[labs/observers] ResizeController "observe" is available publicly, but "unobserve" is not.

Should this be an RFC?

[X] This is not a substantial change

Which package is this a feature request for?
Observers (@lit-labs/observers)
Description
You can currently ResizeController.observe(...) new elements across the lifecycle of the controller, but you aren't given API to unobserve those elements. This method should likely be made private so that it is less likely to lead someone to leverage it and some sort of setter for the target (hopefully one that is easier to use than at current and accepts an array of elements) should be surfaced to ensure that more content can be easily managed by the observer.
This seems like a common patterns in the observer controllers, so each might benefit the same API structure.
Happy to mark up a PR for this if it's acceptable.
Alternatives and Workarounds
WORKAROUND:
You can force the type today with:
(this.resizeController as unknown as {
    _observer: ResizeObserver
})._observer.unobserve(item);

ALTERANATIVE 1:
Or the observe method could be made private to clarify that this isn't functionality that should be taken advantage of outside of the host and target arguments in the constructor.

Since ResizeObserver has an unobserve() method, it makes sense for the controller to also, for symmetry. I'd maybe be in favor of removing observe(), but that's a different discussion.
This is a great issue for a contributino!


Memorized Payee list data

is this in the scope to be implemented in the future?

Could you describe what you're referring to? I'm not sure I'm familiar with this part of the spec.

Closing due to lack of activity.


structure and design the pop up of the project section

Add the pop-up design and functionality
add the pop-up functionality to show the link to preview the project and to see the source code of the project

LGTM
You can merge, good luck in your next milestone


feat: use hologit to project distribution branch

Why
Keeping sources and build artifacts derived from them in the same source tree is gnarly and error-prone. Hologit offers an elegant and reliable way to define derived trees declaratively and then do as little work as possible to maintain them.
What
In the first version, preserved at commit 66ec63295e5fc076e5f4355ffdb140ae33a162a8, we just use the lens-npm-install lens to merge node_modules/ into the output based on {package.json,package-lock.json}:
$ git ls-tree $(git holo project --ref=66ec63295e5fc076e5f4355ffdb140ae33a162a8 github-action)
info: reading mappings from holobranch: github-action
info: compositing tree...
info: merging background-action:{action.yml,*.js,package*.json} -> /
info: stripping .holo/{branches,sources} tree from output tree...
info: building input tree for lens npm-install from {package.json,package-lock.json}
info: found existing output tree matching holospec(9da7adca354ba98ddba51c8c07b391effb71ae9f)
info: merging lens output tree(f18f6efcbad9a20abf1d0ab8622867bf7585d93d) into /
info: stripping .holo/lenses tree from output tree...
info: writing final output tree...
info: projection ready
100644 blob a4d41cc50ae9eec0e96152083db4524c9ce05b35    action.yml
100755 blob e058ef4d4931ed8bd2e89cf07ef0782b440a0dfa    index.js
100644 blob 413271ecf6848d401994b7263f1eb9b16e127b41    input.js
040000 tree a1d04abea642261a6d7f97c2ca2160471a075eec    node_modules
100644 blob a0e5b7d7f7bbef44de2ae72c584916b6f00daf7d    package-lock.json
100644 blob 9794b77314980c989647cd8c1f776280abb9092c    package.json
100644 blob 69bf4cf0981f2a145bc9c0b422f1afdca6a54682    post-run.js

The second version, which this PR lands at, uses an updated version of lens-npm-run to invoke npm ci; npm run build and replaces the output entirely with the resulting dist/ tree:
$ git ls-tree $(git holo project  --working github-action)
info: indexing working tree (this can take a while under Docker)...
info: using working tree: c57c50b17dd8f55368c4139c81acdec8b8d210ed
info: reading mappings from holobranch: github-action
info: compositing tree...
info: merging background-action:{*.js,package*.json} -> /
info: merging background-action:{action.yml} -> /dist/
info: stripping .holo/{branches,sources} tree from output tree...
info: building input tree for lens build from {**}
info: found existing output tree matching holospec(2bdd0c57696650941c3bfa288208156874c5234a)
info: merging lens output tree(a5c4cc45eeb91c7feb093baf41611fbb076004df) into /
info: stripping .holo/lenses tree from output tree...
info: writing final output tree...
info: projection ready
100644 blob a4d41cc50ae9eec0e96152083db4524c9ce05b35    action.yml
100644 blob fcb14f44d1abb4738f3dec25b16ba741d53f89ab    index.js
100644 blob bf362bb267020e75ae2e7ff2dafbfc4f00922ab9    index.js.map
100644 blob f57a351c6bff1e06117c4a358df77b0d7b597b11    sourcemap-register.js

A further improvement would be to create a dedicated lens-ncc package that came with its own ncc command. That could be applied to any npm project then without modifying it. An idea for later.
I suspect the npm-install approach is actually the overall more efficient given that actions code is deployed to runners via git, so the node_modules tree would get transferred in a packfile anyway. Cramming everything into one blob only achieves inflating git storage space by duplicating infrequently changing dependencies on every build. ncc seems like it would make more sense for web distribution but not git distribution
To put this holobranch into practice you might:
git holo project github-action --commit-to=v1
git push origin v1

Or configure a GitHub action:
    - name: 'Project holobranch github-action to v1'
      uses: JarvusInnovations/hologit@actions/projector/v1
      env:
        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        HAB_LICENSE: accept
      with:
        holobranch: github-action
        commit-to: v1

Looks good to me 👍

@jmealo can you add the github action? There's an example here https://github.com/JarvusInnovations/github-action-js-template


Is it possible to convert the .yaml files given by AL-GO git hub into azure devops style .yaml.

Hello Team,
we've forked the PTE template into azure devops and started running Create  APP .yaml , but it is giving the error , since it is designed for the Git Hub, do we have a way to convert the .yaml of github to azure devops style.
Error:

Kindly please let us know how to approach the problem here.

See #101
AL-Go for GitHub wasn't designed for Azure DevOps. Even if we convert the yaml files the underlying scripts wouldn't work because they also depend on running on GitHub.
I'm afraid you'll either have to run your workflows on GitHub or use another managed devops solution to help with your Azure Devops builds.


Add small demo for simulating a galaxy using galcheat information

This was also requested in the BL meeting. Basically a very short script or even just an simple example in the README showing how to actually generate a single galaxy in galsim using the information in the galcheat survey and filter object. Probably after #24 is merged. What do you think @aboucaud ?
I can definitely create a PR if you think it might be useful since I have to this for BTK anyways.

Sure, this could either be a short notebook or a short script.


International card support

When I hit https://sandbox.tangocard.com/raas/v1/rewards
There are international rewards listed.

When I hit brands they do not have all of the information available.
Specifically they are missing denomination, currency_code, locale
#<Tangocard::Brand:0x007f87963c9a68
  @description="Amazon.ca",
  @image_url="https://dwwvg90koz96l.cloudfront.net/graphics/item-images/amazon-canada-gift-card.png",
  @rewards=
   [#<Tangocard::Reward:0x007f87963c98d8 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$5", @max_price=0, @min_price=0, @sku="AMCA-E-500-STD", @unit_price=486>,
    #<Tangocard::Reward:0x007f87963c9680 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$10", @max_price=0, @min_price=0, @sku="AMCA-E-1000-STD", @unit_price=971>,
    #<Tangocard::Reward:0x007f87963c9428 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$15", @max_price=0, @min_price=0, @sku="AMCA-E-1500-STD", @unit_price=1457>,
    #<Tangocard::Reward:0x007f87963c9180 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$25", @max_price=0, @min_price=0, @sku="AMCA-E-2500-STD", @unit_price=2428>,
    #<Tangocard::Reward:0x007f87963c8de8 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$50", @max_price=0, @min_price=0, @sku="AMCA-E-5000-STD", @unit_price=4856>,
    #<Tangocard::Reward:0x007f87963c8be0 @available=true, @currency_type="USD", @description="Amazon.ca Gift Certificate CAD$100", @max_price=0, @min_price=0, @sku="AMCA-E-10000-STD", @unit_price=9712>]>,
 #<Tangocard::Brand:0x007f87963c89b0

I'm going to monkeypatch rewards and add those fields in because it will make it alot easier for me to support international cards when I can detect those 3 fields. rather than trying to parse them out of the description which can be formed pretty poorly.
After I monkey patch this if it works i'll send it to ya

class Tangocard::Reward
  attr_reader :denomination, :currency_code, :locale
  def initialize(params)
    @description = params['description']
    @sku = params['sku']
    @currency_type = params['currency_type']
    @unit_price = params['unit_price'].to_i
    @available = params['available']
    @min_price = params['min_price'].to_i
    @max_price = params['max_price'].to_i
    @denomination = params['denomination'].to_i
    @currency_code = params['currency_code']
    @locale = params['locale']
  end
end

If you would add those 3 fields, I would be so happy!

Thanks @spencerroan! I would welcome a PR if you'd like to submit one (please include tests where appropriate). If not, I'll try to get to this in the next couple weeks.

It should be fine now, my recent PR #11 fixes this issue

fixed


GraphQL - Order ASC / DESC

I'm trying to sort results by I don't find the GraphQL property that enables it.
In the docs, the order attribute is a string:

Is there any option where we can pass the column & the sort order?
Thanks

Found !
I just needed to add "reverse:ColumnName"


trivy-operator scan error for fluentd-hec image

What steps did you take and what happened:
Helm installation:
helm repo add aqua https://aquasecurity.github.io/helm-charts/
helm repo update
helm install trivy-operator aqua/trivy-operator  --namespace trivy-system  --create-namespace  --version 0.17.1

Create a sample deployment:
kubectl create deployment sample-scan --image=splunk/fluentd-hec:1.3.3 --replicas=1
=> The trivy-operator fails to scan the newly created deployment after approximately 3 minutes with the following error:
{"level":"error","ts":"2023-11-27T11:38:11Z","logger":"reconciler.scan job","msg":"Scan job container","job":"trivy-system/scan-vulnerabilityreport-7599bf8d6c","container":"fluentd-container","status.reason":"Error","status.message":"2023-11-27T11:38:06.075Z\\t\\u001b\[31mFATAL\\u001b\[0m\\timage scan error: scan error: scan failed: failed analysis: analyze error: pipeline error: failed to analyze layer (sha256:d8eb1cd53414391d2ef9211f02391f20bb68285336edb914b8b7c64136485ac5): walk error: failed to extract the archive: read tcp <IP_ADDRESS>:36528->10.X.X.X:443: read: connection reset by peer\\n","stacktrace":"github.com/aquasecurity/trivy-operator/pkg/vulnerabilityreport/controller.(\*ScanJobController).processFailedScanJob\\n\\t/home/runner/work/trivy-operator/trivy-operator/pkg/vulnerabilityreport/controller/scanjob.go:299\\ngithub.com/aquasecurity/trivy-operator/pkg/vulnerabilityreport/controller.(\*ScanJobController).reconcileJobs.func1\\n\\t/home/runner/work/trivy-operator/trivy-operator/pkg/vulnerabilityreport/controller/scanjob.go:81\\nsigs.k8s.io/controller-runtime/pkg/reconcile.Func.Reconcile\\n\\t/home/runner/go/pkg/mod/sigs.k8s.io/controller-runtime@v0.16.3/pkg/reconcile/reconcile.go:111\\nsigs.k8s.io/controller-runtime/pkg/internal/controller.(\*Controller).Reconcile\\n\\t/home/runner/go/pkg/mod/sigs.k8s.io/controller-runtime@v0.16.3/pkg/internal/controller/controller.go:119\\nsigs.k8s.io/controller-runtime/pkg/internal/controller.(\*Controller).reconcileHandler\\n\\t/home/runner/go/pkg/mod/sigs.k8s.io/controller-runtime@v0.16.3/pkg/internal/controller/controller.go:316\\nsigs.k8s.io/controller-runtime/pkg/internal/controller.(\*Controller).processNextWorkItem\\n\\t/home/runner/go/pkg/mod/sigs.k8s.io/controller-runtime@v0.16.3/pkg/internal/controller/controller.go:266\\nsigs.k8s.io/controller-runtime/pkg/internal/controller.(\*Controller).Start.func2.2\\n\\t/home/runner/go/pkg/mod/sigs.k8s.io/controller-runtime@v0.16.3/pkg/internal/controller/controller.go:227"}
What did you expect to happen:
It's interesting that scanning the container image with the trivy-cli without any issues: trivy image splunk/fluentd-hec:1.3.3
I would expect that the trivy-operator behaves more or less similar like the trivy cli? Is it possible that there are some differences in the configuration which could cause this issue?
Anything else you would like to add:
I installed the trivy-operator in several environments with and without adjusted configuration params:

OnPrem airgapped VMware TKG cluster and GKE cluster
Pulling the image splunk/fluentd-hec:1.3.3 from Artifactory (airgapped) and directly from dockerhub
Adjusting the scan time out parameter in the values.yaml file of the trivy-operator helm installation
The issues is also reproducable with other versions of the splunk/fluentd-hec container image. e. g. splunk/fluentd-hec:1.3.2

Environment:

Trivy-Operator version (use trivy-operator version): We tested with v0.16.0 and v0.17.1 (latest)
Kubernetes version (use kubectl version): 1.25-1.28
OS (macOS 10.15, Windows 10, Ubuntu 19.10 etc):

Yes only for this particular image, but it works with the trivy cli.

Sorry for the late answer!

Do you mind try a simple test scan the but image but make sure that the following helm setting is false

Awesome! That did the trick. Now the image was scanned properly 🥳

Closing following to last comment from @thomasroot


Finalize "simpleUI" debug option

Added in https://github.com/microsoft/vscode/issues/128588
	export interface DebugSessionOptions {

		debugUI?: {
			/**
			 * When true, the debug toolbar will not be shown for this session, the window statusbar color will not be changed, and the debug viewlet will not be automatically revealed.
			 */
			simple?: boolean;
		};

Allows to start a debug session which has the debug UI hidden by default. Used for jupyter's Run By Line, and could be useful for other extensions starting atypical-style debug experiences.

Anything holding back this from finalization @roblourens?

No. Could discuss it next month

Moving this to July based on discussions from last week

Maybe inside debugUI into DebugSessionOptions, like the compact property and other?

The only thing that bugs me is flooding DebugSessionOptions with a bunch of properties that feel sort of different from the others, and probably won't be commonly used... but I don't have a strong opinion about it

suppressDebugToolbar?: boolean;
suppressDebugStatusbar?: boolean;
suppressDebugView?: boolean;


PF2e: Hexagonal grid results in NaN measurement

Foundry V12.331
PF2e 6.3.1
Elevation Ruler 0.10.5
libWrapper <IP_ADDRESS>
With a hexagonal grid and Elevation Ruler and libWrapper the only enabled modules, distance always shows up as NaN.
If I flip to a square grid, it works fine.
Elevation ruler options are:
Use Pathfinding
Tokens Block No
Pathfinding Snap to Grid
Use Token Ruler
Hide GM Token Ruler
Use Token Speed Highlighting Always
Track Combat Move History
Round Distance to Multiple 0.1

Should be fixed in v0.10.6. Thanks!

Should be fixed in v0.10.6. Thanks!

Can confirm fix in v0.10.6. Thanks so much, love the comment on the run


Array of objects is not formatted nicely

Describe the bug
Formatting an array of objects, an object is somehow broken across a new line even if this doesn't look very good.
https://dprint.dev/playground/#code/ATDGHsDsGcBdgGYAdIFkCmBbARugTtABYCWSAKsbADbrTAC8wA2gN7CQCGm6AXMAEQAxAAoA5fsAC+AGmBtO3PkLHAAwlQ7RoEmXPZdeAsgE8k6HbPkGlAEVqg8pWMSg6AugG4gA/plugin/typescript
Deno version: 1.44.1
Input Code
const fpnMembershipTitles = [{ name: "FPN" }, { name: "FPN Class" }, { name: "Type" }, { name: "Description" }];

Expected Output
const fpnMembershipTitles = [
  { name: "FPN" },
  { name: "FPN Class" },
  { name: "Type" },
  { name: "Description" },
];

Actual Output
const fpnMembershipTitles = [{ name: "FPN" }, { name: "FPN Class" }, {
  name: "Type",
}, { name: "Description" }];

Hello @dsherret @lucacasonato  can u review my PR,
https://github.com/dprint/dprint-plugin-typescript/pull/649
I realized that there should be a break line '\n' between '[' and '{', so I checked to see whether this was an array, not a part of a comment, or not part of the string, and then if that correct, I placed a break line '\n' between '[' and '{', so that the behavior worked as expected.


Clean up and small fixes for release 0.2.2

Codecov Report

Merging #36 into master will increase coverage by 0.02%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master      #36      +/-   ##
==========================================
+ Coverage   59.15%   59.18%   +0.02%
==========================================
  Files          33       33
  Lines        1442     1443       +1
==========================================
+ Hits          853      854       +1
  Misses        589      589

Impacted Files
Coverage Δ

pycomlink/util/temporal.py
95.45% <100%> (+0.21%)
:arrow_up:

pycomlink/core/comlink.py
63.84% <100%> (ø)
:arrow_up:

Continue to review full report at Codecov.

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[C++][Parquet] FileSerializer: adjust field ordering to prevent from bad dtor order

Describe the enhancement requested
Parquet uses FileSerializer to write File. And the FileSerializer has page_index_builder_ and file_encryptor. In the happy path, the dtor ordering is ok:
  void Close() override {
    if (is_open_) {
      // If any functions here raise an exception, we set is_open_ to be false
      // so that this does not get called again (possibly causing segfault)
      is_open_ = false;
      if (row_group_writer_) {
        num_rows_ += row_group_writer_->num_rows();
        row_group_writer_->Close();
      }
      row_group_writer_.reset();

      WritePageIndex();

      // Write magic bytes and metadata
      auto file_encryption_properties = properties_->file_encryption_properties();

      if (file_encryption_properties == nullptr) {  // Non encrypted file.
        file_metadata_ = metadata_->Finish(key_value_metadata_);
        WriteFileMetaData(*file_metadata_, sink_.get());
      } else {  // Encrypted file
        CloseEncryptedFile(file_encryption_properties);
      }
    }

However, if Close throw exception, the row_group_writer_ may dtor after file_encryption and page_index_builder. Causing the memory issue.
Component(s)
C++, Parquet

Found no problem here, just close it first


named logger and isort imports

right now, all logging outputs from bungiesearch are going into the root logger
instead, have a named logger bungiesearch and direct all logging into that logger so that users of the library can route logging statements from bungiesearch correctly
also ran isort and fixed some indentations in files that pep8 complains about

Coverage remained the same at 100.0% when pulling 1ab4128ca72ebd691833c8f2971ed085f0f25a24 on BetterWorks:namedLogger into d9f09688691aa0a71db42236e6cd1db4ac1c609f on ChristopherRabotin:master.


Added inai.kg

Kyrgyz-German Institute of Applied Informatics

@kb1prb13 Pull request merged. Thank you!


Cluster visualization support in Builder

Context
According to a constant drip of requests from users, we need to support a similar clustering visualization feature as the one in Editor, including full backwards compatibility with Editor maps using that feature.

We'll clarify this in https://github.com/CartoDB/product/issues/27.


add support for e2e testing environment

Can you explain to me, what the difference is between these docker files and the dev docker files?
Dockerfile-backend, Dockerfile-frontend, entrypoint-backend.sh, entrypoint-frontend.sh, proxy.conf look identical to the dev files.
The docker-compose file only seems to use a .env file instead.

Yea, sorry. I should have added a bit of explanation.
Dockerfiles are not exactly the same. For backend, installation of modules moved to entrypoint-backend.sh. The same is for proxy.conf which looks almost the same except for the backend name which is e2e-backend.
I want to be able to run 2 instances of Librephotos on my machine. One is the main one with all my photos and another one just for running e2e tests. Hence, e2e- prefix in test containers which allow having separate instances. In addition to that, e2e tests do not require all volumes to be mapped to host folders. Only location with media files would change frequently. Other volumes such as Postgres, Redis and cache folders can live in named volumes. Maybe that's not the best decision, I don't know. That make sense to me.
I agree that e2e/entrypoint-frontend.sh is exactly the same as in frontend/entrypoint.sh. This is need to make docker image to be built (you can't reference files in Dockerfile that are located outside of build folder).
This solution is still at very initial state. In the future, we could add a way of running e2e in CI where we could test prebuilt images. Or, assuming there will be enough resources available, we could build a PR check with all components pulled from a specific branch.


Improves DMI saving (smaller files, better formatting)

What I did
Actually compresses dmis when saving them
We've been using the write_to wrapper function, which doesn't allow us to pass in a compression level.
This means we've been using the default, which is for some reason the fastest/worst for file size.
This commit instead "inlines" the write_to function, and uses the Best compression method (best case slowest/lowest file size)
This strategy reduces a large file I tested (screen_cyborg.dmi) from 82kb to 21kb (byond's output if you're curious is about 26kb)
I've also added saving to the test suite, to ensure the path gets walked and to allow for visual inspection if changes are made
Improves our image formatting somewhat
We, like byond, arrange our saved DMIs in squares. However unlike byond we currently don't remove extra height that we don't use, which leads to odd looking files. This commit fixes that
What I didn't do
I haven't converted us over to a less cursed image saving format. We don't really need much of what the image crate provides here, but I don't trust myself to deal with the like, requirements for sending empty data that the recommended solution, lodepng, would require.
I can look into it if you'd like I'm just not totally confident in my ability to do it performantly without some oversight.

Do you think you could compare the relative speeds for a large DMI? Just want to make sure we're not paying a 100x increase or something (and if so, we could try and move it up a level).

Do you think you could compare the relative speeds for a large DMI? Just want to make sure we're not paying a 100x increase or something (and if so, we could try and move it up a level).
Honestly it should be a unit test.... :whelm:

Ran it on the biggest dmi I know of, wallening's hierophant walls
All numbers come from the load_and_save test in release mode on my laptop
Fast strategy took 0.61s and produced 3.74mb
Best strategy took 1.31s and produced 992kb
Default strategy took 0.88s and produced 999kb

Pogged

For the record:
It seems like default results in file sizes lower then standard dreammaker for most dmis. It is still way better then the fast option tho, it goes from like 500kb -> 99kb -> 9kb
Idk if that changes the calculus on the speed here but even if we just leave it at default this is a strong win.


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Explicit color mapping only works for string-type category labels

Hi folks,
first thing first -really enjoying getting to grips with holoviews - feels like a breath of fresh air after years of matplotlib, so thanks.
Not sure if this is a proper bug or a documentation issue, but in the styling plots examples explicit color-mapping is mentioned and an example given. However, this only works if the entries in the 'color_index' column (and equivalently, keys in the explicit mapping dictionary) are of string type so e.g. the following code:
categorical_points = hv.Points((np.random.rand(100),
                                np.random.rand(100),
                                np.random.choice(list(range(4)), 100)), vdims='Category')
explicit_mapping = {0: 'blue', 1: 'red', 2: 'green', 3: 'purple'}
categorical_points.sort('Category').options(color_index='Category', cmap=explicit_mapping, size=5, colorbar=True)

Gives
~/code/external/holoviews/holoviews/plotting/util.py in process_cmap(cmap, ncolors, provider, categorical)
    849     if not isinstance(palette, list):
    850         raise TypeError("cmap argument %s expects a list, Cycle or valid %s colormap or palette."
--> 851                         % (cmap,providers_checked))
    852     if ncolors and len(palette) != ncolors:
    853         return [palette[i%len(palette)] for i in range(ncolors)]

TypeError: cmap argument {0: 'blue', 1: 'red', 2: 'green', 3: 'purple'} expects a list, Cycle or valid matplotlib, bokeh, or colorcet colormap or palette

The use-case for this is that I want to set the 'color_index' to a boolean column then pick specific colors. I want to do so because If, e.g I pick 'tab10' then True/False get mapped to the extreme ends of the categorical colormap and then actually get plotted as quite similar colors - perhaps that's worth filing as a separate issue? I guess the larger issue here is that then normalization needs to handled differently for categorical vs continuous colormaps, so I can see that being a pain.

Now fixed.


fix: rankedItem casting to string (#64)

Bug fix. For more information: #64

@all-contributors please add @samyan for code

@all-contributors please add @samyan for bugs

:tada: This PR is included in version 3.1.1 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


(AzureCXP) fixed Event Hubs example header type

Fixed Event Hubs example header type and typo
resolves MicrosoftDocs/azure-docs#92262

@MayankBargali-MSFT : Thanks for your contribution! The author(s) have been notified to review your proposed change.


Changed broken documentation link.

Hi @alexandrunastase, I'm your friendly neighborhood .NET Foundation Pull Request Bot (You can call me DNFBOT). Thanks for your contribution!

This seems like a small (but important) contribution, so no Contribution License Agreement is required at this point. Real humans will now evaluate your PR.

TTYL, DNFBOT;

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

entity-framework/core/providers/npgsql/index.md
:white_check_mark:Succeeded

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.

Duplicate of the fix from #307


Null penetration detection & prevention

I am using https://pypi.org/project/schemathesis/ to smoke test a Django DRF API, and find that NULL is frequently causing problems.
I have run into NULL in

headers https://github.com/Styria-Digital/django-rest-framework-jwt/issues/70 , also oauth2_provider and social-auth2, but I havent raise bugs yet, as I plan to remove those anyway.
URL percent encoding paths https://github.com/django-oscar/django-oscar-api/issues/226

It seems like something this app might detect and prevent at a higher level, rather than every single app needing to have dedicated null detection, or try/except everywhere to reply with an error blaming the client instead of a server error.  Obviously a dedicated frontend processor doing packet analysis is the best approach, but it seems there is room for a simpler solution within Django that could reach the 80/20 rule.
If there are other more suitable projects for active abuse detection in django, I'd be glad to be redirected elsewhere.

It's possible for a middleware to do something like this but I think it would make more sense to handle it in your API, maybe in a base class?

@Gee19 , the problem isnt in my API. It is in an API exposed by a third party app.


[Dapp] Farming Index page - logged in / out information display

Description
Design reference
https://www.figma.com/file/TxyvqO53GlCiTaJOE26AwM/dApp?node-id=0%3A1&t=a2NYxoXVVLR9uNCd-1
The "My Farming Assets" and pool cards don't display the same info when logged in or not
Acceptance Criteria

[ ] Add and describe Acceptance Criteria
[ ] Add and describe Acceptance Criteria

Additional context
Add any other context or screenshots about the feature request here.

Fix #1


UseUI Blackout doesn't always clear when a service gets added

Google Contacts has been added, yet it doesn't let me get to other stuff. Refreshing the page, as you would expect, enables everything.
http://bit.ly/rGlxaQ

@ctide assigned to you to ensure it gets fixed in the new UI, not the current one.


Agents reporting healthy, but not sending any data

Per figure 3.22 (Step 8).
I am able to enroll agents and they show healthy, but do not send any data.

Same problem here. Everything is shown as healthy but no data is coming in. Did you find out why?

You had to copy the certs created into the cert store of the windows and/or linux hosts.

I was running into the same issue and wanted to clarify the recommended steps of copying the certs into the windows cert store.
Steps I took with no success:

Copied the ca.crt that was created in the Project Directory/certs/ca to the Windows host
Opened Microsoft Management Console (MMC) and added Certificate snap in
Navigated and imported the ca.crt through Trusted Root Certification Authorities > Certificates
Restarted the Elastic Agent service and the host with no data still being sent


Unit hydrograph + hourly timestep + more realistic PDFs

Incorporated unit hydrograph functionality

Used the pyflo package to transform q_spigot + q_overflow using the unit hydrograph transformation, producing an output called q_total.
Pyflo is installed with the command $ pip install pyflo. A distribution .csv file is also required to determine the shape of the unit hydrograph. The default is found in distributions/scs484.csv, and it is defined as follows: uh484 = system.array_from_csv('distributions/scs484.csv')
The default distribution (peak rate factor 484) was kept. It would be possible to change this, as well as other parameters like time of concentration and a parameter controlling direct runoff, but I didn't feel like I understood these well enough to change them.

Changed timestep to hour

Allows for longer predictions and easier deployment
Transformed g to be in $\frac{m}{h^{2}}$

Normalized q values by dividing by basin area

Outputs are now yields, not discharges. This keeps them in the same unit (m) as the input, precipitation.
Pyflo outputs everything in $\frac{ft^{3}}{s}$. These had to be transformed into $\frac{m}{h}$.

Incorporated PDFs of geological permeability, ET, and precip for more realistic value generation

Permeability (log 10) generated using a Normal(-13.8857, 1.1835) distribution

ET generated using a Weibull_min(2.2447, 9.9807e-5, 0.0016) distribution

Light precip generated using a Gumbel_r(0.0008108, 0.0009759) distribution

Heavy precip generated using a GenPareto(0.2341, 0.0101, 0.009250) distribution

PDFs fitted using a selection of PDFs identified during literature review, fitting with both the maximum likelihood and method of moments techniques, and goodness of fit evaluated using Kolmogorov-Smirnov test. All calculations done using scipy.stats package

Updated infiltration calculation

My original interpretation of Darcy's law drained the bucket in a suspiciously fast way
New calculation: infiltration = $\frac{10^{K} * (hwaterlevel + soildepth)}{soildepth}$

Updated A_spigot and H_spigot calculations to be a function of H_bucket

Bucket parameters now include rA_spigot and rH_spigot.
A_spigot calculated as area of a circle with diameter rA_spigot * H_bucket
H_spigot calculated as rH_spigot * H_bucket

Updated model validation and results visualization function

Included q_total as an output for visualization and validation
q_spigot and q_overflow still included in bucket output plot and LSTM outputs
Static attributes removed from bucket visualization for clarity. Information printed above plots instead
h_bucket plotted in separate subplot for clarity/scale issues
Calculates NSE for q_total now

Updated water balance calculation

infiltration and q_total added to mass_out
q_spigot and q_overflow removed from mass_out

Example bucket outputs:

Example LSTM result visualizations:

Tasks for the future:

Currently the buckets are very wet. We could adjust the PDFs or the Markov chain to create a somewhat wet bucket.
Initial values of h_bucket might significantly affect bucket results. h_bucket[0] might need to be incorporated as an input in the LSTM

Can you include instructions on how to install pyflo? It isn't a simple pip install? Do we have to clone this and install from the source: https://github.com/benjiyamin/pyflo?

Yep, it's just a pip install, but it also requires a distribution .csv file. An example of one was added in this commit. I also added a line about referencing and defining the distribution in the above comment

I'd like to get some better LSTM results before we merge this. There are a couple of things going on here preventing the LSTM from learning the response. I just made a couple of tweaks to the setup, like using a shorter time series, and a lower bucket, to get an overflow response quicker in the time series.

Is it possible that including the initial value of h_bucket as an input would improve predictions? It seems to have a significant impact on the data generated. Although, that might be data contamination. What do you think?

It probably would, but in a "real world" scenario, we wouldn't know the initial state values. We usually warm up models to get the state values in a reasonable configuration.

Okay, the first couple things coming to mind for improving the LSTM (on top of the two changes you made) would be increasing number of buckets produced, number of epochs, number of layers/hidden states... am I on the right track? Or should I be tweaking something in the bucket model or even the LSTM itself?

You are on the right track, yes. The more complicated your system, the more complex your model needs to be, true.
But what I was thinking is to try to get more of the total hydrologic response, with increasing and decreasing spigot flow and some overflows earlier in the simulation, so we can reduce the total training time steps down to like a few hundred, so we can add more buckets.

Thanks @quinnylee! I am merging this PR, but then I am going to make a few changes immediately after.
Then, we need to make the update to the main code in /src to match :)


Stop-Service xxx fails with "Cannot open xxx service on computer '.'."

Steps to reproduce
On Windows OneCore VM, use PowerShell Direct to connect and attempt to run:

Stop-Service xxx

Expected behavior
Service stops

Actual behavior
cmdlet fails and service doesn't stop

Environment data

> $PSVersionTable
Name                           Value
----                           -----
PSVersion                      6.0.0-beta.8
PSEdition                      Core
GitCommitId                    v6.0.0-beta.8
OS                             Microsoft Windows 10.0.17022
Platform                       Win32NT
PSCompatibleVersions           {1.0, 2.0, 3.0, 4.0...}
PSRemotingProtocolVersion      2.3
SerializationVersion           <IP_ADDRESS>
WSManStackVersion              3.0

this looks account issue..try running command in account having access, or run PS in admin mode.

Should this be closed by now?


http proxy support for ngx_mgmt_module

Describe the feature you'd like to add to nginx
Foresee http(s) proxy support in nginx ngx_mgmt_module for outgoing connections to product.connect.nginx.com.
( to support for example Squid http forward proxy )
example:
http_proxy=http://<IP_ADDRESS>:8080
https_proxy=https://<IP_ADDRESS>:8080
Describe the problem this feature solves
Connect to the mandatory product.connect.nginx.com for setups without direct internet connection.
Additional context
The ngx_mgmt_module module enables NGINX Plus license verification and usage reporting.
This is mandatory for each nginx/1.27.2 (nginx-plus-r33) instance.
usage_report endpoint=product.connect.nginx.com interval=1h;
product.connect.nginx.com => not reachable for setups without direct internet connection

Similar remark for the NGINX Instance Manager, it that one also has no direct connection.

The http/https proxy support in NGINX Plus (via ngx_mgmt_module) and in NGINX Instance Manager to send license telemetry to F5 is currently under active development.
Curious if you are a NGINX Plus prospect or existing customer? We use Github issues mainly for our NGINX open source development work. Licensing telemetry is a commercial feature and if you are a F5 NGINX customer or prospect, suggest reaching out to F5 sales or partner reps for more information and updates.


TypeError: cannot pickle 'paddle.base.libpaddle.PaddleInferPredictor' object

Hi, dear developers!
Thank you very much for code sharing!
Following step-by-step instruction from you readme file tend me to the following issue :
CEDe not imported: No module named 'annotator.labeled_data_utils' Arguments: {'align_rdkit_output': False,  'assign_stereo': False,  'chunk_size': 10000,  'config_dataset_graph_path': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/../../../data/config_dataset_graph_2.json',  'config_dataset_keypoint_path': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/../../../data/config_dataset_keypoint.json',  'config_training_graph_path': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/../../../data/config_training_graph.json',  'config_training_keypoint_path': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/../../../data/config_training_keypoint.json',  'force_cpu': False,  'input_images_paths': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/input_images_paths_default.jsonl',  'num_processes_mp': 10,  'num_threads_pytorch': 10,  'remove_captions': True,  'save_mol_folder': '/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/../../../data/predictions/molgrapher/default/'} Number of images to annotate:  1 PyTorch device: cpu Data module configuration: {'batch_size': 4,  'benchmarks': ['uspto-10k', 'uspto-10k-abb', 'uspto-10k-l'],  'experiment_name': 'exp-default',  'image_size': [3, 1024, 1024],  'nb_atoms_classes': 182,  'nb_bonds_classes': 6,  'nb_duplicates': 1,  'nb_sample': 1000,  'nb_sample_benchmarks': [10, 10, 10],  'nb_sample_fine-tuning': 10000,  'nb_workers': 4,  'num_processes_mp': 10,  'num_threads_pytorch': 10,  'on_fly': False,  'training_dataset': 'molgrapher-synthetic',  'training_dataset_fine-tuning': 'uspto-10k'} Starting Caption Removal Preprocessing 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.32s/it] Caption Removal Preprocessing completed in 1.84 Lightning automatically upgraded your loaded checkpoint from v1.8.1 to v2.1.3. To apply the upgrade to your files permanently, run python -m pytorch_lightning.utilities.upgrade_checkpoint data/models/keypoint_detector/kd_model.ckpt/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/models/../../data/models/graph_classifier/gc_no_stereo_model.ckpt /Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/models/../../data/models/keypoint_detector/kd_model.ckpt Lightning automatically upgraded your loaded checkpoint from v1.8.1 to v2.1.3. To apply the upgrade to your files permanently, runpython -m pytorch_lightning.utilities.upgrade_checkpoint data/models/graph_classifier/gc_no_stereo_model.ckpt Keypoint detector number parameters: 12.8722 M Node classifier number parameters: 26.6585 M GPU available: True (mps), used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs Starting Keypoint Detection + Node Classification Setting up predict dataloader Real benchmarks sets lengths (on-the-fly validation): [1] Number of predict sets: 1 Traceback (most recent call last):   File "/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/predict_molgrapher.py", line 330, in <module>     main()   File "/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/predict_molgrapher.py", line 326, in main     proceed_batch(args, _batch_images_paths)   File "/Users/danil/НТО/AI_HACK/MolGrapher/molgrapher/scripts/annotate/predict_molgrapher.py", line 126, in proceed_batch     predictions_out = trainer.predict(model, dataloaders=data_module.predict_dataloader())   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 864, in predict     return call._call_and_handle_interrupt(   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt     return trainer_fn(*args, **kwargs)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 903, in _predict_impl     results = self._run(model, ckpt_path=ckpt_path)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 989, in _run     results = self._run_stage()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage     return self.predict_loop.run()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator     return loop_run(self, *args, **kwargs)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/loops/prediction_loop.py", line 105, in run     self.reset()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/loops/prediction_loop.py", line 179, in reset     iter(data_fetcher)  # creates the iterator inside the fetcher   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/loops/fetchers.py", line 99, in __iter__     super().__iter__()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/loops/fetchers.py", line 48, in __iter__     self.iterator = iter(self.combined_loader)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/utilities/combined_loader.py", line 335, in __iter__     iter(iterator)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/utilities/combined_loader.py", line 144, in __iter__     self._load_current_iterator()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/pytorch_lightning/utilities/combined_loader.py", line 160, in _load_current_iterator     self.iterators = [iter(self.iterables[self._iterator_idx])]   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 433, in __iter__     self._iterator = self._get_iterator()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 386, in _get_iterator     return _MultiProcessingDataLoaderIter(self)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1039, in __init__     w.start()   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/process.py", line 121, in start     self._popen = self._Popen(self)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/context.py", line 224, in _Popen     return _default_context.get_context().Process._Popen(process_obj)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/context.py", line 284, in _Popen     return Popen(process_obj)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__     super().__init__(process_obj)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__     self._launch(process_obj)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 47, in _launch     reduction.dump(process_obj, fp)   File "/Users/danil/anaconda3/envs/molgrapher/lib/python3.9/multiprocessing/reduction.py", line 60, in dump     ForkingPickler(file, protocol).dump(obj) TypeError: cannot pickle 'paddle.base.libpaddle.PaddleInferPredictor' object
Please help to solve this problem

Hello,
Did you install PaddleOCR on CPU using the following command ?
bash install_paddleocr.sh -d 'cpu'
Or on GPU ?
Best,
Lucas

Hello,
Did you install PaddleOCR on CPU using the following command ?
bash install_paddleocr.sh -d 'cpu'
Or on GPU ?
Best,
Lucas

Yes, i have install PaddleOCR on cpu
OS: Mac OS
But i have install torch==1.13.1, not 1.13.1+cu117, because i had mistake
And can you help with docker for that project to run on the server:)

Best

And i installed PaddleOCR to /MolGrapher

Hello,
Here you can find how to install PaddleOCR (cpu) for Mac M1: https://github.com/DS4SD/MolGrapher/blob/282615be8b0343c9d63f35be456d4469ce6f6a10/install_paddleocr.sh#L17
If it does not work, you may need to read instructions directly on the PaddleOCR repository. (I am not a Mac user myself.)
I just added an example Dockerfile. You can run it with scripts bash docker_build.sh and bash docker_run.sh.
Hopefully it can help,
Best,
Lucas

Thank you very much, I will test that


Feature request: theme

Hope there is a prop on the Chart component that sets the theme. For instance:
希望 Chart 组件上能有一个属性用来设置颜色主题。例如：
<Chart theme="dark">
...
</Chart>

THX!

<Chart theme="dark">
...
</Chart>

Thanks a lot for the quick update. 十分感谢及时的更新！


Release 2.1.0

cc @mastahyeti @ptoomey3 @kbrock @st0012

:+1: thanks!
On Tue, Sep 22, 2015 at 1:56 PM, Jerry Cheung<EMAIL_ADDRESS>wrote:

cc @mastahyeti https://github.com/mastahyeti @ptoomey3
https://github.com/ptoomey3 @kbrock https://github.com/kbrock @st0012
https://github.com/st0012
You can view, comment on, or merge this pull request online at:
https://github.com/jch/html-pipeline/pull/222
Commit Summary

update script/changelog
release 2.1.0

File Changes

M CHANGELOG.md
https://github.com/jch/html-pipeline/pull/222/files#diff-0 (7)
M lib/html/pipeline/version.rb
https://github.com/jch/html-pipeline/pull/222/files#diff-1 (2)
M script/changelog
https://github.com/jch/html-pipeline/pull/222/files#diff-2 (37)

Patch Links:

https://github.com/jch/html-pipeline/pull/222.patch
https://github.com/jch/html-pipeline/pull/222.diff

—
Reply to this email directly or view it on GitHub
https://github.com/jch/html-pipeline/pull/222.

--
-Ben Toews

https://rubygems.org/gems/html-pipeline/versions/2.1.0 :tada:

:+1:

:+1:


Epic 3

Epic
As a user, I'd like the ability to create accounts.
User Stories

As a user, I should be able to engage in a one-step sign-in procedure without the requirement for formal certifications.
As a user, I should have the capability to establish an account utilizing my email address.

Epic
As a user, I'd like the ability to create accounts.
User Stories

[ ] As a user, I should be able to engage in a one-step sign-in procedure without the requirement for formal certifications.
[ ] As a user, I should have the capability to establish an account utilizing my email address.


Geth's checkpoint challenge is ignored by us, causing remote to disconnect

What is wrong?
Upon connecting a geth instance to my local trinity, trinity consistently ignores geth's initial checkpoint challenge (https://github.com/ethereum/go-ethereum/blob/6d74d1e5f762e06a6a739a42261886510f842778/eth/handler.go#L344-L361), even though trinity has the header requested. Because of that geth assumes we're useless as we advertise a certain HEAD and do not provide a header prior to that when asked to
It looks like the checkpoint challenge request (the first msg after the handshake) is being dropped because when it arrives the peer has no subscribers, so https://github.com/ethereum/trinity/blob/50a2c376d49e606aaef2a73f0b8a4be5c13d6246/p2p/peer.py#L316-L321 is a no-op
In the original handshake implementation, I remember dealing with something similar, which was addressed by temporarily storing those messages and manually adding them to the subscriber's queues once the peer was added to the pool: https://github.com/ethereum/trinity/blob/50a2c376d49e606aaef2a73f0b8a4be5c13d6246/p2p/peer_pool.py#L307-L328
But we're no longer using the second argument to _add_peer(): https://github.com/ethereum/trinity/blob/50a2c376d49e606aaef2a73f0b8a4be5c13d6246/p2p/peer_pool.py#L290

But we're no longer using the second argument to _add_peer():
https://github.com/ethereum/trinity/blob/50a2c376d49e606aaef2a73f0b8a4be5c13d6246/p2p/peer_pool.py#L290

Hah, yeah, that looks obviously broken. :clap: for a clear write-up.


Update LICENSE

Just fixed a typo error in licensing
Adds the name of copyright owner in LICENSE file
Fixes #26
Changes
-Copyright [yyyy] [name of copyright owner]
+Copyright 2023 The cdevents Authors

[ ] Tests pass
[ ] Appropriate changes to README are included in PR

Looks great and thank you for the updating the license! One pre-commit failed but I will merge this and resolve the pre-commit in the next pull request. Thank you!


Add additional Declare fix for 64-bit Excel (#84)

@grv87 Thanks for submitting this, I'll get this merged shortly.


Can you train Thai Treebanks Dataset?

I found . thtb_orchidpp.txt file is a treebank dataset from orchid corpus but it is not CoNLLU.

Umm... The dataset seems something like phrase structure. For example, the first line
[S [NP [FIXN การ]] [VP [VACT ประชุม] [PP [RPRE ทาง] [NP [NCMN วิชาการ] [PUNC <space>] [NP [NCMN ครั้ง] [DONM ที่ 1]]]]]]

denotes the phrase tree as shown below.

Well, how do we convert the phrase structure and the dependency tree into one another, @wannaphong?

Sorry, I do not know because it is beyond the scope of my expertise. I think @korakot should help with this.

It's possible in theory. A constituency tree can be converted to a
dependency tree with no ambiguity.
For example
VP = V + NP   can be converted to V -[dobj]-> NP
But there's no package library to do it for Thai (or even many other
languages).
You may need to convert them one by one.
You can search google to find some papers and 1 github for this.
https://www.google.com/search?q=convert+constituency+tree+to+dependency+tree
Korakot
On Mon, Dec 14, 2020 at 10:49 PM Wannaphong Phatthiyaphaibun <
<EMAIL_ADDRESS>wrote:

Well, how do we convert the phrase structure and the dependency tree into
one another, @wannaphong https://github.com/wannaphong?
Sorry, I do not know because it is beyond the scope of my expertise. I
think @korakot https://github.com/korakot should help with this.
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VP = V + NP   can be converted to V -[dobj]-> NP

Oh, it looks very nice. But I'm vague that S = NP + VP can be converted into NP <-[nsubj]- VP or NP <-[vocative]- VP or NP -[acl]-> VP...

For S = NP + VP
It needs to look inside of NP and VP, so that we can know which [rel] it is.
It's not ambiguous, though. You need to do a few if-then cases on PoS and
word groups.
It's a bit labor-intensive to list all cases.
Korakot
On Mon, Dec 21, 2020 at 3:39 PM Koichi Yasuoka<EMAIL_ADDRESS>wrote:

VP = V + NP can be converted to V -[dobj]-> NP
Oh, it looks very nice. But I'm vague that S = NP + VP can be converted
into NP <-[nsubj]- VP or NP <-[vocative]- VP or NP -[acl]-> VP...
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Field alias AttributeError: 'AliasChoices' object has no attribute 'lower' - CliSettingsSource

Description
Field aliases fail when subclassing a model with BaseSettings and using AliasChoices.

Pytest output
/usr/local/lib/python3.11/dist-packages/pydantic_settings/main.py:141: in __init__
    **__pydantic_self__._settings_build_values(
/usr/local/lib/python3.11/dist-packages/pydantic_settings/main.py:260: in _settings_build_values
    CliSettingsSource(
/usr/local/lib/python3.11/dist-packages/pydantic_settings/sources.py:902: in __init__
    self._connect_root_parser(
/usr/local/lib/python3.11/dist-packages/pydantic_settings/sources.py:1236: in _connect_root_parser
    self._add_parser_args(
/usr/local/lib/python3.11/dist-packages/pydantic_settings/sources.py:1255: in _add_parser_args
    for field_name, resolved_name, field_info in self._sort_arg_fields(model):
/usr/local/lib/python3.11/dist-packages/pydantic_settings/sources.py:1154: in _sort_arg_fields
    resolved_name = resolved_name.lower() if not self.case_sensitive else resolved_name
E   AttributeError: 'AliasChoices' object has no attribute 'lower'\

self = EnvSettingsSource(env_nested_delimiter='.', env_prefix_len=0), model = <class 'test_pydantic-settings.test_settings.<locals>.Check'>
    def _sort_arg_fields(self, model: type[BaseModel]) -> list[tuple[str, str, FieldInfo]]:
        positional_args, subcommand_args, optional_args = [], [], []
        fields = model.__pydantic_fields__ if is_pydantic_dataclass(model) else model.model_fields
        for field_name, field_info in fields.items():
            resolved_name = field_name if field_info.alias is None else field_info.alias
>           resolved_name = resolved_name.lower() if not self.case_sensitive else resolved_name
E           AttributeError: 'AliasChoices' object has no attribute 'lower'

Example Code
import pytest

from pydantic import AliasChoices, Field
# from pydantic import BaseModel
from pydantic_settings import BaseSettings

def test_settings():
    class Check(BaseSettings):
    # class Check(BaseModel):  # This works
        field: str = Field(
            "foo",
            alias=AliasChoices(
                "ALIAS_NAME"
            )
        )

    assert Check().field == 'foo'
    assert Check(ALIAS_NAME="bar").field == 'bar'

Expect
For this test to pass with BaseSettings as it does so with BaseModel.
Platform: Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Python version: Python 3.11.9 (main, Apr  6 2024, 17:59:24) [GCC 11.4.0]

pydantic           2.7.3
pydantic_core      2.18.4
pydantic-settings  2.3.0

Thanks @SantiagoG for reporting this.
This is probably related to pydantic-settings new CLI source.
@kschwab Could you please take a look at this? this shouldn't happen because the code does not enable the CLI source and  we mentioned in the doc
To enable CLI parsing, we simply set the `cli_parse_args` flag to a valid value, which retains similar conotations as
defined in `argparse`.

@hramezani yes agreed. It's currently getting initialized; we can move it to only initialize if parsing is necessary.
The above is also a bug in CliSettingsSource related to AliasChoices which we can address separately.

It has been release in new pydantic-settings 2.3.2


About failures due to exceeded resource quota

Motivation
Hello there! Some time back I asked a question on the Slack channel and still haven't got any advice on the problem.
I need some help and advice about the way Argo handles resource quotas. We're hitting the problem repeatedly in our namespace, a workflow fails because of quota limits and is not retried later on.
An example Workflow result:
pods "workflow-test-1578387522-82e06118" is forbidden: exceeded quota: diamand-quota, requested: limits.cpu=2, used: limits.cpu=23750m, limited: limits.cpu=24

Is there any advice with respect to the Workflow reconciliation? Any existing solutions? Does / should workflow-controller take care of that?
Summary
All in all, I need to know
a) whether the problem is on our site
b) whether there is an easy solution how to get around failed Workflows due to resource quotas
c) whether somebody else is hitting the issue
d) whether there are any plans from the Argo site regarding this and/or how can I contribute
I am ready and willing to go ahead and see to the implementation myself, just not experienced enough to be able to tell whether this is something that can be implemented and how to go about this. again, any pointers are welcome! :)
Cheers,
Marek

As far as I'm aware, resource quotas are a K8s concept that Argo does not know about. They are manged by a cluster admin and restrict how much resources can a specific namespace use. If running workflows is providing this error, might it be that your specific namespace in your specific cluster is running out of quotas? If so, this is not a problem that Argo could solve.

Hello @simster7 , thank you for the answer!
Correct, it is a K8s concept.

If running workflows is providing this error, might it be that your specific namespace in your specific cluster is running out of quotas?
That is precisely the issue, the namespace is temporarily out of quotas. For example, due to lots of pods being currently running or lack of memory.

this is not a problem that Argo could solve
I disagree. I believe the correct behaviour would be to wait for the resource quota to be available before executing the next step of a Workflow. Argo is, after all, a workload management engine, isn't it? How I am I supposed to use Argo for container orchestration if it doesn't allow me to follow the very basic Kubernetes rule (which is that pods are created when there are resources to do so) and fails instantly?

Consider the following example:
I have a workflow in which I only submit resources. In the Workflow step, I do submit a resource (let's say a Job) and then the Workflow immediately fails because it is not able to create the Pod for it. However, 1 second later, the Job is run by K8s because the quota had been freed for it. The Workflow, however, stays failed nevertheless. And that, in my opinion, is quite a problem and it makes it nearly impossible to use Argo Workflow in an environment with strict quotas.  :D
Cheers,
Marek

(I edited your comment above to distinguish the quotes and your responses)
Ah, I see what you're saying now. If that is the case as you've described, I agree that we need some sort of spec/logic to fix this.
I haven't tried it here yet, but would using retryStrategy here help at all? If not, what do you think we could add to it to solve this issue?

Thanks for the edit! :)
I actually do see your point now as well after giving it a deeper thought and I think you might be right in the sense that Argo is probably not to blame ( but it might be the saviour :) )
I'll get back to the retryStrategy in a sec. Please, correct me if I am wrong in my reasoning. Let's consider the case of k8s resources first (which is the most trickier one I suppose, because it's basically decoupled from the Workflow):

Workflow resource is created
A pod is created -> which executes kubectl create command -> and here one of the two things happens:
a) this command fails right ahead because of quota violation
b) this command is successful, it submits the resource to the cluster BUT the (for example with Job) resource condition is  temporarily set to Failed due to exceeded quotas

In both cases, the Workflow is considered failed (unless treated otherwise).
Now, in case of a)
This is, in my opinion, something which should be handled on the Argo side. The reason should be detected and there should be a mechanism (which may or may not be configurable) to wait for the required resources.
the b) case is where we're doomed, because it's completely decoupled and I agree that in that particular case, Argo's done it's job and submitted the resource. However, what's missing here is the ability to act upon these failures. I think that the successCondition and failureCondition is not sufficient and neither is retryStrategy because I have no way to detect why the failure happened and I end up retrying workflow steps whose failure was justifiable due to application misbehaviour (also, it is quite confusing, even from the UI, because it looks like the step was failing for some time, but in fact, it is just waiting for the quota).
I think a potential solution to combat the b) problem to introduce a better decision mechanism. I can elaborate a little bit more if you will, but the rough idea would be to have a onSuccess and onFailure branching like:
steps:
- - name: resource-submission
    onFailure:
      template: retry-if-exceeded-quota

and probably to improve the successCondition, resp. failureCondition to allow a more fine-grained control over these?
I am looking forward to your response and comments,
Mark

What sort of condition would {success, failure}Condition use to determine if the issue is caused by resource quotas?
I think this falls in the domain of retryStrategy although I agree it might be a bit off, because this is technically a failure while scheduling, and not a failure of the pod. Any ideas for what would be a good change to retryStrategy to alleviate this?

I'm relatively new to Argo, but I've already seen similar issues.  I'm running computational jobs.  I know that these will occupy, for example, 1 CPU.  If I have a cluster with, say, 10 CPUs but my Workflow will create 100 pods, I'd like to be able to specify the restriction at the Workflow level in the spec that the work described will require 1 CPU.
I see these resource based constraints as important because the current constraints on parallelism (as far as I know) don't really address this issue.  I can set a limit on the total number of Workflows, but that is difficult because I don't know what resources each of the workflows will require.  I can set a parallelism in the Workflow itself, but then I don't know what the cluster is capable of.
What is really needed to scale things is for the workflows themselves to specify their resource constraints and have the scheduler prioritize these nodes and then schedule what the cluster can handle.  Is there some way to accomplish this currently?

@xogeny I'm going to implement it, if you'd like you can be one of beta-testers :) Basically the idea is to alter how pods are scheduled, not to fail when there are no resources, but rather indicate a "Pending" state.

@jamhed I'd be very happy to try it out.  This will be relatively important for us.  BTW, I see you are in Prague.  Turns out I'm in Prague this week.  Let me know if you want to grab lunch this week and talk Argo, my treat.  I'm near Karlin, BTW.

@xogeny sure, would love to, check your mail. actually, i have more things i'd like to discuss with this regard :)

@jamhed consider myself in for the testing :)

@CermakM sure :)

@CermakM jamhed/workflow-controller:v2.4.3-1
If it can't schedule the pod, it just keeps it in Pending state.

@jamhed that sounds interesting! Will give it a shot :)

@CermakM, this is pretty much the changes you need to do in argo helm chart:
images:
  namespace: jamhed
  tag: v2.4.3-1

@jamhed Just tested it out. Works like a charm! What a relief to see that ... are there any plans of merging this to the upstream?!
@simster7 :pray:

@CermakM let me backport it to 2.6.1, and i'll open a pull request.

@CermakM @simster7 https://github.com/argoproj/argo/pull/2385

We are considering making this default behaviour in v2.11. Thoughts?

We are considering making this default behaviour in v2.11. Thoughts?

I would say it's much better behavior in comparison to the current one. So +1 on my side for this usability improvement.

Thank you. I've created a new image for testing if you would like to try it: argoproj/workflow-controller:fix-3791 .

Hey whats the current status on this? Has this feature been released? I can see the flag in the API reference but the default behaviour isn't documented.

I've created another test image: argoproj/workflow-controller:fix-3791.
Can you please try it out to confirm it fixes your problem?

We've installed your build argoproj/workflow-controller:fix-3791 (sha256:2cc4166ce). I can confirm the workflow acts much more stable with respect to resources in comparison to v2.9.5 (I haven't tested newer releases than that).
Is there anything to observe in logs (I didn't see any relevant messages)?

Thank you. I wanted to verify it worked better.

Thank you. I wanted to verify it worked better.

Thank you for this fix.
In what release of argo we can expect this feature to be present?

v2.11

Thank you. I've created a new image for testing if you would like to try it: argoproj/workflow-controller:fix-3791 .

@alexec After some time we spotted an issue. Workflows fail (interestingly, they do not get deleted based on the ttl strategy configuration) and stay in the cluster. I can see pod deleted as a message. This happens for a pod that requires a relatively large amount of resources and that are not available as other workflows use them.
Checking cluster events, there was nothing suspicious. Workflow controler produces the following log:
time="2020-09-01T08:43:41Z" level=info msg="Processing workflow" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="Updated phase  -> Running" namespace=thoth-backend-stage workflow=adviser-c9267fd5
E0901 08:43:41.029779       1 event.go:263] Server rejected event '&v1.Event{TypeMeta:v1.TypeMeta{Kind:"", APIVersion:""}, ObjectMeta:v1.ObjectMeta{Name:"adviser-c9267fd5.16309c64000c72ab", GenerateName:"", Namespace:"thoth-backend-stage", SelfLink:"", UID:"", ResourceVersion:"", Generation:0, CreationTimestamp:v1.Time{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, DeletionTimestamp:(*v1.Time)(nil), DeletionGracePeriodSeconds:(*int64)(nil), Labels:map[string]string(nil), Annotations:map[string]string(nil), OwnerReferences:[]v1.OwnerReference(nil), Finalizers:[]string(nil), ClusterName:"", ManagedFields:[]v1.ManagedFieldsEntry(nil)}, InvolvedObject:v1.ObjectReference{Kind:"Workflow", Namespace:"thoth-backend-stage", Name:"adviser-c9267fd5", UID:"b44d8e83-14f6-482e-b001-42b0c8e1a09b", APIVersion:"argoproj.io/v1alpha1", ResourceVersion:"195448328", FieldPath:""}, Reason:"WorkflowRunning", Message:"Workflow Running", Source:v1.EventSource{Component:"workflow-controller", Host:""}, FirstTimestamp:v1.Time{Time:time.Time{wall:0xbfcba04f41ab50ab, ext:508109826557890, loc:(*time.Location)(0x2994080)}}, LastTimestamp:v1.Time{Time:time.Time{wall:0xbfcba04f41ab50ab, ext:508109826557890, loc:(*time.Location)(0x2994080)}}, Count:1, Type:"Normal", EventTime:v1.MicroTime{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, Series:(*v1.EventSeries)(nil), Action:"", Related:(*v1.ObjectReference)(nil), ReportingController:"", ReportingInstance:""}': 'events is forbidden: User "system:serviceaccount:thoth-backend-stage:argo-server" cannot create resource "events" in API group "" in the namespace "thoth-backend-stage"' (will not retry!)
time="2020-09-01T08:43:41Z" level=info msg="DAG node adviser-c9267fd5 initialized Running" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="All of node adviser-c9267fd5.advise dependencies [] completed" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="Pod node adviser-c9267fd5-3669748408 initialized Pending" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="Mark node adviser-c9267fd5.advise as Pending, due to: pods \"adviser-c9267fd5-3669748408\" is forbidden: exceeded quota: thoth-backend-stage-quota, requested: limits.memory=6400Mi, used: limits.memory=14824Mi, limited: limits.memory=20Gi" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="node adviser-c9267fd5-3669748408 message: Pending 27.047885ms" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="Released all acquired locks" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:41Z" level=info msg="Workflow update successful" namespace=thoth-backend-stage phase=Running resourceVersion=195448332 workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Processing workflow" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=warning msg="pod adviser-c9267fd5-3669748408 deleted" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Skipped node adviser-c9267fd5-2425184343 initialized Omitted (message: omitted: depends condition not met)" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Skipped node adviser-c9267fd5-1685984280 initialized Omitted (message: omitted: depends condition not met)" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Skipped node adviser-c9267fd5-1448319234 initialized Omitted (message: omitted: depends condition not met)" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Skipped node adviser-c9267fd5-3104014563 initialized Omitted (message: omitted: depends condition not met)" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Outbound nodes of adviser-c9267fd5 set to [adviser-c9267fd5-1685984280 adviser-c9267fd5-1448319234 adviser-c9267fd5-3104014563]" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="node adviser-c9267fd5 phase Running -> Error" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="node adviser-c9267fd5 finished: 2020-09-01 08:43:51.160005601 +0000 UTC" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Checking daemoned children of adviser-c9267fd5" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Updated phase Running -> Error" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Marking workflow completed" namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Checking daemoned children of " namespace=thoth-backend-stage workflow=adviser-c9267fd5
time="2020-09-01T08:43:51Z" level=info msg="Released all acquired locks" namespace=thoth-backend-stage workflow=adviser-c9267fd5
E0901 08:43:51.161571       1 event.go:263] Server rejected event '&v1.Event{TypeMeta:v1.TypeMeta{Kind:"", APIVersion:""}, ObjectMeta:v1.ObjectMeta{Name:"adviser-c9267fd5.16309c665bf6cacc", GenerateName:"", Namespace:"thoth-backend-stage", SelfLink:"", UID:"", ResourceVersion:"", Generation:0, CreationTimestamp:v1.Time{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, DeletionTimestamp:(*v1.Time)(nil), DeletionGracePeriodSeconds:(*int64)(nil), Labels:map[string]string(nil), Annotations:map[string]string{"workflows.argoproj.io/node-name":"adviser-c9267fd5", "workflows.argoproj.io/node-type":"DAG"}, OwnerReferences:[]v1.OwnerReference(nil), Finalizers:[]string(nil), ClusterName:"", ManagedFields:[]v1.ManagedFieldsEntry(nil)}, InvolvedObject:v1.ObjectReference{Kind:"Workflow", Namespace:"thoth-backend-stage", Name:"adviser-c9267fd5", UID:"b44d8e83-14f6-482e-b001-42b0c8e1a09b", APIVersion:"argoproj.io/v1alpha1", ResourceVersion:"195448332", FieldPath:""}, Reason:"WorkflowNodeError", Message:"Error node adviser-c9267fd5", Source:v1.EventSource{Component:"workflow-controller", Host:""}, FirstTimestamp:v1.Time{Time:time.Time{wall:0xbfcba051c989c4cc, ext:508119958577099, loc:(*time.Location)(0x2994080)}}, LastTimestamp:v1.Time{Time:time.Time{wall:0xbfcba051c989c4cc, ext:508119958577099, loc:(*time.Location)(0x2994080)}}, Count:1, Type:"Warning", EventTime:v1.MicroTime{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, Series:(*v1.EventSeries)(nil), Action:"", Related:(*v1.ObjectReference)(nil), ReportingController:"", ReportingInstance:""}': 'events is forbidden: User "system:serviceaccount:thoth-backend-stage:argo-server" cannot create resource "events" in API group "" in the namespace "thoth-backend-stage"' (will not retry!)
E0901 08:43:51.166772       1 event.go:263] Server rejected event '&v1.Event{TypeMeta:v1.TypeMeta{Kind:"", APIVersion:""}, ObjectMeta:v1.ObjectMeta{Name:"adviser-c9267fd5.16309c665bf7e5f2", GenerateName:"", Namespace:"thoth-backend-stage", SelfLink:"", UID:"", ResourceVersion:"", Generation:0, CreationTimestamp:v1.Time{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, DeletionTimestamp:(*v1.Time)(nil), DeletionGracePeriodSeconds:(*int64)(nil), Labels:map[string]string(nil), Annotations:map[string]string(nil), OwnerReferences:[]v1.OwnerReference(nil), Finalizers:[]string(nil), ClusterName:"", ManagedFields:[]v1.ManagedFieldsEntry(nil)}, InvolvedObject:v1.ObjectReference{Kind:"Workflow", Namespace:"thoth-backend-stage", Name:"adviser-c9267fd5", UID:"b44d8e83-14f6-482e-b001-42b0c8e1a09b", APIVersion:"argoproj.io/v1alpha1", ResourceVersion:"195448332", FieldPath:""}, Reason:"WorkflowFailed", Message:"", Source:v1.EventSource{Component:"workflow-controller", Host:""}, FirstTimestamp:v1.Time{Time:time.Time{wall:0xbfcba051c98adff2, ext:508119958649585, loc:(*time.Location)(0x2994080)}}, LastTimestamp:v1.Time{Time:time.Time{wall:0xbfcba051c98adff2, ext:508119958649585, loc:(*time.Location)(0x2994080)}}, Count:1, Type:"Warning", EventTime:v1.MicroTime{Time:time.Time{wall:0x0, ext:0, loc:(*time.Location)(nil)}}, Series:(*v1.EventSeries)(nil), Action:"", Related:(*v1.ObjectReference)(nil), ReportingController:"", ReportingInstance:""}': 'events is forbidden: User "system:serviceaccount:thoth-backend-stage:argo-server" cannot create resource "events" in API group "" in the namespace "thoth-backend-stage"' (will not retry!)
time="2020-09-01T08:43:51Z" level=info msg="Workflow update successful" namespace=thoth-backend-stage phase=Error resourceVersion=195448520 workflow=adviser-c9267fd5

Not sure if this line can have any impact on the issue (I suspect not?!):
'events is forbidden: User "system:serviceaccount:thoth-backend-stage:argo-server" cannot create resource "events" in API group "" in the namespace "thoth-backend-stage"' (will not retry!)

Pods may be deleted manually, due to scaledown events in your cluster, or other reasons. When this happens, unless you have 'resubmitPendingPods', the nodes fails.

Pods may be deleted manually, due to scaledown events in your cluster, or other reasons. When this happens, unless you have 'resubmitPendingPods: true`, the nodes fails. See #3918

Thanks! It looks like setting resubmitPendingPods: true on the template level did the trick.

@simster7 @alexec Is this feature released? I still see Workflow getting failed if we have resource quota set on number of pods at namespace level
Tried with both 2.11.8 and  2.12.9
Error message: time="2021-02-23T12:01:43Z" level=info msg="Mark node test-trans-1-pgsdb.onExit[0].e-mail as Pending, due to: pods \"test-trans-1-pgsdb-2584680134\" is forbidden: exceeded quota: pod-demo, requested: pods=1, used: pods=3, limited: pods=3" namespace=pod-demo workflow=test-trans-1-pgsdb


Missing ./ in front of configure

Small, but it tripped me up for a minute when installing fltk from source.

Thanks!

No problem!
On Nov 29, 2016 15:32, "deech"<EMAIL_ADDRESS>wrote:

Merged #41 https://github.com/deech/fltkhs/pull/41.
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CI tests for Windows and MacOS

converts the github workflow to use the matrix notation for Linux tests
run tests on Windows and MacOS
fix some tests failing on Windows (SQLiteJDBCLoaderTest.multipleClassLoader)

You can see a sample execution here: https://github.com/gotson/sqlite-jdbc/actions/runs/1150032640 (edit: or in the checks of this very PR)

Wow. I didn't know GH action supports windows/mac as well :)

Merged


New hire form flow #775

WIP: Attempting to create PR with prev author history.

This PR contains prev commit progress from #775 and serves as my starting point.

Right now:
'hire' = business_forms  (Business hired a developer)
'hired' = developer_forms (Developer found a job)
Both hire/hired follow their own namespace.
Are you satisfied with just the naming change to i18n keys/admin menu buttons? Or would you also like to update the namespaces?

Would you also like to update the namespaces as well?

That would be great, thank you!

That would be great, thank you! Same goes for the routes, too.

Great. On it!

Hey @joemasilotti, I'm currently exploring potential options for changing the namespace from :hire to :business_forms.
Starting with the current :hire namespace:
namespace :hire do
  resources :forms, only: [:index, :show]
end

(Option: 1) We could move take the :hire (business_forms) namespace and move it into :businesses
resources :businesses, only: [] do
  #resources :conversations, only: :index, controller: :business_conversations
  #resources :invisiblizes, only: :create, module: :businesses
  resources :forms, only: [:index, :show]
end

(Option: 2) Rename the :hire namespace to business_forms:
namespace :business_forms do
  resources :forms, only: [:index, :show] 🚿
end

Any thoughts?

URL-wise, I like /businesses/forms and /developers/forms. So whatever routes can make that works feel right to me.

Done.
admin/hire/forms is now admin/businesses/forms
and
admin/hired/forms is now admin/developers/forms
Any specific behavior would you like to see tests for?
Admin views newly created business/developer form for instance?

I recently added tests around the admin CRUD for specialties. Something like that for dev + biz would be perfect!

I recently added tests around the admin CRUD for specialties. Something like that for dev + biz would be perfect!

Sounds good! On it.

Amazing thank you! One more request if you’re down for it - I’d love to see a db seed for each form. I’m planning to review this tomorrow - thanks for picking it up!

I added a seed to hiring.rb in e545ddf1bcc8ea51aa7d6dce68aea65743273f76
Bit tricky to see because at the model level, the business_form still exists as Hire:Form.
https://github.com/joemasilotti/railsdevs.com/blob/486d887c4b0a2bce386548d9cad69099ca8302ef/db/seeds/hiring.rb#L14-L23
The model/migration would be the last remaining piece to change.
I think I can get that done before you review it tomorrow.

Businesses::BusinessForm is a bit redundant, but it prevents us from a potential table conflict from

Developesr::Form
Businesses::Form.

Alternatively, we could create a generic Form model that handles both biz/dev forms with an enum and backfill existing hired_forms. This would also require changing some associations. Thinking that might be a good fit for a future issue.

The model Hire::Form is now Businesses::BusinessForm
Also, the migration from this PR that creates hire_forms will create business_forms instead.
Businesses::BusinessForm is a bit redundant but it prevents us from breaking out of the current table naming scheme and from doing something more complex like setting custom table_names on new models like:

Developers::Form
Businesses::Form

Which AFAIK would require creating a generic Form model that handles both biz/dev forms via an enum and backfilling existing hired_forms. This would also require changing some associations. RN I'm thinking that might be a good fit for a future issue.
I'm open to any/all feedback. Happy reviewing!

Ah! This looks promising!
So this means we could achieve Businesses::Form that uses a table called businesses_forms
Happy to give it a go today! Sound good?

Sounds great - go for it!

In the future, I think it would be feasible to move existing Hired::Form HiringAgreements::Term HiringAgreements::Signature into Forms:: namespace
Something like
Hired::Form -> Forms::Developers::Hired
Business::BusinessForm -> Forms::Businesses::Hired
HiringAgreements::Term -> Forms::HiringAgreements::Term
HiringAgreements::Signature -> Forms::HiringAgreements::Signature
In this PR, I think we have an opportunity to kick that off with Businesses::BusinessForm (since the migration is brand new)
Starting with creating the prefix forms_
# app/models/forms.rb

module Forms
  def self.table_name_prefix
    "forms_"
  end
end

Then the table:
create_table :forms_businesses_hired do |t|
   ...
end

Then we can change: Business::BusinessForm -> Forms::Businesses::Hired
What do you think?

I love this! Great idea.
So this PR would only make changes to the new model and you would open a new issue/PR for the remaining refactor?

Sounds good all around to me. Let's move forward without the refactor for now and get this PR merged.
Is everything we talked about implemented? Or do you still need to add the Forms module?

Sounds good! Still working on Business::BusinessForm -> Forms::Businesses::Hired

Alrighty, I think we've made it to a good place in this PR!
The only thing I might want to add is an integration test for "business creates Forms::Businesses::Hire"
Other than that it's ready for review.

Sweet! Thanks @MikeRayUX. Looking forward to your next revision. :)

I know there's a lot of changes to be made on this one. Please @ me when everything is ready for review again!

@joemasilotti Ready for review!

@joemasilotti Ready for review!

Can you enable commits on your branch by maintainers? I have a few tiny tweaks I want to make before merging. But this is like 99% of the way there!

"Allow edits by maintainers" box was already checked in the sidebar of the PR.
Is there something else I need to enable somewhere?

Weird! No, that should be it.
I'll just add my comments in a review real quick.

I'll just add my comments in a review real quick.

Ok, ready to implement any changes.

Merged! Thanks for taking this over the finish line @MikeRayUX. And thank you @RomanTurner for kicking this off an getting us some solid foundation.
Can both of you please email me how you'd like to receive your bounty? I can do GitHub Sponsorships, Venmo, Zelle, or PayPal.
And @MikeRayUX, whenever you have a minute can you please document the next steps for the refactor? Feel free to assign yourself if you want to take on the work.

Thanks again @RomanTurner for your work in this PR!
It was a pleasure working with you on this Joe!
Happy to file an issue and dive in on the next refactor for Hired::Form -> Developers::CelebrationPackageRequest
Will get on that first thing in the morning.


"incubate_eggs_worker.py" errors

I tried a couple of things, like: ' pip install -e git+https://github.com/tejado/pgoapi.git@81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b#egg=pgoapi' and
`root@server:~/poke/PokemonGo-Bot# sudo pip install -r requirements.txt
Obtaining pgoapi from git+https://github.com/tejado/pgoapi.git@81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b#egg=pgoapi (from -r requirements.txt (line 1))
Updating ./src/pgoapi clone (to 81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b)
Could not find a tag or branch '81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b', assuming commit.
Running setup.py (path:/root/poke/PokemonGo-Bot/src/pgoapi/setup.py) egg_info for package pgoapi
warning: no previously-included files matching '*.dll' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*.pyo' found anywhere in distribution
warning: no previously-included files matching '*.so' found anywhere in distribution

Installing extra requirements: 'egg'
Requirement already satisfied (use --upgrade to upgrade): geopy==1.11.0 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 2))
Requirement already satisfied (use --upgrade to upgrade): protobuf==3.0.0b4 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 3))
Requirement already satisfied (use --upgrade to upgrade): requests==2.10.0 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 4))
Requirement already satisfied (use --upgrade to upgrade): s2sphere==0.2.4 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 5))
Requirement already satisfied (use --upgrade to upgrade): gpsoauth==0.3.0 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 6))
Requirement already satisfied (use --upgrade to upgrade): protobuf-to-dict==0.1.0 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 7))
Requirement already satisfied (use --upgrade to upgrade): googlemaps==2.4.4 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 8))
Requirement already satisfied (use --upgrade to upgrade): colorama==0.3.7 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 9))
Requirement already satisfied (use --upgrade to upgrade): enum34==1.1.6 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 10))
Requirement already satisfied (use --upgrade to upgrade): pyyaml==3.11 in /usr/lib/python2.7/dist-packages (from -r requirements.txt (line 11))
Requirement already satisfied (use --upgrade to upgrade): haversine==0.4.5 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 12))
Requirement already satisfied (use --upgrade to upgrade): polyline==1.3.1 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 13))
Requirement already satisfied (use --upgrade to upgrade): universal-analytics-python==0.2.4 in /usr/local/lib/python2.7/dist-packages (from -r requirements.txt (line 14))
Requirement already satisfied (use --upgrade to upgrade): six in /usr/local/lib/python2.7/dist-packages (from pgoapi->-r requirements.txt (line 1))
Requirement already satisfied (use --upgrade to upgrade): pycryptodomex>=3.0 in /usr/local/lib/python2.7/dist-packages (from gpsoauth==0.3.0->-r requirements.txt (line 6))
Installing collected packages: pgoapi
Running setup.py develop for pgoapi
warning: no previously-included files matching '*.dll' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*.pyo' found anywhere in distribution
warning: no previously-included files matching '*.so' found anywhere in distribution
Creating /usr/local/lib/python2.7/dist-packages/pgoapi.egg-link (link to .)
Removing pgoapi 1.1.0 from easy-install.pth file
Adding pgoapi 1.1.0 to easy-install.pth file

Installed /root/poke/PokemonGo-Bot/src/pgoapi

Successfully installed pgoapi
Cleaning up...
root@server:~/poke/PokemonGo-Bot# pip install git+https://github.com/tejado/pgoapi.git@81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b#egg=pgoapi
Requirement already satisfied (use --upgrade to upgrade): pgoapi from git+https://github.com/tejado/pgoapi.git@81e786cabf027a1c8fbd1e9a07e1c11aa3d8ee8b in /usr/local/lib/python2.7/dist-packages
Requirement already satisfied (use --upgrade to upgrade): geopy==1.11.0 in /usr/local/lib/python2.7/dist-packages (from pgoapi)
Requirement already satisfied (use --upgrade to upgrade): protobuf>=3.0.0a3 in /usr/local/lib/python2.7/dist-packages (from pgoapi)
Requirement already satisfied (use --upgrade to upgrade): requests==2.10.0 in /usr/local/lib/python2.7/dist-packages (from pgoapi)
Requirement already satisfied (use --upgrade to upgrade): s2sphere==0.2.4 in /usr/local/lib/python2.7/dist-packages (from pgoapi)
Requirement already satisfied (use --upgrade to upgrade): gpsoauth==0.3.0 in /usr/local/lib/python2.7/dist-packages (from pgoapi)
Requirement already satisfied (use --upgrade to upgrade): pycryptodomex>=3.0 in /usr/local/lib/python2.7/dist-packages (from gpsoauth==0.3.0->pgoapi)
Cleaning up...
root@server:~/poke/PokemonGo-Bot# ./run.sh
[20:58:03] PokemonGO Bot v1.0
[20:58:03] Configuration initialized
[20:58:03]
[20:58:03] Location Found: #####################
[20:58:03] GeoPosition:<PHONE_NUMBER>343815797,<PHONE_NUMBER>671155104, 0)
[20:58:03]
[20:58:03] [x] Parsing cached location...
[20:58:03]
[20:58:03] [x] Last location flag used. Overriding passed in location
[20:58:03] [x] Last in-game location was set as:<PHONE_NUMBER>783273204,<PHONE_NUMBER>783273204, 0)
[20:58:03]
[20:58:03] Attempting login to Pokemon Go.
[20:58:04] Login to Pokemon Go successful.
[20:58:05]
[20:58:05] --- My Username ---
[20:58:05] Level:
.......
[20:58:05] Starting PokemonGo Bot....
Traceback (most recent call last):
File "pokecli.py", line 294, in
main()
File "pokecli.py", line 260, in main
bot.tick()
File "/root/poke/PokemonGo-Bot/pokemongo_bot/init.py", line 63, in tick
if worker(self).work() == WorkerResult.RUNNING:
File "/root/poke/PokemonGo-Bot/pokemongo_bot/cell_workers/incubate_eggs_worker.py", line 34, in work
incubators.append({"id":incubator.get("id", -1), "used":False})
AttributeError: 'str' object has no attribute 'get'
`
it keep givin me errors
Other Information
OS: Debian Wheezy
Git Commit: 8852f6222f63b23e400b1df594d780121003edbf

fixed: pip uninstall pgoapi && pip install -r requirements.txt


Idea: Use Webdriver instead of Phantom

Hi there, I really like this project...
I needed to use Chrome instead of PhantomJS, as it renders a page a lot differently (even when using PhantomJS 2), so I swapped Phantom to neutral Webdriver which can communicate with any browser - Chrome, Firefox and even PhantomJS – but I can make a choice :)
I'm really not happy with my code, but I'm really not good with browser automation. But it's doing what it should do, so I'm making this PR not with intent to merge it, but to discuss it or maybe somebody can help me with a correct implementation.
Biggest ugliness is IMHO dep on easyimage, as Chrome is not able to resize below 400px (https://code.google.com/p/chromium/issues/detail?id=275686), only with use of mobileEmulation, which causes screenshots to contain crazy amount of whitespace.

Hello Pavel,
first of all, thank you very much for trying out my script and providing feedback, I really appreciate this.
Before I wrote this script, I already tried out several alternatives and the main point of this script was to keep it as easy and accessible as possible.
If we would switch to webdriver this would be much more complicated. You need:

node.js and npm dependencies (and what about io.js?)
Java
A Selenium server running (and way to configure e.g. the port)
A proper configured imagemagick installation (and again, probably more configuration options)

I see the advantages of your solution, but I don't want to introduce this much complexity into the setup and configuration of the script.
I think it's great that you took the time to evaluate another option, but I don't want to trade in the simplicity of the script for this features.
However, I would be very interested to hear about the problems you were facing using PhantomJS. Could you provide some screenshots, expected vs. result?

Hi, thanks for taking time,
I really like the idea of simplicify, I wish sooo much it would just work  :) I totally, agree with complexity, I thought I would take me half an hour to ajdust your script to work with Webdriver, but spend another few hours figuring out how to handle different behavour with different browsers (like being unable to make Chrome window narrower and cropping the image instead)
Posting images why PhantomJS was, sadly, unusable for me:
PhantomJS 2 on the left, Chrome on the right.

It looks like PhantomJS is using another font family to render the text.
Do you use special webfonts? How do you include them (Google Web Fonts, local file)?

Just plain font-family: sans-serif;, you can check it live at https://en.skypicker.com
But that's not an issue of this project, but ours with compatibity and Phantom's with rendering :)
Thanks for your time!

I think the problem could be that you're only specifying a generic font family. This basically leaves the choice of the used font up to the browser. So your website is probably going to be rendered in Helvetica on OSX, Arial on Windows and something like Bitstream Vera on Linux.
You could try to use a font stack like font-family: Arial, 'Helvetica Neue', Helvetica, sans-serif; and see what happens.

Closing this for now - please reopen if there is something new.


Remove note to editors from public view

This PR moves the hyperlink from the heading into the text, and removes the note for ourselves.
Fixes #25.

Updates look good! For my own future reference, if I'm happy with this kinda thing shall I just go ahead and "rebase and merge"?

We haven't agreed on the etiquette explicitly 😅 I'll mention this in #39
If you are requested to review, leaving an approval and merging is 👍 Given the verbal confirmation I'll go ahead and merge.


turn peripherals into scoped singletons

See this RFC for details: japaric/svd2rust#157

@homunkulus r+

:pushpin: Commit 9bab417 has been approved by japaric

:hourglass: Testing commit 9bab4175eb4120612e07470d060d6aca3b5edfd5 with merge e5c7f14862c5d0dd65d471a940261e419917c086...

:broken_heart: Test failed - status-travis

@homunkulus r+

:pushpin: Commit b7a627c has been approved by japaric

:hourglass: Testing commit b7a627cd986cb05e71c456782daaee4f47e26ede with merge 5c75a4e84b3f13d2b3e9a73f8b7f2e0eaf66012e...

@homunkulus r+

:pushpin: Commit 0b45ea4 has been approved by japaric

:hourglass: Testing commit 0b45ea4bd486565da5d78793dcb4718afb4f4387 with merge ce5bee6e37b4127040224523a1af85c275401a18...

:broken_heart: Test failed - status-travis

:sunny: Test successful - status-travis
Approved by: japaric
Pushing ce5bee6e37b4127040224523a1af85c275401a18 to master...


[Bugfix][Hardware] Fix model input for decode

CPU backend is fixed by #9044 , but I think we can make below changes so that no more fix needed for other backends. Verified on XPU backend.
@LiuXiaoxuanPKU

I prefer fixing the issue on each model runner as IMO passing an empty list instead of None is semantically wrong.

Not sure what others think?

I think this will break multi step features(which num_seqs should be num_steps).
I have a fix here  https://github.com/vllm-project/vllm/pull/9145

@DarkLight1337 I agree, it’s potentially more misleading otherwise.

Thanks for everyone's comments. Sorry for not familiar with other path calls about this variable. Let me close it.


icon variant does not sit above footer

Summary of the bug
When displaying a back to top with variant=icon, the element does not remain fixed in position when the footer scrolls into view:

the default variant does remain fixed:

🧐 Expected behaviour
The button should not overlap the footer
📝 Acceptance Criteria
Given an icon variant back to top exists on a page
When  the page footer scrolls into view
Then the back to top button should not overlap the footer
Additional info
This may be due to the tooltip on the button

Can I fix this?
If allowed:
Do I need a reproduction repo to mimic this bug? I'm running storybook locally and have a navigated to "back-to-top" component, but I don't have reproduction steps to follow


Test to reproduced the sub-tree recompile behavior

While the bound element is not re-described, it looks like children of the element are. From reading the Lacona codebase, it looks like the standard practice is to bind to leaf nodes. I discovered the behavior while following the React pattern of binding near the top of the component tree and passing state via props.
If this usage is to be supported, a proposal would be to cache the entire subtree description, rather than just the element description. I could re-architect my usage to bind only to leaf nodes, so a fix is not strictly required. However, given the failure mode is functional but slow, it might be good to add warning if non-leaf bound elements are detected.
I'm happy to assist with either option.

Thanks for the test - this is definitely a bug that we want to fix. Diving into it now.


Integrating dummynet package

Now the two namespaces for running the system is created with Linux command, A cleaner solution to this is to create them with dummynet

Not a high priority right now


Switch to Pulumi blog post

New blog post that is part of TF Takeover campaign

Approval from Eli before her PTO


pull request for e2e test at 11/13/2016 08:15:10

:white_check_mark: Validation status: passed

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