PowderCalibrationFitSigma¶
What the powder ring fit knows about its own answer, from the covariance of the converged problem. Each sigma is in its parameter’s own unit and is scaled by the residual scatter of that fit, so it is the usual “how far could this move before the fit got visibly worse”. The two correlations are the ones that matter: a tilt and a beam-centre shift both displace a ring’s radius as cos(phi) and are told apart only by how that amplitude grows with the ring’s radius, so as the rings run out the pair stops being separable and these approach 1. The rotation sigmas and the correlations are absent when the tilt was not a free parameter.
Properties¶
Name | Type | Description | Notes |
|---|---|---|---|
beam_x_pxl | float | Standard error of the fitted PONI x [pixels] | [optional] |
beam_y_pxl | float | Standard error of the fitted PONI y [pixels] | [optional] |
detector_distance_mm | float | Standard error of the fitted detector distance [mm] | [optional] |
poni_rot1_rad | float | Standard error of the fitted rot1 [rad] | [optional] |
poni_rot2_rad | float | Standard error of the fitted rot2 [rad] | [optional] |
correlation_beam_x_rot1 | float | Correlation between the fitted PONI x and rot1, -1 to 1 | [optional] |
correlation_beam_y_rot2 | float | Correlation between the fitted PONI y and rot2, -1 to 1 | [optional] |
Example¶
from jfjoch_client.models.powder_calibration_fit_sigma import PowderCalibrationFitSigma
# TODO update the JSON string below
json = "{}"
# create an instance of PowderCalibrationFitSigma from a JSON string
powder_calibration_fit_sigma_instance = PowderCalibrationFitSigma.from_json(json)
# print the JSON string representation of the object
print(PowderCalibrationFitSigma.to_json())
# convert the object into a dict
powder_calibration_fit_sigma_dict = powder_calibration_fit_sigma_instance.to_dict()
# create an instance of PowderCalibrationFitSigma from a dict
powder_calibration_fit_sigma_from_dict = PowderCalibrationFitSigma.from_dict(powder_calibration_fit_sigma_dict)