diff --git a/peyote/detector.py b/peyote/detector.py
index 383b44697867c9130aaf6a3285b7b044e72b56a0..4315615dccfb278c726a8d5192023693ac89535a 100644
--- a/peyote/detector.py
+++ b/peyote/detector.py
@@ -1,5 +1,6 @@
from __future__ import division, print_function, absolute_import
import numpy as np
+import peyote
import logging
import os
from scipy.interpolate import interp1d
@@ -210,7 +211,16 @@ class Interferometer(object):
:param waveform_polarizations: dict, polarizations of the waveform
:param parameters: dict, parameters describing position and time of arrival of the signal
"""
- self.data += self.get_detector_response(waveform_polarizations, parameters)
+ signal_ifo = self.get_detector_response(waveform_polarizations, parameters)
+ self.data += signal_ifo
+ opt_snr = np.sqrt(peyote.utils.optimal_snr_squared(signal=signal_ifo, interferometer=self,
+ time_duration=1 / (self.frequency_array[1]
+ - self.frequency_array[0])).real)
+ mf_snr = np.sqrt(peyote.utils.matched_filter_snr_squared(signal=signal_ifo, interferometer=self,
+ time_duration=1 / (self.frequency_array[1]
+ - self.frequency_array[0])).real)
+ logging.info("Injection found with optimal SNR = {:.2f} and matched filter SNR = {:.2f} in {}".format(
+ opt_snr, mf_snr, self.name))
def unit_vector_along_arm(self, arm):
"""
diff --git a/peyote/likelihood.py b/peyote/likelihood.py
index d1bbbb62515960b2e641325989a1c7f812ee7d6f..2208f21db69be1c0db29542ea57622273147bcf2 100644
--- a/peyote/likelihood.py
+++ b/peyote/likelihood.py
@@ -1,4 +1,13 @@
+from __future__ import division, print_function
import numpy as np
+try:
+ from scipy.special import logsumexp
+except ImportError:
+ from scipy.misc import logsumexp
+from scipy.special import i0, i0e
+from scipy.interpolate import interp1d
+import peyote
+import logging
class Likelihood(object):
@@ -30,3 +39,120 @@ class Likelihood(object):
def log_likelihood_ratio(self):
return self.log_likelihood() - self.noise_log_likelihood()
+
+
+class MarginalizedLikelihood(Likelihood):
+ def __init__(self, interferometers, waveform_generator, distance_marginalization=False, phase_marginalization=False,
+ time_marginalization=False, prior=None):
+ Likelihood.__init__(self, interferometers, waveform_generator)
+ self.distance_marginalization = distance_marginalization
+ self.phase_marginalization = phase_marginalization
+ self.time_marginalization = time_marginalization
+ self.prior = prior
+
+ if self.distance_marginalization:
+ self.distance_array = np.array([])
+ self.delta_distance = 0
+ self.distance_prior_array = np.array([])
+ self.setup_distance_marginalization()
+ prior['luminosity_distance'] = 1
+
+ if self.phase_marginalization:
+ self.bessel_function_interped = None
+ self.setup_phase_marginalization()
+ prior['psi'] = 0
+
+ if self.time_marginalization:
+ logging.warning('Time marginalisation not yet implemented.')
+ self.time_marginalization = False
+
+ def setup_distance_marginalization(self):
+ if 'luminosity_distance' not in self.prior.keys():
+ logging.info('No prior provided for distance, using default prior.')
+ self.prior['luminosity_distance'] = peyote.prior.create_default_prior('luminosity_distance')
+ self.distance_array = np.linspace(self.prior['luminosity_distance'].minimum,
+ self.prior['luminosity_distance'].maximum, 1e4)
+ self.delta_distance = self.distance_array[1] - self.distance_array[0]
+ self.distance_prior_array = np.array([self.prior['luminosity_distance'].prob(distance)
+ for distance in self.distance_array])
+
+ def setup_phase_marginalization(self):
+ if 'psi' not in self.prior.keys() or not isinstance(prior['psi'], peyote.prior.Prior):
+ logging.info('No prior provided for polarization, using default prior.')
+ self.prior['psi'] = peyote.prior.create_default_prior('psi')
+ self.bessel_function_interped = interp1d(np.linspace(0, 1e6, 1e5),
+ np.log([i0e(snr) for snr in np.linspace(0, 1e6, 1e5)])
+ + np.linspace(0, 1e6, 1e5),
+ bounds_error=False, fill_value=-np.inf)
+
+ def noise_log_likelihood(self):
+ log_l = 0
+ for interferometer in self.interferometers:
+ log_l -= peyote.utils.noise_weighted_inner_product(interferometer.data, interferometer.data,
+ interferometer.power_spectral_density_array,
+ self.waveform_generator.time_duration) / 2
+ return log_l.real
+
+ def log_likelihood_ratio(self):
+ waveform_polarizations = self.waveform_generator.frequency_domain_strain()
+
+ if waveform_polarizations is None:
+ return np.nan_to_num(-np.inf)
+
+ if self.time_marginalization:
+ signal_times_data = 0 * 1j
+ else:
+ matched_filter_snr_squared = 0
+ optimal_snr_squared = 0
+
+ for interferometer in self.interferometers:
+ signal_ifo = interferometer.get_detector_response(waveform_polarizations,
+ self.waveform_generator.parameters)
+ if self.time_marginalization:
+ signal_times_data += np.conj(signal_ifo) * interferometer.data\
+ / interferometer.power_spectral_density_array
+ else:
+ matched_filter_snr_squared += peyote.utils.matched_filter_snr_squared(
+ signal_ifo, interferometer, self.waveform_generator.time_duration)
+ optimal_snr_squared += peyote.utils.optimal_snr_squared(signal_ifo, interferometer,
+ self.waveform_generator.time_duration)
+
+ if self.time_marginalization:
+ matched_filter_snr_squared = np.abs(np.fft.ifft(signal_times_data))\
+ * 4 / self.waveform_generator.time_duration
+
+ if self.phase_marginalization and not self.distance_marginalization and not self.time_marginalization:
+ matched_filter_snr_squared = self.bessel_function_interped(abs(matched_filter_snr_squared))
+
+ log_l = matched_filter_snr_squared - optimal_snr_squared / 2
+
+ elif self.distance_marginalization and not self.phase_marginalization and not self.time_marginalization:
+ log_l = logsumexp(matched_filter_snr_squared * self.waveform_generator.parameters['luminosity_distance']
+ / self.distance_array
+ - optimal_snr_squared * self.waveform_generator.parameters['luminosity_distance']**2
+ / self.distance_array**2 / 2, b=self.distance_prior_array * self.delta_distance)
+
+ elif self.distance_marginalization and self.phase_marginalization and not self.time_marginalization:
+ log_l = logsumexp(self.bessel_function_interped(abs(matched_filter_snr_squared)
+ * self.waveform_generator.parameters['luminosity_distance']
+ / self.distance_array)
+ - optimal_snr_squared * self.waveform_generator.parameters['luminosity_distance']**2
+ / self.distance_array**2 / 2, b=self.distance_prior_array * self.delta_distance)
+
+ elif self.distance_marginalization and self.phase_marginalization and self.time_marginalization:
+ log_l = logsumexp(np.array([sum(self.bessel_function_interped(
+ matched_filter_snr_squared * self.waveform_generator.parameters['luminosity_distance']
+ / distance)) for distance in self.distance_array])
+ - optimal_snr_squared * self.waveform_generator.parameters['luminosity_distance']**2
+ / self.distance_array**2 / 2, b=self.distance_prior_array * self.delta_distance)\
+ - np.log(self.waveform_generator.sampling_frequency)
+ print(log_l)
+ else:
+ log_l = matched_filter_snr_squared - optimal_snr_squared / 2
+
+ return log_l.real
+
+ def log_likelihood(self):
+ return self.log_likelihood() + self.noise_log_likelihood()
+
+
diff --git a/peyote/source.py b/peyote/source.py
index 749a74d89c5b095d3ce6026dddfdb7d73fcd5bc7..230f876eb9548129768b32efaffe41fc16bf8717 100644
--- a/peyote/source.py
+++ b/peyote/source.py
@@ -15,7 +15,7 @@ def lal_binary_black_hole(
geocent_time, psi):
""" A Binary Black Hole waveform model using lalsimulation """
if mass_2 > mass_1:
- return {'plus': 0, 'cross': 0}
+ return None
luminosity_distance = luminosity_distance * 1e6 * utils.parsec
mass_1 = mass_1 * utils.solar_mass
diff --git a/peyote/utils.py b/peyote/utils.py
index 989fcfebb44f616e9e9faa67a3633e4a9e0caa0c..bf3fa6283048f50cda9ad06b9900e47fa23a58e1 100644
--- a/peyote/utils.py
+++ b/peyote/utils.py
@@ -344,3 +344,38 @@ def inner_product(aa, bb, frequency, PSD):
product = 4. * np.real(integral)
return product
+
+
+def noise_weighted_inner_product(aa, bb, power_spectral_density, time_duration):
+ """
+ Calculate the noise weighted inner product between two arrays.
+
+ Parameters
+ ----------
+ aa: array
+ Array to be complex conjugated
+ bb: array
+ Array not to be complex conjugated
+ power_spectral_density: array
+ Power spectral density
+ time_duration: float
+ time_duration of the data
+
+ Return
+ ------
+ Noise-weighted inner product.
+ """
+
+ # caluclate the inner product
+ integrand = np.conj(aa) * bb / power_spectral_density
+ product = 4 / time_duration * np.sum(integrand)
+ return product
+
+
+def matched_filter_snr_squared(signal, interferometer, time_duration):
+ return noise_weighted_inner_product(signal, interferometer.data, interferometer.power_spectral_density_array,
+ time_duration)
+
+
+def optimal_snr_squared(signal, interferometer, time_duration):
+ return noise_weighted_inner_product(signal, signal, interferometer.power_spectral_density_array, time_duration)
diff --git a/tutorials/BasicTutorial.py b/tutorials/BasicTutorial.py
index a6eb219474ddb367b969e61e3664fef6fbf075cd..fcdeed337c231101838693b3c0282dd60157aeef 100644
--- a/tutorials/BasicTutorial.py
+++ b/tutorials/BasicTutorial.py
@@ -8,14 +8,16 @@ comoving volume prior on luminosity distance, the cosmology is WMAP7.
from __future__ import division, print_function
import peyote
-peyote.utils.setup_logger()
+peyote.utils.setup_logger(log_level="info")
time_duration = 4.
sampling_frequency = 2048.
outdir = 'outdir'
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0, a_2=0, tilt_1=0, tilt_2=0, phi_12=0, phi_jl=0,
- luminosity_distance=2500., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
+np.random.seed(170809)
+
+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=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
# Create the waveform_generator using a LAL BinaryBlackHole source function
@@ -30,9 +32,6 @@ IFOs = [peyote.detector.get_inteferometer_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']]
-# Set up likelihood
-likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
-
# Set up prior
priors = dict()
# These parameters will not be sampled
diff --git a/tutorials/Injection.py b/tutorials/Injection.py
index 25e59c5df4d48ae4bd54517c911cfcfbde0418cf..1060f0467c0c1d53bfbd0d19a3d686feff3cb522 100644
--- a/tutorials/Injection.py
+++ b/tutorials/Injection.py
@@ -4,43 +4,59 @@ Tutorial to show signal injection using new features of detector.py
from __future__ import division, print_function
import numpy as np
import peyote
-
-peyote.utils.setup_logger()
-
-outdir = 'outdir'
-label = 'injection'
-
-# Create the waveform generator
-waveform_generator = peyote.waveform_generator.WaveformGenerator(
- peyote.source.lal_binary_black_hole, sampling_frequency=4096, time_duration=4,
- parameters={'reference_frequency': 50.0, 'waveform_approximant': 'IMRPhenomPv2'})
-
-# Define the prior
-# Merger time is some time in 2018, shame LIGO will never see it...
-time_of_event = np.random.uniform(1198800018, 1230336018)
-prior = dict()
-prior['geocent_time'] = peyote.prior.Uniform(time_of_event-0.01, time_of_event+0.01, name='geocent_time')
-peyote.prior.fill_priors(prior, waveform_generator)
-
-# Create signal injection
-injection_parameters = {name: prior[name].sample() for name in prior}
-print(injection_parameters)
-for parameter in injection_parameters:
- waveform_generator.parameters[parameter] = injection_parameters[parameter]
-injection_polarizations = waveform_generator.frequency_domain_strain()
-
-# Create interferometers and inject signal
-IFOs = [peyote.detector.get_inteferometer_with_fake_noise_and_injection(
- name, injection_polarizations=injection_polarizations, injection_parameters=injection_parameters,
- sampling_frequency=4096, time_duration=4, outdir=outdir) for name in ['H1', 'L1', 'V1', 'GEO600']]
-
-# Define a likelihood
-likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
-
-# Run the sampler
-result = peyote.sampler.run_sampler(
- likelihood, prior, label='injection', sampler='nestle',
- npoints=200, resume=False, outdir=outdir, use_ratio=True, injection_parameters=injection_parameters)
-truth = [injection_parameters[parameter] for parameter in result.search_parameter_keys]
-result.plot_corner(truth=truth)
-print(result)
+import logging
+
+
+def main():
+
+ peyote.utils.setup_logger()
+
+ outdir = 'outdir'
+ label = 'injection'
+
+ # Create the waveform generator
+ waveform_generator = peyote.waveform_generator.WaveformGenerator(
+ peyote.source.lal_binary_black_hole, sampling_frequency=2048, time_duration=4,
+ parameters={'reference_frequency': 50.0, 'waveform_approximant': 'IMRPhenomPv2'})
+
+ # Define the prior
+ # Merger time is some time in 2018, shame LIGO will never see it...
+ time_of_event = np.random.uniform(1198800018, 1230336018)
+ prior = dict()
+ prior['luminosity_distance'] = peyote.prior.PowerLaw(alpha=2, minimum=100, maximum=5000, name='luminosity_distance')
+ prior['geocent_time'] = peyote.prior.Uniform(time_of_event-0.01, time_of_event+0.01, name='geocent_time')
+ prior['mass_1'] = peyote.prior.Gaussian(mu=40, sigma=5, name='mass_1')
+ prior['mass_2'] = peyote.prior.Gaussian(mu=40, sigma=5, name='mass_2')
+ peyote.prior.fill_priors(prior, waveform_generator)
+
+ # Create signal injection
+ injection_parameters = {name: prior[name].sample() for name in prior}
+ if injection_parameters['mass_1'] < injection_parameters['mass_2']:
+ injection_parameters['mass_1'], injection_parameters['mass_2'] =\
+ injection_parameters['mass_2'], injection_parameters['mass_1']
+ logging.info("Injection parameters:\n{}".format("\n".join(["{}: {}".format(key, injection_parameters[key])
+ for key in injection_parameters])))
+ for parameter in injection_parameters:
+ waveform_generator.parameters[parameter] = injection_parameters[parameter]
+ injection_polarizations = waveform_generator.frequency_domain_strain()
+
+ # Create interferometers and inject signal
+ interferometers = [peyote.detector.get_inteferometer_with_fake_noise_and_injection(
+ name, injection_polarizations=injection_polarizations, injection_parameters=injection_parameters,
+ sampling_frequency=2048, time_duration=4, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+
+ # Define a likelihood
+ likelihood = peyote.likelihood.MarginalizedLikelihood(interferometers, waveform_generator, prior=prior,
+ distance_marginalization=True, phase_marginalization=True)
+
+ # Run the sampler
+ result = peyote.sampler.run_sampler(
+ likelihood, prior, label=label, sampler='dynesty', npoints=500, resume=False, outdir=outdir, use_ratio=True,
+ injection_parameters=injection_parameters)
+ truth = [injection_parameters[parameter] for parameter in result.search_parameter_keys]
+ result.plot_corner(truth=truth)
+ print(result)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tutorials/standard_data.txt b/tutorials/standard_data.txt
index a3487b11402c1b49b1665897d619f98ca0ec8f36..f8dc4388f21f877670cf48a765ad947f41ae6963 100644
--- a/tutorials/standard_data.txt
+++ b/tutorials/standard_data.txt
@@ -19,610 +19,610 @@
1.700000000000000000e+01 1.819875165161175548e-24 1.072065808550889356e-23
1.800000000000000000e+01 1.768433004078348058e-23 -8.258509941748371538e-24
1.900000000000000000e+01 -1.148407801160056393e-23 1.119220115942690801e-23
-2.000000000000000000e+01 1.737961378998035643e-22 5.675853961292239793e-23
-2.100000000000000000e+01 1.030543949840635110e-22 1.403569442711740267e-22
-2.200000000000000000e+01 1.065346466626823977e-22 1.488698210860752029e-22
-2.300000000000000000e+01 1.628959790578073603e-22 8.638072120339693225e-23
-2.400000000000000000e+01 1.608400451416332751e-22 -4.891563251469428937e-23
-2.500000000000000000e+01 3.217415342571341055e-23 -1.629160451370713571e-22
-2.600000000000000000e+01 -1.376563906366519596e-22 -3.726049296778877710e-23
-2.700000000000000000e+01 -1.748257421876825366e-23 1.385412284978934168e-22
-2.800000000000000000e+01 1.284546517302394518e-22 -1.630081596178991419e-23
-2.900000000000000000e+01 -7.058017224432897185e-23 -1.071617939832806679e-22
-3.000000000000000000e+01 -3.493374953709433294e-23 1.124392528346560963e-22
-3.100000000000000000e+01 9.764843291224622035e-23 -5.312551117797398107e-23
-3.200000000000000000e+01 -1.083829247095670066e-22 -1.914459698412222240e-23
-3.300000000000000000e+01 8.014101715512701825e-23 6.325121671550935926e-23
-3.400000000000000000e+01 -3.856942772208415436e-23 -9.428346451754767409e-23
-3.500000000000000000e+01 8.626318309209906784e-24 1.013211321176095392e-22
-3.600000000000000000e+01 2.045623680900992003e-23 -9.508865594301758000e-23
-3.700000000000000000e+01 -3.823977545294806123e-23 7.803116176144077294e-23
-3.800000000000000000e+01 5.189277696558813076e-23 -7.283450829661414733e-23
-3.900000000000000000e+01 -5.544694023678743334e-23 6.948326731605941215e-23
-4.000000000000000000e+01 5.665411191473663735e-23 -6.460231882277377773e-23
-4.100000000000000000e+01 -5.436771871144030034e-23 5.999071664757711714e-23
-4.200000000000000000e+01 4.704937032008324872e-23 -5.610071860937896031e-23
-4.300000000000000000e+01 -4.563074618544210052e-23 5.777464696324506395e-23
-4.400000000000000000e+01 3.456319306482809070e-23 -6.211057835318579925e-23
-4.500000000000000000e+01 -2.807458573619520487e-23 6.414707637632119334e-23
-4.600000000000000000e+01 1.554796470438244301e-23 -6.582789811204821780e-23
-4.700000000000000000e+01 -3.160660842990948537e-24 6.306938770272374031e-23
-4.800000000000000000e+01 -7.936704514683460086e-24 -5.970434533677182656e-23
-4.900000000000000000e+01 2.250895801798681448e-23 5.326185152337446806e-23
-5.000000000000000000e+01 -3.550755779787748472e-23 -5.075326651901343814e-23
-5.100000000000000000e+01 4.659687718754687841e-23 3.619204411286843029e-23
-5.200000000000000000e+01 -5.430046260895671391e-23 -1.877624704200213213e-23
-5.300000000000000000e+01 5.738829622680787006e-23 2.827325249509822687e-24
-5.400000000000000000e+01 -5.122646124400852289e-23 1.447018364596800266e-23
-5.500000000000000000e+01 4.599161917106792689e-23 -2.870001310325403706e-23
-5.600000000000000000e+01 -2.732052523549428203e-23 3.821971465602462254e-23
-5.700000000000000000e+01 1.889944589831438204e-23 -5.148304021038646491e-23
-5.800000000000000000e+01 9.600004559094515133e-25 5.180971779865171767e-23
-5.900000000000000000e+01 -1.952831193135860709e-23 -4.749208416392695760e-23
-6.000000000000000000e+01 3.271080534773716480e-23 3.381660511653609357e-23
-6.100000000000000000e+01 -4.379198379016019812e-23 -1.844708712835085003e-23
-6.200000000000000000e+01 4.796067075976542835e-23 1.727702876469330378e-24
-6.300000000000000000e+01 -4.256019745470435912e-23 1.521114183340625242e-23
-6.400000000000000000e+01 2.983719710266772256e-23 -3.002028671754322197e-23
-6.500000000000000000e+01 -1.542054807802608598e-23 4.062382379450694819e-23
-6.600000000000000000e+01 -1.290757798272856274e-24 -4.104506338482793550e-23
-6.700000000000000000e+01 1.723098577300785383e-23 3.304337390325531594e-23
-6.800000000000000000e+01 -3.369882684895019268e-23 -2.065366271446040373e-23
-6.900000000000000000e+01 3.872189686414829912e-23 7.629027456625108150e-24
-7.000000000000000000e+01 -3.671333850493249612e-23 1.098413627342702477e-23
-7.100000000000000000e+01 2.948967347513955931e-23 -2.423800316892933736e-23
-7.200000000000000000e+01 -1.457518414041149083e-23 2.969754031113732428e-23
-7.300000000000000000e+01 -6.896318529965155495e-25 -3.580863090194482331e-23
-7.400000000000000000e+01 1.935349315669330849e-23 3.603833806956736134e-23
-7.500000000000000000e+01 -2.500915935220520129e-23 -2.196913706069111343e-23
-7.600000000000000000e+01 3.178068574235242653e-23 4.102893444016679124e-24
-7.700000000000000000e+01 -3.062997711414782493e-23 1.659564302743775741e-23
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