Avoid using MouseEvent.initMouseEvent

In https://github.com/cibernox/ember-native-dom-helpers/blob/master/addon-test-support/helpers.js#L69
we use MouseEvent.initMouseEvent() which is deprecated in favor of the plain constructor: https://developer.mozilla.org/en-US/docs/Web/API/MouseEvent/MouseEvent
Might have issues down the line.
Also curious about the undocumented +1, +5, +95 here https://github.com/cibernox/ember-native-dom-helpers/blob/master/addon-test-support/helpers.js#L42
I can change out to use the constructors when I get the chance

@alexander-alvarez the fireEvent function is taken right off the Ember event method here: https://github.com/emberjs/ember.js/blob/aeeddf7c00b42a340494465252481b5414d87009/packages/ember-testing/lib/events.js#L29-L51 and likewise the buildMouseEvent function here: https://github.com/emberjs/ember.js/blob/aeeddf7c00b42a340494465252481b5414d87009/packages/ember-testing/lib/events.js#L60-L85
We can improve as needed. However some deprecated methods are still the most compatible across browsers, so will have to consider that as well.
For example, see https://github.com/emberjs/ember.js/blob/aeeddf7c00b42a340494465252481b5414d87009/packages/ember-testing/lib/events.js#L54 and https://github.com/cibernox/ember-native-dom-helpers/blob/master/addon-test-support/helpers.js#L58 notice the shift to native events only, no hep from jQuery. MDN recommends using Event constructors vs document.createEvent here https://developer.mozilla.org/en-US/docs/Web/API/Document/createEvent
So far we started with borrowing the existing acceptance test helper events and made some updates to fix issues regarding using native events and DOM API only (no jQuery).

I can confirm that because I also did the emberjs helpers. The only reason why I use the awful initmouseevent is because of Internet Explorer. We can't drop it until ember 3.0


Remove context menu when you clicked.

This change will remove contenxt menu when you click the item.
My issue was that open a modal dialog by clicking the item from the context menu leaves the menu. I think this should be removed?
Thank you!

I have amended the code that 'active' property can be function that set active = true/false dynamiclly.
It would be great if you merge the change.
Thank you.

@NishiokaTakeo Not sure this plugin could still be of any use :) thanks for edits.


Ground work for #521

More ground work towards integration of the new type checker:

DeepCopy is now copying types.
Add TestDeepCopy to test that DeepCopy does a complete copy, including types, but does not copy the identifiers.
IrGenerators are now generating tag types: either Untyped() or Typed[Int](i) for a random number i.
Move the test (yep, the only one!) for InlinerOfUserOper to its own class.
Add missing annotation @RunWith(classOf[JUnitRunner]) to several tests. They were not running automatically!

[x] Tests added for any new code
[x] Ran make fmt-fix (or had formatting run automatically on all files edited)
[ ] ~Documentation added for any new functionality~
[ ] ~Entry added to UNRELEASED.md for any new functionality~

Thank you for staging these well-sized, reviewable change sets! <3 <3 <3

@shonfeder could you have a look at this PR one more time?


add skipRouter param to hardhat-router deploy task

this is no longer relevant with router being generate only now in hardhat-router


First readStream contains invalid samples

Thanks for this very interesting project!
I have been playing with Soapy and an Airspy when I noticed that the FFTs on my first samples did not always came out as expected. Looking closer I noticed that the 1st few complex samples always contained some 0s and from thereon on never again. This seems a bit strange and almost like either the first buffer has some wrong data in it or I am reading it wrong.  Hopefully this is something I am doing wrong.
I allocate the buffers as such:
	vector<vector<char>> buffMem(1, vector<char>(SoapySDR::formatToSize("CF32") * mtu));
	vector<void *> buffs(1);
	for (size_t i = 0; i < 1; i++) buffs[i] = buffMem[i].data();
	int flags(0);
	long long timeNs(0);
	vector<vector<float>> sampleBuff(5 * 2048, vector<float>(2));		// Holds the IQ samples

then read a couple of sample with:
	for (int l = 0; l < 4; l++)
	{
		// Read stream
		int elementsRead = device->readStream(st, buffs.data(), mtu, flags, timeNs);

		cout << elementsRead * SoapySDR::formatToSize("CF32") << " bytes have been read. Flags = " << flags << endl;

		for (int i = 0; i < buffMem[0].capacity(); i += SoapySDR::formatToSize("CF32"))
		{
			deciCnt++;

			if (((i + 8) <= buffMem[0].capacity()) && deciCnt == 1)
			{
				// Bytes packed back into floats
				float ival;
				float qval;
				unsigned char ci[] = { buffMem[0][i + 0], buffMem[0][i + 1], buffMem[0][i + 2], buffMem[0][i + 3] };
				unsigned char cq[] = { buffMem[0][i + 4], buffMem[0][i + 5], buffMem[0][i + 6], buffMem[0][i + 7] };
				memcpy(&qval, &cq, sizeof(qval));
				memcpy(&ival, &ci, sizeof(ival));

				// Store the IQ values
				sampleBuff[sampleCount][0] = ival;
				sampleBuff[sampleCount][1] = qval;
				sampleCount++;

				deciCnt = 0;

			}
		}
	}

I am reading 2048 (MTU) elements at a time.
Output of the first few samples
0	9.75201e-007	0
1	-1.63161e-006	0
2	2.91294e-006	0
3	-5.7514e-006	0
4	1.00109e-005	0
5	-1.65721e-005	0
6	2.5934e-005	0
7	-4.02493e-005	0
8	6.21305e-005	0
9	-9.65394e-005	0
10	0.000161523	0
11	-0.000401581	0
12	-0.000173937	-0.000727539
13	-0.000135679	0.000232104
14	0.000481409	-0.000474067
15	-0.00017729	0.000708774`
...

Any idea what could be the cause of this?
As a side question is there some builtin byte packing function or is this pretty much the way everyone else also do it?

The IQ conversion routine in libairspy has a delay of 12 samples.

Ah okay, that is an easy fix for me then. Thanks!


Add automatic black formatting to PRs, blacken SDK

This adds auto formatting for PRs (a limitation of the tool), black configuration, and also blackens the SDK code.

Codecov Report

Merging #284 (a5158e2) into main (29e1d8a) will not change coverage.
The diff coverage is 100.00%.

:exclamation: Current head a5158e2 differs from pull request most recent head d77b0d0. Consider uploading reports for the commit d77b0d0 to get more accurate results

@@           Coverage Diff           @@
##             main     #284   +/-   ##
=======================================
  Coverage   97.33%   97.33%
=======================================
  Files          25       25
  Lines        1090     1090
=======================================
  Hits         1061     1061
  Misses         29       29

Files Changed
Coverage Δ

src/vonage/verify.py
100.00% <ø> (ø)

src/vonage/account.py
100.00% <100.00%> (ø)

src/vonage/application.py
100.00% <100.00%> (ø)

src/vonage/client.py
94.24% <100.00%> (ø)

src/vonage/ncco_builder/ncco.py
97.67% <100.00%> (ø)

src/vonage/ncco_builder/pay_prompts.py
100.00% <100.00%> (ø)

src/vonage/number_insight.py
100.00% <100.00%> (ø)

src/vonage/number_management.py
100.00% <100.00%> (ø)

src/vonage/redact.py
100.00% <100.00%> (ø)

src/vonage/short_codes.py
100.00% <100.00%> (ø)

... and 3 more

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


Blocking while popping

Is there a better way to block until you have something to pop other than:
while input.is_empty() {}
let x = input.pop().unwrap();

?

If you expect to be waiting for more than a few hundred nanoseconds, it's probably better to use a queue that has a proper blocking API? Maybe crossbeam::channel?
If you are only waiting for a short time, you can use rtrb, but depending on your application, you might want to add something like spin_loop() or some more fancy way to avoid maxing out the CPU. But other than that, I think your code is fine.
Another, not necessarily better way to do it:
let x = loop {
    if let Ok(item) = input.pop() {
        break item;
    }
    // relax a while
};

And yet another, but this time definitely worse way of doing it:
fn crazy(input: &mut rtrb::Consumer<i32>) -> i32 {
    input.pop().unwrap_or_else(|_| {
        // relax a while
        crazy(input)
    })
}
let x = crazy(&mut input);

Apart from being crazy, this will also quickly lead to a stack overflow.
I was just curious if that would work.

In case anyone finds this useful, here's my approach:
let backoff = crossbeam_utils::Backoff::new();
loop {
    match consumer.pop() {
        Ok(item) => {
            backoff.reset();

            // do stuff with `item`
            // ...
        }
        Err(rtrb::PopError::Empty) => {
            if backoff.is_completed() {
                thread::sleep(Duration::from_micros(/* your desirable duration */));
            } else {
                backoff.snooze();
            }
        }
    }
}

This still keeps Producer::push very fast, while potentially trading off some latency on the consumer side.
As a bonus - if you replace thread::sleep with tokio::time::sleep/tokio_timerfd::sleep (or similar), then you can have a producer in sync code, and a consumer in async code.


hostmux: init at 1.4.0

Description of changes
Small wrapper script for tmux to easily connect to a series of hosts via ssh and open a split pane for each of the hosts.
Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
23.11 Release Notes (or backporting 23.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

You'll need to work a little more on your PR. Currently, when I try to run this, I get an error, that tmux isn't found. If I hadn't installed ssh on my system I would get the same error. You need to edit your derivation such that calls to tmux get replaced by absolute paths to the tmux binary in the nix store. The same needs to be done for ssh. You should be able to use the substituteInPlace function for that.
You should also add meta.mainProgram as lib.getExe will no longer assume the package name to be the name of the main program.
Also, in order to be able to use the added program properly, you should also include it in all-packages.nix.
Read the nixpkgs manual for more information.

@AndersonTorres thanks for your time and your review. I applied all you suggestions.

@antonmosich I thought buildInputs = [ tmux openssh ] solves the problem that ssh + tmux are found, do i really have to patch the shell script?

Yes, you still need to patch the script.
You can try out nix-shell -E 'with import /path/to/your/nixpkgs { }; mkShell { buildInputs = [ hostmux ]; }' --pure. The --pure makes sure that there are no other environment variables "polluting" your shell (such as a pre-populated PATH).
Running hostmux in that shell should fail.


Negative duration in Diarization

I have noticed that sometimes the diarization script calculates a negative duration. After tracking it down it turned out that distribute_overlap method in https://github.com/speechbrain/speechbrain/blob/ff3bca4c05cbf01aff50139b0d61b5687482ad45/speechbrain/processing/diarization.py in which the end is moved back without checking if it crosses the start.

@nauman-daw

Hi @Aya-AlJafari ,
Thanks for pointing this out.  Are you refering to this line?  https://github.com/speechbrain/speechbrain/blob/ff3bca4c05cbf01aff50139b0d61b5687482ad45/speechbrain/processing/diarization.py#L267
Can you please give an example of overlapping segments (just start and end points)  where this can happen?

Hello @Aya-AlJafari,
Could you please give us some news about this issue? Is it still up? Thanks! :-)

Hello,
It seems that there has been no activity for a very long time, so I will close this issue.
Please open a new issue with all the required information so that we can deal with your problem. Thank you!


Kenlm error while running bash scripts/prepare_model.sh config/data.en.config

Hello, there!
I am trying to reproduce the findings from your paper for a school project using Google Colab.
After installing packages including Kenlm, when I type:

bash scripts/prepare_model.sh config/data.en.config

I keep getting the following error:

Traceback (most recent call last): File "kenlm.pyx", line 139, in kenlm.Model.init RuntimeError: util/file.cc:76 in int util::OpenReadOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 00))'. No such file or directory while opening /content/mtnt/models/corpus.en.1000.bpe.lm.5.bin
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File "scripts/eval_kenlm.py", line 14, in model = kenlm.Model(model_file) File "kenlm.pyx", line 142, in kenlm.Model.init OSError: Cannot read model 'models/corpus.en.1000.bpe.lm.5.bin' (util/file.cc:76 in int util::OpenReadOrThrow(const char*) threw ErrnoException because `-1 == (ret = open(name, 00))'. No such file or directory while opening /content/mtnt/models/corpus.en.1000.bpe.lm.5.bin)

Please advise. I would appreciate your input. Thanks

Hello, there!
I am trying to reproduce the findings from your paper for a school project using Google Colab.
After installing packages including Kenlm, when I type:

bash scripts/prepare_model.sh config/data.en.config

I keep getting the following error:

Traceback (most recent call last): File "kenlm.pyx", line 139, in kenlm.Model.init RuntimeError: util/file.cc:76 in int util::OpenReadOrThrow(const char*) threw ErrnoException because -1 == (ret = open(name, 00))'. No such file or directory while opening /content/mtnt/models/corpus.en.1000.bpe.lm.5.bin The above exception was the direct cause of the following exception: Traceback (most recent call last): File "scripts/eval_kenlm.py", line 14, in model = kenlm.Model(model_file) File "kenlm.pyx", line 142, in kenlm.Model.init OSError: Cannot read model 'models/corpus.en.1000.bpe.lm.5.bin' (util/file.cc:76 in int util::OpenReadOrThrow(const char*) threw ErrnoException because -1 == (ret = open(name, 00))'. No such file or directory while opening /content/mtnt/models/corpus.en.1000.bpe.lm.5.bin)

Please advise. I would appreciate your input. Thanks

I'm also facing the same problem...


ipython widgets broken in docker container

our tutorial produces the following error and then fails to run:
ShimWarning: The IPython.html package has been deprecated. You should import from notebook instead. IPython.html.widgets has moved to ipywidgets.
"IPython.html.widgets has moved to ipywidgets.", ShimWarning)

the API has changed iwth ipywidgets 5:  https://ipywidgets.readthedocs.org/en/latest/test.html
that imports without error but fails to run, which makes sense because we include some of the widget JS in beaker and it probably has to be updated to work with the latest version.
for now probably the best fix is to force the docker container (the base container) to use the old version.
however when i look in it, it seems to have ipywidgets 4.1.1 so i'm not really sure what's going on.

with pip i can only go back to ipywidgets 4.0.0 and that is still broken and that is the earliest version.
so this probably broken when it split off to a new package.
may have to move ipython itself back an earlier version.

https://github.com/ContinuumIO/anaconda-issues/issues/470

https://github.com/ipython/ipython/issues/8905


Refactor setSDKReadMany and setResourceForScalar

Issue #, if available: #922
Description of changes:

Refactor setSDKReadMany:

use FindPluralizedIdentifiersInShape helpers to simplify logic

Refactor setResourceForScalar:

Remove unused params
Add targetVar param to be the variable's fully-qualified name (i.e. fieldPath+varName r.ko.Spec.CacheClusterID)

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

/ok-to-test


Bugs in Querying for public microbiome data in Qiita using redbiom tutorial

When selecting the copy button and then pasting to the command line there is an error "command not found".
If the same command is selected (highlighted) using the the keyboard and then pasted to the terminal command prompt it works fine.
$ cat > features << END
TACGTAGGTGGCGAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGATTGGCAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGG
TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAGGGAGCGCAGGCGGTCTGGCAAGTCTGATGTGAAATACCGGGGCTTAACCCCGGAGCTGCATCCAAAACTGTAGTTCTTGAGTGGAGTAGAGGTAAGCGGAATTCCGAGT
TACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTTGTTAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGCAGGCGGAATTCCGTG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGTAAGACAAGTCAGAAGTGAAAACCCAGGGCTTAACTCTGGGACTGCTTTTGAAACTGTCAGACTGGAGTGCAGGAGAGGTAAGCGGAATTCCTAG
TACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGAGAACAAGTTAGTTGTGAAATACCTCGGCTCAACTGAGGAACTGCAACTAAAACTGTACTTCTTGAGTGCAGGAGAGGTAAGTGGAATTACTAG
TACGTAGGTGACAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGCGTAGGCGGGATAGCAAGTCAGTCGTGAAATACCGGAGCTCAACTCCGGGGCTGCGATTGAAACTGTTATTCTTGAGTATCGGAGAGGAAAGCGGAATTCCTGG
TACGTAGGGGGCAAGCGTTATCCGGAATTACTGGGTGTAAAGGGAGCGTAGACGGTGATGTAAGTCTGATGTGAAAGCCTCCGGCTCAACCGGAGAATTGCATCAGAAACTGTTGAACTTGAGTGCAGAAGAGGAGAGTGGAACTCTATG
TACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCATGGGCTCAACCTCCAGCCTGCATTTGAAACTGTAGTTCTTGAGTGTCGGAGAGGCAATCGGAATTCCGTGT
TACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGCAGCCGGGCTGACAAGTCAGATGTGAAATCCCGAGGCTTAACCTCGGAACTGCATTTGAAACTGTTAGTCTTGAGTATCGGAGAGGTCATCGGAATTCCTTG
TACGTAGGTGGCGAGCGTTATCCGGAATTATTGGGCGTAAAGGGAGCGTAGACGGCTGTGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTGCAGCTAGAGTGTCGGAGAGGTAAGCGGAATTCCTAG
END
-bash: $: command not found
Another related issue a little further down the tutorial
$ redbiom summarize features --from features --context $CTX --category qiita_empo_3
Animal distal gut	10
Total samples	10
This is the results when using the copy button.
The results should actually be
$ redbiom summarize features --from features --context $CTX --category qiita_empo_3
In addition it would be nice if the $ could be drop from the copy button results.
Hope this help
Thanks

Thank you for reporting.
Just so I can reproduce; what's the copy button? Do you have a screenshot?

Hi Antonio,
The copy button appears in the upper right hand corner of the second rectangle when the cursor is placed in the box. When you select the botton (hotspot) is flashes “copied!”.

On Sep 17, 2021, at 3:49 PM, Antonio Gonzalez @.***> wrote:
Thank you for reporting.
Just so I can reproduce; what's the copy button? Do you have a screenshot?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub https://github.com/qiita-spots/qiita/issues/3148#issuecomment-922158602, or unsubscribe https://github.com/notifications/unsubscribe-auth/AVVYWNSMQKPW72FZMTKX2RTUCPVZTANCNFSM5EIN4REQ.
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Thank you; however, I'm still not sure what we are talking about; do you have a link to the tutorial you are talking about? Note that I searched for redbiom summarize features and cat > features in the Qiita's documentation and I couldn't find it ...

Aloha Antonio,
Here is the link you ask for:
https://forum.qiime2.org/t/querying-for-public-microbiome-data-in-qiita-using-redbiom/4653
Thanks.,
John

On Sep 17, 2021, at 3:49 PM, Antonio Gonzalez @.***> wrote:
Thank you for reporting.
Just so I can reproduce; what's the copy button? Do you have a screenshot?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub https://github.com/qiita-spots/qiita/issues/3148#issuecomment-922158602, or unsubscribe https://github.com/notifications/unsubscribe-auth/AVVYWNSMQKPW72FZMTKX2RTUCPVZTANCNFSM5EIN4REQ.
Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

Thank you for the link, now it makes sense: that's in the QIIME 2 forum and not part of the Qiita basic tutorials; thus it needs to be fixed directly there.
(1) @AlohaPropolis, do you have a pencil beside the title so you can edit that tutorial? Basically, the $ needs to be deleted from the code text.
(2) pinging @wasade as he might need to fix as he's the original author, if (1) doesn't work.
BTW, AFAIK you can add comments directly to that tutorial raising this bugs.

Ah I see, thanks. The "$" is used to denote a prompt, and I think is beneficial in that context to differentiate from standard input and output. I'm not sure what the semantics are of the copy button on there TBH and may be something with discourse?

Closing as this report is not actually related to the Qiita's system but the help in the Q2 forum.
@AlohaPropolis; thank you for your report.


sti --run=true test

@bparees here's a script for testing sti --run=true
In another git newbie instance, it also includes the cherry picked gofmt related change from test, but the change is the same, so it should be no harm, right?
I'm also working on the vagrant-openshift change to call this script, which of course will be a separate PR.
thanks

the cherry-picked commit would normally not be a problem except that it appears your cherry-picked commit id is different from the commit id that actually got merged.  not entirely sure why, my only theory is that in the original PR the commit was updated/force-pushed after you cherry-picked and before the PR was merged.
so as it is you'll need to rebase and delete the cherry-picked commit during the rebase.
regarding the PR itself....  i'm wary of adding a standalone shell script for a single test, but i guess we can live with it for now and refactor is we find ourselves adding more tests of this nature (which we probably should, someday... or move to the extended test model we're doing in openshift).
however in order to get this test run on test/merges you'll need to go edit the vagrant plugin so it actually invokes this script when testing PRs.  you can do that after this PR merges.

2 comments but generally lgtm.  seems like you've mastered your shell-script fu :)

re: cherry pick ... ahhhh, I was worried about that ... ok, I'm looking at various git man pages / doc, but I'll probably look for a consult before pulling the trigger on the next git operation ;-)
re: script vs. extended model ... yeah, this crossed my mind, but given how we landed here, and the lack of tests in general, I shied away from heavy investment at this time and went quick and cheap.  If you want to simply punt on this script at this time, let me know.

Oh, and as I noted in the description, yep, already started on the vagrant plugin, and will merge after this one does.

whoops. glossed over that last sentence..cool.

ok ... those couple of edits are now pushed (minus the cherry pick commit)

lgtm [merge]

[test]

[merge]


[FP]: form-filler-addon has been reported wrongly

Package URl
<EMAIL_ADDRESS>CPE
cpe:2.3:a:vaadin:flow:1.0.0:pha1::::::
CVE
CVE-2019-25027, CVE-2021-31412, CVE-2018-25007, CVE-2021-31404
ODC Integration
{"label"=>"Maven Plugin"}
ODC Version
8.4.0
Description
This version of form-filler-addon is the new product by Vaadin, it should not be included in the reported CVE. as it is a prerelease version, the artifact is in our prerelease nexus instead of maven central.

I made a PR that combine this fix with previous ones.


devenv container  --docker-run  sometimes fails (doesn't cause an image to be put into the local docker-daemon)

The symptom is that in certain configurations (yet unknown), skopeo fails to copy the container from the Nix json to the local docker-daemon registry upon devenv container <name> --docker-run with a "too many levels of symbolic links" kind of error under skopeo 1.14.1 and a silent failure under skopeo 1.13.X
I filed these upstream reports:
https://github.com/nlewo/nix2container/issues/112
https://github.com/containers/skopeo/issues/2204

:sob: : This turned out to be because I had a directory named "nix" as a subdir of my devenv project.  Any devenv project with a "nix" subdir will have the same issue.
I've created the minimal possible reproduction case at https://github.com/mcdonc/devenv-nix2container-bugrepro
Its readme demonstrates how to reproduce the problem.

As I think about this, I suppose it makes sense.  It's trying to overlay "/nix" on the container image.  :sob:

I suppose this makes sense as it's trying to overlay /nix on the container image.  :sob:

This will be fixed by https://github.com/cachix/devenv/pull/963


ERROR TypeError: dropListRef._cacheOwnPosition is not a function - Angular 10

After force upgrade to angular 10, It has the following console error when drag and drop:
core.js:4352 ERROR TypeError: dropListRef._cacheOwnPosition is not a function
at cdk-drag-scroll.js:401
at ZoneDelegate.invokeTask (zone.js:407)
at Object.onInvokeTask (core.js:27474)
at ZoneDelegate.invokeTask (zone.js:406)
at Zone.runTask (zone.js:179)
at invokeTask (zone.js:488)
at ZoneTask.invoke (zone.js:477)
at timer (zone.js:2540)

For whoever is still using this package, the latest angular CDK has official support for the scroll(just use cdkScrollable directive on the container) - I have removed this package from my project.

I have the same issue. Angular 9


Matrix table output updates

Make sure the final soft-filtered matrix table is annotated with LGT (see https://github.com/populationgenomics/ancestry/pull/82), cc @KatalinaBobowik
Save the raw matrix table (that comes from the combiner and doesn't have soft-filters) to a temporary bucket.
Add a version of the matrix table without the reference blocks (-nonref.mt), cc @tiboloic.

Sucessful run in test: https://batch.hail.populationgenomics.org.au/batches/3353, output matrix table without reference blocks, annotated with LGT: gs://cpg-tob-wgs-test/mt/v3.2-nonref.mt/

Looks great :D


Fix code scanning alert - Database query built from user-controlled sources

Tracking issue for:

[ ] https://github.com/erasmosoares/sample-node/security/code-scanning/3

Fixed


Categorical features use causes crash returning from train function

Hi,
I am using the Python version of LGBM on windows 7 ultimate.
I will try to get a reproduction for this, but essentially, I find that if I use the categorical_feature override to the train function (specifying the columns as a list of strings), then the model trains fine, and  prints out:
Early stopping, best iteration is:
[97]	valid_0's l1: 0.047323
But then dies with:
File "C:/Projects/Zillow/ZillowHandStacker/SearchTreeParams.py", line 468, in search_lgbm_zillow
model = lgbm.train(train_set=d_train, params=lgbm_params, categorical_feature = cat_cols_for_lgbm, num_boost_round=2000, valid_sets=d_cv, early_stopping_rounds=50)
File "C:\Users\Tom\Anaconda3\lib\site-packages\lightgbm\engine.py", line 223, in train
booster._load_model_from_string(booster._save_model_to_string())
File "C:\Users\Tom\Anaconda3\lib\site-packages\lightgbm\basic.py", line 1608, in _save_model_to_string
ptr_string_buffer))
OSError: exception: access violation writing 0x000000000AC71000
Lowering the number of booster rounds helps somewhat, but it's still slow. I have also found that using categorical features makes the CV function blow up on memory.
What's odd with the above is it seems to be in the creation of the return data, as the actual fitting has already been done.
I have some fairly large categorical features - not sure exactly but a couple will be about 20-30k or so.
When I don't use this parameter all is fine, and in fact training seems better.
Perhaps some guidelines on when to use this feature/what it does under the hood might be useful if this is expected behaviour (though I guess access violation is never expected :) ) If these categories with very many distinct values are being one-hotted under the hood I can see why that would blow up memory.

I think the reason is the string model is too large.
@wxchan can you confirm this ?

BTW, current master branch has a bug of categorical feature in python-package.
When use validation data, the validation result may is fake (it should be better) .
refer to this PR: https://github.com/Microsoft/LightGBM/pull/993

@wxchan updates ?

@tomcwalker you can try the latest code.


Base game swap timer on realtime

When playing with my friends, the games would desync.
This is cause by two issues. 1 the shuffler counts frames and our computers would fun the games at a wide variety of frame-rates. For example, I would run Oot at 60 fps and theirs would run at 30 fps, causing  my game to count to the frame limit faster.
The second cause is load times. My computer loads Spyro age of Dragon on the PlayStation significantly faster than my friend's laptops. Thus my game would start earlier each time the game loads.
This fix starts a timer when the shuffler starts and switches when the timer has been reached. The time limit is increased before the next game is launched, causing no time loss between each computer.

Note: This commit also changes the total-time.txt to output total play time, rather than total running time.

Planning to pull this. The only thing I want to work out is whether there is an easy solution for pausing the shuffler like currently exists (pausing the emulator stops the frameadvance, so that works well). Not a necessary feature, just considering it carefully.

The only thing I want to work out is whether there is an easy solution for pausing the shuffler like currently exists (pausing the emulator stops the frameadvance, so that works well). Not a necessary feature, just considering it carefully.
I've gotten it to work if the pause button on the keyboard. Otherwise, I couldn't find anyway for lua to know the game was paused. Also not in this branch, but I also implemented key bindings to move the timer backward and forward if people wanted to have control over the timer.


Add juliusvonkohout to the 1.8 release team

@DnPlas

Thanks @juliusvonkohout , I have added you as a release team member in https://github.com/kubeflow/internal-acls/pull/603. We can close this PR.


build(main): release 1.3.7

:robot: I have created a release beep boop
1.3.7 (2024-10-16)
Build System

deps-dev: bump @babel/core from 7.25.7 to 7.25.8 (798d2cf)
deps-dev: bump @babel/eslint-parser from 7.25.7 to 7.25.8 (7408dda)

Continuous Integration

update permissions in release-please workflow to include checks (218e71b)

This PR was generated with Release Please. See documentation.

:robot: Created releases:

v1.3.7
:sunflower:


Format error: [Errno 32] Broken pipe error: CodeFormatter

I got this when I try to format a PHP script
Format error:
[Errno 32] Broken pipe
error: CodeFormatter
My system is win 7 64 bits and sublime text 2
And with another file I got this :
Traceback (most recent call last):
File ".\sublime_plugin.py", line 362, in run_
File ".\code_formatter.py", line 60, in run
File ".\codeformatter\formatter.py", line 62, in format
File ".\codeformatter\formatter.py", line 83, in clean
File ".\encodings\utf_8.py", line 16, in decode
UnicodeDecodeError: 'utf8' codec can't decode byte 0x82 in position 96: unexpected code byte

Getting same error.

i just installed php on my windows.
and provided path.
"codeformatter_php_path": "C:/php/php.exe",
and it works for me then.


New version: Brave.Brave.Nightly version <IP_ADDRESS>

Created by 🥟 Dumplings in workflow run #6444.

Log
Updated: <IP_ADDRESS> → <IP_ADDRESS>
Submitting WinGet manifests
Creating manifests
Uploading manifests and making commits
Creating a pull request

Microsoft Reviewers: Open in CodeFlow

/azp run

/azp run

@wingetbot run

/azp run

https://github.com/microsoft/winget-pkgs/issues/142854


AAC library discrepancy

The AAC Audio format relies on libfdk_aac, whereas the X264 Video format relies on libfaac.
It seems unlikely that anyone would have both installed, so it would seem that one or the other will always be failing. Is there perhaps a different way to specify which library is available, or should we update one or the other? My thought would be to update both to "libfdk_aac" for the moment as this is the library ffmpeg is currently recommending in its install process.

It's supported in the latest version.

I think I'm running into the same problem here. My FFMpeg\Format\Video\X264() video save is failing out with FFMpeg not recognizing the audio format. It does seem to work in the command line if I force it to use libfdk_aac. The incoming file format is MP4/MP3.
Trying to update PHP-FFMpeg via Composer in my project isn't yielding much luck though. It's claiming that I already have the latest version. Is there something else I need to do here to make this happen? I do see that there is a 0.6 tag in the repo, but changing to require-dev isn't working either. Is that the latest version you're referring to? I originally grabbed PHP-FFMpeg in November through Composer with this exact same setup...
{
    "require": {
        "php-ffmpeg/php-ffmpeg": "~0.5"
    }
}

Thoughts?

@romainneutron Please reopen. Not fixed. #231 and #222


change the color of the name of the month in week mode

i want to change the color of the text of the month and also make the circle of the selected day bigger

I would like to customise the color of the text of the month, looked through the JTCalendarMenuView but couldn't figure out how to do it. Any tips?

For the menu view one of the two methods:
- (UIView *)buildMenuItemView;
- (void)prepareMenuItemView:(UIView *)menuItemView date:(NSDate *)date;

For the day view
- (void)prepareDayView:(JTCalendarDayView*)dayView;

Hey thanks for the reply, but which property for the menuItemView would modify the text color?

menuItemView is just a UILabel so use textColor property. It's a UIView in the delegate for letting other people override this.


Run nnUNet model

@thuhoainguyen  We are ready to train the first model. Let's setup first:
Note: Please observe any unexpected error during installing and let me know!
Setup optimus:
Go to hnguyen@optimus:/data/hnguyen/kits23_training in optimus. And if you use command ls, there should be nnUNet_preprocessed nnUNet_raw nnUNet_results presented.

Install Pytorch:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Install libraries for nnUNet (note that only install nnUNet after installing Pytorch above)
pip3 install -e nnUNet/

Move the nnUNet_preprocessed, nnUNet_results and nnUNet_results inside kits23:
mv ../nnUNet_preprocessed/ .

mv ../nnUNet_results/ .

mv ../nnUNet_raw/ .

Done setting up! This need to be done only once.

Experimental traning
There are multile configurations in nnUNet. First let's try running nnUNet to train the Dataset079_KiTS2023_preprocessed (the one we dealed with preprocess.py, not the dataset with histology one, I still need to plan for this later). Do you notice that the ID of the dataset I set to 79? Because it's your birthday 🤗
In this experimental training, we will train with configuration 2D U-Net with fold number 0. Please observe the speed so we can plan for all configurations later!
Activate the virtual environment if not yet activated:
source venv-kits23/bin/activate

Setup paths (if it prints out the location of the paths, this means succeed):
source envar.zshrc

Run nnUNet preprocess and planning:
(reference)
nnUNetv2_plan_and_preprocess -d 79 --verify_dataset_integrity

Run the training with configuration 2D U-Net with fold number 0:
nnUNetv2_train 79 2d 0 --npz -device cuda

That's it! Let it run and observe the speed!
Reference: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/how_to_use_nnunet.md#model-training
Reference: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md

@anhtuduong i have an error git clone the kits23 to optimus:

@thuhoainguyen I just fixed the type for cloning git using http, let's try:
git clone https://github.com/thuhoainguyen/kits23.git

@anhtuduong I have error regarding the code to run nnUNet preprocess and planning.
It said "only 3d images are supported by Nibabel I0"

@thuhoainguyen Ok please standby, I will investigate this!

@thuhoainguyen I wrote a script that convert 4D images to 3D images for this. Please:
In /data/hnguyen/kits23:
git pull

Activate the virtual environment if not yet activated:
source venv-kits23/bin/activate

Setup paths (if it prints out the location of the paths, this means succeed):
source envar.zshrc

Convert images:
python3 segmentation/convert_4d_to_3d.py

After the conversion, you can continue to run:
nnUNetv2_plan_and_preprocess -d 79 --verify_dataset_integrity

and train:
nnUNetv2_train 79 2d 0 --npz -device cuda

@anhtuduong I'm running the code nnUNetv2_plan_and_preprocess -d 79 --verify_dataset_integrity
But looks like they are running all the dataset ? Because I see "208/489". I don't quite understand.

@anhtuduong After that code above, this info show up:
############################
INFO: You are using the old nnU-Net default planner. We have updated our recommendations. Please consider using those instead! Read more here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md
############################

@anhtuduong I have error regarding the code: nnUNetv2_train 79 2d 0 --npz -device cuda
(venv-kits23) hnguyen@optimus:/data/hnguyen/kits23$ nnUNetv2_train 79 2d 0 --npz -device cuda

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/data/hnguyen/kits23/venv-kits23/bin/nnUNetv2_train", line 5, in <module>
    from nnunetv2.run.run_training import run_training_entry
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/run/run_training.py", line 13, in <module>
    from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 25, in <module>
    from batchgeneratorsv2.transforms.nnunet.remove_connected_components import \
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/nnunet/remove_connected_components.py", line 5, in <module>
    from acvl_utils.morphology.morphology_helper import label_with_component_sizes
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/acvl_utils/morphology/morphology_helper.py", line 7, in <module>
    import cc3d
Traceback (most recent call last):
  File "/data/hnguyen/kits23/venv-kits23/bin/nnUNetv2_train", line 5, in <module>
    from nnunetv2.run.run_training import run_training_entry
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/run/run_training.py", line 13, in <module>
    from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 25, in <module>
    from batchgeneratorsv2.transforms.nnunet.remove_connected_components import \
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/nnunet/remove_connected_components.py", line 5, in <module>
    from acvl_utils.morphology.morphology_helper import label_with_component_sizes
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/acvl_utils/morphology/morphology_helper.py", line 7, in <module>
    import cc3d
  File "cc3d.pyx", line 1, in init cc3d
ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).

@thuhoainguyen seems like a difficult one... Ok let me have some times with this

@thuhoainguyen Let's try this:
Activate the virtual environment if not yet activated:
source venv-kits23/bin/activate

Setup paths (if it prints out the location of the paths, this means succeed):
source envar.zshrc

Update numpy version:
pip3 install --upgrade numpy==1.26.4

Run the training:
nnUNetv2_train 79 2d 0 --npz -device cuda

The problem is the conflict of numpy version. If the problem still shows up, please run this command and copy the output for me:
pip3 install -e nnUNet/

@anhtuduong Still error.
(venv-kits23) hnguyen@optimus:/data/hnguyen/kits23$ pip3 install --upgrade numpy==1.26.4
Collecting numpy==1.26.4
  Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.2/18.2 MB 19.7 MB/s eta 0:00:00
Installing collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 2.0.0
    Uninstalling numpy-2.0.0:
      Successfully uninstalled numpy-2.0.0
Successfully installed numpy-1.26.4

(venv-kits23) hnguyen@optimus:/data/hnguyen/kits23$ nnUNetv2_train 79 2d 0 --npz -device cuda

############################
INFO: You are using the old nnU-Net default plans. We have updated our recommendations. Please consider using those instead! Read more here: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md
############################

Using device: cuda:0

#######################################################################
Please cite the following paper when using nnU-Net:
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
#######################################################################

2024-06-17 18:33:02.567160: do_dummy_2d_data_aug: False
2024-06-17 18:33:02.578484: Creating new 5-fold cross-validation split...
2024-06-17 18:33:02.582555: Desired fold for training: 0
2024-06-17 18:33:02.582652: This split has 391 training and 98 validation cases.
using pin_memory on device 0
Exception in background worker 5:
Exception in background worker 7:
 local variable 'region_labels' referenced before assignment
 local variable 'region_labels' referenced before assignment
Exception in background worker 4:
 local variable 'region_labels' referenced before assignment
Traceback (most recent call last):
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 53, in producer
    item = next(data_loader)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/data_loader.py", line 126, in __next__
    return self.generate_train_batch()
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/dataloading/data_loader_2d.py", line 100, in generate_train_batch
    tmp = self.transforms(**{'image': data_all[b], 'segmentation': seg_all[b]})
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/compose.py", line 13, in apply
    data_dict = t(**data_dict)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 67, in apply
    data_dict['segmentation'] = self._apply_to_segmentation(data_dict['segmentation'], **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/seg_to_regions.py", line 17, in _apply_to_segmentation
    if isinstance(region_labels, int) or len(region_labels) == 1:
UnboundLocalError: local variable 'region_labels' referenced before assignment
Traceback (most recent call last):
Traceback (most recent call last):
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 53, in producer
    item = next(data_loader)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/data_loader.py", line 126, in __next__
    return self.generate_train_batch()
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 53, in producer
    item = next(data_loader)
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/dataloading/data_loader_2d.py", line 100, in generate_train_batch
    tmp = self.transforms(**{'image': data_all[b], 'segmentation': seg_all[b]})
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/data_loader.py", line 126, in __next__
    return self.generate_train_batch()
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/dataloading/data_loader_2d.py", line 100, in generate_train_batch
    tmp = self.transforms(**{'image': data_all[b], 'segmentation': seg_all[b]})
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/compose.py", line 13, in apply
    data_dict = t(**data_dict)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 67, in apply
    data_dict['segmentation'] = self._apply_to_segmentation(data_dict['segmentation'], **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/seg_to_regions.py", line 17, in _apply_to_segmentation
    if isinstance(region_labels, int) or len(region_labels) == 1:
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/compose.py", line 13, in apply
    data_dict = t(**data_dict)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 18, in __call__
    return self.apply(data_dict, **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/base/basic_transform.py", line 67, in apply
    data_dict['segmentation'] = self._apply_to_segmentation(data_dict['segmentation'], **params)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgeneratorsv2/transforms/utils/seg_to_regions.py", line 17, in _apply_to_segmentation
    if isinstance(region_labels, int) or len(region_labels) == 1:
UnboundLocalError: local variable 'region_labels' referenced before assignment
UnboundLocalError: local variable 'region_labels' referenced before assignment
Traceback (most recent call last):
  File "/data/hnguyen/kits23/venv-kits23/bin/nnUNetv2_train", line 8, in <module>
    sys.exit(run_training_entry())
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/run/run_training.py", line 275, in run_training_entry
    run_training(args.dataset_name_or_id, args.configuration, args.fold, args.tr, args.p, args.pretrained_weights,
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/run/run_training.py", line 211, in run_training
    nnunet_trainer.run_training()
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 1362, in run_training
    self.on_train_start()
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 903, in on_train_start
    self.dataloader_train, self.dataloader_val = self.get_dataloaders()
  File "/data/hnguyen/kits23/nnUNet/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py", line 696, in get_dataloaders
    _ = next(mt_gen_train)
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 196, in __next__
    item = self.__get_next_item()
  File "/data/hnguyen/kits23/venv-kits23/lib/python3.10/site-packages/batchgenerators/dataloading/nondet_multi_threaded_augmenter.py", line 181, in __get_next_item
    raise RuntimeError("One or more background workers are no longer alive. Exiting. Please check the "
RuntimeError: One or more background workers are no longer alive. Exiting. Please check the print statements above for the actual error message

(venv-kits23) hnguyen@optimus:/data/hnguyen/kits23$ pip3 install -e nnUNet/
Obtaining file:///data/hnguyen/kits23/nnUNet
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Installing backend dependencies ... done
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Collecting argparse
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Successfully built nnunetv2
Installing collected packages: argparse, nnunetv2
  Attempting uninstall: nnunetv2
    Found existing installation: nnunetv2 2.5
    Uninstalling nnunetv2-2.5:
      Successfully uninstalled nnunetv2-2.5
Successfully installed argparse-1.4.0 nnunetv2-2.5

@thuhoainguyen oke we solved the numpy error! This is another error with the machine. Be patient, we are getting closer!

@thuhoainguyen please ask Jacopo to let me access to your workspace in optimus. Let him know that I will help you on some difficult technicals. My email is<EMAIL_ADDRESS>


cmd/go go get not showing progress when downloading

Please answer these questions before submitting your issue. Thanks!
What version of Go are you using (go version)?
go1.9
Does this issue reproduce with the latest release?
Yes
What operating system and processor architecture are you using (go env)?
Linux, amd64
What did you do?
run go get github.com/astaxie/beego in my terminal
What did you expect to see?
I was expecting a progressbar to see the progress of download in golang.
What did you see instead?
A blank line showing (possible) execution of command.

Most go tools are quiet by default, and this is unlikely to change, but you can use the -v flag to make the tool more verbose.

Alright, thanks :smiley:


feat(operator): Add skip-tls-verify flag for opm render

Description
Add skip-tls-verify flag for opm render to be able to build index image with insecure registry.
I added this flag for development purposes, similar to the --use-http flag.
It is needed to pull bundle images from insecure registries, in particular OpenShift internal registries, namely crc and infra. This allows you to test local changes on OpenShift clusters without pushing the changes, open a PR and wait for CI to push the images, speeding up development time.
It seems like opm render does not use docker daemon and fails to pull the images even if I specify an insecure registry in the docker configuration.
Checklist

[ ] Investigated and inspected CI test results
[ ] Unit test and regression tests added
[ ] Evaluated and added CHANGELOG entry if required
[ ] Determined and documented upgrade steps
[ ] Documented user facing changes (create PR based on openshift/openshift-docs and merge into rhacs-docs)

If any of these don't apply, please comment below.
Testing Performed
Tested locally

@porridge I updated the description and added a rationale.

gke-qa-e2e-tests failed because of the flake (ROX-16290), merging


Retrofits SpanId to be effectively portable with Finagle's TraceId

Finagle's TraceId is a propagation value and used in many places, such
as indexing data for collection. It also has a concise fixed-length
serialized form, and similarly concise toString.
The serialized form could be helpful when propagating across systems,
most notably Finagle, but also cassandra, whose baggage includes opaque
values.
See https://github.com/thelastpickle/cassandra-zipkin-tracing/tree/mck/brave-3.5
See #166

👍

+1


Unit Testing for linear_fit.py and edits to python to make it pylint compliant.

Added several edits to linear_fit.py such as the following:

Added functionality to specify headers in csv uploaded, doesn't have to be 'x', 'y', and 'sigma' anymore.
Added many error cases to add specific print in error output in common error cases.
Removed the 'weight' variable as it was unnecessary, and there were too many defined variables for pylint.
Redefined some variable like "S" to be pylint compliant.
Added section to only call on the example usage if the module itself is being ran directly. Avoids unit testing errors.

Added test_linear_fit.py
Added a blank CSV. THIS IS ON PURPOSE, DO NOT DELETE IT
Unit testing uses this blank CSV to add custom data and remove it, to test for specific cases the code handles.

Okay, pylint likes my code but flake8 doesn't so I will make edits.

The remaining issues from flake8 actually are from the cython code @avgagliardo. Is there any suggestions on what to do about that?

I can review this.

Do we check for both code and linting?

Yes

Make sure to check the code itself and also check that there are not any minor linting errors like whitespace or indentation errors etc...

@ojha-aditya I added you as a reviewer, so it should be available now

@ojha-aditya Make sure to review this as soon as possible so it can be merged.

Great, merging.


"User Menu" onboarding modal has incorrect description

🐛
Steps to reproduce:

Sign up for a new account
Close the "Modeling app" banner
In the "Welcome to Modeling App!" modal, click the next button until the user is brought to the "User Menu" modal
Expected: Description states "Click the hamburger menu in the upper right corner to open the user menu. You can change your settings, sign out, report a bug, or request a feature."
Actual: "Click your avatar" (not visible, hamburger menu is in its place), and "report a bug" is not listed in the things the user can do in the user menu although it is an option visible in the user menu.

Video:
https://www.loom.com/share/156adcd54ec24d9494da344d107295ed?sid=a2a04a3c-8140-4653-b314-8c4178841a61 (https://www.loom.com/share/156adcd54ec24d9494da344d107295ed?sid=a2a04a3c-8140-4653-b314-8c4178841a61)
Bug report:
https://app.qawolf.com/zoo/bug-reports/8fe72575-9bb4-44a4-938c-23610826ef6b
Affected workflows:
If you are aware of this bug you can set the priority to low which will prevent it from causing a run failure.

our test for this must not be working anymore cc @franknoirot

Yeah you know what I bet our test for this didn't cover the case if the user had no avatar image on their profile, only if the avatar image URL 404'd.
@lf94 I think this'll be dead easy for you to switch since you helped me with that test before. Here's some context refresher in case it helps:

The test is "Avatar text updates depending on image load success" in flow tests. I think this test is missing a case where the user object has an undefined image instead of a 404ing one, and I hypothesize it will fail if we add one
The UserMenu.tsx step in onboarding doesn't currently have a way to know that no image loading was attempted if the user's avatar is nonexistent. Maybe we should fire that error over in UserSidebarMenu, and allow it to try and load even if !user.image?

https://github.com/KittyCAD/modeling-app/pull/2959 should close it

this is still failing in qawolf

"the "report a bug" feature is still not mentioned in the user menu modal, which caused the test to fail on our end"

"the "report a bug" feature is still not mentioned in the user menu modal, which caused the test to fail on our end"

Oh okay easy fix, should take me just a moment.


feat(issueVariant): Allow for issue variant creation with just severity rating

Task Description
Currently the severity input type for creating issue variants only accepts a cvss vector. Add a rating field alongside vector and update the resolver methods to allow for creating a new issue variant where vector is not provided.
Acceptance Criteria:

[x] Add a "rating" field of type SeverityValues to SeverityInput
[x] Add a new "NewSeverityFromRating" function that creates a severity with only the rating field and not the vector
[x] Add logic to handle between when a vector is provided and when only a rating is provided
[x] Be able to create a new IssueVariant with only a rating

Review comments have been added


UNMET PEER DEPENDENCY<EMAIL_ADDRESS>

npm WARN<EMAIL_ADDRESS>requires a peer of<EMAIL_ADDRESS>but none was installed.

Grunt 0.4.x is something of the past! Please, update your package to Grunt v1.x.x

@gleero: Is this project still maintained?
@japrescott has created a fork which solves this issue (at least it looks like that, when I'm reading the code).
Would you accept a fix for this issue as a Pull request?

+1