Merge pull request matplotlib#29028 from Kaustbh/issue3

Update colormap usage documentation to prioritize string colormap names

Author: QuLogic

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")
 
 
 

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__":

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))

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)

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

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:

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)

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.

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)

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()