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Backport PR #20935 on branch v3.5.x (Add ColormapsRegistry as experimental and add it to pyplot)#21099

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3 changes: 2 additions & 1 deletiondoc/api/pyplot_summary.rst
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Expand Up@@ -29,4 +29,5 @@ For a more in-depth look at colormaps, see the

.. currentmodule:: matplotlib.pyplot

.. autofunction:: colormaps
.. autodata:: colormaps
:no-value:
24 changes: 14 additions & 10 deletionsdoc/users/next_whats_new/colormaps.rst
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@@ -1,18 +1,22 @@
Colormap registry
------------------
Colormap registry (experimental)
--------------------------------

Colormaps are now managed via `matplotlib.colormaps`, which is a
`.ColormapRegistry`.
Colormaps are now managed via `matplotlib.colormaps` (or `.pyplot.colormaps`),
which is a `.ColormapRegistry`. While we are confident that the API is final,
we formally mark it as experimental for 3.5 because we want to keep the option
to still adapt the API for 3.6 should the need arise.

Colormaps can be obtained using item access::

import matplotlib asmpl
cmap =mpl.colormaps['viridis']
import matplotlib.pyplot asplt
cmap =plt.colormaps['viridis']

To register new colormaps use::

mpl.colormaps.register(my_colormap)
plt.colormaps.register(my_colormap)

The use of `matplotlib.cm.get_cmap` and `matplotlib.cm.register_cmap` is
discouraged in favor of the above. Within `.pyplot` the use of
``plt.get_cmap()`` and ``plt.register_cmap()`` will continue to be supported.
We recommend to use the new API instead of the `~.cm.get_cmap` and
`~.cm.register_cmap` functions for new code. `matplotlib.cm.get_cmap` and
`matplotlib.cm.register_cmap` will eventually be deprecated and removed.
Within `.pyplot` ``plt.get_cmap()`` and ``plt.register_cmap()`` will continue
to be supported for backward compatibility.
3 changes: 1 addition & 2 deletionsexamples/images_contours_and_fields/contourf_demo.py
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Expand Up@@ -6,7 +6,6 @@
How to use the `.axes.Axes.contourf` method to create filled contour plots.
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

origin = 'lower'
Expand DownExpand Up@@ -87,7 +86,7 @@

# Illustrate all 4 possible "extend" settings:
extends = ["neither", "both", "min", "max"]
cmap =mpl.colormaps["winter"].with_extremes(under="magenta", over="yellow")
cmap =plt.colormaps["winter"].with_extremes(under="magenta", over="yellow")
# Note: contouring simply excludes masked or nan regions, so
# instead of using the "bad" colormap value for them, it draws
# nothing at all in them. Therefore the following would have
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3 changes: 1 addition & 2 deletionsexamples/images_contours_and_fields/demo_bboximage.py
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Expand Up@@ -7,7 +7,6 @@
a bounding box. This demo shows how to show an image inside a `.text.Text`'s
bounding box as well as how to manually create a bounding box for the image.
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
Expand DownExpand Up@@ -39,7 +38,7 @@
a = np.vstack((a, a))

# List of all colormaps; skip reversed colormaps.
cmap_names = sorted(m for m inmpl.colormaps if not m.endswith("_r"))
cmap_names = sorted(m for m inplt.colormaps if not m.endswith("_r"))

ncol = 2
nrow = len(cmap_names) // ncol + 1
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Expand Up@@ -94,7 +94,7 @@

# pick the desired colormap, sensible levels, and define a normalization
# instance which takes data values and translates those into levels.
cmap = plt.get_cmap('PiYG')
cmap = plt.colormaps['PiYG']
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig, (ax0, ax1) = plt.subplots(nrows=2)
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3 changes: 1 addition & 2 deletionsexamples/images_contours_and_fields/quadmesh_demo.py
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Expand Up@@ -9,7 +9,6 @@
This demo illustrates a bug in quadmesh with masked data.
"""

import matplotlib as mpl
from matplotlib import pyplot as plt
import numpy as np

Expand All@@ -30,7 +29,7 @@
axs[0].set_title('Without masked values')

# You can control the color of the masked region.
cmap =mpl.colormaps[mpl.rcParams['image.cmap']].with_extremes(bad='y')
cmap =plt.colormaps[plt.rcParams['image.cmap']].with_extremes(bad='y')
axs[1].pcolormesh(Qx, Qz, Zm, shading='gouraud', cmap=cmap)
axs[1].set_title('With masked values')

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Expand Up@@ -43,7 +43,7 @@ def survey(results, category_names):
labels = list(results.keys())
data = np.array(list(results.values()))
data_cum = data.cumsum(axis=1)
category_colors = plt.get_cmap('RdYlGn')(
category_colors = plt.colormaps['RdYlGn'](
np.linspace(0.15, 0.85, data.shape[1]))

fig, ax = plt.subplots(figsize=(9.2, 5))
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2 changes: 1 addition & 1 deletionexamples/mplot3d/polys3d.py
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Expand Up@@ -32,7 +32,7 @@ def polygon_under_graph(x, y):

# verts[i] is a list of (x, y) pairs defining polygon i.
verts = [polygon_under_graph(x, poisson.pmf(l, x)) for l in lambdas]
facecolors = plt.get_cmap('viridis_r')(np.linspace(0, 1, len(verts)))
facecolors = plt.colormaps['viridis_r'](np.linspace(0, 1, len(verts)))

poly = PolyCollection(verts, facecolors=facecolors, alpha=.7)
ax.add_collection3d(poly, zs=lambdas, zdir='y')
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5 changes: 2 additions & 3 deletionsexamples/pie_and_polar_charts/nested_pie.py
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Expand Up@@ -8,7 +8,6 @@

"""

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

Expand All@@ -31,7 +30,7 @@
size = 0.3
vals = np.array([[60., 32.], [37., 40.], [29., 10.]])

cmap =mpl.colormaps["tab20c"]
cmap =plt.colormaps["tab20c"]
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap([1, 2, 5, 6, 9, 10])

Expand DownExpand Up@@ -62,7 +61,7 @@
# Obtain the ordinates of the bar edges
valsleft = np.cumsum(np.append(0, valsnorm.flatten()[:-1])).reshape(vals.shape)

cmap =mpl.colormaps["tab20c"]
cmap =plt.colormaps["tab20c"]
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap([1, 2, 5, 6, 9, 10])

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2 changes: 1 addition & 1 deletionexamples/userdemo/colormap_interactive_adjustment.py
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Expand Up@@ -54,7 +54,7 @@ def adjust_colorbar(mouseevent):
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (0.9*Z1 - 0.5*Z2) * 2

cmap = plt.get_cmap('viridis').with_extremes(
cmap = plt.colormaps['viridis'].with_extremes(
over='xkcd:orange', under='xkcd:dark red')
axesimage = plt.imshow(Z, cmap=cmap)
colorbar = plt.colorbar(axesimage, ax=ax, use_gridspec=True)
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16 changes: 16 additions & 0 deletionslib/matplotlib/cm.py
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Expand Up@@ -102,6 +102,12 @@ class ColormapRegistry(Mapping):
r"""
Container for colormaps that are known to Matplotlib by name.

.. admonition:: Experimental

While we expect the API to be final, we formally mark it as
experimental for 3.5 because we want to keep the option to still adapt
the API for 3.6 should the need arise.

The universal registry instance is `matplotlib.colormaps`. There should be
no need for users to instantiate `.ColormapRegistry` themselves.

Expand DownExpand Up@@ -136,6 +142,16 @@ def __str__(self):
return ('ColormapRegistry; available colormaps:\n' +
', '.join(f"'{name}'" for name in self))

def __call__(self):
"""
Return a list of the registered colormap names.

This exists only for backward-compatibilty in `.pyplot` which had a
``plt.colormaps()`` method. The recommended way to get this list is
now ``list(colormaps)``.
"""
return list(self)

def register(self, cmap, *, name=None, force=False):
"""
Register a new colormap.
Expand Down
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