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Here's a sample python script, how you can analyze a run with an integrating detector (in this example, it's 'andor'). For more detail about the technical design, visit this page.
Two keys variables when running in integrating-detector mode: PS_SMD_N_EVENTS (and environment variable) which is how many events SMD0 will send to each EB core, and the batch_size kwarg which controls how many events the EB cores send to the BD cores. When running integrating detectors then the "units" of these two variables change from being "high-rate events" to being "low-rate integrating-detector events". batch_size should be set to less than PS_SMD_N_EVENTS. We can consider trying to simplify this interface in the future.
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# This `batch_size`The PS_SMD_N_EVENTS should be set to a small number (e.g. 1) # since all other events which are part of this intg. event will be sent # in the same batch. batch. import os os.environ['PS_SMD_N_EVENTS'] = '1' batch_size = 1 from psana import DataSource ds = DataSource(exp='xpptut15', run=1, dir='/cds/data/psdm/prj/public01/xtc/intg_det', intg_det='andor', batch_size=batch_size) run = next(ds.runs()) hsd = run.Detector('hsd') andor = run.Detector('andor') # Test calculating sum of the hsd for each integrating event. sum_hsd = 0 for i_evt, evt in enumerate(run.events()): hsd_calib = hsd.raw.calib(evt) andor_calib = andor.raw.calib(evt) # Keep summing the value of the other detector (hsd in this case) sum_hsd += np.sum(hsd_calib[:])/np.prod(hsd_calib.shape) # When an integrating event is found, print out and reset the sum variable if andor_calib is not None: val_andor = np.sum(andor_calib[:])/np.prod(andor_calib.shape) print(f'i_evt: {i_evt} andor: {val_andor} sum_hsd:{sum_hsd}') sum_hsd = 0 |
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from psana import DataSource from psmon import publish from psmon.plots import Image,XYPlot import os from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() # The PS_SMD_N_EVENTS should be set to a small number (e.g. 1) # since all other events which are part of this intg. event will be sent # in the same batch. os.environ['PS_SMD_N_EVENTS'] = '1' os.environ['PS_SRV_NODES']='1' if rank==4: # hack for now to eliminate use of publish.local below 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/psdm/prj/public01/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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