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

8.6 Encapsulation

Sometimes the use of inheritance is impractical or even impossible. This motivates the use of encapsulation.

We will explain the concept of encapsulation by considering Python functions, that is, objects of the Python type function, which we encapsulate in a new class, Function, and provide with some relevant methods:

class Function:
    def __init__(self, f):
        self.f = f
    def __call__(self, x):
        return self.f(x)
    def __add__(self, g):
        def sum(x):
            return self(x) + g(x)
        return type(self)(sum) 
    def __mul__(self, g): 
        def prod(x):
            return self.f(x) * g(x)
        return type(self)(prod)
    def __radd__(self, g):
        return self + g
    def __rmul__(self, g):
        return self * g

Note that the operations __add__ and __mul__ should return an instance of the same class. This is achieved by the statement return type(self)(sum), which in this case is a more general form of writing return Function...