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@jrleeman
Created August 8, 2017 21:27
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{
"cells": [
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def cart2pol(x, y):\n",
" rho = np.sqrt(x**2 + y**2)\n",
" phi = np.arctan2(y, x)\n",
" return(rho, phi)\n",
"\n",
"def pol2cart(rho, phi):\n",
" phi = np.deg2rad(phi)\n",
" x = rho * np.cos(phi)\n",
" y = rho * np.sin(phi)\n",
" return(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"theta = np.arange(0,360, 1)\n",
"r = 34/2 * np.ones_like(theta)\n",
"x_offset = 100.\n",
"y_offset = 100."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, y = pol2cart(r, theta)\n",
"x += x_offset\n",
"y += y_offset"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x11acac908>"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MbIuZPW1mnwuO3WZmL5rZ5uDjsu6U2pyFlaksmff+I0NZEZEsmD97CpfM6ktlC3jbQW9m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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11abe66d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x,y,s=1)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"117.0 100.0\n",
"116.997410818 100.296690909\n",
"116.989644059 100.593291444\n",
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]
}
],
"source": [
"n_per_row = 4\n",
"i = 0\n",
"f = open('circ_coords.txt', 'w')\n",
"for a, b in zip(x,y):\n",
" print(a, b)\n",
" f.write('(xy %.3f %.3f) '%(a, b))\n",
" i += 1\n",
" if i ==n_per_row:\n",
" f.write('\\n')\n",
" i = 0\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11adb65c0>]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IdhGZYW0LF5GlIpJqPbZxTVTljf58WW8M1fO3VeP65IdDbD6Qz/2X9KS1Tqds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\npXzc/wP5jVFyowSoJwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a982d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"360"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(x)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 117. , 116.99741082, 116.98964406, 116.97670209,\n",
" 116.95858885, 116.93530987, 116.90687222, 116.87328458,\n",
" 116.83455717, 116.79070179, 116.7417318 , 116.68766212,\n",
" 116.62850921, 116.5642911 , 116.49502735, 116.42073905,\n",
" 116.34144883, 116.25718085, 116.16796078, 116.07381579,\n",
" 115.97477455, 115.87086725, 115.76212553, 115.64858251,\n",
" 115.53027278, 115.40723238, 115.27949879, 115.14711091,\n",
" 115.01010908, 114.86853502, 114.72243186, 114.57184411,\n",
" 114.41681763, 114.25739966, 114.09363873, 113.92558475,\n",
" 113.7532889 , 113.57680367, 113.39618281, 113.21148134,\n",
" 113.02275553, 112.83006286, 112.63346203, 112.43301293,\n",
" 112.22877661, 112.02081528, 111.8091923 , 111.59397212,\n",
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