Texts in Computational Science and Engineering

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1 Texts in Computational Science and Engineering 3 Editors Timothy J. Barth, Moffett Field, CA Michael Griebel, Bonn David E. Keyes, Norfolk Risto M. Nieminen, Espoo Dirk Roose, Leuven Tamar Schlick, New York

2 Hans Petter Langtangen Python Scripting tor Computational Science With 62 Figures ~ Springer

3 Hans Petter Langtangen Simula Research Laboratory Martin Linges vei 17, Fornebu P.O. Box Lysaker, Norway On leave from: Department of Informatics University of Os10 P.O. Box 1080 Blindern 0316 Oslo, Norway The author of this book has received financial support from the NFF - Norsk faglitterrer forfatter- og oversetterforening. Library of Congress Control Number: Mathematics Subject Classification (2000): 65Y 99, 68NOl, 68N15, 68N19, 68N30, 97U50, 97U70 ISSN ISBN ISBN (ebook) DOI / This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfllm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag Berlin Heidelberg GmbH. Violations are liable for prosecution under the German Copyright Law. springeronline.com Springer-Verlag Berlin Heidelberg 2004 Originally published by Springer-Verlag Berlin Heidelberg New Y ork in 2004 Softcover reprint ofthe hardcover 1st edition 2004 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover Design: Friedhelm Steinen-Broo, Estudio Calamar, Spain Cover production: design & production GmbH, Heidelberg Production: LE-TEX Jelonek, Schmidt &V6ckler GbR, Leipzig Typeset by the author. Printed on acid-free paper 46/31421YL

4 Preface The primary purpose of this book is to help scientists and engineers working 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 programming 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 computing environments also fall into this category, but these environments are mainly used for computing and visualization with built-in tools, while scripting aims at gluing a range of different tools for computing, visualization, data analysis, file/directory management, user interfaces, and Internet communication. So, although Matlab is perhaps the scripting language of choiee in computational science today, my use of the term scripting goes beyond typical Matlab scripts. Python stands out as the language of choice for scripting in computational science because of its very elean syntax, rieh modularization features, good support for numerical computing, and rapidly growing popularity. What Scripting is About. The simplest application of scripting is to write short programs (scripts) that automate manual interaction with the computer. That is, scripts often glue stand-alone applications and operating system commands. A primary example is automating simulation and visualization: from an effective user interface the script extracts information and generates input files for a simulation program, runs the program, archive data files, prepares input for a visualization program, creates plots and animations, and perhaps performs some data analysis. More advanced use of scripting ineludes rapid construction of graphical user interfaces (GUIs), searching and manipulating text (data) files, managing files and directories, tailoring visualization and image processing environments to your own needs, administering large sets of computer experiments, and managing your existing Fortran, C, or C++ libraries and applications directly from scripts. Scripts are often considerably fast er to develop than the corresponding programs in a traditionallanguage like Fortran, C, C++, or Java, and the code is normally much shorter. In fact, the high-level programming style and tools used in scripts open up new possibilities you would hardly consider as a Fortran or C programmer. Furthermore, scripts are for the most part truly cross-platform, so what you write on Windows runs without modifications

5 VI Preface on Unix and Macintosh, also when graphical user interfaces and operating system interactions are involved. The interest in scripting with Python has exploded among Internet service developers and computer system administrators. However, Python scripting has a significant potential in computational science and engineering (CSE) as weil. Software systems such as Maple, Mathematica, Matlab, and S-Plus/R are primary examples of very popular, widespread tools because of their simple and effective user interface. Python resembles the nature of these interfaces, but is a fuil-fledged, advanced, and very powerful programming language. With Python and the techniques explained in this book, you can actually create your own easy-to-use computational environment, which mirrors the working style of Matlab-like tools, but tailored to your own number crunching codes and favorite visualization systems. Scripting enables you to develop scientific software that combines "the best of all worlds", i.e., highly different tools and programming styles for accomplishing a task. As a simple example, one can think of using a C++ library for creating a computational grid, a Fortran 77 library for solving partial differential equations on the grid, a C code for visualizing the solution, and Python for gluing the tools together in a high-level program, perhaps with an easy-to-use graphical interface. Special Features of This Book. The current book addresses applications of scripting in CSE and is tailored to professionals and students in this field. The book differs from other scripting books on the market in that it has a different pedagogical strategy, a different composition of topics, and a different target audience. Practitioners in computational science and engineering seldom have the interest and time to sit down with a pure computer language book and figure out how to apply the new tools to their problem areas. Instead, they want to get quickly started with examples from their own world of applications and learn the tools while using them. The present book is written in this spirit - we dive into simple yet useful examples and learn about syntax and programming techniques during dissection of the examples. The idea is to get the reader started such that further development of the examples towards real-life applications can be done with the aid of online manuals or Python reference books. Contents. The contents of the book can be briefly sketched as follows. Chapter 1 gives an introduction to what scripting is and what it can be good for in a computational science context. A quick introduction to scripting with Python, using examples of relevance to computational scientists and engineers, is provided in Chapter 2. Chapter 3 presents an overview of basic Python functionality, including file handling, data structures, functions, and operating system interaction. Numerical computing in Python, with particular focus on efficient array processing, is the subject of Chapter 4. Python can easily call up Fortran, C, and C++ code, which is demonstrated in Chapter 5.

6 Preface VII A quiek tutorial on building graphie al user interfaces appears in Chapter 6, while Chapter 7 builds the same user interfaces as interactive Web pages. Chapters 8-12 concern more advanced features of Python. In Chapter 8 we discuss regular expressions, persistent data, elass programming, and efficiency issues. Migrating slow loops over large array structures to Fortran, C, and C++ is the topic of Chapters 9 and 10. More advanced GUI programming, involving plot widgets, event bindings, animated graphics, and automatie generation of GUIs are treated in Chapter 11. More advanced tools and examples of relevance for problem solving environments in science and engineering, tying together many techniques from previous chapters, are presented in Chapter 12. Readers of this book need to have a considerable amount of software installed in order to be able to run all examples successfully. Appendix A explains how to install Python and many of its modules as well as other software packages. All the software needed for this book is available for free over the Internet. Good software engineering practice is outlined in a scripting context in Appendix B. This ineludes building modules and packages, documentation techniques and tools, co ding styles, verification of programs through automated regression tests, and application of version control systems. Required Background. This book is aimed at readers with programming experience. Many of the comments throughout the text address Fortran or C programmers and try to show how much fast er and more convenient Python code development turns out to be. Other comments, especially in the parts of the book that deal with elass programming, are meant for C++ and Java programmers. No previous experience with scripting languages like Perl or Tel is assumed, but there are scattered remarks on technical differences between Python and other scripting languages (Perl in particular). I hope to convince computational scientists having experience with Perl that Python is a preferable alternative, especially for large long-term projects. Matlab programmers constitute an important target audience. These will pick up simple Python programming quite easily, but to take advantage of elass programming at the level of Chapter 12 they probably need another source for introducing object-oriented programming and get experience with the dominating languages in that field, C++ or Java. Most of the examples are relevant for computational science. This means that the examples have a root in mathematical subjects, but the amount of mathematieal details is kept as low as possible to enlarge the audience and allow focusing on software and not mathematics. To appreciate and see the relevance of the examples, it is advantageous to be familiar with basie mathematical modeling and numerical computations. The usefulness of the book is meant to scale with the reader's amount of experience with numerical simulations.

7 VIII Preface Acknowledgements. The author appreciates the constructive comments from Arild Burud, Roger Hansen, and Tom Thorvaldsen on an earlier version of the manuscript. I will in particular thank the anonymous Springer referees of an even earlier version who made very useful suggestions, which led to a major revision and improvement of the book. Sylfest Glimsdal is thanked for his careful reading and detection of many errors in the present version of the book. I will also acknowledge all the input I have received from our enthusiastic team of scripters at Simula Research Laboratory: Are Magnus Bruaset, Xing Cai, Kent-Andre Mardal, Halvard Moe, Ola Skavhaug, Gunnar Staff, Magne Westlie, and Asmund 0degiird. As always, the prompt support and advice from Martin Peters, Frank Holzwarth, Leonie Kunz, Peggy Glauch, and Thanh-Ha Le Thi at Springer have been essential to complete the book project. Software, updates, and an errata list associated with this book can be found on the Web page From this page you can also download a PDF version of the book. The PDF version is searchable, and references are hyperlinks, thus making it convenient to navigate in the text during software development. Oslo, April 2004 Hans Petter Langtangen

8 Table of Contents 1 Introduction Scripting versus Ttaditional Programming Why Scripting is Useful in Computational Science Classification of Programming Languages Productive Pairs of Programming Languages Gluing Existing Applications Scripting Yields Shorter Code Efficiency Type-Specification (Declaration) of Variables Flexible Function Interfaces Interactive Computing Creating Code at Run Time Nested Heterogeneous Data Structures GUI Programming Mixed Language Programming When to Choose a Dynamically Typed Language Why Python? Script or Program? Preparations for Working with This Book Getting Started with Python Scripting A Scientific Hello World Script Executing Python Scripts Dissection of the Scientific Hello World Script Reading and Writing Data Files Problem Specification The Complete Code Dissection Working with Files in Memory Efficiency Measurements Exercises Automating Simulation and Visualization The Simulation Code Using Gnuplot to Visualize Curves Functionality of the Script The Complete Code Dissection Exercises Conducting Numerical Experiments Wrapping a Loop Around Another Script

9 X Table of Contents Generating an HTML Report Making Animations Varying Any Parameter Exercises File Format Conversion The First Version of the Script The Second Version of the Script Basic Python Introductory Topics Recommended Python Documentation Testing Statements in the Interactive Shell Control Statements Running an Application File Reading and Writing Output Formatting Variables of Different Types Boolean Types The None Variable Numbers and Numerical Expressions Lists and TupIes Dictionaries Splitting and Joining Text String Operations Text Processing The Basics of a Python Class Determining a Variable's Type Exercises Functions Keyword Arguments Doc Strings Variable Number of Arguments Call by Reference Treatment of Input and Output Arguments Function Objects Working with Files and Directories Listing Files in a Directory Testing File Types Removing Files and Directories Copying and Renaming Files Splitting Pathnames Creating and Moving to Directories Traversing Directory Trees Exercises

10 Table of Contents XI 4 N umerical Computing in Python A Quick NumPy Primer Creating Arrays Array Indexing Array Computations Type Testing Hidden Temporary Arrays Exercises Vectorized Algorithms From Scalar to Array Function Arguments Slicing Remark on Efficiency Exercises More Advanced Array Computing Random Numbers Linear Algebra The Gnuplot Module Example: Curve Fitting Arrays on Structured Grids File 1/0 with NumPy Arrays Reading and Writing Tables with NumPy Arrays Functionality in the Numpytools Module Exercises Other Tools for Numerical Computations The ScientificPython Package The SciPy Package The Python-Matlab Interface Some Useful Python Modules Combining Python with Fortran, C, and C About Mixed Language Programming Applications of Mixed Language Programming Calling C from Python Automatie Generation of Wrapper Code Scientific Hello World Examples Combining Python and Fortran Combining Python and C Combining Python and C++ Functions Combining Python and C++ Classes Exercises A Simple Computational Steering Example Modified Time Loop for Repeated Simulations Creating a Python Interface The Steering Python Script Equipping the Steering Script with a GUI Scripting Interfaces to Large Libraries

11 XII Table of Contents 6 Introduction to GUI Programming Scientific Hello World GUI Introductory Topics The First Python/Tkinter Encounter Binding Events Changing the Layout The Final Scientific Hello World GUI An Alternative to Tkinter Variables About the Pack Command An Introduction to the Grid Geometry Manager Implementing a GUI as a Class A Simple Graphical Function Evaluator Exercises Adding GUIs to Scripts A Simulation and Visualization Script with a GUI Improving the Layout Exercises A List of Common Widget Operations Frame Label Button Text Entry Balloon Help Option Menu Slider Check Button Making a Simple Megawidget Menu Bar List Data Listbox Radio Button Combo Box Message Box User-Defined Dialogs Color-Picker Dialogs File Selection Dialogs Toplevel Some Other Types of Widgets Adapting Widgets to the User's Resize Actions Customizing Fonts and Colors Widget Overview Exercises

12 Table of Contents XIII 7 Web Interfaces and CGI Programming Introductory CGI Scripts Web Forms and CGI Scripts Generating Forms in CGI Scripts Debugging CGI Scripts A General Shell Script Wrapper for CGI Scripts Security Issues Adding Web Interfaces to Scripts A Class for Form Parameters Calling Other Programs Running Simulations Getting a CGI Script to Work Using Web Applications from Scripts Exercises Advanced Python Miscellaneous Topics Parsing Command-Line Arguments Platform-Dependent Operations Run-Time Generation of Code Exercises Regular Expressions and Text Processing Motivation Special Characters Regular Expressions for Real Numbers Using Groups to Extract Parts of a Text Extracting Interval Limits Extracting Multiple Matches Splitting Text Pattern-Matching Modifiers Substitution and Backreferences Example: Swapping Arguments in Function Calls A General Substitution Script Debugging Regular Expressions Exercises Tools for Handling Data in Files Writing and Reading Python Data Structures Pickling Objects Shelving Objects Writing and Reading Zip Archive Files Downloading Internet Files Binary Input/Output Exercises A Database for NumPy Arrays The Structure of the Database Pickling

13 XIV Table of Contents Formatted ASCII Storage Shelving Comparing the Various Techniques Scripts Involving Local and Remote Hosts Sec ure Shell Commands Distributed Simulation and Visualization ClientjServer Programming Threads Classes Class Programming Checking the Class Type Private Data Static Data Special Attributes Special Methods Multiple Inheritance Using a Class as a C-like Structure Attribute Access via String Names Example: Turning String Formulas into Functions Example: Class for Structured Grids New-Style Classes Implementing GetjSet Functions via Properties Subclassing Built-in Types Copy and Assignment Building Class Interfaces at Run Time Building Flexible Class Interfaces Exercises Scope of Variables Global, Local, and Class Variables Nested Functions Dictionaries of Variables in Namespaces Exceptions Handling Exceptions Raising Exceptions Iterators Constructing an Iterator A Pointwise Grid Iterator A Vectorized Grid Iterator Generators Some Aspects of Generic Programming Exercises Investigating Efficiency CPU-Time Measurements Profiling Python Scripts Optimization of Python Code

14 Table of Contents XV 9 Fortran Programming with N umpy Arrays Problem Definition Filling an Array in Fortran The Fortran Subroutine Building and Inspecting the Extension Module Array Storage Issues Generating an Erroneous Interface Array Storage in C and Fortran Input and Output Arrays as Function Arguments F2PY Interface Files Hiding Work Arrays Increasing Callback Efficiency Callbacks to Vectorized Python Functions Avoiding Callbacks to Python Compiled Inline Callback Functions Summary Exercises C and C++ Programming with NumPy Arrays C Programming with NumPy Arrays The Basics of the NumPy C API The Handwritten Extension Code Sending Arguments from Python to C Consistency Checks Computing Array Values Returning an Output Array Convenient Macros Module Initialization Extension Module Template Compiling, Linking, and Debugging the Module Writing a Wrapper for a C Function C++ Programming with NumPy Arrays Wrapping a NumPy Array in a C++ Object Using SCXX NumPy-C++ Class Conversion Comparison of the Implementations Efficiency Error Handling Summary Exercises More Advanced GUI Programming Adding Plot Areas in GUIs The BLT Graph Widget Animation of Functions in BLT Graph Widgets Other Tools for Making GUIs with Plots

15 XVI Table of Contents Exercises Event Bindings Binding Events to Functions with Arguments A Text Widget with Tailored Keyboard Bindings A Fancy List Widget Animated Graphics with Canvas Widgets The First Canvas Encounter Co ordinate Systems The Mathematical Model Class The Planet Class Drawing and Moving Planets Dragging Planets to New Positions Using Pmw's SerolIed Canvas Widget Simulation and Visualization Scripts Restructuring the Script Representing a Parameter by a Class Improved Command-Line Script Improved GU! Script Improved CGI Script Parameters with Physical Dimensions Adding a Curve Plot Area Automatie Generation of Scripts Applications of the Tools Allowing Physical Units in Input Files Converting Input Files to GUIs Tools and Examples Running Series of Computer Experiments Multiple Values of Input Parameters Implementation Details Further Applications Tools for Representing Functions Functions Defined by String Formulas A Unified Interface to Functions Interactive Drawing of Functions A Notebook for Selecting Functions Solving Partial Differential Equations Numerical Methods for ID Wave Equations Implementations of ID Wave Equations Classes for Solving ID Wave Equations A Problem Solving Environment Numerical Methods for 2D Wave Equations Implementations of 2D Wave Equations Exercises

16 Table of Contents XVII A Setting up the Required Software Environment A.1 Installation on Unix Systems A.l.1 A Suggested Direetory Strueture A.l.2 Setting Some Environment Variables A.l.3 Installing Tcl/Tk and Additional Modules A.l.4 Installing Python A.l.5 Installing Python Modules A.l.6 Installing Gnuplot A.l.7 Installing SWIG A.l.8 Summary of Environment Variables A.l.9 Testing the Installation of Seripting Utilities A.2 Installation on Windows Systems B Elements of Software Engineering B.1 Building and Using Modules B.l.1 Single-File Modules B.l.2 Multi-File Modules B.l.3 Debugging and Troubleshooting B.2 Tools for Doeumenting Python Software B.2.1 Doe Strings B.2.2 Tools for Automatie Doeumentation B.3 Coding Standards B.3.1 Style Guide B.3.2 Pythonie Programming B.4 Verifieation of Seripts BA.1 Automating Regression Tests BA.2 Implementing a Tool for Regression Tests BA.3 Writing a Test Seript BAA Verifying Output from Numerieal Computations BA.5 Automatie Doe String Testing BA.6 Unit Testing B.5 Version Control Management B.5.1 Getting Started with CVS B.5.2 Building Seripts to Simplify the Use of CVS B.6 Exereises Bibliography Index

17 List of Exercises Exercise 2.1 Exercise 2.2 Exercise 2.3 Exercise 2.4 Exercise 2.5 Exercise 2.6 Exercise 2.7 Exercise 2.8 Exercise 2.9 Exercise 2.10 Exercise 2.11 Exercise 2.12 Exercise 2.13 Exercise 2.14 Exercise 2.15 Exercise 2.16 Exercise 2.17 Exercise 3.1 Exercise 3.2 Exercise 3.3 Exercise 3.4 Exercise 3.5 Exercise 3.6 Exercise 3.7 Exercise 3.8 Exercise 3.9 Exercise 3.10 Exercise 3.11 Exercise 3.12 Exercise 3.13 Exercise 3.14 Exercise 3.15 Exercise 3.16 Exercise 3.17 Exercise 3.18 Exercise 3.19 Exercise 3.20 Exercise 4.1 Exercise 4.2 Exercise 4.3 Become familiar with the electronie documentation Extend Exercise 2.1 with a loop Find five errors in a script Basie use of control structures Replace exception handling by an if-test Use standard input/output instead of files Read streams of (x, y) pairs from the command line Estimate the chance of an event in a diee game Determine if you win or loose a hazard game Generate an HTML report from the simviz1.py script.. 50 Generate a Jb.'IE;X report from the simvizl. py script Compute time step values in the simviz1.py script Use Matlab for curve plotting in the simvizl. py script.. 51 Combine curves from two simulations in one plot Make an animated oscillating system figure Improve an automatieally generated HTML report Combine two-column data files to a multi-column file Write format specifications in printf-style Write your own function for joining strings Write an improved function for joining strings Never modify a list you are iterating on Pack a collection of files Make a specialized sort function Check if your system has a specific program Find the paths to a collection of programs Use Exercise 3.8 to improve the simviz1.py script Use Exercise 3.8 to improve the loop4simviz2.py script. 99 Find the version number of a utility Automate execution of a family of similar commands Remove temporary files in a directory tree Find old and large files in a directory tree Remove redundant files in a directory tree Annotate a filename with the current date Automatie backup of recently modified files Search for a text in files with certain extensions Search directories for plots and make HTML report Fix Unix/Windows Line Ends Matrix-vector multiply with NumPy arrays Replace lists by NumPy arrays Assignment and in-place NumPy array modifications

18 XX List of Exercises Exercise 4.4 Exercise 4.5 Exercise 4.6 Exercise 4.7 Exercise 4.8 Exercise 4.9 Exercise 4.10 Exercise 4.11 Exercise 4.12 Exercise 4.13 Exercise 4.14 Exercise 4.15 Exercise 4.16 Exercise 4.17 Exercise 5.1 Exercise 5.2 Exercise 5.3 Exercise 6.1 Exercise 6.2 Exercise 6.3 Exercise 6.4 Exercise 6.5 Exercise 6.6 Exercise 6.7 Exercise 6.8 Exercise 6.9 Exercise 6.10 Exercise 6.11 Exercise 6.12 Exercise 6.13 Exercise 6.14 Exercise 6.15 Exercise 6.16 Exercise 6.17 Exercise 6.18 Exercise 6.19 Exercise 7.1 Exercise 7.2 Exercise 7.3 Exercise 7.4 Exercise 7.5 Exercise 8.1 Exercise 8.2 Exercise 8.3 Exercise 8.4 Process comma-separated numbers in a file Vectorized constant function Vectorize a numerical integration rule Vectorize a formula containing an if condition Vectorized Box-Müller method for normal variates Implement Exercise 2.8 using NumPy arrays Implement Exercise 2.9 using NumPy arrays Use the Gnuplot module in the simviz1.py script NumPy arrays and binary files One-dimensional Monte Carlo integration Higher-dimensional Monte Carlo integration Load data file into NumPy array and visualize Analyze trends in the data from Exercise Computing a function over a 3D grid Implement a numerical integration rule in F Implement a numerical integration rule in C Implement a numerical integration rule in C Modify the Scientific Hello World Gur Change the layout of the GUI in Exercise Control a layout with the grid geometry manager Make a demo of Newton's method Program with Pmw. EntryField in hwgui10. py Program with Pmw. EntryField in simvizgui2. py Replace Tkinter variables by set / get-like functions Use simviz1.py as a module in simvizgui2.py Apply Matlab for visualization in simvizgui2. py Program with Pmw. OptionMenu in simvizgui2. py Study the nonlinear motion of a pendulum Add error handling with an associated message box Add a message bar to a balloon help Select a file from a list and perform an action Make a GUI for finding and selecting font names Launch a GUI when command-line options are missing. 271 Write a GUI for Exercise Write a GUI for selecting files to be plotted Write an easy-to-use GUI generator Write a CGI debugging tool Make a Web calculator Make a Web application for registering participants Make a Web application for numerical experiments Become a "nobody" user on a Web server Use the getopt/optparse module in simviz1.py Store command-line options in a dictionary TUrn files with commands into Python variables A grep script

19 List of Exercises XXI Exercise 8.5 Exercise 8.6 Exercise 8.7 Exercise 8.8 Exercise 8.9 Exercise 8.10 Exercise 8.11 Exercise 8.12 Exercise 8.13 Exercise 8.14 Exercise 8.15 Exercise 8.16 Exercise 8.17 Exercise 8.18 Exercise 8.19 Exercise 8.20 Exercise 8.21 Exercise 8.22 Exercise 8.23 Exercise 8.24 Exercise 8.25 Exercise 8.26 Exercise 8.27 Exercise 8.28 Exercise 8.29 Experiment with a regex for real numbers Find errors in regular expressions Generate data from a user-supplied formula Explain the behavior of regular expressions Edit extensions in filenames Extract info from a program code Regex for splitting a pathname Rename a collection of files according to a pattern Reimplement the re. findall function Interpret a regex code and find programming errors Automatie fine tuning of PostScript figures Prefix name of digital image files with date and time Transform a list of lines to a list of paragraphs Copy computer codes into documents A very useful script for all writers Read Fortran 90 files with namelists Regex for matching fb.'ie;x commands Automatie update of function calls in C++ files Read/write (x, y) pairs from/to binary files Use the XDR format in the script from Exercise Archive all files needed in a fb.'ie;x document Using a Web site for distributed simulation Convert data structures to/from strings Implement a dass for vectors in 3D Extend the dass from Exericse Exercise 8.30 Make a dictionary type with ordered keys Exercise 8.31 Make a smarter integration function Exercise 8.32 Extend the Grid2D dass Exercise 8.33 Exercise 8.34 Exercise 8.35 Exercise 8.36 Exercise 9.1 Exercise 9.2 Exercise 9.3 Exercise 9.4 Exercise 9.5 Exercise 9.6 Exercise 10.1 Exercise 10.2 Exercise 10.3 Exercise 10.4 Exercise 10.5 Exercise 10.6 Exercise 10.7 Extend the functionality of dass Grid2D at run time Make a boundary iterator in a 2D grid Make a generator for odd numbers Make a dass for sparse vectors Extend Exercise 5.1 with a callback to Python Compile callback functions in Exercise Smoothing of time series Smoothing of 3D data Type incompatibility between Python and Fortran Problematic callbacks to Python from Fortran Extend Exercise 5.2 or 5.3 with a callback to Python Apply C/C++ function pointers in Exercise Debug a C extension module Investigate the efficiency of vector operations Make callbacks to vectorized Python functions Avoid Python callbacks in extension modules Extend Exercise 9.4 with C and C++ code

20 XXII List of Exercises Exercise 10.8 Apply SWIG to an array dass in C Exercisc 10.9 Build a dictionary in C Exercise Make a C module for computing random numbers Exercise Almost automatie generation of C extension modules Exercise Introduce C++ array objects in Exercise Exercise Introduce SCXX in Exercise Exercise ncorporate a BLT graph widget in simvizl.py Exercise 11.2 Plot a two-column datafile in a Pmw.Blt widget Exercise 11.3 Use a BLT graph widget in simvizgui2.py Exercise 11.4 Extend Exercise 11.3 to handle multiple curves Exercise 11.5 Use a BLT graph widget in Exercise Exercise nteractive dump of snapshot plots in an animation Exercise 11.7 Extend the animate.py GU Exercise 11.8 Animate a curve in a BLT graph widget Exercise 11.9 Add animations to the GU1 in Exercise Exercise Extend the GU1 in Exercise 6.17 with a fancy list Exercise Remove canvas items Exercise ntroduce properties in dass Parameters Exercise 12.1 Allow multiple values of parameters in input files Exercise 12.2 Turn mathematical formulas into Fortran functions Exercise 12.3 Exercise 12.4 Exercise 12.5 Exercise 12.6 Exercise B.1 Exercise B. 2 Exercise B.3 Exercise B.4 Exercise B. 5 Exercise B.6 Exercise B. 7 Exercise B. 8 Exercise B. 9 Exercise B.lO Exercise B.11 Exercise B.12 Move a wave source during simulation ndude damping in a 1D wave simulator Add a NumPy database to a PDE simulator Use iterators in finite difference schemes Pack modules and packages using Distutils Distribute mixed-ianguage code using Distutils Make a Python module of simviz1.py Use tools to document the script in Exercise Make a regression test for a trivial script Repeat Exercise B.5 using the test script tools Make a regression test for a script with 1/ Make a regression test for the script in Exercise Approximate fioats in Exercise B Make tests for grid iterators Make a tar/zip archive of files associated with a script Semi-automatic evaluation of a student project