path.simplify was added some time after 0.98.3. You'll have to upgrade
to 0.98.5.x for that feature.
pdf.compression should have some impact on file size, but I doubt it
will have much impact on display times, since it doesn't actually remove
any data. I'm surprised this isn't having any effect -- perhaps the
matplotlibrc file you're editing is not the one being loaded? You can
see where the file is being loaded from with:
import matplotlib
matplotlib.get_configdir()
agg.path.chunksize has no effect on PDF output.
Is it possible you're using the Cairo backend, and not matplotlib's own
Python-based PDF backend?
As a cheap workaround, you can also easily decimate your data using
Numpy with something like:
data = data[::skip]
where 'skip' is the number of data points to skip.
Cheers,
Mike
Daniel Soto wrote:
> thanks for the suggestion. i'm running 0.98.3 and have tried
>
> pdf.compression
> path.simplify
> agg.path.chunksize
>
> without any change in filesize (176KB) or time to open file (13 sec).
>
> are there any other options or backends that might help?
>
> drs
>
> On 3 Mar 2009, at 05:29, Michael Droettboom wrote:
>
>> With recent versions of matplotlib, you can set the "path.simplify"
>> rcParam to True, which should reduce the data so that vertices that
>> have no impact on the plot appearance (at the given dpi) are removed.
>>
>> You can do either, in your script:
>>
>> from matplotlib import rcParam
>> rcParam['path.simplify'] = True
>>
>> or in your matplotlibrc file:
>>
>> path.simplify: True
>>
>> Hope that helps. The amount of reduction this produces is somewhat
>> data-dependent.
>>
>> Cheers,
>> Mike
>>
>> Daniel Soto wrote:
>>> hello,
>>>
>>> i'm using matplotlib on os x and am having issues with plots of
>>> large data sets. i have some plots which contain about ~10000
>>> points and the pdf files generated bring preview.app and quicklook
>>> to their knees when they open the pdf files.
>>>
>>> here is a small file that reproduces my issues. at 1000 points it
>>> is snappy and at 10000 it is a pig.
>>>
>>> is there a setting to downsample or otherwise compress?
>>>
>>> best,
>>> drs
>>>
>>>
>>>
>>> import matplotlib.pyplot
>>> import scipy
>>>
>>> x = scipy.rand(10000)
>>> matplotlib.pyplot.plot(x)
>>> matplotlib.pyplot.savefig('rand.pdf')
>>>
>>> ------------------------------------------------------------------------------
>>>
>>>
>>> Open Source Business Conference (OSBC), March 24-25, 2009, San
>>> Francisco, CA
>>> -OSBC tackles the biggest issue in open source: Open Sourcing the
>>> Enterprise
>>> -Strategies to boost innovation and cut costs with open source
>>> participation
>>> -Receive a $600 discount off the registration fee with the source
>>> code: SFAD
>>> http://p.sf.net/sfu/XcvMzF8H
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Matplotlib-users@lists.sourceforge.net
>>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>>
>>
>> --
>> Michael Droettboom
>> Science Software Branch
>> Operations and Engineering Division
>> Space Telescope Science Institute
>> Operated by AURA for NASA
>>
>
--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
------------------------------------------------------------------------------
Open Source Business Conference (OSBC), March 24-25, 2009, San Francisco, CA
-OSBC tackles the biggest issue in open source: Open Sourcing the Enterprise
-Strategies to boost innovation and cut costs with open source participation
-Receive a $600 discount off the registration fee with the source code: SFAD
http://p.sf.net/sfu/XcvMzF8H
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