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OUT OF DATE. Use as reference for what we plan on doing.

List of Program Components

MAUS follows a Map-Reduce dataflow which is discussed in detail in the MAUS Design page. The core of MAUS will handle some of these steps (in the lingo: the partition and comparison functions) but it's up to the user to specify the following three things:

  1. The input reader (ROOT file, socket, database, your favourite file format, etc.)
  2. The map workers, which means what operation you want performed on each event
  3. The reduce workers, which performs operations on all events after the map step is done (histograming, fitting, etc.)
  4. The output writer (same as input reader in term of formats)

Input Readers

Input readers yield events to be processed.

Reader type Comments
BytestreamFile Read prewritten DAQ bytestream data from a file
CouchDBServer Read JSON documents that are spills from a CouchDB server
DATEServer Read DAQ bytestream data that needs to be unpacked from the DAQ DATE
JSONFile Read JSON documents that are spills sequentially from a file
ROOTFile Write ROOT file of run

Map Workers

The mapping workers are broken into three categories:
  1. MC
  2. Data
  3. Both MC and Data
  4. Testing

MC

Worker name Comments
BeamMaker Make a MC beam with certain properties
CovarianceEvolver Evolve a covariance matrix. Either run worker once per run, or use cached evolved covariance matrix, or be recomputed per spill since we get those currents.
Digitization Digitize MC into ADC/TDC counts
Simulation Track particles and get energy deposited for those particles. Don't run this in parallel (default is default, so don't worry) unless you're really sure what you're doing with random number seeds.
Spill Create a spill out of many triggers
Trigger Trigger simulation. Make triggers out of digitized MC information.
TransferMatrices Track particle with transfer matrices
TransferMatricesCreate Create the transfer matrices from simulation events
VirtualPlanes Generate virtual planes at certain points using MC tracks. This interpolates between steps assuming no material.

Data

Worker name Comments
DAQUnpacker Unpack DAQ bytestream data
InstrumentalDataQuality Perform low level checks on the data. Examples: dead channels or do the currents in the bytestream agree with EPICS

Both MC and Data

Worker name Comments
ApplyCalibration Apply the calibration on either MC or data to go from ADC and TDC counts to energy deposited and time, respectively. Specify if MC or data.
Cut Remove data based on a cut string
EPICSAlarm Create an EPICS alarm, if detected, and post to alarm handler if enabled
FitGlobalTrack Fit a track using information from all detectors
FitSciFiTrack Fit a track using just the SciFi tracker with recpack
FitTOFTrack Fit a track using just the SciFi tracker using Mark Rayner's method

Testing

Worker name Comments
FakeDAQData Spit out precomputed DAQ bytestream data
FakeFitTrack Spit out precomputed tracks
FakeMCTruth Spit out precomputed MC truth
FakeMCDigitized Spit out precomputed digitized MC information

Reduce Workers

Whatever histogram, correction, etc. you want. Current MAUS efforts are focused on mappers and only 'example' reducers will be in v1.0.

Output Writers

Reader type Comments
CouchDBServer Write JSON documents that are spills from a CouchDB server
JSONFile Write JSON documents that are spills sequentially to a file
ROOTFile Write ROOT file of run

Updated by Tunnell, Christopher over 9 years ago ยท 19 revisions