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potamides

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About

potamides is a Python package for constraining gravitational potentials using stellar stream curvature analysis. The name is inspired by Greek ποταμίδες ("potamídes", meaning "river streams"), with the initial "P" representing $\Phi$, the conventional symbol for gravitational potential in astronomy.

Key Features

  • 🌊 Spline-based stream modeling: Smooth parametric representation of stellar streams with cubic spline interpolation
  • 📐 Curvature analysis: Compute geometric properties including tangent vectors, curvature, principal normals, and arc-length
  • 🌌 Gravitational field fitting: Match stream curvature to potential models with customizable halo and disk components
  • JAX-accelerated: Fast, GPU-compatible computations with automatic differentiation and JIT compilation
  • 📊 Likelihood framework: Bayesian inference for potential parameters using curvature-acceleration alignment
  • 📈 Visualization tools: Built-in plotting methods for tracks, geometry vectors, and gravitational fields

Installation

Using pip (recommended)

pip install potamides

From source

git clone https://github.com/xggs-dev/potamides.git
cd potamides
uv pip install -e .

Requirements

  • Python >= 3.11
  • JAX >= 0.5.3
  • For GPU support, install JAX with CUDA support separately
  • See pyproject.toml for full dependency list

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This package builds upon excellent open-source scientific software:

  • JAX: High-performance numerical computing with automatic differentiation
  • galax: Galactic dynamics in JAX
  • interpax: Interpolation library for JAX
  • Astropy: Community Python library for astronomy
  • unxt: Unitful quantities for JAX

AI Usage Disclosure

Portions of this codebase (including tests and documentation) were refactored and generated with the assistance of Language Models. All AI contributions have been and will continue to be reviewed and verified by the human maintainers.


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Constrain gravitational potentials using stellar stream curvature analysis

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