This script lives in /reg/g/psdm/tutorials/examplePython/areaDetAccess.py and demonstrates how to access calibrated "unassembled data" (3D array, no geometry applied) and "assembled images" (2D array, geometry applied). Assembled/unassembled are only different for multi-panel detectors. For monolithic 2D area detectors they are the same.
NOTE: there are many other python methods of the Detector object for Area Detectors (e.g. x,y pixel coordinates, pedestal values, masks). One can see all the methods of a class using ipython tab completion, or /reg/g/psdm/sw/releases/ana-current/Detector/examples/ex_all_dets.py.
from psana import *
ds = DataSource('exp=xpptut15:run=54:smd')
det = Detector('cspad')
for nevent,evt in enumerate(ds.events()):
# includes pedestal subtraction, common-mode correction, bad-pixel
# suppresion, and returns an "unassembled" 3D array of cspad panels
calib_array = det.calib(evt)
# this is the same as the above, but also uses geometry to
# create an "assembled" 2D image (including "fake pixels" in gaps)
img = det.image(evt)
break
import matplotlib.pyplot as plt
plt.imshow(img,vmin=-2,vmax=2)
plt.show()
You can find a version with more advanced graphics here.
The "image" method can also be used to convert any array with the "unassembled" shape into "assembled" format (that is, applying the geometry) with a line like this:
assembled_array = det.image(evt, unassembled_array)
This can be useful for plotting masks, or determining which pixels are "real" in the assembled image:
real_pixels = det.image(evt, np.ones_like(unassembled_array))
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