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bajes-0.3.0


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توضیحات

Bayesian Jenaer Software
ویژگی مقدار
سیستم عامل -
نام فایل bajes-0.3.0
نام bajes
نسخه کتابخانه 0.3.0
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Matteo Breschi
ایمیل نویسنده matteo.breschi@uni-jena.de
آدرس صفحه اصلی https://github.com/matteobreschi/bajes
آدرس اینترنتی https://pypi.org/project/bajes/
مجوز MIT
<img src="docs/bajes.png" height=140> *bajes* [baɪɛs] is a Python software for Bayesian inference developed at Friedrich-Schiller-Universtät Jena and specialized in the analysis of gravitational-wave and multi-messenger transients. The software is designed to be state-of-art, simple-to-use and light-weighted with minimal dependencies on external libraries. ## Installation *bajes* is compatible with Python v3.7 (or higher) and it is built on modules that can be easily installed via `pip`. The mandatory dependencies are `numpy`, `scipy` and `astropy`. However, the user might need to download some further packages. See [`INSTALL`](INSTALL.md) for more information. ## Modules *bajes* provides an homonymous Python module that includes: * `bajes.inf`: implementation of the statistical objects and Bayesian workflow, * `bajes.obs`: tools and methods for data analysis of multi-messenger signals. For more details, visit [`gw_tutorial`](docs/gw_tutorial.ipynb). ## Inference The *bajes* package provides a user-friendly interface capable to easily set up a Bayesian analysis for an arbitrary model. Providing a prior file and a likelihood function, the command python -m bajes -p prior.ini -l like.py -o /path/to/outdir/ will run a parameter estimation job, inferring the properties of the input model. For more details, visit [`inf_tutorial`](docs/inf_tutorial.ipynb) or type `python -m bajes --help`. ## Pipeline The *bajes* infrastructure allows the user to set up a pipeline for parameters estimation of multi-messenger transients. This can be easily done writing a configuration file, that contains the information to be passed to the executables. Subsequently, the following command, bajes_pipe.py config.ini will generates the requested output directory, if it does not exists, and the pipeline will be written into a bash executable (`/path/to/outdir/jobname.sub`). For more details, visit [`conifg_example`](docs/config_example.ini). The pipeline incorporates an interface with reduced-order-quadratude (ROQ) interpolants. In particular, the ROQ pipeline relies on the output provided by [`PyROQ-refactored`](https://github.com/bernuzzi/PyROQ). ## Credits *bajes* is developed at the Friedrich-Schiller-Universität Jena, visit [`CREDITS`](CREDITS.md) for more details. If you find *bajes* useful in your research, please include the following [citation](https://arxiv.org/abs/2102.00017) in your publication, @article{Bajes:2021, author = "Breschi, Matteo and Gamba, Rossella and Bernuzzi, Sebastiano", title = "{Bayesian inference of multimessenger astrophysical data: Methods and applications to gravitational waves}", eprint = "2102.00017", archivePrefix = "arXiv", primaryClass = "gr-qc", doi = "10.1103/PhysRevD.104.042001", journal = "Phys. Rev. D", volume = "104", number = "4", pages = "042001", year = "2021" } ## Acknowledgement *bajes* has benefited from open source libraries, including the samplers, * [`cpnest`](https://johnveitch.github.io/cpnest/) * [`dynesty`](https://dynesty.readthedocs.io/) * [`emcee`](https://emcee.readthedocs.io/) * [`ultranest`](https://johannesbuchner.github.io/UltraNest/) and the gravitational-wave analysis packages, * [`bilby`](https://lscsoft.docs.ligo.org/bilby/) * [`gwbinning`](https://bitbucket.org/dailiang8/gwbinning/) * [`lalsuite`](https://lscsoft.docs.ligo.org/lalsuite/) * [`pycbc`](https://pycbc.org) We also acknowledge the LIGO-Virgo-KAGRA Collaboration for maitaining the [GWOSC archive](https://www.gw-openscience.org).


زبان مورد نیاز

مقدار نام
>=3.7 Python


نحوه نصب


نصب پکیج whl bajes-0.3.0:

    pip install bajes-0.3.0.whl


نصب پکیج tar.gz bajes-0.3.0:

    pip install bajes-0.3.0.tar.gz