matplotlib matshow labels

I start using matplotlib a month ago, so I'm still learning.

I'm trying to do a heatmap with matshow. My code is the following:

data = numpy.array(a).reshape(4, 4)  
cax = ax.matshow(data, interpolation='nearest', cmap=cm.get_cmap('PuBu'), norm=LogNorm())  
cbar = fig.colorbar(cax)

ax.set_xticklabels(alpha)  
ax.set_yticklabels(alpha)

where alpha is a model from django with 4fields: 'ABC', 'DEF', 'GHI', 'JKL'

the thing is that I don't know why, the label 'ABC' doe开发者_如何学Pythonsn't appear, leaving the last cell without label.

If someone would have a clue how to modify my script in a way to appear the 'ABC' I would be grateful :)

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What's happening is that the xticks actually extend outside of the displayed figure when using matshow. (I'm not quite sure exactly why this is. I've almost never used matshow, though.)

To demonstrate this, look at the output of ax.get_xticks(). In your case, it's array([-1., 0., 1., 2., 3., 4.]). Therefore, when you set the xtick labels, "ABC" is at <-1, -1>, and isn't displayed on the figure.

The easiest solution is just to prepend a blank label to your list of labels, e.g.

ax.set_xticklabels(['']+alpha)
ax.set_yticklabels(['']+alpha)

As a full example:

import numpy as np
import matplotlib.pyplot as plt

alpha = ['ABC', 'DEF', 'GHI', 'JKL']

data = np.random.random((4,4))

fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(data, interpolation='nearest')
fig.colorbar(cax)

ax.set_xticklabels(['']+alpha)
ax.set_yticklabels(['']+alpha)

plt.show()

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The other way to do this is to specify the ticks, and then set the corresponding labels. Then you don't have to worry about the extra out of bounds tick. This comes up in lots of matplotlib demos. So here, note the extra calls the ax.set_xticks and ax.set_yticks

import numpy as np
import matplotlib.pyplot as plt

alpha = ['ABC', 'DEF', 'GHI', 'JKL']

data = np.random.random((4,4))

fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(data, interpolation='nearest')
fig.colorbar(cax)

xaxis = np.arange(len(alpha))
ax.set_xticks(xaxis)
ax.set_yticks(xaxis)
ax.set_xticklabels(alpha)
ax.set_yticklabels(alpha)

plt.show()