AngleCalibration¶

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

Example Usage for Conversion:¶

from angcal import MythenDetectorSpecifications, FlatField, AngleCalibration, EpicsMythenFileReader, MythenFrame
from pathlib import Path
import numpy as np

data_path = Path("/path/to/your/data/files")  # adjust this path

# setup mythen detector specifications - stores all relevant parameters of the detector setup
mythendetectorspecifications = MythenDetectorSpecifications(offset=0, num_counters=1)

# setup flatfield
flatfield = FlatField(mythendetectorspecifications)

flatfield.normalized_flatfield = np.loadtxt(data_path / "Flatfield_E17p5keV_T12500eV_up_AUGCAL2_Sep2023_open_WS_C_X_X.raw", dtype=np.double, usecols=[1,2])

# setup mythen data file reader to read EPICS mythen hdf5 files
mythenfilereader = EpicsMythenFileReader()

# setup angle calibration - has everything to do conversion and calibration
anglecalibration = AngleCalibration(mythendetectorspecifications, flatfield, mythenfilereader)

anglecalibration.read_initial_calibration_from_file(str(data_path / "Angcal_2E_Feb2023_P29.off"))

anglecalibration.read_bad_channels_from_file(str(data_path / "bc2023_003_RING.chans"))

#set scale factor to get reasonable scales
frame = mythenfilereader.read_frame(str(data_path / "Fructose_0p2_60_0060.h5"))
anglecalibration.scale_factor = frame.incident_intensity

file_list = [str(data_path / f"Fructose_0p2_60_006{i}.h5") for i in range(0,4)]

redistributed_photon_counts = anglecalibration.convert(file_list)

API:¶

Note

The method read_initial_calibration_from_file expects a text file in the format below. Each line denotes the DG parameters for each module.

module 0 center  643.320033188550 +- 0.0000 conversion 0.657661167459868E-04 +- 0.0000 offset  0.00000000000000     +- 0.0000
module 1 center  633.044070300816 +- 0.0000 conversion 0.657618957538105E-04 +- 0.0000 offset  5.00486981153634     +- 0.0000

If youre file format deviates from the above format read your file into a np.ndarray(,3) where the first dimension stores the centers the second the conversions and the third the offsets and set DGparameters directly.

Note

The method read_bad_channels_from_file expects a text file using the format below. Each line denotes the index of a bad channel. Consecutive bad channels can be stored on one line e.g. channels with 20-42 are bad channels.

If youre file format deviates from the above format read your file into a boolean np.ndarray and set bad_channels directly.

class angcal._angcal.AngleCalibration¶

Bases: pybind11_object

__init__(self: angcal._angcal.AngleCalibration, MythenDetectorSpecifications: angcal::MythenDetectorSpecifications, FlatField: angcal::FlatField, MythenFileReader: angcal::MythenFileReader) → None¶

Parameters:

MythenDetectorSpecifications (MythenDetectorSpecifications) – storing all mythen specific parameters
FlatField (FlatField) – class storing inverse normalized flatfield
MythenFileReader (File Reader to read Mythen acquisition files)

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.

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: float
Angle of base peak center [degree].

module_index: int
Index of module
calibrate(self: angcal._angcal.AngleCalibration, file_list: list[str], base_peak_angle: float, output_filename: Optional[str]) -> None

calibrates BC parameters for all modules

file_listlist
list of paths to acquisition files

base_peak_angledouble
angle of base peak center [degree]

output_filename : str, optional

convert(self: angcal._angcal.AngleCalibration, file_list: list[str]) → numpy.ndarray¶

performs angular conversion e.g. calculates diffraction pattern from raw photon counts

Parameters:: file_list (list) – list of paths to acquisition files
Returns:: photon counts redistributed to fixed angle width bins, flatfield corrected and variance scaled photon counts
Return type:: numpy.ndarray (,num_fixed_angle_width_bins)

module_is_disconnected(self: angcal._angcal.AngleCalibration, module_index: int) → bool¶

check if a module only has bad channels or is disconnected

Parameters:: module_index (int) – Index of the module.
Returns:: True if module is disconnected, False otherwise.
Return type:: bool

rate_correction(self: angcal._angcal.AngleCalibration, photon_count: float, photon_count_error: float, exposure_time: float) → tuple[float, float]¶

performs rate correction

Parameters:

photon_count (float) – measured photon counts
photon_count_error (float) – propagated error of measured photon counts
exposure_time (float) – exposure time of acquisition [s]

Returns:

pair {rate corrected photon counts, propagated_error}

Return type:

tuple of float

read_bad_channels_from_file(self: angcal._angcal.AngleCalibration, filename: str) → None¶: reads bad channels from file

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

write_DG_parameters_to_file(self: angcal._angcal.AngleCalibration, filename: str, parameters: angcal::DGParameters) → None¶

writes DG parameters to file

Parameters:

filename: str: path to output file
parameters: DGParameters: DGParameters object containing the parameters to write to file

property BCparameters¶: current “Best Computing” BC parameters

property DGparameters¶: historic DG parameters

property angular_range¶: angular range for conversion [degrees] :returns: (min_angle, max_angle) :rtype: tuple of float

property bad_channels¶

numpy.ndarray of bool, shape (n_channels,) Expected size: number of channels/strips in the detector. Each element is True if the channel is bad, otherwise False.

Type:: bad_channels

property base_peak_ROI_width¶: width of base peak region of interest [degrees] e.g. [base_peak - base_peak_ROI_width, base_peak + base_peak_ROI_width] given in angles default: ‘0.05°’

property base_peak_angle¶: center of chosen base peak for calibration [degrees]

property histogram_bin_width¶: bin width of fixed angle width histogram [degrees] default: ‘0.0036°’

property num_fixed_angle_width_bins¶: number of bins in fixed angle width histogram

property scale_factor¶: scale factor to scale correction to reasonable values