Programming language processors
Programming language processors
Building parsers with Java
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
The Mathematica Guidebook: Programming with CD-ROM
The Mathematica Guidebook: Programming with CD-ROM
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Core Java 2, Volume 1: Fundamentals
Core Java 2, Volume 1: Fundamentals
Technology Reviews: 3Ms for Instruction: Reviews of Maple, Mathematica, and Matlab
Computing in Science and Engineering
Matlab Guide
3Ms for Instruction, Part 2: Maple, Mathematica, and Matlab
Computing in Science and Engineering
Java 6 New Features
Groovy in Action
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computer Languages, Systems and Structures
Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4
Modeling and Simulation in Scilab/Scicos with ScicosLab 4.4
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The jLab environment extends the potential of Java for scientific computing. It provides a Matlab/Scilab like scripting language that is executed by an interpreter implemented in the Java language. The scripting language supports the basic programming constructs with Matlab like matrix manipulation operators. The jLab "core" provides the general purpose functionality with an extensive set of built in mathematical routines that cover all the basic numerical analysis tasks. The important advantage of jLab compared to other similar environments is the potentiality to dynamically and automatically integrate Java code to the system in order to obtain both execution speed and to reduce the programming effort. This task is supported both by an easy to use extension Java class wizard and by application specific class wizards that automate the utilization of jLab's scientific libraries. However, the incorporation of external Java general purpose code is not as convenient as the scripting code development is. Also, j-scripting is relatively slow compared to Groovy scripting that operates by compiling the scripts to Java classes. This was the motivation for the adaptation of the general purpose Groovy "scripting SuperJava" language as a parallel and cooperative scripting option in the jLab environment. The paper concentrates on the issues involved in the implementation of the multiscripting environment and on the benefits that can be obtained by the combination of these two very different scripting frameworks.