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Will average event data either over an infinite time ("infinite" checked in configuration) or a settable finite number of events (generated internally using the SumN pattern).  For the finite case be careful not to set N too large as it can consume a lot of memory.  The PickN produces an array of numbers/1D-arrays/2D-arrays which are, respectively, 1D/2D/3D arrays (with "time" being the added dimension).  AMI allows you to average over any of those axes, but users will typically want to average over axis=0 ("time"), unless you want to see an average projection (axis=1 or 2) vs. time.

This pattern is similar to an MPI "reduce" operation.

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Used to generate 1D and 2D summed histograms from per-event x (1D) or x/y (2D) coordinates.  This is an infinite sum, i.e. there is currently no support for a time window).  Internally uses the Accumulator pattern (see below).  See the numpy.histogram documentation for a description of the "density" parameter.  This pattern is similar to an MPI "reduce" operation.

MeanVsScan, MeanWaveformVsScan

Useful for either step-scans or fly-scans.  Compute the mean of a value or waveform as a function of step number (for a step-scan) or the fly-scanned value (for a fly-scan).  Internally uses the ReduceByKey pattern below.

SumN

Sum N events with a pattern very similar to the PickN pattern.  See the PickN section below for some integer-arithmetic related subtleties.  This pattern is similar to an MPI "reduce" operation.

Global Operations Typically Used Internally By AMI

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