Book Image

Scientific Computing with Python 3

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
References

Measuring execution time


In order to take decisions on code optimization, one often has to compare several code alternatives and decide which code should be preferred based on the execution time. Furthermore, discussing execution time is an issue when comparing different algorithms. In this section, we present a simple and easy way to measure execution time.

Timing with a magic function

The easiest way to measure the execution time of a single statement is to use IPython’s magic function %timeit.

Note

The shell IPython adds additional functionality to standard Python. These extra functions are called magic functions.

As the execution time of a single statement can be extremely short, the statement is placed in a loop and executed several times. By taking the minimum measured time, one makes sure that other tasks running on the computer do not influence the measured result too much. Let's consider four alternative ways to extract nonzero elements from an array as follows:

A=zeros((1000,1000))&...