Special types

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 2.3, h5py fully supports HDF5 enums and VL types.

How special types are represented

Since there is no direct NumPy dtype for variable-length strings, enums or references, h5py extends the dtype system slightly to let HDF5 know how to store these types. Each type is represented by a native NumPy dtype, with a small amount of metadata attached. NumPy routines ignore the metadata, but h5py can use it to determine how to store the data.

There are two functions for creating these “hinted” dtypes:


Create a NumPy dtype object containing type hints. Only one keyword may be specified.

  • vlen – Base type for HDF5 variable-length datatype.
  • enum – 2-tuple (basetype, values_dict). basetype must be an integer dtype; values_dict is a dictionary mapping string names to integer values.
  • ref – Provide class h5py.Reference or h5py.RegionReference to create a type representing object or region references respectively.

Determine if the given dtype object is a special type. Example:

>>> out = h5py.check_dtype(vlen=mydtype)
>>> if out is not None:
...     print "Vlen of type %s" % out
  • vlen – Check for an HDF5 variable-length type; returns base class
  • enum – Check for an enumerated type; returns 2-tuple (basetype, values_dict).
  • ref – Check for an HDF5 object or region reference; returns either h5py.Reference or h5py.RegionReference.

Variable-length strings

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, 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.

Here’s an example showing how to create a VL array of strings:

>>> f = h5py.File('foo.hdf5')
>>> dt = h5py.special_dtype(vlen=str)
>>> ds = f.create_dataset('VLDS', (100,100), dtype=dt)
>>> ds.dtype.kind
>>> h5py.check_dtype(vlen=ds.dtype)
<type 'str'>

Arbitrary vlen data

Starting with h5py 2.3, variable-length types are not restricted to strings. For example, you can create a “ragged” array of integers:

>>> dt = h5py.special_dtype(vlen=np.dtype('int32'))
>>> dset = f.create_dataset('vlen_int', (100,), dtype=dt)
>>> dset[0] = [1,2,3]
>>> dset[1] = [1,2,3,4,5]

Single elements are read as NumPy arrays:

>>> dset[0]
array([1, 2, 3], dtype=int32)

Multidimensional selections produce an object array whose members are integer arrays:

>>> dset[0:2]
array([array([1, 2, 3], dtype=int32), array([1, 2, 3, 4, 5], dtype=int32)], dtype=object)

Enumerated types

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.

Here’s an example of creating an enumerated type:

>>> dt = h5py.special_dtype(enum=('i', {"RED": 0, "GREEN": 1, "BLUE": 42}))
>>> h5py.check_dtype(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
>>> ds[0,:] = 42
>>> ds[0,0]
>>> ds[1,0]

Object and region references

References have their own section.