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from psana import *
ds  = DataSource('exp=xpptut15:run=54:idx') # run online/offline
det = Detector('cspad', ds.env()) # simple detector interface
from mpi4py import MPI # large-scale parallelization
rank = MPI.COMM_WORLD.Get_rank()
size = MPI.COMM_WORLD.Get_size()
for run in ds.runs():
    times = run.times()
    nevents = len(times)
    mytimes= times[rank*nevents:(rank+1)*nevents]
    for n,t in enumerate(timesmytimes):
        evt = run.event(t) # random access
        if 'image' not in locals():
            img = det.image(evt) # many complex run-dependent calibrations
        else:
            img += det.image(evt)
        if n>5: break
import numpy as np
img_all = np.empty_like(img)
MPI.COMM_WORLD.Reduce(img,img_all)
if rank==0:
    from pypsalg.AngularIntegrationM import *  # algorithms
    ai = AngularIntegratorM()
    ai.setParameters(img_all.shape[0],img_all.shape[1],
                     mask=np.ones_like(img_all))
    bins,intensity = ai.getRadialHistogramArrays(img_all)
    from psmon import publish # real time plotting
    from psmon.plots import Image
    publish.local = True
    img = Image(0,"CsPad",img_all)
    publish.send('image',img)
MPI.Finalize()