Universal Spectroscopy and Imaging Data (h5USID)#


Universal Spectroscopy and Imaging Data (USID) is an open, community-driven, self-describing, and standardized schema for representing imaging and spectroscopy data of any size, dimensionality, precision, instrument of origin, or modality. USID data is typically stored in Hierarchical Data Format Files (HDF5) and the combination of USID within .hdf5 files is referred to as h5USID.

pyUSID provides a convenient interface to I/O operations on such h5USID files. USID (via pyUSID) forms the foundation for other materials microscopy scientific python package called pycroscopy. If you have any questions regarding this module, please consider contacting the developers of pyUSID.

Also see the HDF5 utility functions for inspecting HDF5 files.


h5USID files can contain multiple USID datasets within the same file. RosettaSciIO supports reading in one or more USID datasets.


When writing files with this plugin, the model and other secondary data artifacts linked to the signal are not written to the file but these can be implemented at a later stage.


Reading and writing h5USID files requires the installation of pyUSID.

In HyperSpy, files must use the .h5 file extension in order to use this IO plugin or the reader=usid_hdf5 parameter of the load function needs to be set explicitly. Otherwise, using the .hdf5 extension will default to the HyperSpy plugin.

API functions#

rsciio.usid.file_reader(filename, lazy=False, dataset_path=None, ignore_non_uniform_dims=True)#

Read a USID Main dataset present in an HDF5 file into a HyperSpy Signal.

filenamestr, pathlib.Path

Filename of the file to read or corresponding pathlib.Path.

lazybool, default=False

Whether to open the file lazily or not.

dataset_pathstr, optional

Absolute path of USID Main HDF5 dataset. Default is None - all Main Datasets will be read. Given that HDF5 files can accommodate very large datasets, lazy reading is strongly recommended. If a string like "/Measurement_000/Channel_000/My_Dataset" is provided, the specific dataset will be loaded.

ignore_non_uniform_dimsbool, optional

If True (default), parameters that were varied non-uniformly in the desired dataset will result in Exceptions. Else, all such non-uniformly varied parameters will be treated as uniformly varied parameters and a Signal object will be generated.

list of dict

List of dictionaries containing the following fields:

  • ‘data’ – multidimensional numpy.ndarray or dask.array.Array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the parsed metadata

  • ‘original_metadata’ – dictionary containing the full metadata tree from the input file

rsciio.usid.file_writer(filename, signal, **kwds)#

Write a HyperSpy Signal object to a HDF5 file formatted according to USID.

filenamestr, pathlib.Path

Filename of the file to write to or corresponding pathlib.Path.


Dictionary containing the signal object. Should contain the following fields:

  • ‘data’ – multidimensional numpy array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the metadata tree

**kwdsdict, optional

All other keyword arguments will be passed to pyUSID.io.hdf_utils.model.write_main_dataset().

Usage examples#

Reading the sole dataset within a h5USID file using HyperSpy:

>>> import hyperspy.api as hs
>>> hs.load("sample.h5")
<Signal2D, title: HAADF, dimensions: (|128, 128)>

If multiple datasets are present within the h5USID file, all available datasets will be loaded.


Given that HDF5 files can accommodate very large datasets, setting lazy=True is strongly recommended if the contents of the HDF5 file are not known apriori. This prevents issues with regard to loading datasets far larger than memory.

Also note that setting lazy=True leaves the file handle to the HDF5 file open. If it is important that the files be closed after reading, set lazy=False.

>>> hs.load("sample.h5")
[<Signal2D, title: HAADF, dimensions: (|128, 128)>,
<Signal1D, title: EELS, dimensions: (|64, 64, 1024)>]

We can load a specific dataset using the dataset_path keyword argument. Setting it to the absolute path of the desired dataset will cause the single dataset to be loaded.

>>> # Loading a specific dataset
>>> hs.load("sample.h5", dataset_path='/Measurement_004/Channel_003/Main_Data')
<Signal2D, title: HAADF, dimensions: (|128, 128)>

h5USID files support the storage of HDF5 dataset with compound data types. As an (oversimplified) example, one could store a color image using a compound data type that allows each color channel to be accessed by name rather than an index. Naturally, reading in such a compound dataset into HyperSpy will result in a separate signal for each named component in the dataset:

>>> hs.load("file_with_a_compound_dataset.h5")
[<Signal2D, title: red, dimensions: (|128, 128)>,
Signal2D, title: blue, dimensions: (|128, 128)>,
Signal2D, title: green, dimensions: (|128, 128)>]

h5USID files also support parameters or dimensions that have been varied non-uniformly. Currently, the reading of non-uniform axes is not implemented in RosettaSciIO, the USID plugin will default to a warning when it encounters a parameter that has been varied non-uniformly:

>>> hs.load("sample.h5")
UserWarning: Ignoring non-uniformity of dimension: Bias
<BaseSignal, title: , dimensions: (|7, 3, 5, 2)>

In order to prevent accidental misinterpretation of information downstream, the keyword argument ignore_non_uniform_dims can be set to False which will result in a ValueError instead.

>>> hs.load("sample.h5")
ValueError: Cannot load provided dataset. Parameter: Bias was varied non-uniformly.
Supply keyword argument "ignore_non_uniform_dims=True" to ignore this error