Python Programming and Numerical Methods
A Guide for Engineers and Scientists
- 1st Edition - November 27, 2020
- Latest edition
- Authors: Qingkai Kong, Timmy Siauw, Alexandre Bayen
- Language: English
Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the go… Read more
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- Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice
- Summaries at the end of each chapter allow for quick access to important information
- Includes code in Jupyter notebook format that can be directly run online
PART 1 INTRODUCTION TO PYTHON PROGRAMMING
CHAPTER 1 Python Basics
1.1 Getting Started With Python
1.2
Python as a Calculator1.3
Managing Packages1.4
Introduction to Jupyter Notebook1.5
Logical Expressions and Operators1.6
Summary and ProblemsCHAPTER 2 Variables and Basic Data Structures
2.1
Variables and Assignment2.2
Data Structure – String2.3
Data Structure – List2.4
Data Structure – Tuple2.5
Data Structure – Set2.6
Data Structure – Dictionary2.7 Introducing Numpy Arrays
2.8
Summary and ProblemsCHAPTER 3 Functions
3.1
Function Basics3.2
Local Variables and Global Variables3.3 Nested Functions
3.4
Lambda Functions3.5 Functions as Arguments to Functions
3.6
Summary and ProblemsCHAPTER 4 Branching Statements
4.1
If-Else Statements4.2 Ternary Operators
4.3 Summary and Problems
CHAPTER 5 Iteration
5.1
For-Loops5.2 While Loops
5.3
Comprehensions5.4
Summary and ProblemsCHAPTER 6 Recursion
6.1
Recursive Functions6.2
Divide-and-Conquer6.3
Summary and ProblemsCHAPTER 7 Object-Oriented Programming
7.1
Introduction to OOP7.2
Class and Object7.3
Inheritance, Encapsulation, and Polymorphism7.4
Summary and ProblemsCHAPTER 8 Complexity
8.1 Complexity and Big-ONotation
8.2
Complexity Matters8.3
The Profiler8.4
Summary and ProblemsCHAPTER 9 Representation of Numbers
9.1
Base-N and Binary9.2
Floating Point Numbers9.3
Round-Off Errors9.4
Summary and ProblemsCHAPTER 10 Errors, Good Programming Practices, and Debugging
10.1
Error Types10.2
Avoiding Errors10.3
Try/Except10.4
Type Checking10.5
Debugging10.6
Summary and ProblemsCHAPTER 11 Reading and Writing Data
11.1
TXT Files11.2
CSVFiles11.3
Pickle Files11.4
JSONFiles11.5
HDF5 Files11.6
Summary and ProblemsCHAPTER 12 Visualization and Plotting
12.1
2D Plotting12.2 3D Plotting
12.3 Working With Maps
12.4
Animations and Movies12.5
Summary and ProblemsCHAPTER 13 Parallelize Your Python
13.1
Parallel Computing Basics13.2
Multiprocessing13.3
Using Joblib13.4
Summary and ProblemsPART 2 INTRODUCTION TO NUMERICAL METHODS
CHAPTER 14 Linear Algebra and Systems of Linear Equations
14.1
Basics of Linear Algebra14.2
Linear Transformations14.3
Systems of Linear Equations14.4 Solutions to Systems of Linear Equations
14.5
Solving Systems of Linear Equations in Python14.6
Matrix Inversion14.7
Summary and ProblemsCHAPTER 15 Eigenvalues and Eigenvectors
15.1
Eigenvalues and Eigenvectors Problem Statement15.2
The Power Method15.3
The QR Method15.4
Eigenvalues and Eigenvectors in Python15.5
Summary and ProblemsCHAPTER 16 Least Squares Regression
16.1
Least Squares Regression Problem Statement16.2
Least Squares Regression Derivation (Linear Algebra)16.3
Least Squares Regression Derivation (Multivariate Calculus)16.4
Least Squares Regression in Python16.5
Least Squares Regression for Nonlinear Functions16.6
Summary and ProblemsCHAPTER 17 Interpolation
17.1
Interpolation Problem Statement17.2 Linear Interpolation
17.3
Cubic Spline Interpolation17.4
Lagrange Polynomial Interpolation17.5
Newton’s Polynomial Interpolation17.6
Summary and ProblemsCHAPTER 18 Taylor Series
18.1
18.2 Approximations Using Taylor Series
18.3
Discussion About Errors18.4
Summary and ProblemsCHAPTER 19 Root Finding
19.1
19.2
Tolerance19.3
Bisection Method19.4
Newton–Raphson Method19.5 Root Finding in Python
19.6 Summary and Problems
CHAPTER 20 Numerical Differentiation
20.1
Numerical Differentiation Problem Statement20.2
Using Finite Difference to Approximate Derivatives20.3
Approximating of Higher Order Derivatives20.4
Numerical Differentiation With Noise20.5
Summary and ProblemsCHAPTER 21 Numerical Integration
21.1
21.2
Riemann Integral21.3 Trapezoid Rule
21.4 Simpson’s Rule
21.5
Computing Integrals in Python21.6 Summary and Problems
CHAPTER 22 Ordinary Differential Equations (ODEs) Initial-Value Problems
22.1
ODE Initial Value Problem Statement22.2
Reduction of Order22.3 The Euler Method
22.4 Numerical Error and Instability
22.5 Predictor–Corrector and Runge–Kutta Methods
22.6
Python ODE Solvers22.7
Advanced Topics22.8
Summary and ProblemsCHAPTER 23 Boundary-Value Problems for Ordinary Differential Equations (ODEs)
23.1
ODE Boundary Value Problem Statement23.2
The Shooting Method23.3
The Finite Difference Method23.4
Numerical Error and Instability23.5
Summary and ProblemsCHAPTER 24 Fourier Transform
24.1
24.2
Discrete Fourier Transform (DFT)24.3
Fast Fourier Transform (FFT)24.4
FFT in Python24.5
Summary and ProblemsAppendix A Getting Started With Python in Windows
Index
- Edition: 1
- Latest edition
- Published: December 2, 2020
- Language: English
QK
Qingkai Kong
Qingkai Kong is currently a seismologist at Lawrence Livermore National Laboratory. He is actively working on using machine learning approaches for seismic simulations and explosion discrimination. Dr. Kong has a Master’s degree in Structural Engineering and a PhD. in Earth Science. Before joining the lab, he worked at Berkeley Division of Data Sciences and Berkeley Seismology Lab as a researcher to develop the MyShake project. He also worked as a visiting researcher at Google's visiting Faculty program to help build the Android Earthquake Alerting system. In his spare time, he loves playing soccer and kayaking in SF bay. Play better, work better.
TS
Timmy Siauw
AB