@@ -162,14 +162,14 @@ class Learner2D(BaseLearner):

         return np.delete(self.values_combined,

                          list(self._interp.values()), axis=0)

-    def ip(self):

-        points = self.scale(self.points)

-        return interpolate.LinearNDInterpolator(points, self.values)

-

     @property

     def n_real(self):

         return self.n - len(self._interp)

+    def ip(self):

+        points = self.scale(self.points)

+        return interpolate.LinearNDInterpolator(points, self.values)

+

     def ip_combined(self):

         points = self.scale(self.points_combined)

         values = self.values_combined

@@ -223,7 +223,6 @@ class Learner2D(BaseLearner):

             self._values = np.resize(self._values, (nmax, self.vdim))

             self._values[n] = value

-        # Remove the point if in the stack.

         self._stack.pop(point, None)

     def _fill_stack(self, stack_till=None):

@@ -288,8 +287,9 @@ class Learner2D(BaseLearner):

                 # The while loop is needed because `stack_till` could be larger

                 # than the number of triangles between the points. Therefore

                 # it could fill up till a length smaller than `stack_till`.

-                if self.n >= 2**self.ndim:

-                    # Only fill the stack if no more bounds left in _stack

+                no_bounds_in_stack = not any(p in self._stack

+                                             for p in self._bounds_points)

+                if no_bounds_in_stack:

                     self._fill_stack(stack_till=n_left)

                 new_points, new_loss_improvements = self._split_stack(n_left)

                 points += new_points