Abstract
AlphaTwirl is a Python library that summarizes large event data into multivariate categorical data, which can be regarded as generalizations of histograms. The output can be imported as data frames in R and pandas. With their rich set of data wrangling tools, users can develop flexible and configurable analysis code. The multivariate categorical data loaded as data frames are readily visualized by graphic tools available in R and Python. AlphaTwirl can process event data concurrently with multiple cores or batch systems. Users can extend and customize nearly any functionality of AlphaTwirl with reusable code. AlphaTwirl is released under the BSD license.
Highlights
AlphaTwirl is used in the CMS experiment [3] to analyze event data in ROOT trees [4], including Delphes trees [5], Heppy trees [6], and CMS MiniAOD [7] and NanoAOD [8]
This paper starts by distinguishing event data and categorical data, followed by the discussion of how data frames with categorical data can be regarded as generalizations of histograms and their advantages
As can be seen from the above example, data frames can express arbitrary dimensions of histograms with categories specified by combinations of different types of variables such as strings, integers, and floats
Summary
AlphaTwirl is used in the CMS experiment [3] to analyze event data in ROOT trees [4], including Delphes trees [5], Heppy trees [6], and CMS MiniAOD [7] and NanoAOD [8]. This paper starts by distinguishing event data and categorical data, followed by the discussion of how data frames with categorical data can be regarded as generalizations of histograms and their advantages. The paper, describes how AlphaTwirl summarizes event data as summarize. The paper mentions features of the implementation, such as dependency injection, framework independent modules, and concurrency
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