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This pattern does not provide a solution for all possible LCLS analyses, so it is also possible to call MPI directly from python in these advanced examples.

Missing Detectors: How Not To Crash

The default behavior of the Detector class is to crash when a requested Detector is not present (so users will be alerted if they make a typo, for example).  But this is often not desired behavior during production running when Detectors are being added/removed from the data stream on a run-to-run basis. To change that behavior use a constructor like Detector('MyDetectorName',accept_missing=True).  With this flag, all methods of the Detector will return None, just as it would if it will missing in every event.

Accessing Data While Running

If one wishes to examine the data gathered while the analysis is in progress, users can register a "monitor" function will will be called every time data is gathered by all the cores (set with the "gather_interval" parameter in the above example).  The monitor function will be passed a dictionary of all gathered values, and can be registered like this:

def my_monitor(results):
    # process results dictionary here
 
smldata.add_monitor_function(my_monitor)

Note that after each gather interval the data is removed from memory, so it is the user's responsibility to "remember" any data from previous callbacks that they want to keep (be careful not to use up all machine memory).

Job Submission Script

The following script can be a useful pattern to follow for submitting batch jobs.  Change the script to use the appropriate directory, experiment name, and analysis python-script name.   Then make the script executable with a command like "chmod +x submit.sh" and submit a job with a command like "./submit.sh 123" where "123" is the run number to be analyzed.

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