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

9.6 Iterator objects

As we mentioned earlier, a for loop only needs an iterable object. Lists, in particular, are iterable objects. This means that a list is able to create an iterator from its contents. In fact, this is true for any object (not only lists): any object may be made iterable.

This is achieved via the method __iter__, which should return an iterator. Here we give an example where the method __iter__ is a generator:

class OdeStore:
    """
    Class to store results of ode computations
    """
    def __init__(self, data):
        "data is a list of the form [[t0, u0], [t1, u1],...]"
        self.data = data
    
    def __iter__(self):
        "By default, we iterate on the values u0, u1,..."
        for t, u in self.data:
            yield u

store = OdeStore([[0, 1], [0.1, 1.1], [0.2, 1.3]])
for u in store:
    print(u)
# result: 1, 1.1, 1.3
list(store) # [1, 1.1, 1...