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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd 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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Toc

Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with Jupyter and IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Jupyter Notebook 4. Profiling and Optimization 5. High-Performance Computing 6. Data 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

Computing the Voronoi diagram of a set of points

The Voronoi diagram of a set of seed points divides space into several regions. Each region contains all points closer to one seed point than to any other seed point.

The Voronoi diagram is a fundamental structure in computational geometry. It is widely used in computer science, robotics, geography, and other disciplines. For example, the Voronoi diagram of a set of metro stations gives us the closest station from any point in the city.

In this recipe, we compute the Voronoi diagram of the set of metro stations in Paris using SciPy.

Getting ready

You need the Smopy module to display the OpenStreetMap map of Paris. You can install this package with pip install git+https://github.com/rossant/smopy.git.

How to do it...

  1. Let's import the packages:
    >>> import numpy as np
        import pandas as pd
        import scipy.spatial as spatial
        import matplotlib.pyplot as plt
        import matplotlib.path as path
        import matplotlib as mpl
        import...
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