[Bug]: Show and Hide Button is not working

The Show and Hide Button is not working in the password of the Login and Sign-up pages
I am GSSOC24 contributor
Please assign me, I would like to work on it
https://github.com/khushi-joshi-05/Food-ordering-website/assets/121508628/dd9db640-d27e-4539-bda9-f5e9cc3c37d0

Hello Ma'am
Just saw an issue in your "Food-ordering-website"
that during login , when we click on eye icon
It should show the password but the icon is not working
I can fix the issue by making it to work
So, Kindly allow it to me
Thanks


feat: code copied button

#59

NIce!But when i check in preview site: https://island-5wudfrvlq-sanyuan0704.vercel.app/zh/guide/getting-started.html, i found the click is invalid. Because in Island.js, all components in default theme will not execute useEffect in
production.Maybe you should call setupCopyCodeButton in setupEffects function instead of useEffect hook.

NIce!But when i check in preview site: https://island-5wudfrvlq-sanyuan0704.vercel.app/zh/guide/getting-started.html, i found the click is invalid. Because in Island.js, all components in default theme will not execute useEffect in production.Maybe you should call setupCopyCodeButton in setupEffects function instead of useEffect hook.

Okay, I'll try to fix it later

I'm done with the above questions, please review again

LGTM, thank you


Video Pauses on Resize

When dynamically increasing the width of the video player, it stops playing. I would expect it to continue playing as the size changes. This would allow a user to minimize/maximize videos to their own sizes instead of using the fullscreen functionality.
import 'package:flutter/material.dart';
import 'package:youtube_player_flutter/youtube_player_flutter.dart';

void main() => runApp(MyApp());

class MyApp extends StatefulWidget {
  @override
  _MyAppState createState() => _MyAppState();
}

class _MyAppState extends State<MyApp> {
  double width = 100.0;

  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Transition Demo',
      home: Scaffold(
          appBar: AppBar(
            title: Text('Demo'),
          ),
          body: Column(
            children: <Widget>[
              YoutubePlayer(
                context: context,
                videoId: "iLnmTe5Q2Qw",
                flags: YoutubePlayerFlags(
                  autoPlay: true,
                  showVideoProgressIndicator: true,
                ),
                width: width,
              ),
              FlatButton(child: Text('Increase'), onPressed: () => setState(()=> width += 10)),
            ],
          ),
        ),
    );
  }
}

After clicking the button to change the width, the console outputs the following. It looks like it is unintializing the video, then reloading it.
D/ViewRootImpl@d4a2009[MainActivity](12807): ViewPostIme pointer 0
D/ViewRootImpl@d4a2009[MainActivity](12807): ViewPostIme pointer 1
D/Dialog  (12807): mIsSamsungBasicInteraction = false, isMetaDataInActivity = false
D/InputTransport(12807): Input channel constructed: fd=189
D/ViewRootImpl@9528d35[MainActivity](12807): setView = DecorView@b2a75ca[MainActivity] TM=true MM=false
D/OpenGLRenderer(12807): eglDestroySurface = 0x74c3bedb80, 0x74c3ad8000
D/ViewRootImpl@4ba4055[MainActivity](12807): dispatchDetachedFromWindow
D/ViewRootImpl@4ba4055[MainActivity](12807): Surface release. android.view.ViewRootImpl.doDie:7931 android.view.ViewRootImpl.die:7899 android.view.WindowManagerGlobal.removeViewLocked:497 android.view.WindowManagerGlobal.removeView:435 android.view.WindowManagerImpl.removeViewImmediate:124 android.app.Dialog.dismissDialog:518 android.app.Dialog.dismiss:501 android.app.Dialog.cancel:1462
D/InputTransport(12807): Input channel destroyed: fd=207
V/InputMethodManager(12807): Not IME target window, ignoring
D/ViewRootImpl@9528d35[MainActivity](12807): dispatchAttachedToWindow
D/ViewRootImpl@9528d35[MainActivity](12807): Relayout returned: old=[0,0][499,281] new=[0,0][499,281] result=0x7 surface={valid=true<PHONE_NUMBER>52} changed=true
D/OpenGLRenderer(12807): eglCreateWindowSurface = 0x74c3bedc80, 0x74c3ad8010
D/ViewRootImpl@9528d35[MainActivity](12807): Relayout returned: old=[0,0][499,281] new=[0,0][499,281] result=0x1 surface={valid=true<PHONE_NUMBER>52} changed=false
D/ViewRootImpl@9528d35[MainActivity](12807): MSG_RESIZED: frame=Rect(0, 0 - 499, 281) ci=Rect(0, 0 - 0, 0) vi=Rect(0, 0 - 0, 0) or=2
I/flutter (12807): PlaybackQualityChange large
D/        (12807): PlayerBase::PlayerBase()
D/        (12807): TrackPlayerBase::TrackPlayerBase()
I/libOpenSLES(12807): Emulating old channel mask behavior (ignoring positional mask 0x3, using default mask 0x3 based on channel count of 2)
I/cr_MediaCodecBridge(12807): create MediaCodec video decoder, mime video/x-vnd.on2.vp9
I/ACodec  (12807):  [] Now uninitialized
I/ACodec  (12807): [] onAllocateComponent
I/OMXClient(12807): IOmx service obtained
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Loaded
I/MediaCodec(12807): MediaCodec will operate in async mode
D/SurfaceUtils(12807): connecting to surface 0x74b9e5e010, reason connectToSurface
I/MediaCodec(12807): [OMX.qcom.video.decoder.vp9] setting surface generation to 13114379
D/SurfaceUtils(12807): disconnecting from surface 0x74b9e5e010, reason connectToSurface(reconnect)
D/SurfaceUtils(12807): connecting to surface 0x74b9e5e010, reason connectToSurface(reconnect)
I/ACodec  (12807): [HW_HDR] app-pid : 12807
W/DirectStreamingProxy(12807): app-pid not found. use getpid(). pid = 12807
D/DirectStreamingProxy(12807): pid = 12807
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] OMX_QCOM_FramePacking_OnlyOneCompleteFrame is setting
I/ACodec  (12807): Success to set FramePackingMode
I/SmartFittingClass(12807): Create SmartFitting Version 2.0
I/SmartFittingClass(12807): Init, [State:UNINITIALIZED] pid: 12807
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Loaded->Idle
I/SmartFittingClass(12807): InitialCheck()
I/SmartFittingClass(12807): Create SmartFittingManagerServiceProxy!!
I/SmartFittingClass(12807): SmartFittingManagerServiceProxy::init
I/SmartFittingClass(12807): Create SmartFittingManagerServiceProxy::EventHandler
I/SmartFittingClass(12807): SmartFittingManagerServiceProxy::onAddSmartFittingListener pid: 12807
I/SmartFittingClass(12807): Create SmartFittingListener
I/SmartFittingClass(12807): InitialCheck, WhiteListStatus returned from CodecSolution : 0
I/SmartFittingClass(12807): InitialCheck, [State:FINISHED] SmartFitting has not been activated by App. Shut Down SmartFitting.
D/SurfaceUtils(12807): set up nativeWindow 0x74b9e5e010 for 854x450, color 0x7fa30c06, rotation 0, usage 0x20402900
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] configureOutputBuffersFromNativeWindow setBufferCount : 20, minUndequeuedBuffers : 5
I/MediaCodec(12807): setCodecState state : 0
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Idle->Executing
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Executing
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now handling output port settings change
D/SurfaceUtils(12807): set up nativeWindow 0x74b9e5e010 for 856x456, color 0x7fa30c06, rotation 0, usage 0x20402900
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] configureOutputBuffersFromNativeWindow setBufferCount : 20, minUndequeuedBuffers : 5
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Executing
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] OMX_EventPortSettingsChanged 0x7f000062
I/MediaCodec(12807): setCodecState state : 1
D/        (12807): TrackPlayerBase::~TrackPlayerBase()
D/        (12807): PlayerBase::~PlayerBase()
I/MediaCodec(12807): setCodecState state : 0
D/        (12807): PlayerBase::stop() from IPlayer
D/AudioTrack(12807): stop() called with 482924 frames delivered
I/MediaCodec(12807): setCodecState state : 1
W/cr_MediaCodecBridge(12807): Releasing: OMX.qcom.video.decoder.vp9
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Executing->Idle
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Idle->Loaded
I/ACodec  (12807): [OMX.qcom.video.decoder.vp9] Now Loaded
I/SmartFittingClass(12807): ShutDownSmartFitting!!
I/SmartFittingClass(12807): SmartFittingManagerServiceProxy::Deinit
I/SmartFittingClass(12807): Destroy SmartFittingListener
I/SmartFittingClass(12807): Destroy ~SmartFittingManagerServiceProxy::EventHandler
I/SmartFittingClass(12807): Destroy SmartFitting!!
I/SmartFittingClass(12807): Destroy SmartFittingManagerServiceProxy!!
I/ACodec  (12807):  [OMX.qcom.video.decoder.vp9] Now uninitialized
I/ACodec  (12807):  [] Now kWhatShutdownCompleted event : 8522
D/SurfaceUtils(12807): disconnecting from surface 0x74b9e5e010, reason disconnectFromSurface
I/MediaCodec(12807): Codec shutdown complete
W/cr_MediaCodecBridge(12807): Codec released
D/        (12807): TrackPlayerBase::~TrackPlayerBase()
D/        (12807): PlayerBase::~PlayerBase()

@kulture-rob I couldn't figure out why it's pausing. May be it's caused by the webview itself.

I also had this problem. It looked like resizing caused the change of the
playback quality change and then it paused. I still couldn't figure out how
to fix it.
On Sun, Aug 11, 2019 at 11:36 PM Sarbagya Dhaubanjar <
<EMAIL_ADDRESS>wrote:

@kulture-rob https://github.com/kulture-rob I couldn't figure out why
it's pausing. May be it's caused by the webview itself.
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.

I've noticed also that when you change orientation this behavior also happens (probably due to the resizing).

Any update on this issue?

try adding the key "forceHD: true", it solved the problem on Android, I haven't checked it yet on ios
_controller = YoutubePlayerController(       initialVideoId: state.lessonItem.courseVideo.video.url,       flags: const YoutubePlayerFlags(         autoPlay: false,         controlsVisibleAtStart: false,         disableDragSeek: false,         enableCaption: false,         forceHD: true,         hideControls: false,         hideThumbnail: false,         isLive: false,         loop: false,         mute: false,       ),     )..addListener(listener);

any update on this issue?

@sarbagyastha , could you please reopen the issue?
It's still ocurring even if we add "forceHD:true" (see https://github.com/sarbagyastha/youtube_player_flutter/issues/50#issuecomment-643379598).
Thank you in advance!


Http Get Request throw “HttpException: Connection closed before full  header was received”

In certain circumstances it throws “HttpException: Connection closed before full  header was received” when i use http get request. When i try to remove the request header paramer "content-length" that it solved. So I suggest that it would be necessary to remove header paramer "content-length" in http get request.

Hi @porco-zheng
Can you please provide your flutter doctor -v and a minimal complete reproducible code sample?
Thank you

i cannt offer the api here，but the code works as：
var request = await _httpClient.getUrl(Uri.parse("http://xxxx"));
request.headers.removeAll("content-length");
var response = await request.close();
[√] Flutter (Channel unknown, v1.17.2, on Microsoft Windows [Version 10.0.16299.2166], locale zh-CN)
• Flutter version 1.17.2 at D:\flutter\sdk\flutterSDK\flutter
• Framework revision 5f21edf8b6 (10 months ago), 2020-05-28 12:44:12 -0700
• Engine revision b851c71829
• Dart version 2.8.3
[√] Android toolchain - develop for Android devices (Android SDK version 29.0.3)
• Android SDK at D:\Android\SDK
• Platform android-30, build-tools 29.0.3
• ANDROID_HOME = D:\Android\SDK
• Java binary at: D:\Program Files\Android\Android Studio\jre\bin\java
• Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b01)
• All Android licenses accepted.
[√] Android Studio (version 4.0)
• Android Studio at D:\Program Files\Android\Android Studio
• Flutter plugin version 47.1.2
• Dart plugin version 193.6911.15
• Java version OpenJDK Runtime Environment (build 1.8.0_242-release-1644-b01)

Hi @porco-zheng
We need a complete reproducible code sample to verify the issue!
Looks like your Flutter version is ten months old, please upgrade and try again.
If you are able to reproduce this issue with the http package, then please file this issue in the Dart repository https://github.com/dart-lang/sdk/issues as this package has nothing to do with Flutter itself.
Thank you


CB-7009 Azure single RG: env service create RG

how can we handle the new status in case of a production rollback? Can you pleasse implement an enum converter like FeatureStateConverter.java into 2.21?

how can we handle the new status in case of a production rollback? Can you pleasse implement an enum converter like FeatureStateConverter.java into 2.21?
Great idea.
However, do you mean as a separate PR before this one? Even then it will most probably have an effect for later flow changes only. This EnumConverter should be already rolled out before the present feature of RG create makes it to an env.


iPad: Is setIPadPopupCoordinates working with newer iOS versions on iPad?

I'm a bit confused, because setIPadPopupCoordinates was mentioned for the use with iPads, but since iOS 13 it seems not to work at all.
Can anyone give me a clue, if I'm missing something, or it just doesn't work anymore with iPads?
Thanks!

Got it: The problem was a step before: Cordova was not detecting the iPad as an "ios" devide with this.platform.is("ios")
(like here: https://forum.ionicframework.com/t/platform-ios-13-4-no-detect/189754/8)
My workaround so far:
if(!this.platform.is('android) && this.platform.is('core'){


Rule Request: UseCompatibleStrings

Summary of the new feature
The `e and `u{xxxx} special characters in double-quoted strings were added in Powershell 6. It would be nice to have a compatibility rule that detects these escape characters when writing for Powershell <6 and suggests a different syntax (ex. suggesting $([char]0x1b) for `e)
What is the latest version of PSScriptAnalyzer at the point of writing
v1.22

I would name it UseCompatibleEscapeSequences as it specifically concerns the use of compatible escape sequences

Thanks... would want to make sure this came with great test coverage 😄

Specifically for the escape character (`e):
$([char]0x1b) is a nice suggestion for the auto correction (-fix), yet my recommendation for a (documented) more idiomatic (and presumably more efficient) compatible correction would be to define a new (common) $Esc variable also knowing that in most cases this doesn't require any Subexpression operator $( ) as it is usually followed by a special character:
$Esc = $([char]0x1b)
"This is $Esc[7mInverse$Esc[27m text."

The same might apply to any specific unicode escape sequences (although less common):
$Smiley = $([char]0x263a)
"Please $Smiley, I am taking a picture"

@iRon7 I'm not sure I agree that we should be defining a new variable.
There's a minutia of complexities with doing so. Not least of all:

What do you call the variable so that it doesn't clash or shadow an existing one?

Where do you define the variable?

With PowerShell's dynamic scopes, lexical analysis would be insufficient to find a suitable name or place. Though perhaps I'm over-thinking it? 🤔
I've made a first stab at the MVP of this and learned a fair bit.

As Sydney says, much testing is going to be needed; There are many edge cases.


Fix: Rating in info box are shown correctly

As it is a small change it is incorporated in the following pr #205


Add orElseW

🚀 Feature request
Current Behavior
There is no orElseW for Either, TaskEither, ReaderTaskEither
Desired Behavior
orElseW acts like orElse, but with automatic widen type
Suggested Solution

Who does this impact? Who is this for?
All users
Describe alternatives you've considered
Explicit annotate all generic parameter with widened type
Additional context

Your environment

Software
Version(s)

fp-ts
2.9.3

TypeScript
4.1

No need to widen
export declare function orElse<E, A, M>(onLeft: (e: E) => Either<M, A>): (ma: Either<E, A>) => Either<M, A>

The M type always overrides the E type

No need to widen
export declare function orElse<E, A, M>(onLeft: (e: E) => Either<M, A>): (ma: Either<E, A>) => Either<M, A>

The M type always overrides the E type

Sorry, what I meant is to widen the A type
function orElse<E, B, M>(onLeft: (e: E) => Either<M, B>): <A>(ma: Either<E, A>) => Either<M, A|B>

Sorry, what I meant is to widen the A type
function orElse<E, B, M>(onLeft: (e: E) => Either<M, B>): <A>(ma: Either<E, A>) => Either<M, A|B>


Animations not working as expected in Svelte

I'm trying to await code block changes in Svelte.
console.log('start')
await renderer.render(result.current)
console.log('end')

Instead of resolving the promises as expected it instantly logs start and end.
https://github.com/shikijs/shiki-magic-move/assets/38083522/8c7dbdb4-718f-4250-9be0-176974e184dd
I tried Svelte's tick lifecycle function as the Vue example used it, because I thought it might be related to DOM updates but it made no difference.
await tick()
await renderer.render(result.current)
await tick()

After some debugging I noticed these styles in one of my reproductions made it work, so I decided to investigate further.
body {
  display: grid;
  place-content: center;
}

I'm not sure how grid fixes it but the actual reason seems to be that <pre> is block-level, and making it inline solves the problem — another solution I found was to set containerStyle to false.
pre {
  display: inline-block;
}

It works as expected.
https://github.com/shikijs/shiki-magic-move/assets/38083522/a5b63dd5-47ce-4f71-ad43-494cfc491971
I also found this code el.addEventListener('transitionend', finish) from Shiki Magic Move never ran because of this line — which I had to remove to make it work before.
const finish = (e) => {
  // if (!e || e.target !== el) return
  resolve()
}

You can look at the reproduction on StackBlitz where the entire code is located in src/app.svelte.
It might be a skill issue though.

I've identified the problem: when containerStyle is set to true and it's not the first render shiky-magic-move does this
https://github.com/shikijs/shiki-magic-move/blob/b67b003b9f7010f84e519955b82a996944d4522d/src/renderer.ts#L359-L368
to apply the style change and wait for the transition to end before resolving the whole render promise.
However this poses a problem when the bg doesn't actually change because if you apply the same style the transition never start (and thus never end). This means that now we are awaiting for a promise that will never resolve (hence the error)
Here's an even more minimal reproduction of the situation in codepen.
https://codepen.io/paoloricciuti/pen/eYoKZKo
I wonder if this could happen for other stuff too and not only the bg.
A possible solution could be to set a timeout for options.duration * options.delayContainer and resolve the promise in this timeout and also set a transitionstart that cancel the timeout. Basically this:
  private registerTransitionEnd(el: HTMLElement, cb: () => void) {
    let resolved = false
    let resolve = () => { }
    const promise = new Promise<void>((_resolve) => {
      const finish = (e: TransitionEvent) => {
        if (!e || e.target !== el)
          return
        resolve()
      }
      resolve = () => {
        if (resolved)
          return
        resolved = true
        el.removeEventListener('transitionend', finish)
        cb()
        _resolve()
      }
      const timeout = setTimeout(()=>{
        resolve();
      }, this.options.duration * this.options.delayContainer);
      el.addEventListener('transitionstart', ()=> clearTimeout(timeout))
      el.addEventListener('transitionend', finish)
    }) as PromiseWithResolve<void>
    promise.resolve = resolve
    return promise
  }

this basically should ensure that if the transition never starts the promise will be resolved nonetheless.
I can PR this change if needed.

I think it's likely the integration issue as the core works well with vanilla JavaScript. I am not familiar with Svelte enough to help sadly

I think it's likely the integration issue as the core works well with vanilla JavaScript. I am not familiar with Svelte enough to help sadly

Do you have a vanilla example? I investigated further and from what i'm seeing the problem is exactly what i've explained. But that should be a problem with vanilla too. It's possible that the integration with svelte surfaced this problem but i'm confident enough to say that the problem is there in vanilla too.

@antfu the moment you add containerStyle it breaks also for vue (i didn't test react but i would assume the same)

go here https://stackblitz.com/edit/github-b1z8zy
open the console
change something in the playground
observe that Animating continues to blink even after the animation is done and that in the console you can see the start log but not the end log
from this moment on every change will show a start log followed immediately by an end log (this is the previous unfinished promises that are resolved when the new render starts)

i will try to PR this change...i have a decent idea of how to fix it but it might require some change when the promise is created. Will see

I think it's likely the integration issue as the core works well with vanilla JavaScript. I am not familiar with Svelte enough to help sadly

In the reproduction I provided I'm using shiki-magic-move/core and there's no Svelte magic besides running render() when code updates.
<script>
  // ...
  async function render() {
    const result = machine.commit(code)
    if (result.previous) renderer.replace(result.previous)
    console.log('start')
    await renderer.render(result.current)
    console.log('end')
  }

  $: if (code) {
    render()
  }
</script>

Also I want to thank you for making this wonderful project since this is something I wanted for a while to integrate into Animotion which is made possible due to your efforts. 🙏


add topics

I suggest adding the topics mariadb, mysql, database, fortran in the About section at https://github.com/v-h-giang/Mariadb_connector_fortran

Thanks, Done !


Make the download directory configurable instead of using hardcoded /tmp

My domain hosting is not allowing to use /tmp and I need to choose another directory to download the installation files. Please provide the capability to make the folder configurable

I think this is already configurable - see in config.
You can use the env var SELF_UPDATER_DOWNLOAD_PATH


Angle Conversion Calculator

interconverts radians and degrees.
please assign this issue to me under JWOC.

@SHYALI Go for it.

Please take a git pull before make your PR. Otherwise some conflicts can be occurred as the folder structure has been changed.


Add missing container to whaletainership. fixes #821

Can you edit the description on this page so that it includes the text "closes #821"? Otherwise GitHub won't automatically close the issue if this is merged. (GitHub doesn't look at the title of the pull-request)

:+1:

I actually managed to extend the whale without adding the container. Thanks for the heads up, @tianon

LGTM


GOPATH is not being respected on windows

GOPATH and GS_GOPATH do not seem to work at all on Windows.
Setting GOPATH globally works correctly for go build, but I get no completion inside of Sublime. The same thing happens with using GS_GOPATH, or a sublime project file.
Completion only ever works for standard library, and the current file. This is with a full fledged src, bin, pkg structure.

After much debugging, I got it to work by putting the following into my user-specific GoSublime settings:
{
    "env": { "GOPATH": "$GS_GOPATH;" },
}

Note that the semicolon is important. MarGo complains about missing GOPATH otherwise, when not in a .go file (e.g. when sublime starts).

Thanks @prencher :+1:
Nice trick, this solution is work, it solve the annoying MarGo error.


Music editor: Row indicator

It would be nice if we had a way to tell which row in currently edited pattern we're at. Cuurently I have to count by hand what row I'm at, which if I, for example, am transcribing music from a module file to Tic, is a real chore.

done in .30


[chore] Add shortId lib

What needs to be done
This lib will be used to generate unique ids on the fly for the game.

Added with
10ec90e0335c1449c3e868492f61c8d515e9312a


Improve warning about view snapshot discrepancies

I think it's better to be conservative, as the list of variables that can cause discrepancies is incomplete and unlikely to ever be exhaustive.
Other things that seem to cause differences are the architecture of the Mac running the simulator causing subtle "sub" pixel differences, and optical font sizing adjustments that either don't respect the displayScale UI trait or deliberately ignore it because it's truly driven by the device display's points per inch (a product of the display's scale and pixels per inch).
I think a broad caution is more prudent than extending the warning to include more device characteristics, as even if that list could be complete now, it would be very hard to track.

More alternatives, same as the proposed in this PR but removing the emphatic/redundant "exact":

⚠ Warning: Snapshots must be compared using the same simulator that originally took the reference to avoid discrepancies between images.

Strengthened to also mention the architecture of the hosting Mac:

⚠ Warning: To avoid discrepancies between images, snapshots must be compared using the same simulator and Mac system architecture (i.e. Apple Silicon vs. Intel) that originally took the reference image.


TypeError: t is null

Describe the bug
To Reproduce
Expected behavior
Screenshots
Applicable Versions:

Netlify CMS version<EMAIL_ADDRESS>Git provider: git-gateway
Browser version: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:121.0) Gecko/20100101 Firefox/121.0

CMS configuration
backend:
  name: git-gateway
  branch: main
  commit_messages:
    create: Create {{collection}} “{{slug}}”
    update: Update {{collection}} “{{slug}}”
    delete: Delete {{collection}} “{{slug}}”
    uploadMedia: "[skip ci] Upload “{{path}}”"
    deleteMedia: "[skip ci] Delete “{{path}}”"
media_folder: static/assets
public_folder: /assets
collections:
  - name: posts
    label: Posts
    label_singular: Post
    description: |
      Create awesome content here.
    folder: src/content/posts
    slug: "{{year}}-{{month}}-{{day}}-{{slug}}"
    summary: "{{title}} — {{year}}/{{month}}/{{day}}"
    create: true
    fields:
      - label: Template
        name: template
        widget: hidden
        default: blog-post
      - label: Title
        name: title
        widget: string
        tagname: h1
      - label: Slug
        name: slug
        widget: string
        hint: Start slug with /. Use only alphanumeric characters, - and _. Avoid spaces
          in any cases.
      - label: Publish Date
        name: date
        widget: datetime
        dateFormat: YYYY-MM-DD
        timeFormat: HH:mm
        format: YYYY-MM-DD HH:mm
        date_format: YYYY-MM-DD
        time_format: HH:mm
      - label: SEO Description
        name: description
        widget: text
      - label: Featured Image
        name: featuredImage
        widget: image
        required: false
        tagname: ""
      - label: Body
        name: body
        widget: markdown
        hint: Main content goes here.
    publish: true
    type: folder_based_collection
    sortable_fields:
      - commit_date
      - title
      - date
      - commit_author
      - description
    view_filters: []
    view_groups: []
  - label: Pages
    name: pages
    files:
      - label: Home
        name: home
        file: src/content/pages/index.md
        fields:
          - label: Template
            name: template
            widget: hidden
            default: index-page
          - label: Slug
            name: slug
            widget: hidden
            default: /
          - label: Title
            name: title
            widget: string
          - label: Tagline
            name: tagline
            widget: string
          - label: Featured Image
            name: featuredImage
            widget: image
          - label: Body
            name: body
            widget: markdown
          - label: CTA
            name: cta
            widget: object
            fields:
              - name: ctaText
                label: CTA text
                widget: string
              - name: ctaLink
                label: CTA Link
                widget: string
      - label: About
        name: about
        file: src/content/pages/about.md
        fields:
          - label: Template
            name: template
            widget: hidden
            default: about-page
          - label: Slug
            name: slug
            widget: hidden
            default: /about
          - label: Title
            name: title
            widget: string
          - label: Body
            name: body
            widget: markdown
      - label: Contact
        name: contact
        file: src/content/pages/contact.md
        fields:
          - label: Template
            name: template
            widget: hidden
            default: contact-page
          - label: Slug
            name: slug
            widget: hidden
            default: /contact
          - label: Title
            name: title
            widget: string
          - label: Body
            name: body
            widget: markdown
    publish: true
    type: file_based_collection
    sortable_fields:

...

Hi @learner-susu, please fill out all the fields in the issue template. The information you provided is not enough to debug the problem.
I'm closing the issue for now. Once you add the missing information, I can open it again.


Configuration File XSD

As developer
I want an XSD definition for the CI's build file
to generate serialization classes for the project.

[x] An XSD definition exists.
[x] The XSD definition can be versioned.

Closed with #5


New action that gets features from OGC API server.

See https://www.ogc.org/standards/ogcapi-features
You can either call for a full geoJSON with all the results or iterate over each result using the split parameter.
This is a new geospatial API standard that replaces WFS servers.
Kamelet Binding used to test:
apiVersion: camel.apache.org/v1alpha1
kind: KameletBinding
metadata:
  name: integration
spec:
  source:
    ref:
      apiVersion: camel.apache.org/v1alpha1
      name: timer-source
      kind: Kamelet
    properties:
      period: '5000000'
      message: 'NONLD_AREA=0.0&NAME=Croydon'
  steps:
  - ref:
      apiVersion: camel.apache.org/v1alpha1
      name: ogcapi-features-action
      kind: Kamelet
    properties:
      collection: activity_level_ldn
      url: https://emotional.byteroad.net/
  sink:
    ref:
      apiVersion: camel.apache.org/v1alpha1
      name: log-sink
      kind: Kamelet
    properties:
      showAll: true

This is not a source because there is no default "timestamp" parameter from which to query only the last results.


Fix broken mixin link on user-guide testing

Problem
See here, on almost bottom page, on section
To make use of another ScalaTest test style, such as FunSpec or others, see Test Mixins.

There's link to Test Mixins, but the link is broken.
Solution
It's because missing s on the html link.
Result
That fix should be solve that problem.

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

Codecov Report

Merging #443 into develop will increase coverage by 0.03%.
The diff coverage is n/a.

@@             Coverage Diff             @@
##           develop     #443      +/-   ##
===========================================
+ Coverage    93.52%   93.55%   +0.03%
===========================================
  Files          217      217
  Lines         3213     3213
  Branches       249      255       +6
===========================================
+ Hits          3005     3006       +1
+ Misses         208      207       -1

Impacted Files
Coverage Δ

...on/internal/caseclass/jackson/CaseClassField.scala
98.83% <0%> (+1.16%)
:arrow_up:

Continue to review full report at Codecov.

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@hellowin thanks for the PR -- we'll get this merged later this week. Thanks!

Merged in 5cc30b2bee943d806f4c9ebe84e1f1fd0bf52d9b. Thanks again!


Cannot create disk on CephFS

I'm trying to create a QEMU VM on Proxmox with CephFS storage:
resource "proxmox_vm_qemu" "my_vm" {
  name = "my-vm"
  ...
  disk {
    id           = 0
    type         = "virtio"
    storage      = "vm-images"
    storage_type = "cephfs"
    iothread     = true
    format       = "raw"
    size         = 1 * 1024 * 1024 * 1024 # KiB
  }

However when I apply this I get the following error:
$ terraform apply
...
proxmox_vm_qemu.my_vm: Creating...

Error: Error creating VM: Cannot create VM disk vm-images:103/vm-103-disk-0.raw, error status:  (params: map[agent:0 boot:cd bootdisk:virtio0 cores:24 cpu:host description:My VM hotplug:network,disk ide2:local:iso/my-iso.iso,media=cdrom memory:189440 name:my-vm net0:virtio=5A:87:D4:F3:EA:26,bridge=vmbr0 numa:false onboot:true ostype:l26 scsihw:lsi sockets:1 virtio0:media=disk,size=1073741824,file=vm-images:103/vm-103-disk-0.raw,format=raw,iothread=1 vmid:103])

Surprisingly, after running that command, the disk vm-103-disk-0.raw on vm-images has been created.
Note that creating the VM with a disk using storage_type=lvm works.
How can I get this working on CephFS?

Hello @basvandijk
i'm using this configuration for cephfs
  disk {
    id           = 0
    size         = var.disk_size
    type         = "scsi"
    storage      = "pool"
    storage_type = "cephfs"
    iothread     = true
  }

I discovered that the following condition (diskName != fullDiskName) is failing since in my case:
diskName     == "vm-images:vm-100-disk-0.raw"
fullDiskName == "vm-images:100/vm-100-disk-0.raw"

Any idea where this discrepancy is coming from?

I think the discrepancy is caused by a missing cephfs on this line.
I'm going to patch that line and see if that fixes my problem...


get code-server to show in HA

Hi,
forgive my ignorance with this question, I'm getting started with snap and HA and code-server. I've got HA up and running, got the code-server up and running and can access it on port 8080. How do I get code-server to show up inside HA? It's a new install of each and the only changes to any configs I've made is to code-server to change the IP address.
thanks

https://www.home-assistant.io/integrations/panel_iframe/


Archive merge

Thank you for submitting a contribution to Apache NiFi - MiNiFi C++.
In order to streamline the review of the contribution we ask you
to ensure the following steps have been taken:
For all changes:

[ ] Is there a JIRA ticket associated with this PR? Is it referenced
in the commit message?

[ ] Does your PR title start with MINIFI-XXXX where XXXX is the JIRA number you are trying to resolve? Pay particular attention to the hyphen "-" character.

[ ] Has your PR been rebased against the latest commit within the target branch (typically master)?

[ ] Is your initial contribution a single, squashed commit?

For code changes:

[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?
[ ] If applicable, have you updated the LICENSE file?
[ ] If applicable, have you updated the NOTICE file?

For documentation related changes:

[ ] Have you ensured that format looks appropriate for the output in which it is rendered?

Note:
Please ensure that once the PR is submitted, you check travis-ci for build issues and submit an update to your PR as soon as possible.

@phrocker please review the same
@apiri @jdye64 please review the LICENSE file for libarchive to see whether it is OK. Joe raise some concern in last PR.

@phrocker @apiri please let me know if you have comments.

The incremental read already been added to address Marc comments.

@apiri remove test and doc from lib archieve, update LICENSE to add each author.

@apiri @phrocker rebased.


improve generating documentation for ES2015 code syntax

Especially the ES2015 module syntax isn't supported well by laxar-dox. There may also be a newer dox release with improved ES2015 support.

Seems that there was no real issue with laxar-dox, but with dox itself and a string within the laxar code. See here for the according dox issue: https://github.com/tj/dox/issues/184
Removing the superfluous single tick within the template literal in storage.js did the job.


Contribution opportunities link change

Description
Updated the "How to Contribute" button link in the Support popup. It now redirects users to the guide site for the Organised app.
Fixes # (issue)
Since we now have a dedicated site with detailed guides for the Organised app, the "How to Contribute" button was updated to redirect users there. Previously, the button directed people to the guide on GitHub.
Please delete options that are not relevant.

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

:tada: This PR is included in version 2.130.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Nan loss Value

Hello,
Thank you for publishing this package, it is really helpful.
I tried to follow the starter example https://deepdow.readthedocs.io/en/latest/auto_examples/end_to_end/getting_started.html#sphx-glr-auto-examples-end-to-end-getting-started-py, and I applied it for my own portfolio dataset but I am getting Nan Values in the training phase.
How to fix this problem ? is there any other networks that I could experiment ?
Thank you in advance

Hey @MrBeastgeek and thank you for your interest!
I remember seeing a couple of similar issues and in all of the cases the problem was in the input data (e.g. prices equal to 0 which would lead to division by 0 when computing returns).
Anyway, I can definitely help further if you provide a minimal reproducible example.

Hey @MrBeastgeek and thank you for your interest!
I remember seeing a couple of similar issues and in all of the cases the problem was in the input data (e.g. prices equal to 0 which would lead to division by 0 when computing returns).
Anyway, I can definitely help further if you provide a minimal reproducible example.

Hello, @jankrepl . In your example "Basic example", you are setting X to be standardized returns per asset, and y as well. So n_channels will be 1, apparently, because we have just one feature which is the return (per asset).
I understand you have done this for the sake of the example.
In my case, I want to feed the model other data with multiple channels (12 in my case). I didn't change y (I just let it be the returns per coin), but I got an error:
35, in __init__
    raise ValueError('X and y need to have the same number of input channels.')
ValueError: X and y need to have the same number of input channels.

so what I did is basically change y to be the same as X, but with the gaps.
Now I get the same error with the loss, because I have 0 values for some of the features (which are not returns) sometimes.
So my question is basically is it possible to use different shapes of X and y - I want X to be some feature matrix independent of the returns (maybe also include the returns), and y to be solely the returns.
If yes, how to specify which dimension is the return column so that the benchmarks are computed.
If no, it seems that the model will try to predict y which is basically features, and I only want it to have returns as y.

To add to my question, do you think this framework is appropriate for the following problem:
I have historical positions (multiple per asset) that I want to use as features to generate final positions. Because if I have 50 positions for an asset, I would like to narrow it down to a single position using ML. And I want to optimize for + return and - risk.

To add to my question, do you think this framework is appropriate for the following problem:
I have historical positions (multiple per asset) that I want to use as features to generate final positions. Because if I have 50 positions for an asset, I would like to narrow it down to a single position using ML. And I want to optimize for + return and - risk.

So my understanding is that your features are basically portfolio weights/absolute positions (I would imagine coming from some 3rd party portfolio managers) in the past and you are trying to predict optimal future portfolio weights?
Sounds reasonable to me and doable with deepdow (but hacky). And yes, this usecase is not the primary usecase I had in mind when building the tool.
In your case X does not contain historical returns, whereas y needs to contain them since you want to compute losses.
My understanding is that you are hitting this exception: https://github.com/jankrepl/deepdow/blob/341b34d324cf8054f2f59f092b3afcdd031bc828/deepdow/data/load.py#L38
In theory you can simply try to remove that condition and the training will still work out? I am not 100%, you need to play around with it and see:)

Thank you for your fast response and understanding my use case. You are right, I've got positions (n positions per asset, per hour) and I want these to be my actual features and y - the returns. Based on the positions that I already have (multiple per asset), my goal is to "combine" them, in other words, just make one final position per asset.
It seems that it is possible to do it with your library and I will play around with it. Thanks for the assitance!


Scrolling over membership in Gitter historical room jumps calendar around because those events weren't timestamp massaged

Scrolling over membership in Gitter historical room jumps calendar around because those events weren't timestamp massaged.
.../r/gitter_gitter:gitter.im/date/2015/11/15

In the future, it would be nice to timestamp massage /join membership events (see MSC3998) so we don't have to do this type of ignoring and so the room is more semantic.
Potential solution
Ignore membership events in the IntersectionObserver (because they aren’t timestamp massaged) so you don’t jump around in the calendar

Solved instead by ignoring events which appear in the future compared to timestamp in the URL
See https://github.com/matrix-org/matrix-public-archive/pull/196


Upgrade version of youtube_player_iframe dependencies

Thank you, @filipedfs and @jimmff for the workaround. I'm concerned, though about whether or not this package is being maintained. The last commit appears to have been 3 months ago. @sarbayastha, thank you for sharing this plugin, but should I be looking elsewhere for something to use in production?

Already in master. Thank you for the PR.

Thank you for updating. When will it be available in pub.dev?


Add permission for exporting interactions

Description of change
Adds a permission to control access to the interaction search export results feature.
Checklist

[x] Have any relevant search models been updated?
[x] Have any relevant fixtures (fixtures/test_data.yaml) been updated?
[x] Have any relevant select-/prefetch-related field lists in the views and search apps been updated?
[x] Has the admin site been updated (for new models, fields etc.)?
[x] Has the README been updated (if needed)?

Codecov Report

Merging #1070 into develop will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           develop   #1070      +/-   ##
==========================================
+ Coverage     96.2%   96.2%   +<.01%
==========================================
  Files          217     217
  Lines         6190    6196       +6
  Branches       588     589       +1
==========================================
+ Hits          5955    5961       +6
  Misses         166     166
  Partials        69      69

Impacted Files
Coverage Δ

datahub/search/views.py
94.81% <100%> (+0.03%)
:arrow_up:

datahub/search/permissions.py
100% <100%> (ø)
:arrow_up:

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No longer need to do a mkdir for local repo

dockerfile is even smaller ;-)

rebased and merged as https://github.com/spring-cloud/spring-cloud-stream/commit/5a74f88b228112185ea23164cd82cadd272369eb


Automatic forge publishing

I heard CI is cool for that

Added in e6f87badeb472d6ba127787cadaf5b127a757f5b

This change appears to have broken Travis releasing, so I'm rolling it back and setting the milestone to 1.0.3.

Rolled back in db5e71b6ec6749cb4bec08e7d3fea95082a1d73b

Closing this for now since it's not a priority for me - will revisit in the future.


Stop output window automatically opening

This is driving me crazy but just started happening today.. anytime I work on a file the output window for BASH IDE opens.. it always starts with text like:
Initialized server v. 1.5.6 for file:///c%3A/Users/robert.bram/work/notes
Analyzing file://c:\Users\robert.bram\work\notes\bin\filexxx.sh
Analyzing file://c:\Users\robert.bram\work\notes\bin\fileyyy.sh

That sounds weird. Could it be a configuration option in vs code, that you by mistake enabled?

I googled VS Code auto opening the output console, and the responses I read say that it is linked to whatever extension is writing to that console - and VS Code says it's a bash IDE console.. so I am not sure what conf options relate to this.

Thanks. Can I get you to record a gif/movie showing the problem? That might make it easier for us to debug.
You can depending on your OS, use https://licecap.en.softonic.com/ for it.
Thanks.

@robertmarkbram do you still have this problem? If so then please reopen with a gif/screenshot.


Required Go version in README is wrong

Bug Report
What did you do?
Tried to go_get promtool in the golang:1.11.5 container. According to the README, go 1.11+ should be sufficient to do this.
What did you expect to see?
Sucessful docker build
What did you see instead? Under which circumstances?

This step worked in golang:1.12.3, and judging by this file which has recently been added, I think this is because you now need go 1.12+ to build from source, so the README needs updating.

Do you want to submit the PR for this?

fixed in #5471


feat: feature in feed page links to website

closes #665
Summary:
On the Feed page, when you click on a feature it will open up the documentation page on gtfs.org
Expected behavior:
On the Feed page, when you click on a feature it should open the correct documentation page based on the feature
Testing tips:
Use Chaimaa's analytics tool (https://qa.mobilitydatabase.org/metrics/gtfs/features) to find the features you want to test and make sure the features when clicked go to the right spot
Discussion
Right now we are able to map the features to the correct link using the name of the feature ex: Levels. This is not great as the name of the feature can change, and in certain instances the names are different from the documentation and the validation report ex: Bikes Allowance vs Bike Allowed. A possible solution to this issue would be to assign each feature an id. This would help with displaying information about the feature on the UI, but would also help track feature data if the name of the feature ever changes. Thoughts?
Please make sure these boxes are checked before submitting your pull request - thanks!

[ ] Run the unit tests with ./scripts/api-tests.sh to make sure you didn't break anything
[ ] Add or update any needed documentation to the repo
[X] Format the title like "feat: [new feature short description]". Title must follow the Conventional Commit Specification(https://www.conventionalcommits.org/en/v1.0.0/).
[X] Linked all relevant issues
[ ] Include screenshot(s) showing how this pull request works and fixes the issue(s)
[ ]

This LGTM however i'm worried that the UI will need modification each time we introduce a feature on the validator side. If there's a problem with the naming, my opinion is that it should be fixed on the validator side and not mapped on the UI.

Fair point. We'll have to add the documentation link anyways on the validator side as part of the next release. I'd suggest keeping this PR for now and then refactoring this once the feature links are actually in the validator for 6.0.


Curations that would enable Escher visualizations

Hi folks,
This is an attempt to convert my email from May 11th, 2019 to an issue that can be tracked.
There is a jupyter notebook that contains all the suggested fixes. I will attempt to break each suggestion into a separate comment below.

Hi Jeremy,
Some good news – Ed Kerkhoven from Chalmers has been working with Christian Lieven from DTU to add BiGG IDs to their Yarrowia model (https://github.com/ChristianLieven/Yarrowia_lipolytica_W29-GEM/tree/bigg_ids). They regularly post updates and we can link to their latest release for our paper.
Ed and Christian plan to publish an updated model in another paper that is associated with some data that was produced at EMSL awhile ago and I think there is some interest is collaborating with you on visualization as they develop their new model. You can follow-up with Ed directly to assess how you could collaborate.
Cheers,
Scott
Hi Scott,
That is fantastic news!
Ed, what kind of visualizations you are interested in?
Sincerely,
Jeremy
Hi Jeremy,
You're probably aware of Escher (https://escher.github.io/)? It would be nice if the model works with that.
Some other curations that we're planning to include are BIGG anntations, Yali1 gene identifiers, curate with reactions from iYLI647 and iMK735, SLIMEr formulation of lipid metabolism (https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0673-8), do you have some additional ideas after working with the model?
Best, Ed

iMM904 central carbon metabolism reactions missing in YALI4

FRUK:  f16_c and fdp_c have no other reactions.
ALCD2x_copy1   iMM904 had two copies of this reaction for no good reason.  ACLD2ir is the correct reaction in yarrowia.  Recommend replacing the reaction in the map
ETOHt:   y001762 (etoh_c[c] <=> etoh_e[e])  is the equivalent reaction in YALI4. Is there any reason not to rename this reaction to ETOHt?
ACt2r: Closest reaction is y001106 but no proton symport and very different GPR
ACtm: No ability to directly transport acetate from mitochondria to cytosol without converting to Acetyl-CoA in Yali4
MALt2r: y001901 converts L-malate from extracellular to cytosol without proton symport
CITt2r: y001686 transports citrate from extracellular to cytoplasm without proton symport
ACONTm: Explicit representation of citrate -> cis-aconitate -> isocitrate in Yali4. Recommend modifying escher map to accomodate
SUCD1m: YALI4 does not contain a SUCD1m reaction, but all homologs to yeast SUCD1m genes are present in Yali4. Recommend adding this reaction to YALI4
FRDm: YALI4 does not contain a FRDm reaction, but homologs to yeast FRDm genes are present in Yali4. Recommend adding reaction to YALI4
FUMt2r: Closest reaction is y001796	fum_e[e] <=> fum_c[c] with no proton symport. Recommend altering YALI4 to accommodate proton symport
SUCOASm: Identical in stoichiometry to y001022: adp_m[m] + pi_m[m] + succoa_m[m] --> atp_m[m] + coa_m[m] + succ_m[m]
Yeast is reversible, but Yarrowia reaction is not. Recommend renaming y001022 to SUCOASm
AKGDbm: Identical stoichiometry and reaction direction to y000831. Recommend renaming
GCC2cm_copy1: GCC2cm_copy1 is duplicated in iMM904 for no good reason. Identical stoichiometry to y000505.  Reaction is reversible in Yeast and irreversible in yarrowia. Recommend renaming to GCC2cm
AKGDam: Nearly identical stoichiometry to y000832 with the exception of sdhlam_m, which has one less hydrogen in its chemical formula than HC00695_m.  Note that HC00695 is a universal BIGG metabolite with an instance in the iCHOv1 model.
Recommend aligning with Yeast sdhlam_m metabolite. See missing sdhlam_m discussion below
CO2t: Identical stoichiometry to y001697. Recommend renaming

Thanks @djinnome. It is not always entirely clear what curation you propose. For instance about ALCD2x_copy1. However, some are just changes of a reaction ID to the one used in the Escher map? Others seem to be changes in the reaction equation, e.g. changing fumarate to proton symport. How sure are you that these reactions in iMM904 (and then specifically in that Escher map) is correct? The Yarrowia lipolytica model is based on yeast-GEM 8, which is a more up-to-date version than iMM904, so is it not possible that those reactions have since been curated? It seems a bit abitrary to make the model fit a map of an old model?


Please suggest using Homebrew for OS X install since OS X installer is no longer being maintained

See: https://github.com/timcharper/git_osx_installer/issues/41

They are still being updated, just got a little behind.

@timcharper totally understand, but I think it might be a good idea for git-scm.com to display both well-used means of staying up-to-date with Git. There have been times in the past when Homebrew wasn't up-to-date and for the past few months git_os_installer was out-of-date, so unless git_os_installer is going to be continually tracking the latest version of Git, I think there should be other options.

If there is an urgent reason for upgrade (such as a import security vulnerability), I am quick to update. Giving additional options may introduce more confusion for the users. I'd argue that most people looking for a quick way to install git aren't as concerned if they are installing a version a couple months old. I'll try and keep it more up to date in the future (maybe set a reminder to build and deploy weekly).

@timcharper I had been using the git_osx_installer version provided by git-scm.com for a while and suggesting it to others, but unless it is tracked to the latest, I couldn't continue to recommend that.
I understand at times an earlier version could be more stable- but if that were the case, I'd expect that to be noted in the page.
The confusing part to me is that git-scm on the front page says "Latest source Release..." as if it tracks the latest, but it sounds like that is not the goal of the git_osx_installer project, and that is fine, but I think in that case, git-scm.com should be much more careful about how it labels things.

This no longer seems to be an issue and should be closed.


GPL License declaration found in your code

In the license declaration of class PYXScanner.java you declare the GPL 2.0 license applies for this file (among Apache 2.0). This is contradictory to the your declaration of Apache 2.0 to be applied on the software package since GPL 2.0 permissions and limitations also apply to the whole software package once it is used within your code.
Please remove the notion of GPL 2.0 from the code or release the package under multi license  that allows the user to choose which license to apply.
Thanks,
Alex

This was probably done by the original author of Tagsoup. Orbeon has not modified this file.
Generally, when files are licensed under multiple licenses, the user of the file gets to choose which applies. Orbeon chooses to interpret this file, and all files in that package as being under the Apache 2.0 license only. You, as a user, can make that same choice.
In any case, this page clarifies the intent: "TagSoup is free and Open Source software. As of version 1.2, it is licensed under the Apache License, Version 2.0, which allows proprietary re-use as well as use with GPL 3.0 or GPL 2.0-or-later projects.".

Hi ebruchez,
thank you for clarifying.
I just have two concerns on this: whatever is declared in the license declaration may be rendered invalid as soon as a more restrictive license (like GPL 2.0) is used somewhere in your code or any of your dependencies without.
You are completely right when stating that a user may choose which license to apply when software is released under multiple licenses. However, it has to be made clear in the license declaration that this choice is possible, which is not the case in PYXScanner.java.
Thanks to your comment, this is clear to me now.
br,
Alex


Add chromium-vaapi-wayland patch

Can you please add this patch which enables hardware acceleration in Wayland compositors like sway?
https://aur.archlinux.org/packages/chromium-wayland-vaapi/
https://www.reddit.com/r/swaywm/comments/rxrccc/wayland_hardware_acceleration_in_chromium/
Thank you very much

Sorry but no, I try to keep this build as close to void as possible. You could try opening an issue on void-packages github to see if they accept it.


Ability to set a response time

I propose to add the ability to set a waiting time for a response to a request sendDataAndWaitResponse.

Fixed


Can't exponentiate BigInts

In normal JavaScript, this works BigInt(2) ** BigInt(2)
But writing the above in TypeScript, compiles to Math.pow(BigInt(2), BigInt(2)), which doesn't work.
According to MDN:

[BigInt] cannot be used with methods in the built-in Math object

TypeScript code
BigInt(2) ** BigInt(2)

Expected behavior:
Return 2.
Actual behavior:
TypeError: Cannot convert a BigInt value to a number
    at Math.pow (<anonymous>)

Why is this an issue? Environments that support BigInt also know the exponentiation operator.
Therefore you don't need to downlevel your code. The only target that makes sense for BigInt is ESnext.
IIRC there's even a compile error if you use BigInt with a different target.

@ajafff Oh, it didn't occur to me that I was downleveling. I just ran tsc without any configurations. Maybe that's why I was running into this issue. I'll test again later  today after setting the target to ESNext.

Technically I guess we could give an error when using bigints in exponentiation expressions when targeting anything earlier than ES2017.

Hi @DanielRosenwasser ,
can i take this up.

yup

Hi @DanielRosenwasser ,
When we target es5 , we are getting "error TS2304: Cannot find name 'BigInt'.", if we change the error code to TS2737 which stands for "BigInt literals are not available when targeting lower than ESNext." it will fix the issue, let me know if we can change.

This looks like it was separately already solved. With this file:
3 ** 4n;
BigInt(2) ** BigInt(2);

Targeting es5, you get these errors:
index.ts:1:1 - error TS2365: Operator '**' cannot be applied to types '3' and '4n'.

1 3 ** 4n;
  ~~~~~~~

index.ts:1:6 - error TS2737: BigInt literals are not available when targeting lower than ESNext.

1 3 ** 4n;
       ~~

index.ts:2:1 - error TS2304: Cannot find name 'BigInt'.

2 BigInt(2) ** BigInt(2);
  ~~~~~~

index.ts:2:14 - error TS2304: Cannot find name 'BigInt'.

2 BigInt(2) ** BigInt(2);
               ~~~~~~


🐛 Bug Report: Custom Entity Providers documentation incomplete

📜 Description
Referencing my issue #25404, from my work account, the Custom Entity Providers doc is incomplete and needs to specify properly what you should do in the new Backend.
After following the guide and creating the FrobsProvider class in the old Backend you install the provider like so
  const frobs = new FrobsProvider('production', env.reader);
  builder.addEntityProvider(frobs);

  const { processingEngine, router } = await builder.build();
  await processingEngine.start();

  await env.scheduler.scheduleTask({
    id: 'run_frobs_refresh',
    fn: async () => {
      await frobs.run();
    },
    frequency: { minutes: 30 },
    timeout: { minutes: 10 },
  });

while in the new Backend it say to do it like so:
export const catalogModuleFrobsProvider = createBackendModule({
  pluginId: 'catalog',
  moduleId: 'frobs-provider',
  register(env) {
    env.registerInit({
      deps: {
        catalog: catalogProcessingExtensionPoint,
        reader: coreServices.urlReader,
      },
      async init({ catalog, reader }) {
        catalog.addEntityProvider(new FrobsProvider('dev', reader));
      },
    });
  },
});

As you can see, the whole scheduler is missing but especially this bit of code inside the scheduler is missing await frobs.run(); in the new Backend and there is no mention of how to add it
👍 Expected behavior
The new Backend documentation should mention how to trigger the run function after installation, and to be completed, it would be nice to add also how you can add a scheduler.
👎 Actual Behavior with Screenshots
After following the documentation you end up with the new entity provider not working and without knowing the real reason why only the logger inside this function is triggered.
  async connect(connection: EntityProviderConnection): Promise<void> {
    this.logger.info('Establishing connection.');
    this.connection = connection;
  }

👟 Reproduction steps

migrate the catalog to the new Backend
add a new entity provider following the documentation above

📃 Provide the context for the Bug.
No response
🖥 Your Environment
No response
👀 Have you spent some time to check if this bug has been raised before?

[X] I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

[X] I have read the Code of Conduct

Are you willing to submit PR?
None

👋 Would you be willing to submit a PR to fix this? 🙏

Hey @aramissennyeydd I'm not sure my solution is correct, I mean I understood that async connect is the only thing being called once the module is installed but is that the correct way to add all the logic plus the scheduler, or is there a different and better way of doing it?

I don't know if you saw my last comment in the other issue, this is my current async connect
  async connect(connection: EntityProviderConnection): Promise<void> {
    this.logger.info('Establishing connection with OKTA.');
    this.connection = connection;
    // load user on the 1st connection
    await this.run();

    // create scheduler task
    await this.scheduler.scheduleTask({
      id: 'refresh_users',
      fn: async () => {
        await this.run();
      },
      frequency: { cron: '0 0 * * *' },
      timeout: { minutes: 2 },
      initialDelay: { seconds: 3 },
    });
  }

Looks like you would just need to update the snippet to something like,
export const catalogModuleFrobsProvider = createBackendModule({
  pluginId: 'catalog',
  moduleId: 'frobs-provider',
  register(env) {
    env.registerInit({
      deps: {
        catalog: catalogProcessingExtensionPoint,
        reader: coreServices.urlReader,
        scheduler: coreServices.scheduler,
      },
      async init({ catalog, reader, scheduler }) {
        const frobs = new FrobsProvider('dev', reader);
        catalog.addEntityProvider(frobs);
        await scheduler.scheduleTask({
          id: 'run_frobs_refresh',
          fn: async () => {
            await frobs.run();
          },
          frequency: { minutes: 30 },
          timeout: { minutes: 10 },
        });
      },
    });
  },
});

Adding in the scheduler core service and setting its fn. I'd recommend the above for any custom catalog providers instead of doing it in connect as you have more clear control in the above example. I'll open a PR to adjust the documentation here :)

waiting for your PR to be merged and then I'll update my code accordingly :) TY for the PR

hey @aramissennyeydd @freben today I externalised the user provider in a BE module and yarn tsc don't pass anymore.
if I follow the documentation at some point it says to use this command yarn new --select backend-module --option id=catalog to create a proper module, it creates everything and adds the module to the backend package.json which works perfectly but then once I commit the pipeline fail on the tsc part Cannot find module '@internal/backstage-plugin-catalog-backend-module-okta-user-provider' or its corresponding type declarations.


libnd4j CUDA partial sync

Since we're using asymmetric allocation in CUDA now, we should provide partial synchronization mechanics
Aha! Link: https://skymindai.aha.io/features/ND4J-132

Not actual anymore.


Reccommended method for running as a service?

I saw some mentions of nohup and screen, is there a recommended method in use at pivotal? supervisord perhaps? Or is there a service script somewhere?
Thanks.

At this time we do not have any suggestions or recommendations for this. You are welcome to develop your own service or init.d script to use.


[v2.4] KDM release: k8s march patches

This is a KDM release for new k8s versions: v1.18.17. It's not going out with any Rancher release, it's a KDM + RKE release only.

All existing k8s versions in dev-v2.4 have been released (are present in release-v2.4) so we need to introduce new k8s versions. To introduce a new k8s version, basecopy the latest existing version in a separate commit, followed by another commit to update what's changed. Following versions need to be created with their compatible rancher server versions:

basecopy version from
create
set rancher version constraints

v1.18.16-rancher1-1
v1.18.17-rancher1-1
2.4.5+

Following system images need to be updated:

Name
Kubernetes Version
Version

Add docker version if a new docker version was introduced in docker info

Kubernetes Version
Docker Version

Update constraints for all addon versions so the new Kubernetes versions use the right template. Refer to the table below to confirm the template name.

 Confirm the versions for System Images and Addon templates are correct. Update this table if new versions get introduced. Click to view the full list.

Name
Kubernetes Version
Version
Update constraints for addon versions if required

etcd
v1.18.17
v3.4.3

kube-dns
v1.18.17
v1.15.2
kubeDnsv116

core-dns
v1.18.17
v1.6.9
coreDnsv117

flannel
v1.18.17
v0.12.0
flannelv116

calico, canal
v1.18.17
v3.13.4
canalv117PrivilegedCalico3134, calicov117Privileged

weave
v1.18.17
v2.6.4
weavev116

aci
-
-
-

metrics-server
v1.18.17
v0.3.6
metricsServerv18

podInfra
v1.18.17
3.1

node local
v1.18.17
1.15.7
nodelocalv115

rancher forks

dns-autoscaler
v1.18.17
v1.7.1

flannel-cni
all
v0.3.0-rancher6

ingress
v1.18.17
nginx-0.35.0-rancher2
nginxIngressV11512

ingress-backend
all
1.5-rancher1

rke-tools
all
v0.1.71

windowsPodInfra
v1.18.17
v0.1.6

Version v2.4.5 min version checks
metadata config pointing to dev-v2.4
KDM job on the k8s version v1.18.17-rancher1-1 on all the network [calico,weave,flannel,canal] for cluster creation
All the clusters have come up with the k8s version v1.18.17-rancher1-1 on all networks

v2.4.15 Fresh Install checks

2.4.15 - pointing to dev-v2.4 (fresh install checks)

KDM job (Cluster creation succeeds for all k8s versions with all network provider) and run network related test cases

run Validation tests for all the Canal clusters
all clusters came up with k8s v1.18.17-rancher1-1 with each provider. validation tests passed

v2.4.15 upgrade tests:
On 2.4.15 KDM job pointing to release-v2.4:

Run pre upgrade checks

Upgrade dev-v2.4

Upgrade K8s versions to new versions

Run post upgrade checks

all validation steps passed with each provider, each using v1.18.17-rancher1-1

2.5.3 pointing to dev-v2.5
KDM job (Cluster creation succeeds for k8s versions -  All k8s with all network provider - 1.19, 1.18) and run network related test cases- 2.5.3 pointing to dev-v2.5

Validation has passed for all network providers on v1.18.17-rancher1-1 and v1.19.9-rancher1-1

RKE checks using v1.1.16-rc1 look good.


LHS use getSortedFavoriteChannelIds instead of getFavoriteChannelIds

Summary
When a favorite channel had an unread message it was displayed in the unread section and not in the favorite section. BUT if there was only one favorite channel with unread messages it was also keeping the favorite section empty. The reason for this is that getFavoriteChannelIds does not filter the channels in the unread section but getSortedFavoriteChannelIds does.
Ticket Link
https://mattermost.atlassian.net/browse/MM-13517

@enahum Cherrypick candidate?

@sudheerDev yes, ticket says 1.17 but the fix is super low risk and will avoid confusion I don't think this needs to wait for one more month. I propose to have it merged and cherry-picked for 1.16
@DHaussermann @esethna thoughts?

Fine with me, I'll update the fix version


Create an example where a non-native js function is called

Please write an example where the rust code is calling a javascript non-native method.  Thanks.

👍


(Fatal) Blocking queue is killed because the data reader raises an exception.

跑caddn报错，没有做任何改动
------------Environment Information-------------
platform:
Linux-4.14.0_1-0-0-36-x86_64-with-Ubuntu-18.04-bionic
gcc (GCC) 8.2.0
Python - 3.7.10 (default, Feb 20 2021, 21:17:23)  [GCC 7.5.0]
Science Toolkits:
cv2 - 4.6.0
numpy - 1.21.6
numba - 0.56.4
pandas - 1.3.5
pillow - 8.3.2
skimage - 0.19.3
PaddlePaddle:
paddle - 2.4.0
paddle3d - 0.5.0
paddleseg - 2.6.0
FLAGS_cudnn_deterministic - Not set.
FLAGS_cudnn_exhaustive_search - Not set.
CUDA:
cudnn - None
nvcc - Build cuda_11.0_bu.TC445_37.28845127_0
GPUs:
Not Found.
2022-11-24 14:56:07,165 -     INFO -
---------------Config Information---------------
batch_size: 4
iters: 74240
lr_scheduler:
div_factor: 10
lr_max: 0.001
moms:

0.95
0.85
pct_start: 0.4
total_step: 74240
type: OneCycle
model:
backbone_3d:
layers: 101
return_idx:

0
3
type: ResNet
bev_cfg:
input_channels: 64
layer_nums:
10
10
10
layer_strides:
2
2
2
num_filters:
64
128
256
num_upsample_filters:
128
128
128
upsample_strides:
1
2
4
class_head:
align_corners: false
aspp_out_channels: 256
aspp_ratios:
1
12
24
36
backbone_channels: 2048
backbone_indices:
1
num_classes: 81
type: DeepLabV3Head
dense_head:
anchor_generator_cfg:
align_center: false
anchor_bottom_heights:

-1.78
anchor_rotations:
0
1.57
anchor_sizes:

3.9
1.6
1.56
class_name: Car
feature_map_stride: 2
matched_threshold: 0.6
unmatched_threshold: 0.45

align_center: false
anchor_bottom_heights:

-0.6
anchor_rotations:
0
1.57
anchor_sizes:

0.8
0.6
1.73
class_name: Pedestrian
feature_map_stride: 2
matched_threshold: 0.5
unmatched_threshold: 0.35

align_center: false
anchor_bottom_heights:

-0.6
anchor_rotations:
0
1.57
anchor_sizes:

1.76
0.6
1.73
class_name: Cyclist
feature_map_stride: 2
matched_threshold: 0.5
unmatched_threshold: 0.35
anchor_target_cfg:
match_height: false
norm_by_num_examples: false
pos_fraction: -1.0
sample_size: 512
class_names:

Car
Pedestrian
Cyclist
grid_size:
280
376
25.0
input_channels: 384
loss_weights:
cls_weight: 1.0
code_weights:

1.0
1.0
1.0
1.0
1.0
1.0
1.0
dir_weight: 0.2
loc_weight: 2.0
model_cfg:
class_agnostic: false
dir_limit_offset: 0.0
dir_offset: 0.78539
use_direction_classifier: true
num_dir_bins: 2
point_cloud_range:

2
-30.08
-3.0
46.8
30.08
1.0
predict_boxes_when_training: false
type: AnchorHeadSingle
disc_cfg:
depth_max: 46.8
depth_min: 2.0
mode: LID
num_bins: 80
f2v_cfg:
grid_size:
280
376
25.0
pc_range:
2
-30.08
-3.0
46.8
30.08
1.0
sample_cfg:
mode: bilinear
padding_mode: zeros
ffe_cfg:
channel_reduce_cfg:
bias_attr: false
in_channels: 256
kernel_size: 1
out_channels: 64
padding: 0
stride: 1
ddn_loss:
alpha: 0.25
beta: 2.0
bg_weight: 1
fg_weight: 13
weight: 3.0
downsample_factor: 4
map_to_bev_cfg:
bias_attr: false
in_channels: 1600
kernel_size: 1
out_channels: 64
padding: 0
stride: 1
post_process_cfg:
eval_metric: kitti
nms_config:
nms_post_maxsize: 500
nms_pre_maxsize: 4096
nms_thresh: 0.01
recall_thresh_list:
0.3
0.5
0.7
score_thresh: 0.1
pretrained: https://bj.bcebos.com/paddle3d/pretrained/deeplabv3_resnet101.pdparams
type: CADDN
optimizer:
beta1: 0.95
beta2: 0.99
clip_grad_by_norm: 10.0
type: AdamWOnecycle
weight_decay: 0.01
sync_bn: true
train_dataset:
class_names:

Car
Pedestrian
Cyclist
dataset_root: data/kitti
depth_downsample_factor: 4
mode: train
point_cloud_range:
2
-30.08
-3.0
46.8
30.08
1.0
type: KittiDepthDataset
voxel_size:
0.16
0.16
0.16
val_dataset:
class_names:
Car
Pedestrian
Cyclist
dataset_root: data/kitti
depth_downsample_factor: 4
mode: val
point_cloud_range:
2
-30.08
-3.0
46.8
30.08
1.0
type: KittiDepthDataset
voxel_size:
0.16
0.16
0.16

2022-11-24 14:56:10,256 -  WARNING - No custom op grid_sample_3d found, try JIT build
Compiling user custom op, it will cost a few seconds.....
2022-11-24 14:56:12,770 -     INFO - grid_sample_3d builded success!
2022-11-24 14:56:12,772 -  WARNING - There is a file with the same name locally, we directly load the local file
2022-11-24 14:56:13,348 -  WARNING - class_head.cls.weight is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - class_head.cls.bias is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - ffe.channel_reduce._conv.weight is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - ffe.channel_reduce._batch_norm.weight is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - ffe.channel_reduce._batch_norm.bias is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - ffe.channel_reduce._batch_norm._mean is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - ffe.channel_reduce._batch_norm._variance is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - map_to_bev._conv.weight is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - map_to_bev._batch_norm.weight is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - map_to_bev._batch_norm.bias is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - map_to_bev._batch_norm._mean is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - map_to_bev._batch_norm._variance is not in pretrained model
2022-11-24 14:56:13,349 -  WARNING - backbone_2d.blocks.level_0.1.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.2.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.2.bias is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.2._mean is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.2._variance is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.4.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.5.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.5.bias is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.5._mean is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.5._variance is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.7.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.8.weight is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.8.bias is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.8._mean is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.8._variance is not in pretrained model
2022-11-24 14:56:13,350 -  WARNING - backbone_2d.blocks.level_0.10.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.11.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.11.bias is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.11._mean is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.11._variance is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.13.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.14.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.14.bias is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.14._mean is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.14._variance is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.16.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.17.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.17.bias is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.17._mean is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.17._variance is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.19.weight is not in pretrained model
2022-11-24 14:56:13,351 -  WARNING - backbone_2d.blocks.level_0.20.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.20.bias is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.20._mean is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.20._variance is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.22.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.23.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.23.bias is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.23._mean is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.23._variance is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.25.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.26.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.26.bias is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.26._mean is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.26._variance is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.28.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.29.weight is not in pretrained model
2022-11-24 14:56:13,352 -  WARNING - backbone_2d.blocks.level_0.29.bias is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.29._mean is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.29._variance is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.31.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.32.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.32.bias is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.32._mean is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_0.32._variance is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.1.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.2.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.2.bias is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.2._mean is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.2._variance is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.4.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.5.weight is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.5.bias is not in pretrained model
2022-11-24 14:56:13,353 -  WARNING - backbone_2d.blocks.level_1.5._mean is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.5._variance is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.7.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.8.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.8.bias is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.8._mean is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.8._variance is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.10.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.11.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.11.bias is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.11._mean is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.11._variance is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.13.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.14.weight is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.14.bias is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.14._mean is not in pretrained model
2022-11-24 14:56:13,354 -  WARNING - backbone_2d.blocks.level_1.14._variance is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.16.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.17.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.17.bias is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.17._mean is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.17._variance is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.19.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.20.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.20.bias is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.20._mean is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.20._variance is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.22.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.23.weight is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.23.bias is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.23._mean is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.23._variance is not in pretrained model
2022-11-24 14:56:13,355 -  WARNING - backbone_2d.blocks.level_1.25.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.26.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.26.bias is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.26._mean is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.26._variance is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.28.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.29.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.29.bias is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.29._mean is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.29._variance is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.31.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.32.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.32.bias is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.32._mean is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_1.32._variance is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_2.1.weight is not in pretrained model
2022-11-24 14:56:13,356 -  WARNING - backbone_2d.blocks.level_2.2.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.2.bias is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.2._mean is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.2._variance is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.4.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.5.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.5.bias is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.5._mean is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.5._variance is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.7.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.8.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.8.bias is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.8._mean is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.8._variance is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.10.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.11.weight is not in pretrained model
2022-11-24 14:56:13,357 -  WARNING - backbone_2d.blocks.level_2.11.bias is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.11._mean is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.11._variance is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.13.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.14.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.14.bias is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.14._mean is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.14._variance is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.16.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.17.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.17.bias is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.17._mean is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.17._variance is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.19.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.20.weight is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.20.bias is not in pretrained model
2022-11-24 14:56:13,358 -  WARNING - backbone_2d.blocks.level_2.20._mean is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.20._variance is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.22.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.23.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.23.bias is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.23._mean is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.23._variance is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.25.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.26.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.26.bias is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.26._mean is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.26._variance is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.28.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.29.weight is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.29.bias is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.29._mean is not in pretrained model
2022-11-24 14:56:13,359 -  WARNING - backbone_2d.blocks.level_2.29._variance is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.blocks.level_2.31.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.blocks.level_2.32.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.blocks.level_2.32.bias is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.blocks.level_2.32._mean is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.blocks.level_2.32._variance is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_0.0.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_0.1.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_0.1.bias is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_0.1._mean is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_0.1._variance is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_1.0.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_1.1.weight is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_1.1.bias is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_1.1._mean is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_1.1._variance is not in pretrained model
2022-11-24 14:56:13,360 -  WARNING - backbone_2d.deblocks.level_2.0.weight is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - backbone_2d.deblocks.level_2.1.weight is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - backbone_2d.deblocks.level_2.1.bias is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - backbone_2d.deblocks.level_2.1._mean is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - backbone_2d.deblocks.level_2.1._variance is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_cls.weight is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_cls.bias is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_box.weight is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_box.bias is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_dir_cls.weight is not in pretrained model
2022-11-24 14:56:13,361 -  WARNING - dense_head.conv_dir_cls.bias is not in pretrained model
2022-11-24 14:56:13,626 -     INFO - There are 555/753 variables loaded into CADDN.
Traceback (most recent call last):
File "tools/train.py", line 189, in
main(args)
File "tools/train.py", line 184, in main
trainer.train()Exception in thread Thread-2:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 536, in _thread_loop
batch = self._get_data()
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 674, in _get_data
batch.reraise()
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/worker.py", line 172, in reraise
raise self.exc_type(msg)
AssertionError: DataLoader worker(0) caught AssertionError with message:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/worker.py", line 339, in _worker_loop
batch = fetcher.fetch(indices)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/fetcher.py", line 125, in fetch
data.append(self.dataset[idx])
File "/root/paddlejob/workspace/liujingkai/Paddle3D-develop/paddle3d/datasets/kitti/kitti_depth_det.py", line 458, in getitem
data_dict = self.update_data(data_dict=input_dict)
File "/root/paddlejob/workspace/liujingkai/Paddle3D-develop/paddle3d/datasets/kitti/kitti_depth_det.py", line 295, in update_data
data_dict['depth_maps'] = self.get_depth_map(data_dict["frame_id"])
File "/root/paddlejob/workspace/liujingkai/Paddle3D-develop/paddle3d/datasets/kitti/kitti_depth_det.py", line 143, in get_depth_map
assert os.path.exists(depth_file)
AssertionError
File "/root/paddlejob/workspace/liujingkai/Paddle3D-develop/paddle3d/apis/trainer.py", line 217, in train
for sample in self.train_dataloader:
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 745, in next
self.reader.read_next_list()[0])
SystemError: (Fatal) Blocking queue is killed because the data reader raises an exception.
[Hint: Expected killed != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:175)

看报错是depth_file不存在，可以检查一下自己的数据集～

该issue较长时间无反馈，先关闭，如有问题请重新打开或者建立新的issue


set charset manually

First of all, thank you very much for your convenient and pretty Jsoup. It gives me a lot of help and inspiration. It's very useful. Thx a lot ^ ^
This is the problem I encountered：
I need extract data from a old page with tag-soup. This is a copy of the the pages: http://ilaww.mezoka.com/aInvalidPage.html .
It is encoded with GB2312(a Chinese charset). But as you can see, its content-type meta tag is terrible：
<meta HTTP-EQUIV="Pragma" CONTENT="no-cache" ; charset="gb2312">

So I have to set charset manually.
I think I must set charset before Jsoup parse and save content to String. So I modified Response Interface a little to add a method which can set charset before parse() uses it.
ILAww/jsoup@9135ff0a5c3654baa7ecdd05f69841fbb4f52b79

(Closing out old, dormant bugs. If you are still impacted by this, please reopen & vote.)