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NumPy Beginner's Guide

You're reading from   NumPy Beginner's Guide An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.

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Product type Paperback
Published in Apr 2013
Publisher Packt
ISBN-13 9781782166085
Length 310 pages
Edition 2nd Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (19) Chapters Close

Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. NumPy Quick Start FREE CHAPTER 2. Beginning with NumPy Fundamentals 3. Get in Terms with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Move Further with NumPy Modules 7. Peeking into Special Routines 8. Assure Quality with Testing 9. Plotting with Matplotlib 10. When NumPy is Not Enough – SciPy and Beyond 11. Playing with Pygame Pop Quiz Answers Index

Time for action – shifting frequencies


We will create a signal, transform it, and then shift the signal. In order to shift the frequencies, perform the following steps:

  1. Create a cosine wave with 30 points.

    x =  np.linspace(0, 2 * np.pi, 30)
    wave = np.cos(x)
  2. Transform the cosine wave with the fft function.

    transformed = np.fft.fft(wave)
  3. Shift the signal with the fftshift function.

    shifted = np.fft.fftshift(transformed)
  4. Reverse the shift with the ifftshift function. This should undo the shift.

    print np.all((np.fft.ifftshift(shifted) - transformed) < 10 ** -9)

    The result is shown as follows:

    True
    
  5. Plot the signal and transform it with Matplotlib.

    plot(transformed, lw=2)
    plot(shifted, lw=3)
    show()

    The following screenshot shows the shift in the fast Fourier transform:

What just happened?

We applied the fftshift function to a cosine wave. After applying the ifftshift function, we got our signal back (see fouriershift.py).

import numpy as np
from matplotlib.pyplot import plot, show

x =  np.linspace(0...
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