2010/3/30 Ariel Rokem <aro...@berkeley.edu>: > I ended up with the code below, using Chloe's previously posted > 'subcolormap' and, in order to make the colorbar nicely attached to the main > imshow plot, I use make_axes_locatable in order to generate the colorbar > axes. I tried it out with a couple of use-cases and it seems to do what it > is supposed to, (with ticks only for the edges of the range of the data and > 0, if that is within that range), but I am not entirely sure. Do you think > it works?
I think even Chloe would agree that you should avoid the subcolormap()
if you can. I tried to create an as minimalistic as possible but
working self-contained example, please find the code also attached as
.py file:
from matplotlib import pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid import make_axes_locatable
import numpy as np
fig = plt.figure()
ax_im = fig.add_subplot(1, 1, 1)
divider = make_axes_locatable(ax_im)
ax_cb = divider.new_vertical(size = '20%', pad = 0.2, pack_start = True)
fig.add_axes(ax_cb)
x = np.linspace(-5, 5, 101)
y = x
Z = np.sin(x*y[:,None]).clip(-1,1-0.1)
# Leave out if you want:
Z += 2
min_val = Z.min()
max_val = Z.max()
bound = max(np.abs(Z.max()), np.abs(Z.min()))
patch = ax_im.imshow(Z, origin = 'upper', interpolation = 'nearest',
vmin = -bound, vmax = bound)
cb = fig.colorbar(patch, cax = ax_cb, orientation = 'horizontal',
norm = patch.norm,
boundaries = np.linspace(-bound, bound, 256),
ticks = [min_val, 0, max_val],
format = '%.2f')
plt.show()
Friedrich
cbar.py
Description: Binary data
<<attachment: cbar.png>>
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