Book Image

Scientific Computing with Python 3

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

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
References

Basic elements of SymPy


Here we introduce the basic elements of SymPy. You will find it favorable to be already familiar with classes and data types in Python.

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 following command:

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

followed by a 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 is often chosen 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...