DOC: add an example and tune up docs

jklymak · jklymak · commit 08a98be3cf3e · 2019-07-17T13:40:36.000-07:00
diff --git a/examples/images_contours_and_fields/interpolation_methods.py b/examples/images_contours_and_fields/interpolation_methods.py
@@ -9,11 +9,14 @@
 If `interpolation` is None, it defaults to the :rc:`image.interpolation`
 (default: ``'nearest'``). If the interpolation is ``'none'``, then no
 interpolation is performed for the Agg, ps and pdf backends. Other backends
-will default to ``'nearest'``.
+will default to ``'antialiased'``.
 
 For the Agg, ps and pdf backends, ``interpolation = 'none'`` works well when a
 big image is scaled down, while ``interpolation = 'nearest'`` works well when
 a small image is scaled up.
+
+See :doc:`/gallery/images_contours_and_fields/image_antialiasing` for a
+discussion on the default `interpolation="antialiased"` option. 
 """
 
 import matplotlib.pyplot as plt
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -5503,7 +5503,9 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
 
             See
             :doc:`/gallery/images_contours_and_fields/interpolation_methods`
-            for an overview of the supported interpolation methods.
+            for an overview of the supported interpolation methods, and
+            :doc:`/gallery/images_contours_and_fields/image_antialiasing` for
+            a discussion of image antialiasing.
 
             Some interpolation methods require an additional radius parameter,
             which can be set by *filterrad*. Additionally, the antigrain image
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -180,13 +180,11 @@ def _resample(
         # do antialiasing....
         # compare the number of displayed pixels of the image to the number of
         # the data pixels.
-        print(image_obj.origin)
         shape = list(data.shape)
         if image_obj.origin == 'upper':
             shape[0] = 0
         dispx, dispy = transform.transform(shape[::-1])
         #dispy = transform.transform([data.shape[0], 0])[0]
-        print('d', dispx, dispy, data.shape[1])
         if ((dispx < 3 * data.shape[1] and
                 dispx != data.shape[1] and
                 dispx != 2 * data.shape[1]) or
