Hello List,
a while ago, I proposed an extension to pytables
which would allow for HDF5 dimension scales to
be read. Not on a high level, though, just
giving access to the kind of attributes that is used
in their implementation, especially references
and variable-length list attributes.
After some more suggestions from this list, I figured
out that a completely different approach is better,
and so I reimplemented the same as follows:
I introduced a new numpy data type, conveniently
called reference, which corresponds to a HDF5
reference. Now one can make arrays of references,
even record arrays of references, and also store them
(via pytables) into a HDF5 file.
Secondly, I modified setAttr so that if it is given
a list of numpy arrays of all same datatype, a variable
length array is created as an attribute.
To illustrate that, I give a little example:
-------------- snip ----------------
from __future__ import with_statement
import tables
from tables import reference
from numpy import array, arange
with tables.openFile("test.h5", mode="w", title="test") as file:
group = file.createGroup("/", "G", "group")
dataA = file.createArray(group, "A", array([1,2,3]), "data")
dataB = file.createArray(group, "B", array([4,5,6]), "data")
dataC = file.createArray(group, "C", array([7,8,9]), "data")
file.flush()
ref = tables.reference(dataA)
print array([ref])
dataB.attrs.dataA = array([ref])
a = array([(2, ref), (3, ref)], dtype=[("l", "i"), ("s", "r")])
print a.dtype
print a.dtype.base
dataB.attrs.dataL = a
dataB.attrs.list = [ arange(7), arange(9) ]
dataB.attrs.reflist = [ array([ref]),
array([ref, tables.reference(dataB)]) ]
print dataB.attrs.reflist
dataB.attrs.dataR = ref
print ref
print ref.deref(file)
l = ref
ref = tables.reference(dataB)
print "-----------"
------------------------------------------------------------------------------
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