@@ -27,7 +27,7 @@ def deviations(ip):

     Returns

     -------

-    numpy array

+    deviations : numpy.ndarray

         The deviation per triangle.

     """

     values = ip.values / (ip.values.ptp(axis=0).max() or 1)

@@ -65,7 +65,7 @@ def areas(ip):

     Returns

     -------

-    numpy array

+    areas : numpy.ndarray

         The area per triangle in ``ip.tri``.

     """

     p = ip.tri.points[ip.tri.vertices]

@@ -79,6 +79,15 @@ def uniform_loss(ip):

     Works with `~adaptive.Learner2D` only.

+    Parameters

+    ----------

+    ip : `scipy.interpolate.LinearNDInterpolator` instance

+

+    Returns

+    -------

+    losses : numpy.ndarray

+        Loss per triangle in ``ip.tri``.

+

     Examples

     --------

     >>> from adaptive.learner.learner2D import uniform_loss

@@ -103,6 +112,10 @@ def resolution_loss_function(min_distance=0, max_distance=1):

     The arguments `min_distance` and `max_distance` should be in between 0 and 1

     because the total area is normalized to 1.

+    Returns

+    -------

+    loss_function : callable

+

     Examples

     --------

     >>> def f(xy):

@@ -133,12 +146,21 @@ def resolution_loss_function(min_distance=0, max_distance=1):

 def minimize_triangle_surface_loss(ip):

-    """Loss function that is similar to the default loss function in the

+    """Loss function that is similar to the distance loss function in the

     `~adaptive.Learner1D`. The loss is the area spanned by the 3D

     vectors of the vertices.

     Works with `~adaptive.Learner2D` only.

+    Parameters

+    ----------

+    ip : `scipy.interpolate.LinearNDInterpolator` instance

+

+    Returns

+    -------

+    losses : numpy.ndarray

+        Loss per triangle in ``ip.tri``.

+

     Examples

     --------

     >>> from adaptive.learner.learner2D import minimize_triangle_surface_loss

@@ -170,6 +192,19 @@ def minimize_triangle_surface_loss(ip):

 def default_loss(ip):

+    """Loss function that combines

+

+    Works with `~adaptive.Learner2D` only.

+

+    Parameters

+    ----------

+    ip : `scipy.interpolate.LinearNDInterpolator` instance

+

+    Returns

+    -------

+    losses : numpy.ndarray

+        Loss per triangle in ``ip.tri``.

+    """

     dev = np.sum(deviations(ip), axis=0)

     A = areas(ip)

     losses = dev * np.sqrt(A) + 0.3 * A

@@ -425,6 +460,12 @@ class Learner2D(BaseLearner):

         Returns

         -------

         interpolate : `scipy.interpolate.LinearNDInterpolator`

+

+        Examples

+        --------

+        >>> xs, ys = [np.linspace(*b, n=100) for b in learner.bounds]

+        >>> ip = learner.interpolator()

+        >>> zs = ip(xs[:, None], ys[None, :])

         """

         if scaled:

             if self._ip is None: