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Python Version](https://img.shields.io/pypi/pyversions/bajes)](https://pypi.org/project/bajes/)\n[![PyPI - License](https://img.shields.io/pypi/l/bajes)](https://pypi.org/project/bajes/)\n\n*bajes* [baɪɛs] is a Python software for Bayesian inference developed at Friedrich-Schiller-Universtät Jena\nand specialized in the analysis of gravitational-wave and multi-messenger transients.\nThe software is designed to be state-of-art, simple-to-use and light-weighted\nwith minimal dependencies on external libraries.\n\n## Installation\n\n*bajes* is compatible with Python v3.7 (or higher)\nand it is built on modules that can be easily installed via `pip`.\nThe mandatory dependencies are `numpy`, `scipy` and `astropy`.\nHowever, the user might need to download some further packages.\nSee [`INSTALL`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/INSTALL.md) for more information.\n\n## Modules\n\n*bajes* provides an homonymous Python module that includes:\n* `bajes.inf`: implementation of the statistical objects and Bayesian workflow,\n* `bajes.obs`: tools and methods for data analysis of multi-messenger signals.\nFor more details, visit [`gw_tutorial`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/gw_tutorial.ipynb).\n\n## Inference\n\nThe *bajes* package  provides a user-friendly interface capable to easily set up a\nBayesian analysis for an arbitrary model. Providing a prior file and a likelihood function, the command\n\n    python -m bajes -p prior.ini -l like.py -o /path/to/outdir/\n\nwill run a parameter estimation job, inferring the properties of the input model.\nFor more details, visit [`inf_tutorial`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/inf_tutorial.ipynb)\nor type `python -m bajes --help`.\n\n## Pipeline\n\n![](https://raw.githubusercontent.com/matteobreschi/bajes/release/v1.2.0/docs/figs/pipe.png)\n\nThe *bajes*  infrastructure allows the user to set up a pipeline for parameters\nestimation of multi-messenger transients.\nThis can be easily done writing a configuration file,\nthat contains the information to be passed to the executables.\nSubsequently, the following command,\n\n    bajes_pipe config.ini\n\nwill generates the requested output directory, if it does not exists, and\nthe pipeline will be written into a bash executable (`/path/to/outdir/jobname.sub`).\nFor more details, visit [`conifg_example`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/docs/config_example.ini).\n\nThe GW pipeline incorporates an interface with reduced-order-quadratude (ROQ) interpolants.\nIn particular, the ROQ pipeline relies on the output provided by [`JenpyROQ`](https://github.com/gcarullo/JenpyROQ).\n\n## Credits\n\n*bajes* is developed at the Friedrich-Schiller-Universität Jena,\nvisit [`CREDITS`](https://github.com/matteobreschi/bajes/tree/release/v1.2.0/CREDITS.md) for more details.\n\nIf you find *bajes* useful in your research, please include the following [citation](https://arxiv.org/abs/2102.00017) in your publication,\n\n    @article{Bajes:2021,\n             author = \"Breschi, Matteo and Gamba, Rossella and Bernuzzi, Sebastiano\",\n             title = \"{Bayesian inference of multimessenger astrophysical data: Methods and applications to gravitational waves}\",\n             eprint = \"2102.00017\",\n             archivePrefix = \"arXiv\",\n             primaryClass = \"gr-qc\",\n             doi = \"10.1103/PhysRevD.104.042001\",\n             journal = \"Phys. Rev. D\",\n             volume = \"104\",\n             number = \"4\",\n             pages = \"042001\",\n             year = \"2021\"\n            }\n\n## Acknowledgement\n\n*bajes* has benefited from open source libraries, including the samplers,\n* [`cpnest`](https://johnveitch.github.io/cpnest/)\n* [`dynesty`](https://dynesty.readthedocs.io/)\n* [`emcee`](https://emcee.readthedocs.io/)\n* [`ultranest`](https://johannesbuchner.github.io/UltraNest/)\n\nand the gravitational-wave analysis packages,\n* [`bilby`](https://lscsoft.docs.ligo.org/bilby/)\n* [`gwbinning`](https://bitbucket.org/dailiang8/gwbinning/)\n* [`lalsuite`](https://lscsoft.docs.ligo.org/lalsuite/)\n* [`pycbc`](https://pycbc.org)\n\nWe also acknowledge the LIGO-Virgo-KAGRA Collaboration for maitaining the [GWOSC](https://www.gw-openscience.org).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatteobreschi%2Fbajes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatteobreschi%2Fbajes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatteobreschi%2Fbajes/lists"}