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-1.0.6.tar.gz (26.8 kB view details)

Uploaded Source

Built Distribution

surfinBH-1.0.6-py2-none-any.whl (29.5 kB view details)

Uploaded Python 2

File details

Details for the file surfinBH-1.0.6.tar.gz.

File metadata

  • Download URL: surfinBH-1.0.6.tar.gz
  • Upload date:
  • Size: 26.8 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-1.0.6.tar.gz
Algorithm Hash digest
SHA256 f373d8b5a78328b8859e1115ec7be59271ac1b84166220e46bb560771f7d9bbc
MD5 789eb4cb28ea9400bb1173f9d6f88eae
BLAKE2b-256 f41401fee046c43b37498868a2493c174a2f0f2f2cb45c46aee79eb12740470d

See more details on using hashes here.

File details

Details for the file surfinBH-1.0.6-py2-none-any.whl.

File metadata

  • Download URL: surfinBH-1.0.6-py2-none-any.whl
  • Upload date:
  • Size: 29.5 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-1.0.6-py2-none-any.whl
Algorithm Hash digest
SHA256 e065fcb3a4c378dd90b6f8e658d1ebda2df20c80f59362d23a4332f10e711968
MD5 af7fb41b989de9a08b7fdeee3c3d3df2
BLAKE2b-256 fc60c47fc41699afe620ff70c8e4464853b90cab5d468141af6d2670c1508ee4

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