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

8.4.2 Class methods

We saw in Section 8.3: Bound and unbound methods how methods are either bound to an instance of a class or remain in a state as unbound methods. Class methods are different. They are always bound methods. They are bound to the class itself.

We will first describe the syntactic details and then give some examples to show what these methods can be used for.

To indicate that a method is a class method, the decorator line precedes the method definition:

@classmethod

While standard methods make a reference to an instance by the use of their first argument, the first argument of a class method refers to the class itself. By convention, the first argument is called self for standard methods and cls for class methods.

The following is an example of the standard case:

class A:
    def func(self,*args):
         <...>

This is contrasted by an example of the classmethod case:

class B:
    @classmethod
    def func(cls,*args):
         <...>

In practice, class methods...