unimpeded

unimpeded:: Universal model comparison & parameter estimation distributed over every dataset
Author:: Dily Ong & Will Handley
Version:: 1.2.8
Homepage:: https://github.com/handley-lab/unimpeded
Documentation:: http://unimpeded.readthedocs.io/

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:

Python 3.10+
anesthetic

Documentation:

Tests:

pytest

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:

Software paper: arXiv:2511.05470
Tension statistics: arXiv:2511.04661

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

Metadata

Release files for unimpeded 1.2.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for unimpeded 1.2.8
File	Size	Uploaded
unimpeded-1.2.8.tar.gz	28.5 kB	Aug 16, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for unimpeded 1.2.8
File	Interpreter	ABI	Platform	Reset
unimpeded-1.2.8-py3-none-any.whl 16.5 kB Aug 16, 2026	Python 3	none	any	Details

Total release size: 45.0 kB

Release files / unimpeded-1.2.8.tar.gz

Download URL	unimpeded-1.2.8.tar.gz
Size	28.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`7a63388699a84a42944676afd6634e218c98e7db95d6b142346fed141ea0b0bd`
BLAKE2b-256 checksum How to use checksums	`27ceff0be69aa05f97982712ed85c59af5a8a88e960f4ddd02e83680323f7286`
Upload date	Aug 16, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Release files / unimpeded-1.2.8-py3-none-any.whl

Download URL	unimpeded-1.2.8-py3-none-any.whl
Size	16.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`bbb151c87831def9afb2e094990334ae34f46427a66df0d35f7fc4fdf821753e`
BLAKE2b-256 checksum How to use checksums	`fbb4c79497cb9c4c1c7cf1e07443ca896d3c419f5ac551f97c85606c47a01a19`
Upload date	Aug 16, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`