diff --git a/peyote/detector.py b/peyote/detector.py
index 4fdc1abb46dbcba63f571be065bb5e5f22dfabbd..bd63aabdbc9dfe30abb2f4729cbefeaf5a5af6b2 100644
--- a/peyote/detector.py
+++ b/peyote/detector.py
@@ -75,27 +75,27 @@ class Interferometer:
detector_tensor = 0.5 * (np.einsum('i,j->ij', self.x, self.x) - np.einsum('i,j->ij', self.y, self.y))
return detector_tensor
- def inject_signal(self, source, params):
+ def inject_signal(self, source):
"""
Inject a signal into noise.
Adds the requested signal to self.data
:param source: source type
- :param params: parameters
+
"""
- signal = source.frequency_domain_strain(params)
+ signal = source.frequency_domain_strain()
for mode in signal.keys():
det_response = self.antenna_response(
- params['ra'], params['dec'], params['geocent_time'],
- params['psi'], mode)
+ source.ra, source.dec, source.geocent_time,
+ source.psi, mode)
signal[mode] *= det_response
signal_ifo = sum(signal.values())
time_shift = self.time_delay_from_geocenter(
- params['ra'], params['dec'], params['geocent_time'])
+ source.ra, source.dec, source.geocent_time)
signal_ifo *= np.exp(-1j*2*np.pi*time_shift*source.frequency_array)
self.data += signal_ifo
diff --git a/peyote/likelihood.py b/peyote/likelihood.py
index 1a08cecd313afcbf2e3cf4224acbad0288d6203a..443469bf72a52cbb1389d8e0c029653f5898f7c2 100644
--- a/peyote/likelihood.py
+++ b/peyote/likelihood.py
@@ -1,79 +1,77 @@
import numpy as np
-class likelihood:
+class Likelihood:
def __init__(self, interferometers, source):
self.interferometers = interferometers
self.source = source
- self.parameter_keys = set(self.source.parameter_keys +
- ['ra', 'dec', 'geocent_time', 'psi'])
- def get_interferometer_signal(self, parameters, waveform_polarizations, interferometer):
+ def get_interferometer_signal(self, waveform_polarizations, interferometer):
h = []
for mode in waveform_polarizations:
det_response = interferometer.antenna_response(
- parameters['ra'], parameters['dec'],
- parameters['geocent_time'], parameters['psi'], mode)
+ self.source.ra, self.source.dec,
+ self.source.geocent_time, self.source.psi, mode)
h.append(waveform_polarizations[mode] * det_response)
signal = np.sum(h, axis=0)
time_shift = interferometer.time_delay_from_geocenter(
- parameters['ra'], parameters['dec'],
- parameters['geocent_time'])
+ self.source.ra, self.source.dec,
+ self.source.geocent_time)
signal *= np.exp(-1j * 2 * np.pi * time_shift * self.source.frequency_array)
return signal
- def loglikelihood(self, parameters):
+ def log_likelihood(self):
log_l = 0
- waveform_polarizations = self.source.frequency_domain_strain(parameters)
+ waveform_polarizations = self.source.frequency_domain_strain()
for interferometer in self.interferometers:
- log_l += self.log_likelihood_interferometer(parameters, waveform_polarizations, interferometer)
+ log_l += self.log_likelihood_interferometer(waveform_polarizations, interferometer)
return log_l.real
- def log_likelihood_interferometer(self, parameters, waveform_polarizations, interferometer):
- signal_ifo = self.get_interferometer_signal(parameters, waveform_polarizations, interferometer)
+ def log_likelihood_interferometer(self, waveform_polarizations, interferometer):
+ signal_ifo = self.get_interferometer_signal(waveform_polarizations, interferometer)
log_l = - 4. / self.source.time_duration * np.vdot(interferometer.data - signal_ifo,
(interferometer.data - signal_ifo)
- / (interferometer.power_spectral_density_array))
+ / interferometer.power_spectral_density_array)
return log_l.real
-class likelihoodB(likelihood):
+class LikelihoodB(Likelihood):
def __init__(self, interferometers, source):
- likelihood.__init__(self, interferometers, source)
+ Likelihood.__init__(self, interferometers, source)
for interferometer in self.interferometers:
interferometer.whiten_data()
- def loglikelihood(self, parameters):
+ def log_likelihood(self):
log_l = 0
- waveform_polarizations = self.source.frequency_domain_strain(parameters)
+ waveform_polarizations = self.source.frequency_domain_strain()
for interferometer in self.interferometers:
for mode in waveform_polarizations.keys():
det_response = interferometer.antenna_response(
- parameters['ra'], parameters['dec'],
- parameters['geocent_time'], parameters['psi'], mode)
+ self.source.ra, self.source.dec,
+ self.source.geocent_time, self.source.psi, mode)
waveform_polarizations[mode] *= det_response
signal_ifo = np.sum(waveform_polarizations.values(), axis=0)
time_shift = interferometer.time_delay_from_geocenter(
- parameters['ra'], parameters['dec'],
- parameters['geocent_time'])
- signal_ifo *= np.exp(-1j*2*np.pi*time_shift)
+ self.source.ra, self.source.dec,
+ self.source.geocent_time)
+ signal_ifo *= np.exp(-1j * 2 * np.pi * time_shift)
signal_ifo_whitened = signal_ifo / (
- interferometer.amplitude_spectral_density_array)
+ interferometer.amplitude_spectral_density_array)
log_l -= 4. * self.source.sampling_frequency * (
np.real(sum(
- (interferometer.whitened_data - signal_ifo_whitened)**2)))
+ (interferometer.whitened_data - signal_ifo_whitened) ** 2)))
return log_l
diff --git a/peyote/sampler.py b/peyote/sampler.py
index cdacc7e1f434d2a9218963cc47b68e01cbfbd9b3..60cff8efd18dabf9a3e1f28f72532ddb3d5daa46 100644
--- a/peyote/sampler.py
+++ b/peyote/sampler.py
@@ -9,8 +9,6 @@ import peyote
class Result(dict):
- def __init__(self):
- pass
def __getattr__(self, name):
try:
@@ -34,8 +32,8 @@ class Result(dict):
if os.path.isfile(file_name):
logging.info(
'Renaming existing file {} to {}.old'
- .format(file_name, file_name))
- os.rename(file_name, file_name+'.old')
+ .format(file_name, file_name))
+ os.rename(file_name, file_name + '.old')
logging.info("Saving result to {}".format(file_name))
with open(file_name, 'w+') as f:
@@ -71,12 +69,20 @@ class Sampler:
self.label = label
self.outdir = outdir
self.kwargs = kwargs
-
self.sampler_string = sampler_string
+
+ self.external_sampler = None
self.import_external_sampler()
+ self.active_parameter_values = self.prior.__dict__.copy()
+
+ self.search_parameter_keys = []
+ self.ndim = 0
self.initialise_parameters()
+
self.verify_prior()
+
+ self.result = Result()
self.add_initial_data_to_results()
self.set_kwargs()
@@ -89,24 +95,21 @@ class Sampler:
pass
def add_initial_data_to_results(self):
- self.result = Result()
self.result.search_parameter_keys = self.search_parameter_keys
- self.result.labels = [
- self.prior[k].latex_label for k in self.search_parameter_keys]
+ self.result.labels = [self.prior.__dict__[k].latex_label for k in self.search_parameter_keys]
def initialise_parameters(self):
- self.active_parameter_values = self.prior.copy()
- self.search_parameter_keys = []
- for key in self.likelihood.parameter_keys:
- if key in self.prior:
- p = self.prior[key]
- CA = isinstance(p, numbers.Real)
- CB = hasattr(p, 'prior')
- CC = getattr(p, 'is_fixed', False) is True
- if CA is False and CB and CC is False:
+
+ for key in self.likelihood.source.__dict__:
+ if key in self.prior.__dict__:
+ p = self.prior.__dict__[key]
+ ca = isinstance(p, numbers.Real)
+ cb = hasattr(p, 'prior')
+ cc = getattr(p, 'is_fixed', False) is True
+ if ca is False and cb and cc is False:
self.search_parameter_keys.append(key)
self.active_parameter_values[key] = np.nan
- elif CC:
+ elif cc:
self.active_parameter_values[key] = p.value
else:
try:
@@ -119,24 +122,24 @@ class Sampler:
logging.info("Search parameters:")
for key in self.search_parameter_keys:
- logging.info(' {} ~ {}'.format(key, self.prior[key].prior))
+ logging.info(' {} ~ {}'.format(key, self.prior.__dict__[key]))
def verify_prior(self):
- required_keys = self.likelihood.parameter_keys
+ required_keys = self.likelihood.source.__dict__
unmatched_keys = [
- r for r in required_keys if r not in self.prior]
+ r for r in required_keys if r not in self.prior.__dict__]
if len(unmatched_keys) > 0:
raise ValueError(
"Input prior is missing keys {}".format(unmatched_keys))
def prior_transform(self, theta):
- return [self.prior[k].prior.rescale(t)
- for k, t in zip(self.search_parameter_keys, theta)]
+ return [self.prior.__dict__[key].prior.rescale(t)
+ for key, t in zip(self.search_parameter_keys, theta)]
def loglikelihood(self, theta):
for i, k in enumerate(self.search_parameter_keys):
- self.active_parameter_values[k] = theta[i]
- return self.likelihood.loglikelihood(self.active_parameter_values)
+ self.likelihood.source.__dict__[k] = theta[i]
+ return self.likelihood.log_likelihood()
def run_sampler(self):
pass
@@ -226,13 +229,12 @@ class Pymultinest(Sampler):
def run_sampler(likelihood, prior, label='label', outdir='outdir',
sampler='nestle', **sampler_kwargs):
if hasattr(peyote.sampler, sampler.title()):
- SamplerClass = getattr(peyote.sampler, sampler.title())
- sampler = SamplerClass(likelihood, prior, sampler, outdir=outdir,
- label=label, **sampler_kwargs)
+ sampler_class = getattr(peyote.sampler, sampler.title())
+ sampler = sampler_class(likelihood, prior, sampler, outdir=outdir,
+ label=label, **sampler_kwargs)
result = sampler.run_sampler()
result.save_to_file(outdir=outdir, label=label)
return result
else:
raise ValueError(
"Sampler {} not yet implemented".format(sampler))
-
diff --git a/peyote/source.py b/peyote/source.py
index 492734d1368bf972c07d152a30fdfea593ab9760..dfddbad8e07e89eedd89ead065a48987be4a94e7 100644
--- a/peyote/source.py
+++ b/peyote/source.py
@@ -1,15 +1,13 @@
from __future__ import division, print_function
-import peyote
-import numpy as np
-import os.path
+
+import os
+
+import lal
+import lalsimulation as lalsim
from astropy.table import Table
-from peyote.utils import sampling_frequency, nfft
-try:
- import lal
- import lalsimulation as lalsim
-except ImportError:
- print("lal is not installed")
+import peyote
+from peyote.utils import sampling_frequency, nfft
class Source:
@@ -49,41 +47,59 @@ class BinaryBlackHole(Source):
"""
A way of getting a BBH waveform from lal
"""
- parameter_keys = ['mass_1', 'mass_2', 'luminosity_distance', 'spin_1',
- 'spin_1', 'iota', 'phase', 'waveform_approximant',
- 'reference_frequency', 'ra', 'dec', 'geocent_time',
- 'psi']
- def frequency_domain_strain(self, parameters):
- luminosity_distance = parameters['luminosity_distance'] * 1e6 * lal.PC_SI
- mass_1 = parameters['mass_1'] * lal.MSUN_SI
- mass_2 = parameters['mass_2'] * lal.MSUN_SI
+ def __init__(self, name, sampling_frequency, time_duration, mass_1, mass_2, luminosity_distance, spin_1, spin_2,
+ iota, phase, waveform_approximant, reference_frequency, ra, dec, geocent_time, psi):
+ Source.__init__(self, name, sampling_frequency, time_duration)
+ self.mass_1 = mass_1
+ self.mass_2 = mass_2
+ self.luminosity_distance = luminosity_distance
+ self.spin_1 = spin_1
+ self.spin_2 = spin_2
+ self.iota = iota
+ self.phase = phase
+ self.waveform_approximant = waveform_approximant
+ self.reference_frequency = reference_frequency
+ self.ra = ra
+ self.dec = dec
+ self.geocent_time = geocent_time
+ self.psi = psi
+
+ def copy(self):
+ copy = BinaryBlackHole(self.name, self.sampling_frequency, self.time_duration, self.mass_1, self.mass_2,
+ self.luminosity_distance, self.spin_1, self.spin_2, self.iota, self.phase,
+ self.waveform_approximant, self.reference_frequency, self.ra, self.dec,
+ self.geocent_time, self.psi)
+ return copy
+
+ def frequency_domain_strain(self):
+ luminosity_distance = self.luminosity_distance * 1e6 * lal.PC_SI
+ mass_1 = self.mass_1 * lal.MSUN_SI
+ mass_2 = self.mass_2 * lal.MSUN_SI
longitude_ascending_nodes = 0.0
eccentricity = 0.0
- meanPerAno = 0.0
+ mean_per_ano = 0.0
waveform_dictionary = lal.CreateDict()
- approximant = lalsim.GetApproximantFromString(
- parameters['waveform_approximant'])
+ approximant = lalsim.GetApproximantFromString(self.waveform_approximant)
frequency_minimum = 20
frequency_maximum = self.frequency_array[-1]
delta_frequency = self.frequency_array[1] - self.frequency_array[0]
- hplus, hcross = lalsim.SimInspiralChooseFDWaveform(
- mass_1, mass_2, parameters['spin_1'][0], parameters['spin_1'][1],
- parameters['spin_1'][2], parameters['spin_2'][0],
- parameters['spin_2'][1], parameters['spin_2'][2],
- luminosity_distance, parameters['iota'],
- parameters['phase'], longitude_ascending_nodes,
- eccentricity, meanPerAno, delta_frequency, frequency_minimum,
- frequency_maximum, parameters['reference_frequency'],
+ h_plus, h_cross = lalsim.SimInspiralChooseFDWaveform(
+ mass_1, mass_2, self.spin_1[0], self.spin_1[1], self.spin_1[2],
+ self.spin_2[0], self.spin_2[1], self.spin_2[2],
+ luminosity_distance, self.iota,
+ self.phase, longitude_ascending_nodes,
+ eccentricity, mean_per_ano, delta_frequency, frequency_minimum,
+ frequency_maximum, self.reference_frequency,
waveform_dictionary, approximant)
- h_plus = hplus.data.data
- h_cross = hcross.data.data
+ h_plus = h_plus.data.data
+ h_cross = h_cross.data.data
return {'plus': h_plus, 'cross': h_cross}
@@ -122,15 +138,6 @@ class Supernova(AstrophysicalSource):
AstrophysicalSource.__init__(self, name, right_ascension, declination, luminosity_distance)
-# class BinaryBlackHole(CompactBinaryCoalescence):
-# def __init__(self, name, right_ascension, declination, luminosity_distance, mass_1, mass_2, spin_1, spin_2,
-# coalescence_time, inclination_angle, waveform_phase, polarisation_angle, eccentricity):
-# CompactBinaryCoalescence.__init__(self, name, right_ascension, declination, luminosity_distance, mass_1,
-# mass_2, spin_1, spin_2, coalescence_time, inclination_angle, waveform_phase,
-# polarisation_angle, eccentricity)
-
-
-
class BinaryNeutronStar(CompactBinaryCoalescence):
def __init__(self, name, right_ascension, declination, luminosity_distance, mass_1, mass_2, spin_1, spin_2,
coalescence_time, inclination_angle, waveform_phase, polarisation_angle, eccentricity,
@@ -171,10 +178,10 @@ class BinaryNeutronStarMergerNumericalRelativity(Source):
full_filename = '{}/{}'.format(directory_path, file_name)
if not os.path.isfile(full_filename):
- print('{} does not exist'.format(full_filename)) # add exception
- return(-1)
- else: # ok file exists
+ print('{} does not exist'.format(full_filename)) # add exception
+ return (-1)
+ else: # ok file exists
strain_table = Table.read(full_filename)
Hplus, _ = nfft(strain_table["hplus"], sampling_frequency(strain_table['time']))
Hcross, frequency = nfft(strain_table["hcross"], sampling_frequency(strain_table['time']))
- return(strain_table['time'],strain_table["hplus"],strain_table["hcross"],frequency,Hplus,Hcross)
+ return (strain_table['time'], strain_table["hplus"], strain_table["hcross"], frequency, Hplus, Hcross)
diff --git a/requirements.txt b/requirements.txt
index e388207bf87985fb01086b86ec97232dfbbc9528..c4de1e3edc475b556f1110f7d081a932c1ee028b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,5 @@
+dynesty
+corner
numpy
matplotlib>=1.4
scipy
diff --git a/tests.py b/tests.py
index 83e9d59a17684c8a7a3d13c9614eb084b1ecebaf..fa5d9ca5a9f01f314673513638f60f8c2ff1ac67 100644
--- a/tests.py
+++ b/tests.py
@@ -45,12 +45,12 @@ class Test(unittest.TestCase):
self.assertAlmostEqual(all(self.msd['hf_signal_and_noise'] - hf_signal_and_noise_saved), 0.00000000, 5)
def test_recover_luminosity_distance(self):
- likelihood = peyote.likelihood.likelihood(
+ likelihood = peyote.likelihood.Likelihood(
[self.msd['IFO']], self.msd['source'])
- prior = self.msd['simulation_parameters'].copy()
- dL = self.msd['simulation_parameters']['luminosity_distance']
- prior['luminosity_distance'] = peyote.parameter.Parameter(
+ prior = self.msd['source'].copy()
+ dL = self.msd['source'].luminosity_distance
+ prior.luminosity_distance = peyote.parameter.Parameter(
'luminosity_distance',
prior=peyote.prior.Uniform(lower=dL-10, upper=dL+10))
diff --git a/tutorials/BasicTutorial.py b/tutorials/BasicTutorial.py
index 2c2a7dda5459d830db633f1714fc33a64ab008f0..9dfdf612657205af696ce566bf4f6f3e5d73803e 100644
--- a/tutorials/BasicTutorial.py
+++ b/tutorials/BasicTutorial.py
@@ -1,39 +1,34 @@
import numpy as np
import pylab as plt
+import dynesty.plotting as dyplot
+import corner
import peyote
import dynesty.plotting as dyplot
import corner
-peyote.setup_logger()
+# peyote.setup_logging()
time_duration = 1.
sampling_frequency = 4096.
-simulation_parameters = dict(
- mass_1=36., mass_2=29.,
- spin_1=[0, 0, 0],
- spin_2=[0, 0, 0],
- luminosity_distance=100.,
- iota=0.4, #np.pi/2,
- phase=1.3,
- waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.,
- ra=1.375,
- dec=-1.2108,
- geocent_time=1126259642.413,
- psi=2.659
- )
-
-source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration)
-hf_signal = source.frequency_domain_strain(simulation_parameters)
+
+
+source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration, mass_1=36., mass_2=29.,
+ spin_1=[0, 0, 0], spin_2=[0, 0, 0], luminosity_distance=100., iota=0.4,
+ phase=1.3, waveform_approximant='IMRPhenomPv2', reference_frequency=50.,
+ ra=1.375, dec=-1.2108, geocent_time=1126259642.413, psi=2.659)
+
+# source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration)
+hf_signal = source.frequency_domain_strain()
+
# Simulate the data in H1
H1 = peyote.detector.H1
H1_hf_noise, frequencies = H1.power_spectral_density.get_noise_realisation(
sampling_frequency, time_duration)
H1.set_data(sampling_frequency, time_duration, frequency_domain_strain=H1_hf_noise)
-H1.inject_signal(source, simulation_parameters)
+H1.inject_signal(source)
H1.set_spectral_densities()
# Simulate the data in L1
@@ -41,7 +36,7 @@ L1 = peyote.detector.L1
L1_hf_noise, frequencies = L1.power_spectral_density.get_noise_realisation(
sampling_frequency, time_duration)
L1.set_data(sampling_frequency, time_duration, frequency_domain_strain=L1_hf_noise)
-L1.inject_signal(source, simulation_parameters)
+L1.inject_signal(source)
L1.set_spectral_densities()
IFOs = [H1, L1]
@@ -57,32 +52,26 @@ plt.xlabel(r'frequency')
plt.ylabel(r'strain')
fig.savefig('data')
-likelihood = peyote.likelihood.likelihood(IFOs, source)
+likelihood = peyote.likelihood.Likelihood(IFOs, source)
-prior = simulation_parameters.copy()
-prior['mass_1'] = peyote.parameter.Parameter(
+prior = source.copy()
+prior.mass_1 = peyote.parameter.Parameter(
'mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
latex_label='$m_1$')
-#prior['mass_2'] = peyote.parameter.Parameter(
-# 'mass_2', prior=peyote.prior.Uniform(lower=27, upper=31),
-# latex_label='$m_2$')
-#prior['iota'] = peyote.parameter.Parameter(
+# prior.mass_2 = peyote.parameter.Parameter(
+# 'mass_2', prior=peyote.prior.Uniform(lower=27, upper=31),
+# latex_label='$m_2$')
+#prior.iota = peyote.parameter.Parameter(
# 'iota', prior=peyote.prior.Uniform(lower=0, upper=np.pi),
# latex_label='$iota$')
-#prior['phase'] = peyote.parameter.Parameter(
-# 'phase', prior=peyote.prior.Uniform(lower=0, upper=np.pi))
-#prior['geocent_time'] = peyote.parameter.Parameter(
-# 'geocent_time', prior=peyote.prior.Uniform(
-# lower=simulation_parameters['geocent_time']-100,
-# upper=simulation_parameters['geocent_time']+100))
-prior['luminosity_distance'] = peyote.parameter.Parameter(
+prior.luminosity_distance = peyote.parameter.Parameter(
'luminosity_distance', prior=peyote.prior.Uniform(lower=30, upper=200),
latex_label='$d_L$')
result = peyote.run_sampler(likelihood, prior, sampler='nestle',
n_live_points=200, verbose=True)
-truths = [simulation_parameters[x] for x in result.search_parameter_keys]
+truths = [source.__dict__[x] for x in result.search_parameter_keys]
fig = corner.corner(result.samples, truths=truths, labels=result.search_parameter_keys)
fig.savefig('corner')
diff --git a/tutorials/GW150914.py b/tutorials/GW150914.py
index 8936d11d758ba8d7fb379c7716085fff067df5d9..35ec7b419c90ee4d3583222a0776f0587cb706e3 100644
--- a/tutorials/GW150914.py
+++ b/tutorials/GW150914.py
@@ -1,4 +1,3 @@
-
# coding: utf-8
# # GW150914 analysis
@@ -15,12 +14,12 @@ import peyote
import corner
import logging
+
logging.getLogger().addHandler(logging.StreamHandler())
logging.getLogger().setLevel('DEBUG')
import matplotlib.mlab as mlab
-
data_file = 'tutorial_data/GW150914_strain_data.npy'
time_series, strain_H1, strain_L1 = np.load(data_file)
time_duration = time_series[-1] - time_series[0]
@@ -29,9 +28,8 @@ time_of_event = 1126259462.44
sampling_frequency = np.int(peyote.utils.sampling_frequency(time_series))
NFFT = 4 * sampling_frequency
-power_spectral_density_H1, frequency_series = mlab.psd(strain_H1, Fs = sampling_frequency, NFFT = NFFT)
-power_spectral_density_L1, frequency_series = mlab.psd(strain_L1, Fs = sampling_frequency, NFFT = NFFT)
-
+power_spectral_density_H1, frequency_series = mlab.psd(strain_H1, Fs=sampling_frequency, NFFT=NFFT)
+power_spectral_density_L1, frequency_series = mlab.psd(strain_L1, Fs=sampling_frequency, NFFT=NFFT)
with open('150914_PSD_H1.txt', 'w+') as file:
for f, p in zip(frequency_series, power_spectral_density_H1):
@@ -40,7 +38,6 @@ with open('150914_PSD_L1.txt', 'w+') as file:
for f, p in zip(frequency_series, power_spectral_density_L1):
file.write('{} {}\n'.format(f, p))
-
search_idxs = (time_series > time_of_event - 0.5) * (time_series < time_of_event + 0.5)
time_series = time_series[search_idxs]
strain_H1 = strain_H1[search_idxs]
@@ -58,27 +55,19 @@ L1.set_data(sampling_frequency, time_duration,
IFOs = [H1, L1]
-source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration)
-likelihood = peyote.likelihood.likelihood(IFOs, source)
-
-prior = dict(
- spin_1 = [0, 0, 0],
- spin_2 = [0, 0, 0],
- luminosity_distance = 410.,
- iota = 2.97305,
- phase = 1.145,
- waveform_approximant = 'IMRPhenomPv2',
- reference_frequency = 50.,
- ra = 1.375,
- dec = -1.2108,
- geocent_time = 1126259642.413,
- psi=2.659
- )
-
-prior['mass_1'] = peyote.parameter.Parameter(
+source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration, spin_1=[0, 0, 0], spin_2=[0, 0, 0],
+ luminosity_distance=410., iota=2.97305, phase=1.145,
+ waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375,
+ dec=-1.2108, geocent_time=1126259642.413, psi=2.659, mass_1=32, mass_2=32)
+# ignore the fact that I hardcoded in some masses
+likelihood = peyote.likelihood.Likelihood(IFOs, source)
+
+prior = source.copy()
+
+prior.mass_1 = peyote.parameter.Parameter(
'mass_1', prior=peyote.prior.Uniform(lower=32, upper=41),
latex_label='$m_1$')
-prior['mass_2'] = peyote.parameter.Parameter(
+prior.mass_2 = peyote.parameter.Parameter(
'mass_2', prior=peyote.prior.Uniform(lower=25, upper=33),
latex_label='$m_2$')
diff --git a/tutorials/Injection.py b/tutorials/Injection.py
index 5a12fe42d3aac7510d845b090805f5ee7c76c8ad..6a0d9f0e5c9886092199488718ca156652e91ee6 100644
--- a/tutorials/Injection.py
+++ b/tutorials/Injection.py
@@ -2,32 +2,18 @@
Tutorial to show signal injection using new features of detector.py
"""
from __future__ import print_function, division
-import numpy as np
-import matplotlib.pyplot as plt
-import peyote
import corner
+import peyote
+
time_duration = 1.
sampling_frequency = 4096.
-simulation_parameters = dict(
- mass_1=36.,
- mass_2=29.,
- spin_1=[0, 0, 0],
- spin_2=[0, 0, 0],
- luminosity_distance=5000.,
- iota=0.,
- phase=0.,
- waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.,
- ra=0,
- dec=1,
- geocent_time=0,
- psi=1
- )
-
-source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration)
+source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration, mass_1=36., mass_2=29.,
+ spin_1=[0, 0, 0], spin_2=[0, 0, 0], luminosity_distance=5000., iota=0., phase=0.,
+ waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=0, dec=1,
+ geocent_time=0, psi=1)
IFO_1 = peyote.detector.H1
IFO_2 = peyote.detector.L1
@@ -35,35 +21,19 @@ IFO_3 = peyote.detector.V1
IFOs = [IFO_1, IFO_2, IFO_3]
for IFO in IFOs:
IFO.set_data(from_power_spectral_density=True, sampling_frequency=sampling_frequency, duration=time_duration)
- IFO.inject_signal(source, simulation_parameters)
-
-# ff = peyote.utils.create_fequency_series(sampling_frequency, time_duration)
-# for IFO in IFOs:
-# plt.loglog(ff, np.abs(IFO.data), label=IFO.name)
-#
-# plt.xlim(10, 1e3)
-# plt.ylim(1e-30, 1e-18)
-#
-# plt.xlabel(r'frequency [Hz]')
-# plt.legend(loc='best')
-#
-# plt.tight_layout()
-# plt.show()
-# plt.close()
+ IFO.inject_signal(source)
-likelihood = peyote.likelihood.likelihoodB(source=source, interferometers=IFOs)
-prior = simulation_parameters.copy()
-prior['mass_1'] = peyote.parameter.Parameter('mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
- latex_label='$m_1$')
-# prior['mass_2'] = peyote.parameter.Parameter('mass_2', prior=peyote.prior.Uniform(lower=28, upper=30),
-# latex_label='$m_2$')
+likelihood = peyote.likelihood.LikelihoodB(source=source, interferometers=IFOs)
-result = peyote.sampler.run_sampler(likelihood, prior, sampler='nestle', npoints=100, print_progress=True)
+prior = source.copy()
+prior.mass_1 = peyote.parameter.Parameter('mass_1', prior=peyote.prior.Uniform(lower=35, upper=37),
+ latex_label='$m_1$')
+prior.mass_2 = peyote.parameter.Parameter('mass_2', prior=peyote.prior.Uniform(lower=28, upper=30),
+ latex_label='$m_2$')
-print(result.samples)
+result = peyote.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=100, print_progress=True)
-truths = [simulation_parameters[k] for k in result.parameter_keys]
+truths = [source.__dict__[x] for x in result.search_parameter_keys]
fig = corner.corner(result.samples, labels=result.labels, truths=truths)
-fig.show()
-
+fig.savefig('Injection Test')
diff --git a/tutorials/make_standard_data.py b/tutorials/make_standard_data.py
index 3d285ad20856c62db3dfc85946e1647d57f809cd..aeab6c071625ae53dd52560ff7ac3761f69824f2 100644
--- a/tutorials/make_standard_data.py
+++ b/tutorials/make_standard_data.py
@@ -1,34 +1,21 @@
import peyote
import os
import numpy as np
+
np.random.seed(10)
time_duration = 1.
sampling_frequency = 4096.
-simulation_parameters = dict(
- mass_1=36.,
- mass_2=29.,
- spin_1=[0, 0, 0],
- spin_2=[0, 0, 0],
- luminosity_distance=410.,
- iota=0.,
- phase=0.,
- waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.,
- ra=0,
- dec=1,
- geocent_time=0,
- psi=1
- )
-
-source = peyote.source.BinaryBlackHole(
- 'BBH', sampling_frequency, time_duration)
-hf_signal = source.frequency_domain_strain(simulation_parameters)
+source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration, mass_1=36., mass_2=29.,
+ spin_1=[0, 0, 0], spin_2=[0, 0, 0], luminosity_distance=410., iota=0., phase=0.,
+ waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=0, dec=1,
+ geocent_time=0, psi=1)
+hf_signal = source.frequency_domain_strain()
IFO = peyote.detector.H1
IFO.set_data(from_power_spectral_density=True, sampling_frequency=sampling_frequency, duration=time_duration)
-IFO.inject_signal(source, simulation_parameters)
+IFO.inject_signal(source)
hf_signal_and_noise = IFO.data
frequencies = peyote.utils.create_fequency_series(sampling_frequency=sampling_frequency, duration=time_duration)
diff --git a/tutorials/standard_data.txt b/tutorials/standard_data.txt
index 76e9cbc18eb3f247e35eb6e0af613046f92801df..8dbdad2ff6cec56f94302f5e00baf0dbf9c84b46 100644
--- a/tutorials/standard_data.txt
+++ b/tutorials/standard_data.txt
@@ -1,2050 +1,2050 @@
# frequency hf_real hf_imag
0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00
-1.000000000000000000e+00 9.933456595828239568e-21 2.098524393547731511e-20
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-3.000000000000000000e+00 -1.394460275010800684e-20 -2.971194975952633996e-21
-4.000000000000000000e+00 3.036479661330466611e-23 -2.182089083568393127e-21
-5.000000000000000000e+00 5.471668506597554423e-21 8.828848777034429263e-22
-6.000000000000000000e+00 -7.150569363003309757e-21 8.939960477942127316e-22
-7.000000000000000000e+00 2.226413574376279703e-21 -9.313102937858531695e-21
-8.000000000000000000e+00 1.240046321485982703e-21 4.467181605797375830e-21
-9.000000000000000000e+00 -5.511952834957475950e-23 -1.598129965822042759e-23
-1.000000000000000000e+01 -9.718950407385646147e-23 1.906543591049877074e-22
-1.100000000000000000e+01 1.511033652210179443e-22 -9.851257374399057077e-23
-1.200000000000000000e+01 -1.303651586721324933e-22 5.185711924136239927e-23
-1.300000000000000000e+01 -3.527396163214312426e-23 1.422686499115795385e-22
-1.400000000000000000e+01 -1.118521160203876922e-22 -5.516666499000578922e-23
-1.500000000000000000e+01 -9.632519643114311015e-23 6.087021176417338272e-23
-1.600000000000000000e+01 1.064495560707787644e-22 -2.959444555723925695e-23
-1.700000000000000000e+01 3.327601586295464127e-25 1.014752299020472595e-22
-1.800000000000000000e+01 -4.428290144554814452e-23 8.587707035909331481e-23
-1.900000000000000000e+01 1.471300654502695082e-24 8.575736510641451208e-23
-2.000000000000000000e+01 7.089936574531354660e-23 3.600925369544416307e-23
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