cogwheel.pn_coordinates.IntrinsicParameterProposal

class cogwheel.pn_coordinates.IntrinsicParameterProposal(inspiral_analysis, merger_analysis, mchirp_range, q_min=0.05, resolution=128, beta_temperature=0.1)

Bases: object

Provide a method generate_intrinsic_samples that generates samples of intrinsic parameters (per .params) from an importance-sampling proposal using Quasi Monte Carlo.

The importance-sampling proposal is informed by the inspiral and the merger frequency via Fisher analysis using post-Newtonian models. Provide a constructor from_posterior.

Methods

from_posterior
generate_intrinsic_samples	Return pd.DataFrame with 2**log2n_qmc Quasi Monte Carlo samples of params.

Attributes

params

classmethod from_posterior(posterior, **kwargs)

Parameters:

posterior: cogwheel.posterior.Posterior

Posterior instance from which to take best-fit parameters, parameter ranges, and detector PSDs.

**kwargs

generate_intrinsic_samples(log2n_qmc: int)

Return pd.DataFrame with 2**log2n_qmc Quasi Monte Carlo samples of params. A Sobol sequence is used.