Surrogate Final BH properties.
Welcome to surfinBH!
surfinBH provides fits for Surrogate Final Black Hole properties from mergers of binary black holes (BBH). Just like Point Break, but with black holes (BH)! This package lives on GitHub.
These fits are described in the following papers:
 Vijay Varma, Davide Gerosa, Francois Hebert and Leo C. Stein, in preparation.
If you find this package useful in your work, please cite reference  and, if available, the relevant paper describing the particular model.
surfinBH is available through PyPi.
pip install surfinBH
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
All of these can be installed through pip or conda.
- scikit-learn (at least 0.19.1)
See list of available fits
print(surfinBH.fits_collection.keys())) >>> ['7dq2', '3dq8']
Pick your favorite fit and get some basic information about it.
fit_name = '7dq2' 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.
Evaluate the fit. Here we show the evaluation for the surfinBH7dq2 model.
q = 1.2 chiA = [0.1, 0.2, 0.3] chiB = [0.2, -0.5, 0.3] x = [q] + chiA + chiB print(x) >>> [1.2, 0.1, 0.2, 0.3, 0.2, -0.5, 0.3] # Final mass and its 1-sigma error etimate mC, mC_err_est = fit('mC', x) # Final spin vector and its 1-sigma error estimate chiC, chiC_err_est = fit('chiC', x) # Final kick vector and its 1-sigma error estimate velC, velC_err_est = fit('velC', x)
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