Add time prior and increase number of live points

examples/supernova_example/supernova_example.py

 #!/bin/python
 """
-Tutorial to demonstrate running parameter estimation/model selection on an NR supernova injected signal. 
-Signal model is made by applying PCA to a set of supernova waveforms. The first few PCs are then linearly
-combined with a scale factor. (See https://arxiv.org/pdf/1202.3256.pdf)
+
+Tutorial to demonstrate running parameter estimation/model selection on an NR
+supernova injected signal.  Signal model is made by applying PCA to a set of
+supernova waveforms. The first few PCs are then linearly combined with a scale
+factor. (See https://arxiv.org/pdf/1202.3256.pdf)
 
 """
 from __future__ import division, print_function
@@ -21,49 +23,51 @@ tupak.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 supernova waveform.  We first establish a dictionary of parameters that
-# includes all of the different waveform parameters. It will read in a signal to inject from a txt file.
-injection_parameters = dict(file_path = 'MuellerL15_example_inj.txt', luminosity_distance = 10.0, ra = 1.375,
-                             dec = -1.2108, geocent_time = 1126259642.413, psi= 2.659)
+# We are going to inject a supernova waveform.  We first establish a dictionary
+# of parameters that includes all of the different waveform parameters. It will
+# read in a signal to inject from a txt file.
+
+injection_parameters = dict(file_path='MuellerL15_example_inj.txt',
+                            luminosity_distance=10.0, ra=1.375,
+                            dec=-1.2108, geocent_time=1126259642.413,
+                            psi=2.659)
 
 # Create the waveform_generator using a supernova source function
-waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
-                                                                sampling_frequency=sampling_frequency,
-                                                                frequency_domain_source_model=tupak.source.supernova,
-                                                                parameters=injection_parameters)
+waveform_generator = tupak.waveform_generator.WaveformGenerator(
+    time_duration=time_duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.source.supernova,
+    parameters=injection_parameters)
 hf_signal = waveform_generator.frequency_domain_strain()
 
-# 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 = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
-    name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
-    sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+    name, injection_polarizations=hf_signal,
+    injection_parameters=injection_parameters, time_duration=time_duration,
+    sampling_frequency=sampling_frequency, outdir=outdir)
+    for name in ['H1', 'L1', 'V1']]
 
 # read in from a file the PCs used to create the signal model.
 realPCs = np.loadtxt('SupernovaRealPCs.txt')
 imagPCs = np.loadtxt('SupernovaImagPCs.txt')
 
-# now we have to do the waveform_generator again because the signal model is not the same as the injection in this case.
-simulation_parameters = dict(realPCs=realPCs, imagPCs=imagPCs, pc_coeff1 = 0.1, pc_coeff2 = 0.1, 
-                            pc_coeff3 = 0.1, pc_coeff4 = 0.1, pc_coeff5 = 0.1, luminosity_distance = 10.0,
-                            ra = 1.375, dec = -1.2108, geocent_time = 1126259642.413, psi=2.659)
+# Now we make another waveform_generator because the signal model is
+# not the same as the injection in this case.
+simulation_parameters = dict(
+    realPCs=realPCs, imagPCs=imagPCs)
 
-waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
-                                                                sampling_frequency=sampling_frequency,
-                                                                frequency_domain_source_model=tupak.source.supernova_pca_model,
-                                                                parameters=simulation_parameters)
+search_waveform_generator = tupak.waveform_generator.WaveformGenerator(
+    time_duration=time_duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.source.supernova_pca_model,
+    parameters=simulation_parameters)
 
-# Set up prior, which is a dictionary
+# Set up prior
 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', 'geocent_time']:
     priors[key] = injection_parameters[key]
-
-# don't use default for luminosity distance because we want kpc not Mpc
-priors['luminosity_distance'] = tupak.prior.Uniform(2, 20, 'luminosity_distance') 
+priors['luminosity_distance'] = tupak.prior.Uniform(
+    2, 20, 'luminosity_distance')
 priors['pc_coeff1'] = tupak.prior.Uniform(-1, 1, 'pc_coeff1')
 priors['pc_coeff2'] = tupak.prior.Uniform(-1, 1, 'pc_coeff2')
 priors['pc_coeff3'] = tupak.prior.Uniform(-1, 1, 'pc_coeff3')
@@ -71,13 +75,20 @@ priors['pc_coeff4'] = tupak.prior.Uniform(-1, 1, 'pc_coeff4')
 priors['pc_coeff5'] = tupak.prior.Uniform(-1, 1, 'pc_coeff5')
 priors['ra'] = tupak.prior.create_default_prior(name='ra')
 priors['dec'] = tupak.prior.create_default_prior(name='dec')
-
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
-
-# Run sampler.  In this case we're going to use the `dynesty` sampler
-result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=100,
-                                   outdir=outdir, label=label)
+priors['geocent_time'] = tupak.prior.Uniform(
+    injection_parameters['geocent_time'] - 1,
+    injection_parameters['geocent_time'] + 1,
+    'geocent_time')
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and
+# the waveoform generator
+likelihood = tupak.GravitationalWaveTransient(
+    interferometers=IFOs, waveform_generator=search_waveform_generator)
+
+# Run sampler.
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+    outdir=outdir, label=label)
 
 # make some plots of the outputs
 result.plot_corner()