Python scripting for computational science:
Saved in:
| Format: | Electronic eBook |
|---|---|
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2006
|
| Edition: | 2. ed. |
| Series: | Texts in computational science and engineering
3 |
| Subjects: | |
| Online Access: | BTU01 TUM01 UBA01 UBM01 UBR01 UBT01 UPA01 Volltext Inhaltsverzeichnis |
| Physical Description: | 1 Online-Ressource (XXIV, 736 S.) graph. Darst. |
| ISBN: | 3540294155 9783540294153 9783540312697 |
| DOI: | 10.1007/3-540-31269-2 |
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MARC
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| adam_text | Table of Contents
1 Introduction 1
1.1 Scripting versus Traditional Programming 1
1.1.1 Why Scripting is Useful in Computational Science. .. 2
1.1.2 Classification of Programming Languages 4
1.1.3 Productive Pairs of Programming Languages 5
1.1.4 Gluing Existing Applications 6
1.1.5 Scripting Yields Shorter Code 7
1.1.6 Efficiency 8
1.1.7 Type Specification (Declaration) of Variables 9
1.1.8 Flexible Function Interfaces 11
1.1.9 Interactive Computing 12
1.1.10 Creating Code at Run Time 13
1.1.11 Nested Heterogeneous Data Structures 14
1.1.12 GUI Programming 16
1.1.13 Mixed Language Programming 17
1.1.14 When to Choose a Dynamically Typed Language ... 19
1.1.15 Why Python? 20
1.1.16 Script or Program? 21
1.2 Preparations for Working with This Book 22
2 Getting Started with Python Scripting 27
2.1 A Scientific Hello World Script 27
2.1.1 Executing Python Scripts 28
2.1.2 Dissection of the Scientific Hello World Script 29
2.2 Working with Files and Data 32
2.2.1 Problem Specification 32
2.2.2 The Complete Code 33
2.2.3 Dissection 33
2.2.4 Working with Files in Memory 36
2.2.5 Array Computing 37
2.2.6 Interactive Computing and Debugging 39
2.2.7 Efficiency Measurements 42
2.2.8 Exercises 43
2.3 Gluing Stand Alone Applications 46
2.3.1 The Simulation Code 47
2.3.2 Using Gnuplot to Visualize Curves 49
2.3.3 Functionality of the Script 50
2.3.4 The Complete Code 51
2.3.5 Dissection 53
2.3.6 Exercises 56
2.4 Conducting Numerical Experiments 58
2.4.1 Wrapping a Loop Around Another Script 59
XII Table of Contents
2.4.2 Generating an HTML Report 60
2.4.3 Making Animations 61
2.4.4 Varying Any Parameter 63
2.5 File Format Conversion 66
2.5.1 A Simple Read/Write Script 66
2.5.2 Storing Data in a Dictionaries and Lists 68
2.5.3 Making a Module with Functions 69
2.5.4 Exercises 71
3 Basic Python 73
3.1 Introductory Topics 74
3.1.1 Recommended Python Documentation 74
3.1.2 Control Statements 75
3.1.3 Running an Application 76
3.1.4 File Reading and Writing 78
3.1.5 Output Formatting 79
3.2 Variables of Different Types 81
3.2.1 Boolean Types 81
3.2.2 The None Variable 81
3.2.3 Numbers and Numerical Expressions 82
3.2.4 Lists and Tuples 84
3.2.5 Dictionaries 90
3.2.6 Splitting and Joining Text 93
3.2.7 String Operations 94
3.2.8 Text Processing 96
3.2.9 The Basics of a Python Class 98
3.2.10 Copy and Assignment 100
3.2.11 Determining a Variable s Type 104
3.2.12 Exercises 105
3.3 Functions 109
3.3.1 Keyword Arguments 110
3.3.2 Doc Strings Ill
3.3.3 Variable Number of Arguments 112
3.3.4 Call by Reference 113
3.3.5 Treatment of Input and Output Arguments 115
3.3.6 Function Objects 116
3.4 Working with Files and Directories 117
3.4.1 Listing Files in a Directory 118
3.4.2 Testing File Types 118
3.4.3 Removing Files and Directories 119
3.4.4 Copying and Renaming Files 120
3.4.5 Splitting Pathnames 121
3.4.6 Creating and Moving to Directories 122
3.4.7 Traversing Directory Trees 122
3.4.8 Exercises 125
Table of Contents XIII
4 Numerical Computing in Python 131
4.1 A Quick NumPy Primer 133
4.1.1 Creating Arrays 133
4.1.2 Array Indexing 134
4.1.3 Array Computations 136
4.1.4 Type Testing 138
4.1.5 Hidden Temporary Arrays 139
4.1.6 Exercises 140
4.2 Vectorized Algorithms 141
4.2.1 From Scalar to Array in Function Arguments 141
4.2.2 Slicing 143
4.2.3 Remark on Efficiency 144
4.2.4 Exercises 146
4.3 More Advanced Array Computing 147
4.3.1 Random Numbers 147
4.3.2 Linear Algebra 148
4.3.3 The Gnuplot Module 150
4.3.4 Example: Curve Fitting 152
4.3.5 Arrays on Structured Grids 153
4.3.6 File I/O with NumPy Arrays 157
4.3.7 Reading and Writing Tables with NumPy Arrays ... 158
4.3.8 Functionality in the Numpytools Module 160
4.3.9 Exercises 163
4.4 Other Tools for Numerical Computations 168
4.4.1 The Scientific Python Package 168
4.4.2 The SciPy Package 174
4.4.3 The Python Matlab Interface 179
4.4.4 Some Useful Python Modules 180
5 Combining Python with Fortran, C, and C++ 183
5.1 About Mixed Language Programming 183
5.1.1 Applications of Mixed Language Programming 184
5.1.2 Calling C from Python 184
5.1.3 Automatic Generation of Wrapper Code 186
5.2 Scientific Hello World Examples 188
5.2.1 Combining Python and Fortran 189
5.2.2 Combining Python and C 194
5.2.3 Combining Python and C++ Functions 199
5.2.4 Combining Python and C++ Classes 202
5.2.5 Exercises 205
5.3 A Simple Computational Steering Example 206
5.3.1 Modified Time Loop for Repeated Simulations 207
5.3.2 Creating a Python Interface 208
5.3.3 The Steering Python Script 210
5.3.4 Equipping the Steering Script with a GUI 213
5.4 Scripting Interfaces to Large Libraries 214
XIV Table of Contents
6 Introduction to GUI Programming 219
6.1 Scientific Hello World GUI 220
6.1.1 Introductory Topics 220
6.1.2 The First Python/Tkinter Encounter 222
6.1.3 Binding Events 225
6.1.4 Changing the Layout 226
6.1.5 The Final Scientific Hello World GUI 230
6.1.6 An Alternative to Tkinter Variables 232
6.1.7 About the Pack Command 233
6.1.8 An Introduction to the Grid Geometry Manager .... 235
6.1.9 Implementing a GUI as a Class 237
6.1.10 A Simple Graphical Function Evaluator 239
6.1.11 Exercises 240
6.2 Adding GUIs to Scripts 243
6.2.1 A Simulation and Visualization Script with a GUI . . 243
6.2.2 Improving the Layout 246
6.2.3 Exercises 248
6.3 A List of Common Widget Operations 249
6.3.1 Frame 252
6.3.2 Label 252
6.3.3 Button 254
6.3.4 Text Entry 254
6.3.5 Balloon Help 256
6.3.6 Option Menu 257
6.3.7 Slider 257
6.3.8 Check Button 258
6.3.9 Making a Simple Megawidget 258
6.3.10 Menu Bar 259
6.3.11 List Data 261
6.3.12 Listbox 262
6.3.13 Radio Button 265
6.3.14 Combo Box 266
6.3.15 Message Box 267
6.3.16 User Defined Dialogs 269
6.3.17 Color Picker Dialogs 270
6.3.18 File Selection Dialogs 273
6.3.19 Toplevel 274
6.3.20 Some Other Types of Widgets 275
6.3.21 Adapting Widgets to the User s Resize Actions 276
6.3.22 Customizing Fonts and Colors 278
6.3.23 Widget Overview 280
6.3.24 Exercises 282
Table of Contents XV
7 Web Interfaces and CGI Programming 289
7.1 Introductory CGI Scripts 290
7.1.1 Web Forms and CGI Scripts 291
7.1.2 Generating Forms in CGI Scripts 293
7.1.3 Debugging CGI Scripts 295
7.1.4 A General Shell Script Wrapper for CGI Scripts .... 296
7.1.5 Security Issues 298
7.2 Adding Web Interfaces to Scripts 300
7.2.1 A Class for Form Parameters 301
7.2.2 Calling Other Programs 303
7.2.3 Running Simulations 304
7.2.4 Getting a CGI Script to Work 305
7.2.5 Using Web Applications from Scripts 308
7.2.6 Exercises 310
8 Advanced Python 313
8.1 Miscellaneous Topics 313
8.1.1 Parsing Command Line Arguments 313
8.1.2 Platform Dependent Operations 316
8.1.3 Run Time Generation of Code 317
8.1.4 Exercises 318
8.2 Regular Expressions and Text Processing 320
8.2.1 Motivation 320
8.2.2 Special Characters 323
8.2.3 Regular Expressions for Real Numbers 325
8.2.4 Using Groups to Extract Parts of a Text 328
8.2.5 Extracting Interval Limits 329
8.2.6 Extracting Multiple Matches 333
8.2.7 Splitting Text 338
8.2.8 Pattern Matching Modifiers 339
8.2.9 Substitution and Backreferences 341
8.2.10 Example: Swapping Arguments in Function Calls .. . 342
8.2.11 A General Substitution Script 345
8.2.12 Debugging Regular Expressions 347
8.2.13 Exercises 349
8.3 Tools for Handling Data in Files 357
8.3.1 Writing and Reading Python Data Structures 358
8.3.2 Pickling Objects 359
8.3.3 Shelving Objects 361
8.3.4 Writing and Reading Zip and Tar Archive Files 362
8.3.5 Downloading Internet Files 362
8.3.6 Binary Input/Output 364
8.3.7 Exercises 366
8.4 A Database for NumPy Arrays 367
8.4.1 The Structure of the Database 367
8.4.2 Pickling 370
8.4.3 Formatted ASCII Storage 370
XVI Table of Contents
8.4.4 Shelving 372
8.4.5 Comparing the Various Techniques 373
8.5 Scripts Involving Local and Remote Hosts 373
8.5.1 Secure Shell Commands 374
8.5.2 Distributed Simulation and Visualization 375
8.5.3 Client/Server Programming 377
8.5.4 Threads 378
8.6 Classes 379
8.6.1 Class Programming 380
8.6.2 Checking the Class Type 383
8.6.3 Private Data 384
8.6.4 Static Data 385
8.6.5 Special Attributes 386
8.6.6 Special Methods 386
8.6.7 Multiple Inheritance 388
8.6.8 Using a Class as a C like Structure 388
8.6.9 Attribute Access via String Names 389
8.6.10 New Style Classes 390
8.6.11 Implementing Get/Set Functions via Properties 390
8.6.12 Subclassing Built in Types 392
8.6.13 Building Class Interfaces at Run Time 394
8.6.14 Building Flexible Class Interfaces 398
8.6.15 Exercises 404
8.7 Scope of Variables 408
8.7.1 Global, Local, and Class Variables 408
8.7.2 Nested Functions 410
8.7.3 Dictionaries of Variables in Namespaces 411
8.8 Exceptions 413
8.8.1 Handling Exceptions 413
8.8.2 Raising Exceptions 415
8.9 Iterators 415
8.9.1 Constructing an Iterator 416
8.9.2 A Pointwise Grid Iterator 418
8.9.3 A Vectorized Grid Iterator 421
8.9.4 Generators 423
8.9.5 Some Aspects of Generic Programming 424
8.9.6 Exercises 428
8.10 Investigating Efficiency 429
8.10.1 CPU Time Measurements 430
8.10.2 Profiling Python Scripts 433
8.10.3 Optimization of Python Code 434
8.10.4 Case Study on Numerical Efficiency 437
Table of Contents XVII
9 Fortran Programming with Numerical Python
Arrays 443
9.1 Problem Definition 443
9.2 Filling an Array in Fortran 445
9.2.1 The Fortran Subroutine 445
9.2.2 Building and Inspecting the Extension Module 446
9.3 Array Storage Issues 448
9.3.1 Generating an Erroneous Interface 448
9.3.2 Array Storage in C and Fortran 450
9.3.3 Input and Output Arrays as Function Arguments .. . 451
9.3.4 F2PY Interface Files 458
9.3.5 Hiding Work Arrays 462
9.4 Increasing Callback Efficiency 463
9.4.1 Callbacks to Vectorized Python Functions 463
9.4.2 Avoiding Callbacks to Python 466
9.4.3 Compiled Inline Callback Functions 466
9.5 Summary 470
9.6 Exercises 470
10 C and C++ Programming with Numerical
Python Arrays 475
10.1 C Programming with NumPy Arrays 476
10.1.1 The Basics of the NumPy C API 476
10.1.2 The Handwritten Extension Code 478
10.1.3 Sending Arguments from Python to C 479
10.1.4 Consistency Checks 480
10.1.5 Computing Array Values 480
10.1.6 Returning an Output Array 483
10.1.7 Convenient Macros 484
10.1.8 Module Initialization 485
10.1.9 Extension Module Template 486
10.1.10 Compiling, Linking, and Debugging the Module 488
10.1.11 Writing a Wrapper for a C Function 489
10.2 C++ Programming with NumPy Arrays 492
10.2.1 Wrapping a NumPy Array in a C++ Object 493
10.2.2 Using SCXX 495
10.2.3 NumPy C++ Class Conversion 497
10.2.4 Migrating Loops to C++ with Weave 505
10.3 Comparison of the Implementations 506
10.3.1 Efficiency 506
10.3.2 Error Handling 509
10.3.3 Summary 510
10.4 Exercises 511
XVIII Table of Contents
11 More Advanced GUI Programming 515
11.1 Adding Plot Areas in GUIs 515
11.1.1 The BLT Graph Widget 516
11.1.2 Animation of Functions in BLT Graph Widgets 522
11.1.3 Other Tools for Making GUIs with Plots 524
11.1.4 Exercises 525
11.2 Event Bindings 527
11.2.1 Binding Events to Functions with Arguments 528
11.2.2 A Text Widget with Tailored Keyboard Bindings ... 530
11.2.3 A Fancy List Widget 533
11.3 Animated Graphics with Canvas Widgets 536
11.3.1 The First Canvas Encounter 537
11.3.2 Coordinate Systems 538
11.3.3 The Mathematical Model Class 542
11.3.4 The Planet Class 543
11.3.5 Drawing and Moving Planets 545
11.3.6 Dragging Planets to New Positions 546
11.3.7 Using Pmw s Scrolled Canvas Widget 550
11.4 Simulation and Visualization Scripts 552
11.4.1 Restructuring the Script 553
11.4.2 Representing a Parameter by a Class 555
11.4.3 Improved Command Line Script 569
11.4.4 Improved GUI Script 570
11.4.5 Improved CGI Script 571
11.4.6 Parameters with Physical Dimensions 572
11.4.7 Adding a Curve Plot Area 574
11.4.8 Automatic Generation of Scripts 576
11.4.9 Applications of the Tools 577
11.4.10 Allowing Physical Units in Input Files 582
11.4.11 Converting Input Files to GUIs 587
12 Tools and Examples 591
12.1 Running Series of Computer Experiments 591
12.1.1 Multiple Values of Input Parameters 592
12.1.2 Implementation Details 595
12.1.3 Further Applications 600
12.2 Tools for Representing Functions 604
12.2.1 Functions Defined by String Formulas 604
12.2.2 A Unified Interface to Functions 609
12.2.3 Interactive Drawing of Functions 615
12.2.4 A Notebook for Selecting Functions 619
12.3 Solving Partial Differential Equations 626
12.3.1 Numerical Methods for ID Wave Equations 627
12.3.2 Implementations of ID Wave Equations 630
12.3.3 Classes for Solving ID Wave Equations 637
12.3.4 A Problem Solving Environment 643
12.3.5 Numerical Methods for 2D Wave Equations 649
Table of Contents XIX
12.3.6 Implementations of 2D Wave Equations 652
12.3.7 Exercises 661
A Setting up the Required Software Environment... 663
A.I Installation on Unix Systems 663
A. 1.1 A Suggested Directory Structure 663
A. 1.2 Setting Some Environment Variables 664
A. 1.3 Installing Tcl/Tk and Additional Modules 665
A.1.4 Installing Python 666
A.1.5 Installing Python Modules 668
A.1.6 Installing Gnuplot 671
A.I.7 Installing SWIG 672
A. 1.8 Summary of Environment Variables 672
A. 1.9 Testing the Installation of Scripting Utilities 673
A.2 Installation on Windows Systems 673
B Elements of Software Engineering 679
B.I Building and Using Modules 679
B.I.I Single File Modules 679
B.1.2 Multi File Modules 683
B.I.3 Debugging and Troubleshooting 684
B.2 Tools for Documenting Python Software 686
B.2.1 Doc Strings 686
B.2.2 Tools for Automatic Documentation 688
B.3 Coding Standards 692
B.3.1 Style Guide 692
B.3.2 Pythonic Programming 696
B.4 Verification of Scripts 701
B.4.1 Automating Regression Tests 701
B.4.2 Implementing a Tool for Regression Tests 705
B.4.3 Writing a Test Script 709
B.4.4 Verifying Output from Numerical Computations .... 710
B.4.5 Automatic Doc String Testing 714
B.4.6 Unit Testing 716
B.5 Version Control Management 718
B.6 Exercises 720
Bibliography 725
Index 727
|
| adam_txt |
Table of Contents
1 Introduction 1
1.1 Scripting versus Traditional Programming 1
1.1.1 Why Scripting is Useful in Computational Science. . 2
1.1.2 Classification of Programming Languages 4
1.1.3 Productive Pairs of Programming Languages 5
1.1.4 Gluing Existing Applications 6
1.1.5 Scripting Yields Shorter Code 7
1.1.6 Efficiency 8
1.1.7 Type Specification (Declaration) of Variables 9
1.1.8 Flexible Function Interfaces 11
1.1.9 Interactive Computing 12
1.1.10 Creating Code at Run Time 13
1.1.11 Nested Heterogeneous Data Structures 14
1.1.12 GUI Programming 16
1.1.13 Mixed Language Programming 17
1.1.14 When to Choose a Dynamically Typed Language . 19
1.1.15 Why Python? 20
1.1.16 Script or Program? 21
1.2 Preparations for Working with This Book 22
2 Getting Started with Python Scripting 27
2.1 A Scientific Hello World Script 27
2.1.1 Executing Python Scripts 28
2.1.2 Dissection of the Scientific Hello World Script 29
2.2 Working with Files and Data 32
2.2.1 Problem Specification 32
2.2.2 The Complete Code 33
2.2.3 Dissection 33
2.2.4 Working with Files in Memory 36
2.2.5 Array Computing 37
2.2.6 Interactive Computing and Debugging 39
2.2.7 Efficiency Measurements 42
2.2.8 Exercises 43
2.3 Gluing Stand Alone Applications 46
2.3.1 The Simulation Code 47
2.3.2 Using Gnuplot to Visualize Curves 49
2.3.3 Functionality of the Script 50
2.3.4 The Complete Code 51
2.3.5 Dissection 53
2.3.6 Exercises 56
2.4 Conducting Numerical Experiments 58
2.4.1 Wrapping a Loop Around Another Script 59
XII Table of Contents
2.4.2 Generating an HTML Report 60
2.4.3 Making Animations 61
2.4.4 Varying Any Parameter 63
2.5 File Format Conversion 66
2.5.1 A Simple Read/Write Script 66
2.5.2 Storing Data in a Dictionaries and Lists 68
2.5.3 Making a Module with Functions 69
2.5.4 Exercises 71
3 Basic Python 73
3.1 Introductory Topics 74
3.1.1 Recommended Python Documentation 74
3.1.2 Control Statements 75
3.1.3 Running an Application 76
3.1.4 File Reading and Writing 78
3.1.5 Output Formatting 79
3.2 Variables of Different Types 81
3.2.1 Boolean Types 81
3.2.2 The None Variable 81
3.2.3 Numbers and Numerical Expressions 82
3.2.4 Lists and Tuples 84
3.2.5 Dictionaries 90
3.2.6 Splitting and Joining Text 93
3.2.7 String Operations 94
3.2.8 Text Processing 96
3.2.9 The Basics of a Python Class 98
3.2.10 Copy and Assignment 100
3.2.11 Determining a Variable's Type 104
3.2.12 Exercises 105
3.3 Functions 109
3.3.1 Keyword Arguments 110
3.3.2 Doc Strings Ill
3.3.3 Variable Number of Arguments 112
3.3.4 Call by Reference 113
3.3.5 Treatment of Input and Output Arguments 115
3.3.6 Function Objects 116
3.4 Working with Files and Directories 117
3.4.1 Listing Files in a Directory 118
3.4.2 Testing File Types 118
3.4.3 Removing Files and Directories 119
3.4.4 Copying and Renaming Files 120
3.4.5 Splitting Pathnames 121
3.4.6 Creating and Moving to Directories 122
3.4.7 Traversing Directory Trees 122
3.4.8 Exercises 125
Table of Contents XIII
4 Numerical Computing in Python 131
4.1 A Quick NumPy Primer 133
4.1.1 Creating Arrays 133
4.1.2 Array Indexing 134
4.1.3 Array Computations 136
4.1.4 Type Testing 138
4.1.5 Hidden Temporary Arrays 139
4.1.6 Exercises 140
4.2 Vectorized Algorithms 141
4.2.1 From Scalar to Array in Function Arguments 141
4.2.2 Slicing 143
4.2.3 Remark on Efficiency 144
4.2.4 Exercises 146
4.3 More Advanced Array Computing 147
4.3.1 Random Numbers 147
4.3.2 Linear Algebra 148
4.3.3 The Gnuplot Module 150
4.3.4 Example: Curve Fitting 152
4.3.5 Arrays on Structured Grids 153
4.3.6 File I/O with NumPy Arrays 157
4.3.7 Reading and Writing Tables with NumPy Arrays . 158
4.3.8 Functionality in the Numpytools Module 160
4.3.9 Exercises 163
4.4 Other Tools for Numerical Computations 168
4.4.1 The Scientific Python Package 168
4.4.2 The SciPy Package 174
4.4.3 The Python Matlab Interface 179
4.4.4 Some Useful Python Modules 180
5 Combining Python with Fortran, C, and C++ 183
5.1 About Mixed Language Programming 183
5.1.1 Applications of Mixed Language Programming 184
5.1.2 Calling C from Python 184
5.1.3 Automatic Generation of Wrapper Code 186
5.2 Scientific Hello World Examples 188
5.2.1 Combining Python and Fortran 189
5.2.2 Combining Python and C 194
5.2.3 Combining Python and C++ Functions 199
5.2.4 Combining Python and C++ Classes 202
5.2.5 Exercises 205
5.3 A Simple Computational Steering Example 206
5.3.1 Modified Time Loop for Repeated Simulations 207
5.3.2 Creating a Python Interface 208
5.3.3 The Steering Python Script 210
5.3.4 Equipping the Steering Script with a GUI 213
5.4 Scripting Interfaces to Large Libraries 214
XIV Table of Contents
6 Introduction to GUI Programming 219
6.1 Scientific Hello World GUI 220
6.1.1 Introductory Topics 220
6.1.2 The First Python/Tkinter Encounter 222
6.1.3 Binding Events 225
6.1.4 Changing the Layout 226
6.1.5 The Final Scientific Hello World GUI 230
6.1.6 An Alternative to Tkinter Variables 232
6.1.7 About the Pack Command 233
6.1.8 An Introduction to the Grid Geometry Manager . 235
6.1.9 Implementing a GUI as a Class 237
6.1.10 A Simple Graphical Function Evaluator 239
6.1.11 Exercises 240
6.2 Adding GUIs to Scripts 243
6.2.1 A Simulation and Visualization Script with a GUI . . 243
6.2.2 Improving the Layout 246
6.2.3 Exercises 248
6.3 A List of Common Widget Operations 249
6.3.1 Frame 252
6.3.2 Label 252
6.3.3 Button 254
6.3.4 Text Entry 254
6.3.5 Balloon Help 256
6.3.6 Option Menu 257
6.3.7 Slider 257
6.3.8 Check Button 258
6.3.9 Making a Simple Megawidget 258
6.3.10 Menu Bar 259
6.3.11 List Data 261
6.3.12 Listbox 262
6.3.13 Radio Button 265
6.3.14 Combo Box 266
6.3.15 Message Box 267
6.3.16 User Defined Dialogs 269
6.3.17 Color Picker Dialogs 270
6.3.18 File Selection Dialogs 273
6.3.19 Toplevel 274
6.3.20 Some Other Types of Widgets 275
6.3.21 Adapting Widgets to the User's Resize Actions 276
6.3.22 Customizing Fonts and Colors 278
6.3.23 Widget Overview 280
6.3.24 Exercises 282
Table of Contents XV
7 Web Interfaces and CGI Programming 289
7.1 Introductory CGI Scripts 290
7.1.1 Web Forms and CGI Scripts 291
7.1.2 Generating Forms in CGI Scripts 293
7.1.3 Debugging CGI Scripts 295
7.1.4 A General Shell Script Wrapper for CGI Scripts . 296
7.1.5 Security Issues 298
7.2 Adding Web Interfaces to Scripts 300
7.2.1 A Class for Form Parameters 301
7.2.2 Calling Other Programs 303
7.2.3 Running Simulations 304
7.2.4 Getting a CGI Script to Work 305
7.2.5 Using Web Applications from Scripts 308
7.2.6 Exercises 310
8 Advanced Python 313
8.1 Miscellaneous Topics 313
8.1.1 Parsing Command Line Arguments 313
8.1.2 Platform Dependent Operations 316
8.1.3 Run Time Generation of Code 317
8.1.4 Exercises 318
8.2 Regular Expressions and Text Processing 320
8.2.1 Motivation 320
8.2.2 Special Characters 323
8.2.3 Regular Expressions for Real Numbers 325
8.2.4 Using Groups to Extract Parts of a Text 328
8.2.5 Extracting Interval Limits 329
8.2.6 Extracting Multiple Matches 333
8.2.7 Splitting Text 338
8.2.8 Pattern Matching Modifiers 339
8.2.9 Substitution and Backreferences 341
8.2.10 Example: Swapping Arguments in Function Calls . . 342
8.2.11 A General Substitution Script 345
8.2.12 Debugging Regular Expressions 347
8.2.13 Exercises 349
8.3 Tools for Handling Data in Files 357
8.3.1 Writing and Reading Python Data Structures 358
8.3.2 Pickling Objects 359
8.3.3 Shelving Objects 361
8.3.4 Writing and Reading Zip and Tar Archive Files 362
8.3.5 Downloading Internet Files 362
8.3.6 Binary Input/Output 364
8.3.7 Exercises 366
8.4 A Database for NumPy Arrays 367
8.4.1 The Structure of the Database 367
8.4.2 Pickling 370
8.4.3 Formatted ASCII Storage 370
XVI Table of Contents
8.4.4 Shelving 372
8.4.5 Comparing the Various Techniques 373
8.5 Scripts Involving Local and Remote Hosts 373
8.5.1 Secure Shell Commands 374
8.5.2 Distributed Simulation and Visualization 375
8.5.3 Client/Server Programming 377
8.5.4 Threads 378
8.6 Classes 379
8.6.1 Class Programming 380
8.6.2 Checking the Class Type 383
8.6.3 Private Data 384
8.6.4 Static Data 385
8.6.5 Special Attributes 386
8.6.6 Special Methods 386
8.6.7 Multiple Inheritance 388
8.6.8 Using a Class as a C like Structure 388
8.6.9 Attribute Access via String Names 389
8.6.10 New Style Classes 390
8.6.11 Implementing Get/Set Functions via Properties 390
8.6.12 Subclassing Built in Types 392
8.6.13 Building Class Interfaces at Run Time 394
8.6.14 Building Flexible Class Interfaces 398
8.6.15 Exercises 404
8.7 Scope of Variables 408
8.7.1 Global, Local, and Class Variables 408
8.7.2 Nested Functions 410
8.7.3 Dictionaries of Variables in Namespaces 411
8.8 Exceptions 413
8.8.1 Handling Exceptions 413
8.8.2 Raising Exceptions 415
8.9 Iterators 415
8.9.1 Constructing an Iterator 416
8.9.2 A Pointwise Grid Iterator 418
8.9.3 A Vectorized Grid Iterator 421
8.9.4 Generators 423
8.9.5 Some Aspects of Generic Programming 424
8.9.6 Exercises 428
8.10 Investigating Efficiency 429
8.10.1 CPU Time Measurements 430
8.10.2 Profiling Python Scripts 433
8.10.3 Optimization of Python Code 434
8.10.4 Case Study on Numerical Efficiency 437
Table of Contents XVII
9 Fortran Programming with Numerical Python
Arrays 443
9.1 Problem Definition 443
9.2 Filling an Array in Fortran 445
9.2.1 The Fortran Subroutine 445
9.2.2 Building and Inspecting the Extension Module 446
9.3 Array Storage Issues 448
9.3.1 Generating an Erroneous Interface 448
9.3.2 Array Storage in C and Fortran 450
9.3.3 Input and Output Arrays as Function Arguments . . 451
9.3.4 F2PY Interface Files 458
9.3.5 Hiding Work Arrays 462
9.4 Increasing Callback Efficiency 463
9.4.1 Callbacks to Vectorized Python Functions 463
9.4.2 Avoiding Callbacks to Python 466
9.4.3 Compiled Inline Callback Functions 466
9.5 Summary 470
9.6 Exercises 470
10 C and C++ Programming with Numerical
Python Arrays 475
10.1 C Programming with NumPy Arrays 476
10.1.1 The Basics of the NumPy C API 476
10.1.2 The Handwritten Extension Code 478
10.1.3 Sending Arguments from Python to C 479
10.1.4 Consistency Checks 480
10.1.5 Computing Array Values 480
10.1.6 Returning an Output Array 483
10.1.7 Convenient Macros 484
10.1.8 Module Initialization 485
10.1.9 Extension Module Template 486
10.1.10 Compiling, Linking, and Debugging the Module 488
10.1.11 Writing a Wrapper for a C Function 489
10.2 C++ Programming with NumPy Arrays 492
10.2.1 Wrapping a NumPy Array in a C++ Object 493
10.2.2 Using SCXX 495
10.2.3 NumPy C++ Class Conversion 497
10.2.4 Migrating Loops to C++ with Weave 505
10.3 Comparison of the Implementations 506
10.3.1 Efficiency 506
10.3.2 Error Handling 509
10.3.3 Summary 510
10.4 Exercises 511
XVIII Table of Contents
11 More Advanced GUI Programming 515
11.1 Adding Plot Areas in GUIs 515
11.1.1 The BLT Graph Widget 516
11.1.2 Animation of Functions in BLT Graph Widgets 522
11.1.3 Other Tools for Making GUIs with Plots 524
11.1.4 Exercises 525
11.2 Event Bindings 527
11.2.1 Binding Events to Functions with Arguments 528
11.2.2 A Text Widget with Tailored Keyboard Bindings . 530
11.2.3 A Fancy List Widget 533
11.3 Animated Graphics with Canvas Widgets 536
11.3.1 The First Canvas Encounter 537
11.3.2 Coordinate Systems 538
11.3.3 The Mathematical Model Class 542
11.3.4 The Planet Class 543
11.3.5 Drawing and Moving Planets 545
11.3.6 Dragging Planets to New Positions 546
11.3.7 Using Pmw's Scrolled Canvas Widget 550
11.4 Simulation and Visualization Scripts 552
11.4.1 Restructuring the Script 553
11.4.2 Representing a Parameter by a Class 555
11.4.3 Improved Command Line Script 569
11.4.4 Improved GUI Script 570
11.4.5 Improved CGI Script 571
11.4.6 Parameters with Physical Dimensions 572
11.4.7 Adding a Curve Plot Area 574
11.4.8 Automatic Generation of Scripts 576
11.4.9 Applications of the Tools 577
11.4.10 Allowing Physical Units in Input Files 582
11.4.11 Converting Input Files to GUIs 587
12 Tools and Examples 591
12.1 Running Series of Computer Experiments 591
12.1.1 Multiple Values of Input Parameters 592
12.1.2 Implementation Details 595
12.1.3 Further Applications 600
12.2 Tools for Representing Functions 604
12.2.1 Functions Defined by String Formulas 604
12.2.2 A Unified Interface to Functions 609
12.2.3 Interactive Drawing of Functions 615
12.2.4 A Notebook for Selecting Functions 619
12.3 Solving Partial Differential Equations 626
12.3.1 Numerical Methods for ID Wave Equations 627
12.3.2 Implementations of ID Wave Equations 630
12.3.3 Classes for Solving ID Wave Equations 637
12.3.4 A Problem Solving Environment 643
12.3.5 Numerical Methods for 2D Wave Equations 649
Table of Contents XIX
12.3.6 Implementations of 2D Wave Equations 652
12.3.7 Exercises 661
A Setting up the Required Software Environment. 663
A.I Installation on Unix Systems 663
A. 1.1 A Suggested Directory Structure 663
A. 1.2 Setting Some Environment Variables 664
A. 1.3 Installing Tcl/Tk and Additional Modules 665
A.1.4 Installing Python 666
A.1.5 Installing Python Modules 668
A.1.6 Installing Gnuplot 671
A.I.7 Installing SWIG 672
A. 1.8 Summary of Environment Variables 672
A. 1.9 Testing the Installation of Scripting Utilities 673
A.2 Installation on Windows Systems 673
B Elements of Software Engineering 679
B.I Building and Using Modules 679
B.I.I Single File Modules 679
B.1.2 Multi File Modules 683
B.I.3 Debugging and Troubleshooting 684
B.2 Tools for Documenting Python Software 686
B.2.1 Doc Strings 686
B.2.2 Tools for Automatic Documentation 688
B.3 Coding Standards 692
B.3.1 Style Guide 692
B.3.2 Pythonic Programming 696
B.4 Verification of Scripts 701
B.4.1 Automating Regression Tests 701
B.4.2 Implementing a Tool for Regression Tests 705
B.4.3 Writing a Test Script 709
B.4.4 Verifying Output from Numerical Computations . 710
B.4.5 Automatic Doc String Testing 714
B.4.6 Unit Testing 716
B.5 Version Control Management 718
B.6 Exercises 720
Bibliography 725
Index 727 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author_GND | (DE-588)1019109599 |
| building | Verbundindex |
| bvnumber | BV022353625 |
| classification_rvk | ST 250 ST 260 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA |
| ctrlnum | (OCoLC)315814860 (DE-599)BVBBV022353625 |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| doi_str_mv | 10.1007/3-540-31269-2 |
| edition | 2. ed. |
| format | Electronic eBook |
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| index_date | 2024-07-02T17:00:38Z |
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| spelling | Python scripting for computational science Hans Petter Langtangen 2. ed. Berlin [u.a.] Springer 2006 1 Online-Ressource (XXIV, 736 S.) graph. Darst. txt rdacontent c rdamedia cr rdacarrier Texts in computational science and engineering 3 Wissenschaftliches Rechnen (DE-588)4338507-2 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Wissenschaftliches Rechnen (DE-588)4338507-2 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Langtangen, Hans Petter 1962- Sonstige (DE-588)1019109599 oth Texts in computational science and engineering 3 (DE-604)BV035421282 3 https://doi.org/10.1007/3-540-31269-2 Verlag Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015563077&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Python scripting for computational science Texts in computational science and engineering Wissenschaftliches Rechnen (DE-588)4338507-2 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4338507-2 (DE-588)4434275-5 |
| title | Python scripting for computational science |
| title_auth | Python scripting for computational science |
| title_exact_search | Python scripting for computational science |
| title_exact_search_txtP | Python scripting for computational science |
| title_full | Python scripting for computational science Hans Petter Langtangen |
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| title_short | Python scripting for computational science |
| title_sort | python scripting for computational science |
| topic | Wissenschaftliches Rechnen (DE-588)4338507-2 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
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