Dev/combinators

Added a node to address multiple/input/output of nodes.

I think the name "Projection" is still a little misleading. I much prefer the mathematical / categorical nomenclature around "Braiding". Because that's precisely what it is! We're weaving threads together. And if there are any "loose ends", they are simply initial/terminal objects (0's in the current code). A final benefit is that it's much less ambiguous than "project/projection".
Would anyone be opposed if we used that term instead?

Following up on the discussion we had around dictionaries vs. tuples as types for inputs/outputs, let me quickly sketch the two scenarios using the following NIR graph:
graph TD;
A --> C;
B --> C

1: Dictionaries
Assume C has two ports for "a" and "b". Then, it's input type dictionary would be something like:
{"a": ..., "b": ...}. But that means the edge coming into C would have to know which wire to connect where because dictionaries are not ordered (by default). So, there would have to be some form or "named braiding" that wires A to "a" and B to "b". This would be similar to a function call, where we would very specifically set the inputs f(a=..., b=...).
2: Tuples
Assume C has two ports. Then, the connection only needs to understand the arity because any two inputs, will work (assuming we don't type-check). So, the braiding would be "unnamed" in the sense that the wires feeding in would simply be a tuple (..., ...). This has the disadvantage that we cannot name the inputs. Specifically, we can't say f(a=..., b=...), we have to say f(..., ...).
Curious to hear your thoughts.

Yes that is accurate. So once again, both work, we just have to settle on one that makes most sense. I believe the dictionary solution is more powerful/complete than Tuples.

TL;DR

dictionaries are nice - naming multiple inputs will avoid bugs and make NIR code more understandable
most NIR graphs will be sequential, so making nodes with single I/O easy is important
in code, braiding has more complicated syntax than named ports, while achieving the same
code readability is important - NIR will be mostly used for generic network conversions

I don't know how dictionaries would be more powerful (given we'd only use flat dicts, so basically named tuples). But still, it's nice UX and will probably make NIR developers' lives easier.
Here are some concrete NIR graphs that we will definitely have to support, first the simple case, then the more complicated one.
1) Sequential architectures.
One input and one output for every node. I would imagine that the vast majority of NIR graphs in the near future will look like this.
graph TD;
Input --> Conv2d_a;
Conv2d_a --> ReLU_a;
ReLU_a --> AvgPool2d_a;
AvgPool2d_a --> AndSoOnAndOn;
AndSoOnAndOn --> Flatten;
Flatten --> Linear;
Linear --> ReLU;
ReLU --> Output;

Input_rnn --> Dense_in;
Dense_in --> LIF;
LIF --> Dense_rec;
Dense_rec --> LIF;
LIF --> Dense_out;
Dense_out --> Output_rnn;

2) Example of a rSTDP-LIF population.
graph TD;
Input_signal --> Dense_s;
Dense_s --> LIF_pre;
LIF_pre --> LearningDense;
LearningDense --> LIF_post;
LIF_post --> LearningDense;
Input_reward --> Dense_r;
Dense_r --> LearningDense;

(we still need to add the Braids, i.e. all three inputs to LearningDense must go through their own Braid node).
where now LearningDense has

tuple signature (array(N,), array(N,), array(N,)) -> (array(N,))
dictionary signature {'input_spk': array(N,), 'reward_spk': array(N,), 'post_spk': array(N,)} -> {'output_spk': array(N,)}.

For the developer, this will look like
rstdp_demo = nir.NIRGraph(
    nodes={
        "input": nir.Input(
            shape=(array(N,)),
            shape={'...': array(N,)},
        ),
        "input_rew": nir.Input(
            shape=(array(N,)),
            shape={'...': array(N,)},
        ),
        "inp_dense": nir.Dense(
            input_shape=(array(N,)) or {"inp": array(N,)},
            output_shape=(array(N,)) or {"out": array(N,)}
        ),
        "rew_dense": nir.Dense(
            input_shape=(array(N,)) or {"inp": array(N,)},
            output_shape=(array(N,)) or {"out": array(N,)}
        ),
        "pre_lif:": nir.LIF(...),
        "pre_spk_braid": nir.Braid(
            output_indices=array([0, np.nan, np.nan])
        ),
        "post_spk_braid": nir.Braid(
            output_indices=array([np.nan, 0, np.nan])
        ),
        "rew_spk_braid": nir.Braid(
            output_indices=array([np.nan, np.nan, 0])
        ),
        "learning_dense": LearningDense(
            input_shape=(array(N,), array(N,), array(N,)),
            input_shape={
                'pre_spk': array(N,),
                'post_spk': array(N,),
                'rew_spk': array(N,)
            },
            output_shape=(array(N,)) or {"out": array(N,)}
        ),
        "post_lif": nir.LIF(...),
        "output": nir.Output(...)
    },
    edges=[
        ("input", "inp_dense"),
        ("inp_dense", "pre_lif"),
        ("input_rew", "rew_dense"),
        ("rew_dense", "rew_spk_braid"),
        ("rew_spk_braid", "learning_dense"),
        ("pre_lif", "pre_spk_braid"),
        ("pre_spk_braid", "learning_dense"),
        ("learning_dense", "post_lif"),
        ("post_lif", "post_spk_braid"),
        ("post_spk_braid", "learning_dense"),
        ("post_lif", "output")
    ]
)

I think tuples or dictionaries don't make a big difference here.
If we do go for dictionaries, it would be convenient to have some default key naming that we can use whenever we don't care about the key names (i.e., most of the time).
(Something like dd: tuple -> dict with dd(input_tuple) = dict(zip(def_keys[:len(input_tuple)], input_tuple)) such that dd((input1, input2)) -> {'a': input1, 'b': input2} (for some def_keys='abcd...'))
To be fully exhaustive here, let's also take a look at this using named ports instead (similar to Matthias's proposal in #28, #29). This looks pretty much the same as above, but instead of using nir.Braid, we specify in the edges what ports to connect. By default, a NIR node can have one input and one output port, and then we don't have to specify any ports (neither in the node shapes, nor in the edges). We can assume that every I/O port accepts a single tensor.
rstdp_demo = nir.NIRGraph(
    nodes={
        "input": nir.Input(shape=(N,)),
        "input_rew": nir.Input(shape=(N,)),
        "inp_dense": nir.Dense(
            input_shape=array(N,),
            output_shape=array(N,),
        ),
        "rew_dense": nir.Dense(
            input_shape=array(N,),
            output_shape=array(N,),
        ),
        "pre_lif:": nir.LIF(...),
        # can also define LearningDense as NIR graph, like in #28
        "learning_dense": LearningDense(
            input_shape={
                'pre_spk': array(N,),
                'post_spk': array(N,),
                'rew_spk': array(N,)
            },
            output_shape=array(N,)
        ),
        "post_lif": nir.LIF(...),
        "output": nir.Output(...)
    },
    edges=[
        ("input", "inp_dense"),
        ("inp_dense", "pre_lif"),
        ("input_rew", "rew_dense"),
        ("pre_lif", "learning_dense.pre_spk"),
        ("rew_dense", "learning_dense.rew_spk"),
        ("learning_dense", "post_lif"),
        ("post_lif", "learning_dense.post_spk"),
        ("post_lif", "output")
    ]
)

To be honest, this looks a lot cleaner and simpler than the Braiding business while keeping all the functionality (right?), and we would spare developers from having to read and understand a "NIR Braiding tutorial".
While it's true that only developers will ever use this (and not end users), it's also true that these devs will work with generic networks and won't be converting single concrete networks into NIR. This already makes debugging and testing a bit harder, so anything we can do to make the feedback/debugging loop faster and easier would save lots of pain.
Thoughts?

Excellent comment @stevenabreu7!

* code readability is important - NIR will be mostly used for generic network conversions

Maybe we agree here, but let me quickly clarify that I actually don't believe that NIR graphs should be readable. The code should be readable (which I also think is what you're writing?) and it should be easy to use and create graphs (100%). Actually, I think that's one of the most important features. But. The graphs itself should foremost be complete and unambiguous. I think it's less essential that they are small than they are complete and disambiguates as much as possible.
With that opinion in mind, I'd like to turn to your point around braiding. I reckon that's essential to decide before we can take a decision on how to implement either braiding or ports.

* in code, braiding has more complicated syntax than named ports, while achieving the same

I agree that they essentially achieve the same thing, but I think the methods are different in one significant way. Ports use edges to couple nodes together. Meaning, the responsibility for sending signals to the correct places lies in the edges, not in the nodes. Conversely, braids uses nodes to rearrange the wiring internally. Meaning, the responsibility for sending signals to the correct places lies in the nodes.
Let me try to raise two issues around (a) compartmentalization and (b) disambiguation.
Compartmentalization
Consider the following graph:
graph TD;
subgraph F
  C --> D;
end
subgraph G
  B --> F;
end
A --> G;

With ports, we can describe the connectivity as [(A, G), (B, F), (C, D)] because we know that B is the input node of G and C is the input node of F. But we'd also be able to subgraph into G with (A, G.B) and similarly with (B, F.C) because the . notation allows us to "look up" a node inside a subgraph. Essentially, . "exposes" the internal nodes of subgraphs, because the outside world needs to know that before they can (precisely) create edges to the subgraph. Now, what if that node itself if a subgraph? Then this has to be legal: G.F.C.
I may be wrong, but this seems to follow from the necessity to "open up" subgraphs. And I'm not certain that it's a good idea to allow arbitrary lookups like this. Although, I'm not 100% convinced. Maybe it's fine to allow arbitrary nesting?
One thing I like about the braiding (but cannot formally prove why I like) is that it compartmentalizes that connections. If we, for instance, force connections to only be dictionary of named tensors, any node connecting to a subgraph doesn't have to know how the subgraph looks like, it just needs to know its "type signature". That is, which inputs does it have. (In some sense this is closer to the notion of "ports", oddly enough).
Disambiguation of signals
Consider your graph on rSTDP-LIF. If we used ports, how would the LearningDense node distinguish from the inputs? We'd have edges looking something like [(LIF_pre, LearningDense), (Dense_r, LearningDense), ...]. In which order should they arrive? Or, which edge would map to which input in LearningDense?
One solution would be to give names to the inputs of each subgraph, so we could say (LIF_pre, LearningDense.a) and so on. But then each edge would have to have information about the type of the target node, which sounds messy to me. Or, rather, that sounds like mixing up the responsibility of connectivity I mentioned above.
Curious to hear your thoughts.

I see your point in the compartmentalisation with "opening up" subgraphs, I agree that this could become too messy. I was thinking we just allow the . notation only for Input nodes, so in that case, G.B works, but G.F doesn't work because F is not an Input node. (your last paragraph about braiding then just as well applies to named ports)
Re: the connectivity becoming a responsibility of the edges and not nodes. The edges will still only be "identity transformations", so I don't feel that they get more responsibility/compute. I think of edges with named ports as just "syntactic sugar" that is more concise than Braiding. If we use multiple Braid instances (as in the rSTDP example), the edges still need to know which Braid to connect to, so won't the edges have just as much responsibility as in the named ports case?
I don't think I understand the questions in "disambiguation of signals", are they still valid also for the "input-restricted . notation"?

Man. I'm sorry about the shitty writing. Let me try to clarify.
I see your point about "restricting" edges to subgraphs to only operate on input nodes. What worries me is that we now have "classes" of nodes. We would have to treat Input (and Output) as a very different thing. But maybe that's ok?
Regarding disambiguation, I'm trying to say that we would have to expose the types of the nodes inside the graph. That's not necessarily a problem, but I wonder whether that's a good separation of concern. I would disagree that using Braid puts responsibility on the edges because I cannot imagine a scenario where any node would have more than one braid. Meaning, N inputs will, at most need N braids that all output the same type. Essentially, the wiring is now happening inside the braid. To me, this is different from named ports because the wires wouldn't know any details about the source or destination of their connections. In ports, they would have to know the structure of the node, somehow.
This is getting hairy ;-) Wanna jump on a call to sort this out later today?

in the example with the LearningDense (i.e., a non-nested node with complex input type), for making an edge we only need to know the output_type of the pre-node and the input_type of the post-node (same as for edges between nodes with simple input types)
but working with a nested graph, when defining the edges, we would need to also know what nodes are subgraphs, and what the name (and type) of its input nodes are.
buuuuut, we also want to enforce that every NIRNode has an input_type and output_type defined, right? in that case, a NIR graph (being a NIR node) will also have to have an input_type and output_type (probably makes sense to compute that in the __post_init__ function - creating an input_type which is a dictionary of input_node_key -> input_node_shape).
so in the end, even with nested graphs, for making an edge, we still only need to know the output_type of the pre-node and the input_type of the post-node, right? so then the separation of concern is the same as with braiding?

so in the end, even with nested graphs, for making an edge, we still only need to know the output_type of the pre-node and the input_type of the post-node, right? so then the separation of concern is the same as with braiding?

Ah, because you're assuming dictionaries as input types? Yes, in that case the input types of the subgraph would be sufficient to connect to, and they would correspond 1:1 to the input nodes in the subgraph.
I'd still argue that ports moves the concern of "connecting to the right input/from the right output port" into the edges. The braiding approach keeps this in the nodes.
Honestly, I can see the two approaches are doing mostly the same thing. I do think the braiding approach is slightly more "clean". More importantly, though, I think we should take a decision and move on :)

Closed in favour of #50


Public project file

Sorry, please forgive my unreasonable request. I am a newbie. I don't know how to integrate the files of hw and sw into a project. So I want to modify the project of ZC706 development board based on your project. then I can implement your project on the ZC706 development board. Thank you very much. I hope you have the time to help me. Thank you very much!

Hello, what was the VS version of the project you were doing at the time?
Thank you!


rubysrc2cpg: Complete List of Built-in Functions

Is your feature request related to a problem? Please describe.
In a recent PR, I have included the detection for built-in calls which are then later given a method full name prefixed with __builtin. This list is small for now, but is incomplete.
Describe the solution you'd like
Under io.joern.rubysrc2cpg.astcreation.AstCreatorHelper there is an object GlobalTypes with a note on what needs to be filled out and a link to all the built-in calls. Some of these calls are object constructors and I think they should also be included as the basic types are also considered "built in" in other frontends such as Python and JS.

Fixed in #3088


list of extra types to put in both places

Or, in fact, there should be one list and we detect where they're needed.

Done!


feat: Test windows

Codecov Report

Merging #155 into master will increase coverage by 1.55%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #155      +/-   ##
==========================================
+ Coverage   64.53%   66.09%   +1.55%
==========================================
  Files          28       27       -1
  Lines        2746     2197     -549
  Branches      389      374      -15
==========================================
- Hits         1772     1452     -320
+ Misses        818      620     -198
+ Partials      156      125      -31

Impacted Files
Coverage Δ

sentry_sdk/integrations/flask.py
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sentry_sdk/integrations/rq.py
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sentry_sdk/integrations/pyramid.py
0% <0%> (-58.72%)
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sentry_sdk/integrations/celery.py
0% <0%> (-55.23%)
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sentry_sdk/integrations/aws_lambda.py
0% <0%> (ø)
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sentry_sdk/integrations/sanic.py

sentry_sdk/_compat.py
52% <0%> (+2%)
:arrow_up:

sentry_sdk/hub.py
79.67% <0%> (+4.02%)
:arrow_up:

sentry_sdk/worker.py
87.34% <0%> (+4.2%)
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sentry_sdk/integrations/excepthook.py
86.36% <0%> (+6.36%)
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Travis support for Windows seems dysfunctional.


If resolution of the window is changed, the vh and vw property are not updated

I think this might be the only ReactUnityUIDocument related.
When you start your application, and you use 100vh or 100vw you get a component over the full screen. But after you change the resolution of the window. For example through resolution picker:

Size in pixels of 100vh or 100vw property is not updated and left as is.
Example:
We start with a height of 720 pixels. We use 100vh on a div, and this div is set to 720 pixels.
Then we change the size of the window and by extension, its resolution, so the height is now1080 pixels.
But we notice that the div of 100vh is still of size 720 pixels.

Fixed on main branch.

Fixed in v0.10.0

This issue is still reproducible when a custom aspect is selected in the editor:

Steps to reproduce

Create a component with 100vh
Perform code edit (and hot reload)
Size of the 100vh is not honored until resolution changes again

It looks like it tries to keep the resolution of the editor window instead of set aspect for this given window

I just confirmed that hot-reload brakes this even with "Free Aspect" and the results of the window are unclear to me.

Can't reproduce. Note that the vw and vh units are based on the root component's size, rather than the editor windows size.


refactor: Use optional instead of shared_ptr where it is semantically correct

Standards checklist:

[x] The PR title is descriptive.
[x] The PR doesn't replicate another PR which is already open.
[x] The PR has self-explained commits history.

[x] The code is mine or it's from somewhere with an Apache-2.0 compatible license.
[ ] The code is efficient, to the best of my ability, and does not waste computer resources.
[ ] The code is stable and I have tested it myself, to the best of my abilities.
[x] If the code introduces new classes, methods, I provide a valid use case for all of them.

Changes:

[...]

Other comments:
Theoretically using optional instead of shared_ptr should have a performance boost because of all allocations happen at the stack, not the heap. Also, using optional by reference without copying should be much faster than copying the shared_ptr.
This PR should be tested very carefully, because:

Previously shared_ptr was copied in a lot of places and used to obtain temporal reference to internal object, e.x. in TryBindStatementText. In this example sqlite doesn't obtain memory itself and just uses pointer the user give to it, so with optional we should be care that the object we pass to such functions will live until the function finished. To prevent error with TryBindStatementText optional is passed by rvalue-reference that prevents from passing a temporal copy of optional that could have been resulted in corrupted data in db. However, there can be potentially more places like this.
We should avoid copying optional object where possible, because even that copying shared_ptr is long, it is faster than copying the entire object that is the case with optionals. Double check that there are no more unnecessary copies of optionals and everything is accessed by reference

I suggest testing with basic tests (full sync and highload) before accepting the PR.


nwipe: 0.28 -> 0.30

Motivation for this change
Update to latest upstream release 0.30
Change log: https://github.com/martijnvanbrummelen/nwipe/releases/tag/v0.30
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.


HTTPS - Add "custom headers" after CONNECT method

Can it possible to add "custom headers" after CONNECT method when using HTTPS? I need it because I have to add some headers that can be read by proxy and if I add on HTTP Headers the proxy can't read because of the headers are encrypted.
Example:
CONNECT example.com:443 HTTP/1.1
My-Custom-Header: Some value goes here
actual request headers
PS: My proxy can read custom headers after CONNECT method

I would try using CURLOPT_PROXYHEADER.
$curl = new Curl();
$curl->setOpt(CURLOPT_PROXYHEADER, ...);

See also the documentation for CURLOPT_HEADEROPT.

https://secure.php.net/manual/en/function.curl-setopt.php


fix: Add springboot sample to e2e-demos

Description
Issue ticket number and link
https://issues.redhat.com/browse/STONE-441
Type of change

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration
Checklist:

[ ] I have performed a self-review of my code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] I have added meaningful description with JIRA/GitHub issue key(if applicable), for example HASSuiteDescribe("[test_id:01] DEVHAS-62 devfile source")
[ ] I have updated labels (if needed)

Conventional Commits PR Check most unusefull check in the history

https://github.com/redhat-appstudio/infra-deployments/pull/1251
forcing push for


Add client parser

Now all messages received from the server have a central parsing point, with a respective object as result for the parsed values

Amazing work


Broken Build

https://github.com/cryptonomex/graphene/commit/9f9c1e7895fe55a394f3848a802042c0e4692fdb
I was able to build the witness_node after I did a git revert 9f9c1e7 ...  that fixes the following:
Compiler Error:
[ 86%] Building CXX object libraries/wallet/CMakeFiles/graphene_wallet.dir/cache.cpp.o
/home/jcalfee/bitshares/graphene/libraries/egenesis/embed_genesis.cpp: In function ‘void load_genesis(const boost::program_options::variables_map&, egenesis_info&)’:
/home/jcalfee/bitshares/graphene/libraries/egenesis/embed_genesis.cpp:201:69: error: ‘read_file_contents’ was not declared in this scope
       read_file_contents( genesis_json_filename, *info.genesis_json );

Still having a problem building the cli_wallet though:
/home/jcalfee/bitshares/graphene/programs/cli_wallet/main.cpp:35:42: fatal error: graphene/egenesis/egenesis.hpp: No such file or directory
 #include <graphene/egenesis/egenesis.hpp>
                                          ^

https://github.com/cryptonomex/fc/commit/c16bb206a39b09b03423088c831bc193de89056c


Can't login with firefox

Node-RED version: 0.19.5 (docker)
node.js version: v8.12.0
npm version: 6.4.1
Platform/OS: ubuntu 18.04
Browser: Firefox 63.0 (64 bits)
Uncomment the section "adminAuth" in the file "settings.js", use firefox.
Try to enter your password, the focus come back to the Username.
In the console there is the following error :

Une chaîne vide a été transmise à « getElementById() ».

I tried with chrome and its was working. The error is not in the console.

Can you please provide the full error message from the javascript console? Are there any line numbers etc?
There is another user on the discourse forum who has reported some similar behaviour, but no-one has been able to reproduce.

This is the full error. The error is in vendor.js 2:24487
No, this is the default firefox on ubuntu 18.04 without any extensions.
We just try with one of my colleague and he haven't the error with Firefox Quantum 63.0.1 on Windows.
On my laptop with ubuntu 18.10 and firefox Firefox Quantum 63.0, I haven't the error.

So its just one browser on one operating system. Not sure how far we'll get debugging that unless someone is able to reproduce and dig into it for us.

Works in OSX 10.13.6 with FireFox 63.0.1

Works in the following environments.
Node-RED version: 0.19.5 (master branch)
node.js version: v8.11.2
npm version: 5.6.0
Platform/OS: Windows 8.1
Browser: Firefox 63.0.3 (64 bits)
Node-RED version: 0.19.5 (master branch)
node.js version: v10.13.0
npm version: 6.4.1
Platform/OS: Ubuntu 16.04.5
Browser: Firefox 63.0 (64 bits)

Seems problem with GNOME on Ubuntu 18.04
I updated system to Ubuntu 18.10 then everything works.

I'm on GNOME/Ubuntu 18.10 and noticed, that focus stays in the password input field for only half a second. So in order to log in, i have to click into the password field and paste the password right away as typing it takes too long. Chrome is not affected.

The problem disappeared for me as well. I guess it was a problem relevant to the specific ff build.

Closing due to inactivity.


Win-Acme v<IP_ADDRESS>9 hangs at get request

I am having an issue with renewing a certificate from an AWS Lightsail windows server.  It has worked fine in the past.  I upgraded to the latest version to see if that helped, but it did not.  I'm attaching an image of the output when running in verbose mode, but it just hangs on the initial get request.

Please refer to this discussion, I think you have the same problem: https://github.com/win-acme/win-acme/issues/1127


bibtext compatible articles

Hi, I see that btac adds publisher to @article entry type.
It's not compatible neither with BibTeX nor BiBLaTeX.
Invocation of biber --validate-datamodel thesis yields warnings about invalid field.
i.e. WARN - Datamodel: article entry 'teokarevic2011ten' (refs.bib): Invalid field 'publisher' for entrytype 'article' which indeed is not required by APA style.
Do you think --strict mode could be implemented?

I've added this feature in 1.3.2. The new option is --filter-fields-by-entrytype or -b for short. It takes a single argument: either required (only add fields marked as required), optional (required and optional fields) and all (require, optional, and non-standard fields like doi, issn, isbn).
I used this list as reference of what fields are required/optional by entrytype. Note that this is restricted to bibtex's fields and entrytypes, not biblatex's extension.


Unable to see border in table

Hi JiHong,
I am unable to see border in table when converting HTML input into *.docx.
How to add the below style to a table  in SunEditor?
border="1" style=" border: 1px solid black;
border-collapse: collapse;"
Version
2.42.0

https://github.com/JiHong88/SunEditor/blob/master/src/assets/css/suneditor-contents.css#L295
"suneditor-contents.css" is all define editor's contents style.
Try edit the css at the this file.


How to fine-tune with 'TSM_kinetics_RGB_mobilenetv2_shift8_blockres_avg_segment8_e100_dense.pth'

I want to fine-tune on my dataset with 'TSM_kinetics_RGB_mobilenetv2_shift8_blockres_avg_segment8_e100_dense.pth'
I can train successfully with 'TSM_kinetics_RGB_mobilenetv2_shift8_blockres_avg_segment8_e100_dense.pth' , but when I train with command:
python main.py mydata RGB
--arch mobilenetv2 --num_segments 8
--gd 20 --lr 0.001 --lr_steps 10 20 --epochs 25
--batch-size 8 -j 16 --dropout 0.8 --consensus_type=avg --eval-freq=1
--shift --shift_div=8 --shift_place=blockres
--tune_from=pretrained/TSM_kinetics_RGB_mobilenetv2_shift8_blockres_avg_segment8_e100_dense.pth
I got error:
Traceback (most recent call last):
File "main.py", line 378, in
main()
File "main.py", line 123, in main
model.load_state_dict(model_dict)
File "/home/cgim/anaconda3/envs/centernet/lib/python3.6/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DataParallel:
Unexpected key(s) in state_dict: "base_model.features.0.0.weight",...
What should I change?

Are you training on GPU? I remember having a DataParallel issue when I didn't initialize my GPUs.

Are you training on GPU? I remember having a DataParallel issue when I didn't initialize my GPUs.

Any solution for this problem?

how to download TSM_kinetics_RGB_mobilenetv2_shift8_blockres_avg_segment8_e100_dense.pth??


Dividend Date as time.Time

Is your feature request related to a problem? Please describe.
The date values from the ListDividend() should be time.Time not string's. Similar to your other APIs like GetAgg()
Describe the solution you'd like
It would be amazing if the type was change from a string to time.Time.

@nathanhack thanks for bringing this to our attention. I’m taking note and we will review to see if this change in necessary.
Only issue I see with a type change in this circumstance is that it would be a breaking change and possibly require multiple changes for some end users.

@nathanhack I think this makes sense and I'm seeing a few other cases where we could use our time aliases as well. I'll discuss it with the team and merge this change if we decide to allow the breaking change. Thanks for the request!

Closed by #248 and released in v1.10.0. Thanks again for bringing this issue up @nathanhack!


optimize code more readable.

I didn't get the meaning when I first read this loop,so I think this cound be  better.

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.


Update input format

Command and prefix are now case insensitive and all command words are full words
close #194

LGTM


pilot discovery cpu usage is too high

Describe the bug
pilot-discovery cpu uasage is about 20% running bookinfo sample.
  PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND
 32607 root      20   0   96340  83696  26556 S  19.3  0.5 311:59.26 pilot-discovery

Expected behavior
not too much cpu
Steps to reproduce the bug
run sample bookinfo
Version
What version of istio and Kubernetes are you using? Use istioctl version and kubectl version
Version: 1.0.0-snapshot.0
root@szvp000201060:/home/paas# kubectl version
Client Version: version.Info{Major:"1", Minor:"12+", GitVersion:"v1.12.0-alpha.0.1799+fd222e64412e4c-dirty", GitCommit:"fd222e64412e4c402759b49f1f130e1727ef7a77", GitTreeState:"dirty", BuildDate:"2018-07-09T06:05:40Z", GoVersion:"go1.10.2", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"12+", GitVersion:"v1.12.0-alpha.0.1799+fd222e64412e4c-dirty", GitCommit:"fd222e64412e4c402759b49f1f130e1727ef7a77", GitTreeState:"dirty", BuildDate:"2018-07-09T06:05:40Z", GoVersion:"go1.10.2", Compiler:"gc", Platform:"linux/amd64"}

Is Istio Auth enabled or not?
no
Environment
linux

NO, it is always so since I noticed.

go-torch created a flame graph
https://gist.githubusercontent.com/hzxuzhonghu/43afa1ef7f93e2b88453e6ab635277aa/raw/dcae6909df49c8391779770175dff7a1d445b4d2/torch.svg

/kind bug

It still exist in 1.0 branch

   PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND
105035 root      20   0   69808  58400  26196 S  17.1  0.4 221:15.49 /usr/local/bin/pilot-discovery discovery

/cc @andraxylia @ayj @costinm @kyessenov @ijsnellf @nmittler @rshriram @vadimeisenbergibm @ZackButcher

@costinm any update on the fix that you mentioned in https://github.com/istio/istio/issues/7595#issuecomment-410417910?

Did a bit of digging around and I think the idle CPU usage is due to https://github.com/istio/istio/blob/adb21944b0a79fabe9c67eba55549b6308b7f1d1/pilot/pkg/serviceregistry/kube/queue.go#L108.  The queue appears to run at the ratelimit all the time.  So something like 10 times a second it wakes up and does nothing.  I changed the implementation of queue.go to use a channel and my CPU usage went to 0.3% from a consistent 12% on my system.
I'm not sure why the queue is implemented with a slice and sleeping as opposed to a channel, but the channel approach seems to work better at doing nothing :)

@ibuildthecloud this is great find, ship it!

Maybe we should make use of sync.Cond to notify instead of checking if len(q.queue) == 0 { every time.
I can test this today.

Yes, that's been a problem for a long time. We planned to get rid of it as
we refactor the serviceregustry to event based ( there will be no queue),
and for config galley would take over k8s integration, so lower priority.
On Tue, Aug 21, 2018, 10:27 Darren Shepherd<EMAIL_ADDRESS>wrote:

Did a bit of digging around and I think the idle CPU usage is due to
https://github.com/istio/istio/blob/adb21944b0a79fabe9c67eba55549b6308b7f1d1/pilot/pkg/serviceregistry/kube/queue.go#L108.
The queue appears to run at the ratelimit all the time. So something like
10 times a second it wakes up and does nothing. I changed the
implementation of queue.go to use a channel and my CPU usage went to 0.3%
from a consistent 12% on my system.
I'm not sure why the queue is implemented with a slice and sleeping as
opposed to a channel, but the channel approach seems to work better at
doing nothing :)
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@all I opened a pr #8123 to fix this.
reduce pilot cpu usage from about 20% down to below 1%

We planned to get rid of it as
we refactor the serviceregustry to event based ( there will be no queue),
and for config galley would take over k8s integration, so lower priority.

@costinm  I have interest in this.

I see #8123 is only in master, it will be in 1.1 in November time-frame.

Ahh thanks - I see now, it's in 1.1.0 snapshot 1 but not snapshot 0.

yes!
I have rise to 1.0.2 and the cpu is still high.


Some questions about TGAN

While reading the paper, I have a few questions about TGAN:

Can TGAN generate labels as well or just features?
How much of synthetic data should I set to generate? (For example, I have tabular data with 100.000rows and 6 columns)
Could you give me the description of this dataset? https://s3.amazonaws.com/hdi-demos/tgan-demo/census-train.csv

Can TGAN generate labels as well or just features?

Yes. Both features and labels are considered columns in a table, so there's nothing different between labels and features.

How much of synthetic data should I set to generate? (For example, I have tabular data with 100.000rows and 6 columns)

You can generate as much as you like.

Could you give me the description of this dataset?

Here is the original dataset. https://archive.ics.uci.edu/ml/datasets/census+income.

Thank you.
A few more questions:
I am using TGAN for KDD dataset, here are 6 futures that I have chosen and 1 label(duration: numerical, protocol_type: categorical, Src_bytes: numerical, Dst_bytes: numerical, Count: numerical, Srv_Count: numerical, label: categorical)
And here is sample one row which I extracted from synthetic0_1.npz and read it using numpy:
>>> a.shape
(5000, 6)
>>> a[0]
array([-9.9999529e-01,  1.0000000e+00,  6.7457329e-20,  1.1078949e-17,
        1.4016924e-16,  9.3192446e-24], dtype=float32)

This is real data
[5607, tcp, 0, 0, 223,23, normal]
My question for this is why the synthetic data was being scaled so small? And why synthetic data missing 1 column? My .csv input file has 7 columns.

Also, there is one problem occurred. Which I have not trained TGAN successfully yet
https://github.com/tensorpack/tensorpack/issues/1123, please help me this.

The questions about training your model are being answered here, so I'll close this issue.

Please close this issue https://github.com/DAI-Lab/TGAN/issues/9 not this one. Because the question above has not been answered. Thank you.

Hi @TrinhDinhPhuc,

Please close this issue #9 not this one. Because the question above has not been answered. Thank you.

Sorry, I had not seen it. Let's get it answered.

My question for this is why the synthetic data was being scaled so small?

This is because the npz contents are not yet the final output.
This file contains an intermediate output which still needs to be post-processed in order to produce
the actual sampling output, which will end up being stored in a .csv file.
The file which you should be looking for is called synthesized0_1.csv. If you cannot find it, the training
process might have not finished succesfully.
Did any errors show up in the logs while training?

And why synthetic data missing 1 column? My .csv input file has 7 columns.

I can't be sure but it will help if you show us some raw lines of your CSV file, so we can figure it out together.

Yes, you're right. I could not find synthesized0_1.csv file. Seems like training process haven't finished successfully yet.
Sure this is some raw lines of my CSV file:

0 | tcp | 491 | 0 | 2 | 2 | normal
0 | udp | 146 | 0 | 13 | 1 | normal
0 | tcp | 0 | 0 | 123 | 6 | neptune
0 | tcp | 232 | 8153 | 5 | 5 | normal
0 | tcp | 199 | 420 | 30 | 32 | normal
0 | tcp | 0 | 0 | 121 | 19 | neptune
0 | tcp | 0 | 0 | 166 | 9 | neptune
0 | tcp | 0 | 0 | 117 | 16 | neptune
0 | tcp | 0 | 0 | 270 | 23 | neptune
0 | tcp | 0 | 0 | 133 | 8 | neptune
0 | tcp | 0 | 0 | 205 | 12 | neptune
0 | tcp | 0 | 0 | 199 | 3 | neptune
0 | tcp | 287 | 2251 | 3 | 7 | normal
0 | tcp | 334 | 0 | 2 | 2 | warezclient

Oh, finally I have trained successfully. Would you mind review a little bit about this synthetic data (table on the right)? The result quite bad actually. As you can see in the real data (table on the left), there are no negative values. And the value of column A & C, seems like all rows their value is the average number of the whole column.
Here are those metrics I used:
[
{
"name": "KDD2",
"num_random_search": 2,
"train_csv": "data/KDD2.csv",
"continuous_cols": [0, 2, 3, 4, 5],
"epoch": 5,
"steps_per_epoch": 200,
"output_epoch": 3,
"sample_rows": 200
}
]

Also, could you explain to me these values in exp-result.csv file?

0.062684896457984 | 0.030622851203326 | 0.327656512353082
0.051571120172703 | 0.327536579515471 | 0.456424402334693

Hi @TrinhDinhPhuc,
I think that in order for the model to be able to generate valid values, you should increase steps_per_epoch to values similar to  the ones present in the  demo_config.json, that is, 10000 steps per epoch, which in turn will increase the training time.

Thank you. What about my question "Also, could you explain to me these values in exp-result.csv file?" above?

Thank you. What about my question "Also, could you explain to me these values in exp-result.csv file?" above?

@TrinhDinhPhuc this does not seem to be related to the questions which you opened this issue for,
and also this is clearly explained in the README, so I think this issue can be closed already.
If you have any other doubt about TGAN which is not already responded on the README or the paper, please feel free to open a new issue to ask about it!


Error encode/decode frame with list value signals

Describe the bug
Unable to encode/decode frame with signal of list type.
Signal in LDF file:
  BSBatt2Current: 24, {0, 0, 2}, BMS2, GWM ;

To Reproduce
Steps to reproduce the behavior:
The issue can be reproduced from running the following 2 unit tests.
def test_encode_array():
    signal = LinSignal('BattCurr', 24, [0, 0, 2])
    encoding_type = LinSignalEncodingType(
        "BattCurrCoding",
        [PhysicalValue(0, 182272, 0.00390625, -512, "A")]
    )

    signal.encoding_type = encoding_type
    frame = LinUnconditionalFrame(0x20, "LinStatus", 3, {0: signal})

    encoded = frame.encode({"BattCurr": [-511.99609375, -511.99609375, -511.99609375]})

def test_decode_array():
    signal = LinSignal('BattCurr', 24, [0, 0, 2])
    encoding_type = LinSignalEncodingType(
        "BattCurrCoding",
        [PhysicalValue(0, 182272, 0.00390625, -512, "A")]
    )

    signal.encoding_type = encoding_type
    frame = LinUnconditionalFrame(0x20, "LinStatus", 3, {0: signal})

    decoded = frame.decode(frame.encode_raw({"BattCurr": [1, 1, 1]}))

Expected behavior
Encode/decode should work without errors.

In your examples you're providing scalar (float) values when encoding array signals, in the standard the initial value provided to array type signals must be an integer array and all the examples there point to this limitation.
Do you have a productive example of scalars encoded/decoded into array signals? Is this something that's allowed in ISO 17987 or SAE J2602?

In the example, the initial value is an integer array: [0, 0, 2].  The scalar values are the encoded value.
I think this one can better demo the issue. If I encode a frame with default values, I cannot decode it back.
def test_encode_array():
    signal = LinSignal('BattCurr', 24, [0, 0, 2])
    encoding_type = LinSignalEncodingType(
        "BattCurrCoding",
        [PhysicalValue(0, 182272, 0.00390625, -512, "A")]
    )

    signal.encoding_type = encoding_type
    frame = LinUnconditionalFrame(0x20, "LinStatus", 3, {0: signal})

    encoded = frame.encode({})    # encoded = bytearray(b'\x00\x00\x02')
    frame.decode(encoded)           # Error

If you do provide a value in the encode step you would also encounter errors because the PhysicalValue encoder cannot encode list types. So even a line like this encoded = frame.encode({"BattCurr": [0, 1, 2]}) will lead to an exception.
I'd be hesitant to merge #120 since we don't have an example LDF for it, the standard doesn't mention anything about arrays being encoded into scalars, only ASCII and BCD converter examples are available. There's a slight hint at it in the NCF example.
I'll check the ISO standard tomorrow to see if there's anything in there that would confirm this use case. If it does I would prefer the approach where support for array signals is done in the value converters, rather than the frame encoding algorithm.

The ISO standard that I read didn't mention anything new regarding array signals. If this is something you know exists in your LDF files then we can add support but as previously mentioned it should done in the converter that way we won't introduce any breaking changes.

Thank you for pushing back. I realize my original implementation is quite wrong. I've proposed a new solution, please review and let me know if this one is ok.


testdata/case: Git submodule update failed

Git submodule cannot update because url	of submodule was wrong.
Cloning into 'testdata/case/tiny-test-repo-with-readme'...
fatal: Unable to find remote helper for 'ttps'
Clone of 'ttps://github.com/sgtest/tiny-test-repo-wth-readme.git' into submodule path 'testdata/case/tiny-test-repo-with-readme' failed

There is pending PR #5. It will fix Travis issues once merged

@alexsaveliev Thanks, I will close my PR.

Fixed by #5


DB2 Date and Timestamp specific functions

Actual Behavior
I am trying to parse below DB2 SQL : DELETE FROM TABLE_DEF WHERE COLUMN1=:COLUMN1 AND COLUMN3 BETWEEN CURRENT TIMESTAMP - 1 DAY AND CURRENT TIMESTAMP
Exception:
Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "BETWEEN" "BETWEEN"
at line 1, column 58.
Was expecting one of:
"."
";"
"AND"
"LIMIT"
"ORDER"
<EOF>

at net.sf.jsqlparser.parser.CCJSqlParser.generateParseException(CCJSqlParser.java:18047)
at net.sf.jsqlparser.parser.CCJSqlParser.jj_consume_token(CCJSqlParser.java:17900)
at net.sf.jsqlparser.parser.CCJSqlParser.Statement(CCJSqlParser.java:91)
at net.sf.jsqlparser.parser.CCJSqlParserManager.parse(CCJSqlParserManager.java:38)
... 24 more

Caused by:
net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "BETWEEN" "BETWEEN"
at line 1, column 58.
Expected Behavior
Should be parsed since it works fine against DB2.
Steps to Reproduce the Problem

Specifications

Version: 1.2
Platform: Windows 7
Subsystem:

Solved via PR #1252


iodine sliding window - stable

This pull request adds most of the features as outlined in https://github.com/yarrick/iodine/pull/16, and is the most stable version prior to some major refactoring and other features unrelated to the sliding window protocol. (including the commits that have already been reviewed - up to ac60bf66a98b486bb6616698f978026f636b64bc)
I've taken the comments from https://github.com/yarrick/iodine/pull/16#issuecomment-192330087 into account.
At some point I'll rebase the other commits from #16 on top of this, but for now it can be an intermediate stage for fixing issues with this particular version.

This branch fails to build on my Arch server (used to build fine up to recently) :
CC iodine.c
CC client.c
LD ../bin/iodine
Package libsystemd-daemon was not found in the pkg-config search path.
Perhaps you should add the directory containing `libsystemd-daemon.pc'
to the PKG_CONFIG_PATH environment variable
No package 'libsystemd-daemon' found
CC iodined.c
CC user.c
CC fw_query.c
CC server.c
LD ../bin/iodined
Package libsystemd-daemon was not found in the pkg-config search path.
Perhaps you should add the directory containing `libsystemd-daemon.pc'
to the PKG_CONFIG_PATH environment variable
No package 'libsystemd-daemon' found
iodined.o: In function `main':
iodined.c:(.text.startup+0x854): undefined reference to `sd_listen_fds'
collect2: error: ld returned 1 exit status
Makefile:35: recipe for target '../bin/iodined' failed
make[1]: *** [../bin/iodined] Error 1
make[1]: Leaving directory '/root/tmp/iodine-frekky/src'
Makefile:20: recipe for target 'all' failed
make: *** [all] Error 2
[root@vps193617 iodine-frekky]#

According to : https://bbs.archlinux.org/viewtopic.php?id=209316
There is no libsystemd-daemon in Arch anymore, only one libsystemd

So, it may be coming from a bug on Arch? I did not have /usr/lib64/pkgconfig/libsystemd-daemon.pc on my Arch system. Not sure why, since when, nor if by default.
After copying one from Fedora, this PR builds fine.

@sheepdestroyer you have that problem compiling master also right? I guess the osflags code or similar needs to be adjusted if this (libsystemd-daemon -> libsystemd) is coming to all distros eventually.

sorry for this very late reply
Yes you are right, without libsystemd-daemon.pc copied from a fedora system on my arch srv, master will not build either.
Should I open a new bug entry for that?

@sheepdestroyer please do.

done so : http://dev.kryo.se/iodine/ticket/123

This branch of my iodine fork isn't really stable enough to be merged anyway, and it's too much work to maintain it separately. Any further features/fixes would be better added in the master branch. Closing pull request.

This branch of my iodine fork isn't really stable enough to be merged anyway, and it's too much work to maintain it separately. Any further features/fixes would be better added in the master branch. Closing pull request.


repoUrl has no protocol

repoUrl does not have a protocol in publish:github:pull-request

honestly it wouldn't hurt to support both in the reading end just to be forgiving, but that's a separate issue


Draft PR and testing governance policy

In order to alleviate a lot of cognitive overhead, let's write down the guidelines we've been applying and/or want to apply when managing pull requests and feature testing in some of the projects in the Asciidoctor organization. This should improve consistency and transparency.  First, @mojavelinux wants to sort the guidelines that apply to Core versus, for example, Asciidoctor Epub. Also, we've kicked around the following project terms and their meanings which may or may not help with this issue:
Run = Lead
Assist = Partner
Coordinate = Mediate
Support = Laissez-Faire
This draft doesn't have cover all the scenarios. First, let's just get down what we've been doing that we want to keep doing.
This document will be stored in the new governance component that will, for now, be stored in this repository since it won't be a versioned document.

Materials for reference:

https://www.mozilla.org/en-US/about/governance/policies/module-ownership/
https://www.softwaretestingnews.co.uk/open-source-governance-accelerates-innovation/
http://nerdstein.net/blog/community-governance-considerations-open-source-projects
https://opensource.guide/leadership-and-governance/
https://opensource.com/open-organization/18/4/new-governance-model-research
https://github.com/moby/moby/blob/master/CONTRIBUTING.md
https://gist.github.com/calebamiles/c578f88403b2fcb203deb5c9ef941d98
https://github.com/rust-lang/rfcs
https://jenkins.io/project/governance/
https://github.com/nodejs/TSC/blob/master/Project-Lifecycle.md


Problems encountered in the reproduction process

I am having problems reproducing the test example, the program does not go down, my configuration file and running code are as follows, what is wrong with me that causes the program to fail to reproduce
(nope-nerf) lijingbo@Super-Server:~/nope-nerf-main$ CUDA_VISIBLE_DEVICES=1 python preprocess/dpt_depth.py configs/preprocess.yaml
/home/lijingbo/anaconda3/envs/nope-nerf/lib/python3.8/site-packages/timm/models/_factory.py:114: UserWarning: Mapping deprecated model name vit_base_resnet50_384 to current vit_base_r50_s16_384.orig_in21k_ft_in1k.
model = create_fn(

Hi, it seems you ran the pre-processing step to generate the depth maps. Can you give some more details about the problem?


Why are Azure APIs incomplete??

Why doesn't the "machine sizes" API response payload include the number of supported NICS???
Does someone need to go back to API-dev school or did they just forget to include that information in the response payload???   Obviously the system knows if I can or cannot add a NIC to a VM because the UI prevents it.  BUT, I cannot programmatically determine the maximum number of NICs per VM without going to the Docs site.
I need this functionality because I have to be able to analyze my customers' Azure presence and determine if it's possible to simply add NICs to a VM (no downtime) or if they have to resize the VM first (down time).
Please ensure this (and other APIs lacking information) return complete information in their response payloads.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: bf1062f1-616d-acc2-abeb-b04e4cad28ad
Version Independent ID: 44068874-d5de-c2bf-d33b-22b649c7637c
Content: VM sizes - Azure Virtual Machines
Content Source: articles/virtual-machines/sizes.md
Service: virtual-machines
Sub-service: sizes
GitHub Login: @ju-shim
Microsoft Alias: jushiman

Thanks for the feedback! I have assigned the issue to the content author to investigate further and update the document as appropriate.

This isn't an issue with the document, it's an issue with the API itself.  The docs page is just fine.  The problem I have is the information I need cannot be obtained programmatically through the API; it ONLY exists on the docs site.

Please send my comments to the DEVELOPERS.

-----Original Message-----
From: MicrosoftDocs/azure-docs
Sent: May 24, 2022 8:05 PM
To: MicrosoftDocs/azure-docs
Cc: dcc1165 , Author
Subject: Re: [MicrosoftDocs/azure-docs] Why are Azure APIs incomplete?? (Issue #93401)

Thanks for the feedback! I have assigned the issue to the content author to investigate further and update the document as appropriate.
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Current handling of grid types is hard-coded to an integer value

I should look into whether there's a more graceful way of handling different grid types, and whether different handling methods are required.

It's not the hard-coding to integer values that's the issue, they just represent the hexgrids. It's the fact that hex grid support is not great now, likely due to API changes. Also #10 is a problem.


feature request: Better Custom Sort Behavior

Did you check existing requests?

[X] I have searched the existing issues

Describe the feature
I would like to be able to further customize the sorting behavior of the buffer. Specifically, I would like files to first be sorted by their extension and then again by their name.
Ex:
foo/
a.c
z.c
a.h
z.h

Not:
foo/
a.c
a.h
z.c
z.h

If this is already possible, please let me know how to do it. If this is the case, please consider this feature requesting improved documentation surrounding sorting instead.
Provide background
I find it visually hard to keep track of files when the filetypes are mixed with each other.
Additional details
I've searched for similar issues and couldn't find anything. :help oil-columns doesn't seem to expose the file extension.
Nvim-tree allows you to pass a function to implement whatever custom sorting you want, exposing table elements for each items attributes (name, extension, type, etc). It also allows strings to simplify common sorting methods.

A lot of the components of oil are tightly coupled (e.g. sorting, columns, performance optimizations), which means they are much more difficult to customize. The somewhat limited sorting capabilities that are currently exposed are structured in a way that will play nice with the other systems. While it's technically possible to enable custom sorting, the API would be unfriendly and it would have to some with a lot of notes about edge cases and unexpected behavior. That kind of API will add more maintenance burden for me than it's worth. Closing this as out of scope.


Feature Request: HANA Cloud Integration

Hi there,
is there a plan for making HANA Cloud available via the BTP service operator?
BR,
Tobias

Hi,
It's up to HANA, BTP service operator supports any service that supports K8S and has OSB broker


Enhancement to address issue-337 to query the final TLSVersion

Motivation:
To provide an enhancment to resolve issue-337 to query the final TLSVersion.
This allows a client channel to grab the final TLSVersion used for the connection.
Modifications:

Added extension points to allow querying the TLSVersion from the SSLConnection based on the Channel and ChannelPipeline.

Added an unknown type to TLSVersion to account for a default case where the TLSVersion cannot be derived from the conenction.

Added a unit test testObtainingTLSVersionOnClientChannel to query the final TLSVersion when the client channel is closed.

Result:
2 APIs added to query the TLSVersion when on a channel.
Additional Notes:
Note that I have 2 concerns about this PR that I would like some additional feedback on:

First is the unknown case for TLSVersion.  This may not be needed as BoringSSL may just return tlsv12 each time the version cannot be derived from the connection.  Let me know your thoughts.
I am not wild about the way this is being unit-tested, but I wanted to do it this way because it provides the TLSVersion on the Channel after it is closed.  Let me know your thoughts.

Scratch the unknown case comments above, this created an API Breakage which will never do.  I refactored this code to default to tlsv12 as this is what BoringSSL will default to anyways.

Fix for #337

This can happen if the builder takes this opportunity to rebuild the docker image. I can usually just kick it again.

@swift-nio-bot test this please

Nice one!

Thank you @Lukasa !


feat: Add flag columns to Exercise

Related to #1990
This PR adds the flagged and flaggedReason. flagged refers to exercises that were flagged by students or mentors to be reviewed by the Admin. flaggedReason is the reason for the flag.

The flaggedBy column you had proposed would be useful I suggest you add that in this PR as well.

The flaggedBy column you had proposed would be useful I suggest you add that in this PR as well.

I had some issues with having two columns with User type. I'll look into Prisma doc and add it.


Error building robotiq_driver

Description
Trying to follow the tutorial for setting up moveit2 on humble. Ran into some issues building the robotiq packages - ignoring those packages results in a proper build.
Your environment

ROS Distro: Humble
OS Version: Ubuntu 22.04
Binary build
If binary, which release version?

Steps to reproduce
Followed tutorial for Getting Started up to colcon build.
Expected behaviour
robotiq_driver package builds
Actual behaviour
robotiq_driver package fails to build
Backtrace or Console output
Stack Trace

The main branch of that repo is incompatible with Humble since this PR: https://github.com/PickNikRobotics/ros2_robotiq_gripper/pull/54
You will need to grab the humble branch instead and that should fix the error.

Closing this as it looks like it is resolved


[Action] Integrate Lokalise

Feature Request
Integrate Lokalise actions bundled in the Fastlane repository.
See: https://github.com/lokalise/lokalise-fastlane-actions.
Motivation Behind Feature

I'd like to see Lokalise (a widely used translation service) actions to be part of the Fastlane repository.
This would mean having the Lokalise actions available in Swift as well.
See: https://github.com/lokalise/lokalise-fastlane-actions.

Actions like those are prime candidates for a fastlane plugin, as only a small part of the users will be interested in those, and they are coupled to an external service. With the actions already existing, it should be pretty straight forward to turn them into a plugin: https://docs.fastlane.tools/plugins/create-plugin/
(As far as I know, this won't actually solve your problem as fastlane swift doesn't support plugins yet, correct?)

Got it 👍
Yes, exactly, Fastlane Swift does not support plugins yet.
Guess we’ll have to support Swift plugins then 😉
Should we close this and reopen only once plugins are supported?

Well, someone would have to create that plugin first anyway... Do you want to take a shot at it?

For sure! 👍
Will only be able to do so after the 1st of Jan though...

We are not in a hurry here ;) Just post a link to your repo when done, then we can close this issue.


int32 instead of int32_t in  tensorflow/tensorflow/lite/kernels/internal/common.h

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Devuan Beowulf (Debian 10)
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): source
TensorFlow version (use command below): 2.5.0
Python version: 3.7.3
Bazel version (if compiling from source): 3.6.0
GCC/Compiler version (if compiling from source): (Debian 8.3.0-6) 8.3.0
CUDA/cuDNN version: NA
GPU model and memory: NA

Describe the current behavior
Line 192 of  tensorflow/tensorflow/lite/kernels/internal/common.h  references int_32
 int32x4x4_t input_val, int32 quantized_multiplier, int shift) {
                                          ^^^^^

compilation shows int32 is undefined
Describe the expected behavior
It should use int32_t
int32x4x4_t input_val, int32_t quantized_multiplier, int shift) {
^^^^^^
Standalone code to reproduce the issue
Any compilation attempt should fail
Other info / logs
NA

This should be fixed already. You might want to get the latest code version.


Deploy Playground production

Created loose testing plan for playground frontend: https://docs.google.com/document/d/1pwzyEebCbA-EfGmo3nL_ToGz2Wu8Y8maMuJh6cUeJxU/edit?usp=sharing if there are any specific flows missing then add them to the doc please
Setup Cypress dashboard for playground
Investigating using this public library to workaround cypress not selecting react components : https://www.npmjs.com/package/cypress-react-selector let me know if there are any concerns here because i think im going to followthrough

@MrDSGC I believe this comment should be somewhere else? :D


Feature Request: tf.assign() support tuples

I have recently updated to V1.3 of Tensorflow.  I have some code that I use for dynamic_rnn which copies the STATE of the cell so it persists to the next .run(), I can also INIT that value as well.  Since the update, I am getting a "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7f278c196940>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.".  I have tried to enable state_is_tuple but then the assign() commands fail as they don't support the tuple structures.
I have an open StackOverflow question with the details:
https://stackoverflow.com/questions/46576194/how-do-i-assign-a-lstmstatetuple-using-tf-assign
Since it seems like the RNN core is moving in the direction of the tuple for the state, it would be nice if the .assign() can handle this transparently.

@ebrevdo do we expect to add tuple support soon?

you can perform an assign on a tuple by using
variables = (var1, var2)  # or whatever; you can use map_structure with tf.get_variable here as well
updates = tf.contrib.framework.nest.map_structure(
  lambda state, var: tf.assign(var, state),
  final_lstm_state,
  variable_tuple,
  check_types=False)

@Mazecreator does @ebrevdo 's suggestion get you back in business?

@cy89 this might do it but it doesn't seem like "tf.contrib.framework.nest.map_structure()" exists.  I am using TF1.3 but checked the HEAD and didn't see that "nest" folder.  I tried to find if it moved to another location but couldn't find it.
I am sure this is a good short term solution within the active LSTM graph but will greatly complicate copying variables from one Net to another as the transparency of the .assign() would be appreciated.
Once we have a short-term solution I will post it on Stack Overflow in response to my open issue.

It might not be the version expected but I found a "map_structure" here:
from tensorflow.python.util.nest import *
Running the above code failed with this error so I must have missed something:
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in map_structure
  structure[0], [func(*x) for x in entries])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in <listcomp>
  structure[0], [func(*x) for x in entries])
TypeError: <lambda>() missing 1 required positional argument: 'var'

I have been working on this for a while now.  I was not certain what needs to replace "#variable_tuple," in the map_structure but have a better guess:
    output, self.new_state = tf.nn.dynamic_rnn(self.cell_L1,hidden_input,time_major=True,\
            initial_state=self.state,  dtype=tf.float32, swap_memory=True)

    update = tf.contrib.framework.nest.map_structure(
            lambda state, var: tf.assign(var, state),
            self.new_state,
            self.state,
            check_types=False)

    with tf.control_dependencies([update]):
        outputs = tf.identity(output)

The problem now is I get a different error which doesn't really make sense to me as Tensors should allow an assign() should it?
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in map_structure
structure[0], [func(*x) for x in entries])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in <listcomp>
structure[0], [func(*x) for x in entries])
File "/home/greg/DFP_trading_sequence/test6/Network_sequence_new1.py", line 143, in <lambda>
lambda state, var: tf.assign(var,state) ,
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/state_ops.py", line 272, in assign
return ref.assign(value)
AttributeError: 'Tensor' object has no attribute 'assign'

You can only assign something to a Variable object.  So if you want an
initial state it has to be a tuple of Variables (and cell.zero_state()
doesn't return Variables - only Tensors).  You can then assign to those in
your update.
On Tue, Oct 10, 2017 at 11:54 AM, Greg Peatfield<EMAIL_ADDRESS>wrote:

I have been working on this for a while now. I was not certain what needs
to replace "#variable_tuple," in the map_structure but have a better guess:
output, self.new_state = tf.nn.dynamic_rnn(self.cell_L1,hidden_input,time_major=True,\
        initial_state=self.state,  dtype=tf.float32, swap_memory=True)

update = tf.contrib.framework.nest.map_structure(
        lambda state, var: tf.assign(var, state),
        self.new_state,
        self.state,
        check_types=False)

with tf.control_dependencies([update]):
    outputs = tf.identity(output)

The problem now is I get a different error which doesn't really make sense
to me as Tensors should allow an assign() should it?
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in map_structure
structure[0], [func(*x) for x in entries])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/nest.py", line 325, in
structure[0], [func(*x) for x in entries])
File "/home/greg/DFP_trading_sequence/test6/Network_sequence_new1.py", line 143, in
lambda state, var: tf.assign(var,state) ,
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/state_ops.py", line 272, in assign
return ref.assign(value)
AttributeError: 'Tensor' object has no attribute 'assign'
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@ebrevdo @Mazecreator is this issue resolved? (I don't know anything about the RNN API).

I'm rather surprised that tf.Variable(self.init_state) even works!  Sounds
like a bug.  That should have failed in python-land right away! +lukasz
You need to do something like:
counter = [0]
def create_variable(init):
r = tf.get_variable(initializer=init, name="rnn_state_%d" % counter[0])
counter[0] += 1
return r
self.state = tf.contrib.framework.nest.map_structure(create_variable,
self.init_state)
On Thu, Oct 12, 2017 at 5:32 PM, Greg Peatfield<EMAIL_ADDRESS>wrote:

I think I understand the approach although I have yet to code the
solution. It seems like the more an more I dig into this approach the more
complexity and issue the rise. I may need to wait for the actual support
for assign() with Tuples. I ran into another issue as I was trying to
prototype this and it was initializing a variable with a Tuple only returns
a single combined tensor. Hopefully if Assign() supports tuples the
"variable()" will support Tuples as well to initialize. I find the biggest
problem is the complexity of other libraries by manually copying like this.
Also, I need to figure out how to screen for a tuple so when I "assign()"
the variables I don't collapse the tuple into a single tensor variable as
this was tough to find even in a short segment of code.
So I think I might be able to do a short-term solution if needed based
upon the comments from @ebrevdo https://github.com/ebrevdo , but I
really think if the RNN training is going to force the state to be a tuple
then the rest of TensorFlow should help support this new requirement.
Another option is to remove the warning statement and just document that
Tuples are handled faster and support both formats moving forward. I am
sure this is an easier solution for now, not sure about the long term
support issues as I am sure the team will need to think through.
Here is what I am talking about when the RNN cells are
"state_is_tuple=True"
    self.init_state = self.cell_L1.zero_state(batch_size, tf.float32)
    self.state = tf.Variable(self.init_state, trainable=False)

In the code above, this silently functions as you would expect. The
self.state is initialized with the Tuple value from self.init_state, but
what you get for self.state is actually a single Tensor variable with the
Tuple collapsed. This causes the RNN functions to fail later as they are
expecting a Tuple for the state.
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What you saw looks like a bug in tf.convert_to_tensor.  It treats complex
data structures, like LSTMStateTuple and nested LSTMStateTuples, as nested
tuples and converts them to initializers.  We'll look at fixing that bug
separately.
On Thu, Oct 12, 2017 at 7:57 PM, ebrevdo<EMAIL_ADDRESS>wrote:

I'm rather surprised that tf.Variable(self.init_state) even works! Sounds
like a bug. That should have failed in python-land right away! +lukasz
You need to do something like:
counter = [0]
def create_variable(init):
r = tf.get_variable(initializer=init, name="rnn_state_%d" % counter[0])
counter[0] += 1
return r
self.state = tf.contrib.framework.nest.map_structure(create_variable,
self.init_state)
On Thu, Oct 12, 2017 at 5:32 PM, Greg Peatfield<EMAIL_ADDRESS>wrote:

I think I understand the approach although I have yet to code the
solution. It seems like the more an more I dig into this approach the
more
complexity and issue the rise. I may need to wait for the actual support
for assign() with Tuples. I ran into another issue as I was trying to
prototype this and it was initializing a variable with a Tuple only
returns
a single combined tensor. Hopefully if Assign() supports tuples the
"variable()" will support Tuples as well to initialize. I find the
biggest
problem is the complexity of other libraries by manually copying like
this.
Also, I need to figure out how to screen for a tuple so when I "assign()"
the variables I don't collapse the tuple into a single tensor variable as
this was tough to find even in a short segment of code.
So I think I might be able to do a short-term solution if needed based
upon the comments from @ebrevdo https://github.com/ebrevdo , but I
really think if the RNN training is going to force the state to be a
tuple
then the rest of TensorFlow should help support this new requirement.
Another option is to remove the warning statement and just document that
Tuples are handled faster and support both formats moving forward. I am
sure this is an easier solution for now, not sure about the long term
support issues as I am sure the team will need to think through.
Here is what I am talking about when the RNN cells are
"state_is_tuple=True"
self.init_state = self.cell_L1.zero_state(batch_size, tf.float32)
self.state = tf.Variable(self.init_state, trainable=False)
In the code above, this silently functions as you would expect. The
self.state is initialized with the Tuple value from self.init_state, but
what you get for self.state is actually a single Tensor variable with the
Tuple collapsed. This causes the RNN functions to fail later as they are
expecting a Tuple for the state.
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I would like to still request either tf.assign() be updated to support Tuples to drastically reduce the complexity of updating the LSTM Tuple (as well as any future changes) or that the current merged tensor remain as supported.
For cases where the network is copied for parallel Reinforcement Learning threads where the global network is copied, the complexity is going to increase due to the unique nature or handling Tuple Named Tensors.

The current merged tensor behavior is broken since even though you can
assign a merged tensor to a variable, but then you have to slice it back up
when you want to read the states back out again; and in the meantime you've
lost the structure and shape of the intermediate states.
Having tf.assign support tuples may make sense; we'll look into it.  In the
meantime, you should be able to get pretty far using nest:
updated_variables = tf.contrib.framework.nest.map_structure(lambda v, s:
v.assign(s), variables, states)
where variables are created via map_structure using the rnn_cell's
state_size property or by looping over the zero_state tensors.
On Wed, Oct 18, 2017 at 10:06 AM, Greg Peatfield<EMAIL_ADDRESS>wrote:

I would like to still request either tf.assign() be updated to support
Tuples to drastically reduce the complexity of updating the LSTM Tuple (as
well as any future changes) or that the current merged tensor remain as
supported.
For cases where the network is copied for parallel Reinforcement Learning
threads where the global network is copied, the complexity is going to
increase due to the unique nature or handling Tuple Named Tensors.
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Thanks, I understand the concern.
The introduction of the Tuple ultimately creates yet another class of Variable which requires special handling, it would be nice to get this new internal structure to work like the other variables.  Maybe the nest.map_structure() can be include within the Assign() if there is some test to detect the input/output are tuples()?

Yes this is a possibility.  Are you interested in sending a PR?

nest.is_sequence can be used to test the variables

I am very tight on time at the moment and think this might need some more holistic thinking.  My thought is it is going to be more than just an Assign update to better integrate the Tuple into TensorFlow "seamlessly".  There is likely to be more issues as well which I am  not sure I will be able to handle as I am not familiar with Named Tuples so it is more than just moving the Tensor as the Name needs to go with it.
My thought right now is a few things need to change to integrate the Tuple():

Assign() (_add() and _subtract() should be considered as well, but don't think that is urgent)
Variable()       - Would be nice to Init a Tuple variable with another Tuple Tensor (Initial State for example)
get_variable() - Not sure if the Tuple would be be supported here
Not sure if Run() will be impacted and if Placeholder needs a face-lift as well...
How with other Ops handle a Tuple if it is passed in place of a Tensor?  Is this a good way to concatenate different inputs

I am tied up until about January/Feb of next year, so if the basic Assign() needs to wait until then I can hopefully tackle it at that time (I may need to come back for hints).  I think the more high level integration would be best within the development team as they would have better visibility of issues that maybe be caused or ways to handle the Tuple Tensor.
For now I need to use the Concatenated State until I have time to try to convert all the code to Tuples() as described above.

It has been 14 days with no activity and this issue has an assignee.Please update the label and/or status accordingly.

Nagging Assignee: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @ebrevdo: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Let's move this discussion to<EMAIL_ADDRESS>which is more appropriate for design docs and proposals.


Suggestion for performance.

Perhaps we should not send the entire reference model and reference sequence to the JavaScript client by default. This could be done on request, if it is absolutely needed.

The sequence is no longer included in the response.


Using Pulumi

I had a question around what else is needed to use Pulumi.  It says to install, but are there any other requirements.
I'd suggest adding this to the README is something else is needed.
I'm getting an error when trying to run simulation 1
ModuleNotFoundError: No module named 'pulumi'

Concur. I have never touched Pulumi before this and I can't get it working. I setup and account and I'm getting this error when trying to run AWS scenario

@aseto-pan I'm updating the readme to include some more details on what is needed.


Add DamagePopup, Target Entities, And Practice Laser Gun

About the PR
Adds Targets for security

that when damaged, show the amount of damage they took, and their total damage. they can be repaired with a welding tool and crafted at a security techfab.

Adds a damage popup component and system.
And finally, adds a practice laser gun, identical to the normal laser gun, except its projectiles do no damage.

Changelog
:cl: Daemon

add: Added security target dummies.
add: Added practice laser gun.

I have not read the code conventions and you cannot make me


Feature/replication ui

This PR introduces a new replication UI.
A new view and menu entry will appear if you are logged in into the _system database:
Replication
This view will change its content depending on what replication setup is running (mode):

No replication running
Master/Slave (default single applier - per database)
Master/Slave (global replier - per server)
Active Failover

Not edited CHANGELOG.md by now, because it is not clear whether this feature will make it into 3.3.9 or 3.3.10. Keep In mind!
Also after merge we need a new PR for devel.

Do we also need to add the stateAll function to the applier in<EMAIL_ADDRESS>Otherwise the function will not be present in arangosh, but only in arangod.
For consistency I think it should be added in arangosh too.


SELECT command denied to user 'username'@'logstash_server' exception

I am not sure if it's an issue but more of a use case I think. So here's what's happening -
i have installed Logstash on one server and my database on which I am writing the SELECT query is on a different server. So when I run Logstash with JDBC plugin, I get the SELECT command denied for my username exception. Here's my config -
	input {
		jdbc {
			jdbc_connection_string => "jdbc:mysql://database_server:port/"
			jdbc_user => "username"
			jdbc_password => "password"
			jdbc_validate_connection => true
			jdbc_driver_library => "/tmp/mysql-connector-java-5.0.8-bin.jar"
			jdbc_driver_class => "com.mysql.jdbc.Driver"
			statement => "SELECT * from database.table where time > now() - interval 1 minute"
		}
	}

	output {

	   elasticsearch {
									cluster => "ClusterName"
									host => "hostName"
									protocol => "transport"
									port => "9300"
									index => "logstash-%{+YYYY.MM.dd}"
									workers => 4
	   }
	   stdout {
			codec => rubydebug
	   }
	}

Wehn I run logstash process with this configuration on logstash_server, I get the following exception -
 Exception when executing JDBC query {:exception=>#<Sequel::DatabaseError: Java::ComMysqlJdbcExceptions::MySQLSyntaxErrorException: SELECT command denied to user 'username'@'logstash_server' for table 'table'>, :level=>:warn}

Does that mean I need Logstash to be installed on database_server itself? Can't Logstash installed on a remote server connect to the database_server using the jdbc_connection_string?
Thanks!

same issue here

Of course LS can be installed on a separate server from the DB. Are you sure you have the right permissions setup on the DB end?

Also, can we move this to our forum? The forum is also the best place to debug config issues, getting help etc. Github is mainly used for issues/enhancements. Thanks!


[XXX] add: github workflow

[MTB-XXX] CI update.
Description
GitHhub Actions Workflow
What does this PR do?
Adds New GitHhub Actions Workflow
Why is this change necessary?
Why is this change necessary?
This change is necessary to automate testing and ensure code quality through continuous integration.
Related Issue
If applicable, please link to the issue that this PR addresses: Trello issue

Closes #[x42x]

Change Type
Please select the relevant options:

[ ] Bug fix
[x] New feature
[ ] Documentation update
[ ] Other (please specify):

Testing Information
Provide details about the tests you have conducted to validate your changes.
Review Checklist

[x] I have reviewed the contributing guidelines.
[x] I have updated the documentation (if applicable).
[x] I have added tests to cover my changes (if applicable).
[x] All new and existing tests have passed.

Additional Notes
Please provide any additional information or context that may be helpful for the reviewer.

In order to win you must be prepared to lose sometime. And leave one or two cards showing.

Van Morrison


Implementing Totem commands support

What does this command do?

Just get the device imei.
if you want, give me a second, I'll add more commands to this protocol

Do we actually need this command? What's the practical application for it? I think it would be better to have location request command or something like that.

Also, I noticed there is some problem with frame decoder. We need to fix it properly instead of catching the exception. Can you please provide a log file with HEX messages to and from device.

just for to test it and check if everything works, I'll remove then
I will remove the try-catch too

OK, please update the pull request when you implement some useful commands so I can integrate it into the Traccar project. Thanks.

For this protocol, needs to set a device password to send a command, so I added this key KEY_GPS_PASSWORD to  Command.java
this is an example how it works:
jQuery.ajax({
        type: "POST",
        url: "http://URL:8082/api/command/send",
        dataType: "text",
        data: JSON.stringify({
            deviceId: 2,
            type: "engineStop",
            other: {"gpsPassword": "000000"}
        })
    });

but I don't know what changes should I make on your website for this to work

That's a good point. Many devices require password. I need to think of some generic solution.
By the way, your unit test fails.

sorry, I forgot update it
I already fix it, try again

Following code is incorrect. Need to implement it properly.
buf.readBytes(buf.readableBytes());
Everything else looks good. What is the default password? For now I think we can hardcode the default one and I will fix it properly in the next version. I want to release a new version next week, so I don't have time to add it into this version.

Sorry sir, I don't know how implement a properly solution
the gps response is like these:
for stop engine
Receive:'025'OK
*000000,025,C,1#

for resume engine
Receive:'025'OK
*000000,025,C,0#

Default password is 000000

Can you please provide original HEX messages of the tracker's response.

Stop engine response
526563656976653a27303235274f4b0d0a2a3030303030302c3032352c432c3123

resume engine response
526563656976653a27303235274f4b0d0a2a3030303030302c3032352c432c3023

So, there is no line break at the end?

No, there isn't

Anyway, thanks for the pull request. I will think how to fix it properly and merge it to the main branch as soon as I find some free time to do it.

Great!
Thanks for support

I have made some minor modifications and added default password. You can pull the changes from master if you want to be in sync.

already did it, thanks


Could hospitalization data be added?

At least one province (BC) has started publishing hospitalization numbers. This seems like an important statistic to follow. Could the available data be added to the spreadsheet?
For BC the daily reports are listed on http://www.bccdc.ca/health-info/diseases-conditions/covid-19/case-counts-press-statements. An example report is http://www.bccdc.ca/Health-Info-Site/Documents/BC_Surveillance_Summary_March_27_final.pdf. I've extracted some of the data here https://docs.google.com/spreadsheets/d/1uz-hq7ncFff92iSh63_G-oFew2O1F_2eHOfr1_zdREg/edit?usp=sharing

For Quebec, hospitalization numbers have been given at the daily press conference for a few days. There’s a transcript, so it should be easy to find.
Start here to check each day’s transcript:
http://www.assnat.qc.ca/fr/calendrier-detaille.html

Hi there-- Thanks for your message. We recognize that collecting hospitalization information is important however at this time we are not able to collect this as this is not readily available at the individual level across the whole country.


Created the Tower of Hanoi Video and file

Description 📜
I have recorded and added the video for The Tower of Hanoi problem and created the file as well.
Kindly check
Fixes #3247

Type of change 📝

[ ] Au#dio (Should be in mp3 format Includes speech clarity, Concise ,Low distortion)
[x] Video (Animations, screen-recordings, presentations and regular explanatory films are all possibilties etc)
[ ] Doc#umentation (Content Creation in the form of codes or tutorials)
[ ] Other (If you choose other, Please mention changes below)

Domain of Contribution 📊

[ ] Android Dev #(Flutter)
[ ] Android Dev #(Java)
[ ] Android Dev #(Kotlin)
[ ] Backend Dev #(Java)
[ ] Backend Dev #(.NET)
[ ] Backend Dev #(PHP)
[ ] Backend Dev #(Python)
[ ] C/#CPP
[ ] Competitive #Programming
[ ] Cyber #Security
[x] DSA
[ ] Data#base
[ ] Datascience with #Python
[ ] Datascience with #R
[ ] Frontend Dev #HTML/CSS/JS
[ ] Frontend Dev #React/Angular/Vue
[ ] Go#lang
[ ] Interview #Prep
[ ] Java_#Domain
[ ] JavaScript_#Domain
[ ] ME#RN
[ ] Machine #Learning
[ ] Open #Source
[ ] Python_#Domain
[ ] Ru#st
[ ] Statis#tics
[ ] UI/#UX

Checklist ✅

[x] I follow Contributing Guidelines & Code of conduct of this project.
[x] I have performed a self-review of my own code or work.
[x] I have commented my code, particularly in hard-to-understand areas.
[x] My changes generates no new warnings.
[x] I'm GWOC'21 contributor

Screenshots / Gif (Optional) 📸

@nimisha-yadav Kindly Review!

@nimisha-yadav  Kindly Review the PR!

@nimisha-yadav All Done!
Kindly Check!!

Hey @nimisha-yadav!
I think something wrong happened while pushing into the repository. I am closing this PR as it has been a mess now and making a new PR for the same.
Sorry for the inconvenience.
Thank You

Kindly review pr #8441


Frida: Get typing hint for frida Command Error (Cannot create process, error code : 193)

The terminal process failed to launch: A native exception occurred during launch (Cannot create process, error code: 193).

I solved it by changing this code :
from \codecolorist.vscode-frida-0.7.0\out\commands\typing.js :
function npmInstall() {
    const name = `npm`;
    const shellPath = 'npm';

...

that
function npmInstall() {
    const name = `npm`;
    const shellPath = 'npm.cmd';

...

any other solution ?

Good catch.
https://github.com/ChiChou/vscode-frida/blob/main/src/commands/typing.ts#L11
It should be executable("npm"), like this
https://github.com/ChiChou/vscode-frida/blob/main/src/commands/lldb.ts#L32

It's not a .exe file, but a .bat file.

Windows is 💩 for real