gw.likelihood.StationaryGaussianGWLikelihood

gw.likelihood.StationaryGaussianGWLikelihood(
    wfg_kwargs,
    wfg_domain,
    data_domain,
    event_data,
    t_ref=None,
    time_marginalization_kwargs=None,
    phase_marginalization_kwargs=None,
    calibration_marginalization_kwargs=None,
    phase_grid=None,
    use_base_domain=False,
    frequency_update=None,
)

Implements GW likelihood for stationary, Gaussian noise.

Parameters

Name	Type	Description	Default
wfg_kwargs	dict	Waveform generator parameters (at least approximant and f_ref).	required
wfg_domain	dingo.gw.domains.Domain	Domain used for waveform generation. This can potentially deviate from the final domain, having a wider frequency range needed for waveform generation.	required
data_domain	UniformFrequencyDomain | MultibandedFrequencyDomain	Domain object for event data.	required
event_data	dict	GW data. Contains strain data in event_data[“waveforms”] and asds in event_data[“asds”].	required
t_ref	Optional[float]	Reference time; true geocent time for GW is t_ref + theta[“geocent_time”].	None
time_marginalization_kwargs	Optional[dict]	Time marginalization parameters. If None, no time marginalization is used.	None
calibration_marginalization_kwargs	Optional[dict]	Calibration marginalization parameters. If None, no calibration marginalization is used.	None
phase_marginalization_kwargs	Optional[dict]	Phase marginalization parameters. If None, no phase marginalization is used.	None
use_base_domain	bool	When the domain is a MultibandedFrequencyDomain, whether to use the associated base UniformFrequencyDomain for likelihood computations.	False
frequency_update	Optional[dict[str, float | dict[str, float | list[float]]]]	Specifies settings for updating the frequency range example: {‘minimum_frequency’: {‘H1’: 30., ‘L1’: 20.}, maximum_frequency: 1024.}	None

Attributes

Name	Description
asd
calibration_marginalization_kwargs
log_Zn
phase_grid
phase_marginalization
pm_approx_22_mode
psi
time_marginalization
use_base_domain
whiten
whitened_strains

Methods

Name	Description
d_inner_h_complex	Calculate the complex inner product (d | h(theta)) between the stored data d
d_inner_h_complex_multi	Calculate the complex inner product (d | h(theta)) between the stored data d
initialize_time_marginalization	Initialize time marginalization. Time marginalization can be performed via FFT,
log_likelihood
log_likelihood_phase_grid

d_inner_h_complex

gw.likelihood.StationaryGaussianGWLikelihood.d_inner_h_complex(theta)

Calculate the complex inner product (d | h(theta)) between the stored data d and a simulated waveform with given parameters theta.

Parameters

Name	Type	Description	Default
theta	dict	Parameters at which to evaluate h.	required

Returns

Name	Type	Description
complex	Inner product

d_inner_h_complex_multi

gw.likelihood.StationaryGaussianGWLikelihood.d_inner_h_complex_multi(
    theta,
    num_processes=1,
)

Calculate the complex inner product (d | h(theta)) between the stored data d and a simulated waveform with given parameters theta. Works with multiprocessing.

Parameters

Name	Type	Description	Default
theta	pd.DataFrame	Parameters at which to evaluate h.	required
num_processes	int	Number of parallel processes to use.	1

Returns

Name	Type	Description
complex	Inner product

initialize_time_marginalization

gw.likelihood.StationaryGaussianGWLikelihood.initialize_time_marginalization(
    t_lower,
    t_upper,
    n_fft=1,
)

Initialize time marginalization. Time marginalization can be performed via FFT, which is super fast. However, this limits the time resolution to delta_t = 1/self.data_domain.f_max. In order to allow for a finer time resolution we compute the time marginalized likelihood n_fft via FFT on a grid of n_fft different time shifts [0, delta_t, 2delta_t, …, (n_fft-1)delta_t] and average over the time shifts. The effective time resolution is thus

delta_t_eff = delta_t / n_fft = 1 / (f_max * n_fft).

Note: Time marginalization in only implemented for uniform time priors.

Parameters

Name	Type	Description	Default
t_lower		Lower time bound of the uniform time prior.	required
t_upper		Upper time bound of the uniform time prior.	required
n_fft		Size of grid for FFT for time marginalization.	1

log_likelihood

gw.likelihood.StationaryGaussianGWLikelihood.log_likelihood(theta)

log_likelihood_phase_grid

gw.likelihood.StationaryGaussianGWLikelihood.log_likelihood_phase_grid(
    theta,
    phases=None,
)