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AMI can display the raw/calibrated detector data in real time and it can calculate and display some simple quantities without writing any code, just with .  Analyses are configured through a GUI with just a few mouse clicks. This is the AMI guideA guide to using AMI can be found here.

Note that it 's is possible to augment the core AMI capabilities by writing a plug-in following a well defined C++ API, but this is not trivial. If you are interested in adding new capabilities to AMI, but you don't have enough C++ expertise in your group to create an AMI plug-in, contact us before coming to LCLS.

Pros: excellent real time capabilities (< 1s); very simple to use for simple analysis.

Cons: non trivial augmenting capabilities through additional C++ code.

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The LCLS data acquisition system can allocate processes, called shared memory servers, which receive the detector data through the network, build these contributions into events, and then hand over these events to one or more clients running on the same monitoring machine. The clients can be written in any language that can access shared memory. The most common frameworks adopted in LCLS as shared memory clients are CASS, OnDA and psana. 

Pros: excellent real time capabilities (< 1s); ability to run the same code online and offline.

Cons: competes with AMI resources (monitoring nodes); requires setting up the shared memory servers on the DAQ side.

Fast Feedback

This method allows to run the analysis by reading of analysis reads the detector data from a fast , dedicated storage layer called fast feedback (FFB), called fast feedback, dedicated to the running experiment, and runs a prepared analysis. Any framework which can read XTC files can be adopted to run as a fast feedback application. The most common framework used with this method is psana.

Pros: trivial to setup; independent from of the DAQ; ability to run the same code online and offline.

Cons: not quite real time (< 10s); may compete with the HDF5 translator if the translation services are run against on the FFB storage layer.