Fixed bugs with changing max/min of priors within Cosmological and Interped classes

Within the Interped (core) class, it now yields an error if the maximum is increased (or minimum decreased) post-initialization. The attributes min_limit and max_limit keep track of the original boundaries of the provided arrays, such that the maximum can be decreased and then later increased back to the original value without yielding errors.

Within the Cosmological (gw) class, the distance arrays are recalculated when the boundaries are extended, so the maximum can be increased (or minimum decreased) post-initialization.

bilby/core/prior/interpolated.py

@@ -44,6 +44,8 @@ class Interped(Prior):
 
         """
         self.xx = xx
+        self.min_limit = min(xx)
+        self.max_limit = max(xx)
         self._yy = yy
         self.YY = None
         self.probability_density = None
@@ -97,6 +99,8 @@ class Interped(Prior):
 
         Updates the prior distribution if minimum is set to a different value.
 
+        Yields an error if value is set below instantiated x-array minimum.
+
         Returns
         -------
         float: Minimum of the prior distribution
@@ -106,6 +110,8 @@ class Interped(Prior):
 
     @minimum.setter
     def minimum(self, minimum):
+        if minimum < self.min_limit:
+            raise ValueError('Minimum cannot be set below {}.'.format(round(self.min_limit, 2)))
         self._minimum = minimum
         if '_maximum' in self.__dict__ and self._maximum < np.inf:
             self._update_instance()
@@ -116,6 +122,8 @@ class Interped(Prior):
 
         Updates the prior distribution if maximum is set to a different value.
 
+        Yields an error if value is set above instantiated x-array maximum.
+
         Returns
         -------
         float: Maximum of the prior distribution
@@ -125,6 +133,8 @@ class Interped(Prior):
 
     @maximum.setter
     def maximum(self, maximum):
+        if maximum > self.max_limit:
+            raise ValueError('Maximum cannot be set above {}.'.format(round(self.max_limit, 2)))
         self._maximum = maximum
         if '_minimum' in self.__dict__ and self._minimum < np.inf:
             self._update_instance()