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

Methods mimicking function calls and iterables

Using a class instance together with parentheses or brackets, () or [], invokes a call to one of the special methods __call__ or __getitem__, giving the instance the behavior of a function or of an iterable; see also Table 8.1.

class Polynomial:
    ...
    def __call__(self, x):
        return self.eval(x)

Which now may be used as follows:

p = Polynomial(...)    # Creating a polynomial object
p(3.) # value of p at 3.

The special method __getitem__ makes sense if the class provides an iterator (it is recommended that you review Section 9.2.1: Generators before you consider the following example).

The recursion is called a three-term recursion. It plays an important role in applied mathematics, in particular in the construction of orthogonal polynomials. We can set up a three-term recursion as a class in the following way:

import itertools

class  Recursion3Term:
    def __init__(self, a0, a1, u0, u1):
        self.coeff = [a1, a0]
     ...