I want to overlay many line plots using alpha transparency. However, plotting

them in Matplotlib takes about O(n**2) time, and I think I may be running into

memory limitations as well.

As a simple benchmark, I used IPython to run alco.ipy (below), which runs

alco.py for an increasing number of data series. Extrapolating from this,

plotting 60000 series would take something like 200 minutes. This is similar to

my actual use case, which takes about 3 hours to finish a plot. Zooming in and

saving again is much faster, taking only about 30 seconds.

I would appreciate suggestions on how to speed this up. For instance:

Is there a memoryless "canvas" object that I could draw on, just accumulating

the alpha in each pixel: new_alpha = old_alpha + (1 - old_alpha) * this_alpha.

Failing that, I could do it manually by keeping a Numpy array of the pixels in

the image. For each series, find the x values corresponding to each column

index, then interpolate to find the row index corresponding to each y value.

Finally, use imshow() or something to add axes and annotation.

That you in advance for any help.

Best regards,

Jon Olav

== Output of alco.ipy ==

The columns are "number of series" and "seconds".

In [8]: run alco.ipy

1000 9.07

2000 24.8

3000 44.73

4000 67.85

5000 95.67

6000 135.1

7000 177.82

8000 226.03

9000 278.32

10000 340.81

== alco.ipy ==

n, t = [], []

for i in range(1000, 10001, 1000):

n.append(i)

ti = !python alco.py $i

t.append(float(ti.s))

print n[-1], t[-1]

plot(n, t, '.-')

== alco.py ==

"""Alpha compositing of line plots. Usage: python alco.py NSERIES ALPHA"""

from sys import argv

import numpy as np

import matplotlib as mpl

mpl.use("agg") # noninteractive plotting

from pylab import *

n = int(argv[1])

try:

alpha = float(argv[2])

except IndexError:

alpha = 0.02

# generate some data

x = np.arange(200)

for i in range(n):

y = np.sin(x / (2 * np.pi * x[-1] * i))

plot(x, y, 'k-', alpha=alpha)

savefig("test.png")