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

@@ -410,14 +410,7 @@ class PriorDict(dict):
         prob = np.product([self[key].prob(sample[key])
                            for key in sample], **kwargs)
 
-        ratio = 1
-        outsample = self.conversion_function(sample)
-        # Check if there is a constraint in sample/outsample
-        if (np.any(isinstance([self[key] for key in sample.keys()], Constraint)) or
-                np.any(isinstance([self[key] for key in outsample.keys()], Constraint))):
-            # If constraint exists in keys, caclulate the cached normalization constant
-            ratio = self.normalize_constraint_factor(sample.keys())
-
+        ratio = self.normalize_constraint_factor(sample.keys())
         if np.all(prob == 0.):
             return prob
         else:
@@ -451,14 +444,7 @@ class PriorDict(dict):
         ln_prob = np.sum([self[key].ln_prob(sample[key])
                           for key in sample], axis=axis)
 
-        ratio = 1
-        outsample = self.conversion_function(sample)
-        # Check if there is a constraint in sample/outsample
-        if (np.any(isinstance([self[key] for key in sample.keys()], Constraint)) or
-                np.any(isinstance([self[key] for key in outsample.keys()], Constraint))):
-            # If constraint exists in keys, caclulate the cached normalization constant
-            ratio = self.normalize_constraint_factor(sample.keys())
-
+        ratio = self.normalize_constraint_factor(sample.keys())
         if np.all(np.isinf(ln_prob)):
             return ln_prob
         else: