Resolve #430 (Add normalisation flag to constrained prior)

This MR resolves issue #430 (closed) to properly normalize ConstrainedPriorDict.prob() when sampling the prior. This MR allows for users to input a normalisation flag when creating an instance of a Constraint prior and when calling ConstrainedPriorDict.prob() if there are normalisation constants given will return the normalisation*prob and otherwise use the ratio of total samples to kept samples as this normalisation constant. Pass normalisation=1 when creating a Constraint Prior for behaviour before this MR.

bilby/core/prior/dict.py

@@ -41,7 +41,7 @@ class PriorDict(dict):
             self.from_file(filename)
         elif dictionary is not None:
             raise ValueError("PriorDict input dictionary not understood")
-        self._cached_normalizations= {}
+        self._cached_normalizations = {}
 
         self.convert_floats_to_delta_functions()
 
@@ -390,7 +390,6 @@ class PriorDict(dict):
             if len(keep) == 1:
                 return 1
             all_samples = {key: np.array([]) for key in keys}
-            _first_key = list(all_samples.keys())[0]
             while np.count_nonzero(keep) < min_accept:
                 samples = self.sample_subset(keys=keys, size=sampling_chunk)
                 for key in samples: