toolbox_scs.detectors.dssc_processing

DSSC-related sub-routines.

comment: contributions should comply with pep8 code structure guidelines.

Module Contents

Functions

process_dssc_data(proposal, run_nr, module, chunksize, ...)

Collects and reduces DSSC data for a single module.
toolbox_scs.detectors.dssc_processing.process_dssc_data(proposal, run_nr, module, chunksize, info, dssc_binners, path='./', pulsemask=None, dark_image=None, xgm_mnemonic='SCS_SA3', xgm_normalization=False, normevery=1)[source]

Collects and reduces DSSC data for a single module.

Copyright (c) 2020, SCS-team

Parameters:

  • proposal (int) – proposal number

  • run_nr (int) – run number

  • module (int) – DSSC module to process

  • chunksize (int) – number of trains to load simultaneously

  • info (dictionary) – dictionary containing keys ‘dims’, ‘frames_per_train’, ‘total_frames’, ‘trainIds’, ‘number_of_trains’.

  • dssc_binners (dictionary) – a dictionary containing binner objects created by the ToolBox member function “create_binner()”

  • path (str) – location in which the .h5 files, containing the binned data, should be stored.

  • pulsemask (numpy.ndarray) – array of booleans to be used to mask dssc data according to xgm data.

  • dark_image (xarray.DataArray) – an xarray dataarray with matching coordinates with the loaded data. If dark_image is not None it will be subtracted from each individual dssc frame.

  • xgm_normalization (bool) – true if the data should be divided by the corresponding xgm value.

  • xgm_mnemonic (str) – Mnemonic of the xgm data to be used for normalization.

  • normevery (int) – One out of normevery dssc frames will be normalized.

Returns:

module_data – xarray datastructure containing data binned according to bins.

Return type:

xarray.Dataset