diff --git a/examples/injection_examples/australian_detector.py b/examples/injection_examples/australian_detector.py
index 29ad908bddebab0c38a185cf36aabd4fc360334e..9fa165e03e765c65f41e52ffa705bb3e13a7af48 100644
--- a/examples/injection_examples/australian_detector.py
+++ b/examples/injection_examples/australian_detector.py
@@ -9,9 +9,11 @@ from __future__ import division, print_function
import numpy as np
-import bilby, gwinc
+import bilby
+import gwinc
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 4.
sampling_frequency = 2048.
@@ -35,7 +37,7 @@ Aplus_psd = gwinc_noises['Total']
# The location of this detector is not defined in Bilby, so we need to add it
AusIFO = bilby.gw.detector.Interferometer(
power_spectral_density=bilby.gw.detector.PowerSpectralDensity(
- frequency_array=frequencies, psd_array=Aplus_psd),
+ frequency_array=frequencies, psd_array=Aplus_psd),
name='AusIFO', length=4,
minimum_frequency=min(frequencies), maximum_frequency=max(frequencies),
latitude=-31.34, longitude=115.91,
@@ -44,8 +46,9 @@ AusIFO = bilby.gw.detector.Interferometer(
# Set up two other detectors at Hanford and Livingston
interferometers = bilby.gw.detector.InterferometerList(['H1', 'L1'])
for interferometer in interferometers:
- interferometer.power_spectral_density = bilby.gw.detector.PowerSpectralDensity(
- frequency_array=frequencies, psd_array=Aplus_psd)
+ interferometer.power_spectral_density =\
+ bilby.gw.detector.PowerSpectralDensity(
+ frequency_array=frequencies, psd_array=Aplus_psd)
# append the Australian detector to the list of other detectors
interferometers.append(AusIFO)
@@ -54,9 +57,10 @@ interferometers.append(AusIFO)
# Inject a gravitational-wave signal into the network
# as we're using a three-detector network of A+, we inject a GW150914-like
# signal at 4 Gpc
-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,
- ra=1.375, dec=0.2108)
+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, ra=1.375, dec=0.2108)
# Fixed arguments passed into the source model
@@ -79,17 +83,19 @@ for interferometer in interferometers:
interferometer.inject_signal(
parameters=injection_parameters, waveform_generator=waveform_generator)
- ## plot the data for sanity
+ # plot the data for sanity
signal = interferometer.get_detector_response(
waveform_generator.frequency_domain_strain(), injection_parameters)
interferometer.plot_data(signal=signal, outdir=outdir, label=label)
# set up priors
priors = bilby.gw.prior.BBHPriorSet()
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'geocent_time', 'phase']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi',
+ 'geocent_time', 'phase']:
priors[key] = injection_parameters[key]
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
+# Initialise the likelihood by passing in the interferometer data (IFOs)
+# and the waveoform generator
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=interferometers, waveform_generator=waveform_generator,
time_marginalization=False, phase_marginalization=False,
diff --git a/examples/injection_examples/binary_neutron_star_example.py b/examples/injection_examples/binary_neutron_star_example.py
index 76cf0c1ec9d27f79099f2798df6847a963070ee1..f46eb0e6e0defe874917a0756fa0c2b95478e8b8 100644
--- a/examples/injection_examples/binary_neutron_star_example.py
+++ b/examples/injection_examples/binary_neutron_star_example.py
@@ -35,7 +35,7 @@ injection_parameters = dict(
# to inject the signal into. For the
# TaylorF2 waveform, we cut the signal close to the isco frequency
duration = 8
-sampling_frequency = 2*1570.
+sampling_frequency = 2 * 1570.
start_time = injection_parameters['geocent_time'] + 2 - duration
# Fixed arguments passed into the source model. The analysis starts at 40 Hz.
@@ -94,4 +94,3 @@ result = bilby.run_sampler(
conversion_function=bilby.gw.conversion.generate_all_bns_parameters)
result.plot_corner()
-
diff --git a/examples/injection_examples/calibration_example.py b/examples/injection_examples/calibration_example.py
index 9f2052ffb55d019aa1509ae251aa6f57d4bc9c14..ec3f3491e9ad61fda38512557d91a5345cf68f2d 100644
--- a/examples/injection_examples/calibration_example.py
+++ b/examples/injection_examples/calibration_example.py
@@ -39,7 +39,7 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameters=injection_parameters,waveform_arguments=waveform_arguments)
+ parameters=injection_parameters, waveform_arguments=waveform_arguments)
# Set up interferometers. In this case we'll use three interferometers
# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)).
@@ -83,4 +83,3 @@ result = bilby.run_sampler(
# make some plots of the outputs
result.plot_corner()
-
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index f629b2c91e9350dfc4a36434415935a45a501701..06dfa174033d12e53b02bb0f033eceeb64609da7 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -29,22 +29,25 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50.)
# Create the waveform_generator using a LAL BinaryBlackHole source function
+# We specify a function which transforms a dictionary of parameters into the
+# appropriate parameters for the source model.
waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
sampling_frequency=sampling_frequency, duration=duration,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameter_conversion=
- bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
+ parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
waveform_arguments=waveform_arguments)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1', 'K1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
# Set up prior
+# Note it is possible to sample in different parameters to those that were
+# injected.
priors = bilby.gw.prior.BBHPriorSet()
priors.pop('mass_1')
priors.pop('mass_2')
@@ -69,9 +72,10 @@ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
+# Note we've added a post-processing conversion function, this will generate
+# many useful additional parameters, e.g., source-frame masses.
result = bilby.core.sampler.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
injection_parameters=injection_parameters, label='DifferentParameters',
conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
-
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 254fdfc52f4dd76aed5df74fbb9b31c79b134787..ded59e157ee098bd7c765dae01e0b5b55790d408 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -16,19 +16,20 @@ duration = 1
# Here we define out source model - this is the sine-Gaussian model in the
# frequency domain.
def sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
- arg = -(np.pi * tau * (f-f0))**2 + 1j * phi0
+ arg = -(np.pi * tau * (f - f0))**2 + 1j * phi0
plus = np.sqrt(np.pi) * A * tau * np.exp(arg) / 2.
- cross = plus * np.exp(1j*np.pi/2)
+ cross = plus * np.exp(1j * np.pi / 2)
return {'plus': plus, 'cross': cross}
-# We now define some parameters that we will inject and then a waveform generator
+# We now define some parameters that we will inject
injection_parameters = dict(A=1e-23, f0=100, tau=1, phi0=0, geocent_time=0,
ra=0, dec=0, psi=0)
-waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(duration=duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=sine_gaussian,
- parameters=injection_parameters)
+
+# Now we pass our source function to the WaveformGenerator
+waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=sine_gaussian)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -41,13 +42,13 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Here we define the priors for the search. We use the injection parameters
# except for the amplitude, f0, and geocent_time
prior = injection_parameters.copy()
-prior['A'] = bilby.core.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
+prior['A'] = bilby.core.prior.LogUniform(minimum=1e-25, maximum=1e-21, name='A')
prior['f0'] = bilby.core.prior.Uniform(90, 110, 'f')
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(ifos, waveform_generator)
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
result = bilby.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
resume=False, sample='unif', injection_parameters=injection_parameters)
result.plot_corner()
-
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index 5ea57fb58897f5e0cace5f9a9ad22c40c0ba3afb..82ca24d187775fb8213910498fa2dddd940b24e7 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -1,41 +1,41 @@
#!/usr/bin/env python
"""
A script to show how to create your own time domain source model.
-A simple damped Gaussian signal is defined in the time domain, injected into noise in
-two interferometers (LIGO Livingston and Hanford at design sensitivity),
-and then recovered.
+A simple damped Gaussian signal is defined in the time domain, injected into
+noise in two interferometers (LIGO Livingston and Hanford at design
+sensitivity), and then recovered.
"""
-import bilby
import numpy as np
-
+import bilby
# define the time-domain model
-def time_domain_damped_sinusoid(time, amplitude, damping_time, frequency, phase, ra, dec, psi, geocent_time):
+def time_domain_damped_sinusoid(
+ time, amplitude, damping_time, frequency, phase):
"""
- This example only creates a linearly polarised signal with only plus polarisation.
+ This example only creates a linearly polarised signal with only plus
+ polarisation.
"""
-
- plus = amplitude * np.exp(-time / damping_time) * np.sin(2.*np.pi*frequency*time + phase)
+ plus = amplitude * np.exp(-time / damping_time) *\
+ np.sin(2 * np.pi * frequency * time + phase)
cross = np.zeros(len(time))
-
return {'plus': plus, 'cross': cross}
+
# define parameters to inject.
injection_parameters = dict(amplitude=5e-22, damping_time=0.1, frequency=50,
- phase=0,
- ra=0, dec=0, psi=0, geocent_time=0.)
+ phase=0, ra=0, dec=0, psi=0, geocent_time=0.)
duration = 0.5
sampling_frequency = 2048
-outdir='outdir'
-label='time_domain_source_model'
+outdir = 'outdir'
+label = 'time_domain_source_model'
# call the waveform_generator to create our waveform model.
-waveform = bilby.gw.waveform_generator.WaveformGenerator(duration=duration, sampling_frequency=sampling_frequency,
- time_domain_source_model=time_domain_damped_sinusoid,
- parameters=injection_parameters)
+waveform = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ time_domain_source_model=time_domain_damped_sinusoid)
# inject the signal into three interferometers
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -47,7 +47,7 @@ ifos.inject_signal(waveform_generator=waveform,
# create the priors
prior = injection_parameters.copy()
-prior['amplitude'] = bilby.core.prior.Uniform(1e-23, 1e-21, r'$h_0$')
+prior['amplitude'] = bilby.core.prior.LogUniform(1e-23, 1e-21, r'$h_0$')
prior['damping_time'] = bilby.core.prior.Uniform(
0, 1, r'damping time', unit='$s$')
prior['frequency'] = bilby.core.prior.Uniform(0, 200, r'frequency', unit='Hz')
@@ -57,9 +57,8 @@ prior['phase'] = bilby.core.prior.Uniform(-np.pi / 2, np.pi / 2, r'$\phi$')
likelihood = bilby.gw.likelihood.GravitationalWaveTransient(ifos, waveform)
# launch sampler
-result = bilby.core.sampler.run_sampler(likelihood, prior, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+result = bilby.core.sampler.run_sampler(
+ likelihood, prior, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/injection_examples/eccentric_inspiral.py b/examples/injection_examples/eccentric_inspiral.py
index 75ff93b572e85f9e2abfae62b341057d9c51b2f3..2709220fc7b07e91af9597bff6872178447c7a43 100644
--- a/examples/injection_examples/eccentric_inspiral.py
+++ b/examples/injection_examples/eccentric_inspiral.py
@@ -1,8 +1,8 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation on a reduced parameter space
-for an injected eccentric binary black hole signal with masses & distnace similar
-to GW150914.
+Tutorial to demonstrate running parameter estimation on a reduced parameter
+space for an injected eccentric binary black hole signal with masses & distnace
+similar to GW150914.
This uses the same binary parameters that were used to make Figures 1, 2 & 5 in
Lower et al. (2018) -> arXiv:1806.05350.
@@ -10,14 +10,11 @@ Lower et al. (2018) -> arXiv:1806.05350.
For a more comprehensive look at what goes on in each step, refer to the
"basic_tutorial.py" example.
"""
-from __future__ import division, print_function
+from __future__ import division
import numpy as np
-
import bilby
-import matplotlib.pyplot as plt
-
duration = 64.
sampling_frequency = 256.
@@ -28,13 +25,15 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility.
np.random.seed(150914)
-injection_parameters = dict(mass_1=35., mass_2=30., eccentricity=0.1,
- luminosity_distance=440., iota=0.4, psi=0.1, phase=1.2,
- geocent_time=1180002601.0, ra=45, dec=5.73)
+injection_parameters = dict(
+ mass_1=35., mass_2=30., eccentricity=0.1, luminosity_distance=440.,
+ iota=0.4, psi=0.1, phase=1.2, geocent_time=1180002601.0, ra=45, dec=5.73)
-waveform_arguments = dict(waveform_approximant='EccentricFD', reference_frequency=10., minimum_frequency=10.)
+waveform_arguments = dict(waveform_approximant='EccentricFD',
+ reference_frequency=10., minimum_frequency=10.)
-# Create the waveform_generator using the LAL eccentric black hole no spins source function
+# Create the waveform_generator using the LAL eccentric black hole no spins
+# source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_eccentric_binary_black_hole_no_spins,
@@ -43,8 +42,8 @@ waveform_generator = bilby.gw.WaveformGenerator(
# Setting up three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1), and
# Virgo (V1)) at their design sensitivities. The maximum frequency is set just
-# prior to the point at which the waveform model terminates. This is to avoid any
-# biases introduced from using a sharply terminating waveform model.
+# prior to the point at which the waveform model terminates. This is to avoid
+# any biases introduced from using a sharply terminating waveform model.
minimum_frequency = 10.
maximum_frequency = 128.
@@ -59,13 +58,13 @@ ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
# Now we set up the priors on each of the binary parameters.
-priors = dict()
+priors = bilby.core.prior.PriorSet()
priors["mass_1"] = bilby.core.prior.Uniform(
name='mass_1', minimum=5, maximum=60, unit='$M_{\\odot}$')
priors["mass_2"] = bilby.core.prior.Uniform(
name='mass_2', minimum=5, maximum=60, unit='$M_{\\odot}$')
-priors["eccentricity"] = bilby.core.prior.PowerLaw(
- name='eccentricity', latex_label='$e$', alpha=-1, minimum=1e-4, maximum=0.4)
+priors["eccentricity"] = bilby.core.prior.LogUniform(
+ name='eccentricity', latex_label='$e$', minimum=1e-4, maximum=0.4)
priors["luminosity_distance"] = bilby.gw.prior.UniformComovingVolume(
name='luminosity_distance', minimum=1e2, maximum=2e3)
priors["dec"] = bilby.core.prior.Cosine(name='dec')
@@ -79,16 +78,13 @@ priors["geocent_time"] = bilby.core.prior.Uniform(
1180002600.9, 1180002601.1, name='geocent_time', unit='s')
# Initialising the likelihood function.
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(interferometers=ifos,
- waveform_generator=waveform_generator, time_marginalization=False,
- phase_marginalization=False, distance_marginalization=False,
- prior=priors)
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Now we run sampler (PyMultiNest in our case).
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='pymultinest',
- npoints=1000, injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='pymultinest', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
# And finally we make some plots of the output posteriors.
result.plot_corner()
-
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index b92288d90237f57fceb84b48704ed296e67bf9bf..6a5eb033729cb1d3cb7f442c594084e8decd27f9 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -1,12 +1,12 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate how to specify the prior distributions used for parameter estimation.
+Tutorial to demonstrate how to specify the prior distributions used for
+parameter estimation.
"""
from __future__ import division, print_function
-import bilby
-import numpy as np
-import bilby.gw.prior
+import numpy as np
+import bilby
duration = 4.
@@ -15,18 +15,19 @@ outdir = 'outdir'
np.random.seed(151012)
-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,
- ra=1.375, dec=-1.2108)
+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, ra=1.375, dec=-1.2108)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
- reference_frequency=50.)
+ reference_frequency=50., minimum_frequency=20.)
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameters=injection_parameters, waveform_arguments=waveform_arguments)
+ waveform_arguments=waveform_arguments)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
@@ -58,14 +59,16 @@ priors['a_2'] = bilby.core.prior.Interped(
name='a_2', xx=a_2, yy=p_a_2, minimum=0, maximum=0.5)
# Additionally, we have Gaussian, TruncatedGaussian, Sine and Cosine.
# It's also possible to load an interpolate a prior from a file.
-# Finally, if you don't specify any necessary parameters it will be filled in from the default when the sampler starts.
+# Finally, if you don't specify any necessary parameters it will be filled in
+# from the default when the sampler starts.
# Enjoy.
# Initialise GravitationalWaveTransient
-likelihood = bilby.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
+likelihood = bilby.gw.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='specify_prior')
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
+ injection_parameters=injection_parameters, label='specify_prior')
result.plot_corner()
-
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 83259d627e80909796d646be386ced0fbd2e2e99..05c1def463d2ddad61bbff15997e49c50f0233ec 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate how to improve the speed and efficiency of parameter estimation on an injected signal using
-phase and distance marginalisation.
+Tutorial to demonstrate how to improve the speed and efficiency of parameter
+estimation on an injected signal using time, phase and distance marginalisation.
"""
from __future__ import division, print_function
import bilby
@@ -11,12 +11,14 @@ import numpy as np
duration = 4.
sampling_frequency = 2048.
outdir = 'outdir'
+label = 'marginalized_likelihood'
np.random.seed(170608)
-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,
- ra=1.375, dec=-1.2108)
+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, ra=1.375, dec=-1.2108)
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
reference_frequency=50.)
@@ -24,7 +26,7 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
# Create the waveform_generator using a LAL BinaryBlackHole source function
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
- frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, parameters=injection_parameters,
+ frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
waveform_arguments=waveform_arguments)
# Set up interferometers.
@@ -38,19 +40,21 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Set up prior
priors = bilby.gw.prior.BBHPriorSet()
# These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra', 'dec', 'geocent_time']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
+ 'dec']:
priors[key] = injection_parameters[key]
# Initialise GravitationalWaveTransient
-# Note that we now need to pass the: priors and flags for each thing that's being marginalised.
-# This is still under development so care should be taken with the marginalised likelihood.
+# Note that we now need to pass the: priors and flags for each thing that's
+# being marginalised. A lookup table is used fro distance marginalisation which
+# takes a few minutes to build.
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator, prior=priors,
- distance_marginalization=False, phase_marginalization=True,
- time_marginalization=False)
+ distance_marginalization=True, phase_marginalization=True,
+ time_marginalization=True)
# Run sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
- injection_parameters=injection_parameters, outdir=outdir, label='MarginalisedLikelihood')
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty',
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/injection_examples/non_tensor.py b/examples/injection_examples/non_tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..c75fcf0dbcaad9eb589e700f8baccdb393818a89
--- /dev/null
+++ b/examples/injection_examples/non_tensor.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+"""
+A demonstration of a simple model with more than just the two polarization modes
+allowed in general relativity.
+
+We adapt the sine-Gaussian burst model to include vector polarizations with an
+unknown contribution from the vector modes.
+"""
+from __future__ import division, print_function
+import bilby
+import numpy as np
+
+
+def vector_tensor_sine_gaussian(frequency_array, hrss, Q, frequency, epsilon):
+ """
+ Vector-Tensor sine-Gaussian burst model
+
+ This is just like the bilby.gw.source.sinegaussian function but adds a
+ vector-polarized component.
+
+ Parameters
+ ----------
+ frequency_array: array-like
+ Frequency array on which to calculate the waveform.
+ hrss: float
+ Q: float
+ frequency: float
+ epsilon: float
+ Relative size of the vector modes compared to the tensor modes.
+ """
+ waveform_polarizations = bilby.gw.source.sinegaussian(
+ frequency_array, hrss, Q, frequency, 0, 0, 0, 0)
+
+ waveform_polarizations['x'] = epsilon * waveform_polarizations['plus']
+ waveform_polarizations['y'] = epsilon * waveform_polarizations['cross']
+ return waveform_polarizations
+
+
+duration = 4.
+sampling_frequency = 2048.
+
+outdir = 'outdir'
+label = 'vector_tensor'
+bilby.core.utils.setup_logger(outdir=outdir, label=label)
+
+np.random.seed(170801)
+
+injection_parameters = dict(
+ hrss=1e-22, Q=5.0, frequency=200.0, ra=1.375, dec=-1.2108,
+ geocent_time=1126259642.413, psi=2.659, epsilon=0.2)
+
+waveform_generator =\
+ bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=vector_tensor_sine_gaussian)
+
+ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
+ifos.set_strain_data_from_power_spectral_densities(
+ sampling_frequency=sampling_frequency, duration=duration,
+ start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+ parameters=injection_parameters)
+
+priors = dict()
+for key in ['psi', 'geocent_time', 'hrss', 'Q', 'frequency']:
+ priors[key] = injection_parameters[key]
+priors['ra'] = bilby.core.prior.Uniform(0, 2 * np.pi, latex_label='$\\alpha$')
+priors['dec'] = bilby.core.prior.Cosine(latex_label='$\\delta$')
+priors['epsilon'] = bilby.core.prior.Uniform(0, 1, latex_label='$\\epsilon$')
+
+vector_tensor_likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
+
+# Run sampler. In this case we're going to use the `dynesty` sampler
+vector_tensor_result = bilby.core.sampler.run_sampler(
+ likelihood=vector_tensor_likelihood, priors=priors, sampler='nestle',
+ npoints=1000, injection_parameters=injection_parameters,
+ outdir=outdir, label='vector_tensor')
+
+vector_tensor_result.plot_corner()
+
+tensor_likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
+
+priors['epsilon'] = 0
+
+# Run sampler. In this case we're going to use the `dynesty` sampler
+tensor_result = bilby.core.sampler.run_sampler(
+ likelihood=tensor_likelihood, priors=priors, sampler='nestle', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label='tensor')
+
+# make some plots of the outputs
+tensor_result.plot_corner()
+
+bilby.result.plot_multiple(
+ [tensor_result, vector_tensor_result], labels=['Tensor', 'Vector + Tensor'],
+ parameters=dict(ra=1.375, dec=-1.2108), evidences=True)
diff --git a/examples/injection_examples/sine_gaussian_example.py b/examples/injection_examples/sine_gaussian_example.py
index 4bbf7c6b0181c28212c26731acbff17539c29f28..07ac1ff0b2a12f1fe446e3f08e1fc85f70783c1f 100644
--- a/examples/injection_examples/sine_gaussian_example.py
+++ b/examples/injection_examples/sine_gaussian_example.py
@@ -1,13 +1,14 @@
#!/usr/bin/env python
"""
-Tutorial to demonstrate running parameter estimation on a sine gaussian injected signal.
-
+Tutorial to demonstrate running parameter estimation on a sine gaussian
+injected signal.
"""
from __future__ import division, print_function
import bilby
import numpy as np
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 4.
sampling_frequency = 2048.
@@ -19,61 +20,46 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(170801)
-# We are going to inject a sine gaussian waveform. We first establish a dictionary of parameters that
-# includes all of the different waveform parameters
-injection_parameters = dict(hrss = 1e-22, Q = 5.0, frequency = 200.0, ra = 1.375, dec = -1.2108,
- geocent_time = 1126259642.413, psi= 2.659)
+# We are going to inject a sine gaussian waveform. We first establish a
+# dictionary of parameters that includes all of the different waveform
+# parameters
+injection_parameters = dict(
+ hrss=1e-22, Q=5.0, frequency=200.0, ra=1.375, dec=-1.2108,
+ geocent_time=1126259642.413, psi=2.659)
# Create the waveform_generator using a sine Gaussian source function
-waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(duration=duration,
- sampling_frequency=sampling_frequency,
- frequency_domain_source_model=bilby.gw.source.sinegaussian,
- parameters=injection_parameters)
+waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
+ duration=duration, sampling_frequency=sampling_frequency,
+ frequency_domain_source_model=bilby.gw.source.sinegaussian)
-# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
-# and Virgo (V1)). These default to their design sensitivity
+# Set up interferometers. In this case we'll use three interferometers
+# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). These default to
+# their design sensitivity
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
# Set up prior, which is a dictionary
priors = dict()
-# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
-# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
-# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
-# not sample any parameter that has a delta-function prior.
for key in ['psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
-# The above list does *not* include frequency and Q, which means those are the parameters
-# that will be included in the sampler. If we do nothing, then the default priors get used.
-#priors['Q'] = bilby.prior.create_default_prior(name='Q')
-#priors['frequency'] = bilby.prior.create_default_prior(name='frequency')
priors['Q'] = bilby.core.prior.Uniform(2, 50, 'Q')
priors['frequency'] = bilby.core.prior.Uniform(30, 1000, 'frequency', unit='Hz')
priors['hrss'] = bilby.core.prior.Uniform(1e-23, 1e-21, 'hrss')
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = bilby.gw.likelihood.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator)
+# Initialise the likelihood by passing in the interferometer data (IFOs) and
+# the waveoform generator
+likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator)
# Run sampler. In this case we're going to use the `dynesty` sampler
-result = bilby.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters, outdir=outdir, label=label)
+result = bilby.core.sampler.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
# make some plots of the outputs
result.plot_corner()
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/examples/injection_examples/using_gwin.py b/examples/injection_examples/using_gwin.py
index a213c490c6a30bd51dda582baa1675b6c300f3c3..1539195e60cf0e68f1b3d565f35161e91fbce84b 100644
--- a/examples/injection_examples/using_gwin.py
+++ b/examples/injection_examples/using_gwin.py
@@ -48,7 +48,8 @@ fixed_parameters = injection_parameters.copy()
for key in priors:
fixed_parameters.pop(key)
-# These lines generate the `model` object - see https://gwin.readthedocs.io/en/latest/api/gwin.models.gaussian_noise.html
+# These lines generate the `model` object - see
+# https://gwin.readthedocs.io/en/latest/api/gwin.models.gaussian_noise.html
generator = FDomainDetFrameGenerator(
FDomainCBCGenerator, 0.,
variable_args=variable_parameters, detectors=['H1', 'L1'],
@@ -64,6 +65,7 @@ model.update(**injection_parameters)
# This create a dummy class to convert the model into a bilby.likelihood object
class GWINLikelihood(bilby.core.likelihood.Likelihood):
+
def __init__(self, model):
""" A likelihood to wrap around GWIN model objects
@@ -89,4 +91,3 @@ result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
label=label)
result.plot_corner()
-
diff --git a/examples/logo/sample_logo.py b/examples/logo/sample_logo.py
index b82733ebe78f7cfd582a3af0901f69144f728d4b..59153aeeffeecc1fb4780d0ffccf5535c69d02e6 100644
--- a/examples/logo/sample_logo.py
+++ b/examples/logo/sample_logo.py
@@ -11,11 +11,11 @@ class Likelihood(bilby.Likelihood):
self.parameters = dict(x=None, y=None)
def log_likelihood(self):
- return -1/(self.interp(self.parameters['x'], self.parameters['y'])[0])
+ return -1 / (self.interp(self.parameters['x'], self.parameters['y'])[0])
for letter in ['t', 'u', 'p', 'a', 'k']:
- img = 1-io.imread('{}.jpg'.format(letter), as_grey=True)[::-1, :]
+ img = 1 - io.imread('{}.jpg'.format(letter), as_grey=True)[::-1, :]
x = np.arange(img.shape[0])
y = np.arange(img.shape[1])
interp = si.interpolate.interp2d(x, y, img.T)
diff --git a/examples/open_data_examples/GW150914.py b/examples/open_data_examples/GW150914.py
index aa5ca44af19f6575feb8ed7e8b4e373b001bec0a..f44c9dd1cb4c82867f8880e612bf2cf0b1c711d2 100644
--- a/examples/open_data_examples/GW150914.py
+++ b/examples/open_data_examples/GW150914.py
@@ -51,4 +51,3 @@ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
result = bilby.run_sampler(likelihood, prior, sampler='dynesty',
outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/other_examples/add_multiple_results.py b/examples/other_examples/add_multiple_results.py
index f4c565555ad6250390622cd71fd1f433a7599c64..83f57b1a62ca15b16735bd6677e11e96a3bb260c 100644
--- a/examples/other_examples/add_multiple_results.py
+++ b/examples/other_examples/add_multiple_results.py
@@ -17,14 +17,14 @@ injection_parameters = dict(m=0.5, c=0.2)
sigma = 1
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
likelihood = bilby.core.likelihood.GaussianLikelihood(
time, data, model, sigma=sigma)
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 1, 'm')
priors['c'] = bilby.core.prior.Uniform(-2, 2, 'c')
@@ -40,5 +40,3 @@ resultA.plot_walkers()
result = resultA + resultB
result.plot_corner()
print(result)
-
-
diff --git a/examples/other_examples/gaussian_example.py b/examples/other_examples/gaussian_example.py
index bf7730286f12ddc70ac9bfe647e6e637fd4004ac..2d9e51938dbcc1c871f4d44ef46f3df12366cc5c 100644
--- a/examples/other_examples/gaussian_example.py
+++ b/examples/other_examples/gaussian_example.py
@@ -39,8 +39,8 @@ class SimpleGaussianLikelihood(bilby.Likelihood):
mu = self.parameters['mu']
sigma = self.parameters['sigma']
res = self.data - mu
- return -0.5 * (np.sum((res / sigma)**2)
- + self.N*np.log(2*np.pi*sigma**2))
+ return -0.5 * (np.sum((res / sigma)**2) +
+ self.N * np.log(2 * np.pi * sigma**2))
likelihood = SimpleGaussianLikelihood(data)
diff --git a/examples/other_examples/get_LOSC_event_data.py b/examples/other_examples/get_LOSC_event_data.py
index 6197426eb6e02869fb73e18b349d8dde92ca1fc5..9c7b314797480aa4f4f6df5b767144490a84ca19 100644
--- a/examples/other_examples/get_LOSC_event_data.py
+++ b/examples/other_examples/get_LOSC_event_data.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
""" Helper script to faciliate downloading data from LOSC
-Usage: To download the GW150914 data from https://losc.ligo.org/events/
+Usage: To download the GW150914 data from https://losc.ligo.org/events/
$ python get_LOSC_event_data -e GW150914
@@ -52,7 +52,7 @@ for det, in ['H', 'L']:
event, detector, sampling_frequency, starttime, duration))
os.remove(filename)
-time = np.arange(0, int(duration), 1/int(sampling_frequency)) + int(starttime)
+time = np.arange(0, int(duration), 1 / int(sampling_frequency)) + int(starttime)
arr = [time] + data
outfile = '{}/{}_strain_data.npy'.format(args.outdir, args.event)
diff --git a/examples/other_examples/hyper_parameter_example.py b/examples/other_examples/hyper_parameter_example.py
index 21ad6c24dcba6937ab79d3732dfbe7f198644290..796b8e057171b84bf6ad9853ae5df9a144074535 100644
--- a/examples/other_examples/hyper_parameter_example.py
+++ b/examples/other_examples/hyper_parameter_example.py
@@ -15,7 +15,7 @@ outdir = 'outdir'
# Define a model to fit to each data set
def model(x, c0, c1):
- return c0 + c1*x
+ return c0 + c1 * x
N = 10
@@ -64,8 +64,8 @@ fig2.savefig('outdir/hyper_parameter_combined_posteriors.png')
def hyper_prior(data, mu, sigma):
- return np.exp(- (data['c0'] - mu)**2 / (2 * sigma**2))\
- / (2 * np.pi * sigma**2)**0.5
+ return np.exp(- (data['c0'] - mu)**2 / (2 * sigma**2)) /\
+ (2 * np.pi * sigma**2)**0.5
def run_prior(data):
diff --git a/examples/other_examples/linear_regression_pymc3.py b/examples/other_examples/linear_regression_pymc3.py
index c10d5d3c93480254bc6ca2fa4d05ea128f0e2718..eb98be1edd7a7092dec74fd4b2eaa4dc32997bbe 100644
--- a/examples/other_examples/linear_regression_pymc3.py
+++ b/examples/other_examples/linear_regression_pymc3.py
@@ -9,7 +9,6 @@ from __future__ import division
import bilby
import numpy as np
import matplotlib.pyplot as plt
-import inspect
from bilby.core.likelihood import GaussianLikelihood
@@ -18,10 +17,12 @@ label = 'linear_regression_pymc3'
outdir = 'outdir'
bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
+
# First, we define our "signal model", in this case a simple linear function
def model(time, m, c):
return time * m + c
+
# Now we define the injection parameters which we make simulated data with
injection_parameters = dict(m=0.5, c=0.2)
@@ -32,7 +33,7 @@ sigma = 1
# contents of the injection_paramsters when calling the model function.
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
@@ -51,7 +52,7 @@ likelihood = GaussianLikelihood(time, data, model, sigma=sigma)
# From hereon, the syntax is exactly equivalent to other bilby examples
# We make a prior
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 5, 'm')
priors['c'] = bilby.core.prior.Uniform(-2, 2, 'c')
diff --git a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
index 9321ea4c82621250201b3af8b97484845cd2e9a7..4270e696891056c9638a6fd2084b0c99fc3daf58 100644
--- a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
+++ b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
@@ -20,10 +20,12 @@ label = 'linear_regression_pymc3_custom_likelihood'
outdir = 'outdir'
bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
+
# First, we define our "signal model", in this case a simple linear function
def model(time, m, c):
return time * m + c
+
# Now we define the injection parameters which we make simulated data with
injection_parameters = dict(m=0.5, c=0.2)
@@ -34,7 +36,7 @@ sigma = 1
# contents of the injection_paramsters when calling the model function.
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
@@ -51,6 +53,7 @@ fig.savefig('{}/{}_data.png'.format(outdir, label))
# Parameter estimation: we now define a Gaussian Likelihood class relevant for
# our model.
class GaussianLikelihoodPyMC3(bilby.Likelihood):
+
def __init__(self, x, y, sigma, function):
"""
A general Gaussian likelihood - the parameters are inferred from the
@@ -105,6 +108,7 @@ class GaussianLikelihoodPyMC3(bilby.Likelihood):
# set the likelihood distribution
pm.Normal('likelihood', mu=mu, sd=self.sigma, observed=self.y)
+
# Now lets instantiate a version of our GaussianLikelihood, giving it
# the time, data and signal model
likelihood = GaussianLikelihoodPyMC3(time, data, sigma, model)
@@ -135,15 +139,16 @@ class PriorPyMC3(bilby.core.prior.Prior):
return pm.Uniform(self.name, lower=self.minimum,
upper=self.maximum)
+
# From hereon, the syntax is exactly equivalent to other bilby examples
# We make a prior
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 5, 'm')
priors['c'] = PriorPyMC3(-2, 2, 'c')
# And run sampler
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='pymc3', draws=1000,
- tune=1000, discard_tuned_samples=True, injection_parameters=injection_parameters,
- outdir=outdir, label=label)
+ tune=1000, discard_tuned_samples=True,
+ injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
diff --git a/examples/other_examples/linear_regression_unknown_noise.py b/examples/other_examples/linear_regression_unknown_noise.py
index be44d994f0ffea88fa91666d850cfbd31a03879b..e4427c9e0b062a880327527dbfbdf7541a962261 100644
--- a/examples/other_examples/linear_regression_unknown_noise.py
+++ b/examples/other_examples/linear_regression_unknown_noise.py
@@ -31,7 +31,7 @@ sigma = 1
# contents of the injection_parameters when calling the model function.
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
diff --git a/examples/other_examples/occam_factor_example.py b/examples/other_examples/occam_factor_example.py
index b977eaeadf53086326ca4047a8a9aa64bb607b0c..7785a540f956db8886311dc67b308e0ab390b98d 100644
--- a/examples/other_examples/occam_factor_example.py
+++ b/examples/other_examples/occam_factor_example.py
@@ -84,8 +84,8 @@ class Polynomial(bilby.Likelihood):
def log_likelihood(self):
res = self.y - self.polynomial(self.x, self.parameters)
- return -0.5 * (np.sum((res / self.sigma)**2)
- + self.N*np.log(2*np.pi*self.sigma**2))
+ return -0.5 * (np.sum((res / self.sigma)**2) +
+ self.N * np.log(2 * np.pi * self.sigma**2))
def fit(n):
diff --git a/examples/other_examples/radioactive_decay.py b/examples/other_examples/radioactive_decay.py
index b59fad8a96e318ec85a4a661b9703d8a5989cfcd..aceabd156ff1a68bdbb4bf500d0718e6167241ee 100644
--- a/examples/other_examples/radioactive_decay.py
+++ b/examples/other_examples/radioactive_decay.py
@@ -45,8 +45,8 @@ def decay_rate(delta_t, halflife, n_init):
n_atoms = n_init * atto * n_avogadro
- rates = (np.exp(-np.log(2) * (times[:-1] / halflife))
- - np.exp(- np.log(2) * (times[1:] / halflife))) * n_atoms / delta_t
+ rates = (np.exp(-np.log(2) * (times[:-1] / halflife)) -
+ np.exp(- np.log(2) * (times[1:] / halflife))) * n_atoms / delta_t
return rates
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index b6eeb99a15cef7af635f75f155824a7c64e85c28..507c4e4ed1c4c4907b7b9f153649d0634897c953 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -1,4 +1,4 @@
-#!/bin/python
+#!/usr/bin/env python
"""
Tutorial to demonstrate running parameter estimation/model selection on an NR
@@ -9,10 +9,10 @@ factor. (See https://arxiv.org/pdf/1202.3256.pdf)
"""
from __future__ import division, print_function
import numpy as np
+import bilby
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
-import bilby.gw.likelihood
-
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 3.
sampling_frequency = 4096.
@@ -45,7 +45,7 @@ waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
diff --git a/setup.cfg b/setup.cfg
index b1e3bbe886b74ad4ac8fba910a11027596dab54b..b3838851e991b3f1fe06cc09925e7fc6991dca8b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
[flake8]
-exclude = .git,docs,build,dist,test,examples,*__init__.py
+exclude = .git,docs,build,dist,test,*__init__.py
max-line-length = 160
ignore = E129