Opps! I forgot to mention CArray!
On Mon, Jun 3, 2013 at 10:35 PM, Tim Burgess <timburg...@mac.com> wrote:
> My thoughts are:
>
> - try it without any compression. Assuming 32 bit floats, your monthly
> 5760 x 2880 is only about 65MB. Uncompressed data may perform well and at
> the least it will give you a baseline to work from - and will help if you
> are investigating IO tuning.
>
> - I have found with CArray that the auto chunksize works fairly well.
> Experiment with that chunksize and with some chunksizes that you think are
> more appropriate (maybe temporal rather than spatial in your case).
>
>
> On Jun 03, 2013, at 10:45 PM, Andreas Hilboll <li...@hilboll.de> wrote:
>
> On 03.06.2013 14:43, Andreas Hilboll wrote:
> > Hi,
> >
> > I'm storing large datasets (5760 x 2880 x ~150) in a compressed EArray
> > (the last dimension represents time, and once per month there'll be one
> > more 5760x2880 array to add to the end).
> >
> > Now, extracting timeseries at one index location is slow; e.g., for four
> > indices, it takes several seconds:
> >
> > In [19]: idx = ((5000, 600, 800, 900), (1000, 2000, 500, 1))
> >
> > In [20]: %time AA = np.vstack([_a[i,j] for i,j in zip(*idx)])
> > CPU times: user 4.31 s, sys: 0.07 s, total: 4.38 s
> > Wall time: 7.17 s
> >
> > I have the feeling that this performance could be improved, but I'm not
> > sure about how to properly use the `chunkshape` parameter in my case.
> >
> > Any help is greatly appreciated :)
> >
> > Cheers, Andreas.
>
> PS: If I could get significant performance gains by not using an EArray
> and therefore re-creating the whole database each month, then this would
> also be an option.
>
> -- Andreas.
>
>
>
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