...

intensity = alg.intensity_of_pix_above_thr(data, thr=12)

Peak finders

Peak finder works on calibrated, background subtracted n-d array of data in the region of interest specified by the list of windows and using only good pixels from mask n-d array. All algorithms implemented here have three major stages

1. find a list of seed peak candidates
2. process peak candidates and evaluate their parameters
3. apply selection criteria to the peak candidates and return the list of peaks with their parameters

The list of peaks contains 17 (float for uniformity) parameters per peak:

• seg - segment index beginning from 0, example for CSPAD this index should be in the range (0,32)
• row - index of row beginning from 0
• col - index of column beginning from 0
• npix - number of pixels accounted in the peak
• amp_max - pixel with maximal intensity
• amp_total - total intensity of all pixels accounted in the peak
• row_cgrav - row coordinate of the peak evaluated as a "center of gravity" over pixels accounted in the peak using their intensities as weights
• col_cgrav - column coordinate of the peak evaluated as a "center of gravity" over pixels accounted in the peak using their intensities as weights
• raw_sigma - row sigma evaluated in the "center of gravity" algorithm
• col_sigma - column sigma evaluated in the "center of gravity" algorithm
• row_min - minimal row of the pixel group accounted in the peak
• row_max - maximal row of the pixel group accounted in the peak
• col_min - minimal column of the pixel group accounted in the peak
• col_max - maximal column of the pixel group accounted in the peak
• bkgd - background level estimated as explained in section below
• noise - r.m.s. of the background estimated  as explained in  section below
• son - signal over noise ratio estimated  as explained in  section below

There is a couple of classes helping to save/retrieve peak parameter records in/from the text file:

• pyimgalgos.PeakStore
• pyimgalgos.TDFileContainer

Peak selection parameters

Internal peak selection is done at the end of each peak finder, but all peak selection parameters need to be defined right after algorithm object is created.  These peak selection parameters are set for all peak-finders:

...

• ImgAlgos.PyAlgos - code example in Sphinx documentation
• Peak Finding - short announcement about peak finders
• Hit and Peak Finders - examples in Chris' tutorial
• Peak Finding Module - (depricated) psana module, it demonstaration examples and results
• Psana Module Catalog - (depricated) peak finding  psana modules
• Psana Module Examples - (depricated) peak finding examples in psana modules
• GUI for tuning peak finding - Chun's page in development

• pyimgalgos.PeakStore - class helping to save peak parameter records in the text file

• pyimgalgos.TDFileContainer - class helping to retrieve peak parameter records from the text file