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CCL Development Environment Setup Guide

Based on the project analysis, here's how to set up your shell session for CCL development:

Project Overview

CCL (Core Cosmology Library) is a Python package with C/C++ extensions built using:

  • Python 3.10+ with setuptools
  • CMake build system for C/C++ components
  • SWIG for Python-C bindings
  • GSL (GNU Scientific Library) and FFTW dependencies
  • pytest for testing
  • flake8 for linting

Development Setup and Workflow

For detailed instructions on setting up your development environment, testing, and benchmarking, please refer to the project documentation in readthedocs/source:

Quick Start (Conda)

# Create conda environment from the project specification
conda env create -f .github/environment.yml
conda activate test

# Install CCL in development mode
pip install -v -e .

Quick Development Cycle

# Data cycle
conda activate test
# ... edit files ...
pip install -v -e .  # Rebuild if C files changed
flake8 pyccl/
OMP_NUM_THREADS=2 pytest -vv pyccl

Updating dependencies

To upgrade the project dependencies defined in .github/environment.yml:

# Update the conda environment from the YAML (reinstalls pip entries too)
conda env update -f .github/environment.yml --prune

# Activate the environment (name is `test` by default in the YAML)
conda activate test
  • Notes:
    • --prune removes packages that are no longer listed in the YAML.
    • Re-running env update will also (re)install the pip:-listed packages from the YAML, but if you need to explicitly upgrade pip-installed packages afterwards, run:
# Reinstall/upgrade pip packages listed in the YAML (example)
pip install --upgrade classy isitgr \
   'velocileptors @ git+https://github.com/sfschen/velocileptors' \
   'baccoemu @ git+https://bitbucket.org/rangulo/baccoemu.git@master' \
   MiraTitanHMFemulator dark_emulator colossus
  • If you prefer to recreate the environment from scratch:
conda env remove -n test
conda env create -f .github/environment.yml
conda activate test
  • Reminder: pip-installed packages come from PyPI or the specified VCS URLs and are unaffected by conda channels; the YAML's channels: controls only conda package resolution.