8000 Deemphasize comment about extremely old Matplotlib versions in example. by anntzer · Pull Request #13196 · matplotlib/matplotlib · GitHub
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Deemphasize comment about extremely old Matplotlib versions in example. #13196

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Jan 16, 2019
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Deemphasize comment about extremely old Matplotlib versions in example. #13196

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34 changes: 15 additions & 19 deletions examples/images_contours_and_fields/image_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,22 +84,17 @@
# the pixel center will have the same color as it does with nearest, but
# other pixels will be interpolated between the neighboring pixels.
#
# Earlier versions of matplotlib (<0.63) tried to hide the edge effects
# from you by setting the view limits so that they would not be visible.
# A recent bugfix in antigrain, and a new implementation in the
# matplotlib._image module which takes advantage of this fix, no longer
# makes this necessary. To prevent edge effects, when doing
# interpolation, the matplotlib._image module now pads the input array
# with identical pixels around the edge. e.g., if you have a 5x5 array
# with colors a-y as below::
# To prevent edge effects when doing interpolation, Matplotlib pads the input
# array with identical pixels around the edge: if you have a 5x5 array with
# colors a-y as below::
#
# a b c d e
# f g h i j
# k l m n o
# p q r s t
# u v w x y
#
# the _image module creates the padded array,::
# Matplotlib computes the interpolation and resizing on the padded array ::
#
# a a b c d e e
# a a b c d e e
Expand All @@ -109,16 +104,17 @@
# o u v w x y y
# o u v w x y y
#
# does the interpolation/resizing, and then extracts the central region.
# This allows you to plot the full range of your array w/o edge effects,
# and for example to layer multiple images of different sizes over one
# another with different interpolation methods - see
# :doc:`/gallery/images_contours_and_fields/layer_images`.
# It also implies a performance hit, as this
# new temporary, padded array must be created. Sophisticated
# interpolation also implies a performance hit, so if you need maximal
# performance or have very large images, interpolation='nearest' is
# suggested.
# and then extracts the central region of the result. (Extremely old versions
# of Matplotlib (<0.63) did not pad the array, but instead adjusted the view
# limits to hide the affected edge areas.)
#
# This approach allows plotting the full extent of an array without
# edge effects, and for example to layer multiple images of different
# sizes over one another with different interpolation methods -- see
# :doc:`/gallery/images_contours_and_fields/layer_images`. It also implies
# a performance hit, as this new temporary, padded array must be created.
# Sophisticated interpolation also implies a performance hit; for maximal
# performance or very large images, interpolation='nearest' is suggested.

A = np.random.rand(5, 5)

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