ds = Dataset(Dict(:A => 1:3, :B => 4:6))\n    @inferred Dataset(Dict(:A => 1:3, :B => 4:6))\n    @test ds == Dataset(A = 1:3, B = 4:6)\n    @test eltype.(eachcol(ds)) == Union[Union{Missing, Int}, Union{Missing, Int}]\n\n    a=allowmissing([1, 2, 3])\n    ds = Dataset(Dict(:a=>a, :b=>1, :c=>1:3))\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.a == a\n    @test ds.a.val !== a\n\n    ds = Dataset(Dict(:a=>a, :b=>1, :c=>1:3))\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.a.val == a\n\n    ds = Dataset(Dict(\"A\" => 1:3, \"B\" => 4:6))\n    @inferred Dataset(Dict(\"A\" => 1:3, \"B\" => 4:6))\n    @test ds == Dataset(A = 1:3, B = 4:6)\n    @test eltype.(eachcol(ds)) == Union[Union{Missing, Int}, Union{Missing, Int}]\n\n    a=allowmissing([1, 2, 3])\n    ds = Dataset(Dict(\"a\"=>a, \"b\"=>1, \"c\"=>1:3))\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.\"a\" == a\n    @test ds.\"a\".val !== a\n    ds = Dataset(Dict(\"a\"=>a, \"b\"=>1, \"c\"=>1:3))\n    @test propertynames(ds) == [:a, :b, :c]\n    @test ds.\"a\".val == a\nend\n\n@testset \"vector constructors\" begin\n    x = allowmissing([1, 2, 3])\n    y = allowmissing([1, 2, 3])\n\n    ds = Dataset([x, y], :auto)\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1 == x\n    @test ds.x2 == y\n    @test ds.x1.val !== x\n    @test ds.x2.val !== y\n    ds = Dataset([x, y], :auto, copycols=true)\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1 == x\n    @test ds.x2 == y\n    @test ds.x1.val !== x\n    @test ds.x2.val !== y\n    ds = Dataset([x, y], :auto, copycols=false)\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1.val === x\n    @test ds.x2.val === y\n\n    ds = Dataset([x, y], [:x1, :x2])\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1 == x\n    @test ds.x2 == y\n    @test ds.x1.val !== x\n    @test ds.x2.val !== y\n    ds = Dataset([x, y], [:x1, :x2], copycols=true)\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1 == x\n    @test ds.x2 == y\n    @test ds.x1.val !== x\n    @test ds.x2.val !== y\n    ds = Dataset([x, y], [:x1, :x2], copycols=false)\n    @test propertynames(ds) == [:x1, :x2]\n    @test ds.x1.val === x\n    @test ds.x2.val === y\n\n    ds = Dataset([x, y], [\"x1\", \"x2\"])\n    @test names(ds) == [\"x1\", \"x2\"]\n    @test ds.\"x1\" == x\n    @test ds.\"x2\" == y\n    @test ds.\"x1\".val !== x\n    @test ds.\"x2\".val !== y\n    ds = Dataset([x, y], [\"x1\", \"x2\"], copycols=true)\n    @test names(ds) == [\"x1\", \"x2\"]\n    @test ds.\"x1\" == x\n    @test ds.\"x2\" == y\n    @test ds.\"x1\".val !== x\n    @test ds.\"x2\".val !== y\n    ds = Dataset([x, y], [\"x1\", \"x2\"], copycols=false)\n    @test names(ds) == [\"x1\", \"x2\"]\n    @test ds.\"x1\".val === x\n    @test ds.\"x2\".val === y\n\n    n = [:x1, :x2]\n    v = AbstractVector[1:3, [1, 2, 3]]\n    @test Dataset(v, n).x1.val isa Vector{Union{Int, Missing}}\n    @test v[1] isa AbstractRange\n\n    n = [\"x1\", \"x2\"]\n    v = AbstractVector[1:3, [1, 2, 3]]\n    @test Dataset(v, n).\"x1\".val isa Vector{Union{Int, Missing}}\n    @test v[1] isa AbstractRange\nend\n\n@testset \"recyclers\" begin\n    @test Dataset(a = 1:5, b = 1) == Dataset(a = collect(1:5), b = fill(1, 5))\n    @test Dataset(a = 1, b = 1:5) == Dataset(a = fill(1, 5), b = collect(1:5))\n    @test size(Dataset(a=1, b=[])) == (0, 2)\n    @test size(Dataset(a=1, b=[], copycols=false)) == (0, 2)\nend\n\n@testset \"constructor thrown exceptions\" begin\n    for copycolsarg in (true, false)\n        @test_throws DimensionMismatch Dataset(Any[collect(1:10)], InMemoryDatasets.Index([:A, :B]), copycols=copycolsarg)\n        @test_throws ArgumentError Dataset(A = rand(2, 2), copycols=copycolsarg)\n        @test_throws ArgumentError Dataset(A = rand(2, 1), copycols=copycolsarg)\n        @test_throws ArgumentError Dataset([1, 2, 3], :auto, copycols=copycolsarg)\n        @test_throws DimensionMismatch Dataset(AbstractVector[1:3, [1, 2]], :auto, copycols=copycolsarg)\n        @test_throws ArgumentError Dataset([1:3, 1], [:x1, :x2], copycols=copycolsarg)\n        @test_throws ArgumentError Dataset([1:3, 1], [\"x1\", \"x2\"], copycols=copycolsarg)\n        @test_throws ErrorException Dataset([1:3, 1], copycols=copycolsarg)\n    end\n\n    @test_throws MethodError Dataset([1 2; 3 4], :auto, copycols=false)\nend\n\n@testset \"column types\" begin\n    ds = Dataset(A = 1:3, B = 2:4, C = 3:5)\n    answer = [Array{Union{Missing, Int}, 1}, Array{Union{Missing, Int}, 1}, Array{Union{Missing, Int}, 1}]\n    @test typeof.(eachcol(ds)) == answer\n    ds[!, :D] = [4, 5, missing]\n    push!(answer, Vector{Union{Int, Missing}})\n    @test typeof.(eachcol(ds)) == answer\n    ds[!, :E] .= 'c'\n    push!(answer, Vector{Union{Missing, Char}})\n    @test typeof.(eachcol(ds)) == answer\nend\n\n@testset \"expansion of Ref and 0-dimensional arrays\" begin\n    @test Dataset(a=Ref(1), b=fill(1)) == Dataset(a=[1], b=[1])\n    @test Dataset(a=Ref(1), b=fill(1), c=1:3) ==\n          Dataset(a=[1, 1, 1], b=[1, 1, 1], c=1:3)\nend\n\n@testset \"broadcasting into 0 rows\" begin\n    for ds in [Dataset(x1=1:0, x2=1), Dataset(x1=1, x2=1:0)]\n        @test size(ds) == (0, 2)\n        @test ds.x1.val isa Vector{Union{Missing, Int}}\n        @test ds.x2.val isa Vector{Union{Missing, Int}}\n    end\nend\n\n@testset \"Dict constructor corner case\" begin\n    @test_throws ArgumentError Dataset(Dict('a' => 1, true => 2))\n    @test_throws ArgumentError Dataset(Dict(:z => 1, \"true\" => 2))\n    @test Dataset(Dict(\"z\" => 1, \"true\" => 2)) == Dataset(\"true\" => 2, \"z\" => 1)\n    @test Dataset(Dict([Symbol(c) => i for (i, c) in enumerate('a':'z')])) ==\n          Dataset(Dict([string(c) => i for (i, c) in enumerate('a':'z')])) ==\n          Dataset([Symbol(c) => i for (i, c) in enumerate('a':'z')])\n    @test Dataset(OrderedDict(:z => 1, :a => 2)) == Dataset(z=1, a=2)\n\nend\n\n@testset \"removed constructors\" begin\n    @test_throws ArgumentError Dataset([1 2; 3 4])\n    @test_throws ArgumentError Dataset([[1, 2], [3, 4]])\n    @test_throws ArgumentError Dataset([Int, Float64], [:a, :b])\n    @test_throws ArgumentError Dataset([Int, Float64], [:a, :b], 2)\n    @test_throws ArgumentError Dataset([Int, Float64], [\"a\", \"b\"])\n    @test_throws ArgumentError Dataset([Int, Float64], [\"a\", \"b\"], 2)\nend\n\n@testset \"threading correctness tests\" begin\n    for x in (10, 2*10^6), y in 1:4\n        ds = Dataset(rand(x, y), :auto)\n        InMemoryDatasets.insertcols!(ds, 1, :g => PooledArray(rand(1:10, nrow(ds))))\n        InMemoryDatasets.insertcols!(ds, 1, :g2 => CategoricalArray(rand(1:10, nrow(ds))))\n        @test ds == copy(ds)\n        @test ds == ds[1:nrow(ds), :]\n        p = shuffle(1:nrow(ds))\n        @test ds[p, :] == ds[p, :]\n    end\nend\n\nend # module\n", "meta": {"hexsha": "231a036c6118f7222a783c20931a1351b8785961", "size": 13383, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/constructors.jl", "max_stars_repo_name": "stephenll-forks/InMemoryDatasets.jl", "max_stars_repo_head_hexsha": "4220d4bdc03e95098c498ed4012b6bee8fe50eb1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 44, "max_stars_repo_stars_event_min_datetime": "2021-09-28T06:06:44.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T19:07:11.000Z", "max_issues_repo_path": "test/constructors.jl", "max_issues_repo_name": "stephenll-forks/InMemoryDatasets.jl", "max_issues_repo_head_hexsha": "4220d4bdc03e95098c498ed4012b6bee8fe50eb1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 29, "max_issues_repo_issues_event_min_datetime": "2021-06-11T11:22:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T09:40:38.000Z", "max_forks_repo_path": "test/constructors.jl", "max_forks_repo_name": "stephenll-forks/InMemoryDatasets.jl", "max_forks_repo_head_hexsha": "4220d4bdc03e95098c498ed4012b6bee8fe50eb1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2021-10-21T12:24:56.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-30T13:45:13.000Z", "avg_line_length": 35.4047619048, "max_line_length": 122, "alphanum_fraction": 0.5472614511, "num_tokens": 4957, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.11920292515117029, "lm_q1q2_score": 0.052648714235532766}}
{"text": "using Pkg\nPkg.add(\"IJulia\")\nPkg.add(\"DifferentialEquations\")\nPkg.add(\"Plots\")\nPkg.add(\"Interact\")\nPkg.add(\"Mux\")\nPkg.add(\"Blink\")\nPkg.add(PackageSpec(name=\"GenericSVD\",rev=\"master\"));\nPkg.add(\"PyCall\")\nPkg.add(\"ForwardDiff\")\n\nPkg.activate(\".\")\nPkg.add(\"IJulia\")\nPkg.add(\"DifferentialEquations\")\nPkg.add(\"Plots\")\nPkg.add(\"Interact\")\nPkg.add(\"Mux\")\nPkg.add(\"Blink\")\nPkg.add(PackageSpec(name=\"GenericSVD\",rev=\"master\"));\nPkg.add(\"PyCall\")\nPkg.add(\"ForwardDiff\")\nPkg.activate()\n", "meta": {"hexsha": "33313941dfc0e771731b95bcef7d0e39da8155b1", "size": 474, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "install.jl", "max_stars_repo_name": "raphbacher/julia-intro-bio", "max_stars_repo_head_hexsha": "d4897b1d3e5a6447a3e634d0b9d30fa319956765", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "install.jl", "max_issues_repo_name": "raphbacher/julia-intro-bio", "max_issues_repo_head_hexsha": "d4897b1d3e5a6447a3e634d0b9d30fa319956765", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "install.jl", "max_forks_repo_name": "raphbacher/julia-intro-bio", "max_forks_repo_head_hexsha": "d4897b1d3e5a6447a3e634d0b9d30fa319956765", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6086956522, "max_line_length": 53, "alphanum_fraction": 0.7088607595, "num_tokens": 163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44167300566462553, "lm_q2_score": 0.11920291732853862, "lm_q1q2_score": 0.05264871078048752}}
{"text": "println(uppercase(\"julia123 \") * lowercase(\"LOWER\"))\n\nstr = \"SEARCHING FOR CHARACTERS AND SUBSTRINGS\"\ntitlecase(str)\n\n'!' \u2208 str\n\nstr = \"Hello, World! These are my words.\"\npattern = \"wor\"\nidx1 = findfirst(pattern, lowercase(str))\n\nidx2 = findnext(pattern, lowercase(str), idx1[end])\n\nprintln(str)\nprintln(replace(str, \", World\"=>\" there\"))\n\nparse(Int, \"1234\")\n\nparse(Int, \"011010\", base = 2)\n\nparse(Float64, \"1.2e-3\")\n\nstrings = [\"123.4\", \"4590.12\", \"0.456\"]\nparse.(Float64, strings)\n", "meta": {"hexsha": "fcb0bff1be1554bc1c95676d71b3047fdeb1133f", "size": 483, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "textbook/_build/jupyter_execute/content/Strings_and_File_Processing/String_Functions.jl", "max_stars_repo_name": "NoseKnowsAll/NoseKnowsAll.github.io", "max_stars_repo_head_hexsha": "b2cff3e33cc2087770fb4aecb38b7925ad8d6e5a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "textbook/_build/jupyter_execute/content/Strings_and_File_Processing/String_Functions.jl", "max_issues_repo_name": "NoseKnowsAll/NoseKnowsAll.github.io", "max_issues_repo_head_hexsha": "b2cff3e33cc2087770fb4aecb38b7925ad8d6e5a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "textbook/_build/jupyter_execute/content/Strings_and_File_Processing/String_Functions.jl", "max_forks_repo_name": "NoseKnowsAll/NoseKnowsAll.github.io", "max_forks_repo_head_hexsha": "b2cff3e33cc2087770fb4aecb38b7925ad8d6e5a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.32, "max_line_length": 52, "alphanum_fraction": 0.66873706, "num_tokens": 146, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.44167299096624174, "lm_q2_score": 0.11920291889306492, "lm_q1q2_score": 0.05264870971940631}}
{"text": "\"\"\"\n    EasyPlot\n\nMasked APIs of plotting; based on PyPlot\n\"\"\"\nmodule EasyPlot\n   import PyPlot  # based on PyPlot\n\n# -----------------------------------------------------------------------------\n# a public Dict of the attributes of fonts; modify a copy of it in functions\npublic_fontdict = Dict(\n   \"family\" => \"Times New Roman\",\n   \"weight\" => \"normal\",\n   \"color\" => \"black\",\n   \"size\" => 16\n)\n\n\n\n# ----------\n\"\"\"\n    Plot_SteadyState( t::Int, Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ; outpdf::Union{Nothing,String} = nothing )\n\nplot 4 sub-plots: capital distribution, consumption distribution, labor distribution, asset & UEBMI-Indi distribution;\nthe keyword parameter outpdf= indicates whether to output to a pdf figure & the file directory,\nit is nothing (\"not output\") by default;\n\"\"\"\nfunction Plot_SteadyState( t::Int, Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ;\n      outpdf::Union{Nothing,String} = nothing, picsize::Tuple = (12,8) )\n   # define an age-based x-axis\n   local Xval = env.START_AGE : env.START_AGE + env.S - 1\n   # plotting\n   PyPlot.figure( figsize = picsize )\n   PyPlot.subplot(2,2,1)\n      PyPlot.plot(Xval, Dst[:\ud835\udc9c][t,:] .* Ps[:N][t,:] )\n      PyPlot.xlabel(\"Age\"); PyPlot.ylabel(\"k Distribution among ages\"); PyPlot.grid(true)\n   PyPlot.subplot(2,2,2)\n      PyPlot.plot(Xval, Dst[:c][t,:] .* Ps[:N][t,:] )\n      PyPlot.xlabel(\"Age\"); PyPlot.ylabel(\"Consumption Distribution\"); PyPlot.grid(true)\n   PyPlot.subplot(2,2,3)\n      PyPlot.plot(Xval[1:env.Sr], Dst[:Lab][t,:] .* Ps[:N][t,1:env.Sr] )\n      PyPlot.xlabel(\"Age\"); PyPlot.ylabel(\"Labor Distribution\"); PyPlot.grid(true)\n   PyPlot.subplot(2,2,4)\n      PyPlot.plot(Xval, Dst[:a][t,:] .* Ps[:N][t,:] )\n      PyPlot.plot(Xval, Dst[:\u03a6][t,:] .* Ps[:N][t,:] )\n      PyPlot.legend([\"Asset Distri\",\"UEBMI-Indi Distri\"]); PyPlot.grid(true)\n   PyPlot.tight_layout()\n\n   if isa(outpdf, String)\n   PyPlot.savefig( outpdf, format = \"pdf\" )\n   end\n\n   return nothing\nend\n# ----------\n\"\"\"\n   Plot_PerformProfile( PerfLog::Dict ; startiter::Int = 1 )\n\nPlots for the Dict of performance/convergence returned by EasySearch.Transition!(ReturnLog=true);\nstartiter= receives an integer indicating which round to start to plot;\nreturns nothing\n\"\"\"\nfunction Plot_PerformProfile( PerfLog::Dict ; startiter::Int = 1 )\n   local XVAL = startiter:length(PerfLog[:K])\n   PyPlot.figure( figsize = (8,4) )\n   PyPlot.subplot(1,2,1)\n      PyPlot.plot( XVAL, PerfLog[:K][XVAL], linewidth = 1.0 )\n      PyPlot.title(\"Capital (K)\")\n      PyPlot.xlabel(\"Iteration Round\")\n      PyPlot.ylabel(\"Relative Error (%)\")\n      PyPlot.grid(true)\n   PyPlot.subplot(1,2,2)\n      PyPlot.plot( XVAL, PerfLog[:L][XVAL], linewidth = 1.0 )\n      PyPlot.title(\"Labor (L)\")\n      PyPlot.xlabel(\"Iteration Round\")\n      PyPlot.grid(true)\n   PyPlot.suptitle(\"Convergence Profile\")\n   # nominal returns\n   return nothing\nend\n# ----------\n\"\"\"\n   Plot_Transition( t::Int, Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ; outpdf::Union{Nothing,String} = nothing, picsize::Tuple = (12,8) )\n\n\nthe keyword parameter outpdf= indicates whether to output to a pdf figure & the file directory,\nit is nothing (\"not output\") by default;\n\nPlots:\n1. Y,   \u0394Y/Y,   LI/Y,   \u0394(LI/Y)/(LI/Y)\n2. r,   w\u0304,      K,      L\n3. C,   I,      G,      SubstituionRate(Pension)\n\"\"\"\nfunction Plot_Transition( Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ;\n   YearRange::Tuple = ( env.START_YEAR, env.START_YEAR + env.T - 1 ),  # the range of years to plot\n   LineWidth::Float64 = 1.0,  # the width of lines to plot\n   outpdf::Union{Nothing,String} = nothing, picsize::Tuple = (12,8) )\n   # -----------------\n   # assertion: years\n   @assert( env.START_YEAR <= YearRange[1] < YearRange[2] < (env.START_YEAR + env.T - 1)  ,  \"out of the range of years! Maximum T-1 years\" )\n   # prepare the values of X axis\n   local tmpDisplaceYear = 1 - env.START_YEAR  # the shift to convert real years to index\n   local XYEAR = YearRange[1]:YearRange[2]  # the years to plot on x-axis\n   local XLOC = XYEAR .+ tmpDisplaceYear  # the index to slice data\n\n   # prepare the growth rate of LI/Y\n   local LI2Ygrowth = (Dt[:LI2Y][2:env.T] ./ Dt[:LI2Y][1:env.T-1] .- 1.0) .* 100\n\n   # maximum layout\n   local tmpLayout = (3,4)\n   # Plotting\n   PyPlot.figure( figsize = picsize )\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 1)\n      PyPlot.plot( XYEAR, Dt[:Y][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"GDP\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 2)\n      PyPlot.plot( XYEAR, Dt[:GDPgrowth][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"GDP Growth Rate (%)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 3)\n      PyPlot.plot( XYEAR, Dt[:LI2Y][XLOC] .* 100, linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"UEBMI-POOL Gaps / GDP (%)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 4)\n      PyPlot.plot( XYEAR, LI2Ygrowth[XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Growth of UEBMI-POOL Gaps / GDP (%)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 5)\n      PyPlot.plot( XYEAR, Dt[:r][XLOC] .* 100, linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Net Interest Rate (%)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 6)\n      PyPlot.plot( XYEAR, Dt[:w\u0304][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Average Wage Level\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 7)\n      PyPlot.plot( XYEAR, Dt[:K][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Aggregated Capital (K)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 8)\n      PyPlot.plot( XYEAR, Dt[:L][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Aggregated Labor (L)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 9)\n      PyPlot.plot( XYEAR, Dt[:C][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Aggregated Consumption (C)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 10)\n      PyPlot.plot( XYEAR, Dt[:I][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Aggregated Investment (I)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 11)\n      PyPlot.plot( XYEAR, Dt[:G][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Government Purchase (G)\")\n      # ------------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 12)\n      PyPlot.plot( XYEAR, Dt[:SubstiRat][XLOC] .* 100, linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Substitution Rate of Pension (%)\")\n\n      # tight layout\n      PyPlot.tight_layout()\n\n      # output\n      if isa(outpdf, String)\n      PyPlot.savefig( outpdf, format = \"pdf\" )\n      end\n\n   return nothing\nend\n# --------------\n\"\"\"\n   Plot_Calibrate( Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ;\n      YearRange::Tuple = ( env.START_YEAR, env.START_YEAR + env.T - 1 ),  # the range of years to plot\n      LineWidth::Float64 = 1.0,  # the width of lines to plot\n      outpdf::Union{Nothing,String} = nothing, picsize::Tuple = (12,8) )\n\nplots key indicators in calibration:\n   1. LI/GDP\n   2. LI/pool account expenditure\n   3. LI/pool account incomes\n\"\"\"\nfunction Plot_Calibrate( Dt::Dict, Dst::Dict, Pt::Dict, Ps::Dict, Pc::Dict, env::NamedTuple ;\n   YearRange::Tuple = ( env.START_YEAR, env.START_YEAR + env.T - 1 ),  # the range of years to plot\n   LineWidth::Float64 = 1.0,  # the width of lines to plot\n   outpdf::Union{Nothing,String} = nothing, picsize::Tuple = (12,8), tmpLayout::Tuple = (1,2),\n   GlobalFontSize::Int = 12 )\n   #  ---------------\n   # assertion: years\n   @assert( env.START_YEAR <= YearRange[1] < YearRange[2] < (env.START_YEAR + env.T - 1)  ,  \"out of the range of years! Maximum T-1 years\" )\n   # prepare the values of X axis\n   local tmpDisplaceYear = 1 - env.START_YEAR  # the shift to convert real years to index\n   local XYEAR = YearRange[1]:YearRange[2]  # the years to plot on x-axis\n   local XLOC = XYEAR .+ tmpDisplaceYear  # the index to slice data\n\n   # prepare the growth rate of LI/Y\n   local LI2Ygrowth = (Dt[:LI2Y][2:env.T] ./ Dt[:LI2Y][1:env.T-1] .- 1.0) .* 100\n\n   # maximum layout\n   # local tmpLayout = (1,2)\n   # Plotting\n   PyPlot.figure( figsize = picsize )\n      # PyPlot.subplot(tmpLayout[1],tmpLayout[2], 1)\n      # PyPlot.plot( XYEAR, 100.0 .* Dt[:LI][XLOC] ./ Dt[:Y][XLOC], linewidth = LineWidth )\n      #    PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n      #    PyPlot.ylabel(\"LI/Y (%)\")\n      # -----\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 1)\n      PyPlot.plot( XYEAR, 100.0 .* Dt[:LI][XLOC] ./ Dt[:AggPoolExp][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Percentage (%)\")\n      # ------\n      PyPlot.subplot(tmpLayout[1],tmpLayout[2], 2)\n      PyPlot.plot( XYEAR, 100.0 .* Dt[:LI][XLOC] ./ Dt[:AggPoolIn][XLOC], linewidth = LineWidth )\n         PyPlot.xlabel(\"Year\"); PyPlot.grid(true)\n         PyPlot.ylabel(\"Percentage (%)\")\n\n      # tight layout\n      PyPlot.tight_layout()\n\n      # output\n      if isa(outpdf, String)\n      PyPlot.savefig( outpdf, format = \"pdf\" )\n      end\n\n\nend  # function ends\n#  -----------\n\n\n\n\n\n\nend  # module ends\n#\n", "meta": {"hexsha": "594e3e213e98874bd0636b4842ffcae96c685439", "size": 9851, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/EasyPlot.jl", "max_stars_repo_name": "Clpr/OLG4UEBMI", "max_stars_repo_head_hexsha": "53b7e0afab1490e3eba34455c06cbc74277a5953", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/EasyPlot.jl", "max_issues_repo_name": "Clpr/OLG4UEBMI", "max_issues_repo_head_hexsha": "53b7e0afab1490e3eba34455c06cbc74277a5953", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/EasyPlot.jl", "max_forks_repo_name": "Clpr/OLG4UEBMI", "max_forks_repo_head_hexsha": "53b7e0afab1490e3eba34455c06cbc74277a5953", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-16T02:59:22.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-16T02:59:22.000Z", "avg_line_length": 39.562248996, "max_line_length": 163, "alphanum_fraction": 0.6078570703, "num_tokens": 2937, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.10521054091152958, "lm_q1q2_score": 0.05260527045576479}}
{"text": "\"\"\"\n# Plotting\n\n```julia\nplot(ts::TS, cols::Vector{Int} = collect(1:TSx.ncol(ts)))\nplot(ts::TS, cols::Vector{T}) where {T<:Union{String, Symbol}}\nplot(ts::TS, cols::T) where {T<:Union{Int, String, Symbol}}\n```\n\nPlots a TS object with the index on the x-axis and selected `cols` on\nthe y-axis. By default, plot all the columns. Columns can be selected\nusing Int indexes, String(s), or Symbol(s).\n\n# Example\n```jldoctest; setup = :(using TSx, DataFrames, Dates, Plots, Random, Statistics)\njulia> using Random;\njulia> random(x) = rand(MersenneTwister(123), x);\njulia> dates = Date(\"2022-01-01\"):Month(1):Date(\"2022-01-01\")+Month(11);\n\njulia> df = DataFrame(Index = dates,\n        val1 = random(12),\n        val2 = random(12),\n        val3 = random(12));\n\njulia> ts = TS(df)\njulia> show(ts)\n(12 x 3) TS with Dates.Date Index\n\n Index       val1        val2        val3\n Date        Float64     Float64     Float64\n\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n 2022-01-01  -0.319954    0.974594   -0.552977\n 2022-02-01  -0.0386735  -0.171675    0.779539\n 2022-03-01   1.67678    -1.75251     0.820462\n 2022-04-01   1.69702    -0.0130037   1.0507\n 2022-05-01   0.992128    0.76957    -1.28008\n 2022-06-01  -0.315461   -0.543976   -0.117256\n 2022-07-01  -1.18952    -1.12867    -0.0829082\n 2022-08-01   0.159595    0.450044   -0.231828\n 2022-09-01   0.501436    0.265327   -0.948532\n 2022-10-01  -2.10516    -1.11489     0.285194\n 2022-11-01  -0.781082   -1.20202    -0.639953\n 2022-12-01  -0.169184    1.34879     1.33361\n\n\njulia> using Plots\n\njulia> # plot(ts)\n\n# plot first 6 rows with selected columns\njulia> # plot(ts[1:6], [:val1, :val3]);\n\n# plot columns 1 and 2 on a specified window size\njulia> # plot(ts, [1, 2], size=(600, 400));\n```\n\"\"\"\n@recipe function f(ts::TS, cols::Vector{Int} = collect(1:TSx.ncol(ts)))\n    seriestype := :line\n    size --> (1200, 1200)\n    xlabel --> :Index\n    ylabel --> join(TSx.names(ts)[cols], \", \")\n    legend := true\n    label := permutedims(TSx.names(ts)[cols])\n    (TSx.index(ts), Matrix(ts.coredata[!, cols.+1])) # increment to account for Index\nend\n\n@recipe function f(ts::TS, cols::Vector{T}) where {T<:Union{String, Symbol}}\n    colindices = [DataFrames.columnindex(ts.coredata, i) for i in cols]\n    colindices .-= 1            # decrement to account for Index\n    (ts, colindices)\nend\n\n@recipe function f(ts::TS, cols::T) where {T<:Union{Int, String, Symbol}}\n    (ts, [cols])\nend\n", "meta": {"hexsha": "ddfbe121747a17bf13fbac7039c27f13610a174c", "size": 2427, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plot.jl", "max_stars_repo_name": "xKDR/TSx.jl", "max_stars_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/plot.jl", "max_issues_repo_name": "xKDR/TSx.jl", "max_issues_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/plot.jl", "max_forks_repo_name": "xKDR/TSx.jl", "max_forks_repo_head_hexsha": "82189a7475159b3c1ded8448c548e79b0e14b25b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9342105263, "max_line_length": 85, "alphanum_fraction": 0.6126905645, "num_tokens": 901, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10521054091152958, "lm_q1q2_score": 0.05260527045576479}}
{"text": "function to_rna(dna)\n    mapping = Dict('G' => 'C', 'C' => 'G', 'T' => 'A', 'A' => 'U')\n    !issubset(Set(collect(dna)), keys(mapping)) && throw(ErrorException(\"Invalid DNA\"))\n    map(c->mapping[c], dna)\nend\n\n", "meta": {"hexsha": "8f8eae5d46669231f004701d3d1de45e6f9a4700", "size": 209, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "rna-transcription/rna-transcription.jl", "max_stars_repo_name": "koladilip/julia-exercism", "max_stars_repo_head_hexsha": "1e67c319ae866a26365da8380659724c5cd472fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "rna-transcription/rna-transcription.jl", "max_issues_repo_name": "koladilip/julia-exercism", "max_issues_repo_head_hexsha": "1e67c319ae866a26365da8380659724c5cd472fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rna-transcription/rna-transcription.jl", "max_forks_repo_name": "koladilip/julia-exercism", "max_forks_repo_head_hexsha": "1e67c319ae866a26365da8380659724c5cd472fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8571428571, "max_line_length": 87, "alphanum_fraction": 0.5502392344, "num_tokens": 72, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4532618627863437, "lm_q2_score": 0.11596071978154188, "lm_q1q2_score": 0.052560571858226886}}
{"text": "\"\"\"\n    MyAwesomePackage\n\nHere you are, you found an awesome package :-)\n\n\"\"\"\nmodule MyAwesomePackage\n\nexport plusTwo\n\n\n\"\"\"\n    plusTwo(x)\n\nSum the numeric \"2\" to whatever it receives as input\n\nA more detailed explanation can go here, altought I guess it is not needed in this case\n\n# Arguments\n* `x`: The amount to which we want to add 2\n\n# Notes\n* Notes can go here\n\n# Examples\n```julia\njulia> five = plusTwo(3)\n5\n```\n\"\"\"\nplusTwo(x) = return x+2\n\nend # module\n", "meta": {"hexsha": "5cb17c4df2a4b1fd1c0118aebb4c354a3b7de903", "size": 462, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MyAwesomePackage.jl", "max_stars_repo_name": "sylvaticus/MyAwesomePackage", "max_stars_repo_head_hexsha": "c4c49b7d830dc0c98f2e825e9f50a88c1c7dec4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-08T11:02:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-01T04:09:52.000Z", "max_issues_repo_path": "src/MyAwesomePackage.jl", "max_issues_repo_name": "sylvaticus/MyAwesomePackage", "max_issues_repo_head_hexsha": "c4c49b7d830dc0c98f2e825e9f50a88c1c7dec4e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-25T10:38:02.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-22T01:10:54.000Z", "max_forks_repo_path": "src/MyAwesomePackage.jl", "max_forks_repo_name": "sylvaticus/MyAwesomePackage", "max_forks_repo_head_hexsha": "c4c49b7d830dc0c98f2e825e9f50a88c1c7dec4e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-05-04T12:46:42.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-08T01:16:16.000Z", "avg_line_length": 13.5882352941, "max_line_length": 87, "alphanum_fraction": 0.6861471861, "num_tokens": 129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.35936413143782797, "lm_q2_score": 0.14608725076600915, "lm_q1q2_score": 0.05249851798566705}}
{"text": "# This file was generated, do not modify it. # hide\ncoerce!(df, :zipcode => Multiclass)\ndf.isrenovated  = @. !iszero(df.yr_renovated)\ndf.has_basement = @. !iszero(df.sqft_basement)\nschema(df)", "meta": {"hexsha": "d49feaf4d5359518749282cfa03c2a305350a6a7", "size": 191, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/end-to-end/HouseKingCounty/code/ex3.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "__site/assets/end-to-end/HouseKingCounty/code/ex3.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "__site/assets/end-to-end/HouseKingCounty/code/ex3.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 38.2, "max_line_length": 51, "alphanum_fraction": 0.722513089, "num_tokens": 66, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4225046348141882, "lm_q2_score": 0.1242130051100335, "lm_q1q2_score": 0.052480570363187595}}
{"text": "\n# mapobs\n\nstruct MappedData{F,D}\n    f::F\n    data::D\nend\n\nBase.show(io::IO, data::MappedData) = print(io, \"mapobs($(data.f), $(summary(data.data)))\")\nBase.show(io::IO, data::MappedData{F,<:AbstractArray}) where {F} =\n    print(io, \"mapobs($(data.f), $(ShowLimit(data.data, limit=80)))\")\nLearnBase.nobs(data::MappedData) = nobs(data.data)\nLearnBase.getobs(data::MappedData, idx::Int) = data.f(getobs(data.data, idx))\nLearnBase.getobs(data::MappedData, idxs::AbstractVector) = data.f.(getobs(data.data, idxs))\n\n\n\"\"\"\n    mapobs(f, data)\n\nLazily map `f` over the observations in a data container `data`.\n\n```julia\ndata = 1:10\ngetobs(data, 8) == 8\nmdata = mapobs(-, data)\ngetobs(mdata, 8) == -8\n```\n\"\"\"\nmapobs(f, data) = MappedData(f, data)\nmapobs(f::typeof(identity), data) = data\n\n\n\"\"\"\n    mapobs(fs, data)\n\nLazily map each function in tuple `fs` over the observations in data container `data`.\nReturns a tuple of transformed data containers.\n\"\"\"\nmapobs(fs::Tuple, data) = Tuple(mapobs(f, data) for f in fs)\n\n\nstruct NamedTupleData{TData,F}\n    data::TData\n    namedfs::NamedTuple{F}\nend\n\nLearnBase.nobs(data::NamedTupleData) = nobs(getfield(data, :data))\n\nfunction LearnBase.getobs(data::NamedTupleData{TData,F}, idx::Int) where {TData,F}\n    obs = getobs(getfield(data, :data), idx)\n    namedfs = getfield(data, :namedfs)\n    return NamedTuple{F}(f(obs) for f in namedfs)\nend\n\nBase.getproperty(data::NamedTupleData, field::Symbol) =\n    mapobs(getproperty(getfield(data, :namedfs), field), getfield(data, :data))\n\nBase.show(io::IO, data::NamedTupleData) =\n    print(io, \"mapobs($(getfield(data, :namedfs)), $(getfield(data, :data)))\")\n\n\"\"\"\n    mapobs(namedfs::NamedTuple, data)\n\nMap a `NamedTuple` of functions over `data`, turning it into a data container\nof `NamedTuple`s. Field syntax can be used to select a column of the resulting\ndata container.\n\n```julia\ndata = 1:10\nnameddata = mapobs((x = sqrt, y = log), data)\ngetobs(nameddata, 10) == (x = sqrt(10), y = log(10))\ngetobs(nameddata.x, 10) == sqrt(10)\n```\n\"\"\"\nfunction mapobs(namedfs::NamedTuple, data)\n    return NamedTupleData(data, namedfs)\nend\n\n# filterobs\n\n\"\"\"\n    filterobs(f, data)\n\nReturn a subset of data container `data` including all indices `i` for\nwhich `f(getobs(data, i)) === true`.\n\n```julia\ndata = 1:10\nnobs(data) == 10\nfdata = filterobs(>(5), data)\nnobs(fdata) == 5\n```\n\"\"\"\nfunction filterobs(f, data; iterfn = _iterobs)\n    return datasubset(data, [i for (i, obs) in enumerate(iterfn(data)) if f(obs)])\nend\n\n_iterobs(data) = [getobs(data, i) for i = 1:nobs(data)]\n\n\n# groupobs\n\n\"\"\"\n    groupobs(f, data)\n\nSplit data container data `data` into different data containers, grouping\nobservations by `f(obs)`.\n\n```julia\ndata = -10:10\ndatas = groupobs(>(0), data)\nlength(datas) == 2\n```\n\"\"\"\nfunction groupobs(f, data)\n    groups = Dict{Any,Vector{Int}}()\n    for i = 1:nobs(data)\n        group = f(getobs(data, i))\n        if !haskey(groups, group)\n            groups[group] = [i]\n        else\n            push!(groups[group], i)\n        end\n    end\n    return Dict(group => datasubset(data, idxs) for (group, idxs) in groups)\nend\n\n# joinobs\n\nstruct JoinedData{T,N}\n    datas::NTuple{N,T}\n    ns::NTuple{N,Int}\nend\n\nJoinedData(datas) = JoinedData(datas, nobs.(datas))\n\nLearnBase.nobs(data::JoinedData) = sum(data.ns)\nfunction LearnBase.getobs(data::JoinedData, idx)\n    for (i, n) in enumerate(data.ns)\n        if idx <= n\n            return getobs(data.datas[i], idx)\n        else\n            idx -= n\n        end\n    end\nend\n\n\"\"\"\n    joinobs(datas...)\n\nConcatenate data containers `datas`.\n\n```julia\ndata1, data2 = 1:10, 11:20\njdata = joinobs(data1, data2)\ngetobs(jdata, 15) == 15\n```\n\"\"\"\njoinobs(datas...) = JoinedData(datas)\n\n\n# ## Tests\n\n@testset \"Data container transformations\" begin\n    @testset \"mapobs\" begin\n        data = 1:10\n        mdata = mapobs(-, data)\n        @test getobs(mdata, 8) == -8\n\n        mdata2 = mapobs((-, x -> 2x), data)\n        @test getobs(mdata2, 8) == (-8, 16)\n\n        nameddata = mapobs((x = sqrt, y = log), data)\n        @test getobs(nameddata, 10) == (x = sqrt(10), y = log(10))\n        @test getobs(nameddata.x, 10) == sqrt(10)\n    end\n\n    @testset \"filterobs\" begin\n        data = 1:10\n        fdata = filterobs(>(5), data)\n        @test nobs(fdata) == 5\n    end\n\n    @testset \"groupobs\" begin\n        data = -10:10\n        datas = groupobs(>(0), data)\n        @test length(datas) == 2\n    end\n\n    @testset \"joinobs\" begin\n        data1, data2 = 1:10, 11:20\n        jdata = joinobs(data1, data2)\n        @test getobs(jdata, 15) == 15\n    end\nend\n", "meta": {"hexsha": "cec32cbc65c4da8c54be5c3c24a4181c5a2a5431", "size": 4561, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/transformations.jl", "max_stars_repo_name": "Chandu-4444/FastAI.jl", "max_stars_repo_head_hexsha": "290ae32e7b1ca502fc93e1a86aa250658cf1f72a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 448, "max_stars_repo_stars_event_min_datetime": "2020-08-18T00:12:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T13:29:38.000Z", "max_issues_repo_path": "src/datasets/transformations.jl", "max_issues_repo_name": "Chandu-4444/FastAI.jl", "max_issues_repo_head_hexsha": "290ae32e7b1ca502fc93e1a86aa250658cf1f72a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 72, "max_issues_repo_issues_event_min_datetime": "2020-08-18T12:35:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:50:04.000Z", "max_forks_repo_path": "src/datasets/transformations.jl", "max_forks_repo_name": "Chandu-4444/FastAI.jl", "max_forks_repo_head_hexsha": "290ae32e7b1ca502fc93e1a86aa250658cf1f72a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 40, "max_forks_repo_forks_event_min_datetime": "2020-08-17T23:59:47.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T17:48:12.000Z", "avg_line_length": 23.2704081633, "max_line_length": 91, "alphanum_fraction": 0.6274939706, "num_tokens": 1437, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.42250463481418826, "lm_q2_score": 0.1242130018680137, "lm_q1q2_score": 0.05248056899341921}}
{"text": "VERSION < v\"0.7.0-beta2.199\" && __precompile__()\n\nmodule OffsetArrays\n\nusing Base: Indices, tail, @propagate_inbounds\n\nexport OffsetArray, OffsetVector\n\nstruct OffsetArray{T,N,AA<:AbstractArray} <: AbstractArray{T,N}\n    parent::AA\n    offsets::NTuple{N,Int}\nend\nOffsetVector{T,AA<:AbstractArray} = OffsetArray{T,1,AA}\n\nOffsetArray(A::AbstractArray{T,N}, offsets::NTuple{N,Int}) where {T,N} =\n    OffsetArray{T,N,typeof(A)}(A, offsets)\nOffsetArray(A::AbstractArray{T,N}, offsets::Vararg{Int,N}) where {T,N} =\n    OffsetArray(A, offsets)\n\nOffsetArray{T,N}(::UndefInitializer, inds::Indices{N}) where {T,N} =\n    OffsetArray{T,N,Array{T,N}}(Array{T,N}(undef, map(indexlength, inds)), map(indexoffset, inds))\nOffsetArray{T}(::UndefInitializer, inds::Indices{N}) where {T,N} = OffsetArray{T,N}(undef, inds)\nOffsetArray{T,N}(::UndefInitializer, inds::Vararg{AbstractUnitRange,N}) where {T,N} = OffsetArray{T,N}(undef, inds)\nOffsetArray{T}(::UndefInitializer, inds::Vararg{AbstractUnitRange,N}) where {T,N} = OffsetArray{T,N}(undef, inds)\nOffsetArray(A::AbstractArray{T,0}) where {T} = OffsetArray{T,0,typeof(A)}(A, ())\n\n# OffsetVector constructors\nOffsetVector(A::AbstractVector, offset) = OffsetArray(A, offset)\nOffsetVector{T}(::UndefInitializer, inds::AbstractUnitRange) where {T} = OffsetArray{T}(undef, inds)\n\n# deprecated constructors\nusing Base: @deprecate\n\n@deprecate OffsetArray(::Type{T}, inds::Vararg{UnitRange{Int},N}) where {T,N} OffsetArray{T}(undef, inds)\n@deprecate OffsetVector(::Type{T}, inds::AbstractUnitRange) where {T} OffsetVector{T}(undef, inds)\n@deprecate OffsetArray{T,N}(inds::Indices{N}) where {T,N} OffsetArray{T,N}(undef, inds)\n@deprecate OffsetArray{T}(inds::Indices{N}) where {T,N} OffsetArray{T}(undef, inds)\n@deprecate OffsetArray{T,N}(inds::Vararg{AbstractUnitRange,N}) where {T,N} OffsetArray{T,N}(undef, inds)\n@deprecate OffsetArray{T}(inds::Vararg{AbstractUnitRange,N}) where {T,N} OffsetArray{T}(undef, inds)\n@deprecate OffsetVector{T}(inds::AbstractUnitRange) where {T} OffsetVector{T}(undef, inds)\n\n# The next two are necessary for ambiguity resolution. Really, the\n# second method should not be necessary.\nOffsetArray(A::AbstractArray{T,0}, inds::Tuple{}) where {T} = OffsetArray{T,0,typeof(A)}(A, ())\nOffsetArray(A::AbstractArray{T,N}, inds::Tuple{}) where {T,N} = error(\"this should never be called\")\nfunction OffsetArray(A::AbstractArray{T,N}, inds::NTuple{N,AbstractUnitRange}) where {T,N}\n    lA = map(indexlength, axes(A))\n    lI = map(indexlength, inds)\n    lA == lI || throw(DimensionMismatch(\"supplied axes do not agree with the size of the array (got size $lA for the array and $lI for the indices\"))\n    OffsetArray(A, map(indexoffset, inds))\nend\nOffsetArray(A::AbstractArray{T,N}, inds::Vararg{AbstractUnitRange,N}) where {T,N} =\n    OffsetArray(A, inds)\n\nBase.IndexStyle(::Type{OA}) where {OA<:OffsetArray} = IndexStyle(parenttype(OA))\nparenttype(::Type{OffsetArray{T,N,AA}}) where {T,N,AA} = AA\nparenttype(A::OffsetArray) = parenttype(typeof(A))\n\nBase.parent(A::OffsetArray) = A.parent\n\nBase.eachindex(::IndexCartesian, A::OffsetArray) = CartesianIndices(axes(A))\nBase.eachindex(::IndexLinear, A::OffsetVector)   = axes(A, 1)\n\nBase.size(A::OffsetArray) = size(parent(A))\nBase.size(A::OffsetArray, d) = size(parent(A), d)\n\n# Implementations of axes and indices1. Since bounds-checking is\n# performance-critical and relies on axes, these are usually worth\n# optimizing thoroughly.\n@inline Base.axes(A::OffsetArray, d) =\n    1 <= d <= length(A.offsets) ? Base.Slice(axes(parent(A))[d] .+ A.offsets[d]) : (1:1)\n@inline Base.axes(A::OffsetArray) =\n    _axes(axes(parent(A)), A.offsets)  # would rather use ntuple, but see #15276\n@inline _axes(inds, offsets) =\n    (Base.Slice(inds[1] .+ offsets[1]), _axes(tail(inds), tail(offsets))...)\n_axes(::Tuple{}, ::Tuple{}) = ()\nBase.axes1(A::OffsetArray{T,0}) where {T} = 1:1  # we only need to specialize this one\n\nconst OffsetAxis = Union{Integer, UnitRange, Base.Slice{<:UnitRange}, Base.OneTo}\nfunction Base.similar(A::OffsetArray, ::Type{T}, dims::Dims) where T\n    B = similar(parent(A), T, dims)\nend\nfunction Base.similar(A::AbstractArray, ::Type{T}, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) where T\n    B = similar(A, T, map(indexlength, inds))\n    OffsetArray(B, map(indexoffset, inds))\nend\n\nBase.reshape(A::AbstractArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) =\n    OffsetArray(reshape(A, map(indexlength, inds)), map(indexoffset, inds))\n\n# Reshaping OffsetArrays can \"pop\" the original OffsetArray wrapper and return\n# an OffsetArray(reshape(...)) instead of an OffsetArray(reshape(OffsetArray(...)))\nBase.reshape(A::OffsetArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) =\n    OffsetArray(reshape(parent(A), map(indexlength, inds)), map(indexoffset, inds))\n# And for non-offset axes, we can just return a reshape of the parent directly\nBase.reshape(A::OffsetArray, inds::Tuple{Union{Integer,Base.OneTo},Vararg{Union{Integer,Base.OneTo}}}) = reshape(parent(A), inds)\nBase.reshape(A::OffsetArray, inds::Dims) = reshape(parent(A), inds)\n\nBase.similar(::Type{T}, shape::Tuple{OffsetAxis,Vararg{OffsetAxis}}) where {T<:AbstractArray} =\n    OffsetArray(T(undef, map(indexlength, shape)), map(indexoffset, shape))\n\nBase.fill(v, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(OffsetArray(Array{typeof(v), N}(undef, map(indexlength, inds)), map(indexoffset, inds)), v)\nBase.zeros(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =\n    fill!(OffsetArray(Array{T, N}(undef, map(indexlength, inds)), map(indexoffset, inds)), zero(T))\nBase.ones(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =\n    fill!(OffsetArray(Array{T, N}(undef, map(indexlength, inds)), map(indexoffset, inds)), one(T))\nBase.trues(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(OffsetArray(BitArray{N}(undef, map(indexlength, inds)), map(indexoffset, inds)), true)\nBase.falses(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =\n    fill!(OffsetArray(BitArray{N}(undef, map(indexlength, inds)), map(indexoffset, inds)), false)\n\n@inline @propagate_inbounds function Base.getindex(A::OffsetArray{T,N}, I::Vararg{Int,N}) where {T,N}\n    @boundscheck checkbounds(A, I...)\n    @inbounds ret = parent(A)[offset(A.offsets, I)...]\n    ret\nend\n@inline @propagate_inbounds function Base.getindex(A::OffsetVector, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds ret = parent(A)[offset(A.offsets, (i,))[1]]\n    ret\nend\n@inline @propagate_inbounds function Base.getindex(A::OffsetArray, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds ret = parent(A)[i]\n    ret\nend\n@inline @propagate_inbounds function Base.setindex!(A::OffsetArray{T,N}, val, I::Vararg{Int,N}) where {T,N}\n    @boundscheck checkbounds(A, I...)\n    @inbounds parent(A)[offset(A.offsets, I)...] = val\n    val\nend\n@inline @propagate_inbounds function Base.setindex!(A::OffsetVector, val, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds parent(A)[offset(A.offsets, (i,))[1]] = val\n    val\nend\n@inline @propagate_inbounds function Base.setindex!(A::OffsetArray, val, i::Int)\n    @boundscheck checkbounds(A, i)\n    @inbounds parent(A)[i] = val\n    val\nend\n\n### Convenience functions ###\n\nBase.fill(x, inds::Tuple{UnitRange,Vararg{UnitRange}}) =\n    fill!(OffsetArray{typeof(x)}(undef, inds), x)\n@inline Base.fill(x, ind1::UnitRange, inds::UnitRange...) = fill(x, (ind1, inds...))\n\n### Low-level utilities ###\n\n# Computing a shifted index (subtracting the offset)\n@inline offset(offsets::NTuple{N,Int}, inds::NTuple{N,Int}) where {N} =\n    (inds[1]-offsets[1], offset(Base.tail(offsets), Base.tail(inds))...)\noffset(::Tuple{}, ::Tuple{}) = ()\n\n# Support trailing 1s\n@inline offset(offsets::Tuple{Vararg{Int}}, inds::Tuple{Vararg{Int}}) =\n    (offset(offsets, Base.front(inds))..., inds[end])\noffset(offsets::Tuple{Vararg{Int}}, inds::Tuple{}) = error(\"inds cannot be shorter than offsets\")\n\nindexoffset(r::AbstractRange) = first(r) - 1\nindexoffset(i::Integer) = 0\nindexlength(r::AbstractRange) = length(r)\nindexlength(i::Integer) = i\n\n@eval @deprecate $(Symbol(\"@unsafe\")) $(Symbol(\"@inbounds\"))\n\nfunction Base.showarg(io::IO, a::OffsetArray, toplevel)\n    print(io, \"OffsetArray(\")\n    Base.showarg(io, parent(a), false)\n    if ndims(a) > 0\n        print(io, \", \")\n        printindices(io, axes(a)...)\n    end\n    print(io, ')')\n    toplevel && print(io, \" with eltype \", eltype(a))\nend\nprintindices(io::IO, ind1, inds...) =\n    (print(io, _unslice(ind1), \", \"); printindices(io, inds...))\nprintindices(io::IO, ind1) = print(io, _unslice(ind1))\n_unslice(x) = x\n_unslice(x::Base.Slice) = x.indices\n\nend # module\n", "meta": {"hexsha": "d72ecb1352bcc34f20bbd136e12e3893afa8c758", "size": 8680, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/OffsetArrays.jl", "max_stars_repo_name": "vchuravy/OffsetArrays.jl", "max_stars_repo_head_hexsha": "e814c7a77dd15d02e827d8084312124492046712", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/OffsetArrays.jl", "max_issues_repo_name": "vchuravy/OffsetArrays.jl", "max_issues_repo_head_hexsha": "e814c7a77dd15d02e827d8084312124492046712", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/OffsetArrays.jl", "max_forks_repo_name": "vchuravy/OffsetArrays.jl", "max_forks_repo_head_hexsha": "e814c7a77dd15d02e827d8084312124492046712", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.6666666667, "max_line_length": 149, "alphanum_fraction": 0.7026497696, "num_tokens": 2561, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.43398146480389854, "lm_q2_score": 0.12085324040654355, "lm_q1q2_score": 0.05244806629792947}}
{"text": "\"\"\"\n    frule([::RuleConfig,] (\u0394f, \u0394x...), f, x...)\n\nExpressing the output of `f(x...)` as `\u03a9`, return the tuple:\n\n    (\u03a9, \u0394\u03a9)\n\nThe second return value is the differential w.r.t. the output.\n\nIf no method matching `frule((\u0394f, \u0394x...), f, x...)` has been defined, then return `nothing`.\n\nExamples:\n\nunary input, unary output scalar function:\n\n```jldoctest frule\njulia> dself = NoTangent();\n\njulia> x = rand()\n0.8236475079774124\n\njulia> sinx, \u0394sinx = frule((dself, 1), sin, x)\n(0.7336293678134624, 0.6795498147167869)\n\njulia> sinx == sin(x)\ntrue\n\njulia> \u0394sinx == cos(x)\ntrue\n```\n\nUnary input, binary output scalar function:\n\n```jldoctest frule\njulia> sincosx, \u0394sincosx = frule((dself, 1), sincos, x);\n\njulia> sincosx == sincos(x)\ntrue\n\njulia> \u0394sincosx[1] == cos(x)\ntrue\n\njulia> \u0394sincosx[2] == -sin(x)\ntrue\n```\n\nNote that techically speaking julia does not have multiple output functions, just functions\nthat return a single output that is iterable, like a `Tuple`.\nSo this is actually a [`Tangent`](@ref):\n```jldoctest frule\njulia> \u0394sincosx\nTangent{Tuple{Float64, Float64}}(0.6795498147167869, -0.7336293678134624)\n```\n\nThe optional [`RuleConfig`](@ref) option allows specifying frules only for AD systems that\nsupport given features. If not needed, then it can be omitted and the `frule` without it\nwill be hit as a fallback. This is the case for most rules.\n\nSee also: [`rrule`](@ref), [`@scalar_rule`](@ref), [`RuleConfig`](@ref)\n\"\"\"\nfrule(\u0227rgs, f, ::Vararg{Any}) = nothing\n\n# if no config is present then fallback to config-less rules\nfrule(::RuleConfig, args...) = frule(args...)\n\n# Manual fallback for keyword arguments. Usually this would be generated by\n#\n#   frule(::Any, ::Vararg{Any}; kwargs...) = nothing\n#\n# However - the fallback method is so hot that we want to avoid any extra code\n# that would be required to have the automatically generated method package up\n# the keyword arguments (which the optimizer will throw away, but the compiler\n# still has to manually analyze). Manually declare this method with an\n# explicitly empty body to save the compiler that work.\nconst frule_kwfunc = Core.kwftype(typeof(frule)).instance\n(::typeof(frule_kwfunc))(::Any, ::typeof(frule), \u0227rgs, f, ::Vararg{Any}) = nothing\nfunction (::typeof(frule_kwfunc))(kws::Any, ::typeof(frule), ::RuleConfig, args...)\n    return frule_kwfunc(kws, frule, args...)\nend\n\n\"\"\"\n    rrule([::RuleConfig,] f, x...)\n\nExpressing `x` as the tuple `(x\u2081, x\u2082, ...)` and the output tuple of `f(x...)`\nas `\u03a9`, return the tuple:\n\n    (\u03a9, (\u03a9\u0304\u2081, \u03a9\u0304\u2082, ...) -> (s\u0304elf, x\u0304\u2081, x\u0304\u2082, ...))\n\nWhere the second return value is the the propagation rule or pullback.\nIt takes in differentials corresponding to the outputs (`x\u0304\u2081, x\u0304\u2082, ...`),\nand `s\u0304elf`, the internal values of the function itself (for closures)\n\nIf no method matching `rrule(f, xs...)` has been defined, then return `nothing`.\n\nExamples:\n\nunary input, unary output scalar function:\n\n```jldoctest\njulia> x = rand();\n\njulia> sinx, sin_pullback = rrule(sin, x);\n\njulia> sinx == sin(x)\ntrue\n\njulia> sin_pullback(1) == (NoTangent(), cos(x))\ntrue\n```\n\nbinary input, unary output scalar function:\n\n```jldoctest\njulia> x, y = rand(2);\n\njulia> hypotxy, hypot_pullback = rrule(hypot, x, y);\n\njulia> hypotxy == hypot(x, y)\ntrue\n\njulia> hypot_pullback(1) == (NoTangent(), (x / hypot(x, y)), (y / hypot(x, y)))\ntrue\n```\n\nThe optional [`RuleConfig`](@ref) option allows specifying rrules only for AD systems that\nsupport given features. If not needed, then it can be omitted and the `rrule` without it\nwill be hit as a fallback. This is the case for most rules.\n\nSee also: [`frule`](@ref), [`@scalar_rule`](@ref), [`RuleConfig`](@ref)\n\"\"\"\nrrule(::Any, ::Vararg{Any}) = nothing\n\n# if no config is present then fallback to config-less rules\nrrule(::RuleConfig, args...) = rrule(args...)\n\n# Manual fallback for keyword arguments. See above\nconst rrule_kwfunc = Core.kwftype(typeof(rrule)).instance\n(::typeof(rrule_kwfunc))(::Any, ::typeof(rrule), ::Any, ::Vararg{Any}) = nothing\nfunction (::typeof(rrule_kwfunc))(kws::Any, ::typeof(rrule), ::RuleConfig, args...)\n    return rrule_kwfunc(kws, rrule, args...)\nend\n", "meta": {"hexsha": "0abc8120506e2cb9195abbb8f82f9643d52713d5", "size": 4118, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rules.jl", "max_stars_repo_name": "pierre-haessig/ChainRulesCore.jl", "max_stars_repo_head_hexsha": "ea252f0651c11e1018c3fe79a6fe7e736eff71fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/rules.jl", "max_issues_repo_name": "pierre-haessig/ChainRulesCore.jl", "max_issues_repo_head_hexsha": "ea252f0651c11e1018c3fe79a6fe7e736eff71fa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/rules.jl", "max_forks_repo_name": "pierre-haessig/ChainRulesCore.jl", "max_forks_repo_head_hexsha": "ea252f0651c11e1018c3fe79a6fe7e736eff71fa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0, "max_line_length": 92, "alphanum_fraction": 0.6918406994, "num_tokens": 1270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4649015713733885, "lm_q2_score": 0.11279540479169524, "lm_q1q2_score": 0.05243876093135655}}
{"text": "# ---\n# title: 529. Minesweeper\n# id: problem529\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: Depth-first Search, Breadth-first Search\n# link: <https://leetcode.com/problems/minesweeper/description/>\n# hidden: true\n# ---\n# \n# Let's play the minesweeper game\n# ([Wikipedia](https://en.wikipedia.org/wiki/Minesweeper_\\(video_game\\)),\n# [online game](http://minesweeperonline.com))!\n# \n# You are given a 2D char matrix representing the game board. **' M'**\n# represents an **unrevealed** mine, **' E'** represents an **unrevealed** empty\n# square, **' B'** represents a **revealed** blank square that has no adjacent\n# (above, below, left, right, and all 4 diagonals) mines, **digit** ('1' to '8')\n# represents how many mines are adjacent to this **revealed** square, and\n# finally **' X'** represents a **revealed** mine.\n# \n# Now given the next click position (row and column indices) among all the\n# **unrevealed** squares ('M' or 'E'), return the board after revealing this\n# position according to the following rules:\n# \n#   1. If a mine ('M') is revealed, then the game is over - change it to **' X'**.\n#   2. If an empty square ('E') with **no adjacent mines** is revealed, then change it to revealed blank ('B') and all of its adjacent **unrevealed** squares should be revealed recursively.\n#   3. If an empty square ('E') with **at least one adjacent mine** is revealed, then change it to a digit ('1' to '8') representing the number of adjacent mines.\n#   4. Return the board when no more squares will be revealed.\n# \n# \n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: \n#     \n#     [['E', 'E', 'E', 'E', 'E'],\n#      ['E', 'E', 'M', 'E', 'E'],\n#      ['E', 'E', 'E', 'E', 'E'],\n#      ['E', 'E', 'E', 'E', 'E']]\n#     \n#     Click : [3,0]\n#     \n#     Output: \n#     \n#     [['B', '1', 'E', '1', 'B'],\n#      ['B', '1', 'M', '1', 'B'],\n#      ['B', '1', '1', '1', 'B'],\n#      ['B', 'B', 'B', 'B', 'B']]\n#     \n#     Explanation:\n#     ![](https://assets.leetcode.com/uploads/2018/10/12/minesweeper_example_1.png)\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: \n#     \n#     [['B', '1', 'E', '1', 'B'],\n#      ['B', '1', 'M', '1', 'B'],\n#      ['B', '1', '1', '1', 'B'],\n#      ['B', 'B', 'B', 'B', 'B']]\n#     \n#     Click : [1,2]\n#     \n#     Output: \n#     \n#     [['B', '1', 'E', '1', 'B'],\n#      ['B', '1', 'X', '1', 'B'],\n#      ['B', '1', '1', '1', 'B'],\n#      ['B', 'B', 'B', 'B', 'B']]\n#     \n#     Explanation:\n#     ![](https://assets.leetcode.com/uploads/2018/10/12/minesweeper_example_2.png)\n#     \n# \n# \n# \n# **Note:**\n# \n#   1. The range of the input matrix's height and width is [1,50].\n#   2. The click position will only be an unrevealed square ('M' or 'E'), which also means the input board contains at least one clickable square.\n#   3. The input board won't be a stage when game is over (some mines have been revealed).\n#   4. For simplicity, not mentioned rules should be ignored in this problem. For example, you **don 't** need to reveal all the unrevealed mines when the game is over, consider any cases that you will win the game or flag any squares.\n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "ecf09bf08078520f0c576880277c653913b225f6", "size": 3206, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/529.minesweeper.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/unresolved/529.minesweeper.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/unresolved/529.minesweeper.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 33.0515463918, "max_line_length": 235, "alphanum_fraction": 0.5611353712, "num_tokens": 1041, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490158620112276, "lm_q2_score": 0.11279540031810142, "lm_q1q2_score": 0.05243876052407598}}
{"text": "# This file was generated, do not modify it. # hide\nX = MLJ.table(X)\ny = categorical(y);", "meta": {"hexsha": "6ee05e30693a8882e3cfc5b7f399d15cc628cf52", "size": 88, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/isl/lab-9/code/ex4.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/isl/lab-9/code/ex4.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/isl/lab-9/code/ex4.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 29.3333333333, "max_line_length": 51, "alphanum_fraction": 0.6818181818, "num_tokens": 26, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46490157137338856, "lm_q2_score": 0.11279540031810142, "lm_q1q2_score": 0.05243875885157576}}
{"text": "\n@eval struct SiteType{T}\n  (f::Type{<:SiteType})() = $(Expr(:new, :f))\nend\n\n# Note that the complicated definition of\n# SiteType above is a workaround for performance\n# issues when creating parameterized types\n# in Julia 1.4 and 1.5-beta. Ideally we\n# can just use the following in the future:\n# struct SiteType{T}\n# end\n\n\"\"\"\nSiteType is a parameterized type which allows\nmaking Index tags into Julia types. Use cases\ninclude overloading functions such as `op`,\n`siteinds`, and `state` which generate custom\noperators, Index arrays, and IndexVals associated\nwith Index objects having a certain tag.\n\nTo make a SiteType type, you can use the string\nmacro notation: `SiteType\"MyTag\"`\n\nTo make a SiteType value or object, you can use\nthe notation: `SiteType(\"MyTag\")`\n\nThere are currently a few built-in site types\nrecognized by `ITensors.jl`. The system is easily extensible\nby users. To add new operators to an existing site type,\nyou can follow the instructions [here](http://itensor.org/docs.cgi?vers=julia&page=formulas/sitetype_extending).\nTo create new site types, you can follow the instructions\n[here](https://itensor.org/docs.cgi?vers=julia&page=formulas/sitetype_basic) and \n[here](https://itensor.org/docs.cgi?vers=julia&page=formulas/sitetype_qns).\n\nThe current built-in site types are:\n\n- `SiteType\"S=1/2\"` (or `SiteType\"S=\u00bd\"`)\n- `SiteType\"S=1\"`\n- `SiteType\"Fermion\"`\n- `SiteType\"tJ\"`\n- `SiteType\"Electron\"`\n\n# Examples\n\nTags on indices get turned into SiteTypes internally, and then\nwe search for overloads of functions like `op` and `siteind`.\nFor example:\n```julia\njulia> s = siteind(\"S=1/2\")\n(dim=2|id=862|\"S=1/2,Site\")\n\njulia> @show op(\"Sz\", s);\nop(s, \"Sz\") = ITensor ord=2\nDim 1: (dim=2|id=862|\"S=1/2,Site\")'\nDim 2: (dim=2|id=862|\"S=1/2,Site\")\nNDTensors.Dense{Float64,Array{Float64,1}}\n 2\u00d72\n 0.5   0.0\n 0.0  -0.5\n\njulia> @show op(\"Sx\", s);\nop(s, \"Sx\") = ITensor ord=2\nDim 1: (dim=2|id=862|\"S=1/2,Site\")'\nDim 2: (dim=2|id=862|\"S=1/2,Site\")\nNDTensors.Dense{Float64,Array{Float64,1}}\n 2\u00d72\n 0.0  0.5\n 0.5  0.0\n\njulia> @show op(\"Sy\", s);\nop(s, \"Sy\") = ITensor ord=2\nDim 1: (dim=2|id=862|\"S=1/2,Site\")'\nDim 2: (dim=2|id=862|\"S=1/2,Site\")\nNDTensors.Dense{Complex{Float64},Array{Complex{Float64},1}}\n 2\u00d72\n 0.0 + 0.0im  -0.0 - 0.5im\n 0.0 + 0.5im   0.0 + 0.0im\n\njulia> s = siteind(\"Electron\")\n(dim=4|id=734|\"Electron,Site\")\n\njulia> @show op(\"Nup\", s);\nop(s, \"Nup\") = ITensor ord=2\nDim 1: (dim=4|id=734|\"Electron,Site\")'\nDim 2: (dim=4|id=734|\"Electron,Site\")\nNDTensors.Dense{Float64,Array{Float64,1}}\n 4\u00d74\n 0.0  0.0  0.0  0.0\n 0.0  1.0  0.0  0.0\n 0.0  0.0  0.0  0.0\n 0.0  0.0  0.0  1.0\n```\n\nMany operators are available, for example:\n\n- `SiteType\"S=1/2\"`: `\"Sz\"`, `\"Sx\"`, `\"Sy\"`, `\"S+\"`, `\"S-\"`, ...\n- `SiteType\"Electron\"`: `\"Nup\"`, `\"Ndn\"`, `\"Nupdn\"`, `\"Ntot\"`, `\"Cup\"`, `\"Cdagup\"`, `\"Cdn\"`, `\"Cdagup\"`, `\"Sz\"`, `\"Sx\"`, `\"Sy\"`, `\"S+\"`, `\"S-\"`, ...\n- ...\n\nYou can view the source code for the internal SiteType definitions\nand operators that are defined [here](https://github.com/ITensor/ITensors.jl/tree/master/src/physics/site_types).\n\"\"\"\nSiteType(s::AbstractString) = SiteType{Tag(s)}()\n\nSiteType(t::Tag) = SiteType{t}()\n\ntag(::SiteType{T}) where {T} = T\n\nmacro SiteType_str(s)\n  SiteType{Tag(s)}\nend\n\n# Keep TagType defined for backwards\n# compatibility; will be deprecated later\nconst TagType = SiteType\nmacro TagType_str(s)\n  TagType{Tag(s)}\nend\n\n#---------------------------------------\n#\n# op system\n#\n#---------------------------------------\n\n@eval struct OpName{Name}\n  (f::Type{<:OpName})() = $(Expr(:new, :f))\nend\n\n# Note that the complicated definition of\n# OpName above is a workaround for performance\n# issues when creating parameterized types\n# in Julia 1.4 and 1.5-beta. Ideally we\n# can just use the following in the future:\n# struct OpName{Name}\n# end\n\n\"\"\"\nOpName is a parameterized type which allows\nmaking strings into Julia types for the purpose\nof representing operator names.\nThe main use of OpName is overloading the \n`ITensors.op!` method which generates operators \nfor indices with certain tags such as \"S=1/2\".\n\nTo make a OpName type, you can use the string\nmacro notation: `OpName\"MyTag\"`. \n\nTo make an OpName value or object, you can use\nthe notation: `OpName(\"myop\")`\n\"\"\"\nOpName(s::AbstractString) = OpName{Symbol(s)}()\nOpName(s::Symbol) = OpName{s}()\nname(::OpName{N}) where {N} = N\n\nmacro OpName_str(s)\n  OpName{Symbol(s)}\nend\n\n# Default implementations of op and op!\nop(::OpName, ::SiteType; kwargs...) = nothing\nop(::OpName, ::SiteType, ::Index...; kwargs...) = nothing\nop(::OpName, ::SiteType, ::SiteType,\n   sitetypes_inds::Union{SiteType, Index}...; kwargs...) = nothing\nop!(::ITensor, ::OpName, ::SiteType, ::Index...; kwargs...) = nothing \nop!(::ITensor, ::OpName, ::SiteType, ::SiteType,\n    sitetypes_inds::Union{SiteType, Index}...; kwargs...) = nothing \n\n# Deprecated version, for backwards compatibility\nop(::SiteType, ::Index, ::AbstractString; kwargs...) = nothing\n\nfunction _sitetypes(ts::TagSet)\n  Ntags = length(ts)\n  return SiteType[SiteType(ts[n]) for n in 1:Ntags]\nend\n\n_sitetypes(i::Index) = _sitetypes(tags(i))\n\n\"\"\"\n    op(opname::String, s::Index; kwargs...)\n\nReturn an ITensor corresponding to the operator\nnamed `opname` for the Index `s`. The operator\nis constructed by calling an overload of either\nthe `op` or `op!` methods which take a `SiteType`\nargument that corresponds to one of the tags of\nthe Index `s` and an `OpName\"opname\"` argument\nthat corresponds to the input operator name.\n\nOperator names can be combined using the `\"*\"`\nsymbol, for example `\"S+*S-\"` or `\"Sz*Sz*Sz\"`. \nThe result is an ITensor made by forming each operator \nthen contracting them together in a way corresponding\nto the usual operator product or matrix multiplication.\n\nThe `op` system is used by the AutoMPO\nsystem to convert operator names into ITensors,\nand can be used directly such as for applying\noperators to MPS.\n\n# Example\n```julia\ns = Index(2, \"Site,S=1/2\")\nSz = op(\"Sz\", s)\n```\n\"\"\"\nfunction op(name::AbstractString,\n            s::Index...;\n            kwargs...)\n\n  name = strip(name)\n\n  # TODO: filter out only commons tags\n  # if there are multiple indices\n  commontags_s = commontags(s...)\n\n  # Interpret operator names joined by *\n  # as acting sequentially on the same site\n  starpos = findfirst(\"*\", name)\n  if !isnothing(starpos)\n    op1 = name[1:starpos.start-1]\n    op2 = name[starpos.start+1:end]\n    return product(op(op1, s...; kwargs...),\n                   op(op2, s...; kwargs...))\n  end\n\n  common_stypes  = _sitetypes(commontags_s)\n  push!(common_stypes,SiteType(\"Generic\"))\n  opn = OpName(name)\n\n\n  #\n  # Try calling a function of the form:\n  #    op(::OpName, ::SiteType, ::Index; kwargs...)\n  #\n  for st in common_stypes\n    res = op(opn, st, s...; kwargs...)\n    if !isnothing(res)\n      return res\n    end\n  end\n\n  # otherwise try calling a function of the form:\n  #    op!(::ITensor, ::OpName, ::SiteType, ::Index; kwargs...)\n  #\n  Op = emptyITensor(prime.(s)..., dag.(s)...)\n  for st in common_stypes\n    op!(Op, opn, st, s...; kwargs...)\n    if !isempty(Op)\n      return Op\n    end\n  end\n\n  #\n  # otherwise try calling a function of the form:\n  #    op(::OpName, ::SiteType; kwargs...)\n  # which returns a Julia matrix\n  #\n  for st in common_stypes\n    op_mat = op(opn, st; kwargs...)\n    if !isnothing(op_mat)\n      rs = reverse(s)\n      return itensor(op_mat, prime.(rs)..., dag.(rs)...)\n    end\n  end\n\n  if length(s) > 1\n    # No overloads for common tags found. It might be a\n    # case of making an operator with mixed site types,\n    # searching for overloads like:\n    #   op(::OpName,\n    #      ::SiteType...,\n    #      ::Index...;\n    #      kwargs...)\n    #   op!(::ITensor, ::OpName,\n    #       ::SiteType...,\n    #       ::Index...;\n    #       kwargs...)\n    stypes = _sitetypes.(s)\n\n    for st in Iterators.product(stypes...)\n      res = op(opn, st..., s...; kwargs...)\n      if !isnothing(res)\n        return res\n      end\n    end\n\n    Op = emptyITensor(prime.(s)..., dag.(s)...)\n    for st in Iterators.product(stypes...)\n      op!(Op, opn, st..., s...; kwargs...)\n      if !isempty(Op)\n        return Op\n      end\n    end\n    error(\"Older op interface does not support multiple indices with mixed site types. You may want to overload `op(::OpName, ::SiteType..., ::Index...)` or `op!(::ITensor, ::OpName, ::SiteType..., ::Index...) for the operator \\\"$name\\\" and Index tags $(tags.(s)).\")\n  end\n\n  #\n  # otherwise try calling a function of the form:\n  #   op(::SiteType, ::Index, ::AbstractString)\n  #\n  # (Note: this version is for backwards compatibility\n  #  after version 0.1.10, and may be eventually\n  #  deprecated)\n  #\n  for st in common_stypes\n    res = op(st, s[1], name; kwargs...)\n    if !isnothing(res)\n      return res\n    end\n  end\n\n  throw(ArgumentError(\"Overload of \\\"op\\\" or \\\"op!\\\" functions not found for operator name \\\"$name\\\" and Index tags: $(commontags_s))\"))\nend\n\n# For backwards compatibility, version of `op`\n# taking the arguments in the other order:\nop(s::Index,\n   opname::AbstractString;\n   kwargs...) = op(opname, s; kwargs...)\n\n\n# To ease calling of other op overloads,\n# allow passing a string as the op name\nop(opname::AbstractString,t::SiteType) = op(OpName(opname),t)\n\n\"\"\"\n    op(opname::String,sites::Vector{<:Index},n::Int; kwargs...)\n\nReturn an ITensor corresponding to the operator\nnamed `opname` for the n'th Index in the array \n`sites`.\n\n# Example\n\n```julia\ns = siteinds(\"S=1/2\", 4)\nSz2 = op(\"Sz\", s, 2)\n```\n\"\"\"\nop(opname::AbstractString, s::Vector{<:Index},\n   ns::Vararg{Int, N}; kwargs...) where {N} =\n  op(opname, ntuple(n -> s[ns[n]], Val(N))...; kwargs...)\n\nop(s::Vector{ <: Index}, opname::AbstractString,\n   ns::Int...; kwargs...) =\n  op(opname, s, ns...; kwargs...)\n\nop(s::Vector{ <: Index}, opname::AbstractString,\n   ns::Tuple{Vararg{Int}}, kwargs::NamedTuple) =\n  op(opname, s, ns...; kwargs...)\n\nop(s::Vector{ <: Index}, opname::AbstractString,\n   ns::Int, kwargs::NamedTuple) =\n  op(opname, s, ns; kwargs...)\n\n# This version helps with call like `op.(Ref(s), os)` where `os`\n# is a vector of tuples.\nop(s::Vector{ <: Index}, os::Tuple{String, Vararg}) =\n  op(s, os...)\n\n# Here, Ref is used to not broadcast over the vector of indices\n# TODO: consider overloading broadcast for `op` with the example\n# here: https://discourse.julialang.org/t/how-to-broadcast-over-only-certain-function-arguments/19274/5\n# so that `Ref` isn't needed.\nops(s::Vector{ <: Index},\n    os::AbstractArray{ <: Tuple{String, Vararg}}) =\n  op.(Ref(s), os)\n\nops(os::Vector{ <: Tuple{String, Vararg}}, s::Vector{ <: Index}) =\n  op.(Ref(s), os)\n\n#---------------------------------------\n#\n# state system\n#\n#---------------------------------------\n\n@eval struct StateName{Name}\n  (f::Type{<:StateName})() = $(Expr(:new, :f))\nend\n\nStateName(s::AbstractString) = StateName{SmallString(s)}()\nStateName(s::SmallString) = StateName{s}()\nname(::StateName{N}) where {N} = N\n\nmacro StateName_str(s)\n  StateName{SmallString(s)}\nend\n\nstate(::SiteType, ::StateName) = nothing\nstate(::SiteType, ::AbstractString) = nothing\n\nfunction state(s::Index,\n               name::AbstractString)::IndexVal\n  stypes  = _sitetypes(s)\n  sname = StateName(name)\n\n  # Try calling state(::SiteType\"Tag\",::StateName\"Name\")\n  for st in stypes\n    res = state(st,sname)\n    !isnothing(res) && return s(res)\n  end\n\n  # Try calling state(::SiteType\"Tag\",\"Name\")\n  for st in stypes\n    res = state(st,name)\n    !isnothing(res) && return s(res)\n  end\n\n  throw(ArgumentError(\"Overload of \\\"state\\\" function not found for Index tags $(tags(s))\"))\nend\n\nstate(s::Index,n::Integer) = s[n]\n\nstate(sset::Vector{<:Index},j::Integer,st) = state(sset[j],st)\n\n#---------------------------------------\n#\n# siteind system\n#\n#---------------------------------------\n\nspace(st::SiteType; kwargs...) = nothing\n\nspace(st::SiteType, n::Int; kwargs...) =\n  space(st; kwargs...)\n\nfunction space_error_message(st::SiteType)\n  return \"Overload of \\\"space\\\",\\\"siteind\\\", or \\\"siteinds\\\" functions not found for Index tag: $(tag(st))\"\nend\n\nfunction siteind(st::SiteType; addtags = \"\", kwargs...) \n  sp = space(st; kwargs...)\n  isnothing(sp) && return nothing\n  return Index(sp, \"Site, $(tag(st)), $addtags\")\nend\n\nfunction siteind(st::SiteType, n; kwargs...)\n  s = siteind(st; kwargs...)\n  !isnothing(s) && return addtags(s, \"n=$n\")\n  sp = space(st, n; kwargs...)\n  isnothing(sp) && error(space_error_message(st))\n  return Index(sp, \"Site, $(tag(st)), n=$n\")\nend\n\nsiteind(tag::String; kwargs...) =\n  siteind(SiteType(tag); kwargs...)\n\nsiteind(tag::String, n; kwargs...) =\n  siteind(SiteType(tag), n; kwargs...)\n\n# Special case of `siteind` where integer (dim) provided\n# instead of a tag string\nsiteind(d::Integer,n::Integer; kwargs...) = Index(d,\"Site,n=$n\")\n\n#---------------------------------------\n#\n# siteinds system\n#\n#---------------------------------------\n\nsiteinds(::SiteType, N; kwargs...) = nothing\n\n\"\"\"\n  siteinds(tag::String, N::Integer; kwargs...)\n\nCreate an array of `N` physical site indices of type `tag`.\nKeyword arguments can be used to specify quantum number conservation,\nsee the `space` function corresponding to the site type `tag` for\nsupported keyword arguments.\n\"\"\"\nfunction siteinds(tag::String,\n                  N::Integer; kwargs...)\n  st = SiteType(tag)\n\n  si = siteinds(st,N;kwargs...)\n  if !isnothing(si)\n    return si\n  end\n\n  return [siteind(st,j; kwargs...) for j=1:N]\nend\n\n\"\"\"\n  siteinds(f::Function, N::Integer; kwargs...)\n\nCreate an array of `N` physical site indices where the site type at site `n` is given\nby `f(n)` (`f` should return a string).\n\"\"\"\nfunction siteinds(f::Function,\n                  N::Integer; kwargs...)\n  [siteind(f(n),n; kwargs...) for n=1:N]\nend\n\n# Special case of `siteinds` where integer (dim)\n# provided instead of a tag string\nfunction siteinds(d::Integer,\n                  N::Integer; kwargs...)\n  return [siteind(d,n; kwargs...) for n=1:N]\nend\n\n#---------------------------------------\n#\n# has_fermion_string system\n#\n#---------------------------------------\n\nhas_fermion_string(::OpName, ::SiteType) = nothing\n\nfunction has_fermion_string(opname::AbstractString,\n                            s::Index;\n                            kwargs...)::Bool\n  opname = strip(opname)\n\n  # Interpret operator names joined by *\n  # as acting sequentially on the same site\n  starpos = findfirst(\"*\", opname)\n  if !isnothing(starpos)\n    op1 = opname[1:starpos.start-1]\n    op2 = opname[starpos.start+1:end]\n    return xor(has_fermion_string(op1,s; kwargs...),\n               has_fermion_string(op2,s; kwargs...))\n  end\n\n  Ntags = length(tags(s))\n  stypes = _sitetypes(s)\n  opn = OpName(opname)\n  for st in stypes\n    res = has_fermion_string(opn, st)\n    !isnothing(res) && return res\n  end\n  return false\nend\n", "meta": {"hexsha": "a847b3508b56105bb024280c51bc2c146ef7f501", "size": 14738, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/physics/sitetype.jl", "max_stars_repo_name": "saolof/ITensors.jl", "max_stars_repo_head_hexsha": "ae84b80ef55271dca1aa39dfcd4db350c4e864e1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/physics/sitetype.jl", "max_issues_repo_name": "saolof/ITensors.jl", "max_issues_repo_head_hexsha": "ae84b80ef55271dca1aa39dfcd4db350c4e864e1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/physics/sitetype.jl", "max_forks_repo_name": "saolof/ITensors.jl", "max_forks_repo_head_hexsha": "ae84b80ef55271dca1aa39dfcd4db350c4e864e1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.4962686567, "max_line_length": 266, "alphanum_fraction": 0.6327181436, "num_tokens": 4441, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.11279539733570564, "lm_q1q2_score": 0.05243875746505526}}
{"text": "#=\nThis file is part of the replication code for: Hasenzagl, T., Pellegrino, F., Reichlin, L., & Ricco, G. (2020). A Model of the Fed's View on Inflation.\nPlease cite the paper if you are using any part of the code for academic work (including, but not limited to, conference and peer-reviewed papers).\n=#\n\nfunction get_progress(chain::Array{Float64, 2}, istart::Int64, iend::Int64, print_progress=1::Int64, acc_str=\"\"::String)\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Get acceptance rate and print current status of the estimation (if enabled)\n# ----------------------------------------------------------------------------------------------------------------------\n\n     chain_iter = chain[:, istart:iend];\n     acc_rate         = 100*mean(chain_iter[1, 2:end] .!= chain_iter[1, 1:end-1]);\n     acc_rate         = round(acc_rate, digits=2);\n\n     if print_progress == 1\n          if acc_str != \"\"\n               print(\"- $acc_str: $acc_rate%\\n\");\n          else\n               print(\"- Acceptance rate: $acc_rate%\\n\");\n          end\n     end\n\n     return acc_rate;\nend\n", "meta": {"hexsha": "574f922eae3d118c7ca59ee8962a58abde9f03fe", "size": 1153, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "code/Metropolis-Within-Gibbs/subroutines/get/get_progress.jl", "max_stars_repo_name": "thasenzagl/replication-hasenzagl-et-al-2020", "max_stars_repo_head_hexsha": "86a873a77cc63f1f14309254997421b998a94fa4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-08-18T19:23:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-12T14:09:41.000Z", "max_issues_repo_path": "code/Metropolis-Within-Gibbs/subroutines/get/get_progress.jl", "max_issues_repo_name": "GRicco/replication-hasenzagl-et-al-2020", "max_issues_repo_head_hexsha": "82de51ba05f1569f3e6481e038997e585a00f31e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/Metropolis-Within-Gibbs/subroutines/get/get_progress.jl", "max_forks_repo_name": "GRicco/replication-hasenzagl-et-al-2020", "max_forks_repo_head_hexsha": "82de51ba05f1569f3e6481e038997e585a00f31e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2020-08-14T16:57:30.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-12T14:09:46.000Z", "avg_line_length": 44.3461538462, "max_line_length": 151, "alphanum_fraction": 0.501300954, "num_tokens": 255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10818895599979557, "lm_q1q2_score": 0.052404575625019933}}
{"text": "function clean(phone_number)\n    new = replace(phone_number, r\"[^0-9]\"  => \"\")\n    noone = new[1] == '1' ? new[2:end] : new\n    if length(noone) != 10\n        return nothing\n    elseif '1' \u2208 [noone[1], noone[4]] \n        return nothing\n    elseif '0' \u2208 [noone[1], noone[4]]\n        return nothing\n    else\n        noone\n    end\nend\n", "meta": {"hexsha": "078a62946b2bcc88a81c64d09fb5f86a8e5db161", "size": 332, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/phone-number/phone-number-1st-try.jl", "max_stars_repo_name": "Rigante-pl/Exercism-Julia", "max_stars_repo_head_hexsha": "002bdb61b8aaeea5d70579a3882870d0175cd36d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia/phone-number/phone-number-1st-try.jl", "max_issues_repo_name": "Rigante-pl/Exercism-Julia", "max_issues_repo_head_hexsha": "002bdb61b8aaeea5d70579a3882870d0175cd36d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia/phone-number/phone-number-1st-try.jl", "max_forks_repo_name": "Rigante-pl/Exercism-Julia", "max_forks_repo_head_hexsha": "002bdb61b8aaeea5d70579a3882870d0175cd36d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.7142857143, "max_line_length": 49, "alphanum_fraction": 0.5331325301, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4843800842769843, "lm_q2_score": 0.10818894881116972, "lm_q1q2_score": 0.05240457214299273}}
{"text": "using Base: @propagate_inbounds, @_inline_meta\n\n\"\"\"\n    Window{X}(k::Kernel, a::DenseArray, pos::CartesianIndex)\n\nCreate a stack-allocated view on `a` with interior axes `X` and cartesian indexing\nrelative to `pos` in the parent array.\nWhen indexing outside `X` the returned value is determined by the extension\nspecified in `k`.\nThe total axes of the window will span the full axes of kernel `k` except for\nwhen `k` was created with an `ExtensionNothing` in which case they will be\ntrimmed.\n\"\"\"\nfunction Window end\n\n\n# AbstractArray interface\n\nBase.axes(::Type{W}) where W<:Window =\n    W.parameters[5] <: ExtensionArray &&\n    W.parameters[5].parameters[4] != ExtensionNothing &&\n    W.parameters[5].parameters[4] != ExtensionNone ?\n        axes(W.parameters[4]) :\n        W.parameters[3]\n\nBase.axes(w::Window) = axes(typeof(w))\nBase.size(w::Window) = length.(axes(w))\n\n@propagate_inbounds Base.getindex(w::Window, wi::Int...) = getindex(w, CartesianIndex(wi))\n@propagate_inbounds function Base.getindex(w::Window{<:Any,N}, wi::CartesianIndex{N}) where N\n    # central piece to get efficient boundary handling.\n    # we rely on the compiler to constant propagate this check away\n    checkbounds_inner(Bool, w, wi) || return getindex_extension(w, wi)\n\n    return parent(w)[position(w) + wi]\nend\n\n@propagate_inbounds Base.setindex!(w::Window, x, wi::Int...) = setindex!(w, x, CartesianIndex(wi))\n@propagate_inbounds function Base.setindex!(w::Window{<:Any,N}, x, wi::CartesianIndex{N}) where N\n    # central piece to get efficient boundary handling.\n    # we rely on the compiler to constant propagate this check away\n    checkbounds_inner(Bool, w, wi) || return setindex_extension!(w, x, wi)\n\n    return parent(w)[position(w) + wi] = x\nend\n\n\n# Window interface\n\nBase.eltype(::Type{<:Window{T}}) where T = T\n\n\"\"\"\n    checkbounds_inner(Bool, w::Window, i::CartesianIndex)\n\nReturn true if `i` indexes `w`'s parent in the interior and false if an\nextension would be involved.\n\"\"\"\n@inline checkbounds_inner(::Type{Bool}, w::Window, i::CartesianIndex) =\n    in(i, CartesianIndices(axes_inner(w)))\n\n\"\"\"\n    parent(w::Window)\n\nReturn reference to parent array.\n\"\"\"\nBase.parent(w::Window) = w.parent\n\n\"\"\"\n    axes_inner(w::Window)\n\nReturn the inner axes of the window where no extension handling is active.\n\"\"\"\naxes_inner(w::Window{<:Any,<:Any,X}) where X = X\n\n\"\"\"\n    position(w::Window)::CartesianIndex\n\nReturn the position of `w`, i.e. its center coordinate within the parent array.\n\"\"\"\nBase.position(w::Window) = w.position\n\n\"\"\"\n    Tuple(w::Window)\n\nCreate a tuple from the statically sized window `w`.\n\nNOTE: this doesn't check bounds and thus assumes the window was properly\n      created.\n\"\"\"\n@generated function Base.Tuple(w::Window)\n    els = [ :( w[$i] ) for i in CartesianIndices(axes(w)) ]\n    return :( @_inline_meta; @inbounds ($(els...),) )\nend\n\n## think twice before defining `iterate` like this, rather encourage use of\n## mapreduce functions\n#\n#@generated function Base.iterate(w::Window, st = nothing)\n#    ci = CartesianIndices(axes(w))\n#    return quote\n#        @_inline_meta\n#        it = isnothing(st) ? iterate($ci) : iterate($ci, st)\n#        isnothing(it) && return nothing\n#        return @inbounds w[it[1]], it[2]\n#    end\n#end\n\n# specialized mapfoldl, since Julia base has an upper limit on tuple size for\n# inlining it\n\n# since generated keyword functions may not inline, we forward to an internal\n# generated non-keyword function\n#   FIXME: file a julia bug\n# this workaround does not seem to work when actually using non-default keyword\n# arguments. explicitly specializing type arguments (F, Op) helps with the allocations\n# but inlining still doesn't happen.\n#   FIXME: file a julia bug\n\nstruct _DefaultValue end\n\n@inline function Base.mapfoldl(f::F, op::Op, w::W...;\n        dims = :, init = _DefaultValue()) where {F, Op, W<:Window}\n    return _mapfoldl(f, op, dims, init, w...)\nend\n\n@generated function _mapfoldl(f, op, dims::Colon, init, w...)\n    wax = same(axes, w...)\n    weltype = same(eltype, w...)\n    ws(i) = [ :( @inbounds w[$j][$i] ) for j in eachindex(w) ]\n    stm_init = init === _DefaultValue ?\n        :( acc = Base.mapreduce_empty(f, op, $weltype) ) :\n        :( acc = init )\n    stm_accs = [ :( acc = op(acc, f($(ws(i)...))) ) for i in CartesianIndices(wax) ]\n    return :( @_inline_meta; $stm_init; $(stm_accs...); acc )\nend\n\n# Set of workarounds for not being able to subtype AbstractArray\n#   see also `src/types.jl`\n#   TODO: remove these as soon as Window <: AbstractArray\n\nBase.ndims(w::Window) = length(axes(w))\nBase.eltype(w::Window) = eltype(typeof(w))\nBase.length(w::Window) = prod(size(w))\nBase.CartesianIndices(w::Window) = CartesianIndices(axes(w))\nBase.keys(w::Window) = CartesianIndices(w)\n@inline Base.checkbounds(::Type{Bool}, w::Window, i::CartesianIndex) =\n    in(i, keys(w))\n\n# redefine inlined variations of various operations on arrays\n# TODO: Julia base should define inlined dispatch function\n\n@inline Base.mapreduce(f, op, w::Window...; dims = :, init = _DefaultValue()) =\n    _mapfoldl(f, op, dims, init, w...)\n@inline Base.in(x, w::Window) = _mapfoldl(==(x), |, :, _DefaultValue(), w)\n\n@inline Base.sum(w::Window) = _mapfoldl(identity, +, :, _DefaultValue(), w)\n@inline Base.sum(f, w::Window) = _mapfoldl(f, +, :, _DefaultValue(), w)\n@inline Base.prod(w::Window) = _mapfoldl(identity, *, :, _DefaultValue(), w)\n@inline Base.prod(f, w::Window) = _mapfoldl(f, *, :, _DefaultValue(), w)\n\n# typemax/typemin usage here is not strictly compatible with julia base but\n# simplifies the mapreduce implementation considerably\n@inline Base.minimum(w::Window) = _mapfoldl(identity, min, :, typemax(eltype(w)), w)\n@inline Base.minimum(f, w::Window) = _mapfoldl(f, min, :, typemax(eltype(w)), w)\n@inline Base.maximum(w::Window) = _mapfoldl(identity, max, :, typemin(eltype(w)), w)\n@inline Base.maximum(f, w::Window) = _mapfoldl(f, max, :, typemin(eltype(w)), w)\n\n@inline Base.count(w::Window{Bool}) = _mapfoldl(identity, +, :, _DefaultValue(), w)\n@inline Base.count(f, w::Window) = _mapfoldl(x->f(x)::Bool, +, :, _DefaultValue(), w)\n@inline Base.all(w::Window{Bool}) = _mapfoldl(identity, &, :, _DefaultValue(), w)\n@inline Base.all(f, w::Window) = _mapfoldl(x->f(x)::Bool, &, :, _DefaultValue(), w)\n@inline Base.any(w::Window{Bool}) = _mapfoldl(identity, |, :, _DefaultValue(), w)\n@inline Base.any(f, w::Window) = _mapfoldl(x->f(x)::Bool, |, :, _DefaultValue(), w)\n", "meta": {"hexsha": "df14028dc80e9f7a82b8dd2741c66eda06e7e254", "size": 6425, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/window.jl", "max_stars_repo_name": "stev47/StaticKernels.jl", "max_stars_repo_head_hexsha": "bf238cab33d456dd18563baa415327eb6e27f06a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2020-04-15T18:23:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-05T21:13:45.000Z", "max_issues_repo_path": "src/window.jl", "max_issues_repo_name": "stev47/StaticKernels.jl", "max_issues_repo_head_hexsha": "bf238cab33d456dd18563baa415327eb6e27f06a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-04-16T05:33:53.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-19T08:10:07.000Z", "max_forks_repo_path": "src/window.jl", "max_forks_repo_name": "stev47/StaticKernels.jl", "max_forks_repo_head_hexsha": "bf238cab33d456dd18563baa415327eb6e27f06a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-18T17:32:47.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-18T17:32:47.000Z", "avg_line_length": 36.9252873563, "max_line_length": 98, "alphanum_fraction": 0.6821789883, "num_tokens": 1784, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295350395727, "lm_q2_score": 0.11757214127540647, "lm_q1q2_score": 0.0523818614360388}}
{"text": "\"\"\"\nDescription\n\n      The first() method returns the first n number of observations in a TimeArray\n\nUsage\n\n      using MarketData\n      first(cl)     # returns the first observation\n      first(cl, 10) # returns the first 10 observations\n\nMethod Signature(s)\n\n      first(ts::TimeSeries.TimeArray, n::Int=1)\n\nDetails\n\n      The first function subsets a TimeSeries.Timearray by the first n observations. When n is not supplied, it\n      defaults to n=1.\n\nSee Also\n \n      last() which takes the last n observations from a TimeSeries.TimeArray\n\n\"\"\"\n\nfunction first(ts::TimeSeries.TimeArray, n::Int=1)\n   ts[1:n] \nend\n", "meta": {"hexsha": "47735015ac02f6bf49800a7ff0e010b134ad599b", "size": 616, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/first.jl", "max_stars_repo_name": "milktrader/TimeSeriesAnalysis.jl", "max_stars_repo_head_hexsha": "9614b73fc0201f34b68846ba636eee7686d27ac6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2015-10-28T00:02:24.000Z", "max_stars_repo_stars_event_max_datetime": "2016-03-06T09:42:34.000Z", "max_issues_repo_path": "src/first.jl", "max_issues_repo_name": "milktrader/TimeSeriesAnalysis.jl", "max_issues_repo_head_hexsha": "9614b73fc0201f34b68846ba636eee7686d27ac6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/first.jl", "max_forks_repo_name": "milktrader/TimeSeriesAnalysis.jl", "max_forks_repo_head_hexsha": "9614b73fc0201f34b68846ba636eee7686d27ac6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.5333333333, "max_line_length": 111, "alphanum_fraction": 0.6931818182, "num_tokens": 149, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.10970578261038684, "lm_q1q2_score": 0.05228354359612565}}
{"text": "module TestSets\n\nusing Test\nusing VecMathOptInterface\nconst MOI = VecMathOptInterface\n\ninclude(\"dummy.jl\")\n\n\"\"\"\n    MutLessThan{T<:Real} <: MOI.AbstractScalarSet\n\nA mutable `LessThan`-like set to test `copy` of indicator set\n\"\"\"\nmutable struct MutLessThan{T<:Real} <: MOI.AbstractScalarSet\n    upper::T\n    MutLessThan(v::T) where {T<:Real} = new{T}(v)\nend\n\nBase.copy(mlt::MutLessThan) = MutLessThan(Base.copy(mlt.upper))\n\nfunction test_sets_equals()\n    # By default, `==` redirects to `===`, it works for bits type\n    # but not for `BigInt`s. We define functions creating different\n    # instances so that `a() !== a()`.\n    a() = big(1)\n    b() = big(2)\n    @test a() !== a()\n    @test b() !== b()\n    for S in [\n        MOI.LessThan,\n        MOI.GreaterThan,\n        MOI.EqualTo,\n        MOI.PowerCone,\n        MOI.DualPowerCone,\n    ]\n        @test S(a()) == S(a())\n        @test S(a()) != S(b())\n        @test S(b()) == S(b())\n        @test S(b()) != S(a())\n    end\n    for S in [MOI.Interval, MOI.Semicontinuous, MOI.Semiinteger]\n        @test S(a(), b()) == S(a(), b())\n        @test S(a(), b()) != S(b(), a())\n        @test S(a(), b()) != S(b(), b())\n        @test S(a(), b()) != S(a(), a())\n        @test S(a(), a()) != S(b(), b())\n        @test S(a(), a()) == S(a(), a())\n    end\n    S = MOI.Indicator\n    A() = MOI.LessThan(a())\n    B() = MOI.LessThan(b())\n    @test S{MOI.ACTIVATE_ON_ZERO}(A()) == S{MOI.ACTIVATE_ON_ZERO}(A())\n    @test S{MOI.ACTIVATE_ON_ZERO}(A()) != S{MOI.ACTIVATE_ON_ONE}(A())\n    @test S{MOI.ACTIVATE_ON_ZERO}(A()) != S{MOI.ACTIVATE_ON_ZERO}(B())\n    @test S{MOI.ACTIVATE_ON_ONE}(A()) != S{MOI.ACTIVATE_ON_ONE}(B())\nend\n\nfunction test_sets_sos1_copy()\n    s = MOI.SOS1([1.0])\n    s_copy = copy(s)\n    s_copy.weights[1] = 2.0\n    @test s.weights[1] == 1.0\nend\n\nfunction test_sets_sos2_copy()\n    s = MOI.SOS2([1.0])\n    s_copy = copy(s)\n    s_copy.weights[1] = 2.0\n    @test s.weights[1] == 1.0\nend\n\nfunction test_sets_indicator_copy()\n    s1 = MOI.Indicator{MOI.ACTIVATE_ON_ONE}(MOI.LessThan(4.0))\n    s2 = MOI.Indicator{MOI.ACTIVATE_ON_ZERO}(MOI.GreaterThan(4.0))\n    s1_copy = copy(s1)\n    s2_copy = copy(s2)\n    @test s1_copy isa MOI.Indicator{MOI.ACTIVATE_ON_ONE}\n    @test s1 == s1_copy\n    @test s2_copy isa MOI.Indicator{MOI.ACTIVATE_ON_ZERO}\n    @test s2 == s2_copy\n    s3 = MOI.Indicator{MOI.ACTIVATE_ON_ZERO}(MutLessThan(4.0))\n    s3_copy = copy(s3)\n    @test s3.set.upper \u2248 4.0\n    s3_copy.set.upper = 5.0\n    @test s3.set.upper \u2248 4.0\n    @test s3_copy.set.upper \u2248 5.0\nend\n\nfunction test_sets_broadcast()\n    model = DummyModelWithAdd()\n    x = MOI.add_variables(model, 3)\n    cis = MOI.add_constraint.(model, x, MOI.EqualTo(0.0))\n    @test cis isa\n          Vector{MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}}\n    @test length(cis) == 3\nend\n\nfunction test_sets_dimension()\n    @test MOI.dimension(MOI.ExponentialCone()) == 3\n    @test MOI.dimension(MOI.DualExponentialCone()) == 3\n    @test MOI.dimension(MOI.PowerCone(1 / 2)) == 3\n    @test MOI.dimension(MOI.DualPowerCone(1 / 2)) == 3\n    @test MOI.dimension(MOI.PositiveSemidefiniteConeTriangle(5)) == 15\n    @test MOI.dimension(MOI.PositiveSemidefiniteConeSquare(5)) == 25\n    @test MOI.dimension(MOI.LogDetConeTriangle(5)) == 17\n    @test MOI.dimension(MOI.LogDetConeSquare(5)) == 27\n    @test MOI.dimension(MOI.RootDetConeTriangle(5)) == 16\n    @test MOI.dimension(MOI.RootDetConeSquare(5)) == 26\n    @test MOI.dimension(MOI.SOS1([1.0, 2.0])) == 2\n    @test MOI.dimension(MOI.SOS2([1.0, 2.0])) == 2\n    @test MOI.dimension(\n        MOI.Indicator{MOI.ACTIVATE_ON_ONE}(MOI.LessThan(1.0)),\n    ) == 2\n    @test MOI.dimension(MOI.Complements(10)) == 10\nend\n\nfunction test_sets_DimensionMismatch()\n    for (S, min_dimension) in (\n        (MOI.Reals, 0),\n        (MOI.Zeros, 0),\n        (MOI.Nonnegatives, 0),\n        (MOI.Nonpositives, 0),\n        (MOI.NormInfinityCone, 1),\n        (MOI.NormOneCone, 1),\n        (MOI.SecondOrderCone, 1),\n        (MOI.RotatedSecondOrderCone, 2),\n        (MOI.GeometricMeanCone, 2),\n        (MOI.Complements, 0),\n        (MOI.RelativeEntropyCone, 1),\n        (MOI.PositiveSemidefiniteConeTriangle, 0),\n        (MOI.PositiveSemidefiniteConeSquare, 0),\n        (MOI.LogDetConeTriangle, 0),\n        (MOI.LogDetConeSquare, 0),\n        (MOI.RootDetConeTriangle, 0),\n        (MOI.RootDetConeSquare, 0),\n    )\n        @test_throws DimensionMismatch S(min_dimension - 1)\n        @test S(min_dimension) isa S\n    end\n    @test_throws DimensionMismatch MOI.NormSpectralCone(-1, 0)\n    @test_throws DimensionMismatch MOI.NormSpectralCone(0, -1)\n    @test MOI.NormSpectralCone(0, 0) isa MOI.NormSpectralCone\n    @test_throws DimensionMismatch MOI.NormNuclearCone(-1, 0)\n    @test_throws DimensionMismatch MOI.NormNuclearCone(0, -1)\n    @test MOI.NormNuclearCone(0, 0) isa MOI.NormNuclearCone\n    # Other dimension checks\n    @test_throws DimensionMismatch MOI.RelativeEntropyCone(2)\n    @test_throws DimensionMismatch MOI.Complements(-3)\n    @test_throws DimensionMismatch MOI.Complements(3)\n    return\nend\n\nfunction _dual_set_test(set1, set2)\n    @test MOI.dual_set(set1) == set2\n    @test MOI.dual_set_type(typeof(set1)) == typeof(set2)\n    @test MOI.dual_set(set2) == set1\n    @test MOI.dual_set_type(typeof(set2)) == typeof(set1)\nend\n\nfunction _self_dual_set_test(set)\n    @test MOI.dual_set(set) == set\n    @test MOI.dual_set_type(typeof(set)) == typeof(set)\nend\n\nfunction test_sets_dual_nonpositives()\n    nonpositives3 = MOI.Nonpositives(3)\n    nonpositives4 = MOI.Nonpositives(4)\n    _self_dual_set_test(nonpositives3)\n    @test MOI.dual_set(nonpositives3) != nonpositives4\n    _self_dual_set_test(nonpositives4)\n    return\nend\n\nfunction test_sets_dual_nonnegatives()\n    nonnegatives3 = MOI.Nonnegatives(3)\n    nonnegatives4 = MOI.Nonnegatives(4)\n    _self_dual_set_test(nonnegatives3)\n    @test MOI.dual_set(nonnegatives3) != nonnegatives4\n    _self_dual_set_test(nonnegatives4)\n    return\nend\n\nfunction test_sets_dual_zeroreal()\n    zeros3 = MOI.Zeros(3)\n    zeros4 = MOI.Zeros(4)\n    reals3 = MOI.Reals(3)\n    reals4 = MOI.Reals(4)\n    _dual_set_test(zeros3, reals3)\n    @test MOI.dual_set(reals3) != zeros4\n    _dual_set_test(zeros4, reals4)\n    @test MOI.dual_set(zeros4) != reals3\n    return\nend\n\nfunction test_sets_dual_norm()\n    norminf2 = MOI.NormInfinityCone(2)\n    norminf3 = MOI.NormInfinityCone(3)\n    normone2 = MOI.NormOneCone(2)\n    normone3 = MOI.NormOneCone(3)\n    _dual_set_test(norminf2, normone2)\n    _dual_set_test(norminf3, normone3)\n    @test MOI.dual_set(norminf2) != normone3\n    @test MOI.dual_set(normone2) != norminf3\n    return\nend\n\nfunction test_sets_dual_soc()\n    soc2 = MOI.SecondOrderCone(2)\n    soc3 = MOI.SecondOrderCone(3)\n    _self_dual_set_test(soc2)\n    @test MOI.dual_set(soc2) != soc3\n    _self_dual_set_test(soc3)\n    return\nend\n\nfunction test_sets_dual_rsoc()\n    rsoc2 = MOI.RotatedSecondOrderCone(2)\n    rsoc3 = MOI.RotatedSecondOrderCone(3)\n    _self_dual_set_test(rsoc2)\n    @test MOI.dual_set(rsoc2) != rsoc3\n    _self_dual_set_test(rsoc3)\n    return\nend\n\nfunction test_sets_dual_normspectral()\n    normspec22 = MOI.NormSpectralCone(2, 2)\n    normspec23 = MOI.NormSpectralCone(2, 3)\n    normnuc22 = MOI.NormNuclearCone(2, 2)\n    normnuc23 = MOI.NormNuclearCone(2, 3)\n    _dual_set_test(normspec23, normnuc23)\n    _dual_set_test(normspec22, normnuc22)\n    @test MOI.dual_set(normspec22) != normnuc23\n    @test MOI.dual_set(normnuc22) != normspec23\n    return\nend\n\nfunction test_sets_dual_psdtriangle()\n    psd2 = MOI.PositiveSemidefiniteConeTriangle(2)\n    psd3 = MOI.PositiveSemidefiniteConeTriangle(3)\n    _self_dual_set_test(psd2)\n    @test MOI.dual_set(psd2) != psd3\n    _self_dual_set_test(psd3)\n    return\nend\n\nfunction test_sets_dual_exponential()\n    exp = MOI.ExponentialCone()\n    dual_exp = MOI.DualExponentialCone()\n    _dual_set_test(exp, dual_exp)\n    @test MOI.dual_set(exp) != exp\n    _dual_set_test(dual_exp, exp)\n    @test MOI.dual_set(dual_exp) != dual_exp\n    return\nend\n\nfunction test_sets_dual_power()\n    pow03 = MOI.PowerCone(0.3)\n    pow04 = MOI.PowerCone(0.4)\n    dual_pow03 = MOI.DualPowerCone(0.3)\n    _dual_set_test(pow03, dual_pow03)\n    @test MOI.dual_set(pow03) != pow03\n    _dual_set_test(dual_pow03, pow03)\n    @test MOI.dual_set(dual_pow03) != pow04\n    @test MOI.dual_set(dual_pow03) != dual_pow03\n    return\nend\n\nfunction test_sets_dual_psdsquare()\n    s = MOI.PositiveSemidefiniteConeSquare(4)\n    err = ErrorException(\n        \"\"\"Dual of `PositiveSemidefiniteConeSquare` is not defined in VecMathOptInterface.\n            For more details see the comments in `src/Bridges/Constraint/square.jl`.\"\"\",\n    )\n    @test_throws err MOI.dual_set(MOI.PositiveSemidefiniteConeSquare(4))\n    @test_throws err MOI.dual_set_type(MOI.PositiveSemidefiniteConeSquare)\nend\n\nfunction test_sets_dual_nonimplemented()\n    s = MOI.LogDetConeTriangle(4)\n    err = ErrorException(\"Dual of $s is not implemented.\")\n    @test_throws err MOI.dual_set(MOI.LogDetConeTriangle(4))\n    err = ErrorException(\"Dual type of $(typeof(s)) is not implemented.\")\n    @test_throws err MOI.dual_set_type(MOI.LogDetConeTriangle)\nend\n\nfunction test_sets_Interval()\n    @test MOI.Interval(MOI.GreaterThan(1.0)) === MOI.Interval(1.0, Inf)\n    @test MOI.Interval(MOI.LessThan(2.0)) === MOI.Interval(-Inf, 2.0)\n    @test MOI.Interval(MOI.EqualTo(3.0)) === MOI.Interval(3.0, 3.0)\n\n    @test MOI.Interval(MOI.GreaterThan(1.0f0)) === MOI.Interval(1.0f0, Inf32)\n    @test MOI.Interval(MOI.LessThan(2.0f0)) === MOI.Interval(-Inf32, 2.0f0)\n    @test MOI.Interval(MOI.EqualTo(3.0f0)) === MOI.Interval(3.0f0, 3.0f0)\n\n    @test_throws MethodError MOI.Interval(MOI.GreaterThan(false))\n    @test_throws MethodError MOI.Interval(MOI.LessThan(true))\n    @test MOI.Interval(MOI.EqualTo(true)) === MOI.Interval(true, true)\n\n    @test_throws MethodError MOI.Interval(MOI.GreaterThan(1))\n    @test_throws MethodError MOI.Interval(MOI.LessThan(2))\n    @test MOI.Interval(MOI.EqualTo(3)) === MOI.Interval(3, 3)\n\n    @test MOI.Interval(MOI.Interval(1, 2)) == MOI.Interval(1, 2)\nend\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$name\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\nend\n\nend\n\nTestSets.runtests()\n", "meta": {"hexsha": "f49631b4fb13bedb4bccf115021217c7394ca9be", "size": 10308, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/sets.jl", "max_stars_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_stars_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/sets.jl", "max_issues_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_issues_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/sets.jl", "max_forks_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_forks_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3134796238, "max_line_length": 90, "alphanum_fraction": 0.6695770276, "num_tokens": 3496, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.10970578261038684, "lm_q1q2_score": 0.052283543596125644}}
{"text": "\"\"\"\n    struct Alphabet{T}\n\nA basic struct for storing alphabets. An alphabet consists of the symbols of a common type `T`.\n\n# Example\n```julia-repl\njulia> Alphabet{Char}()\nEmpty alphabet of Char\n\njulia> Alphabet([\"a\", \"b\", \"c\"])\nAlphabet of String:\n    1.  \"a\"\n    2.  \"b\"\n    3.  \"c\"\n```\n\"\"\"\nstruct Alphabet{T}\n    letters::Vector{T}\n    inversions::Vector{Int}\n\n    function Alphabet(letters::AbstractVector{T}, inversions::AbstractVector{<:Integer}) where T\n        @assert length(unique(letters)) == length(letters) \"Non-unique set of letters\"\n        @assert !(T<:Integer) \"Alphabets of integers are not supported.\"\n        @assert length(letters) == length(inversions)\n        @assert all(i-> 0 \u2264 i \u2264 length(letters), inversions)\n        return new{T}(letters, inversions)\n    end\nend\n\nAlphabet{T}() where T = Alphabet(T[])\nAlphabet(init::AbstractVector{T}) where T = Alphabet(init, fill(0, length(init)))\n\nletters(A::Alphabet) = A.letters\n\nBase.length(A::Alphabet) = length(letters(A))\nBase.isempty(A::Alphabet) = isempty(letters(A))\nBase.:(==)(A::Alphabet, B::Alphabet) =\n    letters(A) == letters(B) && A.inversions == B.inversions\nBase.hash(A::Alphabet{T}, h::UInt) where T =\n    hash(letters(A), hash(A.inversions, hash(h, hash(Alphabet{T}))))\n\nBase.show(io::IO, A::Alphabet) = print(io, Alphabet, \" \", letters(A))\n\nhasinverse(i::Integer, A::Alphabet) = A.inversions[i] > 0\nhasinverse(l::T, A::Alphabet{T}) where T = hasinverse(A[l], A)\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", A::Alphabet{T}) where T\n    if isempty(A)\n        print(io, \"Empty alphabet of $(T)\")\n    else\n        print(io, \"Alphabet of $(T):\")\n        for (i, l) in pairs(letters(A))\n            print(io, \"\\n\\t$(i).\\t\")\n            show(io, l)\n            if hasinverse(i, A)\n                print(io, \" = (\")\n                show(io, A[-i])\n                print(io, \")\u207b\u00b9\")\n            end\n        end\n    end\nend\n\n\"\"\"\n    push!(A::Alphabet{T}, symbols::T...) where T\n\nPush one or more elements of type `T` at the end of the alphabet `A`.\n\n# Example\n```julia-repl\njulia> A = Alphabet{String}()\nEmpty alphabet of String\n\njulia> push!(A, \"a\", \"b\")\nAlphabet of String:\n    1.  \"a\"\n    2.  \"b\"\n```\n\"\"\"\nfunction Base.push!(A::Alphabet{T}, symbols::T...) where T\n    for s in symbols\n        if findfirst(symbol -> symbol == s, letters(A)) !== nothing\n            error(\"Symbol $(s) already in the alphabet.\")\n        end\n        push!(A.letters, s)\n        push!(A.inversions, 0)\n    end\n    A\nend\n\n\"\"\"\n    set_inversion!(A::Alphabet{T}, x::T, y::T) where T\n\nSet the inversion of `x` to `y` (and vice versa).\n\n# Example\n```julia-repl\njulia> A = Alphabet([\"a\", \"b\", \"c\"])\nAlphabet of String:\n    1. \"a\"\n    2. \"b\"\n    3. \"c\"\n\njulia> set_inversion!(A, \"a\", \"c\")\nAlphabet of String:\n    1. \"a\" = (\"c\")\u207b\u00b9\n    2. \"b\"\n    3. \"c\" = (\"a\")\u207b\u00b9\n\njulia> set_inversion!(A, \"a\", \"b\")\nAlphabet of String:\n    1. \"a\" = (\"b\")\u207b\u00b9\n    2. \"b\" = (\"a\")\u207b\u00b9\n    3. \"c\"\n```\n\"\"\"\nfunction set_inversion!(A::Alphabet{T}, x::T, y::T) where T\n    if (ix = findfirst(symbol -> symbol == x, letters(A))) === nothing\n        error(\"Element $(x) not found in the alphabet.\")\n    end\n    if (iy = findfirst(symbol -> symbol == y, letters(A))) === nothing\n        error(\"Element $(y) not found in the alphabet.\")\n    end\n\n    if A.inversions[ix] > 0\n        A.inversions[A.inversions[ix]] = 0\n    end\n    if A.inversions[iy] > 0\n        A.inversions[A.inversions[iy]] = 0\n    end\n\n    A.inversions[ix] = iy\n    A.inversions[iy] = ix\n    A\nend\n\n\"\"\"\n    getindex(A::Alphabet{T}, x::T) where T\n\nReturn the position of the symbol `x` in the alphabet `A`.\n\n# Example\n```julia-repl\njulia> A = Alphabet([\"a\", \"b\", \"c\"])\nAlphabet of String:\n    1. \"a\"\n    2. \"b\"\n    3. \"c\"\n\njulia> A[\"c\"]\n3\n```\n\"\"\"\nfunction Base.getindex(A::Alphabet{T}, x::T) where T\n    if (index = findfirst(symbol -> symbol == x, letters(A))) === nothing\n        throw(DomainError(\"Element '$(x)' not found in the alphabet\"))\n    end\n    return index\nend\n\n\"\"\"\n    getindex(A::Alphabet{T}, p::Integer) where T\n\nReturn the symbol that holds the `p`th position in the alphabet `A`. If `p < 0`, then the inversion of the `|p|`th symbol is returned.\n\n# Example\n```julia-repl\njulia> A = Alphabet([\"a\", \"b\", \"c\"])\nAlphabet of String:\n    1. \"a\"\n    2. \"b\"\n    3. \"c\"\n\njulia> set_inversion!(A, \"a\", \"c\")\nAlphabet of String:\n    1. \"a\" = (\"c\")\u207b\u00b9\n    2. \"b\"\n    3. \"c\" = (\"a\")\u207b\u00b9\n\njulia> A[\"a\"]\n1\n\njulia> A[-A[\"a\"]]\n\"c\"\n```\n\"\"\"\nBase.@propagate_inbounds function Base.getindex(A::Alphabet, p::Integer)\n    @boundscheck checkbounds(letters(A), abs(p))\n    if p > 0\n        return @inbounds letters(A)[p]\n    elseif p < 0 && hasinverse(-p, A)\n        return @inbounds letters(A)[A.inversions[-p]]\n    end\n\n    throw(DomainError(\"Inversion of $(letters(A)[-p]) is not defined\"))\nend\n\nfunction Base.inv(A::Alphabet, i::Integer)\n    hasinverse(i, A) && return A.inversions[i]\n\n    throw(DomainError(A[i], \"is not invertible over $A\"))\nend\n\n\"\"\"\n    inv(A::Alphabet, w::AbstractWord)\nReturn the inverse of a word `w` in the context of alphabet `A`.\n\"\"\"\nfunction Base.inv(A::Alphabet, w::AbstractWord)\n    res = similar(w)\n    n = length(w)\n    for (i, l) in zip(eachindex(res), Iterators.reverse(w))\n        res[i] = inv(A, l)\n    end\n    return res\nend\n\nBase.inv(A::Alphabet{T}, a::T) where {T} = A[-A[a]]\n\nfunction _print_syllable(io, symbol, pow)\n    str = string(symbol)\n    if length(str) > 3 && endswith(str, \"^-1\")\n        print(io, first(str, length(str)-3), \"^-\", pow)\n    else\n        if pow == 1\n            print(io, str)\n        else\n            print(io, str, \"^\", pow)\n        end\n    end\nend\n\nfunction print_repr(io::IO, w::AbstractWord, A::Alphabet, sep=\"*\")\n    if isone(w)\n        print(io, w)\n    else\n        first_syllable = true\n        idx = 1\n        pow = 1\n        while idx < length(w)\n            if w[idx] == w[idx+1]\n                pow += 1\n                idx += 1\n            else\n                first_syllable || print(io, sep)\n                _print_syllable(io, A[w[idx]], pow)\n                first_syllable = false\n                pow = 1\n                idx += 1\n            end\n        end\n        @assert idx == length(w)\n        first_syllable || print(io, sep)\n        _print_syllable(io, A[w[idx]], pow)\n    end\nend\n", "meta": {"hexsha": "8b6be26184a0cd83edc6f241229a340eec99369b", "size": 6251, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/alphabets.jl", "max_stars_repo_name": "kalmarek/KnuthBendix.jl", "max_stars_repo_head_hexsha": "74f6bdbb804723f88cb8ed1b60bb27b3e839576b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-10-31T19:26:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T07:15:53.000Z", "max_issues_repo_path": "src/alphabets.jl", "max_issues_repo_name": "kalmarek/KnuthBendix.jl", "max_issues_repo_head_hexsha": "74f6bdbb804723f88cb8ed1b60bb27b3e839576b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 51, "max_issues_repo_issues_event_min_datetime": "2020-05-12T12:14:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-18T17:12:31.000Z", "max_forks_repo_path": "src/alphabets.jl", "max_forks_repo_name": "kalmarek/KnuthBendix.jl", "max_forks_repo_head_hexsha": "74f6bdbb804723f88cb8ed1b60bb27b3e839576b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-19T13:07:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-19T13:07:13.000Z", "avg_line_length": 24.3229571984, "max_line_length": 134, "alphanum_fraction": 0.5610302352, "num_tokens": 1977, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713671682749474, "lm_q2_score": 0.11436853221906972, "lm_q1q2_score": 0.052282055327005084}}
{"text": "# # GARTEUR SM-AG19 Testbed: Construction of the geometry\n\n# Source code: [`garteur_geometry_vis_tut.jl`](garteur_geometry_vis_tut.jl)\n\n# ## Description\n\n# This virtual test application is based on the test article  \n# used by the GARTEUR Structures & Materials Action Group 19  \n# which organized a Round Robin exercise where 12 European laboratories  \n# tested a single structure between 1995 and 1997. The benchmark structure   \n# was a laboratory structure built to simulate the dynamic behaviour  \n# of an aeroplane. The structure was initially built for a benchmark  \n# study on experimental modal analysis conducted by the  \n# Structures and Materials Action Group (SM-AG19) of the Group  \n# for Aeronautical Research and Technology in EURope (GARTEUR).  \n# The test-bed was designed and manufactured by ONERA, France.\n\n# ![](IMAC97photo.png)\n\n# ### References\n\n# - [GARTEUR] Ground Vibration Test Techniques, compiled by A Gravelle, GARTEUR\n#   Structures & Materials Action Group 19 Technical report TP-115, 1999.\n# - [BW] Etienne Balmes, Jan R. Wright, GARTEUR GROUP ON GROUND VIBRATION\n#   TESTING | RESULTS FROM THE TEST OF A SINGLE STRUCTURE BY 12 LABORATORIES IN\n#   EUROPE, Proceedings of DETC'97, 1997 ASME Design Engineering Technical\n#   Conferences, September 14-17, 1997, Sacramento, California.\n\n# ## Goals\n\n# - Show how to construct model from multiple connected beams.\n# - Demonstrate the use of massless connectors.\n# - Visualize the structure interactively.\n# \n\n##\n# ## Geometry of the testbed airplane.\n\n# The aluminum testbed was a rather simple structure which was reasonably\n# dynamically representative of a simple airplane structure [GARTEUR](@ref\n# References). It was composed of several beams simulating a fuselage with wings\n# and a tail. Wing tip drums allowed to adjust bending and torsion frequencies\n# similarly to airplane ones, with some very close modal frequencies. \n\n# ![](garteur-geom.png)\n\n# The script included below defines the geometry of the structure, the\n# cross-sectional properties, the connectivity, and the location of the nodes.\n\ninclude(\"garteur_geometry_tut.jl\")\n\n##\n# ## Basic visualization\n\n# Here we use the `PlotlyJS` plotting library, with some simple utilities for\n# generating geometry of beams.\nusing PlotlyJS\nusing FinEtoolsFlexBeams.VisUtilModule: plot_solid, plot_space_box, render, default_layout_3d, save_to_json\n\n# The colors are used to help distinguish between the individual parts of the model.\ncolors = [\n\"rgb(125, 155, 155)\",  # 1 body\n\"rgb(125, 155, 155)\",  # 2 wing\n\"rgb(125, 155, 155)\",  # 3 drums\n\"rgb(125, 155, 155)\",  # 4 vertical tail\n\"rgb(125, 155, 155)\",  # 5 horizontal tail\n\"rgb(125, 155, 155)\",  # 6 constraining plate\n\"rgb(15, 15, 155)\",  # 7 massless connectors: wing beam to plate\n\"rgb(125, 15, 15)\",  # 8 massless connectors: wing beam to drums\n\"rgb(125, 175, 15)\",  # 9 massless connectors: fuselage to wing beam\n\"rgb(125, 175, 15)\",  # 10 massless connectors: fuselage to tail\n\"rgb(15, 155, 155)\",  # 11 massless connectors: sensors, point masses\n]\n\n# The geometry is defined in terms of \"traces\" (a trace is in the `PlotlyJS`\n# parlance a graphical object; it could be a curve, surface, and a lot of other\n# things). It is the name for a graphical object in `PlotlyJS`. The two points\n# below define a box, which is helpful when setting the extents of the graphics\n# display.\ntbox = plot_space_box([[-1.2 * L -1.2 * L -1.2 * L]; [+1.2 * L +1.2 * L +1.2 * L]])\n# For each finite element set in the array `fesa`, generate the graphics to\n# represent that object.\ntraces = let traces = tbox\n    for fes in fesa\n        labl  = fes.label[1]\n        tm = plot_solid(fens, fes; facecolor=colors[labl]);\n        traces = cat(traces, tm; dims = 1)\n    end\n    traces\nend\n# The layout of the plot is defined with simple defaults.\nlayout = default_layout_3d(;width=900, height=900)\n# Next, the graphics is rendered, and may be interacted with by zooming,\n# panning, etc.\npl = render(traces; layout = layout)\n\n##\n# ## Visualizing the nodes\n\n# In order to be able to discern the nodes we will reduce the opacity of the\n# surfaces representing the beams, otherwise the nodes would be hidden by these\n# surfaces. Otherwise the geometries defined in the same way as above.\n\ntbox = plot_space_box([[-1.2 * L -1.2 * L -1.2 * L]; [+1.2 * L +1.2 * L +1.2 * L]])\ntraces = let traces = tbox\n    for fes in fesa\n        labl  = fes.label[1]\n        tm = plot_solid(fens, fes; facecolor=colors[labl], opacity = 0.3);\n        traces = cat(traces, tm; dims = 1)\n    end\n    traces\nend\n# Next we add to the \"traces\" the graphics representing all the nodes in the\n# model as bright red dots.\nusing FinEtoolsFlexBeams.VisUtilModule: plot_nodes\ntraces = cat(traces, plot_nodes(fens; color = \"rgb(255, 15, 5)\"); dims = 1)\n\n# Finally, the graphics is presented.\nlayout = default_layout_3d(;width=900, height=900)\npl = render(traces; layout = layout)\n\ntrue", "meta": {"hexsha": "d245863253e02760cf7b2126cab4fc61263ce3f2", "size": 4921, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/src/tutorials/garteur_geometry_vis_tut.jl", "max_stars_repo_name": "PetrKryslUCSD/FinEtoolsFlexBeamsTutorials.jl", "max_stars_repo_head_hexsha": "970460483aa01ebcc86d6840070825752633ad8e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/src/tutorials/garteur_geometry_vis_tut.jl", "max_issues_repo_name": "PetrKryslUCSD/FinEtoolsFlexBeamsTutorials.jl", "max_issues_repo_head_hexsha": "970460483aa01ebcc86d6840070825752633ad8e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "docs/src/tutorials/garteur_geometry_vis_tut.jl", "max_forks_repo_name": "PetrKryslUCSD/FinEtoolsFlexBeamsTutorials.jl", "max_forks_repo_head_hexsha": "970460483aa01ebcc86d6840070825752633ad8e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3360655738, "max_line_length": 107, "alphanum_fraction": 0.7236334078, "num_tokens": 1394, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4571367168274948, "lm_q2_score": 0.11436852165386972, "lm_q1q2_score": 0.05228205049726425}}
{"text": "# Copyright (c) 2019 Arpit Bhatia and contributors                               #src\n#                                                                                #src\n# Permission is hereby granted, free of charge, to any person obtaining a copy   #src\n# of this software and associated documentation files (the \"Software\"), to deal  #src\n# in the Software without restriction, including without limitation the rights   #src\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      #src\n# copies of the Software, and to permit persons to whom the Software is          #src\n# furnished to do so, subject to the following conditions:                       #src\n#                                                                                #src\n# The above copyright notice and this permission notice shall be included in all #src\n# copies or substantial portions of the Software.                                #src\n#                                                                                #src\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     #src\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       #src\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    #src\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         #src\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  #src\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  #src\n# SOFTWARE.                                                                      #src\n\n# # Variables, constraints, and objective functions\n\n# **Originally Contributed by**: Arpit Bhatia\n\n# While the last tutorial introduced you to basics of of JuMP code, this\n# tutorial will go in depth focusing on how to work with different parts of a\n# JuMP program.\n\nusing JuMP\nmodel = Model();\n\n# ## Variable basics\n\n# ### Variable bounds\n\n# All of the variables we have created till now have had a bound. We can also\n# create a free variable.\n\n@variable(model, free_x)\n\n# While creating a variable, instead of using the <= and >= syntax, we can also\n# use the `lower_bound` and `upper_bound` keyword arguments.\n\n@variable(model, keyword_x, lower_bound = 1, upper_bound = 2)\n\n# We can query whether a variable has a bound using the `has_lower_bound` and\n# `has_upper_bound` functions. The values of the bound can be obtained using the\n# `lower_bound` and `upper_bound` functions.\n\nhas_upper_bound(keyword_x)\n\n#-\n\nupper_bound(keyword_x)\n\n# Note querying the value of a bound that does not exist will result in an error.\n\ntry #hide\nlower_bound(free_x)\ncatch err; showerror(stderr, err); end  #hide\n\n# JuMP also allows us to change the bounds on variable. We will learn this in\n# the problem modification tutorial.\n\n# ### [Containers](@id tutorial_variable_container)\n\n# We have already seen how to add a single variable to a model using the\n# [`@variable`](@ref) macro. Let's now look at more ways to add variables to a\n# JuMP model.\n\n# JuMP provides data structures for adding collections of variables to a model.\n# These data structures are referred to as Containers and are of three types:\n# `Arrays`, `DenseAxisArrays`, and `SparseAxisArrays`.\n\n# #### Arrays\n\n# JuMP arrays are created in a similar syntax to Julia arrays with the addition\n# of specifying that the indices start with 1. If we do not tell JuMP that the\n# indices start at 1, it will create a `DenseAxisArray` instead.\n\n@variable(model, a[1:2, 1:2])\n\n# An n-dimensional variable $x \\in {R}^n$ having a bound $l \\preceq x \\preceq u$\n# ($l, u \\in {R}^n$) is added in the following manner.\n\nn = 10\nl = [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]\nu = [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]\n\n@variable(model, l[i] <= x[i = 1:n] <= u[i])\n\n# Note that while working with Containers, we can also create variable bounds\n# depending upon the indices:\n\n@variable(model, y[i = 1:2, j = 1:2] >= 2i + j)\n\n# #### DenseAxisArrays\n\n# `DenseAxisArrays` are used when the required indices are not one-based integer\n# ranges. The syntax is similar except with an arbitrary vector as an index as\n# opposed to a one-based range.\n\n# An example where the indices are integers but do not start with one.\n\n@variable(model, z[i = 2:3, j = 1:2:3] >= 0)\n\n# Another example where the indices are an arbitrary vector.\n\n@variable(model, w[1:5,[\"red\", \"blue\"]] <= 1)\n\n# #### SparseAxisArrays\n\n# `SparseAxisArrays` are created when the indices do not form a rectangular set.\n# For example, this applies when indices have a dependence upon previous indices\n# (called triangular indexing).\n\n@variable(model, u[i = 1:3, j = i:5])\n\n# We can also conditionally create variables by adding a comparison check that\n# depends upon the named indices and is separated from the indices by a\n# semi-colon (;).\n\n@variable(model, v[i = 1:9; mod(i, 3) == 0])\n\n# ### Variable types\n\n# The last arguement to the `@variable` macro is usually the variable type. Here\n# we'll look at how to specifiy the variable type.\n\n# #### Integer variables\n\n# Integer optimization variables are constrained to the set $x \\in {Z}$\n\n@variable(model, integer_x, Int)\n\n# or\n\n@variable(model, integer_z, integer = true)\n\n# #### Binary variables\n\n# Binary optimization variables are constrained to the set $x \\in \\{0, 1\\}$.\n\n@variable(model, binary_x, Bin)\n\n# or\n\n@variable(model, binary_z, binary = true)\n\n# #### Semidefinite variables\n\n# JuMP also supports modeling with semidefinite variables. A square symmetric\n# matrix X is positive semidefinite if all eigenvalues are nonnegative.\n\n@variable(model, psd_x[1:2, 1:2], PSD)\n\n# We can also impose a weaker constraint that the square matrix is only\n# symmetric (instead of positive semidefinite) as follows:\n\n@variable(model, sym_x[1:2, 1:2], Symmetric)\n\n# ## Constraint basics\n\nmodel = Model()\n@variable(model, x)\n@variable(model, y)\n@variable(model, z[1:10]);\n\n# ### Constraint references\n\n# While calling the `@constraint` macro, we can also set up a constraint\n# reference. Such a referrence is useful for obtaining additional information\n# about the constraint, such as its dual solution.\n\n@constraint(model, con, x <= 4)\n\n# ### [Containers](@id tutorial_constraint_container)\n\n# Just as we had containers for variables, JuMP also provides `Arrays`,\n# `DenseAxisArrays`, and `SparseAxisArrays` for storing collections of\n# constraints. Examples for each container type are given below.\n\n# #### Arrays\n\n@constraint(model, [i = 1:3], i * x <= i + 1)\n\n# #### DenseAxisArrays\n\n@constraint(model, [i = 1:2, j = 2:3], i * x <= j + 1)\n\n# #### SparseAxisArrays\n\n@constraint(model, [i = 1:2, j = 1:2; i != j], i * x <= j + 1)\n\n# ### Constraints in a loop\n\n# We can add constraints using regular Julia loops\n\nfor i in 1:3\n    @constraint(model, 6x + 4y >= 5i)\nend\n\n# or use for each loops inside the `@constraint` macro.\n\n@constraint(model, [i in 1:3], 6x + 4y >= 5i)\n\n# We can also create constraints such as $\\sum _{i = 1}^{10} z_i \\leq 1$\n\n@constraint(model, sum(z[i] for i in 1:10) <= 1)\n\n# ## Objective functions\n\n# While the recommended way to set the objective is with the [`@objective`](@ref)\n# macro, the functions [`set_objective_sense`](@ref) and [`set_objective_function`](@ref)\n# provide an equivalent lower-level interface.\n\nusing GLPK\n\nmodel = Model(GLPK.Optimizer)\n@variable(model, x >= 0)\n@variable(model, y >= 0)\nset_objective_sense(model, MOI.MIN_SENSE)\nset_objective_function(model, x + y)\n\noptimize!(model)\n\n#-\n\nobjective_value(model)\n\n# To query the objective function from a model, we use the [`objective_sense`](@ref),\n# [`objective_function`](@ref), and [`objective_function_type`](@ref) functions.\n\nobjective_sense(model)\n\n#-\n\nobjective_function(model)\n\n#-\n\nobjective_function_type(model)\n\n# # Vectorized syntax\n\n# We can also add constraints and an objective to JuMP using vectorized linear\n# algebra. We'll illustrate this by solving an LP in standard form i.e.\n\n# ```math\n# \\begin{aligned}\n# & \\min & c^T x \\\\\n# & \\;\\;\\text{s.t.} & A x = b \\\\\n# & & x \\succeq 0 \\\\\n# & & x \\in \\mathbb{R}^n\n# \\end{aligned}\n# ```\n\nvector_model = Model(GLPK.Optimizer)\n\nA= [ 1 1 9  5;\n     3 5 0  8;\n     2 0 6 13]\n\nb = [7; 3; 5]\n\nc = [1; 3; 5; 2]\n\n@variable(vector_model, x[1:4] >= 0)\n@constraint(vector_model, A * x .== b)\n@objective(vector_model, Min, c' * x)\n\noptimize!(vector_model)\n\n#-\n\nobjective_value(vector_model)\n", "meta": {"hexsha": "8dc74228e448e799955d495b6ac0a1761cc337ab", "size": 8400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/variables_constraints_objective.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lang/Julia/variables_constraints_objective.jl", "max_issues_repo_name": "ethansaxenian/RosettaDecode", "max_issues_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lang/Julia/variables_constraints_objective.jl", "max_forks_repo_name": "ethansaxenian/RosettaDecode", "max_forks_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3249097473, "max_line_length": 89, "alphanum_fraction": 0.6747619048, "num_tokens": 2227, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489886026626094, "lm_q2_score": 0.1259227631950178, "lm_q1q2_score": 0.05224521093119116}}
{"text": "__precompile__()\n\n\"\"\"\nThis is a package I use to handle numerical-model parameters,\nthus the name. However, it should be useful otherwise too.\nIt has two main features:\n- keyword type constructors with default values, and\n- unpacking and packing of composite types and dicts.\n\nThe macro `@with_kw` which decorates a type definition to\nallow default values and a keyword constructor:\n\n```\njulia> using Parameters\n\njulia> @with_kw struct A\n           a::Int = 6\n           b::Float64 = -1.1\n           c::UInt8\n       end\n\njulia> A(c=4)\nA\n  a: 6\n  b: -1.1\n  c: 4\n```\n\nUnpacking is done with `@unpack` (`@pack!` is similar):\n```\nstruct B\n    a\n    b\n    c\nend\n@unpack a, c = B(4,5,6)\n# is equivalent to\nBB = B(4,5,6)\na = BB.a\nc = BB.c\n```\n\"\"\"\nmodule Parameters\nimport Base: @__doc__\nimport OrderedCollections: OrderedDict\nusing UnPack: @unpack, @pack!\n\nexport @with_kw, @with_kw_noshow, type2dict, reconstruct, @unpack, @pack!, @pack\n\n## Parser helpers\n#################\n\n# To iterate over code blocks dropping the line-number bits:\nstruct Lines\n    block::Expr\nend\nstart(lns::Lines) = 1\nfunction next(lns::Lines, nr)\n    for i=nr:length(lns.block.args)\n        if lns.block.args[i] isa LineNumberNode\n            continue\n        end\n        if ( lns.block.args[i] isa Symbol\n             || lns.block.args[i] isa String # doc-string\n             || !(lns.block.args[i].head==:line))\n            return lns.block.args[i], i+1\n        end\n    end\n    return -1\nend\nfunction done(lns::Lines, nr)\n    if next(lns::Lines, nr)==-1\n        true\n    else\n        false\n    end\nend\nfunction Base.iterate(lns::Lines, nr=start(lns))\n    nr = next(lns, nr)\n    return nr == -1 ? nothing : nr\nend\n\n# This is not O(1) but hey...\nfunction Base.setindex!(lns::Lines, val, ind)\n    ii = 1\n    for i=1:length(lns.block.args)\n        if lns.block.args[i] isa LineNumberNode\n            continue\n        end\n        if lns.block.args[i] isa Symbol || !(lns.block.args[i].head==:line)\n            if ind==ii\n                lns.block.args[i] = val\n                return nothing\n            end\n            ii +=1\n        end\n    end\n    throw(BoundsError(\"Attempted to set line $ind of $(ii-1) length code-block\"))\nend\n\n# Transforms :(a::b) -> :a\ndecolon2(a::Expr) = (@assert a.head==:(::);  a.args[1])\ndecolon2(a::Symbol) = a\n\n# Keep the ::T of the args if T \u2208 typparas,\n# leave symbols as is, drop field-doc-strings.\nfunction keep_only_typparas(args, typparas)\n    args = copy(args)\n    tokeep = Int[]\n    typparas_ = map(stripsubtypes, typparas)\n    for i=1:length(args)\n        isa(args[i], String) && continue # do not keep field doc-strings\n        push!(tokeep, i)\n        isa(args[i], Symbol) && continue\n        # keep the typepara if \u2208 typparas\n        @assert args[i].head==:(::)\n        T = args[i].args[2]\n        if !(symbol_in(typparas_, T))\n            args[i] = decolon2(args[i])\n        end\n    end\n    args[tokeep]\nend\n\n# check whether a symbol is contained in an expression\nsymbol_in(s::Symbol, ex::Symbol) = s==ex\nsymbol_in(s::Symbol, ex) = false\nfunction symbol_in(s::Symbol, ex::Expr)\n    for a in ex.args\n        symbol_in(s,a) && return true\n    end\n    return false\nend\nsymbol_in(s::Symbol, ex::Vector) = any(map(e->symbol_in(s, e), ex))\nsymbol_in(s::Vector, ex) = any(map(ss->symbol_in(ss, ex), s))\n\n# Returns the name of the type as Symbol\nfunction typename(typedef::Expr)\n    if typedef.args[2] isa Symbol\n        return typedef.args[2]\n    elseif typedef.args[2].args[1] isa Symbol\n        return typedef.args[2].args[1]\n    elseif typedef.args[2].args[1].args[1] isa Symbol\n        return typedef.args[2].args[1].args[1]\n    else\n        error(\"Could not parse type-head from: $typedef\")\n    end\nend\n\n# Transforms:  Expr(:<:, :A, :B) -> :A\nstripsubtypes(s::Symbol) = s\nfunction stripsubtypes(e::Expr)\n    e.args[1]\nend\nstripsubtypes(vec::Vector) = [stripsubtypes(v) for v in vec]\n\nfunction check_inner_constructor(l)\n    if l.args[1].head==:where\n        fnhead = l.args[1].args[1]\n    else\n        fnhead = l.args[1]\n    end\n    if length(fnhead.args)==1\n        error(\"No inner constructors with zero positional arguments allowed!\")\n    elseif (length(fnhead.args)==2 #1<length(fnhead.args)<=3\n            && fnhead.args[2] isa Expr\n            && fnhead.args[2].head==:parameters)\n        error(\"No inner constructors with zero positional arguments plus keyword arguments allowed!\")\n    end\n    nothing\nend\n\n## Exported helper functions\n#####################\n\"\"\"\nTransforms a type-instance into a dictionary.\n\n```\njulia> type T\n           a\n           b\n       end\n\njulia> type2dict(T(4,5))\nDict{Symbol,Any} with 2 entries:\n  :a => 4\n  :b => 5\n```\n\nNote that this uses `getproperty`.\n\"\"\"\nfunction type2dict(dt)\n    di = Dict{Symbol,Any}()\n    for n in propertynames(dt)\n        di[n] = getproperty(dt, n)\n    end\n    di\nend\n\n\"\"\"\nMake a new instance of a type with the same values as the input type\nexcept for the fields given in the AbstractDict second argument or as\nkeywords.  Works for types, Dicts, and NamedTuples.\n\nNote: this is not very performant.  Check Setfield.jl for a faster &\nnicer implementation.\n\n```jldoctest\njulia> using Parameters\n\njulia> struct A\n           a\n           b\n       end\n\njulia> a = A(3,4)\nA(3, 4)\n\njulia> b = reconstruct(a, b=99)\nA(3, 99)\n```\n\"\"\"\nfunction reconstruct(pp::T, di) where T\n    if pp isa AbstractDict\n        pp = deepcopy(pp)\n        for (k,v) in di\n            !haskey(pp, k) && error(\"Field $k not in type $T\")\n            pp[k] = v\n        end\n        return pp\n    else\n        di = !isa(di, AbstractDict) ? Dict(di) : copy(di)\n        ns = fieldnames(T)\n        args = []\n        for (i,n) in enumerate(ns)\n            push!(args, pop!(di, n, getfield(pp, n)))\n        end\n        length(di)!=0 && error(\"Fields $(keys(di)) not in type $T\")\n        return pp isa NamedTuple ? T(Tuple(args)) : T(args...)\n    end\nend\nreconstruct(pp; kws...) = reconstruct(pp, kws)\n\n\n###########################\n# Keyword constructors with @with_kw\n##########################\n\n# A type with fields (r,a) in variable aa becomes\n# quote\n#     r = aa.r\n#     a = aa.a\n# end\n_unpack(binding, fields) = Expr(:block, [:($f = $binding.$f) for f in fields]...)\n# Pack fields back into binding using reconstruct:\nfunction _pack_mutable(binding, fields)\n    e = Expr(:block, [:($binding.$f = $f) for f in fields]...)\n    push!(e.args, binding)\n    e\nend\nfunction _pack_new(T, fields)\n    Expr(:call, T, fields...)\nend\n\nconst macro_hidden_nargs = length(:(@m).args) - 1 # ==1 on Julia 0.6, ==2 on Julia 0.7\n\n\"\"\"\nThis function is called by the `@with_kw` macro and does the syntax\ntransformation from:\n\n```julia\n@with_kw struct MM{R}\n    r::R = 1000.\n    a::R\nend\n```\n\ninto\n\n```julia\nstruct MM{R}\n    r::R\n    a::R\n    MM{R}(r,a) where {R} = new(r,a)\n    MM{R}(;r=1000., a=error(\"no default for a\")) where {R} = MM{R}(r,a) # inner kw, type-paras are required when calling\nend\nMM(r::R,a::R) where {R} = MM{R}(r,a) # default outer positional constructor\nMM(;r=1000,a=error(\"no default for a\")) =  MM(r,a) # outer kw, so no type-paras are needed when calling\nMM(m::MM; kws...) = reconstruct(mm,kws)\nMM(m::MM, di::Union{AbstractDict, Tuple{Symbol,Any}}) = reconstruct(mm, di)\nmacro unpack_MM(varname)\n    esc(quote\n    r = varname.r\n    a = varname.a\n    end)\nend\nmacro pack_MM(varname)\n    esc(quote\n    varname = $Parameters.reconstruct(varname,r=r,a=a)\n    end)\nend\n```\n\"\"\"\nfunction with_kw(typedef, mod::Module, withshow=true)\n    if typedef.head==:tuple # named-tuple\n        withshow==false && error(\"`@with_kw_noshow` not supported for named tuples\")\n        return with_kw_nt(typedef, mod)\n    elseif typedef.head != :struct\n        error(\"\"\"Only works on type-defs or named tuples.\n              Make sure to have a space after `@with_kw`, e.g. `@with_kw (a=1,)\n              Also, make sure to use a trailing comma for single-field NamedTuples.\n              \"\"\")\n    end\n    err1str = \"Field \\'\"\n    err2str = \"\\' has no default, supply it with keyword.\"\n\n    inner_constructors = Any[]\n\n    # parse a few things\n    tn = typename(typedef) # the name of the type\n    ismutable = typedef.args[1]\n    # Returns M{...} (removes any supertypes)\n    if typedef.args[2] isa Symbol\n        typparas = Any[]\n    elseif typedef.args[2].head==:<:\n        if typedef.args[2].args[1] isa Symbol\n            typparas = Any[]\n        else\n            typparas = typedef.args[2].args[1].args[2:end]\n        end\n    else\n        typparas = typedef.args[2].args[2:end]\n    end\n\n    # error on types without fields\n    lns = Lines(typedef.args[3])\n    if done(lns, start(lns))\n        error(\"@with_kw only supported for types which have at least one field.\")\n    end\n    # default type @deftype\n    l, i = next(lns, start(lns))\n    if l isa Expr && l.head == :macrocall && l.args[1] == Symbol(\"@deftype\")\n        has_deftyp = true\n        if length(l.args) != (2 + macro_hidden_nargs)\n            error(\"Malformed `@deftype` line $l\")\n        end\n        deftyp = l.args[2 + macro_hidden_nargs]\n        if done(lns, i)\n            error(\"@with_kw only supported for types which have at least one field.\")\n        end\n    else\n        has_deftyp = false\n    end\n\n    # Expand all macros (except @assert) in body now (only works at\n    # top-level)\n    # See issue https://github.com/mauro3/Parameters.jl/issues/21\n    lns2 = Any[] # need new lines as expanded macros may have many lines\n    for (i,l) in enumerate(lns) # loop over body of typedef\n        if i==1 && has_deftyp\n            push!(lns2, l)\n            continue\n        end\n        if l isa Symbol || l isa String\n            push!(lns2, l)\n            continue\n        end\n        if l.head==:macrocall && l.args[1]!=Symbol(\"@assert\")\n            tmp = macroexpand(mod, l)\n            if tmp.head==:block\n                llns = Lines(tmp)\n                for ll in llns\n                    push!(lns2, ll)\n                end\n            else\n                push!(lns2,tmp)\n            end\n        else\n            push!(lns2, l)\n        end\n    end\n    lns = lns2\n\n    # the vars for the unpack macro\n    unpack_vars = Any[]\n    # the type def\n    fielddefs = quote end # holds r::R etc\n    fielddefs.args = Any[] # in julia 0.5 this is [:( # /home/mauro/.julia/v0.5/Parameters/src/Parameters.jl, line 228:)]\n    kws = OrderedDict{Any, Any}()\n    # assertions in the body\n    asserts = Any[]\n    for (i,l) in enumerate(lns) # loop over body of typedef\n        if i==1 && has_deftyp\n            # ignore @deftype line\n            continue\n        end\n        if l isa Symbol  # no default value and no type annotation\n            if has_deftyp\n                push!(fielddefs.args, :($l::$deftyp))\n            else\n                push!(fielddefs.args, l)\n            end\n            sym = l\n            syms = string(sym)\n            kws[sym] = :(error($err1str * $syms * $err2str))\n            # unwrap-macro\n            push!(unpack_vars, sym)\n        elseif l isa String # doc-string\n            push!(fielddefs.args, l)\n        elseif l.head==:(=)  # default value and with or without type annotation\n            if l.args[1] isa Expr && (l.args[1].head==:call || # inner constructor\n                                        l.args[1].head==:where && l.args[1].args[1].head==:call) # inner constructor with `where`\n                check_inner_constructor(l)\n                push!(inner_constructors, l)\n            else\n                fld = l.args[1]\n                if fld isa Symbol && has_deftyp # no type annotation\n                    fld = :($fld::$deftyp)\n                end\n                # add field doc-strings\n                docstring = string(\"Default: \", l.args[2])\n                if i > 1 && lns[i-1] isa String\n                    # if the last line was a docstring, append the default\n                    fielddefs.args[end] *= \" \" * docstring\n                else\n                    # otherwise add a new line\n                    push!(fielddefs.args, docstring)\n                end\n                push!(fielddefs.args, fld)\n                kws[decolon2(fld)] = l.args[2]\n                # unwrap-macro\n                push!(unpack_vars, decolon2(fld))\n            end\n        elseif l.head==:macrocall  && l.args[1]==Symbol(\"@assert\")\n            # store all asserts\n            push!(asserts, l)\n        elseif l.head==:function # inner constructor\n            check_inner_constructor(l)\n            push!(inner_constructors, l)\n        elseif l.head==:block\n            error(\"No nested begin-end allowed in type defintion\")\n        else # no default value but with type annotation\n            push!(fielddefs.args, l)\n            sym = decolon2(l.args[1])\n            syms = string(sym)\n            kws[sym] = :(error($err1str *$syms * $err2str))\n            # unwrap-macro\n            push!(unpack_vars, l.args[1])\n        end\n    end\n    # The type definition without inner constructors:\n    typ = Expr(:struct, typedef.args[1:2]..., copy(fielddefs))\n\n    # Inner keyword constructor.  Note that this calls the positional\n    # constructor under the hood and not `new`.  That way a user can\n    # provide a special positional constructor (say enforcing\n    # invariants) which also gets used with the keywords.\n    args = Any[]\n    kwargs = Expr(:parameters)\n    for (k,w) in kws\n        push!(args, k)\n        push!(kwargs.args, Expr(:kw,k,w))\n    end\n    if length(typparas)>0\n        tps = stripsubtypes(typparas)\n        innerc = :( $tn{$(tps...)}($kwargs) where {$(tps...)} = $tn{$(tps...)}($(args...)))\n    else\n        innerc = :($tn($kwargs) = $tn($(args...)) )\n    end\n    push!(typ.args[3].args, innerc)\n\n    # Inner positional constructor: only make it if no inner\n    # constructors are user-defined.  If one or several are defined,\n    # assume that one has the standard positional signature.\n    if length(inner_constructors)==0\n        if length(typparas)>0\n            tps = stripsubtypes(typparas)\n            innerc2 = :( $tn{$(tps...)}($(args...)) where {$(tps...)} = new{$(tps...)}($(args...)) )\n        else\n            innerc2 = :($tn($(args...)) = new($(args...)))\n        end\n        prepend!(innerc2.args[2].args, asserts)\n        push!(typ.args[3].args, innerc2)\n    else\n        if length(asserts)>0\n            error(\"Assertions are only allowed in type-definitions with no inner constructors.\")\n        end\n        append!(typ.args[3].args, inner_constructors)\n    end\n\n    # Outer positional constructor which does not need explicit\n    # type-parameters when called.  Only make this constructor if\n    #  (1) type parameters are used at all\n    #  (2) all type parameters are used in the fields (otherwise get a\n    #      \"method is not callable\" warning!)\n    #       See also https://github.com/JuliaLang/julia/issues/17186\n    if typparas!=Any[] # condition (1)\n        # fields definitions stripped of ::Int etc., only keep ::T if T\u2208typparas :\n        fielddef_strip_contT = keep_only_typparas(fielddefs.args, typparas)\n        outer_positional = :(  $tn($(fielddef_strip_contT...)) where {$(typparas...)}\n                             = $tn{$(stripsubtypes(typparas)...)}($(args...)))\n        # Check condition (2)\n        checks = true\n        for tp in stripsubtypes(typparas)\n            checks = checks && symbol_in(tp, fielddefs.args)\n        end\n        if !checks\n            outer_positional = :()\n        end\n    else\n        outer_positional = :()\n    end\n\n    # Outer keyword constructor, useful to infer the type parameter\n    # automatically.  This calls the outer positional constructor.\n    # only create if type parameters are used.\n    if typparas==Any[]\n        outer_kw=:()\n    else\n        outer_kw = :($tn($kwargs) = $tn($(args...)) )\n    end\n    # NOTE: The reason to have both outer and inner keyword\n    # constructors are to allow both calls:\n    #   `MT4(r=4, a=5.0)` (outer kwarg-constructor) and\n    #   `MT4{Float32, Int}(r=4, a=5.)` (inner kwarg constructor).\n\n    ## outer copy constructor\n    ###\n    outer_copy = quote\n        $tn(pp::$tn; kws... ) = $Parameters.reconstruct(pp, kws)\n        # $tn(pp::$tn, di::Union(AbstractDict,Vararg{Tuple{Symbol,Any}}) ) = reconstruct(pp, di) # see issue https://github.com/JuliaLang/julia/issues/11537\n        # $tn(pp::$tn, di::Union(AbstractDict, Tuple{Vararg{Tuple{Symbol, Any}}}) ) = reconstruct(pp, di) # see issue https://github.com/JuliaLang/julia/issues/11537\n        $tn(pp::$tn, di::$Parameters.AbstractDict) = $Parameters.reconstruct(pp, di)\n        $tn(pp::$tn, di::Vararg{Tuple{Symbol,Any}} ) = $Parameters.reconstruct(pp, di)\n    end\n\n    # (un)pack macro from https://groups.google.com/d/msg/julia-users/IQS2mT1ITwU/hDtlV7K1elsJ\n    unpack_name = Symbol(\"unpack_\"*string(tn))\n    pack!_name = Symbol(\"pack_\"*string(tn)*\"!\")\n    pack_name = Symbol(\"pack_\"*string(tn))\n    showfn = if withshow\n        :(function Base.show(io::IO, p::$tn)\n              if get(io, :compact, false) || get(io, :typeinfo, nothing)==$tn\n                Base.show_default(IOContext(io, :limit => true), p)\n              else\n                # just dumping seems to give ok output, in particular for big data-sets:\n                dump(IOContext(io, :limit => true), p, maxdepth=1)\n              end\n          end)\n    else\n        :nothing\n    end\n    if ismutable\n        pack_macros = quote\n            macro $pack!_name(ex)\n                esc($Parameters._pack_mutable(ex, $unpack_vars))\n            end\n            macro $pack_name()\n                esc($Parameters._pack_new($tn, $unpack_vars))\n            end\n        end\n    else\n        pack_macros = quote\n            macro $pack_name()\n                esc($Parameters._pack_new($tn, $unpack_vars))\n            end\n        end\n    end\n\n    # Finish up\n    quote\n        Base.@__doc__ $typ\n        $outer_positional\n        $outer_kw\n        $outer_copy\n        $showfn\n        macro $unpack_name(ex)\n            esc($Parameters._unpack(ex, $unpack_vars))\n        end\n        $pack_macros\n        $tn\n    end\nend\n\n\"\"\"\nDo the with-kw stuff for named tuples.\n\"\"\"\nfunction with_kw_nt(typedef, mod)\n    kwargs = []\n    args = []\n    nt = []\n    for a in typedef.args\n        if a isa Expr\n            a.head != :(=) && error(\"NameTuple fields need to be of form: `k=val`\")\n            sy = a.args[1]::Symbol\n            va = a.args[2]\n            push!(kwargs, Expr(:kw, sy, va))\n            push!(args, sy)\n            push!(nt, :($sy=$sy))\n        elseif a isa Symbol  # no default value given\n            sy = a\n            push!(args, sy)\n            push!(nt, :($sy=$sy))\n            push!(kwargs, Expr(:kw, sy, :(error(\"Supply default value for $($(string(sy)))\"))))\n        else\n            error(\"Cannot parse $(string(a))\")\n        end\n    end\n    NT = gensym(:NamedTuple_kw)\n    nt = Expr(:tuple, nt...)\n    quote\n        $NT(; $(kwargs...)) =$nt\n        $NT($(args...)) = $nt\n        $NT\n    end\nend\n\n\"\"\"\nMacro which allows default values for field types and a few other features.\n\nBasic usage:\n\n```julia\n@with_kw struct MM{R}\n    r::R = 1000.\n    a::Int = 4\nend\n```\n\nFor more details see manual.\n\"\"\"\nmacro with_kw(typedef)\n    return esc(with_kw(typedef, __module__, true))\nend\n\nmacro with_kw(args...)\n    error(\"\"\"Only works on type-defs or named tuples.\n          Did you try to construct a NamedTuple but omitted the space between the macro and the NamedTuple?\n          Do `@with_kw (a=1, b=2)` and not `@with_kw(a=1, b=2)`.\n          \"\"\")\nend\n\n\"\"\"\nAs `@with_kw` but does not define a `show` method to avoid annoying\nredefinition warnings.\n\n```julia\n@with_kw_noshow struct MM{R}\n    r::R = 1000.\n    a::Int = 4\nend\n```\n\nFor more details see manual.\n\"\"\"\nmacro with_kw_noshow(typedef)\n    return esc(with_kw(typedef, __module__, false))\nend\n\nend # module\n", "meta": {"hexsha": "eafab426f920d5beb9efac512395d437a1b9826f", "size": 19746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Parameters.jl", "max_stars_repo_name": "portugueslab/Parameters.jl", "max_stars_repo_head_hexsha": "699dfd8ef0d5211e5f8ddbe820e246a4edcc9b52", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Parameters.jl", "max_issues_repo_name": "portugueslab/Parameters.jl", "max_issues_repo_head_hexsha": "699dfd8ef0d5211e5f8ddbe820e246a4edcc9b52", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Parameters.jl", "max_forks_repo_name": "portugueslab/Parameters.jl", "max_forks_repo_head_hexsha": "699dfd8ef0d5211e5f8ddbe820e246a4edcc9b52", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8729198185, "max_line_length": 165, "alphanum_fraction": 0.5734832371, "num_tokens": 5521, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "module TestDeprecated\n\nusing Test, DataFrames, Random, Logging, Statistics\n\nconst \u2245 = isequal\n\nold_logger = global_logger(NullLogger())\n\n@testset \"deprecated tuple in sort\" begin\n    dv1 = [9, 1, 8, missing, 3, 3, 7, missing]\n    dv2 = [9, 1, 8, missing, 3, 3, 7, missing]\n    dv3 = Vector{Union{Int, Missing}}(1:8)\n    cv1 = CategoricalArray(dv1, ordered=true)\n\n    d = DataFrame(dv1 = dv1, dv2 = dv2, dv3 = dv3, cv1 = cv1)\n    @test sort(d, (:dv1, :cv1))[!, :dv3] == sortperm(dv1)\n    @test sort(d, (:dv1, :dv3))[!, :dv3] == sortperm(dv1)\n\n    df = DataFrame(rank=rand(1:12, 1000),\n                   chrom=rand(1:24, 1000),\n                   pos=rand(1:100000, 1000))\n    ds = sort(df, (order(:rank, rev=true),:chrom,:pos))\n    @test issorted(ds, (order(:rank, rev=true),:chrom,:pos))\n\n    ds2 = sort(df, (:rank, :chrom, :pos), rev=(true, false, false))\n    @test issorted(ds2, (order(:rank, rev=true), :chrom, :pos))\n\n    sort!(df, (:rank, :chrom, :pos), rev=(true, false, false))\n    @test issorted(df, (order(:rank, rev=true), :chrom, :pos))\nend\n\n@testset \"categorical constructor\" begin\n    df = DataFrame([Int, String], [:a, :b], [false, true], 3)\n    @test !(df[:a] isa CategoricalVector)\n    @test df[:b] isa CategoricalVector\n    @test_throws DimensionMismatch DataFrame([Int, String], [:a, :b], [true], 3)\nend\n\n@testset \"DataFrame constructors\" begin\n    df = DataFrame(Union{Int, Missing}, 10, 3)\n    @test size(df, 1) == 10\n    @test size(df, 2) == 3\n    @test typeof(df[1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[2]) == Vector{Union{Int, Missing}}\n    @test typeof(df[3]) == Vector{Union{Int, Missing}}\n    @test all(ismissing, df[1])\n    @test all(ismissing, df[2])\n    @test all(ismissing, df[3])\n    @test typeof(df[:, 1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[:, 2]) == Vector{Union{Int, Missing}}\n    @test typeof(df[:, 3]) == Vector{Union{Int, Missing}}\n    @test all(ismissing, df[:, 1])\n    @test all(ismissing, df[:, 2])\n    @test all(ismissing, df[:, 3])\n\n    df = DataFrame([Union{Int, Missing}, Union{Float64, Missing}, Union{String, Missing}], 100)\n    @test size(df, 1) == 100\n    @test size(df, 2) == 3\n    @test typeof(df[1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[2]) == Vector{Union{Float64, Missing}}\n    @test typeof(df[3]) == Vector{Union{String, Missing}}\n    @test all(ismissing, df[1])\n    @test all(ismissing, df[2])\n    @test all(ismissing, df[3])\n    @test typeof(df[:, 1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[:, 2]) == Vector{Union{Float64, Missing}}\n    @test typeof(df[:, 3]) == Vector{Union{String, Missing}}\n    @test all(ismissing, df[:, 1])\n    @test all(ismissing, df[:, 2])\n    @test all(ismissing, df[:, 3])\n\n    df = DataFrame([Union{Int, Missing}, Union{Float64, Missing}, Union{String, Missing}],\n                [:A, :B, :C], [false, false, true], 100)\n    @test size(df, 1) == 100\n    @test size(df, 2) == 3\n    @test typeof(df[1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[2]) == Vector{Union{Float64, Missing}}\n    @test typeof(df[3]) <: CategoricalVector{Union{String, Missing}}\n    @test all(ismissing, df[1])\n    @test all(ismissing, df[2])\n    @test all(ismissing, df[3])\n    @test typeof(df[:, 1]) == Vector{Union{Int, Missing}}\n    @test typeof(df[:, 2]) == Vector{Union{Float64, Missing}}\n    @test typeof(df[:, 3]) <: CategoricalVector{Union{String, Missing}}\n    @test all(ismissing, df[:, 1])\n    @test all(ismissing, df[:, 2])\n    @test all(ismissing, df[:, 3])\nend\n\ndf = DataFrame(Union{Int, Missing}, 2, 2)\n@test size(df) == (2, 2)\n@test eltype.(eachcol(df)) == [Union{Int, Missing}, Union{Int, Missing}]\n\n@test df \u2245 DataFrame([Union{Int, Missing}, Union{Float64, Missing}], 2)\n\n@testset \"colwise\" begin\n    Random.seed!(1)\n    df = DataFrame(a = repeat(Union{Int, Missing}[1, 3, 2, 4], outer=[2]),\n                   b = repeat(Union{Int, Missing}[2, 1], outer=[4]),\n                   c = Vector{Union{Float64, Missing}}(randn(8)))\n\n    missingfree = DataFrame([collect(1:10)], [:x1])\n\n    @testset \"::Function, ::AbstractDataFrame\" begin\n        cw = colwise(sum, df)\n        answer = [20, 12, -0.4283098098931877]\n        @test isa(cw, Vector{Real})\n        @test size(cw) == (ncol(df),)\n        @test cw == answer\n\n        cw = colwise(sum, missingfree)\n        answer = [55]\n        @test isa(cw, Array{Int, 1})\n        @test size(cw) == (ncol(missingfree),)\n        @test cw == answer\n    end\n\n    @testset \"::Function, ::GroupedDataFrame\" begin\n        gd = groupby(DataFrame(A = [:A, :A, :B, :B], B = 1:4), :A)\n        @test colwise(length, gd) == [[2,2], [2,2]]\n    end\n\n    @testset \"::Vector, ::AbstractDataFrame\" begin\n        cw = colwise([sum], df)\n        answer = [20 12 -0.4283098098931877]\n        @test isa(cw, Array{Real, 2})\n        @test size(cw) == (length([sum]),ncol(df))\n        @test cw == answer\n\n        cw = colwise([sum, minimum], missingfree)\n        answer = reshape([55, 1], (2,1))\n        @test isa(cw, Array{Int, 2})\n        @test size(cw) == (length([sum, minimum]), ncol(missingfree))\n        @test cw == answer\n\n        cw = colwise([Vector{Union{Int, Missing}}], missingfree)\n        answer = reshape([Vector{Union{Int, Missing}}(1:10)], (1,1))\n        @test isa(cw, Array{Vector{Union{Int, Missing}},2})\n        @test size(cw) == (1, ncol(missingfree))\n        @test cw == answer\n\n        @test_throws MethodError colwise([\"Bob\", :Susie], DataFrame(A = 1:10, B = 11:20))\n    end\n\n    @testset \"::Vector, ::GroupedDataFrame\" begin\n        gd = groupby(DataFrame(A = [:A, :A, :B, :B], B = 1:4), :A)\n        @test colwise([length], gd) == [[2 2], [2 2]]\n    end\n\n    @testset \"::Tuple, ::AbstractDataFrame\" begin\n        cw = colwise((sum, length), df)\n        answer = Any[20 12 -0.4283098098931877; 8 8 8]\n        @test isa(cw, Array{Real, 2})\n        @test size(cw) == (length((sum, length)), ncol(df))\n        @test cw == answer\n\n        cw = colwise((sum, length), missingfree)\n        answer = reshape([55, 10], (2,1))\n        @test isa(cw, Array{Int, 2})\n        @test size(cw) == (length((sum, length)), ncol(missingfree))\n        @test cw == answer\n\n        cw = colwise((CategoricalArray, Vector{Union{Int, Missing}}), missingfree)\n        answer = reshape([CategoricalArray(1:10), Vector{Union{Int, Missing}}(1:10)],\n                         (2, ncol(missingfree)))\n        @test typeof(cw) == Array{AbstractVector,2}\n        @test size(cw) == (2, ncol(missingfree))\n        @test cw == answer\n\n        @test_throws MethodError colwise((\"Bob\", :Susie), DataFrame(A = 1:10, B = 11:20))\n    end\n\n    @testset \"::Tuple, ::GroupedDataFrame\" begin\n        gd = groupby(DataFrame(A = [:A, :A, :B, :B], B = 1:4), :A)\n        @test colwise((length), gd) == [[2,2],[2,2]]\n    end\nend\n\n@testset \"deletecols and deletecols!\" begin\n    df = DataFrame(a=[1,2], b=[3.0, 4.0])\n    @test deletecols(df, :a) == DataFrame(b=[3.0, 4.0])\n    deletecols!(df, :a)\n    @test df == DataFrame(b=[3.0, 4.0])\n\n    df = DataFrame(a=[1,2], b=[3.0, 4.0])\n    @test deletecols(df, Not(2)) == DataFrame(b=[3.0, 4.0])\n    deletecols!(df, Not(2))\n    @test df == DataFrame(b=[3.0, 4.0])\n\n    df = DataFrame(a=[1,2], b=[3.0, 4.0])\n    @test deletecols(df, :a, copycols=false)[1] === df.b\n    @test deletecols(df, []) == df\n    @test deletecols(df, Not([])) == DataFrame()\nend\n\n@testset \"df[col] and df[col] for getindex, view, and setindex\" begin\n    @testset \"getindex DataFrame\" begin\n        df = DataFrame(a=1:3, b=4:6, c=7:9)\n        @test df[1] == [1, 2, 3]\n        @test df[1] === eachcol(df)[1]\n        @test df[1:2] == DataFrame(a=1:3, b=4:6)\n        @test df[r\"[ab]\"] == DataFrame(a=1:3, b=4:6)\n        @test df[Not(Not(r\"[ab]\"))] == DataFrame(a=1:3, b=4:6)\n        @test df[Not(3)] == DataFrame(a=1:3, b=4:6)\n        @test eachcol(df)[1] === df[1]\n        @test eachcol(view(df,1:2))[1] == eachcol(df)[1]\n        @test eachcol(df[1:2])[1] == eachcol(df)[1]\n        @test eachcol(df[r\"[ab]\"])[1] == eachcol(df)[1]\n        @test eachcol(df[Not(Not(r\"[ab]\"))])[1] == eachcol(df)[1]\n        @test eachcol(df[Not(r\"[c]\")])[1] == eachcol(df)[1]\n        @test eachcol(df[1:2])[1] !== eachcol(df)[1]\n        @test df[:] == df\n        @test df[r\"\"] == df\n        @test df[Not(Not(r\"\"))] == df\n        @test df[Not(1:0)] == df\n        @test df[:] !== df\n        @test df[r\"\"] !== df\n        @test df[Not(Not(r\"\"))] !== df\n        @test df[Not(1:0)] !== df\n        @test eachcol(view(df, :))[1] == eachcol(df)[1]\n        @test eachcol(df[:])[1] == eachcol(df)[1]\n        @test eachcol(df[r\"\"])[1] == eachcol(df)[1]\n        @test eachcol(df[Not(1:0)])[1] == eachcol(df)[1]\n        @test eachcol(df[:])[1] !== eachcol(df)[1]\n        @test eachcol(df[r\"\"])[1] !== eachcol(df)[1]\n        @test eachcol(df[Not(1:0)])[1] !== eachcol(df)[1]\n    end\n    @testset \"getindex df[col] and df[cols]\" begin\n        x = [1, 2, 3]\n        df = DataFrame(x=x, copycols=false)\n        @test df[:x] === x\n        @test df[[:x]].x !== x\n        @test df[:].x !== x\n        @test df[r\"x\"].x !== x\n        @test df[r\"\"].x !== x\n        @test df[Not(1:0)].x !== x\n    end\n    @testset \"view DataFrame\" begin\n        df = DataFrame(a=1:3, b=4:6, c=7:9)\n        @test view(df, 1) == [1, 2, 3]\n        @test view(df, 1) isa SubArray\n        @test view(df, 1:2) isa SubDataFrame\n        @test view(df, 1:2) == df[1:2]\n        @test view(df, r\"[ab]\") isa SubDataFrame\n        @test view(df, r\"[ab]\") == df[1:2]\n        @test view(df, Not(Not(r\"[ab]\"))) isa SubDataFrame\n        @test view(df, Not(Not(r\"[ab]\"))) == df[1:2]\n        @test view(df, :) isa SubDataFrame\n        @test view(df, :) == df\n        @test parent(view(df, :)) === df\n        @test view(df, r\"\") isa SubDataFrame\n        @test view(df, r\"\") == df\n        @test parent(view(df, r\"\")) === df\n        @test view(df, Not(1:0)) isa SubDataFrame\n        @test view(df, Not(1:0)) == df\n        @test parent(view(df, Not(1:0))) === df\n    end\n    @testset \"getindex SubDataFrame\" begin\n        df = DataFrame(x=-1:3, a=0:4, b=3:7, c=6:10, d=9:13)\n        sdf = view(df, 2:4, 2:4)\n        @test sdf[1] == [1, 2, 3]\n        @test sdf[1] isa SubArray\n        @test sdf[1:2] == DataFrame(a=1:3, b=4:6)\n        @test sdf[1:2] isa SubDataFrame\n        @test sdf[r\"[ab]\"] == DataFrame(a=1:3, b=4:6)\n        @test sdf[r\"[ab]\"] isa SubDataFrame\n        @test sdf[Not(Not(r\"[ab]\"))] == DataFrame(a=1:3, b=4:6)\n        @test sdf[Not(Not(r\"[ab]\"))] isa SubDataFrame\n        @test sdf[:] == df[2:4, 2:4]\n        @test sdf[:] isa SubDataFrame\n        @test sdf[r\"\"] == df[2:4, 2:4]\n        @test sdf[r\"\"] isa SubDataFrame\n        @test sdf[Not(1:0)] == df[2:4, 2:4]\n        @test sdf[Not(1:0)] isa SubDataFrame\n        @test parent(sdf[:]) === parent(sdf)\n        @test parent(sdf[r\"\"]) === parent(sdf)\n        @test parent(sdf[Not([])]) === parent(sdf)\n    end\n    @testset \"view SubDataFrame\" begin\n        df = DataFrame(x=-1:3, a=0:4, b=3:7, c=6:10, d=9:13)\n        sdf = view(df, 2:4, 2:4)\n        @test view(sdf, 1) == [1, 2, 3]\n        @test view(sdf, 1) isa SubArray\n        @test view(sdf, 1:2) isa SubDataFrame\n        @test view(sdf, 2:3) == df[2:4, 3:4]\n        @test view(sdf, r\"[ab]\") isa SubDataFrame\n        @test view(sdf, r\"[ab]\") == df[2:4, r\"[ab]\"]\n        @test view(sdf, Not(Not(r\"[ab]\"))) isa SubDataFrame\n        @test view(sdf, Not(Not(r\"[ab]\"))) == df[2:4, Not(Not(r\"[ab]\"))]\n        @test view(sdf, :) isa SubDataFrame\n        @test view(sdf, :) == df[2:4, 2:4]\n        @test view(sdf, r\"\") isa SubDataFrame\n        @test view(sdf, r\"\") == df[2:4, 2:4]\n        @test view(sdf, Not(1:0)) isa SubDataFrame\n        @test view(sdf, Not(1:0)) == df[2:4, 2:4]\n        @test parent(view(sdf, :)) == parent(sdf)\n        @test parent(view(sdf, r\"\")) == parent(sdf)\n        @test parent(view(sdf, Not(1:0))) == parent(sdf)\n    end\n\n    @testset \"old setindex! tests\" begin\n        df = DataFrame(reshape(1:12, 4, :))\n        df[1, :] = df[1:1, :]\n\n        df = DataFrame(reshape(1:12, 4, :))\n\n        # Scalar broadcasting assignment of rows\n        df[1:2, :] = 1\n        df[[true,false,false,true], :] = 3\n\n        # Vector broadcasting assignment of rows\n        df[1:2, :] = [2,3]\n        df[[true,false,false,true], :] = [2,3]\n\n        # Broadcasting assignment of columns\n        df[:, 1] = 1\n        df[:x3] = 2\n\n        # assignment of subtables\n        df[1, 1:2] = df[2:2, 2:3]\n        df[1:2, 1:2] = df[2:3, 2:3]\n        df[[true,false,false,true], 2:3] = df[1:2,1:2]\n\n        # scalar broadcasting assignment of subtables\n        df[1:2, 1:2] = 3\n        df[[true,false,false,true], 2:3] = 3\n\n        # vector broadcasting assignment of subtables\n        df[1:2, 1:2] = [3,2]\n        df[[true,false,false,true], 2:3] = [2,3]\n\n        # test of 1-row DataFrame assignment\n        df = DataFrame([1 2 3])\n        df[1, 2:3] = DataFrame([11 12])\n        @test df == DataFrame([1 11 12])\n\n        df = DataFrame([1 2 3])\n        df[1, [false, true, true]] = DataFrame([11 12])\n        @test df == DataFrame([1 11 12])\n    end\n\n    @testset \"old test/dataframes.jl tests\" begin\n        df = DataFrame(a = Union{Int, Missing}[2, 3],\n                    b = Union{DataFrame, Missing}[DataFrame(c = 1), DataFrame(d = 2)])\n        df[1, :b][:e] = 5\n\n        x = DataFrame(a = [1, 2, 3], b = [4, 5, 6])\n\n        #test_group(\"DataFrame assignment\")\n        # Insert single column\n        x0 = x[Int[], :]\n        @test_throws ArgumentError x0[:d] = [1]\n        @test_throws ArgumentError x0[:d] = 1:3\n\n        # Insert single value\n        x[:d] = 3\n        @test x[:d] == [3, 3, 3]\n    end\n\n    @testset \"test getindex using df[col] and df[cols] syntax\" begin\n        x = [1]\n        y = [1]\n        df = DataFrame(x=x, y=y, copycols=false)\n        @test df.x === x\n        @test df[:y] === y\n        @test df[1] === x\n        @test df[1:1][1] == x\n        @test df[r\"x\"][1] == x\n        @test df[1:1][1] !== x\n        @test df[r\"x\"][1] !== x\n        @test df[1:2][:y] == y\n        @test df[1:2][:y] !== y\n        @test df[r\"\"][:y] == y\n        @test df[r\"\"][:y] !== y\n        @test df[:][:x] == x\n        @test df[:][:x] !== x\n        @test df[[:y,:x]][:x] == x\n        @test df[[:y,:x]][:x] !== x\n    end\n\n    @testset \"setindex! special cases\" begin\n        df = DataFrame(rand(3,2), [:x3, :x3_1])\n        @test_throws ArgumentError df[3] = [1, 2]\n        @test_throws ArgumentError df[4] = [1, 2, 3]\n        df[3] = [1,2,3]\n        df[4] = [1,2,3]\n        @test propertynames(df) == [:x3, :x3_1, :x3_2, :x4]\n        df = DataFrame()\n        @test_throws MethodError df[true] = 1\n        @test_throws MethodError df[true] = [1,2,3]\n        @test_throws MethodError df[1:2, true] = [1,2]\n        @test_throws MethodError df[1, true] = 1\n        @test_throws ArgumentError df[1, 100] = 1\n        @test_throws BoundsError df[1:2, 100] = [1,2]\n    end\n\n    @testset \"handling of end in indexing\" begin\n        z = DataFrame(rand(4,5))\n        for x in [z, view(z, 1:4, :)]\n            y = deepcopy(x)\n            @test x[end] == x[5]\n            @test x[end:end] == x[5:5]\n            @test x[end, :] == x[4, :]\n            x[end] = 1:4\n            y[5] = 1:4\n            @test x == y\n            x[4:end] = DataFrame([11:14, 21:24])\n            y[4] = [11:14;]\n            y[5] = [21:24;]\n            @test x == y\n        end\n    end\n\n    @testset \"aliasing in indexing\" begin\n        # columns should not alias if scalar broadcasted\n        df = DataFrame(A=[0], B=[0])\n        df[1:end] = 0.0\n        df[1, :A] = 1.0\n        @test df[1, :B] === 0\n\n        df = DataFrame(A=[0], B=[0])\n        df[:, 1:end] = 0.0\n        df[1, :A] = 1.0\n        @test df[1, :B] === 0\n\n        # columns should not alias if vector assigned\n        df = DataFrame(A=[0], B=[0])\n        x = [0.0]\n        df[1:end] = x\n        x[1] = 1.0\n        @test df[1, :A] === 0.0\n        @test df[1, :B] === 0.0\n        df[1, :A] = 1.0\n        @test df[1, :B] === 0.0\n\n        df = DataFrame(A=[0], B=[0])\n        x = [0.0]\n        df[:, 1:end] = x\n        x[1] = 1.0\n        @test df[1, :A] === 0.0\n        @test df[1, :B] === 0.0\n        df[1, :A] = 1.0\n        @test df[1, :B] === 0.0\n    end\nend\n\n@testset \"eltypes\" begin\n    @test eltypes(DataFrame(x=[1], y=[\"a\"])) == [Int, String]\nend\n\n@testset \"melt\" begin\n    mdf = DataFrame(id=[missing,1,2,3], a=1:4, b=1:4)\n    @test unstack(melt(mdf, :id), :id, :variable, :value)[1:3,:] == sort(mdf)[1:3,:]\n    @test unstack(melt(mdf, :id), :id, :variable, :value)[:, 2:3] == sort(mdf)[:, 2:3]\n    @test unstack(melt(mdf, Not(Not(:id))), :id, :variable, :value)[1:3,:] == sort(mdf)[1:3,:]\n    @test unstack(melt(mdf, Not(Not(:id))), :id, :variable, :value)[:, 2:3] == sort(mdf)[:, 2:3]\n\n    Random.seed!(1234)\n    x = DataFrame(rand(100, 50))\n    x[!, :id] = [1:99; missing]\n    x[!, :id2] = string.(\"a\", x[!, :id])\n    x[!, :s] = [i % 2 == 0 ? randstring() : missing for i in 1:100]\n    allowmissing!(x, :x1)\n    x[1, :x1] = missing\n    y = melt(x, [:id, :id2])\n    @test y \u2245 melt(x, r\"id\")\n    @test y \u2245 melt(x, Not(Not(r\"id\")))\n\n    d1 = DataFrame(a = Array{Union{Int, Missing}}(repeat([1:3;], inner = [4])),\n                b = Array{Union{Int, Missing}}(repeat([1:4;], inner = [3])),\n                c = Array{Union{Float64, Missing}}(randn(12)),\n                d = Array{Union{Float64, Missing}}(randn(12)),\n                e = Array{Union{String, Missing}}(map(string, 'a':'l')))\n    d1s = stack(d1, [:a, :b])\n    d1m = melt(d1, [:c, :d, :e])\n    @test d1m == melt(d1, r\"[cde]\")\n    @test d1s == d1m\n    d1m = melt(d1[:, [1,3,4]], :a)\n    @test propertynames(d1m) == [:a, :variable, :value]\n    d1m_named = melt(d1[:, [1,3,4]], :a, variable_name=:letter, value_name=:someval)\n    @test propertynames(d1m_named) == [:a, :letter, :someval]\n    dx = melt(d1, [], [:a])\n    @test dx == melt(d1, r\"xxx\", r\"a\")\n    @test size(dx) == (12, 2)\n    @test propertynames(dx) == [:variable, :value]\n    dx = melt(d1, :a, [])\n    @test dx == stack(d1, r\"xxx\", r\"a\")\n    @test size(dx) == (0, 3)\n    @test propertynames(dx) == [:a, :variable, :value]\n    d1m = melt(d1, [:c, :d, :e], view=true)\n    @test d1m == melt(d1, r\"[cde]\", view=true)\n    d1m = melt(d1[:, [1,3,4]], :a, view=true)\n    @test propertynames(d1m) == [:a, :variable, :value]\n    d1m_named = melt(d1, [:c, :d, :e], variable_name=:letter, value_name=:someval, view=true)\n    @test d1m_named == melt(d1, r\"[cde]\", variable_name=:letter, value_name=:someval, view=true)\n    @test propertynames(d1m_named) == [:c, :d, :e, :letter, :someval]\n    df1 = melt(DataFrame(rand(10,10)))\n    df1[!, :id] = 1:100\n    @test size(unstack(df1, :variable, :value)) == (100, 11)\n    @test_throws ArgumentError unstack(melt(DataFrame(rand(3,2))), :variable, :value)\nend\n\n@testset \"insertcols!\" begin\n    df = DataFrame(x = 1:2)\n    @test insertcols!(df, 2, y=2:3) == DataFrame(x=1:2, y=2:3)\nend\n\n@testset \"join\" begin\n    name = DataFrame(ID = Union{Int, Missing}[1, 2, 3],\n                    Name = Union{String, Missing}[\"John Doe\", \"Jane Doe\", \"Joe Blogs\"])\n    job = DataFrame(ID = Union{Int, Missing}[1, 2, 2, 4],\n                    Job = Union{String, Missing}[\"Lawyer\", \"Doctor\", \"Florist\", \"Farmer\"])\n    @test_throws ArgumentError join(name, job)\n    @test_throws ArgumentError join(name, job, on=:ID, kind=:other)\n\n    df1 = DataFrame(id=[1,2,3], x=[1,2,3])\n    df2 = DataFrame(id=[1,2,4], y=[1,2,4])\n    df3 = DataFrame(id=[1,3,4], z=[1,3,4])\n    @test_throws ArgumentError join(df1, df2, df3, on=:id, kind=:xxx)\nend\n\n@testset \"eachcol(df, true)\" begin\n    df = DataFrame(a=1:3, b=4:6, c=7:9)\n    @test eachcol(df)[1] === last(eachcol(df, true)[1])\n    @test eachcol(df)[1] === last(eachcol(df, true)[1])\n\n    df = DataFrame(rand(3,4), [:a, :b, :c, :d])\n    df2 = DataFrame(eachcol(df, true))\n    @test df == df2\n    df2 = DataFrame(eachcol(df, true), copycols=false)\n    @test df == df2\n    @test all(((a,b),) -> a === b, zip(eachcol(df), eachcol(df2)))\n\n    @test Tables.rowtable(df) == Tables.rowtable((;eachcol(df, true)...))\n    @test Tables.columntable(df) == Tables.columntable((;eachcol(df, true)...))\n\n    for (a, b, c, d) in zip(Tables.rowtable(df),\n                            Tables.namedtupleiterator(eachrow(df)),\n                            Tables.namedtupleiterator(eachcol(df)),\n                            Tables.namedtupleiterator((;eachcol(df, true)...)))\n        @test a isa NamedTuple\n        @test a === b === c === d\n    end\n\n    @test Tables.getcolumn((;eachcol(df, true)...), 1) == Tables.getcolumn(df, 1)\n    @test Tables.getcolumn((;eachcol(df, true)...), :a) == Tables.getcolumn(df, :a)\n    @test Tables.columnnames((;eachcol(df, true)...)) == Tuple(Tables.columnnames(df))\n\n    df = DataFrame(A = Vector{Union{Int, Missing}}(1:2), B = Vector{Union{Int, Missing}}(2:3))\n    @test size(eachcol(df, true)) == (size(df, 2),)\n    @test parent(DataFrame(eachcol(df, true))) == df\n    @test names(DataFrame(eachcol(df, true))) == names(df)\n    @test IndexStyle(eachcol(df, true)) == IndexLinear()\n    @test size(eachcol(df, false)) == (size(df, 2),)\n    @test IndexStyle(eachcol(df, false)) == IndexLinear()\n    @test length(eachcol(df, true)) == size(df, 2)\n    @test length(eachcol(df, false)) == size(df, 2)\n    @test eachcol(df, true)[1] == (:A => df[:, 1])\n    @test eachcol(df, false)[1] == df[:, 1]\n    @test collect(eachcol(df, true)) isa Vector{Pair{Symbol, AbstractVector}}\n    @test collect(eachcol(df, true)) == [:A => [1, 2], :B => [2, 3]]\n    @test collect(eachcol(df, false)) isa Vector{AbstractVector}\n    @test collect(eachcol(df, false)) == [[1, 2], [2, 3]]\n    @test eltype(eachcol(df, true)) == Pair{Symbol, AbstractVector}\n    @test eltype(eachcol(df, false)) == AbstractVector\n    for col in eachcol(df, true)\n        @test typeof(col) <: Pair{Symbol, <:AbstractVector}\n    end\n    for col in eachcol(df, false)\n        @test isa(col, AbstractVector)\n    end\n    @test map(minimum, eachcol(df, false)) == [1, 2]\n    @test eltype(map(Vector{Float64}, eachcol(df, false))) == Vector{Float64}\n\n    df = DataFrame([11:16 21:26 31:36 41:46])\n    sdf = view(df, [3,1,4], [3,1,4])\n    @test eachcol(sdf, true) == eachcol(df[[3,1,4], [3,1,4]], true)\n    @test eachcol(sdf, false) == eachcol(df[[3,1,4], [3,1,4]], false)\n    @test size(eachcol(sdf, true)) == (3,)\n    @test size(eachcol(sdf, false)) == (3,)\n\n    df_base = DataFrame([11:16 21:26 31:36 41:46])\n    for df in (df_base, view(df_base, 1:3, 1:3))\n        @test df == DataFrame(eachcol(df, true))\n    end\nend\n\n@testset \"deprecated by/combine\" begin\n    vexp = x -> exp.(x)\n    Random.seed!(1)\n    df = DataFrame(a = repeat([1, 3, 2, 4], outer=[2]),\n                   b = repeat([2, 1], outer=[4]),\n                   c = rand(Int, 8))\n\n    @test combine(groupby(df, :a), [:c => sum]) == by(df, :a, c_sum = :c => sum)\n    @test combine(groupby(df, :a), [:c => vexp]) == by(df, :a, c_function = :c => vexp)\n    @test combine(groupby(df, :a), [:b => sum, :c => sum]) ==\n        by(df, :a, b_sum = :b => sum, c_sum = :c => sum)\n    @test combine(groupby(df, :a), [:b => vexp, :c => identity]) ==\n        by(df, :a, b_function = :b => vexp, c_identity = :c => identity)\n\n    gd = groupby(df, :a)\n\n    @test combine(gd, [:c => sum]) == combine(gd, c_sum = :c => sum)\n    @test combine(gd, [:c => vexp]) == combine(gd, c_function = :c => vexp)\n    @test combine(gd, [:b => sum, :c => sum]) ==\n        combine(gd, b_sum = :b => sum, c_sum = :c => sum) ==\n        combine(gd, (:b,) => sum, (:c,) => sum)\n    @test combine(gd, [:b => vexp, :c => identity]) ==\n        combine(gd, b_function = :b => vexp, c_identity = :c => identity)\n\n    @test map(identity, gd)  == combine(identity, gd, ungroup=false)\n    @test map(:b => mean, gd)  == combine(:b => mean, gd, ungroup=false)\n    @test map([:b,:c] => x -> x.b+x.c, gd) ==\n          combine(AsTable([:b, :c]) => x -> x.b+x.c, gd, ungroup=false)\n    @test by(identity, df, :a) == combine(identity, gd)\n    @test by(:b => mean, df, :a) == combine(:b => mean, gd)\n    @test by([:b,:c] => x -> x.b+x.c, df, :a) ==\n          combine(AsTable([:b, :c]) => x -> x.b+x.c, gd)\n    @test by(df, :a, identity) == combine(identity, gd)\n    @test by(df, :a, :b => mean) == combine(:b => mean, gd)\n    @test by(df, :a, [:b,:c] => x -> x.b+x.c) ==\n          combine(AsTable([:b, :c]) => x -> x.b+x.c, gd)\n    @test by(df, :a, :b => mean, [:b,:c] => x -> x.b+x.c) ==\n          combine(gd, :b => mean, AsTable([:b, :c]) => x -> x.b+x.c)\n    @test by(df, :a, p = :b => mean, q = [:b,:c] => x -> x.b+x.c) ==\n          combine(gd, :b => mean => :p, AsTable([:b, :c]) => (x -> x.b+x.c) => :q)\nend\n\n@testset \"deprecated aggregate\" begin\n    df = DataFrame(x1=Int64[1,2,2], x2=Int64[1,1,2], y=Int64[1,2,3])\n    @test aggregate(df, sum) == aggregate(df, [], sum) == aggregate(df, 1:0, sum)\n    @test aggregate(df, sum) == aggregate(df, [], sum, sort=true, skipmissing=true)\n\n    Random.seed!(1)\n    N = 20\n    d1 = Vector{Union{Int64, Missing}}(rand(1:2, N))\n    d2 = CategoricalArray([\"A\", \"B\", missing])[rand(1:3, N)]\n    d3 = randn(N)\n    d4 = randn(N)\n    df7 = DataFrame([d1, d2, d3], [:d1, :d2, :d3])\n\n    @test aggregate(DataFrame(a=1), identity) == DataFrame(a_identity=1)\n\n    df8 = aggregate(df7[:, [1, 3]], sum)\n    @test df8[1, :d1_sum] == sum(df7[!, :d1])\n\n    df8 = aggregate(df7, :d2, [sum, length], sort=true)\n    @test df8[1:2, :d2] == [\"A\", \"B\"]\n    @test size(df8, 1) == 3\n    @test size(df8, 2) == 5\n    @test sum(df8[!, :d1_length]) == N\n    @test all(df8[!, :d1_length] .> 0)\n    @test df8[!, :d1_length] == [sum(isequal.(d2, \"A\")), sum(isequal.(d2, \"B\")), sum(ismissing.(d2))]\n    df8\u2032 = aggregate(df7, 2, [sum, length], sort=true)\n    @test df8 \u2245 df8\u2032\n    adf = aggregate(groupby(df7, :d2, sort=true), [sum, length])\n    @test df8 \u2245 adf\n    adf\u2032 = aggregate(groupby(df7, 2, sort=true), [sum, length])\n    @test df8 \u2245 adf\u2032\n    adf = aggregate(groupby(df7, :d2), [sum, length], sort=true)\n    @test sort(df8, [:d1_sum, :d3_sum, :d1_length, :d3_length]) \u2245 adf\n    adf\u2032 = aggregate(groupby(df7, 2), [sum, length], sort=true)\n    @test adf \u2245 adf\u2032\n\n    df = DataFrame(a = [3, missing, 1], b = [100, 200, 300])\n    for dosort in (true, false), doskipmissing in (true, false)\n        @test aggregate(df, :a, sum, sort=dosort, skipmissing=doskipmissing) \u2245\n              aggregate(groupby(df, :a, sort=dosort, skipmissing=doskipmissing), sum)\n    end\n\n    # Check column names\n    anonf = x -> sum(x)\n    adf = aggregate(df7, :d2, [mean, anonf])\n    @test propertynames(adf) == [:d2, :d1_mean, :d3_mean,\n                                 :d1_function, :d3_function]\n    adf = aggregate(df7, :d2, [mean, mean, anonf, anonf])\n    @test propertynames(adf) == [:d2, :d1_mean, :d3_mean, :d1_mean_1, :d3_mean_1,\n                                 :d1_function, :d3_function, :d1_function_1, :d3_function_1]\n\n    df9 = aggregate(df7, :d2, [sum, length], sort=true)\n    @test df9 \u2245 df8\n    df9\u2032 = aggregate(df7, 2, [sum, length], sort=true)\n    @test df9\u2032 \u2245 df8\nend\n\n@testset \"deprecated deleterows!\" begin\n    @test deleterows!(DataFrame(x=[1, 2]), 1) ==\n        deleterows!(DataFrame(x=[1, 2]), [1]) ==\n        deleterows!(DataFrame(x=[1, 2]), [true, false]) == DataFrame(x=[2])\nend\n\n@testset \"by skipmissing and sort\" begin\n    df = DataFrame(a=[2, 2, missing, missing, 1, 1, 3, 3], b=1:8)\n    for dosort in (false, true), doskipmissing in (false, true)\n        @test by(df, :a, :b=>sum, sort=dosort, skipmissing=doskipmissing) \u2245\n            combine(groupby(df, :a, sort=dosort, skipmissing=doskipmissing), :b=>sum)\n    end\nend\n\n@testset \"map skipmissing and sort\" begin\n    df = DataFrame(a=[2, 2, missing, missing, 1, 1, 3, 3], b=1:8)\n    for dosort in (false, true), doskipmissing in (false, true)\n        gdf = groupby(df, :a, sort=dosort, skipmissing=doskipmissing)\n        @test map(identity, gdf) \u2245 combine(identity, gdf, ungroup=false)\n        @test map(:b => sum, gdf) \u2245 combine(:b => sum, gdf, ungroup=false)\n    end\nend\n\n@testset \"DataFrame!\" begin\n    x = [1,2,3]\n    y = [4,5,6]\n    # we allow this to make deprecation message simpler\n    @test DataFrame!(x=x, y=y, copycols=true) == DataFrame(x=x,y=y)\n    df1 = DataFrame(x=x, y=y)\n    df2 = DataFrame!(df1)\n    @test df1 == df2\n    @test df1.x === df2.x\n    @test df1.y === df2.y\n\n    a=[1,2,3]\n    df = DataFrame!(:a=>a, :b=>1, :c=>1:3)\n    @test propertynames(df) == [:a, :b, :c]\n    @test df.a === a\n\n    df = DataFrame!(\"a\"=>a, \"b\"=>1, \"c\"=>1:3)\n    @test propertynames(df) == [:a, :b, :c]\n    @test df.\"a\" === a\n\n    df = DataFrame!(Dict(:a=>a, :b=>1, :c=>1:3))\n    @test propertynames(df) == [:a, :b, :c]\n    @test df.a === a\n\n    df = DataFrame!(Dict(\"a\"=>a, \"b\"=>1, \"c\"=>1:3))\n    @test propertynames(df) == [:a, :b, :c]\n    @test df.\"a\" === a\n\n    df = DataFrame!((x, y))\n    @test propertynames(df) == [:x1, :x2]\n    @test df.x1 === x\n    @test df.x2 === y\n\n    df = DataFrame!((x, y), (:x1, :x2))\n    @test propertynames(df) == [:x1, :x2]\n    @test df.x1 === x\n    @test df.x2 === y\n\n    df = DataFrame!((x, y), (\"x1\", \"x2\"))\n    @test names(df) == [\"x1\", \"x2\"]\n    @test df.\"x1\" === x\n    @test df.\"x2\" === y\n\n    @test_throws MethodError DataFrame!([1 2; 3 4], copycols=false)\n    @test_throws MethodError DataFrame!([1 2; 3 4])\n    @test_throws MethodError DataFrame!([Union{Int, Missing}, Union{Float64, Missing}],\n                                        [:x1, :x2], 2)\nend\n\nglobal_logger(old_logger)\n\nend # module\n", "meta": {"hexsha": "a5116df546f5e5c0c70ae7044fc4b4e4d41cc30a", "size": 29376, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/deprecated.jl", "max_stars_repo_name": "AliMalik9599/DataFrames.jl", "max_stars_repo_head_hexsha": "da79269d107cd49c478f097f481e91c995956b3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/deprecated.jl", "max_issues_repo_name": "AliMalik9599/DataFrames.jl", "max_issues_repo_head_hexsha": "da79269d107cd49c478f097f481e91c995956b3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/deprecated.jl", "max_forks_repo_name": "AliMalik9599/DataFrames.jl", "max_forks_repo_head_hexsha": "da79269d107cd49c478f097f481e91c995956b3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5511811024, "max_line_length": 101, "alphanum_fraction": 0.5286968954, "num_tokens": 10509, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.40356685373537454, "lm_q2_score": 0.1294027383065593, "lm_q1q2_score": 0.05222265596312017}}
{"text": "####################################\n# The AbstractBlockArray interface #\n####################################\n\n\"\"\"\n    abstract AbstractBlockArray{T, N} <: AbstractArray{T, N}\n\nThe abstract type that represents a blocked array. Types that implement\nthe `AbstractBlockArray` interface should subtype from this type.\n\n** Typealiases **\n\n* `AbstractBlockMatrix{T}` -> `AbstractBlockArray{T, 2}`\n\n* `AbstractBlockVector{T}` -> `AbstractBlockArray{T, 1}`\n\n* `AbstractBlockVecOrMat{T}` -> `Union{AbstractBlockMatrix{T}, AbstractBlockVector{T}}`\n\"\"\"\nabstract type AbstractBlockArray{T, N} <: LayoutArray{T, N} end\nconst AbstractBlockMatrix{T} = AbstractBlockArray{T, 2}\nconst AbstractBlockVector{T} = AbstractBlockArray{T, 1}\nconst AbstractBlockVecOrMat{T} = Union{AbstractBlockMatrix{T}, AbstractBlockVector{T}}\n\nblock2string(b, s) = string(join(map(string,b), '\u00d7'), \"-blocked \", Base.dims2string(s))\n_block_summary(a) = string(block2string(blocksize(a), size(a)), \" \", typeof(a))\nBase.summary(a::AbstractBlockArray) = _block_summary(a)\n_show_typeof(io, a) = show(io, typeof(a))\nfunction _block_summary(io, a)    \n    print(io, block2string(blocksize(a), size(a)))\n    print(io, ' ')\n    _show_typeof(io, a)\nend\nBase.summary(io::IO, a::AbstractBlockArray) = _block_summary(io, a)\n\n# avoid to_shape which complicates axes\nBase.similar(a::AbstractBlockArray{T}) where {T}                             = similar(a, T)\nBase.similar(a::AbstractBlockArray, ::Type{T}) where {T}                     = similar(a, T, axes(a))\nBase.similar(a::AbstractBlockArray{T}, dims::Tuple) where {T}                = similar(a, T, dims)\n\nBase.IndexStyle(::Type{<:AbstractBlockArray}) = IndexCartesian()\n\n# need to overload axes to return BlockAxis\n@inline size(block_array::AbstractBlockArray) = map(length, axes(block_array))\n@inline axes(block_array::AbstractBlockArray) = throw(error(\"axes for \", typeof(block_array), \" is not implemented\"))\n\n\"\"\"\n    getblock(A, inds...)\n\nReturns the block at blockindex `inds...`. An alternative syntax is `A[Block(inds...)].\nThrows a `BlockBoundsError` if this block is out of bounds.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> v = Array(reshape(1:6, (2, 3)))\n2\u00d73 Array{Int64,2}:\n 1  3  5\n 2  4  6\n\njulia> A = BlockArray(v, [1,1], [2,1])\n2\u00d72-blocked 2\u00d73 BlockArray{Int64,2}:\n 1  3  \u2502  5\n \u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\n 2  4  \u2502  6\n\njulia> getblock(A, 2, 1)\n1\u00d72 Array{Int64,2}:\n 2  4\n\njulia> A[Block(1, 2)]\n1\u00d71 Array{Int64,2}:\n 5\n```\n\"\"\"\nfunction getblock(A::AbstractBlockArray{T,N}, ::Vararg{Integer, N}) where {T,N}\n    throw(error(\"getblock for \", typeof(A), \" is not implemented\"))\nend\n\n\n\"\"\"\n    getblock!(X, A, inds...)\n\nStores the block at blockindex `inds` in `X` and returns it. Throws a `BlockBoundsError` if the\nattempted assigned block is out of bounds.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> A = PseudoBlockArray(ones(2, 3), [1, 1], [2, 1])\n2\u00d72-blocked 2\u00d73 PseudoBlockArray{Float64,2}:\n 1.0  1.0  \u2502  1.0\n \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\n 1.0  1.0  \u2502  1.0\n\njulia> x = zeros(1, 2);\n\njulia> getblock!(x, A, 2, 1);\n\njulia> x\n1\u00d72 Array{Float64,2}:\n 1.0  1.0\n```\n\"\"\"\ngetblock!(X, A::AbstractBlockArray{T,N}, ::Vararg{Integer, N}) where {T,N} = throw(error(\"getblock! for \", typeof(A), \" is not implemented\"))\n\n@inline getblock!(X, A::AbstractBlockArray{T,N}, block::Block{N}) where {T,N}             = getblock!(X, A, block.n...)\n@inline getblock!(X, A::AbstractBlockVector, block::Block{1})                       = getblock!(X, A, block.n[1])\n@inline getblock!(X, A::AbstractBlockArray{T, N}, block::Vararg{Block{1}, N}) where {T,N} = getblock!(X, A, (Block(block).n)...)\n\n\"\"\"\n    setblock!(A, v, inds...)\n\nStores the block `v` in the block at block index `inds` in `A`. An alternative syntax is `A[Block(inds...)] = v`.\nThrows a `BlockBoundsError` if this block is out of bounds.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> A = PseudoBlockArray(zeros(2, 3), [1, 1], [2, 1]);\n\njulia> setblock!(A, [1 2], 1, 1);\n\njulia> A[Block(2, 1)] = [3 4];\n\njulia> A\n2\u00d72-blocked 2\u00d73 PseudoBlockArray{Float64,2}:\n 1.0  2.0  \u2502  0.0\n \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\n 3.0  4.0  \u2502  0.0\n```\n\"\"\"\nsetblock!(A::AbstractBlockArray{T,N}, v, ::Vararg{Integer, N}) where {T,N} = throw(error(\"setblock! for \", typeof(A), \" is not implemented\"))\n\n@inline setblock!(A::AbstractBlockArray{T, N}, v, block::Block{N}) where {T,N}      = setblock!(A, v, block.n...)\n@inline setblock!(A::AbstractBlockVector, v, block::Block{1})                       = setblock!(A, v, block.n[1])\n@inline setblock!(A::AbstractBlockArray{T, N}, v, block::Vararg{Block{1}, N}) where {T,N} = setblock!(A, v, (Block(block).n)...)\n\n\n\"\"\"\n    BlockBoundsError([A], [inds...])\n\nThrown when a block indexing operation into a block array, `A`, tried to access an out-of-bounds block, `inds`.\n\"\"\"\nstruct BlockBoundsError <: Exception\n    a::Any\n    i::Any\n    BlockBoundsError() = new()\n    BlockBoundsError(a::AbstractArray) = new(a)\n    BlockBoundsError(a::AbstractArray, @nospecialize(i)) = new(a,i)\nend\n\nBlockBoundsError(a::AbstractArray, I::Block) = BlockBoundsError(a, I.n)\n\nfunction Base.showerror(io::IO, ex::BlockBoundsError)\n    print(io, \"BlockBoundsError\")\n    if isdefined(ex, :a)\n        print(io, \": attempt to access \")\n            print(io, summary(ex.a))\n        if isdefined(ex, :i)\n            print(io, \" at block index [\")\n            join(io, ex.i, ',')\n            print(io, ']')\n        end\n    end\nend\n\n\"\"\"\n    blockcheckbounds(A, inds...)\n\nThrow a `BlockBoundsError` if the specified block indexes are not in bounds for the given block array.\nSubtypes of `AbstractBlockArray` should\nspecialize this method if they need to provide custom block bounds checking behaviors.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> A = BlockArray(rand(2,3), [1,1], [2,1]);\n\njulia> blockcheckbounds(A, 3, 2)\nERROR: BlockBoundsError: attempt to access 2\u00d72-blocked 2\u00d73 BlockArray{Float64,2,Array{Array{Float64,2},2},Tuple{BlockedUnitRange{Array{Int64,1}},BlockedUnitRange{Array{Int64,1}}}} at block index [3,2]\n[...]\n```\n\"\"\"\n@inline function blockcheckbounds(A::AbstractArray{T, N}, i::Vararg{Integer, N}) where {T,N}\n    if blockcheckbounds(Bool, A, i...)\n        return\n    else\n        throw(BlockBoundsError(A, i))\n    end\nend\n\n@inline function blockcheckbounds(::Type{Bool}, A::AbstractArray{T, N}, i::Vararg{Integer, N}) where {T,N}\n    n = blockaxes(A)\n    k = 0\n    for idx in 1:N # using enumerate here will allocate\n        k += 1\n        @inbounds _i = i[idx]\n        Block(_i) in n[k] || return false\n    end\n    return true\nend\n\nblockcheckbounds(A::AbstractArray{T, N}, i::Block{N}) where {T,N} = blockcheckbounds(A, i.n...)\n\n# Convert to @generated...\n@propagate_inbounds Base.getindex( block_arr::AbstractBlockArray{T, N}, block::Block{N}) where {T,N}       =  getblock(block_arr, block.n...)\n@propagate_inbounds Base.setindex!(block_arr::AbstractBlockArray{T, N}, v, block::Block{N}) where {T,N}    =  setblock!(block_arr, v, block.n...)\n@propagate_inbounds Base.getindex( block_arr::AbstractBlockVector, block::Block{1})                  =  getblock(block_arr, block.n[1])\n@propagate_inbounds Base.setindex!(block_arr::AbstractBlockVector, v, block::Block{1})               =  setblock!(block_arr, v, block.n[1])\n@inline Base.getindex(block_arr::AbstractBlockArray{T,N}, block::Vararg{Block{1}, N}) where {T,N}     =  getblock(block_arr, (Block(block).n)...)\n@inline Base.setindex!(block_arr::AbstractBlockArray{T,N}, v, block::Vararg{Block{1}, N}) where {T,N} =  setblock!(block_arr, v, (Block(block).n)...)\n\n\"\"\"\n    eachblock(A::AbstractBlockArray)\n\nCreate a generator that iterates over each block of an `AbstractBlockArray`\nreturning views.\n\n```jldoctest; setup = quote using BlockArrays end\njulia> v = Array(reshape(1:6, (2, 3)))\n2\u00d73 Array{Int64,2}:\n 1  3  5\n 2  4  6\n\njulia> A = BlockArray(v, [1,1], [2,1])\n2\u00d72-blocked 2\u00d73 BlockArray{Int64,2}:\n 1  3  \u2502  5\n \u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\n 2  4  \u2502  6\n\njulia> Matrix.(collect(eachblock(A)))\n2\u00d72 Array{Array{Int64,2},2}:\n [1 3]  [5]\n [2 4]  [6]\n\njulia> sum.(eachblock(A))\n2\u00d72 Array{Int64,2}:\n 4  5\n 6  6\n```\n\"\"\"\nfunction eachblock(A::AbstractBlockArray)\n    # blockinds = CartesianIndices(blocksize(A))\n    blockinds = CartesianIndices(axes.(blocklasts.(axes(A)),1))\n    (view(A, Block(Tuple(I))) for I in blockinds)\nend\n", "meta": {"hexsha": "7621d6e28712d5fbc0d53442b93011c80ab98a08", "size": 8245, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abstractblockarray.jl", "max_stars_repo_name": "pierrejoyot/BlockArrays.jl", "max_stars_repo_head_hexsha": "1bf7e9bc3c58997b258be4b1b6b0d473873b3a60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/abstractblockarray.jl", "max_issues_repo_name": "pierrejoyot/BlockArrays.jl", "max_issues_repo_head_hexsha": "1bf7e9bc3c58997b258be4b1b6b0d473873b3a60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/abstractblockarray.jl", "max_forks_repo_name": "pierrejoyot/BlockArrays.jl", "max_forks_repo_head_hexsha": "1bf7e9bc3c58997b258be4b1b6b0d473873b3a60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.4979079498, "max_line_length": 200, "alphanum_fraction": 0.6468162523, "num_tokens": 2642, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938443711712, "lm_q2_score": 0.10521054511997295, "lm_q1q2_score": 0.052194303796953945}}
{"text": "using AdventOfCode2018\nusing AdventOfCode2018.Day7\n\ninput = get_aoc_input(7)\nparsed_input = parse_input(input)\ndag = build_dag(parsed_input)\n\nprintln(\"Solution 1: \", part_1(dag))\nprintln(\"Solution 2: \", part_2(dag, workers=5, per_task=60))\n", "meta": {"hexsha": "a925ced60f88d1e49b0f78d320bbc783409faf34", "size": 240, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/day7.jl", "max_stars_repo_name": "MaxNoe/adventofcode2018", "max_stars_repo_head_hexsha": "5b6a553361f98ee886550e45004917394f47d251", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/day7.jl", "max_issues_repo_name": "MaxNoe/adventofcode2018", "max_issues_repo_head_hexsha": "5b6a553361f98ee886550e45004917394f47d251", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/day7.jl", "max_forks_repo_name": "MaxNoe/adventofcode2018", "max_forks_repo_head_hexsha": "5b6a553361f98ee886550e45004917394f47d251", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0, "max_line_length": 60, "alphanum_fraction": 0.7708333333, "num_tokens": 75, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.10521053950871516, "lm_q1q2_score": 0.052194299445579975}}
{"text": "import ChainRulesCore: rrule, no_rrule\nimport ChainRulesCore: rrule_via_ad, RuleConfig, NoForwardsMode, HasReverseMode\nimport Umlaut: make_name, Input, to_expr, BcastCtx\n\n\n###############################################################################\n#                              Primitives                                     #\n###############################################################################\n\n\nstruct ChainRulesCtx end\n\n\nfunction isprimitive(::ChainRulesCtx, f, args...)\n    F = Core.Typeof(f)\n    Args = Core.Typeof.(args)\n    Core.Compiler.return_type(rrule, Tuple{YotaRuleConfig, F, Args...}) !== Nothing && return true\n    if is_kwfunc(F)\n        Args_kwrrule = Tuple{Any, typeof(Core.kwfunc(f)), YotaRuleConfig, Args...,}\n        Core.Compiler.return_type(Core.kwfunc(rrule), Args_kwrrule) !== Nothing && return true\n    end\n    return false\nend\n\n###############################################################################\n#                              RuleConfig                                     #\n###############################################################################\n\n\"\"\"\n    YotaRuleConfig()\n\nChainRules.RuleConfig passed to all `rrule`s in Yota.\nExtends RuleConfig{Union{NoForwardsMode,HasReverseMode}}.\n\"\"\"\nstruct YotaRuleConfig <: RuleConfig{Union{NoForwardsMode,HasReverseMode}} end\n\n\n###############################################################################\n#                              rrule_via_ad                                   #\n###############################################################################\n\n\"\"\"\n    bcast_rrule(::YotaRuleConfig, ::typeof(broadcasted), f, args...; kw...)\n\nSimilar to rrule(config, broadcasted, f, args...), but works on for ChainRule-primitive\nfunctions. For a more flexible handling of broadcasting use rrule(...) directly.\n\"\"\"\nfunction bcast_rrule(::YotaRuleConfig, ::typeof(broadcasted), f::F, args...; kw...) where F\n    ys, pbs = unzip(rrule.(YOTA_RULE_CONFIG, f, args...; kw...))\n    function pullback(\u0394)\n        if \u0394 isa NoTangent || \u0394 isa ZeroTangent\n            return (NoTangent(), [\u0394 for _=1:length(pbs) + 1]...,)\n        end\n        \u0394 = unthunk(\u0394)\n        dxs = map((pb, \u0394) -> pb(\u0394), pbs, \u0394) |> unzip\n        dxs = [all(dx .== NoTangent()) ? NoTangent() : dx for dx in dxs]\n        return (NoTangent(), dxs...,)\n    end\n    return ys, pullback\nend\n\n\nfunction to_rrule_expr(tape::Tape)\n    # TODO (maybe): add YotaRuleConfig() as the first argument for consistency\n    fn_name = gensym(\"rrule_$(tape[V(1)].val)\")\n    header = Expr(:call, fn_name)\n    push!(header.args, Expr(:(::), :config, YotaRuleConfig))\n    for v in inputs(tape)\n        op = tape[v]\n        push!(header.args, Expr(:(::), make_name(op), op.typ))\n    end\n    body = Expr(:block)\n    # generate transformed forward pass\n    seed_id = tape.meta[:seed].id\n    for op in tape.ops[1:seed_id - 1]\n        op isa Input && continue\n        ex = to_expr(op)\n        if ex isa Vector\n            push!(body.args, ex...)\n        else\n            push!(body.args, ex)\n        end\n    end\n    # generate pullback\n    pb_name = gensym(\"pullback_$(tape[V(1)].val)\")\n    pb_ex = :(function $pb_name(dy) end)\n    pb_body = pb_ex.args[2]\n    empty!(pb_body.args)  # clean from useless linenumber nodes\n    push!(pb_body.args, Expr(:(=), make_name(tape.meta[:seed].id), :dy))\n    for op in tape.ops[seed_id + 1:length(tape) - 2]\n        op isa Input && continue\n        ex = to_expr(op)\n        if ex isa Vector\n            push!(pb_body.args, ex...)\n        else\n            push!(pb_body.args, ex)\n        end\n    end\n    push!(body.args, pb_ex)\n    # generate return\n    result_name = make_name(tape[tape.result].args[1].id)\n    push!(body.args, Expr(:tuple, result_name, pb_name))\n    fn_ex = Expr(:function, header, body)\n    return fn_ex\nend\n\n\n\"\"\"\n    make_rrule(tape::Tape)\n    make_rrule(f, args...)\n\nGenerate a function equivalent to (but not extending) ChainRulesCore.rrule(),\ni.e. returning the primal value and the pullback.\n\n\nExamples:\n=========\n\n    foo(x) = 2x + 1\n    rr = make_rrule(foo, 2.0)\n    val, pb = rr(foo, 3.0)\n    pb(1.0)\n\n\"\"\"\nmake_rrule(tape::Tape) = Base.eval(@__MODULE__, to_rrule_expr(tape))\n\nfunction make_rrule(f, args...)\n    return make_rrule(gradtape(f, args...; seed=:auto, ctx=GradCtx()))\nend\n\nfunction make_rrule(::typeof(broadcasted), f, args...)\n    if isprimitive(GradCtx(), f, map(first, args)...)\n        return bcast_rrule # (YOTA_RULE_CONFIG, broadcasted, f, args...)\n    end\n    ctx = BcastGradCtx(GradCtx())\n    _, tape = trace(f, args...; ctx=ctx, fargtypes=(f, map(eltype, args)))\n    tape = Tape(tape; ctx=ctx.inner)\n    gradtape!(tape, seed=:auto)\n    # insert imaginary broadcasted to the list of inputs\n    insert!(tape, 1, Umlaut.Input(broadcasted))\n    # insert ZeroTangent to the result to account for the additional argument\n    grad_tuple_op = tape[V(tape.result.id - 2)]\n    @assert grad_tuple_op isa Call && grad_tuple_op.fn == tuple\n    grad_tuple_op.args = [ZeroTangent(), grad_tuple_op.args...]\n    for id=grad_tuple_op.id:grad_tuple_op.id + 2\n        Umlaut.exec!(tape, tape[V(id)])\n    end\n    return make_rrule(tape)\nend\n\n\nconst GENERATED_RRULE_CACHE = Dict()\n\n\n\"\"\"\n    rrule_via_ad(::YotaRuleConfig, f, args...)\n\nGenerate `rrule` using Yota.\n\"\"\"\nfunction ChainRulesCore.rrule_via_ad(::YotaRuleConfig, f, args...)\n    res = rrule(f, args...)\n    !isnothing(res) && return res\n    sig = map(typeof, (f, args...))\n    if haskey(GENERATED_RRULE_CACHE, sig)\n        rr = GENERATED_RRULE_CACHE[sig]\n        # return Base.invokelatest(rr, f, args...)\n        val, pb = Base.invokelatest(rr, YOTA_RULE_CONFIG, f, args...)\n        return val, dy -> Base.invokelatest(pb, dy)\n    else\n        rr = make_rrule(f, args...)\n        GENERATED_RRULE_CACHE[sig] = rr\n        # return Base.invokelatest(rr, f, args...)\n        val, pb = Base.invokelatest(rr, YOTA_RULE_CONFIG, f, args...)\n        return val, dy -> Base.invokelatest(pb, dy)\n    end\nend", "meta": {"hexsha": "ee7a3503ebdea49a19c67b656fde822b0c9ba5e4", "size": 5967, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cr_api.jl", "max_stars_repo_name": "dfdx/Gendo.jl", "max_stars_repo_head_hexsha": "7bc3e672cd1aed9a2e4f32aa87fc4632b8e196d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/cr_api.jl", "max_issues_repo_name": "dfdx/Gendo.jl", "max_issues_repo_head_hexsha": "7bc3e672cd1aed9a2e4f32aa87fc4632b8e196d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cr_api.jl", "max_forks_repo_name": "dfdx/Gendo.jl", "max_forks_repo_head_hexsha": "7bc3e672cd1aed9a2e4f32aa87fc4632b8e196d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9034090909, "max_line_length": 98, "alphanum_fraction": 0.5649405061, "num_tokens": 1568, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.11124119914140751, "lm_q1q2_score": 0.052148831452155195}}
{"text": "using Formatting\nusing MKL\nusing Base.LinAlg.BLAS\nusing Gadfly\nusing DataFrames\nusing Colors\n\n# ------------------------------------------------------------------------------\n#                              INDIVIDUAL BENCH\n# ------------------------------------------------------------------------------\n\n# custom colors\nTblue = RGB(104/255,135/255,255/255)\nTred = RGB(255/255,110/255,103/255)\nTlightgray = RGB(0.7,0.7,0.7)\nTdarkgray = RGB(0.5,0.5,0.5)\n\n\"\"\"\n`bstat`retrieves basic statistics for a collection of numbers.\n'\u03b1' is the risk error (defaults to 0.05).\n`v_conf` is the *confidence interval* regarding `\u03b1` : v = \u03bc +/- \u03b4\u03bc with probability 1-\u03b1\n\"\"\"\nfunction bstat{T<:Number}(v::Vector{T}, \u03b1=0.05)\n    n = length(v)\n    \u03bc = mean(v) # mean\n    v_min , v_max = extrema(v) # min and max\n    \u03c3 =  stdm(v, \u03bc) # standard deviation\n    \u03b4\u03bc = sqrt(2)*erfinv(1-\u03b1)*(\u03c3/sqrt(n)) # confidence interval : v = \u03bc +/- \u03b4\u03bc\n    return \u03bc, v_min, v_max, \u03c3, \u03b4\u03bc\nend\n\n\"\"\"\n`\u00ecbench` performs a benchmark on a function `f` with arguments `arg`.\n`f` is evaluated sevral times so the benchmark last approximatively `timing`.\n`JIT` is triggered and results are stored in the return arrays at item `1`.\n`gcdisable` allows to disable the GC during evaluations (defaults to `true`).\n\"\"\"\nfunction ibench(f, args, timing=0.0001, gcdisable=true ; \u03b1=0.05, print_header = true, print_eval=false, print_stats=false, print_detail=false)\n\n    # TRIGGER JIT : first compilation will be ignored\n    res = @timed f(args...)\n\n    # eval nrun to match timing\n    t = @elapsed f(args...)\n    nrun = max(2,convert(Int64,div(timing,t))) # at least one run\n\n    # allocate results arrays\n    res_cpu = Array(Float64,nrun+1)\n    res_gc = Array(Float64,nrun+1)\n    res_alloc = Array(Int64,nrun+1)\n\n    # write JIT results\n    res_cpu[1] = res[2]\n    res_gc[1] = res[4]\n    res_alloc[1] = res[3]\n\n    # write PROFILING results : call f n times\n    gcdisable ? gc() : \"\"\n    for i=1:nrun\n        # gcdisable ? gc_enable(false) : \"\"\n        res = @timed f(args...)\n        # gcdisable ? gc_enable(true) : \"\"\n        res_cpu[i+1] = res[2]\n        res_gc[i+1] = res[4]\n        res_alloc[i+1] = res[3]\n    end\n\n    # if print_header==true || print_stats==true || print_detail==true\n    #     idisplay(f, args, timing, res_cpu, res_gc, res_alloc ; \u03b1=\u03b1, print_eval=print_eval, print_stats=print_stats, print_detail=print_detail)\n    # end\n    return res_cpu, res_gc, res_alloc\nend\n\n\"\"\"\n`\u00ecdisplay` prints the results of an individual bench `\u00ecbench` performed on a\nfunction `f` with the given input arguments `arg`.\n`detail` printing defaults to `false`.\n`res` prints a sample evaluation (for checking) is `true` ; defaults to `false`\n\"\"\"\nfunction idisplay(f, args, timing, res_cpu::Vector{Float64}, res_gc::Vector{Float64}, res_alloc::Vector{Int64} ; \u03b1=0.05, print_eval=false, print_stats=true, print_detail=false)\n\n    nrun = length(res_cpu)-1\n\n    # FORMATER\n    fe_sep1 = FormatExpr(\"{:=>44}\")\n    fe_sep2 = FormatExpr(\"{:->44}\")\n    fe_head = FormatExpr(\"{:<10s}{:>12s}{:>8s}{:>14s}\")\n    fe_line = FormatExpr(\"{:<10s}{:>12.2e}{:>8.2f}{:>14d}\")\n\n    # GET STATS\n    stat_cpu = bstat(res_cpu[2:end],\u03b1)\n    stat_gc = bstat(res_gc[2:end],\u03b1)\n    stat_alloc = bstat(res_alloc[2:end],\u03b1)\n\n    # SUMMARY\n    println(\"\")\n    printfmtln(fe_sep1,\"\")\n    println(\"Running $f for about $timing s\")\n    println(\"Evaluations : $nrun\")\n    println(\"CPU time : \",sum(res_cpu),\"s\")\n    # PRINT EVAL\n    if print_eval\n        println(string(f),\" = \")\n        println(f(args...))\n    end\n\n    # HEADER\n    printfmtln(fe_sep1,\"\")\n    printfmtln(fe_head,\"NAME\",\"CPU (s)\", \"GC (%)\", \"ALLOC (byt)\")\n    printfmtln(fe_sep2,\"\")\n\n    # RESULTS : MEAN\n    conf = convert(Int32,100*(1-\u03b1))\n    printfmtln(fe_line, \"MEAN\", stat_cpu[1], stat_gc[1], stat_alloc[1])\n    if print_stats\n        printfmtln(fe_line, \"MIN\", stat_cpu[2], stat_gc[2], stat_alloc[2])\n        printfmtln(fe_line, \"MAX\", stat_cpu[3], stat_gc[3], stat_alloc[3])\n        printfmtln(fe_line, \"STD\", stat_cpu[4], stat_gc[4], stat_alloc[4])\n        printfmtln(fe_line, \"CONF($conf%)\", stat_cpu[5], stat_gc[5], stat_alloc[5])\n        printfmtln(fe_sep2,\"\")\n    end\n\n    # RESULTS : JIT\n    printfmtln(fe_line, \"JIT\", res_cpu[1], res_gc[1], res_alloc[1])\n    printfmtln(fe_sep2,\"\")\n\n    # RESULTS : details\n    if print_detail == true\n        for i=1:nrun\n            printfmtln(fe_line, i, res_cpu[i+1], res_gc[i+1], res_alloc[i+1])\n        end\n    end\n    return\nend\n\nfunction idisplay2(f, args, n, timing, neval, res_cpu::Vector{Float64}, res_gc::Vector{Float64}, res_alloc::Vector{Int64}; print_eval=false, print_scale=true)\n\n    # FORMATER\n    fe_sep1 = FormatExpr(\"{:=>44}\")\n    fe_sep2 = FormatExpr(\"{:->44}\")\n    fe_head = FormatExpr(\"{:<10s}{:>12s}{:>8s}{:>14s}\")\n\n    # SUMMARY\n    println(\"\")\n    printfmtln(fe_sep1,\"\")\n    println(\"Running '$f' for about $timing s\")\n    println(\"Evaluations : $neval\")\n    # PRINT EVAL\n    if print_eval\n        println(string(f),\" = \")\n        println(f(args...))\n    end\n\n    # HEADER\n\n    if print_scale\n        fe_line = FormatExpr(\"{:<10s}{:>12.1f}{:>8.1f}{:>14d}\")\n        printfmtln(fe_sep1,\"\")\n        printfmtln(fe_head,\"n=$n\",\"CPU (ns/el)\", \"GC (%)\", \"ALLOC (by/el)\")\n        printfmtln(fe_sep2,\"\")\n        printfmtln(fe_line, \"MEAN\", res_cpu[2]*(1e9/n), res_gc[2]*(1e9/n), res_alloc[2]\u00f7n)\n        printfmtln(fe_line, \"JIT\", res_cpu[1]*(1e9/n), res_gc[1]*(1e9/n), res_alloc[1]\u00f7n)\n        printfmtln(fe_sep2,\"\")\n    else\n        fe_line = FormatExpr(\"{:<10s}{:>12.1e}{:>8.1f}{:>14d}\")\n        printfmtln(fe_sep1,\"\")\n        printfmtln(fe_head,\"n=$n\",\"CPU (s)\", \"GC (%)\", \"ALLOC (by)\")\n        printfmtln(fe_sep2,\"\")\n        printfmtln(fe_line, \"MEAN\", res_cpu[2], res_gc[2], res_alloc[2])\n        printfmtln(fe_line, \"JIT\", res_cpu[1], res_gc[1], res_alloc[1])\n        printfmtln(fe_sep2,\"\")\n    end\n\n\n\n    return\nend\n\nfunction ibench2(f, args, n, nrep=500, gcdisable=true)\n\n    # TRIGGER JIT : first compilation will be ignored\n    res = @timed f(args...)\n\n    # eval nrun to match timing\n    t = @elapsed f(args...)\n\n    # allocate results arrays\n    res_cpu = Array(Float64,2)\n    res_gc = Array(Float64,2)\n    res_alloc = Array(Int64,2)\n\n    # write JIT results\n    res_cpu[1] = res[2]\n    res_gc[1] = res[4]\n    res_alloc[1] = res[3]\n\n    tcpu = 0.0\n    tgc = 0.0\n    alloc = 0\n\n    gc()\n\n    if n <= 10_000\n        nchunk = 10_000 \u00f7 n + 1\n        @inbounds for j in 1:nrep\n            res = @timed for k in 1:nchunk f(args...) end\n            tcpu += res[2]\n            tgc += res[4]\n            alloc += res[3]\n        end\n    else\n        nchunk = 1.0\n        @inbounds for j in 1:nrep\n            res = @timed f(args...)\n            tcpu += res[2]\n            tgc += res[4]\n            alloc += res[3]\n        end\n    end\n\n    res_cpu[2] = tcpu / (nrep * nchunk)\n    res_gc[2] = tgc / (nrep * nchunk)\n    res_alloc[2] = alloc \u00f7 (nrep * nchunk)\n\n    return res_cpu, res_gc, res_alloc\nend\n\n# ------------------------------------------------------------------------------\n#                              FUNCTIONS\n# ------------------------------------------------------------------------------\n\n\n# FOR NON EXISTING BLAS FUNCTIONS\nnanf() = \"\"\n\n# DEFINE VECTORIZED JULIA FONCTIONS\nvj_dot(a,b) = sum(a.*b)\nvj_axpy(\u03bb,a,b) = \u03bb*a+b\nvj_invsqrt{T<:Number}(a::Vector{T}) = 1 ./ sqrt(a)\n\n# sqrt(X)\nfunction lj_sqrt{T<:Number}(dest,x::T)\n    for i in eachindex(x) dest[i] = sqrt(x[i]) end\n    dest\nend\nfunction ilj_sqrt{T<:Number}(dest,x::T)\n    @inbounds for i in eachindex(x) dest[i] = sqrt(x[i]) end\n    dest\nend\n", "meta": {"hexsha": "4e11877d00de900045a8203c7f5b8bf876965bac", "size": 7545, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/utility.jl", "max_stars_repo_name": "lionpeloux/MKL.jl", "max_stars_repo_head_hexsha": "44420c0b5d0959dc8eb1fab5fb4b84f0232f058c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "benchmark/utility.jl", "max_issues_repo_name": "lionpeloux/MKL.jl", "max_issues_repo_head_hexsha": "44420c0b5d0959dc8eb1fab5fb4b84f0232f058c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmark/utility.jl", "max_forks_repo_name": "lionpeloux/MKL.jl", "max_forks_repo_head_hexsha": "44420c0b5d0959dc8eb1fab5fb4b84f0232f058c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.18, "max_line_length": 176, "alphanum_fraction": 0.5732273028, "num_tokens": 2364, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.10669060104662921, "lm_q1q2_score": 0.05209524892520144}}
{"text": "module TestErrors\n\nusing Test\nusing MathOptInterface\nconst MOI = MathOptInterface\n\ninclude(\"dummy.jl\")\n\nfunction test_errors_fallback_AddVariableNotAllowed()\n    model = DummyModel()\n    @test_throws MOI.AddVariableNotAllowed MOI.add_variable(model)\n    try\n        MOI.add_variable(model)\n    catch err\n        @test sprint(showerror, err) ==\n              \"MathOptInterface.AddVariableNotAllowed:\" *\n              \" Adding variables cannot be performed. You may want to use a\" *\n              \" `CachingOptimizer` in `AUTOMATIC` mode or you may need to call\" *\n              \" `reset_optimizer` before doing this operation if the\" *\n              \" `CachingOptimizer` is in `MANUAL` mode.\"\n    end\n    @test_throws MOI.AddVariableNotAllowed MOI.add_variables(model, 2)\n    return\nend\n\nfunction test_errors_inconsistent_vectorscalar()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    @test_throws(\n        MOI.ErrorException,\n        MOI.add_constraint(\n            model,\n            MOI.VectorOfVariables([vi, vi]),\n            MOI.EqualTo(0),\n        )\n    )\n    @test_throws(\n        MOI.ErrorException,\n        MOI.add_constraint(model, vi, MOI.Nonnegatives(2))\n    )\n    return\nend\n\nfunction _test_errors_UnsupportedConstraint(f)\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    @test_throws(MOI.UnsupportedConstraint, f(model, vi, MOI.EqualTo(0)),)\n    try\n        f(model, vi, MOI.EqualTo(0))\n    catch err\n        @test sprint(showerror, err) ==\n              \"$(MOI.UnsupportedConstraint{MOI.VariableIndex,MOI.EqualTo{Int}}):\" *\n              \" `$MOI.VariableIndex`-in-`$MOI.EqualTo{$Int}` constraint is\" *\n              \" not supported by the model.\"\n    end\n    return\nend\n\nfunction test_errors_UnsupportedConstraint()\n    _test_errors_UnsupportedConstraint(MOI.add_constraint)\n    return\nend\n\nfunction test_errors_UnsupportedConstraint_shortcut()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    @test_throws MOI.UnsupportedConstraint begin\n        MOI.add_constraint(model, vi, MOI.EqualTo(0))\n    end\n    @test_throws MOI.UnsupportedConstraint begin\n        MOI.add_constraint(model, [vi, vi], MOI.Nonnegatives(2))\n    end\n    @test_throws MOI.UnsupportedConstraint begin\n        MOI.add_constraints(model, [vi, vi], [MOI.EqualTo(0), MOI.EqualTo(0)])\n    end\nend\n\nfunction test_errors_add_constraint()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    @test_throws(\n        MOI.AddConstraintNotAllowed,\n        MOI.add_constraint(model, vi, MOI.EqualTo(0.0)),\n    )\n    try\n        MOI.add_constraint(model, vi, MOI.EqualTo(0.0))\n    catch err\n        @test sprint(showerror, err) ==\n              \"$(MOI.AddConstraintNotAllowed{MOI.VariableIndex,MOI.EqualTo{Float64}}):\" *\n              \" Adding `$MOI.VariableIndex`-in-`$MOI.EqualTo{Float64}`\" *\n              \" constraints cannot be performed. You may want to use a\" *\n              \" `CachingOptimizer` in `AUTOMATIC` mode or you may need to call\" *\n              \" `reset_optimizer` before doing this operation if the\" *\n              \" `CachingOptimizer` is in `MANUAL` mode.\"\n    end\n    @test_throws(\n        MOI.AddConstraintNotAllowed,\n        MOI.add_constraint(model, vi, MOI.EqualTo(0.0)),\n    )\n    @test_throws(\n        MOI.AddConstraintNotAllowed,\n        MOI.add_constraint(model, [vi, vi], MOI.Zeros(2)),\n    )\n    @test_throws(\n        MOI.AddConstraintNotAllowed,\n        MOI.add_constraints(\n            model,\n            [vi, vi],\n            [MOI.EqualTo(0.0), MOI.EqualTo(0.0)],\n        ),\n    )\n    return\nend\n\nfunction test_errors_DeleteNotAllowed()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    @test_throws MOI.DeleteNotAllowed{typeof(vi)} MOI.delete(model, vi)\n    try\n        MOI.delete(model, vi)\n    catch err\n        @test sprint(showerror, err) ==\n              \"MathOptInterface.DeleteNotAllowed{MathOptInterface.VariableIndex}:\" *\n              \" Deleting the index MathOptInterface.VariableIndex(1) cannot be\" *\n              \" performed. You may want to use a `CachingOptimizer` in\" *\n              \" `AUTOMATIC` mode or you may need to call `reset_optimizer`\" *\n              \" before doing this operation if the `CachingOptimizer` is in\" *\n              \" `MANUAL` mode.\"\n    end\n    @test_throws MOI.DeleteNotAllowed{typeof(ci)} MOI.delete(model, ci)\n    try\n        MOI.delete(model, ci)\n    catch err\n        @test sprint(showerror, err) ==\n              \"$(MathOptInterface.DeleteNotAllowed{MathOptInterface.ConstraintIndex{MathOptInterface.VariableIndex,MathOptInterface.EqualTo{Float64}}}):\" *\n              \" Deleting the index $(MathOptInterface.ConstraintIndex{MathOptInterface.VariableIndex,MathOptInterface.EqualTo{Float64}}(1))\" *\n              \" cannot be performed. You may want to use a `CachingOptimizer`\" *\n              \" in `AUTOMATIC` mode or you may need to call `reset_optimizer`\" *\n              \" before doing this operation if the `CachingOptimizer` is in\" *\n              \" `MANUAL` mode.\"\n    end\n    return\nend\n\nfunction _test_unsupported_attribute(f)\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    @test_throws(\n        MOI.UnsupportedAttribute,\n        f(model, MOI.ObjectiveFunction{MOI.VariableIndex}(), vi),\n    )\n    @test_throws(\n        MOI.UnsupportedAttribute,\n        f(model, MOI.ConstraintDualStart(), ci, 0.0),\n    )\n    return\nend\n\nfunction test_errors_unsupported_attribute()\n    _test_unsupported_attribute(MOI.set)\n    return\nend\n\nfunction test_errors_SetAttributeNotAllowed()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    model = DummyModel()\n    @test_throws(\n        MOI.SetAttributeNotAllowed,\n        MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE),\n    )\n    @test_throws(\n        MOI.SetAttributeNotAllowed,\n        MOI.set(model, MOI.ConstraintPrimalStart(), ci, 0.0),\n    )\n    return\nend\n\nfunction test_errors_ConstraintFunction_NotAllowed()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    @test_throws(\n        MOI.SetAttributeNotAllowed,\n        MOI.set(model, MOI.ConstraintFunction(), ci, vi)\n    )\n    @test_throws(\n        ArgumentError,\n        MOI.set(\n            model,\n            MOI.ConstraintFunction(),\n            ci,\n            MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, vi)], 0.0),\n        ),\n    )\n    return\nend\n\nfunction test_errors_ConstraintSet_NotAllowed()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    @test_throws(\n        MOI.SetAttributeNotAllowed,\n        MOI.set(model, MOI.ConstraintSet(), ci, MOI.EqualTo(1.0))\n    )\n    @test_throws(\n        ArgumentError,\n        MOI.set(model, MOI.ConstraintSet(), ci, MOI.EqualTo(1))\n    )\n    @test_throws(\n        ArgumentError,\n        MOI.set(model, MOI.ConstraintSet(), ci, MOI.GreaterThan(1.0))\n    )\nend\n\nfunction test_errors_ModifyNotAllowed_constraint()\n    model = DummyModel()\n    vi = MOI.VariableIndex(1)\n    ci = MOI.ConstraintIndex{MOI.VariableIndex,MOI.EqualTo{Float64}}(1)\n    change = MOI.ScalarConstantChange(1.0)\n    err = MOI.ModifyConstraintNotAllowed(ci, change)\n    @test_throws err MOI.modify(model, ci, change)\n    @test sprint(showerror, err) ==\n          \"$(MathOptInterface.ModifyConstraintNotAllowed{MathOptInterface.VariableIndex,MathOptInterface.EqualTo{Float64},MathOptInterface.ScalarConstantChange{Float64}}):\" *\n          \" Modifying the constraints $(MathOptInterface.ConstraintIndex{MathOptInterface.VariableIndex,MathOptInterface.EqualTo{Float64}}(1))\" *\n          \" with MathOptInterface.ScalarConstantChange{Float64}(1.0) cannot\" *\n          \" be performed. You may want to use a `CachingOptimizer` in\" *\n          \" `AUTOMATIC` mode or you may need to call `reset_optimizer`\" *\n          \" before doing this operation if the `CachingOptimizer` is in\" *\n          \" `MANUAL` mode.\"\nend\n\nfunction test_errors_ModifyNotAllowed_objective()\n    model = DummyModel()\n    change = MOI.ScalarConstantChange(1.0)\n    attr = MOI.ObjectiveFunction{MOI.VariableIndex}()\n    err = MOI.ModifyObjectiveNotAllowed(change)\n    @test_throws err MOI.modify(model, attr, change)\n    @test sprint(showerror, err) ==\n          \"$(MathOptInterface.ModifyObjectiveNotAllowed{MathOptInterface.ScalarConstantChange{Float64}}):\" *\n          \" Modifying the objective function with $(MathOptInterface.ScalarConstantChange{Float64}(1.0))\" *\n          \" cannot be performed. You may want to use a `CachingOptimizer`\" *\n          \" in `AUTOMATIC` mode or you may need to call `reset_optimizer`\" *\n          \" before doing this operation if the `CachingOptimizer` is in\" *\n          \" `MANUAL` mode.\"\nend\n\nfunction test_errors_show_SetAttributeNotAllowed()\n    @test sprint(showerror, MOI.UnsupportedAttribute(MOI.Name())) ==\n          \"$MOI.UnsupportedAttribute{$MOI.Name}:\" *\n          \" Attribute $MOI.Name() is not supported by the model.\"\n    @test sprint(showerror, MOI.UnsupportedAttribute(MOI.Name(), \"Message\")) ==\n          \"$MOI.UnsupportedAttribute{$MOI.Name}:\" *\n          \" Attribute $MOI.Name() is not supported by the model: Message\"\n    @test sprint(showerror, MOI.SetAttributeNotAllowed(MOI.Name())) ==\n          \"$MOI.SetAttributeNotAllowed{$MOI.Name}:\" *\n          \" Setting attribute $MOI.Name() cannot be performed. You may want to use\" *\n          \" a `CachingOptimizer` in `AUTOMATIC` mode or you may need to call\" *\n          \" `reset_optimizer` before doing this operation if the\" *\n          \" `CachingOptimizer` is in `MANUAL` mode.\"\n    @test sprint(\n              showerror,\n              MOI.SetAttributeNotAllowed(MOI.Name(), \"Message\"),\n          ) ==\n          \"$MOI.SetAttributeNotAllowed{$MOI.Name}:\" *\n          \" Setting attribute $MOI.Name() cannot be performed: Message You may want\" *\n          \" to use a `CachingOptimizer` in `AUTOMATIC` mode or you may need to call\" *\n          \" `reset_optimizer` before doing this operation if the `CachingOptimizer`\" *\n          \" is in `MANUAL` mode.\" ==\n          \"$MOI.SetAttributeNotAllowed{$MOI.Name}:\" *\n          \" Setting attribute $MOI.Name() cannot be performed: Message You may want\" *\n          \" to use a `CachingOptimizer` in `AUTOMATIC` mode or you may need to call\" *\n          \" `reset_optimizer` before doing this operation if the `CachingOptimizer`\" *\n          \" is in `MANUAL` mode.\"\n    return\nend\n\nfunction test_errors_ResultIndexBoundsError()\n    @test sprint(\n        showerror,\n        MOI.ResultIndexBoundsError(MOI.VariablePrimal(1), 0),\n    ) ==\n          \"Result index of attribute MathOptInterface.VariablePrimal(1) out of\" *\n          \" bounds. There are currently 0 solution(s) in the model.\"\nend\n\nfunction test_errors_InvalidCalbackUsage()\n    @test sprint(\n        showerror,\n        MOI.InvalidCallbackUsage(MOI.LazyConstraintCallback(), MOI.UserCut(1)),\n    ) ==\n          \"InvalidCallbackUsage: Cannot submit $(MOI.UserCut(1)) inside a MathOptInterface.LazyConstraintCallback().\"\nend\n\nfunction runtests()\n    for name in names(@__MODULE__; all = true)\n        if startswith(\"$name\", \"test_\")\n            @testset \"$(name)\" begin\n                getfield(@__MODULE__, name)()\n            end\n        end\n    end\nend\n\nend\n\nTestErrors.runtests()\n", "meta": {"hexsha": "5fa7c704e3c5d59707b54111269204582aede9ac", "size": 11480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/errors.jl", "max_stars_repo_name": "dourouc05/MathOptInterface.jl", "max_stars_repo_head_hexsha": "8f1bc2a1874265974919c2257ef2bfa27fd2bee0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/errors.jl", "max_issues_repo_name": "dourouc05/MathOptInterface.jl", "max_issues_repo_head_hexsha": "8f1bc2a1874265974919c2257ef2bfa27fd2bee0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/errors.jl", "max_forks_repo_name": "dourouc05/MathOptInterface.jl", "max_forks_repo_head_hexsha": "8f1bc2a1874265974919c2257ef2bfa27fd2bee0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7948717949, "max_line_length": 174, "alphanum_fraction": 0.6515679443, "num_tokens": 2888, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.1066905939456512, "lm_q1q2_score": 0.05209524545791179}}
{"text": "# MD5 hash tests from:\n# https://git.lysator.liu.se/nettle/nettle/blob/master/testsuite/md5-test.c\nfor (text, hash) in [\n    (\n        \"\",\n        \"d41d8cd98f00b204e9800998ecf8427e\"\n    ),(\n        \"a\",\n        \"0cc175b9c0f1b6a831c399e269772661\"\n    ),(\n        \"abc\",\n        \"900150983cd24fb0d6963f7d28e17f72\"\n    ),(\n        \"message digest\",\n        \"f96b697d7cb7938d525a2f31aaf161d0\"\n    ),(\n        \"abcdefghijklmnopqrstuvwxyz\",\n        \"c3fcd3d76192e4007dfb496cca67e13b\"\n    ),(\n        \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdef\" *\n        \"ghijklmnopqrstuvwxyz0123456789\",\n        \"d174ab98d277d9f5a5611c2c9f419d9f\"\n    ),(\n        \"12345678901234567890123456789012\" *\n        \"34567890123456789012345678901234\" *\n        \"5678901234567890\",\n        \"57edf4a22be3c955ac49da2e2107b67a\"\n    )\n]\n    @test digest(\"md5\", text) == hex2bytes(hash)\nend\n\n\n# SHA1 hash tests from:\n# https://git.lysator.liu.se/nettle/nettle/blob/master/testsuite/sha1-test.c\nfor (text, hash) in [\n    (\n        \"\",\n        \"da39a3ee5e6b4b0d3255bfef95601890afd80709\",\n    ),(\n        \"a\",\n        \"86f7e437faa5a7fce15d1ddcb9eaeaea377667b8\",\n    ),(\n        \"abc\",\n        \"a9993e364706816aba3e25717850c26c9cd0d89d\",\n    ),(\n        \"abcdefghijklmnopqrstuvwxyz\",\n        \"32d10c7b8cf96570ca04ce37f2a19d84240d3a89\",\n    ),(\n        \"message digest\",\n        \"c12252ceda8be8994d5fa0290a47231c1d16aae3\",\n    ),(\n        \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmn\" *\n        \"opqrstuvwxyz0123456789\",\n        \"761c457bf73b14d27e9e9265c46f4b4dda11f940\",\n    ),(\n        \"1234567890123456789012345678901234567890\" *\n        \"1234567890123456789012345678901234567890\",\n        \"50abf5706a150990a08b2c5ea40fa0e585554732\",\n    ),(\n        \"38\",\n        \"5b384ce32d8cdef02bc3a139d4cac0a22bb029e8\"\n    )\n]\n    @test hexdigest(\"sha1\", text) == hash\nend\n\n\n# SHA256 hash tests from:\n# https://git.lysator.liu.se/nettle/nettle/blob/master/testsuite/sha256-test.c\nfor (text,hash) in [\n    (\n        \"abc\",\n        \"ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad\"\n    ),(\n        \"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq\",\n        \"248d6a61d20638b8e5c026930c3e6039a33ce45964ff2167f6ecedd419db06c1\"\n    ),(\n        \"abcdefghbcdefghicdefghijdefghijkefghijklfghijklmghijklmnhijklmno\" *\n        \"ijklmnopjklmnopqklmnopqrlmnopqrsmnopqrstnopqrstu\",\n        \"cf5b16a778af8380036ce59e7b0492370b249b11e8f07a51afac45037afee9d1\"\n    ),(\n        \"\",\n        \"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855\"\n    ),(\n        \"a\",\n        \"ca978112ca1bbdcafac231b39a23dc4da786eff8147c4e72b9807785afee48bb\"\n    ),(\n        \"38\",\n        \"aea92132c4cbeb263e6ac2bf6c183b5d81737f179f21efdc5863739672f0f470\"\n    ),(\n        \"message digest\",\n        \"f7846f55cf23e14eebeab5b4e1550cad5b509e3348fbc4efa3a1413d393cb650\"\n    ),(\n        \"abcdefghijklmnopqrstuvwxyz\",\n        \"71c480df93d6ae2f1efad1447c66c9525e316218cf51fc8d9ed832f2daf18b73\"\n    ),(\n        \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789\",\n        \"db4bfcbd4da0cd85a60c3c37d3fbd8805c77f15fc6b1fdfe614ee0a7c8fdb4c0\"\n    ),(\n        \"1234567890123456789012345678901234567890123456789012345678901234\" *\n        \"5678901234567890\",\n        \"f371bc4a311f2b009eef952dd83ca80e2b60026c8e935592d0f9c308453c813e\"\n    )\n]\n    h = Hasher(\"sha256\")\n    update!(h, text)\n    @test hexdigest!(h) == hash\nend\n\n\n# Test that digest!() actually resets the HashState\nh = Hasher(\"SHA1\")\nupdate!(h,\"\")\n@test hexdigest!(h) == \"da39a3ee5e6b4b0d3255bfef95601890afd80709\"\nupdate!(h,\"The quick brown fox jumps over the lazy dog\")\n@test hexdigest!(h) == \"2fd4e1c67a2d28fced849ee1bb76e7391b93eb12\"\nh = Hasher(\"SHA256\")\nupdate!(h,\"The quick brown fox jumps over the lazy dog\")\n@test hexdigest!(h) == \"d7a8fbb307d7809469ca9abcb0082e4f8d5651e46d3cdb762d02d0bf37c9e592\"\n\n# Test invalid parameters\n@test_throws ArgumentError Hasher(\"this is not a hash name\")\n\n# Test show methods\nprintln(\"Testing hash show methods:\")\nprintln(get_hash_types()[\"SHA256\"])\nprintln(Hasher(\"SHA256\"))\n", "meta": {"hexsha": "bfc2a1b3b2ca2cd4fa609de30de7a310e5a81221", "size": 4013, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hash_tests.jl", "max_stars_repo_name": "SamSchlegel/Nettle.jl", "max_stars_repo_head_hexsha": "ed4d932486b2bb5490b15f2fa894eb964459fc48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-28T21:40:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-28T21:40:27.000Z", "max_issues_repo_path": "test/hash_tests.jl", "max_issues_repo_name": "SamSchlegel/Nettle.jl", "max_issues_repo_head_hexsha": "ed4d932486b2bb5490b15f2fa894eb964459fc48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/hash_tests.jl", "max_forks_repo_name": "SamSchlegel/Nettle.jl", "max_forks_repo_head_hexsha": "ed4d932486b2bb5490b15f2fa894eb964459fc48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1085271318, "max_line_length": 89, "alphanum_fraction": 0.6949912783, "num_tokens": 1540, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.10669058968506458, "lm_q1q2_score": 0.05209524337753809}}
{"text": "module JPVT\n\nusing Roots\n\nexport R\n\nconst R= 8.314 # J/(mol.K) gas constant\n\n\nend # module\n", "meta": {"hexsha": "e554afb294f241d0b588d1c7040b08a2abb8aaf3", "size": 91, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/JPVT.jl", "max_stars_repo_name": "simulkade/JPVT.jl", "max_stars_repo_head_hexsha": "b401762eb40b7e88fd53f8891605ad2000e2dfc8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/JPVT.jl", "max_issues_repo_name": "simulkade/JPVT.jl", "max_issues_repo_head_hexsha": "b401762eb40b7e88fd53f8891605ad2000e2dfc8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/JPVT.jl", "max_forks_repo_name": "simulkade/JPVT.jl", "max_forks_repo_head_hexsha": "b401762eb40b7e88fd53f8891605ad2000e2dfc8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.2727272727, "max_line_length": 39, "alphanum_fraction": 0.6813186813, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.10669058613457583, "lm_q1q2_score": 0.05209524164389339}}
{"text": "\nusing CUDA\nusing CUDA: cufunc, curand_rng\nusing CUDA.CUSPARSE: CuSparseMatrix, CuSparseMatrixCSR, CuSparseMatrixCOO\nusing CUDA.CUSOLVER: CuQR\n\nexport cuda_gc, gpu, @gpu!, @cu!\n\nconst CuBaseField{B,M,T,A<:CuArray} = BaseField{B,M,T,A}\n\ntypealias(::Type{CuArray{T,N}}) where {T,N} = \"CuArray{$T,$N}\"\n\n# a function version of @cuda which can be referenced before CUDA is\n# loaded as long as it exists by run-time (unlike the macro @cuda which must\n# exist at compile-time)\nfunction cuda(f, args...; threads=256)\n    @cuda threads=threads f(args...)\nend\n\nis_gpu_backed(::BaseField{B,M,T,A}) where {B,M,T,A<:CuArray} = true\nglobal_rng_for(::Type{<:CuArray}) = curand_rng()\n\n# handy conversion functions and macros\n@doc doc\"\"\"\n\n    @gpu! x y\n\nEquivalent to `x = gpu(x)`, `y = gpu(y)`, etc... for any number of\nlisted variables. See [`gpu`](@ref).\n\"\"\"\nmacro gpu!(vars...)\n    :(begin; $((:($(esc(var)) = gpu($(esc(var)))) for var in vars)...); nothing; end)\nend\n@doc doc\"\"\"\n\n    gpu(x)\n\nRecursively moves x to GPU, but unlike `CUDA.cu`, doesn't also convert\nto Float32. Equivalent to `adapt_structure(CuArray, x)`. Returns nothing.\n\"\"\"\ngpu(x) = adapt_structure(CuArray, x)\n\n\n@doc doc\"\"\"\n\n    @cu! x y\n    \nEquivalent to `x = cu(x)`, `y = cu(y)`, etc... for any number of\nlisted variables. See `CUDA.cu`. Returns nothing.\n\"\"\"\nmacro cu!(vars...)\n    :(begin; $((:($(esc(var)) = cu($(esc(var)))) for var in vars)...); nothing; end)\nend\n\n\n\nadapt_structure(::CUDA.Float32Adaptor, proj::ProjLambert) = adapt_structure(CuArray{Float32}, proj)\n\n\nfunction C\u2113_to_2D(C\u2113, proj::ProjLambert{T,<:CuArray}) where {T}\n    # todo: remove needing to go through cpu here:\n    gpu(Complex{T}.(nan2zero.(C\u2113.(cpu(proj.\u2113mag)))))\nend\n\n\n### misc\n# the generic versions of these trigger scalar indexing of CUDA, so provide\n# specialized versions: \npinv(D::Diagonal{T,<:CuBaseField}) where {T} = Diagonal(@. ifelse(isfinite(inv(D.diag)), inv(D.diag), $zero(T)))\ninv(D::Diagonal{T,<:CuBaseField}) where {T} = any(Array((D.diag.==0)[:])) ? throw(SingularException(-1)) : Diagonal(inv.(D.diag))\nfill!(f::CuBaseField, x) = (fill!(f.arr,x); f)\n==(a::CuBaseField, b::CuBaseField) = (==)(promote(a.arr, b.arr)...)\nsum(f::CuBaseField; dims=:) = (dims == :) ? sum(f.arr) : (1 in dims) ? error(\"Sum over invalid dims of CuFlatS0.\") : f\n\n\n# these only work for Reals in CUDA\n# with these definitions, they work for Complex as well\nCUDA.isfinite(x::Complex) = Base.isfinite(x)\nCUDA.sqrt(x::Complex) = CUDA.sqrt(CUDA.abs(x)) * CUDA.exp(im*CUDA.angle(x)/2)\nCUDA.culiteral_pow(::typeof(^), x::Complex, ::Val{2}) = x * x\nCUDA.pow(x::Complex, p) = x^p\n\n# until https://github.com/JuliaGPU/CUDA.jl/pull/618 (CUDA 2.5)\nCUDA.cufunc(::typeof(angle)) = CUDA.angle\n\n# adapting of SparseMatrixCSC \u2194 CuSparseMatrixCSR (otherwise dense arrays created)\nadapt_structure(::Type{<:CuArray}, L::SparseMatrixCSC)   = CuSparseMatrixCSR(L)\nadapt_structure(::Type{<:Array},   L::CuSparseMatrixCSR) = SparseMatrixCSC(L)\nadapt_structure(::Type{<:CuArray}, L::CuSparseMatrixCSR) = L\nadapt_structure(::Type{<:Array},   L::SparseMatrixCSC)   = L\n\n\n# CUDA somehow missing this one\n# see https://github.com/JuliaGPU/CuArrays.jl/issues/103\n# and https://github.com/JuliaGPU/CuArrays.jl/pull/580\nldiv!(qr::CuQR, x::CuVector) = qr.R \\ (CuMatrix(qr.Q)' * x)\n\n# some Random API which CUDA doesn't implement yet\nRandom.randn(rng::CUDA.CURAND.RNG, T::Random.BitFloatType) = \n    cpu(randn!(rng, CuVector{T}(undef,1)))[1]\n\n# perhaps minor type-piracy, but this lets us simulate into a CuArray using the\n# CPU random number generator\nRandom.randn!(rng::MersenneTwister, A::CuArray) = \n    (A .= adapt(CuArray, randn!(rng, adapt(Array, A))))\n\n# CUDA makes some copies here as a workaround for JuliaGPU/CuArrays.jl#345 &\n# NVIDIA/cuFFT#2714055 but it doesn't appear to be needed in the R2C case, and\n# in the C2R case we pre-allocate the memory only once (via memoization) as well\n# as doing the copy asynchronously\nimport CUDA.CUFFT: unsafe_execute!\nusing CUDA.CUFFT: rCuFFTPlan, cufftReal, cufftComplex, CUFFT_R2C, cufftExecR2C, cufftExecC2R, CUFFT_C2R, unsafe_copyto!, CuDefaultStream, pointer\n\nplan_buffer(x) = plan_buffer(eltype(x),size(x))\n@memoize plan_buffer(T,dims) = CuArray{T}(undef,dims...)\n\nfunction unsafe_execute!(plan::rCuFFTPlan{cufftReal,K,false,N},\n                            x::CuArray{cufftReal,N}, y::CuArray{cufftComplex,N}\n                            ) where {K,N}\n    @assert plan.xtype == CUFFT_R2C\n    cufftExecR2C(plan, x, y)\nend\nfunction unsafe_execute!(plan::rCuFFTPlan{cufftComplex,K,false,N},\n                            x::CuArray{cufftComplex,N}, y::CuArray{cufftReal}\n                            ) where {K,N}\n    @assert plan.xtype == CUFFT_C2R\n    cufftExecC2R(plan, unsafe_copyto!(pointer(plan_buffer(x)),pointer(x),length(x),async=true,stream=CuDefaultStream()), y)\nend\n\n# monkey-patched version of https://github.com/JuliaGPU/CUDA.jl/pull/436\n# until it hits a release\nusing CUDA.CURAND: curandSetPseudoRandomGeneratorSeed, curandSetGeneratorOffset, \n    CURAND_STATUS_ALLOCATION_FAILED, CURAND_STATUS_PREEXISTING_FAILURE, CURAND_STATUS_SUCCESS,\n    unsafe_curandGenerateSeeds, throw_api_error, @retry_reclaim, RNG\n\nfunction Random.seed!(rng::RNG, seed=Base.rand(UInt64), offset=0)\n    curandSetPseudoRandomGeneratorSeed(rng, seed)\n    curandSetGeneratorOffset(rng, offset)\n    res = @retry_reclaim err->isequal(err, CURAND_STATUS_ALLOCATION_FAILED) ||\n                              isequal(err, CURAND_STATUS_PREEXISTING_FAILURE) begin\n        unsafe_curandGenerateSeeds(rng)\n    end\n    if res != CURAND_STATUS_SUCCESS\n        throw_api_error(res)\n    end\n    return\nend\n\n\n\"\"\"\n    cuda_gc()\n\nGargbage collect and reclaim GPU memory (technically should never be\nneeded to do this by hand, but sometimes helps with GPU OOM errors)\n\"\"\"\nfunction cuda_gc()\n    GC.gc(true)\n    CUDA.reclaim()\nend\n", "meta": {"hexsha": "4ac71fb2d4ef1e8dd242bd656a8e822ef886659c", "size": 5848, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/gpu.jl", "max_stars_repo_name": "bthorne93/CMBLensing.jl", "max_stars_repo_head_hexsha": "afe15ffff51e165fb5f87a41fede5d479c2b8359", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/gpu.jl", "max_issues_repo_name": "bthorne93/CMBLensing.jl", "max_issues_repo_head_hexsha": "afe15ffff51e165fb5f87a41fede5d479c2b8359", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/gpu.jl", "max_forks_repo_name": "bthorne93/CMBLensing.jl", "max_forks_repo_head_hexsha": "afe15ffff51e165fb5f87a41fede5d479c2b8359", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.55, "max_line_length": 145, "alphanum_fraction": 0.6927154583, "num_tokens": 1758, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.11279541821247763, "lm_q1q2_score": 0.05200058042550218}}
{"text": "# AbstractVec\n#   - VecSeq: wrap\n#   - VecMPI (TODO)\n#   - VecGhost (TODO)\n# for the MPI variants we won't be able to attach finalizers, as destroy needs to be called collectively.\n\nconst CVec = Ptr{Cvoid}\n\nabstract type AbstractVec{T} <: AbstractVector{T} end\nscalartype(::AbstractVec{T}) where {T} = T\n\n# allows us to pass XXVec objects directly into CVec ccall signatures\nBase.cconvert(::Type{CVec}, obj::AbstractVec) = obj.ptr\n# allows us to pass XXVec objects directly into Ptr{CVec} ccall signatures\nBase.unsafe_convert(::Type{Ptr{CVec}}, obj::AbstractVec) =\n    convert(Ptr{CVec}, pointer_from_objref(obj))\n\n\n\"\"\"\n    VecSeq(v::Vector)\n\nA standard, sequentially-stored serial PETSc vector, wrapping the Julia vector `v`.\n\nThis reuses the array `v` as storage, and so `v` should not be `resize!`-ed or otherwise have its length modified while the PETSc object exists.\n\nThis should only be need to be called for more advanced uses, for most simple usecases, users should be able to pass `Vector`s directly and have the wrapping performed automatically\n\n# External Links\n$(_doc_external(\"Vec/VecCreateSeqWithArray\"))\n\"\"\"\nmutable struct VecSeq{T} <: AbstractVec{T}\n    ptr::CVec\n    array::Vector{T}\nend\n\n\"\"\"\n    Vec(v::CVec)\n\nContainer for an abstract PETSc vector\n\n# External Links\n$(_doc_external(\"Vec/Vec\"))\n\"\"\"\nmutable struct Vec{T} <: AbstractVec{T}\n    ptr::CVec\nend\n\nBase.eltype(::Type{V}) where {V<:AbstractVec{T}} where T = T\nBase.eltype(v::AbstractVec{T}) where {T} = T\nBase.size(v::AbstractVec) = (length(v),)\nBase.parent(v::AbstractVec) = v.array\n\n# this allows setting V[1:2] = 3:4 on a PetscVec (more convenient)\nfunction Base.setindex!(v::AbstractVec, val, I)\n    v.array[I]=val\nend\nBase.getindex(v::AbstractVec, I) = v.array[I]\n\n\n@for_libpetsc begin\n    function VecSeq(comm::MPI.Comm, X::Vector{$PetscScalar}; blocksize=1)\n        @assert initialized($petsclib)\n        v = VecSeq(C_NULL, X)\n        @chk ccall((:VecCreateSeqWithArray, $libpetsc), PetscErrorCode,\n                (MPI.MPI_Comm, $PetscInt, $PetscInt, Ptr{$PetscScalar}, Ptr{CVec}),\n                comm, blocksize, length(X), X, v)\n        finalizer(destroy, v)\n        return v\n    end\n    function destroy(v::AbstractVec{$PetscScalar})\n        finalized($petsclib) ||\n        @chk ccall((:VecDestroy, $libpetsc), PetscErrorCode, (Ptr{CVec},), v)\n        return nothing\n    end\n    function Base.length(v::AbstractVec{$PetscScalar})\n        r_sz = Ref{$PetscInt}()\n        @chk ccall((:VecGetSize, $libpetsc), PetscErrorCode,\n          (CVec, Ptr{$PetscInt}), v, r_sz)\n        return r_sz[]\n    end\n    function LinearAlgebra.norm(v::AbstractVec{$PetscScalar}, normtype::NormType=NORM_2)\n        r_val = Ref{$PetscReal}()\n        @chk ccall((:VecNorm, $libpetsc), PetscErrorCode,\n                   (CVec, NormType, Ptr{$PetscReal}),\n                   v, normtype,r_val)\n        return r_val[]\n    end\n\n    function assemblybegin(V::AbstractVec{$PetscScalar})\n        @chk ccall((:VecAssemblyBegin, $libpetsc), PetscErrorCode, (CVec,), V)\n        return nothing\n    end\n    function assemblyend(V::AbstractVec{$PetscScalar})\n        @chk ccall((:VecAssemblyEnd, $libpetsc), PetscErrorCode, (CVec,), V)\n        return nothing\n    end\n    function assemble(V::AbstractVec{$PetscScalar})\n        assemblybegin(V)\n        assemblyend(V)\n    end\n\n    function ownershiprange(vec::AbstractVec{$PetscScalar})\n        r_lo = Ref{$PetscInt}()\n        r_hi = Ref{$PetscInt}()\n        @chk ccall((:VecGetOwnershipRange, $libpetsc), PetscErrorCode,\n          (CVec, Ptr{$PetscInt}, Ptr{$PetscInt}), vec, r_lo, r_hi)\n        r_lo[]:(r_hi[]-$PetscInt(1))\n    end\n\n    function view(vec::AbstractVec{$PetscScalar}, viewer::AbstractViewer{$PetscLib}=ViewerStdout($petsclib, getcomm(vec)))\n        @chk ccall((:VecView, $libpetsc), PetscErrorCode,\n                    (CVec, CPetscViewer),\n                vec.ptr, viewer);\n        return nothing\n    end\n\n    function localsize(cv::CVec)\n        r_sz = Ref{$PetscInt}()\n        @chk ccall((:VecGetLocalSize, $libpetsc), PetscErrorCode,\n            (CVec, Ptr{$PetscInt}), cv, r_sz)\n        return r_sz[]\n    end\n\n    function setvalues!(vec::AbstractVec{$PetscScalar},idxs,vals, insertmode::InsertMode)\n        idxs = Vector(Int32,idxs);\n        vals = Vector(vals);\n  \n        @chk ccall((:VecSetValues, $libpetsc), PetscErrorCode, \n                 (CVec, $PetscInt, Ptr{$PetscInt}, Ptr{$PetscScalar},InsertMode), vec, length(idxs), idxs, vals, insertmode)\n        return nothing\n\n    end\n\n    function unsafe_localarray(::Type{$PetscScalar}, cv::CVec; read::Bool=true, write::Bool=true)\n        r_pv = Ref{Ptr{$PetscScalar}}()\n        if write\n            if read\n                @chk ccall((:VecGetArray, $libpetsc), PetscErrorCode,\n                    (CVec, Ptr{Ptr{$PetscScalar}}), cv, r_pv)\n            else\n                @chk ccall((:VecGetArrayWrite, $libpetsc), PetscErrorCode,\n                    (CVec, Ptr{Ptr{$PetscScalar}}), cv, r_pv)\n            end\n        else\n            @chk ccall((:VecGetArrayRead, $libpetsc), PetscErrorCode,\n                (CVec, Ptr{Ptr{$PetscScalar}}), cv, r_pv)\n        end\n        r_sz = Ref{$PetscInt}()\n        \n        @chk ccall((:VecGetLocalSize, $libpetsc), PetscErrorCode,\n            (CVec, Ptr{$PetscInt}), cv, r_sz)\n        v = unsafe_wrap(Array, r_pv[], r_sz[]; own = false)\n\n        if write\n            if read\n                finalizer(v) do v\n                    @chk ccall((:VecRestoreArray, $libpetsc), PetscErrorCode, (CVec, Ptr{Ptr{$PetscScalar}}), cv, Ref(pointer(v)))\n                    return nothing\n                end\n            else\n                finalizer(v) do v\n                    @chk ccall((:VecRestoreArrayWrite, $libpetsc), PetscErrorCode, (CVec, Ptr{Ptr{$PetscScalar}}), cv, Ref(pointer(v)))\n                    return nothing\n                end\n            end\n        else\n            finalizer(v) do v\n                @chk ccall((:VecRestoreArrayRead, $libpetsc), PetscErrorCode, (CVec, Ptr{Ptr{$PetscScalar}}), cv, Ref(pointer(v)))\n                return nothing\n            end\n        end\n        return v\n    end\n\n    function Base.fill!(v::AbstractVec{$PetscScalar}, x)\n        @chk ccall((:VecSet, $libpetsc),\n                   PetscErrorCode,\n                   (CVec, $PetscScalar),\n                   v, $PetscScalar(x))\n        return v\n    end\n\n    function Base.setindex!(\n        v::AbstractVec{$PetscScalar},\n        val,\n        i::Integer,\n    )\n        @chk ccall(\n            (:VecSetValues, $libpetsc),\n            PetscErrorCode,\n            (CVec, $PetscInt, Ptr{$PetscInt}, Ptr{$PetscScalar}, InsertMode),\n            v,\n            1,\n            Ref{$PetscInt}(i - 1),\n            Ref{$PetscScalar}(val),\n            INSERT_VALUES,\n        )\n\n        return val\n    end\n\n    function Base.getindex(\n        v::AbstractVec{$PetscScalar},\n        i::Integer,\n    )\n        vals = [$PetscScalar(0)]\n        @chk ccall(\n            (:VecGetValues, $libpetsc),\n            PetscErrorCode,\n            (CVec, $PetscInt, Ptr{$PetscInt}, Ptr{$PetscScalar}),\n            v,\n            1,\n            Ref{$PetscInt}(i - 1),\n            vals,\n        )\n\n        return vals[1]\n    end\nend\n\n\"\"\"\n    unsafe_localarray(PetscScalar, ptr:CVec; read=true, write=true)\n    unsafe_localarray(ptr:AbstractVec; read=true, write=true)\n\nReturn an `Array{PetscScalar}` containing local portion of the PETSc data.\n\nUse `read=false` if the array is write-only; `write=false` if read-only.\n\n!!! note\n    `Base.finalize` should be called on the `Array` before the data can be used.\n\"\"\"\nunsafe_localarray\n\nunsafe_localarray(v::AbstractVec{T}; kwargs...) where {T} =\n    unsafe_localarray(T, v.ptr; kwargs...)\n\n\"\"\"\n    map_unsafe_localarray!(f!, x::AbstractVec{T}; read=true, write=true)\n\nConvert `x` to an `Array{T}` and apply the function `f!`.\n\nUse `read=false` if the array is write-only; `write=false` if read-only.\n\n# Examples\n```julia-repl\njulia> map_unsafe_localarray(x; write=true) do x\n   @. x .*= 2\nend\n\n!!! note\n    `Base.finalize` should is automatically called on the array.\n\"\"\"\nfunction map_unsafe_localarray!(f!, v::AbstractVec{T}; kwargs...) where {T}\n    array = unsafe_localarray(T, v.ptr; kwargs...)\n    f!(array)\n    Base.finalize(array)\nend\n\n\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", vec::AbstractVec)\n    _show(io, vec)\nend\n\nVecSeq(X::Vector{T}; kwargs...) where {T} = VecSeq(MPI.COMM_SELF, X; kwargs...)\nAbstractVec(X::AbstractVector) = VecSeq(X)\n\n\n\"\"\"\n    ownershiprange(vec::AbstractVec)\n\nThe range of indices owned by this processor, assuming that the vectors are laid out with the first n1 elements on the first processor, next n2 elements on the second, etc. For certain parallel layouts this range may not be well defined.\n\nNote: unlike the C function, the range returned is inclusive (`idx_first:idx_last`)\n\n# External Links\n$(_doc_external(\"Vec/VecGetOwnershipRange\"))\n\"\"\"\nownershiprange\n\n\"\"\"\n    setvalues!(\n        vector::AbstractVec{PetscScalar},\n        indices::Vector{PetscInt},\n        vals::Vector{PetscScalar},\n        mode::InsertMode;\n        num_vals = length(ind)\n    )\n\nInsert a set of values into the `vector`. Equivalent to one of the following\ndepending on the `mode`\n```julia\nvector[indices[1:num_vals]] .= vals[1:num_vals]\nvector[indices[1:num_vals]] .+= vals[1:num_vals]\n```\n\n!!! warning\n    `indices` should use 0-based indexing!\n\n# External Links\n$(_doc_external(\"Vec/VecSetValues\"))\n\"\"\"\nfunction setvalues!(::AbstractVec) end\n\n@for_libpetsc function setvalues!(\n    vec::AbstractVec{$PetscScalar},\n    inds::Vector{$PetscInt},\n    vals::Vector{$PetscScalar},\n    mode::InsertMode;\n    num_vals = length(inds)\n)\n    @chk ccall(\n         (:VecSetValues, $libpetsc),\n         PetscErrorCode,\n         (CVec, $PetscInt, Ptr{$PetscInt}, Ptr{$PetscScalar}, InsertMode),\n         vec,\n         num_vals,\n         inds,\n         vals,\n         mode,\n    )\n    return vals\nend\n\n\"\"\"\n    getvalues!(\n        vector::AbstractVec{PetscScalar},\n        indices::Vector{PetscInt},\n        vals::Vector{PetscScalar};\n        num_vals = length(inds)\n    )\n\nGet a set of values from the `vector`. Equivalent to one of the following\n```julia\nvals[1:num_vals] .= vector[indices[1:num_vals]]\n```\n\n!!! warning\n    `indices` should use 0-based indexing!\n\n# External Links\n$(_doc_external(\"Vec/VecGetValues\"))\n\"\"\"\nfunction getvalues!(::AbstractVec) end\n\n@for_libpetsc function getvalues!(\n    vec::AbstractVec{$PetscScalar},\n    inds::Vector{$PetscInt},\n    vals::Vector{$PetscScalar};\n    num_vals = length(inds)\n)\n    @chk ccall(\n         (:VecGetValues, $libpetsc),\n         PetscErrorCode,\n         (CVec, $PetscInt, Ptr{$PetscInt}, Ptr{$PetscScalar}),\n         vec,\n         num_vals,\n         inds,\n         vals,\n    )\n    return vals\nend\n", "meta": {"hexsha": "e53680da2771cd09ea2993d2e71e163282efa10c", "size": 10795, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/vec.jl", "max_stars_repo_name": "psanan/PETSc.jl", "max_stars_repo_head_hexsha": "ff709d1267b258ff5df1860155a5973137a4a755", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 87, "max_stars_repo_stars_event_min_datetime": "2015-10-19T06:43:08.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-18T07:17:42.000Z", "max_issues_repo_path": "src/vec.jl", "max_issues_repo_name": "psanan/PETSc.jl", "max_issues_repo_head_hexsha": "ff709d1267b258ff5df1860155a5973137a4a755", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 118, "max_issues_repo_issues_event_min_datetime": "2015-10-13T01:42:06.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-07T22:13:21.000Z", "max_forks_repo_path": "src/vec.jl", "max_forks_repo_name": "psanan/PETSc.jl", "max_forks_repo_head_hexsha": "ff709d1267b258ff5df1860155a5973137a4a755", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 37, "max_forks_repo_forks_event_min_datetime": "2015-10-24T18:21:20.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T14:20:35.000Z", "avg_line_length": 29.738292011, "max_line_length": 237, "alphanum_fraction": 0.6136174155, "num_tokens": 2937, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.11279539882690351, "lm_q1q2_score": 0.05200057148842724}}
{"text": "\nfunction adder(x, y)\n    a = x + y\n    return a\nend", "meta": {"hexsha": "942e3d7243c064229a570452565ef308598d1f8d", "size": 52, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/testing/adder.jl", "max_stars_repo_name": "wigging/julia-computing", "max_stars_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/testing/adder.jl", "max_issues_repo_name": "wigging/julia-computing", "max_issues_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/testing/adder.jl", "max_forks_repo_name": "wigging/julia-computing", "max_forks_repo_head_hexsha": "927ee1667d5aa88a9f634fb4135a58d1244613eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.4, "max_line_length": 20, "alphanum_fraction": 0.5384615385, "num_tokens": 20, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.10818896318842185, "lm_q1q2_score": 0.05198249001214661}}
{"text": "# Translating\n# https://proplot.readthedocs.io/en/latest/subplots.html#Subplot-numbers-and-a-b-c-labels\n# to Julia\n\n# Import proplot using PyCall\nusing PyCall\nproplot = pyimport(\"proplot\")\n\nf, axs = proplot.subplots(nrows=8, ncols=8, axwidth=0.7, space=0)\n[ax.format(\n    abc=true, abcloc=\"ur\", xlabel=\"x axis\", ylabel=\"y axis\",\n    xticks=[], yticks=[], suptitle=\"Subplot labels demo\"\n) for ax in axs]\nf.savefig(\"figs/Subplot-numbers-and-a-b-c-labels.svg\", transparent=false)\n", "meta": {"hexsha": "ecb59412d510ca4eedd18e75b57c4dc904a73dfa", "size": 477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/subplots/Subplot-numbers-and-a-b-c-labels.jl", "max_stars_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_stars_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-04-24T06:43:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T02:39:31.000Z", "max_issues_repo_path": "src/subplots/Subplot-numbers-and-a-b-c-labels.jl", "max_issues_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_issues_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-04-24T07:43:14.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-07T07:15:47.000Z", "max_forks_repo_path": "src/subplots/Subplot-numbers-and-a-b-c-labels.jl", "max_forks_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_forks_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-07T01:32:42.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-07T01:32:42.000Z", "avg_line_length": 31.8, "max_line_length": 89, "alphanum_fraction": 0.714884696, "num_tokens": 148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.35577489351363034, "lm_q2_score": 0.14608725448372273, "lm_q1q2_score": 0.05197417740764507}}
{"text": "#Julia Control Flow\n\nfunction larger(x, y) \n    if (x>y) \n        return x\n    end\n    return y\nend\nprintln(larger(7,8))", "meta": {"hexsha": "a8e3529aa036c309e6447eec1cb17e23e516c09b", "size": 120, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ch4/11-control-flow.jl", "max_stars_repo_name": "PacktPublishing/Learning-Jupyter", "max_stars_repo_head_hexsha": "734ef16ade5f9874e5187e483746524a675bf915", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2017-02-02T08:47:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-15T18:04:01.000Z", "max_issues_repo_path": "ch4/11-control-flow.jl", "max_issues_repo_name": "PacktPublishing/Learning-Jupyter", "max_issues_repo_head_hexsha": "734ef16ade5f9874e5187e483746524a675bf915", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2016-12-02T04:43:11.000Z", "max_issues_repo_issues_event_max_datetime": "2016-12-02T04:43:57.000Z", "max_forks_repo_path": "ch4/11-control-flow.jl", "max_forks_repo_name": "PacktPublishing/Learning-Jupyter", "max_forks_repo_head_hexsha": "734ef16ade5f9874e5187e483746524a675bf915", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-12-02T04:39:10.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-01T22:58:19.000Z", "avg_line_length": 13.3333333333, "max_line_length": 22, "alphanum_fraction": 0.5916666667, "num_tokens": 41, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.35577489351363034, "lm_q2_score": 0.14608724333058223, "lm_q1q2_score": 0.0519741734396377}}
{"text": "#=\nThis julia script converts fortran 90 code into julia.\nIt uses naive regex replacements to do as much as possible,\nbut the output WILL need further cleanup.\n\nKnown conversion problems such as GOTO are commented and marked with FIXME\n\nMost variable declaration lines are entirely deleted, which may or\nmay not be useful. \n\n\nTo run from a shell: \n\njulia fortran-julia.jl filename.f90\n\nOutput is written to filename.jl.\n=#\n\nusing DataStructures\n\n# Regex/substitution pairs for replace(). Order matters here.\nreplacements = OrderedDict(\n  # Lowercase everything not commented\n  r\"^(?!.*!).*\"m => lowercase,\n  # Lowercase start of lines with comments\n  r\"^.*!\"m => lowercase,\n  # Remove '&' multiline continuations\n  r\"\\s*&\\s*\" => \"\",\n  # Comments use # not !\n  \"!\" => \"#\",\n  # Powers use ^ not **\n  \"**\" => \"^\",\n  # Only double quotes allowed for strings\n  \"'\" => \"\\\"\",\n  # DO loop to for loop\n  r\"do (.*),(.*)\" => s\"for \\1:\\2\",\n  # Spaces around math operators\n  r\"([\\*\\+\\/=])(?=\\S)\" => s\"\\1 \",\n  r\"(?<=\\S)([\\*\\+\\/=])\" => s\" \\1\",\n  # Spaces around - operators, except after e\n  # r\"([^e][\\-])(\\S)\" => s\"\\1 \\2\",\n  r\"(?<!\\W\\de)(\\h*\\-\\h*)\" => s\" - \",\n  # Space after all commas\n  r\"(,)(\\S)\" => s\"\\1 \\2\",\n  # Replace ELSEIF/ELSE IF with elseif \n  r\"(\\s+)else if\" => s\"\\1elseif\",\n  # Replace IF followed by ( to if (\n  r\"(\\s+)(elseif|if)\\(\" => s\"\\1\\2 (\",\n  # Remove THEN\n  r\"([)\\s])then(\\s+)\" => s\"\\1\\2\",\n  # Relace END XXXX with end\n  r\"(\\s+)end\\h*.*\" => s\"\\1end\",\n  # Replace expnent function\n  r\"(\\W)exp\\(\" => s\"\\1exp(\",\n  # Reorganise functions and doc strings. This may be very project specific.\n  r\"#\\^\\^+\\s*subroutine\\s*(\\w+)([^)]+\\))\\s*(.*?)#\\^\\^\\^+\"sm => \n      Base.SubstitutionString(\"\\\"\\\"\\\"\\n\\\\3\\\"\\\"\\\"\\nfunction \\\\1\\\\2::Void\"),\n  r\"\\#\\^\\^+\\s*real function\\s*(\\w+)([^)]+\\))\\s*(.*?)\\#\\^\\^\\^+\"sm => \n      Base.SubstitutionString(\"\\\"\\\"\\\"\\n\\\\3\\\"\\\"\\\"\\nfunction \\\\1\\\\2::Float64\"),\n  # Don't need CALL\n  r\"(\\s*)call(\\h+)\" => s\"\\1\",\n  # Use real math symbols\n  \"gamma\" => \"\u0393\",\n  \"theta\" => \"\u0398\",\n  \"epsilon\" => \"\u03f5\",\n  \"lambda\" => \"\u03bb\",\n  \"alpha\" => \"\u03b1\",\n  # Swap logical symbols\n  \".true.\" => \"true\",\n  \".false.\" => \"false\",\n  r\"\\s*\\.or\\.\\s*\" => \" || \",\n  r\"\\s*\\.and\\.\\s*\" => \" && \",\n  r\"\\s*\\.not\\.\\s*\" => \" ! \",\n  r\"\\s*\\.eq\\.\\s*\" => \" == \",\n  r\"\\s*\\.ne\\.\\s*\" => \" != \",\n  r\"\\s*\\.le\\.\\s*\" => \" <= \",\n  r\"\\s*\\.ge\\.\\s*\" => \" >= \",\n  r\"\\s*\\.gt\\.\\s*\" => \" > \",\n  r\"\\s*\\.lt\\.\\s*\" => \" < \",\n  # Remove (expression) brackets after if\n  # r\"if \\((.*)\\)(\\s*\\n)\" => s\"if \\1\\2\",\n  # Add end after single line if with an = assignment\n  r\"if\\s*(.*?) = (.*?)(\\n)\" => s\"if \\1 = \\2 end\\3\",\n  # Format floats as \"5.0\" not \"5.\"\n  r\"(\\W\\d+)\\.(\\D)\" => s\"\\1.0\\2\",\n  # Tab to 4 spaces\n  r\"\\t\" => \"    \",\n  # Relace suberror with error and mark for fixup\n  r\"(\\W)suberror\\((.*?),.*?\\)\" => s\"\\1 error(\\2)\",\n  # Mark #FIXME the various things this script can't handle\n  r\"(write|goto|while\\s)\" => s\"#FIXME \\1\",\n)\n\n# Patterns to remove\nremoval = OrderedDict(\n  # Trailing whitespace\n  r\"\\h*$\"m => \"\",\n  # Variable declarations\n  r\"\\n\\s*real\\s.*\" => \"\",\n  r\"\\n\\s*real, external\\s.*\" => \"\",\n  r\"\\n\\s*integer\\s.*\" => \"\",\n  r\"\\n\\s*implicit none\" => \"\",\n  r\"\\n\\s*logical\\s.*\" => \"\",\n  # Import statements\n  r\"\\n\\s*use\\s.*\" => \"\",\n)\n\n# Load the file from the first command line argument\nfilename = string(ARGS[1])\ncode = read(filename, String)\n\n# Process replacements and removals.\nfor f in replacements\n  global code = replace(code, f)\nend\nfor r in removal\n  global code = replace(code, r)\nend\nprintln(code)\n\n\n# Write the output to a .jl file with the same filename stem.\nstem = split(filename, \".\")[1]\noutfile = stem * \".jl\"\nwrite(outfile, code)\n", "meta": {"hexsha": "6e492c06f10117aed5fce4074bc8a8c0bc30a71c", "size": 3613, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/src/gcvspline/fortran-julia.jl", "max_stars_repo_name": "charlesll/gcvspl", "max_stars_repo_head_hexsha": "708ef035b2388107d9e1e802fc97e5738103d0c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "deps/src/gcvspline/fortran-julia.jl", "max_issues_repo_name": "charlesll/gcvspl", "max_issues_repo_head_hexsha": "708ef035b2388107d9e1e802fc97e5738103d0c7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-20T11:07:24.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-20T13:44:18.000Z", "max_forks_repo_path": "deps/src/gcvspline/fortran-julia.jl", "max_forks_repo_name": "charlesll/gcvspl", "max_forks_repo_head_hexsha": "708ef035b2388107d9e1e802fc97e5738103d0c7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6746031746, "max_line_length": 77, "alphanum_fraction": 0.5363963465, "num_tokens": 1262, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3702253925955866, "lm_q2_score": 0.1403362440969662, "lm_q1q2_score": 0.05195604106618939}}
{"text": "first = 1\nsecond = 2.1\nstring = \"I am a string\"\nboolvar = true\n\nprintln(first)\nprintln(second)\nprintln(string)\nprintln(boolvar)\n\nconst MYNAME = \"Richard\"\nprintln(MYNAME)\n\nfunction f1()\n    x::Int64 = 21\n    println(x)\nend\nf1()\n\nfunction f2()\n    y = Int64(1234)\n    z = Int(2)\n    println(typeof(y))\n    println(typeof(z))\nend\nf2()\n\nfunction f3()\n    println(typemax(Int64))\n    println(typemin(Int64))\nend\nf3()\n\nprintln(Sys.WORD_SIZE)\nprintln(zero(Int64))\n\nfunction f4()\n    x1 = 'x'\n    println(Int(x1))\n    println(Char(120))\nend\nf4()\n\nmyStr = \"\"\"\nHello from\nthe \nother \nside of the room\n\"\"\"\nprintln(myStr)\nprintln(length(myStr))\nprintln(myStr[1:10])\n\nfor c in myStr\n    print(Int64(c))\nend\nprintln()\n\nx2 = Int64(12)\nx3 = Int64(119)\nprintln(\"The result of $x2 + $x3 is $(x2 + x3)\")\n\n", "meta": {"hexsha": "47213d0782b2d69ca5171f9854b10aaaae131b57", "size": 786, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "variables.jl", "max_stars_repo_name": "rpillar/JuliaExamples", "max_stars_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "variables.jl", "max_issues_repo_name": "rpillar/JuliaExamples", "max_issues_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "variables.jl", "max_forks_repo_name": "rpillar/JuliaExamples", "max_forks_repo_head_hexsha": "261c629de45ed573ae6c3888f21b88e1238d6954", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.4761904762, "max_line_length": 48, "alphanum_fraction": 0.641221374, "num_tokens": 257, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681520167196, "lm_q2_score": 0.13117323225300487, "lm_q1q2_score": 0.05195353969250759}}
{"text": "\n# Vertex traits w.r.t whether size changes propagates\nabstract type Direction end\n\"\"\"\n    Input\n\nRepresents the input direction, i.e coming from the output of another vertex.\n\"\"\"\nstruct Input <: Direction end\n\"\"\"\n    Output\n\nRepresents the output direction, i.e coming from the input of another vertex.\n\"\"\"\nstruct Output <: Direction end\n\"\"\"\n    Both\n\nRepresents both directions (`Input` and `Output`).\n\"\"\"\nstruct Both <: Direction end\n\nstruct Tight\u0394SizeGraph end\nstruct Loose\u0394SizeGraph end\n\n\n\"\"\"\n    opposite(d::Direction)\n\nReturn the opposite direction of `d`.\n\"\"\"\nopposite(::Input) = Output()\nopposite(::Output) = Input()\nopposite(b::Both) = b\n\n\"\"\"\n    neighbours(d::Direction, v)\n\nReturn vertices connected to `v` in direction `d`.\n\"\"\"\nneighbours(::Input, v) = inputs(v)\nneighbours(::Output, v) = outputs(v)\nneighbours(::Both, v) = vcat(inputs(v), outputs(v))\n\n\"\"\"\n    all_in_graph(v::AbstractVertex)\n\nReturn an array of vertices in the same graph (or connected component) as `v`\n\"\"\"\nfunction all_in_graph(v::AbstractVertex, visited = AbstractVertex[])\n    v in visited && return visited\n    push!(visited, v)\n    foreach(vi -> all_in_graph(vi, visited), inputs(v))\n    foreach(vo -> all_in_graph(vo, visited), outputs(v))\n    return visited\nend\n\n\"\"\"\n    all_in_\u0394size_graph(v::AbstractVertex, d::Direction)\n\nReturn an array of vertices which will be affected if `v` changes size in direction `d`.\n\"\"\"\nall_in_\u0394size_graph(vs::AbstractDict{<:AbstractVertex}, d::Direction, mode=Loose\u0394SizeGraph()) = all_in_\u0394size_graph(keys(vs), d, mode)\nall_in_\u0394size_graph(vs::Tuple{Vararg{Pair{<:AbstractVertex}}}, d::Direction, mode=Loose\u0394SizeGraph()) = all_in_\u0394size_graph(first.(vs), d, mode)\nall_in_\u0394size_graph(vs, d::Direction, mode=Loose\u0394SizeGraph()) = unique(mapreduce(v -> all_in_\u0394size_graph(mode, v, d), vcat, vs; init=AbstractVertex[]))\n\nall_in_\u0394size_graph(v::AbstractVertex, d::Direction) = all_in_\u0394size_graph(Loose\u0394SizeGraph(), v, d)\n\nfunction all_in_\u0394size_graph(mode, v::AbstractVertex, d::Direction, visited=[])\n    (v, d) in visited && return visited\n    push!(visited, (v, d))\n    all_in_\u0394size_graph(mode, trait(v), d, v, visited)\n    return unique(map(e -> e[1], visited))\nend\nfunction all_in_\u0394size_graph(mode, v::AbstractVertex, d::Both, visited=[])\n    all_in_\u0394size_graph(v, Input(), visited)\n    foreach(vout -> all_in_\u0394size_graph(mode, vout, Input(), visited), outputs(v))\n    return unique(map(e -> e[1], visited))\nend\n\nall_in_\u0394size_graph(mode, t::DecoratingTrait, d, v, visited) = all_in_\u0394size_graph(mode, base(t), d, v, visited)\nfunction all_in_\u0394size_graph(mode, ::Immutable, ::Direction, v, visited) end\nall_in_\u0394size_graph(mode, ::SizeAbsorb, d, v, visited) = foreach(vn -> all_in_\u0394size_graph(mode, vn, opposite(d), visited), neighbours(d, v))\nfunction all_in_\u0394size_graph(mode, ::SizeTransparent, d, v, visited)\n    foreach(vin -> all_in_\u0394size_graph(mode, vin, Output(), visited), inputs(v))\n    foreach(vout -> all_in_\u0394size_graph(mode, vout, Input(), visited), outputs(v))\nend\n\nfunction all_in_\u0394size_graph(mode::Tight\u0394SizeGraph, ::SizeStack, d::Input, v, visited) \n    if any(vx -> vx in inputs(v), first.(visited))\n        push!(visited, (v, opposite(d)))\n        foreach(vin -> all_in_\u0394size_graph(mode, vin, d, visited), neighbours(opposite(d), v))\n    else\n        all_in_\u0394size_graph(mode, SizeInvariant(), d, v, visited)\n    end\nend\n\n\"\"\"\n    findterminating(v::AbstractVertex, direction::Function, other::Function= v -> [], visited = [])\n\nReturn an array of all vertices which terminate size changes (i.e does not propagate them) seen through the given direction\n (typically [`inputs`](@ref) or [`outputs`](@ref)). A vertex will be present once for each unique path through which its seen.\n\nThe `other` direction may be specified and will be traversed if a [`SizeInvariant`](@ref) vertex is encountered.\n\nWill return the given vertex if it is terminating.\n\n# Examples\n```jldoctest\njulia> using NaiveNASlib, NaiveNASlib.Advanced\n\njulia> v1 = inputvertex(\"v1\", 3);\n\njulia> v2 = inputvertex(\"v2\", 3);\n\njulia> v3 = conc(v1,v2,v1,dims=1);\n\njulia> name.(findterminating(v1, outputs, inputs))\n1-element Vector{String}:\n \"v1\"\n\njulia> name.(findterminating(v3, outputs, inputs))\nAny[]\n\njulia> name.(findterminating(v3, inputs, outputs))\n3-element Vector{String}:\n \"v1\"\n \"v2\"\n \"v1\"\n\njulia> v5 = v3 + inputvertex(\"v4\", 9);\n\njulia> name.(findterminating(v3, outputs, inputs))\n1-element Vector{String}:\n \"v4\"\n```\n\"\"\"\nfunction findterminating(v::AbstractVertex, direction::Function, other::Function=v->AbstractVertex[], visited = Set{AbstractVertex}())\n    v in visited && return AbstractVertex[]\n    push!(visited, v)\n    res = findterminating(trait(v), v, direction, other, visited)\n    delete!(visited, v)\n    return res\n end\nfindterminating(t::DecoratingTrait, v, d::Function, o::Function, visited) = findterminating(base(t), v, d, o, visited)\nfindterminating(::SizeAbsorb, v, d::Function, o::Function, visited) = [v]\nfindterminating(::Immutable, v, d::Function, o::Function, visited) = [v]\n\nfindterminating(::SizeStack, v, d::Function, o::Function, visited) = collectterminating(v, d, o, visited)\nfindterminating(::SizeInvariant, v, d::Function, o::Function, visited) = vcat(collectterminating(v, d, o, visited), collectterminating(v, o, d, visited))\ncollectterminating(v, d::Function, o::Function, visited) = mapfoldl(vf -> findterminating(vf, d, o, visited), vcat, d(v), init=[])\n\n# Will be defined later but we need it below\nfunction remove_with_undo! end\n\n# Just a namespace so we don't have to see very generic names in the NaiveNASlib namespace as they are not to be used outside\nmodule SizeCycleDetector\n\n    export isinsizecycle\n\n    using ..NaiveNASlib\n    using ..NaiveNASlib:    AbstractVertex, remove_with_undo!, Input, Output, neighbours, DecoratingTrait, MutationTrait, \n                            SizeAbsorb, SizeStack, SizeInvariant, SizeTransparent, trait, base, findterminating\n\n    struct SeenNothing end\n    struct SeenSizeStack{T}\n        vs::T\n    end\n    SeenSizeStack(v, s) = SeenSizeStack([v])\n    SeenSizeStack(v, s::SeenSizeStack) = SeenSizeStack(vcat(v, s.vs))\n\n    \"\"\"\n        isinsizecycle(v)\n\n    Return true if removing `v` leads to a size cycle.  \n\n    A size cycle is when at least one vertex `vi` has the impossible size constraint that `nout(vi) == nout(vi) + nout(vj)` where `vj` is a vertex in the graph (possibly `vi`).\n    \n    This tends to happen when concatenation is followed by an elementwise operation and removing one input `vi` to the concatenation and replacing it with another vertex `vj` \n    which is also input to the elementwise operation (possibly through any number of size transparent operations). Since the sum of input sizes to the concatenation must be \n    equal to the input size of the elementwise operation and one out of several inputs to the concatenation must also have the same output size as the input to the \n    elementwise operation we are stuck with an impossible constraint.\n\n    As the algorithm is a bit messy (which imo is a sign that it is incorrect), here is a rundown of the idea:\n\n    1. From the queired vertex, trace the graph forwards (output direction) until we hit a non-`SizeTransparent` vertex. If we encounter a `SizeStack` vertex store \n    this vertex in a `SeenSizeStack` and use it as the current state. If we encounter a `SizeInvariant` when we have `SeenSizeStack` as the state, we will store the \n    vertex and the `SeenSizeStack` as the output. The result from this is thus the set of potentially problematic vertices. If there are no potentially problematic\n    vertices there can not be a size cycle and we return false.\n\n    2. As the posed question is \"If I remove `v` and connect its inputs to its outputs, will there be a size cycle?\" we determine if shortcutting `v` creates a size transparent\n    path between any input ancestor to `v` and any of the problematic vertices. To avoid having to check all ancestors, we just examine the set which terminates any size \n    transparency using `findterminating`, knowing that if `v` and a problematic vertex share such a terminating ancestor, then shortcutting `v` creates a size transparent path\n    to the problematic vertex which may not have been there before. If there are no problematic vertices which share a common terminating ancestor with `v` we return false.\n\n    3. For each problematic vertex `vp` and associated set of terminating ancestors `VPa` which passes the check in 2 we now check if any of the vertices in `VPa` sees `vp` as\n    problematic for the same reason as `v` does, meaning that they both pass through the same concatenation at least one time before hitting `vp`. If not, it basically means \n    that `v` is alone in a `noop` concatenation and there is no `vj` from the first paragraph to create an impossible constraint. If this applies to all problematic vertices\n    we don't have a size cycle and return false.\n    \"\"\"\n    function isinsizecycle(v)\n        # Step 1: Find all potentially problematic vertices seen from v. Result is a Dict mapping SizeInvariant vertices to the set of SizeStack vertices passed through on the way\n        problemvertices = Dict(findproblemvertices(v))\n\n        isempty(problemvertices) && return false\n\n        # Begin step 2. Get the set of size change terminating ancestors for v\n        v_ancestors = mapreduce(vi -> findterminating(vi, inputs), vcat, inputs(v); init=AbstractVertex[])\n\n        # We temporaily remove v from the graph without recreating any connections.\n        # This prevents that any size absorbing vertices which are input to v show up as problemancestors below\n        # which would obviously ruin the alg as they would trivially see any vertex in problemvertices as as being problematic\n        # for the exact same reason as v does.\n        undo_rm = remove_with_undo!(v)\n\n        iscycle = any(keys(problemvertices)) do problemvertex\n            # These are not really problematic, but they are the ancestors of a problematic vertex\n            # Note that we have cut off `v` from the graph, so none of its inputs show up in problemancestors \n            # unless it connects to problemvertex through another path\n            problemancestors = findterminating(problemvertex, inputs)\n\n            # Step 2: There can't be any size ccyle if there is no transparent path\n            # from vs terminating ancestors to one of problemvertexs terminating\n            # ancestors. \n            !any(pa -> pa in v_ancestors, problemancestors) && return false\n\n            any(problemancestors) do ancestor\n                # Aaaand step 3: Check if any terminating ancestor of a `problemvertex` sees `problemvertex` as problematic for the same reason as `v` \n                ancestorproblemvertices = Dict(findproblemvertices(ancestor))\n\n                # No intersect -> no problem\n                any(intersect(keys(problemvertices), keys(ancestorproblemvertices))) do commonproblem\n                    s1 = problemvertices[commonproblem]\n                    s2 = ancestorproblemvertices[commonproblem]\n                    # Finally check that there is at least one SizeStack which both `v` and a terminating ancestor of `problemvertex` (after `v` is cut of)\n                    # passes through.\n                    any(vx -> vx in s2.vs, s1.vs)\n                end\n            end\n        end\n        # Reconnect `v` to the graph throgh the undo function\n        undo_rm()\n        return iscycle\n    end\n\n    findproblemvertices(v) = stepforward(SeenNothing(), v)\n    findproblemvertices(state, v::AbstractVertex) = findproblemvertices(state, trait(v), v)\n    findproblemvertices(state, t::DecoratingTrait, v) = findproblemvertices(state, base(t), v)\n    findproblemvertices(state, ::MutationTrait, v) = pvinit()\n    findproblemvertices(state::SeenNothing, ::SizeInvariant, v) = stepforward(state, v)\n    findproblemvertices(state::SeenSizeStack, ::SizeInvariant, v) = vcat(v => state, stepforward(state, v))\n    findproblemvertices(state, ::SizeStack, v) = stepforward(SeenSizeStack(v, state), v)\n\n    stepforward(state, v) = mapreduce(vo -> findproblemvertices(state, vo), vcat, outputs(v); init=pvinit())\n\n    pvinit() = Pair{AbstractVertex, <:SeenSizeStack}[]\n\nend\nusing .SizeCycleDetector", "meta": {"hexsha": "542ee264b44c4df7e45f4f3557b20aa8642164e7", "size": 12240, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mutation/graph.jl", "max_stars_repo_name": "DrChainsaw/NaiveNASlib.jl", "max_stars_repo_head_hexsha": "c94437eb49493e8b5dbbbe9e9bf5a561646bcaed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2019-07-08T23:50:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T15:37:12.000Z", "max_issues_repo_path": "src/mutation/graph.jl", "max_issues_repo_name": "DrChainsaw/NaiveNASlib.jl", "max_issues_repo_head_hexsha": "c94437eb49493e8b5dbbbe9e9bf5a561646bcaed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 89, "max_issues_repo_issues_event_min_datetime": "2019-06-14T00:15:56.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-26T12:46:26.000Z", "max_forks_repo_path": "src/mutation/graph.jl", "max_forks_repo_name": "DrChainsaw/NaiveNASlib.jl", "max_forks_repo_head_hexsha": "c94437eb49493e8b5dbbbe9e9bf5a561646bcaed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:54:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:33:04.000Z", "avg_line_length": 47.2586872587, "max_line_length": 179, "alphanum_fraction": 0.7108660131, "num_tokens": 3121, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44167302036300954, "lm_q2_score": 0.11757214591334526, "lm_q1q2_score": 0.05192844479610767}}
{"text": "\nfunction known_lastindex(::Type{T}) where {T}\n    if known_offset1(T) === nothing || known_length(T) === nothing\n        return nothing\n    else\n        return known_length(T) - known_offset1(T) + 1\n    end\nend\nknown_lastindex(@nospecialize x) = known_lastindex(typeof(x))\n\n@inline static_lastindex(x) = Static.maybe_static(known_lastindex, lastindex, x)\n\nfunction Base.first(x::AbstractVector, n::StaticInt)\n    @boundscheck n < 0 && throw(ArgumentError(\"Number of elements must be nonnegative\"))\n    start = offset1(x)\n    @inbounds x[start:min((start - one(start)) + n, static_lastindex(x))]\nend\n\nfunction Base.last(x::AbstractVector, n::StaticInt)\n    @boundscheck n < 0 && throw(ArgumentError(\"Number of elements must be nonnegative\"))\n    stop = static_lastindex(x)\n    @inbounds x[max(offset1(x), (stop + one(stop)) - n):stop]\nend\n\n\"\"\"\n    ArrayInterface.to_indices(A, I::Tuple) -> Tuple\n\nConverts the tuple of indexing arguments, `I`, into an appropriate form for indexing into `A`.\nTypically, each index should be an `Int`, `StaticInt`, a collection with values of `Int`, or a collection with values of `CartesianIndex`\nThis is accomplished in three steps after the initial call to `to_indices`:\n\n# Extended help\n\nThis implementation differs from that of `Base.to_indices` in the following ways:\n\n*  `to_indices(A, I)` never results in recursive processing of `I` through\n  `to_indices(A, axes(A), I)`. This is avoided through the use of an internal `@generated`\n  method that aligns calls of `to_indices` and `to_index` based on the return values of\n  `ndims_index`. This is beneficial because the compiler currently does not optimize away\n  the increased time spent recursing through\n    each additional argument that needs converting. For example:\n    ```julia\n    julia> x = rand(4,4,4,4,4,4,4,4,4,4);\n\n    julia> inds1 = (1, 2, 1, 2, 1, 2, 1, 2, 1, 2);\n\n    julia> inds2 = (1, CartesianIndex(1, 2), 1, CartesianIndex(1, 2), 1, CartesianIndex(1, 2), 1);\n\n    julia> inds3 = (fill(true, 4, 4), 2, fill(true, 4, 4), 2, 1, fill(true, 4, 4), 1);\n\n    julia> @btime Base.to_indices(\\$x, \\$inds2)\n    1.105 \u03bcs (12 allocations: 672 bytes)\n    (1, 1, 2, 1, 1, 2, 1, 1, 2, 1)\n\n    julia> @btime ArrayInterface.to_indices(\\$x, \\$inds2)\n    0.041 ns (0 allocations: 0 bytes)\n    (1, 1, 2, 1, 1, 2, 1, 1, 2, 1)\n\n    julia> @btime Base.to_indices(\\$x, \\$inds3);\n    340.629 ns (14 allocations: 768 bytes)\n\n    julia> @btime ArrayInterface.to_indices(\\$x, \\$inds3);\n    11.614 ns (0 allocations: 0 bytes)\n\n    ```\n* Recursing through `to_indices(A, axes, I::Tuple{I1,Vararg{Any}})` is intended to provide\n  context for processing `I1`. However, this doesn't tell use how many dimensions are\n  consumed by what is in `Vararg{Any}`. Using `ndims_index` to directly align the axes of\n  `A` with each value in `I` ensures that a `CartesiaIndex{3}` at the tail of `I` isn't\n  incorrectly assumed to only consume one dimension.\n* `Base.to_indices` may fail to infer the returned type. This is the case for `inds2` and\n  `inds3` in the first bullet on Julia 1.6.4.\n* Specializing by dispatch through method definitions like this:\n  `to_indices(::ArrayType, ::Tuple{AxisType,Vararg{Any}}, ::Tuple{::IndexType,Vararg{Any}})`\n  require an excessive number of hand written methods to avoid ambiguities. Furthermore, if\n  `AxisType` is wrapping another axis that should have unique behavior, then unique parametric\n  types need to also be explicitly defined.\n* `to_index(axes(A, dim), index)` is called, as opposed to `Base.to_index(A, index)`. The\n  `IndexStyle` of the resulting axis is used to allow indirect dispatch on nested axis types\n  within `to_index`.\n\"\"\"\nto_indices(A, ::Tuple{}) = ()\n@inline function to_indices(a::A, inds::I) where {A,I}\n    _to_indices(a, inds, IndexStyle(A), static(ndims(A)), IndicesInfo(I))\nend\n@generated function _to_indices(a, inds, ::S, ::StaticInt{N}, ::IndicesInfo{NI,NS,IS}) where {S,N,NI,NS,IS}\n    _to_indices_expr(S, N, NI, NS, IS)\nend\nfunction _to_indices_expr(S::DataType, N::Int, ni, ns, is)\n    blk = Expr(:block, Expr(:meta, :inline))\n    # check to see if we are dealing with linear indexing over a multidimensional array\n    if length(ni) == 1 && ni[1] === 1\n        push!(blk.args, :((to_index(LazyAxis{:}(a), getfield(inds, 1)),)))\n    else\n        indsexpr = Expr(:tuple)\n        ndi = Int[]\n        nds = Int[]\n        isi = Bool[]\n        # 1. unwrap AbstractCartesianIndex, CartesianIndices, Indices\n        for i in 1:length(ns)\n            ns_i = ns[i]\n            if ns_i isa Tuple\n                for j in 1:length(ns_i)\n                    push!(ndi, 1)\n                    push!(nds, ns_i[j])\n                    push!(isi, false)\n                    push!(indsexpr.args, :(getfield(getfield(getfield(inds, $i), 1), $j)))\n                end\n            else\n                push!(indsexpr.args, :(getfield(inds, $i)))\n                push!(ndi, ni[i])\n                push!(nds, ns_i)\n                push!(isi, is[i])\n            end\n        end\n\n        # 2. find splat indices\n        splat_position = 0\n        remaining = N\n        for i in eachindex(ndi, nds, isi)\n            if isi[i] && splat_position == 0\n                splat_position = i\n            else\n                remaining -= ndi[i]\n            end\n        end\n        if splat_position !== 0\n            for _ in 2:remaining\n                insert!(ndi, splat_position, 1)\n                insert!(nds, splat_position, 1)\n                insert!(indsexpr.args, splat_position, indsexpr.args[splat_position])\n            end\n        end\n\n        # 3. insert `to_index` calls\n        dim = 0\n        nndi = length(ndi)\n        for i in 1:nndi\n            ndi_i = ndi[i]\n            if ndi_i == 1\n                dim += 1\n                indsexpr.args[i] = :(to_index($(_axis_expr(N, dim)), $(indsexpr.args[i])))\n            else\n                subaxs = Expr(:tuple)\n                for _ in 1:ndi_i\n                    dim += 1\n                    push!(subaxs.args, _axis_expr(N, dim))\n                end\n                if i == nndi && S <: IndexLinear\n                    indsexpr.args[i] = :(to_index(LinearIndices($(subaxs)), $(indsexpr.args[i])))\n                else\n                    indsexpr.args[i] = :(to_index(CartesianIndices($(subaxs)), $(indsexpr.args[i])))\n                end\n            end\n        end\n        push!(blk.args, Expr(:(=), :axs, :(lazy_axes(a))))\n        push!(blk.args, :(flatten_tuples($(indsexpr))))\n    end\n    return blk\nend\n\nfunction _axis_expr(N::Int, d::Int)\n    if d <= N\n        :(getfield(axs, $d))\n    else  # ndims(a)+ can only have indices 1:1\n        :($(SOneTo(1)))\n    end\nend\n\n@inline function flatten_tuples(inds::I) where {I}\n    if @generated\n        t = Expr(:tuple)\n        for i in 1:fieldcount(I)\n            p = fieldtype(I, i)\n            if p <: Tuple\n                for j in 1:fieldcount(p)\n                    push!(t.args, :(@inbounds(getfield(getfield(inds, $i), $j))))\n                end\n            else\n                push!(t.args, :(@inbounds(getfield(inds, $i))))\n            end\n        end\n        Expr(:block, Expr(:meta, :inline), t)\n    else\n        out = ()\n        for i in inds\n            out = i isa Tuple ? (out..., i...) : (out..., i)\n        end\n        out\n    end\nend\n\n\"\"\"\n    ArrayInterface.to_index([::IndexStyle, ]axis, arg) -> index\n\nConvert the argument `arg` that was originally passed to `ArrayInterface.getindex` for the\ndimension corresponding to `axis` into a form for native indexing (`Int`, Vector{Int}, etc.).\n\n`ArrayInterface.to_index` supports passing a function as an index. This function-index is\ntransformed into a proper index.\n\n```julia\njulia> using ArrayInterface, Static\n\njulia> ArrayInterface.to_index(static(1):static(10), 5)\n5\n\njulia> ArrayInterface.to_index(static(1):static(10), <(5))\nstatic(1):4\n\njulia> ArrayInterface.to_index(static(1):static(10), <=(5))\nstatic(1):5\n\njulia> ArrayInterface.to_index(static(1):static(10), >(5))\n6:static(10)\n\njulia> ArrayInterface.to_index(static(1):static(10), >=(5))\n5:static(10)\n\n```\n\nUse of a function-index helps ensure that indices are inbounds\n\n```julia\njulia> ArrayInterface.to_index(static(1):static(10), <(12))\nstatic(1):10\n\njulia> ArrayInterface.to_index(static(1):static(10), >(-1))\n1:static(10)\n```\n\nNew axis types with unique behavior should use an `IndexStyle` trait:\n```julia\nto_index(axis::MyAxisType, arg) = to_index(IndexStyle(axis), axis, arg)\nto_index(::MyIndexStyle, axis, arg) = ...\n```\n\n\"\"\"\nto_index(x, i::Slice) = i\nto_index(x, ::Colon) = indices(x)\nto_index(::LinearIndices{0,Tuple{}}, ::Colon) = Slice(static(1):static(1))\nto_index(::CartesianIndices{0,Tuple{}}, ::Colon) = Slice(static(1):static(1))\n# logical indexing\nto_index(x, i::AbstractArray{Bool}) = LogicalIndex(i)\nto_index(x::LinearIndices, i::AbstractArray{Bool}) = LogicalIndex{Int}(i)\n# cartesian indexing\n@inline to_index(x, i::CartesianIndices{0}) = i\n@inline to_index(x, i::CartesianIndices) = getfield(i, :indices)\n@inline to_index(x, i::CartesianIndex) = Tuple(i)\n@inline to_index(x, i::NDIndex) = Tuple(i)\n@inline to_index(x, i::AbstractArray{<:AbstractCartesianIndex}) = i\n@inline function to_index(x, i::Base.Fix2{<:Union{typeof(<),typeof(isless)},<:Union{Base.BitInteger,StaticInt}})\n    static_first(x):min(_sub1(canonicalize(i.x)), static_last(x))\nend\n@inline function to_index(x, i::Base.Fix2{typeof(<=),<:Union{Base.BitInteger,StaticInt}})\n    static_first(x):min(canonicalize(i.x), static_last(x))\nend\n@inline function to_index(x, i::Base.Fix2{typeof(>=),<:Union{Base.BitInteger,StaticInt}})\n    max(canonicalize(i.x), static_first(x)):static_last(x)\nend\n@inline function to_index(x, i::Base.Fix2{typeof(>),<:Union{Base.BitInteger,StaticInt}})\n    max(_add1(canonicalize(i.x)), static_first(x)):static_last(x)\nend\n# integer indexing\nto_index(x, i::AbstractArray{<:Integer}) = i\nto_index(x, @nospecialize(i::StaticInt)) = i\nto_index(x, i::Integer) = Int(i)\n@inline to_index(x, i) = to_index(IndexStyle(x), x, i)\nfunction to_index(S::IndexStyle, x, i)\n    throw(ArgumentError(\n        \"invalid index: $S does not support indices of type $(typeof(i)) for instances of type $(typeof(x)).\"\n    ))\nend\n\n\"\"\"\n    unsafe_reconstruct(A, data; kwargs...)\n\nReconstruct `A` given the values in `data`. New methods using `unsafe_reconstruct`\nshould only dispatch on `A`.\n\"\"\"\nfunction unsafe_reconstruct(axis::OneTo, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return OneTo(data)\n    end\nend\nfunction unsafe_reconstruct(axis::UnitRange, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return UnitRange(first(data), last(data))\n    end\nend\nfunction unsafe_reconstruct(axis::OptionallyStaticUnitRange, data; kwargs...)\n    if axis === data\n        return axis\n    else\n        return OptionallyStaticUnitRange(static_first(data), static_last(data))\n    end\nend\nfunction unsafe_reconstruct(A::AbstractUnitRange, data; kwargs...)\n    return static_first(data):static_last(data)\nend\n\n\"\"\"\n    to_axes(A, inds) -> Tuple\n\nConstruct new axes given the corresponding `inds` constructed after\n`to_indices(A, args) -> inds`. This method iterates through each pair of axes and\nindices calling [`to_axis`](@ref).\n\"\"\"\n@inline function to_axes(A, inds::Tuple)\n    if ndims(A) === 1\n        return (to_axis(axes(A, 1), first(inds)),)\n    elseif Base.length(inds) === 1\n        return (to_axis(eachindex(IndexLinear(), A), first(inds)),)\n    else\n        return to_axes(A, axes(A), inds)\n    end\nend\n# drop this dimension\nto_axes(A, a::Tuple, i::Tuple{<:CanonicalInt,Vararg{Any}}) = to_axes(A, _maybe_tail(a), tail(i))\nto_axes(A, a::Tuple, i::Tuple{I,Vararg{Any}}) where {I} = _to_axes(StaticInt(ndims_index(I)), A, a, i)\nfunction _to_axes(::StaticInt{1}, A, axs::Tuple, inds::Tuple)\n    return (to_axis(_maybe_first(axs), first(inds)), to_axes(A, _maybe_tail(axs), tail(inds))...)\nend\n@propagate_inbounds function _to_axes(::StaticInt{N}, A, axs::Tuple, inds::Tuple) where {N}\n    axes_front, axes_tail = Base.IteratorsMD.split(axs, Val(N))\n    if IndexStyle(A) === IndexLinear()\n        axis = to_axis(LinearIndices(axes_front), getfield(inds, 1))\n    else\n        axis = to_axis(CartesianIndices(axes_front), getfield(inds, 1))\n    end\n    return (axis, to_axes(A, axes_tail, tail(inds))...)\nend\nto_axes(A, ::Tuple{Ax,Vararg{Any}}, ::Tuple{}) where {Ax} = ()\nto_axes(A, ::Tuple{}, ::Tuple{}) = ()\n\n_maybe_first(::Tuple{}) = static(1):static(1)\n_maybe_first(t::Tuple) = first(t)\n_maybe_tail(::Tuple{}) = ()\n_maybe_tail(t::Tuple) = tail(t)\n\n\"\"\"\n    to_axis(old_axis, index) -> new_axis\n\nConstruct an `new_axis` for a newly constructed array that corresponds to the\npreviously executed `to_index(old_axis, arg) -> index`. `to_axis` assumes that\n`index` has already been confirmed to be in bounds. The underlying indices of\n`new_axis` begins at one and extends the length of `index` (i.e., one-based indexing).\n\"\"\"\n@inline function to_axis(axis, inds)\n    if !can_change_size(axis) &&\n       (known_length(inds) !== nothing && known_length(axis) === known_length(inds))\n        return axis\n    else\n        return to_axis(IndexStyle(axis), axis, inds)\n    end\nend\n\n# don't need to check size b/c slice means it's the entire axis\n@inline function to_axis(axis, inds::Slice)\n    if can_change_size(axis)\n        return copy(axis)\n    else\n        return axis\n    end\nend\nto_axis(S::IndexLinear, axis, inds) = StaticInt(1):length(inds)\n\n\"\"\"\n    ArrayInterface.getindex(A, args...)\n\nRetrieve the value(s) stored at the given key or index within a collection. Creating\nanother instance of `ArrayInterface.getindex` should only be done by overloading `A`.\nChanging indexing based on a given argument from `args` should be done through,\n[`to_index`](@ref), or [`to_axis`](@ref).\n\"\"\"\nfunction getindex(A, args...)\n    inds = to_indices(A, args)\n    @boundscheck checkbounds(A, inds...)\n    unsafe_getindex(A, inds...)\nend\n@propagate_inbounds function getindex(A; kwargs...)\n    inds = to_indices(A, find_all_dimnames(dimnames(A), static(keys(kwargs)), Tuple(values(kwargs)), :))\n    @boundscheck checkbounds(A, inds...)\n    unsafe_getindex(A, inds...)\nend\n@propagate_inbounds getindex(x::Tuple, i::Int) = getfield(x, i)\n@propagate_inbounds getindex(x::Tuple, ::StaticInt{i}) where {i} = getfield(x, i)\n\n## unsafe_getindex ##\nfunction unsafe_getindex(a::A) where {A}\n    is_forwarding_wrapper(A) || throw(MethodError(unsafe_getindex, (A,)))\n    unsafe_getindex(parent(a))\nend\n\n# TODO Need to manage index transformations between nested layers of arrays\nfunction unsafe_getindex(a::A, i::CanonicalInt) where {A}\n    if IndexStyle(A) === IndexLinear()\n        is_forwarding_wrapper(A) || throw(MethodError(unsafe_getindex, (A, i)))\n        return unsafe_getindex(parent(a), i)\n    else\n        return unsafe_getindex(a, _to_cartesian(a, i)...)\n    end\nend\nfunction unsafe_getindex(a::A, i::CanonicalInt, ii::Vararg{CanonicalInt}) where {A}\n    if IndexStyle(A) === IndexLinear()\n        return unsafe_getindex(a, _to_linear(a, (i, ii...)))\n    else\n        is_forwarding_wrapper(A) || throw(MethodError(unsafe_getindex, (A, i)))\n        return unsafe_getindex(parent(a), i, ii...)\n    end\nend\n\nunsafe_getindex(a, i::Vararg{Any}) = unsafe_get_collection(a, i)\n\nunsafe_getindex(A::Array) = Base.arrayref(false, A, 1)\nunsafe_getindex(A::Array, i::CanonicalInt) = Base.arrayref(false, A, Int(i))\n@inline function unsafe_getindex(A::Array, i::CanonicalInt, ii::Vararg{CanonicalInt})\n    unsafe_getindex(A, _to_linear(A, (i, ii...)))\nend\n\nunsafe_getindex(A::LinearIndices, i::CanonicalInt) = Int(i)\nunsafe_getindex(A::CartesianIndices{N}, ii::Vararg{CanonicalInt,N}) where {N} = CartesianIndex(ii...)\nunsafe_getindex(A::CartesianIndices, ii::Vararg{CanonicalInt}) =\n    unsafe_getindex(A, Base.front(ii)...)\nunsafe_getindex(A::CartesianIndices, i::CanonicalInt) = @inbounds(A[i])\n\nunsafe_getindex(A::ReshapedArray, i::CanonicalInt) = @inbounds(parent(A)[i])\nfunction unsafe_getindex(A::ReshapedArray, i::CanonicalInt, ii::Vararg{CanonicalInt})\n    @inbounds(parent(A)[_to_linear(A, (i, ii...))])\nend\n\nunsafe_getindex(A::SubArray, i::CanonicalInt) = @inbounds(A[i])\nunsafe_getindex(A::SubArray, i::CanonicalInt, ii::Vararg{CanonicalInt}) = @inbounds(A[i, ii...])\n\n# This is based on Base._unsafe_getindex from https://github.com/JuliaLang/julia/blob/c5ede45829bf8eb09f2145bfd6f089459d77b2b1/base/multidimensional.jl#L755.\n#=\n    unsafe_get_collection(A, inds)\n\nReturns a collection of `A` given `inds`. `inds` is assumed to have been bounds-checked.\n=#\nfunction unsafe_get_collection(A, inds)\n    axs = to_axes(A, inds)\n    dest = similar(A, axs)\n    if map(length, axes(dest)) == map(length, axs)\n        Base._unsafe_getindex!(dest, A, inds...)\n    else\n        Base.throw_checksize_error(dest, axs)\n    end\n    return dest\nend\n_ints2range(x::CanonicalInt) = x:x\n_ints2range(x::AbstractRange) = x\n# apply _ints2range to front N elements\n_ints2range_front(::Val{N}, ind, inds...) where {N} =\n    (_ints2range(ind), _ints2range_front(Val(N - 1), inds...)...)\n_ints2range_front(::Val{0}, ind, inds...) = ()\n_ints2range_front(::Val{0}) = ()\n# get output shape with given indices\n_output_shape(::CanonicalInt, inds...) = _output_shape(inds...)\n_output_shape(ind::AbstractRange, inds...) = (Base.length(ind), _output_shape(inds...)...)\n_output_shape(::CanonicalInt) = ()\n_output_shape(x::AbstractRange) = (Base.length(x),)\n@inline function unsafe_get_collection(A::CartesianIndices{N}, inds) where {N}\n    if (Base.length(inds) === 1 && N > 1) || stride_preserving_index(typeof(inds)) === False()\n        return Base._getindex(IndexStyle(A), A, inds...)\n    else\n        return reshape(\n            CartesianIndices(_ints2range_front(Val(N), inds...)),\n            _output_shape(inds...)\n        )\n    end\nend\n_known_first_isone(ind) = known_first(ind) !== nothing && isone(known_first(ind))\n@inline function unsafe_get_collection(A::LinearIndices{N}, inds) where {N}\n    if Base.length(inds) === 1 && ndims_index(typeof(first(inds))) === 1\n        return @inbounds(eachindex(A)[first(inds)])\n    elseif stride_preserving_index(typeof(inds)) === True() &&\n            reduce_tup(&, map(_known_first_isone, inds))\n        # create a LinearIndices when first(ind) != 1 is imposable\n        return reshape(\n            LinearIndices(_ints2range_front(Val(N), inds...)),\n            _output_shape(inds...)\n        )\n    else\n        return Base._getindex(IndexStyle(A), A, inds...)\n    end\nend\n\n\"\"\"\n    ArrayInterface.setindex!(A, args...)\n\nStore the given values at the given key or index within a collection.\n\"\"\"\n@propagate_inbounds function setindex!(A, val, args...)\n    can_setindex(A) || error(\"Instance of type $(typeof(A)) are not mutable and cannot change elements after construction.\")\n    inds = to_indices(A, args)\n    @boundscheck checkbounds(A, inds...)\n    unsafe_setindex!(A, val, inds...)\nend\n@propagate_inbounds function setindex!(A, val; kwargs...)\n    can_setindex(A) || error(\"Instance of type $(typeof(A)) are not mutable and cannot change elements after construction.\")\n    inds = to_indices(A, find_all_dimnames(dimnames(A), static(keys(kwargs)), Tuple(values(kwargs)), :))\n    @boundscheck checkbounds(A, inds...)\n    unsafe_setindex!(A, val, inds...)\nend\n\n## unsafe_setindex! ##\nfunction unsafe_setindex!(a::A, v) where {A}\n    is_forwarding_wrapper(A) || throw(MethodError(unsafe_setindex!, (A, v)))\n    return unsafe_setindex!(parent(a), v)\nend\n# TODO Need to manage index transformations between nested layers of arrays\nfunction unsafe_setindex!(a::A, v, i::CanonicalInt) where {A}\n    if IndexStyle(A) === IndexLinear()\n        is_forwarding_wrapper(A) || throw(MethodError(unsafe_setindex!, (A, v, i)))\n        return unsafe_setindex!(parent(a), v, i)\n    else\n        return unsafe_setindex!(a, v, _to_cartesian(a, i)...)\n    end\nend\nfunction unsafe_setindex!(a::A, v, i::CanonicalInt, ii::Vararg{CanonicalInt}) where {A}\n    if IndexStyle(A) === IndexLinear()\n        return unsafe_setindex!(a, v, _to_linear(a, (i, ii...)))\n    else\n        is_forwarding_wrapper(A) || throw(MethodError(unsafe_setindex!, (A, v, i, ii...)))\n        return unsafe_setindex!(parent(a), v, i, ii...)\n    end\nend\n\nfunction unsafe_setindex!(A::Array{T}, v) where {T}\n    Base.arrayset(false, A, convert(T, v)::T, 1)\nend\nfunction unsafe_setindex!(A::Array{T}, v, i::CanonicalInt) where {T}\n    return Base.arrayset(false, A, convert(T, v)::T, Int(i))\nend\n\nunsafe_setindex!(a, v, i::Vararg{Any}) = unsafe_set_collection!(a, v, i)\n\n# This is based on Base._unsafe_setindex!.\n#=\n    unsafe_set_collection!(A, val, inds)\n\nSets `inds` of `A` to `val`. `inds` is assumed to have been bounds-checked.\n=#\nunsafe_set_collection!(A, v, i) = Base._unsafe_setindex!(IndexStyle(A), A, v, i...)\n", "meta": {"hexsha": "f9852442249b11b4126a9318ae68b11af08a4b55", "size": 20770, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/indexing.jl", "max_stars_repo_name": "JuliaDiffEq/ArrayInterface.jl", "max_stars_repo_head_hexsha": "1c6c72578ef6bae8f5fc6109c2be311cfeca0a19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2018-11-03T15:08:53.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-05T09:12:54.000Z", "max_issues_repo_path": "src/indexing.jl", "max_issues_repo_name": "JuliaDiffEq/ArrayInterface.jl", "max_issues_repo_head_hexsha": "1c6c72578ef6bae8f5fc6109c2be311cfeca0a19", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 26, "max_issues_repo_issues_event_min_datetime": "2018-11-02T10:08:09.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-20T23:24:33.000Z", "max_forks_repo_path": "src/indexing.jl", "max_forks_repo_name": "JuliaDiffEq/ArrayInterface.jl", "max_forks_repo_head_hexsha": "1c6c72578ef6bae8f5fc6109c2be311cfeca0a19", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2019-01-30T22:38:34.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:36:05.000Z", "avg_line_length": 37.7636363636, "max_line_length": 157, "alphanum_fraction": 0.6546461242, "num_tokens": 5890, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4416730203630095, "lm_q2_score": 0.11757212426963223, "lm_q1q2_score": 0.05192843523666356}}
{"text": "module TestVariablesModelInstance\n\nusing Mimi\nusing Test\n\nimport Mimi:\n    variable_names, compinstance, get_var_value, get_param_value,\n    set_param_value, set_var_value, dim_count, compdef, components, parameters, variables,\n    LeafComponentInstance, AbstractComponentInstance, ComponentDef, TimestepArray,\n    ComponentInstanceParameters, ComponentInstanceVariables\n\nmy_model = Model()\n\n@defcomp testcomp1 begin\n    var1 = Variable(index=[time])\n    var2 = Variable(index=[time])\n    par1 = Parameter(index=[time])\n\n    function run_timestep(p, v, d, t)\n        v.var1[t] = p.par1[t]\n    end\nend\n\npar = collect(2015:5:2110)\n\nset_dimension!(my_model, :time, 2015:5:2110)\n@test_throws ErrorException run(my_model) #no components added yet\n\nadd_comp!(my_model, testcomp1)\nset_param!(my_model, :testcomp1, :par1, par)\nrun(my_model)\n#NOTE: this variables function does NOT take in Nullable instances\n@test (variable_names(my_model, :testcomp1) == [:var1, :var2])\n\n#test basic def and instance functions\nmi = my_model.mi\nmd = modeldef(mi)\nci = compinstance(mi, :testcomp1)\ncdef = compdef(md, ci.comp_path)\nciter = components(mi)\n\n@test typeof(md) == Mimi.ModelDef && md == mi.md\n@test typeof(ci) <: LeafComponentInstance && ci == compinstance(mi, :testcomp1)\n@test typeof(cdef) <: ComponentDef && cdef.comp_id == ci.comp_id\n@test ci.comp_name == :testcomp1\n@test typeof(citer) <: Base.ValueIterator && length(citer) == 1 && eltype(citer) <: AbstractComponentInstance\n\n#test convenience functions that can be called with name symbol\n\nparam_value = get_param_value(ci, :par1)\n@test typeof(param_value)<: TimestepArray\n@test_throws ErrorException get_param_value(ci, :missingpar)\n\nvar_value = get_var_value(ci, :var1)\n@test_throws ErrorException get_var_value(ci, :missingvar)\n@test typeof(var_value) <: TimestepArray\n\nparams = parameters(mi, :testcomp1)\nparams2 = parameters(mi, :testcomp1)\n@test typeof(params) <: ComponentInstanceParameters\n@test params == params2\n\nvars = variables(mi, :testcomp1)\nvars2 = variables(ci)\n@test typeof(vars) <: ComponentInstanceVariables\n@test vars == vars2\n\n@test dim_count(mi, :time) == 20\n\nend #module\n", "meta": {"hexsha": "3a972944861e32d05c2163ae06d0a243c97d9908", "size": 2136, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_variables_model_instance.jl", "max_stars_repo_name": "arnavgautam/Mimi.jl", "max_stars_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/test_variables_model_instance.jl", "max_issues_repo_name": "arnavgautam/Mimi.jl", "max_issues_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_variables_model_instance.jl", "max_forks_repo_name": "arnavgautam/Mimi.jl", "max_forks_repo_head_hexsha": "7c6c43bf721166b76b997dd6fd4d167c57225e33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.0845070423, "max_line_length": 109, "alphanum_fraction": 0.7518726592, "num_tokens": 564, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10374863379429106, "lm_q1q2_score": 0.05187431689714553}}
{"text": "# auxiliary/unique2.jl\n#\n# Similar to unique, but removes all occurences of elements that appear more than once.\n\n\"\"\"unique2(itr)\n\nReturns an array containing only those elements that appear exactly once in itr,\nand without any elements that appear more than once.\n\"\"\"\nfunction unique2(itr)\n    out = collect(itr)\n    i = 1\n    while i < length(out)\n        inext = findnext(out,out[i],i+1)\n        if inext == 0\n            i += 1\n            continue\n        end\n        while inext != 0\n            deleteat!(out,inext)\n            inext = findnext(out,out[i],i+1)\n        end\n        deleteat!(out,i)\n    end\n    out\nend\n", "meta": {"hexsha": "9d7ae821c0ab667d2950ab8eb9e8e8382e996459", "size": 625, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/auxiliary/unique2.jl", "max_stars_repo_name": "garrison/TensorOperations.jl", "max_stars_repo_head_hexsha": "4596b33769374c79aa623d3906ffa4c7afea5557", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/auxiliary/unique2.jl", "max_issues_repo_name": "garrison/TensorOperations.jl", "max_issues_repo_head_hexsha": "4596b33769374c79aa623d3906ffa4c7afea5557", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/auxiliary/unique2.jl", "max_forks_repo_name": "garrison/TensorOperations.jl", "max_forks_repo_head_hexsha": "4596b33769374c79aa623d3906ffa4c7afea5557", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.1481481481, "max_line_length": 87, "alphanum_fraction": 0.5952, "num_tokens": 164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.10374862686637971, "lm_q1q2_score": 0.05187431343318986}}
{"text": "import Pkg\nPkg.instantiate() # downloads all dependencies for the current project\n\nPkg.add(\"JSON\")\nPkg.add(\"HTTP\")\nPkg.add(\"JSONTables\")\n\nusing HTTP, JSON, JSONTables\n\n# treat a json object of arrays or array of objects as a \"table\"\njtable = jsontable(json_source)\n\n# turn json table into DataFrame\ndf = DataFrame(jtable)\n\n# turn DataFrame back into json object of arrays\nobjecttable(df)\n\n# turn DataFrame back into json array of objects\narraytable(df)\n", "meta": {"hexsha": "cb76fc3dc9564dbc9abd7cad0434ec515da1db5b", "size": 453, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "samples/data/acquisition-collection/load-read/filesystem/JSON/JSON_to_tables.jl", "max_stars_repo_name": "Samples-Playgorunds/Samples.Julia", "max_stars_repo_head_hexsha": "c2a547248611529ce40d44cae5f6a32a55d1376b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "samples/data/acquisition-collection/load-read/filesystem/JSON/JSON_to_tables.jl", "max_issues_repo_name": "Samples-Playgorunds/Samples.Julia", "max_issues_repo_head_hexsha": "c2a547248611529ce40d44cae5f6a32a55d1376b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "samples/data/acquisition-collection/load-read/filesystem/JSON/JSON_to_tables.jl", "max_forks_repo_name": "Samples-Playgorunds/Samples.Julia", "max_forks_repo_head_hexsha": "c2a547248611529ce40d44cae5f6a32a55d1376b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.5714285714, "max_line_length": 70, "alphanum_fraction": 0.7637969095, "num_tokens": 111, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.5, "lm_q2_score": 0.10374861716730452, "lm_q1q2_score": 0.05187430858365226}}
{"text": "function parse_allocation( filename :: String ) :: Dict\n  n = 0\n  key = \"x\"\n  out=Dict{String,Tuple}()\n  oldv = -1\n  newv = -1\n  for line in eachline( filename )\n    n += 1\n    r = (n % 3)\n    if r==1\n      key = line\n      if key == \"#\"\n        break;\n      end\n    elseif r == 2\n      oldv = parse(Int64, line )\n    else\n      newv = parse(Int64, line )\n      out[key]=(oldv,newv)\n    end;\n    print(\"$key\\n\");\n  end # loop\n  out\nend # parse_allocation\n", "meta": {"hexsha": "408082186020242ef212cc20047d36371044bb51", "size": 455, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parse_allocation.jl", "max_stars_repo_name": "grahamstark/staying_put_sim", "max_stars_repo_head_hexsha": "d6a05d1aa3a145ba9a85176807262825a8314115", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/parse_allocation.jl", "max_issues_repo_name": "grahamstark/staying_put_sim", "max_issues_repo_head_hexsha": "d6a05d1aa3a145ba9a85176807262825a8314115", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/parse_allocation.jl", "max_forks_repo_name": "grahamstark/staying_put_sim", "max_forks_repo_head_hexsha": "d6a05d1aa3a145ba9a85176807262825a8314115", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.2, "max_line_length": 55, "alphanum_fraction": 0.5120879121, "num_tokens": 154, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.10970578406578958, "lm_q1q2_score": 0.051856111417587905}}
{"text": "using MyFirstPkg\nusing Test\n\n@testset \"MyFirstPkg.jl\" begin\n    @test my_f(3) == 9\nend\n", "meta": {"hexsha": "f8d07f91021fe4ff6a79bff2263fa7378dae85bb", "size": 87, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "caiprozect/MyFirstPkg.jl", "max_stars_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "caiprozect/MyFirstPkg.jl", "max_issues_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "caiprozect/MyFirstPkg.jl", "max_forks_repo_head_hexsha": "6a2c02ce3ce9dcaf32b66ff16bb94389414fb20a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.4285714286, "max_line_length": 30, "alphanum_fraction": 0.7011494253, "num_tokens": 35, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.10970577096716555, "lm_q1q2_score": 0.051856105226084755}}
{"text": "module TestPaperSelfgravitatingGasDynamics\n\nusing Test\nusing Trixi\n\ninclude(\"test_trixi.jl\")\n\n# Start with a clean environment: remove Trixi output directory if it exists\noutdir = \"out\"\nisdir(outdir) && rm(outdir, recursive=true)\n\n# pathof(Trixi) returns /path/to/Trixi/src/Trixi.jl, dirname gives the parent directory\nconst EXAMPLES_DIR = joinpath(pathof(Trixi) |> dirname |> dirname, \"examples\", \"paper-self-gravitating-gas-dynamics\")\n\n# Numerical examples from the Euler-gravity paper\n@testset \"paper-self-gravitating-gas-dynamics\" begin\n  @testset \"elixir_euler_eoc_test.jl\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_euler_eoc_test.jl\"),\n      l2   = [0.0001740977055972079, 0.0003369355182519592, 0.0003369355182518708, 0.0006099171220334989],\n      linf = [0.001079347149189669, 0.0018836938381321389, 0.001883693838132583, 0.003971575376718217])\n  end\n\n  @testset \"elixir_euler_eoc_test.jl with polydeg=4\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_euler_eoc_test.jl\"),\n      l2   = [1.7187201161597772e-5, 2.678065111772951e-5, 2.678065111783027e-5, 4.952504160091526e-5],\n      linf = [0.0001501749544159381, 0.00016549482504535362, 0.00016549482504601976, 0.0004372960291432193],\n      polydeg = 4)\n  end\n\n\n  @testset \"elixir_hypdiff_eoc_test.jl\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_hypdiff_eoc_test.jl\"),\n      l2   = [0.003154024896093942, 0.012394432074951856, 0.02185973823794725],\n      linf = [0.01731850928579215, 0.07843510773347553, 0.11242300176349201])\n  end\n\n  @testset \"elixir_hypdiff_eoc_test.jl with polydeg=4\" begin\n  @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_hypdiff_eoc_test.jl\"),\n    l2   = [0.0002511283012128458, 0.0008808243846610255, 0.0016313343228567005],\n    linf = [0.0017290715087938668, 0.003129184465704738, 0.01000728849316701],\n    polydeg = 4)\n  end\n\n\n  @testset \"elixir_eulergravity_eoc_test.jl\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_eoc_test.jl\"),\n      l2   = [0.00024871265138964204, 0.0003370077102132591, 0.0003370077102131964, 0.0007231525513793697],\n      linf = [0.0015813032944647087, 0.0020494288423820173, 0.0020494288423824614, 0.004793821195083758],\n      tspan = (0.0, 0.1))\n  end\n\n  @testset \"elixir_eulergravity_eoc_test.jl with polydeg=4\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_eoc_test.jl\"),\n      l2   = [1.9537712148648045e-5, 2.7564396197947587e-5, 2.7564396197967635e-5, 5.688838772067586e-5],\n      linf = [0.00012335710672761735, 0.00020086268350816283, 0.00020086268350727465, 0.0004962155455632278],\n      tspan = (0.0, 0.1), polydeg = 4)\n  end\n\n  @testset \"elixir_eulergravity_eoc_test.jl with 1st order RK3S*\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_eoc_test.jl\"),\n      l2   = [0.00024871265138959434, 0.000337007710281087, 0.0003370077102811394, 0.0007231525515231289],\n      linf = [0.0015813032941613958, 0.002049428843978518, 0.0020494288439798503, 0.004793821198143977],\n      tspan = (0.0, 0.1), timestep_gravity=Trixi.timestep_gravity_erk51_3Sstar!)\n  end\n\n  @testset \"elixir_eulergravity_eoc_test.jl with 3rd order RK3S*\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_eoc_test.jl\"),\n      l2   = [0.0002487126513894034, 0.00033700771023049785, 0.00033700771023048245, 0.0007231525514158737],\n      linf = [0.0015813032943847727, 0.002049428842844314, 0.0020494288428452023, 0.004793821195971937],\n      tspan = (0.0, 0.1), timestep_gravity=Trixi.timestep_gravity_erk53_3Sstar!)\n  end\n\n\n  @testset \"elixir_eulergravity_jeans_instability.jl\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_jeans_instability.jl\"),\n      l2   = [10733.63378538114, 13356.780607423452, 1.6721408750434403e-6, 26834.076821148774],\n      linf = [15194.296424901113, 18881.481685044182, 6.809717915492584e-6, 37972.99700513482],\n      tspan = (0.0, 0.1))\n  end\n\n  @testset \"elixir_eulergravity_jeans_instability.jl with RK3S*\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_jeans_instability.jl\"),\n      l2   = [10734.598193238024, 13358.217234481384, 1.9105446886104614e-6, 26836.487841241516],\n      linf = [15195.661004798487, 18883.512035906537, 7.864715319982429e-6, 37976.408478975296],\n      tspan = (0.0, 0.1),\n      parameters=ParametersEulerGravity(background_density=1.5e7,\n                                        gravitational_constant=6.674e-8,\n                                        cfl=2.4,\n                                        resid_tol=1.0e-4,\n                                        n_iterations_max=1000,\n                                        timestep_gravity=timestep_gravity_erk52_3Sstar!))\n  end\n\n  @testset \"Printing\" begin\n    # These tests rely on the previous tests above and re-use some of their variables.\n    show(stdout, parameters)\n    show(stdout, semi)\n    show(stdout, semi_euler.boundary_conditions)\n    show(stdout, TrivialCallback())\n    show(stdout, equations_euler)\n  end\n\n\n  @testset \"elixir_eulergravity_sedov_blast_wave.jl\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_sedov_blast_wave.jl\"),\n      l2   = [0.046315994852653024, 0.0650818006233669, 0.06508180062336677, 0.4896707211656037],\n      linf = [2.3874843337593776, 4.07876384374792, 4.07876384374792, 16.23914384809855],\n      tspan = (0.0, 0.05))\n  end\n\n  @testset \"elixir_eulergravity_sedov_blast_wave.jl with ref-level=8 and no AMR\" begin\n    @test_trixi_include(joinpath(EXAMPLES_DIR, \"elixir_eulergravity_sedov_blast_wave.jl\"),\n      l2   = [0.00289222135995042, 0.013724813590853825, 0.013724813590853832, 0.05822904710548214],\n      linf = [0.26361780693997594, 1.3908873830688688, 1.3908873830688688, 4.066701303607613],\n      tspan = (0.0, 0.005), initial_refinement_level=8, amr_callback=TrivialCallback())\n  end\nend\n\n# Clean up afterwards: delete Trixi output directory\n@test_nowarn rm(outdir, recursive=true)\n\nend #module\n", "meta": {"hexsha": "906cd2b97cd3d108e49a0e40dbf792431ca8a185", "size": 5984, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_paper-self-gravitating-gas-dynamics.jl", "max_stars_repo_name": "jlchan/Trixi.jl", "max_stars_repo_head_hexsha": "33657f4390699c16fc3e50b2e6f2d3ebe63b52eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-13T11:10:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-13T11:10:36.000Z", "max_issues_repo_path": "test/test_paper-self-gravitating-gas-dynamics.jl", "max_issues_repo_name": "jlchan/Trixi.jl", "max_issues_repo_head_hexsha": "33657f4390699c16fc3e50b2e6f2d3ebe63b52eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_paper-self-gravitating-gas-dynamics.jl", "max_forks_repo_name": "jlchan/Trixi.jl", "max_forks_repo_head_hexsha": "33657f4390699c16fc3e50b2e6f2d3ebe63b52eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.6504065041, "max_line_length": 117, "alphanum_fraction": 0.7369652406, "num_tokens": 2196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618627863437, "lm_q2_score": 0.11436853674701282, "lm_q1q2_score": 0.05183889601009944}}
{"text": "cell_to_vtkcell(::Type{Line}) = VTKCellTypes.VTK_LINE\ncell_to_vtkcell(::Type{QuadraticLine}) = VTKCellTypes.VTK_QUADRATIC_EDGE\n\ncell_to_vtkcell(::Type{Quadrilateral}) = VTKCellTypes.VTK_QUAD\ncell_to_vtkcell(::Type{QuadraticQuadrilateral}) = VTKCellTypes.VTK_BIQUADRATIC_QUAD\ncell_to_vtkcell(::Type{Triangle}) = VTKCellTypes.VTK_TRIANGLE\ncell_to_vtkcell(::Type{QuadraticTriangle}) = VTKCellTypes.VTK_QUADRATIC_TRIANGLE\ncell_to_vtkcell(::Type{Cell{2,8,4}}) = VTKCellTypes.VTK_QUADRATIC_QUAD\n\ncell_to_vtkcell(::Type{Hexahedron}) = VTKCellTypes.VTK_HEXAHEDRON\ncell_to_vtkcell(::Type{Tetrahedron}) = VTKCellTypes.VTK_TETRA\ncell_to_vtkcell(::Type{QuadraticTetrahedron}) = VTKCellTypes.VTK_QUADRATIC_TETRA\n\n\"\"\"\n    vtk_grid(filename::AbstractString, grid::Grid)\n\nCreate a unstructured VTK grid from a `Grid`. Return a `DatasetFile`\nwhich data can be appended to, see `vtk_point_data` and `vtk_cell_data`.\n\"\"\"\nfunction WriteVTK.vtk_grid(filename::AbstractString, grid::Grid{dim,C,T}; compress::Bool=true) where {dim,C,T}\n    cls = MeshCell[]\n    for cell in grid.cells\n        celltype = Ferrite.cell_to_vtkcell(typeof(cell))\n        push!(cls, MeshCell(celltype, collect(cell.nodes)))\n    end\n    coords = reshape(reinterpret(T, getnodes(grid)), (dim, getnnodes(grid)))\n    return vtk_grid(filename, coords, cls; compress=compress)\nend\n\n\"\"\"\n    vtk_point_data(vtk, data::Vector{<:Tensor}, name)\n\nWrite the tensor field data to the vtk file. Only writes the tensor values available in `data`.\nIn the vtu-file, ordering of the tensor components is coulumn-wise (just like Julia).\n[1 2\n 3 4] => 1, 3, 2, 4\n\"\"\"\nfunction WriteVTK.vtk_point_data(\n    vtk::WriteVTK.DatasetFile,\n    data::Union{\n        Vector{Tensor{order,dim,T,M}},\n        Vector{SymmetricTensor{order,dim,T,M}}\n        },\n    name::AbstractString\n    ) where {order,dim,T,M}\n\n    npoints = length(data)\n    out = zeros(T, M, npoints)\n    out[1:M, :] .= reshape(reinterpret(T, data), (M, npoints))\n    return vtk_point_data(vtk, out, name)\nend\n\n\"\"\"\n    vtk_point_data(vtk, data::Vector{<:Vec}, name)\n\nWrite the vector field data to the vtk file.\n\"\"\"\nfunction WriteVTK.vtk_point_data(vtk::WriteVTK.DatasetFile, data::Vector{Vec{dim,T}}, name::AbstractString) where {dim,T}\n    npoints = length(data)\n    out = zeros(T, (dim == 2 ? 3 : dim), npoints)\n    out[1:dim, :] .= reshape(reinterpret(T, data), (dim, npoints))\n    return vtk_point_data(vtk, out, name)\nend\n\nfunction vtk_nodeset(vtk::WriteVTK.DatasetFile, grid::Grid{dim}, nodeset::String) where {dim}\n    z = zeros(getnnodes(grid))\n    z[collect(getnodeset(grid, nodeset))] .= 1.0\n    vtk_point_data(vtk, z, nodeset)\nend\n\n\n\n\n\"\"\"\n    vtk_cellset(vtk, grid::Grid)\n\nExport all cell sets in the grid. Each cell set is exported with\n`vtk_cell_data` with value 1 if the cell is in the set, and 0 otherwise.\n\"\"\"\nfunction vtk_cellset(vtk::WriteVTK.DatasetFile, grid::AbstractGrid, cellsets=keys(grid.cellsets))\n    z = zeros(getncells(grid))\n    for cellset in cellsets\n        z .= 0.0\n        z[collect(getcellset(grid, cellset))] .= 1.0\n        vtk_cell_data(vtk, z, cellset)\n    end\n    return vtk\nend\n\n\"\"\"\n    vtk_cellset(vtk, grid::Grid, cellset::String)\n\nExport the cell set specified by `cellset` as cell data with value 1 if\nthe cell is in the set and 0 otherwise.\n\"\"\"\nvtk_cellset(vtk::WriteVTK.DatasetFile, grid::AbstractGrid, cellset::String) =\n    vtk_cellset(vtk, grid, [cellset])\n\nimport Ferrite.field_offset\nfunction WriteVTK.vtk_point_data(vtkfile, dh::MixedDofHandler, u::Vector, suffix=\"\")\n\n    fieldnames = Ferrite.getfieldnames(dh)  # all primary fields\n\n    for name in fieldnames\n        @debug println(\"exporting field $(name)\")\n        field_dim = getfielddim(dh, name)\n        space_dim = field_dim == 2 ? 3 : field_dim\n        data = fill(NaN, space_dim, getnnodes(dh.grid))  # set default value\n\n        for fh in dh.fieldhandlers\n            # check if this fh contains this field, otherwise continue to the next\n            field_pos = findfirst(i->i == name, getfieldnames(fh))\n            if field_pos == 0 && continue end\n\n            cellnumbers = sort(collect(fh.cellset))  # TODO necessary to have them ordered?\n            offset = field_offset(fh, name)\n\n            for cellnum in cellnumbers\n                cell = dh.grid.cells[cellnum]\n                n = ndofs_per_cell(dh, cellnum)\n                eldofs = zeros(Int, n)\n                _celldofs = celldofs!(eldofs, dh, cellnum)\n                counter = 1\n\n                for node in cell.nodes\n                    for d in 1:field_dim\n                        data[d, node] = u[_celldofs[counter + offset]]\n                        @debug println(\"  exporting $(u[_celldofs[counter + offset]]) for dof#$(_celldofs[counter + offset])\")\n                        counter += 1\n                    end\n                    if field_dim == 2\n                        # paraview requires 3D-data so pad with zero\n                        data[3, node] = 0\n                    end\n                end\n            end\n        end\n        vtk_point_data(vtkfile, data, string(name, suffix))\n    end\n\n    return vtkfile\nend\n", "meta": {"hexsha": "d53db764d78a9378abdd57826100d85b8a93dae3", "size": 5108, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Export/VTK.jl", "max_stars_repo_name": "koehlerson/JuAFEM.jl", "max_stars_repo_head_hexsha": "73ec778233856e3fc5751ea06e9c665d3f6f8bb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Export/VTK.jl", "max_issues_repo_name": "koehlerson/JuAFEM.jl", "max_issues_repo_head_hexsha": "73ec778233856e3fc5751ea06e9c665d3f6f8bb3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Export/VTK.jl", "max_forks_repo_name": "koehlerson/JuAFEM.jl", "max_forks_repo_head_hexsha": "73ec778233856e3fc5751ea06e9c665d3f6f8bb3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-11T18:17:24.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-11T18:17:24.000Z", "avg_line_length": 35.7202797203, "max_line_length": 126, "alphanum_fraction": 0.6552466719, "num_tokens": 1432, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45326186278634367, "lm_q2_score": 0.11436852316318397, "lm_q1q2_score": 0.05183888985306786}}
{"text": "# # DrWatson Workflow Tutorial\n\n# ```@setup workflow\n# cd(@__DIR__)\n# ```\n\n# *Disclaimer: DrWatson assumes basic knowledge of how Julia's\n# [project manager](https://pkgdocs.julialang.org/v1/environments/) works.*\n\n# This example page demonstrates how DrWatson's functions help a typical scientific\n# workflow, as illustrated below:\n\n# ![](workflow.png)\n\n# Blue text comes from DrWatson. Of course, not all of DrWatson's functionality\n# will be highlighted in this tutorial nor is shown in the above figure!\n\n# ## 1. Setup the project\n\n# So, let's start a new scientific project. You want your project to be contained\n# in a folder. So let's create a new project, located at current working directory\nusing DrWatson\ninitialize_project(\"DrWatson Example\"; authors=\"Datseris\", force=true)\n\n# Alright now we have a project set up. The project has a default reasonable structure,\n# as illustrated in the [Default Project Setup](@ref) page:\n#\n# ```@setup workflow\n# using DrWatson\n# struct ShowFile\n#     file::String\n# end\n# function Base.show(io::IO, ::MIME\"text/plain\", f::ShowFile)\n#     write(io, read(f.file))\n# end\n# ```\n# ```@example workflow\n# ShowFile(dirname(pathof(DrWatson))*\"/defaults/project_structure.txt\") # hide\n# ```\n\n# For example, folders exist for data, plots, scripts, source code, etc.\n# Three files are noteworthy:\n# * Project.toml: Defines project\n# * Manifest.toml: Contains exact list of project dependencies\n# * .git (hidden folder): Contains reversible and searchable history of the project\n\n# The scientific project we have created is also a [Julia project environment](https://docs.julialang.org/en/v1/manual/code-loading/#Environments-1).\n# This means that it has its own dedicated dependencies and versions of dependencies.\n# This project is now active by default so we can start adding packages\n# that we will be using in the project. We'll add the following for demonstrating\nusing Pkg\nPkg.add([\"Statistics\", \"JLD2\"])\n\n# ## 2. Write some scripts\n\n# We start by writing some script for our project that will do some dummy calculations.\n# Let's create `scripts/example.jl` in our project. All following code is supposed to\n# exist in that file.\n\n# ```@setup workflow\n# cd(joinpath(@__DIR__, \"DrWatson Example\"))\n# ```\n\n\n# Now, with DrWatson every script (typically) starts with the following two lines:\n# ```@setup workflow\n# quickactivate(\"DrWatson Example\", \"DrWatson Example\")\n# ```\n\n# ```julia\n# using DrWatson\n# @quickactivate \"DrWatson Example\" # <- project name\n# ```\n# This command does something simple: it searches the folder of the script, and its\n# parent folders, until it finds a Project.toml. It activates that project, but\n# if the project name doesn't match the given name (here `\"DrWatson Example\"`)\n# it throws an error. Let's see the project we activated:\nprojectname()\n\n# This is **extremely useful** for two reasons. First, it is guaranteed\n# that our scripts run within the context of the project and thus use the correct\n# package versions.\n\n# Second, DrWatson provides the powerful function [`projectdir`](@ref) and its derivatives\n# like `datadir, plotsdir, srcdir`, etc.\nprojectdir()\n\n# `projectdir` will **always** return the path to the currently active project. It doesn't\n# matter where its called from, or where the active project actually is.\n# So, by using DrWatson, you don't care anymore where your current script is, you only\n# care about the target directory.\n\ndatadir()\n\n#\n\ndatadir(\"sims\", \"electron_gas\")\n\n# Giving arguments to `projectdir` and derivatives joins paths.\n\n# ## 3. Prepare simulations\n\n# Let's say we write a simple simulation function, that creates some data\nfunction fakesim(a, b, v, method = \"linear\")\n    if method == \"linear\"\n        r = @. a + b * v\n    elseif method == \"cubic\"\n        r = @. a*b*v^3\n    end\n    y = sqrt(b)\n    return r, y\nend\n\n# and we create some parameters in our scripts and run the simulation\na, b = 2, 3\nv = rand(5)\nmethod = \"linear\"\nr, y = fakesim(a, b, v, method)\n\n# Okay, that is fine, but it is typically the case that in scientific context\n# some simulations are done for several different combinations of parameters.\n# It is convenient to group all parameters in a dictionary, with the keys\n# being the parameters. Depending on the package you will use to actually save the data,\n# the key type should be either `String` or `Symbol`. Here we will be using\n# JLD2.jl and therefore the key type will be `String`.\nparams = @strdict a b v method\n\n# Above we used the ultra-cool [`@strdict`](@ref) macro, which creates a\n# dictionary from existing variables!\n\n# Now, for every simulation we want to do, we would create such a container.\n# We can use the [`dict_list`](@ref) to ease up the process of preparing several\n# of these parameter containers\n\nallparams = Dict(\n    \"a\" => [1, 2], # it is inside vector. It is expanded.\n    \"b\" => [3, 4],\n    \"v\" => [rand(5)],     # single element inside vector; no expansion\n    \"method\" => \"linear\", # not in vector = not expanded, even if naturally iterable\n)\n\ndicts = dict_list(allparams)\n\n# using `dict_list` is great, because it has a very clear design on how to expand\n# containers, while not caring whether the parameter values are iterable or not.\n# In short **everything in a `Vector` is expanded once** (`Vector`s of length 1\n# are not expanded naturally). See [`dict_list`](@ref) for more details.\n\n# The resulting dictionaries are then typically given into a `main` or `makesim`\n# function that actually does the simulation given some input parameters.\n\n# ## 4. Run and save\n# Alright, we now have to actually save the results, so we first define:\n\nfunction makesim(d::Dict)\n    @unpack a, b, v, method = d\n    r, y = fakesim(a, b, v, method)\n    fulld = copy(d)\n    fulld[\"r\"] = r\n    fulld[\"y\"] = y\n    return fulld\nend\n\n# and then we can save the results by once again leveraging [`projectdir`](@ref)\n# ```julia\n# for (i, d) in enumerate(dicts)\n#     f = makesim(d)\n#     wsave(datadir(\"simulations\", \"sim_$(i).jld2\"), f)\n# end\n# ```\n\n# *(`wsave` is a function from DrWatson, that ensures that the directory you try to\n# save the data exists. It then calls `FileIO.save`)*\n\n# Here each simulation was named according to a number.\n# But this is not how we do it in science... We typically want the input parameters\n# to be part of the file name. E.g. here we would want the file name to be something like\n# `a=2_b=3_method=linear.jld2`. It would be also nice that such a naming scheme would\n# apply to arbitrary input parameters so that we don't have to manually write\n# `a=$(a)_b=$(b)_method=$(method)` and change this code every time we change\n# a parameter name...\n\n# Enter [`savename`](@ref):\n\nsavename(params)\n\n# `savename` takes as an input pretty much *any* Julia composite container with key-value\n# pairs and transforms it into such a name. We can even do\n\nsavename(dicts[1], \"jld2\")\n\n# `savename` is flexible and smart. As you noticed, even though the vector `v` with\n# 5 numbers is part of the input, it wasn't included in the name (on purpose).\n# See the [`savename`](@ref) documentation for more.\n\n# We now transform our make+save loop into\n\nfor (i, d) in enumerate(dicts)\n    f = makesim(d)\n    wsave(datadir(\"simulations\", savename(d, \"jld2\")), f)\nend\n\nreaddir(datadir(\"simulations\"))\n\n# That is cool, but we can do better. In fact, **much better**.\n# Remember that the project initialized by DrWatson is a git repository.\n# So, now we quickly go into git and commit the script we have created and all changes\n# (not shown here).\n\n# Then we make+save again, but now instead of `wsave` we use [`@tagsave`](@ref):\n\n# ```@setup workflow\n# for (i, d) in enumerate(dicts)\n#     f = makesim(d)\n#     @tagsave(datadir(\"simulations\", savename(d, \"jld2\")), f; gitpath = \"../..\")\n# end\n# ```\n\n# ```julia\n# for (i, d) in enumerate(dicts)\n#     f = makesim(d)\n#     @tagsave(datadir(\"simulations\", savename(d, \"jld2\")), f)\n# end\n# ```\n\n# and let's load the first simulation\n\nfirstsim = readdir(datadir(\"simulations\"))[1]\n\nwload(datadir(\"simulations\", firstsim))\n\n# So what happened is that `tagsave` automatically added git-related information\n# into the file we saved (the field `:gitcommit`), enabling reproducibility!\n\n# It gets even better! Because [`@tagsave`](@ref) is a macro, it deduced automatically\n# where the script that called `@tagsave` was located. It even includes the exact line of code\n# that called the `@tagsave` command. This information is in the `:script` field of the\n# saved data!\n\n# ## 5. Analyze results\n\n# Cool, now we can start analyzing some simulations. The actual analysis is your job,\n# but DrWatson can help you get started with the [`collect_results`](@ref) function.\n# Notice that you need to be `using DataFrames` to access the function!\nPkg.add([\"DataFrames\"])\nusing DataFrames\n\ndf = collect_results(datadir(\"simulations\"))\n\n# Some things to note:\n# * the returned object is a `DataFrame` for further analysis.\n# * the input to `collect_result` is a folder, **not** a dataframe!\n#   The function does the loading and combining for you.\n# * If you create new simulations, you can iteratively (or all from scratch) add them to\n#   this dataframe.\n# * If you create new simulations that have **new parameters**, that don't exist in the\n#   simulations already saved, that's no problem. `collect_results` will appropriately\n#   and automatically add `missing` to all parameter values that don't exist in previews\n#   and/or current simulations. This is demonstrated explicitly in the\n#   [Adapting to new data/parameters](@ref) real world example, so it is not repeated here.\n# * Similarly with e.g. `savename`, `collect_results` is a flexible function. It has\n#   several configuration options.\n\n# Great! so now we are doing some analysis and we want to save some results...\n# It is very often the case in science that the same analysis may be done again, and again,\n# and some times even with the same parameters... And poor scientitists sometimes forget\n# to change the name of the output file, and end up overwritting previous work! Devastating!\n\n# To avoid such scenarios, we can use the function [`safesave`](@ref), e.g.\n\nanalysis = 42\n\nsafesave(datadir(\"ana\", \"linear.jld2\"), @strdict analysis)\n\n# If a file `linear.jld2` exists in that folder, it is not overwritten. Instead, it is\n# renamed to `linear#1.jld2`, and a new `linear.jld2` file is made!\n\n# ## 6. Share your project\n\n# This is already discussed in the [Reproducibility](@ref) section of the docs\n# so there is no reason to copy/paste everything here.\n# What is demonstrated there is that it is truly trivial to share your project\n# with a colleague, and this project is guaranteed to work for them!\n\n\n# ---\n\n# And that's it! We hope that DrWatson will take some stress out of the absurdly stressfull\n# scientific life!\n", "meta": {"hexsha": "b511ea44e361d7fa3b3f93a593ac7391611bc608", "size": 10797, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/src/workflow.jl", "max_stars_repo_name": "junglegobs/DrWatson.jl", "max_stars_repo_head_hexsha": "b6d8251f1606410cca83f136cb06c218ea03138f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 607, "max_stars_repo_stars_event_min_datetime": "2019-01-30T09:52:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T11:13:41.000Z", "max_issues_repo_path": "docs/src/workflow.jl", "max_issues_repo_name": "junglegobs/DrWatson.jl", "max_issues_repo_head_hexsha": "b6d8251f1606410cca83f136cb06c218ea03138f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 284, "max_issues_repo_issues_event_min_datetime": "2019-01-29T20:14:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T03:48:51.000Z", "max_forks_repo_path": "docs/src/workflow.jl", "max_forks_repo_name": "junglegobs/DrWatson.jl", "max_forks_repo_head_hexsha": "b6d8251f1606410cca83f136cb06c218ea03138f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 81, "max_forks_repo_forks_event_min_datetime": "2019-02-27T09:09:55.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-16T13:12:18.000Z", "avg_line_length": 37.2310344828, "max_line_length": 149, "alphanum_fraction": 0.7190886357, "num_tokens": 2812, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.35936414516010196, "lm_q2_score": 0.14414884567931804, "lm_q1q2_score": 0.05180192670336359}}
{"text": "using Mimi\n\n# Define a simple component\n# =========================\n\n# First define the state this component will hold\n\n@defcomp component1 begin\n    savingsrate = Parameter()\n\nend\n\n# Second, define the run_timestep function for the component\n\nfunction run_timestep(s::component1, t::Int)\nend\n\n# Create a model uses the component\n# =================================\n\nm = Model()\n\naddcomponent(m, component1)\n\n# Run model\n# =========\n\nrun(m)\n\n# Access the variables in the model\n# =================================\n", "meta": {"hexsha": "65a1a4c59b754dd01ba0e954c15bf559fab18dea", "size": 514, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/01-onecomponent.jl", "max_stars_repo_name": "JuliaPackageMirrors/Mimi.jl", "max_stars_repo_head_hexsha": "141d933d30d4d085a765dc96f75dd87752172c48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/01-onecomponent.jl", "max_issues_repo_name": "JuliaPackageMirrors/Mimi.jl", "max_issues_repo_head_hexsha": "141d933d30d4d085a765dc96f75dd87752172c48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/01-onecomponent.jl", "max_forks_repo_name": "JuliaPackageMirrors/Mimi.jl", "max_forks_repo_head_hexsha": "141d933d30d4d085a765dc96f75dd87752172c48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.0625, "max_line_length": 60, "alphanum_fraction": 0.5856031128, "num_tokens": 111, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10521054582138019, "lm_q1q2_score": 0.05178338240599245}}
{"text": "using Adapt\nusing Test\n\n\n# custom array type\n\nstruct CustomArray{T,N} <: AbstractArray{T,N}\n    arr::Array\nend\n\nCustomArray(x::Array{T,N}) where {T,N} = CustomArray{T,N}(x)\nAdapt.adapt_storage(::Type{<:CustomArray}, xs::Array) = CustomArray(xs)\n\nBase.size(x::CustomArray, y...) = size(x.arr, y...)\nBase.getindex(x::CustomArray, y...) = getindex(x.arr, y...)\nBase.count(x::CustomArray) = count(x.arr)\n\nconst mat = CustomArray{Float64,2}(rand(2,2))\nconst vec = CustomArray{Float64,1}(rand(2))\n\nconst mat_bools = CustomArray{Bool,2}(rand(Bool,2,2))\n\nmacro test_adapt(to, src_expr, dst_expr, typ=nothing)\n    quote\n        src = $(esc(src_expr))\n        dst = $(esc(dst_expr))\n\n        @test adapt($(esc(to)), src) == dst\n        @test typeof(adapt($(esc(to)), src)) == typeof(dst)\n        if $(esc(typ)) !== nothing\n            @test typeof(dst) <: $(esc(typ))\n        end\n    end\nend\n\nAnyCustomArray{T,N} = Union{CustomArray,WrappedArray{T,N,CustomArray,CustomArray{T,N}}}\n\n\n# basic adaption\n@test_adapt CustomArray mat.arr mat\n\n# idempotency\n@test_adapt CustomArray mat mat\n\n# custom wrapper\nstruct Wrapper{T}\n    arr::T\nend\nWrapper(x::T) where T = Wrapper{T}(x)\nAdapt.adapt_structure(to, xs::Wrapper) = Wrapper(adapt(to, xs.arr))\n@test_adapt CustomArray Wrapper(mat.arr) Wrapper(mat)\n\n\n@testset \"base structures\" begin\n\n@test @inferred(adapt(nothing, NamedTuple())) == NamedTuple()\n@test_adapt CustomArray (mat.arr,) (mat,)\n@test @allocated(adapt(nothing, ())) == 0\n@test @allocated(adapt(nothing, (1,))) == 0\n@test @allocated(adapt(nothing, (1,2,3,4,5,6,7,8,9,10))) == 0\n\n@test_adapt CustomArray (a=mat.arr,) (a=mat,)\n\nend\n\n# NOTE: if we put this in the preceding testset, unrelated tests start to allocate\n@testset \"closures\" begin\n\n# basic capture of a variable\nfunction closure1(x)\n    function foobar()\n        x\n    end\n    foobar\nend\n\nf = closure1(mat)\n@test f() == mat\nf\u2032 = adapt(CustomArray, f)\n@test f\u2032() == mat.arr\n@test @inferred(adapt(nothing, f)()) == f()\n\n# closure with sparams\nfunction closure2(A::CustomArray{T}=zeros(1), b::Number=0) where {T}\n    function f()\n        convert(T, 0)\n        A, A[1] == b\n    end\n    f\nend\n\nf = closure2(mat)\n@test f() == (mat, false)\nf\u2032 = adapt(CustomArray, f)\n@test f\u2032() == (mat.arr, false)\n\nend\n\n\n@testset \"array wrappers\" begin\n\n@test_adapt CustomArray view(mat.arr,:,:) view(mat,:,:) AnyCustomArray\ninds = CustomArray{Int,1}([1,2])\n@test_adapt CustomArray view(mat.arr,inds.arr,:) view(mat,inds,:) AnyCustomArray\n\n# NOTE: manual creation of PermutedDimsArray because permutedims collects\n@test_adapt CustomArray PermutedDimsArray(mat.arr,(2,1)) PermutedDimsArray(mat,(2,1)) AnyCustomArray\n\n# NOTE: manual creation of ReshapedArray because Base.Array has an optimized `reshape`\n@test_adapt CustomArray Base.ReshapedArray(mat.arr,(2,2),()) reshape(mat,(2,2)) AnyCustomArray\n\n@test_adapt CustomArray Base.LogicalIndex(mat_bools.arr) Base.LogicalIndex(mat_bools) AnyCustomArray\n\n@test_adapt CustomArray reinterpret(Int64,mat.arr) reinterpret(Int64,mat) AnyCustomArray\n\n\n## doubly-wrapped\n\n@test_adapt CustomArray reinterpret(Int64,view(mat.arr,:,:)) reinterpret(Int64,view(mat,:,:)) AnyCustomArray\n\n@test_adapt CustomArray reshape(view(mat.arr,:,:), (2,2)) reshape(view(mat,:,:), (2,2)) AnyCustomArray\n@test_adapt CustomArray reshape(reinterpret(Int64,mat.arr), (2,2)) reshape(reinterpret(Int64,mat), (2,2)) AnyCustomArray\n@test_adapt CustomArray reshape(reinterpret(Int64,view(mat.arr,:,:)), (2,2)) reshape(reinterpret(Int64,view(mat,:,:)), (2,2)) AnyCustomArray\n\n@test_adapt CustomArray view(reinterpret(Int64,mat.arr), :, :) view(reinterpret(Int64,mat), :, :) AnyCustomArray\n@test_adapt CustomArray view(reinterpret(Int64,view(mat.arr,:,:)), :, :) view(reinterpret(Int64,view(mat,:,:)), :, :) AnyCustomArray\n@test_adapt CustomArray view(Base.ReshapedArray(mat.arr,(2,2),()), :, :) view(reshape(mat, (2,2)), :, :) AnyCustomArray\n@test_adapt CustomArray view(reshape(view(mat.arr,:,:), (2,2)), :, :) view(reshape(view(mat,:,:), (2,2)), :, :) AnyCustomArray\n@test_adapt CustomArray view(reshape(reinterpret(Int64,mat.arr), (2,2)), :, :) view(reshape(reinterpret(Int64,mat), (2,2)), :, :) AnyCustomArray\n@test_adapt CustomArray view(reshape(reinterpret(Int64,view(mat.arr,:,:)), (2,2)), :, :) view(reshape(reinterpret(Int64,view(mat,:,:)), (2,2)), :, :) AnyCustomArray\n\n\nusing LinearAlgebra\n\n@test_adapt CustomArray mat.arr' mat' AnyCustomArray\n\n@test_adapt CustomArray transpose(mat.arr) transpose(mat) AnyCustomArray\n\n@test_adapt CustomArray LowerTriangular(mat.arr) LowerTriangular(mat) AnyCustomArray\n@test_adapt CustomArray UnitLowerTriangular(mat.arr) UnitLowerTriangular(mat) AnyCustomArray\n@test_adapt CustomArray UpperTriangular(mat.arr) UpperTriangular(mat) AnyCustomArray\n@test_adapt CustomArray UnitUpperTriangular(mat.arr) UnitUpperTriangular(mat) AnyCustomArray\n\n@test_adapt CustomArray Diagonal(vec.arr) Diagonal(vec) AnyCustomArray\n\ndl = CustomArray{Float64,1}(rand(2))\ndu = CustomArray{Float64,1}(rand(2))\nd = CustomArray{Float64,1}(rand(3))\n@test_adapt CustomArray Tridiagonal(dl.arr, d.arr, du.arr) Tridiagonal(dl, d, du) AnyCustomArray\n\nend\n\n\n@testset \"type information\" begin\n    @test Adapt.ndims(LinearAlgebra.Transpose{Float64,Array{Float64,1}}) == 2\n    @test Adapt.ndims(Adapt.WrappedSubArray{Float64,3,Array{Float64,3}}) == 3\n\n    @test Adapt.parent(LinearAlgebra.Transpose{Float64,Array{Float64,1}}) == Array\n    @test Adapt.parent(Adapt.WrappedSubArray{Float64,3,Array{Float64,3}}) == Array\nend\n", "meta": {"hexsha": "b860e6d8c23cf9d18b88cce6b1a6304a5139d90c", "size": 5475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "simeonschaub/Adapt.jl", "max_stars_repo_head_hexsha": "c7dad7faed4cf95aaab2ff986cbcb90cecf7f453", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "simeonschaub/Adapt.jl", "max_issues_repo_head_hexsha": "c7dad7faed4cf95aaab2ff986cbcb90cecf7f453", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "simeonschaub/Adapt.jl", "max_forks_repo_head_hexsha": "c7dad7faed4cf95aaab2ff986cbcb90cecf7f453", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0062111801, "max_line_length": 164, "alphanum_fraction": 0.7119634703, "num_tokens": 1586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10521053810590074, "lm_q1q2_score": 0.051783378608525006}}
{"text": "using Test\ninclude(\"module-2-exercise-1.jl\")\n\n@testset \"$(get_module_2_excersise_1_author_name()): powers of three tests\" begin\n     powers_of_three = get_powers_of_three(0)\n     @test powers_of_three == []\n     powers_of_three = get_powers_of_three(1)\n     @test powers_of_three == [1]\n     powers_of_three = get_powers_of_three(2)\n     @test [3^0, 3^1] == powers_of_three\n     powers_of_three = get_powers_of_three(5)\n     @test [3^0, 3^1, 3^2, 3^3, 3^4] == powers_of_three\nend\n", "meta": {"hexsha": "b14895167a1f8f038bcf96a3ce0d78d83a3a5ffc", "size": 480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "module2/module-2-exercise-1b-test-runner.jl", "max_stars_repo_name": "j-hayes/chem-324-programming-tutorials", "max_stars_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-15T01:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T01:54:45.000Z", "max_issues_repo_path": "module2/module-2-exercise-1b-test-runner.jl", "max_issues_repo_name": "j-hayes/chem-324-programming-tutorials", "max_issues_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "module2/module-2-exercise-1b-test-runner.jl", "max_forks_repo_name": "j-hayes/chem-324-programming-tutorials", "max_forks_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.2857142857, "max_line_length": 81, "alphanum_fraction": 0.6958333333, "num_tokens": 160, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10521052828620037, "lm_q1q2_score": 0.051783373775384986}}
{"text": "using Test\nusing JSON\nusing Random\nimport Jupyter2Pluto: PlutoCell, JupyterMarkdownCell, JupyterCodeCell, generate_code\n\nconst markdown_arr = [\n    \"# Data structures\\n\",\n    \"\\n\",\n    \"Once we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>\\n\",\n    \"\\n\",\n    \"Types of data structures covered:\\n\",\n    \"1. Tuples\\n\",\n    \"2. Dictionaries\\n\",\n    \"3. Arrays\\n\",\n    \"\\n\",\n    \"<br>\\n\",\n    \"As an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.\\n\",\n    \"We'll explain this more below!\"\n]\n\n##TODO: fix remove \\n from the last jupyter cell\nconst code_arr = [\"# First, restore fibonacci\\n\", \"fibonacci[1] = 1\\n\", \"fibonacci\"]\nRandom.seed!(1234)\n\nfunction test_jupyter2pluto()\n    #@info join(markdown_arr)\n    jmark_cell = JupyterMarkdownCell(join(markdown_arr))\n    pmc = PlutoCell(jmark_cell)\n    @testset \"test pluto markdown codegen\" begin\n        @test string(pmc) == \"\"\"# \u2554\u2550\u2561 196f2941-2d58-45ba-9f13-43a2532b2fa8\nmd\\\"\\\"\\\"\n# Data structures\n\nOnce we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>\n\nTypes of data structures covered:\n1. Tuples\n2. Dictionaries\n3. Arrays\n\n<br>\nAs an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.\nWe'll explain this more below!\\\"\\\"\\\"\n\n\"\"\"\n    end\n    pcc = PlutoCell(JupyterCodeCell(1, code_arr))\n    @testset \"Pluto code block codegen\" begin\n        @test string(pcc) == \"\"\"# \u2554\u2550\u2561 bd85187e-0531-4a3e-9fea-713204a818a2\nbegin\n    # First, restore fibonacci\n    fibonacci[1] = 1\n    fibonacci\nend\n\n\"\"\"\n    end\n    @test string(generate_code(PlutoCell[pmc, pcc])) == \"\"\"### A Pluto.jl notebook ###\n\n# \u2554\u2550\u2561 196f2941-2d58-45ba-9f13-43a2532b2fa8\nmd\\\"\\\"\\\"\n# Data structures\n\nOnce we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>\n\nTypes of data structures covered:\n1. Tuples\n2. Dictionaries\n3. Arrays\n\n<br>\nAs an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.\nWe'll explain this more below!\\\"\\\"\\\"\n\n# \u2554\u2550\u2561 bd85187e-0531-4a3e-9fea-713204a818a2\nbegin\n    # First, restore fibonacci\n    fibonacci[1] = 1\n    fibonacci\nend\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500196f2941-2d58-45ba-9f13-43a2532b2fa8\n# \u2560\u2550bd85187e-0531-4a3e-9fea-713204a818a2\n\"\"\"\nend\n\ntest_jupyter2pluto()\n", "meta": {"hexsha": "ba81bc60d597277387930b207a11b6f36889af70", "size": 2735, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_jupyter2pluto.jl", "max_stars_repo_name": "vdayanand/Jupyter2Pluto.jl", "max_stars_repo_head_hexsha": "a138934db0b338ba7a9a54849a65e8d0f515c817", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "max_stars_repo_stars_event_min_datetime": "2020-07-31T13:53:02.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-21T14:33:36.000Z", "max_issues_repo_path": "test/test_jupyter2pluto.jl", "max_issues_repo_name": "vdayanand/Jupyter2Pluto.jl", "max_issues_repo_head_hexsha": "a138934db0b338ba7a9a54849a65e8d0f515c817", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-08-16T18:19:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-17T17:35:28.000Z", "max_forks_repo_path": "test/test_jupyter2pluto.jl", "max_forks_repo_name": "vdayanand/Jupyter2Pluto.jl", "max_forks_repo_head_hexsha": "a138934db0b338ba7a9a54849a65e8d0f515c817", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.7303370787, "max_line_length": 194, "alphanum_fraction": 0.7093235832, "num_tokens": 821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41111086923216794, "lm_q2_score": 0.12592276483513232, "lm_q1q2_score": 0.051768217307489116}}
{"text": "using Pkg, PackageCompiler\n\n# packages to install \npackages_to_install = [\"Plots\",\n    \"DataFrames\",\n    \"GR\",\n    \"Query\",\n    \"Parameters\"]\nPkg.add(packages_to_install)\n\n# packages to bake in\ncompile_package(\"Plots\", \"DataFrames\", \"Query\", \"GR\", force = true)\n\n", "meta": {"hexsha": "b33a5fda69dbb981543d1ac4274753d1541f56ae", "size": 263, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "init.jl", "max_stars_repo_name": "davidanthoff/compiled-binder-postbuild", "max_stars_repo_head_hexsha": "dde33cb40d1818b5b4c9520706fcd0b63ede95b4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "init.jl", "max_issues_repo_name": "davidanthoff/compiled-binder-postbuild", "max_issues_repo_head_hexsha": "dde33cb40d1818b5b4c9520706fcd0b63ede95b4", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "init.jl", "max_forks_repo_name": "davidanthoff/compiled-binder-postbuild", "max_forks_repo_head_hexsha": "dde33cb40d1818b5b4c9520706fcd0b63ede95b4", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.7857142857, "max_line_length": 67, "alphanum_fraction": 0.6768060837, "num_tokens": 71, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.12592275171421674, "lm_q1q2_score": 0.051768211913338115}}
{"text": "function check_vote(name)\n\tif voted.get(name)\n\t\tprintln(\"kick them out!\")\n\telse\n\t\tvoted[name] = true\n\t\tprintln(\"let them vote!\")\n\tend\nend\n\nvoted = Dict()\ncheck_vote(\"tom\")\ncheck_vote(\"mike\")\ncheck_vote(\"mike\")\n", "meta": {"hexsha": "700a52d63600eede90a9d8dac0f9f6f9a9f92782", "size": 210, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "05_hash_tables/julia/02_check_voter.jl", "max_stars_repo_name": "filchyboy/grokking_algorithms_work", "max_stars_repo_head_hexsha": "16dace97610e2cb0938704e2b8cfd6e92d6b024d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2021-03-11T00:25:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T00:19:23.000Z", "max_issues_repo_path": "book04grokkingAlgo/05_hash_tables/julia/02_check_voter.jl", "max_issues_repo_name": "mcuallen/CodeLrn2019", "max_issues_repo_head_hexsha": "adc727d92904c5c5d445a2621813dfa99474206d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 160, "max_issues_repo_issues_event_min_datetime": "2021-04-26T19:04:15.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-26T20:18:37.000Z", "max_forks_repo_path": "book04grokkingAlgo/05_hash_tables/julia/02_check_voter.jl", "max_forks_repo_name": "mcuallen/CodeLrn2019", "max_forks_repo_head_hexsha": "adc727d92904c5c5d445a2621813dfa99474206d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2021-04-26T19:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T08:36:29.000Z", "avg_line_length": 15.0, "max_line_length": 27, "alphanum_fraction": 0.6904761905, "num_tokens": 60, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3998116407397951, "lm_q2_score": 0.12940274502147983, "lm_q1q2_score": 0.0517367238032712}}
{"text": "printstyled(stdout,\"  merge! behavior and intent\\n\", color=:light_green)\nnx = 100\nfs = 100.0\n\u0394 = round(Int64, 1000000/fs)\n\nid = \"VV.STA1.00.EHZ\"\nloc = GeoLoc(lat=46.8523, lon=121.7603, el=4392.0)\nloc1 = GeoLoc(lat=rand(), lon=rand(), el=rand())\nresp1 = fctoresp(0.2, 1.0)\nresp2 = fctoresp(2.0, 1.0)\nunits = \"m/s\"\nsrc = \"test\"\nt0 = 0\n\nmkC1() = SeisChannel( id = id, name = \"Channel 1\",\n                  loc = loc, fs = fs, resp = deepcopy(resp1), units = units,\n                  src = \"test channel 1\",\n                  notes = [tnote(\"New channel 1.\")],\n                  misc = Dict{String,Any}( \"P\" => 6.1 ),\n                  t = [1 t0; nx 0],\n                  x = randn(nx) )\nmkC2() = SeisChannel( id = id, name = \"Channel 2\",\n                  loc = loc, fs = fs, resp = deepcopy(resp1), units = units,\n                  src = \"test channel 2\",\n                  notes = [tnote(\"New channel 2.\")],\n                  misc = Dict{String,Any}( \"S\" => 11.0 ),\n                  t = [1 t0+nx*\u0394; nx 0],\n                  x = randn(nx) )\nmkC4() = (C = mkC2(); C.loc = loc1;\n                  C.name = \"Channel 4\";\n                  C.src = \"test channel 4\";\n                  C.notes = [tnote(\"New channel 4.\")]; C)\nmkC5() = (C = mkC2(); C.loc = loc1;\n                  C.name = \"Channel 5\";\n                  C.src = \"test channel 5\";\n                  C.notes = [tnote(\"New channel 5.\")];\n                  C.t = [1 t0+nx*2\u0394; nx 0];\n                  C)\nC2_ov() = (nov = 3; C = mkC2(); C.t = [1 t0+(nx-nov)*\u0394; nx 0]; C)\nC3_ov() = (nov = 3; C = mkC2(); C.t = [1 t0+2*(nx-nov)*\u0394; nx 0]; C)\n\nfunction prandSC(c::Bool)\n  if c == true\n    C = randSeisChannel(c=true, nx=1000)\n  else\n    C = randSeisChannel(c=false, nx=10000)\n  end\n  C.name = randstring(20)\n  C.misc = Dict{String,Any}()\n  return C\nend\n\nfunction mk_tcat(T::Array{Array{Int64,2},1}, fs::Float64)\n  L = length(T)\n  \u03c4 = Array{Array{Int64,1},1}(undef,L)\n  for i = 1:L\n    \u03c4[i] = t_expand(T[i], fs)\n  end\n  tt = sort(unique(vcat(\u03c4...)))\n  return t_collapse(tt, fs)\nend\n\n# ===========================================================================\nprintstyled(stdout,\"    xtmerge!\\n\", color=:light_green)\n\u03b4 = 20000\ntmax = div(typemax(Int64),2)-2\u03b4\nx = randn(12)\nt = sort(rand(0:2\u03b4:tmax, 12))\nwhile (length(unique(t)) < 12)\n  push!(t, rand(0:2\u03b4:tmax))\n  t = sort(unique(t))\nend\nx = vcat(x, x[1:6])\nt = vcat(t, t[1:6])\nxtmerge!(t, x, div(\u03b4,2))\n@test length(t) == 12\n@test length(x) == 12\n\n# ===========================================================================\nprintstyled(stdout,\"    removal of traces with no data or time info\\n\", color=:light_green)\nS = SeisData(prandSC(false), prandSC(false), prandSC(false), prandSC(false))\nS.x[2] = Float64[]\nS.x[3] = Float64[]\nS.t[4] = Array{Int64,2}(undef,0,0)\nmerge!(S, v=1)\nbasic_checks(S)\nsizetest(S, 1)\n\nprintstyled(stdout,\"    ability to handle irregularly-sampled data\\n\", color=:light_green)\nC = prandSC(true)\nnamestrip!(C)\nS = SeisData(C, prandSC(true), prandSC(true))\nnamestrip!(S)\nfor i = 2:3\n  S.id[i] = identity(S.id[1])\n  S.resp[i] = copy(S.resp[1])\n  S.loc[i] = deepcopy(S.loc[1])\n  S.units[i] = identity(S.units[1])\nend\nT = merge(S, v=1)\nbasic_checks(T)\nsizetest(T, 1)\n\n# ===========================================================================\nprintstyled(stdout,\"    simple merges\\n\", color=:light_green)\nprintstyled(stdout,\"      three channels, two w/same params, no overlapping data\\n\", color=:light_green)\nS = SeisData(mkC1(), mkC2(), prandSC(false))\n\n# Is the group merged correctly?\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test vcat(U.x[1], U.x[2])==S.x[i]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n# Do the notes log the extra source?\n@test findfirst([occursin(\"New channel 1\", i) for i in S.notes[i]]) != nothing\n@test findfirst([occursin(\"test channel 1\", i) for i in S.notes[i]]) != nothing\n\n# Are dictionaries merging correctly?\n@test haskey(S.misc[i], \"P\")\n@test haskey(S.misc[i], \"S\")\n\nprintstyled(stdout, \"      \\\"zipper\\\" merge I: two channels, staggered time windows, no overlap\\n\", color=:light_green)\nS = SeisData(mkC1(), mkC2())\nW = Array{Int64,2}(undef, 8, 2);\nfor i = 1:8\n  W[i,:] = t0 .+ [(i-1)*nx*\u0394 ((i-1)*nx + nx-1)*\u0394]\nend\nw1 = W[[1,3,5,7], :]\nw2 = W[[2,4,6,8], :]\nS.t[1] = w_time(w1, \u0394)\nS.t[2] = w_time(w2, \u0394)\nS.x[1] = randn(4*nx)\nS.x[2] = randn(4*nx)\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 1\n@test S.t[1] == [1 t0; 8*nx 0]\n\n# ===========================================================================\n# (II) as (I) with another channel at a new location\nprintstyled(stdout,\"    channels must have identical :fs, :loc, :resp, and :units to merge \\n\", color=:light_green)\nS = SeisData(mkC1(), mkC2(), prandSC(false), mkC4())\n\nU = deepcopy(S)\nmerge!(S)\n@test S.n == 3\nbasic_checks(S)\n# Are there two channels with the same ID?\n@test length(findall(S.id.==id)) == 2\n\n# Is the subgroup merged correctly?\ni = findfirst(S.src.==\"test channel 2\")\n@test S.loc[i] == loc\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n@test vcat(U.x[1], U.x[2]) == S.x[i]\n\n# Is the odd channel left alone?\nj = findfirst(S.src.==\"test channel 4\")\n@test S.loc[j] == loc1\nfor i in datafields\n  if i != :notes\n    @test getfield(U, i)[4] == getfield(S, i)[j]\n  end\nend\n\n# with a second channel at the new location\nS = SeisData(deepcopy(U), mkC5())\nU = deepcopy(S)\nmerge!(S)\n@test S.n == 3\nbasic_checks(S)\n\n# Are there two channels with the same ID?\n@test length(findall(S.id.==id)) == 2\n\n# Is the first subgroup merged correctly?\ni = findfirst(S.src.==\"test channel 2\")\n@test S.loc[i] == loc\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n@test vcat(U.x[1], U.x[2]) == S.x[i]\n\n# Is the second subgroup merged correctly?\nj = findfirst(S.src.==\"test channel 5\")\n@test S.loc[j] == loc1\n@test mk_tcat(U.t[4:5], fs) == S.t[j]\n@test vcat(U.x[4], U.x[5]) == S.x[j]\n\n# with resp, not loc\nS = deepcopy(U)\nS.loc[4] = deepcopy(loc)\nS.loc[5] = deepcopy(loc)\nS.resp[4] = deepcopy(resp2)\nS.resp[5] = deepcopy(resp2)\n\nU = deepcopy(S)\nmerge!(S)\n@test S.n == 3\nbasic_checks(S)\n\n# Are there two channels with the same ID?\n@test length(findall(S.id.==id)) == 2\n\n# Is the first subgroup merged correctly?\ni = findfirst(S.src.==\"test channel 2\")\n@test S.loc[i] == loc\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n@test vcat(U.x[1], U.x[2]) == S.x[i]\n\n# Is the second subgroup merged correctly?\nj = findfirst(S.src.==\"test channel 5\")\n@test S.resp[j] == resp2\n@test mk_tcat(U.t[4:5], fs) == S.t[j]\n@test vcat(U.x[4], U.x[5]) == S.x[j]\n\n# ===========================================================================\nprintstyled(stdout,\"    one merging channel with a time gap\\n\", color=:light_green)\nS = SeisData(mkC1(), mkC2(), prandSC(false))\nS.x[2] = rand(2*nx)\nS.t[2] = vcat(S.t[2][1:1,:], [nx 2*\u0394], [2*nx 0])\n\n# Is the group merged correctly?\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test U.x[1] == S.x[i][1:nx]\n@test U.x[2][1:nx] == S.x[i][nx+1:2nx]\n@test U.x[2][nx+1:2nx] == S.x[i][2nx+1:3nx]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\nprintstyled(stdout,\"      merge window is NOT the first\\n\", color=:light_green)\nos = 2\nS = SeisData(mkC1(), mkC2(), prandSC(false))\nS.x[1] = rand(2*nx)\nS.t[1] = vcat(S.t[1][1:1,:], [nx os*\u0394], [2*nx 0])\nS.t[2][1,2] += (os+nx)*\u0394\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test U.x[1] == S.x[i][1:2nx]\n@test U.x[2] == S.x[i][2nx+1:3nx]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n# ===========================================================================\nprintstyled(stdout,\"    one merge group has non-duplication time overlap\\n\", color=:light_green)\nprintstyled(stdout,\"      check for averaging\\n\", color=:light_green)\nnov = 3\nS = SeisData(mkC1(), C2_ov(), prandSC(false))\n\n# Is the group merged correctly?\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n\n@test S.n == 2\ni = findid(id, S)\n@test S.x[i][1:nx-nov] == U.x[1][1:nx-nov]\n@test S.x[i][nx-nov+1:nx] == 0.5*(U.x[1][nx-nov+1:nx] + U.x[2][1:nov])\n@test S.x[i][nx+1:2nx-nov] == U.x[2][nov+1:nx]\n@test S.t[i] == [1 U.t[1][1,2]; 2nx-nov 0]\n\n# Do the notes log the extra source?\n@test findfirst([occursin(\"New channel 1\", i) for i in S.notes[i]]) != nothing\n@test findfirst([occursin(\"test channel 1\", i) for i in S.notes[i]]) != nothing\n\n# Are dictionaries merging correctly?\n@test haskey(S.misc[i], \"P\")\n@test haskey(S.misc[i], \"S\")\n\nprintstyled(stdout,\"      src overlap window is NOT first\\n\", color=:light_green)\nos = 2\nS = SeisData(mkC1(), C2_ov(), prandSC(false))\nS.x[1] = rand(2*nx)\nS.t[1] = vcat(S.t[1][1:1,:], [nx os*\u0394], [2*nx 0])\nS.t[2][1,2] += (os+nx)*\u0394\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test S.x[i][1:2nx-nov] == U.x[1][1:2nx-nov]\n@test S.x[i][2nx-nov+1:2nx] == 0.5*(U.x[1][2nx-nov+1:2nx] + U.x[2][1:nov])\n@test S.x[i][2nx+1:3nx-nov] == U.x[2][nov+1:nx]\n@test S.t[i] == vcat(U.t[1][1:2,:], [length(S.x[i]) 0])\n\nprintstyled(stdout,\"      dest overlap window is NOT first\\n\", color=:light_green)\nnov = 3\nS = SeisData(mkC1(), C2_ov(), prandSC(false))\nS.x[2] = rand(2*nx)\nS.t[2] = [1 t0-nx*\u0394; nx+1 \u0394*(nx-nov); 2*nx 0]\n\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test S.x[i][1:nx] == U.x[2][1:nx]\n@test S.x[i][nx+1:2nx-nov] == U.x[1][1:nx-nov]\n@test S.x[i][2nx-nov+1:2nx] == 0.5*(U.x[1][nx-nov+1:nx] + U.x[2][nx+1:nx+nov])\n@test S.x[i][2nx+1:3nx-nov] == U.x[2][nx+nov+1:2nx]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n# ===========================================================================\nprintstyled(stdout,\"    overlap with time mismatch\\n\", color=:light_green)\n\n#= mk_tcat stops working here as merge shifts one window back in\ntime one sample to account for the intentional one-\u0394 time mismatch =#\n\nprintstyled(stdout,\"      data overlap one sample off of S.t\\n\", color=:light_green)\n\n# (a) 3_sample overlap with wrong time (C2[1:2] == C1[99:100])\nnov = 2\nS = SeisData(mkC1(), C2_ov())\nS.x[2] = vcat(copy(S.x[1][nx-nov+1:nx]), rand(nx-nov))\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 1\ni = findid(id, S)\n@test length(S.x[i]) == 2nx-nov\n@test S.x[i][1:nx-nov] == U.x[1][1:nx-nov]\n@test S.x[i][nx-nov+1:nx] == U.x[1][nx-nov+1:nx] == U.x[2][1:nov]\n@test S.x[i][nx+1:2nx-nov] == U.x[2][nov+1:nx]\n@test S.t[i] == [1 U.t[1][1,2]-\u0394; 2nx-nov 0]\n\nprintstyled(stdout,\"      src overlap window is NOT first\\n\", color=:light_green)\nS = deepcopy(U)\nS.x[1] = rand(2*nx)\nS.t[1] = vcat(S.t[1][1:1,:], [nx os*\u0394], [2*nx 0])\nS.x[2] = vcat(copy(S.x[1][2nx-nov+1:2nx]), rand(nx-nov))\nS.t[2][1,2] += (os+nx)*\u0394\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 1\ni = findid(id, S)\n@test S.x[i][1:2nx-nov] == U.x[1][1:2nx-nov]\n@test S.x[i][2nx-nov+1:2nx] == U.x[1][2nx-nov+1:2nx] == U.x[2][1:nov]\n@test S.x[i][2nx+1:3nx-nov] == U.x[2][nov+1:nx]\n@test S.t[i] == [1 0; U.t[1][2,1] U.t[1][2,2]-\u0394; 3nx-nov 0]\n\n#= mk_tcat starts working again here as the time shift is now\napplied to the second window, rather than the first. =#\n\nprintstyled(stdout,\"      dest overlap window is NOT first\\n\", color=:light_green)\nS = SeisData(mkC1(), C2_ov())\nS.t[2] = [1 t0-nx*\u0394; nx+1 \u0394*(nx-nov); 2*nx 0]\nS.x[2] = vcat(randn(nx), copy(S.x[1][nx-nov+1:nx]), randn(nx-nov))\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 1\ni = findid(id, S)\n@test S.x[i][1:nx] == U.x[2][1:nx]\n@test S.x[i][nx+1:2nx-nov] == U.x[1][1:nx-nov]\n@test S.x[i][2nx-nov+1:2nx] == U.x[1][nx-nov+1:nx] == U.x[2][nx+1:nx+nov]\n@test S.x[i][2nx+1:3nx-nov] == U.x[2][nx+nov+1:2nx]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n\n# ===========================================================================\nprintstyled(stdout,\"    multichannel merge with overlap\\n\", color=:light_green)\nbreakpt_1 = nx-nov        # 97\nbreakpt_2 = 2*(nx-nov)-1  # 195\nbreakpt_3 = 2*(nx-nov)+2  # 198\n\nS = SeisData(mkC1(), C2_ov(), C3_ov(), prandSC(false))\nS.x[2] = vcat(copy(S.x[1][nx-nov+1:nx]), rand(nx-nov))\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\ni = findid(id, S)\n@test S.n == 2\n@test S.x[i][1:breakpt_1] == U.x[1][1:nx-nov]\n@test S.x[i][breakpt_1+1:nx] == U.x[1][nx-nov+1:nx] == U.x[2][1:nov]\n@test S.x[i][nx+1:breakpt_2] == U.x[2][nov+1:nx-nov-1]\n@test S.x[i][breakpt_2+1:breakpt_3] == 0.5*(U.x[2][nx-nov:nx] + U.x[3][1:nov+1])\n@test S.x[i][breakpt_3+1:end] == U.x[3][nov+2:end]\n@test S.t[i] == [1 U.t[1][1,2]-\u0394; 3nx-2nov-1 0]\n\n# What happens when there's a gap in one trace?\nbreakpt_4 = length(S.x[i])\nS = deepcopy(U)\nS.x[3] = rand(2*nx)\nS.t[3] = vcat(S.t[3][1:1,:], [nx 2*\u0394], [2*nx 0])\n@test w_time(t_win(S.t[3], \u0394), \u0394)  == S.t[3]\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\ni = findid(id, S)\n@test S.t[i] == [1 t0-\u0394; 295 2*\u0394; 395 0]\n@test S.x[i][1:breakpt_1] == U.x[1][1:nx-nov]\n@test S.x[i][breakpt_1+1:nx] == U.x[1][nx-nov+1:nx] == U.x[2][1:nov]\n@test S.x[i][nx+1:breakpt_2] == U.x[2][nov+1:nx-nov-1]\n@test S.x[i][breakpt_2+1:breakpt_3] == 0.5*(U.x[2][nx-nov:nx] + U.x[3][1:nov+1])\n@test S.x[i][breakpt_3+1:breakpt_4] == U.x[3][nov+2:nx]\n@test S.x[i][breakpt_4+1:end] == U.x[3][nx+1:end]\n\n# ===========================================================================\nprintstyled(stdout,\"    \\\"zipper\\\" merges II\\n\", color=:light_green)\nprintstyled(stdout,\"      two traces with staggered time windows, some with overlap\\n\", color=:light_green)\n\n# (a) One overlap in a late window should not shift back previous windows\nnov = 2\nS = SeisData(mkC1(), mkC2())\nW = Array{Int64,2}(undef, 8, 2);\nfor i = 1:8\n  W[i,:] = t0 .+ [(i-1)*nx*\u0394 ((i-1)*nx + nx-1)*\u0394]\nend\nW[8,1] -= nov*\u0394\nW[8,2] -= nov*\u0394\nw1 = W[[1,3,5,7], :]\nw2 = W[[2,4,6,8], :]\nS.t[1] = w_time(w1, \u0394)\nS.t[2] = w_time(w2, \u0394)\nS.x[1] = randn(4*nx)\nS.x[2] = randn(4*nx)\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\ni = findid(id, S)\n@test S.n == 1\n@test S.t[i] == [1 t0; 8*nx-nov 0]\n\n# These should be untouched\nfor j = 1:6\n  si = 1 + nx*(j-1)\n  ei = nx*j\n  k = div(j,2)\n  if isodd(j)\n    usi = k*nx + 1\n    uei = usi + nx - 1\n    @test S.x[i][si:ei] == U.x[1][usi:uei]\n  else\n    usi = (k-1)*nx + 1\n    uei = usi + nx - 1\n    @test S.x[i][si:ei] == U.x[2][usi:uei]\n  end\nend\n\n# The only overlap should be here:\n@test S.x[i][1+6nx:7nx-nov] == U.x[1][1+3nx:4nx-nov]\n@test S.x[i][7nx-nov+1:7nx] == 0.5*(U.x[1][4nx-nov+1:4nx] .+ U.x[2][3nx+1:3nx+nov])\n@test S.x[i][7nx+1:8nx-nov] == U.x[2][3nx+nov+1:4nx]\n\nprintstyled(stdout,\"      one overlap, late window, time mismatch\\n\", color=:light_green)\nnov = 3\ntrue_nov = 2\nS = SeisData(mkC1(), mkC2())\nW = Array{Int64,2}(undef, 8, 2);\nfor i = 1:8\n  W[i,:] = t0 .+ [(i-1)*nx*\u0394 ((i-1)*nx + nx-1)*\u0394]\nend\nW[8,1] -= nov*\u0394\nW[8,2] -= nov*\u0394\nw1 = W[[1,3,5,7], :]\nw2 = W[[2,4,6,8], :]\nS.t[1] = w_time(w1, \u0394)\nS.t[2] = w_time(w2, \u0394)\nS.x[1] = randn(4*nx)\nS.x[2] = randn(4*nx)\nS.x[2][3nx+1:3nx+2] = copy(S.x[1][4nx-1:4nx])\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 1\nfor j = 1:5\n  si = 1 + nx*(j-1)\n  ei = nx*j\n  k = div(j,2)\n  if isodd(j)\n    usi = k*nx + 1\n    uei = usi + nx - 1\n    @test S.x[i][si:ei] == U.x[1][usi:uei]\n  else\n    usi = (k-1)*nx + 1\n    uei = usi + nx - 1\n    @test S.x[i][si:ei] == U.x[2][usi:uei]\n  end\nend\n\u03b4i = nov - true_nov\n\n# Because we moved the start time of 2:3 back by 1, we expect:\n@test S.x[i][1+5nx:6nx-\u03b4i] == U.x[2][1+2nx:3nx-\u03b4i]\n\n# Thus there's a one-point overlap (that gets resolved by averaging) at:\n@test S.x[i][6nx-\u03b4i+1:6nx] == 0.5*(U.x[2][3nx-\u03b4i+1:3nx] + U.x[1][3nx+1:3nx+\u03b4i])\n\n# Proceeding through the merged time series, we expect:\n@test S.x[i][6nx+1:7nx-nov] == U.x[1][3nx+\u03b4i+1:4nx-true_nov]\n@test S.x[i][7nx-nov+1:7nx-\u03b4i] == U.x[1][4nx-true_nov+1:4nx] == U.x[2][3nx+1:3nx+true_nov]\n@test S.x[i][7nx-\u03b4i+1:end] == U.x[2][3nx+true_nov+1:end]\n\n# ============================================================================\nprintstyled(stdout,\"    distributivity: S1*S3 + S2*S3 == (S1+S2)*S3\\n\", color=:light_green)\nimax = 10\nprintstyled(\"      trial \", color=:light_green)\nfor i = 1:imax\n  if i > 1\n    print(\"\\b\\b\\b\\b\\b\")\n  end\n  printstyled(string(lpad(i, 2), \"/\", imax), color=:light_green)\n  S1 = randSeisData()\n  S2 = randSeisData()\n  S3 = randSeisData()\n  # M1 = (S1+S2)*S3\n  # M2 = S1*S3 + S2*S3\n  @test ((S1+S2)*S3) == (S1*S3 + S2*S3)\n  if i == imax\n    println(\"\")\n  end\nend\n\n# ============================================================================\nprintstyled(stdout,\"    checking (formerly-breaking) end-member cases\\n\", color=:light_green)\nprintstyled(stdout,\"      time windows not in chronological order\\n\", color=:light_green)\nC1 = mkC1()\nC2 = mkC2()\nC3 = deepcopy(C1)\nC3.t = [1 0; 101 -2000000; 200 0]\nappend!(C3.x, randn(100))\nS = SeisData(C3, C2, prandSC(false))\n\n# Is the group merged correctly?\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test S.t[i] == [1 -1000000; 300 0]\n@test vcat(U.x[1][101:200], U.x[1][1:100], U.x[2]) == S.x[i]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n# Do the notes log the extra source?\n@test findfirst([occursin(\"New channel 1\", j) for j in S.notes[i]]) != nothing\n@test findfirst([occursin(\"test channel 1\", j) for j in S.notes[i]]) != nothing\n\n# Are dictionaries merging correctly?\n@test haskey(S.misc[i], \"P\")\n@test haskey(S.misc[i], \"S\")\n\nprintstyled(stdout,\"      sequential one-sample windows\\n\", color=:light_green)\nC1 = mkC1()\nC2 = mkC2()\nC2.t = [1 1200000; 99 90059; 100 90210]\nC2.t = [1 1000000; 99 90059; 100 90210]\nS = SeisData(C1, C2, prandSC(false))\n\n# Is the group merged correctly?\nU = deepcopy(S)\nmerge!(S)\nbasic_checks(S)\n@test S.n == 2\ni = findid(id, S)\n@test S.t[i] == [1  0; 199  90059; 200  90210]\n@test vcat(U.x[1], U.x[2])==S.x[i]\n@test mk_tcat(U.t[1:2], fs) == S.t[i]\n\n# Do the notes log the extra source?\n@test findfirst([occursin(\"New channel 1\", j) for j in S.notes[i]]) != nothing\n@test findfirst([occursin(\"test channel 1\", j) for j in S.notes[i]]) != nothing\n\n# Are dictionaries merging correctly?\n@test haskey(S.misc[i], \"P\")\n@test haskey(S.misc[i], \"S\")\n\n# ============================================================================\nprintstyled(stdout,\"  merge! and new/extended methods\\n\", color=:light_green)\nfs1 = 50.0\n\u0394 = round(Int64, s\u03bc/fs1)\n\n# t1 = round(Int64,time()/\u03bcs)\nt1 = 0\n\n# s1 and s2 represent data from a fictitious channel\n# s2 begins 1 second later, but has a gap of 1s after sample 25\ns1 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\n  t = [1 t1; 100 0], x=randn(100))\ns2 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\n  t = [1 t1+1000000; 26 1000000; 126 200000; 150 0], x=randn(150))\n\n\ns3 = SeisChannel(fs = 100.0, gain = 5.0, name = \"DEAD.STA.EHE\", id = \"DEAD.STA..EHE\",\n  t = [1 t1; 100 0], x=rand(100).-0.5)\ns4 = SeisChannel(fs = 100.0, gain = 50.0, name = \"DEAD.STA.EHE\", id = \"DEAD.STA..EHE\",\n  t = [1 t1+1000000; 100 1000000; 150 0], x=randn(150))\n\n# We expect:\n# (1) LAST 25-26 points in s1 will be averaged with first 25 points in s3\n# (2) s3 will be ungapped with exactly 50 samples of junk\n# (3) after sync of a seisobj formed from [s1+s3, s2], s2 will be padded with 0.5s\n# at start and 3s at end\n\nS = SeisData()\nfor i in fieldnames(typeof(S))\n  if i != :n\n    @test \u2248(isempty([]), isempty(getfield(S,i)))\n  end\nend\n\nprintstyled(\"    fixing data gaps\\n\", color=:light_green)\ns2u = ungap(s2)\n@test length(s2.x) / s2.fs + \u03bcs * sum(s2.t[2:end - 1, 2]) \u2248 length(s2u.x) / s2u.fs\n\nprintstyled(\"    channel add and simple merges\\n\", color=:light_green)\nS = S + (s1 * s2)\n\n# Do in-place operations only change leftmost variable?\n@test length(s1.x) \u2248 100\n@test length(s2.x) \u2248 150\n\n# expected behavior for S = s1+s2\n# (0) length[s.x[1]] = 250\n# (1) S.x[1][1:50] = s1.x[1:50]\n# (2) S.x[1][51:75] = 0.5.*(s1.x[51:75] + s2.x[1:25])\n# (3) S.x[1][76:100] = s1.x[76:100]\n# (4) S.x[1][101:125] = mean(S.x[1])\n# (5) S.x[1][126:250] = s2.x[126:250]\n\n# Basic merge ops\n@test S.n == 1\n@test length(S.fs) == 1\n@test ==(S.fs[1], s1.fs)\n@test ==(length(S.gain), 1)\n@test ==(S.gain[1], s1.gain)\n@test ==(length(S.name), 1)\n@test ==(length(S.t),1)\n@test ==(length(S.x),1)\n@test S.id[1]==\"DEAD.STA..EHZ\"\nU = deepcopy(S)\nungap!(S, m=false, tap=false) # why do I have to force type here\n@test ==(length(S.x[1]), 260)\n@test ==(S.x[1][1:50], s1.x[1:50])\n@test ==(S.x[1][51:75], 0.5.*(s1.x[51:75] .+ s2.x[1:25]))\n@test ==(S.x[1][76:100], s1.x[76:100])\n@test minimum(isnan.(S.x[1][101:125]))\n@test ==(S.x[1][126:225], s2.x[26:125])\n@test minimum(isnan.(S.x[1][226:235]))\n@test ==(S.x[1][236:260], s2.x[126:150])\n\n# Auto-tapering after a merge\nT = deepcopy(S)\nii = findall(isnan.(S.x[1]))\nnanfill!(S)\ntaper!(S, N_min = 0)\n@test length(findall(isnan.(S.x[1])))==0          # No more NaNs?\n@test sum(diff(S.x[1][ii]))==0                    # All NaNs filled w/same val?\n@test \u2248(T.x[1][15:90], S.x[1][15:90])             # Un-windowed vals untouched?\n\nprintstyled(\"    channel delete\\n\", color=:light_green)\nS -= 1\nfor i in fieldnames(typeof(S))\n  if i != :n\n    @test \u2248(isempty([]), isempty(getfield(S,i)))\n  end\nend\n@test isempty(S)\n\nprintstyled(\"    a more difficult merge\\n\", color=:light_green)\nS = SeisData()\nS *= (s1 * s3)\nS *= (s2 * s4)\n@test \u2248(S.n, 2)\n@test \u2248(length(S.fs), 2)\n@test \u2248(length(S.gain), 2)\n@test \u2248(length(S.name), 2)\n@test \u2248(length(S.t),2)\n@test \u2248(length(S.x),2)\ni = findid(\"DEAD.STA..EHE\", S)\nj = findid(\"DEAD.STA..EHZ\", S)\n@test \u2248(S.fs[i], 100.0)\n@test \u2248(S.gain[j], 10.0)\nungap!(S, m=false, tap=false)\n@test \u2248(length(S.x[j]), 260)\n@test \u2248(length(S.x[i]), 350)\n@test \u2248(S.x[i][101]/S.gain[i], s4.x[1]/s4.gain)\n\nprintstyled(\"      repeating data\\n\", color=:light_green)\nloc1 = GeoLoc(lat=45.28967, lon=-121.79152, el=1541.0)\nloc2 = GeoLoc(lat=48.78384, lon=-121.90093, el=1676.0)\ns1 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\nt = [1 t1; 100 0], x=randn(100))\ns2 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\n  t = [1 t1+1000000; 150 0], x=vcat(s1.x[51:100], randn(100)))\nC = (s1 * s2)[1]\n@test length(C.x) == 200\n@test C.x[1:100] == s1.x\n@test C.x[101:200] == s2.x[51:150]\n\nprintstyled(\"    overlapping times\\n\", color=:light_green)\nos = 50\nnx = length(s1.x)\nlx = length(s1.x)/s1.fs\n\u03c4 = round(Int, s\u03bc*(2.0-os/fs1))\ns2 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\n  t = [1 t1+\u03c4; 150 0], x=randn(150))\nC = (s1 * s2)[1]\n@test length(C.x) == 250-os\n@test C.x[1:nx-os] == s1.x[1:os]\n@test C.x[nx-os+1:nx] == 0.5.*(s1.x[nx-os+1:nx]+s2.x[1:os])\n@test C.x[nx+1:200] == s2.x[os+1:150]\n\nprintstyled(\"    two-sample offset\\n\", color=:light_green)\nos = 2\nnx = length(s1.x)\nlx = length(s1.x)/s1.fs\n\u03c4 = round(Int, s\u03bc*(2.0-(os-1)/fs1))\ns2 = SeisChannel(fs = fs1, gain = 10.0, name = \"DEAD.STA.EHZ\", id = \"DEAD.STA..EHZ\",\n  t = [1 t1+\u03c4; 150 0], x=vcat(copy(s1.x[nx-os+1:nx]), randn(150-os)))\n@test s1.x[nx] == s2.x[os]\nC = (s1 * s2)[1]\n@test length(C.x) == 250-os+1\n@test C.x[1:99] == s1.x[1:99]\n@test C.x[100] == 0.5*(s2.x[1] + s1.x[100])\n@test C.x[101:249] == s2.x[2:150]\n\n# Ensure merge works correctly with traces separated in time\nprintstyled(\"    operator \\\"*\\\"\\n\", color=:light_green)\ns5 = SeisChannel(fs = 100.0, loc=loc1, gain = 32.0, name = \"DEAD.STA.EHE\", id = \"DEAD.STA..EHE\",\n  t = [1 t1; 100 0], x=randn(100))\ns6 = SeisChannel(fs = 100.0, loc=loc1, gain = 16.0, name = \"UNNAMED\", id = \"DEAD.STA..EHE\",\n  t = [1 t1+30000000; 200 0], x=randn(200))\nT = (s5 * s6)\nungap!(T)\n@test \u2248(length(T.x[1]),3200)\n\nprintstyled(\"    one repeating segment in two channels\\n\", color=:light_green)\nfs1 = 50.0\n\u0394 = round(Int64, s\u03bc/fs1)\ng1 = 10.0\nns = 2\nn1 = \"DEAD.STA..EHZ\"\nid = \"DEAD.STA..EHZ\"\nt_in = [1 0; 300 0]\nnx = round(Int64, ns*fs1)\nseg_1 = randn(nx)\nseg_2 = randn(nx)\nseg_3 = randn(nx)\nt = t_expand(t_in, fs1)\ni1 = vcat(collect(1:100), collect(201:300))\ni2 = collect(101:300)\nt1 = t_collapse(t[i1], fs1)\nt2 = t_collapse(t[i2], fs1)\ns1 = SeisChannel(fs = fs1, gain = g1, name = n1, id = id, t = t1, x = vcat(seg_1, seg_3))\ns2 = SeisChannel(fs = fs1, gain = g1, name = n1, id = id, t = t2, x = vcat(seg_2, seg_3))\nC = (s1 * s2)[1]\n@test length(C.x) == 300\n@test C.x == vcat(seg_1, seg_2, seg_3)\n@test C.t == t_in\n\nprintstyled(stdout,\"    SeisData * SeisChannel ==> SeisData\\n\", color=:light_green)\n(S,T) = mktestseis()\nmerge!(S,T)\nC = prandSC(false)\nD = prandSC(false)\nU = merge(S,C)\nU = sort(U)\nV = merge(C,S)\nV = sort(V)\n@test U == V\nU = merge(C,D)\n\n(S,T) = mktestseis()\nA = deepcopy(S[5])\nB = deepcopy(T[4])\nT*=S[1]\nsizetest(T, 5)\n\nprintstyled(stdout,\"    SeisData merge tests\\n\", color=:light_green)\nprintstyled(stdout,\"      one common channel (fast, no gaps)\\n\", color=:light_green)\n(S,T) = mktestseis()\nt = t_expand(S.t[4], S.fs[4])\nT.t[3][1,2] = t[end] + t[end]-t[end-1]\nmerge!(T, S[4])\n\nprintstyled(stdout,\"      one common channel (slow, has gaps)\\n\", color=:light_green)\n(S,T) = mktestseis()\nA = deepcopy(S[5])\nB = deepcopy(T[4])\nT*=S[1]\nsizetest(T, 5)\n\nT = sort(T)\nS*=T[2]\nsizetest(S, 5)\ni = findid(A, S)\n@test \u2248(S.t[i][2,1], 1+length(A.x))\n@test \u2248(S.t[i][2,2], (5-1/S.fs[i])*s\u03bc)\n\nprintstyled(stdout,\"      common channels and \\\"splat\\\" notation (mseis!)\\n\", color=:light_green)\n(S,T) = mktestseis()\nU = merge(S,T)\nsizetest(U, 7)\n\nV = SeisData(S,T)\nmerge!(V)\n@test U == V\n\nmseis!(S,T)\n@test S == V\n\nprintstyled(stdout,\"      mseis! with Types from SeisIO.Quake\\n\", color=:light_green)\nS = randSeisData()\nEv = randSeisEvent(2)\nC = ungap(deepcopy(Ev.data[1]))\nEv.data[1] = deepcopy(C)\nC.t[1,2] += 1000000\nC.x = randn(eltype(C.x), length(C.x))\nC.az = 0.0\nC.baz = 0.0\nC.dist = 0.0\nEv.data[2] = deepcopy(C)\nmseis!(S,   randSeisChannel(),\n            convert(EventChannel, randSeisChannel()),\n            rand(Float64, 23),  # should warn\n            convert(EventTraceData, randSeisData()),\n            Ev,\n            randSeisEvent())\n\nprintstyled(stdout,\"      two independent channels ==> same as \\\"+\\\"\\n\", color=:light_green)\n(S,T) = mktestseis()\nU = S[1] * T[2]\nsizetest(U, 2)\n@test U == S[1]+T[2]\n\nprintstyled(stdout,\"      two identical channel IDs\\n\", color=:light_green)\nU = S[4] * T[3]\n@test typeof(U)==SeisData\n@test U.id[1]==S.id[4]\n@test U.id[1]==T.id[3]\n\nprintstyled(stdout,\"    pull\\n\", color=:light_green)\nux = Float64.(randperm(800))\nC = pull(S,4)\nC.x = ux[1:600]\n@test C.name==\"Longmire\"\n@test S.n==4\n@test length(findall(S.name.==\"Longmire\"))==0\n\nprintstyled(stdout,\"      notes still faithfully track names in modified objects\\n\", color=:light_green)\nstr1 = \"ADGJALMGFLSFMGSLMFLSeptember Lobe sucks\"\nstr2 = \"HIGH SNR ON THIS CHANNEL\"\nnote!(S,str2)\nnote!(S,str1)\n@test (findall([maximum([occursin(str1, S.notes[i][j]) for j=1:length(S.notes[i])]) for i = 1:S.n]) == [4])\n@test (length(findall([maximum([occursin(str2, S.notes[i][j]) for j = 1:length(S.notes[i])]) for i = 1:S.n]))==S.n)\n\n# merge test: when we merge, does each field have exactly 7 entries?\nprintstyled(stdout,\"      merge! after pull doesn't affect pulled channel\\n\", color=:light_green)\nT.x[3] = ux[601:800]\nmerge!(S,T)\nn_targ = 7\n@test \u2248(S.n, n_targ)\n@test \u2248(maximum([length(getfield(S,i)) for i in datafields]), n_targ)\n@test \u2248(minimum([length(getfield(S,i)) for i in datafields]), n_targ)\n@test any(maximum([C.x.==i for i in S.x[1]])) == false\n\n# Old: (Test Passed, 1.42978256,  154864097, 0.038663895, Base.GC_Diff(154864097, 109, 0, 1845386, 4165, 0,  38663895, 7, 0))\n# New: (Test Passed, 1.263168574, 128490661, 0.108295874, Base.GC_Diff(128490661,  81, 0, 1324714, 3857, 0, 108295874, 6, 1))\n\nprintstyled(stdout,\"    purge!\\n\", color=:light_green)\n(S,T) = mktestseis()\nS.t[5] = Array{Int64,2}(undef,0,2)\nS.x[5] = Array{Float32,1}(undef,0)\nU = purge(S)\npurge!(S)\n@test S == U\n@test S.n == 4\n\nprintstyled(stdout,\"    issue 29\\n\", color=:light_green)\nS = read_data(path * \"/SampleFiles/SEED/CIRIO__BHE___2017101.mseed\")\nmerge!(S)\nC2 = S[1]\nungap!(C2)\n\n#=  In Issue #29, ObsPy handled the merge without repeated data, so this is the\n    basis for comparison\n=#\nio = open(path * \"/SampleFiles/SEED/obspy.dat\", \"r\")\nX2 = Array{Int32,1}(undef, length(C2.x))\nread!(io, X2)\nclose(io)\nX = map(Float32, X2)\n@test isapprox(C2.x, X)\n\nprintstyled(stdout,\"      merge_ext! on SeisData == nothing\\n\", color=:light_green)\nC = randSeisChannel(s=true)\nS = randSeisData(3, s=1.0)\nS.id = [C.id, C.id, C.id]\nS.fs = [C.fs, C.fs, C.fs]\nU = deepcopy(S)\n\u03a9 = 3\nrest = [1,2]\n@test SeisIO.merge_ext!(S, \u03a9, rest) == nothing\n@test S==U\n", "meta": {"hexsha": "2359c16a60212392242e491872d134bead9e2cb0", "size": 28027, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Processing/test_merge.jl", "max_stars_repo_name": "tclements/SeisIO.jl", "max_stars_repo_head_hexsha": "2bc58ef028f0b811b02340239c940fdec1288339", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-12-16T17:10:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-02T15:07:05.000Z", "max_issues_repo_path": "test/Processing/test_merge.jl", "max_issues_repo_name": "tclements/SeisIO.jl", "max_issues_repo_head_hexsha": "2bc58ef028f0b811b02340239c940fdec1288339", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/Processing/test_merge.jl", "max_forks_repo_name": "tclements/SeisIO.jl", "max_forks_repo_head_hexsha": "2bc58ef028f0b811b02340239c940fdec1288339", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.0033185841, "max_line_length": 125, "alphanum_fraction": 0.5786562957, "num_tokens": 11077, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39981164073979497, "lm_q2_score": 0.12940274502147983, "lm_q1q2_score": 0.051736723803271184}}
{"text": "#=\nInstall script for the required packages\n=#\nusing Pkg\n\nprintln(\"Installing necessary packages. This may take some time.\")\nPkg.add(\"DifferentialEquations\")\nPkg.add(\"DynamicalSystems\")\nPkg.add(\"ChaosTools\")\nPkg.add(\"Plots\")\nPkg.add(\"Pyplot\")\nPkg.add(\"PlotlyJS\")\nPkg.add(\"Images\")\nPkg.add(\"ImageMagick\")\nPkg.add(\"StatsBase\")\n", "meta": {"hexsha": "82907bbe7a4e41155b1494bd5531a60261b2f587", "size": 325, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "dependencies.jl", "max_stars_repo_name": "csimal/chaos-and-fractals", "max_stars_repo_head_hexsha": "4b189b0fde62d480f1f2e750b976de54bd9d2297", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dependencies.jl", "max_issues_repo_name": "csimal/chaos-and-fractals", "max_issues_repo_head_hexsha": "4b189b0fde62d480f1f2e750b976de54bd9d2297", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dependencies.jl", "max_forks_repo_name": "csimal/chaos-and-fractals", "max_forks_repo_head_hexsha": "4b189b0fde62d480f1f2e750b976de54bd9d2297", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.3125, "max_line_length": 66, "alphanum_fraction": 0.7446153846, "num_tokens": 96, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3998116407397951, "lm_q2_score": 0.1294027198405294, "lm_q1q2_score": 0.05173671373563409}}
{"text": "# This file was generated, do not modify it. # hide\nusing MLJ, StatsBase, Random, PyPlot, CategoricalArrays, PrettyPrinting, DataFrames\nX, y = @load_crabs\nX = DataFrame(X)\n@show size(X)\n@show y[1:3]\nfirst(X, 3) |> pretty", "meta": {"hexsha": "6ca794040e5f7e36ec55dd2410a72928c0b69761", "size": 220, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "assets/pages/end-to-end/crabs-xgb/code/ex1.jl", "max_stars_repo_name": "vdayanand/MLJTutorials", "max_stars_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "assets/pages/end-to-end/crabs-xgb/code/ex1.jl", "max_issues_repo_name": "vdayanand/MLJTutorials", "max_issues_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assets/pages/end-to-end/crabs-xgb/code/ex1.jl", "max_forks_repo_name": "vdayanand/MLJTutorials", "max_forks_repo_head_hexsha": "702effdcfadb44fb7c4fe61355f0c481595c95bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4285714286, "max_line_length": 83, "alphanum_fraction": 0.7227272727, "num_tokens": 69, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.46490157137338844, "lm_q2_score": 0.11124121534690626, "lm_q1q2_score": 0.051716215816262213}}
{"text": "### A Pluto.jl notebook ###\n# v0.15.1\n\nusing Markdown\nusing InteractiveUtils\n\n# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).\nmacro bind(def, element)\n    quote\n        local el = $(esc(element))\n        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing\n        el\n    end\nend\n\n# \u2554\u2550\u2561 78e82534-3101-11eb-2eda-43b2de63543b\nusing Pkg, DrWatson\n\n# \u2554\u2550\u2561 7fcdc6c2-3101-11eb-306d-b32a138fa475\nbegin\n\t#@quickactivate \"SR2StanPluto\"\n\tusing StatisticalRethinking\n\tusing PlutoUI\nend\n\n# \u2554\u2550\u2561 69c4c56a-3101-11eb-0c7c-8372134bc6a3\nmd\"## Intro-pluto-10.jl\"\n\n# \u2554\u2550\u2561 ad970090-3102-11eb-2aa8-6122b50d6069\n@bind x Slider(1:20, default=3)\n\n# \u2554\u2550\u2561 964f8476-3101-11eb-168e-438a10661092\ns1 = s2 = [sqrt(i) for i in 1:x]\n\n# \u2554\u2550\u2561 0aaeaef0-318e-11eb-289b-978a9a86418b\nmd\"<details><summary>CLICK ME</summary>\n<p>\n\n#### yes, even hidden code blocks!\n\n```julia\nText('hello world!')\n```\n\n</p>\n</details>\"\n\n# \u2554\u2550\u2561 48625f5c-3103-11eb-3d51-4b70ff04ba9b\nmd\"## End of intro-pluto-10.jl\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u250069c4c56a-3101-11eb-0c7c-8372134bc6a3\n# \u2560\u255078e82534-3101-11eb-2eda-43b2de63543b\n# \u2560\u25507fcdc6c2-3101-11eb-306d-b32a138fa475\n# \u2560\u2550ad970090-3102-11eb-2aa8-6122b50d6069\n# \u2560\u2550964f8476-3101-11eb-168e-438a10661092\n# \u2560\u25500aaeaef0-318e-11eb-289b-978a9a86418b\n# \u255f\u250048625f5c-3103-11eb-3d51-4b70ff04ba9b\n", "meta": {"hexsha": "5ca9094a0d29d2613080f5223195a47633090a8c", "size": 1426, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/intros/intro-pluto/intro-pluto-10.jl", "max_stars_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_stars_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-12-17T01:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T00:23:37.000Z", "max_issues_repo_path": "notebooks/intros/intro-pluto/intro-pluto-10.jl", "max_issues_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_issues_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "notebooks/intros/intro-pluto/intro-pluto-10.jl", "max_forks_repo_name": "StatisticalRethinkingJulia/SR2StanPluto.jl", "max_forks_repo_head_hexsha": "6eea864f4ca098320d7bc295c6254304c86d7d19", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-21T21:01:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-21T21:01:50.000Z", "avg_line_length": 24.1694915254, "max_line_length": 195, "alphanum_fraction": 0.7089761571, "num_tokens": 641, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10669059962643358, "lm_q1q2_score": 0.051678801638613894}}
{"text": "#=\nP05 (*) Reverse a list.\n=#\n\nfunction my_reverse(coll)\n    # With built-in `reverse` function\n    reverse(coll)\nend\n\nfunction reverse_with_reversed(coll, reversed)\n    # With tail recursion\n    if isempty(coll)\n        reversed\n    else\n        insert!(reversed, 1, coll[1])\n        reverse_with_reversed(coll[2:end], reversed)\n    end\nend\n\nfunction my_reverse_2(coll)\n    # Reversed collection initially empty\n    reverse_with_reversed(coll, [])\nend\n\n@assert my_reverse([]) == []\n@assert my_reverse([:a, :b, :c, :d]) == [:d, :c, :b, :a]\n@assert my_reverse(Vector{Symbol}[[:a, :b], [:c, :d]]) == Vector{Symbol}[[:c, :d], [:a, :b]]\n\n@assert my_reverse_2([]) == []\n@assert my_reverse_2([:a, :b, :c, :d]) == [:d, :c, :b, :a]\nmy_reverse_2(Vector{Symbol}[[:a, :b], [:c, :d]])\n@assert my_reverse_2(Vector{Symbol}[[:a, :b], [:c, :d]]) == Vector{Symbol}[[:c, :d], [:a, :b]]\n\nprintln(\"Tests passed: JL-05.jl\")\n", "meta": {"hexsha": "d0d6081b14c574c353198e84630c4f8dc8a06312", "size": 903, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "JL-05.jl", "max_stars_repo_name": "microamp/jl-99", "max_stars_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "JL-05.jl", "max_issues_repo_name": "microamp/jl-99", "max_issues_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "JL-05.jl", "max_forks_repo_name": "microamp/jl-99", "max_forks_repo_head_hexsha": "5d49a7e1617394e6cbc06f1a94fe6230b3025d73", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8, "max_line_length": 94, "alphanum_fraction": 0.5991140642, "num_tokens": 300, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44552953503957277, "lm_q2_score": 0.11596071825396677, "lm_q1q2_score": 0.05166392488654471}}
{"text": "# <img src=\"https://raw.githubusercontent.com/UCLAMAEThreads/MAE103/main/notebook/logo_UCLA_blue_boxed.svg\" alt=\"ucla logo\" width=\"150\" align=\"left\"/>\n\n# ### **MAE 103 - Elementary Fluid Mechanics**\n# <hr />\n", "meta": {"hexsha": "209b379ff8fc490d2789877f2370a8153030383c", "size": 208, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/literate/header.jl", "max_stars_repo_name": "jdeldre/MAE103", "max_stars_repo_head_hexsha": "1417d573636e7e88fc0dce795073b8f0a82634f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/literate/header.jl", "max_issues_repo_name": "jdeldre/MAE103", "max_issues_repo_head_hexsha": "1417d573636e7e88fc0dce795073b8f0a82634f2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-04-03T22:18:15.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-03T22:18:15.000Z", "max_forks_repo_path": "test/literate/header.jl", "max_forks_repo_name": "jdeldre/MAE103", "max_forks_repo_head_hexsha": "1417d573636e7e88fc0dce795073b8f0a82634f2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.6, "max_line_length": 150, "alphanum_fraction": 0.7163461538, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.1919327864472368, "lm_q2_score": 0.26894141551050293, "lm_q1q2_score": 0.05161867526999494}}
{"text": "export  execute,\n        Environment,\n        Action, Action_NoOp,\n        Percept,\n        Agent,\n        AgentProgram,\n            TableDrivenAgentProgram,\n            ReflexVacuumAgentProgram,\n            SimpleReflexAgentProgram,\n            ModelBasedReflexAgentProgram,\n        State,\n        Rule\n\n\"\"\"\nAn agent perceives an *environment* through sensors and acts with actuators.\n\nSensors provide agent the *percepts*, based on which the agent delivers\n*actions*\n\nPg. 35, AIMA 3ed\n\"\"\"\nabstract type Environment end\n\n\"\"\"\n*AgentProgram* is an internal representation of an agent function with a \nconcrete implementation. While *agent function* can be abstract *AgentProgram*\nprovides clear direction to the implementation.\n\nPg. 35, AIMA 3ed\n\"\"\"\nabstract type AgentProgram end\n\n\"\"\"\n*Action* is an agent's response to the environment through actuators.\n\nAlthough the representation of a string may suffice for more most sample\nprograms, an abstract type is introduced to emphasize the need for\nproviding a concrete type based on the environment or agent at hand.\n\nIn most problems we try to solve, the *Action* may be driven by the choice\nof the *Environment*\n\"\"\"\nabstract type Action end\n\n\"\"\"\n*NoOp* is a directive where the agent does not take any further action.\n\"\"\"\nstruct NoOpActionType <: Action\n  val::Symbol\n  NoOpActionType() = new(Symbol(\"NoOp\"))\nend\n\nconst Action_NoOp = NoOpActionType()\n\n\"\"\"\n*Percept* is an input to the *Agent* from environment through sensors.\n\nAlthough the representation of a Tuple may suffice for more most sample\nprograms, an abstract type is introduced to emphasize the need for\nproviding a concrete type based on the environment or agent at hand.\n\nIn most problems we try to solve, the *Percept* may be driven by the choice\nof the *Environment*\n\"\"\"\nabstract type Percept end\n\n\"\"\"\nGiven a *Percept* returns an *Action* apt for the agent.\n\nDepending on the agent program the function may respond with different *Action*\nevaluation strategies.\n\"\"\"\nexecute(ap::AP, p::Percept) where {AP <: AgentProgram} = error(E_ABSTRACT)\n\n\"\"\"\n*TableDrivenAgentProgram* is a simple model of an agent program where all\npercept sequences are well-known ahead in time and can be organized as a\nmapping from percepts to action.\n\nLook at the corresponding execute method for *Action* evaluation strategy.\n\nThe implementation must have the following methods:\n\n1. append - percept to the list of percepts seen my the AgentProgram\n2. lookup - the percepts in the tables of the AgentProgram\n\nFig 2.7 Pg. 47, AIMA 3ed\n\"\"\"\nabstract type TableDrivenAgentProgram <: AgentProgram end\n\nfunction execute(ap::TableDrivenAgentProgram, percept::Percept)\n    append(ap.percepts, percept)\n    action = lookup(ap.table, ap.percepts)\n    return action\nend\n\n\"\"\"\n*Rule* is an abstract representation of a framework that associates a *State*\ncondition to the appropriate action.\n\nDefinition of a condition can be implementation dependent.\n\"\"\"\nabstract type Rule end\n\n\"\"\"\n*State* is an internal evaluated position of the Environment. In the context\nof the problem the *Environment* can be one of the stated states. Any input or'\naction may lead to change in *Environment* state.\n\"\"\"\nabstract type State end\n\n\"\"\"\n*SimpleReflexAgentProgram* is a simple *Percept* to *Action* matching state\nbased rules.\n\nIt does not depend on the historical percept data.\n\nIt needs to implement two methods\n\n1. interpret_input\n2. rule_match\n\nfor all the concrete implementations.\n\n3. rules - Will provide all the rules associated with the\nAgentProgram.\n\nFig 2.10 Pg. 49, AIMA 3ed\n\"\"\"\nabstract type SimpleReflexAgentProgram <: AgentProgram end\n\nfunction execute(ap::SimpleReflexAgentProgram, percept::Percept)\n    state = interpret_input(percept);\n    rule = rule_match(state, ap.rules);\n    action = rule.action;\n    return action;\nend\n\ninterpret_input(percept) = error(E_ABSTRACT)\nrule_match(state, rules) = error(E_ABSTRACT)\n\n\"\"\"\n*ModelBasedReflexAgentProgram* uses a model which is close to the\nunderstanding of the world.\n\nThe *AgentProgram* updates the states based on the *Percepts* received.\n\"\"\"\nabstract type ModelBasedReflexAgentProgram <: AgentProgram end\n\nfunction execute(ap::ModelBasedReflexAgentProgram, percept::Percept)\n    ap.state = update_state(ap.state, ap.action, percept, ap.model)\n    rule = rule_match(ap.state, ap.rules)\n    ap.action = rule.action\n    return ap.action\nend\n\nupdate_state(state, action, percept, model) = error(E_ABSTRACT)\n\n\"\"\"\n*SimpleProblemSolvingAgentProgram* formulates the goal then the problem.\nSearches for the sequence of *Actions* that would solve the problem.\n\npg.67 AIMA 3e\n\"\"\"\nabstract type SimpleProblemSolvingAgentProgram <: AgentProgram end\n\nfunction execute(ap::SimpleProblemSolvingAgentProgram, percept::Percept)\n    #persistent: seq, an action sequence, initially empty\n    #state, some description of the current world state\n    #goal , a goal, initially null\n    #problem, a problem formulation\n    ap.state = update_state(ap.state, percept)\n    if isempty(ap.seq)\n        ap.goal = formulate_goal(ap.state)\n        ap.problem = formulate_problem(ap.state, ap.goal)\n        ap.seq = search(ap.problem)\n        if ap.seq == failure\n            return nothing\n        end\n    end\n    action = first(ap.seq)\n    ap.seq = rest(ap.seq)\n    return action\nend\n\nupdate_state(state, percept) = error(E_ABSTRACT)\nformulate_goal(state) = error(E_ABSTRACT)\nformulate_problem(state, goal) = error(E_ABSTRACT)\n\n\"\"\"\nAgent perceives *Environment* through sensors and acts based on actuators.\n\nWhile this code may not be there in the book this is a general outline one can\ngather from the description in the book.\n\npg. 35 Fig.2.1 AIMA 3e\n\"\"\"\nstruct Agent{AP}\n    program::AP\n    Agent{AP}(ap::AP) where{AP <: AgentProgram} = new(ap)\nend\n\nexecute(a::Agent, percept::Percept) = execute(a.program, percept)\n", "meta": {"hexsha": "6d6992e769f29a153ef45452417d9d3825346ef5", "size": 5822, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/agents.jl", "max_stars_repo_name": "JuliaTagBot/AIMACore.jl", "max_stars_repo_head_hexsha": "3067f36840e500dcb6b62dda8fd635c73f8218f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-02-20T22:20:32.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-01T18:49:30.000Z", "max_issues_repo_path": "src/agents.jl", "max_issues_repo_name": "JuliaTagBot/AIMACore.jl", "max_issues_repo_head_hexsha": "3067f36840e500dcb6b62dda8fd635c73f8218f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2018-02-16T02:32:46.000Z", "max_issues_repo_issues_event_max_datetime": "2018-04-08T08:32:04.000Z", "max_forks_repo_path": "src/agents.jl", "max_forks_repo_name": "JuliaTagBot/AIMACore.jl", "max_forks_repo_head_hexsha": "3067f36840e500dcb6b62dda8fd635c73f8218f1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2018-02-13T15:45:23.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-08T11:49:49.000Z", "avg_line_length": 28.5392156863, "max_line_length": 79, "alphanum_fraction": 0.7447612504, "num_tokens": 1356, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3775406828054583, "lm_q2_score": 0.13660840232900245, "lm_q1q2_score": 0.05157522949225435}}
{"text": "using MaxwellGuidedMode\nusing Test\n\n@testset \"MaxwellGuidedMode.jl\" begin\n    # Write your tests here.\nend\n", "meta": {"hexsha": "809951d8c53c2a829412b8b6744245d85d5dd9b7", "size": 107, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "wsshin/MaxwellGuide.jl", "max_stars_repo_head_hexsha": "47dea497bd7ff834b2a11e96ec784bdb28fefe0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2021-09-12T22:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T01:09:55.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "wsshin/MaxwellGuide.jl", "max_issues_repo_head_hexsha": "47dea497bd7ff834b2a11e96ec784bdb28fefe0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-08-12T18:46:15.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-13T22:19:05.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "wsshin/MaxwellGuide.jl", "max_forks_repo_head_hexsha": "47dea497bd7ff834b2a11e96ec784bdb28fefe0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-05T13:24:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-05T13:24:40.000Z", "avg_line_length": 15.2857142857, "max_line_length": 37, "alphanum_fraction": 0.7757009346, "num_tokens": 31, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.47657965106367595, "lm_q2_score": 0.10818895312434518, "lm_q1q2_score": 0.051560653528944816}}
{"text": "import SparseArrays\n\n\"\"\"\n    abstract type AbstractIndexing end\n\nIndexing to be used for storing the row and column indices of\n`MutableSparseMatrixCSC`. See [`ZeroBasedIndexing`](@ref) and\n[`OneBasedIndexing`](@ref).\n\"\"\"\nabstract type AbstractIndexing end\n\n\"\"\"\n    struct ZeroBasedIndexing <: AbstractIndexing end\n\nZero-based indexing: the `i`th row or column has index `i - 1`. This is useful\nwhen the vectors of row and column indices need to be communicated to a\nlibrary using zero-based indexing such as C libraries.\n\"\"\"\nstruct ZeroBasedIndexing <: AbstractIndexing end\n\n\"\"\"\n    struct ZeroBasedIndexing <: AbstractIndexing end\n\nOne-based indexing: the `i`th row or column has index `i`. This enables an\nallocation-free conversion of [`MutableSparseMatrixCSC`](@ref) to\n`SparseArrays.SparseMatrixCSC`.\n\"\"\"\nstruct OneBasedIndexing <: AbstractIndexing end\n\n_first_index(::ZeroBasedIndexing) = 0\n_first_index(::OneBasedIndexing) = 1\n\n_shift(x, ::T, ::T) where {T<:AbstractIndexing} = x\n_shift(x::Integer, ::ZeroBasedIndexing, ::OneBasedIndexing) = x + 1\n_shift(x::Integer, ::OneBasedIndexing, ::ZeroBasedIndexing) = x - 1\n_shift(x::Array{<:Integer}, ::ZeroBasedIndexing, ::OneBasedIndexing) = x .+ 1\n\n\"\"\"\n    mutable struct MutableSparseMatrixCSC{Tv,Ti<:Integer,I<:AbstractIndexing}\n        indexing::I\n        m::Int\n        n::Int\n        colptr::Vector{Ti}\n        rowval::Vector{Ti}\n        nzval::Vector{Tv}\n    end\n\nMatrix type loading sparse matrices in the Compressed Sparse Column format.\nThe indexing used is `indexing`, see [`AbstractIndexing`](@ref). The other\nfields have the same meaning than for `SparseArrays.SparseMatrixCSC` except\nthat the indexing is different unless `indexing` is `OneBasedIndexing`.\n\nThe matrix is loaded in 5 steps:\n1) `MOI.empty!` is called.\n2) `MOI.Utilities.add_column` and `MOI.Utilities.allocate_terms` are called in\n   any order.\n3) `MOI.Utilities.set_number_of_rows` is called.\n4) `MOI.Utilities.load_terms` is called for each affine function.\n5) `MOI.Utilities.final_touch` is called.\n\"\"\"\nmutable struct MutableSparseMatrixCSC{Tv,Ti<:Integer,I<:AbstractIndexing}\n    indexing::I\n    m::Int # Number of rows\n    n::Int # Number of columns\n    colptr::Vector{Ti}\n    rowval::Vector{Ti}\n    nzval::Vector{Tv}\n    function MutableSparseMatrixCSC{Tv,Ti,I}() where {Tv,Ti<:Integer,I}\n        return new{Tv,Ti,I}(I(), 0, 0, [zero(Ti)], Ti[], Tv[])\n    end\nend\n\nfunction SparseArrays.nzrange(A::MutableSparseMatrixCSC, col)\n    start = _shift(A.colptr[col], A.indexing, OneBasedIndexing())\n    stop = _shift(A.colptr[col+1], A.indexing, ZeroBasedIndexing())\n    return start:stop\nend\n\nfunction MOI.empty!(A::MutableSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}\n    A.m = 0\n    A.n = 0\n    resize!(A.colptr, 1)\n    A.colptr[1] = zero(Ti)\n    empty!(A.rowval)\n    empty!(A.nzval)\n    return\nend\n\nfunction add_column(A::MutableSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}\n    A.n += 1\n    push!(A.colptr, zero(Ti))\n    return\nend\n\nfunction add_columns(A::MutableSparseMatrixCSC{Tv,Ti}, n) where {Tv,Ti}\n    A.n += n\n    for _ in 1:n\n        push!(A.colptr, zero(Ti))\n    end\n    return\nend\n\nfunction set_number_of_rows(A::MutableSparseMatrixCSC, num_rows)\n    A.m = num_rows\n    for i in 3:length(A.colptr)\n        A.colptr[i] += A.colptr[i-1]\n    end\n    resize!(A.rowval, A.colptr[end])\n    resize!(A.nzval, A.colptr[end])\n    return\nend\n\nfunction final_touch(A::MutableSparseMatrixCSC)\n    for i in length(A.colptr):-1:2\n        A.colptr[i] = _shift(A.colptr[i-1], ZeroBasedIndexing(), A.indexing)\n    end\n    A.colptr[1] = _first_index(A.indexing)\n    return\nend\n\n_variable(term::MOI.ScalarAffineTerm) = term.variable\n_variable(term::MOI.VectorAffineTerm) = _variable(term.scalar_term)\n\nfunction allocate_terms(\n    A::MutableSparseMatrixCSC,\n    index_map,\n    func::Union{MOI.ScalarAffineFunction,MOI.VectorAffineFunction},\n)\n    for term in func.terms\n        A.colptr[index_map[_variable(term)].value+1] += 1\n    end\n    return\nend\n\n_output_index(::MOI.ScalarAffineTerm) = 1\n_output_index(term::MOI.VectorAffineTerm) = term.output_index\n\nfunction load_terms(\n    A::MutableSparseMatrixCSC,\n    index_map,\n    func::Union{MOI.ScalarAffineFunction,MOI.VectorAffineFunction},\n    offset::Int,\n)\n    new_offset = _shift(offset, OneBasedIndexing(), A.indexing)\n    for term in func.terms\n        ptr = A.colptr[index_map[_variable(term)].value] += 1\n        A.rowval[ptr] = new_offset + _output_index(term)\n        A.nzval[ptr] = MOI.coefficient(term)\n    end\n    return\nend\n\n\"\"\"\n    Base.convert(\n        ::Type{SparseMatrixCSC{Tv,Ti}},\n        A::MutableSparseMatrixCSC{Tv,Ti,I},\n    ) where {Tv,Ti,I}\n\nConverts `A` to a `SparseMatrixCSC`. Note that the field `A.nzval` is **not\ncopied** so if `A` is modified after the call of this function, it can still\naffect the value returned. Moreover, if `I` is `OneBasedIndexing`, `colptr`\nand `rowval` are not copied either, i.e., the conversion is allocation-free.\n\"\"\"\nfunction Base.convert(\n    ::Type{SparseArrays.SparseMatrixCSC{Tv,Ti}},\n    A::MutableSparseMatrixCSC{Tv,Ti},\n) where {Tv,Ti}\n    return SparseArrays.SparseMatrixCSC{Tv,Ti}(\n        A.m,\n        A.n,\n        _shift(A.colptr, A.indexing, OneBasedIndexing()),\n        _shift(A.rowval, A.indexing, OneBasedIndexing()),\n        A.nzval,\n    )\nend\n\nfunction _first_in_column(\n    A::MutableSparseMatrixCSC{Tv,Ti},\n    row::Integer,\n    col::Integer,\n) where {Tv,Ti}\n    range = SparseArrays.nzrange(A, col)\n    row = _shift(row, OneBasedIndexing(), A.indexing)\n    idx = searchsortedfirst(view(A.rowval, range), row)\n    return get(range, idx, last(range) + 1)\nend\n\nfunction extract_function(\n    A::MutableSparseMatrixCSC{T},\n    row::Integer,\n    constant::T,\n) where {T}\n    func = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm{T}[], constant)\n    for col in 1:A.n\n        idx = _first_in_column(A, row, col)\n        if idx > last(SparseArrays.nzrange(A, col))\n            continue\n        end\n        r = _shift(A.rowval[idx], A.indexing, OneBasedIndexing())\n        if r == row\n            push!(\n                func.terms,\n                MOI.ScalarAffineTerm(A.nzval[idx], MOI.VariableIndex(col)),\n            )\n        end\n    end\n    return func\nend\n\nfunction extract_function(\n    A::MutableSparseMatrixCSC{T},\n    rows::UnitRange,\n    constants::Vector{T},\n) where {T}\n    func = MOI.VectorAffineFunction(MOI.VectorAffineTerm{T}[], constants)\n    if isempty(rows)\n        return func\n    end\n    idx = [_first_in_column(A, first(rows), col) for col in 1:A.n]\n    for output_index in eachindex(rows)\n        for col in 1:A.n\n            if idx[col] > last(SparseArrays.nzrange(A, col))\n                continue\n            end\n            row = _shift(A.rowval[idx[col]], A.indexing, OneBasedIndexing())\n            if row != rows[output_index]\n                continue\n            end\n            push!(\n                func.terms,\n                MOI.VectorAffineTerm(\n                    output_index,\n                    MOI.ScalarAffineTerm(\n                        A.nzval[idx[col]],\n                        MOI.VariableIndex(col),\n                    ),\n                ),\n            )\n            idx[col] += 1\n        end\n    end\n    return func\nend\n", "meta": {"hexsha": "88f5f600ac05537e7d0e674373484ffa02d72508", "size": 7206, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utilities/sparse_matrix.jl", "max_stars_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_stars_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 132, "max_stars_repo_stars_event_min_datetime": "2020-06-20T00:45:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T22:06:34.000Z", "max_issues_repo_path": "src/Utilities/sparse_matrix.jl", "max_issues_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_issues_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 460, "max_issues_repo_issues_event_min_datetime": "2020-06-08T14:12:55.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:03:39.000Z", "max_forks_repo_path": "src/Utilities/sparse_matrix.jl", "max_forks_repo_name": "manuelbb-upb/MathOptInterface.jl", "max_forks_repo_head_hexsha": "54b6bcb723acb2b2d79584e2f27ea56fd4c7777c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 28, "max_forks_repo_forks_event_min_datetime": "2020-06-08T01:45:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T10:36:25.000Z", "avg_line_length": 29.5327868852, "max_line_length": 78, "alphanum_fraction": 0.6550097141, "num_tokens": 2040, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4263215925474903, "lm_q2_score": 0.12085324357297283, "lm_q1q2_score": 0.05152234726455952}}
{"text": "#=\nStack\n=#\n\nusing SimpleDataStructures\n\nss = SimpleStack{Int}()\n\npush!(ss, 1)\npush!(ss, 2)\npush!(ss, 3)\npop!(ss)\npop!(ss)\npop!(ss)\npop!(ss)\n", "meta": {"hexsha": "a7fdd44aa0e672d40418418b9ffee63e649b3cab", "size": 141, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/stack.jl", "max_stars_repo_name": "harryscholes/SimpleDataStructures.jl", "max_stars_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/stack.jl", "max_issues_repo_name": "harryscholes/SimpleDataStructures.jl", "max_issues_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/stack.jl", "max_forks_repo_name": "harryscholes/SimpleDataStructures.jl", "max_forks_repo_head_hexsha": "bce776a723aa1026c37246b128203afab2d2424c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.8125, "max_line_length": 26, "alphanum_fraction": 0.6170212766, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.42632159254749036, "lm_q2_score": 0.12085322774082716, "lm_q1q2_score": 0.05152234051497397}}
{"text": "#--------------------------------------------------------------------\n# Spacetime Discretization methods in Julia\n# Soham 01-2018\n#--------------------------------------------------------------------\n\nstruct Patch\n    loc::Array{Int,1}\n    value::Array{Float64,2}\nend\n\nstruct Boundary\n    kind::Int\n    value::Array{Float64,1}\nend\n", "meta": {"hexsha": "a73aeec4f2cc005f8a888a179f1eb9fde9a0a36f", "size": 331, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Types.jl", "max_stars_repo_name": "mukherjeesoham/ScalarWave.jl", "max_stars_repo_head_hexsha": "4058b2738159036cfb9d98ad461d49f9e0a8609c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Types.jl", "max_issues_repo_name": "mukherjeesoham/ScalarWave.jl", "max_issues_repo_head_hexsha": "4058b2738159036cfb9d98ad461d49f9e0a8609c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Types.jl", "max_forks_repo_name": "mukherjeesoham/ScalarWave.jl", "max_forks_repo_head_hexsha": "4058b2738159036cfb9d98ad461d49f9e0a8609c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.0666666667, "max_line_length": 69, "alphanum_fraction": 0.4048338369, "num_tokens": 68, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.40356685373537454, "lm_q2_score": 0.12765262532179067, "lm_q1q2_score": 0.051516368372175665}}
{"text": "#---------------------------------------------------------------------#\n#This function plots the solution\n#Written by F.X. Giraldo on April 19 2019\n#           Department of Applied Mathematics\n#           Naval Postgraduate School\n#           Monterey; CA 93943-5216\n#---------------------------------------------------------------------#\nfunction plot_solution(q,coord,space_method,time)\n\n    #Plot Solution\n    text=string(space_method,\", time= \",\"$time\")\n    plot_handle=plot(coord,q,xlabel=\"x\",ylabel=\"q(x,t)\",legend=true,lw=3,label=[space_method],title=text)\n    display(plot_handle)\n\nend\n", "meta": {"hexsha": "d47e52d16386d66f07b39ffb84ec61f78ac03edc", "size": 595, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Projects/Project_02_1D_Wave/For_Students/julia/plot_solution.jl", "max_stars_repo_name": "fxgiraldo/Element-based-Galerkin-Methods", "max_stars_repo_head_hexsha": "3e7cd28b5d6000a95b00710f15a282b9320b0007", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 31, "max_stars_repo_stars_event_min_datetime": "2019-12-28T06:19:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T14:40:33.000Z", "max_issues_repo_path": "Projects/Project_02_1D_Wave/For_Instructors/julia/RHS_Vector_approach/plot_solution.jl", "max_issues_repo_name": "fxgiraldo/Element-based-Galerkin-Methods", "max_issues_repo_head_hexsha": "3e7cd28b5d6000a95b00710f15a282b9320b0007", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-01-29T19:12:22.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T21:53:41.000Z", "max_forks_repo_path": "Projects/Project_02_1D_Wave/For_Instructors/julia/RHS_Vector_approach/plot_solution.jl", "max_forks_repo_name": "fxgiraldo/Element-based-Galerkin-Methods", "max_forks_repo_head_hexsha": "3e7cd28b5d6000a95b00710f15a282b9320b0007", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2020-09-10T21:35:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T06:53:34.000Z", "avg_line_length": 37.1875, "max_line_length": 105, "alphanum_fraction": 0.5260504202, "num_tokens": 124, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.40356683938849797, "lm_q2_score": 0.12765262034372557, "lm_q1q2_score": 0.05151636453177721}}
{"text": "#' # MNIST: Knet.jl CNN\ninfo(\"MNIST: Knet.jl CNN example\") #jl-only\n\n#' In this tutorial we will adapt the\n#' [MNIST example](http://denizyuret.github.io/Knet.jl/latest/tutorial.html#Convolutional-neural-network-1)\n#' from [Knet.jl](https://github.com/denizyuret/Knet.jl)\n#' to utilize a custom augmentation pipeline.\n#' In order to showcase the effect that image augmentation can\n#' have on a neural network's ability to generalize, we will\n#' limit the training set to just the first 500 images (of the\n#' available 60,000!). For more information on the dataset see\n#' [^MNIST1998].\n\n#md #' !!! note\n#md #'\n#md #'     This tutorial is also available as a\n#md #'     [Juypter](https://jupyter.org/) notebook. You can\n#md #'     find a link to the Juypter version of this tutorial\n#md #'     in the top right corner of this page.\n\n#' ## Preparing the MNIST dataset\ninfo(\"Preparing the MNIST dataset\") #jl-only\n\n#' In order to access, prepare, and visualize the MNIST images we\n#' employ the help of three additional Julia packages. In the\n#' interest of time and space we will not go into great detail\n#' about their functionality. Feel free to click on their\n#' respective names to find out more information about the\n#' utility they can provide.\n#'\n#' - [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl)\n#'   has an MNIST submodule that offers a convenience interface\n#'   to read the MNIST database.\n#'\n#' - [Images.jl](https://github.com/JuliaImages/Images.jl) will\n#'   provide us with the necessary tools to process and display\n#'   the image data in Julia / Juypter.\n#'\n#' - [MLDataUtils.jl](https://github.com/JuliaML/MLDataUtils.jl)\n#'   implements a variety of functions to convert and partition\n#'   Machine Learning datasets. This will help us prepare the\n#'   MNIST data to be used with Knet.jl.\n\nusing Images, MLDatasets, MLDataUtils, Random\nRandom.seed!(42);\n#md nothing # hide\n\n#' As you may have seen previously in the\n#' [elastic distortions tutorial](@ref elastic), the function\n#' `MNIST.traintensor` returns the MNIST training images\n#' corresponding to the given indices as a multi-dimensional\n#' array. These images are stored in the native horizontal-major\n#' memory layout as a single array. Because we specify that\n#' the `eltype` of that array should be `Float32`, all the\n#' individual values are scaled to be between `0.0` and `1.0`.\n#' Also note, how the observations are laid out along the last\n#' array dimension\n\n@show summary(MNIST.traintensor(Float32, 1:500));\n#md nothing # hide\n\n#' The corresponding label of each image is stored as an integer\n#' value between `0` and `9`. That means that if the label has\n#' the value `3`, then the corresponding image is known to be a\n#' handwritten \"3\". To show a more concrete example, the\n#' following code reveals that the first training image denotes a\n#' \"5\" and the second training image a \"0\" (etc).\n\n@show summary(MNIST.trainlabels(1:500))\nprintln(\"First eight labels: \", join(MNIST.trainlabels(1:8),\", \"))\n\n#' For Knet we will require a slightly format for the images\n#' and also the labels. More specifically, we add an additional\n#' singleton dimension of length 1 to our image array. Think of\n#' this as our single color channel (because MNIST images are gray).\n#' Additionally we will convert our labels to proper 1-based indices.\n#' This is because some functions provided by Knet expect the labels\n#' to be in this format. We will do all this by creating a little\n#' utility function that we will name `prepare_mnist`.\n\n\"\"\"\n    prepare_mnist(images, labels) -> (X, Y)\n\nChange the dimension layout x1\u00d7x2\u00d7N of the given array\n`images` to x1\u00d7x2\u00d71\u00d7N and return the result as `X`.\nThe given integer vector `labels` is transformed into\nan integer vector denoting 1-based class indices.\n\"\"\"\nfunction prepare_mnist(images, labels)\n    X = reshape(images, (28, 28, 1, :))\n    Y = convertlabel(LabelEnc.Indices{Int8}, labels, 0:9)\n    X, Y\nend\n#md nothing # hide\n\n#' With `prepare_mnist` defined, we can now use it in conjunction\n#' with the functions in the `MLDatasets.MNIST` sub-module to load\n#' and prepare our training set. Recall that for this tutorial only\n#' the first 500 images of the training set will be used.\n\ntrain_x, train_y = prepare_mnist(MNIST.traintensor(Float32, 1:500), MNIST.trainlabels(1:500))\n@show summary(train_x) summary(train_y);\n[MNIST.convert2image(train_x[:,:,1,i]) for i in 1:8]\n#md tmp = hcat(ans...) # hide\n#md save(\"mnist_knet_train.png\",repeat(tmp, inner=(4,4))) # hide\n#md nothing # hide\n\n#md #' ![training images](mnist_knet_train.png)\n\n#' Similarly, we use `MNIST.testtensor` and `MNIST.testlabels`\n#' to load the full MNIST test set. We will utilize that data to\n#' measure how well the network is able to generalize with and\n#' without augmentation.\n\ntest_x, test_y = prepare_mnist(MNIST.testtensor(Float32), MNIST.testlabels())\n@show summary(test_x) summary(test_y);\n[MNIST.convert2image(test_x[:,:,1,i]) for i in 1:8]\n#md tmp = hcat(ans...) # hide\n#md save(\"mnist_knet_test.png\",repeat(tmp, inner=(4,4))) # hide\n#md nothing # hide\n\n#md #' ![test images](mnist_knet_test.png)\n\n#' ## Defining the Network\ninfo(\"Defining the Network\") #jl-only\n\n#' With the dataset prepared, we can now define and instantiate\n#' our neural network. To keep things simple, we will use the same\n#' convolutional network as defined in the\n#' [MNIST example](http://denizyuret.github.io/Knet.jl/latest/tutorial.html#Convolutional-neural-network-1)\n#' of the Knet.jl package.\n\nusing Knet\n#md nothing # hide\n\n#' The first thing we will do is define the forward pass through\n#' the network. This will effectively outline the computation\n#' graph of the network architecture. Note how this does not\n#' define some details, such as the number of neurons per layer.\n#' We will define those later when initializing our\n#' vector of weight arrays `w`.\n\n\"\"\"\n    forward(w, x) -> a\n\nCompute the forward pass for the given minibatch `x` by using the\nneural network parameters in `w`. The resulting (unnormalized)\nactivations of the last layer are returned as `a`.\n\"\"\"\nfunction forward(w, x)\n    # conv1 (2x2 maxpool)\n    a1 = pool(relu.(conv4(w[1], x)  .+ w[2]))\n    # conv2 (2x2 maxpool)\n    a2 = pool(relu.(conv4(w[3], a1) .+ w[4]))\n    # dense1 (relu)\n    a3 = relu.(w[5] * mat(a2) .+ w[6])\n    # dense2 (identity)\n    a4 = w[7] * a3 .+ w[8]\n    return a4\nend\n#md nothing # hide\n\n#' In order to be able to train our network we need to choose a\n#' cost function. Because this is a classification problem we will\n#' use the negative log-likelihood (provided by `Knet.nll`).\n#' With the cost function defined we can the simply use the\n#' higher-order function `grad` to create a new function `costgrad`\n#' that computes us the corresponding gradients.\n\n\"\"\"\n    cost(w, x, y) -> AbstractFloat\n\nCompute the per-instance negative log-likelihood for the data\nin the minibatch `(x, y)` given the network with the current\nparameters in `w`.\n\"\"\"\ncost(w, x, y) = nll(forward(w, x), y)\ncostgrad = grad(cost)\n#md nothing # hide\n\n#' Aside from the cost function that we need for training, we\n#' would also like a more interpretable performance measurement.\n#' In this tutorial we will use \"accuracy\" for its simplicity\n#' and because we know that the class distribution for MNIST\n#' is close to uniform.\n\n\"\"\"\n    acc(w, X, Y; [batchsize]) -> Float64\n\nCompute the accuracy for the data in `(X,Y)` given the network\nwith the current parameters in `w`. The resulting value is\ncomputed by iterating over the data in minibatches of size\n`batchsize`.\n\"\"\"\nfunction acc(w, X, Y; batchsize = 100)\n    sum = 0; count = 0\n    for (x_cpu, y) in eachbatch((X, Y), maxsize = batchsize)\n        x = KnetArray{Float32}(x_cpu)\n        sum += Int(accuracy(forward(w,x), y, average = false))\n        count += length(y)\n    end\n    return sum / count\nend\n#md nothing # hide\n\n#' Before we can train or even just use our network, we need to\n#' define how we initialize `w`, which is our the vector of\n#' parameter arrays. The dimensions of these individual arrays\n#' specify the filter sizes and number of neurons.\n#' It can be helpful to compare the indices here with the indices\n#' used in our `forward` function to see which array corresponds\n#' to which computation node of our network.\n\nfunction weights(atype = KnetArray{Float32})\n    w = Array{Any}(undef, 8)\n    # conv1\n    w[1] = xavier(5,5,1,20)\n    w[2] = zeros(1,1,20,1)\n    # conv2\n    w[3] = xavier(5,5,20,50)\n    w[4] = zeros(1,1,50,1)\n    # dense1\n    w[5] = xavier(500,800)\n    w[6] = zeros(500,1)\n    # dense2\n    w[7] = xavier(10,500)\n    w[8] = zeros(10,1)\n    return map(a->convert(atype,a), w)\nend\n#md nothing # hide\n\n\n#' ## Training without Augmentation\ninfo(\"Training baseline network without augmentation\") #jl-only\n\n#' In order to get an intuition for how useful augmentation can\n#' be, we need a sensible baseline to compare to. To that end, we\n#' will first train the network we just defined using only the\n#' (unaltered) 500 training examples.\n\n#' The package\n#' [ValueHistories.jl](https://github.com/JuliaML/ValueHistories.jl)\n#' will help us record the accuracy during the training process.\n#' We will use those logs later to visualize the differences\n#' between having augmentation or no augmentation.\n\nusing ValueHistories\nusing ProgressMeter #jl-only\n\n#' To keep things simple, we will not overly optimize our\n#' training function. Thus, we will be content with using a\n#' closure. Because both, the baseline and the augmented version,\n#' will share this \"inefficiency\", we should still get a decent\n#' enough picture of their performance differences.\n\nfunction train_baseline(; epochs = 500, batchsize = 100, lr = .03)\n    w = weights()\n    log = MVHistory()\n    p = Progress(epochs, desc = \"Baseline: \") #jl-only\n    for epoch in 1:epochs\n        for (batch_x_cpu, batch_y) in eachbatch((train_x ,train_y), batchsize)\n            batch_x = KnetArray{Float32}(batch_x_cpu)\n            g = costgrad(w, batch_x, batch_y)\n            Knet.update!(w, g, lr = lr)\n        end\n\n        next!(p) #jl-only\n        if (epoch % 5) == 0\n            train = acc(w, train_x, train_y)\n            test  = acc(w, test_x,  test_y)\n            @trace log epoch train test\n            msg = \"epoch \" * lpad(epoch,4) * \": train accuracy \" * rpad(round(train, digits=3),5,\"0\") * \", test accuracy \" * rpad(round(test,digits=3),5,\"0\")\n            cancel(p, msg, :blue) #jl-only\n#md             println(msg)\n#jp             println(msg)\n        end\n    end\n    finish!(p) #jl-only\n    log\nend\n#md nothing # hide\n\n#' Aside from the accuracy, we will also keep an eye on the\n#' training time. In particular we would like to see if and how\n#' the addition of augmentation causes our training time to\n#' increase.\n\ntrain_baseline(epochs=1) # warm-up\nbaseline_log = @time train_baseline(epochs=200);\n#md nothing # hide\n\n#' As we can see, the accuracy on the training set is around a\n#' 100%, while the accuracy on the test set peaks around 90%. For\n#' a mere 500 training examples, this isn't actually that bad of\n#' a result.\n\n#' ## Integrating Augmentor\ninfo(\"Training network with augmentation\") #jl-only\n\n#' Now that we have a network architecture with a baseline to\n#' compare to, let us finally see what it takes to add Augmentor\n#' to our experiment. First, we need to include the package to\n#' our experiment.\n\nusing Augmentor\n\n#' The next step, and maybe the most human-hour consuming part of\n#' adding image augmentation to a prediction problem, is to\n#' design and select a sensible augmentation pipeline. Take a\n#' look at the [elastic distortions tutorial](@ref elastic) for\n#' an example of how to do just that.\n\n#' For this example, we already choose a quite complicated but\n#' promising augmentation pipeline for you. This pipeline was\n#' designed to yield a large variation of effects as well as to\n#' showcase how even deep pipelines are quite efficient in terms\n#' of performance.\n\npl = Reshape(28,28) |>\n     PermuteDims(2,1) |>\n     ShearX(-5:5) * ShearY(-5:5) |>\n     Rotate(-15:15) |>\n     CropSize(28,28) |>\n     Zoom(0.9:0.1:1.2) |>\n     CacheImage() |>\n     ElasticDistortion(10) |>\n     PermuteDims(2,1) |>\n     Reshape(28,28,1)\nprintln(pl) #jl-only\n\n#' Most of the used operations are quite self explanatory, but\n#' there are some details about this pipeline worth pointing out\n#' explicitly.\n\n#' 1. We use the operation [`PermuteDims`](@ref) to convert the\n#'    horizontal-major MNIST image to a julia-native\n#'    vertical-major image. The vertical-major image is then\n#'    processed and converted back to a horizontal-major array.\n#'    We mainly do this here to showcase the option, but it is\n#'    also to keep consistent with how the data is usually used\n#'    in the literature. Alternatively, one could just work with\n#'    the MNIST data in a vertical-major format all the way\n#'    through without any issue.\n\n#' 2. As counter-intuitive as it sounds, the operation\n#'    [`CacheImage`](@ref) right before\n#'    [`ElasticDistortion`](@ref) is actually used to improve\n#'    performance. If we were to omit it, then the whole pipeline\n#'    would be applied in one single pass. In this case, applying\n#'    distortions on top of affine transformations lazily is in\n#'    fact less efficient than using a temporary variable.\n\n#' With the pipeline now defined, let us quickly peek at what\n#' kind of effects we can achieve with it. In particular, lets\n#' apply the pipeline multiple times to the first training image\n#' and look at what kind of results it produces.\n\n#jp [MNIST.convert2image(reshape(augment(train_x[:,:,:,1], pl), (28, 28))) for i in 1:2, j in 1:8]\n#md [MNIST.convert2image(reshape(augment(train_x[:,:,:,1], pl), (28, 28))) for i in 1:8, j in 1:2]\n#md tmp = vcat(hcat(ans[:,1]...), hcat(ans[:,2]...)) # hide\n#md save(\"mnist_knet_aug.png\",repeat(tmp, inner=(4,4))) # hide\n#md nothing # hide\n\n#md #' ![augmented samples](mnist_knet_aug.png)\n\n#' As we can see, we can achieve a wide range of effects, from\n#' more subtle to more pronounced. The important part is that all\n#' examples are still clearly representative of the true label.\n\n#' Next, we have to adapt the function `train_baseline` to make\n#' use of our augmentation pipeline. To integrate Augmentor\n#' efficiently, there are three necessary changes we have to\n#' make.\n\n#' 1. Preallocate a buffer with the same size and element type\n#'    that each batch has.\n#'\n#'    ```\n#'    batch_x_aug = zeros(Float32, 28, 28, 1, batchsize)\n#'    ```\n\n#' 2. Add a call to [`augmentbatch!`](@ref) in the inner loop of\n#'    the batch iterator using our pipeline and buffer.\n#'\n#'    ```\n#'    augmentbatch!(batch_x_aug, batch_x_org, pl)\n#'    ```\n\n#' 3. Replace `batch_x_org` with `batch_x_aug` in the constructor\n#'    of `KnetArray`.\n#'\n#'    ```\n#'    batch_x = KnetArray{Float32}(batch_x_aug)\n#'    ```\n\n\n#' Applying these changes to our `train_baseline` function\n#' will give us something similar to the following function.\n#' Note how all the other parts of the function remain exactly\n#' the same as before.\n\nfunction train_augmented(; epochs = 500, batchsize = 100, lr = .03)\n    w = weights()\n    log = MVHistory()\n    p = Progress(epochs, desc = \"Augmented: \") #jl-only\n    batch_x_aug = zeros(Float32, size(train_x,1), size(train_x,2), 1, batchsize)\n    for epoch in 1:epochs\n        for (batch_x_cpu, batch_y) in eachbatch((train_x ,train_y), batchsize)\n            augmentbatch!(CPUThreads(), batch_x_aug, batch_x_cpu, pl)\n            batch_x = KnetArray{Float32}(batch_x_aug)\n            g = costgrad(w, batch_x, batch_y)\n            Knet.update!(w, g, lr = lr)\n        end\n\n        next!(p) #jl-only\n        if (epoch % 5) == 0\n            train = acc(w, train_x, train_y)\n            test  = acc(w, test_x,  test_y)\n            @trace log epoch train test\n            msg = \"epoch \" * lpad(epoch,4) * \": train accuracy \" * rpad(round(train, digits=3),5,\"0\") * \", test accuracy \" * rpad(round(test,digits=3),5,\"0\")\n            cancel(p, msg, :blue) #jl-only\n#md             println(msg)\n#jp             println(msg)\n        end\n    end\n    finish!(p) #jl-only\n    log\nend\n#md nothing # hide\n\n#' You may have noticed in the code above that we also pass a\n#' `CPUThreads()` as the first argument to [`augmentbatch!`](@ref).\n#' This instructs Augmentor to process the images of the batch in\n#' parallel using multi-threading. For this to work properly you\n#' will need to set the environment variable `JULIA_NUM_THREADS`\n#' to the number of threads you wish to use. You can check how\n#' many threads are used with the function `Threads.nthreads()`\n\n@show Threads.nthreads();\n#md nothing # hide\n\n#' Now that all pieces are in place, let us train our network\n#' once more. We will use the same parameters except that now\n#' instead of the original training images we will be using\n#' randomly augmented images. This will cause every epoch to be\n#' different.\n\ntrain_augmented(epochs=1) # warm-up\naugmented_log = @time train_augmented(epochs=200);\n#md nothing # hide\n\n#' As we can see, our network reaches far better results on our\n#' testset than our baseline network did. However, we can also\n#' see that the training took quite a bit longer than before.\n#' This difference generally decreases as the complexity of the\n#' utilized neural network increases. Yet another way to improve\n#' performance (aside from simplifying the augmentation pipeline)\n#' would be to increase the number of available threads.\n\n#' ## Improving Performance\ninfo(\"Improving Performance\") #jl-only\n\n#' One of the most effective ways to make the most out of the\n#' available resources is to augment the next (couple) mini-batch\n#' while the current minibatch is being processed on the GPU.\n#' We can do this via julia's build in parallel computing\n#' capabilities\n\n#' First we need a worker process that will be responsible for\n#' augmenting our dataset each epoch. This worker also needs\n#' access to a couple of our packages\n\nusing Distributed\naddprocs(1)\n@everywhere using Augmentor, MLDataUtils\n#md nothing # hide\n\n\n#' Next, we replace the inner `eachbatch` loop with a more\n#' complicated version using a `RemoteChannel` to exchange and\n#' queue the augmented data.\n\nfunction async_train_augmented(; epochs = 500, batchsize = 100, lr = .03)\n    w = weights()\n    log = MVHistory()\n    p = Progress(epochs, desc = \"Async Augmented: \") #jl-only\n    for epoch in 1:epochs\n        @sync begin\n            local_ch = Channel{Tuple}(4) # prepare up to 4 minibatches in adavnce\n            remote_ch = RemoteChannel(()->local_ch)\n            @spawn begin\n                # This block is executed on the worker process\n                batch_x_aug = zeros(Float32, size(train_x,1), size(train_x,2), 1, batchsize)\n                for (batch_x_cpu, batch_y) in eachbatch((train_x ,train_y), batchsize)\n                    # we are still using multithreading\n                    augmentbatch!(CPUThreads(), batch_x_aug, batch_x_cpu, pl)\n                    put!(remote_ch, (batch_x_aug, batch_y))\n                end\n                close(remote_ch)\n            end\n            @async begin\n                # This block is executed on the main process\n                for (batch_x_aug, batch_y) in local_ch\n                    batch_x = KnetArray{Float32}(batch_x_aug)\n                    g = costgrad(w, batch_x, batch_y)\n                    Knet.update!(w, g, lr = lr)\n                end\n            end\n        end\n\n        next!(p) #jl-only\n        if (epoch % 5) == 0\n            train = acc(w, train_x, train_y)\n            test  = acc(w, test_x,  test_y)\n            @trace log epoch train test\n            msg = \"epoch \" * lpad(epoch,4) * \": train accuracy \" * rpad(round(train,digits=3),5,\"0\") * \", test accuracy \" * rpad(round(test,digits=3),5,\"0\")\n            cancel(p, msg, :blue) #jl-only\n#md             println(msg)\n#jp             println(msg)\n        end\n    end\n    finish!(p) #jl-only\n    log\nend\n#md nothing # hide\n\n#' Note that for this toy example the overhead of this approach\n#' is greater than the benefit.\n\nasync_train_augmented(epochs=1) # warm-up\naugmented_log = @time async_train_augmented(epochs=200);\n#md nothing # hide\n\n#' ## Visualizing the Results\ninfo(\"Visualizing the Results\") #jl-only\n\n#' Before we end this tutorial, let us make use the\n#' [Plots.jl](https://github.com/JuliaPlots/Plots.jl) package to\n#' visualize and discuss the recorded training curves.\n#' We will plot the accuracy curves of both networks side by side\n#' in order to get a good feeling about their differences.\n\nusing Plots\n#jp gr()\n#md gr()\n#md nothing # hide\nunicodeplots() #jl-only\n\n#+\n\n#md default(bg_outside=colorant\"#FFFFFF\") # hide\nplt = plot(\n    plot(baseline_log,  title=\"Baseline\",  ylim=(.5,1)),\n    plot(augmented_log, title=\"Augmented\", ylim=(.5,1)),\n    size = (900, 400),\n    xlab = \"Epoch\",\n    ylab = \"Accuracy\",\n    markersize = 1\n)\n#jp plt\n#md png(plt, \"mnist_knet_curves.png\") # hide\n#md nothing # hide\n\n#md #' ![learning curves](mnist_knet_curves.png)\n\n#' Note how the accuracy on the (unaltered) training set\n#' increases faster for the baseline network than for the\n#' augmented one. This is to be expected, since our augmented\n#' network doesn't actually use the unaltered images for\n#' training, and thus has not actually seen them. Given this\n#' information, it is worth pointing out explicitly how the\n#' accuracy on training set is still greater than on the test set\n#' for the augmented network as well. This is also not a\n#' surprise, given that the augmented images are likely more\n#' similar to their original ones than to the test images.\n\n#' For the baseline network, the accuracy on the test set\n#' plateaus quite quickly (around 90%). For the augmented network\n#' on the other hand, it the accuracy keeps increasing for quite\n#' a while longer.\n\n#' ## References\n#'\n#' [^MNIST1998]: LeCun, Yan, Corinna Cortes, Christopher J.C. Burges. [\"The MNIST database of handwritten digits\"](http://yann.lecun.com/exdb/mnist/) Website. 1998.\n", "meta": {"hexsha": "3dcac49afc7641209e94edae3dd92a41533c599d", "size": 21980, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/mnist_knet.jl", "max_stars_repo_name": "baggepinnen/Augmentor.jl", "max_stars_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/mnist_knet.jl", "max_issues_repo_name": "baggepinnen/Augmentor.jl", "max_issues_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/mnist_knet.jl", "max_forks_repo_name": "baggepinnen/Augmentor.jl", "max_forks_repo_head_hexsha": "9652e66b5db5b431cccb9799615ae3e98d55ccfd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5726495726, "max_line_length": 164, "alphanum_fraction": 0.6873066424, "num_tokens": 5857, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4378234991142019, "lm_q2_score": 0.11757212272365286, "lm_q1q2_score": 0.05147583816915406}}
{"text": "@testset \"1013.partition-array-into-three-parts-with-equal-sum.jl\" begin\n    @test can_three_parts_equal_sum([0, 2, 1, -6, 6, 7, 9, -1, 2, 0, 1]) == false\n    @test can_three_parts_equal_sum([0, 2, 1, -6, 6, -7, 9, 1, 2, 0, 1]) == true\n    @test can_three_parts_equal_sum([3, 3, 6, 5, -2, 2, 5, 1, -9, 4]) == true\n    @test can_three_parts_equal_sum([0, 0, 0]) == true\n    @test can_three_parts_equal_sum([0, 0]) == false\nend", "meta": {"hexsha": "92d101eb913dfeaaebb78baa387b639318c337db", "size": 425, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/1013.partition-array-into-three-parts-with-equal-sum.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "test/problems/1013.partition-array-into-three-parts-with-equal-sum.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "test/problems/1013.partition-array-into-three-parts-with-equal-sum.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 60.7142857143, "max_line_length": 81, "alphanum_fraction": 0.6235294118, "num_tokens": 187, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49609382947091946, "lm_q2_score": 0.10374862894475306, "lm_q1q2_score": 0.05146905463556002}}
{"text": "# To be implemented by user.\r\n\r\n\"\"\"\r\n    ASTInterface.Simulation\r\n\r\nParent type for user simulation.\r\n\"\"\"\r\nabstract type Simulation <: AbstractSimulation end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.reset!\r\n\r\nResets simulation.\r\n\"\"\"\r\nfunction reset!(sim::Simulation)::Nothing end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.environment\r\n\r\nReturns Environment object constructed in simulation.\r\nEnvironment is an alias of Dict{Symbol, Sampleable}.\r\n\"\"\"\r\nfunction environment(sim::Simulation)::Environment end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.observe\r\n\r\nReturns quasi-normalized observation of simulation.\r\n\"\"\"\r\nfunction observe(sim::Simulation)::Vector{<:Real} end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.step!\r\n\r\nSteps simulation given an EnvironmentValue object.\r\nEnvironmentValue is an alias of Dict{Symbol, Any}.\r\n\"\"\"\r\nfunction step!(sim::Simulation, x::EnvironmentValue)::Nothing end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.isterminal\r\n\r\nChecks whether simulation has finished due to time limit or terminal state, independent of event status.\r\n\"\"\"\r\nfunction isterminal(sim::Simulation)::Bool end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.isevent\r\n\r\nChecks whether simulation is in an event state.\r\n\"\"\"\r\nfunction isevent(sim::Simulation)::Bool end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.distance\r\n\r\nReturns custom metric of distance to event. For best results, metric should depend only on current state.\r\n\"\"\"\r\nfunction distance(sim::Simulation)::Real end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.flatten\r\n\r\nFlattens environment variable to quasi-normalized array.\r\n\"\"\"\r\nfunction flatten(distribution::Any, value::Any)::Vector{<:Real} end\r\n\r\n\r\n\"\"\"\r\n    ASTInterface.unflatten\r\n\r\nReconstructs environment variable from quasi-normalized array.\r\n\"\"\"\r\nfunction unflatten(distribution::Any, array::Vector{<:Real})::Any end\r\n", "meta": {"hexsha": "91e8b1007476604d281e2d601e2a1cb7ceb18299", "size": 1718, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/interface.jl", "max_stars_repo_name": "jatucker4/ASTInterface_baseline.jl", "max_stars_repo_head_hexsha": "c550a8ab7cac06f76b38d8e013a8b52ce55d8ef4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-14T06:04:41.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-14T06:04:41.000Z", "max_issues_repo_path": "src/interface.jl", "max_issues_repo_name": "jatucker4/ASTInterface.jl", "max_issues_repo_head_hexsha": "79f5ff4d42380752d6c78f635f33e3d40ec9487e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/interface.jl", "max_forks_repo_name": "jatucker4/ASTInterface.jl", "max_forks_repo_head_hexsha": "79f5ff4d42380752d6c78f635f33e3d40ec9487e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6987951807, "max_line_length": 106, "alphanum_fraction": 0.7142025611, "num_tokens": 340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10374862132405092, "lm_q1q2_score": 0.051469050854976724}}
{"text": "using Weave\n\nweave(\"01_WhyJulia.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"02_Functions.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"03_Graphs.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"04_Transformations.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"05_Inverse.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"06_Polynomials.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"07_PolynomialRoots.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"08_Rational-Functions.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"09_Exp-Log.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"10_Trigonometric-Functions.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"11_Limits.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"12_Limit-Extensions.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"13_Continuity.jmd\", doctype=:\"github\", out_path=:pwd)\nweave(\"13_Continuity.jmd\", doctype=:\"github\", out_path=:pwd)\n", "meta": {"hexsha": "fb3f664caeb91f58d9b32d98ab1e74e37c4cb473", "size": 888, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Deep Into to Julia/build.jl", "max_stars_repo_name": "bmoretz/Statistical-Computing", "max_stars_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-27T08:18:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-09T09:20:49.000Z", "max_issues_repo_path": "Deep Into to Julia/build.jl", "max_issues_repo_name": "bmoretz/Statistical-Computing", "max_issues_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Deep Into to Julia/build.jl", "max_forks_repo_name": "bmoretz/Statistical-Computing", "max_forks_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-07-27T08:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-02T11:31:27.000Z", "avg_line_length": 52.2352941176, "max_line_length": 73, "alphanum_fraction": 0.7398648649, "num_tokens": 303, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.39233683016710835, "lm_q2_score": 0.13117321866710235, "lm_q1q2_score": 0.0514640848146679}}
{"text": "## 2.1 - Strings\n\n# Q1\nhi1 = repeat(\"hi\", 1000)\nhi2 = \"hi\"^1000\n\n@assert hi1 == hi2\n\n# Q2\n\na = 3\nb = 4\n\nstr1 = \"$a + $b\"\nstr2 = \"$(a+b)\"\n\n## 2.2 - Data Structures\n\n# Q1\n\na_ray = [1, 2, 3]\npush!(a_ray, 4)\nprintln(a_ray)\npop!(a_ray)\nprintln(a_ray)\n\n\n# Q2\n\n# This dictionary can only contain strings (i.e. type is Dict{String, String})\nmyphonebook = Dict(\"Jenny\" => \"867-5309\", \"Ghostbusters\" => \"555-2368\") \n\n# This dictionary can contain any type of object (i.e. type is Dict{String, Any})\nflexible_phonebook = Dict(\"Jenny\" => 8675309, \"Ghostbusters\" => \"555-2368\")", "meta": {"hexsha": "6f5dbf802ceaff3ad3a7475a38c8cca150ffd1f5", "size": 564, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Exercise Solutions/2 - Basics.jl", "max_stars_repo_name": "wruth1/JuliaTutorials", "max_stars_repo_head_hexsha": "88c49bd58a1141f1a7a2c51e4c84f190b6cfe3ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Exercise Solutions/2 - Basics.jl", "max_issues_repo_name": "wruth1/JuliaTutorials", "max_issues_repo_head_hexsha": "88c49bd58a1141f1a7a2c51e4c84f190b6cfe3ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Exercise Solutions/2 - Basics.jl", "max_forks_repo_name": "wruth1/JuliaTutorials", "max_forks_repo_head_hexsha": "88c49bd58a1141f1a7a2c51e4c84f190b6cfe3ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.5882352941, "max_line_length": 81, "alphanum_fraction": 0.6258865248, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3522017820478896, "lm_q2_score": 0.1460872526248659, "lm_q1q2_score": 0.05145219070895801}}
{"text": "# Translating\n# https://proplot.readthedocs.io/en/latest/subplots.html#Automatic-subplot-spacing\n# to Julia\n\n# Import proplot using PyCall\nusing PyCall\nproplot = pyimport(\"proplot\")\n\nf, axs = proplot.subplots(\n    nrows=3, ncols=3, axwidth=1.1, share=0\n)\naxs[5].format(\n    title=\"title\\ntitle\\ntitle\",\n    suptitle=\"Tight layout with variable row-column spacing\"\n)\naxs[2].format(ylabel=\"ylabel\\nylabel\\nylabel\")\n[ax.format(xlabel=\"xlabel\\nxlabel\\nxlabel\") for ax in axs[1:2:5]]\n[ax.format(\n    rowlabels=[\"Row 1\", \"Row 2\", \"Row 3\"],\n    collabels=[\"Column 1\", \"Column 2\", \"Column 3\"]\n) for ax in axs]\nf.savefig(\"figs/Automatic-subplot-spacing-1.svg\", transparent=false)\n", "meta": {"hexsha": "64dfff8f509e02f342f7fc4b5ed0dcbabcddb9f7", "size": 671, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/subplots/Automatic-subplot-spacing-1.jl", "max_stars_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_stars_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-04-24T06:43:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T02:39:31.000Z", "max_issues_repo_path": "src/subplots/Automatic-subplot-spacing-1.jl", "max_issues_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_issues_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-04-24T07:43:14.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-07T07:15:47.000Z", "max_forks_repo_path": "src/subplots/Automatic-subplot-spacing-1.jl", "max_forks_repo_name": "natgeo-wong/JuliaProPlotExamples", "max_forks_repo_head_hexsha": "e2735080a7827be66201db8a494e1c83192ac64b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-07T01:32:42.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-07T01:32:42.000Z", "avg_line_length": 29.1739130435, "max_line_length": 82, "alphanum_fraction": 0.7078986587, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.38121956625615, "lm_q2_score": 0.13477590699708825, "lm_q1q2_score": 0.051379212807209196}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.18\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 a4f0a914-1ec9-11eb-3c22-db87a9776aaa\nbegin\n\tusing JSON\n\tusing DataFrames\nend\n\n# \u2554\u2550\u2561 5f83510e-1fc5-11eb-3c40-83a844bf2907\nusing Turing\n\n# \u2554\u2550\u2561 2414104e-1d41-11eb-37cb-9b5cee78a98b\nmd\"## Creating our conjectures\"\n\n# \u2554\u2550\u2561 3b7f8a1e-1d42-11eb-1ab1-c11fda416e80\nmd\"\"\"If there is one thing we can all agree on, it is that the reality in which we live is complex. The explanation for the things we usually see, and have naturalized in our daily lives, is usually quite complex and requires abstraction from what \"is simply seen\".\n\nIn order to give an explanation and gain a deeper understanding of the things we see, we tend to generate models that seek to explain them in a simple and generalized way. In this way we can reduce the noise of our observations to general rules that \"govern\" them.\n\nFor example, it is obvious to everyone that if we push a glass it will move in the same direction as we did. We also know that if we keep pushing it and it goes beyond the limits of the table, it will fall to the floor. But one thing is to have the intuition of what's going to happen, and another is to have an understanding of the laws that govern that movement. In this case, they are the Newton\u00b4s Law\u00b4s of motion:\n\n\"\"\"\n\n# \u2554\u2550\u2561 4e4385b4-203b-11eb-0c5b-cbe41156401b\nmd\"$ \\vec{F}^{\\} = m*\\vec{a}^{\\} $\"\n\n# \u2554\u2550\u2561 2dca2f42-203b-11eb-2cb3-0d6a3823cdcd\nmd\"\"\"In this way, and with only one formula, it is possible to gain an understanding that is generalizable to many aspects of reality. \n\n#### Observable variables vs Latent variables\n\nNow, it is worth noting that in this case all the variables that make up our model are observable. This means that they can be measured directly.\n\nIn the case of a glass, we could weigh it with a scale. Then, by pushing it, we could measure the acceleration it acquired and from these two measurements we could obtain the force we applied to it. So, every parameter of the model is fully defined.\n\nHowever, as we try to advance in our understanding of reality, we arrive at more and more complex models and many times we are not so lucky to be able to define them with simple observable variables.\n\nFor example, this is very common in the economic sciences, where models are created with variables such as \"quality of life\". Economists will try to measure this latent variable with other variables that can be observed (such as quality of life, schooling rate, number of hospitals for a certain number of inhabitants, etc), but that do not have an obvious and direct relationship as if they had newton's equations.\n\nThis type of latent variables are used in different models to gain greater abstraction and to be able to obtain information that a priori is not found at first sight in the data. For example, in the case of economics, from concrete measures of a country's economy it is possible to generalize knowledge and be able to infer an abstract variable such as quality of life.\n\n### Bayesian hierarchical models\n\nThe Bayesian framework allows us to build statistical models that can generalize the information obtained from the data and make inferences from latent variables. \n\nA nice way to think about this kind of models is that they allow us to build our \"story\" about which are the variables that generate the data we are observing. Basically, they allow us to increase the \"depth\" of our model by indicating that the parameters of our prior distributions also follow other probability distributions.\n\nThis sure is sounding very strange. Don't worry, let's move on to an example to clarify it.\n\n#### Football analysis\n\nLet's imagine for a moment that we are brilliant statisticians. We find ourselves looking for new interesting challenges to solve and we come across a sports bookmaker. They tell us that they want to expand into football betting and that they would like us to be able to build a model that allows them to analyze the strengths and weaknesses of English Premier League teams. They are interested because they want to be able to predict possible outcomes and thus be able to price the bets. \n\nThe problem is that, as they have never worked in this sector before, they only have the results of the league matches. So what can we do? \"\"\"\n\n# \u2554\u2550\u2561 0c0268dc-1ed3-11eb-2f73-b3706305b298\nmd\"We have the data stored in a specific format called JSON, so the first thing to do is to parse and visualize it\"\n\n# \u2554\u2550\u2561 cbaaf72c-1eb4-11eb-3509-932b337f270b\nbegin\nengland_league = JSON.parsefile(\"matches_England.json\")\nmatches_df = DataFrame(home = [], away = [], score_home = [], score_away = [])\nend;\n\n# \u2554\u2550\u2561 ab6fcb22-1ed2-11eb-2749-3bef16911972\nbegin\n\tmatches = []\n\tfor match in england_league\n\t\tpush!(matches, split(match[\"label\"], \",\"))\nend\nend\n\n# \u2554\u2550\u2561 d2a81a14-1ed2-11eb-1307-bfb169aebbb4\nbegin\nfor match in matches\n\thome, away = split(match[1], \" - \")\n\tscore_home, score_away = split(match[2], \" - \")\n\t\n\tpush!(matches_df,[home, away, parse(Int,score_home), parse(Int,score_away)])\nend\nend\t\n\n# \u2554\u2550\u2561 dd80fc38-1ed2-11eb-0e8c-49859d27c72c\nmatches_df\n\n# \u2554\u2550\u2561 7f0c72d4-1f9e-11eb-06f1-cb587d7e5436\nteams = unique(collect(matches_df[1]))\n\n# \u2554\u2550\u2561 868e5c8c-1ed3-11eb-291c-fb26d512c103\nmd\"\"\"So, we have the data of the 380 matches that were played in the Premier League 2017/2018 and our challenge is to be able to analyze the characteristics of these teams. \n\nA priori it may seem that we are missing data, that with the data we have we cannot infer \"characteristics\" specific to each team. At most, it might be possible to see who the teams that scored the most goals, the averages of goals per game or how the positions were after the tournament, but to obtain characteristics of the teams? how could we face this problem?\n\n#### Creating our stories\n\nOkay, let's see what information we have from our data: \nOn one hand we have specified the names of each team and which one is local. On the other hand, we have the number of goals scored.\n\nA possible approach to this data is to realize that the goals scored by each team can be modeled with a poisson distribution. \n\nWhy? You have to remember that this distribution describes \"arrivals\" - discrete events - in a continuum. For example, it is widely used to describe customer arrivals to a location as time passes or failures in continuous industrial processes (e.g. failure in the production of a pipe). \n\nIn this particular case, we could propose that the goals scored by a team are the discrete events that occur in the time continuum that the game last:\n\n\"\"\"\n\n# \u2554\u2550\u2561 04a8f16c-1ed5-11eb-3979-af35c6d9dea1\nmd\"$Score \\sim Poisson(\u03b8)$\"\n\n# \u2554\u2550\u2561 9f1e1618-1edb-11eb-02af-9fa5c8904163\nmd\"\"\"Well, we have an improvement. We've already told our model how to think about goals scored. \n\nNow we can use the flexibility of Bayesianism to indicate what the \"goal rate\" of our Poisson depends on.  You can think of it literally as the number of goals a team scores per unit of time. And this is where we have to take advantage of all the information provided by the data set.\n\nAs expected, this rate has to be particular to each match the team plays and take into account the opponent. We can therefore propose that the scoring rate of each team (in each particular match) depends on the \"attacking power\" of the team on the one hand, and the \"defensive power\" of the opponent on the other:\n\n\"\"\"\n\n# \u2554\u2550\u2561 42104900-1f80-11eb-019f-a7c561ca3e4b\nmd\"$\u03b8_{team1} \\sim att_{team1} + def_{team2}$\" \n\n# \u2554\u2550\u2561 9d0f3a1e-1f80-11eb-3422-fb7b4696cc13\nmd\"\"\"In this way we could be capturing, from the results of each game, the attack and defence strengths of each team. \n\nAnother latent variable that we could obtain, given the data, is if there is an effect that increases (or decreases) the goal rate related to whether the team is local or not. This would also help - in case there is indeed an effect - in not the attack and defence parameters be disrupted by having to \"contain\" that information.\"\"\"\n\n# \u2554\u2550\u2561 0b1f9c68-1f83-11eb-11e7-e7614c021c05\nmd\"$\u03b8_{home} \\sim home + att_{home} + def_{away}$\n$\u03b8_{away} \\sim att_{away} + def_{home}$\"\n\n# \u2554\u2550\u2561 043c438c-1f84-11eb-2cd7-6dd07a125910\nmd\"\"\"This leaves one attack and one defense parameter for each team, and a global league parameter that indicates the effect of being local on the scoring rate.\n\n#### Letting the information flow \n\nOkay, we are already getting much closer to the initial goal we set. As a last step, we must be able to make the information flow between the two independent poissons that we proposed to model the score of each of the two teams that are playing. We need to do that precisely because we have proposed that the poissons are independent, but we need that when making the inference of the parameters the model can access the information from both scores so it can catch the correlation between them. In other words, we have to find a way to interconnect our model.\n\nAnd that is exactly what hierarchical Bayesian models allow us to do. How? By letting us choose probability distributions for the parameters that represent the characteristics of both equipment. With the addition that these parameters will share the same prior distributions. Let's see how:\n\nThe first thing to do, as we already know, is to assign the prior distributions of our attack and defense parameters. A reasonable idea would be to propose that they follow a normal distribution since it is consistent that there are some teams that have a very good defense, so the parameter would take negative values; or there may be others that have a very bad one, taking positive values (since they would \"add up\" to the goal rate of the opposing team). The normal distribution allows us to contemplate both cases.\n\nNow, when choosing the parameters we are not going to stop and assign fixed numbers, but we will continue to deepen the model and add another layer of distributions:\n\n\"\"\"\n\n# \u2554\u2550\u2561 4a31541e-1f97-11eb-038e-736446224c21\nmd\"$att_{t} \\sim Normal(\u03bc_{att}, \u03c3_{att})$\n$def_{t} \\sim Normal(\u03bc_{def}, \u03c3_{def})$\"\n\n# \u2554\u2550\u2561 e3a99868-1f97-11eb-1872-79c8f6abe1be\nmd\"Where the t sub-index is indicating us that there are a couple of these parameters for each team.\n\nThen, as a last step to have our model defined, we have to assign the priority distributions that follow the parameters of each normal distribution. We have to define our hyper priors.\n\n\"\n\n# \u2554\u2550\u2561 c3ca1cc4-1f98-11eb-3c23-33acb95fbc03\nmd\"$\u03bc_{att}, \u03bc_{def} \\sim Normal(0, 0.1)$\n$\u03c3_{att}, \u03c3_{def} \\sim Exponential(1)$\"\n\n# \u2554\u2550\u2561 4106e55a-1f99-11eb-110e-b79221774f12\nmd\"We must not forget the parameter that represents the advantage of being local\"\n\n# \u2554\u2550\u2561 bff4fdae-1f99-11eb-2d94-aba7bf9d097f\nmd\"$home \\sim Normal(0,1)$\"\n\n# \u2554\u2550\u2561 2b135202-1fa7-11eb-324d-93a5eda41e3e\nmd\"\"\"Now that our model is fully define, let's add one last restriction to the characteristics of the teams to make it easier to compare them: subtract the average of all the attack and defence powers from each one. In this way we will have the features centred on zero, with negative values for the teams that have less attacking power than the average and positive values for those that have more. As we already said, the opposite analysis applies to the defence, negative values are the ones that will indicate that a team has a strong defence as they will be \"subtracting\" from the scoring rate of the opponent. This is equivalent to introducing the restriction:\n\"\"\"\n\n# \u2554\u2550\u2561 ddc7b166-203a-11eb-0ac2-056c309bb590\nmd\"$\\sum att_{t} = 0$\n$\\sum def_{t} = 0$\"\n\n# \u2554\u2550\u2561 8e164be0-203b-11eb-293a-6b32dba96133\nmd\"Let's translate all this into Turing code:\"\n\n# \u2554\u2550\u2561 51a309bc-2033-11eb-10c0-ed17545df33d\nbegin\n\t@model function football_matches(home_teams, away_teams, score_home, score_away, teams)\n\t#hyper priors\n\t\u03c3att ~ Exponential(1)\n\t\u03c3deff ~ Exponential(1)\n\t\u03bcatt ~ Normal(0,0.1)\n\t\u03bcdef ~ Normal(0,0.1)\n\t\n\thome ~ Normal(0,1)\n\t\t\n\t#Team-specific effects\t\n\tatt ~ filldist(Normal(\u03bcatt, \u03c3att), length(teams))\n\tdef ~ filldist(Normal(\u03bcatt, \u03c3deff), length(teams))\n\t\n\tdict = Dict{String, Int64}()\n\tfor (i, team) in enumerate(teams)\n\t\tdict[team] = i\n\tend\n\t\t\n\t#Zero-sum constrains\n\toffset = mean(att) + mean(def)\n\t\n\tlog_\u03b8_home = Vector{Real}(undef, length(home_teams))\n\tlog_\u03b8_away = Vector{Real}(undef, length(home_teams))\n\t\t\n\t#Modeling score-rate and scores (as many as there were games in the league) \n\tfor i in 1:length(home_teams)\n\t\t#score-rate\n\t\tlog_\u03b8_home[i] = home + att[dict[home_teams[i]]] + def[dict[away_teams[i]]] - offset\n\t\tlog_\u03b8_away[i] = att[dict[away_teams[i]]] + def[dict[home_teams[i]]] - offset\n\t\t#scores\n\t\tscore_home[i] ~ LogPoisson(log_\u03b8_home[i])\n\t\tscore_away[i] ~ LogPoisson(log_\u03b8_away[i])\n\tend\n\t\n\tend\nend\n\n# \u2554\u2550\u2561 f3f0b6f8-2033-11eb-0f9e-d951d791001d\nmd\"\"\"As you can see, the turing code is very clear and direct. In the first block we define our hyperpriors for the distributions of the characteristics of the equipment.\n\nIn the second one, we define the priors distributions that will encapsulate the information about the attack and defense powers of the teams. With the *filldist* function we are telling Turing that we need as many of these parameters as there are teams in the league *length(teams)*\n\nThen, we calculate the average of the defense and attack parameters that we are going to use to centralize those variables, and we use the LogPoisson distribution to allow the theta to take some negative value in the inference process and give more sensitivity to the parameters that make it up.\n\nAs we said before, we will model the thetas for each game played in the league, that's why the *for* of the last block goes from 1 to *length(home_teams)*, which is the list that contains who was the local team of each game played.\n\nSo let\u00b4s run it and see if all of this effort was worth it:\n\"\"\"\n\n# \u2554\u2550\u2561 f3f4bfba-203f-11eb-142c-a187112744d2\nmodel = football_matches(matches_df[1], matches_df[2], matches_df[3], matches_df[4], teams)\n\n# \u2554\u2550\u2561 2ce4435c-2040-11eb-1670-e39ad4cc690c\nposterior = sample(model, NUTS(),3000);\n\n# \u2554\u2550\u2561 9e50e56e-5674-11eb-2919-4bde8964bc59\nrand(LogPoisson(0.2), 100)\n\n# \u2554\u2550\u2561 8c1dd9de-2040-11eb-35a3-39e8196577a1\nmd\"#### Analyzing the results\nIn order to compare and corroborate that the inference of our model makes sense, it is key to have the ranking table of how the teams actually performed in the 2017/2018 Premier League.\n\"\n\n# \u2554\u2550\u2561 0f9406ae-2054-11eb-3f0c-1d03ba779f18\nbegin\n\ttable_positions = \n\t[11, 5, 9, 4, 13, 14, 1, 15, 12, 6, 2, 16, 10, 17, 20, 3, 7, 8, 19, 18]\n\t\n\tgames_won = \n\t[32, 25, 23, 21, 21, 19, 14, 13, 12, 12, 11, 11, 10, 11, 9, 9, 7, 8, 7, 6]\n\t\n\tteams_ = []\n\tfor i in table_positions\n\t\tpush!(teams_, teams[i])\n\tend\n\t\n\ttable_position_df = DataFrame(Table_of_positions = teams_, Wins = games_won)\n\nend\n\n# \u2554\u2550\u2561 099e4188-2054-11eb-1e7e-69bdb2b0202c\nmd\"Let's now explore a little bit the a posteriori values we obtained.\"\n\n# \u2554\u2550\u2561 16572944-2045-11eb-38d0-fb20d981e3e9\nbegin\n\tpost_att = collect(get(posterior, :att)[1])\n\tpost_def = collect(get(posterior, :def)[1])\n\tpost_home = collect(get(posterior, :home)[1])\nend;\n\n# \u2554\u2550\u2561 4ae7d968-206f-11eb-1925-c5b5a31e6940\nbegin\nusing Plots\nhistogram(post_home, legend=false, title=\"Posterior distribution of home parameter\")\nend\n\n# \u2554\u2550\u2561 93d3bf98-5684-11eb-361e-c7b0f1a3bb0e\nget(posterior, :att)[:att]\n\n# \u2554\u2550\u2561 18f36e1a-5682-11eb-2826-ab4d4c457af3\nposterior.value.data\n\n# \u2554\u2550\u2561 6f096138-5682-11eb-179f-f973803b9268\ncollect(get(posterior, :att)[1])\n\n# \u2554\u2550\u2561 8cec9318-206e-11eb-3caa-3bad9d788d3c\nmd\"As a first measure to analyze, it is interesting to see and quantify (if any) the effect that being local has on the score rate:\"\n\n# \u2554\u2550\u2561 8b77d12c-206f-11eb-19a5-f557157ac05e\nmean(post_home)\n\n# \u2554\u2550\u2561 b56df7ea-206f-11eb-18b9-0544cc596ca5\nmd\"So, to include in the model the parameter home was a good idea. indeed being local provides a very big advantage. \n\nBeyond the fact that it is interesting to be able to quantify how much the location influences the scoring rate of the teams, including it in the analysis allow us to have better estimates of the defense and attack parameters of the teams. This is true because if it had not been included, this positive effect would have manifested itself in the only parameters it would have found, the attack and defense parameters, deforming the real measure of these.\n\nSo, being confident that we are on the right track, let\u00b4s find the attack and defence parameters of each team.\"\n\n# \u2554\u2550\u2561 554b4ef0-2045-11eb-3218-050ec936f1aa\nbegin\n\tteams_att = []\n\tteams_def = []\n\tfor i in 1:length(post_att)\n\t\tpush!(teams_att, post_att[i])\n\t\tpush!(teams_def, post_def[i])\n\tend\nend\n\n# \u2554\u2550\u2561 8a4df438-2045-11eb-2798-af592dbbffb5\nmd\"This way we obtain all the samples of the posterior distributions for each one of the parameters of each equipment. Scroll right to explore the entire array.\"\n\n# \u2554\u2550\u2561 6b1a1520-2045-11eb-1302-0778b4e7a836\nteams_att\n\n# \u2554\u2550\u2561 da93bf52-2045-11eb-2b6f-3f31f57d8653\nmd\"For example, if we would like to se the posterior distribution of the attack parameter for Burnley:\"\n\n# \u2554\u2550\u2561 1fa92570-2046-11eb-3541-e5fccf7afabd\nteams[1]\n\n# \u2554\u2550\u2561 255c4d08-2046-11eb-1cf0-b32d2cd711bf\nhistogram(teams_att[1], legend=false, title=\"Posterior distribution of Burnley\u00b4s attack power\")\n\n# \u2554\u2550\u2561 473e6a46-205b-11eb-2731-7b47b99d86a5\nmean(teams_att[1])\n\n# \u2554\u2550\u2561 917d4550-2058-11eb-174b-65436a4e6cc8\nmd\"Comparing it to the attacking power of Manchester City, champion of the Premier league:\"\n\n# \u2554\u2550\u2561 c2de4714-2058-11eb-1a0d-f1bf96649844\nteams[11]\n\n# \u2554\u2550\u2561 0103406e-2059-11eb-333f-7b5814f21a54\nbegin\n\thistogram(teams_att[11], legend=false, title=\"Posterior distribution of Manchester City\u00b4s attack power\")\nend\n\n# \u2554\u2550\u2561 579a4d9e-205b-11eb-2352-83beca145211\nmean(teams_att[11])\n\n# \u2554\u2550\u2561 6924f372-2059-11eb-2b98-6f4b65b777f4\nmd\"Okay, when comparing the league champion against a mid-table team, we can clearly see the superiority in attack. For now, it seems that the inference comes in handy. \n\nLet's try now to have an overview of the attacking powers of each team. To do this, just take the average of each and plot it next to the standard deviation \n\"\n\n# \u2554\u2550\u2561 ece1d052-2060-11eb-2b50-ef46663ed88f\nbegin\n\tteams_att_\u03bc = mean.(teams_att)\n\tteams_def_\u03bc = mean.(teams_def)\n\tteams_att_\u03c3 = std.(teams_att)\n\tteams_def_\u03c3 = std.(teams_def)\nend;\n\n# \u2554\u2550\u2561 43a39b30-2061-11eb-28c5-37cbc4aed84b\nmd\"\"\"Remember that the \".\" operator is used for broadcasting. This means that it will apply the function to each component of the array\"\"\"\n\n# \u2554\u2550\u2561 41ca2316-2062-11eb-3fe6-f33a789fd578\nbegin\n\tteams_att_\u03bc\n\tsorted_att = sortperm(teams_att_\u03bc)\n\tabbr_names = [t[1:3] for t in teams]\nend;\n\n# \u2554\u2550\u2561 0ef8a16e-2063-11eb-1391-6b3b959de541\nbegin\n\tabbr_names[5] = \"Mun\"\n\tabbr_names[10] = \"Whu\"\n\tabbr_names[11] = \"Mci\"\n\tabbr_names[16] = \"Bou\"\n\tabbr_names[18] = \"Wba\"\n\tabbr_names[19] = \"Stk\"\n\tabbr_names[20] = \"Bha\"\nend;\n\n# \u2554\u2550\u2561 1eb73246-2063-11eb-0f20-416e433b5144\nsorted_names = abbr_names[sorted_att]\n\n# \u2554\u2550\u2561 abace764-2062-11eb-2949-fd9e86b50f17\nbegin\n\tscatter(1:20, teams_att_\u03bc[sorted_att], grid=false, legend=false, yerror=teams_att_\u03c3[sorted_att], color=:blue, title=\"Premier league 17/18 teams attack power\")\n\tannotate!(collect(1:20), teams_att_\u03bc[sorted_att] .+ 0.238, text.(sorted_names, :black, :center, 8))\n\tylabel!(\"Mean team attack\")\nend\n\n# \u2554\u2550\u2561 ebd267a4-2064-11eb-02a7-b93465de6319\nmd\"\"\"Although there is a high correlation between the attacking power of each team and its position on the table after the league ends, it is clear that this is not enough to explain the results. For example, Manchester City was the league's sub-champion, but only appeared in fifth place.\n\nLet's explore what happens to the defence power: \"\"\"\n\n# \u2554\u2550\u2561 f6ad5f52-2065-11eb-0be7-f159941e0d89\nbegin\n\tsorted_def = sortperm(teams_def_\u03bc)\n\tsorted_names_def = abbr_names[sorted_def]\nend\n\n# \u2554\u2550\u2561 539e68b4-2066-11eb-09c2-b337241c36bc\nbegin\n\tscatter(1:20, teams_def_\u03bc[sorted_def], grid=false, legend=false, yerror=teams_def_\u03c3[sorted_def], color=:blue, title=\"Premier league 17/18 teams defence power\")\n\tannotate!(collect(1:20), teams_def_\u03bc[sorted_def] .+ 0.2, text.(sorted_names_def, :black, :center, 8))\n\tylabel!(\"Mean team defence\")\nend\n\n# \u2554\u2550\u2561 69e7e54c-2067-11eb-21a1-cb72f4b423cd\nmd\"To read this graph we have to remember that the defense effect is better the more negative it is, since it is representing the scoring rate that takes away from the opponent team. As we already said:\"\n\n# \u2554\u2550\u2561 f59d488a-2067-11eb-1d4b-1d9dd97ea39e\nmd\"$\u03b8_{team1} \\sim att_{team1} + def_{team2}$\"\n\n# \u2554\u2550\u2561 27d5a290-2068-11eb-06f5-0b16f6ec6cc5\nmd\"As the $def_{team2}$ is adding up in the equation, if it take negative values, it is going to start substracting the scoring rate of the oponent.\n\nThings, then, begin to make a little more sense. Now we can see that Manchester United is the team with the strongest defence, so being second in the overall is not extrange.\n\nTo gain a deeper understanding of what\u00b4s going on here, let's chart both characteristics together. This is going to let us see the combined effect they have. Also i\u00b4m going to add the final position of each team to improve the interpretability.\"\n\n# \u2554\u2550\u2561 b8f9bae8-206a-11eb-1426-031f6fd05fd6\nbegin\n\ttable_position = \n\t[11, 5, 9, 4, 13, 14, 1, 15, 12, 6, 2, 16, 10, 17, 20, 3, 7, 8, 19, 18]\n\tposition = sortperm(table_position)\nend\n\n# \u2554\u2550\u2561 ee45d48e-206a-11eb-0edf-2b8b893bb583\nbegin\nscatter(teams_att_\u03bc, teams_def_\u03bc, legend=false)\nannotate!(teams_att_\u03bc, teams_def_\u03bc.+ 0.016, text.(abbr_names, :black, :center, 6))\nannotate!(teams_att_\u03bc, teams_def_\u03bc.- 0.016, text.(position, :left, :center, 5))\n\nxlabel!(\"Mean team attack\")\nylabel!(\"Mean team defence\")\nend\n\n# \u2554\u2550\u2561 c794f45e-206b-11eb-33c4-05bc429ba846\nmd\"Well, great! Now we have some interesting information to analyze the teams and the league in general. It\u00b4s easier now to perceive how the two features interact with each other, comparing between teams and being able to see how that affects the final position. \n\nFor example, looking at the cases of Liverpool and Tottenham, or Leicester City and Everton; one could say (against general common sense) that the power of defense has a greater effect on the performance of each team than the attack. But we leave you to do those analysis for the betting house.\n\nWell, we went from having a problem that seemed almost impossible to have a solid solution, with a quantitative analysis of the characteristics of each team. We even know how much the localization of the teams increases the scoring rate. We were able to achieve this thanks to the hierarchical framework that Bayesianism provides us. Using this tool allows us to create models proposing latent variables that cannot be observed, to infer them and to gain a much deeper and more generalized knowledge than we had at first. You just have to imagine a good story.\"\n\n# \u2554\u2550\u2561 fcb7e8d2-2071-11eb-020d-7bb1d53f8a6d\nmd\"### Bibliography \n\n- [Paper of Gianluca Baio and Marta A. Blangiardo](https://discovery.ucl.ac.uk/id/eprint/16040/1/16040.pdf)\n- [Post of Daniel Weitzenfeld](http://danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/)\n\n\"\n\n# \u2554\u2550\u2561 fac413ec-5684-11eb-3167-d56d967247fc\nmun_att_post = collect(get(posterior, :att)[:att])[5][:,1];\n\n# \u2554\u2550\u2561 cc6681a2-5688-11eb-1d8e-e1edcb80a20d\nmun_def_post = collect(get(posterior, :def)[:def])[5][:,1];\n\n# \u2554\u2550\u2561 f01e7438-5688-11eb-2900-8df62e9feb18\nliv_att_post = collect(get(posterior, :att)[:att])[4][:,1];\n\n# \u2554\u2550\u2561 fae4e938-5688-11eb-3ab8-e3a8a4a61692\nliv_def_post = collect(get(posterior, :def)[:def])[4][:,1];\n\n# \u2554\u2550\u2561 84d5e7a2-56dc-11eb-0d54-b790c7c714fb\n\n\n# \u2554\u2550\u2561 7704b9e6-56a0-11eb-0ade-2194a0845f61\npost_home;\n\n# \u2554\u2550\u2561 03f063cc-5689-11eb-3d90-b7a5991332ba\n# This function simulates matches given the attach, defense and home parameters.\n# The first pair of parameters alwas correspond to the home team.\nfunction simulate_matches_(att\u2081, def\u2081, att\u2082, def\u2082, home, n_matches, home_team=1)\n\tif home_team == 1\n\t\tlog\u03b8\u2081 = home + att\u2081 + def\u2082\n\t\tlog\u03b8\u2082 = att\u2082 + def\u2081\n\t\t\t\t\n\telseif home_team == 2\n\t\tlog\u03b8\u2081 = att\u2081 + def\u2082\n\t\tlog\u03b8\u2082 = home + att\u2082 + def\u2081\n\telse\n\t\treturn DomainError(home_team, \"Invalid home_team value\")\n\tend\n\t\n\tscores\u2081 = rand(LogPoisson(log\u03b8\u2081), n_matches)\n\tscores\u2082 = rand(LogPoisson(log\u03b8\u2082), n_matches)\n\t\n\tresults = [(s\u2081, s\u2082) for (s\u2081, s\u2082) in zip(scores\u2081, scores\u2082)]\n\t\n\treturn results\nend\n\n# \u2554\u2550\u2561 a8a9179c-5693-11eb-1758-e7d34722c7fa\nmat = simulate_matches_(0.2, -0.1, 0.4, -0.3, 0.2, 1000, 2)\n\n# \u2554\u2550\u2561 7e82f15c-5695-11eb-20f1-85763cbfa572\nmax_h = maximum(map(x -> x[1], mat))\n\n# \u2554\u2550\u2561 bd11306c-5697-11eb-22ed-99ae7703be59\nmax_a = maximum(map(x -> x[2], mat))\n\n# \u2554\u2550\u2561 d99e52fa-5697-11eb-12fe-ab4ae36ab362\nfunction simulate_matches(team1_att_post, team1_def_post, team2_att_post,\t\t\t\tteam2_def_post, home_post, n_matches)\n\t\n\tteam1_as_home_results = Tuple{Int64,Int64}[]\n\tteam2_as_home_results = Tuple{Int64,Int64}[]\n\t\n\tfor (t1_att, t1_def, t2_att, t2_def, home) in zip(team1_att_post, team1_def_post, team2_att_post, team2_def_post, home_post)\n\t\t\n\t\tteam1_as_home_results = vcat(team1_as_home_results, simulate_matches_(t1_att, t1_def, t2_att, t2_def, home, n_matches, 1))\n\t\t\n\t\tteam2_as_home_results = vcat(team2_as_home_results, simulate_matches_(t1_att, t1_def, t2_att, t2_def, home, n_matches, 2))\n\tend\n\t\n\treturn team1_as_home_results, team2_as_home_results\nend\n\n# \u2554\u2550\u2561 5a3d6966-56a0-11eb-0db1-c5a06ea2dd83\nkjj, kjjj = simulate_matches(mun_att_post, mun_def_post, liv_att_post, liv_def_post, post_home, 1000)\n\n# \u2554\u2550\u2561 9d1f1e7e-56a1-11eb-2539-f5da33710a32\nfunction match_heatmaps(team1_as_home_results, team2_as_home_results)\n\t\n\tmax_t1_as_home = maximum(map(x -> x[1], team1_as_home_results))\n\tmax_t2_as_away = maximum(map(x -> x[2], team1_as_home_results))\n\t\n\tmax_t1_as_away = maximum(map(x -> x[1], team2_as_home_results))\n\tmax_t2_as_home = maximum(map(x -> x[2], team2_as_home_results))\n\t\n\tmatrix_t1_as_home = zeros(Int64, (max_t1_as_home + 1, max_t2_as_away + 1))\n\tmatrix_t2_as_home = zeros(Int64, (max_t1_as_away + 1, max_t2_as_home + 1))\n\t\n\tfor match in team1_as_home_results\n\t\tmatrix_t1_as_home[match[1] + 1, match[2] + 1] += 1\n\tend\n\t\n\tfor match in team2_as_home_results\n\t\tmatrix_t2_as_home[match[1] + 1, match[2] + 1] += 1\n\tend\n\t\n\tgr()\n\t#heat_t1_home = heatmap(0:max_t1_as_home,\n\theatmap(0:max_t1_as_home,\n\t\t\t\t\t\t\t\t0:max_t2_as_away,\n\t\t\t\t\t\t\t\tmatrix_t1_as_home,\n\t\t\t\t\t\t\t\tc=cgrad([:blue, :white,:red, :yellow]),\n\t\t\t\t\t\t\t\txlabel=\"Team1 score\", ylabel=\"Team2 score\",\n\t\t\t\t\t\t\t\ttitle=\"Team1 as home\")\n\tsavefig(\"./t1_as_home\")\n\t\n\t#heat_t2_home = heatmap(0:max_t1_as_away,\n\theatmap(0:max_t1_as_away,\n\t\t\t\t\t\t\t\t0:max_t2_as_home,\n\t\t\t\t\t\t\t\tmatrix_t2_as_home,\n\t\t\t\t\t\t\t\tc=cgrad([:blue, :white,:red, :yellow]),\n\t\t\t\t\t\t\t\txlabel=\"Team1 Score\", ylabel=\"Team2 Score\",\n\t\t\t\t\t\t\t\ttitle=\"Team2 as home\")\n\tsavefig(\"./t2_as_home\")\n\t#plot(heat_t1_home, heat_t2_home, layout=(2,1), size=(900, 400))\n\t#current()\n\t\n\treturn matrix_t1_as_home, matrix_t2_as_home\nend\n\n# \u2554\u2550\u2561 d7bfc106-56d0-11eb-21b3-3b9036905041\nm_1_home, m_t2_home = match_heatmaps(kjj, kjjj)\n\n# \u2554\u2550\u2561 9ceba0fa-56d4-11eb-2696-21f4babf699c\nbegin\n\tgr()\n\theatmap(collect(0:(size(m_t2_home, 1)-1)),\t\t\n\t\t\tcollect(0:(size(m_t2_home, 2)-1)),\t\n\t\t\tm_t2_home,\n\t\t\tc=cgrad([:blue, :white,:red, :yellow]),\n\t\t\txlabel=\"Team1 score\", ylabel=\"Team2 score\",\n\t\t\ttitle=\"Team1 as home\")\nend\n\n# \u2554\u2550\u2561 0da9d3a0-56d5-11eb-08a9-c9e83106dfc3\nm_t1_home\n\n# \u2554\u2550\u2561 db8ed140-5735-11eb-2078-2f90d15d6325\n\"hola\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u25002414104e-1d41-11eb-37cb-9b5cee78a98b\n# \u255f\u25003b7f8a1e-1d42-11eb-1ab1-c11fda416e80\n# \u255f\u25004e4385b4-203b-11eb-0c5b-cbe41156401b\n# \u255f\u25002dca2f42-203b-11eb-2cb3-0d6a3823cdcd\n# \u2560\u2550a4f0a914-1ec9-11eb-3c22-db87a9776aaa\n# \u2560\u25500c0268dc-1ed3-11eb-2f73-b3706305b298\n# \u2560\u2550cbaaf72c-1eb4-11eb-3509-932b337f270b\n# \u2560\u2550ab6fcb22-1ed2-11eb-2749-3bef16911972\n# \u2560\u2550d2a81a14-1ed2-11eb-1307-bfb169aebbb4\n# \u2560\u2550dd80fc38-1ed2-11eb-0e8c-49859d27c72c\n# \u2560\u25507f0c72d4-1f9e-11eb-06f1-cb587d7e5436\n# \u255f\u2500868e5c8c-1ed3-11eb-291c-fb26d512c103\n# \u255f\u250004a8f16c-1ed5-11eb-3979-af35c6d9dea1\n# \u255f\u25009f1e1618-1edb-11eb-02af-9fa5c8904163\n# \u255f\u250042104900-1f80-11eb-019f-a7c561ca3e4b\n# \u255f\u25009d0f3a1e-1f80-11eb-3422-fb7b4696cc13\n# \u255f\u25000b1f9c68-1f83-11eb-11e7-e7614c021c05\n# \u255f\u2500043c438c-1f84-11eb-2cd7-6dd07a125910\n# \u255f\u25004a31541e-1f97-11eb-038e-736446224c21\n# \u255f\u2500e3a99868-1f97-11eb-1872-79c8f6abe1be\n# \u255f\u2500c3ca1cc4-1f98-11eb-3c23-33acb95fbc03\n# \u255f\u25004106e55a-1f99-11eb-110e-b79221774f12\n# \u255f\u2500bff4fdae-1f99-11eb-2d94-aba7bf9d097f\n# \u255f\u25002b135202-1fa7-11eb-324d-93a5eda41e3e\n# \u255f\u2500ddc7b166-203a-11eb-0ac2-056c309bb590\n# \u255f\u25008e164be0-203b-11eb-293a-6b32dba96133\n# \u2560\u25505f83510e-1fc5-11eb-3c40-83a844bf2907\n# \u2560\u255051a309bc-2033-11eb-10c0-ed17545df33d\n# \u255f\u2500f3f0b6f8-2033-11eb-0f9e-d951d791001d\n# \u2560\u2550f3f4bfba-203f-11eb-142c-a187112744d2\n# \u2560\u25502ce4435c-2040-11eb-1670-e39ad4cc690c\n# \u2560\u25509e50e56e-5674-11eb-2919-4bde8964bc59\n# \u255f\u25008c1dd9de-2040-11eb-35a3-39e8196577a1\n# \u255f\u25000f9406ae-2054-11eb-3f0c-1d03ba779f18\n# \u255f\u2500099e4188-2054-11eb-1e7e-69bdb2b0202c\n# \u2560\u255016572944-2045-11eb-38d0-fb20d981e3e9\n# \u2560\u255093d3bf98-5684-11eb-361e-c7b0f1a3bb0e\n# \u2560\u255018f36e1a-5682-11eb-2826-ab4d4c457af3\n# \u2560\u25506f096138-5682-11eb-179f-f973803b9268\n# \u255f\u25008cec9318-206e-11eb-3caa-3bad9d788d3c\n# \u2560\u25504ae7d968-206f-11eb-1925-c5b5a31e6940\n# \u2560\u25508b77d12c-206f-11eb-19a5-f557157ac05e\n# \u255f\u2500b56df7ea-206f-11eb-18b9-0544cc596ca5\n# \u2560\u2550554b4ef0-2045-11eb-3218-050ec936f1aa\n# \u255f\u25008a4df438-2045-11eb-2798-af592dbbffb5\n# \u2560\u25506b1a1520-2045-11eb-1302-0778b4e7a836\n# \u255f\u2500da93bf52-2045-11eb-2b6f-3f31f57d8653\n# \u2560\u25501fa92570-2046-11eb-3541-e5fccf7afabd\n# \u2560\u2550255c4d08-2046-11eb-1cf0-b32d2cd711bf\n# \u2560\u2550473e6a46-205b-11eb-2731-7b47b99d86a5\n# \u255f\u2500917d4550-2058-11eb-174b-65436a4e6cc8\n# \u2560\u2550c2de4714-2058-11eb-1a0d-f1bf96649844\n# \u2560\u25500103406e-2059-11eb-333f-7b5814f21a54\n# \u2560\u2550579a4d9e-205b-11eb-2352-83beca145211\n# \u255f\u25006924f372-2059-11eb-2b98-6f4b65b777f4\n# \u2560\u2550ece1d052-2060-11eb-2b50-ef46663ed88f\n# \u255f\u250043a39b30-2061-11eb-28c5-37cbc4aed84b\n# \u2560\u255041ca2316-2062-11eb-3fe6-f33a789fd578\n# \u255f\u25000ef8a16e-2063-11eb-1391-6b3b959de541\n# \u2560\u25501eb73246-2063-11eb-0f20-416e433b5144\n# \u2560\u2550abace764-2062-11eb-2949-fd9e86b50f17\n# \u255f\u2500ebd267a4-2064-11eb-02a7-b93465de6319\n# \u2560\u2550f6ad5f52-2065-11eb-0be7-f159941e0d89\n# \u2560\u2550539e68b4-2066-11eb-09c2-b337241c36bc\n# \u255f\u250069e7e54c-2067-11eb-21a1-cb72f4b423cd\n# \u255f\u2500f59d488a-2067-11eb-1d4b-1d9dd97ea39e\n# \u255f\u250027d5a290-2068-11eb-06f5-0b16f6ec6cc5\n# \u2560\u2550b8f9bae8-206a-11eb-1426-031f6fd05fd6\n# \u2560\u2550ee45d48e-206a-11eb-0edf-2b8b893bb583\n# \u255f\u2500c794f45e-206b-11eb-33c4-05bc429ba846\n# \u255f\u2500fcb7e8d2-2071-11eb-020d-7bb1d53f8a6d\n# \u2560\u2550fac413ec-5684-11eb-3167-d56d967247fc\n# \u2560\u2550cc6681a2-5688-11eb-1d8e-e1edcb80a20d\n# \u2560\u2550f01e7438-5688-11eb-2900-8df62e9feb18\n# \u2560\u2550fae4e938-5688-11eb-3ab8-e3a8a4a61692\n# \u2560\u255084d5e7a2-56dc-11eb-0d54-b790c7c714fb\n# \u2560\u25507704b9e6-56a0-11eb-0ade-2194a0845f61\n# \u2560\u255003f063cc-5689-11eb-3d90-b7a5991332ba\n# \u2560\u2550a8a9179c-5693-11eb-1758-e7d34722c7fa\n# \u2560\u25507e82f15c-5695-11eb-20f1-85763cbfa572\n# \u2560\u2550bd11306c-5697-11eb-22ed-99ae7703be59\n# \u2560\u2550d99e52fa-5697-11eb-12fe-ab4ae36ab362\n# \u2560\u25505a3d6966-56a0-11eb-0db1-c5a06ea2dd83\n# \u2560\u25509d1f1e7e-56a1-11eb-2539-f5da33710a32\n# \u2560\u2550d7bfc106-56d0-11eb-21b3-3b9036905041\n# \u2560\u25509ceba0fa-56d4-11eb-2696-21f4babf699c\n# \u2560\u25500da9d3a0-56d5-11eb-08a9-c9e83106dfc3\n# \u2560\u2550db8ed140-5735-11eb-2078-2f90d15d6325\n", "meta": {"hexsha": "39b66e7fd31e105849066a81e87403d74f1019b1", "size": 30672, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "football_simulation_chapter/football-analysis-chapter.jl", "max_stars_repo_name": "lambdaclass/julia_playground", "max_stars_repo_head_hexsha": "53e50d251e752d77c0f1cba4ac559db45d45bb01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "football_simulation_chapter/football-analysis-chapter.jl", "max_issues_repo_name": "lambdaclass/julia_playground", "max_issues_repo_head_hexsha": "53e50d251e752d77c0f1cba4ac559db45d45bb01", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "football_simulation_chapter/football-analysis-chapter.jl", "max_forks_repo_name": "lambdaclass/julia_playground", "max_forks_repo_head_hexsha": "53e50d251e752d77c0f1cba4ac559db45d45bb01", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.5074183976, "max_line_length": 666, "alphanum_fraction": 0.7565858112, "num_tokens": 11142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.10521054231434403, "lm_q1q2_score": 0.05137256082216398}}
{"text": "@testset \"21.merge-two-sorted-lists.jl\" begin\n    list1 = ListNode{Int}([1, 2, 4])\n    list2 = ListNode{Int}([1, 3, 4])\n    expected = ListNode{Int}([1, 1, 2, 3, 4, 4])\n    @test merge_two_lists(list1, list2) == expected\n    @test merge_two_lists(nothing, ListNode(0)) == ListNode(0)\n    @test merge_two_lists(nothing, nothing) === nothing\nend\n", "meta": {"hexsha": "1018a9bda3a51fb8b7915885c22bb8a422d950a1", "size": 344, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/21.merge-two-sorted-lists.jl", "max_stars_repo_name": "RexWzh/LeetCode.jl", "max_stars_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/problems/21.merge-two-sorted-lists.jl", "max_issues_repo_name": "RexWzh/LeetCode.jl", "max_issues_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/problems/21.merge-two-sorted-lists.jl", "max_forks_repo_name": "RexWzh/LeetCode.jl", "max_forks_repo_head_hexsha": "d86109b9d5a15491304ff9d2ee66e506f60d2146", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.2222222222, "max_line_length": 62, "alphanum_fraction": 0.6453488372, "num_tokens": 123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48828339529583464, "lm_q2_score": 0.10521053670308636, "lm_q1q2_score": 0.051372558082280036}}
{"text": "# ---\n# title: 769. Max Chunks To Make Sorted\n# id: problem769\n# author: Tian Jun\n# date: 2020-10-31\n# difficulty: Medium\n# categories: Array\n# link: <https://leetcode.com/problems/max-chunks-to-make-sorted/description/>\n# hidden: true\n# ---\n# \n# Given an array `arr` that is a permutation of `[0, 1, ..., arr.length - 1]`,\n# we split the array into some number of \"chunks\" (partitions), and individually\n# sort each chunk.  After concatenating them, the result equals the sorted\n# array.\n# \n# What is the most number of chunks we could have made?\n# \n# **Example 1:**\n# \n#     \n#     \n#     Input: arr = [4,3,2,1,0]\n#     Output: 1\n#     Explanation:\n#     Splitting into two or more chunks will not return the required result.\n#     For example, splitting into [4, 3], [2, 1, 0] will result in [3, 4, 0, 1, 2], which isn't sorted.\n#     \n# \n# **Example 2:**\n# \n#     \n#     \n#     Input: arr = [1,0,2,3,4]\n#     Output: 4\n#     Explanation:\n#     We can split into two chunks, such as [1, 0], [2, 3, 4].\n#     However, splitting into [1, 0], [2], [3], [4] is the highest number of chunks possible.\n#     \n# \n# **Note:**\n# \n#   * `arr` will have length in range `[1, 10]`.\n#   * `arr[i]` will be a permutation of `[0, 1, ..., arr.length - 1]`.\n# \n# \n# \n# \n## @lc code=start\nusing LeetCode\n\n## add your code here:\n## @lc code=end\n", "meta": {"hexsha": "27bdfc52fa8af1abced8f48970c4d6f591b10e3a", "size": 1329, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/unresolved/769.max-chunks-to-make-sorted.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2020-10-27T18:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:27:49.000Z", "max_issues_repo_path": "src/unresolved/769.max-chunks-to-make-sorted.jl", "max_issues_repo_name": "jmmshn/LeetCode.jl", "max_issues_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 57, "max_issues_repo_issues_event_min_datetime": "2020-11-01T07:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T11:57:53.000Z", "max_forks_repo_path": "src/unresolved/769.max-chunks-to-make-sorted.jl", "max_forks_repo_name": "jmmshn/LeetCode.jl", "max_forks_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-10-30T11:52:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T10:35:11.000Z", "avg_line_length": 24.6111111111, "max_line_length": 103, "alphanum_fraction": 0.5914221219, "num_tokens": 446, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882834101888134, "lm_q2_score": 0.10521053319605045, "lm_q1q2_score": 0.05137255793675087}}
{"text": "using Test\n\nmodule Foo\n    foo(x, y) = 1\n    foo(s::Integer, y) = 2\n    foo(s, y::Integer) = 3\n    foo(x::Integer, y::Integer) = 4\nend\n\n\nmodule Foo2\n    foo(x, y) = 1\n    foo(x::Integer, y) = 2\n    foo(s, y::Integer) = 3\nend\n\nmodule Foo4\n    import Main.Foo2\n    Foo2.foo(x::Integer, y::Integer) = 4\nend", "meta": {"hexsha": "5562658a65c0d3fa6af9d1653d3ee64aa32a4e01", "size": 303, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Chapter3/ambiguities.jl", "max_stars_repo_name": "diegomestre2/julia_exercises", "max_stars_repo_head_hexsha": "24368e2fa8093401d1b501ce9ba7ed6c2e4ff299", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Chapter3/ambiguities.jl", "max_issues_repo_name": "diegomestre2/julia_exercises", "max_issues_repo_head_hexsha": "24368e2fa8093401d1b501ce9ba7ed6c2e4ff299", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Chapter3/ambiguities.jl", "max_forks_repo_name": "diegomestre2/julia_exercises", "max_forks_repo_head_hexsha": "24368e2fa8093401d1b501ce9ba7ed6c2e4ff299", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.15, "max_line_length": 40, "alphanum_fraction": 0.5577557756, "num_tokens": 124, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4882833804028559, "lm_q2_score": 0.10521053600167916, "lm_q1q2_score": 0.05137255617289627}}
{"text": "## Exercise 8-1\n## Write a function that takes a string as an argument and displays the letters backward, one per line.\nprintln(\"Ans: \")\n\nfunction reverse(s::AbstractString)\n    s = s[end: -1: 1]\n\n    for i in s\n        println(i)\n    end\nend\n\nreverse(\"Hello world\")\n\nprintln(\"End.\")\n", "meta": {"hexsha": "a59dcdd9edacde4c24bb844c222518824b6a9ef9", "size": 284, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Chapter8/ex1.jl", "max_stars_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_stars_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-13T14:11:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-13T14:11:30.000Z", "max_issues_repo_path": "Chapter8/ex1.jl", "max_issues_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_issues_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter8/ex1.jl", "max_forks_repo_name": "yashppawar/ThinkJuliaExercises.jl", "max_forks_repo_head_hexsha": "72145b969dc51ebcac413a10004175ebc63cd5c2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.75, "max_line_length": 103, "alphanum_fraction": 0.6549295775, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.2422056177854002, "lm_q2_score": 0.21206880435710534, "lm_q1q2_score": 0.05136425577232387}}
{"text": "# runtests.jl\n\nusing Test: @test, @testset, detect_ambiguities\nusing Sinograms\n\n@testset \"Sinograms\" begin\n    # todo\n    include(\"fbp-par.jl\")\n    include(\"bdd_2d.jl\")\n    \n    @test length(detect_ambiguities(Sinograms)) == 0\nend\n", "meta": {"hexsha": "72485d60263b3de9a784da23c0a8b4f52db323eb", "size": 231, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "SoniaMinseoKim/Sinograms.jl", "max_stars_repo_head_hexsha": "48fb4fca1a846f710b0ac8e0a61e5b44c91966ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "SoniaMinseoKim/Sinograms.jl", "max_issues_repo_head_hexsha": "48fb4fca1a846f710b0ac8e0a61e5b44c91966ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "SoniaMinseoKim/Sinograms.jl", "max_forks_repo_head_hexsha": "48fb4fca1a846f710b0ac8e0a61e5b44c91966ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.7692307692, "max_line_length": 52, "alphanum_fraction": 0.683982684, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4186969093556867, "lm_q2_score": 0.12252320610998799, "lm_q1q2_score": 0.05130008772260176}}
{"text": "#EXAMPLE: broadcasting\n\nusing MPI\n\n\nMPI.Init()\ncomm = MPI.COMM_WORLD\nrank = MPI.Comm_rank(comm)\n\n\nif rank == 0\n    data = [7.0,8.0,9.0,10.0]\nelse\n    data = Float64[]\nend\n\ndata = MPI.bcast(data, 0, comm)\n@show rank, data\n\n\nMPI.Finalize()", "meta": {"hexsha": "1e924a961306066079c81659ceabf574f466fec7", "size": 237, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "broadcast2.jl", "max_stars_repo_name": "zhaotianjing/MPI_testing", "max_stars_repo_head_hexsha": "48472252e5421aee708ee89abdc9ba05ba1d416a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-04-26T18:13:43.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-14T18:36:57.000Z", "max_issues_repo_path": "broadcast2.jl", "max_issues_repo_name": "zhaotianjing/MPI_testing", "max_issues_repo_head_hexsha": "48472252e5421aee708ee89abdc9ba05ba1d416a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "broadcast2.jl", "max_forks_repo_name": "zhaotianjing/MPI_testing", "max_forks_repo_head_hexsha": "48472252e5421aee708ee89abdc9ba05ba1d416a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-09-18T16:01:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-09-18T16:01:43.000Z", "avg_line_length": 11.2857142857, "max_line_length": 31, "alphanum_fraction": 0.6455696203, "num_tokens": 83, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10230471678790089, "lm_q1q2_score": 0.051152358393950444}}
{"text": "module TestExpressionsFusion\n\nimport Test: @testset, @test\nimport Volcanito: fuse_conjunction\n\n@testset \"fuse_conjunction\" begin\n    test_cases = (\n        (\n            input = (:(x > y || z == 0), :(y < z)),\n            output = :((x > y || z == 0) && y < z),\n        ),\n        (\n            input = (:(x), :(y)),\n            output = :(x && y),\n        ),\n        (\n            input = (:(x > y), :(y < z)),\n            output = :(x > y && y < z),\n        ),\n        (\n            input = (:(a > 0), :(b < 1)),\n            output = :(a > 0 && b < 1),\n        ),\n    )\n    for case in test_cases\n        @test fuse_conjunction(case.input) == case.output\n    end\nend\n\nend\n", "meta": {"hexsha": "63f257070ef6729debb25e311c53e9c28aee054f", "size": 674, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/query/expression_operations/fusion.jl", "max_stars_repo_name": "johnmyleswhite/Volcanito.jl", "max_stars_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2020-08-28T14:55:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-21T09:54:01.000Z", "max_issues_repo_path": "test/query/expression_operations/fusion.jl", "max_issues_repo_name": "johnmyleswhite/Volcanito.jl", "max_issues_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 29, "max_issues_repo_issues_event_min_datetime": "2020-08-28T16:24:08.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-29T16:26:01.000Z", "max_forks_repo_path": "test/query/expression_operations/fusion.jl", "max_forks_repo_name": "johnmyleswhite/Volcanito.jl", "max_forks_repo_head_hexsha": "d182ea3715016e3ef215cd54e8737ef422132350", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-09-06T14:21:02.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-12T10:14:07.000Z", "avg_line_length": 21.7419354839, "max_line_length": 57, "alphanum_fraction": 0.3857566766, "num_tokens": 196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.10230470926115266, "lm_q1q2_score": 0.05115235463057633}}
{"text": "using Printf\n\n_drop_moi(s) = replace(string(s), \"MathOptInterface.\" => \"\")\n\nstruct _PrintOptions{T<:MIME}\n    simplify_coefficients::Bool\n    default_name::String\n    print_types::Bool\n\n    \"\"\"\n        _PrintOptions(\n            mime::MIME;\n            simplify_coefficients::Bool = false,\n            default_name::String = \"v\",\n            print_types::Bool = true,\n        )\n\n    A struct to control options for printing.\n\n    ## Arguments\n\n     * `simplify_coefficients` : Simplify coefficients if possible by omitting\n       them or removing trailing zeros.\n     * `default_name` : The name given to variables with an empty name.\n     * `print_types` : Print the MOI type of each function and set for clarity.\n    \"\"\"\n    function _PrintOptions(\n        mime::MIME;\n        simplify_coefficients::Bool = false,\n        default_name::String = \"v\",\n        print_types::Bool = true,\n    )\n        return new{typeof(mime)}(\n            simplify_coefficients,\n            default_name,\n            print_types,\n        )\n    end\nend\n\nfunction _to_string(mime::MIME, args...; kwargs...)\n    return _to_string(_PrintOptions(mime), args...; kwargs...)\nend\n\n#------------------------------------------------------------------------\n# Math Symbols\n#------------------------------------------------------------------------\n\n# REPL-specific symbols\n# Anything here: https://en.wikipedia.org/wiki/Windows-1252\n# should probably work fine on Windows\n\n_to_string(::_PrintOptions, ::typeof(*)) = \"*\"\n_to_string(::_PrintOptions{MIME\"text/latex\"}, ::typeof(*)) = \"\\\\times \"\n\nfunction _to_string(::_PrintOptions, ::typeof(^), n::Int)\n    return n == 2 ? \"\u00b2\" : string('^', n)\nend\n\nfunction _to_string(::_PrintOptions{MIME\"text/latex\"}, ::typeof(^), n::Int)\n    return string('^', n)\nend\n\n_to_string(::_PrintOptions, ::typeof(in)) = @static Sys.iswindows() ? \"in\" : \"\u2208\"\n\n#------------------------------------------------------------------------\n# Functions\n#------------------------------------------------------------------------\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    v::MOI.VariableIndex,\n)\n    var_name = MOI.get(model, MOI.VariableName(), v)\n    if isempty(var_name)\n        return string(options.default_name, \"[\", v.value, \"]\")\n    else\n        return var_name\n    end\nend\n\nfunction _to_string(\n    options::_PrintOptions{MIME\"text/latex\"},\n    model::MOI.ModelLike,\n    v::MOI.VariableIndex,\n)\n    var_name = MOI.get(model, MOI.VariableName(), v)\n    if isempty(var_name)\n        return string(options.default_name, \"_{\", v.value, \"}\")\n    end\n    # We need to escape latex math characters that appear in the name.\n    # However, it's probably impractical to catch everything, so let's just\n    # escape the common ones:\n    # Escape underscores to prevent them being treated as subscript markers.\n    var_name = replace(var_name, \"_\" => \"\\\\_\")\n    # Escape carets to prevent them being treated as superscript markers.\n    var_name = replace(var_name, \"^\" => \"\\\\^\")\n    # Convert any x[args] to x_{args} so that indices on x print as subscripts.\n    m = match(r\"^(.*)\\[(.+)\\]$\", var_name)\n    if m !== nothing\n        var_name = m[1] * \"_{\" * m[2] * \"}\"\n    end\n    return var_name\nend\n\nfunction _shorten(options::_PrintOptions, x::Float64)\n    if options.simplify_coefficients && isinteger(x)\n        return string(round(Int, x))\n    end\n    return string(x)\nend\n\n_shorten(::_PrintOptions, x) = string(\"(\", x, \")\")\n\n\"\"\"\n    _to_string(options::_PrintOptions, c::Real, x::String)\n\nWrite a coefficient-name pair to string. There are a few cases to handle.\n\n          | is_first | !is_first\n    -----------------------------\n    +2.1x | \"2.1 x\"  | \" + 2.1 x\"\n    -2.1x | \"-2.1 x\" | \" - 2.1 x\"\n    -----------------------------\n    +2.0x | \"2 x\"    | \" + 2 x\"\n    -2.0x | \"-2 x\"   | \" - 2 x\"\n    +1.0x | \"x\"      | \" + x\"\n    -1.0x | \"-x\"     | \" - x\"\n\"\"\"\nfunction _to_string(options::_PrintOptions, c::Real, x::String; is_first::Bool)\n    prefix = if is_first\n        c < 0 ? \"-\" : \"\"\n    else\n        c < 0 ? \" - \" : \" + \"\n    end\n    s = _shorten(options, abs(c))\n    if options.simplify_coefficients && s == \"1\"\n        return string(prefix, x)\n    else\n        return string(prefix, s, \" \", x)\n    end\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    c::Number,\n    x::String;\n    is_first::Bool,\n)\n    return string(is_first ? \"\" : \" + \", _shorten(options, c), \" \", x)\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    term::MOI.ScalarAffineTerm;\n    is_first::Bool,\n)\n    name = _to_string(options, model, term.variable)\n    return _to_string(options, term.coefficient, name; is_first = is_first)\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    f::MOI.ScalarAffineFunction,\n)\n    s = _shorten(options, f.constant)\n    if options.simplify_coefficients && iszero(f.constant)\n        s = \"\"\n    end\n    is_first = isempty(s)\n    for term in f.terms\n        s *= _to_string(options, model, term; is_first = is_first)\n        is_first = false\n    end\n    return s\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    term::MOI.ScalarQuadraticTerm;\n    is_first::Bool,\n)\n    name_1 = _to_string(options, model, term.variable_1)\n    name_2 = _to_string(options, model, term.variable_2)\n    # Be careful here when printing the coefficient. ScalarQuadraticFunction\n    # assumes an additional 0.5 factor!\n    coef = term.coefficient\n    name = if term.variable_1 == term.variable_2\n        coef /= 2\n        string(name_1, _to_string(options, ^, 2))\n    else\n        string(name_1, _to_string(options, *), name_2)\n    end\n    return _to_string(options, coef, name; is_first = is_first)\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    f::MOI.ScalarQuadraticFunction,\n)\n    s = _shorten(options, f.constant)\n    if options.simplify_coefficients && iszero(f.constant)\n        s = \"\"\n    end\n    is_first = isempty(s)\n    for term in f.affine_terms\n        s *= _to_string(options, model, term; is_first = is_first)\n        is_first = false\n    end\n    for term in f.quadratic_terms\n        s *= _to_string(options, model, term; is_first = is_first)\n        is_first = false\n    end\n    return s\nend\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    f::MOI.AbstractVectorFunction,\n)\n    rows = map(fi -> _to_string(options, model, fi), eachscalar(f))\n    max_length = maximum(length.(rows))\n    s = join(map(r -> string(\"\u2502\", rpad(r, max_length), \"\u2502\"), rows), '\\n')\n    return string(\n        \"\u250c\",\n        rpad(\"\", max_length),\n        \"\u2510\\n\",\n        s,\n        \"\\n\u2514\",\n        rpad(\"\", max_length),\n        \"\u2518\",\n    )\nend\n\nfunction _to_string(\n    options::_PrintOptions{MIME\"text/latex\"},\n    model::MOI.ModelLike,\n    f::MOI.AbstractVectorFunction,\n)\n    return string(\n        \"\\\\begin{bmatrix}\\n\",\n        join(\n            map(fi -> _to_string(options, model, fi), eachscalar(f)),\n            \"\\\\\\\\\\n\",\n        ),\n        \"\\\\end{bmatrix}\",\n    )\nend\n\n#------------------------------------------------------------------------\n# Sets\n#------------------------------------------------------------------------\n\nfunction _to_string(options::_PrintOptions, set::MOI.LessThan)\n    return string(\"<= \", _shorten(options, set.upper))\nend\n\nfunction _to_string(options::_PrintOptions{MIME\"text/latex\"}, set::MOI.LessThan)\n    return string(\"\\\\le \", _shorten(options, set.upper))\nend\n\nfunction _to_string(options::_PrintOptions, set::MOI.GreaterThan)\n    return string(\">= \", _shorten(options, set.lower))\nend\n\nfunction _to_string(\n    options::_PrintOptions{MIME\"text/latex\"},\n    set::MOI.GreaterThan,\n)\n    return string(\"\\\\ge \", _shorten(options, set.lower))\nend\n\nfunction _to_string(options::_PrintOptions, set::MOI.EqualTo)\n    return string(\"== \", _shorten(options, set.value))\nend\n\nfunction _to_string(options::_PrintOptions{MIME\"text/latex\"}, set::MOI.EqualTo)\n    return string(\"= \", _shorten(options, set.value))\nend\n\nfunction _to_string(options::_PrintOptions, set::MOI.Interval)\n    return string(\n        _to_string(options, in),\n        \" [\",\n        _shorten(options, set.lower),\n        \", \",\n        _shorten(options, set.upper),\n        \"]\",\n    )\nend\n\nfunction _to_string(options::_PrintOptions{MIME\"text/latex\"}, set::MOI.Interval)\n    return string(\n        \"\\\\in \\\\[\",\n        _shorten(options, set.lower),\n        \", \",\n        _shorten(options, set.upper),\n        \"\\\\]\",\n    )\nend\n\nfunction _to_string(options::_PrintOptions, ::MOI.ZeroOne)\n    return string(_to_string(options, in), \" {0, 1}\")\nend\n\n_to_string(::_PrintOptions{MIME\"text/latex\"}, ::MOI.ZeroOne) = \"\\\\in \\\\{0, 1\\\\}\"\n\nfunction _to_string(options::_PrintOptions, ::MOI.Integer)\n    return string(_to_string(options, in), \" \u2124\")\nend\n\nfunction _to_string(::_PrintOptions{MIME\"text/latex\"}, ::MOI.Integer)\n    return \"\\\\in \\\\mathbb{Z}\"\nend\n\nfunction _to_string(options::_PrintOptions, set::MOI.AbstractSet)\n    return string(_to_string(options, in), \" \", _drop_moi(set))\nend\n\nfunction _to_string(::_PrintOptions{MIME\"text/latex\"}, set::MOI.AbstractSet)\n    set_str = replace(replace(_drop_moi(set), \"{\" => \"\\\\{\"), \"}\" => \"\\\\}\")\n    return string(\"\\\\in \\\\text{\", set_str, \"}\")\nend\n\n#------------------------------------------------------------------------\n# Constraints\n#------------------------------------------------------------------------\n\nfunction _to_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    cref::MOI.ConstraintIndex,\n)\n    f = MOI.get(model, MOI.ConstraintFunction(), cref)\n    s = MOI.get(model, MOI.ConstraintSet(), cref)\n    return string(_to_string(options, model, f), \" \", _to_string(options, s))\nend\n\n#------------------------------------------------------------------------\n# Nonlinear constraints\n#------------------------------------------------------------------------\n\n\"\"\"\n    _VariableNode\n\nA type used to work-around the default printing of Julia expressions.\n\nWithout this type, if we subsititued the variable names into the expression\nand then converted to a string, each variable would be printed with enclosing\n`\"`.\n\nTo work-around this, create a new type and overload `show`.\n\"\"\"\nstruct _VariableNode\n    x::String\nend\nBase.show(io::IO, x::_VariableNode) = print(io, x.x)\n\n_replace_names(::_PrintOptions, ::MOI.ModelLike, x, ::Any) = x\nfunction _replace_names(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    x::Expr,\n    lookup,\n)\n    if x.head == :ref\n        return get!(lookup, x.args[2]) do\n            return _VariableNode(_to_string(options, model, x.args[2]))\n        end\n    else\n        for (i, arg) in enumerate(x.args)\n            x.args[i] = _replace_names(options, model, arg, lookup)\n        end\n    end\n    return x\nend\n\nfunction _replace_nonlinear_latex(::_PrintOptions{MIME\"text/latex\"}, s::String)\n    s = replace(s, \" * \" => \" \\\\times \")\n    s = replace(s, \" >= \" => \" \\\\ge \")\n    s = replace(s, \" <= \" => \" \\\\le \")\n    s = replace(s, \" == \" => \" = \")\n    return s\nend\n_replace_nonlinear_latex(::_PrintOptions, s::String) = s\n\nfunction _print_nonlinear_constraints(\n    io::IO,\n    options::_PrintOptions{MIME\"text/plain\"},\n    model::MOI.ModelLike,\n    block::MOI.NLPBlockData,\n)\n    lookup = Dict{MOI.VariableIndex,_VariableNode}()\n    if options.print_types\n        println(io, \"\\nNonlinear\")\n    end\n    has_expr = :ExprGraph in MOI.features_available(block.evaluator)\n    for (i, bound) in enumerate(block.constraint_bounds)\n        if has_expr\n            ex = MOI.constraint_expr(block.evaluator, i)\n            println(io, \" \", _replace_names(options, model, ex, lookup))\n        else\n            println(io, \" \", bound.lower, \" <= g_$(i)(x) <= \", bound.upper)\n        end\n    end\nend\n\nfunction _print_nonlinear_constraints(\n    io::IO,\n    options::_PrintOptions{MIME\"text/latex\"},\n    model::MOI.ModelLike,\n    block::MOI.NLPBlockData,\n)\n    lookup = Dict{MOI.VariableIndex,_VariableNode}()\n    if options.print_types\n        println(io, \" & \\\\text{Nonlinear} \\\\\\\\\")\n    end\n    has_expr = :ExprGraph in MOI.features_available(block.evaluator)\n    for (i, bound) in enumerate(block.constraint_bounds)\n        if has_expr\n            ex = MOI.constraint_expr(block.evaluator, i)\n            nl_c = string(_replace_names(options, model, ex, lookup))\n            println(io, \" & \", _replace_nonlinear_latex(options, nl_c), \" \\\\\\\\\")\n        else\n            println(\n                io,\n                \"& \",\n                bound.lower,\n                \" \\\\le g_$(i)(x) \\\\le \",\n                bound.upper,\n                \" \\\\\\\\\",\n            )\n        end\n    end\nend\n\nfunction _print_nonlinear_constraints(\n    ::IO,\n    ::_PrintOptions,\n    ::MOI.ModelLike,\n    ::Nothing,\n)\n    return\nend\n\n#------------------------------------------------------------------------\n# ObjectiveFunction\n#------------------------------------------------------------------------\n\nfunction _objective_function_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    ::Nothing,\n)\n    F = MOI.get(model, MOI.ObjectiveFunctionType())\n    f = MOI.get(model, MOI.ObjectiveFunction{F}())\n    return _drop_moi(F), _to_string(options, model, f)\nend\n\nfunction _objective_function_string(\n    options::_PrintOptions,\n    model::MOI.ModelLike,\n    block::MOI.NLPBlockData,\n)\n    lookup = Dict{MOI.VariableIndex,_VariableNode}()\n    if block.has_objective\n        f = \"f(x)\"\n        if :ExprGraph in MOI.features_available(block.evaluator)\n            ex = MOI.objective_expr(block.evaluator)\n            f = string(_replace_names(options, model, ex, lookup))\n            f = _replace_nonlinear_latex(options, f)\n        end\n        return \"Nonlinear\", f\n    else\n        return _objective_function_string(options, model, nothing)\n    end\nend\n\n#------------------------------------------------------------------------\n# MOI.ModelLike\n#------------------------------------------------------------------------\n\nfunction _nlp_block(model::MOI.ModelLike)\n    try\n        block = MOI.get(model, MOI.NLPBlock())\n        if block === nothing\n            return\n        end\n        if :ExprGraph in MOI.features_available(block.evaluator)\n            MOI.initialize(block.evaluator, [:ExprGraph])\n        end\n        return block\n    catch\n        return nothing\n    end\nend\n\n\"\"\"\n    _print_model(\n        io::IO,\n        options::_PrintOptions{MIME\"text/plain\"},\n        model::MOI.ModelLike,\n    )\n\nPrint a plain-text formulation of `model` to `io`.\n\"\"\"\nfunction _print_model(\n    io::IO,\n    options::_PrintOptions{MIME\"text/plain\"},\n    model::MOI.ModelLike,\n)\n    nlp_block = _nlp_block(model)\n    sense = MOI.get(model, MOI.ObjectiveSense())\n    if sense == MOI.FEASIBILITY_SENSE\n        println(io, \"Feasibility\")\n    else\n        F, f = _objective_function_string(options, model, nlp_block)\n        sense_s = sense == MOI.MIN_SENSE ? \"Minimize\" : \"Maximize\"\n        if options.print_types\n            println(io, sense_s, \" \", F, \":\\n \", f)\n        else\n            println(io, sense_s, \": \", f)\n        end\n    end\n    println(io, \"\\nSubject to:\")\n    for (F, S) in MOI.get(model, MOI.ListOfConstraintTypesPresent())\n        if options.print_types\n            println(io, \"\\n$(_drop_moi(F))-in-$(_drop_moi(S))\")\n        end\n        for cref in MOI.get(model, MOI.ListOfConstraintIndices{F,S}())\n            s = _to_string(options, model, cref)\n            println(io, \" \", replace(s, '\\n' => \"\\n \"))\n        end\n    end\n    _print_nonlinear_constraints(io, options, model, nlp_block)\n    return\nend\n\n\"\"\"\n    _print_model(\n        io::IO,\n        options::_PrintOptions{MIME\"text/latex\"},\n        model::MOI.ModelLike,\n    )\n\nPrint a LaTeX formulation of `model` to `io`.\n\"\"\"\nfunction _print_model(\n    io::IO,\n    options::_PrintOptions{MIME\"text/latex\"},\n    model::MOI.ModelLike,\n)\n    nlp_block = _nlp_block(model)\n    println(io, \"\\$\\$ \\\\begin{aligned}\")\n    sense = MOI.get(model, MOI.ObjectiveSense())\n    if sense == MOI.FEASIBILITY_SENSE\n        println(io, \"\\\\text{feasibility}\\\\\\\\\")\n    else\n        F, f = _objective_function_string(options, model, nlp_block)\n        sense_s = sense == MOI.MIN_SENSE ? \"min\" : \"max\"\n        println(io, \"\\\\\", sense_s, \"\\\\quad & \", f, \" \\\\\\\\\")\n    end\n    println(io, \"\\\\text{Subject to}\\\\\\\\\")\n    for (F, S) in MOI.get(model, MOI.ListOfConstraintTypesPresent())\n        if options.print_types\n            println(io, \" & \\\\text{$(_drop_moi(F))-in-$(_drop_moi(S))} \\\\\\\\\")\n        end\n        for cref in MOI.get(model, MOI.ListOfConstraintIndices{F,S}())\n            println(io, \" & \", _to_string(options, model, cref), \" \\\\\\\\\")\n        end\n    end\n    _print_nonlinear_constraints(io, options, model, nlp_block)\n    return print(io, \"\\\\end{aligned} \\$\\$\")\nend\n\n#------------------------------------------------------------------------\n# Latex\n#------------------------------------------------------------------------\n\nstruct _LatexModel{T<:MOI.ModelLike}\n    model::T\n    kwargs::Any\nend\n\n\"\"\"\n    latex_formulation(model::MOI.ModelLike; kwargs...)\n\nWrap `model` in a type so that it can be pretty-printed as `text/latex` in a\nnotebook like IJulia, or in Documenter.\n\nTo render the model, end the cell with `latex_formulation(model)`, or call\n`display(latex_formulation(model))` in to force the display of the model from\ninside a function.\n\nPossible keyword arguments are:\n\n * `simplify_coefficients` : Simplify coefficients if possible by omitting\n   them or removing trailing zeros.\n * `default_name` : The name given to variables with an empty name.\n * `print_types` : Print the MOI type of each function and set for clarity.\n\"\"\"\nlatex_formulation(model::MOI.ModelLike; kwargs...) = _LatexModel(model, kwargs)\n\nfunction Base.show(io::IO, model::_LatexModel)\n    return _print_model(\n        io,\n        _PrintOptions(MIME(\"text/latex\"); model.kwargs...),\n        model.model,\n    )\nend\n\nBase.show(io::IO, ::MIME\"text/latex\", model::_LatexModel) = show(io, model)\n\nfunction Base.print(model::MOI.ModelLike; kwargs...)\n    for d in Base.Multimedia.displays\n        if Base.Multimedia.displayable(d, \"text/latex\") &&\n           startswith(\"$(typeof(d))\", \"IJulia.\")\n            return display(d, \"text/latex\", latex_formulation(model; kwargs...))\n        end\n    end\n    return print(stdout, model; kwargs...)\nend\n\nfunction Base.print(io::IO, model::MOI.ModelLike; kwargs...)\n    return _print_model(io, _PrintOptions(MIME(\"text/plain\"); kwargs...), model)\nend\n", "meta": {"hexsha": "017779fcd66603dccd3b12daf1153c43f66e02d5", "size": 18453, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utilities/print.jl", "max_stars_repo_name": "jump-dev/MathOptInterface.jl", "max_stars_repo_head_hexsha": "dfc7c613ce5193f3ae872921668bd0f267f37c60", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 132, "max_stars_repo_stars_event_min_datetime": "2020-06-20T00:45:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T22:06:34.000Z", "max_issues_repo_path": "src/Utilities/print.jl", "max_issues_repo_name": "jump-dev/MathOptInterface.jl", "max_issues_repo_head_hexsha": "dfc7c613ce5193f3ae872921668bd0f267f37c60", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 460, "max_issues_repo_issues_event_min_datetime": "2020-06-08T14:12:55.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:03:39.000Z", "max_forks_repo_path": "src/Utilities/print.jl", "max_forks_repo_name": "jump-dev/MathOptInterface.jl", "max_forks_repo_head_hexsha": "dfc7c613ce5193f3ae872921668bd0f267f37c60", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 28, "max_forks_repo_forks_event_min_datetime": "2020-06-08T01:45:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T10:36:25.000Z", "avg_line_length": 29.1516587678, "max_line_length": 80, "alphanum_fraction": 0.5820191839, "num_tokens": 4706, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834617637482, "lm_q2_score": 0.10818896462614713, "lm_q1q2_score": 0.051139134324122926}}
{"text": "module Traversals\n\n# This module does not export much. The primary reasons are that exporting would increase the\n# likelihood of name conflicts, and because the functions here should be used only by developers\n# to create new graph algorithms that rely on breadth-first or depth-first search traversals.\n\nusing LightGraphs\nusing SimpleTraits\n\"\"\"\n    abstract type TraversalAlgorithm\n\n`TraversalAlgorithm` is the abstract type used to specify breadth-first traversal (`BFS`) or\ndepth-first traversal (`DFS`) for the various traversal functions.\n\"\"\"\nabstract type TraversalAlgorithm end\n\n\"\"\"\n    abstract type AbstractTraversalState\n\n`AbstractTraversalState` is the abstract type used to hold mutable states\nfor various traversal algorithms (see [`traverse_graph!`](@ref)).\n\nWhen creating concrete types, you should override the following functions where relevant. These functions\nare listed in order of occurrence in the traversal:\n- [`initfn!(<:AbstractTraversalState, u::Integer)`](@ref): runs prior to traversal, used to set initial state.\n- [`previsitfn!(<:AbstractTraversalState, u::Integer)`](@ref): runs prior to neighborhood discovery for vertex `u`.\n- [`visitfn!(<:AbstractTraversalState, u::Integer, v::Integer)`](@ref): runs for each neighbor `v` (newly-discovered or not) of vertex `u`.\n- [`newvisitfn!(<:AbstractTraversalState, u::Integer, v::Integer)`](@ref): runs when a new neighbor `v` of vertex `u` is discovered.\n- [`postvisitfn!(<:AbstractTraversalState, u::Integer)`](@ref): runs after neighborhood discovery for vertex `u`.\n- [`postlevelfn!(<:AbstractTraversalState)`](@ref): runs after each traversal level.\n\nEach of these functions should return a boolean. If the return value of the function is `false`, the traversal will return the state\nimmediately. Otherwise, the traversal will continue.\n\nFor better performance, use the `@inline` directive and make your functions branch-free.\n\"\"\"\nabstract type AbstractTraversalState end\n\n\"\"\"\n    initfn!(state, u)\n\nModify [`AbstractTraversalState`](@ref) `state` on initialization of traversal\nwith source vertices, and return `true` if successful; `false` otherwise.\n`initfn!` will be called once for each vertex passed to [`traverse_graph!`](@ref).\n\"\"\"\n@inline initfn!(::AbstractTraversalState, u) = true\n\n\"\"\"\n    previsitfn!(state, u)\n\nModify [`AbstractTraversalState`](@ref) `state` before examining neighbors of vertex `u`,\nand return `true` if successful; `false` otherwise.\n\"\"\"\n@inline previsitfn!(::AbstractTraversalState, u) = true\n\n\"\"\"\n    newvisitfn!(state, u, v)\n\nModify [`AbstractTraversalState`](@ref) `state` when the first edge between `u` and `v` is encountered,\nand return `true` if successful; `false` otherwise.\n\"\"\"\n@inline newvisitfn!(::AbstractTraversalState, u, v) = true\n\n\"\"\"\n    visitfn!(state, u, v)\n\nModify [`AbstractTraversalState`](@ref) `state` when the edge between `u` and `v` is encountered,\nand return `true` if successful; `false` otherwise. Note: `visitfn!` may be called multiple times\nper edge, depending on the traversal algorithm, for a function that operates on the first occurrence\nonly, use [`newvisitfn!`](@ref).\n\"\"\"\n@inline visitfn!(::AbstractTraversalState, u, v) = true\n\n\"\"\"\n    postvisitfn!(state, u)\n\nModify [`AbstractTraversalState`](@ref) `state` after having examined all neighbors of vertex `u`,\nand return `true` if successful; `false` otherwise.\n\"\"\"\n@inline postvisitfn!(::AbstractTraversalState, u) = true\n\"\"\"\n    postlevelfn!(state)\n\nModify [`AbstractTraversalState`](@ref) `state` before moving to the next vertex in the traversal algorithm,\nand return `true` if successful; `false` otherwise.\n\"\"\"\n@inline postlevelfn!(::AbstractTraversalState) = true\n\n##############\n# functions common to both BFS and DFS\n##############\n\"\"\"\n    traverse_graph!(g, s, alg, state, neighborfn=outneighbors)\n    traverse_graph!(g, ss, alg, state, neighborfn=outneighbors)\n\nTraverse a graph `g` from source vertex `s` / vertices `ss` keeping track of `state`. Return `true` if\ntraversal finished normally; `false` if one of the visit functions returned `false` (see )\n\n\"\"\"\ntraverse_graph!(g::AbstractGraph, s::Integer, alg, state, neighborfn) = traverse_graph!(g, [s], alg, state, neighborfn)\ntraverse_graph!(g::AbstractGraph, s::Integer, alg, state) = traverse_graph!(g, [s], alg, state)\n\nstruct VisitState{T<:Integer} <: AbstractTraversalState\n    visited::Vector{T}\nend\n\n@inline function initfn!(s::VisitState, u)\n    push!(s.visited, u)\n    return true\nend\n@inline function newvisitfn!(s::VisitState, u, v)\n    push!(s.visited, v)\n    return true\nend\n\n\"\"\"\n    visited_vertices(g, s, alg)\n    visited_vertices(g, ss, alg)\n\nReturn a vector representing the vertices of `g` visited in order by [`TraversalAlgorithm`](@ref) `alg`\nstarting at vertex `s` (vertices `ss`).\n\"\"\"\nfunction visited_vertices(\n    g::AbstractGraph{U},\n    ss::AbstractVector,\n    alg::TraversalAlgorithm\n    ) where U<:Integer\n\n    v = Vector{U}()\n    sizehint!(v, nv(g))  # actually just the largest connected component, but we'll use this.\n    state = VisitState(v)\n    traverse_graph!(g, ss, alg, state)\n\n    return state.visited\nend\n\nvisited_vertices(g::AbstractGraph, s::Integer, alg::TraversalAlgorithm) = visited_vertices(g, [s], alg)\n\n\nmutable struct ParentState{T<:Integer} <: AbstractTraversalState\n    parents::Vector{T}\nend\n\n@inline function newvisitfn!(s::ParentState, u, v) \n    s.parents[v] = u\n    return true\nend\n\n\"\"\"\n    parents(g, s, alg, neighborfn=outneighbors)\n\nReturn a vector of parent vertices indexed by vertex using [`TraversalAlgorithm`](@ref) `alg` starting with\nvertex `s`. If `neighborfn` is specified, use the corresponding neighbor-generation function ([`inneighbors`](@ref)r\nand [`outneighbors`](@ref) are valid values).\n\n### Performance\nThis implementation is designed to perform well on large graphs. There are\nimplementations which are marginally faster in practice for smaller graphs,\nbut the performance improvements using this implementation on large graphs\ncan be significant.\n\"\"\"\nfunction parents(g::AbstractGraph{T}, s::Integer, alg::TraversalAlgorithm, neighborfn=outneighbors) where T\n    parents = zeros(T, nv(g))\n    state = ParentState(parents)\n\n    traverse_graph!(g, s, alg, state, neighborfn)\n    return state.parents\nend\n\n\ninclude(\"bfs.jl\")\ninclude(\"dfs.jl\")\n\n\"\"\"\n    tree(p)\n\nReturn a directed acyclic graph based on a [`parents`](@ref) vector `p`.\n\"\"\"\nfunction tree(p::AbstractVector{T}) where T <: Integer\n    n = T(length(p))\n    t = DiGraph{T}(n)\n    for (v, u) in enumerate(p)\n        if u > zero(T) && u != v\n            add_edge!(t, u, v)\n        end\n    end\n    return t\nend\n\n\"\"\"\n    tree(g, s, alg, neighborfn=outneighbors)\n\nReturn a directed acyclic graph based on traversal of the graph `g` starting with source vertex `s`\nusing algorithm `alg` with neighbor function `neighborfn`.\n\"\"\"\nfunction tree(g::AbstractGraph, s::Integer, alg::TraversalAlgorithm, neighborfn::Function=outneighbors)\n    p = parents(g, s, alg, neighborfn)\n    return tree(p)\nend\n\nexport visited_vertices, parents, distances, topological_sort, tree\n\nend  # module\n", "meta": {"hexsha": "e60cec674f5c7b9c232a1c3afbc7d7ce8cbb1b3c", "size": 7077, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Experimental/Traversals/Traversals.jl", "max_stars_repo_name": "pitsianis/LightGraphs.jl", "max_stars_repo_head_hexsha": "90989ef1248115c8735bff2f5d5775329b7f4eb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 745, "max_stars_repo_stars_event_min_datetime": "2015-03-19T03:29:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-07T00:59:06.000Z", "max_issues_repo_path": "src/Experimental/Traversals/Traversals.jl", "max_issues_repo_name": "pitsianis/LightGraphs.jl", "max_issues_repo_head_hexsha": "90989ef1248115c8735bff2f5d5775329b7f4eb0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1491, "max_issues_repo_issues_event_min_datetime": "2015-03-19T17:04:59.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-08T14:47:57.000Z", "max_forks_repo_path": "src/Experimental/Traversals/Traversals.jl", "max_forks_repo_name": "pitsianis/LightGraphs.jl", "max_forks_repo_head_hexsha": "90989ef1248115c8735bff2f5d5775329b7f4eb0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 288, "max_forks_repo_forks_event_min_datetime": "2015-04-04T14:31:40.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-30T10:37:21.000Z", "avg_line_length": 34.6911764706, "max_line_length": 139, "alphanum_fraction": 0.7234703971, "num_tokens": 1768, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.10818895456207035, "lm_q1q2_score": 0.05113913117432945}}
{"text": "n = parse(Int64, readline())\n\ndict = Dict{String, Int64}() \n\nfor i in 1:n\n    A = split(readline(), \" \")\n    dict[A[1]] = parse(Int64, A[2])\nend\n\nfor i in 1:n\n    key = readline()\n    val = get(dict,key,\"-1\")\n    if val == \"-1\"\n        println(\"Not found\")\n    else\n        println(key,\"=\",val)\n    end\nend", "meta": {"hexsha": "a7b751c130de81c92ec2c8497790a7f892e00ece", "size": 306, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Hackerrank/30 Days of Code/Julia/day 08.jl", "max_stars_repo_name": "Next-Gen-UI/Code-Dynamics", "max_stars_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Hackerrank/30 Days of Code/Julia/day 08.jl", "max_issues_repo_name": "Next-Gen-UI/Code-Dynamics", "max_issues_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Hackerrank/30 Days of Code/Julia/day 08.jl", "max_forks_repo_name": "Next-Gen-UI/Code-Dynamics", "max_forks_repo_head_hexsha": "a9b9d5e3f27e870b3e030c75a1060d88292de01c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.0, "max_line_length": 35, "alphanum_fraction": 0.5163398693, "num_tokens": 101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4726834766204328, "lm_q2_score": 0.10818894737344459, "lm_q1q2_score": 0.05113912777638483}}
{"text": "export AbstractAgent, @agent, GraphAgent, GridAgent, ContinuousAgent, OSMAgent\n\n\"\"\"\n    YourAgentType <: AbstractAgent\nAgents participating in Agents.jl simulations are instances of user-defined Types that\nare subtypes of `AbstractAgent`. It is almost always the case that mutable Types make\nfor a simpler modelling experience.\n\nYour agent type(s) **must have** the `id` field as first field.\nDepending on the space structure there might be a `pos` field of appropriate type\nand a `vel` field of appropriate type.\nEach space structure quantifies precicely what extra fields (if any) are necessary,\nhowever we recommend to use the [`@agent`](@ref) macro to help you create the agent type.\n\nYour agent type may have other additional fields relevant to your system,\nfor example variable quantities like \"status\" or other \"counters\".\n\nAs an example, a [`GraphSpace`](@ref) requires an `id::Int` field and a `pos::Int` field.\nTo make an agent with two additional properties, `weight, happy`, we'd write\n```julia\nmutable struct ExampleAgent <: AbstractAgent\n    id::Int\n    pos::Int\n    weight::Float64\n    happy::Bool\nend\n```\n\"\"\"\nabstract type AbstractAgent end\n\n\"\"\"\n    @agent YourAgentType{X, Y} AgentSupertype begin\n        some_property::X\n        other_extra_property::Y\n        # etc...\n    end\n\nCreate a struct for your agents which includes the mandatory fields required to operate\nin a particular space. Depending on the space of your model, the `AgentSupertype` is\nchosen appropriately from [`GraphAgent`](@ref), [`GridAgent`](@ref),\n[`ContinuousAgent`](@ref), [`OSMAgent`](@ref).\n\n## Example\nUsing\n```julia\n@agent Person{T} GridAgent{2} begin\n    age::Int\n    moneyz::T\nend\n```\nwill in fact create an agent appropriate for using with 2-dimensional [`GridSpace`](@ref)\n```julia\nmutable struct Person{T} <: AbstractAgent\n    id::Int\n    pos::NTuple{2, Int}\n    age::Int\n    moneyz::T\nend\n```\n\"\"\"\nmacro agent(name, base, fields)\n    base_type = Core.eval(@__MODULE__, base)\n    base_fieldnames = fieldnames(base_type)\n    base_types = [t for t in base_type.types]\n    base_fields = [:($f::$T) for (f, T) in zip(base_fieldnames, base_types)]\n    res = :(mutable struct $(esc(name)) <: AbstractAgent end)\n    push!(res.args[end].args, base_fields...)\n    push!(res.args[end].args, map(esc,fields.args)...)\n    return res\nend\n\n\"\"\"\n    GraphAgent\nCombine with [`@agent`](@ref) to create an agent type for [`GraphSpace`](@ref).\nIt attributes the fields `id::Int, pos::Int` to the start of the agent type.\n\"\"\"\nmutable struct GraphAgent <: AbstractAgent\n    id::Int\n    pos::Int\nend\n\n\"\"\"\n    GridAgent{D}\nCombine with [`@agent`](@ref) to create an agent type for `D`-dimensional\n[`GridSpace`](@ref). It attributes the fields `id::Int, pos::NTuple{D,Int}`\nto the start of the agent type.\n\"\"\"\nmutable struct GridAgent{D} <: AbstractAgent\n    id::Int\n    pos::Dims{D}\nend\n\n\"\"\"\n    ContinuousAgent{D}\nCombine with [`@agent`](@ref) to create an agent type for `D`-dimensional\n[`ContinuousSpace`](@ref). It attributes the fields\n`id::Int, pos::NTuple{D,Float64}, vel::NTuple{D,Float64}`\nto the start of the agent type.\n\"\"\"\nmutable struct ContinuousAgent{D} <: AbstractAgent\n    id::Int\n    pos::NTuple{D,Float64}\n    vel::NTuple{D,Float64}\nend\n\n\"\"\"\n    OSMAgent\nCombine with [`@agent`](@ref) to create an agent type for [`OpenStreetMapSpace`](@ref).\nIt attributes the fields\n`id::Int, pos::Tuple{Int,Int,Float64}, route::Vector{Int}, destination::Tuple{Int,Int,Float64}`\nto the start of the agent type.\n\"\"\"\nmutable struct OSMAgent <: AbstractAgent\n    id::Int\n    pos::Tuple{Int,Int,Float64}\n    route::Vector{Int}\n    destination::Tuple{Int,Int,Float64}\nend\n", "meta": {"hexsha": "7e77beff7ad6abcf878a8e3399d7342a59a2d7e9", "size": 3649, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core/agents.jl", "max_stars_repo_name": "Alim-faraji/Agents.jl", "max_stars_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 395, "max_stars_repo_stars_event_min_datetime": "2019-10-19T01:22:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T00:56:36.000Z", "max_issues_repo_path": "src/core/agents.jl", "max_issues_repo_name": "Alim-faraji/Agents.jl", "max_issues_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 506, "max_issues_repo_issues_event_min_datetime": "2019-10-09T09:53:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T18:14:17.000Z", "max_forks_repo_path": "src/core/agents.jl", "max_forks_repo_name": "Alim-faraji/Agents.jl", "max_forks_repo_head_hexsha": "139095939bcc4efbaa84a4e58a50c04792ee6b0f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 85, "max_forks_repo_forks_event_min_datetime": "2019-10-26T19:44:36.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-22T09:34:42.000Z", "avg_line_length": 30.6638655462, "max_line_length": 95, "alphanum_fraction": 0.7032063579, "num_tokens": 991, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10374863517987337, "lm_q1q2_score": 0.05106384733275882}}
{"text": "# eachslice, eachcol, eachrow exactly as in Julia 1.1\n# https://github.com/JuliaLang/julia/pull/29749/files\n# https://github.com/JuliaLang/julia/blob/master/HISTORY.md#new-library-functions\n\nexport eachrow, eachcol, eachslice\n\n\"\"\"\n    eachrow(A::AbstractVecOrMat)\nCreate a generator that iterates over the first dimension of vector or matrix `A`,\nreturning the rows as views.\nSee also [`eachcol`](@ref) and [`eachslice`](@ref).\n\"\"\"\neachrow(A::AbstractVecOrMat) = (view(A, i, :) for i in axes(A, 1))\n\n\n\"\"\"\n    eachcol(A::AbstractVecOrMat)\nCreate a generator that iterates over the second dimension of matrix `A`, returning the\ncolumns as views.\nSee also [`eachrow`](@ref) and [`eachslice`](@ref).\n\"\"\"\neachcol(A::AbstractVecOrMat) = (view(A, :, i) for i in axes(A, 2))\n\n\"\"\"\n    eachslice(A::AbstractArray; dims)\nCreate a generator that iterates over dimensions `dims` of `A`, returning views that select all\nthe data from the other dimensions in `A`.\nOnly a single dimension in `dims` is currently supported. Equivalent to `(view(A,:,:,...,i,:,:\n...)) for i in axes(A, dims))`, where `i` is in position `dims`.\nSee also [`eachrow`](@ref), [`eachcol`](@ref), and [`selectdim`](@ref).\n\"\"\"\n@inline function eachslice(A::AbstractArray; dims)\n    length(dims) == 1 || throw(ArgumentError(\"only single dimensions are supported\"))\n    dim = first(dims)\n    dim <= ndims(A) || throw(DimensionMismatch(\"A doesn't have $dim dimensions\"))\n    idx1, idx2 = ntuple(d->(:), dim-1), ntuple(d->(:), ndims(A)-dim)\n    return (view(A, idx1..., i, idx2...) for i in axes(A, dim))\nend\n\n# also from 1.1, like === not ==\nisnothing(::Any) = false\nisnothing(::Nothing) = true\n", "meta": {"hexsha": "e4223b36df46ebb7175bd3013b8eda73a94eb733", "size": 1650, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/eachslice.jl", "max_stars_repo_name": "ericphanson/TensorCast.jl", "max_stars_repo_head_hexsha": "7f8e9b7f1117608db4bd3cae8d7cea2e1ff48c45", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/eachslice.jl", "max_issues_repo_name": "ericphanson/TensorCast.jl", "max_issues_repo_head_hexsha": "7f8e9b7f1117608db4bd3cae8d7cea2e1ff48c45", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-07-12T08:19:56.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-12T08:19:56.000Z", "max_forks_repo_path": "src/eachslice.jl", "max_forks_repo_name": "ericphanson/TensorCast.jl", "max_forks_repo_head_hexsha": "7f8e9b7f1117608db4bd3cae8d7cea2e1ff48c45", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-10T20:00:34.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-10T20:00:34.000Z", "avg_line_length": 38.3720930233, "max_line_length": 95, "alphanum_fraction": 0.6793939394, "num_tokens": 475, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10374862409521529, "lm_q1q2_score": 0.05106384187702163}}
{"text": "# https://discourse.julialang.org/t/union-splitting-vs-c/61772/20\nmodule Lines\n\nusing BenchmarkTools\nusing Test\nusing SingleDispatchArrays\n\nabstract type AbstractLine end\n\nfor T in [:Line1, :Line2, :Line3, :Line4, :Line5]\n    @eval begin\n        struct $T <:AbstractLine\n            length::Float64\n        end\n        paint(l::$T) = l.length\n    end\nend\n\nstruct Picture{T<:AbstractLine}\n       lines::Vector{T}\nend\n\n# Dynamical dispatch at runtime\n\nfunction paint1(p)\n  s = 0.\n  for l in p.lines\n    s += paint(l)\n  end\n  s\nend\n\n# Union splitting\n\nfunction paint2(p)\n  s = 0.\n  for l in p.lines\n    if l isa Line1\n      s += paint(l)\n    elseif l isa Line2\n      s += paint(l)\n    elseif l isa Line3\n      s += paint(l)\n    elseif l isa Line4\n      s += paint(l)\n    elseif l isa Line5\n      s += paint(l)\n    end\n  end\n  s\nend\n\nfunction paint3(a::SingleDispatchVector)\n    # Will be lowered to `Core.Box` if s is not constant\n    s = Ref(0.)\n    foreach(a) do l\n        s[] += paint(l)\n    end\n    s[]\nend\n\n\nfunction run(n)\n\n  line_types = [ Line1, Line2, Line3, Line4, Line5 ]\n  p = Picture([line_types[rand(1:5)](rand()) for i in 1:n])\n  \n  SingleDispatchArrays.add_subtype.(AbstractLine, line_types)\n  a = SingleDispatchVector{AbstractLine}(p.lines)\n  \n  @test paint1(p) \u2248 paint3(a)\n  println(\"result = \", paint1(p))\n\n  print(\" with runtime dispatch: \"); @btime paint1($p)\n  print(\" with splitting: \"); @btime paint2($p)\n  print(\" with SingleDispatchArrays.jl: \"); @btime paint3($a)\nend\n\nend\n\n# running\nLines.run(1_000_000)", "meta": {"hexsha": "e28534afd0a980114ea8eae061eec25835a996f4", "size": 1528, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/lines.jl", "max_stars_repo_name": "melonedo/SingleDispatchArrays.jl", "max_stars_repo_head_hexsha": "f8c37dc90ed0b475fc90179ab73dd46e4a1c072c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-15T08:06:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-15T08:06:09.000Z", "max_issues_repo_path": "benchmark/lines.jl", "max_issues_repo_name": "melonedo/SingleDispatchArrays.jl", "max_issues_repo_head_hexsha": "f8c37dc90ed0b475fc90179ab73dd46e4a1c072c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmark/lines.jl", "max_forks_repo_name": "melonedo/SingleDispatchArrays.jl", "max_forks_repo_head_hexsha": "f8c37dc90ed0b475fc90179ab73dd46e4a1c072c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.6341463415, "max_line_length": 65, "alphanum_fraction": 0.6289267016, "num_tokens": 475, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4921881357207955, "lm_q2_score": 0.1037486234024242, "lm_q1q2_score": 0.05106384153603807}}
{"text": "f(x) = x + 100\n\nmacro g()         # According to Julia docs, ...\n  :(f(x) + 5)     # ... f is global, x is local, right?\nend               # if so, f should refer to the f above?\n\n(function main()\n  local x = 3\n  f(x) = x - 100  # f in the call environment subtracts 100\n#  println(@g())   # So why does this do -92?\nend)()\n", "meta": {"hexsha": "f71de03d26b7e90564930575a83df2a8503c3496", "size": 324, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/eval_and_scope.jl", "max_stars_repo_name": "idrougge/ple", "max_stars_repo_head_hexsha": "fcf645e23fec68fea93a068eb3bb78a88ca8af46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 59, "max_stars_repo_stars_event_min_datetime": "2016-10-27T03:33:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-03T06:10:10.000Z", "max_issues_repo_path": "julia/eval_and_scope.jl", "max_issues_repo_name": "idrougge/ple", "max_issues_repo_head_hexsha": "fcf645e23fec68fea93a068eb3bb78a88ca8af46", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2017-01-07T19:27:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-11-10T21:29:59.000Z", "max_forks_repo_path": "julia/eval_and_scope.jl", "max_forks_repo_name": "idrougge/ple", "max_forks_repo_head_hexsha": "fcf645e23fec68fea93a068eb3bb78a88ca8af46", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2016-09-20T16:22:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-15T09:28:06.000Z", "avg_line_length": 27.0, "max_line_length": 59, "alphanum_fraction": 0.5339506173, "num_tokens": 106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10374862132405094, "lm_q1q2_score": 0.051063840513087405}}
{"text": "# Starting custom types example file for Learning Julia\n\n# Create a new custom type using the struct keyword\nstruct MyType\n    field1::Int\n    field2::String\nend\n\n# instantiate the type\nx = MyType(10, \"ABC\")\nprintln(x)\nprintln(\"field1 of x is \", x.field1, \"\\n\")\n\n# by default, fields in a type is immutable\n# ex. x.field1 = 20 will result in an error\n\n# use \"mutable\" to make a type that can be altered\nmutable struct MyMutableType\n    field1::Int\n    field2::String\nend\n\ny = MyMutableType(10, \"ABC\")\ny.field2 = \"CDE\"\nprintln(y, \"\\n\")\n\n# use the isa() method to see if a variable is a given type\nprintln(\"Is x a MyMutableType: \", isa(x, MyMutableType))\nprintln(\"Is y a MyMutableType: \", isa(y, MyMutableType))\n", "meta": {"hexsha": "a542eb2f7a3c6da88c73710b4f98177e4859e5b5", "size": 710, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "6_packages-and-builtin-fns/05_types.jl", "max_stars_repo_name": "scaotravis/Julia-Course", "max_stars_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "6_packages-and-builtin-fns/05_types.jl", "max_issues_repo_name": "scaotravis/Julia-Course", "max_issues_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "6_packages-and-builtin-fns/05_types.jl", "max_forks_repo_name": "scaotravis/Julia-Course", "max_forks_repo_head_hexsha": "df88edf368d4eeae5f8c6105853b334d38481ed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.6666666667, "max_line_length": 59, "alphanum_fraction": 0.7042253521, "num_tokens": 207, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3311197396289915, "lm_q2_score": 0.1540575685174147, "lm_q1q2_score": 0.051011501975361875}}
{"text": "using Pkg\nPkg.activate(\".\")\n\nENV[\"LINES\"] = 20\n\nusing IJulia\nnotebook(dir=\".\")\n", "meta": {"hexsha": "6eb7a90b7bee0501eeed54b99c1126b877739a3f", "size": 79, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "day4/2_start_jupyter.jl", "max_stars_repo_name": "pszufe/ComplexNetworks2019", "max_stars_repo_head_hexsha": "2dba61f6235fac17714340e4fe9c4da42c28c608", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2019-08-18T11:15:47.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-16T08:08:46.000Z", "max_issues_repo_path": "day4/2_start_jupyter.jl", "max_issues_repo_name": "pszufe/ComplexNetworks2019", "max_issues_repo_head_hexsha": "2dba61f6235fac17714340e4fe9c4da42c28c608", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "day4/2_start_jupyter.jl", "max_forks_repo_name": "pszufe/ComplexNetworks2019", "max_forks_repo_head_hexsha": "2dba61f6235fac17714340e4fe9c4da42c28c608", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-01-30T22:47:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-13T03:26:47.000Z", "avg_line_length": 9.875, "max_line_length": 17, "alphanum_fraction": 0.6455696203, "num_tokens": 28, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3886180267058489, "lm_q2_score": 0.13117321187415154, "lm_q1q2_score": 0.050976274755201}}
{"text": "data = readline()\n\n\nfunction solve_1()\n    count(==('('), data) - count(==(')'), data)\nend\n\nfunction solve_2()\n    pos = 0\n    for (i, c) in enumerate(data)\n        pos += c == '(' ? 1 : -1\n        if pos == -1\n            return i\n        end\n    end\nend\n\nsolve_1() |> println\nsolve_2() |> println", "meta": {"hexsha": "9a927c8c11ecea493d5e13471069de39a0afa278", "size": 298, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2015/1.jl", "max_stars_repo_name": "pLOPeGG/JuliaOfCode", "max_stars_repo_head_hexsha": "894bca6427174b84ed385b8a008b79d15d63bd7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2015/1.jl", "max_issues_repo_name": "pLOPeGG/JuliaOfCode", "max_issues_repo_head_hexsha": "894bca6427174b84ed385b8a008b79d15d63bd7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2015/1.jl", "max_forks_repo_name": "pLOPeGG/JuliaOfCode", "max_forks_repo_head_hexsha": "894bca6427174b84ed385b8a008b79d15d63bd7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.6842105263, "max_line_length": 47, "alphanum_fraction": 0.4798657718, "num_tokens": 94, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10521053670308636, "lm_q1q2_score": 0.05096188863506773}}
{"text": "# Variables in functions have local scope\n\nfunction sumPlusTwo(x, y)\n  a = 2\n  x + a + y\nend\n\n# println(a) # UndefVarError - a not defined outside its local scope\nprintln(sumPlusTwo(1, 1))\n\n# Loops have local scoped Variables\nfor i = 1:10\n  a = 2\n  m = a * i\n  println(\"$a * $i = $m\")\nend\n\n# UndefVarError for both of these\n# println(a)\n# println(m)\n\nfor i = 1:1\n  a = 2\n  println(\"Outer loop: a = $a\")\n  for i = 1:1\n    a = 3\n    local b = 1\n    println(\"Inner loop a = $a\")\n    println(\"Inner loop b = $b\")\n  end\n  println(\"Outer loop: a = $a\")\n  # println(\"Outer loop: b = $b\") - b not defined here\nend", "meta": {"hexsha": "f0b0143f0c6713ba5f483a1c67cab91ae08ded33", "size": 605, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-programming/8-variable-scope/local-scope.jl", "max_stars_repo_name": "cadamsmith/julia-programming", "max_stars_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "julia-programming/8-variable-scope/local-scope.jl", "max_issues_repo_name": "cadamsmith/julia-programming", "max_issues_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "julia-programming/8-variable-scope/local-scope.jl", "max_forks_repo_name": "cadamsmith/julia-programming", "max_forks_repo_head_hexsha": "56435144a2775f5c5e75b3eec9023983caa86d3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.3333333333, "max_line_length": 68, "alphanum_fraction": 0.6016528926, "num_tokens": 219, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3345894279828469, "lm_q2_score": 0.15203224546424318, "lm_q1q2_score": 0.0508683820448289}}
{"text": "__precompile__() # this module is safe to precompile\nmodule IMinuit\n\nusing PyCall: PyObject, pycall, PyNULL, PyAny, PyVector, pyimport_conda, pyimport, pytype_mapping\nimport PyCall: PyObject, pycall\n# import PyCall: hasproperty # Base.hasproperty in Julia 1.2\nimport Base: convert, ==, isequal, hash, hasproperty,  haskey\n\nusing ForwardDiff: gradient\n\nexport Minuit, migrad, minos, hesse, matrix, iminuit, args, model_fit, @model_fit\nexport AbstractFit, Fit, ArrayFit, func_argnames, Data, chisq, @plt_data, @plt_data!, @plt_best, @plt_best!\nexport gradient, LazyHelp, contour, mncontour, mnprofile, draw_mncontour, profile,\n    draw_contour, draw_profile\nexport get_contours, get_contours_all, contour_df, get_contours_given_parameter\nexport contour_df_given_parameter, get_contours_samples, contour_df_samples\n\n# copied from PyPlot.jl\n# that lazily looks up help from a PyObject via zero or more keys.\n# This saves us time when loading iminuit, since we don't have\n# to load up all of the documentation strings right away.\nstruct LazyHelp\n    o # a PyObject or similar object supporting getindex with a __doc__ property\n    keys::Tuple{Vararg{String}}\n    LazyHelp(o) = new(o, ())\n    LazyHelp(o, k::AbstractString) = new(o, (k,))\n    LazyHelp(o, k1::AbstractString, k2::AbstractString) = new(o, (k1,k2))\n    LazyHelp(o, k::Tuple{Vararg{AbstractString}}) = new(o, k)\nend\nfunction show(io::IO, ::MIME\"text/plain\", h::LazyHelp)\n    o = h.o\n    for k in h.keys\n        o = getproperty(o, k) \n    end\n    if hasproperty(o, \"__doc__\")\n        print(io, \"Docstring pulled from the Python `iminuit`:\\n\\n\")\n        print(io, convert(AbstractString, o.\"__doc__\"))\n    else\n        print(io, \"no Python docstring found for \", h.k)\n    end\nend\n\nBase.show(io::IO, h::LazyHelp) = show(io, \"text/plain\", h)\nfunction Base.Docs.catdoc(hs::LazyHelp...)\n    Base.Docs.Text() do io\n        for h in hs\n            show(io, MIME\"text/plain\"(), h)\n        end\n    end\nend\n\n\n@doc raw\"\"\"\n    method_argnames(m::Method)\n\nExtracting the argument names of a method as an array.\nModified from [`methodshow.jl`](https://github.com/JuliaLang/julia/blob/master/base/methodshow.jl) \n(`Vector{Any}` changed to `Vector{Symbol}`).\n\"\"\"\nfunction method_argnames(m::Method)\n    argnames = ccall(:jl_uncompress_argnames, Vector{Symbol}, (Any,), m.slot_syms)\n    isempty(argnames) && return argnames\n    return argnames[1:m.nargs]\nend\n\n\"\"\"\n    func_argnames(f::Function)\n\nExtracting the argument names of a function as an array.\n\"\"\"\nfunction func_argnames(f::Function)\n    ms = collect(methods(f))\n    return method_argnames(last(ms))[2:end]\nend\n\n\n###########################################################################\n\ninclude(\"init.jl\")\ninclude(\"FitStructs.jl\")\n\n###########################################################################\n\n\n# Wrappers of the iminuit functions\n@doc raw\"\"\"\n    Minuit(fcn; kwds...)\n    Minuit(fcn, start; kwds...)\n    Minuit(fcn, m::AbatractFit; kwds...)\n\nWrapper of the `iminuit` function `Minuit`.\n* `fcn` is the function to be optimized.\n* `start`: an array/tuple of the starting values of the parameters.\n* `kwds` is the list of keyword arguments of `Minuit`. For more information, refer to the `iminuit` manual.\n* `m`: a fit that was previously defined; its parameters at the latest stage can be passed to the new fit.\n\nExample:\n```\nfit = Minuit(fcn, [1, 0]; name = [\"a\", \"b\"], error = 0.1*ones(2), \n            fix_a = true, limit_b = (0, 50) )\nmigrad(fit)\n```\nwhere the parameters are collected in an array `par` which is the argument of `fcn(par)`. In this case,\none can use external code (e.g., `using ForwardDiff: gradient`) to compute the gradient as \n`gradfun(par) = gradient(fcn, par)`, and include `grad = gradfun` as a keyword argument.\n\n\nIf `fcn` is defined as `fcn(a, b)`, then the starting values need to be\nset as `Minuit(fcn, a = 1, b = 0)`.\n\nFrom `iminuit`:\n\n`Minuit(fcn, throw_nan=False, pedantic=True, forced_parameters=None, print_level=0, \n        errordef=None, grad=None, use_array_call=False, **kwds)`\n\n\"\"\"\nfunction Minuit(fcn; kwds...)::Fit\n    forced_parameters = Tuple(func_argnames(fcn)) # get the argument lists of fcn\n    return iminuit.Minuit(fcn; forced_parameters= forced_parameters, pedantic = false, kwds...)\nend\n\nMinuit(fcn, start::AbstractVector; kwds...)::ArrayFit =  iminuit.Minuit.from_array_func(fcn, start; pedantic = false, kwds...)\nMinuit(fcn, start::Tuple; kwds...)::ArrayFit =  iminuit.Minuit.from_array_func(fcn, start; pedantic = false, kwds...)\n\nMinuit(fcn, m::ArrayFit; kwds...) = Minuit(fcn, args(m); (Symbol(k) => v for (k,v) in m.fitarg)...,  kwds...)\nMinuit(fcn, m::Fit; kwds...) = Minuit(fcn; (Symbol(k) => v for (k,v) in m.fitarg)...,  kwds...)\n\n# nsplit = 1, nsplit option removed since iminuit v1.5.0\nfunction migrad(f::AbstractFit; ncall = 1000, resume = true, precision = nothing) \n    return pycall(f.migrad, PyObject, ncall, resume, precision)\nend\n\n\nhesse(f::AbstractFit; maxcall = 0) = pycall(f.hesse, PyObject, maxcall)\n\n\nfunction minos(f::AbstractFit; var = nothing, sigma = 1, maxcall = 0)\n    return pycall(f.minos, PyObject, var, sigma, maxcall)\nend\n\n\nmatrix(f::AbstractFit; kws...) = pycall(PyObject(f).matrix, PyObject, kws...)\n\nfunction args(o::AbstractFit)::Vector{Float64}\n    _a::PyObject = o.args;  _n::Int = length(_a)\n    _res = zeros(_n)\n    @views for i = 1:_n\n        _res[i] = get(_a, i-1)\n    end\n    return _res\nend\n\nfor fun in [:contour, :mncontour, :draw_contour, :draw_mncontour]\n    :( ($fun)(f::AbstractFit, par1, par2; kws...) = f.$fun(par1, par2; kws...) ) |> eval\nend\n\nfor fun in [:profile, :draw_profile, :mnprofile, :draw_mnprofile]\n    :( ($fun)(f::AbstractFit, par1; kws...) = f.$fun(par1; kws...) ) |> eval\nend\n\n\nfor f in [:migrad, :minos, :hesse, :matrix, :args, :contour, :mncontour, :profile, \n          :mnprofile, :draw_mncontour, :draw_contour, :draw_profile, :draw_mnprofile]\n    sf = string(f)\n    @eval @doc LazyHelp(mMinuit, $sf)  function $f(ars...; kws...) \n        if !hasproperty(mMinuit, $sf)\n            error(\"iminuit \", version, \" does not have iminuit.Minuit\", $sf)\n        end\n        return pycall(mMinuit.$sf, PyAny, ars...; kws...)\n    end\nend\n\n#########################################################################\n\ninclude(\"Data.jl\")\ninclude(\"contour.jl\")\n\n#########################################################################\n\n\n\"\"\"\n    model_fit(model::Function, data::Data, start_values; kws...)\n\nconvenient wrapper for fitting a `model` to `data`; the returning stype is `ArrayFit`, which can be passed\nto `migrad`, `minos` etc.\n* `model` is the function to be fitted to `data`; it should be of the form `model(x, params)` with `params` given either as an array or a tuple.\n* `start_values` can be either the initial values of parameters (`<: AbstractArray` or `<:Tuple`) or a previous fit of type `AbstractFit`.\n\"\"\"\nfunction model_fit(model::Function, data::Data, start_values; kws...)\n    # _model(x, par) = length(func_argnames(model)) > 2 ? model(x, par...) : model(x, par)\n    _chisq(par) = chisq(model, data, par)\n    _fit = Minuit(_chisq, start_values; kws...)\n    return _fit\nend\n\n\"\"\"\n    @model_fit model data start_values kws...\n\nconvenient wrapper for fitting a `model` to `data`; see [`model_fit`](@ref).\n\"\"\"\nmacro model_fit(model, data, start_values, kws...)\n    _expr = quote\n        _chisq(par) = chisq($model, $data, par)\n        _fit = isempty($kws) ? Minuit(_chisq, $start_values) : Minuit(_chisq, $start_values; $(kws...))\n    end\n    esc(_expr)\nend\n\n\nend\n", "meta": {"hexsha": "a3de4389b4da9d319929e187defb24d8311499ff", "size": 7512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IMinuit.jl", "max_stars_repo_name": "zhanglm79/IMinuit.jl", "max_stars_repo_head_hexsha": "3d5a685d6b955b32aa88dd3c5aa7bbfc83605aca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-07-09T06:44:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-17T14:18:40.000Z", "max_issues_repo_path": "src/IMinuit.jl", "max_issues_repo_name": "zhanglm79/IMinuit.jl", "max_issues_repo_head_hexsha": "3d5a685d6b955b32aa88dd3c5aa7bbfc83605aca", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/IMinuit.jl", "max_forks_repo_name": "zhanglm79/IMinuit.jl", "max_forks_repo_head_hexsha": "3d5a685d6b955b32aa88dd3c5aa7bbfc83605aca", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-11-24T12:21:23.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-25T09:12:08.000Z", "avg_line_length": 35.6018957346, "max_line_length": 144, "alphanum_fraction": 0.6518903088, "num_tokens": 2205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713670203584295, "lm_q2_score": 0.11124121092722458, "lm_q1q2_score": 0.05085244029374502}}
{"text": "# Exercise 20-1\nfunction avoids(word, banned)\n    return length(intersect(word, banned)) == 0\nend\n\n# println(avoids(\"abcdefghi\", \"wxyz\"))\n# println(avoids(\"abcdefghi\", \"d\"))", "meta": {"hexsha": "fcd51ef1e57c17a2d044e6b7570c711a04bc96e5", "size": 173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/exercises/ex20.jl", "max_stars_repo_name": "EnzioKam/learning_julia", "max_stars_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/exercises/ex20.jl", "max_issues_repo_name": "EnzioKam/learning_julia", "max_issues_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/exercises/ex20.jl", "max_forks_repo_name": "EnzioKam/learning_julia", "max_forks_repo_head_hexsha": "a673e2d721d734333c79bcc4cf04f678ed378bdb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.7142857143, "max_line_length": 47, "alphanum_fraction": 0.6878612717, "num_tokens": 51, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713671682749485, "lm_q2_score": 0.11124120356108878, "lm_q1q2_score": 0.05085243857185515}}
{"text": "using Test\nusing SymDiff\n\n@test subs(1, :u => 1) == 1\n", "meta": {"hexsha": "b79de43bdea2c557d1b4f494d465e4d1ccbbb8b5", "size": 54, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_subs_num.jl", "max_stars_repo_name": "ahojukka5/SymDiff.jl", "max_stars_repo_head_hexsha": "1c33d5a998c1cb4e49e15a8a810ae624a937cf70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-22T14:09:23.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-22T14:09:23.000Z", "max_issues_repo_path": "test/test_subs_num.jl", "max_issues_repo_name": "ahojukka5/SymDiff.jl", "max_issues_repo_head_hexsha": "1c33d5a998c1cb4e49e15a8a810ae624a937cf70", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-06-22T16:26:37.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-12T14:41:50.000Z", "max_forks_repo_path": "test/test_subs_num.jl", "max_forks_repo_name": "ahojukka5/SymDiff.jl", "max_forks_repo_head_hexsha": "1c33d5a998c1cb4e49e15a8a810ae624a937cf70", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-09-28T21:43:02.000Z", "max_forks_repo_forks_event_max_datetime": "2020-09-28T21:43:02.000Z", "avg_line_length": 10.8, "max_line_length": 27, "alphanum_fraction": 0.6111111111, "num_tokens": 22, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45713671682749474, "lm_q2_score": 0.11124119472172639, "lm_q1q2_score": 0.05085243453105804}}
{"text": "function __init__iris()\n    DEPNAME = \"Iris\"\n    LINK = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/\"\n    DOCS = \"https://archive.ics.uci.edu/ml/datasets/Iris\"\n    DATA = \"iris.data\"\n\n    register(DataDep(\n        DEPNAME,\n        \"\"\"\n        Dataset: The Iris dataset\n        Website: $DOCS\n        \"\"\",\n        LINK .* [DATA],\n        \"6f608b71a7317216319b4d27b4d9bc84e6abd734eda7872b71a458569e2656c0\"  # if checksum omitted, will be generated by DataDeps\n    ))\nend\n\n\"\"\"\n    Iris(; as_df = true, dir = nothing)\n\nFisher's classic iris dataset. \n\nMeasurements from 3 different species of iris: setosa, versicolor and\nvirginica. There are 50 examples of each species.\n\nThere are 4 measurements for each example: sepal length, sepal width, petal\nlength and petal width.  The measurements are in centimeters.\n\nThe module retrieves the data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).\n\nNOTE: no pre-defined train-test split for this dataset. \n\n# Arguments\n\n$ARGUMENTS_SUPERVISED_TABLE\n\n# Fields\n\n$FIELDS_SUPERVISED_TABLE\n\n# Methods\n\n$METHODS_SUPERVISED_TABLE\n\n# Examples\n\n```julia-repl\njulia> dataset = Iris()\nIris:\n  metadata => Dict{String, Any} with 4 entries\n  features => 150\u00d74 DataFrame\n  targets => 150\u00d71 DataFrame\n  dataframe => 150\u00d75 DataFrame\n\n\njulia> dataset[1:2]\n(2\u00d74 DataFrame\n Row \u2502 sepallength  sepalwidth  petallength  petalwidth \n     \u2502 Float64      Float64     Float64      Float64    \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502         5.1         3.5          1.4         0.2\n   2 \u2502         4.9         3.0          1.4         0.2, 2\u00d71 DataFrame\n Row \u2502 class       \n     \u2502 String15    \n\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n   1 \u2502 Iris-setosa\n   2 \u2502 Iris-setosa)\n\njulia> X, y = Iris(as_df=false)[]\n([5.1 4.9 \u2026 6.2 5.9; 3.5 3.0 \u2026 3.4 3.0; 1.4 1.4 \u2026 5.4 5.1; 0.2 0.2 \u2026 2.3 1.8], InlineStrings.String15[\"Iris-setosa\" \"Iris-setosa\" \u2026 \"Iris-virginica\" \"Iris-virginica\"])\n```\n\"\"\"\nstruct Iris <: SupervisedDataset\n    metadata::Dict{String, Any}\n    features\n    targets\n    dataframe\nend\n\nfunction Iris(; dir = nothing, as_df = true)\n    path = datafile(\"Iris\", \"iris.data\", dir)\n    df = read_csv(path, header=0)\n    rename!(df, [\"sepallength\", \"sepalwidth\", \"petallength\", \"petalwidth\", \"class\"])\n\n    features = df[!, Not(:class)]\n    targets = df[!, [:class]]\n\n    metadata = Dict{String, Any}()\n    metadata[\"path\"] = path\n    metadata[\"n_observations\"] = size(df, 1)\n    metadata[\"feature_names\"] = names(features)\n    metadata[\"target_names\"] = names(targets)\n\n    if !as_df\n        features = df_to_matrix(features)\n        targets = df_to_matrix(targets)\n        df = nothing\n    end\n\n    return Iris(metadata, features, targets, df)\nend\n\n# deprecated in v0.6\nfunction Base.getproperty(::Type{Iris}, s::Symbol)\n    if s == :features\n        @warn \"Iris.features() is deprecated, use `Iris().features` instead.\"\n        return () -> Iris(as_df=false).features\n    elseif s == :labels\n        @warn \"Iris.labels() is deprecated, use `Iris().targets` instead.\"\n        return () -> Iris(as_df=false).targets |> vec\n    else \n        return getfield(Iris, s)\n    end\nend\n", "meta": {"hexsha": "3fbee5896c920ecfc4fa589e61ec8aed83b00ed5", "size": 3157, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/misc/iris.jl", "max_stars_repo_name": "soham-chitnis10/MLDatasets.jl", "max_stars_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/datasets/misc/iris.jl", "max_issues_repo_name": "soham-chitnis10/MLDatasets.jl", "max_issues_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/datasets/misc/iris.jl", "max_forks_repo_name": "soham-chitnis10/MLDatasets.jl", "max_forks_repo_head_hexsha": "795ad8d1be3695024091e4ca8c0162397d0b525b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.452173913, "max_line_length": 167, "alphanum_fraction": 0.6214760849, "num_tokens": 966, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.10669059536584677, "lm_q1q2_score": 0.0508465667112311}}
{"text": "#=##############################################################################\n# DESCRIPTION\n    Tools for formatting PSU-WOPWOP's inputs/outputs.\n\n# AUTHORSHIP\n  * Author    : Eduardo J. Alvarez and Tyler Critchfield\n  * Email     : Edo.AlvarezR@gmail.com\n  * Created   : Nov 2019\n  * License   : MIT\n=###############################################################################\n\n\n\"\"\"\n    `read_wopwopoutput(outputname; read_path=\"\", tec=false)`\n\nReads an output PSU-WOPWOP field of observer data. It returns `(header, field)`\nwhere `header[k]` is a string with the name of the k-th field, and\n`field[i, j, t, k]` is the value at the node of coordinates (i, j) at time t\nin the k-th field.\n\nGive it `tec=true` to read a TecPlot file (output of a single observer), and it\nwill return `(header, field)` where `field[t, k]` is the value at time t in the\nk-th field.\n\n# ARGUMENTS\n* `outputname::String`           : PSU-WOPWOP output file to read. For instance,\n                                    `outputname=pressure` will look for the files\n                                    pressure.nam and pressure.fn under path\n                                    `read_path`.\n\n# OPTIONAL ARGUMENTS\n* `read_path::String`           : Path from where to read the inputs files.\n\"\"\"\nfunction read_wopwopoutput(outputname::String; read_path=\"\",\n                            verbose=true, verbose_level=1,\n                            v_lvl=0, tec=false)\n\n    if tec\n        if verbose && verbose_level>=0\n            println(\"\\t\"^v_lvl*\"Reading output file $(outputname*\".tec\") ...\")\n        end\n\n        f = open(joinpath(read_path, outputname*\".tec\"), \"r\")\n\n        readline(f) # Title\n        header = split(replace(readline(f)[13:end], \"\\\"\" => \"\"), \", \")\n        readline(f) # Info\n        readline(f) # Empty line\n\n        # Data\n        data = []\n\n        for ln in eachline(f)\n\n            # Get rid of space at start of line\n            while ln[1] == ' '\n                ln = ln[2:end]\n            end\n\n            # Get rid of space at end of line\n            while ln[end] == ' '\n                ln = ln[1:end-1]\n            end\n\n            # Get rid of double spaces\n            while occursin(\"  \", ln)\n                ln = replace(ln, \"  \" => \" \")\n            end\n\n            # Parse data\n            push!(data, Meta.parse.(split(ln, \" \")))\n        end\n\n        field = hcat(data...)'\n\n        close(f)\n    else\n        if verbose && verbose_level>=0\n            println(\"\\t\"^v_lvl*\"Reading output file $(outputname*\".nam\") ...\")\n        end\n\n        # Read header file\n        f = open(joinpath(read_path, outputname*\".nam\"), \"r\")\n        header = readlines(f)\n        close(f)\n\n        # Remove invalid characters from headers\n        for str in [\" \", \"(\", \")\"]\n            for i in 1:length(header)\n                header[i] = replace(header[i], str => \"\")\n            end\n        end\n\n        # Read function file\n        f = open(joinpath(read_path, outputname*\".fn\"), \"r\")\n        imax, jmax, tmax, fieldmax = [Meta.parse(elem) for elem in split(readline(f))]\n\n        if verbose && verbose_level>=0\n            println(\"\\t\"^(v_lvl+1)*\"Found $(imax)x$(jmax) grid in WOPWOP output with\"*\n                        \" $tmax time steps.\")\n        end\n\n        # Read fields\n        field = zeros(imax, jmax, tmax, fieldmax)\n        for fieldi in 1:fieldmax # Iterate over fields\n\n            if verbose && verbose_level>=1\n                println(\"\\t\"^(v_lvl+1)*\"Reading field $(header[fieldi])...\")\n            end\n\n            for t in 1:tmax # Iterate over time steps\n\n                if verbose && (t-1)%ceil(Int, tmax/4)==0 && verbose_level>=1\n                    println(\"\\t\"^(v_lvl+2)*\"Reading time t=$t out of $tmax\")\n                end\n\n                for j in 1:jmax\n                    for i in 1:imax\n                        # NOTE: Shouldn't I be iterating over in the inner loop?\n                        field[i, j, t, fieldi] = Meta.parse(readline(f))\n                    end\n                end\n            end\n        end\n\n        close(f)\n    end\n\n    return header, field\nend\n\n\"\"\"\n    `read_wopwoploading(filename; read_path=\"\", verbose=false, v_lvl=0)`\n\nRead a PSU-WOPWOP functional file containing loading data (v1.0). Returns\n`(hdr, names, timeinfo, dims, time, data)` where data[k][:, i, j] is the value at\nthe i-th node (or face) at the j-th time in the k-th zone\n\"\"\"\nfunction read_wopwoploading(filename; read_path=\"\",\n                                        verbose=false, verbose_lvl=0, v_lvl=0)\n\n    # Open PSU-WOPWOP file\n    f = open(joinpath(read_path, filename), \"r\")\n\n    # Read format header\n    header = []\n    push!(header, read(f, Int32, 1))                #1 Magic number\n    push!(header, read(f, Int32, 2))                #2 Version\n    chars = [Char(c) for c in read(f, Int8, 1024)]  #3 Comments\n    str = \"\"; for c in chars; str *= c; end;\n    push!(header, str)\n    push!(header, read(f, Int32, 1))                #4 Functional file\n    nz = read(f, Int32, 1)[1]                       #5 Number of zones\n    push!(header, nz)\n    push!(header, read(f, Int32, 1))                #6 1==structured, 2==unstructured\n    push!(header, read(f, Int32, 1))                #7 1==Constant, 2==Periodic, 3==Aperiodic\n    ncnt = read(f, Int32, 1)[1]                     #8 1==node-centered, 2==face-centered\n    push!(header, ncnt)\n    push!(header, read(f, Int32, 1))                #9 1==pressure, 2==loading, 3==flow\n    push!(header, read(f, Int32, 1))                #10 1==stationary ground fixed, 2==rotating ground fixed, 3==patch fixed\n    push!(header, read(f, Int32, 1))                #11 1==single, 2==double\n    push!(header, read(f, Int32, 1))                #12 something\n    push!(header, read(f, Int32, 1))                #13 something else\n\n    if verbose\n        println(\"\\t\"^(v_lvl)*\"$(header[1])\\t # Magic number\")\n        println(\"\\t\"^(v_lvl)*\"$(header[2])\\t # Version\")\n        println(\"\\t\"^(v_lvl)*\"$(header[3])\\t # Comment\")\n        println(\"\\t\"^(v_lvl)*\"$(header[4])\\t # Functional file\")\n        println(\"\\t\"^(v_lvl)*\"$(header[5])\\t\\t # Number of zones\")\n        println(\"\\t\"^(v_lvl)*\"$(header[6])\\t # 1==structured, 2==unstructured\")\n        println(\"\\t\"^(v_lvl)*\"$(header[7])\\t # 1==Constant, 2==Periodic, 3==Aperiodic\")\n        println(\"\\t\"^(v_lvl)*\"$(header[8])\\t\\t # 1==node-centered, 2==face-centered\")\n        println(\"\\t\"^(v_lvl)*\"$(header[9])\\t # 1==pressure, 2==loading, 3==flow\")\n        println(\"\\t\"^(v_lvl)*\"$(header[10])\\t # 1==stationary ground, 2==rotating ground, 3==patch\")\n        println(\"\\t\"^(v_lvl)*\"$(header[11])\\t # 1==single, 2==double\")\n        println(\"\\t\"^(v_lvl)*\"$(header[12])\\t # something\")\n        println(\"\\t\"^(v_lvl)*\"$(header[13])\\t # something else\")\n    end\n\n    # Error cases: Current implementation only takes unstructured, constant,\n    # face centered patches\n    Int(header[2][1]) != 1 ? error(\"Only v1.0 is supported\") :\n    Int(header[4][1]) != 2 ? error(\"File is not flagged as a functional file\") :\n    # Int(header[6][1]) != 2 ? error(\"Only unstructured patches are supported\") :\n    # Int(header[7][1]) != 1 ? error(\"Only constant patches are supported\") :\n    # Int(header[8][1]) != 2 ? error(\"Only face-centered patches are supported\") :\n    Int(header[9][1]) != 2 ? error(\"Only loading vectors are supported\") :\n    Int(header[11][1]) != 1 ? error(\"Only single-precision floats are supported\") :\n    nothing;\n\n    # Zone specification\n    zone_specs = zeros(Int, read(f, Int32))     # Read number of zones\n    for i in 1:length(zone_specs)               # Read each zone number\n        zone_specs[i] = Int(read(f, Int32))\n    end\n\n    if verbose\n        println(\"\\t\"^(v_lvl+1)*\"Zone specification: $zone_specs\")\n    end\n\n    names = String[]\n    timeinfo = []\n    dims = []\n    ndata = zeros(Int, length(zone_specs))\n    nt = nothing\n\n    # Data header\n    for zi in 1:length(zone_specs)\n\n        # Name\n        chars = [Char(c) for c in read(f, Int8, 32)]\n        str = \"\"; for c in chars; str *= c; end;\n        push!(names, str)\n\n        # Time information\n        if Int(header[7][1])==2         # Periodic case\n            push!(timeinfo, Any[read(f, Float32), read(f, Int32)])\n            ntimes = timeinfo[end][2]\n\n        elseif Int(header[7][1])==3     # Aperiodic case\n            push!(timeinfo, read(f, Int32))\n            ntimes = timeinfo[end]\n\n        else                            # Constant case\n            push!(timeinfo, 1)\n            ntimes = 1\n        end\n\n        if nt != nothing && nt != ntimes\n            error(\"Logic error: Got different times ($(nt)!=$(ntimes))\")\n        end\n        nt = ntimes\n\n        # Dimensions\n        if Int(header[6][1])==1         # Structured case\n            push!(dims, read(f, Int32, 2))\n            ndata[zi] = dims[end][1]*dims[end][2]\n        else\n            push!(dims, read(f, Int32))\n            ndata[zi] = dims[end]\n        end\n\n        if verbose\n            println(\"\\t\"^(v_lvl+1)*\"$(replace(names[end], \" \" => \"\"))\"*\n                    \"\\t$(timeinfo[end])\\t$(dims[end]) # Name, Time info, dims\")\n        end\n    end\n\n    # Time of every data block of every zone\n    time = [zeros(nt) for i in 1:length(zone_specs)]\n\n    # Data of every zone, where data[k][:, i, j] is the value at the i-th node\n    # (or face) at the j-th time in the k-th zone\n    data = [zeros(3, nd, nt) for nd in ndata]\n\n    for ti in 1:nt                          # Iterate over time entries\n        for zi in 1:length(zone_specs)      # Iterate over zones\n\n            if Int(header[7][1])!=1\n                time[zi][ti] = Float64(read(f, Float32))\n            end\n\n            # NOTE: Here I assume is loading data (three-dimensional)\n            for dim in 1:3                  # Iterate over spatial dimension\n                for di in 1:ndata[zi]       # Iterate over nodes / faces\n\n                    data[zi][dim, di, ti] = Float64(read(f, Float32))\n\n                end\n            end\n        end\n    end\n\n\n    hdr = Dict(\n                \"version\"   => header[2],\n                \"comment\"   => header[3],\n                \"zones\"     => Int(header[5][1]),\n                \"structured\"=> Int(header[6][1])==1,\n                \"time\"      => Int(header[7][1]),\n                \"ntimes\"    => nt,\n                \"node-centered\"=> Int(header[8][1])==1,\n                \"data-type\" => Int(header[9][1]),\n                \"frame\"     => Int(header[10][1])\n              )\n\n    return hdr, names, timeinfo, dims, time, data\nend\n\n\n\"\"\"\n    `wopwop2vtk(grid, outputname, save_path; read_path=\"\", paraview=false)`\n\nConverts an output field from PSU-WOPWOP generated over a grid of observers to\nvtk format. The user must make sure that the `grid` matches the exact grid that\nWOPWOP used as an observer to generate the output fields.\n\n# ARGUMENTS\n* `grid::gt.AbstractGrid`        : Grid.\n* `outputname::String`           : PSU-WOPWOP output file to read. For instance,\n                                    `outputname=pressure` will look for the files\n                                    pressure.nam and pressure.fn under path\n                                    `read_path`.\n* `save_path::String`           : Where to save vtk files.\n\n# OPTIONAL ARGUMENTS\n* `read_path::String`           : Path from where to read the inputs files.\n\n\"\"\"\nfunction wopwop2vtk(grid::gt.AbstractGrid, outputname::String, save_path::String;\n                        pref=\"\",\n                        read_path=\"\", prompt=true,\n                        verbose=true, v_lvl=0, createpath=true,\n                        paraview=false)\n\n    if createpath\n        gt.create_path(save_path, prompt)\n    end\n\n    header, field = read_wopwopoutput(outputname; read_path=read_path,\n                                                verbose=verbose, v_lvl=v_lvl)\n\n    tmax = size(field, 3)\n    fieldmax = size(field, 4)\n\n    # Add field to grid\n    if verbose\n        println(\"\\t\"^v_lvl*\"Generating vtk files...\")\n    end\n\n    for t in 1:tmax # Iterates over time steps\n\n        if verbose && (t-1)%ceil(Int, tmax/10)==0\n            println(\"\\t\"^(v_lvl+2)*\"Saving vtk time t=$t out of $tmax\")\n        end\n\n        for fieldi in 1:fieldmax # Iterates over fields\n\n            field_name = header[fieldi]\n            field_type = \"scalar\"\n            entry_type = \"node\"\n            field_data = reshape(field[:, :, t, fieldi], grid.nnodes)\n\n            gt.add_field(grid, field_name, field_type, field_data,\n                            entry_type; raise_warn=false)\n        end\n\n        # Save vtk of this time step\n        gt.save(grid, pref*outputname; path=save_path, num=t-1)\n\n    end\n\n    str = pref*outputname*\"...vtk;\"\n\n    if paraview\n        run(`paraview --data=$(save_path)/$(str)`)\n    end\n\n    return str\nend\n\n\"\"\"\n    `geomwopwop2vtk(filename; read_path=\"\", save_path=nothing,\npref_save_fname::String=\"automatic\", paraview=false, verbose=false, v_lvl=0)`\n\nConverts an input patch file in PSU-WOPWOP format to VTK format.\n\"\"\"\nfunction geomwopwop2vtk(filename; read_path=\"\", save_path=nothing,\n                        pref_save_fname::String=\"automatic\", paraview=false,\n                        verbose=false, v_lvl=0, loading_file=nothing)\n\n    # String carrying vtk files\n    if save_path!=nothing && paraview\n        vtk_str = save_path*\"/\"\n    end\n\n    # Build output file name prefix\n    if pref_save_fname==\"automatic\"\n        doti = findfirst(x->x=='.', filename)\n        if length(doti)!=0\n            # Get rid of extension\n            preff = filename[1:doti[end]-1]\n        else\n            preff = filename\n        end\n    else\n        preff = pref_save_fname\n    end\n\n    # Open PSU-WOPWOP file\n    f = open(joinpath(read_path, filename), \"r\")\n\n    # Read format header\n    header = []\n    push!(header, read(f, Int32, 1))                #1 Magic number\n    push!(header, read(f, Int32, 2))                #2 Version\n    chars = [Char(c) for c in read(f, Int8, 32)]    #3 Units\n    str = \"\"; for c in chars; str *= c; end;\n    push!(header, str)\n    chars = [Char(c) for c in read(f, Int8, 1024)]  #4 Comments\n    str = \"\"; for c in chars; str *= c; end;\n    push!(header, str)\n    push!(header, read(f, Int32, 1))                #5 Geometry file\n    nz = read(f, Int32, 1)[1]                       #6 Number of zones\n    push!(header, nz)\n    push!(header, read(f, Int32, 1))                #7 1==structured, 2==unstructured\n    push!(header, read(f, Int32, 1))                #8 1==Constant, 2==Periodic, 3==Aperiodic\n    ncnt = read(f, Int32, 1)[1]                     #9 Normal centered 1==node, 2==face\n    push!(header, ncnt)\n    push!(header, read(f, Int32, 1))                #10 1==single, 2==double\n    push!(header, read(f, Int32, 1))                #11 iblank\n    push!(header, read(f, Int32, 1))                #12 something\n\n    # Open loading file\n    if loading_file != nothing\n        if loading_file == \"automatic\"\n            # Construct loading file identifier\n            iden = filename[1:findfirst(\"compact.wop\", filename)[1]-1]\n            iden *= \"loading_\"\n\n            # Find files that contain the identifier\n            loading_files = [fname for fname in readdir(read_path)\n                                                       if occursin(iden, fname)]\n            # Identify loading file\n            if length(loading_files)==0\n                @warn(\"No loading file was found for $(fname).\")\n                lfile = nothing\n            elseif length(loading_files)>1\n                @warn(\"More than one loading file for $(fname) was found.\"*\n                        \" The first one will be used: $(loading_files).\")\n                lfile = loading_files[1]\n            else\n                lfile = loading_files[1]\n            end\n\n        else\n            lfile = loading_file\n        end\n\n        if lfile == nothing\n            loading_file = nothing\n        else\n            (load_hdr, load_names,\n            load_timeinfo, load_dims,\n            load_time,\n            load_data) = read_wopwoploading(lfile; read_path=read_path,\n                                            verbose=verbose, v_lvl=v_lvl+1)\n        end\n    end\n\n    if verbose\n        println(\"\\t\"^(v_lvl)*\"$(header[1])\\t # Magic number\")\n        println(\"\\t\"^(v_lvl)*\"$(header[2])\\t # Version\")\n        println(\"\\t\"^(v_lvl)*\"$(header[3])\\t # Units\")\n        println(\"\\t\"^(v_lvl)*\"$(header[4])\\t # Comment\")\n        println(\"\\t\"^(v_lvl)*\"$(header[5])\\t # Geometry file\")\n        println(\"\\t\"^(v_lvl)*\"$(header[6])\\t\\t # Number of zones\")\n        println(\"\\t\"^(v_lvl)*\"$(header[7])\\t # 1==structured, 2==unstructured\")\n        println(\"\\t\"^(v_lvl)*\"$(header[8])\\t # 1==Constant, 2==Periodic, 3==Aperiodic\")\n        println(\"\\t\"^(v_lvl)*\"$(header[9])\\t\\t # Normal centered 1==node, 2==face\")\n        println(\"\\t\"^(v_lvl)*\"$(header[10])\\t # 1==single, 2==double\")\n        println(\"\\t\"^(v_lvl)*\"$(header[11])\\t # iblank\")\n        println(\"\\t\"^(v_lvl)*\"$(header[12])\\t # something\")\n    end\n\n    # Error cases:\n    Int(header[2][1]) != 1 ? error(\"Only v1.0 is supported\") :\n    # Int(header[7][1]) != 2 ? error(\"Only unstructured patches are supported\") :\n    # Int(header[8][1]) != 1 ? error(\"Only constant patches are supported\") :\n    # Int(header[9][1]) != 2 ? error(\"Only face-centered patches are supported\") :\n    Int(header[10][1]) != 1 ? error(\"Only single-precision floats are supported\") :\n    nothing;\n\n    if loading_file != nothing\n        if load_hdr[\"structured\"] == (Int(header[7][1]) == 1)\n            error(\"Geometry and loading must both be either structured or unstructured!\")\n        end\n    end\n\n    Tflag = Int(header[8][1])       # 1==Constant, 2==Periodic, 3==Aperiodic\n\n    # Read patch headers\n    names = []\n    nbnodes = []\n    nbfaces = []\n    imaxs = []\n    jmaxs = []\n    connectivity = []\n    periods = []\n    nts = []\n    celltypes = []\n\n    for zonei in 1:nz\n        # NOTE: Here I assume it is UNSTRUCTURED and constant\n\n        chars = [Char(c) for c in read(f, Int8, 32)]\n        str = \"\"; for c in chars; str *= c; end;\n\n        push!(names, str)\n        push!(connectivity, [])\n\n        if Tflag==2\n            # Period\n            push!(periods, Float64(read(f, Float32)))\n        end\n        if Tflag==2 || Tflag==3\n            # nt\n            push!(nts, read(f, Int32))\n        end\n\n        # Unstructured case\n        if Int(header[7][1]) == 2\n            push!(nbnodes, read(f, Int32))\n            push!(nbfaces, read(f, Int32))\n            if verbose\n                println(\"\\t\"^(v_lvl)*\"$(names[end])\\t$(nbnodes[end]) $(nbfaces[end])\\t# Name, nbNodes, nbFaces\")\n            end\n\n            for facei in 1:nbfaces[end]       # Iterate over faces (cells)\n\n                this_nbnodes = read(f, Int32) # Number of node indices to read\n                push!(connectivity[end], [])\n\n                for nodei in 1:this_nbnodes   # Collect node indices\n                    push!(connectivity[end][end], read(f, Int32))\n                end\n            end\n\n            push!(celltypes, -1)\n\n        # Structured case\n        else\n            push!(imaxs, read(f, Int32))\n            push!(jmaxs, read(f, Int32))\n            push!(nbnodes, imaxs[end]*jmaxs[end])\n\n            if verbose\n                println(\"\\t\"^(v_lvl)*\"$(names[end])\\t$(imaxs[end]) $(jmaxs[end])\\t# Name, iMax, jMax\")\n            end\n\n            # Create connectivity: Quadrilateral-faces case\n            if imaxs[end]!=1 && jmaxs[end]!=1\n                push!(nbfaces, (imaxs[end]-1)*(jmaxs[end]-1))\n                for ni in 1:imaxs[end]-1\n                    for nj in 1:jmaxs[end]-1\n                        # Subscripts of every node\n                        subs = ( (ni, nj), (ni+1, nj), (ni+1, nj+1), (ni, nj+1) )\n                        # Convert subscripts to linear index of every node\n                        this_cell = [Base._sub2ind((imaxs[end], jmaxs[end]), sub...) for sub in subs]\n                        push!(connectivity[end], this_cell)\n                    end\n                end\n\n                push!(celltypes, -1)\n\n            # Create connectivity: Compact-patch (line) case\n            else\n                push!(nbfaces, imaxs[end]*jmaxs[end]-1)\n                for ni in 1:(imaxs[end]*jmaxs[end]-1)\n                    push!(connectivity[end], [ni, ni+1])\n                end\n\n                push!(celltypes, 4)\n            end\n        end\n\n        if verbose && Tflag==2\n            println(\"\\t\"^(v_lvl+1)*\"$(periods[end])\\t$(nts[end])\\t# Period, nt\")\n        elseif verbose && Tflag==3\n            println(\"\\t\"^(v_lvl+1)*\"$(nts[end])\\t# nt\")\n        end\n\n    end\n\n\n    # -------------------- READ POINTS AND NORMALS -----------------------------\n\n    Nflag = Int(header[9][1]) # Normal centered 1==node, 2==face\n\n    if Tflag != 1\n        points = [zeros(3, nbnodes[zi], nts[zi]) for zi in 1:nz]\n        normals = [zeros(3, Nflag==1 ? nbnodes[zi] : nbfaces[zi], nts[zi]) for zi in 1:nz]\n        times = [zeros(nts[zi]) for zi in 1:nz]\n    else\n        points = [zeros(3, nbnodes[zi]) for zi in 1:nz]\n        normals = [zeros(3, Nflag==1 ? nbnodes[zi] : nbfaces[zi]) for zi in 1:nz]\n        times = []\n    end\n\n    # Format cells as vtks (0-indexed)\n    vtk_cells = [[Int[ind-1 for ind in cell] for cell in connectivity[zi]] for zi in 1:nz]\n\n    # Zone of every point\n    point_zones = [[zi for pi in 1:nbnodes[zi]] for zi in 1:nz]\n\n    str = \"\"\n\n    # NOTE: I assume that all zones have the same number of time steps, so here\n    # I'm checking that assumption\n    if Tflag != 1\n        for zi in 1:nz-1\n            if nts[zi]!=nts[zi+1]\n                error(\"Found that not all zones have the same number of time\"*\n                        \" steps ($(nts[zi])!=$(nts[zi+1])).\")\n            end\n        end\n    end\n\n    for ti in 1:(Tflag != 1 ? nts[1] : 1)   # Iterate over time steps\n        for zonei in 1:nz                   # Iterate over zones\n\n            # Read time signature\n            if Tflag != 1\n                times[zonei][ti] = Float64(read(f, Float32))\n            end\n\n            # Read points\n            for k in 1:3\n                for pi in 1:nbnodes[zonei]\n                    points[zonei][k, pi, ti] = Float64(read(f, Float32))\n                end\n            end\n\n            # Read normals\n            for k in 1:3\n                for ni in 1:(Nflag==1 ? nbnodes[zonei] : nbfaces[zonei])\n                    normals[zonei][k, ni, ti] = Float64(read(f, Float32))\n                end\n            end\n\n            if save_path != nothing\n\n                # Formats points as vtks\n                vtk_points = [ points[zonei][:, pi, ti] for pi in 1:nbnodes[zonei] ]\n\n                # Format normals as a vtk field\n                vtk_normals = [ normals[zonei][:, pi, ti] for pi in 1:(\n                                    Nflag==1 ? nbnodes[zonei] : nbfaces[zonei]) ]\n\n                # Data fields\n                point_data = [Dict( \"field_name\" => \"zone\",\n                                    \"field_type\" => \"scalar\",\n                                    \"field_data\" => point_zones[zonei])]\n\n                if Nflag == 2\n                    cell_data = [Dict( \"field_name\"  => \"normals\",\n                                        \"field_type\" => \"vector\",\n                                        \"field_data\" => vtk_normals)]\n                else\n                    cell_data = nothing\n                    push!(point_data, Dict( \"field_name\" => \"normals\",\n                                            \"field_type\" => \"vector\",\n                                            \"field_data\" => vtk_normals))\n                end\n\n                # Loading data\n                if loading_file != nothing\n\n                    # NOTE: Here I assume that the loading file has data for\n                    # the same number of zones that the geometry file (even\n                    # though that is not necessarely true, but it is for\n                    # files generated in my framework)\n                    loading_vectors = [load_data[zonei][:, ni, ti] for ni in 1:size(load_data[zonei], 2)]\n\n                    loading_field = Dict(   \"field_name\"  => load_names[zonei],\n                                            \"field_type\" => \"vector\",\n                                            \"field_data\" => loading_vectors)\n\n                    if load_hdr[\"node-centered\"]\n                        push!(point_data, loading_field)\n                    else\n                        if cell_data == nothing\n                            cell_data = []\n                        end\n                        push!(cell_data, loading_field)\n                    end\n                end\n\n                filename = preff*\"_\"*replace(names[zonei], \" \" => \"\")\n\n                gt.generateVTK(filename, vtk_points; cells=vtk_cells[zonei],\n                                point_data=point_data, cell_data=cell_data,\n                                path=save_path, num=Tflag!=1 ? ti : nothing,\n                                override_cell_type=celltypes[zonei])\n\n                if ti==1\n                    str *= filename*(Tflag==1 ? \".vtk;\" : \"...vtk;\")\n                end\n            end\n\n        end\n    end\n\n    close(f)\n\n    if paraview && save_path != nothing\n        vtk_str *= str\n        run(`paraview --data=$(vtk_str)`)\n    end\n\n    return names, points, vtk_cells, periods, times, str\nend\n\n\nfunction save_geomwopwop2vtk(read_path::String, save_path::String; prompt=true,\n                                         verbose=true, verbose_level=0, v_lvl=0)\n    # Create save path\n    gt.create_path(save_path, prompt)\n\n    # Identify loft and compact PSW files\n    wopfiles = [fname for fname in readdir(read_path)\n                if occursin(\"loft.wop\", fname) || occursin(\"compact.wop\", fname)]\n\n    vtk_str = \"$save_path/\"\n\n    for fname in wopfiles\n\n        println(\"\\t\"^(v_lvl)*\"*\"^73)\n        println(\"\\t\"^(v_lvl)*\"*\\t\\tREADING $fname\")\n        println(\"\\t\"^(v_lvl)*\"*\"^73)\n\n        # Loading file if compact patch\n        lfname = occursin(\"compact.wop\", fname) ? \"automatic\" : nothing\n\n        # Convert from PSW to VTK\n        vtk_str *= geomwopwop2vtk(fname; read_path=read_path,\n                                        loading_file=lfname,\n                                        verbose=verbose && verbose_level>0,\n                                        v_lvl=v_lvl+1,\n                                        save_path=save_path)[end]\n    end\n\n    return vtk_str\nend\n\n\"\"\"\nReads a time-sequency or single legacy ASCII unstructured VTK file and formats\nthem into a single file in WOPWOP's geometry format v1.0.\n\"\"\"\nfunction vtk2wopwop(in_filename, out_filename; read_path=\"\", nums=nothing,\n                            save_path=\"\", wopbin=true,\n                            t0=0.0, tf=1.0, period=nothing, compact=false)\n\n    _in_filename = in_filename*(in_filename[end-3:end]==\".vtk\" ? \"\" : \".vtk\")\n\n    # Time type: 1==Constant, 2==periodic, 3==aperiodic\n    nums==nothing   ? Tflag = 1 :\n    period!=nothing ? Tflag = 2 :\n                      Tflag = 3\n\n    # Read first file\n    (points, cells, cell_types,\n        data) = gt.read_vtk(Tflag==1 ? _in_filename :\n                                replace(_in_filename, \".vtk\" => \".$(nums[1]).vtk\");\n                                path=read_path)\n\n    nnodes = size(points, 2)\n    ncells = size(cells, 1)\n\n    # Create output file\n    f = open(joinpath(save_path, out_filename), \"w\")\n\n    # Binary / ASCII printing\n    prnt(x) = wopbin ? write(f, x) : print(f, x)\n    prntln(x) = wopbin ? write(f, x) : print(f, x, \"\\n\")\n\n    # Convertion to 4-bytes numbers\n    # NOTE: 4 bytes = 4*8 bites = 32 bites\n    fl(x) = Float32(x)\n    nt(x) = Int32(x)\n    # Convertion to n-bytes string\n    st(x::String, n) = x * \" \"^(n-length(x))\n\n    # Magic number\n    prntln(nt(42))\n    # Version number\n    prnt(nt(1))\n    prntln(nt(0))\n    # Units for Tecplot\n    prntln(st(\"N/m^2\", 32))\n    # Comments\n    prntln(st(\"Geometry input file for PSU-WOPWOP (Format v1.0)\\n\"*\n              \"------------------------------------------------\\n\"*\n              \"Created by FLOWNoise (written by Eduardo Alvarez)\\n\"*\n              \"https://github.com/byuflowlab/FLOWNoise\\n\"*\n              \"Creation date: $(Dates.now())\\n\"*\n              \"Units: SI\\n\"*\n              (\n                compact ? \"Format: Structured grid, node-centered\" :\n                          \"Format: Unstructured grid, face-centered\"\n              ), 1024))\n\n    # Format string\n    prntln(nt(1))               # Geometry file flag\n    prntln(nt(1))               # Number of zones\n    prntln(nt(2^!compact))      # 1==structured, 2==unstructured\n    prntln(nt(Tflag))           # Geometry 1==constant, 2==periodic, 3==aperiodic\n    prntln(nt(2^!compact))      # Normal vectors 1==node, 2==face\n    prntln(nt(1))               # Floating point 1==single, 2==double\n    prntln(nt(0))               # iblank values 1==included, 0==not\n    prntln(nt(0))               # WOPWOP secret conspiracy\n\n    # ----------------- PATCH ----------------------------------------\n    # Name\n    prntln(st(compact ? \"vtkcompactpatch\" : \"vtkpatch\", 32))\n    # TimeInformation\n    if Tflag==2\n        # Period\n        prntln(fl(period))\n        # nt\n        prntln(nt( length(nums) ))\n    elseif Tflag==3\n        # nt\n        prntln(nt( length(nums) ))\n    end\n\n    if compact\n        # iMax\n        prntln(nt( 1 ))\n        # jMax\n        prntln(nt( nnodes ))\n    else\n        # nbNodes\n        prntln(nt( nnodes ))\n        # nbFaces\n        prntln(nt( ncells ))\n        # Connectivity\n        # NOTE: Here I assume that connectivity is the same in all VTK files\n        for cell in cells\n            prnt(nt( size(cell, 1) ))         # Number of nodes in this cell\n            # for pi in reverse(cell)         # Clockwise node ordering\n            for pi in cell                    # NOTE: Turns out that FLOWVLM's VTKs are already clockwise\n                prnt(nt( pi+1 ))              # 1-indexed node index\n            end\n            if !wopbin; prntln(\"\"); end;\n        end\n    end\n\n\n\n    # ----------------- DATA OF PATCH -------------------------------\n\n    Ns = zeros(3, compact ? nnodes : ncells)\n\n    for ti in 1:(Tflag==1 ? 1 : length(nums))\n        # Read vtk\n        if ti != 1\n            # NOTE: Here I assume that `nums` is given sequentally\n            (points, cells, cell_types,\n             data) = gt.read_vtk(replace(_in_filename, \".vtk\" => \".$(nums[ti]).vtk\");\n                                    path=read_path)\n\n            Ns[:] .= 0\n        end\n\n        # Calculate normals\n        if compact\n            # NOTE: Here I assume that the lifting line is defined sequentially\n            for pi in 1:nnodes-1\n                # NOTE: Here I give it the vector that connect the points\n                # as the normal, crossing my fingers that PSU-WOPWOP will never\n                # use the actual normal direction for anything (I think that\n                # it only expects the magnitude to be the length of this segment\n                # of the line)\n                D = points[:, pi+1] - points[:, pi]\n\n                # Here I split the length in two for each node\n                Ns[:, pi] += D/2\n                Ns[:, pi+1] += D/2\n            end\n\n            # # Here I'm normalizing the normal by the length\n            # for pi in 1:nnodes\n            #     Ns[:, pi] /= norm(Ns[:, pi])\n            # end\n\n        else\n            for (ci, cell) in enumerate(cells)\n\n                if length(cell) != 4\n                    error(\"Only quadrilateral cells are supported!\")\n                end\n\n                crss1 = -cross( points[:, cell[2]+1] - points[:, cell[1]+1],\n                                points[:, cell[3]+1] - points[:, cell[1]+1])\n                crss2 = -cross( points[:, cell[4]+1] - points[:, cell[3]+1],\n                                points[:, cell[1]+1] - points[:, cell[3]+1])\n\n                Ns[:, ci] .= crss1/2 + crss2/2\n            end\n        end\n\n        # Time signature\n        if Tflag != 1\n            prntln(fl( (tf-t0)*(ti-1)/(length(nums)-1) ))\n        end\n\n        # nbNodes floating point x coordinates\n        # nbNodes floating point y coordinates\n        # nbNodes floating point z coordinates\n        for k in 1:3\n            for pi in 1:nnodes\n                prntln(fl(points[k, pi]))\n            end\n        end\n\n        if compact\n            # imax \u00d7 jmax floating point normal vector x coordinates\n            # imax \u00d7 jmax floating point normal vector y coordinates\n            # imax \u00d7 jmax floating point normal vector z coordinates\n            for k in 1:3\n                for j in 1:nnodes\n                    prntln(fl(Ns[k, j]))\n                end\n            end\n\n        else\n            # nbFaces floating point normal vector x coordinates\n            # nbFaces floating point normal vector y coordinates\n            # nbFaces floating point normal vector z coordinates\n            for k in 1:3\n                for j in 1:ncells\n                    prntln(fl(Ns[k, j]))\n                end\n            end\n        end\n    end\n\n    close(f)\n\n    return nothing\nend\n\n\n\n\"\"\"\nRead the solution field created by PSU-WOPWOP named `fieldname` (i.e., pressure,\nspl_spectrum, OASPLdB, or OASPLdBA) under `read_path`.\n\"\"\"\nfunction read_pswfield(fieldname::String, read_path; re20=false, tec=false,\n                                                                     optargs...)\n    header, field = read_wopwopoutput(fieldname; read_path=read_path, tec=tec,\n                                                                    optargs...)\n\n    if re20\n        header_hash = Dict( (occursin(\",re20\", h) ? h[1:findfirst(\",re20\", h)[1]-1] : h, i)\n                                                            for (i,h) in enumerate(header))\n    else\n        header_hash = Dict((h, i) for (i,h) in enumerate(header))\n    end\n\n    return field, header, header_hash\nend\n\n\nfunction read_pswfield(fieldnames::Array{String, 1}, read_path;\n                            re20crit=[\"OASPLdB\", \"OASPLdBA\"], tec=false,\n                            optargs...)\n\n    datas = Dict()\n\n    for fieldname in fieldnames\n        data = read_pswfield(fieldname, read_path; re20=fieldname in re20crit,\n                                                       tec=tec, optargs...)\n\n        datas[fieldname] = Dict(\"field\"=>data[1], \"hd\"=>data[2], \"hs\"=>data[3])\n    end\n\n    return datas\nend\n\nfunction fetch_pswfield(read_path::String, args...;\n                        # fieldnames=[\"pressure\", \"spl_spectrum\", \"OASPLdB\", \"OASPLdBA\"],\n                        fieldnames=[\"spl_spectrum\", \"OASPLdB\", \"OASPLdBA\"],\n                        datasets=def_datasets_psw, verbose=true, v_lvl=0,\n                        verbose_level=1, optargs...)\n\n    if verbose; println(\"\\t\"^v_lvl*\n                        \"*\"^72*\"\\n*\\tReading dataset $read_path\\n\"*\"*\"^72); end;\n\n    datasets[read_path] = read_pswfield(fieldnames, read_path, args...;\n                                            verbose=verbose,\n                                            verbose_level=verbose_level-1,\n                                            v_lvl=v_lvl+1,\n                                            optargs...)\n\n    return datasets[read_path]\nend\n\nfunction fetch_pswdataset(read_path, args...; datasets=def_datasets_psw,\n                                                verbose=true, v_lvl=0,\n                                                force_read=false,\n                                                optargs...)\n\n    if !(read_path in keys(datasets)) || force_read\n        return fetch_pswfield(read_path, args...; verbose=verbose, v_lvl=v_lvl,\n                                                  datasets=datasets, optargs...)\n    else\n        if verbose; println(\"\\t\"^v_lvl*\n                            \"Dataset $(read_path) has already been loaded.\"*\n                            \" It will not be read again.\"); end;\n        return datasets[read_path]\n    end\n\nend\n\n# ------------------------- FUNCTIONS FOR BPM ----------------------------------\nfunction read_bpmoutput(args...; datasets=def_datasets_bpm, optargs...)\n    return read_wopwopoutput(args...; tec=true, datasets=datasets, optargs...)\nend\nfunction fetch_bpmdataset(args...; datasets=def_datasets_bpm, optargs...)\n    return fetch_pswdataset(args...; tec=true, datasets=datasets,\n                                     fieldnames=[\"spl_spectrum\", \"splA_spectrum\",\n                                                          \"OASPLdB\", \"OASPLdBA\"],\n                                                                    optargs...)\nend\n", "meta": {"hexsha": "6fe0f993e188deee04aca8b7af3415862057055c", "size": 36521, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FLOWNoise_wopwop.jl", "max_stars_repo_name": "byuflowlab/FLOWNoise", "max_stars_repo_head_hexsha": "1d6d7ada765f5067411527a1235b87d6eec3737f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2019-11-25T16:38:03.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T15:10:24.000Z", "max_issues_repo_path": "src/FLOWNoise_wopwop.jl", "max_issues_repo_name": "byuflowlab/FLOWNoise", "max_issues_repo_head_hexsha": "1d6d7ada765f5067411527a1235b87d6eec3737f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-05-12T14:21:43.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-12T16:56:08.000Z", "max_forks_repo_path": "src/FLOWNoise_wopwop.jl", "max_forks_repo_name": "byuflowlab/FLOWNoise", "max_forks_repo_head_hexsha": "1d6d7ada765f5067411527a1235b87d6eec3737f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2020-05-06T03:23:25.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-23T12:46:27.000Z", "avg_line_length": 35.8752455796, "max_line_length": 124, "alphanum_fraction": 0.4980422223, "num_tokens": 9478, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.10669058968506456, "lm_q1q2_score": 0.0508465640038859}}
{"text": "using PyPlot, ProgressMeter;\n\noutput_dir = \"pic\"\nif ! ispath(output_dir); mkdir(output_dir); end\n\nfunction pic()\n    L = 5.0 # system size\n    steps = 500;\n    progress = Progress(steps);\n    for i in 0:steps-1\n        filename = @sprintf(\"./data/%05d.csv\", i);\n        data = readcsv(filename);\n        x = data[:,1]; y = data[:,2]; vx = data[:,3]; vy = data[:,4];\n        plot(x,y,\"b.\");\n        quiver(x,y,vx,vy);\n        axis(\"square\"); axis([0, L, 0, L]);\n        picname = @sprintf(\"./pic/%05d.png\", i);\n        savefig(picname, dpi=60);\n\n        clf();\n        next!(progress);\n    end\nend\npic()\ngc();\n", "meta": {"hexsha": "f5dbb2af0a96caca17de60fdfaf9ccb117cfa53f", "size": 609, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "pic.jl", "max_stars_repo_name": "goropikari/vicsek-model", "max_stars_repo_head_hexsha": "1bbcd13be0cdedd959a799ef679c95f6bf549a34", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-02T17:23:22.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-02T17:23:22.000Z", "max_issues_repo_path": "pic.jl", "max_issues_repo_name": "goropikari/vicsek-model", "max_issues_repo_head_hexsha": "1bbcd13be0cdedd959a799ef679c95f6bf549a34", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pic.jl", "max_forks_repo_name": "goropikari/vicsek-model", "max_forks_repo_head_hexsha": "1bbcd13be0cdedd959a799ef679c95f6bf549a34", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.4230769231, "max_line_length": 69, "alphanum_fraction": 0.5188834154, "num_tokens": 184, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.10669058826486906, "lm_q1q2_score": 0.05084656332704962}}
{"text": "\nmin = 0\n\nfunction getMin(arr)\n#\n  global min\n  if length(arr) == 0\n    return Nothing\n  end\n\n  min = arr[1] #\n  for ele in arr\n    if ele < min\n      min = ele #\n    end\n  end\n\n  return min\nend", "meta": {"hexsha": "6671f5b02a40b5366accc02317b98581fe9980f3", "size": 194, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/general/general2.jl", "max_stars_repo_name": "judy-vscode/judy", "max_stars_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_stars_repo_stars_event_min_datetime": "2019-01-17T05:54:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-28T17:46:08.000Z", "max_issues_repo_path": "test/general/general2.jl", "max_issues_repo_name": "judy-vscode/judy", "max_issues_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2018-12-25T11:20:16.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T18:49:04.000Z", "max_forks_repo_path": "test/general/general2.jl", "max_forks_repo_name": "judy-vscode/judy", "max_forks_repo_head_hexsha": "9bc57e1905aeba0d9a4d0b1f208bd6cfb3d9db1c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2019-01-17T05:53:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-28T17:46:09.000Z", "avg_line_length": 10.2105263158, "max_line_length": 21, "alphanum_fraction": 0.5567010309, "num_tokens": 68, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489886026626094, "lm_q2_score": 0.12252322373245951, "lm_q1q2_score": 0.050834745882745544}}
{"text": "__precompile__(true)\nmodule YourSubmodule\n\nexport your_function\n\"\"\"\nWrite something about the purpose of the function up top.\n\nArguments:\n- `arg_1::Integer` Desribute first function argument. [input_units]\n- `arg_2::Integer` Desribute second function argument. [input_units]\n\nReturns:\n- `ret_1::Float64` Describe first return value. [input_units]\n- `ret_2::Float64` Describe second return value. [input_units]\n\nNotes:\n1. Make specific comments about corner cases or things to know about usage here.\n\nReferences:\n1. List citations to references used in writing the function here\n\"\"\"\nfunction your_function(arg_1::Integer, arg_2::Real)\n    if arg_2 < 0\n        return 0, 0\n    end\n\n    return arg_1 + arg_2, arg_1 - arg_2\nend\n\nend # End of module YourSubmodule (This comment is nice to distinguish from an end)", "meta": {"hexsha": "c9b0bf952022431cc083c3d16874f01b03d8dd6e", "size": 808, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/submodule.jl", "max_stars_repo_name": "logankilpatrick/JuliaPackageTemplate.jl", "max_stars_repo_head_hexsha": "2f7fd04d6d643ebaf14f6f152a745a3964421419", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2019-02-21T00:43:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-21T04:20:31.000Z", "max_issues_repo_path": "src/submodule.jl", "max_issues_repo_name": "logankilpatrick/JuliaPackageTemplate.jl", "max_issues_repo_head_hexsha": "2f7fd04d6d643ebaf14f6f152a745a3964421419", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/submodule.jl", "max_forks_repo_name": "logankilpatrick/JuliaPackageTemplate.jl", "max_forks_repo_head_hexsha": "2f7fd04d6d643ebaf14f6f152a745a3964421419", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-01-25T03:56:07.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-07T02:08:13.000Z", "avg_line_length": 26.9333333333, "max_line_length": 83, "alphanum_fraction": 0.7561881188, "num_tokens": 213, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489886026626094, "lm_q2_score": 0.12252320771203076, "lm_q1q2_score": 0.05083473923588792}}
{"text": "\n\nmutable struct TestRobot1d <: AbstractRobot\n    \"Device name\"\n    devname::String\n    \"Position\"\n    x::Float64\n    \"Reference Position\"\n    x\u1d63::Float64\n    \"Name of the axis\"\n    axis::String\n    \"Time that the program should sleep for each move\"\n    \u0394t::Float64\nend\n\n\"\"\"\n`TestRobot1d(devname, axis=\"\u03b8\";dt=0.0)`\n\nCreates a test robot with a single degree of freedom. Useful for testing the\ninterfaces and simulating experiments.\n\n * `devname`: a string with the device name\n * `axis`: a string with the name of the axis\n * `dt`: number of seconds that the program should wait before moving on. Useful to simulate an actual system that is not instantaneous.\n\nThis test actuator tries to simulate the wind tunnel turntable so that it has\na position and a reference that can be changed.\n\"\"\"\nTestRobot1d(devname, axis=\"\u03b8\";dt=0.0) = TestRobot1d(devname, 0.0, 0.0, axis, dt)\n\n\"\"\"\n`move(dev::TestRobot1d, x; a=false, r=false, sync=true)`\n\nMove the test robot to a new position. \nIt tries to emulate the `WMesaP` interface.\n\"\"\"\nfunction move(dev::TestRobot1d, x; a=false, r=false, sync=true)\n    if r\n        dev.x += x\n    elseif a\n        dev.x = x\n    else\n        dev.x = x + dev.x\u1d63\n    end\n\n    sync && sleep(dev.\u0394t)\n\n    p = dev.x - dev.x\u1d63\n    println(\"Movement: \u03b8 = $p\")\nend\n\n\nnumaxes(dev::TestRobot1d) = 1\naxesnames(dev::TestRobot1d) = [dev.axis]\n\nmoveto(dev::TestRobot1d, x) = move(dev, x[1]; a=false, r=false, sync=true)\n\nrmove(dev::TestRobot1d, x; sync=true) = move(dev, x, r=true, sync=sync)\ndevposition(dev::TestRobot1d) = dev.x - dev.x\u1d63\nabsposition(dev::TestRobot1d) = dev.x\n\nsetreference(dev::TestRobot1d, x=0) = (dev.x\u1d63 = dev.x - x)\nsetabsreference(dev::TestRobot1d) = (dev.x\u1d63 = 0)\n\n\nwaituntildone(dev::TestRobot1d) = sleep(dev.\u0394t)\nstopmotion(dev::TestRobot1d) = sleep(dev.\u0394t/5)\n\n\n\n\n\n\nmutable struct TestRobot <: AbstractCartesianRobot\n    devname::String\n    n::Int\n    x::Vector{Float64}\n    xr::Vector{Float64}\n    axes::Vector{String}\n    axidx::Dict{String,Int}\n    \u0394t::Float64\nend\n\n\"\"\"\n`TestRobot(devname, axes=[\"x\", \"y\", \"z\"]; dt=0.0)`\n\nCreates a cartesian robot with several axes. It tries to emulate the interface\nused by the wind tunnel's cartesian robot (see the `RoboSimples` package at\n<https://github.com/pjsjipt/RoboSimples.jl>).\n\"\"\"\nfunction TestRobot(devname, axes=[\"x\", \"y\", \"z\"]; dt=0.0)\n    n = length(axes)\n    axidx = Dict{String,Int}()\n    axes = axes[1:n]\n    for (i, ax) in enumerate(axes)\n        axidx[ax] = i\n    end\n    \n    TestRobot(devname, n, zeros(n), zeros(n), axes, axidx, dt)\nend\n\nnumaxes(dev::TestRobot) = dev.n\naxesnames(dev::TestRobot) = dev.axes\n\nfunction move(dev::TestRobot, ax::Integer, mm; r=false)\n    if r\n        dev.x[ax] += mm\n    else\n        dev.x[ax] = mm\n    end\n    sleep(dev.\u0394t)\n    println(\"Position: $ax -> $(dev.axes[ax]) = $(dev.x[ax])\")\nend\n\nmove(dev::TestRobot, ax, mm; r=false) =\n    move(dev, dev.axidx[string(ax)], mm; r=r)\n\n\nfunction move(dev::TestRobot, axes::AbstractVector,\n                                x::AbstractVector; r=false)\n    ndof = length(axes)\n\n    for i in 1:ndof\n        move(dev, axes[i], x[i]; r=r)\n    end\n    return\nend\n\nmoveto(dev::TestRobot, x::AbstractVector) = move(dev, dev.axes, x, r=false)\n\nmoveX(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"x\"]; r=false)\nmoveY(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"y\"]; r=false)\nmoveZ(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"z\"]; r=false)\n\nrmoveX(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"x\"]; r=true)\nrmoveY(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"y\"]; r=true)\nrmoveZ(dev::TestRobot, mm) = move(dev, mm, dev.axidx[\"z\"]; r=true)\n\ndevposition(dev::TestRobot, ax) = dev.x[dev.axidx[string(ax)]]\ndevposition(dev::TestRobot, ax::Integer) = dev.x[ax]\n\ndevposition(dev::TestRobot, axes::AbstractVector) = dev.x[axes]\n\nfunction devposition(dev::TestRobot)\n    pos = Dict{String,Float64}()\n\n    for i in 1:numaxes(dev)\n        pos[dev.axes[i]] = dev.x[i]\n    end\n    return pos\nend\n\npositionX(dev::TestRobot) = devposition(dev, \"x\")\npositionY(dev::TestRobot) = devposition(dev, \"y\")\npositionZ(dev::TestRobot) = devposition(dev, \"z\")\n\nsetreference(dev::TestRobot, ax::Integer, mm=0) = dev.x[ax] = mm\nsetreference(dev::TestRobot, ax, mm=0) = dev.x[dev.axidx[string(ax)]] = mm\n\nfunction setreference(dev::TestRobot, ax::AbstractVector, mm=0)\n    nax = length(ax)\n    if length(mm) == 1\n        mm = fill(mm[1], nax)\n    end\n\n    for i in 1:nax\n        setreference(dev, ax[i], mm[i])\n    end\n    \nend\n\nsetreferenceX(dev::TestRobot, mm=0) = dev.x[dev.axidx[\"x\"]] = mm\nsetreferenceY(dev::TestRobot, mm=0) = dev.x[dev.axidx[\"y\"]] = mm\nsetreferenceZ(dev::TestRobot, mm=0) = dev.x[dev.axidx[\"z\"]] = mm\n\n\n", "meta": {"hexsha": "41b31c21d84b69388a1dc2554e0153a5c80eea0b", "size": 4660, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/testdevices.jl", "max_stars_repo_name": "pjsjipt/AbstractActuators.jl", "max_stars_repo_head_hexsha": "898f53d75b126342cbe941141408f3a6c695425d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/testdevices.jl", "max_issues_repo_name": "pjsjipt/AbstractActuators.jl", "max_issues_repo_head_hexsha": "898f53d75b126342cbe941141408f3a6c695425d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/testdevices.jl", "max_forks_repo_name": "pjsjipt/AbstractActuators.jl", "max_forks_repo_head_hexsha": "898f53d75b126342cbe941141408f3a6c695425d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.3276836158, "max_line_length": 136, "alphanum_fraction": 0.6478540773, "num_tokens": 1518, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4148988457967689, "lm_q2_score": 0.12252321091611637, "lm_q1q2_score": 0.05083473879241076}}
{"text": "\"\"\"\n    AbstractBridgeOptimizer\n\nA bridge optimizer applies given constraint bridges to a given optimizer thus\nextending the types of supported constraints. The attributes of the inner\noptimizer are automatically transformed to make the bridges transparent, e.g.\nthe variables and constraints created by the bridges are hidden.\n\nBy convention, the inner optimizer should be stored in a `model` field and\nthe dictionary mapping constraint indices to bridges should be stored in a\n`bridges` field. If a bridge optimizer deviates from these conventions, it\nshould implement the functions `MOI.optimize!` and `bridge` respectively.\n\"\"\"\nabstract type AbstractBridgeOptimizer <: MOI.AbstractOptimizer end\n\n# AbstractBridgeOptimizer interface\n\n\"\"\"\n    is_bridged(b::AbstractBridgeOptimizer, F::Type{<:MOI.AbstractFunction},\n              S::Type{<:MOI.AbstractSet})::Bool\n\nReturn a `Bool` indicating whether `b` tries to bridge `F`-in-`S` constraints\ninstead of passing it as is to its internal model.\n\"\"\"\nfunction is_bridged end\n# Syntactic sugar\nfunction is_bridged(b::AbstractBridgeOptimizer, ::Type{CI{F, S}}) where {F, S}\n    return is_bridged(b, F, S)\nend\n# We don't bridge variables.\nis_bridged(b::AbstractBridgeOptimizer, ::Type{VI}) = false\n\n\"\"\"\n    supports_bridging_constraint(b::AbstractBridgeOptimizer,\n                               F::Type{<:MOI.AbstractFunction},\n                               S::Type{<:MOI.AbstractSet})::Bool\n\nReturn a `Bool` indicating whether `b` supports bridging `F`-in-`S` constraints.\n\"\"\"\nfunction supports_bridging_constraint(::AbstractBridgeOptimizer,\n                                    ::Type{<:MOI.AbstractFunction},\n                                    ::Type{<:MOI.AbstractSet})\n    return false\nend\n\n\"\"\"\n    bridge_type(b::AbstractBridgeOptimizer,\n                F::Type{<:MOI.AbstractFunction},\n                S::Type{<:MOI.AbstractSet})\n\nReturn the `AbstractBridge` type to be used to bridge `F`-in-`S` constraints.\nThis function should only be called if `is_bridged(b, F, S)`.\n\"\"\"\nfunction bridge_type end\n\nfunction concrete_bridge_type(b::AbstractBridgeOptimizer,\n                              F::Type{<:MOI.AbstractFunction},\n                              S::Type{<:MOI.AbstractSet})\n    return concrete_bridge_type(bridge_type(b, F, S), F, S)\nend\n\n\"\"\"\n    bridge(b::AbstractBridgeOptimizer, ci::CI)\n\nReturn the `AbstractBridge` used to bridge the constraint with index `ci`.\n\"\"\"\nbridge(b::AbstractBridgeOptimizer, ci::CI) = b.bridges[ci]\n# By convention, they should be stored in a `bridges` field using a\n# dictionary-like object.\n\n# Implementation of the MOI interface for AbstractBridgeOptimizer\n\nMOI.optimize!(b::AbstractBridgeOptimizer) = MOI.optimize!(b.model)\n# By convention, the model should be stored in a `model` field\n\nfunction MOI.is_empty(b::AbstractBridgeOptimizer)\n    return isempty(b.bridges) && MOI.is_empty(b.model)\nend\nfunction MOI.empty!(b::AbstractBridgeOptimizer)\n    MOI.empty!(b.model)\n    MOI.empty!(b.bridged)\n    empty!(b.bridges)\nend\nfunction MOI.supports(b::AbstractBridgeOptimizer,\n                      attr::Union{MOI.AbstractModelAttribute,\n                                  MOI.AbstractOptimizerAttribute})\n    return MOI.supports(b.model, attr)\nend\nfunction MOI.copy_to(b::AbstractBridgeOptimizer, src::MOI.ModelLike;\n                   copy_names = true)\n    return MOIU.default_copy_to(b, src, copy_names)\nend\n\n# References\nMOI.is_valid(b::AbstractBridgeOptimizer, vi::VI) = MOI.is_valid(b.model, vi)\nfunction MOI.is_valid(b::AbstractBridgeOptimizer, ci::CI)\n    if is_bridged(b, typeof(ci))\n        return MOI.is_valid(b.bridged, ci)\n    else\n        return MOI.is_valid(b.model, ci)\n    end\nend\nMOI.delete(b::AbstractBridgeOptimizer, vi::VI) = MOI.delete(b.model, vi)\nfunction MOI.delete(b::AbstractBridgeOptimizer, ci::CI)\n    if is_bridged(b, typeof(ci))\n        if !MOI.is_valid(b, ci)\n            throw(MOI.InvalidIndex(ci))\n        end\n        MOI.delete(b, bridge(b, ci))\n        delete!(b.bridges, ci)\n        MOI.delete(b.bridged, ci)\n    else\n        MOI.delete(b.model, ci)\n    end\nend\n\n# Attributes\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::MOI.ListOfConstraintIndices{F, S}) where {F, S}\n    if is_bridged(b, F, S)\n        list = MOI.get(b.bridged, attr)\n    else\n        list = MOI.get(b.model, attr)\n    end\n    for bridge in values(b.bridges)\n        for c in MOI.get(bridge, attr)\n            i = something(findfirst(isequal(c), list), 0)\n            if !iszero(i)\n                MOI.deleteat!(list, i)\n            end\n        end\n    end\n    return list\nend\nfunction _numberof(b::AbstractBridgeOptimizer, model::MOI.ModelLike,\n                   attr::Union{MOI.NumberOfConstraints, MOI.NumberOfVariables})\n    s = MOI.get(model, attr)\n    for v in values(b.bridges)\n        s -= MOI.get(v, attr)\n    end\n    return s\nend\nfunction MOI.get(b::AbstractBridgeOptimizer, attr::MOI.NumberOfVariables)\n    return _numberof(b, b.model, attr)\nend\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::MOI.NumberOfConstraints{F, S}) where {F, S}\n    if is_bridged(b, F, S)\n        # The constraints contained in `b.bridged` may have been added by\n        # bridges\n        return _numberof(b, b.bridged, attr)\n    else\n        return _numberof(b, b.model, attr)\n    end\nend\nfunction MOI.get(b::AbstractBridgeOptimizer, attr::MOI.ListOfConstraints)\n    list_of_types = [MOI.get(b.model, attr); MOI.get(b.bridged, attr)]\n    # Some constraint types show up in `list_of_types` even when all the\n    # constraints of that type have been created by bridges and not by the user.\n    # The code in `NumberOfConstraints` takes care of removing these constraints\n    # from the counter so we can rely on it to remove these constraint types.\n    types_to_remove = findall(iszero.(\n        map(FS -> MOI.get(b, MOI.NumberOfConstraints{FS...}()), list_of_types)))\n    deleteat!(list_of_types, types_to_remove)\n    return list_of_types\nend\n\n# Model an optimizer attributes\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::Union{MOI.AbstractModelAttribute,\n                             MOI.AbstractOptimizerAttribute})\n    return MOI.get(b.model, attr)\nend\nfunction MOI.set(b::AbstractBridgeOptimizer,\n                  attr::Union{MOI.AbstractModelAttribute,\n                              MOI.AbstractOptimizerAttribute},\n                  value)\n    return MOI.set(b.model, attr, value)\nend\n\n# Variable attributes\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::MOI.AbstractVariableAttribute,\n                 index::VI)\n    return MOI.get(b.model, attr, index)\nend\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::MOI.AbstractVariableAttribute,\n                 indices::Vector{VI})\n    return MOI.get(b.model, attr, indices)\nend\nfunction MOI.supports(b::AbstractBridgeOptimizer,\n                      attr::MOI.AbstractVariableAttribute,\n                      IndexType::Type{<:MOI.Index})\n    return MOI.supports(b.model, attr, IndexType)\nend\nfunction MOI.set(b::AbstractBridgeOptimizer,\n                  attr::MOI.AbstractVariableAttribute,\n                  index::MOI.Index, value)\n    return MOI.set(b.model, attr, index, value)\nend\nfunction MOI.set(b::AbstractBridgeOptimizer,\n                  attr::MOI.AbstractVariableAttribute,\n                  indices::Vector{<:MOI.Index}, values::Vector)\n    return MOI.set(b.model, attr, indices, values)\nend\n\n# Constraint attributes\nfunction MOI.get(b::AbstractBridgeOptimizer,\n                 attr::MOI.AbstractConstraintAttribute,\n                 ci::CI)\n    if is_bridged(b, typeof(ci))\n        if MOI.is_set_by_optimize(attr)\n            MOI.get(b, attr, bridge(b, ci))\n        else\n            MOI.get(b.bridged, attr, ci)\n        end\n    else\n        MOI.get(b.model, attr, ci)\n    end\nend\n## Setting names\nfunction MOI.supports(b::AbstractBridgeOptimizer, attr::MOI.ConstraintName,\n                      Index::Type{<:CI})\n    if is_bridged(b, Index)\n        return MOI.supports(b.bridged, attr, Index)\n    else\n        return MOI.supports(b.model, attr, Index)\n    end\nend\nfunction MOI.set(b::AbstractBridgeOptimizer, attr::MOI.ConstraintName,\n                  constraint_index::CI, name::String)\n    if is_bridged(b, typeof(constraint_index))\n        MOI.set(b.bridged, attr, constraint_index, name)\n    else\n        MOI.set(b.model, attr, constraint_index, name)\n    end\nend\n## Setting functions and sets\nfunction MOI.set(b::AbstractBridgeOptimizer, ::MOI.ConstraintSet,\n                  constraint_index::CI{F, S}, set::S) where {F, S}\n    if is_bridged(b, typeof(constraint_index))\n        MOI.set(b, MOI.ConstraintSet(), bridge(b, constraint_index), set)\n        MOI.set(b.bridged, MOI.ConstraintSet(), constraint_index, set)\n    else\n        MOI.set(b.model, MOI.ConstraintSet(), constraint_index, set)\n    end\nend\nfunction MOI.set(b::AbstractBridgeOptimizer, ::MOI.ConstraintFunction,\n                  constraint_index::CI{F, S}, func::F) where {F, S}\n    if is_bridged(b, typeof(constraint_index))\n        MOI.set(b, MOI.ConstraintFunction(), bridge(b, constraint_index), func)\n        MOI.set(b.bridged, MOI.ConstraintFunction(), constraint_index, func)\n    else\n        MOI.set(b.model, MOI.ConstraintFunction(), constraint_index, func)\n    end\nend\n\n# Name\nfunction MOI.get(b::AbstractBridgeOptimizer, IdxT::Type{<:MOI.Index},\n                 name::String)\n    if is_bridged(b, IdxT)\n        return MOI.get(b.bridged, IdxT, name)\n    else\n        return MOI.get(b.model, IdxT, name)\n    end\nend\n\n# We have no information as to whether the constraint is in the bridge or the\n# model. Therefore, we try the model first, and then the bridge if that fails.\nfunction MOI.get(b::AbstractBridgeOptimizer, IdxT::Type{CI},\n                 name::String)\n    ci = MOI.get(b.model, IdxT, name)\n    if ci === nothing\n        return MOI.get(b.bridged, IdxT, name)\n    else\n        return ci\n    end\nend\n\n# Constraints\nfunction MOI.supports_constraint(b::AbstractBridgeOptimizer,\n                                F::Type{<:MOI.AbstractFunction},\n                                S::Type{<:MOI.AbstractSet})\n    if is_bridged(b, F, S)\n        return supports_bridging_constraint(b, F, S) &&\n            MOI.supports_constraint(b.bridged, F, S)\n    else\n        return MOI.supports_constraint(b.model, F, S)\n    end\nend\nfunction MOI.add_constraint(b::AbstractBridgeOptimizer, f::MOI.AbstractFunction,\n                            s::MOI.AbstractSet)\n    if is_bridged(b, typeof(f), typeof(s))\n        ci = MOI.add_constraint(b.bridged, f, s)\n        @assert !haskey(b.bridges, ci)\n        b.bridges[ci] = concrete_bridge_type(b, typeof(f), typeof(s))(b, f, s)\n        return ci\n    else\n        return MOI.add_constraint(b.model, f, s)\n    end\nend\nfunction MOI.modify(b::AbstractBridgeOptimizer, ci::CI,\n                     change::MOI.AbstractFunctionModification)\n    if is_bridged(b, typeof(ci))\n        MOI.modify(b, bridge(b, ci), change)\n        MOI.modify(b.bridged, ci, change)\n    else\n        MOI.modify(b.model, ci, change)\n    end\nend\n\n# Objective\nfunction MOI.modify(b::AbstractBridgeOptimizer, obj::MOI.ObjectiveFunction,\n                     change::MOI.AbstractFunctionModification)\n    MOI.modify(b.model, obj, change)\nend\n\n# Variables\nMOI.add_variable(b::AbstractBridgeOptimizer) = MOI.add_variable(b.model)\nMOI.add_variables(b::AbstractBridgeOptimizer, n) = MOI.add_variables(b.model, n)\n", "meta": {"hexsha": "d70f4e162c8bb208212c9f40ff97175a8b980b92", "size": 11451, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Bridges/bridgeoptimizer.jl", "max_stars_repo_name": "migarstka/MathOptInterface.jl", "max_stars_repo_head_hexsha": "c50524522d3551ab7819124b6a2e220ccd4091d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Bridges/bridgeoptimizer.jl", "max_issues_repo_name": "migarstka/MathOptInterface.jl", "max_issues_repo_head_hexsha": "c50524522d3551ab7819124b6a2e220ccd4091d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Bridges/bridgeoptimizer.jl", "max_forks_repo_name": "migarstka/MathOptInterface.jl", "max_forks_repo_head_hexsha": "c50524522d3551ab7819124b6a2e220ccd4091d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.5621118012, "max_line_length": 80, "alphanum_fraction": 0.6562745612, "num_tokens": 2853, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4263215925474903, "lm_q2_score": 0.11920292984474948, "lm_q1q2_score": 0.05081878288774036}}
{"text": "# This file was generated, do not modify it. # hide\nr3 = x -> round(x, sigdigits=3)\nr3(pi)", "meta": {"hexsha": "de4efdc95ea4d9f572f9930734209c2d5e49e7c3", "size": 90, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "_assets/pages/isl/lab-4/code/ex2.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2021-08-09T11:35:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T06:20:43.000Z", "max_issues_repo_path": "_assets/pages/isl/lab-4/code/ex2.jl", "max_issues_repo_name": "giordano/DataScienceTutorials.jl", "max_issues_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 56, "max_issues_repo_issues_event_min_datetime": "2019-10-22T00:06:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-21T14:38:09.000Z", "max_forks_repo_path": "_assets/pages/isl/lab-4/code/ex2.jl", "max_forks_repo_name": "giordano/DataScienceTutorials.jl", "max_forks_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2019-11-20T16:25:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-05T11:55:15.000Z", "avg_line_length": 30.0, "max_line_length": 51, "alphanum_fraction": 0.6666666667, "num_tokens": 31, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.40356685373537454, "lm_q2_score": 0.12592276647524683, "lm_q1q2_score": 0.05081825468006966}}
{"text": "### A Pluto.jl notebook ###\n# v0.17.1\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 00f8bf47-47d2-4a99-86ba-d7cd0b7f268b\nmd\"\"\" ## First steps\n\nUse this [template](https://github.com/sapal6/nbdev.jl-template.git) to create a new repository. This will create a repository with the directory structure required for nbdev.\"\"\"\n\n# \u2554\u2550\u2561 fbb6eadc-80a1-4cd9-ad27-bebcdd6e5981\nmd\"\"\"\n!!! note\n\n*Right now nbdev is highly opinionated about the folder structure like presence of \"src\" and \"docs\" directory.*\"\"\"\n\n# \u2554\u2550\u2561 170cd996-3652-4157-bcd5-cf782ce74896\nmd\"\n!!! tip\n\n*Refer to this [link](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template) to understand how to use a template repository.*\"\n\n# \u2554\u2550\u2561 b0c1fe8f-e796-4835-95f8-17990c7421a7\nmd\"## First notebook\"\n\n# \u2554\u2550\u2561 e0694cd4-7d07-494d-a010-f705d1cbeb2c\nmd\"\"\"As a first steps clone your repository to your local machine. \n\nOnce you have cloned the repository, start Pluto the usual way as you use to do and create a notebook inside the \"nbs\" directory.\n\nEach Pluto notebook maintains it's own environment. You can directly import a package and Pluto will install it from the registry. However, in our case Nbdev.jl is not yet registered. So, you can install Nbdev in the following way\"\"\"\n\n# \u2554\u2550\u2561 15a5c4fd-301e-4f7c-9ad3-28ae2d214224\nmd\"\"\"In the first cell of your notebook type in the following code \ud83d\udc47\"\"\"\n\n# \u2554\u2550\u2561 5c86332a-db85-4326-9f17-215a8a0188c8\nmd\"\"\"\n```\nusing Nbdev\n```\n\"\"\"\n\n# \u2554\u2550\u2561 e89e5c16-845e-4d0a-a346-80586c100be0\nmd\"This will install Nbdev in our notebook environment.\"\n\n# \u2554\u2550\u2561 2ac2c281-ad4a-4364-8f62-5c8202d4e9c1\nmd\"!!! note\n\n*If you don't want to install Nbdev as part of every notebook that you create in your project, then you can follow the other options to install packages for your notebook projects by following the instructions in the [Pluto wiki](https://github.com/fonsp/Pluto.jl/wiki/%F0%9F%8E%81-Package-management).*\"\n\n# \u2554\u2550\u2561 9258500a-0a33-41cd-b32a-e09e820f84ee\nmd\"## Start building your project\"\n\n# \u2554\u2550\u2561 dedef829-e43c-41c9-8238-b9d631dccb35\nmd\"There are some anchors which tell Nbdev about which portion of your notebook should be exported as code, which portion should be part of documentation and which portion should not be touched.\"\n\n# \u2554\u2550\u2561 99d43364-5ac6-43bb-a70c-20a79a463fdf\nmd\"### #export\"\n\n# \u2554\u2550\u2561 fb11c61e-2b26-4b2a-9a34-835163ac7f87\nmd\"when you put the comment #export at the begining of a cell, it tells nbdev to export that cell as a code into the source file.\"\n\n# \u2554\u2550\u2561 c9f87989-522c-4adc-9a50-af95e3fa9cfa\nmd\"\n```\n#export\nfunction somefunc(x)\n\tx+1\nend\n```\"\n\n# \u2554\u2550\u2561 0d4fb029-bfc8-4208-a6f4-bd5ea981ff82\nmd\"Nbdev will now put the above cell as part of the source file (with the same name as the current notebook) inside the src directory.\"\n\n# \u2554\u2550\u2561 ac65836e-6f4f-4212-b7f4-a648de9c761b\nmd\"### #hide\"\n\n# \u2554\u2550\u2561 789ebdb8-0017-4662-9120-a780584f8368\nmd\"Suppose that you have included soem cells as par of experment while developing your package but you want that the cell should neither be exported as a code nor that should be exported as part of documentation.\n\nIn such a case use the `#hide` anchor at the begining of the cell.\"\n\n# \u2554\u2550\u2561 809102dd-b91a-458f-b5c7-2a49c7536d3b\nmd\"\n```\n#hide\nmyexperiment = 1+1\n```\"\n\n# \u2554\u2550\u2561 f8391a08-edf6-499c-8e8d-8f731dad8fd4\nmd\"such a cell won't be exported.\"\n\n# \u2554\u2550\u2561 4d12cf7f-54d7-4a06-9749-a97246e7564e\nmd\"Any other cell which neither has the `#hide` nor the `#export` anchor, is exported as an example block. For such a block the cell is executed and the output is captured.\n\nThe output and the code of an example cell appears in the final document.\"\n\n# \u2554\u2550\u2561 c40eeb9c-dbda-4dc0-9fc3-e91be447ac25\nmd\"### #noop\"\n\n# \u2554\u2550\u2561 929d68db-fac5-4507-8316-a0dcf71df403\nmd\"Nbdev captures the output for cells which are not marked with `#export` by default. but there might be times when you have an example for which you just want to show the code and not the output. Such cases can be handled by the `#noop` anchor. Just start such cells with `#noop` and nbdev will not capture output for such cells.\"\n\n# \u2554\u2550\u2561 fac3e16d-76ff-476e-a2c4-ff34f7c3f1cc\nmd\"\"\"\n```\nbegin\n#noop\n\"\nsome experiment\n\"\nfunction myexperiment\n    1+1\nend\nmyexperiment\nend\n```\"\"\"\n\n# \u2554\u2550\u2561 9fcf1320-0ddf-471c-a584-092586122347\nmd\"In the above case even though Pluto displays the docstrign as an output, that output won't be displayed in your document.\n\nAs another example let's consider something like the code below\"\n\n# \u2554\u2550\u2561 a3ea3f66-a66c-41d1-8c3c-c1cd3a879871\nmd\"\n```\n#noop \nadd = 1+1\nadd\n````\n\"\n\n# \u2554\u2550\u2561 77b75b82-c684-4377-882a-ff0a4283c7f5\nmd\"The output for such code cell won't be captured in the documentation. Only the code will be displayed once you build the document.\"\n\n# \u2554\u2550\u2561 a7714f66-3eff-481a-af7f-ae26c2b8e8e2\nmd\"## Docstrings\"\n\n# \u2554\u2550\u2561 926b30a2-1a86-4952-a991-d75b5ce6e545\nmd\"Define a docstring for functions, structs etc. and use the special function `showDoc` to document that function or struct\"\n\n# \u2554\u2550\u2561 5bd889ce-9522-462b-a843-508061926306\n\"\"\"\n> This is a function docstring\n\"\"\" \nfunction somefunc()\n\t1+1\nend\n\n# \u2554\u2550\u2561 8c9f1b35-8f88-44ba-8a37-57b92b42980b\nmd\"`showdoc(somefunc)` would show the function like this \ud83d\udc47\"\n\n# \u2554\u2550\u2561 52e3492a-c27d-476f-9010-7d009fd0eeaf\nmd\"`showdoc(somefunc)`\"\n\n# \u2554\u2550\u2561 306239c9-7fd6-442e-8b97-db674398b307\nmd\"## Exporting code\"\n\n# \u2554\u2550\u2561 a8955cbd-2530-405f-b876-18bb35e0b008\nmd\"\"\"Once you are done with your notebook and you can run `notebook2script()`. This reads the current notebook in the \"nbs\" folder, scrubs out all the cells which are do not start with `#export` and creates a module file in the src folder.\n\nThe module file in the src folder will have the same name as the current file with the first letter in uppercase.\n\nThe exported code file automatically has all the code inside the module with the same name as the file.\n\nFor example, the exported source code file would have the following structure \ud83d\udc47\n\n```\nmodule Example\n\nfunction somefunc(x)\n    x+1\nend\n\nend\n```\"\"\"\n\n# \u2554\u2550\u2561 84f7040c-0ca9-4d61-abfe-789fc446ebb2\nmd\"\"\"\nOnce you are happy with the code you can export the source code by calling \n\n`notebook2script(nbsdir, srcdir)`\n\nHere \"nbs_dir\" is the directory where your notebooks are stored and the \"src_dir\" is the directory where you want your source code files to be exported. This is usually the `src` directory.\"\"\"\n\n# \u2554\u2550\u2561 7b1fdf81-870a-4977-8a9d-c867ee2861ff\nmd\"## Tests\"\n\n# \u2554\u2550\u2561 6ace0489-21ae-488f-8ea5-d3087651394c\nmd\"\"\"Nbdev comes with support for [PlutoTests(in alpha)](https://github.com/JuliaPluto/PlutoTest.jl) out of the box.\n\nWhat this means is that you can write reactive tests within the same notebook where you are writing your code. when you run that particular notebook, the affected tests are executed. So, you can write tests as you write your code.\n\nIn addition to reactivity you can also take advantage of other goodies in PlutoTests like \"time travel\" to see the different stages of your code (follow the link to PlutoTests for further details).\"\"\"\n\n# \u2554\u2550\u2561 50ef31b0-b5f2-4a8e-b123-1681645a031f\nmd\"!!! note\nCurrently PlutoTests is in **alpha** so use it with caution. However, you can even use the native test suite in Julia to write your tests but won't get features like time travel while using the native test suite.\n\nAutomatic execution of tests using github actions or Travis CI is planned for future versions of PlutoTest\"\n\n# \u2554\u2550\u2561 bc79b045-8969-4014-b7ac-01282d969c8c\nmd\"## Building documentation\"\n\n# \u2554\u2550\u2561 eb26aca4-4b12-435d-941f-edb18bda0c3c\nmd\"\"\"\nDo not forget to edit the \"index\" notebook available in the \"nbs\" folder. This notebook would be used as your homepage and the contents of this notebook are also used in the README file.\"\"\"\n\n# \u2554\u2550\u2561 547f7e70-71bf-44a6-ba62-0e35edf688fc\nmd\"!!! note\n\n*You will get the index notebook in the nbs directory when you create a new project using the nbdev template.*\n\"\n\n# \u2554\u2550\u2561 b1e0024c-aa32-46a3-baea-534b1187fb7a\nmd\"\"\"To build documentation, goto the project root and launch the Julia prompt and then import Nbdev with `import Nbdev` and then call the following function `Nbdev.build(<your notebook directory)`. Your code at the Julia prompt should look like this \n\n```\nimport Nbdev\nNbdev.build(\"nbs\")\n```\n\nThis will run all your notebooks in the notebook directory (/nbs usually) and pick up the cells which are not marked as `#export` or #hide and then create markdown files in the `/docs` directory.\"\"\"\n\n# \u2554\u2550\u2561 fa7161ef-219f-47b5-b4a0-b18e0d3b5b54\nmd\"## Creating the Table of contents\"\n\n# \u2554\u2550\u2561 54817dac-18b6-40cc-9ef6-b51370164839\nmd\"\"\"The default configuration in the \"mkdocs.yml\" file (available to you when you create your repository from the template) contains all the necessary default configuration required to create you project page.\n\nIn order to test how your website looks like, go to the project root and type `mkdocs serve` . This would serve the project documentation to view locally (localhost).\n\nThe table of contents in the default case would be created by mkdocs on the basis of the documents available under the \"docs\" directory.\n\nIf you want to give your own spin to the table of contents then add a config similar to the following in the mkdocs.yml file-->\n\n```\nnav:\n    - Home: index.md\n    - Docs:\n      - Nav1: nav1.md\n      - Nav2: nav2.md\n```\n\"\"\"\n\n# \u2554\u2550\u2561 2b4992b3-f1a4-4587-b0d6-cb30efb1dfa1\nmd\"\"\"\n!!! note\n*Make sure that the \"Home\" navigation should always be the index.md file*\"\"\"\n\n# \u2554\u2550\u2561 394597e3-42f8-4bd3-8f10-206b5ea5f225\nmd\"For more details about setting up navigation refer [this](https://www.mkdocs.org/user-guide/writing-your-docs/) and [this](https://squidfunk.github.io/mkdocs-material/setup/extensions/python-markdown/?h=table+con+of#table-of-contents) \"\n\n# \u2554\u2550\u2561 094e214e-6681-4de8-b9a4-e73c4b23ff1c\nmd\"## Other required edits\"\n\n# \u2554\u2550\u2561 23ac38d5-c44a-4b8b-86c4-0bc3833add14\nmd\"\"\"In the mkdocs.yml file, edit the \"site_name\" config to the name that you want to give to your site. The \"repo_url\" and \"repo_name\" should be your git repository url and the name of your git repository respectively.\"\"\"\n\n# \u2554\u2550\u2561 33eaee48-fc83-4c71-949d-1c9b9a2b32aa\nmd\"## Optional edits\"\n\n# \u2554\u2550\u2561 952bc125-e3b6-474c-90c4-786fdf300dcc\nmd\"There are loads f other optional edits which you can make to the config file in order to customize your project website. Refer the documentation [here](https://squidfunk.github.io/mkdocs-material/getting-started/) to know about the other options.\"\n\n# \u2554\u2550\u2561 7429a0be-4f40-4d36-89cd-df56377c4502\nmd\"## Publishing your project website\"\n\n# \u2554\u2550\u2561 8fa0648a-599b-4ed5-870f-6b3d74e25fcd\nmd\"\"\"The first step is to checkin all your project contents to your repoitory. As soon as you push your changes to the remote branch, the github action (placed under \"/.github/workflows\" creates a new \"gh-pages\" branch under your project repository, builds the project webpages and pushes the created pages to the \"\"gh-pages\" branch.\n\nIf you go to the \"Actions\" tab in your github repository then you can view the Action under the \"All workflows\" section. Here, a green tick mark before the action name suggests that all went fine.\n\nNow, go to the Settings tab in your repository, click on Pages menu item from the left menu and in the \"Source\" drop-down select \"gh-pages\" as the source and root as the source folder, Click on save.\n\nA detailed step by step description about configuring github pages can be read [here](https://docs.github.com/en/pages/getting-started-with-github-pages/configuring-a-publishing-source-for-your-github-pages-site).\n\nYou project website would be published at the following url --> \"<username>.github.io/<project reporsitory name>\"\n\"\"\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u2560\u255000f8bf47-47d2-4a99-86ba-d7cd0b7f268b\n# \u2560\u2550fbb6eadc-80a1-4cd9-ad27-bebcdd6e5981\n# \u2560\u2550170cd996-3652-4157-bcd5-cf782ce74896\n# \u2560\u2550b0c1fe8f-e796-4835-95f8-17990c7421a7\n# \u2560\u2550e0694cd4-7d07-494d-a010-f705d1cbeb2c\n# \u2560\u255015a5c4fd-301e-4f7c-9ad3-28ae2d214224\n# \u2560\u25505c86332a-db85-4326-9f17-215a8a0188c8\n# \u2560\u2550e89e5c16-845e-4d0a-a346-80586c100be0\n# \u2560\u25502ac2c281-ad4a-4364-8f62-5c8202d4e9c1\n# \u2560\u25509258500a-0a33-41cd-b32a-e09e820f84ee\n# \u2560\u2550dedef829-e43c-41c9-8238-b9d631dccb35\n# \u2560\u255099d43364-5ac6-43bb-a70c-20a79a463fdf\n# \u2560\u2550fb11c61e-2b26-4b2a-9a34-835163ac7f87\n# \u2560\u2550c9f87989-522c-4adc-9a50-af95e3fa9cfa\n# \u2560\u25500d4fb029-bfc8-4208-a6f4-bd5ea981ff82\n# \u2560\u2550ac65836e-6f4f-4212-b7f4-a648de9c761b\n# \u2560\u2550789ebdb8-0017-4662-9120-a780584f8368\n# \u2560\u2550809102dd-b91a-458f-b5c7-2a49c7536d3b\n# \u2560\u2550f8391a08-edf6-499c-8e8d-8f731dad8fd4\n# \u2560\u25504d12cf7f-54d7-4a06-9749-a97246e7564e\n# \u2560\u2550c40eeb9c-dbda-4dc0-9fc3-e91be447ac25\n# \u2560\u2550929d68db-fac5-4507-8316-a0dcf71df403\n# \u2560\u2550fac3e16d-76ff-476e-a2c4-ff34f7c3f1cc\n# \u2560\u25509fcf1320-0ddf-471c-a584-092586122347\n# \u2560\u2550a3ea3f66-a66c-41d1-8c3c-c1cd3a879871\n# \u2560\u255077b75b82-c684-4377-882a-ff0a4283c7f5\n# \u2560\u2550a7714f66-3eff-481a-af7f-ae26c2b8e8e2\n# \u2560\u2550926b30a2-1a86-4952-a991-d75b5ce6e545\n# \u2560\u25505bd889ce-9522-462b-a843-508061926306\n# \u2560\u25508c9f1b35-8f88-44ba-8a37-57b92b42980b\n# \u2560\u255052e3492a-c27d-476f-9010-7d009fd0eeaf\n# \u2560\u2550306239c9-7fd6-442e-8b97-db674398b307\n# \u2560\u2550a8955cbd-2530-405f-b876-18bb35e0b008\n# \u2560\u255084f7040c-0ca9-4d61-abfe-789fc446ebb2\n# \u2560\u25507b1fdf81-870a-4977-8a9d-c867ee2861ff\n# \u2560\u25506ace0489-21ae-488f-8ea5-d3087651394c\n# \u2560\u255050ef31b0-b5f2-4a8e-b123-1681645a031f\n# \u2560\u2550bc79b045-8969-4014-b7ac-01282d969c8c\n# \u2560\u2550eb26aca4-4b12-435d-941f-edb18bda0c3c\n# \u2560\u2550547f7e70-71bf-44a6-ba62-0e35edf688fc\n# \u2560\u2550b1e0024c-aa32-46a3-baea-534b1187fb7a\n# \u2560\u2550fa7161ef-219f-47b5-b4a0-b18e0d3b5b54\n# \u2560\u255054817dac-18b6-40cc-9ef6-b51370164839\n# \u2560\u25502b4992b3-f1a4-4587-b0d6-cb30efb1dfa1\n# \u2560\u2550394597e3-42f8-4bd3-8f10-206b5ea5f225\n# \u2560\u2550094e214e-6681-4de8-b9a4-e73c4b23ff1c\n# \u2560\u255023ac38d5-c44a-4b8b-86c4-0bc3833add14\n# \u2560\u255033eaee48-fc83-4c71-949d-1c9b9a2b32aa\n# \u2560\u2550952bc125-e3b6-474c-90c4-786fdf300dcc\n# \u2560\u25507429a0be-4f40-4d36-89cd-df56377c4502\n# \u2560\u25508fa0648a-599b-4ed5-870f-6b3d74e25fcd\n", "meta": {"hexsha": "08a4aa8222d538f6110cf2e42828b3b33d7434c2", "size": 13608, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "nbs/tutorial.jl", "max_stars_repo_name": "sapal6/Nbdev.jl", "max_stars_repo_head_hexsha": "7523ad44543c9425c0e256dd12a71f1d701801c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2021-12-27T08:01:52.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-11T06:45:30.000Z", "max_issues_repo_path": "nbs/tutorial.jl", "max_issues_repo_name": "sapal6/Nbdev.jl", "max_issues_repo_head_hexsha": "7523ad44543c9425c0e256dd12a71f1d701801c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "nbs/tutorial.jl", "max_forks_repo_name": "sapal6/Nbdev.jl", "max_forks_repo_head_hexsha": "7523ad44543c9425c0e256dd12a71f1d701801c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.7425149701, "max_line_length": 333, "alphanum_fraction": 0.7577895356, "num_tokens": 5114, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "# ------------------------------------------------------------------------------------------\n# # Estructuras de datos\n#\n# Una vez que empecemos a trabajar con muchos datos a la vez, ser\u00e1 conveniente guardar\n# nuestros datos en estructuras como arreglos o diccionarios (m\u00e1s all\u00e1 que s\u00f3lo\n# variables).<br>\n#\n# Tipos de estructuras de datos que cubrimos\n# 1. Tuplas\n# 2. Diccionarios\n# 3. Arreglos\n#\n# <br>\n# Como repaso, las tuplas y los arreglos ambos son secuencias ordenadas de elementos\n# (entonces podemos accesarlos por medio de un \u00edndice).\n# Los diccionarios y los arreglos son mutables.\n#\n# \u00a1Explicaremos m\u00e1s brevemente!\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## Tuplas / Tuples\n#\n# Podemos crear una tupla encerrando una secuencia ordenada de elementos con `( )`.\n#\n# Sintaxis: <br>\n# ```julia\n# (item1, item2, ...)```\n# ------------------------------------------------------------------------------------------\n\nmis_animales_favoritos = (\"ping\u00fcino\", \"gato\", \"petauro_del_az\u00facar\")\n\n# ------------------------------------------------------------------------------------------\n# Podemos accesar con el \u00edndice a esta tupla,\n# ------------------------------------------------------------------------------------------\n\nmyfavoriteanimals[1]\n\n# ------------------------------------------------------------------------------------------\n# Pero como las tuplas con inmutables, no la podemos modificar\n# ------------------------------------------------------------------------------------------\n\nmis_animales_favoritos[1] = \"nutria\"\n\n# ------------------------------------------------------------------------------------------\n# ## Diccionarios\n#\n# Si tenemos conjuntos de datos relacionados entre s\u00ed, podemos guardar los datos en un\n# diccionario. Un buen ejemplo es una lista de contactos, donde asociamos nombres a n\u00fameros\n# de tel\u00e9fono.\n#\n# Sintaxis:\n# ```julia\n# Dict(llave1 => valor1, llave2 => valor2, ....)```\n# ------------------------------------------------------------------------------------------\n\nmiagenda = Dict(\"Jenny\" => \"867-5309\", \"Cazafantasmas\" => \"555-2368\")\n\n# ------------------------------------------------------------------------------------------\n# En este ejemplo, cada nombre y n\u00famero es un par de \"llave\" y \"valor\". Podemos tomar el\n# n\u00famer de Jenny (un valor) usando la llave asociada.\n# ------------------------------------------------------------------------------------------\n\nmiagenda[\"Jenny\"]\n\n# ------------------------------------------------------------------------------------------\n# Podemos agregar otra entrada al diccionario de la manera siguiente\n# ------------------------------------------------------------------------------------------\n\nmiagenda[\"Kramer\"] = \"555-FILK\"\n\n# ------------------------------------------------------------------------------------------\n# Veamos como se ve nuestro diccionario ahora...\n# ------------------------------------------------------------------------------------------\n\nmiagenda\n\n# ------------------------------------------------------------------------------------------\n# Para borrar a Kramer de nuestro diccionario - y simult\u00e1neamente tomar su n\u00famero - usamos\n# pop!\n# ------------------------------------------------------------------------------------------\n\npop!(miagenda, \"Kramer\")\n\nmiagenda\n\n# ------------------------------------------------------------------------------------------\n# A diferencia de las tuplas y los arreglos, los diccionarios no est\u00e1n ordenados y no\n# podemos accesarlos con un \u00edndice\n# ------------------------------------------------------------------------------------------\n\nmiagenda[1]\n\n# ------------------------------------------------------------------------------------------\n# En el ejemplo anterior, `julia` piensa que est\u00e1s tratando de accesar a un valor asociado a\n# la llave `1`.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ## Arreglos\n#\n# A diferencia de las tuplas, los arreglos son mutables. A diferencia de los diccionarios,\n# los arreglos contienen secuencias ordenadas de elementos. <br>\n# Podemos crear un arreglo encapsulando esta secuencia de elementos con `[ ]`.\n#\n# Sintaxis: <br>\n# ```julia\n# [item1, item2, ...]```\n#\n#\n# Por ejemplo, podemos usar un arreglo para recordar a mis amigos\n# ------------------------------------------------------------------------------------------\n\nmyfriends = [\"Ted\", \"Robyn\", \"Barney\", \"Lily\", \"Marshall\"]\n\n# ------------------------------------------------------------------------------------------\n# O guardar una secuencia de n\u00fameros\n# ------------------------------------------------------------------------------------------\n\nfibonacci = [1, 1, 2, 3, 5, 8, 13]\n\nmezcla = [1, 1, 2, 3, \"Ted\", \"Robyn\"]\n\n# ------------------------------------------------------------------------------------------\n# Una vez que tenenmos un arreglo, podemos tomar los datos individualmente dentro del\n# arreglo acces\u00e1ndolos por su \u00edndice. Por ejemplo, si queremos al tercer amigo en myfriends,\n# escribimos\n# ------------------------------------------------------------------------------------------\n\nmyfriends[3]\n\n# ------------------------------------------------------------------------------------------\n# Podemos usar el \u00edndice para mutar la entrada del arreglo\n# ------------------------------------------------------------------------------------------\n\nmyfriends[3] = \"Baby Bop\"\n\n# ------------------------------------------------------------------------------------------\n# Tambi\u00e9n se puede editar con las fuciones de `push!` y `pop!`. `push!` agrega un elemento\n# al final del arreglo y `pop!` quita al \u00faltimo elemento del arreglo.\n#\n# Podemos agregar otro n\u00famero a la secuencia fibonacci\n# ------------------------------------------------------------------------------------------\n\npush!(fibonacci, 21)\n\n# ------------------------------------------------------------------------------------------\n# y quitarlo\n# ------------------------------------------------------------------------------------------\n\npop!(fibonacci)\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# Hasta ahora hemos dado ejemplos de arreglos de escalars unidimensionales, pero los\n# arreglos pueden tener un n\u00famero arbitrario de dimensiones y tambi\u00e9n pueden guardar otros\n# arreglos.\n# <br><br>\n# Por ejemplo, estos son arreglos de arreglos\n# ------------------------------------------------------------------------------------------\n\nfavoritos = [[\"koobideh\", \"chocolate\", \"eggs\"],[\"penguins\", \"cats\", \"sugargliders\"]]\n\nnumbers = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]\n\n# ------------------------------------------------------------------------------------------\n# Abajo hay arreglos de 2D y 3D poblados con valores aleatorios\n# ------------------------------------------------------------------------------------------\n\nrand(4, 3)\n\nrand(4, 3, 2)\n\n# ------------------------------------------------------------------------------------------\n# \u00a1Cuidado copiando los arreglos!\n# ------------------------------------------------------------------------------------------\n\nfibonacci\n\nsomenumbers = fibonacci\n\nsomenumbers[1] = 404\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# Editar `somenumbers` causa que `fibonacci` se edite tambi\u00e9n!\n#\n# En el ejemplo superior, en realidad no hicimos una copia de `fibonacci`. S\u00f3lo creamos una\n# nueva manera de accesar las entradas del arreglo relacionado a `fibonacci`.\n#\n# Si queremos hacer una copia de un arreglo amarrado a `fibonacci`, usamos la funci\u00f3n de\n# `copy`.\n# ------------------------------------------------------------------------------------------\n\n# Primero restauramos a fibonnaci\nfibonacci[1] = 1\nfibonacci\n\nsomemorenumbers = copy(fibonacci)\n\nsomemorenumbers[1] = 404\n\nfibonacci\n\n# ------------------------------------------------------------------------------------------\n# En el \u00faltimo ejemplo, no se edit\u00f3 a fibonacci. Entonces vemos que los arreglos amarrados a\n# `somemorenumbers` y `fibonacci` son distintos.\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Exercises\n#\n# 3.1 Crea un arreglo, `arreglo`, que sea un arreglo de 1D de 2-elementos de 1-elemento de\n# 1D, cada uno guardando el n\u00famero 0.\n# Accesa a `arreglo` para agregar un `1` a cada uno de los arreglos que contiene.\n# ------------------------------------------------------------------------------------------\n\n\n\n# ------------------------------------------------------------------------------------------\n# 3.2 Trata de agregar \"Emergencia\" a `miagenda` con el valor `911`. Trata de agregar `911`\n# como un entero y no como cadena. \u00bfPorqu\u00e9 no funciona?\n# ------------------------------------------------------------------------------------------\n\n\n\n# ------------------------------------------------------------------------------------------\n# 3.3 Crea un nuevo diccionario que se llame `agenda_flexible` que tenga el n\u00famero de Jenny\n# guardado como cadena y el de los Cazafantasmas como entero.\n# ------------------------------------------------------------------------------------------\n\n\n\n# ------------------------------------------------------------------------------------------\n# 3.4 Agrega la llave de \"Emergencia al valor (entero) `911` a `agenda_flexible`.\n# ------------------------------------------------------------------------------------------\n\n\n\n# ------------------------------------------------------------------------------------------\n# 3.5 \u00bfPorqu\u00e9 podemos agregar un entero como valor a `agenda_flexible` pero no a\n# `miagenda`? \u00bfC\u00f3mo pudimos haber inicializado `miagenda` para que aceptara enteros como\n# valores?\n# ------------------------------------------------------------------------------------------\n\n\n", "meta": {"hexsha": "dae8787ab87bc13a2e4360d9e1f553f0698070a2", "size": 10152, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/intro-to-julia-ES/ 3. 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{"text": "module RocketCollectLatestObservableTest\n\nusing Test\nusing Rocket\n\ninclude(\"../test_helpers.jl\")\n\n@testset \"CollectLatestObservable\" begin\n\n    println(\"Testing: collectLatest\")\n\n    run_testset([\n        (\n            source = collectLatest([ of(1), from(1:5) ]),\n            values = @ts([ [1, 1], [1, 2], [1, 3], [1, 4], [1, 5], c ]),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest([ of(1), from(1:5) ], (v) -> v .^ 2),\n            values = @ts([ [1, 1], [1, 4], [1, 9], [1, 16], [1, 25], c ]),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest(Int, Int, [ of(1), from(1:5) ], sum),\n            values = @ts([ 2, 3, 4, 5, 6, c ]),\n            source_type = Int\n        ),\n        (\n            source = collectLatest([ of(1) |> async(0), from(1:5) ]),\n            values = @ts([ [1, 5] ] ~ c),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest([ of(1) |> async(0), from(1:5) |> async(0) ]),\n            values = @ts([ [1, 1] ] ~ [ [1, 2] ] ~ [ [1, 3] ] ~ [ [1, 4] ] ~ [ [1, 5] ] ~ c),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest([ from(1:5), of(2.0) ]),\n            values = @ts([ [5, 2], c ]),\n            source_type = Vector{Union{Float64, Int}}\n        ),\n        (\n            source = collectLatest([ from(1:5) |> async(0), of(2.0) ]),\n            values = @ts([ [1, 2.0] ] ~ [ [2, 2.0] ] ~ [ [3, 2.0] ] ~ [ [4, 2.0] ] ~ [ [5, 2.0] ] ~ c),\n            source_type = Vector{Union{Float64, Int}}\n        ),\n        (\n            source = collectLatest([ completed(Int), of(1) ]),\n            values = @ts(c),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest([ completed(Int), completed(Int) ]),\n            values = @ts(c),\n            source_type = Vector{Int}\n        ),\n        (\n            source = collectLatest([ faulted(Float64, \"err\"), completed(Int) ]),\n            values = @ts(e(\"err\")),\n            source_type = Vector{Union{Float64, Int}}\n        ),\n        (\n            source = collectLatest([ completed(Int), faulted(Float64, \"err\") ]),\n            values = @ts(c),\n            source_type = Vector{Union{Float64, Int}}\n        ),\n        (\n            source = collectLatest([ faulted(Float64, \"err1\"), faulted(Float64, \"err2\") ]),\n            values = @ts(e(\"err1\")),\n            source_type = Vector{Float64}\n        ),\n    ])\n\n    somenumbers    = Union{Float64, Int}[ 1, 1, 1, 1, 1.0, 1.0, 1.0, 1.0 ]\n    expectedoutput = somenumbers\n\n    collected100 = collectLatest(map(n -> of(n), somenumbers))\n\n    run_testset([\n        (\n            source = collected100,\n            values = @ts([ [ 1, 1, 1, 1, 1.0, 1.0, 1.0, 1.0 ], c ]),\n            source_type = Vector{Union{Float64, Int}}\n        )\n    ])\n\n\n    @testset begin\n        s1 = Subject(Int)\n        s2 = Subject(Float64)\n        s3 = Subject(String)\n        s4 = of(5)\n        s5 = from(1:10)\n\n        latest = collectLatest([ s1, s2, s3, s4, s5 ])\n        values = Vector{Any}()\n        subscription = subscribe!(latest, lambda(\n            on_next     = (d) -> push!(values, d),\n            on_error    = (e) -> push!(values, e),\n            on_complete = ()  -> push!(values, \"completed\")\n        ))\n\n        @test values == [ ]\n\n        next!(s1, 1)\n\n        @test values == [ ]\n\n        next!(s2, 2.0)\n\n        @test values == [ ]\n\n        next!(s3, \"Hello\")\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10] ]\n\n        next!(s1, 2)\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10] ]\n\n        next!(s3, \"Hello, world!\")\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10]  ]\n\n        complete!(s1);\n\n        next!(s2, 3.0)\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10], [2, 3.0, \"Hello, world!\", 5, 10]  ]\n\n        complete!(s2)\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10], [2, 3.0, \"Hello, world!\", 5, 10] ]\n\n        next!(s3, \"Something\")\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10], [2, 3.0, \"Hello, world!\", 5, 10], [2, 3.0, \"Something\", 5, 10] ]\n\n        complete!(s3)\n\n        @test values == [ [1, 2.0, \"Hello\", 5, 10], [2, 3.0, \"Hello, world!\", 5, 10], [2, 3.0, \"Something\", 5, 10], \"completed\" ]\n\n        unsubscribe!(subscription)\n    end\n\nend\n\nend\n", "meta": {"hexsha": "36d3bfa1a92646d98962374bdc7733018653bc9a", "size": 4310, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/observable/test_observable_collect_latest.jl", "max_stars_repo_name": "hgeorgako/Rocket.jl", "max_stars_repo_head_hexsha": "9661dad340e9a079ebd6ed57dcf9e5db31af637f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/observable/test_observable_collect_latest.jl", "max_issues_repo_name": "hgeorgako/Rocket.jl", "max_issues_repo_head_hexsha": "9661dad340e9a079ebd6ed57dcf9e5db31af637f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/observable/test_observable_collect_latest.jl", "max_forks_repo_name": "hgeorgako/Rocket.jl", "max_forks_repo_head_hexsha": "9661dad340e9a079ebd6ed57dcf9e5db31af637f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7333333333, "max_line_length": 129, "alphanum_fraction": 0.4382830626, "num_tokens": 1410, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4960938294709195, "lm_q2_score": 0.10230471815640063, "lm_q1q2_score": 0.0507527394031519}}
{"text": "\n# ------------------------------------------------------------------------------------------\n# # \u8f6f\u4ef6\u5305\n#\n# Julia \u6709\u8d85\u8fc7 2000 \u4e2a\u767b\u8bb0\u5728\u6848\u7684\u5305\uff0c\u8fd9\u4f7f\u5f97\u7b2c\u4e09\u65b9\u7684\u8f6f\u4ef6\u5305\u6210\u4e3a Julia \u8bed\u8a00\u751f\u6001\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002\n# \u5c3d\u7ba1\u5982\u6b64\uff0c\u8f6f\u4ef6\u5305\u751f\u6001\u4f9d\u7136\u6709\u5f88\u5927\u7684\u6210\u957f\u7a7a\u95f4\u3002\n#\n# \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u5728 Julia \u4e2d\u8c03\u7528\u5176\u5b83\u8bed\u8a00\u7684\u51fd\u6570\u4e5f\u662f\u4e00\u7b49\u516c\u6c11\u3002Julia \u63d0\u4f9b\u4e86\u5f88\u68d2\u7684\u5916\u90e8\u51fd\u6570\u63a5\u53e3\uff08FFI\uff09\u3002\n# \u6211\u4eec\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u901a\u8fc7 `PyCall` \u6216 `Rcall` \u8c03\u7528 Python \u6216 R\u3002\n# \u8fd9\u610f\u5473\u7740\u4f60\u4e0d\u5fc5\u7b49\u5230 Julia \u7684\u751f\u6001\u5b8c\u5168\u6210\u719f\u5c31\u80fd\u8fc1\u79fb\u73b0\u6709\u9879\u76ee\u5230 Julia\uff0c\n# \u800c\u4e14\u8fc1\u79fb\u5230 Julia \u5e76\u4e0d\u610f\u5473\u7740\u653e\u5f03\u4f60\u5728\u5176\u4ed6\u8bed\u8a00\u4e2d\u60ef\u7528\u7684\u5305\u6216\u8005\u5e93\uff01\n#\n# \u67e5\u770b\u5168\u90e8\u53ef\u83b7\u5f97\u7684\u5305\uff0c\u8bf7\u770b\n# https://pkg.julialang.org/\n# \u6216\u8005\n# https://juliaobserver.com/\n#\n# \u73b0\u5728\u6211\u4eec\u5148\u5b66\u4e60\u4e00\u4e0b\u8be5\u5982\u4f55\u4f7f\u7528\u5305\u3002\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# \u5728\u5b89\u88c5\u597d\u7684 Julia \u4e2d\u7b2c\u4e00\u6b21\u8981\u4f7f\u7528\u4e00\u4e2a\u5305\u7684\u65f6\u5019\uff0c\u4f60\u9700\u8981\u7528\u5305\u7ba1\u7406\u5668\uff08package manager\uff09\u660e\u786e\u6dfb\u52a0\uff08add\uff09\u5b83\uff1a\n# ------------------------------------------------------------------------------------------\n\nusing Pkg\nPkg.add(\"Example\")\n\n# ------------------------------------------------------------------------------------------\n# \u4e4b\u540e\u6bcf\u6b21\u4f7f\u7528 Julia \u65f6\uff08\u6bd4\u5982\u6253\u5f00\u4e00\u4e2a\u65b0\u7684 REPL \u4f1a\u8bdd\u6216\u8005\u7b2c\u4e00\u6b21\u6253\u5f00\u4e00\u4e2a notebook\uff09\uff0c\n# \u4f60\u9700\u8981\u901a\u8fc7 `using` \u5173\u952e\u5b57\u8f7d\u5165\u8981\u7528\u7684\u5305\n# ------------------------------------------------------------------------------------------\n\nusing Example\n\n# ------------------------------------------------------------------------------------------\n# \u5728 `Example.jl` \u7684\u6e90\u7801\n# https://github.com/JuliaLang/Example.jl/blob/master/src/Example.jl\n# \u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u58f0\u660e\u4e86\u4e0b\u9762\u8fd9\u4e2a\u51fd\u6570\n#\n# ```julia\n# hello(who::String) = \"Hello, $who\"\n# ```\n#\n# \u8f7d\u5165\u4e86 `Example` \u6211\u4eec\u73b0\u5728\u5e94\u8be5\u53ef\u4ee5\u8c03\u7528 `hello` \u51fd\u6570\u4e86\n# ------------------------------------------------------------------------------------------\n\nhello(\"it's me. I was wondering if after all these years you'd like to meet.\")\n\n# ------------------------------------------------------------------------------------------\n# \u73b0\u5728\u8ba9\u6211\u4eec\u6765\u73a9 `Colors` \u5305\u5427\n# ------------------------------------------------------------------------------------------\n\nPkg.add(\"Colors\")\n\nusing Colors\n\n# ------------------------------------------------------------------------------------------\n# \u9996\u5148\u521b\u5efa\u4e00\u4e2a 100 \u4e2a\u4e0d\u540c\u989c\u8272\u7684\u8c03\u8272\u677f\n# ------------------------------------------------------------------------------------------\n\npalette = distinguishable_colors(100)\n\n# ------------------------------------------------------------------------------------------\n# \u7136\u540e\u901a\u8fc7 `rand` \u51fd\u6570\u6765\u521b\u5efa\u968f\u673a\u77e9\u9635\n# ------------------------------------------------------------------------------------------\n\nrand(3, 3)\n\nrand(1:10, 3, 3)\n\nrand(palette, 3, 3)\n", "meta": {"hexsha": "d5be2b6475f1982dae8eb0c1d164d61fe429077f", "size": 2356, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/03.\u5305\u7684\u4f7f\u7528.jl", "max_stars_repo_name": "NahsiN/JuliaTutorials", "max_stars_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, "max_stars_repo_stars_event_min_datetime": "2020-07-15T14:56:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T12:50:32.000Z", "max_issues_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/03.\u5305\u7684\u4f7f\u7528.jl", "max_issues_repo_name": "NahsiN/JuliaTutorials", "max_issues_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 42, "max_issues_repo_issues_event_min_datetime": "2018-02-25T22:53:47.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-14T02:15:50.000Z", "max_forks_repo_path": ".nbexports/zh-cn/intro-to-julia-ZH/\u7b80\u77ed\u7248/03.\u5305\u7684\u4f7f\u7528.jl", "max_forks_repo_name": "NahsiN/JuliaTutorials", "max_forks_repo_head_hexsha": "6d61077a0f6565e19305cddab0adde79d232e147", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 394, "max_forks_repo_forks_event_min_datetime": "2020-07-14T23:22:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 32.7222222222, "max_line_length": 92, "alphanum_fraction": 0.3225806452, "num_tokens": 650, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4493926344647597, "lm_q2_score": 0.11279539733570564, "lm_q1q2_score": 0.05068942076419209}}
{"text": "### A Pluto.jl notebook ###\n# v0.12.8\n\nusing Markdown\nusing InteractiveUtils\n\n# \u2554\u2550\u2561 e077ab16-22e5-11eb-15ed-5f0654bf7328\nusing Pkg, DrWatson\n\n# \u2554\u2550\u2561 e75a9a06-22e5-11eb-2e36-8d4f62b830ed\nbegin\n\t@quickactivate \"StatisticsWithJuliaPlutoNotebooks\"\n\tusing DataFrames, CSV\nend\n\n# \u2554\u2550\u2561 ca5fbdc8-22e5-11eb-0a60-cb0a127a3ca5\nmd\"## Listing4.06\"\n\n# \u2554\u2550\u2561 fa68607e-22e5-11eb-0558-c9a4d9f77426\nbegin\n\tdata1 = CSV.read(datadir(\"purchaseData.csv\"), copycols=true)\n\tText(\"Original value: $(data1.Name[1])\")\nend\n\n# \u2554\u2550\u2561 e303495a-243f-11eb-1440-a1d6ef299e6a\nbegin\n\tdata2 = data1\n\tdata2.Name[1] = \"EMILY\"\n\tdata1.Name[1]\nend\n\n# \u2554\u2550\u2561 e30380ae-243f-11eb-351f-058cb130306d\nbegin\n\tdata3 = CSV.read(datadir(\"purchaseData.csv\"), copycols=true)\n\tdata4 = copy(data3)\n\tdata4.Name[1] = \"EMILY\"\n\tdata3.Name[1]\nend\n\n# \u2554\u2550\u2561 e30411fc-243f-11eb-25b0-a33492c9b3fb\nbegin\n\tdata5 = DataFrame()\n\tdata5.X = [[0,1],[100,101]]\n\tdata6 = copy(data5)\n\tdata6.X[1][1] = -1\n\tdata5.X[1][1]\nend\n\n# \u2554\u2550\u2561 e30b3752-243f-11eb-2e8d-bb6dc37d5c08\nbegin\n\tdata7 = DataFrame(X = [[0,1],[100,101]])\n\tdata8 = deepcopy(data7)\n\tdata8.X[1][1] = -1\n\tdata7.X[1][1]\nend\n\n# \u2554\u2550\u2561 475ff888-22e6-11eb-2354-09f8f40a8e12\nmd\"## End of listing4.06\"\n\n# \u2554\u2550\u2561 Cell order:\n# \u255f\u2500ca5fbdc8-22e5-11eb-0a60-cb0a127a3ca5\n# \u2560\u2550e077ab16-22e5-11eb-15ed-5f0654bf7328\n# \u2560\u2550e75a9a06-22e5-11eb-2e36-8d4f62b830ed\n# \u2560\u2550fa68607e-22e5-11eb-0558-c9a4d9f77426\n# \u2560\u2550e303495a-243f-11eb-1440-a1d6ef299e6a\n# \u2560\u2550e30380ae-243f-11eb-351f-058cb130306d\n# \u2560\u2550e30411fc-243f-11eb-25b0-a33492c9b3fb\n# \u2560\u2550e30b3752-243f-11eb-2e8d-bb6dc37d5c08\n# \u255f\u2500475ff888-22e6-11eb-2354-09f8f40a8e12\n", "meta": {"hexsha": "8df588a8354a46f713d8ff484adf3be1d7d093f1", "size": 1551, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/04/listing4.06.jl", "max_stars_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_stars_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 68, "max_stars_repo_stars_event_min_datetime": "2020-11-08T07:32:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T16:58:01.000Z", "max_issues_repo_path": "notebooks/04/listing4.06.jl", "max_issues_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_issues_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-12-07T08:07:30.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-20T16:16:06.000Z", "max_forks_repo_path": "notebooks/04/listing4.06.jl", "max_forks_repo_name": "pitmonticone/StatisticsWithJuliaPlutoNotebooks.jl", "max_forks_repo_head_hexsha": "47a10ee915d87dd3241d2863e47b5f9630a03409", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 14, "max_forks_repo_forks_event_min_datetime": "2020-11-14T05:07:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T21:05:35.000Z", "avg_line_length": 22.1571428571, "max_line_length": 61, "alphanum_fraction": 0.7137330754, "num_tokens": 805, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4882833952958347, "lm_q2_score": 0.10374861855288664, "lm_q1q2_score": 0.05065872772425592}}
{"text": "module NamedTupleUtilities\n\n\"\"\"\n    select(a::NamedTuple, v::Val{n})\nSelect a field `n` from `a` if it is in `a`.\n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.select((a=1,b=2,c=3),Val(:a))\n(a = 1,)\n```\n\"\"\"\n@generated function select(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if i == bn)...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    remove(a::NamedTuple, v::Val{n})\nRemove a field `n` from the `a` if it is in `a`.\n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.remove((a=1,b=2,c=3),Val(:c))\n(a = 1, b = 2)\n```\n\"\"\"\n@generated function remove(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if i != bn)...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    range(a::NamedTuple, b::Val{n}, c::Val{n})\nReturn a NamedTuple which retains the fields from `b` to `c` in `a`. \nIf `b` is not in `a`, then it will return the empty NamedTuple. \nIf `c` is not in `a`, then it will return everything starting with `b`.\n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.range((a=1,b=2,c=3),Val(:a),Val(:b))\n(a = 1, b = 2)\n```\n\"\"\"\n@generated function range(a::NamedTuple{an}, ::Val{bn}, ::Val{cn}) where {an, bn, cn}\n    rangeStarted = false\n    names = Symbol[]\n    for n in an\n        if n == bn\n            rangeStarted = true\n        end\n        if rangeStarted\n            push!(names, n)\n        end\n        if n == cn\n            rangeStarted = false\n            break\n        end\n    end\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :( NamedTuple{$(names...,),$types}(($(vals...),)) )\nend\n\n\"\"\"\n    rename(a::NamedTuple, b::Val{n}, c::Val{n})\nReturn a NamedTuple derived from `a` in which the the field from `b` is renamed to `c`. \nIf `b` is not in `a`, then it will return the original NamedTuple. \nIf `c` is in `a`, then `ERROR: duplicate field name in NamedTuple: \"c\" is not unique` will occur.\n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.rename((a = 1, b = 2, c = 3),Val(:a),Val(:d))\n(d = 1, b = 2, c = 3)\njulia> QueryOperators.NamedTupleUtilities.rename((a = 1, b = 2, c = 3),Val(:m),Val(:d))\n(a = 1, b = 2, c = 3)\njulia> QueryOperators.NamedTupleUtilities.rename((a = 1, b = 2, c = 3),Val(:a),Val(:c))\nERROR: duplicate field name in NamedTuple: \"c\" is not unique\n```\n\"\"\"\n@generated function rename(a::NamedTuple{an}, ::Val{bn}, ::Val{cn}) where {an, bn, cn}\n    names = Symbol[]\n    typesArray = DataType[]\n    vals = Expr[]\n    for n in an\n        if n == bn\n            push!(names, cn)\n        else\n            push!(names, n)\n        end\n        push!(typesArray, fieldtype(a, n))\n        push!(vals, :(getfield(a, $(QuoteNode(n)))))\n    end\n    types = Tuple{typesArray...}\n    return :(NamedTuple{$(names...,),$types}(($(vals...),)))\nend\n\n\"\"\"\n    startswith(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields with names starting with `b` in `a`. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.startswith((abc=1,bcd=2,cde=3),Val(:a))\n(abc = 1,)\n```\n\"\"\"\n@generated function startswith(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if Base.startswith(String(i), String(bn)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    not_startswith(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields with names that do not start with `b` in `a`. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.not_startswith((abc=1,bcd=2,cde=3),Val(:a))\n(bcd = 2, cde = 3)\n```\n\"\"\"\n@generated function not_startswith(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if !Base.startswith(String(i), String(bn)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    endswith(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields with names that do not end with `b` in `a`. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.not_endswith((abc=1,bcd=2,cde=3),Val(:d))\n(abc = 1, cde = 3)\n```\n\"\"\"\n@generated function endswith(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if Base.endswith(String(i), String(bn)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    not_endswith(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields with names ending with `b` in `a`. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.endswith((abc=1,bcd=2,cde=3),Val(:d))\n(bcd = 2,)\n```\n\"\"\"\n@generated function not_endswith(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if !Base.endswith(String(i), String(bn)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    occursin(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields with names containing `b` as a substring. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.occursin((abc=1,bcd=2,cde=3),Val(:d))\n(bcd = 2, cde = 3)\n```\n\"\"\"\n@generated function occursin(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if Base.occursin(String(bn), String(i)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    not_occursin(a::NamedTuple, b::Val{n})\nReturn a NamedTuple which retains the fields without names containing `b` as a substring. \n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.not_occursin((abc=1,bcd=2,cde=3),Val(:d))\n(abc = 1,)\n```\n\"\"\"\n@generated function not_occursin(a::NamedTuple{an}, ::Val{bn}) where {an, bn}\n    names = ((i for i in an if !Base.occursin(String(bn), String(i)))...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\n\"\"\"\n    oftype(a::NamedTuple, b::DataType)\nReturns a NamedTuple which retains the fields whose elements have type `b`.\n```jldoctest\njulia> QueryOperators.NamedTupleUtilities.oftype((a = [4,5,6], b = [3.,2.,1.], c = [\"He\",\"llo\",\"World!\"]), Val(Int64))\n(a = [4, 5, 6],)\njulia> QueryOperators.NamedTupleUtilities.oftype((a = [4,5,6], b = [3.,2.,1.], c = [\"He\",\"llo\",\"World!\"]), Val(Number))\n(a = [4, 5, 6], b = [3., 2., 1.])\njulia> QueryOperators.NamedTupleUtilities.oftype((a = [4,5,6], b = [3.,2.,1.], c = [\"He\",\"llo\",\"World!\"]), Val(Float32))\nNamedTuple()\n```\n\"\"\"\n@generated function oftype(a::NamedTuple{an}, ::Val{b}) where {an, b}\n    names = ((i for i in an if fieldtype(a, i) <: b)...,)\n    types = Tuple{(fieldtype(a, n) for n in names)...}\n    vals = Expr[:(getfield(a, $(QuoteNode(n)))) for n in names]\n    return :(NamedTuple{$names,$types}(($(vals...),)))\nend\n\nend", "meta": {"hexsha": "1ebbb707d46a27f2cdf31b6ebbee9f0beb9492f6", "size": 7472, "ext": "jl", "lang": "Julia", "max_stars_repo_path": 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{"text": "\r\n\"\"\"\r\n    nullgraph!(net)\r\n\r\nRemove all nodes and links from the network. \r\n\r\nThe first argument *net* is a FastNet structure that is be used in the simulation. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia> net=FastNet(1000,2000,1,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> randomgraph!(net)\r\nNetwork of 1000 nodes and 2000 links\r\n\r\njulia> nullgraph!(net)\r\nNetwork of 0 nodes and 0 links\r\n```\r\n\"\"\"\r\nfunction nullgraph!(net::FastNet)\r\n    while countnodes_f(net)>0\r\n        destroynode_f!(net,net.nid[1])\r\n    end\r\n    net\r\nend\r\n\r\n\"\"\"\r\n    randomgraph!(net;<keyword arguments>)\r\n\r\nCreate an ER random graph in the network *net*. \r\n\r\nThe network isn't guaranteed to be a simple graph, but in large sparse \r\nnetworks it is simple with high probability. \r\n\r\nBy default all nodes and links that the network can accommodate will be used and \r\nall nodes will be set to state one. This behavior can be controlled by the following \r\nkeyword arguments:\r\n\r\n- N : The number of nodes that will be used in the creation of the random graph.\r\n  All other nodes will be removed from the network. \r\n- K : The number of links that will be used in the creation of the random graph.\r\n  All other links will be removed from the network. \r\n- S : The state of the nodes. All nodes will be set to this state. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia> net=FastNet(1000,2000,1,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> randomgraph!(net)\r\nNetwork of 1000 nodes and 2000 links\r\n\r\njulia> nullgraph!(net)\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> randomgraph!(net,N=100,K=10)\r\nNetwork of 100 nodes and 10 links\r\n```\r\n\"\"\"\r\nfunction randomgraph!(net::FastNet; N::Int=0,K::Int=0,S::Int=1)\r\n    nullgraph!(net)\r\n    n=N;\r\n    k=K;\r\n    s=S;\r\n    if n===0\r\n        n=net.N\r\n    end \r\n    if k===0\r\n        k=net.K\r\n    end\r\n    if n<1 && k>0\r\n        throw(ArgumentError(\"In order to create links the net has to have at least one node\"))\r\n    end\r\n    if n>net.N\r\n        throw(ArgumentError(\"Trying to create more nodes than maximum allowed by the net\"))\r\n    end\r\n    if k>net.K\r\n        throw(ArgumentError(\"Trying to create more links than maximum allowed by the net\"))\r\n    end\r\n    if s<1 || s>net.C-1\r\n        msg=\"The net passed to randomgraph! only supports node states between 1 and $(net.C-1),\"\r\n        msg*=\" but you are asking it to set nodes to state $s.\"\r\n        throw(ArgumentError(msg))\r\n    end\r\n    makenodes!(net,n,s)\r\n    for i=1:k\r\n        src=randomnode_f(net,s)\r\n        dst=randomnode_f(net,s)\r\n        makelink_f!(net,src,dst)\r\n    end\r\n    net\r\nend\r\n\r\n\r\n\"\"\"\r\n    configmodel!(net::FastNet,degreedist;<keyword arguments>)\r\n\r\nCreate a configuration model style network with prescribed degree distribution, *degreedist*.\r\n\r\nThe network in *net* is replaced with the new topology. The degree distribution is specified in  \r\nterms of a vector of Float64 variables such that *degreedist[k]* is specifies, p<sub>k</sub>, the probability\r\nthat a randomly drawn node has degree k. If the elements of *degreedist* add up to less than 1.0 the \r\nremaining nodes will have degree zero.   \r\n \r\nThe network generation is fast and unbiased but isn't guaranteed to result in a simple graph. \r\nThe algorithm will try to match the desired degree distribution as closely as possible,\r\nbut small discrepancies can appear if the degree distribution would result in an odd degree sum \r\nor non integer numbers of nodes of certain degrees. \r\n\r\nIf there FastNet is not large enough to accomodate the desired number of links or nodes an argument error \r\nwill be thrown. \r\n\r\nThe keyword arguments are \r\n- N : The number of nodes that will be used in the creation of the network\r\n- S : The state of the nodes. All nodes will be set to this state. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia> net=FastNet(1000,2000,2,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> configmodel!(net,[0.5,0.25,0.25],N=200)\r\nNetwork of 200 nodes and 175 links\r\n\r\njulia> degreedist(net)\r\n3-element Vector{Float64}:\r\n 0.5\r\n 0.25\r\n 0.25\r\n\r\njulia> configmodel!(net,[0.5,0.25],N=200)\r\nNetwork of 200 nodes and 100 links\r\n\r\njulia> degreedist(net)\r\n2-element Vector{Float64}:\r\n 0.5\r\n 0.25\r\n```\r\n\"\"\"\r\nfunction configmodel!(net::FastNet,degreedist;N=0,S=1)\r\n    (counts,totalstubs)=_configmodelsetup!(net,degreedist,N,S)\r\n    nn=sum(counts);\r\n    l=length(counts)\r\n    nde=Array{Int,1}(undef,totalstubs)\r\n    curnode=1\r\n    curstub=1\r\n    for i=1:l\r\n        for j=1:counts[i]\r\n            for s=1:i\r\n                nde[curstub]=curnode\r\n                curstub+=1\r\n            end\r\n            curnode+=1\r\n        end\r\n    end\r\n    cutoff=totalstubs\r\n    rng=net.rng\r\n    while cutoff>1\r\n        stub=rand(rng,1:cutoff)\r\n        src=node_f(net,nde[stub])\r\n        nde[stub]=nde[cutoff]\r\n        cutoff-=1\r\n        stub=rand(rng,1:cutoff)\r\n        dst=node_f(net,nde[stub])\r\n        nde[stub]=nde[cutoff]\r\n        cutoff-=1\r\n        makelink_f!(net,src,dst)\r\n    end\r\n    net\r\nend\r\n\r\n\r\n\"\"\"\r\n    regulargraph!(net::FastNet,deg;<keyword arguments>)\r\n\r\nCreate a regular graph with node degree *deg*. \r\n\r\nThe network in *net* is replaced with the new topology in which all nodes have degree *deg*\r\nand are randomly connected. If the number of nodes is not specified the function will\r\ntry to use all nodes allowed by net. \r\n \r\nThe network generation is fast and unbiased, but isn't guaranteed to result in a simple graph. \r\n\r\nNote that finite regular graphs with odd node degree and odd number of nodes do not exist. Hence \r\neither *deg* or the number of nodes must be even. \r\n\r\nIf there FastNet is not large enough to accomodate the desired number of links or nodes an argument error \r\nwill be thrown. \r\n\r\nThe keyword arguments are \r\n- N : The number of nodes that will be used in the creation of the network\r\n- S : The state of the nodes. All nodes will be set to this state. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia>  net=FastNet(1000,2000,2,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> regulargraph!(net,4)\r\nNetwork of 1000 nodes and 2000 links\r\n\r\njulia> degreedist(net)\r\n4-element Vector{Float64}:\r\n 0.0\r\n 0.0\r\n 0.0\r\n 1.0\r\n```\r\n\"\"\"\r\nfunction regulargraph!(net::FastNet,deg;N=0,S=1)\r\n    s=0\r\n    n=0\r\n    d=0\r\n    try \r\n        s=convert(Int,S)\r\n    catch e\r\n        throw(ArgumentError(\"regulargraph expects s to be an integer\"))\r\n    end\r\n    try \r\n        n=convert(Int,N)\r\n    catch e\r\n        throw(ArgumentError(\"regulargraph expects N to be an integer\"))\r\n    end\r\n    try \r\n        d=convert(Int,deg)\r\n    catch e\r\n        throw(ArgumentError(\"regulargraph expects deg to be an integer\"))\r\n    end\r\n    if n==0\r\n        n=net.N\r\n    end\r\n    checknodestate(net,s,\"Trying to create regular graph\")\r\n    if n<1\r\n        throw(ArgumentError(\"regulargraph expects number of nodes n to be positive\"))\r\n    end\r\n    if d<0\r\n        throw(ArgumentError(\"regulargraph expects nodedegree deg to be non-negative\"))\r\n    end\r\n    if n>net.N\r\n        throw(ArgumentError(\"Requested size for regulargraph exceeds max node count of underlyng FastNet\"))\r\n    end\r\n    if (n*d)\u00f72>net.K\r\n        throw(ArgumentError(\"Requested regulargraph exceeds max link count of underlyng FastNet\"))\r\n    end\r\n    if isodd(n) && isodd(d)\r\n        throw(ArgumentError(\"A regular graph of odd node degree must have an even number of nodes.\"))\r\n    end\r\n    nullgraph!(net)\r\n    makenodes!(net,n,s)\r\n    totalstubs=n*d\r\n    nde=Array{Int,1}(undef,totalstubs)\r\n    for i=1:totalstubs\r\n        nde[i]=((i-1)\u00f7deg)+1 \r\n    end\r\n    cutoff=totalstubs\r\n    rng=net.rng\r\n    while cutoff>0\r\n        srcs=rand(rng,1:cutoff)         # pick the source\r\n        src=node_f(net,nde[srcs])\r\n        nde[srcs]=nde[cutoff]\r\n        cutoff-=1\r\n        dsts=rand(rng,1:cutoff)         # pick the source\r\n        dst=node_f(net,nde[dsts])\r\n        nde[dsts]=nde[cutoff]\r\n        cutoff-=1\r\n        makelink_f!(net,src,dst)\r\n    end\r\n    net\r\nend\r\n\r\n\"\"\"\r\n    rectlattice!(net::FastNet,dims, <keyword arguments>)\r\n\r\nCreate a rectangular lattice with given dimensions *dims*. \r\n\r\nThe network in *net* is replaced with the new topology, that is a lattice specified by dims.  \r\n*dims* can be a number, in this case it indicates the number of nodes to be arranged into a 1D lattice. \r\nAlternatively, *dims* can be a vector of Ints. In this case the dimension of the lattice is identical to the \r\nlength of *dims* and each element of *dims* specifies the length of the lattice in one of these dimensions. \r\n\r\nIf there FastNet is not large enough to accomodate the desired number of nodes or links an argument error \r\nwill be thrown. \r\n\r\nKeyword arguments are \r\n- periodic : If this argument is true the lattice is generated with periodic boundary conditions in all dimensions. \r\n  Alternatively a Vector of Bool of the same length as *dims* can be supplied. In this case the n'th argument of \r\n  the vector specifies if the lattice is periodic in the n'th dimension. \r\n- S : The state of the nodes. All nodes will be set to this state. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia>  net=FastNet(2000,6000,2,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> rectlattice!(net,[10,20,10],periodic=[true,false,true])\r\nNetwork of 2000 nodes and 5900 links\r\n\r\njulia> degreedist(net)\r\n6-element Vector{Float64}:\r\n 0.0\r\n 0.0\r\n 0.0\r\n 0.0\r\n 0.1\r\n 0.9\r\n```\r\n\"\"\"\r\nfunction rectlattice!(\r\n        net         ::FastNet,\r\n        dims        ::Union{Int,Tuple,AbstractVector};\r\n        S           ::Integer=1,\r\n        periodic    ::Union{AbstractArray,Bool,Tuple}=false\r\n    )\r\n    task=\"Trying to create a rectangular lattice\"\r\n    d=[dims...]\r\n    nd=length(d)\r\n    per=[false]\r\n    if isa(periodic,Bool)\r\n        per=fill(periodic,nd)\r\n    else\r\n        per=[periodic...]\r\n    end\r\n    if length(per)!=length(d)\r\n        throw(ArgumentError(task*\", but the number of values passed for parameter periodic does not agree with dims\"))\r\n    end\r\n    for x in per\r\n        if !isa(x,Bool)\r\n            throw(ArgumentError(task*\", but $x in periodic is not of type Bool\"))\r\n        end\r\n    end\r\n    n=1\r\n    s=checknodestate(net,S,task)\r\n    for i=1:nd\r\n        n*=d[i]\r\n    end\r\n    nullgraph!(net)\r\n    makenodes!(net,n,s)\r\n    linksneeded=0\r\n    for i=1:nd\r\n        linksneeded+=n \r\n        if !per[i]\r\n            linksneeded-=n\u00f7d[i]\r\n        end\r\n    end    \r\n    for i=1:n\r\n        lowmult=1\r\n        for j=1:nd\r\n            if ((i-1)\u00f7lowmult)%d[j]==d[j]-1\r\n                if per[j]\r\n                    src=node_f(net,i)\r\n                    dst=node_f(net,i+lowmult-d[j]*lowmult)                    \r\n                    makelink_f!(net,src,dst)\r\n                end\r\n            else\r\n                src=node_f(net,i)\r\n                dst=node_f(net,i+lowmult)\r\n                makelink_f!(net,src,dst)\r\n            end\r\n            lowmult*=d[j]\r\n        end    \r\n    end\r\n    net\r\nend\r\n\r\n\"\"\"\r\n    adjacency!(net,mat;S=1)\r\n\r\nCreate a network with given adjacency matrix.\r\n\r\nThe network in *net* is replaced with the new topology that is specified by the adjacency matrix *mat*. \r\nIf direction of links matters note that the element *mat[i,j]* corresponds to the link from j to i. \r\n\r\nSymmeric matrices will not result in parallel links, instead the link is placed in an arbitrary direction. \r\n\r\nNote that node *n* in the matrix will be the node in position *n* in *net* after creation, which is \r\nnot necessarily the node with ID *n*, if you need to find a particular node at a later time then it\r\nis best to save its id using the node(net,pos) function directly after calling adjacency!(net,mat). \r\n\r\nIf *net* is not large enough to accomodate the desired number of nodes or links an argument error \r\nwill be thrown. \r\n\r\nKeyword arguments are \r\n- S : The state of the nodes. All nodes will be set to this state. \r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia> net=FastNet(1000,2000,2,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> mat=[0 1 0; 1 0 1; 0 1 0]\r\n3\u00d73 Matrix{Int64}:\r\n 0  1  0\r\n 1  0  1\r\n 0  1  0\r\n\r\njulia> adjacency!(net,mat)\r\nNetwork of 3 nodes and 2 links\r\n```\r\n\"\"\"\r\nfunction adjacency!(\r\n        net     ::FastNet,\r\n        mat     ::AbstractMatrix;\r\n        S       ::Integer=1\r\n    )\r\n    task=\"Trying to create topology from adjacency matrix\"\r\n    x,y=size(mat)\r\n    if x!=y\r\n        err=task*\", but adjacency! expects a square matrix as its second argument and received a rectangular one\"\r\n        throw(ArgumentError(err)) \r\n    end\r\n    if x>net.N \r\n        throw(ArgumentError(task*\", but the matrix is larger than the maximum number of allowed nodes by the network\")) \r\n    end\r\n    s=checknodestate(net,S,task)\r\n    count=0\r\n    b1=false\r\n    b2=false\r\n    for i=1:x-1\r\n        for j=i+1:x\r\n            try \r\n                b1=convert(Bool,mat[i,j])\r\n                b2=convert(Bool,mat[i,j])\r\n            catch e\r\n                throw(ArgumentError(task*\", but was unable to convert an element of mat to Bool\"))\r\n            end\r\n            if b1||b2\r\n                count+=1 \r\n            end\r\n        end\r\n    end\r\n    if count>net.K\r\n        throw(ArgumentError(task*\", but the matrix contains more links than permitted by net\"))\r\n    end\r\n    nullgraph!(net)\r\n    makenodes!(net,x,s)\r\n    for i=1:x\r\n        dst=node_f(net,i)\r\n        for j=i+1:x\r\n            src=node_f(net,j)\r\n            linked::Bool = mat[i,j]\r\n            if linked\r\n                makelink_f!(net,src,dst) \r\n            else \r\n                linked = mat[j,i]\r\n                if linked\r\n                    makelink_f!(net,dst,src) \r\n                end\r\n            end\r\n        end\r\n    end\r\n    net\r\nend\r\n\r\n### WIP\r\nfunction configmodel_DG!(net::FastNet,degreedist,N::Int=0,S::Int=1)\r\n    (counts,totalstubs)=_configmodelsetup!(net,degreedist,N,S)    \r\n\r\n    println(\"On track\")\r\nend\r\n\r\n\"\"\"\r\n    randomgeometricgraph!(net::FastNet; N::Int,K::Int,S::Int, dim::Int, deg::Float64)\r\n\r\nCreate a random geometric graph with mean degree *deg*. \r\n\r\nThe network in *net* is replaced with the new topology in which each node has *dim* vector entries uniformly drawn\r\nfrom (0,1). A maximal connection distance is calculated from the given mean degree and nodes get connected,\r\nwhen their euclidean distance is smaller than the maximal connection distance. \r\n\r\nIf there FastNet is not large enough to accomodate the desired number of links or nodes an argument error \r\nwill be thrown.\r\n\r\nAdditionaly, if the calculated maximal connection distance results in more links being generated than supported by the net,\r\nan argument error will be thrown as well.\r\n\r\nThe keyword arguments are \r\n- N : The number of nodes that will be used in the creation of the network\r\n- S : The state of the nodes. All nodes will be set to this state.\r\n- dim : Dimensions of the random geometric graph.\r\n- deg : The graph\u00b4s mean degree which is used to calculate the maximal connection distance between nodes.\r\n\r\n# Examples \r\n```jldoctest\r\njulia> using Fastnet\r\n\r\njulia>  net=FastNet(1000,2000,2,[])\r\nNetwork of 0 nodes and 0 links\r\n\r\njulia> randomgeometricgraph!(net,dim=2,meandegree=2.0)\r\nNetwork of 1000 nodes and 970 links\r\n\r\n```\r\n\"\"\"\r\nfunction randomgeometricgraph!(net::FastNet; N::Int=0,K::Int=0,S::Int=1, dim::Int=2, deg::Float64=2.0)\r\n    nullgraph!(net)\r\n    n=N;\r\n    k=K;\r\n    s=S;\r\n    if n===0\r\n        n=net.N\r\n    end \r\n    if k===0\r\n        k=net.K\r\n    end\r\n    if n<1 && k>0\r\n        throw(ArgumentError(\"In order to create links the net has to have at least one node\"))\r\n    end\r\n    if n>net.N\r\n        throw(ArgumentError(\"Trying to create more nodes than maximum allowed by the net\"))\r\n    end\r\n    if k>net.K\r\n        throw(ArgumentError(\"Trying to create more links than maximum allowed by the net\"))\r\n    end\r\n    if s<1 || s>net.C-1\r\n        msg=\"The net passed to randomgeometricgraph! only supports node states between 1 and $(net.C-1),\"\r\n        msg*=\" but you are asking it to set nodes to state $s.\"\r\n        throw(ArgumentError(msg))\r\n    end\r\n\r\n    r = (1/2)*((deg/n) * double_factorial(dim) / (pi/2)^(floor(dim/2)))^(1/dim)\r\n    makenodes!(net,n,s)\r\n    loc = rand(n,dim)\r\n    for i = 1:n\r\n        for j = i+1:n\r\n            dist = sqrt(sum((loc[i,:].-loc[j,:]).^2))\r\n            if (dist < r)\r\n                if countlinks_f(net)>=k\r\n                    throw(ArgumentError(\"For the given mean degree, you would create more links than allowed by the net\"))\r\n                end\r\n                makelink_f!(net,i,j)\r\n            end\r\n        end\r\n    end\r\n    net\r\nend\r\n\r\nfunction double_factorial(n::Int)\r\n    if isodd(n)\r\n        k=Int((n+1)/2)\r\n        return factorial(2*k-1)/(2^(k-1)*factorial(k-1))\r\n    else\r\n        k=Int(n/2)\r\n        return 2^k*factorial(k)\r\n    end\r\nend\r\n\r\n\r\n", "meta": {"hexsha": "116df1823b1ebdee3042f6236845c962b4f3dbfd", "size": 16691, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nettools.jl", "max_stars_repo_name": "bridgewalker/Quicknet.jl", "max_stars_repo_head_hexsha": "2dc354f0901bb336648245731bb587898b6ad208", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-09-03T08:28:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T19:32:14.000Z", "max_issues_repo_path": "src/nettools.jl", "max_issues_repo_name": "bridgewalker/Quicknet.jl", "max_issues_repo_head_hexsha": "2dc354f0901bb336648245731bb587898b6ad208", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2021-09-03T07:22:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-13T20:18:26.000Z", "max_forks_repo_path": "src/nettools.jl", "max_forks_repo_name": "bridgewalker/Quicknet.jl", "max_forks_repo_head_hexsha": "2dc354f0901bb336648245731bb587898b6ad208", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-03T11:05:48.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-14T11:56:35.000Z", "avg_line_length": 29.752228164, "max_line_length": 124, "alphanum_fraction": 0.6254867893, "num_tokens": 4544, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46101677931231594, "lm_q2_score": 0.10970576660095784, "lm_q1q2_score": 0.05057619919036222}}
{"text": "\"\"\"\nProvides a dynamically typed singly-linked list implementation, `List` similar to that\navailable in most lisp dialects. This module provides basic Julia functionality to\nmanipulate such lists, exported under both the Julia and Scheme names where they differ\n(such as `Base.first` vs. `car`.\n\"\"\"\nmodule Lists\n\nimport FunctionalCollections: append\nusing Base.Iterators\n\nconst car = Base.first\n\nexport Cons, List, isnil, ispair, car, cdr, caar, cadr, cddr, nil, append, ++, cons, list,\n       islist, Nil\n\n\"\"\"\nAn object representing nothing, or an empty list.\n\nThe singleton instance of this type is called `nil`. This type is isomorphic to, and very\nsimilar, to `Nothing`. However, it is often useful to distinguish the `nil` used within many\nlisp dialects from a true `Nothing`, because `nil` is iterable and represents an empty list.\n\"\"\"\nstruct Nil end\nconst nil = Nil.instance\n\n\"\"\"\nAn object which is essentially a pair of two objects.\n\nThe two objects are referred to, for historical reasons, as `car` and `cdr`.These\nabbreviations are not semantically relevant today, so can generally be thought of as the\nhead object and the tail object. For `Cons` objects which are (proper) lists, the `car`\n(head) object will be the first element of the list, and the `cdr` (tail) object will be a\nlist representing the remaining elements.\n\"\"\"\nstruct Cons\n    car\n    cdr\nend\nconst cons = Cons\n\ncar(\u03b1::Cons) = \u03b1.car\n\"\"\"\nThe tail of a `List`.\n\nNote that the tail need not be a list (or even a `List`) if the list is improper.\n\"\"\"\ncdr(\u03b1::Cons) = \u03b1.cdr\ncaar(\u03b1::Cons) = car(car(\u03b1))\ncadr(\u03b1::Cons) = car(cdr(\u03b1))\ncddr(\u03b1::Cons) = cdr(cdr(\u03b1))\n\nBase.map(f, ::Nil, ::Nil...) = nil\nBase.map(f, \u03b1::Cons, \u03b2s::Cons...) =\n    Cons(f(car(\u03b1), map(car, \u03b2s)...), map(f, cdr(\u03b1), map(cdr, \u03b2s)...))\nBase.filter(p, ::Nil) = nil\nBase.filter(p, \u03b1::Cons) = let \u03b2 = filter(p, cdr(\u03b1))\n    p(car(\u03b1)) ? Cons(car(\u03b1), \u03b2) : \u03b2\nend\n\nBase.:(==)(\u03b1::Cons, \u03b2::Cons) = car(\u03b1) == car(\u03b2) && cdr(\u03b1) == cdr(\u03b2)\n\n\"\"\"\nAn immutable singly linked list, which may be improper.\n\nA list is either `Nil` (empty) or `Cons`, where the first element is an arbitrary object and\nthe second element is a list. The `List` type, however, includes all instances of `Cons`,\nincluding those for which the second element is not a list. Such `List` instances are called\nimproper lists, which are allowed because they are used in many lisp dialects.\n\"\"\"\nconst List = Union{Cons, Nil}\n\nList() = nil\nList(x, xs...) = Cons(x, List(xs...))\nconst list = List\n\nisnil(::Nil) = true\nisnil(::Cons) = false\n\nispair(\u03b1::List) = !isnil(\u03b1)\n\n\"\"\"\nReturn `true` if the provided `List` is in fact a list, i.e., it is not improper.\n\nThis is the case if the list is a `Nil` (empty list) or a `Cons` with a proper list as its\ntail.\n\"\"\"\nislist(::Nil) = true\nislist(\u03b1::Cons) = islist(cdr(\u03b1))\n\nBase.iterate(\u03b1::List, \u03b2=\u03b1) = isnil(\u03b2) ? nothing : (car(\u03b2), cdr(\u03b2))\n\nBase.length(::Nil) = 0\nBase.length(\u03b1::Cons) = 1 + length(cdr(\u03b1))\n\nBase.getindex(\u03b1::Nil, b) = throw(BoundsError(\u03b1, b))\nBase.getindex(\u03b1::Cons, b) = b == 1 ? car(\u03b1) : cdr(\u03b1)[b - 1]\n\nBase.convert(::Type{List}, xs::List) = xs\nBase.convert(::Type{List}, xs) = List(xs...)\nappend(::Nil, \u03b2::List) = \u03b2\nappend(\u03b1::Cons, \u03b2::List) = Cons(car(\u03b1), append(cdr(\u03b1), \u03b2))\nappend(\u03b1::List, \u03b2::List, \u03b3::List, \u03b3s::List...) = append(append(\u03b1, \u03b2), \u03b3, \u03b3s...)\nconst (++) = append\n\ninclude(\"broadcast.jl\")\ninclude(\"show.jl\")\n\nend  # Lists module\n", "meta": {"hexsha": "0f9acdb63d0769fc230627e67cb7abe1d0df755b", "size": 3381, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Lists/Lists.jl", "max_stars_repo_name": "TotalVerb/SExpressions.jl", "max_stars_repo_head_hexsha": "172276055a9d94b1f03dbee0a44715133e4922ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "max_stars_repo_stars_event_min_datetime": "2017-05-02T14:21:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-25T15:10:17.000Z", "max_issues_repo_path": "src/Lists/Lists.jl", "max_issues_repo_name": "TotalVerb/SExpressions.jl", "max_issues_repo_head_hexsha": "172276055a9d94b1f03dbee0a44715133e4922ef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2016-11-02T23:02:55.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-18T00:24:15.000Z", "max_forks_repo_path": "src/Lists/Lists.jl", "max_forks_repo_name": "TotalVerb/SExpressions.jl", "max_forks_repo_head_hexsha": "172276055a9d94b1f03dbee0a44715133e4922ef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-06-11T06:32:32.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-26T11:03:10.000Z", "avg_line_length": 30.7363636364, "max_line_length": 92, "alphanum_fraction": 0.6752440106, "num_tokens": 1028, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4301473339755163, "lm_q2_score": 0.11757213972942693, "lm_q1q2_score": 0.05057334245440987}}
{"text": "#\n# Algebra of queries.\n#\n\nimport Base:\n    convert,\n    getindex,\n    show,\n    >>\n\n\n#\n# Query interface.\n#\n\nabstract type AbstractQuery end\n\n\"\"\"\n    Query(op, args...)\n\nA query is implemented as a pipeline transformation that preserves pipeline\nsource.  Specifically, a query takes the input pipeline that maps the *source*\nto the *input target* and generates a pipeline that maps the *source* to the\n*output target*.\n\nParameter `op` is a function that performs the transformation; `args` are extra\narguments passed to the function.\n\nThe query transforms an input pipeline `p` by invoking `op` with the following\narguments:\n\n    op(env::Environment, q::Pipeline, args...)\n\nThe result of `op` must be the output pipeline.\n\"\"\"\nstruct Query <: AbstractQuery\n    op\n    args::Vector{Any}\n\n    Query(op; args::Vector{Any}=Any[]) =\n        new(op, args)\nend\n\nQuery(op, args...) =\n    Query(op; args=collect(Any, args))\n\nquoteof(F::Query) =\n    quoteof(F.op, F.args)\n\nshow(io::IO, F::Query) =\n    print_expr(io, quoteof(F))\n\n\n#\n# Navigation sugar.\n#\n\nstruct Navigation <: AbstractQuery\n    __path::Tuple{Vararg{Symbol}}\nend\n\nBase.getproperty(nav::Navigation, s::Symbol) =\n    let path = getfield(nav, :__path)\n        Navigation((path..., s))\n    end\n\nshow(io::IO, nav::Navigation) =\n    let path = getfield(nav, :__path)\n        print(io, join((:It, path...), \".\"))\n    end\n\n\"\"\"\n    It :: AbstractQuery\n\nIn a query expression, use `It` to refer to the query's input.\n\n```jldoctest\njulia> unitknot[Lift(3) >> (It .+ 1)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 4 \u2502\n```\n\n`It` is the identity with respect to query composition.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> It]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\n`It` provides a shorthand notation for data navigation using\n`Get`, so that `It.a.x` is equivalent to `Get(:a) >> Get(:x)`.\n\n```jldoctest\njulia> unitknot[Lift((a=(x=1,y=2),)) >> It.a]\n\u2502 a    \u2502\n\u2502 x  y \u2502\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 1  2 \u2502\n\njulia> unitknot[Lift((a=(x=1,y=2),)) >> It.a.x]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\"\"\"\nconst It = Navigation(())\n\ntranslate(mod::Module, ::Val{:it}) = It\n\n\n#\n# Querying a DataKnot.\n#\n\n\"\"\"\n    db::DataKnot[F::Query; params...] :: DataKnot\n\nQueries `db` with `F`.\n\"\"\"\ngetindex(db::DataKnot, F; kws...) =\n    query(db, Each(F); kws...)\n\nquery(db, F; kws...) =\n    query(convert(DataKnot, db), Lift(F), sort(collect(Pair{Symbol,DataKnot}, kws), by=first))\n\nfunction query(db::DataKnot, F::AbstractQuery, params::Vector{Pair{Symbol,DataKnot}}=Pair{Symbol,DataKnot}[])\n    db = pack(db, params)\n    q = assemble(shape(db), F)\n    db\u2032 = q(db)\n    return db\u2032\nend\n\nfunction pack(db::DataKnot, params::Vector{Pair{Symbol,DataKnot}})\n    !isempty(params) || return db\n    ctx_lbls = first.(params)\n    ctx_cols = collect(AbstractVector, cell.(last.(params)))\n    ctx_shps = collect(AbstractShape, shape.(last.(params)))\n    scp_cell = TupleVector(1, AbstractVector[cell(db), TupleVector(ctx_lbls, 1, ctx_cols)])\n    scp_shp = TupleOf(shape(db), TupleOf(ctx_lbls, ctx_shps)) |> IsScope\n    return DataKnot(scp_shp, scp_cell)\nend\n\nassemble(db::DataKnot, F::AbstractQuery, params::Vector{Pair{Symbol,DataKnot}}=Pair{Symbol,DataKnot}[]) =\n    assemble(shape(pack(db, params)), F)\n\nassemble(src::AbstractShape, F::AbstractQuery) =\n    optimize(uncover(assemble(nothing, cover(src), F)))\n\nassemble(::Nothing, p::Pipeline, F) =\n    assemble(Environment(), p, F)\n\n#\n# External syntax.\n#\n\"\"\"\n     @query expr\n\nCreates a query object from a specialized path-like notation:\n\n* bare identifiers are translated to navigation with `Get`;\n* query combinators, such as `Count(X)`, use lower-case names;\n* the period (`.`) is used for query composition (`>>`);\n* aggregate queries, such as `Count`, require parentheses;\n* records can be constructed using curly brackets, `{}`; and\n* functions and operators are lifted automatically.\n\n```jldoctest\njulia> @query 2x+1\nLift(+, (Lift(*, (Lift(2), Get(:x))), Lift(1)))\n```\n\"\"\"\nmacro query(ex)\n    return :( translate($__module__, $(Expr(:quote, ex)) ) )\nend\n\n\"\"\"\n     @query dataset expr param=...\n\nApplies the query to a dataset with a given set of parameters.\n\n```jldoctest\njulia> @query unitknot 2x+1 x=1\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\"\"\"\nmacro query(db, exs...)\n    exs = map(exs) do ex\n        if Meta.isexpr(ex, :(=), 2)\n            esc(Expr(:kw, ex.args...))\n        else\n            :( Each(translate($__module__, $(Expr(:quote, ex)) )) )\n        end\n    end\n    return quote\n        query($(esc(db)), $(exs...))\n    end\nend\n\nfunction translate(mod::Module, ex::Expr)::AbstractQuery\n    head = ex.head\n    args = ex.args\n    if head === :block\n        return Compose(translate.(Ref(mod), filter(arg -> !(arg isa LineNumberNode), args))...)\n    elseif head === :. && length(args) == 2 && Meta.isexpr(args[2], :tuple, 1)\n        return Compose(translate(mod, args[1]), translate(mod, args[2].args[1]))\n    elseif head === :.\n        return Compose(translate.(Ref(mod), args)...)\n    elseif head === :let && length(args) == 2\n        return Given(translate.(Ref(mod), Meta.isexpr(args[1], :block) ? args[1].args : (args[1],))...,\n                     translate(mod, args[2]))\n    elseif head === :braces\n        return Record(translate.(Ref(mod), args)...)\n    elseif head === :quote && length(args) == 1 && Meta.isexpr(args[1], :braces)\n        return Record(translate.(Ref(mod), args[1].args)...)\n    elseif head === :curly && length(args) >= 1\n        return Compose(translate(mod, args[1]), Record(translate.(Ref(mod), args[2:end])...))\n    elseif head === :call && length(args) >= 1\n        call = args[1]\n        if call isa QuoteNode\n            call = call.value\n        end\n        if call === :(=>) && length(args) == 3 && args[2] isa Symbol\n            return Compose(translate(mod, args[3]), Label(args[2]))\n        elseif call isa Symbol\n            return translate(mod, Val(call), (args[2:end]...,))\n        elseif Meta.isexpr(call, :.) && !isempty(call.args)\n            return Compose(translate.(Ref(mod), call.args[1:end-1])...,\n                           translate(mod, Expr(:call, call.args[end], args[2:end]...)))\n        elseif call isa Base.Callable\n            return Lift(call(translate.(Ref(mod), args[2:end])...))\n        end\n    elseif head == :comparison && length(args) == 3\n        return translate(mod, Expr(:call, args[2], args[1], args[3]))\n    elseif head == :comparison && length(args) > 3\n        return translate(mod, Expr(:&&, Expr(:call, args[2], args[1], args[3]), Expr(head, args[3:end]...)))\n    elseif head == :&&\n        return Lift(&, (translate.(Ref(mod), args)...,))\n    elseif head == :||\n        return Lift(|, (translate.(Ref(mod), args)...,))\n    end\n    error(\"invalid query expression: $(repr(ex))\")\nend\n\ntranslate(mod::Module, sym::Symbol) =\n    translate(mod, Val(sym))\n\ntranslate(::Module, ::Val{:nothing}) =\n    Lift(nothing)\n\ntranslate(::Module, ::Val{:missing}) =\n    Lift(missing)\n\ntranslate(mod::Module, qn::QuoteNode) =\n    translate(mod, qn.value)\n\ntranslate(mod::Module, @nospecialize(v::Val{N})) where {N} =\n    Get(N)\n\nfunction translate(mod::Module, @nospecialize(v::Val{N}), args::Tuple) where {N}\n    fn = getfield(mod, N)\n    Lift(fn, translate.(Ref(mod), args))\nend\n\ntranslate(mod::Module, val) =\n    Lift(val)\n\n\n#\n# Compiling a query.\n#\n\n\"\"\"\n    Environment()\n\nQuery compilation state.\n\"\"\"\nmutable struct Environment\nend\n\nassemble(env::Environment, p::Pipeline, F)::Pipeline =\n    assemble(env, p, Lift(F))\n\nassemble(env::Environment, p::Pipeline, F::Query)::Pipeline =\n    F.op(env, p, F.args...)\n\nfunction assemble(env::Environment, p::Pipeline, nav::Navigation)::Pipeline\n    for name in getfield(nav, :__path)\n        p = Get(env, p, name)\n    end\n    p\nend\n\n\n#\n# Adapters.\n#\n\n# The underlying data shape below flow and scope containers.\n\ndomain(shp::AbstractShape) =\n    shp\n\ndomain(shp::IsFlow) =\n    domain(elements(shp))\n\ndomain(shp::IsScope) =\n    domain(column(shp))\n\nreplace_domain(shp::AbstractShape, f) =\n    f isa AbstractShape ? f : f(shp)\n\nreplace_domain(shp::IsFlow, f) =\n    replace_elements(shp, elts -> replace_domain(elts, f))\n\nreplace_domain(shp::IsScope, f) =\n    replace_column(shp, col -> replace_domain(col, f))\n\n# Finds the output label.\n\ngetlabel(p::Pipeline, default) =\n    getlabel(target(p), default)\n\ngetlabel(shp::AbstractShape, default) =\n    default\n\ngetlabel(shp::IsLabeled, default) =\n    label(shp)\n\ngetlabel(shp::IsFlow, default) =\n    getlabel(elements(shp), default)\n\ngetlabel(shp::IsScope, default) =\n    getlabel(column(shp), default)\n\n# Reassigns the output label.\n\nrelabel(p::Pipeline, lbl::Union{Symbol,Nothing}) =\n    p |> designate(source(p), relabel(target(p), lbl))\n\nrelabel(shp::AbstractShape, ::Nothing) =\n    shp\n\nrelabel(shp::AbstractShape, lbl::Symbol) =\n    shp |> IsLabeled(lbl)\n\nrelabel(shp::IsLabeled, ::Nothing) =\n    subject(shp)\n\nrelabel(shp::IsLabeled, lbl::Symbol) =\n    subject(shp) |> IsLabeled(lbl)\n\nrelabel(shp::IsFlow, lbl::Symbol) =\n    replace_elements(shp, elts -> relabel(elts, lbl))\n\nrelabel(shp::IsFlow, ::Nothing) =\n    replace_elements(shp, elts -> relabel(elts, nothing))\n\nrelabel(shp::IsScope, lbl::Symbol) =\n    replace_column(shp, col -> relabel(col, lbl))\n\nrelabel(shp::IsScope, ::Nothing) =\n    replace_column(shp, col -> relabel(col, nothing))\n\n# Removes the flow annotation and strips the scope container from the query output.\n\nfunction uncover(p::Pipeline)\n    q = uncover(target(p))\n    chain_of(p, q) |> designate(source(p), target(q))\nend\n\nfunction uncover(src::IsFlow)\n    p = uncover(elements(src))\n    lbl = getlabel(p, nothing)\n    p = relabel(p, nothing)\n    tgt = relabel(replace_domain(target(p), dom -> BlockOf(dom, cardinality(src))), lbl)\n    with_elements(p) |> designate(src, tgt)\nend\n\nuncover(src::IsScope) =\n    column(1) |> designate(src, column(src))\n\nuncover(src::AbstractShape) =\n    pass() |> designate(src, src)\n\n# Finds or creates a flow container and clones the scope container.\n\nfunction cover(cell::BlockVector, sig::Signature)\n    elts = elements(cell)\n    card = cardinality(cell)\n    q = elts isa Vector{Union{}} && card == x0to1 ? null_filler() : block_filler(elts, card)\n    cover(q |> designate(sig))\nend\n\ncover(cell::AbstractVector, sig::Signature) =\n    cover(filler(cell[1]) |> designate(sig))\n\ncover(p::Pipeline) =\n    cover(source(p), p)\n\nfunction cover(src::IsScope, p::Pipeline)\n    ctx = context(src)\n    tgt = TupleOf(target(p), ctx) |> IsScope\n    p = tuple_of(p, column(2)) |> designate(src, tgt)\n    cover(nothing, p)\nend\n\ncover(::AbstractShape, p::Pipeline) =\n    cover(nothing, p)\n\nfunction cover(::Nothing, p::Pipeline)\n    q = cover(target(p))\n    chain_of(p, q) |> designate(source(p), target(q))\nend\n\ncover(src::AbstractShape) =\n    wrap() |> designate(src, BlockOf(src, x1to1) |> IsFlow)\n\ncover(src::BlockOf) =\n    pass() |> designate(src, src |> IsFlow)\n\nfunction cover(src::ValueOf)\n    ty = eltype(src)\n    if ty <: AbstractVector\n        ty\u2032 = eltype(ty)\n        adapt_vector() |> designate(src, BlockOf(ty\u2032, x0toN) |> IsFlow)\n    elseif Missing <: ty\n        ty\u2032 = Base.nonmissingtype(ty)\n        adapt_missing() |> designate(src, BlockOf(ty\u2032, x0to1) |> IsFlow)\n    else\n        wrap() |> designate(src, BlockOf(src, x1to1) |> IsFlow)\n    end\nend\n\nfunction cover(src::IsLabeled)\n    p = cover(subject(src))\n    tgt = replace_elements(target(p), IsLabeled(label(src)))\n    p |> designate(src, tgt)\nend\n\ncover(src::IsFlow) =\n    pass() |> designate(src, src)\n\nfunction cover(src::IsScope)\n    p = cover(column(src))\n    tgt = target(p)\n    tgt = replace_elements(tgt, TupleOf(elements(tgt), context(src)) |> IsScope)\n    chain_of(with_column(1, p), distribute(1)) |> designate(src, tgt)\nend\n\n\n#\n# Elementwise composition.\n#\n\n# Trivial pipes at the source and target endpoints of a pipeline.\n\nsource_pipe(p::Pipeline) =\n    trivial_pipe(source(p))\n\ntarget_pipe(p::Pipeline) =\n    trivial_pipe(target(p))\n\ntrivial_pipe(src::IsFlow) =\n    trivial_pipe(elements(src))\n\ntrivial_pipe(src::AbstractShape) =\n    cover(src)\n\ntrivial_pipe(db::DataKnot) =\n    cover(shape(db))\n\n# Align pipelines for composition.\n\nrealign(p::Pipeline, ::AbstractShape) =\n    p\n\nrealign(p::Pipeline, ref::IsScope) =\n    realign(p, target(p), ref)\n\nrealign(p::Pipeline, ::AbstractShape, ::IsScope) =\n    p\n\nrealign(p::Pipeline, tgt::IsFlow, ref::IsScope) =\n    realign(p, elements(tgt), tgt, ref)\n\nrealign(p::Pipeline, ::IsScope, ::IsFlow, ::IsScope) =\n    p\n\nfunction realign(p::Pipeline, elts::AbstractShape, tgt::IsFlow, ref::IsScope)\n    p\u2032 = chain_of(with_column(1, p), distribute(1))\n    ctx = context(ref)\n    src\u2032 = TupleOf(source(p), ctx) |> IsScope\n    tgt\u2032 = replace_elements(tgt, elts -> TupleOf(elts, ctx) |> IsScope)\n    p\u2032 |> designate(src\u2032, tgt\u2032)\nend\n\nrealign(::AbstractShape, p::Pipeline) =\n    p\n\nrealign(ref::IsFlow, p::Pipeline) =\n    realign(ref, source(p), p)\n\nrealign(::IsFlow, ::IsFlow, p::Pipeline) =\n    p\n\nrealign(ref::IsFlow, ::AbstractShape, p::Pipeline) =\n    realign(ref, p, target(p))\n\nfunction realign(ref::IsFlow, p::Pipeline, ::AbstractShape)\n    p\u2032 = with_elements(p)\n    src\u2032 = replace_elements(ref, source(p))\n    tgt\u2032 = replace_elements(ref, target(p))\n    p\u2032 |> designate(src\u2032, tgt\u2032)\nend\n\nfunction realign(ref::IsFlow, p::Pipeline, tgt::IsFlow)\n    p\u2032 = chain_of(with_elements(p), flatten())\n    src\u2032 = replace_elements(ref, source(p))\n    card\u2032 = cardinality(ref)|cardinality(tgt)\n    tgt\u2032 = BlockOf(elements(tgt), card\u2032) |> IsFlow\n    p\u2032 |> designate(src\u2032, tgt\u2032)\nend\n\n# Composition.\n\ncompose(p::Pipeline) = p\n\ncompose(p1::Pipeline, p2::Pipeline, p3::Pipeline, ps::Pipeline...) =\n    foldl(compose, ps, init=compose(compose(p1, p2), p3))\n\nfunction compose(p1::Pipeline, p2::Pipeline)\n    p1 = realign(p1, source(p2))\n    p2 = realign(target(p1), p2)\n    @assert fits(target(p1), source(p2)) \"cannot fit\\n$(target(p1))\\ninto\\n$(source(p2))\"\n    chain_of(p1, p2) |> designate(source(p1), target(p2))\nend\n\n>>(X::Union{DataKnot,AbstractQuery,Pair{Symbol,<:Union{DataKnot,AbstractQuery}}}, Xs...) =\n    Compose(X, Xs...)\n\nCompose(Xs...) =\n    Query(Compose, Xs...)\n\nquoteof(::typeof(Compose), args::Vector{Any}) =\n    quoteof(>>, args)\n\nfunction Compose(env::Environment, p::Pipeline, Xs...)\n    for X in Xs\n        p = assemble(env, p, X)\n    end\n    p\nend\n\n\n#\n# Record combinator.\n#\n\nfunction assemble_record(p::Pipeline, xs::Vector{Pipeline})\n    lbls = Symbol[]\n    cols = Pipeline[]\n    seen = Dict{Symbol,Int}()\n    for (i, x) in enumerate(xs)\n        x = uncover(x)\n        lbl = getlabel(x, nothing)\n        if lbl !== nothing\n            x = relabel(x, nothing)\n        else\n            lbl = ordinal_label(i)\n        end\n        if lbl in keys(seen)\n            lbls[seen[lbl]] = ordinal_label(seen[lbl])\n        end\n        seen[lbl] = i\n        push!(lbls, lbl)\n        push!(cols, x)\n    end\n    src = elements(target(p))\n    tgt = TupleOf(lbls, target.(cols))\n    lbl = getlabel(p, nothing)\n    if lbl !== nothing\n        tgt = relabel(tgt, lbl)\n    end\n    q = tuple_of(lbls, cols) |> designate(src, tgt)\n    q = cover(q)\n    compose(p, q)\nend\n\n\"\"\"\n    Record(X\u2081, X\u2082 \u2026 X\u2099) :: Query\n\nThis query emits a record with fields generated by `X\u2081`, `X\u2082` \u2026 `X\u2099`.\n\n```jldoctest\njulia> unitknot[Lift(1:3) >> Record(It, It .* It)]\n  \u2502 #A  #B \u2502\n\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n1 \u2502  1   1 \u2502\n2 \u2502  2   4 \u2502\n3 \u2502  3   9 \u2502\n```\n\nField labels are inherited from queries.\n\n```jldoctest\njulia> unitknot[Lift(1:3) >> Record(:x => It,\n                                    :x\u00b2 => It .* It)]\n  \u2502 x  x\u00b2 \u2502\n\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n1 \u2502 1   1 \u2502\n2 \u2502 2   4 \u2502\n3 \u2502 3   9 \u2502\n```\n\"\"\"\nRecord(Xs...) =\n    Query(Record, Xs...)\n\nfunction Record(env::Environment, p::Pipeline, Xs...)\n    xs = assemble.(Ref(env), Ref(target_pipe(p)), collect(AbstractQuery, Xs))\n    assemble_record(p, xs)\nend\n\ntranslate(mod::Module, ::Val{:record}, args::Tuple) =\n    Record(translate.(Ref(mod), args)...)\n\n\n#\n# Mix combinator.\n#\n\nfunction assemble_mix(p::Pipeline, xs::Vector{Pipeline})\n    lbls = Symbol[]\n    cols = Pipeline[]\n    card = x1to1\n    seen = Dict{Symbol,Int}()\n    for (i, x) in enumerate(xs)\n        card |= cardinality(target(x))\n        x = uncover(x)\n        lbl = getlabel(x, nothing)\n        if lbl !== nothing\n            x = relabel(x, nothing)\n        else\n            lbl = ordinal_label(i)\n        end\n        if lbl in keys(seen)\n            lbls[seen[lbl]] = ordinal_label(seen[lbl])\n        end\n        seen[lbl] = i\n        push!(lbls, lbl)\n        x = chain_of(\n                x,\n                with_elements(wrap()),\n        ) |> designate(source(x), replace_elements(target(x), elts -> BlockOf(elts, x1to1)))\n        push!(cols, x)\n    end\n    src = elements(target(p))\n    tgt = BlockOf(TupleOf(lbls, elements.(target.(cols))), card)\n    lbl = getlabel(p, nothing)\n    if lbl !== nothing\n        tgt = relabel(tgt, lbl)\n    end\n    q = chain_of(\n            tuple_of(lbls, cols),\n            distribute_all(),\n    ) |> designate(src, tgt)\n    q = cover(q)\n    compose(p, q)\nend\n\n\"\"\"\n    Mix(X\u2081, X\u2082 \u2026 X\u2099) :: Query\n\nThis query emits records containing every combination of elements\ngenerated by `X\u2081`, `X\u2082` \u2026 `X\u2099`.\n\n```jldoctest\njulia> unitknot[Mix(Lift(1:2), Lift('a':'c'))]\n  \u2502 #A  #B \u2502\n\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n1 \u2502  1  a  \u2502\n2 \u2502  1  b  \u2502\n3 \u2502  1  c  \u2502\n4 \u2502  2  a  \u2502\n5 \u2502  2  b  \u2502\n6 \u2502  2  c  \u2502\n```\n\"\"\"\nMix(Xs...) =\n    Query(Mix, Xs...)\n\nfunction Mix(env::Environment, p::Pipeline, Xs...)\n    xs = assemble.(Ref(env), Ref(target_pipe(p)), collect(AbstractQuery, Xs))\n    assemble_mix(p, xs)\nend\n\ntranslate(mod::Module, ::Val{:mix}, args::Tuple) =\n    Mix(translate.(Ref(mod), args)...)\n\n\n#\n# Collect combinator.\n#\n\nfunction as_record(p::Pipeline)\n    q = as_record(elements(target(p)))\n    q !== nothing ?\n        compose(p, cover(q)) :\n        p\nend\n\nas_record(src::AbstractShape) = nothing\n\nas_record(::TupleOf) = nothing\n\nfunction as_record(src::IsLabeled)\n    p = as_record(subject(src))\n    p !== nothing ?\n        p |> designate(src, target(p) |> IsLabeled(label(src))) :\n        nothing\nend\n\nas_record(src::IsFlow) =\n    as_record(elements(src))\n\nfunction as_record(src::IsScope)\n    p = as_record(column(src))\n    p !== nothing ?\n        chain_of(column(1), p) |> designate(src, target(p)) :\n        nothing\nend\n\nas_record(src::ValueOf) =\n    as_record(src.ty)\n\nas_record(::Type) =\n    nothing\n\nfunction as_record(ity::Type{<:NamedTuple})\n    lbls = collect(Symbol, ity.parameters[1])\n    cols = collect(AbstractShape, ity.parameters[2].parameters)\n    adapt_tuple() |> designate(ity, TupleOf(lbls, cols))\nend\n\nfunction as_record(ity::Type{<:Tuple})\n    cols = collect(AbstractShape, ity.parameters)\n    adapt_tuple() |> designate(ity, TupleOf(cols))\nend\n\nfunction assemble_collect(p, x)\n    p = as_record(p)\n    src = elements(target(p))\n    dom = deannotate(domain(src))\n    dom isa TupleOf || error(\"expected a record; got\\n$(syntaxof(dom))\")\n    x = uncover(x)\n    x_lbl = getlabel(x, nothing)\n    x = relabel(x, nothing)\n    cols = Pipeline[]\n    lbls = Symbol[]\n    x_pos = width(dom)+1\n    for i in 1:width(dom)\n        lbl = label(dom, i)\n        if lbl == x_lbl\n            x_pos = i\n            continue\n        end\n        col = lookup(src, i)\n        push!(cols, col)\n        if lbl == ordinal_label(i)\n            lbl = ordinal_label(length(cols))\n        end\n        push!(lbls, lbl)\n    end\n    if !fits(target(x), BlockOf(Nothing, x1to1))\n        splice!(cols, x_pos:x_pos-1, Ref(x))\n        splice!(lbls, x_pos:x_pos-1, Ref(x_lbl !== nothing ? x_lbl : ordinal_label(length(cols))))\n    end\n    tgt = TupleOf(lbls, target.(cols))\n    lbl = getlabel(p, nothing)\n    if lbl !== nothing\n        tgt = relabel(tgt, lbl)\n    end\n    q = tuple_of(lbls, cols) |> designate(src, tgt)\n    q = cover(q)\n    compose(p, q)\nend\n\n\"\"\"\n    Collect(X\u2081, X\u2082 \u2026 X\u2099) :: Query\n\nIn the combinator form, `Collect(X\u2081, X\u2082 \u2026 X\u2099)` adds fields `X\u2081`,\n`X\u2082` \u2026 `X\u2099` to the input record.\n\n```jldoctest\njulia> unitknot[Record(:x => 1) >> Collect(:y => 2 .* It.x)]\n\u2502 x  y \u2502\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 1  2 \u2502\n```\n\nIf a field already exists, it is replaced.\n\n```jldoctest\njulia> unitknot[Record(:x => 1) >> Collect(:x => 2 .* It.x)]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 2 \u2502\n```\n\nTo remove a field, assign it the value `nothing`.\n\n```jldoctest\njulia> unitknot[Record(:x => 1) >> Collect(:y => 2 .* It.x, :x => nothing)]\n\u2502 y \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 2 \u2502\n```\n\n---\n\n    Each(X >> Record) :: Query\n\nIn the query form, `Collect` appends a field to the source record.\n\n```jldoctest\njulia> unitknot[Lift(1) >> Label(:x) >> Collect]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\"\"\"\nCollect(X, Ys...) =\n    Query(Collect, X, Ys...)\n\nLift(::typeof(Collect)) =\n    Then(Collect)\n\nCollect(env::Environment, p::Pipeline, X, Ys...) =\n    Collect(env, Collect(env, p, X), Ys...)\n\nfunction Collect(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    assemble_collect(p, x)\nend\n\ntranslate(mod::Module, ::Val{:collect}, args::Tuple) =\n    Collect(translate.(Ref(mod), args)...)\n\ntranslate(mod::Module, ::Val{:collect}, ::Tuple{}) =\n    Then(Collect)\n\n\n#\n# Join combinator.\n#\n\njoin_pipe(p::Pipeline) =\n    join_pipe(source(p), target(p))\n\njoin_pipe(src::AbstractShape, dst::AbstractShape) =\n    trivial_pipe(replace_domain(dst, domain(src)))\n\nfunction assemble_join(p::Pipeline, x::Pipeline)\n    p = as_record(p)\n    dom = deannotate(domain(target(p)))\n    dom isa TupleOf || error(\"expected a record; got\\n$(syntaxof(dom))\")\n    p0 = uncover(source(p))\n    q = assemble_join(target(p), target(p0), x)\n    chain_of(tuple_of(p, p0), q) |> designate(source(p), target(q))\nend\n\nfunction assemble_join(src::IsFlow, src0::AbstractShape, x::Pipeline)\n    q = assemble_join(elements(src), src0, x)\n    q\u2032 = chain_of(distribute(1), with_elements(q))\n    q\u2032 |> designate(TupleOf(src, src0), replace_elements(src, target(q)))\nend\n\nfunction assemble_join(src::IsScope, src0::AbstractShape, x::Pipeline)\n    q = assemble_join(column(src), replace_column(src, src0), x)\n    q\u2032 = tuple_of(chain_of(tuple_of(chain_of(column(1), column(1)),\n                                    tuple_of(column(2),\n                                             chain_of(column(1), column(2)))),\n                           q),\n                  chain_of(chain_of(column(1), column(2))))\n    q\u2032 |> designate(TupleOf(src, src0), replace_column(src, target(q)))\nend\n\nfunction assemble_join(src::AbstractShape, src0::AbstractShape, x::Pipeline)\n    dom = deannotate(domain(src))::TupleOf\n    x = uncover(x)\n    x_lbl = getlabel(x, nothing)\n    x = relabel(x, nothing)\n    cols = Pipeline[]\n    col_shps = AbstractShape[]\n    lbls = Symbol[]\n    x_pos = width(dom)+1\n    for i in 1:width(dom)\n        lbl = label(dom, i)\n        if lbl == x_lbl\n            x_pos = i\n            continue\n        end\n        col = lookup(src, i)\n        push!(cols, chain_of(column(1), col))\n        push!(col_shps, target(col))\n        if lbl == ordinal_label(i)\n            lbl = ordinal_label(length(cols))\n        end\n        push!(lbls, lbl)\n    end\n    splice!(cols, x_pos:x_pos-1, Ref(chain_of(column(2), x)))\n    splice!(col_shps, x_pos:x_pos-1, Ref(target(x)))\n    splice!(lbls, x_pos:x_pos-1, Ref(x_lbl !== nothing ? x_lbl : ordinal_label(length(cols))))\n    tgt = TupleOf(lbls, col_shps)\n    lbl = getlabel(src, nothing)\n    if lbl !== nothing\n        tgt = relabel(tgt, lbl)\n    end\n    tuple_of(lbls, cols) |> designate(TupleOf(src, src0), tgt)\nend\n\n\"\"\"\n    Join(X) :: Query\n\n`Join(X)` evaluates `X` in the source context and adds it\nas a field to the input record.\n\n```jldoctest\njulia> unitknot[Record(:x => 1) >> Each(Record(:y => 2 .* It.x) >> Join(It.x))]\n\u2502 y  x \u2502\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 2  1 \u2502\n```\n\"\"\"\nJoin(X) =\n    Query(Join, X)\n\nfunction Join(env::Environment, p::Pipeline, X)\n    x = assemble(env, join_pipe(p), X)\n    assemble_join(p, x)\nend\n\ntranslate(mod::Module, ::Val{:join}, (arg,)::Tuple{Any}) =\n    Join(translate(mod, arg))\n\n\n#\n# Lifting Julia values and functions.\n#\n\nfunction assemble_lift(p::Pipeline, f, xs::Vector{Pipeline})\n    cols = uncover.(xs)\n    ity = Tuple{eltype.(target.(cols))...}\n    oty = Core.Compiler.return_type(f, ity)\n    oty != Union{} || error(\"cannot apply $f to $ity\")\n    src = elements(target(p))\n    tgt = ValueOf(oty)\n    q = if length(cols) == 1\n            card = cardinality(target(xs[1]))\n            if fits(x1toN, card) && !(oty <: AbstractVector)\n                chain_of(cols[1], block_lift(f))\n            else\n                chain_of(cols[1], lift(f))\n            end\n        else\n            chain_of(tuple_of(Symbol[], cols), tuple_lift(f))\n        end |> designate(src, tgt)\n    q = cover(q)\n    compose(p, q)\nend\n\nLift(X::AbstractQuery) = X\n\n\"\"\"\n    Lift(val) :: Query\n\nThis converts any value to a constant query.\n\n```jldoctest\njulia> unitknot[Lift(\"Hello\")]\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 Hello \u2502\n```\n\n`AbstractVector` objects become plural queries.\n\n```jldoctest\njulia> unitknot[Lift('a':'c')]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\nTo specify the vector cardinality, add `:x0to1`, `:x0toN`,\n`:x1to1`, or `:x1toN`.\n\n```jldoctest\njulia> unitknot[Lift('a':'c', :x1toN)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\nThe `missing` value makes an query with no output.\n\n```jldoctest\njulia> unitknot[Lift(missing)]\n(empty)\n```\n\"\"\"\nLift(val) =\n    Query(Lift, val)\n\nLift(elts::AbstractVector, card::Union{Cardinality,Symbol}) =\n    Query(Lift, elts, card)\n\n\"\"\"\n    Lift(f, (X\u2081, X\u2082 \u2026 X\u2099)) :: Query\n\n`Lift` lets you use a function as a query combinator.\n\n```jldoctest\njulia> unitknot[Lift((x=1, y=2)) >> Lift(+, (It.x, It.y))]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\n`Lift` is implicitly used when a function is broadcast over\nqueries.\n\n```jldoctest\njulia> unitknot[Lift((x=1, y=2)) >> (It.x .+ It.y)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\nFunctions accepting a `AbstractVector` can be used with plural\nqueries.\n\n```jldoctest\njulia> unitknot[sum.(Lift(1:3))]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 6 \u2502\n```\n\nFunctions returning `AbstractVector` become plural queries.\n\n```jldoctest\njulia> unitknot[Lift((x='a', y='c')) >> Lift(:, (It.x, It.y))]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\"\"\"\nLift(f, Xs::Tuple) =\n    Query(Lift, f, Xs)\n\nconvert(::Type{AbstractQuery}, val) =\n    Lift(val)\n\nconvert(::Type{AbstractQuery}, F::AbstractQuery) =\n    F\n\nLift(env::Environment, p::Pipeline, val) =\n    Lift(env, p, convert(DataKnot, val))\n\nLift(env::Environment, p::Pipeline, elts::AbstractVector, card::Union{Cardinality,Symbol}) =\n    Lift(env, p, DataKnot(Any, elts, card))\n\nfunction Lift(env::Environment, p::Pipeline, f, Xs::Tuple)\n    xs = assemble.(Ref(env), Ref(target_pipe(p)), collect(AbstractQuery, Xs))\n    assemble_lift(p, f, xs)\nend\n\nfunction Lift(env::Environment, p::Pipeline, db::DataKnot)\n    q = cover(cell(db), Signature(elements(target(p)), shape(db)))\n    compose(p, q)\nend\n\n# Broadcasting.\n\nstruct QueryStyle <: Base.BroadcastStyle\nend\n\nBase.BroadcastStyle(::Type{<:Union{AbstractQuery,DataKnot,Pair{Symbol,<:Union{AbstractQuery,DataKnot}}}}) =\n    QueryStyle()\n\nBase.BroadcastStyle(s::QueryStyle, ::Broadcast.DefaultArrayStyle) =\n    s\n\nBase.broadcastable(X::Union{AbstractQuery,DataKnot,Pair{Symbol,<:Union{AbstractQuery,DataKnot}}}) =\n    X\n\nBase.Broadcast.instantiate(bc::Broadcast.Broadcasted{QueryStyle}) =\n    bc\n\nBase.copy(bc::Broadcast.Broadcasted{QueryStyle}) =\n    BroadcastLift(bc)\n\nBroadcastLift(bc::Broadcast.Broadcasted{QueryStyle}) =\n    BroadcastLift(bc.f, (BroadcastLift.(bc.args)...,))\n\nBroadcastLift(val) = val\n\nBroadcastLift(f, Xs) = Query(BroadcastLift, f, Xs)\n\nBroadcastLift(env::Environment, p::Pipeline, args...) =\n    Lift(env, p, args...)\n\nquoteof(::typeof(BroadcastLift), args::Vector{Any}) =\n    quoteof(broadcast, Any[args[1], quoteof.(args[2])...])\n\nLift(bc::Broadcast.Broadcasted{QueryStyle}) =\n    BroadcastLift(bc)\n\n\n#\n# Each combinator.\n#\n\n\"\"\"\n    Each(X) :: Query\n\nThis evaluates `X` elementwise.\n\n```jldoctest\njulia> X = Lift('a':'c') >> Count;\n\njulia> unitknot[Lift(1:3) >> Each(X)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 3 \u2502\n2 \u2502 3 \u2502\n3 \u2502 3 \u2502\n```\n\nCompare this with the query without `Each`.\n\n```jldoctest\njulia> X = Lift('a':'c') >> Count;\n\njulia> unitknot[Lift(1:3) >> X]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 9 \u2502\n```\n\"\"\"\nEach(X) = Query(Each, X)\n\nEach(env::Environment, p::Pipeline, X) =\n    compose(p, assemble(env, target_pipe(p), X))\n\ntranslate(mod::Module, ::Val{:each}, (arg,)::Tuple{Any}) =\n    Each(translate(mod, arg))\n\n\n#\n# Assigning labels.\n#\n\n\"\"\"\n    Label(lbl::Symbol) :: Query\n\nThis assigns a label to the output.\n\n```jldoctest\njulia> unitknot[Lift(\"Hello World\") >> Label(:greeting)]\n\u2502 greeting    \u2502\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 Hello World \u2502\n```\n\nA label could also be assigned using the `=>` operator.\n\n```jldoctest\njulia> unitknot[:greeting => Lift(\"Hello World\")]\n\u2502 greeting    \u2502\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 Hello World \u2502\n```\n\"\"\"\nLabel(lbl::Symbol) =\n    Query(Label, lbl)\n\nLabel(env::Environment, p::Pipeline, lbl::Symbol) =\n    relabel(p, lbl)\n\nLift(p::Pair{Symbol}) =\n    Compose(p.second, Label(p.first))\n\ntranslate(mod::Module, ::Val{:label}, (arg,)::Tuple{Symbol}) =\n    Label(arg)\n\n\n#\n# Assigning a name to a query.\n#\n\n\"\"\"\n    Tag(name::Symbol, F) :: Query\n\nThis provides a substitute name for a query.\n\n```jldoctest\njulia> IncIt = It .+ 1\nIt .+ 1\n\njulia> IncIt = Tag(:IncIt, It .+ 1)\nIncIt\n```\n\n---\n\n    Tag(name::Symbol, (X\u2081, X\u2082 \u2026 X\u2099), F) :: Query\n\nThis provides a substitute name for a query combinator.\n\n```jldoctest\njulia> Inc(X) = Lift(+, (X, 1));\n\njulia> Inc(It)\nLift(+, (It, 1))\n\njulia> Inc(X) = Tag(:Inc, (X,), Lift(+, (X, 1)));\n\njulia> Inc(It)\nInc(It)\n```\n\"\"\"\nTag(name::Symbol, X) =\n    Query(Tag, name, X)\n\nTag(name::Symbol, args::Tuple, X) =\n    Query(Tag, name, args, X)\n\nTag(F::Union{Function,DataType}, args::Tuple, X) =\n    Tag(nameof(F), args, X)\n\nTag(env::Environment, p::Pipeline, name, X) =\n    assemble(env, p, X)\n\nTag(env::Environment, p::Pipeline, name, args, X) =\n    assemble(env, p, X)\n\nquoteof(::typeof(Tag), args::Vector{Any}) =\n    quoteof(Tag, args...)\n\nquoteof(::typeof(Tag), name::Symbol, X) =\n    name\n\nquoteof(::typeof(Tag), name::Symbol, args::Tuple, X) =\n    Expr(:call, name, quoteof.(args)...)\n\n\n#\n# Attributes and parameters.\n#\n\n\"\"\"\n    Get(lbl::Symbol) :: Query\n\nThis query extracts a field value by its label.\n\n```jldoctest\njulia> unitknot[Lift((x=1, y=2)) >> Get(:x)]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\nThis has a shorthand form using `It`.\n\n```jldoctest\njulia> unitknot[Lift((x=1, y=2)) >> It.x]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\nWith unlabeled fields, ordinal labels (A, B, ...) can be used.\n\n```jldoctest\njulia> unitknot[Lift((1,2)) >> It.B]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 2 \u2502\n```\n\"\"\"\nGet(name) =\n    Query(Get, name)\n\nfunction Get(env::Environment, p::Pipeline, name)\n    tgt = target(p)\n    q = lookup(tgt, name)\n    q !== nothing || error(\"cannot find \\\"$name\\\" at\\n$(syntaxof(tgt))\")\n    q = cover(q)\n    compose(p, q)\nend\n\nlookup(::AbstractShape, ::Any) = nothing\n\nlookup(src::IsLabeled, name::Any) =\n    lookup(subject(src), name)\n\nlookup(src::IsFlow, name::Any) =\n    lookup(elements(src), name)\n\nfunction lookup(src::IsScope, name::Any)\n    q = lookup(column(src), name)\n    q !== nothing ?\n        chain_of(column(1), q) |> designate(src, target(q)) :\n        nothing\nend\n\nfunction lookup(src::IsScope, name::Symbol)\n    q = lookup(context(src), name)\n    q === nothing || return chain_of(column(2), q) |> designate(src, target(q))\n    q = lookup(column(src), name)\n    q === nothing || return chain_of(column(1), q) |> designate(src, target(q))\n    nothing\nend\n\nfunction lookup(lbls::Vector{Symbol}, name::Symbol)\n    j = findlast(isequal(name), lbls)\n    if j === nothing\n        j = findlast(isequal(Symbol(\"#$name\")), lbls)\n    end\n    j\nend\n\nlookup(src::TupleOf, j::Int) =\n    column(j) |> designate(src, column(src, j))\n\nfunction lookup(src::TupleOf, name::Symbol)\n    lbls = labels(src)\n    if isempty(lbls)\n        lbls = Symbol[ordinal_label(i) for i = 1:width(src)]\n    end\n    j = lookup(lbls, name)\n    j !== nothing || return nothing\n\n    tgt = relabel(column(src, j), name == lbls[j] ? name : nothing)\n    column(lbls[j]) |> designate(src, tgt)\nend\n\nlookup(src::ValueOf, name) =\n    lookup(src.ty, name)\n\nlookup(::Type, ::Any) =\n    nothing\n\nfunction lookup(ity::Type{<:NamedTuple}, name::Symbol)\n    j = lookup(collect(Symbol, ity.parameters[1]), name)\n    j !== nothing || return nothing\n    oty = ity.parameters[2].parameters[j]\n    lift(getindex, j) |> designate(ity, oty |> IsLabeled(name))\nend\n\nfunction lookup(ity::Type{<:Tuple}, name::Symbol)\n    lbls = Symbol[ordinal_label(i) for i = 1:length(ity.parameters)]\n    j = lookup(lbls, name)\n    j !== nothing || return nothing\n    oty = ity.parameters[j]\n    lift(getindex, j) |> designate(ity, oty)\nend\n\n\n#\n# Specifying context parameters.\n#\n\nfunction assemble_keep(p::Pipeline, q::Pipeline)\n    q = uncover(q)\n    tgt = target(q)\n    name = getlabel(tgt, nothing)\n    name !== nothing || error(\"parameter name is not specified\")\n    tgt = relabel(tgt, nothing)\n    lbls\u2032 = Symbol[]\n    cols\u2032 = AbstractShape[]\n    perm = Int[]\n    src = source(q)\n    if src isa IsScope\n        ctx = context(src)\n        for j = 1:width(ctx)\n            lbl = label(ctx, j)\n            if lbl != name\n                push!(lbls\u2032, lbl)\n                push!(cols\u2032, column(ctx, j))\n                push!(perm, j)\n            end\n        end\n    end\n    push!(lbls\u2032, name)\n    push!(cols\u2032, tgt)\n    ctx\u2032 = TupleOf(lbls\u2032, cols\u2032)\n    qs = Pipeline[chain_of(column(2), column(j)) for j in perm]\n    push!(qs, q)\n    tgt = BlockOf(TupleOf(src isa IsScope ? column(src) : src, ctx\u2032) |> IsScope,\n                  x1to1) |> IsFlow\n    q = chain_of(tuple_of(src isa IsScope ? column(1) : pass(),\n                          tuple_of(lbls\u2032, qs)),\n                 wrap(),\n    ) |> designate(src, tgt)\n    compose(p, q)\nend\n\n\"\"\"\n    Keep(X\u2081, X\u2082 \u2026 X\u2099) :: Query\n\n`Keep` evaluates named queries, making their results available for\nsubsequent computation.\n\n```jldoctest\njulia> unitknot[Keep(:x => 2) >> It.x]\n\u2502 x \u2502\n\u253c\u2500\u2500\u2500\u253c\n\u2502 2 \u2502\n```\n\n`Keep` does not otherwise change its input.\n\n```jldoctest\njulia> unitknot[Lift(1) >> Keep(:x => 2) >> (It .+ It.x)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\"\"\"\nKeep(P, Qs...) =\n    Query(Keep, P, Qs...)\n\nKeep(env::Environment, p::Pipeline, P, Qs...) =\n    Keep(env, Keep(env, p, P), Qs...)\n\nfunction Keep(env::Environment, p::Pipeline, P)\n    q = assemble(env, target_pipe(p), P)\n    assemble_keep(p, q)\nend\n\ntranslate(mod::Module, ::Val{:keep}, args::Tuple{Any,Vararg{Any}}) =\n    Keep(translate.(Ref(mod), args)...)\n\n\n#\n# Setting the scope for context parameters.\n#\n\nfunction assemble_given(p::Pipeline, q::Pipeline)\n    q = cover(uncover(q))\n    compose(p, q)\nend\n\n\"\"\"\n    Given(X\u2081, X\u2082 \u2026 X\u2099, Q) :: Query\n\nThis evaluates `Q` in a context augmented with named parameters\nadded by a set of queries.\n\n```jldoctest\njulia> unitknot[Given(:x => 2, It.x .+ 1)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\"\"\"\nGiven(P, Xs...) =\n    Query(Given, P, Xs...)\n\nGiven(env::Environment, p::Pipeline, Xs...) =\n    Given(env, p, Keep(Xs[1:end-1]...) >> Each(Xs[end]))\n\nfunction Given(env::Environment, p::Pipeline, X)\n    q = assemble(env, target_pipe(p), X)\n    assemble_given(p, q)\nend\n\nconst Let = Given\n\ntranslate(mod::Module, ::Val{:given}, args::Tuple{Any,Vararg{Any}}) =\n    Given(translate.(Ref(mod), args)...)\n\n\n#\n# Then assembly.\n#\n\nThen(q::Pipeline) =\n    Query(Then, q)\n\nThen(env::Environment, p::Pipeline, q::Pipeline) =\n    compose(p, q)\n\nThen(ctor) =\n    Query(Then, ctor)\n\nThen(ctor, args::Tuple) =\n    Query(Then, ctor, args)\n\nThen(env::Environment, p::Pipeline, ctor, args::Tuple=()) =\n    assemble(env, source_pipe(p), ctor(Then(p), args...))\n\n\n#\n# Count and other aggregate combinators.\n#\n\nfunction assemble_count(p::Pipeline)\n    p = uncover(p)\n    q = chain_of(p,\n                 block_length(),\n    ) |> designate(source(p), Int)\n    cover(q)\nend\n\nfunction assemble_exists(p::Pipeline)\n    p = uncover(p)\n    q = chain_of(p,\n                 block_not_empty(),\n    ) |> designate(source(p), Bool)\n    cover(q)\nend\n\n\"\"\"\n    Count(X) :: Query\n\nIn the combinator form, `Count(X)` emits the number of elements\nproduced by `X`.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[Count(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\n---\n\n    Each(X >> Count) :: Query\n\nIn the query form, `Count` emits the number of elements in its input.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[X >> Count]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\nTo limit the scope of aggregation, use `Each`.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[Lift(1:3) >> Each(X >> Count)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 3 \u2502\n2 \u2502 3 \u2502\n3 \u2502 3 \u2502\n```\n\"\"\"\nCount(X) =\n    Query(Count, X)\n\nLift(::typeof(Count)) =\n    Then(Count)\n\nfunction Count(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    compose(p, assemble_count(x))\nend\n\n\"\"\"\n    Exists(X) :: Query\n\nIn the combinator form, `Exists(X)` emits a boolean testing if `X` produces any elements.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[Exists(X)]\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 true \u2502\n```\n\nWhen the query argument `X` is empty, `Exists(X)` produces `false`.\n\n```jldoctest\njulia> X = Lift([]);\n\njulia> unitknot[Exists(X)]\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 false \u2502\n```\n\n---\n\n    Each(X >> Exists) :: Query\n\nIn the query form, `Exists` emits a boolean testing if its input has any elements.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[X >> Exists]\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 true \u2502\n```\n\nWhen the query input is empty, `Exists` produces `false`.\n\n```jldoctest\njulia> X = Lift([]);\n\njulia> unitknot[X >> Exists]\n\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n\u2502 false \u2502\n```\n\"\"\"\nExists(X) =\n    Query(Exists, X)\n\nLift(::typeof(Exists)) =\n    Then(Exists)\n\nfunction Exists(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    compose(p, assemble_exists(x))\nend\n\n\"\"\"\n    Sum(X) :: Query\n\nIn the combinator form, `Sum(X)` emits the sum of elements\nproduced by `X`.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[Sum(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 6 \u2502\n```\n\nThe `Sum` of an empty input is `0`.\n\n```jldoctest\njulia> unitknot[Sum(Int[])]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 0 \u2502\n```\n\n---\n\n    Each(X >> Sum) :: Query\n\nIn the query form, `Sum` emits the sum of input elements.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[X >> Sum]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 6 \u2502\n```\n\"\"\"\nSum(X) =\n    Query(Sum, X)\n\nLift(::typeof(Sum)) =\n    Then(Sum)\n\n\"\"\"\n     Max(X) :: Query\n\nIn the combinator form, `Max(X)` finds the maximum among the\nelements produced by `X`.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[Max(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\nThe `Max` of an empty input is empty.\n\n```jldoctest\njulia> unitknot[Max(Int[])]\n(empty)\n```\n\n---\n\n    Each(X >> Max) :: Query\n\nIn the query form, `Max` finds the maximum of its input elements.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[X >> Max]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 3 \u2502\n```\n\"\"\"\nMax(X) =\n    Query(Max, X)\n\nLift(::typeof(Max)) =\n    Then(Max)\n\n\"\"\"\n     Min(X) :: Query\n\nIn the combinator form, `Min(X)` finds the minimum among the\nelements produced by `X`.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[Min(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\nThe `Min` of an empty input is empty.\n\n```jldoctest\njulia> unitknot[Min(Int[])]\n(empty)\n```\n\n---\n\n    Each(X >> Min) :: Query\n\nIn the query form, `Min` finds the minimum of its input elements.\n\n```jldoctest\njulia> X = Lift(1:3);\n\njulia> unitknot[X >> Min]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 1 \u2502\n```\n\"\"\"\nMin(X) =\n    Query(Min, X)\n\nLift(::typeof(Min)) =\n    Then(Min)\n\nfunction Sum(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    assemble_lift(p, sum, Pipeline[x])\nend\n\nmaximum_missing(v) =\n    !isempty(v) ? maximum(v) : missing\n\nfunction Max(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    card = cardinality(target(x))\n    optional = fits(x0to1, card)\n    assemble_lift(p, optional ? maximum_missing : maximum, Pipeline[x])\nend\n\nminimum_missing(v) =\n    !isempty(v) ? minimum(v) : missing\n\nfunction Min(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    card = cardinality(target(x))\n    optional = fits(x0to1, card)\n    assemble_lift(p, optional ? minimum_missing : minimum, Pipeline[x])\nend\n\ntranslate(mod::Module, ::Val{:count}, (arg,)::Tuple{Any}) =\n    Count(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:count}, ::Tuple{}) =\n    Then(Count)\n\ntranslate(mod::Module, ::Val{:exists}, (arg,)::Tuple{Any}) =\n    Exists(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:exists}, ::Tuple{}) =\n    Then(Exists)\n\ntranslate(mod::Module, ::Val{:sum}, (arg,)::Tuple{Any}) =\n    Sum(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:sum}, ::Tuple{}) =\n    Then(Sum)\n\ntranslate(mod::Module, ::Val{:max}, (arg,)::Tuple{Any}) =\n    Max(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:max}, ::Tuple{}) =\n    Then(Max)\n\ntranslate(mod::Module, ::Val{:min}, (arg,)::Tuple{Any}) =\n    Min(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:min}, ::Tuple{}) =\n    Then(Min)\n\n\n#\n# Filter combinator.\n#\n\nfunction assemble_filter(p::Pipeline, x::Pipeline)\n    x = uncover(x)\n    fits(target(x), BlockOf(ValueOf(Bool))) || error(\"expected a predicate\")\n    q = chain_of(tuple_of(pass(),\n                          chain_of(x, block_any())),\n                 sieve_by(),\n    ) |> designate(source(x), BlockOf(source(x), x0to1) |> IsFlow)\n    compose(p, q)\nend\n\n\"\"\"\n    Filter(X) :: Query\n\nThis query emits the elements from its input that satisfy a given\ncondition.\n\n```jldoctest\njulia> unitknot[Lift(1:5) >> Filter(isodd.(It))]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 1 \u2502\n2 \u2502 3 \u2502\n3 \u2502 5 \u2502\n```\n\nWhen the predicate query produces an empty output, the condition\nis presumed to have failed.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Filter(missing)]\n(empty)\n```\n\nWhen the predicate produces plural output, the condition succeeds\nif at least one output value is `true`.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Filter([true,false])]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\"\"\"\nFilter(X) =\n    Query(Filter, X)\n\nfunction Filter(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    assemble_filter(p, x)\nend\n\ntranslate(mod::Module, ::Val{:filter}, (arg,)::Tuple{Any}) =\n    Filter(translate(mod, arg))\n\n\n#\n# First, Last, Nth.\n#\n\nassemble_first(p::Pipeline, inv::Bool=false) =\n    assemble_nth(p, !inv ? 1 : -1, cardinality(target(p))&x0to1)\n\nfunction assemble_nth(p::Pipeline, n::Int, card::Cardinality=x0to1)\n    elts = elements(target(p))\n    chain_of(\n        p,\n        get_by(n, card),\n    ) |> designate(source(p), BlockOf(elts, card) |> IsFlow)\nend\n\nfunction assemble_nth(p::Pipeline, n::Pipeline)\n    n = uncover(n)\n    fits(target(n), BlockOf(ValueOf(Int), x1to1)) || error(\"expected a singular mandatory integer\")\n    src = source(p)\n    tgt = BlockOf(elements(target(p)), x0to1) |> IsFlow\n    chain_of(\n        tuple_of(p, n),\n        get_by(),\n    ) |> designate(src, tgt)\nend\n \n\"\"\"\n    First(X) :: Query\n\nIn the combinator form, `First(X)` emits the first element produced by its argument `X`.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[First(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 a \u2502\n```\n---\n\n    Each(X >> First) :: Query\n\nIn the query form, `First` emits the first element of its input.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[X >> First]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 a \u2502\n```\n\"\"\"\nFirst(X) = Query(First, X)\n\n\"\"\"\n    Last(X) :: Query\n\nIn the combinator form, `Last(X)` emits the last element produced by its argument `X`.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[Last(X)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 c \u2502\n```\n---\n\n    Each(X >> Last) :: Query\n\nIn the query form, `Last` emits the last element of its input.\n\n```jldoctest\njulia> X = Lift('a':'c');\n\njulia> unitknot[X >> Last]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 c \u2502\n```\n\"\"\"\nLast(X) = Query(Last, X)\n\n\"\"\"\n    Nth(X, N) :: Query\n\nIn the combinator form, `Nth(X, N)` emits the `N`th element produced by its argument `X`.\n\n```jldoctest\njulia> X = Lift('a':'d');\n\njulia> N = Count(X) .\u00f7 2;\n\njulia> unitknot[Nth(X, N)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 b \u2502\n```\n---\n\n    Each(X >> Nth(N)) :: Query\n\nIn the query form, `Nth(N)` emits the `N`th element produced by its input.\n\n```jldoctest\njulia> X = Lift('a':'d');\n\njulia> N = Count(X) .\u00f7 2;\n\njulia> unitknot[X >> Nth(N)]\n\u253c\u2500\u2500\u2500\u253c\n\u2502 b \u2502\n```\n\"\"\"\nNth(X, N) = Query(Nth, X, N)\n\nLift(::typeof(First)) =\n    Then(First)\n\nLift(::typeof(Last)) =\n    Then(Last)\n\nNth(N) = Query(Nth, N)\n\nfunction First(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    compose(p, assemble_first(x))\nend\n\nfunction Last(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    compose(p, assemble_first(x, true))\nend\n\nfunction Nth(env::Environment, p::Pipeline, X, N::Int)\n    x = assemble(env, target_pipe(p), X)\n    compose(p, assemble_nth(x, N))\nend\n\nfunction Nth(env::Environment, p::Pipeline, X, N)\n    p0 = target_pipe(p)\n    x = assemble(env, p0, X)\n    n = assemble(env, p0, N)\n    compose(p, assemble_nth(x, n))\nend\n\nNth(env::Environment, p::Pipeline, N::Int) =\n    assemble_nth(p, N)\n\nNth(env::Environment, p::Pipeline, N) =\n    assemble_nth(p, assemble(env, source_pipe(p), N))\n\ntranslate(mod::Module, ::Val{:first}, (arg,)::Tuple{Any}) =\n    First(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:first}, ::Tuple{}) =\n    Then(First)\n\ntranslate(mod::Module, ::Val{:last}, (arg,)::Tuple{Any}) =\n    Last(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:last}, ::Tuple{}) =\n    Then(Last)\n\ntranslate(mod::Module, ::Val{:nth}, args::Tuple{Any,Any}) =\n    Nth(translate.(Ref(mod), args)...)\n\ntranslate(mod::Module, ::Val{:nth}, (arg,)::Tuple{Any}) =\n    Nth(translate(mod, arg))\n\n\n#\n# Take and Drop combinators.\n#\n\nfunction assemble_take(p::Pipeline, n::Union{Int,Missing}, inv::Bool)\n    elts = elements(target(p))\n    card = cardinality(target(p))|x0to1\n    chain_of(\n        p,\n        slice_by(n, inv),\n    ) |> designate(source(p), BlockOf(elts, card) |> IsFlow)\nend\n\nfunction assemble_take(p::Pipeline, n::Pipeline, inv::Bool)\n    n = uncover(n)\n    fits(target(n), BlockOf(ValueOf(Int), x0to1)) || error(\"expected a singular integer\")\n    src = source(p)\n    tgt = BlockOf(elements(target(p)), cardinality(target(p))|x0to1) |> IsFlow\n    chain_of(\n        tuple_of(p, n),\n        slice_by(inv),\n    ) |> designate(src, tgt)\nend\n\n\"\"\"\n    Take(N) :: Query\n\nThis query preserves the first `N` elements of its input, dropping\nthe rest.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Take(2)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n```\n\n`Take(-N)` drops the last `N` elements.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Take(-2)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n```\n\"\"\"\nTake(N) =\n    Query(Take, N)\n\n\"\"\"\n    Drop(N) :: Query\n\nThis query drops the first `N` elements of its input, preserving\nthe rest.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Drop(2)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 c \u2502\n```\n\n`Drop(-N)` takes the last `N` elements.\n\n```jldoctest\njulia> unitknot[Lift('a':'c') >> Drop(-2)]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 b \u2502\n2 \u2502 c \u2502\n```\n\"\"\"\nDrop(N) =\n    Query(Drop, N)\n\nTake(env::Environment, p::Pipeline, n::Union{Int,Missing}, inv::Bool=false) =\n    assemble_take(p, n, inv)\n\nfunction Take(env::Environment, p::Pipeline, N, inv::Bool=false)\n    n = assemble(env, source_pipe(p), N)\n    assemble_take(p, n, inv)\nend\n\nDrop(env::Environment, p::Pipeline, N) =\n    Take(env, p, N, true)\n\ntranslate(mod::Module, ::Val{:take}, (arg,)::Tuple{Any}) =\n    Take(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:drop}, (arg,)::Tuple{Any}) =\n    Drop(translate(mod, arg))\n\n\n#\n# Unique and Group combinators.\n#\n\nfunction assemble_unique(p::Pipeline, x::Pipeline)\n    x = uncover(x)\n    q = chain_of(x, unique_by()) |> designate(source(x), target(x)) |> cover\n    compose(p, q)\nend\n\n\"\"\"\n    Unique(X) :: Query\n\nThis query produces all distinct elements emitted by `X`.\n\n```jldoctest\njulia> unitknot[Unique(['a','b','b','c','c','c'])]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\n---\n\n    Each(X >> Unique) :: Query\n\nIn the query form, `Unique` produces all distinct elements of its input.\n\n```jldoctest\njulia> unitknot[Lift(['a','b','b','c','c','c']) >> Unique]\n\u2500\u2500\u253c\u2500\u2500\u2500\u253c\n1 \u2502 a \u2502\n2 \u2502 b \u2502\n3 \u2502 c \u2502\n```\n\"\"\"\nUnique(X) =\n    Query(Unique, X)\n\nLift(::typeof(Unique)) =\n    Then(Unique)\n\nfunction Unique(env::Environment, p::Pipeline, X)\n    x = assemble(env, target_pipe(p), X)\n    assemble_unique(p, x)\nend\n\ntranslate(mod::Module, ::Val{:unique}, (arg,)::Tuple{Any}) =\n    Unique(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:unique}, ::Tuple{}) =\n    Then(Unique)\n\nfunction assemble_group(p::Pipeline, xs::Vector{Pipeline})\n    lbls = Symbol[]\n    ks = Pipeline[]\n    seen = Dict{Symbol,Int}()\n    for (i, x) in enumerate(xs)\n        x = uncover(x)\n        lbl = getlabel(x, nothing)\n        if lbl !== nothing\n            x = relabel(x, nothing)\n        else\n            lbl = ordinal_label(i)\n        end\n        if lbl in keys(seen)\n            lbls[seen[lbl]] = ordinal_label(seen[lbl])\n        end\n        seen[lbl] = i\n        push!(lbls, lbl)\n        push!(ks, x)\n    end\n    p0 = uncover(target_pipe(p))\n    lbl = getlabel(p0, nothing)\n    if lbl !== nothing\n        p0 = relabel(p0, nothing)\n    else\n        lbl = ordinal_label(length(xs)+1)\n    end\n    if lbl in keys(seen)\n        lbls[seen[lbl]] = ordinal_label(seen[lbl])\n    end\n    push!(lbls, lbl)\n    src = elements(target(p))\n    tgt = TupleOf(target(p0), TupleOf(target.(ks)))\n    q = tuple_of(p0, tuple_of(Symbol[], ks)) |> designate(src, tgt)\n    r = uncover(compose(p, cover(q)))\n    cols = Pipeline[]\n    for (i, k) in enumerate(ks)\n        col = chain_of(column(2), column(i)) |> designate(elements(target(r)), target(k))\n        push!(cols, col)\n    end\n    card = cardinality(target(r)) & x1toN\n    col = chain_of(column(1), flatten()) |> designate(elements(target(r)),\n                                                      BlockOf(elements(target(p0)), card))\n    push!(cols, col)\n    src = source(r)\n    tgt = replace_elements(target(r), TupleOf(lbls, target.(cols)))\n    chain_of(\n        r,\n        group_by(),\n        with_elements(tuple_of(lbls, cols)),\n    ) |> designate(src, tgt) |> cover\nend\n\n\"\"\"\n    Group(X\u2081, X\u2082 \u2026 X\u2099) :: Query\n\nThis query groups the input data by the keys `X\u2081`, `X\u2082` \u2026 `X\u2099`.\n\n```jldoctest\njulia> unitknot[Lift(1:5) >> Group(isodd.(It))]\n  \u2502 #A     #B      \u2502\n\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\n1 \u2502 false  2; 4    \u2502\n2 \u2502  true  1; 3; 5 \u2502\n```\n\"\"\"\nGroup(Xs...) =\n    Query(Group, Xs...)\n\nfunction Group(env::Environment, p::Pipeline, Xs...)\n    xs = assemble.(Ref(env), Ref(target_pipe(p)), collect(AbstractQuery, Xs))\n    assemble_group(p, xs)\nend\n\ntranslate(mod::Module, ::Val{:group}, args::Tuple) =\n    Group(translate.(Ref(mod), args)...)\n\n\n#\n# Cardinality assertions.\n#\n\nfunction assemble_cardinality(p::Pipeline, card::Cardinality)\n    src = source(p)\n    tgt = BlockOf(elements(target(p)), card) |> IsFlow\n    q = block_cardinality(card, getlabel(src, nothing), getlabel(tgt, nothing))\n    chain_of(p, q) |> designate(src, tgt)\nend\n\n\"\"\"\n    Is0to1(X) :: Query\n\nThis query asserts that `X` emits 0 or 1 element.\n\n---\n\n    Each(X >> Is0to1) :: Query\n\nIn this form, `Is0to1` asserts that its input contains 0 or 1 element.\n\"\"\"\nIs0to1(X) = Query(Is0to1, X)\n\n\"\"\"\n    Is0toN(X) :: Query\n\nThis query asserts that `X` may emit any number of elements.\n\n---\n\n    Each(X >> Is0toN) :: Query\n\nIn this form, `Is0toN` asserts that its input contains any number of elements.\n\"\"\"\nIs0toN(X) = Query(Is0toN, X)\n\n\"\"\"\n    Is1to1(X) :: Query\n\nThis query asserts that `X` emits 1 element.\n\n---\n\n    Each(X >> Is1to1) :: Query\n\nIn this form, `Is1to1` asserts that its input contains 1 element.\n\"\"\"\nIs1to1(X) = Query(Is1to1, X)\n\n\"\"\"\n    Is1toN(X) :: Query\n\nThis query asserts that `X` emits 1 or more elements.\n\n---\n\n    Each(X >> Is1toN) :: Query\n\nIn this form, `Is1toN` asserts that its input contains 1 or more elements.\n\"\"\"\nIs1toN(X) = Query(Is1toN, X)\n\nLift(::typeof(Is0to1)) = Then(Is0to1)\n\nLift(::typeof(Is0toN)) = Then(Is0toN)\n\nLift(::typeof(Is1to1)) = Then(Is1to1)\n\nLift(::typeof(Is1toN)) = Then(Is1toN)\n\nIs0to1(env::Environment, p::Pipeline, X) =\n    assemble_cardinality(assemble(env, p, X), x0to1)\n\nIs0toN(env::Environment, p::Pipeline, X) =\n    assemble_cardinality(assemble(env, p, X), x0toN)\n\nIs1to1(env::Environment, p::Pipeline, X) =\n    assemble_cardinality(assemble(env, p, X), x1to1)\n\nIs1toN(env::Environment, p::Pipeline, X) =\n    assemble_cardinality(assemble(env, p, X), x1toN)\n\ntranslate(mod::Module, ::Val{:is0to1}, (arg,)::Tuple{Any}) =\n    Is0to1(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:is0toN}, (arg,)::Tuple{Any}) =\n    Is0toN(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:is1to1}, (arg,)::Tuple{Any}) =\n    Is1to1(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:is1toN}, (arg,)::Tuple{Any}) =\n    Is1toN(translate(mod, arg))\n\ntranslate(mod::Module, ::Val{:is0to1}, ::Tuple{}) =\n    Then(Is0to1)\n\ntranslate(mod::Module, ::Val{:is0toN}, ::Tuple{}) =\n    Then(Is0toN)\n\ntranslate(mod::Module, ::Val{:is1to1}, ::Tuple{}) =\n    Then(Is1to1)\n\ntranslate(mod::Module, ::Val{:is1toN}, ::Tuple{}) =\n    Then(Is1toN)\n\n", "meta": {"hexsha": "945b763495af0c3d5055c902a8eae4e848619e35", "size": 52223, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/queries.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/DataKnots.jl-f3f2b2ad-91c8-5588-b964-d77e2d3bb090", "max_stars_repo_head_hexsha": "9547884c325a12fb3a9ec78caf099da2d1266b9d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, 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"Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4301473485858429, "lm_q2_score": 0.11757212581561163, "lm_q1q2_score": 0.05057333818718648}}
{"text": "# ------------------------------------------------------------------------------------------\n# # Introduction to DataFrames\n# **[Bogumi\u0142 Kami\u0144ski](http://bogumilkaminski.pl/about/), May 23, 2018**\n# ------------------------------------------------------------------------------------------\n\nusing DataFrames # load package\n\n# ------------------------------------------------------------------------------------------\n# ## Manipulating columns of DataFrame\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Renaming columns\n#\n# Let's start with a `DataFrame` of `Bool`s that has default column names.\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame(Bool, 3, 4)\n\n# ------------------------------------------------------------------------------------------\n# With `rename`, we create new `DataFrame`; here we rename the column `:x1` to `:A`.\n# (`rename` also accepts collections of Pairs.)\n# ------------------------------------------------------------------------------------------\n\nrename(x, :x1 => :A)\n\n# ------------------------------------------------------------------------------------------\n# With `rename!` we do an in place transformation.\n#\n# This time we've applied a function to every column name.\n# ------------------------------------------------------------------------------------------\n\nrename!(c -> Symbol(string(c)^2), x)\n\n# ------------------------------------------------------------------------------------------\n# We can also change the name of a particular column without knowing the original.\n#\n# Here we change the name of the third column, creating a new `DataFrame`.\n# ------------------------------------------------------------------------------------------\n\nrename(x, names(x)[3] => :third)\n\n# ------------------------------------------------------------------------------------------\n# With `names!`, we can change the names of all variables.\n# ------------------------------------------------------------------------------------------\n\nnames!(x, [:a, :b, :c, :d])\n\n# ------------------------------------------------------------------------------------------\n# We get an error when we try to provide duplicate names\n# ------------------------------------------------------------------------------------------\n\nnames!(x, fill(:a, 4))\n\n# ------------------------------------------------------------------------------------------\n#  unless we pass `makeunique=true`, which allows us to handle duplicates in passed names.\n# ------------------------------------------------------------------------------------------\n\nnames!(x, fill(:a, 4), makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# ### Reordering columns\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# We can reorder the names(x) vector as needed, creating a new DataFrame.\n# ------------------------------------------------------------------------------------------\n\nsrand(1234)\nx[shuffle(names(x))]\n\n# ------------------------------------------------------------------------------------------\n# also `permutecols!` will be introduced in next release of DataFrames\n# ------------------------------------------------------------------------------------------\n\n# ------------------------------------------------------------------------------------------\n# ### Merging/adding columns\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame([(i,j) for i in 1:3, j in 1:4])\n\n# ------------------------------------------------------------------------------------------\n# With `hcat` we can merge two `DataFrame`s. Also [x y] syntax is supported but only when\n# DataFrames have unique column names.\n# ------------------------------------------------------------------------------------------\n\nhcat(x, x, makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# We can also use `hcat` to add a new column; a default name `:x1` will be used for this\n# column, so `makeunique=true` is needed.\n# ------------------------------------------------------------------------------------------\n\ny = hcat(x, [1,2,3], makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# You can also prepend a vector with `hcat`.\n# ------------------------------------------------------------------------------------------\n\nhcat([1,2,3], x, makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# Alternatively you could append a vector with the following syntax. This is a bit more\n# verbose but cleaner.\n# ------------------------------------------------------------------------------------------\n\ny = [x DataFrame(A=[1,2,3])]\n\n# ------------------------------------------------------------------------------------------\n# Here we do the same but add column `:A` to the front.\n# ------------------------------------------------------------------------------------------\n\ny = [DataFrame(A=[1,2,3]) x]\n\n# ------------------------------------------------------------------------------------------\n# A column can also be added in the middle. Here a brute-force method is used and a new\n# DataFrame is created.\n# ------------------------------------------------------------------------------------------\n\nusing BenchmarkTools\n@btime [$x[1:2] DataFrame(A=[1,2,3]) $x[3:4]]\n\n# ------------------------------------------------------------------------------------------\n# We could also do this with a specialized in place method `insert!`. Let's add `:newcol` to\n# the `DataFrame` `y`.\n# ------------------------------------------------------------------------------------------\n\ninsert!(y, 2, [1,2,3], :newcol)\n\n# ------------------------------------------------------------------------------------------\n# If you want to insert the same column name several times `makeunique=true` is needed as\n# usual.\n# ------------------------------------------------------------------------------------------\n\ninsert!(y, 2, [1,2,3], :newcol, makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# We can see how much faster it is to insert a column with `insert!` than with `hcat` using\n# `@btime`.\n# ------------------------------------------------------------------------------------------\n\n@btime insert!(copy($x), 3, [1,2,3], :A)\n\n# ------------------------------------------------------------------------------------------\n# Let's use `insert!` to append a column in place,\n# ------------------------------------------------------------------------------------------\n\ninsert!(x, ncol(x)+1, [1,2,3], :A)\n\n# ------------------------------------------------------------------------------------------\n# and to in place prepend a column.\n# ------------------------------------------------------------------------------------------\n\ninsert!(x, 1, [1,2,3], :B)\n\n# ------------------------------------------------------------------------------------------\n# With `merge!`, let's merge the second DataFrame into first, but overwriting duplicates.\n# ------------------------------------------------------------------------------------------\n\ndf1 = DataFrame(x=1:3, y=4:6)\ndf2 = DataFrame(x='a':'c', z = 'd':'f', new=11:13)\ndf1, df2, merge!(df1, df2)\n\n# ------------------------------------------------------------------------------------------\n#  For comparison: merge two `DataFrames`s but renaming duplicate names via `hcat`.\n# ------------------------------------------------------------------------------------------\n\ndf1 = DataFrame(x=1:3, y=4:6)\ndf2 = DataFrame(x='a':'c', z = 'd':'f', new=11:13)\nhcat(df1, df2, makeunique=true)\n\n# ------------------------------------------------------------------------------------------\n# ### Subsetting/removing columns\n#\n# Let's create a new `DataFrame` `x` and show a few ways to create DataFrames with a subset\n# of `x`'s columns.\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame([(i,j) for i in 1:3, j in 1:5])\n\n# ------------------------------------------------------------------------------------------\n# First we could do this by index\n# ------------------------------------------------------------------------------------------\n\nx[[1,2,4,5]]\n\n# ------------------------------------------------------------------------------------------\n# or by column name.\n# ------------------------------------------------------------------------------------------\n\nx[[:x1, :x4]]\n\n# ------------------------------------------------------------------------------------------\n# We can also choose to keep or exclude columns by `Bool`. (We need a vector whose length is\n# the number of columns in the original `DataFrame`.)\n# ------------------------------------------------------------------------------------------\n\nx[[true, false, true, false, true]]\n\n# ------------------------------------------------------------------------------------------\n# Here we create a single column `DataFrame`,\n# ------------------------------------------------------------------------------------------\n\nx[[:x1]]\n\n# ------------------------------------------------------------------------------------------\n# and here we access the vector contained in column `:x1`.\n# ------------------------------------------------------------------------------------------\n\nx[:x1]\n\n# ------------------------------------------------------------------------------------------\n# We could grab the same vector by column number\n# ------------------------------------------------------------------------------------------\n\nx[1]\n\n# ------------------------------------------------------------------------------------------\n# and remove everything from a `DataFrame` with `empty!`.\n# ------------------------------------------------------------------------------------------\n\nempty!(y)\n\n# ------------------------------------------------------------------------------------------\n# Here we create a copy of `x` and delete the 3rd column from the copy with `delete!`.\n# ------------------------------------------------------------------------------------------\n\nz = copy(x)\nx, delete!(z, 3)\n\n# ------------------------------------------------------------------------------------------\n# ### Modify column by name\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame([(i,j) for i in 1:3, j in 1:5])\n\n# ------------------------------------------------------------------------------------------\n# With the following syntax, the existing column is modified without performing any copying.\n# ------------------------------------------------------------------------------------------\n\nx[:x1] = x[:x2]\nx\n\n# ------------------------------------------------------------------------------------------\n# We can also use the following syntax to add a new column at the end of a `DataFrame`.\n# ------------------------------------------------------------------------------------------\n\nx[:A] = [1,2,3]\nx\n\n# ------------------------------------------------------------------------------------------\n# A new column name will be added to our `DataFrame` with the following syntax as well (7 is\n# equal to `ncol(x)+1`).\n# ------------------------------------------------------------------------------------------\n\nx[7] = 11:13\nx\n\n# ------------------------------------------------------------------------------------------\n# ### Find column name\n# ------------------------------------------------------------------------------------------\n\nx = DataFrame([(i,j) for i in 1:3, j in 1:5])\n\n# ------------------------------------------------------------------------------------------\n# We can check if a column with a given name exists via\n# ------------------------------------------------------------------------------------------\n\n:x1 in names(x) \n\n# ------------------------------------------------------------------------------------------\n# and determine its index via\n# ------------------------------------------------------------------------------------------\n\nfindfirst(names(x), :x2)\n", "meta": {"hexsha": "76457047c97d6ec3a53fba26795de7583252e91b", "size": 12498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".nbexports/introductory-tutorials/broader-topics-and-ecosystem/intro-to-julia-DataFrames/05_columns.jl", "max_stars_repo_name": "grenkoca/JuliaTutorials", "max_stars_repo_head_hexsha": "3968e0430db77856112521522e10f7da0d7610a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 535, 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"max_forks_repo_forks_event_max_datetime": "2022-03-28T20:12:57.000Z", "avg_line_length": 45.2826086957, "max_line_length": 92, "alphanum_fraction": 0.2522003521, "num_tokens": 1720, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3960681662740417, "lm_q2_score": 0.12765261868437058, "lm_q1q2_score": 0.05055913860239813}}
{"text": "module TikzCDs\n\nusing TikzPictures\n\nimport TikzPictures: PDF, TEX, TIKZ, SVG, save, TikzDocument, push!, tikzCommand\nexport PDF, TEX, TIKZ, SVG, save, TikzDocument, push!, tikzCommand\nimport LaTeXStrings: LaTeXString, @L_str, @L_mstr\nexport LaTeXString, @L_str, @L_mstr\n\nexport TikzCD\n\nfunction TikzCD(data::AbstractString; options=\"\", preamble=\"\", enableWrite18=true)\n  # check for tikz-cd package being imported\n  if !occursin(\"\\\\usepackage{tikz-cd}\", preamble)\n    preamble = strip(string(\"\\\\usepackage{tikz-cd}\\n\", preamble))\n  end\n  # tikz-cd doesn't support the content being in a $$, so this helps when using LaTeXStrings\n  if typeof(data) == LaTeXString\n    data = strip(data[2:end-1])\n  end\n  TikzPicture(data, options=options, preamble=preamble, environment=\"tikzcd\", enableWrite18=enableWrite18)\nend\n\nend\n", "meta": {"hexsha": "4571ca39cc41e1b569f86230435bb9d7936df420", "size": 816, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TikzCDs.jl", "max_stars_repo_name": "epatters/TikzCDs.jl", "max_stars_repo_head_hexsha": "869328e22db58bd93e1ee1e5fc1be67a179ef7ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/TikzCDs.jl", "max_issues_repo_name": "epatters/TikzCDs.jl", "max_issues_repo_head_hexsha": "869328e22db58bd93e1ee1e5fc1be67a179ef7ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/TikzCDs.jl", "max_forks_repo_name": "epatters/TikzCDs.jl", "max_forks_repo_head_hexsha": "869328e22db58bd93e1ee1e5fc1be67a179ef7ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.64, "max_line_length": 106, "alphanum_fraction": 0.7450980392, "num_tokens": 244, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4804786780479071, "lm_q2_score": 0.1052105416129368, "lm_q1q2_score": 0.050551421950888194}}
{"text": "@show Threads.nthreads()\nimport Pkg\nPkg.instantiate()\nPkg.status(mode=Pkg.PKGMODE_MANIFEST)\nusing NEOs\n\nusing EarthOrientation, SatelliteToolbox\n#EarthOrientation.update()\nEarthOrientation.update(force = true)\nget_iers_eop(force_download = true)\n", "meta": {"hexsha": "2348d8fc6c90ff97bd552270603a251cc975308d", "size": 246, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/setup.jl", "max_stars_repo_name": "LuEdRaMo/NEOs.jl", "max_stars_repo_head_hexsha": "d64a74741983bdb0692dc14ad3e1a4166e711a89", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/setup.jl", "max_issues_repo_name": "LuEdRaMo/NEOs.jl", "max_issues_repo_head_hexsha": "d64a74741983bdb0692dc14ad3e1a4166e711a89", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-10-01T16:45:42.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-12T02:23:25.000Z", "max_forks_repo_path": "scripts/setup.jl", "max_forks_repo_name": "LuEdRaMo/NEOs.jl", "max_forks_repo_head_hexsha": "d64a74741983bdb0692dc14ad3e1a4166e711a89", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-25T13:48:41.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T13:48:41.000Z", "avg_line_length": 22.3636363636, "max_line_length": 40, "alphanum_fraction": 0.825203252, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4804786780479071, "lm_q2_score": 0.10521052688338609, "lm_q1q2_score": 0.05055141487365314}}
{"text": "\"\"\"\n    print_sol!(x::BnBSolver,y::BnBModel,ubdcnt::Int64,lbdcnt::Int64,ubdtime::Float64,lbdtime::Float64)\n\nPrints solution information for the B&B problem. Displays first node found, solution value,\nsolution, and time spent solving subproblems.\n\"\"\"\nfunction print_sol!(x::BnBSolver,y::BnBModel,\n                           ubdcnt::Int64,lbdcnt::Int64,\n                           ubdtime::Float64,lbdtime::Float64)\n  temp = 0.0\n  println(\"First Solution Found at Node $(y.first_num)\")\n  if (x.Verbosity==\"Normal\"||x.Verbosity==\"Full\")\n    println(\"UBD = $(y.soln_val)\")\n    println(\"Solution is :\")\n    if (y.feas_fnd)\n      for i=1:length(y.Init_Box)\n        temp = y.soln[i]\n        println(\"    X[$i] = $temp\")\n      end\n    end\n    println(\"Total LBD problems solved = $lbdcnt in $lbdtime seconds.\")\n    println(\"Total UBD problems solved = $ubdcnt in $ubdtime seconds.\")\n    println(\"Total time spent preprocessing =  $(y.Pretime[end]) seconds.\")\n    println(\"Total time spent postprocessing = $(y.Posttime[end]) seconds.\")\n  end\nend\n\n\"\"\"\n    print_node!(x::BnBSolver,id::Int64,lbd::Float64,box::Vector{Interval{Float64}})\n\nPrints node information for the B&B problem. Node id, bound, and interval box.\n\"\"\"\nfunction print_node!(x::BnBSolver,id::Int64,lbd::Float64,\n                            box::Vector{Interval{V}}) where {V<:AbstractFloat}\n  if (x.Verbosity == \"Full\")\n    println(\"Node ID: $(id), Lower Bound: $(lbd), IntervalBox: $(box)\")\n  end\nend\n\nfunction print_node!(x::BnBSolver,id::Int64,lbd::Float64,\n                            box::Vector{MCInterval{V}}) where {V<:AbstractFloat}\n  if (x.Verbosity == \"Full\")\n    println(\"Node ID: $(id), Lower Bound: $(lbd), IntervalBox: $(box)\")\n  end\nend\n\n\"\"\"\n    print_int!(B::BnBSolver,k_int::Int64,k_nod::Int64,nid::Int64,lbdp::Float64,lbd::Float64,ubd::Float64,feasL::Bool,feasU::Bool)\n\nPrints the iteration information if the Verbosity is set to \"Normal\" or \"Full\".\nThe header is displayed every hdr_intv, the iteration info is displayed every\nitr_intv\n\"\"\"\nfunction print_int!(B::BnBSolver,k_int::Int64,k_nod::Int64,\n                           nid::Int64,lbdp::Float64,lbd::Float64,ubd::Float64,feasL::Bool,feasU::Bool)\n  if ((B.Verbosity == \"Full\")||(B.Verbosity == \"Normal\"))\n    # prints header line every B.hdr_intv times\n    if (mod(k_int,B.hdr_intv)==0||k_int==1)\n      println(\"Iteration   NodeID    Current_LBD     Global_LBD     Global_UBD      NodesLeft     Absolute_Gap    Absolute_Ratio     LBD_Feas     UBD_Feas\")\n    end\n    # prints iteration summary every B.itr_intv times\n    sbool1 = feasL ? \"true\" : \"false\"\n    sbool2 = feasU ? \"true\" : \"false\"\n    if ((mod(k_int,B.itr_intv)==0))\n      ptr_arr_temp = [k_int nid lbdp lbd ubd k_nod abs(ubd-lbd) abs(ubd-lbd)/(min(abs(lbd),abs(ubd))) sbool1 sbool2]\n      ptr_arr1 = join([@sprintf(\"%6u\",x) for x in ptr_arr_temp[1:2]], \",   \")\n      ptr_arr2 = join([@sprintf(\"%3.7f\",x) for x in ptr_arr_temp[3:5]], \",     \")\n      ptr_arr3 = join([@sprintf(\"%6u\",x) for x in ptr_arr_temp[6:6]], \",\")\n      ptr_arr4 = join([@sprintf(\"%3.7f\",x) for x in ptr_arr_temp[7:8]], \",       \")\n      ptr_arr5 = join([@sprintf(\"%s\",x) for x in ptr_arr_temp[9:10]], \",       \")\n      println(string(ptr_arr1,\",      \",ptr_arr2,\",      \",ptr_arr3),\",        \", ptr_arr4,\",        \", ptr_arr5)\n    end\n  end\nend\n\n\"\"\"\n    print_results!(B::BnBSolver,sol::Float64,pnt,feas::Bool,lbd_bool::Bool)\n\nPrints the results of a single bounding problem.\n\"\"\"\nfunction print_results!(B::BnBSolver,sol::Float64,pnt,feas::Bool,lbd_bool::Bool)\n  if (B.Verbosity == \"Full\")\n    if (lbd_bool)\n      println(\"Lower Bound: $(sol), Solution: $(pnt), Feasibility: $(feas)\")\n    else\n      println(\"Upper Bound: $(sol), Solution: $(pnt), Feasibility: $(feas)\")\n    end\n  end\nend\n", "meta": {"hexsha": "fd6c5cb812b818010031fe823a014b6e2975622e", "size": 3755, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/src/utils/Display.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/EAGOBranchBound.jl-9145a7a4-36c4-5f80-8e8e-59721dad6c2e", "max_stars_repo_head_hexsha": "99da31dfe81429b7648091463258b0e8328dc915", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-07-18T14:15:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-05T16:54:27.000Z", "max_issues_repo_path": "src/src/utils/Display.jl", "max_issues_repo_name": "UnofficialJuliaMirrorSnapshots/EAGOBranchBound.jl-9145a7a4-36c4-5f80-8e8e-59721dad6c2e", "max_issues_repo_head_hexsha": "99da31dfe81429b7648091463258b0e8328dc915", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2018-03-05T01:19:57.000Z", "max_issues_repo_issues_event_max_datetime": "2018-03-06T03:14:22.000Z", "max_forks_repo_path": "src/src/utils/Display.jl", "max_forks_repo_name": "PSORLab/EAGOBranchBound.jl", "max_forks_repo_head_hexsha": "f3eaa3a4c9d970e3efc00515466f48a018eb7ded", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.7222222222, "max_line_length": 156, "alphanum_fraction": 0.6292942743, "num_tokens": 1187, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1008786267847576, "lm_q1q2_score": 0.0504393133923788}}
{"text": "# Date: July 1, 2018\n# Author: Bernhard K\u00f6nig\n\n############################################################\n# Title: Boosting Model for the French MTPL Data\n############################################################\n\n# We suggest that you first go through the frenchMTPLdata_single_tree.jl tutorial before doing this tutorial\n\n##############################\n# Load packages\n##############################\nt0 = time_ns()\ncd(joinpath(GLOBAL_julia_code_folder, \"DecisionTrees.jl\"))\n# @info(\"You may want to run 'pkg> instantiate' when you first run this. Use ] to enter the package mode.\")\n\nusing Distributed\n# Distributed.@everywhere using Revise\nDistributed.@everywhere import CSV\nDistributed.@everywhere import DataFrames\nDistributed.@everywhere import DataFrames: DataFrame\n\nDistributed.@everywhere using DecisionTrees  \n\ntela = (time_ns() - t0) / 1e9\n@show tela # precompilation may take some time\n\n\n##############################\n# Read the data\n##############################\ndatafile = joinpath(\"data\", \"freMTPL2\", \"freMTPL2.csv\")\n@assert isfile(datafile);\n@time fullData = CSV.read(datafile, DataFrame, strict=true);\n\n# the data is described in the R package CASdatasets\n# also the Noll, Salzmann, W\u00fcthrich Paper has some descriptive graphs of the data.\n\n# add AreaInteger as new variable, i.e. A:F -> 1:6 (as suggested in the paper of W\u00fcthrich, Noll, Salzmann)\nareasSorted = sort(unique(fullData[:Area]))\nAreaInteger = map(x->findall((in)([x]), areasSorted)[1], fullData[:Area])\nfullData[:AreaInteger] = AreaInteger\n\n# correct for unreasonable observations\nfor i = 1:size(fullData, 1)\n    fullData[:ClaimNb][i] = min(4, fullData[:ClaimNb][i])\n    fullData[:Exposure][i] = min(1.0, fullData[:Exposure][i])\nend\n    \n# set independent variables\nselected_explanatory_vars = [\"Area\",\"AreaInteger\",\"VehPower\",\"VehAge\",\"DrivAge\",\"BonusMalus\",\"VehBrand\",\"VehGas\",\"Density\",\"Region\"]\n# note: we refrain from using the exposure as explanatory variable, as this is not meaningful in practical applications\n# where one aims to predict the claim frequency of a given policy (without knowing how long that policy will remain within a certain portfolio)\n\n# check type of each column\nfor x in selected_explanatory_vars\n    println(x)\n    println(eltype(fullData[Symbol(x)]))\n    println(\"first ten values\")\n    print(fullData[1:10,Symbol(x)])\n    println(\"\")\nend\n\n# consider unique elements for each variable\nfor x in 1:size(fullData, 2)\n    println(x)\n    @show size(unique(fullData[:,x]))\nend\n\n##############################\n# Prepare the data\n##############################\n\n# dtmtable::DTMTable is a custom data format for DecisionTrees\n# sett are the model settings\n# dfprepped is an intermediary dataframe which is generally not needed\ndtmtable, sett, dfprepped = prepare_dataframe_for_dtm!(fullData, keycol=\"IDpol\", trnvalcol=\"trnTest\", numcol=\"ClaimNb\", denomcol=\"Exposure\", weightcol=\"Exposure\", independent_vars=selected_explanatory_vars);\n# consider \nfieldnames(typeof(dtmtable))\ndtmtable.key # an identifier (String type), ideally it is a unique identifier for each row\ndtmtable.numerator # a vector\ndtmtable.denominator # a vector\ndtmtable.weight # a vector\n# each of the above have the same length\n\ndtmtable.trnidx # an index vector for the training data. This is an integer vector\n# therefore we generally have\nlength(dtmtable.trnidx) < length(dtmtable.numerator)\n# dtmtable.validx is the corresponding validation data\n# we trnidx and validx should generally be a partition of 1:n (where n==length(dtmtable.weights))\n\ndtmtable.features # explanatory variables\n# note that the data is generally stored in a compressed format\n# for numerical variables, the algorithm considers sett.maxSplittingPoints  (default 250) splitting points which are uniformly spaced\n# you can increase this number and re-prepare the data if you want\n#= \n    sett.maxSplittingPoints=100\n    dtmtable=prep_data_from_df(df_prepped,sett,fn) =#\n# we realize that it may be sutiable to define the splitting in a different manner (than uniformly spaced).\n# this feature might be added at a later time. You can consider the function add_coded_numdata!(...) to see how splitting points are chosen\n\noriginalTrnValIndex = deepcopy(fullData[:trnTest])\n\n# Redefine trn and val data sets\n# if you prefer train on X% of the data, you can use this function to adjust the training set. By default it is sampled randomly\n# resample_trnvalidx!(dtmtable,.85)\n\n##############################\n# Define model SETTINGS\n##############################\n\n# the minimum weight of each individual tree shall be 3% of the exposure per leaf\n# You can set minWeight to any positive value, e.g. sett.minWeight=20000\n# if minWeight is set to a value in [-1,0] its absolute value is interpreted as a percentage\n# thus with sett.minWeight=-0.03 we define the min weight of each leaf as 3% of the training data\n\n# model_type: currently only \"build_tree\" and \"boosted_tree\" are supported\n# we might add the implementation of bagging at a later stage\n\n# iterations is the number of trees\n\n# learningRate is the moderation factor which is also know as shrinkage or learning rate\n\n# subsampling_features_prop must be between 0 and 1 and defines the subsampling probability of the predictors for each tree \n# i.e. with subsampling_features_prop=0.5 each tree will only use half of the predictors\n\n# calculatePoissonError, is a boolean which we need to enable here in order for output to show the poisson error of the estimates\nupdateSettingsMod!(sett,minWeight=-0.03,model_type=\"boosted_tree\",iterations=40,learningRate=0.05,subsampling_features_prop=.7,calculatePoissonError=true)\n\n##############################\n# Run single Boosting Model\n##############################\n\n# This model might take few minutes to run.\n# resM is the resulting Model\nresultingFiles, resM = dtm(dtmtable, sett)\n# consider the resulting Excel file for a descritipon of the model \n# it should be located here:\n@show resultingFiles[2]\n\n# if you prefer to analyze the data in Julia instead of excel, consider\nsheetnames = map(x->x.name, resM.exceldata.sheets)\n# the following is the ModelStatistics sheets as a Dataframe \nresM.exceldata.sheets[3].data\n# this is the lift in the training data for each iteration of the boosting model\nresM.exceldata.sheets[3].data[:x6][1:sett.iterations + 1]\n\n############################################################\n# Investigate the predictions (fitted values)\n############################################################\n\n# DecisionTrees Boosting Models provide a number of different estimates\n# 1. Fitted values based on the \"raw relativities\"\n# this is simply the combination of all decision trees\n\n# 2. Smoothed fitted values per Score. \n# the user can specify the number of scores for the output (see sett.nScores which defaults to 1000)\n# DecisionTrees runs a moving average over the observed data to smooth the noise\n\n# 3. Unsmoothed fitted values per Score. \n# As the score is merely an aggregation of the resulting 'relativities' (i.e. the spread of the fitted frequencies by the ensbemble)\n# one can calculate the observed frequency for each score. Here, no smoothing is performed\n\n\n# If you want, you can consider the function below to get the fitted value\n# rawFittedValues,smoothedFittedValuesPerScore,unsmoothedFittedValuesPerScore=getFittedValues(reM)\n# in the following lines we derive the fitted values from the resM struct \n\n# We will show the different fitted values in the following\n\n# 1. Fitted values based on the \"raw relativities\"\n# for the fitted values, consider:\nfitted = resM.meanobserved .* resM.rawrelativities\n# resM.meanobserved is the mean observed value of the training data\n# rawrelativities is the estimated relative deviation from the mean for each observation\n# Note:as for the input data the fitted vector corresponds to all the data.\n# dtmtable.validx determines which observations correspond to the validation data\n\n# Let us calculate the Poisson Error for these estimates\nerrs = poissonError(dtmtable.numerator, fitted .* dtmtable.denominator);\ntrnAvgError = sum(errs[dtmtable.trnidx]) / length(dtmtable.trnidx) # should be around 0.3104379280001656\nvalAvgError = sum(errs[dtmtable.validx]) / length(dtmtable.validx) # should be around 0.32060219905526594\n\n# 2. Smoothed fitted values per Score. \nscores = resM.scores\nfittedThroughScores = zeros(Float64, length(scores))\nfor i = 1:length(scores)\n    @inbounds sc = scores[i]\n    @inbounds fittedThroughScores[i] = resM.ScoreToSmoothedEstimate[sc]\nend\n\nfitted = fittedThroughScores\n# notably for these fitted values we have less unique values\n@show unique(fitted)\n# this should correspond to sett.nScores\nerrs = poissonError(dtmtable.numerator, fitted .* dtmtable.denominator);\ntrnAvgError = sum(errs[dtmtable.trnidx]) / length(dtmtable.trnidx) # 0.30909682928383053\nvalAvgError = sum(errs[dtmtable.validx]) / length(dtmtable.validx) # 0.3192856166040531\n\n# 3. (raw) Observed frequency per Score\n# As the score is merely an aggregation of the resulting 'relativities' (i.e. the spread of the fitted frequencies by the ensbemble)\n# one can calculate the observed frequency for each score. Here, no smoothing is performed\nscores = resM.scores\nrawFittedThroughScores = zeros(Float64, length(scores))\nfor i = 1:length(scores)\n    @inbounds sc = scores[i]\n    @inbounds rawFittedThroughScores[i] = resM.rawObservedRatioPerScore[sc]\nend\n\n# Again, let us consider the Poisson Error\nfitted = rawFittedThroughScores\nerrs = poissonError(dtmtable.numerator, fitted .* dtmtable.denominator);\ntrnAvgError = sum(errs[dtmtable.trnidx]) / length(dtmtable.trnidx) # 0.30523053930420263\nvalAvgError = sum(errs[dtmtable.validx]) / length(dtmtable.validx) # 0.31953356919603665\n\n# Unseen data\n# Note: For any out of sample data (which MUST have exactly the same structure as dtmtable.features!)\n# you can also use the predict function\nestimatedFrequencieForFirst20Obs = predict(resM, dtmtable.features[1:20,:])\n# getting new/unseen data into the right format is not trivial at the moment. It is easiset to use prepare_dataframe_for_dtm! to the \n# whole data set and then discrard possible hold out observations  (or mark as validation through validx)\n\n# Let us try a more granular model with more iterations and a higher learning rate\nupdateSettingsMod!(sett,minWeight=-0.01,iterations=100,learningRate=0.1,subsampling_features_prop=1.0,calculatePoissonError=true)\nresultingFiles, resMGranular = dtm(dtmtable, sett)\n\n# todo write down error metrics of this model\n\n# Let us calculate its error\nfitted = resMGranular.meanobserved .* resMGranular.rawrelativities\n# Let us calculate the Poisson Error for these estimates\nerrs = poissonError(dtmtable.numerator, fitted .* dtmtable.denominator);\ntrnAvgError = sum(errs[dtmtable.trnidx]) / length(dtmtable.trnidx) # should be around 0.296411226074372\nvalAvgError = sum(errs[dtmtable.validx]) / length(dtmtable.validx) # should be around 0.3129144168276613\n\n# The model is slightly better than the previous model.\n# Let us consider a cross validation of this model.\ncvsampler = CVOptions(-5, 0.0, true)\n# this will run 5 models and thus might take some time\nstatsdf, settsdf, cvModels = dtm(dtmtable, sett, cvsampler)\n\n# todo continue here\n# Let us attach the scores to the data and write it to disk.\n# We will then fit a GLM in R with and without the scores as predictor\nfullDataWithScores = deepcopy(fullData);\nfullDataWithScores[:scores] = resMGranular.scores;\nwritetable(joinpath(\"data\", \"freMTPL2\", \"freMTPL2_withJuliaBoostingScores1000.csv\"),fullDataWithScores)\n\n############################################################\n# Additional options\n############################################################\n\n# You can modify the settings directly, but should generally not do so.\nsett.iterations = 4\n# It is preferred to use the update function to avoid creating an 'invalid' setting (such as a negative number of iterations)\n# e.g. this will throw an error (whereas sett.iterations=-3 does not)\n# updateSettingsMod!(sett,iterations=-3) \n\n# you can modify the number of scores which are derived\nupdateSettingsMod!(sett,nScores=10000) \n# let us set it back to 1000\nupdateSettingsMod!(sett,nScores=1000) \n# The resulting Excel ouput shows a number of graphs per score band\n# if you are interested in the tails of the data, you many want to pick more granular score bands\n# the default is this\nsett.scorebandsstartingpoints\n# you can set them manually by defining the 'start' of each band\nupdateSettingsMod!(sett,scorebandsstartingpoints=[1,25,50,75,100,300,500,800,900,925,950,975])\nsett.scorebandsstartingpoints\n# at the moment you will need to re run the hwole model if you amend nScores or scorebands\nupdateSettingsMod!(sett,iterations=20)\nresultingFiles, resM = dtm(dtmtable, sett)\n\n# SOME COMMENT ON MANY UNUSED AND EXPERMIANTAL options\n# if you want to use the resutling tree in a different programming language you may want to\n# consider these options which give you the core structure of the model in other languages\n# updateSettingsMod!(sett,writeCsharpCode=\"true\",writeVbaCode=\"true\",writeSasCode=\"true\")\n\n# mention DOT graph\n\n############################################################\n# Prepare a grid search over the parameter space\n############################################################\n\n# set some default settings\nupdateSettingsMod!(sett,\niterations=250,\nmodel_type=\"boosted_tree\",\nnScores=\"1000\",\nwriteStatistics=\"true\",\nwriteSasCode=\"false\",\nwriteIterationMatrix=\"false\",\nwriteResult=\"false\",\nwriteCsharpCode=\"false\",\nwriteVbaCode=\"false\",\nsaveJLDFile=\"false\",\nsaveResultAsJLDFile=\"false\",\n\nshowProgressBar_time=\"true\",\nprroduceEstAndLeafMatrices=\"false\",\nwrite_dot_graph=\"false\",\n\ncalculatePoissonError=\"true\",\nperformanceMeasure=\"Average Poisson Error Val\"\n)\n\n# performanceMeasure will determine which metric is considered for the output summary \n# performanceMeasure must be in DecisionTrees.global_statsperiter_header\n# you may want to consider the Excel file of any boosting to get an idea of the available options for performanceMeasure \n# consider the first row of the sheet ModelStatistics of the Excel file which lists all measures\n# We note that some missing (or constant) measures are either not fully implemented yet, or were not calculated (e.g. because a boolean in the settings was set to false)\n@show sett.performanceMeasure\n@assert in(sett.performanceMeasure, DecisionTrees.global_statsperiter_header)\n\n# create a vector of ModelSettings\n# note, we can provide an arbitrary number of vectors over which we want to loop\n# the function createGridSearchSettings will copy the 'input' settings X times into a vector of settings\n# X is the size of the cartesian product of all parameter vectors over which we want to loop.\n\nsettV = createGridSearchSettings(sett,    \n    minWeight=[-0.03,-0.01,-0.005,-0.0025]\n    ,learningRate=[0.1,0.05],\n    subsampling_features_prop=[.5,.7,1.0],\n    crit=[\"poissondeviance\", \"difference\"])\n    # ,   subsampling_prop=[1.0,.5,.7]);\n\n# consider the length(settV) which is the number of models that will be run\nlength(settV)\n\n# depending on the size of settV, you may want to restart Julia with additional workers for parallelization, i.e. \n# shell> julia -p 8 \n# to start julia with 8 workers. Depending on your CPU you may want to choose a different number\n\nSys.CPU_CORES # might be give you an indication of the number of workers() you could use\n\n############################################################\n# Run grid search\n############################################################\n\n@warn \"This may take quiet some time depending on length(settV)=$(length(settV))\"\n@info \"Starting grid search...\"\n\ntt0 = time_ns()\nstatsdf, settsdf, allmodels = dtm(dtmtable, settV)\n@show ela = (-tt0 + time_ns()) / 1e9\n@info \".....done\"\n\n# Consider the resulting vector of models 'allmodels' \n# We apply addtional functions on these results, e.g.\nfittedValuesRawRelativities = map(x->x.meanobserved .* x.rawrelativities, allmodels);\n\n# in the following we vill evaluate the Poisson Error for the LAST iteration of the model\n# Note: this is not the 'BEST' iteration, but the last one.\nvalAvgErrsMultirun = zeros(length(fittedValuesRawRelativities))\nfor i = 1:length(fittedValuesRawRelativities)    \n    fitted = fittedValuesRawRelativities[i]\n    errs = poissonError(dtmtable.numerator, fitted .* dtmtable.denominator);\n    trnAvgError = sum(errs[dtmtable.trnidx]) / length(dtmtable.trnidx)\n    valAvgError = sum(errs[dtmtable.validx]) / length(dtmtable.validx)\n    valAvgErrsMultirun[i] = valAvgError\nend \n\n@show bestModel = argmin(valAvgErrsMultirun)\nbestSetting = settsdf[bestModel,:]\n# this data should be consistent with the data in the Excel output\n\n    # crit::SplittingCriterion # fn version of 4\n\n\n############################################################\n# Partial Dependence Plots\n############################################################\n# this is currently in development and in need of review  \n# NOTE: the function is very slow. This will take a few minutes\n# pds=partialDependence(resM,dtmtable,listOfFeatures=[:Density,:VehGas])\n# pds[\"Density\"]\n    ", "meta": {"hexsha": "a27593ca21ef70375107c8bcbb7dc9a52f595f8e", "size": 17104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "tutorials/2.frenchMTPLdata_boosting.jl", "max_stars_repo_name": "kafisatz/DecisionTrees.jl", "max_stars_repo_head_hexsha": "f36e55da257d3a58c6eb9ac4ba98e396ddb7e1bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2018-08-05T03:16:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-06T10:00:56.000Z", "max_issues_repo_path": "tutorials/2.frenchMTPLdata_boosting.jl", "max_issues_repo_name": "kafisatz/DecisionTrees.jl", "max_issues_repo_head_hexsha": "f36e55da257d3a58c6eb9ac4ba98e396ddb7e1bc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2018-06-19T07:50:36.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-10T16:08:11.000Z", "max_forks_repo_path": "tutorials/2.frenchMTPLdata_boosting.jl", "max_forks_repo_name": "kafisatz/DecisionTrees.jl", "max_forks_repo_head_hexsha": "f36e55da257d3a58c6eb9ac4ba98e396ddb7e1bc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.3687002653, "max_line_length": 207, "alphanum_fraction": 0.7301800748, "num_tokens": 4244, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.5, "lm_q2_score": 0.1008786186753556, "lm_q1q2_score": 0.0504393093376778}}
{"text": "\"\"\"\n    Queue\n\nA simple queue interface based on a doubly linked list.\n\"\"\"\nstruct Queue{T <: DLinkedList}\n    data::T\nend\n\n\n\"\"\"\n    enqueue!(q::Queue{DLinkedList{T}}, x::T)\n\nEnqueue element (last in) the queue. Time complexity O(1).\n\"\"\"\nfunction enqueue!(q::Queue{DLinkedList{T}}, x::T) where T\n    addlast!(q.data, x)\n    return nothing\nend\n\n\n\"\"\"\n    dequeue!(q::Queue)\n\nRemove first element from the queue. Time complexity O(1).\n\"\"\"\ndequeue!(q::Queue) = removefirst!(q.data)\n\n\n\"\"\"\n    peek(q::Queue)\n\nLookup first queue element, don't remove.\n\"\"\"\npeek(q::Queue) = peekfirst(q.data)\n\n\n\"\"\"\n    length(q::Queue)\n\nGet size of queue.\n\"\"\"\nlength(q::Queue) = length(q.data)\n\n\n\"\"\"\n    size(q::Queue)\n\nGet size of queue.\n\"\"\"\nsize(q::Queue) = length(q)\n\n\n\"\"\"\n    isempty(q::Queue)\n\nCheck if queue is empty.\n\"\"\"\nisempty(q::Queue) = length(q) == 0", "meta": {"hexsha": "c38b500490d7ddadd3b8a998642865e89528f450", "size": 837, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/queue.jl", "max_stars_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_stars_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/queue.jl", "max_issues_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_issues_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/queue.jl", "max_forks_repo_name": "mgcth/DataStructuresAlgorithms.jl", "max_forks_repo_head_hexsha": "3bc8a1eef89cfffcdf977bf6efbd15580777d929", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.186440678, "max_line_length": 58, "alphanum_fraction": 0.6212664277, "num_tokens": 231, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4532618480153861, "lm_q2_score": 0.11124119619495342, "lm_q1q2_score": 0.05042139016276672}}
{"text": "# Display an array in the form\n# [ -3.0e-01 -5.1e-01  1.9e-01 ... -2.3e-01 -4.4e-01  2.4e-01 ]\n# with ndisp/2 elements on each side.\nfunction vec2str(x :: Array{Float64,1}; ndisp :: Int=7)\n  n = length(x);\n  if n <= ndisp\n    ndisp = n;\n    nside = n;\n  else\n    nside = max(1, div(ndisp - 1, 2));\n  end\n  s = \"[ \";\n  i = 1;\n  while i <= nside\n    s *= @sprintf(\"%8.1e \", x[i]);\n    i += 1;\n  end\n  if i <= div(n, 2)\n    s *= \"... \";\n  end;\n  i = max(i, n - nside + 1);\n  while i <= n\n    s *= @sprintf(\"%8.1e \", x[i]);\n    i += 1;\n  end\n  s *= \"]\";\n  return s;\nend\n", "meta": {"hexsha": "bc7266d66a9fb4c303278760311e9a162ccfd316", "size": 566, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/krylov_utils.jl", "max_stars_repo_name": "abelsiqueira/Krylov.jl", "max_stars_repo_head_hexsha": "dc0ca5466f7f1f7e65958fe016e3a06b858e3df0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-06T18:14:15.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-06T18:14:15.000Z", "max_issues_repo_path": "src/krylov_utils.jl", "max_issues_repo_name": "abelsiqueira/Krylov.jl", "max_issues_repo_head_hexsha": "dc0ca5466f7f1f7e65958fe016e3a06b858e3df0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/krylov_utils.jl", "max_forks_repo_name": "abelsiqueira/Krylov.jl", "max_forks_repo_head_hexsha": "dc0ca5466f7f1f7e65958fe016e3a06b858e3df0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-06-05T10:58:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-05T10:58:57.000Z", "avg_line_length": 19.5172413793, "max_line_length": 63, "alphanum_fraction": 0.4681978799, "num_tokens": 248, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45326183324442865, "lm_q2_score": 0.11124119472172639, "lm_q1q2_score": 0.05042138785187016}}
{"text": "# This file contains SDESystem prototypes\n\nimport DifferentialEquations: LambaEM, SDEProblem\nimport UUIDs: uuid4\n\n\"\"\"\n    @def_sde_system ex \n\nwhere `ex` is the expression to define to define a new AbstractSDESystem component type. The usage is as follows:\n```julia\n@def_sde_system mutable struct MySDESystem{T1,T2,T3,...,TN,OP,RH,RO,ST,IP,OP} <: AbstractSDESystem\n    param1::T1 = param1_default                 # optional field \n    param2::T2 = param2_default                 # optional field \n    param3::T3 = param3_default                 # optional field\n        \u22ee\n    paramN::TN = paramN_default                 # optional field \n    drift::DR = drift_function                  # mandatory field\n    diffusion::DF = diffusion_function          # mandatory field\n    readout::RO = readout_functtion             # mandatory field\n    state::ST = state_default                   # mandatory field\n    input::IP = input_default                   # mandatory field\n    output::OP = output_default                 # mandatory field\nend\n```\nHere, `MySDESystem` has `N` parameters. `MySDESystem` is represented by the `drift`, `diffusion` and `readout` function. `state`, `input` and `output` is the initial state, input port and output port of `MySDESystem`.\n\n!!! warning \n    `drift` must have the signature \n    ```julia\n    function drift((dx, x, u, t, args...; kwargs...)\n        dx .= .... # update dx\n    end\n    ```\n    and `diffusion` must have the signature \n    ```julia\n    function diffusion((dx, x, u, t, args...; kwargs...)\n        dx .= .... # update dx\n    end\n    ```\n    and `readout` must have the signature \n    ```julia\n    function readout(x, u, t)\n        y = ...\n        return y\n    end\n    ```\n\n!!! warning \n    New SDE system must be a subtype of `AbstractSDESystem` to function properly.\n\n# Example \n```julia \njulia> @def_sde_system mutable struct MySDESystem{DR, DF, RO, IP, OP} <: AbstractSDESystem\n           \u03b7::Float64 = 1.\n           drift::DR = (dx, x, u, t) -> (dx .= x)\n           diffusion::DF = (dx, x, u, t, \u03b7=\u03b7) -> (dx .= \u03b7)\n           readout::RO = (x, u, t) -> x \n           state::Vector{Float64} = rand(2) \n           input::IP = nothing \n           output::OP = Outport(2)\n       end\n\njulia> ds = MySDESystem();\n```\n\"\"\"\nmacro def_sde_system(ex) \n    checksyntax(ex, :AbstractSDESystem)\n    appendcommonex!(ex)\n    foreach(nex -> appendex!(ex, nex), [\n        :( alg::$ALG_TYPE_SYMBOL = Causal.LambaEM{true}() ), \n        :( integrator::$INTEGRATOR_TYPE_SYMBOL = Causal.construct_integrator(Causal.SDEProblem, input, \n            (drift, diffusion), state, t, modelargs, solverargs; alg=alg, modelkwargs=modelkwargs, solverkwargs=solverkwargs, numtaps=3) ) \n        ])\n    quote \n        Base.@kwdef $ex \n    end |> esc \nend\n\n\n##### Define SDE system library\n\n\"\"\"\n    SDESystem(; drift, diffusion, readout, state, input, output) \n\nConstructs a SDE system. \n\"\"\"\n@def_sde_system mutable struct SDESystem{DR, DF, RO, ST, IP, OP} <: AbstractSDESystem \n    drift::DR \n    diffusion::DF \n    readout::RO \n    state::ST \n    input::IP \n    output::OP \nend\n\n@doc raw\"\"\"\n    NoisyLorenzSystem() \n\nConstructs a noisy Lorenz system \n\"\"\"\n@def_sde_system mutable struct NoisyLorenzSystem{ET, DR, DF, RO, IP, OP} <: AbstractSDESystem\n    \u03c3::Float64 = 10.\n    \u03b2::Float64 = 8 / 3\n    \u03c1::Float64 = 28.\n    \u03b7::ET = 1.\n    \u03b3::Float64 = 1.\n    drift::DR = function lorenzdrift(dx, x, u, t, \u03c3=\u03c3, \u03b2=\u03b2, \u03c1=\u03c1, \u03b3=\u03b3)\n        dx[1] = \u03c3 * (x[2] - x[1])\n        dx[2] = x[1] * (\u03c1 - x[3]) - x[2]\n        dx[3] = x[1] * x[2] - \u03b2 * x[3]\n        dx .*= \u03b3\n    end\n    diffusion::DF = (dx, x, u, t, \u03b7=\u03b7) -> (dx .= \u03b7)\n    readout::RO = (x, u, t) -> x\n    state::Vector{Float64} = rand(3)\n    input::IP = nothing \n    output::OP = Outport(3) \nend  \n\n@doc raw\"\"\"\n    NoisyLorenzSystem() \n\nConstructs a noisy Lorenz system \n\"\"\"\n@def_sde_system mutable struct ForcedNoisyLorenzSystem{ET, CM, DR, DF, RO, IP, OP} <: AbstractSDESystem\n    \u03c3::Float64 = 10.\n    \u03b2::Float64 = 8 / 3\n    \u03c1::Float64 = 28.\n    \u03b7::ET = 1.\n    cplmat::CM = I(3)\n    \u03b3::Float64 = 1.\n    drift::DR = function forcedlorenzdrift(dx, x, u, t, \u03c3=\u03c3, \u03b2=\u03b2, \u03c1=\u03c1, \u03b3=\u03b3, cplmat=cplmat)\n        dx[1] = \u03c3 * (x[2] - x[1])\n        dx[2] = x[1] * (\u03c1 - x[3]) - x[2]\n        dx[3] = x[1] * x[2] - \u03b2 * x[3]\n        dx .*= \u03b3\n        dx .+= cplmat * map(ui -> ui(t), u.itp)   # Couple inputs\n    end\n    diffusion::DF = (dx, x, u, t, \u03b7=\u03b7) -> (dx .= \u03b7)\n    readout::RO = (x, u, t) -> x\n    state::Vector{Float64} = rand(3)\n    input::IP = Inport(3) \n    output::OP = Outport(3) \nend  \n\n\n##### Pretty printing \nshow(io::IO, ds::SDESystem) = print(io, \n    \"SDESystem(drift:$(ds.drift), diffusion:$(ds.diffusion), readout:$(ds.readout), state:$(ds.state), t:$(ds.t), \",\n    \"input:$(ds.input), output:$(ds.output))\")\nshow(io::IO, ds::NoisyLorenzSystem) = print(io, \n    \"NoisyLorenzSystem(\u03c3:$(ds.\u03c3), \u03b2:$(ds.\u03b2), \u03c1:$(ds.\u03c1), \u03b7:$(ds.\u03b7), \u03b3:$(ds.\u03b3), state:$(ds.state), t:$(ds.t), \",\n    \"input:$(ds.input), output:$(ds.output))\")\nshow(io::IO, ds::ForcedNoisyLorenzSystem) = print(io, \n    \"ForcedNoisyLorenzSystem(\u03c3:$(ds.\u03c3), \u03b2:$(ds.\u03b2), \u03c1:$(ds.\u03c1), \u03b7:$(ds.\u03b7), \u03b3:$(ds.\u03b3), cplmat:$(ds.cplmat), \", \n    \"state:$(ds.state), t:$(ds.t), input:$(ds.input), output:$(ds.output))\")\n", "meta": {"hexsha": "f6fc4810a113e466868edfb13dedaf22599ce260", "size": 5217, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/components/systems/dynamicalsystems/sdesystems.jl", "max_stars_repo_name": "gs246/Causal.jl", "max_stars_repo_head_hexsha": "a667a24638415710333af760409566cc6f5dc681", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/components/systems/dynamicalsystems/sdesystems.jl", "max_issues_repo_name": "gs246/Causal.jl", "max_issues_repo_head_hexsha": "a667a24638415710333af760409566cc6f5dc681", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/components/systems/dynamicalsystems/sdesystems.jl", "max_forks_repo_name": "gs246/Causal.jl", "max_forks_repo_head_hexsha": "a667a24638415710333af760409566cc6f5dc681", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2292993631, "max_line_length": 217, "alphanum_fraction": 0.5689093349, "num_tokens": 1771, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.10230470857690284, "lm_q1q2_score": 0.05035316378992509}}
{"text": "using TikzPictures\nusing Test\n\nsvgBackends = [\n    \"testPic.pdf2svg.svg\" => PdfToSvgBackend(),\n    \"testPic.poppler.svg\" => PopplerBackend(),\n    \"testPic.dvisvgm.svg\" => DVIBackend()\n]\nif VERSION < v\"1.3\"\n    deleteat!(svgBackends, 2)\n    @warn \"Not testing PopplerBackend on Julia versions < 1.3\"\nend\n\n# Pre-test cleanup (for repeated tests)\nfor file in [\"testPic.pdf\", \"testPic.svg\", \"testDoc.pdf\", \"testDoc.tex\", first.(svgBackends)...]\n  if isfile(file)\n    rm(file)\n  end\nend\n\n# Run tests\ndata = \"\\\\draw (0,0) -- (10,10);\\n\\\\draw (10,0) -- (0,10);\\n\\\\node at (5,5) {tikz \\$\\\\sqrt{\\\\pi}\\$};\"\ntp = TikzPicture(data, options=\"scale=0.25\", preamble=\"\")\ntd = TikzDocument()\npush!(td, tp, caption=\"hello\")\n\nsave(TEX(\"testPic\"), tp)\n@test isfile(\"testPic.tex\")\n\n# check that the TEX file contains the desired environments\nfunction has_environment(content::String, environment::String)\n    has_begin = occursin(\"\\\\begin{$environment}\", content)\n    has_end = occursin(\"\\\\end{$environment}\", content)\n    if has_begin && has_end\n        return true # has both\n    elseif !has_begin && !has_end\n        return false # has neither\n    else\n        error(\"\\\\begin{$environment} and \\\\end{$environment} do not match\")\n    end\nend\n\nfilecontent = join(readlines(\"testPic.tex\", keep=true)) # read with line breaks\n@test occursin(data, filecontent) # also check that the data is contained\n@test has_environment(filecontent, \"tikzpicture\")\n@test has_environment(filecontent, \"document\")\n\n# same check for include_preamble=false and limit_to=:picture\nsave(TEX(\"testPic\"; include_preamble=false), tp)\nfilecontent = join(readlines(\"testPic.tex\", keep=true))\n@test occursin(data, filecontent)\n@test has_environment(filecontent, \"tikzpicture\") # must occur\n@test !has_environment(filecontent, \"document\") # must not occur\n\n# same check for limit_to=:data\nsave(TEX(\"testPic\"; limit_to=:data), tp)\nfilecontent = join(readlines(\"testPic.tex\", keep=true))\n@test occursin(data, filecontent)\n@test !has_environment(filecontent, \"tikzpicture\")\n@test !has_environment(filecontent, \"document\")\n\nsave(TEX(\"testPic\"), tp) # save again with limit_to=:all\nif success(`lualatex -v`)\n  save(PDF(\"testPic\"), tp)\n  @test isfile(\"testPic.pdf\")\n\n    save(SVG(\"testPic\"), tp)\n    @test isfile(\"testPic.svg\") # default SVG backend\n\n    @testset for (k, v) in svgBackends\n        svgBackend(v)\n        for usetectonic in [true, false]\n            if usetectonic && v == DVIBackend()\n                continue\n            end\n            tikzUseTectonic(usetectonic)\n            save(SVG(k), tp)\n            @test isfile(k)\n            rm(k)\n        end\n    end\n\n    save(PDF(\"testDoc\"), td)\n    @test isfile(\"testDoc.pdf\")\nelse\n    @warn \"lualatex is missing; can not test compilation\"\nend\n\n# Test tikz-cd\n\ndata = \"A\\\\arrow{rd}\\\\arrow{r} & B \\\\\\\\& C\"\ntp = TikzPicture(data, options=\"scale=0.25\", environment=\"tikzcd\", preamble=\"\\\\usepackage{tikz-cd}\")\ntd = TikzDocument()\npush!(td, tp, caption=\"hello\")\n\nsave(TEX(\"testCD\"), tp)\n@test isfile(\"testCD.tex\")\n\nfilecontent = join(readlines(\"testCD.tex\", keep=true)) # read with line breaks\n@test occursin(data, filecontent) # also check that the data is contained\n@test has_environment(filecontent, \"tikzcd\")\n@test has_environment(filecontent, \"document\")\n\nif success(`lualatex -v`)\n    save(PDF(\"testCD\"), tp)\n    @test isfile(\"testCD.pdf\")\n\n    save(PDF(\"testCDDoc\"), td)\n    @test isfile(\"testCDDoc.pdf\")\nelse\n    @warn \"lualatex is missing; can not test compilation\"\nend\n\n# Test adjustbox width/height\nadjustbox_width = TikzPicture(L\"\\node (sigmoid) [circle, draw=black, label=left:{$\\sigma(z)$}] {};\", width=\"2cm\")\nsave(TEX(\"adjustbox_width\"), adjustbox_width)\n@test isfile(\"adjustbox_width.tex\")\n\nadjustbox_height = TikzPicture(L\"\\node (sigmoid) [circle, draw=black, label=left:{$\\sigma(z)$}] {};\", height=\"10cm\")\nsave(TEX(\"adjustbox_height\"), adjustbox_height)\n@test isfile(\"adjustbox_height.tex\")\n\nadjustbox_aspect = TikzPicture(L\"\\node (sigmoid) [circle, draw=black, label=left:{$\\sigma(z)$}] {};\", height=\"10cm\", keepAspectRatio=false)\nsave(TEX(\"adjustbox_aspect\"), adjustbox_aspect)\n@test isfile(\"adjustbox_aspect.tex\")\n", "meta": {"hexsha": "43125eaa2dcaa80ecc014a106c2b3de1f0fa4b6a", "size": 4136, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaTeX/TikzPictures.jl", "max_stars_repo_head_hexsha": "9c35373ab794f5e31bdbf5e53b8ed13b7e37fe4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 56, "max_stars_repo_stars_event_min_datetime": "2018-08-16T09:47:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T06:27:18.000Z", "max_issues_repo_path": "test/runtests.jl", "max_issues_repo_name": "JuliaTeX/TikzPictures.jl", "max_issues_repo_head_hexsha": "9c35373ab794f5e31bdbf5e53b8ed13b7e37fe4c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 43, "max_issues_repo_issues_event_min_datetime": "2018-08-15T22:32:45.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-01T18:36:16.000Z", "max_forks_repo_path": "test/runtests.jl", "max_forks_repo_name": "JuliaTeX/TikzPictures.jl", "max_forks_repo_head_hexsha": "9c35373ab794f5e31bdbf5e53b8ed13b7e37fe4c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 21, "max_forks_repo_forks_event_min_datetime": "2018-08-24T16:55:10.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-09T08:47:59.000Z", "avg_line_length": 32.8253968254, "max_line_length": 139, "alphanum_fraction": 0.6823017408, "num_tokens": 1181, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.49218813572079556, "lm_q2_score": 0.1023047024186547, "lm_q1q2_score": 0.05035316075890842}}
{"text": "using MyPackage\r\nusing Test\r\n\r\n@testset \"my_func.jl\" begin\r\n    # Write your own tests here.\r\n    @test my_func(1,2) == 4\r\n    @test my_func(2,2) == 6\r\n    @test my_func(2,3) == 7\r\nend\r\n\r\n@testset \"derivative tests\" begin\r\n    @test derivative_of_my_func(2,1) == 2\r\nend\r\n", "meta": {"hexsha": "53fbeca0c0e672b5011edc957d7af79b29863ae8", "size": 271, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/my_func_tests.jl", "max_stars_repo_name": "XiaopingWu2020/MyPackage.jl", "max_stars_repo_head_hexsha": "5d3b414a871ff3a5db52a76979414a66284648e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/my_func_tests.jl", "max_issues_repo_name": "XiaopingWu2020/MyPackage.jl", "max_issues_repo_head_hexsha": "5d3b414a871ff3a5db52a76979414a66284648e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-05-16T03:03:54.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-16T03:03:54.000Z", "max_forks_repo_path": "test/my_func_tests.jl", "max_forks_repo_name": "XiaopingWu2020/MyPackage.jl", "max_forks_repo_head_hexsha": "5d3b414a871ff3a5db52a76979414a66284648e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.3571428571, "max_line_length": 42, "alphanum_fraction": 0.6199261993, "num_tokens": 96, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10374863240870875, "lm_q1q2_score": 0.05025377130975222}}
{"text": "include(\"colorscheme.jl\")\nusing PyPlot, DrWatson\nusing3D()\nexport rdspl, axis_zoomin!, add_identifiers!, nice_arrow!, figx, figy\n\nDrWatson._wsave(s, fig::Figure) = fig.savefig(s, dpi = 600, transparent = false)\n\n\"\"\"\n    rdspl(x, n = 3)\nRound `x` with `n` sigdigits for display purposes.\n\"\"\"\nrdspl(x::Real, n = 3) = round(x, sigdigits=n)\nrdspl(x::AbstractVector, n = 3) = Tuple((round.(Float64.(x); sigdigits=n)))\n\nPyPlot.rc(\"lines\", lw = 3)\nPyPlot.rc(\"errorbar\", capsize = 6)\nPyPlot.rc(\"axes\", grid = true)\nPyPlot.rc(\"grid\", color = \"0.75\", alpha = 0.75)\nPyPlot.rc(\"axes\", axisbelow=true) # makes grid behind other plotting elements\n\nMATHFONTSIZE = 34\nPyPlot.rc(\"font\", size = 28) # set default fontsize\nPyPlot.rc(\"xtick\", labelsize = 24)\nPyPlot.rc(\"ytick\", labelsize = 24)\nPyPlot.rc(\"axes\", labelsize = MATHFONTSIZE)\nPyPlot.rc(\"legend\", fontsize = 30, handlelength = 1)\n# PyPlot.rc(\"font\", family = \"Times New Roman\") # Serif main font\nPyPlot.rc(\"font\", family = \"DejaVu Sans\") # sans main font\n# PyPlot.rc(\"mathtext\", rm = \"sanserif\", fontset=\"dejavusans\") # sans math font\nPyPlot.rc(\"mathtext\", rm = \"serif\", fontset=\"cm\") # serif math font\n\nfor z in (\"x\", \"y\")\n    PyPlot.rc(\"$(z)tick.major\", size = 7, width = 1.2)\n    PyPlot.rc(\"$(z)tick.minor\", size = 3, visible = false)\nend\n\nPyPlot.rc(\"figure\", figsize = (figx, figy))\nPyPlot.rc(\"savefig\", dpi = 600, transparent = true, format = \"png\")\n\n# set default color cycle\nPyPlot.rc(\"axes\", prop_cycle = matplotlib.cycler(color=COLORS))\n\nif false # test font\n    figure(figsize=(10,8)); plot(rand(10), label = \"\\$a=\\\\int_0^\\\\infty xdx\\$\")\n    xlabel(\"x label\"); ylabel(\"\\$H_2\\$\"); legend(); tight_layout()\nend\n\nif false # test color scheme\n    fig = figure(figsize = (20, 15)) # show colors\n    ax1 = subplot(231)\n    ax2 = subplot(232)\n    ax3 = subplot(233)\n    ax4 = subplot(223)\n    ax5 = subplot(224)\n    lw = 60\n    L = length(COLORSCHEME)\n    for (i, c) in enumerate(COLORS)\n        chsv = matplotlib.colors.rgb_to_hsv(matplotlib.colors.to_rgb(c))\n        ax1.plot([0, 1], [0, 0] .+ i, color = c, lw = lw)\n        ax1.set_title(\"color\")\n        ax2.plot([0, 1], [0, 0] .+ i, color = string(chsv[3]), lw = lw)\n        ax2.set_title(\"brightness\")\n        ax3.plot([0, 1], [0, 0] .+ i, color = string(chsv[2]), lw = lw)\n        ax3.set_title(\"saturation\")\n        x = 0:0.05:5\u03c0\n        ax4.plot(x, rand(1:3) .* cos.(x .+ i/2) .+ rand(length(x))/5; color=c, lw = 2)\n        ax5.bar(collect(1:4) .+ (i-1)/L, 0.5rand(4) .+ 0.5, 1/L; color=c)\n    end\n    fig = tight_layout()\nend\n\nbbox = Dict(:boxstyle => \"round,pad=0.3\", :facecolor=>\"white\", :alpha => 1.0)\n\n\"`add_identifiers!(fig = gcf(), axs = fig.get_axes(); xloc, yloc)`\"\nfunction add_identifiers!(fig = gcf(), axs = fig.get_axes(); \n        xloc = 0.975, yloc = 1\n    )\n    for (i, ax) in enumerate(axs)\n        l = collect('a':'z')[i]\n        if ax.name \u2260 \"3d\"\n            ax.text(xloc, yloc, \"$(l)\"; transform = ax.transAxes,\n            bbox = bbox, zorder = 99, va = \"top\")\n        end\n    end\nend\n\n\"\"\"\n    nice_arrow!(ax, xc, yc, xspan, yspan;\n        style = \"<->\", tex = \"\", xo = 0.2, yo = -0.2, color = \"C0\"\n    )\n\"\"\"\nfunction nice_arrow!(ax, xc, yc, xspan, yspan;\n    style = \"<->\", tex = \"\", xo = 0.2, yo = -0.2, color = \"C0\")\n\n    ax.annotate(\"\";\n        xy=(xc-xspan/2, yc - yspan/2), xycoords=\"data\",\n        xytext=(xc+xspan/2, yc + yspan/2), textcoords=\"data\",\n        arrowprops = Dict(\n            :arrowstyle=>style,\n            :connectionstyle=>\"arc3\",\n            :lw=>1.5, :color => color),\n        zorder = 99, color\n    )\n    if tex != \"\"\n        ax.text(xc + xo, yc + yo, tex; va = :center, color)\n    end\nend\n\n\"\"\"\n    axis_zoomin!(zoomin, origin, zbox, rbox, co = \"C0\"; kwargs...)\nCreate a zoomin box connecting two axes, the `zoomin` and `origin`.\nThe zoom-in box is in the `origin` axis, while the `zoomin` axis is the\nbox. `rbox` is the enclosing box of the `zoomin` axes, while `zbox`\nis the small \"zoom-in\" box of the `origin` axis. They must be in the form\n((x1, y1), (x2, y2)). `co` is color `\u03b1` the alpha setting.\n\"\"\"\nfunction axis_zoomin!(zoomin, origin, zbox, rbox, co = \"C0\";\n    connect_lines = true, lw = 2.0, \u03b1 = 1.0, dir = :right, set_lims::Bool=true)\n    # plot box in zoomin axis\n    line, = zoomin.plot(\n        [rbox[1][1], rbox[2][1], rbox[2][1], rbox[1][1], rbox[1][1]],\n        [rbox[1][2], rbox[1][2], rbox[2][2], rbox[2][2], rbox[1][2]],\n        color=co, lw = lw, alpha = \u03b1\n    )\n    line.set_clip_on(false)\n    # plot box in origin axis\n    line, = origin.plot(\n        [zbox[1][1], zbox[2][1], zbox[2][1], zbox[1][1], zbox[1][1]],\n        [zbox[1][2], zbox[1][2], zbox[2][2], zbox[2][2], zbox[1][2]],\n        color=co, lw = lw, alpha = \u03b1\n    )\n    line.set_clip_on(false)\n\n    # xyA is zoomin axis, xyB is origin axis\n    if connect_lines\n    if dir == :right\n        for e in 1:2\n            con = matplotlib.patches.ConnectionPatch(\n            xyA = (rbox[1][1], rbox[e][2]), xyB=(zbox[2][1], zbox[e][2]),\n            coordsA=\"data\", coordsB=\"data\",\n            axesA = zoomin, axesB=origin, color=co, lw = lw, alpha = \u03b1)\n            con.set_clip_on(false)\n            zoomin.add_artist(con)\n        end\n    elseif dir == :top\n        for e in 1:2\n            con = matplotlib.patches.ConnectionPatch(\n            xyA = (rbox[e][1], rbox[1][2]), xyB=(zbox[e][1], zbox[2][2]),\n            coordsA=\"data\", coordsB=\"data\",\n            axesA = zoomin, axesB=origin, color=co, lw = lw, alpha = \u03b1)\n            con.set_clip_on(false)\n            zoomin.add_artist(con)\n        end\n    end\n    end\n    # Set limits for zoom axis\n    if set_lims\n        zoomin.set_xlim(zbox[1][1], zbox[2][1])\n        zoomin.set_ylim(zbox[1][2], zbox[2][2])\n    end\n    # remove axis for zoomin\n    zoomin.axis(\"off\")\nend\n\nfunction darken_color(color, amount=0.5)\n    c = matplotlib.colors.to_rgb(color)\n    c = matplotlib.colors.rgb_to_hsv(c)\n    c = (c[1], c[2], clamp(amount*c[3], 0, 1))\n    return matplotlib.colors.hsv_to_rgb(c)\nend\n", "meta": {"hexsha": "5b3d1839cbf311a0d8a33591bf909232ccb40b1b", "size": 5976, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/style.jl", "max_stars_repo_name": "JuliaDynamics/NonlinearDynamicsTextbook", "max_stars_repo_head_hexsha": "bfae8cf867f458f00151da089332f2ce3bea5dd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 88, "max_stars_repo_stars_event_min_datetime": "2021-07-18T20:54:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T09:23:53.000Z", "max_issues_repo_path": "src/style.jl", "max_issues_repo_name": "JuliaDynamics/NonlinearDynamicsTextbook", "max_issues_repo_head_hexsha": "bfae8cf867f458f00151da089332f2ce3bea5dd0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/style.jl", "max_forks_repo_name": "JuliaDynamics/NonlinearDynamicsTextbook", "max_forks_repo_head_hexsha": "bfae8cf867f458f00151da089332f2ce3bea5dd0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2021-07-28T18:49:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-24T08:45:11.000Z", "avg_line_length": 34.9473684211, "max_line_length": 86, "alphanum_fraction": 0.5749665328, "num_tokens": 2070, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.48438008427698437, "lm_q2_score": 0.10374862201684201, "lm_q1q2_score": 0.05025376627613893}}
{"text": "# # Machine Learning in Julia\n\n# An introduction to the\n# [MLJ](https://alan-turing-institute.github.io/MLJ.jl/stable/)\n# toolbox.\n\n\n# ### Set-up\n\n# Inspect Julia version:\n\nVERSION\n\n# The following instantiates a package environment.\n\n# The package environment has been created using **Julia 1.6** and may not\n# instantiate properly for other Julia versions.\n\nusing Pkg\nPkg.activate(\"env\")\nPkg.instantiate()\n\n\n# ## General resources\n\n# - [MLJ Cheatsheet](https://alan-turing-institute.github.io/MLJ.jl/dev/mlj_cheatsheet/)\n# - [Common MLJ Workflows](https://alan-turing-institute.github.io/MLJ.jl/dev/common_mlj_workflows/)\n# - [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/)\n# - [Data Science Tutorials in Julia](https://juliaai.github.io/DataScienceTutorials.jl/)\n\n\n# ## Part 1 - Data Representation\n\n# > **Goals:**\n# > 1. Learn how MLJ specifies it's data requirements using \"scientific\" types\n# > 2. Understand the options for representing tabular data\n# > 3. Learn how to inspect and fix the representation of data to meet MLJ requirements\n\n\n# ### Scientific types\n\n# To help you focus on the intended *purpose* or *interpretation* of\n# data, MLJ models specify data requirements using *scientific types*,\n# instead of machine types. An example of a scientific type is\n# `OrderedFactor`. The other basic \"scalar\" scientific types are\n# illustrated below:\n\n# ![](scitypes.png)\n\n# A scientific type is an ordinary Julia type (so it can be used for\n# method dispatch, for example) but it usually has no instances. The\n# `scitype` function is used to articulate MLJ's convention about how\n# different machine types will be interpreted by MLJ models:\n\nusing ScientificTypes\nscitype(3.141)\n\n#-\n\ntime = [2.3, 4.5, 4.2, 1.8, 7.1]\nscitype(time)\n\n# To fix data which MLJ is interpreting incorrectly, we use the\n# `coerce` method:\n\nheight = [185, 153, 163, 114, 180]\nscitype(height)\n\n#-\n\nheight = coerce(height, Continuous)\n\n# Here's an example of data we would want interpreted as\n# `OrderedFactor` but isn't:\n\nexam_mark = [\"rotten\", \"great\", \"bla\",  missing, \"great\"]\nscitype(exam_mark)\n\n#-\n\nexam_mark = coerce(exam_mark, OrderedFactor)\n\n#-\n\nlevels(exam_mark)\n\n# Use `levels!` to put the classes in the right order:\n\nlevels!(exam_mark, [\"rotten\", \"bla\", \"great\"])\nexam_mark[1] < exam_mark[2]\n\n# When sub-sampling, no levels are lost:\n\nlevels(exam_mark[1:2])\n\n# **Note on binary data.** There is no separate scientific type for\n# binary data. Binary data is `OrderedFactor{2}` or\n# `Multiclass{2}`. If a binary measure like `truepositive` is a\n# applied to `OrderedFactor{2}` then the \"positive\" class is assumed\n# to appear *second* in the ordering. If such a measure is applied to\n# `Multiclass{2}` data, a warning is issued. A single `OrderedFactor`\n# can be coerced to a single `Continuous` variable, for models that\n# require this, while a `Multiclass` variable can only be one-hot\n# encoded.\n\n\n# ### Two-dimensional data\n\n# Whenever it makes sense, MLJ Models generally expect two-dimensional\n# data to be *tabular*. All the tabular formats implementing the\n# [Tables.jl API](https://juliadata.github.io/Tables.jl/stable/) (see\n# this\n# [list](https://github.com/JuliaData/Tables.jl/blob/master/INTEGRATIONS.md))\n# have a scientific type of `Table` and can be used with such models.\n\n# Probably the simplest example of a table is the julia native *column\n# table*, which is just a named tuple of equal-length vectors:\n\ncolumn_table = (h=height, e=exam_mark, t=time)\n\n#-\n\nscitype(column_table)\n\n#-\n\n# Notice the `Table{K}` type parameter `K` encodes the scientific\n# types of the columns. (This is useful when comparing table scitypes\n# with `<:`). To inspect the individual column scitypes, we use the\n# `schema` method instead:\n\nschema(column_table)\n\n# Here are five other examples of tables:\n\ndict_table = Dict(:h => height, :e => exam_mark, :t => time)\nschema(dict_table)\n\n# (To control column order here, instead use `LittleDict` from\n# OrderedCollections.jl.)\n\nrow_table = [(a=1, b=3.4),\n             (a=2, b=4.5),\n             (a=3, b=5.6)]\nschema(row_table)\n\n#-\n\nimport DataFrames\ndf = DataFrames.DataFrame(column_table)\n\n#-\n\nschema(df) == schema(column_table)\n\n#-\n\nusing UrlDownload, CSV\ncsv_file = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                   \"MachineLearningInJulia2020/\"*\n                   \"for-MLJ-version-0.16/data/horse.csv\");\nschema(csv_file)\n\n\n# Most MLJ models do not accept matrix in lieu of a table, but you can\n# wrap a matrix as a table:\n\nusing Tables\nmatrix_table = Tables.table(rand(2,3))\nschema(matrix_table)\n\n# The matrix is *not* copied, only wrapped. Some models may perform\n# better if one wraps the adjoint of the transpose - see\n# [here](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Observations-correspond-to-rows,-not-columns).\n\n\n# **Manipulating tabular data.** In this workshop we assume\n# familiarity with some kind of tabular data container (although it is\n# possible, in principle, to carry out the exercises without this.)\n# For a quick start introduction to `DataFrames`, see [this\n# tutorial](https://juliaai.github.io/DataScienceTutorials.jl/data/dataframe/).\n\n# ### Fixing scientific types in tabular data\n\n# To show how we can correct the scientific types of data in tables,\n# we introduce a cleaned up version of the UCI Horse Colic Data Set\n# (the cleaning work-flow is described\n# [here](https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/horse/#dealing_with_missing_values)).\n# We already downloaded this data set immediately above.q\n\nhorse = DataFrames.DataFrame(csv_file); # convert to data frame\nfirst(horse, 4)\n\n#-\n\n# From [the UCI\n# docs](http://archive.ics.uci.edu/ml/datasets/Horse+Colic) we can\n# surmise how each variable ought to be interpreted (a step in our\n# work-flow that cannot reliably be left to the computer):\n\n# variable                    | scientific type (interpretation)\n# ----------------------------|-----------------------------------\n# `:surgery`                  | Multiclass\n# `:age`                      | Multiclass\n# `:rectal_temperature`       | Continuous\n# `:pulse`                    | Continuous\n# `:respiratory_rate`         | Continuous\n# `:temperature_extremities`  | OrderedFactor\n# `:mucous_membranes`         | Multiclass\n# `:capillary_refill_time`    | Multiclass\n# `:pain`                     | OrderedFactor\n# `:peristalsis`              | OrderedFactor\n# `:abdominal_distension`     | OrderedFactor\n# `:packed_cell_volume`       | Continuous\n# `:total_protein`            | Continuous\n# `:outcome`                  | Multiclass\n# `:surgical_lesion`          | OrderedFactor\n# `:cp_data`                  | Multiclass\n\n# Let's see how MLJ will actually interpret the data, as it is\n# currently encoded:\n\nschema(horse)\n\n# As a first correction step, we can get MLJ to \"guess\" the\n# appropriate fix, using the `autotype` method:\n\nautotype(horse)\n\n#-\n\n# Okay, this is not perfect, but a step in the right direction, which\n# we implement like this:\n\ncoerce!(horse, autotype(horse));\nschema(horse)\n\n# All remaining `Count` data should be `Continuous`:\n\ncoerce!(horse, Count => Continuous);\nschema(horse)\n\n# We'll correct the remaining truant entries manually:\n\ncoerce!(horse,\n        :surgery               => Multiclass,\n        :age                   => Multiclass,\n        :mucous_membranes      => Multiclass,\n        :capillary_refill_time => Multiclass,\n        :outcome               => Multiclass,\n        :cp_data               => Multiclass);\nschema(horse)\n\n\n# ### Resources for Part 1\n#\n# - From the MLJ manual:\n#    - [A preview of data type specification in\n#   MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#A-preview-of-data-type-specification-in-MLJ-1)\n#    - [Data containers and scientific types](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Data-containers-and-scientific-types-1)\n#    - [Working with Categorical Data](https://alan-turing-institute.github.io/MLJ.jl/dev/working_with_categorical_data/)\n# - [Summary](https://juliaai.github.io/ScientificTypes.jl/dev/#Summary-of-the-default-convention) of the MLJ convention for representing scientific types\n# - [ScientificTypes.jl](https://juliaai.github.io/ScientificTypes.jl/dev/)\n# - From Data Science Tutorials:\n#     - [Data interpretation: Scientific Types](https://juliaai.github.io/DataScienceTutorials.jl/data/scitype/)\n#     - [Horse colic data](https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/horse/)\n# - [UCI Horse Colic Data Set](http://archive.ics.uci.edu/ml/datasets/Horse+Colic)\n\n\n# ### Exercises for Part 1\n\n\n# #### Exercise 1\n\n# Try to guess how each code snippet below will evaluate:\n\nscitype(42)\n\n#-\n\nquestions = [\"who\", \"why\", \"what\", \"when\"]\nscitype(questions)\n\n#-\n\nelscitype(questions)\n\n#-\n\nt = (3.141, 42, \"how\")\nscitype(t)\n\n#-\n\nA = rand(2, 3)\n\n# -\n\nscitype(A)\n\n#-\n\nelscitype(A)\n\n#-\n\nusing SparseArrays\nAsparse = sparse(A)\n\n#-\n\nscitype(Asparse)\n\n#-\n\nC = coerce(A, Multiclass)\n\n#-\n\nscitype(C)\n\n#-\n\nelscitype(C)\n\n#-\n\nv = [1, 2, missing, 4]\nscitype(v)\n\n#-\n\nelscitype(v)\n\n#-\n\nscitype(v[1:2])\n\n# Can you guess at the general behavior of\n# `scitype` with respect to tuples, abstract arrays and missing\n# values? The answers are\n# [here](https://github.com/juliaai/ScientificTypesBase.jl#2-the-scitype-and-scitype-methods)\n# (ignore \"Property 1\").\n\n\n# #### Exercise 2\n\n# Coerce the following vector to make MLJ recognize it as a vector of\n# ordered factors (with an appropriate ordering):\n\nquality = [\"good\", \"poor\", \"poor\", \"excellent\", missing, \"good\", \"excellent\"]\n\n#-\n\n\n# #### Exercise 3 (fixing scitypes in a table)\n\n# Fix the scitypes for the [House Prices in King\n# County](https://mlr3gallery.mlr-org.com/posts/2020-01-30-house-prices-in-king-county/)\n# dataset:\n\nhouse_csv = urldownload(\"https://raw.githubusercontent.com/ablaom/\"*\n                        \"MachineLearningInJulia2020/for-MLJ-version-0.16/\"*\n                        \"data/house.csv\");\nhouse = DataFrames.DataFrame(house_csv)\nfirst(house, 4)\n\n# (Two features in the original data set have been deemed uninformative\n# and dropped, namely `:id` and `:date`. The original feature\n# `:yr_renovated` has been replaced by the `Bool` feature `is_renovated`.)\n\n# <a id='part-2-selecting-training-and-evaluating-models'></a>\n", "meta": {"hexsha": "29feebe059b16ec57dd0879562eb408b1dea5d5e", "size": 10312, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/01_data_representation/notebook.jl", "max_stars_repo_name": "roland-KA/MLJTutorial.jl", "max_stars_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2021-11-02T10:46:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:36:21.000Z", "max_issues_repo_path": "notebooks/01_data_representation/notebook.jl", "max_issues_repo_name": "roland-KA/MLJTutorial.jl", "max_issues_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2021-11-11T14:36:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-14T04:31:54.000Z", "max_forks_repo_path": "notebooks/01_data_representation/notebook.jl", "max_forks_repo_name": "roland-KA/MLJTutorial.jl", "max_forks_repo_head_hexsha": "00d2294dbe458504f7e2d200b1ed84097b9ff3e7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2021-11-11T12:08:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-30T14:27:30.000Z", "avg_line_length": 27.6461126005, "max_line_length": 154, "alphanum_fraction": 0.6931730023, "num_tokens": 2792, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3775406687981454, "lm_q2_score": 0.1329642281726931, "lm_q1q2_score": 0.05019940363054776}}
{"text": "\"\"\"\nThis module demonstrates how to write an abstract type hierarchy and explore\nit's shape and behaviours.\n\"\"\"\nmodule PersonalAssets\n\nusing InteractiveUtils\n\nexport Asset\nexport Property\nexport Investment\nexport Cash\nexport House\nexport Apartment\nexport FixedIncome\nexport Equity\n\nabstract type Asset end\n\nabstract type Property <: Asset end\nabstract type Investment <: Asset end\nabstract type Cash <: Asset end\n\nabstract type House <: Property end\nabstract type Apartment <: Property end\n\nabstract type FixedIncome <: Investment end\nabstract type Equity <: Investment end\n\nexport subtypetree\n\nfunction subtypetree(roottype, level=1, indent=2)\n  level == 1 && println(roottype)\n  for type in subtypes(roottype)\n    join(fill(\" \", level*indent)) * string(type) |> println\n    subtypetree(type, level+1, indent)\n  end\nend\n\nend # PersonalAssets\n", "meta": {"hexsha": "f81b9b522858d105e86770a13ee21f222b3a1711", "size": 843, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PersonalAssets.jl", "max_stars_repo_name": "BradLyman/learn-julia", "max_stars_repo_head_hexsha": "135b142d1b5d9ae25547ad2d31b93d09c605d8d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/PersonalAssets.jl", "max_issues_repo_name": "BradLyman/learn-julia", "max_issues_repo_head_hexsha": "135b142d1b5d9ae25547ad2d31b93d09c605d8d0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/PersonalAssets.jl", "max_forks_repo_name": "BradLyman/learn-julia", "max_forks_repo_head_hexsha": "135b142d1b5d9ae25547ad2d31b93d09c605d8d0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.5609756098, "max_line_length": 76, "alphanum_fraction": 0.7722419929, "num_tokens": 189, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.33458942798284697, "lm_q2_score": 0.15002881674163043, "lm_q1q2_score": 0.0501980559745255}}
{"text": "using Test, AlgoritmiStruttureDati.Laboratorio3\na = [0, 1, 2, 2, 4, 5, 6, 6, 6, 9]\n\n@testset \"freqTwo.jl\" begin\n    @test freqTwo(a, 0, 9) == 0\n    @test freqTwo(a, 2, 2) == 1\n    @test freqTwo(a, 6, 6) == 2\nend\n", "meta": {"hexsha": "845c47735df83557c93d610b361480982d09e2e9", "size": 212, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/laboratorio3/freqTwoTest.jl", "max_stars_repo_name": "LorenzoFeroleto98/AlgoritmiStruttureDati.jl", "max_stars_repo_head_hexsha": "2cd88441b5d428b78a5a82b992d03561cf6cc5b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/laboratorio3/freqTwoTest.jl", "max_issues_repo_name": "LorenzoFeroleto98/AlgoritmiStruttureDati.jl", "max_issues_repo_head_hexsha": "2cd88441b5d428b78a5a82b992d03561cf6cc5b4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/laboratorio3/freqTwoTest.jl", "max_forks_repo_name": "LorenzoFeroleto98/AlgoritmiStruttureDati.jl", "max_forks_repo_head_hexsha": "2cd88441b5d428b78a5a82b992d03561cf6cc5b4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5555555556, "max_line_length": 47, "alphanum_fraction": 0.5801886792, "num_tokens": 111, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.45713671682749474, "lm_q2_score": 0.10970578261038684, "lm_q1q2_score": 0.05015054127950311}}
{"text": "module DataDrop\n\nusing JSON\nusing SparseArrays\nusing HDF5\n\nconst _sep = \"-\"\n\n\"\"\"\n    clean_file_name(name)\n\nConstruct a clean file name.\n\nReplace '.', ':', ' ' (dot, colon, space) with underscores.\n\nExample:\n```\ns =  clean_file_name(\"something or other.1=0.5_5:7\")\ns == \"something_or_other_1=0_5_5_7\"\n```\n\"\"\"\nfunction clean_file_name(name)\n    cleanname = replace(replace(replace(name,'.'=>'_'),':'=>'_'),' '=>'_')    \n    return cleanname\nend\n\n\"\"\"\n    file_extension(filename)\n\nExtract extension from the string representing a file name.\n\nExpected result:\n```\np = \"matrices.dir\"\nfile_extension(p) == \".dir\"\np = \"matrices.h5\"\nfile_extension(p) == \".h5\"\np = \"matri.ces.dir\"\nfile_extension(p) == \".dir\"\np = \"matrices.\"\nfile_extension(p) == \".\"\np = \"matrices\"\nfile_extension(p) == \"\"\n```\n\"\"\"\nfunction file_extension(filename)\n    f, e = splitext(filename)\n    return e\nend\n\n\"\"\"\n    with_extension(filename, ext)\n\nMake sure the file name has the desired extension.\n\n`ext` can be either with or without the leading dot. \n\n```\nwith_extension(\"fo_1=0_5_5_7\", \".ext\") == \"fo_1=0_5_5_7.ext\"\nwith_extension(\"fo_1=0_5_5_7\", \"ext\") == \"fo_1=0_5_5_7.ext\"  \nwith_extension(\"fo_1=0_5_5_7.dat\", \"ext\") == \"fo_1=0_5_5_7.ext\"  \n```\n\"\"\"\nfunction with_extension(filename, ext)\n    if ext == \"\"\n        # Remove the extension completely\n        f, e = splitext(filename)\n        return f \n    else        \n        # If the file has the wrong extension, remove it\n        f, e = splitext(filename)\n        # Replace or fix up the extension\n        if ext[1] == '.'\n            ext = ext[2:end]\n        end\n        if match(Regex(\".*\\\\.\" * ext * \"\\$\"), f) == nothing\n            f = f * \".\" * ext\n        end\n        return f\n    end\nend\n\n\"\"\"\n    retrieve_json(j)\n\nRetrieve a dictionary from a JSON file named `j`.\n\nExample:\n```\nd = retrieve_json(\"myfile.json\")\n```\n\"\"\"\nfunction retrieve_json(j)\n    j = with_extension(j, \"json\")\n    return open(j, \"r\") do file\n        JSON.parse(file)\n    end\nend\n\n\"\"\"\n    store_json(j, d)\n\nStore a dictionary `d` into a JSON file named `j`.\n\nThe dictionary is stored into a JSON file. If the file name `fname` is supplied\nwithout an extension, it is appended the \".json\" extension.\n\"\"\"\nfunction store_json(j, d)\n    j = with_extension(j, \"json\")\n    open(j, \"w\") do file\n        JSON.print(file, d, 4)\n    end\nend\n\nconst MATRIX_UNKNOWN = -1\nconst MATRIX_DENSE = 0\nconst MATRIX_SPARSE = 1\n\n\"\"\"\n    retrieve_matrix(fname)\n\nRetrieve a matrix from file `fname`.\n\"\"\"\nfunction retrieve_matrix(fname)\n    return retrieve_matrix(fname, \"\")\nend\n\n\"\"\"\n    empty_hdf5_file(fname)\n\nEmpty an HDF5 file. All contents will be erased.\n\"\"\"\nfunction empty_hdf5_file(fname)\n    # If the file exists, delete all contents\n    h5open(fname, \"w\") do fid\n    end\nend\n\n\"\"\"\n    retrieve_matrix(fname, mpath)\n\nRetrieve a matrix from file `fname`.\n\nThe matrix data is stored under the path `mpath`.\n\nExample:\nStoring a sparse matrix as \"/data\" with\n`store_matrix(\"test/matrices/matrix_SInt64.h5f\", \"/data\", d)` gives:\n```\njulia> f = h5open(\"test/matrices/matrix_SInt64.h5f\", \"r\")      \n\ufffd\n\u2514\u2500 \ufffd data \n   \u251c\u2500 \ufffd I \n   \u251c\u2500 \ufffd J \n   \u251c\u2500 \ufffd V \n   \u251c\u2500 \ufffd matrix_type\n   \u251c\u2500 \ufffd ncols      \n   \u2514\u2500 \ufffd nrows   \n```  \n\nExample:\nStoring a dense matrix under the default path (\"/\") with\n`store_matrix(\"matrix_d_Float32.h5\", d)` gives:\n```\njulia> f = h5open(\"matrix_d_Float32.h5\", \"r\")            \n\ufffd  \n\u251c\u2500 \ufffd matrix      \n\u2514\u2500 \ufffd matrix_type    \n```\n\"\"\"\nfunction retrieve_matrix(fname, mpath)\n    if file_extension(fname) == \"\" \n        fname = with_extension(fname, \"h5\")\n    end\n    if mpath != \"\" && mpath[end:end] != \"/\"\n        mpath = mpath * \"/\"\n    end\n    typ = MATRIX_UNKNOWN\n    return h5open(fname, \"r\") do f\n        typ = read(f, mpath * \"matrix_type\")\n        if typ == MATRIX_DENSE\n            v = read(f, mpath * \"matrix\")\n        elseif typ == MATRIX_SPARSE\n            I = read(f, mpath * \"I\")\n            J = read(f, mpath * \"J\")\n            V = read(f, mpath * \"V\")\n            nrows = read(f, mpath * \"nrows\")\n            ncols = read(f, mpath * \"ncols\")\n            v = sparse(I, J, V, nrows, ncols)\n        end\n        v\n    end\nend\n\n\"\"\"\n    store_matrix(fname, matrix)\n\nStore a dense matrix into the file named `fname`.\n\"\"\"\nfunction store_matrix(fname, matrix)\n    store_matrix(fname, \"\", matrix)\nend\n\n\n\"\"\"\n    store_matrix(fname, mpath, matrix)\n\nStore a dense matrix under the path `mpath` into the file `fname`.\n\"\"\"\nfunction store_matrix(fname, mpath, matrix)\n    if file_extension(fname) == \"\"\n        fname = with_extension(fname, \"h5\")\n    end\n    if mpath != \"\" && mpath[end:end] != \"/\"\n        mpath = mpath * \"/\"\n    end\n    h5open(fname, \"cw\") do file\n        write(file, mpath * \"matrix_type\", MATRIX_DENSE) \n        write(file, mpath * \"matrix\", matrix) \n    end\nend\n\n\"\"\"\n    store_matrix(fname, mpath, matrix::SparseArrays.SparseMatrixCSC{T, Int64}) where {T}\n\nStore a sparse matrix under the path `mpath` into the file named `fname`.\n\nThe matrix is stored into an HDF5 file. If the file name `fname` is supplied\nwithout an extension, it is appended the \".h5\" extension.\n\"\"\"\nfunction store_matrix(fname, mpath, matrix::SparseArrays.SparseMatrixCSC{T, Int64}) where {T}\n    I, J, V = findnz(matrix)\n    if file_extension(fname) == \"\"\n        fname = with_extension(fname, \"h5\")\n    end\n    if mpath != \"\" && mpath[end:end] != \"/\"\n        mpath = mpath * \"/\"\n    end\n    h5open(fname, \"cw\") do file\n        write(file, mpath * \"matrix_type\", MATRIX_SPARSE) \n        write(file, mpath * \"I\", I) \n        write(file, mpath * \"J\", J) \n        write(file, mpath * \"V\", V) \n        write(file, mpath * \"nrows\", size(matrix, 1)) \n        write(file, mpath * \"ncols\", size(matrix, 2)) \n    end\nend\n\n\"\"\"\n    store_matrix(fname, matrix::SparseArrays.SparseMatrixCSC{T, Int64}) where {T}\n\nStore a sparse matrix into the file named `fname`.\n\nThe matrix is stored into an HDF5 file. If the file name `fname` is supplied\nwithout an extension, it is appended the \".h5\" extension.\n\"\"\"\nfunction store_matrix(fname, matrix::SparseArrays.SparseMatrixCSC{T, Int64}) where {T}\n    store_matrix(fname, \"\", matrix)\nend\n\n\"\"\"\n    store_value(fname, vpath, num) \n\nStore a number or a string into the file named `fname` under the name `vpath`.\n\"\"\"\nfunction store_value(fname, vpath, num) \n    if file_extension(fname) == \"\"\n        fname = with_extension(fname, \"h5\")\n    end\n    if vpath == \"\"\n        vpath = \"number\"\n    end\n    if vpath != \"\" && vpath[end:end] != \"/\"\n        vpath = vpath * \"/\"\n    end\n\n    h5open(fname, \"cw\") do file\n        write(file, vpath, num) \n    end\nend\n\n\n\"\"\"\n    retrieve_value(fname, vpath)\n\nRetrieve a number or a string known as `vpath` from the file named `fname`.\n\"\"\"\nfunction retrieve_value(fname, vpath)\n    if file_extension(fname) == \"\" \n        fname = with_extension(fname, \"h5\")\n    end\n    if vpath != \"\" && vpath[end:end] != \"/\"\n        vpath = vpath * \"/\"\n    end\n    return h5open(fname, \"r\") do f\n        v = read(f, vpath)\n        v\n    end\nend\n\nend # module\n", "meta": {"hexsha": "6c1ea8264e1d224132afae24413eda3b93453f52", "size": 6971, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DataDrop.jl", "max_stars_repo_name": "PetrKryslUCSD/DataValet.jl", "max_stars_repo_head_hexsha": "a119173b66683cb61a96de61b8b8b174d3f82cf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-02T17:37:59.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-26T16:28:25.000Z", "max_issues_repo_path": "src/DataDrop.jl", "max_issues_repo_name": "PetrKryslUCSD/DataValet.jl", "max_issues_repo_head_hexsha": "a119173b66683cb61a96de61b8b8b174d3f82cf7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-01T15:16:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-01T15:16:58.000Z", "max_forks_repo_path": "src/DataDrop.jl", "max_forks_repo_name": "PetrKryslUCSD/DataValet.jl", "max_forks_repo_head_hexsha": "a119173b66683cb61a96de61b8b8b174d3f82cf7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.082781457, "max_line_length": 93, "alphanum_fraction": 0.6062257926, "num_tokens": 2001, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.45713671682749474, "lm_q2_score": 0.10970578115498411, "lm_q1q2_score": 0.05015054061418508}}
{"text": "function count_nucleotides(strand)\n    default_count = 0\n    nucleotides = \"ACGT\"\n\n    res = Dict(n => default_count for n in nucleotides)\n\n    for n in strand\n        # https://docs.julialang.org/en/v1/manual/strings/#Common-Operations\n        if occursin(n, nucleotides)\n            res[n] = res[n] + 1\n        else\n            throw(DomainError(\"Invalid strand\"))\n        end\n    end\n\n    return res\nend\n", "meta": {"hexsha": "9cac7d4af5f2fcf240eb1409f1aaa72257cade98", "size": 407, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "exercism/nucleotide-count.jl", "max_stars_repo_name": "joaopalmeiro/learning-julia", "max_stars_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercism/nucleotide-count.jl", "max_issues_repo_name": "joaopalmeiro/learning-julia", "max_issues_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2022-01-07T00:39:37.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-11T08:46:14.000Z", "max_forks_repo_path": "exercism/nucleotide-count.jl", "max_forks_repo_name": "joaopalmeiro/learning-julia", "max_forks_repo_head_hexsha": "12fc7abb712ad94fe1dbf8f02af8a383bfb65750", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.6111111111, "max_line_length": 76, "alphanum_fraction": 0.6044226044, "num_tokens": 113, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4687906266262437, "lm_q2_score": 0.10669059252545564, "lm_q1q2_score": 0.050015549725133585}}
{"text": "pwd()\n\na = 2 + 2;\n\nb = 2 * 38;\n\na\n\nb\n\nc = a + b;\n\nc", "meta": {"hexsha": "df0225d2d37d3f033aab4ce105290887b736a07a", "size": 51, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test.jl", "max_stars_repo_name": "alexandreloures/econometrics-with-Julia", "max_stars_repo_head_hexsha": "4eba87e09227606826802b7b7cac738b528bab0d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test.jl", "max_issues_repo_name": "alexandreloures/econometrics-with-Julia", "max_issues_repo_head_hexsha": "4eba87e09227606826802b7b7cac738b528bab0d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test.jl", "max_forks_repo_name": "alexandreloures/econometrics-with-Julia", "max_forks_repo_head_hexsha": "4eba87e09227606826802b7b7cac738b528bab0d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 3.9230769231, "max_line_length": 11, "alphanum_fraction": 0.3137254902, "num_tokens": 29, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.46879062662624377, "lm_q2_score": 0.10669058400428263, "lm_q1q2_score": 0.050015545730487554}}

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