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

7.8 Functions as decorators

In Section 7.6.1: Partial application, we saw how a function can be used to modify another function. A decorator is a syntax element in Python that conveniently allows us to alter the behavior of a function without changing the definition of the function itself. Let's start with the following situation.

Assume that we have a function that determines tcitehe degree of sparsity of a matrix:

def how_sparse(A):
    return len(A.reshape(-1).nonzero()[0])

This function returns an error if it is not called with an array object as input. More precisely, it will not work with an object that does not implement the method reshape. For instance, the function how_sparse will not work with a list, because lists have no method reshape. The following helper function modifies any function with one input parameter so that it tries to make a type conversion to an array:

def cast2array(f):
    def new_function(obj):
        fA = f(array(obj))
        return fA
    return...