unimpeded:

Universal model comparison & parameter estimation distributed over every dataset

Author:

Dily Ong & Will Handley

Version:
1.2.6
Homepage:

https://github.com/handley-lab/unimpeded

Documentation:

http://unimpeded.readthedocs.io/

Build Status Test Coverage Status Documentation Status PyPi location Permanent DOI for this release License information

unimpeded is a Python package providing access to a comprehensive database of nested sampling and MCMC chains for cosmological analysis. It can be viewed as an extension to the Planck legacy archive across multiple models and datasets.

The package provides:

  • Public Nested Sampling Database: Pre-computed chains for 8 cosmological models across 39 datasets

  • Tension Statistics Calculator: Six tension quantification metrics with proper nested sampling corrections

  • Zenodo Integration: Automated archival and retrieval with permanent DOIs

  • Analysis Tools: Built on anesthetic for visualization and statistical analysis

Features

Installation

unimpeded can be installed via pip

pip install unimpeded

or via the setup.py

git clone https://github.com/handley-lab/unimpeded
cd unimpeded
python -m pip install .

You can check that things are working by running the test suite:

export MPLBACKEND=Agg     # only necessary for OSX users
python -m pytest
flake8 unimpeded tests
pydocstyle --convention=numpy unimpeded

Dependencies

Basic requirements:

Documentation:

Tests:

Documentation

Full Documentation is hosted at ReadTheDocs. To build your own local copy of the documentation you’ll need to install sphinx. You can then run:

python -m pip install ".[all,docs]"
cd docs
make html

and view the documentation by opening docs/build/html/index.html in a browser. To regenerate the automatic RST files run:

sphinx-apidoc -fM -t docs/templates/ -o docs/source/ unimpeded/

Citation

If you use unimpeded in your research, please cite the following papers:

For the software and database:

@article{Ong2025unimpeded,
    author = {Ong, Dily Duan Yi and Handley, Will},
    title = {unimpeded: A Public Nested Sampling Database for Bayesian Cosmology},
    journal = {arXiv e-prints},
    year = {2025},
    note = {arXiv:2511.05470}
}

For the tension statistics methodology:

@article{Ong2025tension,
    author = {Ong, Dily Duan Yi and Handley, Will},
    title = {Tension statistics for nested sampling},
    journal = {arXiv e-prints},
    year = {2025},
    eprint = {2511.04661},
    archivePrefix = {arXiv},
    primaryClass = {astro-ph.CO}
}

Links:

Contributing

There are many ways you can contribute via the GitHub repository.

  • You can open an issue to report bugs or to propose new features.

  • Pull requests are very welcome. Note that if you are going to propose major changes, be sure to open an issue for discussion first, to make sure that your PR will be accepted before you spend effort coding it.

  • Adding models and data to the grid. Contact Will Handley to request models or ask for your own to be uploaded.

Questions/Comments

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.2.8)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for williamjameshandley from gravatar.com williamjameshandley

Credits

Author: Will Handley

License

MIT License (MIT License)

Requires

Python >=3.10

Provides Extra

docs test

Classifiers

Report project as malware

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