Hi,
I am a regular user of matplotlib since I moved from matlab to
python/numpy/scipy. Even if I find matplotlib to be a real help during
the transition from matlab to python, I must confess I found it the most
disappointing compare other packages ( essentially numpy/scipy/ipython).
This is not a rant; I want to know if this slowness is coming from my
lack of matplotlib knowledge or not; I apologize in advance if the
following hurts anyone feelings :)
First, I must admit that whereas I took a significant amount of time
to study numpy and scipy, I didn't take that same time for matplotlib.
So this disappointment may just be a consequences of this laziness.
My main problem with matplotlib is speed: I find it really annoying
to use in an interactive manner. For example, when I need to display
some 2d information, such as spectrogramm or correlogram, this take 1 or
2 seconds for a small signal (~4500 frames of 256 samples). My function
correlogram (similar to specgram, but compute correlation instead of log
spectrum) uses imshow, and this function takes 20 times more time than
imagesc of matlab for the same size.
Also, I found changing the size of the matplotlib window really
'annoying to the eye': I compared to matlab, and this may be due to the
fact that the whole window is redrawn with matplotlib, including the
toolbar, whereas in matlab, the top toolbar is not redrawn.
Finally, plotting many data (using plot(X, Y) with X and Y around
1000/10000 samples) is 'slow' (the '' are because I don't know much
about computer graphics, and I understand that slow in the rendering is
often just a perception)
So, is this a current limitation of matplotlib, is matplotlib
optimized for good rendering for publication, and not for interactive
use, or I am just misguided in my use of matplotlib ?
Config info:
- ubuntu edgy on a bi xeon 3.2 Ghz with 2 Gb of Ram
- numpy SVN (post 1.0)
- matplotlib 0.87.7
- matplotlibrc: uses numpy for numeric, Gtk as a backend (or GtkAdd
for anti aliasing, but this makes the problem worse).
Cheers,
David
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