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

8.7 Classes as decorators

In Section 7.8: Function as decorators, we saw how functions can be modified by applying another function as a decorator. In Section 8.1.5: Special methods, we saw how classes can be made to behave as functions as long as they are provided with the method __call__. We will use this here to show how classes can be used as decorators.

Let's assume that we want to change the behavior of some functions in such a way that before the function is invoked, all input parameters are printed. This could be useful for debugging purposes. We take this situation as an example to explain the use of a decorator class:

class echo:
    text = 'Input parameters of {name}\n'+\
        'Positional parameters {args}\n'+\
        'Keyword parameters {kwargs}\n'
    def __init__(self, f):
        self.f = f
    def __call__(self, *args, **kwargs):
        print(self.text.format(name = self.f.__name__,
              args = args, kwargs = kwargs))
 ...