BboxImage

Resampling

When Matplotlib rasterizes an image to save / display a Figure,

we need to, in general, resample the data (either up or down) in

addition to normalizing and color mapping it. This is because the

exact size of the input, in "data" pixels, will not match the size, in

"screen" pixels, of the output. The details of how we do the

resampling is controlled by the *interpolation* specified. This

resampling process can introduce a variety of artifacts and the

default interpolation is chosen to avoid aliasing in common cases (see

:doc:`/gallery/images_contours_and_fields/image_antialiasing`).

Colormapping

The processing steps for rendering a pseudo color image are:

Resampling and Colormapping

When Matplotlib rasterizes an image to save / display a Figure, we

need to resample the user supplied data because the size of the input,

in "data" pixels, will in general not match the size, in "screen"

pixels, of the output. This resampling process can introduce a

variety of artifacts and the default interpolation is chosen to avoid

aliasing in common cases (see

:doc:`/gallery/images_contours_and_fields/image_antialiasing`). The

details of how we do the resampling is controlled by the

*interpolation* kwarg and, depending on the kernel, may also

implicitly smooth the user data.

The processing steps for rendering a pseudo color image are currently:

1. resample the user input to the required dimensions

2. normalize the user data via a `~.colors.Normalize` instance

3. colormap from the normalized data to RGBA via a `~.colors.Colormap` instance

Prior to Matplotlib 2.0 we did the normalization and colormapping

first and then resampled to fit the screen. However this can produce

full range on thescale of a few screen pixels. Because most

colormaps are notstraight linesinRGB spacetheinterpolated values

"cut thecorner" and produce colors in the output image that are not

present in the colormap. To fix thisproblem we re-ordered the

first and then resampledin RGBA spaceto fit the screen. However,

values may "cut thecorner" and produce colors in the output image

that are notpresentinthe colormap whenthedata is changing close

to thefull range on the scale of a few screen pixels. To fix this

problem we re-ordered the processing in 2.0.

What you need to know about Floating Point Arithmetic for Colormapping