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These recipes won't cover all possible data access scenarios. But we hope they will give a reader an idea where to look further.

The essential functionality which is not implemented in psana

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Parallel data processing

We recognize an importance of supporting some form of parallel processing in the framework. But we still haven't settled on a specific solution for that. There are many pros and cons of different techniques which we're still evaluating one against each other and weighting their benefits versus complications. A problem is that their effectiveness greatly depends on a class of problems solved by a user. In the realm of various data analysis approaches being taken by LCLS users it's hard to find a common solution which would suit all equally well. For now, we leave it up to a user to find an appropriate scheme.

Direct access to events

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Using Modules with Interactive psana

Non-interactive-psana modules (both python and C++) can also be included in an interactive-psana job.  For this mode of operation, the interactive-psana script looks like the "last" module in chain.  One specifies the modules to use by creating a .cfg file (identical to the non-interactive-psana .cfg file) and specifying it with a line similar to the following before the dataset definition line:

setConfigFile("cspad_calib.cfg") 

The essential functionality which is not implemented in psana

Data exchange between external psana modules and Python code

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