OUT OF DATE.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)

• Table of contents
• OUT OF DATE. Use as reference for what we plan on doing.
• List of Program Components
  • Input Readers
  • Map Workers
    • MC
    • Data
    • Both MC and Data
    • Testing
  • Reduce Workers
  • Output Writers

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