[Matplotlib-users] LinearSegmentedColormap, Normalize, and colorbar

[Timothy W. H. <hi...@me...>]

Hello,

I'm trying to teach myself to create custom colormaps to highlight
certain aspects of a dataset I am working with.  The script below
produces two plots -- the first shows a 4x4 array foo of random floats
between 0.0 and 1.0, and the second shows the same array, but normalized
such that [foo.min(), foo.max()] is mapped to [0.0, 1.0].

As I understand it, I am plotting two slightly different datasets using
the same colormap, yet the two colorbars are different -- note the
value at the transition from grayscale to red.

I am not sure whether the colors are being assigned to slightly
different data values in the two plots, or if the problem is in plotting
the colorbar.  I'd appreciate any help!

Thanks,
Tim

--

Timothy W. Hilton
PhD Candidate, Department of Meteorology
The Pennsylvania State University
503 Walker Building, University Park, PA   16802
hi...@me...

=========

import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib as mpl

mycmdata1 = {'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, 1.0, 0.0),
                      (1.0, 0.5, 1.0))}
mycm1 = mpl.colors.LinearSegmentedColormap('mycm1', mycmdata1)

N = 4
np.random.seed(0)
foo = np.random.rand(N, N)

plt.figure()
plt.pcolor(foo, cmap=mycm1)
plt.colorbar()

plt.figure()
norm = mpl.colors.Normalize()
plt.pcolor(norm(foo), cmap=mycm1)
plt.colorbar()