Python for scientists:
"Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from s...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2014
|
| Schlagworte: | |
| Online-Zugang: | Cover image Inhaltsverzeichnis |
| Zusammenfassung: | "Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible".. |
| Beschreibung: | XII, 220 S. Ill., graph. Darst. |
| ISBN: | 9781107061392 9781107686427 |
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Datensatz im Suchindex
| _version_ | 1804152314514112512 |
|---|---|
| adam_text | Python for Scientists
JO HNM STEWA R T
Department of Applied Mathematics amp; Theoretical Physics
University of Cambridge
Contents
Preface page xi
1 Introduction 1
1 1 Scientific Software 1
1 2 The plan of this book 4
1 3 Can Python compete with compiled languages? 8
1 4 Limitations of this book 9
1 5 Installing Python and add-ons 9
2 Getting started with IPython 11
2 1 Generalities 11
2 2 Tab completion 12
2 3 Introspection 12
2 4 History 13
2 5 Magic commands 14
2 6 The magic %run command 15
3 A Short Python tutorial 20
3 1 Typing Python 20
3 2 Objects and identifiers 21
3 3 Numbers 23
331 Integers 23
332 Real numbers 23
333 Boolean numbers 24
334 Complex numbers 25
3 4 Namespaces and modules 25
3 5 Container objects 27
351 Lists 27
352 List indexing 28
353 List slicing 28
354 List mutability 30
355 Tuples 31
356 Strings 31
357 Dictionaries 32
3 6 Python if statements 32
3 7 Loop constructs 33
(371 The Python for loop 34
372 The Python continue Statement 35
373 The Python break Statement 35
374 List comprehensions 36
375 Python white loops 37
3 8 Functions 37
381 Syntax and scope 38
382 Positional arguments 41
383 Keyword arguments 41
384 Variable number of positional arguments 41
385 Variable number of keyword arguments 42
386 The Python print function 42
387 Anonymous functions 44
3 9 Introduction to Python classes 45
3 10 The structure of Python 47
3 11 Prime numbers: a worked example 48
4 Numpy 52
4 1 One-dimensional arrays 54
411 Ab initio constructors 54
412 Look alike constructors 55
413 Arithmetical Operations on vectors 56
414 Ufuncs 58
415 Logical Operations on vectors 59
4 2 Two-dimensional arrays 62
421 Broadcasting 63
422 Ab initio constructors 64
423 Look alike constructors 65
424 Operations on arrays and ufuncs 66
4 3 Higher-dimensional arrays 66
4 4 Domestic input and Output 67
441 Discursive Output and input 67
442 Numpy text Output and input 69
443 Numpy binary Output and input 69
4 5 Foreign input and Output 70
451 Small amounts of data 70
452 Large amounts of data 71
4 6 Miscellaneous ufuncs 71
461 Maxima and minima 71
462 Sums and products 72
463 Simple statistics 72
4 7 Polynomials 73
471 Converting data to coefficients 73
472 Converting coefficients to data 73
473 Manipulating polynomials in coefficient form 74
4 8 Linear algebra 74
481 Basic Operations on matrices 74
482 More specialized Operations on matrices 75
483 Solving linear systems of equations 75
4 9 More numpy and beyond 76
491 Scipy 76
492 Seikits 11
5 Two-dimensional graphics 79
5 1 Introduction 79
5 2 Getting started: simple figures 80
521 Front-ends 80
522 Back-ends 80
523A simple figure 81
524 Interactive controls 82
5 3 Cartesian plots 83
531 The matplotlib plot funetion 83
532 Curve styles 83
533 Marker styles 84
534 Axes, grid, labels and title 85
535A not-so-simple example: partial sums of Fourier series 86
5 4 Polar plots 87
5 5 Error bars 88
5 6 Text and annotations 89
5 7 Displaying mathematical formulae 90
571 Non-MßX users 90
572 I4TgX users 91
573 Alternatives for I#IeX users 92
5 8 Contour plots 92
5 9 Compound figures 95
591 Multiple figures 95
592 Multiple plots 96
5 10 Animations 98
5 10 1 In situ animations 99
5 10 2 Movies 100
5 11 Mandelbrot sets: a worked example 102
6 Three-dimensional graphics 107
6 1 Introduction 107
611 Three-dimensional data sets 107
612 The reduetion to two dimensions 108
6 2 Visualization Software 109
63A three-dimensional curve 110
631 Visualizing the curve with mplot3d 111
632 Visualizing the curve with mlab 112
64A simple surface 114
641 Visualizing the simple surface with mplot3d 114
642 Visualizing the simple surface with ?nlab 116
65A parametrically defined surface 117
651 Visualizing Enneper s surface using mplot3d 117
652 Visualizing Enneper s surface using mlab 119
6 6 Three-dimensional visualization of a Julia set 120
7 Ordinary differential equations 122
7 1 Initial value problems 122
7 2 Basic concepts 122
7 3 The odeint function 125
731 Theoretical background 125
732 Practical usage 127
7 4 Two-point boundary value problems 132
741 Introduction 132
742 Formulation of the boundary value problem 133
743A simple example 135
744A linear eigenvalue problem 136
745A non-linear boundary value problem 138
7 5 Delay differential equations 142
751A model equation 143
752 More general equations and their numerical Solution 144
753 The logistic equation 145
754 The Mackey-Glass equation 147
7 6 Stochastic differential equations 150
761 The Wiener process 150
762 The Itö calculus 152
763 Itö and Stratanovich stochastic integrals 155
764 Numerical Solution of stochastic differential equations 156
8 Partial differential equations: a pseudospectral approach 163
8 1 Initial-boundary value problems 163
8 2 Method of lines 164
8 3 Spatial derivatives via finite differencing 164
8 4 Spatial derivatives by spectral techniques for periodic problems 165
8 5 The IVP for spatially periodic problems 167
8 6 Spectral techniques for non-periodic problems 170
8 7 An introduction to f2py 172
871 Simple examples with scalar arguments 172
872 Vector arguments 174
873A simple example with multi-dimensional arguments 175
874 Undiscussed features of f2py 176
88A real-life f2py example 177
8 9 Worked example: Burgers equation 178
891 Boundary conditions: the traditional approach 179
892 Boundary conditions: the penalty approach 179
9 Case study: multigrid 184
9 1 The one-dimensional case 185
911 Linear elliptic equations 185
912 Smooth and rough modes 186
9 2 The tools of multigrid 186
921 Relaxation methods 186
922 Residual and error 189
923 Prolongation and restriction 190
9 3 Multigrid schemes 191
931 The two-grid algorithm 192
932 The V-cycle scheme 193
933 The füll multigrid scheme (FMG) 194
94A simple Python multigrid implementation 195
941 Utility functions 196
942 Smoothing functions 197
943 Multigrid functions 199
Appendix A Installing a Python environment 205
A 1 Installing Python packages 205
A 2 Communicating with Python 206
A21 Editors for programming 206
A22 The IPython-edhoT interaction 207
A23 The two windows approach 207
A24 Calling the editor from within IPython 208
A25 Calling IPython from within the editor 208
A26 The IPython pager 208
A 3 The Python Package Index 209
Appendix B Fortran77 subroutines for pseudospectral methods 210
References 216
Index 218
|
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