ok. i managed to install 0.98.5.x from source into my enthought
python distribution.
after that, using path.simplify helped considerably.
as far as the pdf.compression not working, i was using rcParams in the
script so i'm
almost certain the options were being loaded.
thanks mike,
drs
On 3 Mar 2009, at 08:11, Michael Droettboom wrote:
> 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
>>>> [email protected]
>>>> 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
_______________________________________________
Matplotlib-users mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/matplotlib-users