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@bmcfee
Created October 16, 2015 00:39
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"sns.set(style='darkgrid')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive\n",
"from IPython.display import clear_output, display, HTML"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Adapted from original code from http://www.johndcook.com/blog/2011/06/21/how-to-fit-an-elephant/\n",
"\n",
"Author: Piotr A. Zolnierczuk (zolnierczukp at ornl dot gov)\n",
" \n",
"Based on a paper by:\n",
"Drawing an elephant with four complex parameters\n",
"Jurgen Mayer, Khaled Khairy, and Jonathon Howard,\n",
"Am. J. Phys. 78, 648 (2010), DOI:10.1119/1.3254017\n",
"\"\"\"\n",
"\n",
"# elephant parameters\n",
"#p1, p2, p3, p4 = (50 - 30j, 18 + 8j, 12 - 10j, -14 - 60j )\n",
"#p5 = 40 + 20j # eyepiece\n",
"\n",
"def fourier(t, C):\n",
" f = np.zeros(t.shape)\n",
" A, B = C.real, C.imag\n",
" for k in range(len(C)):\n",
" f = f + A[k]*np.cos(k*t) + B[k]*np.sin(k*t)\n",
" return f\n",
" \n",
"def elephant(p1r=50, p1i=-30, p2r=18, p2i=8, p3r=12, p3i=-10, p4r=-14, p4i=-60, p5r=40, p5i=20):\n",
" npar = 6\n",
" \n",
" t = np.linspace(0,2*np.pi,1000)\n",
" \n",
" Cx = np.zeros((npar,), dtype='complex')\n",
" Cy = np.zeros((npar,), dtype='complex')\n",
" \n",
" Cx[1] = p1r*1j\n",
" Cx[2] = p2r*1j\n",
" Cx[3] = p3r\n",
" Cx[5] = p4r\n",
" \n",
" Cy[1] = p4i + p1i*1j\n",
" Cy[2] = p2i*1j\n",
" Cy[3] = p3i*1j\n",
" \n",
" x = np.append(fourier(t,Cx), [-p5i])\n",
" y = np.append(fourier(t,Cy), [p5i])\n",
" \n",
" plt.plot(y, -x, '.')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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aa69p1qxZkqQ5c+aoq6srvKNEybV3dPsuy0rP+CaoUYj00Mmx4x+UNTBZzgXbBSqDjx8/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8c58eb28d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interact(elephant,\n",
" p1r=(-100, 100), p1i=(-100, 100),\n",
" p2r=(-100, 100), p2i=(-100, 100),\n",
" p3r=(-100, 100), p3i=(-100, 100),\n",
" p4r=(-100, 100), p4i=(-100, 100),\n",
" p5r=(-100, 100), p5i=(-100, 100));"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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