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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Finding the root of a mathematical function


In this short recipe, we will see how to use SciPy to find the root of a simple mathematical function of a single real variable.

How to do it…

  1. Let's import NumPy, SciPy, scipy.optimize, and matplotlib:

    In [1]: import numpy as np
            import scipy as sp
            import scipy.optimize as opt
            import matplotlib.pyplot as plt
            %matplotlib inline
  2. We define the mathematical function f(x)=cos(x)-x in Python. We will try to find a root of this function numerically. Here, a root corresponds to a fixed point of the cosine function:

    In [2]: f = lambda x: np.cos(x) - x
  3. Let's plot this function on the interval [-5, 5] (using 1000 samples):

    In [3]: x = np.linspace(-5, 5, 1000)
            y = f(x)
            plt.plot(x, y)
            plt.axhline(0, color='k')
            plt.xlim(-5,5)
  4. We see that this function has a unique root on this interval (this is because the function's sign changes on this interval). The scipy.optimize module contains a few root-finding functions...

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