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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 about which method works best for a given experiments is left to the users.
The approaches are:
AMI can display the raw/calibrated detector data in real time and it can calculate and display some simple quantities without writing any code. Analyses are configured through a GUI with just a few mouse clicks. A guide to using AMI can be found here.
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Cons: non trivial augmenting capabilities through additional C++ code.
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This method of analysis reads the detector data from a fast storage layer called fast feedback (FFB), 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 of the DAQ; ability to run the same code online and offline.
Cons: not quite real time (<5 minutes); may compete with the HDF5 translator if the translation services are run on the FFB storage layer.
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Cons: if not using pre-existing software, requires very large time investment from both LCLS and the user. competes with AMI resources (monitoring nodes); requires setting up the shared memory servers on the DAQ side.
This method of analysis reads the detector data from a fast storage layer called fast feedback (FFB), 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 of the DAQ; ability to run the same code online and offline.
Cons: not quite real time (<5 minutes); may compete with the HDF5 translator if the translation services are run on the FFB storage layer.