On Tue, Mar 16, 2010 at 8:46 AM, Jon Olav Vik <jono...@gmail.com> wrote:
> Thank you, thank you, thank you. > > This is just as convenient, 50% faster even for 1000 series, and runtime does > indeed scale as O(n) up to 10000 series. The projected speedup for 60000 > series > was 40x. However, in my actual use case it was at least 400x: Finishing in 2 > min 17 sec rather than not getting past halfway in 16 hours. > > (The extra difference is probably due to better memory usage. Still, > LineCollection requires O(n) memory, whereas manually updating a bitmap would > only use O(1) memory, where 1 = size of bitmap. However, I hope I never have > to > do that...) > > May the hours and hours you have saved me be added to your life! 8-) Since you are granting extra life blessings, I thought I should add something to the mix. You should be able to achieve something close to this using the animation blit API. There is a little hackery at the end to use the renderer to directly dump a PNG and thereby circumvent the normal figure.canvas.draw pipeline, but the advantage is you render directly to the canvas and save no intermediaries. See the examples and tutorial at http://matplotlib.sourceforge.net/examples/animation/index.html http://www.scipy.org/Cookbook/Matplotlib/Animations Here's some example code:: import matplotlib._png as _png import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot(111) n = 10000 line, = ax.plot([],[], alpha=1) x = np.arange(200) fig.canvas.draw() ax.axis([0, 200, -1, 1]) for i in range(n): if (i%100)==0: print i yy = np.sin(x / (2 * np.pi * x[-1] * i)) line.set_data(x, yy) ax.draw_artist(line) fig.canvas.blit(ax.bbox) filename = 'test.png' renderer = fig.canvas.get_renderer() _png.write_png(renderer._renderer.buffer_rgba(0, 0), renderer.width, renderer.height, filename, fig.dpi) JDH ------------------------------------------------------------------------------ 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
