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text:
using DynamicGrids, OffsetArrays, Test, Dates
using DynamicGrids: _initdata!, data, init, mask, boundary, source, dest,
    sourcestatus, deststatus, gridsize, ruleset, grids, SimData, Extent,
    _updatetime, WritableGridData, tspan, extent

inita = [0 1 1
         0 1 1]
initb = [2 2 2
         2 2 2]
initab = (a=inita, b=initb)

life = Life{:a,:a}()
tspan_ = DateTime(2001):Day(1):DateTime(2001, 2)

@testset "_initdata!" begin
    rs = Ruleset(life, timestep=Day(1); opt=SparseOpt());

    ext = Extent(; init=initab, tspan=tspan_)
    simdata = SimData(ext, rs)

    @test simdata isa SimData
    @test init(simdata) == initab
    @test mask(simdata) === nothing
    @test ruleset(simdata) === StaticRuleset(rs)
    @test tspan(simdata) === tspan_
    @test currentframe(simdata) === 1
    @test first(simdata) === simdata[:a]
    @test last(simdata) === simdata[:b]
    @test boundary(simdata) === Remove()
    @test gridsize(simdata) == (2, 3)
    updated = _updatetime(simdata, 2)
    @test currenttimestep(simdata) == Millisecond(86400000)

    gs = grids(simdata)
    grida = gs[:a]
    gridb = gs[:b]

    @test parent(source(grida)) == parent(dest(grida)) ==
        [0 0 0 0 0
         0 0 1 1 0
         0 0 1 1 0
         0 0 0 0 0
         0 0 0 0 0
         0 0 0 0 0]

    wgrida = WritableGridData(grida)
    @test parent(source(grida)) === parent(source(wgrida))
    @test parent(dest(grida)) === parent(dest(wgrida))

    @test parent(grida) == parent(wgrida) ==
        [0 1 1
         0 1 1]

    @test sourcestatus(grida) == deststatus(grida) ==
        [0 1 0 0
         0 1 0 0
         0 0 0 0
         0 0 0 0]

    @test parent(source(gridb)) == parent(dest(gridb)) ==
        [2 2 2
         2 2 2]
    @test sourcestatus(gridb) == deststatus(gridb) == nothing

    @test firstindex(grida) == 1
    @test lastindex(grida) == 6
    @test gridsize(grida) == (2, 3) == size(grida) == (2, 3)
    @test axes(grida) == (1:2, 1:3)
    @test ndims(grida) == 2
    @test eltype(grida) == Int

    output = ArrayOutput(initab; tspan=tspan_)
    _initdata!(simdata, output, extent(output), rs)
end

@testset "initdata! with :_default_" begin
    initx = [1 0]
    rs = Ruleset(Life())
    output = ArrayOutput((_default_=initx,); tspan=tspan_)
    simdata = SimData(output, rs)
    simdata2 = _initdata!(simdata, output, extent(output), rs)
    @test keys(simdata2) == (:_default_,)
    @test DynamicGrids.ruleset(simdata2) == DynamicGrids.StaticRuleset(rs)
    @test DynamicGrids.init(simdata2)[:_default_] == [1 0]
    @test DynamicGrids.source(simdata2[:_default_]) ==
        OffsetArray([0 0 0 0
                     0 1 0 0
                     0 0 0 0
                     0 0 0 0
                     0 0 0 0], (0:4, 0:3))
end

ext: jl


text:
using Base.Test
import WaterData
import WaterData.testhelpers: between

# Tests on tables.jl

@testset "Tabular equations of state" begin
    # make some basic EOS grids
    gridtable = WaterData.GridEOS([1.,2,3], [1.,2,3], [1. 1 1; 1 1 1; 1 1 1])
    nongridtable = WaterData.UnstructuredEOS([1.,2,2,3], [1.,1,2,2], [1.,2,2,3])
    linetable = WaterData.LineEOS([1., 2, 3], [5., 7, 8])

    @testset "Checking if a point is in a table" begin
        @test in(2, 2, gridtable)
        @test in(2, 2, WaterData.BoundingBox(gridtable))
        @test !in(3, 0.5, gridtable)
        @test !in(3, 0.5, WaterData.BoundingBox(gridtable))
        @test in(2, 1.5, nongridtable)
        @test in(2.1, 1.9, nongridtable)
        @test in(1.9, 1.1, nongridtable)
        @test !in(3, 4, nongridtable)  # outside the bounding box
        @test in(1.1, 1.9, WaterData.BoundingBox(nongridtable))
        @test !in(1.1, 1.9, nongridtable) # inside the BB but outside the EOS
    end

    @testset "Log-normalisation based on the range of an entire table" begin
        xn, yn = WaterData.lognorm12(2.5, 1.5, gridtable)
        @test xn |> between(1, 2)
        @test yn |> between(1, 2)
        P, T = WaterData.unlognorm(xn, yn, gridtable)
        @test P ≈ 2.5
        @test T ≈ 1.5

        xn, yn = WaterData.lognorm12(2.5, 1.5, nongridtable)
        @test xn |> between(1, 2)
        @test yn |> between(1, 2)
        P, T = WaterData.unlognorm(xn, yn, nongridtable)
        @test P ≈ 2.5
        @test T ≈ 1.5
    end

    @testset "Linear interpolation on unstructured data" begin
        # interpolating in the region of interest gives appropriate values
        @test WaterData.lininterp(nongridtable, 1.9, 1.1) |> between(1, 2)
        @test WaterData.lininterp(nongridtable, 2.1, 1.9) |> between(2, 3)
        # interpolating outside the bounding box gives NaN
        @test WaterData.lininterp(nongridtable, 0.1, 0.2) |> isnan
        @test WaterData.lininterp(nongridtable, 4, 5) |> isnan
        # interpolating outside the tessellation but within the BB gives NaN
        @test WaterData.lininterp(nongridtable, 1.1, 1.9) |> isnan
        @test WaterData.lininterp(nongridtable, 2.9, 1.1) |> isnan
    end

    @testset "Linear interpolation of 1-D data" begin
        @test linetable(1) == 5
        @test linetable(1.5) == 6
        @test linetable(2) == 7
        @test linetable(2.5) == 7.5
        @test linetable(3) == 8
        @test linetable(-9) == 5  # clips at start
        @test linetable(28) == 8  # clips at end
    end

    @testset "Checking tagging of temperature dependence" begin
        @test WaterData.istempdependent(gridtable)
        @test !WaterData.istempdependent(linetable)
        @test WaterData.istempdependent(nongridtable)
    end

end


text:
using QuantizedStateSystems
using LinearAlgebra
using Test

@testset "QSS1 for scalar systems with no input" begin
    @test begin
        a = -1.0
        f = (x,u,t) -> a*x
        x₀ = 10.0
        tspan = (0.0,1.0)
        Δq = 0.01
        txarray,xarray,tqarray,qarray = qss1(f,x₀,tspan,Δq,[],[])
        xtrue(t) = x₀*exp(a*t)
        xtruearray = xtrue.(txarray)
        relerror = (xarray-xtruearray)./xtruearray
        norm(relerror)<0.01
    end
end

@testset "QSS1 for scalar systems with (control) inputs" begin
    @test begin
        f = (x,u,t) -> -1.0*x + u
        x₀ = 10.0
        tspan = (0.0,5.0)
        tuarray = [1.76]
        uarray = [10.0]
        Δq = 0.01
        heaviside(t,τ) = t>=τ ? 1.0 : 0.0
        xtrue(t) = x₀*exp(-t) + 10.0*(heaviside(t,1.76)*(1.0-exp(-t+1.76)))
        (txarray,xarray,tqarray,qarray) = qss1(f,x₀,tspan,Δq,tuarray,uarray)
        xtruearray = xtrue.(txarray)
        relerror = (xarray-xtruearray)./xtruearray
        norm(relerror)<0.05
    end
end

max_stars_repo_name: hurak/QuantizedStateSystems

max_issues_repo_name: hurak/QuantizedStateSystems

max_forks_repo_name: hurak/QuantizedStateSystems


text:
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule SDL2_mixer_jll
using Base
using Base: UUID
import JLLWrappers

JLLWrappers.@generate_main_file_header("SDL2_mixer")
JLLWrappers.@generate_main_file("SDL2_mixer", UUID("7e88e5ca-120e-58e7-9660-b2187fd466c0"))
end  # module SDL2_mixer_jll


text:
#module plotting
#(for future dev)

#export plotBode, plotNyquist

using DataFrames, Queryverse, DelimitedFiles, CSV, Plots
include("/Users/nirmitdeshpande/.julia/dev/impedance/src/preprocessing.jl")

function plotNyquist()
    cyc1=100.0
    cyc2=103.0

    df = cellSelect(cyc1, cyc2)
   #since plottingbasic() will be in an independant file, add parameters here

    #add plot variation and
    plot(df[!,4], df[!,3], title= "Nyquist Plot", xlabel = "Re(Z)",ylabel="Im(Z)", label="/")
    #savefig("testerplot.png")

    #use DF plotting instead of basic array
end

function plotBode()
    cyc1=100.0
    cyc2=103.0

    df= cellSelect(cyc1, cyc2)

    dff= DataFrame(a=Float64)
    dfa= DataFrame(a=Float64)
    dfb= DataFrame(b=Float64)

    dfa= angle.(df[!,3]+ (df[!,4])im)
    dff= log.(abs.(df[!,2]))

    plot(dfa, dff, title= "Bode Plot (phase)", xlabel = "log(f)",ylabel="phase", label="/")
    #savefig("testerplot.png")
    #dfb= log.(abs.(df[!,3]+ (df[!,4])im))

   #plot(dfb,dff, title= "Bode Plot (magnitude)", xlabel = "log(f)",ylabel="log(magnitude)", label="abc")
    #savefig("testerplot.png")
end

#end

max_stars_repo_name: nirmitdeshpande/EISModeling

max_issues_repo_name: nirmitdeshpande/EISModeling

max_forks_repo_name: nirmitdeshpande/EISModeling


text:
@testset "VectorFold" begin
    vf_dict = Dict([
        VectorFold([1,2], 10, 5) => Dict(
            :pattern => [1,2],
            :gap => 10,
            :folds => 5,
            :length => 10,
            :unfold => [1,2,11,12,21,22,31,32,41,42],
        ),
    ])

    for (vf, results) in vf_dict
        @test pattern(vf) == results[:pattern]
        @test gap(vf) == results[:gap]
        @test folds(vf) == results[:folds]
        @test length(vf) == results[:length]
        @test unfold(vf) == results[:unfold]
    end

end


text:
# Canonical Correlation Analysis

#### CCA Type

struct CCA{T<:Real}
    xmean::Vector{T}  # sample mean of X: of length dx (can be empty)
    ymean::Vector{T}  # sample mean of Y: of length dy (can be empty)
    xproj::Matrix{T}  # projection matrix for X, of size (dx, p)
    yproj::Matrix{T}  # projection matrix for Y, of size (dy, p)
    corrs::Vector{T}  # correlations, of length p

    function CCA(xm::Vector{T},
                 ym::Vector{T},
                 xp::Matrix{T},
                 yp::Matrix{T},
                 crs::Vector{T}) where T<:Real

        dx, px = size(xp)
        dy, py = size(yp)

        isempty(xm) || length(xm) == dx ||
            throw(DimensionMismatch("Incorrect length of xmean."))

        isempty(ym) || length(ym) == dy ||
            throw(DimensionMismatch("Incorrect length of ymean."))

        px == py ||
            throw(DimensionMismatch("xproj and yproj should have the same number of columns."))

        length(crs) == px ||
            throw(DimensionMismatch("Incorrect length of corrs."))

        new{T}(xm, ym, xp, yp, crs)
    end
end

## properties

xindim(M::CCA) = size(M.xproj, 1)
yindim(M::CCA) = size(M.yproj, 1)
outdim(M::CCA) = size(M.xproj, 2)

xmean(M::CCA) = fullmean(xindim(M), M.xmean)
ymean(M::CCA) = fullmean(yindim(M), M.ymean)

xprojection(M::CCA) = M.xproj
yprojection(M::CCA) = M.yproj

correlations(M::CCA) = M.corrs

## use

xtransform(M::CCA, X::AbstractVecOrMat{<:Real}) = transpose(M.xproj) * centralize(X, M.xmean)
ytransform(M::CCA, Y::AbstractVecOrMat{<:Real}) = transpose(M.yproj) * centralize(Y, M.ymean)

## show & dump

function show(io::IO, M::CCA)
    print(io, "CCA (xindim = $(xindim(M)), yindim = $(yindim(M)), outdim = $(outdim(M)))")
end

function dump(io::IO, M::CCA)
    show(io, M)
    println(io)
    println(io, "correlations: ")
    printvecln(io, correlations(M))
    println(io, "xmean:")
    printvecln(io, xmean(M))
    println(io, "ymean:")
    printvecln(io, ymean(M))
    println(io, "xprojection:")
    printarrln(io, xprojection(M))
    println(io, "yprojection:")
    printarrln(io, yprojection(M))
end

#### Perform CCA on data

## ccacov

function ccacov(Cxx::DenseMatrix{T},
                Cyy::DenseMatrix{T},
                Cxy::DenseMatrix{T},
                xmean::Vector{T},
                ymean::Vector{T},
                p::Int) where T<:Real

    # argument checking
    dx, dx2 = size(Cxx)
    dy, dy2 = size(Cyy)
    dx == dx2 || error("Cxx must be a square matrix.")
    dy == dy2 || error("Cyy must be a square matrix.")
    size(Cxy) == (dx, dy) ||
        throw(DimensionMismatch("size(Cxy) should be equal to (dx, dy)"))

    isempty(xmean) || length(xmean) == dx ||
        throw(DimensionMismatch("Incorrect length of xmean."))

    isempty(ymean) || length(ymean) == dy ||
        throw(DimensionMismatch("Incorrect length of ymean."))

    1 <= p <= min(dx, dy) ||
        throw(DimensionMismatch(""))

    _ccacov(Cxx, Cyy, Cxy, xmean, ymean, p)
end

function _ccacov(Cxx, Cyy, Cxy, xmean, ymean, p::Int)
    dx = size(Cxx, 1)
    dy = size(Cyy, 1)

    # solve Px and Py

    if dx <= dy
        # solve Px: (Cxy * inv(Cyy) * Cyx) Px = λ Cxx * Px
        # compute Py: inv(Cyy) * Cyx * Px

        G = cholesky(Cyy) \ Cxy'
        Ex = eigen(Symmetric(Cxy * G), Symmetric(Cxx))
        ord = sortperm(Ex.values; rev=true)
        vx, Px = extract_kv(Ex, ord, p)
        Py = qnormalize!(G * Px, Cyy)
    else
        # solve Py: (Cyx * inv(Cxx) * Cxy) Py = λ Cyy Py
        # compute Px: inv(Cx) * Cxy * Py

        H = cholesky(Cxx) \ Cxy
        Ey = eigen(Symmetric(Cxy'H), Symmetric(Cyy))
        ord = sortperm(Ey.values; rev=true)
        vy, Py = extract_kv(Ey, ord, p)
        Px = qnormalize!(H * Py, Cxx)
    end

    # compute correlations
    # Note: Px' * Cxx * Px == I
    #       Py' * Cyy * Py == I
    crs = coldot(Px, Cxy * Py)

    # construct CCA model
    CCA(xmean, ymean, Px, Py, crs)
end

## ccasvd

function ccasvd(Zx::DenseMatrix{T},
                Zy::DenseMatrix{T},
                xmean::Vector{T},
                ymean::Vector{T},
                p::Int) where T<:Real

    dx, n = size(Zx)
    dy, n2 = size(Zy)
    n == n2 ||
        throw(DimensionMismatch("Zx and Zy must have the same number of columns."))

    isempty(xmean) || length(xmean) == dx ||
        throw(DimensionMismatch("Incorrect length of xmean."))

    isempty(ymean) || length(ymean) == dy ||
        throw(DimensionMismatch("Incorrect length of ymean."))

    1 <= p <= min(dx, dy) ||
        throw(DimensionMismatch(""))

    _ccasvd(Zx, Zy, xmean, ymean, p)
end

# The implementation is partly based on:
#
#   David Weenink.
#   Canonical Correlation Analysis.
#   Institute of Phonetic Sciences, Univ. of Amsterdam,
#   Proceedings 25 (2003), 81-99.
#
#   Note: in this paper, each row is considered as an observation.
#   The algorithm is adpated to the column-major format here.
#
function _ccasvd(Zx::DenseMatrix{T}, Zy::DenseMatrix{T}, xmean::Vector{T}, ymean::Vector{T}, p::Int) where T<:Real
    # svd factorization of Z

    n = size(Zx, 2)

    # svd decomposition
    Sx = svd(Zx)
    Sy = svd(Zy)
    S = svd!(Sx.Vt * transpose(Sy.Vt)) # svd of Vx * Vy'

    # compute Px and Py
    ord = sortperm(S.S; rev=true)
    si = ord[1:p]
    Px = rmul!(Sx.U, Diagonal(1.0 ./ Sx.S)) * S.U[:, si]
    Py = rmul!(Sy.U, Diagonal(1.0 ./ Sy.S)) * S.V[:, si]

    # scale so that Px' * Cxx * Py == I
    #           and Py' * Cyy * Py == I,
    #
    # with Cxx = Zx * Zx' / (n - 1)
    #      Cyy = Zy * Zy' / (n - 1)
    #
    rmul!(Px, sqrt(n-1))
    rmul!(Py, sqrt(n-1))

    # compute correlations
    crs = rmul!(coldot(Zx'Px, Zy'Py), one(T)/(n-1))

    # construct CCA model
    CCA(xmean, ymean, Px, Py, crs)
end

## interface functions

function fit(::Type{CCA}, X::AbstractMatrix{T}, Y::AbstractMatrix{T};
             outdim::Int=min(min(size(X)...), min(size(Y)...)),
             method::Symbol=:svd,
             xmean=nothing,
             ymean=nothing) where T<:Real

    dx, n = size(X)
    dy, n2 = size(Y)

    n2 == n ||
        throw(DimensionMismatch("X and Y should have the same number of columns."))

    (n >= dx && n >= dy) ||
        @warn("CCA would be numerically instable when n < dx or n < dy.")

    xmv = preprocess_mean(X, xmean)
    ymv = preprocess_mean(Y, ymean)

    Zx = centralize(X, xmv)
    Zy = centralize(Y, ymv)

    if method == :cov
        Cxx = rmul!(Zx*transpose(Zx), inv(n - 1))
        Cyy = rmul!(Zy*transpose(Zy), inv(n - 1))
        Cxy = rmul!(Zx*transpose(Zy), inv(n - 1))
        M = ccacov(Cxx, Cyy, Cxy, xmv, ymv, outdim)
    elseif method == :svd
        M = ccasvd(Zx, Zy, xmv, ymv, outdim)
    else
        error("Invalid method name $(method)")
    end

    return M::CCA
end