On Tue, Sep 1, 2009 at 21:11, Jorge Scandaliaris<jorgesmbox...@yahoo.es> wrote:
> David Warde-Farley <dwf <at> cs.toronto.edu> writes:
>> If you actually want to save multiple arrays, you can use
>> savez('fname', *[a,b,c]) and they will be accessible under the names
>> arr_0, arr_1, etc. and a list of these names is in the 'files'
>> attribute on the NpzFile object. To retrieve your list of arrays when
>> you load, you can just do
>>
>> mynewlist = [data[arrname] for arrname in data.files]
>>
>
> Thanks for the tip. I have realized, though, that I might need some more
> flexibility than just the ability to save ndarrays. The data I am dealing with
> is best kept in a hierarchical way (I could represent the structure with
> ndarrays also, but I think it would be messy and difficult). I am having a
> look
> at h5py to see if it fulfill my needs. I know there is pytables, too, but from
> having a quick look it seems h5py is simpler. Am I right on this?. I also get
> a
> nice side-effect, the data would be readable by the de-facto standard software
> used by most people in my field.
If there is a particular format that uses HDF5 that you are trying to
replicate, h5py is the clear answer. However, PyTables will, by and
large, make files that are entirely readable by other HDF5 libraries
when you just use the subset of features that is supported by
HDF5-proper. For example, tables and arrays work just fine. What won't
be supported by non-PyTables libraries are things like dataset
attributes which are pickled objects. Your non-PyTables HDF5 apps will
see some extraneous attributes on the arrays and tables, but those are
typically not necessary for interpretation.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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