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To do analysis with a psana python script execute the following commands.  This requires the ability to ssh to an LCLS unix account and the ability to open up a graphics window on your computer ("X-windows").  Two free options for graphics:We recommend the free NX Technology (for Mac or Windows) for improved performance, but XQuartz (Mac) or XWin32 (Windows) also works.  Replace "YOURACCOUNTNAME" in the first line below with your unix account name.

Note that psana in a package in a larger python-ecosystem called conda.  Conda only supports the "bash" unix shell. Once you login you can see what shell you have by typing "echo $SHELL".  If it is not bash, you can type "bash" to get such a shell, and we recommend that users send email to pcds-it-l@slac.stanford.edu to get your default shell changed to bash.

# you don't need this first line if you are using the NX Technology NoMachine software
ssh -X pslogin.slac.stanford.edu -l YOURACCOUNTNAME
ssh -X psana

# If you don't know which shell you are using
# type "echo $SHELL" at the linux command prompt
 
source /reg/g/psdm/etc/psconda.sh

cp /reg/g/psdm/tutorials/examplePython/firstExample.py firstExample.py
python firstExample.py

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• many of the "building block" tools can be used at FELs around the world: e.g. python, MPI and, in future and scikit-beam algorithms
• part of the world-standard conda python ecosystem
• same code works for online/offline analysis analysis (with real-time plot display), allowing users to do their entire analysis chain with one tool
• ability to use both simple and complex languages (python/C++)
• users don't have to wait for xtc-to-hdf5 conversion time
• access to calibrated images both online/offline (pedestals, common-mode, geometry, bad-pixel mask) for complex detectors (CSPAD, pnCCD, EPIX)
• local support at SLAC from ~10 people (core offline group and instrument engineers)
• simple detector names using experiment-specific "aliases"
• free
• ability to randomly access xtc data ("jump" to event in middle of file)
• support for fast parallel processing of xtc files with hundreds of cores using MPI
• ability to run entire psana code on Windows/Mac machines using virtual box (useful for off-site visualization or code development, for example)