Also, depending on how much meta data you really needed to store you could
just use attributes. That is what they are there for.
On Tue, Jun 25, 2013 at 10:06 AM, Josh Ayers <josh.ay...@gmail.com> wrote:
> Another option is to create a Python object - dict, list, or whatever
> works - containing the metadata and then store a pickled version of it in a
> PyTables array. It's nice for this sort of thing because you have the full
> flexibility of Python's data containers.
>
> For example, if the Python object is called 'fit', then
> numpy.frombuffer(pickle.dumps(fit), 'u1') will pickle it and convert the
> result to a NumPy array of unsigned bytes. It can be stored in a PyTables
> array using a UInt8Atom. To retrieve the Python object, just use
> pickle.loads(hdf5_file.root.data_1.fit[:]).
>
> It gets a little more complicated if you want to be able to modify the
> Python object, because the length of the pickle will change. In that case,
> you can use an EArray (for the case when the pickle grows), and store the
> number of bytes as an attribute. Storing the number of bytes handles the
> case when the pickle shrinks and doesn't use the full length of the on-disk
> array. To load it, use
> pickle.loads(hdf5_file.root.data_1.fit[:num_bytes]), where num_bytes is the
> previously stored attribute. To modify it, just overwrite the array with
> the new version, expanding if necessary, then update the num_bytes
> attribute.
>
> Using a PyTables VLArray with an 'object' atom uses a similar technique
> under the hood, so that may be easier. It doesn't allow resizing though.
>
> Hope that helps,
> Josh
>
>
>
> On Tue, Jun 25, 2013 at 1:33 AM, Andreas Hilboll <li...@hilboll.de> wrote:
>
>> On 25.06.2013 10:26, Andre' Walker-Loud wrote:
>> > Dear PyTables users,
>> >
>> > I am trying to figure out the best way to write some metadata into some
>> files I have.
>> >
>> > The hdf5 file looks like
>> >
>> > /root/data_1/stat
>> > /root/data_1/sys
>> >
>> > where "stat" and "sys" are Arrays containing statistical and systematic
>> fluctuations of numerical fits to some data I have. What I would like to
>> do is add another object
>> >
>> > /root/data_1/fit
>> >
>> > where "fit" is just a metadata key that describes all the choices I
>> made in performing the fit, such as seed for the random number generator,
>> and many choices for fitting options, like initial guess values of
>> parameters, fitting range, etc.
>> >
>> > I began to follow the example in the PyTables manual, in Section 1.2
>> "The Object Tree", where first a class is defined
>> >
>> > class Particle(tables.IsDescription):
>> > identity = tables.StringCol(itemsize=22, dflt=" ", pos=0)
>> > ...
>> >
>> > and then this class is used to populate a table.
>> >
>> > In my case, I won't have a table, but really just want a single object
>> containing my metadata. I am wondering if there is a recommended way to do
>> this? The "Table" does not seem optimal, but I don't see what else I would
>> use.
>>
>> For complex information I'd probably indeed use a table object. It
>> doesn't matter if the table only has one row, but still you have all the
>> information there nicely structured.
>>
>> -- Andreas.
>>
>>
>>
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