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configurationdatabase
Gian Michele Innocenti edited this page Jul 6, 2019
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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.
The package performs the following operations:
- Conversion: the flat ROOT TTrees are converted into Pandas Dataframes saved in a pickle format.