Revert "Merge branch '176-add-neutron-star-merger-to-examples' into 'master'"

This reverts merge request !171 (merged) @paul-lasky was telling me that this is not correct yet. Also it failed the flake8 check. Consider merging master into this branch first so you can do the flake8 check beforehand.

examples/injection_examples/binary_neutron_star_example.py

-#!/bin/python
-"""
-Tutorial to demonstrate running parameter estimation on a binary neutron star
-system taking into account tidal deformabilities.
-
-This example estimates the masses using a uniform prior in both component masses
-and also estimates the tidal deformabilities using a uniform prior in both
-tidal deformabilities
-"""
-
-from __future__ import division, print_function
-
-import numpy as np
-
-import tupak
-
-# Specify the output directory and the name of the simulation.
-outdir = 'outdir'
-label = 'bns_example'
-tupak.core.utils.setup_logger(outdir=outdir, label=label)
-
-# Set up a random seed for result reproducibility.  This is optional!
-np.random.seed(88170235)
-
-# We are going to inject a binary neutron star waveform.  We first establish a
-# dictionary of parameters that includes all of the different waveform
-# parameters, including masses of the two black holes (mass_1, mass_2),
-# spins of both black holes (a_1,a_2) , etc. 
-injection_parameters = dict(
-    mass_1=1.5, mass_2=1.3, a_1=0.0, a_2=0.0, luminosity_distance=50.,
-    iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
-    ra=1.375, dec=-1.2108, lambda1=400, lambda2=450)
-
-# Set the duration and sampling frequency of the data segment that we're going
-# to inject the signal into. For the
-# TaylorF2 waveform, we cut the signal close to the isco frequency
-duration = 8
-sampling_frequency = 2*1570.
-start_time = injection_parameters['geocent_time'] + 2 - duration
-
-# Fixed arguments passed into the source model. The analysis starts at 40 Hz.
-waveform_arguments = dict(waveform_approximant='TaylorF2',
-                          reference_frequency=50., minimum_frequency=40.0)
-
-# Create the waveform_generator using a LAL Binary Neutron Star source function
-waveform_generator = tupak.gw.WaveformGenerator(
-    duration=duration, sampling_frequency=sampling_frequency,
-    frequency_domain_source_model=tupak.gw.source.lal_binary_neutron_star,
-    waveform_arguments=waveform_arguments)
-
-# 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 and start at 40 Hz.
-interferometers = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
-for interferometer in interferometers:
-    interferometer.minimum_frequency = 40
-interferometers.set_strain_data_from_power_spectral_densities(
-    sampling_frequency=sampling_frequency, duration=duration,
-    start_time=start_time)
-interferometers.inject_signal(parameters=injection_parameters,
-                              waveform_generator=waveform_generator)
-
-priors = tupak.gw.prior.BNSPriorSet()
-for key in ['a_1', 'a_2', 'psi', 'geocent_time', 'ra', 'dec',
-            'iota', 'luminosity_distance', 'phase']:
-    priors[key] = injection_parameters[key]
-    
-# Initialise the likelihood by passing in the interferometer data (IFOs)
-# and the waveoform generator
-likelihood = tupak.gw.GravitationalWaveTransient(
-    interferometers=interferometers, 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 `nestle` sampler
-result = tupak.run_sampler(
-    likelihood=likelihood, priors=priors, sampler='nestle', npoints=1000,
-    injection_parameters=injection_parameters, outdir=outdir, label=label)
-
-result.plot_corner()
-