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• gets the pixel coordinates from PixCoords2x1, PixCoordsQuad, and PixCoordsCSPad classes,
• gets data from the event (from Psana::CsPad::DataV#, Psana::CsPad::ElementV# (where # stands for 1 or 2) data objects or  from ndarray<T,3> of shape [N][185][388])
• produces the ndarray<double,2> or Image2D<double> object with CSPad image for each event,
• adds the image object in the event for processing in other modules.
  Time consumed to fill the CSPad image array (currently 1750x1750) is measured to be about 40 msec/event on psana0105.
• produces image size pixel map array where real/fake pixels are marked as 1/0, respectively. This array is saved in the env.calibStore() as ndarray<int16_t ,2> for source. If the file name fname_pixmap is not empty the map will be saved in this file in text format.
• produces image size map of pixel indexes in the flatten [4][8][185][388] array where fake pixels are marked as -1. This array is saved in the env.calibStore() as ndarray<int32_t ,2> for source. If the file name fname_pixnum is not empty the map will be saved in this file in text format.

Remarks:

• By default the empty key corresponds to raw data.If the imgkey is defined as "Image2D", the image is saved in the event as a CSPadPixCoords::Image2D<double> object, otherwise (by default) as a ndarray<double,2> object.

Module CSPadPixCoords::CSPadInterpolImageProducer

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Module ImgAlgos::CSPadArrPeakFinder is a psana-based implementation of the "median algorithm" for peak finding in CSPad data array shaped as [5920=4*8*185][388]. This algorithm was first implemented in myana/Cheetah by Anton Barty and Co. The "median algorithm" assumes that the amplitude level of background and noise for each pixel can be estimated as a mean and RMS of the surrounding pixels, located in the ring with parameters rmin and dr around the pixel in question. The threshold SoNThr_noise on signal over noise (S/N) ratio allow to asset the pixel amplitude as a large noise fluctuation. Statistics of pixels above the S/N threshold accumulated over many images can be used to form the noisy-pixel mask. For example, if the fraction of images where pixel exceeds the S/N threshold grater than certain value (frac_noisy_imgs=0.9), the pixel is considered as noisy. The permanent bad pixel mask (see module ImgAlgos::CSPadMaskApply) and dynamically evaluated noisy pixel mask are used to get rid of bad pixels and improve the image quality. Healthy pixels with S/N above threshold (SoNThr_signal about 3-5) are treated as potential signals. Using recursive flood-filling algorithm the groups of connected signal pixels can be found and considered as a candidate for a diffraction peaks. Peak finding algorithm uses the amplitude, S/N thresholds, and limits on number of pixels in the connected region (parameters peak_amp_tot_thr, peak_SoN_thr, peak_npix_min, and peak_npix_max) in order to define the peak. Finally, the event is selected or discarded depending on number of found peaks and total amplitude threshold, defined by the parameters event_npeak_min, event_npeak_max, and event_amp_tot_thr, respectively.

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See also Examples for package pyimgalgos.

References

• Psana User Manual - Old
• Psana Reference Manual - Old
• Psana Module Examples
• Migration from pyana to psana
• psana - Migration from pyana

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