reformat different parameter example

examples/injection_examples/change_sampled_parameters.py

-#!/bin/python
+#!/usr/bin/env python
 """
-Tutorial to demonstrate running parameter estimation sampling in non-standard parameters for an injected signal.
+Tutorial to demonstrate running parameter estimation sampling in non-standard
+parameters for an injected signal.
 
-This example estimates the masses using a uniform prior in chirp mass, mass ratio and redshift.
+This example estimates the masses using a uniform prior in chirp mass,
+mass ratio and redshift.
 The cosmology is according to the Planck 2015 data release.
 """
 from __future__ import division, print_function
@@ -18,9 +20,10 @@ outdir = 'outdir'
 
 np.random.seed(151226)
 
-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=500, 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=500, 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.)
@@ -29,35 +32,44 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
 waveform_generator = tupak.gw.waveform_generator.WaveformGenerator(
     sampling_frequency=sampling_frequency, duration=duration,
     frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
-    parameter_conversion=tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
-    non_standard_sampling_parameter_keys=['chirp_mass', 'mass_ratio', 'redshift'],
+    parameter_conversion=
+        tupak.gw.conversion.convert_to_lal_binary_black_hole_parameters,
     parameters=injection_parameters, waveform_arguments=waveform_arguments)
 hf_signal = waveform_generator.frequency_domain_strain()
 
 # Set up interferometers.
-IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
-    name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, duration=duration,
-    sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+ifos = tupak.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)
+ifos.inject_signal(waveform_generator=waveform_generator,
+                   parameters=injection_parameters)
 
 # Set up prior
 priors = tupak.gw.prior.BBHPriorSet()
 priors.pop('mass_1')
 priors.pop('mass_2')
 priors.pop('luminosity_distance')
-priors['chirp_mass'] = tupak.prior.Uniform(name='chirp_mass', latex_label='$m_c$', minimum=13, maximum=45)
-priors['mass_ratio'] = tupak.prior.Uniform(name='mass_ratio', latex_label='$q$', minimum=0.1, maximum=1)
-priors['redshift'] = tupak.prior.Uniform(name='redshift', latex_label='$z$', minimum=0, maximum=0.5)
+priors['chirp_mass'] = tupak.prior.Uniform(
+    name='chirp_mass', latex_label='$m_c$', minimum=13, maximum=45)
+priors['mass_ratio'] = tupak.prior.Uniform(
+    name='mass_ratio', latex_label='q', minimum=0.1, maximum=1)
+priors['redshift'] = tupak.prior.Uniform(
+    name='redshift', latex_label='$z$', minimum=0, maximum=0.5)
 # These parameters will not be sampled
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi',
+            'ra', 'dec', 'geocent_time', 'phase']:
     priors[key] = injection_parameters[key]
 print(priors)
 
 # Initialise GravitationalWaveTransient
-likelihood = tupak.gw.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.gw.likelihood.GravitationalWaveTransient(
+    interferometers=ifos, waveform_generator=waveform_generator)
 
 # Run sampler
-result = tupak.core.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
-                                        injection_parameters=injection_parameters, label='DifferentParameters',
-                                        outdir=outdir, conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
+result = tupak.core.sampler.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
+    injection_parameters=injection_parameters, label='DifferentParameters',
+    conversion_function=tupak.gw.conversion.generate_all_bbh_parameters)
 result.plot_corner()