``matplotlib.colorizer.Colorizer`` as container for ``norm`` and ``cmap``

`matplotlib.colorizer.Colorizer` encapsulates the data-to-color pipeline. It makes reuse of colormapping easier, e.g. across multiple images. Plotting methods that support *norm* and *cmap* keyword arguments now also accept a *colorizer* keyword argument.

In the following example the norm and cmap are changed on multiple plots simultaneously:

..plot::

:include-source: true

:alt: Example use of a matplotlib.colorizer.Colorizer object

import matplotlib.pyplot as plt

import matplotlib as mpl

import numpy as np

x = np.linspace(-2, 2, 50)[np.newaxis, :]

y = np.linspace(-2, 2, 50)[:, np.newaxis]

im_0 = 1 * np.exp( - (x**2 + y**2 - x * y))

im_1 = 2 * np.exp( - (x**2 + y**2 + x * y))

colorizer = mpl.colorizer.Colorizer()

fig, axes = plt.subplots(1, 2, figsize=(6, 2))

cim_0 = axes[0].imshow(im_0, colorizer=colorizer)

fig.colorbar(cim_0)

cim_1 = axes[1].imshow(im_1, colorizer=colorizer)

fig.colorbar(cim_1)

colorizer.vmin = 0.5

colorizer.vmax = 2

colorizer.cmap = 'RdBu'

All plotting methods that use a data-to-color pipeline now create a colorizer object if one is not provided. This can be re-used by subsequent artists such that they will share a single data-to-color pipeline:

..plot::

:include-source: true

:alt: Example of how artists that share a ``colorizer`` have coupled colormaps

import matplotlib.pyplot as plt

import matplotlib as mpl

import numpy as np

x = np.linspace(-2, 2, 50)[np.newaxis, :]

y = np.linspace(-2, 2, 50)[:, np.newaxis]

im_0 = 1 * np.exp( - (x**2 + y**2 - x * y))

im_1 = 2 * np.exp( - (x**2 + y**2 + x * y))

fig, axes = plt.subplots(1, 2, figsize=(6, 2))

cim_0 = axes[0].imshow(im_0, cmap='RdBu', vmin=0.5, vmax=2)

fig.colorbar(cim_0)

cim_1 = axes[1].imshow(im_1, colorizer=cim_0.colorizer)

fig.colorbar(cim_1)

cim_1.cmap = 'rainbow'

3D plotting improvements