Add calibration

This MR adds calibration uncertainty to Interferometer objects.

Currently, just the cubic spline model used elsewhere is implemented.

An example of usage has been added.

• Specifying the prior will be super boring so I should add a prior file for this.

examples/injection_examples/calibration_example.py

+#!/bin/python
+"""
+Tutorial to demonstrate running parameter estimation with calibration
+uncertainties included.
+"""
+from __future__ import division, print_function
+
+import numpy as np
+import tupak
+
+# Set the duration and sampling frequency of the data segment
+# that we're going to create and inject the signal into.
+
+duration = 4.
+sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'calibration'
+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.gw.WaveformGenerator(
+    duration=duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
+    parameters=injection_parameters,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
+ifos = tupak.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
+for ifo in ifos:
+    injection_parameters.update({
+        'recalib_{}_amplitude_{}'.format(ifo.name, ii): 0.1 for ii in range(5)})
+    injection_parameters.update({
+        'recalib_{}_phase_{}'.format(ifo.name, ii): 0.01 for ii in range(5)})
+    ifo.calibration_model = tupak.gw.calibration.CubicSpline(
+        prefix='recalib_{}_'.format(ifo.name),
+        minimum_frequency=ifo.minimum_frequency,
+        maximum_frequency=ifo.maximum_frequency, n_points=5)
+ifos.set_strain_data_from_power_spectral_densities(
+    sampling_frequency=sampling_frequency, duration=duration)
+ifos.inject_signal(parameters=injection_parameters,
+                   waveform_generator=waveform_generator)
+
+# Set up prior, which is a dictionary
+# Here we fix the injected cbc parameters and most of the calibration parameters
+# to the injected values.
+# We allow a subset of the calibration parameters to vary.
+priors = injection_parameters.copy()
+for key in injection_parameters:
+    if 'recalib' in key:
+        priors[key] = injection_parameters[key]
+for name in ['recalib_H1_amplitude_0', 'recalib_H1_amplitude_1']:
+    priors[name] = tupak.prior.Gaussian(
+        mu=0, sigma=0.2, name=name, latex_label='H1 $A_{}$'.format(name[-1]))
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and
+# the waveform generator
+likelihood = tupak.gw.GravitationalWaveTransient(
+    interferometers=ifos, waveform_generator=waveform_generator)
+
+# 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()
+