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Commit 22017fa

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jklymakjklymak
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DOC: link palettable
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‎lib/matplotlib/colors.py

Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
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:doc:`/tutorials/colors/colormapnorms` for more details about data
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normalization
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More colormaps are available at palettable_
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The module also provides functions for checking whether an object can be
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interpreted as a color (:func:`is_color_like`), for converting such an object
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to an RGBA tuple (:func:`to_rgba`) or to an HTML-like hex string in the
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cycle does not include color.
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All string specifications of color, other than "CN", are case-insensitive.
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.. _palettable: https://jiffyclub.github.io/palettable/
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"""
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from collections.abc import Sized

‎tutorials/colors/colormap-manipulation.py

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Creating Colormaps in Matplotlib
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********************************
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Creating and manipulating colormaps in Matplotlib is straight-forward
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using the class `.ListedColormap` and a Nx4 numpy array of values
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between 0 and 1 to represent the RGBA values of the colormap. There
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Matplotlib has a number of built-in colormaps accessible via
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`.matplotlib.cm.get_cmap`. There are also external libraries like
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palettable_ that have many extra colormaps.
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.. _palettable: https://jiffyclub.github.io/palettable/
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However, we often want to create or manipulate colormaps in Matplotlib.
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This can be done using the class `.ListedColormap` and a Nx4 numpy array of
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values between 0 and 1 to represent the RGBA values of the colormap. There
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is also a `.LinearSegmentedColormap` class that allows colormaps to be
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specified with far fewer anchor points defining segments, and linearly
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specified with a few anchor points defining segments, and linearly
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interpolating between the anchor points.
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Getting colormaps and accessing their values

‎tutorials/colors/colormaps.py

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"""
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***********************
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Colormaps in Matplotlib
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***********************
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********************************
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Choosing Colormaps in Matplotlib
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********************************
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Matplotlib has a number of built-in colormaps accessible via
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`.matplotlib.cm.get_cmap`. There are also external libraries like
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[palettable]_ that have many extra colormaps. Here we briefly discuss
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how to choose between the many options. For help on creating your
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own colormaps, see :doc:`/tutorials/colors/custom_colormaps`.
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How (and why) to choose a particular colormap.
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Overview
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========
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# Plot colormap L values. Do separately for each category
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# so each plot can be pretty. To make scatter markers change
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# color along plot:
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# http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable
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# http://stackoverflow.com/questions/8202605/
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if cmap_category == 'Sequential':
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# These colormaps all start at high lightness but we want them
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# plots because the grayscale changes unpredictably through the
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# colormap.
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#
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# Conversion to grayscale is done in many different ways [bw]_. Some of the better
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# ones use a linear combination of the rgb values of a pixel, but weighted
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# according to how we perceive color intensity. A nonlinear method of conversion
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# to grayscale is to use the :math:`L^*` values of the pixels. In general, similar
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# principles apply for this question as they do for presenting one's information
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# perceptually; that is, if a colormap is chosen that is monotonically increasing
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# in :math:`L^*` values, it will print in a reasonable manner to grayscale.
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# Conversion to grayscale is done in many different ways [bw]_. Some of the
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# better ones use a linear combination of the rgb values of a pixel, but
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# weighted according to how we perceive color intensity. A nonlinear method of
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# conversion to grayscale is to use the :math:`L^*` values of the pixels. In
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# general, similar principles apply for this question as they do for presenting
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# one's information perceptually; that is, if a colormap is chosen that is
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# monotonically increasing in :math:`L^*` values, it will print in a reasonable
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# manner to grayscale.
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#
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# With this in mind, we see that the Sequential colormaps have reasonable
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# representations in grayscale. Some of the Sequential2 colormaps have decent
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# enough grayscale representations, though some (autumn, spring, summer, winter)
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# have very little grayscale change. If a colormap like this was used in a plot
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# and then the plot was printed to grayscale, a lot of the information may map to
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# the same gray values. The Diverging colormaps mostly vary from darker gray on
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# the outer edges to white in the middle. Some (PuOr and seismic) have noticeably
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# darker gray on one side than the other and therefore are not very symmetric.
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# coolwarm has little range of gray scale and would print to a more uniform plot,
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# losing a lot of detail. Note that overlaid, labeled contours could help
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# differentiate between one side of the colormap vs. the other since color cannot
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# be used once a plot is printed to grayscale. Many of the Qualitative and
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# Miscellaneous colormaps, such as Accent, hsv, and jet, change from darker to
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# lighter and back to darker gray throughout the colormap. This would make it
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# impossible for a viewer to interpret the information in a plot once it is
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# printed in grayscale.
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# enough grayscale representations, though some (autumn, spring, summer,
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# winter) have very little grayscale change. If a colormap like this was used
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# in a plot and then the plot was printed to grayscale, a lot of the
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# information may map to the same gray values. The Diverging colormaps mostly
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# vary from darker gray on the outer edges to white in the middle. Some
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# (PuOr and seismic) have noticeably darker gray on one side than the other
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# and therefore are not very symmetric. coolwarm has little range of gray scale
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# and would print to a more uniform plot, losing a lot of detail. Note that
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# overlaid, labeled contours could help differentiate between one side of the
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# colormap vs. the other since color cannot be used once a plot is printed to
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# grayscale. Many of the Qualitative and Miscellaneous colormaps, such as
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# Accent, hsv, and jet, change from darker to lighter and back to darker gray
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# throughout the colormap. This would make it impossible for a viewer to
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# interpret the information in a plot once it is printed in grayscale.
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mpl.rcParams.update({'font.size': 14})
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# =========================
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#
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# There is a lot of information available about color blindness (*e.g.*,
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# [colorblindness]_). Additionally, there are tools available to convert images to
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# how they look for different types of color vision deficiencies (*e.g.*,
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# [colorblindness]_). Additionally, there are tools available to convert images
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# to how they look for different types of color vision deficiencies (*e.g.*,
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# [vischeck]_).
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#
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# The most common form of color vision deficiency involves differentiating between
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# red and green. Thus, avoiding colormaps with both red and green will avoid many
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# problems in general.
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# The most common form of color vision deficiency involves differentiating
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# between red and green. Thus, avoiding colormaps with both red and green will
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# avoid many problems in general.
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#
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#
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# References
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# .. [colorblindness] http://www.color-blindness.com/
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# .. [vischeck] http://www.vischeck.com/vischeck/
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# .. [IBM] https://dx.doi.org/10.1109/VISUAL.1995.480803
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# .. [palettable] https://jiffyclub.github.io/palettable/

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