Small simplification of waveform generator / likelihood interaction

This allows the waveform generator to be given None for the sampling
frequency and duration (in this state, it therefore can't be used to
generate an injection). Then, when passed to the likelihood, a check if
performed to see if those attributes are set. If they are None, they are
set from the data, and if they disagree with the data, an error is raised.

examples/open_data_examples/GW150914.py

@@ -36,9 +36,7 @@ prior = tupak.gw.prior.BBHPriorSet(filename='GW150914.prior')
 # creates the frequency-domain strain. In this instance, we are using the
 # `lal_binary_black_hole model` source model. We also pass other parameters:
 # the waveform approximant and reference frequency.
-waveform_generator = tupak.WaveformGenerator(duration=interferometers.duration,
-                                             sampling_frequency=interferometers.sampling_frequency,
-                                             frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
+waveform_generator = tupak.WaveformGenerator(frequency_domain_source_model=tupak.gw.source.lal_binary_black_hole,
                                              waveform_arguments={'waveform_approximant': 'IMRPhenomPv2',
                                                                  'reference_frequency': 50})
 

tupak/gw/likelihood.py

@@ -62,6 +62,7 @@ class GravitationalWaveTransient(likelihood.Likelihood):
         self.distance_marginalization = distance_marginalization
         self.phase_marginalization = phase_marginalization
         self.prior = prior
+        self._check_set_duration_and_sampling_frequency_of_waveform_generator()
 
         if self.distance_marginalization:
             self.check_prior_is_set()
@@ -80,6 +81,24 @@ class GravitationalWaveTransient(likelihood.Likelihood):
         if self.time_marginalization:
             self.check_prior_is_set()
 
+    def _check_set_duration_and_sampling_frequency_of_waveform_generator(self):
+        """ Check the waveform_generator has the same duration and
+        sampling_frequency as the interferometers. If they are unset, then
+        set them, if they differ, raise an error
+        """
+
+        attributes = ['duration', 'sampling_frequency', 'start_time']
+        for attr in attributes:
+            wfg_attr = getattr(self.waveform_generator, attr)
+            ifo_attr = getattr(self.interferometers, attr)
+            if wfg_attr is None:
+                setattr(self.waveform_generator, attr, ifo_attr)
+            elif wfg_attr != ifo_attr:
+                logging.debug(
+                    "The waveform_generator {} is not equal to that of the "
+                    "provided interferometers. Overwriting the "
+                    "waveform_generator.".format(attr))
+
     def check_prior_is_set(self):
         if self.prior is None:
             raise ValueError("You can't use a marginalized likelihood without specifying a prior")

tupak/gw/waveform_generator.py

@@ -6,7 +6,7 @@ import numpy as np
 
 class WaveformGenerator(object):
 
-    def __init__(self, duration, sampling_frequency, start_time=0, frequency_domain_source_model=None,
+    def __init__(self, duration=None, sampling_frequency=None, start_time=0, frequency_domain_source_model=None,
                  time_domain_source_model=None, parameters=None, parameter_conversion=None,
                  non_standard_sampling_parameter_keys=None,
                  waveform_arguments=None):
@@ -14,9 +14,9 @@ class WaveformGenerator(object):
 
     Parameters
     ----------
-    sampling_frequency: float
+    sampling_frequency: float, optional
         The sampling frequency
-    duration: float
+    duration: float, optional
         Time duration of data
     start_time: float, optional
         Starting time of the time array