gwinferno.models.spline_perturbation.PowerlawSplineRedshiftModel

class PowerlawSplineRedshiftModel(n_splines, z_pe, z_inj, basis=<class 'gwinferno.interpolation.LogXBSpline'>)

Bases: PowerlawRedshiftModel

Methods

log_prob(z, lamb)
normalization(lamb, cs)	Args:
prob(z, dVdz, lamb, cs)	prob Returns probability

__call__(z, lamb, cs)

Return type:

Array

Args:

z (jnp.ndarray): Redshift lamb (float): Power-law exponent for redshift model cs (jnp.ndarray): B-Spline coefficients

Returns:

jnp.ndarray:

__init__(n_splines, z_pe, z_inj, basis=<class 'gwinferno.interpolation.LogXBSpline'>)

Args:

n_splines (int): Number of basis functions used to create B-Spline z_pe (dict): Redshift parameter estimation z_inj (dict): Redshift injections basis (LogXBSpline, optional): Bases to be used in the spline perturbation. Defaults to LogXBSpline.

normalization(lamb, cs)

Args:

lamb (float): Power-law exponent for the redshift model cs (jnp.ndarray): B-Spline coefficients

Returns:

_type_:

prob(z, dVdz, lamb, cs)

prob Returns probability

Args:

z (jnp.ndarray): Redshift dV_cdz (jnp.ndarray): Differential co-moving volume element with respect to redshift. lamb (float): Power-law exponent for redshift model cs (jnp.ndarray): B-Spline coefficients

Returns:

_type_: