Implemented parameters and non_standard_sampling_parameter_keys as properties...

Implemented parameters and non_standard_sampling_parameter_keys as properties that refer to the respective attribute of the waveform generator. This will ensure consistency between the parameter list of the waveform_generator and the likelihood.

tupak/gw/likelihood.py

@@ -50,10 +50,9 @@ class GravitationalWaveTransient(likelihood.Likelihood):
     def __init__(self, interferometers, waveform_generator, time_marginalization=False, distance_marginalization=False,
                  phase_marginalization=False, prior=None):
 
+        self.waveform_generator = waveform_generator
         likelihood.Likelihood.__init__(self, waveform_generator.parameters)
         self.interferometers = interferometers
-        self.waveform_generator = waveform_generator
-        self.non_standard_sampling_parameter_keys = self.waveform_generator.non_standard_sampling_parameter_keys
         self.time_marginalization = time_marginalization
         self.distance_marginalization = distance_marginalization
         self.phase_marginalization = phase_marginalization
@@ -82,6 +81,18 @@ class GravitationalWaveTransient(likelihood.Likelihood):
         else:
             self.__prior = dict()
 
+    @property
+    def non_standard_sampling_parameter_keys(self):
+        return self.waveform_generator.non_standard_sampling_parameter_keys
+
+    @property
+    def parameters(self):
+        return self.waveform_generator.parameters
+
+    @parameters.setter
+    def parameters(self, parameters):
+        self.waveform_generator.parameters = parameters
+
     def noise_log_likelihood(self):
         log_l = 0
         for interferometer in self.interferometers: