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

@@ -404,7 +404,11 @@ class PriorDict(dict):
                            for key in sample], **kwargs)
 
         ratio = 1
-        if np.any(isinstance([self[key] for key in sample], Constraint)):
+        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()]))):
+            # If constraint exists in keys, caclulate the cached normalization constant
             ratio = self.normalize_constraint_factor(sample.keys())
 
         if np.all(prob == 0.):
@@ -418,7 +422,7 @@ class PriorDict(dict):
             else:
                 constrained_prob = np.zeros_like(prob)
                 keep = np.array(self.evaluate_constraints(sample), dtype=bool)
-                constrained_prob[keep] = prob[keep]*ratio
+                constrained_prob[keep] = prob[keep] * ratio
                 return constrained_prob
 
     def ln_prob(self, sample, axis=None):