flake examples

examples/injection_examples/basic_tutorial.py

@@ -50,7 +50,7 @@ waveform_generator = bilby.gw.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)
 
@@ -85,4 +85,3 @@ result = bilby.run_sampler(
 
 # Make a corner plot.
 result.plot_corner()
-

examples/injection_examples/binary_neutron_star_example.py

@@ -25,7 +25,7 @@ np.random.seed(88170235)
 # We are going to inject a binary neutron star waveform.  We first establish a
 # dictionary of parameters that includes all of the different waveform
 # parameters, including masses of the two black holes (mass_1, mass_2),
-# spins of both black holes (a_1,a_2) , etc. 
+# spins of both black holes (a_1, a_2), etc.
 injection_parameters = dict(
     mass_1=1.5, mass_2=1.3, a_1=0.0, a_2=0.0, luminosity_distance=50.,
     iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
@@ -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.
@@ -64,7 +64,7 @@ priors = bilby.gw.prior.BNSPriorSet()
 for key in ['a_1', 'a_2', 'psi', 'geocent_time', 'ra', 'dec',
             'iota', 'luminosity_distance', 'phase']:
     priors[key] = injection_parameters[key]
-    
+
 # Initialise the likelihood by passing in the interferometer data (IFOs)
 # and the waveoform generator
 likelihood = bilby.gw.GravitationalWaveTransient(
@@ -78,4 +78,3 @@ result = bilby.run_sampler(
     injection_parameters=injection_parameters, outdir=outdir, label=label)
 
 result.plot_corner()
-

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()
-

examples/injection_examples/change_sampled_parameters.py

@@ -34,15 +34,14 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
 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)
 
@@ -80,4 +79,3 @@ result = bilby.core.sampler.run_sampler(
     injection_parameters=injection_parameters, label='DifferentParameters',
     conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
 result.plot_corner()
-

examples/injection_examples/create_your_own_source_model.py

@@ -16,9 +16,9 @@ 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}
 
 
@@ -52,4 +52,3 @@ 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()
-

examples/injection_examples/create_your_own_time_domain_source_model.py

@@ -62,4 +62,3 @@ result = bilby.core.sampler.run_sampler(
     injection_parameters=injection_parameters, outdir=outdir, label=label)
 
 result.plot_corner()
-

examples/injection_examples/eccentric_inspiral.py

@@ -88,4 +88,3 @@ result = bilby.run_sampler(
 
 # And finally we make some plots of the output posteriors.
 result.plot_corner()
-

examples/injection_examples/how_to_specify_the_prior.py

@@ -72,4 +72,3 @@ result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
     injection_parameters=injection_parameters, label='specify_prior')
 result.plot_corner()
-

examples/injection_examples/marginalized_likelihood.py

@@ -58,4 +58,3 @@ result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty',
     injection_parameters=injection_parameters, outdir=outdir, label=label)
 result.plot_corner()
-

examples/injection_examples/non_tensor.py

@@ -57,7 +57,7 @@ waveform_generator =\
 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)
 

examples/injection_examples/sine_gaussian_example.py

@@ -38,7 +38,7 @@ waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
 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)
 
@@ -63,15 +63,3 @@ result = bilby.core.sampler.run_sampler(
 
 # make some plots of the outputs
 result.plot_corner()
-
-
-
-
-
-
-
-
-
-
-
-

examples/injection_examples/using_gwin.py

@@ -91,4 +91,3 @@ result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
     label=label)
 result.plot_corner()
-

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)

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()
-

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)
-
-

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)

examples/other_examples/get_LOSC_event_data.py

 #!/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)

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):

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
 
@@ -34,7 +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)
 

examples/other_examples/linear_regression_pymc3_custom_likelihood.py

@@ -36,7 +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)
 
@@ -139,9 +139,10 @@ 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')