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Note that LCLS-II psana is not compatible with LCLS-I psana, so environments must activate one or the other, but not both.

Public Practice Data

Publicly accessible practice data is located in the directory /cds/data/psdm/prj/public01/xtc.  Use of this data requires the additional "dir=" keyword to the DataSource object.

ExperimentRunComment
tmoc00118222Generic TMO dark data


Detector Names

Use this command to see non-epics detector names (see "Detector Interface" example below):

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Standard (per-shot) detectors and the slower epics variables can be accessed as shown here using the names discovered with the commands above.  You can use tab-completion in ipython or the jupyternotebook jupyter notebook to explore what you can do with the various detector objects:

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Code Block
languagepy
from psana import DataSource
import numpy as np
import os

# OPTIONAL callback with "gathered" small data from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
  all cores.
# usually used for creating realtime plots when analyzing from
# DAQ shared memory. Called back on each SRV node.
def test_callback(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=[test_callback])

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()

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