bilby.gw.likelihood.roq.ROQGravitationalWaveTransient

class bilby.gw.likelihood.roq.ROQGravitationalWaveTransient(interferometers, waveform_generator, priors, weights=None, linear_matrix=None, quadratic_matrix=None, roq_params=None, roq_params_check=True, roq_scale_factor=1, distance_marginalization=False, phase_marginalization=False, time_marginalization=False, jitter_time=True, delta_tc=None, distance_marginalization_lookup_table=None, reference_frame='sky', time_reference='geocenter', parameter_conversion=None)

Bases: GravitationalWaveTransient

A reduced order quadrature likelihood object

This uses the method described in Smith et al., (2016) Phys. Rev. D 94, 044031. A public repository of the ROQ data is available from https://git.ligo.org/lscsoft/ROQ_data.

Parameters:

interferometers: list, bilby.gw.detector.InterferometerList

A list of bilby.detector.Interferometer instances - contains the detector data and power spectral densities

waveform_generator: `bilby.waveform_generator.WaveformGenerator`

An object which computes the frequency-domain strain of the signal, given some set of parameters

linear_matrix: str, array_like

Either a string point to the file from which to load the linear_matrix array, or the array itself.

quadratic_matrix: str, array_like

Either a string point to the file from which to load the quadratic_matrix array, or the array itself.

roq_params: str, array_like

Parameters describing the domain of validity of the ROQ basis.

roq_params_check: bool

If true, run tests using the roq_params to check the prior and data are valid for the ROQ

roq_scale_factor: float

The ROQ scale factor used.

parameter_conversion: func, optional

Function to update self.parameters before bases are selected based on the values of self.parameters. This enables a user to switch bases based on the values of parameters which are not directly used for sampling.

priors: dict, bilby.prior.PriorDict

A dictionary of priors containing at least the geocent_time prior Warning: when using marginalisation the dict is overwritten which will change the the dict you are passing in. If this behaviour is undesired, pass priors.copy().

time_marginalization: bool, optional

If true, marginalize over time in the likelihood. The spacing of time samples can be specified through delta_tc. If using time marginalisation and jitter_time is True a “jitter” parameter is added to the prior which modifies the position of the grid of times.

jitter_time: bool, optional

Whether to introduce a time_jitter parameter. This avoids either missing the likelihood peak, or introducing biases in the reconstructed time posterior due to an insufficient sampling frequency. Default is False, however using this parameter is strongly encouraged.

delta_tc: float, optional

The spacing of time samples for time marginalization. If not specified, it is determined based on the signal-to-noise ratio of signal.

distance_marginalization_lookup_table: (dict, str), optional

If a dict, dictionary containing the lookup_table, distance_array, (distance) prior_array, and reference_distance used to construct the table. If a string the name of a file containing these quantities. The lookup table is stored after construction in either the provided string or a default location: ‘.distance_marginalization_lookup_dmin{}_dmax{}_n{}.npz’

reference_frame: (str, bilby.gw.detector.InterferometerList, list), optional

Definition of the reference frame for the sky location. - “sky”: sample in RA/dec, this is the default - e.g., “H1L1”, [“H1”, “L1”], InterferometerList([“H1”, “L1”]):

sample in azimuth and zenith, azimuth and zenith defined in the frame where the z-axis is aligned the the vector connecting H1 and L1.

time_reference: str, optional

Name of the reference for the sampled time parameter. - “geocent”/”geocenter”: sample in the time at the Earth’s center,

this is the default

• e.g., “H1”: sample in the time of arrival at H1

__init__(interferometers, waveform_generator, priors, weights=None, linear_matrix=None, quadratic_matrix=None, roq_params=None, roq_params_check=True, roq_scale_factor=1, distance_marginalization=False, phase_marginalization=False, time_marginalization=False, jitter_time=True, delta_tc=None, distance_marginalization_lookup_table=None, reference_frame='sky', time_reference='geocenter', parameter_conversion=None)

Empty likelihood class to be subclassed by other likelihoods

Parameters:

parameters: dict

A dictionary of the parameter names and associated values

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(interferometers, ...[, weights, ...])	Empty likelihood class to be subclassed by other likelihoods
cache_lookup_table()
calculate_snrs(waveform_polarizations, ...)	Compute the snrs for ROQ
calibration_marginalized_likelihood(...)
compute_log_likelihood_from_snrs(total_snrs)
compute_per_detector_log_likelihood()
distance_marginalized_likelihood(d_inner_h, ...)
generate_calibration_sample_from_marginalized_likelihood([...])	Generate a single sample from the posterior distribution for the set of calibration response curves when explicitly marginalizing over the calibration uncertainty.
generate_distance_sample_from_marginalized_likelihood([...])	Generate a single sample from the posterior distribution for luminosity distance when using a likelihood which explicitly marginalises over distance.
generate_phase_sample_from_marginalized_likelihood([...])	Generate a single sample from the posterior distribution for phase when using a likelihood which explicitly marginalises over phase.
generate_posterior_sample_from_marginalized_likelihood()	Reconstruct the distance posterior from a run which used a likelihood which explicitly marginalised over time/distance/phase.
generate_time_sample_from_marginalized_likelihood([...])	Generate a single sample from the posterior distribution for coalescence time when using a likelihood which explicitly marginalises over time.
get_calibration_log_likelihoods([...])
get_sky_frame_parameters([parameters])	Generate ra, dec, and geocenter time for parameters
load_lookup_table(filename)
load_weights(filename[, format])	Load ROQ weights.
log_likelihood()
log_likelihood_ratio()	Difference between log likelihood and noise log likelihood
noise_log_likelihood()
perform_roq_params_check([ifo])	Perform checking that the prior and data are valid for the ROQ
phase_marginalized_likelihood(d_inner_h, ...)
save_weights(filename[, format])	Save ROQ weights into a single file.
time_marginalized_likelihood(...)

Attributes

basis_number_linear
basis_number_quadratic
cached_lookup_table_filename
interferometers
lal_version
lalsimulation_version
marginalized_parameters
meta_data
priors
reference_frame
waveform_generator

calculate_snrs(waveform_polarizations, interferometer, return_array=True)

Compute the snrs for ROQ

Parameters:

waveform_polarizations: waveform

interferometer: bilby.gw.detector.Interferometer

generate_calibration_sample_from_marginalized_likelihood(signal_polarizations=None)

Generate a single sample from the posterior distribution for the set of calibration response curves when explicitly marginalizing over the calibration uncertainty.

Parameters:

signal_polarizations: dict, optional

Polarizations modes of the template.

Returns:

new_calibration: dict

Sample set from the calibration posterior

generate_distance_sample_from_marginalized_likelihood(signal_polarizations=None)

Generate a single sample from the posterior distribution for luminosity distance when using a likelihood which explicitly marginalises over distance.

See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293

Parameters:

signal_polarizations: dict, optional

Polarizations modes of the template. Note: These are rescaled in place after the distance sample is generated to allow further parameter reconstruction to occur.

Returns:

new_distance: float

Sample from the distance posterior.

generate_phase_sample_from_marginalized_likelihood(signal_polarizations=None)

Generate a single sample from the posterior distribution for phase when using a likelihood which explicitly marginalises over phase.

See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293

Parameters:

signal_polarizations: dict, optional

Polarizations modes of the template.

Returns:

new_phase: float

Sample from the phase posterior.

Notes

This is only valid when assumes that mu(phi) propto exp(-2i phi).

generate_posterior_sample_from_marginalized_likelihood()

Reconstruct the distance posterior from a run which used a likelihood which explicitly marginalised over time/distance/phase.

See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293

Returns:

sample: dict

Returns the parameters with new samples.

Notes

This involves a deepcopy of the signal to avoid issues with waveform caching, as the signal is overwritten in place.

generate_time_sample_from_marginalized_likelihood(signal_polarizations=None)

Generate a single sample from the posterior distribution for coalescence time when using a likelihood which explicitly marginalises over time.

In order to resolve the posterior we artificially upsample to 16kHz.

See Eq. (C29-C32) of https://arxiv.org/abs/1809.02293

Parameters:

signal_polarizations: dict, optional

Polarizations modes of the template.

Returns:

new_time: float

Sample from the time posterior.

get_sky_frame_parameters(parameters=None)

Generate ra, dec, and geocenter time for parameters

This method will attempt to convert from the reference time and sky parameters, but if they are not present it will fall back to ra and dec.

Parameters:

parameters: dict, optional

The parameters to be converted. If not specified self.parameters will be used.

Returns:

dict: dictionary containing ra, dec, and geocent_time

load_weights(filename, format=None)

Load ROQ weights. format should be json, npz, or hdf5. json or npz file is assumed to contain weights from a single basis. Support for json format is deprecated as of v2.1 and will be removed in v2.2, another method should be used by default.

Parameters:

filenamestr

The name of the file to save the weights to.

formatstr

The format to save the data to, this should be one of "hdf5", "npz", default=:code:”hdf5”.

Returns:

weights: dict

Dictionary containing the ROQ weights.

log_likelihood()

Returns:

float

log_likelihood_ratio()

Difference between log likelihood and noise log likelihood

Returns:

float

noise_log_likelihood()

Returns:

float

perform_roq_params_check(ifo=None)

Perform checking that the prior and data are valid for the ROQ

Parameters:

ifo: bilby.gw.detector.Interferometer

The interferometer

save_weights(filename, format='hdf5')

Save ROQ weights into a single file. format should be npz, or hdf5. For weights from multiple bases, hdf5 is only the possible option. Support for json format is deprecated as of v2.1 and will be removed in v2.2, another method should be used by default.

Parameters:

filenamestr

The name of the file to save the weights to.

formatstr

The format to save the data to, this should be one of "hdf5", "npz", default=:code:”hdf5”.