make pep happy

examples/injection_examples/binary_neutron_star_example.py

 #!/bin/python
 """
-Tutorial to demonstrate running parameter estimation on a binary neutron star system taking into account tidal deformabilities.
+Tutorial to demonstrate running parameter estimation on a binary neutron star
+system taking into account tidal deformabilities.
 
-This example estimates the masses using a uniform prior in both component masses and also estimates the tidal deformabilities using a uniform prior in both tidal deformabilities
+This example estimates the masses using a uniform prior in both component masses
+and also estimates the tidal deformabilities using a uniform prior in both
+tidal deformabilities
 """
 
 from __future__ import division, print_function
@@ -19,54 +22,60 @@ tupak.core.utils.setup_logger(outdir=outdir, label=label)
 # Set up a random seed for result reproducibility.  This is optional!
 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),
+# 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. 
-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,
-                            ra=1.375, dec=-1.2108, lambda1=400, lambda2=450)
+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,
+    ra=1.375, dec=-1.2108, lambda1=400, lambda2=450)
 
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into. For the 
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into. For the
 # TaylorF2 waveform, we cut the signal close to the isco frequency
 duration = 8
 sampling_frequency = 2*1570.
 start_time = injection_parameters['geocent_time'] + 2 - duration
 
-# Fixed arguments passed into the source model. The analysis is started at 40 Hz.
+# Fixed arguments passed into the source model. The analysis starts at 40 Hz.
 waveform_arguments = dict(waveform_approximant='TaylorF2',
-                          reference_frequency=50.,minimum_frequency=40.0)
+                          reference_frequency=50., minimum_frequency=40.0)
 
 # Create the waveform_generator using a LAL Binary Neutron Star source function
-waveform_generator = tupak.gw.WaveformGenerator(duration=duration,
-                                             sampling_frequency=sampling_frequency,
-                                             frequency_domain_source_model=tupak.gw.source.lal_binary_neutron_star,
-                                             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)).  These default to their design sensitivity and start at 40 Hz.
+waveform_generator = tupak.gw.WaveformGenerator(
+    duration=duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.gw.source.lal_binary_neutron_star,
+    waveform_arguments=waveform_arguments)
+
+# 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 and start at 40 Hz.
 interferometers = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
 for interferometer in interferometers:
     interferometer.minimum_frequency = 40
 interferometers.set_strain_data_from_power_spectral_densities(
     sampling_frequency=sampling_frequency, duration=duration,
-    start_time = start_time)
-interferometers.inject_signal(parameters=injection_parameters, waveform_generator=waveform_generator)
-
+    start_time=start_time)
+interferometers.inject_signal(parameters=injection_parameters,
+                              waveform_generator=waveform_generator)
 
-#priors
 priors = tupak.gw.prior.BNSPriorSet()
-for key in ['a_1', 'a_2', 'psi','geocent_time','ra','dec',
-            'iota','luminosity_distance','phase']:
+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 = tupak.gw.GravitationalWaveTransient(interferometers=interferometers, waveform_generator=waveform_generator, time_marginalization=False, phase_marginalization=False,
-                                              distance_marginalization=False, prior=priors)
+# Initialise the likelihood by passing in the interferometer data (IFOs)
+# and the waveoform generator
+likelihood = tupak.gw.GravitationalWaveTransient(
+    interferometers=interferometers, waveform_generator=waveform_generator,
+    time_marginalization=False, phase_marginalization=False,
+    distance_marginalization=False, prior=priors)
 
 # Run sampler.  In this case we're going to use the `nestle` sampler
-result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='nestle', npoints=1000,
-                           injection_parameters=injection_parameters, outdir=outdir, label=label)
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='nestle', npoints=1000,
+    injection_parameters=injection_parameters, outdir=outdir, label=label)
 
 result.plot_corner()