About this tutorial

The main objective of this session is to introduce and to explain the new Python interface for accessing LCLS data from analysis applications. The new software framework is known as the "Interactive psana" or just ipsana. The first idea of implementing such tool was suggested around 1 and a half years ago at the joint PCDS/SRD meeting (look for ipsana). Though its underlying machinery is largely based on the batch version of psana the interface to the data is much simple, more intuitive, and it requires much less code to be written by a user in order to get to "that CSPad image" (EPICS PV, etc.). The new framework won't work for everyone, especially for those users who have either heavily invested into the modular code of the batch frameworks, or who needs the performance of modules written in C++. Still our intent is to demonstrate the power of the new approach and to encourage using the tool where it seems to be appropriate.

What is beyond a scope of this tutorial

• This isn't an explanation how to do the data analysis
• This is not a Users or Reference Guide for the interactive framework

Attention pyana users

As it's been announced earlier, the pyana framework will be phased out at some point. There is a variety of reason why:

• first of all, as our understanding of what kind of analysis framework would work better for our users and for ourselves as developers grew over time we realized that we needed to develop a tool which would have a better interface to the data, a better internal architecture and be much easy to maintain and extend for new data types of LCLS instruments. Hence we ended up with psana.
• for its users, the psana framework has a number of advantages, among which:
  • better API to the data
  • a possibility of writing (mixing) modules in C++ and Python. Modules written in different languages will still the same data, and they can also exchange data within the framework.
  • supporting data sets in both XTC and HDF5
  • an ability to read the "live" files (while they're being recorded by the DAQ system or data movers)
  • and ability to read data from shared memory of the DAQ (DSS) machines

The last two features open an interesting possibility of using psana for real-time monitoring of data while benefiting from
the same code which might be developed for the traditional OFFLINE processing/analysis.

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Chapter 2

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