Modifying axes on matplotlib colorbar plot of 2D array

I have a 2D numpy array that I want to plot in a colorbar. I am having trouble changing the axis so that they display my dataset. The vertical axis goes 'down' from 0 to 100, whereas I want it to go 'up' from 0.0 to 0.1. So I need to do two things:

• Flip the array using np.flipud() and then 'flip' the axis as well
• Change the labels to go from 0.0 to 0.1, instead of 0 to 100

Here is an example of what my colorbar plot currently looks like:

And h开发者_StackOverflow中文版ere is the code:

data = np.load('scorr.npy')
(x,y) = np.unravel_index(data.argmax(), data.shape)
max=data[x][y]

fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(data, interpolation='nearest')
cbar = fig.colorbar(cax, ticks=[-max, 0, max])
cbar.ax.set_yticklabels([str(-max), '0', str(max)])
plt.show()

Does anybody have any suggestions? Thanks in advance!

You want to look at the imshow options "origin" and "extent", I think.

import matplotlib.pyplot as plt
import numpy as np

x,y = np.mgrid[-2:2:0.1, -2:2:0.1]
data = np.sin(x)*(y+1.05**(x*np.floor(y))) + 1/(abs(x-y)+0.01)*0.03

fig = plt.figure()
ax = fig.add_subplot(111)
ticks_at = [-abs(data).max(), 0, abs(data).max()]
cax = ax.imshow(data, interpolation='nearest', 
                origin='lower', extent=[0.0, 0.1, 0.0, 0.1],
                vmin=ticks_at[0], vmax=ticks_at[-1])
cbar = fig.colorbar(cax,ticks=ticks_at,format='%1.2g')
fig.savefig('out.png')

The only way I know to change the axis labels on an image plot is manual labelling... If someone has a cleaner method I'd love to learn it.

ax.yaxis.set_ticks(np.arange(0,100,10))
ax.yaxis.set_ticklabels(['%.2f' % 0.1/100*i for i in np.arange(0,100,10)])