DOC: remove cm.get_cmap from examples and tutorials

Author: tacaswell

examples/images_contours_and_fields/contour_image.py

@@ -41,7 +41,7 @@
 axs = _axs.flatten()
 
 cset1 = axs[0].contourf(X, Y, Z, levels, norm=norm,
-                        cmap=cm.get_cmap(cmap, len(levels) - 1))
+                        cmap=cmap.resampled(len(levels) - 1))
 # It is not necessary, but for the colormap, we need only the
 # number of levels minus 1.  To avoid discretization error, use
 # either this number or a large number such as the default (256).

examples/images_contours_and_fields/image_annotated_heatmap.py

@@ -39,6 +39,7 @@
 
 import numpy as np
 import matplotlib
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 # sphinx_gallery_thumbnail_number = 2
 
@@ -272,7 +273,7 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
 fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: qrates[::-1][norm(x)])
 
 im, _ = heatmap(data, y, x, ax=ax3,
-                cmap=plt.get_cmap("PiYG", 7), norm=norm,
+                cmap=mpl.colormaps["PiYG"].resampled(7), norm=norm,
                 cbar_kw=dict(ticks=np.arange(-3, 4), format=fmt),
                 cbarlabel="Quality Rating")
 

examples/lines_bars_and_markers/multivariate_marker_plot.py

@@ -31,7 +31,7 @@
 positions = np.random.normal(size=(N, 2)) * 5
 data = zip(skills, takeoff_angles, thrusts, successful, positions)
 
-cmap = plt.cm.get_cmap("plasma")
+cmap = plt.colormaps["plasma"]
 fig, ax = plt.subplots()
 fig.suptitle("Throwing success", size=14)
 for skill, takeoff, thrust, mood, pos in data:

tutorials/colors/colormap-manipulation.py

@@ -4,7 +4,7 @@
 ********************************
 
 Matplotlib has a number of built-in colormaps accessible via
-`.matplotlib.cm.get_cmap`.  There are also external libraries like
+`.matplotlib.colormaps`.  There are also external libraries like
 palettable_ that have many extra colormaps.
 
 .. _palettable: https://jiffyclub.github.io/palettable/
@@ -24,19 +24,19 @@
 ============================================
 
 First, getting a named colormap, most of which are listed in
-:doc:`/tutorials/colors/colormaps`, may be done using
-`.matplotlib.cm.get_cmap`, which returns a colormap object.
-The second argument gives the size of the list of colors used to define the
-colormap, and below we use a modest value of 8 so there are not a lot of
-values to look at.
+:doc:`/tutorials/colors/colormaps`, may be done using `.matplotlib.colormaps`,
+which returns a colormap object.  The length of the list of colors used
+internally to define the colormap and be adjusted via `.Colormap.resampled`.
+Blow we use a modest value of 8 so there are not a lot of values to look at.
+
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib import cm
+import matplotlib as mpl
 from matplotlib.colors import ListedColormap, LinearSegmentedColormap
 
-viridis = cm.get_cmap('viridis', 8)
+viridis = mpl.colormaps['viridis'].resampled(8)
 
 ##############################################################################
 # The object ``viridis`` is a callable, that when passed a float between
@@ -72,7 +72,7 @@
 # However, one may still call the colormap with an integer array, or with a
 # float array between 0 and 1.
 
-copper = cm.get_cmap('copper', 8)
+copper = mpl.colormaps['copper'].resampled(8)
 
 print('copper(range(8))', copper(range(8)))
 print('copper(np.linspace(0, 1, 8))', copper(np.linspace(0, 1, 8)))
@@ -123,7 +123,7 @@ def plot_examples(colormaps):
 # For example, suppose we want to make the first 25 entries of a 256-length
 # "viridis" colormap pink for some reason:
 
-viridis = cm.get_cmap('viridis', 256)
+viridis = mpl.colormaps['viridis'].resampled(256)
 newcolors = viridis(np.linspace(0, 1, 256))
 pink = np.array([248/256, 24/256, 148/256, 1])
 newcolors[:25, :] = pink
@@ -138,15 +138,15 @@ def plot_examples(colormaps):
 # values that were in the original colormap. This method does not interpolate
 # in color-space to add new colors.
 
-viridis_big = cm.get_cmap('viridis')
+viridis_big = mpl.colormaps['viridis']
 newcmp = ListedColormap(viridis_big(np.linspace(0.25, 0.75, 128)))
 plot_examples([viridis, newcmp])
 
 ##############################################################################
 # and we can easily concatenate two colormaps:
 
-top = cm.get_cmap('Oranges_r', 128)
-bottom = cm.get_cmap('Blues', 128)
+top = mpl.colormaps['Oranges_r'].resampled(128)
+bottom = mpl.colormaps['Blues'].resampled(128)
 
 newcolors = np.vstack((top(np.linspace(0, 1, 128)),
                        bottom(np.linspace(0, 1, 128))))
@@ -268,4 +268,4 @@ def plot_linearmap(cdict):
 #    - `matplotlib.colors.LinearSegmentedColormap`
 #    - `matplotlib.colors.ListedColormap`
 #    - `matplotlib.cm`
-#    - `matplotlib.cm.get_cmap`
+#    - `matplotlib.colormaps`

tutorials/colors/colormaps.py

@@ -4,7 +4,7 @@
 ********************************
 
 Matplotlib has a number of built-in colormaps accessible via
-`.matplotlib.cm.get_cmap`.  There are also external libraries that
+`.matplotlib.colormaps`.  There are also external libraries that
 have many extra colormaps, which can be viewed in the
 `Third-party colormaps`_ section of the Matplotlib documentation.
 Here we briefly discuss how to choose between the many options.  For
@@ -80,7 +80,6 @@
 import numpy as np
 import matplotlib as mpl
 import matplotlib.pyplot as plt
-from matplotlib import cm
 from colorspacious import cspace_converter
 
 
@@ -105,7 +104,7 @@ def plot_color_gradients(category, cmap_list):
     axs[0].set_title(f'{category} colormaps', fontsize=14)
 
     for ax, name in zip(axs, cmap_list):
-        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
+        ax.imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
         ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
                 transform=ax.transAxes)
 
@@ -277,7 +276,7 @@ def plot_color_gradients(category, cmap_list):
 
             # Get RGB values for colormap and convert the colormap in
             # CAM02-UCS colorspace.  lab[0, :, 0] is the lightness.
-            rgb = cm.get_cmap(cmap)(x)[np.newaxis, :, :3]
+            rgb = mpl.colormaps[cmap](x)[np.newaxis, :, :3]
             lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
 
             # Plot colormap L values.  Do separately for each category
@@ -380,14 +379,14 @@ def plot_color_gradients(cmap_category, cmap_list):
     for ax, name in zip(axs, cmap_list):
 
         # Get RGB values for colormap.
-        rgb = cm.get_cmap(plt.get_cmap(name))(x)[np.newaxis, :, :3]
+        rgb = mpl.colormaps[name](x)[np.newaxis, :, :3]
 
         # Get colormap in CAM02-UCS colorspace. We want the lightness.
         lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
         L = lab[0, :, 0]
         L = np.float32(np.vstack((L, L, L)))
 
-        ax[0].imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
+        ax[0].imshow(gradient, aspect='auto', cmap=mpl.colormaps[name])
         ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
         pos = list(ax[0].get_position().bounds)
         x_text = pos[0] - 0.01