remove superfluous example files

examples/injection_examples/basic_tutorial_dist_time_phase_marg.py

-#!/bin/python
-"""
-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.
-"""
-from __future__ import division, print_function
-import tupak
-import numpy as np
-
-# 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_dist_time_phase_marg'
-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 black hole 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, 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=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.)
-
-# 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.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters,
-                                             waveform_arguments=waveform_arguments)
-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.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']]
-
-# Set up prior, which is a dictionary
-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 ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'dec', 'geocent_time', 'phase']:
-    priors[key] = injection_parameters[key]
-
-# 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.core.prior.create_default_prior(name='luminosity_distance')
-
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
-                                              time_marginalization=True, phase_marginalization=True,
-                                              distance_marginalization=True, 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,
-                           injection_parameters=injection_parameters, outdir=outdir, label=label)
-
-# make some plots of the outputs
-result.plot_corner()
-

examples/injection_examples/basic_tutorial_time_phase_marg.py

-#!/bin/python
-"""
-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.
-"""
-from __future__ import division, print_function
-import tupak
-import numpy as np
-
-# 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_time_phase_marg'
-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 black hole 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, 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=2000., iota=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.)
-
-# 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.gw.source.lal_binary_black_hole,
-                                             parameters=injection_parameters,
-                                             waveform_arguments=waveform_arguments)
-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.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']]
-
-# Set up prior, which is a dictionary
-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 ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl','psi', 'ra', 'dec', 'geocent_time', 'phase']:
-    priors[key] = injection_parameters[key]
-
-# 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.core.prior.create_default_prior(name='luminosity_distance')
-
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator,
-                                              time_marginalization=True, phase_marginalization=True,
-                                              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,
-                           injection_parameters=injection_parameters, outdir=outdir, label=label)
-
-# make some plots of the outputs
-result.plot_corner()
-