feat(rabbitmq): adds additional queue options

This enables subscribers to create "exclusive" and/or non-"durable"
queues.

Hey @bugsduggan Thanks for the contribution! At a high level this looks great and makes perfect sense. I just want to take a look at it in the context of integration tests to see if there's anything new that needs to be added on that front to make sure that new behavior is covered by tests.
Will get this merged through ASAP and publish new packages.
In the meantime would love to hear more about how you're using nestjs-plus (work or personal?) and if there's anything missing in terms of functionality that you'd like to see get added

Hi @WonderPanda , thanks for the quick response. I appreciate that I just fired off this PR without even really saying hi first :grin: .
I'm currently using nestjs-plus for a project at work but I'm also planning on using it in a few of my pet projects.
If you do feel like extra integration tests might be needed, let me know. I'm not overly familiar with yarn (I use npm) but I always appreciate an excuse to learn new things.

I was stoked to see a PR get opened! Contributions are definitely always welcome also feel free to open issues for Feature requests etc as well. For the project, yarn is really just the package manager and task runner although it's also using lerna (which ties in better with yarn) to manage the multiple packages that need to get pushed to npm.
I'm going to merge this, as it's just pass through options to the underlying AMQP connection. Don't think it's worth writing a complicated integration scenario around durable and exclusive queues since those are presumed to work and have been tested by the maintainers of amqplib.

@bugsduggan I've published new packages to NPM. @nestjs-plus/rabbitmq version 1.3.0 has your changes. Thanks again for the contribution


Fix DraggableScrollableSheet crash when switching out scrollables

Fixes https://github.com/flutter/flutter/issues/105469
This accounts for when there is more than one position is attached to the scroll controller, and only triggers a snap when appropriate.
Pre-launch Checklist

[x] I read the Contributor Guide and followed the process outlined there for submitting PRs.
[x] I read the Tree Hygiene wiki page, which explains my responsibilities.
[x] I read and followed the Flutter Style Guide, including Features we expect every widget to implement.
[x] I signed the CLA.
[x] I listed at least one issue that this PR fixes in the description above.
[ ] I updated/added relevant documentation (doc comments with ///).
[x] I added new tests to check the change I am making, or this PR is test-exempt.
[x] All existing and new tests are passing.

If you need help, consider asking for advice on the #hackers-new channel on Discord.

I am still working with the customer on this to ensure their issue is resolved, marking WIP for now

@dnfield I've cleaned this up and confirmed with the customer that this does resolve their issue. Can you PTAL when you have a chance? Thanks!

The status or check suite tree status luci-flutter has failed. Please fix the issues identified (or deflake) before re-applying this label.

@keyonghan this is a bug. The auto submit bot should not be removing the label for this check.


Locale/language bug fix in pe module

A change introduced in commit 75babbc0248f14b3ecb135c88d592268f1c1edce
silently broke language check due to integer size and signedness.
Tested using pe.language(0x04) on file 33659578e2f6e143c4e0533d598bf690

@googlebot I signed it!

I’ve become a big fan of test cases in situations like this, to ensure there isn’t a regression in the future. Does any of the existing test PE files exhibit this bug? If so, it would make writing a test super easy.

Hey Wes, I've been thinking about the same. Yes, there is a PE file to test this on, see MD5 33659578e2f6e143c4e0533d598bf690, available on VT.
Here is the yara rule that detects language correctly with Yara 3.10 but doesn't work in 3.11:
import "pe"
rule bugcheck_pe_language {
meta:
description = "This is a test case for bug in Yara 3.11.0"
bug_type = "regression"
introduced_by = "75babbc0248f14b3ecb135c88d592268f1c1edce"
fixed_in = "https://github.com/VirusTotal/yara/pull/1226"
md5 = "33659578e2f6e143c4e0533d598bf690" //test sample
condition:
pe.language(0x04) // LANG_CHINESE
}

Following the discovery of this bug, we built a testing system that uses a few thousand test files and couple dozen Yara rules to test verify new builds of Yara before deploying them internally. This should catch other similar bugs in the future.

Hey Wes, I've been thinking about the same. Yes, there is a PE file to test this on, see MD5 33659578e2f6e143c4e0533d598bf690, available on VT.

Thanks for the pointer! I didn't want to add another test file to the repository if I could trigger this with an existing one, and I discovered I could trigger it with tests/data/mtxex.dll which already exists in the repository. I wrote up a test case for this at https://github.com/wxsBSD/yara/commit/c04f0e92b142c721f485ea577ac46886cc3a7c67. It would be awesome if you could bring this commit into your branch so they are merged together.

Following the discovery of this bug, we built a testing system that uses a few thousand test files and couple dozen Yara rules to test verify new builds of Yara before deploying them internally. This should catch other similar bugs in the future.

We set up something similar a while back to continuously check our rules for regressions. It is difficult to get full regression testing since it entirely depends upon what you're scanning and with what rules, which is why when things like this pop up I try to include test cases so a "make check" will catch these regressions if we ever introduce them again.
Thank you Vitaly and Costin for catching this bug and fixing it!

Thanks for the pointer! I didn't want to add another test file to the repository if I could trigger this with an existing one, and I discovered I could trigger it with tests/data/mtxex.dll which already exists in the repository. I wrote up a test case for this at wxsBSD@c04f0e9. It would be awesome if you could bring this commit into your branch so they are merged together.

Hey, sorry, I misunderstood you about the test files.
I pulled your commit, so it's part of the pull request now. Please see googlebot request above, it requires your consent too.

@googlebot I consent


Runtime.evaluate callback after Runtime.disbale had call baked

I had do Runtime.disbale() in Promise.all and then do CDP.close(), but after that I still got a callback which I had set in the Runtime.evaluate before.

Component
Version

Operating system
ubuntu

Node.js
8.11.3

Chrome/Chromium/...
v67

chrome-remote-interface
0.25.6

Is Chrome running in a container? NO

I'm not sure I'm understanding this issue, please show me some standalone code.

I may do something wrong,sorry.


DotNetCore 3.1 just not working properly...

So I've just installed dotnetcore 3.1 on my web server.
Running dotnet --info produces the following:
.NET Core runtimes installed:
  Microsoft.AspNetCore.All 2.1.2 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.All 2.1.3 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.All 2.2.2 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.App 2.1.2 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 2.1.3 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 2.2.2 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.NETCore.App 2.1.2 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 2.1.3 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 2.2.2 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 3.0.0 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 3.1.0 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.WindowsDesktop.App 3.1.0 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]

The web api kept retuning a 404 so I decided to run the application in the console.
I saw this error:
.\NewsApi.exe
It was not possible to find any compatible framework version
The framework 'Microsoft.AspNetCore.App', version '3.1.0' was not found.
  - The following frameworks were found:
      2.1.2 at [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
      2.1.3 at [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
      2.2.2 at [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]

What's going on? One minute I'm being told 3.1.0 is installed the next minute it isn't.
I've got a tight client deadline on and  I can't deploy my app. Please help.

Looks like ASP.net Core Runtime 3.1 is not installed. You can download it here. It needs to be installed additionally to .net Core 3.1.

Hey, yeah the problem was that I'd installed the desktop version not the hosting one!


Split person class into student and company classes and add new comma…

…nds to add student and company and update some test cases
Close #72 #73

LGTM


Fix typos in user guide

Encountered some typos in the user guide, including a wrong command, which might be confusing otherwise.

Codecov Report

Merging #153 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #153   +/-   ##
=======================================
  Coverage   50.91%   50.91%
=======================================
  Files          13       13
  Lines         711      711
=======================================
  Hits          362      362
  Misses        297      297
  Partials       52       52

Continue to review full report at Codecov.

Legend - Click here to learn more
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Not found from URL and doesn't seem to exists

After following the instructions for setup from the repo I get the following error:
Traceback (most recent call last):
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 286, in hf_raise_for_status
    response.raise_for_status()
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/diffusers/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.bin

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/diffusers/modeling_utils.py", line 600, in _get_model_file
    model_file = hf_hub_download(
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1368, in hf_hub_download
    raise head_call_error
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1238, in hf_hub_download
    metadata = get_hf_file_metadata(
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 1631, in get_hf_file_metadata
    r = _request_wrapper(
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 385, in _request_wrapper
    response = _request_wrapper(
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/file_download.py", line 409, in _request_wrapper
    hf_raise_for_status(response)
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 323, in hf_raise_for_status
    raise RepositoryNotFoundError(message, response) from e
huggingface_hub.utils._errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-65c2558d-7ee52867429c7cb00573f23b;01245222-2121-417b-a172-779cf0e57f42)

Repository Not Found for url: https://huggingface.co/diffusers/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.bin.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/mnt/Samsung980Pro/WORK/AI/arch/AnimateDiff-MotionDirector/train.py", line 1038, in <module>
    main(name=name, use_wandb=args.wandb, **config)
  File "/mnt/Samsung980Pro/WORK/AI/arch/AnimateDiff-MotionDirector/train.py", line 527, in main
    vae          = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/diffusers/modeling_utils.py", line 452, in from_pretrained
    model_file = cls._get_model_file(
  File "/mnt/Samsung980Pro/arch_linux_software/anaconda3/envs/animatediff-motiondirector/lib/python3.10/site-packages/diffusers/modeling_utils.py", line 616, in _get_model_file
    raise EnvironmentError(
OSError: diffusers/stable-diffusion-v1-5 is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login`.

Which is odd to me because I have the entire stable diffusion huggingface repo clone into "models/StableDiffusion" and when I try to follow the link to "https://huggingface.co/diffusers/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.bin" I get a repository not found, I try to search for this in hugging face specifcally and I still cannot find it even though I actually think this is trying to refer to "https://huggingface.co/runwayml/stable-diffusion-v1-5" which I already have installed I believe under the correct folder structure. What am I missing here?

I feel a bit dumb, I needed to change "pretrained_model_path: diffusers/stable-diffusion-v1-5" to "pretrained_model_path: models/StableDiffusion/", but not going to lie, I didn't think that would be something I would need to mess with.

for this download to work I changed at the my_video.yaml file the second line to:
pretrained_model_path: "runwayml/stable-diffusion-v1-5" Then it downloaded it. Seems they really removed those files from that repo.

why can't we just manually download the checkpoints and dispense with the whole downloading from huggingface part.

does anyone know in what script its trying to reach hugging face?

pretrained_model_path: "models/StableDiffusion/stable-diffusion-v1-5"
Thats the answer. Update the dang instructions man.


deprecated forecast - HA 2023.9

Nice work on this integration.
I have just upgraded to HA 2023.9 beta and am receiving this error message.
HA 2023.9 is expected to be released on 6 September, at which point I would envisage you will get more users of your integration seeing this issue as it states the issue needs to be resolved before 2024.3.

2023-08-31 09:28:05.967 DEBUG (MainThread) [custom_components.bureau_of_meteorology] Finished fetching bureau_of_meteorology data in 0.000 seconds (success: True)
2023-08-31 09:28:05.998 WARNING (MainThread) [homeassistant.components.weather] custom_components.bureau_of_meteorology.weather::None is using a forecast attribute on an instance of WeatherEntity, this is deprecated and will be unsupported from Home Assistant 2024.3. Please report it to the custom integration author.
2023-08-31 09:28:06.006 WARNING (MainThread) [homeassistant.components.weather] custom_components.bureau_of_meteorology.weather::None is using a forecast attribute on an instance of WeatherEntity, this is deprecated and will be unsupported from Home Assistant 2024.3. Please report it to the custom integration author.

This is no longer a beta, all HA users that update to 2023.9 are recieving this message.

yep - there will be a flurry of us as we all update

https://github.com/bremor/bureau_of_meteorology/pull/190 seems to address this when I tested locally, am not sure if there's more to change however @bremor ?

It's a little bit bigger of a change to implement I suspect
https://developers.home-assistant.io/blog/2023/08/07/weather_entity_forecast_types

It seems like the WeatherDaily and WeatherHourly needs to get scrapped since the WeatherEntity doesn't need seperate entities for different forecast types. Is the solution to merge the two entities into one and define async_forecast_dailyand async_forecast_hourly (I believe these can be same code from WeatherDaily.forecast and WeatherHourly.forecast?).

Hi folks. I've put up #193 to address this issue. It keeps things AS-IS for the moment but I think longer term need a discussion on unifying WeatherDaily and WeatherHourly and what that transition looks like as it will be a breaking change.

@evilmarty thanks for the work to fix this..
I have merged and created a pre-release v1.2.0. Please test and report any issues.

Installed the update - is there supposed to be a forecast imported now?
I thought in the past it had a 7 day forecast - but I can't seem to see that anymore?

Working for me. Thanks Simon.

The update fixed the notice, but now the hourly forecast is missing..  Any ideas?

I've lost hourly and daily forecasts. Can't seem to get them back despite deleting my sensors, redownloading via HACS and setting up sensors from scratch. Any tips appreciated

The code needs to be fixed to reenable the forecasts again. I can see how to do it but don't have time right now.

No urgency here ...
I tried the v1.2.0 pre-release, and it removed the HA warning but lost forecasts.
I reverted to v1.1.21, and forecasts resumed just fine. The HA warning is that the integration needs to be updated prior to HA 2024.3, so that's a full 5 months away yet.
For anybody who wants forecasts now, just use v1.1.21 of the BOM integration.

No urgency here ...
I tried the v1.2.0 pre-release, and it removed the HA warning but lost forecasts.
I reverted to v1.1.21, and forecasts resumed just fine. The HA warning is that the integration needs to be updated prior to HA 2024.3, so that's a full 5 months away yet.
For anybody who wants forecasts now, just use v1.1.21 of the BOM integration.

Thanks Tatham, did the trick. I didn't think I opted in for pre releases and blindly updated to get rid of the integration warning.

Looks like time is up, I had pinned to 1.1.21 of the BOM integration which has provided me forecasts.
Home Assistant 2024.4.b release seems to no longer present those forecasts ;-(

I don't think you need to pin to an old version any more; I'm not.
Try BOM integration 1.3.0 with 2024.4.
Everything is back in alignment.

Nice work, 1.3.0 has the BoM forecasts.

But no longer available as attributes ;-(
So I have to go from:
{{state_attr('weather.noosa_heads_hourly','forecast')}}

To something that calls a lot of services, stores those values somewhere so I can use them in an automation ;-(

https://community.home-assistant.io/t/petition-dont-delete-the-forecast-attribute/609298/240

That is not the integration developer's fault.
The core HA team have implemented this method for accessing forecasts for performance reasons.


Install failes

Hi there,
I have one instance already running without any problems and wanted to set up another one but I fail at the last step.
It's probably something obvious but I don't get the error:
PHP Notice:  Undefined variable: redisConfig in /var/www/BookStack/config/database.php on line 142

Quick guidance for a blind man?

@broedli Sorry about this issue, Th error ran silent on my setup therefore went under my radar. I've just pushed out a bugfix release. Just update the instance via git and this should be sorted.
Let me know if the update does not fix this and I'll re-open.

looks good.


Support HTTPScaledObject scoped timeout

Proposal
Currently, there is a timeout configuration for setting the request timeout.
Allowing HTTPScaledObjects to override the timeout for specific route can help to users who have some slow routes but they prefer to not increase the global timeout for all the routes just due to some of them
Use-Case
No response
Is this a feature you are interested in implementing yourself?
Maybe
Anything else?
No response

Hi, is this expected in future releases?
We're using shared k8s cluster so it would be easier to use scoped timeout than asking platform team to come with values that fit all teams sharing the cluster. Also what kinds of timeouts is this expected to support?

Hi, is this expected in future releases?

No, this has been closed automatically due to inactivity. Are you willing to contribute with it?

We're using shared k8s cluster so it would be easier to use scoped timeout than asking platform team to come with values that fit all teams sharing the cluster. Also what kinds of timeouts is this expected to support?

The original proposal was to override the "cold start timeout"

No, this has been closed automatically due to inactivity. Are you willing to contribute with it?

Sorry, I don't have any experience with go and this doesn't seem like easy task to start.

The original proposal was to override the "cold start timeout"

Ok, we got API that can take long to answer so I was looking for something to override the responseHeaderTimeout

I've checked and I guess that we could configure all the timeouts at same place from the HTTPScaledObject, so maybe could support all of them.
https://github.com/kedacore/http-add-on/blob/main/interceptor/proxy_handlers.go#L46-L86
@tomkerkhove ?

Having something like this would be great. We were trying to deploy LLMs this way on a cluster, having at most a few requests pending before adding new resources.
But the responses (at least non streamed ones) can take quite some time before they are ready and those are then killed by the 500ms response header timeout and updating this for individual endpoints/pods would be great.

I would be willing to have a closer look at this but I have literally no idea where to start (and no experience whatsoever with coding for keda). Any suggestion or example of a variable that has similar behaviors where I could have a look to start it ?

I found a way to change the default timeout of keda http addon, while installing http addon via the helm we can change the default value from values.yaml file.
interceptor:
responseHeaderTimeout: 300s
tcpConnectTimeout: 300s
keepAlive: 300s
resources:
limits:
memory: 1Gi
requests:
memory: 512Mi


How to set a function repeat count?

I want to run this counter only once when i reached to a particular div, how to do that?
setInterval(financialCounter,0000);             function financialCounter(){               $('.counter__item__count').countTo({                 speed: 2000,               });             };

You'll need to use standard JS API's for this.  Here's an example: https://www.sitepoint.com/five-techniques-lazy-load-images-website-performance/


Python virtual environment isn't "activated" in kubernetes containers

Version report
Jenkins and plugins versions report:

Jenkins: 2.289.2
OS: Linux - 5.4.89+
---
promoted-builds:3.10
ssh-agent:1.23
code-coverage-api:1.4.0
ace-editor:1.1
authentication-tokens:1.4
pipeline-stage-step:2.5
jira:3.5
xunit:3.0.2
bouncycastle-api:2.20
build-timeout:1.20
javadoc:1.6
resource-disposer:0.16
mailer:1.34
kubernetes-client-api:5.4.1
view-job-filters:2.3
job-dsl:1.77
caffeine-api:2.9.1-23.v51c4e2c879c8
buildgraph-view:1.8
ghprb:1.42.2
pyenv-pipeline:2.1.2
shiningpanda:0.24
naginator:1.18.1
windows-slaves:1.0
run-condition:1.5
pam-auth:1.5.1
script-security:1.77
github:1.33.1
google-oauth-plugin:1.0.6
jdk-tool:1.0
google-compute-engine:4.3.8
docker-java-api:<IP_ADDRESS>
junit:1.51
workflow-durable-task-step:2.39
branch-api:2.6.5
workflow-basic-steps:2.23
dtkit-api:3.0.0
bootstrap5-api:5.0.2-1
git:4.7.2
matrix-project:1.19
structs:1.23
antisamy-markup-formatter:1.1
echarts-api:5.1.2-3
checks-api:1.7.0
command-launcher:1.2
docker-plugin:1.2.2
plain-credentials:1.7
pipeline-model-extensions:1.8.5
maven-plugin:3.12
embeddable-build-status:2.0.3
copyartifact:1.46.1
apache-httpcomponents-client-4-api:4.5.13-1.0
git-server:1.10
workflow-step-api:2.24
forensics-api:1.2.1
pipeline-build-step:2.13
throttle-concurrents:2.3
token-macro:2.15
docker-commons:1.17
extra-columns:1.23
pipeline-stage-tags-metadata:1.8.5
bootstrap4-api:4.6.0-3
pipeline-model-definition:1.8.5
data-tables-api:1.10.25-2
snakeyaml-api:1.29.1
pipeline-model-api:1.8.5
workflow-cps-global-lib:2.21
parameterized-trigger:2.41
okhttp-api:3.14.9
handlebars:3.0.8
ldap:2.7
plugin-util-api:2.4.0
conditional-buildstep:1.4.1
pipeline-input-step:2.12
oauth-credentials:0.4
workflow-job:2.41
cobertura:1.16
workflow-support:3.8
workflow-cps:2.93
pipeline-milestone-step:1.3.2
workflow-multibranch:2.26
font-awesome-api:5.15.3-4
ssh-credentials:1.19
jsch:<IP_ADDRESS>
ssh-slaves:1.32.0
greenballs:1.15.1
scm-api:2.6.4
github-api:1.123
lockable-resources:2.11
rebuild:1.32
popper2-api:2.5.4-3
external-monitor-job:1.4
credentials:2.5
jaxb:2.3.0
git-client:3.8.0
workflow-aggregator:2.6
jquery3-api:3.6.0-2
popper-api:1.16.1-2
jackson2-api:2.12.3
workflow-scm-step:2.13
kubernetes:1.30.1
matrix-auth:2.6.6
durable-task:1.38
trilead-api:1.0.13
workflow-api:2.46
pipeline-graph-analysis:1.11
kubernetes-credentials:0.9.0
github-branch-source:2.11.1
metrics:<IP_ADDRESS>
pipeline-rest-api:2.19
dashboard-view:2.17
credentials-binding:1.27
display-url-api:2.3.5
pipeline-stage-view:2.19
configuration-as-code:1.51
jjwt-api:0.11.2-9.c8b45b8bb173
variant:1.4
bulk-builder:1.5
ws-cleanup:0.39
sshd:3.0.4
cloudbees-folder:6.15
momentjs:1.1.1

What Operating System are you using (both controller, and any agents involved in the problem)?

Jenkins controller LTS docker container and docker build agent

Reproduction steps

Set up a kubernetes build cluster (unfortunately I don't know good reproduce steps here as I just used helm install on a test GKE account - you may not want to do the same)
Create a new pipeline job using

pipeline {
    agent {
        kubernetes {
            yaml '''
                apiVersion: v1
                kind: Pod
                spec:
                  containers:
                  - name: python
                    image: python:2.7
                    args: ["sleep", "99d"]
'''
        }
    }

    stages {
        stage("Confirm virtualenv") {
            steps {
                container("python") {
                    sh "pip install virtualenv"
                    withPythonEnv("python") {
                        sh "python -c 'import sys; print(sys.executable)'"
                        sh "python-config --cflags"
                    }
                }
            }
        }
    }
}

Run pipeline job

Results
Expected result:
Expected the commands inside the withPythonEnv block to print the executable and build flags for the virtual environment.
Actual result:
Executing sh script inside container python of pod python-info-29-r3hck-5kdvd-f2x82
Executing command: "python" "--version"
exit
Python 2.7.18
Executing sh script inside container python of pod python-info-29-r3hck-5kdvd-f2x82
Executing command: "python" "-m" "virtualenv" "--python=python" "/home/jenkins/agent/workspace/python-info/.pyenv-python"
exit
created virtual environment CPython2.7.18.final.0-64 in 254ms
  creator CPython2Posix(dest=/home/jenkins/agent/workspace/python-info/.pyenv-python, clear=False, global=False)
  seeder FromAppData(download=False, pip=latest, setuptools=latest, wheel=latest, via=copy, app_data_dir=/root/.local/share/virtualenv/seed-app-data/v1.0.1)
  activators PythonActivator,CShellActivator,FishActivator,PowerShellActivator,BashActivator
[Pipeline] {
[Pipeline] sh
+ python -c import sys; print(sys.executable)
/usr/local/bin/python
[Pipeline] sh
+ python-config --cflags
-I/usr/local/include/python2.7 -I/usr/local/include/python2.7 -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes
[Pipeline] }
[Pipeline] // withPythonEnv

Given what I know about this plugin, I suppose this could be a kubernetes pipeline problem instead, with environment variables not getting passed through properly. And since this plugin hasn't been updated in a couple years, maybe this is no longer maintained? If that's the case, there is an obvious workaround of just not using this plugin, and explicitly handling the virtual environment myself in the sh commands. But thought I'd check if maybe this is a fixable bug, or if there's a known/official workaround.
(and yes, I'm aware that in theory there's no need for the virtualenv inside a container, because the container is isolated, but due to certain combinations of dependencies, scripts, etc, it's not feasible right now to try to set up a build without the virtualenv)

I also see this problem. It appears the PYTHON_PATH envvar gets set, but the correct paths do not get prepended to PATH

Any update on this topic?

I'm having issues with Docker. I assume that the reason might be the same. I'm using
docker.image('imagename:latest').inside('''-m=30G ...'') {
  sh '''python3 -m venv conan_venv'''
  //withPythonEnv('conan_venv/') {
  withPythonEnv(WORKSPACE+'/conan_venv/') {
    ...
  }
}

Do I understand correctly that Pyenv Pipeline Plugin currently doesn't support Docker Pipeline plugin? Or am I doing something wrong?

Indeed it seems that the plugin does not work with Docker :(
    node('linux') {
        docker.image('python:3.8-slim-bullseye').inside {
            sh "echo python"
            withPythonEnv('python') {
                sh("""
                    which python
                    which python3
                """)
            }
        }
    }

16:26:22  [Pipeline] withDockerContainer
16:26:22  ubuntu-agent-18-1 does not seem to be running inside a container
16:26:22  $ docker run -t -d -u 1000:1000 -w /home/jenkins/workspace/sandbox/tmp-python -v /home/jenkins/workspace/sandbox/tmp-python:/home/jenkins/workspace/sandbox/tmp-python:rw,z -v /home/jenkins/workspace/sandbox/tmp-python@tmp:/home/jenkins/workspace/sandbox/tmp-python@tmp:rw,z -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** -e ******** python:3.8-slim-bullseye cat
16:26:30  $ docker top e03c995ba0389789a334303d8f5d0b86213d3f938edcdbc916482b8ba1c13649 -eo pid,comm
16:26:30  [Pipeline] {
16:26:30  [Pipeline] sh
16:26:30  + echo python
16:26:30  python
16:26:30  [Pipeline] withPythonEnv
16:26:30  $ docker exec e03c995ba0389789a334303d8f5d0b86213d3f938edcdbc916482b8ba1c13649 python --version
16:26:30  $ docker exec e03c995ba0389789a334303d8f5d0b86213d3f938edcdbc916482b8ba1c13649 python -m venv /home/jenkins/workspace/sandbox/tmp-python/.pyenv-python
16:26:36  [Pipeline] {
16:26:36  [Pipeline] sh
16:26:37  + which python
16:26:37  /usr/local/bin/python
16:26:37  + which python3
16:26:37  /usr/local/bin/python3
16:26:37  [Pipeline] }
16:26:37  [Pipeline] // withPythonEnv
16:26:37  [Pipeline] }
16:26:37  $ docker stop --time=1 e03c995ba0389789a334303d8f5d0b86213d3f938edcdbc916482b8ba1c13649
16:26:40  $ docker rm -f e03c995ba0389789a334303d8f5d0b86213d3f938edcdbc916482b8ba1c13649
16:26:40  [Pipeline] // withDockerContainer

It successfully creates the venv directory, but fails to use it in sh steps.

The workaround that works for us:
environment {     PATH = "$WORKSPACE/.pyenv-python3/bin:$PATH" }
and in the stage:
python3.11 -m venv $WORKSPACE/.pyenv-python3


Add native registry login tests for EKS, AKS and GKE

It introduces a new package tftestenv. It is similar to testenv but uses
terraform to create test environment. It provides helpers to run terraform
apply and destroy in the test context.
tftestenv is used to create tests similar to the controller tests but with
cloud provider managed cluster provisioned using terraform. The test
setup is written similar to the usual controller test suite_test.go,
providing flexibility in the test setup and execution. The structure tries
to keep the infrastructure code separate from the test code such that
the infrastructure provisioning tool can be easily replaced, without the
need to change the actual tests.
The go tests are also written similar to the usual controller tests,
with a kube client from the tftestenv environment global variable.
Terraform is automatically downloaded using the terraform-exec package.
The test container images are generated using the go-containerregistry
package and pushed to the test container image repository.
tests/integration/README.md contains more details about the test setup and workflow.
The terraform configurations for EKS is based on https://github.com/terraform-aws-modules/terraform-aws-eks/tree/6bb73bbdd469797cb067e9aa22ce2f31a7fb22be/examples/self_managed_node_group .
The terraform configurations for AKS is based on https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/kubernetes_cluster .
The terraform configurations for GKE is based on the official GKE terraform provider examples https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/container_cluster with Terraform Kubernetes Engine Auth Module to be able to easily retrieve the kubeconfig. Both GCR and Artifact Registry are tested.
Average test duration including provisioning and cleanup time:

EKS - 25-30 minutes
AKS - ~13 minutes
GKE - ~9 minutes

We may be able to optimize some configurations to bring down the time of EKS and AKS in the future.
Part of  #264.

Posting some results to provide an idea of what it looks like when run.
With .env
# AWS
export AWS_ACCESS_KEY_ID=<redacted>
export AWS_SECRET_ACCESS_KEY=<redacted>
export AWS_REGION=us-east-2

# Azure
export TF_VAR_azure_location=eastus

# GCP
export TF_VAR_gcp_project_id=<redacted>
# export TF_VAR_gcp_region=us-central1
# export TF_VAR_gcp_zone=us-central1-c
# Leave GCR region empty to use gcr.io. Else set it to `us`, `eu` or `asia`.
# export TF_VAR_gcr_region=

and az login and gcloud auth login being already run:
aws:
$ make test-aws
make test PROVIDER_ARG="-provider aws"
make[1]: Entering directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'
cp kustomization.yaml build/flux
cd build/flux && kustomize edit set image fluxcd/image-reflector-controller=fluxcd/image-reflector-controller
kustomize build build/flux > build/flux.yaml
go test -timeout 30m -v ./...  -provider aws
2022/06/23 02:53:59 Terraform binary:  /usr/bin/terraform
2022/06/23 02:53:59 Init Terraform
2022/06/23 02:54:05 Checking for an empty Terraform state
2022/06/23 02:54:07 Applying Terraform
2022/06/23 03:08:34 pushing test image z.dkr.ecr.us-east-2.amazonaws.com/flux-test-repo-peaceful-ghost:v0.1.0
2022/06/23 03:08:38 pushing test image z.dkr.ecr.us-east-2.amazonaws.com/flux-test-repo-peaceful-ghost:v0.1.2
2022/06/23 03:08:41 pushing test image z.dkr.ecr.us-east-2.amazonaws.com/flux-test-repo-peaceful-ghost:v0.1.3
2022/06/23 03:08:44 pushing test image z.dkr.ecr.us-east-2.amazonaws.com/flux-test-repo-peaceful-ghost:v0.1.4
2022/06/23 03:08:47 Installing flux
=== RUN   TestImageRepositoryScan
=== RUN   TestImageRepositoryScan/ecr
--- PASS: TestImageRepositoryScan (2.01s)
    --- PASS: TestImageRepositoryScan/ecr (2.01s)
PASS
2022/06/23 03:09:20 Destroying environment...
ok      github.com/fluxcd/image-reflector-controller/tests/integration  1592.611s
make[1]: Leaving directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'

azure:
$ make test-azure
make test PROVIDER_ARG="-provider azure"
make[1]: Entering directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'
cp kustomization.yaml build/flux
cd build/flux && kustomize edit set image fluxcd/image-reflector-controller=fluxcd/image-reflector-controller
kustomize build build/flux > build/flux.yaml
go test -timeout 30m -v ./...  -provider azure
2022/06/23 03:23:50 Terraform binary:  /usr/bin/terraform
2022/06/23 03:23:50 Init Terraform
2022/06/23 03:23:52 Checking for an empty Terraform state
2022/06/23 03:23:52 Applying Terraform
2022/06/23 03:29:34 pushing test image fluxtestrepomovingotick.azurecr.io/vynz8:v0.1.0
2022/06/23 03:29:38 pushing test image fluxtestrepomovingotick.azurecr.io/vynz8:v0.1.2
2022/06/23 03:29:40 pushing test image fluxtestrepomovingotick.azurecr.io/vynz8:v0.1.3
2022/06/23 03:29:42 pushing test image fluxtestrepomovingotick.azurecr.io/vynz8:v0.1.4
2022/06/23 03:29:45 Installing flux
=== RUN   TestImageRepositoryScan
=== RUN   TestImageRepositoryScan/acr
--- PASS: TestImageRepositoryScan (3.36s)
    --- PASS: TestImageRepositoryScan/acr (3.36s)
PASS
2022/06/23 03:30:16 Destroying environment...
ok      github.com/fluxcd/image-reflector-controller/tests/integration  749.433s
make[1]: Leaving directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'

gcp:
$ make test-gcp
make test PROVIDER_ARG="-provider gcp"
make[1]: Entering directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'
cp kustomization.yaml build/flux
cd build/flux && kustomize edit set image fluxcd/image-reflector-controller=fluxcd/image-reflector-controller
kustomize build build/flux > build/flux.yaml
go test -timeout 30m -v ./...  -provider gcp
2022/06/23 03:39:18 Terraform binary:  /usr/bin/terraform
2022/06/23 03:39:18 Init Terraform
2022/06/23 03:39:22 Checking for an empty Terraform state
2022/06/23 03:39:23 Applying Terraform
2022/06/23 03:44:37 pushing test image gcr.io/z/ik9ar:v0.1.0
2022/06/23 03:44:46 pushing test image gcr.io/z/ik9ar:v0.1.2
2022/06/23 03:44:54 pushing test image gcr.io/z/ik9ar:v0.1.3
2022/06/23 03:45:02 pushing test image gcr.io/z/ik9ar:v0.1.4
2022/06/23 03:45:10 pushing test image us-central1-docker.pkg.dev/z/flux-test-repo-helpful-kangaroo/8y697:v0.1.0
2022/06/23 03:45:19 pushing test image us-central1-docker.pkg.dev/z/flux-test-repo-helpful-kangaroo/8y697:v0.1.2
2022/06/23 03:45:26 pushing test image us-central1-docker.pkg.dev/z/flux-test-repo-helpful-kangaroo/8y697:v0.1.3
2022/06/23 03:45:32 pushing test image us-central1-docker.pkg.dev/z/flux-test-repo-helpful-kangaroo/8y697:v0.1.4
2022/06/23 03:45:38 Installing flux
=== RUN   TestImageRepositoryScan
=== RUN   TestImageRepositoryScan/gcr
=== RUN   TestImageRepositoryScan/artifact_registry
--- PASS: TestImageRepositoryScan (9.11s)
    --- PASS: TestImageRepositoryScan/gcr (8.29s)
    --- PASS: TestImageRepositoryScan/artifact_registry (0.82s)
PASS
2022/06/23 03:46:17 Destroying environment...
ok      github.com/fluxcd/image-reflector-controller/tests/integration  568.476s
make[1]: Leaving directory '/home/go/src/github.com/fluxcd/image-reflector-controller/tests/integration'

This looks really good but I think some of these things would be good to share with the Azure E2E tests. Maybe we should move some of the packages to pkg instead?

This looks really good but I think some of these things would be good to share with the Azure E2E tests. Maybe we should move some of the packages to pkg instead?

@phillebaba yes, that's the plan after getting these changes reviewed, accepted and merged here initially.

Rebased and verified that it works with go 1.18.


Missing icon on Framer

Hi, I can't find the ranking icon in Framer.

Hi @KerryVN, this is a Framer thing! It looks like there may already be a report, in case you want to +1 it.

Thank you! New to Framer so not sure where to report these things yet. Appreciate your help 😁


source, target, ligand and receptor

Hi,
I am confused with some glossaries (source, target, ligand and receptor), and here is an related issue.
https://github.com/scverse/squidpy/issues/499#issuecomment-1139211386

Hi,
I haven't noticed this issue until now, I hope you've already found a satisfying answer!
Indeed, in OmniPath source and target refers to the flow of information or direction of causality in case of molecular interactions. Of course this simple scheme sometimes is not able to cover biological reality. And about cell-cell communication, ligand is by definition source and receptor is target, just because the information flow is from ligand to receptor. In OmniPath we try to cover other kind of cell-cell communication agents too, hence we call them transmitters and receivers, e.g. a channel or transporter is a receiver, because altering its behaviour potentially has an impact on the expressing cell.
Best,
Denes


Multiple thermostats Beok TGR81

Describe the bug
I have 9 pcs Beok TGR81 thermostats in my house connected to WiFi network. I have added all of them but experiencing Unavalible status for some of them. The Unavalible status of thermostats entities vary betwene thermostats. Sometimes more of them is avalible sometime less.
To Reproduce
I don't know how to reproduce this behavior. The adding process was succesfull without problems. When I was changing the Entities names they appeard avalible.
Expected behavior
All 9 Thermostats and their Entities should be all the time avalible.
Additional context
Its not a context, but when I want to change the set temperature from, for example, 23 to 22 degrees in Home Assistant I can do it, the temperature is changed on the physical thermostat, but just after the chage the indicator of thermostat is going bac to the old vale (23) but on physical thermostat the value is changed (22).
If the bug involves a device, then please include device diagnostics from
Settings / Devices & Services / Tuya Local (your device) / "1 device"
{
"home_assistant": {
"installation_type": "Home Assistant OS",
"version": "2023.3.6",
"dev": false,
"hassio": true,
"virtualenv": false,
"python_version": "3.10.10",
"docker": true,
"arch": "x86_64",
"timezone": "Europe/Warsaw",
"os_name": "Linux",
"os_version": "5.15.90",
"supervisor": "2023.03.3",
"host_os": "Home Assistant OS 9.5",
"docker_version": "20.10.22",
"chassis": "embedded",
"run_as_root": true
},
"custom_components": {
"presence_simulation": {
"version": "2.4",
"requirements": []
},
"hacs": {
"version": "1.31.0",
"requirements": [
"aiogithubapi>=22.10.1"
]
},
"average": {
"version": "2.3.0",
"requirements": []
},
"tuya_local": {
"version": "2022.3.1",
"requirements": [
"pycryptodome~=3.17",
"tinytuya==1.12.0"
]
}
},
"integration_manifest": {
"domain": "tuya_local",
"name": "Tuya Local",
"codeowners": [
"@make-all"
],
"config_flow": true,
"dependencies": [],
"documentation": "https://github.com/make-all/tuya-local",
"integration_type": "device",
"iot_class": "local_push",
"issue_tracker": "https://github.com/make-all/tuya-local/issues",
"requirements": [
"pycryptodome~=3.17",
"tinytuya==1.12.0"
],
"version": "2022.3.1",
"is_built_in": false
},
"data": {
"name": "Termostat pok\u00f3j nad gara\u017cem",
"type": "beok_tgr81_thermostat_c",
"device_id": "REDACTED",
"local_key": "REDACTED",
"host": "REDACTED",
"protocol_version": "auto",
"api_version_set": 3.2,
"api_version_used": 3.2,
"api_working": true,
"status": {
"1": true,
"2": "manual",
"10": true,
"16": 24,
"19": 32,
"24": 210,
"27": -2,
"101": 1,
"102": "52"
},
"cached_state": {
"updated_at":<PHONE_NUMBER>.7124414,
"1": true,
"2": "manual",
"10": true,
"16": 24,
"19": 32,
"24": 210,
"27": -2,
"101": 1,
"102": "52"
},
"pending_state": {},
"connected": true,
"force_dps": [],
"home_assistant": {
"name": "Termostat pok\u00f3j nad gara\u017cem",
"name_by_user": "Termostat pok\u00f3j nad gara\u017cem",
"disabled": false,
"disabled_by": null,
"entities": [
{
"disabled": false,
"disabled_by": null,
"entity_category": null,
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "climate.termostat_pokoj_nad_garazem",
"state": "unavailable",
"attributes": {
"hvac_modes": [
"off",
"heat"
],
"min_temp": null,
"max_temp": null,
"target_temp_step": 1.0,
"preset_modes": [
"Schedule",
"Manual"
],
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem",
"supported_features": 17
},
"last_changed": "2023-04-03T00:01:30.650108+00:00",
"last_updated": "2023-04-03T00:01:30.650108+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": "switch",
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "switch.termostat_pokoj_nad_garazem_anti_frost",
"state": "unavailable",
"attributes": {
"device_class": "switch",
"icon": "mdi:snowflake-melt",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Anti-frost"
},
"last_changed": "2023-04-03T00:01:30.652723+00:00",
"last_updated": "2023-04-03T00:01:30.652723+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": "\u00b0C",
"state": {
"entity_id": "number.termostat_pokoj_nad_garazem_calibration_offset",
"state": "-2",
"attributes": {
"min": -9,
"max": 9,
"step": 1.0,
"mode": "auto",
"unit_of_measurement": "\u00b0C",
"icon": "mdi:thermometer",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Calibration offset"
},
"last_changed": "2023-04-03T00:01:32.713815+00:00",
"last_updated": "2023-04-03T00:01:32.713815+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "lock.termostat_pokoj_nad_garazem_child_lock",
"state": "unavailable",
"attributes": {
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Child lock",
"supported_features": 0
},
"last_changed": "2023-04-03T00:01:30.651073+00:00",
"last_updated": "2023-04-03T00:01:30.651073+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "diagnostic",
"device_class": null,
"original_device_class": "problem",
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "binary_sensor.termostat_pokoj_nad_garazem_error",
"state": "on",
"attributes": {
"device_class": "problem",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Error"
},
"last_changed": "2023-04-03T00:01:32.712808+00:00",
"last_updated": "2023-04-03T00:01:32.712808+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": "temperature",
"icon": null,
"unit_of_measurement": "\u00b0C",
"state": {
"entity_id": "number.termostat_pokoj_nad_garazem_high_temperature_limit",
"state": "32",
"attributes": {
"min": 30,
"max": 90,
"step": 1.0,
"mode": "auto",
"unit_of_measurement": "\u00b0C",
"device_class": "temperature",
"icon": "mdi:thermometer-chevron-up",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem High temperature limit"
},
"last_changed": "2023-04-03T00:01:32.714697+00:00",
"last_updated": "2023-04-03T00:01:32.714697+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": "\u00b0C",
"state": {
"entity_id": "number.termostat_pokoj_nad_garazem_hysteresis_3",
"state": "1",
"attributes": {
"min": 1,
"max": 5,
"step": 1.0,
"mode": "auto",
"unit_of_measurement": "\u00b0C",
"icon": "mdi:arrow-expand-vertical",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Hysteresis"
},
"last_changed": "2023-04-03T00:01:32.716234+00:00",
"last_updated": "2023-04-03T00:01:32.716234+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": "temperature",
"icon": null,
"unit_of_measurement": "\u00b0C",
"state": {
"entity_id": "number.termostat_pokoj_nad_garazem_low_temperature_limit_3",
"state": "unknown",
"attributes": {
"min": 5,
"max": 20,
"step": 1.0,
"mode": "auto",
"unit_of_measurement": "\u00b0C",
"device_class": "temperature",
"icon": "mdi:thermometer-chevron-down",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Low temperature limit"
},
"last_changed": "2023-04-03T00:01:32.715483+00:00",
"last_updated": "2023-04-03T00:01:32.715483+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": "restart",
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "button.termostat_pokoj_nad_garazem_reset_3",
"state": "unavailable",
"attributes": {
"device_class": "restart",
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Reset"
},
"last_changed": "2023-04-03T00:01:30.651899+00:00",
"last_updated": "2023-04-03T00:01:30.651899+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "select.termostat_pokoj_nad_garazem_sensor_selection_3",
"state": "unknown",
"attributes": {
"options": [
"Internal",
"Both",
"External"
],
"friendly_name": "Termostat pok\u00f3j nad gara\u017cem Sensor selection"
},
"last_changed": "2023-04-03T00:01:32.716848+00:00",
"last_updated": "2023-04-03T00:01:32.716848+00:00"
}
},
{
"disabled": false,
"disabled_by": null,
"entity_category": "config",
"device_class": null,
"original_device_class": null,
"icon": null,
"unit_of_measurement": null,
"state": {
"entity_id": "select.termostat_pokoj_nad_garazem_working_week_3",
"state": "unavailable",
"attributes": {
"options": [
"Off",
"5 days",
"6 days",
"7 days"
],
"icon": "mdi:calendar-clock",
"friendly_name": "Working week"
},
"last_changed": "2023-04-03T00:01:07.325464+00:00",
"last_updated": "2023-04-03T00:01:07.325464+00:00"
}
}
]
}
}
}
Please check if there are messages from Tuya Local in the Home
Assistant log (Settings / System / Logs), and include those too.
This error originated from a custom integration.
Logger: custom_components.tuya_local.device
Source: custom_components/tuya_local/device.py:184
Integration: Tuya Local (documentation, issues)
First occurred: 1:44:15 PM (26 occurrences)
Last logged: 2:48:43 PM
Failed to refresh device state for Lampa salon
Lampka sypialnia error reading: Unexpected Payload from Device
Failed to refresh device state for Lampka sypialnia
Lampa salon error reading: Unexpected Payload from Device
If applicable, add screenshots to help explain your problem.
https://user-images.githubusercontent.com/121897721/229567378-b48279fe-ebc0-44e4-8a5c-c71aa4e2d12f.mp4

I can confirm the same behavior.

May be this issue is related to another? #653

Yes, probably

As #653 is fixed in 2023.5.1, please confirm if this bug still exists in the case where there are many devices.

Assuming the issue is fixed, as no feedback was received.

Hi Jason, i did not have time to have a look in to this since then. If issue still persist I will raise defect again.
Greetings
Pobierz aplikację BlueMail dla systemu Android
W dniu 11 cze 2023, 17:22, o 17:22, użytkownik Jason Rumney @.***> napisał:

Assuming the issue is fixed, as no feedback was received.
--
Reply to this email directly or view it on GitHub:
https://github.com/make-all/tuya-local/issues/591#issuecomment-1586213157
You are receiving this because you authored the thread.
Message ID: @.***>


Nothing happen when I generate the EXE file

So I have put my webhook into Spy.y
I have tried to reinstall
So Yes I need help, here is a video of me showing the problem:
Loom

Hey @HarryPottier64, so I'd recommend you to use auto-py-to-exe instead of my crappy builder
Cause I don't support the builder to build it so use Auto-py-to-exe and there's a bunch of tutorials on YouTube to build the python file into a exe
And also if you want to talk to me out of GitHub for whatever reason you can add me in friends on discord my name is pingui_8163 hope I see you soon bye

So I watched the video and the builder isn't working and maybe even the .py file. I will need to do a new version with a new builder since I got new skills in gui making, it would work better.
And also I've made a discord server if you need to join it to talk to me or just support me it would be great. Here's the discord server https://discord.gg/u3ccZkxm8h


Update typing rewrites for typing_extensions 4.12.2

https://github.com/python/typing_extensions/releases/tag/4.12.2

this intentionally does not get added until 3.13 final


Notification not showing up on Taskbar

I have franz app pinned to my taskbar, up until now when I received a message a notification would show up. Now all of a sudden it doesn't. I re-installed franz but still same issue occurs. See screenshot below. you'll see Franz app icon but now message notification

Can anyone help?

@Endofdays2018 what services do you have installed?

WhatsApp, Skype and FB Messenger. But this never caused an issued before

Got a similar problem since a few days, but only with WhatsApp, ICQ still works. I tried restarting Franz, emptying the cache and even re-adding the service. OS is also Windows 10 (v1709), notification not even shown in the system tray. Franz is v4.0.4 - probably unsupported now? I'd upgrade, but it runs without an account.

@HungryFeline Whatsapp has issued an update which unfortunately breaks Franz 4. Only Franz 5 is officially supported, where the update has already been hotfixed! :)

@Endofdays2018 are you receiving notifications after yesterday's hotfix?

@haraldox yes, working now. thx!

Yep, working. Thank you. Franz 5 also looks a lot nicer.

I have recently started using Franz. And I am having trouble in getting notification on taskbar. Am I missing anything?
I see a red dot on notification area (system tray), but there is no highlight or indicator on taskbar.
Franz version: 5.0.0-beta.18
Release: 1.8.4 / win32 / x64
OS: Windows 10
Services added: Hangouts (2 accounts)

I have the same problem, there is system tray notification but no notification on taskbar.
Franz 5.0.0-beta.18, Windows 10, Slack.
I've experimented a bit and found that the taskbar notification appears only when Franz isn't minimized. It doesn't depend on "Minimize to system tray" option, even if it's disabled and I use minimize window button "_" (not close button), the taskbar notification stops working.

I'm experiencing the same problem with Franz 5.0.0-beta.18, Windows 10 for both Skype and Whatsapp. Any chance that this getting fixed?

Same thing here for both Slack and WhatsApp. I'm getting the system tray notification (even when disabled) but I'm not getting taskbar notification.
Also... Neither inside Franz it's showing the red dot notification on the services that do have new messages.

Same problem here in Win 10. No notifications in systray, info center or in app icons. Sounds are playing.

Same problem here in Win 10. No notifications in systray, info center or in app icons. Sounds are playing.

Mine was fixed after latest update to version 5.0.0-beta.22.

Mmh... I can not really tell when notifcations do not work but my guess is, that when I minimzed app to systray notifications will not work and no badge is shown in systray. When app is minimized both seem to work.

I have Franz 5.0.0-beta.22 and win 7 pro 64 bits. Notifications do not work too. I have Franz in two other computers and work fine on them. I have Franz on the taskbar. I've already uninstalled and reinstalled Franz but the problem remains.
Any help ?


Clarify readme with --with-xcoverage

--with-xcover is not the correct option and may cause confusion amongst users.

@cmheisel thanks for the merge


'Uint8List' is restricted and can't be extended or implemented.

I'm getting this error:
/C:/flutter/.pub-cache/hosted/pub.dartlang.org/pinenacl-0.2.1/lib/api/api.dart(24,7): error GB965149E: 'Uint8List' is restricted and can't be extended or implemented.
It might be resolved by upping the pinenacl version number.
https://github.com/redsolver/skynet/blob/9b968889505d596eddfec833f5fd14174e2c4291/pubspec.yaml#L18
https://github.com/ilap/pinenacl-dart/issues/11

@redsolver Can we make this change please? I'm unable to successfully import and access the package without this?


Add later action IAC and definitions

add Event grid IAC
add Logic apps IaC
add action trigger for maintenace(send email)

Done in IaC


Trying to fire an event from a state

Hello,
This code:
var fsm = new StateMachine({

  transitions: [
    {name:'init',from:'none',to:'ready'},
    {name:'start',from:'ready',to:'running'},
    {name:'end',from:'running',to:'over'}
  ],

  methods: {
    onReady:function(lc){
       this.start();
    },
    onRunning:function(){}
  }
});

throws this Exception, transition is invalid while previous transition is still in progress
I'm still trying to wrap my head around the library, so I'm not sure if this is a bug or I'm just doing it wrong. But it is possible to fire events within states in the previous version.
Thanks,

Did you copy/paste this from code? Or retype it by hand as an example. I ask because it doesn't look syntactically correct.  javascript-state-machine defines events, not transitions. Based off your code, I got this to work:
'use strict';

const StateMachine = require('javascript-state-machine');

let fsm = StateMachine.create({
  events: [
    {name:'init',from:'none',to:'ready'},
    {name:'start',from:'ready',to:'running'},
    {name:'end',from:'running',to:'over'}
  ],

  methods: {
    onReady:function(lc){
       this.start();
    },
    onRunning:function(){}
  }
});

console.log('Current state: ' + fsm.current);

fsm.init();

console.log('Current state: ' + fsm.current);

Which correctly outputs:
Current state: none
Current state: ready

So, not sure what your issue is?

@mgoldsborough it was V3 code which is just a release candidate for the moment.

I came here to look for a solution to the same problem. But I realized that the problem is not with V3 being a release candidate, but with having forgotten how state machine's are supposed to work.
Firing a transition within a transition is the result of calling a JSM's state transition method in a transition event handler. This is a form of recursion, an infinite state situation. But a state machine is a 'finite state machine' meaning it has only specifically designated states.
What is missing (only by way of missing explanations) is an understanding of what the GOF book refers to as the Context: the body of values that are affected by state-change event handler code, or are examined by application code in order to establish that the application has reached a combination of application values that justify firing a state transition.
That is, the application code must evaluate it's own 'state' and determine that the 'next step' in the state machine can be taken. That evaluation, along with the result or decision that application produces, becomes the 'trigger' for 'firing a transition'. In the case of Jake's excellent V3, firing the transition is accomplished at the end of the evaluation process by executing an 'fsm.giantStep()' operation, or some such.
Again, what is missing in the documentation, are the examples of the complete process. So it's appropriate to go back to 1997 and review the GOF Book's discussion of the 'Context' and how it becomes the source of decisions to fire transitions, as the application code changes its the state of the context 'in step with' the transitions that the state machine is making in response to changes in the application.
So perhaps Jake will kindly provide such examples and documentation. But if or until then, fire state transitions in application code based on application value changes, not in state-change event handlers.

@kimballjohnson my remark was that @breadam was using a 3.x API whereas @mgoldsborough was answering with code that is 2.x.
And if I am not mistaken @breadam is trying to fire a transition from a state handler (ready is a state) not from a transition handler. This, at least for the understanding I have from the library, should be valid (in 2.x or 3.x).

Unfortunately (in both v2 and v3) the on**** handlers are intended for observing and performing side-effects and are not intended to change the fundamental nature of the current transition. Therefore firing a new transition during any lifecycle event (e.g. any on****) handler is currently forbidden.
The use case we typically see, similar to @breadam example above, is not really intending to perform a new transition during an old one, but actually to perform it immediately upon completion of the previous one, and unfortunately the current design does not allow for that.
This sometimes comes about from a slight misunderstanding of the event lifecycle naming convention because certainly onReady sounds like everything should be ready, but there are actually still 3 more events in the lifecycle after that one Lifecycle Events, so if we allow a new transition to occur here what should happen to those remaining lifecycle events? we'd have to drop them and that has the potential to leave the FSM in an inconsistent state.
In some cases this can also be a sign that you have 2 transitions that should be combined into 1, If you really want to automatically start-when-ready without any outside input then just make this a single transition and go straight from 'init' to 'running'.

Also consider the title of this issue "Trying to fire an event from a state" - I think this is a sign that my terminology is causing some confusion. The onReady method in the original example is not really a state it is a lifecycle event - it is shorthand for onEnterReady which is just one of the Lifecycle Events that occur during a transition. So what is actually happening here is "Trying to fire a transition from a lifecycle event".
Naming things is hard, and I have made it a little more confusing by changing the terminology in v3

+1 for this feature.  I also want to start a transition immediately upon completion of the previous one.
I realize, as @jakesgordon notes, I could combine the two transitions into one, skipping the state 'in the middle'. However the state in the middle, is a real, meaningful state in my business domain, so I'd rather explicitly define what is going on in the statemachine.
In my case, I tried to fire the new transition from an onAfterTRANSITION listener (e.g. onAfterCreate). Reading form the doc, this is event supposed to be fired after the transition has finished, so the error message "transition is invalid while previous transition is still in progress" was confusing to me.

Reading through these "transition is invalid while previous transition is still in progress" issues because i am comparing state machines to achieve a task which i thought simple but turned out not: automatically transition into a next, third state (even with a delay) after the first transition is over. So state A -> transition -> state B -> automatically transition after 2s -> state C. I was trying to figure out a declarative solution, but now i would be happy for even an imperative one with lifecycle methods, but seems like i cannot find a good one where can i call another transition.
Any ideas?


Updated masked image modeling example for Keras 3.

I noticed that the loss is going up during training instead of going down like before. So, I'm not sure what's wrong.
However the MAE metric is going down just like before.


toml2json: 1.3.0 -> 1.3.1

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/woodruffw/toml2json/releases.
meta.description for toml2json is: A very small CLI for converting TOML to JSON
meta.homepage for toml2json is: https://github.com/woodruffw/toml2json
Updates performed

Rust version update

To inspect upstream changes
Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found 1.3.1 with grep in /nix/store/ihxys2sj1zqrdmk3nb49f6mn82bnwavj-toml2json-1.3.1
found 1.3.1 in filename of file in /nix/store/ihxys2sj1zqrdmk3nb49f6mn82bnwavj-toml2json-1.3.1

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
toml2json

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/ihxys2sj1zqrdmk3nb49f6mn82bnwavj-toml2json-1.3.1 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A toml2json https://github.com/r-ryantm/nixpkgs/archive/a0c88cee2dc76a9d3d1d713737b2673a50d5c2ac.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/ihxys2sj1zqrdmk3nb49f6mn82bnwavj-toml2json-1.3.1
ls -la /nix/store/ihxys2sj1zqrdmk3nb49f6mn82bnwavj-toml2json-1.3.1/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

toml2json

Maintainer pings
cc @rvarago for testing.

Result of nixpkgs-review pr 247660 run on aarch64-darwin 1

1 package built:

toml2json


Strange output in main page on self-hosted

Hello, used compose to setup selfhosted version and got this strange output on main page

Duplicate of #274

If it still not solved, then maybe https://github.com/ionelmc/python-hunter can help to trace down what is wrong. Maybe headers, or maybe ANSI converter doesn't work.

Fixed in #316. Please check.


This is the best color picker ever on windows!!

This is the best color picker ever on windows!!
All the other alterantives don't work with new version of windows but yours is perfect!
Thank you very much for your work!

Thanks for the kind words. :)
I would really appreciate it if you could add your opinion to Sourceforge (https://sourceforge.net/projects/colora/reviews) or AlternativeTo (https://alternativeto.net/software/colora/).
(And always feel free to make suggestions what features I could improve or add.)


chore: ran prettier

ran prettier

[!WARNING]
This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

#4  👈
#3
#2
#1
main

This stack of pull requests is managed by Graphite. Learn more about stacking.

Join @kcoderhtml and the rest of your teammates on  Graphite

Merge activity

Jun 25, 10:01 AM EDT: Graphite rebased this pull request as part of a merge.


Quantization aware training does not add weights quantization and fake quantization nodes to Conv2D layers without bias and followed by non-addition ops

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: NA
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 1.14.0
Python version: 3.6.6
Bazel version (if compiling from source): NA
GCC/Compiler version (if compiling from source): NA
CUDA/cuDNN version: 10.1/7
GPU model and memory: GTX 1080ti/12G

Describe the current behavior
Calling tf.contrib.quantize.create_training_graph() or tf.contrib.quantize.create_eval_graph() will not add weight quantization and activation quantization nodes to Conv2D layers with use_bias=False, activation=None and without following batch normalization layer.
Describe the expected behavior
A weights quantization node and an activation quantization node should be added to this layer, even it does not have biases, activation function, and batch normalization.
The issue seems to be related to THIS part
Code to reproduce the issue
import tensorflow as tf
slim = tf.contrib.slim

if __name__ == '__main__':
    with tf.Session() as sess:
        input = tf.ones((2, 5, 5, 10))

        conv_bias = tf.keras.layers.Conv2D(
            15,
            kernel_size=3,
            dilation_rate=(1, 1),
            activation=None,
            use_bias=True,
            kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
            kernel_regularizer=slim.l2_regularizer(0.00005),
            kernel_constraint=tf.keras.constraints.UnitNorm(axis=2))

        conv_no_bias = tf.keras.layers.Conv2D(
            15,
            kernel_size=3,
            dilation_rate=(1, 1),
            activation=None,
            use_bias=False,
            kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
            kernel_regularizer=slim.l2_regularizer(0.00005),
            kernel_constraint=tf.keras.constraints.UnitNorm(axis=2))

        x_2 = conv_bias(input)
        x_1 = conv_no_bias(input)

        output = tf.concat((x_1, x_2), axis=3)
        tf.contrib.quantize.create_training_graph(quant_delay=0)
        writer = tf.summary.FileWriter('./bug_report', sess.graph)

Other info / logs
Graph from tensorboard:

I could reproduce the issue with TF1.14.0. Here is the gist. Thanks!

I also encounter this problem, how do you solve this?@protossw512@jvishnuvardhan
Thanks very much!

@pianogGG Can you please try tf-nightly and share a simple standalone code to reproduce the issue? Thanks!

@protossw512 Can you please try tf-nightly and share the updated code. I think there are no more updates to contrib. So it is better to try TF2.x or tf-nightly. Thanks!

@pianogGG Can you please try tf-nightly and share a simple standalone code to reproduce the issue? Thanks!

Do you mean using tensorflow_model_optimization package to quantize ,right?

@jvishnuvardhan Do you mean using tensorflow_model_optimization package to quantize ,right?

@jvishnuvardhan  I find it can insert fakequant with conv without bias, did you modify something? If so, how modify?

Conv with relu6 but without bias  can insert fakequant node

Hi There,
We are checking to see if you still need help on this, as you are using an older version of tensorflow which is officially considered end of life . We recommend that you upgrade to the latest 2.x version and let us know if the issue still persists in newer versions. Please open a new issue for any help you need against 2.x, and we will get you the right help.
This issue will be closed automatically 7 days from now. If you still need help with this issue, please provide us with more information.


Add finleap connect

adds finleap connect as adopter

Thank you @christianhuening for your contribution. Before I can start building your PR, a member of the organization must set the required label(s) {'reviewed/ok-to-test'}. Once started, you can check the build status in the PR checks section below.


Added function athena.os.is_sudo.

Hi @romanfinkelnburg, could you please add tests for this function?


critical vulnerability in most jwt libraries

I think this library is vulnerable too, since the Secret type is not type-safe wrt algorithm (it is possible for an attacker to change the algorithm).
Either verify should take an additional algorithm parameter, or the Secret type should embed which algorithm it is considered a secret for.
https://auth0.com/blog/2015/03/31/critical-vulnerabilities-in-json-web-token-libraries/

Issue tracking is here and I see you already filed an issue: https://bitbucket.org/ssaasen/haskell-jwt/issue/14/critical-bug-verify-is-insecure
See my response there.


Add MDEV plugin (for extend GVT-g VGPU support)

This is an Intel device plugin for MDEV. Especially on VM-based
container side, this fills the last piece of Kata mediated pass through
solution support for orchestration.
It adds Kata VFIO/MDEV assignment capability for Kubernetes which
is supported from Kata 1.3 e.g vGPU device for Intel GVT-g.
It identify MDEV device created by UUID e.g
"a297db4a-f4c2-11e6-90f6-d3b88d6c9525" for each VFIO device. Then
expose VFIO/mdev device path through device plugin.
Signed-off-by: Terrence Xu<EMAIL_ADDRESS>@rojkov @mythi  The old PR (https://github.com/intel/intel-device-plugins-for-kubernetes/pull/175) has been deleted automatically since I renamed my branch from "terrence/kata" to "terrence/mdev"

Codecov Report

Merging #176 into master will decrease coverage by 0.63%.
The diff coverage is 63.26%.

@@            Coverage Diff             @@
##           master     #176      +/-   ##
==========================================
- Coverage   80.59%   79.95%   -0.64%
==========================================
  Files          10       11       +1
  Lines        1283     1332      +49
==========================================
+ Hits         1034     1065      +31
- Misses        226      243      +17
- Partials       23       24       +1

Impacted Files
Coverage Δ

cmd/mdev_plugin/mdev_plugin.go
63.26% <63.26%> (ø)

Continue to review full report at Codecov.

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@mythi @kad Is this still relevant PR ? It's inactive for a long time.

I've kept this open in case a need for it comes. It's been silent so let's close it and re-open later if needed.


Dependency error when building Cloud Function

When deploying this module, the building of the Cloud Function throws an error related to a package not being pinned in requirements.txt.

I know this error did not happen in the past, so I ran git bisect and determined that the commit that introduced this behavior was Move requirements to use fully resolved set of dependencies plus enforce using hashes.
I ran the pip-compile command mentioned at the top of requirements.txt and came across a warning for this very error.

Adding the --allow-unsafe flag to the command did remove the warning, but the build error when deploying the Cloud Function remained.

Thanks, I've seen that before but weirdly it doesn't happen on the container build. What Python version do you have?

On the Cloud Function, "python38". However, now that you mentioned it, I noticed that requirements.txt was generated using Python 3.9, and when I ran pip-compile myself, I used Python 3.10. Maybe it's related to that.

It wasn't a Python issue, but some additional dependencies needed to be pinned and --allow-unsafe flag to be passed. I've fixed it now and also improved the Terraform code a bit.

It's working fine now. Thank you!
Closing this issue.

I'm running into this issue again. I tried updating the branch with your most recent changes but it didn't work. Also tried running the pip-compile command again. The error message is the exact same as before, but it displays a different link for the googleapis-common-protos package:
googleapis-common-protos<2.0dev,>=1.56.2 from https://files.pythonhosted.org/packages/76/b9/fc6a31a90e3c78cea00d209d48f1e5c6c9edc6b14f3039470c3f7fae60a1/googleapis_common_protos-1.57.1-py2.py3-none-any.whl (from google-api-core[grpc]==2.10.2->-r requirements.txt (line 194)); Error ID: 0ea8a540

Yeah, it's pretty strange. It seems to work fine when the pipeline builds the container or whenever I do it locally. I have googleapis-common-protos also both in requirements.txt.in and the generated requirements.txt. What Python runtime version are you using?

On my machine, I'm running Python 3.10. The Cloud Function is using "python38".

Well, that was a bit of an issue. I think the root cause is because pip-compile generates a dependency to googleapis-common-protos[grpc] no matter how much you try to force it use the one without the grpc qualifier. I fixed it by manually editing the requirements.txt to remove the [grpc] part. I think it's a pip bug but you can't fix it inside the Cloud Function environment.
I've pushed the fix and tagged a new release 1.4.7.
(just leaving the comment here in case someone stumbled upon this by search)

@coachtinho Did you get this to work?

Sorry for the late response, I've been away for a while. Just tested it and seems to be working properly.
Thank you for the updates, closing the issue.


Allow concise and structured output of the Lighthouse results

Clear and concise description of the problem
I would like to use the results of the the Lighthouse scan in CI to automatically comment on PRs with the scores. The static HTML is great, but just having the main scores as output of the CLI program will be awesome.
This action has some examples - https://github.com/foo-software/lighthouse-check-action
Suggested solution
I would imagine a flag --output-scores that saves a scores.json file. For the actual implementation, I am not sure how to achieve it.
Alternative
No response
Additional context
No response

I'd prefer a CSV, but whatever. Both can be good.
I like the output from https://github.com/cloudfour/lighthouse-parade

Thanks for the references, it's a good idea

Great to hear that the data/scores may be exported easily, especially for those of us that may want a historical perspective when publishing new releases to see if any areas have regression in score.
In terms of the data format -- perhaps we can integrate with jq to convert from JSON to CSV, or at least mention it in the docs somewhere, as I'd expect CSV would be more flexible/useful as a reference/comparison format

Just pointing out that I have nothing against CSV and mentioned JSON just as an example format in the OP.

CSV is just easier to import into a spreadsheet, which is how many of these reports are going to need to be processed.
Great about unlighthouse-ci - when I try to run it though I get:
% npx unlighthouse-ci --site https://www.example.com
npm ERR! code E404
npm ERR! 404 Not Found - GET https://registry.npmjs.org/unlighthouse-ci - Not found
npm ERR! 404
npm ERR! 404  'unlighthouse-ci@*' is not in this registry.
npm ERR! 404
npm ERR! 404 Note that you can also install from a
npm ERR! 404 tarball, folder, http url, or git url.

I think there are two issues here:

More advanced reporters, currently we only have two, see https://unlighthouse.dev/integrations/ci#reporting. Ideally a CSV and excel format would be supported
A GitHub action to compare results from these reports. I haven't made a GitHub action before so i think this is a bit further out of scope and would be covered by a seperate repo. I'm guessing it would need some sort of cloud storage for the reports?

For now I've updated this issue to just cover issue 1. Feel free to make a separate issue for the GitHub action if it's more appropriate

CSV is now supported, have moved GitHub Action Support to this issue.
Please let me know you're feedback, will look to improve it.


Allow for prepending of data to angular elements for HTML5 validators

Some HTML5 validators will spit errors on 'ng-' elements in the markup. Prepending 'data-ng-' resolves this. Angularjs also supports this method. Would be nice to see support for this as well.

Actually I see a commit to fix this, however it is not currently working.


Sort results by closest match to requested dimensions

Will need to figure out what happens if someone doesn't enter a dimension.

Fixed by #17


Issue serializing enum related types

Hello,
I am trying to implement a feature in metaplex that requires the usage of an enum  defined this way:
#[derive(Clone)]
pub enum Config {
    V1 { config: ConfigV1 },
    V2 { config: ConfigV2 },
}

#[account]
#[derive(Default)]
pub struct ConfigV1 {
    // ...
}

#[account]
#[derive(Default)]
pub struct ConfigV2 {
    // ...
}

Here's how it's serialized in anchor generated IDL:
{
  "name": "Config",
  "type": {
    "kind": "enum",
    "variants": [
      {
        "name": "V1",
        "fields": [
          {
            "name": "config",
            "type": {
              "defined": "ConfigV1"
            }
          }
        ]
      },
      {
        "name": "V2",
        "fields": [
          {
            "name": "config",
            "type": {
              "defined": "ConfigV2"
            }
          }
        ]
      }
    ]
  }
}

Unfortunately, the related ConfigV1 and ConfigV2 types are not serialized along the base enum. Which cause the following error at runtime:
IdlError: Type not found: {"name":"config","type":{"defined":"ConfigV1"}}

Am I missing something? Shouldn't the related type be seralized because they are referenced by the enum, but also because they are tagged with #[account]? Since I am quite new to this ecosystem, I may be missing something obvious, sorry if I am. 🙂
If you need more context, here is the related pull request in Metaplex: https://github.com/metaplex-foundation/metaplex/pull/399#discussion_r715390786
Many thanks for reading!

i see this is added as a bug; I am having the same issue.  it is driving me insane; is there a work around?

I’d have to see the rest of your code to be sure but my PR #1343 fixes it for me


grpc xds: support calling service using shortnames (#34662)

grpc xds: support calling service using shortnames

Change-Id: Ib41c3706040f0241d87872236fbebd01231d93b5

refactor to shove logic into filter

Change-Id: I47c79086ca55425654db66e73cd880b9df3c703b
Please provide a description of this PR:
To help us figure out who should review this PR, please put an X in all the areas that this PR affects.

[ ] Configuration Infrastructure
[ ] Docs
[ ] Installation
[ ] Networking
[ ] Performance and Scalability
[ ] Policies and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[ ] Developer Infrastructure

Please check any characteristics that apply to this pull request.

[ ] Does not have any user-facing changes. This may include CLI changes, API changes, behavior changes, performance improvements, etc.

@stevenctl: The following test failed, say /retest to rerun all failed tests or /retest-required to rerun all mandatory failed tests:

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release-notes_istio_release-1.11
3c21da33377dee4e0c568acb9f11106414f5eef4
link
/test release-notes_istio_release-1.11

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@stevenctl: The following test failed, say /retest to rerun all failed tests or /retest-required to rerun all mandatory failed tests:

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3c21da33377dee4e0c568acb9f11106414f5eef4
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/test release-notes_istio_release-1.11

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/retest

#34662


Turtle will mine itself if it uses the turtle.digUp() method

If a turtle equipped with a kinetic augment uses the turtle.digUp() method and there is no block above it, the turtle will mine itself.
https://github.com/user-attachments/assets/2225ea92-2385-4bd8-9ace-375d5f82319f
Also the turtle is able to mine 2 blocks under it with turtle.digDown() method.
https://github.com/user-attachments/assets/a278b46a-a3a6-40b2-b5ea-1552fee53725

Duplicate of #20


scipy.stats.loggamma.rvs(c) is not stable for small values of c

Loggamma takes the log of scipy.stats.gamma to sample.
def _rvs(self, c):
    return np.log(self._random_state.gamma(c, size=self._size))

When the shape parameter c is small, the loggamma has a large left tail. We cannot sample in there because the gamma draw samples which are smaller than what can be represented by float 64, so that the actual tail is truncated to values greater than -600. The code below illustrates this behavior.
It happens that for small values of c, the loggamma is well approximated by a negative exponential distribution. Does it seem reasonable to change the sampling procedure so that for smaller values of c, samples are drawn from an exponential, or to use some other sampling scheme that guarantee stability for small c ?
import numpy as np
import matplotlib.pypot as plt
from scipy import stats

n = 1000
c = 1/1000
law = stats.loggamma(c)

# inverse transform samples
uu = np.random.rand(n)
samples = law.ppf(uu)
samples = samples[np.invert(np.isinf(samples))]
plt.hist(samples, bins=20, density=True)

# scipy sampling
samples2 = law.rvs(size=1000)
samples2 = samples2[np.invert(np.isinf(samples2))]
plt.hist(samples2, bins=20, density=True, alpha=.5)

# exponential sampling
samples3 = -stats.expon.rvs(scale=1/c, size=n)
plt.hist(samples3, bins=20, density=True, alpha=.5)

# density plot
xx = np.linspace(np.nanmin(samples3),2, 1000)
plt.plot(xx, law.pdf(xx))

print(len(samples), len(samples2), len(samples3))
pass

It happens that for small values of c, the loggamma is well approximated by a negative exponential distribution.

Hi @remilepriol thanks for your patience on this. Can you provide a reference for this?
Better yet, for a given threshold where we would switch loggamma sample to negative exponential sampling, can you bound the relative difference between the CDFs below the threshold?

Hello Matt,
I am not aware of any reference,  and could not bound the CDF difference myself. I simply looked at the log-pdf curves.

On Dec 19, 2020, at 23:01, Matt Haberland<EMAIL_ADDRESS>wrote:
It happens that for small values of c, the loggamma is well approximated by a negative exponential distribution.
Hi @remilepriol https://github.com/remilepriol thanks for your patience on this. Can you provide a reference for this?
Better yet, for a given threshold where we would switch loggamma sample to negative exponential sampling, can you bound the relative difference between the CDFs below the threshold?
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Hello Matt,
I am not aware of any reference,  and could not bound the CDF difference myself. I simply looked at the log-pdf curves.

On Dec 19, 2020, at 23:01, Matt Haberland<EMAIL_ADDRESS>wrote:
It happens that for small values of c, the loggamma is well approximated by a negative exponential distribution.
Hi @remilepriol https://github.com/remilepriol thanks for your patience on this. Can you provide a reference for this?
Better yet, for a given threshold where we would switch loggamma sample to negative exponential sampling, can you bound the relative difference between the CDFs below the threshold?
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Ah, OK. I don't think we can do this without mathematical justification. I'll leave this open though, as the issue remains that sampling doesn't work well in the left tail for small c. Regarding your plot, though - I am not seeing good agreement from 0 to -600. Is the current sampling method working well there, and everything below that is just cut off, or is it sampling improperly from 0 to -600? Is it over-represented because samples are smaller values are not being drawn?

Ah, OK. I don't think we can do this without mathematical justification. I'll leave this open though, as the issue remains that sampling doesn't work well in the left tail for small c. Regarding your plot, though - I am not seeing good agreement from 0 to -600. Is the current sampling method working well there, and everything below that is just cut off, or is it sampling improperly from 0 to -600? Is it over-represented because samples are smaller values are not being drawn?

Really this isn't a rvs issue. The only thing that seems to work at this small c is the pdf. I was hoping gh-13319 would work, but even the CDF isn't working, because scipy.special.gammainc has trouble with these small arguments.
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gammainc
c = 1/1000
x = np.linspace(-1000, 0, 100)
plt.plot(x, gammainc(1/1000, np.exp(x)))

Really this isn't a rvs issue. The only thing that seems to work at this small c is the pdf. I was hoping gh-13319 would work, but even the CDF isn't working, because scipy.special.gammainc has trouble with these small arguments.
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gammainc
c = 1/1000
x = np.linspace(-1000, 0, 100)
plt.plot(x, gammainc(1/1000, np.exp(x)))

There is a technique that we can use when the shape parameter is small.  The gamma distribution Gamma(c) has the property that
Gamma(c) ~ Gamma(c + 1) * U**(1/c)

where Gamma(c) is the standard gamma distribution (loc=0, scale=1), and U is the uniform distribution on [0, 1].  Then
log Gamma(c) ~ log(Gamma(c + 1)) + log(U)/c

The script below generates the following plot.  The top plot is a histogram of the variates generated by loggamma.rvs with c = 0.001, and the bottom plot is the histogram of a sample generated using the alternative formulation.  The black curve is the PDF, scaled to match the expected histogram values.

Other possible approaches can be found in Devroye's book "Non-Uniform Random Variate Generation", section IV.3.
Here's the script:
import numpy as np
from scipy.stats import loggamma
import matplotlib.pyplot as plt

rng = np.random.default_rng()

n = 10000000
c = 0.001
x = loggamma.rvs(c, size=n)
# Remove -inf from x
x = x[~np.isinf(x)]

# Use the property of the gamma distribution Gamma(c)
#    Gamma(c) ~ Gamma(c + 1)*U**(1/c),
# where U is uniform on [0, 1], so
#    log(Gamma(c)) ~ log(Gamma(c + 1)) + log(U)/c
w = np.log(rng.gamma(c + 1, size=n)) + np.log(rng.uniform(size=n))/c

hist_xmin = -5000
hist_xmax = 5

plt.subplot(2, 1, 1)

h1 = plt.hist(x, bins=500, range=(hist_xmin, hist_xmax), alpha=0.5)

dx1 = h1[1][1] - h1[1][0]
xx = np.linspace(hist_xmin, hist_xmax, 6000)
plt.plot(xx, n*loggamma.pdf(xx, c)*dx1, 'k')

plt.subplot(2, 1, 2)

h2 = plt.hist(w, bins=500, range=(hist_xmin, hist_xmax), alpha=0.5)

dx2 = h2[1][1] - h2[1][0]
plt.plot(xx, n*loggamma.pdf(xx, c)*dx2, 'k')

plt.show()

There is a technique that we can use when the shape parameter is small.  The gamma distribution Gamma(c) has the property that
Gamma(c) ~ Gamma(c + 1) * U**(1/c)

where Gamma(c) is the standard gamma distribution (loc=0, scale=1), and U is the uniform distribution on [0, 1].  Then
log Gamma(c) ~ log(Gamma(c + 1)) + log(U)/c

The script below generates the following plot.  The top plot is a histogram of the variates generated by loggamma.rvs with c = 0.001, and the bottom plot is the histogram of a sample generated using the alternative formulation.  The black curve is the PDF, scaled to match the expected histogram values.

Other possible approaches can be found in Devroye's book "Non-Uniform Random Variate Generation", section IV.3.
Here's the script:
import numpy as np
from scipy.stats import loggamma
import matplotlib.pyplot as plt

rng = np.random.default_rng()

n = 10000000
c = 0.001
x = loggamma.rvs(c, size=n)
# Remove -inf from x
x = x[~np.isinf(x)]

# Use the property of the gamma distribution Gamma(c)
#    Gamma(c) ~ Gamma(c + 1)*U**(1/c),
# where U is uniform on [0, 1], so
#    log(Gamma(c)) ~ log(Gamma(c + 1)) + log(U)/c
w = np.log(rng.gamma(c + 1, size=n)) + np.log(rng.uniform(size=n))/c

hist_xmin = -5000
hist_xmax = 5

plt.subplot(2, 1, 1)

h1 = plt.hist(x, bins=500, range=(hist_xmin, hist_xmax), alpha=0.5)

dx1 = h1[1][1] - h1[1][0]
xx = np.linspace(hist_xmin, hist_xmax, 6000)
plt.plot(xx, n*loggamma.pdf(xx, c)*dx1, 'k')

plt.subplot(2, 1, 2)

h2 = plt.hist(w, bins=500, range=(hist_xmin, hist_xmax), alpha=0.5)

dx2 = h2[1][1] - h2[1][0]
plt.plot(xx, n*loggamma.pdf(xx, c)*dx2, 'k')

plt.show()

gh-13349 uses the alternative formulation for loggamma._rvs.

gh-13349 uses the alternative formulation for loggamma._rvs.

Interesting @WarrenWeckesser  ! This approach you suggest is similar to my initial guess, as log(U)/c is exactly an exponential variable with scale parameter 1/c. The difference is that you add this log(Gamma(c + 1)) which allows for positive values of x. However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c. Does Devroye provide any insights on this topic in his book ?

Interesting @WarrenWeckesser  ! This approach you suggest is similar to my initial guess, as log(U)/c is exactly an exponential variable with scale parameter 1/c. The difference is that you add this log(Gamma(c + 1)) which allows for positive values of x. However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c. Does Devroye provide any insights on this topic in his book ?

However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c

This is an exact relationship that is valid for any c > 0, i.e. the distribution of Gamma(c) is the same as the distribution of Gamma(c + 1)*U**(1/c).  (I used the symbol ~ to mean "is distributed as", not "is approximately equal to".)  The relation is also noted in the last sentence of the section Generating gamma-distributed random variables of the wikipedia article on the gamma distribution.

However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c

This is an exact relationship that is valid for any c > 0, i.e. the distribution of Gamma(c) is the same as the distribution of Gamma(c + 1)*U**(1/c).  (I used the symbol ~ to mean "is distributed as", not "is approximately equal to".)  The relation is also noted in the last sentence of the section Generating gamma-distributed random variables of the wikipedia article on the gamma distribution.

I see ! When c is low, the exponential variable captures large negative values of x and compensates the faulty gamma(c+1) , while for larger c, this relationship is exact. That sounds like a great approach.

On Jan 5, 2021, at 15:54, Warren Weckesser<EMAIL_ADDRESS>wrote:
However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c
This is an exact relationship that is valid for any c > 0, i.e. the distribution of Gamma(c) is the same as the distribution of Gamma(c + 1)U*(1/c). (I used the symbol ~ to mean "is distributed as", not "is approximately equal to".) The relation is also noted in the last sentence of the section Generating gamma-distributed random variables https://en.wikipedia.org/wiki/Gamma_distribution#Generating_gamma-distributed_random_variables of the wikipedia article on the gamma distribution.
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I see ! When c is low, the exponential variable captures large negative values of x and compensates the faulty gamma(c+1) , while for larger c, this relationship is exact. That sounds like a great approach.

On Jan 5, 2021, at 15:54, Warren Weckesser<EMAIL_ADDRESS>wrote:
However we still don't know an error bound on the difference, and it might be inappropriate to use it for all values of c
This is an exact relationship that is valid for any c > 0, i.e. the distribution of Gamma(c) is the same as the distribution of Gamma(c + 1)U*(1/c). (I used the symbol ~ to mean "is distributed as", not "is approximately equal to".) The relation is also noted in the last sentence of the section Generating gamma-distributed random variables https://en.wikipedia.org/wiki/Gamma_distribution#Generating_gamma-distributed_random_variables of the wikipedia article on the gamma distribution.
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Here's a concrete example of the problem that @mdhaber referred to in https://github.com/scipy/scipy/issues/11094#issuecomment-754258491:
In [12]: loggamma.cdf(-800, 0.001)
Out[12]: 0.0

The true value is 0.44958802911019136 (computed with mpmath).  For any x < -745.2, the computed CDF is 0, because exp(x) underflows to 0 and the cdf method computes gammainc(c, np.exp(x)).

Here's a concrete example of the problem that @mdhaber referred to in https://github.com/scipy/scipy/issues/11094#issuecomment-754258491:
In [12]: loggamma.cdf(-800, 0.001)
Out[12]: 0.0

The true value is 0.44958802911019136 (computed with mpmath).  For any x < -745.2, the computed CDF is 0, because exp(x) underflows to 0 and the cdf method computes gammainc(c, np.exp(x)).


Release 0.2.2

Please do not squash this PR

Please do not squash this PR

How did this one get merged? Just curious. Can't we disable squasing for the master branch?

How did this one get merged? Just curious. Can't we disable squasing for the master branch?

We can't disable a branching strategy per branch.

We can't disable a branching strategy per branch.

How did this one get merged?

I merged it, after I tested it.

How did this one get merged?

I merged it, after I tested it.