Scientific Data Analysis using Jython Scripting and Java

By Sergei V. Chekanov

Scientific Data Analysis using Jython Scripting and Java Cover Image

Written by the primary developer of the jHepWork data analysis framework, this practical book, complete with dozens of code snippets, is a reliable reference source that enables readers to lay the foundation for data-analysis applications using Java scripting.

Full Description

  • ISBN13: 978-1-8499-6286-5
  • 468 Pages
  • User Level: Students
  • Publication Date: August 5, 2010
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
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.
Table of Contents

Table of Contents

  1. Introduction.
  2. 1 Jython, Java and jHepWork.
  3. 2 Introduction to Jython.
  4. 3 Mathematical Functions.
  5. 4 One
  6. dimensional Data.
  7. 5 Two
  8. dimensional Data.
  9. 6 Multi
  10. dimensional Data.
  11. 7 Arrays, Matrices and Linear Algebra.
  12. 8 Histograms.
  13. 9 Random Numbers and Statistical Samples.
  14. 10 Graphical Canvases.
  15. 11 Input and Output.
  16. 12 Miscellaneous Analysis Issues Using jHepWork.
  17. 13 Data Clustering.
  18. 14 Linear Regression and Curve Fitting.
  19. 15 Neural Networks.
  20. 16 Steps in Data Analysis.
  21. 17 Real
  22. life Examples.
  23. Index of Examples.
  24. Index
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