Create a Dataset object in Python#

A lot of low-level functions in Darfix takes a Dataset object as input. When testing/debugging these, it can be useful to create such an object in Python to run the functions.

To do this, we use the load_process_data function:

darfix.core.data_selection.load_process_data(detector_url, root_dir=None, dark_detector_url=None, title='', metadata_url=None, progress_callback=None)[source]#

Loads data from detector_url.

Parameters:

  • detector_url (Union[str, DataUrl]) – detector_url to be loaded.

  • metadata_url – path to the scan metadata for HDF5 containing positioner information in order to load metadata for non-edf files

  • root_dir (Optional[str])

  • dark_detector_url (Union[str, DataUrl, None])

  • title (str)

  • progress_callback (Optional[Callable[[], int]])

Here is an example on how to use it to load a HDF5 dataset (here the Silicon_111_reflection_0003 dataset)

from darfix.core.data_selection import load_process_data

filepath = '/data/scisoft/darfix/datasets/darfix/bliss_hdf5/Silicon_111_reflection_0003/Silicon_111_reflection_0003.h5'
scan_index = 1
detector_name = 'pco_ff'

dataset, bg_dataset = load_process_data(
    detector_url=f"silx://{filepath}?/{scan_index}.1/measurement/{detector_name}",
    metadata_url=f"silx://{filepath}?{scan_index}.1/instrument/positioners",


)

Then, we use the find_dimensions method to register the appropriate dimensions in the Dataset. This is mandatory to run most operations on a Dataset

dataset.find_dimensions(tolerance=1e-5)
dataset.reshape_data()

The tolerance need to be changed depending on the dataset used. We use here the value appropriate for Silicon_111_reflection_0003, the default value being 1e-9.

The Dataset is now ready for computations. For example, we can compute the moments (Center of Mass, Skewness, Kurtosis, …) by using apply_moments:

dataset.apply_moments()
moments = dataset.moments_dims