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DOC: Added blurb for colorizer objects in what's new for 3.10 #29478

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56 changes: 56 additions & 0 deletions doc/users/prev_whats_new/whats_new_3.10.0.rst
Original file line number Diff line number Diff line change
Expand Up @@ -292,6 +292,62 @@ the ``set_data`` method, enabling e.g. resampling
fig.savefig("after.png")


``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
========================

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