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github PyPI version Conda Version DOI license Build Status

Welcome to surfinBH!

BHScattering


surfinBH provides surrogate final Black Hole properties for mergers of binary black holes (BBH).

These fits are described in the following papers:
[1] Vijay Varma, D. Gerosa, L. C. Stein, F. Hébert and H. Zhang, arxiv:1809.09125.

[2] Vijay Varma, S. E. Field, M. A. Scheel, J. Blackman, D. Gerosa, L. C. Stein, L. E. Kidder, H. P. Pfeiffer, arxiv:1905.09300.

If you find this package useful in your work, please cite reference [1] and, if available, the relevant paper describing the particular model. Please also cite this package, see the DOI badge at the top of this page for BibTeX keys.

This package is compatible with both python2 and python3. This package lives on GitHub and is tested every day with Travis CI. You can see the current build status of the master branch at the top of this page.

Installation

PyPI

surfinBH is available through PyPI:

pip install surfinBH

Conda

surfinBH is available on conda-forge:

conda install -c conda-forge surfinbh

From source

git clone https://github.com/vijayvarma392/surfinBH
cd surfinBH
git submodule init
git submodule update
python setup.py install

If you do not have root permissions, replace the last step with python setup.py install --user

Dependencies

All of these can be installed through pip or conda.

Usage

import surfinBH

See list of available fits

print(surfinBH.fits_collection.keys())
>>> ['NRSur3dq8Remnant', 'NRSur7dq4Remnant', 'surfinBH7dq2']

Pick your favorite fit and get some basic information about it.

fit_name = 'NRSur7dq4Remnant'

surfinBH.fits_collection[fit_name].desc
>>> 'Fits for remnant mass, spin and kick veclocity for generically precessing BBH systems up to mass ratio 4.'

surfinBH.fits_collection[fit_name].refs
>>> 'arxiv:1905.09300'

Load the fit

This only needs to be done once at the start of your script. If the fit data is not already downloaded, this will also download the data.

fit = surfinBH.LoadFits(fit_name)
>>> Loaded NRSur7dq4Remnant fit.

Evaluation

The evaluation of each fit is different, so be sure to read the documentation. This also describes the frames in which different quantities are defined.

help(fit)

We also provide ipython examples for usage of different fits:

Current fits
Older fits

Animations

We also provide a tool to visualize the binary black hole scattering process, see binary black hole explorer. Here's an example:

Making contributions

See this README for instructions on how to make contributions to this package.

You can find the list of contributors here.

Credits

The code is developed and maintained by Vijay Varma. Please report bugs by raising an issue on our GitHub repository.