Jan 22, 2025 · Jan 22, 2025
diff --git a/doc/users/prev_whats_new/whats_new_3.10.0.rst b/doc/users/prev_whats_new/whats_new_3.10.0.rst
    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
 ========================
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( - (x2 + y2 - x * y))
		im_1 = 2 * np.exp( - (x2 + y2 + 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( - (x2 + y2 - x * y))
		im_1 = 2 * np.exp( - (x2 + y2 + 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
		========================

Expand Down