On Sunday, March 13, 2011, onet <o...@dds.nl> wrote:
> On Fri, 2011-03-11 at 17:08 -1000, Eric Firing wrote:
>> On 03/11/2011 02:54 PM, onet wrote:
>> > Using matplotlib I try to plot satellite observations, which consists of
>> > roughly one million patches that are not gridded regularly.
>> > I first collect the vertices (corner points of the observations) and
>> > colors and then use PolyCollection and ax.add_collection to add these
>> > patches to the figure.
>> >
>> > On my 64bit Linux machine:
>> > # 1M patches will use>  4Gb of memory
>> >
>> > My question: how can I plot more efficiently and use less memory?
>>
>> If your data are on a quadrilateral mesh, as in your example, (or can be
>> approximately mapped onto such a mesh) then pcolormesh should be very
>> much more efficient both in time and in memory than making a PolyCollection.
>
> The data I want to plot is not as regular as in the example (this was
> just to generate lots of non-overlaping patches) but it has different
> shapes along the orbit of the satellite when projected on the map.
> Almost square at the equator and rotated near the poles. See example
> link below from a plot in IDL.
>
> http://temis.nl/o3msaf/vaac/gome2/vaac/daily/images/2011/S-O3M_GOME_NAR_02_M02_20110312000254Z_20110313000254Z_N_O_20110313024518Z.AAI_Global.Unfiltered.png
>
> But I think my satellite data along an orbit is probably piecewise
> regular enough try the pcolormesh approach.
>
> So thanks for the suggestion!
>
> Best regards,
>
>         Olaf.
>

It might be regular with respect to a particular projection.  Have you
considered checking out Basemap?

Ben Root

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