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

16.2.1 Symbols – the basis of all formulas

The basic construction element to build a formula in SymPy is the symbol. As we saw in the introductory example, a symbol is created by the command symbols. This SymPy command generates symbol objects from a given string:

x, y, mass, torque = symbols('x y mass torque')

It is actually a short form of the following command:

symbol_list=[symbols(l) for l in 'x y mass torque'.split()]

Followed by an unpacking step to obtain variables:

 x, y, mass, torque = symbol_list

The arguments of the command define the string representation of the symbol. The variable name of the symbol chosen is often identical to its string representation, but this is not required by the language:

row_index=symbols('i',integer=True)
print(row_index**2) # returns i**2

Here, we also defined that the symbol is assumed to be an integer.

An entire set of symbols can be defined in a very compact way:

integervariables = symbols...