import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

"""
Creating a colormap from a list of colors
-----------------------------------------
Creating a colormap from a list of colors can be done with the `from_list`
method of `LinearSegmentedColormap`. You must pass a list of RGB tuples that
define the mixture of colors from 0 to 1.


Creating custom colormaps
-------------------------
It is also possible to create a custom mapping for a colormap. This is
accomplished by creating dictionary that specifies how the RGB channels
change from one end of the cmap to the other.

Example: suppose you want red to increase from 0 to 1 over the bottom
half, green to do the same over the middle half, and blue over the top
half.  Then you would use:

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))}

If, as in this example, there are no discontinuities in the r, g, and b
components, then it is quite simple: the second and third element of
each tuple, above, is the same--call it "y".  The first element ("x")
defines interpolation intervals over the full range of 0 to 1, and it
must span that whole range.  In other words, the values of x divide the
0-to-1 range into a set of segments, and y gives the end-point color
values for each segment.

Now consider the green. cdict['green'] is saying that for
0 <= x <= 0.25, y is zero; no green.
0.25 < x <= 0.75, y varies linearly from 0 to 1.
x > 0.75, y remains at 1, full green.

If there are discontinuities, then it is a little more complicated.
Label the 3 elements in each row in the cdict entry for a given color as
(x, y0, y1).  Then for values of x between x[i] and x[i+1] the color
value is interpolated between y1[i] and y0[i+1].

Going back to the cookbook example, look at cdict['red']; because y0 !=
y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
but then it jumps down, so that for x from 0.5 to 1, red increases from
0.7 to 1.  Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
back to 0, and ramps back to 1 as x goes from 0.5 to 1.

row i:   x  y0  y1
                /
               /
row i+1: x  y0  y1

Above is an attempt to show that for x in the range x[i] to x[i+1], the
interpolation is between y1[i] and y0[i+1].  So, y0[0] and y1[-1] are
never used.

"""
# Make some illustrative fake data:

x = np.arange(0, np.pi, 0.1)
y = np.arange(0, 2*np.pi, 0.1)
X, Y = np.meshgrid(x, y)
Z = np.cos(X) * np.sin(Y) * 10


# --- Colormaps from a list ---

colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]  # R -> G -> B
n_bins = [3, 6, 10, 100]  # Discretizes the interpolation into bins
cmap_name = 'my_list'
fig, axs = plt.subplots(2, 2, figsize=(6, 9))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)
for n_bin, ax in zip(n_bins, axs.ravel()):
    # Create the colormap
    cm = LinearSegmentedColormap.from_list(
        cmap_name, colors, N=n_bin)
    # Fewer bins will result in "coarser" colomap interpolation
    im = ax.imshow(Z, interpolation='nearest', origin='lower', cmap=cm)
    ax.set_title("N bins: %s" % n_bin)
    fig.colorbar(im, ax=ax)


# --- Custom colormaps ---

cdict1 = {'red':   ((0.0, 0.0, 0.0),
                   (0.5, 0.0, 0.1),
                   (1.0, 1.0, 1.0)),

         'green': ((0.0, 0.0, 0.0),
                   (1.0, 0.0, 0.0)),

         'blue':  ((0.0, 0.0, 1.0),
                   (0.5, 0.1, 0.0),
                   (1.0, 0.0, 0.0))
        }

cdict2 = {'red':   ((0.0, 0.0, 0.0),
                   (0.5, 0.0, 1.0),
                   (1.0, 0.1, 1.0)),

         'green': ((0.0, 0.0, 0.0),
                   (1.0, 0.0, 0.0)),

         'blue':  ((0.0, 0.0, 0.1),
                   (0.5, 1.0, 0.0),
                   (1.0, 0.0, 0.0))
        }

cdict3 = {'red':  ((0.0, 0.0, 0.0),
                   (0.25, 0.0, 0.0),
                   (0.5, 0.8, 1.0),
                   (0.75, 1.0, 1.0),
                   (1.0, 0.4, 1.0)),

         'green': ((0.0, 0.0, 0.0),
                   (0.25, 0.0, 0.0),
                   (0.5, 0.9, 0.9),
                   (0.75, 0.0, 0.0),
                   (1.0, 0.0, 0.0)),

         'blue':  ((0.0, 0.0, 0.4),
                   (0.25, 1.0, 1.0),
                   (0.5, 1.0, 0.8),
                   (0.75, 0.0, 0.0),
                   (1.0, 0.0, 0.0))
        }

# Make a modified version of cdict3 with some transparency
# in the middle of the range.
cdict4 = cdict3.copy()
cdict4['alpha'] = ((0.0, 1.0, 1.0),
                #   (0.25,1.0, 1.0),
                   (0.5, 0.3, 0.3),
                #   (0.75,1.0, 1.0),
                   (1.0, 1.0, 1.0))


# Now we will use this example to illustrate 3 ways of
# handling custom colormaps.
# First, the most direct and explicit:

blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)

# Second, create the map explicitly and register it.
# Like the first method, this method works with any kind
# of Colormap, not just
# a LinearSegmentedColormap:

blue_red2 = LinearSegmentedColormap('BlueRed2', cdict2)
plt.register_cmap(cmap=blue_red2)

# Third, for LinearSegmentedColormap only,
# leave everything to register_cmap:

plt.register_cmap(name='BlueRed3', data=cdict3)  # optional lut kwarg
plt.register_cmap(name='BlueRedAlpha', data=cdict4)

# Make the figure:

fig, axs = plt.subplots(2, 2, figsize=(6, 9))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)

# Make 4 subplots:

im1 = axs[0, 0].imshow(Z, interpolation='nearest', cmap=blue_red1)
fig.colorbar(im1, ax=axs[0, 0])

cmap = plt.get_cmap('BlueRed2')
im2 = axs[1, 0].imshow(Z, interpolation='nearest', cmap=cmap)
fig.colorbar(im2, ax=axs[1, 0])

# Now we will set the third cmap as the default.  One would
# not normally do this in the middle of a script like this;
# it is done here just to illustrate the method.

plt.rcParams['image.cmap'] = 'BlueRed3'

im3 = axs[0, 1].imshow(Z, interpolation='nearest')
fig.colorbar(im3, ax=axs[0, 1])
axs[0, 1].set_title("Alpha = 1")

# Or as yet another variation, we can replace the rcParams
# specification *before* the imshow with the following *after*
# imshow.
# This sets the new default *and* sets the colormap of the last
# image-like item plotted via pyplot, if any.
#

# Draw a line with low zorder so it will be behind the image.
axs[1, 1].plot([0, 10*np.pi], [0, 20*np.pi], color='c', lw=20, zorder=-1)

im4 = axs[1, 1].imshow(Z, interpolation='nearest')
fig.colorbar(im4, ax=axs[1, 1])

# Here it is: changing the colormap for the current image and its
# colorbar after they have been plotted.
im4.set_cmap('BlueRedAlpha')
axs[1, 1].set_title("Varying alpha")
#

fig.suptitle('Custom Blue-Red colormaps', fontsize=16)

plt.show() 

　

　

M:\______\custom_cmap.py:162: MatplotlibDeprecationWarning: 
  The register_cmap function was deprecated in Matplotlib 3.7 and will be removed 
  two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
  plt.register_cmap(cmap=blue_red2)
M:\______\custom_cmap.py:167: MatplotlibDeprecationWarning: 
  The register_cmap function was deprecated in Matplotlib 3.7 and will be removed 
  two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
  plt.register_cmap(name='BlueRed3', data=cdict3)  # optional lut kwarg
Traceback (most recent call last):
  File "M:\______\custom_cmap.py", line 167, in 
    plt.register_cmap(name='BlueRed3', data=cdict3)  # optional lut kwarg
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\______\AppData\Local\Programs\Python\Python311\Lib
    \site-packages\matplotlib\_api\deprecation.py", line 200, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
TypeError: register_cmap() got an unexpected keyword argument 'data' 

E:\______\custom_cmap.py:162: MatplotlibDeprecationWarning: 
  The register_cmap function was deprecated in Matplotlib 3.7 and will be removed 
  two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
  plt.register_cmap(cmap=blue_red2)
E:\______\custom_cmap.py:167: MatplotlibDeprecationWarning: 
  The register_cmap function was deprecated in Matplotlib 3.7 and will be removed 
  two minor releases later. Use ``matplotlib.colormaps.register(name)`` instead.
  plt.register_cmap(name='BlueRed3', data=cdict3)  # optional lut kwarg
Traceback (most recent call last):
  File "E:\______\custom_cmap.py", line 167, in 
    plt.register_cmap(name='BlueRed3', data=cdict3)  # optional lut kwarg
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Program Files\Python312\Lib\site-packages\matplotlib\_api\deprecation.py", 
    line 200, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
TypeError: register_cmap() got an unexpected keyword argument 'data' 

"""
=========================================
Creating a colormap from a list of colors
=========================================

For more detail on creating and manipulating colormaps see
:ref:`colormap-manipulation`.

Creating a :ref:`colormap ` from a list of colors
can be done with the `.LinearSegmentedColormap.from_list` method.  You must
pass a list of RGB tuples that define the mixture of colors from 0 to 1.


Creating custom colormaps
=========================
It is also possible to create a custom mapping for a colormap. This is
accomplished by creating dictionary that specifies how the RGB channels
change from one end of the cmap to the other.

Example: suppose you want red to increase from 0 to 1 over the bottom
half, green to do the same over the middle half, and blue over the top
half.  Then you would use::

    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),
        )
    }

If, as in this example, there are no discontinuities in the r, g, and b
components, then it is quite simple: the second and third element of
each tuple, above, is the same -- call it "``y``".  The first element ("``x``")
defines interpolation intervals over the full range of 0 to 1, and it
must span that whole range.  In other words, the values of ``x`` divide the
0-to-1 range into a set of segments, and ``y`` gives the end-point color
values for each segment.

Now consider the green, ``cdict['green']`` is saying that for:

- 0 <= ``x`` <= 0.25, ``y`` is zero; no green.
- 0.25 < ``x`` <= 0.75, ``y`` varies linearly from 0 to 1.
- 0.75 < ``x`` <= 1, ``y`` remains at 1, full green.

If there are discontinuities, then it is a little more complicated. Label the 3
elements in each row in the ``cdict`` entry for a given color as ``(x, y0,
y1)``. Then for values of ``x`` between ``x[i]`` and ``x[i+1]`` the color value
is interpolated between ``y1[i]`` and ``y0[i+1]``.

Going back to a cookbook example::

    cdict = {
        'red': (
            (0.0,  0.0, 0.0),
            (0.5,  1.0, 0.7),
            (1.0,  1.0, 1.0),
        ),
        'green': (
            (0.0,  0.0, 0.0),
            (0.5,  1.0, 0.0),
            (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),
        )
    }

and look at ``cdict['red'][1]``; because ``y0 != y1``, it is saying that for
``x`` from 0 to 0.5, red increases from 0 to 1, but then it jumps down, so that
for ``x`` from 0.5 to 1, red increases from 0.7 to 1.  Green ramps from 0 to 1
as ``x`` goes from 0 to 0.5, then jumps back to 0, and ramps back to 1 as ``x``
goes from 0.5 to 1. ::

  row i:   x  y0  y1
                 /
                /
  row i+1: x  y0  y1

Above is an attempt to show that for ``x`` in the range ``x[i]`` to ``x[i+1]``,
the interpolation is between ``y1[i]`` and ``y0[i+1]``.  So, ``y0[0]`` and
``y1[-1]`` are never used.

"""
import matplotlib.pyplot as plt
import numpy as np

import matplotlib as mpl
from matplotlib.colors import LinearSegmentedColormap

# Make some illustrative fake data:

x = np.arange(0, np.pi, 0.1)
y = np.arange(0, 2 * np.pi, 0.1)
X, Y = np.meshgrid(x, y)
Z = np.cos(X) * np.sin(Y) * 10


# %%
# Colormaps from a list
# ---------------------

colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]  # R -> G -> B
n_bins = [3, 6, 10, 100]  # Discretizes the interpolation into bins
cmap_name = 'my_list'
fig, axs = plt.subplots(2, 2, figsize=(6, 9))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)
for n_bin, ax in zip(n_bins, axs.flat):
    # Create the colormap
    cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_bin)
    # Fewer bins will result in "coarser" colomap interpolation
    im = ax.imshow(Z, origin='lower', cmap=cmap)
    ax.set_title("N bins: %s" % n_bin)
    fig.colorbar(im, ax=ax)


# %%
# Custom colormaps
# ----------------

cdict1 = {
    'red': (
        (0.0, 0.0, 0.0),
        (0.5, 0.0, 0.1),
        (1.0, 1.0, 1.0),
    ),
    'green': (
        (0.0, 0.0, 0.0),
        (1.0, 0.0, 0.0),
    ),
    'blue': (
        (0.0, 0.0, 1.0),
        (0.5, 0.1, 0.0),
        (1.0, 0.0, 0.0),
    )
}

cdict2 = {
    'red': (
        (0.0, 0.0, 0.0),
        (0.5, 0.0, 1.0),
        (1.0, 0.1, 1.0),
    ),
    'green': (
        (0.0, 0.0, 0.0),
        (1.0, 0.0, 0.0),
    ),
    'blue': (
        (0.0, 0.0, 0.1),
        (0.5, 1.0, 0.0),
        (1.0, 0.0, 0.0),
    )
}

cdict3 = {
    'red': (
        (0.0, 0.0, 0.0),
        (0.25, 0.0, 0.0),
        (0.5, 0.8, 1.0),
        (0.75, 1.0, 1.0),
        (1.0, 0.4, 1.0),
    ),
    'green': (
        (0.0, 0.0, 0.0),
        (0.25, 0.0, 0.0),
        (0.5, 0.9, 0.9),
        (0.75, 0.0, 0.0),
        (1.0, 0.0, 0.0),
    ),
    'blue': (
        (0.0, 0.0, 0.4),
        (0.25, 1.0, 1.0),
        (0.5, 1.0, 0.8),
        (0.75, 0.0, 0.0),
        (1.0, 0.0, 0.0),
    )
}

# Make a modified version of cdict3 with some transparency
# in the middle of the range.
cdict4 = {
    **cdict3,
    'alpha': (
        (0.0, 1.0, 1.0),
        # (0.25, 1.0, 1.0),
        (0.5, 0.3, 0.3),
        # (0.75, 1.0, 1.0),
        (1.0, 1.0, 1.0),
    ),
}


# %%
# Now we will use this example to illustrate 2 ways of
# handling custom colormaps.
# First, the most direct and explicit:

blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)

# %%
# Second, create the map explicitly and register it.
# Like the first method, this method works with any kind
# of Colormap, not just
# a LinearSegmentedColormap:

mpl.colormaps.register(LinearSegmentedColormap('BlueRed2', cdict2))
mpl.colormaps.register(LinearSegmentedColormap('BlueRed3', cdict3))
mpl.colormaps.register(LinearSegmentedColormap('BlueRedAlpha', cdict4))

# %%
# Make the figure, with 4 subplots:

fig, axs = plt.subplots(2, 2, figsize=(6, 9))
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)

im1 = axs[0, 0].imshow(Z, cmap=blue_red1)
fig.colorbar(im1, ax=axs[0, 0])

im2 = axs[1, 0].imshow(Z, cmap='BlueRed2')
fig.colorbar(im2, ax=axs[1, 0])

# Now we will set the third cmap as the default.  One would
# not normally do this in the middle of a script like this;
# it is done here just to illustrate the method.

plt.rcParams['image.cmap'] = 'BlueRed3'

im3 = axs[0, 1].imshow(Z)
fig.colorbar(im3, ax=axs[0, 1])
axs[0, 1].set_title("Alpha = 1")

# Or as yet another variation, we can replace the rcParams
# specification *before* the imshow with the following *after*
# imshow.
# This sets the new default *and* sets the colormap of the last
# image-like item plotted via pyplot, if any.
#

# Draw a line with low zorder so it will be behind the image.
axs[1, 1].plot([0, 10 * np.pi], [0, 20 * np.pi], color='c', lw=20, zorder=-1)

im4 = axs[1, 1].imshow(Z)
fig.colorbar(im4, ax=axs[1, 1])

# Here it is: changing the colormap for the current image and its
# colorbar after they have been plotted.
im4.set_cmap('BlueRedAlpha')
axs[1, 1].set_title("Varying alpha")

fig.suptitle('Custom Blue-Red colormaps', fontsize=16)
fig.subplots_adjust(top=0.9)

plt.show()

# %%
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.axes.Axes.imshow` / `matplotlib.pyplot.imshow`
#    - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`
#    - `matplotlib.colors`
#    - `matplotlib.colors.LinearSegmentedColormap`
#    - `matplotlib.colors.LinearSegmentedColormap.from_list`
#    - `matplotlib.cm`
#    - `matplotlib.cm.ScalarMappable.set_cmap`
#    - `matplotlib.cm.register_cmap` 

　Python 3.11.6 (matplotlib 3.7.1) 及び Python 3.12.0 (matplotlib 3.8.1) 共に、正常実行です。

　

　

• 2023/11/23 Ver=1.04 Python 3.12.0 (matplotlib 3.8.1)で確認
• 2023/11/23 Ver=1.04 Python 3.11.6 (matplotlib 3.7.1)で確認
• 2023/04/01 Ver=1.03 Python 3.11.2 で確認
• 2020/10/31 Ver=1.01 Python 3.7.8 で確認
• 2018/11/28 Ver=1.01 初版リリース

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