8000 Update antialiased default to auto and fix documentation errors by SiddharthKarmokar · Pull Request #29571 · matplotlib/matplotlib · GitHub
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Update antialiased default to auto and fix documentation errors #29571

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Update antialiased default to auto and fix documentation errors #29571

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d9dad87
Update antialiased default to auto and fix documentation errors
SiddharthKarmokar Feb 3, 2025
67f55f2
update image_antialiasing example document to reflect rcParam behavior
SiddharthKarmokar Feb 4, 2025
7ec2b1e
Merge branch 'matplotlib:main' into issue29541
SiddharthKarmokar Feb 4, 2025
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9 changes: 6 additions & 3 deletions galleries/examples/images_contours_and_fields/image_antialiasing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@
#
# ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
#
# For both keyword arguments, Matplotlib has a default "antialiased", that is
# For both keyword arguments, Matplotlib has a default "auto", that is
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Strictly, interpolation has an rcParam that is used. Although in the default rc-file it is indeed "auto". Good find of the doc error though, but ideally it should be rewritten to reflect the rcParam.

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@SiddharthKarmokar SiddharthKarmokar Feb 5, 2025 •
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For both keyword arguments, Matplotlib uses a default value of "auto" as
specified by the rcParam, which is recommended for most situations.This default behavior is explained below. Note that the behavior may differ if the image is being down- or up-sampled, as outlined below.

Updated the 9F97 text to properly reference rcParam, new contributor here, can you please explain to me why the "codecov/project/tests" check fail for the amended request?

# recommended for most situations, and is described below. Note that this
# default behaves differently if the image is being down- or up-sampled, as
# described below.
Expand Down Expand Up @@ -166,16 +166,19 @@
# %%
# A final example shows the desirability of performing the anti-aliasing at the
# RGBA stage when using non-trivial interpolation kernels. In the following,
# the data in the upper 100 rows is exactly 0.0, and data in the inner circle
# the data in the outer circle is exactly 0.0, and data in the inner circle
# is exactly 2.0. If we perform the *interpolation_stage* in 'data' space and
# use an anti-aliasing filter (first panel), then floating point imprecision
# makes some of the data values just a bit less than zero or a bit more than
# 2.0, and they get assigned the under- or over- colors. This can be avoided if
# you do not use an anti-aliasing filter (*interpolation* set set to
# you do not use an anti-aliasing filter (*interpolation* set to
# 'nearest'), however, that makes the part of the data susceptible to Moiré
# patterns much worse (second panel). Therefore, we recommend the default
# *interpolation* of 'hanning'/'auto', and *interpolation_stage* of
# 'rgba'/'auto' for most down-sampling situations (last panel).
# In this example, the data values are clipped at the edges of the color range.
# The interpolation uses the 'nearest' method, and as a result, no
# floating-point imprecision is visible in the first panel.

a = alarge + 1
cmap = plt.get_cmap('RdBu_r')
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