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Another option is to reduce the data by azimuthally averaging the signal. Here, we need to know the center for the integration as well as the detector distance and beam energy. The latter two are mostly important for the q-values of the bins that will also be stored in the smallData. As conditions will change during an experiment, it is convenient to have a function that return the correct integration setup for each (range of) run(s). In addition to a 1-d result as a q-vector, you can also define a number of phi-slices that should be used for integration, resulting in a 2-d average in q-phi space.
The averaging will automatically apply a corrections for the x-ray polarization and geometric acceptance. What is currently not corrected for are signal differences due to different path length of the photons when crossing the detector . Also, the calculations assume that the detector is mounted perpendicular to the beam direction.
Run dependent parameters:
First up are functions that will return e.g. the region-of-interest boundaries for each run. During the experiment, this should be kept up-to-date so if the setup changes, the smallData file will get an entry with new boundaries for a just finished range of runs. This way, the smallDataRun script will always use the correct region of interest for each run.
Run dependent integration parameters
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def getAzIntParams(run): """ Parameters for azimuthal integration See azimuthalBinning.py for more info """ if isinstance(run,str): run=int(run) ret_dict = {} if run>0: az_dict = {'eBeam': 6.5} # keV #az_dict['center'] = [224.367146, 870.269879] # um az_dict['center'] = [-549.943775, 261.418876] # um az_dict['dis_to_sam'] = 80. # mm az_dict['tx'] = 0 # deg az_dict['ty'] = 0 # deg az_dict['phiBins'] = 11 # number of phi bins ret_dict['epix10k2M'] = az_dict return ret_dict |
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