diff --git a/bilby/gw/conversion.py b/bilby/gw/conversion.py
index 793fecb0b8862fd074aa469444e54929c3099634..498c54248109b298cd82e3ad6e21dc3957470485 100644
--- a/bilby/gw/conversion.py
+++ b/bilby/gw/conversion.py
@@ -47,6 +47,53 @@ def luminosity_distance_to_comoving_distance(distance):
return redshift_to_comoving_distance(redshift)
+@np.vectorize
+def transform_precessing_spins(theta_jn, phi_jl, tilt_1, tilt_2, phi_12, a_1,
+ a_2, mass_1, mass_2, reference_frequency, phase):
+ """
+ Vectorized version of
+ lalsimulation.SimInspiralTransformPrecessingNewInitialConditions
+
+ All parameters are defined at the reference frequency
+
+ Parameters
+ ----------
+ theta_jn: float
+ Inclination angle
+ phi_jl: float
+ Spin phase angle
+ tilt_1: float
+ Primary object tilt
+ tilt_2: float
+ Secondary object tilt
+ phi_12: float
+ Relative spin azimuthal angle
+ a_1: float
+ Primary dimensionless spin magnitude
+ a_2: float
+ Secondary dimensionless spin magnitude
+ mass_1: float
+ Primary mass _in SI units_
+ mass_2: float
+ Secondary mass _in SI units_
+ reference_frequency: float
+ phase: float
+ Orbital phase
+
+ Returns
+ -------
+ iota: float
+ Transformed inclination
+ spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z: float
+ Cartesian spin components
+ """
+ iota, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z = \
+ lalsim.SimInspiralTransformPrecessingNewInitialConditions(
+ theta_jn, phi_jl, tilt_1, tilt_2, phi_12, a_1, a_2, mass_1,
+ mass_2, reference_frequency, phase)
+ return iota, spin_1x, spin_1y, spin_1z, spin_2x, spin_2y, spin_2z
+
+
def convert_to_lal_binary_black_hole_parameters(parameters):
"""
Convert parameters we have into parameters we need.
@@ -164,7 +211,7 @@ def convert_to_lal_binary_neutron_star_parameters(parameters):
Mass: mass_1, mass_2
- Spin: a_1, a_2, tilt_1, tilt_2, phi_12, phi_jl
+ Spin: chi_1, chi_2, tilt_1, tilt_2, phi_12, phi_jl
Extrinsic: luminosity_distance, theta_jn, phase, ra, dec, geocent_time, psi
This involves popping a lot of things from parameters.
@@ -406,6 +453,71 @@ def mass_1_and_chirp_mass_to_mass_ratio(mass_1, chirp_mass):
return mass_ratio
+def lambda_1_lambda_2_to_lambda_tilde(lambda_1, lambda_2, mass_1, mass_2):
+ """
+ Convert from individual tidal parameters to domainant tidal term.
+
+ See, e.g., Wade et al., https://arxiv.org/pdf/1402.5156.pdf.
+
+ Parameters
+ ----------
+ lambda_1: float
+ Tidal parameter of more massive neutron star.
+ lambda_2: float
+ Tidal parameter of less massive neutron star.
+ mass_1: float
+ Mass of more massive neutron star.
+ mass_2: float
+ Mass of less massive neutron star.
+
+ Return
+ ------
+ lambda_tilde: float
+ Dominant tidal term.
+ """
+ eta = component_masses_to_symmetric_mass_ratio(mass_1, mass_2)
+ lambda_plus = lambda_1 + lambda_2
+ lambda_minus = lambda_1 - lambda_2
+ lambda_tilde = 8 / 13 * (
+ (1 + 7 * eta - 31 * eta**2) * lambda_plus +
+ (1 - 4 * eta)**0.5 * (1 + 9 * eta - 11 * eta**2) * lambda_minus)
+
+ return lambda_tilde
+
+
+def lambda_1_lambda_2_to_delta_lambda(lambda_1, lambda_2, mass_1, mass_2):
+ """
+ Convert from individual tidal parameters to second domainant tidal term.
+
+ See, e.g., Wade et al., https://arxiv.org/pdf/1402.5156.pdf.
+
+ Parameters
+ ----------
+ lambda_1: float
+ Tidal parameter of more massive neutron star.
+ lambda_2: float
+ Tidal parameter of less massive neutron star.
+ mass_1: float
+ Mass of more massive neutron star.
+ mass_2: float
+ Mass of less massive neutron star.
+
+ Return
+ ------
+ delta_lambda: float
+ Second dominant tidal term.
+ """
+ eta = component_masses_to_symmetric_mass_ratio(mass_1, mass_2)
+ lambda_plus = lambda_1 + lambda_2
+ lambda_minus = lambda_1 - lambda_2
+ delta_lambda = 1 / 2 * (
+ (1 - 4 * eta) ** 0.5 * (1 - 13272 / 1319 * eta + 8944 / 1319 * eta**2)
+ * lambda_plus + (1 - 15910 / 1319 * eta + 32850 / 1319 * eta**2 +
+ 3380 / 1319 * eta**3) * lambda_minus)
+
+ return delta_lambda
+
+
def lambda_tilde_delta_lambda_to_lambda_1_lambda_2(
lambda_tilde, delta_lambda, mass_1, mass_2):
"""
@@ -484,6 +596,29 @@ def lambda_tilde_to_lambda_1_lambda_2(
return lambda_1, lambda_2
+def _generate_all_cbc_parameters(sample, defaults, base_conversion,
+ likelihood=None, priors=None):
+ """Generate all cbc parameters, helper function for BBH/BNS"""
+ output_sample = sample.copy()
+ waveform_defaults = defaults
+ for key in waveform_defaults:
+ try:
+ output_sample[key] = \
+ likelihood.waveform_generator.waveform_arguments[key]
+ except (KeyError, AttributeError):
+ default = waveform_defaults[key]
+ output_sample[key] = default
+ logger.warning('Assuming {} = {}'.format(key, default))
+
+ output_sample = fill_from_fixed_priors(output_sample, priors)
+ output_sample, _ = base_conversion(output_sample)
+ output_sample = generate_mass_parameters(output_sample)
+ output_sample = generate_spin_parameters(output_sample)
+ output_sample = generate_source_frame_parameters(output_sample)
+ compute_snrs(output_sample, likelihood)
+ return output_sample
+
+
def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
"""
From either a single sample or a set of samples fill in all missing
@@ -500,21 +635,42 @@ def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
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']
+ waveform_defaults = {
+ 'reference_frequency': 50.0, 'waveform_approximant': 'IMRPhenomPv2',
+ 'minimum_frequency': 20.0}
+ output_sample = _generate_all_cbc_parameters(
+ sample, defaults=waveform_defaults,
+ base_conversion=convert_to_lal_binary_black_hole_parameters,
+ likelihood=likelihood, priors=priors)
+ return output_sample
- output_sample = fill_from_fixed_priors(output_sample, priors)
- 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)
+
+def generate_all_bns_parameters(sample, likelihood=None, priors=None):
+ """
+ From either a single sample or a set of samples fill in all missing
+ BNS parameters, in place.
+
+ Since we assume BNS waveforms are aligned, component spins won't be
+ calculated.
+
+ Parameters
+ ----------
+ sample: dict or pandas.DataFrame
+ Samples to fill in with extra parameters, this may be either an
+ injection or posterior samples.
+ likelihood: bilby.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.
+ """
+ waveform_defaults = {
+ 'reference_frequency': 50.0, 'waveform_approximant': 'TaylorF2',
+ 'minimum_frequency': 20.0}
+ output_sample = _generate_all_cbc_parameters(
+ sample, defaults=waveform_defaults,
+ base_conversion=convert_to_lal_binary_neutron_star_parameters,
+ likelihood=likelihood, priors=priors)
return output_sample
@@ -540,13 +696,12 @@ def fill_from_fixed_priors(sample, priors):
return output_sample
-def generate_non_standard_parameters(sample):
+def generate_mass_parameters(sample):
"""
- Add the known non-standard parameters to the data frame/dictionary.
+ Add the known mass 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
Parameters
----------
@@ -569,12 +724,47 @@ def generate_non_standard_parameters(sample):
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'])
+ return output_sample
+
+
+def generate_spin_parameters(sample):
+ """
+ Add all spin parameters to the data frame/dictionary.
+
+ We add:
+ cartestian spin components, chi_eff, chi_p cos tilt 1, cos tilt 2
+
+ Parameters
+ ----------
+ sample : dict, pd.DataFrame
+ The input dictionary with some spin parameters
+
+ Returns
+ -------
+ dict: The updated dictionary
+
+ """
+ output_sample = sample.copy()
+
+ output_sample = generate_component_spins(output_sample)
+
+ output_sample['chi_eff'] = (output_sample['spin_1z'] +
+ output_sample['spin_2z'] *
+ output_sample['mass_ratio']) /\
+ (1 + output_sample['mass_ratio'])
+
+ output_sample['chi_p'] = np.maximum(
+ (output_sample['spin_1x'] ** 2 + output_sample['spin_1y']**2)**0.5,
+ (4 * output_sample['mass_ratio'] + 3) /
+ (3 * output_sample['mass_ratio'] + 4) * output_sample['mass_ratio'] *
+ (output_sample['spin_2x'] ** 2 + output_sample['spin_2y']**2)**0.5)
+
+ try:
+ output_sample['cos_tilt_1'] = np.cos(output_sample['tilt_1'])
+ output_sample['cos_tilt_2'] = np.cos(output_sample['tilt_2'])
+ except KeyError:
+ pass
- output_sample['redshift'] =\
- luminosity_distance_to_redshift(sample['luminosity_distance'])
return output_sample
@@ -599,13 +789,12 @@ def generate_component_spins(sample):
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):
+ if all(key in output_sample.keys() for key in spin_conversion_parameters):
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(
+ transform_precessing_spins(
output_sample['iota'], output_sample['phi_jl'],
output_sample['tilt_1'], output_sample['tilt_2'],
output_sample['phi_12'], output_sample['a_1'],
@@ -618,40 +807,73 @@ def generate_component_spins(sample):
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 'chi_1' in output_sample and 'chi_2' in output_sample:
+ output_sample['spin_1x'] = 0
+ output_sample['spin_1y'] = 0
+ output_sample['spin_1z'] = output_sample['chi_1']
+ output_sample['spin_2x'] = 0
+ output_sample['spin_2y'] = 0
+ output_sample['spin_2z'] = output_sample['chi_2']
+ else:
+ logger.warning("Component spin extraction failed.")
+ logger.warning(output_sample.keys())
- 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}
-
- 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] =\
- 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['mass_1'][ii] * solar_mass,
- output_sample['mass_2'][ii] * solar_mass,
- output_sample['reference_frequency'][ii],
- output_sample['phase'][ii])
-
- for name in new_spin_parameters:
- output_sample[name] = new_spins[name]
+ return output_sample
- 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.")
+def generate_tidal_parameters(sample):
+ """
+ Generate all tidal parameters
+
+ lambda_tilde, delta_lambda
+
+ Parameters
+ ----------
+ sample: dict, pd.DataFrame
+ Should contain lambda_1, lambda_2
+
+ Returns
+ -------
+ output_sample: dict, pd.DataFrame
+ Updated sample
+ """
+ output_sample = sample.copy()
+
+ output_sample['lambda_tilde'] =\
+ lambda_1_lambda_2_to_lambda_tilde(
+ output_sample['lambda_1'], output_sample['lambda_2'],
+ output_sample['mass_1'], output_sample['mass_2'])
+ output_sample['delta_lambda'] = \
+ lambda_1_lambda_2_to_delta_lambda(
+ output_sample['lambda_1'], output_sample['lambda_2'],
+ output_sample['mass_1'], output_sample['mass_2'])
+
+ return output_sample
+
+
+def generate_source_frame_parameters(sample):
+ """
+ Generate source frame masses along with redshifts and comoving distance.
+
+ Parameters
+ ----------
+ sample: dict, pd.DataFrame
+
+ Returns
+ -------
+ output_sample: dict, pd.DataFrame
+ """
+ output_sample = sample.copy()
+
+ output_sample['redshift'] =\
+ luminosity_distance_to_redshift(output_sample['luminosity_distance'])
+ output_sample['comoving_distance'] =\
+ redshift_to_comoving_distance(output_sample['redshift'])
+
+ for key in ['mass_1', 'mass_2', 'chirp_mass', 'total_mass']:
+ if key in output_sample:
+ output_sample['{}_source'.format(key)] =\
+ output_sample[key] / (1 + output_sample['redshift'])
return output_sample
@@ -674,7 +896,6 @@ def compute_snrs(sample, likelihood):
if isinstance(temp_sample, dict):
temp = dict()
for key in likelihood.waveform_generator.parameters.keys():
- # likelihood.waveform_generator.parameters[key] = temp_sample[key]
temp[key] = temp_sample[key]
signal_polarizations =\
likelihood.waveform_generator.frequency_domain_strain(temp)
@@ -696,8 +917,6 @@ def compute_snrs(sample, likelihood):
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)