I am having some trouble using Pyplot imshow to plot an image from a numpy ndarray called data keeping both its aspect ratio and square-shaped pixels. The shape of the ndarray is (112, 2182). Here´s the code I´m using:
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
mpl.use('Agg')
import matplotlib.pyplot as plt
data = np.random.random_sample((112, 2182))
plt.figure(figsize=(25, 3.5))
plt.subplots_adjust(left=0.1, right=0.9)
ax = plt.subplot(111)
plt.axis('off')
plt.title('Title', fontweight='bold', y=1.15)
im = ax.imshow(data)
# Add same-width colorbar to the bottom of the image
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="25%", pad=0.3)
cbar = plt.colorbar(im, orientation="horizontal", cax=cax)
# Save image
plt.savefig('image.pdf', dpi=1000, format=pdf)
I thought that by setting the aspect using the shape of the ndarray the image would show square pixels. However, the image seems to keep the aspect ratio but pixels are not squares, as you can see in zoomed image (I still can´t post images, sorry):
zoomed plot
whole plot
These outputs are the same I got when I used im = ax.imshow(data) withouth the aspect argument. I manually counted pixels in the y-axis of the whole plot and there are 112 of them, so it is not a matter of pixel grouping. Any ideas on how to solve this?
EDIT: the image I am plotting will also have a colorbar and a title. Besides, I need to be able to save it to a pdf file. The zoomed plot I uploaded is a screen capture of the plot zoomed in a pdf viewer.
If you use ax.imshow(data) you will automatically get square pixels. If instead you set the aspect to something other than 1/ "equal", then you will not get square pixels. This is however independent of the effect observed in the question.
The problem in the "zoomed image" is that the size of the data pixels is of the order of actual screen pixels. Hence the size of an image pixels might vary by as much as a screen pixel. The solution to this is to save on a figure such that one image pixels is exactly one screen pixel, or any multiple of that.
In this case this would be as simple as
data = np.random.random_sample((2182, 112))
plt.imsave("filename.png", data)
You need to rescale pixels with use of imshow options: shape and extent:
import numpy as np
import matplotlib.pyplot as plt
shape = (112, 2182)
extent = [0, 112, 0, 2182]
data = np.random.random_sample(shape)
plt.figure(figsize=(5, 3.5))
ax = plt.subplot(111)
plt.axis('off')
dx = (extent[1] - extent[0]) / shape[1]
dy = (extent[3] - extent[2]) / shape[0]
dx_dy = dx/dy
im = ax.imshow(data, extent=extent, aspect=dx_dy)
plt.show()
The documentation says that the aspect parameter may be a float or
'equal': Ensures an aspect ratio of 1. Pixels will be square (unless
pixel sizes are explicitly made non-square in data coordinates using
extent).
'auto': The axes is kept fixed and the aspect is adjusted so
that the data fit in the axes. In general, this will result in
non-square pixels.
So an aspect ratio of 1.0 is what you are looking for. It is not expected that you get the output you show above given the value you calculated from the shape of the array.
As @ImportanceOfBeingErnest points out aspect='equal' is the default, so it as easy as
im = ax.imshow(data)
