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

Constant functions

One of the most common examples of broadcasting is the addition of a function and a constant; if  is a scalar, we often write:

This is an abuse of notation since you should not be able to add functions and constants. Constants are, however, implicitly broadcast to functions. The broadcast version of the constant  is the function  defined by:

Now it makes sense to add two functions together:

We are not being pedantic for the sake of it, but because a similar situation may arise for arrays, as in the following code:

vector = arange(4) # array([0.,1.,2.,3.])
vector + 1.        # array([1.,2.,3.,4.])

In this example, everything happens as if the scalar 1. had been converted to an array of the same length as vector, that is, array([1.,1.,1.,1.]), and then added to vector.

This example is exceedingly simple, so we'll proceed to show less obvious situations.