HDF5 for Python Documentation
HDF5 supports a few types which have no direct NumPy equivalent. Among the most useful and widely used are variable-length (VL) types, and enumerated types. As of version 1.2, h5py fully supports HDF5 enums, and has partial support for VL types.
In HDF5, data in VL format is stored as arbitrary-length vectors of a base type. In particular, strings are stored C-style in null-terminated buffers. NumPy has no native mechanism to support this. Unfortunately, this is the de facto standard for representing strings in the HDF5 C API, and in many HDF5 applications.
Thankfully, NumPy has a generic pointer type in the form of the "object" ("O") dtype. In h5py 1.2, variable-length strings are mapped to object arrays. A small amount of metadata attached to an "O" dtype tells h5py that its contents should be converted to VL strings when stored in the file.
Existing VL strings can be read and written to with no additional effort; Python strings and fixed-length NumPy strings can be auto-converted to VL data and stored. However, creating VL data requires the use of a special "hinted" dtype object. Two functions are provided at the package level for this purpose:
Here's an example showing how to create a VL array of strings:
>>> f = h5py.File('foo.hdf5')
>>> dt = h5py.new_vlen(str)
>>> ds = f.create_dataset('VLDS', (100,100), dtype=dt)
>>> ds.dtype.kind
... 'O'
>>> h5py.get_vlen(ds.dtype)
... <type 'str'>
HDF5 has the concept of an enumerated type, which is an integer datatype with a restriction to certain named values. Since NumPy has no such datatype, HDF5 ENUM types are read and written as integers. Like variable-length strings, you can create a new enumerated type from a NumPy integer base type by using convenience functions to attach a small amount of metadata:
Here's an example:
>>> dt = h5py.new_enum('i', {"RED": 0, "GREEN": 1, "BLUE": 42})
>>> h5py.get_enum(dt)
{'BLUE': 42, 'GREEN': 1, 'RED': 0}
>>> f = h5py.File('foo.hdf5','w')
>>> ds = f.create_dataset("EnumDS", (100,100), dtype=dt)
>>> ds.dtype.kind
'i'
>>> ds[0,:] = 42
>>> ds[0,0]
42
>>> ds[1,0]
0