Merge branch 'master' of git.ligo.org:Monash/tupak

README.md

@@ -13,6 +13,7 @@ To get started with `tupak`, we have a few examples and tutorials:
 1. [Examples of injecting and recovering data](https://git.ligo.org/Monash/tupak/tree/master/examples/injection_examples)
     * [A basic tutorial](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/basic_tutorial.py)
     * [Create your own source model](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_source_model.py)
+    * [Create your own time-domain source model](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/create_your_own_time_domain_source_model.py)
     * [How to specify the prior](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/how_to_specify_the_prior.py)
     * [Using a partially marginalized likelihood](https://git.ligo.org/Monash/tupak/blob/master/examples/injection_examples/marginalized_likelihood.py)
 

examples/injection_examples/basic_tutorial.py

@@ -55,7 +55,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi'
 priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
 
 # Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
 
 # Run sampler.  In this case we're going to use the `dynesty` sampler
 result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,

examples/injection_examples/change_sampled_parameters.py

@@ -42,8 +42,8 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi'
     priors[key] = injection_parameters[key]
 priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
 
-# Initialise Likelihood
-likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
+# Initialise GravitationalWaveTransient
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
 
 # Run sampler
 result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',

examples/injection_examples/create_your_own_source_model.py

@@ -46,7 +46,7 @@ prior['A'] = tupak.prior.Uniform(0, 1e-20, 'A')
 prior['f0'] = tupak.prior.Uniform(0, 20, 'f')
 prior['geocent_time'] = tupak.prior.Uniform(-0.01, 0.01, 'geocent_time')
 
-likelihood = tupak.likelihood.Likelihood(IFOs, waveform_generator)
+likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
 
 result = tupak.sampler.run_sampler(
     likelihood, prior, sampler='dynesty', outdir=outdir, label=label,

examples/injection_examples/how_to_specify_the_prior.py

@@ -57,8 +57,8 @@ priors['a_2'] = tupak.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=0, ma
 # Finally, if you don't specify any necessary parameters it will be filled in from the default when the sampler starts.
 # Enjoy.
 
-# Initialise Likelihood
-likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
+# Initialise GravitationalWaveTransient
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
 
 # Run sampler
 result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',

examples/injection_examples/marginalized_likelihood.py

@@ -37,9 +37,9 @@ priors = dict()
 for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'iota', 'ra', 'dec', 'geocent_time']:
     priors[key] = injection_parameters[key]
 
-# Initialise Likelihood
+# Initialise GravitationalWaveTransient
 # Note that we now need to pass the: priors and flags for each thing that's being marginalised.
-likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator, prior=priors,
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator, prior=priors,
                                          distance_marginalization=True, phase_marginalization=True)
 
 # Run sampler

examples/open_data_examples/GW150914.py

@@ -53,7 +53,7 @@ waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=in
 
 # In this step, we define the likelihood. Here we use the standard likelihood
 # function, passing it the data and the waveform generator.
-likelihood = tupak.likelihood.Likelihood(interferometers, waveform_generator)
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
 
 # Finally, we run the sampler. This function takes the likelihood and prio
 # along with some options for how to do the sampling and how to save the data

examples/other_examples/alternative_likelihoods.py

@@ -14,13 +14,13 @@ label = 'test'
 outdir = 'outdir'
 
 
-# Here is minimum requirement for a Likelihood class needed to run tupak. In
-# this case, we setup a GaussianLikelihood, which needs to have a
+# Here is minimum requirement for a GravitationalWaveTransient class needed to run tupak. In
+# this case, we setup a GaussianGravitationalWaveTransient, which needs to have a
 # log_likelihood and noise_log_likelihood method. Note, in this case we make
 # use of the `tupak` waveform_generator to make the signal (more on this later)
 # But, one could make this work without the waveform generator.
 
-class GaussianLikelihood():
+class GaussianGravitationalWaveTransient():
     def __init__(self, x, y, waveform_generator):
         self.x = x
         self.y = y
@@ -74,9 +74,9 @@ waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=ti
                                                                 time_domain_source_model=model)
 
 
-# Now lets instantiate a version of out Likelihood, giving it the time, data
+# Now lets instantiate a version of out GravitationalWaveTransient, giving it the time, data
 # and waveform_generator
-likelihood = GaussianLikelihood(time, data, waveform_generator)
+likelihood = GaussianGravitationalWaveTransient(time, data, waveform_generator)
 
 # From hereon, the syntax is exactly equivalent to other tupak examples
 # We make a prior

test/tests.py

@@ -46,7 +46,7 @@ class Test(unittest.TestCase):
         self.assertAlmostEqual(all(self.msd['hf_signal_and_noise'] - hf_signal_and_noise_saved), 0.00000000, 5)
 
     def test_recover_luminosity_distance(self):
-        likelihood = tupak.likelihood.Likelihood(
+        likelihood = tupak.likelihood.GravitationalWaveTransient(
             [self.msd['IFO']], self.msd['waveform_generator'])
 
         priors = {}

tupak/conversion.py

@@ -106,8 +106,8 @@ def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
     ----------
     sample: dict or pandas.DataFrame
         Samples to fill in with extra parameters, this may be either an injection or posterior samples.
-    likelihood: tupak.likelihood.Likelihood
-        Likelihood used for sampling, used for waveform and likelihood.interferometers.
+    likelihood: tupak.likelihood.GravitationalWaveTransient
+        GravitationalWaveTransient used for sampling, used for waveform and likelihood.interferometers.
     priors: dict, optional
         Dictionary of prior objects, used to fill in non-sampled parameters.
     """

tupak/likelihood.py

@@ -11,10 +11,10 @@ import tupak
 import logging
 
 
-class Likelihood(object):
+class GravitationalWaveTransient(object):
     def __init__(self, interferometers, waveform_generator, distance_marginalization=False, phase_marginalization=False,
                  prior=None):
-        # Likelihood.__init__(self, interferometers, waveform_generator)
+        # GravitationalWaveTransient.__init__(self, interferometers, waveform_generator)
         self.interferometers = interferometers
         self.waveform_generator = waveform_generator
         self.parameters = self.waveform_generator.parameters
@@ -106,7 +106,7 @@ class Likelihood(object):
                                                  bounds_error=False, fill_value=-np.inf)
 
 
-class BasicLikelihood(object):
+class BasicGravitationalWaveTransient(object):
     def __init__(self, interferometers, waveform_generator):
         self.interferometers = interferometers
         self.waveform_generator = waveform_generator
@@ -149,13 +149,13 @@ def get_binary_black_hole_likelihood(interferometers):
         output of either `tupak.detector.get_interferometer_with_open_data`
         or `tupak.detector.get_interferometer_with_fake_noise_and_injection`
     Returns
-    likelihood: tupak.likelihood.Likelihood
+    likelihood: tupak.likelihood.GravitationalWaveTransient
         The likelihood to pass to `run_sampler`
     """
     waveform_generator = tupak.waveform_generator.WaveformGenerator(
         time_duration=interferometers[0].duration, sampling_frequency=interferometers[0].sampling_frequency,
         frequency_domain_source_model=tupak.source.lal_binary_black_hole,
         parameters={'waveform_approximant': 'IMRPhenomPv2', 'reference_frequency': 50})
-    likelihood = tupak.likelihood.Likelihood(interferometers, waveform_generator)
+    likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
     return likelihood
 

tupak/prior.py

@@ -443,7 +443,7 @@ def fill_priors(prior, likelihood, parameters=None):
     ----------
     prior: dict
         dictionary of prior objects and floats
-    likelihood: tupak.likelihood.Likelihood instance
+    likelihood: tupak.likelihood.GravitationalWaveTransient instance
         Used to infer the set of parameters to fill the prior with
     parameters: list
         list of parameters to be sampled in, this can override the default

tupak/result.py

@@ -119,7 +119,7 @@ class Result(dict):
             filename = '{}/{}_corner.png'.format(self.outdir, self.label)
             kwargs['filename'] = kwargs.get('filename', filename)
             logging.info('Saving corner plot to {}'.format(kwargs['filename']))
-        if self.injection_parameters is not None:
+        if getattr(self, 'injection_parameters', None) is not None:
             # If no truth argument given, set these to the injection params
             injection_parameters = [self.injection_parameters[key]
                                     for key in self.search_parameter_keys]
@@ -211,8 +211,8 @@ class Result(dict):
 
         Parameters
         ----------
-        likelihood: tupak.likelihood.Likelihood
-            Likelihood used for sampling.
+        likelihood: tupak.likelihood.GravitationalWaveTransient
+            GravitationalWaveTransient used for sampling.
         priors: dict
             Dictionary of prior object, used to fill in delta function priors.
         conversion_function: function

tupak/sampler.py

@@ -19,7 +19,7 @@ class Sampler(object):
 
     Parameters
     ----------
-    likelihood: likelihood.Likelihood
+    likelihood: likelihood.GravitationalWaveTransient
         A  object with a log_l method
     prior: dict
         The prior to be used in the search. Elements can either be floats
@@ -150,7 +150,7 @@ class Sampler(object):
         try:
             self.likelihood.log_likelihood_ratio()
         except TypeError:
-            raise TypeError('Likelihood evaluation failed. Have you definitely specified all the parameters?\n{}'.format(
+            raise TypeError('GravitationalWaveTransient evaluation failed. Have you definitely specified all the parameters?\n{}'.format(
                 self.likelihood.parameters))
 
     def prior_transform(self, theta):
@@ -408,8 +408,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
 
     Parameters
     ----------
-    likelihood: `tupak.likelihood.Likelihood`
-        A `Likelihood` instance
+    likelihood: `tupak.likelihood.GravitationalWaveTransient`
+        A `GravitationalWaveTransient` instance
     priors: dict
         A dictionary of the priors for each parameter - missing parameters will
         use default priors, if None, all priors will be default