change resolution_loss to a factory function

adaptive/learner/learner2D.py

@@ -89,7 +89,7 @@ def uniform_loss(ip):
     return np.sqrt(areas(ip))
 
 
-def resolution_loss(ip, min_distance=0, max_distance=1):
+def resolution_loss_function(min_distance=0, max_distance=1):
     """Loss function that is similar to the `default_loss` function, but you
     can set the maximimum and minimum size of a triangle.
 
@@ -104,27 +104,24 @@ def resolution_loss(ip, min_distance=0, max_distance=1):
     ...     x, y = xy
     ...     return x**2 + y**2
     >>>
-    >>> from functools import partial
-    >>> loss = partial(resolution_loss, min_distance=0.01)
+    >>> loss = resolution_loss_function(min_distance=0.01, max_distance=1)
     >>> learner = adaptive.Learner2D(f,
     ...                              bounds=[(-1, -1), (1, 1)],
     ...                              loss_per_triangle=loss)
     >>>
     """
-    A = areas(ip)
-    dev = np.sum(deviations(ip), axis=0)
-
-    # similar to the default_loss
-    loss = np.sqrt(A) * dev + A
+    def resolution_loss(ip):
+        loss = default_loss(ip)
 
-    # Setting areas with a small area to zero such that they won't be chosen again
-    loss[A < min_distance**2] = 0
+        # Setting areas with a small area to zero such that they won't be chosen again
+        loss[A < min_distance**2] = 0
 
-    # Setting triangles that have a size larger than max_distance to infinite loss
-    # such that these triangles will be picked
-    loss[A > max_distance**2] = np.inf
+        # Setting triangles that have a size larger than max_distance to infinite loss
+        # such that these triangles will be picked
+        loss[A > max_distance**2] = np.inf
 
-    return loss
+        return loss
+    return resolution_loss
 
 
 def minimize_triangle_surface_loss(ip):