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November 1, 2013 18:04
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A file plotting a 3d plane with ipythong
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{ | |
"metadata": { | |
"name": "planes.ipynb" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Homework by Ryan Swanstrom\n", | |
"\n", | |
"## Create and Plot a Plane.\n", | |
"\n", | |
"Here are equations for the original 2 planes.\n", | |
"\n", | |
"## $ \\hat{Y_1} = -87 + X_1 + 18X_2 $\n", | |
"\n", | |
"## $ \\hat{Y_2} = -7 + 9X_1 + 2 X_2 $\n", | |
"\n", | |
"The equation for the new plane will be \n", | |
"\n", | |
"# $\\hat{Y_{new}} = -\\hat{Y_1} + 2 \\hat{Y_2} = 73 + 17X_1 - 14 X_2$\n", | |
"\n", | |
"The plots can be seen below with the green plane being the new one.\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"from mpl_toolkits.mplot3d import Axes3D\n", | |
"\n", | |
"y1 = np.array([1, 18, -1])\n", | |
"y2 = np.array([9, 2, -1])\n", | |
"ynew = np.array([17,-14, -1])\n", | |
"\n", | |
"# a plane is a*x+b*y+c*z+d=0\n", | |
"# [a,b,c] is the normal. \n", | |
"d1 = -87\n", | |
"d2 = -7\n", | |
"dnew = 73\n", | |
"\n", | |
"# create x,y\n", | |
"x1, x2 = np.meshgrid(range(0,24,10), range(0,24,10))\n", | |
"\n", | |
"# calculate corresponding z\n", | |
"y1 = -87+ x1 + 18*x2\n", | |
"y2 = -7 + 9*x1 + 2*x2\n", | |
"ynew = 73 + 17*x1 -14*x2\n", | |
"\n", | |
"# plot the surface\n", | |
"plt3d = plt.figure().gca(projection='3d')\n", | |
"\n", | |
"#plt3d.plot_surface(x1, x2, y2, color='darkblue')\n", | |
"#plt3d.plot_surface(x1, x2, y1, color='lightblue')\n", | |
"plt3d.plot_surface(x1, x2, ynew, color='green')\n", | |
"plt3d.view_init(10, 99)\n", | |
"plt.show()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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voOFyHK5peQ0L5i4AimsJ2mw2fvrpJ44cOcKLL77IX/7yF2RZJisri27dukU8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\nr78+oiPYO++8w4oVK1i9erW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mQPEtoe7HGm7crRvVlL1VS4TlpWVJNsManWQWXJ/Ph9ls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\nQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x82c3710>" | |
] | |
} | |
], | |
"prompt_number": 56 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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