Steps

LLRHToTauTau framework

1. miniAOD -> flat NTuple

KLUBAnalysis

2. flat Ntuple -> skim

3. make histograms

3.1 hadd of outputs

4. evaluate QCD starting from the histograms

LLRHToTauTau framework

Samples can be processed on the local Tier3 or with CRAB.

Tier3 --> fast, light computations (SVfit turned off or EnrichedMiniAOD to ntuples)

CRAB --> heavy computations (SVfit, many samples in parallel)

Instructions for large productions (CRAB)

1. `datasets.txt` : Define the datasets names you want to run on.

Each block of dataset to be processed is defined by `=== ===`; text after is ignored

Lines *containing* a `#` are ignored.

Separate MC from Data as you have to change the flag.

2. `analyzer.py` :

set the IsMC flag

to produce EnrichedMiniAOD set RUN_NTUPLIZER to False

3. `submitAllDatasetOnCrab.py` :

under *PROCESS* write the list of blocks of datasets to be processed

under *tag* write a tag to be used in outputs and folder names

4. `cmsenv ; source /cvmfs/cms.cern.ch/crab3/crab.sh`

5. `python submitAllDatasetOnCrab.py`

EnrichedMiniAOD to LLRntuples

1. `testAllDatasetOnCrab.py` : set *path* to the folder created by the CRAB submission ("crab3_\<tag\>") 2. `python testAllDatasetOnCrab.py` : will print a list of published datasets names.

For missing names listed at the end, typically CRAB submission failed 3. `datasets_Enriched.txt` : copy the previous list and define a block name between `=== ===` 4. `tools/makeAllFileLists.py` : define *PROCESS* and *tag* as before 5. `cmsenv ; source /cvmfs/cms.cern.ch/crab3/crab.sh ; cd tools; python makeAllFileLists.py` :

will create the file list for each published sample under `inputFilesEMiniAOD\<tag\>`

NOTE : this can take some time depending on how fast the CRAB server responds and might need some retries.

Se proprio non va, do this by hand from DAS interface.

6. TO BE FINISHED

-- ChiaraAmendola - 2017-11-08-

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Topic revision: r1 - 2017-11-10 - ChiaraAmendola
 
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