Databases Reference
In-Depth Information
CHAPTER 6
Storing Metadata with Attributes
Groups and datasets are great for keeping data organized in a file. But the feature that
really turns HDF5 into a scientific database, instead of just a file format, is
attributes
.
Attributes are pieces of metadata you can stick on objects in the file. They can hold
equipment settings, timestamps, computation results, version numbers, virtually any‐
thing you want. They're a key mechanism for making
self-describing
files. Unlike simple
binary formats that just hold arrays of numbers, judicious use of metadata makes your
files scientifically useful all on their own.
Attribute Basics
You can attach attributes to any kind of object that is linked into the HDF5 tree structure:
groups, datasets, and even named datatypes. To demonstrate, let's create a new file con‐
taining a single dataset:
>>>
f
=
h5py
.
File
(
'attrsdemo.hdf5'
,
'w'
)
>>>
dset
=
f
.
create_dataset
(
'dataset'
,(
100
,))
Looking at the properties attached to the
dset
object, there's one called
.attrs
:
>>>
dset
.
attrs
<Attributes of HDF5 object at 73767504>
This is a little proxy object (an instance of
h5py.AttributeManager
) that lets you in‐
teract with attributes in a Pythonic way. As was the case with groups, the main thing to
keep in mind here is that the
attrs
object works mostly like a Python dictionary.
For example, you can create a new attribute simply by assigning a name to a value:
>>>
dset
.
attrs
[
'title'
]
=
"Dataset from third round of experiments"
>>>
dset
.
attrs
[
'sample_rate'
]
=
100e6
# 100 MHz digitizer setting
>>>
dset
.
attrs
[
'run_id'
]
=
144
