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This script lives in /reg/g/psdmsdf/group/lcls/ds/ana/tutorials/examplePython3psana1_examples/radInteg.py and demonstrates how to do an angular integration of a 2D area-detector image. If you don't have a 2D image, but instead have pixel values and positions, consider using the more general BinnedStatistic1D/BinnedStatistic2D/BinnedStatisticDD classes in skbeam.core.accumulators.binned_statistic (not documented here, but documentation is available within within IPython).
This method for doing radial integrations is part of the world-reusable scikit-beam project hosted by BNL. This goal of this python library is to provide low-level building-blocks that can be easily installed around the world enabling scientists to reuse familiar tools at different laboratories.
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from skbeam.core.accumulators.binned_statistic import RadialBinnedStatistic, RPhiBinnedStatistic import numpy as np img = np.reshape(np.arange(9),(3,3)) print ('Image:\n',img) mask = np.ones_like(img) mask[1][1]=0 print(f'\nMask:\n{mask}') radbinstat = RadialBinnedStatistic(img.shape, bins=3, statistic='sum', origin=(0,0), range = (0,2), mask=mask) rphibinstat = RPhiBinnedStatistic(img.shape, bins=(3,1), statistic='sum', origin=(0,0), range = ((0,2),(0,np.pi/3))) rphibinstat_mask = RPhiBinnedStatistic(img.shape, bins=(3,1), statistic='sum', origin=(0,0), range = ((0,2),(0,np.pi/3)), mask=mask) print('\nAngular integration with mask:') print(radbinstat(img)) print('\nBin edges and centers:') print(radbinstat.bin_edges) print(radbinstat.bin_centers) print('\n2D R/Phi Angular integration (1 phi bin) with phi range and mask:') print(rphibinstat_mask(img)) print('\n2D R/Phi Angular integration (1 phi bin) with phi range and no mask:') print(rphibinstat(img)) print('\nR/Phi bin edges:') print(rphibinstat.bin_edges[0]) print(rphibinstat.bin_edges[1]) |
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