Book Image

Scientific Computing with Scala

By : Vytautas Jancauskas
Book Image

Scientific Computing with Scala

By: Vytautas Jancauskas

Overview of this book

Scala is a statically typed, Java Virtual Machine (JVM)-based language with strong support for functional programming. There exist libraries for Scala that cover a range of common scientific computing tasks – from linear algebra and numerical algorithms to convenient and safe parallelization to powerful plotting facilities. Learning to use these to perform common scientific tasks will allow you to write programs that are both fast and easy to write and maintain. We will start by discussing the advantages of using Scala over other scientific computing platforms. You will discover Scala packages that provide the functionality you have come to expect when writing scientific software. We will explore using Scala's Breeze library for linear algebra, optimization, and signal processing. We will then proceed to the Saddle library for data analysis. If you have experience in R or with Python's popular pandas library you will learn how to translate those skills to Saddle. If you are new to data analysis, you will learn basic concepts of Saddle as well. Well will explore the numerical computing environment called ScalaLab. It comes bundled with a lot of scientific software readily available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's powerful parallel collections for safe and convenient parallel programming. Topics such as the Akka concurrency framework will be covered. Finally, you will learn about multivariate data visualization and how to produce professional-looking plots in Scala easily. After reading the book, you should have more than enough information on how to start using Scala as your scientific computing platform
Table of Contents (11 chapters)
10
Index

Scatter plot matrix


Scatter plot matrix is a set of scatter plots. These are constructed by considering each possible pair of attributes and plotting points using just those two attributes as a scatter plot. This will result in n times n scatter plots if n is the number of attributes. It is most convenient to arrange these plots into a rectangular matrix (hence the name).

We looked into this kind of plot earlier when we were discussing the Breeze library and its plotting framework. However, it is informative to see how this could be done in pure JFreeChart. It will also make use of most of the JFreeChart concepts that we have discussed so far:

import scala.collection.mutable.{MutableList, Map}
import scala.math._
import org.jfree.chart._
import org.jfree.data.xy._
import org.jfree.data.statistics._
import java.io.{FileReader, BufferedReader}
import java.awt.GridLayout
import javax.swing.JFrame
import javax.swing.Jpanel

Note that we will need to import some Swing stuff as well here. The reason...