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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 – plotting in three dimensions


We will plot in three dimensions a simple three-dimensional function:

  1. We need to use the 3d keyword to specify a three-dimensional projection for the plot.

    ax = fig.add_subplot(111, projection=’3d’)
  2. To create a square two-dimensional grid, we will use the meshgrid function. This will be used to initialize the x and y values.

    u = np.linspace(-1, 1, 100)
    
    x, y = np.meshgrid(u, u)
  3. We will specify the row strides, column strides, and the color map for the surface plot. The strides determine the size of the "tiles" on the surface. The choice for colormap is a matter of taste.

    ax.plot_surface(x, y, z,  rstride=4, cstride=4,cmap=cm.YlGnBu_r)

    The result is the following 3D plot:

What just happened?

We created a plot of a three dimensional function (see three_d.py):

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm

fig = plt.figure()
ax = fig.add_subplot(111, projection=’3d’)

u = np.linspace...
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