Environment

To obtain the environment to run psana2, execute the following:

source /reg/g/psdm/sw/conda2/manage/bin/psconda.sh

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

Example Script

If you have xtc files (with associated smd files) you can run this script with MPI: PS_SRV_NODES=2; mpirun -n 6 python example.py

It also works on one core with: python example.py.  See MPI rank/task diagram here.

This mechanism by defaults produces "aligned" datasets where missing values are padded (with NaN's for floats, and -99999 for integers).  To create an unaligned dataset (without padding) prefix the name of the variable with "unaligned_".

from psana import DataSource
import numpy as np

# called back on each SRV node, for every smd.event() call below
def test_callback(data_dict):
    print(data_dict)
ds = DataSource(exp='xpptut13', run=1, dir='.tmp')
# batch_size here specifies how often the dictionary of information
# is sent to the SRV nodes
smd = ds.smalldata(filename='my.h5', batch_size=5, callbacks=[test_callback])
run = next(ds.runs())

# necessary (instead of "None") since some ranks may not receive events
# and the smd.sum() below could fail
arrsum = np.zeros((2), dtype=np.int)
for i,evt in enumerate(run.events()):
    myones = np.ones_like(arrsum)
    smd.event(evt, myfloat=2.0, arrint=myones)
    arrsum += myones

if smd.summary:
    smd.sum(arrsum)
    smd.save_summary({'summary_array' : arrsum}, summary_int=1)
smd.done()

MPI Task Structure

To allow for scaling, many hdf5 files are written, one per "SRV" node.  The total number of SRV nodes is defined by the environment variable PS_SRV_NODES (defaults to 0).  These many hdf5 files are joined by psana into what appears to be one file using the hdf5 "virtual dataset" feature.