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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, 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)
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20
About Packt
22
References

9.7.1 The while loop

The while loop may be used to repeat a code block until a condition is fulfilled:

while condition:
    <code>

A while loop is equivalent to the following code:

for iteration in itertools.count():
    if not condition:
        break
    <code>

So a while loop used to repeat a code block until a condition is fulfilled is equivalent to an infinite iterator, which might be stopped if a condition is fulfilled. The danger of such a construction is obvious: the code may be trapped in an infinite loop if the condition is never fulfilled.

The problem in scientific computing is that you are not always sure that an algorithm will converge. Newton iteration, for instance, might not converge at all. If that algorithm were implemented inside a while loop, the corresponding code would be trapped in an infinite loop for some choices of initial conditions.

We, therefore, advise that finite...

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