Feb 3, 2025 · Feb 4, 2025 · Feb 4, 2025 · Feb 11, 2025
diff --git a/galleries/examples/images_contours_and_fields/image_antialiasing.py b/galleries/examples/images_contours_and_fields/image_antialiasing.py
 #
 # ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
 #
 # For both keyword arguments, Matplotlibhas a default"antialiased", that is
 #recommended for most situations, and isdescribed below.  Note that this
 # defaultbehaves differently if the image is being down- or up-sampled, as
 #described below.
 # For both keyword arguments, Matplotlibuses a defaultvalue of "auto" as
 #specified by the rcParam, which isrecommended for most situations.
 #Thisdefaultbehavior 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
 # ====================================
 # %%
 # 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 theupper 100 rows is exactly 0.0, and data in the inner circle
 # the data in theouter 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* setsetto
 # 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')
Original file line number	Diff line number	Diff line change
Expand Up		@@ -63,10 +63,10 @@
		#
		# ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
		#
		# For both keyword arguments, Matplotlibhas a default"antialiased", that is
		#recommended for most situations, and isdescribed below. Note that this
		# defaultbehaves differently if the image is being down- or up-sampled, as
		#described below.
		# For both keyword arguments, Matplotlibuses a defaultvalue of "auto" as
		#specified by the rcParam, which isrecommended for most situations.
Comment on lines +66 to +67 Copy link Member QuLogicFeb 11, 2025 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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.
		#Thisdefaultbehavior 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 DownExpand 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 theupper 100 rows is exactly 0.0, and data in the inner circle
		# the data in theouter 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 setsetto
		# 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')
Expand Down