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Peak finding algorithms locate regions of charge in an area detector. In LCLS data all area detectors are represented by n-d arrays (typically a 2-dimensional array for image, or a 3-dimensional array for multi-panel detectors) so these python-callable algorithms will work on n-d arrays.

Additional This is a short overview.  Additional details are here and simple examples are here. This link here may also be useful.

Algorithms

All peak finders and a few other image processing algorithms are available in a single class suite supporting common infrastructure. All algorithms work with good pixels defined by a mask and inside specified rectangular windows. Both these mechanisms (windows and mask) can be used to describe a region of interest. By default, if mask or/and windows are not specified, all pixels are processed. A few versions of the peak finder use different approach to the peak definition (finding group of pixels forming a peak) at a first stage, but can use the same algorithms at final stage for evaluation of peak parameters, as explained below. 

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In a second stage, all pixels associated with a peak are used to evaluate the row and column of the peak maximum, total intensity, center of gravity coordinates, and edges of the peak region.  A surrounding user-specified ring-region of pixels around the peak is used to evaluate average background level, its rms spread, and signal-over-noise ratio. The user will typically reduce the number of peaks returned from these algorithms by applying cuts to signal levels or signal-over-noise ratio.

References

• Hit /and Peak Finding DetailsAlgorithms
• Package PyAlgos (documentation auto-generated by Sphinx)
• Detector.AreaDetector