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Commit4d6ab93

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Merge pull request#20935 from timhoffm/experimental-colormaps
ENH: Add ColormapsRegistry as experimental and add it to pyplot
2 parentsec3966b +abd8213 commit4d6ab93

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-274
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12 files changed

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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 asmpl
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cmap =mpl.colormaps['viridis']
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import matplotlib.pyplot asplt
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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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importnumpyasnp
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importmatplotlibasmpl
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importmatplotlib.pyplotasplt
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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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importmatplotlibasmpl
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importmatplotlib.pyplotasplt
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importnumpyasnp
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frommatplotlib.imageimportBboxImage
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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(mforminmpl.colormapsifnotm.endswith("_r"))
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cmap_names=sorted(mforminplt.colormapsifnotm.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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importmatplotlibasmpl
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frommatplotlibimportpyplotasplt
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importnumpyasnp
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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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@@ -43,7 +43,7 @@ def survey(results, category_names):
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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))forlinlambdas]
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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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importmatplotlibasmpl
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importmatplotlib.pyplotasplt
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importnumpyasnp
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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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