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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)
# important: always check for missing data
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:
tot_runsum = smd.sum(runsum) # sum (or max/min) across all mpi cores
# pass either dictionary or kwargs
smd.save_summary({'sum_over_run' : tot_runsum}, summary_int=1)
smd.done() |
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Code Block | ||
---|---|---|
| ||
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)
# Use this function to decide to keep/discard this event
# If this detector is needed, make sure to define this
# detector in as_smds argument for DataSource (see below)
def filter_fn(evt):
run = evt.run()
step = run.step(evt)
opal = run.Detector('tmo_opal1')
img = opal.raw.image(evt)
return True
# Use this function to direct an event to process on a
# particular 'rank'. This function should returns a rank
# number between 1 and total no. of ranks - 3 (3 ranks are reserved).
def destination(evt):
# Note that run, step, and det can be accessed
# the same way as shown in filter_fn
n_bd_nodes = 3 # for mpirun -n 6, 3 ranks are reserved so there are 3 bd ranks left
dest = (evt.timestamp % n_bd_nodes) + 1
return dest
# 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,
detectors = ['tmo_opal1', 'ebeam'], # only reads these detectors (faster)
filter = filter_fn, # filter_fn returns True/False
small_xtc = ['tmo_opal1'], # detectors to be used in filter callback
destination = destination) # returns rank no. (send this evt to this rank)
# 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:
tot_runsum = smd.sum(runsum) # sum (or max/min) across all mpi cores
# pass either dictionary or kwargs
smd.save_summary({'sum_over_run' : tot_runsum}, summary_int=1)
smd.done() |
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