Matplotlib canvas as numpy array artefacts

Question

I want to convert a matplotlib figure into a numpy array. I have been able to do this by accessing the contents of the renderer directly. However, when I call imshow on the numpy array it has what looks like aliasing artefacts along the edges which aren't present in the original figure.

I've tried playing around with various parameters but can't figure out how to fix the artefacts from imshow. The differences in the images remain if I save the figures to an image file.

Note that what I want to achieve is a way to confirm that the content of the array is the same as the figure I viewed before. I think probably these artefacts are not present in the numpy array but are created during the imshow call. Perhaps approriate configuration of imshow can resolve the problem.

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
import math

fig = plt.figure(frameon=False)
ax = plt.gca()
ax.add_patch(Rectangle((0,0), 1, 1, angle=45, color="red"))
ax.set_xlim(-2,2)
ax.set_ylim(-2,2)
ax.set_aspect(1)
plt.axis("off")
fig.canvas.draw()
plt.savefig("rec1.png")
plt.show()
X = np.array(fig.canvas.renderer._renderer)

fig = plt.figure(frameon=False)
ax = plt.gca()
plt.axis("off")
plt.imshow(X)
plt.savefig("rec2.png")
plt.show()

Answer 1

These are clearly resampling artefacts, which can be avoided by using plt.figimage which specifically adds a non-resampled image to the figure.

plt.figimage(X)
plt.show()

Note that this will not work with the %matplotlib inline in Jupyter Notebook, but it does work fine with %matplotlib notebook and with GUI backends.