defaultdatabase

Intro

This package is meant to run fast parallel analysis and machine learning optimization using modern servers with Python and Pandas. In order to start your analysis you need a list of unmerged flat ROOT TTrees for data and MC. For full compatibility, it is recommended to produce your TTrees using the same format presented in https://github.com/ginnocen/ALICETreeCreator. The TTrees have be saved in a folder preserving the standard Grid folder structure (E.g.production/child_1/0001/AnalysisResults.root).

In this tutorial we will go step by step through the package and you will learn the main functionalities and how to run a real optimization on a small dataset.

case: LcpK0s_multiplicity_test
download:
  alice:
    activate: false
conversion:
  mc: 
    activate: true 
  data:
    activate: true
skimming: 
  mc:
    activate: true
  data:
    activate: true
merging: 
  mc:
    activate: true
  data:
    activate: true
mergingperiods: 
  mc:
    activate: true
  data:
    activate: true

ml_study:   
  activate: true
  doscancuts: false
  applytodatamc: false
  doroc: true
  doboundary: false
  docorrelation: false
  docrossvalidation: false
  dogridsearch: false
  doimportance: false
  dolearningcurve: true
  dopca: false
  dosignifopt: false
  doefficiency: false
  dotesting: false
  dotraining: true

analysis:
  data:
    doapply: true
    domergeapply: true
    domergeapplyperiods: true
    histomass: true
  mc:
    doapply: true
    domergeapply: true
    domergeapplyperiods: true
    histomass: true
    efficiency: true
'''