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xc_um = 109.92 * np.array( [[ 473.38, 685.26, 155.01, 154.08, 266.81, 53.95, 583.04, 582.15], [ 989.30, 987.12, 1096.93, 884.11, 1413.16, 1414.94, 1500.83, 1288.02], [1142.59, 930.23, 1459.44, 1460.67, 1347.57, 1559.93, 1032.27, 1033.44], [ 626.78, 627.42, 516.03, 729.15, 198.28, 198.01, 115.31, 327.66]]) yc_um = 109.92 * np.array( [[1028.07, 1026.28, 1139.46, 926.91, 1456.78, 1457.35, 1539.71, 1327.89], [1180.51, 967.36, 1497.74, 1498.54, 1385.08, 1598.19, 1069.65, 1069.93], [ 664.89, 666.83, 553.60, 765.91, 237.53, 236.06, 152.17, 365.47], [ 510.38, 722.95, 193.33, 193.41, 308.04, 95.25, 625.28, 624.14]]) orient_deg = np.array( [[ 90., 90., 0., 0., 270., 270., 0., 0.], [ 0., 0., 270., 270., 180., 180., 270., 270.], [ 90., 90., 0., 0., 270., 270., 0., 0.], [ 0., 0., 270., 270., 180., 180., 270., 270.]]) tilt_deg = np.array( [[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.]]) |
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ALL arrays are shown here FOR TEST PURPOSE ONLY !!! any they are not consistent with any real detector. The |
These arrays of alignment parameters are used for initialization of the CSPadPixCoordsWODB
class object, which can be used to get X,Y-coordinate arrays as shown below:
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w = CSPadPixCoordsWODB(xc_um, yc_um, orient_deg, tilt_deg) X,Y = w.get_cspad_pix_coordinate_arrays_pix () |
where X.shape = Y.shape = (4, 8, 185, 388) and obviously can be re-shaped to (32, 185, 388). Then, it is easy to produce image of coordinate arrays using numpy
2D histogramming method:
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xsize = X.max() + 1 ysize = Y.max() + 1 Img, Xedges, Yedges = np.histogram2d(X.flatten(), Y.flatten(), bins=[xsize,ysize], range=[[0,xsize],[0,ysize]], normed=False, weights=None) |
In order to get an image of intensity the weights array needs to be provided.
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