I’m writing a software system that visualizes slices and projections through a 3D dataset. I’m using
matplotlib and specifically
imshow to visualize the image buffers I get back from my analysis code.
Since I’d like to annotate the images with plot axes, I use the extent keyword that
imshow supplies to map the image buffer pixel coordinates to a data space coordinate system.
matplotlib doesn’t know about units. Say (taking an artificial example) that I want to plot an image with dimensions of
1000 m X 1 km. In that case the extent would be something like
[0, 1000, 0, 1]. Even though the image array is square, since the aspect ratio implied by the extent keyword is 1000, the resulting plot axes also have an aspect ratio of 1000.
Is it possible to force the aspect ratio of the plot while still keeping the automatically generated major tick marks and labels I get by using the extent keyword?
You can do it by setting the aspect of the image manually (or by letting it auto-scale to fill up the extent of the figure).
imshow sets the aspect of the plot to 1, as this is often what people want for image data.
In your case, you can do something like:
import matplotlib.pyplot as plt import numpy as np grid = np.random.random((10,10)) fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, figsize=(6,10)) ax1.imshow(grid, extent=[0,100,0,1]) ax1.set_title('Default') ax2.imshow(grid, extent=[0,100,0,1], aspect='auto') ax2.set_title('Auto-scaled Aspect') ax3.imshow(grid, extent=[0,100,0,1], aspect=100) ax3.set_title('Manually Set Aspect') plt.tight_layout() plt.show()