From d9dad877d8bf841f6b26a2c29bd364a362b700f0 Mon Sep 17 00:00:00 2001
From: Siddharth Karmokar
 <155292670+SiddharthKarmokar@users.noreply.github.com>
Date: Mon, 3 Feb 2025 16:51:10 +0000
Subject: [PATCH 1/2] Update antialiased default to auto and fix documentation
 errors

---
 .../images_contours_and_fields/image_antialiasing.py     | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/galleries/examples/images_contours_and_fields/image_antialiasing.py b/galleries/examples/images_contours_and_fields/image_antialiasing.py
index 7f223f6998f2..cfa3c96a4823 100644
--- a/galleries/examples/images_contours_and_fields/image_antialiasing.py
+++ b/galleries/examples/images_contours_and_fields/image_antialiasing.py
@@ -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
 # 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.
@@ -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')

From 67f55f23b070ad89d7da40cfce3e2ec440563d61 Mon Sep 17 00:00:00 2001
From: Siddharth Karmokar
 <155292670+SiddharthKarmokar@users.noreply.github.com>
Date: Tue, 4 Feb 2025 16:48:14 +0000
Subject: [PATCH 2/2] update image_antialiasing example document to reflect
 rcParam behavior

---
 .../images_contours_and_fields/image_antialiasing.py      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/galleries/examples/images_contours_and_fields/image_antialiasing.py b/galleries/examples/images_contours_and_fields/image_antialiasing.py
index cfa3c96a4823..1a9c159e03ab 100644
--- a/galleries/examples/images_contours_and_fields/image_antialiasing.py
+++ b/galleries/examples/images_contours_and_fields/image_antialiasing.py
@@ -63,10 +63,10 @@
 #
 # ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
 #
-# For both keyword arguments, Matplotlib has a default "auto", 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.
+# 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
 # ====================================