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There are three different ways to get quasi real time information about data taking. They offer different pros and cons and the final choice to decide which method works best for a given experiments is left to the users.

The approaches are:

1. AMI (Analysis Monitoring Interface) processes running on the instrument dedicated monitoring nodes
2. Shared memory applications running on the instrument dedicated monitoring nodes
3. Psana based Custom applications running on the experimental hall fast feedback queues and reading the data from disk

AMI

This is the simplest technique for fast feedback. It 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 through with a few mouse clicks. This is the AMI guide.

Note that it's possible to augment the core AMI capabilities by writing a plug-in which follows following a well defined C++ API, but this is not trivial. If you are interested in adding new capabilities to AMI and , but you don't have real time data acquisition 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.

Shared Memory

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

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Todo

Fast Feedback

. 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: takes resources (monitoring nodes) away from AMI; requires setting up the shared memory servers on the DAQ side.

Fast Feedback

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

Pros: trivial to setup; independent from the DAQ; ability to run the same code online and offline. Cons: not quite real time (< 10s).

 Todo