added check for minimum samples in constraint nomralizationa nd propoerlly...

added check for minimum samples in constraint nomralizationa nd propoerlly normalize lnprob as well as prob

bilby/core/prior/dict.py

@@ -381,8 +381,15 @@ class PriorDict(dict):
 
     @lru_cache()
     def normalize_constraint_factor(self, keys):
-        samples = self.sample_subset(keys=keys, size=1000)
+        min_accept = 50
+        sampling_chunk = 250
+        samples = self.sample_subset(keys=keys, size=sampling_chunk)
         keep = np.array(self.evaluate_constraints(samples))
+        while np.count_nonzero(keep) < min_accept:
+            new_samples = self.sample_subset(keys=keys, size=sampling_chunk)
+            for key in samples:
+                samples[key] = np.concatenate(samples[key], new_samples[key])
+            keep = np.array(self.evaluate_constraints(samples))
         return len(keep) / np.count_nonzero(keep)
 
     def prob(self, sample, **kwargs):
@@ -444,6 +451,14 @@ 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())
+
         if np.all(np.isinf(ln_prob)):
             return ln_prob
         else:
@@ -455,7 +470,7 @@ class PriorDict(dict):
             else:
                 constrained_ln_prob = -np.inf * np.ones_like(ln_prob)
                 keep = np.array(self.evaluate_constraints(sample), dtype=bool)
-                constrained_ln_prob[keep] = ln_prob[keep]
+                constrained_ln_prob[keep] = ln_prob[keep] + np.log(ratio)
                 return constrained_ln_prob
 
     def rescale(self, keys, theta):