Install issue on OSX - cannot find package "gopkg.in/alecthomas/kingpin.v2"

Installing on OSX, I received this output when running the go get command:
golang/src/github.com/hanwen/go-fuse/fuse/nodefs/files_darwin.go:5: imported and not used: "time"
golang/src/github.com/hanwen/go-fuse/fuse/nodefs/syscall.go:11: undefined: syscall.SYS_UTIMENSAT```

Then when running ./install.sh I received:

```+ go install -ldflags '-X github.com/zach-klippenstein/adbfs/internal/cli.buildSHA=23ff9c5' -v ./cmd/...
internal/cli/adbfs_config.go:3:8: cannot find package "gopkg.in/alecthomas/kingpin.v2" in any of:
	/usr/local/Cellar/go/1.5.2/libexec/src/gopkg.in/alecthomas/kingpin.v2 (from $GOROOT)
	/Users/jules/Downloads/golang/src/gopkg.in/alecthomas/kingpin.v2 (from $GOPATH)```

Suggestion: would be awesome if this package was added to Homebrew. Then mac users go do brew install adbfs

See https://github.com/zach-klippenstein/adbfs/issues/12

I got it installed - I recommend adding GO15VENDOREXPERIMENT=1 to the installation instructions

@jfrumar I looked at adding it to Homebrew, but the Acceptable Formulae doc requires that packages be "known", "used", and "stable" – none of which describe this project at this point (although an argument could now be made for the first two).
I'll add the vendoring experiment to the instructions.

Cool fair enough - look forwards to the future then! :)
On Thu, Dec 24, 2015 at 4:33 AM, Zach Klippenstein<EMAIL_ADDRESS>
wrote:

@jfrumar https://github.com/jfrumar I looked at adding it to Homebrew,
but the Acceptable Formulae
https://github.com/Homebrew/homebrew/blob/master/share/doc/homebrew/Acceptable-Formulae.md#niche-or-self-submitted-stuff
doc requires that packages be "known", "used", and "stable" – none of which
describe this project at this point (although an argument could now be made
for the first two).
I'll add the vendoring experiment to the instructions.
—
Reply to this email directly or view it on GitHub
https://github.com/zach-klippenstein/adbfs/issues/17#issuecomment-166951149
.


Rename pos to index on Node

Why this change?
When calling Node::move_child() it currently expects to_position which can get very confusing when using GDScript. I was wondering why I was unable to pass an actual position (Vector2) into it. Then I realized that by "position" we actually mean the "index" in the node hierarchy. To avoid mixing concepts together I decided to rename pos to index. This should not break compatibility as it is just an internal rename + documentation update.

Kinda related to: #61418. But in here in case of Node::move_child its parameter seems to already be treated as an index, not as a position in a sense explained in https://github.com/godotengine/godot/pull/61418#issuecomment-1138302339. Hence the renaming done seems correct.
The strange part to me is that child_count is treated as a valid index, it's treated equivalently to child_count - 1. Personally I'd make passing child_count be invalid since it's not a valid index. But oh well, it's already made like that (for whatever reason) so changing it would be breaking and thus it's for a potential separate PR (if changing it is desired at all).
@Calinou I don't think this PR breaks compatibility, it's only renaming the parameter.

The strange part to me is that child_count is treated as a valid index, it's treated equivalently to child_count - 1. Personally I'd make passing child_count be invalid since it's not a valid index. But oh well, it's already made like that (for whatever reason) so changing it would be breaking and thus it's for a potential separate PR (if changing it is desired at all).

I don't know. Compatibility can be kept by changing it to clamp instead of subtracting 1, which is less awkward, may I add.

Thanks!


AOKZOE PRO A1 Several issues

Hi

The battery gets depleted while in sleep mode.
While in desktop mode sometimes the touchscreen stops responding, you have to change the gamepad to "desktop mode" using the dedicated button on the machine, press A and then back to gamepad mode for the touch screen to work again.
While In game mode the device won't always come back from sleep and it has to be shutdown using the power button.
No TDP adjustment ( Is there any way to know at what TDP is the CPU running?)

Other than that, works like a charm!

https://github.com/ChimeraOS/chimeraos/wiki/General-Configuration#tdp-controls

@pastaq I forgot to thank you so thank you!!


Is TrustedGetter subject to replay attack?

Unlike TrustedCallSigned, where a nonce is involved in the construction of signed payload, the payload of TrustedGetter is the encoded getter itself without any salt or randomness.
Does it mean that the attacker, under some circumstances (e.g. without wss connection), can intercept the request and perform replay attack to repeatedly query e.g. some user's balance on the sidechain?

Yes, essentially this is possible; however, a nonce has rather been invented as a simple measure to prevent spam attacks and not replay attacks.
So in general, I would anyhow say: Just don't connect without a secure connection because this offers the attack vector of Man in the middle attacks in general, and you can't even verify if the response is from the TEE once we have implemented https://github.com/integritee-network/worker/issues/1202.

we enforce wss, so I don't see the risk here.


Progress bar to be moved from inner loops to outer loops

Currently the tqdm runs on the local sampling steps and training steps, and no information is provided on the completion of the outer loops. It would more useful to the user to have the info on the progress at the level of the outer loops.

We should also show some current state during the run, such as the current acceptance rate and likelihood value, on a regular basis


mupx: Server unreachable on AWS every couple of weeks

I am facing a strange phenomenon on my AWS instance.
Every two to three weeks, the server is not reachable anymore, neither via HTTP nor via SSH. In the EC2 console, the instance is marked as running, with all statuses set to OK. But still, the server won't respond to any request anymore. This does not depend on the location from which I am trying to access it (office or mobile network).
If I look at the time-point where the server gets down on Kadira, I can see that everything ran normal, with all performance values far below criticality (there's not much traffic so far on the machine), until suddenly all measures fall down to zero.
The only way to recover the instance is to stop and restart it.
I am using a t2.micro instance with the standard Ubuntu Server 14.04 LTS (HVM) image provided by Amazon's free tier. The only additional software I had installed besides Meteor is the letsencrypt tool to get an SSL certificate for the machine. All the rest is "bare-bone" Ubuntu.
Do you have any clues what happens here, or how I could analyze what's going on?
Thanks in advance for any help,
Tom
PS: I am using the mupx branch of mup.

The idea of seeing if it lasts longer with an instance with more RAM comes to mind from your mentioning the micro image. Of course if spending more for no great reason (meaning doesn't make a difference or a poor trade off), isn't ideal it would reveal maybe a memory leak etc. So maybe your RAM can be monitored.
Still you say that doesn't seem to be a problem from the OKs maybe?
Also try the new kadirahq port as that might also give you a different result.
Having extra failover servers in a scalable design should also help. You could have a script see its actually not OK / responding to curl or what have you and that would trigger a restart while a second machine would take over while it's not responding in theory.

Hi @MasterJames, thanks for your quick reply. I am monitoring the RAM using Kadira, so I have a screenshot of the time-point between 7:00 and 9:00 where the problem occurred. As you can see in the image, memory usage was in fact significantly higher (~ 770 MB) before the incident than after I restarted the instance (~ 200 MB). Could be a memory leak, but would this also explain why not even SSH works in this case?

Regarding the kadirahq port: Is this safe for productive use already? Thought it was still in dev stage.
And yes, a failover server would definitely help. Is it easy to set-up this with mupx and meteor cluster?

Easy is relative to all the hard work you do to get it there I suppose. I would say with cluster one of the easiest and will get easier.
It's probably okay to see if its working for your use case. Probable as stable as mupx if not better regardless of official status the othere's is depreciated so i wold sTay for ward as possible (so only you can know). Mainly because you'll get more better help resolving possible issues. Stability in this case means changes so in this modern world you need to keep up whatever the cost. Refactoring on updates and running your own pre-deployment tests etc.

Anyway kadira is the perfect tool to find the leak or up your image size.

I see that the memory goes up in Kadira, but how do I find out the root cause? Is there any place in Kadira that shows which part of the software is actually using up all that memory? BTW: It seems as if I am not the only one with this problem:
http://stackoverflow.com/questions/35794893/how-to-find-a-memory-leak-in-meteor-app
Not sure if this is related, though. Could be a problem in our apps, of course.

@derwaldgeist same issue here, did you ever find a solution for this problem?

There was a guy in the Meteor forums wh told me that this might be related to inefficient garbage collection in NodeJS. That would mean: It isn't a memory leak, but the memory of a micro instance (512mb) is lower than the trigger when Node actually performs its GC. He also described a solution which would require a patch of the mupx code. Since I did not want to touch this, I have filed a feature request for the mupx development version: https://github.com/kadirahq/meteor-up/issues/124. There you can also find links to the forum threads, including the aforementioned patch.

I'm closing this now in favor of https://github.com/kadirahq/meteor-up/issues/124

@derwaldgeist thank you, we're getting this issue on a 1gb ec2 instance.. as a temp solution we forked meteord here (https://github.com/codingways/meteord) and added these flags
node main.js --max_old_space_size=380 --gc_interval=100
I'll report back in a few weeks

Thanks for sharing this!

Looking forward to hear what happened with the new garbage collection :)

@sahanDissanayake so far so good! after 10 days everything's working great.. i'll keep you guys updated

Sounds promising! One question about this: If you do a fork of mupx, how do you actually compile and use it? I know how to install it from npm, but I do not know how to build it myself.

Here is the script I dug up from when I did a PR for Backup & Restore of MongoDB.
I was using an Ubuntu VirtualBox VM on Windows and accessing a file mount.
cd /mnt/PersonalDevFolder/GitHub
rm mupx-1.5.3.tgz
tar cvzf mupx-1.5.3.tgz package/
npm uninstall -g mupx
npm install -g mupx-1.5.3.tgz

I suspect the 1.5.3 was because the latest one was/is likely 1.5.2
I'm seeing 3.9.3 from npm -v mupx so?... you'll figure it out.
the folder "package" is a symbolic link (aka junction) to
C:\PersonalDevFolder\GitHub\meteor-up


Component attributes don't render properly when using refs in combination with SSR

Which package(s) are affected?
Lit Core (lit / lit-html / lit-element / reactive-element), SSR (@lit-labs/ssr)
Description
While upgrading Lit 2.7.2 to the latest version (3.1.0) we encountered a bug. We render our components on the server using the @lit-labs/ssr package and for testing the ssrFixture from @lit-labs/testing/fixtures.js
We encountered a bug with dynamic attributes becoming undefined while making use of the ref directive.
  render() {
    return html`<div
      ${ref(this._testRef)}
      class="some-div"
      id=${this._id}
      title=${this._title}
    >
      Hello World
    </div>`;
  }

For an complete example of our code, please check out our reproduction that gave the following HTML output:

Reproduction
You can find a reproduction here: https://github.com/mwildeboer/lit-ssr-bug. We encountered the bug in both our development environment and the Playwright tests.
Workaround
We fixed our bug by moving the ref declaration below the attributes like so:
  render() {
    return html`<div
      class="some-div"
      id=${this._id}
      title=${this._title}
      ${ref(this._testRef)}
    >
      Hello World
    </div>`;
  }

Is this a regression?
Yes. This used to work, but now it doesn't.
Affected versions
2.7.2 -> 3.1.0
Browser/OS/Node environment
Playwright 1.40.1 and latest Chrome
MacOS 14.2.1
Node version: v18.18.2

Was this actually working before? I can't imagine any change in Lit 3 causing this suddenly breaking. I would think if it's a regression, it was some change to the ssr code. In any case, I can indeed reproduce the issue separately as well and will look into a fix. There must be something going on with counting the binding when an element part is involved.


Change delete keys to use fingerprint instead of short or long id

The issue I foresee here is that the customer manifest may specify any of the key options (short/long/fingerpint) but the delete function always uses the same thing no matter what they have specified.  That means if the customer wants to ensure a short key is absent, this version might not do that because the short key could match more than one fingerprint.
I think the code needs to be refactored to use what the customer manifest specified rather than using the provider, OR we could consider dropping support for short keys entirely (which would definitely be a major version bump)

It suddenly occurred to me that we should also handle short key conflicts when we are matching the manifest to the provider.  I don't think that would work right (and it needs a test).

I believe if they specified the short key and we delete by the fingerprint they might inadvertently delete the wrong key anyways.  We could detect if the ID is a short key and use that when short otherwise use fingerprint.

@WolverineFan also after thinking about this, the conflicts of short keys will be less relevant as far as a prefetch but more so if they are using the short key and we match the first short key.  Are you suggesting we iterate over all and if there by chance two short keys with different fingerprints that we throw a warning?

Yes, I think something like that would be good.  Issue a warning (or even an error?) if we find 2 keys matching the specified key (even a long could theoretically have a conflict, it's just less likely).  The user will almost certainly want to update their manifests when this happens.
In a way it's a shame the colliding puppetlabs key was revoked since that would have made for an excellent test case.  I'm sure there must be other short key collisions out there legitimately though.

Found one!  http://www.asheesh.org/note/debian/short-key-ids-are-bad-news.html
$ gpg --keyserver pgp.mit.edu --recv-key 0x70096AD1
gpg: requesting key 70096AD1 from hkp server pgp.mit.edu
gpg: key 70096AD1: public key "Asheesh Laroia<EMAIL_ADDRESS>imported
gpg: key 70096AD1: public key "Asheesh Laroia<EMAIL_ADDRESS>imported
gpg: no ultimately trusted keys found
gpg: Total number processed: 2
gpg:               imported: 2  (RSA: 1)
$ gpg --fingerprint --with-colon | grep 70096AD1
pub:-:1024:17:37E1C17570096AD1:2005-12-28:::-:Asheesh Laroia <asheesh@asheesh.org>::scESC:
fpr:::::::::D00436A90C4BD12002020A3C37E1C17570096AD1:
pub:-:4096:1:EC4B033C70096AD1:2011-03-11:::-:Asheesh Laroia <asheesh@asheesh.org>::cESCA:
fpr:::::::::97225E589D6B9E01533C485EEC4B033C70096AD1:

Closing, leaving a ticket for future work to get into 2.x https://tickets.puppetlabs.com/browse/MODULES-1661


Pull widget titles from backend config

RHCLOUD-31816

Pull the titles of the widgets from the backend config
Fallback to the widgetType where title is not defined (as was previously used below in the widget drawer).

Before

After

/retry

/retest


[Report page] Change contigs query to use new columns

Description
Update the contigs query used for the new report page to use the new columns (species_taxid_nt, species_taxid_nr, genus_taxid_nt, genus_taxid_nr) rather than lineage_json.
Based on a few local tests, this cuts the time for the query into roughly  third of what it was previously.
Tests

Verified that the contigs are still calculated and displayed as expected.

Checked a few samples locally and it seems that this decreases the time of the query to roughly a third of what it was previously. Will check again on staging and prod.


Proposal: New subtle icon button style

Proposal: Create new subtle icon button style

Summary

The new Microsoft Store created a new style for subtle icon buttons. It would be great if this style was included in winui 2 library so other apps can use it. Here's a gif of the subtle icon button style in the new Microsoft Store.

Rationale

This style is great for scenarios when devs just want to show an icon without any button border around it

Scope

Capability
Priority

Developers can use this style using StaticResource SubtleIconButtonStyle
Must

Important Notes

Because I'm on the store engineering team, I'd be happy to create the PR for this and bring our style over to WinUI. If this sounds good with the WinUI team, just let me know and I'll make the PR asap.
Open Questions

The brushes are there already, and the Command Bar buttons use this style

The brushes are there already, and the Command Bar buttons use this style

Oh, I couldn't find it in WinUI 2.6.1. What is the style's name?

The brushes are there already, and the Command Bar buttons use this style

Oh, I couldn't find it in WinUI 2.6.1. What is the style's name?

The AppBar Buttons are separate controls, but they use the Subtle brush colours, which is the kind of button you are trying to make.

Ah sorry I misread. Thought you meant the style was there, but rather you said the brushes are there.

+1
I have my own style for doing basically the same exact thing (called ClearButtonStyle on my end). It would be great to pull this into the framework itself as it's one less style to keep synced.

@dpaulino +1 for this idea! Would be really useful to have.

DefaultButtonStyle
AccentButtonStyle
SubtleButtonStyle

@chigy @gabbybilka FYI, I think we could do this.

@StephenLPeters  @dpaulino Any updates on this :)? Would be reaaaaally nice to have this!

Bump

Bump

Bump

Don't waste your time here
Use this style:
https://github.com/microsoft/PowerToys/blob/77063e0767a0619abfc339db3a2a564d27310b6c/src/settings-ui/Settings.UI/SettingsXAML/Styles/Button.xaml#L12

I found another way of doing the same with existing resources.
<Button Style="{StaticResource CommandBarFlyoutEllipsisButtonStyle}" Width="40" Height="40">
    <StackPanel>
        <FontIcon Glyph="" FontSize="16"></FontIcon>
    </StackPanel>
</Button>


[nrf fromlist] Bluetooth: shell: Fix shell build with out-of-tree ...

controller
Fix shell build errors when building with combined host and controller
but the selected controller is not the in-tree zephyr controller, i.e
CONFIG_BT_CTLR=y, CONFIG_BT_LL_SW_SPLIT=n and cmd_scanx and others
are not defined.
Signed-off-by: Joakim Andersson<EMAIL_ADDRESS>(cherry picked from commit 7129a68fedbd3bf842833db3b502220681d6b446)
Signed-off-by: Joakim Andersson<EMAIL_ADDRESS>

Manifest PR ?

Manifest PR ?

Sorry, updated


Daemon cli

This PR adds the daemon CLI to launch the ipc agent json rpc server. It takes the config file path as command line input. Sample config file is placed in examples folder.
Launch
cargo build --release
ipc-client daemon ./examples/sample_config.toml

@adlrocha While this PR is being reviewed, I will create another one that handles the versioning of the command line from git commit, to keep the PR small.

@adlrocha I have updated the code based on the review comments, feel free to take a look. As for the injection of version, I have added the issue: #26 to track it.


Best way to add L2 norm to the cost?

Hi devs,
what is the right way on adding squared sum of values of some input layers to the cost?
Please give me a hint, and I'll try to make an implementation.

Could you give me some additional details of what you are trying to do?
By input layers, do you mean the input to the network?  Or do you just mean layers that are not the final cost.  And by values, do you mean the activations?  Or the parameters (weights) themselves?
I imagine you are trying to do some kind of regularization by penalizing the magnitude of the weights -- is that correct?

Yes, I'm trying to do L2 penalization.
By "input" i mean hidden / non-final layers.
By parameters I mean weights themselves.
Something like
http://lasagne.readthedocs.org/en/latest/modules/regularization.html
Ideally if I could sum up several different costs for same or different layers and they would work automagically.
Also I'd like to have weight decay and learning rate change schedules not tied to GradientDescentMomentumWeightDecay , I guess that's in progress for v 1.2 .

I want to do  regularization by penalizing the magnitude of the weights too.
But I didn't find anyway to that in Neon.
Has this issue be fixed?


Fix CTAS bugs

For #2393
Did not take into account that the alias does not exist
For #2394
Because Double is not precise, an exact value is required for the KEY column
use decimal128 for general type

run all

run all

run all

un all

run all


Make ttnctl (and other) OAuth requests RFC compliant

The clientid TTNCTL uses no password(secret) to exchange tokens. However this requires 'public mode' to be enabled. RFC http://tools.ietf.org/html/rfc6749#section-4.4 ... so use secret: ttnctl which is just as easy and don't seem to confuse some proxy/cNAT servers.

@kruisdraad What do you mean with "public mode"? I cannot seem to find any mention of it in the RFC.
To my understanding the RFC actually specifies that for public or native clients (like ttnctl) the Implicit Flow  should be used. (Or the PKCE extension).
Do you mean you want ttnctl to use the Client Credentials Flow (username + password)? We moved away from that because we want to implement 2FA in the future.
PKCE might be nice, but that's also something for the future.

problem list in that the pasword (secret) in the client cli should be used
in addition i found another RFC issue:
The word "bearer" now has a capital "B" in JSON output to match OAuth 2.0 spec

ttnctl now uses the (not-so-secret) "secret" ttnctl.


docs(api): Extend docs about API and database [skip ci]

Minor edits on the docs. Does not touch any source code

@vogelino @dnsos Can you tell why this fails? My branch was based on staging

Hmm, we've had this issue before, remember @vogelino? I'm just surprised that we are encountering it again. I'm trying to remember what was the issue and I kind of recall a mismatch between our code and the installed Next/React types.

@ff6347 & @dnsos There was an improper typing of the nextjs page. We have done that in a much cleaner way in the wegweiser project so I felt free to bring this logic to this project too. It should pass now.

@vogelino can we merge this?

@vogelino can we merge this?

Do it! :)

:tada: This PR is included in version 1.0.0-staging.8 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Implement multi-gradient coloring for surface and scalar-field objects.

Initial implementation without GUI has been completed (gfx/MultiGradient.cpp/hpp/qif)

Initial implementation of multi-gradient editor GUI has been completed.


InvalidOperationException and NullReferenceException

I am having a bit of an issue automating testing on an application. When writing a simple test program, I can get the application to open fine in [ClassInitialize] with the code below, but an InvalidOperationException is thrown with the description "an unknown error occurred in the remote end while processing the command.." once the app opens.
`using Microsoft.VisualStudio.TestTools.UnitTesting;
using OpenQA.Selenium.Appium.Windows;
using OpenQA.Selenium.Remote;
using System;
namespace UnitTestProject1
{
[TestClass]
public class UnitTest1
{
protected const string WindowsApplicationDriverUrl = "http://<IP_ADDRESS>:4723";
protected static WindowsElement Result;
protected static WindowsDriver Session;
protected static string OriginalMode;
    [ClassInitialize]
    public static void Setup(TestContext context)
    {
        if (Session == null)
        {
                DesiredCapabilities appCapabilities = new DesiredCapabilities();
                appCapabilities.SetCapability("app", "Application");
                appCapabilities.SetCapability("deviceName", "WindowsPC");
                Session = new WindowsDriver<WindowsElement>(new Uri(WindowsApplicationDriverUrl), appCapabilities);
                Assert.IsNotNull(Session);
                Session.Manage().Timeouts().ImplicitlyWait(TimeSpan.FromSeconds(7));
        }
    }

    [TestMethod]
    public void TestMethod1()
    {
    }
}

}`
Since the app opened fine, I just used a try-catch to catch the InvalidOperationException, allowing the code to proceed to [TestMethod]. However, in the unit test any attempt to use Session is met with a NullReferenceException with the message "Object reference not set to an instance of an object." Debugging confirms that Session enters the test method as null.
`
using Microsoft.VisualStudio.TestTools.UnitTesting;
using OpenQA.Selenium.Appium.Windows;
using OpenQA.Selenium.Remote;
using System;
namespace UnitTestProject1
{
[TestClass]
public class UnitTest1
{
protected const string WindowsApplicationDriverUrl = "http://<IP_ADDRESS>:4723";
protected static WindowsElement Result;
protected static WindowsDriver Session;
protected static string OriginalMode;
    [ClassInitialize]
    public static void Setup(TestContext context)
    {
        if (Session == null)
        {
           try
           {
                DesiredCapabilities appCapabilities = new DesiredCapabilities();
                appCapabilities.SetCapability("app", "Application");
                appCapabilities.SetCapability("deviceName", "WindowsPC");
                Session = new WindowsDriver<WindowsElement>(new Uri(WindowsApplicationDriverUrl), appCapabilities);
                Assert.IsNotNull(Session);
                Session.Manage().Timeouts().ImplicitlyWait(TimeSpan.FromSeconds(7));
            }
            catch (InvalidOperationException e)
            {
            }
        }
    }

    [TestMethod]
    public void TestMethod1()
    {
        Session.FindElementByName("Name").Click();
    }
}

}`
I find this very strange, as I have successfully run through many of the provided samples without any issues. What is even more strange is that some of these issues seem to come and go without any explanation. During one test, I had the script running fine with the addition of the try-catch and  appCapabilities.SetCapability("app", "Root"); line. Running it again brought me back two steps. Now, the addition of that line prevents the application from opening.
Unfortunately, I cannot provide the name of the application being used. Any suggestions are much appreciated.

https://discuss.appium.io/t/windows-driver-doesnt-connect-to-application-50-of-time/14219 here is another user with the same issue

Workaround identified: Still having issue with InvalidOperationException, but try-catch allows code to proceed with test after app is launched. Creating a new instance of DesiredCapabilities and WindowsDriver  with the SetCapability("app", "Root") addition allows me to find the app once it is opened. Not elegant, but it works for now.

How you resoved this issue?.I'm facing the same issue


Getting querying API failure

Love this workflow, use it every day.
After updating Tod to latest version, getting a Querying API fail when creating a task via Alfred.
In Tod itself, creating a task is successful.
On latest workflow version too.

I'll take a look at this. What output do you get on the command line when running "tod -t testtask" ?

There were some changes to Todoist's API yesterday which may be related

I've determined this is due to a change in the way TOD returns the status code - it returns "Querying API" and then success/failure, which breaks the way I expect response within Alfred. Will investigate further.

In the meantime, you can manually work around this by downgrading to the previous version with the installation command of  cargo install tod --version 0.3.1
Note that you will probably get a task creation error once daily from the update response, but a second task creation will succeed.

Thank you!
On Tue, Jul 04, 2023 at 14:37:00, Jesse @.***> wrote:

Yes - been safe since I closed this issue :)
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Add attachments support to Bot.post

This feature is not documented by GroupMe, but it has been observed previously by others and recently by myself. All attachment types supported in non-bot posts seem to be supported in bot posts. (…possibly excluding the undocumented Events attachment, which I didn't investigate and which is probably of little use anyway since it consists entirely of pointers into the auxiliary API at https://api.groupme.com/v3/conversations/{group_id}/events/{create,show,list,update,rsvp,delete}.)

Hmm, so it looks like a vararg can't be provided by-name as a keyword argument, and also cannot be provided after other keyword arguments. Upon further consideration, I'm not 100% sure how to order *attachments and picture_url in the signatures of responses.Bot.post and endpoints.Bots.post.

On second thought, since picture_url gives a strict subset of the functionality available via attachments, I believe it should be okay for *attachments and picture_url to be mutually exclusive, and for picture_url to be keyword-only (and *attachments to therefore take priority for any callsites that give the arguments positionally).

Sorry for the delay! This looks good, thanks :)

Thanks again for this. It's now available in v0.7.0.


Scroll to show more in Recent topics

The Recent topics view currently shows a limited number of topics. Sometimes, a user may want to see more topics. For example, selecting the "Participated" filter and scrolling through could be a way to find a conversation you vaguely remember, or something you meant to follow up on.
We should let users see more topics by scrolling to the bottom of the screen, matching the UI we have for other narrows (a loading indicator is shown, and then more topics are loaded).
@amanagr FYI

I imagine that we can just call message_fetch.maybe_load_older_messages, plus some loading indicator plumbing to show a loading indicator in both "recent topics" and the top of "All messages" while we're doing a load_old_messages fetch for "Recent topics"; read the logic in message_scroll.js for how we call that function for inspiration.

@zulipbot claim


TX Power

How do i retrieve the tx power using the bglib?

It is not possible to read the current power; the API does not provide a method for doing this. It is only possible to set the power. However, the module never changes the power on its own, so it will always be whatever you set it to last (starting with the  tag in hardware.xml).

Sorry, I meant how do I retrieve the txpower from the ble advertisements using the bled112_scanner.py from the help it seems i only can get the following:
't' (Unix time):\t1364699464.574,<PHONE_NUMBER>.128, etc.
'r' (RSSI value):\t-57, -80, -92, etc.
'p' (Packet type):\t0 (advertisement), 4 (scan response)
's' (Sender MAC):\t000780535BB4,<PHONE_NUMBER>94, etc.
'a' (Address type):\t0 (public), 1 (random)
'b' (Bond status):\t255 (no bond), 0 to 15 if bonded
'd' (Data payload):\t02010603030918, etc.

If the power is present in advertisement data (which is not at all guaranteed), then it will be in a properly formed advertisement field contained in the data payload. The value<PHONE_NUMBER>0918 in your example contains two fields:

02 01 06 (2-byte field, type=1, value=06) - flags field
03 03 0918 (3-byte field, type=3, value= 0918) - 16-bit service UUID list, containing 0x1809

There are no other fields to work with. This is the only way to get ad payload data; the Bluetooth LE specification contains more information about different advertisement field types and their formats/values.


feat(shipper): add Deutsche Post and DPD, updated shippers for german language and general fixes for shippers

Proposed change

Adding Deutsche Post Briefankündigung
Adding DPD (kept dpd_com_pl, so no breaking change)
Added/fixed german language to multiple providers
Fixed german language

Type of change

[ ] Dependency upgrade
[ ] Bugfix (non-breaking change which fixes an issue)
[ ] New feature (which adds functionality)
[ ] Breaking change (fix/feature causing existing functionality to break)
[x] Code quality improvements to existing code or addition of tests
[ ] Documentation update
[x] Adds a new shipper
[x] Update existing shipper

Additional information
I'm not sure if the hermes body excludes non german subjects or not. If yes feel free to change it.

Looks like some tests will need updating on this.

Probably, I've no hass instance for checking against the tests in vscode rn (vm crashed). I only tested it on my production server

Thanks!


Add testing for .tsx files

Fixes #535.

Looks good to me. We might want to pull out the "runLinterWithConfiguration" bit at some later point to use with other tests? I like it. But this is good for now.

@gscshoyru yeah that function can be used in ruleDisableEnableTests.ts as well.
In general, I think we need more testing utilities for running more than one rule on a file.


Fix the .NET SDK installer script used by MRT Core

This is a follow-up to PR #1916.
The old versions.props file used by the script used to have two PropertyGroup XML nodes, and the new file only has one. This means that script ends up not getting the version from the new Versions.props file, which ultimately means that nothing actually gets installed. (Overall the script is a no-op).
We also need to add a check on $dotNetSdkVersionLkg as in the script, as within the Versions.props file it's defined to be $(CsWinRTDependencyDotNetCoreSdkPackageVersion). But this doesn't get interpreted by PowerShell, so the string exists verbatim. This later causes the dotnet-install.ps1 to fail, as it wants a real version number. But if its set to $(CsWinRTDependencyDotNetCoreSdkPackageVersion) we should just skip the second install step, since it's the same version.

/azp run

we should not attempt to install if newer version is already installed.

we should not attempt to install if newer version is already installed.

Sure, but I don't think that will fix the PreFast build though... as it seems to want to use the exact same version of the .NET SDK in the global.json file...

we should not attempt to install if newer version is already installed.

Sure, but I don't think that will fix the PreFast build though... as it seems to want to use the exact same version of the .NET SDK in the global.json file...

for the prefast issue, we can remove sdk from global.json, to be inline with everyone else.

we should not attempt to install if newer version is already installed.

Sure, but I don't think that will fix the PreFast build though... as it seems to want to use the exact same version of the .NET SDK in the global.json file...

for the prefast issue, we can remove sdk from global.json, to be inline with everyone else.

Sure, I can try that in another branch, and see if that works...

/azp run


HAWQ-36. Allow querying pg_locks

After eliminating the difference between writer gang and reader gang, we only need the status of locks in the master now.

Looks good. +1.


KFLUXBUGS-24: bump dev/stage overlay chains deployment to use performance arguments

I'm not sure if these new args are just ignored, or rejected as invalid, if we don't have a version of chains installed that actually processes them.  We should find out with the e2e's, as I've updated the developer overlay with this change
But if it does cause a failure, then getting the correct openshift pipelines version will serve as the verification that everything is correct
FYI I duplicated the policy we use for core tekton controller, which comes from https://github.com/tektoncd/pipeline/blob/main/docs/tekton-controller-performance-configuration.md
@lcarva @enarha @rupalibehera  FYI / PTAL
rh-pre-commit.version: 2.2.0
rh-pre-commit.check-secrets: ENABLED

/lgtm

/lgtm
I suggest we wait with that PR until the nightly bump is merged (still in CI). Otherwise we risk getting conflicts with the deploy.yaml changes.

/hold

/lgtm I suggest we wait with that PR until the nightly bump is merged (still in CI). Otherwise we risk getting conflicts with the deploy.yaml changes.

hold placed on this PR (I think you meant to say "this PR" when you said "that PR" @enarha )

@lcarva @enarha @rupalibehera - turns out the runtime errors out on unrecognized arguments.
Yaml snippet from the deployment with the args on the container look as specified:
                   "spec": {
                        "containers": [
                            {
                                "args": [
                                    "--threads-per-controller=32",
                                    "--kube-api-qps=50",
                                    "--kube-api-burst=50"
                                ],

but the konflux bootstrap fails with pipeline service not being ready, and on the chains pod, the container errors out, the logs have
flag provided but not defined: -threads-per-controller
Usage of /usr/local/bin/openshift-pipelines-chains-controller:
  -namespace string
    	Namespace to restrict informer to. Optional, defaults to all namespaces.

So this error no longer occurring will be the indicator that development is at the correct operator level.
/hold cancel
e2e will pass once @enarha PRs have merged.

/test appstudio-e2e-tests

/test appstudio-e2e-tests-ocp-4-14

I've rebased PR @enarha @lcarva
let's see if chains comes up with the new arguments

chains came up fine now @lcarva @enarha with the new args
hit a seemingly unrelated multi platform build e2e flake
/retest

again with the multi platform build test
 Summarizing 1 Failure:
  [TIMEDOUT] [multi-platform-build-service-suite Multi Platform Controller E2E tests] ibm power pc dynamic allocation when the Component with multi-platform-build is created [It] The multi platform secret is populated [multi-platform, ibmp-dynamic]
  /tmp/tmp.zoJi1i6gzA/tests/build/multi-platform.go:408
Ran 487 of 629 Specs in 5353.496 seconds
FAIL! - Suite Timeout Elapsed -- 486 Passed | 1 Failed | 11 Pending | 131 Skipped

I'm wondering if we can force merge @enarha @lcarva  ... I'll ask build team about it in their channel.
fyi  @hugares not ready just yet but might need a force merge

/test appstudio-e2e-tests

As per slack discussion, the test failures appear to be unrelated. Given the urgency of these changes, let's just merge this.


Zoom or Crop outside

Will it be possible to implement zoom in the future?  or the crop outside the image like in other packages?

What other packages, can you please name some other packages that might be better or as good as crop_image?

?

Actually, rotation was contributed some time ago by a kind user, maybe somebody else in need would tackle the zoom as well. As to the crop outside the image, I don't even undestand the idea. :-)


train MAE stays -1

Hi @YKQ98 !
Thanks for this nice repo! I was training on MP e_form but I kept getting -1 for train MAE. Do you have any suggestions/explanations about this? Looking forward to your reply.

Thanks for using our model, this is because I omit the Mae calculation of train set to speed up the training process, -1 is a value I set, as long as you see a normal loss and validation Mae, it is fine.

Thank you!


Could you share the pre-trained wav2vec 2.0 models using TAPT and P-TAPT?

Thanks very much!

What specific pre-trained model are you looking for? Is it already fine-tuned on the Emotion Classification objective or the one with only pre-training objective? (Say, running the further pre-training on IEMOCAP)
And currently I have models trained on specific splits, for instance, training on Session 1~4 and test on Session 5, but I don't have a model that trained on the whole IEMOCAP. What is your usage there?

Sorry for the misunderstanding, just the one with only pre-training objective. I pre-trained one model using TPAT, however, the average uar is just～71 (~2% gap). So I want to use your pre-trained models in my downstream tasks.
Besides, do I  need to pre-train the model for each session and then fine-tune it on that session or just pre-train only once and finetune it on all sessions (since the pre-training is time-consuming)?

For the purpose of the research experiment, I have to run it for each session respectively to make sure I don't have access to the test set even during the pretraining phase. So the numbers you saw is by running TAPT (for each split) respectively instead of using a single model.
If you have your own dataset, it will be better to just run the continue-pretraining procedure (TAPT) on your own dataset instead of IEMOCAP. Or simply run TPAT with all the data including IEMOCAP...
The checkpoints I currently in hand is the pretrained model of each session after TAPT. Are you requesting all of them (5 checkpoints)?

Great, I see!
Yeah, I request all of them. And could you please upload all your pre-trained models (using TAPT and P-TAPT) to online drive (may be Google, or Baidu) so that the community can use them in their own research.
Thanks very much!

Here you go.
https://cmu.box.com/s/yitaa74udy23vqubm9ox2pyf2kb3sg5n
But I didn't double check checkpoints for you. (The experiments have been done in a while)

OK! I'll try it.

thanks！！！

Here you go. https://cmu.box.com/s/yitaa74udy23vqubm9ox2pyf2kb3sg5n
But I didn't double check checkpoints for you. (The experiments have been done for a while)

Dear Liwei @b04901014,
I finetuned your pre-trained models using TAPT and PTAPT on IEMOCAP.  Since my GPU has only 32GB memory which could not afford a batch size of 64, I slightly modified the trainer setting (i.e., I accumulate gradients every 2 batches with a batch size of 32) in run_downstream_custom_multiple_fold.py, which is shown as below:
trainer = Trainer(
    precision=args.precision,
    amp_backend='native',
    callbacks=[checkpoint_callback] if hasattr(model, 'valid_met') else None,
    checkpoint_callback=hasattr(model, 'valid_met'),
    resume_from_checkpoint=None,
    check_val_every_n_epoch=1,
    accumulate_grad_batches=64//args.batch_size,
    max_epochs=hparams.max_epochs,
    num_sanity_val_steps=2 if hasattr(model, 'valid_met') else 0,
    gpus=1,
    logger=False
)

Besides, I modified the checkpoint loading part in downstream/Custom/trainer.py to make sure that every fold the model initializes from corresponding checkpoint.
if self.hp.pretrained_path is not None:
    import glob
    ckpt_files = sorted(glob.glob(os.path.join(self.hp.pretrained_path, '*.ckpt')))
    if len(ckpt_files) == self.hp.nfolds:
        pretrained_path = ckpt_files[self.hp.ifold]
    else:
        pretrained_path = self.hp.pretrained_path
        assert pretrained_path.endswith('.ckpt')
    print(f"==> Fold {self.hp.ifold +1 } use pre-trained model '{pretrained_path}' ")
    self.model = PretrainedRNNHead.load_from_checkpoint(pretrained_path, strict=False,
                                                        n_classes=self.dataset.nemos,
                                                        backend=self.hp.model_type)
else:
    self.model = PretrainedRNNHead(n_classes=self.dataset.nemos,
                                   backend=self.hp.model_type)

The results (five experiments) are as follows:

Method
Stat Func
UAR
WAR
macroF1
microF1

PTAPT
mean
71.6207
70.5469
70.9655
72.0825

PTAPT
std
0.8163
0.9130
0.9821
0.6793

TAPT
mean
71.4809
70.0057
70.3757
71.8254

TAPT
std
0.5025
0.4757
0.3672
0.1165

TAPT*
mean
71.4191
70.1220
70.4931
71.7437

TAPT*
std
1.1267
1.1062
1.2942
0.8730

*: using my own pre-trained model under the default TAPT setting in README.md except that training_step=16000 with a batch size of 32 (about 1000 epochs on IEMOCAP Session 1 train set), and in the finetuning stage it is used for all sessions.
From the above table, the three kinds of models have similar performance. Since the results have a gap with those reported in the paper, could you help me to figure out what's wrong with my finetuning process?
Best,
Licai

Hmm. I'm a little bit confused. Are you running n-folds with different session as testing set? Or are the performance you reporting on a specific split? (Say all are test on session 1)
I mean, if you are reporting only the session 1 performance (run session 1 five times), you should be only using the session 1 checkpoint?
If you only run 1 run with the 5 splits (test on session 1 ~ 5), the std should be much larger.

Sorry, the results I reported are on all sessions (and each session I ran five times). I use the following command (using TAPT as an example) for finetuning:
CUDA_VISIBLE_DEVICES=0 python run_downstream_custom_multiple_fold.py --precision 16 \
                                                                     --batch_size 32 \
                                                                     --num_exps 5 \
                                                                     --saving_path ./saved/downstream/TAPT/author \
                                                                     --pretrained_path ./saved/pretrain/TAPT/author

So you mean the std is the std of 25 runs or the Run std. printed (first average by fold)?
Also, could you check if the five label file in your labeldir correspond to the 5 splits? It looks like you modified the code to hardcode the labeldir, originally it is passed as a command line argument.

The std is the std (mean(across five sessions) across five experiments).
The five label files in labeldir are generated using your preprocessing code, which can be downloaded from labels_sess.zip.
I checked the label files. The test split for each fold only include the samples in the correponding session.
The oringinal argument for labeldir in downstream finetuning is the path to a specific checkpoint file. As I said above, to make sure that each session uses the correponding pre-trained checkpoint which do not 'see' the test set of that , I modified the checkpoint loading part .

Hmm. Then could you set the gradient_checkpointing in https://github.com/b04901014/FT-w2v2-ser/blob/main/modules/FeatureFuser.py#L103
to True then re-run with batch_size = 64?
With accumulate gradients every 2 steps, you are essentially running only 7.5 epochs instead of 15.
Otherwise I could not find anything weird about your experiment currently.

Could you check if the five label file in your labeldir correspond to the 5 splits? It looks like you modified the code to hardcode the labeldir, originally it is passed as a command line argument.
Also, if you use accumulate_grad_batches to accumulate gradients every two step, then you should double the max_epoch, otherwise you are using only half the training steps. BTW, you can have more memory if you set https://github.com/b04901014/FT-w2v2-ser/blob/main/modules/FeatureFuser.py#L103 to True.
I don't think the performance should differ this much though.

Thanks very much!
I believe that the training steps using a batch size of 32 and accumulate_grad_batches=2 is equal to that using a batch size of 64.  Nevertheless, I will use distributed parallel training to check the influence of differernt batch sizes.

NVM, I think you are right. They should be equivalent. Then I actually don't know where should be the issue then. Do you mind just uploading your whole code as a zip file without the checkpoints? Maybe I could look into it.

My pleasure! The code is here FT-w2v2-ser.zip.
I use the shell in scripts folder to pretrain and finetune models on IEMOCAP.
The results are saved in saved folder (detailed results for finetuning are also included in the subfolders).
Any questions is welcome.

I have seen several numbers below 76% for the session 2 (Fold 2) for individual runs on TAPT and PTAPT. Which I almost haven't seen one throughout my experiments. There is definitely something wrong, will inspect in it.

Thanks very much! I'm looking forward to your message.

Ah. I just ran one run on my machine with the session 2 PTAPT checkpoint using 2e-4 as the learning rate. The UAR is 75.17. Which is somewhat close to your results.
To make sure I didn't upload a bad checkpoint, I re-ran the same stuff using the default learning rate 1e-4. The UAR is 80.42.
If I understand it correctly, the former (2e-4) simulated your accumulated gradients as the loss should be doubled.
While this is just me running a single run on session 2 on my machine, it may be what's happening on your side. Since the first number is close to what you report, and none of your runs can achieve 80%.
The learning rate 2e-4 may be too large for fine-tuning, the training loss is bouncing up and down per step on the last iteration.
I knew this difference when you mentioned the gradient accumulation but I didn't think this would make this much difference. Anyway, hope this could resolve your issue.

I just checked the pytorch-lightning's implementation, they seemed to be dividing the loss by the accumulation step automatically, I'll try to re-run your code though.

I just ran your code on session 2 only, here is what I got:

There is a bad 76 run there, but the most of them are around 78~81%. Compared to what you got in your run:

I think the difference is too huge that they are unlikely to be drawn from the same distribution. The only I code I changed is the path in the script into:
CUDA_VISIBLE_DEVICES=3 python run_downstream_custom_multiple_fold.py --precision 16 \
                                                                      --batch_size 32 \
                                                                      --num_exps 5 \
                                                                      --saving_path /tmp \
                                                                      --pretrained_path /usr1/liweiche/exps_new/IEMOCAP/checkpoints/session2.ckpt \
                                                                      --datadir ../FT-w2v2-ser/Dataset/IEMOCAP/Audio_16k/ \
                                                                      --labeldir /usr1/liweiche/exps_new/IEMOCAP/session2/labels/

And on the "/share/home/sunlicai/transformers/wav2vec2-base" to "facebook/wav2vec2-base". I'm not sure how your get your local pretrained model there. But unless the config.json in it is the same as https://huggingface.co/facebook/wav2vec2-base/blob/main/config.json . It should be fine.
I did not change anything else including the gradient accumulation and seed. If the pretrained model config is not a problem, then I the only thing I could think of is the environment, or there may be bugs during the transition of folds...

Much thanks to your helpful debugging.
I re-ran the code with batch_size=64 by following your suggestion to set gradient_checkpointing to True.
The results of PTAPT are as follows:

Exp
Fold
UAR
WAR
macroF1
microF1

1
1
69.8167
64.3318
64.7215
69.2565

1
2
77.6739
72.9228
74.4119
76.8706

1
3
69.3841
68.5491
69.1264
69.2956

1
4
73.4489
73.9088
73.5648
73.8406

1
5
75.2707
73.1668
73.5307
73.9309

2
1
73.7616
70.4147
71.3115
72.6207

2
2
80.7606
78.0059
78.9644
79.5461

2
3
69.4304
69.9392
70.2692
71.0953

2
4
69.5929
69.7381
69.5926
71.0474

2
5
73.5148
71.8775
72.7360
73.4933

3
1
72.2615
69.3088
70.2676
71.2854

3
2
74.9120
73.0205
74.0394
75.0084

3
3
70.9914
70.4605
70.7650
70.9058

3
4
73.8345
75.3637
74.4753
74.9603

3
5
72.3662
71.9581
72.1026
72.1454

4
1
70.7526
66.4516
67.6090
70.4535

4
2
75.6213
72.8250
74.1808
76.2389

4
3
67.9802
67.8540
68.6463
69.5036

4
4
72.2317
71.2900
71.3094
72.1324

4
5
72.0067
70.5077
70.3071
71.1857

5
1
71.8092
70.2304
71.0657
71.4374

5
2
79.5007
76.8328
77.2310
77.9573

5
3
71.6541
71.5899
71.9562
72.1773

5
4
74.7210
75.1697
75.1347
75.2602

5
5
73.9711
73.3280
74.1058
74.2059

Stat Func
mean
73.0907
71.5618
72.0570
73.0342

Stat Func
std
0.8454
1.2663
1.2180
0.7900

Stat Func
fold_std
2.7925
2.7066
2.6447
2.5047

The results of TAPT are as follows:

Exp
Fold
UAR
WAR
macroF1
microF1

1
1
72.4140
69.2166
69.9198
70.8831

1
2
78.8307
77.1261
77.6667
78.5128

1
3
67.4721
67.0721
67.1881
67.2617

1
4
76.4685
76.3337
76.1041
76.2746

1
5
73.2559
71.3940
71.6633
72.2239

2
1
70.3762
64.3318
65.6699
70.2334

2
2
77.3431
75.8553
77.0228
77.0488

2
3
65.5209
64.5526
64.9570
65.2109

2
4
71.1131
70.9990
71.3480
72.9595

2
5
74.7123
72.2804
73.2582
74.5627

3
1
68.2955
62.0277
62.8583
68.3240

3
2
76.2815
74.6823
75.9064
76.6184

3
3
64.3476
64.3788
64.0973
65.9882

3
4
73.1064
71.8720
71.5928
72.7076

3
5
68.1130
69.1378
69.4952
70.0967

4
1
72.1599
68.6636
69.4446
70.8491

4
2
74.9441
73.4115
74.3779
76.2800

4
3
68.5871
68.2016
68.4900
68.6162

4
4
76.4899
76.6246
76.5327
76.5754

4
5
72.2048
69.7824
70.3533
71.4640

5
1
70.0527
64.8848
65.8348
69.6342

5
2
76.0400
71.0655
71.6846
74.8651

5
3
67.6256
67.6803
67.8072
67.9602

5
4
72.0541
73.9088
73.3376
74.2588

5
5
73.3085
72.2804
72.3810
72.6330

Stat Func
mean
72.0447
70.3105
70.7597
72.0817

Stat Func
std
1.2300
1.3362
1.3040
0.7987

Stat Func
fold_std
3.5281
3.9150
3.8503
3.5022

The results of PTAPT and TAPT are better than previous ones, especially PTAPT.  It seems that using a smaller batches with  gradient accmulation here is not fully equivalent to using larger batches without it. However, these is still ~1.5% performance gap.
I manually installed most packages (except fairseq, which is not used currently) with the versions specified in your dockerfile. You can check the env.txt.
My local pre-trained wav2vec2.0 base model is downloaded from official website about seven months ago. After I checked my local config.json, I found that it was not same to the lastest one (updated six months ago). One noticable difference is the architectures (old 'Wav2Vec2Model' vs. new 'Wav2Vec2ForPreTraining'). I also notice that warning (' Found keys that are not in the model state dict but in the checkpoint') occurred when loads pretrained checkpoint.
==> Fold 1 use pre-trained model './saved/pretrain/TAPT/author/session1.ckpt'
Some weights of Wav2Vec2ForPreTraining were not initialized from the model checkpoint at /share/home/sunlicai/transformers/wav2vec2-base and are newly initialized: ['project_hid.bias', 'project_q.weight', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'quantizer.weight_proj.bias', 'project_hid.weight', 'project_q.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/share/home/sunlicai/anaconda3/envs/pytorch1.9/lib/python3.8/site-packages/pytorch_lightning/core/saving.py:205: UserWarning: Found keys that are in the model state dict but not in the checkpoint: ['rnn_head.weight_ih_l0', 'rnn_head.weight_hh_l0', 'rnn_head.bias_ih_l0', 'rnn_head.bias_hh_l0', 'rnn_head.weight_ih_l0_reverse', 'rnn_head.weight_hh_l0_reverse', 'rnn_head.bias_ih_l0_reverse', 'rnn_head.bias_hh_l0_reverse', 'linear_head.1.weight', 'linear_head.1.bias']
  rank_zero_warn(
/share/home/sunlicai/anaconda3/envs/pytorch1.9/lib/python3.8/site-packages/pytorch_lightning/core/saving.py:209: UserWarning: Found keys that are not in the model state dict but in the checkpoint: ['wav2vec2.wav2vec2PT.wav2vec2.masked_spec_embed', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.0.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.0.layer_norm.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.0.layer_norm.bias', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.1.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.2.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.3.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.4.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.5.conv.weight', 'wav2vec2.wav2vec2PT.wav2vec2.feature_extractor.conv_layers.6.conv.weight', 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  rank_zero_warn(
Weigh losses by prior distribution of each class: tensor([0.2906, 0.2043, 0.2069, 0.2983]).
Using native 16bit precision.

Maybe it's the devil. I'll fix the config issue by using the default facebook/wav2vec2-base instead of my local one and re-run the code. Hope it helps!

Glad that you are getting better results on reproducing, while I'm not sure why there is a difference between batch_size=64 and the accumulate gradient stuff you did earlier... On my machine they ran about the same results.

Really wierd.

Glad that you are getting better results on reproducing, while I'm not sure why there is a difference between batch_size=64 and the accumulate gradient stuff you did earlier... On my machine they ran about the same results.

I re-ran the code using facebook/wav2vec2-base with batch_size=64. However, the results are even worse than those when using my local config. I am totally lost!!!

Method
Stat Func
UAR
WAR
macroF1
microF1

PTAPT
mean
72.2857
70.5583
71.0144
72.3176

PTAPT
std
0.6220
0.4712
0.4265
0.4557

PTAPT
fold_std
2.6346
2.2990
2.3078
2.1766

TAPT
mean
71.6164
69.7006
70.0368
71.5589

TAPT
std
0.8966
0.7048
0.7180
0.3754

TAPT
old_std
4.2064
3.7524
3.9618
3.4980

Hmm. It's really hard for me to debug for this... I think you fixed your own seed and I don't think there is a difference between these three models. One can claim there's a variance merely across the average of 5 runs, but I don't think there's that much difference. And this could not explain why in every run there is a steady gap between PTAPT and TAPT, and I did not observe such a high variance in my local runs; it seems to indicate there do have some factor that changed across your three experiments.
Additionally, I would say as I didn't fix the seed, having above 73% may be somewhat reasonable enough for PTAPT.
Thank you for bringing this up, but I'm afraid that I couldn't help more on this currently.
One thing is that I don't really know what's happening there, another is that I'm occupied with another research project recently.

This issue is getting too lengthy. I'll close this issue. Feel free to open another one if you have questions.


Kia and Hyundai Port

This adds support for Kia and Hyundai.
Lateral Support Only.
No Panda Security.
Currently In Use by
Kia Sorento 2018  @ku7
Kia Stinger 2018  @TK211X
Hyundai Elantra 2017 @killian

this is great! @emmertex , few things for now:

can you make a separate PR for the dbc changes in opendbc repo? We can probably merge them quickly.
Same thing for the panda repo.

temporarily closed until panda safety is done.


Update: RealVNC.VNCServer version <IP_ADDRESS>891

Removing old versions to reduce clutter
Microsoft Reviewers: Open in CodeFlow

/azp run


🐛 [BUG]: Node added in onMounted() is not rendered

Is there an existing issue for this?

[x] I have searched the existing issues and this is a new bug.

Current Behavior
I use the example shown in Adding nodes to the graph.
The node that is pushed in the onMounted() function is not rendered.
When I use Vue dev tools, I see that the event nodesInitialized event does not contain the node that is pushed to the elements array in onMounted(), but only the node that is part of the initial elements definition.
Here is the code I'm using:
<script setup>
import { ref, onMounted } from 'vue'
import { VueFlow } from '@vue-flow/core'

const elements = ref([
  {
    id: '1',
    position: { x: 50, y: 50 },
    label: 'Node 1',
  }
]);

onMounted(() => {
  elements.value.push({
    id: '2',
    label: 'Node 2',
    position: { x: 150, y: 50 },
  })
})
</script>

<template>
  <VueFlow v-model="elements"/>
</template>

Expected Behavior
I expect the node that is pushed to the elements array to be rendered when I refresh the page.
Steps To Reproduce

Use the code from the first example in Adding nodes to the graph
npm run dev in your App folder

Relevant log output
No response
Anything else?
As per my package-lock.json, I am using "@vue-flow/core": "^1.33.4".

Thanks for the report.
Alas, because of the way the v-models (elements, nodes and edges) are setup at the moment I expect that this issue will persist for the remainder of VueFlow 1.
I will address this problem with the next major update - for now you can use onPaneReady if you need to add some nodes after the initial ones, though be aware that fit-view-on-init will not play nicely with this and won't consider nodes that have been added after that. You can always call fitView yourself though, so that's not a big problem :)
A little bit of background on why this happens:
Currently when elements are passed to VueFlow they are "transformed" from Node -> GraphNode / Edge -> GraphEdge, adding some additional pieces of info to these elements that are necessary for VueFlow to work correctly.
To allow for direct mutation of these objects like node.position.x = 100 without having to pass a new array of elements, the transformed elements are pushed into your model ref.
So basically what happens is this

You pass VueFlow your elements ref
The elements get transformed
Transformed elements overwrite your elements ref value
To avoid having infinite loops happening while this is done, a pausable watcher is used that waits until your elements ref is overwritten to continue watching for changes.
When you do elements.value.push(...) during onMounted it ends up happening during the pause of this watcher so the new element is never noticed and consumed by VueFlow :/

What does work is onMounted(() => setTimeout(() => {}, 0)) but obviously this isn't a great pattern to use.
In the next major version I'll see that this binding is solved differently since the current implementation has been giving me headaches for too long but re-doing it would be a breaking change since the whole "transformation" needs to stop and be replaced with an internal map that contains these additional pieces of information that VueFlow needs.
Anyway - sorry that I can't give you a better solution at this point in time.
I'll keep this issue open though.

@bcakmakoglu Thanks a lot for the detailed explanation and the workarounds.


disable progress canvas when waveColor and progressColor are identical

this feature was broken before.

Coverage increased (+0.09%) to 68.537% when pulling 4fe38f1dfba6e45e38ca69253435815e269abce2 on optional-progress-wave into a8c692dfe74ab510cd9a39803aa7fd96d8fe9229 on master.


Add proj:code to support Projection extension v2

When https://github.com/stac-extensions/projection/pull/12 lands in the projection extension and a 2.0 is released, proj:epsg will be replaced by proj:code. I am not sure how this libaray handles different versions of extensions.
I was working on https://github.com/stac-utils/pystac/pull/1287 and noticed that pystac depends on this library for summary fields, so just wanted to open this.

Implemented in ebd9c99a


fatal error: module 'FBSDKCoreKit' not found

Hi, after i upgraded to 3.0.0 i started getting this error:
    ** BUILD FAILED **
Xcode's output:
↳
    In file included from /Users/erolasan/Work/lvlup/lvlupmobile/ios/Pods/FBSDKLoginKit/FBSDKLoginKit/FBSDKLoginKit/Internal/_FBSDKLoginRecoveryAttempter.m:21:
    In file included from /Users/erolasan/Work/lvlup/lvlupmobile/ios/Pods/FBSDKLoginKit/FBSDKLoginKit/FBSDKLoginKit/Internal/FBSDKLoginKit+Internal.h:19:
   In file included from /Users/erolasan/Work/lvlup/lvlupmobile/ios/Pods/FBSDKLoginKit/FBSDKLoginKit/FBSDKLoginKit/Internal/../FBSDKLoginKit.h:27:
    /Users/erolasan/Work/lvlup/lvlupmobile/ios/Pods/FBSDKLoginKit/FBSDKLoginKit/FBSDKLoginKit/FBSDKLoginButton.h:24:9:
fatal error: module 'FBSDKCoreKit' not found
    @import FBSDKCoreKit;
     ~~~~~~~^~~~~~~~~~~~
    1 error generated.
Could not build the application for the simulator.
Error launching application on iPhone 8.

I've done a full clean/reset of Pods and pod cache, but error still shows

I have  the same problem!any guys can tell me how to do?

I have managed to solve this issue by changing the Facebook Pod versions from 5.10.0 to 5.8.0 on the Podfile. The solution is from this issue, you can check it for more details.
My new Podfile looks like below.
PODS:
  - FBSDKCoreKit (5.8.0):
    - FBSDKCoreKit/Basics (= 5.8.0)
    - FBSDKCoreKit/Core (= 5.8.0)
  - FBSDKCoreKit/Basics (5.8.0)
  - FBSDKCoreKit/Core (5.8.0):
    - FBSDKCoreKit/Basics
  - FBSDKLoginKit (5.8.0):
    - FBSDKLoginKit/Login (= 5.8.0)
  - FBSDKLoginKit/Login (5.8.0):
    - FBSDKCoreKit (~> 5.0)
  - Flutter (1.0.0)
....

Same problem here!

See my related pull request here: https://github.com/roughike/flutter_facebook_login/pull/217

This solution worked for me as well.

I am facing the same issue, unfortunately upgrading the pod version causes different errors for me.

The solution of @atavci  suggested didn't work with me :(

Looks like that's https://github.com/facebook/facebook-ios-sdk/issues/1102 and the solution is to have
pod 'FBSDKCoreKit', :modular_headers => true

in this plugins' .podspec.
Until this is done, you can workaround by enabling modular_headers globally in your application's ios/Podspec file with
# Somewhere at the top of the file, I put it next to "platform :ios ..."

use_modular_headers!

Looks like that's facebook/facebook-ios-sdk#1102 and the solution is to have
pod 'FBSDKCoreKit', :modular_headers => true

in this plugins' .podspec.
Until this is done, you can workaround by enabling modular_headers globally in your application's ios/Podspec file with
# Somewhere at the top of the file, I put it next to "platform :ios ..."

use_modular_headers!

Sorry, this didn't work for me as it generated a conflict with other library (Boring-SSL) which is used by some Firebase library in my project.

Any news on this? I'm still running into this issue.

I have managed to solve this issue by changing the Facebook Pod versions from 5.10.0 to 5.8.0 on the Podfile. The solution is from this issue, you can check it for more details.
My new Podfile looks like below.
PODS:
  - FBSDKCoreKit (5.8.0):
    - FBSDKCoreKit/Basics (= 5.8.0)
    - FBSDKCoreKit/Core (= 5.8.0)
  - FBSDKCoreKit/Basics (5.8.0)
  - FBSDKCoreKit/Core (5.8.0):
    - FBSDKCoreKit/Basics
  - FBSDKLoginKit (5.8.0):
    - FBSDKLoginKit/Login (= 5.8.0)
  - FBSDKLoginKit/Login (5.8.0):
    - FBSDKCoreKit (~> 5.0)
  - Flutter (1.0.0)
....

Even using 5.13.0 version the problem persist. This is the only solution that worked for me until now

Neither solution worked for me. flutter clean, clear DerivedData, pod deintegrate, then even tried explicitly stating FBSDK* to be 5.8.0 and set modular header to true in podfile did not do the trick. App is still running perfectly fine in the iOS/Android emulators.
pod 'FBSDKCoreKit', '5.8.0', :modular_headers => true
pod 'FBSDKLoginKit', '5.8.0'

Any other suggestions?

For anyone that this issue still happens, can you guys try to add use_frameworks! after platform :ios, '9.0' as a new line to see whether it fixes the issue? Here is an example Podfile
platform :ios, '9.0'

use_frameworks!

target 'ASRIOS' do

  # Pods for ASRIOS
  pod 'FBSDKCoreKit', '5.11.1'
  pod 'FBSDKLoginKit', '5.11.1'
  pod 'FBSDKShareKit', '5.11.1'

  target 'ASRIOSTests' do
    inherit! :search_paths
    # Pods for testing
  end

  target 'ASRIOSUITests' do
    # Pods for testing
  end

end


SARAALERT-1281 Assessments Job Performance

Please title your PR like SARAALERT-<Jira number>: <title>, and include all numbers if the PR covers more than one, e.g. SARAALERT-1, SARAALERT-2: <title>. If the PR is not related to a Jira task, just give it a descriptive title.
Description
Jira Ticket: SARAALERT-1281

Improve the performance of the send_assessments_job
Write a test file for the send_assessments_job

(Improvement) Evidence
Here are all of there runs I tested on my local machine to get to the last item in the list (which is the implemented version in this PR)
1.24.0:
                          user     system      total        real
SendAssessmentsJob   421.593488  28.610750 450.397578 (553.695276)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .each)
                          user     system      total        real
SendAssessmentsJob   492.477518  57.369191 550.041894 (534.521236)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches 5_000)
                          user     system      total        real
SendAssessmentsJob   382.569628  53.201871 435.938969 (418.032642)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches 15_000)
                          user     system      total        real
SendAssessmentsJob   387.700364  45.913196 433.778385 (415.849046)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches 1_000)
                          user     system      total        real
SendAssessmentsJob   464.031848  64.798865 529.050312 (581.857190)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches patient_batch.size)
USES A LOT OF MEMORY: > 5GB
                          user     system      total        real
SendAssessmentsJob   487.439485  58.728787 546.342603 (528.932165)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches 10_000)
                          user     system      total        real
SendAssessmentsJob   457.062621  63.842703 521.089999 (498.010821)

--------------------------------------------------------------------------------
SA-1281/assessmentsJobPerf: (with .find_in_batches 15_000, AND select)
USES 1GB PEAK
                         user     system      total        real
SendAssessmentsJob   329.659055  44.618899 374.438588 (360.195652)

Important Changes
Please list important files (meaning substantial or integral to the PR) along with a list of the general changes that should be highlighted for reviewers.
send_assessments_job.rb

Made performance improvements to job (threading, find_in_batches, select specific fields)
Now collects summary data for sent items, but still keeps all data for exceptions

assessment_job_email.html.erb

Now expects summary data for the sent area

send_assessments_job_test.rb

New test file for job

Testing
This fix was tested on the following browsers (submitter must check all those that apply):

[ ] Chrome
[ ] Firefox
[ ] Safari
[ ] IE11

bundle exec rails test test/jobs/send_assessments_job_test.rb
 NO_MEMPROF='true' DISEASE_TRAKKER_DATABASE_NAME=1m_disease_trakker_development  bundle exec ruby script/benchmarks/send_assessments_job_benchmark.rb

Based on the CI jobs that have run so far these changes have yielded between a 5% and 15% performance improvement of the job. Based on the local testing in the PR description these changed yield about a 40% performance improvement. My guess is that the differences in % improvements between CI and my local machine have to do with differences in hardware.
New CI Job
Old CI Job
Old CI Job
Again, the biggest performance improvement I think will end up coming from improving the reminder_eligible scope such that we just have to iterate through less patients that we would have skipped anyway. Using the better_reminder_eligible scope from https://github.com/SaraAlert/SaraAlert/pull/671 reduces the number of returned patients in testing from 560k patients to under 100k patients.

Needs rebase


Some items kept in extras after frictionless => CKAN conversion cause CKAN to fail with ValidationError

Overview
When a package is converted from CKAN to frictionless and back again🦶, some items are saved in extras such as id and contributors. Some of the extras may be read and used to set values when converting from frictionless to CKAN, but they are not popped out of the extras list and are kept there.
This can then cause issues when trying to pass the resulting CKAN package dict through CKAN's own validation (e.g. when calling package_update() on the same package), because CKAN will throw an error:
ValidationError: {u'extras': [{}, {}, {'key': [u'There is a schema field with the same name']}]}

This happens because extras now contains keys that are already defined in the CKAN dataset schema such as id.
For ckanext-versioning, we worked around this by filtering out values from extras that already exist as properties of the package dict, but others may be hitting this as well. See our workaround in https://github.com/datopian/ckanext-versioning/pull/19/files#diff-3a58033cd97812191417c36622c5aa7bR123

Please preserve this line to notify @amercader (lead of this repository)

/cc @pdelboca @rufuspollock

contributors is intentional as it is tough to know if there was not some extra stuff in there that does not map across. id is a bug and will get fixed.


Add new third-party tools

We're eager to grow our ecosystem.
To help with this, we can expand our docs around third-party tools.
If you're aware of a tool which can interface with QuestDB, but does not have a 3rd party tools page, please raise it in this issue. We'll respond as soon as we can, and give the thumb ups. After that, create a PR to include it based on the current pattern, and that's it!
Also, auditing the current third-party tools page & confirming they're all in the overview is a helpful task.

Hey @goodroot maybe you can assign this task to me I will be solving the mentioned issue

Hey @abhirajadhikary06 - consider this a meta issue.
If you want to create a third-party tool, please create a new issue - we'll link them back here.

@goodroot After thorough documentation review and research, I identified -----
Airbyte as an open-source data integration platform that enhances QuestDB's ingestion capabilities by seamlessly pulling data from multiple sources.


How to use ?

What are the syntax, the usage, etc. ? Give at least an "Hello world" exemple (or how to declare variable, or some other basic stuff) !

@felko @Mesabloo

Hello (good to start with a simple hello)!
Nihil is currently not usable at all (that's why there is no installation) guide for the moment.
Most of it is nothing but a simple typed lambda calculus interpreter (with simple additions like data-types).
I do plan on writing a wiki (in the Wiki tabs above) on how to use Nihil, everything that's needed to start writing some code. Unfortunately, I'm currently quite busy, and would like to rewrite some of the "compiler" (which is not really a compiler for now) like the parser and the desugarer.
In the end, it should look like the examples in this folder (but without the quantified types, as I'm about to drop this feature, at least for now).
I do not plan on releasing a prototype yet, I'd like to have most of this project done before (+ the compilation stuff that I don't currently have). It really is a lot of study, but eventually I'll get there with a release.
I cannot give a release date, my schedule is very fluctuating, but I'd say that it won't happen before 2021 (if I'm being optimistic, as I'm the only one working on it).
Thanks for your interest in this project anyway. :)

Closing this for inactivity.


[nRF52840-DK] nRFConnect all-clusters-app crashes when left running overnight.

Problem
Testing with a nRF52840 device on SHA 5d8599d1 using the all-clusters-app. Twice now after leaving the device sit overnight (commissioned and on a thread network) it has crashed when checking minicom the next day. Minicom terminal had been steaming the whole time. Logs below:
I: 1970 [DL]advertising srp service: 20EBAACAD67DEC05-000000000000630A._matter._tcp
D: 1978 [DL]Using Thread extended MAC for hostname.
I: 1984 [DIS]Advertise commission parameter vendorID=65521 productID=32769 discriminator=3840/15
I: 2029 [DL]advertising srp service: 9E6C8B30932004FF._matterc._udp
D: 2036 [DIS]Scheduling extended discovery timeout in 900s
E: 2078 [DL]Long dispatch time: 124 ms, for event type 32787
D: 2797 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 902078 [DIS]OnExpiration - valid time out
D: 902082 [DL]Using Thread extended MAC for hostname.
I: 902087 [DIS]Advertise operational node 20EBAACAD67DEC05-000000000000630A
I: 902094 [DL]advertising srp service: 20EBAACAD67DEC05-000000000000630A._matter._tcp
I: 902102 [DL]removing srp service: 9E6C8B30932004FF._matterc._udp
D: 902144 [DIS]Extended discovery timed out
D: 902234 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 939826 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 3387110 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 3390939 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 3394439 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 4602964 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 6407323 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 6481055 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 7871517 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 7982270 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 10981132 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 12093770 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 12785374 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 13879748 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 14551029 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 15062314 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 16351877 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 16352988 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 19804658 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 22142366 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 22690520 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 23708612 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 25894459 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 26555599 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 27825673 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 29222409 [DL]OnSrpClientNotification: Last requested operation completed successfully
D: 29316397 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 29713209 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 31842227 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 33492397 [DL]OpenThread State Changed (Flags: 0x00000200)
D: 35603447 [DL]OpenThread State Changed (Flags: 0x00000200)
ASSERTION FAIL [!arch_is_in_isr()] @ WEST_TOPDIR/zephyr/kernel/mutex.c:101
        mutexes cannot be used inside ISRs
E: r0/a1:  0x00000004  r1/a2:  0x00000065  r2/a3:  0x20007218
E: r3/a4:  0x00000000 r12/ip:  0x00000000 r14/lr:  0x000466cb
E:  xpsr:  0x61000021
E: s[ 0]:  0x00000000  s[ 1]:  0x00000000  s[ 2]:  0x00000000  s[ 3]:  0x00000000
E: s[ 4]:  0x00000000  s[ 5]:  0x00000000  s[ 6]:  0x00000000  s[ 7]:  0x00000000
E: s[ 8]:  0x00000000  s[ 9]:  0x00000000  s[10]:  0x00000000  s[11]:  0x00000000
E: s[12]:  0x00000000  s[13]:  0x00000000  s[14]:  0x00000000  s[15]:  0x00000000
E: fpscr:  0x00000000
E: Faulting instruction address (r15/pc): 0x00096338
E: >>> ZEPHYR FATAL ERROR 4: Kernel panic on CPU 0
E: Fault during interrupt handling

E: Current thread: 0x20007218 (idle)
E: Halting system

ASSERTION FAIL [!arch_is_in_isr()] @ WEST_TOPDIR/zephyr/kernel/mutex.c:101
mutexes cannot be used inside ISRs

This seems to be the key thing here....  @Damian-Nordic


A Thanks Message

Hello, i am Onur, i am a CTO of a community that develop Blockchain based Decentralized Application Network. This repository have a very good idea. All contributor of this project and me should develop this project and use in the other project. Let's not stop developing.
Onur Atakan ULUSOY - CTO of Decentra Network Community

Thank you for your appreciation, we can develop oneFace together.


Anchor tag to download the file that's piped.

Anchor tag should do this :eyes:
I also set some headers and it will download as song.mp3, you can set the name dynamically if you want on line 18.

have your free hacktoberfest point you meme


Effects::Random Section Requires Subscription Section to Compile

Subscriptions are never added in the text of the tutorial.  Without defining main you can compile without the subscriptions section but get an empty view.  If you define main as Html.program you have to add the subscriptions section in order to compile but your view is then correct.

I also ran into this issue

Ooh, this also seems to affect the next tutorial.
This can be fixed (I think, I'm completely new) by:
adding a main declaration:
main =
  Html.program
    { init = init
    , view = view
    , update = update
    , subscriptions = subscriptions
}

Adding a do-nothing subscription:
subscriptions : Model -> Sub Msg
subscriptions model =
  Sub.none

I believe the latest version of https://guide.elm-lang.org/ introduces both main and subscriptions as they appear in programs, if only to mention that we will cover them in more detail soon. Sorry for the trouble!


Unit Convertor Modified

🛠 Issue (1736)

Closes #1736
Description
Negative values are not accepted as input and a proper alert is displayed when a negative value is given as input.
Type of change

[x] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)

Checklist:

[x] I have made this from my own
[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] My changes generate no new warnings
[x] The title of my pull request is a short description of the requested changes.

ATTACH SCREEN-SHOTS / DEPLOYMENT LINK

@TusharKesarwani @zugzwang03 @harshit-paneri I have done the required changes, please merge this pr.


[0.23-rc1][Android] transform scaleY: -1, scaleX: -1 broken

View with style transform: [ { scaleY: -1 } ] or transform: [ { scaleX: -1 } ] not displayed.
In 0.22 this works fine.

@gut4 are you using invertible scroll view?

invertible scroll view not displayed too.
Also I find that transform: [{ rotateY: '100deg' }], transform: [{ rotateX: '10deg' }] broken too. Views inside scroll view or outside both not displayed.

Do you know what commits broke this?

Confirming this is Android-only. Same code on iOS and Android:

Works after reverting 3e760f48c5a28b592cb60c4060ca28a1574f6274:

Releasing 0.23.0-rc3 now, @gut4, @terrysahaidak can you try that please?

Yeah, it works. Thanks.

This seems to be broken in 0.24.1 transform: [ { scaleX: -1 } ]


Builds failing with go error

The acmedns plugin among other builds are always failing at panic: internal error: can't find reason for requirement on<EMAIL_ADDRESS>It seems to be related to this go bug https://github.com/golang/go/issues/55955, which was fixed 2 weeks ago.
Hopefully the fix will roll out to GitHub actions quickly.
https://github.com/caddyserver/caddy/issues/5301
Plugins affected

netcup
acmedns

GoDaddy is also affected.


Wizard: Support editing RBAC

@Haarolean
hi, Is this feature make RBAC be editable without deploy?

@seono this issue is about being able to edit RBAC within ui setup configuration wizard.