This page is about n-d array processing algorithms coded in ImgAlgos.PyAlgos
. Algorithms are implemented on C++ and also have python callable interface using boost/python
wrapper. All examples are shown for python level interface.
import psana from ImgAlgos.PyAlgos import PyAlgos # create object: alg = PyAlgos(windows=winds, mask=mask, pbits=0) # where pbits - is a print info control bit-word: # pbits = 0 - print nothing # + 1 - main results, list of peaks # + 2 - input parameters, index matrix of pixels for S/N algorithm # + 128 - tracking and all details in class PyAlgos.py # + 256 - tracking and all details in class AlgArrProc # + 512 - tracking and all details in class AlgImgProc # set peak-selector parameters: alg.set_peak_selection_pars(npix_min=5, npix_max=5000, amax_thr=0, atot_thr=0, son_min=10) |
# List of windows winds = None # entire size of all segments will be used for peak finding winds = (( 0, 0, 185, 0, 388), ( 1, 20,160, 30,300), ( 7, 0, 185, 0, 388)) # Mask mask = None # (default) all pixels in windows will be used for peak finding mask = det.mask() # see class Detector.PyDetector mask = np.loadtxt(fname_mask) # mask.shape = <should be the same as shape of data n-d array> |
r0=3 dr=0.1 (4 pixels) r0=3 dr=0.5 (12 pixels) r0=3 dr=1 (24 pixels) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 + 0 0 1 0 0 1 0 0 + 0 0 1 0 1 1 0 0 + 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 r0=4 dr=0.2 (12 pixels) r0=4 dr=0.3 (16 pixels) r0=4 dr=0.5 (24 pixels) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 + 0 0 0 1 0 0 1 0 0 0 + 0 0 0 1 0 0 1 0 0 0 + 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
r0=5 dr=0.05 (12 pixels) r0=5 dr=0.5 (28 pixels) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 + 0 0 0 0 1 0 0 1 0 0 0 0 + 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 r0=6 dr=0.2 (12 pixels) r0=6 dr=0.5 (28 pixels) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 + 0 0 0 0 0 1 0 0 1 0 0 0 0 0 + 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
two-threshold peak-finding algorithm in restricted region around pixel with maximal intensity.
peaks = alg.peak_finder_v1(nda, thr_low=10, thr_high=150, radius=5, dr=0.05) |
define peaks for regoins of connected pixels above threshold
peaks = alg.peak_finder_v2(nda, thr=10, r0=5, dr=0.05) |
define peaks in local maximums of specified rank (radius), for example rank=2 means 5x5 pixel region around central pixel.
peaks = alg.peak_finder_v3(nda, rank=2, r0=5, dr=0.05) |
r0(ex.=5.0), dr(ex.=0.05)
evaluates background level, rms of noise, and S/N for the pixel with maximal intensity.set_peak_selection_pars
, for examplealg.set_peak_selection_pars(npix_min=5, npix_max=500, amax_thr=0, atot_thr=1000, son_min=6) |
Test for 100x100 image with random normal distribution of intensities
Example of the map of local maximums found for rank from 1 to 5:
color coding of pixels:
Table for rank, associated 2-d region size, fraction of pixels recognized as local maximums for rank, and time consumption for this algorithm.
rank | 2-d region | fraction | time, ms |
---|---|---|---|
1 | 3x3 | 0.1062 | 5.4 |
2 | 5x5 | 0.0372 | 5.2 |
3 | 7x7 | 0.0179 | 5.1 |
4 | 9x9 | 0.0104 | 5.2 |
5 | 11x11 | 0.0066 | 5.2 |