If you're plotting lots of lines, do not use plot but use LineCollection instead.
http://matplotlib.sourceforge.net/examples/api/collections_demo.html http://matplotlib.sourceforge.net/api/collections_api.html#matplotlib.collections.LineCollection Here is slightly modified version of your code that uses LineCollection (but I haven't check if the code is correct). With my not so good macbook, it took me 3 sec for 6000 lines and it seems like O(n) to me. Regards, -JJ ax = subplot(111) x = np.arange(200) yy = [np.array((x, np.sin(x / (2 * np.pi * x[-1] * i))))) for i in range(n)] yyt = [np.transpose(y1) for y1 in yy] from matplotlib.collections import LineCollection lc = LineCollection(yyt, colors=[(0, 0, 0, alpha)]) ax.add_collection(lc) ax.autoscale_view() On Tue, Mar 16, 2010 at 7:26 AM, Jon Olav Vik <jono...@gmail.com> wrote: > 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") > > > > ------------------------------------------------------------------------------ > Download Intel® Parallel Studio Eval > Try the new software tools for yourself. Speed compiling, find bugs > proactively, and fine-tune applications for parallel performance. > See why Intel Parallel Studio got high marks during beta. > http://p.sf.net/sfu/intel-sw-dev > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > ------------------------------------------------------------------------------ Download Intel® Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
