remove post-processing

examples/injection_examples/basic_tutorial.py

@@ -4,19 +4,16 @@ Tutorial to demonstrate running parameter estimation on a reduced parameter
 space for an injected signal.
 
 This example estimates the masses using a uniform prior in both component masses
-and distance using a uniform in
-comoving volume prior on luminosity distance between luminosity distances of
-100Mpc and 5Gpc, the cosmology is WMAP7.
+and distance using a uniform in comoving volume prior on luminosity distance
+between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
 """
 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
-
+# Set the duration and sampling frequency of the data segment that we're
+# going to inject the signal into
 duration = 4.
 sampling_frequency = 2048.
 
@@ -34,18 +31,18 @@ np.random.seed(88170235)
 # spins of both black holes (a, tilt, phi), etc.
 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,
+    phi_12=1.7, phi_jl=0.3, luminosity_distance=2000., iota=0.4, psi=2.659,
     phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108)
 
 # Fixed arguments passed into the source model
 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.  In this case we'll use two interferometers
 # (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design
@@ -53,42 +50,38 @@ 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)
 
-# Set up PriorSet, which inherits from dict
+# Set up a PriorSet, which inherits from 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 equal to their injected values.  This implies the
+# 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. The above list does *not* include mass_1, mass_2, iota
-# and luminosity_distance, which means those are the parameters that will be
-# included in the sampler.  If we do nothing, then the default priors get used.
+# sampler implementation is smart enough to not sample any parameter that has
+# a delta-function prior.
+# The above list does *not* include mass_1, mass_2, iota and luminosity
+# distance, which means those are the parameters that will be included in the
+# sampler.  If we do nothing, then the default priors get used.
 priors = bilby.gw.prior.BBHPriorSet()
-# priors['geocent_time'] = bilby.core.prior.Uniform(
-#     minimum=injection_parameters['geocent_time'] - 1,
-#     maximum=injection_parameters['geocent_time'] + 1,
-#     name='geocent_time', latex_label='$t_c$', unit='$s$')
+priors['geocent_time'] = bilby.core.prior.Uniform(
+    minimum=injection_parameters['geocent_time'] - 1,
+    maximum=injection_parameters['geocent_time'] + 1,
+    name='geocent_time', latex_label='$t_c$', unit='$s$')
 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]
 
-# 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=ifos, waveform_generator=waveform_generator,
-    time_marginalization=False, phase_marginalization=False,
-    distance_marginalization=False, prior=priors)
+    interferometers=ifos, waveform_generator=waveform_generator)
 
 # Run sampler.  In this case we're going to use the `dynesty` sampler
 result = bilby.run_sampler(
-    likelihood=likelihood, priors=priors, sampler='nestle', npoints=1000,
-    injection_parameters=injection_parameters, outdir=outdir, label=label,
-    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+    injection_parameters=injection_parameters, outdir=outdir, label=label)
 
-
-# make some plots of the outputs
+# Make a corner plot.
 result.plot_corner()
-