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Scientific Computing with Python 3

Scientific Computing with Python 3

By : Claus Führer, Claus Fuhrer, Olivier Verdier, Jan Erik Solem
4 (2)
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Scientific Computing with Python 3

Scientific Computing with Python 3

4 (2)
By: Claus Führer, Claus Fuhrer, Olivier Verdier, Jan Erik Solem

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (17 chapters)
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16
References

Making 3D plots


There are some useful matplotlib tool kits and modules that can be used for a variety of special purposes. In this section, we describe a method for producing 3D-plots.

The mplot3d toolkit provides 3D plotting of points, lines, contours, surfaces, and all other basic components as well as 3D rotation and scaling. Making a 3D plot is done by adding the keyword projection='3d' to the axes object as shown in the following example:

from mpl_toolkits.mplot3d import axes3d

fig = figure()
ax = fig.gca(projection='3d')
# plot points in 3D
class1 = 0.6 * random.standard_normal((200,3))
ax.plot(class1[:,0],class1[:,1],class1[:,2],'o')
class2 = 1.2 * random.standard_normal((200,3)) + array([5,4,0])
ax.plot(class2[:,0],class2[:,1],class2[:,2],'o')
class3 = 0.3 * random.standard_normal((200,3)) + array([0,3,2])
ax.plot(class3[:,0],class3[:,1],class3[:,2],'o')

As you can see, you need to import the axes3D type from mplot3d. The resulting...

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