Langtangen Python Scripting for Computational Science

Erscheinungsjahr 2013
ISBN: 978-3-662-05450-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 732 Seiten, Web PDF

Reihe: Texts in Computational Science and Engineering

The primary purpose of this book is to help scientists and engineers work ing intensively with computers to become more productive, have more fun, and increase the reliability of their investigations. Scripting in the Python programming language can be a key tool for reaching these goals [27,29]. The term scripting means different things to different people. By scripting I mean developing programs of an administering nature, mostly to organize your work, using languages where the abstraction level is higher and program ming is more convenient than in Fortran, C, C++, or Java. Perl, Python, Ruby, Scheme, and Tel are examples of languages supporting such high-level programming or scripting. To some extent Matlab and similar scientific com puting environments also fall into this category, but these environments are mainly used for computing and visualization with built-in tools, while script ing aims at gluing a range of different tools for computing, visualization, data analysis, file/directory management, user interfaces, and Internet communi cation. So, although Matlab is perhaps the scripting language of choiee in computational science today, my use of the term scripting goes beyond typi cal Matlab scripts. Python stands out as the language of choice for scripting in computational science because of its very elean syntax, rieh modulariza tion features, good support for numerical computing, and rapidly growing popularity. What Scripting is About.

Zielgruppe

Research

Autoren/Hrsg.

Langtangen, Hans Petter

Inhaltsverzeichnis

1 Introduction.- 2 Getting Started with Python Scripting.- 3 Basic Python.- 4 Numerical Computing in Python.- 5 Combining Python with Fortran, C, and C++.- 6 Introduction to GUI Programming.- 7 Web Interfaces and CGI Programming.- 8 Advanced Python.- 9 Fortran Programming with NumPy Arrays.- 10 C and C++ Programming with NumPy Arrays.- 11 More Advanced GUI Programming.- 12 Tools and Examples.- A Setting up the Required Software Environment.- A.1 Installation on Unix Systems.- A.1.1 A Suggested Directory Structure.- A.1.2 Setting Some Environment Variables.- A.1.3 Installing Tcl/Tk and Additional Modules.- A.1.4 Installing Python.- A.1.5 Installing Python Modules.- A.1.6 Installing Gnuplot.- A.1.7 Installing SWIG.- A.1.8 Summary of Environment Variables.- A.1.9 Testing the Installation of Scripting Utilities.- A.2 Installation on Windows Systems.- B Elements of Software Engineering.- B.1 Building and Using Modules.- B.1.1 Single-File Modules.- B.1.2 Multi-File Modules.- B.1.3 Debugging and Troubleshooting.- B.2 Tools for Documenting Python Software.- B.2.1 Doc Strings.- B.2.2 Tools for Automatic Documentation.- B.3 Coding Standards.- B.3.1 Style Guide.- B.3.2 Pythonic Programming.- B.4 Verification of Scripts.- B.4.1 Automating Regression Tests.- B.4.2 Implementing a Tool for Regression Tests.- B.4.3 Writing a Test Script.- B.4.4 Verifying Output from Numerical Computations.- B.4.5 Automatic Doc String Testing.- B.4.6 Unit Testing.- B.5 Version Control Management.- B.5.1 Getting Started with CVS.- B.5.2 Building Scripts to Simplify the Use of CVS.- B.6 Exercises.