Bayesian Analysis in Cosmology
Maintainers
Project description
*Cobaya*, a code for Bayesian analysis in Cosmology
===================================================
:Author: `Jesus Torrado`_ and `Antony Lewis`_
:Source: `Source code at GitHub <https://github.com/CobayaSampler/cobaya>`_
:Documentation: `Documentation at Readthedocs <https://cobaya.readthedocs.org>`_
:Licence: `LGPL <https://www.gnu.org/licenses/lgpl-3.0.en.html>`_ + mandatory bug reporting asap + mandatory `arXiv'ing <https://arxiv.org>`_ of publications using it (see `LICENCE.txt <https://github.com/CobayaSampler/cobaya/blob/master/LICENCE.txt>`_ for exceptions). The documentation is licensed under the `GFDL <https://www.gnu.org/licenses/fdl-1.3.en.html>`_.
:E-mail list: https://cosmocoffee.info/cobaya/ – **sign up for important bugs and release announcements!**
:Support: For general support, CosmoCoffee_; for bugs and issues, use the `issue tracker <https://github.com/CobayaSampler/cobaya/issues>`_.
:Installation: ``pip install cobaya --upgrade --user`` (see the `installation instructions <https://cobaya.readthedocs.io/en/latest/installation.html>`_; in general do *not* clone)
.. image:: https://secure.travis-ci.org/CobayaSampler/cobaya.png?branch=master
:target: https://secure.travis-ci.org/CobayaSampler/cobaya
.. image:: https://img.shields.io/pypi/v/cobaya.svg?style=flat
:target: https://pypi.python.org/pypi/cobaya/
.. image:: https://readthedocs.org/projects/cobaya/badge/?version=latest
:target: https://cobaya.readthedocs.org/en/latest
**Cobaya** (**co**\ de for **bay**\ esian **a**\ nalysis, and Spanish for *Guinea Pig*) is a framework for sampling and statistical modelling: it allows you to explore an arbitrary prior or posterior using a range of Monte Carlo samplers (including the advanced MCMC sampler from CosmoMC_, and the advanced nested sampler PolyChord_). The results of the sampling can be analysed with GetDist_. It supports MPI parallelization (and very soon HPC containerization with Docker/Shifter and Singularity).
Its authors are `Jesus Torrado`_ and `Antony Lewis`_. Some ideas and pieces of code have been adapted from other codes (e.g CosmoMC_ by `Antony Lewis`_ and contributors, and `Monte Python`_, by `Julien Lesgourgues`_ and `Benjamin Audren`_).
**Cobaya** has been conceived from the beginning to be highly and effortlessly extensible: without touching **cobaya**'s source code, you can define your own priors and likelihoods, create new parameters as functions of other parameters...
Though **cobaya** is a general purpose statistical framework, it includes interfaces to cosmological *theory codes* (CAMB_ and CLASS_) and *likelihoods of cosmological experiments* (Planck, Bicep-Keck, SDSS... and more coming soon). Automatic installers are included for all those external modules. You can also use **cobaya** simply as a wrapper for cosmological models and likelihoods, and integrate it in your own sampler/pipeline.
The interfaces to most cosmological likelihoods are agnostic as to which theory code is used to compute the observables, which facilitates comparison between those codes. Those interfaces are also parameter-agnostic, so using your own modified versions of theory codes and likelihoods requires no additional editing of **cobaya**'s source.
The overhead per posterior evaluation is ``< 0.1 ms x dimension`` per posterior evaluation (mostly due to evaluating ``scipy.stats`` logpdf's in the prior), which makes it suitable for most cosmological applications (CAMB_ and CLASS_ take seconds to run), but not necessarily for more general statistical applications, if the evaluation time per pdf involved is of that order or smaller.
How to cite us
--------------
As of this version, there is no scientific publication yet associated to this software, so simply mention its `GitHub repository <https://github.com/CobayaSampler/cobaya>`_.
To appropriately cite the modules (samplers, theory codes, likelihoods) that you have used, simply run the script `cobaya-citation` with your input file(s) as argument(s), and you will get *bibtex* references and a short suggested text snippet for each module mentioned in your input file. You can find a usage example :ref:`here <citations>`.
Acknowledgements
----------------
Thanks to `Julien Lesgourgues`_ for support on interfacing CLASS_, and to `Guadalupe Cañas Herrera`_ for extensive and somewhat painful testing.
.. _`Jesus Torrado`: https://astronomy.sussex.ac.uk/~jt386
.. _`Antony Lewis`: https://cosmologist.info
.. _CosmoMC: https://cosmologist.info/cosmomc/
.. _CosmoCoffee: https://cosmocoffee.info/viewforum.php?f=11
.. _`Monte Python`: https://baudren.github.io/montepython.html
.. _`Julien Lesgourgues`: https://www.particle-theory.rwth-aachen.de/cms/Particle-Theory/Das-Institut/Mitarbeiter-TTK/Professoren/~gufe/Lesgourgues-Julien/?lidx=1
.. _`Benjamin Audren`: https://baudren.github.io/
.. _Camb: https://camb.info/
.. _Class: https://class-code.net/
.. _GetDist: https://github.com/cmbant/getdist
.. _PolyChord: https://ccpforge.cse.rl.ac.uk/gf/project/polychord
.. _`Guadalupe Cañas Herrera`: https://gcanasherrera.github.io/pages/about-me.html#about-me
===================================================
:Author: `Jesus Torrado`_ and `Antony Lewis`_
:Source: `Source code at GitHub <https://github.com/CobayaSampler/cobaya>`_
:Documentation: `Documentation at Readthedocs <https://cobaya.readthedocs.org>`_
:Licence: `LGPL <https://www.gnu.org/licenses/lgpl-3.0.en.html>`_ + mandatory bug reporting asap + mandatory `arXiv'ing <https://arxiv.org>`_ of publications using it (see `LICENCE.txt <https://github.com/CobayaSampler/cobaya/blob/master/LICENCE.txt>`_ for exceptions). The documentation is licensed under the `GFDL <https://www.gnu.org/licenses/fdl-1.3.en.html>`_.
:E-mail list: https://cosmocoffee.info/cobaya/ – **sign up for important bugs and release announcements!**
:Support: For general support, CosmoCoffee_; for bugs and issues, use the `issue tracker <https://github.com/CobayaSampler/cobaya/issues>`_.
:Installation: ``pip install cobaya --upgrade --user`` (see the `installation instructions <https://cobaya.readthedocs.io/en/latest/installation.html>`_; in general do *not* clone)
.. image:: https://secure.travis-ci.org/CobayaSampler/cobaya.png?branch=master
:target: https://secure.travis-ci.org/CobayaSampler/cobaya
.. image:: https://img.shields.io/pypi/v/cobaya.svg?style=flat
:target: https://pypi.python.org/pypi/cobaya/
.. image:: https://readthedocs.org/projects/cobaya/badge/?version=latest
:target: https://cobaya.readthedocs.org/en/latest
**Cobaya** (**co**\ de for **bay**\ esian **a**\ nalysis, and Spanish for *Guinea Pig*) is a framework for sampling and statistical modelling: it allows you to explore an arbitrary prior or posterior using a range of Monte Carlo samplers (including the advanced MCMC sampler from CosmoMC_, and the advanced nested sampler PolyChord_). The results of the sampling can be analysed with GetDist_. It supports MPI parallelization (and very soon HPC containerization with Docker/Shifter and Singularity).
Its authors are `Jesus Torrado`_ and `Antony Lewis`_. Some ideas and pieces of code have been adapted from other codes (e.g CosmoMC_ by `Antony Lewis`_ and contributors, and `Monte Python`_, by `Julien Lesgourgues`_ and `Benjamin Audren`_).
**Cobaya** has been conceived from the beginning to be highly and effortlessly extensible: without touching **cobaya**'s source code, you can define your own priors and likelihoods, create new parameters as functions of other parameters...
Though **cobaya** is a general purpose statistical framework, it includes interfaces to cosmological *theory codes* (CAMB_ and CLASS_) and *likelihoods of cosmological experiments* (Planck, Bicep-Keck, SDSS... and more coming soon). Automatic installers are included for all those external modules. You can also use **cobaya** simply as a wrapper for cosmological models and likelihoods, and integrate it in your own sampler/pipeline.
The interfaces to most cosmological likelihoods are agnostic as to which theory code is used to compute the observables, which facilitates comparison between those codes. Those interfaces are also parameter-agnostic, so using your own modified versions of theory codes and likelihoods requires no additional editing of **cobaya**'s source.
The overhead per posterior evaluation is ``< 0.1 ms x dimension`` per posterior evaluation (mostly due to evaluating ``scipy.stats`` logpdf's in the prior), which makes it suitable for most cosmological applications (CAMB_ and CLASS_ take seconds to run), but not necessarily for more general statistical applications, if the evaluation time per pdf involved is of that order or smaller.
How to cite us
--------------
As of this version, there is no scientific publication yet associated to this software, so simply mention its `GitHub repository <https://github.com/CobayaSampler/cobaya>`_.
To appropriately cite the modules (samplers, theory codes, likelihoods) that you have used, simply run the script `cobaya-citation` with your input file(s) as argument(s), and you will get *bibtex* references and a short suggested text snippet for each module mentioned in your input file. You can find a usage example :ref:`here <citations>`.
Acknowledgements
----------------
Thanks to `Julien Lesgourgues`_ for support on interfacing CLASS_, and to `Guadalupe Cañas Herrera`_ for extensive and somewhat painful testing.
.. _`Jesus Torrado`: https://astronomy.sussex.ac.uk/~jt386
.. _`Antony Lewis`: https://cosmologist.info
.. _CosmoMC: https://cosmologist.info/cosmomc/
.. _CosmoCoffee: https://cosmocoffee.info/viewforum.php?f=11
.. _`Monte Python`: https://baudren.github.io/montepython.html
.. _`Julien Lesgourgues`: https://www.particle-theory.rwth-aachen.de/cms/Particle-Theory/Das-Institut/Mitarbeiter-TTK/Professoren/~gufe/Lesgourgues-Julien/?lidx=1
.. _`Benjamin Audren`: https://baudren.github.io/
.. _Camb: https://camb.info/
.. _Class: https://class-code.net/
.. _GetDist: https://github.com/cmbant/getdist
.. _PolyChord: https://ccpforge.cse.rl.ac.uk/gf/project/polychord
.. _`Guadalupe Cañas Herrera`: https://gcanasherrera.github.io/pages/about-me.html#about-me
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
| Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
|---|---|---|---|
| cobaya-1.0.3.tar.gz (168.7 kB) Copy SHA256 hash SHA256 | Source | None |
