AngleCalibration

Class to calibrate the mythen detector parameters. For the calibration the “Best computing” parameters are used. However, as initial parameters it expects the “historic detector group” parameters. See for a more detail description of the different detector parameters.

class angcal._angcal.AngleCalibration

Bases: pybind11_object

Variables:

  • histogram_bin_width (double) – bin width of fixed angle width histogram [degree] (default: 0.0036 deg)

  • base_peak_angle (double) – angle of center of base peak [degree]

  • number_of_bins (int) – Read-only. number of bins for new fixed angle width histogram

__init__(self: angcal._angcal.AngleCalibration, MythenDetectorSpecifications: angcal::MythenDetectorSpecifications, FlatField: angcal::FlatField, MythenFileReader: Optional[angcal::MythenFileReader] = None, file_interface: Optional[SimpleFileInterface] = None) → None
Parameters:

  • MythenDetectorSpecifications (MythenDetectorSpecifications) – storing all mythen specific parameters

  • FlatField (FlatField) – class storing inverse normalized flatfield

  • MythenFileReader (optional[MythenFileReader], default None) – pass if you use custom acquisition files - default: reads hdf5 files

  • file_interafce (optional[SimpleFileInterface], default None) – custom_file_ptr optional, pass if you use custom files to store initial angle parameters - default: initial angle parameters supports following format module [module_index] center [center] +- [error] conversion [conversion] +- [error] offset [offset] +- [error]

base_peak_is_in_module(self: angcal._angcal.AngleCalibration, module_index: int, detector_angle: float) → bool

check if base peak ROI is contained within module region

Parameters:

  • module_index (int) – Index of the module.

  • detector_angle (double) – Detector position, measured as the offset of the first strip from the default detector position [degrees].

Returns:

True if the base peak ROI lies inside the module region, False otherwise.

Return type:

bool

calibrate(*args, **kwargs)

Overloaded function.

  1. calibrate(self: angcal._angcal.AngleCalibration, file_list: list[str], base_peak_angle: float, module_index: int) -> None

    calibrates BC parameters for respective module

    file_list: list

    list of paths to acquisition files

    base_peak_angle: double

    angle of base peak center [degree]

    module_index: int

    index of module

  2. calibrate(self: angcal._angcal.AngleCalibration, file_list: list[str], base_peak_angle: float) -> None

    calibrates BC parameters for all modules

    file_list: list

    list of paths to acquisition files

    base_peak_angle: double

    angle of base peak center [degree]

module_is_disconnected(self: angcal._angcal.AngleCalibration, arg0: int) → bool
read_initial_calibration_from_file(self: angcal._angcal.AngleCalibration, arg0: str) → None
reads the historical Detector Group (DG) parameters from file and

transforms them to Best Computing parameters

redistribute_photon_counts_to_fixed_angle_width_bins(*args, **kwargs)

Overloaded function.

  1. redistribute_photon_counts_to_fixed_angle_width_bins(self: angcal._angcal.AngleCalibration, arg0: angcal::MythenFrame, arg1: int) -> numpy.ndarray

    redistribute photon counts of respective module fixed angle width bins

    numpy.ndarray (,new_number_of_bins)

    to fixed angle width redistributed, flatfield corrected and variance scaled photon counts of respective module

  2. redistribute_photon_counts_to_fixed_angle_width_bins(self: angcal._angcal.AngleCalibration, arg0: angcal::MythenFrame) -> numpy.ndarray

    redistribute photon counts of given frame to fixed angle width bins

    numpy.ndarray (,new_number_of_bins)

    to fixed angle width redistributed, flatfield corrected and variance scaled photon counts

redistributed_photon_counts_in_base_peak_ROI(self: angcal._angcal.AngleCalibration, arg0: angcal::MythenFrame, arg1: int) → numpy.ndarray

redistribute photon counts to fixed angle width bins which are within base peak region

Returns:

to fixed angle width redistributed, flatfield corrected and variance scaled photon counts of respective module within base peak ROI

Return type:

numpy.ndarray (,number_of_bins_in_base_peak_ROI)