Do you want to leave a lasting mark on the open source ecosystem by building awesome tools for other developers?
Apply here: https://apply.workable.com/j/12939AB951

Do you want to leave a lasting mark on the open source ecosystem by building awesome tools for other developers?
Apply here: https://apply.workable.com/j/12939AB951
I think it's quite problematic on #Linux to start off with regular ol' #Python that came with the system (i.e. /usr/bin/python
), and then as you go installing some packages (i.e. on the #AUR if you're on #ArchLinux) which will then install some Python libraries using it, and then you start using something like #Conda or #Miniconda whereby subsequent package installations or updates may be installing these libraries on the Conda environment (i.e. ~/miniconda3/python
) and not the system, so there's some overlap there or so? I'm wondering what's the best way of moving forward from this point - esp since sometime ago, it's no longer possible to raw pip install <package>
anymore for wtv reason.
I released version 0.18.6 oft #boinor today. Though I fixed some open bugs from the #poliastro list of issues, this release is basically done to upload something to #conda and prepare a #debian package.
#python #astronomy #software #release
I know we like to act like space is completely free these days, but maybe we take that too far.
"Hm, I think I should -v
this one tar invocation, just to see what I'm actually backing up from my homedir."
"Sure! Here's your .python
directory, containing thousands of files supporting python libraries. And your .local/python
directory, containing thousands of files supporting python libraries. Oh, and your .conda
directory, never guess what's in there..."
"I get it."
"Do you want to know what's in your .pyenv
director~"
"I. Get. It."
(ETA: 1.2GB at the end. Though most of that is just .pyenv
; the others are much smaller).
As part of a submission to @joss I had to explore using #conda to build #software for the first time.
I have always been reticent to use it (I like pip for python, and much of my work is on #HPC where environment modules are king.
I wrote a short post reflecting on what I learnt and my new opinions on conda: https://jackatkinson.net/post/pondering_conda/
https://bioinfo-fr.net/conda-et-le-piege-de-la-licence-anaconda
#bioinfofr #anaconda #conda
Troubleshooting Python Virtual Environment Errors on Windows 11. Common causes include PATH variable issues, environment creation inconsistencies, and permission problems. Learn how to resolve these errors and improve your workflow using advanced techniques & tools like virtualenvwrapper or conda. #PythonVirtualEnvironmentError #Windows11 #Virtualenv #Conda #PythonError #Programming
https://tech-champion.com/microsoft-windows/troubleshooting-python-virtual-environment-activation-errors-on-windows-11
The Python tools for the Polylith Architecture now has support for the Pixi package management tool, thanks to a contribution from the Community!
https://github.com/DavidVujic/python-polylith/discussions/325
Switch to conda-forge or even better start using #pixi https://pixi.sh/latest/basic_usage/
Anaconda Inc requested research institute where I work to pay for corporate license because researchers are using #anaconda in their daily work. In effect the institute will block access repo.anaconda.com.
I have to say I haven't seen it coming 10+ years ago when I started to use #conda.
#enshittification
What parts of #guix are used when it's hosted?
Survey found:
It's used on top of their #GNU #Linux distro by a third of users
- 50% for package management - same need as #homebrew #conda or #nix usage.
- 41% #dev environments (like #pip or #docker). Super interesting! Lots of comments from users looking for #Nix flakes features
- 28% package their own software.
- 17% for #dotfiles and home environment management. Seems like an opportunity to attract users!?
https://guix.gnu.org/en/blog/2025/guix-user-and-contributor-survey-2024-the-results-part-2/
After programming a good 2 months in #python finally found the tool #poetry which is quite similar to how #nodejs bundles libraries into a directory either locally or globally in the cache directory.
I have tried the other tools from #pyenv to #venv and/or #virtualenv. Where I thought they were used to deal with library dependency management only to realize that they are more like #nvm.
I did use #conda for some time, though preferred a python only solution. I do realize that poetry won't resolve all issues and might need to look into to containerization later on, though for the time period it looks like a good solution.
I am turning slowly into someone who kind of likes #VSCode or its derivatives such as #VSCodium or #OSSCode. Maybe my #SpyderIDE days are over if I can get a similar IPython console and variable explorer set up easily.
Somehow, after multiple attempts over the years, I've managed to get my #conda environments to play nicely with the IDE.
My colleague, who is a #Python guru, has had a play with #uv for managing installations and environments after I suggested it following recommendations from folks on Mastodon.
She has never used it before but says it seems to be faster and more lightweight than #conda and will investigate further.
I'm resisting the temptation just now because I have a case study to write and a project proposal to draft before I finish for Christmas hols on Thursday evening.
Инфраструктура для Data-Engineer виртуальные окружения
В современной Python-разработке управление зависимостями и изоляция проектов являются критически важными аспектами. Независимо от того, работаете ли вы над небольшим скриптом или крупным проектом, правильная организация окружений поможет избежать конфликтов между пакетами и обеспечит воспроизводимость вашего кода.
Well, after hours fighting to get #python + #quarto + #conda working on an HPC server, I decided to give up and try the path of least resistance that is the jupyter notebook environment provided by the IT people.
Now I'm learning that not only #jupyter notebooks suck themselves, but the jupyter IDE is... kind of bad? Setting the correct wd is a PITA, the file explorer is terrible, horizontal scrollbars hide code, no variable inspector?
```
Error: package or namespace load failed for ‘SummarizedExperiment’ in dyn.load(file, DLLpath = DLLpath, ...):
unable to load shared object '/gnu/store/69q48pgylw5fnp916mwwcdpmxx3hcpdw-r-matrix-1.7-0/site-library/Matrix/libs/Matrix.so':
/lib64/libm.so.6: versionGLIBC_2.35' not found (required by /gnu/store/69q48pgylw5fnp916mwwcdpmxx3hcpdw-r-matrix-1.7-0/site-library/Matrix/libs/Matrix.so)
Error: package ‘SummarizedExperiment’ could not be loaded
```