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()
 
@@ -380,8 +380,8 @@ class PriorDict(dict):
             return all_samples
 
     def normalize_constraint_factor(self, keys):
-        if repr(keys) in self.cached_norm_factors.keys():
-            return self._cached_normalizations[repr(keys)]
+        if tuple(keys) in self._cached_normalizations.keys():
+            return self._cached_normalizations[tuple(keys)]
         else:
             min_accept = 1000
             sampling_chunk = 5000
@@ -398,7 +398,7 @@ class PriorDict(dict):
                         [all_samples[key], samples[key].flatten()])
                 keep = np.array(self.evaluate_constraints(all_samples), dtype=bool)
             factor = len(keep) / np.count_nonzero(keep)
-            self._cached_normalizations[repr(keys)] = factor
+            self._cached_normalizations[tuple(keys)] = factor
             return factor
 
     def prob(self, sample, **kwargs):