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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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15 changes: 9 additions & 6 deletions 15 galleries/examples/images_contours_and_fields/image_antialiasing.py
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Expand Up @@ -63,10 +63,10 @@
#
# ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
#
# For both keyword arguments, Matplotlib has a default "antialiased", that is
# 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.
# For both keyword arguments, Matplotlib uses a default value of "auto" as
# specified by the rcParam, which is recommended for most situations.
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If the default is to fall back to the rcParam, you should reference it with:

:rc:`name.of.rcParam`

to get the rcParam default automatically inserted.

# This default behavior is explained below. Note that the behavior may differ if the
# image is being down- or up-sampled, as outlined below.
#
# Down-sampling and modest up-sampling
# ====================================
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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