Book Image

NumPy Cookbook - Second Edition

By : Ivan Idris
Book Image

NumPy Cookbook - Second Edition

By: Ivan Idris

Overview of this book

<p>NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.</p> <p>This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.</p>
Table of Contents (19 chapters)
NumPy Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Debugging with IPython


"If debugging is the process of removing software bugs, then programming must be the process of putting them in." Edsger Dijkstra, Dutch computer scientist, winner of the 1972 Turing Award

Debugging is one of those things nobody really likes, but is very important to master. It can take hours, and because of Murphy's law, you most likely don't have that time. Therefore, it is important to be systematic and know your tools well. After you've found the bug and implemented a fix, you should have a unit test in place (if the bug has a related ID from an issue tracker, I usually name the test by appending the ID at the end). In this way, you will at least not have to go through the hell of debugging again. Unit testing is covered in the next chapter. We will debug the following buggy code. It tries to access an array element that is not present:

import numpy as np

a = np.arange(7)
print(a[8])

The IPython debugger works as the normal Python pdb debugger; it adds features such...