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

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

Creating a :ref:`colormap <colormaps>` 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.ColormapRegistry.register`
#
# .. tags::
#
#    styling: colormap
#    plot-type: imshow
#    level: intermediate