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63 | 63 | #
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64 | 64 | # ``interpolation_stage='data'``: Data -> Interpolate/Resample -> Normalize -> RGBA
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65 | 65 | #
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66 |
| -# For both keyword arguments, Matplotlib has a default "antialiased", that is |
| 66 | +# For both keyword arguments, Matplotlib has a default "auto", that is |
67 | 67 | # recommended for most situations, and is described below. Note that this
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68 | 68 | # default behaves differently if the image is being down- or up-sampled, as
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69 | 69 | # described below.
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166 | 166 | # %%
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167 | 167 | # A final example shows the desirability of performing the anti-aliasing at the
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168 | 168 | # RGBA stage when using non-trivial interpolation kernels. In the following,
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169 |
| -# the data in the upper 100 rows is exactly 0.0, and data in the inner circle |
| 169 | +# the data in the outer circle is exactly 0.0, and data in the inner circle |
170 | 170 | # is exactly 2.0. If we perform the *interpolation_stage* in 'data' space and
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171 | 171 | # use an anti-aliasing filter (first panel), then floating point imprecision
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172 | 172 | # makes some of the data values just a bit less than zero or a bit more than
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173 | 173 | # 2.0, and they get assigned the under- or over- colors. This can be avoided if
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174 |
| -# you do not use an anti-aliasing filter (*interpolation* set set to |
| 174 | +# you do not use an anti-aliasing filter (*interpolation* set to |
175 | 175 | # 'nearest'), however, that makes the part of the data susceptible to Moiré
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176 | 176 | # patterns much worse (second panel). Therefore, we recommend the default
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177 | 177 | # *interpolation* of 'hanning'/'auto', and *interpolation_stage* of
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178 | 178 | # 'rgba'/'auto' for most down-sampling situations (last panel).
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| 179 | +# In this example, the data values are clipped at the edges of the color range. |
| 180 | +# The interpolation uses the 'nearest' method, and as a result, no |
| 181 | +# floating-point imprecision is visible in the first panel. |
179 | 182 |
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180 | 183 | a = alarge + 1
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181 | 184 | cmap = plt.get_cmap('RdBu_r')
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