db uploading attempted error !!

mysql> source /root/dbDump.sql;
Query OK, 0 rows affected (0.01 sec)
Query OK, 1 row affected (0.00 sec)
Query OK, 0 rows affected (0.01 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.02 sec)
Records: 0  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.01 sec)
Records: 0  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.01 sec)
Records: 0  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.01 sec)
Records: 0  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 3 rows affected (0.01 sec)
Records: 3  Duplicates: 0  Warnings: 0
ERROR 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '(id)
) ENGINE=MyISAM  DEFAULT CHARSET=utf8 AUTO_INCREMENT=1' at line 14
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.01 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 4 rows affected (0.01 sec)
Records: 4  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.01 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 2 rows affected (0.00 sec)
Records: 2  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.00 sec)
Query OK, 1 row affected, 1 warning (0.01 sec)
Query OK, 0 rows affected (0.00 sec)
Query OK, 0 rows affected (0.01 sec)
Query OK, 28 rows affected (0.00 sec)
Records: 28  Duplicates: 0  Warnings: 0
Query OK, 0 rows affected (0.01 sec)
Query OK, 0 rows affected (0.00 sec)
ERROR 1075 (42000): Incorrect table definition; there can be only one auto column and it must be defined as a key

@lenesys This is not a problem, will not hinder work.


Need to apply pending rewards

https://github.com/hubbleprotocol/hubble-common/blob/master/packages/hubble-sdk/src/Hubble.ts#L189
this needs to apply pending rewards the same way we do it in webapp

Fixed by #19


High score feature has been added

-Highest score is shown on the main page
-If player's score is within the top 10 scores, user is being presented with a menu to enter his/her name.
After entering his/her name, user will be added to the appropriate rank in the high score table
-Clicking on S will open the top 10 players.
-Scores are stored in a file named as scoreList.txt so that rankings do not disappear after the game is finished

https://youtu.be/ehegChXiizw


[#47] Fix that makes the manager work for write db correctly

works with using
works with routers configured for read/write scenario

Hey, thanks for the PR.
Can you please fix test errors. Thanks.

@aykut: As I see test case errors were version dependent issues. A bit caught up with something right now. Would fix it in some time.

Thanks a lot.


Delete gradle-support branch

Now main project runs on gradle may as well just delete old branch.

Hi @domdomegg ,
I've invited you to be a collaborator on this repo, if you accept you should be able to assign labels to issues, merge your own PRs, and other fun stuff. :) Please feel free to do so.

Yay ty


local file cache of s3 assets

hey @JohnStarich
I was exploring how i can have a file cache.
the idea if that the clients ( web or native) can make requests to S3, but the results are cached, such that future requests use the cache, or on a cache miss use S3. This make writing code on top of hackpadFS much easier if your using it with s3.
https://github.com/NinesStack/filecache seems like a good fit for this.
If you have any thoughts on this before i start on this please feel free to let me know what you think...

Hey @gedw99! Hope you're well.
I think you may be interested in a combination of the s3.FS example and the cache.ReadOnlyFS.
It should be relatively easy to combine the two for a read-only cache. I'd be interested to hear how it goes!

thanks for the tip ! Am in the middle of something else but will come back to this.


Computer ops do not prevent user from selecting out == in

When running a computer op, napari-imagej does not issue an error message when specifying the same layer for both input and output. But some computer ops fail when attempting to use the same image for input and output. Example steps to reproduce:

Launch napari-imagej via Plugins → ImageJ2 (napari-imagej)
Open an image via File → Open File(s)... – I used NESb_C2_TP1.tiff from NESb.zip of the SCIFIO sample images
Type gauss into the napari-imagej search bar
Expand the Ops (13) tree branch
Double click filter.gauss(img "out", img "in", double[] "sigmas") -> (img "out")
Note that the "out" and "in" point to the same layer

It seems like that would be pretty tricky to implement in practice.
Right now, napari-imagej delegates completely to magicgui to make the layer selector boxes. As such, any solution would require overriding the layer ComboBoxes.
I think that if we did this we'd need to add an I/O indicator field to each selector. Then, for each layer, we'd have to pass the Layer choices function to each ComboBox that is created by a module. More specifically, it should probably be a Callable object that knows whether it is an input or output ComboBox, along with a callback mechanism that other ComboBoxes can use to identify the Layers that are off-limits.
While I bet it's possible, it certainly hurts my brain to think about. Do you envision an easier way to do this, @ctrueden?

Yeah, we can just do post-dismissal validation, similar to how we handle the settings dialog. If it's a computer op and the in == out, display an error dialog and do not proceed with the execution.

Yeah, we can just do post-dismissal validation, similar to how we handle the settings dialog. If it's a computer op and the in == out, display an error dialog and do not proceed with the execution.

You know @ctrueden, since this is a problem outside of napari-imagej, maybe this validation should be added to the OpListingModule upstream? Then it would be fixed everywhere...


Enable build with Java 15

There are currently some problems building Metrics 4.x with Java 15:
Error:  Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.8.1:testCompile (default-testCompile) on project metrics-jvm: Compilation failure
Error:  /home/runner/work/metrics/metrics/metrics-jvm/src/test/java/com/codahale/metrics/jvm/FileDescriptorRatioGaugeTest.java:[16,78] <anonymous com.codahale.metrics.jvm.FileDescriptorRatioGaugeTest$1> is not abstract and does not override abstract method getCpuLoad() in com.sun.management.OperatingSystemMXBean

See #1729, 10110e7daf5bd6315e012d6963c66fb1581322f8 and 4527d40573077b3ab5cfea907cb7bc4317a57d56.

Fixed by #1738

Fixed by #1738

Closed by #1738

Closed by #1738


config: linux: update description of PidsLimit

Fix a misleading comment for how PidsLimit works when given a limit of
0. A limit of 0 is identical to a limit of 1, and so there is no reason
to distinguish the two. Since the default value for integers in Go is 0,
it makes more sense to make the default value mean that there is no
limit.
Signed-off-by: Aleksa Sarai<EMAIL_ADDRESS>(github: cyphar)

/cc @crosbymichael @LK4D4 @vishnuk @mrunalp @vbatts @dqminh @philips @tianon

preciously LGTM

LGTM


[Plus][WEBJS] Add a custom capture when sending files (/sendFile)

the  caption can not be changed while attaching Files for uploading
{
"session": "default",
"chatId"<EMAIL_ADDRESS>"file": {
"mimetype": "image/jpeg",
"filename": "filename.jpeg",
"data": "........"
},
"caption": "string"
}
the Caption on whatsapp changes to "filename.jpeg" instead and dosen't consider the "caption": "string" key value
same is the case when using url method the caption is replaced by the File name present in the url.
this is happning in the plus Version.

Hi! It's a pretty new feature for WhatsApp, waiting for the engine to support it https://github.com/pedroslopez/whatsapp-web.js/issues/1849

does this work in other Engines Venom and all ?

@lightningfuzion2 published a new plus version today - it does work in NOWEB engine!

Filename issue is fixed, not filename should be overwritten by filename field!

Hi!
caption has been fixed in WAHA Plus 2024.3.9
Now both caption and filename should work with no issues in both WEBJS and NOWEB engine.


Add Leetcode Wildcard Matching solution in cpp

Issue: Add Leetcode 44. Wildcard Matching solution in C++
Description
Summary:
Add a C++ solution for LeetCode problem 44. Wildcard Matching.
Details:
LeetCode problem 44, "Wildcard Matching," requires implementing a function to determine if a given string (s) can be matched against a pattern string (p) where '?' matches any single character, and '*' matches zero or more characters.
Requirements:

[ ] Provide a detailed solution with comments explaining the logic.
[ ] Ensure the solution passes all test cases on LeetCode.
[ ] Include sample test cases to validate the solution.

Hacktoberfest: Please label this issue as hacktoberfest and assign it to me.

@ayushi-ras  Please assign this issue to me


Error when trying to set persistent breakpoints

Firstly, thank you for making this plugin. I'd like to quickly report an issue here.
When trying to set a breakpoint, I get the following error,

E5107: Error loading lua [string ":lua"]:1: '=' expected near ''

The following is my configuration
local opts = { noremap = true, silent = true }
local keymap = vim.api.nvim_set_keymap
keymap("n", "<F9>", "<cmd>lua require('persistent-breakpoints.api').toggle_breakpoint<cr>", opts)

I get similar errors when trying to use the command-version :PBToggleBreakpoint

E5108: Error executing lua [string ":lua"]:1: attempt to call field 'toggle_break' (a nil value)
stack traceback:
[string ":lua"]:1: in main chunk

Thank you very much for you feedback!

About the kepmap: a pair of brackets should be added to the end of toggle_breakpoint. so it should be keymap("n", "<F9>", "<cmd>lua require('persistent-breakpoints.api').toggle_breakpoint()<cr>", opts).
About the :PBToggleBreakpoint: This bug have been fixed. Now it should work properly.

Details about the change can be found here.

Thank you. This works!


Bugfix 4566

🚀 Pull Request
Description
The standard name of m01s00i023 has been corrected to surface_snow_amount.

Consult Iris pull request check list

We also just need to consult a third-party STASH expert on this change with to ensure it aligns with expectation

@bjlittle  I think I've managed to rebase it. First time working with a forked repo!

We also just need to consult a third-party STASH expert on this change with to ensure it aligns with expectation

@bjlittle have you approached anyone yet. @rcomer suggests Martin Best at the UK Met Office might be a good shout.

I have now reached out for a third-party opinion 🤞

@trexfeathers I think I've got the change to latest.rst done, had a little issue with the updating so it looks like I did it twice!

I've just noticed that we also have this LBFC mapping:
https://github.com/SciTools/iris/blob/68a39a5f5f1edac43e16d35e97a29f350972ca0c/lib/iris/fileformats/um_cf_map.py#L1210
LBFC 93 does indeed correspond to m01s00i023, so should the above mapping also be changed to reference surface_snow_amount?

I would say yes, if possible.

I would say yes, if possible.

OK, two sounds like consensus!
The change would literally be:
- CFName('snowfall_amount', None, 'kg m-2'): 93,
+ CFName('surface_snow_amount', None, 'kg m-2'): 93,

If you could do the honours?

Voila, 8f42c71 sorted

OK, we're in! Thanks for your patience.


chore(main): release uniform 0.2.3

:robot: I have created a release beep boop
0.2.3 (2024-04-29)
Bug Fixes

pixels,uniform: move react to dev deps (ec8e8d4)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/fuf-stack/uniform/releases/tag/uniform-v0.2.3 :sunflower:


[PUI] Notifications

Add actions for notifications tables in PUI:

Mark all as read
Delete read notifications

Ver nice @SchrodingersGat!


Biography section - Image to align on the left

Into "Biography" section, the image is needed to align on the left side, with the text written on the right and at the bottom of the image.

done

did it


hi, if i want to support the giscus

How should I change it or other ways?

This is built into Hugo, its nothing to do with this theme really - follow the instructions in the official guide: https://gohugo.io/content-management/comments/
I suspect you'll be fine adding the template line in the same place in the theme as where gitalk is (/themes/github-style/layouts/_default/single.html).


Open spec swagger

Information

Type
Breaking change

Feature
No

Description

Add OpenSpec interfaces supports (OS3/OS2).
Add @tsed/openspec package
Update @tsed/schema package with the OpenSpec definition

Usage example
Example to use your feature and to improve the documentation after merging your PR:
import {OpenSpec3, OpenSpec2} from "@tsed/openspec";

Todos

[x] Tests
[x] Coverage
[x] Example
[x] Documentation

:tada: This PR is included in version 6.0.0-beta.14 :tada:
The release is available on:

v6.0.0-beta.14
GitHub release

Your semantic-release bot :package::rocket:

:tada: This PR is included in version 6.0.0-rc.1 :tada:
The release is available on:

v6.0.0-rc.1
GitHub release

Your semantic-release bot :package::rocket:


Add project initial documentation

This PR adds project goal documentation. Please check them and consider update the project root if it looks good.

Thanks for your contribution. Looks good


Update meta.yaml

Checklist

[ ] Used a personal fork of the feedstock to propose changes
[ ] Bumped the build number (if the version is unchanged)
[ ] Reset the build number to 0 (if the version changed)
[ ] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[ ] Ensured the license file is being packaged.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

import: 'ipywidgets_bokeh'
Traceback (most recent call last):
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/test_tmp/run_test.py", line 2, in <module>
    import ipywidgets_bokeh
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.11/site-packages/ipywidgets_bokeh/__init__.py", line 1, in <module>
    from .widget import IPyWidget
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.11/site-packages/ipywidgets_bokeh/widget.py", line 13, in <module>
    from .kernel import kernel
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.11/site-packages/ipywidgets_bokeh/kernel.py", line 142, in <module>
    kernel = BokehKernel.instance()
             ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.11/site-packages/traitlets/config/configurable.py", line 551, in instance
    inst = cls(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/conda/feedstock_root/build_artifacts/ipywidgets-bokeh_1669141855638/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.11/site-packages/ipywidgets_bokeh/kernel.py", line 128, in __init__
    self.comm_manager = CommManager(parent=self, kernel=self)


Pre-populated database not imported properly

Expected Behavior
I expect the database to be moved and have the table in it
Current Behavior
I move the database and try to SELECT something from the table, but it returns that the table does not exist.
Possible Solution
I suspect the moving of the database has not worked. SQLite rather creates a completly new database on SQLite.openDatabase({ name: 'foo', location: 'default' }..., and of course, that has no table: bar yet.
Steps to Reproduce (for bugs)
I have a pre-populated database named foo.db located in www/sqlite/. I move this database to the internal storage like this
try {
    await SQLite.openDatabase({ name: foo, createFromLocation: '~/sqlite/foo.db' });
    console.log('Database successfully moved.');
} catch (error) {
    console.log('Error on moving database. Error: ', error);
}

This logs me Database successfully moved. so I suspect it worked.
Then I try to open the database like this.
SQLite.openDatabase({ name: 'foo', location: 'default' },
    db => {
        console.log('Successfully opened Database:', db);
        db.transaction(tx => {
            tx.executeSql('SELECT * FROM bar', [], (_, { rows }) =>
                console.log(JSON.stringify(rows))
            )
        });
    },
    error => {
        console.log('Error on opening database locations.db: ', error);
    });

This logs me Successfully opened Database with this
{
    "dbname": "locations",
    "openError": [Function anonymous],
    "openSuccess": [Function anonymous],
    "openargs": {
        "dblocation": "nosync",
        "location": "default",
        "name": "foo"
        }
}

That looks like a Database Object to me.
But then returns me on tx.executeSql('SELECT * FROM bar'... this

Possible Unhandled Promise Rejection (id: 0):

Object {
  "code": 5,
  "message": "no such table: bar",
}

Your Environment

React Native SQLite Storage Version used: 5.0.0
React Native version used: 0.63.4
Operating System and version (simulator or device): iPhone 11 Pro Max 14.4

@Stophface, did you make any progress with this?

The same issue...
I have the same problem and have been looking for a solution for about a week. I'm downloading a database from the web service, for example, let's say hello.db. After moving it to the databases folder on the android side very comfortably, I took care of all my problems with the explanations in the documentation, it is superb, but I faced a complete disaster on the ios side. There is neither a databases folder that needs to be moved nor the opendatabase command can not read the database file I have. object {code 5, message: no such table table_name}

When you want pre-populate database then every time you have to create a new database file and delete previous database or re build application.
check out below documentation
https://infinitbility.com/react-native-sqlite-storage-examples-of-query

This is working for me.
First time it didn't work.. but after delete the app and rebuilding. It works (I'm on IOS simulator)
  console.log("opening db");
  const db = await SQLite.openDatabase({ name: "hadits.db", location: "default" });
  console.log("db opened");
  const result = await db.executeSql("SELECT * FROM hadits ORDER BY 'position' ASC");
  console.log("table loaded");

For me, there was a little bit of subtlety with this getting this to work on iOS as I kept experiencing the infamous "no such table found" error. I assumed that all I had to do was create a www directory under my project through the file system and it would all work. I created the directory and placed my sqlite db there and verified it in the project viewer in Xcode. However, it did not copy my database into /Users..../Library/Developer/CoreSimulator/Devices/MyApp.app/HASH-ID/Library/LocalDatabase/my-sqlite-db.db
I only discovered the source of this issue when I used Xcode to step through the source code of
react-native-sqlite-storage/platforms/ios/SQLite.m
around line #159 which starts with
RCT_EXPORT_METHOD(open: (NSDictionary *) options success:(RCTResponseSenderBlock)success error:(RCTResponseSenderBlock)error)
"Step 3 - Add file to project
in XCode, right click on the main folder and select Add Files to 'your project name"
in https://github.com/andpor/react-native-sqlite-storage is very important. Use Xcode to add the folder.
Good luck.
Osupa.

Following SnehalAgrawal comment in this Issue solved my Problem on iOS and Android https://github.com/andpor/react-native-sqlite-storage/issues/164


Responses Object keys must be strings?

Responses Object spec says that:  This field MUST be enclosed in quotation marks (for example, “200”) for compatibility between JSON and YAML (https://github.com/OAI/OpenAPI-Specification/blob/157a4c81ae537ef793b2bee368bc00d88b461de8/versions/3.0.2.md#patterned-fields-1)
Is it really needed?
Json only allows strings as keys and at least in PHP numeric keys are auto-encoded to strings.
Yaml allows both, but in PHP numeric keys are always type-casted to an int, like 200, instead of "200"

proof of PHP array typecasting - https://3v4l.org/F8slo
my tests showing how keys are encoded into json and yaml - https://github.com/orisai/openapi/blob/314b0ce647bd5b5878eced14cfe67b82915d1f12/tests/Unit/Spec/ResponsesTest.php#L109-L139 (current build failures are related to caching issues, code is correct)

Because it is probably not possible to create workaround for this in PHP, we could solve it only by adding a flag to yaml encoder to make integer keys strings (in case array is not auto-indexed from 0)
But the preferred way would be to remove string key requirement from specification.

This was previously discussed in https://github.com/OAI/OpenAPI-Specification/pull/2147#issuecomment-588302204 and https://github.com/OAI/OpenAPI-Specification/issues/2171 and the decision was to keep string keys.

There is already quite a lot of validations dependent on multiple properties or even multiple objects, would it be a big problem to add one more in next major? So far I found no PHP library that prints codes as strings, because it's very hard to do...

Maybe spec could encourage use of json for cross compatiblity instead of yaml? json works well in practically every language, unlike way more complex yaml.

Maybe spec could encourage use of json for cross compatiblity instead of yaml? json works well in practically every language, unlike way more complex yaml.

@mabar the OpenAPI specification is defined as a JSON object that can be encoded as either JSON or YAML, so this is pretty much already the case.  This is, in fact, why the keys in YAML need to be a string as the issues linked by @hkosova note.
Any time you are maintaining JSON compatibility in YAML (not just for OpenAPI), you have to convert non-string keys to strings before serializing to YAML.

Well, some work is being done on Symfony side to solve this problem in PHP.
I hope assumption that all numeric keys could be encoded as strings in yaml, except in arrays that are auto-indexed from zero, will work.
Otherwise we would have to write a whole new yaml encoder with AST support for PHP, to be able explicitly tell, which keys should be strings.

This has been inactive for a while and I don't think it can be improved by changes in OpenAPI itself, so I'm closing this issue. Thanks for the great discussion, some really useful information in this thread.


RTMP unsupported video codec: 2 (MP3)

Which version are you using?
v0.20.0
Which operating system are you using?

[x] Linux amd64 standard
[ ] Linux amd64 Docker
[ ] Linux arm64 standard
[ ] Linux arm64 Docker
[ ] Linux arm7 standard
[ ] Linux arm7 Docker
[ ] Linux arm6 standard
[ ] Linux arm6 Docker
[ ] Windows amd64 standard
[ ] Windows amd64 Docker (WSL backend)
[ ] macOS amd64 standard
[ ] macOS amd64 Docker
[ ] Other (please describe)

Describe the issue
RTMP Published stream from DJI device results in closed session. See log excerpt below (ips obfuscated):
Aug 18 21:54:49 HOSTNAME rtsp-simple-server[114]: 2022/08/18 21:54:49 INF [RTMP] [conn <IP_ADDRESS>:10751] closed (unsupported video codec: 2)
ffmpeg version 4.2.7-0ubuntu0.1
Installed all media codecs with sudo apt install ubuntu-restricted-extras
Describe how to replicate the issue

start the server
publish with dji go application (RTMP)
read with VLC (obviously shows no alias/stream available)

Did you attach the server logs?

no
Did you attach a network dump?

no

Hello, please provide a network dump, that can be generated in this way:

Download wireshark (https://www.wireshark.org/)
Start capturing on the interface used for exchanging RTSP (if the server and the target software are both installed on your pc, the interface is probably "loopback", otherwise it's the one of your network card)
Start the server and replicate the issue
Stop capturing, save the result in .pcap format
Attach

Thanks

The server now supports publishing reading and proxying MP3 tracks with RTMP.

added in v0.23.0


Package conflicts found when creating the new environment on stellar

I was trying to install the updated postgkyl on stellar. I started with a clean state (i.e. only anaconda is loaded, no paths or modules loaded in bashrc, no old conda envs), but package conflicts are found in the step of creating the new environment with the command below:
conda env create -f environment.yml
The error message is attached in the file.
error_message.txt

we removed some stuff that was in the $HOME directory, including a .condarc file, which might've been causing this. The installation worked then.
There are still warnings and errors that show up, but pgkyl plots still.

@manauref what kind of warnings?

Like this
Successfully built postgkyl
Installing collected packages: sympy, numpy, ndindex, click, scipy, matplotlib, blosc2, tables, postgkyl
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
gensim 4.3.0 requires FuzzyTM>=0.4.0, which is not installed.
numba 0.57.1 requires numpy<1.25,>=1.21, but you have numpy 1.26.4 which is incompatible.
python-lsp-black 1.2.1 requires black>=22.3.0, but you have black 0.0 which is incompatible.
Successfully installed blosc2-2.6.2 click-8.1.7 matplotlib-3.9.0 ndindex-1.8 numpy-1.26.4 postgkyl-1.7.1.dev87+gf876908 scipy-1.13.1 sympy-1.12.1 tables-3.9.2

but like I said, plotting works despite it


L2 announcements stop working after a time

Is there an existing issue for this?

[X] I have searched the existing issues

What happened?
I created LoadBalancer service with the new L2 announcement feature and could access to it from my local network.
After a some time this service stopped working.
Cilium agent doesn't answer ARP requests for the IP of the service.
12:19:04.985743 eth0  B   ARP, Request who-has <IP_ADDRESS> tell <IP_ADDRESS>, length 46

Cilium Version
Client: 1.14.0-rc.0 08239f59 2023-06-28T15:18:32-07:00 go version go1.20.5 linux/amd64
Daemon: 1.14.0-rc.0 08239f59 2023-06-28T15:18:32-07:00 go version go1.20.5 linux/amd64
Kernel Version
Linux talos-xa1-p9c 6.1.35-talos #1 SMP PREEMPT_DYNAMIC Wed Jun 28 13:58:51 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
Kubernetes Version
1.27.3
Sysdump
No response
Relevant log output
No response
Anything else?
No response
Code of Conduct

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

Could you provide us a sysdump? Otherwise, it's hard to know how we can reproduce this issue.

cc: @dylandreimerink

That sounds quite serious. I am not aware of any known issue that would cause this. Very keen on a sysdump :pray:

@dylandreimerink @YutaroHayakawa
cilium-sysdump-20230704-200609.zip

@dylandreimerink @YutaroHayakawa can I send it privately?

I believe you uploaded it in a now deleted message, but I am still able to access it via the mail notification it sent out initially, so I hope it did not contain very sensitive info. :fearful:
I did take a look. At the time of capturing the sysdump there were at least some IPs we are responding to:
{
  "l2-announce-entries": [
    {"IP":"<IP_ADDRESS>","NetworkInterface":"eth0","Origins":[{"Name":"{redacted}","Namespace":"media"}],"Deleted":false,"Revision":46},
    {"IP":"<IP_ADDRESS>","NetworkInterface":"eth0","Origins":[{"Name":"{redacted}","Namespace":"media"}],"Deleted":false,"Revision":41},
    {"IP":"<IP_ADDRESS>","NetworkInterface":"eth0","Origins":[{"Name":"{redacted}","Namespace":"{redacted}"}],"Deleted":false,"Revision":40},
    {"IP":"<IP_ADDRESS>","NetworkInterface":"eth0","Origins":[{"Name":"{redacted}","Namespace":"media"}],"Deleted":false,"Revision":32}
  ]
}

The revision numbers are very high, indicating a lot of churn in the list.
The agent log is full of rate limit messages:
2023-07-04T14:59:51.959400310Z level=info msg="Waited for 1.395439596s due to client-side throttling, not priority and fairness, request: GET:https://<IP_ADDRESS>:6443/apis/coordination.k8s.io/v1/namespaces/kube-system/leases/cilium-l2announce-kube-system-hubble-peer" subsys=klog
2023-07-04T15:00:12.159409007Z level=info msg="Waited for 1.398748976s due to client-side throttling, not priority and fairness, request: PUT:https://<IP_ADDRESS>:6443/apis/coordination.k8s.io/v1/namespaces/kube-system/leases/cilium-l2announce-default-kubernetes" subsys=klog
2023-07-04T15:00:22.160304629Z level=info msg="Waited for 1.191052918s due to client-side throttling, not priority and fairness, request: GET:https://<IP_ADDRESS>:6443/apis/coordination.k8s.io/v1/namespaces/kube-system/leases/cilium-l2announce-kube-system-hubble-ui" subsys=klog
2023-07-04T15:00:42.360058569Z level=info msg="Waited for 1.399530216s due to client-side throttling, not priority and fairness, request: PUT:https://<IP_ADDRESS>:6443/apis/coordination.k8s.io/v1/namespaces/kube-system/leases/cilium-l2announce-kube-system-kube-dns" subsys=klog

So what is likely happening is that the increased API traffic for the leases is triggering the default QPS limit of 5 requests a second. The throttling causes the agent to not update the lease quick enough, so it stops announcing, assuming another node is taking over. The default burst limit is 10 QPS which could explain why its initially not an issue.
I did not account for the client rate limit during design and development of the feature :disappointed:.
For now I see two possible workarounds:

Increase the lease duration and renew deadline to decrease the amount of API calls needed to keep the lease. This can be done via the l2announcements.leaseDuration and l2announcements.leaseRenewDeadline helm values.
Increase the client rate limit. There currently is no Helm value for this, but it should be doable by setting the k8s-client-qps option in the ConfigMap and restarting the agent.

We unfortunately do not have time to come up with a permanent solution before the v1.14 release. So what I have done is open a PR to add helm values for the client rate limit so they can be configured via helm. The PR also adds warnings to the docs about setting the rate limits and a section on picking good values.
Since this is all we will be able to do before v1.14, I am removing the release-blocker label (the PR has one). But I would like this keep this issue open to track progress on the more permanent fix.

Adding to Dylan's https://github.com/cilium/cilium/issues/26586#issuecomment-1620641960 , increasing l2announcements.leaseRetryPeriod will help in your case. Increasing only leaseDuration and leaseRenewDeadline might not be sufficient -  https://pkg.go.dev/k8s.io/client-go/tools/leaderelection package does not describe different parameters well, but most significant in terms of the number of calls issued is actually RetryPeriod rather than leaseDuration/leaseRenewDeadline

I am seeing this type of event behavior with 1.14.0 L2 load balancer..  Have the following settings:
l2announcements.leaseDuration=300s
l2announcements.leaseRenewDeadline=60s
l2announcements.leaseRetryPeriod=10s
two l2 announcement leases at this time.  both are showing the same.  a arp/mac address is learned on the local segment for awhile but then times out and is not re-learned.

same problem. not work after a while.

So far, we did not experience missing leases after having configure high values for base qps/burst qps and leaseduration/renew.
however, the "Waited for 1s due to client-side throttling, not priority and fairness" log line is regularly found in one of nodes cilium agent. A few days ago the log volume grew a lot (15/20 per hour), which is very surprising as the number of services is low (35 on this node) and qps high (150; burst: 500).
lease config:  leaseRetryPeriod: 3s / leaseDuration: 30s / leaseRenewDeadline: 10s
This is confusing

Same question, not work after a while.
Is it possible not to create lease for not LB services ? There are too many leases.

This is tracked here: https://github.com/cilium/cilium/issues/28752 and a patch has been submitted

I'm still seeing this. There is now a job running on the cluster that does an arping every few seconds and if it goes down, we restart a random cilium pod. The leases simply expire and never get recreated.

I still have this issue even though I've limited the announcement to only 2 LB services using the namespace filter and increased the qps significantly beyond the default.

I also am seeing this. I've been fixing it temporarily by deleting the impacted Kubernetes LoadBalancer service but that is becoming annoying for homelab use. Are people not experiencing this in prod?

Are people not experiencing this in prod?
L2announce is experimental so I would expect almost no one uses this in prod. Most probably just use bgp in prod which is not experimental. I switched to bgp at home due to this issue as well.

Same here, after about 30-40 mins after the cilium pod starts (or after I create a new service), it stops responding to ARP. If I restart cilium pod, it operates for some time and then stops again.

Also experiencing this behavior, Cilium 1.14.5

If you are able to try 1.15.0-pre.3 version, it contains this fix that limits amount of leases created (so also decreases qps used): https://github.com/cilium/cilium/pull/29446
Except for increasing qps/burst I would recommend increasing l2announcements.leaseRetryPeriod per this https://github.com/cilium/cilium/issues/26586#issuecomment-1628615612

Do other pods watch these leases even though they aren't the "master"?

Unfortunately, that's not how leases work :cry: If they were just watching leases everything would be fine. Nodes that are not leaders are performing GET operation on lease every leaseRetryPeriod, which burns qps limit quite significantly on all nodes and this is the main root cause of the issues.

Put QPS/BURST to 50/100 and it started working for me, thanks.

I've noticed this requires near weekly tuning. No matter what numbers I set, eventually, a lease would be lost. Usually, within a week or two. I've had to switch back to metallb.

Same for me, randomly having to restart Cilium pods to get it working again, even with the 50/100 setting. This is with 1.15rc1 release.

I think it its fairly clear that this problem is quite consistently effecting a number of users. Keeping an open mind the causes can be:

Client rate limiting delaying lease related updates to the points leases are lost (my primary suspect)
A bug in the k8s.io/client-go/tools/leaderelection library
A bug in L2 announcements

It would be great if users with this issue could check the agent logs to see if the Waited for ...s due to client-side throttling, not priority and fairness messages appear when this happens. Or even better record and share a sysdump which should include that info as well.
With regards to this happening even after changing the QPS settings, I believe this requires very specific tuning. The rate is shared with all other agent features on that instance, so not only do the lease settings and the amount of leases play a role, any uptick in other API traffic might also variably take up part of the limit. Granted this is speculation, but I suspect situations like this can explain the delay in the failure.
In any case, the current situation isn't workable. But a fix doesn't seem straightforward, so I would like to get some discussion/feedback going around these possible solutions:

Change some of the k8s client plumbing internally to remove the rate limit for the lease API calls specifically. The downside to this is that we might just shift the problem to the API server and overload it in some way (what the rate limit attempts to avoid).
Ditch the k8s leases and do leader election via the KVStore instread. The downside being that this makes the Cilium deployment more complicated, which isn't desirable for the smaller setups this feature was intended for.
Ditch the k8s leases and do leader election via some sort of agent-to-agent raft protocol. The downside being that this would take the longest to develop, would be major maintenance burden due to it being only used for this feature, and require additional ports to be opened in our firewall scheme.

No fix that I can come up with has zero downsides, I am open for ideas on alternative fixes and feedback on which direction sounds least bad.

It's not due to throttling, but due to just randomly losing the lease and nothing anywhere ever checking for the lease to exist. There's no self-healing; it assumes that if it is the leader, it will never fail to not be the leader.
At least that was my case; there was nothing in the logs to hint at what was going on. Maybe someone else has fresher logs that shows something different 🤞

@withinboredom I think you are completely right :facepalm:. The RunOrDie method exits when the context is canceled but also when it somehow loses its lease while the agent is still running. I have no clue how that made it so far without notice. I think https://github.com/cilium/cilium/pull/30340 should be the fix.

I appreciate the team effort that went into resolving this ticket. Thank you to @withinboredom for providing the illuminating fact that enabled @dylandreimerink to find and fix #30340 .
I know this ticket had been opened for many months with lots of different ideas and workarounds proposed and used. It's the consistent review of the ticket and interest in the ticket that I really appreciate. It would have been too easy to get frustrated and upset leading to a lack of cooperation so I am thankful for the wonderful contributors to this product.

So I did some additional testing that's relevant to my problem.

I'm using L2announce for a single IP via ARP
I have NGINX Ingress and librespeed pods as my test services, the LB IP is used by the Service for NGINX Ingress
I am using Cilium v1.15.1 which was installed on my behalf by RKE2 and I am using Rancher to help create/manage the k8s cluster
I timed each scenario and had the librespeed page up being refreshed every 5 minutes

First scenario:

NGINX Ingress and librespeed pods are on the same k8s node as the node holding the ARP IP
The ARP IP did notg change nodes at all so far as I can tell.
The librespeed page did not break even after 2h 15min, so I stopped and went to the Second scenario

Second scenario:

NGINX Ingress and librespeed pods are on a DIFFERENT k8s node from the node holding the ARP IP
The ARP IP did notg change nodes at all so far as I can tell.
The librespeed page broke in under 5 minutes (I did not measure precisely as I was running this test passively while doing other things.)

So I have a hunch this is related to whatever handles the traffic with kube-proxy disabled (which I have as true).

For the second scenario, can you try setting
loadBalancer:
  bpfSocketLBHostnsOnly: true
socketLB:
  hostNamespaceOnly: true

I believe restricting the LB to the host namespace fixed this issue for me (also rke2 with ingress-nginx).

For the second scenario, can you try setting
loadBalancer:
  bpfSocketLBHostnsOnly: true
socketLB:
  hostNamespaceOnly: true

I believe restricting the LB to the host namespace fixed this issue for me (also rke2 with ingress-nginx).

Oh I tried the hostNamespaceOnly thing, but I may have missed the bpfSocketLBHostnsOnly thing... I'll retry with those, thanks! :D Yay! Oh I'm excited this might work wheee :DDD let's find out...

Okay so turned-on the test nodes (turned the cluster off when not testing). Cilium initially already was broken on initial init (shhh not redundant).
So about to start the test, and to set it up had to restart all cilium pods. Kinda dumb, hopefully I can find out why that is... preferably some way Cilium can fix itself in times like that... but may not necessarily be the test I'm about to do. Just wanted to add it to this topic for discussion if anyone has any ideas on that! I did not have this kind of init weirdness when using MetalLB btw (but also could not find a way to get SourceIP with that either).
About to test!

For the second scenario, can you try setting
loadBalancer:
  bpfSocketLBHostnsOnly: true
socketLB:
  hostNamespaceOnly: true

I believe restricting the LB to the host namespace fixed this issue for me (also rke2 with ingress-nginx).

Yeah it broke again after about 1hr duration (not sure on exact timing measurement). So that did not seem to correct the matter :(
Any other ideas? Ugh probably going to switch away from Cilium at this point with how fragile it seems to be...

@PaulRitzkat0110 I'm confused too there bud! :((((


[RFC] replace big data/model files with script for downloading

This PR is an attempt to address the problem of checking in large files to Git, most recently brought up in this discussion: https://robustintelligencehq.slack.com/archives/C03GL6FAP7V/p1654886534062429
Moved NYC TLC datasets (total ~150mb) and model (100mb) to Dropbox + added a download_files.sh script to download these files locally in the right directories. What are people's thoughts? The thinking is that adding a download_files.sh script would allow us to also download unzip large image directories / NLP directories.
Intended usage: ./download_files.sh <output_dir>
Considerations:

This does beg the question of why we don't just migrate everything to Dropbox. But there was probably a reason we tried to create a git repo in the first place right? @msanvido @n8cha
As a result I didn't check in the small model_extras files or the configs, is that ok or do people think it's inconsistent?
The script is downloading each data/model file individually, seemed the simplest another option is to just upload a zip file dump to dropbox and unzip it in the right directories.

Closes RIME-8831
@RobustIntelligence/ml

I like this better than the others.
The only other alternative I'd really think of would be doing something like in datasets and have an interface that is similar to our custom dataloaders functions, (e.g., get_ref_data, get_eval_data, get_incremental_data, etc.)

I just tried this script out! both in a python script and in a python notebook. It doesn't seem like there's permission issues, but if someone wants they can quickly verify using this script:
test_download_files.py.zip

Assuming this looks good I'll do this for all other examples in here as well.

Closing in favor of #12


Cordentity - https://github.com/hyperledger-labs/cordentity

We always use cordentity to integrate Hyperledger Indy and Corda. It's pretty useful utility for many use cases.

Thanks!


[besu] node monitoring

besu node monitoring

Added service monitor to scrape
updated helm charts to enable/disable metrics

Fixes #2184

The lesser fields we have in our charts (+ placed logically in the values.yaml file, with their example key:value pair), the easier it becomes to use the chart manually (by creating the value files for charts manually) (The easier the charts are to use manually, the easier we can decouple them from ansible in the future)

@jagpreetsinghsasan this can be reviewed.

Please merge all the commits into 1.
I can still see 4 of them

I'm squash merging this, but kindly ensure to squash merge commits while pushing changes to PR


Download page doesn't link to PyPI

http://www.scipy.org/scipylib/download.html links to SF and to GitHub Releases (which for numpy does not have the right tarballs, see https://github.com/numpy/numpy/issues/8117), but not to PyPI. Needs fixing.
And the release procedure for numpy needs looking at as well, GitHub Releases needs to have the release tarballs.

addressed in gh-186


Cannot deserialize instance of com.github.tomakehurst.wiremock.stubbing.StubMapping out of START_ARRAY token

Hi,
I'm currently facing an issue when I try to run  the below code:
@SpringBootTest(classes = { AppConfig.class })
@AutoConfigureWireMock(stubs="classpath:/stubs", port = 8089)
public class PhotoServiceTest {
@Autowired
private Service service;

@Test
public void contextLoads() throws Exception {
	assertThat(this.service.getFooList()).isNot(null);
}

}
I get :
Caused by: org.springframework.beans.factory.BeanCreationException:
Error creating bean with name 'org.springframework.cloud.contract.wiremock.WireMockConfiguration': Invocation of init method failed; nested exception is com.github.tomakehurst.wiremock.common.JsonException: {
"errors" : [ {
"code" : 10,
"source" : {
"pointer" : "/"
},
"title" : "Error parsing JSON",
"detail" : "Cannot deserialize instance of com.github.tomakehurst.wiremock.stubbing.StubMapping out of START_ARRAY token"
} ]
}
Inside stubs, response is defined in a json file (i'm using "bodyFileName" ) having an array of objects :
[ {"idobj : "1"}, {idobj : "2"}]
With the spring-cloud-contract-wiremock 2.1.3.RELEASE.
Could you please check and advise ?
Thanks

Apparently inside the stubs folder you have a json file which is not a stub. Can you create a sample that replicates this?

@marcingrzejszczak Thanks for getting back quickly. You're right ! I fixed it by moving my ref file response.json to __files.


file name containing unicode

Just started using opencvsharp, and is amazed to how awesome and efficient the whole development process has become since using it.
However, Cv2.imread and Cv2.imwrite doesn't seem to take non-ascii characters in the file name
I'm using Windows 10 en-US and the error message I get is
System.ArgumentException: 'Cannot marshal: Encountered unmappable character.'
I get the same error when I use Mat.SaveImage
Is there ways to get around the problem?
Or is there a plan to fix this in the future?
Thanks

To answer to my original question,
I guess one workaround will be to open file stream to the original file, and use Mat.FromStream to the file stream.
thanx

Hi magicsgram
It is not OpenCVSharp issue. As you know, OpenCVSharp is just OpenCV wrapper.
But OpenCV uses fopen function in imread. Please check and trace https://github.com/opencv/opencv/blob/master/modules/imgcodecs/src/loadsave.cpp.
Refer: https://github.com/opencv/opencv/issues/4292
Thanks

I see, then we'll have to wait until OpenCV gets it fixed.
In a mean time, opening a file stream to a file within C# and using stream based methods work well in OpenCVSharp (with the minimum performance impact fortunately).
I'll just stick to it for now. Thanks:)


Litellm integration

Have you seen the backend-rc branch? It also uses LiteLLM and has a new approach to formatting prompts/parsing.

See #1385 and I think the rc branch (LiteLLM + backends) https://github.com/stanfordnlp/dspy/tree/rc

See new dspy.LM implementation that is not yet documented, but was merged in this recent release.


Additional guidance needed

Hi @JaMe76,
Sorry to interrupt you. I really appreciate your work. I have been investigating different tools for data extraction and I find your work to be the best so far. I haven't had the opportunity to try out the Gradio example that many people are excited about. That said, I have a few questions.
I'm new to this information extraction field, so there are many things that are a bit out of my scope. I've seen that several people have been asking about your model "xrf_layout/model_final_inf_only.pt". I understand that this model is private, but I've seen that you've made available the possibility of training a very similar model in https://deepdoctection.readthedocs.io/en/latest/tutorials/training_and_logging/.
I got a bit confused, though, with one of your comments on Hugging Face, where you mentioned that the cell/row models used are available, but I don't see where they are actually available.
Finally, I just want to confirm that the example training procedure for the layout model is the correct one, and that it's not just a toy example.
Thank you in advance for your help.

Thanks for your comments about this repo.
Regarding your questions: The training of the private layout model follows exactly the training scripts you were referring to with the only difference that when merging datasets I had one additional datasets containing around 6k images labelled by hand. Looking at the training script, the number of samples taken from Doclaynet is around 75k and Publaynet 25k. That means, you can train with this script a model on a dataset that differs by 6%. It takes 5-6 days on a RTX 3090.
Do these 6k images really matters? To be honest, I don't know. I did not train a model on this reduced dataset. Adding some good data can change the game a lot. So, I would like to qualify my last statement about "getting a similar model". It refers to the fact that you can train a model on a slightly smaller publicly available dataset.
The model cell/row models haven't been released either. I trained these model on Pubtabnet + ~1k hand labelled data. With the release of Table transformer v2, I  don't think they are any better.

Thank you for your invaluable insights. I've been experimenting with Table Transformer v2 based on your guidelines in the documentation, but I haven't noticed a significant improvement in my specific cases compared to other baseline methods. It's possible that my limited understanding of Table Transformer is hindering my progress, as its parameterization appears to rely heavily on trial and error. Admittedly, my tables are somewhat complex, adorned with embellishments. Any guidance you could provide, @JaMe76, would be greatly appreciated, though I hope not to cause any inconvenience.

Tatr v2 does not require padding as far as I remember. So, you reducing the default padding values to 0 in the configs should improve the segmentation results.

Hi,
Thank you for the feedback @JaMe76 . After a bit of research and also making a lot of trials wit Tatr v2, a found this example https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Table%20Transformer/Inference_with_Table_Transformer_(TATR)_for_parsing_tables.ipynb which works really well. But i did found out that the model tends to at least crop the table too short and latter on gives problems when doing table column and row detection. Also by Tatr v2 you mean the base Table transformer from huggingface https://huggingface.co/microsoft/table-transformer-detection?


Cannot use WinRT with MFC?

Version
C++/WinRT v2.0.220110.5
Summary
I am unable to use WinRT with MFC - "this function must be called from a UI thread"
Reproducible example
// here is the MFC OnInitDialog

BOOL MainDlg::OnInitDialog() {
     CDialogEx::OnInitDialog();
     HWND hwnd = GetSafeHwnd();

     winrt::init_apartment();
     CheckForAppUpdates(hwnd);
     // snip
}

winrt::fire_and_forget MainDlg::CheckForAppUpdates(HWND hWnd) {
try {
    // Capture the UI thread context
    // co_await winrt::resume_foreground(); // needs a Dispatcher.. which needs ?
    winrt::apartment_context ui_thread;
    // Switch to a background thread
    co_await winrt::resume_background();

    if (auto storeContext{ winrt::Windows::Services::Store::StoreContext::GetDefault() })
    {
        const auto updates =  co_await storeContext.GetAppAndOptionalStorePackageUpdatesAsync();

        if (updates) {
            const auto numUpdates = updates.Size();

            if (numUpdates > 0) {

                co_await ui_thread;
                // This method must be called on the UI thread. See docs..
                auto result = co_await storeContext.RequestDownloadAndInstallStorePackageUpdatesAsync(updates);

                auto overallState = result.OverallState();

                CString text;
                switch (overallState)
                {
                case StorePackageUpdateState::OtherError:
                    text = L"Updates: Other Error";
                    break;
                case StorePackageUpdateState::Downloading:
                    text = L"Updates: Downloading";
                    break;
                case StorePackageUpdateState::Pending:
                    text = L"Updates: Pending";
                    break;
                case StorePackageUpdateState::Canceled:
                    text = L"Updates: Cancelled";
                    break;
                case StorePackageUpdateState::Deploying:
                    text = L"Updates: Deploying";
                    break;
                default:
                    text.Format(L"Updates: %d", overallState);
                    break;
                }

                ::PostMessage(hWnd, WM_USER_APP_MSG, 0, (LPARAM)new CString(text));
                co_return;
            }
        }
    }
}
catch (winrt::hresult_error const& ex)
{
    // Handle WinRT exceptions
    ::PostMessage(hWnd, WM_USER_APP_MSG, 1, (LPARAM)new CString(ex.message().c_str()));
}
catch (std::exception const& ex)
{
    // Handle other exceptions
    ::PostMessage(hWnd, WM_USER_APP_MSG, 1, (LPARAM)new CString(ex.what()));
}
}

Expected behavior
No response
Actual behavior
No response
Additional comments
This is not an actual bug, a simple example of MFC and WinRT would be helpful for me to use WinRT.
Thank you.

In a desktop app, before using an instance of this class in a way that displays UI, you'll need to associate the object with its owner's window handle. For more info, and code examples, see Display WinRT UI objects that depend on CoreWindow.

https://learn.microsoft.com/en-us/uwp/api/windows.services.store.storecontext?view=winrt-22621

Hi @sylveon , I had a look at the examples you referred to and it appears it's for WinUI 3, and not for MFC.
Do you perhaps know of an MFC example I can refer to?

The example is pretty much the same without WinUI 3, you associate the StoreContext object to a window by querying it for IInitializeWithWindow (using storeContext.as<IInitializeWithWindow>()), then call Initialize on the returned object, passing in your HWND.
I don't know why they said this was for an example for WinUI 3 when it applies to all Win32 apps.
Also, your transition to a background thread isn't really needed here.


Random motion vectors at the bottom and motion vectors shifted up

I capture motion vectors on a V1 camera on raspberry 3 B+ using Python 3.7.3:
with picamera.PiCamera( sensor_mode = 2, resolution = (2592//2, 1944//2), framerate = 12) as camera:
    camera.start_recording( 'test.h264', format='h264', motion_output='test.data' )
    try:
        camera.wait_recording(30)
    finally:
        camera.stop_recording()

Then I annotated the frames extracted (ffmpeg) from the h264 with the vectors loaded from the test.data file:
motion_data = np.fromfile(
    'test.data', dtype=[
        ('x', 'i1'),
        ('y', 'i1'),
        ('sad', 'u2'),
        ])

cols = (2592//2 + 15) // 16
cols += 1
rows = (1944//2 + 15) // 16
frame_count = motion_data.shape[0] // (cols * rows)
motion_data = motion_data.reshape((frame_count, rows, cols))
index=0
for f in motion_data:
    index +=1
    img = Image.open( 'frame{0:03d}.jpg'.format(index) )
    draw = ImageDraw.Draw(img)
    for r in range(rows):
        for c in range(cols):
            x, y, sad = f[r,c]
            if x or y:
                cx = c*16+8
                cy = r*16+8
                draw.line( [ cx, cy, cx+x, cy+y ], fill='#ff0000' )
                draw.ellipse( [ cx-1, cy-1, cx+1, cy+1 ], fill='#ffffff' )
     img.save( 'annot{0:03d}.jpg'.format(index) )

I see two issues:

all the frames have a bunch of motion vectors at the bottom where there is no motion:

on real motion, the motion vectors seem shifted up by 3 or 4 macro blocks (which about the height of the MV noise at the bottom).

I verified that motion_data.shape[0] % (cols * rows) is indeed 0 before the reshape(). The motion shift does not seem a timelag because on horizontal motion it is just shifted up (not left or right).
Now to be transparent, I am a beginner with Linux, the Rasberry, PiCamera, Numpy, image processing, Python... and github.

I changed the camera and the pi: i cannot reproduce.


[rhel-8] test: Fix check package release issue

Test should check release field when filter package by release, not
package name
Cherry-picked from master commit 7b416df4dd06

[ ] Needs some more backports: PR #1221

This is a nice and unintrusive test stabilization for the RHEL 8 branch. But I mostly want a PR for running tests against rhel-8-4 now, as deadlines for 8.3.1 have passed. So let's switch over to 8.4 ASAP.

Ugh, what a disaster.. Job for osbuild-composer.socket failed. Did that somehow break in 8.4? (which shows how c-composer should have begun to start testing on 8.4 months ago..) @jkozol , @larskarlitski , does that ring a bell? Known breakage or API change?

Ugh, what a disaster.. Job for osbuild-composer.socket failed. Did that somehow break in 8.4? (which shows how c-composer should have begun to start testing on 8.4 months ago..) @jkozol , @larskarlitski , does that ring a bell? Known breakage or API change?

Hm, this seems like a potential regression introduced in dc1b84fcfeb946a1a0553c7af9df0c448cacc21a. osbuild-composer.service needs to Require/After=osbuild-composer.socket, which used to be pulled in transitively by the worker. What do you think @ondrejbudai ?

Hm, this seems like a potential regression introduced in dc1b84fcfeb946a1a0553c7af9df0c448cacc21a. osbuild-composer.service needs to Require/After=osbuild-composer.socket, which used to be pulled in transitively by the worker. What do you think @ondrejbudai ?

I don't understand what's the issue here. osbuild-composer.service can run just alright without osbuild-composer. socket. Also, the local worker.socket/.service doesn't imply that the osbuild-composer.socket must be enabled.

I don't understand what's the issue here. osbuild-composer.service can run just alright without osbuild-composer. socket. Also, the local worker.socket/.service doesn't imply that the osbuild-composer.socket must be enabled.

@ondrejbudai : FYI, @henrywang backported a few unit related fixes in #1221. He'll cherry-pick this test robustification as well, then I'll close my PR and we sort it all out in #1221.

@ondrejbudai : FYI, @henrywang backported a few unit related fixes in #1221. He'll cherry-pick this test robustification as well, then I'll close my PR and we sort it all out in #1221.

Ah, thanks, @martinpitt and @henrywang!

Ah, thanks, @martinpitt and @henrywang!


Docs @ Docker readme or image is wrong

Problem description
Docs Readme on the docker.hub is incorrect. https://hub.docker.com/r/docs/docker.github.io
"Read these docs offline" topic suggest use the latest tag, but it does not work.

run
docker run -it -p 4000:4000 docs/docker.github.io:latest
to：https://hub.docker.com/r/docs/docker.github.io#read-these-docs-offline
same to can not start，no log，Can you help me？

docker run -it -p 4000:4000 --entrypoint "nginx" docs/docker.github.io:latest -g 'daemon off;'

Thanks for sharing this issue. As the issue is old, it seems to have been resolved on newer images. If the issue still persists on newer images, please let us know.


Add some cfg checks on some types for the Rust sdk

Mainly adding sdk = [] as feature in casper types
Add "sdk" cfg feature checks into some types that use io/fs or system calls
Most of the changes are induced by compiling, linting and cargo check on the new sdk feature flag
https://github.com/casper-ecosystem/rustSDK/issues/2

@SaiProServ please follow up on this with the stakeholders


config: fix path to set interface in ceph.conf

need to use hostvars[host]['XXX'] to retrieve the monitor
interface and/or radosgw interface.
Fixes: https://bugzilla.redhat.com/show_bug.cgi?id=1493920
Signed-off-by: Guillaume Abrioux<EMAIL_ADDRESS>

jenkins test luminous-ansible2.3-bluestore_dmcrypt_journal_collocation

jenkins test luminous-ansible2.3-purge_cluster

jenkins test luminous-ansible2.3-docker_cluster

jenkins test luminous-ansible2.3-docker_cluster

can you also fix this for https://github.com/ceph/ceph-ansible/blob/master/roles/ceph-mon/templates/ceph-mon.service.j2 and https://github.com/ceph/ceph-ansible/blob/master/roles/ceph-rgw/templates/ceph-radosgw.service.j2? Thanks!

@leseb done, lets wait for the CI

jenkins test this please


Read sensors based on Schedules

Support Schedules for more accurate reading & reporting

System timer tracks read events and if they complete before the next schedule
Hopefully less missed events #163
Deprecated filter & sensor modifiers
This allows slow updates for the solarman driver added in #144

Supports additional sensors for the first inverter (typically settings)
Support sensor groups in configuration to reduce long lists of sensors (see here for the full mapping.

Groups: energy_management, power_flow_card, settings

Fix #165
Moved to the HA Python 3.11 base containers & init system
Made ha_addon_sunsynk_multi part of the sunsynk pypi package. Improved tests & hopefully versioning
Added a translation file #164 (Applies to the Settings screen, not 100% complete)

New configuration (optional)
  READ_ALLOW_GAP: 5
  SCHEDULES:
    - KEY: "W"
      READ_EVERY: 60
      REPORT_EVERY: 60
      CHANGE_ANY: false
      CHANGE_SIGNIFICANT: 80
      CHANGE_SIGNIFICANT_PERCENT: 100

Schedules
Schedules will be assigned in the following order:

Sensor name (the slug: lowercase and spaces replaced with an underscore, _)
RWSensors (aka configuration) - rw
Sensor unit
Any unit - any_unit
No unit - no_unit

This is AWESOME

👍  Please try the schedules and once you have a set that works well for solarman we should probably include it in the docs

@kellerza will do


CustomPrediction Exception

I run the FirstCustomPrediction.py code , and get the exception . The model file path and json file path is correct
Traceback (most recent call last):
File "C:\Users\admin\Anaconda3\lib\site-packages\imageai\Prediction\Custom_init_.py", line 385, in loadModel
model = ResNet50(model_path=self.modelPath, weights="trained", model_input=image_input, num_classes=self.numObjects)
File "C:\Users\admin\Anaconda3\lib\site-packages\imageai\Prediction\ResNet\resnet50.py", line 111, in ResNet50
model.load_weights(weights_path)
File "C:\Users\admin\Anaconda3\lib\site-packages\tensorflow\python\keras_impl\keras\engine\topology.py", line 1101, in load_weights
f = h5py.File(filepath, mode='r')
File "C:\Users\admin\Anaconda3\lib\site-packages\h5py_hl\files.py", line 271, in init
fid = make_fid(name, mode, userblock_size, fapl, swmr=swmr)
File "C:\Users\admin\Anaconda3\lib\site-packages\h5py_hl\files.py", line 101, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File "h5py_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py\h5f.pyx", line 78, in h5py.h5f.open
OSError: Unable to open file (Truncated file: eof = 78022201, sblock->base_addr = 0, stored_eof = 94851792)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:/tensorflow/pyimages/day2-object-detect/train_test/test.py", line 14, in
prediction.loadModel(num_objects=10)
File "C:\Users\admin\Anaconda3\lib\site-packages\imageai\Prediction\Custom_init_.py", line 389, in loadModel
raise ValueError("You have specified an incorrect path to the ResNet model file.")
ValueError: You have specified an incorrect path to the ResNet model file.

Please paste the code you are running in the comment below for me to review .

`from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "resnet_model_ex-020_acc-0.651714.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "4.jpg"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction + " : " + eachProbability)`

The code you are using is not fully compatible with the latest version of ImageAI. The README you read was not properly updated ( will fix that ASAP).
I will encourage you to follow the newly created Official Documentation of ImageAI , via the link below.
http://imageai.readthedocs.io/en/latest/custom/index.html

Please note that when printing results, use "," instead of "+"

You can also find this tutorial helpful
https://towardsdatascience.com/train-image-recognition-ai-with-5-lines-of-code-8ed0bdd8d9ba

Thanks for you advice , it works when i use idenprof_061-0.7933.h5 model file.

You are welcome.

Hello,
I am facing the same problem except I am using my own custom trained model with it's associated json file.
This is the code,
from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "vigil.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "896.png"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction , " : " , eachProbability)
Please suggest a solution

Hello,
I am facing the same problem except I am using my own custom trained model with it's associated json file.
This is the code,
from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "vigil.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "896.png"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction , " : " , eachProbability)
Please suggest a solution

try this model "idenprof_061-0.7933.h5"

Hello, i randomly started to face the same issue with a program that worked fine ~6 months ago.
from imageai.Prediction.Custom import CustomImagePrediction
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "model_ex-166_acc-0.994712.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)

The model is there, the type is resnet, and the num_objects is correct. i tried to omit the full execution path and just pass the names of the files as is shown in documentation, but result is the same.
Please help.

I also tried the model and code from https://github.com/OlafenwaMoses/ImageAI/blob/e4e8430250b1a236fb0730e5af4f1d1c50bf803d/imageai/Prediction/CUSTOMPREDICTION.md, with same results.

created a new virtual env and freshy installed everything, and the problem seems to be resolved. Still would like to know what happened though.

okay, there is something really weird going on - i just encountered the same exact issue on a completely different machine that even has a different os. just like previously, it worked fine ~6 months ago. i installed imageai ~8 months ago, and the version on both machines is the newest one. Im really lost

I also tried the model and code from https://github.com/OlafenwaMoses/ImageAI/blob/e4e8430250b1a236fb0730e5af4f1d1c50bf803d/imageai/Prediction/CUSTOMPREDICTION.md, with same results.

I install the newest version and run the demo codes without any errors. I did't train the model with my own data.

Hello,
I am facing the same problem except I am using my own custom trained model with it's associated json file.
This is the code,
from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "vigil.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "896.png"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction , " : " , eachProbability)
Please suggest a solution

try this model "idenprof_061-0.7933.h5"

Hey, it worked for me. Turns out if you rename your model file(h5) it won't work. Thanks a ton!!!!!.
I have another query can we use this resnet model for live video object detection? If let's say I use YOLO for object detection then use the ResNet model for classification. Can this happen?

Hello,
I am facing the same problem except I am using my own custom trained model with it's associated json file.
This is the code,
from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "vigil.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "896.png"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction , " : " , eachProbability)
Please suggest a solution

try this model "idenprof_061-0.7933.h5"

Hey, it worked for me. Turns out if you rename your model file(h5) it won't work. Thanks a ton!!!!!.
I have another query can we use this resnet model for live video object detection? If let's say I use YOLO for object detection then use the ResNet model for classification. Can this happen?

Yeah, it can work for live video object detection. But why you use YOLO for object detection and use the ResNet model for classification ? Object detection already recognize the object. In my opinion you just need object detection.

Hello,
I am facing the same problem except I am using my own custom trained model with it's associated json file.
This is the code,
from imageai.Prediction.Custom import CustomImagePrediction
import os
execution_path = os.getcwd()
prediction = CustomImagePrediction()
prediction.setModelTypeAsResNet()
prediction.setModelPath(os.path.join(execution_path, "vigil.h5"))
prediction.setJsonPath(os.path.join(execution_path, "model_class.json"))
prediction.loadModel(num_objects=10)
predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "896.png"), result_count=5)
for eachPrediction, eachProbability in zip(predictions, probabilities):
print(eachPrediction , " : " , eachProbability)
Please suggest a solution

try this model "idenprof_061-0.7933.h5"

Hey, it worked for me. Turns out if you rename your model file(h5) it won't work. Thanks a ton!!!!!.
I have another query can we use this resnet model for live video object detection? If let's say I use YOLO for object detection then use the ResNet model for classification. Can this happen?

Yeah, it can work for live video object detection. But why you use YOLO for object detection and use the ResNet model for classification ? Object detection already recognize the object. In my opinion you just need object detection.

Could you provide more details on how I can use the ResNet for Live Video Object Detection? I trained a YOLO model (using darkflow) on custom dataset. While the model can correctly detect objects in images I can find no bounding boxes in the video (I am on Windows 10 btw). The ResNet is another model I trained which has got around 93.5% accuracy. So, I hope to use this ResNet on videos.
My initial thoughts were that, since YOLO is already trained to detect some objects, I wish to use ResNet to classify the detected objects into certain classes.

@randomarchive101 Did you ever solve your issue? I have a similar problem: ImageAI works fine on Windows but has this error on a Raspberry Pi running Linux.

Hola, estuve con el mismo problema, casi una semana intentando, por fin lo solucioné.
Al parecer existen incompatibilidades con la libreria h5py en versiones recientes.
En mi caso apliqué el siguiente comando de instalacion:
pip install h5py==2.10.0
Espero les sirva.


document environment variables

We need a document describing what the supported environment variables are (print_defaults ...) and what they do
for example:
DEVITO_DEBUG_COMPILER: writes to standard output the command line used to jit-compile an operator (eg gcc ... -O3 ...
DEVITO_JIT_BACKDOOR: escape hatch for custom kernels. The typical use case is as follows: one runs a Devito program; then, once the session is over, the generated file(s) is(are) modified manually (e.g., for debugging or for performance experimentation); finally, when re-running the same program, if DEVITO_JIT_BACKDOOR is set, Devito won't overwrite the user-modified files, but rather it will compile and call them (thus it's pretty much like bypassing code generation)
DEVITO_FIRST_TOUCH: first touch upon data allocation. Works around numa issue (though this is a fake issue thanks to MPI) and allows parallel initialisation by multiple threads

Should that be a typical document, or like a cheatsheet-poster ?

Closing as now the wiki page is updated with all env vars


[Task]: Add client unit test

SubTasks
Add client unit test to verify that when authentication type if oauth2, we see expires_in field

This can be done once we merge this external contributor's PR for moving position of this field in the form


Change some chapter 2 exercises to fit fingerboard

Exercise 9 is hitting a real high range on the F# locrian. We need to move this lower down the fingerboard.
Rearrange the scales on exercises 8 and 9 (aka 2E and 2F) to fit better on both sides!

DONE!


meltano cli config support for mappings

Migrated from GitLab: https://gitlab.com/meltano/meltano/-/issues/3270
Originally created by @pandemicsyn on 2022-02-22 15:07:16

During one of the early review cycles for the stream maps MR we realized we'd forgotten to spec meltano config for mappers. So wanted to circle back and talk about that /cc @aaronsteers @tayloramurphy
List/set/reset/unset
At the moment configuring mappers is totally manual, but we should consider supporting manipulating mapping's config opts via:

meltano config <mapper name>
meltano config <mapping name>

There's some issues with either path.
For the first, it's probably the route folks would expect to take when first installing a mapper - but also a bit weird as when invoking it, you'll be instead using the mapping name. Things like listing's might also work a bit unexpected. Do we list the config opts of the mapper itself, the config for all the existing mappings, or both?
The second makes a lot of sense but seems problematic and addresses some of the issues of th first. Its biggest drawback would be around adding a new mapping. How would we know what mapper is being referenced when dealing with a not yet defined mapping?
Test
@aaronsteers @tayloramurphy not how we want to handle config tests for mappers/mappings - is there any special handling that would be useful? Or is parity good enough?

View 4 previous comments from the original issue on GitLab

Still relevant


ENH: Add download_all utility method & script

Reference issue
#16983
What does this implement/fix?
_download_all.py can be run as a standalone script to download all the data files required by scipy.datasets at once without the need to build SciPy first. This could be useful; for example, SciPy Debian, Fedora etc. packages may have build time constraints for downloading from external links.
As per the discussion with Ralf earlier today, the steps might look something like this:
git clone ...
git submodule update ...
# Download dataset files
python _download_all.py

...continue the build process

Although I couldn't find the exact SciPy Debian build script in https://salsa.debian.org/python-team/packages/scipy, where something like this should be placed, it would be great to hear @drew-parsons, your thoughts and if could help us review this. Thanks!
Besides, download_all can also be used as a utility method within the scipy.datasets module if required.
from scipy import datasets

# Cache all datasets
datasets.download_all()

# Now work without an internet connection

cc @rgommers
Additional Information
Sharing Network access by Debian package builds by Nathan Willis, talks about some constraints when using the internet at build time in Debian packages for people who didn't know about this strict rule.
I found it a nice read since I recently learnt about the constraint when working on scipy.datasets module.

@rgommers, a little reminder requesting your review, in case it was missed. I'll raise the clear_cache utility method PR (already implemented) after this lands to avoid unnecessary merge conflicts. Thanks!


Add support for receipt templates (for non-tax deductible payments)

Related to https://github.com/opencollective/opencollective/issues/5368
Related Front-end PR: https://github.com/opencollective/opencollective-frontend/pull/7985
Related PDF PR: https://github.com/opencollective/opencollective-pdf/pull/844

Just to note that the GraphQL Inspector issue is not related to this; I've opened an issue and fixed this at, https://github.com/opencollective/opencollective/issues/5724