jax-healpy 0.5

Healpix JAX implementation.

jax-healpy

jax-healpy: A JAX-based implementation of HEALPix functions for high-performance scientific computing.

This project provides a comprehensive JAX-native implementation of HEALPix (Hierarchical Equal Area isoLatitude Pixelization) functions, designed for modern scientific computing with GPU acceleration, automatic differentiation, and seamless integration with machine learning workflows.

⚠️ WARNING: BETA STAGE - This project is in active development. APIs may change and some features are still under development.

Key Features

• 🚀 GPU Acceleration: Leverage JAX's XLA compilation for high-performance computing on CPUs and GPUs
• 🔄 Automatic Differentiation: Full support for forward and reverse-mode automatic differentiation
• 📊 Vectorized Operations: Efficient batch processing of HEALPix operations
• 🔧 HEALPix Compatibility: Drop-in replacement for many healpy functions
• 🌐 Spherical Harmonics: Integration with s2fft for spherical harmonic transforms
• 🎯 Clustering Tools: Advanced clustering algorithms for astronomical data analysis

Installation

Prerequisites

First, install JAX following the official documentation for your target architecture (CPU/GPU).

Install jax-healpy

Install via PyPI:

pip install jax-healpy

For spherical harmonics functionality, install with recommended dependencies:

pip install jax-healpy[recommended]

Development Installation

Clone the repository and install in editable mode:

git clone https://github.com/pchanial/jax-healpy.git
cd jax-healpy
pip install -e .

Quick Start

import jax.numpy as jnp
import jax_healpy as hp

# Create a HEALPix map
nside = 64
npix = hp.nside2npix(nside)

# Convert pixel indices to sky coordinates
pixels = jnp.arange(npix)
theta, phi = hp.pix2ang(nside, pixels)

# Convert sky coordinates back to pixels
recovered_pixels = hp.ang2pix(nside, theta, phi, nest=False)

# Spherical harmonics transform (requires s2fft)
alm = hp.map2alm(skymap, lmax=128)
reconstructed_map = hp.alm2map(alm, nside=nside)

Performance Benchmarks

Execution time measured on high-performance computing systems:

Test System:

• CPU: Intel(R) Xeon(R) Gold 2648 @ 2.50GHz
• GPU: NVIDIA Tesla V100-SXM2-16GB

jax-healpy demonstrates significant performance improvements, especially for GPU-accelerated workloads and batch operations.

Documentation

Complete documentation is available at jax-healpy.readthedocs.io

• Getting Started Guide
• API Reference
• Examples and Tutorials

Development

Setting up Development Environment

Install development dependencies:

pip install -e .[test]

Running Tests

Execute the test suite:

pytest

Code Quality

This project uses pre-commit hooks for code quality:

pip install pre-commit
pre-commit install

High-Performance Computing

Environment Setup

For HPC systems, load required modules:

module load python/3.10
python -m venv venv
source venv/bin/activate
pip install jax-healpy

GPU Support

Ensure JAX is properly configured for your GPU architecture. See the JAX GPU installation guide for details.

Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• Setting up the development environment
• Code style and testing requirements
• Submitting pull requests
• Reporting issues

Citation

If you use jax-healpy in your research, please cite:

@software{jax_healpy,
  author = {Chanial, Pierre and Biquard, Simon and Kabalan, Wassim},
  title = {jax-healpy: JAX-based HEALPix implementation},
  url = {https://github.com/pchanial/jax-healpy},
  year = {2024}
}

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Acknowledgments

• Built on JAX for high-performance computing
• Compatible with HEALPix pixelization scheme
• Integrates with s2fft for spherical harmonics
• Inspired by the original healpy package