DynamicsAnalysisSettings (extraReflectanceId, …)

These settings determine the behavior of the DynamicsAnalysis class. :type extraReflectanceId: Optional [str ] :param extraReflectanceId: The unique IDTag of the extraReflectance calibration that was used on this analysis. :type referenceMaterial: Material :param referenceMaterial: The material that was imaged in the reference image of this analysis. Found as an in pwspy.moduleConst.Material. The theoretically predicted reflectance of the reference image is used in the extraReflectance correction. :type numericalAperture: float :param numericalAperture: The illumination NA of the system. This is used for two purposes. First, we want to make sure that the NA of our data matches the NA of our extra reflectance correction cube. Second, the theoretically predicted reflectance of our reference is based not only on what our refereMaterial is but also the NA since reflectance is angle dependent. :type relativeUnits: bool :param relativeUnits: If True then all calculation are performed such that the reflectance is 1 if it matches the reference. If False then we use the theoretical reflectance of the reference (based on NA and reference material) to normalize our results to the actual physical reflectance of the sample (about 0.4% for water) :type cameraCorrection: Optional [CameraCorrection ] :param cameraCorrection: An object describing the dark counts and nonlinearity of the camera used. If the data supplied to the DynamicsAnalysis class has already been corrected then this setting will not be used. Setting this to None will result in the camera correcting being automatically determined based on the image files’ metadata. :type diffusionRegressionLength: int :param diffusionRegressionLength: The original matlab scripts for analysis of dynamics data determined the slope of the log(ACF) by looking only at the first two indices, (log(ACF)[1]log(ACF)[0])/dt. This results in very noisy results. However as you at higher index value of the log(ACF) the noise becomes much worse. A middle ground is to perform linear regression on the first 4 indices to determine the slope. You can adjust that number here. 