DebrisPy

Welcome to DebrisPy — a lightweight Python package designed to compute the azimuthally averaged (radial) profiles of surface density profiles in debris discs.

Installation

1. The package can be installed via PyPI directly:

pip install debrispy

2. For development, clone the repository and install in editable mode:

git clone https://github.com/DenizAkansoy/DebrisPy.git
cd DebrisPy
pip install -e .

Important: DebrisPy requires Python 3.8 or higher.

Features

DebrisPy provides tools for:

• defining semi-major-axis surface-density profiles;
• specifying unique eccentricity profiles or eccentricity distributions;
• constructing eccentricity kernels for ASD calculations;
• computing azimuthally averaged surface-density profiles;
• validating and visualising results with Monte Carlo sampling;
• using built-in profiles or arbitrary user-defined functions;
• optional adaptive gridding, interpolation, and parallelisation for more demanding calculations.

Important Note: custom functions must be vectorised

User-supplied functions should be vectorised. DebrisPy evaluates many profiles and distributions on NumPy arrays, so scalar Python conditionals such as if/else will usually fail or behave incorrectly.

For example, avoid:

def bad_profile(a):
    if a < 50:
        return 0.0
    return a**-1

Use NumPy-aware operations instead:

import numpy as np

def good_profile(a):
    return np.where(a < 50, 0.0, a**-1)

Boolean masks and array arithmetic are also suitable.

Documentation

The full documentation is available at debrispy.readthedocs.io.

The documentation includes worked examples, API references, implementation notes, and notebook-based tutorials.

The documentation source files are also located in:

docs/source/

Repository structure

debrispy/              Core package code
docs/source/           Sphinx documentation source
examples/              Example notebooks and scripts
tests/                 Test suite
assets/                README/demo assets

Examples

Example notebooks are provided in the examples/ directory. These demonstrate how to define input profiles, construct eccentricity kernels, compute ASD profiles, and compare semi-analytic calculations with Monte Carlo realisations.

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Dependencies

Core dependencies are installed automatically when installing DebrisPy with:

pip install debrispy

These include:

• numpy
• scipy
• matplotlib
• fast_histogram
• adaptive
• tqdm
• joblib

Additional optional dependencies are needed for development, testing, and building the documentation.

For development and testing:

pip install -e ".[dev]"

This installs additional packages such as:

• pytest
• ipykernel
• notebook

For building the documentation locally:

pip install -e ".[docs]"

This installs additional packages such as:

• sphinx
• sphinx-rtd-theme
• myst-parser
• nbsphinx

Testing

To run the test suite, clone the repository and install the optional development dependencies:

git clone https://github.com/DenizAkansoy/DebrisPy.git
cd DebrisPy
pip install -e ".[dev]"
pytest tests/

Acknowledgments

If you use DebrisPy (or parts of its code) in your research, we would greatly appreciate it if you cited the paper introducing the package:

@article{Rafikov2026,
  author        = {Roman R. Rafikov and Deniz Akansoy and Antranik A. Sefilian},
  title         = {Debris Disc Substructures Induced by Secular Planetary Perturbations},
  journal       = {arXiv e-prints},
  year          = {2026},
  eprint        = {2607.08750},
  archiveprefix = {arXiv},
  primaryclass  = {astro-ph.EP},
  doi           = {10.48550/arXiv.2607.08750}
}

Contact

For questions, bug reports, or feedback, please open an issue on GitHub or contact Deniz Akansoy at da619@cam.ac.uk.