diff --git a/bilby/gw/likelihood.py b/bilby/gw/likelihood.py
index 44aef9cda01edd136c04689024056100ff98d91f..1f0d6d129421f8539515d051ca8d3e8961d55398 100644
--- a/bilby/gw/likelihood.py
+++ b/bilby/gw/likelihood.py
@@ -835,8 +835,7 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
If true, run tests using the roq_params to check the prior and data are
valid for the ROQ
roq_scale_factor: float
- The ROQ scale factor used. WARNING: this does not apply the scaling,
- but is only used for checking that the ROQ basis is appropriate.
+ The ROQ scale factor used.
priors: dict, bilby.prior.PriorDict
A dictionary of priors containing at least the geocent_time prior
distance_marginalization_lookup_table: (dict, str), optional
@@ -1005,22 +1004,22 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
logger.info(msg)
roq_params = self.roq_params
- roq_params['flow'] *= self.roq_scale_factor
- roq_params['fhigh'] *= self.roq_scale_factor
- roq_params['seglen'] /= self.roq_scale_factor
- roq_params['chirpmassmin'] /= self.roq_scale_factor
- roq_params['chirpmassmax'] /= self.roq_scale_factor
- roq_params['compmin'] /= self.roq_scale_factor
-
- if ifo.maximum_frequency > roq_params['fhigh']:
+ roq_minimum_frequency = roq_params['flow'] * self.roq_scale_factor
+ roq_maximum_frequency = roq_params['fhigh'] * self.roq_scale_factor
+ roq_segment_length = roq_params['seglen'] / self.roq_scale_factor
+ roq_minimum_chirp_mass = roq_params['chirpmassmin'] / self.roq_scale_factor
+ roq_maximum_chirp_mass = roq_params['chirpmassmax'] / self.roq_scale_factor
+ roq_minimum_component_mass = roq_params['compmin'] / self.roq_scale_factor
+
+ if ifo.maximum_frequency > roq_maximum_frequency:
raise BilbyROQParamsRangeError(
"Requested maximum frequency {} larger than ROQ basis fhigh {}"
- .format(ifo.maximum_frequency, roq_params['fhigh']))
- if ifo.minimum_frequency < roq_params['flow']:
+ .format(ifo.maximum_frequency, roq_maximum_frequency))
+ if ifo.minimum_frequency < roq_minimum_frequency:
raise BilbyROQParamsRangeError(
"Requested minimum frequency {} lower than ROQ basis flow {}"
- .format(ifo.minimum_frequency, roq_params['flow']))
- if ifo.strain_data.duration != roq_params['seglen']:
+ .format(ifo.minimum_frequency, roq_minimum_frequency))
+ if ifo.strain_data.duration != roq_segment_length:
raise BilbyROQParamsRangeError(
"Requested duration differs from ROQ basis seglen")
@@ -1031,27 +1030,27 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
if priors.minimum_chirp_mass is None:
logger.warning("Unable to check minimum chirp mass ROQ bounds")
- elif priors.minimum_chirp_mass < roq_params["chirpmassmin"]:
+ elif priors.minimum_chirp_mass < roq_minimum_chirp_mass:
raise BilbyROQParamsRangeError(
"Prior minimum chirp mass {} less than ROQ basis bound {}"
.format(priors.minimum_chirp_mass,
- roq_params["chirpmassmin"]))
+ roq_minimum_chirp_mass))
if priors.maximum_chirp_mass is None:
logger.warning("Unable to check maximum_chirp mass ROQ bounds")
- elif priors.maximum_chirp_mass > roq_params["chirpmassmax"]:
+ elif priors.maximum_chirp_mass > roq_maximum_chirp_mass:
raise BilbyROQParamsRangeError(
"Prior maximum chirp mass {} greater than ROQ basis bound {}"
.format(priors.maximum_chirp_mass,
- roq_params["chirpmassmax"]))
+ roq_maximum_chirp_mass))
if priors.minimum_component_mass is None:
logger.warning("Unable to check minimum component mass ROQ bounds")
- elif priors.minimum_component_mass < roq_params["compmin"]:
+ elif priors.minimum_component_mass < roq_minimum_component_mass:
raise BilbyROQParamsRangeError(
"Prior minimum component mass {} less than ROQ basis bound {}"
.format(priors.minimum_component_mass,
- roq_params["compmin"]))
+ roq_minimum_component_mass))
def _set_weights(self, linear_matrix, quadratic_matrix):
""" Setup the time-dependent ROQ weights.
@@ -1077,11 +1076,15 @@ class ROQGravitationalWaveTransient(GravitationalWaveTransient):
for ifo in self.interferometers:
if self.roq_params is not None:
self.perform_roq_params_check(ifo)
+ # Get scaled ROQ quantities
+ roq_scaled_minimum_frequency = self.roq_params['flow'] * self.roq_scale_factor
+ roq_scaled_maximum_frequency = self.roq_params['fhigh'] * self.roq_scale_factor
+ roq_scaled_segment_length = self.roq_params['seglen'] * self.roq_scale_factor
# Generate frequencies for the ROQ
roq_frequencies = create_frequency_series(
- sampling_frequency=self.roq_params['fhigh'] * 2,
- duration=self.roq_params['seglen'])
- roq_mask = roq_frequencies >= self.roq_params['flow']
+ sampling_frequency=roq_scaled_maximum_frequency * 2,
+ duration=roq_scaled_segment_length)
+ roq_mask = roq_frequencies >= roq_scaled_minimum_frequency
roq_frequencies = roq_frequencies[roq_mask]
overlap_frequencies, ifo_idxs, roq_idxs = np.intersect1d(
ifo.frequency_array[ifo.frequency_mask], roq_frequencies,
diff --git a/examples/gw_examples/injection_examples/roq_example.py b/examples/gw_examples/injection_examples/roq_example.py
index da639805e3a476071a0ce9a4f08bf697138e6f27..0ffdbf33817fa0813e630264608a38b06df6f7cc 100644
--- a/examples/gw_examples/injection_examples/roq_example.py
+++ b/examples/gw_examples/injection_examples/roq_example.py
@@ -32,12 +32,11 @@ freq_nodes_quadratic = np.load("fnodes_quadratic.npy") * scale_factor
# Load the parameters describing the valid parameters for the basis.
params = np.genfromtxt("params.dat", names=True)
-params['flow'] *= scale_factor
-params['fhigh'] *= scale_factor
-params['seglen'] /= scale_factor
-params['chirpmassmin'] /= scale_factor
-params['chirpmassmax'] /= scale_factor
-params['compmin'] /= scale_factor
+
+# Get scaled ROQ quantities
+minimum_chirp_mass = params['chirpmassmin'] / scale_factor
+maximum_chirp_mass = params['chirpmassmax'] / scale_factor
+minimum_component_mass = params['compmin'] / scale_factor
np.random.seed(170808)
@@ -84,10 +83,10 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'theta_jn', 'phase', 'psi', 'ra',
'dec', 'phi_12', 'phi_jl', 'luminosity_distance']:
priors[key] = injection_parameters[key]
for key in ['mass_1', 'mass_2']:
- priors[key].minimum = max(priors[key].minimum, params['compmin'])
+ priors[key].minimum = max(priors[key].minimum, minimum_component_mass)
priors['chirp_mass'] = bilby.core.prior.Constraint(
- name='chirp_mass', minimum=float(params['chirpmassmin']),
- maximum=float(params['chirpmassmax']))
+ name='chirp_mass', minimum=float(minimum_chirp_mass),
+ maximum=float(maximum_chirp_mass))
priors['mass_ratio'] = bilby.core.prior.Constraint(0.125, 1, name='mass_ratio')
priors['geocent_time'] = bilby.core.prior.Uniform(
injection_parameters['geocent_time'] - 0.1,
@@ -96,7 +95,7 @@ priors['geocent_time'] = bilby.core.prior.Uniform(
likelihood = bilby.gw.likelihood.ROQGravitationalWaveTransient(
interferometers=ifos, waveform_generator=search_waveform_generator,
linear_matrix=basis_matrix_linear, quadratic_matrix=basis_matrix_quadratic,
- priors=priors, roq_params=params)
+ priors=priors, roq_params=params, roq_scale_factor=scale_factor)
# write the weights to file so they can be loaded multiple times
likelihood.save_weights('weights.npz')
diff --git a/test/gw_likelihood_test.py b/test/gw_likelihood_test.py
index c95c4a0dfceee341811f286c340cbeab34736bb8..a63706b0fbfebe5f009927b2a6ed81d0d223690c 100644
--- a/test/gw_likelihood_test.py
+++ b/test/gw_likelihood_test.py
@@ -772,12 +772,6 @@ class TestRescaledROQLikelihood(unittest.TestCase):
scale_factor = 0.5
params = np.genfromtxt(self.params_file, names=True)
- params['flow'] *= scale_factor
- params['fhigh'] *= scale_factor
- params['seglen'] /= scale_factor
- params['chirpmassmin'] /= scale_factor
- params['chirpmassmax'] /= scale_factor
- params['compmin'] /= scale_factor
self.duration = 4 / scale_factor
self.sampling_frequency = 2048 * scale_factor
@@ -807,8 +801,9 @@ class TestRescaledROQLikelihood(unittest.TestCase):
self.roq = bilby.gw.likelihood.ROQGravitationalWaveTransient(
interferometers=ifos, waveform_generator=self.roq_wfg,
- linear_matrix=linear_matrix_file, roq_params=params,
- quadratic_matrix=quadratic_matrix_file, priors=self.priors)
+ linear_matrix=linear_matrix_file, roq_params=params,
+ roq_scale_factor=scale_factor, quadratic_matrix=quadratic_matrix_file,
+ priors=self.priors)
class TestBBHLikelihoodSetUp(unittest.TestCase):