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from psana import DataSource import numpy as np import os # OPTIONAL callback with "gathered" small data from all cores. # usually used for creating realtime plots when analyzing from # DAQ shared memory. Called back on each SRV node. def my_smalldata(data_dict): print(data_dict) # sets the number of h5 files to write. 1 is sufficient for 120Hz operation # optional: only needed if you are saving h5. os.environ['PS_SRV_NODES']='1' ds = DataSource(exp='tmoc00118', run=222, dir='/cds/data/psdm/prj/public01/xtc', max_events=10) # batch_size is optional. specifies how often the dictionary of small # user data is gathered smd = ds.smalldata(filename='mysmallh5.h5', batch_size=5, callbacks=[my_smalldata]) for run in ds.runs(): opal = run.Detector('tmo_opal1') ebeam = run.Detector('ebeam') runsum = 0 for evt in run.events(): img = opal.raw.image(evt) photonEnergy = ebeam.raw.ebeamPhotonEnergy(evt) if img is None or photonEnergy is None: continue evtsum = np.sum(img) # pass either dictionary or kwargs smd.event(evt, evtsum=evtsum, photonEnergy=photonEnergy) runsum += evtsum # beware of datatypes when summing: can overflow # optional summary data for whole run if smd.summary: smd.sum(runsum) # sum across all mpi cores # pass either dictionary or kwargs smd.save_summary({'sum_over_run' : runsum}, summary_int=1) smd.done() |
Full-Featured Example with Callbacks and Detector Selection
You can run this script with MPI the same way as shown in the previous example: mpirun -n 6 python example.py
Three more These additional arguments for DataSource were added to this example
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