How to avoid running out of memory in Python plotting?

To draw a large quantity of similar objects you have to use one of the different matplotlib.collections classes — alas, their usage is a bit arcane, at least when it is my understanding that is involved...

Anyway, starting from the docs and this official example I was able to put together the following code

$ cat ellipses.py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import EllipseCollection

N = 10**5

# centres of ellipses —  uniform distribution, -5<=x<5, -3<=y<3
xy = np.random.random((N,2))*np.array((5*2,3*2))-np.array((5,3))

# width, height of ellipses
w, h = np.random.random(N)/10, np.random.random(N)/10

# rotation angles, anticlockwise
a = np.random.random(N)*180-90

# we need an axes object for the correct scaling of the ellipses
fig, ax = plt.subplots()

# create the collection
ec = EllipseCollection(w, h, a,
                    units='x',
                    offsets=xy,
                    transOffset=ax.transData)

ax.add_collection(ec)
ax.autoscale(tight=True)

plt.savefig('el10^5.png')

I timed it on my almost low-end notebook

$ time python -c 'import numpy; import matplotlib.pyplot as p; f, a = p.subplots()'

real    0m0.697s
user    0m0.620s
sys     0m0.072s
$ time python ellipses.py 

real    0m5.704s
user    0m5.616s
sys     0m0.080s
$

As you can see, when you discount the staging required for every plot, it takes about 5 seconds — and what is the result?

I think that the details about eccentricity and angle are lost in such a dense representation, but I don't know the specifics of your task and won't comment further.