A Friday 20 February 2009, Gabriel Beckers escrigué:
> OK, thanks. I think in a previous version this was possible. Or am I
> getting something wrong? I have many files with tables of 1002 rows,
> with one column that holds a 32 x 3488 float32 matrix, for example:
>
> /bird1/IJ_5000/erp/lfp (Table(1002L,)) ''
> description := {
> "signal": Float32Col(shape=(32, 3488), dflt=0.0, pos=0)}
> byteorder := 'little'
> chunkshape := (1L,)
>
> I admit that it is a weird thing to do, but I didn't know a better
> solution.
>
> Here is my application:
>
> I store long 32-channel recordings (say 1.5 hours at 14000 Hz) of
> brain activity in a PyTables file. In this recording there are
> relatively short episodes (events, n=1002) where I want to study
> brain activity, across the 32 channels. Ideally I would 'cut out'
> these 1002 events from the recording (say 32 x 3488 samples per
> event) and store the result in a big 1002 x 32 x 3488 array for
> further analyses. This is no problem of course, but for my analyses I
> very often I need to make selections of events that are not
> sequential. Say, event numbers [3, 8, 23, 57, 576, 578]. But usually
> the sets are much bigger. I cannot access those with normal slicing
> in Array types. So as an alternative I stored the 32 x 3488 events in
> a 1002-row Table. Tables have a readCoordinates method that does
> exactly what I want. If there is a better solution I would be glad to
> learn about it.
I see. Well, you should know that I'm working in providing fancy
selection features to *Array objects. So hopefully, for PyTables 2.2
you will be able to use:
array[[1,3,500]]
or
array[:, [1,3,500], 100:300]
I expect to commit the patches to the PyTables repository (trunk) by the
next week, so stay tuned.
Cheers,
--
Francesc Alted
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