SourceChangeWarning

Problem:
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torchvision.models.resnet.Bottleneck' has changed. yo
u can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to r
evert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed.
you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to
revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torchvision.models.resnet.Bottleneck' has changed. yo
u can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to r
evert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.DecoderWithAttention' has changed. you can ret
rieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the
changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.Attention' has changed. you can retrieve the o
riginal source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can
retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert
the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.sparse.Embedding' has changed. you c
an retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to reve
rt the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.rnn.LSTMCell' has changed. you can r
etrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert t
he changes.
warnings.warn(msg, SourceChangeWarning)
Traceback (most recent call last):
File "caption.py", line 222, in
seq, alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size)
File "caption.py", line 45, in caption_image_beam_search
image = transform(img)  # (3, 256, 256)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 60, in call
img = t(img)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 163, in call
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\functional.py", line 208, in normalize
tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'other'
(empty) E:\Program Files\a-PyTorch-Tutorial-to-Image-Captioning>
(empty) E:\Program Files\a-PyTorch-Tutorial-to-Image-Captioning>python caption.py --img=e:/paper.jpg --model=fake/BEST_checkpoint_coco_5_cap_per_img_5_min_
word_freq.pth.tar --word_map=fake/WORDMAP_coco_5_cap_per_img_5_min_word_freq.json --beam_size=5
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.Encoder' has changed. you can retrieve the ori
ginal source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can re
trieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert th
e changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed.
you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to
revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torchvision.models.resnet.Bottleneck' has changed. yo
u can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to r
evert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.DecoderWithAttention' has changed. you can ret
rieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the
changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.Attention' has changed. you can retrieve the o
riginal source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can
retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert
the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.sparse.Embedding' has changed. you c
an retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to reve
rt the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.rnn.LSTMCell' has changed. you can r
etrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert t
he changes.
warnings.warn(msg, SourceChangeWarning)
Traceback (most recent call last):
File "caption.py", line 222, in
seq,alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size)
File "caption.py", line 45, in caption_image_beam_search
image = transform(img)  # (3, 256, 256)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 60, in call
img = t(img)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 163, in call
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\functional.py", line 208, in normalize
tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'other'
(empty) E:\Program Files\a-PyTorch-Tutorial-to-Image-Captioning>python caption.py --img=e:/paper.jpg --model=fake/BEST_checkpoint_coco_5_cap_per_img_5_min_
word_freq.pth.tar --word_map=fake/WORDMAP_coco_5_cap_per_img_5_min_word_freq.json --beam_size=5
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.Encoder' has changed. you can retrieve the ori
ginal source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can re
trieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert th
e changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed.
you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to
revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torchvision.models.resnet.Bottleneck' has changed. yo
u can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to r
evert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.DecoderWithAttention' has changed. you can ret
rieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the
changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'models.Attention' has changed. you can retrieve the o
riginal source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can
retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert
the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.sparse.Embedding' has changed. you c
an retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to reve
rt the changes.
warnings.warn(msg, SourceChangeWarning)
C:\Anaconda3\lib\site-packages\torch\serialization.py:425: SourceChangeWarning: source code of class 'torch.nn.modules.rnn.LSTMCell' has changed. you can r
etrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert t
he changes.
warnings.warn(msg, SourceChangeWarning)
Traceback (most recent call last):
File "caption.py", line 222, in
seq,alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size)
File "caption.py", line 45, in caption_image_beam_search
image = transform(img)  # (3, 256, 256)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 60, in call
img = t(img)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\transforms.py", line 163, in call
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "C:\Anaconda3\lib\site-packages\torchvision\transforms\functional.py", line 208, in normalize
tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
RuntimeError: Expected object of type torch.cuda.FloatTensor but found type torch.FloatTensor for argument #4 'other'
Pytorch and python are installed according to the version you provide.
Why does this happen?
Thank you for your answer.

@672401341 Hi, were you able to fix this?
It looks like the image (which is being normalized) is on the GPU, whereas the mean and standard deviation tensors are on the CPU. We're supplying the mean and standard deviations as lists, so they're converted to tensors by PyTorch internally. It's weird – I don't get the error when I try it on my computer.
You could try putting the image on the GPU after the normalization:
    img = torch.FloatTensor(img)
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    transform = transforms.Compose([normalize])
    image = transform(img)  # (3, 256, 256)
    img = img.to(device)

Let me know if this fixes it.
(You can ignore the other warnings, they're only because some layer definitions were changed after the model was trained.)

I also get this warning.It may be the reason as you said.Some definitions about model are changed after the training

@JunlongFeng Yes, it's because I might have modified the code after I trained my checkpoints.

Reopen if needed.

Source-code Change Warning.
i get error after running caption.py file:::
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'models.Encoder' has changed. Saved a reverse patch to Encoder.patch. Run `patch -p0 < Encoder.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. Saved a reverse patch to Sequential.patch. Run `patch -p0 < Sequential.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. Saved a reverse patch to Conv2d.patch. Run `patch -p0 < Conv2d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. Saved a reverse patch to BatchNorm2d.patch. Run `patch -p0 < BatchNorm2d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.activation.ReLU' has changed. Saved a reverse patch to ReLU.patch. Run `patch -p0 < ReLU.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.pooling.MaxPool2d' has changed. Saved a reverse patch to MaxPool2d.patch. Run `patch -p0 < MaxPool2d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torchvision.models.resnet.Bottleneck' has changed. Saved a reverse patch to Bottleneck.patch. Run `patch -p0 < Bottleneck.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.pooling.AdaptiveAvgPool2d' has changed. Saved a reverse patch to AdaptiveAvgPool2d.patch. Run `patch -p0 < AdaptiveAvgPool2d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'models.DecoderWithAttention' has changed. Saved a reverse patch to DecoderWithAttention.patch. Run `patch -p0 < DecoderWithAttention.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'models.Attention' has changed. Saved a reverse patch to Attention.patch. Run `patch -p0 < Attention.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. Saved a reverse patch to Linear.patch. Run `patch -p0 < Linear.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.activation.Softmax' has changed. Saved a reverse patch to Softmax.patch. Run `patch -p0 < Softmax.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.sparse.Embedding' has changed. Saved a reverse patch to Embedding.patch. Run `patch -p0 < Embedding.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.dropout.Dropout' has changed. Saved a reverse patch to Dropout.patch. Run `patch -p0 < Dropout.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.rnn.LSTMCell' has changed. Saved a reverse patch to LSTMCell.patch. Run `patch -p0 < LSTMCell.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py:657: SourceChangeWarning: source code of class 'torch.nn.modules.activation.Sigmoid' has changed. Saved a reverse patch to Sigmoid.patch. Run `patch -p0 < Sigmoid.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)

here i set torch.nn.Module.dump_patches=True as per first error but both the time i am getting same output for every image.
output is ::   a close up of a black and white background
can you help me through this error and please you specifi the version of pytorch, torch, torchvision that is compitable with current source code. i think upgraded version of this package is remove or deprecated some functions.

看不懂啊。这个怎么操作

Getting the same error as @purveshpatel511. Any fix?

@ragvri @baitongda
I also get this warning.
Ubuntu18.04
PyTorch1.6
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.upsampling.UpsamplingNearest2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)

Thus, run in Python Terminal:
torch.nn.Module.dump_patches = True

There will be BatchNorm2d.patch, Conv2d.patch and UpsamplingNearest2d.patch, 3 patch file, in your current work dir, so you can use tool patch
If you dont know patch, run in Terminal:
man patch

Then:
patch -p0 < [your patch file]
# eg: patch -p0 < BatchNorm2d.patch
# eg: patch -p0 < Conv2d.patch
# eg: patch -p0 < UpsamplingNearest2d.patch

However, my model is so old that I give up using it, thus I dont know Follow-up operation. : ）

@ragvri @baitongda
I also get this warning.
Ubuntu18.04
PyTorch1.6
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/xx/lib/python3.7/site-packages/torch/serialization.py:649: SourceChangeWarning: source code of class 'torch.nn.modules.upsampling.UpsamplingNearest2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)

Thus, run in Python Terminal:
torch.nn.Module.dump_patches = True

There will be BatchNorm2d.patch, Conv2d.patch and UpsamplingNearest2d.patch, 3 patch file, in your current work dir, so you can use tool patch
If you dont know patch, run in Terminal:
man patch

Then:
patch -p0 < [your patch file]
# eg: patch -p0 < BatchNorm2d.patch
# eg: patch -p0 < Conv2d.patch
# eg: patch -p0 < UpsamplingNearest2d.patch

However, my model is so old that I give up using it, thus I dont know Follow-up operation. : ）

checkpoint = torch.load(checkpoint)
torch.save(checkpoint, 'BEST_checkpoint_coco_5_cap_per_img_5_min_word_freq.pth.tar')
Save and recall model to resolve


fix: fix typecasting problem when using old version mysql

query return buffer without typecasting
so, this library can not find table pk
Table table_name has no PK.

Can you provide details on the problem? Something that could be reproduced so others can be sure that problem exists and this is the correct fix for it.

@Kononnable
It seems to be a problem with the mysql character setting.
In the case of a specific mysql server, the character setting is binary and type conversion is not performed.
problem case:
| character_set_client     | binary                                 |
| character_set_connection | binary                                 |
| character_set_database   | utf8                                   |
| character_set_filesystem | binary                                 |
| character_set_results    | binary                                 |
| character_set_server     | utf8                                   |
| character_set_system     | utf8                                   |

I think it would be better to solve it through a simple modification of the library rather than changing mysql settings
ps. I got help from Google Translate because I couldn't speak English well.

Sorry for the delay.
I'll merge it, but for now it will not be released in a new version. It will be available only for people installing directly from github.
I hope it doesn't break workflow of someone with different settings - it doesn't look like it would break but there is no easy way to test it really.

@Kononnable
Is it possible to use it with the sql.js driver?


Qbittorrent sort by

Description
Would like to see a sort by torrent date added
Priority
Medium (Would be very useful)

Already in the works.
Related PR https://github.com/ajnart/homarr/pull/1557
Duplicate of many

Great work as always
On Fri, Nov 24, 2023, 9:09 AM Tagaishi @.***> wrote:

Already in the works.
Related PR #1557 https://github.com/ajnart/homarr/pull/1557
Duplicate of many
—
Reply to this email directly, view it on GitHub
https://github.com/ajnart/homarr/issues/1704#issuecomment-1825036501,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ACGD6KV7YSYVRGZLCISZGNLYF7XT5AVCNFSM6AAAAAA7YNTXV2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMRVGAZTMNJQGE
.
You are receiving this because you authored the thread.Message ID:
@.***>


Control doesn't always refresh

Great control, typically works.
When opening records the control doesn't always refresh with what is checked, it still has the correct values, they just are indicated. Refreshing the for solves the problem. Also, reloading the record again typically works indicating it may be a timing issues where the control is not ready!
Look forward to an update :-)
CB

After additional testing I could recreate the issue without this control on the form :-)


Can't run on windows-latest

I can't run it only on windows-latest
log:
Chocolatey v2.2.2
Upgrading the following packages:
mingw
By upgrading, you accept licenses for the packages.
mingw is not installed. Installing...

mingw v13.2.0
mingw package files upgrade completed. Performing other installation steps.
Downloading mingw 64 bit
  from 'https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev0/x86_64-13.2.0-release-posix-seh-ucrt-rt_v11-rev0.7z'

Download of x86_64-13.2.0-release-posix-seh-ucrt-rt_v11-rev0.7z (69.57 MB) completed.
Hashes match.
Extracting C:\Users\runneradmin\AppData\Local\Temp\chocolatey\mingw\13.2.0\x86_64-13.2.0-release-posix-seh-ucrt-rt_v11-rev0.7z to C:\ProgramData\chocolatey\lib\mingw\tools\install...
C:\ProgramData\chocolatey\lib\mingw\tools\install
Testing path: C:\ProgramData\mingw64\mingw32\bin
Testing path: C:\ProgramData\mingw64\mingw64\bin
PATH environment variable does not have C:\ProgramData\mingw64\mingw64\bin in it. Adding...
Environment Vars (like PATH) have changed. Close/reopen your shell to
 see the changes (or in powershell/cmd.exe just type `refreshenv`).
 The upgrade of mingw was successful.
  Software installed to 'C:\ProgramData\chocolatey\lib\mingw\tools\install'

Chocolatey upgraded 1/1 packages.
 See the log for details (C:\ProgramData\chocolatey\logs\chocolatey.log).
Remove-Item: D:\a\_temp\26a2006e-59c9-41e5-bcb8-2abcadb1ba96.ps1:140
Line |
 140 |          Remove-Item (Join-Path $mingw_lib 'libpthread.dll.a')
     |          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     | Cannot find path
     | 'C:\ProgramData\chocolatey\lib\mingw\tools\install\mingw64\x86_64-w64-mingw32\lib\libpthread.dll.a' because it
     | does not exist.

Error: Process completed with exit code 1.

Also getting this. Does anyone know a fix?

Looks like using
      uses<EMAIL_ADDRESS>      with:
        version: 12.2.0

Re https://github.com/egor-tensin/setup-mingw/issues/14 works

windows-latest image already includes mingw by default. If you're using cmake you could add -G "MinGW Makefiles" to cmake command. It's located in C:\mingw64 and is already added to github actions by default


bar's height is unchangable from config

Hi, I'm trying to change bar's height from 18 to 12 or 14 but it seems not change at all, do I need to configure it somewhere else beside ~/.config/wapanel.toml ? thanks.

After some research, I found that the panel always has at least 46 height forced by some GTK elements in it. There is not much I can do, code I wrote and decisions I took are just bad.

Nevermind, thanks for the info.


OutLineDataPointExtraLineView causing a huge performance impact

First of off thanks for this project, I like it a lot.
I have recently run into an issue where loading a View takes an abnormally long time (like 4-5 seconds delay).
I profiled my application using the SwiftUI tool in Instruments and was able to narrow down the issue to the OutLineDataPointExtraLineView struct:

The columns in the table are Module / View Type, Count, Total Duration, Min Duration, Avg. Duration, and Max Duration.
For a bit of context, the chart is rendering a LineView with an extra line, and there are about 1000 data points in each line (is this a reasonable amount of data? I don't see anything in the docs about what is reasonable).
What is really weird about this is that it only became an issue after adding Firebase Analytics to the app.  Here is a profile of rendering the same view without Firebase in the project:

What I'd like to know is:

Is there any stress testing / benchmarking to see what is a reasonable amount of data to render
Any clues as how the addition of another third party library (Firebase Analytics) to a totally unrelated section of the app could cause OutLineDataPointExtraLineView to be rendered so many times and with such poor render times ( I realize it is unreasonable to expect an answer to this one, but any hints to help drive my investigation would be appreciated)

Hey @hahmed1, I can't think what Firebase Analytics might be doing to OutLineDataPointExtraLineView.
One thing to try adding .drawingGroup() to the end of the declaration. drawingGroup(opaque:colorMode:)

1000 data points should be fine, I have no specific metrics for how many is too many.

we are also seeing this performance hit and .drawingGroup() did not help immediately
#8	0x0000000104c0bcb4 in ExtraLineData.getMaxValue() at checkouts/SwiftUICharts/Sources/SwiftUICharts/SharedLineAndBar/Models/ExtraLineData.swift:116
#9	0x0000000104c0b9d4 in ExtraLineData.range.getter at checkouts/SwiftUICharts/Sources/SwiftUICharts/SharedLineAndBar/Models/ExtraLineData.swift:81
#10	0x0000000104c8753c in OutLineDataPointExtraLineView.body.getter at checkouts/SwiftUICharts/Sources/SwiftUICharts/SharedLineAndBar/ViewModifiers/ExtraLine.swift:515

looks like
    internal func getMaxValue() -> Double {
        self.dataPoints
            .map(\.value)
            .max() ?? 0
    }

is slow on device (not simulator) when we have 1000 points


Reorganize languages in the examples section

Currently the order of languages in the right side examples section feels random

We should:

Prepare a comprehensive list of languages that we want to support there
Decide whether we want to remove some of them
Reorganize the order
Add syntax highlighting

I guess we can close this one, the only missing piece is considering if we want to drop some of the languages.


Recovering "split axis" from MiniTreeBuilder

Hello! I was wondering whether it would be possible to either include as part of the data, or to recover based on the tree, which access a node was split on?

If you need that information for traversal, I recommend using a method that intersects the two children and sorts them based on their distance instead. Otherwise, this information can be obtained by either one of two options:

Recovering it from the two bounding boxes and choosing the axis which minimizes min(max1 - min2, max2 - min1) (the overlap between the two children)
Storing it somewhere by packing it in the BVH node during construction. Since this is not used in the current design, and would take up useful bits from the BVH layout, it is not currently done and would require hacking the library a little.

Also, keep in mind that there might not be any split axis for other builders (e.g. ordered clustering), and that reinsertion will interfere with option 2. In general, BVHs need not be split along one axis, they just have to partition the set of objects in some way.
I hope this answered your question. If not, please re-open.


Follow-up to 1708

Signed-off-by: bermudezmt<EMAIL_ADDRESS>
Proposed changes
A few follow-up edits, and have started editing the Architecture & Concepts overview, with plans to add a diagram later.
Unreleased product version (optional)

Related issues (optional)
Relates to #1322

@mikhailshilkov I can't tag you for a review somehow but can you please have a look?

Looks great! 👍


How use vue-slider-component?

Nuxtjs always return document is not defined.
Can you show me example?
Please:3

@olinium  This is because of SSR. on server side window is not available. As an option you can guard all slider usages in a if( typeof window !== 'undefined' ) condition :)

https://nuxtjs.org/faq/window-document-undefined/#window-or-document-undefined-

@olinium Could you solve your problem? I am having the exact problem and the proposed solution does not work.
@alexchopin
What is not clear to me from your FAQ is how the slider would be used as a component as in
<template>
...
    <vue-slider></vue-slider>
</template>

I tried to add it as a plugin (and vendor) in the nuxt config with ssr: false, so that I can use it as <vue-slider> without having to explicitly specify it as a component, but in none of these cases the vue-slider element is expanded as is it specified in the plugin.
I also tried to dynamically set components:
...

</template>

I would pretty much appreciate your help.

Hi, @tillkolter.
I'm kind having the same issue with vue-color. I have tried to create a plugin with it, to take advantage of the new ssr: false property, but that didn't work as expected:
nuxt.config.js
{
    vendor: ['vue-color'],
    plugins: [{ src: '~plugins/color', ssr: false }],
}

~plugins/color.js
import { Sketch } from 'vue-color';
export default Sketch;

~pages/test.vue
<template>

        <color-picker v-model="bg"></color-picker>

</template>

With the code above, I was still getting this error message:
Nuxt.js Error:

ReferenceError: window is not defined
    at /MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:3482
    at /MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:3414
    at e.exports (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:3700)
    at Object.<anonymous> (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:3:5927)
    at t (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:388)
    at Object.<anonymous> (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:2:20632)
    at t (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:388)
    at Object.e.exports.e (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:568)
    at t (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:388)
    at e.__esModule.default (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:475)
    at /MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:480
    at o.(anonymous function).exports (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:143)
    at Object.<anonymous> (/MY_APP/node_modules/vue-color/dist/vue-color.min.js:1:259)
    at Module._compile (module.js:570:32)
    at Object.Module._extensions..js (module.js:579:10)
    at Module.load (module.js:487:32)

So, I have managed to stop the errors with a solution based on yours:
~pages/test.vue
<template>

        <color-picker v-if="ssr" v-model="bg"></color-picker>

</template>

No errors this time. It works fine on client side, but the vue-color component is not rendered from server side. The question here is: There is a way to add this component to the page later, during component mount maybe?

Has anyone figured out a solution for this ?

Hi @tillkolter
Did you make ir work?
I've been trying the @josantana solution but still cannot make it
Cheers!

@epartipilo @veebuv I stopped trying and implemented my own css slider directly in the component and put the handler stuff in the mounted hook.

@tillkolter well done, I'm about to do the same, got it working with @josantana solution but with the vue-warn of different HTML in client and server so it behaves weird
Thanks!

Hi @tillkolter @veebuv, I managed to use the vue-slider-component
With the help of this tutorial https://alligator.io/vuejs/hide-no-ssr/ to keep client and server DOM equal and Nuxt.js proposal for window undefined https://nuxtjs.org/faq/window-document-undefined.
Seems prety good in 'dev' mode, I have not prove it on production yet.
Cheers!

Awesome thanks @epartipilo 💃

@epartipilo can you show code, how you have resolved this problem?


HARK.utilities missing approxUniform?

Hello,
I'm new to HARK (kind of). I was going through some of the tutorials and noticed that I do not have approxUniform in my HARK.utilities
or
when I import approxUniform from HARK.utilities, it cannot be found.
I just installed HARK today from conda. I have other things in HARK.utilities like utility functions, but not this approximated uniform distribution. I'm on a windows machine, running ananconda
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)]

approxUniform exists in HARK.distribution in the latest release v0.10.6.
from HARK.distribution import approxUniform

The way to approximate distributions has changed since the v0.10.6 release. This breaks some of the materials that have been built on top of HARK.
@merajindc would you be able to tell us which tutorials gave you this error?
If you would like to approximate a Uniform distribution using the unreleased master branch (or in future version v0.10.7 or later), the code is:
from HARK.distribution import Uniform
Uniform(bot=0, top=1).approx(10)
for example.

Sebastian,
I found this problem in /DemARK-master/notebooks/Chinese-Growth.ipynb
i changed it to HARK.distribution and it worked fine.
im just getting started in HARK, and will be entering the dissertation
planning phase with Rob Axtell this summer or fall. I'm a macro guy that
went back to get a phd. I've had conversations with Chris about this
project, but im getting up to speed on python first before i can commit to
helping out. I will likely be going through all of the DemARK notebooks so
i can keep a running list of bugs. Im not using git so i just downloaded
the zip file.
thanks,
Meraj
On Thu, May 14, 2020 at 7:28 AM Sebastian Benthall<EMAIL_ADDRESS>wrote:

The way to approximate distributions has changed since the v0.10.6
release. This breaks some of the materials that have been built on top of
HARK.
@merajindc https://github.com/merajindc would you be able to tell us
which tutorials gave you this error?
If you would like to approximate a Uniform distribution using the
unreleased master branch (or in future version v0.10.7 or later), the
code is:
from HARK.distribution import Uniform
Uniform(bot=0, top=1).approx(10)
for example.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/econ-ark/HARK/issues/687#issuecomment-628571236, or
unsubscribe
https://github.com/notifications/unsubscribe-auth/APSJ7XTJOQCMZC5PHE33YKDRRPIUVANCNFSM4NAFGNJA
.

Ah, ok, thank you.
good luck on your dissertation planning. A dissertation that combines Econ-ARK with Robert Axtell's expertise would be fascinating. @llorracc and I have been discussing ways to synthesize Econ-ARK with Mesa, an ABM toolkit that was developed by some people that worked with Axtell.
I'll try to keep you in the loop if there's any progress on that front on our end.
I believe the DemARK issue you've raised is fixed in this PR, which hasn't yet been merged, but could be.
https://github.com/econ-ark/DemARK/pull/116
I'd encourage you to post problems you find with DemARKs on that issue tracker; this one is intended for the HARK library.
Also, while git/GitHub can be confusing, there's a lot of upside to learning them and I'd encourage you to take it up.


feat: adds timestamp and auto generate createdAt and updatedAt fields

type: 'timestamp with time zone'
the above is type is not by "better-sqlite3" so we are using type:datetime as it has date-time with timezone.

For auto generating the createdAt and updatedAt fields:

The routes are generated by ez-router (Because ez-model is a combination of ez-router and ez-model)
In ez-router you can see the setHandler(Add Create Schema) and setHandler(Add Form Create Schema) which create the validation schemas for the POST request
In getCreateSchema the non-generated-columns are identified using !isGeneratedCol

By right the isGeneratedCol should be catching the createdAt and updatedAt fields, but it's probably not working since it's returning a validation error, so you could have a look at that


Ignore end-to-end IPA test

I don't have enough patience to wait for 50 seconds. We should move it to end-to-end test folder if it is going to take that long

+1. The test is taking too long, and it is unlikely that this will catch bugs that individual unit tests cannot. I think this should be ran as an integration test, separately from unit tests.


chore: cleanup animations and add type safety

Pull Request

📝 Description (short summary, TL;DR)

What changed?

📌 Additional Notes

🔗 Related Issues

🔄 Closes #
🔍 Type of Change

[ ] 🐛 Bug fix (non-breaking change which fixes an issue)
[ ] ✨ New feature (non-breaking change which adds functionality)
[ ] 💥 Breaking change (would cause existing functionality to not work)
[ ] 📚 Documentation update
[ ] 🎨 Style/UI update
[ ] ♻ Code refactoring
[ ] 📦 Dependency update
[ ] 🧪 Test update
[ ] 🔧 Configuration change

✅ Checklist

Code Quality

[ ] 👀 I have performed a self-review
[ ] 💬 I have added necessary comments
[ ] ⚠ No new warnings or errors are generated
[ ] ⚡ I have added/updated tests

Git Hygiene

[ ] 🔍 My commits are small and have clear messages
[ ] 🔀 I have rebased on the latest main branch
[ ] 🧩 This PR is small and focused on a single change

🧪 Testing

[ ] 👉 Manual testing

📸 Screenshots

#56  👈 (View in Graphite)
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v8: 4.5.107 -> 5.4.232

Motivation for this change
Upgrade to latest v8 so WebAssembly support is up to date
Things done

[ ] Tested using sandboxing
(nix.useChroot on NixOS,
or option build-use-chroot in nix.conf
on non-NixOS)
Built on platform(s)

[ ] NixOS
[ ] OS X
[x] Linux

[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[x] Fits CONTRIBUTING.md.

Upgraded to latest v8. Updated v8 version and deps, also created a new
patch similar to what existed for v4.5

builds on top of #17212


plumb error info to Deliverer to fail immediately, and send error type to frontend CORE-4352

The key features of this PR are the following:
1.) Add a new interface DelivererErrorInfo which has one function, IsImmediateFail, which lets Deliverer know whether or not an error should immediately put the Outbox into a failed state.
2.) Modify OutboxRecord to include information about the error type, which allows us to get it to the frontend reliably.
3.) Add a new exportable error type, IdentifySummaryError, which is what seems to come back from identify failures from GetTLFCryptKeys, and make it act like a immediate fail error.
4.) Add a few more immediate fail errors (although there might be more out there), like for too long messages.
5.) Refactor all the local chat related errors out of libkb and into the chat package.
6.) Attach DebugLabeler to some more structs, and give it a "verbose" mode that we might want to eventually control from the environment. This is to stop things like blockEngine from cluttering up the logs.

cc @chrisnojima for the new error codes coming in to Electron.


Bridge point cloud packed

See: https://bitbucket.org/ignitionrobotics/ign-msgs/pull-requests/144 and https://bitbucket.org/ignitionrobotics/ign-sensors/pull-requests/78/output-a-pointcloudpacked-message/diff

I tried adding a demo in on branch packed_demo using the new message but the point cloud is not showing on RViz. The ROS message data is suspiciously short:
header:
  seq: 105
  stamp:
    secs: 10
    nsecs: 500000000
  frame_id: "model_with_lidar/link/gpu_lidar"
height: 1
width: 1
fields:
  -
    name: "x"
    offset: 0
    datatype: 7
    count: 1
  -
    name: "y"
    offset: 4
    datatype: 7
    count: 1
  -
    name: "z"
    offset: 8
    datatype: 7
    count: 1
is_bigendian: False
point_step: 12
row_step: 12
data: [0, 0, 128, 127, 0, 0, 128, 255, 0, 0, 192, 255]
is_dense: True

So is the Ignition data:
header {
  stamp {
    sec: 13
    nsec: 500000000
  }
  data {
    key: "frame_id"
    value: "model_with_lidar::link::gpu_lidar"
  }
}
field {
  name: "x"
  datatype: FLOAT32
  count: 1
}
field {
  name: "y"
  offset: 4
  datatype: FLOAT32
  count: 1
}
field {
  name: "z"
  offset: 8
  datatype: FLOAT32
  count: 1
}
height: 1
width: 1
point_step: 12
row_step: 12
data: "\000\000\200\177\000\000\200\377\000\000\300\377"
is_dense: true

I'm still looking into what could be going on. It could be a problem with my local setup.

Your width and height are 1. Is that intentional?

Oh I hadn't noticed that detail on the message. I'm running this example world. The laser scans are going through fine. It could be an issue with my setup, I'll try your updated branches soon.

The GPU lidar works for me with the new branches. The depth cloud looks mirrored though. This is using the demo on #29 :

The mirror should be fixed: https://bitbucket.org/ignitionrobotics/ign-sensors/commits/3f70f5615bd01cc8a7639d80500a065e20858d5e?at=ign-sensors2

Are you having trouble with the depth_image in rviz? I see all black.

Are you having trouble with the depth_image in rviz? I see all black.

You need this fix, it hasn't been released yet: https://github.com/ros-visualization/rviz/pull/1378

Ahh, thanks.

Add other direction and tests.


Proof of concept for pytest_regression

See #1793
Good points:

The general use case works nicely and the API is clean.
It plays nicely with the automatic seeding we've put in place here: https://github.com/tensorflow/addons/blob/master/tensorflow_addons/utils/test_utils.py#L85
It simplifies of our tests a lot and will make them way more robusts. We'll be able to sleep at night knowing that the numbers didn't change after a commit in master :)

Left to do:

Add some documentation to our contributing explaining what this fixture does and how it works.
Fix bazel complaining about files not being declared.
Pytest-regression needs python 3.6+. py 3.5 has only 4 months left to live. Do we drop support at the same time as tf? or in 4 months when it reaches EOL?
In the docker image, for python 3.7, we get a weird error: cannot import _bz2

Thank you for your contribution. We sincerely apologize for any delay in reviewing, but TensorFlow Addons is transitioning to a minimal maintenance and release mode. New features will not be added to this repository. For more information, please see our public messaging on this decision:
TensorFlow Addons Wind Down
Please consider sending feature requests / contributions to other repositories in the TF community with a similar charters to TFA:
Keras
Keras-CV
Keras-NLP


⚠ Bofrost.fr has degraded performance

In e2925d9, Bofrost.fr (https://www.bofrost.fr) experienced degraded performance:

HTTP code: 200
Response time: 1521 ms

Resolved: Bofrost.fr performance has improved in 3b49af0.


Dotnet try command fails on Manjaro.

I was following the guide given here but running dotnet try juypter install gives the following exception.
[yucked@SR98 ~]$ dotnet try juypter install
A fatal error occurred. The required library libhostfxr.so could not be found.
If this is a self-contained application, that library should exist in [/home/yucked/.dotnet/tools/.store/dotnet-try/1.0.19553.4/dotnet-try/1.0.19553.4/tools/netcoreapp3.0/any/].
If this is a framework-dependent application, install the runtime in the global location [/usr/share/dotnet] or use the DOTNET_ROOT environment variable to specify the runtime location or register the runtime location in [/etc/dotnet/install_location].

The .NET Core runtime can be found at:
  - https://aka.ms/dotnet-core-applaunch?missing_runtime=true&arch=x64&rid=manjaro-x64

I've installed .NET Core 3.1 via dotnet-install-scripts
I've added dotnet core path in my bashrc:
# ANACONDA
PATH=/home/yucked/anaconda3/bin:$PATH

# DOTNET
PATH=/home/yucked/.dotnet:$PATH
PATH=/home/yucked/.dotnet/tools:$PATH

export PATH

And my dotnet info is as follow:
.NET Core SDK (reflecting any global.json):
 Version:   3.1.100
 Commit:    cd82f021f4

Runtime Environment:
 OS Name:     manjaro
 OS Version:
 OS Platform: Linux
 RID:         linux-x64
 Base Path:   /home/yucked/.dotnet/sdk/3.1.100/

Host (useful for support):
  Version: 3.1.0
  Commit:  65f04fb6db

.NET Core SDKs installed:
  3.1.100 [/home/yucked/.dotnet/sdk]

.NET Core runtimes installed:
  Microsoft.AspNetCore.App 3.1.0 [/home/yucked/.dotnet/shared/Microsoft.AspNetCore.App]
  Microsoft.NETCore.App 3.1.0 [/home/yucked/.dotnet/shared/Microsoft.NETCore.App]

To install additional .NET Core runtimes or SDKs:
  https://aka.ms/dotnet-download

I've read couple of solutions and all of them end up being about PATH not configured but mine works properly AFAIK.

Do you get this issue as well if you don't customize your path manually via .bashrc? That's not typically needed.

If I don't add it manually to the path, it doesn't recognize dotnet or dotnet try. dotnet-install-scripts doesn't add dotnet to PATH and I've to manually do that. Writing export PATH=....:$PATH only works for the console window I type it in and I'd have to type that in each time I open a new console window or restart my machine. Also problem stated on this SO.

Is the issue specific to dotnet-try or are other .NET global tools also affected?

I haven't tried any other tools besides dotnet-try. If there is a list of any other tools that I can install and report back on, I can do so when I wake up. That will definitely help narrow down the problem.

Here's a good simple one: https://www.nuget.org/packages/dotnet-cowsay/

Okay, it seems to be a problem with dotnet itself.
[yucked@SR98 ~]$ dotnet tool install --global dotnet-cowsay --version 1.3.0
You can invoke the tool using the following command: dotnet-cowsay
Tool 'dotnet-cowsay' (version '1.3.0') was successfully installed.
[yucked@SR98 ~]$ dotnet-cowsay
A fatal error occurred. The required library libhostfxr.so could not be found.
If this is a self-contained application, that library should exist in [/home/yucked/.dotnet/tools/.store/dotnet-cowsay/1.3.0/dotnet-cowsay/1.3.0/tools/netcoreapp2.1/any/].
If this is a framework-dependent application, install the runtime in the global location [/usr/share/dotnet] or use the DOTNET_ROOT environment variable to specify the runtime location or register the runtime location in [/etc/dotnet/install_location].

The .NET Core runtime can be found at:
  - https://aka.ms/dotnet-core-applaunch?missing_runtime=true&arch=x64&rid=manjaro-x64

Okay, I see to have fixed it. It seems it was looking for DOTNET_ROOT env variable and I added it to bashrc which fixed it. Thank you!


Run DRC and correct spacing,ie via next to R11

@aShure one DRC error I'm getting a lot is Drill size, one some vias I put into the ground pours to connect them to each other (last time I made a PCB I read that it's good practice to put vias into the ground planes). I used the default drill size for vias (outer diameter=1.0064, drill=0.6).
Questions:

should I include vias connecting the ground pours?
what size is appropriate?

Check your net classes, it could be that the power class needs bigger values (which is wrong in this application, but might be the default)

Yes
same diameter as the holes that are used for the THT headers OR a via diameter that is the maximum which is tented by common PCB manufacturers. Tented via = buried and sealed under solder mask, hole partly or fully bridged by solder mask print.

Okay, the spacing issues and other DRC issues are fixed, except for hole sizes on vias.
I'm still looking into tented via sizes, and then I'll update via sizes and make sure the DRC settings correspond. After that, the DRC should be happy :-)

I heard back from one potential manufacturer (Seeed) and their max tented hole size is D=0.6mm. Coincidentally, that's what the hole size is currently.
@aShure Could you let me know what German manufacturer you're thinking about using for the first run of 3-4 test PCBS? I'll contact them to see what their max tented via size is. If it's smaller than D=0.6mm, I'll adjust the layout accordingly. If it's D>=0.6mm, I'll keep the vias at D=0.6mm.
Or, if you happen to know this number off hand, that would be cool, too :-)
We're getting close!

Okay sounds good. 0.6mm sounds big for finished hole size, so I guess it's drill diameter.. I know at least http://www.multi-circuit-boards.eu/ don't guarantee tenting over 300µm finished hole size, so that's after plating.
I suggest https://www.haka-lp.de/ for the first run, please download the DRC file from their homepage and see if everything is okay.
I think Haka have 2mm routing for free but they charge for anything smaller.

@aShure Great!
My German is a little rough, so I want to confirm which of haka's DRU files I should use. I'm thinking "zwilling" because leo translates it as "twin". Is that right? Here are their options:

haka_35_l2 and zwilling.
35µm copper, 2 layers and the budget version "zwilling" where you get a
pair of your prototype for a cheaper price if it can be processed in a
stack (hence the bigger diameter and worse tolerances, boards are
stacked and then drilled and routed - the more boards in the stack, the
lower the accuracy)

Marvellous. Now the file nomenclature makes sense to me. Thanks for clarifying. And that's good to know about the
I'll run the two DRC sets you mentioned, and make adjustments.

DRC results for haka's haka_35_l2 and zwilling
all three branches ( master, CPver2, CPvar6 ) had two sets of issues across both DRCs:

solderable jumpers give 9 overlap errors on layer 1
R9,R10 square arrangement gave 6 keepout errors, as already discussed and approved in issue 11

The DRC errors are all fine. And yes, you are correct that normal DRC settings are for untented vias. I will inquire about the tented via size.

Can't reach Haka right now.
Set the tented via size to e.g. 0.6mm - thus the headers won't be tented, but all the vias. If the vias can't be tented in production, they will be altered automatically.

Thanks, Alex!


Apply a label when template validation fails.

When the template fails to validate we could apply a label like needs-triage or needs-more-info.
Open questions:

[ ] Should this label name be configurable per repo?  If so, what is the config format?
[ ] Should this be in addition to the comment or instead of the comment?

Having a good default would be a chance for the bot to bring consistency across the repositories managed with the bot. On the other hand there should be the option for configuring the label. Be together, not the same. :)

For the ExoPlayer repository it should be in addition to the comment. The comment gives a clear hint to the reporter, while the label can be more easily overlooked.


Hide only disputed boundaries of target country

Currently disputed boundaries worldwide are hidden causing gaps in borders.
A better approach is to add the new borders on top of the disputed borders covering up the dotted lines There does not seem to be a mapbox-gl-js method to add a layer after another layer with a known id admin-2-boundaries-dispute.
Alternative is to hide only select sections of the disputed boundaries by country code ["!in", "iso_3166_1", "IN", "CN-IN", "IN-PK"]
https://www.mapbox.com/mapbox-gl-js/api/#Map#addLayer

Once we upgrade to Streets source v8 (#4), we’ll be able to show the target country’s claim lines, dotting the disputed lines while avoiding gaps.


Store failed steps during deprovisioning

Description
If a step cannot finish his job and goes to the next one, the step needs to be marked. The idea is to store those steps
Changes proposed in this pull request:

Store the step that was retried via RetryOperationWithoutFail without success in the DB

Related issue(s)

/test all

/test all


Add initial version v0.1.0 for dynatrace-operator

New Submissions

[x] Does your operator have nested directory structure?
[x] Have you selected the Project Community Operator Submissions in your PR on the right-hand menu bar?
[x] Are you familiar with our contribution guidelines?
[x] Have you packaged and deployed your Operator for Operator Framework?

[x] Is your submission signed?
[x] Is operator icon set?

Updates to existing Operators

[ ] Did you create a ci.yaml file according to the update instructions?
[ ] Is your new CSV pointing to the previous version with the replaces property if you chose replaces-mode via the updateGraph property in ci.yaml?
[ ] Is your new CSV referenced in the appropriate channel defined in the package.yaml or annotations.yaml ?
[ ] Have you tested an update to your Operator when deployed via OLM?
[ ] Is your submission signed?

Your submission should not

[x] Modify more than one operator
[x] Modify an Operator you don't own
[x] Rename an operator - please remove and add with a different name instead
[x] Submit operators to both upstream-community-operators and community-operators at once
[x] Modify any files outside the above mentioned folders
[x] Contain more than one commit. Please squash your commits.

Operator Description must contain (in order)

[x] Description about the managed Application and where to find more information
[x] Features and capabilities of your Operator and how to use it
[x] Any manual steps about potential pre-requisites for using your Operator

Operator Metadata should contain

[x] Human readable name and 1-liner description about your Operator
[x] Valid category name1
[x] One of the pre-defined capability levels2
[x] Links to the maintainer, source code and documentation
[x] Example templates for all Custom Resource Definitions intended to be used
[x] A quadratic logo

@DTMad Required operator bundle annotation(s) not found:  operators.operatorframework.io.bundle.channel.default.v1

@DTMad Required operator bundle annotation(s) not found:  operators.operatorframework.io.bundle.channel.default.v1

@DTMad Required operator bundle annotation(s) not found: operators.operatorframework.io.bundle.channel.default.v1

Added to annotations and dockerfile

@DTMad Required operator bundle annotation(s) not found: operators.operatorframework.io.bundle.channel.default.v1

Added to annotations and dockerfile


Add AppVeyor CI

How to configure:

Open https://ci.appveyor.com/signup/free and click on Github button
Open https://ci.appveyor.com/projects/new and choose this repository on the Github list
Replace auth_token for Github with your own ( public_repo is enough ) with the encrypted value obtained from here: https://ci.appveyor.com/tools/encrypt ( paste your token in the page and use the encrypted value in the YAML replacing mine )
Add and push the new tag
Enjoy!

Example:
See https://github.com/julianxhokaxhiu/7h/releases/tag/1.5.6
How do the binaries inherit the version?
The version field in the YAML is the one which governs the overall build version. By changing that you will update it for the entire build. The way Veyor works unfortunately can't make it inherit from the Tag name. And even if it would have worked, when you create a normal build ( not created from a tag push ) that it would create a weird version. Therefore the smartest way I found was using the native .NET binary version, using the build part inherited from the CI build number.
By default, it will increase everytime a new tag will trigger a push. On PRs it will stay the same, but they will append the commit hash. Although no new deployment will be triggered.
What does it brings:
This PR adds the capability to automatically build your repository and provide release artifacts as soon as you create a tag. By default it will build a Debug and Release artifact and both will be added to the release. I think personally that both are useful for various purposes:

I may want to report you a stack trace if it crashes so I'll use the debug version
I want to not waste disk space so I'll use the release version

Of course to ensure your configuration works, you need to merge this PR first.
If you need support in configuring this let me know!
I hope you will appreciate this.

hey this is very usefull merging in 1, 2 ,3

Thanks! I'm glad you merged this :) As a FF7 fan, I found out about this only now that I was willing to play again the game. I'm very happy this is now FOSS and I will contribute again as soon as I see something that may fit the vision and the mission. Cheers!

you will find us in our discord group
https://discord.gg/aXQ6uv
Un Saludo!


Users SNBAT create a cycle in LinkedList

Expected Behavior
In insert(), check that key is not equal to previousKey or nextKey
Current Behavior
Users can create a cycle by setting previousKey equal to key

This is my github handle


Enable ZLIB and LZMA compression while writing

Fixes #181
I have only tested creating compressed ROOT files containing TObjStrings so far. Once that works perfectly I will start looking at files containing TH1s.
ZLIB and LZMA compressed files can be read by uproot but not by ROOT.
The current interface only allows the user to set the compression algorithm and compression level while creating the file and does not let the user change it after.

(Some notes for myself - )
As far as I understand,
TObjStrings are not compressed
THist is compressed
Streamers are compressed
Current status -
Waiting for a reply to this - https://github.com/python-lz4/python-lz4/issues/168 to figure out the lz4 checksum.
Need to figure out how THist is compressed.
uproot is able to read (uproot created)zlib and lzma compressed files. (Not dealing with THist yet)
ROOT is able to read (uproot created)zlib and lzma compressed objects which have less 10000 bytes.

@reikdas This might be helpful: https://github.com/python-lz4/python-lz4/blob/master/lz4/frame/init.py#L133-L136

I couldn't figure out how to get an 8 byte checksum from the python-lz4 package (issue - https://github.com/python-lz4/python-lz4/issues/168) and I decided to try using the xxhash package to calculate the checksum instead. (But it is failing due to checksum mismatch - I am still working on it)
Currently, ROOT is only able to read those ZLIB and LZMA compressed files (written by uproot) where the length of the object being compressed is less than 10000 bytes. @jpivarski Do you know why this might be?

I'm glad you're testing large objects. The 9-byte header is 3-byte identity, 3-byte compressed size, and 3-byte uncompressed size. (How it gets read.) Those 3-byte numbers have a maximum of 2**24, an unusual number. If either the uncompressed or the compressed size exceeds this number, then you can only write a chunk that maximizes this size, followed by another chunk with the rest (or more chunks, if necessary). Each chunk is separately compressed.
The differences you're seeing among classes, whether TObjString is compressed or not, probably has more to do with the object sizes. If the compressed size is larger than the uncompressed size—which can easily happen if the object is small, due to the fixed size of the compression dictionary—it won't be stored in a compressed way. In fact, that's very important for the reading code (whether ROOT, uproot, groot, or root4j) to know if the buffer is compressed or not: our only indication is whether fObjlen == fNbytes - fKeylen.
A checksum from one library isn't going to work for another: they're all different kinds of hashes. It has to come from the LZ4 library. Are you having trouble calling the function at all or getting the right answer?

A checksum from one library isn't going to work for another: they're all different kinds of hashes. It has to come from the LZ4 library. Are you having trouble calling the function at all or getting the right answer?

I don't think ROOT generates the checksum from the LZ4 library. They use the xxhash library - https://github.com/root-project/root/blob/master/core/lz4/src/ZipLZ4.cxx#L63
According to ROOT, the checksum generated by uproot (xxhash) is not the right answer.
This fails even in uproot. The uproot error is - _block.LZ4BlockError: Decompression failed: corrupt input or insufficient space in destination buffer.

Those 3-byte numbers have a maximum of 2**24, an unusual number.

10000 < 2**24 . So that is probably not the issue.
Also, I am assuming the limit to be 10000 (with the extra digit causing the problem perhaps?) The closest object length I was able to create was ~11500 which failed when trying to be read by ROOT.

Oh, it's supposed to be xxhash; okay.
I also checked the size and saw that it's less than 2**24, but I didn't know if you were talking about the compressed or uncompressed size. I didn't see any hint of a sign bit in the code that I pointed you to, but I didn't mentally exclude the possibility.
For both issues, the thing to do is to see what ROOT does in both cases. Maybe the best testbed is to make TTree branches of char type, since you can more finely control both the size of the baskets (with AutoFlush) and its contents (because you fill exactly the bits you want to fill). If you've never made a TTree before, you know that you'll need to soon!
With a testbed like this, you can see where ROOT turns over from writing a single "page" of compressed data or multiple "pages" ("page" is a word I made up because I don't know what else to call it: a chunk of compressed data with a 9-byte header). You can do that for a compression type you understand, such as ZLIB. Also, you can separately test the LZ4 hash—I mean xxhash—by controlling the content of the data written to the TBasket.
Oh—maybe the issue with computing the hash is because the TKey is included... or not included... Because you remember how an object saved in a ROOT file has a TKey in the TDirectory and then also the same TKey right before the object? That might be included in the calculation of the hash or not, and maybe you've been testing the wrong case.

An update - I am waiting for answers to either https://github.com/python-lz4/python-lz4/issues/170 or https://stackoverflow.com/questions/56191696/what-is-lz4s-corresponding-python-api-function-for-the-c-apis-function-lz4-com

If it's been this long, don't expect an answer. (Sometimes very old threads are answered, but you can't rely on it—it could happen any year now or never.) Is it the case that we can do gzip and lzma? If so, let's restrict the interface to only accept those two options and close this pull request, opening an issue for lz4 for later.
(A lot of users would be happy with just gzip—that already makes a difference.)

I moved lz4 compression into a different branch.
For some reason, the Github files changed diff seems to show changes that have already been committed into master even after I fixed the merge conflicts.

Once I've reviewed this, I plan to include it in uproot 3.6. Thanks!

I think the differences you're talking about are because this branch is behind master (i.e. reverse-differences). I'll do a git merge.


StartContinuousRecognitionAsync Not Working on Windows 7 x86

Describe the bug
While using StartContinuousRecognitionAsync on Windows 7, the connection to the Cognitive server suddenly dies during start.
Was able to trace the error using SDK 1.4+:
(893): 5203ms SPX_TRACE_ERROR: AZ_LOG_ERROR: WS open operation failed with result=8(WS_OPEN_ERROR_INVALID_BYTES_RECEIVED_ARGUMENTS), code=998[0x000003e6]
(893): 5204ms SPX_TRACE_ERROR: Error: File:D:\a\1\s\source\core\usp\transport.cpp Func:?OnWSOpened@@YAXPAXU_WS_OPEN_RESULT_DETAILED@@@Z Line:422
(893): 5204ms SPX_TRACE_INFO: AZ_LOG_INFO: TS:1223, TransportError: connection:0x6304a8, reason=2, code=8 [0x00000008], string=998
(893): 5204ms SPX_DBG_TRACE_VERBOSE: Response: On Error: Code:5, Message: Connection failed (no connection to the remote host). Internal error: 8. Error details: 998. Please check network connection, firewall setting, and the region name used to create speech factory..
To Reproduce
Steps to reproduce the behavior:

Start a new Console project targeting 4.6.2 Framework

Add a Nuget reference to Microsoft.CognitiveServices.Speech v1.3.1. (Newer releases throws a CreateFile2 entrypoint missing on Kernel32"

SpeechConfig speechConfig = SpeechConfig.FromSubscription(
                ConfigurationManager.AppSettings["Microsoft.SubscriptionKey"],
                ConfigurationManager.AppSettings["Microsoft.ServiceRegion"]);

speechConfig.SpeechRecognitionLanguage = "pt-BR";

SpeechRecognizer speechRecognizer = new SpeechRecognizer(speechConfig);

speechRecognizer.Recognized += (sender, recognitionResult) =>
{
                Console.WriteLine($"{recognitionResult.Result.Text}");
};

await speechRecognizer.StartContinuousRecognitionAsync().ConfigureAwait(false);

Target platform: x86

Expected behavior
Sentences being recognized as it does while running on Windows 10
Version of the Cognitive Services Speech SDK
1.0.0~1.5.0
Platform, Operating System, and Programming Language

OS: Windows 7 Enterprise x68 7601 SP1
Hardware - Core i7-7700HQ
Programming language: C#

Exactly same code works flawlessly on Windows 10 build 1903

I'm sorry, @RodrigoRVieira -- Windows 7 is not a supported platform for the Speech SDK.
Please checkout https://docs.microsoft.com/azure/cognitive-services/speech-service/ for the platform support matrix.
Best, Mark

You have no plans on opening this component? Perhaps we (the community) can make it compatible.

we do have plans to open-source this component.  in ~2-3 months.

i have similar problem but my OS is Ubuntu 18.04.3 LTS. what is problem?
Say something...
Speech Recognition canceled: CancellationReason.Error
Error details: Connection failed (no connection to the remote host). Internal error: 1. Error details: -2. Please check network connection, firewall setting, and the region name used to create speech factory.

Hi, is this sdk now compatible in windows 7?

Any progress?

Unfortunately, we have not been able to prioritize compatibility with Windows 7 yet.

There should be an older version that supports win7
this is from 2011 https://stackoverflow.com/questions/5813746/microsoft-speech-api-installation

This one works great, no dependency, only REST API:
https://github.com/mediatechlab/tts-wrapper


Numba Dispatch error when Number of keyword args > 3 for nested numba calls

Reporting a bug

[x] I have tried using the latest released version of Numba (most recent is
visible in the change log (https://github.com/numba/numba/blob/main/CHANGE_LOG).
[x] I have included a self contained code sample to reproduce the problem.
i.e. it's possible to run as 'python bug.py'.

This error happens when defining function * is used. I can start with three function definition cases, The first two cases are passed, and the third one which is a minor modification of the second test case fails.
Numba version: '0.57.1'
Python version: '3.9.17'
Pass Test 1
import numba as nb
def test_1(a, b, c, d, e, f, g):
    return a + b + c + d + e + f + g
test_1 = nb.njit(test_1)

def test_2(a, b, c, d, e, f, g):
    return test_1(a, b, c, d, e, f, g)
test_2 = nb.njit(test_2)
test_2(1, 2, 3, 4, 5, 6, 7)

Pass Test 2
import numba as nb
def test_1(a, b, c, *, d, e, f):
    return a + b + c + d + e + f
test_1 = nb.njit(test_1)

def test_2(a, b, c, d, e, f, g):
    return test_1(a, b, c, d, e, f)
test_2 = nb.njit(test_2)
test_2(1, 2, 3, 4, 5, 6, 7)

Fail Test 3
import numba as nb
def test_1(a, b, c, *, d, e, f, g):
    return a + b + c + d + e + f
test_1 = nb.njit(test_1)

def test_2(a, b, c, d, e, f, g):
    return test_1(a, b, c, d, e, f, g)
test_2 = nb.njit(test_2)
test_2(1, 2, 3, 4, 5, 6, 7)

Error traceback
---------------------------------------------------------------------------
TypingError                               Traceback (most recent call last)
Cell In[10], line 9
      7 test_2 = nb.njit(test_2)
      8 # test_1(1, 2, 3, 4, 5, 6, 7)
----> 9 test_2(1, 2, 3, 4, 5, 6, 7)

File ~/.cache/pypoetry/virtualenvs/indices-ldm-post-process-danSbNDA-py3.9/lib/python3.9/site-packages/numba/core/dispatcher.py:467, in _DispatcherBase._compile_for_args(self, *args, **kws)
    464         msg = (f"{str(e).rstrip()} \n\nThis error may have been caused "
    465                f"by the following argument(s):\n{args_str}\n")
    466         e.patch_message(msg)
--> 467     error_rewrite(e, 'typing')
    468 except errors.UnsupportedError as e:
    469     # Something unsupported is present in the user code, add help info
    470     error_rewrite(e, 'unsupported_error')

File ~/.cache/pypoetry/virtualenvs/indices-ldm-post-process-danSbNDA-py3.9/lib/python3.9/site-packages/numba/core/dispatcher.py:409, in _DispatcherBase._compile_for_args..error_rewrite(e, issue_type)
    407     raise e
    408 else:
--> 409     raise e.with_traceback(None)

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Internal error at .
tuple index out of range
During: resolving callee type: type(CPUDispatcher())
During: typing of call at /tmp/ipykernel_3435313/3644502072.py (6)

Enable logging at debug level for details.

Hi @rishabh1212, thanks for the report. I can confirm the bug in Numba main branch.

However, with arguments passed as keyword arguments:
from numba import njit

@njit
def test_1(a, b, c, *, d, e, f, g):
    return a + b + c + d + e + f

@njit
def test_2(a, b, c, d, e, f, g):
    return test_1(a, b, c, d=d, e=e, f=f, g=g)
test_2(1, 2, 3, 4, 5, 6, 7)

We do still have a problem:
$ python nbtest.py
Traceback (most recent call last):
  File "/home/gmarkall/numbadev/issues/9185/nbtest.py", line 10, in <module>
    test_2(1, 2, 3, 4, 5, 6, 7)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 487, in _compile_for_args
    raise e
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 420, in _compile_for_args
    return_val = self.compile(tuple(argtypes))
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 965, in compile
    cres = self._compiler.compile(args, return_type)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 125, in compile
    status, retval = self._compile_cached(args, return_type)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 139, in _compile_cached
    retval = self._compile_core(args, return_type)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 152, in _compile_core
    cres = compiler.compile_extra(self.targetdescr.typing_context,
  File "/home/gmarkall/numbadev/numba/numba/core/compiler.py", line 770, in compile_extra
    return pipeline.compile_extra(func)
  File "/home/gmarkall/numbadev/numba/numba/core/compiler.py", line 461, in compile_extra
    return self._compile_bytecode()
  File "/home/gmarkall/numbadev/numba/numba/core/compiler.py", line 529, in _compile_bytecode
    return self._compile_core()
  File "/home/gmarkall/numbadev/numba/numba/core/compiler.py", line 504, in _compile_core
    raise e
  File "/home/gmarkall/numbadev/numba/numba/core/compiler.py", line 495, in _compile_core
    pm.run(self.state)
  File "/home/gmarkall/numbadev/numba/numba/core/compiler_machinery.py", line 364, in run
    raise e
  File "/home/gmarkall/numbadev/numba/numba/core/compiler_machinery.py", line 356, in run
    self._runPass(idx, pass_inst, state)
  File "/home/gmarkall/numbadev/numba/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
    return func(*args, **kwargs)
  File "/home/gmarkall/numbadev/numba/numba/core/compiler_machinery.py", line 311, in _runPass
    mutated |= check(pss.run_pass, internal_state)
  File "/home/gmarkall/numbadev/numba/numba/core/compiler_machinery.py", line 273, in check
    mangled = func(compiler_state)
  File "/home/gmarkall/numbadev/numba/numba/core/typed_passes.py", line 110, in run_pass
    typemap, return_type, calltypes, errs = type_inference_stage(
  File "/home/gmarkall/numbadev/numba/numba/core/typed_passes.py", line 88, in type_inference_stage
    errs = infer.propagate(raise_errors=raise_errors)
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 1078, in propagate
    errors = self.constraints.propagate(self)
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 177, in propagate
    raise e
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 155, in propagate
    constraint(typeinfer)
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 578, in __call__
    self.resolve(typeinfer, typevars, fnty)
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 601, in resolve
    sig = typeinfer.resolve_call(fnty, pos_args, kw_args)
  File "/home/gmarkall/numbadev/numba/numba/core/typeinfer.py", line 1557, in resolve_call
    return self.context.resolve_function_type(fnty, pos_args, kw_args)
  File "/home/gmarkall/numbadev/numba/numba/core/typing/context.py", line 196, in resolve_function_type
    res = self._resolve_user_function_type(func, args, kws)
  File "/home/gmarkall/numbadev/numba/numba/core/typing/context.py", line 248, in _resolve_user_function_type
    return func.get_call_type(self, args, kws)
  File "/home/gmarkall/numbadev/numba/numba/core/types/functions.py", line 541, in get_call_type
    self.dispatcher.get_call_template(args, kws)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 359, in get_call_template
    pysig, args = self._compiler.fold_argument_types(args, kws)
  File "/home/gmarkall/numbadev/numba/numba/core/dispatcher.py", line 118, in fold_argument_types
    args = fold_arguments(self.pysig, args, kws,
  File "/home/gmarkall/numbadev/numba/numba/core/typing/templates.py", line 218, in fold_arguments
    bind_kws[n] = args[len(kwonly) + idx]
IndexError: tuple index out of range

@gmarkall for https://github.com/numba/numba/issues/9185#issuecomment-1715806562
Look at Pass Test 2, even without passing as kwargs it is successfull.

@guilhermeleobas

It fails in the interpreter:

Yes, it is expected and thus not part of my tests. I would expect same results for Test2 and Test3 but one is pass another is fail


Speed up CI by compiling with -j6 and run tests with -j100 or -j12

So, @ted-ross says -j12? Phew! :100:
I am only half-serious about this, but there actually is some merit to these crazy parallel settings.
The downsides are obvious: tests fail more and logs are a mess.
But the upside is that the same tests tend to fail more, over and over again, so they show themselves as the least reliable tests from the whole bunch, and can be fixed. Also, the CI does run a little bit faster, though not by all that much.
The -j6 for compilations is interesting, though. Most of the compilation is essentially fetching stuff from ccache, so there it does make sense to initiate more fetches in parallel. When an actual compilation needs to happen, it will be arrived at faster. Dispatch will "compile" this way in about a second!.
Edit: looking at the durations also shows my folly in doing the split compile and test jobs for Ubuntu. The compressing of the artifacts takes longer than ccache restore and the actual build.
Edit Edit: the natural continuation in this direction would be to run every test multiple times

@jiridanek can you please rebase this PR against master ? Thanks.

I test using -j12 because that's a good match for the number of CPUs available on my system (8).  If you're looking for speed, you need to tune to the available CPUs.  I agree that using parallelism to flush out bugs is also valuable.


Releases not standard?

I am trying to use this tool to grab the latest version of an asset binary but it doesn't work.
https://github.com/dvershinin/lastversion/
See here.
https://github.com/dvershinin/lastversion/discussions/116
Anything you can/willing to  "fix" at your end?

Even with --pre  it won't work as you want, I guess, because all the latest "versions" are nothing but references to commits. To me, it looks like they are misusing release tags for tracking upstream commits, without providing a clear version string... probably won't be supported.

if you install lastversion and try
lastversion --pre tasmota/install
you will get some "bogus"  version/release  29  which will then download a way old release from 2023
Program Version	<IP_ADDRESS>(lvgl-haspmota)
Build Date & Time	2023-03-13T15:09:39

without fix I have to manually download the binary I want to install and can't use that tool as part of an install script I have.
I could just as well use https://ota.tasmota.com/tasmota32/  but for some reason it does not include all the assest binaries available at tasmota/install.   Why is that?
In particular at this time I need tasmotas2-lvgl.factory.bin and that is not at https://ota.tasmota.com/tasmota32/ but is at https://github.com/tasmota/install/releases/tag/2024-08-01.d.836ce9ef9ba64677861a173cc7687be380acef6b

Sorry no, our resources are very limited and we can not implement support for the many existing tools out there.

I know you closed this but a few points.   https://github.com/tasmota/install/releases/latest  only takes you to the latest release not the pre-releases.   That program "lastversion" works fine for your releases but not pre-releases which it does on other repos fine (like wled).  That is what he is saying about not having a proper "tag/pre-release".    Ok you can't do anything about it.
SO my issue is then.
I'd be happy to use a release instead of a pre-release but for whatever reason the release does NOT include tasmotas2-lvgl.factory.bin but it is in the daily pre-releases.  Further I asked about about why   https://ota.tasmota.com/tasmota32/  which has daily releases does not match the pre-releases at github (i.e. there is NO tasmotas2-lvgl.factory.bin).   Seems weird shouldn't the CI produce the same set of assets whether it is a pre-release or release?
Anyway....can you get bins at  https://ota.tasmota.com/tasmota32/  to match the assets of the pre-release at github?  (i.e. include tasmotas2-lvgl.factory.bin ?   If so my issue is solved cause I can just curl it.

Came up with one liner that works so I'm good.
 BIN=tasmota32s2-lvgl.factory.bin; wget $(curl --silent https://api.github.com/repos/tasmota/install/releases | grep "browser_download_url\": \"" | grep $BIN | head -1 | sed 's/^.*: //' | xargs)
will download latest release pre or regular whichever is first from curl command
https://github.com/tasmota/install/releases/download/2024-08-04.d.bdec6aed67387e4b1233c442ac77977665884c0e/tasmota32s2-lvgl.factory.bin

The OTA server provides only the firmware variants build in official Tasmota repo.
The Tasmota Install repo provides more build variants. All "unofficial" builds are available here. The tasmota32s2-lvgl.factory.bin is not a official standard build provided from Tasmota.


Broken search form filter

This filter on get_search_form breaks plugins that filter the search form:
    function get_search_form( $form ) {
	$form = '';
	locate_template( '/searchform.php', true, false );
	return $form;
}

Because you're force-loading the searchform.php template, any plugin filtering this will cause a second search form to appear instead of overwriting the theme's search form.

Fixed - This was actually also causing issues with the search widget, resulting in an internal server error. The offending files have been removed with core functionality now working as expected.


Remove invalid install_swiftsyntax_parser preset

It seems that build-libparser-only hasn't existed since 2019 and this
change https://github.com/apple/swift/pull/27871

@swift-ci please smoke test

Tested this here https://ci.swift.org/job/swift-PR-osx-preset/7/console https://github.com/apple/swift/pull/41859

@swift-ci please smoke test Linux Platform

@swift-ci please smoke test macOS Platform

@swift-ci Please smoke test Linux


improve Adding Reports documentation

reverse was used but not imported in the wagtail_hooks.py examples, causing them to be invalid
I switched the Django url calls to use path instead since that seems both cleaner and officially recommended, the url function is "likely to be deprecated in a future release" per Django docs and  path is starting to replace it in urls.py files now

@phette23 - thank you for the fixes for the docs, this has been merged in. Really appreciate your time on making these improvements.


Override composer task parameters from your environment (stage|production) config file

Hi, I ran into the same problems as described here #39 #22
In the composer task, shared_path and release_path are not evaluated accordingly to the environment.
The bug is simple to reproduce:
When you override deploy_to path in your stage|production ruby file with a path different from your deploy.rb file, the composer task will run with the deploy.rb deploy_topath so that both release and shared path will be wrong when launching composer tasks.
The workaround I found is to load default paths using lambdas:
namespace :load do
  task :defaults do
    ...
    # use ruby global variables in a lambda
    set :composer_working_dir, -> { "#{release_path}" }
    set :composer_path, -> { "#{shared_path}" }
    set :composer_use_global, false
  end
end

I added the variable composer_path so that I can override the SSHKit command_map with a lambda (since SSHkit.config.command_map = ->{ fetch(:myvar) } doesn't seem to work).
Also, instead of overriding the command in the deploy.rb, I override the variable directly in the rake tasks so that the variable is set accordingly to the environment we are deploying in.
With these changes, you can do whatever you want:

override the deploy_to path
override the composer_path
override the composer_working_dir
From your stage or deploy.rb files

I also added other stuffs:

I added a check which checks whether composer is installed globally or not
In case composer is not installed, I trigger composer:install_executable task (with a ask)
This check is triggered whenever a composer task is triggered
(before 'composer:run', 'composer:check')
I added a flag which indicate that we prefer to use a globally installed composer instead of a locally installed version of composer

What do you thing about this ?

Any feedback ?

This PR caught me while I was away on vacation so it got lost in the shuffle. Despite this being a limitation of Capistrano, I'd be open to potentially including a solution for it in this extension.
But I'd prefer if the code changes could be limited to only addressing that problem for one. There seems to be other changes going on in this PR. Can it be reduced to the minimum required to fix the original issue?


Mapping DD2 data

What data do they have
Where are they each located at (server location, institution responsible, etc)
Who owns what data

Action items:

[ ] Alisa to write the first version of the interview guide
[ ] Annelli to present the idea to DD2 Wednesday Mar 30th

Misc items to do:

(Luke & Alisa) Contact others (F, S, S, T) for a meeting to involve them in project for mapping DD2 (first get confirmation for collaborating with them)

F-Frederik, S- Sia, S-Sidsel, T-Torben Hansen


Create temp folder in two places

Move creation of temp folder to a helper method and call from no-arg
constructor and before. The creation of the folder is idempotent so it
can be called multiple times. Calling this method in before() allows
tests to be run multiple times in IntelliJ.
Thank you for submitting a contribution to Apache Geode.
In order to streamline the review of the contribution we ask you
to ensure the following steps have been taken:
For all changes:

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

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

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

[x] Does gradlew build run cleanly?

[x] Have you written or updated unit tests to verify your changes?

[x] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?

Note:
Please ensure that once the PR is submitted, you check travis-ci for build issues and
submit an update to your PR as soon as possible. If you need help, please send an
email to<EMAIL_ADDRESS>

Precheckin is green. I can pull this in now.

This is now creating two separate temporary folders, one of which doesn't end up getting cleaned up.

@jdeppe-pivotal That's not good... I'll look at it today


[BUG]: Missing one button in UI

Hi @Alitindrawan24 , there is no button of comma(,) present in button tab which is required for AND OR XOR operation.
I can include that please assign me this issue under hacktoberfest.
Thank You
Below is the image of existing app.

already assigned @SubramanyaKS


Composer: Adds composer.json for package registration

Composer is a package manager for php based packages which doesnt fit for this lib. Why do you want it in there?

Yes, but it's not only a package manager. Composer allows you to declare libraries in your project. It would be nice if we support people to download this lib when they already have composer installed. There are lots of packages in composer which are not PHP such as jQuery and have 600k+ download using composer.

jquery and other js libs are not provided by the source repos but by so called shim reposes which distribute whatever think makes sense.
I dont think we should invest time to support another package manager, but concentrate on actual library features.
feel free to create such a shim repos on your own. thanks for the suggestion.


Example how to include prometheus-operator/contrib/kube-prometheus/examples/alertmanager-config-external.jsonnet

I'am trying to change the alertmanager config with the example file at "prometheus-operator/contrib/kube-prometheus/examples/alertmanager-config-external.jsonnet".
How can I include this code inside my main.jsonnet?
For example the prometheus-additional-alert-rule-example.jsonnet
file uses works with merging the _config+::
Thanks for helping

I think the full example you are looking for would be:
local kp =
  (import 'kube-prometheus/kube-prometheus.libsonnet') + {
    _config+:: {
      namespace: 'monitoring',
      alertmanager+: {
        config: importstr 'alertmanager-config.yaml',
      },
    },
  };

{ ['00namespace-' + name]: kp.kubePrometheus[name] for name in std.objectFields(kp.kubePrometheus) } +
{ ['0prometheus-operator-' + name]: kp.prometheusOperator[name] for name in std.objectFields(kp.prometheusOperator) } +
{ ['node-exporter-' + name]: kp.nodeExporter[name] for name in std.objectFields(kp.nodeExporter) } +
{ ['kube-state-metrics-' + name]: kp.kubeStateMetrics[name] for name in std.objectFields(kp.kubeStateMetrics) } +
{ ['alertmanager-' + name]: kp.alertmanager[name] for name in std.objectFields(kp.alertmanager) } +
{ ['prometheus-' + name]: kp.prometheus[name] for name in std.objectFields(kp.prometheus) } +
{ ['prometheus-adapter-' + name]: kp.prometheusAdapter[name] for name in std.objectFields(kp.prometheusAdapter) } +
{ ['grafana-' + name]: kp.grafana[name] for name in std.objectFields(kp.grafana) }

Let us know if that helps! :slightly_smiling_face:

Thanks!!
That worked.
I will have to invest a little but more time into jsonnet


Dev: Class GoogleDriveFile

Hi @vperezb,
as I am not sure if I can work directly in your repo, I am sending you a PR for the work I have done so far. I have put the files into a new folder called "dev", but if you prefer not having it in the master branch, we can check alternative methods.

utils: a few functions to simply work in the class definition
drive: I have tried mapping all the functions you had created related to simple files (not folders) into this class
TEST: just for checking that all the methods work

TODO: create a GoogleDriveFolder class which should inherit methods from GoogleDriveFile.
Names are provisional, they can be changed.

I added you as a collaborator, tell me if you can merge the PR yourself, if not I can check it !


Posibility to format "formatted" output with variable number of zeros

Coverage decreased (-8.3%) to 87.582% when pulling 0b07b32b4e18bd4f3c7e5f27a48ee202554f46ac on psanchezg:master into 2118a067359ffc96784b02fcd26f312e2bd886db on kevinkhill:master.

Thank you, and I am really happy that you want to contribute! All I ask is that you please write tests for the feature so I can merge the PR with an easy conscience?


Unpin animation

Adds an animation on pinning/unpinning course tiles.
Also refactors common behavior of My Courses/All Courses into Polymer behavior, so they can handle course entity management independently; d2l-all-courses was previously depending on d2l-my-courses for state management.

Many :+1:s!


Cleave is broken with Vue

Cleave is not working with vue/vuetify
https://github.com/vuetifyjs/vuetify/issues/6910#issuecomment-479798524

There is really nothing wrong with Cleave or Vuetify. Here is my v-text-field wrapper component which works perfectly with Cleave.
<script>
import Cleave from 'cleave.js';

export default {
  props: {
    value: {
      type: [String, Number],
      required: false,
      default: '',
    },
  },
  data() {
    return {
      currentValue: this.value,
    };
  },
  mounted() {
    this.cleave = new Cleave(this.$el.querySelector('input'), {
      numeral: true,
      onValueChanged: this.onValueChanged.bind(this),
    });

    this.cleave.setRawValue(this.value);
  },
  methods: {
    onValueChanged({ target }) {
      this.$nextTick(() => {
        this.currentValue = target.value;
        this.$emit('input', target.rawValue);
      });
    },
  },
};
</script>

<template>
  <v-text-field
    v-model="currentValue"
    v-bind="$attrs"
    :label="$t(domain)"
    height="22"
    class="caption rbx-text-input"
  />
</template>

https://github.com/ankurk91/vue-cleave-component

For those that are still having issues binding Cleave to this.$refs.input (or whatever) -- I found a surprisingly easy fix that hasn't been documented yet:
v-model.lazy="foo"
The .lazy modifier makes Vue wait for Cleave to validate by binding to change events (instead of input events).

Also try just
:value="value"
@input.native="value = $event.target.value"

@fatihacet thanks this works for me..  for other people reference :)
`
`

Or as directive (Vuetify):
Vue.directive('cleave', {
  bind(el, binding, vnode) {
    const input = el.querySelector('input')
    // @ts-ignore
    input._vCleave = new Cleave(input, binding.value)
  },
  update: (el) => {
    const input = el.querySelector('input')
    const event = new Event('input', {bubbles: true});
    setTimeout(function () {
      input.value = input._vCleave.properties.result
      input.dispatchEvent(event)
    }, 100);
  },
  unbind(el) {
    const input = el.querySelector('input')
    // @ts-ignore
    input._vCleave.destroy()
  }
})

@harlet That works good but keep in mind that you cannot fill form data with a click anymore. The data shows up for a second and disappears. But for now its a decent workaround to the issue. Just wondering if this is gonna get properly fixed any time?!

For those that are still having issues binding Cleave to this.$refs.input (or whatever) -- I found a surprisingly easy fix that hasn't been documented yet:
v-model.lazy="foo"
The .lazy modifier makes Vue wait for Cleave to validate by binding to change events (instead of input events).

This worked. Thanks!

For those that are still having issues binding Cleave to this.$refs.input (or whatever) -- I found a surprisingly easy fix that hasn't been documented yet:
v-model.lazy="foo"
The .lazy modifier makes Vue wait for Cleave to validate by binding to change events (instead of input events).

This worked. Thanks!

Vue 2.6 did introduce a probably breaking change by using MicroTasks instead of MacroTasks, see
https://github.com/vuejs/vue/pull/8450#issuecomment-1608252244
for the problem and a solution.


src: support V8 v8.0.x.y

../src/cpu_profile_node.cc:58:95: warning: 'GetCallUid' is deprecated: Use GetScriptId, GetLineNumber, and GetColumnNumber instead.

GetCallUid() was removed in V8 8.0, let's add the last missing replacement – a column number.

LGTM, thans for your pr :)

@hyj1991 Any chance you could release a new version with this change?

@alexeykuzmin (Release v1.2.2)[99350cf]


austin-sns44

Give @ATLi2001 permission for sns44

Should take effect on the hour (8pm Princeton time)


[TOPI][Hexagon] Implement quantized elementwise for hexagon

Thanks for contributing to TVM!   Please refer to guideline https://tvm.apache.org/docs/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from Reviewers by @ them in the pull request thread.
cc @mehrdadh

Hi, @mehrdadh @kparzysz-quic @TejashShah this PR is ready for review. Thank you

@trahman-quic @jverma-quic thanks for the contribution and review!
I'll wait for @jverma-quic's review. Also, please change the PR description to show the purpose of this PR.

Thanks for the PR, Tasmia! It looks good to me.

Thank you @jverma-quic .
@kparzysz-quic @mehrdadh, please let me know if it looks good to merge now. Thanks


Connector stop working with new-generated API keys

Hi team. Starting from 10.04 connector stop working with keys which were generated on 10.04 and later.
I've tested it both with Demo EA enrollment 8608480 (which was re-generated (not by me) on the EA portal on 10.04) and with several production EA enrollments, which were generated from 10.04 till 14.04.
If you try to go through interface https://docs.microsoft.com/en-us/power-bi/desktop-connect-azure-consumption-insights#connect-with-azure-consumption-insights, on the step where you choose tables, system (again) asks for API KEY and here always thinks that it is incorrect.
If you go with code https://docs.microsoft.com/en-us/power-bi/desktop-connect-azure-consumption-insights#migrate-from-the-azure-enterprise-connector it just thinks that the API Key is incorrect.
Tried to re-enter keys, to delete them from data sources, nothing helped.
Accounts, whose API Keys were generated before (have 5 to test) are fine, with both regular update and if you try to re-build connection from scratch.
Reported it also to Issue forum and microsoft service-now.

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

ID: 3e71361e-a9bb-89ee-27ee-7e38a60914a0
Version Independent ID: df5a9b26-4eb1-a396-1c84-9f3ec6232753
Content: Connect to Azure Consumption Insights data in Power BI Desktop - Power BI
Content Source: powerbi-docs/desktop-connect-azure-consumption-insights.md
Service: powerbi
Sub-service: powerbi-desktop
GitHub Login: @davidiseminger
Microsoft Alias: davidi

Did you ever get this solved?  Seeing the same issue.

Thanks for your comment, @chizhova . Did you find your answer on the Issues site? If not, this looks like a support request or product question rather than documentation feedback. You might find a solution in the Power BI Community, at https://community.powerbi.com. If not, you can get support by going to https://support.powerbi.com.