PT Run crashes when trying to open in non-administrator mode

Environment
Windows build number: 10.0.18363.900
PowerToys version: 0.19.2
PowerToy module for which you are reporting the bug (if applicable): PT Run
Dual monitor setup

Steps to reproduce

Restart PT in non-administrator mode
Toggle PT Run off then back on
Press Alt + Space to open Run

Expected behavior
PT Run pops up
Actual behavior
I notice PT Run attempting to open (current active window loses focus), but after a few seconds PowerLauncher.exe crashes. The process disappears from task manager detail view. In administrator mode, I face a similar issue with #3650.
Screenshots
The errors that show up in event viewer:

Does this still happen with 0.23.2?  We've done a lot of fixes here.

So far it seems like it's fixed in the 0.23.2. It works without administrator mode. I'm thinking it might be some sort of conflict with either MSI Afterburner or RTSS because after updating those programs, PT Run starts working.

https://github.com/microsoft/PowerToys/issues/3190 has a reference to RTSS and seems to be fixed as well.


Add ai-jam-js demo.

Removed .DS_Store files. We could decide to move the demos later on. For now, we have them outside of the magenta directory.


The new fontSizes function doesn't work with items media config

Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior:
Set fontSizes with non-default setting and use data items.. some with media elements and others without. Observe that the media items don't respect the custom font sizes but the items without media elements do.
**App.js**

        fontSizes={{
           cardSubtitle: "0.85rem",
           cardText: "0.8rem",
           cardTitle: "10rem"
         }}

**data.js**

const items = [
 {
   title: "May 1940",
   cardTitle: "Dunkirk",
   cardSubtitle:
     "Men of the British Expeditionary Force (BEF...`,
   media: {
     name: "dunkirk beach",
     source: {
       url: "http://someurl/image.jpg"
     },
     type: "IMAGE"
   }
 },
 {
   title: "25 July 1940",
   cardTitle: "The Battle of Britain",
   cardSubtitle: `RAF Spitfire pilots scramble for their planes`,
   cardDetailedText: `After France’s surrender in June 1940...`
 },

Expected behavior
The custom font size should always be respected, regardless of the media config.
Screenshots

Desktop (please complete the following information):

OS: OSX
Browser: Chrome
Version: Version 101.0.4951.64 (Official Build) (arm64)

Additional context
Add any other context about the problem here.

@stephenhibbert this is now fixed in the new release.

Awesome, thanks for the note - all working as expected now! Thanks for the quick fix and for the awesome project :)


fail to insert secondary citation

when I insert Markdown citation, I would like to cite multiple authors in a bracket; I press "shift+enter", but there is no reaction. How can I realize this?

duplicate of #117


Update adls gen1 module to 0.0.48

This checklist is used to make sure that common guidelines for a pull request are followed.

[x] The PR has modified HISTORY.rst describing any customer-facing, functional changes. Note that this does not include changes only to help content. (see Modifying change log).

[x] I adhere to the Command Guidelines.

@Juliehzl  please help with this PR.

@akharit Let's talk about this after Ignite.

@Juliehzl  I am fine with merging this later when you have cycles. Please ping me so I can resolve any merge conflicts.

@akharit  Can you resolve all conflicts? If CI is green, I will agree.

@Juliehzl CI is all green. Can you merge this?

@Juliehzl CI is all green. Can you merge this?

Sorry for the delay. Could you rebase dev again and let CI pass? I will merge for you ASAP. If dropped, please ping me.


WIP: chore(deps): Upgrade Patternfly to version 5

Fixes: https://github.com/janus-idp/backstage-plugins/issues/648
/cc @invincibleJai

Thanks @jeff-phillips-18. Some type errors are shown here on topology https://github.com/janus-idp/backstage-plugins/actions/runs/6101300353/job/16557365720?pr=715

LGTM


Add env variables IMSUB_OUT and ROOTDIR.

This can override the hard coded DCC values. DCC values are still the default.

Will probably manually change this and reject the pull request because I've made some additional changes this morning.
Other to-dos:

Change /work/lna18/ to /work/snpit/ (and add a check that this folder exists at the beginning of the module)
Pull Lei's SFFT repo to check for compatibility w/o my forked repo

Ok.
I am currently setting up /hpc/group/cosmology/roman-sn-pit as a long term storage home. So /work/roman-sn-pit/ is likely a good temp file home. Also, the joy of environment variables is that I will define them in the PIT Conda environment, so we can easily change them for the group without changing this package.

Made these changes manually.


Update s3, sdk-core, sqs to 2.20.22

Updates

software.amazon.awssdk:s3
software.amazon.awssdk:sdk-core
software.amazon.awssdk:sqs

from 2.17.295 to 2.20.22.
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "software.amazon.awssdk" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "software.amazon.awssdk" }
}]

labels: library-update, early-semver-minor, semver-spec-minor, commit-count:1

Superseded by #583.


Userlog Service Improvements

Improves userlog service with proper messages and enhances API

:boom: Acceptance test cs3ApiTests-ocis failed. Further test are cancelled...


NHS QR code not recognized in 1.1.2

Describe the bug
I downloaded the VerificaC19 App today (in the UK) and I tried to scan a freshly generated NHS Covid Pass QR code (for a second vaccination, done in June). It recognizes my name and date of birth, but tells me "Certificazione non valida" without giving a reason (and screen shots are disabled due to "security policy", hmmmm). The same QR code is recognized and converted into a Digital Covid Certificate without issue by the French TousAntiCovid app.

Expected behaviour
QR code recognized as, as far as I am aware Italy is recognizing UK NHS vaccinations now.

Steps to reproduce the issue

Open app, click "scan QR code" button, scan NHS QR code, receive error. (It would be nice to be able to at least screen shot the error screen, even if screenshots are blocked otherwise.)
Technical details

Host Machine OS (Windows/Linux/Mac): Samsung Galaxy A51, Android version 11

Possible Fix

Additional context

I can confirm the bug.
I've been denied entry today at my workplace due to the QR Code being considered invalid.

I can confirm this. None of the codes available on the NHS England App are now recognised. Before it was reading the code of the second dose on the app-generated PDF.

It's not our fault, there was a signature problem on some QR code generated by NHS. I suggest you to download a newly DCC, let me know if it works!


e2e: add support for multiple clusters

In the current E2E tests only one Elemental cluster is deployed, so we need to add the support for multiple cluster.

It could be a good idea to be able to configure both K3s and RKE2 client clusters.

First implementation done in PR #1007.


 Not sure if tanks can survive it, but it killed me at around 90% hp.

I'm sorry your run ended that way. However, I think "don't get hit by telegraphed aoes on higher floor sets because there is a good chance they will one-shot" should be implicitly understood by Deep Dungeon players, and seems superfluous to add.


Task Management Integration

[ ] Allow users to create and manage a list of tasks to work on during each Pomodoro session.


diagram added

add component diagram and update readme

add component diagram to project, display in readme and add link to deployed site.


How to compile tensorflow using SSE4.1, SSE4.2, and AVX.

Just got tensorflow running. Now running into this error.
Currently using Mac Yosemite, downloaded tensorflow using pip3 through anaconda, using python 3.5.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
So since anaconda has a special set of commands, how do you get tensorflow to run on SSE4.1, SSE4.2, and AVX via the anaconda command system ?  I am really confused how to go about this.

You need to build TensorFlow from source.
SO question about this: http://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions
TensorFlow guide to build from source: https://www.tensorflow.org/install/install_sources

Just like @Carmezim these are simply warning messages.
For each of your programs, you will only see them once.
And just like the warnings say, you should only compile TF with these flags if you need to make TF faster.
You can follow our guide to install TensorFlow from sources to compile TF with support for SIMD instruction sets.

Ok, thanks. I get it.

The only way to silence these warning messages is to build from sources, using --config opt option.

A sort of "workaround" (albeit imperfect) that redirects the messages on Unix/Linux/OSX:
python myscript.py 2>/dev/null

@CGTheLegend you can use TF environment variable TF_CPP_MIN_LOG_LEVEL. It defaults to 0, showing all logs. To filter out INFO set to 1, WARNINGS additionally 2 and to additionally filter out ERROR logs set to 3.
So you can do the following to silence those warnings:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

I install from tensorflow install guide, also got this warning.
pip3 install --upgrade tensorflow

@jadeydi  Instead of compiling from source, "pip" just install the binary as well, so that you'll still got these warnings.

I just compiled tensorflow with support for SSE4.1 SSE4.2 AVX AVX2 and FMA. The build is available here: https://github.com/lakshayg/tensorflow-build . I hope this is useful.

Hi @lakshayg, thanks for sharing. You might want to check https://github.com/yaroslavvb/tensorflow-community-wheels

Approximately much faster is the build compared to the standard pip install tensorflow-gpu on Ubuntu?  Is it only faster for CPU computations, or is there any benefit to GPU computations?

http://www.anandtech.com/show/2362/5
This came up on google and has some decent technical details.

test is a DivX encode using VirtualDub 1.7.6 and DivX 6.7. SSE4 comes in if you choose to enable a new full search algorithm for motion estimation, which is accelerated by two SSE4 instructions: MPSADBW and PHMINPOSUW. The idea is that motion estimation (figuring out what will happen in subsequent frames of video) requires a lot of computation of sums of absolute differences, as well as finding the minimum values of the results of those computations. The SSE2 instruction PSADBW can compute two sums of differences from a pair of 16B unsigned integers; the SSE4 instruction MPSADBW can do eight.

...

On our QX9650, the full search with SSE4 enabled runs about 45% faster than with SSE2 only

Now sure what functions tensorflow is using, but might be worth the effort.

Sorry but this is a ridiculous thing to have output in all TF scripts by default. Most people probably aren't compiling TF from source nor want to.

@TomAshley303, this is a pretty awesome info to get! I don't plan to recompile from source. I don't want to. But the info tells me what to do if my model becomes big and slow and will need a performance boost. It's usually cheaper to recompile with extensions than to buy new hardware, given that having good walkthroughts (which we do have) minimizes the labour cost of recompiling (CPU time does not matter, can run overnight).

I went through the process... Was straight-forward and took no time at all. Not your usual cmake C++ kinda nightmare.

I have a small bash script to compile TF under MacOS. It dynamically calculates CPU features and put them as build parameters. Can be easily extended to support Linux. I was thinking to create PR but didn't find folder with scripts (helpers) for local builds, only ci_build. If it makes sense I will do it
gist
https://gist.github.com/venik/9ba962c8b301b0e21f99884cbd35082f

A note to @Carmezim and @gunan
I've encountered this issue when I was installing TensorFlow for the first time. Now I am having to figure out how to resolve it again because I'm installing TensorFlow on a new machine. It's a pain in the neck, and the documentation you've provided is not clear at all.

Thank you, according to @Carmezim answer, I get the cpu speed up version based on avx and sse. I'v tested faster-rcnn(resnet-101) on Intel. Cost time speeds up about 30%, it is truly useful.

You can silence the warnings.
Just add these code at the top.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
As mentioned here: https://stackoverflow.com/a/44984610

@SayimTR options make TF faster

you could easily add a user variable in System Environment Variable:
TF_CPP_MIN_LOG_LEVEL, value = 2. Then restart your IDE

@mikalyoung improvements for GPU computations cannot be expected, since those instructions set are CPU only, and they allow for vectorized operations.
So if you compare two codes running (ideally) 100% on GPUs, one on a Tensorflow instance compiled with SIMD support and one without, you should get the same results in terms of speed (and hopefully numerically also).

Why are there no windows compiles? I am having the same issues, but instead of muting the warnings I would like to use my GPU, I also have an and graphics card and not Nvidia what do I do?

*I Do not have a Nvidia graphics card, I have an and one what do I do?

*AMD graphics card.. autocorrect

These are not merely warnings as it kills the process on my test boxes. Since I also use AMD GPUs I spun up a Digital Ocean tensorflow box to give this a go, but it seems there is no GPU support there either, and it's failing miserably.
`# Job id 0
Loading hparams from /home/science/tf-demo/models/nmt-chatbot/model/hparams
saving hparams to /home/science/tf-demo/models/nmt-chatbot/model/hparams
saving hparams to /home/science/tf-demo/models/nmt-chatbot/model/best_bleu/hparams
attention=scaled_luong
attention_architecture=standard
batch_size=128
beam_width=10
best_bleu=0
best_bleu_dir=/home/science/tf-demo/models/nmt-chatbot/model/best_bleu
check_special_token=True
colocate_gradients_with_ops=True
decay_factor=1.0
decay_steps=10000
dev_prefix=/home/science/tf-demo/models/nmt-chatbot/data/tst2012
dropout=0.2
encoder_type=bi
eos=
epoch_step=0
forget_bias=1.0
infer_batch_size=32
init_op=uniform
init_weight=0.1
learning_rate=0.001
learning_rate_decay_scheme=
length_penalty_weight=1.0
log_device_placement=False
max_gradient_norm=5.0
max_train=0
metrics=['bleu']
num_buckets=5
num_embeddings_partitions=0
num_gpus=1
num_layers=2
num_residual_layers=0
num_train_steps=500000
num_translations_per_input=10
num_units=512
optimizer=adam
out_dir=/home/science/tf-demo/models/nmt-chatbot/model
output_attention=True
override_loaded_hparams=True
pass_hidden_state=True
random_seed=None
residual=False
share_vocab=False
sos=
source_reverse=False
src=from
src_max_len=50
src_max_len_infer=None
src_vocab_file=/home/science/tf-demo/models/nmt-chatbot/data/vocab.from
src_vocab_size=15003
start_decay_step=0
steps_per_external_eval=None
steps_per_stats=100
subword_option=
test_prefix=/home/science/tf-demo/models/nmt-chatbot/data/tst2013
tgt=to
tgt_max_len=50
tgt_max_len_infer=None
tgt_vocab_file=/home/science/tf-demo/models/nmt-chatbot/data/vocab.to
tgt_vocab_size=15003
time_major=True
train_prefix=/home/science/tf-demo/models/nmt-chatbot/data/train
unit_type=lstm
vocab_prefix=/home/science/tf-demo/models/nmt-chatbot/data/vocab
warmup_scheme=t2t
warmup_steps=0
creating train graph ...
num_bi_layers = 1, num_bi_residual_layers=0
cell 0  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
cell 1  LSTM, forget_bias=1  DropoutWrapper, dropout=0.2   DeviceWrapper, device=/gpu:0
learning_rate=0.001, warmup_steps=0, warmup_scheme=t2t
decay_scheme=, start_decay_step=0, decay_steps 10000, decay_factor 1
Trainable variables
embeddings/encoder/embedding_encoder:0, (15003, 512),
embeddings/decoder/embedding_decoder:0, (15003, 512),
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/memory_layer/kernel:0, (1024, 512),
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (1536, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0
dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (1536, 512), /device:GPU:0
dynamic_seq2seq/decoder/output_projection/kernel:0, (512, 15003), /device:GPU:0
creating eval graph ...
num_bi_layers = 1, num_bi_residual_layers=0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
Trainable variables
embeddings/encoder/embedding_encoder:0, (15003, 512),
embeddings/decoder/embedding_decoder:0, (15003, 512),
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/memory_layer/kernel:0, (1024, 512),
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (1536, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0
dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (1536, 512), /device:GPU:0
dynamic_seq2seq/decoder/output_projection/kernel:0, (512, 15003), /device:GPU:0
creating infer graph ...
num_bi_layers = 1, num_bi_residual_layers=0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 0  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
cell 1  LSTM, forget_bias=1  DeviceWrapper, device=/gpu:0
Trainable variables
embeddings/encoder/embedding_encoder:0, (15003, 512),
embeddings/decoder/embedding_decoder:0, (15003, 512),
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/memory_layer/kernel:0, (1024, 512),
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0, (1536, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0, (1024, 2048), /device:GPU:0
dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0, (2048,), /device:GPU:0
dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0, (), /device:GPU:0
dynamic_seq2seq/decoder/attention/attention_layer/kernel:0, (1536, 512), /device:GPU:0
dynamic_seq2seq/decoder/output_projection/kernel:0, (512, 15003),
log_file=/home/science/tf-demo/models/nmt-chatbot/model/log_1519669184
2018-02-26 18:19:44.862736: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Killed`

what command needs to run and where to run these commands and how please tell.  I desperately need help.

But does it mean that the system is not using GPU for the process?

Just chiming in on this thread that you shouldn't just silence these warnings - I'm getting about 43% faster training time by building from source, I think it's worth the effort.

Tensorflow's instructions on building from source are pretty clear
Use this thread to get an idea of how to set your Bazel build flags for SSE/AVX/FMA etc

how to install tensorflow using this file" tensorflow-1.6.0-cp36-cp36m-win_amd64.whl"

@anozele pip3 install --upgrade *path to wheel file*

@gunan --config=opt is not enough, you should also add, e.g., --copt="-msse4.2", when you build TensorFlow from source.

According to Intel, https://software.intel.com/en-us/articles/intel-optimization-for-tensorflow-installation-guide, If you use intel built Tensorflow, you can ignore those warning since all available instruction set would be used by the backend MKL. Can anyone from Tensorflow confirm this?

This isn't an error, just warnings saying if you build TensorFlow from source it can be faster on your machine.
SO question about this: http://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions
TensorFlow guide to build from source: https://www.tensorflow.org/install/install_sources

However,it is not faster than i do not use -FMA -AVX -SSE https://stackoverflow.com/questions/57197854/fma-avx-sse-flags-did-not-bring-me-good-performance

Hi. Sorry if I am beating a dead horse. Just wondering why are binaries compiled with advance instructions not the default pip wheel? Who wouldn't prefer faster performance? It just seems a bit odd that the default behavior is to use the user a warning about them having compiled the binary in a less than optimal manner.

Hi. Sorry if I am beating a dead horse. Just wondering why is the default pip wheel not the binaries compiled with advance instructions?

This is because old cpu architectures don't support advanced instruction set. See wiki for the detailed list of cpus supporting  AVX, AVX2 or AVX512. If the default pip binary is compiled with these instruction sets then tensorflow cannot work on old CPUs.

Traceback (most recent call last):
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 1, in
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/init.py", line 24, in
from tensorflow.python import *
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/init.py", line 49, in
from tensorflow.python import pywrap_tensorflow
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 72, in
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "/home/kamran/py_env/dl_py36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/usr/lib/python3.6/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

But does it mean that the system is not using GPU for the process?

Nope, It shows even if you are using GPU, if you haven`t silenced the messages you should also see Tensorflor loading your GPU device in command prompt.

If you check with this repo:
请查看下面代码：
https://github.com/fo40225/tensorflow-windows-wheel
He has compiled almost all version of TF with SSE and AVX
他已经将几乎所有TF版本编译好了！

This article was a good tutorial on how to build from source including the flags
https://medium.com/@pierreontech/setup-a-high-performance-conda-tensorflow-environment-976995158cb1

try forcing the inclusion of the appropriate extensions using additional bazel options like --copt=-mavx --copt=-msse4.1 --copt=-msse4.2


[fe_*_adversarial_cifar] Pin albumentations version

CI fix.
Console output: http://<IP_ADDRESS>/job/Federated-Learning/job/func_tests/job/fe_tf_adversarial_cifar/200/console

@itrushkin could you raise a ticket in https://github.com/fastestimator/fastestimator in order to notify guys that new albumentations break some functionality?

They have pinned it in the latest release: https://github.com/fastestimator/fastestimator/issues/1031. Can we update to fastestimator==1.2.0?


tilemap.removeTile etc operations don't modify plus.tileAnimations

I realize this is a nice-to-have, but would make using animations more plug-and-play:
When calling this.tilemap.removeTile(x, y, layer) (or "swap", "randomize", etc...), the corresponding this.tilemap.plus.tileAnimations[*].tileLocations[*] doesn't get "corrected".
I've currently got a crude external approach:
// keys: 23-27
// spear: 28
// dagger: 29
// money: 30
if (itemsTile && itemsTile.index >= 23 && itemsTile.index <= 30) {
    this.tilemap.removeTile(
        this.player.x,
        this.player.y,
        this.itemsLayer,
    );

    // remove rotating "key" animation?
    if (itemsTile.index >= 23 && itemsTile.index <= 27) {
        const anim = this.tilemap.plus.animation.tileAnimations.find(
            animation => animation.frames[0].tileId === 22,
        );
        const ind = anim.tileLocations.findIndex(
            location =>
                location.x === this.player.x &&
                location.y === this.player.y,
        );

        anim.tileLocations.splice(ind, 1);
    }
}

But would be sweet to have removeTile patched to remove "any tileLocations at given coordinates where tileAnimations' frames contains index".
And while typing this, I just realized that I could just as well override TileMap.prototype.removeTile and make this less clunky...
Maybe I'll send along a PR 😜

You seem to have a good grasp of the code already! :)
As you figured out yourself, the system builds a structure that keeps track of the animated tiles. If you are confident you can submit a PR, please do go ahead! I think we should also look at other layer modification code besides removeTile(..).

Yup, I mentioned "swap", "randomize" etc above.
Tilemap.swap will need to check for removal and potentially add a new tileLocation (and maybe even a whole new tileAnimation... (haven't checked if you add all available ones in the tileset, regardless of whether it's used.))
Tilemap.random will only need to correct tileLocation coordinates.
But yeah, nothing trivial in those 2...
Then apart from the obvious "add", there's "copy", "paste", "replace",...
Damn...
I understand why tile animations weren't made part of the core lib yet.
Would be a whole lot easier if it were the Tile object itself that was responsible for managing its own animations...


feat: improved getting-started section

Guided experience for the Getting Started section

Let me catch up with this PR once we merge #734 =) Thanks both of you for your reviews!

Finally gone through the changes - @AlejandroFernandezLuces @PipKat please do a final review and approve whenever you can :)

Looks good to me, nice work 😄

Thanks @AlejandroFernandezLuces for your review!


Update xion-testnet-1 <=> osmo-test-5 IBC

Had to recreate IBC transfer between Xion and Osmosis testnets
Update connection / client /channel info
Update denom

Please resolve conflicts

rebased and resolved conflicts, thanks


Ensure flags are correctly loaded/saved to/from NetCDF

Part of #3358
According to CF, flags contain flag_values, flag_masks and flag_meanings attributes.
This ticket is to check whether flags require any special handling to ensure that they are correctly loaded/saved.

Acceptance Criteria

[ ] Added tests for loading a NetCDF file with a flag variable
[ ] Added tests for saving a cube with a flag Ancillary Variable to NetCDF file

This ticket is best tackled after #3473

The convention with status_flags is to be unitless. An example of a status flag being described within CF conventions is given here: https://github.com/cf-convention/cf-conventions/issues/216
variables:

    float sea_water_practical_salinity(time, z);
        sea_water_practical_salinity:units = "1";
        sea_water_practical_salinity:long_name = "Salinity";
        sea_water_practical_salinity:standard_name = "sea_water_practical_salinity";
        sea_water_practical_salinity:ancillary_variables = "sea_water_practical_salinity_qc_agg sea_water_practical_salinity_qc_flat_line_test";

    int sea_water_practical_salinity_qc_agg(time, z);
        sea_water_practical_salinity_qc_agg:long_name = "Salinity QARTOD Aggregate Flag";
        sea_water_practical_salinity_qc_agg:standard_name = "status_flag qartod_aggregate_quality_flag";
        sea_water_practical_salinity_qc_agg:missing_value = 2;
        sea_water_practical_salinity_qc_agg:flag_meanings = "PASS NOT_EVALUATED SUSPECT FAIL MISSING";
        sea_water_practical_salinity_qc_agg:flag_values = 1, 2, 3, 4, 9;

    int sea_water_practical_salinity_qc_flat_line_test(time, z);
        sea_water_practical_salinity_qc_flat_line_test:long_name = "Salinity QARTOD Flat Line Test Flag";
        sea_water_practical_salinity_qc_flat_line_test:standard_name = "status_flag qartod_flat_line_test_quality_flag";
        sea_water_practical_salinity_qc_flat_line_test:missing_value = 2;
        sea_water_practical_salinity_qc_flat_line_test:flag_meanings = "PASS NOT_EVALUATED SUSPECT FAIL MISSING";
        sea_water_practical_salinity_qc_flat_line_test:flag_values = 1, 2, 3, 4, 9;

When such a file is loaded by Iris, the current behaviour is to assign the unit to be "1" (In fact, this is the behaviour when using iris to directly instatiate an AncillaryVariable without specifying the unit). As such, a round trip (loading then saving) would result in a unit being assigned to the status_flag against current convention.
It seems as though the unit convention of status_flags in CF conventions is described by the standard name modifier. See #3408 for a discussion on handling of standard name modifiers.

In order to preserve unitless cf variables, it may be worthwhile changing the general behaviour defined here:
https://github.com/SciTools/iris/blob/242a62f719213eb315074621ccb8d00e51c466cf/lib/iris/fileformats/_pyke_rules/fc_rules_cf.krb#L1654-L1656
Changing cf_units._UNIT_DIMENSIONLESS to "no unit", combined with fixing #3394, ought to make round tripping consistent.


context based auto dash-at-point

Depends on point(cursor) location, dynamically call dash to show documentation.

Not necessary


select not returning undefined or null

Why are we converting undefined and null to empty string?
Again breaking changes that I (or my users) only notice in production. Null and undefined are valid values!
I really don't want to add test to my forms that null's aren't suddenly converted to strings...
https://github.com/cdmoro/bootstrap-vue-3/blob/6e7729739af0eac0e0bca0b7bf86106ac805193d/packages/bootstrap-vue-3/src/components/BFormSelect/BFormSelectOption.vue#L2

When doing <option :value="null" :disabled="true"> Vue will only render <option disabled=""> Which breaks the component when using null or undefined values. https://github.com/cdmoro/bootstrap-vue-3/issues/124
https://vuejs.org/guide/essentials/template-syntax.html#attribute-bindings

The v-bind directive instructs Vue to keep the element's id attribute in sync with the component's dynamicId property. If the bound value is null or undefined, then the attribute will be removed from the rendered element.

Which previously would mean any null value would not be capable of being selected.
Perhaps a better option should be to cast null and undefined as strings, and render it as value="undefined" or value="null"

https://stackblitz.com/edit/vitejs-vite-ycvvwn?file=src/App.vue
seems that native works and b-form-select doesn't. Vue binds the value ref (even null) to the dom object. That is only problem if you are rendering it to html (in ssr, but after that you hydrate it to a working vue version).
btw. null was working in b-form-select in previous versions... don't know what the problem in #124 was.
undefined = value missing
null = unset value
I already removed all the b-form-select:s where the value could be null from my code, so it's same to me. It's just really weird to return a value that's different from what the user specified.
We're not building markdown, we're building dom. I might be wrong...

It's all html in the end. The same mysterious reason why disabled="false" will still be disabled. html doesn't know what any of this stuff is, it's all just strings.
I'll re-evaluate the component, probably tomorrow, as Bootstrap-vue also follows the behavior you described. It may be possible that Vue had made a change between now and the almost year ago issue we had. Or not and it is and always has been a Bootstrap-vue-3 problem.

it's not html, it's dom manipulations. Object renders in html as: value="[object Object]"
As you can see in my example, bootstra-vue-3 doesn't even select the right option if the v-model ref is null, it only seems to work on undefined.
disabled="false" is html, :disabled="false" is vue and binds to dom and works.

@xvaara with the latest changes, do you find your example you posted to be acceptable now? I recall finding undefined behavior out of line with how native selects work, but I think it still outputs undefined. Do you think the behavior is acceptable? Or is there still things I should fix regarding this component?

I upgraded the dependencies in the example and it seems fine. I also think undefined is fine the way it is currently.


No images for amd64

Did you check the FAQ & Troubleshooting section for answers to common questions and issues?

Yes

Describe the issue

When trying to run Lemmy-Easy-Deploy, I get an error saying there are no Lemmy Backend images available for amd64 architecture

Finding the best available Backend image, please wait...
! ghcr.io/ubergeek77/lemmy:0.19.3 is not available for amd64
! Checking backup image at dessalines/lemmy:0.19.3...
ERROR: A Lemmy Backend image for your architecture is not available (amd64)
If you are confident that this image exists for 'amd64', please report this as an issue:
    https://github.com/ubergeek77/Lemmy-Easy-Deploy/issue

As far as I know, there should be backend images for amd64. I'm running this on a mostly-default Oracle Cloud VM with Ubuntu. The only previous issue I had with Lemmy-Easy-Deploy was that my user wasn't added to the Docker group, but I fixed that easily. I previously had issues trying to set up Lemmy with Docker where Docker would try to autolaunch D-Bus and fail, if that's relevant at all.
Diagnostic Information

Run ./deploy.sh -d and paste the output below:

ubuntu@lemmy-soulism-net-v1:~/lemmy-easy/Lemmy-Easy-Deploy$ ./deploy.sh -d

==== Docker Information ====
Detected runtime: docker (Docker version 24.0.2, build cb74dfc)
Detected compose: docker compose (Docker Compose version v2.18.1)
   Runtime state: OK

==== System Information ====
      OS: Linux
  KERNEL: 5.4.0-1100-oracle (x86_64)
HOSTNAME: OK
   SHELL: bash
  MEMORY:
              total        used        free      shared  buff/cache   available
Mem:           970M        211M        101M        1.8M        658M        598M
Swap:            0B          0B          0B

DISTRO:
----------------------------
NAME="Ubuntu"
PRETTY_NAME="Ubuntu 18.04.6 LTS"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic
----------------------------

==== Lemmy-Easy-Deploy Information ====
Version: 1.3.3

IMAGE     CREATED   STATUS

Integrity:
    2cd5fe103e2d1d703edd5b2368a302e2aa56f005044b5aa182dfcb50e4803b02  ./deploy.sh
    92c95dfc886792b8df2e9fffb540fc71a35c3bc6fd6c7662134da1545a79457a  ./templates/Caddy-Dockerfile.template
    c1202e70662dd2228da36a35a0f38ec8fc81bec8964d7315d02e8671a58dd7d7  ./templates/Caddyfile.template
    2537678c7971df36c1ed95f4228d3cfcb15bb4a28a60d939eaf8dd75b5d64a36  ./templates/cloudflare.snip
    c494a610bcb4cd1cfc0a4fe4fb0f6d437b2a84a0ad1625daee240e6dd6f1c910  ./templates/compose-email-volumes.snip
    c9cb4c5fee12930e17798a02ae1bd12e2dc69e149a394c24511bc9d4e6b776d4  ./templates/compose-email.snip
    09b990908d667411c9089b6848cf63d30ad3e76b1e560747833c3cc241ab95a4  ./templates/docker-compose.yml.template
    1c202b1b6e87c65b2fcda6035c9fe3f8631d76662907ffd38f24b14686e30647  ./templates/lemmy-email.snip
    c834cdce9eaf77f38155b404724fdfe66845575386ee516987452aa715642a6f  ./templates/lemmy.hjson.template

Custom Files:
*** No custom files ***

==== Settings ====
        CLOUDFLARE: No
 CADDY_DISABLE_TLS: false
   CADDY_HTTP_PORT: 80
  CADDY_HTTPS_PORT: 443
 LEMMY_TLS_ENABLED: true
      ENABLE_EMAIL: false
         SMTP_PORT: 25
    ENABLE_POSTFIX: false
POSTGRES_POOL_SIZE: 5

==== Generated Files ====
*** No files generated ***

ubuntu@lemmy-soulism-net-v1:~/lemmy-easy/Lemmy-Easy-Deploy$

I believe this may have been either an intermittent network issue, or perhaps 0.19.3 images weren't ready yet.
My images are up to date for 0.19.3 for all architectures, and I now have 0.19.4 images as well.
Please let me know if you continue to have issues with ARM images!


prepare for api-15 compat, some view API compat stuff change.

According to PR 12396, I commit two compatibily change about View:

replace view.setBackground by view.setBackgroundDrawable;
fix ReactToolbarManager setRTL api compat error(it should only enabled in API 17);

@astreet

Maybe @mkonicek can help land this, because I think @astreet is up to other stuff these days

This PR doesn't have a test plan, can you fix that?


refactor: Crypter/Wallet crypto operations

The current wallet is calling Pack/Unpack
This behavior has now changed and delegated to a
newer Packager interface

In addition, operations involving private keys have been
moved to the wallet. Namely, DerviveKEK() is the only function
in the crypto functionality that deals with private keys

Aries framework has been updated with new context functions
to support the new structure. In addtion to WithWallet(),
2 new function are added to the context provider:
WithCrypter() and WithPackager().

JWE Crypto functionality depends on these providers in
the following order:
1. WithWallet()
2. WithCrypter()
3. WithPackager()

Finally the recipient key is no longer needed in Crypter.Decrypt()
as it will be matched with the recipients's list in the envelope
and the wallet's first key found.

closes #385
Signed-off-by: Baha Shaaban<EMAIL_ADDRESS>

Separating packaging concerns (envelope.Packager) from encryption (crypto.Crypter) seems like a good idea. However, this PR leaves Crypter with knowledge that, I think, belongs to the Packager. The Crypter still knows stuff about envelopes. It's doing the actual packaging of the envelopes.

@Moopli as discussed in today's morning call, it's probably better to separate the concerns of packaging vs. encryption:

Support at least two envelope formats (legacy and the new one)
Support different alg and enc

@Moopli as discussed in today's morning call, it's probably better to separate the concerns of packaging vs. encryption:

Support at least two envelope formats (legacy and the new one)
Support different alg and enc

this will be done as part of a separate PR

@llorllale @Baha-sk opened #475


"aren't" shows up as "aren" in subtitles-1k typing test

Expected Behavior
In subtitles-1k list, aren't should appear.
Actual Behavior
aren is shown instead.
Steps to Reproduce the Problem
Inconvenient to reproduce since words in typing test are random, but aren't is showing up as aren in the subtitles-1k list, so the word lists should be checked for this typo (2k, 5k, and 10k lists as well).

Another incidence of this discovered was couldn't showing as couldn.


State of the Project

Hey everyone,
I've just taken over this project after the original author disappeared. It's quite a challenge, especially because I'm still getting the hang of Dart/Flutter. Also, I'm an Android user, so unfortunately, I can neither test nor build on iOS devices.
Challenges & Action Items:
Learning the ropes of Dart/Flutter is my top priority right now. If you've got tips or see areas for improvement in the code, I'm all ears! Also, if you're an iOS user, your insights into how things work on Apple devices would be super helpful.
I'm still working on getting a Google Play Account setup, so I can publish it officially, until then, all I can offer are the prebuilt bundles in the releases.
Additionally, I've successfully renamed the project from "git+" to "lab+". Your feedback on this change would be greatly appreciated.
How You Can Help:
If you're experienced with Flutter, your guidance could save me a lot of headaches.
Bugs happen, so if you spot any, please report them. I'll do my best to squash them ASAP.
While I can't build or test for iOS, your feedback on its performance is invaluable.
Community Spirit:
Let's work together to make this project the best it can be. Your support means a lot!
Thanks,
thelooter

The App is now available on fdroid

I've now worked on this codebase for several months, and I'm gonna be honest, maintaining it in it's current state isn't really feasible. I am currently considering a complete rewrite so I can implement a more maintainable and more well tested architecture. I still wanna maintain the multiplatform nature of the app, but I might consider a different tech stack. The current contender is Kotlin Multiplatform in combination with Compose Multiplatform. This stack is close to normal android development and therefore more approachable for potential maintainer. I need to conduct further research before making a final decision, so any feedback and questions are more than welcome


Error running dsSimulate in MATLAB 2019b

I receive the following error when I attempt to run the dsTutorial provided in the demos directory. it looks like this prior issue might be related as well
Error:File: dsSimulate.m Line: 812 Column: 64
Identifier 'sim_id' is not a function or a shared variable. To share 'sim_id' with nested function,
initialize it in the current scope. For more information, see Sharing Variables Between Parent and Nested
Functions.
Error in dsTutorial (line 50)
data=dsSimulate(eqns,'tspan',[0 100],'ic',[1 2 .5],'solver','rk4');

fixed by pull request #578


Add filtering to analytics charts and tables

Ensure that analytics charts and tables can be filtered based on individual fields, such as users, date, time, location. Related to #183
Definition of done

[ ] Determine how to add faceted filtering to analytics data.
[ ] Add UI widgets for faceting data.
[ ] Determine how to connect UI widgets with analytics data query.
[ ] Update data table according to faceted data.
[ ] Update chart according to faceted data.
[ ] Optionally, update URL to include data faceting parameters.
[ ] Document how to write functional tests for faceted filtering.

See the tutorial Interactive Data Visualization using D3.js, DC.js, Nodejs and MongoDB for a great example of how to implement this feature.

@brylie Do you think it is better to use D3.js charting library rather than Chart.js?

Yeppers, or even DC.js, as in the tutorial.

DC.js package for meteor.

Crossfiler.js

D3.js(official) or DC.js(unofficial)

Perhaps you can use this documentation as an inspiration?
https://tyk.io/dashboard-v0-9/analytics/

@bajiat Thanks! Filtering approach is useful on that resource.

Here is related branch - feature/dc-d3-dynamic-charting.
So far, having:

Added package charts:dc. It includes d3.js + dc.js + crossfilter.js.
Line chart with real data rendered within a package mentioned above.
Dynamically filtering data by month and year.


Example via nestjs - not working

I ported openssl-ts to a sample nodejs/nestjs app
NodeJs ver:  18.6.0
NestJs ver: 9.1.1
import { Injectable } from '@nestjs/common';
import { openssl } from 'openssl-ts';

@Injectable()
export class OpenSslService {

  async generateKey() {
	const output = await openssl(['genrsa', '-out', 'private.key', '2048']);
	console.log(output.toString());
  }

}

I get an error on the line:
const output = await openssl(['genrsa', '-out', 'private.key', '2048']);

Here is the error:
Error: spawn openssl ENOENT
	at Process.onexit (node:internal/child_process:283:19)
	at onErrorNT (node:internal/child_process:476:16)
	at processTicksAndRejections (node:internal/process/task_queues:82:21) {
  errno: -4058,
  code: 'ENOENT',
  syscall: 'spawn openssl',
  path: 'openssl',
  spawnargs: [ 'genrsa', '-out', 'private.key', '2048' ]
}

Hey! @felixunivers do you actually have openssl on your PATH?
This package does not provide the openssl binary, it is just a wrapper for it.
Execute: which openssl to find out if you actually have it.

Added a NestJs example: https://github.com/marianozunino/openssl-ts/tree/master/example/nestjs-example
Updated the Readme to remark that this pkg is a wrapper


Install fails in OSX

Hi, i trying to install the last version in OSX but it fails with this error:
`Tesla:black-screen juan$ npm i

<EMAIL_ADDRESS>preinstall /Users/juan/black-screen
npm prune

|

<EMAIL_ADDRESS>install /Users/juan/black-screen/node_modules/pty.js
node-gyp rebuild

CXX(target) Release/obj.target/pty/src/unix/pty.o
SOLINK_MODULE(target) Release/pty.node

<EMAIL_ADDRESS>postinstall /Users/juan/black-screen
npm run recompile

<EMAIL_ADDRESS>recompile /Users/juan/black-screen
export ELECTRON_VERSION=$(npm run electron-version --silent); HOME=~/.electron-gyp cd node_modules/pty.js; node-gyp rebuild --target=$ELECTRON_VERSION --arch=x64 --dist-url=https://atom.io/download/atom-shell

CXX(target) Release/obj.target/pty/src/unix/pty.o
SOLINK_MODULE(target) Release/pty.node
npm WARN engineStrict Per-package engineStrict (found in this package's package.json)
npm WARN engineStrict won't be used in npm 3+. Use the config setting engine-strict instead.
npm ERR! Darwin 15.0.0
npm ERR! argv "/usr/local/bin/node" "/usr/local/bin/npm" "i"
npm ERR! node v4.4.4
npm ERR! npm  v2.15.1
npm ERR! code EPEERINVALID
npm ERR! peerinvalid The package<EMAIL_ADDRESS>does not satisfy its siblings' peerDependencies requirements!
npm ERR! peerinvalid Peer<EMAIL_ADDRESS>wants<EMAIL_ADDRESS>npm ERR! Please include the following file with any support request:
npm ERR!     /Users/juan/black-screen/npm-debug.log`
It is probably that the problem was my environment and not the release but i doesn't have experience with npm and related stuff so any help would be appreciated.
Thanks.

Same issue for me.

No problems here

I'm getting the same error, node 4.4.4 and npm 2.15.1

I was having a similar problem and updating npm (npm update -g npm) seems to have fixed it for me

Weird, I updated npm and re-ran install.  Worked for me. 4.4.4, npm 3.9.2. Thanks @amwmedia

Removing the node_modules subfolder and doing npm install again should help.


Validity principality

Close those proofs as well (missing projections in both, and the case case in principality).
Principality relies on SR for products, at least there is an easy proof using it. It does not seem entirely moral to me.
This required again a bit of moving around lemmas at the right places.

Cool!
There is no circularity between SR/validity/inversion_mkApps ?
Is it ready to merge? (looks okay to me)

No, no circularity, the only apparent one was due to inductives, but we can prove the special case:
 Lemma invert_type_mkApps_ind Σ Γ ind u args T mdecl idecl :
    wf Σ.1 ->
    declared_inductive Σ.1 mdecl ind idecl ->
    Σ ;;; Γ |- mkApps (tInd ind u) args : T ->
    (typing_spine Σ Γ (subst_instance_constr u (ind_type idecl)) args T)
    * consistent_instance_ext Σ (ind_universes mdecl) u.
  Proof.

before validity. This is ready!


 It uses way too many resources, and does too much than what a forum should normally do.
phpbb: avail says it's good. tbh I still don't think it's very good software, and despite the fact that the new version has quite good php code, I think the design still sucks, and that good themes for it are a rarity.
SimpleMachines always sounded to me like something of 1998. idk, it looks very old.
Personally I'd either go for esoTalk, Flarum or NodeBB.

I'd go for Flarum but let's see what the others have to say :D


Modification in Welcome to lucknow Project

Issue Type:

[x] Bug Report
[x] Feature Request

Describe the bug
Some required Modifications

elaborate @Abh1shekSingh

Like some UI , colors, Navbar, cards ....like that
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elaborate @Abh1shekSingh https://github.com/Abh1shekSingh
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Update project tt_um_wokwi_414120248222232577 (mng2/tt09-wokwi)

Update project tt_um_wokwi_414120248222232577 to commit mng2/tt09-wokwi@0079ed4eb7acbc0a6191d6566ae5aa55709855b6
Project title: Test
Tiles: 1x1
Workflow: https://github.com/mng2/tt09-wokwi/actions/runs/11760730953

View in Wokwi

@mng2 I see that the project you submitted is just the default 4 inverters - if this was intentional, no problem. If not, please make sure to submit a new revision before the deadline (you can hop on Tiny Tapeout discord chat for help))

@urish Thanks, I am working on something more interesting and will hopefully get it in.


Text is cut

Cyberiada, page 176

The problem is probably in the PdfBookPage#getAsReflownBitmap.
Bitmap.Config bitmapConfig = Bitmap.Config.ALPHA_8;
final ByteBuffer bb = ByteBuffer.allocateDirect(bytes.length);
bb.put(bytes);
Bitmap bm = Bitmap.createBitmap(width, height, bitmapConfig);
bb.rewind();
bm.copyPixelsFromBuffer(bb);

The native method transfers the image as byte array of grayscale pixels, this byte array should be converted to android bitmap, the only way I found how to do it, is using a bitmap with ALPHA Config.

One possible solution is committed in the branch "compressed_buffer" 243d9bfb6d9c49f53f49e4e681a1cff5553ec8c2
The byte buffer is now png compressed and BitmapFactory.decodeByteArray undestands such a buffer.

compressed_buffer merged into master


python3: add support for netbsd

Description of changes
This is needed for https://github.com/NixOS/nix/pull/11566

Things done

Built on platform(s)

[x] x86_64-linux (pkgsCross.x86_64-freebsd.python3 and pkgsCross.x86_64-netbsd.python3)
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

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

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

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

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

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

[ ] Fits CONTRIBUTING.md.

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

cc @rhelmot

Have you tested the NetBSD binary actually works?  Last time I tried this it didn't, which suggests upstream has this disabled for a reason.

Why does Nix need a cross compiled Python anyway?

We cannot build libpsl -> libcurl without it:
nix-env % nix build .\#packages.x86_64-linux.nix-x86_64-unknown-netbsd -L --show-trace
error:
       … while calling the 'derivationStrict' builtin
         at <nix/derivation-internal.nix>:34:12:
           33|
           34|   strict = derivationStrict drvAttrs;
             |            ^
           35|

       … while evaluating derivation 'nix-x86_64-unknown-netbsd-2.25.0pre20240924_dirty'
         whose name attribute is located at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:336:7

       … while evaluating attribute 'buildInputs' of derivation 'nix-x86_64-unknown-netbsd-2.25.0pre20240924_dirty'
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:383:7:
          382|       depsHostHost                = elemAt (elemAt dependencies 1) 0;
          383|       buildInputs                 = elemAt (elemAt dependencies 1) 1;
             |       ^
          384|       depsTargetTarget            = elemAt (elemAt dependencies 2) 0;

       … while calling the 'getAttr' builtin
         at <nix/derivation-internal.nix>:44:19:
           43|       value = commonAttrs // {
           44|         outPath = builtins.getAttr outputName strict;
             |                   ^
           45|         drvPath = strict.drvPath;

       … while calling the 'derivationStrict' builtin
         at <nix/derivation-internal.nix>:34:12:
           33|
           34|   strict = derivationStrict drvAttrs;
             |            ^
           35|

       … while evaluating derivation 'curl-x86_64-unknown-netbsd-8.9.1'
         whose name attribute is located at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:336:7

       … while evaluating attribute 'propagatedBuildInputs' of derivation 'curl-x86_64-unknown-netbsd-8.9.1'
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:390:7:
          389|       depsHostHostPropagated      = elemAt (elemAt propagatedDependencies 1) 0;
          390|       propagatedBuildInputs       = elemAt (elemAt propagatedDependencies 1) 1;
             |       ^
          391|       depsTargetTargetPropagated  = elemAt (elemAt propagatedDependencies 2) 0;

       … while calling the 'getAttr' builtin
         at <nix/derivation-internal.nix>:44:19:
           43|       value = commonAttrs // {
           44|         outPath = builtins.getAttr outputName strict;
             |                   ^
           45|         drvPath = strict.drvPath;

       … while calling the 'derivationStrict' builtin
         at <nix/derivation-internal.nix>:34:12:
           33|
           34|   strict = derivationStrict drvAttrs;
             |            ^
           35|

       … while evaluating derivation 'libpsl-x86_64-unknown-netbsd-0.21.5'
         whose name attribute is located at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:336:7

       … while evaluating attribute 'buildInputs' of derivation 'libpsl-x86_64-unknown-netbsd-0.21.5'
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/make-derivation.nix:383:7:
          382|       depsHostHost                = elemAt (elemAt dependencies 1) 0;
          383|       buildInputs                 = elemAt (elemAt dependencies 1) 1;
             |       ^
          384|       depsTargetTarget            = elemAt (elemAt dependencies 2) 0;

       … in the condition of the assert statement
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/lib/customisation.nix:352:23:
          351|             drvPath = assert condition; drv.${outputName}.drvPath;
          352|             outPath = assert condition; drv.${outputName}.outPath;
             |                       ^
          353|           } //

       … while evaluating the attribute 'handled'
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/check-meta.nix:507:7:
          506|       # or, alternatively, just output a warning message.
          507|       handled =
             |       ^
          508|         (

       … from call site
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/check-meta.nix:511:13:
          510|           else if valid == "no" then (
          511|             handleEvalIssue { inherit meta attrs; } { inherit (validity) reason errormsg; }
             |             ^
          512|           )

       … while calling 'handleEvalIssue'
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/check-meta.nix:269:38:
          268|
          269|   handleEvalIssue = { meta, attrs }: { reason , errormsg ? "" }:
             |                                      ^
          270|     let

       … while calling the 'throw' builtin
         at /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/stdenv/generic/check-meta.nix:281:8:
          280|         else throw;
          281|     in handler msg;
             |        ^
          282|

       error: Package ‘python3-3.12.5’ in /nix/store/5n0kgjjm07z3gvz912i5gviig1qh4d4w-source/pkgs/development/interpreters/python/cpython/default.nix:675 is not available on the requested hostPlatform:
         hostPlatform.config = "x86_64-unknown-netbsd"
         package.meta.platforms = [
           "aarch64-linux"
           "armv5tel-linux"
           "armv6l-linux"
           "armv7a-linux"
           "armv7l-linux"
           "i686-linux"
           "loongarch64-linux"
           "m68k-linux"
           "microblaze-linux"
           "microblazeel-linux"
           "mips-linux"
           "mips64-linux"
           "mips64el-linux"
           "mipsel-linux"
           "powerpc64-linux"
           "powerpc64le-linux"
           "riscv32-linux"
           "riscv64-linux"
           "s390-linux"
           "s390x-linux"
           "x86_64-linux"
           "x86_64-darwin"
           "i686-darwin"
           "aarch64-darwin"
           "armv7a-darwin"
           "i686-cygwin"
           "x86_64-cygwin"
           "x86_64-windows"
           "i686-windows"
           "i686-freebsd"
           "x86_64-freebsd"
         ]
         package.meta.badPlatforms = [ ]
       , refusing to evaluate.

       a) To temporarily allow packages that are unsupported for this system, you can use an environment variable
          for a single invocation of the nix tools.

            $ export NIXPKGS_ALLOW_UNSUPPORTED_SYSTEM=1

          Note: When using `nix shell`, `nix build`, `nix develop`, etc with a flake,
                then pass `--impure` in order to allow use of environment variables.

       b) For `nixos-rebuild` you can set
         { nixpkgs.config.allowUnsupportedSystem = true; }
       in configuration.nix to override this.

       c) For `nix-env`, `nix-build`, `nix-shell` or any other Nix command you can add
         { allowUnsupportedSystem = true; }
       to ~/.config/nixpkgs/config.nix.

I haven't tested the actual interpreter because it's not actually used in nix.
However if basic things like python already don't cross compile than we might need to disable netbsd cross compilation for nix.

It's the second time this psl-make-dafsa script makes life as a cross-compiler hard...
I think it would be overall useful not depend on python for libcurl and have the script package separately.

Before we've talked about not including the Python parts of libpsl when we can't cross compile Python.  Can't we do that?

https://github.com/NixOS/nixpkgs/pull/344209

Here it's just disabled for cross-compilation: https://github.com/NixOS/nixpkgs/pull/344211

Opened an issue instead: https://github.com/NixOS/nixpkgs/issues/344423

Sure, but I actually, I don't want this dependency at all in libcurl. As far as I can see, no package really needs it.

Why not just disable it universally then?

I did this here: https://github.com/NixOS/nixpkgs/pull/344209

I don't understand from that what it's needed for.


Navigator Rotate and Click Zoom

This PR adds functionality to rotate the image of the navigator along with the subject viewer. It also adds a click zoom to the navigator.

Ah yes. Ok, some good notes above that should be considered. Initially, my construction of this PR was based on #28, but I notice there isn't a zoom functionality there, it was only reposition. Good point about the separation between the subject viewer and navigator.
Concerning the three issues:

I could make a change to keep the image stationary at all times and only move the red rectangle when there is a rotation, pan, etc.

Based off issue #28, is the idea that a click should reset the image, or center the image to the spot where it was clicked?

Yes. I did notice this issue, which seems to be present on master, so a separate PR is probably the best solution here.

Awesomeness 👍  Replying your replies:

I think that would be the best - when panning and zooming, keep the image static in the centre of the Navigator, then move the red rectangle around. As for rotation, I'll leave it to you whether you'd like to rotate the image (which may be easier for a human to interpret - check with a UI expert) or the red rectangle (which is way easier to do code-wise).

Clicking on the Navigator should centre the image to the spot where it was clicked - basically, just remove that this.props.dispatch(setScaling(this.props.scaling + ZOOM_STEP)); line of code. 👍

I'll also help look into this once I can free up some time, but that'd be late September at best. Hopefully this issue won't be a breaking matter for our users.

I think the most recent commit covers all the issues we discussed, but I'm open to making any other tweaks, if necessary. Also, I'm curious if issue 3 above is due to some miscalculations on panning the image? It's definitely worth investigating in a future branch.


Port rdp extension code

@beloni: I have just ported the extension plugin interface to FreeRDP 1.0, but I haven't tested it. Since you're one of the few using that interface, could you please test it and report if it works so that we can close this ticket?

@beloni: ping, O.S.Systems is the only one using the extension code that I know of, so we'd need you to confirm that it works! Let me know when you'll have the time

@beloni: ok then, how about we close this ticket which was to port the code, and you open a new one if it turns out to not work properly when you end up testing it?

Agreed


Start ruler server in constructor

Fixes #238

LGTM

Do you want to stick a should for this in cmd/ingester/main.go?

Sorry, hit merge before I saw your message.


Schedule Finder | Fix stop list for opposite Direction

Summary of changes
Asana Ticket: ⏱🐞 Schedule Finder | Stop Selection Bug
[Please include a brief description of what was changed]

Assigned to: @NAME

@ingridpierre This is ready for product review
I still need to fix up some tests.

@meagonqz This looks great, looked at it on our longest routes and ones with complicated variants. For what the original ticket was trying to accomplish, I was able to see the stops I would expect and observed that the lists changed significantly depending on the route and direction. 👍
However, I was able to break the tool again using a particular minor edge case (saturday-only stop). I'll file a new ticket for that as well.


Views with '>' stop at clock

When going through the views using '>', the help says that events view will appear after the clock. But the view does not change after clock.

This is an expected behavior. There's no sentence in doc where event view comes after the clock view.

Here's an extract from the help file for calendar:
  ------------------------------------------------------------------------------
  VIEW						*calendar-view*

  This application has some different views. You can switch views with |>| and
  |<| keys. The default views setting is the following.

  	1. Year view
  	2. Month view
  	3. Week view
  	4. Days view
  	5. Day view
  	6. Clock view
  	7. Event view

  1. Year view					*calendar-view-year*
  	This view shows the months. If the window is not wide enough to view
  	all the months in the year, it shows the nearest 3 or 5 months.
  	The name of the year view is 'year'.

  ~
  ~
  ~

  6. Clock view					*calendar-view-clock*
  	This view shows a digital clock.
  	The name of the clock view is 'clock'.

  7. Event view					*calendar-view-event*
  	This view shows a event list. There are two available event views:
  	'event' and 'agenda'.

  ------------------------------------------------------------------------------
  KEY MAPPINGS					*calendar-key-mappings*


Qobj.__mul__ not compatible with jit

Input
op = qutip.num(3, dtype="jax")

@jax.jit
def f(op, A):
    return op * A

f(op, 2.)

Output:
---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

[<ipython-input-69-1460d0a528c8>](https://localhost:8080/#) in <cell line: 7>()
      5     return op * A
      6
----> 7 f(op, 2.)

    [... skipping hidden 12 frame]

6 frames

    [... skipping hidden 2 frame]

    [... skipping hidden 5 frame]

[/usr/local/lib/python3.10/dist-packages/qutip/core/qobj.py](https://localhost:8080/#) in _initialize_data(self, arg, dims, copy)
    298             )
    299         if self._dims.shape != self._data.shape:
--> 300             raise ValueError('Provided dimensions do not match the data: ' +
    301                              f"{self._dims.shape} vs {self._data.shape}")
    302

ValueError: Provided dimensions do not match the data: (3, 3) vs (1, 1)

Hi @Ericgig ! Have you been able to find the reason for this particular issue?

No.
I guess is JaxArray(mul_jax(self.data, other)) is seen as a 1x1 matrix when tracer are used instead of real array. But I don't know why.

@Ericgig this works for now for all the test cases that are valid for jit and grad. We can close this issue.

I would like to know why it's suddenly fixed, but yes it seems to work fine.
Maybe we should let this open until we have tests that would detect if we randomly breaks it...


Use SubMesh for wave port boundary mode calculations

Once https://github.com/mfem/mfem/pull/3379 is merged, we can redesign the WavePortOperator class to solve the eigenmode problem for each port on a SubMesh object and transfer the computed solution back onto the global mesh for the boundary condition/excitation.

Will be completed as part of #47.


report all compilation errors when performing incremental recompilation

If you recompiling code with errors incrementally front-end server will only report errors in the code that gets recompiled, but will swallow the errors in the code (libraries) which did not change.
This might be misleading to users. See flutter/flutter#23121

Should be fixed with d9072d52725774f1f422327a731fa616806fe927.


ng serve - error on vendor.bundle.js

Please provide us with the following information:

OS?

cublinux

Versions.

angular-cli: 1.0.0-beta.26
node: 7.2.1
os: linux x64
@angular/common: 2.4.4
@angular/compiler: 2.4.4
@angular/core: 2.4.4
@angular/forms: 2.4.4
@angular/http: 2.4.4
@angular/platform-browser: 2.4.4
@angular/platform-browser-dynamic: 2.4.4
@angular/router: 3.4.4
@angular/compiler-cli: 2.4.4

Repro steps.

It was an application created by angular-cli.
I'm working with routes, making some changer to deal with two routes-outlet.
Now in the great part of times that I run >ng serve I got an error with vendor.bundle.js, in diferent lines that freezes my browser and I cant run the application.

The log given by the failure.

This error shows in browser console:

Angular is running in the development mode. Call enableProdMode() to enable the production mode.  vendor.bundle.js:1102:5
Error: Script terminated by timeout at:
ZoneDelegate.prototype.scheduleTask@http://localhost:4200/vendor.bundle.js:111027:21
Zone.prototype.scheduleMicroTask@http://localhost:4200/vendor.bundle.js:110927:20
scheduleResolveOrReject@http://localhost:4200/vendor.bundle.js:111295:9
ZoneAwarePromise.prototype.then@http://localhost:4200/vendor.bundle.js:111384:17
Router.prototype.executeScheduledNavigation@http://localhost:4200/vendor.bundle.js:25320:13
Router.prototype.processNavigations/<@http://localhost:4200/vendor.bundle.js:25264:17
MergeMapSubscriber.prototype._tryNext@http://localhost:4200/vendor.bundle.js:17758:22
MergeMapSubscriber.prototype._next@http://localhost:4200/vendor.bundle.js:17748:13
MergeMapSubscriber.prototype.notifyComplete@http://localhost:4200/vendor.bundle.js:17800:13
InnerSubscriber.prototype._complete@http://localhost:4200/vendor.bundle.js:95107:9
Subscriber.prototype.complete@http://localhost:4200/vendor.bundle.js:457:13
subscribeToResult/<@http://localhost:4200/vendor.bundle.js:671:17
ZoneDelegate.prototype.invoke@http://localhost:4200/vendor.bundle.js:111004:17
NgZone.prototype.forkInnerZoneWithAngularBehavior/this.inner<.onInvoke@http://localhost:4200/vendor.bundle.js:32639:28
ZoneDelegate.prototype.invoke@http://localhost:4200/vendor.bundle.js:111003:17
Zone.prototype.run@http://localhost:4200/vendor.bundle.js:110875:24
scheduleResolveOrReject/<@http://localhost:4200/vendor.bundle.js:111297:52
ZoneDelegate.prototype.invokeTask@http://localhost:4200/vendor.bundle.js:111037:21
NgZone.prototype.forkInnerZoneWithAngularBehavior/this.inner<.onInvokeTask@http://localhost:4200/vendor.bundle.js:32630:28
ZoneDelegate.prototype.invokeTask@http://localhost:4200/vendor.bundle.js:111036:21
Zone.prototype.runTask@http://localhost:4200/vendor.bundle.js:110913:28
drainMicroTaskQueue@http://localhost:4200/vendor.bundle.js:111195:25
vendor.bundle.js:111027:21

Mention any other details that might be useful.
The ng server seems to build a valid version:
** NG Live Development Server is running on http://localhost:4200. **
Hash: 29ebe35476fd098584c8
Time: 15880ms
chunk    {0} main.bundle.js, main.bundle.map (main) 48.5 kB {2} [initial] [rendered]
chunk    {1} styles.bundle.js, styles.bundle.map (styles) 9.96 kB {3} [initial] [rendered]
chunk    {2} vendor.bundle.js, vendor.bundle.map (vendor) 3.67 MB [initial] [rendered]
chunk    {3} inline.bundle.js, inline.bundle.map (inline) 0 bytes [entry] [rendered]
webpack: bundle is now VALID.

Thanks! We'll be in touch soon.

This seems like a problem in your application, not in the CLI. You have some logic that is causing that timeout error.
If you can find a simple repro that shows this is a CLI issue I can reopen, but right now this looks like a question for Stack Overflow.

Thanks @Meligy !
Your example works fine to me !
But my example keeps getting problems ...
I removed the guards in the routes an it backs to normal, runing ng serve !

Okay Recently I fetched this problem I have very simple solution for solve this Issue , follow these steps:
go to these directories  src-> app-> index.html open the index.html and find
<base href="your app name "> change this to <base href="/">

Hello @Sachin124 neither ./ nor / worked for me. The problem seems to be caused by my file structure. But I'm not quite sure what is the root cause. My server also has a src and dist folders. My file structure is like this:
/dist
    /www (angular application)
        /src
             - index.html
        /dist
              - index.html
              - bundled js/css files that are not served when
              when the index.html is served by Express)
    /js
        - index.js
        - compiled js files from TS server
/src (ExpressJS server code folder with TS)
    /routes
        - IndexRoute.ts
    - App.ts
    - index.ts

In my IndexRoute.ts I send in response the index.html of the Angular app like so res.sendfile("./dist/www/dist/index.html");
I seem to be getting the index.html file but not the bundled scripts and css files.
Thank you in advance for the help.


MAX not working with int? in an empty database

When I call this MAX function and the database is empty, I get this error:
Exception has occurred.
_TypeError (type 'Null' is not a subtype of type 'int' in type cast)
@Query('SELECT MAX(position) FROM mytable')
Future<int?> getMaxPosition();

position is an int?

int? position;

The database.g.dart looks like this:
@override
Future<int?> getMaxPosition() async {
    return _queryAdapter.query('SELECT MAX(position) FROM csinventoryitem',
        mapper: (Map<String, Object?> row) => row.values.first as int);
  }

Workaround is to check if the table is empty beforehand.

Hey Stensan,
good catch, in this case the code is supposed to be next:
  @override
  Future<int?> getMaxPosition() async {
    return _queryAdapter.query<int?>('SELECT MAX(id) FROM csinventoryitem',
        mapper: (Map<String, Object?> row) => row.values.first as int?);
  }

if there are any concerns please let me know 😉

Thanks, I will use rawQuery for this

@Stensan ok, no problem
thank you for reporting the issue!

@dkaera hello👋, I'm having this issue too, when will it be fixed


Made change to get the JSONEditorDocuemnt from text buffer

What was the change?
Made a small change to get the JSONEditorDocument from TextBufer instead of TextView
Why?
FromTextView(..) was made internal recently. Using public API instead - FromTextBuffer(..) instead
How was it tested?
Verified that completion works for the below:

Files
Library
Path
Provider

Merged via https://github.com/aspnet/LibraryManager/commit/766487cc55554028eec5a1d97c3e8732b40e56d4


Error when subtracting with floating number

https://stackoverflow.com/questions/66220369/flutter-error-when-subtracting-with-floating-number
issue when two floating number are subtracted
print('result ${200.83 - 100.12}');
output -> result<PHONE_NUMBER>0000001

Duplicate of #45019

That's simply how floating point arithmetic works. (And that's why for example you should never use floating point to perform operations with monetary values)


Use-Case and Misuse-case 1

For each use-case, derive security requirements using misuse case analysis.
The misusers should be contextualized in your environment of operation and relevant to the interactions identified above. Use names for mis-users that help the reader understand their motives, resources, attack of choice, and the available access to the system to carry out their attack.
Iterate between use and misuse cases to elaborate additional security functions. Embed the final diagram in your report.
Diagraming Advice: Use proper notation as discussed in class materials. Use cases should focus on software features. Go back and forth between use case and misuse case analysis until specific functional security requirements can be identified that are implemented by the software. For misuse cases, prioritize mitigations that are implemented in the OSS project, instead of the environment. Make sure all misuse cases are addressed by use cases. Arrange the diagram to reduce clutter

@Chrs987 I have a good portion of my diagram complete. Would you like to review my rough draft tonight in a discord meeting?


feat(krew-website): deprecation fix

fixed krew website astro deprecation warning

Nice, thanks 👍🏽

@nratering I do recognize the needed changes. Check: did you verify the website on both serve and build?

@nratering I do recognize the needed changes. Check: did you verify the website on both serve and build?

Yes, both.


Write full test suite in Mocha & Chai

A full test suite using Mocha & Chai should be developed to allow a better development workflow and CI testing with Travis.

Unit testing isn't feasible with discord.js due to the impracticality of mocking the entire discord API.


Error: implode(): Argument #2 ($array) must be of type ?array, string given

I have been working on a Laravel project and I had to use PAMI, by the way it's a great library! However, recently I got this error
[2022-01-21 09:55:06] ERROR: implode(): Argument #2 ($array) must be of type ?array, string given {"userId":8,"exception":"[object] (TypeError(code: 0): implode(): Argument #2 ($array) must be of type ?array, string given at .../marcelog/pami/src/PAMI/Message/Event/Factory/Impl/EventFactoryImpl.php:72)

I'm using the following settings:
OS: Ubuntu 20.04
Laravel 8
PHP 8+ (at least 8 or newer)

After a few days of headache and tests, I finally found what was occurring.  I went to the error line on EventFactoryImpl.php. At beginning, I was thinking Asterisk server was returning a wrong message, after some tests and verifications I concluded it was not the server. In order that, I went to php documentation and found the order of implode function arguments was wrong. According to the documentation the function signature is as follows:
implode(string $separator, array $array): string

As we can see on code, the order is inverted:
    public static function createFromRaw($message)
    {
        $eventStart = strpos($message, 'Event: ') + 7;
        $eventEnd = strpos($message, Message::EOL, $eventStart);
        if ($eventEnd === false) {
            $eventEnd = strlen($message);
        }
        $name = substr($message, $eventStart, $eventEnd - $eventStart);
        $parts = explode('_', $name);
        $totalParts = count($parts);
        for ($i = 0; $i < $totalParts; $i++) {
            $parts[$i] = ucfirst($parts[$i]);
        }
        $name = implode($parts, '');    # trouble line
        $className = '\\PAMI\\Message\\Event\\' . $name . 'Event';
        if (class_exists($className, true)) {
            return new $className($message);
        }
        return new UnknownEvent($message);
    }

Because of that I made a slightly change, reverting the order.
    public static function createFromRaw($message)
    {
        $eventStart = strpos($message, 'Event: ') + 7;
        $eventEnd = strpos($message, Message::EOL, $eventStart);
        if ($eventEnd === false) {
            $eventEnd = strlen($message);
        }
        $name = substr($message, $eventStart, $eventEnd - $eventStart);
        $parts = explode('_', $name);
        $totalParts = count($parts);
        for ($i = 0; $i < $totalParts; $i++) {
            $parts[$i] = ucfirst($parts[$i]);
        }
        $name = implode('', $parts);    # changed line
        $className = '\\PAMI\\Message\\Event\\' . $name . 'Event';
        if (class_exists($className, true)) {
            return new $className($message);
        }
        return new UnknownEvent($message);
    }

Hope this can help someone. And also be fixed in this wonderful project.
Thanks!

Confirmed!

They only need to Implement the Change in the repository, when they believe that this

@lorismich where did you merged this PR because I can't find it in your fork?

@lorismich where did you merged this PR because I can't find it in your fork?

I reopened the PR here: https://github.com/marcelog/PAMI/pull/213


AzureDNS Provider Issue

Seeing this error when trying to use with the AzureDNS provider.  Would anyone happen to have a clue as to what the issue might be?  As far as I can tell, all my info for Azure is correct.
Traceback (most recent call last):
  File "/usr/local/bin/octodns-sync", line 11, in <module>
    sys.exit(main())
  File "/usr/local/lib/python2.7/site-packages/octodns/cmds/sync.py", line 37, in main
    manager = Manager(args.config_file)
  File "/usr/local/lib/python2.7/site-packages/octodns/manager.py", line 116, in __init__
    self.providers[provider_name] = _class(provider_name, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/octodns/provider/azuredns.py", line 252, in __init__
    client_id, secret=key, tenant=directory_id
  File "/usr/local/lib/python2.7/site-packages/msrestazure/azure_active_directory.py", line 440, in __init__
    self.set_token()
  File "/usr/local/lib/python2.7/site-packages/msrestazure/azure_active_directory.py", line 473, in set_token
    raise_with_traceback(AuthenticationError, "", err)
  File "/usr/local/lib/python2.7/site-packages/msrest/exceptions.py", line 48, in raise_with_traceback
    raise error
msrest.exceptions.AuthenticationError: , MissingTokenError: (missing_token) Missing access token parameter.
ERROR: Job failed: exit code 1

[update]: verified the credentials that I am using; and they are correct with the correct Azure permissions.

/cc @h-hwang @mohitsuley

I work with Mohit. We'll take a look at this.
Aaron
On Sun, Mar 4, 2018 at 6:42 AM, Ross McFarland<EMAIL_ADDRESS>wrote:

/cc @h-hwang https://github.com/h-hwang @mohitsuley
https://github.com/mohitsuley
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/github/octodns/issues/217#issuecomment-370234363, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AILDf-on-pFpCAAw_xjFgEv44rwU-d8Xks5ta_0-gaJpZM4SbUol
.

ACK.
Checked to make sure the API wasn't acting up or anything - looks good.
While I look deeper, can you try running a test from az command-set directly to make sure general command-line access is working for you?
az network dns zone list [--resource-group]

@omarkilani any specific thoughts on whether https://github.com/github/octodns/pull/331 fixes this?
@chriskacerguis you mind giving it a test to see?

@ross nope... just figured they might have fixed some bug or something. A lot of code churn...


tensorboard can support when mediapipe_model_maker train model?

Have I written custom code (as opposed to using a stock example script provided in MediaPipe)
None
OS Platform and Distribution
windows
MediaPipe Tasks SDK version
No response
Task name (e.g. Image classification, Gesture recognition etc.)
.Image classification
Programming Language and version (e.g. C++, Python, Java)
Python
Describe the actual behavior
tensorboard can support when mediapipe_model_maker train model?
Describe the expected behaviour
tensorboard can support when mediapipe_model_maker train model?tensorboard can support when mediapipe_model_maker train model?
Standalone code/steps you may have used to try to get what you need
tensorboard can support when mediapipe_model_maker train model?

Other info / Complete Logs
No response

Hi @lonngxiang,
Considering the issue outlined by our team, you can utilize TensorBoard to visualize various aspects of machine learning experimentation. This includes tracking and visualizing metrics like loss and accuracy, visualizing the model graph, and displaying images, text, and audio data. You can find information on setting up TensorBoard with MediaPipe Model Maker in the same thread as described below:

Unfortunately the callback for object detection was not implemented yet - so you may need to hack together something by calling ObjectDetector.init separately instead of using the ObjectDetector.create method, and then adding the tensorboard callback to model._callbacks after you've initialized the object detector model. After adding the callback, you can call _create_and_train_model just like how it is done in ObjectDetector.create.
You can see how the tensorboard logging is set in ImageClassifier: https://github.com/google/mediapipe/blob/master/mediapipe/model_maker/python/vision/image_classifier/image_classifier.py#L59. There is a util function called get_default_callbacks which will get you a tensorboard logging and model checkpointing callback.
Alternatively, the metrics should be printed every epoch so you can also try to capture the logs and use regex to capture the metric values by epoch.

For a clearer understanding, we kindly ask you to review the same thread.
Thank you


Add support for the diamonds dataset in ggplot2

https://ggplot2.tidyverse.org/reference/diamonds.html

Maybe easier to get it from https://openml.org/d/42225 ?

Done by #110.

@kou https://community.rstudio.com/t/licenses-for-data-in-tidyverse/129334

Thanks!
It seems that we can use CC0 for this!


fix(css-resource): ensure only global styles get injected globally

Fixes #304

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


Change the order of inline client check

Change the order of inline client check to improve direct publish performance and avoid potential errors from custom ACLCheck.

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

11 of 11 (100.0%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.0003%) to 98.653%

Totals

Change from base Build<PHONE_NUMBER>:
0.0003%

Covered Lines:
5055

Relevant Lines:
5124

💛  - Coveralls


no workflow labels in table

The user defined workflow labels no longer show up in the "Workflows Run" table in the app.

Just noticed this as well, the label only shows up in the "Workflow Details" table when you select one of the jobs in "Workflows Run". And even then, you have to scroll all the way to the right (see screenshot below). Wondering if we should also make it a searchable field at the top along with history, name, and status? And allow partial matches? (i.e. if I submitted "testrun_1" and "testrun_2", I should be able to search for "testrun_" and see both)

Hey @vortexing @tefirman it's been a month since this opened. Seems like the details of what you want are still up in the air. Any further thoughts on what you want this to look like in the tracking page?

Hey @sckott ! Thanks for following up on this! I think I was envisioning moving the "Label" column from the "Workflow Description" table up to the "Workflows Run" table (see screenshot below), maybe renamed as "workflow_label" just to the right of "workflow_name"? This way if I submit multiple simultaneous analyses, I can easily differentiate between them without having to select and scroll down for each one. How much lift would something like that require?

I think it'd be easy, but I haven't checked yet. I'll have a look see

@tefirman PR at https://github.com/FredHutch/shiny-cromwell/pull/117 to look at


Upgrade org.junit:* to >= 5.3.0 and upgrade tests annotations

In the last issue about upgrading to JUnit 5 (#616), version was upgraded in poms but not test annotations in Java test classes. See more at this link.
Make sure that:

JUnit version is upgraded to 5.3.0

JUnit 5 anotations are used in tests.

The project still builds

The tests are still passing

The version is controlled via the strongbox-parent/pom.xml.
The tests can be executed using:
mvn clean install -Dintegration.tests
These must all be passing successfully.

I'm taking it if there's no problem about it.

Great! Glad to see you're back!
Please, feel free to proceed!

(Closed by accident).


Exclude building from idle power

The building "AI manager" from the AutoSeller mod should always consume it's total power. Never idle.
But it's always at 10w.
Thanks.

I assume you mean the mod Colony Manager, I have excluded it now.


LSTM: Many to many sequence prediction with different sequence length

First of all, I know that there are already issues open regarding that topic, but their solutions don't solve my problem and I'll explain why.
The problem is to predict the next n_post steps of a sequence given n_pre steps of it, with n_pre < n_post. I've built a toy example using a simple sine wave to illustrate it. The many to one forecast (n_pre=50, n_post=1) works perfectly:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])

Also, the many to many forecast with (n_pre=50, n_post=50) gives a near perfect fit:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])

But now assume we have data that looks like this:
dataX or input: (nb_samples, nb_timesteps, nb_features) -> (1000, 50, 1)
dataY or output: (nb_samples,  nb_timesteps, nb_features) -> (1000, 10, 1)
The solution given in #2403 is to build the model like this:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])

Well, it compiles and trains, but the prediction is really bad:

My explanation to this is: The network has only one piece of information (no return_sequences) at the end of the LSTM layer, repeats this output_dimension-times and then tries to fit. The best guess it can give is the average of all the points to predict as it doesn't know whether it is currently going down or up in the sinus wave, it loses this information with return_sequences=False!
So, my final question is: How can I keep this information and let the LSTM layer return a part of its sequence? Because I don't want to fit it to n_pre=50 time steps but only to 10 because in my problem, the points are not so nicely correlated  as in the sine wave of course. Currently I just give 50 points and then crop the output (after training) to 10 but it still tries to fit to all 50, which distorts the result.
Any help would be greatly appreciated!

I think you need to do something like this:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])

otherwise you are just repeating the the last Dense layer and getting a constant value.

Thank you very much. I tried your suggestion and the predictions now look like this:

The number of epochs and hidden neurons is the same as in the other testcases, but the prediction is worse for 10 steps compared to 50. Is there a (simple) explanation why it gets worse with more layers? Or does it just need to train longer because it has more parameters to adjust?

I would say that the modeling assumptions of both approaches are different. In the later model, it is assumed that the model sees the complete input sequence (first 50 steps), somehow creates a summary and uses this summary to generate a new signal (last 10 steps).
On the other hand, your initial model estimated the last 50 steps while reading the input signal, no summarisation of the original signal was used.

That's a perfect and clear answer, thank you very much.

HI, I have been studying how to use the many to many model of lstm to predict time series data, and now I have the same problem that you once had, could you share your demo py files about predicting  a simple sine wave to me ?   I mean i want to learn your code and replace your data with mine just to have a try.  it will be very nice of you if you could do me a favor! thanks first !
my email<EMAIL_ADDRESS>thank you !

Here you go!
test_sine.txt

@Ironbell ,
Could you please post a copy of the ful code on your rep? That would be great.

HI there!
It seems in new versions of Keras the input_dim and output_dim arguments are replaced with input_shape() function. So may you edit this parts of code to match to the new version:

model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))

I also have another question. what is the reason of using model.add(Activation('linear'))  ?
Thanks in advanced!

Hi @bestazad ,
You can obtain the same result using input_dim() or input_shape(), to my knowledge both these two "alternatives" has been used for quite some time
https://stackoverflow.com/questions/53106111/in-keras-when-should-i-use-input-shape-instead-of-input-dim
The reasoning the why model.add(Activation('linear')) is used is most likely because this is (only) tentative example, other activation functions can probably give similar results here.

How would you train the model on variable input length?

Hi @gustavz
Two options/suggestions:

Padding https://machinelearningmastery.com/data-preparation-variable-length-input-sequences-sequence-prediction/
Sequence bucketing https://arxiv.org/ftp/arxiv/papers/1708/1708.05604.pdf

Padding looks easier but I would guess that this method also decreases the usefulness of the model.
If you (or anybode else) could help me with a good explanation of what RepeatVector() does here I would be happy, this is the best reference https://stackoverflow.com/questions/51749404/how-to-connect-lstm-layers-in-keras-repeatvector-or-return-sequence-true , however, this is for a Encode/Decoder network and I'm not sure if this is the same for a LSTM network. E.g., does RepeatVector() that the original input (from the very first layer) or does RepeatVector() work with the inputs/outputs between hidden layers?

what is the difference between:
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=False))
model.add(RepeatVector(10))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.add(Activation('linear'))

and
model = Sequential()
model.add(LSTM(input_dim=1, output_dim=hidden_neurons, return_sequences=True))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=False))
model.add(Dense(10))

maybe best explained with this image

Thanks for this; which is which? I've added some numbers to your image to better reference the variants. I assume that the code that contains RepeatVector() is represented by variant 4 and that the code that does not contains RepeatVector() is represented by variant 5. Is this correct?

Thanks! :-)

This is also what I am wondering about. I am not sure what difference actually is.
And if you are right, whats the use of repeating the lstm blocks?
Coming back to the variable input length question. Padding the input is trivial, but i am not sure if this really a go-for method, as you mess a lot with the data.
How are variable input lengths dealt with in NLP tasks where sentences nearly always have different input lenghts, how would this be implemented in Keras?

Option 1 is part 4 of the image?

Thanks for this; which is which? I've added some numbers to your image to better reference the variants. I assume that the code that contains RepeatVector() is represented by variant 4 and that the code that does not contains RepeatVector() is represented by variant 5. Is this correct?

Thanks! :-)

that the code that does not contains RepeatVector() is a many-to-one architecture (variant 3). To have a many-to-many architecture you have to mdofy the code that does not contain RepeatVector() to have return_sequences=True in both LSTM layer and not only only the first layer.

Can anybody will help me in how to write the code for 5th case of the above image?

Anyone to help me with forecasting Time Series using CNN-LSTM, I tried but it doesn't attach the forecasting part to the testing Data.
You can reach me via<EMAIL_ADDRESS>