I think using the profiler is the best bet here. We've used that in the
past to track down things that take a long time to import quite
successfully. I'm not seeing any slowness here, so that is likely do to
an environmental difference on your machine, implying you'll really need
to run the profiler yourself. I recommend runsnakerun to examine the
profile output -- if you have trouble interpreting it, feel free to send
me your raw profiler data to me off list.
Mike
On 12/31/2012 02:21 PM, C M wrote:
Resurrecting an old thread here....
On Tue, Mar 29, 2011 at 3:23 PM, David Kremer <da...@david-kremer.fr
<mailto:da...@david-kremer.fr>> wrote:
> I would recommend running the import in the Python profiler to
determine
> where most of the time is going. When I investigated this a few
years
> back, it was mainly due to loading the GUI toolkits, which are
> understandably quite large. You can avoid most of that by using
the Agg
> backend. If you're using the Agg backend and still experiencing
> slowness, it may be that load-up issues have crept back into
matplotlib
> since then -- but we need profiling data to figure out where and
how.
Importing Matplotlib is very slow for me, too. For a wxPython
application with embedded Matplotlib, I am getting "load" times of >
20 seconds when "cold" importing matplotlib, with this (circa mid
2004) computer setup: Windows XP, sp3, Intel Pentium, 1.70 Ghz, 1 GB
RAM.
This is, by the way, an import well after Python and wxPython have
already been loaded into RAM, as it happens by a user action, so none
of the time involved here is due to loading Python or wxPython (they
both load more quickly--about 10 seconds to cold import them, my code,
images, and some other libraries).
First of all: does that amount of time seem appropriate for that fast
of a system--or is that too long? It definitely *feels* way too long
from a user perspective (for comparison Word or PowerPoint loads on
this computer in about 2.5 seconds).
Trying to improve it and following this old thread, I have switched to
matplotlib.use('Agg')
instead of
matplotlib.use('wxAgg')
as suggested to speed things up...but it is no faster.
I see, though, that I also have lines such as:
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg
Would the presence of these imports obviate the fact that I switched
to using the Agg instead of the wxAgg? If so, is there any way to use
something faster here (I suspect not but thought I'd ask).
Also, what else should I consider doing to reduce the import time
significantly? (I have to learn how to use the profiler, so I haven't
done that yet).
Thanks,
Che
>
> Mike
Thank you a lot for your answer.
I noticed than _matplotlib.pyplot_ is longer to be imported the
first time than
if it has already been imported previously (maybe things are
already loaded in
ram memory), and we don't need to fetch it from the hard drive
thanks to the
kernel.
As far I see, the function calls are the same for the two logs I
obtained,
except than the first took 6s instead of 1.4s.
The two logs have been obtained using :
<code>
python -m cProfile temp.py
</code>
where temp.py consist of two lines :
<code>
#!/usr/bin/env python2
import matplotlib.pyplot
</code>
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