More About Types - Python and HDF5 - page 87

CHAPTER 7
More About Types

One of the best features of HDF5 is the huge variety of datatypes it supports. In some
cases, the HDF5 feature set goes beyond NumPy. To maintain performance and create
interoperable files, it's important to understand exactly what's going on when you use
each type.

The HDF5 Type System
As with NumPy, all data in HDF5 has an associated type. The HDF5 type system is quite
flexible and includes the usual suspects like integers and floats of various precisions, as
well as strings and vector types.

Table 7-1 shows the native HDF5 datatypes and how they map to NumPy. Keep in mind
that most of the types (integers and floats, for example) support a number of different
precisions. For example, on most NumPy installations integers come in 1-, 2-, 4-, and
8-byte widths.
Table 7-1. HDF5 types

Native HDF5 type NumPy equivalent

Integer

dtype("i")

Float

dtype("f")

Strings (fixed width)

dtype("S10")

Strings (variable width)

h5py.special_dtype(vlen=bytes)

Compound

dtype([ ("field1": "i"), ("field2": "f") ])

Enum

h5py.special_dtype(enum=("i",{"RED":0, "GREEN":1, "BLUE":2}))

Array

dtype("(2,2)f")

Opaque

dtype("V10")

Reference

h5py.special_dtype(ref=h5py.Reference)