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

Raising exceptions

Creating an error is referred to as raising an exception. You saw some examples of exceptions in the previous section. You can also define your own exceptions of a predefined type or use an exception of an unspecified type. Raising an exception is done with a command like this:

raise Exception("Something went wrong")

Here an exception of an unspecified type was raised.

It might be tempting to print out error messages when something goes wrong, for example, like this:

print("The algorithm did not converge.")

This is not recommended for a number of reasons. Firstly, printouts are easy to miss, especially if the message is buried in many other messages being printed to your console. Secondly, and more importantly, it renders your code unusable by other code. The calling code will not read what you printed and will not have a way of knowing that an error occurred and therefore has no way of taking care of it.

For these reasons, it is always better...