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Any algorithm object can be created as shown below.

import psananumpy as np
from ImgAlgos.PyAlgos import PyAlgos 

# create object:
alg = PyAlgos(windows=winds, mask=mask, pbits=0)

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• nda - calibrated n-d array of data, pedestals and background should be subtracted, common mode - corrected
• thr_* - different type of thresholds
• rank - peak rank as explained in  section below.
• r0, dr - ring internal radius and width to evaluate background and noise rms as explained in  section below.

Two threshold "Droplet finder"

two-threshold peak-finding algorithm in restricted region around pixel with maximal intensity.

peak_finder_v1

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Two threshold allows to speed-up this algorithms. It is assumed that only pixels with intensity above thr_high are pretending to be peak candidate centers. Candidates are considered as a peak if their intensity is maximal in the (square) region of radius around them. Low threshold in the same region is used to account for contributing to peak pixels.

peak_finder_v1

peaks = alg.peak_finder_v1(nda, thr_low=10, thr_high=150, radius=5, dr=0.05)

Parameter radius in this algorithm is used for two purpose:

• defines (square) region to search for local maximum with intensity above thr_high and contributing pixels with intensity above  thr_lo,
• is used as a r0 parameter to evaluate background and noise rms as explained in section below.

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Flood filling algorithm

define peaks for regions of connected pixels above threshold

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peaks = alg.peak_finder_v2(nda, thr=10, r0=5, dr=0.05)

Two neighbor pixels are assumed connected if have common side. Pixels with intensity above threshold thr are considered only.

Local maximums search algorithm

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alg.set_peak_selection_pars(npix_min=5, npix_max=500, amax_thr=0, atot_thr=1000, son_min=6)

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Demonstration for local maximum map

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