CODEWITHSUNDEEP
imageplotmatplotliblogarithm
endolith
endolith

Reputation: 26813

Non-linear axes for imshow in matplotlib

I am generating 2D arrays on log-spaced axes (for instance, the x pixel coordinates are generated using logspace(log10(0.95), log10(2.08), n).

I want to display the image using a plain old imshow, in its native resolution and scaling (I don't need to stretch it; the data itself is already log scaled), but I want to add ticks, labels, lines that are in the correct place on the log axes. How do I do this?

Ideally I could just use commands line axvline(1.5) and the line would be in the correct place (58% from the left), but if the only way is to manually translate between logscale coordinates and image coordinates, that's ok, too.

For linear axes, using extents= in the call to imshow does what I want, but I don't see a way to do the same thing with a log axis.

Example:

from matplotlib.colors import LogNorm

x = logspace(log10(10), log10(1000), 5)
imshow(vstack((x,x)), extent=[10, 1000, 0, 100], cmap='gray', norm=LogNorm(), interpolation='nearest')
axvline(100, color='red')

This example does not work, because extent= only applies to linear scales, so when you do axvline at 100, it does not appear in the center. I'd like the x axis to show 10, 100, 1000, and axvline(100) to put a line in the center at the 100 point, while the pixels remain equally spaced.

Upvotes: 27

Views: 35638

Answers (3)

Matt
Matt

Reputation: 7

To display imshow with abscisse log scale:

ax = fig.add_subplot(nrow, ncol, i+1)
ax.set_xscale('log')

Upvotes: -2

endolith
endolith

Reputation: 26813

Actually, it works fine. I'm confused.

Previously I was getting errors about "Images are not supported on non-linear axes" which is why I asked this question. But now when I try it, it works:

import matplotlib.pyplot as plt
import numpy as np

x = np.logspace(1, 3, 5)
y = np.linspace(0, 2, 3)
z = np.linspace(0, 1, 4)
Z = np.vstack((z, z))

plt.imshow(Z, extent=[10, 1000, 0, 1], cmap='gray')
plt.xscale('log')

plt.axvline(100, color='red')

plt.show()

This is better than pcolor() and pcolormesh() because

  1. it's not insanely slow and
  2. is interpolated nicely without misleading artifacts when the image is not shown at native resolution.

Upvotes: 12

imsc
imsc

Reputation: 7840

In my view, it is better to use pcolor and regular (non-converted) x and y values. pcolor gives you more flexibility and regular x and y axis are less confusing.

import pylab as plt
import numpy as np
from matplotlib.colors import LogNorm
from matplotlib.ticker import LogFormatterMathtext

x=np.logspace(1, 3, 6)
y=np.logspace(0, 2,3)
X,Y=np.meshgrid(x,y)
z = np.logspace(np.log10(10), np.log10(1000), 5)
Z=np.vstack((z,z))

im = plt.pcolor(X,Y,Z, cmap='gray', norm=LogNorm())
plt.axvline(100, color='red')

plt.xscale('log')
plt.yscale('log')

plt.colorbar(im, orientation='horizontal',format=LogFormatterMathtext())
plt.show()

enter image description here

As pcolor is slow, a faster solution is to use pcolormesh instead.

im = plt.pcolormesh(X,Y,Z, cmap='gray', norm=LogNorm())

Upvotes: 22

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