Book Image

NumPy Cookbook

Book Image

NumPy Cookbook

Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
Table of Contents (17 chapters)
NumPy Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Calling C functions


We can call C functions from Cython. For instance, in this example, we will call the C log function. This function works on a single number only. Remember that the NumPy log function can also work with arrays. We will compute the so-called log returns of stock prices.

How to do it...

We will start by writing some Cython code:

  1. Write the .pyx file.

    First, we need to import the C log function from the libc namespace. Second, we will apply this function to numbers in a for loop. Finally, we will use the NumPy diff function to get the first order difference between the log values in the second step.

    from libc.math cimport log
    import numpy
    
    def logrets(numbers):
       logs = [log(x) for x in numbers] 
       return numpy.diff(logs)

    Building has been covered in the previous recipes already. We only need to change some values in the setup.py file.

  2. Plot the log returns.

    Let's download stock price data with matplotlib, again. Apply the Cython logrets function that we just created on the...