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  • Book Overview & Buying Scientific Computing with Scala
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Scientific Computing with Scala

Scientific Computing with Scala

By : Vytautas Jancauskas
4 (1)
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Scientific Computing with Scala

Scientific Computing with Scala

4 (1)
By: Vytautas Jancauskas
Buy this Book

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)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introducing Scientific Computing with Scala
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1. Introducing Scientific Computing with Scala
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Why Scala for scientific computing?
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Numerical computing packages for Scala
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Data analysis packages for Scala
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Other scientific software
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Alternatives for doing plotting
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Using Emacs as the Scala IDE
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Profiling Scala code
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Debugging Scala code
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Building, testing, and distributing your Scala software
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Mixing Java and Scala code
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Summary
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2. Storing and Retrieving Data
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2. Storing and Retrieving Data
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Reading and writing CSV files
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Reading and writing JSON files
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Reading and writing XML files
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Database access using JDBC
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Database access using Slick
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Reading and writing HDF5 files
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Summary
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3. Numerical Computing with Breeze
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3. Numerical Computing with Breeze
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Using Breeze in your project
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Basic Breeze data structures
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Statistical computing with Breeze
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Optimization
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Signal processing
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Cheat sheet
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Summary
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4. Using Saddle for Data Analysis
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4. Using Saddle for Data Analysis
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Installing Saddle
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Basic Saddle data structures
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Data analysis with Saddle
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Summary
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5. Interactive Computing with ScalaLab
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5. Interactive Computing with ScalaLab
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Installing and running ScalaLab
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Basic ScalaSci data structures
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Data storage and retrieval
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Plotting with ScalaLab
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Other ScalaLab features
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Summary
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6. Parallel Programming in Scala
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6. Parallel Programming in Scala
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Programming with Scala threads
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Using Scala's parallel collections
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Agent-based concurrency with the Akka framework
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Summary
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7. Cluster Computing Using Scala
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7. Cluster Computing Using Scala
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Using MPJ Express for distributed computing
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Using an Akka cluster for distributed computing
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Summary
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8. Scientific Plotting with Scala
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8. Scientific Plotting with Scala
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Plotting with JFreeChart
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Plotting with scala-chart
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Plotting with Wisp
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Summary
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9. Visualizing Multi-Dimensional Data in Scala
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9. Visualizing Multi-Dimensional Data in Scala
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Obtaining data to visualize
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Andrews curve
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Parallel coordinates
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Scatter plot matrix
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Sammon mapping
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Improving the program
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Summary
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Index
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Index

Numerical computing packages for Scala

Let's now look through linear algebra and numerical computing software that is available for Scala. A linear algebra software package would involve being able to perform operations on matrices and vectors, solving linear systems of equations, finding the determinant, and performing other operations associated with the discipline of linear algebra in the field of mathematics.

MATLAB started as a linear algebra package and evolved into a whole programming language and interactive computing environment. The NumPy library for Python can do most of the things expected from a linear algebra package and a lot more. In this section, we will provide an overview of what is available in Scala in this regard. We will examine the packages briefly, tell you where to get them, how actively they are developed, and very briefly discuss the main functionality available in them.

Scalala

Scalala is a linear algebra package for Scala. It is currently not actively maintained, having been superseded by Breeze. It can be found at the following website:

https://github.com/scalala/Scalala

It is right now mostly of historic interest; however, Scalala has rich MATLAB-like operators on vectors and matrices and a library of numerical routines. It also has basic support for plotting.

Breeze

Breeze is the biggest and best maintained numerical computing library for Scala. It can be found at the ScalaNLP website:

http://www.scalanlp.org/

It is developed along with Epic and Puck, the former of which is a powerful statistical parser and the latter is a GPU-powered parser for natural languages. These two later libraries will be of less concern to us. Breeze, however, is a big part of this book. It provide functionality that is roughly equivalent to the famous and widely used NumPy package for Python. It is actively maintained and is likely to remain so in the near future.

Breeze is modeled on Scalala, which was mentioned previously. It supports all the matrix and vector operations you would expect. It provides a large number of probability distributions. It also provides routines for optimization and linear equation solving as well as routines for plotting. In a later chapter, we will introduce Breeze in detail and explain to readers how to do things they have grown accustomed to in other systems in Breeze.

ScalaLab

ScalaLab is a numerical computing environment aiming to replicate the functionality of MATLAB. The website is given here:

https://code.google.com/p/scalalab/

ScalaLab will be discussed in a section dedicated to it. It supports Scala-based scripting and is written mostly in Java with some speed-critical sections written in C/C++. It allows you to access the results of MATLAB scripts. It can use dozens of Scala and Java libraries for scientific computing. There is a basic support for plotting:

ScalaLab

The ScalaLab window with a plotting example

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