Surrogate Final BH properties.
Project description
Welcome to surfinBH!
surfinBH provides surrogate final Black Hole properties from mergers of binary black holes (BBH). Just like Point Break, but with black holes! This package lives on GitHub.
These fits are described in the following papers:
[1] Vijay Varma, Davide Gerosa, Francois Hebert and Leo C. Stein, 2018, in preparation.
If you find this package useful in your work, please cite reference [1] and, if available, the relevant paper describing the particular model.
Installation
PyPi
surfinBH is available through PyPi.
pip install surfinBH
From source
git clone https://github.com/vijayvarma392/surfinBH
cd surfinBH
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.
- numpy
- scipy
- scikit-learn (at least 0.19.1)
- h5py
Usage
import surfinBH
See list of available fits
print(surfinBH.fits_collection.keys())
>>> ['surfinBH3dq8', 'surfinBH7dq2']
Pick your favorite fit and get some basic information about it.
fit_name = 'surfinBH7dq2'
surfinBH.fits_collection[fit_name].desc
>>> 'Fits for remnant mass, spin and kick veclocity for generically precessing BBH systems.'
surfinBH.fits_collection[fit_name].refs
>>> 'Varma:2018_inprep'
surfinBH.fits_collection[fit_name].refs_url
>>> 'arxiv.2018.xxxx'
Get data for the fit. This only needs to done once, ever.
surfinBH.DownloadData(fit_name)
>>> fit_7dq2.h5 100%[======================>] 42.85M 495KB/s in 60s
Load the fit. This only needs to be done once at the start of your script.
fit = surfinBH.LoadFits(fit_name)
>>> Loaded surfinBH7dq2 fit.
The evaluation of each fit is different, so be sure to read the documentation. This also defines the frames in which different quantities are defined.
help(fit)
We also provide ipython examples for usage of different fits:
Credits
The code is developed and maintained by Vijay Varma. Please, report bugs to vvarma@caltech.edu.
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