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Merge pull request matplotlib#23393 from QuLogic/custom-cmap
Clean up formatting of custom cmap example
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‎examples/color/custom_cmap.py‎

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@@ -12,7 +12,7 @@
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Creating custom colormaps
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-------------------------
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=========================
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It is also possible to create a custom mapping for a colormap. This is
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accomplished by creating dictionary that specifies how the RGB channels
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change from one end of the cmap to the other.
@@ -21,51 +21,78 @@
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half, green to do the same over the middle half, and blue over the top
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half. Then you would use::
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cdict = {'red': ((0.0, 0.0, 0.0),
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(0.5, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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'blue': ((0.0, 0.0, 0.0),
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(0.5, 0.0, 0.0),
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(1.0, 1.0, 1.0))}
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cdict = {
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'red': (
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(0.0, 0.0, 0.0),
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(0.5, 1.0, 1.0),
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(1.0, 1.0, 1.0),
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),
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'green': (
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(0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0),
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),
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'blue': (
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(0.0, 0.0, 0.0),
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(0.5, 0.0, 0.0),
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(1.0, 1.0, 1.0),
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)
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}
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If, as in this example, there are no discontinuities in the r, g, and b
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components, then it is quite simple: the second and third element of
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each tuple, above, is the same--call it "y". The first element ("x")
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each tuple, above, is the same--call it "``y``". The first element ("``x``")
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defines interpolation intervals over the full range of 0 to 1, and it
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must span that whole range. In other words, the values of x divide the
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0-to-1 range into a set of segments, and y gives the end-point color
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must span that whole range. In other words, the values of ``x`` divide the
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0-to-1 range into a set of segments, and ``y`` gives the end-point color
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values for each segment.
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Now consider the green. cdict['green'] is saying that for
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0 <= x <= 0.25, y is zero; no green.
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0.25 < x <= 0.75, y varies linearly from 0 to 1.
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x > 0.75, y remains at 1, full green.
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If there are discontinuities, then it is a little more complicated.
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Label the 3 elements in each row in the cdict entry for a given color as
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(x, y0, y1). Then for values of x between x[i] and x[i+1] the color
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value is interpolated between y1[i] and y0[i+1].
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Going back to the cookbook example, look at cdict['red']; because y0 !=
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y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
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but then it jumps down, so that for x from 0.5 to 1, red increases from
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0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
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back to 0, and ramps back to 1 as x goes from 0.5 to 1.::
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Now consider the green, ``cdict['green']`` is saying that for:
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- 0 <= ``x`` <= 0.25, ``y`` is zero; no green.
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- 0.25 < ``x`` <= 0.75, ``y`` varies linearly from 0 to 1.
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- 0.75 < ``x`` <= 1, ``y`` remains at 1, full green.
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If there are discontinuities, then it is a little more complicated. Label the 3
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elements in each row in the ``cdict`` entry for a given color as ``(x, y0,
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y1)``. Then for values of ``x`` between ``x[i]`` and ``x[i+1]`` the color value
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is interpolated between ``y1[i]`` and ``y0[i+1]``.
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Going back to a cookbook example::
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cdict = {
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'red': (
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(0.0, 0.0, 0.0),
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(0.5, 1.0, 0.7),
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(1.0, 1.0, 1.0),
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),
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'green': (
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(0.0, 0.0, 0.0),
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(0.5, 1.0, 0.0),
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(1.0, 1.0, 1.0),
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),
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'blue': (
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(0.0, 0.0, 0.0),
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(0.5, 0.0, 0.0),
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(1.0, 1.0, 1.0),
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)
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}
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and look at ``cdict['red'][1]``; because ``y0 != y1``, it is saying that for
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``x`` from 0 to 0.5, red increases from 0 to 1, but then it jumps down, so that
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for ``x`` from 0.5 to 1, red increases from 0.7 to 1. Green ramps from 0 to 1
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as ``x`` goes from 0 to 0.5, then jumps back to 0, and ramps back to 1 as ``x``
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goes from 0.5 to 1. ::
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row i: x y0 y1
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/
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/
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/
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row i+1: x y0 y1
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Above is an attempt to show that for x in the range x[i] to x[i+1], the
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interpolation is between y1[i] and y0[i+1]. So, y0[0] and y1[-1] are
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never used.
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Above is an attempt to show that for ``x`` in the range ``x[i]`` to ``x[i+1]``,
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the interpolation is between ``y1[i]`` and ``y0[i+1]``. So, ``y0[0]`` and
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``y1[-1]`` are never used.
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"""
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import numpy as np
@@ -82,7 +109,8 @@
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###############################################################################
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# --- Colormaps from a list ---
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# Colormaps from a list
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# ---------------------
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colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> B
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n_bins = [3, 6, 10, 100] # Discretizes the interpolation into bins
@@ -99,60 +127,79 @@
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###############################################################################
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# --- Custom colormaps ---
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cdict1 = {'red': ((0.0, 0.0, 0.0),
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(0.5, 0.0, 0.1),
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(1.0, 1.0, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(1.0, 0.0, 0.0)),
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'blue': ((0.0, 0.0, 1.0),
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(0.5, 0.1, 0.0),
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(1.0, 0.0, 0.0))
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}
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cdict2 = {'red': ((0.0, 0.0, 0.0),
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(0.5, 0.0, 1.0),
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(1.0, 0.1, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(1.0, 0.0, 0.0)),
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'blue': ((0.0, 0.0, 0.1),
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(0.5, 1.0, 0.0),
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(1.0, 0.0, 0.0))
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}
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cdict3 = {'red': ((0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.5, 0.8, 1.0),
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(0.75, 1.0, 1.0),
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(1.0, 0.4, 1.0)),
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'green': ((0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.5, 0.9, 0.9),
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(0.75, 0.0, 0.0),
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(1.0, 0.0, 0.0)),
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'blue': ((0.0, 0.0, 0.4),
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(0.25, 1.0, 1.0),
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(0.5, 1.0, 0.8),
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(0.75, 0.0, 0.0),
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(1.0, 0.0, 0.0))
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}
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# Custom colormaps
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# ----------------
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cdict1 = {
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'red': (
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(0.0, 0.0, 0.0),
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(0.5, 0.0, 0.1),
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(1.0, 1.0, 1.0),
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),
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'green': (
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(0.0, 0.0, 0.0),
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(1.0, 0.0, 0.0),
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),
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'blue': (
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(0.0, 0.0, 1.0),
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(0.5, 0.1, 0.0),
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(1.0, 0.0, 0.0),
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)
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}
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cdict2 = {
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'red': (
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(0.0, 0.0, 0.0),
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(0.5, 0.0, 1.0),
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(1.0, 0.1, 1.0),
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),
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'green': (
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(0.0, 0.0, 0.0),
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(1.0, 0.0, 0.0),
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),
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'blue': (
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(0.0, 0.0, 0.1),
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(0.5, 1.0, 0.0),
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(1.0, 0.0, 0.0),
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)
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}
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cdict3 = {
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'red': (
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(0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.5, 0.8, 1.0),
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(0.75, 1.0, 1.0),
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(1.0, 0.4, 1.0),
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),
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'green': (
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(0.0, 0.0, 0.0),
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(0.25, 0.0, 0.0),
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(0.5, 0.9, 0.9),
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(0.75, 0.0, 0.0),
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(1.0, 0.0, 0.0),
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),
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'blue': (
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(0.0, 0.0, 0.4),
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(0.25, 1.0, 1.0),
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(0.5, 1.0, 0.8),
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(0.75, 0.0, 0.0),
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(1.0, 0.0, 0.0),
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)
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}
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# Make a modified version of cdict3 with some transparency
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# in the middle of the range.
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cdict4 = {**cdict3,
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'alpha': ((0.0, 1.0, 1.0),
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# (0.25, 1.0, 1.0),
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(0.5, 0.3, 0.3),
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# (0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0)),
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}
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cdict4 = {
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**cdict3,
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'alpha': (
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(0.0, 1.0, 1.0),
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# (0.25, 1.0, 1.0),
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(0.5, 0.3, 0.3),
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# (0.75, 1.0, 1.0),
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(1.0, 1.0, 1.0),
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),
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}
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###############################################################################
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mpl.colormaps.register(LinearSegmentedColormap('BlueRedAlpha', cdict4))
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###############################################################################
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# Make the figure:
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# Make the figure, with 4 subplots:
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fig, axs = plt.subplots(2, 2, figsize=(6, 9))
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fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)
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# Make 4 subplots:
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im1 = axs[0, 0].imshow(Z, cmap=blue_red1)
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fig.colorbar(im1, ax=axs[0, 0])
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# colorbar after they have been plotted.
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im4.set_cmap('BlueRedAlpha')
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axs[1, 1].set_title("Varying alpha")
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#
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fig.suptitle('Custom Blue-Red colormaps', fontsize=16)
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fig.subplots_adjust(top=0.9)

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