Scientific Data Analysis using Jython Scripting and Java

by Sergei V. Chekanov

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.

Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation.

This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.

700 pages, Kindle Edition

First published August 5, 2010

About the author

Dr. S.V. Chekanov dedicated 30 years to high-energy physics, studying the creation of the smallest and most fundamental particles that comprise all matter. He has published over two hundred professional articles, many of which are based on data analysis obtained at multinational particle accelerators at CERN (European Organization for Nuclear Research) and DESY (German Electron Synchrotron). Dr. S.Chekanov is also the author of two books on scientific data analysis. He contributed to Standard Model measurements and led a group of scientists measuring photons (light quanta) at the highest energy ever created in particle accelerators. In recent years, Dr. S. V. Chekanov has coordinated the search for new particles beyond the Standard Model at the Large Hadron Collider (CERN) using artificial intelligence and deep neural networks.