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willycs40/Simulate GPS Problem 2.ipynb

Last active August 29, 2015 14:20
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Simulate GPS Grid Problems
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Simulate GPS Problem 2.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:2588f533a851c024e017048b1e3c2f777050a4196550283c1e856bce111e8881"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import math as m\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# set the box range\n",
"box_dim = 100\n",
"\n",
"# create random pairs of coordinates in the box (x1, y1, x2, x2)\n",
"randoms = np.random.rand(10000, 4)*box_dim\n",
"randoms[0:10,]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 111,
"text": [
"array([[ 10.67703215, 87.27675791, 11.56526361, 79.92198867],\n",
" [ 81.2283512 , 52.24879191, 75.5042816 , 55.0166546 ],\n",
" [ 32.70622064, 67.98464488, 27.73900492, 95.52083663],\n",
" [ 66.46936307, 77.90811741, 16.87401609, 24.82937002],\n",
" [ 11.60415264, 99.9630587 , 4.45027578, 36.35778503],\n",
" [ 15.53375625, 17.49650387, 4.81442386, 38.56435318],\n",
" [ 33.14291848, 41.30859325, 28.09719459, 11.53435108],\n",
" [ 45.74869111, 58.96177641, 67.22882704, 57.9946143 ],\n",
" [ 65.06045955, 24.43825205, 60.75214046, 72.82288794],\n",
" [ 49.19464056, 74.05560444, 10.22045352, 11.78019158]])"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# calculute the true line lengths - vectorised pythag\n",
"line_lengths = np.sqrt(np.square(randoms[:,2]-randoms[:,0])+np.square(randoms[:,3]-randoms[:,1]))\n",
"\n",
"#plot the true line length distribution\n",
"plt.hist(line_lengths, bins = np.arange(1, 150, 1))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0xb716330>"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# make a new copy of the coordinates\n",
"randoms_truncated = randoms.copy()\n",
"\n",
"x_truncation = 1.0\n",
"y_truncation = 11.0\n",
"\n",
"# truncate the x's to nearest 1m, and the y's to the nearest 11m\n",
"randoms_truncated[:,0] = np.around(randoms[:,0]/x_truncation,0)*x_truncation\n",
"randoms_truncated[:,2] = np.around(randoms[:,2]/x_truncation,0)*x_truncation\n",
"randoms_truncated[:,1] = np.around(randoms[:,1]/y_truncation,0)*y_truncation\n",
"randoms_truncated[:,3] = np.around(randoms[:,3]/y_truncation,0)*y_truncation"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 160
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# recalc the new line lengths \n",
"truncated_line_lengths = np.sqrt(np.square(randoms_truncated[:,2]-randoms_truncated[:,0])+np.square(randoms_truncated[:,3]-randoms_truncated[:,1]))\n",
"\n",
"#plot as before\n",
"plt.hist(truncated_line_lengths, bins = np.arange(1, 150, 1))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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DtAvmC319TX7dJZ8DWj5b9gtX2N2xXIt9w/V/q9Vg2C+Brf2dZiBLhv2IneljNXwk7azO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\n6gDDXpI64P8DXexRNCL4U5MAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xefa2110>"
]
}
],
"prompt_number": 161
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(20,10))\n",
"ax1.set_title('Original')\n",
"ax2.set_title('Truncated')\n",
"\n",
"ax1.hist(line_lengths, bins = np.arange(1, 150, 1), alpha=0.5)\n",
"ax2.hist(truncated_line_lengths, bins = np.arange(1, 150, 1), alpha=0.5)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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AAACgc5aYAQDAJQz76Cx6ryAA2IuACAAALmHYR2fRewUBwF4ERFxkeLds67md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\nzMzMKucFIjMzMzMzMzOzyv0HpOfZeScZ+qgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10a5a330>"
]
}
],
"prompt_number": 220
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"residuals = randoms - randoms_truncated"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# take a look at the residuals which we cut off when we truncated. These are flat distributions...\n",
"\n",
"f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey=True, figsize=(20,10))\n",
"ax1.set_title('Residual Y1')\n",
"ax2.set_title('Residual Y2')\n",
"ax3.set_title('Residual X1')\n",
"ax4.set_title('Residual X2')\n",
"\n",
"ax1.hist(residuals[:,1], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax2.hist(residuals[:,3], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax3.hist(residuals[:,0], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax4.hist(residuals[:,2], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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nq+rjVfWaebdnt6iqf1VVd1XVw+bdlp2uqn5z+Ix+tKr+uKq+e95t2omq6ryq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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10fe0950>"
]
}
],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ok, so can we fix the broken distribution\n",
"\n",
"num_rows = len(randoms[:,1])\n",
"\n",
"simulated_residuals = np.random.rand(10000, 4)\n",
"\n",
"simulated_residuals[:,0] = (simulated_residuals[:,0] * x_truncation) - (x_truncation/2) \n",
"simulated_residuals[:,2] = (simulated_residuals[:,2] * x_truncation) - (x_truncation/2) \n",
"simulated_residuals[:,1] = (simulated_residuals[:,1] * y_truncation) - (y_truncation/2) \n",
"simulated_residuals[:,3] = (simulated_residuals[:,3] * y_truncation) - (y_truncation/2) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# take a look at our simulated residuals and check they look like the real resilduals\n",
"\n",
"f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey=True, figsize=(20,10))\n",
"ax1.set_title('Random Residual Y1')\n",
"ax2.set_title('Random Residual Y2')\n",
"ax3.set_title('Random Residual X1')\n",
"ax4.set_title('Random Residual X2')\n",
"\n",
"ax1.hist(simulated_residuals[:,1], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax2.hist(simulated_residuals[:,3], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax3.hist(simulated_residuals[:,0], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"ax4.hist(simulated_residuals[:,2], bins = np.arange(-6, 6, 0.1), alpha=0.5)\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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OmGVnOI+eOvNo605s050oeJKduQxrpw44m7WRM4arE/jNbq/d1N17vUBjlnZv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\nS/Liqvp8kiNVdVZ3P1ZVL0/y+LT/o0l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"text": [
"<matplotlib.figure.Figure at 0x114d9d90>"
]
}
],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# add the simulated residuals and take another look at the lengths histogram!\n",
"\n",
"fixed_data = randoms_truncated + simulated_residuals\n",
"\n",
"# recalc the new line lengths \n",
"fixed_line_lengths = np.sqrt(np.square(fixed_data[:,2]-fixed_data[:,0])+np.square(fixed_data[:,3]-fixed_data[:,1]))\n",
"\n",
"#plot as before\n",
"plt.hist(fixed_line_lengths, bins = np.arange(1, 150, 1))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x129046f0>"
]
}
],
"prompt_number": 174
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(20,10))\n",
"ax1.set_title('Original')\n",
"ax2.set_title('Truncated')\n",
"ax3.set_title('Fixed')\n",
"\n",
"ax1.hist(line_lengths, bins = np.arange(1, 150, 1), alpha=0.5)\n",
"ax2.hist(truncated_line_lengths, bins = np.arange(1, 150, 1), alpha=0.5)\n",
"ax3.hist(fixed_line_lengths, bins = np.arange(1, 150, 1), alpha=0.5)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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3neg8w7kBrCYB0QZahUAIAADYTAIe2EyWmHEkykABjubixTtz440jTZYBAFgr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\nMzMzMzMzG3IeIDIzMzMzMzMzG3L/A90repuwVpCPAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107e5e30>"
]
}
],
"prompt_number": 218
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.optimize import curve_fit"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 230
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Define model function to be used to fit to the data above:\n",
"def gauss(x, *p):\n",
" A, mu, sigma = p\n",
" return A*np.exp(-(x-mu)**2/(2.*sigma**2))\n",
"\n",
"# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)\n",
"p0 = [1., 50., 25.]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 231
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bins = np.arange(0, 150, 5)\n",
"bin_centres = (bins[:-1] + bins[1:])/2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 232
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create histograms for fitting and fit curves\n",
"hist_original, bin_edges = np.histogram(line_lengths, bins=bins, density=True)\n",
"hist_truncated, bin_edges = np.histogram(truncated_line_lengths, bins=bins, density=True)\n",
"hist_fixed, bin_edges = np.histogram(fixed_line_lengths, bins=bins, density=True)\n",
"\n",
"coeff_original, var_matrix = curve_fit(gauss, bin_centres, hist_original, p0=p0)\n",
"coeff_truncated, var_matrix = curve_fit(gauss, bin_centres, hist_truncated, p0=p0)\n",
"coeff_fixed, var_matrix = curve_fit(gauss, bin_centres, hist_fixed, p0=p0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 233
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create histograms of fitted curves\n",
"hist_original_fit = gauss(bin_centres, *coeff_original)\n",
"hist_truncated_fit = gauss(bin_centres, *coeff_truncated)\n",
"hist_fixed_fit = gauss(bin_centres, *coeff_fixed)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 234
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(20,5))\n",
"ax1.set_title('Original')\n",
"ax2.set_title('Truncated')\n",
"ax3.set_title('Fixed')\n",
"\n",
"ax1.plot(bin_centres, hist_original, color='r',label='Test data')\n",
"ax1.plot(bin_centres, hist_original_fit, color='b', label='Fitted data')\n",
"\n",
"ax2.plot(bin_centres, hist_truncated, color='r',label='Test data')\n",
"ax2.plot(bin_centres, hist_truncated_fit, color='b', label='Fitted data')\n",
"\n",
"ax3.plot(bin_centres, hist_fixed, color='r',label='Test data')\n",
"ax3.plot(bin_centres, hist_fixed_fit, color='b', label='Fitted data')\n",
"\n",
"plt.show()\n",
"\n",
"print 'Original - Mean: {}, SD: {}'.format(coeff_original[1], coeff_original[2])\n",
"print 'Truncated - Mean: {}, SD: {}'.format(coeff_truncated[1], coeff_truncated[2])\n",
"print 'Fixed - Mean: {}, SD: {}'.format(coeff_fixed[1], coeff_fixed[2])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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IiEhMSiSJiIhIcKIlksqWhTp1YOHCYGISERERkZiUSBIREZHgRGttA7W3iYiIiGQoJZJE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bRqlb1uugnuvjvvv30REYlS0aLQsKG9wH6vz51rM9Nz5sCkSfZnoUL7J5eqVIEtW2xX0Q0b9vyZ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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11599350>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Original - Mean: 48.9664426296, SD: 29.1530505025\n",
"Truncated - Mean: 49.3492839645, SD: 29.4436943482\n",
"Fixed - Mean: 49.2043883025, SD: 29.328475245\n"
]
}
],
"prompt_number": 235
}
],
"metadata": {}
}
]
}
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Simulate GPS Problem.ipynb
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