Initial commit: v0.1.0

docs/source/index.rst

@@ -0,0 +1,95 @@
+SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys
+===================================================================
+
+.. image:: https://img.shields.io/badge/astro--ph.CO-arxiv%3A2412.04443-B31B1B.svg
+   :target: https://arxiv.org/abs/2412.04443
+   :alt: arXiv
+
+.. image:: https://img.shields.io/github/v/tag/hoellin/selfisys_public.svg?label=version
+   :target: https://github.com/hoellin/selfisys_public/releases
+   :alt: GitHub Release
+
+.. image:: https://img.shields.io/github/last-commit/hoellin/selfisys_public
+   :target: https://github.com/hoellin/selfisys_public/commits/main
+   :alt: Last Commit
+
+.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg
+   :target: https://github.com/hoellin/selfisys_public/blob/main/LICENSE
+   :alt: License
+
+**SelfiSys** is a Python package designed to address the issue of model misspecification in field-based, implicit likelihood cosmological inference.
+
+It leverages the inferred initial matter power spectrum, enabling a thorough diagnosis of systematic effects in large-scale spectroscopic galaxy surveys.
+
+Key Features
+------------
+
+- **Custom hidden-box forward models**
+   We provide a `HiddenBox` class to simulate realistic spectroscopic galaxy surveys. It accommodates fully non-linear gravitational evolution, and incorporates multiple systematic effects observed in real-world survey, e.g., misspecified galaxy bias, survey mask, selection functions, dust extinction, line interlopers, or inaccurate gravity solver.
+- **Diagnosis of systematic effects**
+   Diagnose the impact of systematic effects using the inferred initial matter power spectrum, prior to performing cosmological inference.
+- **Cosmological inference**
+   Perform inference of cosmological parameters using Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.
+
+For practical examples demonstrating how to use SelfiSys, visit the `SelfiSys Examples Repository <https://github.com/hoellin/selfisys_examples>`_.
+
+References
+----------
+
+If you use the SelfiSys package in your research, please cite the following paper and feel free to `contact the authors <mailto:tristan.hoellinger@iap.fr>`_ for feedback, collaboration opportunities, or other inquiries.
+
+**Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum**
+*Hoellinger, T. and Leclercq, F., arXiv e-prints*, 2024
+`arXiv:2412.04443 <https://arxiv.org/abs/2412.04443>`_  
+`[astro-ph.CO] <https://arxiv.org/abs/2412.04443>`_  
+`[ADS] <https://ui.adsabs.harvard.edu/abs/arXiv:2412.04443>`_  
+`[pdf] <https://arxiv.org/pdf/2412.04443>`_
+
+Contributors
+------------
+
+- **Tristan Hoellinger**  
+  `tristan.hoellinger@iap.fr <mailto:tristan.hoellinger@iap.fr>`_
+  
+  Principal developer and maintainer, Institut d’Astrophysique de Paris (IAP).
+
+License
+-------
+
+This software is distributed under the GPLv3 Licence. Please review the `LICENSE <https://github.com/hoellin/selfisys_public/blob/main/LICENSE>`_ file in the repository to understand the terms of use and ensure compliance. By downloading and using this software, you agree to the terms of the licence.
+
+Requirements
+------------
+
+The code is written in Python 3.10 and depends on the following packages:
+
+- `pySELFI <https://pyselfi.readthedocs.io/en/latest/>`_: Python implementation of the Simulator Expansion for Likelihood-Free Inference.
+- `Simbelmynë <https://simbelmyne.readthedocs.io/en/latest/>`_: A hierarchical probabilistic simulator for generating synthetic galaxy survey data.
+- `ELFI <https://elfi.readthedocs.io/en/latest/>`_: A statistical software package for likelihood-free inference, implementing Approximate Bayesian Computation (ABC) with a Population Monte Carlo (PMC) sampler.
+
+A comprehensive list of dependencies, along with installation instructions, will be provided in a future release.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API Documentation
+
+   selfisys.hiddenbox
+   selfisys.normalise_hb
+   selfisys.prior
+   selfisys.selection_functions
+   selfisys.selfi_interface
+   selfisys.sbmy_interface
+   selfisys.grf
+   selfisys.utils
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contribute
+
+   ../../CONTRIBUTING.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: References
+
+   ../../REFERENCES.md