matplotlib · QuLogic · Jan 15, 2025 · Oct 27, 2024
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
@@ -56,9 +56,9 @@ colour maps version 8.0.1 (DOI: https://doi.org/10.5281/zenodo.1243862).
     img = np.sin(x*y)
 
     _, ax = plt.subplots(1, 3)
-    ax[0].imshow(img, cmap=plt.cm.berlin)
-    ax[1].imshow(img, cmap=plt.cm.managua)
-    ax[2].imshow(img, cmap=plt.cm.vanimo)
+    ax[0].imshow(img, cmap="berlin")
+    ax[1].imshow(img, cmap="managua")
+    ax[2].imshow(img, cmap="vanimo")
 
 
 

diff --git a/galleries/examples/axisartist/demo_curvelinear_grid2.py b/galleries/examples/axisartist/demo_curvelinear_grid2.py
@@ -41,7 +41,7 @@ def inv_tr(x, y):
     # itself (i.e., transData) is not affected by the given transform.
 
     ax1.imshow(np.arange(25).reshape(5, 5),
-               vmax=50, cmap=plt.cm.gray_r, origin="lower")
+               vmax=50, cmap="gray_r", origin="lower")
 
 
 if __name__ == "__main__":

diff --git a/galleries/examples/images_contours_and_fields/contour_demo.py b/galleries/examples/images_contours_and_fields/contour_demo.py
@@ -13,8 +13,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-import matplotlib.cm as cm
-
 delta = 0.025
 x = np.arange(-3.0, 3.0, delta)
 y = np.arange(-2.0, 2.0, delta)
@@ -79,7 +77,7 @@
 
 fig, ax = plt.subplots()
 im = ax.imshow(Z, interpolation='bilinear', origin='lower',
-               cmap=cm.gray, extent=(-3, 3, -2, 2))
+               cmap="gray", extent=(-3, 3, -2, 2))
 levels = np.arange(-1.2, 1.6, 0.2)
 CS = ax.contour(Z, levels, origin='lower', cmap='flag', extend='both',
                 linewidths=2, extent=(-3, 3, -2, 2))

diff --git a/galleries/examples/images_contours_and_fields/contour_image.py b/galleries/examples/images_contours_and_fields/contour_image.py
@@ -35,7 +35,7 @@
 levels = np.arange(-2.0, 1.601, 0.4)
 
 norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
-cmap = cm.PRGn
+cmap = plt.colormaps["PRGn"]
 
 fig, _axs = plt.subplots(nrows=2, ncols=2)
 fig.subplots_adjust(hspace=0.3)

diff --git a/galleries/examples/images_contours_and_fields/contourf_demo.py b/galleries/examples/images_contours_and_fields/contourf_demo.py
@@ -39,7 +39,7 @@
 # for purposes of illustration.
 
 fig1, ax2 = plt.subplots(layout='constrained')
-CS = ax2.contourf(X, Y, Z, 10, cmap=plt.cm.bone)
+CS = ax2.contourf(X, Y, Z, 10, cmap="bone")
 
 # Note that in the following, we explicitly pass in a subset of the contour
 # levels used for the filled contours.  Alternatively, we could pass in

diff --git a/galleries/examples/images_contours_and_fields/contourf_log.py b/galleries/examples/images_contours_and_fields/contourf_log.py
@@ -10,7 +10,7 @@
 import numpy as np
 from numpy import ma
 
-from matplotlib import cm, ticker
+from matplotlib import ticker
 
 N = 100
 x = np.linspace(-3.0, 3.0, N)
@@ -36,7 +36,7 @@
 # Automatic selection of levels works; setting the
 # log locator tells contourf to use a log scale:
 fig, ax = plt.subplots()
-cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
+cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap="PuBu_r")
 
 # Alternatively, you can manually set the levels
 # and the norm:

diff --git a/galleries/examples/images_contours_and_fields/image_demo.py b/galleries/examples/images_contours_and_fields/image_demo.py
@@ -12,7 +12,6 @@
 import numpy as np
 
 import matplotlib.cbook as cbook
-import matplotlib.cm as cm
 from matplotlib.patches import PathPatch
 from matplotlib.path import Path
 
@@ -30,7 +29,7 @@
 Z = (Z1 - Z2) * 2
 
 fig, ax = plt.subplots()
-im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlBu,
+im = ax.imshow(Z, interpolation='bilinear', cmap="RdYlBu",
                origin='lower', extent=[-3, 3, -3, 3],
                vmax=abs(Z).max(), vmin=-abs(Z).max())
 
@@ -58,7 +57,7 @@
 ax['hopper'].imshow(image)
 ax['hopper'].axis('off')  # clear x-axis and y-axis
 
-im = ax['mri'].imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent)
+im = ax['mri'].imshow(A, cmap="hot", origin='upper', extent=extent)
 
 markers = [(15.9, 14.5), (16.8, 15)]
 x, y = zip(*markers)
@@ -163,7 +162,7 @@
 fig, ax = plt.subplots()
 ax.add_patch(patch)
 
-im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+im = ax.imshow(Z, interpolation='bilinear', cmap="gray",
                origin='lower', extent=[-3, 3, -3, 3],
                clip_path=patch, clip_on=True)
 im.set_clip_path(patch)

diff --git a/galleries/examples/images_contours_and_fields/image_masked.py b/galleries/examples/images_contours_and_fields/image_masked.py
@@ -25,7 +25,7 @@
 Z = (Z1 - Z2) * 2
 
 # Set up a colormap:
-palette = plt.cm.gray.with_extremes(over='r', under='g', bad='b')
+palette = plt.colormaps["gray"].with_extremes(over='r', under='g', bad='b')
 # Alternatively, we could use
 # palette.set_bad(alpha = 0.0)
 # to make the bad region transparent.  This is the default.

diff --git a/galleries/examples/images_contours_and_fields/image_nonuniform.py b/galleries/examples/images_contours_and_fields/image_nonuniform.py
@@ -11,7 +11,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from matplotlib import cm
 from matplotlib.image import NonUniformImage
 
 interp = 'nearest'
@@ -30,7 +29,7 @@
 fig.suptitle('NonUniformImage class', fontsize='large')
 ax = axs[0, 0]
 im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
-                     cmap=cm.Purples)
+                     cmap="Purples")
 im.set_data(x, y, z)
 ax.add_image(im)
 ax.set_xlim(-4, 4)
@@ -39,7 +38,7 @@
 
 ax = axs[0, 1]
 im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
-                     cmap=cm.Purples)
+                     cmap="Purples")
 im.set_data(x2, y, z)
 ax.add_image(im)
 ax.set_xlim(-64, 64)
@@ -50,7 +49,7 @@
 
 ax = axs[1, 0]
 im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
-                     cmap=cm.Purples)
+                     cmap="Purples")
 im.set_data(x, y, z)
 ax.add_image(im)
 ax.set_xlim(-4, 4)
@@ -59,7 +58,7 @@
 
 ax = axs[1, 1]
 im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
-                     cmap=cm.Purples)
+                     cmap="Purples")
 im.set_data(x2, y, z)
 ax.add_image(im)
 ax.set_xlim(-64, 64)

diff --git a/galleries/examples/images_contours_and_fields/layer_images.py b/galleries/examples/images_contours_and_fields/layer_images.py
@@ -31,12 +31,12 @@ def func3(x, y):
 fig = plt.figure(frameon=False)
 
 Z1 = np.add.outer(range(8), range(8)) % 2  # chessboard
-im1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',
+im1 = plt.imshow(Z1, cmap="gray", interpolation='nearest',
                  extent=extent)
 
 Z2 = func3(X, Y)
 
-im2 = plt.imshow(Z2, cmap=plt.cm.viridis, alpha=.9, interpolation='bilinear',
+im2 = plt.imshow(Z2, cmap="viridis", alpha=.9, interpolation='bilinear',
                  extent=extent)
 
 plt.show()

diff --git a/galleries/examples/images_contours_and_fields/shading_example.py b/galleries/examples/images_contours_and_fields/shading_example.py
@@ -25,10 +25,10 @@ def main():
     dem = cbook.get_sample_data('jacksboro_fault_dem.npz')
     elev = dem['elevation']
 
-    fig = compare(z, plt.cm.copper)
+    fig = compare(z, plt.colormaps["copper"])
     fig.suptitle('HSV Blending Looks Best with Smooth Surfaces', y=0.95)
 
-    fig = compare(elev, plt.cm.gist_earth, ve=0.05)
+    fig = compare(elev, plt.colormaps["gist_earth"], ve=0.05)
     fig.suptitle('Overlay Blending Looks Best with Rough Surfaces', y=0.95)
 
     plt.show()

diff --git a/galleries/examples/lines_bars_and_markers/gradient_bar.py b/galleries/examples/lines_bars_and_markers/gradient_bar.py
@@ -56,15 +56,15 @@ def gradient_bar(ax, x, y, width=0.5, bottom=0):
     for left, top in zip(x, y):
         right = left + width
         gradient_image(ax, extent=(left, right, bottom, top),
-                       cmap=plt.cm.Blues_r, cmap_range=(0, 0.8))
+                       cmap="Blues_r", cmap_range=(0, 0.8))
 
 
 fig, ax = plt.subplots()
 ax.set(xlim=(0, 10), ylim=(0, 1))
 
 # background image
    gradient_image(ax, direction=1, extent=(0, 1, 0, 1), transform=ax.transAxes,
-               cmap=plt.cm.RdYlGn, cmap_range=(0.2, 0.8), alpha=0.5)
+               cmap="RdYlGn", cmap_range=(0.2, 0.8), alpha=0.5)
 
 N = 10
 x = np.arange(N) + 0.15

diff --git a/galleries/examples/lines_bars_and_markers/multivariate_marker_plot.py b/galleries/examples/lines_bars_and_markers/multivariate_marker_plot.py
@@ -39,7 +39,7 @@
     t = Affine2D().scale(skill).rotate_deg(takeoff)
     m = MarkerStyle(SUCCESS_SYMBOLS[mood], transform=t)
     ax.plot(pos[0], pos[1], marker=m, color=cmap(thrust))
-fig.colorbar(plt.cm.ScalarMappable(norm=Normalize(0, 1), cmap=cmap),
+fig.colorbar(plt.cm.ScalarMappable(norm=Normalize(0, 1), cmap="plasma"),
              ax=ax, label="Normalized Thrust [a.u.]")
 ax.set_xlabel("X position [m]")
 ax.set_ylabel("Y position [m]")

diff --git a/galleries/examples/misc/contour_manual.py b/galleries/examples/misc/contour_manual.py
@@ -8,7 +8,6 @@
 
 import matplotlib.pyplot as plt
 
-import matplotlib.cm as cm
 from matplotlib.contour import ContourSet
 from matplotlib.path import Path
 
@@ -30,12 +29,12 @@
 fig, ax = plt.subplots()
 
 # Filled contours using filled=True.
-cs = ContourSet(ax, [0, 1, 2], [filled01, filled12], filled=True, cmap=cm.bone)
+cs = ContourSet(ax, [0, 1, 2], [filled01, filled12], filled=True, cmap="bone")
 cbar = fig.colorbar(cs)
 
 # Contour lines (non-filled).
 lines = ContourSet(
-    ax, [0, 1, 2], [lines0, lines1, lines2], cmap=cm.cool, linewidths=3)
+    ax, [0, 1, 2], [lines0, lines1, lines2], cmap="cool", linewidths=3)
 cbar.add_lines(lines)
 
 ax.set(xlim=(-0.5, 3.5), ylim=(-0.5, 4.5),

diff --git a/galleries/examples/misc/demo_agg_filter.py b/galleries/examples/misc/demo_agg_filter.py
@@ -14,7 +14,6 @@
 import numpy as np
 
 from matplotlib.artist import Artist
-import matplotlib.cm as cm
 from matplotlib.colors import LightSource
 import matplotlib.transforms as mtransforms
 
@@ -179,7 +178,7 @@ def filtered_text(ax):
 
     # draw
     ax.imshow(Z, interpolation='bilinear', origin='lower',
-              cmap=cm.gray, extent=(-3, 3, -2, 2), aspect='auto')
+              cmap="gray", extent=(-3, 3, -2, 2), aspect='auto')
     levels = np.arange(-1.2, 1.6, 0.2)
     CS = ax.contour(Z, levels,
                     origin='lower',

diff --git a/galleries/examples/misc/hyperlinks_sgskip.py b/galleries/examples/misc/hyperlinks_sgskip.py
@@ -13,8 +13,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-import matplotlib.cm as cm
-
 # %%
 
 fig = plt.figure()
@@ -32,7 +30,7 @@
 Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
 Z = (Z1 - Z2) * 2
 
-im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
+im = plt.imshow(Z, interpolation='bilinear', cmap="gray",
                 origin='lower', extent=(-3, 3, -3, 3))
 
 im.set_url('https://www.google.com/')

diff --git a/galleries/examples/misc/table_demo.py b/galleries/examples/misc/table_demo.py
@@ -21,7 +21,7 @@
 value_increment = 1000
 
 # Get some pastel shades for the colors
-colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))
+colors = plt.colormaps["BuPu"](np.linspace(0, 0.5, len(rows)))
 n_rows = len(data)
 
 index = np.arange(len(columns)) + 0.3

diff --git a/galleries/examples/mplot3d/contour3d.py b/galleries/examples/mplot3d/contour3d.py
@@ -9,13 +9,12 @@
 
 import matplotlib.pyplot as plt
 
-from matplotlib import cm
 from mpl_toolkits.mplot3d import axes3d
 
 ax = plt.figure().add_subplot(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
 
-ax.contour(X, Y, Z, cmap=cm.coolwarm)  # Plot contour curves
+ax.contour(X, Y, Z, cmap="coolwarm")  # Plot contour curves
 
 plt.show()
 

diff --git a/galleries/examples/mplot3d/contour3d_2.py b/galleries/examples/mplot3d/contour3d_2.py
@@ -9,12 +9,11 @@
 
 import matplotlib.pyplot as plt
 
-from matplotlib import cm
 from mpl_toolkits.mplot3d import axes3d
 
 ax = plt.figure().add_subplot(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
-ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
+ax.contour(X, Y, Z, extend3d=True, cmap="coolwarm")
 
 plt.show()
 

diff --git a/galleries/examples/mplot3d/contourf3d.py b/galleries/examples/mplot3d/contourf3d.py
@@ -12,12 +12,11 @@
 
 import matplotlib.pyplot as plt
 
-from matplotlib import cm
 from mpl_toolkits.mplot3d import axes3d
 
 ax = plt.figure().add_subplot(projection='3d')
 X, Y, Z = axes3d.get_test_data(0.05)
-ax.contourf(X, Y, Z, cmap=cm.coolwarm)
+ax.contourf(X, Y, Z, cmap="coolwarm")
 
 plt.show()
 

diff --git a/galleries/examples/mplot3d/custom_shaded_3d_surface.py b/galleries/examples/mplot3d/custom_shaded_3d_surface.py
@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from matplotlib import cbook, cm
+from matplotlib import cbook
 from matplotlib.colors import LightSource
 
 # Load and format data
@@ -29,7 +29,7 @@
 ls = LightSource(270, 45)
 # To use a custom hillshading mode, override the built-in shading and pass
 # in the rgb colors of the shaded surface calculated from "shade".
-rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')
+rgb = ls.shade(z, cmap=plt.colormaps["gist_earth"], vert_exag=0.1, blend_mode='soft')
 surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,
                        linewidth=0, antialiased=False, shade=False)
 

diff --git a/galleries/examples/mplot3d/subplot3d.py b/galleries/examples/mplot3d/subplot3d.py
@@ -9,7 +9,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from matplotlib import cm
 from mpl_toolkits.mplot3d.axes3d import get_test_data
 
 # set up a figure twice as wide as it is tall
@@ -27,7 +26,7 @@
 X, Y = np.meshgrid(X, Y)
 R = np.sqrt(X**2 + Y**2)
 Z = np.sin(R)
-surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
+surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap="coolwarm",
                        linewidth=0, antialiased=False)
 ax.set_zlim(-1.01, 1.01)
 fig.colorbar(surf, shrink=0.5, aspect=10)

diff --git a/galleries/examples/mplot3d/surface3d.py b/galleries/examples/mplot3d/surface3d.py
@@ -13,7 +13,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from matplotlib import cm
 from matplotlib.ticker import LinearLocator
 
 fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
@@ -26,7 +25,7 @@
 Z = np.sin(R)
 
 # Plot the surface.
-surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
+surf = ax.plot_surface(X, Y, Z, cmap="coolwarm",
                        linewidth=0, antialiased=False)
 
 # Customize the z axis.

diff --git a/galleries/examples/mplot3d/surface3d_radial.py b/galleries/examples/mplot3d/surface3d_radial.py
@@ -26,7 +26,7 @@
 X, Y = R*np.cos(P), R*np.sin(P)
 
 # Plot the surface.
-ax.plot_surface(X, Y, Z, cmap=plt.cm.YlGnBu_r)
+ax.plot_surface(X, Y, Z, cmap="YlGnBu_r")
 
 # Tweak the limits and add latex math labels.
 ax.set_zlim(0, 1)

diff --git a/galleries/examples/mplot3d/tricontour3d.py b/galleries/examples/mplot3d/tricontour3d.py
@@ -37,7 +37,7 @@
                 < min_radius)
 
 ax = plt.figure().add_subplot(projection='3d')
-ax.tricontour(triang, z, cmap=plt.cm.CMRmap)
+ax.tricontour(triang, z, cmap="CMRmap")
 
 # Customize the view angle so it's easier to understand the plot.
 ax.view_init(elev=45.)