diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1fdaae3edc4c09906e1af38f9702a232be97a9d1..a5f45836dce4a12637061f1cb292e722de210f1c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -26,7 +26,9 @@ Changes currently on master, but not under a tag.
- Autocorrelation calculation moved into parent class
- Fix interpretation of kwargs for dynesty
- PowerSpectralDensity structure modified
-- Fixed bug in get_open_data
+- Fixed bug in get_open_data
+- Modified how sampling in non-standard parameters is done, the
+ `non_standard_sampling_parameter_keys` kwarg has been removed
- .prior files are no longer created. The prior is stored in the result object.
- Changed the way repr works for priors. The repr can now be used to
re-instantiate the Prior in most cases
diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index a8575e17b28fb9cea657e979fb048edab8a00e65..2c18ef1c33fba11302dd09a6a892196a2e8861dd 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -40,18 +40,16 @@ waveform_generator = tupak.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters, waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers. In this case we'll use two interferometers
# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design
# sensitivity
-IFOs = []
-for name in ["H1", "L1"]:
- IFOs.append(
- tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal,
- injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir))
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time']-3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Set up prior, which is a dictionary
# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
@@ -68,7 +66,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', '
priors[key] = injection_parameters[key]
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.gw.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
+likelihood = tupak.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator,
time_marginalization=False, phase_marginalization=False,
distance_marginalization=False, prior=priors)
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index 9f95dce802453bbd838f760db3d164b22a409b17..106ace05c782ec8dc485e46c288527722e8731f8 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -1,8 +1,10 @@
-#!/bin/python
+#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation sampling in non-standard parameters for an injected signal.
+Tutorial to demonstrate running parameter estimation sampling in non-standard
+parameters for an injected signal.
-This example estimates the masses using a uniform prior in chirp mass, mass ratio and redshift.
+This example estimates the masses using a uniform prior in chirp mass,
+mass ratio and redshift.
The cosmology is according to the Planck 2015 data release.
"""
from __future__ import division, print_function
@@ -18,9 +20,10 @@ outdir = 'outdir'
np.random.seed(151226)
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
- luminosity_distance=500, iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
- ra=1.375, dec=-1.2108)
+injection_parameters = dict(
+ total_mass=66., mass_ratio=0.9, a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0,
+ phi_12=1.7, phi_jl=0.3, luminosity_distance=2000, iota=0.4, psi=2.659,
+ phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50.)
@@ -29,35 +32,45 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
sampling_frequency=sampling_frequency, duration=duration,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
- parameter_conversion=tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
- non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio', 'redshift'],
- parameters=injection_parameters, waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
+ parameter_conversion=
+ tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
+ waveform_arguments=waveform_arguments)
# Set up interferometers.
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1', 'K1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time']-3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Set up prior
priors = tupak.gw.prior.BBHPriorSet()
priors.pop('mass_1')
priors.pop('mass_2')
priors.pop('luminosity_distance')
-priors['chirp_mass'] = tupak.prior.Uniform(name='chirp_mass', latex_label='$m_c$', minimum=13, maximum=45)
-priors['mass_ratio'] = tupak.prior.Uniform(name='mass_ratio', latex_label='$q$', minimum=0.1, maximum=1)
-priors['redshift'] = tupak.prior.Uniform(name='redshift', latex_label='$z$', minimum=0, maximum=0.5)
+priors['chirp_mass'] = tupak.prior.Uniform(
+ name='chirp_mass', latex_label='$m_c$', minimum=13, maximum=45)
+priors['symmetric_mass_ratio'] = tupak.prior.Uniform(
+ name='symmetric_mass_ratio', latex_label='q', minimum=0.1, maximum=0.25)
+priors['redshift'] = tupak.prior.Uniform(
+ name='redshift', latex_label='$z$', minimum=0, maximum=0.5)
# These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi',
+ 'ra', 'dec', 'geocent_time', 'phase']:
priors[key] = injection_parameters[key]
+priors.pop('iota')
+priors['cos_iota'] = np.cos(injection_parameters['iota'])
print(priors)
# Initialise GravitationalWaveTransient
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
-result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, label='DifferentParameters',
- outdir=outdir, conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
+result = tupak.core.sampler.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
+ injection_parameters=injection_parameters, label='DifferentParameters',
+ conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 604a513ef6bb9a70ba60a7db02e4d3dfecf6696f..8ee965ab9382370a71ea96ad42b22e882d7adc31 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -29,14 +29,14 @@ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(duration=dura
sampling_frequency=sampling_frequency,
frequency_domain_source_model=sine_gaussian,
parameters=injection_parameters)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal,
- injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir)
- for name in ['H1', 'L1', 'V1']]
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Here we define the priors for the search. We use the injection parameters
# except for the amplitude, f0, and geocent_time
@@ -44,7 +44,7 @@ prior = injection_parameters.copy()
prior['A'] = tupak.core.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
prior['f0'] = tupak.core.prior.Uniform(90, 110, 'f')
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(ifos, waveform_generator)
result = tupak.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index be48be57ab9c23f8594da2751f03e5be586fb698..b1f814c6e9cf3bc7fe6f2c5146cdf2f10fed49d8 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -36,23 +36,13 @@ waveform = tupak.gw.waveform_generator.WaveformGenerator(duration=duration, samp
time_domain_source_model=time_domain_damped_sinusoid,
parameters=injection_parameters)
-hf_signal = waveform.frequency_domain_strain()
-#note we could plot the time domain signal with the following code
-# import matplotlib.pyplot as plt
-# plt.plot(waveform.time_array, waveform.time_domain_strain()['plus'])
-
-# or the frequency-domain signal:
-# plt.loglog(waveform.frequency_array, abs(waveform.frequency_domain_strain()['plus']))
-
-
-
# inject the signal into three interferometers
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal,
- injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir)
- for name in ['H1', 'L1']]
-
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform,
+ parameters=injection_parameters)
# create the priors
prior = injection_parameters.copy()
@@ -61,9 +51,8 @@ prior['damping_time'] = tupak.core.prior.Uniform(0, 1, r'damping time')
prior['frequency'] = tupak.core.prior.Uniform(0, 200, r'frequency')
prior['phase'] = tupak.core.prior.Uniform(-np.pi / 2, np.pi / 2, r'$\phi$')
-
# define likelihood
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(IFOs, waveform)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(ifos, waveform)
# launch sampler
result = tupak.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
diff --git a/examples/injection_examples/eccentric_inspiral.py b/examples/injection_examples/eccentric_inspiral.py
index 79bdf8088e8cee636b389132ff09a5e0d0ff1937..f4077b640c2d3446f7c32699e58e4bdfda3f3215 100644
--- a/examples/injection_examples/eccentric_inspiral.py
+++ b/examples/injection_examples/eccentric_inspiral.py
@@ -40,7 +40,6 @@ waveform_generator = tupak.gw.WaveformGenerator(
frequency_domain_source_model=tupak.gw.source.lal_eccentric_binary_black_hole_no_spins,
parameters=injection_parameters, waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and
# Virgo (V1)) at their design sensitivities. The maximum frequency is set just
@@ -49,13 +48,15 @@ hf_signal = waveform_generator.frequency_domain_strain()
minimum_frequency = 10.
maximum_frequency = 128.
-IFOs = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
-for IFO in IFOs:
- IFO.minimum_frequency = minimum_frequency
- IFO.maximum_frequency = maximum_frequency
-
-IFOs.set_strain_data_from_power_spectral_densities(sampling_frequency, duration)
-IFOs.inject_signal(waveform_generator=waveform_generator, parameters=injection_parameters)
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+for ifo in ifos:
+ ifo.minimum_frequency = minimum_frequency
+ ifo.maximum_frequency = maximum_frequency
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Now we set up the priors on each of the binary parameters.
priors = dict()
@@ -71,7 +72,7 @@ priors["phase"] = tupak.core.prior.Uniform(name='phase', minimum=0, maximum=2 *
priors["geocent_time"] = tupak.core.prior.Uniform(1180002600.9, 1180002601.1, name='geocent_time')
# Initialising the likelihood function.
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs,
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=ifos,
waveform_generator=waveform_generator, time_marginalization=False,
phase_marginalization=False, distance_marginalization=False,
prior=priors)
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index 450250db63b3c141ca87e9ef1689328b278dd857..51b7877dde665ece58094500d25e9504e81c94d7 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -27,12 +27,14 @@ waveform_generator = tupak.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters, waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Set up prior
# This loads in a predefined set of priors for BBHs.
@@ -56,7 +58,7 @@ priors['a_2'] = tupak.core.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=
# Enjoy.
# Initialise GravitationalWaveTransient
-likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index b91cee3e109f257d55b53c8238b924dbef870d79..8c05d0f977af14b6cd19c9614c2be956db71dcf3 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -26,16 +26,14 @@ waveform_generator = tupak.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole, parameters=injection_parameters,
waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
-IFOs = []
-for name in ["H1", "L1", "V1"]:
- IFOs.append(
- tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal,
- injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir))
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Set up prior
priors = tupak.gw.prior.BBHPriorSet()
@@ -47,7 +45,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
# Note that we now need to pass the: priors and flags for each thing that's being marginalised.
# This is still under development so care should be taken with the marginalised likelihood.
likelihood = tupak.gw.GravitationalWaveTransient(
- interferometers=IFOs, waveform_generator=waveform_generator, prior=priors,
+ interferometers=ifos, waveform_generator=waveform_generator, prior=priors,
distance_marginalization=False, phase_marginalization=True,
time_marginalization=False)
diff --git a/examples/injection_examples/plot_time_domain_data.py b/examples/injection_examples/plot_time_domain_data.py
index a0b1a61a889cafb1569030680fec478f12d4da5b..37a0868c84157b2261a57499b9985252fc63c4c5 100644
--- a/examples/injection_examples/plot_time_domain_data.py
+++ b/examples/injection_examples/plot_time_domain_data.py
@@ -25,7 +25,7 @@ waveform_generator = tupak.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
parameters=injection_parameters, waveform_arguments=waveform_arguments)
-hf_signal = waveform_generator.frequency_domain_strain()
+hf_signal = waveform_generator.frequency_domain_strain(injection_parameters)
H1 = tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
'H1', injection_polarizations=hf_signal,
diff --git a/examples/injection_examples/sine_gaussian_example.py b/examples/injection_examples/sine_gaussian_example.py
index 8761cf6b388542a34717ec52c1c71c637d6fac1f..aba4081847b49c4f7388df6ccf348c4558c8c5d7 100644
--- a/examples/injection_examples/sine_gaussian_example.py
+++ b/examples/injection_examples/sine_gaussian_example.py
@@ -29,13 +29,15 @@ waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(duration=dura
sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.sinegaussian,
parameters=injection_parameters)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
# and Virgo (V1)). These default to their design sensitivity
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, duration=duration,
- sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time']-3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# Set up prior, which is a dictionary
priors = dict()
@@ -55,7 +57,7 @@ priors['frequency'] = tupak.core.prior.Uniform(30, 1000, 'frequency')
priors['hrss'] = tupak.core.prior.Uniform(1e-23, 1e-21, 'hrss')
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index 86ba46c80afb207a4833570666c9eb14417edf7f..2365bd55ef652f47819214acf7635615e5ab82b2 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -13,7 +13,7 @@ import numpy as np
# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
import tupak.gw.likelihood
-time_duration = 3.
+duration = 3.
sampling_frequency = 4096.
# Specify the output directory and the name of the simulation.
@@ -35,19 +35,19 @@ injection_parameters = dict(file_path='MuellerL15_example_inj.txt',
# Create the waveform_generator using a supernova source function
waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
- time_duration=time_duration, sampling_frequency=sampling_frequency,
+ duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.supernova,
parameters=injection_parameters)
-hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers. In this case we'll use three interferometers
# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). These default to
# their design sensitivity
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
- name, injection_polarizations=hf_signal,
- injection_parameters=injection_parameters, time_duration=time_duration,
- sampling_frequency=sampling_frequency, outdir=outdir)
- for name in ['H1', 'L1', 'V1']]
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time']-3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
# read in from a file the PCs used to create the signal model.
realPCs = np.loadtxt('SupernovaRealPCs.txt')
@@ -59,7 +59,7 @@ simulation_parameters = dict(
realPCs=realPCs, imagPCs=imagPCs)
search_waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
- time_duration=time_duration, sampling_frequency=sampling_frequency,
+ duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=tupak.gw.source.supernova_pca_model,
waveform_arguments=simulation_parameters)
@@ -85,7 +85,7 @@ priors['geocent_time'] = tupak.core.prior.Uniform(
# Initialise the likelihood by passing in the interferometer data (IFOs) and
# the waveoform generator
likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
- interferometers=IFOs, waveform_generator=search_waveform_generator)
+ interferometers=ifos, waveform_generator=search_waveform_generator)
# Run sampler.
result = tupak.run_sampler(
diff --git a/test/conversion_test.py b/test/conversion_test.py
index f0e4409f2d360c93ba93a96f6ac33c7b4089dc88..6a57a177359fd08c92df411ebf9551745c51513f 100644
--- a/test/conversion_test.py
+++ b/test/conversion_test.py
@@ -1,7 +1,7 @@
from __future__ import division, absolute_import
import unittest
import mock
-import tupak
+from tupak.gw import conversion
import numpy as np
@@ -47,52 +47,52 @@ class TestBasicConversions(unittest.TestCase):
del self.symmetric_mass_ratio
def test_total_mass_and_mass_ratio_to_component_masses(self):
- mass_1, mass_2 = tupak.gw.conversion.total_mass_and_mass_ratio_to_component_masses(self.mass_ratio, self.total_mass)
+ mass_1, mass_2 = conversion.total_mass_and_mass_ratio_to_component_masses(self.mass_ratio, self.total_mass)
self.assertTrue(all([abs(mass_1 - self.mass_1) < 1e-5,
abs(mass_2 - self.mass_2) < 1e-5]))
def test_symmetric_mass_ratio_to_mass_ratio(self):
- mass_ratio = tupak.gw.conversion.symmetric_mass_ratio_to_mass_ratio(self.symmetric_mass_ratio)
+ mass_ratio = conversion.symmetric_mass_ratio_to_mass_ratio(self.symmetric_mass_ratio)
self.assertAlmostEqual(self.mass_ratio, mass_ratio)
def test_chirp_mass_and_total_mass_to_symmetric_mass_ratio(self):
- symmetric_mass_ratio = tupak.gw.conversion.chirp_mass_and_total_mass_to_symmetric_mass_ratio(self.chirp_mass, self.total_mass)
+ symmetric_mass_ratio = conversion.chirp_mass_and_total_mass_to_symmetric_mass_ratio(self.chirp_mass, self.total_mass)
self.assertAlmostEqual(self.symmetric_mass_ratio, symmetric_mass_ratio)
def test_chirp_mass_and_mass_ratio_to_total_mass(self):
- total_mass = tupak.gw.conversion.chirp_mass_and_mass_ratio_to_total_mass(self.chirp_mass, self.mass_ratio)
+ total_mass = conversion.chirp_mass_and_mass_ratio_to_total_mass(self.chirp_mass, self.mass_ratio)
self.assertAlmostEqual(self.total_mass, total_mass)
def test_component_masses_to_chirp_mass(self):
- chirp_mass = tupak.gw.conversion.component_masses_to_chirp_mass(self.mass_1, self.mass_2)
+ chirp_mass = conversion.component_masses_to_chirp_mass(self.mass_1, self.mass_2)
self.assertAlmostEqual(self.chirp_mass, chirp_mass)
def test_component_masses_to_total_mass(self):
- total_mass = tupak.gw.conversion.component_masses_to_total_mass(self.mass_1, self.mass_2)
+ total_mass = conversion.component_masses_to_total_mass(self.mass_1, self.mass_2)
self.assertAlmostEqual(self.total_mass, total_mass)
def test_component_masses_to_symmetric_mass_ratio(self):
- symmetric_mass_ratio = tupak.gw.conversion.component_masses_to_symmetric_mass_ratio(self.mass_1, self.mass_2)
+ symmetric_mass_ratio = conversion.component_masses_to_symmetric_mass_ratio(self.mass_1, self.mass_2)
self.assertAlmostEqual(self.symmetric_mass_ratio, symmetric_mass_ratio)
def test_component_masses_to_mass_ratio(self):
- mass_ratio = tupak.gw.conversion.component_masses_to_mass_ratio(self.mass_1, self.mass_2)
+ mass_ratio = conversion.component_masses_to_mass_ratio(self.mass_1, self.mass_2)
self.assertAlmostEqual(self.mass_ratio, mass_ratio)
def test_mass_1_and_chirp_mass_to_mass_ratio(self):
- mass_ratio = tupak.gw.conversion.mass_1_and_chirp_mass_to_mass_ratio(self.mass_1, self.chirp_mass)
+ mass_ratio = conversion.mass_1_and_chirp_mass_to_mass_ratio(self.mass_1, self.chirp_mass)
self.assertAlmostEqual(self.mass_ratio, mass_ratio)
def test_lambda_tilde_to_lambda_1_lambda_2(self):
lambda_1, lambda_2 =\
- tupak.gw.conversion.lambda_tilde_to_lambda_1_lambda_2(
+ conversion.lambda_tilde_to_lambda_1_lambda_2(
self.lambda_tilde, self.mass_1, self.mass_2)
self.assertTrue(all([abs(self.lambda_1 - lambda_1) < 1e-5,
abs(self.lambda_2 - lambda_2) < 1e-5]))
def test_lambda_tilde_delta_lambda_to_lambda_1_lambda_2(self):
lambda_1, lambda_2 =\
- tupak.gw.conversion.lambda_tilde_delta_lambda_to_lambda_1_lambda_2(
+ conversion.lambda_tilde_delta_lambda_to_lambda_1_lambda_2(
self.lambda_tilde, self.delta_lambda, self.mass_1, self.mass_2)
self.assertTrue(all([abs(self.lambda_1 - lambda_1) < 1e-5,
abs(self.lambda_2 - lambda_2) < 1e-5]))
@@ -113,17 +113,10 @@ class TestConvertToLALBBHParams(unittest.TestCase):
def test_cos_angle_to_angle_conversion(self):
with mock.patch('numpy.arccos') as m:
m.return_value = 42
- self.search_keys.append('cos_tilt_1')
self.parameters['cos_tilt_1'] = 1
- self.parameters, _ = tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters(self.parameters, self.search_keys)
- self.assertEqual(42, self.parameters['tilt_1'])
-
- def test_cos_angle_to_angle_conversion_removal(self):
- with mock.patch('numpy.arccos') as m:
- m.return_value = 42
- self.search_keys.append('cos_tilt_1')
- self.parameters['cos_tilt_1'] = 1
- self.parameters, _ = tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters(self.parameters, self.search_keys, remove=True)
+ self.parameters, _ =\
+ conversion.convert_to_lal_binary_black_hole_parameters(
+ self.parameters)
self.assertDictEqual(self.parameters, dict(tilt_1=42, cos_tilt_1=1))
diff --git a/test/detector_test.py b/test/detector_test.py
index a69ca162378220c3149148d31784c46972f2f69c..e9ff0e11af6dfa47f480c1e412574924dcb186b4 100644
--- a/test/detector_test.py
+++ b/test/detector_test.py
@@ -723,16 +723,6 @@ class TestInterferometerList(unittest.TestCase):
self.ifo_list.inject_signal(parameters=None, waveform_generator=waveform_generator)
self.assertTrue(m.called)
- @patch.object(tupak.gw.waveform_generator.WaveformGenerator, 'frequency_domain_strain')
- def test_inject_signal_pol_none_sets_wg_parameters(self, m):
- waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
- frequency_domain_source_model=lambda x, y, z: x)
- parameters = dict(y=1, z=2)
- self.ifo1.inject_signal = MagicMock(return_value=None)
- self.ifo2.inject_signal = MagicMock(return_value=None)
- self.ifo_list.inject_signal(parameters=parameters, waveform_generator=waveform_generator)
- self.assertDictEqual(parameters, waveform_generator.parameters)
-
@patch.object(tupak.gw.detector.Interferometer, 'inject_signal')
def test_inject_signal_with_inj_pol(self, m):
self.ifo_list.inject_signal(injection_polarizations=dict(plus=1))
diff --git a/test/gw_likelihood_test.py b/test/gw_likelihood_test.py
index fa8e53b6ec255b5defd1ad98ccd7da0f9f39b46b..e04f16a453291684ceb620d6e5346bbd210a6ec4 100644
--- a/test/gw_likelihood_test.py
+++ b/test/gw_likelihood_test.py
@@ -1,9 +1,7 @@
from __future__ import division, absolute_import
import unittest
-import mock
import tupak
import numpy as np
-from scipy.special import logsumexp
class TestBasicGWTransient(unittest.TestCase):
@@ -19,7 +17,7 @@ class TestBasicGWTransient(unittest.TestCase):
self.interferometers.set_strain_data_from_power_spectral_densities(
sampling_frequency=2048, duration=4)
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
- duration=4, sampling_frequency=2048, parameters=self.parameters,
+ duration=4, sampling_frequency=2048,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -27,6 +25,7 @@ class TestBasicGWTransient(unittest.TestCase):
interferometers=self.interferometers,
waveform_generator=self.waveform_generator
)
+ self.likelihood.parameters = self.parameters.copy()
def tearDown(self):
del self.parameters
@@ -56,10 +55,10 @@ class TestBasicGWTransient(unittest.TestCase):
def test_likelihood_zero_when_waveform_is_none(self):
"""Test log likelihood returns np.nan_to_num(-np.inf) when the
waveform is None"""
- self.likelihood.waveform_generator.parameters['mass_2'] = 32
+ self.likelihood.parameters['mass_2'] = 32
self.assertEqual(self.likelihood.log_likelihood_ratio(),
np.nan_to_num(-np.inf))
- self.likelihood.waveform_generator.parameters['mass_2'] = 29
+ self.likelihood.parameters['mass_2'] = 29
def test_repr(self):
expected = 'BasicGravitationalWaveTransient(interferometers={},\n\twaveform_generator={})'.format(
@@ -83,7 +82,6 @@ class TestGWTransient(unittest.TestCase):
sampling_frequency=self.sampling_frequency, duration=self.duration)
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=self.duration, sampling_frequency=self.sampling_frequency,
- parameters=self.parameters.copy(),
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -96,12 +94,7 @@ class TestGWTransient(unittest.TestCase):
interferometers=self.interferometers,
waveform_generator=self.waveform_generator, prior=self.prior.copy()
)
-
- # self.distance = tupak.gw.likelihood.GravitationalWaveTransient(
- # interferometers=self.interferometers,
- # waveform_generator=self.waveform_generator,
- # distance_marginalization=True, prior=self.prior
- # )
+ self.likelihood.parameters = self.parameters.copy()
def tearDown(self):
del self.parameters
@@ -109,7 +102,6 @@ class TestGWTransient(unittest.TestCase):
del self.waveform_generator
del self.prior
del self.likelihood
- # del self.distance
def test_noise_log_likelihood(self):
"""Test noise log likelihood matches precomputed value"""
@@ -133,10 +125,10 @@ class TestGWTransient(unittest.TestCase):
def test_likelihood_zero_when_waveform_is_none(self):
"""Test log likelihood returns np.nan_to_num(-np.inf) when the
waveform is None"""
- self.likelihood.waveform_generator.parameters['mass_2'] = 32
+ self.likelihood.parameters['mass_2'] = 32
self.assertEqual(self.likelihood.log_likelihood_ratio(),
np.nan_to_num(-np.inf))
- self.likelihood.waveform_generator.parameters['mass_2'] = 29
+ self.likelihood.parameters['mass_2'] = 29
def test_repr(self):
expected = 'GravitationalWaveTransient(interferometers={},\n\twaveform_generator={},\n\t' \
@@ -163,7 +155,6 @@ class TestTimeMarginalization(unittest.TestCase):
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=self.duration, sampling_frequency=self.sampling_frequency,
- parameters=self.parameters.copy(),
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -182,6 +173,8 @@ class TestTimeMarginalization(unittest.TestCase):
waveform_generator=self.waveform_generator,
time_marginalization=True, prior=self.prior.copy()
)
+ for like in [self.likelihood, self.time]:
+ like.parameters = self.parameters.copy()
def tearDown(self):
del self.duration
@@ -199,12 +192,12 @@ class TestTimeMarginalization(unittest.TestCase):
times = np.linspace(self.prior['geocent_time'].minimum,
self.prior['geocent_time'].maximum, 4097)[:-1]
for time in times:
- self.waveform_generator.parameters['geocent_time'] = time
+ self.likelihood.parameters['geocent_time'] = time
like.append(np.exp(self.likelihood.log_likelihood_ratio()))
marg_like = np.log(np.trapz(like, times)
/ self.waveform_generator.duration)
- self.waveform_generator.parameters = self.parameters.copy()
+ self.time.parameters = self.parameters.copy()
self.assertAlmostEqual(marg_like, self.time.log_likelihood_ratio(),
delta=0.5)
@@ -228,7 +221,6 @@ class TestMarginalizedLikelihood(unittest.TestCase):
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=self.duration, sampling_frequency=self.sampling_frequency,
- parameters=self.parameters.copy(),
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -292,7 +284,6 @@ class TestPhaseMarginalization(unittest.TestCase):
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=self.duration, sampling_frequency=self.sampling_frequency,
- parameters=self.parameters.copy(),
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -311,6 +302,8 @@ class TestPhaseMarginalization(unittest.TestCase):
waveform_generator=self.waveform_generator,
phase_marginalization=True, prior=self.prior.copy()
)
+ for like in [self.likelihood, self.phase]:
+ like.parameters = self.parameters.copy()
def tearDown(self):
del self.duration
@@ -327,11 +320,11 @@ class TestPhaseMarginalization(unittest.TestCase):
like = []
phases = np.linspace(0, 2 * np.pi, 1000)
for phase in phases:
- self.waveform_generator.parameters['phase'] = phase
+ self.likelihood.parameters['phase'] = phase
like.append(np.exp(self.likelihood.log_likelihood_ratio()))
marg_like = np.log(np.trapz(like, phases) / (2 * np.pi))
- self.waveform_generator.parameters = self.parameters.copy()
+ self.phase.parameters = self.parameters.copy()
self.assertAlmostEqual(marg_like, self.phase.log_likelihood_ratio(),
delta=0.5)
@@ -354,7 +347,6 @@ class TestTimePhaseMarginalization(unittest.TestCase):
self.waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=self.duration, sampling_frequency=self.sampling_frequency,
- parameters=self.parameters.copy(),
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
)
@@ -386,6 +378,8 @@ class TestTimePhaseMarginalization(unittest.TestCase):
time_marginalization=True, phase_marginalization=True,
prior=self.prior.copy()
)
+ for like in [self.likelihood, self.time, self.phase, self.time_phase]:
+ like.parameters = self.parameters.copy()
def tearDown(self):
del self.duration
@@ -405,12 +399,12 @@ class TestTimePhaseMarginalization(unittest.TestCase):
times = np.linspace(self.prior['geocent_time'].minimum,
self.prior['geocent_time'].maximum, 4097)[:-1]
for time in times:
- self.waveform_generator.parameters['geocent_time'] = time
+ self.phase.parameters['geocent_time'] = time
like.append(np.exp(self.phase.log_likelihood_ratio()))
marg_like = np.log(np.trapz(like, times)
/ self.waveform_generator.duration)
- self.waveform_generator.parameters = self.parameters.copy()
+ self.time_phase.parameters = self.parameters.copy()
self.assertAlmostEqual(marg_like,
self.time_phase.log_likelihood_ratio(),
delta=0.5)
@@ -418,11 +412,11 @@ class TestTimePhaseMarginalization(unittest.TestCase):
like = []
phases = np.linspace(0, 2 * np.pi, 1000)
for phase in phases:
- self.waveform_generator.parameters['phase'] = phase
+ self.time.parameters['phase'] = phase
like.append(np.exp(self.time.log_likelihood_ratio()))
marg_like = np.log(np.trapz(like, phases) / (2 * np.pi))
- self.waveform_generator.parameters = self.parameters.copy()
+ self.time_phase.parameters = self.parameters.copy()
self.assertAlmostEqual(marg_like,
self.time_phase.log_likelihood_ratio(),
delta=0.5)
@@ -435,7 +429,6 @@ class TestBBHLikelihoodSetUp(unittest.TestCase):
def tearDown(self):
del self.ifos
- del self.like
def test_instantiation(self):
self.like = tupak.gw.likelihood.get_binary_black_hole_likelihood(
diff --git a/test/waveform_generator_test.py b/test/waveform_generator_test.py
index a7cd95ebe9ee6ce43f4bbe5dbbf5a04bf44f9c2a..a03f250a3db219bbc810c8da700e007ad65ceaa1 100644
--- a/test/waveform_generator_test.py
+++ b/test/waveform_generator_test.py
@@ -20,8 +20,9 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
def setUp(self):
self.waveform_generator = \
- tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
- frequency_domain_source_model=dummy_func_dict_return_value)
+ tupak.gw.waveform_generator.WaveformGenerator(
+ 1, 4096,
+ frequency_domain_source_model=dummy_func_dict_return_value)
self.simulation_parameters = dict(amplitude=1e-21, mu=100, sigma=1,
ra=1.375,
dec=-1.2108,
@@ -34,16 +35,14 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
def test_repr(self):
expected = 'WaveformGenerator(duration={}, sampling_frequency={}, start_time={}, ' \
- 'frequency_domain_source_model={}, time_domain_source_model={}, parameters={}, ' \
- 'parameter_conversion={}, non_standard_sampling_parameter_keys={}, waveform_arguments={})'\
+ 'frequency_domain_source_model={}, time_domain_source_model={}, ' \
+ 'parameter_conversion={}, waveform_arguments={})'\
.format(self.waveform_generator.duration,
self.waveform_generator.sampling_frequency,
self.waveform_generator.start_time,
self.waveform_generator.frequency_domain_source_model.__name__,
self.waveform_generator.time_domain_source_model,
- self.waveform_generator.parameters,
None,
- self.waveform_generator.non_standard_sampling_parameter_keys,
self.waveform_generator.waveform_arguments)
self.assertEqual(expected, repr(self.waveform_generator))
@@ -52,16 +51,14 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
tupak.gw.waveform_generator.WaveformGenerator(1, 4096,
time_domain_source_model=dummy_func_dict_return_value)
expected = 'WaveformGenerator(duration={}, sampling_frequency={}, start_time={}, ' \
- 'frequency_domain_source_model={}, time_domain_source_model={}, parameters={}, ' \
- 'parameter_conversion={}, non_standard_sampling_parameter_keys={}, waveform_arguments={})'\
+ 'frequency_domain_source_model={}, time_domain_source_model={}, ' \
+ 'parameter_conversion={}, waveform_arguments={})'\
.format(self.waveform_generator.duration,
self.waveform_generator.sampling_frequency,
self.waveform_generator.start_time,
self.waveform_generator.frequency_domain_source_model,
self.waveform_generator.time_domain_source_model.__name__,
- self.waveform_generator.parameters,
None,
- self.waveform_generator.non_standard_sampling_parameter_keys,
self.waveform_generator.waveform_arguments)
self.assertEqual(expected, repr(self.waveform_generator))
@@ -71,16 +68,14 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
self.waveform_generator.parameter_conversion = conversion_func
expected = 'WaveformGenerator(duration={}, sampling_frequency={}, start_time={}, ' \
- 'frequency_domain_source_model={}, time_domain_source_model={}, parameters={}, ' \
- 'parameter_conversion={}, non_standard_sampling_parameter_keys={}, waveform_arguments={})'\
+ 'frequency_domain_source_model={}, time_domain_source_model={}, ' \
+ 'parameter_conversion={}, waveform_arguments={})'\
.format(self.waveform_generator.duration,
self.waveform_generator.sampling_frequency,
self.waveform_generator.start_time,
self.waveform_generator.frequency_domain_source_model.__name__,
self.waveform_generator.time_domain_source_model,
- self.waveform_generator.parameters,
conversion_func.__name__,
- self.waveform_generator.non_standard_sampling_parameter_keys,
self.waveform_generator.waveform_arguments)
self.assertEqual(expected, repr(self.waveform_generator))
@@ -100,6 +95,7 @@ class TestWaveformGeneratorInstantiationWithoutOptionalParameters(unittest.TestC
self.assertIsInstance(self.waveform_generator.time_array, np.ndarray)
def test_source_model_parameters(self):
+ self.waveform_generator.parameters = self.simulation_parameters.copy()
self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
sorted(list(self.simulation_parameters.keys())))
@@ -136,14 +132,15 @@ class TestSetters(unittest.TestCase):
del self.simulation_parameters
def test_parameter_setter_sets_expected_values_with_expected_keys(self):
- self.waveform_generator.parameters = self.simulation_parameters
+ self.waveform_generator.parameters = self.simulation_parameters.copy()
for key in self.simulation_parameters:
self.assertEqual(self.waveform_generator.parameters[key], self.simulation_parameters[key])
def test_parameter_setter_none_handling(self):
- self.waveform_generator.parameters = None
- self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
- sorted(list(self.simulation_parameters.keys())))
+ with self.assertRaises(TypeError):
+ self.waveform_generator.parameters = None
+ # self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
+ # sorted(list(self.simulation_parameters.keys())))
def test_frequency_array_setter(self):
new_frequency_array = np.arange(1, 100)
@@ -157,11 +154,13 @@ class TestSetters(unittest.TestCase):
def test_parameters_set_from_frequency_domain_source_model(self):
self.waveform_generator.frequency_domain_source_model = dummy_func_dict_return_value
+ self.waveform_generator.parameters = self.simulation_parameters.copy()
self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
sorted(list(self.simulation_parameters.keys())))
def test_parameters_set_from_time_domain_source_model(self):
self.waveform_generator.time_domain_source_model = dummy_func_dict_return_value
+ self.waveform_generator.parameters = self.simulation_parameters.copy()
self.assertListEqual(sorted(list(self.waveform_generator.parameters.keys())),
sorted(list(self.simulation_parameters.keys())))
@@ -195,10 +194,10 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
def test_parameter_conversion_is_called(self):
self.waveform_generator.parameter_conversion = MagicMock(side_effect=KeyError('test'))
with self.assertRaises(KeyError):
- self.waveform_generator.frequency_domain_strain()
+ self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
def test_frequency_domain_source_model_call(self):
- self.waveform_generator.parameters = self.simulation_parameters
expected = self.waveform_generator.frequency_domain_source_model(self.waveform_generator.frequency_array,
self.simulation_parameters['amplitude'],
self.simulation_parameters['mu'],
@@ -207,36 +206,39 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
self.simulation_parameters['dec'],
self.simulation_parameters['geocent_time'],
self.simulation_parameters['psi'])
- actual = self.waveform_generator.frequency_domain_strain()
+ actual = self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_time_domain_source_model_call_with_ndarray(self):
self.waveform_generator.frequency_domain_source_model = None
self.waveform_generator.time_domain_source_model = dummy_func_array_return_value
- self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2):
return value
with mock.patch('tupak.core.utils.nfft') as m:
m.side_effect = side_effect
- expected = self.waveform_generator.time_domain_strain()
- actual = self.waveform_generator.frequency_domain_strain()
+ expected = self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
+ actual = self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected, actual))
def test_time_domain_source_model_call_with_dict(self):
self.waveform_generator.frequency_domain_source_model = None
self.waveform_generator.time_domain_source_model = dummy_func_dict_return_value
- self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2):
return value, self.waveform_generator.frequency_array
with mock.patch('tupak.core.utils.nfft') as m:
m.side_effect = side_effect
- expected = self.waveform_generator.time_domain_strain()
- actual = self.waveform_generator.frequency_domain_strain()
+ expected = self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
+ actual = self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
@@ -244,7 +246,8 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = None
with self.assertRaises(RuntimeError):
- self.waveform_generator.frequency_domain_strain()
+ self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
def test_key_popping(self):
self.waveform_generator.parameter_conversion = MagicMock(return_value=(dict(amplitude=1e-21, mu=100, sigma=1,
@@ -253,7 +256,8 @@ class TestFrequencyDomainStrainMethod(unittest.TestCase):
psi=2.659, c=None, d=None),
['c', 'd']))
try:
- self.waveform_generator.frequency_domain_strain()
+ self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
except RuntimeError:
pass
self.assertListEqual(sorted(self.waveform_generator.parameters.keys()),
@@ -279,10 +283,10 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
def test_parameter_conversion_is_called(self):
self.waveform_generator.parameter_conversion = MagicMock(side_effect=KeyError('test'))
with self.assertRaises(KeyError):
- self.waveform_generator.time_domain_strain()
+ self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
def test_time_domain_source_model_call(self):
- self.waveform_generator.parameters = self.simulation_parameters
expected = self.waveform_generator.time_domain_source_model(self.waveform_generator.time_array,
self.simulation_parameters['amplitude'],
self.simulation_parameters['mu'],
@@ -291,36 +295,39 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
self.simulation_parameters['dec'],
self.simulation_parameters['geocent_time'],
self.simulation_parameters['psi'])
- actual = self.waveform_generator.time_domain_strain()
+ actual = self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
def test_frequency_domain_source_model_call_with_ndarray(self):
self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = dummy_func_array_return_value
- self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2):
return value
with mock.patch('tupak.core.utils.infft') as m:
m.side_effect = side_effect
- expected = self.waveform_generator.frequency_domain_strain()
- actual = self.waveform_generator.time_domain_strain()
+ expected = self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
+ actual = self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected, actual))
def test_frequency_domain_source_model_call_with_dict(self):
self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = dummy_func_dict_return_value
- self.waveform_generator.parameters = self.simulation_parameters
def side_effect(value, value2):
return value
with mock.patch('tupak.core.utils.infft') as m:
m.side_effect = side_effect
- expected = self.waveform_generator.frequency_domain_strain()
- actual = self.waveform_generator.time_domain_strain()
+ expected = self.waveform_generator.frequency_domain_strain(
+ parameters=self.simulation_parameters)
+ actual = self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
self.assertTrue(np.array_equal(expected['plus'], actual['plus']))
self.assertTrue(np.array_equal(expected['cross'], actual['cross']))
@@ -328,7 +335,8 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
self.waveform_generator.time_domain_source_model = None
self.waveform_generator.frequency_domain_source_model = None
with self.assertRaises(RuntimeError):
- self.waveform_generator.time_domain_strain()
+ self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
def test_key_popping(self):
self.waveform_generator.parameter_conversion = MagicMock(return_value=(dict(amplitude=1e-2,
@@ -339,7 +347,8 @@ class TestTimeDomainStrainMethod(unittest.TestCase):
psi=2.659, c=None, d=None),
['c', 'd']))
try:
- self.waveform_generator.time_domain_strain()
+ self.waveform_generator.time_domain_strain(
+ parameters=self.simulation_parameters)
except RuntimeError:
pass
self.assertListEqual(sorted(self.waveform_generator.parameters.keys()),
diff --git a/tupak/core/prior.py b/tupak/core/prior.py
index cfa01fb2e78f287a72643f5aa32ff79dc69afd39..3cc6f12f6b85150e52a358444c91477893b25ca7 100644
--- a/tupak/core/prior.py
+++ b/tupak/core/prior.py
@@ -135,12 +135,6 @@ class PriorSet(OrderedDict):
missing_keys = set(likelihood.parameters) - set(self.keys())
- if getattr(likelihood, 'non_standard_sampling_parameter_keys', None) is not None:
- for parameter in likelihood.non_standard_sampling_parameter_keys:
- if parameter in self:
- continue
- self[parameter] = create_default_prior(parameter, default_priors_file)
-
for missing_key in missing_keys:
if not self.test_redundancy(missing_key):
default_prior = create_default_prior(missing_key, default_priors_file)
diff --git a/tupak/gw/conversion.py b/tupak/gw/conversion.py
index 28c5b057da7fc99ad74d53729fb53db4c4e41f06..5f78db028e631ec5d6e0daf2ac8ffb221a48c9aa 100644
--- a/tupak/gw/conversion.py
+++ b/tupak/gw/conversion.py
@@ -47,7 +47,7 @@ def luminosity_distance_to_comoving_distance(distance):
return redshift_to_comoving_distance(redshift)
-def convert_to_lal_binary_black_hole_parameters(parameters, search_keys, remove=True):
+def convert_to_lal_binary_black_hole_parameters(parameters):
"""
Convert parameters we have into parameters we need.
@@ -65,10 +65,6 @@ def convert_to_lal_binary_black_hole_parameters(parameters, search_keys, remove=
----------
parameters: dict
dictionary of parameter values to convert into the required parameters
- search_keys: list
- parameters which are needed for the waveform generation
- remove: bool, optional
- Whether or not to remove the extra key, necessary for sampling, default=True.
Return
------
@@ -78,100 +74,89 @@ def convert_to_lal_binary_black_hole_parameters(parameters, search_keys, remove=
keys which are added to parameters during function call
"""
- added_keys = []
converted_parameters = parameters.copy()
-
- if 'mass_1' not in search_keys and 'mass_2' not in search_keys:
- if 'chirp_mass' in converted_parameters.keys():
- if 'total_mass' in converted_parameters.keys() and 'total_mass' not in added_keys:
- converted_parameters['symmetric_mass_ratio'] = chirp_mass_and_total_mass_to_symmetric_mass_ratio(
- converted_parameters['chirp_mass'], converted_parameters['total_mass'])
- if remove:
- added_keys.append('chirp_mass')
- if 'symmetric_mass_ratio' in converted_parameters.keys() and 'symmetric_mass_ratio' not in added_keys:
- converted_parameters['mass_ratio'] = \
- symmetric_mass_ratio_to_mass_ratio(converted_parameters['symmetric_mass_ratio'])
- if remove:
- added_keys.append('symmetric_mass_ratio')
- if 'mass_ratio' in converted_parameters.keys() and 'mass_ratio' not in added_keys:
- if 'total_mass' not in converted_parameters.keys() and 'total_mass' not in added_keys:
- converted_parameters['total_mass'] = chirp_mass_and_mass_ratio_to_total_mass(
- converted_parameters['chirp_mass'], converted_parameters['mass_ratio'])
- if remove:
- added_keys.append('chirp_mass')
- converted_parameters['mass_1'], converted_parameters['mass_2'] = \
- total_mass_and_mass_ratio_to_component_masses(converted_parameters['mass_ratio'],
- converted_parameters['total_mass'])
- if remove:
- added_keys.append('total_mass')
- added_keys.append('mass_ratio')
- added_keys.append('mass_1')
- added_keys.append('mass_2')
- elif 'total_mass' in converted_parameters.keys() and 'mass_ratio' not in added_keys:
- if 'symmetric_mass_ratio' in converted_parameters.keys() and 'symmetric_mass_ratio' not in added_keys:
- converted_parameters['mass_ratio'] = \
- symmetric_mass_ratio_to_mass_ratio(converted_parameters['symmetric_mass_ratio'])
- if remove:
- added_keys.append('symmetric_mass_ratio')
- if 'mass_ratio' in converted_parameters.keys() and 'mass_ratio' not in added_keys:
- converted_parameters['mass_1'], converted_parameters['mass_2'] = \
- total_mass_and_mass_ratio_to_component_masses(
- converted_parameters['mass_ratio'], converted_parameters['total_mass'])
- if remove:
- added_keys.append('total_mass')
- added_keys.append('mass_ratio')
- added_keys.append('mass_1')
- added_keys.append('mass_2')
-
- elif 'mass_1' in search_keys and 'mass_2' not in search_keys:
- if 'chirp_mass' in converted_parameters.keys() and 'chirp_mass' not in added_keys:
+ original_keys = list(converted_parameters.keys())
+
+ if 'chirp_mass' in converted_parameters.keys():
+ if 'total_mass' in converted_parameters.keys():
+ converted_parameters['symmetric_mass_ratio'] =\
+ chirp_mass_and_total_mass_to_symmetric_mass_ratio(
+ converted_parameters['chirp_mass'],
+ converted_parameters['total_mass'])
+ if 'symmetric_mass_ratio' in converted_parameters.keys():
+ converted_parameters['mass_ratio'] =\
+ symmetric_mass_ratio_to_mass_ratio(
+ converted_parameters['symmetric_mass_ratio'])
+ if 'total_mass' not in converted_parameters.keys():
+ converted_parameters['total_mass'] =\
+ chirp_mass_and_mass_ratio_to_total_mass(
+ converted_parameters['chirp_mass'],
+ converted_parameters['mass_ratio'])
+ converted_parameters['mass_1'], converted_parameters['mass_2'] = \
+ total_mass_and_mass_ratio_to_component_masses(
+ converted_parameters['mass_ratio'],
+ converted_parameters['total_mass'])
+ elif 'total_mass' in converted_parameters.keys():
+ if 'symmetric_mass_ratio' in converted_parameters.keys():
converted_parameters['mass_ratio'] = \
- mass_1_and_chirp_mass_to_mass_ratio(parameters['mass_1'], parameters['chirp_mass'])
- temp = (parameters['chirp_mass'] / parameters['mass_1']) ** 5
- parameters['mass_ratio'] = (
- (2 * temp / 3 / (
- (51 * temp ** 2 - 12 * temp ** 3) ** 0.5 + 9 * temp)) ** (1 / 3) +
- (((51 * temp ** 2 - 12 * temp ** 3) ** 0.5 + 9 * temp) / 9 / 2 ** 0.5) ** (1 / 3))
- if remove:
- added_keys.append('chirp_mass')
- elif 'symmetric_mass_ratio' in converted_parameters.keys() and 'symmetric_mass_ratio' not in added_keys:
- converted_parameters['mass_ratio'] = symmetric_mass_ratio_to_mass_ratio(parameters['symmetric_mass_ratio'])
- if remove:
- added_keys.append('symmetric_mass_ratio')
- if 'mass_ratio' in converted_parameters.keys() and 'mass_ratio' not in added_keys:
- converted_parameters['mass_2'] = converted_parameters['mass_1'] * converted_parameters['mass_ratio']
- if remove:
- added_keys.append('mass_ratio')
- added_keys.append('mass_2')
- elif 'total_mass' in converted_parameters.keys() and 'total_mass' not in added_keys:
- converted_parameters['mass_2'] = parameters['total_mass'] - parameters['mass_1']
- if remove:
- added_keys.append('total_mass')
- added_keys.append('mass_2')
+ symmetric_mass_ratio_to_mass_ratio(
+ converted_parameters['symmetric_mass_ratio'])
+ if 'mass_ratio' in converted_parameters.keys():
+ converted_parameters['mass_1'], converted_parameters['mass_2'] =\
+ total_mass_and_mass_ratio_to_component_masses(
+ converted_parameters['mass_ratio'],
+ converted_parameters['total_mass'])
+ elif 'mass_1' in converted_parameters.keys():
+ converted_parameters['mass_2'] =\
+ converted_parameters['total_mass'] -\
+ converted_parameters['mass_1']
+ elif 'mass_2' in converted_parameters.keys():
+ converted_parameters['mass_1'] = \
+ converted_parameters['total_mass'] - \
+ converted_parameters['mass_2']
+ elif 'symmetric_mass_ratio' in converted_parameters.keys():
+ converted_parameters['mass_ratio'] =\
+ symmetric_mass_ratio_to_mass_ratio(
+ converted_parameters['symmetric_mass_ratio'])
+ if 'mass_1' in converted_parameters.keys():
+ converted_parameters['mass_2'] =\
+ converted_parameters['mass_1'] *\
+ converted_parameters['mass_ratio']
+ elif 'mass_2' in converted_parameters.keys():
+ converted_parameters['mass_1'] =\
+ converted_parameters['mass_2'] /\
+ converted_parameters['mass_ratio']
+ elif 'mass_ratio' in converted_parameters.keys():
+ if 'mass_1' in converted_parameters.keys():
+ converted_parameters['mass_2'] =\
+ converted_parameters['mass_1'] *\
+ converted_parameters['mass_ratio']
+ if 'mass_2' in converted_parameters.keys():
+ converted_parameters['mass_1'] = \
+ converted_parameters['mass_2'] /\
+ converted_parameters['mass_ratio']
for angle in ['tilt_1', 'tilt_2', 'iota']:
cos_angle = str('cos_' + angle)
if cos_angle in converted_parameters.keys():
- converted_parameters[angle] = np.arccos(converted_parameters[cos_angle])
- added_keys.append(angle)
-
- if 'luminosity_distance' not in search_keys:
- if 'redshift' in converted_parameters.keys():
- converted_parameters['luminosity_distance'] = redshift_to_luminosity_distance(parameters['redshift'])
- if remove:
- added_keys.append('redshift')
- elif 'comoving_distance' in converted_parameters.keys():
- converted_parameters['luminosity_distance'] = \
- comoving_distance_to_luminosity_distance(parameters['comoving_distance'])
- if remove:
- added_keys.append('comoving_distance')
-
- added_keys = [key for key in added_keys if key not in search_keys]
+ converted_parameters[angle] =\
+ np.arccos(converted_parameters[cos_angle])
+
+ if 'redshift' in converted_parameters.keys():
+ converted_parameters['luminosity_distance'] =\
+ redshift_to_luminosity_distance(parameters['redshift'])
+ elif 'comoving_distance' in converted_parameters.keys():
+ converted_parameters['luminosity_distance'] = \
+ comoving_distance_to_luminosity_distance(
+ parameters['comoving_distance'])
+
+ added_keys = [key for key in converted_parameters.keys()
+ if key not in original_keys]
return converted_parameters, added_keys
-def convert_to_lal_binary_neutron_star_parameters(parameters, search_keys, remove=True):
+def convert_to_lal_binary_neutron_star_parameters(parameters):
"""
Convert parameters we have into parameters we need.
@@ -189,10 +174,6 @@ def convert_to_lal_binary_neutron_star_parameters(parameters, search_keys, remov
----------
parameters: dict
dictionary of parameter values to convert into the required parameters
- search_keys: list
- parameters which are needed for the waveform generation
- remove: bool, optional
- Whether or not to remove the extra key, necessary for sampling, default=True.
Return
------
@@ -202,36 +183,34 @@ def convert_to_lal_binary_neutron_star_parameters(parameters, search_keys, remov
keys which are added to parameters during function call
"""
converted_parameters = parameters.copy()
- converted_parameters, added_keys = convert_to_lal_binary_black_hole_parameters(
- converted_parameters, search_keys, remove=remove)
-
- if 'lambda_1' not in search_keys and 'lambda_2' not in search_keys:
- if 'delta_lambda' in converted_parameters.keys():
- converted_parameters['lambda_1'], converted_parameters['lambda_2'] =\
- lambda_tilde_delta_lambda_to_lambda_1_lambda_2(
- converted_parameters['lambda_tilde'], parameters['delta_lambda'],
- converted_parameters['mass_1'], converted_parameters['mass_2'])
- added_keys.append('lambda_1')
- added_keys.append('lambda_2')
- elif 'lambda_tilde' in converted_parameters.keys():
- converted_parameters['lambda_1'], converted_parameters['lambda_2'] =\
- lambda_tilde_to_lambda_1_lambda_2(
- converted_parameters['lambda_tilde'],
- converted_parameters['mass_1'], converted_parameters['mass_2'])
- added_keys.append('lambda_1')
- added_keys.append('lambda_2')
+ original_keys = list(converted_parameters.keys())
+ converted_parameters, added_keys =\
+ convert_to_lal_binary_black_hole_parameters(converted_parameters)
+
+ if 'delta_lambda' in converted_parameters.keys():
+ converted_parameters['lambda_1'], converted_parameters['lambda_2'] =\
+ lambda_tilde_delta_lambda_to_lambda_1_lambda_2(
+ converted_parameters['lambda_tilde'],
+ parameters['delta_lambda'], converted_parameters['mass_1'],
+ converted_parameters['mass_2'])
+ elif 'lambda_tilde' in converted_parameters.keys():
+ converted_parameters['lambda_1'], converted_parameters['lambda_2'] =\
+ lambda_tilde_to_lambda_1_lambda_2(
+ converted_parameters['lambda_tilde'],
+ converted_parameters['mass_1'], converted_parameters['mass_2'])
if 'lambda_2' not in converted_parameters.keys():
converted_parameters['lambda_2'] =\
converted_parameters['lambda_1']\
* converted_parameters['mass_1']**5\
/ converted_parameters['mass_2']**5
- added_keys.append('lambda_2')
elif converted_parameters['lambda_2'] is None:
converted_parameters['lambda_2'] =\
converted_parameters['lambda_1']\
* converted_parameters['mass_1']**5\
/ converted_parameters['mass_2']**5
- added_keys.append('lambda_2')
+
+ added_keys = [key for key in converted_parameters.keys()
+ if key not in original_keys]
return converted_parameters, added_keys
@@ -338,7 +317,7 @@ def component_masses_to_chirp_mass(mass_1, mass_2):
Chirp mass of the binary
"""
- return (mass_1 * mass_2) ** 0.6 / (component_masses_to_total_mass(mass_1, mass_2)) ** 0.2
+ return (mass_1 * mass_2) ** 0.6 / (mass_1 + mass_2) ** 0.2
def component_masses_to_total_mass(mass_1, mass_2):
@@ -420,8 +399,10 @@ def mass_1_and_chirp_mass_to_mass_ratio(mass_1, chirp_mass):
Mass ratio of the binary
"""
temp = (chirp_mass / mass_1) ** 5
- mass_ratio = (2 / 3 / (3 ** 0.5 * (27 * temp ** 2 - 4 * temp ** 3) ** 0.5 + 9 * temp)) ** (1 / 3) * temp + \
- ((3 ** 0.5 * (27 * temp ** 2 - 4 * temp ** 3) ** 0.5 + 9 * temp) / (2 * 3 ** 2)) ** (1 / 3)
+ mass_ratio = (2 / 3 / (3 ** 0.5 * (27 * temp ** 2 - 4 * temp ** 3) ** 0.5 +
+ 9 * temp)) ** (1 / 3) * temp + \
+ ((3 ** 0.5 * (27 * temp ** 2 - 4 * temp ** 3) ** 0.5 +
+ 9 * temp) / (2 * 3 ** 2)) ** (1 / 3)
return mass_ratio
@@ -505,25 +486,32 @@ def lambda_tilde_to_lambda_1_lambda_2(
def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
"""
- From either a single sample or a set of samples fill in all missing BBH parameters, in place.
+ From either a single sample or a set of samples fill in all missing
+ BBH parameters, in place.
Parameters
----------
sample: dict or pandas.DataFrame
- Samples to fill in with extra parameters, this may be either an injection or posterior samples.
- likelihood: tupak.gw.likelihood.GravitationalWaveTr, optional
- GravitationalWaveTransient used for sampling, used for waveform and likelihood.interferometers.
+ Samples to fill in with extra parameters, this may be either an
+ injection or posterior samples.
+ likelihood: tupak.gw.likelihood.GravitationalWaveTransient, optional
+ GravitationalWaveTransient used for sampling, used for waveform and
+ likelihood.interferometers.
priors: dict, optional
Dictionary of prior objects, used to fill in non-sampled parameters.
"""
output_sample = sample.copy()
if likelihood is not None:
- output_sample['reference_frequency'] = likelihood.waveform_generator.waveform_arguments['reference_frequency']
- output_sample['waveform_approximant'] = likelihood.waveform_generator.waveform_arguments['waveform_approximant']
+ output_sample['reference_frequency'] =\
+ likelihood.waveform_generator.waveform_arguments[
+ 'reference_frequency']
+ output_sample['waveform_approximant'] =\
+ likelihood.waveform_generator.waveform_arguments[
+ 'waveform_approximant']
output_sample = fill_from_fixed_priors(output_sample, priors)
- output_sample, _ = convert_to_lal_binary_black_hole_parameters(
- output_sample, [key for key in output_sample.keys()], remove=False)
+ output_sample, _ =\
+ convert_to_lal_binary_black_hole_parameters(output_sample)
output_sample = generate_non_standard_parameters(output_sample)
output_sample = generate_component_spins(output_sample)
compute_snrs(output_sample, likelihood)
@@ -557,7 +545,8 @@ def generate_non_standard_parameters(sample):
Add the known non-standard parameters to the data frame/dictionary.
We add:
- chirp mass, total mass, symmetric mass ratio, mass ratio, cos tilt 1, cos tilt 2, cos iota
+ chirp mass, total mass, symmetric mass ratio, mass ratio, cos tilt 1,
+ cos tilt 2, cos iota
Parameters
----------
@@ -570,16 +559,22 @@ def generate_non_standard_parameters(sample):
"""
output_sample = sample.copy()
- output_sample['chirp_mass'] = component_masses_to_chirp_mass(sample['mass_1'], sample['mass_2'])
- output_sample['total_mass'] = component_masses_to_total_mass(sample['mass_1'], sample['mass_2'])
- output_sample['symmetric_mass_ratio'] = component_masses_to_symmetric_mass_ratio(sample['mass_1'], sample['mass_2'])
- output_sample['mass_ratio'] = component_masses_to_mass_ratio(sample['mass_1'], sample['mass_2'])
+ output_sample['chirp_mass'] =\
+ component_masses_to_chirp_mass(sample['mass_1'], sample['mass_2'])
+ output_sample['total_mass'] =\
+ component_masses_to_total_mass(sample['mass_1'], sample['mass_2'])
+ output_sample['symmetric_mass_ratio'] =\
+ component_masses_to_symmetric_mass_ratio(sample['mass_1'],
+ sample['mass_2'])
+ output_sample['mass_ratio'] =\
+ component_masses_to_mass_ratio(sample['mass_1'], sample['mass_2'])
output_sample['cos_tilt_1'] = np.cos(output_sample['tilt_1'])
output_sample['cos_tilt_2'] = np.cos(output_sample['tilt_2'])
output_sample['cos_iota'] = np.cos(output_sample['iota'])
- output_sample['redshift'] = luminosity_distance_to_redshift(sample['luminosity_distance'])
+ output_sample['redshift'] =\
+ luminosity_distance_to_redshift(sample['luminosity_distance'])
return output_sample
@@ -592,7 +587,8 @@ def generate_component_spins(sample):
Parameters
----------
sample: A dictionary with the necessary spin conversion parameters:
- 'iota', 'phi_jl', 'tilt_1', 'tilt_2', 'phi_12', 'a_1', 'a_2', 'mass_1', 'mass_2', 'reference_frequency', 'phase'
+ 'iota', 'phi_jl', 'tilt_1', 'tilt_2', 'phi_12', 'a_1', 'a_2', 'mass_1',
+ 'mass_2', 'reference_frequency', 'phase'
Returns
-------
@@ -600,45 +596,59 @@ def generate_component_spins(sample):
"""
output_sample = sample.copy()
- spin_conversion_parameters = ['iota', 'phi_jl', 'tilt_1', 'tilt_2', 'phi_12', 'a_1', 'a_2', 'mass_1',
- 'mass_2', 'reference_frequency', 'phase']
+ spin_conversion_parameters =\
+ ['iota', 'phi_jl', 'tilt_1', 'tilt_2', 'phi_12', 'a_1', 'a_2', 'mass_1',
+ 'mass_2', 'reference_frequency', 'phase']
if all(key in output_sample.keys() for key in spin_conversion_parameters)\
and isinstance(output_sample, dict):
- output_sample['iota'], output_sample['spin_1x'], output_sample['spin_1y'], output_sample['spin_1z'], \
- output_sample['spin_2x'], output_sample['spin_2y'], output_sample['spin_2z'] = \
+ output_sample['iota'], output_sample['spin_1x'],\
+ output_sample['spin_1y'], output_sample['spin_1z'], \
+ output_sample['spin_2x'], output_sample['spin_2y'],\
+ output_sample['spin_2z'] =\
lalsim.SimInspiralTransformPrecessingNewInitialConditions(
output_sample['iota'], output_sample['phi_jl'],
output_sample['tilt_1'], output_sample['tilt_2'],
- output_sample['phi_12'], output_sample['a_1'], output_sample['a_2'],
+ output_sample['phi_12'], output_sample['a_1'],
+ output_sample['a_2'],
output_sample['mass_1'] * tupak.core.utils.solar_mass,
output_sample['mass_2'] * tupak.core.utils.solar_mass,
output_sample['reference_frequency'], output_sample['phase'])
- output_sample['phi_1'] = np.arctan(output_sample['spin_1y'] / output_sample['spin_1x'])
- output_sample['phi_2'] = np.arctan(output_sample['spin_2y'] / output_sample['spin_2x'])
+ output_sample['phi_1'] =\
+ np.arctan(output_sample['spin_1y'] / output_sample['spin_1x'])
+ output_sample['phi_2'] =\
+ np.arctan(output_sample['spin_2y'] / output_sample['spin_2x'])
elif all(key in output_sample.keys() for key in spin_conversion_parameters)\
and isinstance(output_sample, pd.DataFrame):
logger.debug('Extracting component spins.')
- new_spin_parameters = ['spin_1x', 'spin_1y', 'spin_1z', 'spin_2x', 'spin_2y', 'spin_2z']
- new_spins = {name: np.zeros(len(output_sample)) for name in new_spin_parameters}
+ new_spin_parameters =\
+ ['spin_1x', 'spin_1y', 'spin_1z', 'spin_2x', 'spin_2y', 'spin_2z']
+ new_spins =\
+ {name: np.zeros(len(output_sample)) for name in new_spin_parameters}
for ii in range(len(output_sample)):
- new_spins['iota'], new_spins['spin_1x'][ii], new_spins['spin_1y'][ii], new_spins['spin_1z'][ii], \
- new_spins['spin_2x'][ii], new_spins['spin_2y'][ii], new_spins['spin_2z'][ii] = \
+ new_spins['iota'], new_spins['spin_1x'][ii],\
+ new_spins['spin_1y'][ii], new_spins['spin_1z'][ii], \
+ new_spins['spin_2x'][ii], new_spins['spin_2y'][ii],\
+ new_spins['spin_2z'][ii] =\
lalsim.SimInspiralTransformPrecessingNewInitialConditions(
output_sample['iota'][ii], output_sample['phi_jl'][ii],
output_sample['tilt_1'][ii], output_sample['tilt_2'][ii],
- output_sample['phi_12'][ii], output_sample['a_1'][ii], output_sample['a_2'][ii],
+ output_sample['phi_12'][ii], output_sample['a_1'][ii],
+ output_sample['a_2'][ii],
output_sample['mass_1'][ii] * tupak.core.utils.solar_mass,
output_sample['mass_2'][ii] * tupak.core.utils.solar_mass,
- output_sample['reference_frequency'][ii], output_sample['phase'][ii])
+ output_sample['reference_frequency'][ii],
+ output_sample['phase'][ii])
for name in new_spin_parameters:
output_sample[name] = new_spins[name]
- output_sample['phi_1'] = np.arctan(output_sample['spin_1y'] / output_sample['spin_1x'])
- output_sample['phi_2'] = np.arctan(output_sample['spin_2y'] / output_sample['spin_2x'])
+ output_sample['phi_1'] =\
+ np.arctan(output_sample['spin_1y'] / output_sample['spin_1x'])
+ output_sample['phi_2'] =\
+ np.arctan(output_sample['spin_2y'] / output_sample['spin_2x'])
else:
logger.warning("Component spin extraction failed.")
@@ -647,7 +657,9 @@ def generate_component_spins(sample):
def compute_snrs(sample, likelihood):
- """Compute the optimal and matched filter snrs of all posterior samples and print it out.
+ """
+ Compute the optimal and matched filter snrs of all posterior samples
+ and print it out.
Parameters
----------
@@ -660,41 +672,51 @@ def compute_snrs(sample, likelihood):
temp_sample = sample
if likelihood is not None:
if isinstance(temp_sample, dict):
+ temp = dict()
for key in likelihood.waveform_generator.parameters.keys():
- likelihood.waveform_generator.parameters[key] = temp_sample[key]
- signal_polarizations = likelihood.waveform_generator.frequency_domain_strain()
- for interferometer in likelihood.interferometers:
- signal = interferometer.get_detector_response(signal_polarizations,
- likelihood.waveform_generator.parameters)
- sample['{}_matched_filter_snr'.format(interferometer.name)] = \
- interferometer.matched_filter_snr_squared(signal=signal) ** 0.5
- sample['{}_optimal_snr'.format(interferometer.name)] = \
- interferometer.optimal_snr_squared(signal=signal) ** 0.5
+ # likelihood.waveform_generator.parameters[key] = temp_sample[key]
+ temp[key] = temp_sample[key]
+ signal_polarizations =\
+ likelihood.waveform_generator.frequency_domain_strain(temp)
+ for ifo in likelihood.interferometers:
+ signal = ifo.get_detector_response(
+ signal_polarizations,
+ likelihood.waveform_generator.parameters)
+ sample['{}_matched_filter_snr'.format(ifo.name)] =\
+ ifo.matched_filter_snr_squared(signal=signal) ** 0.5
+ sample['{}_optimal_snr'.format(ifo.name)] = \
+ ifo.optimal_snr_squared(signal=signal) ** 0.5
else:
- logger.info('Computing SNRs for every sample, this may take some time.')
+ logger.info(
+ 'Computing SNRs for every sample, this may take some time.')
all_interferometers = likelihood.interferometers
- matched_filter_snrs = {interferometer.name: [] for interferometer in all_interferometers}
- optimal_snrs = {interferometer.name: [] for interferometer in all_interferometers}
+ matched_filter_snrs = {ifo.name: [] for ifo in all_interferometers}
+ optimal_snrs = {ifo.name: [] for ifo in all_interferometers}
for ii in range(len(temp_sample)):
- for key in set(temp_sample.keys()).intersection(likelihood.waveform_generator.parameters.keys()):
- likelihood.waveform_generator.parameters[key] = temp_sample[key][ii]
- if likelihood.waveform_generator.non_standard_sampling_parameter_keys is not None:
- for key in likelihood.waveform_generator.non_standard_sampling_parameter_keys:
- likelihood.waveform_generator.parameters[key] = temp_sample[key][ii]
- signal_polarizations = likelihood.waveform_generator.frequency_domain_strain()
- for interferometer in all_interferometers:
- signal = interferometer.get_detector_response(signal_polarizations,
- likelihood.waveform_generator.parameters)
- matched_filter_snrs[interferometer.name]\
- .append(interferometer.matched_filter_snr_squared(signal=signal) ** 0.5)
- optimal_snrs[interferometer.name].append(interferometer.optimal_snr_squared(signal=signal) ** 0.5)
-
- for interferometer in likelihood.interferometers:
- sample['{}_matched_filter_snr'.format(interferometer.name)] = matched_filter_snrs[interferometer.name]
- sample['{}_optimal_snr'.format(interferometer.name)] = optimal_snrs[interferometer.name]
+ temp = dict()
+ for key in set(temp_sample.keys()).intersection(
+ likelihood.waveform_generator.parameters.keys()):
+ # likelihood.waveform_generator.parameters[key] =\
+ # temp_sample[key][ii]
+ temp[key] = temp_sample[key][ii]
+ signal_polarizations =\
+ likelihood.waveform_generator.frequency_domain_strain(temp)
+ for ifo in all_interferometers:
+ signal = ifo.get_detector_response(
+ signal_polarizations,
+ likelihood.waveform_generator.parameters)
+ matched_filter_snrs[ifo.name].append(
+ ifo.matched_filter_snr_squared(signal=signal) ** 0.5)
+ optimal_snrs[ifo.name].append(
+ ifo.optimal_snr_squared(signal=signal) ** 0.5)
+
+ for ifo in likelihood.interferometers:
+ sample['{}_matched_filter_snr'.format(ifo.name)] =\
+ matched_filter_snrs[ifo.name]
+ sample['{}_optimal_snr'.format(ifo.name)] =\
+ optimal_snrs[ifo.name]
likelihood.interferometers = all_interferometers
- print([interferometer.name for interferometer in likelihood.interferometers])
else:
logger.debug('Not computing SNRs.')
diff --git a/tupak/gw/detector.py b/tupak/gw/detector.py
index b571d7cc55e8912e3948db7d8dba62ad338e9798..924e8a8782149439c956381d431edd0114ac80dd 100644
--- a/tupak/gw/detector.py
+++ b/tupak/gw/detector.py
@@ -107,8 +107,8 @@ class InterferometerList(list):
"""
if injection_polarizations is None:
if waveform_generator is not None:
- waveform_generator.parameters = parameters
- injection_polarizations = waveform_generator.frequency_domain_strain()
+ injection_polarizations =\
+ waveform_generator.frequency_domain_strain(parameters)
else:
raise ValueError(
"inject_signal needs one of waveform_generator or "
@@ -1241,8 +1241,8 @@ class Interferometer(object):
if injection_polarizations is None:
if waveform_generator is not None:
- waveform_generator.parameters = parameters
- injection_polarizations = waveform_generator.frequency_domain_strain()
+ injection_polarizations =\
+ waveform_generator.frequency_domain_strain(parameters)
else:
raise ValueError(
"inject_signal needs one of waveform_generator or "
diff --git a/tupak/gw/likelihood.py b/tupak/gw/likelihood.py
index ec89d11f08655f85b845b8c24135b1fb47a1a4d4..3d7a77197c89cde2caa64042dd663ab7567ddb1d 100644
--- a/tupak/gw/likelihood.py
+++ b/tupak/gw/likelihood.py
@@ -56,7 +56,7 @@ class GravitationalWaveTransient(likelihood.Likelihood):
phase_marginalization=False, prior=None):
self.waveform_generator = waveform_generator
- likelihood.Likelihood.__init__(self, waveform_generator.parameters)
+ likelihood.Likelihood.__init__(self, dict())
self.interferometers = tupak.gw.detector.InterferometerList(interferometers)
self.time_marginalization = time_marginalization
self.distance_marginalization = distance_marginalization
@@ -135,18 +135,6 @@ class GravitationalWaveTransient(likelihood.Likelihood):
else:
self.__prior = None
- @property
- def non_standard_sampling_parameter_keys(self):
- return self.waveform_generator.non_standard_sampling_parameter_keys
-
- @property
- def parameters(self):
- return self.waveform_generator.parameters
-
- @parameters.setter
- def parameters(self, parameters):
- self.waveform_generator.parameters = parameters
-
def noise_log_likelihood(self):
log_l = 0
for interferometer in self.interferometers:
@@ -159,7 +147,7 @@ class GravitationalWaveTransient(likelihood.Likelihood):
def log_likelihood_ratio(self):
waveform_polarizations =\
- self.waveform_generator.frequency_domain_strain()
+ self.waveform_generator.frequency_domain_strain(self.parameters)
if waveform_polarizations is None:
return np.nan_to_num(-np.inf)
@@ -171,7 +159,7 @@ class GravitationalWaveTransient(likelihood.Likelihood):
dtype=np.complex128)
for interferometer in self.interferometers:
signal_ifo = interferometer.get_detector_response(
- waveform_polarizations, self.waveform_generator.parameters)
+ waveform_polarizations, self.parameters)
matched_filter_snr_squared += interferometer.matched_filter_snr_squared(signal=signal_ifo)
optimal_snr_squared += interferometer.optimal_snr_squared(signal=signal_ifo)
@@ -220,10 +208,10 @@ class GravitationalWaveTransient(likelihood.Likelihood):
def _setup_rho(self, matched_filter_snr_squared, optimal_snr_squared):
rho_opt_ref = (optimal_snr_squared.real *
- self.waveform_generator.parameters['luminosity_distance'] ** 2 /
+ self.parameters['luminosity_distance'] ** 2 /
self._ref_dist ** 2.)
rho_mf_ref = matched_filter_snr_squared * \
- self.waveform_generator.parameters['luminosity_distance'] / self._ref_dist
+ self.parameters['luminosity_distance'] / self._ref_dist
return rho_mf_ref, rho_opt_ref
def log_likelihood(self):
@@ -310,7 +298,7 @@ class BasicGravitationalWaveTransient(likelihood.Likelihood):
given some set of parameters
"""
- likelihood.Likelihood.__init__(self, waveform_generator.parameters)
+ likelihood.Likelihood.__init__(self, dict())
self.interferometers = interferometers
self.waveform_generator = waveform_generator
@@ -342,7 +330,9 @@ class BasicGravitationalWaveTransient(likelihood.Likelihood):
"""
log_l = 0
- waveform_polarizations = self.waveform_generator.frequency_domain_strain()
+ waveform_polarizations =\
+ self.waveform_generator.frequency_domain_strain(
+ self.parameters.copy())
if waveform_polarizations is None:
return np.nan_to_num(-np.inf)
for interferometer in self.interferometers:
@@ -367,7 +357,7 @@ class BasicGravitationalWaveTransient(likelihood.Likelihood):
"""
signal_ifo = interferometer.get_detector_response(
- waveform_polarizations, self.waveform_generator.parameters)
+ waveform_polarizations, self.parameters)
log_l = - 2. / self.waveform_generator.duration * np.vdot(
interferometer.frequency_domain_strain - signal_ifo,
@@ -394,5 +384,5 @@ def get_binary_black_hole_likelihood(interferometers):
waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
duration=interferometers.duration, sampling_frequency=interferometers.sampling_frequency,
frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
- parameters={'waveform_approximant': 'IMRPhenomPv2', 'reference_frequency': 50})
+ waveform_arguments={'waveform_approximant': 'IMRPhenomPv2', 'reference_frequency': 50})
return tupak.gw.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
diff --git a/tupak/gw/waveform_generator.py b/tupak/gw/waveform_generator.py
index b5e08cc15548c53ba661ab3393db8925509f281c..0134ffbb5f0c7ae6caeffbb830a4308ff5086c50 100644
--- a/tupak/gw/waveform_generator.py
+++ b/tupak/gw/waveform_generator.py
@@ -7,7 +7,6 @@ class WaveformGenerator(object):
def __init__(self, duration=None, sampling_frequency=None, start_time=0, frequency_domain_source_model=None,
time_domain_source_model=None, parameters=None,
parameter_conversion=None,
- non_standard_sampling_parameter_keys=None,
waveform_arguments=None):
""" A waveform generator
@@ -33,8 +32,6 @@ class WaveformGenerator(object):
Function to convert from sampled parameters to parameters of the
waveform generator. Default value is the identity, i.e. it leaves
the parameters unaffected.
- non_standard_sampling_parameter_keys: list, optional
- List of parameter name for *non-standard* sampling parameters.
waveform_arguments: dict, optional
A dictionary of fixed keyword arguments to pass to either
`frequency_domain_source_model` or `time_domain_source_model`.
@@ -49,25 +46,21 @@ class WaveformGenerator(object):
self.start_time = start_time
self.frequency_domain_source_model = frequency_domain_source_model
self.time_domain_source_model = time_domain_source_model
- self.__parameters_from_source_model()
+ self.source_parameter_keys = self.__parameters_from_source_model()
self.duration = duration
self.sampling_frequency = sampling_frequency
if parameter_conversion is None:
- self.parameter_conversion = lambda params, search_keys: (params, [])
+ self.parameter_conversion = lambda params: (params, [])
else:
self.parameter_conversion = parameter_conversion
- self.non_standard_sampling_parameter_keys = non_standard_sampling_parameter_keys
- self.parameters = parameters
if waveform_arguments is not None:
self.waveform_arguments = waveform_arguments
else:
self.waveform_arguments = dict()
+ if isinstance(parameters, dict):
+ self.parameters = parameters
self.__frequency_array_updated = False
self.__time_array_updated = False
- self.__full_source_model_keyword_arguments = {}
- self.__full_source_model_keyword_arguments.update(self.waveform_arguments)
- self.__full_source_model_keyword_arguments.update(self.parameters)
- self.__added_keys = []
def __repr__(self):
if self.frequency_domain_source_model is not None:
@@ -85,17 +78,24 @@ class WaveformGenerator(object):
return self.__class__.__name__ + '(duration={}, sampling_frequency={}, start_time={}, ' \
'frequency_domain_source_model={}, time_domain_source_model={}, ' \
- 'parameters={}, parameter_conversion={}, ' \
- 'non_standard_sampling_parameter_keys={}, waveform_arguments={})'\
- .format(self.duration, self.sampling_frequency, self.start_time, fdsm_name, tdsm_name, self.parameters,
- param_conv_name, self.non_standard_sampling_parameter_keys, self.waveform_arguments)
+ 'parameter_conversion={}, ' \
+ 'waveform_arguments={})'\
+ .format(self.duration, self.sampling_frequency, self.start_time, fdsm_name, tdsm_name,
+ param_conv_name, self.waveform_arguments)
- def frequency_domain_strain(self):
- """ Rapper to source_model.
+ def frequency_domain_strain(self, parameters=None):
+ """ Wrapper to source_model.
Converts self.parameters with self.parameter_conversion before handing it off to the source model.
Automatically refers to the time_domain_source model via NFFT if no frequency_domain_source_model is given.
+ Parameters
+ ----------
+ parameters: dict, optional
+ Parameters to evaluate the waveform for, this overwrites
+ `self.parameters`.
+ If not provided will fall back to `self.parameters`.
+
Returns
-------
array_like: The frequency domain strain for the given set of parameters
@@ -107,17 +107,25 @@ class WaveformGenerator(object):
"""
return self._calculate_strain(model=self.frequency_domain_source_model,
model_data_points=self.frequency_array,
+ parameters=parameters,
transformation_function=utils.nfft,
transformed_model=self.time_domain_source_model,
transformed_model_data_points=self.time_array)
- def time_domain_strain(self):
- """ Rapper to source_model.
+ def time_domain_strain(self, parameters=None):
+ """ Wrapper to source_model.
Converts self.parameters with self.parameter_conversion before handing it off to the source model.
Automatically refers to the frequency_domain_source model via INFFT if no frequency_domain_source_model is
given.
+ Parameters
+ ----------
+ parameters: dict, optional
+ Parameters to evaluate the waveform for, this overwrites
+ `self.parameters`.
+ If not provided will fall back to `self.parameters`.
+
Returns
-------
array_like: The time domain strain for the given set of parameters
@@ -129,13 +137,15 @@ class WaveformGenerator(object):
"""
return self._calculate_strain(model=self.time_domain_source_model,
model_data_points=self.time_array,
+ parameters=parameters,
transformation_function=utils.infft,
transformed_model=self.frequency_domain_source_model,
transformed_model_data_points=self.frequency_array)
def _calculate_strain(self, model, model_data_points, transformation_function, transformed_model,
- transformed_model_data_points):
- self._apply_parameter_conversion()
+ transformed_model_data_points, parameters):
+ if parameters is not None:
+ self.parameters = parameters
if model is not None:
model_strain = self._strain_from_model(model_data_points, model)
elif transformed_model is not None:
@@ -143,16 +153,10 @@ class WaveformGenerator(object):
transformation_function)
else:
raise RuntimeError("No source model given")
- self._remove_added_keys()
return model_strain
- def _apply_parameter_conversion(self):
- self.parameters, self.__added_keys = self.parameter_conversion(self.parameters,
- self.non_standard_sampling_parameter_keys)
- self.__full_source_model_keyword_arguments.update(self.parameters)
-
def _strain_from_model(self, model_data_points, model):
- return model(model_data_points, **self.__full_source_model_keyword_arguments)
+ return model(model_data_points, **self.parameters)
def _strain_from_transformed_model(self, transformed_model_data_points, transformed_model, transformation_function):
transformed_model_strain = self._strain_from_model(transformed_model_data_points, transformed_model)
@@ -169,10 +173,6 @@ class WaveformGenerator(object):
model_strain[key] = transformation_function(transformed_model_strain[key], self.sampling_frequency)
return model_strain
- def _remove_added_keys(self):
- for key in self.__added_keys:
- self.parameters.pop(key)
-
@property
def frequency_array(self):
""" Frequency array for the waveforms. Automatically updates if sampling_frequency or duration are updated.
@@ -228,15 +228,44 @@ class WaveformGenerator(object):
@parameters.setter
def parameters(self, parameters):
- if isinstance(parameters, dict):
- for key in parameters.keys():
- self.__parameters[key] = parameters[key]
+ """
+ Set parameters, this applies the conversion function and then removes
+ any parameters which aren't required by the source function.
+
+ (set.symmetric_difference is the opposite of set.intersection)
+
+ Parameters
+ ----------
+ parameters: dict
+ Input parameter dictionary, this is copied, passed to the conversion
+ function and has self.waveform_arguments added to it.
+ """
+ if not isinstance(parameters, dict):
+ raise TypeError('"parameters" must be a dictionary.')
+ new_parameters = parameters.copy()
+ new_parameters, _ = self.parameter_conversion(new_parameters)
+ for key in self.source_parameter_keys.symmetric_difference(
+ new_parameters):
+ new_parameters.pop(key)
+ self.__parameters = new_parameters
+ self.__parameters.update(self.waveform_arguments)
def __parameters_from_source_model(self):
+ """
+ Infer the named arguments of the source model.
+
+ Returns
+ -------
+ set: The names of the arguments of the source model.
+ """
if self.frequency_domain_source_model is not None:
- self.__parameters = dict.fromkeys(utils.infer_parameters_from_function(self.frequency_domain_source_model))
+ model = self.frequency_domain_source_model
elif self.time_domain_source_model is not None:
- self.__parameters = dict.fromkeys(utils.infer_parameters_from_function(self.time_domain_source_model))
+ model = self.time_domain_source_model
+ else:
+ raise AttributeError('Either time or frequency domain source '
+ 'model must be provided.')
+ return set(utils.infer_parameters_from_function(model))
@property
def duration(self):