Project restructuring

There are still some issues to be resolved on the branch. Some additional suggestions are:

• The hyperparameter likelihood isn't really a GW thing, it probably doesn't belong in the core either.
• The default functionality in prior should be in GW.
• The test_redundancy should be in GW.
• I think references to injection_parameters in core should be changed to true_parameters for generality.
• Result.construct_cbc_derived_parameters should be in gw/conversion, this is something I think I put in the wrong place.

examples/injection_examples/basic_tutorial.py

@@ -6,17 +6,20 @@ This example estimates the masses using a uniform prior in both component masses
 comoving volume prior on luminosity distance between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
 """
 from __future__ import division, print_function
-import tupak
+
 import numpy as np
 
+import tupak
+
 # Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+
 time_duration = 4.
 sampling_frequency = 2048.
 
 # Specify the output directory and the name of the simulation.
 outdir = 'outdir'
 label = 'basic_tutorial'
-tupak.utils.setup_logger(outdir=outdir, label=label)
+tupak.core.utils.setup_logger(outdir=outdir, label=label)
 
 # Set up a random seed for result reproducibility.  This is optional!
 np.random.seed(88170235)
@@ -31,13 +34,13 @@ injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5
 # Create the waveform_generator using a LAL BinaryBlackHole source function
 waveform_generator = tupak.WaveformGenerator(time_duration=time_duration,
                                              sampling_frequency=sampling_frequency,
-                                             frequency_domain_source_model=tupak.source.lal_binary_black_hole,
+                                             frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
                                              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
-IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+IFOs = [tupak.gw.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']]
 
@@ -53,13 +56,15 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'd
 
 # 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['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
-priors['geocent_time'] = tupak.prior.Uniform(injection_parameters['geocent_time'] - 1,
-                                            injection_parameters['geocent_time'] + 1,
+priors['luminosity_distance'] = tupak.gw.prior.create_default_prior(name='luminosity_distance')
+priors['geocent_time'] = tupak.core.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.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,time_marginalization=False, phase_marginalization=False, distance_marginalization=False, prior=priors)
+likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, 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 `dynesty` sampler
 result = tupak.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,