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DOC: update colors from colormaps example#27708

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story645 merged 1 commit intomatplotlib:mainfromrcomer:color-from-cmap-2
Feb 4, 2024
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1 change: 0 additions & 1 deletion.flake8
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Original file line numberDiff line numberDiff line change
Expand Up@@ -63,7 +63,6 @@ per-file-ignores =
galleries/users_explain/text/text_props.py: E501

galleries/examples/animation/frame_grabbing_sgskip.py: E402
galleries/examples/color/individual_colors_from_cmap.py: E402
galleries/examples/images_contours_and_fields/tricontour_demo.py: E201
galleries/examples/images_contours_and_fields/tripcolor_demo.py: E201
galleries/examples/images_contours_and_fields/triplot_demo.py: E201
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46 changes: 25 additions & 21 deletionsgalleries/examples/color/individual_colors_from_cmap.py
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Expand Up@@ -7,50 +7,54 @@
cycle provides. Selecting individual colors from one of the provided colormaps can be a
convenient way to do this.

Once we have hold of a `.Colormap` instance, the individual colors can be accessed
by passing it an index. If we want a specific number of colors taken at regular
intervals from a continuous colormap, we can create a new colormap using the
`~.Colormap.resampled` method.
We can retrieve colors from any `.Colormap` by calling it with a float or a list of
floats in the range [0, 1]; e.g. ``cmap(0.5)`` will give the middle color. See also
`.Colormap.__call__`.

For more details about manipulating colormaps, see :ref:`colormap-manipulation`.
Extracting colors from a continuous colormap
--------------------------------------------
"""

import matplotlib.pyplot as plt
import numpy as np

import matplotlib as mpl

n_lines = 21
cmap = mpl.colormaps['plasma']

cmap = mpl.colormaps.get_cmap('plasma').resampled(n_lines)
# Take colors at regular intervals spanning the colormap.
colors = cmap(np.linspace(0, 1, n_lines))

fig, ax = plt.subplots(layout='constrained')

for iinrange(n_lines):
ax.plot([0, i], color=cmap(i))
for i, colorinenumerate(colors):
ax.plot([0, i], color=color)

plt.show()

# %%
# Instead of passing colors one by one to `~.Axes.plot`, we can replace the default
# color cycle with a different set of colors. Specifying a `~cycler.cycler` instance
# within `.rcParams` achieves that. See :ref:`color_cycle` for details.


from cycler import cycler

cmap = mpl.colormaps.get_cmap('Dark2')
colors = cmap(range(cmap.N)) # cmap.N is number of unique colors in the colormap
#
# Extracting colors from a discrete colormap
# ------------------------------------------
# The list of all colors in a `.ListedColormap` is available as the ``colors``
# attribute.

withmpl.rc_context({'axes.prop_cycle': cycler(color=colors)}):
colors =mpl.colormaps['Dark2'].colors
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@rcomerrcomerJan 26, 2024•
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#27678 started out using thecolors attribute but@jklymak said it could be confusing asLinearSegmentedColormap doesn't have it. Then@timhoffm suggested using it. I am hoping that using it after specifically stating that it's onListedColormaps is a reasonable compromise.

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maybe turn that into a section heading to make it more obvious?

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Sectioning: The above should be something "Extracting values from a continuous colormap" (though technically most of the visually continous colormaps are still densely sampled ListedColormaps) and here "Extracting values from a discrete colormap".

I also suggest to not combine both theListedColormap.colors explanation and the therc_context into one plot. That's confusing. Either drop therc_context, or make it a separate plot.


fig, ax = plt.subplots(layout='constrained')
fig, ax = plt.subplots(layout='constrained')

for iinrange(n_lines):
ax.plot([0, i])
for i, colorinenumerate(colors):
ax.plot([0, i], color=color)

plt.show()

# %%
# See Also
# --------
#
# For more details about manipulating colormaps, see :ref:`colormap-manipulation`. To
# change the default color cycle, see :ref:`color_cycle`.
#
# .. admonition:: References
#
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