Название: Python for Engineering and Scientific ComputingАвтор: Veit Steinkamp
Издательство: Rheinwerk Publishing Inc.
Год: 2024
Страниц: 511
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
It’s finally here—your guide to Python for engineers and scientists, by an engineer and scientist! Get to know your development environments and the key Python modules you’ll need: NumPy, SymPy, SciPy, Matplotlib, and VPython. Understand basic Python program structures and walk through practical exercises that start simple and increase in complexity as you work your way through the book. With information on statistical calculations, Boolean algebra, and interactive programming with Tkinter, this Python guide belongs on every scientist’s shelf!
If you need to perform extensive calculations for your scientific work and also want to present the results in a graphically appealing way, then you should seriously consider using Python. Python is a programming language whose functionality is similar to that of MATLAB when extended with appropriate modules. In addition, Python and all its extension modules are provided free of charge. Using Python, you can, for example, solve systems of equations, create function plots, differentiate, integrate, and also solve differential equations. You can also create graphical user interfaces (GUIs). For almost every problem in engineering and natural sciences, solutions exist that not only cover a wide range of applications, but also excel in their user-friendliness and performance.
Matplotlib is a program library for plotting mathematical functions and geometric figures. With just a few statements, you can easily create meaningful diagrams for scientific papers and publications. The Matplotlib module is usually imported together with the pyplot submodule. The pyplot submodule serves as an interface, specifically an application programming interface (API), to the matplotlib module. Matplotlib contains a collection of functions similar to the functionality of MATLAB. Matplotlib methods create drawing areas for diagrams (plots), draw lines or points in a predefined drawing area, specify line styles, and provide numerous options for labels and the scaling of coordinate axes. Several mathematical functions can be represented in one plot or in different subplots. Extensive design options for labels (legends) of the individual function plots support the reader in finding their way around.
Highlights include:
1) Program structures
2) NumPy
3) Matplotlib
4) SymPy
5) SciPy
6) VPython
7) Tkinter
8) Numerical calculations
9) Statistical calculations
10) Boolean algebra
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