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# This `batch_size` 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_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 |
RIX Example: Deadtime and Integrating Detectors
Sometimes data for a particular shot can not be read out because DAQ data buffers are unavailable. This is called "deadtime". Deadtime can make it impossible to reliably use high-rate shots to perform operations like normalizations for lower-rate integrating detectors. Matt Weaver is adding counters to the timing system so the DAQ can count how many shots whose data has been dropped due to deadtime so experimenters can make a decision about whether there is sufficient data for a normalization-style analysis.
This example
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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() 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 |
AMI and Integrating Detectors
AMI is also affected by the DAQ deadtime issue described above but, in addition, events can also be thrown away going into the AMI shared memory (this feature is what allows AMI analysis to stay real-time). This can cause additional issues with AMI integrating-detector normalization analyses that try to use high-rate shots to do normalizations for low-rate integrating detectors, since not all high-rate shots are guaranteed to be available. Also, unlike psana, AMI doesn't currently have the idea of shots that "belong together" which will make an AMI normalization-style analysis of an integrating detector impossible.
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