The model we believe to be way of the future for LCLS analysis (and FEL/lightsource analysis around the world) is that users be able to put together simple/short python "building blocks" to quickly express the complexity of their experiment. Many of these building blocks are publicly available on the web, and so can be reused around the world.
The section will show short/simple working example scripts that demonstrate the most common building blocks (at the time of this writing, the longest is 26 lines). These examples do not show all possible complex uses of the building blocks, but we include links to more detailed documentation where appropriate.
All scripts shown in this section can be copied from this directory:
/reg/g/psdm/tutorials/examplePython
They are maintained in the svn user repository named "examplePython".
For reference, here we include a 39-line script that incorporates several of the building blocks:
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(times):
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()
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