DOC: colormap-manipulation tutorial #11905
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| """ | ||
| ******************************** | ||
| Creating Colormaps in Matplotlib | ||
| ******************************** | ||
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| Matplotlib colormaps are implimented as a class, which makes them quite | ||
| flexible, but opaque to users as to how to create and/or | ||
| manipulate them. This opacity is not helped in the library by the fact that | ||
| the named colormaps are accessed via `.matplotlib.cm.get_cmap` module, whereas | ||
| the colormap class itself is defined in `.matplotlib.colors.Colormap`! | ||
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| Fortunately, the way to create colormaps is quite straight forward, by creating | ||
| an instance of class `.ListedColormap` using a Nx4 numpy array of values | ||
| between 0 and 1 to represent the RGBA values of the colormap. | ||
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| Getting colormaps and accessing their values | ||
| ============================================ | ||
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| First, getting a named colormap, most of which are listed in | ||
| :doc:`/tutorials/colors/colormaps` requires the use of | ||
| `.matplotlib.cm.get_cmap`, which returns a | ||
| :class:`.matplotlib.colors.ListedColormap` 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 12 so there are not a lot of values to look at. | ||
| """ | ||
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| import numpy as np | ||
| import matplotlib as mpl | ||
| import matplotlib.pyplot as plt | ||
| from matplotlib import cm | ||
| from matplotlib.colors import ListedColormap, LinearSegmentedColormap | ||
| from collections import OrderedDict | ||
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| viridis = cm.get_cmap('viridis', 12) | ||
| print(viridis) | ||
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| ############################################################################## | ||
| # The object ``viridis`` is a callable, that when passed a float between | ||
| # 0 and 1 returns an RGBA value from the colormap: | ||
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| print(viridis(0.56)) | ||
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| ############################################################################## | ||
| # The list of colors that comprise the colormap can be directly accessed using | ||
| # the ``colors`` property, | ||
| # or it can be acccessed indirectly by calling ``viridis`` with an array | ||
| # of values matching the length of the colormap. Note that the returned list | ||
| # is in the form of an RGBA Nx4 array, where N is the length of the colormap. | ||
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| print('viridis.colors', viridis.colors) | ||
| print('viridis(range(12))', viridis(range(12))) | ||
| print('viridis(np.linspace(0, 1, 12))', viridis(np.linspace(0, 1, 12))) | ||
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| ############################################################################## | ||
| # The colormap is a lookup table, so "oversampling" the colormap returns | ||
| # nearest-neighbor interpolation (note the repeated colors in the list below) | ||
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| print('viridis(np.linspace(0, 1, 15))', viridis(np.linspace(0, 1, 15))) | ||
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| ############################################################################## | ||
| # Creating a new ListedColormap | ||
| # ============================= | ||
| # | ||
| # This is essential the inverse operation of the above where we supply a | ||
| # Nx4 numpy array with all values between 0 and 1, | ||
| # to `.ListedColormap` to make a new colormap. This means that | ||
| # any numpy operations that we can do on a Nx4 array make carpentry of | ||
| # new colormaps from existing colormaps quite straight forward. | ||
| # | ||
| # Suppose we want to make the first 25 entries of a 256-length "viridis" | ||
| # colormap pink for some reason: | ||
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| viridis = cm.get_cmap('viridis', 256) | ||
| newcolors = viridis(np.linspace(0, 1, 256)) | ||
| pink = np.array([248/256, 24/256, 148/256, 1]) | ||
| newcolors[:25, :] = pink | ||
| newcmp = ListedColormap(newcolors) | ||
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| def plot_examples(cms): | ||
| """ | ||
| helper function to plot two colormaps | ||
| """ | ||
| np.random.seed(19680801) | ||
| data = np.random.randn(30, 30) | ||
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| fig, axs = plt.subplots(1, 2, figsize=(6, 3), constrained_layout=True) | ||
| for [ax, cmap] in zip(axs, cms): | ||
| psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4) | ||
| fig.colorbar(psm, ax=ax) | ||
| plt.show() | ||
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| plot_examples([viridis, newcmp]) | ||
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| ############################################################################## | ||
| # We can easily reduce the range of a colormap; here we choose the middle | ||
| # 0.5 of the colormap. | ||
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| viridis = cm.get_cmap('viridis', 256) | ||
| newcmp = ListedColormap(viridis(np.linspace(0.25, 0.75, 256))) | ||
|
There was a problem hiding this comment. Will this have over-sampling issues? There was a problem hiding this comment. Ooops, yes it will. Not really noticeable at 256 resolution, but would certainly be a problem for small colormaps. Changed to # We can easily reduce the dynamic range of a colormap; here we choose the middle
# 0.5 of the colormap. However, we need to interpolate from a larger
# colormap, otherwise the new colormap will have repeated values.
viridisBig = cm.get_cmap('viridis', 512)
newcmp = ListedColormap(viridisBig(np.linspace(0.25, 0.75, 256)))
plot_examples([viridis, newcmp]) |
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| plot_examples([viridis, newcmp]) | ||
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| ############################################################################## | ||
| # and we can easily concatenate two colormaps: | ||
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| top = cm.get_cmap('Oranges_r', 128) | ||
| bottom = cm.get_cmap('Blues', 128) | ||
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| newcolors = np.vstack((top(np.linspace(0, 1, 128)), | ||
| bottom(np.linspace(0, 1, 128)))) | ||
| newcmp = ListedColormap(newcolors, name='OrangeBlue') | ||
| plot_examples([viridis, newcmp]) | ||
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| ############################################################################## | ||
| # Of course we need not start from a named colormap, we just need to create | ||
| # the Nx4 array to pass to `.ListedColormap`. Here we create a | ||
| # brown colormap that goes to white.... | ||
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| N = 256 | ||
| vals = np.ones((N, 4)) | ||
| vals[:, 0] = np.linspace(90/256, 1, N) | ||
| vals[:, 1] = np.linspace(39/256, 1, N) | ||
| vals[:, 2] = np.linspace(41/256, 1, N) | ||
| newcmp = ListedColormap(vals) | ||
| plot_examples([viridis, newcmp]) | ||
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| ############################################################################## | ||
| # LinearSegmented colormaps | ||
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There was a problem hiding this comment.
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| # ========================= | ||
| # | ||
| # `.LinearSegmentedColormap` have an alternate way to specify colormaps that | ||
|
There was a problem hiding this comment. Alternative wording:
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| # specify anchor points for linear ramps for each of RGB, and optionally, alpha | ||
| # (RGBA). | ||
| # | ||
| # The format to specify these colormaps is a bit complicated to allow | ||
| # discontinuities at the anchor points. First, with no discontinuities: | ||
|
There was a problem hiding this comment. I think the format needs a description here, or at least a "See LinearSegmentedColormap for a description of the format." There was a problem hiding this comment. new version describes the format. |
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| cdict = {'red': [[0.0, 0.0, 0.0], | ||
| [0.5, 1.0, 1.0], | ||
| [1.0, 1.0, 1.0]], | ||
| 'green': [[0.0, 0.0, 0.0], | ||
| [0.25, 0.0, 0.0], | ||
| [0.75, 1.0, 1.0], | ||
| [1.0, 1.0, 1.0]], | ||
| 'blue': [[0.0, 0.0, 0.0], | ||
| [0.5, 0.0, 0.0], | ||
| [1.0, 1.0, 1.0]]} | ||
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| def plot_linearmap(cdict): | ||
| newcmp = LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256) | ||
| rgba = newcmp(np.linspace(0, 1, 256)) | ||
| fig, ax = plt.subplots(figsize=(4, 3), constrained_layout=True) | ||
| col = ['r', 'g', 'b'] | ||
| for xx in [0.25, 0.5, 0.75]: | ||
| ax.axvline(xx, color='0.7', linestyle='--') | ||
| for i in range(3): | ||
| ax.plot(np.arange(256)/256, rgba[:, i], color=col[i]) | ||
| ax.set_xlabel('index') | ||
| ax.set_ylabel('RGB') | ||
| plt.show() | ||
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| plot_linearmap(cdict) | ||
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| ############################################################################# | ||
| # However, consider the case where the third column is different than the | ||
| # second. The linear interpolation between red[i, 0] and red[i+1, 0] is | ||
| # from red[i, 2] to red[i+1, 1]. This format allows us to have | ||
| # discontinuities in the colormap at the anchor points; in this case | ||
| # between 0 and 0.5, the linear interpolation goes from 0.3 to 1, and | ||
| # between 0.5 and 1 it goes from 0.9 to 1. Note that red[0, 1], and red[2, 2] | ||
| # are both superfluous to the interpolation, which happens between the last | ||
| # element of the first anchor and the first element of the second anchor. | ||
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| cdict['red'] = [[0.0, 0.0, 0.3], | ||
| [0.5, 1.0, 0.9], | ||
| [1.0, 1.0, 1.0]] | ||
| plot_linearmap(cdict) | ||
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| ############################################################################# | ||
| # | ||
| # ------------ | ||
| # | ||
| # References | ||
| # """""""""" | ||
| # | ||
| # The use of the following functions, methods, classes and modules is shown | ||
| # in this example: | ||
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| import matplotlib | ||
| matplotlib.axes.Axes.pcolormesh | ||
| matplotlib.figure.Figure.colorbar | ||
| matplotlib.colors | ||
| matplotlib.colors.LinearSegmentedColormap | ||
| matplotlib.colors.ListedColormap | ||
| matplotlib.cm | ||
| matplotlib.cm.get_cmap | ||
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Don't indent bullet points. RestructuredText interprets this as a blockquote. This leads to over-indented bullet points in the HTML.
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No problem - shoudl this be a
seealso::?There was a problem hiding this comment.
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Yeah, you could make this a
seealso::.If you switch to
seealso::the following is just for information: Make sure to write bullet lists exactly like this:I know this is a bit picky, but that's how restructered text wants it.