Linear Simulation Based Inference
Project description
- lsbi:
Linear Simulation Based Inference
- Version:
- 0.0.0
- Homepage:
- Documentation:
lsbi
It can be viewed as an extension to the Planck legacy archive across models and datasets
It provides mcmc and nested sampling chains, allowing parameter estimation, model comparison and tension quantification.
Current functionality includes:
UNDER CONSTRUCTION
Features
Installation
lsbi can be installed via pip
pip install lsbior via the setup.py
git clone https://github.com/handley-lab/lsbi
cd lsbi
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 lsbi tests
pydocstyle --convention=numpy lsbiDependencies
Basic requirements:
Python 3.6+
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 htmland 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/ lsbi/Citation
If you use lsbi to generate results for a publication, please cite as:
Handley et al, (2024) lsbi: Linear Simulation Based Inference.
or using the BibTeX:
@article{lsbi,
year = {2023},
author = {Will Handley et al},
title = {lsbi: Linear Simulation Based Inference},
journal = {In preparation}
}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
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