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Introduction to Scientific Computing in Python

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Introduction to Scientific Computing in Python Introduction to Scientific Computing in Python Robert Johansson April 16, 2016 Contents 1 Introduction to scientific computing with Python4 1.1 The role of computing in science..................................4 1.1.1 References...........................................4 1.2 Requirements on scientific computing................................5 1.2.1 Tools for managing source code...............................5 1.3 What is Python?...........................................6 1.4 What makes python suitable for scientific computing?......................6 1.4.1 The scientific python software stack............................7 1.4.2 Python environments....................................7 1.4.3 Python interpreter......................................7 1.4.4 IPython............................................7 1.4.5 IPython notebook......................................7 1.4.6 Spyder.............................................8 1.5 Versions of Python..........................................8 1.6 Installation..............................................8 1.6.1 Conda.............................................8 1.6.2 Linux.............................................8 1.6.3 MacOS X...........................................9 1.6.4 Windows...........................................9 1.7 Further reading............................................9 1.8 Python and module versions.....................................9 2 Introduction to Python programming 11 2.1 Python program files......................................... 11 2.1.1 Example:........................................... 11 2.1.2 Character encoding...................................... 12 2.2 IPython notebooks.......................................... 12 2.3 Modules................................................ 12 2.3.1 References........................................... 12 2.3.2 Looking at what a module contains, and its documentation............... 13 2.4 Variables and types.......................................... 14 2.4.1 Symbol names........................................ 14 2.4.2 Assignment.......................................... 14 2.4.3 Fundamental types...................................... 15 2.4.4 Type utility functions.................................... 16 2.4.5 Type casting......................................... 16 2.5 Operators and comparisons..................................... 17 2.6 Compound types: Strings, List and dictionaries.......................... 18 2.6.1 Strings............................................. 18 2.6.2 List.............................................. 20 2.6.3 Tuples............................................. 23 2.6.4 Dictionaries.......................................... 24 1 2.7 Control Flow............................................. 24 2.7.1 Conditional statements: if, elif, else............................. 24 2.8 Loops................................................. 26 2.8.1 for loops:.......................................... 26 2.8.2 List comprehensions: Creating lists using for loops:................... 27 2.8.3 while loops:......................................... 27 2.9 Functions............................................... 28 2.9.1 Default argument and keyword arguments......................... 29 2.9.2 Unnamed functions (lambda function)........................... 30 2.10 Classes................................................. 30 2.11 Modules................................................ 31 2.12 Exceptions.............................................. 33 2.13 Further reading............................................ 35 2.14 Versions................................................ 35 3 Numpy - multidimensional data arrays 36 3.1 Introduction.............................................. 36 3.2 Creating numpy arrays........................................ 36 3.2.1 From lists........................................... 36 3.2.2 Using array-generating functions.............................. 38 3.3 File I/O................................................ 40 3.3.1 Comma-separated values (CSV).............................. 40 3.3.2 Numpy's native file format................................. 41 3.4 More properties of the numpy arrays................................ 42 3.5 Manipulating arrays......................................... 42 3.5.1 Indexing............................................ 42 3.5.2 Index slicing......................................... 43 3.5.3 Fancy indexing........................................ 44 3.6 Functions for extracting data from arrays and creating arrays.................. 45 3.6.1 where............................................. 45 3.6.2 diag.............................................. 46 3.6.3 take.............................................. 46 3.6.4 choose............................................. 46 3.7 Linear algebra............................................. 46 3.7.1 Scalar-array operations................................... 47 3.7.2 Element-wise array-array operations............................ 47 3.7.3 Matrix algebra........................................ 48 3.7.4 Array/Matrix transformations............................... 49 3.7.5 Matrix computations..................................... 50 3.7.6 Data processing........................................ 50 3.7.7 Computations on subsets of arrays............................. 52 3.7.8 Calculations with higher-dimensional data......................... 53 3.8 Reshaping, resizing and stacking arrays.............................. 54 3.9 Adding a new dimension: newaxis................................. 55 3.10 Stacking and repeating arrays.................................... 55 3.10.1 tile and repeat........................................ 55 3.10.2 concatenate.......................................... 55 3.10.3 hstack and vstack...................................... 56 3.11 Copy and \deep copy"........................................ 56 3.12 Iterating over array elements.................................... 57 3.13 Vectorizing functions......................................... 58 3.14 Using arrays in conditions...................................... 59 3.15 Type casting............................................. 59 3.16 Further reading............................................ 60 2 3.17 Versions................................................ 60 4 SciPy - Library of scientific algorithms for Python 61 4.1 Introduction.............................................. 61 4.2 Special functions........................................... 62 4.3 Integration.............................................. 63 4.3.1 Numerical integration: quadrature............................. 63 4.4 Ordinary differential equations (ODEs)............................... 65 4.5 Fourier transform........................................... 70 4.6 Linear algebra............................................. 71 4.6.1 Linear equation systems................................... 72 4.6.2 Eigenvalues and eigenvectors................................ 72 4.6.3 Matrix operations...................................... 73 4.6.4 Sparse matrices........................................ 73 4.7 Optimization............................................. 75 4.7.1 Finding a minima...................................... 76 4.7.2 Finding a solution to a function............................... 77 4.8 Interpolation............................................. 78 4.9 Statistics............................................... 79 4.9.1 Statistical tests........................................ 80 4.10 Further reading............................................ 81 4.11 Versions................................................ 81 5 matplotlib - 2D and 3D plotting in Python 82 5.1 Introduction.............................................. 82 5.2 MATLAB-like API.......................................... 83 5.2.1 Example............................................ 83 5.3 The matplotlib object-oriented API................................. 84 5.3.1 Figure size, aspect ratio and DPI.............................. 89 5.3.2 Saving figures......................................... 90 5.3.3 Legends, labels and titles.................................. 90 5.3.4 Formatting text: LaTeX, fontsize, font family....................... 92 5.3.5 Setting colors, linewidths, linetypes............................. 95 5.3.6 Control over axis appearance................................ 97 5.3.7 Placement of ticks and custom tick labels......................... 98 5.3.8 Axis number and axis label spacing............................. 100 5.3.9 Axis grid........................................... 102 5.3.10 Axis spines.......................................... 103 5.3.11 Twin axes........................................... 103 5.3.12 Axes where x and y is zero................................. 104 5.3.13 Other 2D plot styles..................................... 105 5.3.14 Text annotation....................................... 107 5.3.15 Figures with multiple subplots and insets......................... 107 5.3.16 Colormap and contour figures................................ 111 5.4 3D figures............................................... 114 5.4.1 Animations.......................................... 117 5.4.2 Backends........................................... 119 5.5 Further reading............................................ 121 5.6 Versions................................................ 122 3 6 Sympy - Symbolic algebra in Python 123 6.1 Introduction.............................................. 123 6.2 Symbolic variables.........................................
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