gwinferno.models.bsplines.separable.BSplineEffectiveSpinDims

class BSplineEffectiveSpinDims(n_splines_e, n_splines_p, chieff, chip, chieff_inj, chip_inj, kwargs_e={}, kwargs_p={}, **kwargs)

Bases: object

B-Spline models for the effective spin (\(\chi_\mathrm{eff}\)) and effective precession (\(\chi_\mathrm{p}\)) of binaries,

\[p(\chi_\mathrm{eff}, \chi_\mathrm{p} \mid \mathbf{c}_\mathrm{eff}, \mathbf{c}_\mathrm{p}) = p(\chi_\mathrm{eff} \mid \mathbf{c}_\mathrm{eff}) p(\chi_\mathrm{p} \mid \mathbf{c}_\mathrm{p}),\]

where \(\mathbf{c}_\mathrm{eff}\) and \(\mathbf{c}_\mathrm{p}\) are vectors of the n_splines_e and n_splines_p basis spline coefficients for the effective spin and effective precession, respectively.

Parameters:

n_splines_e, n_splines_pint

Number of basis functions, i.e., the number of degrees of freedom, of the (e)ffective spin and effective (p)recession spline models.

chieff, chiparray_like

Effective spin and effective precession parameter estimation samples for basis evaluation.

chieff_inj, chip_injarray_like

Effective spin and effective precession injection samples for basis evaluation.

kwargs_e, kwargs_pdict, optional

Additional keyword arguments to pass to the basis spline models for the effective spin and effective precession.

**kwargsdict, optional

Additional keyword arguments to pass to both basis spline models.

Methods

__call__(ecoefs, pcoefs, pe_samples=True)

Evaluate the joint probability density over the parameter estimation or injection samples. Use flag pe_samples to specify which samples are being evaluated (parameter estimation or injection).

Parameters:

ecoefs, pcoefsarray_like

Spline coefficients for the effective spin and effective precession.

pe_samplesbool, default=True

If True, design matrix is evaluated across parameter estimation samples. If False, design matrix is evaluated across injection samples.

Returns:

array_like

Joint probability density for parameter estimation or injection samples.

__init__(n_splines_e, n_splines_p, chieff, chip, chieff_inj, chip_inj, kwargs_e={}, kwargs_p={}, **kwargs)