Hi Chris,
"
I've used some hacky tricks to get around this, which mostly involve
downsampling the image on the fly based on screen resolution. One such
effort is at https://github.com/ChrisBeaumont/mpl-modest-image
(https://github.com/ChrisBeaumont/mpl-modest-image).
"
I tried your code
Hi Martin,
"Hi,
I knw you asked for memory profiling but I could not resist and did CPU
profiling on your testcase. I have attached some screenshots and in words:
"
thanks for these tips about profiling.
Stepan
---
"
""
You could look at whether or not you actually need 64-bit precision. Often
times, 8-bit precision per color channel is justifiable, even in grayscale.
My advice is to play with the dtype of your array or, as you mentioned,
resample.
"
thanks, this helped me significantly, uint
Hi,
"
You could, before plotting, sum the different image arrays? Depending on
whether you are plotting RGB(A) images or greyscale images, you could take
the sum of the color channels, or take a weighted average.
"
Yes, I will probably merge the images (RGBA) before plotting. I want to
Hi,
I would like to plot multiple overlayed 4096x4096 images in one axes. If I
run this code the plot takes 300 MB of memory:
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
img = np.zeros((4096, 4096))
img[100: 300, 100:1500] = 200
imgplo
