hyperspy.io_plugins.usid_hdf5 module
- hyperspy.io_plugins.usid_hdf5._assemble_dim_list(dim_dict, dim_names)
Assembles a list of dictionary objects (axes) in the same order as specified in dim_names
- hyperspy.io_plugins.usid_hdf5._convert_to_signal_dict(ndim_form, quantity, units, dim_dict_list, h5_path, h5_dset_path, name, sig_type='', group_attrs={})
Packages required components that make up a Signal object
- Parameters:
ndim_form (numpy.ndarray) – N-dimensional form of the main dataset
quantity (str) – Physical quantity of the measurement
units (str) – Corresponding units
dim_dict_list (list) – List of dictionaries that instruct the axes corresponding to the main dataset
h5_path (str) – Absolute path of the original USID HDF5 file
h5_dset_path (str) – Absolute path of the USIDataset within the HDF5 file
name (str) – Name of the HDF5 dataset
sig_type (str, Optional) – Type of measurement
group_attrs (dict, Optional. Default = {}) – Any attributes at the channel and group levels
- hyperspy.io_plugins.usid_hdf5._flatten_dict(nested_dict, parent_key='', sep='-')
Flattens a nested dictionary
- Parameters:
- Returns:
Dictionary whose keys are flattened to a single level
- Return type:
Notes
Taken from https://stackoverflow.com/questions/6027558/flatten-nested- dictionaries-compressing-keys
- hyperspy.io_plugins.usid_hdf5._get_dim_dict(labels, units, val_func, ignore_non_uniform_dims=True)
Gets a list of dictionaries that correspond to axes for HyperSpy Signal objects
- Parameters:
labels (list) – List of strings denoting the names of the dimension
units (list) – List of strings denoting the units for the dimensions
val_func (callable) – Function that will return the values over which a dimension was varied
ignore_non_uniform_dims (bool, Optional. Default = True) – If set to True, a warning will be raised instead of a ValueError when a dimension is encountered which was non-uniformly.
- Returns:
Dictionary of dictionaries that correspond to axes for HyperSpy Signal objects
- Return type:
Notes
For a future release of HyperSpy: If a dimension was varied non-uniformly, one would need to set the appropriate quantity in the quantity equal to dim_vals. At that point, the typical offset and scale parameters would be (hopefully) ignored.
- hyperspy.io_plugins.usid_hdf5._split_descriptor(desc)
Splits a string such as “Quantity [units]” or “Quantity (units)” into the quantity and unit strings
- Parameters:
desc (str) – Descriptor of a dimension or the main dataset itself
- Returns:
quant (str) – Name of the physical quantity
units (str) – Units corresponding to the physical quantity
- hyperspy.io_plugins.usid_hdf5._usidataset_to_signal(h5_main, ignore_non_uniform_dims=True, lazy=True, *kwds)
Converts a single specified USIDataset object to one or more Signal objects
- Parameters:
h5_main (pyUSID.USIDataset object) – USID Main dataset
ignore_non_uniform_dims (bool, Optional) – If True, 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.
lazy (bool, Optional) – If set to True, data will be read as a Dask array. Else, data will be read in as a numpy array
- Returns:
USIDatasets with compound datatypes are broken down to multiple Signal objects.
- Return type:
list of hyperspy.signals.BaseSignal objects
- hyperspy.io_plugins.usid_hdf5.file_reader(filename, dataset_path=None, ignore_non_uniform_dims=True, lazy=False, **kwds)
Reads a USID Main dataset present in an HDF5 file into a HyperSpy Signal
- Parameters:
filename (str) – path to HDF5 file
dataset_path (str, Optional) – Absolute path of USID Main HDF5 dataset. Default - 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_dims (bool, Optional) – If True, 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.
- Return type:
list of hyperspy.signals.Signal object