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

Combining images


In this recipe, we will combine the famous Mandelbrot fractal (see http://en.wikipedia.org/wiki/Mandelbrot_set) and the image of Lena. The Mandelbrot set was invented by the mathematician Benoit Mandelbrot. These types of fractals are defined by a recursive formula, where you calculate the next complex number in a series by multiplying the current complex number you have by itself and adding a constant to it. More details will be covered in this recipe.

Getting ready

Install SciPy if necessary. The See also section has a reference to the related recipe.

How to do it...

Start by initializing the arrays, followed by generating and plotting the fractal, and finally combining the fractal with the Lena image:

  1. Initialize the x, y, and z arrays, corresponding to the pixels in the image area with the meshgrid(), zeros(), and linspace() functions:

    x, y = np.meshgrid(np.linspace(x_min, x_max, SIZE),
                       np.linspace(y_min, y_max, SIZE))
    c = x + 1j * y
    z = c.copy()
    fractal...