<jupyter_start><jupyter_text>Example usage of NRSur3dq8Remnant fit.<jupyter_code>import surfinBH
fit_name = 'NRSur3dq8Remnant'<jupyter_output><empty_output><jupyter_text>Load the fit, this only needs to be done once at the start of a script<jupyter_code>fit = surfinBH.LoadFits(fit_name)<jupyter_output>Loaded NRSur3dq8Remnant fit.<jupyter_text>Read the documentation<jupyter_code>help(fit)<jupyter_output>Help on Fit3dq8 in module surfinBH._fit_evaluators.fit_3dq8 object:

class Fit3dq8(surfinBH.surfinBH.SurFinBH)
 |  A class for the NRSur3dq8Remnant model presented in Varma et al.,
 |  arxiv:1809.09125. This model is referred to as surfinBH3dq8 in the paper.
 |  
 |  This model predicts the final mass mf, final
 |  spin chif and final kick velocity vf, for the remnants of nonprecessing
 |  binary black hole systems. The fits are done using Gaussian Process
 |  Regression (GPR) and also provide an error estimate along with the fit
 |  value.
 |  
 |  This model has been trained in the parameter space:
 |      q <= 8, |chiAz| <= 0.8, |chiBz| <= 0.8
 |  
 |  However, it extrapolates reasonably to:
 |      q <= 10, |chiAz| <= 1, |chiBz| <= 1
 |  
 |  =========================================================================
 |  Usage:
 |  
 |  import surfinBH
 |  
 |  # Load the fit
 |  fit = surfinBH.LoadFits('NRSur3dq8Remnant')
 |  
 |  We provide the following call methods:
 |      # rem[...]<jupyter_text>Evaluate the fits<jupyter_code>q = 4.3             # Mass ratio q>=1
chiA = [0,0,0.6]    # Spin of larger BH (z-direction only)
chiB = [0,0,-0.7]   # Spin of smaller BH (z-direction only)

## Evaluate the fits and GPR error estimate.

# remnant mass and 1-sigma error estimate
mf, mf_err = fit.mf(q, chiA, chiB)

# remnant spin and 1-sigma error estimate
chif, chif_err = fit.chif(q, chiA, chiB)

# remnant recoil kick and 1-sigma error estimate
vf, vf_err = fit.vf(q, chiA, chiB)

# All of these together
mf, chif, vf, mf_err, chif_err, vf_err = fit.all(q, chiA, chiB)<jupyter_output><empty_output>
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