batch for FWHM / Gaussian spatial distribution evaluation (divergence and pointing)

• Gaussian fit for beamwaist and Pointing

How it works: Reads images, determines position of maximum via center of mass method (maximum pix -> roi of 100 px compared in binned mode), creates lineout in vertical and horizontal through max position, Gaussian fit of both -> determines sigma, center, amp vertical and horizontal. Batch_list does this for each picture in folder and determines from center-position mean value of position and calculates for each center-position deviation from mean value. Batch_list includes save and plots for sigma and pointing.

• background substraction method with statistical mean back -img from image stack included

• initial guess for maximum can be given in coordinates

• crop picture (ROI method)

• PointingDistribution - how the pointing is distributed over space for long shot series

Note: this depends on a particular binsize, meaning: a high numerical accuracy with a low number of statistical events (pictures) will lead to single events in the distribution. In order to account for this and to collect the events in a particular paremetric range, the distribution evaluation needs to decrease the accuracy (decimal_bin). Example: the pointing is given up to a accuracy of 0.0000001 (or else given by datatype not by measurement accuracy), we want to summarize all events in relative bins of an accuracy of 0.01 -> set decimal_bin to 2. The implementation is simply done by np.round(data, decimal_bin) in the script.