diff --git a/tupak/gw/conversion.py b/tupak/gw/conversion.py
index 28c5b057da7fc99ad74d53729fb53db4c4e41f06..858d145b6f5566d138d6ee52ed02d7d3449a0ad4 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 +74,85 @@ 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()).copy()
+
+ 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 +170,6 @@ 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 +179,34 @@ 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()).copy()
+ 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 +313,7 @@ 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 +395,10 @@ 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 +482,30 @@ 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 +539,8 @@ 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 +553,22 @@ 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 +581,8 @@ 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 +590,59 @@ 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 +651,9 @@ 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
----------
@@ -662,39 +668,45 @@ def compute_snrs(sample, likelihood):
if isinstance(temp_sample, 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
+ signal_polarizations =\
+ likelihood.waveform_generator.frequency_domain_strain()
+ 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]
+ for key in set(temp_sample.keys()).intersection(
+ likelihood.waveform_generator.parameters.keys()):
+ likelihood.waveform_generator.parameters[key] =\
+ temp_sample[key][ii]
+ signal_polarizations =\
+ likelihood.waveform_generator.frequency_domain_strain()
+ 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.')