Object and Region References¶
In addition to soft and external links, HDF5 supplies one more mechanism to refer to objects and data in a file. HDF5 references are low-level pointers to other objects. The great advantage of references is that they can be stored and retrieved as data; you can create an attribute or an entire dataset of reference type.
References come in two flavors, object references and region references. As the name suggests, object references point to a particular object in a file, either a dataset, group or named datatype. Region references always point to a dataset, and additionally contain information about a certain selection (dataset region) on that dataset. For example, if you have a dataset representing an image, you could specify a region of interest, and store it as an attribute on the dataset.
Using object references¶
It’s trivial to create a new object reference; every high-level object in h5py has a read-only property “ref”, which when accessed returns a new object reference:
>>> myfile = h5py.File('myfile.hdf5') >>> mygroup = myfile['/some/group'] >>> ref = mygroup.ref >>> print ref <HDF5 object reference>
“Dereferencing” these objects is straightforward; use the same syntax as when opening any other object:
>>> mygroup2 = myfile[ref] >>> print mygroup2 <HDF5 group "/some/group" (0 members)>
Using region references¶
Region references always contain a selection. You create them using the dataset property “regionref” and standard NumPy slicing syntax:
>>> myds = myfile.create_dataset('dset', (200,200)) >>> regref = myds.regionref[0:10, 0:5] >>> print regref <HDF5 region reference>
The reference itself can now be used in place of slicing arguments to the dataset:
>>> subset = myds[regref]
For selections which don’t conform to a regular grid, h5py copies the behavior of NumPy’s fancy indexing, which returns a 1D array. Note that for h5py release before 2.2, h5py always returns a 1D array.
In addition to storing a selection, region references inherit from object references, and can be used anywhere an object reference is accepted. In this case the object they point to is the dataset used to create them.
Storing references in a dataset¶
HDF5 treats object and region references as data. Consequently, there is a special HDF5 type to represent them. However, NumPy has no equivalent type. Rather than implement a special “reference type” for NumPy, references are handled at the Python layer as plain, ordinary python objects. To NumPy they are represented with the “object” dtype (kind ‘O’). A small amount of metadata attached to the dtype tells h5py to interpret the data as containing reference objects.
These dtypes are available from h5py for references and region references:
>>> type(h5py.ref_dtype) <type 'numpy.dtype'> >>> ref_dtype.kind 'O' >>> type(h5py.regionref_dtype) <type 'numpy.dtype'>
The types accepted by this “ref=” keyword argument are h5py.Reference (for object references) and h5py.RegionReference (for region references).
To create an array of references, use this dtype as you normally would:
>>> ref_dataset = myfile.create_dataset("MyRefs", (100,), dtype=h5py.ref_dtype)
You can read from and write to the array as normal:
>>> ref_dataset = myfile.ref >>> print ref_dataset <HDF5 object reference>
Storing references in an attribute¶
Simply assign the reference to a name; h5py will figure it out and store it with the correct type:
>>> myref = myfile.ref >>> myfile.attrs["Root group reference"] = myref
When you create a dataset of reference type, the uninitialized elements are “null” references. H5py uses the truth value of a reference object to indicate whether or not it is null:
>>> print bool(myfile.ref) True >>> nullref = ref_dataset >>> print bool(nullref) False