Merge branch 'add-gwin-example' into 'master'

Adds a simple example of using gwin for an injection

See merge request Monash/tupak!166

examples/injection_examples/using_gwin.py

+#!/bin/python
+"""
+An example of how to use tupak with `gwin` (https://github.com/gwastro/gwin) to
+perform CBC parameter estimation.
+
+To run this example, it is sufficient to use the pip-installable pycbc package,
+but the source installation of gwin. You can install this by cloning the
+repository (https://github.com/gwastro/gwin) and running
+
+$ python setup.py install
+
+A practical difference between gwin and tupak is that while fixed parameters
+are specified via the prior in tupak, in gwin, these are fixed at instantiation
+of the model. So, in the following, we only create priors for the parameters
+to be searched over.
+
+"""
+from __future__ import division, print_function
+import numpy as np
+import tupak
+
+import gwin
+from pycbc import psd as pypsd
+from pycbc.waveform.generator import (FDomainDetFrameGenerator,
+                                      FDomainCBCGenerator)
+
+label = 'using_gwin'
+
+# Search priors
+priors = dict()
+priors['distance'] = tupak.core.prior.Uniform(500, 2000, 'distance')
+priors['polarization'] = tupak.core.prior.Uniform(0, np.pi, 'iota')
+
+# Data variables
+seglen = 4
+sample_rate = 2048
+N = seglen * sample_rate / 2 + 1
+fmin = 30.
+
+# Injected signal variables
+injection_parameters = dict(mass1=38.6, mass2=29.3, spin1z=0, spin2z=0,
+                            tc=0, ra=3.1, dec=1.37, polarization=2.76,
+                            distance=1500)
+
+# These lines figure out which parameters are to be searched over
+variable_parameters = priors.keys()
+fixed_parameters = injection_parameters.copy()
+for key in priors:
+    fixed_parameters.pop(key)
+
+# These lines generate the `model` object - see https://gwin.readthedocs.io/en/latest/api/gwin.models.gaussian_noise.html
+generator = FDomainDetFrameGenerator(
+    FDomainCBCGenerator, 0.,
+    variable_args=variable_parameters, detectors=['H1', 'L1'],
+    delta_f=1. / seglen, f_lower=fmin,
+    approximant='IMRPhenomPv2', **fixed_parameters)
+signal = generator.generate(**injection_parameters)
+psd = pypsd.aLIGOZeroDetHighPower(int(N), 1. / seglen, 20.)
+psds = {'H1': psd, 'L1': psd}
+model = gwin.models.GaussianNoise(
+    variable_parameters, signal, generator, fmin, psds=psds)
+model.update(**injection_parameters)
+
+
+# This create a dummy class to convert the model into a tupak.likelihood object
+class GWINLikelihood(tupak.core.likelihood.Likelihood):
+    def __init__(self, model):
+        """ A likelihood to wrap around GWIN model objects
+
+        Parameters
+        ----------
+        model: gwin.model.GaussianNoise
+            A gwin model
+
+        """
+        self.model = model
+        self.parameters = {x: None for x in self.model.variable_params}
+
+    def log_likelihood(self):
+        self.model.update(**self.parameters)
+        return self.model.loglikelihood
+
+
+likelihood = GWINLikelihood(model)
+
+
+# Now run the inference
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+    label=label)
+result.plot_corner()
+