Wow Jeff! You save me again! I remember looking at it last year and thinking it
would be awesome if there would be a windows installer for it!
I will install and play with it tonight! Thanks a lot!
Anton
________________________________
From: Jeff Whitaker <jsw...@fastmail.fm>
To: antonv <vasilescu_an...@yahoo.com>
Cc: matplotlib-users@lists.sourceforge.net
Sent: Wednesday, April 8, 2009 4:02:22 PM
Subject: Re: [Matplotlib-users] Computer specs for fast matplotlib and basemap
processing
antonv wrote:
> I know that using the csv files is very slow but I have no knowledge of
> working with the netcdf format and I was in a bit of a rush when I wrote
> this. I will take a look again at it. How would you translate a grib in
> netcdf? Are there any secific applications or straight through numpy?
>
> As for pyngl, if i remember correctly I looked at it but it was not working
> on windows.
>
> Thanks,
> Anton
>
Anton: If these are grib version 2 files, another option is
http://code.google.com/p/pygrib2. I have made a windows installer.
-Jeff
>
>
> efiring wrote:
>
>> antonv wrote:
>>
>>> I have a bit of experience programming and I am pretty sure I get my
>>> parts of
>>> the code pretty well optimized. I made sure that in the loop I have only
>>> the
>>> stuff needed and I'm loading all the stuff before.
>>>
>>> The biggest bottleneck is happening because I'm unpacking grib files to
>>> csv
>>> files using Degrib in command line. That operation is usually around half
>>> an
>>>
>> Instead of going to csv files--which are *very* inefficient to write, store,
>> and then read in again--why not convert directly to netcdf, and then read
>> your data in from netcdf as needed for plotting? I suspect this will speed
>> things up quite a bit. Numpy support for netcdf is very good. Of course,
>> direct numpy-enabled access to the grib files might be even better,
>> eliminating the translation phase entirely. Have you looked into
>> http://www.pyngl.ucar.edu/Nio.shtml?
>>
>> Eric
>>
>>
>>
>>> hour using no more than 50% of the processor but it maxes out the memory
>>> usage and it definitely is hard drive intensive as it ends up writing
>>> over 4
>>> GB of data. I have noticed also that on a lower spec AMD desktop this
>>> runs
>>> faster than on my P4 Intel Laptop, my guess being that the laptop hdd is
>>> 5400 rpm and the desktop is 7200 rpm.
>>>
>>> Next step is to take all those csv files and make images from them. For
>>> this
>>> one I haven't dug too deep to see what is happening but it seems to be
>>> the
>>> other way, using the cpu a lot more while keeping the memory usage high
>>> too.
>>>
>>> Thanks,
>>> Anton
>>>
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