Peak Calling

Peak calling analyzers identify and extract local maxima from correlation score maps using various detection algorithms. These analyzers can handle distance constraints, boundary conditions, and score thresholding to produce filtered lists of candidate template locations with their corresponding orientations.

PeakCaller(shape[, num_peaks, min_distance, ...])	Base class for peak calling algorithms.
PeakCallerSort(shape[, num_peaks, ...])	A PeakCaller subclass that first selects num_peaks highest scores.
PeakCallerMaximumFilter(shape[, num_peaks, ...])	Find local maxima by applying a maximum filter and enforcing a distance constraint subsequently.
PeakCallerFast(shape[, num_peaks, ...])	Subdivides the score space into squares with edge length min_distance and determiens maximum value for each.
PeakCallerRecursiveMasking(shape[, ...])	Identifies peaks iteratively by selecting the top score and masking a region around it.
PeakCallerScipy(shape[, num_peaks, ...])	Peak calling using skimage.feature.peak_local_max to compute local maxima.

PeakCaller provides the base class for peak detection algorithms with configurable distance and boundary constraints.

PeakCallerSort selects the highest scoring peaks by sorting and taking the top N candidates.

PeakCallerMaximumFilter finds local maxima using maximum filtering with distance constraints, similar to skimage’s peak_local_max.

PeakCallerFast subdivides the score space into grids and identifies maxima within each subdivision for efficient processing.

PeakCallerRecursiveMasking iteratively identifies peaks by masking regions around detected maxima to prevent duplicate detections.

PeakCallerScipy uses scikit-image’s peak_local_max implementation for local maxima detection of well defined peaks.