Skip to main content

Surrogate Final BH properties.

Project description

github PyPI 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

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

surfinBH-1.0.9.tar.gz (25.5 kB view details)

Uploaded Source

Built Distribution

surfinBH-1.0.9-py2-none-any.whl (28.5 kB view details)

Uploaded Python 2

File details

Details for the file surfinBH-1.0.9.tar.gz.

File metadata

  • Download URL: surfinBH-1.0.9.tar.gz
  • Upload date:
  • Size: 25.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/2.7.15

File hashes

Hashes for surfinBH-1.0.9.tar.gz
Algorithm Hash digest
SHA256 9d3df12a256e06c2c3d72983ac8355f02184d0faf2d8c8ac7728585b8b8effc5
MD5 09e96bafdf1883dd1510a43c36b1698b
BLAKE2b-256 fbe3013de7d618e2cc6d7b747a7ef7beb1b079a90358d3a834e9445b4edb0969

See more details on using hashes here.

File details

Details for the file surfinBH-1.0.9-py2-none-any.whl.

File metadata

  • Download URL: surfinBH-1.0.9-py2-none-any.whl
  • Upload date:
  • Size: 28.5 kB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/2.7.15

File hashes

Hashes for surfinBH-1.0.9-py2-none-any.whl
Algorithm Hash digest
SHA256 8d7c04becf521eba58786e6617a7cd71178adefc6cade216e1289fecb0d0614f
MD5 9dac3bab9c43d697319ab428f9bbbbe7
BLAKE2b-256 e603c28a0b3a997e11ef9981849257b71f9162962c23f80a3f14a61aa8ae2e81

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page