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

For more detail on creating and manipulating colormaps see
:doc:`/tutorials/colors/colormap-manipulation`.

Creating a :doc:`colormap </tutorials/colors/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.
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.

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

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, 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`
