[FIXED] python imshow pixel size varies within plot

Issue

Dear stackoverflow community!

I need to plot a 2D-map in python using imshow. The command used is

plt.imshow(ux_map, interpolation='none', origin='lower', extent=[lonhg_all.min(), lonhg_all.max(), lathg_all.min(), lathg_all.max()])

The image is then saved as follows

plt.savefig('rdv_cr%s_cmlon%s_ux.png' % (2097, cmlon_ref))

and looks like

The problem is that when zooming into the plot one can notice that the pixels have different shapes (e.g. different width). This is illustrated in the zoomed part below (taken from the top region of the the first image):

Is there any reason for this behaviour? I input a rectangular grid for my data, but the problem does not have to do with the data itself, I suppose. Instead it is probably something related to rendering. I'd expect all pixels to be of equal shape, but as could be seen they have both different widths as well as heights. By the way, this also occurs in the interactive plot of matplotlib. However, when zooming in there, they become equally shaped all of a sudden.

I'm not sure as to whether https://github.com/matplotlib/matplotlib/issues/3057/ and the link therein might be related, but I can try playing around with dpi values. In any case, if anybody knows why this happens, could that person provide some background on why the computer cannot display the plot as intended using the commands from above?

Thanks for your responses!

Solution

This is related to the way the image is mapped to the screen. To determine the color of a pixel in the screen, the corresponding color is sampled from the image. If the screen area and the image size do not match, either upsampling (image too small) or downsampling (image too large) occurs.

You observed a case of upsampling. For example, consider drawing a 4x4 image on a region of 6x6 pixels on the screen. Sometimes two screen pixels fall into an image pixel, and sometimes only one. Here, we observe an extreme case of differently sized pixels.

When you zoom in in the interactive view, this effect seems to disapear. That is because suddenly you map the image to a large number of pixels. If one image pixel is enlarged to, say, 10 screen pixels and another to 11, you hardly notice the difference. The effect is most apparent when the image nearly matches the screen resolution.

A solution to work around this effect is to use interpolation, which may lead to an undesirable blurred look. To reduce the blur you can...

• play with different interpolation functions. Try for example 'kaiser'

• or up-scale the image by a constant factor using nearest neighbor interpolation (e.g. replace each pixel in the image by a block of pixels with the same color). Then any blurring will only affect the edges of the block.

Answered By - MB-F