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• det = run.Detector(det_name)
• array = det.raw.calibraw(evt) for single segment detectors, dict=det.raw.calib(evt) for multi-segment detectors.  similarly for det.raw.raw(evt)
• we will return a natural shape for the above (2D for opal, dict for cspad/epix) and have a global-flag if we want everything to be dict (e.g. for psocake)
• maybe image(evt, array=array) should be an algorithm? det.raw.image(evt,array=None)? or could split det.raw.image(evt), det.raw.image(array=array)? ideally shouldn't have to pass evt.
• get_calib_const(det_name, timestamp or run or evt).  Mikhail suggests we should consider det.get_calib_const(timestamp or run or evt)
• flexible number of segments (no hardwired "32" segments for a cspad).  e.g. a "full detector" may have segments 2,3,4,7,8,9.  one run may have segments 3,4,7 another run may have segments 2,3,8,9
  • calibration data must be taken and stored with all segments in the "full detector" (i.e. 2,3,4,7,8,9)
  • should det.raw.calib pad the missing segments in a given run? (e.g. for a run with segments 3,4,7 should we put zeros for 2,8,9?)
• try to use same common-mode algorithm for all detectors (stripes/banks).  multiple passes needed for different directions. (e.g. Jungfrau and epix are currently same)
• default geometries for single panel detectors
• what do do about ROIs (future problem)?
  
  • dict approach for multi-segment detectors could also be used for ROIs
  • ROIs also need the information about the "corner" of the ROI
  • ROIs are really "feature extracted" data i.e. instead det.raw.image() it's det.fex.image()
• how do we reuse the python code in the DRP which is multi-threaded C++?  cpo suggests maybe we have a python multi-process DRP.  another labor-intensive option is supporting multi-threaded C++. or write C++ and wrap with python (was complicated for the hsd, but perhaps just because we needed to avoid 1MHz malloc)
• DRP will have to work on "segments" of a full detector (probably in units of panels)