#!/usr/bin/env python

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

"""

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.

"""



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))
        }

# 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

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)

plt.figure(figsize=(10,4))
plt.subplots_adjust(wspace=0.3)

plt.subplot(1,3,1)
plt.imshow(Z, interpolation='nearest', cmap=blue_red1)
plt.colorbar()

plt.subplot(1,3,2)
cmap = plt.get_cmap('BlueRed2')
plt.imshow(Z, interpolation='nearest', cmap=cmap)
plt.colorbar()

# 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'

# Also see below for an alternative, particularly for
# interactive use.

plt.subplot(1,3,3)
plt.imshow(Z, interpolation='nearest')
plt.colorbar()

# Or as yet another variation, we could replace the rcParams
# specification *before* the imshow with the following *after*
# imshow:
#
# plt.set_cmap('BlueRed3')
#
# This sets the new default *and* sets the colormap of the last
# image-like item plotted via pyplot, if any.


plt.suptitle('Custom Blue-Red colormaps')

plt.show()

