Dimension ScalesΒΆ

Datasets are multidimensional arrays. HDF5 provides support for labeling the dimensions and associating one or “dimension scales” with each dimension. A dimension scale is simply another HDF5 dataset. In principle, the length of the multidimensional array along the dimension of interest should be equal to the length of the dimension scale, but HDF5 does not enforce this property.

The HDF5 library provides the H5DS API for working with dimension scales. H5py provides low-level bindings to this API in h5py.h5ds. These low-level bindings are in turn used to provide a high-level interface through the Dataset.dims property. Suppose we have the following data file:

f = File('foo.h5', 'w')
f['data'] = np.ones((4, 3, 2), 'f')

HDF5 allows the dimensions of data to be labeled, for example:

f['data'].dims[0].label = 'z'
f['data'].dims[2].label = 'x'

Note that the first dimension, which has a length of 4, has been labeled “z”, the third dimension (in this case the fastest varying dimension), has been labeled “x”, and the second dimension was given no label at all.

We can also use HDF5 datasets as dimension scales. For example, if we have:

f['x1'] = [1, 2]
f['x2'] = [1, 1.1]
f['y1'] = [0, 1, 2]
f['z1'] = [0, 1, 4, 9]

We are going to treat the x1, x2, y1, and z1 datasets as dimension scales:

f['data'].dims.create_scale(f['x2'], 'x2 name')
f['data'].dims.create_scale(f['y1'], 'y1 name')
f['data'].dims.create_scale(f['z1'], 'z1 name')

When you create a dimension scale, you may provide a name for that scale. In this case, the x1 scale was not given a name, but the others were. Now we can associate these dimension scales with the primary dataset:


Note that two dimension scales were associated with the third dimension of data. You can also detach a dimension scale:


but for now, lets assume that we have both x1 and x2 still associated with the third dimension of data. You can attach a dimension scale to any number of HDF5 datasets, you can even attach it to multiple dimensions of a single HDF5 dataset.

Now that the dimensions of data have been labeled, and the dimension scales for the various axes have been specified, we have provided much more context with which data can be interpreted. For example, if you want to know the labels for the various dimensions of data:

>>> [dim.label for dim in f['data'].dims]
['z', '', 'x']

If you want the names of the dimension scales associated with the “x” axis:

>>> f['data'].dims[2].keys()
['', 'x2 name']

items() and values() methods are also provided. The dimension scales themselves can also be accessed with:



f['data'].dims[2]['x2 name']

such that:

>>> f['data'].dims[2][1] == f['x2']

though, beware that if you attempt to index the dimension scales with a string, the first dimension scale whose name matches the string is the one that will be returned. There is no guarantee that the name of the dimension scale is unique.