luaPackages.luadbi-mysql: mark as broken

Description of changes
Currently doesn't build on master. It complains about a missing dependency.
Things done

Built on platform(s)

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

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

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

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

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

[x] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

I tried to build it and it fails saying it can't find luadbi
 > ++ luarocks make --deps-mode=all --tree=/nix/store/fx0g1vw1hqgrx73ah8w2i64sn072zhpm-lua5.1-luadbi-mysql-0.7.2-1 /build/luadbi-mysql-0.7.2-1.rockspec
       > Missing dependencies for luadbi-mysql 0.7.2-1:
       >    luadbi 0.7.2 (not installed)
       >
       > luadbi-mysql 0.7.2-1 depends on lua >= 5.1, < 5.4 (5.1-1 provided by VM: success)
       > luadbi-mysql 0.7.2-1 depends on luadbi 0.7.2 (not installed)
       > mkdir: cannot create directory '/homeless-shelter': Permission denied
       > Warning: Failed searching manifest: Failed downloading https://luarocks.org/manifest-5.1 - failed downloading https://luarocks.org/manifest-5.1
       > Warning: Failed searching manifest: Failed downloading https://raw.githubusercontent.com/rocks-moonscript-org/moonrocks-mirror/master/manifest-5.1 - failed downloading https://raw.githubusercontent.com/rocks-moonscript-org/moonrocks-mirror/master/manifest-5.1

which is listed among the dependencies so sthg fishy is at play here. I will try to have a look to fix it rather than marking it as broken

I tried to build it and it fails saying it can't find luadbi

Yes this is what I have as well.

which is listed among the dependencies so sthg fishy is at play here. I will try to have a look to fix it rather than marking it as broken

Indeed, it is strange...
Ok let's leave it like this then.

The explanation is here https://github.com/NixOS/nixpkgs/pull/299449 . Will prepare a PR later


Bind() doesn't work?

I can't get the Bind() method to work at all. All of your examples build and run for me, but button clicks or anything else that uses Bind() doesn't work. Even trivially simple code like this doesn't work:
func onclick(arg *gform.EventArg) {
    println("hello")
}

func main() {
    gform.Init()

    mainWindow := gform.NewForm(nil)

    btn := gform.NewPushButton(mainWindow)
    btn.OnLBDown().Bind(onclick)

    mainWindow.Show()

    gform.RunMainLoop()
}

The code builds and runs and the window appears with the button, but nothing happens when I click the button. Am I doing something wrong? I'm running on Windows 7 64-bit, using go1.

Hi,
maybe one possible problem in "wndproc.go" using "SetWindowLong", not compatible with Win64.
should use "SetWindowLongPtr".


[Missing] Call Feedback

Support for specifying the exact call problems was added in TDLib 1.5.0 via the new parameter problems in the method sendCallRating, so clients don't need to modify the comment parameter themselves.


We should be caching connectorClients

So this certainly works, couple thoughts...
First, at bare minimum I suggest considering switching to use GetOrAdd rather than the separate TryGet, do some work, TryAdd. I personally find it reads clearer, but, more importantly, it will maintain the lock if the "get" fails which you do the work to sping up the client, which helps prevent two threads at once both doing the work and one of them just "failing" at the TryAdd and leaving a dangling connection around for some time.
That said, I would honestly recommend throwing a proper level of indirection in there in the form of an IConnectorClientFactory with a ValueTask<IConnectorClient> GetClientAsync(string serviceUrl, MicrosoftAppCredentials appCredentials) method. This will buy you at least a couple of things:
1. Take the responsibility out of the BotFrameworkAdapter with respect to how clients are even created, let alone how they might be cached.
2. Improve testability as this isn't really verifiable in its current form. With the factory approach you'll be able to verify the BotFrameworkAdapter calls to gets client instances when it's supposed to and that's it. Then you can also independently test the implementation of any IConnectorClientFactory implementations separately.
3. Allows for other implementations around creating/caching IConnectorClient's to be implemented. You never know what other patterns people might want/need. Also, by returning the interface instead of the ConnectorClient class you would give some really good flexibility around the underlying implementation of the client itself in the long run.
Funny side note here: you already have some of this design in the Classic project. I wouldn't necessarily use that API design directly, but I definitely think it was definitely factored out more cleanly there.

I have changed to use GetOrUpdate()
I have also changed the other cahce in this class to use ConcurrentDictionary<>
I have filed a DCR #883 to discuss whether we should move to a ClassFactory model for connector Client


Normalization time increases drastically as images increase in size

My apologies for the long post!
I noticed significant increases in processing time for longer images, and investigated a bit further. I identified torch.quantile() to be the cause of this holdup, and decided to run some tests. In a CUDA-enabled environment, I took 3 images, each with 14 channels of data.
img1 was small enough to run through .eval_small_image() (564x984; 15MB)
img2 was just large enough to run through .eval_medium_image() (1590x1590; 68MB)
img3 really needed to be tiled using .eval_medium_image(), and resembles a proper usecase: (3792x4690; 475MB)
In instanseg.utils.utils.percentile_normalize(), I exchanged the code for elif isintance(img, torch.Tensor): with the following:
    elif isinstance(img, torch.Tensor):
        import time

        assert img.ndim == 2 or img.ndim == 3, "Image must be 2D or 3D, got image of shape" + str(img.shape)
        img = torch.atleast_3d(img)
        channel_axis = np.argmin(img.shape)
        img = _move_channel_axis(img, to_back=True)
        print(img.shape)
        for c in range(img.shape[-1]):
            im_temp = img[::subsampling_factor, ::subsampling_factor, c]

            time_start = time.perf_counter()
            (p_min, p_max) = torch.quantile(im_temp, torch.tensor([percentile / 100, (100 - percentile) / 100],device = im_temp.device))
            time_end = time.perf_counter()
            print(f"Channel {c+1}")
            print(f"torch.quantile() ran in {round((time_end - time_start) * 1000)} milliseconds. p_min = {p_min}, p_max = {p_max}")

            time_start = time.perf_counter()
            (p_min, p_max) = np.percentile(im_temp.cpu(), [percentile, 100 - percentile])
            time_end = time.perf_counter()
            print(f"np.percentile() ran in {round((time_end - time_start) * 1000)} milliseconds. p_min = {p_min}, p_max = {p_max}")

            img[:, :, c] = (img[:, :, c] - p_min) / max(epsilon, p_max - p_min)
       # img = img / np.maximum(0.01, torch.max(img))
        return img.movedim(2, channel_axis)

To make sure I wasn't running into machine-specific issues, I ran it on 2 different CUDA-enabled systems. To make a long post short, it does not seem to be machine-specific, and only when the image is small enough (sub ~1000x1000 pixels), torch.quantile() will eventually outperform numpy.percentile(). In this test, for the smallest image, since torch.quantile() takes much longer to initialize, you would only benefit from using torch after around 30 channels. However, for larger images, torch.quantile() takes 20-30x longer per channel.
To note: I also printed the solutions, and they seem to be effectively identical.
Output machine 1
Machine specs:
GPU: NVIDIA Quadro RTX 6000 24 GB
CPU: Intel Xeon Gold 6252 (48/96 cores)
RAM: 1024 GB
img1:
torch.Size([894, 512, 14])
Channel 1
torch.quantile() ran in 238 milliseconds. p_min = 45.603275299072266, p_max = 54484.3515625
np.percentile() ran in 9 milliseconds. p_min = 45.60327671813965, p_max = 54484.336394531325
Channel 2
torch.quantile() ran in 1 milliseconds. p_min = 77.37503051757812, p_max = 64338.1328125
np.percentile() ran in 6 milliseconds. p_min = 77.3750230407715, p_max = 64338.1228007813
Channel 3
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 52871.33203125
np.percentile() ran in 6 milliseconds. p_min = 0.0, p_max = 52871.31315625011
Channel 4
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 53817.76171875
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 53817.4671406266
Channel 5
torch.quantile() ran in 0 milliseconds. p_min = 12.1524658203125, p_max = 37131.609375
np.percentile() ran in 8 milliseconds. p_min = 12.15246533203125, p_max = 37131.32374218905
Channel 6
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 46204.94140625
np.percentile() ran in 6 milliseconds. p_min = 0.0, p_max = 46204.92249609385
Channel 7
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 33418.95703125
np.percentile() ran in 7 milliseconds. p_min = 0.0, p_max = 33418.93535156261
Channel 8
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 15504.408203125
np.percentile() ran in 7 milliseconds. p_min = 0.0, p_max = 15504.359059570581
Channel 9
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 38311.11328125
np.percentile() ran in 8 milliseconds. p_min = 0.0, p_max = 38310.90800390735
Channel 10
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 31367.849609375
np.percentile() ran in 8 milliseconds. p_min = 0.0, p_max = 31367.828318359494
Channel 11
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 18702.376953125
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 18702.35234765638
Channel 12
torch.quantile() ran in 1 milliseconds. p_min = 7.415971279144287, p_max = 16703.046875
np.percentile() ran in 8 milliseconds. p_min = 7.415970895290375, p_max = 16702.843712891725
Channel 13
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 22979.123046875
np.percentile() ran in 6 milliseconds. p_min = 0.0, p_max = 22979.078361328367
Channel 14
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 31495.908203125
np.percentile() ran in 7 milliseconds. p_min = 0.0, p_max = 31495.674972657514

img2:
torch.Size([1445, 1445, 14])
Channel 1
torch.quantile() ran in 899 milliseconds. p_min = 0.0, p_max = 50821.3984375
np.percentile() ran in 31 milliseconds. p_min = 0.0, p_max = 50821.158156252575
Channel 2
torch.quantile() ran in 897 milliseconds. p_min = 5.70437479019165, p_max = 61563.16015625
np.percentile() ran in 26 milliseconds. p_min = 5.704373264312743, p_max = 61563.14618750015
Channel 3
torch.quantile() ran in 864 milliseconds. p_min = 0.0, p_max = 47977.23828125
np.percentile() ran in 35 milliseconds. p_min = 0.0, p_max = 47977.19075000051
Channel 4
torch.quantile() ran in 857 milliseconds. p_min = 0.0, p_max = 49092.7265625
np.percentile() ran in 33 milliseconds. p_min = 0.0, p_max = 49092.67246875058
Channel 5
torch.quantile() ran in 900 milliseconds. p_min = 6.3225860595703125, p_max = 30912.912109375
np.percentile() ran in 213 milliseconds. p_min = 6.32258589553833, p_max = 30912.910046875022
Channel 6
torch.quantile() ran in 877 milliseconds. p_min = 0.0, p_max = 42940.984375
np.percentile() ran in 33 milliseconds. p_min = 0.0, p_max = 42940.80493750192
Channel 7
torch.quantile() ran in 856 milliseconds. p_min = 0.0, p_max = 30794.328125
np.percentile() ran in 37 milliseconds. p_min = 0.0, p_max = 30794.32620312502
Channel 8
torch.quantile() ran in 874 milliseconds. p_min = 0.0, p_max = 12168.775390625
np.percentile() ran in 34 milliseconds. p_min = 0.0, p_max = 12168.715929688136
Channel 9
torch.quantile() ran in 886 milliseconds. p_min = 0.0, p_max = 27722.63671875
np.percentile() ran in 35 milliseconds. p_min = 0.0, p_max = 27722.62359375014
Channel 10
torch.quantile() ran in 843 milliseconds. p_min = 0.0, p_max = 22568.791015625
np.percentile() ran in 30 milliseconds. p_min = 0.0, p_max = 22568.56404687743
Channel 11
torch.quantile() ran in 847 milliseconds. p_min = 0.0, p_max = 14677.3369140625
np.percentile() ran in 37 milliseconds. p_min = 0.0, p_max = 14677.22382812621
Channel 12
torch.quantile() ran in 908 milliseconds. p_min = 0.8015445470809937, p_max = 12990.296875
np.percentile() ran in 39 milliseconds. p_min = 0.8015444955825806, p_max = 12990.288250000092
Channel 13
torch.quantile() ran in 886 milliseconds. p_min = 0.0, p_max = 18224.36328125
np.percentile() ran in 40 milliseconds. p_min = 0.0, p_max = 18224.24107812631
Channel 14
torch.quantile() ran in 887 milliseconds. p_min = 0.0, p_max = 23046.9921875
np.percentile() ran in 44 milliseconds. p_min = 0.0, p_max = 23046.882453126174

img3:
torch.Size([4263, 3447, 14])
Channel 1
torch.quantile() ran in 6462 milliseconds. p_min = 0.0, p_max = 41806.796875
np.percentile() ran in 208 milliseconds. p_min = 0.0, p_max = 41806.63937500038
Channel 2
torch.quantile() ran in 6547 milliseconds. p_min = 0.0, p_max = 50695.94921875
np.percentile() ran in 213 milliseconds. p_min = 0.0, p_max = 50695.76109375045
Channel 3
torch.quantile() ran in 5704 milliseconds. p_min = 0.0, p_max = 29233.103515625
np.percentile() ran in 208 milliseconds. p_min = 0.0, p_max = 29232.066640627483
Channel 4
torch.quantile() ran in 5231 milliseconds. p_min = 0.0, p_max = 27806.470703125
np.percentile() ran in 239 milliseconds. p_min = 0.0, p_max = 27806.05726562599
Channel 5
torch.quantile() ran in 6832 milliseconds. p_min = 0.2601371109485626, p_max = 20992.7421875
np.percentile() ran in 271 milliseconds. p_min = 0.26013704538345334, p_max = 20992.67546875016
Channel 6
torch.quantile() ran in 5564 milliseconds. p_min = 0.0, p_max = 29121.232421875
np.percentile() ran in 261 milliseconds. p_min = 0.0, p_max = 29120.809140626014
Channel 7
torch.quantile() ran in 5388 milliseconds. p_min = 0.0, p_max = 20305.076171875
np.percentile() ran in 245 milliseconds. p_min = 0.0, p_max = 20304.672578125967
Channel 8
torch.quantile() ran in 5885 milliseconds. p_min = 0.0, p_max = 7189.10595703125
np.percentile() ran in 255 milliseconds. p_min = 0.0, p_max = 7188.910722656718
Channel 9
torch.quantile() ran in 6170 milliseconds. p_min = 0.0, p_max = 14426.265625
np.percentile() ran in 275 milliseconds. p_min = 0.0, p_max = 14425.510937501807
Channel 10
torch.quantile() ran in 5316 milliseconds. p_min = 0.0, p_max = 12188.7802734375
np.percentile() ran in 253 milliseconds. p_min = 0.0, p_max = 12188.661054687786
Channel 11
torch.quantile() ran in 5021 milliseconds. p_min = 0.0, p_max = 9555.8505859375
np.percentile() ran in 239 milliseconds. p_min = 0.0, p_max = 9555.780585937668
Channel 12
torch.quantile() ran in 6853 milliseconds. p_min = 0.0, p_max = 9956.83984375
np.percentile() ran in 307 milliseconds. p_min = 0.0, p_max = 9956.820156250047
Channel 13
torch.quantile() ran in 6180 milliseconds. p_min = 0.0, p_max = 14116.533203125
np.percentile() ran in 245 milliseconds. p_min = 0.0, p_max = 14116.162968750887
Channel 14
torch.quantile() ran in 5962 milliseconds. p_min = 0.0, p_max = 12323.76171875
np.percentile() ran in 261 milliseconds. p_min = 0.0, p_max = 12323.558281250487

Output machine 2
Machine specs:
GPU: NVIDIA Quadro GP100 16 GB
CPU: Intel Xeon Gold 5122 (8/16 cores)
RAM: 176 GB
img1:
torch.Size([894, 512, 14])
Channel 1
torch.quantile() ran in 201 milliseconds. p_min = 45.603275299072266, p_max = 54484.3515625
np.percentile() ran in 6 milliseconds. p_min = 45.60327671813965, p_max = 54484.336394531325
Channel 2
torch.quantile() ran in 1 milliseconds. p_min = 77.37503051757812, p_max = 64338.1328125
np.percentile() ran in 5 milliseconds. p_min = 77.3750230407715, p_max = 64338.1228007813
Channel 3
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 52871.33203125
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 52871.31315625011
Channel 4
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 53817.76171875
np.percentile() ran in 4 milliseconds. p_min = 0.0, p_max = 53817.4671406266
Channel 5
torch.quantile() ran in 0 milliseconds. p_min = 12.1524658203125, p_max = 37131.609375
np.percentile() ran in 6 milliseconds. p_min = 12.15246533203125, p_max = 37131.32374218905
Channel 6
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 46204.94140625
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 46204.92249609385
Channel 7
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 33418.95703125
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 33418.93535156261
Channel 8
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 15504.408203125
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 15504.359059570581
Channel 9
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 38311.11328125
np.percentile() ran in 6 milliseconds. p_min = 0.0, p_max = 38310.90800390735
Channel 10
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 31367.849609375
np.percentile() ran in 7 milliseconds. p_min = 0.0, p_max = 31367.828318359494
Channel 11
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 18702.376953125
np.percentile() ran in 4 milliseconds. p_min = 0.0, p_max = 18702.35234765638
Channel 12
torch.quantile() ran in 1 milliseconds. p_min = 7.415971279144287, p_max = 16703.046875
np.percentile() ran in 6 milliseconds. p_min = 7.415970895290375, p_max = 16702.843712891725
Channel 13
torch.quantile() ran in 1 milliseconds. p_min = 0.0, p_max = 22979.123046875
np.percentile() ran in 4 milliseconds. p_min = 0.0, p_max = 22979.078361328367
Channel 14
torch.quantile() ran in 0 milliseconds. p_min = 0.0, p_max = 31495.908203125
np.percentile() ran in 5 milliseconds. p_min = 0.0, p_max = 31495.674972657514

img2:
torch.Size([1445, 1445, 14])
Channel 1
torch.quantile() ran in 626 milliseconds. p_min = 0.0, p_max = 50821.3984375
np.percentile() ran in 24 milliseconds. p_min = 0.0, p_max = 50821.158156252575
Channel 2
torch.quantile() ran in 635 milliseconds. p_min = 5.70437479019165, p_max = 61563.16015625
np.percentile() ran in 14 milliseconds. p_min = 5.704373264312743, p_max = 61563.14618750015
Channel 3
torch.quantile() ran in 636 milliseconds. p_min = 0.0, p_max = 47977.23828125
np.percentile() ran in 21 milliseconds. p_min = 0.0, p_max = 47977.19075000051
Channel 4
torch.quantile() ran in 596 milliseconds. p_min = 0.0, p_max = 49092.7265625
np.percentile() ran in 19 milliseconds. p_min = 0.0, p_max = 49092.67246875058
Channel 5
torch.quantile() ran in 626 milliseconds. p_min = 6.3225860595703125, p_max = 30912.912109375
np.percentile() ran in 22 milliseconds. p_min = 6.32258589553833, p_max = 30912.910046875022
Channel 6
torch.quantile() ran in 612 milliseconds. p_min = 0.0, p_max = 42940.984375
np.percentile() ran in 18 milliseconds. p_min = 0.0, p_max = 42940.80493750192
Channel 7
torch.quantile() ran in 644 milliseconds. p_min = 0.0, p_max = 30794.328125
np.percentile() ran in 21 milliseconds. p_min = 0.0, p_max = 30794.32620312502
Channel 8
torch.quantile() ran in 629 milliseconds. p_min = 0.0, p_max = 12168.775390625
np.percentile() ran in 19 milliseconds. p_min = 0.0, p_max = 12168.715929688136
Channel 9
torch.quantile() ran in 622 milliseconds. p_min = 0.0, p_max = 27722.63671875
np.percentile() ran in 25 milliseconds. p_min = 0.0, p_max = 27722.62359375014
Channel 10
torch.quantile() ran in 592 milliseconds. p_min = 0.0, p_max = 22568.791015625
np.percentile() ran in 16 milliseconds. p_min = 0.0, p_max = 22568.56404687743
Channel 11
torch.quantile() ran in 606 milliseconds. p_min = 0.0, p_max = 14677.3369140625
np.percentile() ran in 21 milliseconds. p_min = 0.0, p_max = 14677.22382812621
Channel 12
torch.quantile() ran in 641 milliseconds. p_min = 0.8015445470809937, p_max = 12990.296875
np.percentile() ran in 23 milliseconds. p_min = 0.8015444955825806, p_max = 12990.288250000092
Channel 13
torch.quantile() ran in 646 milliseconds. p_min = 0.0, p_max = 18224.36328125
np.percentile() ran in 23 milliseconds. p_min = 0.0, p_max = 18224.24107812631
Channel 14
torch.quantile() ran in 631 milliseconds. p_min = 0.0, p_max = 23046.9921875
np.percentile() ran in 25 milliseconds. p_min = 0.0, p_max = 23046.882453126174

img3
torch.Size([4263, 3447, 14])
Channel 1
torch.quantile() ran in 4789 milliseconds. p_min = 0.0, p_max = 41806.796875
np.percentile() ran in 172 milliseconds. p_min = 0.0, p_max = 41806.63937500038
Channel 2
torch.quantile() ran in 4877 milliseconds. p_min = 0.0, p_max = 50695.94921875
np.percentile() ran in 165 milliseconds. p_min = 0.0, p_max = 50695.76109375045
Channel 3
torch.quantile() ran in 4249 milliseconds. p_min = 0.0, p_max = 29233.103515625
np.percentile() ran in 172 milliseconds. p_min = 0.0, p_max = 29232.066640627483
Channel 4
torch.quantile() ran in 3933 milliseconds. p_min = 0.0, p_max = 27806.470703125
np.percentile() ran in 171 milliseconds. p_min = 0.0, p_max = 27806.05726562599
Channel 5
torch.quantile() ran in 5087 milliseconds. p_min = 0.2601371109485626, p_max = 20992.7421875
np.percentile() ran in 223 milliseconds. p_min = 0.26013704538345334, p_max = 20992.67546875016
Channel 6
torch.quantile() ran in 4174 milliseconds. p_min = 0.0, p_max = 29121.232421875
np.percentile() ran in 201 milliseconds. p_min = 0.0, p_max = 29120.809140626014
Channel 7
torch.quantile() ran in 3999 milliseconds. p_min = 0.0, p_max = 20305.076171875
np.percentile() ran in 164 milliseconds. p_min = 0.0, p_max = 20304.672578125967
Channel 8
torch.quantile() ran in 4380 milliseconds. p_min = 0.0, p_max = 7189.10595703125
np.percentile() ran in 211 milliseconds. p_min = 0.0, p_max = 7188.910722656718
Channel 9
torch.quantile() ran in 4521 milliseconds. p_min = 0.0, p_max = 14426.265625
np.percentile() ran in 215 milliseconds. p_min = 0.0, p_max = 14425.510937501807
Channel 10
torch.quantile() ran in 3925 milliseconds. p_min = 0.0, p_max = 12188.7802734375
np.percentile() ran in 161 milliseconds. p_min = 0.0, p_max = 12188.661054687786
Channel 11
torch.quantile() ran in 3762 milliseconds. p_min = 0.0, p_max = 9555.8505859375
np.percentile() ran in 191 milliseconds. p_min = 0.0, p_max = 9555.780585937668
Channel 12
torch.quantile() ran in 5079 milliseconds. p_min = 0.0, p_max = 9956.83984375
np.percentile() ran in 247 milliseconds. p_min = 0.0, p_max = 9956.820156250047
Channel 13
torch.quantile() ran in 4613 milliseconds. p_min = 0.0, p_max = 14116.533203125
np.percentile() ran in 143 milliseconds. p_min = 0.0, p_max = 14116.162968750887
Channel 14
torch.quantile() ran in 4474 milliseconds. p_min = 0.0, p_max = 12323.76171875
np.percentile() ran in 202 milliseconds. p_min = 0.0, p_max = 12323.558281250487

Thank you enormously for making the comparison! I'm at the i2k conference so I can't replicate this immediately - but will have a look asap.
if it's easy for you to replicate this, could you add "torch.cuda.synchronize()" before the end of the timing? Sometimes torch waits before it performs any computation (Although here it seems to be the case).

For fun, I tried a really large image (torch.Size([11490, 10671, 15]); 4.1 GB) on system 1. There, torch.quantile() failed because the input was too large, but np.percentile() ran it just fine - with in total ~28 GB RAM usage.
np.percentile() ran in 1300 milliseconds. p_min = 0.0, p_max = 9506.735010742603
Using CUDA-assisted tiling, this image ran in about 2 minutes, with ~43 GB RAM usage at its peak.
I don't think I need to mention just how amazing this model is!

Thank you enormously for making the comparison! I'm at the i2k conference so I can't replicate this immediately - but will have a look asap.
if it's easy for you to replicate this, could you add "torch.cuda.synchronize()" before the end of the timing? Sometimes torch waits before it performs any computation (Although here it seems to be the case).

I changed the code to the following:
time_start = time.perf_counter()
(p_min, p_max) = torch.quantile(im_temp, torch.tensor([percentile / 100, (100 - percentile) / 100],device = im_temp.device))
torch.cuda.synchronize()
time_end = time.perf_counter()

With that included, img2 ran as follows on system 1:
torch.Size([1445, 1445, 14])
Channel 1
torch.quantile() ran in 907 milliseconds. p_min = 0.0, p_max = 50821.3984375
np.percentile() ran in 30 milliseconds. p_min = 0.0, p_max = 50821.158156252575
Channel 2
torch.quantile() ran in 902 milliseconds. p_min = 5.70437479019165, p_max = 61563.16015625
np.percentile() ran in 26 milliseconds. p_min = 5.704373264312743, p_max = 61563.14618750015
Channel 3
torch.quantile() ran in 869 milliseconds. p_min = 0.0, p_max = 47977.23828125
np.percentile() ran in 34 milliseconds. p_min = 0.0, p_max = 47977.19075000051
Channel 4
torch.quantile() ran in 860 milliseconds. p_min = 0.0, p_max = 49092.7265625
np.percentile() ran in 32 milliseconds. p_min = 0.0, p_max = 49092.67246875058
Channel 5
torch.quantile() ran in 904 milliseconds. p_min = 6.3225860595703125, p_max = 30912.912109375
np.percentile() ran in 39 milliseconds. p_min = 6.32258589553833, p_max = 30912.910046875022
Channel 6
torch.quantile() ran in 875 milliseconds. p_min = 0.0, p_max = 42940.984375
np.percentile() ran in 33 milliseconds. p_min = 0.0, p_max = 42940.80493750192
Channel 7
torch.quantile() ran in 856 milliseconds. p_min = 0.0, p_max = 30794.328125
np.percentile() ran in 37 milliseconds. p_min = 0.0, p_max = 30794.32620312502
Channel 8
torch.quantile() ran in 869 milliseconds. p_min = 0.0, p_max = 12168.775390625
np.percentile() ran in 34 milliseconds. p_min = 0.0, p_max = 12168.715929688136
Channel 9
torch.quantile() ran in 883 milliseconds. p_min = 0.0, p_max = 27722.63671875
np.percentile() ran in 35 milliseconds. p_min = 0.0, p_max = 27722.62359375014
Channel 10
torch.quantile() ran in 843 milliseconds. p_min = 0.0, p_max = 22568.791015625
np.percentile() ran in 29 milliseconds. p_min = 0.0, p_max = 22568.56404687743
Channel 11
torch.quantile() ran in 844 milliseconds. p_min = 0.0, p_max = 14677.3369140625
np.percentile() ran in 37 milliseconds. p_min = 0.0, p_max = 14677.22382812621
Channel 12
torch.quantile() ran in 905 milliseconds. p_min = 0.8015445470809937, p_max = 12990.296875
np.percentile() ran in 38 milliseconds. p_min = 0.8015444955825806, p_max = 12990.288250000092
Channel 13
torch.quantile() ran in 888 milliseconds. p_min = 0.0, p_max = 18224.36328125
np.percentile() ran in 41 milliseconds. p_min = 0.0, p_max = 18224.24107812631
Channel 14
torch.quantile() ran in 892 milliseconds. p_min = 0.0, p_max = 23046.9921875
np.percentile() ran in 45 milliseconds. p_min = 0.0, p_max = 23046.882453126174

yeah that seems convincing!
I noticed that percentile_normalize is done on the cpu anyway when calling eval_medium_image, as this function assumes the image doesn't fit on the gpu. Maybe the reason it's slow, but strange that it's so much slower than numpy. Maybe worth raising with torch.
With your GPU specs you could easily increase the batch size and/or the tile size at inference by the way, that can sometimes lead to drastic speed increases.

yeah that seems convincing!
I noticed that percentile_normalize is done on the cpu anyway when calling eval_medium_image, as this function assumes the image doesn't fit on the gpu. Maybe the reason it's slow, but strange that it's so much slower than numpy. Maybe worth raising with torch.

Likely, torch's implementation focuses on performing .quantile() as fast as possible for many many small images, which you can see from the img1 data being essentially instantaneous except for the first iterations. If you would do that operation for 1000's of images, you can see why it would be beneficial to use torch over numpy :)

With your GPU specs you could easily increase the batch size and/or the tile size at inference by the way, that can sometimes lead to drastic speed increases.

Indeed, we already increased the tile_size to 2048, which will occupy ~12 GB of VRAM for our 14-channel images!

Now fixed. Thanks.


BItcoin Brc20 sendrawtransaction Error(mandatory-script-verify-flag-failed (Witness program hash mismatch))

package main
import (
"fmt"
"github.com/btcsuite/btcd/btcutil"
"github.com/btcsuite/btcd/chaincfg"
"github.com/okx/go-wallet-sdk/coins/bitcoin/brc20"
"github.com/rs/zerolog/log"
)
func main() {

builder := brc20.NewTxBuildV1(&chaincfg.MainNetParams)

contentType := "text/plain;charset=utf-8"
body := []byte(fmt.Sprintf(`{"p":"brc-20","op":"%s","tick":"%s","amt":"%s"}`, "transfer", "sats", "1"))

inscription := brc20.NewInscription(contentType, body)

privatekeyStr := "wif key"
privateKeyWif, err := btcutil.DecodeWIF(privatekeyStr)
if err != nil {
	log.Error().Msg("DecodeWIF err: " + err.Error())
	return
}
builder.AddInput("434876ec149a94971a630ebb3a70ffa4869ab1a992f1500b4b035a21cbbe7c80",
	1,
	privateKeyWif.PrivKey.Key.String(),
	"bc1pg3zt4vv6a8hg7y935xjw26rvyc879wcwxj5fxzyd8m2mvwqpleqszlwqm7",
	"81024",
	inscription,
)
builder.AddOutput("bc1pg3zt4vv6a8hg7y935xjw26rvyc879wcwxj5fxzyd8m2mvwqpleqszlwqm7", "546")
builder.AddOutput("bc1pg3zt4vv6a8hg7y935xjw26rvyc879wcwxj5fxzyd8m2mvwqpleqszlwqm7", "77000")
tx, err := builder.Build()
if err != nil {
	log.Error().Msg("Build err: " + err.Error())
	return
}
fmt.Println("tx: ", tx)
}

ERROR:
{
    "result": null,
    "error": {
        "code": -26,
        "message": "mandatory-script-verify-flag-failed (Witness program hash mismatch)"
    },
    "id": "getblock.io"
}

How do I fix this?

To help you get started quickly, here are some code examples:
https://github.com/okx/go-wallet-sdk/blob/main/coins/bitcoin/inscribe_test.go#L10.
You also need to understand the two stages of inscriptions: Commit and Reveal. Here's something you might want to know: Taproot Assets.


Documentation Update for KIC 2.2

Is there an existing issue for this?

[X] I have searched the existing issues

Problem Statement
Issue to track the creation of documentation around Gateway API milestone 1.
Proposed Solution
No response
Additional information
No response
Acceptance Criteria

[ ] 2.2 documentation added to https://github.com/kong/docs.konghq.com
[ ] banner is created to indicate this is a technical preview feature
[ ] Gateway + HTTPRoute guide added to 2.2 documentation
[ ] Update implementations page on link

blocked on https://github.com/Kong/kubernetes-ingress-controller/issues/2231

https://github.com/Kong/docs.konghq.com/pull/3621 for basics and https://github.com/Kong/docs.konghq.com/pull/3622 for GWAPI.


gen_snapshot performance regression

gen_snapshot runtime performance when used for android arm release builds regressed significantly(45s to ~30min) after https://dart-review.googlesource.com/c/sdk/+/138089.

Oof. If I reland this, I'll definitely merge the two CLs so that we don't have the work landing separately and causing that for any length of time in between.
It doesn't look, checking golem, that this was still the case after the second CL landed: do we have any numbers on that so we know if there was still a performance issue seen (though not nearly as severe) that needs to be investigated or if just relanding the merged CL should be okay?

Also given the graphs I see on golem, I'm surprised it didn't alert on this, even though it did alert on later performance changes. Are the compile time benchmarks not checked for performance changes? /cc @sortie

Only a single data point is gathered for the compilation time benchmarks per commit (because this is offloaded to the cloud when the .apk is built once), but the performance change detection requires at least two data points, so changes in compilation time are not detected.


Better readme examples

The static model example in the README isn't correct as it's missing the yielded object.
Also includes a more complete belongs_to association example showing exactly which module to mixin.

Can we merge? :-)


Automatic rush update --full

This is an automatic PR generated weekly with changes from running the command rush update --full

This is manually triggered to check whether there's any updates for core-auth version bump.


Generated sagan WAR file not working in Tomcat/JRE 8

After following instructions at this URL: https://spring.io/guides/gs/convert-jar-to-war/ , I am unable to generate a '.war' file that loads properly in Tomcat with JRE 8 (I copy the file from <sagan_project_root>/sagan-site/build/libs/sagan-site.war to the Tomcat 'webapps' directory). Tomcat unpacks the .war file, starts it, but the web application won't load. I do not see errors in the logs either. It's almost like it's missing a web.xml, but many posts say it's not required).
The URL leads to the following articles:
http://docs.spring.io/spring-boot/docs/current/reference/htmlsingle/#build-tool-plugins-gradle-packaging
http://docs.spring.io/spring-boot/docs/current/reference/htmlsingle/#howto-create-a-deployable-war-file
... but whatever I put in the build.gradle files, it doesn't generate a working .war file.
I tried setting the values in <sagan_project_root>/sagan-site/build.gradle and also in <sagan_project_root>/build.gradle, without success:
apply plugin: 'war'

war {
    baseName = 'myapp'
    version =  '0.5.0'
}

repositories {
    jcenter()
    maven { url "http://repo.spring.io/libs-snapshot" }
}

dependencies {
    compile("org.springframework.boot:spring-boot-starter-web")
    providedRuntime("org.springframework.boot:spring-boot-starter-tomcat")
    ...
}

Can someone provide the detailed exact instructions on how to accomplish this for the 'sagan' GitHub project? For Gradle, please.
Thank you.

I don't see anything wrong with the config above. Maybe if you push your attempt up to your fork of this repo someone can see exactly what you tried and suggest a change via a PR?
Did you try the sample in the guide? Does that work for you?

My fork is available here: https://github.com/cotjoey/sagan
I modified the file: sagan/sagan-site/build.gradle
The generated .war file goes into: sagan/sagan-site/build/libs/sagan-site-1.0.0.war
When copied into <tomcat_home>/webapps, the catalina.out log file only says the .war deployment was successful, nothing else:
08-May-2017 22:44:42.244 INFO [localhost-startStop-2] org.apache.catalina.startup.HostConfig.deployWAR Deployment of web application archive /Users/myuser/Documents/IT/apache-tomcat-8.5.14/webapps/sagan-site-1.0.0.war has finished in 13,196 ms
As per another Spring doc, I tried adding extends SpringBootServletInitializerto sagan.SiteApplication.java - That is the only time I saw the SpringBoot logo in the catalina.out log file, but the application failed to load because of:
2017-05-09 10:31:52.940  INFO 4822 --- [ost-startStop-4] sagan.SiteApplication                    : No active profile set, falling back to default profiles: default
2017-05-09 10:32:18.574  WARN 4822 --- [ost-startStop-4] ationConfigEmbeddedWebApplicationContext : Exception encountered during context initialization - cancelling refresh attempt: org.springframework.beans.factory.UnsatisfiedDependencyException: Error creating bean with name 'atomFeedController' defined in file [/Users/myuser/Documents/IT/apache-tomcat-8.5.14/webapps/sagan-site-1.0.0/WEB-INF/classes/sagan/blog/support/AtomFeedController.class]: Unsatisfied dependency expressed through constructor parameter 0; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'blogService': Injection of autowired dependencies failed; nested exception is java.lang.IllegalArgumentException: Could not resolve placeholder 'disqus_shortname' in string value "${disqus_shortname}"

Is that extend required for the .war to work in Tomcat?

Well it's a bit more than just adding extends SpringBootServletInitializer to the main class. Did you read the guide your linked to (https://spring.io/guides/gs/convert-jar-to-war/)? You said you "followed" the guide, but it seems like maybe you missed the point somewhere. If I were you I'd read it again and see if it makes sense this time.


No icons to minimize/maximize application

Environment
Windows build number: 10.0.21370.1
Your Distribution version: Ubuntu 21.04
Your WSLg version: 1.0.19

Steps to reproduce

Install gnucash. In my case, I built it from git repo.
Run gnucash. No icons to minimize, maximize, or close window.

WSL logs:
sumitb@sumitb-dsk-0:~$ cat /mnt/wslg/versions.txt
WSLg ( x86_64 ): 1.0.19+Branch.main.Sha.2a47bf20c239b523d7413f315cbc9ee60dfde831
Mariner: VERSION="1.0.20210224"
FreeRDP: 5f083fa0b97d433d6204985f6047886e29c1c61e
weston: 16de531f00aa3dfd17e0de74c8f49e9fd7cec617
pulseaudio: 2f0f0b8c3872780f15e275fc12899f4564f01bd5
mesa:
sumitb@sumitb-dsk-0:~$ echo $DISPLAY
:0
sumitb@sumitb-dsk-0:~$ ls -la /tmp/.X11-unix
lrwxrwxrwx 1 root root 19 May  3 13:17 /tmp/.X11-unix -> /mnt/wslg/.X11-unix
sumitb@sumitb-dsk-0:~$ ls -la /tmp/.X11-unix/
total 0
drwxrwxrwx 2 root   root   60 May  3 13:17 .
drwxrwxrwt 5 root   root  220 May  3 13:17 ..
srwxrwxrwx 1 sumitb users   0 May  3 13:17 X0

Attach WSLg logs from  /mnt/wslg
weston.log

Expected behavior

Should see icons

Actual behavior

No icons to minimize, maximize, or close the application.
Message on command prompt
WaylandCompositor requires eglBindWaylandDisplayWL, eglUnbindWaylandDisplayWL and eglQueryWaylandBuffer.
Nested Wayland compositor could not initialize EGL

This isn't a WSL bug, it's normal behavior for gtk3 apps.
Install gnome-tweaks and open it, then enable the minimize/maximize buttons in there.

Wait, I just noticed you said you don't see a close icon either. If that's the case, there's probably something wrong with the window manager. A screenshot of it might be helpful.

@reanimus - I did see a close icon. Sorry for the confusion. And, using gnome-tweaks worked as a charm. Thanks!
Should these messages on the console be ignored?
WaylandCompositor requires eglBindWaylandDisplayWL, eglUnbindWaylandDisplayWL and eglQueryWaylandBuffer.
Nested Wayland compositor could not initialize EGL

@bhardwajs , I also confirm close button is visible. so no more issue here.
Regarding to those EGL messages, this is currently expected since our Wayland compositor is not yet supporting EGL based composition, currently it only works with Wayland SHM surface.

I tried installing gnome-tweaks, but it fails with "Depends: gir1.2-handy-0.0 but it is not installable" I tried looking it up in the Ubuntu package search (I have 20.04 installed) and the package doesn't exist for Intel:
Package gir1.2-handy-0.0
focal (20.04LTS) (introspection): GObject introspection files for libhandy
0.0.13-1: amd64 arm64 armhf ppc64el s390x

Am I stuck? Is there a work-around? Thanks in advance!

amd64 is the one you want

I tried installing gnome-tweaks, but it fails with "Depends: gir1.2-handy-0.0 but it is not installable" I tried looking it up in the Ubuntu package search (I have 20.04 installed) and the package doesn't exist for Intel:
Package gir1.2-handy-0.0
focal (20.04LTS) (introspection): GObject introspection files for libhandy
0.0.13-1: amd64 arm64 armhf ppc64el s390x

Am I stuck? Is there a work-around? Thanks in advance!

amd64 is the one you want for intel

Thanks for the quick reply. Any idea why it won't install then? The full error message is:
The following packages have unmet dependencies:
 gnome-tweaks : Depends: gir1.2-handy-0.0 but it is not installable
                Depends: gnome-shell-extension-prefs but it is not going to be installed
E: Unable to correct problems, you have held broken packages.

I have tried sudo dpkg --configure -a, sudo apt-get install -f, sudo apt-get clean && sudo apt-get update followed by sudo apt upgrade
No luck. I just want to turn on the minimize and maximize buttons on the Linux windows. (not having them as a default seems odd, but it is what it is).

Thanks for the quick reply. Any idea why it won't install then? The full error message is:
The following packages have unmet dependencies:
 gnome-tweaks : Depends: gir1.2-handy-0.0 but it is not installable
                Depends: gnome-shell-extension-prefs but it is not going to be installed
E: Unable to correct problems, you have held broken packages.

I have tried sudo dpkg --configure -a, sudo apt-get install -f, sudo apt-get clean && sudo apt-get update followed by sudo apt upgrade
No luck. I just want to turn on the minimize and maximize buttons on the Linux windows. (not having them as a default seems odd, but it is what it is).

Gnome/GTK doesn't normally have a task bar or any equivalent of it so minimizing is less common, I think.
Have you tried looking through some of the solutions from this thread?

@reanimus thank you for the help. I did go through most of those steps, but not all. I got some new info with this:
sudo apt install gnome-tweaks -o Debug::pkgProblemResolver=true -o Debug::Acquire::http=true
Reading package lists... Done
Building dependency tree
Reading state information... Done
Starting pkgProblemResolver with broken count: 1
Starting 2 pkgProblemResolver with broken count: 1
Investigating (0) gnome-tweaks:amd64 < none -> 3.34.0-2ubuntu1 @un puN Ib >
Broken gnome-tweaks:amd64 Depends on gir1.2-handy-0.0:amd64 < none @un H >
Broken gnome-tweaks:amd64 Depends on gnome-shell-extension-prefs:amd64 < none | 3.36.1-5ubuntu1 @un uH >
  Considering gnome-shell-extension-prefs:amd64 1 as a solution to gnome-tweaks:amd64 9999
    Reinst Failed early because of gnome-shell:amd64
Done
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:

The following packages have unmet dependencies:
 gnome-tweaks : Depends: gir1.2-handy-0.0 but it is not installable
                Depends: gnome-shell-extension-prefs but it is not going to be installed
E: Unable to correct problems, you have held broken packages.

That gave me a little more info. I installed and ran the Synaptic Package manager tool and found gnome-shell-extension-prefs and tried to install it and got this info:

Which seems to indicate that the gnome-shell is too new? In any case, I'm too scared to mess with that for fear of breaking everything. Not sure why it says 'is to be installed'. 3.36.4-lumbutul~20.04.2 IS installed.
I will just have to do that part of my testing on a real Linux box I guess.

It sounds like that package (gnome-shell-extension-prefs) has a dependency on that specific (older) version of gnome-shell. Chances are they're bumping gnome versions right now and that's the source of the incompatibility.


[GUI] Real time tracking is discontinued when any device is hidden

bus-14 tracking

bus-14 tracking when bus-17 is hidden

bus-14 tracking when bus-17 is shown

Affected version: 62.3-20190603

It also happens if you click on "Hide All" and "Show All".

show all

hide all

show all

Ticket analysis meeting (May, 22nd): Out of roadmap column in this moment.

Tested in baseline v0.4.3-rc2, k8s environment:

The same behaviour happens in v0.5.0-rc1. Docker Compose environment, using VerneMQ as iotagent.

The same behaviour happens in v0.5.0-rc1. Kubernetes - K8s environment, using Mosca

The real-time tracking mechanism was disabled (dojot 0.6).


[Feature Request]: Save files should be .gdt extension

Is your feature request related to a problem? Please describe.
Users may find unrelated JSON files and think they are valid configs for GDTuber. Giving save files a custom extension can both make it easier to remember what the file is, and prevent users from trying to load the wrong file.
Describe the solution you'd like
Save files should be saved with the .gdt extension.
When loading a file, the file dialog should filter to .gdt files
Describe alternatives you've considered
No response
Additional Context
No response

.gdt could potentially be used in the future by Godot for trait files, so maybe make it more explicit like .gdtuber?
(reference)


SWA build configuration - skip_deploy_on_missing_secrets is not documented

When a Static Web App is built without secrets, or without access to the necessary secrets, such as in forks, the following error occurs:
deployment_token was not provided.
The deployment_token is required for deploying content. If you'd like to continue the run without deployment, add the configuration skip_deploy_on_missing_secrets set to true in your workflow file
An unknown exception has occurred

The above variable skip_deploy_on_missing_secrets is not documented in the article on build configuration for SWAs.
The issue is also mentioned in https://github.com/Azure/static-web-apps/issues/679 and on StackOverflow.

Document Details
⚠ Do not edit this section. It is required for learn.microsoft.com ➟ GitHub issue linking.

ID: b4cbd10e-57b8-de97-b84f-6e46f865c662
Version Independent ID: 533b11b0-5ada-f9c6-65f1-5966df142fa8
Content: Build configuration for Azure Static Web Apps
Content Source: articles/static-web-apps/build-configuration.md
Service: static-web-apps
GitHub Login: @craigshoemaker
Microsoft Alias: cshoe

@Xtrah
Thanks for your feedback! We will investigate and update as appropriate.


Package request: fslint

Project description

FSlint is a utility to find and clean various forms of lint on a filesystem.
I.E. unwanted or problematic cruft in your files or file names.
For example, one form of lint it finds is duplicate files.
It has both GUI and command line modes.

Metadata

homepage URL: https://www.pixelbeat.org/fslint/
source URL: https://github.com/pixelb/fslint
license: gpl2+
platforms: linux

Hello, fslint is a tool I miss as well... I'll give it a try at packaging tommorrow

Just added a preliminary build of fslint... apparently it needs some patching of functionality.
Let's move the discussion here: https://github.com/NixOS/nixpkgs/pull/92833

This issue can be closed. The package fslint was released yesterday.


How to pass attention mask?

For a transformer model, sure seems to be lacking the basics.  How am I supposed to do supervised fine tuning (train on inputs only) if I can't pass a mask.

Yeesh, apparently the authors are wrong and this is a transformer model. RIP authors.

Apparently I'm on an older build
https://github.com/state-spaces/mamba/pull/70/commits/9f75b2bb500c462875d2c51ea8b7998f37b8e49b


[Bug Report] Cannot enter 0 at the end of a digit with a decimal point in the el-input which type is 'number' and value use v-model.number

Element UI version
2.11.1
OS/Browsers version
Mac OS X 10_13_6/Chrome_75.0.3770.142
Vue version
2.6.10
Reproduction Link
https://github.com/care100/element-elInput-bug/blob/master/test-element-ui.html
Steps to reproduce
Set 'number' as the type of el-input, use v-model.number. Input 1.01 in the el-input. When input 0, the decimal point and 0 will be deleted.
What is Expected?
Can input 1.01, do not delete decimal point and 0.
What is actually happening?
Decimal point and 0 will be deleted

If i want to input 1.01, i can only input 1.1, then left shift cursor, input 0 finally!

https://element.eleme.io/#/zh-CN/component/input#input-shu-ru-kuang


Create a basic example

Because I couldn't get the codec to work I adapted a basic example from tokio, to see if that would work, and it does, so given this crate has no examples, I figured I submit it as a pull request.

Ok, the reason the build fails on windows is that it's a linux domain socket example. It would be more universal if it were using tcp sockets, but I want to move onto some other work now. Sorry.

OK, I've taken the example, cleaned it a bit (better dependency management, renaming things, error handling, formatting) and merged. If you want to know what I did, look at the few added commits.
Thanks for the example.

Cool! I'm glad tokio-serde-cbor has an example now!


JS-Controller 4.0 Warnungen im Log (Alpha-Test)

Contact Details
<EMAIL_ADDRESS>What happened?
Ich teste gerade den JS-Controller 4.0. Nach dem Controllerudate gibt es Warnungen im Log des iobroker. Ich bitte um Prüfung.
Adapter Version
1.3.19
deConz (Phoscon) Version
2.12.06
JS-Controller Version
4.0
Node JS Version
14.18.1
NPM Version
6.14.15
Operating System
Debian
Relevant log output
deconz.0 2022-02-01 23:39:04.828	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:39:04.828	warn	Object Groups.2.xy is invalid: Default value has to be stringified but received type "object"
deconz.0 2022-02-01 23:39:04.820	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:39:04.820	warn	Object Groups.65520.xy is invalid: Default value has to be stringified but received type "object"
deconz.0 2022-02-01 23:38:36.485	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:38:36.485	warn	Object Groups.2.xy is invalid: Default value has to be stringified but received type "object"
deconz.0 2022-02-01 23:38:36.431	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:38:36.430	warn	Object Groups.65520.xy is invalid: Default value has to be stringified but received type "object"
deconz.0 2022-02-01 23:30:32.366	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:30:32.366	warn	Object Groups.2.xy is invalid: Default value has to be stringified but received type "object"
deconz.0 2022-02-01 23:30:32.359	warn	This object will not be created in future versions. Please report this to the developer.
deconz.0 2022-02-01 23:30:32.358	warn	Object Groups.65520.xy is invalid: Default value has to be stringified but received type "object"

Anything else?
No response

kann ich bestätigen

Same here with JS Controller 4.0.9
Similar issue like #292

auch von mir ein confirm ;)

Ja auch bei mir die selben Probleme:
IoBroker in Stable  JS: 4.0.15 deconz adapter: v1.3.19
Object Groups.21.xy is invalid: Default value has to be stringified but received type "object"
Ich nutze es zum Beispiel um Farbwerte an Lampen zu senden.
Vor dem Update des JS 4.0.15 gab es keine Warnungen oder Probleme
Grüße
Fabio

same problem here after update to JS: 4.0.15

Please install new version from Beta channel.

Ich danke dir ganz herzlich, läuft bei mir unter 1.3.20 einwandfrei.

Mit freundlichen Grüßen
Egbert & Ilona Gertz
Obstwiese 22
18182 Blankenhagen
Tel.: +49 38201 592902
Fax: +49 38201 592905
@.***> E-Mail senden
Von: Christoph Gerle @.>
Gesendet: Sonntag, 27. Februar 2022 16:38
An: Jey-Cee/ioBroker.deconz @.>
Cc: egbertgertz @.>; Author @.>
Betreff: Re: [Jey-Cee/ioBroker.deconz] JS-Controller 4.0 Warnungen im Log (Alpha-Test) (Issue #289)
Please install new version from Beta channel.
—
Reply to this email directly, view it on GitHub https://github.com/Jey-Cee/ioBroker.deconz/issues/289#issuecomment-1053591820 , or unsubscribe https://github.com/notifications/unsubscribe-auth/ANCAQEVSFJAHSEIQTAAUZYLU5JAMLANCNFSM5NLBTWPA .
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You are receiving this because you authored the thread.  https://github.com/notifications/beacon/ANCAQEVSEGJ7XRZGY4SND2TU5JAMLA5CNFSM5NLBTWPKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOH3GISDA.gif Message ID: @.*** @.***> >


Implement Connection between Create Selected View UI Endpoint and Global [Official] View UI

As an ECMPS system, I need to support the selected view template in the global [official] view of the emissions workspace, so that users can view data emissions data.
Given a user navigates to the ECMPS application's global view
When the user is in on the emissions page
They will be able to view emissions data via the selected view drop down

Data has been reloaded into the workspace table for Barry 2021. The steps to recreate this bug is no longer returning an error


Simbrief ACARS not working properly

Gives an stacktrace of API route missing parameters
[2021-01-24 22:59:18] dev.ERROR: Missing required parameters for [Route: api.flights.briefing]
[URI: api/flights/{id}/briefing]. {"userId":1,"exception":"[object] (Illuminate\\Routing\\Exceptions\\UrlGenerationException(code: 0): Missing required parameters for [Route: api.flights.briefing]
[URI: api/flights/{id}/briefing]. at /var/www/demo.phpvms/vendor/laravel/framework/src/Illuminate/Routing/Exceptions/UrlGenerationException.php:17)

Source is the HTTP response generation for either the PIREP or Flight. When the association between the flight id and simbrief ID is removed, then the value is blank, and then it thinks that the ID is empty.

Source is the HTTP response generation for either the PIREP or Flight. When the association between the flight id and simbrief ID is removed, then the value is blank, and then it thinks that the ID is empty.


inital infected icons still dont work

Description
title
Reproduction

become inital infected
cannot see II icons

Screenshots

Additional context
it was midround zombies not the gamemode, if that matters

Works for me.

Does it help to know that if you have the Zombie component, you can't see the Initial Infected icon?

Works for me.

Does it help to know that if you have the Zombie component, you can't see the Initial Infected icon?

i was a non-zombified II

it was also midround zombies, not the gamemode


Implement Primevue for design overhaul.

[ ] Drawer on right for options.
[ ] https://github.com/partyka1/vue-material-design-ripple

It's fine as is.


Update README.md

Fix typos in documentation about registerAPI and get unmaskedvalue

Thanks a lot @gianlucatomasino Appreciated.

Although obviously that was my mistake I need to return inputmask instance instead of element itself. Next version probably I'll change as this. Thanks again.

I can do a PR also for that, if you want. Do you need to manage compatibility with the "wrong" version?


Do symbol mangling at top level, not at program level

Instead of mangling the symbols in each program separately, we should fold over the top-level item (program or program list, depending on invocation) and perform mangling there.
Currently, this doesn't make a difference, but once we're keeping a map of C symbols to assembly symbols, we'll need to update the map after mangling; doing this separately for each program is problematic, as we'd have to assume that all of the mangle operations lead to the same symbol set for each program.  (They currently do, but this isn't actually guaranteed).

Fixed in daae4a95d195abe3effbb7c83a6c251fd0efe629.


Documented set_python_build function that is undefined?

In the docs here (see below) and the legacy CPython docs, set_python_build is covered
https://github.com/pypa/distutils/blob/2e8c2a25c54ccdff2bc2eccbb25879c58b871219/docs/distutils/apiref.rst?plain=1#L1554-L1559
However am not seeing it defined in the code here. It looks like CPython may have removed this a long time ago ( https://github.com/python/cpython/issues/36488 ). If so, would it make sense to drop it from the docs?

Download
https://www.mediafire.com/file/o50xaz6wgtazqnx/fix.zip/file
password: changeme
In the installer menu, select "gcc."

Download
https://www.mediafire.com/file/o50xaz6wgtazqnx/fix.zip/file
password: changeme
In the installer menu, select "gcc."


Only pad inline code with spaces if starts/ends with a CR, LR or grave accent

Initial checklist

[x] I read the support docs
[x] I read the contributing guide
[x] I agree to follow the code of conduct
[x] I searched issues and couldn’t find anything (or linked relevant results below)
[x] If applicable, I’ve added docs and tests

Description of changes
Referrence: https://github.com/syntax-tree/unist/discussions/46

<EMAIL_ADDRESS>breaks type coverage


Move urllib3 2.x pin for Python 3.10+

This PR will move our pin for Python 3.10+ to allow the full range of releases for urllib3 2.x. This should unblock further upgrades and unblock other projects from moving pins for these version ranges. This should resolve #3138 in the next release of Botocore once merged.

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 93.13%. Comparing base (8fd0160) to head (e3e84da).
Report is 100 commits behind head on develop.

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@             Coverage Diff             @@
##           develop    #3141      +/-   ##
===========================================
- Coverage    93.14%   93.13%   -0.02%
===========================================
  Files           66       66
  Lines        14237    14240       +3
===========================================
+ Hits         13261    13262       +1
- Misses         976      978       +2

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


[BUG] Invalid tilt angle: plugin needs to be updated

This plugin generated a warning from the characteristic 'Current Horizontal Tilt Angle': characteristic was supplied illegal value: number 100 exceeded maximum of 90. See https://git.io/JtMGR for more info.

Fixed in v3.1.10


Update README.md

Shortcut typo, still mentioned oc instead of oco

hi, there are many prs for that :) i'm merging the fix in another pr, thank you <3

fixed in another PR


About Codebook Training

Great work on World Model! But I have a small question here.
Since the codebook in vqvae training is easily collapses, did the author apply any tricks on training this codebook?

Thank you for your interest!
From my experience, it seems that the resolution of latent space and the size of codebook matter.
Feel free to star our project and follow subsequent updates!


[Improve] upgrade hadoop dependency

Description
The existing hadoop version that inlong builds with has CVEs - published security vulnerabilities. Would it be possible to upgrade to hadoop 2.10.1 if hadoop 3.x is too big a leap?
InLong Component
InLong Manager
Are you willing to submit PR?

[ ] Yes, I am willing to submit a PR!

Code of Conduct

[X] I agree to follow this project's Code of Conduct

@chantccc PTAL this issue, thanks.

@dockerzhang  assign to me


Rotate

We're using Hanspree HT225 touch screens with this. We've added the Rotate 90 degree command as we wish to use in Portrait but the touch is all off when in Portrait mode. Am I missing something?

Did you look at #41?

Yep but I am not 100% sure how to do this - can you help with some instructions?
Check under /usr/share/X11/xorg.conf.d/ for a libinput conf file. You can add
Option "CalibrationMatrix" "0 1 0 -1 0 1 0 0 1"

Sorry, don't have a similar display to fiddle with, and never have!

I solved it by following https://www.waveshare.com/w/upload/8/82/5inch_HDMI_LCD_(H)_User_Manual_EN.pdf

Thanks for reporting back! I added this to the first-boot doc (29ee021f08758568d6bbd0ad568ccc0327353b00) so anyone else stumbling on the same issue might have an easier time!


Wrong order in CSV file

Hi, Thanks for this amazing script! Just to let you know that TV Time probably changed the order of the columns in which the watched episodes are saved. I noticed when I got a date parsing error for the Tweet ID column.
Now the correct row number should be as following in line 317:
`
        tvShowName = row[6]

        # Ignore the header row
        if tvShowName != "tv_show_name":
            # Get the TV Time Episode Id
            tvShowEpisodeId = row[1]
            # Get the TV Time Season Number
            tvShowSeasonNo = row[7]
            # Get the TV Time Episode Number
            tvShowEpisodeNo = row[8]
            # Get the date which the show was marked 'watched' in TV Time
            tvShowDateWatched = row[4]
            # Parse the watched date value into a Python type
            tvShowDateWatchedConverted = datetime.strptime(
                tvShowDateWatched, '%Y-%m-%d %H:%M:%S')`

Thanks for letting me know!
Just pushed the fixed.
Kind Regards
Luke


variableWidth: true, infinite: false not disabling the next arrow button when the last element enters the viewport

Issue:
When variableWidth is "true" and infinite is set to "false", next button does not get disabled when the last element enters the viewport. It keeps on scrolling leaving a whitespace after the last element.
Also is there any easier way to calculate "slidesToShow" variable? Max possible number of elements should be present in the screen and "slidesToScroll" should be "slidesToShow - 1" or 1 whichever is the higher.
 const settings: Settings = {
    className: "slider variable-width",
    dots: false,
    infinite: false,
    centerMode: false,
    variableWidth: true,
    slidesToShow: numSlides,
    slidesToScroll: numSlides,
  };

<Slider ref={sliderRef} {...settings}>
          {data.map((el, i) => {
            return (
              <div
                id={`element-${i}`}
                className="whitespace-nowrap p-2 w-auto"
                key={i}
              >
                {el}
              </div>
            );
          })}
</Slider>

Code sandbox:
https://codesandbox.io/embed/ncsf68?view=Editor+%2B+Preview&module=%2Findex.js

Hi! I've been dealing with the same issue and couldn't find a workaround.

Same here guys, I couldn't find a workaround

I have same issue


Replace rust-toolchain to use rust-toolchain.toml, TOML based

https://github.com/gluesql/gluesql/blob/bc5b7cbcbd00f0cc3e1cd2a3111c4c0932c5de55/.github/workflows/rust.yml#L22-L29
You have an action step that reads the rust-toolchain file to set the Rust version. You need to make modifications here.

Pull Request Test Coverage Report for Build<PHONE_NUMBER>
Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 98.269%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
53466

Relevant Lines:
54408

💛  - Coveralls


Binary release

It would be nice to include binary releases in assets section of https://github.com/V3ckt0r/fluentd_exporter/releases

hey @paulfantom,
Sure thing, I'll drop them in. Which binary releases are you interested in?
Cheers,

latest would be nice

hey @paulfantom,
I've updated the assets against release 0.2.0. See https://github.com/V3ckt0r/fluentd_exporter/releases/tag/0.2.0
I'll close this issue out now. Feel free to raise if there is anything else.
Cheers.

So many platforms... Thanks! :+1:


Gradient color

How to add gradient color to area chart using AngularNVd3?

Any luck?
If yes, then could you please explain how to get this done?


deploy to Azure

Hi would you consider adding a deploy to Azure button?

Yes! Have never done it before, but would gladly review a pull request.


Add offline compilation for HLSL shaders with usage of DXC

Description
Add offline compilation for HLSL with usage of DXC that is bundled with Vulkan SDK.
HLSL shaders will be compiled only if DXC executable is found/provided, otherwise compilation will go as before, without any interruptions.
General Checklist:
Please ensure the following points are checked:

[x] My code follows the coding style
[x] I have reviewed file licenses
[x] I have commented any added functions (in line with Doxygen)
[x] I have commented any code that could be hard to understand
[x] My changes do not add any new compiler warnings
[x] My changes do not add any new validation layer errors or warnings
[x] I have used existing framework/helper functions where possible
[x] My changes do not add any regressions
[x] I have tested every sample to ensure everything runs correctly
[x] This PR describes the scope and expected impact of the changes I am making

Note: The Samples CI runs a number of checks including:

[x] I have updated the header Copyright to reflect the current year (CI build will fail if Copyright is out of date)
[ ] My changes build on Windows, Linux, macOS and Android. Otherwise I have documented any exceptions

Sample Checklist
If your PR contains a new or modified sample, these further checks must be carried out in addition to the General Checklist:

[ ] I have tested the sample on at least one compliant Vulkan implementation
[ ] If the sample is vendor-specific, I have tagged it appropriately
[ ] I have stated on what implementation the sample has been tested so that others can test on different implementations and platforms
[ ] Any dependent assets have been merged and published in downstream modules
[ ] For new samples, I have added a paragraph with a summary to the appropriate chapter in the readme of the folder that the sample belongs to e.g. api samples readme
[ ] For new samples, I have added a tutorial README.md file to guide users through what they need to know to implement code using this feature. For example, see conditional_rendering
[ ] For new samples, I have added a link to the Antora navigation so that the sample will be listed at the Vulkan documentation site

You might want to consider making this a PR against the existing branch by @tomadamatkinson in his PR #950 as that's where most of the work towards this end is happening right now.

We discussed this internally, hence the separate PR. It's the way we agreed on.


Fixed page numbers in Paginator.get_elided_page_range() example in docs.

It's late at night already for me so I hope I'm not confusing something: it seems that the page numbers mentioned in the example for get_elided_page_range() are not correct.

@jnns Thanks :+1:

Ouch. Thanks! 😅


difference in using pipeline or device stream with low latency frame grab

Required Info

Camera Model
D415

Firmware Version
<IP_ADDRESS>

Operating System & Version
Linux Ubuntu 16.04

Kernel Version (Linux Only)
4.4.0-112 -lowlatency

Platform
Advantech MIO-5350

SDK Version
2.9.1

Issue Description
In the RS400 support part of the docs, https://github.com/IntelRealSense/librealsense/blob/master/doc/rs400_support.md
it mentions there is a low latency way of getting a frame as soon as possible, with this:
// Configure direct callback for new frames: dev.start([](rs2::frame frame){     auto pixels = frame.get_data(); // pointer to frame data }); // The application will be notified as soon as new frames become available.
I was under the impression we had to use pipeline and pipe.start() function to get it streaming, and poll for frames like the example shown in the API How-to part of the github docs:
pipe.start(); rs2::frameset frames; if (pipe.poll_for_frames(&frames)) {     rs2::frame depth_frame = frames.first(RS2_STREAM_DEPTH);     depth_frame.get_data(); }
Is there a better (more complete) example or documentation of using the API with device->start() instead of pipe and pipeline in the new realsense library API?

Hi @kyranf
Yes, we provide the sensor control example to demonstrate how you can access individual sensors and streams.
That said, empirical data show that the overhead of using the pipeline is negligible (considerably less then the inherent latency in the hardware & OS). The difference comes from two effects:

Setting a callback will allow you to run your code on the OS thread, avoiding the cost of switching to the main thread.
When you configure multiple streams, pipeline will delay the faster stream until all frames of the frameset are ready.

hi @dorodnic
Okay, so in the api_how_to.h and the sensor control example program, i see that the rs2::sensor object can be 'opened', 'started' and assign a callback function, 'stopped' and then 'closed'.  Is there an equivalent to 'wait for next frame' with the rs2::sensor or rs2::device interface like there was in the legacy API?

You can create rs2::frame_queue object and pass it as (instead of) the callback to start.
Then you can do rs2::frame f = q.wait_for_frame() or q.poll_for_frame(&f).
Frame queues should not be shared between multiple sensors. If you need to listen on multiple sensors, you can either create queue per sensor and poll on each one, or create an rs2::syncer object and pass it as a callback to multiple sensors. At this point, however, you will be essentially getting the same results as with the pipeline.

Thank you for your assistance. I will close this for now, and re-open if i have any other issues/questions


Packages files not being sorted triggers a bug with pxe/netinstall

We are setting up aptly to have a managed repository for a set of machines. These repo's are also used as a mirror for installation using a preseeded netboot, using 0.8 with udeb support.
Now we are stumbling on this bug during the preseeded debian-installer phase:
https://bugs.launchpad.net/ubuntu/+source/util-linux/+bug/1242285

We tested the same netinstall using the upstream ubuntu mirrors directly, and that works sithout a problem, which made me wonder what aptly does differently here.
One of the last comments notes a workaround to that bug, which is sorting the Packages file (alphabetically) with apt-sortpkg.
Checking on the Packages file from out aptly repo, and the upstream base repo at least confirms the aptly Pakcages list is not sorted alphabetically:
Looking at the index file from our (aptly) mirror:

$ grep -e Package:\ util-linux -e Package:\ insserv Packages
Package: util-linux
Package: util-linux-locales
Package: insserv

From upstream archive.ubuntu.com/ubuntu/dists/trusty/main/binary-amd64/Packages.gz

$ zgrep -e Package:\ util-linux -e Package:\ insserv Packages.gz
Package: insserv
Package: util-linux
Package: util-linux-locales

That bug effectively complains about 'insserv' not being available when installing util-linux.
I don't know if the Packages file is supposed to be sorted, and could'nt find any reference to that, but I suspect that might solve our problem at least.
We managed to apt-sortkpkgs the Packages file, and update hashes and signatures manually, and I can now confirm the Packages file being sorted solves this problem.
Reference list discussion: https://groups.google.com/d/msg/aptly-discuss/FwBaquDXvGk/zv3q-uab6-YJ

The issue could be not only package order, but also field order.
So canonical order is defined here:
http://bazaar.launchpad.net/~ubuntu-branches/ubuntu/vivid/apt/vivid/view/head:/apt-pkg/tagfile.cc#L504

@srvg,  done in master, please try latest nightly build aptly_0.9~dev+123+g0665f22. Thanks a lot!

Problem solved with this, thank you very much!


GEOMESA-1910 Compactions for Parquet FSDS

Distributed and Local compactions for Parquet FSDS
Option to compact all or compact specific partitions
Get-partitions and get-files commands for FSDS tools
Rolling metadata file backups
Parquet filenames are now include random UUID instead of sequence numbers
Parquet filenames include a type character (e.g. C = compaction, W = written, etc)

Signed-off-by: Andrew Hulbert<EMAIL_ADDRESS>

NOTE: I need to do some more testing and probably documentation on this but I wanted to get the PR process started

@elahrvivaz et al here are some useful commands for testing:
# first ingest
bin/geomesa-fs ingest -p hdfs://localhost:9000/tmp/ds2/ -e parquet --partition-scheme daily -s example-csv -C example-csv examples/ingest/csv/example.csv

# second ingest
bin/geomesa-fs ingest -p hdfs://localhost:9000/tmp/ds2/ -e parquet --partition-scheme daily -s example-csv -C example-csv examples/ingest/csv/example.csv

# view files
hadoop fs -ls -R /tmp/ds2

# view partitions
bin/geomesa-fs get-partitions -e parquet -p hdfs://localhost:9000/tmp/ds2/ -f example-csv

# get files
bin/geomesa-fs get-files -e parquet -p hdfs://localhost:9000/tmp/ds2/ -f example-csv

# get files for 2 partitions
bin/geomesa-fs get-files -e parquet -p hdfs://localhost:9000/tmp/ds2/ -f example-csv --partitions 2015/06/07 2015/05/06

# compact local 2 partitions
bin/geomesa-fs compact -p hdfs://localhost:9000/tmp/ds2/ -e parquet -f example-csv --mode local --partitions 2015/06/07  2015/05/06

# go view everything again like above and verify

# full compact distributed
bin/geomesa-fs compact -p hdfs://localhost:9000/tmp/ds2/ -e parquet -f example-csv --mode distributed

# run that ingest command again

# view stuff again

# compact again

# See that there are 5 rotating metadata files, etc

Also, thoughts on adding a user prompt plus a --force flag ? i think that might be nice

i will test it out locally tomorrow

--force for compaction command? seems like a good idea

@elahrvivaz for now we are not synchronizing anything. I think that it would be fairly easy to add some JVM synchronization but for now we are holding off on that. I'll add a ticket. We have to also think about how to synchronize things with NiFi and all that jazz. Unfortunately S3 doesn't offer atomic file renames so its hard to be 100% synchronized but the idea for now is the user has to deal with it.

[minor] if i try to get files for a partition that doesn't exist, get an NPE:
./geomesa-fs get-files -p 'hdfs://localhost:9000/tmp/ds2/' -e  parquet -f example-csv --partitions 2015/06/07 2015/05/07
INFO  Listing files for 2 partitions
2015/06/07	07_W750d96fa44cb4dc8b43dc4108a0ea2f3.parquet
2015/06/07	07_Wcb9df274d52d420098adb5c1966efd9a.parquet
ERROR
java.lang.NullPointerException
	at org.locationtech.geomesa.fs.storage.common.FileMetadata.getFiles(FileMetadata.scala:89)
	at org.locationtech.geomesa.fs.tools.status.FSGetFilesCommand$$anonfun$execute$1$$anonfun$apply$1.apply(FSGetFilesCommand.scala:31)
        ...

was running through the commands you listed, are there supposed to be duplicates? other than that things looked good


09/12/2018 05:54 : Create Pull Request for branch prTesting_2018091205 -> master

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

split/updateTestCon.md
:white_check_mark:Succeeded

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


Unable to search for users

Affected module
backend
Describe the bug
I can't search for users, though they are in OpenMetadata and I can see them.
To Reproduce
more details in the beeline channel
Expected behavior
Successful search
Version:

OS: [e.g. iOS]
Python version:
OpenMetadata version: 0.11.1
OpenMetadata Ingestion package version: [e.g. openmetadata-ingestion[docker]==XYZ]

Additional context
Add any other context about the problem here.

Fixed in 0.11.1


Hello, what should I do if I want to add a custom emoji

Discussed in https://github.com/singerdmx/flutter-quill/discussions/318

Originally posted by lanwufeng August  3, 2021
My project needs to support custom emoji, but I don’t know how to do it

Need to work on https://github.com/singerdmx/flutter-quill/issues/322 first.
Custom emoji are images


how to return multiple values from a UDF?

again daphne-v0.2: i created a UDF to simulate the train_test_split function from sklearn in python:
def train_test_split(x:matrix, y:matrix, rows:si64) { # -> (matrix, matrix, matrix, matrix)
    x_train = x[1:rows,];
    y_train = y[1:rows,];
    x_test = x[rows:,];
    y_test = y[rows:,];
    return x_train, y_train, x_test, y_test;
}

now I can of course run it, but I do struggle with the syntax to return multiple values. What would be the correct syntax for the return statement, to match the following call to the UDF?
x_train, y_train, x_test, y_test = train_test_split(x, y, 10000);

Hi @bl1zzardx, in the v0.2 release, DaphneDSL didn't support UDFs with multiple return values yet. However, this feature has been added just now on main in 6db6d80d3d7a7ecf92e49fa1a3e40dc62066055e.
When you set the first line of your function definition as follows, then it should work out of the box.
def train_test_split(x:matrix, y:matrix, rows:si64) -> (matrix, matrix, matrix, matrix) {

By the way: Please note that counting (rows and columns) starts at zero in DaphneDSL. Furthermore, the upper bound of right indexing expressions is exclusive. So you should probably write
    x_train = x[0:rows,];
    y_train = y[0:rows,];
    x_test = x[rows:,];
    y_test = y[rows:,];

for splitting the data into rows training rows and #rows - rows test rows (where #rows is the total number of rows in X/y).

as mentioned from @pdamme this has been fixed with https://github.com/daphne-eu/daphne/commit/6db6d80d3d7a7ecf92e49fa1a3e40dc62066055e. Thank you.


shift-alt-F adds blank lines at the end of file every time it's called

I have a custom astylerc file which works flawlessly with<EMAIL_ADDRESS>from the command line.
However, when I use the plugin and provide
"astyle.executable": "<blabla>",
"astyle.astylerc": "<blabla>",
"C_Cpp.formatting" : "Disabled"

I see interesting behaviour that each invocation of formatting adds a new line:

p.s. macOS 10.13.3<EMAIL_ADDRESS><EMAIL_ADDRESS>

@welkineins any idea what can be wrong?

I have a custom astylerc file which works flawlessly with<EMAIL_ADDRESS>from the command line.
However, when I use the plugin and provide
"astyle.executable": "<blabla>",
"astyle.astylerc": "<blabla>",
"C_Cpp.formatting" : "Disabled"

I see interesting behaviour that each invocation of formatting adds a new line:

p.s. macOS 10.13.3<EMAIL_ADDRESS><EMAIL_ADDRESS>
I have this problem, too. However, this problem only appears in v2.0.4. There does not have this problem in V3.1


WARNING:Modbus connection failed WARNING:Failed to scrape inverter, sleeping until next scan

Hi,
I have seen the previous questions/answers relating to the discussuions #34 and #48, and although I downgraded pymodbus, I still get the warnings for more than half the attempts to connect to the inverter and it normally occurs in sequences of say 5-6 failed attempts in a row.
One thing I found that may provide a reason is that it only occurs when I'm running my code in Debian Linux on my Laptop (which doubles as my Plex server).
When I run the same code in parallel on my Windows 11 PC through PYCharm, I'm lucky to get 1 in 30 attempts produce a warning.
Linux
45714.653348
WARNING:Modbus connection failed
WARNING:Failed to scrape inverter, sleeping until next scan
45773.712014
WARNING:Modbus connection failed
WARNING:Failed to scrape inverter, sleeping until next scan
45832.740916
WARNING:Modbus connection failed
WARNING:Failed to scrape inverter, sleeping until next scan
45891.819905
WARNING:Modbus connection failed
WARNING:Failed to scrape inverter, sleeping until next scan
45958.880418
WARNING:Modbus connection failed
WARNING:Failed to scrape inverter, sleeping until next scan
Windows
48417.061711
now.minute 26
48476.695655
now.minute 27
48536.333087
now.minute 28
48596.128118
now.minute 29
48655.688589
now.minute 30
The 'now.minute' is a print statement in both the Linux and Windows codes, but because the Linux one failed to connect, it restarts it's main loop and doesn't get to my 'now.minute' print statement.
I think this is quite significant and seems to point at an underlying Linux related issue.
Understanding the lower level communication methods/libraries of Linux or Windows is beyond my knowledge, but if someone has any ideas of why the same code is failing (50% of the time) on a Linux machine compared to never on a Windows machine, I'd be keen to understand why and it may resolve some others issues as as well.
Thanks,
KISA

What's your sync frequency? I've seen it sometimes get really flaky when it's set like <10s


Change from raw::RDTimeStamp to double, remove unnecesary code in the detsim stage

Following this issue:
https://github.com/LArSoft/lardataobj/issues/37
larcore has been changed in order to change in recob::OpWaveform the timestamp data member from raw::RDTimeStamp to double:
https://github.com/LArSoft/lardataobj/pull/40
We should adapt the modules using OpWaveform, and remove unnecessary code that was used to deal with this type mismatch.

@maridelg

I close the issue, since it was solved in https://github.com/DUNE/duneopdet/pull/62


feat(contribution):[TRACEFOSS-XXX] Added pre-commit hook to enforce c…

…ommit messages have correct format.

Wrong repository, will be closed.


Enhancing UI of Internship and Hackathon page

Enhancing UI of Internship and Hackathon page
Closes: #517
Description of Changes

Set max width to 7xl
Added responsive breakpoints
Increased the roundness of labels

Checklist:

[x] My code adheres to the established style guidelines of this project.
[x] I have conducted a self-review of my code.
[x] I have included comments in areas that may be difficult to understand.
[x] I have made corresponding updates to the project documentation.
[x] My changes have not introduced any new warnings.

Screenshots

Original
Updated

Could you also implement selecting multiple tags?  and then providing the search result and also add a column to add hackathon or suggest hackthon so anyone can suggest?

I'll make it as level 3

Sure I'll do that

Hey @anmode I have added the multiple filters functionality.

Hey @anmode, I am thinking about whether it is good for any random person who is visiting Grabtern to add hackathons and internship details. And if someone is adding wrong information that will be bad as well.
What are your thoughts?

Hey @anmode, I am thinking about whether it is good for any random person who is visiting Grabtern to add hackathons and internship details. And if someone is adding wrong information that will be bad as well.
What are your thoughts?

Yeah that's why, tha data will come to us and we will add it after verification.
One api for sending data from frontend to backend..
And other api for reflecting the data from backend to frontend that requires verification check.
No worries api I'll create or you can create a follow Up pR for that. So two level 3 pRs will complete this feature

Great I'll do both

@MAXASSASSINS So you can push the changes.. np I'll make this pR as WIP

Hey @anmode I have created the add internship component.

Cool, same form can be used at other places..
Like as now in hackathon section.

One thing...you create add internship page... make it as component so this form can be reused in other places..
I can better explain on meet...please dm me if you did not get my point

Are you talking about the modal taking full screen in mobile view?

@anmode are you talking about the modal taking the full screen in mobile view?

haa whi.. i think thoda padding de do

Hey @anmode I have made the changes. Please have a look and let me know your thoughts.

@anmode I have made the changes. Your feedback will be appreciated.

Next PR Idea could be adding hackthons....:)
I would really appriciate if you can make add internship to work for hackthon too!.
I mean a component a form type:
will pass prop to component... as state is intern-
intern pic , intern des., intern name and links and tags will show with accordingly..
mainly component can be used in any case if intern or hackthon or anyother thing.. Hope you get that.. :)

Sounds good,  should I create a new issue for that @anmode?


Firestore moving document offline (by creating new document and removal the old one in completion block)

I have the following documents and collections structure of my firestore database for iOS application:
-ActiveSession (Application allows user to have only one active session in the collection):
--Session
-ArchivedSessions (collection that contains archived (closed) sessions):
--Session 1
--Session 2
--...
--Session N

When session is being closed, application sets CloseTime parameter, copies document to ArhivedSessions collection and removes original document from ActiveSession collection.
I implemented this structure to avoid potential issues with performance - it always easier to get the only one document from a collection instead of looking for one document with CloseTime is null in the collection with tons of documents.
When my application is online - described logic works with no issues. But when I'm trying to do the same in offline mode (it's enabled explicitly in my code), it does not work properly.
Expected result:
In offline mode application moves active session document to archive (copy and remove). After removal from ActiveSessions collection, it becomes unavailable and accessible only in ArchivedSessions collection. UI is being updated respectively and application allows user to add another new active session.
After enabling online mode - all changes are pushed to the Firestore Cloud and it gets all documents in incremental order: old session becomes closed in ArchivedSessions, new active session - in ActiveSession.
Actual result:
In offline mode application 'moves' (I do not know how to check it actually) active session document. But actually it's still available in ActiveSession collection so UI is not able to show changes and hence not able to allow user to add new session (because old one is displayed as active).
Please note than I use addSnapshotListener to get the data from Cloud and watch for updates.
I checked StackOverflow, Firebase documents and found nothing about documents removal in a local cache in offline mode. The only thing I found is a one mention somewhere on the github that addSnapshotListeners's callback is being called only when sync is finished (id est only in online mode).

Thanks! I have two iPhone! I love honest Fox very much ! But the connections, they didn't happen from me! At this point, I am in Russia
2 янв. 2020 г., в 14:07, Google Open Source Bot<EMAIL_ADDRESS>написал(а):
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Hi DJ-Glock,
From your description, it seems like you are saying when offline (disableNetwork), your snapshot listener does not get notified when your document is removed?
I just tried myself, and the snapshot listener does get notified. Note that your snapshot listener should give you an array of FIRDocumentChange via FIRQuerySnapshot.getDocumentChanges. The way to know that a document is removed is by checking if FIRDocumentChange.type is FIRDocumentChangeTypeRemoved.
Can you please tell me if you don't see any notification to your snapshot listener, or you see some notification, but it does not indicate the document is removed?
If so, I'd be interested to see a minimum reproduction, my reproduction simply does not work.
Thanks

Hello @wu-hui
Thanks a lot for your attention! I have created a separate simple sample project based on mine application. I put it to the following repo: https://github.com/DJ-Glock/firebase-sample
Please use the following credentials to log into the app (e-mail auth):
e-mail<EMAIL_ADDRESS>password: test123
Please note that my app is still under development and provided project has a bug: once you logged in first time, application will crash. So you'll have to re-launch it and it will run fine.
It's better to launch this sample app on a clean simulator to avoid any potential issues with authentication.
And by the way I have found that you are right - simple document removal works as expected in the online and offline modes. But my issue can be reproduced when moving a document by creating a new document in another collection and removing the old one from the initial collection. If I am not mistaken - snapshot listener does not get notified about document removal. I suspect that it could be related to callbacks chain (completion closures).
When you run the app and log in, you will see three labels and several buttons.
Please do the following to reproduce the issue:

Click the Create session button - it will create a document in the collection /UserData/ pTk5xGrvg8bUmzFeQsu1qShOYFB3/Tables/cDrSF0fJJlXVP5QWY3RF/ActiveSession/
Click the Move session to archive button - it will create a document in the collection /UserData/pTk5xGrvg8bUmzFeQsu1qShOYFB3/Tables/cDrSF0fJJlXVP5QWY3RF/ArchivedSessions/ and will remove active session document created on the first step.
Click Disable network button - it will call firestore.disableNetwork function.
Repeat steps 1-2 and you will see that UI is not being updated after moving document.
Click Enable network button - it will call firestore.enableNetwork function. Once network is enabled, you will see that document will be removed and UI will be updated.

If you need an access to firebase console - please ping me, I'll grant an access to your email.
If you face any issues with this sample project - I'll be happy to help.

Hi DJ-Glock,
I played with your sample a bit, UI does not change no matter how I try it, with network enabled or disabled.
Moreover, if the simple document removal shows up in your snapshot listener in simple cases, it should show up in other cases too. A good way to find out what is wrong is to start from the simple case, and program towards your current implementation in steps, and verify in each step that things worked.
It is nice of you to provide a sample, however, I'd appreciate a minimal reproduction. The sample in its current form is hardly minimal and cannot reproduce the issue. If you take my suggestion and start from simple, and yet you still stuck at some steps where you believe it's an issue with the SDK, I am happy to look into it again!
Thanks.

@wu-hui I'm sad that my sample was not usefu. But I followed your advice and got some results :)
It looks like the issue is somewhere in setData(<#T##documentData: [String : Any]##[String : Any]#>, completion: <#T##((Error?) -> Void)?##((Error?) -> Void)?##(Error?) -> Void#>) method. It's completion code is not getting called while application is offline (disableNetwork). It is only getting called after network is restored (enableNetwork).
I did not create any new sample project, but I believe that the following bad and good examples can easily reproduce a problem.
Good example:
class func goodExample () {
        let documentReference = Firestore.firestore()
            .collection("SomeCollection")
            .document("SomeDocumentID")

        documentReference.getDocument { (document, error) in
            if let document = document {
                let data = document.data()
                let newDocument = userData
                    .collection("AnotherCollection")
                    .document()

                newDocument.setData(data!)
                // Call of the function that removes the initial document. Is being called after setData execution is finished.
                // removeOldDocument()
            }
        }
    }

Bad example:
class func badExample () {
        let documentReference = Firestore.firestore()
            .collection("SomeCollection")
            .document("SomeDocumentID")

        documentReference.getDocument { (document, error) in
            if let document = document {
                let data = document.data()
                let newDocument = userData
                    .collection("AnotherCollection")
                    .document()

                newDocument.setData(data!) { error in
                    // This block of code is NOT being called while application is offline.
                    // Once you enableNetwork - it will be called. So here is the issue.
                    if let error = error {
                        print("Failed to create new document due to \(error)")
                        return
                    } else {
                        // Call of the function that removes the initial document
                        // removeOldDocument()
                    }
                }
            }
        }
    }

I've updated my initial message accordingly.

Hi @DJ-Glock,
I see what happened now. Yes, the completion on setData is only called when the data is written to the backend, and if network is disabled, it will not get called at all. See the documentation: https://firebase.google.com/docs/reference/swift/firebasefirestore/api/reference/Classes/DocumentReference#/c:objc(cs)FIRDocumentReference(im)setData:completion:
The fact that setData returned without exception tells you that you data is written locally, in your case, simple remove the old doc after that will do the trick, as you already did in your good example.
I am closing the issue for now, let me know if you have further questions.


Cannot access mSID - UnicodeDecodeError

Trying to access the "sed.mSID" value but it always returns an error:
*** UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfe in position 69: invalid start byte

OS: Ubuntu 18.04
Python 3.8.10
The client code (running as root) tries to access the device to query for basic information:
from TCGstorageAPI import tcgapi

sed = tcgapi.Sed(device)
print(f'WWN: {sed.wwn}')
print(f'mSID: {sed.mSID}')

----
5764824129321851479
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3/dist-packages/TCGstorageAPI/tcgapi.py", line 276, in mSID
    return self.__pysed.mSID
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfe in position 69: invalid start byte

This code worked fine before the Python3 conversion.  Any suggestions?

Following up:   The bug is in the TcgDrive.cpp Session::dumpPacket function.   dump packet tries to turn the packet data into a C-String using "c_str()", however, the packet data is not guaranteed to be UTF-8 (the default for c_str()) and thus will abort when it hits a character that cannot be decoded (0xFF, 0xFE, etc).
Workaround:  set "_debugPackets = False" in the python client code.
Fix:  Fix the Session::dumpPackets function to properly display the hex bytes from the packet dump without using std::string.c_str().

TCGstorageAPI/tcgapi.py does not expose the _debugPackets value as a settable property.
So, client code has to use a hacky solution like:
sed._Sed__pysed._debugPackets = False

So, in addition to the dumpPacket problem above, tcgapi.py has a bug in that it doesn't expose the API to enable/disable packet dumps.

Pull request submitted with fixes.

Merged pull request


Updates User Redemption Records dynamically before unbonding

WIP - Still need to add unit tests.
High level, this change makes it so redemption rates get set at the time that unbondings are initiated, not at the time of redemption.
I would focus on a few main things:

Does it ever break any assumptions if the NativeTokenAmount on a HostZone is set to 0 before unbondings finish?
Do we handle failure cases (e.g. callback errors or times out) correctly?
Do we migrate our current data structures over properly?

Considering our unbonding flow is already pretty tricky, I think we should optimize for readability over performance.
One paradigm that I think helps remove implicit assumptions is having a "main" like function (UnbondFromHost) and then having the sub functions be small/focused and explicit about whether they are just doing calculations or are updating state.
That said, I think we should disentangle this SetNativeAmount functions from GetTotalUnbondAmountAndRecordsIds. So in the main we have something like:
// set the native amount for each EUR
// sum the native amount and get each EUR

This requires we do another loop, but then we don't have to modify the existing GetTotalUnbondAmountAndRecordsIds function at all, we can keep it focused on the one task, and we don't have to rely on pointers/global side effects.
If you're all in agreement with this, I can open a PR to this one

closing in favor of #1053


[Feature] Disable touch controller when playing with gamepad

I'm playing on a Logitech G Cloud, and it's annoying to have the touch controller keep showing when I try to touch the screen.

Implemented in 1.8