Create own colormap using matplotlib and plot color scale

I have the following problem, I want to create my own colormap (red-mix-violet-mix-blue) that maps to values between -2 and +2 and want to use it to color points in my plot. The plot should then have the colorscale to the right.

That is how I create the map so far. But I am not really sure if it mixes the colors.

cmap = matplotlib.colors.ListedColormap(["red","violet","blue"], name='from_list', N=None)
m = cm.ScalarMappable(norm=norm, cmap=cmap)

That way I map the colors to the values.

colors = itertools.cycle([m.to_rgba(1.22), ..])

Then I plot it:

for i in range(0, len(array_dg)):
  plt.plot(array_dg[i], markers.next(),alpha=alpha[i], c=colors.next())

My problems are:
1. I can't plot the color scale.
2. I am not completely sure if my scale is creating a continues (smooth) colorscale.

Solution 1:

There is an illustrative example of how to create custom colormaps here. The docstring is essential for understanding the meaning of cdict. Once you get that under your belt, you might use a cdict like this:

cdict = {'red':   ((0.0, 1.0, 1.0), 
                   (0.1, 1.0, 1.0),  # red 
                   (0.4, 1.0, 1.0),  # violet
                   (1.0, 0.0, 0.0)), # blue

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

         'blue':  ((0.0, 0.0, 0.0),
                   (0.1, 0.0, 0.0),  # red
                   (0.4, 1.0, 1.0),  # violet
                   (1.0, 1.0, 0.0))  # blue
          }

Although the cdict format gives you a lot of flexibility, I find for simple gradients its format is rather unintuitive. Here is a utility function to help generate simple LinearSegmentedColormaps:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors


def make_colormap(seq):
    """Return a LinearSegmentedColormap
    seq: a sequence of floats and RGB-tuples. The floats should be increasing
    and in the interval (0,1).
    """
    seq = [(None,) * 3, 0.0] + list(seq) + [1.0, (None,) * 3]
    cdict = {'red': [], 'green': [], 'blue': []}
    for i, item in enumerate(seq):
        if isinstance(item, float):
            r1, g1, b1 = seq[i - 1]
            r2, g2, b2 = seq[i + 1]
            cdict['red'].append([item, r1, r2])
            cdict['green'].append([item, g1, g2])
            cdict['blue'].append([item, b1, b2])
    return mcolors.LinearSegmentedColormap('CustomMap', cdict)


c = mcolors.ColorConverter().to_rgb
rvb = make_colormap(
    [c('red'), c('violet'), 0.33, c('violet'), c('blue'), 0.66, c('blue')])
N = 1000
array_dg = np.random.uniform(0, 10, size=(N, 2))
colors = np.random.uniform(-2, 2, size=(N,))
plt.scatter(array_dg[:, 0], array_dg[:, 1], c=colors, cmap=rvb)
plt.colorbar()
plt.show()

By the way, the for-loop

for i in range(0, len(array_dg)):
  plt.plot(array_dg[i], markers.next(),alpha=alpha[i], c=colors.next())

plots one point for every call to plt.plot. This will work for a small number of points, but will become extremely slow for many points. plt.plot can only draw in one color, but plt.scatter can assign a different color to each dot. Thus, plt.scatter is the way to go.

Solution 2:

Since the methods used in other answers seems quite complicated for such easy task, here is a new answer:

Instead of a ListedColormap, which produces a discrete colormap, you may use a LinearSegmentedColormap. This can easily be created from a list using the from_list method.

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

x,y,c = zip(*np.random.rand(30,3)*4-2)

norm=plt.Normalize(-2,2)
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["red","violet","blue"])

plt.scatter(x,y,c=c, cmap=cmap, norm=norm)
plt.colorbar()
plt.show()

More generally, if you have a list of values (e.g. [-2., -1, 2]) and corresponding colors, (e.g. ["red","violet","blue"]), such that the nth value should correspond to the nth color, you can normalize the values and supply them as tuples to the from_list method.

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

x,y,c = zip(*np.random.rand(30,3)*4-2)

cvals  = [-2., -1, 2]
colors = ["red","violet","blue"]

norm=plt.Normalize(min(cvals),max(cvals))
tuples = list(zip(map(norm,cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)

plt.scatter(x,y,c=c, cmap=cmap, norm=norm)
plt.colorbar()
plt.show()