Python

Disclaimer: Based on Google Gemini outputs! When building complex, multi-module Python projects, especially in data science, deep learning, or physics, managing console output is a nightmare. You are often caught in a binary trap: either you turn debugging off and see nothing, or you turn it on and get drowned in a wall of INFO and DEBUG noise from third-party libraries and internal modules. To make matters worse, many heavy frameworks (like PyTorch, Bilby, or Hugging Face) initialize their own logging configurations right at import time....

I restructured all my Python installation in the system and have now committed to use only Anaconda conda environments to manage my Pythons. Below are some steps I followed to clean my directories! Fixing Pandas pytables Errors and Making Python/Brew/Conda Play Nicely on macOS This quick post covers two things that bite many of us on macOS: Why pandas.read_hdf() raises ImportError: Missing optional dependency 'pytables' even when you “installed it,” and A clean ~/....

Sphinx documentation build also requires configuring the source pathname inside docs/conf.py So, the process for building documentation is: Run sphinx-quickstart, e.g. sphinx-quickstart docs/ -p xmmPipeline -a Suyog –ext-autodoc –ext-napolean –ext-doctest –ext-intersphinx –ext-todoint Edit docs/conf.py file to have the correct documentation path. Run autodoc to automatically import the docstring from the code modules and scripts, e.g. sphinx-apidoc -o ./docs ../src/xmmPipeline/ –force Use make html for HTML, make latex for LaTeX, make latexpdf for PDF and make epub for epub...