On Thu, Jul 5, 2012 at 7:29 AM, Benjamin Root <ben.r...@ou.edu> wrote:
>
>
> On Wed, Jul 4, 2012 at 1:17 PM, Gökhan Sever <gokhanse...@gmail.com>wrote:
>
>> Hello,
>>
>> I am working on creating some distribution plots to analyze cloud droplet
>> and drop features. You can see one such plot at
>> http://atmos.uwyo.edu/~gsever/data/rf06_1second/rf06_belowcloud_SurfaceArea_1second.pdf
>> This file contains 38 pages and each page has 16 panels created via
>> MPL's AxesGrid toolkit. I am using PdfPages from pdf backend profile to
>> construct this multi-page plot. The original code that is used to create
>> this plot is in
>> http://code.google.com/p/ccnworks/source/browse/trunk/parcel_drizzle/rf06_moments.py
>>
>> The problem I am reporting is due to the lengthier plot creation times.
>> It takes about 4 minutes to create such plot in my laptop. To better
>> demonstrate the issue I created a sample script which you can use to
>> reproduce my timing results --well based on pseudo/random data points. All
>> my data points in the original script are float64 so I use float64 in the
>> sample script as well.
>>
>> The script is at
>> http://atmos.uwyo.edu/~gsever/data/matplotlib/test_speed.py I also
>> included 2 pages output running the script with nums=2 setting
>> http://atmos.uwyo.edu/~gsever/data/matplotlib/test_speed.pdf
>> Comparing my original output, indeed cloud particles are not from a
>> normal distribution :)
>>
>> Joke aside, running with nums=2 for 2 pages
>>
>> time run test_speed.py
>> CPU times: user 12.39 s, sys: 0.10 s, total: 12.49 s
>> Wall time: 12.84 s
>>
>> when nums=38, just like my original script, then I get similar timing to
>> my original run
>>
>> time run test.py
>> CPU times: user 227.39 s, sys: 1.74 s, total: 229.13 s
>> Wall time: 234.87 s
>>
>> In addition to these longer plot creation times, 38 pages plot creation
>> consumes about 3 GB memory. I am wondering if there are tricks to improve
>> plot creation times as well as more efficiently using the memory.
>> Attempting to create two such distributions blocks my machine eating 6 GB
>> of ram space.
>>
>> Using Python 2.7, NumPy 2.0.0.dev-7e202a2, IPython
>> 0.13.beta1, matplotlib 1.1.1rc on Fedora 16 (x86_64)
>>
>> Thanks.
>>
>> --
>> Gökhan
>>
>>
> Gokhan,
>
> Looking through your code, I see that you have all of the figure objects
> available all at once, rather than one at a time. In belowcloud_M0(), you
> create all of your figure objects and AxesGrid objects in list
> comprehensions, and then you have multiple for-loops that performs a
> particular action on each of these. Then you create your PdfPages object
> and loop over each of the figures, saving it to the page.
>
> I would do it quite differently. At the beginning of the function, create
> your PdfPages object. Then have a single loop over "range(nums)" where you
> create a figure object and an AxesGrid object. Do your 16 (or less) plots,
> and any other text you need for that figure. Save it to the PdfPage
> object, and then close the figure object. When the loop is done, close the
> PdfPages object.
>
> I think you will see huge performance improvement that way.
>
> Cheers!
> Ben Root
>
>
Hi,
Could you try the files again? I believe I have given read permission for
outside access.
http://atmos.uwyo.edu/~gsever/data/matplotlib/test_speed.py
http://atmos.uwyo.edu/~gsever/data/matplotlib/test_speed.pdf
Ben,
Thanks for your suggestion. I will give it a try and report back here.
--
Gökhan
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