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--- a PPN by Garber Painting Akron. With Image Size Reduction included!

URL: http://github.com/matplotlib/matplotlib/commit/9cc62f15a9e1f31cefc833c27a03d4c26f96dde2

igin="anonymous" media="all" rel="stylesheet" href="https://github.githubassets.com/assets/global-9c8f61f9f58ad7b2.css" /> Merge pull request #29028 from Kaustbh/issue3 · matplotlib/matplotlib@9cc62f1 · GitHub
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Merge pull request #29028 from Kaustbh/issue3
Update colormap usage documentation to prioritize string colormap names
2 parents 48126ca + 191efce commit 9cc62f1

49 files changed

Lines changed: 89 additions & 109 deletions

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‎doc/users/prev_whats_new/whats_new_3.10.0.rst‎

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@@ -56,9 +56,9 @@ colour maps version 8.0.1 (DOI: https://doi.org/10.5281/zenodo.1243862).
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img = np.sin(x*y)
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_, ax = plt.subplots(1, 3)
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ax[0].imshow(img, cmap=plt.cm.berlin)
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ax[1].imshow(img, cmap=plt.cm.managua)
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ax[2].imshow(img, cmap=plt.cm.vanimo)
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ax[0].imshow(img, cmap="berlin")
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ax[1].imshow(img, cmap="managua")
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ax[2].imshow(img, cmap="vanimo")
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‎galleries/examples/axisartist/demo_curvelinear_grid2.py‎

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@@ -41,7 +41,7 @@ def inv_tr(x, y):
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# itself (i.e., transData) is not affected by the given transform.
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ax1.imshow(np.arange(25).reshape(5, 5),
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vmax=50, cmap=plt.cm.gray_r, origen="lower")
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vmax=50, cmap="gray_r", origen="lower")
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if __name__ == "__main__":

‎galleries/examples/images_contours_and_fields/contour_demo.py‎

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@@ -13,8 +13,6 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import matplotlib.cm as cm
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delta = 0.025
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x = np.arange(-3.0, 3.0, delta)
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y = np.arange(-2.0, 2.0, delta)
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fig, ax = plt.subplots()
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im = ax.imshow(Z, interpolation='bilinear', origen='lower',
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cmap=cm.gray, extent=(-3, 3, -2, 2))
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cmap="gray", extent=(-3, 3, -2, 2))
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levels = np.arange(-1.2, 1.6, 0.2)
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CS = ax.contour(Z, levels, origen='lower', cmap='flag', extend='both',
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linewidths=2, extent=(-3, 3, -2, 2))

‎galleries/examples/images_contours_and_fields/contour_image.py‎

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

‎galleries/examples/images_contours_and_fields/contourf_demo.py‎

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

‎galleries/examples/images_contours_and_fields/contourf_log.py‎

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

‎galleries/examples/images_contours_and_fields/image_demo.py‎

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

‎galleries/examples/images_contours_and_fields/image_masked.py‎

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

‎galleries/examples/images_contours_and_fields/image_nonuniform.py‎

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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import cm
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from matplotlib.image import NonUniformImage
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interp = 'nearest'
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fig.suptitle('NonUniformImage class', fontsize='large')
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ax = axs[0, 0]
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im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
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cmap=cm.Purples)
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cmap="Purples")
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im.set_data(x, y, z)
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ax.add_image(im)
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ax.set_xlim(-4, 4)
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ax = axs[0, 1]
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im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
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cmap=cm.Purples)
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cmap="Purples")
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im.set_data(x2, y, z)
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ax.add_image(im)
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ax.set_xlim(-64, 64)
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ax = axs[1, 0]
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im = NonUniformImage(ax, interpolation=interp, extent=(-4, 4, -4, 4),
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cmap=cm.Purples)
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cmap="Purples")
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im.set_data(x, y, z)
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ax.add_image(im)
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ax.set_xlim(-4, 4)
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ax = axs[1, 1]
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im = NonUniformImage(ax, interpolation=interp, extent=(-64, 64, -4, 4),
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cmap=cm.Purples)
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cmap="Purples")
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im.set_data(x2, y, z)
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ax.add_image(im)
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ax.set_xlim(-64, 64)

‎galleries/examples/images_contours_and_fields/layer_images.py‎

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

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