Hi, When improving the performance of plotting high-dimensional data using faceted scatter plots, I noticed that much of time was spent on the axis creation (even 50%!).
On my machine creating 20x20 array of subplots without actually plotting anything takes about 11 seconds (for comparison plotting 5000 points on all of them takes only 0.6s!): import matplotlib matplotlib.interactive(True) import matplotlib.pyplot as plt fig, axes = plt.subplots(20,20) plt.show() Profiling shows that 50% of computation time is spent on axis/ticks creation [1], which I have to remove anyways. Is there any easy way of creating thinned axes without ticks and spines? So far I solved the problem by subclassing Axes class (see this gist [2]) and removing all spines and ticks. Running the above example gives a 10x boost in performance (from 11s to 0.9s). import thin_axes fig, axes = plt.subplots(20,20, subplot_kw=dict(projection='thin')) plt.show() Profiling results show more uniform distribution of computing time across functions (most time is spent on creating and applying transforms [3]). The thinned class seems a bit hacky. Is there any other way to create a raw Axes object without spines, ticks, labels etc., just pure canvas with appropriate transforms? Yours, Bartosz [1] profiling results of vanilla Axes: http://pbrd.co/1jlovoo [2] https://gist.github.com/btel/a6b97e50e0f26a1a5eaa [3] profiling results of thined Axes: ------------------------------------------------------------------------------ Open source business process management suite built on Java and Eclipse Turn processes into business applications with Bonita BPM Community Edition Quickly connect people, data, and systems into organized workflows Winner of BOSSIE, CODIE, OW2 and Gartner awards http://p.sf.net/sfu/Bonitasoft _______________________________________________ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
