smd_producer.py
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.
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def getROIs(run):
""" Set parameter for ROI analysis. Set writeArea to True to write the full ROI in the h5 file.
See roi_rebin.py for more info
"""
if isinstance(run,str):
run=int(run)
ret_dict = {}
if run<21:
roi_dict = {}
roi_dict['ROIs'] = [ [[1,2], [127,394], [655,923]]] # can define more than one ROI
roi_dict['writeArea'] = True
roi_dict['thresADU'] = None
ret_dict['jungfrau1M'] = roi_dict
elif run>20 and run<43:
roi_dict = {}
roi_dict['ROIs'] = [chip22] # can define more than one ROI
roi_dict['writeArea'] = True
roi_dict['thresADU'] = None
ret_dict['jungfrau1M'] = roi_dict
elif run>42:
roi_dict = {}
roi_dict['ROIs'] = [chip22] # can define more than one ROI
roi_dict['writeArea'] = True
roi_dict['thresADU'] = None
ret_dict['jungfrau1M'] = roi_dict
return ret_dict |
Besides, the ROI definition, the parameters are the following:
writeArea: whether to write the full ROI to file or only the statistics (intensity sum, and other statistics)
thresADU: pixel intensity threshold. Any pixel below that value are set to 0.
See the following link to learn how to start an interactive iPython session and make an average image for a given detector:
1.2 Area Detector treatment with DetObject#1.2AreaDetectortreatmentwithDetObject-InteractiveSmallDataAnasession
Once an average image has been created, a ROI can be selected by running:
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