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sample code:
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from psana import DataSource from psmon import publish from psmon.plots import Image,XYPlot import os os.environ['PS_SRV_NODES']='1 # hack for use with "mpirun -n 5 python andor.py"to eliminate use of publish.local (allows use of "psplot ANDOR") if rank==4: # rank of SRV node when running with 5 cores. Mona probably has a more elegant way to do this. publish.init() # we will remove this for batch processing and use "psplot" instead # publish.local = True def my_smalldata(data_dict): if 'unaligned_andor_norm' in data_dict: andor_norm = data_dict['unaligned_andor_norm'][0] myplot = XYPlot(0,"Andor (normalized)",range(len(andor_norm)),andor_norm) publish.send('ANDOR',myplot) ds = DataSource(exp='rixx1003821',run=46,dir='/cds/data/drpsrcf/rix/rixx1003821/xtc/',intg_det='andor_vls',batch_size=1) for myrun in ds.runs(): andor = myrun.Detector('andor_vls') timing = myrun.Detector('timing') smd = ds.smalldata(filename='mysmallh5.h5',batch_size=1000, callbacks=[my_smalldata]) norm = 0 ndrop_inhibit = 0 for nstep,step in enumerate(myrun.steps()): print('step:',nstep) for nevt,evt in enumerate(step.events()): andor_img = andor.raw.value(evt) # also need to check for events missing due to damage # (or compare against expected number of events) ndrop_inhibit += timing.raw.inhibitCounts(evt) smd.event(evt, mydata=nevt) # high rate data saved to h5 # need to check Matt's new timing-system data on every # event to make sure we haven't missed normalization # data due to deadtime norm+=nevt # fake normalization if andor_img is not None: print('andor data on evt:',nevt,'ndrop_inhibit:',ndrop_inhibit) # check that the high-read readout group (2) didn't # miss any events due to deadtime if ndrop_inhibit[2]!=0: print('*** data lost due to deadtime') # need to prefix the name with "unaligned_" so # the low-rate andor dataset doesn't get padded # to align with the high rate datasets smd.event(evt, mydata=nevt, unaligned_andor_norm=(andor_img/norm)) norm=0 ndrop_inhibit=0 |
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