Failed at the<EMAIL_ADDRESS>postinstall script 'yodoctor'.

shaunstanislaus@Master ~ (master) $ yo
-bash: yo: command not found
shaunstanislaus@Master ~ (master) $ grunt
grunt-cli: The grunt command line interface. (v0.1.13)
Fatal error: Unable to find local grunt.
If you're seeing this message, either a Gruntfile wasn't found or grunt
hasn't been installed locally to your project. For more information about
installing and configuring grunt, please see the Getting Started guide:
http://gruntjs.com/getting-started
shaunstanislaus@Master ~ (master) $ sudo npm install -g yo
/usr/local/bin/yo -> /usr/local/lib/node_modules/yo/lib/cli.js

<EMAIL_ADDRESS>postinstall /usr/local/lib/node_modules/yo
yodoctor

env: node: No such file or directory
npm ERR! Darwin 14.3.0
npm ERR! argv "node" "/usr/local/bin/npm" "install" "-g" "yo"
npm ERR! node v0.12.2
npm ERR! npm  v2.9.1
npm ERR! file sh
npm ERR! code ELIFECYCLE
npm ERR! errno ENOENT
npm ERR! syscall spawn
npm ERR<EMAIL_ADDRESS>postinstall: yodoctor
npm ERR! spawn ENOENT
npm ERR!
npm ERR! Failed at the<EMAIL_ADDRESS>postinstall script 'yodoctor'.
npm ERR! This is most likely a problem with the yo package,
npm ERR! not with npm itself.
npm ERR! Tell the author that this fails on your system:
npm ERR!     yodoctor
npm ERR! You can get their info via:
npm ERR!     npm owner ls yo
npm ERR! There is likely additional logging output above.

Please search for issues before opening new one. This have been reported multiple time.
This is an issue with npm. Open an issue on their repository.
Also, make sure you're using the latest version and retry installing. If it doesn't work, you can alternatively install yodoctor globally manually before installing yo:
npm -g install yodoctor
npm -g install yo

You asked me to install yo doctor, yo doctor was installed fine and didnt detect any issues but doing the second command after installing yo, i still can't install. NPM said it was Yo's issue and wasn't NPM.
please kindly help to resolve this issue
shaunstanislaus@Master /usr/local/lib/node_modules (master) $ yodoctor
Yeoman Doctor
Running sanity checks on your system
✔ Global configuration file is valid
✔ NODE_PATH matches the npm root
✔ No .bowerrc file in home directory
✔ No .yo-rc.json file in home directory
Everything looks all right!
shaunstanislaus@Master /usr/local/lib/node_modules (master) $ sudo npm -g install yo
/usr/local/bin/yo -> /usr/local/lib/node_modules/yo/lib/cli.js

<EMAIL_ADDRESS>postinstall /usr/local/lib/node_modules/yo
yodoctor

env: node: No such file or directory
npm ERR! Darwin 14.3.0
npm ERR! argv "node" "/usr/local/bin/npm" "-g" "install" "yo"
npm ERR! node v0.12.2
npm ERR! npm  v2.9.1
npm ERR! file sh
npm ERR! code ELIFECYCLE
npm ERR! errno ENOENT
npm ERR! syscall spawn
npm ERR<EMAIL_ADDRESS>postinstall: yodoctor
npm ERR! spawn ENOENT
npm ERR!
npm ERR! Failed at the<EMAIL_ADDRESS>postinstall script 'yodoctor'.
npm ERR! This is most likely a problem with the yo package,
npm ERR! not with npm itself.
npm ERR! Tell the author that this fails on your system:
npm ERR!     yodoctor
npm ERR! You can get their info via:
npm ERR!     npm owner ls yo
npm ERR! There is likely additional logging output above.
npm ERR! Please include the following file with any support request:
npm ERR!     /usr/local/lib/node_modules/npm-debug.log
shaunstanislaus@Master /usr/local/lib/node_modules (master) $

NPM said it was Yo's issue and wasn't NPM.

npm just assume any failures here is due to the package. But in this case it's apparently a race condition in their dependency install process.

so for now i can't use yo, and there isn't any other way to fix it?

Install yodoctor globally.

i installed it globally, yet it isn't working.

Well I gave you some direction of potential troubleshooting. It's now time for you to help yourself further.

npm -g install yeoman-doctor
can fix it

I tried your fix tosspig! It worked in the sense that even $npm install -g yodoctor was not working. So with your link I did get yo doctor installed but I still have the same problem as OP. It will not install Yo.
Jesses-Air:~ jesselawson$ npm install -g yo
/Users/jesselawson/.node_modules_global/bin/yo -> /Users/jesselawson/.node_modules_global/lib/node_modules/yo/lib/cli.js

<EMAIL_ADDRESS>postinstall /Users/jesselawson/.node_modules_global/lib/node_modules/yo
yodoctor

sh: yodoctor: command not found
npm ERR! Darwin 15.0.0
npm ERR! argv "/usr/local/Cellar/node/5.0.0/bin/node" "/usr/local/bin/npm" "install" "-g" "yo"
npm ERR! node v5.0.0
npm ERR! npm  v3.3.9
npm ERR! file sh
npm ERR! code ELIFECYCLE
npm ERR! errno ENOENT
npm ERR! syscall spawn
npm ERR<EMAIL_ADDRESS>postinstall: yodoctor
npm ERR! spawn ENOENT
npm ERR!
npm ERR! Failed at the<EMAIL_ADDRESS>postinstall script 'yodoctor'.
npm ERR! This is most likely a problem with the yo package,
npm ERR! not with npm itself.
npm ERR! Tell the author that this fails on your system:
npm ERR!     yodoctor
npm ERR! You can get their info via:
npm ERR!     npm owner ls yo
npm ERR! There is likely additional logging output above.
npm ERR! Please include the following file with any support request:
npm ERR!     /Users/jesselawson/npm-debug.log
Jesses-Air:~ jesselawson$

yeah this doesn't work. posters telling people to look up issues are not helping at all, considering the real issue is with YEOMAN's website, which claims installing yeoman is as simple as just typing in npm install -g yo. this is wrong.


Sniffing encrypted console game packets

Do you have any information to add concerning how to sniff encrypted console game (PS4/XBOX1) packets?  Like how to get a BurpSuite cert installed on a game console?

I do not, unfortunately.


Build conversion to new netflixoss plugin for 1.2.x branch

This branch should be changed to 1.2.x after merging

Priam-pull-requests #161 SUCCESS
This pull request looks good


examples/minikube: Allow container to mount bpffs itself

Containers with the SYS_ADMIN capability are able to create mounts
(even for bpffs) and mounting it from host is not needed. The
requirement of mounting bppfs on host is invalid in case of
Minikube, because Minikube VMs don't mount bpffs.
Signed-off-by: Michal Rostecki<EMAIL_ADDRESS>
Summary of changes:
Fixes: #3717
<!-- Enter the release note text here if needed -->

How to test (optional):
Run the modified cilium-ds.yaml file on Minikube, then demo.yaml and l3_l4_l7_policy.xaml, after that check whether you got rid of errors like that (which occur every time you run the demo on Minikube without changes from this PR!):
level=error msg="bpf: Unable to update in tunnel endpoint map" error="Unable to get object /sys/fs/bpf/tc/globals/tunnel_endpoint_map: no such file or directory" ipAddr=172.16.xxx.xxx/23

test-me-please

@mrostecki If we want to do this then we should add it to examples/kubernetes directly. This shouldn't be minikube specific, correct?

I've just reaized that BPF mounts on host are needed for persistency after Cilium pod restarts. I will submit an another, proper fix for the issue today.


IntegerLookup fails for XLA

System information.

Have I written custom code (as opposed to using a stock example script provided in Keras): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.9.1
Python version: 3.9.5
Bazel version (if compiling from source):
GPU model and memory: Nvidia A100, 16 GB
Exact command to reproduce:

def make_model():
  vocabulary = list(range(1, 11))

  return keras.Sequential([
    keras.layers.IntegerLookup(vocabulary=vocabulary),

    keras.layers.Lambda(lambda x: tf.cast(x, tf.dtypes.float32), name="cast"),
    keras.layers.Reshape([1,]),

    # keras.layers.Embedding(len(vocabulary) + 2, 8, input_length=1),

    tf.keras.layers.Dense(1, activation='sigmoid')
  ])

xla_model = make_model()
xla_model.compile(
  loss=tf.losses.BinaryCrossentropy(),
  optimizer=opt,
  metrics=['AUC'],
  jit_compile=True
)

xla_model.fit(
  dataset,
  steps_per_epoch=100,
  epochs=2,
  verbose=1
)

Describe the problem.
XLA can dramatically improve train speed.  Lookup tables are useful for going from sparse indices to dense indices for embeddings.  However, this lookup fails when XLA is enabled.
Describe the current behavior.
keras.layers.IntegerLookup preprocessing layer fail when XLA is enabled:
Describe the expected behavior.
XLA supports Keras preprocessing and embedding layers.
Contributing.

Do you want to contribute a PR? (yes/no): no
If yes, please read this page for instructions
Briefly describe your candidate solution(if contributing):

Standalone code to reproduce the issue.
For a runnable copy, see https://colab.research.google.com/gist/jeisinge/db516d15b0d7be31c098d9c83aeca235/integerlookup-xla.ipynb .
Source code / logs.
InvalidArgumentError                      Traceback (most recent call last)

[<ipython-input-8-2e968a73609f>](https://localhost:8080/#) in <module>
      3   steps_per_epoch=100,
      4   epochs=2,
----> 5   verbose=1
      6 )

1 frames

[/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

[/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py](https://localhost:8080/#) in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     53     ctx.ensure_initialized()
     54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
     57     if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node 'StatefulPartitionedCall' defined at (most recent call last):
    File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
      "__main__", mod_spec)
    File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
      exec(code, run_globals)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
      app.start()
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 612, in start
      self.io_loop.start()
    File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
      self.asyncio_loop.run_forever()
    File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
      self._run_once()
    File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
      handle._run()
    File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
      self._context.run(self._callback, *self._args)
    File "/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py", line 758, in _run_callback
      ret = callback()
    File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1233, in inner
      self.run()
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1147, in run
      yielded = self.gen.send(value)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 381, in dispatch_queue
      yield self.process_one()
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 346, in wrapper
      runner = Runner(result, future, yielded)
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1080, in __init__
      self.run()
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1147, in run
      yielded = self.gen.send(value)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
      yield gen.maybe_future(dispatch(*args))
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
      yield gen.maybe_future(handler(stream, idents, msg))
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 545, in execute_request
      user_expressions, allow_stdin,
    File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
      yielded = next(result)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 306, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
      raw_cell, store_history, silent, shell_futures)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
      return runner(coro)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
      coro.send(None)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
      interactivity=interactivity, compiler=compiler, result=result)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
      if (await self.run_code(code, result,  async_=asy)):
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "<ipython-input-8-2e968a73609f>", line 5, in <module>
      verbose=1
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
      tmp_logs = self.train_function(iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function
      return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
Node: 'StatefulPartitionedCall'
Trying to access resource 1743 located in device /job:localhost/replica:0/task:0/device:CPU:0 from device /job:localhost/replica:0/task:0/device:GPU:0
	 [[{{node StatefulPartitionedCall}}]] [Op:__inference_train_function_2676]

See Also:

https://github.com/tensorflow/tensorflow/issues/43687 - Embdding layer issues with TF 2.5 - 2.6
https://github.com/keras-team/keras/issues/15508 - IntegerLookup layer serialization issues
https://github.com/NVIDIA-Merlin/HugeCTR/tree/master/sparse_operation_kit - possible work around?

I believe XLA with IntegerLookup is working now with Keras 3 on a test train on a toy model.
If someone else can confirm, we can close this one out.  Thank you!
Changes:
pip install keras==3.0.4

# import tensorflow.keras as keras
import keras # Keras 3.0

From Keras 3, JIT compile will be made True by default if the model supports it, else it will fallback to false.

From Keras 3, JIT compile will be made True by default if the model supports it, else it will fallback to false.

So, does that mean that when I set jit_compile=True, it might not be honored in TensorFlow?

From Keras 3, JIT compile will be made True by default if the model supports it, else it will fallback to false.

So, does that mean that when I set jit_compile=True, it might not be honored in TensorFlow?

If you set jit_compile explicitly, it will be honored.

Correction.  This is still an issue.  I thought it was compiling with XLA, but it was not.  The message being printed is:
/usr/local/lib/python3.10/dist-packages/keras/src/trainers/trainer.py:205: UserWarning: Model doesn't support `jit_compile=True`. Proceeding with `jit_compile=False`.

See updated colab at: https://colab.research.google.com/drive/1neTKZ5v2ip06997A2-HdOtEoogRYRDKn?usp=sharing

What is the process for re-opening this issue?  Should I create a new issue?

@jacob-talroo , You can create a new issue with the complete details.


Fix timeout error when delete condition contains invalid datetime format

Proposed changes
Fix timeout when delete condition contains invalid datetime format  like
delete from table1 where k2=20210102   k2 is date type
MySQL [test]> delete from table1 where k2=20210102;
ERROR 1064 (HY000): errCode = 2, detailMessage = failed to execute delete. transaction id 5, timeout(ms) 30000, unfinished replicas: 10002=10017, 10002=10019, 10002=10021, 10002=10007, 10002=10023

use std::regex replace boost::regex
Types of changes
What types of changes does your code introduce to Doris?
Put an x in the boxes that apply

[x] Bugfix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Documentation Update (if none of the other choices apply)
[ ] Code refactor (Modify the code structure, format the code, etc...)

Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your code.

[ ] I have created an issue on (Fix #ISSUE) and described the bug/feature there in detail
[ ] Compiling and unit tests pass locally with my changes
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] If these changes need document changes, I have updated the document
[ ] Any dependent changes have been merged

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

Please add some UT for this

DONE


Map Needed

Need to come up with a map to display that won't infringe on copyrights for the regions of inazuma.

Thinking to start go off the official map, and use snagit to create a large panoramic image.
Then from that image create a rough version without detail.
This should be enough to start working
Get the kids to create artwork to replace the images with actual art in it.

Issues

Unsure if it should include the entire map in general, there are no spin puzzles over there.
Are there X/Y coordinates in the game that are accessible, instead of just eyeballing where it would go?

Maybe just use the coordinates from the official map

Got a capture of the map

Have a capture of the x/y locations, now it should be possible to make the temp json file.

I have it loading waypoints and the tree, but I think the units are reversed

Think I have it mostly aligned.  Now I need to clean up the map and make it worse

Now it seems more on brand

New icons, styled the same

Map has been implemented


Fix examples, improve their code style and use modern styling features

Fix all broken examples
Improve their code style (auto formatting)
Use modern styling features and modern function aliases.

I think we should write the expressions multi-line to increase legibilty

I did that on some of the longest examples, I didn't do that in the ships example since it should be transformed into a torque expression anyway, but that requires https://github.com/CartoDB/renderer-prototype/pull/135

As you can see in the Kanban I'm working on the examples right now so in order to avoid conflicts merge this changes into master and I will update my branch from it.
I'm with @IagoLast in increasing the legibility of the expressions. I would use by default string expressions and only the interactive methods when required.


feat: Support hermetic patching

Allows Bazel native patch to be used with npm_import(). This removes the existing dependency on patch when enabled. The proviso is that patch diff headers must have an unstripped 'package/' prefix when use_native_patch is true. The dirname 'package' is the de facto standard for npm packages, though this isn't enforced.
Type of change

New feature or functionality (change which adds functionality)
Documentation (updates to documentation or READMEs)

For changes visible to end-users
Suggested release notes are provided below:

use_native_patch flag added to npm_import, allowing for hermetic patching.

Test plan
Manual testing steps:

set BAZEL_SH var to a nonexistent path.
Create valid patch. Patch diff header should contain package prefix, e.g. "diff --git package/lib/font.coffee ..."
verify that npm_import() with patch applied fails "Error applying patch". System 'patch' cannot be found.
verify that npm_import() with patch applied and use_native_patch set to 'True' succeeds.

@gregmagolan Just wanted to gauge if there's any interest in this feature? I know Windows is a lesser used platform, but I'm sure there's teams out there (like mine) who would find this useful.

@gregmagolan  any thoughts on this MR? I tried to run a rules_js project on Windows, and it blew up while resolving NPM packages due to the "patch" dependency.

Ran into this issue as well on our Macs which have an older version of patch which is stricter than the one on Linux. Would be nice to use the native patch implementation as opt-in as this PR does, (although for our usecase a note in the docs for npm_translate_lock regarding this would also suffice)


fix(proxy-cache): disable grpc in proxy-cache plugin

Summary

Checklist

[ ] The Pull Request has tests
[ ] There's an entry in the CHANGELOG
[ ] There is a user-facing docs PR against https://github.com/Kong/docs.konghq.com - PUT DOCS PR HERE

Full changelog

[Implement ...]

Issue reference

Fix #8962
KAG-846

Should we disallow grpc and grpcs here: https://github.com/Kong/kong/blob/master/kong/plugins/proxy-cache/schema.lua#L20

Should we disallow grpc and grpcs her

I think it would be great, check if each request is a gRPC request at runtime is always a bit delayed.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.11 out of 12 committers have signed the CLA.:white_check_mark: oowl:white_check_mark: hanshuebner:white_check_mark: samugi:white_check_mark: tysoekong:white_check_mark: catbro666:white_check_mark: StarlightIbuki:white_check_mark: jschmid1:white_check_mark: acgoldsmith:white_check_mark: vm-001:white_check_mark: flrgh:white_check_mark: windmgc:x: sabertobihwyYou have signed the CLA already but the status is still pending? Let us recheck it.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.11 out of 12 committers have signed the CLA.:white_check_mark: StarlightIbuki:white_check_mark: tysoekong:white_check_mark: hanshuebner:white_check_mark: oowl:white_check_mark: acgoldsmith:white_check_mark: jschmid1:white_check_mark: windmgc:white_check_mark: vm-001:white_check_mark: catbro666:white_check_mark: samugi:white_check_mark: flrgh:x: sabertobihwyYou have signed the CLA already but the status is still pending? Let us recheck it.

I will reopen other PR to fix this.


Update side-by-side-migrate.md

I am a Microsoft CSS Engineer<EMAIL_ADDRESS>I confirmed with Jarod Aerts<EMAIL_ADDRESS>that the 14 day auto-reversion of side-by-side migration no longer occurs. Updating docs to reflect the current behavior.

@seligj95
Can you review the proposed changes?
Important: When the changes are ready for publication, adding a #sign-off comment is the best way to signal that the PR is ready for the review team to merge.
NOTE: the merge conflicts must be resolved before we can merge.
#label:"aq-pr-triaged"
@MicrosoftDocs/public-repo-pr-review-team

Hi folks. I made this change earlier today and the changes are already published. We can just close this as duplicate.
https://github.com/MicrosoftDocs/azure-docs-pr/pull/281551


Missing warning for unknown CHEBI term

There is no warning or error for this annotation.  The annotation_extension is slightly dropped.
FT                   /controlled_curation="term=gene expression, RNA level;
FT                   annotation_extension=in_presence_of(CHEBI:142517);
FT                   qualifier=increased ; evidence=ECO:0000231;
FT                   db_xref=PMID:29900664; date=20181113"

This is just a timing issue - there was a warning in the logs I saw yesterday, so I added the term to mini-chebi, and the warning went away as expected. The annotations show up on the gene pages (ctt1 & gpd1) with the extensions today.
https://curation.pombase.org/dumps/builds/pombase-build-2018-11-18/logs/qualifier_problems.txt
and pombe-embl/mini-ontologies/chebi.obo r5210


Config Catalog (missed UX changes)

The following issues are remaining in the configuration catalog [1].

'Show Example' text doesn't show:

When the code block takes up a lot of horizontal space, there is an error:

Additional Improvements:

Making the 'Hide example/Show example' button static.
The bracket ('[]') needs to searchable in the docs. Currently the search doesn't  work for toml configurations that contain the brackets.

[1] https://ei.docs.wso2.com/en/latest/micro-integrator/references/config-catalog/

Fixed.


speeddial-spacing incompatible with Vivaldi 3.7 Snapshot

the new version has a new grid system on express panel, please fix it

fix proposed by potmeklecbohdan on
https://forum.vivaldi.net/topic/11298/vivaldihooks-more-useful-mods/311
https://github.com/tiosgz/VivaldiHooks/blob/v37/vivaldi/hooks/speeddial-spacing.js
seems correctly working with Vivaldi 3.7.2218.3

@ian-coog nice, it`s working!


Implement additional source options in EchoPro.Survey

Currently only source=3 has been implemented for EchoPro.Survey that uses both the US and Canada sections of the survey. However, the Matlab version does allow the user to run the entire workflow using either the US or Canada sections only. To allow for this, the Python version must make the following changes:

Modify biological_data.py so that all files account for the other source options. This should be relatively straightforward. For example, if source=1 (US only) then to correctly assign the length data one would do self.survey.length_df = length_us_df.
After quickly attempting to implement this, it was found that there are a couple of instances where index elements of DataFrames are selected when they do not exist. These errors are currently occurring when survey_2019.compute_transect_results() is ran. To correct this all instances where subsets of DataFrames are selected should be examined and one should ensure that the index exists in the DataFrame before using it. It is possible that correctly modifying the input strata_df to remove all unused stratum could resolve some of the initial errors produced.

With the new changes in #161 and #164 this is pretty much addressed. I'll close this now since the description is outdated. If needed later we'll add another issue to track the source/country option.


FHIR deployment revision

I think it would be good to review the deployment of both FHIR server deployments (ePI R5 and IPS R4). FHIR is highly configurable and we may be ignoring some interesting options, and we might discover some misconfigurations. It would be good if someone at Trifork or HL7 could review it. @jkiddo @joofio What do you think about this?

the config file is here in this repo?

Yes:

application.yaml
Helm configuration values.yaml

The same files in the same path are present in the other fhir ePI repo

couldnt you benefit from a external database like postgres?

It's currently using postgres with an independent deployment.

I didn't see this. It is using postgres. The application.yaml has defined internal database but the flag enabled is set to false. It uses external postgres deployment.
I'm closing this issue as now we are using the official image, and configuration is passed to customize our server.
Deployment is moved to https://github.com/Gravitate-Health/hapi-fhir-deployments


Excellent ! Keep it going !

Just wanted to say thank you for this amazing library !

No problem! I've some plans for it's future ;)


docstring example / plot_directive / sphinx-gallery using incorrect versions

First, thanks for building this tool!  It's scratching a serious itch.
The problem that I'm running into is with sphinx-multiversion==0.2.3 (though I tried to fix it with the current dev/master and it also failed).
I use matplotlib.sphinxext.plot_directive to generate figures from docstring example code.  These code examples change from one version to the next, so they need to be generated independently for each version.  (In case it's relevant, these are discovered by autodoc / autosummary.)
As the issue title suggests, I'm seeing incorrect docstrings for some version builds.  (E.g., my development branch is getting docstrings from several tags back.)
My hunch is that autodoc/summary is getting stuck at runtime with whatever the first build version was, rather than reloading the library at each build.  Does this sound plausible?  And if this is the case, is it possible to work around (or fix), or would a fix here go entirely counter to the "no-monkey-patching" ethos of this project?

Quick follow-up: confirmed that this is due to autodoc using the previously imported module rather than the version corresponding to the copied source folder.
A hacky workaround for this is to put the following at the head of conf.py:
import sys
removals = [_ for _ in sys.modules if 'YOURPACKAGENAME' in _]
for _ in removals:
    del sys.modules[_]

to force a reload for each documentation build.  This combined with the usual sys.path hackery does the trick to force the correct version loading.  This only works because the main config is reused (with some config-level patching) for each build, so it feels pretty awkward.
A better solution might be to do something like sphinx-gallery's reset_modules config option, which lets the user (conf.py) provide either a list of modules to be reloaded, or a custom callback function to be executed in between each run.  This conceivably could accept a version or tag as a parameter, which could enable custom reset logic depending on which version is being built.

I think it would also be possible to actually use a subprocess instead of importing and invoking the build method directly. I think the performance penality would be neglible.

Please let me know if #28 resolves the issue.

Thanks!  This seems to work, at least in the sense of the original bug I reported.
The one problem that I'm still seeing is that the version number on each build is stuck at the latest.  (I didn't check this when testing #27, so that problem may have crept in then.)  This may be due to the way that i'm pulling version in from the imported module in conf.py, so it could be out of scope for this issue.

That should be fixed by #30, you can check this without actually building using --dump-metadata.

If your want to test both, you need to merge both branches. Please let me know if that resolves your issues.

I locally rebased #30 onto #28 and checked dump-metadata; everything looks good to me. Thanks!


"Max NICs / Expected network performance (Mbps)."

Hi, can you please clarify what this means "Max NICs / Expected network performance (Mbps)."
The value for this for our Azure VMs is "2/2000"
If we can have 2 NICs, does that make the total 2000Mbps or 4000Mbps ?
In otherwords, what is the bandwidth per NIC.
Thanks
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: c7c054b7-422c-c3b0-fc8e-91314231ee0c
Version Independent ID: 42825540-2137-92e3-08b0-0dbd84089eda
Content: Azure virtual machine network throughput
Content Source: articles/virtual-network/virtual-machine-network-throughput.md
Service: virtual-network
GitHub Login: @steveesp
Microsoft Alias: steveesp

@sarah026, expected network performance is the maximum aggregated bandwidth allocated per VM size across all NICs.

The VM size is Standard D4s v3, and the "Max NICs / Expected network performance (Mbps)." value is "2/2000" in the documentations here
From what you've just said, it is the aggregate value, so if I have 2 NIC cards, then the expected net performance is 2000Mbps
and if I only have 1 NIC, it is 1000Mbps
Correct?

@sarah026, The MAX NIC is the capability of the VM where it can hold up to 2 NIC's or the limit based on the size. The bandwidth is not calculated based on the number of NIC. The bandwidth is for the whole VM as per the article. Irrespective of the number of NIC's it is holding. If you consider Standard_D16_v3 size,

then at a given instance VM can send or receive 8Gbps via one or more nic. You can find more details about this on the link.
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-machine-network-throughput#expected-network-throughput.

@sarah026, we are closing this issue for now. If there are further questions regarding this matter, please reply and we will gladly continue the discussion.


Don't treat string as a pane

In some cases options.pane consists string instead of pane of markers.

So what is the reason of options.pane being a string? Maybe we should find the roots of the problem?

@bardt I don't have so much time regarding this for now. Maybe we should create ticket in Jira?

I can't accept this request. Looks like we added an option pane which conflicts with built-in options. Try to rename it and check if it helps.

It's seems like we must add default pane like markerPane and use this.getPane() instead this._getPane()

@bardt @ktoto see again.

@ovvost looks right. Does demo page work with and without passing pane as an option?

@bardt Yes.

I'd prefer @ilfa to see it and then merge.

:+1:

:+1:


AMI does not exist

Is it possible for you to update the repo with a working AMI?

Just a heads up for future readers: After changing the code to use an AMI that actually exists I ran into DB errors. Which probably correlates with PRs being raised.
What a pity that PRs and issues are seemingly being ignored by AWs.


Update Admission Control docs and add others.

Address most of the comments from #8936 review.
Adds minimal documentation for securityContext and serviceAccounts,
which I will expand in a future PR.
Adds analytics.
Links admission_controllers.md from cluster-admin-guide.

Small typo, otherwise lgtm
Sent from my iPhone

On Jun 2, 2015, at 8:23 PM, Eric Tune<EMAIL_ADDRESS>wrote:
Address most of the comments from #8936 review.
Adds minimal documentation for securityContext and serviceAccounts,
which I will expand in a future PR.
Adds analytics.
Links admission_controllers.md from cluster-admin-guide.
You can view, comment on, or merge this pull request online at:
https://github.com/GoogleCloudPlatform/kubernetes/pull/9156
Commit Summary
Update Admission Control docs and add others.
File Changes
M docs/admission_controllers.md (30)
M docs/cluster-admin-guide.md (2)
A docs/security_context.md (6)
A docs/service_accounts.md (14)
Patch Links:
https://github.com/GoogleCloudPlatform/kubernetes/pull/9156.patch
https://github.com/GoogleCloudPlatform/kubernetes/pull/9156.diff
—
Reply to this email directly or view it on GitHub.

Rerunning Shippable.

fixed

Breaks the build:
diffing /root/workspace/src/github.com/GoogleCloudPlatform/kubernetes/docs/ against freshly generated docs
Some md files are missing ga-beacon analytics link:
/root/workspace/src/github.com/GoogleCloudPlatform/kubernetes/docs/admission_controllers.md
/root/workspace/src/github.com/GoogleCloudPlatform/kubernetes/docs/ is out of date. Please run hack/run-gendocs.sh

I saw the beacon at the end of the doc, had trouble thinking that Eric wrote that by hand.
Any chance we can fail Jenkins on these things?

Things are sorted out.

@derekwaynecarr because of the revert of #8936 and me not understanding what was happening on a recent rebase, this PR now has your changes from  #8936 listed as my commit.  Do you care?

@erictune - You can buy me a GitHub beer and we can call it a day ;-)  I am more concerned on the project having the right documentation than me having more internet points.  I was more bummed that I did not catch the url whackiness of the analytics beacon in your PR after making a point of looking for it.

:moneybag:  :arrow_right: :beers:


An option to provide space (margin) between pages in PageView

In many cases, we have things using the full width of pages in PageView.
For example:

I think an option to provide some space(margin) between PageVIew could be handy.
Example:
Consider pages with 2 colors:
PageView.builder(
          itemCount: _pagesList.length,
          itemBuilder: (BuildContext context, int index) {
            return Container(
                    color: _pagesList[index],
                           );
                        }
                     )

Here we have no space between 2 pages, it may seem congested in many cases.
A simple parameter of margin could be very useful:
PageView.builder(
          margin: 35,
          itemCount: _pagesList.length,
          itemBuilder: (BuildContext context, int index) {
            return Container(
                    color: _pagesList[index],
                          );
                        }
                      )

Now it looks much better. This would be useful in many cases.

Does adding Padding(padding: EdgeInsets.symmetric(horizontal: 20, child: )) work?

I believe adding Padding(padding: EdgeInsets.symmetric(horizontal: 20, child: )) does the job

I hope you understand that this margin/space will be visible between pages when we scroll through pages.
Padding would be visible within each page's boundaries. The margin/space property I have requested is outside the page. Hope I am clear now.

Ah, I understand, my bad

Have you try viewportFraction
PageController({int initialPage: 0, bool keepPage: true, double viewportFraction: 1.0 })

@amankataria Please report if @Solido comment solved your issue.

Without additional information, we are unfortunately not sure how to resolve this issue. We are therefore reluctantly going to close this bug for now. Please don't hesitate to comment on the bug if you have any more information for us; we will reopen it right away!
Thanks for your contribution.
Could everyone who still has this problem please file a new issue with the exact descriptions what happens, logs and the output of ' flutter doctor -v'  please.
All system setups can be slightly different so its always better to open new issues and reference related issues.

This should work fine ✨✨
final pageController = PageController(viewportFraction: 1.1);

PageView.builder(
  controller: pageController,
  itemCount: _pagesList.length,
  itemBuilder: (BuildContext context, int index) {
    return FractionallySizedBox(
      widthFactor: 1 / pageController.viewportFraction,
      child: Container(
        color: _pagesList[index],
      ),
    );
  },
);

Thanks @mono0926 , this work for me!

final pageController = PageController(viewportFraction: 1.1);
this line gave me a sweet night sleep

Seems initialPage not working correct for above solution

@jlavyan Can you please explain where you added this, I'm still facing the same issue. Thanks.

@iapicca I would like to request that this issue be reopened.
The proposed solution using viewportFraction doesn't allow fine control of the margin between pages (it will be relative to the width of the parent widget) and seems to break the initialPage parameter.
Ideally we would have a simple double pageMargin parameter that allows specifying the exact spacing between pages.

@cachapa
this issue was closed more than a year ago,
please open a new issue provide your flutter doctor -v and a complete runnable minimal reproducible code sample

Opened #78200


OpenFHE-HEXL Build Error

OpenFHE development not able to find HEXL headers when running scripts/build-openfhe-development-hexl.sh on Ubuntu 20.04
Example error:
In file included from /home/joserochh/openfhe-configurator/openfhe-staging/openfhe-development/src/core/include/lattice/hal/hexl/lat-backend-hexl.h:39,
from /home/joserochh/openfhe-configurator/openfhe-staging/openfhe-development/src/core/include/lattice/lat-hal.h:40,
from /home/joserochh/openfhe-configurator/openfhe-staging/openfhe-development/src/core/lib/lattice/be2-poly-impl.cpp:36:
/home/joserochh/openfhe-configurator/openfhe-staging/openfhe-development/src/core/include/lattice/hal/hexl/hexldcrtpoly.h:47:10: fatal error: hexl/hexl.hpp: No such file or directory
47 | #include "hexl/hexl.hpp"
|          ^~~~~~~~~~~~~~~
compilation terminated.

This was closed shortly after report.  Officially closing now.


Introduce GitHub issue forms

Closes #585
I've taken an initial stab at porting our issue templates to the new GitHub forms.
I've tried to keep the intent of the originals but also threw some of the nice features of the new templates at them for good measure!
I've also added a new form for a generic question about Nox as I believe this is useful for people who want to know something but don't want to raise a "scary" sounding bug or issue.
More than happy to tweak the contents if we want, just kicking things off 👍🏻
You can view the new forms by clicking on the files and GitHub will render it how it will look when filing the issue 🙂

I've removed the question form from this as we're planning to include a permalink to the nox discord channel as discussed in #534

@crwilcox I think a sensible thing to do with making things required might be to use these templates for a period of time and see if new issues are lacking important information, then we can make those fields required or adjust other aspects of the forms going forward?


Scala implementation for the column buffering and serialization of Vector columns.

Migrating the Java written column buffering (and serialization) lib to Scala.
Unit tests checked.
Fastload_spark tests checked.

All the comments are address, except the one I commented back.

I tested again the code (unit + fastload). No regressions were added.


Show better warning when dependency recipe is overridden

We currently have a warning
Two different recipes given for org-noter (:flavor cannot be both melpa and nil)

which is produced when a recipe conflict happens, see #518. However this is not very informative to the end user in the most common case, which is that the package was pulled in as a dependency unbeknownst to them, using the default recipe, and then they try to specify a custom recipe for it. We have the data available to detect when this has happened, so we should use it and display a different warning, like
Custom recipe given for org-noter, but it was already pulled in as a dependency using the default recipe

Is it possible to also specify the package that pulled in the dependency? This would make it easier to rearrange an init file so that the custom recipe is evaluated first.

Yep, sounds like a good idea and simple to implement (the data already exists at the right place as it is used when displaying messages about build system status).

As things stand today, is there any good way to inspect the tree of transitive dependencies? I have no idea which of my packages is pulling in cider via the default recipe.
The only way I know to find it is commenting out packages, which is tedious.

We don't have anything graphical at the moment, but all the needed information is available in the straight--build-cache variable. For example, in mine there's an entry for
               "cider"
               ("2021-05-02 05:30:15"
                ("emacs" "clojure-mode" "parseedn" "pkg-info" "queue" "spinner" "seq" "sesman")
                (:type git :flavor melpa :files
                       ("*.el"
                        (:exclude ".dir-locals.el")
                        "cider-pkg.el")
                       :host github :repo "clojure-emacs/cider" :package "cider" :local-repo "cider"))

which indicates that the cider package has dependencies on all of ("emacs" "clojure-mode" "parseedn" "pkg-info" "queue" "spinner" "seq" "sesman"). In my installation, since cider doesn't appear anywhere else, I know nothing else depends on it. Presumably, in your installation, you'd find a different result.

This variable might be worth mentioning in the README file. I had a hard time finding it.

@jasonhemann the straight-dependents command is available for this now.

Right, we want to avoid documenting such internal variables, and instead we want to provide user-facing commands that have a stable and well-designed API, like the one @progfolio mentioned.


Make tables a queryable object

Since tables are somehow already a data structure, why not make them also queryable as a data object?
Proposal for the spec & docs in Objects.md:

table
Markdown table rows are indexed using the table tag, any additional tags can be added using [[Tags]] in any of its cells.

Title
Description Text

This is some key
The value contains a #table-tag

Another key
This time without a tag

table-tag

This would render the result as

ref
tag
page
pos
title
description_text
tags

someref@23
table
ExamplePage
23
This is some key
The value contains a #table-tag
table-tag

I love it. I was thinking of supporting this for CSV embedded in fenced code blocks already, but also doing this for markdown tables makes perfect sense.

Glad you like it. I am going to prepare a PR. Feel free to assign this issue to me.

Implemented by... you!


Update to ibc-go 1.2.0 and upgrade wasmd addresses

Summary
Currently, the wasm module truncates contract addresses to the length of 20.  This was due to ibc-go not supporting the ADR-028 address length.  There is now a new version of ibc-go that now allows addresses to have a longer length key to support the ADR-028.  The related issue was: https://github.com/cosmos/ibc-go/issues/339
Problem Definition

Wasmd contract address need to support the ADR-028 standard and a migration upgrade handler needs to be added to migrate the old addresses into the new ones.
Proposal

[ ] Update the wasmd project to use ibc-go 1.2.0, remove truncation of contract addresses, and add migration plan
[ ] Update provenance project to use newly created wasmd tag and add upgrade handler to upgrade wasmd to new ConsensusVersion

For Admin Use

[ ] Not duplicate issue
[ ] Appropriate labels applied
[ ] Appropriate contributors tagged
[ ] Contributor assigned/self-assigned

ibc-2.0 requires these address changes now... and given cosmos hub/osmosis/others are on ibc-2.0 that is where we will want to end up as well... so instead of targeting 1.2 we should target 2.0 and update our fork as needed to bring our setup into alignment.  Unfortunately I do not believe the cosmwasm team will have their 1.0.0 release updated on the schedule we need for 1.8.0


READABILITY_PARSER_TOKEN not found in environment, even if supplied in args

There is error, when no READABILITY_PARSER_TOKEN exported, but it is supplied in args.
    readability_parser = ParserClient(token=READABILITY_PARSER_TOKEN)
  File "/Users/support/Virtualenvs/dja18/lib/python2.7/site-packages/readability/clients.py", line 329, in __init__
    self.token = xargs.get('token', required_from_env('READABILITY_PARSER_TOKEN'))
  File "/Users/support/Virtualenvs/dja18/lib/python2.7/site-packages/readability/core.py", line 14, in required_from_env
    "Required argument '{}' not supplied and not found in environment variables".format(key))
ValueError: Required argument 'READABILITY_PARSER_TOKEN' not supplied and not found in environment variables

@skabbit I'll take a look, sorry about that

IT'S WRONG.
self.token = xargs.get('token', required_from_env('READABILITY_PARSER_TOKEN'))
required_from_env() will run anyway.


Cachi2 task

Maintainers will complete the following section

[ ] Commit messages are descriptive enough
[ ] Code coverage from testing does not decrease and new code is covered
[ ] Python type annotations added to new code
[ ] JSON/YAML configuration changes are updated in the relevant schema
[ ] Changes to metadata also update the documentation for the metadata
[ ] Pull request has a link to an osbs-docs PR for user documentation updates
[ ] New feature can be disabled from a configuration file

Rebased and removed DO NOT MERGE commit


Initialise objects in get_events function to avoid warnings

This is to fix the issue I described at https://github.com/rileydutton/Exchange-Web-Services-for-PHP/issues/34
@rileydutton Would be nice if you could take a look. Thanks!

Replaced by https://github.com/rileydutton/Exchange-Web-Services-for-PHP/pull/38


Mergesort algorithm occasionally generates garbage values in sorted vector

Hello there,
I hope this message finds you well. I am reaching out because I believe I may have encountered a bug in the algorithm mergesort of the containers-accelerate project.
While using this project to build a bioinspired library for optimizing continuous functions, I have noticed that occasionally after sorting a vector, the vector becomes filled with some garbage. Specifically, when creating a new point, I constrain it to the search space. However, at the end of the execution, there are some points that are outside the bounds.
I am not entirely certain if this issue is related to the sortBy function and the initialization of the output vector with undefined values. However, it is a possibility that I wanted to bring to your attention.
I apologize if I have overlooked something or if this issue has already been reported. As someone who is new to this subject, I appreciate your patience and understanding. Thank you for your time and consideration.
Best regards,
Sergio Domínguez

Hi Sergio,
Thanks for your message, and your interest in Accelerate! That sounds like a cool project you are working on (:
There might indeed be a problem with the merge-sort function. If you can give me further details on your setup (which backend, OS, etc.) and a small reproducible test case, I can try to look into it.
In the mean time, you could try using the quicksort method instead, and see if that works around the problem for you (that one is a bit better tested so I am slightly more confident that it is correct).

Hi Trevor,
Thanks for getting back to me! I'm glad to hear that you're interested in my project :).
Regarding the merge-sort function, I appreciate your willingness to help. My setup is running on GHC 8.10.7, using LLVM.Native as the backend, and I'm running it on Linux 6.1.22-1-lts. Unfortunately, I don't have a reproducible test case at the moment, but I'll work on creating one and will send it to you as soon as I can.
I tried the quicksort method that you suggested, and it works well for my purposes. However, I was using merge sort because at each iteration the previous vector is already ordered, and the algorithm just needs to insert the new values while maintaining the sorted order. This makes it more efficient than quicksort in some cases.
In any case, I appreciate your help and suggestions, and I'll keep working on finding a solution to this problem.
Thanks again for your help!

Here it is the reproducible test case:
import Data.Array.Accelerate as A
import qualified Data.Array.Accelerate.LLVM.Native as CPU
import Data.Array.Accelerate.Data.Sort.Merge

testMerge :: IO ()
testMerge = do
  let xs = use $ A.fromList (Z :. 1000) [0, 1 ..] :: Acc (Vector Int)
      ys = use $ A.fromList (Z :. 1000) [2, 4 ..] :: Acc (Vector Int)

      T2 loop _ =
        A.awhile
          (\(T2 _ i) -> A.map (A.< 1000) i)
          ( \(T2 xs' i) ->
              let i' = the i :: Exp Int
                  i'' = i' + 1
                  ys' = A.take i' ys :: Acc (Vector Int)
                  sorted = A.take 1000 $ sort (xs' A.++ ys') :: Acc (Vector Int)
               in T2 sorted (unit i'')
          )
          (T2 xs (unit 0))

  print $ CPU.run $ A.take 20 loop
  print $ CPU.run $ A.take 20 xs
  print $ CPU.run $ A.take 20 ys

Results:
Vector (Z :. 20) [-8888828770332863923,-8888828770332863923,-8888828770332863923,-7072078906498459620,-7072078906498459620,-707207890649845
9620,-7072078906498459620,-6948079798528238165,-6948079798528238165,-6948079798528238165,-6332140206658740844,-6332140206658740844,-6332140
206658740844,-6332140206658740844,-6332140206658740844,-6332140206658740844,-6332140206658740844,-6332140206658740844,-6332140206658740844,
-6332140206658740844]
Vector (Z :. 20) [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]
Vector (Z :. 20) [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40]


Feature Request: Add Open Transcript button in Feature Menu

Recently Youtube has shifted the "Open Transcript" button from the "3 dots menu" below the player to in the bottom of description. Now we have to scroll and search for this "Open Transcript" button.
Could you please add "Open Transcript" button  in the Feature Menu in the player?

@lamyergeier Let me look into it and I'll get back to you.

@lamyergeier The feature is done being coded it will be available in the next release. I still have a couple things to finish up before then.

@lamyergeier the feature is available now

@all-contributors please add @lamyergeier for ideas

@VampireChicken12 I am using v1.17.1. But I don't see the show transcript, as can be seen below:

@lamyergeier is the option enabled in the settings and does that video actually have a transcript? The button only appears when the video has a transcript

I just tested with the same video, it does have one but the button didn't appear let me look into the code and figure out what is going wrong that the button isn't appearing


Added JointAxis Python interface

Signed-off-by: ahcorde<EMAIL_ADDRESS>🎉 New feature
This PR is part of this meta ticket https://github.com/ignitionrobotics/sdformat/issues/931
Summary
Added JointAxis Python interface
Test it
from sdformat import JointAxis

jointAxis = JointAxis()

Checklist

[x] Signed all commits for DCO
[x] Added tests
[ ] Added example and/or tutorial
[ ] Updated documentation (as needed)
[ ] Updated migration guide (as needed)
[x] codecheck passed (See contributing)
[x] All tests passed (See test coverage)
[x] While waiting for a review on your PR, please help review another open pull request to support the maintainers

Note to maintainers: Remember to use Squash-Merge and edit the commit message to match the pull request summary while retaining Signed-off-by messages.

Codecov Report

Merging #960 (633e35f) into main (0494785) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #960   +/-   ##
=======================================
  Coverage   66.66%   66.66%
=======================================
  Files           2        2
  Lines          27       27
=======================================
  Hits           18       18
  Misses          9        9

Continue to review full report at Codecov.

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http: cleaning up redispatch logic

Now that we kick off a faux event for pipelining correctly, we don't need the redispatch logic in the HCM any more.
Risk Level: High (changing HCM flow)
Testing: prior PR added pipeline integration test
Docs Changes: n/a
Release Notes: n/a

I'd picked this up today hoping it'd address #9083 but realized it probably won't, as we still want the read disable after dispatch to make sure we don't get more data until we're ready for it :-(


[FEATURE] Foreign script notification

Notify the user when they're editing a foreign script (not known to any content in the workspace) that they need to create a script project to use code features more advanced than just syntax highlighting and syntax analysis.
This could be done when trying to use "go to" feature and others symbol table dependent features.
Feature connected with #12 as that one should display "No project selected" or something similar when editing a foreign script.

The warning should be shown when:

[ ] using goto definition
[ ] using goto declaration
[ ] using goto type definition
[ ] importing vanilla file when no projects are available

The warning should be shown in any other new similar cases moving forward.

Released with v0.3.0


Disable default values

Wonderful plugin!
I would like to disable some default values, i.e. enabled <-> disabled
Is there a way to accomplish that
The use case is editing a lazy.nvim plugin spec, when editing the enabled line
return {
  "nat-418/boole.nvim",
  enabled = true,         -- i want to toggle this value
  opts = {
    mappings = {
      increment = "<C-a>",
      decrement = "<C-x>",
    },
  },
  keys = {
    { mode = { "n", "v" }, "<C-a>" },
    { mode = { "n", "v" }, "<C-x>" },
  },
}

When I goto that line, if I'm on the LHS and I want to change true to false, the plugin changes enabled to disabled

Thanks for this report, I will look into adding a disable option.


chore: reduce Binding variant usages

This PR further reduces the amount of usages of Binding::* variants, mostly by using helper functions.
Usages of Binding::* variants internal to the struct have been renamed to Self::*, so I can quickly filter them out to see which ones remain.
It looks like after this, the only remaining cases are construction of Binding::List and Binding::Empty. These cannot be abstracted, but that should not be a problem once we have an abstract Binding trait, since instances will still be created of the concrete type (probably MarzanoBinding then).

Summary by CodeRabbit

Refactor

Enhanced pattern matching and binding creation logic for improved efficiency and readability.
Optimized pattern resolution processes to enhance performance.
Streamlined text unparser for more accurate output generation.

Can you just fix the clippy complaints?

Oh, that's odd. The fix for the Clippy seems pushed, and locally it's indeed gone. Maybe the CI incorrectly cached the result?


Help to make object glow

i found a way to make object glow like this
        // SUPER SIMPLE GLOW EFFECT
	// use sprite because it appears the same from all angles
	var spriteMaterial = new THREE.SpriteMaterial(
	{
		map: new THREE.ImageUtils.loadTexture( 'images/glow.png' ),
		useScreenCoordinates: false, alignment: THREE.SpriteAlignment.center,
		color: 0x0000ff, transparent: false, blending: THREE.AdditiveBlending
	});
	var sprite = new THREE.Sprite( spriteMaterial );
	sprite.scale.set(200, 200, 1.0);
	mesh.add(sprite); // this centers the glow at the mesh

full code : https://github.com/stemkoski/stemkoski.github.com/blob/master/Three.js/Simple-Glow.html
demo : http://stemkoski.github.io/Three.js/Simple-Glow.html
and glow.png

but i don't know how to make this with react-three-fiber . Can u help me

there is no difference between r3f and threejs, you can copy and use that code 1:1 without changes. put the mesh into the scene via . if you still have problems, better ask on spectrum so we can keep GH for bugs.

@drcmda
can u give me an example of this ? . i'm newbie
you mean put it in a function an use this in
<mesh>
   {glow()}
</mesh>

in react never put sideeffects into the render loop. sideeffects can be run in useEffect for instance.


how to prepare the dataset?

How to make a dataset for train and validation?
The instruction provide the shape of dataset, but which tool or command i need to run to prepare the dataset?

Hi!
Sorry for the late reply.
The process of converting wav files to numpy array is as follows:
speech_dataset = []

# read wav files
for addr_speech in noisy_speech_list:
    noisy_speech, fs = soundfile.read(addr_speech[0])
    if fs != cfg.fs:
        noisy_speech = librosa.resample(noisy_speech, fs, cfg.fs)
    clean_speech, fs = soundfile.read(addr_speech[1])
    if fs != cfg.fs:
        clean_speech = librosa.resample(clean_speech, fs, cfg.fs)
    speech_dataset.append([noisy_speech, clean_speech])

And normalize the speech_dataset.
Additionally, without making .np, you can directly use wav files as follows:
class Wave_Dataset(Dataset):
    def __init__(self, mode):
        # load data
        if mode == 'train':
            print('<Training dataset>')
            print('Load the data...')
            # load the wav addr
            self.noisy_dirs = scan_directory(cfg.noisy_dirs_for_train)
            self.clean_dirs = find_pair(self.noisy_dirs)

        elif mode == 'valid':
            print('<Validation dataset>')
            print('Load the data...')
            # load the wav addr
            self.noisy_dirs = scan_directory(cfg.noisy_dirs_for_valid)
            self.clean_dirs = find_pair(self.noisy_dirs)

    def __len__(self):
        return len(self.noisy_dirs)

    def __getitem__(self, idx):
        # read the wav
        inputs = addr2wav(self.noisy_dirs[idx])
        targets = addr2wav(self.clean_dirs[idx])

        # transform to torch from numpy
        inputs = torch.from_numpy(inputs)
        targets = torch.from_numpy(targets)

        return inputs, targets

The difference is whether the wav file is read each time it is used or whether it is read ahead.
I hope this answer was helpful :)


Fix blake

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@@           Coverage Diff           @@
##              dev     #790   +/-   ##
=======================================
  Coverage   92.07%   92.07%
=======================================
  Files          91       91
  Lines       11993    11993
  Branches    11993    11993
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  Misses        849      849
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Consolidation and cleanup

premise: I've been working on this intermittently for over two months. That's a long list of commits, the PR is huge (though I've seen worse), but unfortunately, most of the changes are intertwined, so... also, some orthogonal changes, originally in this branch, have already been merged through #1115
I honestly don't know where to start, but IMHO the amount of cleanup that this PR brings in is unprecedented. I'll try to list the major points:

Loop blocking is now a Cluster-level transformation, rather than an IET-level transformation. This allowed dropping one of the few (the last?) remaining horrible hacks in Devito (changing the state of a DSE-introduced Array during a DLE pass. Also, it allowed dropping pretty much all of the routines in passes/iet/blocking.py which were legacy, horrible, weak, unmaintainable code

Extirpate both DSE and DLE from the whole codebase (code, tests, comments, docstrings, examples, ...). Now optimizations are just "compiler passes" -- some are Cluster-level optimizations, other are IET-level optimizations, but they are just "passes". From a user perspective, this means that optimizations are controlled by means of a single switch, called opt (much more intuitive name than dse or dle). So one, for example, can do Operator(... opt='advanced' ...). Or Operator(... opt=advanced`, platform='nvidiaX', ...). A positive side effect is that now understanding how the compilation pipeline is structured is vastly easier than before. See for example here

Several improvements to the CSE and optimize_pows (Cluster-level) passes (equipped with tests) -- resulting in better operation counts reductions

More robust and more efficient MPI-level optimizations, plus several minor fixes concerning code generation. All equipped with new tests.

Homogeneize semantics of symbolic_min/symbolic_max/offsets/... across different Dimension types and, in particular, how this information is used to construct an IterationSpace and (later on in the compilation pipeline) an IET.Iteration

Thanks to this top-to-bottom generalization effort, the OI estimated by the profiler is now much more reliable. For example, with TTI we will now see slightly larger OIs (which I checked to be correct). This is because in TTI (not just TTI of course, but this is a notable case) the OI strongly depends on the chosen block size, an information that is now available when the profiler kicks in at compilation time. In master, instead, the profiler kicks in before loop blocking is applied (being loop blocking an IET-level transformation), so the symbolic block size is unavailable

The dichotomy between dse=advanced and dse=aggressive is now gone. As I said earlier, now it's just opt=noop or opt=advanced, and opt=advanced always does the best thing in all problems.

Looking into it, small question, is there backward compatibility, i.e does dse=.. still work?

is there backward compatibility, i.e does dse=.. still work?

yes, all should be backward compatible and, where necessary, suitable deprecation warnings are emitted

Except the few comments left, I think it's gtg.

@mloubout actually, I think the ball is on your pitch now :)
but unfortunately we can't merge it yet, I need to add a small batch of tests and a rewrite of CIRE, since the current rewrite has exposed a bug through an external app (failing on this branch in one particular case).

the current rewrite has exposed a bug through an external app

Can you detail?

Should the test_dse/test_dle be merged?

@georgebisbas I've planned that rewrite already, but it's gonna be a separate PR

the current rewrite has exposed a bug through an external app

Can you detail?

due to the cire() pass causing OOB in extremely unlikely yet possible scenarios
this forced a massive rewrite of cire, which I hope is now nearly done, in consolidate-aliases-3, a branch that sits right on top of this one. I guess I'm gonna close this PR soon and open a new one

closing in favor to #1171


Removed appc support

What are your changes?
Removed appc support
What is the urgency level of this change?

[ ] Blocker
[ x] High
[ ] Medium

I have completed these items:

[ x] I have built locally and tested for broken links and formatting.
[ x] If appropriate, I have added redirects.

If you included a comment with your commit, it appears here:

LGTM


Track still plays in bg despite 'continuesToPlayInBackground' being false

The issue is addressed as is in the title. It happens on iOS, haven't tested on Android yet.
Here's my code:
this.player = new Player(data.preview_url, {continuesToPlayInBackground: false})

I back out of the app and it's still playing.
Note that I do not have the background audio permission on.

When I tested on iOS with 1.0.5, it stopped playing when the app went background by pressing Home button. Tested on iOS 10 device.

Yeah, now it started stopping... wonder why. Closing this.


🚚 Move helper functions from inside classmethod to module root

What was your motivation to move them outside the class instead of marking them as private?
They are not getting used somewhere else as far as I see.

they don't use any instance attributes and could be static. and if they're static, they could just be functions.
the respective pylint-warning is: no-self-use


what is the camera_optimizer? 'CameraOptimizerConfig' object has no attribute 'param_group'?

hello, when I use the kitti dataset train mars, I met the problem. When I remove the camera_optimizer in the config, I found the result is normal? Therefore, the module is necessary?
Thanks a lot.

To my understanding, the module is disabled by default in their work.

Thank you. I want to ask another questions. Did you meet the problem of background blur in private dataset, and can not separate the dynamic objects and background? I didn't using depth and semantic mask.

I met similar problem, caused by wrong bbox position(wrong coordinate). Maybe you can check if your bbox pos is correct.

@Nplace-su Thank you for your reply. Can you tell me where to check the bbox pose, and what is the process for the bbox check? through checking the visible_objects_ls by visualivation?

From the train results, the bbox pose seems right?  but the result of objects_rgb is very strange. could you provide me some help, very grateful.

@szhang963  It seems not 100% right? There are lots of potential issues when you use your own data, I suggest you make sure your coordinate and other data conventions 100% align with their original dataparsers.
BTW, could you share your training config?

Thank you for your patient reply. This is my config.yaml:
!!python/object:nerfstudio.engine.trainer.TrainerConfig
_target: !!python/name:nerfstudio.engine.trainer.Trainer ''
data: &id003 !!python/object/apply:pathlib.PosixPath
- data
- my_kitti
- training
- image_02
- '0001'
experiment_name: KITTI_my_Recon_Mars_focal_cxy_boxscal_camdebug_noscale-001
gradient_accumulation_steps: 1
load_checkpoint: null
load_config: null
load_dir: null
load_scheduler: true
load_step: null
log_gradients: true
logging: !!python/object:nerfstudio.configs.base_config.LoggingConfig
  local_writer: !!python/object:nerfstudio.configs.base_config.LocalWriterConfig
    _target: !!python/name:nerfstudio.utils.writer.LocalWriter ''
    enable: true
    max_log_size: 10
    stats_to_track: !!python/tuple
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - Train Iter (time)
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - Train Rays / Sec
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - Test PSNR
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - Vis Rays / Sec
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - Test Rays / Sec
    - !!python/object/apply:nerfstudio.utils.writer.EventName
      - ETA (time)
  max_buffer_size: 20
  profiler: basic
  relative_log_dir: !!python/object/apply:pathlib.PosixPath []
  steps_per_log: 10
machine: !!python/object:nerfstudio.configs.base_config.MachineConfig
  device_type: cuda
  dist_url: auto
  machine_rank: 0
  num_devices: 1
  num_machines: 1
  seed: 42
max_num_iterations: 100000
method_name: KITTI_my_Recon_Mars_focal_cxy_boxscal_camdebug_noscale
mixed_precision: false
optimizers:
  background_model:
    optimizer: !!python/object:nerfstudio.engine.optimizers.RAdamOptimizerConfig
      _target: &id001 !!python/name:torch.optim.radam.RAdam ''
      eps: 1.0e-15
      lr: 0.001
      max_norm: null
      weight_decay: 0
    scheduler: !!python/object:nerfstudio.engine.schedulers.ExponentialDecaySchedulerConfig
      _target: &id002 !!python/name:nerfstudio.engine.schedulers.ExponentialDecayScheduler ''
      lr_final: 1.0e-05
      lr_pre_warmup: 1.0e-08
      max_steps: 200000
      ramp: cosine
      warmup_steps: 0
  learnable_global:
    optimizer: !!python/object:nerfstudio.engine.optimizers.RAdamOptimizerConfig
      _target: *id001
      eps: 1.0e-15
      lr: 0.001
      max_norm: null
      weight_decay: 0
    scheduler: !!python/object:nerfstudio.engine.schedulers.ExponentialDecaySchedulerConfig
      _target: *id002
      lr_final: 1.0e-05
      lr_pre_warmup: 1.0e-08
      max_steps: 200000
      ramp: cosine
      warmup_steps: 0
  object_model:
    optimizer: !!python/object:nerfstudio.engine.optimizers.RAdamOptimizerConfig
      _target: *id001
      eps: 1.0e-15
      lr: 0.005
      max_norm: null
      weight_decay: 0
    scheduler: !!python/object:nerfstudio.engine.schedulers.ExponentialDecaySchedulerConfig
      _target: *id002
      lr_final: 1.0e-05
      lr_pre_warmup: 1.0e-08
      max_steps: 200000
      ramp: cosine
      warmup_steps: 0
output_dir: !!python/object/apply:pathlib.PosixPath
- work_dirs
pipeline: !!python/object:mars.mars_pipeline.MarsPipelineConfig
  _target: !!python/name:mars.mars_pipeline.MarsPipeline ''
  datamanager: !!python/object:mars.data.mars_datamanager.MarsDataManagerConfig
    _target: !!python/name:mars.data.mars_datamanager.MarsDataManager ''
    camera_optimizer: null
    camera_res_scale_factor: 1.0
    collate_fn: !!python/name:nerfstudio.data.utils.nerfstudio_collate.nerfstudio_collate ''
    data: *id003
    dataparser: !!python/object:mars.data.mars_kitti_dataparser_phi.MarsKittiDataParserConfig
      _target: !!python/name:mars.data.mars_kitti_dataparser_phi.MarsKittiParser ''
      add_input_rows: -1
      alpha_color: white
      bckg_only: false
      box_scale: 1.0
      car_nerf_state_dict_path: !!python/object/apply:pathlib.PosixPath
      - pretrain
      - car_nerf
      - car_nerf.ckpt
      car_object_latents_path: !!python/object/apply:pathlib.PosixPath
      - pretrain
      - car_nerf
      - latent_codes.pt
      chunk: 32768
      data: !!python/object/apply:pathlib.PosixPath
      - data
      - kitti
      - training
      - image_02
      - '0006'
      dataset_type: kitti
      far_plane: 150.0
      first_frame: 0
      last_frame: 50
      max_input_objects: -1
      near_plane: 0.5
      netchunk: 65536
      novel_view: left
      obj_only: false
      obj_opaque: true
      object_setting: 0
      render_only: false
      scale_factor: 1
      scene_scale: 1.0
      semantic_mask_classes: []
      semantic_path: !!python/object/apply:pathlib.PosixPath []
      split_setting: reconstruction
      use_car_latents: false
      use_depth: false
      use_obj: true
      use_object_properties: true
      use_semantic: false
    eval_image_indices: !!python/tuple
    - 0
    eval_num_images_to_sample_from: -1
    eval_num_rays_per_batch: 8192
    eval_num_times_to_repeat_images: -1
    images_on_gpu: false
    masks_on_gpu: false
    patch_size: 1
    pixel_sampler: !!python/object:nerfstudio.data.pixel_samplers.PixelSamplerConfig
      _target: !!python/name:nerfstudio.data.pixel_samplers.PixelSampler ''
      is_equirectangular: false
      keep_full_image: false
      num_rays_per_batch: 4096
    train_num_images_to_sample_from: -1
    train_num_rays_per_batch: 8192
    train_num_times_to_repeat_images: -1
  model: !!python/object:mars.models.scene_graph.SceneGraphModelConfig
    _target: !!python/name:mars.models.scene_graph.SceneGraphModel ''
    background_color: black
    background_model: !!python/object:mars.models.nerfacto.NerfactoModelConfig
      _target: &id004 !!python/name:mars.models.nerfacto.NerfactoModel ''
      appearance_embed_dim: 32
      background_color: black
      base_res: 16
      collider_params:
        far_plane: 6.0
        near_plane: 2.0
      disable_scene_contraction: false
      distortion_loss_mult: 0.002
      enable_collider: true
      eval_num_rays_per_chunk: 4096
      far_plane: 150.0
      features_per_level: 2
      hidden_dim: 64
      hidden_dim_color: 64
      hidden_dim_transient: 64
      implementation: tcnn
      interlevel_loss_mult: 1.0
      log2_hashmap_size: 19
      loss_coefficients:
        rgb_loss_coarse: 1.0
        rgb_loss_fine: 1.0
      max_res: 2048
      near_plane: 0.05
      num_levels: 16
      num_nerf_samples_per_ray: 97
      num_proposal_iterations: 2
      num_proposal_samples_per_ray: &id005 !!python/tuple
      - 256
      - 128
      obj_feat_dim: 0
      orientation_loss_mult: 0.0001
      pred_normal_loss_mult: 0.001
      predict_normals: false
      prompt: null
      proposal_initial_sampler: piecewise
      proposal_net_args_list:
      - hidden_dim: 16
        log2_hashmap_size: 17
        max_res: 128
        num_levels: 5
        use_linear: false
      - hidden_dim: 16
        log2_hashmap_size: 17
        max_res: 256
        num_levels: 5
        use_linear: false
      proposal_update_every: 5
      proposal_warmup: 5000
      proposal_weights_anneal_max_num_iters: 1000
      proposal_weights_anneal_slope: 10.0
      use_average_appearance_embedding: true
      use_gradient_scaling: false
      use_proposal_weight_anneal: true
      use_same_proposal_network: false
      use_single_jitter: true
    collider_params:
      far_plane: 6.0
      near_plane: 2.0
    debug_object_pose: false
    depth_loss_mult: 0.01
    depth_loss_type: !!python/object/apply:nerfstudio.model_components.losses.DepthLossType
    - 1
    depth_sigma: 0.05
    enable_collider: true
    eval_num_rays_per_chunk: 4096
    far_plane: 1000.0
    interlevel_loss_mult: 1.0
    is_euclidean_depth: false
    latent_size: 256
    loss_coefficients:
      rgb_loss_coarse: 1.0
      rgb_loss_fine: 1.0
    max_num_obj: -1
    mono_depth_loss_mult: 0.0
    near_plane: 0.05
    object_model_template: !!python/object:mars.models.nerfacto.NerfactoModelConfig
      _target: *id004
      appearance_embed_dim: 32
      background_color: black
      base_res: 16
      collider_params:
        far_plane: 6.0
        near_plane: 2.0
      disable_scene_contraction: false
      distortion_loss_mult: 0.002
      enable_collider: true
      eval_num_rays_per_chunk: 4096
      far_plane: 150.0
      features_per_level: 2
      hidden_dim: 64
      hidden_dim_color: 64
      hidden_dim_transient: 64
      implementation: tcnn
      interlevel_loss_mult: 1.0
      log2_hashmap_size: 19
      loss_coefficients:
        rgb_loss_coarse: 1.0
        rgb_loss_fine: 1.0
      max_res: 2048
      near_plane: 0.05
      num_levels: 16
      num_nerf_samples_per_ray: 97
      num_proposal_iterations: 2
      num_proposal_samples_per_ray: *id005
      obj_feat_dim: 0
      orientation_loss_mult: 0.0001
      pred_normal_loss_mult: 0.001
      predict_normals: false
      prompt: null
      proposal_initial_sampler: piecewise
      proposal_net_args_list:
      - hidden_dim: 16
        log2_hashmap_size: 17
        max_res: 128
        num_levels: 5
        use_linear: false
      - hidden_dim: 16
        log2_hashmap_size: 17
        max_res: 256
        num_levels: 5
        use_linear: false
      proposal_update_every: 5
      proposal_warmup: 5000
      proposal_weights_anneal_max_num_iters: 1000
      proposal_weights_anneal_slope: 10.0
      use_average_appearance_embedding: true
      use_gradient_scaling: false
      use_proposal_weight_anneal: true
      use_same_proposal_network: false
      use_single_jitter: true
    object_ray_sample_strategy: remove-bg
    object_representation: class-wise
    object_warmup_steps: 1000
    orientation_loss_mult: 0.0001
    pred_normal_loss_mult: 0.001
    predict_normals: false
    prompt: null
    ray_add_input_rows: -1
    semantic_loss_mult: 1.0
    should_decay_sigma: false
    sigma_decay_rate: 0.9998
    sky_model: !!python/object:mars.models.sky_model.SkyModelConfig
      _target: !!python/name:mars.models.sky_model.SkyModel ''
      collider_params:
        far_plane: 6.0
        near_plane: 2.0
      enable_collider: true
      eval_num_rays_per_chunk: 4096
      hidden_dim: 128
      loss_coefficients:
        rgb_loss_coarse: 1.0
        rgb_loss_fine: 1.0
      num_layers: 5
      prompt: null
    starting_depth_sigma: 4.0
    use_interlevel_loss: true
    use_sky_model: false
project_name: nerfstudio-project
prompt: null
relative_model_dir: !!python/object/apply:pathlib.PosixPath
- nerfstudio_models
save_only_latest_checkpoint: false
steps_per_eval_all_images: 5000
steps_per_eval_batch: 500
steps_per_eval_image: 500
steps_per_save: 10000
timestamp: 2023-12-24_200822
use_grad_scaler: true
viewer: !!python/object:nerfstudio.configs.base_config.ViewerConfig
  camera_frustum_scale: 0.1
  default_composite_depth: true
  image_format: jpeg
  jpeg_quality: 90
  make_share_url: false
  max_num_display_images: 512
  num_rays_per_chunk: 32768
  quit_on_train_completion: false
  relative_log_filename: viewer_log_filename.txt
  websocket_host: <IP_ADDRESS>
  websocket_port: null
  websocket_port_default: 7007
vis: wandb

I convert my data to kitti format, and it is right and unshifted for projecting 3d bbox onto image. So, I am confused for the align in train result. Thank you for your help.

here's a tool for visualizing your camera & obj poses that may be helpful: https://github.com/wuzirui/mars_pose_visualizer/
you wanna make sure your camera & obj coord axes is the same as the KITTI/VKITTI's.
p.s. if you have multiple issues (next time), please raise them separately, so that others can refer :)

@wuzirui Than you a lot. I am trying the check of camera & obj coord axes using the visualizer.  Sorry for asking another problem in the same issue, and I will be careful for it.

hi, all. I visualized the camera & obj pose in the same coord system. It seems a little error in Y axis?

I found the conversion of camrect2cam_i is by matmul the inverse intrinsic  to the translation of camrect2img (i.e. P), and the rotation is unit matrix. But in my dataset, the P is obtained by np.matmul(np.matmul(intrinsic, ego2cam_[:3, :]), np.linalg.inv(mycoord2kitticam)), then I used the same operation as following to get the camrect2cam_i. Is it correct for my data format?
        # Get camera Poses   camare id: 02, 03
        for cam_i in range(2):
            transformation = np.eye(4)
            projection = tracking_calibration["P" + str(cam_i + 2)]  # rectified camera coordinate system -> image
            K_inv = np.linalg.inv(projection[:3, :3])
            R_t = projection[:3, 3]

            t_crect2c = np.matmul(K_inv, R_t)
            # t_crect2c = 1./projection[[0, 1, 2],[0, 1, 2]] * projection[:, 3]
            transformation[:3, 3] = R_t
            tracking_calibration["Tr_camrect2cam0" + str(cam_i + 2)] = transformation

Thank you for your reply.

Bbox pos in your data stands for the center of the 3d bbox, or center of bottom face of 3D bbox? @szhang963

@Nplace-su It is in the center of the 3d bbox, but I have converted it to kitti by
pos = np.matmul(mycoord2kitticam[:3,:3], pos)
pos[1] += h/2

hi, @Nplace-su . I fix a bug in Tr_camrect2cam_i, the objets can be learned, but the backgroud and the objects still can not be separate? what causes it?

hi, all. I visualized the camera & obj pose in the same coord system. It seems a little error in Y axis?

I found the conversion of camrect2cam_i is by matmul the inverse intrinsic to the translation of camrect2img (i.e. P), and the rotation is unit matrix. But in my dataset, the P is obtained by np.matmul(np.matmul(intrinsic, ego2cam_[:3, :]), np.linalg.inv(mycoord2kitticam)), then I used the same operation as following to get the camrect2cam_i. Is it correct for my data format?
        # Get camera Poses   camare id: 02, 03
        for cam_i in range(2):
            transformation = np.eye(4)
            projection = tracking_calibration["P" + str(cam_i + 2)]  # rectified camera coordinate system -> image
            K_inv = np.linalg.inv(projection[:3, :3])
            R_t = projection[:3, 3]

            t_crect2c = np.matmul(K_inv, R_t)
            # t_crect2c = 1./projection[[0, 1, 2],[0, 1, 2]] * projection[:, 3]
            transformation[:3, 3] = R_t
            tracking_calibration["Tr_camrect2cam0" + str(cam_i + 2)] = transformation

Thank you for your reply.

the camera 0 in the first image has the x-axis pointing down(?), which should be pointing to its right, maybe you can check your axes systems?

hi, @Nplace-su . I fix a bug in Tr_camrect2cam_i, the objets can be learned, but the backgroud and the objects still can not be separate? what causes it?

these beam-like artifacts in the background image usually indicate that your scale factor is not good, maybe scaling it down would help

@wuzirui Thank you for your reply. The initial kitti data also has the same coord system (x-axis down).

i mean pointing right (relative) to the image is preferred. if your camera & world system aligns with this, it should be correct~

Thank you, Iwill check it. But it seems the Y-axis and z-axis reversed?

Thank you, Iwill check it. But it seems the Y-axis and z-axis reversed?

from this figure, your camera coordinate does not align with your world coordinate(object coordinate), maybe you need to flip them

OK I will check it. Thank you a lot.

Hi, @wuzirui , I fixed the scale_factor from 1.0 to 0.1, and the background and objects are separated. What is the reason？Could you introduce the principle？ Thanks a lot.

The result exists still some ghost shadow as the figure. Could you provide some solutions to solve it?


Inaccurate roc version on macOS x86

When I install the Roc nightly build, roc version returns roc (built from source) instead of saying "nightly" and/or a timestamp.
jan@mackey ~ % which roc
roc not found
jan@mackey ~ % mv ~/Downloads/roc_nightly-macos_x86_64-2022-09-16-525cde8.tar.gz .local/bin && ( cd .local/bin && tar -xf roc_nightly-macos_x86_64-2022-09-16-525cde8.tar.gz )
jan@mackey ~ % which roc
/Users/jan/.local/bin/roc
jan@mackey ~ % roc version # this will not work because I need to bless this executable in System Preferences
zsh: killed     roc version
jan@mackey ~ % roc version # I just blessed it, so it should work now
roc (built from source)

I've set up PR #4079 for this, I will close this issue after we have confirmed it is fixed with tomorrow's nightly release.

Can you try this again with today's nightly @JanCVanB ?

@Anton-4 Success! Thank you :)
[jan@framey ~]$ roc version
roc: /lib64/libtinfo.so.6: no version information available (required by roc)
roc nightly pre-release, built from commit d926143 on Mi 21 Sep 2022 09:14:30 UTC
[jan@framey ~]$

🤦🏻 Well, that didn't actually confirm the issue was resolved... as that was in Linux. Happily, it is actually fixed in macOS:
jan@mackey ~ % roc version
roc nightly pre-release, built from commit 7697824 on Mon Sep 26 09:16:34 UTC 2022
jan@mackey ~ %


Bot Recording from form page

Looks like a dup of https://github.com/aws-samples/amazon-chime-sdk-recording-demo/issues/90


global encoding...

would there be a way to add a global encoding to the config.js file?
For example:
        language: "en", 	timeFormat: 24, 	units: "imperial",         encoding: "UTF-8", // "ISO-8859-9", "ISO-8859-15" support European Characters             // http://scratchpad.wikia.com/wiki/Character_Encoding_Recommendation_for_Languages
I would think it would solve a lot of the issues with showing the correct characters within MM if this could be done globally somehow. There are quite a few modules that don't include encoding and it seems those are the ones that people have the most issues with and this seems like it would be the best way to solve the issues.

Feel free to send a PR.


Auto-upgrade to <IP_ADDRESS>

Base PullRequest
default branch (https://github.com/http4k/http4k-connect/tree/master)
Command results

Details:

add path
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/build/src/bin

./upgrade_http4k.sh <IP_ADDRESS>

Changed files

Changed file:

gradle.properties

:octocat: Repo | :memo: Issues | :department_store: Marketplace

Codecov Report

Merging #129 (22bb5b0) into master (9dc8823) will increase coverage by 0.08%.
The diff coverage is n/a.

@@             Coverage Diff              @@
##             master     #129      +/-   ##
============================================
+ Coverage     62.83%   62.91%   +0.08%
- Complexity      666      668       +2
============================================
  Files           396      396
  Lines          4251     4247       -4
  Branches        291      293       +2
============================================
+ Hits           2671     2672       +1
+ Misses         1411     1404       -7
- Partials        169      171       +2

Impacted Files
Coverage Δ

.../kotlin/org/http4k/connect/example/action/Split.kt
57.14% <0.00%> (-9.53%)
:arrow_down:

...rg/http4k/connect/amazon/sns/action/CreateTopic.kt
84.00% <0.00%> (-3.50%)
:arrow_down:

...in/kotlin/org/http4k/connect/amazon/sns/HttpSNS.kt
26.66% <0.00%> (-2.75%)
:arrow_down:

...onnect/amazon/secretsmanager/HttpSecretsManager.kt
31.25% <0.00%> (-2.09%)
:arrow_down:

...ect/amazon/cloudfront/action/CreateInvalidation.kt
89.47% <0.00%> (-0.53%)
:arrow_down:

...http4k/connect/google/analytics/action/PageView.kt
100.00% <0.00%> (ø)

...org/http4k/connect/amazon/sts/action/AssumeRole.kt
77.55% <0.00%> (+0.46%)
:arrow_up:

...http4k/connect/amazon/cloudfront/HttpCloudFront.kt
50.00% <0.00%> (+2.17%)
:arrow_up:

...rg/http4k/connect/amazon/s3/action/DeleteBucket.kt
69.23% <0.00%> (+2.56%)
:arrow_up:

...in/kotlin/org/http4k/connect/amazon/kms/HttpKMS.kt
33.33% <0.00%> (+3.92%)
:arrow_up:

... and 2 more

:mega: Codecov can now indicate which changes are the most critical in Pull Requests. Learn more


Add CRD compare Git Action

Signed-off-by: Joshua Packer<EMAIL_ADDRESS>
Description of the change(s):

This action will fail when there is change in the main CRDs and we have NOT updated hypershift-deployment-contorller

Why do we need this PR:

So we can catch changes quicker

Issue reference:

(https://issues.redhat.com/browse/ACM-1354)

Test API/Unit - Success
?       github.com/stolostron/hypershift-deployment-controller/api/v1alpha1     [no test files]
?       github.com/stolostron/hypershift-deployment-controller/pkg      [no test files]
?       github.com/stolostron/hypershift-deployment-controller/pkg/constant     [no test files]
ok      github.com/stolostron/hypershift-deployment-controller/pkg/controllers  2.358s  coverage: 77.0% of statements
ok      github.com/stolostron/hypershift-deployment-controller/pkg/controllers/autoimport       0.025s  coverage: 61.5% of statements
?       github.com/stolostron/hypershift-deployment-controller/pkg/helper       [no test files]

/retest

/lgtm


Pick a name?

I know it's nice having an umbrella but it is a bit weird doing @msafi/ for the npm install. I am unsure how npm handles redirects but you could deprecate it in favor of name packages?
I grabbed placeholders just in case.
https://www.npmjs.com/package/vanilla-text-mask
https://www.npmjs.com/package/angular-text-mask
https://www.npmjs.com/package/react-text-mask
Not sure the naming scheme you wanted. {library}-text-mask or text-mask-{library}

Interesting. I don't know why I didn't think of that 😄
I like the names you suggested. We can go with {library}-text-mask. And we can start looking into how to move them over.

I can transfer ownership, then I think you can run npm deprecate on the existing name, then change it to the new name and trigger a publish? Should be good to go after that, whoever is using it just need to change over to the new name and fix their code.

Also should help with library adoption! Having to type more makes it harder to install ;)

Added you to the packages

Cool, thanks! I also registered a bunch of {name}-text-mask packages for the future.

Awesome, I wasn't sure how to transfer ownership rather than have you as a collaborator. I think maybe if you remove me, then add me back it should transfer it?

I don't think it matters https://docs.npmjs.com/cli/owner

Note that there is only one level of access. Either you can modify a package, or you can't. Future versions may contain more fine-grained access levels, but that is not implemented at this time.

I think we both have the same level of access

I'll get to this task sometime this week or next.


Update recording-server-csharp.md

@dbasantes : Thanks for your contribution! The author(s) have been notified to review your proposed change. @ravithanneeru

@bharat-kalyan-namburi FYI

@GrantMeStrength
Can you review this PR?
IMPORTANT: When this content is ready to merge, you must add #sign-off in a comment or the approval may get overlooked.
#label:"aq-pr-triaged"
@MicrosoftDocs/public-repo-pr-review-team

#sign-off

Invalid command: '#sign-off'. Only the assigned author of one or more file in this PR can sign off. @ravithanneeru

#sign-off

Invalid command: '#sign-off'. Only the assigned author of one or more file in this PR can sign off. @ravithanneeru

This should be assigned to chpalm or team.

@GrantMeStrength , I'm Jorge Garcia,  member of chpalm team and ACS DevEx team. Can we approve this change and merged?
CC. @tophpalmer


investigate Shark cluster

To run large scale automatic registration evaluations. We see two options

shark (lkeb, via Michele or Peter)
cartesius (Stefan could ask for pilot project)

action points:

Ask Peter how to use the Shark cluster to run our registrations; estimate amount of work
Kasper will make an estimate how much computation time we need
Ask Michele how much computation time LKEB has on Shark
Investigate if it is possible and how much work it is to use Fastr on Shark.
Fastr requires:

qsub
sge
slur not yet supported (in progress for cartesius)

From the Shark wiki
FAQ on [https://git.lumc.nl/shark/SHARK/wikis/FAQ#when-stuff-doesnt-work]
The cluster uses a ticketing system for the prioritisation of jobs. Tickets are distributed over the departments by ratio of investment. Any overcapacity is available for incidental processing or for people that have used up their tickets. If you find that your jobs are not scheduled in a timely manner, your department has probably used its share of tickets. If this happens on a structural basis, investment from your department is the solution.
Fortunately this is not a big problem in practice, since there are many workers node available.
The Shark cluster runs with Open Grid Scheduler/Grid Engine (also known as SGE, and the command to submit a job is qsub).
Extra:
We are in the process of adding a GPU server to the Shark cluster as a worker node. This worker node will get a separated queue.
For new project members the introduction course for using Shark is mandatory.

Rough estimate of registration time: 10 toolboxes * 5 different settings * (10+21+30) datasets * 20 minuten = 61000 min = 1016 hour.
Initially, we start with 3 toolboxes.

Update: I did follow the Shark introduction course by Michel, yesterday, and also received a researchlumc account. On Thursday afternoon, Oct 5, Floris and I will have a chat with Peter on jenkins+shark.

From the discussion:

we will have low priority on shark, but jobs of around 20min are considered to be small and are typically scheduled easilly.
since we plan to use sacs's worker node that is connected to shark, we will need to sort out how jobs can be scheduled with our own (superelastix) credentials instead of those of sasc.

A minimal working setup will be evaulated in #101


Old Screenshot not relevant now.

[Enter feedback here]

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

ID: 1e25899c-1ed8-7cfc-2c37-73bb010a87f0
Version Independent ID: 8b366c42-43f8-d5eb-b619-b356e75a9916
Content: Configure apps - Azure App Service
Content Source: articles/app-service/configure-common.md
Service: app-service
GitHub Login: @cephalin
Microsoft Alias: cephalin

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

This has been resolved by a recent PR. #please-close


No longer working :(

Well it was nice while it lasted, but after the latest update the map appears to be broke. Not seeing any pokemon anymore. It'd be great if there was an update, but I'm not holding my breath since it's been so long.

....Mine is still working.

You're lucky @Rayenous. Even after completely removing the app, doing a fresh reinstall and creating a new PTC account and API key I'm still only getting pokestops and gyms to pop up. I'm kinda in the middle of nowhere, so this app was very helpful in my little town.

You might be doing something wrong, maybe the accounts have captcha or are banned. Mine is still working and I recently installed it on another PC and works as well.

Something I noticed recently is that using a default config.ini (or no config.ini) file will allow each account to scan once... subsequent scans will then only show stops (I'm not scanning close enough to gyms to know those are being scanned)
In my current config.ini, I have set the scan speed to 44 seconds... which seems high, buy I am scanning a relatively small area with a relatively large number of accounts.  -  Whole area is still scanned fast.
TL;DR:  Try editing the scan speed in your config.ini
Info on this can be found here: https://github.com/mchristopher/PokemonGo-DesktopMap/wiki/Config.INI

Rayenous, are you using the 0.53/0.55 hash keys while running this? my understanding (though i haven't kept up on this project since it hasn't been updated in so long, and i had the same problem as described in here when i last tried it months ago) was that it has no captcha integration, so you have no way of knowing if your accounts have been captcha'd aside from checking the logs, no way of filling out the captchas, and no way of important the new api key so you don't get captchas/bans in the first place. have any of those issues been addressed somehow?

@Rayenous What's a good scan speed? I've only got 2 accounts right now

Has anyone figured out how to make it work again?