ArtStudies/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Quick Commands

Python Projects

• Install dependencies: uv sync (root) or uv sync in a subdirectory with its own pyproject.toml
• Run linter: ruff check . (includes all files via extend-include)
• Auto-fix: ruff check . --fix
• Format imports: ruff format .

R Projects

• Load project: RStudio/.Rprofile uses renv for isolation
• Check style: lintr::lint("script.R")
• Format code: styler::style_file("script.R")

SQL (M2/SQL)

docker compose -f M2/SQL/docker-compose.yml up -d
make tp1       # Execute TP1.sql
make tp2       # Execute TP2.sql
make tp3       # Execute TP3.sql
make project   # Execute DANJOU_Arthur.sql

Project Structure

L3/                    # Bachelor's degree (3rd year)
M1/                    # Master's degree (1st year)
M2/                    # Master's degree (2nd year)
└── <Course>/          # e.g., "Deep Learning", "Data Visualisation"
    ├── TP{n}/         # Practical work (numbered)
    ├── Project/       # Final project
    └── data/          # Course-specific data

Python Conventions

• Package manager: uv (workspace configured at root)
• Linting: Ruff with strict rules (select = ["ALL"])
• Import ordering: Custom sections in pyproject.toml:
  • data-science: numpy, pandas, scipy, matplotlib, seaborn, plotly
  • ml: tensorflow, keras, torch, sklearn, xgboost, catboost, shap
• Reproducibility: Use np.random.seed(42) for random seeds
• Notebooks: Jupyter with descriptive markdown cells

R Conventions

• Package management: renv (autoloading via .Rprofile)
• Linting: lintr configured in .lintr
• Documents: RMarkdown (.Rmd) for reproducible reports
• Visualization: ggplot2, plotly, FactoMineR

Key Technologies

• Data Science: numpy, pandas, scipy, matplotlib, seaborn, plotly, geopandas
• Machine Learning: scikit-learn, xgboost, catboost, tensorflow, keras, shap
• LLM/RAG: langchain, sentence-transformers, faiss-cpu
• R: tidyverse, ggplot2, FactoMineR, caret, glmnet, RShiny

Notes

• Some subprojects (e.g., M2/Reinforcement Learning/project/) have isolated pyproject.toml files
• Large datasets are not versioned—download via notebook code when needed
• Course materials and documentation are primarily in French