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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 3. Chapter 3: Calculus and Differential Equations 4. Chapter 4: Working with Randomness and Probability 5. Chapter 5: Working with Trees and Networks 6. Chapter 6: Working with Data and Statistics 7. Chapter 7: Using Regression and Forecasting 8. Chapter 8: Geometric Problems 9. Chapter 9: Finding Optimal Solutions 10. Chapter 10: Improving Your Productivity 11. Index 12. Other Books You May Enjoy

Triangulating planar figures

As we saw in Chapter 3, Calculus and Differential Equations, we often need to break down a continuous region into smaller, simpler regions. In earlier recipes, we reduced an interval of real numbers into a collection of smaller intervals, each with a small length. This process is usually called discretization. In this chapter, we are working with two-dimensional figures, so we need a two-dimensional version of this process. For this, we’ll break a two-dimensional figure (in this recipe, a polygon) into a collection of smaller and simpler polygons. The simplest of all polygons are triangles, so this is a good place to start for two-dimensional discretization. The process of finding a collection of triangles that tiles a geometric figure is called triangulation.

In this recipe, we will learn how to triangulate a polygon (with a hole) using the Shapely package.

Getting ready

For this recipe, we will need the NumPy package imported as np, the...

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