Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
20
About Packt
22
References

6.1.2 Formatting

The appearance of figures and plots can be styled and customized to look how you want them to look. Some important variables are linewidth, which controls the thickness of plot lines, xlabel and ylabel, which set the axis labels, color for plot colors, and transparent for transparency.

This section will tell you how to use some of them. The following is an example with more keywords:

k = 0.2
x = [sin(2*n*k) for n in range(20)]
plot(x, color='green', linestyle='dashed', marker='o', 
                       markerfacecolor='blue', markersize=12, linewidth=6)

There are short commands that can be used if you only need basic style changes, for example, setting the color and line style. The following table (Table 6.1) shows some examples of these formatting commands. You may use either the short string syntax plot(...,'ro-'), or the more explicit syntax plot(.....