Cleanup principles

I didn't really change much, just moved somes stuff around and expanded
a little more in number 5.
I moved all of the physical shipping container stuff to just the into
because while its a cute analogy, repeating it over and over just got
in the way of the real points - and by number 5 we ended up having more text
about shipping containers than our containers - which was just weird.
Signed-off-by: Doug Davis<EMAIL_ADDRESS>

On Fri, Oct 02, 2015 at 06:48:28AM -0700, Doug Davis wrote:

I moved all of the physical shipping container stuff to just the
[intro] because while its a cute analogy, repeating it over and over
just got in the way of the real points…

+1 to this.

and by number 5 we ended up having more text about shipping
containers than our containers - which was just weird.

I still think it would be nice to have more clarity about what
“Industrial-grade delivery” is about (both for the container ecosystem
as a whole, and for this spec in particular), but that's been
contentious in the past [1,2], so it's probably best to leave it out
of this PR ;).

@duglin across the whole doc, we do formatting of a sentence on each line (no line wraps in a sentence).

okie dokie - one sentence per line.

all-in-all, I'm not opposed. The traditional container is referenced early, but then the similarities are expressed in the sections. For verbosity sake, every 1:1 analogy is not needed.

Looks good overall. @crosbymichael @philips PTAL

couple of nits, but LGTM

fixed the multiple sentences per line issues that @vbatts found.

LGTM

@rgardler fixed

LGTM ping @crosbymichael @philips @LK4D4 @vishh

waiting for one more wording switch

done

cool.


Main source-code in a Mathematica project is recognized as MATLAB

When you look at the language statistics in this repo on GitHub

halirutan/HeyexImport

you find that two of the three Mathematica *.m files are correctly identified. The largest of the three files,  HeyexImport.m which contains the complete implementation is recognized as MATLAB code.
I have already pushed a new commit to GitHub since I heard that it restarts the recognition-algorithm, but no luck. Any help?

Hi @halirutan - I'm sorry for the bad results from Linguist here. We're actually already tracking this issue over here: https://github.com/github/linguist/pull/1652


GEOMESA-633 fixing time zone of delimited exports

looks good to me

Committed as 2d95a759c686ae147c46ef2786c84afbe40cfda1 on 1.5.


POS implementation ?

Hello,
Thank you very much for this very interesting work.
Would a proof of stake implementation concept be possible on this project ?
Also what make this project not suitable for real world usage on basic dapps like proof of existence or things like that ?
Thank you

I implemented it on branch v1.0.0 (I'm waiting for someone to test it)
There are a lot of features that would need to be implemented, especially regarding security, difficulty formula and so on. We discuss that in other issues that you can find here.
Best,

Thanks a lot for your reply, how can we help testing it ?do we have to deploy online ? im willing to help as im interested to learn at the same time :)
Will search for the topic that discuss the needed work for it eventually work on real world then, thanks again.

You could start by understanding the inner work of this coin using branch v1.0.0 as a reference.

Ok will start doing that thank you :)

I did check as well but didn't see any mention of POS, is it the Proof of authority ?


[Bug]: AgentSkills should not be globally stateful

Is there an existing issue for the same bug?

[X] I have checked the troubleshooting document at https://opendevin.github.io/OpenDevin/modules/usage/troubleshooting
[X] I have checked the existing issues.

Describe the bug
Currently, there are places in agentskills that store the current line and current file in global variables: https://github.com/OpenDevin/OpenDevin/blob/b5e6ba8239907ef093c75cb7ba9bca93a2016349/opendevin/runtime/plugins/agent_skills/agentskills.py#L33-L35
This will not work well if we want to have different sessions within one running instance of OpenDevin (e.g. to have two separate windows open doing different things with the same server running).
To fix this issue, we should remove all these globally stateful variables from agentskills and instead store them in the agent state.
Current OpenDevin version
main

Installation and Configuration
na

Model and Agent
No response
Operating System
No response
Reproduction Steps
No response
Logs, Errors, Screenshots, and Additional Context
No response

Can take care of this after the next round of agentskills updates.
Since we now have path parameters, I'd almost go as far as to say, we could remove them altogether.
Even the WINDOW one has a default value and the LLM can decide to use it or not, but happy to hear counterarguments to keep them.

@neubig I lost track of this completely, assigned myself now

Thanks @tobitege !


Restore window view in get_gutter_width()

Now restores window view state, such as curswant.
Should solve the issue in #11.
Let me know if there's any more issues! 👍

Interesting, but I still worry that calling a cursor-movement function in a CursorMoved autocmd is a bit intense, even with the view restore :/
I thought I had found something with wincol() - col('.') but then I realized it deosn't work when scrolled.
I've also opened https://github.com/vim/vim/issues/7190.

I FOUND :D
let gutter_width = wincol() - virtcol('.') + winsaveview().leftcol

It works right?

leftcol represents the left-most column of the buffer being displayed, which is usually only useful when nowrap is set and you've horizontally scrolled the view. It's not a virtual column number.

I still worry that calling a cursor-movement function in a CursorMoved autocmd is a bit intense

I don't believe so. The cursor movement shouldn't trigger a screen redraw or any extra CursorMoved events. It's relatively lightweight.

Indeed I see :/
Two last questions:

What was wrong with this one?

augroup gutter
  au!
  au CursorMoved * :echom GutterWidth()
augroup END

function! GutterWidth()
  let numberwidth = max([&numberwidth, strlen(line('$'))+1])
  let numwidth = (&number || &relativenumber)? numberwidth : 0
  let foldwidth = &foldcolumn

  if &signcolumn == 'yes'
    let signwidth = 2
  elseif &signcolumn == 'auto'
    let signs = execute(printf('sign place buffer=%d', bufnr('')))
    let signs = split(signs, "\n")
    let signwidth = len(signs)>2? 2: 0
  else
    let signwidth = 0
  endif
  let gutter_width =  numwidth + foldwidth + signwidth
  return gutter_width
endfunc

Do you have any opinion on winsaveview/winrestview vs getcurpos/setcurpos? Could the *curpos ones be more performant?

Sorry to be so picky on this one, it's just that a function that runs on every cursor movement needs to be as efficient as possible, otherwise overtime it accumulates and you end up with a slow cursor because people don't care enough about performance :/ Just let me know your thoughts on the 3 propositions above, I'll do some profiling.

Ah, I forgot about getcurpos() with setpos(); that method seems to work!

What was wrong with this one?

This image from PR #4 shows the issue with the old method:

That method didn't consider scl settings such as number and neovim's yes:X and auto:X settings. It was also leaving 2 columns with auto set even if there were no signs to display for me (at least the Lua version was).
Anyway, I wrote a crude benchmark to test each approach out:
local function winrestview_method()
  local saved_view = vim.api.nvim_call_function('winsaveview', {})

  vim.api.nvim_call_function('cursor', { 0, 1 })
  local gutter_width = vim.api.nvim_call_function('wincol', {}) - 1

  vim.api.nvim_call_function('winrestview', { saved_view })
  return gutter_width
end

local function setpos_method()
  local saved_cursor = vim.api.nvim_call_function('getcurpos', {})

  vim.api.nvim_call_function('cursor', { 0, 1 })
  local gutter_width = vim.api.nvim_call_function('wincol', {}) - 1

  vim.api.nvim_call_function('setpos', { '.', saved_cursor })
  return gutter_width
end

local function old_method()
  local width = vim.api.nvim_win_get_width(0)
  local number_width = math.max(
    vim.api.nvim_win_get_option(0, 'numberwidth'),
    #tostring(vim.api.nvim_call_function('line', { '$' })) + 1
  )
  local number = vim.api.nvim_win_get_option(0, 'number')
  local relative_number = vim.api.nvim_win_get_option(0, 'relativenumber')
  number_width = (number or relative_number) and number_width or 0
  local fold_width = vim.api.nvim_win_get_option(0, 'foldcolumn')

  local sign_width = 0
  local sign_column = vim.api.nvim_win_get_option(0, 'signcolumn')

  if sign_column == 'yes' then
    sign_width = 2
  elseif sign_column == 'auto' then
    local signs = vim.api.nvim_call_function('execute', {
      'sign place buffer=' .. vim.api.nvim_get_current_buf(),
    })
    local signs = vim.split(signs, '\n', true)
    sign_width = #signs > 2 and 2 or 0
  else
    sign_width = 0
  end

  return number_width + fold_width + sign_width
end

function bench(func, name, tries)
  print('benchmarking ' .. name .. ' for ' .. tries .. ' tries')

  local start = vim.api.nvim_call_function('reltime', {})

  for i = 1, tries do
    func()
  end

  local elapsed = vim.api.nvim_call_function('reltime', { start })
  elapsed = vim.api.nvim_call_function('reltimestr', { elapsed })

  print(name .. ' took ' .. elapsed .. ' seconds')
end

function bench_all(tries)
  bench(old_method, 'old method', tries)
  bench(winrestview_method, 'winrestview() method', tries)
  bench(setpos_method, 'setpos() method', tries)
end

Here are the results after 100,000 runs of each function while viewing a 10k lines buffer with a few windows open on neovim compiled with Lua JIT:

Each method runs very fast, with the winrestview() method being a bit slower as expected. The old and winrestview() methods seem to fluctuate a few seconds after every run, but there's seemingly a clear winner with getcurpos() and setpos(), which always has a consistently low running time!
I've updated this PR to use that method instead. Let me know what you think. 👍

Ok, I think it runs fast enough :smile:
I'll merge as is, thanks for running the benchmarks!


Fixes Build System for the Frontend.

Description of the change
This PR fixes the build system for the frontend that was setup improperly leading to typescript to not be evaluated.
This entails:

Fixing the TSConfig setup to properly handle all files.
Fixed TSC not compiling with Typescript checks.
Updated ESLint to version 9, and migrated config files to new version.
Added new type checks on ESLint for things like, async errors, unused variables, undefined checks and such.

Additional context
This was done with help of @PThorpe92, if anything needs to be discussed please lead the conversation with him.

closes #440

@jtucholski @calisio
just a note: I have had these changes locally, I spent the day getting this ready with some assorted fixes i needed to merge for open issues with the middleware/scheduling, so i combined into one PR. Carolina and I were working out the last bugs, we will revisit tomorrow and I'm gonna get all these changes merged at once, so we can start to adapt all the other PR's to the new build/linting


Unable to run

This is awesome! Can't wait to have it running.
$ python -V
Python 2.7.15+

$ pip -V
pip 9.0.1 from /usr/lib/python2.7/dist-packages (python 2.7)

$ pip install git+https://github.com/smacke/subsync
Collecting git+https://github.com/smacke/subsync
  Cloning https://github.com/smacke/subsync to /tmp/pip-knBFFV-build
Collecting argparse (from subsync==0.2.0)
  Using cached https://files.pythonhosted.org/packages/f2/94/3af39d34be01a24a6e65433d19e107099374224905f1e0cc6bbe1fd22a2f/argparse-1.4.0-py2.py3-none-any.whl
Collecting ffmpeg-python (from subsync==0.2.0)
  Using cached https://files.pythonhosted.org/packages/d0/76/d04cf269e20bbb0357d2641e20a4d8b9e1bec7cb8485389a001a6ace6018/ffmpeg_python-0.1.17-py2-none-any.whl
Collecting future (from subsync==0.2.0)
Collecting numpy>=1.12.0 (from subsync==0.2.0)
  Using cached https://files.pythonhosted.org/packages/c4/33/8ec8dcdb4ede5d453047bbdbd01916dbaccdb63e98bba60989718f5f0876/numpy-1.16.2-cp27-cp27mu-manylinux1_x86_64.whl
Collecting srt (from subsync==0.2.0)
Collecting tqdm (from subsync==0.2.0)
  Using cached https://files.pythonhosted.org/packages/6c/4b/c38b5144cf167c4f52288517436ccafefe9dc01b8d1c190e18a6b154cd4a/tqdm-4.31.1-py2.py3-none-any.whl
Collecting webrtcvad (from subsync==0.2.0)
Installing collected packages: argparse, future, ffmpeg-python, numpy, srt, tqdm, webrtcvad, subsync
  Running setup.py install for subsync ... done
Successfully installed argparse-1.4.0 ffmpeg-python-0.1.17 future-0.17.1 numpy-1.16.2 srt-1.10.0 subsync-0.2.0 tqdm-4.31.1 webrtcvad-2.0.10

My opinion of pip (and every other poor implementation of apt) is it's only useful for Docker containers or while working on the code.   I run Debian, it uses apt. Once I figure out how to run this utility I'll Dockerize it so I won't have to deal with this down the road and save others the headache known as pip. I'm running guessit (utility that downloads subtitles) in a docker container, utilities like this are awesome in Docker.
$ pip show subsync
Name: subsync
Version: 0.2.0
Summary: Language-agnostic synchronization of subtitles with video via speech detection.
Home-page: https://github.com/smacke/subsync
Author: Stephen Macke
Author-email<EMAIL_ADDRESS>License: MIT
Location: /home/michael/.local/lib/python2.7/site-packages
Requires: argparse, ffmpeg-python, future, numpy, srt, tqdm, webrtcvad

Looks like I can install some of them the right way:
$ sudo apt-get install python-future
$ sudo apt-get install python-numpy
$ sudo apt-get install python-tqdm

$ ls -al /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py
-rw-rw-r-- 1 michael michael 6104 Mar  7 18:42 /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py

$ /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py
zsh: permission denied: /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py

$ chmod 764 /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py

$ /home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py
Traceback (most recent call last):
  File "/home/michael/.local/lib/python2.7/site-packages/subsync/subsync.py", line 13, in <module>
    from .utils import read_srt_from_file, write_srt_to_file, srt_offset
ValueError: Attempted relative import in non-package

No idea how to run it...

Ugg I just read #1, this utility isn't what I thought it was. :(  Subslider.py already does this good enough. syncing continuously is what I was looking for.

Hi, when you did the initial pip install, it already grabbed all the dependencies; there's no need to look for them manually via apt.
It should have installed a subsync executable in /home/michael/.local/bin; if you decide you want to try this out after all, then if you use that one you should see that error go away.

Thanks @smacke you're right. Since it's installed I'll play with it. I wish it could sync every line!

@mabushey me too :) this problem is a lot more challenging, but I want to handle this kind of thing in a robust manner once I have a good set of test cases against which to try out different algorithms.

@smacke I'm not much on python (php/bash coder), but if there's anything related to testing or the like I can do to help let me know.

@mabushey thanks for offering! if you happen to run across any free movies or TV episodes that have out-of-sync subs where the problem points happen in the middle (e.g. due to one or more commercial breaks), I definitely appreciate any pointers as these would make great test cases.


Deployment performance problems

The deployed application has been experiencing some major downtime in the last few weeks. I haven't been able to track down whats happening, and its very sporadic.
Here's an example screenshot of 24 hours of metrics:

During those large spikes, I'm guessing that TODO is just returning 500s before even trying to do its thing. I've take steps to prevent spammy accounts from taking up too much load, and from parsing diffs that are too large in #185. This doesn't seem to have made a significant difference though.
If you're here to ask why TODO isn't doing anything - this is probably why 😞
Deploying your own TODO
In the meantime, you may choose to deploy your own instance of TODO. You can follow the Probot deployment docs, paired with this project's requirements.
How you can help
If you're interested in digging in to see whats happening, I'd greatly appreciate it. I can't grant you access to the deployed thing, so you'll have to administer your own load testing.
First reported in #188, #186

Going to call this closed by #194 - I'll continue to monitor though and see where we're at in a couple of days.


ci: Add TS version tests

Uses the strategy implemented in TanStack Form and Store to take full advantage of Nx.
This strategy can be tested and debugged locally rather than needing to run in CI (e.g. the matrix approach). It also all runs in a single GitHub Action job, making it easier to work out which task has failed (all logs are in the Nx cloud report).
Currently tests TS 5.0 to 5.3. Adding pre-5.0 tests is a bit more involved, but TanStack Form and Store have a workaround which I'll implement in a subsequent PR.

I think a better way would be to just have CI install a different version of typescript, like we do in router:
<EMAIL_ADDRESS>then we add the TS_VERSION as input to nx to avoid cache hits<EMAIL_ADDRESS>also: what's all the file moves about 😅 ?

Yeah I've seen that option too, I prefer how its been implemented for TanStack Form, as it can be tested and debugged locally too rather than needing to run in CI. It also all runs in a single GitHub Action job, making it easier to work out which task has failed (everything is in the Nx cloud report).
As for the file moves, it makes it easier to target different files for different tsconfigs. The "main" TS version can check all files (src, tests, vite config, etc.), while the "legacy" TS versions only need to check src, which speeds up testing. The same thing can be achieved with slightly more annoying "include" and "exclude" config.

Looks good, I like that this strategy allows us to check different versions of TS per package. For example the lowest version of Angular supported by angular-query is v16, which requires TS 4.9 or higher. So unlike react-query we wouldn't need to check either 4.7 or 4.8.


Opening index.html showing nothing

I'm working on this weeks p5 drawing project, but when i open my index.html its showing nothing even though I know the code I have in my sketch.js should work. My index.html file from last week in my hw-3 folder is working fine, I even tried copying the exact code from that sketch.js into the sketch.js in my hw-4 folder, but it shows up blank in the browser, even though it works perfectly in the hw-3 folder. Considering my hw-4 folder is literally a duplicated version of my hw-3 folder, I have no clue what could be causing this. It's just weird because the exact code that works using the index.html from my hw-3 folder does not work in my hw-4 folder. No matter how much I change the code, I can't get the browser to show anything but a blank screen.

Try clearing your browser cache and reloading.  I get:

@DVSnell Yeah, I ended up actually just deleting that folder and making a new one, so now it works fine. Thanks though!

@lilyj521 Hey I'm having this same problem now, I tried deleting my folder and making a new one but nothing seems to be working. Anyone have any tips?

@daylinscott In your hw-4 Readme you wrote that you're getting a 404 error. I think there could be a couple of reasons for that.
What does your URL look like? If you are trying to use your github web page it is important to add the hw-4 directory to the end of the address. For example your git hub web page URL should look like:
https://yourusername.github.io/120-work/hw-4/
and not like:
https://yourusername.github.io/120-work/
When I looked through your repo, I noticed that your hw-4 directory was inside of your coursework directory instead of directly in your 120_work directory. Both your hw-2 and hw-3 directories are in your 120_work directory. I don't know if moving your hw-4 directory will get rid of the error, but it's worth a try.
And finally, when I looked at your hw-4 sketch.js I noticed that you are missing a lot of semi-colons in your code. Adding semi-colon's after all of your fill(); and `ellipse(); functions might also help.
I hope this helps or at least gives you a few places to start looking.


Lamping RC is still disabled after SOTW ended

SOTW has ended and we're not able to lamp the skill still. Please and thank you.

Fix is merged, will be live soon if it isn't already.


Improve option for get unmasked full dataset.

#129 Implements this as new_dataset.unmask()ordataset.unmask(inplace=True).


Fix html not syntax highlight

Fixes #429
This is weird, because I couldn't find the html language from the demo list react-syntax-highlighter. Currently I can see it is default use text type for the html from developer tools.
I trying to use htmlbars to solve this issue.

Yeah, sure, somehow I find this deployed still not working.

It may at least exists long time, see here the screenshots from my PR from October https://github.com/tarasglek/chatcraft.org/assets/133393905/49bb9503-69dd-4b59-a24a-249ecb4e19b7

Amazing.  Maybe we're wrong and this never worked (cc @tarasglek)?  Looks like people use xml as the language for HTML content with highlight.js

It works! xml more suitable 👍  There is some difference between local and cloud, maybe from OS, my htmlbars version is also working locally, and I don't know why first try fails. Maybe we can solve by this way for now.


Final review

To be reviewed:

[ ] the dates in field entry_into_force in raw data (tsv file) some are in the future, e.g. for Celastrus orbiculatus is this field set to 2027-08-02, while for Fundulus heteroclitus and_Xenopus laevis_ is 2024-08-02. I see also that in 2022 we have two very near dates: 2022-07-12 and 2022-08-02. Maybe is correct, but still worth a final check
[ ] the link to the most actual consolidated pdf version (I modified the link to point the 2022 list, please @timadriaens double check this) in Workflow section of README.
[ ] The webpage with English common names is outdated. I couldn't find any other similar webpage. The only link with common names is the PDF brochure to download on circabc but I don't find it good enough.
[ ] metadata. I went through the document and seems good, but still, @timadriaens, we need a sentence mentioning the issues encountered while harvesting data from EASIN API, see #9 (+ #10), I think, to justify our approach.

[ ] future dates are correct, this is because of prolonged transitionary measures foreseen in the Regulation. Still, I would already list those species as Member States are already working on them.
[ ] Link to the consolidated list in the README is fine
[ ] Common names document: the IAS webpage of IUCN was refurbished recently (and the resources page for the EU Reg you find here) and doesn't seem to have a link to the file anymore. I filed a request with IUCN colleagues.
[ ] I have mentioned EASIN in the additional information sentence. We will communicate with them after we published the checklist to see if we can indeed in the future harvest it from their api instead (but for now I would refrain from mentioning any data gaps there).

I had answer for IUCN, here are the files with common names:
https://circabc.europa.eu/ui/group/4cd6cb36-b0f1-4db4-915e-65cd29067f49/library/2ed2ee2a-730f-4583-a3ac-bb2a1815ad6a/details
https://circabc.europa.eu/ui/group/4cd6cb36-b0f1-4db4-915e-65cd29067f49/library/1ac00d12-613b-447c-ab91-3016af071bcf/details
https://circabc.europa.eu/ui/group/4cd6cb36-b0f1-4db4-915e-65cd29067f49/library/fad036d3-e2df-4adb-9c7a-7b9593a4c2f8/details
IUCN have not yet merged those files...

@timadriaens: I will add the link to those three files in README. Could you please check that these names are the same as the ones in our tsv? Or did you already done it? Thanks.

should we not include the common names in other languages? If you want I can do that.

yes, we can. If you do, I am happy to check and map it further to DwC 👍  I also noticed that the names in the spreadsheets are not always correct as we think they should be, e.g. capital letters in French and English common names should be removed unless referring to a country, sin't? I also see that we are not always applying this rule: see e.g. "Pumpkinseed" which probably shoul be "pumpkinseed"? I let this issue to you as it is not my domain of expertise 😄

We can discuss this further if you need in #14. We leave this issue for final review.

Data and metadata have been reviewed.


Feature/nav header - A draft Nav..

Cloned the repo but couldn't find a logo so I've just included a name :+1: (to be changed)

Cloned the repo but couldn't find a logo so I've just included a name :+1: (to be changed)

There is a logo.tsx file already.......and an exported icon in the images ......

Cloned the repo but couldn't find a logo so I've just included a name +1 (to be changed)

There is a logo.tsx file already.......and an exported icon in the images ......

My bad. Didn't check the open PR here. Hey but all this are unmerged so when I clone the repo(dev branch) I can't access those.

Cloned the repo but couldn't find a logo so I've just included a name +1 (to be changed)

There is a logo.tsx file already.......and an exported icon in the images ......

My bad. Didn't check the open PR here. Hey but all this are unmerged so when I clone the repo(dev branch) I can't access those.

There is an images folder from the dev branch.....

Sure I see. Don't merge this first, I'll make some changes on icons and such 💯


Unsigned Short Segfault

Hello,
I may have come across a bug, or at least an odd limitation. I've found that flagser segfaults when reading in a network with too many nodes. In particular, I've found that networks with >65,535 nodes immediately segfault on at least the 3 machines that I have used to test this. Of course, this is a special number, as it is the maximum value for an unsigned short integer. This implies to me that, perhaps, something related to node/matrix indexing is being held in this number format and needs to be allocated more bytes for representation.
To reproduce this error, I'm attaching two flagser files. One of them works fine (fine.flag), the other segfaults (segfault.flag). The fine.flag file has 65535 nodes while the segfault.flag file has 65536 nodes. I have tested this behavior across a variety of parameter values, but the default behavior of the ./flagser command should produce this issue as well.
flagser_files.zip

My current thought is that this line may be producing this behavior. I'm not sure if this max dimension is a ripser constraint or a flagser constraint, however.

Okay, I've refactored so that all data types related to dimensionality are unsigned int instead of unsigned short. This seems to have resolved the problem. I have updated this on a fork: https://github.com/tgebhart/flagser. I do not know what the performance ramifications are, so I will hold off on a pull request unless @luetge determines this is desirable.

Hi,
Sorry for the late reply. Happy to accept a pull request, although I would like it to be opt-in instead of default (i.e. defining an alias dimension_t for either unsigned short or unsigned int, depending on what the user wants). Also, there should not be any reason to change vertex_index_t to something larger, the flag MANY_VERTICES is meant for that.
Let me know if you want to create a pull request accordingly, otherwise I am happy you found a modification that works for you!
Best,
Daniel


Big images crashes my pc (cpu)

I do not have a nvidea-card so its only gpu that works. Wierdly, small images does work as long as I do them one by one but not by batch and not bigger images. Sometimes even small images does make my pc crash, first the mouse get frozen, then the screen gets black. I get no error messages at all. All drivers are up to date as well through the Auto-Detect and Install Updates for Radeon™ Graphics and Ryzen™ Chipset Drivers for Windows®
I have a CPU AMD A10-7800 Radeon R7, 12 Compute Cores 4C+8G, I do have GPU AMD Raden(TM) R7 Graphics (but thats not supported).

Your GPU is simply to old and weak for anything serious.

Do you meen GPU or CPU?


ci: configure dependabot

Welcome to Codecov :tada:
Once merged to your default branch, Codecov will compare your coverage reports and display the results in this comment.
Thanks for integrating Codecov - We've got you covered :open_umbrella:


The images for the map have been updated

The images for the Fuel level monitoring solution have been updated

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


Is it possible to avoid installing autodl-irssi?

Thanks for the script! I tested on Debian 11 and is working fine, but I'm looking to avoid autodl. I'd like to have just a basic rtorrent, rutorrent + nginx! Is it possible to avoid the installation of this plugin? Thanks!

Use swizzin

Thanks for the script! I tested on Debian 11 and is working fine, but I'm looking to avoid autodl. I'd like to have just a basic rtorrent, rutorrent + nginx! Is it possible to avoid the installation of this plugin? Thanks!

https://swizzin.ltd/


Apple Store Review rejected

Attachment:

I checked this issue and then found that actually Github has been filtered out, the feedback from the reviewer is that Google Ad related to Github is not filtered out (as shown in the attachment)
I would like to confirm if this is a bug, or if Google Ad is indeed not filtered out. Thank you.

Thank you for your report.
Intentionally I have not added code to filter ads for the following reasons:

it is controversial whether it is acceptable to hide ads, which are a source of revenue for search engines, so I think it should be done by each user with an ad/content blocker.
filtering by URL may be difficult because ads may not expose their actual URLs.

However, if the Apple Store does not accept this explanation, I will add code to filter ads.

Thanks for the explanation, I will tell this information to apple reviewer.

Apple accepted this clarification and the review has been approved.🎉

Thank you!


is there a way to loop through an audio?

Yes, set the repeat mode to true.

thanks, and is there a plan to add an gape-less audio loop, also playing from assets will be great.

it is gape less in playlist mode. but it's not crossfading like Spotify if that's what you mean? and playing from assets is a bit tricky.

okay great, i hope assets be added in the future, good luck.


Fixup Handling of \ in REPL to Prevent Crash

When parsing the string token if a \ was used it would
unconditionally consume the next character. This would led to a token
with an invalid lenght causing a crash when it was printed. This fixes
that by only consuming the token after the \ if there is one to
consume.
Fixes #603

bump

Thanks for the PR, looks simple and makes sense.
And thanks for the bump post-hand-over :)

No worries. Thought I'd rebase my PRs and bump them to get the ball rolling again now Wren activity is increasing.


Unrelease mrpt2 in ROS Noetic

It looks like the builds have always failed on all platforms https://github.com/MRPT/mrpt/issues/1076
Partially reverts https://github.com/ros/rosdistro/pull/25550 and https://github.com/ros/rosdistro/pull/24945
@jlblancoc FYI

@sloretz Should we go ahead and merge this?

@sloretz Should we go ahead and merge this?

Yeah, I should have asked for a review. @jlblancoc looks like another bloom release is needed to apply https://github.com/MRPT/mrpt/issues/1076#issuecomment-668438050
Merging.

@sloretz Should we go ahead and merge this?

Yeah, I should have asked for a review. @jlblancoc looks like another bloom release is needed to apply MRPT/mrpt#1076 (comment)
Merging.

Yes, thanks for the reminder! Next release is to come out soon...


[Feature Request] Instead of overlapping the notifications, show the notifications like a list

Hi @SchabanBo, can you tell me whether the package supports the feature where if we trigger two notifications at one single time can the notifications will appear in the list view fashion instead of overlapping the notifications on top of each other. Because with this what is happening 1st notification is not being visible as 2nd notifications is showing on top of the first one in a stack manner, so it would be great if the notifications shows in a listview manner like the below package. I was trying the Simple notification that is present in your online demo
https://www.npmjs.com/package/react-toastify

Hi @tejHackerDEV
This would be a nice feature to have, thanks for the suggestion.

Added in V0.2.0


Un-style

Hi,
Any way to do it not to look like a button? I would like it to show up just like an icon from an icon font :)
Thanks!

I'm using this for semantic UI
Example

    <button class="ui button update"></button>
    {{#quickRemoveButton collection="myCollection" _id=this._id onError=onError onSuccess=onSuccess beforeRemove=beforeRemove class="ui button"}}{{/quickRemoveButton}}

Hope it helps!


Update layer.rb

Just a suggestion....

@ktec Thanks so much for looking around and providing a PR. I get failing specs with your change. Can you see what's up?

Sorry man, my bad, it should be product not zip. Will send a new PR, its totally up to you though, just a little suggestion, i wont be offended if you prefer to keep your stuff. Love the repo though, super clean and simple.

Hey, I have updated the branch so it passes tests.

hey @ktec sorry it has taken me so long. I'm traveling now but will review this in the next day or two.


Remove ServiceWorker cache clear

Coverage: 93.994% (+0.02%) from 93.972% when pulling ddeb973b9bb7e7bb256c237e15aa5cf808a36007 on cody-remove-service-worker-cache-clear into 662b140ce3ef5b90edabb41ccb4bf875d67a0ae4 on main.


Add agenda and timings

Have added the draft agenda to the page on the design system site.

@stevenjmesser Just going to take a look at those HTML validation issues, what caused them etc

For anyone who's wondering, I've used <br> to break the long list items for readability.

Had a few non-blocking things but I'll open another PR
Mainly to add some improved semantics for screen readers etc

Move key facts into <dl> using the Summary list component
Mark up "In the morning" and "After lunch" as headings for screen readers
Text editor has automatically flipped quote direction in "We’re feeling ~‘appy~ ’appy" 🤦♂


FasterRCNN to ONNX model

Hi there,
I tried to convert a fasterrcnn model to onnx format, and followed the instruction from test/test_onnx.py https://github.com/pytorch/vision/blob/master/test/test_onnx.py.
Here is my code:
model=models.detection.faster_rcnn.fasterrcnn_resnet50_fpn(pretrained=True,min_size=800,max_size=1333)
image=cv2.imread("test.jpg")
image=cv2.resize(image,(1333,800))
image1 = Image.fromarray(cv2.cvtColor(image.copy(),cv2.COLOR_BGR2RGB))
image_tensor=to_tensor(image1)
model.eval()
onnx_io = io.BytesIO()
torch.onnx.export(model, [image_tensor], "faster_rcnn.onnx",do_constant_folding=True, opset_version=_onnx_opset_version)
I have succeed convert the model with the above code, however, when I tried to convert the tensor and model to cuda tensor with .to(device), there is an error that is line 359, in _get_top_n_idx r.append(top_n_idx + offset) RuntimeError: expected device cuda:0 but got device cpu.
I don't know how to solve it.
Please help me with that.
Cheers!

Hi,
Sorry for the delay in replying.
My advise would be to make sure you convert your inputs and model to CUDA before exporting to ONNX, this is the safest way.
So it would look something like:
model=models.detection.faster_rcnn.fasterrcnn_resnet50_fpn(pretrained=True,min_size=800,max_size=1333)
image=cv2.imread("test.jpg")
image=cv2.resize(image,(1333,800))
image1 = Image.fromarray(cv2.cvtColor(image.copy(),cv2.COLOR_BGR2RGB))
image_tensor=to_tensor(image1)
model.eval()
model.cuda()
image_tensor = image_tensor.cuda()
# just to be safe, run it once to initialize all buffers
out = model([image_tensor])
# now export it
onnx_io = io.BytesIO()
torch.onnx.export(model, [image_tensor], "faster_rcnn.onnx",do_constant_folding=True, opset_version=_onnx_opset_version)

Let me know if you still have issues.

I also get this error with both FasterRCNN and MaskRCNN, and I'm sure that the model and input tensor are on the GPU. I also run the model once before exporting. Exporting with device = 'cpu' works.
img = Image.open(sys.argv[1]).convert('RGB')
img = np.array(img)

device = 'cuda'
model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True, min_size=800, max_size=800)
model.eval()
model.to(device)

img_ = transforms.ToTensor()(img)
img_ = img_.to(device)

out = model([img_])

torch.onnx.export(model, ([img_],), "/tmp/mask_rcnn.onnx", verbose=True, do_constant_folding=True, opset_version=11)

  File "segment_image.py", line 119, in <module>
    torch.onnx.export(model, ([img_],), "/tmp/mask_rcnn.onnx", verbose=True, do_constant_folding=True, opset_version=11)
  File "/opt/env/lib/python3.7/site-packages/torch/onnx/__init__.py", line 156, in export
    custom_opsets)
  File "/opt/env/lib/python3.7/site-packages/torch/onnx/utils.py", line 67, in export
    custom_opsets=custom_opsets)
  File "/opt/env/lib/python3.7/site-packages/torch/onnx/utils.py", line 466, in _export
    fixed_batch_size=fixed_batch_size)
  File "/opt/env/lib/python3.7/site-packages/torch/onnx/utils.py", line 319, in _model_to_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args, training)
  File "/opt/env/lib/python3.7/site-packages/torch/onnx/utils.py", line 276, in _trace_and_get_graph_from_model
    torch.jit._get_trace_graph(model, args, _force_outplace=False, _return_inputs_states=True)
  File "/opt/env/lib/python3.7/site-packages/torch/jit/__init__.py", line 282, in _get_trace_graph
    outs = ONNXTracedModule(f, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 539, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torch/jit/__init__.py", line 365, in forward
    self._force_outplace,
  File "/opt/env/lib/python3.7/site-packages/torch/jit/__init__.py", line 352, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/opt/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 537, in __call__
    result = self._slow_forward(*input, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 523, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torchvision/models/detection/generalized_rcnn.py", line 70, in forward
    proposals, proposal_losses = self.rpn(images, features, targets)
  File "/opt/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 537, in __call__
    result = self._slow_forward(*input, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torch/nn/modules/module.py", line 523, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/opt/env/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 472, in forward
    boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
  File "/opt/env/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 379, in filter_proposals
    top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level)
  File "/opt/env/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 359, in _get_top_n_idx
    r.append(top_n_idx + offset)
RuntimeError: expected device cuda:0 but got device cpu

>>> import torchvision; torchvision.__version__
'0.5.0.dev20200108+cu100'
>>> import torch; torch.__version__
'1.5.0.dev20200109+cu100'

@janstrohbeck Thanks for the detailed report!
After digging a bit further, there seems to be a couple of issues. The first one is that torch.onnx.operators.shape_as_tensor doesn't take the device of the original tensor into account, so that https://github.com/pytorch/vision/blob/61763fa955ef74077a1d3e1aa5da36f7c606943a/torchvision/models/detection/rpn.py#L21
is always a CPU tensor, and the second one is that once we fix the above we also need to fix https://github.com/pytorch/vision/blob/61763fa955ef74077a1d3e1aa5da36f7c606943a/torchvision/models/detection/rpn.py#L24-L26 to use the device of the original tensor.
@lara-hdr do you think we should change shape_as_tensor in pytorch ONNX to take the device of the original tensor into account as well? Otherwise we can just add casts in the model right away as a workaround solution.
@janstrohbeck @Finniu in the mean time, please convert the model to CPU before exporting to ONNX.

Although exporting the model in GPU mode fails, exporting it in CPU mode and then loading it into GPU-enabled onnx runtime (using onnxruntime-gpu PyPi package) works just fine.
I'm using torch==1.5.0 and torchvision==0.6.0

Although exporting the model in GPU mode fails, exporting it in CPU mode and then loading it into GPU-enabled onnx runtime (using onnxruntime-gpu PyPi package) works just fine.
I'm using torch==1.5.0 and torchvision==0.6.0

Thanks, I will try. Another question is have you tried to convert the onnx model to tensorrt?

@Finniu No, I don't use tensorrt.
BTW, if you want to export maskrcnn_resnet50_fpn so that it accepts any input size, do:
dynamic_axes = {'input': [0, 2, 3], 'output': [0, 2, 3]}
torch.onnx.export(net,  ..., dynamic_axes=dynamic_axes)

Exporting Faster R-CNN model:
...
device = torch.device('cuda')

model.to(device)
input = torch.randn((1, 3, 600, 600), device = device)

torch.onnx.export(model, input, 'model.onnx', opset_version = 11)
...

Error:
/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/rpn.py in _get_top_n_idx(self, objectness, num_anchors_per_level)
   372                 pre_nms_top_n = min(self.pre_nms_top_n(), num_anchors)
   373             _, top_n_idx = ob.topk(pre_nms_top_n, dim=1)
--> 374             r.append(top_n_idx + offset)
   375             offset += num_anchors
   376         return torch.cat(r, dim=1)

RuntimeError: expected device cuda:0 but got device cpu

I exported successfully when moving everything to CPU. GPU is not supported?

@danilopeixoto This error has been reported above.
My solution was to export using CPU.
On inference you can use CPU, GPU, TensorRT, etc. depending on onnx runtime you use.
I'm using this one https://microsoft.github.io/onnxruntime/ and very happy with it.

Hello Team,
I am trying to convert the FasterRCNN to ONNX. I was able to successfully export to ONNX, but I am not able to infer any image. I tried to export the model to have a dynamic input size for image as well. Still with no luck.
I am unable to get clear instructions on what should be image data input to the model.
I think I am messing up somewhere in the input to the model
Below is the code I am trying to implement to export and infer.
# **This piece of code is implemented from the test_onnx.py file**

def get_image_from_url(url, size=None):
    import requests
    from PIL import Image
    from io import BytesIO
    from torchvision import transforms as T

    data = requests.get(url)
    image = Image.open(BytesIO(data.content)).convert("RGB")

    if size is None:
        size = (300, 200)
    image = image.resize(size, Image.BILINEAR)
#     plt.imshow(image)

#     img = Image.open(img_path) # Load the image

    transform = T.Compose([T.ToTensor()]) # Defing PyTorch Transform
    image = transform(image)

#     to_tensor = transforms.ToTensor()
    return image

def get_test_images():
    image_url = "http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg"
    image = get_image_from_url(url=image_url, size=(100, 320))

    image_url2 = "https://pytorch.org/tutorials/_static/img/tv_tutorial/tv_image05.png"
    image2 = get_image_from_url(url=image_url2, size=(250, 380))

    images = image
    test_images = [image2]
    return images, test_images

images, test_images = get_test_images()
dummy_input = torch.randn(1, 3, 224, 224)

model_name = r"fasterrcnn_resnet50_fpn_dynamic_try4_with_image_input"
final_path = model_name + ".onnx"
dynamic_axes = {'input': [0, 2, 3], 'output': [0, 2, 3]}

torch.onnx.export(model, images.unsqueeze(0),final_path ,
                  do_constant_folding=True, opset_version=11,
                  dynamic_axes=dynamic_axes, input_names=['input'], output_names=['output'])

Below is the code I am using to use the onnx model to infer.
folder = my path

model_name = r"fasterrcnn_resnet50_fpn_dynamic_try4_with_image_input.onnx"

final_path = os.path.join(folder,model_name)

# Load the ONNX model
model_onnx = onnx.load(final_path)

# Check that the IR is well formed
onnx.checker.check_model(model_onnx)

# Print a human readable representation of the graph
print(onnx.helper.printable_graph(model_onnx.graph))

import onnxruntime as rt

sess = rt.InferenceSession(final_path)

input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name

pred = sess.run([output_name], {input_name:images})
This crashes my kernel and restarts it. I don't know why?
I think I am messing up with the input type and dimensions.
Could you please help me to get this onnx model up and running!!
Attached is the graph log for the converted model.
Let me know if I am missing somewhere in the conversion procedure as well.
torchvision_frcnn_try4_dynamic_onnx_log.docx
Thanks a lot!!

Torchvision Faster R-CNN model does not support dynamic input shape according to documentation.

Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

@danilopeixoto Dynamic shape support should now work on ONNX if using a very recently torchvision nightly

Hey @danilopeixoto, @fmassa
Thank you for the suggestions.
But I am still not able to get any output either for a fix or dynamic input image.
Could you please have a look into the code let me know where am I going wrong?
Also, I tried to tun the test_faster_rcnn from the latest test_onnx.py (here) file and I got the following error.
As I am a newbie here, I don't exactly know what this error means:
log:
>>> test_object = test_onnx.ONNXExporterTester()
>>> test_object.test_faster_rcnn()
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\nn\functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
  warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
..\torch\csrc\utils\python_arg_parser.cpp:756: UserWarning: This overload of nonzero is deprecated:
        nonzero(Tensor input, *, Tensor out)
Consider using one of the following signatures instead:
        nonzero(Tensor input, *, bool as_tuple)
..\aten\src\ATen\native\BinaryOps.cpp:81: UserWarning: Integer division of tensors using div or / is deprecated, and in a future release div will perform true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torchvision\models\detection\rpn.py:164: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(image_size[1] / g[1], dtype=torch.int64, device=device)] for g in grid_sizes]
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\tensor.py:467: RuntimeWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
  'incorrect results).', category=RuntimeWarning)
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torchvision\ops\boxes.py:117: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torchvision\ops\boxes.py:119: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torchvision\models\detection\transform.py:217: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  for s, s_orig in zip(new_size, original_size)
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\onnx\symbolic_opset9.py:2115: UserWarning: Exporting aten::index operator of advanced indexing in opset 11 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.
  "If indices include negative values, the exported graph will produce incorrect results.")
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\onnx\utils.py:915: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input images_tensors
  'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))
C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\onnx\utils.py:915: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input outputs
  'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\test_onnx.py", line 357, in test_faster_rcnn
    tolerate_small_mismatch=True)
  File "C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\test_onnx.py", line 49, in run_model
    self.ort_validate(onnx_io, test_inputs, test_ouputs, tolerate_small_mismatch)
  File "C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\test_onnx.py", line 71, in ort_validate
    torch.testing.assert_allclose(outputs[i], ort_outs[i], rtol=1e-03, atol=1e-05)
  File "C:\Users\msjmf59\Documents\VirtualEnvironments\pytorch_gpu2\Lib\site-packages\torch\testing\__init__.py", line 24, in assert_allclose
    expected = expected.expand_as(actual)
RuntimeError: The expanded size of the tensor (52) must match the existing size (54) at non-singleton dimension 0.  Target sizes: [52, 4].  Tensor sizes: [54, 4]

Thanks a lot!

cc @neginraoof if you could have a look

cc @neginraoof if you could have a look

Hi @fmassa, is there any tool that I can convert the faster rcnn onnx to tensorrt? I have tried with onnx-tensorrt, but failed in nms converting. Thanks

Hi. I am also experiencing the original RuntimeError: expected device cuda:0 but got device cpu message when JIT tracing any rcnn model (on the same line as OP). Strangely, the first iteration through for ob in objectness.split(num_anchors_per_level, 1) succeeds, but the second one fails.
I am working on a JIT related project that requires the model to be traced on the gpu, so the export on cpu workaround does not apply to me. I am not concerned with ONNX right now, only Torchscript. Is there a timeline on a fix for this? Even guidance on how to fix this myself would be appreciated. @fmassa

Hi,
I was able to export the model to ONNX and it was working fine, but now only empty detections are returned. I've tried to downgrade the package versions, change the opset, check the code for changes.
Model inference using PyTorch still works.
Has anyone experienced this issue exporting Faster RCNN model to ONNX?

I replace the dummy input:
input_data = [torch.rand((3, 600, 600), device = cpu_device)]

with:
input_data = [torch.randn((3, 600, 600), device = cpu_device)]

and it worked.
This issue may be related to Export object detection model to ONNX:empty output by ONNX inference.

I replace the dummy input:
input_data = [torch.rand((3, 600, 600), device = cpu_device)]

with:
input_data = [torch.randn((3, 600, 600), device = cpu_device)]

and it worked.
This issue may be related to Export object detection model to ONNX:empty output by ONNX inference.

Hi @danilopeixoto, do I get this right that your only change was from rand to randn?

I replace the dummy input:
input_data = [torch.rand((3, 600, 600), device = cpu_device)]

with:
input_data = [torch.randn((3, 600, 600), device = cpu_device)]

and it worked.
This issue may be related to Export object detection model to ONNX:empty output by ONNX inference.

Hi @danilopeixoto, do I get this right that your only change was from rand to randn?

@FraPochetti Yes, that was the only change in the code.

@FraPochetti Yes, that was the only change in the code.

@danilopeixoto Thanks! Do you happen to have the code snippet you used by any chance?
(as you probably did yourself) I tried a ton of things and nothing is really working, yours included, unfortunately.

@danilopeixoto Thanks!

I replace the dummy input:
input_data = [torch.rand((3, 600, 600), device = cpu_device)]

with:
input_data = [torch.randn((3, 600, 600), device = cpu_device)]

and it worked.
This issue may be related to Export object detection model to ONNX:empty output by ONNX inference.

Thanks @danilopeixoto it worked! Pushing model and dummy input into cpu and converting it into onnx solved my problem. Later was able to do inference on gpu with onnxruntime 'CUDAExecutionProvider'. 👍

I replace the dummy input:
input_data = [torch.rand((3, 600, 600), device = cpu_device)]

with:
input_data = [torch.randn((3, 600, 600), device = cpu_device)]

and it worked.
This issue may be related to Export object detection model to ONNX:empty output by ONNX inference.

Thanks @danilopeixoto it worked! Pushing model and dummy input into cpu and converting it into onnx solved my problem. Later was able to do inference on gpu with onnxruntime 'CUDAExecutionProvider'. 👍

Hi @Ratansairohith great you found a way!
Would you be so kind to share the entire code snippet you used to get to work?
Reading your comment, I just retried on my own but I don't seem to get it right.
This is the Colab notebook I have put together for reference.

Hi @Ratansairohith great you found a way!
Would you be so kind to share the entire code snippet you used to get to work?
Reading your comment, I just retried on my own but I don't seem to get it right.
This is the Colab notebook I have put together for reference.

Hey @FraPochetti! Yeah my code was pretty straight forward. I trained my own faster rcnn with custom dataset just llike official pytorch tutorial https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html. Then i used the following code snippet to convert my torch model to onnx.
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
import torch
num_classes = 10
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
model.load_state_dict(torch.load('/content/drive/MyDrive/images/fasterrcnn_resnet50_fpn_9.pth'))
model.eval()

# set device to cpu
cpu_device = torch.device('cpu')
x = [torch.randn((3, 384, 384), device = cpu_device)]
model.to(cpu_device)

# finally convert pytorch model to onnx
torch.onnx.export(model, x , "faster_rcnn_9.onnx", verbose=True, do_constant_folding=True, opset_version=11)

Hey @FraPochetti! Yeah my code was pretty straight forward. I trained my own faster rcnn with custom dataset just llike official pytorch tutorial https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html. Then i used the following code snippet to convert my torch model to onnx.
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
import torch
num_classes = 10
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
model.load_state_dict(torch.load('/content/drive/MyDrive/images/fasterrcnn_resnet50_fpn_9.pth'))
model.eval()

# set device to cpu
cpu_device = torch.device('cpu')
x = [torch.randn((3, 384, 384), device = cpu_device)]
model.to(cpu_device)

# finally convert pytorch model to onnx
torch.onnx.export(model, x , "faster_rcnn_9.onnx", verbose=True, do_constant_folding=True, opset_version=11)

Thanks a lot. I am doing the same (I think) but for whatever reason, my masks are still empty.
Quite frustrating.
Let me start with a blank notebook again.

Thanks a lot. I am doing the same (I think) but for whatever reason, my masks are still empty.
Quite frustrating.
Let me start with a blank notebook again.

Hi
when I try to load converted model (faster_rcnn_9.onnx) with onnx runtime I'm getting:
`
import onnxruntime as rt
sess = rt.InferenceSession("faster_rcnn_9.onnx")
`
self._sess.load_model(providers)
onnxruntime.capi.onnxruntime_pybind11_state.NotImplemented: [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for the node Greater_1812:Greater(9)
Any suggestions ?

Hello @fmassa, @lara-hdr.
Thanks you for your work. I was wandering if there a way of exporting faster rcnn model without transformation layers and make 2 static output tensors (boxes and scores)?
Any direction of digging would be appreciated.
Thank you kindly.


Replacing File.renameTo with ant.move

File.renameTo fails silently on Linux across file systems.

ping

@mikea Thank you for your PR and your patience! I'm working getting a new version out shortly.


api not Available any more ?

They changed their endpoints.
Search: https://r6.apitab.com/search/${platform}/${name}
Player: https://r6.apitab.com/player/${id}
Image CDN: https://cdn.tab.one/r6/images/

They changed their endpoints.
Search: https://r6.apitab.com/search/${platform}/${name}
Player: https://r6.apitab.com/player/${id}
Image CDN: https://cdn.tab.one/r6/images/

Thank you man

1fd6635 has everything docuemented now! Hope this helps 👍


[Testing] Add tests for new Pulser SimConfig API

Since Pulser API is changing, there must be some tests to check compliance with SimConfig for mitigation purposes.

@rajaiitp was that solved by #410 or #427 ?

@dominikandreasseitz I don't think it was with either. I should add it.

@rajaiitp @Roland-djee @gvelikova i just tried to run the tests on protocols using the newest qadence and a lot of them fail. could it be related to this?

@rajaiitp @Roland-djee @gvelikova i just tried to run the tests on protocols using the newest qadence and a lot of them fail. could it be related to this?

I fixed some of them in a local branch. Will push it soon.

@rajaiitp Do you have something specific in mind about this ? Otherwise, I will close it.


Consider separating as-cell-summaries by sceneGraphNode

@bherr2
@katyb suggested that we should bucket as-cell-summaries by individual nodes/meshes who share an UBERON ID to make full usage of the precise RUI registrations. Let's discuss?

@katyb
@bherr2 and I talked; Separating AS cell summaries by sceneGraphNode (aka renal_pyramid_a, renal_pyramid_b, etc.) introduces challenges when computing cell summaries and is only relevant for 4-5 AS where multiple 3D meshes share an ontology ID. We should discuss this in a meeting to see if it is worth the change to our process. Additionally, this reduces the amount of data per AS (now bucketed an additional time).

How many AS have this issue?
AS-a | Dataset IDs // maybe they are all the same?
AS-b
K
From: Andreas Bueckle @.>
Sent: Friday, July 26, 2024 11:15 AM
To: x-atlas-consortia/hra-pop @.>
Cc: Borner, Katy @.>; Mention @.>
Subject: Re: [x-atlas-consortia/hra-pop] Consider separating as-cell-summaries by sceneGraphNode (Issue #96)
@katybhttps://github.com/katyb
@bherr2https://github.com/bherr2 and I talked; Separating AS cell summaries by sceneGraphNode (aka renal_pyramid_a, renal_pyramid_b, etc.) introduces challenges when computing cell summaries and is only relevant for 4-5 AS where multiple 3D meshes share an ontology ID. We should discuss this in a meeting to see if it is worth the change to our process. Additionally, this reduces the amount of data per AS (now bucketed an additional time).
—
Reply to this email directly, view it on GitHubhttps://github.com/x-atlas-consortia/hra-pop/issues/96#issuecomment-2252976864, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AAP6S77DZYHPKOZAZWEDVXLZOJRX5AVCNFSM6AAAAABLNCACHOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENJSHE3TMOBWGQ.
You are receiving this because you were mentioned.Message ID<EMAIL_ADDRESS>

Here is a CSV with as3d | ontology_id | sex | ref_organ: https://github.com/cns-iu/hra-cell-type-populations-supporting-information/blob/main/validations/TEMP/output/as3d_uberon_sex.csv
Code to produce it using SPARQL: https://github.com/cns-iu/hra-cell-type-populations-supporting-information/blob/main/validations/TEMP/make_table_as3d_uberon_sex.ipynb


Support Readiness and Liveness Probe for Dapr Runtime

In a scenario,  where user app is ready to serve but Dapr sidecar is having issue to come up or taking time to start then requests will start failing even when the pod is ready to serve.
This will become critical when the app is running in production, and there is need to scale the pod.  User requests might start coming to new ready pod but the requests might start failing when calling Dapr as Dapr indeed is not ready.
To avoid this scenario, Dapr need to provide a way for readiness and liveness probe so user app
can "deterministically" declare that Dapr is ready and so ready to serve requests.

/area runtime

cc @yaron2 , @youngbupark , @amanbha ,
Let me know if this capability already exists. If not, please chime in your opinion on this feature.

I was thinking about this when working on Java SDK. I was considering having a method in the SDK to wait for Dapr's sidecar health (listening on HTTP and GRPC port).
Maybe the sidecar can have a health API and the Apps (and SDKs) can probe for that. Health is not only in the startup but also help during operation.

Yeah we need a formal healthz endpoint for our API. (HTTP only is enough here).
Outside of the scope of this issue, we need readiness and liveness probes for:

Placement Service
Sidecar Injector
Operator

This is for upgrade purposes. Once those probes are in, we can ensure zero downtime upgrades.

yes, proposal is to have a readiness API and liveness API that can be probed.

Yeah we need a formal healthz endpoint for our API. (HTTP only is enough here).
Outside of the scope of this issue, we need readiness and liveness probes for:

Placement Service
Sidecar Injector
Operator
Sentry

This is for upgrade purposes. Once those probes are in, we can ensure zero downtime upgrades.

Agree, we need for system services as well which is very well needed to ensure zero downtime upgrades.

Closed the existing issue #725


New Project Addition: Alzheimer's Disease Detection with Web App

Alzheimer's Detection Using ML with Web App
Description
This project aims to develop a machine learning-based system for Alzheimer's disease prediction. It includes preprocessing and analyzing the dataset, implementing ML algorithms, and creating a web application for users to access the prediction results easily.
Tasks

Preprocess and analyze the Alzheimer's dataset.
Implement ML algorithms for disease prediction.
Create a web application interface for uploading data and displaying prediction results.
Evaluate the performance of ML models for accuracy and reliability.
Enhance the web application for better usability and visualization of results.

Aditya Khamitkar
github.com/TheNaiveSamosa
<EMAIL_ADDRESS>GSSoC 24' Contributor

@sanjay-kv Please assign this issue to me under GSSOC 2024

@Ar710, Hey, I've brought up this Issue earlier and I'm eagerly waiting for it to be assigned. It's important to respect project boundaries and not overlap onto other projects. This has happened with two other projects I'm involved in. You've commented to get it assigned to you. Let's avoid crossing boundaries and focus on our respective projects. Try Creating other projects if you want and raise issues for that.
@sanjay-kv, could you please assign this issue to me as soon as you can? I'm actively working on the code and will commit and create a PR as soon as it's assigned. Also, I'd appreciate it if you could assign all the issues I've raised promptly. Once you've had a chance to review the code, let's reconsider the level. Thanks!

@TheNaiveSamosa Sorry for the delay in assigning.  It has mentioned in the readme file of this repo, this is for beginner, all library adding files will be given only level1 and max 10 issues per person on the repository to avoid spams.
@Ar7109 Feel free to raise new issue, it was my mistake , I missed to assign the assignee.


Your request to provision "Benefits"  template failed!

Provisioning Status
Unfortunately your site provisioning at least partially failed!

Your request to provision "Benefits" template failed!
Please report the issue here providing the following provisioning action correlation ID: 622a5c0e-8c2e-4cb4-a325-362f0b921c3d !

Please report the issue here providing the following provisioning action correlation ID: c77293af-b0ac-4820-887e-6272bf5ff3f9 !


ToDo app in Python

ToDo Bash example for reference<EMAIL_ADDRESS>

done


[Ignore] Testing integration with newest PackageManagement

This PR is to test integration with the newest PackageManagement. Please do not review or merge.

@brywang-msft,
Thanks for your contribution as a Microsoft full-time employee or intern. You do not need to sign a CLA.
Thanks,
Microsoft Pull Request Bot


Add tests for lz4 orc compression

This is kind of a feature request, but kind of not.  Spark added in LZ4 compression support for ORC with https://github.com/apache/spark/commit/878527d9fa
cudf already supports lz4 compression for orc. So we should add in tests to verify it works.

I just ran the integration test with LZ4, And cudf threw an exception with "Unsupported compression type"
https://github.com/rapidsai/cudf/blob/branch-21.10/cpp/src/io/comp/uncomp.cpp#L570-L578

@Salonijain27 Hi Saloni, since cuDF hasn't supported LZ4, we should remove it from 21.10 target.

Putting this into the backlog, as far as I understand cudf does not support lz4 for ORC.  @revans2 please correct me if I am mistaken.


When using sql.Int parameter type  = parameter.type.validate is not a function

I'm attempting to execute a query with a number(.Int) parameter type.
My parameter/query looks like this:
var arg = { name: 'id', type: sql.Int, value: 1 };
req.input(arg.name, arg.type, arg.value);

req.query(myQuery, cb);

When it tries to execute that query I get an error in tedious:
.../node_modules/mssql/node_modules/tedious/lib/request.js:144
      value = parameter.type.validate(parameter.value);
                             ^
TypeError: parameter.type.validate is not a function

When I omit the type from the "req.input" it runs fine, it also runs find if I use NVarChar as the type.  The SQL column I'm using that parameter to check against in a WHERE clause is a Int, identity column so I feel like sql.Int is the right type for me.
Can anyone explain why I'm getting this error?

Can't reproduce the issue. sql.Int as a parameter for both input and output is covered by this integration test.

I'm actually having this issue with setting any type at the moment, but it may be my design pattern for doing so.  I create a mssql parameter ready object in an express middleware function I wrote.  (Just pulls the query string/url components and makes mssql ready parameters to sent with the query down the chain.
So before I execute my query I perform the following:
var request = new sql.Request(connection),
  attachInputParametersToSqlRequest = function (parameterObj) {
          request.input(parameterObj.name, parameterObj.type, parameterObj.value);
          // request.input(parameterObj.name, parameterObj.value);
        };

sqlQuery.queryInputParameters.forEach(attachInputParametersToSqlRequest);

My queryInputParameters are defined correctly and when I debug the type is set correctly, ie:  sql.NVarChar, or sql.Int.  (where sql === mssql)
However it throws the 'parameter.type.validate is not a function' error.
What is interesting is if I hardcode sql.NVarchar which is exactly what parameterObj.type is, it works, but when I use the type I created in another module with a different instance of mssql it fails.
Leaving type off the request.input allows it work properly but I would like to be explicit and provide the type if possible.  Will research more later when I have time to come back to it.

Closing due to inactivity.

I can also confirm with @CollinEstes that having two different imported versions of mssql results in this exact same issue.

I ran into this recently after doing a case insensitive global replace which had the intended side effect of changing require('mssql') into require('MSSQL').  I have had other classeswhere I still correctly had require('mssql') and if I then instantiated an instance of the class that required 'mssql' from the class that required 'MSSQL' this problem would arise. Once I corrected the require statement the problem went away.

I can also confirm with @CollinEstes that having two different imported versions of mssql results in this exact same issue.

I found out this happens when using knex and mssql and having two different modules using mssql. Even though it's the same version it fails, I'm having to require mssql in the same module that starts the knex connection and then inject it in the other module. If I do require mssql in the other module that doesn't start knex it fails. This is crazy and took me a lot of time to figure out. My suspicion is that might modify the mssql dependency.

I can also confirm with @CollinEstes that having two different imported versions of mssql results in this exact same issue.

I found out this happens when using knex and mssql and having two different modules using mssql. Even though it's the same version it fails, I'm having to require mssql in the same module that starts the knex connection and then inject it in the other module. If I do require mssql in the other module that doesn't start knex it fails. This is crazy and took me a lot of time to figure out. My suspicion is that might modify the mssql dependency.

I had the same issue. Thanks for sharing your workaround!
In my case, I have two modules using the exact same mssql version. Module B uses the connection pool of module A. So module B does NOT import mssql directly in order to create connections or to submit a a bulk-query (my use case). But module B creates a bulk-query, which is then submitted by module A. The mssql library is designed to use a Class named "Table" to create a bulk-query. The Table class and some column types where the only things imported by my module B. However I run into this issue.
I think this is still an issue and should be fixed. So please reopen this issue.
It has something to do with the replacement of the driver implementation. The type instances (TYPES) exported by the mssql package are somehow wrong. They are missing the validation function. The types exported by the tedious package do have such a function.


Visual bug

I nearly changed everything I needed and made a pull request, so all the things regarding my changes can be discussed in the pull request and I change this issue.
If I have a long menu that allows scrolling in the items, I see a visual glitch. Seems like the reveal animation and the recycler view do have problems because when I scroll down in the menu some items are not drawn...
I attached a small gif showing the issue

Made a pull request with a few changes and the solution for 1 and 2

@MFlisar maybe you add empty row to your recycler view.

@Salman-Samian the video shows that when closing the menu the items "Test 4" and "Test 5" (at the beginning I open it and click Test 4 - the empty icon - and the menu closes and while closing you see the 2 additional menu items drawn correctly) are drawn correctly, so no, I'm adding no empty items. I assume it's a problem with the animation but I could not find it

@MFlisar I will try on my end and try to resolve it


Swiper dont work if I include popper

If i Include the popper js in bootstrap 4.0 beta, the swiper js stop working. But if i remove the popper, i get a warning.
I should be oppening this in the popper git?

Did you mean nolimits4web/Swiper?
Because i'm using both swiper js and popper js together without any problem.

I changed to the CDN version and it worked like a charm, dont know what was happening...


feat: migrate deployment to official jenkins repo

@jenkins-infra yes, i'm thinking transfer not fork.


Summer/Space Ereshkigal

Hi, I plan on summon Summer/Space Ereshkigal in two years. Can you add her to the planner please? So I can see her materials requirements and plan accordingly. I would appreciate it. Thank you.
VictoriaPkmn :D

Thanks for the report! Confusingly she's just called "Ereshkigal" in the game file, so won't show up if you search for "Space" or "Summer".
I've made a couple of changes so she should show up whichever you search now :).

Thanks for the report! Confusingly she's just called "Ereshkigal" in the game file, so won't show up if you search for "Space" or "Summer".
I've made a couple of changes so she should show up whichever you search now :).

Thank you!
VictoriaPkmn :D


Uncaught Exception: Attempting to call a function in a renderer window that has been closed or released. Function provid

An uncaught exception was reported.
Info:
OS: win32
Arch: ia32
GPMDP Version: 3.0.1
Time: Fri Mar 18 2016 15:39:53 GMT-0500 (Central Daylight Time)
Error:
Attempting to call a function in a renderer window that has been closed or released. Function provid
Error: Attempting to call a function in a renderer window that has been closed or released. Function provided here: Object.<anonymous> (C:\Users\zjgom\AppData\Local\GPMDP_3\app-3.0.1\resources\app\build\inject\GPMInject\interface\mouseButtonNavigation.js:8:8.
    at BrowserWindow.callIntoRenderer (C:\Users\zjgom\AppData\Local\GPMDP_3\app-3.0.1\resources\atom.asar\browser\lib\rpc-server.js:201:19)
    at emitTwo (events.js:92:20)
    at BrowserWindow.emit (events.js:172:7)

This issue was created automatically inside the uncaughtException handler

Closing this automatically reported issue as a duplicate of #470


Thruster interface should adjust to batteries charge state

The battery charge state has a significant impact on the thrust delivered for a given PWM signal. Making the thruster interface choose the correct thrust to PWM mapping for the given charge state would ensure much more precise delivery of thrust.

Change has been implemented in PCA9685_thruster_interface


Feature: provide optional password to ssh config

NOT TESTED
ATTENTION, PLEASE CHECK BEFORE MERGING
Adding an optional password attribute seemed very straight forward. I just looked up how other attributes were handled and added a new attribute named password. I hope it really is as easy as it seemed and works the way I thought it would.
However this was the first time I saw Go code so please double check.
Also there were some things I wasn't quite sure about:

Should I use a Sensitive or ForceNew tag as I don't know what they do?
Should I add the password attribute to resource_resource_vX.go as well or just to resource_resource.go?
Not sure if I should make sure that only one, either password or private key is used for authentication. If I should do it, I don't know how to.

@jalemann sorry for getting back this late. I'll merge this and do some additional changes before releasing. Thank you for your contribution!


feat: Add helm chart for nimbus-netpol adapter

Description
Helm chart to deploy the nimbus-netpol adapter as a Kubernetes deployment.

Fixes # (issue)
Does this PR introduce a breaking change?
No
Checklist

[x] PR title follows the <type>: <description> convention
[x] I use conventional commits in my commit messages
[x] I have updated the documentation accordingly
[x] I Keep It Small and Simple: The smaller the PR is, the easier it is to review and have it merged
[x] I have performed a self-review of my code
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes

Additional information for reviewer
Mention if this PR is part of any design or a continuation of previous PRs

Converted this to draft because we need to rebase it after https://github.com/5GSEC/nimbus/pull/42 gets merged into main.


Support "owner" field for Template kinds

Feature Suggestion
It would be nice to let Template entities be associated with an owner so that users/teams can track them along with the components they own or allow another consumer to look up the owner to report any issues with the scaffold.
Possible Implementation
It was suggested by @Rugvip here that "we'd need the BuiltinKindsEntityProcessor to add an owner relation as it does for the other entity kinds"
Context
See above.

Yep we do indeed want this. The missing piece is to create the ownership relations when the field is present in the entity. It should be handled the same way as the other entity types and added here: https://github.com/backstage/backstage/blob/master/plugins/catalog-backend/src/ingestion/processors/BuiltinKindsEntityProcessor.ts

@kuangp Is this something you would like to contribute? :)

@freben sure I can take a stab at it

Thanks, assigned!

Closed via #5578


Update nodemailer to 6.4.6

A minor code change was needed to get the returned type from
addressParser correct.

Thanks!


cosmic-tweaks

When https://github.com/NixOS/nixpkgs/issues/345258 will have a Nixos package, I can ping here if you're willing to add it to this flake.

Nixpkgs COSMIC packages are still generally unhelpful for packaging for use in the desktop environment (even if already packaged in nixpkgs given the problems they have), so I've gone ahead and added cosmic-tweaks in 338c717e405358b46288e666b0baf8403f3f6c52


remove the provenance file from the dataset

removes the provenance files, moves the folder into the dataset and writes the file again if something went wrong.
See #3 for details on the reason for this change.

@wow-such-code so the recovery is not needed?

@wow-such-code so the recovery is not needed?

Yes, it will recover by itself in an error case. The only case where files can vanish is if the script runs through without any errors and without moving files.


[BUG] dynamic readme guesses incorrect content-type

setuptools version
65.6.3
Python version
Python 3.11
OS
linux
Additional environment information
Run within an isolated build via python -m build
Description
This is an issue for a project that uses pyproject.toml to define metadata using the dynamic readme feature provided by setuptools.
The contents of pyproject.toml contain these excerpts:
[build-system]
requires = ["setuptools >=61"]
build-backend = "setuptools.build_meta"

[project]
# ...
dynamic = ["readme"]

[tool.setuptools.dynamic]
readme = {file = ["docs/release.md", "LICENSE.md"]}

With the rendered METADATA file, "Description-Content-Type: text/x-rst" is found, which is incorrect. The concatenated description files are normal, but remain as Markdown format. This inconsistency of content-type is not detected by (e.g.) twine check --strict dist/*.
This is not an issue with declarative configurations with (e.g.) setup.cfg, which allows a field to explicitly state the content-type of the concatenated content:
[metadata]
# ...
long_description = file: docs/release.md, LICENSE.md
long_description_content_type = text/markdown

Expected behavior
The content-type of the files should be auto-detected, and rendered as "Description-Content-Type: text/markdown". Inconsistencies or ambiguous guesses should raise warnings.
How to Reproduce

create a demo project with the pyproject.toml content, and two Markdown files with *.md suffix
run python -m build --wheel to build the wheel and generate METADATA

Output
METADATA contains "Description-Content-Type: text/x-rst"

Hi @mwtoews, currently there is no auto-detection for the content-type (and due to the complexity to cover all edge cases it is very unlikely such feature would be implemented). What is likely to be happening is that the content type is getting a default value.
I belive you can add another content-type key to the TOML table (but it's been a while, so I might be wrong).

Isn't the content-type already auto-detected for pyproject.toml content like readme = "README.md"?
A non-dynamic readme that specifies content-type is:
[project.readme]
text = """\
This is a long description of the package.
"""
content-type = "text/markdown"

but taking only content-type with a dynamic readme appears to be incompatible. Suggestions welcome!

In the case of a non-dynamic readme, the content type is taken from the file extension. With dynamic, you can concatenate multiple files, which brings a lot of edge cases, so no guessing is made.
Have you tried using readme = {file = ..., content-type = ...} inside tool.setuptools.dynamic?

Thanks @abravalheri that works! I'll see if I can close this issue with a documentation enhancement.


Crash on generated world load

Crashes on world load until this mod is disabled, I can then re-enable it in-game and it works as if nothing hapenned
crash-2024-02-19_06.14.45-client.txt

I haven't found out the specific reason yet.
But this problem doesn't seem to exist in the latest version because I did some refactoring in later versions.

I haven't found out the specific reason yet. But this problem doesn't seem to exist in the latest version because I did some refactoring in later versions.

It's honestly a pain to reproduce as this crash appears to be somewhat random - I can play for a while, log off at a certain spot, then when I come back I get a crash until I disable the mod temporarily. Then I keep on playing, and I can log on and off multiple times without any issues even when in previously "problematic" chunks, as well as throughout the world
But that's my story as of 1.19.2, crashing was a lot more frequent in 1.18.2. Anyway, thanks for your attempted support.


Update install-powershell.ps1 to include LTS and Version parameters

Summary of the new feature / enhancement
This will enable easier install of any version or the LTS version which can be used for Side/Side testing very nicely.
Proposed technical implementation details (optional)
New Parameters

LTS - Switch
Version - String

Changing this issue title to better represent that there has been more updates to this than just the LTS & Version


bug: copier update throws error if previously selected value is  disabled

Describe the problem
When trying to update the project created from template, we ran into the scenario where previously selected value choice was disabled, causing an error.
We have a setup similar to this:
version:
  type: str
  help: Choose your Python version
  choices:
    - "3.8"
    - "3.9"

image:
  type: str
  help: Choose a base Docker image
  choices:
    slim (python==3.8):
      value: slim-3.8
      validator: '{% if version == "3.8" %}{% else %}does not apply{% endif %}'
    bullseye (python==3.8):
      value: bullseye-3.8
      validator: '{% if version == "3.8" %}{% else %}does not apply{% endif %}'
    alpine (python==3.9):
      value: alpine-3.9
      validator: '{% if version == "3.9" %}{% else %}does not apply{% endif %}'
    bullseye (python==3.9):
      value: bullseye-3.9
      validator: '{% if version == "3.9" %}{% else %}does not apply{% endif %}'

Basic idea is to show different docker images based on previous versions, and there might be scenario where some images only available only certain versions.
What would be best way to overcome this error? Is this a bug on copier side?
Template
Minimal examples to reproduce the issue:

template: https://github.com/MantasPetrikasVinted/copier-playground
filled project: https://github.com/MantasPetrikasVinted/copier-playground-filled

To Reproduce

Run copier copy git@github.com:MantasPetrikasVinted/copier-playground.git
Select 3.8 and slim-3.8
Run copier update
Select 3.9
Get error message

or

Run git clone git@github.com:MantasPetrikasVinted/copier-playground-filled
Run copier update
Select 3.9
Get error message

Logs
copier-playground-filled git:(main) copier update
No git tags found in template; using HEAD as ref
Updating to template version 0.0.0.post3.dev0+1b21cd9
No git tags found in template; using HEAD as ref
🎤 Choose your Python version
   3.9
Traceback (most recent call last):
  File "/Users/mantas/.local/bin/copier", line 8, in <module>
    sys.exit(copier_app_run())
             ^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/plumbum/cli/application.py", line 638, in run
    inst, retcode = subapp.run(argv, exit=False)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/plumbum/cli/application.py", line 633, in run
    retcode = inst.main(*tailargs)
              ^^^^^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/decorator.py", line 232, in fun
    return caller(func, *(extras + args), **kw)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/cli.py", line 71, in handle_exceptions
    return method(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/cli.py", line 402, in main
    with self._worker(
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 205, in __exit__
    raise value
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/cli.py", line 410, in main
    worker.run_update()
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 826, in run_update
    self._apply_update()
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 882, in _apply_update
    with replace(
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 205, in __exit__
    raise value
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 890, in _apply_update
    current_worker.run_copy()
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 745, in run_copy
    self._ask()
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/copier/main.py", line 454, in _ask
    new_answer = unsafe_prompt(
                 ^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/questionary/prompt.py", line 221, in unsafe_prompt
    question = create_question_func(**_kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/questionary/prompts/select.py", line 146, in select
    ic = InquirerControl(
         ^^^^^^^^^^^^^^^^
  File "/Users/mantas/.local/pipx/venvs/copier/lib/python3.12/site-packages/questionary/prompts/common.py", line 274, in __init__
    raise ValueError(
ValueError: Invalid 'initial_choice' value ('<questionary.prompts.common.Choice object at 0x108887410>'). It must be a selectable value.

Expected behavior
First selectable value is selected
Operating system distribution and version
macOS Ventura 13.6
Copier version
copier 9.1.1
Python version
Python 3.9.17
Installation method
pipx+pypi

It seems like the issue is pretty obvious, thanks for this clear bug report.
For now, it seems to me that you'll be able to workaround the issue by passing the new answers through the -d flag.

Ran into the same issue on windows.

I've submitted a PR that fixes this problem: #1589


Giving credit where credit is due

I've been meaning to do this for a while... I shows on the lower-right in the explore view, under the visualization.
@michellethomas @williaster

Coverage increased (+0.2%) to 72.335% when pulling 0ce25a347d43aa7cd2aadf10266fbbc57b83767f on credits into 866e00d78cf90a0022f9cfa21d098784e5d27c04 on master.

nice LGTM


Datatree release blog post

[x] Closes #708, and a bullet from https://github.com/pydata/xarray/issues/8572

I would like to merge this soon, ideally before AGU next week. Note the post is actually much more about the NASA x Xarray collaboration than it is about the DataTree class itself, as that should be covered by the docs. Let me know if you agree with my summary of how the migration project went! 😀
cc @shoyer @keewis @owenlittlejohns @flamingbear @eni-awowale @aladinor @briannapagan

Thanks for the comments - I've addressed them and am planning to publish this tomorrow.


fixed event card scrolling

Related Issue

Fixes #51

Proposed Changes

Continues Event Card Scrolling from Right to Left without Breaks.
Hover to Puase functionality retained

Checklist

[x] Documentation of the code
[x] Add your name to the CONTRIBUTORS file
[x] Testing your code in the local machine

Screenshots

@TheCodeVenturer  Have a look!


[SPARK-30802][ML] Use Summarizer instead of MultivariateOnlineSummarizer in Aggregator test suite

What changes were proposed in this pull request?
There are three changes in this PR:

use Summarizer instead of MultivariateOnlineSummarizer in Aggregator test suites (similar to https://github.com/apache/spark/pull/26396)
Put common code in Summarizer.getRegressionSummarizers and Summarizer.getClassificationSummarizers.
Move MultiClassSummarizer from LogisticRegression to ml.stat (this seems to be a better place since MultiClassSummarizer is not only used by LogisticRegression but also several other classes).

Why are the changes needed?
Minimize code duplication and improve performance
Does this PR introduce any user-facing change?
No
How was this patch tested?
existing test suites.

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Merged to master

Thanks! @srowen @zhengruifeng


Components initialization

[ ] Initialize react app
[ ] Add components to project
[ ] Install necessary dependencies
[ ] Add linters

Thank you very much for the approval @Wes-Isaac


feat(cli): add "gas tag" command to create a tag

Pull Request Test Coverage Report for Build 880627029

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 81.64%

Totals

Change from base Build 878579213:
0.0%

Covered Lines:
4300

Relevant Lines:
5267

💛  - Coveralls


[bazel] Check targets that depend on Verilator and Vivado are tagged

Either a manual tag, a verilator tag or a vivado tag can keep these from getting brought into wildcards when people are on systems that don't support them.

I'll merge this first as a non-critical check to monitor for a while.
I've dropped the jtag check because that can be done as part of the rule we're querying for (the problem is running jtag tests, not building them, just like cw310 tests)


Index Option AM Settlement

Expected Behavior
Supports AM settlement. For example, the standard SPX options are AM-settled.

PM settlement: Exercise settlement values based on the reported level of the index calculated with the last reported prices of the index's component stocks at the close of market hours on the day of exercise.
AM settlement: Exercise settlement values based on the reported level of the index calculated with the opening prices of the index's component stocks on the day of exercise.

Actual Behavior
All options are settled with the closing price.
Potential Solution
Implements AM settlement (it will change the transaction time).
We use the close for PM settled and could use the open for AM settlement, however, there are official settlement prices
Reproducing the Problem
N/A
Checklist

[x] I have completely filled out this template
[x] I have confirmed that this issue exists on the current master branch
[x] I have confirmed that this is not a duplicate issue by searching issues
[x] I have provided detailed steps to reproduce the issue

Dupliacate of https://github.com/QuantConnect/Lean/issues/7728


Single/Multiple

There are words that can be splited in two words, like あーあ.
This can be stored with enum, like
enum Word {
    Single(NodeDetails),
    Multiple(Vec<(String,NodeDetails)>),
}

Also, this may be able to remove orig from NodeDetails, and reduce the size of naist-jdic dictionary.

あーあ,3,3,4740,感動詞,,,,,*,あー:あ,アー:ア,アー:ア,1/2:1/1,C1


Translation data

The translations for the presets used to be part of the main translation files here https://github.com/openstreetmap/iD/tree/develop/dist/locales
Is this going to change with the move of the presets to a new repository? It looks like translations would be here:
https://github.com/openstreetmap/id-tagging-schema/tree/main/dist/translations
but there is only English.

@westnordost Yes! I already added the code for this here. Just need to publish a new version of the builder and update this repo.

Nice! Would you mind dropping a comment here when this is done or closing this issue when it is done so I am notified of it?

All the translation files are now included at https://github.com/openstreetmap/id-tagging-schema/tree/main/dist/translations.
There's also an index.json file that shows the completion percentage for every language. We'll use this in iD to avoid downloading backup langauges if one of the user's preferred languages is already complete.