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Applying Math with Python

You're reading from   Applying Math with Python Practical recipes for solving computational math problems using Python programming and its libraries

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838989750
Length 358 pages
Edition 1st Edition
Languages
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Authors (2):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (12) Chapters Close

Preface 1. Basic Packages, Functions, and Concepts 2. Mathematical Plotting with Matplotlib FREE CHAPTER 3. Calculus and Differential Equations 4. Working with Randomness and Probability 5. Working with Trees and Networks 6. Working with Data and Statistics 7. Regression and Forecasting 8. Geometric Problems 9. Finding Optimal Solutions 10. Miscellaneous Topics 11. 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...

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