I've never needed to ask this question, so I don't know the answer, but
I would suspect pylab, in reality, adds very little overhead to matplotlib.
But -- I would modify your test script to actually perform a plot using
pylab vs. matplotlib API. Until you actually "do" something, you're
just measuring the cost of importing and initialization, which is likely
not the significant part of memory usage.
Also, make sure you aren't using a GUI backend. Use Agg, Pdf, Svg, Ps
etc. depending on your desired output. The GUI backends will not only
take up more memory, but they can break inside of a CGI/mod_python
environment where you don't have an X server.
Cheers,
Mike
Larsen, Jesper wrote:
>
> Hi matplotlib users and developers,
>
> I am trying to run a web application using matplotlib in a memory
> constrained environment. I have therefore tried to figure out what
> memory overhead matplotlib incurs. When I run the following method
> prior to and after importing pylab and matplotlib respectively I get:
>
> def report_memory():
> import os
> pid = os.getpid()
> a2 = os.popen('ps -p %d -o rss,vsz,%%mem' % pid).readlines()
> print a2[1],
> return int(a2[1].split()[1])
>
> import numpy
> report_memory()
> #import pylab
> #import matplotlib
> report_memory()
>
> $ python test.py
> 5976 17872 0.5
> 15608 41924 1.5
> $ python test.py
> 5972 17824 0.5
> 7608 20608 0.7
>
> I am importing numpy separately since I need it for other purposes. So
> pylab uses ~24 MB while matplotlib uses 2.8 MB. Does this mean that I
> should rewrite my application so that it does not depend on pylab or
> will the matplotlib memory usage ramp up as I import sub modules? What
> is your experience?
>
> Regards,
> Jesper
>
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--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
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