Quick Start - FNAL Teststand

git clone https://github.com/zeratul87/hcalraw.git
cd hcalraw
source env/fnal-teststand.sh
pushd cpp; make -j 5; popd

Analyze data from run X

./fnal_analyze.sh X plugin_name

Output will be stored in output/runX-plugin_name.root

More Examples

./look.py 284928
./look.py 289555 --nevents=1 --dump=8
./look.py 294542 --feds1=1115 --nevents=1000

# analyze AMC13/DCC2 monitor buffer dumps
# HF (USC FEDs):
./dumps2root2pdf-HF.py data/239713_*.dat
# from a single AMC13:
export FEDID=999
cat foo.dat | cpp/badcoffee ${FEDID}
./oneRun.py --file1=badcoffee${FEDID}.root --feds1=${FEDID} --progress

# compare payloads of different sets of FEDs, within one file
./look.py 239480 --feds1=718,719 --feds2=HF --dump=0 --utca-bcn-delta=-131 --no-warn-quality --plugins=histogram,compare

# dump decoded data to stdout
./look.py 55 --feds1=702,931 --nevents=1 --dump=4

# compare payloads of different sets of FEDs, across two files
./oneRun.py --file1=data/B904_Integration_000055.root --feds1=702 \
--file2=data/mol_run55.root --feds2=931 --dump=0 --any-emap \
--no-warn-quality --ignore-ce --utca-bcn-delta=-119

export EOS=root://eoscms.cern.ch//store/group/dpg_hcal/comm_hcal/archival/20160914_USC_Run1_runs133054-220908; \
./oneRun.py --nevents=51 --progress --utca-bcn-delta=-119 --any-emap --no-warn-quality --dump=0 \
--file1=${EOS}/USC_211154.root --feds1=714,722 \
--file2=${EOS}/USC_211155.root --feds2=989

# analyze FE pattern runs
# (before HO refibering) ./look.py 235576 --feds1=HO  --plugins=unpack,patterns --dump=0 | ./diff.py data/ref_2014.txt
# (before move to uTCA ) ./look.py 236631 --feds1=7xy --plugins=unpack,patterns --dump=0 | ./diff.py data/ref_vme_G.txt
# ./look.py --quiet --progress --plugins=unpack,patterns,bitwalk --nevents=8000 --feds1=1115 --last-n-amcs=2 300703
./look.py 260773 --feds1=HBEF --plugins=unpack,patterns --dump=0 | ./diff.py data/ref_utca_G.txt
./look.py 270688 --feds1=HBEF --plugins=unpack,patterns --dump=0 | ./diff.py data/ref_utca_G.txt
./fiberId.sh 288606

# read about usage
./oneRun.py --help

License

GPLv3

Design Goals

• unpack both VME and uTCA data
• digest global, local, or monitor buffer data without configuration
• compare payloads across two .root files containing unsorted events
• enable evaluation of board performance in only one page of plots
• provide flexibility in analysis
• not require CMSSW

Dependencies

• python (2.x, x>=6)
• ROOT (>=5.32)

Environment

(use exactly one of these)

• env/cc7-cvmfs.sh sets up a ROOT 6 environment on cc7
• env/lxplus6.sh sets up CMSSW and EOS
• env/slc6-pypy.sh sets up pypyROOT (outside of CMSSW)

Files

• cpp/CDF*.h are copied from CMSSW (IORawData/HcalTBInputService/src)
• cpp/FED* are copied from CMSSW (DataFormats/FEDRawData)
• cpp/[cdf,cms].h define helper functions
• cpp/[mol,deadbeef,badcoffee,rooter].cpp make .root files from binary event dumps
• analyze.py loops over .root file(s) for one run and produces output/Runxxx.root
• autoBook.py is copied from github.com/elaird/supy/__autoBook__.py
• configuration/ holds some settings used by analyze.py, plugins/, etc.
• decode.py interprets a FED's bytes in an event (called by raw.unpacked)
• diff.py compares the decoded output of a FiberID run to data/ref.txt
• dumps2root2pdf-HF.py converts binary event dumps to .root files and analyzes them
• graphs.py reads in output/Runxxx.root, makes plots, and outputs a .pdf
• look.py will find a files in EOS (or locally) for given runs and loop over them
• make_fiberid_references.py produces FiberID reference files from HCAL logical maps
• oneRun.py is used to analyze one run (see examples above)
• options.py parses command line options for oneRun.py, look.py, or dumps2root2pdf-HF.py
• printer.py contains utility functions for printing messages
• processUSC.py loops over available USC local runs and processes them
• raw.py reads and unpacks raw data
• test_transformation.py tests configuration.hw.transformed* and utils.shortList
• utils.py contains helper functions

plugins/

• compare.py compares the payloads within two .root files for a given event
• histogram.py books and fills many per-FED and per-event histograms
• patterns.py interprets the raw data as front-end patterns
• printraw.py dumps to stdout the payload(s) in an event