heron.models package¶

Submodules¶

heron.models.georgebased module¶

Models utilising the george GPR library in Python and C++.

class heron.models.georgebased.Heron2dHodlr[source]¶

Bases: heron.models.georgebased.HodlrGPR, heron.models.gw.BBHNonSpinSurrogate, heron.models.gw.HofTSurrogate

Produce a BBH waveform generator using the Hodlr method.

Methods

bilby(self, time, mass_1, mass_2, …[, …])	Return a waveform from the GPR in a format expected by the Bilby ecosystem
build(self[, mean, white_noise, tol])	Construct the GP object
distribution(self, p, times[, samples, …])	Return the mean waveform and the variance at a given location in the BBH parameter space.
eval(self)	Prepare the model to be evaluated.
log_evidence(self, k, n)	Evaluate the log-evidence of the model at a hyperparameter location k.
mean(self, p, times)	Return the mean waveform at a given location in the BBH parameter space.
train(self)	Prepare the model to be trained.

class heron.models.georgebased.Heron2dHodlrIMR[source]¶

Bases: heron.models.georgebased.HodlrGPR, heron.models.gw.BBHNonSpinSurrogate, heron.models.gw.HofTSurrogate

Produce a BBH waveform generator using the Hodlr method with IMRPhenomPv2 training data.

Methods

bilby(self, time, mass_1, mass_2, …[, …])	Return a waveform from the GPR in a format expected by the Bilby ecosystem
build(self[, mean, white_noise, tol])	Construct the GP object
distribution(self, p, times[, samples, …])	Return the mean waveform and the variance at a given location in the BBH parameter space.
eval(self)	Prepare the model to be evaluated.
log_evidence(self, k, n)	Evaluate the log-evidence of the model at a hyperparameter location k.
mean(self, p, times)	Return the mean waveform at a given location in the BBH parameter space.
train(self)	Prepare the model to be trained.

class heron.models.georgebased.HeronHodlr[source]¶

Bases: heron.models.georgebased.HodlrGPR, heron.models.gw.BBHSurrogate, heron.models.gw.HofTSurrogate

Produce a BBH waveform generator using the Hodlr method.

Methods

bilby(self, time, mass_1, mass_2, …[, …])	Return a waveform from the GPR in a format expected by the Bilby ecosystem
build(self[, mean, white_noise, tol])	Construct the GP object
distribution(self, p, times[, samples, …])	Return the mean waveform and the variance at a given location in the BBH parameter space.
eval(self)	Prepare the model to be evaluated.
log_evidence(self, k, n)	Evaluate the log-evidence of the model at a hyperparameter location k.
mean(self, p, times)	Return the mean waveform at a given location in the BBH parameter space.
train(self)	Prepare the model to be trained.

class heron.models.georgebased.HodlrGPR[source]¶

Bases: heron.models.Model

A GPR model using the hierarchical matrix approximation.

Methods

build(self[, mean, white_noise, tol])	Construct the GP object
distribution(self, p, times[, samples, …])	Return the mean waveform and the variance at a given location in the BBH parameter space.
eval(self)	Prepare the model to be evaluated.
log_evidence(self, k, n)	Evaluate the log-evidence of the model at a hyperparameter location k.
mean(self, p, times)	Return the mean waveform at a given location in the BBH parameter space.
train(self)	Prepare the model to be trained.

build(self, mean=0.0, white_noise=0, tol=1e-06)[source]¶

Construct the GP object

distribution(self, p, times, samples=100, polarisation='h+')[source]¶

Return the mean waveform and the variance at a given location in the BBH parameter space.

Parameters

pdict

A dictionary of parameter locations.

timesarray-like

The timestamps at which the model should be evaluated.

samplesint

The number of samples to draw from the GP.

polarisationstr {“h+”, “hx”}

The polarisation which should be evaluated.

eval(self)[source]¶

Prepare the model to be evaluated.

log_evidence(self, k, n)[source]¶

Evaluate the log-evidence of the model at a hyperparameter location k.

Parameters

nint

The number of points to be used to calculate the log likelihood.

mean(self, p, times)[source]¶

Return the mean waveform at a given location in the BBH parameter space.

train(self)[source]¶

Prepare the model to be trained.

heron.models.georgebased.train(model, batch_size=100, algorithm='adam', max_iter=1000)[source]¶

Train a george-based Gaussian process model.

heron.models.gw module¶

This module contains objects which provide the specifically-GW parts of waveform surrogate models.

class heron.models.gw.BBHNonSpinSurrogate[source]¶

Bases: object

c_ind = {'h+': 8, 'hx': 9, 'mass ratio': 1, 'time': 0}¶

columns = {0: 'time', 1: 'mass ratio', 8: 'h+', 9: 'hx'}¶

problem_dims = 2¶

class heron.models.gw.BBHSurrogate[source]¶

Bases: object

c_ind = {'h+': 8, 'hx': 9, 'mass ratio': 1, 'spin 1x': 2, 'spin 1y': 3, 'spin 1z': 4, 'spin 2x': 5, 'spin 2y': 6, 'spin 2z': 7, 'time': 0}¶

columns = {0: 'time', 1: 'mass ratio', 2: 'spin 1x', 3: 'spin 1y', 4: 'spin 1z', 5: 'spin 2x', 6: 'spin 2y', 7: 'spin 2z', 8: 'h+', 9: 'hx'}¶

parameters = ('mass ratio', 'spin 1x', 'spin 1y', 'spin 1z', 'spin 2x', 'spin 2y', 'spin 2z')¶

problem_dims = 8¶

class heron.models.gw.HofTSurrogate[source]¶

Bases: object

Methods

bilby(self, time, mass_1, mass_2, …[, …])

Return a waveform from the GPR in a format expected by the Bilby ecosystem

bilby(self, time, mass_1, mass_2, luminosity_distance, theta_jn=0, phase=0, psi=0)[source]¶

Return a waveform from the GPR in a format expected by the Bilby ecosystem

Parameters

timefloat or ndarray

The times at which the model should be evaluated.

mass_1, mass_2float

The mass (in solar masses) of the primary and secondary components.

luminosity_distancefloat

The distance (in megaparsec) to the source

theta_jnfloat

The orientation angle of the source.

phasefloat

The initial phase of the signal

psifloat

The polarisation angle of the signal

Module contents¶

class heron.models.Model[source]¶

Bases: object

This is the factory class for statistical models used for waveform generation.

A model class must expose the followi*** TODO Email about Away Dayc_ind = {j:i for i,j in columns.items()}c_ind = {j:i for i,j in columns.items()}ng methods: - distribution : produce a distribution of waveforms at a given point in the parameter space - mean : produce a mean waveform at a given point in the parameter space - train : provide an interface for training the model