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Commit 9ace1e2

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tacaswellericpre
authored andcommitted
Merge pull request matplotlib#20935 from timhoffm/experimental-colormaps
ENH: Add ColormapsRegistry as experimental and add it to pyplot
2 parents b604450 + abd8213 commit 9ace1e2

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‎doc/api/pyplot_summary.rst‎

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@@ -29,4 +29,5 @@ For a more in-depth look at colormaps, see the
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.. currentmodule:: matplotlib.pyplot
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.. autofunction:: colormaps
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.. autodata:: colormaps
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:no-value:
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Colormap registry
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------------------
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Colormap registry (experimental)
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--------------------------------
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Colormaps are now managed via `matplotlib.colormaps`, which is a
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`.ColormapRegistry`.
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Colormaps are now managed via `matplotlib.colormaps` (or `.pyplot.colormaps`),
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which is a `.ColormapRegistry`. While we are confident that the API is final,
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we formally mark it as experimental for 3.5 because we want to keep the option
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to still adapt the API for 3.6 should the need arise.
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Colormaps can be obtained using item access::
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import matplotlib as mpl
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cmap = mpl.colormaps['viridis']
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import matplotlib.pyplot as plt
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cmap = plt.colormaps['viridis']
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To register new colormaps use::
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mpl.colormaps.register(my_colormap)
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plt.colormaps.register(my_colormap)
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The use of `matplotlib.cm.get_cmap` and `matplotlib.cm.register_cmap` is
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discouraged in favor of the above. Within `.pyplot` the use of
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``plt.get_cmap()`` and ``plt.register_cmap()`` will continue to be supported.
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We recommend to use the new API instead of the `~.cm.get_cmap` and
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`~.cm.register_cmap` functions for new code. `matplotlib.cm.get_cmap` and
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`matplotlib.cm.register_cmap` will eventually be deprecated and removed.
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Within `.pyplot` ``plt.get_cmap()`` and ``plt.register_cmap()`` will continue
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to be supported for backward compatibility.

‎examples/images_contours_and_fields/contourf_demo.py‎

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How to use the `.axes.Axes.contourf` method to create filled contour plots.
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"""
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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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origin = 'lower'
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# Illustrate all 4 possible "extend" settings:
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extends = ["neither", "both", "min", "max"]
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cmap = mpl.colormaps["winter"].with_extremes(under="magenta", over="yellow")
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cmap = plt.colormaps["winter"].with_extremes(under="magenta", over="yellow")
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# Note: contouring simply excludes masked or nan regions, so
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# instead of using the "bad" colormap value for them, it draws
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# nothing at all in them. Therefore the following would have

‎examples/images_contours_and_fields/demo_bboximage.py‎

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a bounding box. This demo shows how to show an image inside a `.text.Text`'s
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bounding box as well as how to manually create a bounding box for the image.
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"""
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.image import BboxImage
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a = np.vstack((a, a))
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# List of all colormaps; skip reversed colormaps.
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cmap_names = sorted(m for m in mpl.colormaps if not m.endswith("_r"))
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cmap_names = sorted(m for m in plt.colormaps if not m.endswith("_r"))
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ncol = 2
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nrow = len(cmap_names) // ncol + 1

‎examples/images_contours_and_fields/pcolormesh_levels.py‎

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# pick the desired colormap, sensible levels, and define a normalization
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# instance which takes data values and translates those into levels.
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cmap = plt.get_cmap('PiYG')
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cmap = plt.colormaps['PiYG']
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norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
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fig, (ax0, ax1) = plt.subplots(nrows=2)

‎examples/images_contours_and_fields/quadmesh_demo.py‎

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This demo illustrates a bug in quadmesh with masked data.
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"""
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import matplotlib as mpl
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from matplotlib import pyplot as plt
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import numpy as np
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axs[0].set_title('Without masked values')
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# You can control the color of the masked region.
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cmap = mpl.colormaps[mpl.rcParams['image.cmap']].with_extremes(bad='y')
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cmap = plt.colormaps[plt.rcParams['image.cmap']].with_extremes(bad='y')
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axs[1].pcolormesh(Qx, Qz, Zm, shading='gouraud', cmap=cmap)
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axs[1].set_title('With masked values')
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‎examples/lines_bars_and_markers/horizontal_barchart_distribution.py‎

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labels = list(results.keys())
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data = np.array(list(results.values()))
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data_cum = data.cumsum(axis=1)
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category_colors = plt.get_cmap('RdYlGn')(
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category_colors = plt.colormaps['RdYlGn'](
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np.linspace(0.15, 0.85, data.shape[1]))
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fig, ax = plt.subplots(figsize=(9.2, 5))

‎examples/mplot3d/polys3d.py‎

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# verts[i] is a list of (x, y) pairs defining polygon i.
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verts = [polygon_under_graph(x, poisson.pmf(l, x)) for l in lambdas]
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facecolors = plt.get_cmap('viridis_r')(np.linspace(0, 1, len(verts)))
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facecolors = plt.colormaps['viridis_r'](np.linspace(0, 1, len(verts)))
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poly = PolyCollection(verts, facecolors=facecolors, alpha=.7)
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ax.add_collection3d(poly, zs=lambdas, zdir='y')

‎examples/pie_and_polar_charts/nested_pie.py‎

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"""
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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size = 0.3
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vals = np.array([[60., 32.], [37., 40.], [29., 10.]])
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cmap = mpl.colormaps["tab20c"]
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cmap = plt.colormaps["tab20c"]
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outer_colors = cmap(np.arange(3)*4)
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inner_colors = cmap([1, 2, 5, 6, 9, 10])
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# Obtain the ordinates of the bar edges
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valsleft = np.cumsum(np.append(0, valsnorm.flatten()[:-1])).reshape(vals.shape)
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cmap = mpl.colormaps["tab20c"]
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cmap = plt.colormaps["tab20c"]
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outer_colors = cmap(np.arange(3)*4)
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inner_colors = cmap([1, 2, 5, 6, 9, 10])
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‎examples/userdemo/colormap_interactive_adjustment.py‎

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Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = (0.9*Z1 - 0.5*Z2) * 2
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cmap = plt.get_cmap('viridis').with_extremes(
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cmap = plt.colormaps['viridis'].with_extremes(
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over='xkcd:orange', under='xkcd:dark red')
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axesimage = plt.imshow(Z, cmap=cmap)
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colorbar = plt.colorbar(axesimage, ax=ax, use_gridspec=True)

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