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.
This 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 example script that incorporates several of the building blocks (described with the comments after the "#" character):
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(mytimes): 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()