Resolve "Introduce conditional prior sets"

Closes #270 (closed)

PP test show that the new capabilities do not interfere with the results we are getting so far. This test is done without any conditional priors, it just shows that everything still works as expected.

https://ldas-jobs.ligo.caltech.edu/~moritz.huebner/conditional_prior_pp/outdir_pp_test_4s/pp.png

examples/gw_examples/injection_examples/conditional_prior.py

 import matplotlib.pyplot as plt
 import numpy as np
-import corner
 
 import bilby.gw.prior
 
 
-mass_1_min = 2
-mass_1_max = 50
-
 def condition_function(reference_params, mass_1):
-    return dict(mu=reference_params['mu'])
-    # return dict(minimum=np.maximum(reference_params['minimum'], mass_1_min / mass_1), maximum=reference_params['maximum'])
+    return dict(minimum=reference_params['minimum'], maximum=mass_1)
 
-# condition_function = lambda reference_params, mass_1: dict(minimum=np.maximum(reference_params['minimum'], mass_1_min / mass_1), maximum=reference_params['maximum'])
+
+mass_1_min = 5
+mass_1_max = 50
 
 mass_1 = bilby.core.prior.PowerLaw(alpha=-2.5, minimum=mass_1_min, maximum=mass_1_max, name='mass_1')
-mass_ratio = bilby.core.prior.ConditionalExponential(mu=2, name='mass_ratio',
-                                                     condition_func=condition_function)
+mass_2 = bilby.core.prior.ConditionalPowerLaw(alpha=-2.5, minimum=mass_1_min, maximum=mass_1_max, name='mass_2',
+                                              condition_func=condition_function)
 
-correlated_dict = bilby.core.prior.ConditionalPriorDict(dictionary=dict(mass_1=mass_1, mass_ratio=mass_ratio))
+correlated_dict = bilby.core.prior.ConditionalPriorDict(dictionary=dict(mass_1=mass_1, mass_2=mass_2))
 
 res = correlated_dict.sample(100000)
 
@@ -33,9 +30,9 @@ plt.show()
 plt.clf()
 
 
-plt.hist(res['mass_ratio'], bins='fd', alpha=0.6, density=True, label='Sampled')
+plt.hist(res['mass_2'], bins='fd', alpha=0.6, density=True, label='Sampled')
 plt.xlabel('$q$')
-plt.ylabel('$p(q | m_1)$')
+plt.ylabel('$p(m_2 | m_1)$')
 plt.loglog()
 plt.legend()
 plt.tight_layout()
@@ -43,6 +40,55 @@ plt.show()
 plt.clf()
 
 
+duration = 4.
+sampling_frequency = 2048.
+outdir = 'outdir'
+label = 'conditional_prior'
+bilby.core.utils.setup_logger(outdir=outdir, label=label)
+
+np.random.seed(88170235)
+
+injection_parameters = dict(
+    mass_1=9., mass_2=7., 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=400., theta_jn=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., minimum_frequency=20.)
+
+waveform_generator = bilby.gw.WaveformGenerator(
+    duration=duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
+    waveform_arguments=waveform_arguments)
+
+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)
+ifos.inject_signal(waveform_generator=waveform_generator,
+                   parameters=injection_parameters)
+
+priors = bilby.core.prior.ConditionalPriorDict()
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra',
+            'dec', 'geocent_time', 'phase', 'luminosity_distance', 'theta_jn']:
+    priors[key] = injection_parameters[key]
+priors['mass_1'] = mass_1
+priors['mass_2'] = mass_2
+
+# Initialise the likelihood by passing in the interferometer data (ifos) and
+# the waveform generator
+likelihood = bilby.gw.GravitationalWaveTransient(
+    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='dynesty', npoints=300,
+    injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# Make a corner plot.
+result.plot_corner()
+
+
 # mass_1 = bilby.core.prior.Uniform(5, 100)
 # mass_2 = bilby.gw.prior.CorrelatedSecondaryMassPrior(minimum=5, maximum=100)
 #